data_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-mdES-BSh4P4"
   },
   "source": [
    "### Set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 479
    },
    "colab_type": "code",
    "id": "F7KPTx60Xa2I",
    "outputId": "4781d0c5-8961-476a-9d5a-7ecc6bbf2b53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting git+https://github.com/ficusss/PyGMNormalize.git\n",
      "  Cloning https://github.com/ficusss/PyGMNormalize.git to /tmp/pip-req-build-az7tbxp_\n",
      "  Running command git clone -q https://github.com/ficusss/PyGMNormalize.git /tmp/pip-req-build-az7tbxp_\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pygmnormalize==0.2) (1.18.4)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from pygmnormalize==0.2) (1.4.1)\n",
      "Building wheels for collected packages: pygmnormalize\n",
      "  Building wheel for pygmnormalize (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pygmnormalize: filename=pygmnormalize-0.2-cp36-none-any.whl size=4669 sha256=061118b2c00b2d1b4d350785de147df8ce8a024fc6eeb6be529d2bf44296b6b8\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-e_0708v9/wheels/22/f9/56/db07076bd0b3b986414f211fac814066ed164011fbf3596e2e\n",
      "Successfully built pygmnormalize\n",
      "Installing collected packages: pygmnormalize\n",
      "Successfully installed pygmnormalize-0.2\n",
      "Collecting scikit-fuzzy\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6c/f0/5eb5dbe0fd8dfe7d4651a8f4e591a196623a22b9e5339101e559695b4f6c/scikit-fuzzy-0.4.2.tar.gz (993kB)\n",
      "\u001b[K     |████████████████████████████████| 1.0MB 2.7MB/s \n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: numpy>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from scikit-fuzzy) (1.18.4)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from scikit-fuzzy) (1.4.1)\n",
      "Requirement already satisfied, skipping upgrade: networkx>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from scikit-fuzzy) (2.4)\n",
      "Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=1.9.0->scikit-fuzzy) (4.4.2)\n",
      "Building wheels for collected packages: scikit-fuzzy\n",
      "  Building wheel for scikit-fuzzy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for scikit-fuzzy: filename=scikit_fuzzy-0.4.2-cp36-none-any.whl size=894070 sha256=0040a8f15dc263c9366edf2f1c4f022165594fe69c6ad3c6b098f48a90950d36\n",
      "  Stored in directory: /root/.cache/pip/wheels/b9/4e/77/da79b16f64ef1738d95486e2731eea09d73e90a72465096600\n",
      "Successfully built scikit-fuzzy\n",
      "Installing collected packages: scikit-fuzzy\n",
      "Successfully installed scikit-fuzzy-0.4.2\n"
     ]
    }
   ],
   "source": [
    "## Run this if using Colab. \n",
    "#If not, do the same commands in the computer terminal (without the !)\n",
    "\n",
    "!pip install git+https://github.com/ficusss/PyGMNormalize.git\n",
    "!pip install -U scikit-fuzzy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "KYdFwd4QUTfG",
    "outputId": "a769fcc3-d22b-4c8e-9e7b-615e7a1ff539"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, FunctionTransformer\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "import seaborn as sb\n",
    "from scipy.cluster import hierarchy\n",
    "from scipy.stats import variation\n",
    "import pygmnormalize as gmn\n",
    "from skfuzzy.cluster import cmeans, cmeans_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W_VGU_cnUTfM"
   },
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "g42qxRejXDfx"
   },
   "source": [
    "325 patients; expression data and clinical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VdUsLFkWYU40"
   },
   "outputs": [],
   "source": [
    "## Use this if running on Colab. Upload files to Colab first\n",
    "clinical_data = pd.read_csv('clinical_data.csv')\n",
    "gene_data = pd.read_csv('expression_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ytYpgybZUTfN"
   },
   "outputs": [],
   "source": [
    "## Use this if running on Jupyter and have the files in the same directory\n",
    "# gene_data = pd.read_csv('data/study_1/expression_data.csv')\n",
    "# clinical_data = pd.read_csv('data/study_1/clinical_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v4hBjzcEUTfV"
   },
   "source": [
    "#### Sample Data Samples - Expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "colab_type": "code",
    "id": "67k5KZHJUTfV",
    "outputId": "3d467737-cac7-48ed-fbbc-f61328505e0a"
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_Name</th>\n",
       "      <th>CGGA_1001</th>\n",
       "      <th>CGGA_1006</th>\n",
       "      <th>CGGA_1007</th>\n",
       "      <th>CGGA_1011</th>\n",
       "      <th>CGGA_1015</th>\n",
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       "      <th>CGGA_1024</th>\n",
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       "      <th>CGGA_1049</th>\n",
       "      <th>CGGA_1052</th>\n",
       "      <th>CGGA_1072</th>\n",
       "      <th>CGGA_1077</th>\n",
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       "      <th>CGGA_1171</th>\n",
       "      <th>CGGA_1177</th>\n",
       "      <th>CGGA_1180</th>\n",
       "      <th>CGGA_1188</th>\n",
       "      <th>CGGA_1214</th>\n",
       "      <th>CGGA_1216</th>\n",
       "      <th>CGGA_1218</th>\n",
       "      <th>CGGA_1224</th>\n",
       "      <th>CGGA_1234</th>\n",
       "      <th>CGGA_1237</th>\n",
       "      <th>CGGA_1240</th>\n",
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       "      <th>CGGA_D46</th>\n",
       "      <th>CGGA_D47</th>\n",
       "      <th>CGGA_D48</th>\n",
       "      <th>CGGA_D52</th>\n",
       "      <th>CGGA_D56</th>\n",
       "      <th>CGGA_D59</th>\n",
       "      <th>CGGA_277</th>\n",
       "      <th>CGGA_518</th>\n",
       "      <th>CGGA_J023</th>\n",
       "      <th>CGGA_J024</th>\n",
       "      <th>CGGA_J042</th>\n",
       "      <th>CGGA_J100</th>\n",
       "      <th>CGGA_1004</th>\n",
       "      <th>CGGA_1008</th>\n",
       "      <th>CGGA_1053</th>\n",
       "      <th>CGGA_1059</th>\n",
       "      <th>CGGA_1070</th>\n",
       "      <th>CGGA_1071</th>\n",
       "      <th>CGGA_1073</th>\n",
       "      <th>CGGA_1074</th>\n",
       "      <th>CGGA_1095</th>\n",
       "      <th>CGGA_1099</th>\n",
       "      <th>CGGA_1114</th>\n",
       "      <th>CGGA_1116</th>\n",
       "      <th>CGGA_1118</th>\n",
       "      <th>CGGA_1119</th>\n",
       "      <th>CGGA_1197</th>\n",
       "      <th>CGGA_1246</th>\n",
       "      <th>CGGA_1275</th>\n",
       "      <th>CGGA_1450</th>\n",
       "      <th>CGGA_1460</th>\n",
       "      <th>CGGA_1475</th>\n",
       "      <th>CGGA_243</th>\n",
       "      <th>CGGA_247</th>\n",
       "      <th>CGGA_738</th>\n",
       "      <th>CGGA_759</th>\n",
       "      <th>CGGA_D30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1BG</td>\n",
       "      <td>12.64</td>\n",
       "      <td>7.03</td>\n",
       "      <td>30.09</td>\n",
       "      <td>6.64</td>\n",
       "      <td>1.83</td>\n",
       "      <td>39.03</td>\n",
       "      <td>12.96</td>\n",
       "      <td>9.28</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3.18</td>\n",
       "      <td>49.16</td>\n",
       "      <td>9.99</td>\n",
       "      <td>40.35</td>\n",
       "      <td>14.70</td>\n",
       "      <td>4.48</td>\n",
       "      <td>11.04</td>\n",
       "      <td>48.61</td>\n",
       "      <td>13.12</td>\n",
       "      <td>9.59</td>\n",
       "      <td>30.45</td>\n",
       "      <td>18.72</td>\n",
       "      <td>47.06</td>\n",
       "      <td>21.99</td>\n",
       "      <td>10.63</td>\n",
       "      <td>18.90</td>\n",
       "      <td>23.58</td>\n",
       "      <td>9.62</td>\n",
       "      <td>11.87</td>\n",
       "      <td>38.88</td>\n",
       "      <td>55.84</td>\n",
       "      <td>23.07</td>\n",
       "      <td>18.49</td>\n",
       "      <td>18.82</td>\n",
       "      <td>20.02</td>\n",
       "      <td>21.39</td>\n",
       "      <td>6.78</td>\n",
       "      <td>4.97</td>\n",
       "      <td>10.89</td>\n",
       "      <td>...</td>\n",
       "      <td>10.13</td>\n",
       "      <td>17.68</td>\n",
       "      <td>8.97</td>\n",
       "      <td>16.73</td>\n",
       "      <td>9.92</td>\n",
       "      <td>28.25</td>\n",
       "      <td>7.06</td>\n",
       "      <td>21.27</td>\n",
       "      <td>20.50</td>\n",
       "      <td>22.14</td>\n",
       "      <td>7.52</td>\n",
       "      <td>9.38</td>\n",
       "      <td>14.35</td>\n",
       "      <td>21.13</td>\n",
       "      <td>2.24</td>\n",
       "      <td>20.74</td>\n",
       "      <td>15.97</td>\n",
       "      <td>26.36</td>\n",
       "      <td>20.84</td>\n",
       "      <td>5.25</td>\n",
       "      <td>11.83</td>\n",
       "      <td>39.87</td>\n",
       "      <td>20.71</td>\n",
       "      <td>10.94</td>\n",
       "      <td>5.74</td>\n",
       "      <td>13.74</td>\n",
       "      <td>14.75</td>\n",
       "      <td>19.61</td>\n",
       "      <td>21.50</td>\n",
       "      <td>14.90</td>\n",
       "      <td>4.46</td>\n",
       "      <td>28.42</td>\n",
       "      <td>19.32</td>\n",
       "      <td>2.24</td>\n",
       "      <td>18.89</td>\n",
       "      <td>4.03</td>\n",
       "      <td>5.29</td>\n",
       "      <td>4.64</td>\n",
       "      <td>7.07</td>\n",
       "      <td>31.41</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1BG-AS1</td>\n",
       "      <td>2.12</td>\n",
       "      <td>1.13</td>\n",
       "      <td>6.64</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.39</td>\n",
       "      <td>4.46</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>7.75</td>\n",
       "      <td>1.61</td>\n",
       "      <td>9.49</td>\n",
       "      <td>5.02</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2.73</td>\n",
       "      <td>5.59</td>\n",
       "      <td>4.37</td>\n",
       "      <td>2.07</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.99</td>\n",
       "      <td>5.55</td>\n",
       "      <td>4.27</td>\n",
       "      <td>1.15</td>\n",
       "      <td>2.71</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.88</td>\n",
       "      <td>2.97</td>\n",
       "      <td>6.18</td>\n",
       "      <td>9.10</td>\n",
       "      <td>1.90</td>\n",
       "      <td>4.85</td>\n",
       "      <td>4.01</td>\n",
       "      <td>5.10</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.98</td>\n",
       "      <td>2.95</td>\n",
       "      <td>...</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2.38</td>\n",
       "      <td>4.89</td>\n",
       "      <td>2.56</td>\n",
       "      <td>3.82</td>\n",
       "      <td>1.03</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.86</td>\n",
       "      <td>5.46</td>\n",
       "      <td>2.62</td>\n",
       "      <td>1.62</td>\n",
       "      <td>2.14</td>\n",
       "      <td>3.48</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.23</td>\n",
       "      <td>4.52</td>\n",
       "      <td>2.61</td>\n",
       "      <td>4.16</td>\n",
       "      <td>1.62</td>\n",
       "      <td>1.04</td>\n",
       "      <td>6.93</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.41</td>\n",
       "      <td>5.29</td>\n",
       "      <td>1.41</td>\n",
       "      <td>3.19</td>\n",
       "      <td>1.97</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.57</td>\n",
       "      <td>8.36</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.60</td>\n",
       "      <td>5.31</td>\n",
       "      <td>2.47</td>\n",
       "      <td>3.69</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2M</td>\n",
       "      <td>452.92</td>\n",
       "      <td>106.54</td>\n",
       "      <td>206.70</td>\n",
       "      <td>707.17</td>\n",
       "      <td>824.32</td>\n",
       "      <td>155.14</td>\n",
       "      <td>538.33</td>\n",
       "      <td>26.39</td>\n",
       "      <td>219.35</td>\n",
       "      <td>302.00</td>\n",
       "      <td>139.67</td>\n",
       "      <td>143.45</td>\n",
       "      <td>79.09</td>\n",
       "      <td>202.67</td>\n",
       "      <td>345.41</td>\n",
       "      <td>117.38</td>\n",
       "      <td>126.56</td>\n",
       "      <td>412.72</td>\n",
       "      <td>170.82</td>\n",
       "      <td>136.63</td>\n",
       "      <td>345.27</td>\n",
       "      <td>763.49</td>\n",
       "      <td>235.32</td>\n",
       "      <td>60.24</td>\n",
       "      <td>561.11</td>\n",
       "      <td>61.97</td>\n",
       "      <td>56.12</td>\n",
       "      <td>483.18</td>\n",
       "      <td>614.56</td>\n",
       "      <td>422.29</td>\n",
       "      <td>151.96</td>\n",
       "      <td>67.55</td>\n",
       "      <td>76.20</td>\n",
       "      <td>78.47</td>\n",
       "      <td>234.72</td>\n",
       "      <td>484.48</td>\n",
       "      <td>903.16</td>\n",
       "      <td>134.09</td>\n",
       "      <td>508.32</td>\n",
       "      <td>...</td>\n",
       "      <td>70.55</td>\n",
       "      <td>67.67</td>\n",
       "      <td>115.30</td>\n",
       "      <td>173.03</td>\n",
       "      <td>182.86</td>\n",
       "      <td>116.36</td>\n",
       "      <td>69.12</td>\n",
       "      <td>183.79</td>\n",
       "      <td>248.62</td>\n",
       "      <td>119.77</td>\n",
       "      <td>51.07</td>\n",
       "      <td>94.13</td>\n",
       "      <td>177.72</td>\n",
       "      <td>255.94</td>\n",
       "      <td>125.46</td>\n",
       "      <td>277.46</td>\n",
       "      <td>260.36</td>\n",
       "      <td>298.05</td>\n",
       "      <td>145.93</td>\n",
       "      <td>256.82</td>\n",
       "      <td>214.55</td>\n",
       "      <td>249.91</td>\n",
       "      <td>452.45</td>\n",
       "      <td>276.66</td>\n",
       "      <td>194.31</td>\n",
       "      <td>384.38</td>\n",
       "      <td>174.69</td>\n",
       "      <td>108.87</td>\n",
       "      <td>65.29</td>\n",
       "      <td>78.92</td>\n",
       "      <td>87.20</td>\n",
       "      <td>243.89</td>\n",
       "      <td>73.80</td>\n",
       "      <td>111.50</td>\n",
       "      <td>340.53</td>\n",
       "      <td>108.06</td>\n",
       "      <td>97.24</td>\n",
       "      <td>160.70</td>\n",
       "      <td>263.66</td>\n",
       "      <td>192.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2M-AS1</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.34</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.32</td>\n",
       "      <td>2.07</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.93</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.44</td>\n",
       "      <td>...</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3.69</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A2ML1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.33</td>\n",
       "      <td>4.96</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.84</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.44</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.25</td>\n",
       "      <td>4.57</td>\n",
       "      <td>7.31</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.04</td>\n",
       "      <td>9.13</td>\n",
       "      <td>1.01</td>\n",
       "      <td>5.71</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.48</td>\n",
       "      <td>...</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4.07</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.79</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2.09</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2.41</td>\n",
       "      <td>3.34</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2.58</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.71</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.60</td>\n",
       "      <td>3.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 326 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gene_Name  CGGA_1001  CGGA_1006  ...  CGGA_738  CGGA_759  CGGA_D30\n",
       "0      A1BG      12.64       7.03  ...      4.64      7.07     31.41\n",
       "1  A1BG-AS1       2.12       1.13  ...      0.85      1.99      2.65\n",
       "2       A2M     452.92     106.54  ...    160.70    263.66    192.54\n",
       "3   A2M-AS1       3.30       0.13  ...      0.38      0.48      2.51\n",
       "4     A2ML1       0.04       0.33  ...      3.33      0.60      3.86\n",
       "\n",
       "[5 rows x 326 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ozvQ6cW_UTfZ"
   },
   "source": [
    "#### Sample Data Samples - Clinical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "HvNFUSOBUTfZ",
    "outputId": "e42e0ec4-9334-4d20-f08f-d53fad3c80f8"
   },
   "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>CGGA_ID</th>\n",
       "      <th>PRS_type</th>\n",
       "      <th>Histology</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>OS</th>\n",
       "      <th>Censor</th>\n",
       "      <th>Radio_status</th>\n",
       "      <th>Chemo_status</th>\n",
       "      <th>IDH_mutation_status</th>\n",
       "      <th>1p19q_codeletion_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CGGA_1001</td>\n",
       "      <td>Primary</td>\n",
       "      <td>GBM</td>\n",
       "      <td>WHO IV</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>3428.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Wildtype</td>\n",
       "      <td>Non-codel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CGGA_1006</td>\n",
       "      <td>Primary</td>\n",
       "      <td>AA</td>\n",
       "      <td>WHO III</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>254.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Wildtype</td>\n",
       "      <td>Non-codel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CGGA_1007</td>\n",
       "      <td>Primary</td>\n",
       "      <td>GBM</td>\n",
       "      <td>WHO IV</td>\n",
       "      <td>Female</td>\n",
       "      <td>57</td>\n",
       "      <td>345.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Wildtype</td>\n",
       "      <td>Non-codel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CGGA_1011</td>\n",
       "      <td>Primary</td>\n",
       "      <td>GBM</td>\n",
       "      <td>WHO IV</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>109.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Wildtype</td>\n",
       "      <td>Non-codel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CGGA_1015</td>\n",
       "      <td>Primary</td>\n",
       "      <td>GBM</td>\n",
       "      <td>WHO IV</td>\n",
       "      <td>Male</td>\n",
       "      <td>62</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Wildtype</td>\n",
       "      <td>Non-codel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CGGA_ID PRS_type  ... IDH_mutation_status 1p19q_codeletion_status\n",
       "0  CGGA_1001  Primary  ...            Wildtype               Non-codel\n",
       "1  CGGA_1006  Primary  ...            Wildtype               Non-codel\n",
       "2  CGGA_1007  Primary  ...            Wildtype               Non-codel\n",
       "3  CGGA_1011  Primary  ...            Wildtype               Non-codel\n",
       "4  CGGA_1015  Primary  ...            Wildtype               Non-codel\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinical_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TpayupZtUTfi"
   },
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lXDbCINEUTfd"
   },
   "source": [
    "#### Data Dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "Ut7j64k4UTfd",
    "outputId": "f151de9e-396e-4975-cdc7-9c58d32f37eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Data Samples: 325\n",
      "Number of Genes: 24326\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of Data Samples: {len(gene_data.columns)-1}')\n",
    "print(f'Number of Genes: {len(gene_data.index)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6gdFEHpsUTfm"
   },
   "source": [
    "### Class (Grade) Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "miRt3aAfUTfj"
   },
   "source": [
    "- Impute the class labels (tumor grade) and histology data as some of the values are 'NaN'\n",
    "- Impute using the most frequent value in the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9cxHgGQjUTfj"
   },
   "outputs": [],
   "source": [
    "class_label_col = ['Grade']\n",
    "histology_col = ['Histology']\n",
    "\n",
    "imputer = SimpleImputer(missing_values = np.nan, strategy = 'most_frequent')\n",
    "\n",
    "class_labels = imputer.fit_transform(clinical_data[class_label_col])\n",
    "histology = imputer.fit_transform(clinical_data[histology_col])\n",
    "\n",
    "class_labels_categories = list(np.unique(class_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 520
    },
    "colab_type": "code",
    "id": "k8Is5dO3UTfn",
    "outputId": "78ba660b-a22b-4890-ef1b-70e26becddef"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.hist(class_labels, bins=range(4),  color=['red'], rwidth=0.90, align='left')\n",
    "ax.set_xlabel('Grade', size=15)\n",
    "ax.set_ylabel('Count', size=15)\n",
    "ax.set_title('Distribution of Class Labels', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "Z7q7BdF5UTfq",
    "outputId": "961f3c35-ecd5-4c7f-c918-98680fcd3839"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts for labels: \n",
      "\tWHO II: 103\n",
      "\tWHO III: 79\n",
      "\tWHO IV: 143\n"
     ]
    }
   ],
   "source": [
    "unique, counts = np.unique(class_labels, return_counts=True)\n",
    "print('Counts for labels: ')\n",
    "for i in range(len(unique)):\n",
    "    print(f'\\t{unique[i]}: {counts[i]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SBicN_GtUTfu"
   },
   "source": [
    "### Histology/Subtype Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 628
    },
    "colab_type": "code",
    "id": "tY0EpP6pUTfv",
    "outputId": "6d34b467-08b9-438f-9ae4-9916a6f0360d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "ax.hist(histology, bins=range(11),  color=['red'], rwidth=0.90, align='left')\n",
    "ax.set_xlabel('Glioma Type', size=15)\n",
    "ax.set_ylabel('Count', size=15)\n",
    "ax.set_title('Distribution of Glioma Types', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "iqiNPaL1UTfx",
    "outputId": "c8ba500f-7b00-4fe9-f38c-3e551c75c90c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts for glioma subtypes: \n",
      "\tA: 46\n",
      "\tAA: 38\n",
      "\tAO: 12\n",
      "\tGBM: 89\n",
      "\tO: 48\n",
      "\trA: 10\n",
      "\trAA: 24\n",
      "\trGBM: 24\n",
      "\trO: 4\n",
      "\tsGBM: 30\n"
     ]
    }
   ],
   "source": [
    "unique, counts = np.unique(histology, return_counts=True)\n",
    "print('Counts for glioma subtypes: ')\n",
    "for i in range(len(unique)):\n",
    "    print(f'\\t{unique[i]}: {counts[i]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "orsucIU4wSLp"
   },
   "source": [
    "### Age Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "gVNNIA8ilVRD",
    "outputId": "20f5fec6-f7db-4a4b-bcf8-902244ff65b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts for ages: \n",
      "\tUnder 18: 7\n",
      "\t18 - 40: 136\n",
      "\t40 - 60: 158\n",
      "\tOver 60: 24\n"
     ]
    }
   ],
   "source": [
    "unique, counts = np.unique(clinical_data['Age'], return_counts=True)\n",
    "print('Counts for ages: ')\n",
    "kid = 0\n",
    "young_adult = 0\n",
    "adult = 0\n",
    "senior = 0\n",
    "for i in range(len(unique)):\n",
    "    if unique[i] <= 18:\n",
    "        kid += counts[i]\n",
    "    elif 18 < unique[i] <= 40:\n",
    "        young_adult += counts[i]\n",
    "    elif 40 < unique[i] <= 60:\n",
    "        adult += counts[i]\n",
    "    else:\n",
    "        senior += counts[i]\n",
    "print(f'\\tUnder 18: {kid}')\n",
    "print(f'\\t18 - 40: {young_adult}')\n",
    "print(f'\\t40 - 60: {adult}')\n",
    "print(f'\\tOver 60: {senior}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2b5nZg5zwW4_"
   },
   "source": [
    "### Gender Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "bR_-bqTwm9KI",
    "outputId": "9d31deb8-7294-474e-8bc4-28e9da3db6d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts for genders: \n",
      "\tFemale: 122\n",
      "\tMale: 203\n"
     ]
    }
   ],
   "source": [
    "unique, counts = np.unique(clinical_data['Gender'], return_counts=True)\n",
    "print('Counts for genders: ')\n",
    "for i in range(len(unique)):\n",
    "    print(f'\\t{unique[i]}: {counts[i]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "KMOYA7VnnGVY",
    "outputId": "1fdfb520-b0d0-40fa-f1fd-3a40adc6df7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Grades by gender:\n",
      "Male:\n",
      "\tWHO II: 62\n",
      "\tWHO III: 49\n",
      "\tWHO IV: 92\n",
      "Female:\n",
      "\tWHO II: 41\n",
      "\tWHO III: 30\n",
      "\tWHO IV: 51\n",
      "\n",
      "Proportion of Males/Total per Grade:\n",
      "\tWHO II:  0.60194\n",
      "\tWHO III:  0.62025\n",
      "\tWHO VI:  0.64336\n"
     ]
    }
   ],
   "source": [
    "males = clinical_data[clinical_data['Gender']=='Male']\n",
    "females = clinical_data[clinical_data['Gender']=='Female']\n",
    "\n",
    "gen_label_col = ['Grade']\n",
    "imputer = SimpleImputer(missing_values = np.nan, strategy = 'most_frequent')\n",
    "males_grade = imputer.fit_transform(males[gen_label_col])\n",
    "females_grade = imputer.fit_transform(females[gen_label_col])\n",
    "\n",
    "print(f'\\nGrades by gender:')\n",
    "\n",
    "print(f'Male:')\n",
    "unique_m, counts_m = np.unique(males_grade, return_counts=True)\n",
    "for i in range(len(unique_m)):\n",
    "    print(f'\\t{unique_m[i]}: {counts_m[i]}')\n",
    "\n",
    "print(f'Female:')\n",
    "unique_f, counts_f = np.unique(females_grade, return_counts=True)\n",
    "for i in range(len(unique_f)):\n",
    "    print(f'\\t{unique_f[i]}: {counts_f[i]}')\n",
    "\n",
    "print('\\nProportion of Males/Total per Grade:')\n",
    "print(f'\\tWHO II: ', round(counts_m[0]/(counts_m[0] + counts_f[0]),5))\n",
    "print(f'\\tWHO III: ', round(counts_m[1]/(counts_m[1] + counts_f[1]),5))\n",
    "print(f'\\tWHO VI: ', round(counts_m[2]/(counts_m[2] + counts_f[2]),5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I8yKvRaZwZeW"
   },
   "source": [
    "*This may cause problems if the model misattributes male charactersitics with malignancy characteristics*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TWJNcM4uUTf0"
   },
   "source": [
    "### Visualize Read Counts by Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zli-xOasUTf1"
   },
   "outputs": [],
   "source": [
    "# Store the gene_names in a separate column so that we can work on the gene_data matrix directly\n",
    "gene_names = gene_data['Gene_Name']\n",
    "gene_data = gene_data.drop(columns=['Gene_Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "DH3sH2nFUTf3",
    "outputId": "35b9282e-6f89-4533-ee67-642ca3a6434f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_sums = gene_data.sum(axis = 0)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(range(325), sample_sums, 'r.', markersize=5)\n",
    "ax.set_xlabel('Sample Number', size=15)\n",
    "ax.set_ylabel('Number of Reads', size=15)\n",
    "ax.set_title('Read Counts per Sample', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5Wdh7eJcUTf6"
   },
   "source": [
    "### Visualize Read Counts by Gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "ZCtSF4hwUTf6",
    "outputId": "f078e75e-0ab1-4edc-ce04-64cac771e627"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gene_sums = gene_data.sum(axis = 1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(range(len(gene_sums)), gene_sums, 'b.', alpha = .75, markersize=3)\n",
    "ax.set_xlabel('Gene Number', size=15)\n",
    "ax.set_ylabel('Number of Reads', size=15)\n",
    "ax.set_title('Read Counts per Gene', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GBug3opdUTf9"
   },
   "source": [
    "## Train-Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "b8_x5zTwUTf-"
   },
   "outputs": [],
   "source": [
    "# Train-test split 90/10 as this is train/test. \n",
    "# stratify=class_labels will make sure that the different classes are equally represented between the training\n",
    "# and testing data\n",
    "train_genes, test_genes, train_lbl, test_lbl = train_test_split(gene_data.T, class_labels, \n",
    "                                                                test_size=0.10, \n",
    "                                                                random_state=0,\n",
    "                                                                shuffle=True, \n",
    "                                                                stratify=class_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Da8wdcb1UTf9"
   },
   "source": [
    "## Create Preprocessing Pipeline on Train Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-z2e_9d8UTgB"
   },
   "source": [
    "### Filter based on read count (i.e. 70% of samples have < 1 count for this gene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y1o1lQ2aUTgB"
   },
   "outputs": [],
   "source": [
    "low_exp = []\n",
    "for i in train_genes.columns:\n",
    "    count = 0\n",
    "    for j in train_genes[i]:\n",
    "        if j < 1: \n",
    "            count += 1\n",
    "    if count/325 >= 0.70:\n",
    "        low_exp.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WhXQI5DgUTgG"
   },
   "outputs": [],
   "source": [
    "cols_filtered = [x for x in train_genes.columns if x not in low_exp]\n",
    "train_genes = train_genes[cols_filtered]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LBb6PUX7UTgJ"
   },
   "source": [
    "### Trimmed Mean of M Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "fx5eeH2fUTgK",
    "outputId": "dd87399d-9767-42cc-d317-6e2600faad1f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>7</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>19</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>35</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>50</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>...</th>\n",
       "      <th>24282</th>\n",
       "      <th>24283</th>\n",
       "      <th>24284</th>\n",
       "      <th>24285</th>\n",
       "      <th>24287</th>\n",
       "      <th>24288</th>\n",
       "      <th>24289</th>\n",
       "      <th>24290</th>\n",
       "      <th>24291</th>\n",
       "      <th>24292</th>\n",
       "      <th>24293</th>\n",
       "      <th>24294</th>\n",
       "      <th>24295</th>\n",
       "      <th>24296</th>\n",
       "      <th>24297</th>\n",
       "      <th>24298</th>\n",
       "      <th>24299</th>\n",
       "      <th>24300</th>\n",
       "      <th>24301</th>\n",
       "      <th>24302</th>\n",
       "      <th>24304</th>\n",
       "      <th>24305</th>\n",
       "      <th>24307</th>\n",
       "      <th>24308</th>\n",
       "      <th>24309</th>\n",
       "      <th>24310</th>\n",
       "      <th>24311</th>\n",
       "      <th>24312</th>\n",
       "      <th>24313</th>\n",
       "      <th>24314</th>\n",
       "      <th>24315</th>\n",
       "      <th>24316</th>\n",
       "      <th>24317</th>\n",
       "      <th>24318</th>\n",
       "      <th>24319</th>\n",
       "      <th>24320</th>\n",
       "      <th>24322</th>\n",
       "      <th>24323</th>\n",
       "      <th>24324</th>\n",
       "      <th>24325</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CGGA_171</th>\n",
       "      <td>12.66</td>\n",
       "      <td>1.21</td>\n",
       "      <td>188.46</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.93</td>\n",
       "      <td>25.41</td>\n",
       "      <td>4.26</td>\n",
       "      <td>7.95</td>\n",
       "      <td>3.18</td>\n",
       "      <td>12.23</td>\n",
       "      <td>2.63</td>\n",
       "      <td>80.28</td>\n",
       "      <td>99.64</td>\n",
       "      <td>17.27</td>\n",
       "      <td>84.17</td>\n",
       "      <td>2.71</td>\n",
       "      <td>63.44</td>\n",
       "      <td>4.74</td>\n",
       "      <td>26.34</td>\n",
       "      <td>1.89</td>\n",
       "      <td>24.75</td>\n",
       "      <td>9.99</td>\n",
       "      <td>92.23</td>\n",
       "      <td>11.66</td>\n",
       "      <td>8.13</td>\n",
       "      <td>4.30</td>\n",
       "      <td>7.04</td>\n",
       "      <td>52.29</td>\n",
       "      <td>17.44</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.74</td>\n",
       "      <td>4.39</td>\n",
       "      <td>3.59</td>\n",
       "      <td>10.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>1.38</td>\n",
       "      <td>56.69</td>\n",
       "      <td>9.50</td>\n",
       "      <td>10.62</td>\n",
       "      <td>4.18</td>\n",
       "      <td>...</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.82</td>\n",
       "      <td>9.71</td>\n",
       "      <td>5.46</td>\n",
       "      <td>2.33</td>\n",
       "      <td>0.71</td>\n",
       "      <td>4.79</td>\n",
       "      <td>18.03</td>\n",
       "      <td>42.40</td>\n",
       "      <td>6.59</td>\n",
       "      <td>0.74</td>\n",
       "      <td>9.38</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.88</td>\n",
       "      <td>2.45</td>\n",
       "      <td>5.29</td>\n",
       "      <td>6.14</td>\n",
       "      <td>3.82</td>\n",
       "      <td>9.94</td>\n",
       "      <td>3.54</td>\n",
       "      <td>7.55</td>\n",
       "      <td>4.74</td>\n",
       "      <td>3.36</td>\n",
       "      <td>2.54</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3.79</td>\n",
       "      <td>101.39</td>\n",
       "      <td>57.10</td>\n",
       "      <td>0.86</td>\n",
       "      <td>9.90</td>\n",
       "      <td>4.43</td>\n",
       "      <td>2.06</td>\n",
       "      <td>3.90</td>\n",
       "      <td>1.57</td>\n",
       "      <td>3.05</td>\n",
       "      <td>2.68</td>\n",
       "      <td>4.59</td>\n",
       "      <td>35.90</td>\n",
       "      <td>3.56</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_1024</th>\n",
       "      <td>2.35</td>\n",
       "      <td>1.22</td>\n",
       "      <td>219.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>4.32</td>\n",
       "      <td>25.78</td>\n",
       "      <td>8.05</td>\n",
       "      <td>6.68</td>\n",
       "      <td>6.91</td>\n",
       "      <td>22.71</td>\n",
       "      <td>4.05</td>\n",
       "      <td>23.22</td>\n",
       "      <td>64.38</td>\n",
       "      <td>21.58</td>\n",
       "      <td>62.23</td>\n",
       "      <td>4.67</td>\n",
       "      <td>14.11</td>\n",
       "      <td>3.14</td>\n",
       "      <td>21.65</td>\n",
       "      <td>10.05</td>\n",
       "      <td>14.01</td>\n",
       "      <td>11.03</td>\n",
       "      <td>64.62</td>\n",
       "      <td>2.70</td>\n",
       "      <td>5.33</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.49</td>\n",
       "      <td>20.05</td>\n",
       "      <td>35.24</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.08</td>\n",
       "      <td>5.55</td>\n",
       "      <td>6.06</td>\n",
       "      <td>4.29</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.51</td>\n",
       "      <td>13.48</td>\n",
       "      <td>18.28</td>\n",
       "      <td>7.63</td>\n",
       "      <td>...</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0.53</td>\n",
       "      <td>7.90</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.26</td>\n",
       "      <td>4.62</td>\n",
       "      <td>6.32</td>\n",
       "      <td>32.99</td>\n",
       "      <td>4.24</td>\n",
       "      <td>1.17</td>\n",
       "      <td>8.78</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.13</td>\n",
       "      <td>5.05</td>\n",
       "      <td>7.82</td>\n",
       "      <td>10.49</td>\n",
       "      <td>4.05</td>\n",
       "      <td>8.84</td>\n",
       "      <td>4.76</td>\n",
       "      <td>13.25</td>\n",
       "      <td>5.96</td>\n",
       "      <td>2.14</td>\n",
       "      <td>5.79</td>\n",
       "      <td>2.73</td>\n",
       "      <td>4.95</td>\n",
       "      <td>22.05</td>\n",
       "      <td>51.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.95</td>\n",
       "      <td>6.84</td>\n",
       "      <td>6.93</td>\n",
       "      <td>18.18</td>\n",
       "      <td>1.43</td>\n",
       "      <td>3.08</td>\n",
       "      <td>9.56</td>\n",
       "      <td>7.66</td>\n",
       "      <td>147.99</td>\n",
       "      <td>5.79</td>\n",
       "      <td>10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_835</th>\n",
       "      <td>7.11</td>\n",
       "      <td>1.73</td>\n",
       "      <td>82.60</td>\n",
       "      <td>3.43</td>\n",
       "      <td>1.36</td>\n",
       "      <td>36.54</td>\n",
       "      <td>3.99</td>\n",
       "      <td>7.10</td>\n",
       "      <td>4.36</td>\n",
       "      <td>10.29</td>\n",
       "      <td>6.66</td>\n",
       "      <td>37.94</td>\n",
       "      <td>86.70</td>\n",
       "      <td>18.17</td>\n",
       "      <td>64.68</td>\n",
       "      <td>3.35</td>\n",
       "      <td>26.98</td>\n",
       "      <td>5.64</td>\n",
       "      <td>27.62</td>\n",
       "      <td>4.99</td>\n",
       "      <td>23.56</td>\n",
       "      <td>16.95</td>\n",
       "      <td>125.59</td>\n",
       "      <td>1.33</td>\n",
       "      <td>10.82</td>\n",
       "      <td>4.37</td>\n",
       "      <td>3.08</td>\n",
       "      <td>91.61</td>\n",
       "      <td>18.58</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.89</td>\n",
       "      <td>9.01</td>\n",
       "      <td>5.84</td>\n",
       "      <td>3.97</td>\n",
       "      <td>0.43</td>\n",
       "      <td>25.18</td>\n",
       "      <td>6.29</td>\n",
       "      <td>11.49</td>\n",
       "      <td>4.21</td>\n",
       "      <td>...</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.17</td>\n",
       "      <td>7.61</td>\n",
       "      <td>6.91</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.29</td>\n",
       "      <td>2.93</td>\n",
       "      <td>11.84</td>\n",
       "      <td>36.43</td>\n",
       "      <td>4.75</td>\n",
       "      <td>0.33</td>\n",
       "      <td>8.71</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.28</td>\n",
       "      <td>3.23</td>\n",
       "      <td>6.44</td>\n",
       "      <td>7.71</td>\n",
       "      <td>3.21</td>\n",
       "      <td>9.67</td>\n",
       "      <td>5.34</td>\n",
       "      <td>9.32</td>\n",
       "      <td>5.07</td>\n",
       "      <td>1.95</td>\n",
       "      <td>5.16</td>\n",
       "      <td>1.92</td>\n",
       "      <td>9.79</td>\n",
       "      <td>40.77</td>\n",
       "      <td>76.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.70</td>\n",
       "      <td>4.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>5.92</td>\n",
       "      <td>2.14</td>\n",
       "      <td>4.49</td>\n",
       "      <td>6.74</td>\n",
       "      <td>5.21</td>\n",
       "      <td>45.83</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_738</th>\n",
       "      <td>4.64</td>\n",
       "      <td>0.85</td>\n",
       "      <td>160.70</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.69</td>\n",
       "      <td>38.59</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.09</td>\n",
       "      <td>5.38</td>\n",
       "      <td>9.99</td>\n",
       "      <td>8.77</td>\n",
       "      <td>21.58</td>\n",
       "      <td>66.48</td>\n",
       "      <td>15.06</td>\n",
       "      <td>91.22</td>\n",
       "      <td>6.22</td>\n",
       "      <td>34.98</td>\n",
       "      <td>6.17</td>\n",
       "      <td>22.85</td>\n",
       "      <td>22.54</td>\n",
       "      <td>21.13</td>\n",
       "      <td>15.24</td>\n",
       "      <td>97.35</td>\n",
       "      <td>6.29</td>\n",
       "      <td>15.05</td>\n",
       "      <td>3.57</td>\n",
       "      <td>2.32</td>\n",
       "      <td>55.90</td>\n",
       "      <td>33.28</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2.45</td>\n",
       "      <td>8.01</td>\n",
       "      <td>10.80</td>\n",
       "      <td>15.89</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.53</td>\n",
       "      <td>25.69</td>\n",
       "      <td>13.40</td>\n",
       "      <td>27.94</td>\n",
       "      <td>3.34</td>\n",
       "      <td>...</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.70</td>\n",
       "      <td>9.59</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.46</td>\n",
       "      <td>3.32</td>\n",
       "      <td>5.84</td>\n",
       "      <td>6.95</td>\n",
       "      <td>35.81</td>\n",
       "      <td>7.03</td>\n",
       "      <td>2.51</td>\n",
       "      <td>10.07</td>\n",
       "      <td>1.18</td>\n",
       "      <td>2.90</td>\n",
       "      <td>7.84</td>\n",
       "      <td>15.36</td>\n",
       "      <td>15.79</td>\n",
       "      <td>8.87</td>\n",
       "      <td>13.42</td>\n",
       "      <td>2.65</td>\n",
       "      <td>12.58</td>\n",
       "      <td>7.58</td>\n",
       "      <td>3.81</td>\n",
       "      <td>5.56</td>\n",
       "      <td>3.33</td>\n",
       "      <td>9.19</td>\n",
       "      <td>32.85</td>\n",
       "      <td>60.04</td>\n",
       "      <td>1.07</td>\n",
       "      <td>6.17</td>\n",
       "      <td>3.52</td>\n",
       "      <td>2.85</td>\n",
       "      <td>6.89</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3.89</td>\n",
       "      <td>12.87</td>\n",
       "      <td>9.91</td>\n",
       "      <td>72.42</td>\n",
       "      <td>15.13</td>\n",
       "      <td>12.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_479</th>\n",
       "      <td>13.11</td>\n",
       "      <td>2.65</td>\n",
       "      <td>133.27</td>\n",
       "      <td>1.14</td>\n",
       "      <td>6.01</td>\n",
       "      <td>27.09</td>\n",
       "      <td>4.40</td>\n",
       "      <td>7.01</td>\n",
       "      <td>3.63</td>\n",
       "      <td>8.20</td>\n",
       "      <td>3.78</td>\n",
       "      <td>27.87</td>\n",
       "      <td>57.60</td>\n",
       "      <td>14.21</td>\n",
       "      <td>78.23</td>\n",
       "      <td>4.07</td>\n",
       "      <td>26.50</td>\n",
       "      <td>3.59</td>\n",
       "      <td>22.19</td>\n",
       "      <td>12.13</td>\n",
       "      <td>28.88</td>\n",
       "      <td>94.33</td>\n",
       "      <td>106.33</td>\n",
       "      <td>15.66</td>\n",
       "      <td>14.19</td>\n",
       "      <td>3.55</td>\n",
       "      <td>2.53</td>\n",
       "      <td>233.53</td>\n",
       "      <td>30.24</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1.76</td>\n",
       "      <td>3.86</td>\n",
       "      <td>22.20</td>\n",
       "      <td>18.24</td>\n",
       "      <td>3.19</td>\n",
       "      <td>1.06</td>\n",
       "      <td>20.44</td>\n",
       "      <td>7.66</td>\n",
       "      <td>17.06</td>\n",
       "      <td>4.80</td>\n",
       "      <td>...</td>\n",
       "      <td>1.09</td>\n",
       "      <td>2.63</td>\n",
       "      <td>8.23</td>\n",
       "      <td>3.89</td>\n",
       "      <td>2.91</td>\n",
       "      <td>4.81</td>\n",
       "      <td>3.36</td>\n",
       "      <td>6.42</td>\n",
       "      <td>53.43</td>\n",
       "      <td>4.79</td>\n",
       "      <td>0.92</td>\n",
       "      <td>6.92</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.13</td>\n",
       "      <td>3.45</td>\n",
       "      <td>10.67</td>\n",
       "      <td>12.78</td>\n",
       "      <td>2.77</td>\n",
       "      <td>8.03</td>\n",
       "      <td>1.38</td>\n",
       "      <td>5.07</td>\n",
       "      <td>4.93</td>\n",
       "      <td>1.94</td>\n",
       "      <td>3.04</td>\n",
       "      <td>3.74</td>\n",
       "      <td>8.00</td>\n",
       "      <td>28.26</td>\n",
       "      <td>39.67</td>\n",
       "      <td>0.58</td>\n",
       "      <td>4.41</td>\n",
       "      <td>4.04</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.48</td>\n",
       "      <td>3.81</td>\n",
       "      <td>11.88</td>\n",
       "      <td>5.88</td>\n",
       "      <td>51.67</td>\n",
       "      <td>11.34</td>\n",
       "      <td>7.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 17717 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1       2      4      ...  24322   24323  24324  24325\n",
       "CGGA_171   12.66   1.21  188.46   1.14  ...   4.59   35.90   3.56   5.64\n",
       "CGGA_1024   2.35   1.22  219.35   0.68  ...   7.66  147.99   5.79  10.36\n",
       "CGGA_835    7.11   1.73   82.60   3.43  ...   5.21   45.83   4.60   4.43\n",
       "CGGA_738    4.64   0.85  160.70   3.33  ...   9.91   72.42  15.13  12.23\n",
       "CGGA_479   13.11   2.65  133.27   1.14  ...   5.88   51.67  11.34   7.31\n",
       "\n",
       "[5 rows x 17717 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training Data before applying TMM\n",
    "train_genes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "ifUDEoy-UTgN",
    "outputId": "74a6da93-cb6c-47f9-8feb-da7918c3143c"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>7</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>19</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>35</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>50</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>...</th>\n",
       "      <th>24282</th>\n",
       "      <th>24283</th>\n",
       "      <th>24284</th>\n",
       "      <th>24285</th>\n",
       "      <th>24287</th>\n",
       "      <th>24288</th>\n",
       "      <th>24289</th>\n",
       "      <th>24290</th>\n",
       "      <th>24291</th>\n",
       "      <th>24292</th>\n",
       "      <th>24293</th>\n",
       "      <th>24294</th>\n",
       "      <th>24295</th>\n",
       "      <th>24296</th>\n",
       "      <th>24297</th>\n",
       "      <th>24298</th>\n",
       "      <th>24299</th>\n",
       "      <th>24300</th>\n",
       "      <th>24301</th>\n",
       "      <th>24302</th>\n",
       "      <th>24304</th>\n",
       "      <th>24305</th>\n",
       "      <th>24307</th>\n",
       "      <th>24308</th>\n",
       "      <th>24309</th>\n",
       "      <th>24310</th>\n",
       "      <th>24311</th>\n",
       "      <th>24312</th>\n",
       "      <th>24313</th>\n",
       "      <th>24314</th>\n",
       "      <th>24315</th>\n",
       "      <th>24316</th>\n",
       "      <th>24317</th>\n",
       "      <th>24318</th>\n",
       "      <th>24319</th>\n",
       "      <th>24320</th>\n",
       "      <th>24322</th>\n",
       "      <th>24323</th>\n",
       "      <th>24324</th>\n",
       "      <th>24325</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CGGA_171</th>\n",
       "      <td>18.586436</td>\n",
       "      <td>1.776429</td>\n",
       "      <td>276.682446</td>\n",
       "      <td>1.673660</td>\n",
       "      <td>2.833477</td>\n",
       "      <td>37.305003</td>\n",
       "      <td>6.254204</td>\n",
       "      <td>11.671577</td>\n",
       "      <td>4.668631</td>\n",
       "      <td>17.955143</td>\n",
       "      <td>3.861163</td>\n",
       "      <td>117.860908</td>\n",
       "      <td>146.283768</td>\n",
       "      <td>25.354483</td>\n",
       "      <td>123.571906</td>\n",
       "      <td>3.978613</td>\n",
       "      <td>93.137718</td>\n",
       "      <td>6.958903</td>\n",
       "      <td>38.670358</td>\n",
       "      <td>2.774752</td>\n",
       "      <td>36.336042</td>\n",
       "      <td>14.666548</td>\n",
       "      <td>135.404977</td>\n",
       "      <td>17.118313</td>\n",
       "      <td>11.935839</td>\n",
       "      <td>6.312929</td>\n",
       "      <td>10.335585</td>\n",
       "      <td>76.768148</td>\n",
       "      <td>25.604064</td>\n",
       "      <td>1.952603</td>\n",
       "      <td>1.086411</td>\n",
       "      <td>6.445060</td>\n",
       "      <td>5.270561</td>\n",
       "      <td>15.268478</td>\n",
       "      <td>7.135077</td>\n",
       "      <td>2.026010</td>\n",
       "      <td>83.227888</td>\n",
       "      <td>13.947168</td>\n",
       "      <td>15.591465</td>\n",
       "      <td>6.136754</td>\n",
       "      <td>...</td>\n",
       "      <td>1.791110</td>\n",
       "      <td>2.671984</td>\n",
       "      <td>14.255474</td>\n",
       "      <td>8.015951</td>\n",
       "      <td>3.420726</td>\n",
       "      <td>1.042367</td>\n",
       "      <td>7.032309</td>\n",
       "      <td>26.470256</td>\n",
       "      <td>62.248412</td>\n",
       "      <td>9.674930</td>\n",
       "      <td>1.086411</td>\n",
       "      <td>13.770993</td>\n",
       "      <td>0.792786</td>\n",
       "      <td>1.291948</td>\n",
       "      <td>3.596901</td>\n",
       "      <td>7.766370</td>\n",
       "      <td>9.014275</td>\n",
       "      <td>5.608230</td>\n",
       "      <td>14.593142</td>\n",
       "      <td>5.197155</td>\n",
       "      <td>11.084328</td>\n",
       "      <td>6.958903</td>\n",
       "      <td>4.932893</td>\n",
       "      <td>3.729032</td>\n",
       "      <td>1.703023</td>\n",
       "      <td>5.564186</td>\n",
       "      <td>148.852983</td>\n",
       "      <td>83.829819</td>\n",
       "      <td>1.262586</td>\n",
       "      <td>14.534417</td>\n",
       "      <td>6.503785</td>\n",
       "      <td>3.024333</td>\n",
       "      <td>5.725679</td>\n",
       "      <td>2.304953</td>\n",
       "      <td>4.477775</td>\n",
       "      <td>3.934569</td>\n",
       "      <td>6.738684</td>\n",
       "      <td>52.705613</td>\n",
       "      <td>5.226518</td>\n",
       "      <td>8.280213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_1024</th>\n",
       "      <td>2.436149</td>\n",
       "      <td>1.264724</td>\n",
       "      <td>227.391214</td>\n",
       "      <td>0.704928</td>\n",
       "      <td>4.478368</td>\n",
       "      <td>26.725076</td>\n",
       "      <td>8.345107</td>\n",
       "      <td>6.924884</td>\n",
       "      <td>7.163316</td>\n",
       "      <td>23.542532</td>\n",
       "      <td>4.198470</td>\n",
       "      <td>24.071229</td>\n",
       "      <td>66.740125</td>\n",
       "      <td>22.371107</td>\n",
       "      <td>64.511307</td>\n",
       "      <td>4.841199</td>\n",
       "      <td>14.627263</td>\n",
       "      <td>3.255110</td>\n",
       "      <td>22.443674</td>\n",
       "      <td>10.418426</td>\n",
       "      <td>14.523597</td>\n",
       "      <td>11.434352</td>\n",
       "      <td>66.988923</td>\n",
       "      <td>2.798980</td>\n",
       "      <td>5.525394</td>\n",
       "      <td>2.871546</td>\n",
       "      <td>0.507963</td>\n",
       "      <td>20.785019</td>\n",
       "      <td>36.531873</td>\n",
       "      <td>3.700874</td>\n",
       "      <td>0.238432</td>\n",
       "      <td>2.156251</td>\n",
       "      <td>5.753459</td>\n",
       "      <td>6.282155</td>\n",
       "      <td>4.447268</td>\n",
       "      <td>0.891527</td>\n",
       "      <td>15.041926</td>\n",
       "      <td>13.974167</td>\n",
       "      <td>18.950132</td>\n",
       "      <td>7.909710</td>\n",
       "      <td>...</td>\n",
       "      <td>1.928186</td>\n",
       "      <td>0.549429</td>\n",
       "      <td>8.189608</td>\n",
       "      <td>1.243991</td>\n",
       "      <td>3.192911</td>\n",
       "      <td>0.269531</td>\n",
       "      <td>4.789366</td>\n",
       "      <td>6.551687</td>\n",
       "      <td>34.199390</td>\n",
       "      <td>4.395435</td>\n",
       "      <td>1.212891</td>\n",
       "      <td>9.101869</td>\n",
       "      <td>1.617188</td>\n",
       "      <td>1.171425</td>\n",
       "      <td>5.235129</td>\n",
       "      <td>8.106676</td>\n",
       "      <td>10.874556</td>\n",
       "      <td>4.198470</td>\n",
       "      <td>9.164068</td>\n",
       "      <td>4.934498</td>\n",
       "      <td>13.735736</td>\n",
       "      <td>6.178489</td>\n",
       "      <td>2.218451</td>\n",
       "      <td>6.002257</td>\n",
       "      <td>2.830080</td>\n",
       "      <td>5.131463</td>\n",
       "      <td>22.858337</td>\n",
       "      <td>53.740417</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.168123</td>\n",
       "      <td>7.090750</td>\n",
       "      <td>7.184049</td>\n",
       "      <td>18.846466</td>\n",
       "      <td>1.482423</td>\n",
       "      <td>3.192911</td>\n",
       "      <td>9.910463</td>\n",
       "      <td>7.940810</td>\n",
       "      <td>153.415208</td>\n",
       "      <td>6.002257</td>\n",
       "      <td>10.739790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_835</th>\n",
       "      <td>8.503976</td>\n",
       "      <td>2.069181</td>\n",
       "      <td>98.794437</td>\n",
       "      <td>4.102481</td>\n",
       "      <td>1.626640</td>\n",
       "      <td>43.703980</td>\n",
       "      <td>4.772274</td>\n",
       "      <td>8.492016</td>\n",
       "      <td>5.214815</td>\n",
       "      <td>12.307443</td>\n",
       "      <td>7.965750</td>\n",
       "      <td>45.378462</td>\n",
       "      <td>103.698277</td>\n",
       "      <td>21.732384</td>\n",
       "      <td>77.361068</td>\n",
       "      <td>4.006796</td>\n",
       "      <td>32.269660</td>\n",
       "      <td>6.745770</td>\n",
       "      <td>33.035138</td>\n",
       "      <td>5.968332</td>\n",
       "      <td>28.179140</td>\n",
       "      <td>20.273193</td>\n",
       "      <td>150.212995</td>\n",
       "      <td>1.590758</td>\n",
       "      <td>12.941354</td>\n",
       "      <td>5.226776</td>\n",
       "      <td>3.683860</td>\n",
       "      <td>109.570925</td>\n",
       "      <td>22.222768</td>\n",
       "      <td>1.363507</td>\n",
       "      <td>0.729596</td>\n",
       "      <td>2.260551</td>\n",
       "      <td>10.776488</td>\n",
       "      <td>6.984982</td>\n",
       "      <td>4.748352</td>\n",
       "      <td>0.514305</td>\n",
       "      <td>30.116755</td>\n",
       "      <td>7.523208</td>\n",
       "      <td>13.742713</td>\n",
       "      <td>5.035407</td>\n",
       "      <td>...</td>\n",
       "      <td>0.813320</td>\n",
       "      <td>2.595447</td>\n",
       "      <td>9.102006</td>\n",
       "      <td>8.264765</td>\n",
       "      <td>2.559565</td>\n",
       "      <td>2.738974</td>\n",
       "      <td>3.504452</td>\n",
       "      <td>14.161333</td>\n",
       "      <td>43.572413</td>\n",
       "      <td>5.681278</td>\n",
       "      <td>0.394699</td>\n",
       "      <td>10.417670</td>\n",
       "      <td>1.016650</td>\n",
       "      <td>0.334896</td>\n",
       "      <td>3.863269</td>\n",
       "      <td>7.702617</td>\n",
       "      <td>9.221612</td>\n",
       "      <td>3.839348</td>\n",
       "      <td>11.565886</td>\n",
       "      <td>6.386953</td>\n",
       "      <td>11.147266</td>\n",
       "      <td>6.064017</td>\n",
       "      <td>2.332314</td>\n",
       "      <td>6.171662</td>\n",
       "      <td>2.296432</td>\n",
       "      <td>11.709413</td>\n",
       "      <td>48.763308</td>\n",
       "      <td>91.307111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.013592</td>\n",
       "      <td>5.370303</td>\n",
       "      <td>2.607408</td>\n",
       "      <td>7.080667</td>\n",
       "      <td>2.559565</td>\n",
       "      <td>5.370303</td>\n",
       "      <td>8.061435</td>\n",
       "      <td>6.231465</td>\n",
       "      <td>54.815364</td>\n",
       "      <td>5.501869</td>\n",
       "      <td>5.298539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_738</th>\n",
       "      <td>4.307423</td>\n",
       "      <td>0.789075</td>\n",
       "      <td>149.181639</td>\n",
       "      <td>3.091318</td>\n",
       "      <td>2.497191</td>\n",
       "      <td>35.824017</td>\n",
       "      <td>8.076417</td>\n",
       "      <td>7.510140</td>\n",
       "      <td>4.994382</td>\n",
       "      <td>9.273955</td>\n",
       "      <td>8.141400</td>\n",
       "      <td>20.033228</td>\n",
       "      <td>61.714968</td>\n",
       "      <td>13.980557</td>\n",
       "      <td>84.681700</td>\n",
       "      <td>5.774174</td>\n",
       "      <td>32.472768</td>\n",
       "      <td>5.727758</td>\n",
       "      <td>21.212199</td>\n",
       "      <td>20.924419</td>\n",
       "      <td>19.615483</td>\n",
       "      <td>14.147655</td>\n",
       "      <td>90.372325</td>\n",
       "      <td>5.839157</td>\n",
       "      <td>13.971274</td>\n",
       "      <td>3.314116</td>\n",
       "      <td>2.153711</td>\n",
       "      <td>51.893302</td>\n",
       "      <td>30.894617</td>\n",
       "      <td>2.014463</td>\n",
       "      <td>2.274393</td>\n",
       "      <td>7.435874</td>\n",
       "      <td>10.025897</td>\n",
       "      <td>14.751066</td>\n",
       "      <td>3.481214</td>\n",
       "      <td>1.420335</td>\n",
       "      <td>23.848639</td>\n",
       "      <td>12.439539</td>\n",
       "      <td>25.937368</td>\n",
       "      <td>3.100602</td>\n",
       "      <td>...</td>\n",
       "      <td>1.977330</td>\n",
       "      <td>2.506474</td>\n",
       "      <td>8.902626</td>\n",
       "      <td>2.562174</td>\n",
       "      <td>2.283677</td>\n",
       "      <td>3.082035</td>\n",
       "      <td>5.421411</td>\n",
       "      <td>6.451851</td>\n",
       "      <td>33.243276</td>\n",
       "      <td>6.526117</td>\n",
       "      <td>2.330093</td>\n",
       "      <td>9.348221</td>\n",
       "      <td>1.095422</td>\n",
       "      <td>2.692139</td>\n",
       "      <td>7.278059</td>\n",
       "      <td>14.259054</td>\n",
       "      <td>14.658233</td>\n",
       "      <td>8.234232</td>\n",
       "      <td>12.458106</td>\n",
       "      <td>2.460058</td>\n",
       "      <td>11.678314</td>\n",
       "      <td>7.036695</td>\n",
       "      <td>3.536914</td>\n",
       "      <td>5.161480</td>\n",
       "      <td>3.091318</td>\n",
       "      <td>8.531296</td>\n",
       "      <td>30.495438</td>\n",
       "      <td>55.736563</td>\n",
       "      <td>0.993306</td>\n",
       "      <td>5.727758</td>\n",
       "      <td>3.267700</td>\n",
       "      <td>2.645723</td>\n",
       "      <td>6.396151</td>\n",
       "      <td>2.413642</td>\n",
       "      <td>3.611180</td>\n",
       "      <td>11.947528</td>\n",
       "      <td>9.199689</td>\n",
       "      <td>67.229212</td>\n",
       "      <td>14.045540</td>\n",
       "      <td>11.353400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_479</th>\n",
       "      <td>16.415838</td>\n",
       "      <td>3.318228</td>\n",
       "      <td>166.875575</td>\n",
       "      <td>1.427464</td>\n",
       "      <td>7.525491</td>\n",
       "      <td>33.921058</td>\n",
       "      <td>5.509511</td>\n",
       "      <td>8.777653</td>\n",
       "      <td>4.545347</td>\n",
       "      <td>10.267725</td>\n",
       "      <td>4.733171</td>\n",
       "      <td>34.897744</td>\n",
       "      <td>72.124508</td>\n",
       "      <td>17.793216</td>\n",
       "      <td>97.956601</td>\n",
       "      <td>5.096298</td>\n",
       "      <td>33.182282</td>\n",
       "      <td>4.495260</td>\n",
       "      <td>27.785466</td>\n",
       "      <td>15.188720</td>\n",
       "      <td>36.162427</td>\n",
       "      <td>118.116403</td>\n",
       "      <td>133.142342</td>\n",
       "      <td>19.608851</td>\n",
       "      <td>17.768173</td>\n",
       "      <td>4.445174</td>\n",
       "      <td>3.167969</td>\n",
       "      <td>292.417297</td>\n",
       "      <td>37.865367</td>\n",
       "      <td>4.645520</td>\n",
       "      <td>2.203804</td>\n",
       "      <td>4.833344</td>\n",
       "      <td>27.797987</td>\n",
       "      <td>22.839427</td>\n",
       "      <td>3.994395</td>\n",
       "      <td>1.327291</td>\n",
       "      <td>25.594183</td>\n",
       "      <td>9.591558</td>\n",
       "      <td>21.361877</td>\n",
       "      <td>6.010376</td>\n",
       "      <td>...</td>\n",
       "      <td>1.364856</td>\n",
       "      <td>3.293185</td>\n",
       "      <td>10.305290</td>\n",
       "      <td>4.870909</td>\n",
       "      <td>3.643790</td>\n",
       "      <td>6.022897</td>\n",
       "      <td>4.207263</td>\n",
       "      <td>8.038877</td>\n",
       "      <td>66.902994</td>\n",
       "      <td>5.997854</td>\n",
       "      <td>1.151989</td>\n",
       "      <td>8.664958</td>\n",
       "      <td>0.851470</td>\n",
       "      <td>2.667104</td>\n",
       "      <td>4.319957</td>\n",
       "      <td>13.360564</td>\n",
       "      <td>16.002625</td>\n",
       "      <td>3.468488</td>\n",
       "      <td>10.054858</td>\n",
       "      <td>1.727983</td>\n",
       "      <td>6.348459</td>\n",
       "      <td>6.173157</td>\n",
       "      <td>2.429193</td>\n",
       "      <td>3.806571</td>\n",
       "      <td>4.683084</td>\n",
       "      <td>10.017293</td>\n",
       "      <td>35.386087</td>\n",
       "      <td>49.673250</td>\n",
       "      <td>0.726254</td>\n",
       "      <td>5.522033</td>\n",
       "      <td>5.058733</td>\n",
       "      <td>4.620476</td>\n",
       "      <td>4.758214</td>\n",
       "      <td>3.105361</td>\n",
       "      <td>4.770736</td>\n",
       "      <td>14.875680</td>\n",
       "      <td>7.362710</td>\n",
       "      <td>64.699189</td>\n",
       "      <td>14.199512</td>\n",
       "      <td>9.153301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 17717 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0         1           2      ...       24323      24324      24325\n",
       "CGGA_171   18.586436  1.776429  276.682446  ...   52.705613   5.226518   8.280213\n",
       "CGGA_1024   2.436149  1.264724  227.391214  ...  153.415208   6.002257  10.739790\n",
       "CGGA_835    8.503976  2.069181   98.794437  ...   54.815364   5.501869   5.298539\n",
       "CGGA_738    4.307423  0.789075  149.181639  ...   67.229212  14.045540  11.353400\n",
       "CGGA_479   16.415838  3.318228  166.875575  ...   64.699189  14.199512   9.153301\n",
       "\n",
       "[5 rows x 17717 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training data after applying TMM\n",
    "# Expect genes as rows and samples as columns\n",
    "normalized_tmm_train_genes = gmn.tmm_normalization(train_genes.T).T\n",
    "normalized_tmm_train_genes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DJFMVCwQUTgQ"
   },
   "outputs": [],
   "source": [
    "gene_indices = list(normalized_tmm_train_genes.columns.values) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FJY46q8JUTgS"
   },
   "source": [
    "### Log Transformation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "JF-ynZ7HUTgT",
    "outputId": "923b29d3-5793-417c-8954-ebf34de1873d"
   },
   "outputs": [
    {
     "data": {
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       "      <th>24305</th>\n",
       "      <th>24307</th>\n",
       "      <th>24308</th>\n",
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       "      <th>24311</th>\n",
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       "      <th>0</th>\n",
       "      <td>4.216178</td>\n",
       "      <td>0.828980</td>\n",
       "      <td>8.112087</td>\n",
       "      <td>0.743007</td>\n",
       "      <td>1.502574</td>\n",
       "      <td>5.221297</td>\n",
       "      <td>2.644826</td>\n",
       "      <td>3.544928</td>\n",
       "      <td>2.223000</td>\n",
       "      <td>4.166325</td>\n",
       "      <td>1.949036</td>\n",
       "      <td>6.880941</td>\n",
       "      <td>7.192626</td>\n",
       "      <td>4.664169</td>\n",
       "      <td>6.949207</td>\n",
       "      <td>1.992266</td>\n",
       "      <td>6.541294</td>\n",
       "      <td>2.798860</td>\n",
       "      <td>5.273156</td>\n",
       "      <td>1.472359</td>\n",
       "      <td>5.183329</td>\n",
       "      <td>3.874457</td>\n",
       "      <td>7.081137</td>\n",
       "      <td>4.097469</td>\n",
       "      <td>3.577228</td>\n",
       "      <td>2.658309</td>\n",
       "      <td>3.369548</td>\n",
       "      <td>6.262436</td>\n",
       "      <td>4.678301</td>\n",
       "      <td>0.965399</td>\n",
       "      <td>0.119570</td>\n",
       "      <td>2.688194</td>\n",
       "      <td>2.397957</td>\n",
       "      <td>3.932484</td>\n",
       "      <td>2.834929</td>\n",
       "      <td>1.018641</td>\n",
       "      <td>6.378995</td>\n",
       "      <td>3.801900</td>\n",
       "      <td>3.962685</td>\n",
       "      <td>2.617476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.840854</td>\n",
       "      <td>1.417911</td>\n",
       "      <td>3.833444</td>\n",
       "      <td>3.002874</td>\n",
       "      <td>1.774303</td>\n",
       "      <td>0.059864</td>\n",
       "      <td>2.813998</td>\n",
       "      <td>4.726300</td>\n",
       "      <td>5.959965</td>\n",
       "      <td>3.274251</td>\n",
       "      <td>0.119570</td>\n",
       "      <td>3.783561</td>\n",
       "      <td>-0.334996</td>\n",
       "      <td>0.369548</td>\n",
       "      <td>1.846755</td>\n",
       "      <td>2.957240</td>\n",
       "      <td>3.172211</td>\n",
       "      <td>2.487545</td>\n",
       "      <td>3.867219</td>\n",
       "      <td>2.377722</td>\n",
       "      <td>3.470449</td>\n",
       "      <td>2.798860</td>\n",
       "      <td>2.302434</td>\n",
       "      <td>1.898801</td>\n",
       "      <td>0.768098</td>\n",
       "      <td>2.476171</td>\n",
       "      <td>7.217744</td>\n",
       "      <td>6.389392</td>\n",
       "      <td>0.336381</td>\n",
       "      <td>3.861401</td>\n",
       "      <td>2.701279</td>\n",
       "      <td>1.596617</td>\n",
       "      <td>2.517447</td>\n",
       "      <td>1.204737</td>\n",
       "      <td>2.162782</td>\n",
       "      <td>1.976206</td>\n",
       "      <td>2.752467</td>\n",
       "      <td>5.719885</td>\n",
       "      <td>2.385850</td>\n",
       "      <td>3.049668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.284603</td>\n",
       "      <td>0.338823</td>\n",
       "      <td>7.829033</td>\n",
       "      <td>-0.504452</td>\n",
       "      <td>2.162973</td>\n",
       "      <td>4.740122</td>\n",
       "      <td>3.060931</td>\n",
       "      <td>2.791790</td>\n",
       "      <td>2.840628</td>\n",
       "      <td>4.557198</td>\n",
       "      <td>2.069864</td>\n",
       "      <td>4.589238</td>\n",
       "      <td>6.060482</td>\n",
       "      <td>4.483565</td>\n",
       "      <td>6.011480</td>\n",
       "      <td>2.275364</td>\n",
       "      <td>3.870588</td>\n",
       "      <td>1.702706</td>\n",
       "      <td>4.488237</td>\n",
       "      <td>3.381065</td>\n",
       "      <td>3.860327</td>\n",
       "      <td>3.515303</td>\n",
       "      <td>6.065851</td>\n",
       "      <td>1.484901</td>\n",
       "      <td>2.466077</td>\n",
       "      <td>1.521828</td>\n",
       "      <td>-0.977205</td>\n",
       "      <td>4.377472</td>\n",
       "      <td>5.191084</td>\n",
       "      <td>1.887866</td>\n",
       "      <td>-2.068352</td>\n",
       "      <td>1.108525</td>\n",
       "      <td>2.524430</td>\n",
       "      <td>2.651260</td>\n",
       "      <td>2.152919</td>\n",
       "      <td>-0.165650</td>\n",
       "      <td>3.910917</td>\n",
       "      <td>3.804690</td>\n",
       "      <td>4.244136</td>\n",
       "      <td>2.983625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.947244</td>\n",
       "      <td>-0.863994</td>\n",
       "      <td>3.033794</td>\n",
       "      <td>0.314976</td>\n",
       "      <td>1.674872</td>\n",
       "      <td>-1.891475</td>\n",
       "      <td>2.259835</td>\n",
       "      <td>2.711866</td>\n",
       "      <td>5.095899</td>\n",
       "      <td>2.136006</td>\n",
       "      <td>0.278450</td>\n",
       "      <td>3.186163</td>\n",
       "      <td>0.693488</td>\n",
       "      <td>0.228265</td>\n",
       "      <td>2.388225</td>\n",
       "      <td>3.019110</td>\n",
       "      <td>3.442885</td>\n",
       "      <td>2.069864</td>\n",
       "      <td>3.195988</td>\n",
       "      <td>2.302903</td>\n",
       "      <td>3.779862</td>\n",
       "      <td>2.627254</td>\n",
       "      <td>1.149553</td>\n",
       "      <td>2.585505</td>\n",
       "      <td>1.500843</td>\n",
       "      <td>2.359370</td>\n",
       "      <td>4.514649</td>\n",
       "      <td>5.747936</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.624831</td>\n",
       "      <td>2.825938</td>\n",
       "      <td>2.844797</td>\n",
       "      <td>4.236222</td>\n",
       "      <td>0.567957</td>\n",
       "      <td>1.674872</td>\n",
       "      <td>3.308952</td>\n",
       "      <td>2.989286</td>\n",
       "      <td>7.261298</td>\n",
       "      <td>2.585505</td>\n",
       "      <td>3.424894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.088138</td>\n",
       "      <td>1.049060</td>\n",
       "      <td>6.626358</td>\n",
       "      <td>2.036497</td>\n",
       "      <td>0.701895</td>\n",
       "      <td>5.449693</td>\n",
       "      <td>2.254677</td>\n",
       "      <td>3.086107</td>\n",
       "      <td>2.382616</td>\n",
       "      <td>3.621459</td>\n",
       "      <td>2.993810</td>\n",
       "      <td>5.503936</td>\n",
       "      <td>6.696248</td>\n",
       "      <td>4.441775</td>\n",
       "      <td>6.273536</td>\n",
       "      <td>2.002449</td>\n",
       "      <td>5.012106</td>\n",
       "      <td>2.753983</td>\n",
       "      <td>5.045929</td>\n",
       "      <td>2.577328</td>\n",
       "      <td>4.816556</td>\n",
       "      <td>4.341501</td>\n",
       "      <td>7.230866</td>\n",
       "      <td>0.669714</td>\n",
       "      <td>3.693917</td>\n",
       "      <td>2.385921</td>\n",
       "      <td>1.881218</td>\n",
       "      <td>6.775721</td>\n",
       "      <td>4.473967</td>\n",
       "      <td>0.447322</td>\n",
       "      <td>-0.454831</td>\n",
       "      <td>1.176674</td>\n",
       "      <td>3.429815</td>\n",
       "      <td>2.804256</td>\n",
       "      <td>2.247427</td>\n",
       "      <td>-0.959303</td>\n",
       "      <td>4.912494</td>\n",
       "      <td>2.911348</td>\n",
       "      <td>3.780595</td>\n",
       "      <td>2.332108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.298105</td>\n",
       "      <td>1.375983</td>\n",
       "      <td>3.186184</td>\n",
       "      <td>3.046974</td>\n",
       "      <td>1.355899</td>\n",
       "      <td>1.453636</td>\n",
       "      <td>1.809189</td>\n",
       "      <td>3.823885</td>\n",
       "      <td>5.445343</td>\n",
       "      <td>2.506216</td>\n",
       "      <td>-1.341174</td>\n",
       "      <td>3.380961</td>\n",
       "      <td>0.023823</td>\n",
       "      <td>-1.578213</td>\n",
       "      <td>1.949822</td>\n",
       "      <td>2.945349</td>\n",
       "      <td>3.205019</td>\n",
       "      <td>1.940861</td>\n",
       "      <td>3.531804</td>\n",
       "      <td>2.675128</td>\n",
       "      <td>3.478618</td>\n",
       "      <td>2.600274</td>\n",
       "      <td>1.221762</td>\n",
       "      <td>2.625659</td>\n",
       "      <td>1.199394</td>\n",
       "      <td>3.549597</td>\n",
       "      <td>5.607724</td>\n",
       "      <td>6.512655</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.002449</td>\n",
       "      <td>2.425003</td>\n",
       "      <td>1.382616</td>\n",
       "      <td>2.823885</td>\n",
       "      <td>1.355899</td>\n",
       "      <td>2.425003</td>\n",
       "      <td>3.011037</td>\n",
       "      <td>2.639571</td>\n",
       "      <td>5.776508</td>\n",
       "      <td>2.459922</td>\n",
       "      <td>2.405595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.106825</td>\n",
       "      <td>-0.341765</td>\n",
       "      <td>7.220926</td>\n",
       "      <td>1.628222</td>\n",
       "      <td>1.320306</td>\n",
       "      <td>5.162855</td>\n",
       "      <td>3.013715</td>\n",
       "      <td>2.908840</td>\n",
       "      <td>2.320306</td>\n",
       "      <td>3.213185</td>\n",
       "      <td>3.025277</td>\n",
       "      <td>4.324323</td>\n",
       "      <td>5.947549</td>\n",
       "      <td>3.805350</td>\n",
       "      <td>6.403978</td>\n",
       "      <td>2.529615</td>\n",
       "      <td>5.021158</td>\n",
       "      <td>2.517971</td>\n",
       "      <td>4.406822</td>\n",
       "      <td>4.387116</td>\n",
       "      <td>4.293921</td>\n",
       "      <td>3.822491</td>\n",
       "      <td>6.497809</td>\n",
       "      <td>2.545760</td>\n",
       "      <td>3.804392</td>\n",
       "      <td>1.728624</td>\n",
       "      <td>1.106825</td>\n",
       "      <td>5.697476</td>\n",
       "      <td>4.949284</td>\n",
       "      <td>1.010395</td>\n",
       "      <td>1.185482</td>\n",
       "      <td>2.894502</td>\n",
       "      <td>3.325659</td>\n",
       "      <td>3.882747</td>\n",
       "      <td>1.799591</td>\n",
       "      <td>0.506232</td>\n",
       "      <td>4.575835</td>\n",
       "      <td>3.636861</td>\n",
       "      <td>4.696960</td>\n",
       "      <td>1.632548</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983553</td>\n",
       "      <td>1.325659</td>\n",
       "      <td>3.154231</td>\n",
       "      <td>1.357368</td>\n",
       "      <td>1.191358</td>\n",
       "      <td>1.623883</td>\n",
       "      <td>2.438668</td>\n",
       "      <td>2.689713</td>\n",
       "      <td>5.054991</td>\n",
       "      <td>2.706225</td>\n",
       "      <td>1.220387</td>\n",
       "      <td>3.224692</td>\n",
       "      <td>0.131487</td>\n",
       "      <td>1.428753</td>\n",
       "      <td>2.863554</td>\n",
       "      <td>3.833806</td>\n",
       "      <td>3.873639</td>\n",
       "      <td>3.041634</td>\n",
       "      <td>3.639013</td>\n",
       "      <td>1.298692</td>\n",
       "      <td>3.545760</td>\n",
       "      <td>2.814898</td>\n",
       "      <td>1.822491</td>\n",
       "      <td>2.367785</td>\n",
       "      <td>1.628222</td>\n",
       "      <td>3.092765</td>\n",
       "      <td>4.930522</td>\n",
       "      <td>5.800552</td>\n",
       "      <td>-0.009689</td>\n",
       "      <td>2.517971</td>\n",
       "      <td>1.708275</td>\n",
       "      <td>1.403662</td>\n",
       "      <td>2.677204</td>\n",
       "      <td>1.271212</td>\n",
       "      <td>1.852470</td>\n",
       "      <td>3.578640</td>\n",
       "      <td>3.201585</td>\n",
       "      <td>6.071016</td>\n",
       "      <td>3.812040</td>\n",
       "      <td>3.505053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.037017</td>\n",
       "      <td>1.730413</td>\n",
       "      <td>7.382629</td>\n",
       "      <td>0.513455</td>\n",
       "      <td>2.911786</td>\n",
       "      <td>5.084109</td>\n",
       "      <td>2.461924</td>\n",
       "      <td>3.133835</td>\n",
       "      <td>2.184390</td>\n",
       "      <td>3.360045</td>\n",
       "      <td>2.242807</td>\n",
       "      <td>5.125062</td>\n",
       "      <td>6.172418</td>\n",
       "      <td>4.153255</td>\n",
       "      <td>6.614071</td>\n",
       "      <td>2.349450</td>\n",
       "      <td>5.052341</td>\n",
       "      <td>2.168405</td>\n",
       "      <td>4.796259</td>\n",
       "      <td>3.924928</td>\n",
       "      <td>5.176420</td>\n",
       "      <td>6.884066</td>\n",
       "      <td>7.056826</td>\n",
       "      <td>4.293433</td>\n",
       "      <td>4.151223</td>\n",
       "      <td>2.152240</td>\n",
       "      <td>1.663558</td>\n",
       "      <td>8.191885</td>\n",
       "      <td>5.242807</td>\n",
       "      <td>2.215840</td>\n",
       "      <td>1.139996</td>\n",
       "      <td>2.273022</td>\n",
       "      <td>4.796909</td>\n",
       "      <td>4.513455</td>\n",
       "      <td>1.997977</td>\n",
       "      <td>0.408485</td>\n",
       "      <td>4.677744</td>\n",
       "      <td>3.261765</td>\n",
       "      <td>4.416966</td>\n",
       "      <td>2.587455</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448749</td>\n",
       "      <td>1.719484</td>\n",
       "      <td>3.365313</td>\n",
       "      <td>2.284191</td>\n",
       "      <td>1.865440</td>\n",
       "      <td>2.590458</td>\n",
       "      <td>2.072882</td>\n",
       "      <td>3.006994</td>\n",
       "      <td>6.063999</td>\n",
       "      <td>2.584446</td>\n",
       "      <td>0.204127</td>\n",
       "      <td>3.115193</td>\n",
       "      <td>-0.231973</td>\n",
       "      <td>1.415274</td>\n",
       "      <td>2.111017</td>\n",
       "      <td>3.739909</td>\n",
       "      <td>4.000237</td>\n",
       "      <td>1.794307</td>\n",
       "      <td>3.329821</td>\n",
       "      <td>0.789089</td>\n",
       "      <td>2.666407</td>\n",
       "      <td>2.626008</td>\n",
       "      <td>1.280477</td>\n",
       "      <td>1.928492</td>\n",
       "      <td>2.227459</td>\n",
       "      <td>3.324421</td>\n",
       "      <td>5.145110</td>\n",
       "      <td>5.634397</td>\n",
       "      <td>-0.461454</td>\n",
       "      <td>2.465199</td>\n",
       "      <td>2.338776</td>\n",
       "      <td>2.208042</td>\n",
       "      <td>2.250420</td>\n",
       "      <td>1.634761</td>\n",
       "      <td>2.254212</td>\n",
       "      <td>3.894884</td>\n",
       "      <td>2.880237</td>\n",
       "      <td>6.015676</td>\n",
       "      <td>3.827769</td>\n",
       "      <td>3.194292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 17717 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0         1         2      ...     24323     24324     24325\n",
       "0  4.216178  0.828980  8.112087  ...  5.719885  2.385850  3.049668\n",
       "1  1.284603  0.338823  7.829033  ...  7.261298  2.585505  3.424894\n",
       "2  3.088138  1.049060  6.626358  ...  5.776508  2.459922  2.405595\n",
       "3  2.106825 -0.341765  7.220926  ...  6.071016  3.812040  3.505053\n",
       "4  4.037017  1.730413  7.382629  ...  6.015676  3.827769  3.194292\n",
       "\n",
       "[5 rows x 17717 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_normalized_tmm_train_genes = pd.DataFrame(np.log2(normalized_tmm_train_genes, out=np.zeros_like(normalized_tmm_train_genes), where=(normalized_tmm_train_genes!=0)))\n",
    "log_normalized_tmm_train_genes.columns = gene_indices\n",
    "log_normalized_tmm_train_genes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WLggA2XHUTgV"
   },
   "source": [
    "#### Visualize Distribution of Counts per Gene after Normalized and Log-transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "gmqZc3pMUTgW",
    "outputId": "135c846d-990b-4527-bb14-d8c077815fff"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_norm_sums = log_normalized_tmm_train_genes.sum(axis = 0)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(range(len(log_norm_sums)), log_norm_sums, 'b.', alpha = .75, markersize=3)\n",
    "ax.set_xlabel('Gene Number', size=15)\n",
    "ax.set_ylabel('Log-Normalized Read Counts', size=15)\n",
    "ax.set_title('Log-Normalized Read Counts per Gene', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qrWyU2ZZUTgZ"
   },
   "source": [
    "### Selecting Features based on Variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z75tldZmUTga"
   },
   "source": [
    "#### Visualize Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8420-OdNUTga",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "selector = VarianceThreshold(threshold=0.5)\n",
    "high_var_train_genes = pd.DataFrame(selector.fit_transform(log_normalized_tmm_train_genes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "jW5aR2djUTgd",
    "outputId": "ff120a82-ed83-4117-da19-731b2f70891f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(selector.variances_, 'c.', alpha=0.25, markersize=3)\n",
    "ax.set_xlabel('Gene Number', size=15)\n",
    "ax.set_ylabel('Variance', size=15)\n",
    "ax.set_title('Distribution of Gene Variance', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lGzOPIKZUTgf"
   },
   "source": [
    "#### Close-up of the distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "20tmwZIPUTgg",
    "outputId": "0965247a-51f2-4f9d-84e4-b0e382203c74"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(selector.variances_, 'c.', alpha=0.25, markersize=3)\n",
    "ax.set_xlabel('Gene Number', size=15)\n",
    "ax.set_ylabel('Variance', size=15)\n",
    "ax.set_ylim(bottom=0, top = 5)\n",
    "ax.set_title('Distribution of Gene Variance', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1rWjVXxzUTgl"
   },
   "source": [
    "#### Remove Genes with Low Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "aA92s_AWUTgl",
    "outputId": "140d43ea-ef24-4cf4-b927-59bc32968194"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter reduced number of genes by 7781 from 17717 to 9936\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>38</th>\n",
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       "      <th>42</th>\n",
       "      <th>43</th>\n",
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       "      <th>24256</th>\n",
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       "      <td>0.828980</td>\n",
       "      <td>8.112087</td>\n",
       "      <td>0.743007</td>\n",
       "      <td>1.502574</td>\n",
       "      <td>1.949036</td>\n",
       "      <td>3.874457</td>\n",
       "      <td>7.081137</td>\n",
       "      <td>4.097469</td>\n",
       "      <td>3.577228</td>\n",
       "      <td>3.369548</td>\n",
       "      <td>6.262436</td>\n",
       "      <td>0.965399</td>\n",
       "      <td>0.119570</td>\n",
       "      <td>2.688194</td>\n",
       "      <td>2.397957</td>\n",
       "      <td>3.932484</td>\n",
       "      <td>1.018641</td>\n",
       "      <td>2.617476</td>\n",
       "      <td>1.195519</td>\n",
       "      <td>1.678301</td>\n",
       "      <td>2.059864</td>\n",
       "      <td>1.441498</td>\n",
       "      <td>0.353060</td>\n",
       "      <td>2.600115</td>\n",
       "      <td>6.097778</td>\n",
       "      <td>0.910117</td>\n",
       "      <td>1.997579</td>\n",
       "      <td>0.780481</td>\n",
       "      <td>3.653268</td>\n",
       "      <td>4.198983</td>\n",
       "      <td>0.495079</td>\n",
       "      <td>1.479972</td>\n",
       "      <td>0.730296</td>\n",
       "      <td>6.285156</td>\n",
       "      <td>0.898801</td>\n",
       "      <td>4.378741</td>\n",
       "      <td>4.320568</td>\n",
       "      <td>4.560271</td>\n",
       "      <td>4.237669</td>\n",
       "      <td>...</td>\n",
       "      <td>1.596617</td>\n",
       "      <td>-0.135687</td>\n",
       "      <td>2.332181</td>\n",
       "      <td>0.582542</td>\n",
       "      <td>1.749320</td>\n",
       "      <td>2.684904</td>\n",
       "      <td>3.543112</td>\n",
       "      <td>1.276439</td>\n",
       "      <td>0.158044</td>\n",
       "      <td>4.227529</td>\n",
       "      <td>1.596617</td>\n",
       "      <td>1.852631</td>\n",
       "      <td>1.441498</td>\n",
       "      <td>-2.389444</td>\n",
       "      <td>-0.207240</td>\n",
       "      <td>-0.880430</td>\n",
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       "      <td>2.921344</td>\n",
       "      <td>0.678301</td>\n",
       "      <td>-0.732331</td>\n",
       "      <td>3.568328</td>\n",
       "      <td>1.553973</td>\n",
       "      <td>0.553973</td>\n",
       "      <td>1.943540</td>\n",
       "      <td>1.393932</td>\n",
       "      <td>1.810983</td>\n",
       "      <td>-1.089883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.018641</td>\n",
       "      <td>1.417911</td>\n",
       "      <td>3.002874</td>\n",
       "      <td>0.059864</td>\n",
       "      <td>5.959965</td>\n",
       "      <td>0.369548</td>\n",
       "      <td>1.898801</td>\n",
       "      <td>0.768098</td>\n",
       "      <td>0.336381</td>\n",
       "      <td>1.596617</td>\n",
       "      <td>2.517447</td>\n",
       "      <td>5.719885</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.338823</td>\n",
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       "      <td>-0.504452</td>\n",
       "      <td>2.162973</td>\n",
       "      <td>2.069864</td>\n",
       "      <td>3.515303</td>\n",
       "      <td>6.065851</td>\n",
       "      <td>1.484901</td>\n",
       "      <td>2.466077</td>\n",
       "      <td>-0.977205</td>\n",
       "      <td>4.377472</td>\n",
       "      <td>1.887866</td>\n",
       "      <td>-2.068352</td>\n",
       "      <td>1.108525</td>\n",
       "      <td>2.524430</td>\n",
       "      <td>2.651260</td>\n",
       "      <td>-0.165650</td>\n",
       "      <td>2.983625</td>\n",
       "      <td>2.069864</td>\n",
       "      <td>3.652449</td>\n",
       "      <td>1.182873</td>\n",
       "      <td>-1.382461</td>\n",
       "      <td>0.108525</td>\n",
       "      <td>0.408086</td>\n",
       "      <td>0.149553</td>\n",
       "      <td>1.419313</td>\n",
       "      <td>2.474175</td>\n",
       "      <td>0.266067</td>\n",
       "      <td>2.326949</td>\n",
       "      <td>3.837492</td>\n",
       "      <td>-0.084120</td>\n",
       "      <td>1.129185</td>\n",
       "      <td>-0.810555</td>\n",
       "      <td>3.564169</td>\n",
       "      <td>-1.504452</td>\n",
       "      <td>2.981733</td>\n",
       "      <td>5.905189</td>\n",
       "      <td>4.365913</td>\n",
       "      <td>4.091957</td>\n",
       "      <td>...</td>\n",
       "      <td>2.725498</td>\n",
       "      <td>0.915880</td>\n",
       "      <td>1.632087</td>\n",
       "      <td>-3.591914</td>\n",
       "      <td>2.895926</td>\n",
       "      <td>2.983625</td>\n",
       "      <td>3.212217</td>\n",
       "      <td>2.542512</td>\n",
       "      <td>0.915880</td>\n",
       "      <td>3.245714</td>\n",
       "      <td>0.362282</td>\n",
       "      <td>3.022795</td>\n",
       "      <td>0.808965</td>\n",
       "      <td>0.993048</td>\n",
       "      <td>-0.863994</td>\n",
       "      <td>-0.343987</td>\n",
       "      <td>-1.462631</td>\n",
       "      <td>3.340300</td>\n",
       "      <td>-0.052756</td>\n",
       "      <td>-0.234362</td>\n",
       "      <td>3.601611</td>\n",
       "      <td>0.240976</td>\n",
       "      <td>0.954980</td>\n",
       "      <td>2.972235</td>\n",
       "      <td>-5.591914</td>\n",
       "      <td>1.597910</td>\n",
       "      <td>-0.547520</td>\n",
       "      <td>0.646490</td>\n",
       "      <td>0.516610</td>\n",
       "      <td>-0.863994</td>\n",
       "      <td>0.314976</td>\n",
       "      <td>-1.891475</td>\n",
       "      <td>5.095899</td>\n",
       "      <td>0.228265</td>\n",
       "      <td>2.585505</td>\n",
       "      <td>1.500843</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.844797</td>\n",
       "      <td>4.236222</td>\n",
       "      <td>7.261298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.088138</td>\n",
       "      <td>1.049060</td>\n",
       "      <td>6.626358</td>\n",
       "      <td>2.036497</td>\n",
       "      <td>0.701895</td>\n",
       "      <td>2.993810</td>\n",
       "      <td>4.341501</td>\n",
       "      <td>7.230866</td>\n",
       "      <td>0.669714</td>\n",
       "      <td>3.693917</td>\n",
       "      <td>1.881218</td>\n",
       "      <td>6.775721</td>\n",
       "      <td>0.447322</td>\n",
       "      <td>-0.454831</td>\n",
       "      <td>1.176674</td>\n",
       "      <td>3.429815</td>\n",
       "      <td>2.804256</td>\n",
       "      <td>-0.959303</td>\n",
       "      <td>2.332108</td>\n",
       "      <td>1.236484</td>\n",
       "      <td>-1.298105</td>\n",
       "      <td>2.625659</td>\n",
       "      <td>0.258288</td>\n",
       "      <td>0.521322</td>\n",
       "      <td>1.122226</td>\n",
       "      <td>4.837830</td>\n",
       "      <td>1.728174</td>\n",
       "      <td>2.247427</td>\n",
       "      <td>0.963160</td>\n",
       "      <td>4.262789</td>\n",
       "      <td>3.552541</td>\n",
       "      <td>-0.713143</td>\n",
       "      <td>2.375983</td>\n",
       "      <td>0.447322</td>\n",
       "      <td>5.775249</td>\n",
       "      <td>0.927315</td>\n",
       "      <td>4.345751</td>\n",
       "      <td>4.108287</td>\n",
       "      <td>4.883559</td>\n",
       "      <td>4.531804</td>\n",
       "      <td>...</td>\n",
       "      <td>1.608785</td>\n",
       "      <td>-2.385568</td>\n",
       "      <td>1.122226</td>\n",
       "      <td>-0.045718</td>\n",
       "      <td>2.605954</td>\n",
       "      <td>3.208756</td>\n",
       "      <td>2.998136</td>\n",
       "      <td>2.180486</td>\n",
       "      <td>0.328677</td>\n",
       "      <td>1.106285</td>\n",
       "      <td>-0.959303</td>\n",
       "      <td>1.603117</td>\n",
       "      <td>0.057375</td>\n",
       "      <td>0.733373</td>\n",
       "      <td>-0.431372</td>\n",
       "      <td>-0.830979</td>\n",
       "      <td>3.746289</td>\n",
       "      <td>3.247427</td>\n",
       "      <td>0.909053</td>\n",
       "      <td>-2.385568</td>\n",
       "      <td>2.878874</td>\n",
       "      <td>0.073863</td>\n",
       "      <td>0.355899</td>\n",
       "      <td>2.725568</td>\n",
       "      <td>1.057375</td>\n",
       "      <td>1.963160</td>\n",
       "      <td>-0.454831</td>\n",
       "      <td>1.161326</td>\n",
       "      <td>-2.137641</td>\n",
       "      <td>1.375983</td>\n",
       "      <td>3.046974</td>\n",
       "      <td>1.453636</td>\n",
       "      <td>5.445343</td>\n",
       "      <td>-1.578213</td>\n",
       "      <td>2.625659</td>\n",
       "      <td>1.199394</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.382616</td>\n",
       "      <td>2.823885</td>\n",
       "      <td>5.776508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.106825</td>\n",
       "      <td>-0.341765</td>\n",
       "      <td>7.220926</td>\n",
       "      <td>1.628222</td>\n",
       "      <td>1.320306</td>\n",
       "      <td>3.025277</td>\n",
       "      <td>3.822491</td>\n",
       "      <td>6.497809</td>\n",
       "      <td>2.545760</td>\n",
       "      <td>3.804392</td>\n",
       "      <td>1.106825</td>\n",
       "      <td>5.697476</td>\n",
       "      <td>1.010395</td>\n",
       "      <td>1.185482</td>\n",
       "      <td>2.894502</td>\n",
       "      <td>3.325659</td>\n",
       "      <td>3.882747</td>\n",
       "      <td>0.506232</td>\n",
       "      <td>1.632548</td>\n",
       "      <td>2.298692</td>\n",
       "      <td>1.320306</td>\n",
       "      <td>2.013715</td>\n",
       "      <td>-0.036911</td>\n",
       "      <td>1.487249</td>\n",
       "      <td>-0.844266</td>\n",
       "      <td>3.788003</td>\n",
       "      <td>0.928324</td>\n",
       "      <td>2.677204</td>\n",
       "      <td>-0.706762</td>\n",
       "      <td>4.018682</td>\n",
       "      <td>3.469222</td>\n",
       "      <td>1.030204</td>\n",
       "      <td>0.949284</td>\n",
       "      <td>-1.023236</td>\n",
       "      <td>3.558320</td>\n",
       "      <td>1.265652</td>\n",
       "      <td>3.528454</td>\n",
       "      <td>5.152726</td>\n",
       "      <td>4.540015</td>\n",
       "      <td>3.350820</td>\n",
       "      <td>...</td>\n",
       "      <td>1.952747</td>\n",
       "      <td>0.700055</td>\n",
       "      <td>0.477663</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>1.952747</td>\n",
       "      <td>3.478864</td>\n",
       "      <td>3.227554</td>\n",
       "      <td>1.910622</td>\n",
       "      <td>0.997037</td>\n",
       "      <td>0.885468</td>\n",
       "      <td>1.413751</td>\n",
       "      <td>2.724577</td>\n",
       "      <td>1.708275</td>\n",
       "      <td>1.570772</td>\n",
       "      <td>-0.107300</td>\n",
       "      <td>0.561727</td>\n",
       "      <td>1.712368</td>\n",
       "      <td>3.806308</td>\n",
       "      <td>1.243197</td>\n",
       "      <td>-0.078731</td>\n",
       "      <td>2.116123</td>\n",
       "      <td>1.649723</td>\n",
       "      <td>0.649723</td>\n",
       "      <td>1.776321</td>\n",
       "      <td>-4.429228</td>\n",
       "      <td>2.396049</td>\n",
       "      <td>-0.642632</td>\n",
       "      <td>1.756639</td>\n",
       "      <td>-1.259303</td>\n",
       "      <td>1.325659</td>\n",
       "      <td>1.357368</td>\n",
       "      <td>1.623883</td>\n",
       "      <td>5.054991</td>\n",
       "      <td>1.428753</td>\n",
       "      <td>2.367785</td>\n",
       "      <td>1.628222</td>\n",
       "      <td>-0.009689</td>\n",
       "      <td>1.403662</td>\n",
       "      <td>2.677204</td>\n",
       "      <td>6.071016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.037017</td>\n",
       "      <td>1.730413</td>\n",
       "      <td>7.382629</td>\n",
       "      <td>0.513455</td>\n",
       "      <td>2.911786</td>\n",
       "      <td>2.242807</td>\n",
       "      <td>6.884066</td>\n",
       "      <td>7.056826</td>\n",
       "      <td>4.293433</td>\n",
       "      <td>4.151223</td>\n",
       "      <td>1.663558</td>\n",
       "      <td>8.191885</td>\n",
       "      <td>2.215840</td>\n",
       "      <td>1.139996</td>\n",
       "      <td>2.273022</td>\n",
       "      <td>4.796909</td>\n",
       "      <td>4.513455</td>\n",
       "      <td>0.408485</td>\n",
       "      <td>2.587455</td>\n",
       "      <td>2.387924</td>\n",
       "      <td>-2.071508</td>\n",
       "      <td>2.204127</td>\n",
       "      <td>0.235153</td>\n",
       "      <td>-0.461454</td>\n",
       "      <td>1.487919</td>\n",
       "      <td>4.568547</td>\n",
       "      <td>3.416966</td>\n",
       "      <td>2.072882</td>\n",
       "      <td>1.257993</td>\n",
       "      <td>4.040314</td>\n",
       "      <td>4.756712</td>\n",
       "      <td>-0.795873</td>\n",
       "      <td>-0.297068</td>\n",
       "      <td>1.115193</td>\n",
       "      <td>4.427079</td>\n",
       "      <td>0.646349</td>\n",
       "      <td>4.772000</td>\n",
       "      <td>4.322616</td>\n",
       "      <td>4.823948</td>\n",
       "      <td>3.579921</td>\n",
       "      <td>...</td>\n",
       "      <td>1.652108</td>\n",
       "      <td>0.394810</td>\n",
       "      <td>0.956689</td>\n",
       "      <td>-0.169688</td>\n",
       "      <td>2.921356</td>\n",
       "      <td>3.182402</td>\n",
       "      <td>3.267404</td>\n",
       "      <td>1.227459</td>\n",
       "      <td>0.984345</td>\n",
       "      <td>2.428757</td>\n",
       "      <td>-0.365239</td>\n",
       "      <td>2.320810</td>\n",
       "      <td>-0.275041</td>\n",
       "      <td>-1.109982</td>\n",
       "      <td>1.317189</td>\n",
       "      <td>0.055604</td>\n",
       "      <td>1.046887</td>\n",
       "      <td>3.521342</td>\n",
       "      <td>1.550929</td>\n",
       "      <td>0.020415</td>\n",
       "      <td>3.004745</td>\n",
       "      <td>0.408485</td>\n",
       "      <td>0.550929</td>\n",
       "      <td>2.215840</td>\n",
       "      <td>0.928492</td>\n",
       "      <td>1.933230</td>\n",
       "      <td>-0.734473</td>\n",
       "      <td>0.918969</td>\n",
       "      <td>-1.275041</td>\n",
       "      <td>1.719484</td>\n",
       "      <td>2.284191</td>\n",
       "      <td>2.590458</td>\n",
       "      <td>6.063999</td>\n",
       "      <td>1.415274</td>\n",
       "      <td>1.928492</td>\n",
       "      <td>2.227459</td>\n",
       "      <td>-0.461454</td>\n",
       "      <td>2.208042</td>\n",
       "      <td>2.250420</td>\n",
       "      <td>6.015676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9936 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0         1         2      ...     24316     24317     24323\n",
       "0  4.216178  0.828980  8.112087  ...  1.596617  2.517447  5.719885\n",
       "1  1.284603  0.338823  7.829033  ...  2.844797  4.236222  7.261298\n",
       "2  3.088138  1.049060  6.626358  ...  1.382616  2.823885  5.776508\n",
       "3  2.106825 -0.341765  7.220926  ...  1.403662  2.677204  6.071016\n",
       "4  4.037017  1.730413  7.382629  ...  2.208042  2.250420  6.015676\n",
       "\n",
       "[5 rows x 9936 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Note: Already fit the VarianceThreshold with the visualizations above\n",
    "\n",
    "#create a mask of features selected\n",
    "mask = selector.get_support(indices = True)\n",
    "\n",
    "#select the same indexes from the header\n",
    "reduced_header = np.take(gene_indices, mask)\n",
    "\n",
    "high_var_train_genes.columns = reduced_header\n",
    "\n",
    "print(f\"Filter reduced number of genes by {len(log_normalized_tmm_train_genes.columns) - len(high_var_train_genes.columns)} from {len(log_normalized_tmm_train_genes.columns)} to {len(high_var_train_genes.columns)}\")\n",
    "high_var_train_genes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GskiWh8-UTgn"
   },
   "source": [
    "#### Remove highly correlated features\n",
    "- This can decrease the dimensionality of the data while eliminating redundancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ko8vz_79UThI"
   },
   "outputs": [],
   "source": [
    "cov = abs(high_var_train_genes.corr().to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y38kALImUThL"
   },
   "outputs": [],
   "source": [
    "order = np.array(hierarchy.dendrogram(hierarchy.ward(cov),no_plot=True)['ivl'], dtype=int)\n",
    "plt.figure(figsize=(8, 8), dpi=100)\n",
    "plt.imshow(cov[order, :][:, order])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UZbfghxWUThR"
   },
   "outputs": [],
   "source": [
    "filtered_cov_high_var = []\n",
    "for i in range(len(high_var_train_genes.columns)):\n",
    "    row = []\n",
    "    for j in range(len(high_var_train_genes.columns)):\n",
    "        if cov[i][j] > 0.70 and i != j:\n",
    "            row.append(j)\n",
    "    filtered_cov_high_var.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ECs0893mUThU"
   },
   "outputs": [],
   "source": [
    "filtered = []\n",
    "for i in range(len(filtered_cov_high_var)):\n",
    "    if len(filtered_cov_high_var[i]) > 0:\n",
    "        filtered.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dvij82JDUThZ"
   },
   "outputs": [],
   "source": [
    "to_remove = []\n",
    "to_keep = []\n",
    "for gene in filtered:\n",
    "    # The gene is not correlated to anything we've seen so far\n",
    "    if gene not in to_remove:\n",
    "        # remove all values \n",
    "        vals = filtered_cov_high_var[gene]\n",
    "        for val in vals:\n",
    "            if val not in to_remove:\n",
    "                to_remove.append(val)\n",
    "        to_keep.append(gene)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_0VtdJMUUThc"
   },
   "source": [
    "###### Get the new filtered features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rHBHWxdTUThd"
   },
   "outputs": [],
   "source": [
    "cols_filtered = [x for x in high_var_train_genes.columns if x not in to_remove]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N8nT4QmWUThf"
   },
   "outputs": [],
   "source": [
    "high_var_train_genes = high_var_train_genes[cols_filtered]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "colab_type": "code",
    "id": "uuAZDsOrUThm",
    "outputId": "1328c023-82ce-434e-86e8-fce6c080e154"
   },
   "outputs": [
    {
     "data": {
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       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>47</th>\n",
       "      <th>50</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>82</th>\n",
       "      <th>86</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>96</th>\n",
       "      <th>98</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>106</th>\n",
       "      <th>107</th>\n",
       "      <th>...</th>\n",
       "      <th>24150</th>\n",
       "      <th>24155</th>\n",
       "      <th>24156</th>\n",
       "      <th>24160</th>\n",
       "      <th>24163</th>\n",
       "      <th>24164</th>\n",
       "      <th>24165</th>\n",
       "      <th>24168</th>\n",
       "      <th>24184</th>\n",
       "      <th>24191</th>\n",
       "      <th>24202</th>\n",
       "      <th>24203</th>\n",
       "      <th>24207</th>\n",
       "      <th>24214</th>\n",
       "      <th>24215</th>\n",
       "      <th>24220</th>\n",
       "      <th>24223</th>\n",
       "      <th>24224</th>\n",
       "      <th>24237</th>\n",
       "      <th>24244</th>\n",
       "      <th>24253</th>\n",
       "      <th>24255</th>\n",
       "      <th>24256</th>\n",
       "      <th>24260</th>\n",
       "      <th>24261</th>\n",
       "      <th>24271</th>\n",
       "      <th>24272</th>\n",
       "      <th>24273</th>\n",
       "      <th>24277</th>\n",
       "      <th>24283</th>\n",
       "      <th>24285</th>\n",
       "      <th>24288</th>\n",
       "      <th>24291</th>\n",
       "      <th>24296</th>\n",
       "      <th>24308</th>\n",
       "      <th>24309</th>\n",
       "      <th>24313</th>\n",
       "      <th>24316</th>\n",
       "      <th>24317</th>\n",
       "      <th>24323</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.216178</td>\n",
       "      <td>8.112087</td>\n",
       "      <td>0.743007</td>\n",
       "      <td>1.502574</td>\n",
       "      <td>1.949036</td>\n",
       "      <td>3.874457</td>\n",
       "      <td>7.081137</td>\n",
       "      <td>4.097469</td>\n",
       "      <td>3.577228</td>\n",
       "      <td>3.369548</td>\n",
       "      <td>6.262436</td>\n",
       "      <td>0.965399</td>\n",
       "      <td>0.119570</td>\n",
       "      <td>2.688194</td>\n",
       "      <td>2.397957</td>\n",
       "      <td>3.932484</td>\n",
       "      <td>1.018641</td>\n",
       "      <td>2.617476</td>\n",
       "      <td>1.195519</td>\n",
       "      <td>1.678301</td>\n",
       "      <td>2.059864</td>\n",
       "      <td>1.441498</td>\n",
       "      <td>0.353060</td>\n",
       "      <td>2.600115</td>\n",
       "      <td>6.097778</td>\n",
       "      <td>0.910117</td>\n",
       "      <td>1.997579</td>\n",
       "      <td>0.780481</td>\n",
       "      <td>4.198983</td>\n",
       "      <td>0.495079</td>\n",
       "      <td>1.479972</td>\n",
       "      <td>0.730296</td>\n",
       "      <td>6.285156</td>\n",
       "      <td>0.898801</td>\n",
       "      <td>4.378741</td>\n",
       "      <td>4.320568</td>\n",
       "      <td>4.560271</td>\n",
       "      <td>4.237669</td>\n",
       "      <td>2.898801</td>\n",
       "      <td>3.731891</td>\n",
       "      <td>...</td>\n",
       "      <td>1.596617</td>\n",
       "      <td>-0.135687</td>\n",
       "      <td>2.332181</td>\n",
       "      <td>0.582542</td>\n",
       "      <td>1.749320</td>\n",
       "      <td>2.684904</td>\n",
       "      <td>3.543112</td>\n",
       "      <td>1.276439</td>\n",
       "      <td>0.158044</td>\n",
       "      <td>4.227529</td>\n",
       "      <td>1.596617</td>\n",
       "      <td>1.852631</td>\n",
       "      <td>1.441498</td>\n",
       "      <td>-2.389444</td>\n",
       "      <td>-0.207240</td>\n",
       "      <td>-0.880430</td>\n",
       "      <td>4.119570</td>\n",
       "      <td>2.921344</td>\n",
       "      <td>0.678301</td>\n",
       "      <td>-0.732331</td>\n",
       "      <td>3.568328</td>\n",
       "      <td>1.553973</td>\n",
       "      <td>0.553973</td>\n",
       "      <td>1.943540</td>\n",
       "      <td>1.393932</td>\n",
       "      <td>1.810983</td>\n",
       "      <td>-1.089883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.018641</td>\n",
       "      <td>1.417911</td>\n",
       "      <td>3.002874</td>\n",
       "      <td>0.059864</td>\n",
       "      <td>5.959965</td>\n",
       "      <td>0.369548</td>\n",
       "      <td>1.898801</td>\n",
       "      <td>0.768098</td>\n",
       "      <td>0.336381</td>\n",
       "      <td>1.596617</td>\n",
       "      <td>2.517447</td>\n",
       "      <td>5.719885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.284603</td>\n",
       "      <td>7.829033</td>\n",
       "      <td>-0.504452</td>\n",
       "      <td>2.162973</td>\n",
       "      <td>2.069864</td>\n",
       "      <td>3.515303</td>\n",
       "      <td>6.065851</td>\n",
       "      <td>1.484901</td>\n",
       "      <td>2.466077</td>\n",
       "      <td>-0.977205</td>\n",
       "      <td>4.377472</td>\n",
       "      <td>1.887866</td>\n",
       "      <td>-2.068352</td>\n",
       "      <td>1.108525</td>\n",
       "      <td>2.524430</td>\n",
       "      <td>2.651260</td>\n",
       "      <td>-0.165650</td>\n",
       "      <td>2.983625</td>\n",
       "      <td>2.069864</td>\n",
       "      <td>3.652449</td>\n",
       "      <td>1.182873</td>\n",
       "      <td>-1.382461</td>\n",
       "      <td>0.108525</td>\n",
       "      <td>0.408086</td>\n",
       "      <td>0.149553</td>\n",
       "      <td>1.419313</td>\n",
       "      <td>2.474175</td>\n",
       "      <td>0.266067</td>\n",
       "      <td>3.837492</td>\n",
       "      <td>-0.084120</td>\n",
       "      <td>1.129185</td>\n",
       "      <td>-0.810555</td>\n",
       "      <td>3.564169</td>\n",
       "      <td>-1.504452</td>\n",
       "      <td>2.981733</td>\n",
       "      <td>5.905189</td>\n",
       "      <td>4.365913</td>\n",
       "      <td>4.091957</td>\n",
       "      <td>3.085805</td>\n",
       "      <td>2.244136</td>\n",
       "      <td>...</td>\n",
       "      <td>2.725498</td>\n",
       "      <td>0.915880</td>\n",
       "      <td>1.632087</td>\n",
       "      <td>-3.591914</td>\n",
       "      <td>2.895926</td>\n",
       "      <td>2.983625</td>\n",
       "      <td>3.212217</td>\n",
       "      <td>2.542512</td>\n",
       "      <td>0.915880</td>\n",
       "      <td>3.245714</td>\n",
       "      <td>0.362282</td>\n",
       "      <td>3.022795</td>\n",
       "      <td>0.808965</td>\n",
       "      <td>0.993048</td>\n",
       "      <td>-0.863994</td>\n",
       "      <td>-0.343987</td>\n",
       "      <td>-1.462631</td>\n",
       "      <td>3.340300</td>\n",
       "      <td>-0.052756</td>\n",
       "      <td>-0.234362</td>\n",
       "      <td>3.601611</td>\n",
       "      <td>0.240976</td>\n",
       "      <td>0.954980</td>\n",
       "      <td>2.972235</td>\n",
       "      <td>-5.591914</td>\n",
       "      <td>1.597910</td>\n",
       "      <td>-0.547520</td>\n",
       "      <td>0.646490</td>\n",
       "      <td>0.516610</td>\n",
       "      <td>-0.863994</td>\n",
       "      <td>0.314976</td>\n",
       "      <td>-1.891475</td>\n",
       "      <td>5.095899</td>\n",
       "      <td>0.228265</td>\n",
       "      <td>2.585505</td>\n",
       "      <td>1.500843</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.844797</td>\n",
       "      <td>4.236222</td>\n",
       "      <td>7.261298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.088138</td>\n",
       "      <td>6.626358</td>\n",
       "      <td>2.036497</td>\n",
       "      <td>0.701895</td>\n",
       "      <td>2.993810</td>\n",
       "      <td>4.341501</td>\n",
       "      <td>7.230866</td>\n",
       "      <td>0.669714</td>\n",
       "      <td>3.693917</td>\n",
       "      <td>1.881218</td>\n",
       "      <td>6.775721</td>\n",
       "      <td>0.447322</td>\n",
       "      <td>-0.454831</td>\n",
       "      <td>1.176674</td>\n",
       "      <td>3.429815</td>\n",
       "      <td>2.804256</td>\n",
       "      <td>-0.959303</td>\n",
       "      <td>2.332108</td>\n",
       "      <td>1.236484</td>\n",
       "      <td>-1.298105</td>\n",
       "      <td>2.625659</td>\n",
       "      <td>0.258288</td>\n",
       "      <td>0.521322</td>\n",
       "      <td>1.122226</td>\n",
       "      <td>4.837830</td>\n",
       "      <td>1.728174</td>\n",
       "      <td>2.247427</td>\n",
       "      <td>0.963160</td>\n",
       "      <td>3.552541</td>\n",
       "      <td>-0.713143</td>\n",
       "      <td>2.375983</td>\n",
       "      <td>0.447322</td>\n",
       "      <td>5.775249</td>\n",
       "      <td>0.927315</td>\n",
       "      <td>4.345751</td>\n",
       "      <td>4.108287</td>\n",
       "      <td>4.883559</td>\n",
       "      <td>4.531804</td>\n",
       "      <td>2.286857</td>\n",
       "      <td>4.448113</td>\n",
       "      <td>...</td>\n",
       "      <td>1.608785</td>\n",
       "      <td>-2.385568</td>\n",
       "      <td>1.122226</td>\n",
       "      <td>-0.045718</td>\n",
       "      <td>2.605954</td>\n",
       "      <td>3.208756</td>\n",
       "      <td>2.998136</td>\n",
       "      <td>2.180486</td>\n",
       "      <td>0.328677</td>\n",
       "      <td>1.106285</td>\n",
       "      <td>-0.959303</td>\n",
       "      <td>1.603117</td>\n",
       "      <td>0.057375</td>\n",
       "      <td>0.733373</td>\n",
       "      <td>-0.431372</td>\n",
       "      <td>-0.830979</td>\n",
       "      <td>3.746289</td>\n",
       "      <td>3.247427</td>\n",
       "      <td>0.909053</td>\n",
       "      <td>-2.385568</td>\n",
       "      <td>2.878874</td>\n",
       "      <td>0.073863</td>\n",
       "      <td>0.355899</td>\n",
       "      <td>2.725568</td>\n",
       "      <td>1.057375</td>\n",
       "      <td>1.963160</td>\n",
       "      <td>-0.454831</td>\n",
       "      <td>1.161326</td>\n",
       "      <td>-2.137641</td>\n",
       "      <td>1.375983</td>\n",
       "      <td>3.046974</td>\n",
       "      <td>1.453636</td>\n",
       "      <td>5.445343</td>\n",
       "      <td>-1.578213</td>\n",
       "      <td>2.625659</td>\n",
       "      <td>1.199394</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.382616</td>\n",
       "      <td>2.823885</td>\n",
       "      <td>5.776508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.106825</td>\n",
       "      <td>7.220926</td>\n",
       "      <td>1.628222</td>\n",
       "      <td>1.320306</td>\n",
       "      <td>3.025277</td>\n",
       "      <td>3.822491</td>\n",
       "      <td>6.497809</td>\n",
       "      <td>2.545760</td>\n",
       "      <td>3.804392</td>\n",
       "      <td>1.106825</td>\n",
       "      <td>5.697476</td>\n",
       "      <td>1.010395</td>\n",
       "      <td>1.185482</td>\n",
       "      <td>2.894502</td>\n",
       "      <td>3.325659</td>\n",
       "      <td>3.882747</td>\n",
       "      <td>0.506232</td>\n",
       "      <td>1.632548</td>\n",
       "      <td>2.298692</td>\n",
       "      <td>1.320306</td>\n",
       "      <td>2.013715</td>\n",
       "      <td>-0.036911</td>\n",
       "      <td>1.487249</td>\n",
       "      <td>-0.844266</td>\n",
       "      <td>3.788003</td>\n",
       "      <td>0.928324</td>\n",
       "      <td>2.677204</td>\n",
       "      <td>-0.706762</td>\n",
       "      <td>3.469222</td>\n",
       "      <td>1.030204</td>\n",
       "      <td>0.949284</td>\n",
       "      <td>-1.023236</td>\n",
       "      <td>3.558320</td>\n",
       "      <td>1.265652</td>\n",
       "      <td>3.528454</td>\n",
       "      <td>5.152726</td>\n",
       "      <td>4.540015</td>\n",
       "      <td>3.350820</td>\n",
       "      <td>4.756639</td>\n",
       "      <td>3.470431</td>\n",
       "      <td>...</td>\n",
       "      <td>1.952747</td>\n",
       "      <td>0.700055</td>\n",
       "      <td>0.477663</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>1.952747</td>\n",
       "      <td>3.478864</td>\n",
       "      <td>3.227554</td>\n",
       "      <td>1.910622</td>\n",
       "      <td>0.997037</td>\n",
       "      <td>0.885468</td>\n",
       "      <td>1.413751</td>\n",
       "      <td>2.724577</td>\n",
       "      <td>1.708275</td>\n",
       "      <td>1.570772</td>\n",
       "      <td>-0.107300</td>\n",
       "      <td>0.561727</td>\n",
       "      <td>1.712368</td>\n",
       "      <td>3.806308</td>\n",
       "      <td>1.243197</td>\n",
       "      <td>-0.078731</td>\n",
       "      <td>2.116123</td>\n",
       "      <td>1.649723</td>\n",
       "      <td>0.649723</td>\n",
       "      <td>1.776321</td>\n",
       "      <td>-4.429228</td>\n",
       "      <td>2.396049</td>\n",
       "      <td>-0.642632</td>\n",
       "      <td>1.756639</td>\n",
       "      <td>-1.259303</td>\n",
       "      <td>1.325659</td>\n",
       "      <td>1.357368</td>\n",
       "      <td>1.623883</td>\n",
       "      <td>5.054991</td>\n",
       "      <td>1.428753</td>\n",
       "      <td>2.367785</td>\n",
       "      <td>1.628222</td>\n",
       "      <td>-0.009689</td>\n",
       "      <td>1.403662</td>\n",
       "      <td>2.677204</td>\n",
       "      <td>6.071016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.037017</td>\n",
       "      <td>7.382629</td>\n",
       "      <td>0.513455</td>\n",
       "      <td>2.911786</td>\n",
       "      <td>2.242807</td>\n",
       "      <td>6.884066</td>\n",
       "      <td>7.056826</td>\n",
       "      <td>4.293433</td>\n",
       "      <td>4.151223</td>\n",
       "      <td>1.663558</td>\n",
       "      <td>8.191885</td>\n",
       "      <td>2.215840</td>\n",
       "      <td>1.139996</td>\n",
       "      <td>2.273022</td>\n",
       "      <td>4.796909</td>\n",
       "      <td>4.513455</td>\n",
       "      <td>0.408485</td>\n",
       "      <td>2.587455</td>\n",
       "      <td>2.387924</td>\n",
       "      <td>-2.071508</td>\n",
       "      <td>2.204127</td>\n",
       "      <td>0.235153</td>\n",
       "      <td>-0.461454</td>\n",
       "      <td>1.487919</td>\n",
       "      <td>4.568547</td>\n",
       "      <td>3.416966</td>\n",
       "      <td>2.072882</td>\n",
       "      <td>1.257993</td>\n",
       "      <td>4.756712</td>\n",
       "      <td>-0.795873</td>\n",
       "      <td>-0.297068</td>\n",
       "      <td>1.115193</td>\n",
       "      <td>4.427079</td>\n",
       "      <td>0.646349</td>\n",
       "      <td>4.772000</td>\n",
       "      <td>4.322616</td>\n",
       "      <td>4.823948</td>\n",
       "      <td>3.579921</td>\n",
       "      <td>2.192317</td>\n",
       "      <td>5.387061</td>\n",
       "      <td>...</td>\n",
       "      <td>1.652108</td>\n",
       "      <td>0.394810</td>\n",
       "      <td>0.956689</td>\n",
       "      <td>-0.169688</td>\n",
       "      <td>2.921356</td>\n",
       "      <td>3.182402</td>\n",
       "      <td>3.267404</td>\n",
       "      <td>1.227459</td>\n",
       "      <td>0.984345</td>\n",
       "      <td>2.428757</td>\n",
       "      <td>-0.365239</td>\n",
       "      <td>2.320810</td>\n",
       "      <td>-0.275041</td>\n",
       "      <td>-1.109982</td>\n",
       "      <td>1.317189</td>\n",
       "      <td>0.055604</td>\n",
       "      <td>1.046887</td>\n",
       "      <td>3.521342</td>\n",
       "      <td>1.550929</td>\n",
       "      <td>0.020415</td>\n",
       "      <td>3.004745</td>\n",
       "      <td>0.408485</td>\n",
       "      <td>0.550929</td>\n",
       "      <td>2.215840</td>\n",
       "      <td>0.928492</td>\n",
       "      <td>1.933230</td>\n",
       "      <td>-0.734473</td>\n",
       "      <td>0.918969</td>\n",
       "      <td>-1.275041</td>\n",
       "      <td>1.719484</td>\n",
       "      <td>2.284191</td>\n",
       "      <td>2.590458</td>\n",
       "      <td>6.063999</td>\n",
       "      <td>1.415274</td>\n",
       "      <td>1.928492</td>\n",
       "      <td>2.227459</td>\n",
       "      <td>-0.461454</td>\n",
       "      <td>2.208042</td>\n",
       "      <td>2.250420</td>\n",
       "      <td>6.015676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 8675 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0         2         4      ...     24316     24317     24323\n",
       "0  4.216178  8.112087  0.743007  ...  1.596617  2.517447  5.719885\n",
       "1  1.284603  7.829033 -0.504452  ...  2.844797  4.236222  7.261298\n",
       "2  3.088138  6.626358  2.036497  ...  1.382616  2.823885  5.776508\n",
       "3  2.106825  7.220926  1.628222  ...  1.403662  2.677204  6.071016\n",
       "4  4.037017  7.382629  0.513455  ...  2.208042  2.250420  6.015676\n",
       "\n",
       "[5 rows x 8675 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_var_train_genes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sMF0fLO1UTho"
   },
   "source": [
    "## Apply Preprocessing Pipeline to Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "oNPbqEW4UThp",
    "outputId": "9d4a3966-d594-4f47-ef99-21b56afef39b"
   },
   "outputs": [
    {
     "data": {
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       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>...</th>\n",
       "      <th>24282</th>\n",
       "      <th>24283</th>\n",
       "      <th>24284</th>\n",
       "      <th>24285</th>\n",
       "      <th>24287</th>\n",
       "      <th>24288</th>\n",
       "      <th>24289</th>\n",
       "      <th>24290</th>\n",
       "      <th>24291</th>\n",
       "      <th>24292</th>\n",
       "      <th>24293</th>\n",
       "      <th>24294</th>\n",
       "      <th>24295</th>\n",
       "      <th>24296</th>\n",
       "      <th>24297</th>\n",
       "      <th>24298</th>\n",
       "      <th>24299</th>\n",
       "      <th>24300</th>\n",
       "      <th>24301</th>\n",
       "      <th>24302</th>\n",
       "      <th>24304</th>\n",
       "      <th>24305</th>\n",
       "      <th>24307</th>\n",
       "      <th>24308</th>\n",
       "      <th>24309</th>\n",
       "      <th>24310</th>\n",
       "      <th>24311</th>\n",
       "      <th>24312</th>\n",
       "      <th>24313</th>\n",
       "      <th>24314</th>\n",
       "      <th>24315</th>\n",
       "      <th>24316</th>\n",
       "      <th>24317</th>\n",
       "      <th>24318</th>\n",
       "      <th>24319</th>\n",
       "      <th>24320</th>\n",
       "      <th>24322</th>\n",
       "      <th>24323</th>\n",
       "      <th>24324</th>\n",
       "      <th>24325</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CGGA_747</th>\n",
       "      <td>13.15</td>\n",
       "      <td>2.36</td>\n",
       "      <td>114.84</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.18</td>\n",
       "      <td>59.20</td>\n",
       "      <td>6.66</td>\n",
       "      <td>12.63</td>\n",
       "      <td>4.84</td>\n",
       "      <td>11.29</td>\n",
       "      <td>6.26</td>\n",
       "      <td>17.50</td>\n",
       "      <td>65.84</td>\n",
       "      <td>20.04</td>\n",
       "      <td>79.47</td>\n",
       "      <td>6.32</td>\n",
       "      <td>31.03</td>\n",
       "      <td>9.99</td>\n",
       "      <td>30.03</td>\n",
       "      <td>13.28</td>\n",
       "      <td>37.84</td>\n",
       "      <td>5.28</td>\n",
       "      <td>82.41</td>\n",
       "      <td>8.23</td>\n",
       "      <td>6.43</td>\n",
       "      <td>5.14</td>\n",
       "      <td>0.66</td>\n",
       "      <td>40.82</td>\n",
       "      <td>33.86</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.35</td>\n",
       "      <td>3.61</td>\n",
       "      <td>5.92</td>\n",
       "      <td>5.12</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1.70</td>\n",
       "      <td>31.23</td>\n",
       "      <td>14.48</td>\n",
       "      <td>13.77</td>\n",
       "      <td>1.40</td>\n",
       "      <td>...</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.43</td>\n",
       "      <td>9.13</td>\n",
       "      <td>3.84</td>\n",
       "      <td>5.27</td>\n",
       "      <td>0.41</td>\n",
       "      <td>6.85</td>\n",
       "      <td>3.92</td>\n",
       "      <td>38.26</td>\n",
       "      <td>6.90</td>\n",
       "      <td>1.63</td>\n",
       "      <td>9.30</td>\n",
       "      <td>1.13</td>\n",
       "      <td>3.03</td>\n",
       "      <td>7.10</td>\n",
       "      <td>16.85</td>\n",
       "      <td>11.45</td>\n",
       "      <td>4.83</td>\n",
       "      <td>9.87</td>\n",
       "      <td>4.34</td>\n",
       "      <td>12.40</td>\n",
       "      <td>11.02</td>\n",
       "      <td>2.73</td>\n",
       "      <td>10.28</td>\n",
       "      <td>1.05</td>\n",
       "      <td>23.88</td>\n",
       "      <td>22.18</td>\n",
       "      <td>169.12</td>\n",
       "      <td>0.44</td>\n",
       "      <td>5.99</td>\n",
       "      <td>7.58</td>\n",
       "      <td>5.83</td>\n",
       "      <td>44.67</td>\n",
       "      <td>3.34</td>\n",
       "      <td>5.03</td>\n",
       "      <td>17.74</td>\n",
       "      <td>8.42</td>\n",
       "      <td>97.58</td>\n",
       "      <td>17.90</td>\n",
       "      <td>20.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_1320</th>\n",
       "      <td>10.64</td>\n",
       "      <td>3.94</td>\n",
       "      <td>33.99</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1.47</td>\n",
       "      <td>53.74</td>\n",
       "      <td>4.26</td>\n",
       "      <td>5.10</td>\n",
       "      <td>11.60</td>\n",
       "      <td>12.27</td>\n",
       "      <td>20.75</td>\n",
       "      <td>30.73</td>\n",
       "      <td>58.54</td>\n",
       "      <td>9.72</td>\n",
       "      <td>56.40</td>\n",
       "      <td>3.97</td>\n",
       "      <td>43.36</td>\n",
       "      <td>6.22</td>\n",
       "      <td>21.80</td>\n",
       "      <td>23.45</td>\n",
       "      <td>23.02</td>\n",
       "      <td>11.36</td>\n",
       "      <td>123.97</td>\n",
       "      <td>26.05</td>\n",
       "      <td>14.18</td>\n",
       "      <td>7.71</td>\n",
       "      <td>3.60</td>\n",
       "      <td>81.55</td>\n",
       "      <td>14.22</td>\n",
       "      <td>35.24</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.08</td>\n",
       "      <td>7.54</td>\n",
       "      <td>4.26</td>\n",
       "      <td>1.18</td>\n",
       "      <td>74.98</td>\n",
       "      <td>5.47</td>\n",
       "      <td>9.17</td>\n",
       "      <td>3.91</td>\n",
       "      <td>...</td>\n",
       "      <td>1.49</td>\n",
       "      <td>9.69</td>\n",
       "      <td>21.11</td>\n",
       "      <td>18.46</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.07</td>\n",
       "      <td>4.81</td>\n",
       "      <td>4.50</td>\n",
       "      <td>147.65</td>\n",
       "      <td>4.69</td>\n",
       "      <td>0.63</td>\n",
       "      <td>13.47</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.35</td>\n",
       "      <td>5.62</td>\n",
       "      <td>6.55</td>\n",
       "      <td>15.30</td>\n",
       "      <td>1.56</td>\n",
       "      <td>7.72</td>\n",
       "      <td>16.78</td>\n",
       "      <td>8.27</td>\n",
       "      <td>3.88</td>\n",
       "      <td>1.07</td>\n",
       "      <td>5.75</td>\n",
       "      <td>1.02</td>\n",
       "      <td>6.84</td>\n",
       "      <td>33.71</td>\n",
       "      <td>179.14</td>\n",
       "      <td>7.39</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2.43</td>\n",
       "      <td>3.70</td>\n",
       "      <td>16.48</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.05</td>\n",
       "      <td>9.31</td>\n",
       "      <td>3.92</td>\n",
       "      <td>60.85</td>\n",
       "      <td>6.87</td>\n",
       "      <td>8.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_760</th>\n",
       "      <td>3.99</td>\n",
       "      <td>1.10</td>\n",
       "      <td>81.73</td>\n",
       "      <td>1.28</td>\n",
       "      <td>3.48</td>\n",
       "      <td>29.48</td>\n",
       "      <td>7.02</td>\n",
       "      <td>5.69</td>\n",
       "      <td>3.09</td>\n",
       "      <td>8.99</td>\n",
       "      <td>6.37</td>\n",
       "      <td>19.45</td>\n",
       "      <td>59.26</td>\n",
       "      <td>14.14</td>\n",
       "      <td>67.61</td>\n",
       "      <td>4.39</td>\n",
       "      <td>19.10</td>\n",
       "      <td>3.91</td>\n",
       "      <td>23.42</td>\n",
       "      <td>6.15</td>\n",
       "      <td>21.37</td>\n",
       "      <td>52.74</td>\n",
       "      <td>165.86</td>\n",
       "      <td>22.09</td>\n",
       "      <td>4.42</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.41</td>\n",
       "      <td>155.23</td>\n",
       "      <td>34.43</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.47</td>\n",
       "      <td>9.15</td>\n",
       "      <td>5.69</td>\n",
       "      <td>2.38</td>\n",
       "      <td>0.62</td>\n",
       "      <td>19.24</td>\n",
       "      <td>6.38</td>\n",
       "      <td>14.40</td>\n",
       "      <td>4.75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.96</td>\n",
       "      <td>6.96</td>\n",
       "      <td>4.47</td>\n",
       "      <td>2.29</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.11</td>\n",
       "      <td>4.96</td>\n",
       "      <td>41.45</td>\n",
       "      <td>3.58</td>\n",
       "      <td>0.57</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.15</td>\n",
       "      <td>3.31</td>\n",
       "      <td>6.53</td>\n",
       "      <td>7.53</td>\n",
       "      <td>2.51</td>\n",
       "      <td>7.28</td>\n",
       "      <td>2.10</td>\n",
       "      <td>5.61</td>\n",
       "      <td>5.29</td>\n",
       "      <td>1.44</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.80</td>\n",
       "      <td>12.73</td>\n",
       "      <td>18.07</td>\n",
       "      <td>46.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.03</td>\n",
       "      <td>5.04</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.57</td>\n",
       "      <td>2.51</td>\n",
       "      <td>4.45</td>\n",
       "      <td>10.72</td>\n",
       "      <td>5.64</td>\n",
       "      <td>43.71</td>\n",
       "      <td>10.47</td>\n",
       "      <td>6.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_538</th>\n",
       "      <td>6.02</td>\n",
       "      <td>2.28</td>\n",
       "      <td>85.14</td>\n",
       "      <td>3.19</td>\n",
       "      <td>1.97</td>\n",
       "      <td>33.14</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.36</td>\n",
       "      <td>3.45</td>\n",
       "      <td>11.14</td>\n",
       "      <td>5.62</td>\n",
       "      <td>30.61</td>\n",
       "      <td>76.84</td>\n",
       "      <td>17.40</td>\n",
       "      <td>80.96</td>\n",
       "      <td>5.91</td>\n",
       "      <td>30.41</td>\n",
       "      <td>6.10</td>\n",
       "      <td>31.16</td>\n",
       "      <td>12.03</td>\n",
       "      <td>22.15</td>\n",
       "      <td>33.11</td>\n",
       "      <td>151.11</td>\n",
       "      <td>11.35</td>\n",
       "      <td>14.91</td>\n",
       "      <td>3.43</td>\n",
       "      <td>2.46</td>\n",
       "      <td>93.15</td>\n",
       "      <td>26.74</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.90</td>\n",
       "      <td>5.18</td>\n",
       "      <td>24.03</td>\n",
       "      <td>8.12</td>\n",
       "      <td>4.04</td>\n",
       "      <td>0.90</td>\n",
       "      <td>25.23</td>\n",
       "      <td>9.45</td>\n",
       "      <td>16.56</td>\n",
       "      <td>3.72</td>\n",
       "      <td>...</td>\n",
       "      <td>1.49</td>\n",
       "      <td>2.77</td>\n",
       "      <td>6.88</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3.85</td>\n",
       "      <td>8.75</td>\n",
       "      <td>3.20</td>\n",
       "      <td>6.14</td>\n",
       "      <td>38.00</td>\n",
       "      <td>6.27</td>\n",
       "      <td>1.56</td>\n",
       "      <td>10.39</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.10</td>\n",
       "      <td>5.47</td>\n",
       "      <td>14.49</td>\n",
       "      <td>12.59</td>\n",
       "      <td>8.23</td>\n",
       "      <td>11.85</td>\n",
       "      <td>3.24</td>\n",
       "      <td>8.88</td>\n",
       "      <td>6.21</td>\n",
       "      <td>3.07</td>\n",
       "      <td>6.48</td>\n",
       "      <td>3.47</td>\n",
       "      <td>8.40</td>\n",
       "      <td>25.68</td>\n",
       "      <td>55.03</td>\n",
       "      <td>0.91</td>\n",
       "      <td>6.28</td>\n",
       "      <td>4.59</td>\n",
       "      <td>2.81</td>\n",
       "      <td>4.17</td>\n",
       "      <td>3.69</td>\n",
       "      <td>4.25</td>\n",
       "      <td>9.90</td>\n",
       "      <td>6.92</td>\n",
       "      <td>29.07</td>\n",
       "      <td>15.45</td>\n",
       "      <td>7.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_909</th>\n",
       "      <td>3.76</td>\n",
       "      <td>1.35</td>\n",
       "      <td>73.51</td>\n",
       "      <td>3.66</td>\n",
       "      <td>1.76</td>\n",
       "      <td>37.69</td>\n",
       "      <td>5.31</td>\n",
       "      <td>11.02</td>\n",
       "      <td>4.76</td>\n",
       "      <td>10.61</td>\n",
       "      <td>4.93</td>\n",
       "      <td>25.59</td>\n",
       "      <td>71.01</td>\n",
       "      <td>13.94</td>\n",
       "      <td>81.34</td>\n",
       "      <td>5.98</td>\n",
       "      <td>33.18</td>\n",
       "      <td>6.71</td>\n",
       "      <td>29.74</td>\n",
       "      <td>15.80</td>\n",
       "      <td>17.35</td>\n",
       "      <td>26.78</td>\n",
       "      <td>198.63</td>\n",
       "      <td>2.08</td>\n",
       "      <td>17.04</td>\n",
       "      <td>4.06</td>\n",
       "      <td>3.68</td>\n",
       "      <td>74.21</td>\n",
       "      <td>33.63</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.60</td>\n",
       "      <td>6.97</td>\n",
       "      <td>9.52</td>\n",
       "      <td>12.73</td>\n",
       "      <td>4.42</td>\n",
       "      <td>1.78</td>\n",
       "      <td>29.87</td>\n",
       "      <td>8.76</td>\n",
       "      <td>29.48</td>\n",
       "      <td>4.32</td>\n",
       "      <td>...</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.56</td>\n",
       "      <td>5.91</td>\n",
       "      <td>2.16</td>\n",
       "      <td>4.47</td>\n",
       "      <td>5.27</td>\n",
       "      <td>2.45</td>\n",
       "      <td>6.96</td>\n",
       "      <td>38.08</td>\n",
       "      <td>6.81</td>\n",
       "      <td>1.90</td>\n",
       "      <td>14.48</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.66</td>\n",
       "      <td>8.03</td>\n",
       "      <td>14.12</td>\n",
       "      <td>11.44</td>\n",
       "      <td>6.45</td>\n",
       "      <td>15.36</td>\n",
       "      <td>3.21</td>\n",
       "      <td>10.15</td>\n",
       "      <td>6.36</td>\n",
       "      <td>3.67</td>\n",
       "      <td>5.72</td>\n",
       "      <td>2.81</td>\n",
       "      <td>12.11</td>\n",
       "      <td>32.51</td>\n",
       "      <td>56.69</td>\n",
       "      <td>1.28</td>\n",
       "      <td>7.85</td>\n",
       "      <td>4.84</td>\n",
       "      <td>3.05</td>\n",
       "      <td>5.78</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4.38</td>\n",
       "      <td>10.17</td>\n",
       "      <td>5.73</td>\n",
       "      <td>60.01</td>\n",
       "      <td>18.22</td>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 17717 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1       2      4      ...  24322  24323  24324  24325\n",
       "CGGA_747   13.15   2.36  114.84   1.36  ...   8.42  97.58  17.90  20.35\n",
       "CGGA_1320  10.64   3.94   33.99   2.82  ...   3.92  60.85   6.87   8.74\n",
       "CGGA_760    3.99   1.10   81.73   1.28  ...   5.64  43.71  10.47   6.10\n",
       "CGGA_538    6.02   2.28   85.14   3.19  ...   6.92  29.07  15.45   7.97\n",
       "CGGA_909    3.76   1.35   73.51   3.66  ...   5.73  60.01  18.22   8.15\n",
       "\n",
       "[5 rows x 17717 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing Data before applying TMM\n",
    "test_genes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1DetoydgUTht"
   },
   "source": [
    "#### Filter low expression columns determined by training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y41zKWccUThu"
   },
   "outputs": [],
   "source": [
    "cols_filtered = [x for x in test_genes.columns if x not in low_exp]\n",
    "test_genes = test_genes[cols_filtered]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "25gVVKPKUThx"
   },
   "source": [
    "#### Normalize and log transform the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "DoZQHMaEUThy",
    "outputId": "dbaf9519-2dbe-43c8-8df7-dcf52a8f4a52"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>4</th>\n",
       "      <th>7</th>\n",
       "      <th>9</th>\n",
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       "      <th>13</th>\n",
       "      <th>14</th>\n",
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       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>19</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
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       "      <th>35</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
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       "      <th>44</th>\n",
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       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>...</th>\n",
       "      <th>24282</th>\n",
       "      <th>24283</th>\n",
       "      <th>24284</th>\n",
       "      <th>24285</th>\n",
       "      <th>24287</th>\n",
       "      <th>24288</th>\n",
       "      <th>24289</th>\n",
       "      <th>24290</th>\n",
       "      <th>24291</th>\n",
       "      <th>24292</th>\n",
       "      <th>24293</th>\n",
       "      <th>24294</th>\n",
       "      <th>24295</th>\n",
       "      <th>24296</th>\n",
       "      <th>24297</th>\n",
       "      <th>24298</th>\n",
       "      <th>24299</th>\n",
       "      <th>24300</th>\n",
       "      <th>24301</th>\n",
       "      <th>24302</th>\n",
       "      <th>24304</th>\n",
       "      <th>24305</th>\n",
       "      <th>24307</th>\n",
       "      <th>24308</th>\n",
       "      <th>24309</th>\n",
       "      <th>24310</th>\n",
       "      <th>24311</th>\n",
       "      <th>24312</th>\n",
       "      <th>24313</th>\n",
       "      <th>24314</th>\n",
       "      <th>24315</th>\n",
       "      <th>24316</th>\n",
       "      <th>24317</th>\n",
       "      <th>24318</th>\n",
       "      <th>24319</th>\n",
       "      <th>24320</th>\n",
       "      <th>24322</th>\n",
       "      <th>24323</th>\n",
       "      <th>24324</th>\n",
       "      <th>24325</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CGGA_747</th>\n",
       "      <td>13.161391</td>\n",
       "      <td>2.362044</td>\n",
       "      <td>114.939478</td>\n",
       "      <td>1.361178</td>\n",
       "      <td>1.181022</td>\n",
       "      <td>59.251281</td>\n",
       "      <td>6.665769</td>\n",
       "      <td>12.640940</td>\n",
       "      <td>4.844193</td>\n",
       "      <td>11.299780</td>\n",
       "      <td>6.265423</td>\n",
       "      <td>17.515159</td>\n",
       "      <td>65.897033</td>\n",
       "      <td>20.057359</td>\n",
       "      <td>79.538839</td>\n",
       "      <td>6.325475</td>\n",
       "      <td>31.056879</td>\n",
       "      <td>9.998654</td>\n",
       "      <td>30.056013</td>\n",
       "      <td>13.291504</td>\n",
       "      <td>37.872778</td>\n",
       "      <td>5.284574</td>\n",
       "      <td>82.481386</td>\n",
       "      <td>8.237129</td>\n",
       "      <td>6.435570</td>\n",
       "      <td>5.144452</td>\n",
       "      <td>0.660572</td>\n",
       "      <td>40.855360</td>\n",
       "      <td>33.889331</td>\n",
       "      <td>1.261091</td>\n",
       "      <td>0.350303</td>\n",
       "      <td>3.613127</td>\n",
       "      <td>5.925128</td>\n",
       "      <td>5.124435</td>\n",
       "      <td>3.422963</td>\n",
       "      <td>1.701473</td>\n",
       "      <td>31.257052</td>\n",
       "      <td>14.492543</td>\n",
       "      <td>13.781928</td>\n",
       "      <td>1.401213</td>\n",
       "      <td>...</td>\n",
       "      <td>2.251949</td>\n",
       "      <td>0.430372</td>\n",
       "      <td>9.137909</td>\n",
       "      <td>3.843326</td>\n",
       "      <td>5.274565</td>\n",
       "      <td>0.410355</td>\n",
       "      <td>6.855934</td>\n",
       "      <td>3.923396</td>\n",
       "      <td>38.293142</td>\n",
       "      <td>6.905977</td>\n",
       "      <td>1.631412</td>\n",
       "      <td>9.308056</td>\n",
       "      <td>1.130979</td>\n",
       "      <td>3.032625</td>\n",
       "      <td>7.106150</td>\n",
       "      <td>16.864596</td>\n",
       "      <td>11.459918</td>\n",
       "      <td>4.834184</td>\n",
       "      <td>9.878550</td>\n",
       "      <td>4.343759</td>\n",
       "      <td>12.410741</td>\n",
       "      <td>11.029546</td>\n",
       "      <td>2.732365</td>\n",
       "      <td>10.288905</td>\n",
       "      <td>1.050910</td>\n",
       "      <td>23.900686</td>\n",
       "      <td>22.199213</td>\n",
       "      <td>169.266497</td>\n",
       "      <td>0.440381</td>\n",
       "      <td>5.995189</td>\n",
       "      <td>7.586566</td>\n",
       "      <td>5.835050</td>\n",
       "      <td>44.708695</td>\n",
       "      <td>3.342893</td>\n",
       "      <td>5.034357</td>\n",
       "      <td>17.755367</td>\n",
       "      <td>8.427294</td>\n",
       "      <td>97.664527</td>\n",
       "      <td>17.915506</td>\n",
       "      <td>20.367628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_1320</th>\n",
       "      <td>10.489705</td>\n",
       "      <td>3.884346</td>\n",
       "      <td>33.509877</td>\n",
       "      <td>2.780166</td>\n",
       "      <td>1.449236</td>\n",
       "      <td>52.980899</td>\n",
       "      <td>4.199826</td>\n",
       "      <td>5.027960</td>\n",
       "      <td>11.436145</td>\n",
       "      <td>12.096681</td>\n",
       "      <td>20.456897</td>\n",
       "      <td>30.295926</td>\n",
       "      <td>57.713097</td>\n",
       "      <td>9.582701</td>\n",
       "      <td>55.603326</td>\n",
       "      <td>3.913922</td>\n",
       "      <td>42.747521</td>\n",
       "      <td>6.132140</td>\n",
       "      <td>21.492066</td>\n",
       "      <td>23.118759</td>\n",
       "      <td>22.694833</td>\n",
       "      <td>11.199535</td>\n",
       "      <td>122.218871</td>\n",
       "      <td>25.682033</td>\n",
       "      <td>13.979701</td>\n",
       "      <td>7.601093</td>\n",
       "      <td>3.549148</td>\n",
       "      <td>80.398071</td>\n",
       "      <td>14.019136</td>\n",
       "      <td>34.742220</td>\n",
       "      <td>0.325339</td>\n",
       "      <td>1.725281</td>\n",
       "      <td>2.050619</td>\n",
       "      <td>7.433494</td>\n",
       "      <td>4.199826</td>\n",
       "      <td>1.163332</td>\n",
       "      <td>73.920875</td>\n",
       "      <td>5.392734</td>\n",
       "      <td>9.040470</td>\n",
       "      <td>3.854770</td>\n",
       "      <td>...</td>\n",
       "      <td>1.468953</td>\n",
       "      <td>9.553125</td>\n",
       "      <td>20.811812</td>\n",
       "      <td>18.199245</td>\n",
       "      <td>1.350648</td>\n",
       "      <td>1.054886</td>\n",
       "      <td>4.742057</td>\n",
       "      <td>4.436436</td>\n",
       "      <td>145.564380</td>\n",
       "      <td>4.623752</td>\n",
       "      <td>0.621101</td>\n",
       "      <td>13.279730</td>\n",
       "      <td>1.784433</td>\n",
       "      <td>0.345056</td>\n",
       "      <td>5.540615</td>\n",
       "      <td>6.457478</td>\n",
       "      <td>15.083881</td>\n",
       "      <td>1.537964</td>\n",
       "      <td>7.610952</td>\n",
       "      <td>16.542975</td>\n",
       "      <td>8.153183</td>\n",
       "      <td>3.825193</td>\n",
       "      <td>1.054886</td>\n",
       "      <td>5.668779</td>\n",
       "      <td>1.005592</td>\n",
       "      <td>6.743382</td>\n",
       "      <td>33.233832</td>\n",
       "      <td>176.609571</td>\n",
       "      <td>7.285613</td>\n",
       "      <td>3.933640</td>\n",
       "      <td>2.395675</td>\n",
       "      <td>3.647736</td>\n",
       "      <td>16.247213</td>\n",
       "      <td>1.488671</td>\n",
       "      <td>1.035168</td>\n",
       "      <td>9.178492</td>\n",
       "      <td>3.864628</td>\n",
       "      <td>59.990468</td>\n",
       "      <td>6.772958</td>\n",
       "      <td>8.616544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_760</th>\n",
       "      <td>5.483920</td>\n",
       "      <td>1.511858</td>\n",
       "      <td>112.331028</td>\n",
       "      <td>1.759253</td>\n",
       "      <td>4.782968</td>\n",
       "      <td>40.517787</td>\n",
       "      <td>9.648401</td>\n",
       "      <td>7.820428</td>\n",
       "      <td>4.246946</td>\n",
       "      <td>12.356001</td>\n",
       "      <td>8.755031</td>\n",
       "      <td>26.732393</td>\n",
       "      <td>81.447898</td>\n",
       "      <td>19.434244</td>\n",
       "      <td>92.924273</td>\n",
       "      <td>6.033687</td>\n",
       "      <td>26.251348</td>\n",
       "      <td>5.373967</td>\n",
       "      <td>32.188825</td>\n",
       "      <td>8.452659</td>\n",
       "      <td>29.371272</td>\n",
       "      <td>72.486705</td>\n",
       "      <td>227.960655</td>\n",
       "      <td>30.360852</td>\n",
       "      <td>6.074919</td>\n",
       "      <td>2.295275</td>\n",
       "      <td>3.312343</td>\n",
       "      <td>213.350612</td>\n",
       "      <td>47.321146</td>\n",
       "      <td>2.982483</td>\n",
       "      <td>1.772997</td>\n",
       "      <td>3.394808</td>\n",
       "      <td>12.575907</td>\n",
       "      <td>7.820428</td>\n",
       "      <td>3.271110</td>\n",
       "      <td>0.852138</td>\n",
       "      <td>26.443766</td>\n",
       "      <td>8.768775</td>\n",
       "      <td>19.791592</td>\n",
       "      <td>6.528476</td>\n",
       "      <td>...</td>\n",
       "      <td>1.346928</td>\n",
       "      <td>2.693856</td>\n",
       "      <td>9.565936</td>\n",
       "      <td>6.143640</td>\n",
       "      <td>3.147413</td>\n",
       "      <td>3.449784</td>\n",
       "      <td>2.900018</td>\n",
       "      <td>6.817104</td>\n",
       "      <td>56.969548</td>\n",
       "      <td>4.920410</td>\n",
       "      <td>0.783417</td>\n",
       "      <td>8.782519</td>\n",
       "      <td>1.264463</td>\n",
       "      <td>1.580579</td>\n",
       "      <td>4.549317</td>\n",
       "      <td>8.974937</td>\n",
       "      <td>10.349353</td>\n",
       "      <td>3.449784</td>\n",
       "      <td>10.005749</td>\n",
       "      <td>2.886274</td>\n",
       "      <td>7.710474</td>\n",
       "      <td>7.270661</td>\n",
       "      <td>1.979159</td>\n",
       "      <td>3.449784</td>\n",
       "      <td>2.473949</td>\n",
       "      <td>17.496317</td>\n",
       "      <td>24.835699</td>\n",
       "      <td>64.460115</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.538897</td>\n",
       "      <td>6.927057</td>\n",
       "      <td>2.858785</td>\n",
       "      <td>3.532249</td>\n",
       "      <td>3.449784</td>\n",
       "      <td>6.116152</td>\n",
       "      <td>14.733741</td>\n",
       "      <td>7.751707</td>\n",
       "      <td>60.075728</td>\n",
       "      <td>14.390137</td>\n",
       "      <td>8.383938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_538</th>\n",
       "      <td>5.503522</td>\n",
       "      <td>2.084390</td>\n",
       "      <td>77.835519</td>\n",
       "      <td>2.916318</td>\n",
       "      <td>1.800986</td>\n",
       "      <td>30.296795</td>\n",
       "      <td>7.048530</td>\n",
       "      <td>6.728558</td>\n",
       "      <td>3.154012</td>\n",
       "      <td>10.184257</td>\n",
       "      <td>5.137839</td>\n",
       "      <td>27.983853</td>\n",
       "      <td>70.247607</td>\n",
       "      <td>15.907189</td>\n",
       "      <td>74.014137</td>\n",
       "      <td>5.402959</td>\n",
       "      <td>27.801012</td>\n",
       "      <td>5.576658</td>\n",
       "      <td>28.486666</td>\n",
       "      <td>10.997901</td>\n",
       "      <td>20.249668</td>\n",
       "      <td>30.269369</td>\n",
       "      <td>138.145705</td>\n",
       "      <td>10.376241</td>\n",
       "      <td>13.630815</td>\n",
       "      <td>3.135727</td>\n",
       "      <td>2.248947</td>\n",
       "      <td>85.158311</td>\n",
       "      <td>24.445875</td>\n",
       "      <td>2.047822</td>\n",
       "      <td>0.822786</td>\n",
       "      <td>4.735588</td>\n",
       "      <td>21.968376</td>\n",
       "      <td>7.423355</td>\n",
       "      <td>3.693393</td>\n",
       "      <td>0.822786</td>\n",
       "      <td>23.065423</td>\n",
       "      <td>8.639249</td>\n",
       "      <td>15.139255</td>\n",
       "      <td>3.400847</td>\n",
       "      <td>...</td>\n",
       "      <td>1.362167</td>\n",
       "      <td>2.532351</td>\n",
       "      <td>6.289739</td>\n",
       "      <td>3.473984</td>\n",
       "      <td>3.519694</td>\n",
       "      <td>7.999305</td>\n",
       "      <td>2.925460</td>\n",
       "      <td>5.613226</td>\n",
       "      <td>34.739837</td>\n",
       "      <td>5.732073</td>\n",
       "      <td>1.426162</td>\n",
       "      <td>9.498603</td>\n",
       "      <td>0.886780</td>\n",
       "      <td>4.662452</td>\n",
       "      <td>5.000708</td>\n",
       "      <td>13.246848</td>\n",
       "      <td>11.509857</td>\n",
       "      <td>7.523917</td>\n",
       "      <td>10.833344</td>\n",
       "      <td>2.962028</td>\n",
       "      <td>8.118151</td>\n",
       "      <td>5.677221</td>\n",
       "      <td>2.806613</td>\n",
       "      <td>5.924056</td>\n",
       "      <td>3.172296</td>\n",
       "      <td>7.679332</td>\n",
       "      <td>23.476816</td>\n",
       "      <td>50.308769</td>\n",
       "      <td>0.831928</td>\n",
       "      <td>5.741215</td>\n",
       "      <td>4.196207</td>\n",
       "      <td>2.568920</td>\n",
       "      <td>3.812240</td>\n",
       "      <td>3.373421</td>\n",
       "      <td>3.885377</td>\n",
       "      <td>9.050642</td>\n",
       "      <td>6.326307</td>\n",
       "      <td>26.575975</td>\n",
       "      <td>14.124486</td>\n",
       "      <td>7.286224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGGA_909</th>\n",
       "      <td>3.713109</td>\n",
       "      <td>1.333164</td>\n",
       "      <td>72.593265</td>\n",
       "      <td>3.614357</td>\n",
       "      <td>1.738051</td>\n",
       "      <td>37.219972</td>\n",
       "      <td>5.243780</td>\n",
       "      <td>10.882571</td>\n",
       "      <td>4.700639</td>\n",
       "      <td>10.477684</td>\n",
       "      <td>4.868519</td>\n",
       "      <td>25.270870</td>\n",
       "      <td>70.124442</td>\n",
       "      <td>13.766156</td>\n",
       "      <td>80.325618</td>\n",
       "      <td>5.905424</td>\n",
       "      <td>32.766216</td>\n",
       "      <td>6.626320</td>\n",
       "      <td>29.369116</td>\n",
       "      <td>15.602960</td>\n",
       "      <td>17.133630</td>\n",
       "      <td>26.446030</td>\n",
       "      <td>196.152908</td>\n",
       "      <td>2.054061</td>\n",
       "      <td>16.827496</td>\n",
       "      <td>4.009368</td>\n",
       "      <td>3.634107</td>\n",
       "      <td>73.284536</td>\n",
       "      <td>33.210604</td>\n",
       "      <td>2.231816</td>\n",
       "      <td>1.580047</td>\n",
       "      <td>6.883078</td>\n",
       "      <td>9.401277</td>\n",
       "      <td>12.571246</td>\n",
       "      <td>4.364879</td>\n",
       "      <td>1.757802</td>\n",
       "      <td>29.497495</td>\n",
       "      <td>8.650755</td>\n",
       "      <td>29.112358</td>\n",
       "      <td>4.266126</td>\n",
       "      <td>...</td>\n",
       "      <td>1.757802</td>\n",
       "      <td>1.540545</td>\n",
       "      <td>5.836297</td>\n",
       "      <td>2.133063</td>\n",
       "      <td>4.414255</td>\n",
       "      <td>5.204278</td>\n",
       "      <td>2.419446</td>\n",
       "      <td>6.873203</td>\n",
       "      <td>37.605109</td>\n",
       "      <td>6.725073</td>\n",
       "      <td>1.876305</td>\n",
       "      <td>14.299422</td>\n",
       "      <td>0.987529</td>\n",
       "      <td>4.601886</td>\n",
       "      <td>7.929859</td>\n",
       "      <td>13.943911</td>\n",
       "      <td>11.297333</td>\n",
       "      <td>6.369563</td>\n",
       "      <td>15.168447</td>\n",
       "      <td>3.169968</td>\n",
       "      <td>10.023421</td>\n",
       "      <td>6.280685</td>\n",
       "      <td>3.624232</td>\n",
       "      <td>5.648667</td>\n",
       "      <td>2.774957</td>\n",
       "      <td>11.958978</td>\n",
       "      <td>32.104572</td>\n",
       "      <td>55.983026</td>\n",
       "      <td>1.264037</td>\n",
       "      <td>7.752104</td>\n",
       "      <td>4.779641</td>\n",
       "      <td>3.011964</td>\n",
       "      <td>5.707918</td>\n",
       "      <td>2.853959</td>\n",
       "      <td>4.325378</td>\n",
       "      <td>10.043171</td>\n",
       "      <td>5.658542</td>\n",
       "      <td>59.261622</td>\n",
       "      <td>17.992780</td>\n",
       "      <td>8.048362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 17717 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0         1           2      ...      24323      24324      24325\n",
       "CGGA_747   13.161391  2.362044  114.939478  ...  97.664527  17.915506  20.367628\n",
       "CGGA_1320  10.489705  3.884346   33.509877  ...  59.990468   6.772958   8.616544\n",
       "CGGA_760    5.483920  1.511858  112.331028  ...  60.075728  14.390137   8.383938\n",
       "CGGA_538    5.503522  2.084390   77.835519  ...  26.575975  14.124486   7.286224\n",
       "CGGA_909    3.713109  1.333164   72.593265  ...  59.261622  17.992780   8.048362\n",
       "\n",
       "[5 rows x 17717 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing data after applying TMM\n",
    "# Expect genes as rows and samples as columns\n",
    "normalized_tmm_test_genes = gmn.tmm_normalization(test_genes.T).T\n",
    "normalized_tmm_test_genes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "IQpaI55CUThz",
    "outputId": "a2521d39-4f5d-4755-91c5-b82cdfb851fd"
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>7</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
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       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>19</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>35</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>50</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>...</th>\n",
       "      <th>24282</th>\n",
       "      <th>24283</th>\n",
       "      <th>24284</th>\n",
       "      <th>24285</th>\n",
       "      <th>24287</th>\n",
       "      <th>24288</th>\n",
       "      <th>24289</th>\n",
       "      <th>24290</th>\n",
       "      <th>24291</th>\n",
       "      <th>24292</th>\n",
       "      <th>24293</th>\n",
       "      <th>24294</th>\n",
       "      <th>24295</th>\n",
       "      <th>24296</th>\n",
       "      <th>24297</th>\n",
       "      <th>24298</th>\n",
       "      <th>24299</th>\n",
       "      <th>24300</th>\n",
       "      <th>24301</th>\n",
       "      <th>24302</th>\n",
       "      <th>24304</th>\n",
       "      <th>24305</th>\n",
       "      <th>24307</th>\n",
       "      <th>24308</th>\n",
       "      <th>24309</th>\n",
       "      <th>24310</th>\n",
       "      <th>24311</th>\n",
       "      <th>24312</th>\n",
       "      <th>24313</th>\n",
       "      <th>24314</th>\n",
       "      <th>24315</th>\n",
       "      <th>24316</th>\n",
       "      <th>24317</th>\n",
       "      <th>24318</th>\n",
       "      <th>24319</th>\n",
       "      <th>24320</th>\n",
       "      <th>24322</th>\n",
       "      <th>24323</th>\n",
       "      <th>24324</th>\n",
       "      <th>24325</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.718240</td>\n",
       "      <td>1.240036</td>\n",
       "      <td>6.844731</td>\n",
       "      <td>0.444856</td>\n",
       "      <td>0.240036</td>\n",
       "      <td>5.888774</td>\n",
       "      <td>2.736771</td>\n",
       "      <td>3.660032</td>\n",
       "      <td>2.276256</td>\n",
       "      <td>3.498223</td>\n",
       "      <td>2.647412</td>\n",
       "      <td>4.130532</td>\n",
       "      <td>6.042142</td>\n",
       "      <td>4.326060</td>\n",
       "      <td>6.313588</td>\n",
       "      <td>2.661174</td>\n",
       "      <td>4.956841</td>\n",
       "      <td>3.321734</td>\n",
       "      <td>4.909582</td>\n",
       "      <td>3.732432</td>\n",
       "      <td>5.243089</td>\n",
       "      <td>2.401787</td>\n",
       "      <td>6.365997</td>\n",
       "      <td>3.042142</td>\n",
       "      <td>2.686068</td>\n",
       "      <td>2.363018</td>\n",
       "      <td>-0.598213</td>\n",
       "      <td>5.352453</td>\n",
       "      <td>5.082759</td>\n",
       "      <td>0.334673</td>\n",
       "      <td>-1.513324</td>\n",
       "      <td>1.853248</td>\n",
       "      <td>2.566846</td>\n",
       "      <td>2.357393</td>\n",
       "      <td>1.775245</td>\n",
       "      <td>0.766784</td>\n",
       "      <td>4.966110</td>\n",
       "      <td>3.857239</td>\n",
       "      <td>3.784706</td>\n",
       "      <td>0.486676</td>\n",
       "      <td>...</td>\n",
       "      <td>1.171174</td>\n",
       "      <td>-1.216342</td>\n",
       "      <td>3.191864</td>\n",
       "      <td>1.942355</td>\n",
       "      <td>2.399052</td>\n",
       "      <td>-1.285055</td>\n",
       "      <td>2.777353</td>\n",
       "      <td>1.972103</td>\n",
       "      <td>5.259014</td>\n",
       "      <td>2.787846</td>\n",
       "      <td>0.706121</td>\n",
       "      <td>3.218480</td>\n",
       "      <td>0.177572</td>\n",
       "      <td>1.600567</td>\n",
       "      <td>2.829068</td>\n",
       "      <td>4.075926</td>\n",
       "      <td>3.518525</td>\n",
       "      <td>2.273272</td>\n",
       "      <td>3.304299</td>\n",
       "      <td>2.118944</td>\n",
       "      <td>3.633517</td>\n",
       "      <td>3.463301</td>\n",
       "      <td>1.450150</td>\n",
       "      <td>3.363018</td>\n",
       "      <td>0.071638</td>\n",
       "      <td>4.578980</td>\n",
       "      <td>4.472437</td>\n",
       "      <td>7.403153</td>\n",
       "      <td>-1.183175</td>\n",
       "      <td>2.583805</td>\n",
       "      <td>2.923447</td>\n",
       "      <td>2.544745</td>\n",
       "      <td>5.482484</td>\n",
       "      <td>1.741097</td>\n",
       "      <td>2.331808</td>\n",
       "      <td>4.150183</td>\n",
       "      <td>3.075069</td>\n",
       "      <td>6.609763</td>\n",
       "      <td>4.163137</td>\n",
       "      <td>4.348206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.390902</td>\n",
       "      <td>1.957672</td>\n",
       "      <td>5.066514</td>\n",
       "      <td>1.475171</td>\n",
       "      <td>0.535292</td>\n",
       "      <td>5.727400</td>\n",
       "      <td>2.070329</td>\n",
       "      <td>2.329973</td>\n",
       "      <td>3.515529</td>\n",
       "      <td>3.596539</td>\n",
       "      <td>4.354515</td>\n",
       "      <td>4.921052</td>\n",
       "      <td>5.850827</td>\n",
       "      <td>3.260432</td>\n",
       "      <td>5.797099</td>\n",
       "      <td>1.968615</td>\n",
       "      <td>5.417769</td>\n",
       "      <td>2.616391</td>\n",
       "      <td>4.425732</td>\n",
       "      <td>4.530992</td>\n",
       "      <td>4.504292</td>\n",
       "      <td>3.485367</td>\n",
       "      <td>6.933323</td>\n",
       "      <td>4.682687</td>\n",
       "      <td>3.805262</td>\n",
       "      <td>2.926207</td>\n",
       "      <td>1.827473</td>\n",
       "      <td>6.329089</td>\n",
       "      <td>3.809326</td>\n",
       "      <td>5.118618</td>\n",
       "      <td>-1.619986</td>\n",
       "      <td>0.786831</td>\n",
       "      <td>1.036060</td>\n",
       "      <td>2.894041</td>\n",
       "      <td>2.070329</td>\n",
       "      <td>0.218263</td>\n",
       "      <td>6.207910</td>\n",
       "      <td>2.431017</td>\n",
       "      <td>3.176398</td>\n",
       "      <td>1.946645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.554788</td>\n",
       "      <td>3.255973</td>\n",
       "      <td>4.379331</td>\n",
       "      <td>4.185807</td>\n",
       "      <td>0.433652</td>\n",
       "      <td>0.077087</td>\n",
       "      <td>2.245513</td>\n",
       "      <td>2.149401</td>\n",
       "      <td>7.185514</td>\n",
       "      <td>2.209064</td>\n",
       "      <td>-0.687100</td>\n",
       "      <td>3.731154</td>\n",
       "      <td>0.835466</td>\n",
       "      <td>-1.535097</td>\n",
       "      <td>2.470046</td>\n",
       "      <td>2.690971</td>\n",
       "      <td>3.914936</td>\n",
       "      <td>0.621022</td>\n",
       "      <td>2.928077</td>\n",
       "      <td>4.048147</td>\n",
       "      <td>3.027363</td>\n",
       "      <td>1.935533</td>\n",
       "      <td>0.077087</td>\n",
       "      <td>2.503038</td>\n",
       "      <td>0.008045</td>\n",
       "      <td>2.753472</td>\n",
       "      <td>5.054581</td>\n",
       "      <td>7.464420</td>\n",
       "      <td>2.865050</td>\n",
       "      <td>1.975865</td>\n",
       "      <td>1.260432</td>\n",
       "      <td>1.867001</td>\n",
       "      <td>4.022120</td>\n",
       "      <td>0.574025</td>\n",
       "      <td>0.049865</td>\n",
       "      <td>3.198257</td>\n",
       "      <td>1.950330</td>\n",
       "      <td>5.906661</td>\n",
       "      <td>2.759786</td>\n",
       "      <td>3.107109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.455208</td>\n",
       "      <td>0.596322</td>\n",
       "      <td>6.811613</td>\n",
       "      <td>0.814963</td>\n",
       "      <td>2.257906</td>\n",
       "      <td>5.340483</td>\n",
       "      <td>3.270290</td>\n",
       "      <td>2.967247</td>\n",
       "      <td>2.086426</td>\n",
       "      <td>3.627140</td>\n",
       "      <td>3.130112</td>\n",
       "      <td>4.740517</td>\n",
       "      <td>6.347806</td>\n",
       "      <td>4.280529</td>\n",
       "      <td>6.537984</td>\n",
       "      <td>2.593040</td>\n",
       "      <td>4.714320</td>\n",
       "      <td>2.425987</td>\n",
       "      <td>5.008488</td>\n",
       "      <td>3.079405</td>\n",
       "      <td>4.876334</td>\n",
       "      <td>6.179645</td>\n",
       "      <td>7.832641</td>\n",
       "      <td>4.924140</td>\n",
       "      <td>2.602865</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>1.727852</td>\n",
       "      <td>7.737082</td>\n",
       "      <td>5.564413</td>\n",
       "      <td>1.576514</td>\n",
       "      <td>0.826190</td>\n",
       "      <td>1.763330</td>\n",
       "      <td>3.652591</td>\n",
       "      <td>2.967247</td>\n",
       "      <td>1.709780</td>\n",
       "      <td>-0.230841</td>\n",
       "      <td>4.724856</td>\n",
       "      <td>3.132375</td>\n",
       "      <td>4.306816</td>\n",
       "      <td>2.706746</td>\n",
       "      <td>...</td>\n",
       "      <td>0.429673</td>\n",
       "      <td>1.429673</td>\n",
       "      <td>3.257906</td>\n",
       "      <td>2.619094</td>\n",
       "      <td>1.654166</td>\n",
       "      <td>1.786506</td>\n",
       "      <td>1.536062</td>\n",
       "      <td>2.769159</td>\n",
       "      <td>5.832119</td>\n",
       "      <td>2.298778</td>\n",
       "      <td>-0.352147</td>\n",
       "      <td>3.134635</td>\n",
       "      <td>0.338525</td>\n",
       "      <td>0.660453</td>\n",
       "      <td>2.185650</td>\n",
       "      <td>3.165902</td>\n",
       "      <td>3.371469</td>\n",
       "      <td>1.786506</td>\n",
       "      <td>3.322757</td>\n",
       "      <td>1.529208</td>\n",
       "      <td>2.946820</td>\n",
       "      <td>2.862087</td>\n",
       "      <td>0.984888</td>\n",
       "      <td>1.786506</td>\n",
       "      <td>1.306816</td>\n",
       "      <td>4.128979</td>\n",
       "      <td>4.634343</td>\n",
       "      <td>6.010335</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.469599</td>\n",
       "      <td>2.792243</td>\n",
       "      <td>1.515402</td>\n",
       "      <td>1.820587</td>\n",
       "      <td>1.786506</td>\n",
       "      <td>2.612624</td>\n",
       "      <td>3.881052</td>\n",
       "      <td>2.954514</td>\n",
       "      <td>5.908710</td>\n",
       "      <td>3.847008</td>\n",
       "      <td>3.067628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.460355</td>\n",
       "      <td>1.059625</td>\n",
       "      <td>6.282357</td>\n",
       "      <td>1.544148</td>\n",
       "      <td>0.848787</td>\n",
       "      <td>4.921093</td>\n",
       "      <td>2.817322</td>\n",
       "      <td>2.750297</td>\n",
       "      <td>1.657188</td>\n",
       "      <td>3.348269</td>\n",
       "      <td>2.361162</td>\n",
       "      <td>4.806523</td>\n",
       "      <td>6.134377</td>\n",
       "      <td>3.991607</td>\n",
       "      <td>6.209729</td>\n",
       "      <td>2.433750</td>\n",
       "      <td>4.797065</td>\n",
       "      <td>2.479401</td>\n",
       "      <td>4.832215</td>\n",
       "      <td>3.459156</td>\n",
       "      <td>4.339826</td>\n",
       "      <td>4.919787</td>\n",
       "      <td>7.110047</td>\n",
       "      <td>3.375212</td>\n",
       "      <td>3.768800</td>\n",
       "      <td>1.648800</td>\n",
       "      <td>1.169250</td>\n",
       "      <td>6.412075</td>\n",
       "      <td>4.611519</td>\n",
       "      <td>1.034090</td>\n",
       "      <td>-0.281412</td>\n",
       "      <td>2.243544</td>\n",
       "      <td>4.457356</td>\n",
       "      <td>2.892071</td>\n",
       "      <td>1.884947</td>\n",
       "      <td>-0.281412</td>\n",
       "      <td>4.527660</td>\n",
       "      <td>3.110906</td>\n",
       "      <td>3.920222</td>\n",
       "      <td>1.765894</td>\n",
       "      <td>...</td>\n",
       "      <td>0.445904</td>\n",
       "      <td>1.340478</td>\n",
       "      <td>2.653000</td>\n",
       "      <td>1.796591</td>\n",
       "      <td>1.815450</td>\n",
       "      <td>2.999875</td>\n",
       "      <td>1.548663</td>\n",
       "      <td>2.488830</td>\n",
       "      <td>5.118519</td>\n",
       "      <td>2.519057</td>\n",
       "      <td>0.512138</td>\n",
       "      <td>3.247715</td>\n",
       "      <td>-0.173352</td>\n",
       "      <td>2.221089</td>\n",
       "      <td>2.322132</td>\n",
       "      <td>3.727577</td>\n",
       "      <td>3.524798</td>\n",
       "      <td>2.911484</td>\n",
       "      <td>3.437407</td>\n",
       "      <td>1.566585</td>\n",
       "      <td>3.021151</td>\n",
       "      <td>2.505185</td>\n",
       "      <td>1.488830</td>\n",
       "      <td>2.566585</td>\n",
       "      <td>1.665527</td>\n",
       "      <td>2.940981</td>\n",
       "      <td>4.553165</td>\n",
       "      <td>5.652738</td>\n",
       "      <td>-0.265470</td>\n",
       "      <td>2.521356</td>\n",
       "      <td>2.069086</td>\n",
       "      <td>1.361162</td>\n",
       "      <td>1.930639</td>\n",
       "      <td>1.754212</td>\n",
       "      <td>1.958054</td>\n",
       "      <td>3.178020</td>\n",
       "      <td>2.661364</td>\n",
       "      <td>4.732051</td>\n",
       "      <td>3.820127</td>\n",
       "      <td>2.865171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.892628</td>\n",
       "      <td>0.414855</td>\n",
       "      <td>6.181764</td>\n",
       "      <td>1.853739</td>\n",
       "      <td>0.797471</td>\n",
       "      <td>5.218005</td>\n",
       "      <td>2.390607</td>\n",
       "      <td>3.443948</td>\n",
       "      <td>2.232857</td>\n",
       "      <td>3.389248</td>\n",
       "      <td>2.283483</td>\n",
       "      <td>4.659403</td>\n",
       "      <td>6.131845</td>\n",
       "      <td>3.783054</td>\n",
       "      <td>6.327788</td>\n",
       "      <td>2.562041</td>\n",
       "      <td>5.034137</td>\n",
       "      <td>2.728208</td>\n",
       "      <td>4.876228</td>\n",
       "      <td>3.963748</td>\n",
       "      <td>4.098759</td>\n",
       "      <td>4.724979</td>\n",
       "      <td>7.615835</td>\n",
       "      <td>1.038479</td>\n",
       "      <td>4.072749</td>\n",
       "      <td>2.003375</td>\n",
       "      <td>1.861601</td>\n",
       "      <td>6.195437</td>\n",
       "      <td>5.053572</td>\n",
       "      <td>1.158218</td>\n",
       "      <td>0.659967</td>\n",
       "      <td>2.783054</td>\n",
       "      <td>3.232857</td>\n",
       "      <td>3.652056</td>\n",
       "      <td>2.125942</td>\n",
       "      <td>0.813772</td>\n",
       "      <td>4.882521</td>\n",
       "      <td>3.112826</td>\n",
       "      <td>4.863560</td>\n",
       "      <td>2.092927</td>\n",
       "      <td>...</td>\n",
       "      <td>0.813772</td>\n",
       "      <td>0.623441</td>\n",
       "      <td>2.545053</td>\n",
       "      <td>1.092927</td>\n",
       "      <td>2.142170</td>\n",
       "      <td>2.379698</td>\n",
       "      <td>1.274677</td>\n",
       "      <td>2.780982</td>\n",
       "      <td>5.232857</td>\n",
       "      <td>2.749550</td>\n",
       "      <td>0.907895</td>\n",
       "      <td>3.837885</td>\n",
       "      <td>-0.018105</td>\n",
       "      <td>2.202225</td>\n",
       "      <td>2.987295</td>\n",
       "      <td>3.801563</td>\n",
       "      <td>3.497910</td>\n",
       "      <td>2.671194</td>\n",
       "      <td>3.923001</td>\n",
       "      <td>1.664468</td>\n",
       "      <td>3.325303</td>\n",
       "      <td>2.650922</td>\n",
       "      <td>1.857675</td>\n",
       "      <td>2.497910</td>\n",
       "      <td>1.472465</td>\n",
       "      <td>3.580022</td>\n",
       "      <td>5.004707</td>\n",
       "      <td>5.806918</td>\n",
       "      <td>0.338039</td>\n",
       "      <td>2.954588</td>\n",
       "      <td>2.256902</td>\n",
       "      <td>1.590704</td>\n",
       "      <td>2.512965</td>\n",
       "      <td>1.512965</td>\n",
       "      <td>2.112826</td>\n",
       "      <td>3.328143</td>\n",
       "      <td>2.500430</td>\n",
       "      <td>5.889026</td>\n",
       "      <td>4.169346</td>\n",
       "      <td>3.008695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 17717 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0         1         2      ...     24323     24324     24325\n",
       "0  3.718240  1.240036  6.844731  ...  6.609763  4.163137  4.348206\n",
       "1  3.390902  1.957672  5.066514  ...  5.906661  2.759786  3.107109\n",
       "2  2.455208  0.596322  6.811613  ...  5.908710  3.847008  3.067628\n",
       "3  2.460355  1.059625  6.282357  ...  4.732051  3.820127  2.865171\n",
       "4  1.892628  0.414855  6.181764  ...  5.889026  4.169346  3.008695\n",
       "\n",
       "[5 rows x 17717 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_normalized_tmm_test_genes = pd.DataFrame(np.log2(normalized_tmm_test_genes, out=np.zeros_like(normalized_tmm_test_genes), where=(normalized_tmm_test_genes!=0)))\n",
    "log_normalized_tmm_test_genes.columns = gene_indices\n",
    "log_normalized_tmm_test_genes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NmsYFNyJUTh1"
   },
   "source": [
    "#### Visualize Distribution of Counts per Gene after Normalized and Log-transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "eWcQsoGaUTh1",
    "outputId": "92a9c05f-9612-4758-a6fe-35cd4df0a876"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YDAaDoS9YWlra55w7OPTbyDH5LVu2YM+eygmgDAaDwWAYShDRlbHfzFxvMBgMBsOIwpi8wWAwGAwjCmPyBoPBYDCMKIzJGwwGg8EwojAmbzAYDAbDiMKYvMFgMBgMIwpj8gaDwWAwjCiMyRsMBoPBMKIwJm8wGAwGw4jCmLzBYDAYDCMKY/IGg8FgMIwojMkbDAaDwTCiMCZvMBgMBsOIwpi8wWAwGAwjCmPyBoPBYDCMKIzJGwwGg8EwojAmbzAYDAbDiMKYvMFgqAUf/Shw1FH+r8FgaAaMyRsMhlqwsABceqn/azAYmgFj8gaDoRbs3AkceaT/azAYmoHxQVfAYDCMBk46yV8Gg6E5ME3eYDAYDIYRhTF5g8FgMBhGFMbkDQZDLdi7Fzj5ZP/XYDA0A+aTNxgMtWDXLuAzn/H/n3baYOtiMBg8jMkbDIZaMD/f/tdgMAweZq43GAwGg2FEYZq8wWCoBQsLwDnnAPv2AWefPejaGAwGwDR5g8FQI5wbdA0MBoOEafIGg6EW7NwJHHSQ+eQNhibBNHmDwWAwGEYUpskbDIZaYFvoDIbmwZi8wWCoBbaFzmBoHozJGwyGWrB9u2nwBkPTYD55g8FgMBhGFMbkDQaDwWAYURiTNxgMBoNhRGFM3mAwGAyGEYUxeYPBUAvsqFmDoXmw6HqDwVALLHe9wdA8mCZvMBhqg+WuNxiaBWPyBoOhFuzYAbRa/q/BYGgGjMkbDIZasHs3sH+//2swGJoB88kbDIZaYGltDYbmwZi8wWCoBZbW1mBoHsxcbzAYDAbDiMKYvKEj2J5og8FgaD6MyRs6Ap8dvmvXoGtiaApM8DMYmgfzyRs6ggVZGTRY8APMN28wNAXG5A0dwYKsDBom+BkMzYMxeYPBUAtM8DMYmgfzyRsMBoPBMKIwJm8wGGqBBd4ZDM2DmesNBkMtsMA7g6F5MCZvMBhqgQXeGQzNg5nrDQZDLbjoIuD88/1fg8HQDBiTNxgMtWBhAbj0Uv/XYDA0AwNh8kQ0RkQXEtFns88PJ6JvEtFlRPRxIlqdfb8m+3xZ9vuWQdTXYDCUY+dO4Mgj/V+DwdAMDEqTfyUAadR7G4B3OeceAeDnAF6Wff8yAD/Pvn9Xdp/BYGggTjoJuPhi/9dgMDQDfWfyRHQYgGcCOD37TACOB3BWdsuHAMxm//929hnZ70/J7jcYDAaDwVCCQWjy7wbwagD3Z58nAex3zt2bfb4awMOy/x8G4CoAyH6/KbvfYDAYDAZDCfrK5InoWQB+5pxbqrncU4hoDxHtuf766+ss2mBogyV8MRgMw4R+a/K/CuC3iOgKAGfCm+n/FsBmIuI9+4cBuCb7/xoAhwNA9vsmADfoQp1z73POHeOcO+bggw/ubQsMKxp2xK7BYBgm9JXJO+de65w7zDm3BcALAHzFOXcSgPMA7MhuewmAf8v+/3T2GdnvX3HOuT5W2WBow/w88OxnW8IXg8EwHGhKxrs/A3AmEb0ZwIUA3p99/34AHyGiywDcCC8YGAwDg520ZjAYhgkDY/LOud0Admf//wjA4wP33AngeX2tmMFgMBgMIwLLeGcYSlgAnMFgMJSjKeZ6g6ES7MQzg8FgKIcxecNQwk48MxgMhnIYkzcMJSwAzmAwGMphPnmDwWAwGEYUxuQNBoPBYBhRGJM3GAwGg2FEYUzeYDDUgo9+FDjqKP/XYDA0A8bkDQZDLVhYAC691P81GAzNgDF5g8FQC3buBI480v81GAzNgDF5g8FQC046Cbj4Yv/XYOgXLPtlMYzJDwlsIhsMBsNy2PHPxbBkOEMCS+NqMBgMy2HZL4thTH5IYBPZYDAYlsOyXxbDzPVDAp7I27cPuiYrB+YiMRgMww7T5A2GCMxFYjAYhh3G5A2GCMxFYjAYhh3G5A2GCMzXZzAYhh3mkzcYDIYVgH7HmFhMSzNgTN5giMCIlGGU0O/95LZ/vRkwc73BEIEF3hlGCf2OMbGYlmaAnHODrkOtOOaYY9yePXsG8u69ez1jmJ+3rW6jAB7PmRlg924bV4PB0EwQ0ZJz7pjQb2aurxFmnhotcODd7t02rgaDYThh5voaYeap0YSNq8FgGFaYud5gMBgMhiGGmesbDIvgNhgMvYTRmJUNM9cPGBbBbTAYeomFBeCcc4B9+4Czzx50bQz9hjH5AcP8vQaDodcYMa+soQKMyQ8YljrVYDD0Ejt3AgcdZIrESoX55A21w3yAKxM27gZD82CavKF2WJzByoSNezNh47KyYUzeUDsszmBlwsa9mbBxWdmwffIGg8FgGBmsxPTitk++TzCfpMFgMAwWll68Hcbka4RNLoPB0FSsFCVkfh549rPNPcEwn3yNMN+XwWBoKlZKUhzbltwO0+RrBE+uleIHMhgMzYbW3kcsBKsSVoolQ8M0eYPBYBhRsAtx3z7/+elP98lxViJW6lZCY/IGg8EwomDX4b59wAUXeF/1SrU0rlR3qjF5g8FgGHHs2GGpbVeqr96YvMFgMIwodu0CPvUp4PzzgTPOWLla/EqGBd4ZDAbDiGJ+Hjj4YOD6621r70pFx0yeiI4iolkiemidFTKMBlZqJKvB0CRs3+4D7Q4+GGi1bE2uRCQxeSL6JyJ6r/j8fADfAfApABcT0a/0qH6GIYUlBjKsVDRNwD39dOAHPwDe8x5bkysRqZr80wF8TXxeAPAxAA8FcE722WB4AJZ1ytBk9JIRN03Avfhi4P77gXvusTXJaJog1kukBt49BMBVAEBEjwTwCAAnOueuI6L3Afh4j+pnGFKs1EhWw3Cgl3umm7RVi5kYkf/L++VXOlbSnvlUJn8jgEOy//8XgOucc9/NPhOAsborZjAYDL1CLxlxkwTcXbuA228HxseBm27KGdsopbXt5NS5JglivUYqk/8CgL8kokMAvBrAJ8RvjwFwRc31MhgMhp6hSYy4l5if99vnrr4auOsuYNUI7qfqRCtfKeMPpPvkTwXwDQAvh/fN/4X47TnwfnmDwWAwNAzbtvlEOGvWAAcc4BPjjBI6if8xn7yCc+4mAP8n8tuTaq2RwWAwGGrBrl3Aued6LX79euCOO4CzzgJOOmnQNasPnWjlK8knn7qF7kdE9MuR3x5DRD+qt1oGg8Fg6BYzM94nf++9wM035wF4Kx0rafdPqrl+C4A1kd8OAHBYLbUZYawk89BKgY2poenYvdub6Rnj46Nnru8EK+lY8CiTJ6KNRHQEER2RfTXFn8V1JIAXALimL7UdYjRt76yhO+zdC7zoRT4vuI2poamYnwcOyfZFOQfcfbdn/IaVgyKf/B8DeAMAl12xTRcEH5hnKEC/t2x0sq3EkI5du4Brr/Xmz5mZQdfGYAhj+3bg6KOBq67yCXFWr7b5utJQxOTPALAHnol/GsCrAFyi7rkbwCXOuR/3pnqjg35v2VhJgSWDAG9Nuv56rxmNUiCTYbSwYwdw4YXArbf6a9QC7wzFiDJ559wPAPwAAIjoNwDsdc7d0q+KGbrDSkr2MAhs3+6P7mRriaEd/bQkmdUqjo9+FHjFK3zgHZE32RviGMW5lBR455z7KjN4IhojogP01dtqGqpiJQSWDDrwbSX0cafoZwxKne8a9JyqG698pc90d9ttXos/8EB/Kl0/sHcv8Jzn+GtY+nMUY6eS9skT0UYAbwVwInwe+9BGDEtta+grzCXRXPTTklTnu0ZtTt188/Lv+iWU7toFfPGL3oJw0EHD0Z+jaAFNTWv7TwCeBeB0AN+H98UbDLWgUxPZKC7IUUE/Y1DqfNeozamNG4Ebbmj/bu/e/jD6+fn8QJxh6c+yuTSM5nxyCU4aIroRwKudc6f3vkrd4ZhjjnF79uwZdDUMFXDyyV57evazh0PaN6RjGIniKIF98nfc4bfPjY8Dz3rWaB1Qk4o65mJTaRURLTnnjgn9lqrJ3wbg6vqqZDDkGDXtyZBj1Mzfw4aTTvK5648/3jP5e+8F9u8fdK0Ggzrm4jDSqtSMd+8E8AdENIJnGBnK0OtgJAtgG12spPShTcX27cCrXuW1+LEx4PLLhycQrk7UMReHkValavIPA/DLAC4hovMAaFnQOef+rNaaGRqDpmpjTTcFN71+/cBKOtKzidi7F1hY8PvkDzjAB8HdcouflyttXFbqXEzVzHcAuB9eKHgqgOcFLsMIIKS1N1Uba/p2l6L6jdpWLUMzwRHu110HHHqo1+gPPtiy3q0kpB41+/BeV8TQDIS09qZKwE33jxXVr6nWEcNoYX4euOwyb6LfudNnZ9y/f2VnaVxpFrZUc71hhaCMcTZpgTRV+GAU1U/2c5P61DBa2L4deMQjgG9/25vtORFOUwXjfmClCdipyXD+oOwe59w/JpSzFsDX4I+tHQdwlnPuDUT0cABnApgEsATgfzvn7iaiNQA+DGAawA0Anu+cuyKlzobOUMY4R2mBDJK5yn7mbTnA8PepoXmYmQE++Ul/oNLu3TbHmm4BrBupmvw/FPzGG+1LmTyAuwAc75y7lYgmAPwHEX0BwJ8AeJdz7kwiei+AlwHYlf39uXPuEUT0AgBvA/D8xDobOkAZ4xulBZIisPRDEBilPjU0D2ed5ffJH3qozTGg+RbAupGau36VvgAcCOCFAP4HwKMSy3HOuVuzjxPZ5QAcD+Cs7PsPAZjN/v/t7DOy359CRKGUuoaasLDgE2gsLIR/H8YtJDGkBBT2I7hvlPp0paOpAZVjY/7IWZtjKw8d++Sdc/sBfJyINsGnvZ1JeY6IxuBN8o8A8B4APwSw3zl3b3bL1fBb9pD9vSp7371EdBO8SX9fp/U2lGOYTqqSmjZQTetOkehNyzZUQRPdWTt3+tzxNodXJuoIvLscQDCdXgjOufsAPI6INgM4G8BR3VaAiE4BcAoAHHHEEd0Wt6IxbARBElWgfgK70kx7Bg87T8EwKuiKyRPRoQBOhWf0leCc258l1jkOwGYiGs+0+cMAXJPddg2AwwFcTUTjADbBB+Dpst4H4H2Az13fSVsMHsPG1EJE1QjsaGCQgZGdauRNXD8LC8DnPgcsLgJPfCLwznea2X4lIcknT0TXE9HP1LUf3rT+JACvSizn4EyDBxGtg0+scxGA8+AT7gDASwD8W/b/p7PPyH7/iks5UcewYiD92ebbbkdT/cOpGGSyo6YmgKqKvXt9trt77gHuvBP46lfj8TZl5QzzXFrJSNXk34M8ip5xJzyT/6Jzbpl2HcGhAD6U+eVXAfiEc+6zRPR9AGcS0ZsBXAjg/dn97wfwESK6DMCNAF6Q+B6DYcWjG/9wE/buD9L03USNvBPs2uXT2E5N+WNf770XuOqqzsppWqyBIQ2pGe/eWMfLnHPfBnB04PsfAXh84Ps7YSlza0MTCLehf+iGSTaBqA+a0Y7CepFz4JnP9Oltr7mm+JmycgzDhUo+eSJ6KLwP/UB4zfoC59xPelExQ/1oAuE29A/dMEkj6qOxXuQcOOoo4Prr/d9uyjEMF1J98mNE9I8ArgTwSfgtc58EcCURvceOoO0c/fR1jYqfcRTQdB/nsMU3hPqz2z4etfXyzncCv/d7/m8MTZ+XhupIZc5vAvB/APw5gC0A1mV//zz7/o31V21loJ/BRcNGuEcZnYx70wnwIOsX6s9u19aorZeU9jT9ZEdDdaSa638XwOudc+8Q3/0YwF8TkQPwRwD+ou7KrQQM04EwhvrQiTm86ebjQdavaCulHQKUDnPTjB4oZUcaEd0J4Lecc/8e+O03AXzaObe2B/WrjGOOOcbt2bNn0NWoDXx4ybOf3UzCLmGEtLdoev92U79et22Y1pHBUBVEtOScCyalS9XkL4XfvraMyWffX9Jh3QwlGCbJuuma5rBjlIOfej13hmkdGQx1ItUn/2YALyWiLxHRy4noOUT0+0T0JfhkNW/uXRVXNobJLzgzA2ze7P8a6kfTffJl/tyi+vc6yG2Y1lE/0PS5VCdWUltDSD2F7hMAng5gPYC/BfAvAP4OwAEAnu6c+2TPamh4AE2frLt3A/v3+7+G+tH0oKgyRl1U/zImPIi53/T1loJYG5o+lzpFqL2j2tZUJO+Tz/zx/55tlzsIwD7n3P09q5lhGZpuDjeTaG/R9P4tcycMW3Kepq+3FCvApRYAACAASURBVMTa0PS51ClC7R3VtibDORe9APwSgMMKfn8YgF8qKqPf1/T0tBtVLC05Nzfn/w7i+brQlHrUhVEZlyZjEH00CuMyCm1gpLSlSntHqW8A7HExPh39AXgugDsAPKbgnscAuB3Ab8fu6fc1yky+W8zNOXfIIf5vL5C6aHpdj36jk/bIvhq1/jAYeoG618korbsiJl/kkz8FwAecc98tsAJ8F/4QmZd3a1EwhFHmF6ziN+x1cFOq76vbejTNV9pJe2RfjVpmtV6jaePfZIxSX9W9TqqWN7R9GeP+8Lnpnxn7Xdz3TAA3lN3Xr2vUNPkyabNJ0mi/zF9NanMRivpjlEyF/cawjH8TYH2Vo9s11+S+RIEmXxR4dwCAmxPkhJuzew09wMwMcP758W1pTQoq6dc+7vl5f2zmvn1eqm7qtqiiwC3ZV01PctM0NGnONx0rva/k2uo2kHJY+7LIXH81gG0JZTwKQAeHFxpS0LRtaUUmq36Zs7ZvBw46CLjggmZvi0k1B/Zyi0+vx2QQJkzb856Old5XdbrFhrYvYyo+gHfDZ7JbX3DPBgAXA3hX7J5+X8Nmri8zIZX9Lk1IvTIBpwaJzc46t26d/9trLC3598zODr/JW/Zv3WPYaxNjk02YhtFHt/RzVIAOo+sfAq+h/w+AEwCsEb+tBvAMABfCa/wPiZXT72vYmHy3RLJKlHanEz5VkJiddW7t2v4weV2vYUOsH+tuU6+JXKcCSp314rIWF1cGQa8Do8L8hpkG1ImOmLx/DlsB7AFwP4C7M6Z/NYC7su++BeDIojL6fQ0bk69TEq2i9ddZx07qWgeGmVDFxmIU29Ttvallbd1aP8Ef5vHQGOS2zV5Zq0ZpfLpBx0z+gZuAJwN4PYBd2fV6AL+W8my/r2Fj8mWoczE2fUE0vX4xdFLvYW1rEUZNk19a8oLDgQcOv6ao29Lv+SetfKZ914+umfwwXaPG5AfFDAbBuHptaaiKXiX3WVz0BHdxsfs6GrpHkevkwAP9WA17BrXUtvSqDTJeZ5ho2rDAmLyhMlIYl140qcxOP9etJtYrzaDb9sTasXWrc0T+bxOxEvb3y3bEYkmqtrUfQbCdolcCa93v7yVG2YJQxOSTD6gxrCyk7AnV+05T95Hq57rdv1qWS6BTpLaHt9bwdrJ9+/z2PiDcnp07gYUF/7eJKBqPUTi0BWhvBwAQLb+nat4HOV+a1k+pbenVXvB+5dAowrDuc+8aMe4/rJdp8v1Dp9J5Vc23rIymSOhcD/Y7NkWLq4qi8ak7gKpqGXVphL0KBAuV31QM2s1lqA8wc33/kcLIbDG0o0p/DMI0WvaeUR3PmBDViXDVqYunzncaPAbt5qoKG8c4jMkPAHqihyZ+UzTQpqBKf/RjwXfLkEYFsb7uZAx0H9apyRcF0K3EcStDVcUjday6XZs2jtXREZMH8LtVrlg5/b4GweRTFotp8uUYZH+E3t0NQxqUGbrp6GU7Y0ygKZaeYUARI+0Xk61zHEdhTFLQKZO/X133ZVfou/ti5fT7GgSTHxYJkyOJRyEVbN0IjWE3BKIfZuhhQK9937F3VfmtLozCGA66D+t+T9GYjJLQ0CmTXy+uaQA/BPDnAI4CcGD293XZ99Oxcvp9NUWTbyLm5vxWoXXr4oSom7YMqh8GEQBWZ3mjLHxJIjtIJlg3sQ9hWOjAsNSzDhS1tZP52FRBrmufPIDdAE6N/HYqgK+mlNOPqyk++SYg5DIoYybdTOJBLYCmLrxUcP2HPSo/BJ6Di4vOzcw412oNJglQN8R+1JjisK+XurDiNfm2m4DbATwt8tvTANyeUk4/rmFg8r307Ur0OxJ5UOa8pi68ECTT02bs2dnRJb4pVqQYBmnmdy59HQ3LPByWejYRTe27Opj8JQDOjPz2CQCXppTTj2sYmHyVgK5uTnZr6oTsFr3SRKr2VyfpaXk8W616YwCajm5cEv3WPDsVIodVQ+7HvBuVud3UMa6DyT8XwD0AvgvgrQD+b/b3uwDuBfDclHL6cQ0Dk9fBSEWHYMzMODc25v9WLbvXGJRG3av3Vl3AOj1tSj04h/fMzGgQvSrodJxSn+vHPCgSVoaFkel6Vpn3nbaxCcwxVvd+WVZ7iVr2yQPYnmntlwO4I/v7iSYF3bmGMfmUCTE3V3xwhDzYIQX9XEz6XYNayGV91CsmoTX5srHs5B3DiFgbu5kfqWupjvlX5r/v1O3QFFSxJJY9G0KovCbM+1jdu7GWNgWWDGdASFkQZSbfqotjJWjyGmWLtBviX8UkX2aV0fcOmuh1ik592L2OL+nH6X4pboemjG0nmmtZ3TsRtrjPZmYGu4MkVveq1tImwpj8gNCN9sHPLiyM/rGkoWC0Tp7vluiG7is6Ma4bjaUJ5stOUVb3OsyiqWVWqVe3GLaxlfXoV91DQhBbP8bGemsB6XR+VbWWNhF1meuPA3A6gK8B+C99pZbT66tJTD4FZabNDRv8KLVaA6leIer2gW7dOljiGCJwRdphJwSxW4GmCeh03HutMfVag04d7yZq8v2qe8hlVUWT70ZA7FRA6YcFqNeoI/DuqVng3RezTHefA/AVAHdlyXA+kFJOP646mXy/F6uUgpkJTE/315RUt3+uyjt7wfjqCqop09q70ZSawhQkelGnVisssDax/SEMSz1D6FfdYy6rbi0JncYCpKAplpduUAeTvwDAOwCMZUx+e/Z9C8DFo5q7vp+Dz4tj9ep2k1Y3puZOUKXNgyB6Vd/J7ek20UxZv3SjKTWRyPSiTlJj6kTLNAwHYib7biwJddCaqmUPk1BXB5O/CcBTAFCWq/5J4rcXALgkpZx+XMOqybOZq9XKF0dIs4/VJdX/Vtamfpmu6pS6U9qbkmimm37rhlA0kZj0ok6asbNZd5Cui07HdaWjrF/0Og3d329aUzXZ1DAJn3Uw+Z8CeGr2/9UAXip+OwHAbSnl9OMaNp88I6bh8XadMn91qmZUl0aa2oYYOnnP4qIXgmZm2v19KRHtvfTpFUFH/vfTbNrJe3rhOgkR2aJxq7OPqjKjqr+PIupYKyllFAW11olOrXnDJODVweT/HcAfZv9/KMuA91QAvw5gD4BvpJTTj2tYmXwIS0t5vu+FhXp8y3Vv2ZPotak/RBSkRtjtYgyZGas8y+2Rfawjd/vlk++UOfFzrVZ9e4djRDbW7joZa1l/mya/HHX5v/U9+nPTrYbDhDqY/AkAXpH9/zAAe5EfN/vjJiXEGUYmXzQJ69YkeqmZ9HoxhYhC6Lt+CSqx56QwohlKSIjoVNNwrn5/Ij83M1O+raiMiHdalzrvD1kmVqJ2XoSUORq6twy6n63fe4fa98lnvvlHAngsgNWdlNGraxiZfFEyl1iwUgwrTTMJEY5+uRxiz8U0lFi9mLC2WmmJdFLKrIpUhl307jI/bCd9W5ebRd5Txzqq+7lBQvZNne6LToTAYey/JqBWJp8x+IcCGK/6bD+uYWXyMa0ptgB7bepM0Y7kb4NanHXWpddtKLPYdOJ2qKvOncybEBGXgaLa595JG1PcG71g2nVYdJqMmPbOFg/9N7WvO5mPZUJGr4TDUUJdyXBOAPDNbG/8fWIb3WkAXpxaTq+vQTL5XjCXmJRbpBX2mujHtGdJwPu5yAbB6Lp9Zx1abp2oyz/KfcgMXidFSU37ywj1S68ZaSjAMxWDHscYdL3KrDByHOuKSO9Uk+9kvIdF2KoLdfjkfzc7be5DAF6i9sr/KYDzUsrpxzVIJt/PiZVKTDq9rxNNXhLwqgyz04A35+rLpNaNv7EqpIumV4yhn+3R7wxF5/M4d5vDvK5yQvVeWmpP2lO3MDcoVHWlFI1jDGXruFOakFqHTp4ZFdTB5C8B8FfZ/zohzgkAfppSTj+uJmjy3UywKkQhhRGn7g0tW4D6XWUBbwsLzm3a5P+WYW4u7WSvWHtjmdQ6QZ3CU5HmMjXl3KpV+ZGzde0QkO+oojWXaVOdvD+mkWntsKpAyWV1cxpckWYrhcZuhZ+maJT9EDaq0pCysqqOb8oz3azbJqMOJn8ngONdmMkfD+COlHL6cTXBJ9+N2bfKs0X+K2buqRHbZRNbv6tsj2uVPbAxzSy1b+rcilMnUeay9BjMzfnMhmNjeXurmrHLMDvr3Jo1uTbaSb27qUvR3NRCcNH7Yr91a/0p0mxT/k9FpwxjGBlNnXXuZHzLnomts25ocFNQB5P/AYBTXZjJvxrAd1LK6cfVBCbfjZlUE5Wq21lijKXbOpdp8rrevL8/xHiLtLMiP2E/9tWmvqOKRqCtKaFxrZtAtlpekOgkCU+vNPnYfC6yeHTDyFPqV8XqVmbyrhN1asWdoNvye1G/2JyS3xW9N2Yx0zSzinugKYJYHUz+NQBuBvBiABsyJj+dpbrdB+CPUsrpx9UEJl8FRRN3dta58XE/SpOTaQy404lXdiZ7GSRRKiNQqdqZbktsF0Kd2lKqFN+NtabXmJtzbuNG7y5hYaUJ2snMjJ/LExOdW6rqRpV3aAGwl0y/qmWtLnDQ4eRku5BYFb2oX6jMMsUghTbGhPFO6jMo1MHkCcB7sqj6uzMmf1cWjPeelDL6dQ0bkw9BSpbM5ImqM80QYhO92zOVq+znL5O2Y22JCSKx7zshlKmEuhNNMFZ2lWjuVAsCmyWraCe9FkY4dmL9+t4KrEUo0vzKxrSMqfeT6HdrmQn1w8xMTm+Y5nQazNoUTb5b2pgyX0ZCk3/gZuAXAZwC4M8BvBzAkVWe78c1CkxeTp6FBX+m/PR0fGJVmWxFWnQRgatzMRWhqC1lAgoHSqXWqSpDryqUFN0ry9y61a/EsbFygSP1fZ1oJ3X1VwwprpBeM8qi8vm3Vqszi1Engl8/kKIBz87mzH1szMdzTEzUk9Z4kIjRyyKLoUSZpaApqD3jXVsBnvHv6racuq4mMfkyxli1DKmhaUYRe6cuq8zHGZvEVcxiVdrUCTSz4M8zM/WYT8sEmFRpvmj8ZZmsyU9Plx/PWZeloddaUVGdBmlNKGo399XMjLcMTU9XyzbJiAmdg0LKWLPVcP16X+9B5byI1beb+/QzrZbf2bJmjX+e53UojqkOGt4PdMzkAYwDeCKA5wE4Rv12LICzMpP9DUXl9PNqEpOvKgWGmLB8Zm6uffGFzNTynipRzIyYtlXnZO+WWejI/aJFqpFS7zIBJrX+Mc3duXA/dyM4lbUh9vvMTFy7Tq1PqrDZbdxH3ZDWjhBTa7XyeVal7nxvq9VMrc+54vVcl3DX6fxNfV8nLsa5OR9rsGpVvvOkE6tX09ARkwfQAvDdzA9/f/b3swA2AvhY9vlnAF4L4EGxcvp9NYnJMyPmrWFFCUL4kvs8y8xKoUkutf0q+5EZqQusG6bf7b2hyP7UKOyU9pXVL7X+XM+FheU+8hBBqVNLSG3D1JSL5hhIrU+oT0PfpSYt6qUmF6r37Gy7hYxzPOzYET9NsGhtlllRmoBOBOOq7ZHvqLJLIpW2dCI0hmiFbF+snqmC7KDQKZM/A8BPADwfwFEAngZ/+twPsuC71wNYF3t+UFeTmLxzfhIUJWhgyZKJn5xkWnNnFC06OYk7ITKpJtVQXvJuc5+HkLqQOxVOegmp0fE4MnNnDVofIVxFW64DdWQLLCKAcg5KM7aco0XWqyJ0axGKMWYOPtuwId5Gva7rtML0AjHGWWfMRuyds7PteSFS6pfy3jr6WNOy2LtCQlG3869OdMrkrwLwUvXdYzOt/tTYc4O+msbkiyRHJi56T7N8NvRb0eSKCQYp9SxiNvJz6B2dLLiURSI1qBQT4yC0p1jbmYFOT7fXc3a2nfGnuHR6RVA6GbdQv8eel/WWRJ8Z5Naty4Xg1LGMCRdVc0vI37Zu9aZcwI9b7LkiTV63uwkIjUPK+El0ylSZjk1M5LkzdDlFwnzZmHYCuQ43biyPPwgJRU0S5Dpl8vcBeKL6bjxj8k+IPTfoqx9MvtvB1QuuaALHTPJlhCqWPS1mmtJEqehzt4RBmrFTTYW6jrKd09N5Ct0qxLXMIpLaxtg7Yz5DFpJ421xKUpzU+nQr7KQwANnesv6OMUdu98KCdxlMTZUfzZvSB7OzeXR4qE4hs7sWXqemlidykhaPlD5qEgNwLryOtKl+acmvpfXrnZufr7f+cr2GDr0psyjVKTRxXVavzrNCpkTa87NNGldGp0z+fgDHqu84293RsecGffWDyXc74WRe96KyqkiwqdI5v09rUFU0+dA7Y/UJmQOr5JqPtUtaE9audQ+YVzthziGiU0WoKSL0IW2PGXCZqbRqn8t6T011ZoaXTDA2NzvVBGX9pPAYc2elaMf6HjYNx9L5ao0xNM4zM8uFM45dmJpqLyPGMJuM2Nqcm3MPbKObmOjuPIXQHIkdX+tcuVtOj3PRdsyyOTk765k7j6XsB1mPbgXmfqIbJv89AP+lrvuzgLy272Pl9PtqmiYfuldGh5cxZJ25LIZUwSOmyXeCIkZYRPxCUnuK1hhqB2sfRMXm1bK+KGKmMY08hQkXab2pDFv2YapZsyigLtYPuvyq1oOUeRTa/hjT5FPeo/u0bC++FLC1VUEKCrqP5ZxlJiFNzzpCv2komquS+UpNPuU8hU6tPSlKRBFiZ2NIi0Gs3np8Y2u9SAloGjpl8v9c5YqV0++raT750ARPzY8ug4BklrAqGl6V76sutCJGWFRWqP2ScFatUxHxr0PDijHW0NhqwafIb1vWvpDGFRM49Lur5PlPea5MGE0lgrr+UpPvZKx0tsCi/nGunTnEiHjZOC0thc8GqPuQoToRY7QyPkQL6yn9EGuzvq9KcGXsBEtZZizD5txcubCVSptGXpMf1qtpTL4q45SYm/NmM53vu4rWHluEIQ1o0yZvOShasDFU2c4Sqv/sbHuCim4RM0l2U1ZVQalonEL1S3lPilBQZa5pAhyzzhRpY1WIYEiL4vdzX1Rh9lJI4P5cuzaeiKZbIs7PhEz63WqnvURMEF671q85aVUsmo9aQ0+1XlSxYm3a5Gnepk3FZYS+73QNpKzZJsOYfAORQqzZpD493R4IlEr0ixahfo5Tq27aVGx666Q9KfeGpP060EtCW1Z2EdPW++ZTtNBeIGR5kFvcUgl+KorGWQs+KcxeliddUNp8XjZWIQtGSBudmvJMMSWbXZMZxtKSb8PUlP8b8pcXCXZVBc7Qc/Jd8nedyjtWJn/XS7ejRF2nYPaCJhmTbyBiDDikfZeZaGPb2Yo0FD3RuglkCd2X8kwn5ZbVtdt3VSmjTDuJvTM29kW+wrrbGJojXK9Nm5YLIDEC20n9yqwrsTTFRe/U60ZbpsoIecjHy8/MzPiyJia8talovzfXJeTrbxK0BSQ0l7thnkUCjp5nofenCkjdCFJV6CTPp5giVAW9EP6MyXeBThlRipanmfnSkpesifJApLk5L9HKQDXNTKskpmF/1/R0fYs6RLxTJnLqZNdCToggl/V3FdNirMwyps6/M3OMacKpfu+qxCD1/tjcY/+s3Dcsy+42N0KZKV23I6TJx9oo68f9G9v/HCqjyIzPu0E2bAhveQz5m4sSYDUBWhDhrIzdHqMb2x4bollF+9NT6OfcXHu9qyoWRZYKvQOG6zsxEXZplqGqAlQVxuS7QKoJNSSJljGVUNAQb2FptfIyW634th/nlhOa2AEuUhrdsGE5QykiTKFJqhdCmeZXVGbosxwD2f4QQS7TDkOMrQxVtRtdF9kfsqwqzLgXmnxobkrGGmpjVWEyVDcpwGqmUtYOOedi9ZN9XbT2ivppcdEHucoT2IosR6F1U+Xo4H4h1GZp7Wi12gNfOxE4ZaCw3tWhBbeqwWxy7bGiwkJCJ4pFaJ4vLbX3g7wvlJq80+DWXqAxTB7A4QDOA/D9bHveK7PvDwRwbpYy91wAD86+JwB/B+AyAN8GsL3sHXUz+elptyz7VQghhlXGVDSBmJnxRHDduuUaRWyiybJiATFS8y2KXC0yMYaYFC+wkMZVREhjRDwlUjfUh0X+2xQJWgstcgxnZtrjIVIWa6i8VEk+JvjUGeEbElYkwYppOCGimDLuPLbMACYm/BXaAhWDnNMxn31KX5UJQhybwoyqbN6E1kyvCbquQ6pgpwVWKfBPTRUHvqa8hy0egHPbtoWfjwnjZeVLWinN5rE1FoKcw3zSoFTe5ubyOTo5GY5NkYht44u9u27tXaJJTP5QZtQAHgTgUgCPAvB2AK/Jvn8NgLdl/58A4AsZs38igG+WvaNuJl8lcYtGmaYqJx1L/2vWxH3vVTRt7Z8skzirLEJJ3FKEglBbQtH1nWyhK2OYKdo7W2tCp4bphVzFQlHUl6FgMa3xO7dcoOqWccTqJploSMsuGk+tRWtmwqZO3g8/NeW15VD+h5gwJMdSukOkMFtFk9MCjNTOWi1fx6J85lyftWu9wCKF5l4T9LL2hBDqSx4DPlo2lOkvBVw2K0TMJEO0LkYzZmeLkxhpTb6TmBxJC7SFlsuXwk6REuNcZ9tNe4VO98l/pcoVK6foAvBvAJ4K4BIAh7pcELgk+/+fALxQ3P/AfbGrbiZfV0Slc2ECKJlwyBeqtflUn3mZ1hpjyjFzbawtMeZTxnx1ZG8sf3+Zpl7U/lifajCB4frENNyUtuk6a8LG5a1eHc7drsuukgI4BbH+XFrKhcypqeW+86L5JImnvk8Kb8yUi5iJrF8obkDXuaowG3JlbdjQbp7XVgstlEkBgyjXKgeBThiKFOjk2MYsJCnl8XPMJKem2unC7GyebjhEM+TvMctgt4GAnEo6ZA2VbWDrHScEqkr7+2nJYXTK5D+prqsB3APgGwA+nf29G/4gm0/EyikofwuAH8MfXbtffE/8Gf5o218Tv30Z6lx7fTUhur5IGwlNLCZUIWaUMmGqMPRYeUXaWNE7Y6bRMoLLFpJVq5a3P9RnsSC2GDQhK+oXySxSNOYybVD35dq1noAxU9fEJmYGLwsg6wRFz3Psw+Skr68m1KGyQoKUfIfU0ObnPTMdHy+3SM3MhC07Re2JCdFF85/N80Q5k5+Z8fNSZmTUQi0zovn55e6vbhlSr1FGL+RaS9l1o03mIY19Zsb387p1ucAaei5mHpdCQCfMs0iQ0eMVU2BS197QaPKunSG/LPOJH6G+PyL7/uSUcsRzGwAsATgx+7xf/f5zV4HJAzgFwB4Ae4444ojaO7Dq4MpJKoPG9GRiDXZ6OkwQeHG0WsWaXIwplzGjWBs7maT6+U2biv1VHH/AuwikbzO0yFOJeZnwUdYvsefKBLeYaV6PcYpvXQt/RVuNQkjpg6LnONFLShS8FFik8Coj3dlCsG6dpzhyPYTer4WDlDkZYqwp839xcXla3ZCLjsvXOStCZbJg18To+qJ+1PNmYaE4dqKM7kgmGWLSMUE8VMcyc758tkjA4vFesya33uh2xKxndWjovRIA6mDyPwIwG/ntRACXp5ST3T8B4BwAfyK+a6y53rl0wsomZyaO2s+uF38ZMZC/6wh7/e4QUefvU3xYqRM4NkmlWXZmxmtr69fHNYAQQeYzpyWzDzGC0LMyKCvEeFLaUATZP5oZFTHEWL8WuQF0YhL5N9QvISajhYMqSGEEkvHOzOTuDumSYA2ff1+zJhfsioQvbuPUVH48aZklJzQ+CwvtUe76OS3UcL9u2eLruWNH+30ssOv1GmISIeF9EBqexNJScXCrdrtwgNvERLEmL/tZ/h/T1GV/hFw9IeE0RTlxLqeZMoufxNxcfpQwW29CZXQSHBhDkYJSF+pg8rcDeH7ktxcAuC2xHALwYQDvVt//NdoD796e/f9MtAfelR6E0wsmn6JFbN3qFwMzKSYKUlLVW2tSpc6pKU8wZKBIiEnMzvo6aOaasq88lbDHpG82x7E/rmjboSTmvKiZ0Maka/2s1s5kUJb0eacsphTNN7ZQpaUmdG+s7KLkK7E669+Xlny/rV/fvneX+yMlHiEVRRYL1tJWrWqPLdBjsnatb/P69XG/q3xHq5UHQcn7yzR0qUmy4BiaU/xZCtBzc+6B4DHeZipNvHq3hX63LFebe3tF4FMRW7uh3+bm0g/HYpfH2FixBS9Ec1LM5KFnZb2lcCfHSCd04nJmZrxLKnYoUqe0MXZvTEGoE3Uw+c8DuEKbygEcC+BKAJ9PLOfXALjMxP/f2XUCgMnMFP8DAF8CcKDLhYL3APghgO+U+eNdj5h8GXiBMAOfmvLEhU178gzqoq0/oQksD++QBCbEJGZncwIlvw+Zn8oIjlyAWosKMRBm8nwaXJHlQGtQfCzq+HhOVFIl99D3vJC5r8oWVlXiIr8LCVshYqr7W29lLBL4YoKC1Ep0Fi5dr7J2xH7XcyDGnGUgJf+m8xmsX+/rOj4ezman3xmLV6jyu7Sk6X5krXPbttwlxtnt1q3LE0bJPkiJDYmNVxM0+Vh9i+JCyuILpPKirScSeg3Ik/1kHYp2d8RoZ2ysY240tsDFFBF9b8yKqutV5mpsMpM/DMBeAPcB+EnGnH+Sfb4QwGEp5fTjGgST11oEaw+sIejIaT0RisxDMohl/fqcsYcSwrBGHItclpO1LDBOCxhyoUstjevMPjOOnE4xE3N9JidzLbCqeTnWDrmIizQY59r7WROzKtq1HIeyyHAdq1G01a9ofsijQUPaZNEckwQq9H49B4oYBPs5JSGU71xc9GWx+TdWXpHWo/uJ6xc7YEX2u2RWvP5Y0wdyV5P0G4esAyGLwsxMvTsgeo3QXNRH58p5FHJRFDGr2JrQwoKMfZDPhOaqVgy00MZ5GHTK4Vg9yyxw+t6YS07T9Ni8DvVNneiayT9ws9e63wBgV/b3hCrP9+MaJJMPJa2J+VA10ywyBzExmphYbj4rYxDyXXKBFDEOLTCwdC6l5JgGFUuLWtRvnJgitmWrqH+kRUPeF2KiXK+yZEKxcYr1kyb6kvDEhChJOLhusTiCcWv0yAAAIABJREFUorJCVghtyZD1jRGokGk21v6QUMuumhjRZJPuhg3lWqEcI60lhdxfvKed2xzTIlkgkDsbWi3PHPi5WLBfSODgvmFmNTHR3KNmnYsrGbOz8b3hmoaVacjyPXJNhGKKpDUrdb3Ldc1riGkOpx1OEbJCSpJk1LOzuSWWhbfUNqeMQeM0+WG6BuGTjxHakFkppmnF3sVaaIwBsAYdizoNaWNFJriQdsdCxqpVy4OgZL079QOHFrh8R8xcJpkZb2XauHG5a0PXLxSQFhrjmFYY0hhTfpdgIjMzk9cpZsIuEh5iwkaV6O6ysSuauwsLnkmuWhUPtHQuJ+ja4hDqDxmZX6Ql6bUxNZVrhqE2MkOOWVj4fzYXF7kUZLwBW9nWr2/vv5R9+7E+7gVC1gjuF53Dvmwuxtw2se9CWyJTFI2id7C5n92DsVwNZfQlJEyy1Um6PvsxRp2iFiYPYA2AeQDvz6LjH5l9/3wA21LL6fXVCyZf5JNxbrnPO2SuLWIYRe8qm1jSLC0XrLYm8HcyKCW0uKQ2JDOgSY0ptMCLTH5VwO2XWn3Mfyb7VGqKvMtBM7nFRc9oNDGW7S+ysMj3FY1LaNtcLD0u+4A3bsy1eh1HoDXislSaRRaIomdihLJo7nK/x7RYTVRjOSG4Xa1W2NURcqdoZjs11R69H2M63Dexecqa+eRkmElL4UIGeOk+j41VikDZC4SYn+w/aS2RSkVsLkpmHBKg9Ls5UY5MgFXkuy/LWaGtdZr2hixAIYFRCmNy7Ya2S0pUEeJ6jTp88kdmAXb7swC5+5Cnp/0HAB9OKacfV6+YfEq0uGToMhBPEpSyfdha4i1jPJoYc+IVTr7CdWKTHBNkJrKh5znrGU9wyXhjBFIG0ZQRrCIGGerrUOCg7hc+g3pyMjd9h3KKF50xXqYBF2kqsrxWK48iZ3Mi973WCkIEiLWLEDOsm7DECHhIoAkFknHAXSzXgxRmmHiGtOmyQEkO7JyYWG49kW4iaaUJzSXJ2GJMhNOzbt4cXvehsQsJQ7GxCq2PonVdN4rm3vS07+O1a8PtCs0RbemLaeAceMnCUZHVRc+3srXHkMqGzPegGb8M7tPKAgubReOxtFSeC6Ss/+sc2zqY/BcBnA9gM4BxAPcLJv88AD9KKacf16C20IUYMRMLmU0rxgClySgUXMTERu7Fj/nFQ/mhmcnzBI5pp6wRycMqJLGUfjAdKcvb58o0eW0e09YGzSxCkr0ug98v91Vrt4ImHixQyf3dVTVg2X+SoW3d6sd/9WoveMS27BRpSUWxFqnQc1czHtmPUoAL9YOeuzwXeDdJyGUkGUosiK/IUsBgprB+fViQ0CZ+Fhx05jrJBGL5I1hgnZwstuB1yphT7uulZh8ac7bWccIiaZlJEf5kbEPInTc3l5/uNz/v+zbl4C/nqq0FqWyENH3ucylkyN9CR3GH3j83l77FMNb/dY5tHUz+Ng6yAzCmmPyTAdyRUk4/rkGltQ1pXuwjBNrN6VIYCEVHa2IvF2Msct+5uMbFv4UId5k0zHVgjW1yMt/qJuvNBDUl7aQk/HI/NPeVJMqxdkkfLpfBGsjkpK9HWYIafi9H9ofcELF+KqqTjgMo27MfI/opWnsZw9BEWZ7gJceW97BzcGfIb63P7maBk7dASgtRqJ7aCqQDVWNMv6wvtHuE2yWDRRmS8IeYnRQOpqfLmXivNO5+aPLStL11a37YDgfDhd4f07KluT60jiSjZEuXDNSU5Wo6FaKtMcixi1kdl5baXQcSMXfh+Hj+fcyyULX/m6bJ3wDguS7M5F8I4NqUcvpxDYrJh6Q9qd1qKdg5/1cm3ijTIqUfK7YYmHnpbSZyYpZp2qFJyL5X1qb4YI7168OEMqW/+H42xfI1NRWvixYQpqc9Udqxw/+dmPB9Ll0HRYRKWhBC28D4XrkzQfa11lZSIvR1m8qsO1qbit0T62dJ0Fmbkslz5NiuW9d+QpwmzvqwGZ6PRbEeGtIyIwVVdrls25bm4mLMzi5PxqMFVd0fcj1I4Vlaq7QGnzLmvWTOdUNqowsL1davtmAV+fEZci2tWeOFcWneD80J58LuuhhClsYQ3eV1oI/cDo19aH7J9jRhrOtg8mfC55rfJJj80Vkw3tcBvD+lnH5cgzDX6/tixCmk6W3b5ifQ5s15lHLIr5VismKGwuZTqa3IyR4LfirC4mK+MJghhrSdVMh6LS76Pghp8qEFKs30bC1Zvz4/4YxN4zpCuEhj3rQp32crhQMpOJXlUk/VyGOMITTmuh5agIjlRQgJEjpORGrynHJ2erpdy5FaFLuf5D53ue2sLN4kZAWRjIWtDBy7wIIEWxZilhZm0ux24XWQsg9arhk2N8fGIbSu9W8hU3UTIbXv1F0Vsd+KFJzJybBLkd+tt78WKS9lgXhcvs5op4WEopz8RZYKbXHi9VDGcvohDNTB5A+HPzHuegBnZIF3Z8OfB38FgKmUcvpxDTJ3vSbIkijFmPTcXB7stn798v2qkrmFgvlCUdtlJ7rxpC86PSz2XJHvPBVau52b8wRWn1IVYqZ6EU5P59tomMim+n4Zeg+3JNS6z8o01ZDmrqOsFxeXH4pSVD9+Rkcj89yp4ufWTEgKMcwUY/7K2dncJcL1YEI3Ph4XWooYnyxfBk/KdLg8T0PlhEz1utxQ/0qtsiyJkxZIeTdESKPXa74pmp6GrK/eVSEtbGVZ4WICkHRVyuA3Fry5/FCwZco20aK+5fXMJ1zyvOF2btrk5/D4uJ83oTMOUtY6BxKuX5/W170U/OraQvdgAAuZ5n4p/FGzbwEwmVpGP65BafIhAiKJUsh8OD/vJ9qhh+aBQDIQJjTRJRNj5iGjVFM1zNBEZt/T+vXtWrAUHPj9sQMgYlqpBGtebK7jdk9N5bn3yxaY9J1pYqHjHsoEhlhfl7UtpnWEzI2SiMzNLY+2L3pvrFzZVs0AYybOWP/EYgtCGpuMMZDEnPf6S6Ia21PNdQkR06L367ZwhPOmTXG3EbdZJjeRLgh9jrkeV/leuWVQnjdeJlQMGrG5z+MtlQeOlF+7NrcIxsqMbRWWuyl4bcsTCNlKw3NUbm2VGn6o/mVMmIVoPmmOLVLczokJP39ZoWIL0tjYcnpXRPNZKJ2eLrZ2lLmb6oAlw+kDyphryHw4MZETSB0FKhdBLFCJtQrAm/1jiGl1evLNzub1kVtI5KKSprYirafItMY+Lp1sYnY296czES0SFDgyniVqnb891HbnlgftlWnmIeLNGoEOqovt/5W+vqWl5RaYUGCZtqAU7dvV9dTjIBltFeYTmjuSAep4CnY3pVh75uaWRyjHNOWy55nhchChtG6w6Zh9qtJdlSqoMWT9yrZQ9UqT76RcbY1g5sprmeexNKPrnTihOZbii3eufQukpiGSya5a5WmZXgta0dBMOFQ3GWfBYDrMLiki557ylHaBJDUWRPerrkc/hbw6zPUfAPBnACjw2y8A+EBKOf24BsXknStefCGNZMcOP+kf/vDl6WalqVISFc1Yi7YWMVJ8wsxEyrZ6lcUbMLEtCpJhRsgHg0gCz5HasQQrsh5bt3rLA0fqEoUj85mocJ2ZiOm9sKFxYoYgkwQtLPj+Hh9v9wOH+pYhhbzQPJEaoh4nZtSxiOAQ+B2cCVBvCUplEqF5q7V+tibpwL0U1wYHA3Lwl47+L6ubNC3rXA5sUWDtbXzcC4ZbtiwXlrRmy+6U0DyWc12beXutsTnXGfOQdWOaMT6exwHpbaQyrif2Xi4zlM2OoftK5klgEz7TDN5WNzUVXpc8jjJgL1Q3nhc6IZikAQsL+bydmAgz5SIGHlOUdD36MR+cq4fJ3w/g3my//IHqtycAuC+lnH5cg8xdH/IZppjxQjmc2d/O6TKZ0Wvtg5/ldK6hSaW1SG2KlMkstOSrEVpM0o/LjDlWF1lGyI/M23iYeRN5IiCflbEJzBSk26JMq5WZ5YraJ7UHbt/YWHHyD92/PAd0DIImHjMzeZtZM9SERBJBjZg5tkigiZVR5CoK5SuQWjxrZqnBXEtLuUa8YUPuLpLZIzVhlc+yEMbCIQtAss95jUhhROfZl2PCWidvFQ3F0WhBXFoRitZPHeiWeXAcxapVOaPndcNWj5A2HXsvW+D0kcBLS35M+V1sJmdGrY9ILrOw8Xt4HHnXkq6btlps2JBvD5VBfrxLiBP/hCylcg5KQTLmLugnY5eoi8n/XuaLvxzA48RvK57J86Sanl7uowlJdjxpWMuanAyfLR7bt8rmbn1aVizASWqRmsGwH37tWh/hX6YpSqvA7Gy+4LZs8XVdvTrXHEPb+Jxrr682m27b5hfftm05Qd60qf39vFWOtUZ5n9yOoyXwFPNbSFpn7U5uX5QC2Pr17Vvv2K/J2hGPi/T5xqwF2goi616kycskIHLeaYGmiAixMChPO5Rlxeovcz2EiGSRgOtczoS3bVueoUy6G0L+X75HB4lJwZYJumQQnKAqpPUxY+KshdJlJseE5y8LNeznleNTRvQHwRR4fvLcHR/PzegszK5dGxdYtHVM9p+kQXNz7S5J7lNWJmIWyFifsBDCz7KgJuekDkbWW3+1oqS3frIgIYVAnrO8NZnv1/NikChi8uNIx/cAHAPgYwD+k4jmnXMfrvD8yGJ+3v8991zg1luB//5vYNcu4LTT8t9mZoAXvQi4/nr/+bTTgM2bgZtuAtatA448Eti5s73MffuACy8EbrkFWFry359yCvC97wFEwNQU0Gr5cvnZiy7y79q719dhft7/dtBBeV1knQG/BO67D7j3XmB83NdLg8vbtw/Yvx/YvRu46ir/LODbtW4dcMcd/rt9+/Lfdu0CPvOZvN3bt/v3LywAq1cDV1wBvO51wJ13+uu223zZ27YBP/4x8KpX5fXYvRsYGwMe+lDg6KOBQw8FPvIRYOtW4H3v82Vv29bexu3b/XsB4KSTwmMo+4vv3bs379OzzvLv27nTl7d7N/DtbwO33+7rDPg6n3yy75/77gN+9jPgnHOApz0NePazfZ985jP+70EH5X1z7bV+POfmfP1OPjnvr337fBnnnuvfdcABed22b8/rf/nl/p2XX97edv67a5fvFz0WGs4BRxwB/OQnwPHH+7rMzACXXebn4lve4uso+3HPnvz/k08GLrjAv+sVr8jnAL9L1wsArrwSWLvWj+HWrf49P/+5H9udO31f79uX99PMTLyNel7z75/8JHD//f7zqlXA4Yf7snftAr74RX/vli1+nJ3z71mzBjjsMOCMM9r7jd+xY4cfx5kZP5d/+EP/3ObN7evlggvi/V02HnVAzu3t2329L7oIePGLgcVFX2/um7e8xbdl1SrgkkuA888HfuM3fJt27PBjcdll/vvxcT9WP/0p8PSnA2efDXz0ozkN2rYtpxcSl18OXH21///YY9vncajuCwu+DEmXxsaAxz3O//+Zz/j6XH+9H+c77vC/797tx/h1rwMe/nC/vhYW/H27d/uxu+AC4Ljjcvq4sJDPmwsv9O+XNPyss/L2bN7sP3/ta/79Z5xR3JaBIcb95QWvyT8++58AvBl+G93fA3gSRlyTD0mWIe1U5vEOZfHSxyFydP22bXGfnvR7yaxwvDd5airXurSpOcV1wFJ5q9XuG5X38b7S9evbzXEs9RM596hHuTYTZ9E2OO4PuXWL+4u1Qu1+CGnjUgsOWUqqSNeh/mJtkDVomc2P3yWjyzkamduiNVp90A/XUW9nlP3FVhjWOEJmUefCgXuhtmnrRMwsv3Fje9Icua2uCFznomDI0DOybqGgqqJgz7IynfNrjc3T0tLGZbMPmvuZ0yNr65PeSim/D2m0RW6rUD17Ad2n0jLDdS5Kgw20b+uVOSNmZnLaELOiaX94aOtvzNzO9WBXoNxmrPuf15EMBNRl6/9D9ZBzTdOgMgvTsJvrH6++ew6Am+APrhlpJi9Nfww9mXkhtVr5IpDZwThYiyifoHNz7RGgIUYj68BbQnjR8YlbMlKWJ3eI6OvFLu+RUcOtVnsgDRPs8fHlQVf8jPR3phBiTRxDhFGa87UrghecFEzktqhYkFuRKVB/z2ZN9iNyf8h75d7uiYlwTAOP69at4e2HIUbP30sC2WrF84OXmThD+5H1/n35Ts2kpTugiJDxnFi7Np7XW5vIi4Rn7j8eV33Gd9m48m/SrcNuFPl+Xl8cEBpKcsJtk6cYalcGz1nNaAYBGUUfYkSyrjrQjsd83bo8wDTkhpF9y4xezre5uXzOy/gGLXgyzZRrlteWNPevXp0rSlqYDzHzmFDGz8SEV6lcSVen3PYX2j5ZRMN7iTqY/HkAjgp8/yh4P/1IMvnQQOvf5ARjTYsZgk4IwYFVvBj05I4F4EmCxHXhM5SZiEq/u3NxTV5OeJ7gk5PtxEtuOWLtmomeFhS4nk95Sr4dRQeeFfWtJLAyUl1qWKE89FwPJhxSwpbRvpoZSoGtTOrWgkSor7VWFGq3JLZFGeq0z132tcwsJ/tHtiOW/EZuB5Pt19v95PwI+fKl5ifrIoVF9oHK3Pia8HKd9EmJRXOF66X3xfOzRdnH5ubat2xKvzy/X76DmZaO8pZt00FZUkANCWy6PamCZjeQQlwRA1y9Ou8TnW1SKi7r14cZrNweNzfX/l6OJVmzpj3AT69/thpIGiD96fJavz4XFEK+ca1xayuGFMKmp5fHUei4oxC9CFmcUsawV5p+10x+mK46mbyW0ssGTm8lkXuIeQLriFK54OS+W639cQBeLAEHT0ZtKtYTUP7G2eLWrMkXDy8SyXA0gQhFlkomkyLNysUWModzGSGtfGFh+RYnSXRj5jbnwgy6SBtmQUymPGVLh07EUzaPyvpGaxXSTcJ9JNOEyrbIvpERylr4KBIGpTartSOpEbVaeS4DHh+utxYcdPnr1uXaeOx4Wtn32trDLg/9bFH2scXFPMBMMnvpJpIaKJ+DwNsOea5woCzPuaLtVVJ41+3RgmdontSBmAtHa9Gzs3nwr5xfWuOXQXStVniXzeKi77e1a/MtsiEhQtKiqal8nsu8E1z/LVvamfz4eC5oaPO5ZP48T9gKIWkbl8NzVbZZbuMMuf647lXcRwy9/upCEZOvEni34sABFxMTPqii1VoeWLF3rw98u/Za4MEPBp7xDB+gsmtX/uzYGPDoR/sglNtv9wFFOijs5JN94NvYGLBhQx5c1GoBp5/ug7huvtnXY+1aH/Bx0UU+iGTfPn/vE57gg1o4QGnLlva67trlA002b/bPHnecDyb7z//0n+++Gzj1VOCd7wSe+EQf8LV5c3vgGpAHIs3P50E9rZav12WX+QCXffv8pQPEZN+ee64PVhsb84FQRx+dl7lvn68fB/vIwMX77vMBi2NjPiCRg9gA31df/zpw4IHAk5+cP8+YngbOO8//PeGE9nEOBUFt3+6Ddq680gejLSz4QMipqbyvOciHA9tkeTMzeVtmZnz9iHxQz3OekwdMnnKKD0ZatSqv1+IicM89PtjuqKP8HPj5z/24cJuJ2vv19NN9sOK+fX4+TU8Dr399/jsHYc7M5EF13AennurnwK23+nZOTwPveIcnrRzYdPDBvozTTwe++U3/LpcFKl13nf979NHAF74AnHiinwubN/v58dnPAnfdBVxzjX/m8MN9XS66yL+Px+fd7/Z12LAhfzfP6R07gKc+1Y/T0Uf78dm71/fPxRcDr3nN8rnG408EPOQhfhwBP66bN/s1cdFFefDiXXf54K1rrvHjtn+/D8475BA/x6+8cnnQHweT8lqQgacMnl/HHecDMWWQoCxLf98pdIAk0L62Zmb8mF98cd4/PL8kOMCw1QLe+lYfaHrrrcCTnpQH8519tr/35JN9YPA99wCXXuqD8w480K/Xe+/NgxJf9CJPK+67z9/zi7/o59VZZ7X3wZOe5N+7uOgDQc84w9PQb34TeMQjclrEQXGXXeafe+xj/Xh+5zs5veC1edNN/p57783nwi23+PnK34+P+wDg170O+NSn8nkmgymPO87fX0TnQtBrtueIcX8APwNwdPb/9dnn6BUrp99XL3zyUrLT0GZjqa2xBiCDQkLbLqRkqDOocVlsPt2xI/cBy/25cmsHm9diAWl6H/TsbC4ly5O4tP8thJjFISWbXMxMz9K51KA4ZzqbvaUELvdus8uErRDa6qDNyc7l2mdMs2TfPPuCuc91IGVIY9caxtq1eSARWy5k/0tNPpRIRGa90yZ01pJlYhHWGmJJO+QcZQ2HzdjsD2X3knYPsIYmA0I5fkKfwcBmYXkAEI8dWwZkilHZF7xdMaZdFWnA3EcyrkTvlef28/rjPfc8PjMzeSyNzGQYc5lwG2RiI76/F6baIui1L60Ic3PtYzc2llswtHVNWivZJSlpmbSwsIWLL9bUWaNutfza4bHgWB8N1nqnptrnsbQUyTZK15S0OklrDVvLeJ7zuRccL8UW11D7NV3qxAffKHM9gDcAeGj2/xuzz9ErVk6/r14w+R07/ODv2LH8N72IpI+SzXoygEwHgjjXTnAl0dBExLnl+z6Z+OozkLX/SAe5yH3dS0vLTWz6UBDJTGT9eBFv2+b/rlsXTr6iJzczFV74Id8zv1sSg5gfU5rr2bwe2hcro/e5/yVzC0W4h/qSE/GEknlwIGNozzvHGUj/5Oxsuxl5aqr9frlvXRMVNoFz4JR2FTHDlsKG7Dd22TAh5hgN6c7hWAwZD8IBauvW+XuYOUrhQ5pK9SEgXAfdH/PzeYApC13c33wKojZ1yn6PuZZ4vNavz+eoPiFNCntynku3mxQyOBiR3Rb8jExQVZfpvVMwbWDzuhRqZmf9GHJgHDN6Fsg42VPIHcg0gpUbGSAn41dYGBwby4UodjtNTvoyuZ80jZBzW+Y80IGN3Eael6GgPFkXzvDHOTmmp/33LPjxWpLKx9zc8hTMzi2v8yAEOec6ZPLDevWCyYeikIugtTgt+enJon2P2meug0J0lH6VYA9JsPQk5jpz8JFMIckEXgZLscbGFzMEzdxkn3A7maEwIZFWCPYL80LlaOdDD10emCiZsGT0HDDH55Nze5hYSOGJtd5Vq8InnoXexcxXnkkd0uRD4xP7LLVJnjNMfMbHl7fbuXYBT0cR81wKbZFicNtZ01q9OmfOk5M54ZTJXphJcu5+nk88T7n/WXjatKnYDx0KjAoJW6xF6mQzst2SCbCfmYUmzcR1UKfUBKUwxEIjC47M2HistC85NMZ1oKjM2G+SOYfWI9Mo7g+Oc5GWPBbEZPS7TAbFfa2D47SiwjuMJiZyoYIFLRbCZPAkt4kFRFYkuC46mFRq/LH+k9ucmY5OTuZ0guuiz7uXO4mKxjik2Rdtb60LxuS7AE+MsuAq/YzW7uVvkijpSE4+pEGbSzUz0xp+rA5aS5HBM8z0JCPiBTU52X6oh9TktRlucjI31TMx0RnIZH3YGsFbc6QJLGTp4GfZ5LpuXXsqXykYSKsDm9y4vWxa599lGlMpoMi9tnrrmhQk9AElso3S1F5GHDQjk1vV2P0gU3LG5maI6Oj5psGMWJqupUmT98pv2pT35ZYtuSDHUe3SzcBl8vySW7D0Niy95SpW35AFyrnc5cPPM/ORZnnWRllDZcsGuxuYeesgLOfahXIeS94eKIM+uQ1FZzbEkCoQFJmGY78VCZTaMhcKCpPar9TqORqe1wm7LLnfOdJemtdlYLLOU8/Bm6HgSf07v1MKIZI5s/ISsq5KZYUFNxY42IUn1za7oLTw6VwuXMj+Co2lVhJ7IQB2aq7/RJUrVk6/r7qZfGjidztI8nkmqDxxpZlIm/30Qi6qB98bItwyslSa3qU2w4tREz1JLCcmPOOcmlquAbdacalam1flgtUauWwf9wcTiA0bcn+u3OIn6yCPtOXyQmZLbuPioi+XBZatW9tzE0jrTNF2OLnlS/tntZYvmZrUzKXJm4UhScSZ+ElTZshfHdvqI82127a1m5vZRCrTurKQxczcuXb30fS0v2/dulxwkK4Q6euW7gNNBEOujlg7WLvng5VkjnQWCLlPpJl3ft6PMxN4mcNdR/PLXPw6N7/UdKWgqQX4IhQxbz2vqmryRe/Sa0EL5lyutC5Jyxdbb7TgIONGeH6sWpWfTSBN5Tt25NYVnov66Faeq4ceutxtym2Qlhm2crZaeZwKr2HW+KV1j7c2c8wPCy1TU+3Cp6ZToa3VIeh5kDreVdApkz+vyhUrp99X3UxealaM1EHSgyvNRbw4pAkypolJaVSa1Io0NCm58iII+Rt1m7QpW9chRFTHx/PFMz/vf1uzJn7OcmzSs6VB+tdC7ZNtY5Pb+Hi7RqIl+Zh1RUrjzHAnJny72HcvhR1+nvfXTk4u/00SCe131P0ZM08zs2f3iHT5yH6QdQu5iEJBnlx/GYDGQpncviR96HrucZkLCzmRZOYtAxI5aI2FpBgj1TEsOmgxZCVhq5Pc9y6PupUEWI8/Pzc15cti1xgHjkqNXydAkWuPA065Dixo8lxKcfPFGHTdGp9eG0wTOCZGCv2hZ7g+0joWUn6ksMDzaGqqnamy4Dsx0T4OcuunbjsLbmNjy+ezVGLYwsBzgK0AfEiNDszT8QXy3ZJmM12TFsOYxTbU95oWDVyTH9arF0yeNZIQ0wtBTjy5yGdn84k4NtYu7cv977F3zM3lxIQZkDYbSiZdpnFKhLQOyXCYoLFmxouII1Bl0I38PwRJ/CQRkfECTAR0bgDdz/IsahaeQgRLay282KQpfnY2Z1CcwY5NlJrYcT/wxQxORvSyANJqpWtgRebUEMHVWmMs2FMyykMOyQkmM3m9d13606X1hP3rMseBHC/253K95+dzS4tOdqKF5ZC2LWMwpBuG1wAHfbFraXKyPfBRzxk2v7PLZ9OmXDhlTXB8PLf+yPaw0CnXmHRvhTLGdWrCl3O2Lo1PlsekmGftAAAgAElEQVQmd47F4KyOMsmWtDJJhqYVFefafdas+UvrGVtEOE6GzeVsEZTWOS5f5xmQFgRt0WELnBYiZ2Zy1yPPbR2jElMAuM+kUAMsP6EvRFt6PZYaxuS7AC8GfTSlhpwoMqKXtR/pc2f/r/SD6ZO/Ym4C1p7ZjMvR01Kr0D506cuOMfvQJJSmMF7A09M54eU6Tk3lC3Zy0vts+bjQEEIaqLQUSHOa9tVJZsjmZu13ZyIT0krl+5jg8ylcMniNNVsdlMXMXGrCHDksCRn7bmVgnpwnMYKvTfna9M6aD0esFyVnYVfG+PjyCGZpieB5wvPjkEPyecYMWsdBSDeGvPg37n+dlpTnakj41K4LFgZbrfYyOaBOxgbII4djWzjlemMTvRRIeXsjj6ecJ9IvLQXo0LYqPY6dEvheaPJc3uysn8syfoGtV3Inh0wrHTpTgNcXj7M8Z0EmqdGMnwNjee1xIBwLHZrmStrA7kHtbpRWCrYoyfUYi/koc4OwRUBaimLZJWNxEdo6WjdqYfIAHgTgxQD+EsDb9ZVaTq+vupn84mI+wNJkr8ELWZrxtMkuNMF4sfFkZK2l1cqDjGKmHjZRSfOUlFDn5nJGw8yBpWCJmImbief0dK7hsm9YajMswGhBpQhSgCjSuDQT4TFgwit/I8qZkKxbLIMV9yEvXtlv3CY25bGWKw/PkVnWpOmdmWHoLPKY8BbSMEKMm4kpmxn5/aFgTGkZ4ZgA+S6pLUt/qLQIsO+affbSNdFqtQtYPA8lAWZhggUC1rSkIMb1YeGOGQET6+np9oAqJrrSbMpbQPk9U1PLLSS89lgjZDfT2Jj387LWp10Si4v5XJuczM31vPWKrT16TUnNt8yS1m9olwdbraRLQs4THRAr+3Pr1lxQWLNmeY4LeYywZNp67UmlQWZe5HtlXIB0qzHt4zkize8bN+bCA89JFi5Y0YpZ+eR6kp9jWRdjdLrXWrxzNTB5AL8I4KcAboY/fe46AHfDH1xzA4AfpZTTj6tuJs/MRCbxCEljMbN7yC+vJ5LWPLWLYHY2P6ddntstCXMoB3PIB8dEjFN3ai1Q/h/SpFiT1e4A1rplcpgiaOlXa0K8kKW2xcSb28WWDGZ0ob6XGrU8WITfIc31mlEy0+bz4qV2whL+2FhuvuO+jBFK+Qz3EY8/t5P7WxMK/l+asNmaQJRbWKTvf3Y2DyiTwZvyUCTpXmEmLokx9yGnhGXTLrdHWlqmp5dre5pRa7cI940UFnQ+e14P7O/WgZnMHOSJgTrGhYVGFlq18Mj10umVmQZw/dhSwDEXbJXTQrKM9Je7WJqCmMVIr8tYXITU8hcXc8GJ99nz+LMgKrV4Bis4er6Hgkfl/noWRljI5ABgpgU6UQ9vweV1IrfvyhiLbdvad9nwnNeKFitPMoYjpKTofkvx3XeKOpj8pwF8FsC6jLFvBzAG4EXwp9Adm1JOP65eaPIhU6+GZMyxQxP0/0xQmbjyIpD+T75PH7DBhJqJB/spp6aW102+W0ZD8zti/kMpoPCJUCz9yjZI4sx9FNPQdZ1CwWrSF8oammRE0grAYxOSmLmtvAglQ5cmS2lClMKWbCPvegj5yLUFQ9aDXTQyu53Mnc8CSCgTW0gbkNoTz7lVq9pN0FrA1Al5pFa0sNBu4dGWBu5DdsdMTbW7MeRWKK6nzKg4O7t8nzYLDnLbGgusW7bk4yGFOS2s6LkktSypoUprGLd7etrXe2ysPWBOzwcW0FiA4v3d8iAndnHITGs8TiwYxLIODgLakiMFXm25KqqzpItyR8W6de0Cnly7ehuorkvoXfIeaTni+vH3WmOXZbGgwcoAxwUwrW61fL25DWvX+rnGAoS0MnCfMW3ioNOQdVS3pZcafR1M/joAzwKwKmPyTxS//RGAr6eU04+rF5p8LOpcgicSMykmiDIyWvvxmDhoLYwJJ5u+eDGx1sXbUaS5SEbpx9rABJiZysSE1+hlQJ1M9yp9vkzs16zJt1zxIpPBZ5Kwh8ymsk762Fu5N5VPGZN9pZmrDs7RC0tHiEuGKy0BbAbW7+H2s4UgFIHLWqlObsRzhQWEmHbO2p5MKsLjyP2o3QjSD8wXa6e8P5kFurLztXU/aU100yZfh/n5djfG1FQeMMXCD5cnhauZmXwfOvcPW6bkoTFsJZHBWyyU8Q4OPT+L1isLbNICp5nymjV50Kg03fK4sEDNa47njhREeU2xr1h+p918/TDbloEFSy3IMhPTR8I6t3y+8NySOxB4fGQWQSkgsiVNl6vLkdbBqak8op3Xw5Ytfhw2b16etptdSzJTIs9ZXlss5MsMjOz+YQ1e7rRgYZUFRimssqWM3axlAlyI8deFOpj8fgBPzv7fB+BE8dvxAG5LKacfVy+S4bCEW3TikNR6mTgzs2YiydK/1vRZQNiwod2PyZNOZzHjusgguFg0MbdBmy3ZVMrvZquADNBisxa/QwdaMQNiAYEZzNxcvuinpuLBKLJtzPSZWOoc2JJJLS7m2rNkiFoA0++QzJcXKBNyXrxSA9B73JlpSndFzGLBRFMShdAiZ4bHjFIzeR2lrM2aTBBD/vyNG/N4jDJzsRRCmehLBqfN1+yjlvuRtYmfYxdYeGu1/LNMRFl75yC6iYk82pqZ+7p1+XbM2O4P6ffWCWp4PukIce43ZvJSA3WuXfvT56mzL5nbI3eZcD9J90DRjpl+Qwt0knlJl4N2W8m5rnewyLwK0sS9uJgzai1AakFRjp123/AalDt55FG2XFemQ/8/e28fXWd93fnuoxdLtmUsIwNHJOTIGSdYSZ0Z7DLFTcNSYFiEtOQKrvsGvtOZqUyi0jWz5sJdN3OpMyQqayUzZHVWuzJugzt3bmOgvfHQ20JpUkrigbSQEtlAXmSIAzhOsI0VLCPb+E0694+tz/y+z9bvOZJtySHTPmudJVt6zvP8XvfLd3/3/mmRnrjXWK/ML/MT95GWZF67NjkDGAS8s+xI6/N9zYWS/zsz+9+m/v2Ymf2VmbWbWauZ3W9m353Nc87HZz7K2kavM14RetZFrLB4hJg1vaRWmy6McnEqFWha4CF6CarQouID9mJzQ8pDaPFOLdvKGFDdrqenWJdcPRkgdoqjlAk2hXcZk1h8BGauerQaR8azQjnEutm50Em9ngwFjSczNlpHHQ9Ex4V5Aa7j79ovjQ3zuxwTG4MQVEHDErQHQaJKQ70gDDQ1OMo8+YhuRMgUYaVojELejB3CG+g7xweJ6xXGO/OpaxihzngCpWsJ2sj1UK+d7ytsitfK+MVjRwcGUhispaU4hzGkoChS7A9zTchC95mGh87nVWZQqDGn8knbWGYcx5RQ9hQoUkuLOw9KkItVIev1IvoHo59sJEItGLwUt9H9DpKpdeebm9M6JIMj7kkNR+DVUwCHuD0OT7WakBstAhU5NWXZFef7mgsl/7+b2Wen/n2VmR2eIt4dN7NTZnbrbJ5zPj7zoeQbecn1eto4wIAsVg7D6Oryak0tLb6Y9MCGmM6lQlwXjb6DxYeABAFQQYQiz5VDjcS+XP/i7xS6r9eLnh5Kls2hgmM2F7AeG5fvX3ttgs80jk3am25+rXIGpBth89wBG3jzCC7GVGHvCFUi4PTeqHxyJ65pLD2eA6+Czyx5xQgVUBtNbwLuJnQSPcwc4145GfxNOQM6ZvFseoQmnlalkgxB+hrjvWp85gxW2qzGMMqRtcWaiogCa3T9+rTPUBCgQXpoCYYANSpUEecg1xyKp0oSZa/rkysa03Pp4c0GESgLDeTWGwcMgZZET135LDpOGIR6wl8k9MbzHer15EWTIYFBzzwjNxTlYh6ZX9YMsfQcMVOdJDVeVTbyXUUOUP7USyCvf3AwvZtKijney4/jmvM8eTO7zMxum4rH/9TZPGO+PvPlyTeCYiKDmBgrdb9VsEL4aGoqsrKBDnPx2Hq9uDlZ+FiuVFXjXlVWbW3Ti0pEKDl6uTlLXlO3EGD0iRgl7VJvNHdF75GDK6hcp4o7VpYijY0Np4qxWi2eMKewMZY5hCuMHyWJ5byvmYytlpYUr47KQT3jWF8gVz4UkpJ6zswVwlZZ+0oUYhwVEVKlxNqlQh0VAhFaQOIoyEZcB4VTFcZkDajxF5nwuZ+6ZsiTBsanShnjosIUxc2BORg/KrBR0OrJayhI0yKVd6FoUM74Zl7KqhGCIuVg47JrtnD+TPJInxUVnhq9rAPS29iD7GfWkSJG9DUWLSLUogS3GOLC6NSyt9pn9nNzc1rjIGk678hTkB5khlYbjGMV0TPQHdae1nNAbtdqRacD2a33rV3b+HCc8xWi+YdiOOd4lU2UbniqaLFZFHLGC8YaREAQx0I4sng4AMPMN2DOkyzzDLlHlW5k8LOBEICaJlOvTw9PDAxMJ9bQXwR5jBM3Emwa09Ucf+LP69cnUmBUsJoyB4SIxa2QHwIGy5yKZCiwCE/ynohYRIHKc2O6TQ6yU6GKcEBg5ZAhnq2eM14UZCINZ2gMkjmOEHNZWiBzq1453jL/J+SiXj3eS7VaLMyjgl9r1zMmxOFRHEruQpi3tzuhij5RnEbbVq0mAc0e6eiYDvMSw83xWvDktBwu+4I+AS9HfoGGIKLBoCzxmfZA2b441xr2jZ6p+5hKf0om0/z/SDyLSGHkp6gyjEiYZoTEYl06RsPDRXQSQ6RS8f/jmDD2vb0J0sc4jIcVcR9zxfywTji/IJ6ihwFAqhzvIDyo9e5xHqJBokbOmRh7Z3PNmZI3s8uniHYfjp8zec58fuZDyeulG0ytRF3kVAerVtPG0Rxz3UxaIQqykcKXLS3TWaQaWwWSjDFLhTLJDda4pQpltazpo3ravAvosb9/uuDMeWllC3p4uLiplOGu7PEYU4M/wLhE7oIaLrQfjzDmzuas/YEBv0eJO5H0pwaawo2MsaaLKRlHUYccupJbY5rWR9xdC/BEwhDKtaWlaCjkIPNoiFBFjnWrJEsY1wjfaJTi4bD+NO4fId1qNe/dMW/qUWmpXYwzULC2ttTfrq6id00GCKiYhp7UwNS4MEgLBnbOk8ewY+4x0PWo5ba2Yly6kfw4k7+dzX3x3uHhIlIGvya2E5gcmVCGwKhxGysbqiyiDVpEplabHubp7U2nwmlxI91bhDiVh8G61foHKl90LcNfiqnEWpobRBGUCANCZYEa+6wf3ZuaGaRnIcxl2EavRkq+xWZxVSqV1Wb2oJn1mlklc0t9Km/+f8prxw6zzZvNBgf958MP++8HB9PPsTGzJ54wa2szO37c7M03zcbHzbZuNTt40Ky72+89eNB/NzZm1ttrdsUV6TlDQ2ZPP2124ID/v1Ixa2kxu+ii6e8eHTXbu9c/ZmaTk2aHDvk9991ntn27v+Oxx8zeeMPvv/pqs127vC21mj+3o8Ns506z5mazu+4y27bNbNMmf+YTT5i1tpotX252zz3+9+XL/W8bNng/WlvNrrnGt8eTT/o7+Wnmbcldx4/7z6Ym/3noUBqDffvMli3zNv/2b/t7urvN/vZvzU6d8nFZutTbfc89ZmvW+ByNjPhzli3zv42Pm11yidmrr5otWOD9uvVW/+jc7t5t1t5u1tfnn6Ehf9+XvuT96ukptr1SMVu1ymzlynT/wYPe3okJs69/3eypp8zWrfPPzp1mP/yhj9WpU2YnTni/d+70969ZU3z+mjU+bhs3prWzYoVZZ6fZCy94P1etKvZl40afLzNvc2env3t01Gz9ev85OprmZMcO/06t5s/r6TE7dsy/d/y4r7tTp/z+lhaz664z+7M/87/t32924YVmr73m6+7IkfQZGvL3XXedj822bb7ODxww+/73zW6/3fty113er23bfKx6e33eV682+4u/8Pn64Q/NTp70Z33+8762azWzLVv8Gb29Zg884G1/+GHv73vfa/bccz4GlYr3h72wfbvvo9FRsy9/2fdoc7PZBz5g9vLLZocPe1+vusqfvWOH37tsma+lvXt9fsfG/J7OzrQHly3zdnzrWz4OTU3++1Wr0rozK+7huDeY95muRs+IV3zmv//3aX+3tpp96EP+7x070rPXrvU1sWmTj9lDD5l98Ys+Vuzt0VEfo95ev4/xNTNbuND3BvtyaMh/Pzrq4zg66nugXvdx2rUrrSWuJUt8LkdHfY/39nrb+vr8PX19vg527fL7jx3ze9va/Nmvv57a8vM/73v5ssvMLr/c10Bfn9mdd/oa7+nxv+3dazY87P1sazM7fdrlzGuvJfk0MmL26KNme/b42hsaMnvlFf/+kiU+PqtWuVzXtm7b5t9H1p/Xq0z768fMnjGznWZ2g5m9y8xq8TOb55yPz3yk0OW81mhFa7yU2CrkDPW28FajVQyk29ycPEW8nRysTFxJGai5GKhWSKvXk1enzHV4BLHqHjBwPHVLGdJYr5WKvwu2KweF6Dhq2xXaUgIT39UDeCqVFEtXDkCs9c8zNU0xHtcbLzzslpbiWOONEucvI0TSNyVi0U48emWOK7msDAnJzTdXGVdj69a07igIkvPmYxpUPAVMixBBetMSocyTVjjs6SmezQ2ZqVpNa01zswcGiqx3RV1qteKpbtVqOeqiSBofuCKgGWXQOfNeqaRwBKl72k6N58IdIaVO2824ajgprtE4p2VraSYv/Ww9ef6vvJZcGmtkyFeraY0oCqkZL4x9hKaZa+7VnPqWluSBK3qJZ61hRN1jEV3TNipSoRyCRiREuD565HLkb9A/QgeKdLC+Zotkzsdlc8CuP2Jm18/m3h/3Zz4OqNHSi43uiwQphfxyJB02FXAOyqBR7ne9Ph0OZWFHwl40UOr19F6tSY6Qg8zHIkdhkCnQ2ppIZgi33t5ixTQ9KETfqe1QOJDNS3xfDQfep8xb+qn8AmK+vLe3t0hy0zhdFLA8V8MizBkCXmvfx3i6ki6JYaPkUDKqfONpWErO02IgZYpA4/yR2EQsFSJarTY9nKFpUMwrcHusAYDAUlhc+QWVSpEg1d5ehEmr1WIMu1bz+VTIVteEZj8An+bS95QoGfcSYQfeD3cFg4U5IbyhFc3og6YX5ows4HkMGxQBBir95J2Re6G8mqh8VI6c65Xb//39KawQiYbwDwgZQpxlTgmLXHhhMRwSja1owMZ4vhrEpLOxdjCQ9QhnNRaam1NoRMcoxtS1KE8Z54Z2YxiyR5kDrShar6fvErYhayIaEdEQPx9XIyU/K7jePE/+HXMKIfyEXGNjDumsWDEdVuX67d82e+SRdP/27Qnq6u11iGb3bocE9+516Gh01OHKQ4ccImxrc6jyhRcc7vvGNxxKBybatCmFDIaGHPpsbzcbGHDocWIivf/ECf95xx1mL77okHVfn8OzfX1+36OP+ntefNHs/e93+Im+fv3rZhdf7O1asMCh+U9/2v/2p39q9gu/4O/t7HTIedEih0a7ux26/8IXEixm5u0+eDCFHcwc9jp40P9m5lvsbW9zeOyaaxwKq9f9nbt2md1/fwoXvPyyj0t3t8NrTz3lYwH0//3vmx09avbJT/rzGJtt2xzSfvLJBP8vWeIQ8MCAj4GGYPi5fr3DzKdPO9w5OWn267/uf7/9dg+HtLf7uD/7rI+Fmbdz2zYfn7Y2b1Ol4jD0N7/pcGlbm9n73udjuWGDt2F0NMGxZg6XPvlkWlNm3ueREYe4jx0ze+kln8d/+S+9P1u3FmFiMx+75mZv65Ej/v/rrvN5/9u/9fYcPOhr9sYbE6T55pv+/SNHvF0LF6Y5u/deh2ZPnfLxMUvvuPde/z/hj+PHfV7feMPHir4DZ15/fYKSv/pVv+fECYfvR0b8uwq933prCj2Mjvpcvvqqt8vMYVYzX3ff/a7P28GDPmZ79vh9l16a9s3EhK8hoGYzb9stt3i7env9d1df7T+/9jXv8+7dDs+ePu1j+NBDZjff7GGNWq04B+wHwgs33jgdwq3kAqJncem+Y//v3evjsGyZ94lQE+uqudn3/IkTvkZvuintH8bjwQd9LpuafN3War629+512Hp8PO31NWuS7DIr/n9gwN/P3HR2+ro5edJl3/PPe1tuvtnvveoqlwWnTvlaHBsz++AH/XdvvFEcu9ZWfw/zSLjzwQe9rZ/4hNnHPubvRG7Q/507/V1co6Muf7Zs8b1wySX+efll7/vjj3u7azUf47GxtAbfEleZ9tePma00h+xvNbNLzWxR/MzmOefjcz7Ok4+XVkaLXplad2qtwoTGI8KL0nO+K5U85KShgcsvTwQWLGasSCxUPDoKd1BtDDQgWtg5AhzjoOl66pHnvBKumWDJnGcTWe7KPFd2thbQoE1DQ8UzzDlghnshHGqJ2xxMHj3/SNZRlvall6bfg0aUsdzJfQcl0cwCJfXEtD5CGPrMOP9lY55j+pN3zpqBiQ5ZUwuEQLDDuwZ10mIhubBHJDJyhDAsesIE8XQzhW/JadaUN05r1HWjfWO+mTuFY5Woqt+NHufAQNpDHR0JJUAWkDq2fn2RmAqyRIrXbPZDo31xtkS8SODUQ3Xw0uNag4TJ3mFt4dn396e5IbuEMdKjeHXvRE83yjJFWbR4laJ3uk6Yp0jeZY5YL/pc5lnXdHt7MctJ65vA7oewDKM+IqaEl2JZ3hwKG1GNubxsDjz5UTN7xcz+qME9/1MS7zo73TpUUlu87rzTvZY77zT78IeLBBG10isVtzArFbNnnjG74YZECDl2zD2MRYvciq5U3Bq+6qrk3Sh56gMfcC923z73iH76p9N9y5f7e0dG3FLesMHvXbfOiX0TE26FfuhD6TtDQ26BdnYmC1jJLqOjZh/5iN/z9NOpT83N7sVgnff1ufVvlp6zZk2ypLHuIyEIj4xnfOxjbq1/7nPudT32mHvox4+7J0Vbzbyfe/Y4ysD1n/6T2Y9+5J7AlVe6p7prl6MFo6P+7He/28d9bMw9Y9px220+Xn/8x/733bvNPvvZRCAbG3OE4MiRRBZ6/XUfi0rF7D3v8XcdO+b3j4wU18u11/r4r1jhzx0a8jnZvz95PGNjiaxz4oS/5+RJXx/j4z7Ht97qbbrjDn/e+vVpDEdHi30aGvK1ph5KW5t7H7t3O3KzZInZD37gBLtTp5yIxtXU5PcfOWL2zne613bkiLe7rS0hGcuXF1GnTZsSArNzp3vak5MJeTl0yOfp0kvTGjl40Il9R4/6sw8f9u88+2widG7d6nP05S+b/czPJORh507fY9Wqe3+spyNHfC28+moi3EGcHBnxscPj3rHDvUdIrZC5jhzx/7/2WpqnCy90j3d83PcxRNo77vC1sGSJTbsaEez0bxs3zo5gV0bEiwTOiy5yRIUx3LjR14kiI8uXe1+amnxNjI/7PCxd6uN5110+dhMT/nn2WZdFZj4++/b5ugYhMJuOjvX1+X7evdvfX6+7bDLzvb1+fUIW9KIPa9e6vMV7p6+XXeZI3WWX+Xzt3+9/O3DA19uiRQk5PX06fdrbfY7NfL0ie/77f/e+Tk76fmhrS0jBzp3e/xUrfM7NvN3sWTPff0NDSR5v3ux7sFLx382GZDknV5n214/5CXQ/MrPPmNlGM/u1+JnNc87HZz5OoSuLrdXr0z3gMgtN79NTyzQW2SinUuNrWIwcm0jOeJk1r54ScTeqPUGGiafa5byASDws88aVZMUVrfn4XWJ0eAAQwjo7vV3d3UVOQFlKGFeOOBiPrSUOz9jxbkVTIhLCvcqFMEvICB6kojuxoAgxS00h0/WjRCbK9VLRLZ5hrWOr3yFWyn3Ke+BwmFzu8sBAvX7llcX+g+BoZTdFdsg5hiCpMVnaB+eESmKQJ0Fcenrce+ruTuVKY7yUOCkeZKwPEYljtIPaAeyVSAyLCBTtVQJXtVrMt6acL2S0uA+I+Z9pXHam/ZXb3zN5h/E5IB5KIGSPwNUxq9dXrChyUAYG0nkIrP3e3vReECola2parsboNR1XSXtwS5qaUjonPApFepiXtrbioTO6p6hcp/uZPHdNmYN0DBoGvweUioOWtC4JyFo83lrleUQnZ9Il53LZHBDvjprZLbO598f9mc/z5HMbVgkhkFnKYGk2AZXuWIxK2CtjgrPBlFAGoQtWcjyZjHfH7wFDKaM95oEqKSUSDyP5K0KmZVCVCikVrgMDxSMkdTNEQg1QLW0vE25btyZBBCFK82oREirAFNIDqlRSUq2WlDlERSUDMieM74IFiT1MYY5c2KDspDkK3mjhIIVJo4GkLGUEZCzWUq1OPxQkzpfC73pUKGSmmNsO65g2cuqYhlq0aBFhB56BoI6hAd6lJEuMJcILGD/AzX19KdsEAibwK0otx5zWtc04dXenkJkarbpegXLVQNUDoM4Uai8jbZUxxBsZz7n3aIhHSb5ksei5EGbFdiCfNPtBTwXE8IsFiOiXGojsL4XpCavpmovjQOgFJathEvYMa4Lx55hsQjW6vskKqlaLhGfdx4sXe0gGmQTkr6W0q9Xp/dT1RGg05wDNxTUXSv7bZtY/m3t/3J/5SKHDO8op+cgWjYpWlZ8WBIkWOopNY37xHeohqbdbrRatzFiLPMahW1uTt6OlcVXYKZNU60ez0HPHQ8Y49kzjyn0w0LV6mnoKegQtMW88w0bGVzzJCu8ddjQFZfAA2YjqVTBHqsgwRohPxvPf1QBDEEbBrx6f1uWPx+uqIaEfshcwMokPxlQk5XawfhBMVCWLl3oyGsPUGL6WRUZo09d4xjZeFQaAGsMIQZ3j5uaioctYaQZIFNYIWbw89gUePYWU4pkHOcSJ9D68f7xd1jR7FU9ei0xt3Tr9TIlGeyHHzD6T8qiNjOfce5Bl7ON4+NHQUJpzYuxqBOvpjy0t/j2MVoyu3t505KumB+O5a2Eo5k7RBdj2lI2N/BRi7syr8oh0TYDAaZojRgy8GdaZGlcqk7X4lhqgrCuep8dsxzmPvJTzHZOfrZL/sDnxrmc292Yaw5cAACAASURBVP84P/NV1nY2kxOFvOYB9/cnQY9SVohdFz5wTvT2FYJVC1E9dDYKv1ePF4IXXhXwqpKoEA4KiXGEpAp7NhhEKohQZSGHRiQilKwKfjYrHqfCa2xSSk3qxlRIEkWbU/aVynSyF23QHHslxun3Yxqc9gslgDcNQQvFl2trhEzVYMsp+c7O4rzzfeabSlwYQhDXBgaKRyDnYGHgUgQuY0i6Jal6eiAI93V0TD/AQ+HSnOJDCKrhyfqFxKbHjDJ/OifUtddxR+mAWumpekrCQ5GxTgmpcWxqTGfV9bp1a0qpIsdaDRo1InJ7IP7uTOH3mf6mBlKtlupqRGWWCxnVasljRl6ppw8iiXPS1eV/Z56WLk2GMHKN/lEBlHUFQkDdeGQj6wrHSA8uyoVEkKOsz3jqoR6YowTm3F7G4GPdsicIGSo5EBJtzsAaHExnBMxXDv1cKPlnzOyA+clzL5qn1BU+s3nO+fjMhydfdqRo7l6EILWM8U7VANAyqFjtEfpV5j0H36hHEWtqA2Oql4SibmoqwtIqlIkzxmI6g4PFmuFajxyouL8/tZkNrZ4iHm69Xu5h5IQaY754cWpnc7O/o6uryKBVpcnzUfAIj2uvTUYBferpKb5XywSjMPUIXp1XBEzMdiiri4Cw1NrbMZaspxgqDK4KUmvwE19XZYvQW7s2wdN6+ApGBEoJhjVCF+9OjQlVrBzhyXur1TQGtVrRoKrV/NmakcDBI1rngbHHuIEvQildPSkwohRqhBBaAXLWA1RAOKrVZDxpDYRYsEaRHYS/MufVKNS+m6X8cTzNqDRmU5t+pvtm+xy9F/mgClAVjSIYzE3cexhtGEDsKbxtVfZ48uq8NDcX6xsgaxg/6g6onGM9EubSkJfm70e+gdbk5z2xMFZ/f/5AMcY1IgMgFxq2YT9glCq3iEvRpVptfs6db6TkZ8uu/9bU5+/VtWOH58i+8IL/v1JxliSlGpWNbub3fu97zthsakrM62ee8Z/33ef5ll/9qi+b0dHE1ly1KrFAr702sUvrdc+rbm93FvKXv+zMzCefdNbx0qV+n5Y/NfP2rV7t7Wlv99KLW7Z4e7dsccasmTPAr7rKn3vVVf7ehx/2+xYtcjZ7Z6czbUdGvL27dyfGeEuLt/HoUWfdv+99idU9OeljpPn5mm0Ai3n/fs80IKd182ZnLMN8bWlxJmu16mP90Y96OyhVa5bKbMIWrtedjd/W5u16801/1iWXOBP+n/wTH8eHH05M6QMHnDl9xRU+RqdO+bvJoTfzkqmnTztLd9067xe58kNDzlCu1fx7l1/u39m3z8eiudlzfycmfFxZV5RaJfPhxAlnZR865PO7aJHPU2trYjKb+RqYmPBx3LHDn3/6tJdWJUNj1SpnCpMT/cILnoN8443OBiavd2Ii5ZVXKj5unZ0pm8PM2/yOdySGNqV9Bwe9bVu2+LiOjPjY3n+/3/vKKylD4HOf83X75pvOXod9/MADPg/1ur+7rc3HdMGCxGqu11MufkuL2T/+x55V0NXlbbvuOt9ry5f7O7//fX/v7bcX6waQgbB7d2Lwd3R4VsimTdPZ4D//8/7u9nZnhddqqaTr4KAzrVkfExO+V9ravByr5sjH5+o+0MyTsvtmek6je/v6nBm/Z4+3cedO33tXXOHrYPfulK2yb59nWdTrLsfa231tbd+eGOEw0JuafK1ecYWvgS98IZWJpbRztep7fGLC53vXrvScW29NNTAOH/bPoUNex8PM7Od+zhnrJ0748264wed3dDSVriXzySzVzKjXfW0MDKQsp6EhX3fd3akGxIkTnrXC3lm0KNUUGB1NpZ3N/JmVynRZRibL8LCPK7KA7KQNG8w+8xl/14oVac+ct6tM+/Mxs1Yze7+ZvW2me98Kn7n05LHUIAsB0+ApKiGHe7EIga6AePDa+vvzpxVpzD3mY0NMUYJPWeWv/v7kSeABtbUVD9bQuHxfX/LwFLbCIgUdwHrXkILGlZV4qFWr4CTkLFdIjcD/2g4l1BDzwwPEqgZCxIOgn+S+wozt6UmehM6levIRNqdqHN5AzrOu1VJerTKJmT9+B/zL4Sd4x4sXF8cC5rKS0oj95djKkZTIR4mUCmlGYideHh4SSInyBpiHSPaLkCOQ++LFxbVAbYaykAPoD548e0O9QiX7aWwXz4xxAE3SA3E0RKIXfQMho96BepF47FQqZF1z2iT3gdTwXe3TTDF05n6uvbvce5RLABrGPMXT4WKsO8b9dd8y5rlMDZAaUKJcqWv2tZI34dpoeKBWK/IitCpiJMcC/eM5s3a0Sh/IhpIFkUGEHJWsqkeBs/ZUjihKS5aChhPnKx5fr5+7Jz9hZl8xr1v/w3mzNt6CV7SYb7kl5bJ3dnrO9WWXpb/v3u1W6qpV/v8nn3TvZXLS/80hEBMTbjVSYe3mm9PhHGbuVfT2uify8Y+nvNOODvfS/s2/cauZ96gXYOZWab2ekITJyYQYvPe97uXw/eefTwfUcHBOZ6cjA0uWeJs4BGLx4mLuNNfAgOfT7trl1eoOH3avcPXqYs40HkutlvJcsbr37DG7++7k7ZmlQz/M3PresiXl+C5enPLlyYc9cSKNb1ube8EHDybvsKXFrf7JSbM/+iP/ef31adxoK5WtWlu9b7297sHt22f2j/6Rj9+iRT4vzz9v9va3uyeqz7n9dn/v9u1uue/Y4R5DvW72Uz/la+XOO4tr7bHH/L3Llrl3tXOnz9uKFT5OVCa79VZv5/j49LloafF7f+EX0triUJXt231stX7D6Ki/5/XX/b6WFp9HqoytWJG8efLEc3UPNmzwOV22zOdy2bJU/bClxceSQ2/wxo8f93aRn6/VD9/xDp/Xw4cdGXj5Zf95++1p39xyi/994ULvMzUPbrwxeZVtbT5uIAbUr+CwlJ4eX1uvvebv3b/f7M//3NuxfHmqQ9HW5kga7W6Zkpx33OGIxMUXe2U+DmW55Rbv0zXX+Jg3OqDmTDzz2V6592zf7mNUqfiaHB9P6Ni2bWlvaaU4rpdf9r7ceWdCnsxSbvi6dT6OX/iCy0QzH2tqNtTrPi+KpIyOel77F7+Y2nXJJS5Xf/hDb88tt/gzly/3cb/ttvR890FT38wcjTt1Knndy5en6nivv54OLaIKKfL6s5/1Pm/c6HP+5pu+H6jcuH27VzXdv9/RomeecRTovvvSoUZmvu4HBvw7R44Uqxdqlc/zliNvNuuY/Lfs72kKXb3uFmJHh1uXpAdFVu7wcDG+R5xTU22UZFerpRgkBB9isjnGL/m/SiJTshIWN4zsCy5wbwavnvQUfQexfD32U2PMkYg2OOjf6+jwdi9cmLxO7TuePTFf7ccll0xP09GPxuHhEmh+NP3HSteYLJ48rHnGGq+WgygWL07HWhIn1z5r/QHGA6+hq6txqqCuB/Vk4jnvET1QsiC/Y73hLeRy6TU/Xcl7eL5K/sy1m3UK10DRASVDabwyR7Dk+aAdyithHkllol9kOsTsCeYTAh8Imcb62Zd6cE4uP5m2gYZwroCSBhlbrfTGWFerCRmJR0Drd/RMAuYnF+9vRJrL3TOb+2d6hv4uElo1zU/Xn8op5B9oWI5n0N9fPGo1olJle4F5oS3MF/yAyC8CSVHEIZJONUUSdFTT2HL9jWsKdE6zbHS9cTRxb28i9cHEX7gw1YNQ/lLuDI25umwOiHf/i5ntMrPVs7n/x/mZDyWvcGOOUFOvJ6EMqSsuPi3ogABCCMYTs3SRqvImnQfBzgEbbDygbCV4aX40RVKi4ouwtZJV9O+MQzzpiUVNCk6tNl3ZRgIMz4CgA4OfEAchAd4PBAeDGSgbwQshDUiPjYXggdUNlKrQOzAz44vgyQkYxg2yDjAkBMZYuKNeT1A8RTW0PoAadUpIU8KSFtSJZD8lb7IOGQdSoRA0mu4GKbK/P+UVQ7DSIkL0laI8HDAD8YpnxZK4GFOESGIJZuYwKs6YRaHlbIFK6/U8M1wPByIPm3nBaItMbyXQ9fcnw6+rq0guY8+g0AkPxVCEKiAyTwjzzVaoK3x/JlD+bNj7GOCE7iLRTuFsTQ2MqZRKakP2qPzT0/hiWEcNejU6dW/GVFbkStk+0xAZz9HaCMg/rQ/CutAwgGZOYJQCuefGU9chTHzIn+o8aUgHw2MuwzNzoeSfMbOD5tD996f+//eCXV+vF73FMs+tv7+42YlX6WLQ2ssw2NVj5FK2rtak1+MNowKGkc4i01gUBgJxb0UPVNDTLqrp6WlNevJUT890tjpKVFOI8GJygkoZ9Jp+pyiFxp9R1lqkgnQglDOenKZAaXEW/s7mp/0YGJpLroYcTPumplQ4g7mJ6Xk8Q+eKuHgu71fnjzghSpCsDorqKIcjGogYNrSNdaWClvgxilk9J13jqkwxCjTTgn8zfqx1DFc95U7rKei4Mi6sqZaWxLwndhpjsTlvENTskkuSt1n2IX6PUaL7QmPwisBheLLXyGqIRyVjlLOmMWJ1bGarqJmTsrzrsiu3z+LvYtoYyoc9p9lA6snH3HYQFkUDUWCKBuHlst7VWKCQDkawphcz1+wllKzuEfYqKCPoUDTIeAbrjPfDNYLjwfjzbuUeaFZNVPCK3Jg514RxxnlQRCxXNO1cr0ZK/h/Y9bO4Vq/2mPr731+sj851//0e11y40GM5sMH37vWY3cqVHv/htKXnnvM41ubN/p1KxePU1HxftMh/8pynn/YYDzEdWK47dqS6yI8+6s818yW8b5/X1R4bS+xuWOOHD/s7K5UUg69Pxbf6+ryv3/mO9+Xeez2m+MUv+jOamjyW19rq8SeYritWeHwNFnVTk382bPC/PflkYqWaefwLdvOnPuU1/x94wO/ftctjYZzid8stKd5+4oQ/i6upycfr9tsTu5WTtogF/+zPeh/Hxjy2ePx4igu/4x3evgMHnANx5ZXO7O/o8DrTZt7HiQl/JqxfM79n4UKPAV58sddFp7786dPejrvu8vE/edLbOjrq76dvzN+aNemEr3rd27typbd3YsJ/B0N8aMifX6n43/btS/HE11/393R1eWxz1y7nWxDvHh/3PlNvGwZ0a2uxPv03v+nr69lni7FPeB5m3qdLLvF45Nq13r7Vq51bMDDgc0oWyvi49/uee9KcvvSS96O52cdszx7/ztKl6f7JyXQCJDyARx7xNdzS4n0/ftyf+eyz3i5qq5ulmuQnTvgzr77ax/SVV1KMdsWKxC944AFv8969fs+xY4lbQSYH62h83NdcZ2cag099ytdDc3Pai2fCqN682efkxhv9ncxP5N1wKTNfmfQbN/r/2c+ckEb2CWc97Njh2QMHDngmA3O9ebNn+WzZYvY7v+Mxcr2OH/c1b+ZzQCbHyEjaW2YuQ/bs8f35wQ+mjJLWVpcrIyO+Xk6d8md2dDhHY/duv2/RIl9nmonyyCPOoVi+3OXEvn3p1MqJCV+7XJWKr0Vkw+iof+fyy/093/ymr4Mnn0zZFcjW3btdhpr5+yYnPSZ/1VV+ih1nJzCm8JXa212+d3c7LwMeyqJF3mbO+ig71XTOrzLt/5P6ma9iOI3gNjxvhZnVm4s1uM2SdY4VzJnybW2J9YlnAoIQczA1poTXBkweUQCNswHL4jEqUx/LH8jt2mtTG7UUJrAoBTEiK1UhqTLIEQ9ZkQyFyzSWxfgQB8NzAoYDhdCylrFsaS4DgvghHmhHx/TTAhWKW7iwWKxG4UUNDVSrxfADiIJCt3Fc6Gu1WvTkmHflSCjkqJ48niSFQDR2qYVf1FOjjG+1WuRL0B5Fp0BOQHI0TAAaAmJBG9kLhCVApVhP0VvXMc3Nn8bV8fq13cT9OzuTh8k4sWeYE8Yqlm4mlKIletWz5FnaN0VXtLJajlFeJkty8XiQirIMlUbeu8LzWplSWeqMl6JkWhsDr5eQGLA8xZGq1WK+vHryHR1F7g5eNuWh4TogM9nP1D1gjTQ3F2PpoHKKQumH31EYjP0Uw2jq2WsVPdoFqkVtCa3syFoCcQMN0fPm4aZoOHOu4/H1emNP/oyVqJl1mdm7zKzrTL97Pj7zUQwnF4PRSVIoi8pbKE5ilJDsVIDyvKgcWUgIUhZpJOWxgYGuib/GYiN6aSw3krI0DY/4pcJNPT2JpKWbiEWv5JdIEitb2DONLwpB685T7CQXitDYOnBcLAaDktZ4ak9PantbW4KiUQ4QwYCiNS6O4FOCGptbY84zkbDoq1YYQ6mTRoiQIO7IWLAmMCa7uop9po1AhbyL3zGHWmGvWk1HwlIGVKt9xbZrP7WIiApPBD1paHE/sHY7O1NdeNL8UALKlyhLI6TN8SwI1rfyVDAUtOJj5HQg1DV1jNAUz2BdKSStz2SczvTgmhiq0MJL8f/6O2LKzCV8GJUNWqiF9alriD2hIUP6xjrVMq/MFfKjpSXxblC0l1+e9jPhD42RV6vp+6zNlpZiyIG1oKRM+oGRiaGulQejEaXlp1HiMQTH3tNCP3CDCDnpulcugPaZkASGx1uKeOfPsF82sxHzuDyfETP7xdk+43x85uOAmrKzkLmGh4sWIUIYwcjmySlfPColk8D8Xr8+CVSN+yl5LlY1U3Jb3Pz1etEKjvmgCGFi+1RP0zgsgguSk1rqaqBotbszuXKEKo0bcpgJ3hmbmP7rfbSFMdIa8gifMnRFn6PxPby0mNGglfLUy4fAtWBB/tCSKHR0TugfyAvvRAHVatPnnZoAnZ3T6+BHAmatljw0zQOnkhyZGWqs1GrFtmv8WNcxMVLOCGBclJMSFQ77SwUv3iX7CAOL+9VQ09oQMJvL8tV1L+iZDMoDgTzH+mE9El9VA0PXO0pSeRn6zlxt+kZ7IUdc03h2mSdPTJiSqur1IpNwPq69NinjOC+6JxlvPd2RvaVeKsgO60bJo9T8AClRY69SKSpGPUBJayXoXoQnA3oHmhX5Qsr3gHOCrFMjvlottimeNqexer1v6dK0JyOayd5WNPRs5GPZdc5K3sx+1cwmzewvzOyfm9n1Uz//YkrZ/8psnnM+PvPhySvcXcZg1RKLyqbUOtwIVfVaIMGp1Y/ViNenLOxaLRG+8ByV5Q0ZDYub+uRckcCnYQXeoYQtJY6pJ6voBV5CJOfEFLo4rqrcNKxAeAPjQkkvWsdci2dofxgbZc9DruOT8/ywrrWoBmPKOMeDcYC0I9wM/MhpdIqYKGSq4QklWsWx5EOpXk1N0vQ/rc+egygj1IlxpyVMEaya4oZB1NtbFOQgGPQvspRBGjQVM+6duL+iclUvKLKq41ypx4jByv5TBELHWj10vC+UcYRn+/rSulSvVomMsOoJmUSHoBEDXhWqMv+5YkGjaGjFPuo+4iwD5lkzNkhVpUgWXjLhNj0AS9cPCjWHltRqxSN+lSWvoZaIdkVZq1lJhFrUc44OGIgpYQqMLM0YQLbRBoxxFHq1WnQCkMX0DYRLPzgO7ClkDacx8l3NYpiray6U/LfM7PdL/vb7Zvat2TznfHzmIyYfrWWNZ2LNskGw+BBKGi9W5vTAQLL4ifuhqEiLIvUpplbhGaKQYxwsHuaB4IreIpsWOF69UVJH1ONHcUW2bU/P9DQS+rV+/XREgTaoEImVsrCwo9EQlR/QuoY/8Cg1PIJwUM+4tzdZ1m1t0zkEGFTUyFd0QZVkPIkOeFzZu8pkj+ER5WVoOldktOMVqyfH+kQIq2eSgydzxhT1BdRgUo4B3hBrTlPK1IBSw04rgkV4P7YjVlvjo9XD6NuCBX6/Mq6Hh4t59DqmeGgoRhSOhnxiXFcPpWFcCGuQtkm2RM4w4F7dL9GYKZMv6oXPtmqeficaFRgwyAMMYGovqBGk4Qn6SBgiGpaECjW7gjlWmRKNMva6pr5yfzSYdB/qQUOgiKqsdY0NDRVPtWTMOLSJkJoidnCLMEyB/RkLFDVtQs6xF4jLs3+Q7RgIEQ15Kyr542Z2XcnfrjOz47N5zvn4zCfxTi1KTbHRQzQUOu7rKxZAiAIWBUrucSQGxRQ3jQER7yK9R2FLYua6cfRIVlWya9cWYW0l9UFgU0GmFj3P1jKflIRVyzx3AEjujHv1RlBWMZVKlR99jYIzChKgPWJ9jIcaWlocRIWV5t7rpQQxjSXSBzZ9e3sxzSsqXp2P3PG2mtvb11cs/RvDGsQhtTStKhCFijEcmV9gU5QX6w2EiPdCrtP1hueqShYDi/UZlRLKEo4HaxsF2d9fVHSK7kRFoAahrlOgagxY2qhCnjnWfqvRqEcuY2xpiiPPjgagpiRGQ1aVsMqXnPfaKG4b741rQ2P4GEiR76GptBjIXV0JuVOPWQ0ONR4woqgdET1g5UHwQUYQ/gGh4J2Mq6JhtF2NDMh+EfFgX2txKZVX0SlBWff1udPS3Fyvd3cn5y06EaxfFDXI1+LFKVSnRh57jz1ddkT22V5zoeT3mNm/K/nbvzOzPbN5zvn4zCfxDoFJXAjFqGSb5uaioI5xyBzkrwIAhADSyOLFvtjMHCLCAMCrU1IZAlljyAiaWPSBNqjFGWGpgYHiUY+xP7RVC7LEmGpPT54YpMUiolcLmQcBEz0iDR9EspRyGBRORTDrsaCKkmgtb4XBI7SrApU+QLJDSfT3F9nVCher0FbSVM7Toz9qXEXvDaFBdUM16GLbFWrVsAZZBsSwWUMak+X7KHQllzE3GrPUimdxjpWspO3NnSqWg581PKD58cwfIQ0yRMjpjuTFoaHk4bJv4/HMOe4MvBg9bTHWAWAPqlFV5smf7RURxujRq6EU17SGvXQ88WQvuKA4R1prQbkXEXGrVJIsw3hThEMVrJJgdW/re1G+kbAWeSnqJLGvVYao0wPJFoWu61CNVxAKPadgYCDdE+tPaIgsGlv9/QndILMg8qXO5ZoLJX+3mZ0ws98ys1VmtszMLp/6/3Ez+/ezec75+My1ku/vz5cjjEKZWB3KkAWhRKwyT07hePXy41nieFtUTtOYOpuY76pVXK1O9xS5tm5Nlclyh+agrLQ8YxRU2h88eW1jVG54AXFMGWvdaEDA+k71ouPvNXVINziKS6t4ceFZMsZU18PSBrlh06sgxEsnPMN3SUnSw2iicFfuRQxh6PoBLVBvJRonKGs8YyDNSLDUGDQeGuNNPJE1lWPiKwGOexHQEVlg3oB7WVsQrmg3CpW5wbuLVwyT0Rc9Kx2vXI1c9oEadtVqcX8xP0D0kKrUOEdQx32pJDSt+oYHfaZs+ly/c159DhHSOUABsr5UzmBQsd8gY6qc2Lq1mLXDXlG5wj4DEWhvL8bhtaRsT08xDKl7oFYrwtkaOtL1p8qW8AKVDWkXiFfk3vA8dYKigaiyh/RHnX/Sa/kOa1d5NeyfarVYJlgRDkUH5uKaCyXfZGb3mNmRwK4/Yma/bWaV2TznfHzmQ8nH9Jqy+6Ji4DQ0FramNKnAVuWEFUisVxcoZA+Nl+nZ4GpBspEhnmBtogzUkoxeWDx/WjfbTJW7YP/i3eS+QyggLvK4iRBCCntGQyMHddJXhDdjicKPcdIYU1u8uNi+SDKMwjPGgBWBUMgTeBkBp3HrnCJWLxUYWmP7WpaUv7FW1cNHEBJT5d5qNWVKKNyq5X01VZHQQWQ9K8ql72WNR2OG+VcjLq7Xer2o1FmbeIkx/qzGuKIM8VOrFbkGIAcYIHi4eKGMgXruuie1n8PDSbFwwqAatGfrwUePvUzpx+9E9Ie9qR4rXnz0VvkOzobGthkH1gxxeYxC1iFcFpSghg9BifQsCYWztWKo7nlFcwi/Ud+i0RzHtYYMJVSqhE3apDUt1FuHF6Xv030JDwQSNP2IfKK3VEz+f9zsHvwHzOyXpn4uO5Pvn4/PfMD1KmjKrgjRIBSA3fHE9PCDnDDD40cII/jUItbNAvGM7yJgtaAECi16yMreR4igeNavTxCZxvGicInjgyWuqW45zz8HTaviUWtaYc8Y244GE8/Wwi/qiasgU6Wo5LeIZlSrRc8kQp+q6IlJIkwgZ+mxl9G4U8tfFWt/f7Eg0tq1xZh1NHZqtSJDmCIcsHz1+RrL7ury+9VD1Zi+wrR40cD9GL/cyzOBVjGgFP6v16eXYNZ4tXpqirywtqrV6eEPVUb9/UUDuafHx5G9yHiS+sm7MJw19ISi1L2jPAINk23dmvaPMqqjdzyTgs7JIP2OGsmz9fKRUcwJIa1aLRmZuZCSOi8RnYwIEPdA3MVjp+iVKkZSEvkdCpF1ohwdXee5uUVJ655nffX2JsMNYzlmLmgYVhEQJWeWIRytrdNrBGievO63rVuLKNNbypOfq4+Z/Rcze03Z+GZ2oZk9Zmbfnfq5bOr3FTP7XTPbbWbPm9ma2bxjPpR8jtgRr1w8DEXC4q1Wp+e1RytbIVPeGQWHChyKngBfa/EHFWgYAHgparSoQMDr0tSnRoKpvz+1BY9SDZHcYlZUASsXLw1yE8zcGO5QQyPC9oyTnhKYU/qqXFGAORY4bVIjKoYH9Jz3HHyK4s9Vu1Jjg9g9njSCV40yxpSYdVyjtIWqf3AWlAGt46U502o4muUFkKIj0dviwlvRGguxfjsGJ2hHhPhR+i0tifyEp4VSWLs2PYv4q4acarViyifv0NMEo/LQUwmjBwk60NFRTBkbGEgKJEcy1PmJhwyVoVBxr0SDGOMUZKgsTS+2Jb5P26SZByqX9DsRrVTHRlEmxh4IXtMzibcTFlPoH2NXQ49kLmm1xMjEjyl80ZhRFr0apaw1PVSJ/R7DcbpvkOkYF6SZ8n817JkjNaJBA84W2cldZ6XkzezqM/mUPSfzzDVByf8HM/v41L8/bmafmfr3h83sL6eU/VVm9vXZvGM+iuGoIK/X8xuybGMpyQxYDHgUb5sKebCBWbQsfFXqFKuAAUu5zRiL49maqsLCz7Vb2xsZ/zMpeYRjtZpiubRbmcr6HS2SoUIkl0Ma4booGCMcqJ4FXg1WAQAAIABJREFUf9P0NIQAudgqkGJ8Hc+CzRyVkhY7yq0D9RoUAYheOM9DCLIG1LtGQdVqxXepEFIWOQiIMsx5jz6XVEwlaubmHeWydOl0z1wNFtIOFcpF2OUOSNJ+IGQ140NTT7W8MAoK44OPHiiDYYNyVXJfrPLIsxlf9r9+T6sDYmBohoKWQ9aLNaeEPlXQZZB8jEezfxodWxodDkWOGAvWOcanluTN7Vnem0MQ+vuLPAZi+yhN9iXrBsMSxdvaWjTCQItyYSGME7JntI4BY8B6YE9rMSiVIRoWVIJvXPs6FzgHiixqSC6mYvI+RR+amho7jGdzna2Sn5yKu082+BCbP132nMxze4KSf8HMuqf+3W1mL0z9+w/M7Fdz9zX6zLWSVyVdBhHHK24APbFKhXJcoECgeLgISbwe9Zj5O5tUGa8KxRGzB/LXOLIu3NgXjX9Wq3nBpX1VRcNmalR1T4tSqIersGt8dlmMX8eOtBruU6gNSxyBDuoBFA6zXGFFPAgEB1wDxmbt2iI0qQJWj6xUNCJ6J/V66jflXDHKFP6P44kCAJaHeAaSAawNv0K9IRVuGmJiXghhxDXDs+JJWhHyJMRRrabUPsJPQN4qzCNDGtZzT08iTHJ2d6xbD5teOSSsH/YFjGYUjxqBrJXcOmdOUU4gZzoHmlqnRloOFdI5VzkR0TodT/Vw+V4jGZRDr+L3gJUhgCmRM1d2VccyEoSBoOH7MPYYBWr4sI9A66LzpKgkISyVJ7VaIvD19Ezncehe0up3MXzCqZpqGLLmogqJYQp1gCLBjz2oYQflcryl2PVm9t4ZPteb2eNTyn607DmZ50YlPyb/rvB/M3vEzH5O/va4mf10yTNvM7NvmNk33vGOd8zdyNWT4KtWixBxVJa5OBaLWkko+p3IOtaqXpA7EBak++Ax8G8EsjKJVRniPcZjbWkjRCrdaAqd6iKtVmeHYOQUXUxH05AECIcqYP5N2VU8W4XrdeyV1KJpcOopo8B1AxJ/A4XAAo+H3Kiiwqhioyt0nUuxq9XyB8+oV4ESgXwUCW/RewLyVg+WNmioR+dUvSGg0qh0NIyC4Gc8VdBjNEaFFVO0QK3UaMLoxIjFMInFVuKa0AOANNzA3iTUxO8wSPAytRCVIix4no0OD1Fyql6sUUV8FIlTUmwOSeNSciLvK0PSchB+vDTUpMaYEntjcS68ewxffs/csj8UmVEUhTFm3lgril7gNJWFBVCk8E+YTyB0Xe+N9jqfmE2jnn3kimilz/gcRUYuvzyhpKxjUBCVpzpmGnJj3ypidK7XnMbkp5T075vZm2a2z8z+DzNbfIbfzyr5qf8fqp+hktfPXHvyLGIUXNxwUenX68mCVSWmgje3SRFSWqQjMtBpC0pQmdxdXdNzglWo8F2MBoS3wpmavqJFPID18ERVsOdS81Tgx1gkUJd6UsCwqshoIzHd3BnMCnHqhtZSknovGy7m1kdlGZEPLSkc09LUOGDM4rrAUFRDj0uNLa0qpmOjqVzq3SEAIYLhIaiyKgst5eatXp+uzNRgVURABWscZ62kB2O/rc2/t359QpgUncK4UQMnhjYiIhI9b42zUkAFLx5vm3vjuo6ky0ZjolfMkNC2Y4SwZspq1mu7eUeZktYxbgT5ImOiwce6xFDD21eCGTHlmDnAmlOeDeEK6hOo8YyHG3k5OfkXQwwqF0AWcmlxoGl4yOSiswZyGRtlCCaOEPycWIVTHRTldtBXkEIlTiOj4cjMhIye7TUnSt48L/7/MbOT5sVxftPM2mb7fXnOTxxcr4IlbqwcdIay5PAW4uZq3ebS8vRZUYgOD6cUOvJ9lTjV21s8HQkPEE9NISwV8NHjZPNgzapHEeFiBIaW741jQ1sUEkb4NzJ6+vtTLLmnJzHGGUP1gmeKkUdkIsKY9JvMBHK4uUfnDcUBWxiB2CjlB+g0EoToJ4JEiUxlcXzGMyeIMVaq1eJ6iso8okY5o4O5oO0IW9CIXHW1ON/6bvWGNFSicGkM+Wj9+rhPBgbcYIDApeNObjpQtBIYEQ8ahuvvT3Ulli6dbshq9b5o1ChRNVZxVAa2olXxyintqKTLECwdj5xXH9EfRYciwQxWOsgKikvXHsWQdC/Gw4OYx5gmmUvVjUjQ4GCSY6BsEYnQ8z5AWbW0MmOuxXcajUtuHsrQEu5BUSNLWKfsB5Wlzc3Tnamy+Trb65yUvJm9z8z+36nY+3fN7NfNrHWm7zV4XlTy/9GKxLv/MPXvn7ci8e7vZvP8+ShrW69P3yz8OwfDDQwUy7GicBRSzjGT43v00jgqm0kVLJARBR4Ulo5ekMaKUWr0AUIUpTrVs4+CirbibUbovywOGSFh4F2NxePFI4gQpFq0RkMbWlYXQYuSU2OnXk9CH6Edv8Om1fFDcEXPnHHTGuC59ByNNUYEJ4d6wLKPMVruqVane+0oo9bW9FzgV8I8g4NJMGuFPhVsWhUPg4y1h5LHq1OlGOc7t2fgi+BhaXyT8BWKRgV2TjBqRTPapPuOw5m2bi2masV9wD05ohdjqOGE2P5omGHIaA2DRoJ9NkpaPc+ogMo8+/jc6MVXq0WuisonZAnGKUQ5yG5xLyrSR9vUCVFji72WC5GoQVYmI5U/oHU/1q8vFivq7i4a840u0v44rU/XRAxl5uSpkguRKR0d3obFi/NneMzldbYx+Z8xs4enlPs3zewWM2squ382HzN7cAriP2VmP5gyGLqmoPjvmtlfm9mFU/dWzOxzZva9qffPCNXX50HJ56xm9VCw2NTKHx5OgkoLbWjcNVqIKgRzFiSeLVB0vJ9iEix8LFy1IGMKWvRwolLUGCdpHznIkbYg+BGc6nXr2IBkoKD1THMEDsK6t7coEFDACG2sZM1tLiPc0Qbydjn+FbhRvVe8HY0xRo9an8lYAU0jXFAIWuIzzgPzq1UAMWx0LBUZUC+H8QHpgb0bvR4UJoxnoEOtyqZKhf5jwIBktLYm+D0WDMmFoXKeJMJYwxT0GdgehYKAjByAWHkMboSiA1wI7Gq16HXGPRghbuBoTmfTmu66XnPIUtzDjeTKTJfeT+gpZidEB0T3ghqSMeSCAasoFoZ/9EhR6pAgSVuMKaS0URW1xuM1RVfbSOYQc6+hGP7OM/Dgtby1MtkrlSLngjbm5gYDuVJJ+0rltfJ3GBclxXK0cktLep+S+7Qq5WwNjzO5zlbJw55/3MxuN7PfaPAZLHvO+f7MRwqdWvyqbLVueTz6UYVw9HS1XjOXEvxiLIjNqxButOTjgR8IZGJ6CrVrmpyS93JQs5Lu9Hs5q1Q9Yh23SMpTAYUQUdayohYqqLVtwHogDlHIqeedE34XXlhUfgij/v4EZUO6i8o1x2qmnC/eDopRFQJtycUE1YNGmDHWur64F/RGT1rDqIPopH2JIQ8N56iRlBtrhUrx7tWY0Iu45vr10/vZ35/S60CglI1OX7WNoB9aOYwxoOBSTH/SWHhU4jH2rX1VGFvnKkf20oI4ufoTM3nnvCuO+WwU/9atRWa8Xrlwi67ZsnnVlF4MNwr9MM6ahgtSofUJ4pjGsc7Jxty+UueD/cd3dM/GkxMZP3UAoiPG+Kh8xbAgO4K1mTvZE3lIqI53q0PEPTgIcf0oujtX17ko+dl+Jsqec74/851CB6TFwTRRgaggw1plQ7MQI5O2Xi8qOzYSAlLJRGzQCIuqxc1z8O5RVrRBPW6s1bLcTSWj8F4lneTgQDZvJN3pGKEEcwYDSIie8BUVdyMlHgWZGkjaBjwGxhwrPbK4eXYZBIvy0/EHrVCOQ+xjLq4alX8MLTDXEJ3wvGKKZtkBJTwDjkEZe1vDUWpsUPIXYRYNPS1Ugqej+fnxzAIN07B+2G9qtMTSsKxFDGc9JCnuWa64PlkjMfwSkQiEfUdH2puxVnkMzzWC0CG3xZPIyuLUjci+ugY1dMZzo5MS26IKFKXFvXpiI6lhyCU9Ia5McUUkoQzFIiSCg6AoqBoUEZVCwVerxfliTmHPgzqQMaBOEufPK7rDGCqBEG8eecl+Y+1plc7YPpXJGEWDg/U5veaUXf9W/5wPTz5XixphSA30eNiLxqFzhR4aHWTDwmJBA7FGYQT0jdJWCKpaTbC4wlexBKX2hZg9Sgvloix19QBVAelmysUjUV6qSPXdSs7BSGDDAzsvXJhybTX/OSIeCDH6wLOV5MWGBt7LVc7DS6QcKu0CAlUFv3ZtMiAitN/oirFhiE4UTKL/9IV3aqgiwpM5xZ/Ldc/dG8/WBsVSwp9eg4MJPkVpELePwj3ONwoQgQ/SENEybSvPYu1HeFcNQDU82VNUh4zKQq+cQcY6V49ZCZ1lXnmMiyuXJyp15kGN5NieuEfiGDdCB9RwjWjfwECxYBJpiF1dib3O+zQXPTfuUbmrgRXDTx0dxboDjCnGqL4PFAcybDQ4kTHMbS6DR1GziDhEucDvovxFJ5R57dVq+u5bypP/Sf2cj5h8tK6Hh4vWYM57yiEAcZPlFj+LmvSLmPcZN5V6aEDIsO/jZlFrVhUDykOt02o1CV/KQPb2FmFUhRERAiqMonDHgFC4jXezMdRI6O8vekx4X3qojEK8eGCMucJ2jAExWuK6tVqqdKdwPX3IlTDNHU+q4RfNGS8TwPwbQwYIHcNM208smPnt6ioqllidTwUZRmhzc/KM8NDXrp3ueUHQIy6uR/XmQjcK07JOGpGOohGdI8DhLeIBxbU0E/wdQzAYL6whrb432wskAQQip0SvvNLfceWV+TaVkRT12SiHMiNR5VGje3N7UBGvaIizP1G4WjkzkmwVgcihETE8gNyD96NpxvCJqP3B+9SR0P5GUmjsAzKst3e6oVcmmzA21chEnsBp4Z2sp4jmIHc1fbC/v8irmstrrvPkm8zsK2b2rjP97vn4nA92fb1eXMxA9JEUF+OR6gUpkaxanQ5rR3gYq1JTWHLvUJRBPQ5VunqICModzwavWMlwQM0Rflc4WGNpqkTVkIhenyptcshVQUWYEbb/0qWpUE2E/fmpMKKmA6pH2N9fJOlQO1/hNVUevF+resW1wFph0ys7ffHi6UpUGfsQ4eJBHYow0H/+Hkue1utF5CcSnNRQ4IhhjTVHIyQKRA09wSpWiFf7zXe1pHDcS3FOcvCytjmX4qZE2JhqyLiBRDQ3+3cxAInT5giiasxHo15DaBHN4j715nLyRJVff3+x+E+uNsJMHjr7KZeXHomLeqHQNcYfQydk7Gg4sawGv/YrF7bAkcCIjp4yf2M/qEzTsWEeMAhUZrAOtAwx+ywis5GP0t+flDnrQmWDQvAgWrRZHawoK2aah3O5Gin5Fjvzq2JmfWa25Cy++xN7bd5s9vDD/u/77jMbHPR/9/WZ3XWXT3lLi9mePWZr1vg9O3b497h3ZMRs3z6zyUmzl18227LF7Phxs3XrzJYv92dt357uHx0127nT7NAhs7Exf8fEhNmJE/78RYvMrrnG7MYb/TtDQ2anT5t9//tmN99stmKF2f79ZtWq2T33+LP7+sz+63/159fr3o7xcbP3vc/szTf92d/5jllnp9nFF5sdO+bfvfVWs95e74+2c2TE39vX55+hIbNNm/z+7dt9zNatS23Ua9Mm/7l3r9m3v2320ktm73+/2b/6V+lZBw/6TzMfi5MnzX7xF/1Zt9zif7/5ZrNTp3yMvvIV/92ePf6dtjazVav8GevXm11xhfe5VjN77DGfs64uv++HP/S5qVR8bMbGzN54w39nZrZtm9nhwz4uzz/v73/gAW/L6Kh/mPPxcbPLLvP3rV1rtnWr2YYN3q7R0TQuF13k7T1+3N+5apXZrl1mr73m//7qV83uv9/bPzDg47pqldmLL3qbu7u93Tt3+rv1qlTMjhwxa29P7+7r83W3a5fZ295mtmSJ2de+5uumo8P7wppds8b7d8cdZo884p9jx3xdbdrkbTpxwtfhpk3+vX37/N3Vqq/NLVvMmpvNLrmkOD7sJTP/+0UXpfX0xS/6c2jHihXej9ZWs9tv936wljZv9vHr7va2bt5s9qUvebvqdW/H+Lj3r7XV7IILzHbvNuvp8X11xRVmf/qnZhs3eps2b/a9RRuffNLsBz8w+8M/9H5yPf20P3901Pfk00+bffCDvrYOHfLvvec9vpeuvHK6PFmzxt+rl/tQPpbLlxf3C2vGrNg+/m/m65X1qxdjxBjrtWOH2cc+Znb0aPH3Kt+2bfNnv/yyz8sbb/j6XLrU/x77NTZm9sQTZi+84GNRqRTv27LFx5Q5Wrcu/a2z0/cCe0flwObNfs/Jk74W9+7156xb5+3asMH3GTJjzx5/h5k/84UXXDYuXerrZWTE9w3jrtfEhFlTk9nKld6GnTt9TpZMab3xcZ+TgYH0zhUr/NkDAy77xsZcLn3962ldsUbjPMzrVab9yz5m1mxOtltzpt89H5/5guvL8lw1tlsWf+bS/E7i1jmoKEJOmnaixBMs0JhipmeKK6SIN05hk66u6eeixxKkZRYnfcF61nibemkRes3BiBABgdwiDI41r0fnKllHoWq8bQ0DKDSYO2UKq5+DRkBL4lG5QPAUHsp5PjGbIDevZfFifubKa+ZO/9I4tublQ6LSA0B0fQIvappYhF4VhcGTJgNBMw6id6mMakV2lMwUQ1IxJBJP4oueT0QCWGP0RbkpwMCMpzKrdU2ynvG6FDViP1B/gDmB5xJj17q2dGzgWEAYbLTv46VjG099UxIj6A1zoF5zbv8pmsXJfBEtiHOnhLwcX4Lx1oI1OWQGeF1Rwly6W8xw4bkdHdPRTP0OBL5KxbNLiOFzkI4ik2VhDMYgV+Mi3ovcgUsAEqfrO+6RubpsjuH6v1dKHti37LE52C0SPGIsKleqMipNNowqM62kBTx1ySWpKAiQlgoD3QQKtWq8VSvyUQoSSLisnremufC8SBICLuNo0mg0oKBUSGoFK9qrsTktcZlLXdR4a1tbMhSA0BhjFcJAgLwXWJxcfhV29Ceyo8ugOJ6v0HHkRuSUHDwDxrGsWhrvUKMB+BpyF0YSgi13TGw0NjRmrG2OseR46frXlEoUL2u2UQgLIa5QqCozeAsKZ8dQlxqA7C09VTAHkROi4hkauqlU3OhQJcIaiUSrWBBJ974+70whW7JyCH/kQjGcKEgIRb8TjTf6ohkjuVPrgOpZL0rWRbFhkNbrKWe8uzt/zkGuYFWsjMj60KwH9hC1Fcj4iRUsdU1oeqkWRGJ96l7MyTndvyrDYkhWlbySYVnPLS1pvSqHYK6uRkr+jOH6er0+UalUPmhmL84ZnPAWvnbt8p/PPeew6bZt/n+gZiCkp55K/wYuy0H8o6MO43R2+u+BLx95xKH1SsXs3e92iGjjRv/55JMORwH57txptnp1goV27jR79lmHi9ascYhpZCTBs3fe6c9dvtxhthUr/N2Tkw4nbdni9z76qMNUb3+7v2983KH6yUlvt8KLmzY5zHf8uP9//frpIYehIX/+xIRDxpdfniDbNWsSjNje7jBwXWCzej21c98+h1vffNP//+qrPhYK8wLTKlTZ1GQ2POxtXLnS//3aaw6jHjniY7l5s8N4PLupyfv9ox+l9jCP69Y5zPuDHzjMv3Sp95s53LSpGG7ZscMh/e99z8fwsssc8tyzx2HDv/mbNA8LFiSIlvnu7k7P4m8xBHTHHf7Me+5JoRMNCzzxhEOMq1f7/zds8Pnev9/Hl2fFtarQ/6ZNPl/0yczX8Oio7wn6DFR78qTD1/z78ce9jc3NHmoAAr3rLp9X9szBgz6vXO3tZh/4QNpfIyP+zPZ2v39szD+nT3tbLr7YQzAf/rDZsmXev95eb9f27f7sSsXfT390zTU3+/dGR31ed+70z+Skz/epUz5Ojz7q/Wpt9Tk9ccLHk4t9unWrh4XYx6Ojvk/37vU9noNsCc0Q8tJrwwaze+/1NfXoox6medvb0rqr1fyd11zjYStkVFwTjz7qY7Zzp68b9u22bb7GVfY89pi/p63N9wUybvFih9OXLPF5IySxcaN/5/RpswMHzN71Lm/Htm1p7+/Z43O4Z08K8VQqfp+GBO+6y/fj5KTvl5YWs3e+0+zzn/d33Xyzj6WZz8XQkH9f5ezhw2avv57CYPv3u6xZv97H7MABlwn79vn9rH+uzZt97a1b5/0dG/Nwz803pzBnZ6f//tix4ny98IKH21jjY2Pex/vvT/LmvFxl2v8n9TPXnvzgYP1/QDAK/SqTVEkxaumXwZqRtEelOlKV8DZyOeeaQsU1PDw9jYU2KLSmhK94wMfWrSkFhApt6l2rx6pIQSMiicKaStaKue3KTs/BW3hZkVmu34mklxzczljTDj04RS1wvP3oyaunrIxsnU/1iGu1lKJFuhW/11zzarUIp+ag24gI9PcnL431AIJBWmJZrQJ+D1M+18cIw8dwi6I0ZYeYMO8U7VH0Ci+xWi2iTsDteuqgwvs5tEHhdLzW3OEi8dhRhejje9nfoB6sI51XPFv2hHqt0bvWsA4oQ8xM2Lp1OpGxkdzQ4kllJLd4EQ6iTK2Gv6JMYp9TqY39FUmIEXLn+OWuruJajeGxuEZVjiqKALs/7mWY/6yhHAKq5zP096e1oiiehjwVQs+t+4GB6YgA65hsBCWyklIKMgeyVJZtcraXnasnX6lU/kuDP0+a2Rtm9qyZPVSv14/Mge3xlrlOnXIr8fRpt3ivuMJ/r+Q4/T+W7tNPu3WqBDSz5NHv3u3e+6lTviR6etwjOHLErc0jR5wIBUGvu9s90slJ//nmm26dbtjgHupzz3kbP/Up92SWL3fPdmLCn3P55dPbqaQa9aoOHy56OK2tbjHfdJP//4knvB2f+YzZT/+0W89q/UPw2749EbS+/GX3rPbtSySa++7zz8aN3ifG9uGHnaiHt8XV3OyeU73u3uGBA+5Vcy1Y4Fb1nj0+NsPDPj8jI/53CE2Qeb73Pe9zb69b84cPJ8RjyZJk2SsRzczJMzff7B7OY4+ZffzjiVi4ebPZQw85eQx0AuRg/Xr/fnu72b/4F+5tdXc7iW/3bp9rHRe9QComJpwANjbm49jW5l6lmb+rtdXH8s//PHlqzAfe1JYt7hlNTPhn165EFjXz+XjoIe/bihWJCNjc7GP5wANpb0xOOvJD3w8d8vsqFZ+Dffv8s2yZj/PISFp77J1Nm/z3W7f671eudI8Pb31kxL97551pPT75pK99M1+bo6O+7r/ylbQnxsZ8H77+uv/9yBGfk61bzX7rt4qe3MqV3s6jRxOhbNs2s5/92YS6PfWU/7ziCv+sX5+QlIEBv4+24E03Nfm+fvpp79PgoNmDD/p7Pv95H/+PftT7B/LV2pqIjA895H2F4Kl7ePnyIiEv/l1RH9bu9u0+P4sX+7vfeMPfy5wODnqfTp3yeT950tfb+Ljff/iw93P5cp8XUBwl94FYdXT4+vnqV/17bW1+z/bt/n3a3dvrn7ExlxOjo97/0VEft+PH3Ys3c9mDjG1q8mfefntCiyD4DQ76Hjx6NJFBedf3v+9rRMmPGzf6+7dvTwgKXrzKIoirZj4mY2M+v+Pj/rtFi1w2T0z42I2M+NofH/e1NzTk4xpRmnm9yrS/fszsGTPbb67Q95nZ81M/J6d+/4L56XR7zezds3nmfH3mo+IdJCslDeVSItQCjqSeaLlpoYaFC4slUSmZixdAPEiLQ5CHSfEInkUcF+KQEtnKvM142IrmnuLJ63nc3EMNc42laZtz71PSSY7UGMk2MXaK90oMEQ8ejx7vsVpNnmbZkarM69q1xUptekZ1jFNyQVKMXpd6enge+gxtPygGnmfuVDh9X/TkeV8kJyn5UtvNGgDB6ezMH3mJR868gxZQvjSmquk617PCme+ch8tcsncgY+EdR+9O452KXtDeHPKAF6rrVmPyGquv1YqedfSUFT3Q2D97D1If+4T1n5sLCJycNdDcnA5HUVQn5nTHOYp7J15KBtWxUZKbkg25QGlIl4tEP7gppK7h3efIoMrd0Vr7GtPmvnhI1eWXJzmjsibKXiXUxpRb5ZLk0jnjeEYZEeX2wEBCFSkMRMorayCijuxzHZ9GJMuzuexciXdmdoOZ7TKzK8Pv/+mUgv+Ima0ws++Y2Z/N5pnz9ZnPincocVVsuom0AIpOYE7BUnsdEh3KRXPglWWubG7IWa2t6axsNpuS0FS46MKiPQhWFqxujmo15aKr8lTYXqF4hCiHn2hblLimbYqEMjYVz8kVCmlkvAwMJGUNlFYmKCF9cdCFGjX67NxPhfhUiOhZ7xgKjBXtR6nDslZjJJITI1yYgw/pS8zVjgpYv4swpaCNQuW6fsgVJgxAuWYlyeVy3DHA+vtTJUDqICh5TvOfIbhq0RIVjr29RWXBftH1rCEqlALzo6GiaGTEsqYxtFNWbW54OIV1UMzsBy37mgtB6VoidAMLPdbHiN+L4152zUQGjQZo/F6OXDowUKxBoYosd0oeYRLqiPAsLjW2q9UioVLz9FlTMYSl4xgh+2jQqAFaRrCLJNhGOf7sYT0WFyiftQ3ptIzwOVfXXCj5b5rZL5f87VfNbGTq3//czA7N5pnz9ZnPindl3hQLRovMxE1TJjzU06lW8/E4hKXGAIlxqSCIQjoe6sGF4osFY/T7UWCXeQywpkl9IWUqZ+Sg/GhTVE58nzi1ehg6HnjD0YNGyEbPKbeB+/qS8iGGiLBFYMdYqp6HjqGHV6Neu2ZDRMMAoYo3TU1w+htRIlX8cU40Ph5PkYvKTz1jkCHWIGNWrRYFNd9hXeLJx+ONta6+emdwG0CJomGibY5tYu4U+QAh0WIkyifAsGUPamnpmN7IvCGI2Q/aJtZMLJSj6zGXRqioXpy76FWjNBl/DEctSR3XYKPz7eMV484oKJVhOcNRlW98P+iEyg19j2Y/0IZo+Orv4XBEx0DHv1HaZnQm+BuUBDt/AAAgAElEQVRxeT2+OCcPuVfLdcdxUUcF+cQapBIppZipMBplLIz7KPfm4mqk5GfLrl9pZmV8wGPmZ8Sbme0xs7ZZPvMn7lqzJsWYzYqsY7MUY6zXPcaqzGONsVYqHqtaudK/U6973EeLycSCIfV6ii/feqvH0CgGs3JlKg6zb5/H8LSghrZxx45UXKK313/33HP+c2goxaEHBz0mu3evs9mJxa5ZU4z1dXZ6LNzM48uHD3ssVq9cYY2hIb+/rc2LxTCeFOY4ccLsqqtS24nr797tsbjmZo97EkveuNHjdQsWOMt61SpnG8NbGBlJDO/ly73dra1+/5IlHpsnU2B0NMX+Yx9qNbPPfc77uW+ff+fUKWfjj4yk+DJM8bExn489e3zer7/e53r3bv/b6tWJp7ByZVonN91k9sorZhde6OPA+5mT/fu9H/fd57HhkRH/qeuGzAzi9xdd5L8fH08FbbZv97jk5KTHEqtV/56u9Y4Oj1s3NXn8vaPDWeKXXuqx0jvvTOvCLLXn4EFnH7/8sreVgkSsgeuvT+/Yt8/7SpyZ2Cp/h3l9zTWJO1Gve3xTsw2eftrb2trqMdAtWzx+fvXVxXfv2uXfv+IKj+Xv2eMchhMn0h6gDxddlLgExJ/JaNm505+hXAPGwCxlIaxf73OhfBTN2rjiivTs48cTP6Ze92fcdJPPe3u780GUPb9xY36f017i38TG9+zx8XnnO4uyRvs3Oup7A/a+ric4D2a+d5kf3kP2A+245RbnalSrqS/r1/v3Xn3V773sMrPPfnY6D+frX/f9fOCA37dzp3NhHnzQ7A/+IPGdkL16IcN++EMvSlSp+DjHd8R7aedTT3k8HXn/1FPeN7gJcE8WLPC9c/Soz1tLi+9v+BVmSWaRWfWhD6VxnferTPvrx8z+1syGzawaft9tZjvM7Gv15Mnvns0z5+szH3C95pdWq9O9BIW5Ya7qKXRl8GvZ7/i9Wqga/+VS6LqvLxVBybGSsYqBlRTGVNar9qevLx02olB9zjPCQ1UWd+wH38XDiX0C/lQPRi1oEJBYv5/v4vniZfMszbdWj0C9276+xCCOfAGdH9rP/ALHzcQGz817mQdSr+ch+OiBKoLE8bba7hxrObfeGs1zWW51hLIjNKzhHMJJWpBIwxJ49aAbkUugF/0aHPT3U0+c8Yg58UuXFr3EeB6AolZkW9BXxjmHxMDqX7iwuO7j+s+F6hqFXrR/urc13q9rIo55RAx13RL2i1knM62Z3JrULBFF1RQx4x4txgS3CDmqcHdkm/M+5TiBwBEmyIX4tO16fwyjxiuOn65bZCUoCAiBFvAZHEzyX/kjmjEVQwpzddkcwPXvMyfaHTezvzGz/2/q53Eze9XMVk/d93Ez+z9n88z5+swXXA8srRMITBMLpgBlUqUuV5lMrwjDaqyXTZirkjQwkBQex2CiwIE/IcEpjEkFsng4Tm5TcwBKPOEr9qUM3oxxLQRNb+/0U7tITdNCLXyfjYRS1e/FeD2nxKFguruLh6zkUnmA4RQyZIy1PjxzgaLX+GEs5nEm66ssRqjjmDvnfni4eH61tjsaYnrSYe4EvrIYbDRMIk+Btmq9b+4FvuT3ZQaPGtCqgBqNGUoDiFUhe4ww9quGiCL8ztrT/Rr5NWrwALFDhC0bk0bzm4Pvh4bSGDJPWmmO8dG9oc/Qvcbe1YNk4tjFdZpbM2UhFiXU5Y45JkShhZdIbUXpYWzlHJi4B7SqJ/uavRoNUW1Df3/RKJjpYnwGB1PqK/yVsqOmlVTHmqRmPvIWoxBS5kxhljO9zlnJ+zNsoZndbmb/t5n95dTP3zCzhbN9xvn4zNcBNWx6yGi1WmLH4gEibOLBCyx4rcKlV4y/KtGJ7+Y8KhSOepHq7cGOVdJSU1PR41QrOUfAQbiUnfCli1Xj1rG9bAhicFqtSwVWjDfHOG/MsY9CJUda0tPuLrggeWzwIfBiQWiUjU/9gEolGTlR8UdmcU6ol13RCOKCqa4V/RCGmgc+MJDml/Wi46bemRLMMPyItceMkNyFAMsJqej56Poq8150rHT+y9ZbfF5cw9yjWRVdXUUya1nf4njTnxxxVdsTFVBO0ce+5t63dWvRgcBYj2hODi2JHrn+m/2maFmjNuqezXEItM96qE00giJ6yLOicdnfn0jIa9dOZ6Gj0CNioIZ9NGJyMlI5Po32KOtQs21wojC61NCq14tcJM0oAaHAeJstonA215wo+Z+Uz3wp+Qif1uuJVBEXBJB5Dh7OpXPV69M3UPTkFU2ITOW4YRHGCDrK3ioCocZKJODo86JHo0IiogtRgPT1JcXZ1+dCQEtSqsDKnWsfxydHHlPllBMsPJviNYwrcLOeQgf7tqUlCS9VXips9Z1lCE1ubnNCVdcI92E0akpRhKKVrVsWQlIPW2FR1gieNmGKMqXBs/C+Ipwb+6LGV5lQ1T2lRhvseQ0/cK9C7Y2UaWTxa9EevU+Rj1wp3Ig+DA4WSZUQ4fQoaCUwckWvWw0mFD77k7XZiJGfQ8pYi7GwkO7RMqMyt8dyaJ3+vZGBwf7UdmpoL46FevUoTPYeSra1tV6/9tqiUm+EZug8KxlwNmPA0dV45JdemoifMVtEHS0MKxAO3jM0lNY15xtEZ+9crjlT8mb2M2Z2h5ndM/Xzn57J98/HZz7g+ugtRKVLRSNgGgRurDTGAoqHwkTlmqu+VK8nYU4evXrMKrBUqbH4sIT1jHjNrdd0NG0zyqarq7hBIxSauxQqq1YTBBmFEj/VG80phgjT5irO1evJ+OKc9bjJVehwVOjatUmw6znQeIY57zU3n2W5tbl68XG8NV6t3ARNJYyMe20HXpJmBUQPW423ajWlcAJvN6rkNxPyE8emkeeMIojM++HhFH6oVqejWDMZzTnDBMQiN/Y6HrHanaZEsiYQ8jDbdb9SJZJ2R+WsSrO/v4h4sK8JKSkbPH5XFRvwNR91MnI54Tl5o2PSKKNG3xvnP6IyVO4D/crtGx0LTTkD4YiZR+yHyAFQgz5nbFB1UmPjuf7FtatGBigtRraet0A4kZg9TlVPTxFx0LBaLjxxLtdcxOQXm9mj5sVvTk7F50+a2YSZ/YWZLZrNc87HZ66VfITt6vVywcOiiye/RY8nblg9GQ0CEPEcPcEtCrZIeqN9KExSOQgBAJHh2SHgIyRZ5slrOtYFFyQyEOkiuTAEix/lHgksZZ589KwiRJdDOmg3m4nCQNEQUkUcBVsktjVSVjkDQFO+orCJ6yjneeVQCTVMMNjwvFX5rl3rz1fhOjycFIYaPZq/DnyvoQx9p3pIEY4t8zTLUs9oE2GShQunKyLWnMb241wwb/HEt5wnV6tNP/UxPisWU4kGJaEuTiDMnSIXuSIzKSA1nlgbKI1aLXm3KCpNV6NPyrfp6ppu3Mf+6BiVkWTLDLgyNAGZqPF89eT1uzljLO7fnFwD7aBugzolOVQpzh1GbExJ1eejwHUOMTSUB4OnriclkgapRgG1R9S7J4ee/TtX11wo+c+Z2SEz+0Uza5r6XdPU/183s9+bzXPOx2c+lHwU5IODaYLjVbaYVXHGBQ48S6xHY+itrUXG+GwgSoX7dXGp9ZtTJDnF0qj9Mc6es0yjYGDzE3YAuo+15tnUKMxoGOWEEL8nJEAd/yiY8AoUKkU4RuLdTGsjx2BWxKJMKWp7Z0Iu+B1tjZUG8f61UqG2iTWgKAmKQ42ZOPdxHqPS0vbzb5QTTPDcaXUR4WnU75xyigZpozi1Pi+nvCBrcupimaGKB4gyiDnf6pkzt7kqbDljSM9dUEWnTkREI9SZUEOavZUjkCp0rKe4xfluxDGJTop+J1Y7LAsrxKtsfqKsyhlRubVa5oSVhUHpkyKcjLO+E0OGc0YqlWIxM5wh0FKImfCj9G9l6OfZXnOh5Peb2W0lf7vNzPbP5jnn4zMfZW2jt0UMfunS4r055a2kk0bwJkKCRaVHcEJGwkIvswBzG2nr1nRMK+SjuJkQGMCNeFhqITeyxHMQtbZJIT5liDM2Go+ECKbIBWzwnFdSNv65dgCZaxya8IN6sLkrJ+yjEMuRihrB2mUKrGw+VciwNhAira0JJly4sOjpKOzJHEU0pGz9aFsjAz2njFE0CovGNaLwNIfF8I4cejFTbD8K/NgP3cMRfdC1lzsKGaNQeQgx9KLIhHJXIiyuUDShGTWIGnm9EQkqM2YaoSv8HqMP9KrRQTfR242KXL8Ty8aqEYwxpQhU/C7t0fkdGEiV5Ag3NjL043yXGTpxjjG0OjuLzP1Y9Idwma4ZlV0YI3wvEu7Yo72909t9LtdcKPk3zeyGkr/dYGbHZvOc8/GZrzx5jZvqOezxXlVoMX2kDNKOEJXCXmxOrZxUFsspE4KxD+pdIpipnw+chAIAMtdYuj6zDCpTjwR4v1otxj352/r1xdrduThtmYEx2wtBrGktmg8cGdK5MWTjNjenPsUUSQwIIPNGbW9kKDXySBHSHR1F772pqWisxLBE5IbMlDKnv2NdltX+1vtzHhZhDAw1JXCpsI/kurhvckY3aziiKqrIeXdUYGp0qkKmH5qBwLP7+5MHhzIgVq8KQdeOVqqDqc2nWi2vTxC9fm1jlBVRoWNgRgcDLhHP05MZo/LO8WZAybQiXk6xKrqh+4dQVhlXKaKGaohpyKJMBvDeWq2czxHXnKKKkd2PzKB/CusPDXnsvVJxOYYOUNhe96f240zk10xXIyXffPfdd89YMOeTn/zkjWb2nk9+8pN/rPdXKpWKmX3WzMbvvvvuP5zxQefh+vznP3/3bbfdNmfPe/vb/aSmq6/208IWLPAqRr/7u35C1W/8htmf/ImfD33llX5S2MGDfpLV/v0+pVdf7dWPnn/eq409/rg/t7vb7BOf8IpNbW1eBevd7/bKSZ/+tFcEe/xx/5w86b9fsMC/+5d/6fd2d6e2dnebfeQjXj3sE5/wd/3e73mlqrY2s1/7Nf95xx1+8tXv/Z6fcnXsmNnP/ZzZL/+yV6B6z3u87e96l1dde+MN78cNN3hlqO7uNC7Hjnn73njD3/2JT/gzH3nEz1M+etTH6wc/8FPA6nWvbvXpT3v1seef9+9OTpr9s39m9ju/49W8Dh3y79Kfb3zDx3ffPq+gNTyc/q7Xjh3eBsaX3w0NeRuOH/cx2LDBK6N96ENevep73/MqVS++6G166aXiPL397d6f48d93Lu6/Hnj42Z/9Vf+78FBn+dXXvHKV/W6n9T3kY+ktjDfb7zhfeLfr7zi721rS+P4+OM+Vt/5jr9/zRr/+Xd/589+73t9viYnzS64wCvjve1tXrVr5UrvH2vu2DGv7Dc56SfUPfust7G52fvzK7+S+rl58/T1edllXt3swx82+/a3vc0PPmj2Z39WXIfd3WnNsibMvHLYt7/tVb6uv973ztGjxfUzOOj9ueQSb9uhQz4vY2NpXH7pl3yOhod9jp54wiuRHTni1epWrjT7t/82jfknPuHvbW/3ubjhBv/eq6965bd63exf/2uzhQu9b0NDaW0dO+ZrorXV7P3v9xP91qzxsfrP/9nbdfy4V+p7/XX/jpmvgW9/2+w3fzPJkJde8j3M6ZEvveSV7v7bf/MqfpyUhhx56SWv3vf1r/t8f/e7PneDg6mNtZq38eBBs699Ld3/3HM+bitXeoU39j17933v8/3d3u6V506e9P4fO+ZjvnBhqnz3jW/4GCA/hoe98uBXvuLvrde9P6y1Sy5xObZvn8/NypU+51de6Wvl6FF/7223+TP7+sx+//fT6YmHDvk7kQsf/aivhWee8fXb3OzP+M53plf54/qTP/E2r17tMmznTv+8+qrvkVde8TX90kve5+XLvb1HjnjlwslJb9tHP+pz8+KLXnHv6FGv3njkiMuqe+81++u/TtUvDxzwfhw6lE6l46pWff90dXm/mpt9LUT5dbbXJz/5yX13333353N/m21Z2//LPDd+V6VS+VMzO2BmF5vZTeYH03xoLhr6Vry0bOqBA76BW1p8o/X2ulKv11N5y02bvJTmM8+kY0HXrvXNsW6dPzMeOzs66h/KgurF38fG0lGsX/qSL3LK1mpZz6Ehb8v4eCppyrvMikcc9vX5cYyHDvmzfuu3/KMlMdeu9cU8MOBCUI+uvO++4r07dqRymD/6kZc9NXPhU6/7Am9rS+WAtdwtx6Fu3pzKreqxuBy52dvrG6pSSSVEzZIip+/a56EhV2wcsdncbPbooz6fW7a4cFm3LpUW3r7dhcSXvuQ/KRv81a/68266yUvsvuc9/vdjx9zooqzt2Fg6fpTjeekzpUH7+lJZYcr9rltXHEddL6OjXtb0ued8/dVq/vyXX3aB/cEPenlanQ+u0VEv2Xn0qH936VJfm2Y+HlpeU8visj5Zh2vWpH2wd68Lw6Ym/ztHdnKv/mTd6drT9TM05ONIeVTmzMyV0PbtqUTx6tXe3+uuM/vmN73E7v79vi8vvtjXq5Z5rdW8nZdf7uO9Zk06hvnUKV+rW7Z4nx57zNfORReltlPaduVK/z/PvueeVJr5qafSXHHUrI4piuiFF/znkiV+P3t+ZMTXVr2eShUvX+6Gw8SE/3tszA3Jj37UlfKll6Z3bN5s9jd/40pj0SI3XPfuTUcN58aeErTLl/uHPnAUMyWzr746tbW318f94YfT2qVtY2N+H/PU3Z2OC16/3t9/771+LOyxY2l/33JLKjd9/fXp/br/9+zxdUr74vG68ThdLem9ebPPaXe3y0/da2bpKFkz35erVqUSw4zVypVudFx0UVE2UWrbzOfuRz/yeWhr833W3u59veAC7zvr+uMf9z7p/pjXq8zFjx8ze6+Z/bGZfc+8Xv33zOwBM3vPbJ9xPj7zkSc/PJxSrYBpNFcUMhkxPWCt1tbpaU0Rzm4UA+NS2CvGLYHk+vv9vVSMm02qiIYXyiC6mdqWe56Wzozx6VhgI34/l7IFT0Gr+UXIGNgtEp0Yv1iiFKIj6TUx9MJ3SIVUWDmyv8u4F7RJY7065mWxwty8KFmNoj7wFRodvMH6jbHB5uZUyassdp67iOVDPtL6AWd7xXGKJLZ6ffqeog9wO5SdruNLaKOpKY1v3ENava4sRKL7VNdC2XjF3/f1pfEH4mfOFQbnvcqZ0FCJpjjqpXH23P9z8XrdX9rnuP7gCC1YMJ2gCEeBNhGWYX0oPA8nR9nxMRafC/eUhWhoG6lxuTUcYf0yjkfZv3NzqfwT0m9JUYYAu3Rp4jgxBkr4nevL5rMYjpl1mdnV5/qcufrMh5KP8aRIqunrS+xJLbvKoswJ4tksNi4lsOQWGMIeggqkLAo6UOY1RzjJkZUQEBRXqdWml1LNPUNja5quonFvrZLFM3J91/gsxCBIS5EHUK8nYk9XV5FMFt/B2MeqVpHnEOOESqDLKaGoOFgXGDoqMOKRl7lxjClIQ0PpDG8tJIRgJa4YiWwIw7J6ACjAXFpRo7Wohtu5xBbjuPH/WHgqjpsKTAwyxlqNwFjMKXep0ab/1jHU9UmaZbWaT5HTzBA1NhhvLVqkee6qxHIZAxrHj8Z7zPjRfvM8OBgYGYxzjnCo7VC+AUYmcflITOQ7KLiWlmKmia7b3PpRHgTykjUZuQUYGKzheGZFzsCJDk9ufnPGSO6MBvLle3qKR24jN2JNkra24tqay2u+lfz/amYT5/qcufrMVzEcyB88XheeKqMciz4ntGdS7LEN0ZsAHajVUvpZV1fKdY5HcrIpo4ceSSl4qnhNsX8sYp6VK8yiwgvjB4tdvRkdx4gO5BCLsrLA9br/HaZyI6GlP7WN0TCo18s9gjhfKhxi3e7odQ0MFA8SUoEVMx6U9axpipEZPzycBPHixcV3sVaikalEToiVKgTLzixX0tZMa3U2a1zXYTSO4736DNpHmqSiKapAylI/dW1BPOUZaszHdLGtWx2pAsXIHXSjh9dEj5UDTDAqUUi8H5kSybdRifB39gTPAQ2glCpGEQgbmQ9lhlqUNbR77VrfJ2TqoOj1XAndl7pOdB9pxkycdwxjTR+OpMK47yLapyiPyhg1ZrSPkRyYm6c4Jox5ZNZjRK9d69/Pke8aGZvncv2Dkj+HKyrVqKSpuwzsCXyoggolnztPWYVTVHS5q1FKX0dH0XsBisZL0Lxz3VzR41YhGL0HhCClXyMkrx49Sg6vRzeFphpFgZ6zvKN3E6/+/iTAct5BPDUtznEujBDh2ZiKVHYfQhZUpZFHmhOu+n8EvwpSRVoYn56e+rTUHJ6h5ZepyBXh+2hkACHDBFcDMFeoJo6FogKKwrDmIpu9vz9fQXE2BlVEXdQY1nErM8z1nhiqimETxr6tLQl1bZsastXq9D4MDaWy0lrTnFMXozKL3iphJtAAUrIUwRsYKIYAopJUoya2L4fkxFBeRB50LnLzllsT9FWVOogZeeWLF6fiN2WGn75LM2RyqBqGhLYnoqTImJzhwruiJ4/HThYGNUA0jY575ppVz/UPSv4crtxC1wWslr9ajDE2W6ZEcjm3jbz6HFQ8OOiLrbe3CP/qItccZ6xoFitKP27W3IbHWNDYIBCb5rCqB1CrJSQEQd7aOl0o5AwPjCONp0cli/Ip8wIVzs19Nxpa0ZDq728cE82lD6kijnM+m5z/+GwVNihtamAz1hHiViVPDrem3FFeUxW2evLEERWtiZ5PI+FO29XLVCUYEaBqdTqcGdcgyryrKylZ5idnNCl0W+bJ55SUrg29B+EPHK3P5X7WeA7tGBhIwl9LQy9cON1DZe+qt79iRdEoUEQGAz4qtXhFhCWGBhoZbpoKicGZC+WVvS+u5/7+6dX82I8gXooa5OZIkQ32gsoQNS50nsrg+zIZE79DyFSVfEdHMlIUSe3tzSNjc3X9g5I/y6tMAOM1ssD1EJHc4omwr3rJGs+q1/OKVd+PJ6VHXOZgc31WLqc7QvQRAo79Vws8xppRojlDBUuZjcCZ5Qod01a8Kwq7aIy0URGMMuNK5zEX46QvGs/t6ytCdRhQnZ2ppGyZIuLdwKvxPoyROHbRaNMr19//v723j7L1LOuDf/c5M+fDc5KTZJJmApE5sZrjwdZFzgFLbIvzSpdCFNwvTa3CstC6oR2xFV/e9kVjMPR0dSVqP5Yfb1zEWjWnFt5GaAlfEtGAVUAyh1gFBEKEBkiA8BVADBDu9497/3x+zzXXfT/P3rNn9p6d+7fWrJnZ+/m4P6/v67rX1xutgcWSvHgJJXrUWK1v3tMcVcux1hHLIDyCqPvEHneqjMEjpCWrjgpbagqlS8Nbt3qOQe5dniCv4+8ValGftwoJ3j6yfeGxpfa4ZX2nHtFLAVkLHgFtAZuaPj9TwdhTTmz/rNBpXVr07Xt58xpo12WO9miFpUXaJq9+haUvtsY/K4QyZkLdbWo5KtXeL9EYfs71bRUYrjcVwJaXG9dlLqh1O6hMfkIMhw1z1EAZMgGtmmYJTIlw5o7ktMKBt9i4SfQAnFy1KBI5EorSWdQqoXvEgBuINZq1BntJG7WE3tO0S4KPZSRdjCCnxdhNTFcCNTKNP2BhDHWFLC1t1Yotk1QC4s2JCjKqGVsN3K4j7b/6hFk0yAqK1IpJfEsaK60OXr16TwCx42/XqH6vftnSGikxWW0fmTkDCemSskxe26TxJdpG6/rJabTqJlJhQ4NI9f0l64YKKZ5Qbvtsg93W1rYey8qYEhWalMbQQkVTeM7iwf8553Rt0fJDAUKv5clqevCV0hAPnA8Wl+H7cz5zD6pwcP9SiLNCoV2bucA/fWaXJm/Hbn19a3Ai+YL1yasLZZqYiMkD+CSAT/T4+dyiMnkSeZqLzj+/kdZ0Uj2tTs2nXAz0x5H46nfeARm5xaamd8s07QahlEkzM4m5mqitdm59drZfDOixKSU5zSXHoJX4WT9dF+PLCSEae+BFzKugtrSU3kPhx3MHXHttU80qxq0EtHSQh+f7tJp8bp6VWejf6ivnfVyLfCYFNs67PfbYvsc7IS9H1HTt5VKUyEg4jzxLgKbKnKDgWWFUILPr0TsRzWqGnlCmQhg16fX1/JGluk7VYuS1SzV5qzUOBs346SFDujbocqKFiTRGhXObBWOtGRQSmMaq+zzH5HTN6gFbPEeD9ErHRN9//Hhakysr5fgiWkB4MJFVckrrz+6vwaCdIWLXlv07F7ehwhxdmfqZVRZsu1QZ4A/n0DL4nTiBLsbJmfwNAH6670/uObv9s1OaPFNUqBWQUWjgkt0sNJFyEas/1JpxyXRtypaVnvVam7/NoC71+5DpXXttm9BfemlDfE+fbpcsLfl3ufC7Tv7yTGL2OpWeLTEsMT5lBhwPji0FEE9a1vvZ39y17IcSRfafpni6TEpakRLMEvOw46fCgJ5hbs3d6rNlrjcjjBm4tLycfqgp2WhnWgOUOKs52jJwCr6MQ7FCqB0fMgM9nrWr3CiRM++rUKdWHPXPUiiledjuKQpsGlOTm0vde9TkPbO8CuxaOpnzqXn99NUOBs0ccpz4DGsNsgqDmoetQJmzQKytbT2BkhZC65rxrDycE2WwFHg1YM0bQ+4JTf3NBcN66X12LlhzXmsc9A2u1fZxTerhVhSWV1fbwZd6CBBpMU/k5NkYFHgYiMc0Oz3lbprYUXP9vP3slE9eGT4nSE3YusE0ApWSMX3NjEq3Jka+j5oyN1uOUZJQaUyARtRa6ddjLtyoDFRRjbZE8JSAlDQzFUCshlXS7D3BQb+3Lg1lZDb/nOks9kQ6ZXY5zSOnLXpm7BgbAqaE1jJxr49WkGOfaNa3lglLAFVjIFFaX28EPPrtyWBoyj9xIt8G1dLUyqEZJHQF2f6SYVH4zdWf597wCLmOnQqg1gqVC3D15tj2UfdpV4Ca/d/Otd5jBXYK3xohz7ElU9BgRHuQDtuqwg6fQUZNhmKVB7vW6JJiLjotchR47JHaXQHDpHLLqRAAACAASURBVFOer7u0h3NKjB1HfXbJlcm2UijqomG5uSZN1LgD3S8qxK6uNuNw6FCT4qi19pnKTBeNV8hoGqhMfptQU5onodoNT2YLtAkiF58SKav92kCzEqGJsU1QPfMS7/E0Y72ejMsLYtIgMo9RemZdStk0P1L7z51Xrm3lc3MRwpYAWGKg5jnVjrxxKI2v/dt7jyLH/EvPtMRL+0eByGOSqkUOBk0/Sfj3728zQo6H1UxyxO/s2UZTZLyCzchYXW0fJkTCS4GzpKV5OdK5a9RyxL1GYZknK3pCI8ey9I5SPIsVDgaD5kAbPa3Mg6cgHD3aMAZr5la/t9cPXctMwaMlgMK+Z12y7VFhTWlQn72hz+oypXcJ8TovXXURKBColcbSHat4eHuxxOj5HfeaHlTj7TfGGXGPsFAV9yvN8idPNhYnO2c5gWMSVCa/TVjNyWr13gI5dKghqN5pZUeONBHkVquzGm+M+Q3DzV0iNmpyy0G1K91I6t+lSdOrOqWndHGT0STJiPTDh9vntq+ttQsE8Xm2IIU1pdr58PqsqVZHjjSblpG3VjtRIStHHHKmQDuGJa3Qe0ZfgqRttBr0+npTrGRlpdEqrObq+RhzBGcwaEcuk0nQ3MwARc+iw+s886QyidyY5pgA76V7Rq1WXl9Ka4Uatroy9D5rxVlfb/teS1qZ507wxoX7TV1pquXyhwF3NPtyjvn+waDJzVZ6oHPeJbRaYbKPQFpa8xbWMkDakkuPs1Ym6yq0a0fb61laNDPJCimWrmkgMsfXFhDi/qAgzDmkNdauDypkhw+X6cgkqEx+m7DawNmz7QpjXCi6MBhZfPJk48PkwmR0vEbY2g1lzeHWrGw3QI7QkQmraY7X6UK3mpNG7jN4x9P8KESQ4CiTpiavEbDqxvDMV+zrgQNNgQ8bzNOlSdiYgRibDWkDkSyzoX/N86PnTIuEZ57UMTp4sJ1OVmLa6j6xa2JtzbcYWEGxS9MqtdleawkomX/uCGXuBV37ubZ6zEOfYe+lmd2Wa/b6YrUw24aSJm/7rpq8lwKooLuDZaWVGTN63ZqXrQ+d+0pTQ2lup6DKcdvYiC1rDvc6rQS03uiZB1xzHDctlqX9ZdyOtpv7SE3ZXbB0LCcI8VpvDXsWFs+cb+/f3GwEQ8aq2EBnK7x42jtjD3QvMFiZ80WhkZktbNfJk412XzX5OWPyMbYJyHDYMHk9oEOlWjJ5BkAxWM9KhF7Esw16shJwjHkNTLVCXYS2nKZqONZ6wECvjY08s1BYgmg1dPqGT57cGuHv9Z/t1k3TN+3EjpWnoVNo47zY+vrW1JzTYry+e9dR0OJ57yRyXFMkEEpQtFoWhUPNHfYYF8d5Y6MZO11n1kTrraM+60oJYE4T14BPTzv0xshj0J4WSuZPBqmE3NPkvfiQ3HzmoHup9D6C+59Fe7gXVJu05mUNfKMQzP9ze5L906BeWhm0lsDqahPbYN1mJesCn8sDauiK4bPV9dMHufVm19E4wqcNtvQUo+GwnQ5N+lLaC0pLPEFCoQIjBXTNatq3r1kLXjzWdlGZ/BRgpTZGyzJXlweDKEPjhFqt3NukupipBaytNe8uMReFmqmUwFm/pLaDDISmVw30KmmnuYVKhsDFzOBAmhipnca49fl2A3ubMdcGa3Hh872AHI4xU9FU489tRG8sPAHM6xP7oRYGK8xo7rxaHZgCp2vGO3FPzZ+0ktAyQR8hCXPJ8uPNt2rvHCMVjnJEzwvaspoQI6Q9bdu7T5m/115l4tZSw/G16a1dBJfvUUbiCdRWALDMgdocy+FybzJYa2UlPX91tX0yHn2/Kyu+0MTCK/v2pXgBZfjW3aIaPf+3VkWuSVoiDx3KB7Tace9Dp/hdLiiyr/DpMWJPMfIsA11r3woKtu06r2rR4vjZ2vUrK75AMw1UJj8F6MRpUBx9dMeOpeu4WVlH3kr8XDi6yeziVTOWXeiWaGnKCq/hPepnLy0q2yaNLSiZOkuStjJnHthBv1XJJ1wi2l1tUOtEbqPyHk2V0XcwxoGM0NPU7VzZtEWPQJGZ2f5bIaRrvfAzWiL0vZ75UwOtOA8UHvl+mqBpWuxjJi1ZeHLts2PD+SIRtAKXfaYV/lRo07FWNxNdZp7plYzFE0Zy79f3WYE6V6KamvKBA+058JjB6mpbMF5dbdfkYJ0NG6eiGjH3XK4CnY6lCpRW2KV52hYE85go77HCU4mhlda+Dez1tO6cJdD2Ud/nZbnoPX3cBronqRhZK8JgsLVAzspKfn9tF5XJTwHDYTNprL61uZnMkUCqKb252T4di9CFaxdHSZpTYsT/1dxNjTTnE+vDQLjgPN8qI3ptSp7dYJ6gQvMjffN9gpV0rDymav+3QT/ehsw90/rBSQDW1poxVqFAN68+3xtjbx40j1YtGX36oCeKqUDjaTDeOPF/b443NxvBdP/+rZpu1/0ecpYGCxV+1HWSE5b4mQZQqtvAMh/GgtCKQcboZWF07cVc+5UR0bJn186JE01swvHjjdldzd5Hj7aDDNVcTy2epXCPHEm/bV683ZN9mImlMV7fNBbEO8yF42aFJy/mwo69FQQ4f8o8tWaABr51WZNsf1QwzM25p2TYvrBQEYXYnBWB5nlmYnAPdwVBT4LK5KcATSkKoVlclkCWJEjdLCUGSVhiqyZTaybSU+lKErASQT6PZl2vIAvPpffM0ZRevUAwK9WTCGrKVRc8Tb2rKpnVBkj4rMRv4x6UkTF6WYNmcmZNPn91tV161baDQgVLgGqftI8U5OiLJzGlJcSz2Hjj5BUZyjGxXJCRFYBUW+nKJBhXy/KEG64hjqkKyRrromWC9dkULo8fL1u0cmuni3EQHB+ug9XVduoV3RHcu+obp+LAPaHWEv6tKYlkbLQMkBZQkOCa9uIvPOSEfu03+7Gy0mZQ1uLoMfAczdA22TacONGu6KkxKzaOSK2ayuxz71dBQmlaznqq7eN7vOBcO6bsA4OH6XrzDriaBiqTnwI8TZ4lH1nchgvFpsfZBaSLsuTzsYTSEnbL8KwE7Gnxyvj5t2VO7K/XRmUI3NRWK2b7rTY5GDSVtpShWIEnF+Syudm0VyVy65vVsaAQYxmBdb0Qg8HWswU4Z3Y8lXB5wpLXJqvx23Gi4KUBehsbjXDA/ioxzlk4yNQ4xtZVUJor73+dDy/y3SPWdm1vbvqH9+TGQs9p4H1kYqwqZ4981bm0RapyhJzQObVm6pzFhJ+r0Mx1Q6ZERq01I6xrjH3xSgZT6OY55hpcyf189GhT5MZG2feBttdqsl4FPgphVkGwUOGhjzXIrh2lRd661/HPFeLJBXESpfXcRZdyfdCiOFrkKCf8bgeVyU8BHmNg5Kkeucqyn6rd2wWnzNamehFdG8hK/fZ5Wu1MA2xUM9AFb7VeK1zoO/rkJyvUb82UHppOmWdKBqbSvjXDcQ40XS+nyZOQUiDR/gwGTd16G29AxkJGYjV0T1vRaPecNsOyyBsb5fHyCItGOFOAs7UJlPipZqlznzuAyK7P0pzycy21S+hzyPCsr3c4bEdml9qh86jmd84NBbdclcfcuFhtMqfJMwCVedy6Lq2wrAIw1yfPe2fwXM7towxhedkXjDzfvQro3LunT7dz+a0Q642Lfu4Jg3o93Qhc77kjnBV2XXiuoJLLKbcWvfXSR5P3+uVlrAyHzXG3SkdL7dLPdO8CbcvcJO6hEiqTnxLU/DYYNMeQMqqVm+zAgSa3NCe9kpmo6d0yG68CncKakZQp0hx98GCzSHNFJGJspHV7hKfV/klE6B/uYvAxts3NGszH8dSAPE8y50ZUc3+Xz1E3sB1f1dat2cxaS2wuvyVSHLuufGsyI45bn+haEnetnkihggFYTMWi2doW61DzurqJvDRDbXdJMNWgNmrjlnFw/Voh9cyZpjqY51e27VANjOb3kycbhk8f9/nnt5mtMlPrNrKaVG4+PHM0x0CLKlkGrtYOCiC6Xm2E+2CwNb1LBYATJ7YGcbGqIa0YnBf181PIsoJsTmPXsbACNjEYtOOC+kSfe+uC/1NQ4TrRtevRxZxAVhJIS2vcWl7smrfClwqIOaHUZlJpwC1pRVeg57ioTH5K8DSBwaAxj2kqzLFj7ettkJRXjEI3Vh9JlvAWO5kDGbf6eD1Jnp+trsaWtkCCwI1orQ+60L3obkq0SojtRugjxFDj1zOzbTqelaL1AJqSxmXfp2NkGYI1AfM+ZbRWALj00nZJS0vEPROkChk2WE+Jk5pqNVBLmYnnJvL67429MhqtWcD+MFI/p1WphUgtQZrel2uDPpf3MjhP08OssGv3pzKVnAuh716zAh37Zw9KsWuQ7pODB5uyvIcPt8+p0LFWvzxjEPbvbzN6ZfYamMa26AmUylRUY/eECtKnXEBeSfu181aymug6pjJDoYwFgLz01FzKqhVM7F61c0s6qfUkrJBeijuwcSm2bV67SrEs20Fl8lOAEn2VzM6ebY6EPH68OQ1KD0Sh6ZzMMcfQPQ07xn6anyVgOYarz/Qkys3N9iliSqCtBsjrVXOheVMDZHiQCVOZci6KHIHlZtSgPatR2PFT6wHN9mpezY2JJ6Xr3JeCyZRg5LQNO8cUosg8WYhEfe/2nZ6Vgr+1zDH7wMBIbVcfAqjEWAt9WJdPae5ibCwnNC2TMfT1z3LcGGehhUZyWSwe0+6jRXl7LaeV6vXqf1U3Cs3b3BcM3GV6HAVwWghZCXN5ufl/aamd+06lglHbjP+xigOv279/q2CjTNjT6q0FxJtb7ne63jyNu6SNe+NphQgvPdUKajn6aa1u3vx30V4P3j1WKWCwLQMxSfO6ypBPisrkp4DhsPHPLC012tX6envzecEbapormZY8gs7vStoF25dLqbLvUW3W8zfnAupKjCDGrYIFF7mOz9JSU0TDM5+VCKx9tzJray63bVHhIrehPY1Oo+o1eK/U/1JdAm8MB4N2nX5r6VDNziOYJXPocNgEiB48mGfKduytkNNH0819ZzMZSvNtn+cJM4NBE8CZO5DFQ1fwlb1G26lBrTomVpPXmvLUVDX4Sn3YvJfM3tKRtbWtlex4FoPV6MlMaBlYXy+PUV9Gy7+t1Yxjtba29Zji0jyqhmvXlkWOppWUgS76dPRou1x07t7cZzE2e0xLVFvLBS17FEZp0dFzQKaJyuSngM3NtvmLmsnqaiOZUwK3wUh9oqrtIrGMztM8bfuUKeU2nW4wSrr0h9l3e6bp3LNy36svkeZGDbJTzc4TTiaxYuh4eBpcH6GJY05Cq7WoPcbYhxnnNJsY2/UVcm33hBNaArwUMkL9+iqAev313AZ9kZur3Drqeoftm95D4fHIkbIPOde+nECZM+eroG4ZlWVGagpfW2tog10/Oj4HDzYM6KlPbZveSWsoPDDbwlZUIzPRGgJdNKNk0eKYMsjRc/mVLHPePForWonh233gzXOf1DfdrxroWxLy7XrR7yzd9IQ+LT7FAF8KZyG0ecQ0UGLy+2+44QYsEl7+8pff8IIXvGDqz73sMuDrvx5497uBf/EvgPPPB77pm4CjR4GPfxz49m8HlpaAr34VeMxjgGc+M913+eXAF78IHDsGvO1twLlzwI03An/4h8Af/AHw0EPpmje+ETjvvHTPl78M/PAPA9/6ren/l74UeNWrgA9+EHj44ebZtn3PfCbwC78AbG4Cl1yS3nPZZen7c+fSc5aXgXe9C7jmGmA4bN518CDw3Oem3+vr6TknTwIf/jDwtKcBt96a2qnPe8UrgC98AfjO70zff/7z6b7LLwfuvz/16dOfBr7yFeAJT0jj83Vfl647cgR40pOA9743veO++4ALLwRuucV/h46H1/dv//Y0zhsb6f+XvhS4/Xbg3nuBv/gLYG0N+PmfB06dasaK7yE4Rp//PPBjPwa8/e3AZz4DfO1riYw+/DDwqU+lsb311jROBw+md958M/COd6Tr/uW/BK67rv38H/kR4NWvBt761nTd614HPOUpzTW33praeumlaU3deivw+McDT3966teNNwLf8R1pvbCPXF8f+xjwjd8IvPjFqX+2b5dcArzlLenvF74wjfX3fz/wnvekey68EPju7wbe/Ob089BDaW7uuiv9tuPkjdnll6drbfsA4Mor09i9+MXtz7150OcdPZrGam0trdsrr0zjfPvtaW0ePJjG9dJL0xyz7d7+4FixfU96UrM2rryyWTN275w7l9753OemNXvjjWmM+az19TROn/50WiuHDycace+9wEc/CrzsZen7L34xrasYgZtuavr67GcDd98NXHVVGqPPfAb4yEfSWn/ve4HHPhY4cAB41rOABx5I9OWOO9KeirHdv337gJ/8yXTNAw+k9913H/Bt37Z17/Dd73xnaqvSFfbtYx8DXv/6RLuWloAQ0nNf/3rg/e9P+/sLX0h08frrgdtua9MIHXeuUSDRwauuAv7pP23PCeeWc6jrg9dxnu+9N/2+5Zb09+Zm+q3zTxqgNPaSS4Djx4G/+TfTerz//rSGXvnKtDd1DeXa9uIXt+nmxkZ63113pWf/1m8BH/hAoudHjwKHDqX+nzqVxu+RRxJNed7z8vtqXLzsZS+7/4Ybbni5+2WO++/Vn2lr8tZUqdHzXspUTuO2GoimD1FitCl6em9Jk9/cbGsPnjmYqWB6RGcuQt0zPdmcUz2i00t9O3GiqRdAUyarflGaVbcBNRwrMVNjsVkGXVqgahp901W03xdd1LZCrK42kjm1R32mBhd676L0T9OsN8dqEdC0MG/++1h3vL4Nh40v1Z6kmKs7UDLX99HS+1pOaF2y2jZjHWwmSsnkXIL3Lttm/l8K1FOrGTVM1eTZVhZB4jpSd5haWM6ebUy8zEyg62dtrZ3/zip5+j8D/Oi+0kI73nroqtRIN8vhw821XLsc7654HW/d2rVQih/yTO7qEmNsiJ7/7r1bLQH2867KeUpfbfyIpa+kNyzUxKwmjjPpfzXXzxGT18Wh/mVNM4sxTzj0u7Nn/UIg/J7Rt9ZfpNfkTFk03XHxaHuUyJw82Y461wpuXnuHw60BUmRCrDXupb6xXkAIW4kRj1v0iIsyFxJBxj94xX9yQYB8lmemzxW3sL+Z57y83BA0Wwvd9lmjrr35sqlU9qAPrhE9RtUjRGxLSVjw1g8r+q2uNr56rsdc1oPuAcv0bHAToZ/3ERg8cywJOuu4275aYbSPIKeMu8vE760x9s3Wdaef3z5bKyjSd81YGB4cpDEqdNssLzdCtM6V/rAwDpBM/Ppuxi30qV+fi/geDNpmei8QlGNh10+Mzf610f0WFDpPnNi6Diw99daJulO4/3LxRFyXtmqlpUVWkLN00j6PWQ5URpjuuX9/oxDa2hXTRGXy24BOppWkuYBV8/EIBwmybnIvwK6UXqGE0v6/udnEBnBDW8l1dbUJONE8XW1LLrjNasNdAUzKUE6c2EqglpZ8YUjv5btZtEZzgfm9TQMbDBrp2Wub1dTt4RKqjSkj1r+5FmyFO2W+Xn64N07DYZtgx7hVgLKaPMdtbc0PYrTrRN/NwCNNPVMtORd5rsw2l7ZotWkNttPvPE3dalcEGQjn3vqzS5p8TtjJMW57D8fc26fsmz0uVp+t60AFNEbbcx+srrY1dtWQtdgW54n1EbzAO95HwZFZG7YstYW1HOl4dPnsuQ5JV3TMqBQxJkG1Z50n1ZKtsmTpaW4faYVLjrsNQM1lq3jrSdP3WBdCNXm7dr1UW5a01RoOfSxNk6Ay+SmAGhbNaaqdkqnkJpGBTysrTTSqFi8pEWjCI0CWCdBklNtMNsqfi1MJvJfepQeN2HZY7U8XPJm8ujhYYpZEqbTo+XxuXLtRrKl4MGjmhAFmnsbCgChbWEc1M5Xc7byQOWpmggo01PZVEMqZfU+eTISAlfBOn26Yt66LkpVB588T1JSRc/1SGLTP9z5TwcISThWgVEOyWpxHtFV48Ez+FKa0xC9hD4Kyc5SzMOSYf4yNMM71y1PjVJC0c+/tQ9UkNeqee41rlJo6I+l1DzMljpHyNrvAWhVXV9v1GDR99PDhtvXQjgGFP1qsusZLGbHNOlCaQcao82vnScecbeE9XfTBtscTyJWuKn3LCQu2rgn3tc2YyQlA2iYbrOnttWmhMvkJ4Ul2rPi2tNRI1MvLeVMhJVWaSBn5yuIlXoSmZaYWVotXabOUg8lrWYyD6T5k4mR+3GClvGDVXEigSFiZasayoPyem90e0tI1B7nN1EcrUOaqhJ/aGMdqOGyXsMxZOKyVgu9hXzmvyphK2qNqBBRoVKjyCA8/10IiZDyq9XLd2mp4pTgFHTPNgLARzHYNemZcCkre2Qjad2q7Xiqnaop6rxad8taCFQBza4tzyfGjlYOmambO8Dm65jmOKrio+Xo4bJ/A6PnRKQhxbHMWIs+6omfMq2DC59KET8bP2AtPAMqNl67dkguKn1P4vOCCrQqHR08o9LBttCqwJPg04LWzy+Jlr7NWPlWOckKDJwiP617qi8rkJ4QlsGSiQJMSc+xYY5JUcDNRO2R6DDVYPT2uZBLr0uo9E6EnjXNhUnJWDcAGCdl8cH2OZaLKNPidlvPlyVVazIMMp0uSLQk6fWCZqxIyS9Q8ydtu8JxWQAGBmmVOgveEFI7ZyZNNYZMjR5JmbzV3Deyzwg8Jt5p4PVNjl9BkrT2qyXsE0SOYnkbrMVtLVD1XhzdPg0Ear9zBNLZ9pfWhhWwopLFQjxV4rXXDS220DNSL4/HM7WtrzX7hATR27GlNpH+fcTUsNkVBWjV57ktdOx5Dz60LtVwwVsTep+NM//rRo/nyzyrsnjnTmLU5zlpQrAuTasd9aYt1oQ4GTUlsTxgvMe5JrBR9UZn8hLASqJbTZFALN63VNNRsxUAaT5siYcsF43URMPt3TmBQ5k3JfmWlTawsYfO0Tmu+HwzaObl2oXuFO4D0ji50CTpdGn5O88gx3txYqvDkPduzKPQhPCQYtOxQq9NDUdj3kjVA36mBddde2zCBXCU/q2nkqiCW+lF6Tt/IdzKTXMVAfTZ9zX38zSVYTZ5709O2PEEmtwe1vRpBr/EXtBooQ1ZTPgNo1cWkVfW4RuzpfAzmXFpqXGTq6/ZM2uqu8iw8OdeFNy5deeueIG3Ph9BAPB3rUh2NaWrHJQGWFkDSTo+25MC1ULL8TorK5KeA4bDRgFdWYrziivYmpfnZbiZqz+q/0zKmKulzYxO5hZPzN7KdOe3Nmp+Z7nP4cLuWthdEs7nZSKIMILSai2W8Z85sLdjBn0OH8mPdh0mS2HuZAbko8T5j6z2jNN72WTkC4QVVUkBaXW3M7Nde27g61NqhY6tWIK8PqqGqu8TOvY0TKAV+5vrqpW7a8evScpT4s60lgWIwaIRH79S2XHtLsAyfGRSaktX1HG8dUFPVte/VoKeQoedg6DhyfHg9tf1jx2J8zGPSZ/v2NeuGrid7cI9d10yr1XMJPKtkbn76KiP2Wbk4HtINjbfg/vOCQ737t4Nc+6zFzBNEu4R665qYJiqT3yZIzA4ebDRu1epV87UEzdMANMKWh31Ysz81Gy+QTrVnj3mpRkBpOMdk6HdUMyI1da0ORZ+x9UF6mhb7q2PkafI0P3r9Lh2ewutyVgvvkAhPO8m5NlTbKB3Coe/0Usx0bGwE8+ZmY2ZdWWkzRM7DykqbeND0zfKYVtvXPpDBqNndxk/k0r9KWoaO53DYCHG2LV1WE/vMUlU/CxUubH50qb12fLQ9XA+MDyi5rHLEXduuGibrz3v+eO4Fa0Xj0cSHDrXnUOeU+9ya/1dWmvdq+hbXtdbI0Nr5VpP3+qqWvy7GnrN+lDRy61rQudE0PaVBub08CTxrlu4dVdZsbJJX9tgKfDuRPhdjZfLbBrVG3ZTU5ENoLy7LTD0NTCV2FoXhhqU2b81YViu0/lndNIPB1lQytp9R1WQaehylWhxIREgAyBTos8xJ8ryPm5UM4PjxZvxYQ52Mr9TvvsgREM9MqRK5fYfd5DovOU23DwHUOaMwQAa5b1/bf8+5Ony4bYpkdDuPaLXavl1vngalmQq59C9abUp9LWny25mzPgTRCgWllLicIGgZv6bF0betmrx3L/+2gYNWAOA4M4iQhzWxUMrp0+3DZDR9LoR8qirja/T8eO7xM2caGuBp8hwXRv9rBL8nsKvG7AUcEjTtezSla66UDlIQGQ4bi8XGRtuFxv3r7eWcYJmzLpQEUrt3rIbPcS2VPZ52oJ1FZfLbhEqL3ESqpXrBJWrW4iRz4zJNhgyPxDJ3PrdlUDltVTeAErbBoE0A9Hq7oJW42zasrW09ttazXNCMfvp0EgoYVMY27NvXEB1aCzhtuX535bh2bSRqLazvnfMZ6jhvbrYzFtRX2EUsPGjKGAM3SQz1Hp65zpPIbN/Vz0trjgp7GnWv47e+3rzzzBk/P5ntsH7RHMbR2C1Kc1Z6v1p7VLuzz7FMqtROu/9yZlm7Pigs51IL2V5m2Jw+3cTz0K9r41b272+yeKjJq+bIdjD48LLL0j379jUMlm7A5eW2oMJ7yaj1OksPVFDh3tdTH72gSloHWSlP7/O0dILvoJtEMx48y5XOlzfmOSsCFRu1gnbRDhVwlPbk6K7drzZos6bQzSGTj7GttWgE68pKP6lvOGwOm+Bmt0w9R4QsU7aLhMyDm847NpK51zYIx75zba1NXFVKppTOtCiVYklIVJOnRcGaJ2lNiHFrKpuCjNm6D3i9R3BzG4cuDga2eczDE1hUmKKvkAFtJFYabORtdivsUSPPWSpUKDt0qGFqWk5Ux9IyAF5LDZOlh9UMvbbmV5EjrLCXG9tJNRYrzOWETevr90y0ufbZtVgy21ttkOvFpkJ6z7AanRfnoGlsKvCurzd0gel6NN9TGWAxGbq1uC+sYLC0lK6nAEFN3s4H9zHja9guu46sUJLlUAAAIABJREFU4MjKk+o+sm5DFWioxWvOvD3MxmrwTJ1TYZVWFXWLWqHMQude53d11T9spySwKx3waCbHjRY3+1vpiVp+puFeICqT3wbsRA4G7WA5e6Z1jFsXmD7L8ylZ5KRQa+7y2qYbzG4k+79qIHyn5vRT+9PIXRVWqOXkyk7adD0y6M3NZoyYYmcXPJmVBgLquK+u+gJN1zyWzMB2nLz0mcOH2/X/OachNNYd1gGwRXVsG8jALJNTIZIakA3IojamVQvJzLz0TNXkGQOi2mRJUMqtR3uP/btkKVCmaoUphb2uVMzEtk2tSt5+Kz2P81pKhdT9TMZkrXhcG9wHF1/cMD6uGWbZsN45o+OtcEwXFs3+yuh5PTVeKhEefaJ7gFYiu1ZUIPWsg/qdZ0Wx46prlcyesRSMF+Ba4fq2VhO7RkrKAdugbj/SIgoKuUwCj9ZSuD9yJP1YemWVppzGrpa8acUQEJXJbwPWbEcpWDcfC3LE6BMHRZcGw2tyn3tRy0rA7OL2YCVKMgj1O1PbpflNNT7VonJaLDcVA44YdETLB/3x1h+tm93GAAwG7QNrlAj0hWfFyIHaHIUmns+t1exi3KqtUWPQugi60fXsAFtpcHOz7QoiYeRYcU6VePIZZGiepeDs2bZZmIyHrifLRL0x88ybubWq1oa1ta330NqgKaUcE41nUXdJl7XGa1tujXTtQ36fcw/pPidT9wLwON9KL5gPz/+PHEnvIaNWBr+83FSjY3CuHn7CvWJ96/p8a7Wy8T8cN6tAUKHJMTVPsOOYWaGAwg9N7zzXgmsjp5DY8baZAV7wMa+31i+vlr3VzClAUdHRks/HjqXvxo0XIkg31QU0LexpJg/gaQDeB+AeAC/pun7aTN6ahaxGCSSirwTQRlpaDIftFBfvmj6aSs7MpIu7ZIrSQEDVikt+eX6vEj7TW/QZFIaoMVITYeCZZ3EYDhvt5OjRxu/adZhEH1Aapx9zba3btEyzKA+f4G+7wTle117bnLqnVhIFhZsQmhxvJTTKHI8f982iyvw0UExNpJagWUZD4UOv7Vpbdv1Yrc5q6tb64Jk/1QrFtUKmpAIrCaN1gZTaVoIKA55ww8+sAKb7g4K8NXPb9lmtPIR2RDyFGM6pXsv6GvoZA/bIiGgNU4FJn8+SyWwf26+ZCZ5yMhw2bc9pzApVCMiQdX0PBk2Z3VwRsdx6suNq25sT1LrWsyosXG+0oJGOcq/QGuYFZPaBFTSmiT3L5AHsB/BBAN8A4ACAPwbw+NI9O6HJa6CGLgT9OX26LRmWUq82N9uBd5ZwWanVoouQqbvAC44hrEmui3l6hJFaY85HbtPoaI60+eO0BNCUtby8NXBsHOnXjhGZHOsB8HCRXIwCn6GaCX2DNmiPjJvljmmxsON55kwTz3DkyNZDLyigabyH167hsH10qZqlPe2aQqV1meg42SBHq6Xmxp33W6HGI8qW4Np1b/OjdfypTXXFEth2eT7U0t+l+8kQtC12bVqBwQpYnDeuE6UDF1zgHyerWjkth+vr7XiX/fub62zEPbVl6xKxwY0e41SFoQuDQfvQpJy7w8b92Lm2wifXsBcPoWmBXbUxPNAVpvEOGlNDOsw1uLpaNvOX3jlOu8bFXmbyVwP4bfn/JwD8ROmeaTN5TX3iRGu1O/4w3YmLhCa1HOEl8+P3SqS9DUl4EreFPk9Nt5aw2WNO+wgPXYzRQv3L1FLImJSp6RntnsSrJr8+mrzVCGwajvrkSlqbMkPVTmyVL5rgSbyZQaCgufzQofQdLUI0m7KPfMbRo/m5s2ZsK6TxM9XwueaOHvXHS4P07KlenOuc4JrTUthu1XbtHPGzLkJOfyb3jRW2PAsDrUFs97ialD7Tc915Wr7uK7tuKfznikR5P+rTV7+71e7tZ2oRIoNSuuGlSXJPliyB3viQQXJfcT16vm/vvZYGWtN2ybVp6aFq511zykwVrULnCYCkU+peVHTR7D4Wsu2gxOSXMN94LID75P+PAPhbu9mAO+8E9u8HzjsPeMc7gC99Kf1/0UXA5z6X/v7KV4DHPx646irgwQeBt74V+NrXgKWl9PmDDwJnzgBve1t65sYGcPw48IEPAN/zPen7Jz8ZuOCC9B2QPvvsZ9N9118PnDqVPr/5ZuCTnwQeeQT40Iea72++Od176lT6/eCD6fprr0192NhI19x+e9O3d70rtfPsWeCnfio96w1vAO64A3jVq5p3njuX7mWb7rwTeM5zgFtuSd8/5zn58fvxHweuuw644grgwx8G/vzPgUOHmu+vuAK47z7gm785jR/7f/HFwPo68Pznp882NoDf//10/yc+kb5nn9hvhY7j7bcDz3gG8Lu/m64HgM9/Ps3pgw+mMWLfTp5M7332s9M4A6mfHPfzzgMuuyxdt7GRxubOO4Ff+qX0e20N+JVfSf1iG4hv/mZgcxP4lm9J3732tUAIwMMPp+fw+te+FnjgAeCrX23u1bm75Rbg534uzddw2IyXjsPNNwNvfCPwl3+Z/j92DPi6r0vr9+DBdrvOnWuvwdOnUx8uvjj1l+vy2c9Oc/iRj6Tnc/6BtAbtnAHNOD7lKWkOdEzW19Ocrq83bbj66vY1HPcLL0xj/tnPAvv2pX7deGPTF84Rx4jPuOee1N4HHkj/h7C171xDQBpToNk3Dz7Y7Fv2keOs9+q73/c+4ItfBG66CXjLW5q9ePp02mv79qV9ZxFCYssWMQJHjyZ68qlPpf+/8AXg8OE0n0B639LS1meEkOYMAB73uLQOPvnJZv64d7UvV12V1k4Xzp1r5vf3fz/NzdVXp+/e9a60x57ylGbMiOc8ZyvNUJq1sQG8/vXA7/1eeoaue4tTp4Df/M1m3t773vTuRx5J7Xn+89P6uu229D1pJZ95wQXAgQOJnj/4YDMO/J5r9JprUpuuuCLtOV0zN9/cXKdjS1i6W+rPjiDH/efhB8C1AH5F/v8hAL/oXPcCAHcBuOtxj3vcVCUk1URUmj52bKtfi9dTk6d0aTVQ9VeuruYj6T0TrBfh3iW52r5QorT+JfqgbYle9ZvmpOmSlE3JnsF31NrVFJsreuIdfKImPZvzarUEq4mpqZ6RvarN5ywpapbuMiP2GX++g1YifdfGRtLmjx/3zd18b6lKnM07poZJ11JpnHPm/77V/3KFarw+lKw3aqHQNUhNWNOgcnNvza92ndp5V2tGac1b6LtZKOuKK5o9oMW0+v545W/1s9y5ENYKQE21lA6p4+BZRFiPwGrfejCTXds5S6Mi1x66wJaWmhiXkgWPsVM8WZOuMBuUqrSAa8JaHLRNXr0Gu2Y8a07OwuRZMaYBVHP99qGmZW4aMsRcChgJI82vJ0+m7zS4Sk3XMbbNzGqCtYRYGfu0AjosUcwxd2sezplZGalKAYfR8idPNkFDjFb1Ik49M7SOkeefJXHQ8rHsE6NlmX6n/bRHx6oJUhkEzd5dZsQukAkfP94+Pz7GtmnQYzJWkPRiKizxoXDS58zwPvPr3cfPcmmintCmRNdGSyuDWVvzj0CmG4ZMJmf+LwmiOte5Ggdd0HepC+rkya2M9+KL/RQ5++OVwaWpfmUlPds70U5/NBOk5GLLjY/NHNGIfJ1ru/dJ+3ICtzduio2NtkLVlS7LMWddAbomuDZK+6MklHguKs/8ruvFE7q7+rtd7GUmvwTgXgBXoAm8+5bSPTvF5HlOMjeoBpR5p9CpFszrlpaa76w/3N5nCa4ts9nXz2Ov84KhvMCuHHP3rAw5HxvTZE6ebCKAKWkzOGhpqcmFz2kSVlsoBTdqDIVqZySGPAGMwTbUNr381+Ewf753qZiRN/42d5Z5vqqV8V4NBrIEg8JTV+VBO4ZaY35cgaSLmeeYf0mj8d5hn8frGajl1erX0sl9rDg5QdgjvuMQZO2zCmCeNs7g02PHmowNjecgrbBZPJbZa6An77Fryu5Jxp50adq61y+9tCkiYzMlSnNqtWBvjeYseZo2evp0vswwoTnoFOBLNUlKfegr5CpIZ3I1Gbz3ThN7lsmntuMaAO9HirK/ruv6nWLyNkJWzW9kWLootNLT8ePpGptq14eAcPHogRl9mTrvV/OpHhXLDWxNkznt1GpWGvTlnTPNoCdrVlxebsqreikwapnY3GxSq5TIW6bgSdQq2HC+lOhqqU1+rqlCzC1npL+Oic011rFRNw5dAhrMpnXMuZ7se632pfOp1ce6tAv+plDJc8pLRKYksHBsdX3ntN2csOi9R59t1x+j7r22a0R+aU91WbtygkxXu0t9HgwauuFVftQfCsHKsGnlYQCvZ7rPafI8ilf7q5ksOYGKc2GzK1Qr72M5JAPXNFlvLdujqwky+f37/WBXrhdrxvf2gZdSmptrjpFXxKq0Z3JBqbl2Tht7msmP+7OTmjwl5SNHUk40T6XT/GlucmpbNCVbAmTThYicVOlVnLImMvUtKcNWiTZ3DTdD7mQ2ZaA2YtYyZbvJmDqmREhrYNuFb4UGJU666bz6/ZYYEKxvQMZ67Fjbr8+zstUMX8pisJua16+u+sU+bFqaRrsvLbUL6+Tqe9u1kDO7elkAHK+jR9sn/HkELGcVGAwaywZdHqV6A1Yb9Kw0uZiIPlH3Xt9zJva+2mcflIQJ2z/Wh8+dyMjzFEoae46Re1H29kc1b50Ha/Ww429rfej3uj9z49q1H3W+chH4zFjhfrHmdio/KiR4VkVLTzwhxVo5clk8OWtRzkSfa+e0UZn8FEDNdP/+xiyjG5V5ljbv21aj4mZQM2OJwNo2kGl6C1lNuOoD15rP+hyPudpANtsmy1zt8/Tas2eThsKzr9X8yHeMo0F5Y2HbUPKR01/PWAjOjw3G04A4+oI9U6HHmGiGvfbarYKJmhQPHGg0Nz0cRDUYqxXo+5SZqy+Q7gdrHeH1LK6ihVM8n6pnrhwMGiGGdQByeei5ubJ9sGNjrRc5xp3bJ6X9U7qmzzq0wq7dU7RwWbcSrT6HDzc15cmkLcOn4sA14NXk4LNId1RIUEuVriuvMmbOveMJ1bn5yQlnnLcuM3tu7PVzj4FSyObzmULI9UkXAYV70ucYfSHFutNybeFckm57Y6v3KA+gC6e0NidFZfJTwHBYjmbNaezDYbNp6SM7caIxA9tJLzG4knRsA5dUkKBEn1u8aib1TOA8LGN1tU3APPOqtl8DFVlDe3U1ETo19Zfyq7sEkxw8pk/C4AX8kZAMBm0NfjBoiLEGHcXoEz61hnAMqDlz/pSRs2wv328Ln6jPX/2XOt8aI9HlD1RhwDOVdmnfjNBfWioz2BLh9uJCclYkjxmVzJ99hD2PsHPOGZXNeA+1euQYGr/TuAcdBwp1x461zzewNISWwgMHGkHKrgdl8kAy6ed89wwy9QLP+tAZHfOu+AtPyBsOJzs2umu+PGGRLj3OH+kLx0ZPj7T7oK/blO640jHfJQGob8bBJKhMfpvgRtUz4JeXG6mRG1uleF1M3KjUsmkOGtdPkyNgZAC0FmhgGoWJnFl1OMwH0alQQQLGRap+q7U1n1Gz0AQJGFNZNL7AE0486d3TvjzTHD9TX7iX4sKCHzZK3gbzkVB4h2F4bWJAHZ9Ls70KNaurzTGizD7ge635VX3+tliIukj6+P08Ddkzg+ZSpmJsR46XGGyOKXiBkRdd1Fg17Pa17VSBxtNCS3Njn6tuDVpvlFErg8oJJ7oHvbgHzpvnT1cN/ElPSn0ibSG96DLrU1AtmexVEOzDbD1m3VXoJec6yu2d3LyVkBMidew1NoMau/r2LT2xQnHOopObe2+scm0cp6/jojL5bUKl9OXl5ixuBsvwsIhcPqVKjVovPmcu6wvVgvQMZ8vYujQfNTMqcVaznadFWpO3JSA0la2sNBGyjG4/fTq9e2OjOY1LNaTS5lPTmR1zRvSqLzwn/XvCBE36qmHnTHnenFlToGqDZ840zJ0WBZpoGcOhGp6eINbFZHS9KYPOaRacH6uVeGZ/fUcuwEhhmYK2QVMcuZasG8d7nloqrFbqMfSu/aRt1PnVvaBrvktoKLXj4oub/h0+3Bx3rExb551+eDJ5xq/k/O96siAPUeH6oUVgdbWc6luiO320/pzv3frDlR7l2pR7b64dw2G7xr7ubSo5Nmgz92xPsy/NvWf18NrYx8K0HVQmv01sbqbI+KWl5rAH3XDKILv8WFxEVlOiP7wUrcy2WKJNZuFZE2LsJoK6UTXoxC54CgQ23zon/WrwoS0IQiJPQra01O3jzY0h+6tuBc8H6AkKdtOR8Wlt8ZI1wT67JAjQ3cO+qkXHavd6jrn3Ls/8ys/INCnoeD76GLcyXOuqyWnjfbTk3PrzLEVra42gbOfNE1L7Ev9ce0qfebEIVivLIXeNMuTcMbL2Z3k50RwtXaxuHqaBqoBgXQDMbFlebgRJO46lKPs+fetiXt5aYJwBy/XmmHzf8T97tolFWF1tv5tCds5dqe/KBdx5+03f3ae4TZcwtF1UJr8NcIJJoHmeOImxTVPRhcmFs7raLlwyHG7Nsx4MGm1cfa2e1GoJJnNDGUTlaV+l6HSrqSoDshqTl2+d0yrJwHk86+nTTSAefd56hnaXBE2Tox4uo5/TNOdpg5bBd6UQkfHYNDUvDsFrq0f8tOKcvk8FvNwhOEpMBoN25oZ9P10G3tnZ+l7Vyq3mXiJsfRieIheRTBeFDQxVeAJFH61a25oTVrx35SqjTarpbm7GeNllad4f8xg/kO748a2a/KFDTeEozaenz9n68nkEs5rx+Z36qbvOyPA+G2cMu3D2bOrX0lKiBbSU5ARXq4jktOncaXm0cq6uln3ipT6OI/BWTX4PMnlufFvUgnm7jJhVAqaMQP3ZLC0ZY7NRyZBUY6H06VV40sViU7J4r12M3Lj2pCYyWT3Dm+/wFrUXqcrne5YCJUY82CGXj99H26KbwGq53Mgkhp7/zwpfLLBjU4i8tnnj4uXtWkHJiwlQrVRTF9mv3LzTDcFgQKv56vutyV0ZeS7Wgd/pddMKFLLxA4yHYCxHqZKZpymNI2Rwrul+Kd3jre8+AkXpGvad+5OxB1rWWg9xAtoV8ax2zjHUzxisp3FD/OFZ9cwgsfEmpTWua6RrDPvME5/tHdHbJWB1CVL2Wl3PtsxtXwuUzm+fw2e61sJOojL5bYALhqZmTZdTJqZmIqsl00SnpUtLZp4+C5wbjxH6ykBtFSm7uXQjeLEEvMe+Mxe8k9scZ882RO3o0XxhjHE0JgY7cg7IJD0N3xtPMsG1Nf8YTM8np2OeS0+zGgffZ9MnlZiRydHtoMVHrJChdRW6NAutEMjn0mKi2pMdG7WIDAb9Iqq7oEyd76DWqQJFjnjm4j36tGdzs6k+pydC5mAZTVdf+2hnZOAHDzZR8/STcy/lIujtz9GjTUyLTZujwPrUpzaf8z1q6evDgHLM2RNAea0XH2MZnmWW3r5UV8mkkfk6j9zrfUz2OXTRajtWXcrKuJawPqhMfgrQ1CEuRt2c+/c3i0ADmrjIPNNvF5MrMR7LuJXQqNbHa231KW5ABsPZojy2jTE2GrOnKXvgRmVksLZJNZwcAfFAv+Tq6lbtO8egNfhH7/GY2LhE3TJcy/BpnlUmR+Z2/Hj6jpUQSxqB/Z9llmn61zYynYjElASYa9WexW4Fky7tqY+2omtXCTutMboXvHHnO3LR2Z5g5Qk8jB/po8mXhM2coN01DprCxWBLy3AuuKDNzG3hHAq1NoWXnzOeY23NT/PlXskJpjlhRa9R145NPeR569YlUzK/d43h5mZDX7tIekmT5999j571UJpn+12fPu6Etl+Z/BRAvykXOkvZrq5uZWAqsQ4G+cjPnKmKsEzMIzweUbKSJ59vS+OW/LX6frZJGXMfSZTCBbX5w4e3FqTg+3MxCCVBw36Xk7itudgjdONAxyXHtNj2tbWth/RQ6yJBPnLEH7sSwWW8w/79W4mFraZIHz01wC4G24U+lpfc2u0SHKy7xNYd8Bh7iUl5VpsScmvII8wlCwI/V03eZjsQFAQYu6KuQR5SY1PueJ4C95jeb035fdxXXlCYFY65TzX2J2fG7svIvHVulSi1knro0vonYeJ96UQXzcz1sWryc8jkdWK4kS64IP1vTalWqlRNnqZlNVVqbnaXn9qiz2aiFK7HjvLermh6b3FaE2qJ2NGqYAPicoS5q3+lzZcj/Ko9b2dj8Znj1PG27aWWpmZUq43b+ywYFb+05GunVjNnqtKk5koP2yGMXv884TfGrQGHHsPXNnlMh+swJ8wqrBUs196+Y6MMWN1JnpZMqwW1dA2k0x/OpWqq3GP0/fN9fZhTbi9a4ZgWN2bLlPYu2zXOvtN3HD7cz4rD/zVVddz56qN9971vJxh4H1QmPyVw4XLz7dvXfMbN4BF9JTJra1tNxypBj6MxaJtypn7VeugDZtts4ZicxcDC2/w5/zCzC1S46OqTfl8iJDntT/vMsZ1G6oo+U33zSsy8VErtE5k8Mwy8qocxli0EnunUI9Yk9ktLW4MrtwtPmO3LSDmWNviRz80JbjmLjN7bJ44gJwzEmNfkJ2Uag0HDnDn3PDfB81fzlEYvzU4/U/86mRwj6cnou0zl4/SD//eJnSGGw+7ASnu9Kh25vW/3vV371kI5CXIZIeMKAruFyuSnBC5aSs0s7UmTHLUzz29tF6EyBJ5YZiX8nHZj4fmcPK3WauDeZtG29tHClGB6Oee5fnWl5OTa5rUjF7OgDG9cC4n3Hs8XScGMJWIpAJKJKxMbDJqoeFp/SPRtWyhM0n+eY36egMgx06j9PgRv3HHRNWYZZx8/qMYOeN9bTdATXEvMvNTPvvnhXl/HARnw8nJTf+HIkUQnNKaC77eR9iyKwzRTTTlVJsRCSzwG2wq1fZSH3BiMy9wnva+rPXYf6r5XhUXLNfdJWfP6bZU1O36TjslOoTL5KYEpNseONfXXmfdJzd5qyx5IEJnSltMy+zC6GH1fubcIuzaNpz11Qd/jaSceA/Ii/T2trI+AY/vVh8j36V+X5mffefJkO7iNFchssKEeAOTVL/DGVZmatVp4wgvHzLNwlDDuuJTG3fbVPmMwKB9eYrXAktA2CUoWA+/7STRh3qcR/mQ+zMzR0yspECiDt7UvrPWNAbWaF28tPLb9HH+r7ebmfzicbg360jovCXKlOaKbRV2PJWFQ3+8pHCqkquVsEhq5G6hMfkrgYtcI5eGwnUNqU0E8cGF5kfH2uj6EJccQS9pUaTOVJHrv85ww4jGBwcAvwNFVSW5SBl4SIMaZn5LlYXOznddMTd0z+Xkm7XEEmWkIPdvR2PoSNzIs9ZPqM3JmXGXmHrEex+0yDmPuYvp94AnNpBlMvVW3DgtDnT7d7AkeaU2GbQ+XocBJZmaFgiNHugMMOf65w4k8AS43l+OMsTemdi3oNeMIWpO6WTwB3t6ncRo5xagvJhUWu1CZ/JTAiGj6vnJ+o+0ymj5MqA8TLgWFeZupS0DJCQ197/faqIxl3CA879n2/ZMQ6+GwqR/A2gYlxjkcNprUykp3GzlWZPhqCSltfmqFOe1Xn892jitUeOM1rqBFlNZLrr+5+epDXPsy6j7CTCk3OgdPENncbALIaLlgOqVqn5rLTc2fKbh2DnXd0CWjgX1drpm+wlzu/AGvz32FLs+6mNPkdxp9xiHG9lrYrga/UxaAyuSnBF34JUKZIzZ9A9u6hIS+mu84RRz43FKgjOcWsN+rH7kPodS+9GHi4woBkxANTVHz/OXe/PQVVAiasyksluIKdN67ApnsGlVC3WcdenM2KWGaZOy3Q+RtO0vpcH3GcTuavDIPCjtk5DR7e5kDDKjjHA0G7dgOXWucX00/nUQ4yfWlr8Awznxtd493XTeuZWHcqo7bFUKqJj/HTH5zs32UpEfUuxZeH1NrjFt9tXpfl/Q5DtP0+lh6dq4f/JxaBf2wTPU6erT8zBKDHGdT9ImI7YPBoF021j5nUkKqfWUQpCXqXRYh3lsaT9X0rNWkSwvuI2zMK6ahyfexGPSBvpvP5AFKjOfx1rkXvGqP5tXnKiO2124XfTVde4/Xj9K46ryVrI+K3Nz2VYD4f84N0dW/LqvDbu+ZyuSngOGwfTiLx0inJaXmpHre33XQQp9FWEKXsKLBTxrAY5kKiU6ppGiXxjCOoGIJxCSSeu45XZHKfaAapDL7LqGqZEnwrpmE+PTR8vtiJwncOEx4knboOIzL4LrezflnUaqusebeOHKkHfOTY56lbI1x26ptHieLgdf3ie733lGKI8pl62ynL10uSDvOKgT37dNuoDL5KWBzs33WtDLScU21fF7u2pJZvIspTgNdUrKaEr0AHm1rFyMbR2jpgt2w42o2XVpISSPu0047HuvrTUqVjbDvy6jHJSbjEu1JMKnA0KcNOSbSF13aqRLzcerY9+kTaYgG4JX6wL1x7Fi57LMKA5PUQ/DWRJcwlZtjva+vr72vYqIxDNNSXnL9zAn22t+qyS8Yky9hEqmtdE/XApmGtl5Cl5SsvsAuBj5OJPg43/V5lgoQfQSwSaXvzc3u6GOvUA/LmVrrUFdbJp3/LgY3LXhaz3avt+PYx7VT0qa7CrR4YzWNfa7/dzHSHMP03rGdEwNz49THKjluEOd2YNMHp6nk2Lb2WUvzhMrkp4RJTETjPmvc63fTLKREZ7tBOeNobZP0TS0vfaNi+zJPT5hg2pNdftQIbYWy4bCpw7+0lOI8+hKVScdlN9dKjONbI0qaf5+2lxiqvndafuZx7vXmdVxhqO87poG+z5yFNjvL/s4rKpOfErajfY+DcSRrjwDt1IL1tJBJ39GHsG8nUngwSAw0VzHOwmPcfTWUzc2mIBJP/OJz6EKYputlHAI8jfSknVhPXWvJflYqHNR1z3YjzXdKOOqzB+YZe50xehg3I2leUJn8lLAT2lVfht61wPSe3JGLzJ6+AAAREklEQVSw45p2uwjvuBhXi9nOmOrpb30YnGU640T8q9XAmhLHFS6mieGwHehXshSU2lEKhpq0H1Y47esTHccX32WC7YtJtP++z51XptEH0xJ+cuMwi/HJHVAUY3d/ZzmflcnvAiYlctNKWdJ7WAVLT32LcTyT9XZNiB7GFV4m3TTD4XhV9ey79P6++euTWDh2migoc7L96GPaJvqkNU1K8HPMOyeEbCe4dTtMaZJ79zoT78K0+pcb252yoJTQVWq6r6Kw26hMfgrYLuO12ElT3SQlHi1z34l2jcvQJx1zT+saR6NWM7oXTZurEzCJMLLb8RSTaPJ9+rcdLXlc5j0ptsOUJrk3N7+LzvzHxSSa/E6P4V4T6iqTnwImmfTSPfO20XdS6Bjn/TpW0x7zEqy2Wqr81jeNpgs8ie7MmfHv3c31M29rdSfbM81n5541S41vr2PagnbXe+ZlzXehMvkpYBJtZp4XyqzaajXmErOctvWkBOt37uta2A7BLvn/urCbmsa8MaXttqePhW0n+zruPOymtWPe25FTRuZtje42KpPfIUzT57fbmFVb9b1dbZilttr33dsxKW4n+ns7B6iMO+fzJqxutz3zZGHr8z62d9ICQNNoA9uhR2TPgs5NYtp/NKAy+SlCF5MlFrP0IY2LWbWnT2lKYi8JTRbq19+J9u81n+E8YB40UYs+QY073e6+a4lrmkcwb9ddNW/osizOMyqTnyJ0Q4yzEPYyw5omxhmHedpo47aFWs+kVcim3Z6K+dyDfdITdxrjrKW9bL3swjhWxnlDickvoaIXzp0Dbr4ZWF9P/29sAKdOAbfckr+W1/B6/T2P8No9bYwzDrnxnQVuvhm4/fb0d6lNuXUybczT2Mw7vDmZF1x/PXDxxbNt0zhryV67F+haX3h9WYR+zVzznvbPPETX7zUpkNir7d4NjOO33Mmo3z6oWn4bdV1XzBK7sR9RNfntYxyJdR6l2z5a+rTbvRuWgd1CH23n3DngwQeBq69uxrCvBWCamMU75xnzuB/3Gs6dA86cSX9ff/3e38+7iZnvxxz336s/s6p4N++YhTazyBpU33S6nZDiu565qJr8ovZrL2A47Hd6X8VWVE2+YlcwC21mVhrUblgQPOnc6+9O+M67NINZ++t3avxnrhE9irGxkaxU/LuiP2a9H0MSAhYHT3ziE+Ndd90162ZUzBDPf35iBs94xs5trlm6IubdDbJT468BdHfeOb/9r1g8zPueCyFsxhif6H1XNfmKhcNuWBBmKZ3PWjPowk6NP/tNIQKY73HYLuadsTyasJetSJXJ70HMavPvFaIz70xw0bHT4/9oCaTby4xl0bCX11xl8nsQs9r8lehUzAMeLULcXmYsi4a9vOYqk9+DmNXmr0SnomL3sJcZyzSxVyyI84oaeFdRUVFRMbfYjUDavY5S4N2+3W5MRUVFRUVFX2xsJAa/Vy2I584lQeXcudm8v5rrKyoqKirmFnvdbTHrWKbK5CsqKioqKnYIs45lqub6irnHrM1dFRUVFZOClohZBQ1WJl8x96C56+ab+99TBYPFQZ3LiorJUZl8xdxjksCbSQSDivlEncuKSVEFxOqTr9gDmCTwZtZ+sIrpoc5lxaSYddDbPKAy+YqFxF6PyK1oUOeyYlJUAbEy+YqKioqKBUUVEKtPvqKioqKiYmFRmXxFRUVFRcWCojL5ioqKioqKBUVl8hUVFRUVFQuKyuQrKioqKioWFJXJV1RUVFRULCgqk6+oqKioqFhQVCZfUVFRUVGxoKhMvqKioqKiYkFRmXxFRUVFRcWCojL5ioqKioqKBUVl8hUVFRUVFQuKyuQrKioqKioWFJXJV1RUVFRULCgqk6+oqKioqFhQVCZfUVFRUVGxoKhMvqKioqKiYkERYoyzbsNUEUL4JIAPT/GRFwN4cIrPm1c8GvpZ+7gYeDT0EXh09LP2cTpYizFe4n2xcEx+2ggh3BVjfOKs27HTeDT0s/ZxMfBo6CPw6Ohn7ePOo5rrKyoqKioqFhSVyVdUVFRUVCwoKpPvxstn3YBdwqOhn7WPi4FHQx+BR0c/ax93GNUnX1FRUVFRsaComnxFRUVFRcWCojL5AkIITwshvC+EcE8I4SWzbs84CCF8fQjh90II7wkhvDuE8GOjz28IIXw0hHD36OcauecnRn19Xwjhu+XzuR2HEMKHQgh/MurLXaPPLgoh3BFC+MDo94Wjz0MI4edH/fhfIYRT8pznjq7/QAjhubPqj0UI4YTM1d0hhIdCCC9ahHkMIfxqCOETIYQ/lc+mNnchhNOjtXHP6N6wuz3M9vFnQwh/NurHq0MIF4w+Px5C+JLM6S939SU3XnPQx6mtzxDCFSGEd4w+f2UI4cDu9a5Bpp+vlD5+KIRw9+jz+ZnLGGP9cX4A7AfwQQDfAOAAgD8G8PhZt2uM9l8G4NTo7/MAvB/A4wHcAOD/dq5//KiPBwFcMer7/nkfBwAfAnCx+exnALxk9PdLANw0+vsaAG8AEAA8GcA7Rp9fBODe0e8LR39fOOu+ZdbkAwDWFmEeATwFwCkAf7oTcwfgj0bXhtG9T5+TPn4XgKXR3zdJH4/rdeY5bl9y4zUHfZza+gTw/wH4gdHfvwxgY17Wq/n+3wF46bzNZdXk8/g2APfEGO+NMX4ZwCsAfN+M29QbMcb7Y4znRn9/HsB7ATy2cMv3AXhFjPHhGOOfA7gHaQz24jh8H4BfH/396wAG8vlvxIS3A7gghHAZgO8GcEeM8dMxxs8AuAPA03a70T3wVAAfjDGWij3tmXmMMb4VwKfNx1OZu9F358cY3x4T1fwNedauwetjjPFNMcavjv59O4DLS8/o6EtuvHYNmXnMYaz1OdJyvxPAbaP7Z9JHoNzPUTu/H8B/LT1jFnNZmXwejwVwn/z/EZSZ5NwihHAcwFUA3jH66EdHpsJfFZNQrr/zPg4RwJtCCJshhBeMPrs0xnj/6O8HAFw6+nuv9pH4AbSJyCLNIzGtuXvs6G/7+bzhnyBpc8QVIYR3hRDeEkL4u6PPSn3Jjdc8YBrrcwXAZ0Uomtd5/LsAPh5j/IB8NhdzWZn8giOEcBTAbwF4UYzxIQA3A/jrAJ4A4H4kE9Next+JMZ4C8HQALwwhPEW/HEnLez6FZOSHfCaA/zb6aNHmcQsWZe5yCCFcB+CrAP7L6KP7ATwuxngVgP8LwG+GEM7v+7w5G6+FX58GP4i2AD43c1mZfB4fBfD18v/lo8/2DEIIy0gM/r/EGF8FADHGj8cYH4kxfg3ALUhmMiDf37kehxjjR0e/PwHg1Uj9+fjILEbz2CdGl+/JPo7wdADnYowfBxZvHgXTmruPom0Gn6v+hhCeB+B7ATxnRNAxMmF/avT3JpKP+kqU+5Ibr5liiuvzU0iumSXz+dxg1LZnAXglP5unuaxMPo93AvimUWTnASRT6Wtm3KbeGPmI/hOA98YY/718fplc9n8CYKToawD8QAjhYAjhCgDfhBQgMrfjEEI4EkI4j38jBTT9KVL7GGX9XAD/Y/T3awD8o5DwZACfG5nHfhvAd4UQLhyZFb9r9Nk8oaUpLNI8Gkxl7kbfPRRCePJoL/wjedZMEUJ4GoB/BeCZMca/kM8vCSHsH/39DUhzd29HX3LjNVNMa32OBKDfA3Dt6P656aPg7wH4sxjjX5nh52oupxG9t6g/SBG970eSwq6bdXvGbPvfQTL3/C8Ad49+rgFwK4A/GX3+GgCXyT3Xjfr6Pkgk8ryOA1Ik7h+Pft7NtiH58d4M4AMAfgfARaPPA4BfGvXjTwA8UZ71T5CCgO4B8I9n3TfTzyNIGs0x+WzPzyOS0HI/gK8g+SZ/eJpzB+CJSMzlgwB+EaPiX3PQx3uQ/M/cl788uvbvj9bx3QDOAXhGV19y4zUHfZza+hzt8z8ajdt/A3BwXtbr6PNfA/DPzLVzM5e14l1FRUVFRcWCoprrKyoqKioqFhSVyVdUVFRUVCwoKpOvqKioqKhYUFQmX1FRUVFRsaCoTL6ioqKiomJBUZl8RcUcIIQwCCG8KYTwqRDCl0M6weu2UU71XCGEEEc/V5vP/8bo8/VdbMudIYTbuq+sqHh0ojL5iooZI4TwH5AqE34UwBCpuMZLABwG8IYQwl+fYfNK+KlZN6CioqKMpe5LKioqdgohhO8D8CKkIi6/Zr6+NYTwDABf2vWGdeNOANeEEJ4QY7x71o3ZCYQQDscY53HsKyp6o2ryFRWzxYsAvNNh8ACAGOPtMcaP8f8Qwr4QwktCCPeEEB4OIbw/hPBcvYcm7BDCs0fXPRRCeEMI4XJz3aEQws+EEO4bPeuPQwjX9Gz3qwC8Bx3a/Mh8/6PmsxtCCA/K/88bXXdq1Pa/CCHcPfr/SAjhP4cQPhdCuDeE8IOZ97wghPChEMKXQgivCyE81nzf2dfR/f8uhHB9COEjAB7qORYVFXOLyuQrKmaEkA62uBrAm8a47ReQGOvLAXwP0qE8vxpC+F5z3d8C8KMAXgzgBQBOje5R3AbgeQD+LYBnINUPf00I4Qk92hFH9z0rhHByjPaX8OtIpUP/PlIZ29uQzl/4GFLt8ncA+A0rrCCN4T9HOu3rhwF8K4D/bq7p29dnA/gOAD8C4B9Oo1MVFbNENddXVMwOKwAOon2ONg8X2i8fPRJjjCGEbwSwgWTa//XRd78zOgzkpwG8Vu45H8D3xBg/M3rmKoD/QBN0COGpSELCeozxLaN73hRCuBKptvg/6NH+VwB4GYCfBPBDvXudx8+xX6MxeB2AO2OM140++yMkZv8MpKNMib8G4OoY4/8eXfdhAP8zhPC0GOMbJ+jr98YY/3IK/amomDmqJl9RMXvYAyRejHQIBn9eOPr8qQC+BuDVIYQl/iAdavEEnno1wjvJ4Ed4z+g3zdh/D8ADAP7AedYTezU6xkcA3AjgB0cnbW0Xb5a/7xn9/l153+cAfBJNH4hzZPCj6/4A6ZhOHm86Tl/fXBl8xSKhavIVFbPDpwA8jPb50kA6wevO0d/vlM8vRtLwP5d53mVIp2MBwGfNd18e/T4kz1pFEiIsHik12uA3ALwUKRvg58e4z4O2+cvOZ/z8kPnMO3f7E0jjAYzX1493N7OiYu+gMvmKihkhxvjVEMLbkM5Af6l8/nGMmE2yWv8VPg3gqwD+NpJGb+Exuxw+jZSyNxiv1W3EGL8cQvhZAD+HFIxn8TCAA+azC7fzTgd/LfPZ/aO/x+lrPZazYqFQmXxFxWzxHwH89xDCD8UYb+249neRNPljMcY7tvneNyO5Bb4QY/yzbT7rFiTf9r9yvvsIgL8KzAsh7ENyO0wTp0IIjxOf/N9GYvJ/NPp+mn2tqNhTqEy+omKGiDH+jxDCfwTwayGE/wPA7QAeRArK+67RZV8YXfu+EMIvA3hFCOFnANyFZLr+FgBXxhiHY7z6DgC/DeCOEMJNAN6NFKz3BACHYow/MUYf/jKE8O8B3OR8/WoALwwhvAvAvUjFfs4fo5198EkArwsh/DTSeNyE5Kd/4+j7qfW1omKvoTL5iooZI8b44yGEtyKlbf0nAOchMa63AbgmxvgGufyFAN4P4PkA/jVSLvd7RveN884YQngWUmT8iwA8DsmsfTdSmt64+H8B/D8ALjKfvwxJq/43SP70X0Risi/E9PCHAH4HySpyCVI8wwv45Q70taJizyDEWF1QFRUVFRUVi4iaQldRUVFRUbGgqEy+oqKioqJiQVGZfEVFRUVFxYKiMvmKioqKiooFRWXyFRUVFRUVC4rK5CsqKioqKhYUlclXVFRUVFQsKCqTr6ioqKioWFBUJl9RUVFRUbGg+P8BPooT8CZ9t1EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_norm_test_sums = log_normalized_tmm_test_genes.sum(axis = 0)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(range(len(log_norm_test_sums)), log_norm_test_sums, 'b.', alpha = .75, markersize=3)\n",
    "ax.set_xlabel('Gene Number', size=15)\n",
    "ax.set_ylabel('Log-Normalized Read Counts', size=15)\n",
    "ax.set_title('Log-Normalized Read Counts per Gene', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BwL4T67kUTh4"
   },
   "source": [
    "#### Remove genes eliminated by variance and covariance filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "5JAtZvUCUTh8",
    "outputId": "56da7c14-cdab-4119-dea9-e85a0d5c9023"
   },
   "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>0</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>7</th>\n",
       "      <th>15</th>\n",
       "      <th>28</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>47</th>\n",
       "      <th>50</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>82</th>\n",
       "      <th>86</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>96</th>\n",
       "      <th>98</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>106</th>\n",
       "      <th>107</th>\n",
       "      <th>...</th>\n",
       "      <th>24150</th>\n",
       "      <th>24155</th>\n",
       "      <th>24156</th>\n",
       "      <th>24160</th>\n",
       "      <th>24163</th>\n",
       "      <th>24164</th>\n",
       "      <th>24165</th>\n",
       "      <th>24168</th>\n",
       "      <th>24184</th>\n",
       "      <th>24191</th>\n",
       "      <th>24202</th>\n",
       "      <th>24203</th>\n",
       "      <th>24207</th>\n",
       "      <th>24214</th>\n",
       "      <th>24215</th>\n",
       "      <th>24220</th>\n",
       "      <th>24223</th>\n",
       "      <th>24224</th>\n",
       "      <th>24237</th>\n",
       "      <th>24244</th>\n",
       "      <th>24253</th>\n",
       "      <th>24255</th>\n",
       "      <th>24256</th>\n",
       "      <th>24260</th>\n",
       "      <th>24261</th>\n",
       "      <th>24271</th>\n",
       "      <th>24272</th>\n",
       "      <th>24273</th>\n",
       "      <th>24277</th>\n",
       "      <th>24283</th>\n",
       "      <th>24285</th>\n",
       "      <th>24288</th>\n",
       "      <th>24291</th>\n",
       "      <th>24296</th>\n",
       "      <th>24308</th>\n",
       "      <th>24309</th>\n",
       "      <th>24313</th>\n",
       "      <th>24316</th>\n",
       "      <th>24317</th>\n",
       "      <th>24323</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.718240</td>\n",
       "      <td>6.844731</td>\n",
       "      <td>0.444856</td>\n",
       "      <td>0.240036</td>\n",
       "      <td>2.647412</td>\n",
       "      <td>2.401787</td>\n",
       "      <td>6.365997</td>\n",
       "      <td>3.042142</td>\n",
       "      <td>2.686068</td>\n",
       "      <td>-0.598213</td>\n",
       "      <td>5.352453</td>\n",
       "      <td>0.334673</td>\n",
       "      <td>-1.513324</td>\n",
       "      <td>1.853248</td>\n",
       "      <td>2.566846</td>\n",
       "      <td>2.357393</td>\n",
       "      <td>0.766784</td>\n",
       "      <td>0.486676</td>\n",
       "      <td>2.951718</td>\n",
       "      <td>0.379761</td>\n",
       "      <td>0.652014</td>\n",
       "      <td>-0.861247</td>\n",
       "      <td>-0.320679</td>\n",
       "      <td>-1.216342</td>\n",
       "      <td>3.853248</td>\n",
       "      <td>0.357393</td>\n",
       "      <td>2.701689</td>\n",
       "      <td>-1.285055</td>\n",
       "      <td>3.571712</td>\n",
       "      <td>-2.642607</td>\n",
       "      <td>-1.998751</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.182352</td>\n",
       "      <td>-0.914687</td>\n",
       "      <td>3.447505</td>\n",
       "      <td>3.539787</td>\n",
       "      <td>3.871121</td>\n",
       "      <td>2.576561</td>\n",
       "      <td>2.569281</td>\n",
       "      <td>2.955445</td>\n",
       "      <td>...</td>\n",
       "      <td>3.726990</td>\n",
       "      <td>2.155055</td>\n",
       "      <td>3.252211</td>\n",
       "      <td>2.423482</td>\n",
       "      <td>3.407242</td>\n",
       "      <td>5.262404</td>\n",
       "      <td>4.764129</td>\n",
       "      <td>0.979445</td>\n",
       "      <td>1.233910</td>\n",
       "      <td>0.444856</td>\n",
       "      <td>-0.199664</td>\n",
       "      <td>1.845233</td>\n",
       "      <td>2.816825</td>\n",
       "      <td>-0.302757</td>\n",
       "      <td>0.098860</td>\n",
       "      <td>-0.233216</td>\n",
       "      <td>1.428855</td>\n",
       "      <td>5.421462</td>\n",
       "      <td>0.015604</td>\n",
       "      <td>-1.057645</td>\n",
       "      <td>3.334673</td>\n",
       "      <td>1.934822</td>\n",
       "      <td>3.167965</td>\n",
       "      <td>4.260522</td>\n",
       "      <td>2.640481</td>\n",
       "      <td>0.942355</td>\n",
       "      <td>-2.057645</td>\n",
       "      <td>1.363018</td>\n",
       "      <td>-1.835252</td>\n",
       "      <td>-1.216342</td>\n",
       "      <td>1.942355</td>\n",
       "      <td>-1.285055</td>\n",
       "      <td>5.259014</td>\n",
       "      <td>1.600567</td>\n",
       "      <td>3.363018</td>\n",
       "      <td>0.071638</td>\n",
       "      <td>-1.183175</td>\n",
       "      <td>2.544745</td>\n",
       "      <td>5.482484</td>\n",
       "      <td>6.609763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.390902</td>\n",
       "      <td>5.066514</td>\n",
       "      <td>1.475171</td>\n",
       "      <td>0.535292</td>\n",
       "      <td>4.354515</td>\n",
       "      <td>3.485367</td>\n",
       "      <td>6.933323</td>\n",
       "      <td>4.682687</td>\n",
       "      <td>3.805262</td>\n",
       "      <td>1.827473</td>\n",
       "      <td>6.329089</td>\n",
       "      <td>5.118618</td>\n",
       "      <td>-1.619986</td>\n",
       "      <td>0.786831</td>\n",
       "      <td>1.036060</td>\n",
       "      <td>2.894041</td>\n",
       "      <td>0.218263</td>\n",
       "      <td>1.946645</td>\n",
       "      <td>3.779599</td>\n",
       "      <td>4.720404</td>\n",
       "      <td>1.495491</td>\n",
       "      <td>3.133281</td>\n",
       "      <td>2.349640</td>\n",
       "      <td>3.176398</td>\n",
       "      <td>5.088000</td>\n",
       "      <td>3.687663</td>\n",
       "      <td>2.310034</td>\n",
       "      <td>0.193601</td>\n",
       "      <td>0.859182</td>\n",
       "      <td>-0.963940</td>\n",
       "      <td>0.380014</td>\n",
       "      <td>-1.664380</td>\n",
       "      <td>4.275199</td>\n",
       "      <td>-2.494455</td>\n",
       "      <td>1.149401</td>\n",
       "      <td>2.532837</td>\n",
       "      <td>1.782703</td>\n",
       "      <td>4.414438</td>\n",
       "      <td>2.532837</td>\n",
       "      <td>2.321462</td>\n",
       "      <td>...</td>\n",
       "      <td>0.666537</td>\n",
       "      <td>-2.020524</td>\n",
       "      <td>0.928077</td>\n",
       "      <td>0.324305</td>\n",
       "      <td>1.500527</td>\n",
       "      <td>4.251499</td>\n",
       "      <td>3.403054</td>\n",
       "      <td>0.630241</td>\n",
       "      <td>-0.188647</td>\n",
       "      <td>1.774412</td>\n",
       "      <td>-1.049670</td>\n",
       "      <td>1.254483</td>\n",
       "      <td>-0.140818</td>\n",
       "      <td>-0.272063</td>\n",
       "      <td>0.103804</td>\n",
       "      <td>-0.020524</td>\n",
       "      <td>-1.576917</td>\n",
       "      <td>2.895953</td>\n",
       "      <td>0.935533</td>\n",
       "      <td>-0.806399</td>\n",
       "      <td>3.400363</td>\n",
       "      <td>-0.757490</td>\n",
       "      <td>2.060134</td>\n",
       "      <td>0.444144</td>\n",
       "      <td>-3.857025</td>\n",
       "      <td>2.346847</td>\n",
       "      <td>-1.806399</td>\n",
       "      <td>0.675470</td>\n",
       "      <td>-0.306828</td>\n",
       "      <td>3.255973</td>\n",
       "      <td>4.185807</td>\n",
       "      <td>0.077087</td>\n",
       "      <td>7.185514</td>\n",
       "      <td>-1.535097</td>\n",
       "      <td>2.503038</td>\n",
       "      <td>0.008045</td>\n",
       "      <td>2.865050</td>\n",
       "      <td>1.867001</td>\n",
       "      <td>4.022120</td>\n",
       "      <td>5.906661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.455208</td>\n",
       "      <td>6.811613</td>\n",
       "      <td>0.814963</td>\n",
       "      <td>2.257906</td>\n",
       "      <td>3.130112</td>\n",
       "      <td>6.179645</td>\n",
       "      <td>7.832641</td>\n",
       "      <td>4.924140</td>\n",
       "      <td>2.602865</td>\n",
       "      <td>1.727852</td>\n",
       "      <td>7.737082</td>\n",
       "      <td>1.576514</td>\n",
       "      <td>0.826190</td>\n",
       "      <td>1.763330</td>\n",
       "      <td>3.652591</td>\n",
       "      <td>2.967247</td>\n",
       "      <td>-0.230841</td>\n",
       "      <td>2.706746</td>\n",
       "      <td>1.589750</td>\n",
       "      <td>-1.430150</td>\n",
       "      <td>2.806485</td>\n",
       "      <td>0.622318</td>\n",
       "      <td>0.257906</td>\n",
       "      <td>1.501463</td>\n",
       "      <td>5.837330</td>\n",
       "      <td>3.282568</td>\n",
       "      <td>2.077058</td>\n",
       "      <td>0.859357</td>\n",
       "      <td>3.870245</td>\n",
       "      <td>1.290696</td>\n",
       "      <td>1.429673</td>\n",
       "      <td>0.583147</td>\n",
       "      <td>5.151469</td>\n",
       "      <td>0.207280</td>\n",
       "      <td>4.726355</td>\n",
       "      <td>4.712052</td>\n",
       "      <td>5.214241</td>\n",
       "      <td>4.723355</td>\n",
       "      <td>2.842869</td>\n",
       "      <td>4.662803</td>\n",
       "      <td>...</td>\n",
       "      <td>2.494443</td>\n",
       "      <td>-0.035290</td>\n",
       "      <td>0.709780</td>\n",
       "      <td>-0.015112</td>\n",
       "      <td>2.647853</td>\n",
       "      <td>3.384818</td>\n",
       "      <td>3.288668</td>\n",
       "      <td>1.933904</td>\n",
       "      <td>1.422293</td>\n",
       "      <td>1.666712</td>\n",
       "      <td>-1.097575</td>\n",
       "      <td>1.837330</td>\n",
       "      <td>-0.076513</td>\n",
       "      <td>0.190002</td>\n",
       "      <td>-1.140643</td>\n",
       "      <td>-0.758773</td>\n",
       "      <td>0.274394</td>\n",
       "      <td>3.936496</td>\n",
       "      <td>1.870245</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>3.202980</td>\n",
       "      <td>0.733826</td>\n",
       "      <td>0.792243</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.792243</td>\n",
       "      <td>1.641511</td>\n",
       "      <td>2.211567</td>\n",
       "      <td>3.125575</td>\n",
       "      <td>-1.661475</td>\n",
       "      <td>1.429673</td>\n",
       "      <td>2.619094</td>\n",
       "      <td>1.786506</td>\n",
       "      <td>5.832119</td>\n",
       "      <td>0.660453</td>\n",
       "      <td>1.786506</td>\n",
       "      <td>1.306816</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.515402</td>\n",
       "      <td>1.820587</td>\n",
       "      <td>5.908710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.460355</td>\n",
       "      <td>6.282357</td>\n",
       "      <td>1.544148</td>\n",
       "      <td>0.848787</td>\n",
       "      <td>2.361162</td>\n",
       "      <td>4.919787</td>\n",
       "      <td>7.110047</td>\n",
       "      <td>3.375212</td>\n",
       "      <td>3.768800</td>\n",
       "      <td>1.169250</td>\n",
       "      <td>6.412075</td>\n",
       "      <td>1.034090</td>\n",
       "      <td>-0.281412</td>\n",
       "      <td>2.243544</td>\n",
       "      <td>4.457356</td>\n",
       "      <td>2.892071</td>\n",
       "      <td>-0.281412</td>\n",
       "      <td>1.765894</td>\n",
       "      <td>2.017898</td>\n",
       "      <td>1.974928</td>\n",
       "      <td>2.961445</td>\n",
       "      <td>-0.380947</td>\n",
       "      <td>-0.707175</td>\n",
       "      <td>1.653000</td>\n",
       "      <td>5.152290</td>\n",
       "      <td>2.363726</td>\n",
       "      <td>1.619053</td>\n",
       "      <td>1.298198</td>\n",
       "      <td>4.047712</td>\n",
       "      <td>0.677946</td>\n",
       "      <td>1.376482</td>\n",
       "      <td>-0.563811</td>\n",
       "      <td>4.504023</td>\n",
       "      <td>0.484123</td>\n",
       "      <td>3.896506</td>\n",
       "      <td>5.056458</td>\n",
       "      <td>4.622805</td>\n",
       "      <td>3.460355</td>\n",
       "      <td>1.431307</td>\n",
       "      <td>4.613676</td>\n",
       "      <td>...</td>\n",
       "      <td>2.316848</td>\n",
       "      <td>-0.188302</td>\n",
       "      <td>0.686167</td>\n",
       "      <td>-0.347000</td>\n",
       "      <td>3.003168</td>\n",
       "      <td>2.819192</td>\n",
       "      <td>3.202583</td>\n",
       "      <td>1.631877</td>\n",
       "      <td>1.431307</td>\n",
       "      <td>0.811698</td>\n",
       "      <td>1.121553</td>\n",
       "      <td>1.694341</td>\n",
       "      <td>0.788978</td>\n",
       "      <td>-3.773265</td>\n",
       "      <td>-0.433415</td>\n",
       "      <td>0.271129</td>\n",
       "      <td>2.663447</td>\n",
       "      <td>3.610440</td>\n",
       "      <td>1.441054</td>\n",
       "      <td>0.512138</td>\n",
       "      <td>2.616904</td>\n",
       "      <td>1.059625</td>\n",
       "      <td>-0.330321</td>\n",
       "      <td>2.237963</td>\n",
       "      <td>1.366287</td>\n",
       "      <td>1.913236</td>\n",
       "      <td>2.127602</td>\n",
       "      <td>1.381553</td>\n",
       "      <td>-1.866374</td>\n",
       "      <td>1.340478</td>\n",
       "      <td>1.796591</td>\n",
       "      <td>2.999875</td>\n",
       "      <td>5.118519</td>\n",
       "      <td>2.221089</td>\n",
       "      <td>2.566585</td>\n",
       "      <td>1.665527</td>\n",
       "      <td>-0.265470</td>\n",
       "      <td>1.361162</td>\n",
       "      <td>1.930639</td>\n",
       "      <td>4.732051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.892628</td>\n",
       "      <td>6.181764</td>\n",
       "      <td>1.853739</td>\n",
       "      <td>0.797471</td>\n",
       "      <td>2.283483</td>\n",
       "      <td>4.724979</td>\n",
       "      <td>7.615835</td>\n",
       "      <td>1.038479</td>\n",
       "      <td>4.072749</td>\n",
       "      <td>1.861601</td>\n",
       "      <td>6.195437</td>\n",
       "      <td>1.158218</td>\n",
       "      <td>0.659967</td>\n",
       "      <td>2.783054</td>\n",
       "      <td>3.232857</td>\n",
       "      <td>3.652056</td>\n",
       "      <td>0.813772</td>\n",
       "      <td>2.092927</td>\n",
       "      <td>1.930496</td>\n",
       "      <td>2.021034</td>\n",
       "      <td>2.662220</td>\n",
       "      <td>0.641820</td>\n",
       "      <td>-0.553437</td>\n",
       "      <td>-0.170108</td>\n",
       "      <td>6.050136</td>\n",
       "      <td>1.888786</td>\n",
       "      <td>1.726056</td>\n",
       "      <td>1.196020</td>\n",
       "      <td>3.595427</td>\n",
       "      <td>1.138939</td>\n",
       "      <td>0.326724</td>\n",
       "      <td>-0.731224</td>\n",
       "      <td>4.887342</td>\n",
       "      <td>1.482697</td>\n",
       "      <td>3.904093</td>\n",
       "      <td>5.000707</td>\n",
       "      <td>4.145394</td>\n",
       "      <td>3.644101</td>\n",
       "      <td>1.730356</td>\n",
       "      <td>4.375586</td>\n",
       "      <td>...</td>\n",
       "      <td>2.593068</td>\n",
       "      <td>0.507964</td>\n",
       "      <td>1.183529</td>\n",
       "      <td>1.262852</td>\n",
       "      <td>2.646378</td>\n",
       "      <td>3.558417</td>\n",
       "      <td>3.219153</td>\n",
       "      <td>2.174089</td>\n",
       "      <td>1.934229</td>\n",
       "      <td>1.789250</td>\n",
       "      <td>1.086232</td>\n",
       "      <td>2.052285</td>\n",
       "      <td>1.552358</td>\n",
       "      <td>0.517948</td>\n",
       "      <td>-0.707765</td>\n",
       "      <td>1.343664</td>\n",
       "      <td>1.996250</td>\n",
       "      <td>4.037611</td>\n",
       "      <td>2.280554</td>\n",
       "      <td>1.132455</td>\n",
       "      <td>3.057428</td>\n",
       "      <td>2.138939</td>\n",
       "      <td>0.414855</td>\n",
       "      <td>2.155023</td>\n",
       "      <td>-0.492036</td>\n",
       "      <td>1.462160</td>\n",
       "      <td>2.035007</td>\n",
       "      <td>1.436071</td>\n",
       "      <td>0.315319</td>\n",
       "      <td>0.623441</td>\n",
       "      <td>1.092927</td>\n",
       "      <td>2.379698</td>\n",
       "      <td>5.232857</td>\n",
       "      <td>2.202225</td>\n",
       "      <td>2.497910</td>\n",
       "      <td>1.472465</td>\n",
       "      <td>0.338039</td>\n",
       "      <td>1.590704</td>\n",
       "      <td>2.512965</td>\n",
       "      <td>5.889026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 8675 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0         2         4      ...     24316     24317     24323\n",
       "0  3.718240  6.844731  0.444856  ...  2.544745  5.482484  6.609763\n",
       "1  3.390902  5.066514  1.475171  ...  1.867001  4.022120  5.906661\n",
       "2  2.455208  6.811613  0.814963  ...  1.515402  1.820587  5.908710\n",
       "3  2.460355  6.282357  1.544148  ...  1.361162  1.930639  4.732051\n",
       "4  1.892628  6.181764  1.853739  ...  1.590704  2.512965  5.889026\n",
       "\n",
       "[5 rows x 8675 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_var_gene_indices = list(high_var_train_genes.columns) \n",
    "cols_filtered = [x for x in log_normalized_tmm_test_genes.columns if x in high_var_gene_indices]\n",
    "high_var_processed_test_genes = log_normalized_tmm_test_genes[cols_filtered]\n",
    "\n",
    "high_var_processed_test_genes.head()"
   ]
  }
 ],
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