CPTAC_VAE_v0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d3b57e4-2cf5-4937-a05d-11568cfc2fe0",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b442f5f8-627f-4722-aa7a-9a656470c8a0",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Goal: predict cancer type based on integrated CPTAC omics data  \n",
    "\n",
    "data url:  \n",
    "https://figshare.com/s/7ffe48478ec907b36dfb  \n",
    "    \n",
    "project repo:  \n",
    "https://github.com/PNNL-CompBio/panCancerModelSystems/tree/main\n",
    "\n",
    "CNV, proteomics, transcriptomics are X  \n",
    "cancer_type (10) is y, from sample file  \n",
    "\n",
    "This notebook encodes a latent feature file with the VAE for each datatype  \n",
    "CPTAC_trnsfrmr_0.ipynb reads the latent feature files to make predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2975d2f0-cb63-4cf3-bd6f-0996f14401ed",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Schematic of experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15481e36-c45c-443e-93fd-0ef9c765043a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "image/png": {
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='CPTAC_schematic_v0.png',\n",
    "      width=1000,\n",
    "      # height=400\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e2358d-849b-41bd-866e-610b1f516649",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "def79b4e-a0ab-4069-ab92-c32798359e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "59e56528-4335-4448-ac60-a90d03bca6fc",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "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>Name</th>\n",
       "      <th>Version</th>\n",
       "      <th>Build</th>\n",
       "      <th>Channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>absl-py</td>\n",
       "      <td>1.3.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anyio</td>\n",
       "      <td>3.6.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>appnope</td>\n",
       "      <td>0.1.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>argon2-cffi</td>\n",
       "      <td>21.3.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>argon2-cffi-bindings</td>\n",
       "      <td>21.2.0</td>\n",
       "      <td>py39h02fc5c5_3</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>asttokens</td>\n",
       "      <td>2.1.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>astunparse</td>\n",
       "      <td>1.6.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>atk-1.0</td>\n",
       "      <td>2.38.0</td>\n",
       "      <td>hcb7b3dd_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>attrs</td>\n",
       "      <td>22.1.0</td>\n",
       "      <td>pyh71513ae_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>babel</td>\n",
       "      <td>2.10.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>backcall</td>\n",
       "      <td>0.2.0</td>\n",
       "      <td>pyh9f0ad1d_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>backports</td>\n",
       "      <td>1.0</td>\n",
       "      <td>py_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>backports.functools_lru_cache</td>\n",
       "      <td>1.6.4</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>beautifulsoup4</td>\n",
       "      <td>4.11.1</td>\n",
       "      <td>pyha770c72_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>bleach</td>\n",
       "      <td>5.0.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>brotlipy</td>\n",
       "      <td>0.7.0</td>\n",
       "      <td>py39h02fc5c5_1005</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>bzip2</td>\n",
       "      <td>1.0.8</td>\n",
       "      <td>h3422bc3_4</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>c-ares</td>\n",
       "      <td>1.18.1</td>\n",
       "      <td>h3422bc3_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ca-certificates</td>\n",
       "      <td>2022.12.7</td>\n",
       "      <td>h4653dfc_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>cached-property</td>\n",
       "      <td>1.5.2</td>\n",
       "      <td>hd8ed1ab_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>cached_property</td>\n",
       "      <td>1.5.2</td>\n",
       "      <td>pyha770c72_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>cachetools</td>\n",
       "      <td>5.2.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>cairo</td>\n",
       "      <td>1.16.0</td>\n",
       "      <td>h73a0509_1014</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>certifi</td>\n",
       "      <td>2022.9.24</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>cffi</td>\n",
       "      <td>1.15.1</td>\n",
       "      <td>py39h7e6b969_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>charset-normalizer</td>\n",
       "      <td>2.1.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>click</td>\n",
       "      <td>8.1.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>contourpy</td>\n",
       "      <td>1.0.6</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>counter</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cryptography</td>\n",
       "      <td>38.0.3</td>\n",
       "      <td>py39he2a39a8_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>cycler</td>\n",
       "      <td>0.11.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>debugpy</td>\n",
       "      <td>1.6.3</td>\n",
       "      <td>py39h23fbdae_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>decorator</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>defusedxml</td>\n",
       "      <td>0.7.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>entrypoints</td>\n",
       "      <td>0.4</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>executing</td>\n",
       "      <td>1.2.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>expat</td>\n",
       "      <td>2.5.0</td>\n",
       "      <td>hb7217d7_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>fitter</td>\n",
       "      <td>1.5.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>flatbuffers</td>\n",
       "      <td>22.10.26</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>flit-core</td>\n",
       "      <td>3.7.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>font-ttf-dejavu-sans-mono</td>\n",
       "      <td>2.37</td>\n",
       "      <td>hab24e00_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>font-ttf-inconsolata</td>\n",
       "      <td>3.000</td>\n",
       "      <td>h77eed37_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>font-ttf-source-code-pro</td>\n",
       "      <td>2.038</td>\n",
       "      <td>h77eed37_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>font-ttf-ubuntu</td>\n",
       "      <td>0.83</td>\n",
       "      <td>hab24e00_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>fontconfig</td>\n",
       "      <td>2.14.2</td>\n",
       "      <td>h82840c6_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>fonts-conda-ecosystem</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>fonts-conda-forge</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>fonttools</td>\n",
       "      <td>4.38.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>freetype</td>\n",
       "      <td>2.12.1</td>\n",
       "      <td>hd633e50_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>fribidi</td>\n",
       "      <td>1.0.10</td>\n",
       "      <td>h27ca646_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>gast</td>\n",
       "      <td>0.4.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>gdk-pixbuf</td>\n",
       "      <td>2.42.10</td>\n",
       "      <td>h9bcf4fe_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>gettext</td>\n",
       "      <td>0.21.1</td>\n",
       "      <td>h0186832_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>giflib</td>\n",
       "      <td>5.2.1</td>\n",
       "      <td>h27ca646_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>google-auth</td>\n",
       "      <td>2.14.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>google-auth-oauthlib</td>\n",
       "      <td>0.4.6</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>google-pasta</td>\n",
       "      <td>0.2.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>graphite2</td>\n",
       "      <td>1.3.13</td>\n",
       "      <td>h9f76cd9_1001</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>graphviz</td>\n",
       "      <td>7.1.0</td>\n",
       "      <td>h4f8fbd6_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>grpcio</td>\n",
       "      <td>1.46.3</td>\n",
       "      <td>py39h365d37b_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>gsea-api</td>\n",
       "      <td>0.3.4</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>gseapy</td>\n",
       "      <td>1.0.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>gtk2</td>\n",
       "      <td>2.24.33</td>\n",
       "      <td>h57013de_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>gts</td>\n",
       "      <td>0.7.6</td>\n",
       "      <td>h4b6d4d6_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>h5py</td>\n",
       "      <td>3.6.0</td>\n",
       "      <td>nompi_py39hd982b79_100</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>harfbuzz</td>\n",
       "      <td>6.0.0</td>\n",
       "      <td>hddbc195_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>hdf5</td>\n",
       "      <td>1.12.1</td>\n",
       "      <td>nompi_hd9dbc9e_104</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>icu</td>\n",
       "      <td>70.1</td>\n",
       "      <td>h6b3803e_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>idna</td>\n",
       "      <td>3.4</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>importlib-metadata</td>\n",
       "      <td>5.0.0</td>\n",
       "      <td>pyha770c72_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>importlib_resources</td>\n",
       "      <td>5.10.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>ipykernel</td>\n",
       "      <td>6.17.0</td>\n",
       "      <td>pyh736e0ef_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>ipython</td>\n",
       "      <td>8.6.0</td>\n",
       "      <td>pyhd1c38e8_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>ipython_genutils</td>\n",
       "      <td>0.2.0</td>\n",
       "      <td>py_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>ipywidgets</td>\n",
       "      <td>8.0.2</td>\n",
       "      <td>pyhd8ed1ab_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>jedi</td>\n",
       "      <td>0.18.1</td>\n",
       "      <td>pyhd8ed1ab_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>jinja2</td>\n",
       "      <td>3.1.2</td>\n",
       "      <td>pyhd8ed1ab_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>joblib</td>\n",
       "      <td>1.2.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>jpeg</td>\n",
       "      <td>9e</td>\n",
       "      <td>he4db4b2_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>json5</td>\n",
       "      <td>0.9.5</td>\n",
       "      <td>pyh9f0ad1d_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>jsonschema</td>\n",
       "      <td>4.17.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>jupyter</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>py39h2804cbe_7</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>jupyter-helpers</td>\n",
       "      <td>0.2.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>jupyter-resource-usage</td>\n",
       "      <td>0.6.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>jupyter_client</td>\n",
       "      <td>7.4.4</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>jupyter_console</td>\n",
       "      <td>6.4.4</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>jupyter_core</td>\n",
       "      <td>4.11.2</td>\n",
       "      <td>py39h2804cbe_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>jupyter_server</td>\n",
       "      <td>1.21.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>jupyterlab</td>\n",
       "      <td>3.5.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>jupyterlab_pygments</td>\n",
       "      <td>0.2.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>jupyterlab_server</td>\n",
       "      <td>2.16.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>jupyterlab_widgets</td>\n",
       "      <td>3.0.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>keras</td>\n",
       "      <td>2.10.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>keras-preprocessing</td>\n",
       "      <td>1.1.2</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>kiwisolver</td>\n",
       "      <td>1.4.4</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>krb5</td>\n",
       "      <td>1.19.3</td>\n",
       "      <td>he492e65_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>lerc</td>\n",
       "      <td>4.0.0</td>\n",
       "      <td>h9a09cb3_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>libblas</td>\n",
       "      <td>3.9.0</td>\n",
       "      <td>16_osxarm64_openblas</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>libcblas</td>\n",
       "      <td>3.9.0</td>\n",
       "      <td>16_osxarm64_openblas</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>libclang</td>\n",
       "      <td>14.0.6</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>libcurl</td>\n",
       "      <td>7.86.0</td>\n",
       "      <td>h1c293e1_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>libcxx</td>\n",
       "      <td>14.0.6</td>\n",
       "      <td>h2692d47_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>libdeflate</td>\n",
       "      <td>1.17</td>\n",
       "      <td>h1a8c8d9_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>libedit</td>\n",
       "      <td>3.1.20191231</td>\n",
       "      <td>hc8eb9b7_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>libev</td>\n",
       "      <td>4.33</td>\n",
       "      <td>h642e427_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>libffi</td>\n",
       "      <td>3.4.2</td>\n",
       "      <td>h3422bc3_5</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>libgd</td>\n",
       "      <td>2.3.3</td>\n",
       "      <td>h90fb8ed_4</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>libgfortran</td>\n",
       "      <td>5.0.0</td>\n",
       "      <td>11_3_0_hd922786_25</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>libgfortran5</td>\n",
       "      <td>11.3.0</td>\n",
       "      <td>hdaf2cc0_25</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>libglib</td>\n",
       "      <td>2.74.1</td>\n",
       "      <td>h4646484_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>libiconv</td>\n",
       "      <td>1.17</td>\n",
       "      <td>he4db4b2_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>liblapack</td>\n",
       "      <td>3.9.0</td>\n",
       "      <td>16_osxarm64_openblas</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>libnghttp2</td>\n",
       "      <td>1.47.0</td>\n",
       "      <td>h519802c_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>libopenblas</td>\n",
       "      <td>0.3.21</td>\n",
       "      <td>openmp_hc731615_3</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>libpng</td>\n",
       "      <td>1.6.39</td>\n",
       "      <td>h76d750c_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>libprotobuf</td>\n",
       "      <td>3.19.4</td>\n",
       "      <td>hccf11d3_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>librsvg</td>\n",
       "      <td>2.54.4</td>\n",
       "      <td>ha2634a2_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>libsodium</td>\n",
       "      <td>1.0.18</td>\n",
       "      <td>h27ca646_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>libsqlite</td>\n",
       "      <td>3.39.4</td>\n",
       "      <td>h76d750c_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>libssh2</td>\n",
       "      <td>1.10.0</td>\n",
       "      <td>h7a5bd25_3</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>libtiff</td>\n",
       "      <td>4.5.0</td>\n",
       "      <td>h5dffbdd_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>libtool</td>\n",
       "      <td>2.4.7</td>\n",
       "      <td>hb7217d7_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>libwebp</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>h999c80f_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>libwebp-base</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>h57fd34a_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>libxml2</td>\n",
       "      <td>2.10.3</td>\n",
       "      <td>h87b0503_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>libzlib</td>\n",
       "      <td>1.2.13</td>\n",
       "      <td>h03a7124_4</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>llvm-openmp</td>\n",
       "      <td>14.0.4</td>\n",
       "      <td>hd125106_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>llvmlite</td>\n",
       "      <td>0.39.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>markdown</td>\n",
       "      <td>3.4.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>markupsafe</td>\n",
       "      <td>2.1.1</td>\n",
       "      <td>py39h02fc5c5_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>matplotlib</td>\n",
       "      <td>3.6.2</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>matplotlib-inline</td>\n",
       "      <td>0.1.6</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>mistune</td>\n",
       "      <td>2.0.4</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>nbclassic</td>\n",
       "      <td>0.4.8</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>nbclient</td>\n",
       "      <td>0.7.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>nbconvert</td>\n",
       "      <td>7.2.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>nbconvert-core</td>\n",
       "      <td>7.2.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>nbconvert-pandoc</td>\n",
       "      <td>7.2.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>nbformat</td>\n",
       "      <td>5.7.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>ncurses</td>\n",
       "      <td>6.3</td>\n",
       "      <td>h07bb92c_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>nest-asyncio</td>\n",
       "      <td>1.5.6</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>notebook</td>\n",
       "      <td>6.5.2</td>\n",
       "      <td>pyha770c72_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>notebook-shim</td>\n",
       "      <td>0.2.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>numba</td>\n",
       "      <td>0.56.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>numpy</td>\n",
       "      <td>1.23.2</td>\n",
       "      <td>py39h3668e8b_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>oauthlib</td>\n",
       "      <td>3.2.2</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>openssl</td>\n",
       "      <td>3.0.7</td>\n",
       "      <td>h03a7124_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>opt-einsum</td>\n",
       "      <td>3.3.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>packaging</td>\n",
       "      <td>21.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>pandas</td>\n",
       "      <td>1.5.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>pandoc</td>\n",
       "      <td>2.19.2</td>\n",
       "      <td>hce30654_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>pandocfilters</td>\n",
       "      <td>1.5.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>pango</td>\n",
       "      <td>1.50.12</td>\n",
       "      <td>h6c112b8_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>parso</td>\n",
       "      <td>0.8.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>pcre2</td>\n",
       "      <td>10.40</td>\n",
       "      <td>hb34f9b4_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>pexpect</td>\n",
       "      <td>4.8.0</td>\n",
       "      <td>pyh1a96a4e_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>pickleshare</td>\n",
       "      <td>0.7.5</td>\n",
       "      <td>py_1003</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>pillow</td>\n",
       "      <td>9.3.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>pip</td>\n",
       "      <td>22.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>pixman</td>\n",
       "      <td>0.40.0</td>\n",
       "      <td>h27ca646_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>pkgutil-resolve-name</td>\n",
       "      <td>1.3.10</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>plotly</td>\n",
       "      <td>5.11.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>prometheus_client</td>\n",
       "      <td>0.15.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>prompt-toolkit</td>\n",
       "      <td>3.0.31</td>\n",
       "      <td>pyha770c72_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>prompt_toolkit</td>\n",
       "      <td>3.0.31</td>\n",
       "      <td>hd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>protobuf</td>\n",
       "      <td>3.19.4</td>\n",
       "      <td>py39hfb83b0d_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>psutil</td>\n",
       "      <td>5.9.3</td>\n",
       "      <td>py39h02fc5c5_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>ptyprocess</td>\n",
       "      <td>0.7.0</td>\n",
       "      <td>pyhd3deb0d_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>pure_eval</td>\n",
       "      <td>0.2.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>pyasn1</td>\n",
       "      <td>0.4.8</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>pyasn1-modules</td>\n",
       "      <td>0.2.8</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>pycparser</td>\n",
       "      <td>2.21</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>pydot</td>\n",
       "      <td>1.4.2</td>\n",
       "      <td>py39h2804cbe_3</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>pydotplus</td>\n",
       "      <td>2.0.2</td>\n",
       "      <td>py_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>pygments</td>\n",
       "      <td>2.13.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>pynndescent</td>\n",
       "      <td>0.5.8</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>pyopenssl</td>\n",
       "      <td>22.1.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>pyparsing</td>\n",
       "      <td>3.0.9</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>pyrsistent</td>\n",
       "      <td>0.19.2</td>\n",
       "      <td>py39h02fc5c5_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>pysocks</td>\n",
       "      <td>1.7.1</td>\n",
       "      <td>pyha2e5f31_6</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>python</td>\n",
       "      <td>3.9.13</td>\n",
       "      <td>h96fcbfb_0_cpython</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>python-dateutil</td>\n",
       "      <td>2.8.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>python-fastjsonschema</td>\n",
       "      <td>2.16.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>python-graphviz</td>\n",
       "      <td>0.20.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>python_abi</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2_cp39</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>pytz</td>\n",
       "      <td>2022.6</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>pyyaml</td>\n",
       "      <td>6.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>pyzmq</td>\n",
       "      <td>24.0.1</td>\n",
       "      <td>py39h0553236_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>readline</td>\n",
       "      <td>8.1.2</td>\n",
       "      <td>h46ed386_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>requests</td>\n",
       "      <td>2.28.1</td>\n",
       "      <td>pyhd8ed1ab_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>requests-oauthlib</td>\n",
       "      <td>1.3.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>rmsd</td>\n",
       "      <td>1.5.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>rsa</td>\n",
       "      <td>4.9</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>scikit-learn</td>\n",
       "      <td>1.1.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>scipy</td>\n",
       "      <td>1.9.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>seaborn</td>\n",
       "      <td>0.12.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>send2trash</td>\n",
       "      <td>1.8.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>setuptools</td>\n",
       "      <td>65.5.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>six</td>\n",
       "      <td>1.16.0</td>\n",
       "      <td>pyh6c4a22f_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>sniffio</td>\n",
       "      <td>1.3.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>soupsieve</td>\n",
       "      <td>2.3.2.post1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>sqlite</td>\n",
       "      <td>3.39.4</td>\n",
       "      <td>h2229b38_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>stack_data</td>\n",
       "      <td>0.6.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>statannot</td>\n",
       "      <td>0.2.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>tenacity</td>\n",
       "      <td>8.1.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>tensorboard</td>\n",
       "      <td>2.10.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>tensorboard-data-server</td>\n",
       "      <td>0.6.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>tensorboard-plugin-wit</td>\n",
       "      <td>1.8.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>tensorflow-deps</td>\n",
       "      <td>2.10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>apple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>tensorflow-estimator</td>\n",
       "      <td>2.10.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>tensorflow-macos</td>\n",
       "      <td>2.10.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>tensorflow-metal</td>\n",
       "      <td>0.6.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>termcolor</td>\n",
       "      <td>2.1.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>terminado</td>\n",
       "      <td>0.17.0</td>\n",
       "      <td>pyhd1c38e8_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>threadpoolctl</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>tinycss2</td>\n",
       "      <td>1.2.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>tk</td>\n",
       "      <td>8.6.12</td>\n",
       "      <td>he1e0b03_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>tomli</td>\n",
       "      <td>2.0.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>tornado</td>\n",
       "      <td>6.2</td>\n",
       "      <td>py39h02fc5c5_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>tqdm</td>\n",
       "      <td>4.64.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>traitlets</td>\n",
       "      <td>5.5.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>typing_extensions</td>\n",
       "      <td>4.4.0</td>\n",
       "      <td>pyha770c72_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>tzdata</td>\n",
       "      <td>2022f</td>\n",
       "      <td>h191b570_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>umap-learn</td>\n",
       "      <td>0.5.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>urllib3</td>\n",
       "      <td>1.26.12</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>wcwidth</td>\n",
       "      <td>0.2.5</td>\n",
       "      <td>pyh9f0ad1d_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>webencodings</td>\n",
       "      <td>0.5.1</td>\n",
       "      <td>py_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>websocket-client</td>\n",
       "      <td>1.4.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>werkzeug</td>\n",
       "      <td>2.2.2</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>wheel</td>\n",
       "      <td>0.37.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>widgetsnbextension</td>\n",
       "      <td>4.0.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>wrapt</td>\n",
       "      <td>1.14.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>xz</td>\n",
       "      <td>5.2.6</td>\n",
       "      <td>h57fd34a_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>zeromq</td>\n",
       "      <td>4.3.4</td>\n",
       "      <td>hbdafb3b_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>zipp</td>\n",
       "      <td>3.10.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>zlib</td>\n",
       "      <td>1.2.13</td>\n",
       "      <td>h03a7124_4</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>zstd</td>\n",
       "      <td>1.5.2</td>\n",
       "      <td>hf913c23_6</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Name       Version                   Build  \\\n",
       "0                          absl-py         1.3.0                  pypi_0   \n",
       "1                            anyio         3.6.2            pyhd8ed1ab_0   \n",
       "2                          appnope         0.1.3            pyhd8ed1ab_0   \n",
       "3                      argon2-cffi        21.3.0            pyhd8ed1ab_0   \n",
       "4             argon2-cffi-bindings        21.2.0          py39h02fc5c5_3   \n",
       "5                        asttokens         2.1.0            pyhd8ed1ab_0   \n",
       "6                       astunparse         1.6.3                  pypi_0   \n",
       "7                          atk-1.0        2.38.0              hcb7b3dd_1   \n",
       "8                            attrs        22.1.0            pyh71513ae_1   \n",
       "9                            babel        2.10.3            pyhd8ed1ab_0   \n",
       "10                        backcall         0.2.0            pyh9f0ad1d_0   \n",
       "11                       backports           1.0                    py_2   \n",
       "12   backports.functools_lru_cache         1.6.4            pyhd8ed1ab_0   \n",
       "13                  beautifulsoup4        4.11.1            pyha770c72_0   \n",
       "14                          bleach         5.0.1            pyhd8ed1ab_0   \n",
       "15                        brotlipy         0.7.0       py39h02fc5c5_1005   \n",
       "16                           bzip2         1.0.8              h3422bc3_4   \n",
       "17                          c-ares        1.18.1              h3422bc3_0   \n",
       "18                 ca-certificates     2022.12.7              h4653dfc_0   \n",
       "19                 cached-property         1.5.2              hd8ed1ab_1   \n",
       "20                 cached_property         1.5.2            pyha770c72_1   \n",
       "21                      cachetools         5.2.0                  pypi_0   \n",
       "22                           cairo        1.16.0           h73a0509_1014   \n",
       "23                         certifi     2022.9.24                  pypi_0   \n",
       "24                            cffi        1.15.1          py39h7e6b969_1   \n",
       "25              charset-normalizer         2.1.1            pyhd8ed1ab_0   \n",
       "26                           click         8.1.3                  pypi_0   \n",
       "27                       contourpy         1.0.6                  pypi_0   \n",
       "28                         counter         1.0.0                  pypi_0   \n",
       "29                    cryptography        38.0.3          py39he2a39a8_0   \n",
       "30                          cycler        0.11.0                  pypi_0   \n",
       "31                         debugpy         1.6.3          py39h23fbdae_1   \n",
       "32                       decorator         5.1.1            pyhd8ed1ab_0   \n",
       "33                      defusedxml         0.7.1            pyhd8ed1ab_0   \n",
       "34                     entrypoints           0.4            pyhd8ed1ab_0   \n",
       "35                       executing         1.2.0            pyhd8ed1ab_0   \n",
       "36                           expat         2.5.0              hb7217d7_0   \n",
       "37                          fitter         1.5.1                  pypi_0   \n",
       "38                     flatbuffers      22.10.26                  pypi_0   \n",
       "39                       flit-core         3.7.1            pyhd8ed1ab_0   \n",
       "40       font-ttf-dejavu-sans-mono          2.37              hab24e00_0   \n",
       "41            font-ttf-inconsolata         3.000              h77eed37_0   \n",
       "42        font-ttf-source-code-pro         2.038              h77eed37_0   \n",
       "43                 font-ttf-ubuntu          0.83              hab24e00_0   \n",
       "44                      fontconfig        2.14.2              h82840c6_0   \n",
       "45           fonts-conda-ecosystem             1                       0   \n",
       "46               fonts-conda-forge             1                       0   \n",
       "47                       fonttools        4.38.0                  pypi_0   \n",
       "48                        freetype        2.12.1              hd633e50_1   \n",
       "49                         fribidi        1.0.10              h27ca646_0   \n",
       "50                            gast         0.4.0                  pypi_0   \n",
       "51                      gdk-pixbuf       2.42.10              h9bcf4fe_0   \n",
       "52                         gettext        0.21.1              h0186832_0   \n",
       "53                          giflib         5.2.1              h27ca646_2   \n",
       "54                     google-auth        2.14.0                  pypi_0   \n",
       "55            google-auth-oauthlib         0.4.6                  pypi_0   \n",
       "56                    google-pasta         0.2.0                  pypi_0   \n",
       "57                       graphite2        1.3.13           h9f76cd9_1001   \n",
       "58                        graphviz         7.1.0              h4f8fbd6_0   \n",
       "59                          grpcio        1.46.3          py39h365d37b_0   \n",
       "60                        gsea-api         0.3.4                  pypi_0   \n",
       "61                          gseapy         1.0.3                  pypi_0   \n",
       "62                            gtk2       2.24.33              h57013de_2   \n",
       "63                             gts         0.7.6              h4b6d4d6_2   \n",
       "64                            h5py         3.6.0  nompi_py39hd982b79_100   \n",
       "65                        harfbuzz         6.0.0              hddbc195_0   \n",
       "66                            hdf5        1.12.1      nompi_hd9dbc9e_104   \n",
       "67                             icu          70.1              h6b3803e_0   \n",
       "68                            idna           3.4            pyhd8ed1ab_0   \n",
       "69              importlib-metadata         5.0.0            pyha770c72_1   \n",
       "70             importlib_resources        5.10.0            pyhd8ed1ab_0   \n",
       "71                       ipykernel        6.17.0            pyh736e0ef_0   \n",
       "72                         ipython         8.6.0            pyhd1c38e8_1   \n",
       "73                ipython_genutils         0.2.0                    py_1   \n",
       "74                      ipywidgets         8.0.2            pyhd8ed1ab_1   \n",
       "75                            jedi        0.18.1            pyhd8ed1ab_2   \n",
       "76                          jinja2         3.1.2            pyhd8ed1ab_1   \n",
       "77                          joblib         1.2.0                  pypi_0   \n",
       "78                            jpeg            9e              he4db4b2_2   \n",
       "79                           json5         0.9.5            pyh9f0ad1d_0   \n",
       "80                      jsonschema        4.17.0            pyhd8ed1ab_0   \n",
       "81                         jupyter         1.0.0          py39h2804cbe_7   \n",
       "82                 jupyter-helpers         0.2.3                  pypi_0   \n",
       "83          jupyter-resource-usage         0.6.3            pyhd8ed1ab_0   \n",
       "84                  jupyter_client         7.4.4            pyhd8ed1ab_0   \n",
       "85                 jupyter_console         6.4.4            pyhd8ed1ab_0   \n",
       "86                    jupyter_core        4.11.2          py39h2804cbe_0   \n",
       "87                  jupyter_server        1.21.0            pyhd8ed1ab_0   \n",
       "88                      jupyterlab         3.5.0            pyhd8ed1ab_0   \n",
       "89             jupyterlab_pygments         0.2.2            pyhd8ed1ab_0   \n",
       "90               jupyterlab_server        2.16.2            pyhd8ed1ab_0   \n",
       "91              jupyterlab_widgets         3.0.3            pyhd8ed1ab_0   \n",
       "92                           keras        2.10.0                  pypi_0   \n",
       "93             keras-preprocessing         1.1.2                  pypi_0   \n",
       "94                      kiwisolver         1.4.4                  pypi_0   \n",
       "95                            krb5        1.19.3              he492e65_0   \n",
       "96                            lerc         4.0.0              h9a09cb3_0   \n",
       "97                         libblas         3.9.0    16_osxarm64_openblas   \n",
       "98                        libcblas         3.9.0    16_osxarm64_openblas   \n",
       "99                        libclang        14.0.6                  pypi_0   \n",
       "100                        libcurl        7.86.0              h1c293e1_1   \n",
       "101                         libcxx        14.0.6              h2692d47_0   \n",
       "102                     libdeflate          1.17              h1a8c8d9_0   \n",
       "103                        libedit  3.1.20191231              hc8eb9b7_2   \n",
       "104                          libev          4.33              h642e427_1   \n",
       "105                         libffi         3.4.2              h3422bc3_5   \n",
       "106                          libgd         2.3.3              h90fb8ed_4   \n",
       "107                    libgfortran         5.0.0      11_3_0_hd922786_25   \n",
       "108                   libgfortran5        11.3.0             hdaf2cc0_25   \n",
       "109                        libglib        2.74.1              h4646484_1   \n",
       "110                       libiconv          1.17              he4db4b2_0   \n",
       "111                      liblapack         3.9.0    16_osxarm64_openblas   \n",
       "112                     libnghttp2        1.47.0              h519802c_1   \n",
       "113                    libopenblas        0.3.21       openmp_hc731615_3   \n",
       "114                         libpng        1.6.39              h76d750c_0   \n",
       "115                    libprotobuf        3.19.4              hccf11d3_0   \n",
       "116                        librsvg        2.54.4              ha2634a2_0   \n",
       "117                      libsodium        1.0.18              h27ca646_1   \n",
       "118                      libsqlite        3.39.4              h76d750c_0   \n",
       "119                        libssh2        1.10.0              h7a5bd25_3   \n",
       "120                        libtiff         4.5.0              h5dffbdd_2   \n",
       "121                        libtool         2.4.7              hb7217d7_0   \n",
       "122                        libwebp         1.2.4              h999c80f_1   \n",
       "123                   libwebp-base         1.2.4              h57fd34a_0   \n",
       "124                        libxml2        2.10.3              h87b0503_0   \n",
       "125                        libzlib        1.2.13              h03a7124_4   \n",
       "126                    llvm-openmp        14.0.4              hd125106_0   \n",
       "127                       llvmlite        0.39.1                  pypi_0   \n",
       "128                       markdown         3.4.1                  pypi_0   \n",
       "129                     markupsafe         2.1.1          py39h02fc5c5_2   \n",
       "130                     matplotlib         3.6.2                  pypi_0   \n",
       "131              matplotlib-inline         0.1.6            pyhd8ed1ab_0   \n",
       "132                        mistune         2.0.4            pyhd8ed1ab_0   \n",
       "133                      nbclassic         0.4.8            pyhd8ed1ab_0   \n",
       "134                       nbclient         0.7.0            pyhd8ed1ab_0   \n",
       "135                      nbconvert         7.2.3            pyhd8ed1ab_0   \n",
       "136                 nbconvert-core         7.2.3            pyhd8ed1ab_0   \n",
       "137               nbconvert-pandoc         7.2.3            pyhd8ed1ab_0   \n",
       "138                       nbformat         5.7.0            pyhd8ed1ab_0   \n",
       "139                        ncurses           6.3              h07bb92c_1   \n",
       "140                   nest-asyncio         1.5.6            pyhd8ed1ab_0   \n",
       "141                       notebook         6.5.2            pyha770c72_0   \n",
       "142                  notebook-shim         0.2.2            pyhd8ed1ab_0   \n",
       "143                          numba        0.56.3                  pypi_0   \n",
       "144                          numpy        1.23.2          py39h3668e8b_0   \n",
       "145                       oauthlib         3.2.2                  pypi_0   \n",
       "146                        openssl         3.0.7              h03a7124_2   \n",
       "147                     opt-einsum         3.3.0                  pypi_0   \n",
       "148                      packaging          21.3            pyhd8ed1ab_0   \n",
       "149                         pandas         1.5.1                  pypi_0   \n",
       "150                         pandoc        2.19.2              hce30654_1   \n",
       "151                  pandocfilters         1.5.0            pyhd8ed1ab_0   \n",
       "152                          pango       1.50.12              h6c112b8_1   \n",
       "153                          parso         0.8.3            pyhd8ed1ab_0   \n",
       "154                          pcre2         10.40              hb34f9b4_0   \n",
       "155                        pexpect         4.8.0            pyh1a96a4e_2   \n",
       "156                    pickleshare         0.7.5                 py_1003   \n",
       "157                         pillow         9.3.0                  pypi_0   \n",
       "158                            pip          22.3            pyhd8ed1ab_0   \n",
       "159                         pixman        0.40.0              h27ca646_0   \n",
       "160           pkgutil-resolve-name        1.3.10            pyhd8ed1ab_0   \n",
       "161                         plotly        5.11.0                  pypi_0   \n",
       "162              prometheus_client        0.15.0            pyhd8ed1ab_0   \n",
       "163                 prompt-toolkit        3.0.31            pyha770c72_0   \n",
       "164                 prompt_toolkit        3.0.31              hd8ed1ab_0   \n",
       "165                       protobuf        3.19.4          py39hfb83b0d_0   \n",
       "166                         psutil         5.9.3          py39h02fc5c5_1   \n",
       "167                     ptyprocess         0.7.0            pyhd3deb0d_0   \n",
       "168                      pure_eval         0.2.2            pyhd8ed1ab_0   \n",
       "169                         pyasn1         0.4.8                  pypi_0   \n",
       "170                 pyasn1-modules         0.2.8                  pypi_0   \n",
       "171                      pycparser          2.21            pyhd8ed1ab_0   \n",
       "172                          pydot         1.4.2          py39h2804cbe_3   \n",
       "173                      pydotplus         2.0.2                    py_2   \n",
       "174                       pygments        2.13.0            pyhd8ed1ab_0   \n",
       "175                    pynndescent         0.5.8                  pypi_0   \n",
       "176                      pyopenssl        22.1.0            pyhd8ed1ab_0   \n",
       "177                      pyparsing         3.0.9            pyhd8ed1ab_0   \n",
       "178                     pyrsistent        0.19.2          py39h02fc5c5_0   \n",
       "179                        pysocks         1.7.1            pyha2e5f31_6   \n",
       "180                         python        3.9.13      h96fcbfb_0_cpython   \n",
       "181                python-dateutil         2.8.2            pyhd8ed1ab_0   \n",
       "182          python-fastjsonschema        2.16.2            pyhd8ed1ab_0   \n",
       "183                python-graphviz        0.20.1                  pypi_0   \n",
       "184                     python_abi           3.9                  2_cp39   \n",
       "185                           pytz        2022.6            pyhd8ed1ab_0   \n",
       "186                         pyyaml           6.0                  pypi_0   \n",
       "187                          pyzmq        24.0.1          py39h0553236_1   \n",
       "188                       readline         8.1.2              h46ed386_0   \n",
       "189                       requests        2.28.1            pyhd8ed1ab_1   \n",
       "190              requests-oauthlib         1.3.1                  pypi_0   \n",
       "191                           rmsd         1.5.1                  pypi_0   \n",
       "192                            rsa           4.9                  pypi_0   \n",
       "193                   scikit-learn         1.1.3                  pypi_0   \n",
       "194                          scipy         1.9.3                  pypi_0   \n",
       "195                        seaborn        0.12.1                  pypi_0   \n",
       "196                     send2trash         1.8.0            pyhd8ed1ab_0   \n",
       "197                     setuptools        65.5.0            pyhd8ed1ab_0   \n",
       "198                            six        1.16.0            pyh6c4a22f_0   \n",
       "199                        sklearn           0.0                  pypi_0   \n",
       "200                        sniffio         1.3.0            pyhd8ed1ab_0   \n",
       "201                      soupsieve   2.3.2.post1            pyhd8ed1ab_0   \n",
       "202                         sqlite        3.39.4              h2229b38_0   \n",
       "203                     stack_data         0.6.0            pyhd8ed1ab_0   \n",
       "204                      statannot         0.2.3                  pypi_0   \n",
       "205                       tenacity         8.1.0                  pypi_0   \n",
       "206                    tensorboard        2.10.1                  pypi_0   \n",
       "207        tensorboard-data-server         0.6.1                  pypi_0   \n",
       "208         tensorboard-plugin-wit         1.8.1                  pypi_0   \n",
       "209                tensorflow-deps        2.10.0                       0   \n",
       "210           tensorflow-estimator        2.10.0                  pypi_0   \n",
       "211               tensorflow-macos        2.10.0                  pypi_0   \n",
       "212               tensorflow-metal         0.6.0                  pypi_0   \n",
       "213                      termcolor         2.1.0                  pypi_0   \n",
       "214                      terminado        0.17.0            pyhd1c38e8_0   \n",
       "215                  threadpoolctl         3.1.0                  pypi_0   \n",
       "216                       tinycss2         1.2.1            pyhd8ed1ab_0   \n",
       "217                             tk        8.6.12              he1e0b03_0   \n",
       "218                          tomli         2.0.1            pyhd8ed1ab_0   \n",
       "219                        tornado           6.2          py39h02fc5c5_1   \n",
       "220                           tqdm        4.64.1                  pypi_0   \n",
       "221                      traitlets         5.5.0            pyhd8ed1ab_0   \n",
       "222              typing_extensions         4.4.0            pyha770c72_0   \n",
       "223                         tzdata         2022f              h191b570_0   \n",
       "224                     umap-learn         0.5.3                  pypi_0   \n",
       "225                        urllib3       1.26.12                  pypi_0   \n",
       "226                        wcwidth         0.2.5            pyh9f0ad1d_2   \n",
       "227                   webencodings         0.5.1                    py_1   \n",
       "228               websocket-client         1.4.1            pyhd8ed1ab_0   \n",
       "229                       werkzeug         2.2.2                  pypi_0   \n",
       "230                          wheel        0.37.1            pyhd8ed1ab_0   \n",
       "231             widgetsnbextension         4.0.3            pyhd8ed1ab_0   \n",
       "232                          wrapt        1.14.1                  pypi_0   \n",
       "233                             xz         5.2.6              h57fd34a_0   \n",
       "234                         zeromq         4.3.4              hbdafb3b_1   \n",
       "235                           zipp        3.10.0            pyhd8ed1ab_0   \n",
       "236                           zlib        1.2.13              h03a7124_4   \n",
       "237                           zstd         1.5.2              hf913c23_6   \n",
       "\n",
       "         Channel  \n",
       "0           pypi  \n",
       "1    conda-forge  \n",
       "2    conda-forge  \n",
       "3    conda-forge  \n",
       "4    conda-forge  \n",
       "5    conda-forge  \n",
       "6           pypi  \n",
       "7    conda-forge  \n",
       "8    conda-forge  \n",
       "9    conda-forge  \n",
       "10   conda-forge  \n",
       "11   conda-forge  \n",
       "12   conda-forge  \n",
       "13   conda-forge  \n",
       "14   conda-forge  \n",
       "15   conda-forge  \n",
       "16   conda-forge  \n",
       "17   conda-forge  \n",
       "18   conda-forge  \n",
       "19   conda-forge  \n",
       "20   conda-forge  \n",
       "21          pypi  \n",
       "22   conda-forge  \n",
       "23          pypi  \n",
       "24   conda-forge  \n",
       "25   conda-forge  \n",
       "26          pypi  \n",
       "27          pypi  \n",
       "28          pypi  \n",
       "29   conda-forge  \n",
       "30          pypi  \n",
       "31   conda-forge  \n",
       "32   conda-forge  \n",
       "33   conda-forge  \n",
       "34   conda-forge  \n",
       "35   conda-forge  \n",
       "36   conda-forge  \n",
       "37          pypi  \n",
       "38          pypi  \n",
       "39   conda-forge  \n",
       "40   conda-forge  \n",
       "41   conda-forge  \n",
       "42   conda-forge  \n",
       "43   conda-forge  \n",
       "44   conda-forge  \n",
       "45   conda-forge  \n",
       "46   conda-forge  \n",
       "47          pypi  \n",
       "48   conda-forge  \n",
       "49   conda-forge  \n",
       "50          pypi  \n",
       "51   conda-forge  \n",
       "52   conda-forge  \n",
       "53   conda-forge  \n",
       "54          pypi  \n",
       "55          pypi  \n",
       "56          pypi  \n",
       "57   conda-forge  \n",
       "58   conda-forge  \n",
       "59   conda-forge  \n",
       "60          pypi  \n",
       "61          pypi  \n",
       "62   conda-forge  \n",
       "63   conda-forge  \n",
       "64   conda-forge  \n",
       "65   conda-forge  \n",
       "66   conda-forge  \n",
       "67   conda-forge  \n",
       "68   conda-forge  \n",
       "69   conda-forge  \n",
       "70   conda-forge  \n",
       "71   conda-forge  \n",
       "72   conda-forge  \n",
       "73   conda-forge  \n",
       "74   conda-forge  \n",
       "75   conda-forge  \n",
       "76   conda-forge  \n",
       "77          pypi  \n",
       "78   conda-forge  \n",
       "79   conda-forge  \n",
       "80   conda-forge  \n",
       "81   conda-forge  \n",
       "82          pypi  \n",
       "83   conda-forge  \n",
       "84   conda-forge  \n",
       "85   conda-forge  \n",
       "86   conda-forge  \n",
       "87   conda-forge  \n",
       "88   conda-forge  \n",
       "89   conda-forge  \n",
       "90   conda-forge  \n",
       "91   conda-forge  \n",
       "92          pypi  \n",
       "93          pypi  \n",
       "94          pypi  \n",
       "95   conda-forge  \n",
       "96   conda-forge  \n",
       "97   conda-forge  \n",
       "98   conda-forge  \n",
       "99          pypi  \n",
       "100  conda-forge  \n",
       "101  conda-forge  \n",
       "102  conda-forge  \n",
       "103  conda-forge  \n",
       "104  conda-forge  \n",
       "105  conda-forge  \n",
       "106  conda-forge  \n",
       "107  conda-forge  \n",
       "108  conda-forge  \n",
       "109  conda-forge  \n",
       "110  conda-forge  \n",
       "111  conda-forge  \n",
       "112  conda-forge  \n",
       "113  conda-forge  \n",
       "114  conda-forge  \n",
       "115  conda-forge  \n",
       "116  conda-forge  \n",
       "117  conda-forge  \n",
       "118  conda-forge  \n",
       "119  conda-forge  \n",
       "120  conda-forge  \n",
       "121  conda-forge  \n",
       "122  conda-forge  \n",
       "123  conda-forge  \n",
       "124  conda-forge  \n",
       "125  conda-forge  \n",
       "126  conda-forge  \n",
       "127         pypi  \n",
       "128         pypi  \n",
       "129  conda-forge  \n",
       "130         pypi  \n",
       "131  conda-forge  \n",
       "132  conda-forge  \n",
       "133  conda-forge  \n",
       "134  conda-forge  \n",
       "135  conda-forge  \n",
       "136  conda-forge  \n",
       "137  conda-forge  \n",
       "138  conda-forge  \n",
       "139  conda-forge  \n",
       "140  conda-forge  \n",
       "141  conda-forge  \n",
       "142  conda-forge  \n",
       "143         pypi  \n",
       "144  conda-forge  \n",
       "145         pypi  \n",
       "146  conda-forge  \n",
       "147         pypi  \n",
       "148  conda-forge  \n",
       "149         pypi  \n",
       "150  conda-forge  \n",
       "151  conda-forge  \n",
       "152  conda-forge  \n",
       "153  conda-forge  \n",
       "154  conda-forge  \n",
       "155  conda-forge  \n",
       "156  conda-forge  \n",
       "157         pypi  \n",
       "158  conda-forge  \n",
       "159  conda-forge  \n",
       "160  conda-forge  \n",
       "161         pypi  \n",
       "162  conda-forge  \n",
       "163  conda-forge  \n",
       "164  conda-forge  \n",
       "165  conda-forge  \n",
       "166  conda-forge  \n",
       "167  conda-forge  \n",
       "168  conda-forge  \n",
       "169         pypi  \n",
       "170         pypi  \n",
       "171  conda-forge  \n",
       "172  conda-forge  \n",
       "173  conda-forge  \n",
       "174  conda-forge  \n",
       "175         pypi  \n",
       "176  conda-forge  \n",
       "177  conda-forge  \n",
       "178  conda-forge  \n",
       "179  conda-forge  \n",
       "180  conda-forge  \n",
       "181  conda-forge  \n",
       "182  conda-forge  \n",
       "183         pypi  \n",
       "184  conda-forge  \n",
       "185  conda-forge  \n",
       "186         pypi  \n",
       "187  conda-forge  \n",
       "188  conda-forge  \n",
       "189  conda-forge  \n",
       "190         pypi  \n",
       "191         pypi  \n",
       "192         pypi  \n",
       "193         pypi  \n",
       "194         pypi  \n",
       "195         pypi  \n",
       "196  conda-forge  \n",
       "197  conda-forge  \n",
       "198  conda-forge  \n",
       "199         pypi  \n",
       "200  conda-forge  \n",
       "201  conda-forge  \n",
       "202  conda-forge  \n",
       "203  conda-forge  \n",
       "204         pypi  \n",
       "205         pypi  \n",
       "206         pypi  \n",
       "207         pypi  \n",
       "208         pypi  \n",
       "209        apple  \n",
       "210         pypi  \n",
       "211         pypi  \n",
       "212         pypi  \n",
       "213         pypi  \n",
       "214  conda-forge  \n",
       "215         pypi  \n",
       "216  conda-forge  \n",
       "217  conda-forge  \n",
       "218  conda-forge  \n",
       "219  conda-forge  \n",
       "220         pypi  \n",
       "221  conda-forge  \n",
       "222  conda-forge  \n",
       "223  conda-forge  \n",
       "224         pypi  \n",
       "225         pypi  \n",
       "226  conda-forge  \n",
       "227  conda-forge  \n",
       "228  conda-forge  \n",
       "229         pypi  \n",
       "230  conda-forge  \n",
       "231  conda-forge  \n",
       "232         pypi  \n",
       "233  conda-forge  \n",
       "234  conda-forge  \n",
       "235  conda-forge  \n",
       "236  conda-forge  \n",
       "237  conda-forge  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conda environment, Apple M1\n",
    "VAE_conda_env = pd.read_csv('CPTAC_VAE_conda_packages.tsv', sep = '\\t',\n",
    "           header = None)\n",
    "headers = ['Name', 'Version', 'Build', 'Channel']\n",
    "VAE_conda_env.columns = headers\n",
    "VAE_conda_env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c9e86e2-673e-4ad5-ab65-6fa5bc41a754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "238"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(VAE_conda_env)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a99b439c-660f-4266-abda-354aac82071f",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Model template\n",
    "https://github.com/greenelab/tybalt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c83e30-483e-4034-9933-af786ed34afe",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d14adf13-27ce-4a3f-aa36-a37e837877b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import collections\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import BatchNormalization, Activation, Layer\n",
    "from tensorflow.keras import metrics, optimizers\n",
    "from tensorflow.keras.losses import binary_crossentropy\n",
    "from tensorflow.keras.callbacks import Callback\n",
    "import tensorflow.compat.v1.keras.backend as K\n",
    "tf.compat.v1.disable_eager_execution()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40fefa3b-7086-4eda-8589-41c9c469e098",
   "metadata": {
    "tags": []
   },
   "source": [
    "### VAE setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1fb9143-46d7-4d7d-bf69-2216d8cdf9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# VAE functions\n",
    "def compute_latent(x):\n",
    "    mu, sigma = x\n",
    "    batch = K.shape(mu)[0]\n",
    "    dim = K.shape(mu)[1]\n",
    "    eps = K.random_normal(shape=(batch,dim), mean=0., stddev=1.0 )\n",
    "    return mu + K.exp(sigma/2)*eps\n",
    "\n",
    "class CustomVariationalLayer(Layer):\n",
    "    \"\"\"\n",
    "    Define a custom layer\n",
    "    \"\"\"\n",
    "    def __init__(self, **kwargs):\n",
    "        self.is_placeholder = True\n",
    "        super(CustomVariationalLayer, self).__init__(**kwargs)\n",
    "\n",
    "    def vae_loss(self, x_input, x_decoded):\n",
    "        reconstruction_loss = original_dim * metrics.binary_crossentropy(\n",
    "            x_input, x_decoded)\n",
    "        kl_loss = - 0.5 * K.sum(1 + z_log_var_encoded - K.square(z_mean_encoded) - \n",
    "                                K.exp(z_log_var_encoded), axis=-1)\n",
    "        return K.mean(reconstruction_loss + (K.get_value(beta) * kl_loss))\n",
    "\n",
    "    def call(self, inputs):\n",
    "        x = inputs[0]\n",
    "        x_decoded = inputs[1]\n",
    "        loss = self.vae_loss(x, x_decoded)\n",
    "        self.add_loss(loss, inputs=inputs)\n",
    "        return x\n",
    "    \n",
    "class WarmUpCallback(Callback):\n",
    "    def __init__(self, beta, kappa):\n",
    "        self.beta = beta\n",
    "        self.kappa = kappa\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        if K.get_value(self.beta) <= 1:\n",
    "            K.set_value(self.beta, K.get_value(self.beta) + self.kappa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a62ab9f-f46d-42fd-9a60-748918c6198b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss plot visualization to determine degree of model fitting\n",
    "def plot_loss(loss_dict, data_type, train_file):\n",
    "\n",
    "    # Create a new figure\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    # Plot loss values\n",
    "    plt.plot(loss_dict['vs0'], marker='o', linestyle='-', color='b')\n",
    "\n",
    "    # Add a grid\n",
    "    plt.grid(True, linestyle='--', alpha=0.6)\n",
    "\n",
    "    # Add titles and labels\n",
    "    plt.title('CPTAC '+data_type+' VAE Loss', fontsize=16, fontweight='bold')\n",
    "    plt.xlabel('Epoch', fontsize=14)\n",
    "    plt.ylabel('Loss', fontsize=14)\n",
    "\n",
    "    # Annotation for epochs and latent dimension\n",
    "    plt.annotate('Samples: {}'.format(len(train_file))+'\\nLatent dim: 250\\nRaw features: {}'.format(len(train_file.columns)), \n",
    "                 xy=(0.7, 0.3), xycoords='axes fraction', \n",
    "                 bbox=dict(boxstyle='round, pad=0.5', fc='white', ec='black'),\n",
    "                 fontsize=12)\n",
    "\n",
    "    # Save the plot\n",
    "    plt.savefig('loss_plots/'+data_type+'_vae_loss.png')\n",
    "    # plt.close()  # Close the figure - not for interactive devel / demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86456c98-8e48-478a-8662-b52ed5ee1e48",
   "metadata": {},
   "source": [
    "### Data structuring and feature encodings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa73c05a-5ac0-4d6c-b7c8-2a15e409514b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To be concatenated on front-end of transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92fb2261-e826-435d-85e3-117787501da3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy number variation\n",
    "CNV = pd.read_csv('data/CNV.csv.gz')\n",
    "CNV.dropna(inplace = True)\n",
    "\n",
    "# Average duplicate CNV values within each sample\n",
    "CNV = CNV.groupby(['entrez_id', 'improve_sample_id'])['CNV'].mean().reset_index()\n",
    "\n",
    "# Restructure into one row per sample with a column for each gene\n",
    "CNV = CNV.pivot(index='improve_sample_id',\n",
    "               columns='entrez_id',\n",
    "               values='CNV')\n",
    "\n",
    "# Remove the introduced sparcity\n",
    "columns_with_NaN = CNV.columns[CNV.isnull().any()]\n",
    "CNV = CNV.drop(columns=columns_with_NaN)\n",
    "\n",
    "# convert columns to strings and append cnv to each entrez id\n",
    "new_column_headers = []\n",
    "for column_header in CNV.columns:\n",
    "    new_column_header = \"cnv_\" + str(column_header)\n",
    "    new_column_headers.append(new_column_header)\n",
    "CNV.columns = new_column_headers\n",
    "\n",
    "# Confirm no nulls\n",
    "print('Null value check', proteomics.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32754654-3d61-4e7f-8c20-d5fb6c1114dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write to disk for read-in by VAE\n",
    "CNV.to_csv('disk_checkpoints/cptac_cnv_to_VAE.tsv',\n",
    "           sep = '\\t',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "32267191-a9f3-4add-8e26-f6f4722584b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Proteomics\n",
    "proteomics = pd.read_csv('data/proteomics.csv.gz')\n",
    "proteomics.dropna(inplace = True)\n",
    "\n",
    "proteomics = proteomics.groupby(['entrez_id', 'improve_sample_id'])['proteomics'].mean().reset_index()\n",
    "\n",
    "proteomics = proteomics.pivot(index='improve_sample_id',\n",
    "               columns='entrez_id',\n",
    "               values='proteomics')\n",
    "\n",
    "columns_with_NaN = proteomics.columns[proteomics.isnull().any()]\n",
    "proteomics = proteomics.drop(columns=columns_with_NaN)\n",
    "\n",
    "new_column_headers = []\n",
    "for column_header in proteomics.columns:\n",
    "    new_column_header = \"prt_\" + str(column_header) # <-- protein columns\n",
    "    new_column_headers.append(new_column_header)\n",
    "proteomics.columns = new_column_headers\n",
    "\n",
    "print('Null value check', proteomics.isnull().sum().sum())\n",
    "\n",
    "proteomics.to_csv('disk_checkpoints/cptac_prt_to_VAE.tsv',\n",
    "           sep = '\\t',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4f6bab19-78e7-4fb0-b5d3-14a5983d82a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Transcriptomics\n",
    "transcriptomics = pd.read_csv('data/transcriptomics.csv.gz')\n",
    "\n",
    "transcriptomics.dropna(inplace = True)\n",
    "\n",
    "transcriptomics = transcriptomics.groupby(['entrez_id', 'improve_sample_id'])['transcriptomics'].mean().reset_index()\n",
    "\n",
    "transcriptomics = transcriptomics.pivot(index='improve_sample_id',\n",
    "               columns='entrez_id',\n",
    "               values='transcriptomics')\n",
    "\n",
    "columns_with_NaN = transcriptomics.columns[transcriptomics.isnull().any()]\n",
    "transcriptomics = transcriptomics.drop(columns=columns_with_NaN)\n",
    "\n",
    "new_column_headers = []\n",
    "for column_header in transcriptomics.columns:\n",
    "    new_column_header = \"trs_\" + str(column_header) # <-- transcriptomics columns\n",
    "    new_column_headers.append(new_column_header)\n",
    "transcriptomics.columns = new_column_headers\n",
    "\n",
    "print(transcriptomics.isnull().sum().sum())\n",
    "\n",
    "# Infinity values, unique to transcriptomics     <--------------------\n",
    "inf_rows = np.isinf(transcriptomics).any(axis=1)\n",
    "inf_rows_indices = inf_rows[inf_rows].index\n",
    "transcriptomics.drop(inf_rows_indices, inplace = True)\n",
    "\n",
    "transcriptomics.to_csv('disk_checkpoints/cptac_trs_to_VAE.tsv',\n",
    "           sep = '\\t',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "23a27bde-a136-44cc-a918-8b2915a5a5e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(325360, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "entrez_id            325360\n",
       "improve_sample_id         0\n",
       "miRNA                   672\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# micro RNA, no gene ids\n",
    "miRNA = pd.read_csv('data/miRNA.csv')\n",
    "print(miRNA.shape)\n",
    "miRNA.isna().sum() # <-- Null values == file len"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b17fe2-dbd5-41b9-88ee-876f313476cc",
   "metadata": {},
   "source": [
    "### VAE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "914891ba-6c02-49bb-8603-ce2ae377a0d4",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Toggle train file from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "49175bc1-d1bc-42d1-a829-3c1c7496c9c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_type = 'cnv'\n",
    "latent_header_prefix = 'ltnt_cnv'\n",
    "epochs = 12 # determined in devel\n",
    "train_file = pd.read_csv('disk_checkpoints/cptac_cnv_to_VAE.tsv',\n",
    "                        sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "180ec7b4-68a1-4fa1-8066-b688b591a277",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_type = 'proteomics'\n",
    "latent_header_prefix = 'ltnt_prt'\n",
    "epochs = 4\n",
    "train_file = pd.read_csv('disk_checkpoints/cptac_prt_to_VAE.tsv',\n",
    "                        sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dc3a5aa2-68f0-433f-9795-0ca6d4a37af9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_type = 'transcriptomics'\n",
    "latent_header_prefix = 'ltnt_trs'\n",
    "epochs = 4\n",
    "train_file = pd.read_csv('disk_checkpoints/cptac_trs_to_VAE.tsv',\n",
    "                        sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2cc5ee3-5348-44a6-9c74-228ce304c521",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>cnv_1.0</th>\n",
       "      <th>cnv_9.0</th>\n",
       "      <th>cnv_10.0</th>\n",
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       "      <th>cnv_13.0</th>\n",
       "      <th>cnv_14.0</th>\n",
       "      <th>cnv_15.0</th>\n",
       "      <th>cnv_16.0</th>\n",
       "      <th>cnv_18.0</th>\n",
       "      <th>cnv_19.0</th>\n",
       "      <th>...</th>\n",
       "      <th>cnv_106480180.0</th>\n",
       "      <th>cnv_106480993.0</th>\n",
       "      <th>cnv_106821730.0</th>\n",
       "      <th>cnv_110599583.0</th>\n",
       "      <th>cnv_112694756.0</th>\n",
       "      <th>cnv_118142757.0</th>\n",
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       "      <th>cnv_122405565.0</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>-0.20200</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.17000</td>\n",
       "      <td>0.72500</td>\n",
       "      <td>-0.642000</td>\n",
       "      <td>0.30700</td>\n",
       "      <td>0.24200</td>\n",
       "      <td>0.08800</td>\n",
       "      <td>-0.26100</td>\n",
       "      <td>0.02200</td>\n",
       "      <td>0.27800</td>\n",
       "      <td>-0.16100</td>\n",
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       "    <tr>\n",
       "      <th>108164.0</th>\n",
       "      <td>-0.10800</td>\n",
       "      <td>-0.30900</td>\n",
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       "      <td>-0.30900</td>\n",
       "      <td>-0.01500</td>\n",
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       "      <td>-0.06900</td>\n",
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       "      <td>0.294000</td>\n",
       "      <td>0.37000</td>\n",
       "      <td>0.61000</td>\n",
       "      <td>1.36400</td>\n",
       "      <td>0.19000</td>\n",
       "      <td>-0.17900</td>\n",
       "      <td>1.02500</td>\n",
       "      <td>0.62100</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",
       "      <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>110445.0</th>\n",
       "      <td>0.05532</td>\n",
       "      <td>-0.13064</td>\n",
       "      <td>-0.13064</td>\n",
       "      <td>0.11672</td>\n",
       "      <td>0.01289</td>\n",
       "      <td>0.03222</td>\n",
       "      <td>0.05075</td>\n",
       "      <td>0.00959</td>\n",
       "      <td>-0.09634</td>\n",
       "      <td>-0.11239</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05439</td>\n",
       "      <td>0.05643</td>\n",
       "      <td>-0.037955</td>\n",
       "      <td>0.04580</td>\n",
       "      <td>0.04080</td>\n",
       "      <td>-0.10505</td>\n",
       "      <td>-0.11300</td>\n",
       "      <td>0.04669</td>\n",
       "      <td>0.05094</td>\n",
       "      <td>0.06456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110446.0</th>\n",
       "      <td>0.20565</td>\n",
       "      <td>-0.18011</td>\n",
       "      <td>-0.18011</td>\n",
       "      <td>0.01911</td>\n",
       "      <td>0.02780</td>\n",
       "      <td>0.03257</td>\n",
       "      <td>0.04025</td>\n",
       "      <td>0.01552</td>\n",
       "      <td>0.01552</td>\n",
       "      <td>-0.17638</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03257</td>\n",
       "      <td>0.00561</td>\n",
       "      <td>0.027375</td>\n",
       "      <td>0.02780</td>\n",
       "      <td>0.01552</td>\n",
       "      <td>-0.00410</td>\n",
       "      <td>0.02273</td>\n",
       "      <td>-0.16608</td>\n",
       "      <td>0.03323</td>\n",
       "      <td>0.20609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110447.0</th>\n",
       "      <td>0.17148</td>\n",
       "      <td>-0.26788</td>\n",
       "      <td>-0.26788</td>\n",
       "      <td>0.15441</td>\n",
       "      <td>0.19446</td>\n",
       "      <td>-0.01371</td>\n",
       "      <td>0.15753</td>\n",
       "      <td>-0.01829</td>\n",
       "      <td>-0.02233</td>\n",
       "      <td>-0.27376</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.04293</td>\n",
       "      <td>0.12338</td>\n",
       "      <td>0.156885</td>\n",
       "      <td>0.17883</td>\n",
       "      <td>-0.02233</td>\n",
       "      <td>-0.27971</td>\n",
       "      <td>0.27415</td>\n",
       "      <td>-0.31231</td>\n",
       "      <td>0.01704</td>\n",
       "      <td>0.29484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110448.0</th>\n",
       "      <td>0.05142</td>\n",
       "      <td>0.03229</td>\n",
       "      <td>0.03229</td>\n",
       "      <td>0.03382</td>\n",
       "      <td>0.02398</td>\n",
       "      <td>0.05281</td>\n",
       "      <td>0.04169</td>\n",
       "      <td>0.04029</td>\n",
       "      <td>0.04023</td>\n",
       "      <td>0.02086</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03116</td>\n",
       "      <td>0.02797</td>\n",
       "      <td>-0.029855</td>\n",
       "      <td>0.03681</td>\n",
       "      <td>0.05461</td>\n",
       "      <td>-0.12708</td>\n",
       "      <td>0.04287</td>\n",
       "      <td>0.05939</td>\n",
       "      <td>0.04928</td>\n",
       "      <td>-0.11042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110449.0</th>\n",
       "      <td>0.06180</td>\n",
       "      <td>0.05127</td>\n",
       "      <td>0.05127</td>\n",
       "      <td>-0.01858</td>\n",
       "      <td>-0.00266</td>\n",
       "      <td>-0.02373</td>\n",
       "      <td>-0.05178</td>\n",
       "      <td>0.02691</td>\n",
       "      <td>-0.05135</td>\n",
       "      <td>0.01071</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03937</td>\n",
       "      <td>-0.05587</td>\n",
       "      <td>0.011510</td>\n",
       "      <td>0.00169</td>\n",
       "      <td>-0.05167</td>\n",
       "      <td>0.12728</td>\n",
       "      <td>0.00504</td>\n",
       "      <td>-0.02157</td>\n",
       "      <td>0.12485</td>\n",
       "      <td>0.02681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1709 rows × 16884 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   cnv_1.0  cnv_9.0  cnv_10.0  cnv_12.0  cnv_13.0  cnv_14.0  \\\n",
       "improve_sample_id                                                             \n",
       "108162.0          -0.27800 -0.41600  -0.41600  -0.43300   0.81900   0.12400   \n",
       "108163.0          -0.17900 -0.16200  -0.16200   0.20800   0.30700  -0.17000   \n",
       "108164.0          -0.10800 -0.30900  -0.30900  -0.30900  -0.01500   0.13000   \n",
       "108165.0          -0.14300  0.45500   0.45500  -0.10000  -0.04800  -0.04900   \n",
       "108166.0           0.55600 -0.25000  -0.25000  -0.12900   1.25100   0.54400   \n",
       "...                    ...      ...       ...       ...       ...       ...   \n",
       "110445.0           0.05532 -0.13064  -0.13064   0.11672   0.01289   0.03222   \n",
       "110446.0           0.20565 -0.18011  -0.18011   0.01911   0.02780   0.03257   \n",
       "110447.0           0.17148 -0.26788  -0.26788   0.15441   0.19446  -0.01371   \n",
       "110448.0           0.05142  0.03229   0.03229   0.03382   0.02398   0.05281   \n",
       "110449.0           0.06180  0.05127   0.05127  -0.01858  -0.00266  -0.02373   \n",
       "\n",
       "                   cnv_15.0  cnv_16.0  cnv_18.0  cnv_19.0  ...  \\\n",
       "improve_sample_id                                          ...   \n",
       "108162.0            0.09700   0.19500  -0.03600   0.00000  ...   \n",
       "108163.0            0.22200   0.24200   0.18100  -0.20200  ...   \n",
       "108164.0            0.28500   0.06100   0.03800  -0.05700  ...   \n",
       "108165.0            0.25600   0.06300  -0.20500  -0.06900  ...   \n",
       "108166.0            0.79100   0.38200  -0.13000  -0.14000  ...   \n",
       "...                     ...       ...       ...       ...  ...   \n",
       "110445.0            0.05075   0.00959  -0.09634  -0.11239  ...   \n",
       "110446.0            0.04025   0.01552   0.01552  -0.17638  ...   \n",
       "110447.0            0.15753  -0.01829  -0.02233  -0.27376  ...   \n",
       "110448.0            0.04169   0.04029   0.04023   0.02086  ...   \n",
       "110449.0           -0.05178   0.02691  -0.05135   0.01071  ...   \n",
       "\n",
       "                   cnv_106480180.0  cnv_106480993.0  cnv_106821730.0  \\\n",
       "improve_sample_id                                                      \n",
       "108162.0                   0.12400         -0.40400        -0.348000   \n",
       "108163.0                  -0.17000          0.72500        -0.642000   \n",
       "108164.0                   0.13000          0.08900        -0.272000   \n",
       "108165.0                  -0.04900         -0.01000         0.092000   \n",
       "108166.0                  -0.26700          0.73000         0.294000   \n",
       "...                            ...              ...              ...   \n",
       "110445.0                   0.05439          0.05643        -0.037955   \n",
       "110446.0                   0.03257          0.00561         0.027375   \n",
       "110447.0                  -0.04293          0.12338         0.156885   \n",
       "110448.0                   0.03116          0.02797        -0.029855   \n",
       "110449.0                  -0.03937         -0.05587         0.011510   \n",
       "\n",
       "                   cnv_110599583.0  cnv_112694756.0  cnv_118142757.0  \\\n",
       "improve_sample_id                                                      \n",
       "108162.0                   0.53300         -0.40600          0.00700   \n",
       "108163.0                   0.30700          0.24200          0.08800   \n",
       "108164.0                  -0.01500         -0.30300          0.11600   \n",
       "108165.0                  -0.04800         -0.20500          0.13000   \n",
       "108166.0                   0.37000          0.61000          1.36400   \n",
       "...                            ...              ...              ...   \n",
       "110445.0                   0.04580          0.04080         -0.10505   \n",
       "110446.0                   0.02780          0.01552         -0.00410   \n",
       "110447.0                   0.17883         -0.02233         -0.27971   \n",
       "110448.0                   0.03681          0.05461         -0.12708   \n",
       "110449.0                   0.00169         -0.05167          0.12728   \n",
       "\n",
       "                   cnv_118568804.0  cnv_122405565.0  cnv_124905743.0  \\\n",
       "improve_sample_id                                                      \n",
       "108162.0                  -0.34800         -0.47700          3.65700   \n",
       "108163.0                  -0.26100          0.02200          0.27800   \n",
       "108164.0                  -0.31600         -0.03800          0.86600   \n",
       "108165.0                   0.27400         -0.07500          0.73800   \n",
       "108166.0                   0.19000         -0.17900          1.02500   \n",
       "...                            ...              ...              ...   \n",
       "110445.0                  -0.11300          0.04669          0.05094   \n",
       "110446.0                   0.02273         -0.16608          0.03323   \n",
       "110447.0                   0.27415         -0.31231          0.01704   \n",
       "110448.0                   0.04287          0.05939          0.04928   \n",
       "110449.0                   0.00504         -0.02157          0.12485   \n",
       "\n",
       "                   cnv_125316803.0  \n",
       "improve_sample_id                   \n",
       "108162.0                  -0.42000  \n",
       "108163.0                  -0.16100  \n",
       "108164.0                  -0.27700  \n",
       "108165.0                  -0.09100  \n",
       "108166.0                   0.62100  \n",
       "...                            ...  \n",
       "110445.0                   0.06456  \n",
       "110446.0                   0.20609  \n",
       "110447.0                   0.29484  \n",
       "110448.0                  -0.11042  \n",
       "110449.0                   0.02681  \n",
       "\n",
       "[1709 rows x 16884 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_file # check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b6247c3-370d-4ee9-81f4-a6e70e96a6de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize\n",
    "scaler = MinMaxScaler()\n",
    "train_file = pd.DataFrame(\n",
    "    scaler.fit_transform(train_file),\n",
    "    columns=train_file.columns,\n",
    "    index=train_file.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c06d8f65-e635-406f-9a21-753c878e7e96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1 Max\n",
      "WARNING:tensorflow:Output {0} missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to custom_variational_layer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-12 16:12:23.704683: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2023-07-12 16:12:23.704803: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
      "2023-07-12 16:12:23.710708: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled\n",
      "2023-07-12 16:12:23.716890: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2023-07-12 16:12:23.717756: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-07-12 16:12:23.736978: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-07-12 16:12:23.853896: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1709 samples\n",
      "Epoch 1/12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-12 16:12:23.989611: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-07-12 16:12:24.013923: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-07-12 16:12:24.060270: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1709/1709 [==============================] - 2s 941us/sample - loss: 9966.7256\n",
      "Epoch 2/12\n",
      " 300/1709 [====>.........................] - ETA: 0s - loss: 8772.4914"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-12 16:12:25.569499: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1709/1709 [==============================] - 1s 480us/sample - loss: 8607.7406\n",
      "Epoch 3/12\n",
      "1709/1709 [==============================] - 1s 458us/sample - loss: 8341.1681\n",
      "Epoch 4/12\n",
      "1709/1709 [==============================] - 1s 462us/sample - loss: 8269.3625\n",
      "Epoch 5/12\n",
      "1709/1709 [==============================] - 1s 461us/sample - loss: 8226.3757\n",
      "Epoch 6/12\n",
      "1709/1709 [==============================] - 1s 458us/sample - loss: 8196.1676\n",
      "Epoch 7/12\n",
      "1709/1709 [==============================] - 1s 460us/sample - loss: 8177.7590\n",
      "Epoch 8/12\n",
      "1709/1709 [==============================] - 1s 459us/sample - loss: 8171.2108\n",
      "Epoch 9/12\n",
      "1709/1709 [==============================] - 1s 457us/sample - loss: 8161.2394\n",
      "Epoch 10/12\n",
      "1709/1709 [==============================] - 1s 457us/sample - loss: 8135.0895\n",
      "Epoch 11/12\n",
      "1709/1709 [==============================] - 1s 455us/sample - loss: 8143.1902\n",
      "Epoch 12/12\n",
      "1709/1709 [==============================] - 1s 456us/sample - loss: 8121.9256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/tf_M1_py3dot9/lib/python3.9/site-packages/keras/engine/training_v1.py:2356: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n",
      "2023-07-12 16:12:34.255209: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VAE done, latent object written to disk\n"
     ]
    }
   ],
   "source": [
    "# Variational autoencoder for feature dimensionality reduction\n",
    "\n",
    "loss_dict = {}\n",
    "vs_list = ['vs0']\n",
    "validation_split = vs_list[0]\n",
    "\n",
    "features = train_file.columns\n",
    "\n",
    "original_dim = len(features)\n",
    "feature_dim = len(features)\n",
    "latent_dim = 250\n",
    "batch_size = 50\n",
    "\n",
    "encoder_inputs = keras.Input(shape=(feature_dim,))\n",
    "z_mean_dense_linear = layers.Dense(\n",
    "    latent_dim, kernel_initializer='glorot_uniform', name=\"encoder_1\")(encoder_inputs)\n",
    "z_mean_dense_batchnorm = layers.BatchNormalization()(z_mean_dense_linear)\n",
    "z_mean_encoded = layers.Activation('relu')(z_mean_dense_batchnorm)\n",
    "\n",
    "z_log_var_dense_linear = layers.Dense(\n",
    "    latent_dim, kernel_initializer='glorot_uniform', name=\"encoder_2\")(encoder_inputs)\n",
    "z_log_var_dense_batchnorm = layers.BatchNormalization()(z_log_var_dense_linear)\n",
    "z_log_var_encoded = layers.Activation('relu')(z_log_var_dense_batchnorm)\n",
    "\n",
    "latent_space = layers.Lambda(\n",
    "    compute_latent, output_shape=(\n",
    "        latent_dim,), name=\"latent_space\")([z_mean_encoded, z_log_var_encoded])\n",
    "\n",
    "decoder_to_reconstruct = layers.Dense(\n",
    "    feature_dim, kernel_initializer='glorot_uniform', activation='sigmoid')\n",
    "decoder_outputs = decoder_to_reconstruct(latent_space)\n",
    "\n",
    "learning_rate = 0.0005\n",
    "\n",
    "kappa = 1\n",
    "beta = K.variable(0)\n",
    "\n",
    "adam = optimizers.Adam(learning_rate=learning_rate)\n",
    "vae_layer = CustomVariationalLayer()([encoder_inputs, decoder_outputs])\n",
    "vae = Model(encoder_inputs, vae_layer)\n",
    "vae.compile(optimizer=adam, loss=None, loss_weights=[beta])\n",
    "\n",
    "history = vae.fit(train_file,\n",
    "            epochs=epochs,\n",
    "                  batch_size=batch_size,\n",
    "                  shuffle=True,\n",
    "                  callbacks=[WarmUpCallback(beta, kappa)],\n",
    "                  verbose=1)\n",
    "loss_dict[validation_split] = history.history['loss']\n",
    "\n",
    "encoder = Model(encoder_inputs, z_mean_encoded)\n",
    "latent_object = pd.DataFrame(\n",
    "    encoder.predict(train_file),\n",
    "    index=train_file.index\n",
    ")\n",
    "latent_object.index.name = train_file.index.name\n",
    "\n",
    "# Convert latent object headers to dtype specific strings for input to transformer\n",
    "new_column_headers = []\n",
    "for column_header in latent_object.columns:\n",
    "    new_column_header = latent_header_prefix+'_' + str(column_header)\n",
    "    new_column_headers.append(new_column_header)\n",
    "latent_object.columns = new_column_headers\n",
    "\n",
    "latent_object.to_csv('latent_features/'+data_type+'_latent.tsv', sep = '\\t')\n",
    "print('VAE done, latent object written to disk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "442b8e6f-9bcf-442a-b91c-1e37aa6f01b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cut epochs once loss flattens to prevent overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b4e79a9b-8e83-4973-8ec5-d82e1cefa721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(loss_dict, data_type, train_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "020544e8-f1d4-4533-8cfb-01c9251d84fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(loss_dict, data_type, train_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e1b377b-289a-4528-847c-9803f8a80aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4xQtub29vPP744xV674ceegg1a9a02HbeLDQ0FL169bJoW7dundWmIvv27bP53t988w0GDBigPl66dCnGjh1b4lgGDRpU4nNjx45Fp06dSnz+di5dunTb905ISEBsbCymTJmCDRs2qMX3t99+i6ioKMTFxeHEiRMWxZROp8OMGTPUxwcPHsT48eOh1+vRuHFj1KtXD15eXkhPT7e6abz5OsOsrCy89NJLeOmll1CvXj00bNgQfn5+yM3Nxe7du22+hojojjn6UB4Rkcxu3rwpxo0bJ/R6famnoNWoUcPitVV9uuPevXutxpOfn1/i+02fPr3UU/HWrl0rQkJCynQK3tq1a8s1n4SEhFK/r7e7mfWdnO5oFh0dbfVZo0aNKtf4S/L888+X6ZRSIYTo3bu3RZ/Zs2eX+L5//fWX1a0LzFv5l2cLfpRyiuCtbj3dsbQ/xTPbtGmTCAwMvG1/Ly8v8emnn1p85nvvvVemz3rwwQeF0WgUQgjx+++/l+k19evXF3/++WeZ509EZAtPdyQiciAPDw988MEHOHnyJKZMmYKOHTsiNDQUHh4ecHd3R506ddC9e3fMmDHDanOHqnbrUbQBAwagWrVqJfYfMmSIxeP169erp5eZ9e3bF2fOnMGSJUswePBgREVFwdfXF3q9HgEBAYiPj8fYsWOxdetW9OnTp1zjnTx5Mg4fPozx48fj7rvvhr+/P9zc3FCzZk20bNkS48ePt9oRsLLYOupX0VMdzUaOHGmz/dZTHdPT0y02ZFEUxSqT4jw9PdGvXz+LNmffQKRHjx44evQo3nrrLbRt2xY1atSAm5sb/Pz80LJlS7zyyis4cuSI1emIAwYMwNy5czF06FDExsYiJCQE1apVg4eHB8LDw9G7d28sW7YM69atU0/LbdSoEZYuXYoxY8YgPj4ederUgaenJ9zc3FCrVi20a9dO/XtaWTeNJyJ5KUJU8g1qiIiIiIiI6I7xSBoREREREZETYZFGRERERETkRFikEREREREROREWaURERERERE6ERRoREREREZETYZFGRERERETkRNwcPQBXZjKZ8Oeff8LX1xeKojh6OERERERE5CBCCOTm5qJ27drqPRhLwiLNjv7880+Eh4c7ehhEREREROQkzp07h7p16962D4s0O/L19QVQFISfn59Dx2I0GpGSkoLY2Fjo9XqHjoWqHvMnrgG5MX+5MX+5MX/nYTAYEB4ertYIt8MizY7Mpzj6+fk5RZHm4+MDPz8//gWVEPMnrgG5MX+5MX+5MX/nU5bLoBQhhKiCsUjJYDDA398fOTk5Di/ShBC4efMmPD09eX2chJg/cQ3IjfnLjfnLjfk7j/LUBtzdUSLu7u6OHgI5EPMnrgG5MX+5MX+5MX/tYZEmCZPJhOTkZJhMJkcPhRyA+RPXgNyYv9yYv9yYvzaxSCMiIiIiInIiLNKIiIiIiIicCIs0IiIiIiIiJ+J0RdqOHTvQp08f1K5dG4qi4Ntvv7V4XgiByZMnIywsDF5eXujWrRtOnDhh0ScrKwuPP/44/Pz8EBAQgNGjR+PatWsWfQ4dOoT27dvD09MT4eHhmDVrltVYVq9ejZiYGHh6eiIuLg7ff/99pc+3quh0OsTFxZV6d3NyTcyfuAbkxvzlxvzlxvy1yenSun79Ou666y58+OGHNp+fNWsWPvjgA3z00UfYvXs3vL290bNnT9y8eVPt8/jjjyMlJQWbN2/Ghg0bsGPHDowZM0Z93mAwoEePHoiIiEBSUhJmz56NqVOnYvHixWqfXbt24dFHH8Xo0aPx+++/4+GHH8bDDz+Mw4cP22/ydpafn+/oIZADMX/iGpAb85cb85cb89cep75PmqIo+Oabb/Dwww8DKDqKVrt2bbz88sv417/+BQDIyclBSEgIli5diqFDh+LIkSNo2rQp9u7di1atWgEANm7ciAcffBDnz59H7dq1sXDhQrz++utIT09XtySdOHEivv32Wxw9ehQAMGTIEFy/fh0bNmxQx3PvvfeiRYsW+Oijj8o0fme6T5rRaERycjLi4uJ4I0MJMX/iGpAb85cb85cb83ce5akN3KpoTJXi9OnTSE9PR7du3dQ2f39/tGnTBomJiRg6dCgSExMREBCgFmgA0K1bN+h0OuzevRv9+/dHYmIiOnToYHHPiJ49e2LmzJnIzs5GjRo1kJiYiJdeesni83v27Gl1+mVxeXl5yMvLUx8bDAYARX85jEYjgKLCU6fTwWQyoXh9bG439yutXafTQVEUm+0ArLZZFUJACGHVX6/XQwhh1V+v11uNsaR2R82ppHbOybrdaDSqX7vKnIqPkXMqfU7mNWD+4wpzKm3snNPfYzfnb/4cV5iTK+ZkrzkBsPoZQOtzcsWc7DUn82tL+jlQi3Myj1FrOd36/O1oqkhLT08HAISEhFi0h4SEqM+lp6cjODjY4nk3NzcEBgZa9ImMjLR6D/NzNWrUQHp6+m0/x5YZM2YgISHBqj0lJQU+Pj4AgMDAQNSrVw/nz59HVlaW2ic0NBShoaFIS0tDbm6u2h4eHo6goCCcOHHC4pTOqKgo+Pn5ITU11SLw6OhouLu7Izk52WIMTZs2hdFoREpKivoPtl6vR1xcHHJzc3Hq1Cm1r6enJ2JiYpCdnY1z586p7b6+vmjQoAEyMzMtvg+OmlNcXBzy8/Nx7NgxtY1zsj0nIQSuX78OAC4zJ8D1crLnnIQQyMrKQl5eHjw9PV1iTq6Yk73mdPbsWWRlZSElJQV+fn4uMSdXzMlecwoICEBOTo7FzwBan5Mr5mSvOZkzv3btGtLS0lxiTlrN6dY9Mm5HU6c77tq1C/fffz/+/PNPhIWFqf0GDx4MRVGwatUqvP3221i2bJlFkAAQHByMhIQEPPPMM+jRowciIyOxaNEi9fnU1FTExsYiNTUVTZo0gbu7O5YtW4ZHH31U7bNgwQIkJCQgIyPD5nhtHUkLDw9HVlaWekjTkUfSUlNTERMTY3GoW4u/hSitnXOyfSTt6NGjiI2NhaIoLjGn4mN0lZzsOSej0YgjR44gNjZW/Vytz6m0sXNOf4+9sLAQR44cQZMmTaDX611iTq6Yk73mJIRASkqKxc8AWp+TK+ZkzyNpx44dQ5MmTdSCTetzMo9RazkZDAYEBga63umOoaGhAICMjAyLIi0jIwMtWrRQ+2RmZlq8rrCwEFlZWerrQ0NDrQot8+PS+pift8XDwwMeHh5W7eb/IRZnDs1WX3u1N2/e3GZfRVFs9i9pjOVtt+ecSmrnnCzb9Xp9iflX5hjL286cqm5Oer0ed9111x29j7POqSLtss3J3d3dIn9zuy1amZMtnFPJYynp/wFanpMr5mSPdvNRp5JocU5mWsuppOdtjqnMPZ1AZGQkQkND8dNPP6ltBoMBu3fvRtu2bQEAbdu2xdWrV5GUlKT22bp1K0wmE9q0aaP22bFjBwoKCtQ+mzdvRnR0NGrUqKH2Kf455j7mz9ESoxHYtk3gk09uYNs2gXKcDksuQggBg8Fg9VslkgfXgNyYv9yYv9yYvzY5XZF27do1HDhwAAcOHABQtFnIgQMHcPbsWSiKghdffBHTpk3DunXrkJycjCeeeAK1a9dWT4ls0qQJevXqhaeeegp79uzBr7/+irFjx2Lo0KGoXbs2AOCxxx6Du7s7Ro8ejZSUFKxatQpz58612CjkhRdewMaNG/Huu+/i6NGjmDp1Kvbt24exY8dW9bekQtasAerXB7p0UTB6dHV06aKgfv2idpKHyWTCqVOnrE4JIHlwDciN+cuN+cuN+WuT053uuG/fPnTu3Fl9bC6chg8fjqVLl+KVV17B9evXMWbMGFy9ehXt2rXDxo0b4enpqb5mxYoVGDt2LLp27QqdToeBAwfigw8+UJ/39/fHjz/+iOeeew7x8fGoWbMmJk+ebHEvtfvuuw+ff/45/v3vf+O1115Do0aN8O2336JZs2ZV8F2oHGvWAIMGAbf+4uTChaL2r74CBgxwzNiIiIiIiMg2p944ROsceZ80o7HoCNr587afVxSgbl3g9GmgHKfHkkbxHinENSA35i835i835u88ylMbON3pjlQ5du4suUADio6unTtX1I/kUPxoM8mJa0BuzF9uzF9uzF97nO50R6ocFy9Wbj/SNr1ej5iYGEcPgxyIa0BuzF9uzF9uzF+beCTNRRW7Q0Gl9CNtM5lMuHLlCi8alhjXgNyYv9yYv9yYvzaxSHNR7dsXXXN2yz0LVYoChIcX9SPXJ4TAuXPnuP2uxLgG5Mb85cb85cb8tYlFmovS64G5c4u+LqlQe/99bhpCRERERORsWKS5sAEDirbZr1PHsj0ggNvvExERERE5KxZpLm7AACAtDdiyxYheva4BAB58kAWajHx9fR09BHIwrgG5MX+5MX+5MX/t4X3S7MiR90mz5Ycfigq06Gjg6FFHj4aIiIiISB68TxpZMZlMqFcvAwBw7BiQk+PgAVGVMplMSE9P585OEuMakBvzlxvzlxvz1yYWaZIQQqCg4CLq1y86cJqU5OABUZUSQiA9PZ07O0mMa0BuzF9uzF9uzF+bWKRJplWror+ge/c6eCBERERERGQTizTJtGpV9F8WaUREREREzolFmiQURUFgYCBaty56zCJNLub8lZJumkcuj2tAbsxfbsxfbsxfm7i7ox052+6OAJCbC/j7A0IAGRlAcLCjR0RERERE5Pq4uyNZMZlMOHv2LLy9TYiJKWrj0TR5mPPnzk7y4hqQG/OXG/OXG/PXJhZpkhBCICsrC0IInvIooeL5k5y4BuTG/OXG/OXG/LWJRZqEWKQRERERETkvFmkSKl6k8ZcqRERERETOhUWaJBRFQWhoKBRFwV13AW5uwKVLwNmzjh4ZVYXi+ZOcuAbkxvzlxvzlxvy1iUWaJHQ6HUJDQ6HT6eDpCTRvXtTOUx7lUDx/khPXgNyYv9yYv9yYvzYxLUkYjUacPHkSRqMRAK9Lk82t+ZN8uAbkxvzlxvzlxvy1iUWaRHJzc9WvWaTJp3j+JCeuAbkxf7kxf7kxf+1hkSYpc5GWlATwthlERERERM6DRZqkmjYFvLwAgwE4ftzRoyEiIiIiIjMWaZJQFAXh4eHqzj5ubkDLlkXP8ZRH13dr/iQfrgG5MX+5MX+5MX9tYpEmCZ1Oh6CgIIudfXhdmjxs5U9y4RqQG/OXG/OXG/PXJqYlCaPRiKNHj1rs7MMiTR628ie5cA3IjfnLjfnLjflrE4s0idy8edPisblIO3AAKCio+vFQ1bo1f5IP14DcmL/cmL/cmL/2sEiTWMOGQEAAcPMmcPiwo0dDREREREQAizSpKQrQqlXR1zzlkYiIiIjIObBIk4ROp0NUVJTVRaO8Lk0OJeVP8uAakBvzlxvzlxvz1yY3Rw+AqoaiKPDz87NqZ5Emh5LyJ3lwDciN+cuN+cuN+WsTS2pJGI1GJCcnW+3sYy7SDh8GbtxwwMCoSpSUP8mDa0BuzF9uzF9uzF+bWKRJxNZfzjp1gNBQwGgs2uWRXBf/cSauAbkxf7kxf7kxf+1hkSY5ReEpj0REREREzoRFGrFIIyIiIiJyIizSJKHT6RAdHW1zZx8Waa7vdvmTHLgG5Mb85cb85cb8tYlpScTd3d1mu/leacePA1evVt14qGqVlD/Jg2tAbsxfbsxfbsxfe1ikScJkMiE5ORkmk8nquZo1gcjIoq+Tkqp4YFQlbpc/yYFrQG7MX27MX27MX5tYpBEAnvJIREREROQsWKQRABZpRERERETOgkUaAWCRRkRERETkLBQhhHD0IFyVwWCAv78/cnJy4Ofn59CxCCFgMpmg0+mgKIrV87m5gL8/IASQng6EhDhgkGQ3peVPro9rQG7MX27MX27M33mUpzbgkTSJ5Ofnl/icry/QpEnR1zya5ppulz/JgWtAbsxfbsxfbsxfe1ikScJkMuHYsWO33dmHpzy6rrLkT66Na0BuzF9uzF9uzF+bWKSRikUaEREREZHjabJIy83NxYsvvoiIiAh4eXnhvvvuw95ilYWiKDb/zJ49W+1Tv359q+ffeecdi885dOgQ2rdvD09PT4SHh2PWrFlVNkdHKF6k8UpFIiIiIiLHcHP0AO7Ek08+icOHD2P58uWoXbs2PvvsM3Tr1g2pqamoU6cOLl68aNH/hx9+wOjRozFw4ECL9jfffBNPPfWU+tjX11f92mAwoEePHujWrRs++ugjJCcnY9SoUQgICMCYMWPsO0E70ev1t33+rruAatWAy5eBM2eA+vWrZlxUNUrLn1wf14DcmL/cmL/cmL/2aG53x7/++gu+vr5Yu3YtevfurbbHx8fjgQcewLRp06xe8/DDDyM3Nxc//fST2la/fn28+OKLePHFF21+zsKFC/H6668jPT0d7u7uAICJEyfi22+/xdGjR8s0Vmfa3bGsWrUCkpKAL78EHnnE0aMhIiIiInIN5akNNHckrbCwEEajEZ6enhbtXl5e+OWXX6z6Z2Rk4LvvvsOyZcusnnvnnXfw1ltvoV69enjssccwfvx4uLkVfUsSExPRoUMHtUADgJ49e2LmzJnIzs5GjRo1rN4vLy8PeXl56mODwQAAMBqNMBqNAIpOxdTpdDCZTCheH5vbzf1Kazdvo2qrHYDVxaGKoiA3Nxfe3t4W26/q9Xp1a1YAaNVKQVKSDnv3AgMHWo7R3L+ksVf1nEpqv3VOtxu7LHMSQuDatWvw9/eHEMIl5lR8jK6Skz3nJIRAbm4u/P39oSiKS8yptLFzTn+P3WQyITc3F76+vlAUxSXm5Io52WtOiqIgJycHPj4+6s8AWp+TK+ZkrzkJIXDjxg34+PiUaexamJN5jFrL6dbnb0dzRZqvry/atm2Lt956C02aNEFISAhWrlyJxMRENGzY0Kr/smXL4OvriwEDBli0P//882jZsiUCAwOxa9cuTJo0CRcvXsScOXMAAOnp6YiMjLR4Tcj/bh6Wnp5us0ibMWMGEhISrNpTUlLg4+MDAAgMDES9evVw/vx5ZGVlqX1CQ0MRGhqKtLQ05Obmqu3h4eEICgrCiRMncPPmTbU9KioKfn5+SE1NtQg8Ojoa7u7uSE5OthhD06ZNceLECej1evUfaL1ej7i4OOTm5uLUqVP/G0cggHrYuxfIzs7GuXPn1Pfw9fVFgwYNkJmZifT0dLXdUXOKi4tDfn4+jh07prbZmhMAeHp6IiYmRto5CSFw/fp1tG3bFhcuXHCJOQGul5M95ySEQFZWFu699154enq6xJxcMSd7zens2bPIyspCYGAg/Pz8XGJOrpiTveYUEBCAAwcOqL+kcYU5uWJO9pqToigQQqB+/fpIS0tziTlpNadr166hrDR3uiMAnDx5EqNGjcKOHTug1+vRsmVLNG7cGElJSThy5IhF35iYGHTv3h3z5s277Xt+8sknePrpp3Ht2jV4eHigR48eiIyMxKJFi9Q+qampiI2NRWpqKpqYbypWjK0jaeHh4cjKylIPaTrqtxBCCCQnJyM2NtbivORbfwuRnAzcfbcevr5AVpYJiuJ8v4UorV2Lv1mx95yMRiNSUlLQvHlz9R9rrc+p+BhdJSd7zsm8BuLi4tTP1fqcShs75/T32AsLC5GSkqL+P8AV5uSKOdlrTkIIHDp0yOJnAK3PyRVzstecjEYjUlNT0axZM4uzqbQ8J/MYtZaTwWBAYGCga57uCAANGjTAzz//jOvXr8NgMCAsLAxDhgxBVFSURb+dO3fi2LFjWLVqVanv2aZNGxQWFiItLQ3R0dEIDQ1FRkaGRR/z49DQUJvv4eHhAQ8PD6t28/8QizOHZquvPdqNRqN6isutz5nbAaBZM6B6dSA3FzhxQgcbtWiJY6/qOd2uvficyjJGGeZU/BQXe42xvO3MqWrnZD7tqaQxlvQ+zjynO22XbU7msyiK/z9A63OyhXOy/Zm3+xlAq3OydzvnxDlV1hiLt5f0vM0xlbmnE/L29kZYWBiys7OxadMm9OvXz+L5jz/+GPHx8bjrrrtKfa8DBw5Ap9MhODgYANC2bVvs2LEDBQUFap/NmzcjOjra5qmOWnDrdXy2uLkBLVsWfc37pbmWsuRPro1rQG7MX27MX27MX3s0WaRt2rQJGzduxOnTp7F582Z07twZMTExGDlypNrHYDBg9erVePLJJ61en5iYiPfffx8HDx7EqVOnsGLFCowfPx7Dhg1TC7DHHnsM7u7uGD16NFJSUrBq1SrMnTsXL730UpXNszLp9XrExMSUqYLnTa1dT3nyJ9fENSA35i835i835q9NmizScnJy8NxzzyEmJgZPPPEE2rVrh02bNqFatWpqny+++AJCCDz66KNWr/fw8MAXX3yBjh07IjY2FtOnT8f48eOxePFitY+/vz9+/PFHnD59GvHx8Xj55ZcxefJkzd4jzWQy4cqVK1bn7drCIs31lCd/ck1cA3Jj/nJj/nJj/tqkyY1DtMKZ7pNmNBqRnJysbhpwO3/8ATRqBHh4AAYDUOwuBKRR5cmfXBPXgNyYv9yYv9yYv/MoT22gySNpZF8NGgA1agB5ecDhw44eDRERERGRXFikkRVFAVq1KvqapzwSEREREVUtFmkS8fX1LXNfXpfmesqTP7kmrgG5MX+5MX+5MX/t4TVpduRM16SV17ffAv37A82bAwcPOno0RERERETaxmvSyIrJZEJ6enqZd/YxH0lLSQFu3LDjwKhKlDd/cj1cA3Jj/nJj/nJj/trEIk0SQgikp6ejrAdO69QBwsIAoxH4/Xc7D47srrz5k+vhGpAb85cb85cb89cmFmlUIl6XRkRERERU9VikUYlYpBERERERVT0WaZJQFAWBgYFQFKXMr2GR5jruJH9yLVwDcmP+cmP+cmP+2sTdHe1Iy7s7AsCVK0DNmkVfZ2cDAQEOHQ4RERERkWZxd0eyYjKZcPbs2XLt7BMUBERFFX29b5+dBkZV4k7yJ9fCNSA35i835i835q9NLNIkIYRAVlZWuXf24SmPruFO8yfXwTUgN+YvN+YvN+avTSzS6LZYpBERERERVS0WaXRbLNKIiIiIiKoWizRJKIqC0NDQcu/s07IloNMB588D6el2GhzZ3Z3mT66Da0BuzF9uzF9uzF+bWKRJQqfTITQ0FDpd+SL38QGaNCn6mkfTtOtO8yfXwTUgN+YvN+YvN+avTUxLEkajESdPnoTRaCz3a3nKo/ZVJH9yDVwDcmP+cmP+cmP+2sQiTSK5ubl39DoWaa7hTvMn18E1IDfmLzfmLzfmrz0s0qhUxYs07t5KRERERGRfLNKoVM2bA9WqAVeuAGlpjh4NEREREZFrY5EmCUVREB4efkc7+3h4AHfdVfQ1T3nUporkT66Ba0BuzF9uzF9uzF+bWKRJQqfTISgo6I539uF1adpW0fxJ+7gG5Mb85cb85cb8tYlpScJoNOLo0aN3vLMPizRtq2j+pH1cA3Jj/nJj/nJj/trEIk0iN2/evOPXmou0pCSAf8e1qSL5k2vgGpAb85cb85cb89ceFmlUJk2aAN7ewLVrwLFjjh4NEREREZHrYpFGZaLXAy1bFn3NUx6JiIiIiOyHRZokdDodoqKiKnTRKK9L067KyJ+0jWtAbsxfbsxfbsxfm9wcPQCqGoqiwM/Pr0LvwSJNuyojf9I2rgG5MX+5MX+5MX9tYkktCaPRiOTk5Art7GMu0g4cAPLzK2dcVDUqI3/SNq4BuTF/uTF/uTF/bWKRJpGK/uWMigICA4sKtOTkShoUVRn+40xcA3Jj/nJj/nJj/trDIo3KTFGAVq2KvuYpj0RERERE9sEijcqF16UREREREdkXizRJ6HQ6REdHV3hnHxZp2lRZ+ZN2cQ3IjfnLjfnLjflrE9OSiLu7e4Xfw1ykpaQA169X+O2oClVG/qRtXANyY/5yY/5yY/7awyJNEiaTCcnJyTCZTBV6n9q1i/6YTMDvv1fS4MjuKit/0i6uAbkxf7kxf7kxf21ikUblxlMeiYiIiIjsh0UalRuLNCIiIiIi+2GRRuXGIo2IiIiIyH4UIYRw9CBclcFggL+/P3JycuDn5+fQsQghYDKZoNPpoChKhd4rKwsICvr76xo1KmGAZFeVmT9pE9eA3Ji/3Ji/3Ji/8yhPbcAjaRLJz8+vlPcJDAQaNCj6et++SnlLqgKVlT9pF9eA3Ji/3Ji/3Ji/9rBIk4TJZMKxY8cqbWcfnvKoLZWdP2kP14DcmL/cmL/cmL82sUijO8IijYiIiIjIPlik0R1hkUZEREREZB8s0iSi1+sr7b1atgR0OuDCBeDixUp7W7KjysyftIlrQG7MX27MX27MX3u4u6MdOdPujvYQFwccPgysXQv07evo0RAREREROS/u7khWhBAwGAyozJqcpzxqhz3yJ23hGpAb85cb85cb89cmTRZpubm5ePHFFxEREQEvLy/cd9992FusUhgxYgQURbH406tXL4v3yMrKwuOPPw4/Pz8EBARg9OjRuHbtmkWfQ4cOoX379vD09ER4eDhmzZpVJfOzB5PJhFOnTlXqzj4s0rTDHvmTtnANyI35y435y435a5Mmi7Qnn3wSmzdvxvLly5GcnIwePXqgW7duuHDhgtqnV69euHjxovpn5cqVFu/x+OOPIyUlBZs3b8aGDRuwY8cOjBkzRn3eYDCgR48eiIiIQFJSEmbPno2pU6di8eLFVTZPZ1e8SOMvZ4iIiIiIKoebowdQXn/99Re+/vprrF27Fh06dAAATJ06FevXr8fChQsxbdo0AICHhwdCQ0NtvseRI0ewceNG7N27F61atQIAzJs3Dw8++CD+85//oHbt2lixYgXy8/PxySefwN3dHbGxsThw4ADmzJljUczJrHlzwN0dyMoCTp8GoqIcPSIiIiIiIu3TXJFWWFgIo9EIT09Pi3YvLy/88ssv6uPt27cjODgYNWrUQJcuXTBt2jQEBQUBABITExEQEKAWaADQrVs36HQ67N69G/3790diYiI6dOgAd3d3tU/Pnj0xc+ZMZGdno0aNGlZjy8vLQ15envrYYDAAAIxGI4xGIwBAURTodDqYTCaLc4PN7eZ+pbXrdDooimKzHYDVIW0hBDw8PKz66/V6CCGs+uv1eqsx3tqu1wPNm+uwb5+CvXuB+vWrdk4ltVdkTreOsapzstecjEYjPDw81Pd2hTkVHyPnVPqcjEYj3N3dIYSwOUYtzqm0sXNOf4/dnL/5c1xhTq6Yk73mBMAif1eYkyvmZK85mX9uFkLY/DlQi3Myj1FrOd36/O1orkjz9fVF27Zt8dZbb6FJkyYICQnBypUrkZiYiIYNGwIoOtVxwIABiIyMxMmTJ/Haa6/hgQceQGJiIvR6PdLT0xEcHGzxvm5ubggMDER6ejoAID09HZGRkRZ9QkJC1OdsFWkzZsxAQkKCVXtKSgp8fHwAAIGBgahXrx7Onz+PrKwstU9oaChCQ0ORlpaG3NxctT08PBxBQUE4ceIEbt68qbZHRUXBz88PqampFoFHR0fD3d0dycnJFmOIi4tD/fr1kZqaqrbp9XrExcUhNzcXp06dUts9PT0RExOD7OxsnDt3zuJ736BBA2RmZqrfp6ioOti3rxb27gXatq36OeXn5+PYsWOVOifAsTnZc056vR5nz551qTm5Yk72nFNBQQEURXGpObliTvaaU2pqqsvNCXC9nOwxJ51OZ/EzgCvMyRVzsuecDAaDy81Jaznduv/F7WhyC/6TJ09i1KhR2LFjB/R6PVq2bInGjRsjKSkJR44csep/6tQpNGjQAFu2bEHXrl3x9ttvY9myZRZhA0BwcDASEhLwzDPPoEePHoiMjMSiRYvU51NTUxEbG4vU1FQ0adLE6nNsHUkLDw9HVlaWus2mo34LAQDZ2dnw9/dX+wAV/y3EsmUKRo/WoUMHYNs2/mbFWedkMplw9epV9WiyK8yp+BhdJSd7zsm8BgIDA9X31/qcShs752R5JO3q1asICAiATqdziTm5Yk72PJJ25coVNX9XmJMr5mSvOZlMJhgMBgQEBOBWWp2TeYxay8lgMCAwMLBMW/Br7kgaADRo0AA///wzrl+/DoPBgLCwMAwZMgRRJVwUFRUVhZo1a+KPP/5A165dERoaiszMTIs+hYWFyMrKUq9jCw0NRUZGhkUf8+OSrnXz8PBQTykrTq/XW91EsHihdGtfe7QbjUacP38eNWrUsHpOURSb71PSGIu3t2lT9N+kJEAIHWwNx15zul17ReZUlnYtzunChQvq0TR7jbG87cypaudkXgMljbGk93HmOd1pu2xzEkJY/Rug9TnZwjnZ/kyj0Vji/wO0Oid7t7vanM6dO4eAgACXmhOgvZxKet7mmMrc0wl5e3sjLCwM2dnZ2LRpE/r162ez3/nz53HlyhWEhYUBANq2bYurV68iKSlJ7bN161aYTCa0+V/V0bZtW+zYsQMFBQVqn82bNyM6OtrmqY6yiokBvL2B69eBo0cdPRoiIiIiIu3TZJG2adMmbNy4EadPn8bmzZvRuXNnxMTEYOTIkbh27RomTJiA3377DWlpafjpp5/Qr18/NGzYED179gQANGnSBL169cJTTz2FPXv24Ndff8XYsWMxdOhQ1K5dGwDw2GOPwd3dHaNHj0ZKSgpWrVqFuXPn4qWXXnLk1J2OXg/Exxd9zfulERERERFVnCaLtJycHDz33HOIiYnBE088gXbt2mHTpk2oVq0a9Ho9Dh06hL59+6Jx48YYPXo04uPjsXPnTotTEVesWIGYmBh07doVDz74INq1a2dxDzR/f3/8+OOPOH36NOLj4/Hyyy9j8uTJmt5+39fX1y7vy5taa4O98ift4BqQG/OXG/OXG/PXHk1uHKIVBoMB/v7+Zbo4UMtWrQKGDi0q1vbscfRoiIiIiIicT3lqA00eSaPyM5lMSE9Pt7nrY0WZj6QdPAjk51f621MlsGf+pA1cA3Jj/nJj/nJj/trEIk0SQgikp6dbbUdaGSIjgaCgogLt0KFKf3uqBPbMn7SBa0BuzF9uzF9uzF+bWKRRhSkK0KpV0de8Lo2IiIiIqGJYpFGl4OYhRERERESVg0WaJBRFUW9iaw8s0pybvfMn58c1IDfmLzfmLzfmr03c3dGOZNndEQAuXgRq1wZ0OsBgKLrBNRERERERFeHujmTFZDLh7NmzdtvZJywMqFMHMJmA/fvt8hFUAfbOn5wf14DcmL/cmL/cmL82sUiThBACWVlZdt3Zh6c8Oq+qyJ+cG9eA3Ji/3Ji/3Ji/NrFIo0rDIo2IiIiIqOJYpFGlYZFGRERERFRxLNIkoSgKQkND7bqzj/leaSdPAllZdvsYugNVkT85N64BuTF/uTF/uTF/bWKRJgmdTofQ0FDodPaLvEYNoGHDoq/37bPbx9AdqIr8yblxDciN+cuN+cuN+WsT05KE0WjEyZMnYTQa7fo5POXROVVV/uS8uAbkxvzlxvzlxvy1iUWaRHJzc+3+GSzSnFdV5E/OjWtAbsxfbsxfbsxfe1ikUaVikUZEREREVDEs0qhS3X03oNMBf/5Z9IeIiIiIiMqHRZokFEVBeHi43Xf28fYGYmOLvubRNOdRVfmT8+IakBvzlxvzlxvz1yYWaZLQ6XQICgqqkp19eMqj86nK/Mk5cQ3IjfnLjfnLjflrE9OShNFoxNGjR6tkZx8Wac6nKvMn58Q1IDfmLzfmLzfmr00s0iRy8+bNKvkcc5G2bx8gRJV8JJVBVeVPzotrQG7MX27MX27MX3tYpFGli4sD3N2BrCzg1ClHj4aIiIiISFtYpFGlc3cHWrQo+pqnPBIRERERlQ+LNEnodDpERUVV2UWjvC7NuVR1/uR8uAbkxvzlxvzlxvy1yc3RA6CqoSgK/Pz8quzzWKQ5l6rOn5wP14DcmL/cmL/cmL82saSWhNFoRHJycpXt7GMu0vbvB7iZkONVdf7kfLgG5Mb85cb85cb8tYlFmkSq8i9ndDTg4wNcvw4cOVJlH0u3wX+ciWtAbsxfbsxfbsxfe1ikkV3o9UB8fNHXPOWRiIiIiKjsWKSR3fC6NCIiIiKi8mORJgmdTofo6Ogq3dmHRZrzcET+5Fy4BuTG/OXG/OXG/LWJaUnE3d29Sj/PXKQdPAjk5VXpR5MNVZ0/OR+uAbkxf7kxf7kxf+1hkSYJk8mE5ORkmEymKvvM+vWBoCCgoAA4dKjKPpZscET+5Fy4BuTG/OXG/OXG/LWJRRrZjaLwlEciIiIiovJikUZ2xSKNiIiIiKh8WKSRXbFIIyIiIiIqH0UIIRw9CFdlMBjg7++PnJwc+Pn5OXQsQgiYTCbodDooilJln5ueDoSFATodkJNTdINrqnqOyp+cB9eA3Ji/3Ji/3Ji/8yhPbcAjaRLJz8+v8s8MDQXq1gVMJmD//ir/eCrGEfmTc+EakBvzlxvzlxvz1x4WaZIwmUw4duyYQ3b24SmPjufI/Mk5cA3IjfnLjfnLjflrE4s0sjsWaUREREREZccijeyORRoRERERUdmxSJOIXq93yOe2alX031OngCtXHDIEguPyJ+fBNSA35i835i835q893N3Rjpxpd0dHa9wYOHEC2LgR6NnT0aMhIiIiIqpa3N2RrAghYDAY4KianKc8Opaj8yfH4xqQG/OXG/OXG/PXJhZpkjCZTDh16pTDdvZhkeZYjs6fHI9rQG7MX27MX27MX5tYpFGVYJFGRERERFQ2LNKoStx9N6DXAxcvAhcuOHo0RERERETOi0WaRDw9PR322dWrA7GxRV/zaJpjODJ/cg5cA3Jj/nJj/nJj/tqjySItNzcXL774IiIiIuDl5YX77rsPe//3k39BQQFeffVVxMXFwdvbG7Vr18YTTzyBP//80+I96tevD0VRLP688847Fn0OHTqE9u3bw9PTE+Hh4Zg1a1aVzbGy6fV6xMTEOHQLVp7y6DjOkD85FteA3Ji/3Ji/3Ji/NmmySHvyySexefNmLF++HMnJyejRowe6deuGCxcu4MaNG9i/fz/eeOMN7N+/H2vWrMGxY8fQt29fq/d58803cfHiRfXPuHHj1OcMBgN69OiBiIgIJCUlYfbs2Zg6dSoWL15clVOtNCaTCVeuXHHoRaMs0hzHGfInx+IakBvzlxvzlxvz1yY3Rw+gvP766y98/fXXWLt2LTp06AAAmDp1KtavX4+FCxdi2rRp2Lx5s8Vr5s+fj3vuuQdnz55FvXr11HZfX1+Ehoba/JwVK1YgPz8fn3zyCdzd3REbG4sDBw5gzpw5GDNmjP0maCdCCJw7dw4BAQEOG4O5SNu3DxACUBSHDUU6zpA/ORbXgNyYv9yYv9yYvzZprkgrLCyE0Wi0OrfWy8sLv/zyi83X5OTkQFEUq8X5zjvv4K233kK9evXw2GOPYfz48XBzK/qWJCYmokOHDnB3d1f79+zZEzNnzkR2djZq1Khh9Tl5eXnIy8tTHxsMBgCA0WiE0WgEACiKAp1OB5PJZHG/CnO7uV9p7TqdDoqi2GwHYPXbEiEEhBBW/fV6PYQQVv31er3VGEtqL+ucmjYFPDx0yM5WcPIkEBlZsTmV1F6Vcyqt3VnmZDQa1a9dZU7Fx8g5lT4n8xow/3GFOZU2ds7p77Gb8zd/jivMyRVzstecAFj9DKD1ObliTvaak/m1Jf0cqMU5mceotZxuff52NFek+fr6om3btnjrrbfQpEkThISEYOXKlUhMTETDhg2t+t+8eROvvvoqHn30UYs7ez///PNo2bIlAgMDsWvXLkyaNAkXL17EnDlzAADp6emIjIy0eK+QkBD1OVtF2owZM5CQkGDVnpKSAh8fHwBAYGAg6tWrh/PnzyMrK0vtExoaitDQUKSlpSE3N1dtDw8PR1BQEE6cOIGbN2+q7VFRUfDz80NqaqpF4NHR0XB3d0dycrLFGJo2bQqj0YiUlBT1H2y9Xo+4uDjk5ubi1KlTal9PT0/ExMQgOzsb586ds/jeN2jQAJmZmUhPT1fbyzOnxo0bITnZG3v3AoWFFZtTXFwc8vPzcezYMbXNEXOqzJzsNSchBK5fvw4ALjMnwPVysuechBDIyspCXl4ePD09XWJOrpiTveZ09uxZZGVlISUlBX5+fi4xJ1fMyV5zCggIQE5OjsXPAFqfkyvmZK85mTO/du0a0tLSXGJOWs3p2rVrKCtFaPD24ydPnsSoUaOwY8cO6PV6tGzZEo0bN0ZSUhKOHDmi9isoKMDAgQNx/vx5bN++3aJIu9Unn3yCp59+GteuXYOHhwd69OiByMhILFq0SO2TmpqK2NhYpKamokmTJlbvYetIWnh4OLKystTPduSRtLS0NNSrV8/iwtGq/i3ECy8o+PBDHcaPB2bP5m9WqvJI2tmzZxEZGQlFUVxiTsXH6Co52XNORqMRZ86cQWRkpPq5Wp9TaWPnnP4ee2FhIc6cOYOIiAjo9XqXmJMr5mSvOQkhcOrUKTV/V5iTK+ZkzyNp586dQ0REhFqwaX1O5jFqLSeDwYDAwEDk5OTcti4BNFqkmV2/fh0GgwFhYWEYMmQIrl27hu+++w5AUYE2ePBgnDp1Clu3bkVQUNBt3yslJQXNmjXD0aNHER0djSeeeAIGgwHffvut2mfbtm3o0qULsrKybB5Ju5XBYIC/v3+ZgpDFp58Cw4cD7doBO3c6ejRERERERFWjPLWBJnd3NPP29kZYWBiys7OxadMm9OvXD8DfBdqJEyewZcuWUgs0ADhw4AB0Oh2Cg4MBAG3btsWOHTtQUFCg9tm8eTOio6PLVKA5G5PJhPT0dKvfNlQ18+Yh+/cDhYUOHYpUnCV/chyuAbkxf7kxf7kxf23SZJG2adMmbNy4EadPn8bmzZvRuXNnxMTEYOTIkSgoKMCgQYOwb98+rFixAkajEenp6UhPT0d+fj6Aok1B3n//fRw8eBCnTp3CihUrMH78eAwbNkwtwB577DG4u7tj9OjRSElJwapVqzB37ly89NJLjpz6HRNCID093erQb1WLjgZ8fYEbN4BiZ6aSnTlL/uQ4XANyY/5yY/5yY/7apLmNQ4Ci3RonTZqE8+fPIzAwEAMHDsT06dNRrVo1pKWlYd26dQCAFi1aWLxu27Zt6NSpEzw8PPDFF19g6tSpyMvLQ2RkJMaPH29RgPn7++PHH3/Ec889h/j4eNSsWROTJ0/W5Pb7zkSnA+Ljge3bi+6XFhfn6BERERERETkXTRZpgwcPxuDBg20+V79+/VJ/U9CyZUv89ttvpX5O8+bNsZMXTlW61q3/LtJGjXL0aIiIiIiInIsmT3ek8lMUBYGBgVa7+jiC+bq0vXsdOw6ZOFP+5BhcA3Jj/nJj/nJj/tqk6d0dnR13d7QtLQ2IjASqVQNycwEPD0ePiIiIiIjIvqTZ3ZHKzmQy4ezZs06xs09EBFCzJlBQABw86OjRyMGZ8ifH4BqQG/OXG/OXG/PXJhZpkhBCICsryyl29lEUnvJY1Zwpf3IMrgG5MX+5MX+5MX9tYpFGDsEijYiIiIjINhZp5BAs0oiIiIiIbGORJglFURAaGuo0O/uYi7QjR4o2DyH7crb8qepxDciN+cuN+cuN+WsTizRJ6HQ6hIaGQqdzjshDQoDwcEAIYP9+R4/G9Tlb/lT1uAbkxvzlxvzlxvy1iWlJwmg04uTJkzAajY4eioqnPFYdZ8yfqhbXgNyYv9yYv9yYvzaxSJNIrpOdV8girWo5W/5U9bgG5Mb85cb85cb8tYdFGjkMizQiIiIiImss0shh4uOL/nv6NHD5smPHQkRERETkLFikSUJRFISHhzvVzj4BAUCjRkVf79vn0KG4PGfMn6oW14DcmL/cmL/cmL82sUiThE6nQ1BQkNPt7MNTHquGs+ZPVYdrQG7MX27MX27MX5uYliSMRiOOHj3qdDv7sEirGs6aP1UdrgG5MX+5MX+5MX9tYpEmkZs3bzp6CFaKF2lCOHYsrs4Z86eqxTUgN+YvN+YvN+avPSzSyKHuvhvQ64H0dODCBUePhoiIiIjI8VikkUNVrw7ExhZ9zVMeiYiIiIhYpElDp9MhKirKKS8a5XVp9ufM+VPV4BqQG/OXG/OXG/PXJqYlCUVR4Ofn55Tbr7JIsz9nzp+qBteA3Ji/3Ji/3Ji/NrFIk4TRaERycrJT7uxjLtL27ePmIfbizPlT1eAakBvzlxvzlxvz1yYWaRJx1r+ccXGAhwdw9Srwxx+OHo3rctb8qepwDciN+cuN+cuN+WsPizRyuGrVgBYtir7mKY9EREREJLsKFWnnzp3D1q1bcePGDbXNZDJh5syZuP/++9GtWzd89913FR4kuT5el0ZEREREVMStIi9+4403sH79eqSnp6tt06dPx5QpU9THP//8M3bt2oXW5p/CySF0Oh2io6OddmcfFmn25ez5k/1xDciN+cuN+cuN+WtThdL69ddf0a1bN1SrVg0AIITA/PnzERMTg7Nnz2LPnj3w9vbG7NmzK2WwVDHu7u6OHkKJzEXa/v1AYaFjx+KqnDl/qhpcA3Jj/nJj/nJj/tpToSItMzMTERER6uMDBw7g0qVLGDduHOrWrYtWrVrh4Ycfxl4eHnE4k8mE5ORkmEwmRw/FpuhowNcX+OsvIDXV0aNxPc6eP9kf14DcmL/cmL/cmL82VahIM5lMFoFv374diqKgS5cualudOnUsTockskWnA+Lji75mTU9EREREMqtQkVavXj3s2bNHffztt98iLCwM0dHRalt6ejoCAgIq8jEkCV6XRkRERERUwSJt4MCB+PXXXzFo0CAMGzYMv/zyCwYOHGjRJzU1FVFRURUaJMmBRRoREREREaAIIcSdvthgMKBHjx7q0bTmzZtj27ZtqFGjBgDgzJkziIqKwsSJEzF9+vTKGbGGGAwG+Pv7IycnB35+fg4dixACJpMJOp0OiqI4dCwlSUsDIiMBNzcgNxfw9HT0iFyHFvIn++IakBvzlxvzlxvzdx7lqQ0qtAW/n58ffvvtNxw+fBgA0KRJE+j1eos+a9asQatWrSryMVRJ8vPz4enElU9EBFCzJnD5MnDwINCmjaNH5FqcPX+yP64BuTF/uTF/uTF/7amUGyY0a9YMzZo1syrQIiIi0K9fP9SpU6cyPoYqwGQy4dixY069s4+i8JRHe9FC/mRfXANyY/5yY/5yY/7aVKEiLTc3F6dOnUJBQYFF+6pVq/D444/jySefxO+//16hAZJcWKQRERERkewqdLrjK6+8gs8++wwZGRnqDa0XLlyIsWPHwnyp28qVK5GUlISYmJiKj5ZcHos0IiIiIpJdhY6k/fzzz+jWrRuqV6+utr3zzjuoU6cOduzYgS+//BJCCMyePbvCA6WKu/V0VGdkLtKOHi3aPIQqjxbyJ/viGpAb85cb85cb89eeChVpFy9eRGRkpPr4yJEjOHfuHJ5//nm0a9cOgwYNQt++fbFjx44KD5QqRq/XIy4uzun/koaEAOHhgBBAUpKjR+M6tJI/2Q/XgNyYv9yYv9yYvzZVqEjLy8uDu7u7+vjnn3+Goijo0aOH2hYVFYULFy5U5GOoEgghYDAYUIE7LlQZnvJY+bSUP9kH14DcmL/cmL/cmL82VahIq1u3Lg4dOqQ+3rBhAwIDA9G8eXO17cqVK/Dx8anIx1AlMJlMOHXqlCZ29mGRVvm0lD/ZB9eA3Ji/3Ji/3Ji/NlVo45AHHngAH374If71r3/B09MTGzduxBNPPGHR5/jx46hXr16FBklyYZFGRERERDKrUJE2adIkrF+/HnPmzAEAhIWF4c0331Sfz8zMxK+//oqxY8dWbJQklfj4ov+mpQGXLgG1ajl0OEREREREVapCRVpoaChSUlLw008/AQA6dOgAPz8/9fnLly9j9uzZ6NmzZ8VGSZVCK3eaDwgAGjcGjh8H9u0DHnjA0SNyDVrJn+yHa0BuzF9uzF9uzF97FMGrCO3GYDDA398fOTk5FsUrlW7YMGDFCiAhAZg82dGjISIiIiKqmPLUBhXaOKS4Cxcu4LvvvsPKlSvx3XffcUdHJ2MymXDlyhXNXDTK69Iql9byp8rHNSA35i835i835q9NFTrdEQD++OMPPPPMM9i6davVc127dsWCBQvQsGHDin4MVZAQAufOnUNAQICjh1ImxYs0IQBFcex4tE5r+VPl4xqQG/OXG/OXG/PXpgoVaefOnUO7du2QmZmJmJgYdOjQAWFhYUhPT8eOHTuwZcsWtG/fHnv27EF4eHhljZkk0KIFoNcDGRnA+fNFN7gmIiIiIpJBhU53TEhIQGZmJhYsWICUlBR89NFHmDJlChYuXIiUlBQsXLgQGRkZFjs+Vobc3Fy8+OKLiIiIgJeXF+677z7sLXZenBACkydPRlhYGLy8vNCtWzecOHHC4j2ysrLw+OOPw8/PDwEBARg9ejSuXbtm0efQoUNo3749PD09ER4ejlmzZlXqPKhk1asDzZoVfc1THomIiIhIJhUq0jZt2oQ+ffrgn//8JxQb56M9/fTT6NOnD3744YeKfIyVJ598Eps3b8by5cuRnJyMHj16oFu3bup1cLNmzcIHH3yAjz76CLt374a3tzd69uyJmzdvqu/x+OOPIyUlBZs3b8aGDRuwY8cOjBkzRn3eYDCgR48eiIiIQFJSEmbPno2pU6di8eLFlTqXquTr6+voIZQLr0urXFrLnyof14DcmL/cmL/cmL8GiQpwd3cXr7322m37vPbaa8Ld3b0iH2Phxo0bQq/Xiw0bNli0t2zZUrz++uvCZDKJ0NBQMXv2bPW5q1evCg8PD7Fy5UohhBCpqakCgNi7d6/a54cffhCKoogLFy4IIYRYsGCBqFGjhsjLy1P7vPrqqyI6OrrMY83JyREARE5Ozh3NVXaLFgkBCNG1q6NHQkRERERUMeWpDSp0TVqtWrWQmpp62z6pqamoVYl3Iy4sLITRaLS634OXlxd++eUXnD59Gunp6ejWrZv6nL+/P9q0aYPExEQMHToUiYmJCAgIQKtWrdQ+3bp1g06nw+7du9G/f38kJiaiQ4cOcHd3V/v07NkTM2fORHZ2NmrUqGE1try8POTl5amPDQYDAMBoNMJoNAIAFEWBTqeDyWSCKHb3A3O7uV9p7TqdDoqi2GwHYHMHn8zMTNSsWVPtAwB6vR5CCKv+er3eaowltdtrTi1bAoAe+/YJFDWZrPrbmqszz6m0sdtrTiaTCZcuXUJoaCgAuMScio/RVXKy55zMayAkJER9f63PqbSxc05/j91oNOLSpUuoVasWdDqdS8zJFXOy15wAID09Xc3fFebkijnZa07m3R1t/Tyu1TmZx6i1nG59/nYqVKT17NkTS5cuxccff4zRo0dbPf/JJ59g/fr1GDFiREU+xoKvry/atm2Lt956C02aNEFISAhWrlyJxMRENGzYEOnp6QCAkJAQi9eFhISoz6WnpyM4ONjieTc3NwQGBlr0iYyMtHoP83O2irQZM2YgISHBqj0lJQU+Pj4AgMDAQNSrVw/nz59HVlaW2ic0NBShoaFIS0tDbm6u2h4eHo6goCCcOHHC4nTNqKgo+Pn5ITU11SLw6OhouLu7Izk52WIMTZs2xYULF5Cenq7+g63X6xEXF4fc3FycOnVK7evp6YmYmBhkZ2fj3Llzaruvry8aNGiAzMxM9ftkzzkJAXh4NEdOjg7HjxuRl2c5p7i4OOTn5+PYsWNqm7PPqbSc7DUnIQSuX7+OkJAQXLhwwSXmBLheTvackxACWVlZ8Pf3h6enp0vMyRVzsteczp49i6ysLGRkZMDPz88l5uSKOdlrTgEBATh+/DgyMjLUnwG0PidXzMlec1IUBUIIeHp6Ii0tzSXmpNWcbt3/4nYqdDPrs2fPolWrVrhy5QqaNm2Kjh07IiQkBBkZGdixYwdSUlIQFBSEpKSkSt3d8eTJkxg1ahR27NgBvV6Pli1bonHjxkhKSsLHH3+M+++/H3/++SfCwsLU1wwePBiKomDVqlV4++23sWzZMouwASA4OBgJCQl45pln0KNHD0RGRmLRokXq86mpqYiNjUVqaiqaNGliNS5bR9LCw8ORlZWl3rDOUb+FEEIgOTkZsbGx0Ov1aruz/xbi/vt12L1bwfLlAo8+6rq/WbH3nIxGI1JSUtC8eXP1H2utz6n4GF0lJ3vOybwG4uLi1M/V+pxKGzvn9PfYCwsLkZKSov4/wBXm5Io52WtOQggcOnTI4mcArc/JFXOy15yMRiNSU1PRrFkzqz0ktDon8xi1lpPBYEBgYGCZbmZdoSNp9erVw6+//oqnn34a27dvR0pKisXznTt3xkcffVTp2+83aNAAP//8M65fvw6DwYCwsDAMGTIEUVFR6ulcGRkZFkVaRkYGWrRoAaCoOs7MzLR4z8LCQmRlZamvDw0NRUZGhkUf82Nzn1t5eHjAw8PDqt38P8TizKHZ6muPdqPRCEVRbI7F3H6rksZY3vaKjP2ee4Ddu4F9+xQMG1b293HmOd1pe0XnZP6H2ZXmVFo752TZriiK+sdV5nSn7bLNSa/XW/0/QOtzsoVzsv2Zt/sZQKtzsnc758Q5VdYYi7eX9LzNMZW5ZwkaNWqErVu34syZM1i7di2WL1+OtWvX4syZM/jpp5+wZs0adO3ataIfY5O3tzfCwsKQnZ2NTZs2oV+/foiMjERoaCh++ukntZ/BYMDu3bvRtm1bAEDbtm1x9epVJCUlqX22bt0Kk8mENm3aqH127NiBgoICtc/mzZsRHR1t81RHZ6coCgIDA61+g+LsuMNj5dBq/lR5uAbkxvzlxvzlxvy1qUKnO5bFyJEj8emnn5brQrnSbNq0CUIIREdH448//sCECRPg6emJnTt3olq1apg5cybeeecdLFu2DJGRkXjjjTdw6NAhpKamqhuOPPDAA8jIyMBHH32EgoICjBw5Eq1atcLnn38OAMjJyUF0dDR69OiBV199FYcPH8aoUaPw3nvvWWzVfzsGgwH+/v5lOqRJth09CjRpAnh5AQYD4FahY79ERERERI5RntqgwkfSHCEnJwfPPfccYmJi8MQTT6Bdu3bYtGkTqlWrBgB45ZVXMG7cOIwZMwatW7fGtWvXsHHjRosdIVesWIGYmBh07doVDz74INq1a2dxDzR/f3/8+OOPOH36NOLj4/Hyyy9j8uTJZS7QnI3JZMLZs2etztt1do0bA35+wF9/AbecTUvloNX8qfJwDciN+cuN+cuN+WuTJo9LDB48GIMHDy7xeUVR8Oabb+LNN98ssU9gYKB61KwkzZs3x86dO+94nM7EvLNbnTp1HD2UctHpgPh4YNu2olMe77rL0SPSJq3mT5WHa0BuzF9uzF9uzF+bNHkkjeTC69KIiIiISCYs0sjpsUgjIiIiIpmwSJOEoigIDQ3V5M4+5iItORkodt9AKgct50+Vg2tAbsxfbsxfbsxfm8q9u+ODDz5Yrg9ITk7Gn3/+Wam7O2oFd3esHEIAISHApUtAYiJw772OHhERERERUfmUpzYo98YhGzduLPeAWLk7ntFoRFpaGurXr1+uG+k5A0UpOpr2/fdFpzyySCs/LedPlYNrQG7MX27MX27MX5vKXaSdPn3aHuOgKpCbm+voIdyx4kUa3Rkt50+Vg2tAbsxfbsxfbsxfe8pdpEVERNhjHES3xc1DiIiIiEgW3DiENMFcpB07BhgMjh0LEREREZE9sUiThKIoCA8P1+z1gcHBQL16RZuIJCU5ejTao/X8qeK4BuTG/OXG/OXG/LWJRZokdDodgoKCoNNpN3Ke8njnXCF/qhiuAbkxf7kxf7kxf20q9zVppE1GoxEnTpxAo0aNNLuzT+vWwNdfs0i7E66QP1UM14DcmL9ry83NxY8//ojz588jJyfH6rZHJpMJWVlZCAwM5A/qEnL2/PV6PQICAhAREYHu3bujevXqjh6SU2CRJpGbGr8TNI+kVYzW86eK4xqQG/N3LUIIrF69GitXrsQPP/yAvLw8eHl5ISAgAG5u1j/eCSF4upvEnDn/goICZGdnIy8vD9WrV8dDDz2EYcOGoU+fPo4emkOxSCPNiI8v+u+ZM0U3tq5Vy7HjISIicgQhBCZMmIB3330X99xzD6ZNm4ZBgwahfv36jh4a0R07ceIEVq9ejdWrV6Nv376YNm0aXn/9dUcPy2Gc75gnUQn8/YHo6KKveTSNiIhkVLxAmzdvHnbv3o1//etfLNBI8xo1aoTXXnsNv//+OxISEvDvf/8b06dPd/SwHIZFmiR0Oh2ioqKc8lzk8uApj3fGVfKnO8c1IDfm7zq2bt2Kd999F3PnzsXYsWMdPRwiu5g8eTKmTJmCf//73/j9998dPRyH4L/WklAUBX5+fk57PnJZsUi7M66SP905rgG5MX/X8eWXX6J+/foYN26co4dCZFevv/46AgMDsXr1akcPxSFYpEnCaDQiOTnZascnrSlepAnh2LFoiavkT3eOa0BuzN81FBYWYs2aNXjkkUdYcJPLq1atGvr374/Vq1dDSPhDH4s0ibjC/5xbtADc3IDMTODcOUePRltcIX+qGK4BuTF/7du3bx8uX76MAQMGOHooRFViwIAB+OOPP3DixAlHD6XKsUgjTfHyApo1K/qapzwSEZFM0tPTAQBRUVEOHglR1TCv9YyMDAePpOqxSCPN4XVpREQkI4PBAADw9/d38EiIqkZAQAAAICcnx7EDcQAWaZLQ6XSIjo52iZ29WKSVnyvlT3eGa0BuzN81mEwmAIBer3fwSOxPURRMnTrV0cMgBzOvdfPalwn/tZaIu7u7o4dQKcxF2r59gIR/Z++Yq+RPd45rQG7MXz7JyckYNGgQIiIi4OnpiTp16qB79+6YN2+eo4emGdeuXcOUKVPQq1cvBAYGQlEULF261GZfRVFK/NO9e3eLviaTCbNmzUJkZCQ8PT3RvHlzrFy50ub7zp8/H02aNIGHhwfq1KmDl156CdevX6/sqZKTYZEmCZPJhOTkZJf4TURsLODpCRgMgITXkd4RV8qf7gzXgNyYv3x27dqFVq1a4eDBg3jqqacwf/58PPnkk9DpdJg7d66jh6cZly9fxptvvokjR47grrvuum3f5cuXW/154YUXAAA9evSw6Pv666/j1VdfVYvmevXq4bHHHsMXX3xh0e/VV1/FuHHj0KxZM8ydOxcDBw7EvHnzuHmMBNwcPQCi8qpWDbj7biAxseiUx+hoR4+IiIjIuUyfPh3+/v7Yu3evel2PWWZmpmMGpUFhYWG4ePEiQkNDsW/fPrQ2n85jw7Bhw6zatm/fDkVR8Oijj6ptFy5cwLvvvovnnnsO8+fPBwA8+eST6NixIyZMmIBHHnkEer0eFy9exJw5c/CPf/wDn376qfr6xo0bY9y4cVi/fj369OlTibMlZ8IjaaRJvC6NiIioZCdPnkRsbKxVgQYAwcHBFo+XLFmCLl26IDg4GB4eHmjatCkWLlxo9br69evjoYcewvbt29GqVSt4eXkhLi4O27dvBwCsWbMGcXFx8PT0RHx8PH7//XeL148YMQI+Pj44deoUevbsCW9vb9SuXRtvvvlmme6DdeHCBYwaNQohISHw8PBAbGwsPvnkE6t+8+bNQ2xsLKpXr44aNWqgVatW+Pzzzy36HD16FGfPni31Mz08PBAaGlpqP1vy8vLw9ddfo2PHjqhbt67avnbtWhQUFODZZ59V2xRFwTPPPIPz588jMTERAJCYmIjCwkIMHTrU4n3Nj2896kauhUUaaRKLNCIiopJFREQgKSkJhw8fLrXvwoULERERgddeew3vvvsuwsPD8eyzz+LDDz+06vvHH3/gscceQ58+fTBjxgxkZ2ejT58+WLFiBcaPH49hw4YhISEBJ0+exODBg61OsTUajejVqxdCQkIwa9YsxMfHY8qUKZgyZcptx5iRkYF7770XW7ZswdixYzF37lw0bNgQo0ePxvvvv6/2+7//+z88//zzaNq0Kd5//30kJCSgRYsW2L17t8X7NWnSBE888USp35uK+P7773H16lU8/vjjFu2///47vL290aRJE4v2e+65R30eKCryAMDLy8uiX/Xq1QEASUlJdhk3OQlBdpOTkyMAiJycHEcPRZhMJlFYWChMJpOjh1Ipjh4VAhDC01OI/HxHj8b5uVr+VH5cA3Jj/q5hyZIlAoAoKCgote+PP/4o9Hq90Ov1om3btuKVV14RmzZtEvk2/qd548YNq7aePXuKqKgoi7aIiAgBQOzatUtt27RpkwAgvLy8xJkzZ9T2RYsWCQBi27Ztatvw4cMFADFu3Di1zWQyid69ewt3d3dx6dIltR2AmDJlivp49OjRIiwsTFy+fNliTEOHDhX+/v7qHPr16ydiY2NL+e4UvX/Hjh1L7Vfc3r17BQCxZMmSMvUfOHCg8PDwENnZ2RbtvXv3tvreCiHE9evXBQAxceJEIYQQSUlJAoB46623LPpt3LhRABA+Pj7lGr8WZWZmCgBi7dq1jh5KpShPbcAjaRLJz8939BAqTaNGgJ8fcPMmkJLi6NFogyvlT3eGa0BuzF8u3bt3R2JiIvr27YuDBw9i1qxZ6NmzJ+rUqYN169ZZ9C1+pCYnJweXL19Gx44dcerUKav7UzVt2hRt27ZVH7dp0wYA0KVLF9SrV8+q/dSpU1ZjGzt2rPq1oigYO3Ys8vPzsWXLFptzEULg66+/Rp8+fSCEwOXLl9U/PXv2RE5ODvbv3w+g6L5a58+fx95STrURQqinadqDwWDAd999hwcffNDqlNO//voLHh4eVq/x9PRUnweAli1bok2bNpg5cyaWLFmCtLQ0/PDDD3j66adRrVo1tR+5JhZpkjCZTDh27JjL7Oyl0wGtWhV9zVMeS+dq+VP5cQ3IjfnLqXXr1lizZg2ys7OxZ88eTJo0Cbm5uRg0aBBSU1PVfr/++iu6desGb29vBAQEoFatWnjttdcAWN9EuHghBvx9Y+3w8HCb7dnZ2RbtOp0OUVFRFm2NGzcGAKSlpdmcx6VLl3D16lUsXrwYtWrVsvgzcuRIAH9vhvLqq6/Cx8cH99xzDxo1aoTnnnsOv/766+2/UXbw9ddf4+bNm1anOgJFRbH5VMbibt68qT5f/H3uuusujBo1CpGRkejTpw8GDx6Mu+++Gz4+PvabADkcd3ckzWrdGti6tahIe+opR4+GiIjIObm7u6N169Zo3bo1GjdujJEjR2L16tWYMmUKTp48ia5duyImJgZz5sxBeHg43N3d8f333+O9996zKuxLupF2Se2iDBuClMY8hmHDhmH48OE2+zRv3hxA0bVmx44dw4YNG7Bx40Z8/fXXWLBgASZPnoyEhIQKj6WsVqxYAX9/fzz00ENWz4WFhWHbtm0QQkBRFLX94sWLAIDatWurbXXq1MEvv/yCEydOID09HY0aNUJoaChq166tFrfkmlikkWZx8xAiIqLyafW/01DMBcH69euRl5eHdevWWRwl27Ztm10+32Qy4dSpUxYFxvHjxwEU7R5pS61ateDr6wuj0Yhu3bqV+hne3t4YMmQIhgwZgvz8fAwYMADTp0/HpEmT1FMK7enixYvYtm0bRowYYfO0xhYtWuC///0vjhw5gqZNm6rt5s1NWrRoYfWaRo0aoVGjRgCA1NRUXLx4ESNGjLDL+Mk58HRHiZT0Wy6tMhdpyckAT8sunavlT+XHNSA35i8X85GaW33//fcAgOj/3WTUvC6K983JycGSJUvsNjbzvcHMnzt//nxUq1YNXbt2tdlfr9dj4MCB+Prrr23uVnnp0iX16ytXrlg85+7ujqZNm0IIgYKCArW9rFvw34kvvvgCJpPJ5qmOANCvXz9Uq1YNCxYsUNuEEPjoo49Qp04d3HfffSW+t8lkwiuvvILq1avjn//8Z6WPnZwHj6RJQq/XIy4uztHDqFTh4UBwMJCZCRw4ABS7jplu4Yr5U/lwDciN+ctn3LhxuHHjBvr374+YmBjk5+dj165dWLVqFerXr69ey9WjRw+4u7ujT58+ePrpp3Ht2jX83//9H4KDg9WjbZXJ09MTGzduxPDhw9GmTRv88MMP+O677/Daa6+hVq1aJb7unXfewbZt29CmTRs89dRTaNq0KbKysrB//35s2bIFWVlZ6nxCQ0Nx//33IyQkBEeOHMH8+fPRu3dv+Pr6qu/XpEkTdOzYsUybh8yfPx9Xr17Fn3/+CaDo6OP58+cBFH2fzdffma1YsQK1a9dGp06dbL5f3bp18eKLL2L27NkoKChA69at8e2332Lnzp1YsWKFxS9UXnjhBdy8eRMtWrRAQUEBPv/8c+zZswfLli2zuj6QXIzd9pgkp9uCPycnx+W2X+7du2gr/rlzHT0S5+aq+VPZcQ3Ijfm7hvJswf/DDz+IUaNGiZiYGOHj4yPc3d1Fw4YNxbhx40RGRoZF33Xr1onmzZsLT09PUb9+fTFz5kzxySefCADi9OnTar+IiAjRu3dvq88CIJ577jmLttOnTwsAYvbs2Wrb8OHDhbe3tzh58qTo0aOHqF69uggJCRFTpkwRRqPR6j2Lb8EvhBAZGRniueeeE+Hh4aJatWoiNDRUdO3aVSxevFjts2jRItGhQwcRFBQkPDw8RIMGDcSECROsfhZDObbgN996wNaf4t8fIYQ4evSoACBeeuml276n0WgUb7/9toiIiBDu7u4iNjZWfPbZZ1b9lixZIu666y7h7e0tfH19RdeuXcXWrVvLNG5XIPMW/IoQlXBFJ9lkMBjg7++PnJwc+Pn5OXQsRqMRycnJiIuLc6lTXhISgKlTgWHDgOXLHT0a5+Wq+VPZcQ3Ijfm7hqVLl2LkyJEoKCiAm5v2ToYaMWIEvvrqK1y7ds3RQyGNuHTpEoKDg7F27Vr07dvX0cOpsPLUBrwmjTSNm4cQERERkathkUaaZi7Sjh0DbrmVCxERERGRJrFIk0hVbDtb1WrVAiIiir5OSnLsWJydK+ZP5cM1IDfmT0SkHSzSJKHX6xETE+OS1yLwlMfSuXL+VDZcA3Jj/uQMli5dyuvRiMqIRZokTCYTrly5ApPJ5OihVDoWaaVz5fypbLgG5Mb8iYi0hUWaJIQQOHfunM0bW2odi7TSuXL+VDZcA3Jj/kRE2sIijTQvPh5QFODs2aIbWxMREZHc0tLSoCgKli5dqrZNnToViqI4blBE5cAijTTPzw+Iji76mkfTiIiIiq7/UhQF+/btq/B73bhxA1OnTsX27dsrPrAyWLBggUVx5SrWrFmDIUOGICoqCtWrV0d0dDRefvllXL161apv/fr1oSiK1Z9//vOfVn2vXr2KMWPGoFatWvD29kbnzp2xf//+KpgR2ZP27oRId8zX19fRQ7Cb1q2Bo0eLirTevR09GufkyvlT2XANyI350526ceMGEhISAACdOnWy++ctWLAANWvWxIgRIyr1ff/9739j4sSJlfqe5TFmzBjUrl0bw4YNQ7169ZCcnIz58+fj+++/x/79++Hl5WXRv0WLFnj55Zct2ho3bmzx2GQyoXfv3jh48CAmTJiAmjVrYsGCBejUqROSkpLQqFEju8+L7INFmiT0ej0aNGjg6GHYTevWwPLlPJJWElfPn0rHNSA35k8EuLm5wc3NcT/6fvXVV1ZFbnx8PIYPH44VK1bgySeftHiuTp06GDZsWKnvuWvXLqxevRqDBg0CAAwePBiNGzfGlClT8Pnnn1fqHKjq8HRHSZhMJqSnp7vszl7FNw/hdfHWXD1/Kh3XgNyYP9mSn5+PyZMnIz4+Hv7+/vD29kb79u2xbds2tU9aWhpq1aoFAEhISFBPu5s6dara5+jRoxg0aBACAwPh6emJVq1aYd26dRafZT798tdff8VLL72knprXv39/XLp0Se1Xv359pKSk4Oeff1Y/q7Sjd1evXsWIESPg7++PgIAADB8+3OYphLauSVMUBWPHjsXq1avRtGlTeHl5oW3btkhOTgYALFq0CA0bNoSnpyc6deqEtLQ0i9ffuHEDR48exeXLl287RsD2Ucj+/fsDAI4cOWLzNfn5+bh+/XqJ7/nVV18hJCQEAwYMUNtq1aqFwYMHY+3atcjLyyt1XOScNFekGY1GvPHGG4iMjISXlxcaNGiAt956y2LHKlvn8CqKgtmzZ6t9bJ3r+84771h81qFDh9C+fXt4enoiPDwcs2bNqrJ5VjYhBNLT0112Z68WLQA3N+DSpaINRMiSq+dPpeMakBvzJ1sMBgP++9//olOnTpg5cyamTp2KS5cuoWfPnjhw4ACAoh/4Fy5cCKCooFi+fDmWL1+uFgUpKSm49957ceTIEUycOBHvvvsuvL298fDDD+Obb76x+sxx48bh4MGDmDJlCp555hmsX78eY8eOVZ9///33UbduXcTExKif9frrr5c4ByEE+vXrh+XLl2PYsGGYNm0azp8/j+HDh5f5+7Bz5068/PLLGD58OKZOnYojR47goYcewocffogPPvgAzz77LCZMmIDExESMGjXK4rV79uxBkyZNMH/+/DJ/XnHp6ekAgJo1a1o9t3XrVlSvXh0+Pj6oX78+5s6da9Xn999/R8uWLaHTWf5If8899+DGjRs4fvz4HY2LHE9zpzvOnDkTCxcuxLJlyxAbG4t9+/Zh5MiR8Pf3x/PPPw8AuHjxosVrfvjhB4wePRoDBw60aH/zzTfx1FNPqY+Ln69vMBjQo0cPdOvWDR999BGSk5MxatQoBAQEYMyYMXacId0JT08gLg74/feio2kREY4eERERkXOrUaMG0tLS4O7urrY99dRTiImJwbx58/Dxxx/D29sbgwYNwjPPPIPmzZtbnX73wgsvoF69eti7dy88PDwAAM8++yzatWuHV199VT1SZBYUFIQff/xRPaJlMpnwwQcfICcnB/7+/nj44Yfx73//GzVr1iz1VD8AWLduHXbs2IFZs2ZhwoQJAIBnnnkGnTt3LvP34dixYzh69Cjq16+vfl+efvppTJs2DcePH1d/PjQajZgxYwbS0tLUvhU1c+ZM6PV69VRFs+bNm6Ndu3aIjo7GlStXsHTpUrz44ov4888/MXPmTLXfxYsX0aFDB6v3DQsLAwD8+eefiIuLq5SxUtXSXJG2a9cu9OvXD73/tztE/fr1sXLlSuzZs0ftExoaavGatWvXonPnzoiKirJo9/X1teprtmLFCuTn5+OTTz6Bu7s7YmNjceDAAcyZM4dFmpNq3frvIu2Wf+uIiIjoFnq9Hnq9HkBRsXT16lWYTCa0atWqTLsDZmVlYevWrXjzzTeRm5uL3Nxc9bmePXtiypQpuHDhAurUqaO2jxkzxuKUw/bt2+O9997DmTNn0Lx583LP4fvvv4ebmxueeeYZi3mNGzcOO3fuLNN7dO3a1aLoatOmDQBg4MCBFr/AN7efOnVK7d+pU6c7PkL9+eef4+OPP8Yrr7xitcHHraeLjhw5Eg888ADmzJmDcePGoW7dugCAv/76Sy2Oi/P09FSfJ23SXJF23333YfHixTh+/DgaN26MgwcP4pdffsGcOXNs9s/IyMB3332HZcuWWT33zjvv4K233kK9evXw2GOPYfz48eoFpYmJiejQoYPFb5d69uyJmTNnIjs7GzVq1LB6v7y8PItzfw0GA4Ci37wYjUYARadi6nQ6mEwmq1M0dTqd2q+0dp1OB0VRbLYDsHndQY0aNaza9Xo9hBA2228dY0ntjprTre3x8QoA3f+uS3ONORUfY0XmZDKZUKNGDSiK4jJzKj5Gzqn0OZlMJgQEBACw/fdDi3Mqbeyc099jN+dvft4V5uSKOZXl3/LKtmzZMrz77rs4evQoCgoK1PbIyMhSX/vHH39ACIE33ngDb7zxhs0+mZmZFkVavXr1LJ43/zyVnZ19J8PHmTNnEBYWBh8fH4v2aPO9ecrg1jH5+/sDAMLDw2223+lYi9u5cydGjx6Nnj17Yvr06aX2VxQF48ePx6ZNm7B9+3b1KKOXl5fN685u3rypPu8KhBBWf/+0+G/Erc/fjuaKtIkTJ8JgMCAmJgZ6vR5GoxHTp0/H448/brP/smXL4Ovra3FBJQA8//zzaNmyJQIDA7Fr1y5MmjQJFy9eVIu99PR0q3+gQkJC1OdsFWkzZsxQt6gtLiUlRf3HIzAwEPXq1cP58+eRlZWl9gkNDUVoaCjS0tIsfhMVHh6OoKAgnDhxQv0LBwBRUVHw8/NDamqqReDR0dFwd3dXL3g1i4uLQ3BwMFJSUtQ2vV6PuLg45Obm4tSpU2q7p6cnYmJikJ2djXPnzqntvr6+aNCgATIzM9VzqB09p/z8fBw7dgwA4O/vCSAGSUlATk4u0tK0PyegcnPS6XQ4e/asS83JFXOy55xCQkJcbk6umJO95nT16lWXmxPgejmVNKfz58+jMn322WcYMWIEHn74YUyYMAHBwcHQ6/WYMWMGTp48WerrzT94/utf/0LPnj1t9mnYsKHFY/ORu1s58nrJksZkr7EePHgQffv2RbNmzfDVV1+VecdJc9FYfC2HhYVZXeYD/H3pT+3atSs0VmeRn59v8fdMq/9GXLt2reyTFhqzcuVKUbduXbFy5Upx6NAh8emnn4rAwECxdOlSm/2jo6PF2LFjS33fjz/+WLi5uYmbN28KIYTo3r27GDNmjEWflJQUAUCkpqbafI+bN2+KnJwc9c+5c+cEAJGVlSUKCwtFYWGhMBqNQgghjEaj2la8vXjb7dpNJlOJ7SaTyaq9sLBQpKWlifz8fIs2IYTN/rbGWFK7o+Z0a/vNm4XCy8skACFSU11jTpWVU35+vkhLSxNGo9Fl5uSKOdlzTvn5+eL06dPqc64wJ1fMyV5zMudv/n+AK8zJFXMqrf3jjz8WAERBQYEozZIlSwQAsXfv3hL79OvXT0RFRanzM7vvvvtERESE+vjy5csCgJgyZYpFv4yMDAFATJo06Y7Hs23bNgFAbNu2TW1r1qyZ6NixY6nvKYQQY8aMEW5ubiI3N9ei/csvvxQAxJIlS9S2KVOmiFt/9AUgnnvuOYu206dPCwBi9uzZNse6evXqMo3Nlj/++EOEhoaKxo0bi8zMzHK9dv369QKA+Pzzz9W2QYMGiZCQEHXtmj311FOievXq6s+1WpWZmSkAiG+//dYl/o3IysoSAEROTk6pc9fckbQJEyZg4sSJGDp0KICi31adOXMGM2bMsNrJZ+fOnTh27BhWrVpV6vu2adMGhYWFSEtLQ3R0NEJDQ5GRkWHRx/y4pOvYPDw8bJ4XXPycb7Nbd+Ep3tce7UajEdnZ2ahbt67Vc4qi2HyfksZY3nZ7zenWdr0euPtuYNcuYN8+BU2aaH9OxVU0p5Lyr8wxlrfdVdZecc48p6tXryI8PLzEMZb0Ps48pzttl21OQgg1f3Mfrc/JFlefU0l97pR5HEII9Tqx3bt3IzEx0eIUwOrVqwOA1bb2wcHB6NSpExYtWoRx48apm1WYXbp0Sd2+vzy8vb1tbqFvy4MPPojFixdj4cKF6sYhRqMR8+bNK/fn3okbN27g7NmzqFmzps0dGotLT09Hjx49oNPpsGnTphK/N1lZWfD397dYJwUFBXjnnXfg7u5usSnKoEGD8NVXX2HNmjXq5iOXL1/G6tWr0adPH5s/l2pRSX9vtPZvREnP26K5Iu3GjRtW30jzOaa3+vjjjxEfH4+77rqr1Pc9cOAAdDodgoODAQBt27bF66+/joKCAlSrVg0AsHnzZkRHR9s81ZGcQ+vWRUXa3r3AP/7h6NEQERE51ieffIKNGzdatb/wwgt46KGHsGbNGvTv3x+9e/fG6dOn8dFHH6Fp06YWp2V5eXmhadOmWLVqFRo3bozAwEA0a9YMzZo1w4cffoh27dohLi4OTz31FKKiopCRkYHExEScP38eBw8eLPeY4+PjsXDhQkybNg0NGzZEcHAwunTpYrNvnz59cP/992PixIlIS0tD06ZNsWbNGuTk5JT7c+/Enj170LlzZ0yZMsXi3nG29OrVC6dOncIrr7yCX375Bb/88ov6XEhICLp37w6gaNOQadOmYdCgQYiMjERWVhY+//xzHD58GG+//bbFwYJBgwbh3nvvxciRI5GamoqaNWtiwYIFMBqNNi/BIe3QXJHWp08fTJ8+HfXq1UNsbCx+//13zJkzx+q+FQaDAatXr8a7775r9R6JiYnYvXs3OnfuDF9fXyQmJmL8+PEYNmyYWoA99thjSEhIwOjRo/Hqq6/i8OHDmDt3Lt57770qmSfdmeI3tSYiIpKd+R5ntxoxYgRGjBiB9PR0LFq0CJs2bULTpk3x2WefYfXq1di+fbtF///+978YN24cxo8fj/z8fEyZMgXNmjVD06ZNsW/fPiQkJGDp0qW4cuUKgoODcffdd2Py5Ml3NObJkyfjzJkzmDVrFnJzc9GxY8cSizSdTod169bhxRdfxGeffQZFUdC3b1+8++67uPvuu+/o8+3FXLDauu9ux44d1SItLi5OzeLSpUtwd3dHixYt8OWXX+KRRx6xeJ1er8f333+PCRMm4IMPPsBff/2F1q1bY+nSpeXaPIWcjyKEtu5smZubizfeeAPffPMNMjMzUbt2bTz66KOYPHmyxU6MixcvxosvvoiLFy+qu/GY7d+/H88++yyOHj2KvLw8REZG4h//+Adeeukli8PChw4dwnPPPYe9e/eiZs2aGDduHF599dUyj9VgMMDf3x85OTnw8/Or+OQrwGQyITMzE8HBwZV+uoQzOX4ciI4uum+awQD87yCo9GTJn0rGNSA35u8ali5dipEjR6KgoKDMm00QadmlS5cQHByMtWvXom/fvo4eToWVpzbQXJGmJc5UpMnCZAICA4GcHGD//qJr1IiIiFwBizSSjcxFGn+dJgmj0YiTJ0+W6/4MWqTTAa1aFX3NUx7/Jkv+VDKuAbkxfyIibWGRJpHi93lwZbwuzTZZ8qeScQ3IjfkTEWkHizRyOSzSiIiIiEjLWKSRyzEXaYcPAzduOHYsRERERETlxSJNEoqiqDexdXV16wIhIYDRCBw44OjROAeZ8ifbuAbkxvyJiLSFRZokdDodgoKCpNh6WVF4yuOtZMqfbOMakBvzJ2eUkZGBQYMGISgoCIqi4P3333f0kIicBv+1loTRaMTRo0el2dmLRZol2fIna1wDcmP+8lm6dCkURVH/uLm5oU6dOhgxYgQuXLjg6OEBAMaPH49NmzZh0qRJWL58OXr16mWXz3n77bfx7bff2uW9HeXatWuYMmUKevXqhcDAQCiKgqVLl5bY32QyYeHChWjRogW8vLwQFBSELl26qDfYNrt48SLGjBmDyMhIeHl5oUGDBnjppZdw5coVq/f88ssvce+99yIgIABBQUHo2LEjvvvuu9uOe8WKFVAUBT4+Pnc0b5nwJhsSuXnzpqOHUGXMRdq+fY4dhzORKX+yjWtAbsxfTm+++SYiIyNx8+ZN/Pbbb1i6dCl++eUXHD58GJ6eng4d29atW9GvXz/861//suvnvP322xg0aBAefvhhu35OVbp8+TLefPNN1KtXD3fddRe2b99+2/6jRo3CihUr8MQTT2Ds2LG4fv06fv/9d2RmZqp9rl27hrZt2+L69et49tlnER4ejoMHD2L+/PnYtm0bkpKS1KPx8+bNw/PPP4/evXvjnXfewc2bN7F06VI89NBD+PrrrzFgwACrMVy7dg2vvPIKvL29K/V74apYpJFLMhdpx44V3dja39+x4yEiInKEBx54AK3+dwPRJ598EjVr1sTMmTOxbt06DB482KFjy8zMREBAgEPHcKdMJhPy8/MdVuiGhYXh4sWLCA0Nxb59+9Da/IOPDV9++SWWLVuGNWvWoH///iX2W7duHc6cOYMNGzagd+/eantgYCDefPNNHDx4EHfffTeAoiKtdevWWL9+vXqt66hRo1CnTh0sW7bMZpE2bdo0+Pr6onPnzi53ZNMeeLojuaSaNYH69Yu+Tkpy6FCIiIicRvv27QEAJ0+eVNvy8/MxefJkxMfHw9/fH97e3mjfvj22bdtm8dqWLVta/fAdFxcHRVFw6NAhtW3VqlVQFAVHjhyxOQbzqZhCCHz44YfqKZlmV69exYsvvojw8HB4eHigYcOGmDlzJkwmk8X7/Oc//8F9992HoKAgeHl5IT4+Hl999ZVFH0VRcP36dSxbtkz9nBEjRgAARowYgfrmHxaKmTp1qtUmO4qiYOzYsVixYgViY2Ph4eGBjRs3AgAuXLiAUaNGISQkBB4eHoiNjcUnn3xi9b7z5s1DbGwsqlevjho1aqBVq1b4/PPPLfocPXoUZ8+etfl9K87DwwOhoaGl9gOAOXPm4J577kH//v1hMplw/fp1m/0MBgMAICQkxKI9LCwMAODl5WXRNzg42OL75OfnBx8fH4t+ZidOnMB7772HOXPmwM2Nx4jKgkWaJHQ6HaKioqS6aJzXpf1NxvzJEteA3Jg/maWlpQEAatSoobYZDAb897//RadOnTBz5kxMnToVly5dQs+ePXGg2DbJ7du3xy+//KI+zsrKQkpKCnQ6HXbu3Km279y5E7Vq1UKTJk1sjqFDhw5Yvnw5AKB79+5Yvny5+vjGjRvo2LEjPvvsMzzxxBP44IMPcP/992PSpEl46aWXLN5n7ty5uPvuu/Hmm2/i7bffhpubGx555BGL66KWL18ODw8PtG/fXv2cp59++o6+d1u3bsX48eMxZMgQzJ07F/Xr10dGRgbuvfdebNmyBWPHjsXcuXPRsGFDjB492mIjlP/7v//D888/j6ZNm+L9999HQkICWrRogd27d1t8RpMmTfDEE0/c0fhsMRgM2LNnD1q3bo3XXnsN/v7+8PHxQVRUFL788kuLvh06dIBOp8MLL7yA3377DefPn8f333+P6dOn4+GHH0ZMTIzat1OnTti4cSPmzZuHtLQ0HD16FM899xxycnLwwgsvWI3jxRdfROfOnfHggw9W2txcniC7ycnJEQBETk6Oo4cipVmzhACEGDjQ0SMhIiKquCVLlggAoqCgoMx9t2zZIi5duiTOnTsnvvrqK1GrVi3h4eEhzp07p/YtLCwUeXl5Fq/Pzs4WISEhYtSoUWrb6tWrBQCRmpoqhBBi3bp1wsPDQ/Tt21cMGTJE7de8eXPRv3//UscIQDz33HMWbW+99Zbw9vYWx48ft2ifOHGi0Ov14uzZs2rbjRs3LPrk5+eLZs2aiS5duli0e3t7i+HDh1t9/vDhw0VERIRV+5QpU8StPyIDEDqdTqSkpFi0jx49WoSFhYnLly9btA8dOlT4+/urY+zXr5+IjY21+qxbARAdO3YstV9xe/fuFQDEkiVLrJ7bv3+/ACCCgoJESEiIWLBggVixYoW45557hKIo4ocffrDo/9///lcEBAQIAOqf4cOHW625jIwM0bVrV4t+NWvWFLt27bIaw4YNG4Sbm5v6vRs+fLjw9vYu09wyMzMFALF27doyfjecW3lqA/5KTRJGoxHJyclS7ezFI2l/kzF/ssQ1IDfmL69u3bqhVq1aCA8Px6BBg+Dt7Y1169ahbt26ah+9Xg93d3cARddaZWVlobCwEK1atcL+/fvVfuZTJXfs2AGg6IhZ69at0b17d/VI2tWrV3H48GG1b3mtXr0a7du3R40aNXD58mX1T7du3WA0GtXPBixPv8vOzkZOTg7at29vMebK1LFjRzRt2lR9LITA119/jT59+kAIYTHenj17IicnRx1LQEAAzp8/j72l/FAihCh1E5DyuHbtGgDgypUrWLt2LZ555hk89thj+OmnnxAUFIRp06ZZ9K9Tpw7uuecevP/++/jmm2/w0ksvYcWKFZg4caJFv+rVqyM6OhrDhw/H6tWr8cknnyAsLAwDBgzAH3/8ofbLz8/H+PHj8c9//tPie0el40mhEpHtf87x8UX3TDt7FsjMBIKDHT0ix5Itf7LGNSA35i+nDz/8EI0bN0ZOTg4++eQT7NixAx4eHlb9li1bhnfffRdHjx5FQUGB2h4ZGal+HRISgkaNGmHnzp14+umnsXPnTnTu3BkdOnTAuHHjcOrUKRw5cgQmk+mOi7QTJ07g0KFDqFWrls3ni+9GuGHDBkybNg0HDhxAXl6e2m6vm7YX/14AwKVLl3D16lUsXrwYixcvvu14X331VWzZsgX33HMPGjZsiB49euCxxx7D/fffb5exmpkL2cjISLRp00Zt9/HxQZ8+ffDZZ5+hsLAQbm5u+PXXX/HQQw/ht99+Uzebefjhh+Hn54eEhASMGjVKLbQeeeQRuLm5Yf369ep79uvXD40aNcLrr7+OVatWAQDee+89XL58GQkJCXadpytikUYuy9cXiIkBjhwpOppWbKMiIiIiKdxzzz0WP3C3a9cOjz32GI4dO6beq+qzzz7DiBEj8PDDD2PChAkIDg6GXq/HjBkzLDYYAYB27drhp59+wl9//YWkpCRMnjwZzZo1Q0BAAHbu3IkjR47Ax8dH3QWwvEwmE7p3745XXnnF5vONGzcGUHQUr2/fvujQoQMWLFiAsLAwVKtWDUuWLLHajKMkJRVzJf1C49YNMcwbmQwbNgzDhw+3+ZrmzZsDKLrW7NixY9iwYQM2btyIr7/+GgsWLMDkyZPtWsDUrl0bgPVmIAAQHByMgoICXL9+Hf7+/li0aBFCQkLU9WLWt29fTJ06Fbt27ULTpk1x6tQpbNy40aowDQwMRLt27fDrr78CAHJycjBt2jQ8++yzMBgM6sYk165dgxACaWlpqF69OoJl/y16CVikkUtr3ZpFGhEREQC18OrcuTPmz5+vnsL21VdfISoqCmvWrLEoXKZMmWL1Hu3bt8eSJUvwxRdfwGg04r777oNOp0O7du3UIu2+++6DXq+/ozE2aNAA165dQ7du3W7b7+uvv4anpyc2bdpkcWRwyZIlVn1LKsZq1KiBq1evWrWfOXOmTGOtVasWfH19YTQaSx0vAHh7e2PIkCEYMmQI8vPzMWDAAEyfPh2TJk2y21b+tWvXRmhoqM0bmP/555/w9PSEr68vACAjI8NmgWo+slpYWKj2A2wXswUFBWq/7OxsXLt2DbNmzcKsWbOs+kZGRqJfv37cjr8EvCZNEjqdDtHR0dLt7MXr0orImj/9jWtAbsyfzDp16qRec2S+wbm5oBJCqP12796NxMREq9ebT2OcOXMmmjdvDv//3Yi0ffv2+Omnn7Bv3747PtURAAYPHozExERs2rTJ6rmrV6+qBYBer4eiKBaFQlpams0f+L29vW0WYw0aNEBOTo7F7QMuXryIb775pkxj1ev1GDhwIL7++mscPnzY6vlLly6pX1+5csXiOXd3dzRt2hRCCIvTS8u6BX95DBkyBOfOncPmzZvVtsuXL2Pt2rXo0qWL+u9C48aNkZGRYXVN3MqVKwFAPTrasGFD6HQ6rFq1ymLNnD9/Hjt37lT7BQcH45tvvrH607lzZ3h6euKbb77BpEmTKnWuroRH0iRivihYJsWLNCGKrlGTlYz5kyWuAbkxfzKbMGECHnnkESxduhT//Oc/8dBDD6k3Ou7duzdOnz6Njz76CE2bNlU3njBr2LAhQkNDcezYMYwbN05t79ChA1599VUAqFCRNmHCBKxbtw4PPfQQRowYgfj4eFy/fh3Jycn46quvkJaWhpo1a6J3796YM2cOevXqhcceewyZmZn48MMP0bBhQ4uiCwDi4+OxZcsWzJkzB7Vr11avzxo6dCheffVV9O/fH88//zxu3LiBhQsXonHjxmXefOSdd97Btm3b0KZNGzz11FNo2rQpsrKysH//fmzZsgVZWVkAgB49eiA0NBT3338/QkJCcOTIEcyfPx+9e/dWj2QBRadFduzYsUybh8yfPx9Xr17Fn3/+CQBYv349zp8/DwAYN26cWkBPmjQJX375JQYOHIiXXnoJ/v7++Oijj1BQUIC3335bfb+xY8diyZIl6NOnD8aNG4eIiAj8/PPPWLlyJbp3765e01arVi2MGjUK//3vf9G1a1cMGDAAubm5WLBgAf766y+18KpevToefvhhq3F/++232LNnj83nqBh7bjMpO2fagr+wsFD8/vvvorCw0NFDqVJ//SWEm1vRVvxpaY4ejePImj/9jWtAbszfNdzJFvx79+61es5oNIoGDRqIBg0aiMLCQmEymcTbb78tIiIihIeHh7j77rvFhg0bStyi/pFHHhEAxKpVq9S2/Px8Ub16deHu7i7++uuvMs0HNrbgF0KI3NxcMWnSJNGwYUPh7u4uatasKe677z7xn//8R+Tn56v9Pv74Y9GoUSPh4eEhYmJixJIlS2xun3/06FHRoUMH4eXlpW4pb/bjjz+KZs2aCXd3dxEdHS0+++yzErfgtzVWIYq2o3/uuedEeHi4qFatmggNDRVdu3YVixcvVvssWrRIdOjQQQQFBQkPDw/RoEEDMWHCBKufEVGOLfgjIiIstsAv/uf06dMWfU+ePCn69+8v/Pz8hJeXl+jSpYvYs2eP1XsePXpUDBo0SJ1LRESE+Ne//iWuX79u0a+goEDMmzdPtGjRQvj4+AgfHx/RuXNnsXXr1lLHzS34y1YbKEIUO05JlcpgMMDf3x85OTnw8/Nz6FjM2y/HxcXd8XniWhUfD+zfD6xeDQwa5OjROIbM+VMRrgG5MX/XsHTpUowcORIFBQVwc+PJUOT6Ll26hODgYKxduxZ9+/Z19HAqrDy1AU9OJ5cXH1/032XLgO3bAe5CTURERETOjEUaubQ1a4Cvvir6esMGoHNnoH79onYiIiIiImfEIk0SOp0OcXFxUu3stWZN0emN2dmW7RcuFLXLVKjJmD9Z4hqQG/N3DdWqVQMAixs3E7ky8w6kMm58xH+tJZKfn+/oIVQZoxF44YWiHR1vZW578UW5Tn2UKX+yjWtAbsxf+2rUqAGg6P5TRDIwr3Xz2pcJizRJmEwmHDt2DCaTydFDqRI7dwL/24XWJiGAc+eK+slAtvzJGteA3Ji/a2jcuDGAonuYEclg9+7d0Ol0iIqKcvRQqhyLNHJJFy9Wbj8iIiJHa9iwIe666y6sXr3a0UMhqhJffvklOnXqhFq1ajl6KFWORRq5pLCwyu1HRETkDAYPHowNGzYgNzfX0UMhsquMjAxs27YNjzzyiKOH4hAs0iQi071x2rcH6tYFFKXkPv7+Rf1kIVP+ZBvXgNyYv2sYOnQoAODhhx/GjRs3HDwaIvu4evUq+vbtCz8/PwwYMMDRw3EIFmmS0Ov1Ut3EVK8H5s4t+rqkQi0nB5gyxfbmIq5GtvzJGteA3Ji/64iKisL333+P3bt3o0+fPjhx4oSjh0RUqVJSUtCzZ0+cOHECW7ZsQXBwsKOH5BCKEDL8iOoY5bmruL0JIZCbmwtfX18otzu85GLWrCna5bH4JiLh4UDHjsBnnxU9fv554L33AFfemVrW/OlvXANyY/6uZ8eOHejTpw8MBgPuuusuPPLII2jSpAkCAgLUrfqJtKCgoADZ2dlITk7G6tWrkZqaipo1a2LTpk1o2bKlo4dXqcpTG7BIsyNnKtKMRiOSk5Ol/E2q0Vi0i+PFi0XXoLVvX3SkbcEC4LnnivqMGgUsXlzU7opkzp+KcA3Ijfm7phs3buCHH37A6tWrsX79ep7+SJrm5+eHfv364ZFHHkGPHj3g4eHh6CFVuvLUBm5VNCYih9HrgU6drNuffRbw8QFGjgQ++QS4dg1YvhyQ8H6JRESkQdWrV8fAgQMxcOBAGI1GXL16FVevXoXxlpuAGo1GHD9+HI0bN2aRLiFnz9/NzQ0BAQHw9/d3yvE5Cos0ktoTTwDe3sCjjwJffglcvw6sXg14eTl6ZERERGWn1+sRFBSEoKAgq+eMRiPy8vKc9od0si/mr00ufBUO3crT09PRQ3BKAwcC69YVFWbffQf07g244s7GzJ+4BuTG/OXG/OXG/LWH16TZkTNdk0al27EDeOihogKtTRvghx+AGjUcPSoiIiIicgXlqQ14JE0SJpMJV65cgclkcvRQnFaHDsBPPwGBgcDu3UXXsWVkOHpUlYP5E9eA3Ji/3Ji/3Ji/NrFIk4QQAufOnQMPnN5e69bAzz8DISHAoUNFhdu5c44eVcUxf+IakBvzlxvzlxvz1yYWaUS3aNasaMv+evWA48eLtuz/4w9Hj4qIiIiIZMEijciGRo2KCrVGjYAzZ4oKtcOHHT0qIiIiIpIBizSJ+Pr6OnoImlKvXlGhFhcHpKcDHTsC+/Y5elR3jvkT14DcmL/cmL/cmL/2cHdHO+Lujq4hKwt44AFgzx7A17dom/727R09KiIiIiLSEu7uSFZMJhPS09O5s88dCAwEtmwp2u0xNxfo2RP48UdHj6p8mD9xDciN+cuN+cuN+WsTizRJCCGQnp7OnX3ukK8v8P33RUfU/voL6NMH+OYbR4+q7Jg/cQ3IjfnLjfnLjflrE4s0ojLy8gK+/RYYNAjIzwceeQT47DNHj4qIiIiIXA2LNKJycHcHVq4ERowAjEbgH/8AFi509KiIiIiIyJWwSJOEoigIDAyEoiiOHormubkBH38MjBtX9PjZZ4HZsx07ptIwf+IakBvzlxvzlxvz1ybNFWlGoxFvvPEGIiMj4eXlhQYNGuCtt96yOM92xIgRUBTF4k+vXr0s3icrKwuPP/44/Pz8EBAQgNGjR+PatWsWfQ4dOoT27dvD09MT4eHhmDVrVpXM0R50Oh3q1asHnU5zkTslnQ6YOxd47bWix6+8ArzxBuCsp3szf+IakBvzlxvzlxvz1ybNpTVz5kwsXLgQ8+fPx5EjRzBz5kzMmjUL8+bNs+jXq1cvXLx4Uf2zcuVKi+cff/xxpKSkYPPmzdiwYQN27NiBMWPGqM8bDAb06NEDERERSEpKwuzZszF16lQsXry4SuZZ2UwmE86ePcudfSqRogDTpwMzZhQ9njYNGD/eOQs15k9cA3Jj/nJj/nJj/trk5ugBlNeuXbvQr18/9O7dGwBQv359rFy5Env27LHo5+HhgdDQUJvvceTIEWzcuBF79+5Fq1atAADz5s3Dgw8+iP/85z+oXbs2VqxYgfz8fHzyySdwd3dHbGwsDhw4gDlz5lgUc1ohhEBWVhbq1Knj6KG4nIkTi3Z/HDu26OjatWvAokWAXu/okf2N+RPXgNyYv9yYv9yYvzZprki77777sHjxYhw/fhyNGzfGwYMH8csvv2DOnDkW/bZv347g4GDUqFEDXbp0wbRp0xAUFAQASExMREBAgFqgAUC3bt2g0+mwe/du9O/fH4mJiejQoQPc3d3VPj179sTMmTORnZ2NGjVqWI0tLy8PeXl56mODwQCg6BRNo9EIoOi8YJ1OB5PJZHGKprnd3K+0dp1OB0VRbLYDsPptiRACQgir/nq9HkIIq/56vd5qjCW1O2pOJbU7Yk7PPKPAx0eHUaMEPv5YQW6uCUuXCnh6OsecjEaj+rXMOck8J/MaMP9xhTmVNnbO6e+xm/M3f44rzMkVc7LXnABY/Qyg9Tm5Yk72mpP5tSX9HKjFOZnHqLWcbn3+djRXpE2cOBEGgwExMTHQ6/UwGo2YPn06Hn/8cbVPr169MGDAAERGRuLkyZN47bXX8MADDyAxMRF6vR7p6ekIDg62eF83NzcEBgYiPT0dAJCeno7IyEiLPiEhIepztoq0GTNmICEhwao9JSUFPj4+AIDAwEDUq1cP58+fR1ZWltonNDQUoaGhSEtLQ25urtoeHh6OoKAgnDhxAjdv3lTbo6Ki4Ofnh9TUVIvAo6Oj4e7ujuTkZIsxNG3aFEajESkpKeo/2Hq9HnFxccjNzcWpU6fUvp6enoiJiUF2djbOnTuntvv6+qJBgwbIzMxUv0+OnFNcXBzy8/Nx7Ngxtc2Rcxo+PAg5OX/i5ZfD8OWXOly8mIPVqxWEhDh+TkIIXL9+HQCkz0nWOZl/k5qXlwdPT0+XmJMr5mSvOZ09exZZWVlISUmBn5+fS8zJFXOy15wCAgKQk5Nj8TOA1ufkijnZa07mzK9du4a0tDSXmJNWc7p1/4vbUYTG7mz3xRdfYMKECZg9e7Z6CuKLL76IOXPmYPjw4TZfc+rUKTRo0ABbtmxB165d8fbbb2PZsmUWYQNAcHAwEhIS8Mwzz6BHjx6IjIzEokWL1OdTU1MRGxuL1NRUNGnSxOpzbB1JCw8PR1ZWFvz8/AA47rcQAJCZmYmaNWtaXDiqxd9ClNbu6Dlt3AgMGqTDzZsKOncWWLdOgZeXY+dkMplw6dIl9RRg5iTfnMxrICQkRH1/rc+ptLFzTpZH0i5duoRatWpBp9O5xJxcMSd7HklLT09X83eFObliTvaak8lkwpUrV1CrVi3cSqtzMo9RazkZDAYEBgYiJydHrQ1KorkjaRMmTMDEiRMxdOhQAEXV9ZkzZzBjxowSi7SoqCjUrFkTf/zxB7p27YrQ0FBkZmZa9CksLERWVpb6Q2xoaCgyMjIs+pgfl3Stm4eHBzw8PKza9Xo99LdcoFS8ULq1r73aw8LCbPZVFMVm/5LGWN52e86ppHZHzql3b2DTJuChh4Bt2xR07w58/70eNg6+Vtmc9Ho9ateubbPfnYylstq59qpuTreuAVeYU0XaZZuTTqez+jdA63OyhXMqeSwl/T9Ay3NyxZzs0a7X60v82fV2Y3TmOZlpLaeSnrc5pjL3dBI3btyw+kaaK+OSnD9/HleuXFGLlLZt2+Lq1atISkpS+2zduhUmkwlt2rRR++zYsQMFBQVqn82bNyM6OtrmqY7Ozmg04uTJk+U6F5buXIcOwE8/ATVqAL/9BnTuDNzye4Eqxfz/v727D4uqzPsA/j0zyIs6vKogAkK+oYSa76kYlKG2la2aV1qtZVtdm6bo5oZb2rZumfVskllZPW31rPVY65O1Vy+2ZqLoplJKogaavAgqovIuIjhznj/uHWBkgMGYOWfm/n6u61wwZ87M3De/wzA/7vv+HeI5IDfGX26Mv9wYf/fkdknaHXfcgeeeew5ffPEFCgoKsGXLFrz88sv49a9/DUDM9Vy2bBn27t2LgoICbN++HdOnT0f//v0xZcoUAMDgwYMxdepUPPzww9i/fz/27NmDhQsX4p577mn8T9PcuXPh7e2Nhx56CEeOHMFHH32EV155BUuXLtWs779U8zm15HyjRwM7dwKhocCPP4rErbhYu/Yw/sRzQG6Mv9wYf7kx/u7H7ZK0V199FbNmzcJjjz2GwYMH44knnsCjjz6KVatWARCjaocOHcKdd96JgQMH4qGHHsLIkSORkZFhMxXxgw8+QGxsLG655RbcdtttmDhxos010AICAvCvf/0L+fn5GDlyJH7/+99j5cqVbll+n7QTHw9kZACRkUBuLjBxInDihNatIiIiIiI9c7s1aSaTCWlpaUhLS7N7v5+fH77++ut2nyc4OBgffvhhm8cMHToUGRkZ19JMokYDBgC7dwOTJwPHjwMJCcC2bUBcnNYtIyIiIiI9cruRNLo2iqIgMjKyscoTuVZUFLBrlxhZO3MGuOkmoNmSSKdj/InngNwYf7kx/nJj/N2T25XgdydVVVUICAhwqMwmyaGsDJg2Ddi/H/D3B774QkyBJCIiIiLP1pHcgCNpkjCbzcjJyWFlH40FBwPffCNG0qqqgORkMfXR2Rh/4jkgN8Zfboy/3Bh/98QkTSLNr4xO2jGZgC+/FCNqly6J66l9+qnzX5fxJ54DcmP85cb4y43xdz9M0og00LWrSMxmzgTq64FZs4CNG7VuFRERERHpAZM0Io14ewObNgEPPACYzcBvfgNs2KB1q4iIiIhIa0zSJGEwGHDdddfBYGDI9cTLC3jnHWDhQkBVgd/9Dnjppc5/HcafeA7IjfGXG+MvN8bfPTFaklAUBf7+/iy/qkMGA7BuHbB8ubj9hz8AK1eKpK2zMP7Ec0BujL/cGH+5Mf7uiUmaJMxmM7Kzs1nZR6cUBXj+ebEBwKpVwNKlnZeoMf7Ec0BujL/cGH+5Mf7uiUmaRPjLqX/LlwOvviq+T0sDHnlErFfrDIw/8RyQG+MvN8Zfboy/+2GSRqQzCxcC774rpkH+938D994LNDRo3SoiIiIichUmaUQ69MADwEcfAV26iK8zZgC8xAkRERGRHBRV7czyBNRcVVUVAgICUFlZCX9/f03boqoq6urq4Ovry4WjbuSrr5oStJtvBj77DOjevePPw/gTzwG5Mf5yY/zlxvjrR0dyA46kScTb21vrJlAHTZsGbN0qErNvvwVuvRUoL7+252L8ieeA3Bh/uTH+cmP83Q+TNElYLBZkZ2fDYrFo3RTqoJtuArZvB4KCgL17xYhaaWnHnoPxJ54DcmP85cb4y43xd09M0ojcwJgxwM6dQGgokJUlErfiYq1bRURERETOwCSNyE3ExwO7dgGRkUBODpCQAJw4oXWriIiIiKizMUkjciMDBwK7dwP9+wMFBSJRO3pU61YRERERUWdidUcn0lt1R4vFAoPBwMo+HqCkRBQROXwYCAkB/vUvYMSI1o9n/InngNwYf7kx/nJj/PWD1R3Jrvr6eq2bQJ0kLEysURs9GrhwAUhKAvbsafsxjD/xHJAb4y83xl9ujL/7YZImCYvFgtzcXFb28SDBwcA33wCTJgFVVUByMrBtm/1jGX/iOSA3xl9ujL/cGH/3xCSNyI35+4sLXk+dCtTWArffLi54TURERETui0kakZvr2lUkZjNnAvX14usHH2jdKiIiIiK6VkzSJGI0GrVuAjmJtzewaRMwbx5gNgP33w+8+abtMYw/8RyQG+MvN8Zfboy/+2F1RyfSU3VHkoPFAixaBLz2mrj90kvAkiVARgZw5gzQu7co28/3aiIiIiLXYnVHakFVVVRVVYE5uWczGIBXXwVSU8XtZctEgZGkJGDuXPE1Ohr45BNNm0ka4HuA3Bh/uTH+cmP83ROTNElYLBbk5eWxso8EFAVYvVokZYCo/NjcqVPArFlM1GTD9wC5Mf5yY/zlxvi7JyZpRB7IbAZ27bJ/n/UfaSkp4jgiIiIi0hcmaUQeKCMDKC5u/X5VBYqKgO3bXdcmIiIiInKMl9YNINfx9fXVugnkImfOOHbc7bcDN90k1qolJQGjRgFduji3baQdvgfIjfGXG+MvN8bf/bC6oxOxuiNpJT1dJF0d1b27qP6YlATcfDMwfDgrQRIRERF1BlZ3pBYsFgsuXLjARaOSSEgAIiJEERF7FAWIjAQOHQLWrxcXwA4OBmpqgK++Av7wBzGq1qMHMH068Mor4liePu6L7wFyY/zlxvjLjfF3T0zSJKGqKoqKilh+VRJGo0isgJaJmvV2WhoQHw8sWABs3gycOwdkZQFr1wJ33AH4+wMVFcA//ymKjAwbBoSGAnffDbz+OvDTT01FSEj/+B4gN8Zfboy/3Bh/98QkjchDzZghkq8+fWz3R0SI/TNm2O43GEQilpIiErOyMiAzE1izBpg6FejWDTh/Xjx2wQJgyBAgPFyU+n/7beDnn5m0EREREXUGFg4h8mAzZojpiunpZuzfX4QxYyKRmGh0aJ2Z0SimPI4aJaY/NjSIpG3HDuDbb4F//xsoKQH+93/FBogplNYiJDffDERFObd/RERERJ6ISZpETCaT1k0gDRiNQGIiEB1tRnT0tRcC6dIFGD9ebE89BdTVAfv2iYRtxw5g715R1v9//kdsAHDddU0JW1IS0Lt3Z/WKrgXfA+TG+MuN8Zcb4+9+WN3RiVjdkWRSWwvs2SMSth07xKjb1RfLjo1tGmlLTAR69tSkqUREREQu15HcgEmaE+kpSbNYLCgtLUWvXr1gMHApomy0iH9VFbB7d9P0yIMHW65Zi49vGmmbNAkICnJJ06TE9wC5Mf5yY/zlxvjrB0vwUwuqqqKkpISVfSSlRfz9/YHbbgNeegn44QfgwgXg00+BRYtEcgYA2dnAunXAXXcBISHAyJHAsmXAl18C1dUua6oU+B4gN8Zfboy/3Bh/98Q1aUTkEkFBoojJ9Oni9rlz4qLb1pG23FzgwAGx/dd/ibVzo0c3TY+cMAHo2lXTLhARERG5BJM0ItJEz57immt33y1unz4tkjZrIZK8PFGMZO9eYPVqUbhk3LimpG3cOMDX17HXMpuBjAzgzBlRvCQh4doLqBARERE5G5M0SSiKguDgYChXX9mYpOAO8bdec23uXHG7sLCpCMmOHaJyZEaG2P78Z5GgjR/ftKZt9GiRyF3tk0+AxYuB4uKmfRER4mLfV18rzpO5wzlAzsP4y43xlxvj755YOMSJ9FQ4hMidqSpw4kRTwvbtt8DZs7bHdOsGTJzYVO5/xAjgs8+AWbNaFiyx/p2yd1FvIiIiImdgdUed0FOSZrFYUFxcjIiICFb2kZCnxV9VgZycpoQtPV0UJmnOZBIX4K6rs/8ciiJG1PLz5Zj66GnnAHUM4y83xl9ujL9+sLojtaCqKsrKyljZR1KeFn9FAQYPBh57TIyGlZYCP/4IrF0L3HknEBAgqkO2lqABItErKhIJngw87RygjmH85cb4y43xd09ck0ZEbs9gAIYOFVtKiigU8sILwNNPt//YKVOAgQOBQYNabiEhTm86ERERUQtuN5JmNpuxYsUKxMTEwM/PD/369cOqVasa/zvQ0NCAJ598EvHx8ejWrRvCw8Pxm9/8BqdPn7Z5nujoaCiKYrO98MILNsccOnQICQkJ8PX1RWRkJF588UWX9ZOIrp3RKEr2O8JsBn76SVzDbc0aYP588dgePcQ2cSLw0EPAiy+KNW45OWIaJREREZGzuN1I2po1a/DGG2/g/fffR1xcHL7//ns8+OCDCAgIwKJFi1BbW4sDBw5gxYoVGDZsGMrLy7F48WLceeed+P77722e689//jMefvjhxtsmk6nx+6qqKiQnJ2Py5MnYsGEDsrOzMX/+fAQGBuKRRx5xWX87i6IoCAsLY2UfSckY/4QEsebs1KmWhUOApjVp6enAzz+L67Q134qKxDq3PXvE1pzRCFx3nRhti421HX3r2bOpMImeyHgOUBPGX26Mv9wYf/fkdoVDbr/9doSGhuKdd95p3Ddz5kz4+flh48aNdh+TmZmJMWPGoLCwEFFRUQDESFpKSgpSUlLsPuaNN97AU089hZKSEnh7ewMAUlNT8emnnyInJ8ehtuqpcAiRjD75RFR3BGwTNUeqO168CBw71jJ5y80V97UmMNA2abMmcf37Az4+ndItIiIickMdyQ3cbiRt/PjxeOutt3Ds2DEMHDgQP/74I3bv3o2XX3651cdUVlZCURQEBgba7H/hhRewatUqREVFYe7cuViyZAm8vMSP5LvvvsOkSZMaEzQAmDJlCtasWYPy8nIEBQW1eJ3Lly/j8uXLjberqqoAiCmaZrMZgPhvhsFggMVisVnAad1vPa69/QaDAYqi2N0PiEo+zamqioKCAkRFRcHYrJSd0WiEqqotjjcajS3a2Np+rfrU2n72qeV+s9mMkydPIiYmBoqieESfmrextf3Tp5vx8cfAkiUGFBc3/QexTx8Va9daMH26mO5or0++vsDw4QYMH27bdlUFSkoMyMkBcnLU/yRyCo4dAwoLFVRUAPv2ic3256AiOlqsfxs4UMWgQcDgwQYMHGhBaKjamDg669wzm80oLCxETExM48/y6uMB/j55ap+uXLmCwsJC9O3bF0aj0SP65IlxclafVFVFXl5eY/w9oU+eGCdn9clsNqOoqAh9+/ZtMZrmrn2yttHd4nT1/W1xuyQtNTUVVVVViI2NhdFohNlsxnPPPYd7773X7vF1dXV48sknMWfOHJuMddGiRRgxYgSCg4Px73//G8uXL8eZM2cak72SkhLExMTYPFdoaGjjffaStNWrV+PZZ59tsf/IkSPo3r07ACA4OBhRUVEoLi5GWVlZ4zFhYWEICwtDQUEBqqurG/dHRkYiJCQEx48fR12zUnXXXXcd/P39cfToUZuADxo0CN7e3sjOzrZpw5AhQ1BRUYHq6urGX1Cj0Yj4+HhUV1cjLy+v8VhfX1/ExsaivLwcRUVFjftNJhP69euH0tJSlJSUNO7Xqk/x8fGor69Hbm5u4z72yX6fVFXFxYsXERMT4zF9AhyLU//+1fj0U+Dgwe6wWEIxcKAJPXvmoqGhDtamXkufQkLq0aNHLiZObOpT//7xyMqqwe7d51BQ4IvCQh+cPOmHwkI/VFUpyMsD8vKArVub/5E0oFs3M/r2vYzo6DrExhowdmwg/P3PIiCgFH5+6i+OU329GQcOdENBwWVMnNiAW25RcPSovuLkieeenvp08uRJlJWVoaamBv7+/h7RJ0+Mk7P6FBgYiFOnTqGmpqbxM4C798kT4+SsPlkT9ZqaGhQUFHhEn9w1TjU1NXCU20133LRpE5YtW4aXXnoJcXFxyMrKQkpKCl5++WXMmzfP5tiGhgbMnDkTxcXFSE9Pb3NY8W9/+xseffRR1NTUwMfHB8nJyYiJicGbb77ZeMzRo0cRFxeHo0ePYvDgwS2ew95IWmRkJMrKyhpfW8uRtOzsbMTFxXEkTcI+mc1mHDlyBEOHDpVqJE0vfTIYjDhzxvKfkTflP9MmFRw7piA/X4XF0vo6gb591cbqk7GxCvr3N2PQILGeTlHa79PmzeYWI4kREWIk8de/vvY+eWKcPLlPV65cwZEjRxr/BnhCnzwxTs4cSTt06JDNZwB375MnxsmZI2lHjx7F9ddfz5E0jftUVVWF4OBgz5zuuGzZMqSmpuKee+4BILLrwsJCrF692iZJa2howOzZs1FYWIhvv/223R/E2LFjceXKFRQUFGDQoEEICwvD2bNnbY6x3g4LC7P7HD4+PvCxs+jE+gexOWvQ7B3rjP1msxmKothti3X/1VprY0f3O6tPbe1nn1rut74xe1Kf2tuvpz6FhxsQHg7cfLPt/suXFZw40XLdW04OUF4uplAWFgLbtjW+KgCga1cxdbKpcIkRgwaJff8ZuMcnnwCzZxtbFE45dUrB7NlGu2vyZI+Tp/bJaDS2+Bvg7n2yh32y/5ptfQZw1z45ez/7xD51Vhub72/tfnvcLkmrra1t8YO0ZsZW1gTt+PHj2LFjB0IcuNhRVlYWDAYDevXqBQC48cYb8dRTT6GhoQFdunQBAGzbtg2DBg2yO9VR7xRFQWRkZIv/oJAcGH/98vEBhgwRW3OqCpw/b79wyYkTQG0tkJUltqv16SOStf377Ve2VFUxCpeSAkyfDnTgbwa5Kb4HyI3xlxvj757cbrrjAw88gG+++QZvvvkm4uLicPDgQTzyyCOYP38+1qxZg4aGBsyaNQsHDhzA559/3riODBDzTr29vfHdd99h3759SEpKgslkwnfffYclS5Zg2rRpeP/99wGIYiODBg1CcnIynnzySRw+fBjz58/H2rVrHS7Bz+qOROQMDQ1ibZu9BO7cuY49129/C0yaJBK7iAjxtVs357SbiIhIZh3JDdwuSauursaKFSuwZcsWlJaWIjw8HHPmzMHKlSvh7e2NgoKCFgU/rHbs2IHExEQcOHAAjz32GHJycnD58mXExMTg/vvvx9KlS22mKx46dAgLFixAZmYmevTogccffxxPPvmkw23VU5JmNptx/PhxDBgwoENDreQZGH95lJWJSwe89x7QbElthwQG2iZt9r4GB+vzenBkH98D5Mb4y43x1w+PTtLcid6StOzsbMTHx/MXVEKMv3zS04GkpPaPmzIFuHJFXPS7uBhwtPCUr69I2Owlcdbvw8IAL7ebVO+Z+B4gN8Zfboy/fnj0ddKIiKh9CQkiUTp1yv66NEUR93/xhe2atKoqkaxZkzZ7X8+dA+rqxNq4Eydab4PBIBK1tkbl+vQRRVCcyWwGMjKAM2eA3r3Fz4afU4iISM+YpBEReSCjEXjlFWDWLJGQNU/UrNMU09JaJiv+/vYLmTR3+TJw+nTbydyZM2KE7vRpsWVmtv58QUFtT63s00cccy3TKz/5BFi8WLTJKiJC/GyurmxJRESkF5zu6ER6mu6oqiqqq6thMplY3UdCjL+87CUpkZEiQXNmkmI2A6WlbY/IFReLKpWO8PNre2plnz5i1K550vnJJyJJvfqvnPVXwN4lCDwV3wPkxvjLjfHXD65J0wk9JWlEJC+9TvdTVaCysv1E7sIFx57PaBSJWkQEEB4uri3X2ho763TP/Hx9/CyIiMjzMUnTCT0ladarzQ8ZMoSLRiXE+JM7nwOXLokpk20lc2fOiGS0oyIjm6ZTBgaKr9at+e3m35tMYr2duzCbgfR0M77//hRGjeqDxEQjE1PJuPPvP/1yjL9+sHAI2WW+lk8w5DEYf3LXc8DPD+jXT2ytMZuBs2ebkrbPPgP+c9nLNhUVia0jDAYgIKD1hK6t24GBrq142TTd1QggCgDX5MnKXX//qXMw/u6HSRoREbk9o1FMcQwPF7eDghxL0tauBaKjgfLypq2iovXbly8DFkvT7WthMjme1F1929fX8ddpbU3eqVNivyxr8vQ63ZeIqC1M0oiIyOM4egmCxx/v2Af2S5faTuKuvt38e+v6uOpqsXV0BA8QSZojSZ3JBPzud/b7rqqi/ykpwPTpnp2wsLonEbkrrklzIj2tSVNVFXV1dfD19WVlHwkx/iTjOWAdSQLsX4LA1SNJDQ2iUIqjCV7z2xUV9hOuXyo4WCR03t4d27p06fhjOrr90uSR1T0FsxnYtUvFyZMNiIrqgkmTFI9OzKklGd//9Ypr0sgub29vrZtAGmL8SbZzYMYM8UHc3kiKsy9BYE+XLkCPHmLrKItFXGjc0VG748fbvtC4VVmZ2PTIYLj2xNDLS6xLbG0kEQAeflhMX+3WTax7bL517Wp7u0sX1/a9szSNJCoAxO8/RxLlJNv7vyfgSJoT6WkkzWw2Izs7G/Hx8azsIyHGn2Q+B2Rck5SeDiQltX/c228Dw4YB9fWu2Roa2r5Pr4zGlolca9vVCV5Ht65dRaL5Swc8OJJIVjK//+sNR9KIiIj+w2gEEhO1boVrObom78EH9ZOwqmrbSVxHtr17gQ8+aP81Bw8W0z0vXbK/WZnNYk1ha9fd62yKcu0Jnp8f4OMDPPss1yQSuTMmaURERB7GaBRT2mbNEh/I7a3JS0vT1wd0RWmarvhLXX+9Y0na66+3nsCrqpgO2Txpq61tPaFrbevIYyyWpteurRWbM6iqKFwzcqRIVMPCxBYa2vR9WBjQs6e+zhHqOHGdRGD//kBcuCDOd8bUPTBJIyIi8kB6W5PnSo6OJCYktP4ciiKqaVorajqbdSSxM5LB3Fxg3772X/PHH8XWGoNBrKFsnrjZS+bCwsTPiDUp9MX2OonRALgm0Z1wTZoT6WlNmqqqsFgsMBgMrOwjIcafeA7Iy1rd7/RpFeHhijTV/fRW3dOVHF2T+PTTosJnSUnTdvas+Fpa2rGKol26tEze7CVzYWFA9+7X3LUOk3FNKsA1iXrVkdyASZoT6S1JY/lVeTH+xHNAbrLG39510iIjPX8k0WwWF2lvbyQxP7/1hOXKFeD8+aakzV4iZ906emH3rl0dG50LDRXr666VjNfJM5vFqGpsLHD6tP1jHIk/OQeTNJ3QU5LGyj5yY/yJ54DcZI6/7CMpgPNHEi9fFiNv7SVzJSXAxYsde+7AQMeSuZ49xaUXrFw5kmRdv2jd6upafm9vnzPuv3LF8XYPHw4MGACEhIhprSEh9r/39+dU1s7C6o5EREREkLO6J+DaNYk+PmKEMjKy/WNramyTt7ZG6urrxfX/KiqAnJy2n1dRRKJmTdr27Gn7OnkPPQQcOSJe45cmSfX1Hf2J6UNWltja4+UlpsW2lchd/X1QkD7+GeLO/6RhkkZERETkgWbMEGX209PN2L+/CGPGRCIx0ajph9Tu3cXWr1/bx6mqSM5aS+aa3y4tFZUxS0vF5oiKCmDlyl/aG/t8fMTm62v7tbXvO/v+/fuB225rv51PPw306gVcuCC28+dbfn/xohid68jPFhBJc1CQ40ldjx4iEezMa267+3RXJmkSkW2KC9li/InngNwYfzlZRxJ79arGkCHuM4pg/ZAfFCQuE9AWs9l2/dyWLcCGDe2/RlISEBfXuUlSZ1yI/JdKTnasuumf/tT++VBX13YSZ+/7ykrxumVlYjt+3PG2m0wdG7ELCRFrHK/W2nTXU6fEfnconMI1aU6kpzVpRERERDJwtLrljh2eOxVWy+qmDQ2imIw1eXMkuSsra7pOYEf5+dkmb0FBwFdftb7+UcvCKSwcohN6StJUVUV1dTVMJpNUlb1IYPyJ54DcGH+5yRb/zqhu6QncqbqpxSKmoDZP3hxJ7hoarv01tUjSWTiEWrBYLMjLy5Oyshcx/sRzQHaMv9xki7/RKNYdzZolEjJ7I0lpaZ6doAH6XJPYGoNBrEkLDhYVJx2hqkB1dcvE7euvgb//vf3Hnznzy9rsbEzSiIiIiMijuLK6pZ5Z1ySGhFQgPj5SlwnatVIUcXkAf38gJqZpf58+jiVpvXs7r22dgUkaEREREXkc60iSu5Zgp2uTkOBY4ZSEBNe3rSOYpEnE19dX6yaQhhh/4jkgN8ZfbrLGX9br5F1Npvh7ynRXFg5xIj0VDiEiIiIikoUeC6d0JDcwuKhNpDGLxYILFy7Acq31TcmtMf7Ec0BujL/cGH+5yRr/GTOAggJRxfHDD8XX/Hz3WY/I6Y6SUFUVRUVFCAwM1LoppAHGn3gOyI3xlxvjLzeZ4+/O0105kkZERERERKQjTNKIiIiIiIh0hEmaREwmk9ZNIA0x/sRzQG6Mv9wYf7kx/u6H1R2diNUdiYiIiIgIYHVHssNisaCkpES6yj4kMP7Ec0BujL/cGH+5Mf7uiUmaJFRVRUlJCThwKifGn3gOyI3xlxvjLzfG3z0xSSMiIiIiItIRJmlEREREREQ6wiRNEoqiIDg4GIqiaN0U0gDjTzwH5Mb4y43xlxvj755Y3dGJWN2RiIiIiIgAVnckOywWC06ePMnKPpJi/InngNwYf7kx/nJj/N0TkzRJqKqKsrIyVvaRFONPPAfkxvjLjfGXG+PvnpikERERERER6YiX1g3wZNb/WFRVVWncEsBsNqOmpgZVVVUwGo1aN4dcjPEnngNyY/zlxvjLjfHXD2tO4MioJpM0J6qurgYAREZGatwSIiIiIiLSg+rqagQEBLR5DKs7OpHFYsHp06dhMpk0L3taVVWFyMhIFBUVsdKkhBh/4jkgN8Zfboy/3Bh//VBVFdXV1QgPD4fB0PaqM46kOZHBYEBERITWzbDh7+/PX1CJMf7Ec0BujL/cGH+5Mf760N4ImhULhxAREREREekIkzQiIiIiIiIdYZImCR8fHzzzzDPw8fHRuimkAcafeA7IjfGXG+MvN8bfPbFwCBERERERkY5wJI2IiIiIiEhHmKQRERERERHpCJM0IiIiIiIiHWGSRkREREREpCNM0iTx2muvITo6Gr6+vhg7diz279+vdZPIBVavXo3Ro0fDZDKhV69euOuuu5Cbm6t1s0gjL7zwAhRFQUpKitZNIRc5deoU7rvvPoSEhMDPzw/x8fH4/vvvtW4WuYDZbMaKFSsQExMDPz8/9OvXD6tWrQLrxXmuXbt24Y477kB4eDgURcGnn35qc7+qqli5ciV69+4NPz8/TJ48GcePH9emsdQuJmkS+Oijj7B06VI888wzOHDgAIYNG4YpU6agtLRU66aRk+3cuRMLFizA3r17sW3bNjQ0NCA5ORkXL17UumnkYpmZmXjzzTcxdOhQrZtCLlJeXo4JEyagS5cu+Oqrr3D06FH89a9/RVBQkNZNIxdYs2YN3njjDaxfvx4//fQT1qxZgxdffBGvvvqq1k0jJ7l48SKGDRuG1157ze79L774ItatW4cNGzZg37596NatG6ZMmYK6ujoXt5QcwRL8Ehg7dixGjx6N9evXAwAsFgsiIyPx+OOPIzU1VePWkSudO3cOvXr1ws6dOzFp0iStm0MuUlNTgxEjRuD111/HX/7yFwwfPhxpaWlaN4ucLDU1FXv27EFGRobWTSEN3H777QgNDcU777zTuG/mzJnw8/PDxo0bNWwZuYKiKNiyZQvuuusuAGIULTw8HL///e/xxBNPAAAqKysRGhqK9957D/fcc4+GrSV7OJLm4err6/HDDz9g8uTJjfsMBgMmT56M7777TsOWkRYqKysBAMHBwRq3hFxpwYIF+NWvfmXzPkCe75///CdGjRqFu+++G7169cINN9yAt99+W+tmkYuMHz8e27dvx7FjxwAAP/74I3bv3o1p06Zp3DLSQn5+PkpKSmz+DgQEBGDs2LH8PKhTXlo3gJzr/PnzMJvNCA0NtdkfGhqKnJwcjVpFWrBYLEhJScGECRNw/fXXa90ccpFNmzbhwIEDyMzM1Lop5GJ5eXl44403sHTpUvzxj39EZmYmFi1aBG9vb8ybN0/r5pGTpaamoqqqCrGxsTAajTCbzXjuuedw7733at000kBJSQkA2P08aL2P9IVJGpEkFixYgMOHD2P37t1aN4VcpKioCIsXL8a2bdvg6+urdXPIxSwWC0aNGoXnn38eAHDDDTfg8OHD2LBhA5M0CXz88cf44IMP8OGHHyIuLg5ZWVlISUlBeHg440/kBjjd0cP16NEDRqMRZ8+etdl/9uxZhIWFadQqcrWFCxfi888/x44dOxAREaF1c8hFfvjhB5SWlmLEiBHw8vKCl5cXdu7ciXXr1sHLywtms1nrJpIT9e7dG0OGDLHZN3jwYJw8eVKjFpErLVu2DKmpqbjnnnsQHx+P+++/H0uWLMHq1au1bhppwPqZj58H3QeTNA/n7e2NkSNHYvv27Y37LBYLtm/fjhtvvFHDlpErqKqKhQsXYsuWLfj2228RExOjdZPIhW655RZkZ2cjKyurcRs1ahTuvfdeZGVlwWg0at1EcqIJEya0uOTGsWPH0LdvX41aRK5UW1sLg8H2Y57RaITFYtGoRaSlmJgYhIWF2XwerKqqwr59+/h5UKc43VECS5cuxbx58zBq1CiMGTMGaWlpuHjxIh588EGtm0ZOtmDBAnz44Yf47LPPYDKZGuedBwQEwM/PT+PWkbOZTKYW6w+7deuGkJAQrkuUwJIlSzB+/Hg8//zzmD17Nvbv34+33noLb731ltZNIxe444478NxzzyEqKgpxcXE4ePAgXn75ZcyfP1/rppGT1NTU4Oeff268nZ+fj6ysLAQHByMqKgopKSn4y1/+ggEDBiAmJgYrVqxAeHh4YwVI0heW4JfE+vXr8dJLL6GkpATDhw/HunXrMHbsWK2bRU6mKIrd/e+++y4eeOAB1zaGdCExMZEl+CXy+eefY/ny5Th+/DhiYmKwdOlSPPzww1o3i1yguroaK1aswJYtW1BaWorw8HDMmTMHK1euhLe3t9bNIydIT09HUlJSi/3z5s3De++9B1VV8cwzz+Ctt95CRUUFJk6ciNdffx0DBw7UoLXUHiZpREREREREOsI1aURERERERDrCJI2IiIiIiEhHmKQRERERERHpCJM0IiIiIiIiHWGSRkREREREpCNM0oiIiIiIiHSESRoREREREZGOMEkjIiIiIiLSESZpREREbiY6OhrR0dFaN4OIiJyESRoREUmpoKAAiqK0uTERIiIiLXhp3QAiIiIt9evXD/fdd5/d+wIDA13bGCIiIjBJIyIiyfXv3x9/+tOftG4GERFRI053JCIicoCiKEhMTERxcTHmzJmDHj16oGvXrpgwYQK++eYbu485f/48UlJSEBMTAx8fH/Tq1QuzZ8/G4cOH7R5fX1+PtWvXYvTo0TCZTOjevTuGDBmCpUuXory8vMXxNTU1WLx4McLDw+Hj44OhQ4di8+bNndpvIiJyPUVVVVXrRhAREblaQUEBYmJiMGXKFGzdurXd4xVFwdChQ1FRUYGePXti8uTJOHfuHD766CPU1dVh8+bNuOuuuxqPP3fuHG688UacOHECiYmJGDduHPLz87F582b4+Pjg66+/xsSJExuPv3TpEm699Vbs2bMHAwYMwNSpU+Hj44Pjx49j27Zt2LNnD4YPHw5AFA5paGhA3759UV5ejsmTJ6O2thabNm3CpUuXsHXrViQnJ3f2j4yIiFyESRoREUnJmqS1tSZt3LhxmDp1KgCRpAHA3LlzsXHjxsbbhw4dwujRoxEQEIDCwkL4+fkBAObPn493330Xy5cvx/PPP9/4nF9++SV+9atfoX///sjNzYXBICa1PPHEE/jrX/+K+++/H++++y6MRmPjYyorK2E0GtG9e3cAIkkrLCzE9OnT8fHHH8Pb2xsAsH37dkyePNnhxJOIiPSJSRoREUnJmqS1ZfHixUhLSwMgkjSj0YgTJ06gb9++Nsf99re/xTvvvIPNmzdj5syZqK+vR0BAALp164aTJ0+ia9euNscnJydj27Zt2LVrFxISEnDlyhUEBwfDYDAgPz8fQUFBbbbLmqTl5eW16EN0dDSqq6tx4cIFB38SRESkN1yTRkREUpsyZQpUVbW7WRM0q6ioqBYJGgAkJCQAAA4ePAgAyMnJQV1dHcaMGdMiQQOApKQkAEBWVlbj8dXV1Rg9enS7CZpVYGCg3SQzIiICFRUVDj0HERHpE5M0IiIiB4WGhra5v7KyEgBQVVXV5vG9e/e2Oc76uD59+jjcloCAALv7vby8YLFYHH4eIiLSHyZpREREDjp79myb+62Jk7+/f5vHl5SU2BxnvR7bqVOnOq2tRETkvpikEREROejkyZMoLCxssT8jIwMAcMMNNwAAYmNj4evri8zMTNTW1rY4Pj09HQAaqzUOGjQI/v7+yMzMtFtqn4iI5MIkjYiIyEFmsxl//OMf0bzm1qFDh/D3v/8dPXv2xG233QYA8Pb2xpw5c3D+/HmsXr3a5jm2bt2Kr7/+Gv3798eECRMAiCmKjz76KCorK7F48WKYzWabx1RWVqKmpsbJvSMiIr1gdUciIpKSIyX4ASA1NRW+vr5tXift0qVL+L//+78W10kbN24c8vLycPPNN2Ps2LEoKCjAP/7xD3h7e7e4TlpdXR2Sk5ORkZGBAQMGYNq0afDx8UFeXh62bt2K3bt321wnzdqHqyUmJmLnzp3gn3ciIvfFJI2IiKTkSAl+ACgvL0dgYCAURcFNN92EjRs34oknnsC2bdtQW1uLG264Ac8++yxuvfXWFo89f/48Vq1ahc8++wynT59GQEAAEhMT8cwzz+D6669vcfzly5exfv16bNy4Ebm5uTAajYiKisK0adPw9NNPN65dY5JGROTZmKQRERE5wJqkWdeTEREROQvXpBEREREREekIkzQiIiIiIiIdYZJGRERERESkI15aN4CIiMgdcAk3ERG5CkfSiIiIiIiIdIRJGhERERERkY4wSSMiIiIiItIRJmlEREREREQ6wiSNiIiIiIhIR5ikERERERER6QiTNCIiIiIiIh1hkkZERERERKQj/w/8RT8t6+jjhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(loss_dict, data_type, train_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1779835-3b7c-4f3c-ad45-f34512203f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# End VAE feature encoding, to CPTAC_trnsfrmr_0.ipynb, separate conda env"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}