schizophrenia_ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb42320c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import validation_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "import os\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e6e3221",
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_file_path = '/Users/shreyamantripragada/Documents/rise2021/data/normal'\n",
    "schizophrenia_file_path = '/Users/shreyamantripragada/Documents/rise2021/data/schizophrenia'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c8054def",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reshape_data (file_path): \n",
    "    '''\n",
    "        Given the file_path of the folder, loop through the files within the folder and \n",
    "        return a combined dataframe consisting of EEG amplitudes for each patient within a single row.\n",
    "        \n",
    "        @param      file_path \n",
    "        \n",
    "    '''\n",
    "    new_df = pd.DataFrame() # create a new, empty data frame\n",
    "    filenames = [] # create an empty list which will eventually contain the file names\n",
    "    \n",
    "    for filename in os.listdir(file_path): # loop through the files within the folder \n",
    "        new_df = new_df.append(pd.read_csv(os.path.join(file_path, filename)).transpose()) # append the values of each file into the data frame\n",
    "        filenames.append(os.path.splitext(filename)[0]) # append the file names to the list 'filenames'\n",
    "        \n",
    "    first_row_values = new_df.index # get the row names and the \n",
    "    new_df.insert(loc = 0, column = '0', value = first_row_values) # push the current row indices to the first column\n",
    "    new_df = new_df.T.reset_index(drop=True).T # reset the column indices\n",
    "    new_df.index = filenames # reassign the new row indices to the filenames\n",
    "    \n",
    "    return new_df, filenames # return the new, combined data frame & filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "549f7bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_data, norm_filenames = reshape_data(normal_file_path)\n",
    "schizo_data, schizo_filenames = reshape_data(schizophrenia_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ecf8b55d",
   "metadata": {},
   "outputs": [
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       "      <th>diagnosis</th>\n",
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       "      <td>158.79</td>\n",
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       "      <td>429.63</td>\n",
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       "      <td>627.92</td>\n",
       "      <td>887.9</td>\n",
       "      <td>808.58</td>\n",
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       "      <td>-321.67</td>\n",
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       "      <td>-491.32</td>\n",
       "      <td>-581.65</td>\n",
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       "      <td>-451.66</td>\n",
       "      <td>-370.14</td>\n",
       "      <td>-121.18</td>\n",
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       "      <td>-191.03</td>\n",
       "      <td>-111.77</td>\n",
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       "      <td>-91.45</td>\n",
       "      <td>-270.29</td>\n",
       "      <td>-331.26</td>\n",
       "      <td>...</td>\n",
       "      <td>138.19</td>\n",
       "      <td>-50.81</td>\n",
       "      <td>-231.68</td>\n",
       "      <td>-591.38</td>\n",
       "      <td>-731.61</td>\n",
       "      <td>-731.61</td>\n",
       "      <td>-611.7</td>\n",
       "      <td>-270.29</td>\n",
       "      <td>-40.64</td>\n",
       "      <td>0</td>\n",
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       "      <td>248.66</td>\n",
       "      <td>229.26</td>\n",
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       "      <td>308.62</td>\n",
       "      <td>449.71</td>\n",
       "      <td>409.15</td>\n",
       "      <td>128.74</td>\n",
       "      <td>29.98</td>\n",
       "      <td>149.9</td>\n",
       "      <td>209.86</td>\n",
       "      <td>...</td>\n",
       "      <td>-430.31</td>\n",
       "      <td>-1010.52</td>\n",
       "      <td>-1171.0</td>\n",
       "      <td>-1331.49</td>\n",
       "      <td>-1331.49</td>\n",
       "      <td>-1070.48</td>\n",
       "      <td>-751.28</td>\n",
       "      <td>98.76</td>\n",
       "      <td>619.01</td>\n",
       "      <td>0</td>\n",
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       "      <td>8.95</td>\n",
       "      <td>-10.07</td>\n",
       "      <td>-170.04</td>\n",
       "      <td>-250.59</td>\n",
       "      <td>-350.16</td>\n",
       "      <td>-330.02</td>\n",
       "      <td>-151.03</td>\n",
       "      <td>-50.34</td>\n",
       "      <td>8.95</td>\n",
       "      <td>-50.34</td>\n",
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       "      <td>-130.89</td>\n",
       "      <td>-350.16</td>\n",
       "      <td>-390.43</td>\n",
       "      <td>-210.32</td>\n",
       "      <td>-30.21</td>\n",
       "      <td>219.27</td>\n",
       "      <td>279.68</td>\n",
       "      <td>199.13</td>\n",
       "      <td>99.57</td>\n",
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       "      <td>-91.29</td>\n",
       "      <td>-74.18</td>\n",
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       "      <td>-353.77</td>\n",
       "      <td>-194.0</td>\n",
       "      <td>-85.59</td>\n",
       "      <td>39.94</td>\n",
       "      <td>108.41</td>\n",
       "      <td>79.88</td>\n",
       "      <td>45.65</td>\n",
       "      <td>1</td>\n",
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       "      <td>349.76</td>\n",
       "      <td>329.87</td>\n",
       "      <td>149.12</td>\n",
       "      <td>169.91</td>\n",
       "      <td>569.37</td>\n",
       "      <td>729.34</td>\n",
       "      <td>709.45</td>\n",
       "      <td>609.14</td>\n",
       "      <td>569.37</td>\n",
       "      <td>609.14</td>\n",
       "      <td>...</td>\n",
       "      <td>69.59</td>\n",
       "      <td>-210.58</td>\n",
       "      <td>-250.34</td>\n",
       "      <td>-170.81</td>\n",
       "      <td>-90.38</td>\n",
       "      <td>159.97</td>\n",
       "      <td>239.5</td>\n",
       "      <td>-30.73</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-30.72</td>\n",
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       "      <td>49.92</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-10.24</td>\n",
       "      <td>8.96</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-151.03</td>\n",
       "      <td>-270.06</td>\n",
       "      <td>...</td>\n",
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       "      <td>-620.76</td>\n",
       "      <td>-481.25</td>\n",
       "      <td>-30.72</td>\n",
       "      <td>139.51</td>\n",
       "      <td>1</td>\n",
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       "      <td>166.85</td>\n",
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       "      <td>129.78</td>\n",
       "      <td>-173.03</td>\n",
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       "      <td>-253.37</td>\n",
       "      <td>-234.83</td>\n",
       "      <td>-191.57</td>\n",
       "      <td>-191.57</td>\n",
       "      <td>...</td>\n",
       "      <td>-185.39</td>\n",
       "      <td>-30.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-55.62</td>\n",
       "      <td>-12.36</td>\n",
       "      <td>154.49</td>\n",
       "      <td>259.55</td>\n",
       "      <td>438.76</td>\n",
       "      <td>574.72</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>022w1</th>\n",
       "      <td>108.01</td>\n",
       "      <td>208.82</td>\n",
       "      <td>388.84</td>\n",
       "      <td>446.45</td>\n",
       "      <td>446.45</td>\n",
       "      <td>349.24</td>\n",
       "      <td>86.41</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>86.41</td>\n",
       "      <td>...</td>\n",
       "      <td>-1231.33</td>\n",
       "      <td>-1342.95</td>\n",
       "      <td>-1213.33</td>\n",
       "      <td>-471.65</td>\n",
       "      <td>-10.8</td>\n",
       "      <td>669.67</td>\n",
       "      <td>716.48</td>\n",
       "      <td>388.84</td>\n",
       "      <td>79.21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 122881 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0       1       2       3       4       5       6       7  \\\n",
       "S42W1   -154.12  -112.09 -154.12 -154.12  -14.01  -51.37 -233.51 -233.51   \n",
       "S31W     308.45   248.96  328.28  429.63  469.29  429.63  508.94  627.92   \n",
       "S167W1   -91.45   -91.45 -150.39 -231.68 -191.03 -111.77  -30.48  -91.45   \n",
       "S50W     248.66   229.26  229.26  308.62  449.71  409.15  128.74   29.98   \n",
       "S165W1     8.95   -10.07 -170.04 -250.59 -350.16 -330.02 -151.03  -50.34   \n",
       "...          ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "33w1     -11.41   -74.18 -171.18  -194.0 -171.18 -154.06  -91.29  -91.29   \n",
       "382w1    349.76   329.87  149.12  169.91  569.37  729.34  709.45  609.14   \n",
       "103w    -130.55   -30.72   49.92   49.92    8.96  -10.24    8.96    8.96   \n",
       "307w1     67.98   166.85  247.19  129.78 -173.03 -253.37 -253.37 -234.83   \n",
       "022w1    108.01   208.82  388.84  446.45  446.45  349.24   86.41     7.2   \n",
       "\n",
       "             8       9  ...   122871   122872   122873   122874   122875  \\\n",
       "S42W1  -112.09 -112.09  ...   317.58   616.47    219.5  -233.51   -32.69   \n",
       "S31W     887.9  808.58  ...  -321.67  -420.82  -491.32  -581.65  -550.81   \n",
       "S167W1 -270.29 -331.26  ...   138.19   -50.81  -231.68  -591.38  -731.61   \n",
       "S50W     149.9  209.86  ...  -430.31 -1010.52  -1171.0 -1331.49 -1331.49   \n",
       "S165W1    8.95  -50.34  ...  -130.89  -350.16  -390.43  -210.32   -30.21   \n",
       "...        ...     ...  ...      ...      ...      ...      ...      ...   \n",
       "33w1    -74.18  -74.18  ...  -450.77  -410.83  -353.77   -194.0   -85.59   \n",
       "382w1   569.37  609.14  ...    69.59  -210.58  -250.34  -170.81   -90.38   \n",
       "103w   -151.03 -270.06  ...   419.81   319.98   139.51  -340.46  -550.37   \n",
       "307w1  -191.57 -191.57  ...  -185.39    -30.9      0.0   -55.62   -12.36   \n",
       "022w1      7.2   86.41  ... -1231.33 -1342.95 -1213.33  -471.65    -10.8   \n",
       "\n",
       "         122876  122877  122878  122879 diagnosis  \n",
       "S42W1    158.79  177.47  317.58   126.1         0  \n",
       "S31W    -451.66 -370.14 -121.18  -50.67         0  \n",
       "S167W1  -731.61  -611.7 -270.29  -40.64         0  \n",
       "S50W   -1070.48 -751.28   98.76  619.01         0  \n",
       "S165W1   219.27  279.68  199.13   99.57         0  \n",
       "...         ...     ...     ...     ...       ...  \n",
       "33w1      39.94  108.41   79.88   45.65         1  \n",
       "382w1    159.97   239.5  -30.73 -310.89         1  \n",
       "103w    -620.76 -481.25  -30.72  139.51         1  \n",
       "307w1    154.49  259.55  438.76  574.72         1  \n",
       "022w1    669.67  716.48  388.84   79.21         1  \n",
       "\n",
       "[84 rows x 122881 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_data['diagnosis'] = 0\n",
    "schizo_data['diagnosis'] = 1\n",
    "combined = normal_data\n",
    "combined = combined.append(schizo_data)\n",
    "#FINAL_data = combined.sample(frac = 1)\n",
    "FINAL_data = combined\n",
    "FINAL_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c929104",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduced_data(data):\n",
    "    '''\n",
    "        Given the data, iterate through each row, take the absolute value of the EEG amplitudes, \n",
    "        and take the average of all the amplitudes for each brain region. For each brain region, append\n",
    "        the average values into a list and append that list to the 'final_summed_values.'\n",
    "        \n",
    "        @param       data     \n",
    "    '''\n",
    "    temp_sum = 0\n",
    "    temp_avg = 0\n",
    "    values_to_stop = [7679, 15359, 23039, 30719, 38399, 46079, 53759, 61439, 69119, 76799, 84479, 92159, 99839, 107519, 115199, 122879]\n",
    "    summed_values = []\n",
    "    final_summed_values = []\n",
    "\n",
    "    for rows in data.index:\n",
    "        row_data = [abs(float(x)) for x in data.loc[rows]]\n",
    "        for index, value in enumerate(row_data):\n",
    "            if index in values_to_stop:\n",
    "                temp_avg = temp_sum/7680\n",
    "                summed_values.append(temp_avg)\n",
    "                temp_sum = 0\n",
    "                temp_avg = 0\n",
    "            else:\n",
    "                temp_sum += value\n",
    "        \n",
    "        final_summed_values.append(summed_values)\n",
    "        summed_values = []\n",
    "        \n",
    "    return final_summed_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55c071a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_auc_curve(false_positive_rate, true_positive_rate):  \n",
    "    plt.subplots(1, figsize=(10,10))\n",
    "    plt.title('Receiver Operating Characteristic - DecisionTree')\n",
    "    plt.plot(false_positive_rate, true_positive_rate)\n",
    "    plt.plot([0, 1], ls=\"--\")\n",
    "    plt.plot([0, 0], [1, 0] , c=\".7\"), plt.plot([1, 1] , c=\".7\")\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2ed6df90",
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_averaged_values = reduced_data(normal_data)\n",
    "schizo_averaged_values = reduced_data(schizo_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd5ee60c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F7</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F8</th>\n",
       "      <th>T3</th>\n",
       "      <th>C3</th>\n",
       "      <th>Cz</th>\n",
       "      <th>C4</th>\n",
       "      <th>T4</th>\n",
       "      <th>T5</th>\n",
       "      <th>P3</th>\n",
       "      <th>Pz</th>\n",
       "      <th>P4</th>\n",
       "      <th>T6</th>\n",
       "      <th>01</th>\n",
       "      <th>02</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S42W1</th>\n",
       "      <td>234.691796</td>\n",
       "      <td>288.619884</td>\n",
       "      <td>297.082391</td>\n",
       "      <td>201.988486</td>\n",
       "      <td>201.117730</td>\n",
       "      <td>308.864366</td>\n",
       "      <td>320.471932</td>\n",
       "      <td>314.160993</td>\n",
       "      <td>196.207816</td>\n",
       "      <td>212.271569</td>\n",
       "      <td>386.414909</td>\n",
       "      <td>366.827124</td>\n",
       "      <td>331.260919</td>\n",
       "      <td>210.572525</td>\n",
       "      <td>316.188987</td>\n",
       "      <td>368.760953</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S31W</th>\n",
       "      <td>209.802646</td>\n",
       "      <td>310.155904</td>\n",
       "      <td>323.160348</td>\n",
       "      <td>227.777398</td>\n",
       "      <td>275.764142</td>\n",
       "      <td>304.740733</td>\n",
       "      <td>310.459188</td>\n",
       "      <td>285.944573</td>\n",
       "      <td>255.524574</td>\n",
       "      <td>419.788974</td>\n",
       "      <td>457.526612</td>\n",
       "      <td>517.576155</td>\n",
       "      <td>517.303068</td>\n",
       "      <td>475.087018</td>\n",
       "      <td>422.721320</td>\n",
       "      <td>486.114878</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S167W1</th>\n",
       "      <td>352.218137</td>\n",
       "      <td>434.144626</td>\n",
       "      <td>355.861077</td>\n",
       "      <td>446.941460</td>\n",
       "      <td>369.187810</td>\n",
       "      <td>351.056225</td>\n",
       "      <td>401.587008</td>\n",
       "      <td>343.221223</td>\n",
       "      <td>281.817039</td>\n",
       "      <td>303.818939</td>\n",
       "      <td>392.647286</td>\n",
       "      <td>431.740410</td>\n",
       "      <td>393.273398</td>\n",
       "      <td>387.944044</td>\n",
       "      <td>542.842977</td>\n",
       "      <td>536.968509</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S50W</th>\n",
       "      <td>269.647852</td>\n",
       "      <td>338.634255</td>\n",
       "      <td>344.864874</td>\n",
       "      <td>234.133116</td>\n",
       "      <td>256.133755</td>\n",
       "      <td>368.086639</td>\n",
       "      <td>368.735112</td>\n",
       "      <td>355.779238</td>\n",
       "      <td>234.084414</td>\n",
       "      <td>258.421585</td>\n",
       "      <td>391.401720</td>\n",
       "      <td>409.314609</td>\n",
       "      <td>358.623829</td>\n",
       "      <td>293.393776</td>\n",
       "      <td>625.496439</td>\n",
       "      <td>629.259563</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S165W1</th>\n",
       "      <td>205.958906</td>\n",
       "      <td>257.508954</td>\n",
       "      <td>265.755559</td>\n",
       "      <td>242.377389</td>\n",
       "      <td>203.218307</td>\n",
       "      <td>274.589074</td>\n",
       "      <td>278.940344</td>\n",
       "      <td>279.626380</td>\n",
       "      <td>257.992473</td>\n",
       "      <td>213.305772</td>\n",
       "      <td>249.756887</td>\n",
       "      <td>298.145780</td>\n",
       "      <td>280.551883</td>\n",
       "      <td>300.361348</td>\n",
       "      <td>294.697612</td>\n",
       "      <td>314.157604</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s158w1</th>\n",
       "      <td>210.156392</td>\n",
       "      <td>243.122535</td>\n",
       "      <td>242.398858</td>\n",
       "      <td>236.778044</td>\n",
       "      <td>206.025147</td>\n",
       "      <td>252.486010</td>\n",
       "      <td>265.404565</td>\n",
       "      <td>246.924285</td>\n",
       "      <td>232.915249</td>\n",
       "      <td>209.578012</td>\n",
       "      <td>267.934919</td>\n",
       "      <td>294.790904</td>\n",
       "      <td>314.715503</td>\n",
       "      <td>271.258555</td>\n",
       "      <td>327.399674</td>\n",
       "      <td>450.594164</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s178w1</th>\n",
       "      <td>233.345544</td>\n",
       "      <td>262.343376</td>\n",
       "      <td>276.535676</td>\n",
       "      <td>240.188206</td>\n",
       "      <td>236.186406</td>\n",
       "      <td>256.556363</td>\n",
       "      <td>262.057082</td>\n",
       "      <td>270.026048</td>\n",
       "      <td>233.934716</td>\n",
       "      <td>231.208828</td>\n",
       "      <td>275.671909</td>\n",
       "      <td>274.158747</td>\n",
       "      <td>293.347750</td>\n",
       "      <td>225.355462</td>\n",
       "      <td>362.525708</td>\n",
       "      <td>455.596702</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S18W1</th>\n",
       "      <td>183.823220</td>\n",
       "      <td>208.668583</td>\n",
       "      <td>246.833941</td>\n",
       "      <td>204.128240</td>\n",
       "      <td>142.202448</td>\n",
       "      <td>237.652893</td>\n",
       "      <td>316.793198</td>\n",
       "      <td>238.867346</td>\n",
       "      <td>200.856008</td>\n",
       "      <td>168.485749</td>\n",
       "      <td>292.000155</td>\n",
       "      <td>320.969673</td>\n",
       "      <td>275.168212</td>\n",
       "      <td>201.090997</td>\n",
       "      <td>412.535669</td>\n",
       "      <td>398.371788</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s27w1</th>\n",
       "      <td>287.675622</td>\n",
       "      <td>325.139793</td>\n",
       "      <td>283.005382</td>\n",
       "      <td>240.012783</td>\n",
       "      <td>245.015546</td>\n",
       "      <td>307.517049</td>\n",
       "      <td>321.980163</td>\n",
       "      <td>294.850973</td>\n",
       "      <td>175.702001</td>\n",
       "      <td>285.849380</td>\n",
       "      <td>412.084990</td>\n",
       "      <td>477.351270</td>\n",
       "      <td>397.399457</td>\n",
       "      <td>239.099220</td>\n",
       "      <td>578.065659</td>\n",
       "      <td>598.894160</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S163W1</th>\n",
       "      <td>207.762145</td>\n",
       "      <td>260.002005</td>\n",
       "      <td>256.324676</td>\n",
       "      <td>197.574137</td>\n",
       "      <td>248.676862</td>\n",
       "      <td>276.740984</td>\n",
       "      <td>286.515740</td>\n",
       "      <td>267.751495</td>\n",
       "      <td>196.478302</td>\n",
       "      <td>284.604995</td>\n",
       "      <td>321.380874</td>\n",
       "      <td>320.558260</td>\n",
       "      <td>332.958268</td>\n",
       "      <td>206.182699</td>\n",
       "      <td>413.562566</td>\n",
       "      <td>343.096845</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s20w1</th>\n",
       "      <td>147.782771</td>\n",
       "      <td>270.373665</td>\n",
       "      <td>248.174302</td>\n",
       "      <td>164.866094</td>\n",
       "      <td>162.434517</td>\n",
       "      <td>226.305784</td>\n",
       "      <td>251.915995</td>\n",
       "      <td>216.597297</td>\n",
       "      <td>114.373884</td>\n",
       "      <td>164.988147</td>\n",
       "      <td>267.253741</td>\n",
       "      <td>337.189439</td>\n",
       "      <td>231.344311</td>\n",
       "      <td>157.555822</td>\n",
       "      <td>311.801208</td>\n",
       "      <td>331.440301</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S164W1</th>\n",
       "      <td>281.700646</td>\n",
       "      <td>310.177616</td>\n",
       "      <td>289.735789</td>\n",
       "      <td>255.653307</td>\n",
       "      <td>268.674207</td>\n",
       "      <td>319.769445</td>\n",
       "      <td>315.296143</td>\n",
       "      <td>314.333275</td>\n",
       "      <td>286.932125</td>\n",
       "      <td>373.642320</td>\n",
       "      <td>452.643918</td>\n",
       "      <td>420.247648</td>\n",
       "      <td>423.259184</td>\n",
       "      <td>340.673021</td>\n",
       "      <td>552.980053</td>\n",
       "      <td>473.332270</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S182W1</th>\n",
       "      <td>240.561294</td>\n",
       "      <td>257.719276</td>\n",
       "      <td>268.663922</td>\n",
       "      <td>269.006473</td>\n",
       "      <td>304.066637</td>\n",
       "      <td>371.140711</td>\n",
       "      <td>355.034129</td>\n",
       "      <td>332.640988</td>\n",
       "      <td>309.211997</td>\n",
       "      <td>277.359837</td>\n",
       "      <td>331.241651</td>\n",
       "      <td>352.364234</td>\n",
       "      <td>334.786615</td>\n",
       "      <td>305.766036</td>\n",
       "      <td>409.443492</td>\n",
       "      <td>361.454595</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s43w1</th>\n",
       "      <td>326.795516</td>\n",
       "      <td>318.867188</td>\n",
       "      <td>353.462038</td>\n",
       "      <td>321.307460</td>\n",
       "      <td>231.690201</td>\n",
       "      <td>360.707176</td>\n",
       "      <td>425.936690</td>\n",
       "      <td>414.537292</td>\n",
       "      <td>310.313387</td>\n",
       "      <td>224.776294</td>\n",
       "      <td>506.125404</td>\n",
       "      <td>682.300913</td>\n",
       "      <td>637.867268</td>\n",
       "      <td>316.340250</td>\n",
       "      <td>1050.375111</td>\n",
       "      <td>852.320971</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S60W</th>\n",
       "      <td>194.210026</td>\n",
       "      <td>222.150624</td>\n",
       "      <td>241.279022</td>\n",
       "      <td>172.894435</td>\n",
       "      <td>132.343818</td>\n",
       "      <td>211.431125</td>\n",
       "      <td>229.013247</td>\n",
       "      <td>217.469091</td>\n",
       "      <td>166.246432</td>\n",
       "      <td>140.000047</td>\n",
       "      <td>266.955217</td>\n",
       "      <td>315.816400</td>\n",
       "      <td>245.146160</td>\n",
       "      <td>164.757775</td>\n",
       "      <td>416.839665</td>\n",
       "      <td>328.217531</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S26W1</th>\n",
       "      <td>296.342263</td>\n",
       "      <td>328.786151</td>\n",
       "      <td>366.891391</td>\n",
       "      <td>380.992835</td>\n",
       "      <td>239.966741</td>\n",
       "      <td>378.872010</td>\n",
       "      <td>430.631947</td>\n",
       "      <td>391.815336</td>\n",
       "      <td>375.639116</td>\n",
       "      <td>236.202167</td>\n",
       "      <td>542.754682</td>\n",
       "      <td>629.906602</td>\n",
       "      <td>532.299876</td>\n",
       "      <td>345.351168</td>\n",
       "      <td>638.262185</td>\n",
       "      <td>599.472014</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S47W1</th>\n",
       "      <td>264.790638</td>\n",
       "      <td>333.373518</td>\n",
       "      <td>327.994910</td>\n",
       "      <td>190.288839</td>\n",
       "      <td>204.384230</td>\n",
       "      <td>320.550438</td>\n",
       "      <td>331.779669</td>\n",
       "      <td>308.773221</td>\n",
       "      <td>185.439796</td>\n",
       "      <td>196.799294</td>\n",
       "      <td>305.105633</td>\n",
       "      <td>327.740318</td>\n",
       "      <td>319.317493</td>\n",
       "      <td>222.812616</td>\n",
       "      <td>457.379887</td>\n",
       "      <td>583.122150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S85W1</th>\n",
       "      <td>272.113160</td>\n",
       "      <td>295.200184</td>\n",
       "      <td>319.971753</td>\n",
       "      <td>249.162530</td>\n",
       "      <td>235.202449</td>\n",
       "      <td>303.822458</td>\n",
       "      <td>324.956053</td>\n",
       "      <td>319.940781</td>\n",
       "      <td>243.296194</td>\n",
       "      <td>229.908083</td>\n",
       "      <td>363.847227</td>\n",
       "      <td>329.659947</td>\n",
       "      <td>329.868052</td>\n",
       "      <td>258.558396</td>\n",
       "      <td>566.683276</td>\n",
       "      <td>503.317306</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S179W1</th>\n",
       "      <td>183.455034</td>\n",
       "      <td>257.494443</td>\n",
       "      <td>247.734924</td>\n",
       "      <td>223.400299</td>\n",
       "      <td>199.758749</td>\n",
       "      <td>261.299923</td>\n",
       "      <td>294.517130</td>\n",
       "      <td>282.269757</td>\n",
       "      <td>196.996012</td>\n",
       "      <td>228.074576</td>\n",
       "      <td>281.298629</td>\n",
       "      <td>298.759270</td>\n",
       "      <td>292.529324</td>\n",
       "      <td>206.190469</td>\n",
       "      <td>305.156831</td>\n",
       "      <td>336.495431</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S78W</th>\n",
       "      <td>230.311833</td>\n",
       "      <td>288.261823</td>\n",
       "      <td>316.428733</td>\n",
       "      <td>284.023038</td>\n",
       "      <td>240.441214</td>\n",
       "      <td>341.605743</td>\n",
       "      <td>352.760816</td>\n",
       "      <td>315.985272</td>\n",
       "      <td>252.532598</td>\n",
       "      <td>253.055806</td>\n",
       "      <td>396.731359</td>\n",
       "      <td>381.690651</td>\n",
       "      <td>343.607728</td>\n",
       "      <td>297.892570</td>\n",
       "      <td>580.001393</td>\n",
       "      <td>511.945663</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S177W1</th>\n",
       "      <td>243.936777</td>\n",
       "      <td>319.006029</td>\n",
       "      <td>319.513790</td>\n",
       "      <td>292.178406</td>\n",
       "      <td>253.916193</td>\n",
       "      <td>362.064560</td>\n",
       "      <td>371.719419</td>\n",
       "      <td>358.125010</td>\n",
       "      <td>282.468072</td>\n",
       "      <td>345.783579</td>\n",
       "      <td>471.585204</td>\n",
       "      <td>492.029939</td>\n",
       "      <td>476.036503</td>\n",
       "      <td>248.525547</td>\n",
       "      <td>618.054892</td>\n",
       "      <td>652.856151</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S153W1</th>\n",
       "      <td>258.592816</td>\n",
       "      <td>314.619374</td>\n",
       "      <td>351.361852</td>\n",
       "      <td>265.194569</td>\n",
       "      <td>262.396504</td>\n",
       "      <td>276.587233</td>\n",
       "      <td>442.066059</td>\n",
       "      <td>389.958831</td>\n",
       "      <td>328.142230</td>\n",
       "      <td>279.397615</td>\n",
       "      <td>566.390319</td>\n",
       "      <td>577.715951</td>\n",
       "      <td>465.274234</td>\n",
       "      <td>331.723406</td>\n",
       "      <td>1035.912033</td>\n",
       "      <td>760.018793</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s157w1</th>\n",
       "      <td>263.363807</td>\n",
       "      <td>265.498470</td>\n",
       "      <td>268.691936</td>\n",
       "      <td>209.978819</td>\n",
       "      <td>244.570796</td>\n",
       "      <td>276.193770</td>\n",
       "      <td>282.520133</td>\n",
       "      <td>241.416328</td>\n",
       "      <td>215.668066</td>\n",
       "      <td>235.924552</td>\n",
       "      <td>266.710372</td>\n",
       "      <td>263.718673</td>\n",
       "      <td>252.403026</td>\n",
       "      <td>196.082199</td>\n",
       "      <td>279.121701</td>\n",
       "      <td>275.778703</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S72W1</th>\n",
       "      <td>192.445177</td>\n",
       "      <td>269.132008</td>\n",
       "      <td>309.593710</td>\n",
       "      <td>289.876029</td>\n",
       "      <td>147.584591</td>\n",
       "      <td>270.954659</td>\n",
       "      <td>305.653466</td>\n",
       "      <td>291.321822</td>\n",
       "      <td>234.816908</td>\n",
       "      <td>166.379111</td>\n",
       "      <td>342.515112</td>\n",
       "      <td>355.598560</td>\n",
       "      <td>394.421258</td>\n",
       "      <td>228.802290</td>\n",
       "      <td>564.260379</td>\n",
       "      <td>588.917255</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s173w1</th>\n",
       "      <td>291.752910</td>\n",
       "      <td>256.103741</td>\n",
       "      <td>263.630473</td>\n",
       "      <td>247.982380</td>\n",
       "      <td>255.688366</td>\n",
       "      <td>307.441845</td>\n",
       "      <td>376.273479</td>\n",
       "      <td>299.506724</td>\n",
       "      <td>204.412462</td>\n",
       "      <td>282.403138</td>\n",
       "      <td>368.398375</td>\n",
       "      <td>385.920725</td>\n",
       "      <td>339.661257</td>\n",
       "      <td>208.522491</td>\n",
       "      <td>468.783569</td>\n",
       "      <td>403.102867</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s94w1</th>\n",
       "      <td>194.460926</td>\n",
       "      <td>281.547832</td>\n",
       "      <td>283.517777</td>\n",
       "      <td>209.737100</td>\n",
       "      <td>195.487525</td>\n",
       "      <td>345.123283</td>\n",
       "      <td>339.923587</td>\n",
       "      <td>308.054522</td>\n",
       "      <td>216.276625</td>\n",
       "      <td>225.690854</td>\n",
       "      <td>369.166438</td>\n",
       "      <td>403.085223</td>\n",
       "      <td>390.819923</td>\n",
       "      <td>211.058046</td>\n",
       "      <td>399.262720</td>\n",
       "      <td>416.438819</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S155W1</th>\n",
       "      <td>311.005400</td>\n",
       "      <td>357.665049</td>\n",
       "      <td>356.807047</td>\n",
       "      <td>312.632046</td>\n",
       "      <td>318.489746</td>\n",
       "      <td>435.374781</td>\n",
       "      <td>479.252160</td>\n",
       "      <td>433.948799</td>\n",
       "      <td>364.579531</td>\n",
       "      <td>348.916324</td>\n",
       "      <td>497.668284</td>\n",
       "      <td>497.625090</td>\n",
       "      <td>485.901863</td>\n",
       "      <td>360.509352</td>\n",
       "      <td>531.961729</td>\n",
       "      <td>527.916669</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s176w1</th>\n",
       "      <td>248.026487</td>\n",
       "      <td>257.546697</td>\n",
       "      <td>299.326345</td>\n",
       "      <td>230.555243</td>\n",
       "      <td>211.930625</td>\n",
       "      <td>267.734699</td>\n",
       "      <td>283.057921</td>\n",
       "      <td>259.588794</td>\n",
       "      <td>210.956659</td>\n",
       "      <td>227.469707</td>\n",
       "      <td>297.331850</td>\n",
       "      <td>283.054904</td>\n",
       "      <td>319.756480</td>\n",
       "      <td>251.821163</td>\n",
       "      <td>375.637059</td>\n",
       "      <td>559.448755</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s53w1</th>\n",
       "      <td>257.428840</td>\n",
       "      <td>286.870264</td>\n",
       "      <td>280.919309</td>\n",
       "      <td>307.215577</td>\n",
       "      <td>243.911398</td>\n",
       "      <td>326.580404</td>\n",
       "      <td>336.251504</td>\n",
       "      <td>314.353065</td>\n",
       "      <td>231.782626</td>\n",
       "      <td>246.093022</td>\n",
       "      <td>386.400382</td>\n",
       "      <td>343.536500</td>\n",
       "      <td>319.652484</td>\n",
       "      <td>256.125180</td>\n",
       "      <td>454.164314</td>\n",
       "      <td>356.870021</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s152w1</th>\n",
       "      <td>283.621900</td>\n",
       "      <td>291.881177</td>\n",
       "      <td>267.420111</td>\n",
       "      <td>171.400437</td>\n",
       "      <td>262.750783</td>\n",
       "      <td>290.793204</td>\n",
       "      <td>277.654607</td>\n",
       "      <td>276.060475</td>\n",
       "      <td>175.756043</td>\n",
       "      <td>256.476059</td>\n",
       "      <td>294.706401</td>\n",
       "      <td>289.584298</td>\n",
       "      <td>281.332246</td>\n",
       "      <td>181.744651</td>\n",
       "      <td>402.214146</td>\n",
       "      <td>365.137797</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S174W1</th>\n",
       "      <td>300.005072</td>\n",
       "      <td>325.837232</td>\n",
       "      <td>300.422954</td>\n",
       "      <td>290.076618</td>\n",
       "      <td>257.038194</td>\n",
       "      <td>274.698163</td>\n",
       "      <td>287.525803</td>\n",
       "      <td>290.185124</td>\n",
       "      <td>280.645440</td>\n",
       "      <td>260.337975</td>\n",
       "      <td>266.657426</td>\n",
       "      <td>273.278233</td>\n",
       "      <td>283.300122</td>\n",
       "      <td>282.162070</td>\n",
       "      <td>283.830268</td>\n",
       "      <td>299.799139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s170w1</th>\n",
       "      <td>182.589126</td>\n",
       "      <td>231.812728</td>\n",
       "      <td>225.907785</td>\n",
       "      <td>210.624896</td>\n",
       "      <td>157.412361</td>\n",
       "      <td>228.781993</td>\n",
       "      <td>262.277732</td>\n",
       "      <td>237.281233</td>\n",
       "      <td>188.039048</td>\n",
       "      <td>156.561721</td>\n",
       "      <td>212.505085</td>\n",
       "      <td>237.641311</td>\n",
       "      <td>216.036612</td>\n",
       "      <td>175.130573</td>\n",
       "      <td>264.623079</td>\n",
       "      <td>254.158253</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S154W1</th>\n",
       "      <td>229.492120</td>\n",
       "      <td>274.738626</td>\n",
       "      <td>276.268072</td>\n",
       "      <td>201.782898</td>\n",
       "      <td>212.835743</td>\n",
       "      <td>277.945600</td>\n",
       "      <td>305.323098</td>\n",
       "      <td>277.739486</td>\n",
       "      <td>231.981599</td>\n",
       "      <td>195.876163</td>\n",
       "      <td>309.220624</td>\n",
       "      <td>320.019533</td>\n",
       "      <td>366.032464</td>\n",
       "      <td>240.286893</td>\n",
       "      <td>438.426023</td>\n",
       "      <td>467.285647</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S169W</th>\n",
       "      <td>201.545688</td>\n",
       "      <td>266.264320</td>\n",
       "      <td>263.293217</td>\n",
       "      <td>249.159771</td>\n",
       "      <td>210.650740</td>\n",
       "      <td>233.773479</td>\n",
       "      <td>254.182624</td>\n",
       "      <td>266.743241</td>\n",
       "      <td>230.181201</td>\n",
       "      <td>185.873434</td>\n",
       "      <td>237.250233</td>\n",
       "      <td>254.454665</td>\n",
       "      <td>289.006150</td>\n",
       "      <td>230.070840</td>\n",
       "      <td>277.976790</td>\n",
       "      <td>280.233451</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S55W1</th>\n",
       "      <td>254.301732</td>\n",
       "      <td>292.047034</td>\n",
       "      <td>280.314758</td>\n",
       "      <td>278.856379</td>\n",
       "      <td>219.074473</td>\n",
       "      <td>287.232943</td>\n",
       "      <td>301.330435</td>\n",
       "      <td>285.363984</td>\n",
       "      <td>211.699786</td>\n",
       "      <td>236.071228</td>\n",
       "      <td>288.861572</td>\n",
       "      <td>319.735379</td>\n",
       "      <td>311.897461</td>\n",
       "      <td>216.970329</td>\n",
       "      <td>382.940629</td>\n",
       "      <td>492.556510</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S10W1</th>\n",
       "      <td>230.766999</td>\n",
       "      <td>271.399865</td>\n",
       "      <td>237.567316</td>\n",
       "      <td>146.835570</td>\n",
       "      <td>215.786254</td>\n",
       "      <td>266.499875</td>\n",
       "      <td>267.092948</td>\n",
       "      <td>244.029880</td>\n",
       "      <td>156.097504</td>\n",
       "      <td>275.024776</td>\n",
       "      <td>318.010314</td>\n",
       "      <td>349.361604</td>\n",
       "      <td>298.436316</td>\n",
       "      <td>118.217077</td>\n",
       "      <td>325.969102</td>\n",
       "      <td>281.002512</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S59LW</th>\n",
       "      <td>238.441238</td>\n",
       "      <td>305.006992</td>\n",
       "      <td>288.872658</td>\n",
       "      <td>189.986962</td>\n",
       "      <td>203.241458</td>\n",
       "      <td>294.620758</td>\n",
       "      <td>303.830634</td>\n",
       "      <td>275.729789</td>\n",
       "      <td>199.671592</td>\n",
       "      <td>179.843025</td>\n",
       "      <td>320.435690</td>\n",
       "      <td>331.179215</td>\n",
       "      <td>346.685854</td>\n",
       "      <td>229.605707</td>\n",
       "      <td>445.262576</td>\n",
       "      <td>519.082555</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S196W1</th>\n",
       "      <td>324.966526</td>\n",
       "      <td>326.767803</td>\n",
       "      <td>303.008684</td>\n",
       "      <td>257.114129</td>\n",
       "      <td>237.625411</td>\n",
       "      <td>325.011531</td>\n",
       "      <td>333.719040</td>\n",
       "      <td>349.301829</td>\n",
       "      <td>273.269822</td>\n",
       "      <td>280.555858</td>\n",
       "      <td>380.583565</td>\n",
       "      <td>396.100579</td>\n",
       "      <td>400.989035</td>\n",
       "      <td>331.142111</td>\n",
       "      <td>453.021273</td>\n",
       "      <td>511.350051</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s12w1</th>\n",
       "      <td>180.226103</td>\n",
       "      <td>207.499172</td>\n",
       "      <td>212.721874</td>\n",
       "      <td>189.212034</td>\n",
       "      <td>162.129931</td>\n",
       "      <td>219.814723</td>\n",
       "      <td>234.199620</td>\n",
       "      <td>238.083526</td>\n",
       "      <td>138.567995</td>\n",
       "      <td>135.117633</td>\n",
       "      <td>223.455811</td>\n",
       "      <td>226.202848</td>\n",
       "      <td>226.772352</td>\n",
       "      <td>175.112195</td>\n",
       "      <td>229.534030</td>\n",
       "      <td>235.926233</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                F7          F3          F4          F8          T3  \\\n",
       "S42W1   234.691796  288.619884  297.082391  201.988486  201.117730   \n",
       "S31W    209.802646  310.155904  323.160348  227.777398  275.764142   \n",
       "S167W1  352.218137  434.144626  355.861077  446.941460  369.187810   \n",
       "S50W    269.647852  338.634255  344.864874  234.133116  256.133755   \n",
       "S165W1  205.958906  257.508954  265.755559  242.377389  203.218307   \n",
       "s158w1  210.156392  243.122535  242.398858  236.778044  206.025147   \n",
       "s178w1  233.345544  262.343376  276.535676  240.188206  236.186406   \n",
       "S18W1   183.823220  208.668583  246.833941  204.128240  142.202448   \n",
       "s27w1   287.675622  325.139793  283.005382  240.012783  245.015546   \n",
       "S163W1  207.762145  260.002005  256.324676  197.574137  248.676862   \n",
       "s20w1   147.782771  270.373665  248.174302  164.866094  162.434517   \n",
       "S164W1  281.700646  310.177616  289.735789  255.653307  268.674207   \n",
       "S182W1  240.561294  257.719276  268.663922  269.006473  304.066637   \n",
       "s43w1   326.795516  318.867188  353.462038  321.307460  231.690201   \n",
       "S60W    194.210026  222.150624  241.279022  172.894435  132.343818   \n",
       "S26W1   296.342263  328.786151  366.891391  380.992835  239.966741   \n",
       "S47W1   264.790638  333.373518  327.994910  190.288839  204.384230   \n",
       "S85W1   272.113160  295.200184  319.971753  249.162530  235.202449   \n",
       "S179W1  183.455034  257.494443  247.734924  223.400299  199.758749   \n",
       "S78W    230.311833  288.261823  316.428733  284.023038  240.441214   \n",
       "S177W1  243.936777  319.006029  319.513790  292.178406  253.916193   \n",
       "S153W1  258.592816  314.619374  351.361852  265.194569  262.396504   \n",
       "s157w1  263.363807  265.498470  268.691936  209.978819  244.570796   \n",
       "S72W1   192.445177  269.132008  309.593710  289.876029  147.584591   \n",
       "s173w1  291.752910  256.103741  263.630473  247.982380  255.688366   \n",
       "s94w1   194.460926  281.547832  283.517777  209.737100  195.487525   \n",
       "S155W1  311.005400  357.665049  356.807047  312.632046  318.489746   \n",
       "s176w1  248.026487  257.546697  299.326345  230.555243  211.930625   \n",
       "s53w1   257.428840  286.870264  280.919309  307.215577  243.911398   \n",
       "s152w1  283.621900  291.881177  267.420111  171.400437  262.750783   \n",
       "S174W1  300.005072  325.837232  300.422954  290.076618  257.038194   \n",
       "s170w1  182.589126  231.812728  225.907785  210.624896  157.412361   \n",
       "S154W1  229.492120  274.738626  276.268072  201.782898  212.835743   \n",
       "S169W   201.545688  266.264320  263.293217  249.159771  210.650740   \n",
       "S55W1   254.301732  292.047034  280.314758  278.856379  219.074473   \n",
       "S10W1   230.766999  271.399865  237.567316  146.835570  215.786254   \n",
       "S59LW   238.441238  305.006992  288.872658  189.986962  203.241458   \n",
       "S196W1  324.966526  326.767803  303.008684  257.114129  237.625411   \n",
       "s12w1   180.226103  207.499172  212.721874  189.212034  162.129931   \n",
       "\n",
       "                C3          Cz          C4          T4          T5  \\\n",
       "S42W1   308.864366  320.471932  314.160993  196.207816  212.271569   \n",
       "S31W    304.740733  310.459188  285.944573  255.524574  419.788974   \n",
       "S167W1  351.056225  401.587008  343.221223  281.817039  303.818939   \n",
       "S50W    368.086639  368.735112  355.779238  234.084414  258.421585   \n",
       "S165W1  274.589074  278.940344  279.626380  257.992473  213.305772   \n",
       "s158w1  252.486010  265.404565  246.924285  232.915249  209.578012   \n",
       "s178w1  256.556363  262.057082  270.026048  233.934716  231.208828   \n",
       "S18W1   237.652893  316.793198  238.867346  200.856008  168.485749   \n",
       "s27w1   307.517049  321.980163  294.850973  175.702001  285.849380   \n",
       "S163W1  276.740984  286.515740  267.751495  196.478302  284.604995   \n",
       "s20w1   226.305784  251.915995  216.597297  114.373884  164.988147   \n",
       "S164W1  319.769445  315.296143  314.333275  286.932125  373.642320   \n",
       "S182W1  371.140711  355.034129  332.640988  309.211997  277.359837   \n",
       "s43w1   360.707176  425.936690  414.537292  310.313387  224.776294   \n",
       "S60W    211.431125  229.013247  217.469091  166.246432  140.000047   \n",
       "S26W1   378.872010  430.631947  391.815336  375.639116  236.202167   \n",
       "S47W1   320.550438  331.779669  308.773221  185.439796  196.799294   \n",
       "S85W1   303.822458  324.956053  319.940781  243.296194  229.908083   \n",
       "S179W1  261.299923  294.517130  282.269757  196.996012  228.074576   \n",
       "S78W    341.605743  352.760816  315.985272  252.532598  253.055806   \n",
       "S177W1  362.064560  371.719419  358.125010  282.468072  345.783579   \n",
       "S153W1  276.587233  442.066059  389.958831  328.142230  279.397615   \n",
       "s157w1  276.193770  282.520133  241.416328  215.668066  235.924552   \n",
       "S72W1   270.954659  305.653466  291.321822  234.816908  166.379111   \n",
       "s173w1  307.441845  376.273479  299.506724  204.412462  282.403138   \n",
       "s94w1   345.123283  339.923587  308.054522  216.276625  225.690854   \n",
       "S155W1  435.374781  479.252160  433.948799  364.579531  348.916324   \n",
       "s176w1  267.734699  283.057921  259.588794  210.956659  227.469707   \n",
       "s53w1   326.580404  336.251504  314.353065  231.782626  246.093022   \n",
       "s152w1  290.793204  277.654607  276.060475  175.756043  256.476059   \n",
       "S174W1  274.698163  287.525803  290.185124  280.645440  260.337975   \n",
       "s170w1  228.781993  262.277732  237.281233  188.039048  156.561721   \n",
       "S154W1  277.945600  305.323098  277.739486  231.981599  195.876163   \n",
       "S169W   233.773479  254.182624  266.743241  230.181201  185.873434   \n",
       "S55W1   287.232943  301.330435  285.363984  211.699786  236.071228   \n",
       "S10W1   266.499875  267.092948  244.029880  156.097504  275.024776   \n",
       "S59LW   294.620758  303.830634  275.729789  199.671592  179.843025   \n",
       "S196W1  325.011531  333.719040  349.301829  273.269822  280.555858   \n",
       "s12w1   219.814723  234.199620  238.083526  138.567995  135.117633   \n",
       "\n",
       "                P3          Pz          P4          T6           01  \\\n",
       "S42W1   386.414909  366.827124  331.260919  210.572525   316.188987   \n",
       "S31W    457.526612  517.576155  517.303068  475.087018   422.721320   \n",
       "S167W1  392.647286  431.740410  393.273398  387.944044   542.842977   \n",
       "S50W    391.401720  409.314609  358.623829  293.393776   625.496439   \n",
       "S165W1  249.756887  298.145780  280.551883  300.361348   294.697612   \n",
       "s158w1  267.934919  294.790904  314.715503  271.258555   327.399674   \n",
       "s178w1  275.671909  274.158747  293.347750  225.355462   362.525708   \n",
       "S18W1   292.000155  320.969673  275.168212  201.090997   412.535669   \n",
       "s27w1   412.084990  477.351270  397.399457  239.099220   578.065659   \n",
       "S163W1  321.380874  320.558260  332.958268  206.182699   413.562566   \n",
       "s20w1   267.253741  337.189439  231.344311  157.555822   311.801208   \n",
       "S164W1  452.643918  420.247648  423.259184  340.673021   552.980053   \n",
       "S182W1  331.241651  352.364234  334.786615  305.766036   409.443492   \n",
       "s43w1   506.125404  682.300913  637.867268  316.340250  1050.375111   \n",
       "S60W    266.955217  315.816400  245.146160  164.757775   416.839665   \n",
       "S26W1   542.754682  629.906602  532.299876  345.351168   638.262185   \n",
       "S47W1   305.105633  327.740318  319.317493  222.812616   457.379887   \n",
       "S85W1   363.847227  329.659947  329.868052  258.558396   566.683276   \n",
       "S179W1  281.298629  298.759270  292.529324  206.190469   305.156831   \n",
       "S78W    396.731359  381.690651  343.607728  297.892570   580.001393   \n",
       "S177W1  471.585204  492.029939  476.036503  248.525547   618.054892   \n",
       "S153W1  566.390319  577.715951  465.274234  331.723406  1035.912033   \n",
       "s157w1  266.710372  263.718673  252.403026  196.082199   279.121701   \n",
       "S72W1   342.515112  355.598560  394.421258  228.802290   564.260379   \n",
       "s173w1  368.398375  385.920725  339.661257  208.522491   468.783569   \n",
       "s94w1   369.166438  403.085223  390.819923  211.058046   399.262720   \n",
       "S155W1  497.668284  497.625090  485.901863  360.509352   531.961729   \n",
       "s176w1  297.331850  283.054904  319.756480  251.821163   375.637059   \n",
       "s53w1   386.400382  343.536500  319.652484  256.125180   454.164314   \n",
       "s152w1  294.706401  289.584298  281.332246  181.744651   402.214146   \n",
       "S174W1  266.657426  273.278233  283.300122  282.162070   283.830268   \n",
       "s170w1  212.505085  237.641311  216.036612  175.130573   264.623079   \n",
       "S154W1  309.220624  320.019533  366.032464  240.286893   438.426023   \n",
       "S169W   237.250233  254.454665  289.006150  230.070840   277.976790   \n",
       "S55W1   288.861572  319.735379  311.897461  216.970329   382.940629   \n",
       "S10W1   318.010314  349.361604  298.436316  118.217077   325.969102   \n",
       "S59LW   320.435690  331.179215  346.685854  229.605707   445.262576   \n",
       "S196W1  380.583565  396.100579  400.989035  331.142111   453.021273   \n",
       "s12w1   223.455811  226.202848  226.772352  175.112195   229.534030   \n",
       "\n",
       "                02  diagnosis  \n",
       "S42W1   368.760953          0  \n",
       "S31W    486.114878          0  \n",
       "S167W1  536.968509          0  \n",
       "S50W    629.259563          0  \n",
       "S165W1  314.157604          0  \n",
       "s158w1  450.594164          0  \n",
       "s178w1  455.596702          0  \n",
       "S18W1   398.371788          0  \n",
       "s27w1   598.894160          0  \n",
       "S163W1  343.096845          0  \n",
       "s20w1   331.440301          0  \n",
       "S164W1  473.332270          0  \n",
       "S182W1  361.454595          0  \n",
       "s43w1   852.320971          0  \n",
       "S60W    328.217531          0  \n",
       "S26W1   599.472014          0  \n",
       "S47W1   583.122150          0  \n",
       "S85W1   503.317306          0  \n",
       "S179W1  336.495431          0  \n",
       "S78W    511.945663          0  \n",
       "S177W1  652.856151          0  \n",
       "S153W1  760.018793          0  \n",
       "s157w1  275.778703          0  \n",
       "S72W1   588.917255          0  \n",
       "s173w1  403.102867          0  \n",
       "s94w1   416.438819          0  \n",
       "S155W1  527.916669          0  \n",
       "s176w1  559.448755          0  \n",
       "s53w1   356.870021          0  \n",
       "s152w1  365.137797          0  \n",
       "S174W1  299.799139          0  \n",
       "s170w1  254.158253          0  \n",
       "S154W1  467.285647          0  \n",
       "S169W   280.233451          0  \n",
       "S55W1   492.556510          0  \n",
       "S10W1   281.002512          0  \n",
       "S59LW   519.082555          0  \n",
       "S196W1  511.350051          0  \n",
       "s12w1   235.926233          0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_normal_data = pd.DataFrame(norm_averaged_values, columns = ['F7', 'F3', 'F4', 'F8', 'T3', 'C3', 'Cz', 'C4', 'T4', 'T5', 'P3', 'Pz', 'P4', 'T6', '01', '02'])\n",
    "reduced_normal_data['diagnosis'] = 0\n",
    "reduced_normal_data.index = norm_filenames\n",
    "reduced_normal_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "506dc9aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F7</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F8</th>\n",
       "      <th>T3</th>\n",
       "      <th>C3</th>\n",
       "      <th>Cz</th>\n",
       "      <th>C4</th>\n",
       "      <th>T4</th>\n",
       "      <th>T5</th>\n",
       "      <th>P3</th>\n",
       "      <th>Pz</th>\n",
       "      <th>P4</th>\n",
       "      <th>T6</th>\n",
       "      <th>01</th>\n",
       "      <th>02</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>312w1</th>\n",
       "      <td>206.320397</td>\n",
       "      <td>281.263372</td>\n",
       "      <td>299.172999</td>\n",
       "      <td>257.392182</td>\n",
       "      <td>211.820132</td>\n",
       "      <td>294.820716</td>\n",
       "      <td>319.786544</td>\n",
       "      <td>345.369290</td>\n",
       "      <td>272.147546</td>\n",
       "      <td>276.660156</td>\n",
       "      <td>449.150581</td>\n",
       "      <td>524.197324</td>\n",
       "      <td>523.097816</td>\n",
       "      <td>435.508664</td>\n",
       "      <td>603.983848</td>\n",
       "      <td>633.859315</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642w1</th>\n",
       "      <td>366.396354</td>\n",
       "      <td>382.963122</td>\n",
       "      <td>418.888553</td>\n",
       "      <td>297.033108</td>\n",
       "      <td>282.734802</td>\n",
       "      <td>330.218452</td>\n",
       "      <td>328.949447</td>\n",
       "      <td>336.694488</td>\n",
       "      <td>316.450935</td>\n",
       "      <td>238.148698</td>\n",
       "      <td>247.791669</td>\n",
       "      <td>248.684117</td>\n",
       "      <td>263.109678</td>\n",
       "      <td>262.007634</td>\n",
       "      <td>296.003741</td>\n",
       "      <td>254.928184</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387-02w1</th>\n",
       "      <td>234.795503</td>\n",
       "      <td>287.451087</td>\n",
       "      <td>178.627186</td>\n",
       "      <td>193.460809</td>\n",
       "      <td>207.708783</td>\n",
       "      <td>281.326902</td>\n",
       "      <td>286.289586</td>\n",
       "      <td>265.439389</td>\n",
       "      <td>262.181173</td>\n",
       "      <td>202.033771</td>\n",
       "      <td>284.241773</td>\n",
       "      <td>292.374388</td>\n",
       "      <td>285.597331</td>\n",
       "      <td>281.038923</td>\n",
       "      <td>293.338473</td>\n",
       "      <td>280.024243</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s351w</th>\n",
       "      <td>235.326948</td>\n",
       "      <td>276.417191</td>\n",
       "      <td>303.862620</td>\n",
       "      <td>242.048113</td>\n",
       "      <td>224.213878</td>\n",
       "      <td>278.223172</td>\n",
       "      <td>293.151385</td>\n",
       "      <td>310.499721</td>\n",
       "      <td>301.374194</td>\n",
       "      <td>240.573027</td>\n",
       "      <td>288.047160</td>\n",
       "      <td>288.309579</td>\n",
       "      <td>360.355677</td>\n",
       "      <td>326.735962</td>\n",
       "      <td>309.567799</td>\n",
       "      <td>414.304186</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423w</th>\n",
       "      <td>271.413695</td>\n",
       "      <td>319.346132</td>\n",
       "      <td>293.445698</td>\n",
       "      <td>245.076982</td>\n",
       "      <td>251.858844</td>\n",
       "      <td>240.822275</td>\n",
       "      <td>274.157577</td>\n",
       "      <td>264.048354</td>\n",
       "      <td>225.329668</td>\n",
       "      <td>226.555383</td>\n",
       "      <td>242.301031</td>\n",
       "      <td>260.228691</td>\n",
       "      <td>260.978638</td>\n",
       "      <td>231.488794</td>\n",
       "      <td>285.456193</td>\n",
       "      <td>283.472827</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485w1</th>\n",
       "      <td>321.123158</td>\n",
       "      <td>366.058307</td>\n",
       "      <td>315.590077</td>\n",
       "      <td>230.039855</td>\n",
       "      <td>273.682033</td>\n",
       "      <td>379.195359</td>\n",
       "      <td>392.642043</td>\n",
       "      <td>379.276591</td>\n",
       "      <td>237.137714</td>\n",
       "      <td>218.613786</td>\n",
       "      <td>365.679971</td>\n",
       "      <td>490.870199</td>\n",
       "      <td>497.869384</td>\n",
       "      <td>373.756691</td>\n",
       "      <td>468.536046</td>\n",
       "      <td>523.482060</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314w1</th>\n",
       "      <td>188.503249</td>\n",
       "      <td>206.377499</td>\n",
       "      <td>210.450124</td>\n",
       "      <td>157.257440</td>\n",
       "      <td>197.954727</td>\n",
       "      <td>250.850112</td>\n",
       "      <td>257.437574</td>\n",
       "      <td>244.540417</td>\n",
       "      <td>166.504868</td>\n",
       "      <td>236.484505</td>\n",
       "      <td>325.113289</td>\n",
       "      <td>350.156646</td>\n",
       "      <td>336.925900</td>\n",
       "      <td>232.581078</td>\n",
       "      <td>432.306539</td>\n",
       "      <td>371.034786</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585w1</th>\n",
       "      <td>256.471974</td>\n",
       "      <td>187.546342</td>\n",
       "      <td>229.976701</td>\n",
       "      <td>251.165103</td>\n",
       "      <td>284.208555</td>\n",
       "      <td>328.489284</td>\n",
       "      <td>306.831605</td>\n",
       "      <td>330.613654</td>\n",
       "      <td>282.786000</td>\n",
       "      <td>281.657758</td>\n",
       "      <td>285.789540</td>\n",
       "      <td>337.777720</td>\n",
       "      <td>296.233518</td>\n",
       "      <td>261.141142</td>\n",
       "      <td>425.521574</td>\n",
       "      <td>420.625659</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113w1</th>\n",
       "      <td>311.777220</td>\n",
       "      <td>282.284711</td>\n",
       "      <td>243.702085</td>\n",
       "      <td>209.386413</td>\n",
       "      <td>230.519184</td>\n",
       "      <td>202.143195</td>\n",
       "      <td>215.298730</td>\n",
       "      <td>266.146725</td>\n",
       "      <td>219.864124</td>\n",
       "      <td>197.267254</td>\n",
       "      <td>204.431280</td>\n",
       "      <td>225.165079</td>\n",
       "      <td>291.204492</td>\n",
       "      <td>237.693068</td>\n",
       "      <td>257.713474</td>\n",
       "      <td>343.016491</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156w1</th>\n",
       "      <td>173.870172</td>\n",
       "      <td>221.301829</td>\n",
       "      <td>201.075190</td>\n",
       "      <td>151.966254</td>\n",
       "      <td>195.879945</td>\n",
       "      <td>270.835018</td>\n",
       "      <td>283.097083</td>\n",
       "      <td>276.803309</td>\n",
       "      <td>190.147621</td>\n",
       "      <td>179.189104</td>\n",
       "      <td>360.347039</td>\n",
       "      <td>419.801158</td>\n",
       "      <td>408.060797</td>\n",
       "      <td>261.521094</td>\n",
       "      <td>469.821441</td>\n",
       "      <td>514.013210</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s083w1</th>\n",
       "      <td>261.245302</td>\n",
       "      <td>307.953362</td>\n",
       "      <td>326.677720</td>\n",
       "      <td>226.410697</td>\n",
       "      <td>230.329172</td>\n",
       "      <td>363.336543</td>\n",
       "      <td>464.088941</td>\n",
       "      <td>405.804865</td>\n",
       "      <td>270.807081</td>\n",
       "      <td>230.348875</td>\n",
       "      <td>382.420915</td>\n",
       "      <td>402.315447</td>\n",
       "      <td>353.052124</td>\n",
       "      <td>223.280934</td>\n",
       "      <td>394.495125</td>\n",
       "      <td>345.571714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387-03w1</th>\n",
       "      <td>284.816371</td>\n",
       "      <td>341.931017</td>\n",
       "      <td>306.802659</td>\n",
       "      <td>271.994405</td>\n",
       "      <td>270.272335</td>\n",
       "      <td>273.050238</td>\n",
       "      <td>278.117245</td>\n",
       "      <td>262.699012</td>\n",
       "      <td>219.752193</td>\n",
       "      <td>265.325355</td>\n",
       "      <td>235.997730</td>\n",
       "      <td>247.531111</td>\n",
       "      <td>239.618544</td>\n",
       "      <td>240.494297</td>\n",
       "      <td>276.671087</td>\n",
       "      <td>355.116046</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540w1</th>\n",
       "      <td>272.317395</td>\n",
       "      <td>367.474717</td>\n",
       "      <td>358.977936</td>\n",
       "      <td>263.742082</td>\n",
       "      <td>311.966984</td>\n",
       "      <td>385.186960</td>\n",
       "      <td>388.477966</td>\n",
       "      <td>351.545504</td>\n",
       "      <td>276.839435</td>\n",
       "      <td>346.381944</td>\n",
       "      <td>366.205023</td>\n",
       "      <td>468.825188</td>\n",
       "      <td>389.886306</td>\n",
       "      <td>315.188745</td>\n",
       "      <td>522.919419</td>\n",
       "      <td>423.323232</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719w1</th>\n",
       "      <td>114.822781</td>\n",
       "      <td>144.747385</td>\n",
       "      <td>156.666669</td>\n",
       "      <td>126.077710</td>\n",
       "      <td>93.408988</td>\n",
       "      <td>158.428544</td>\n",
       "      <td>157.664348</td>\n",
       "      <td>146.569405</td>\n",
       "      <td>116.064589</td>\n",
       "      <td>112.649072</td>\n",
       "      <td>124.491471</td>\n",
       "      <td>153.789216</td>\n",
       "      <td>155.488478</td>\n",
       "      <td>143.695496</td>\n",
       "      <td>163.052439</td>\n",
       "      <td>166.015375</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249w1</th>\n",
       "      <td>129.947992</td>\n",
       "      <td>201.243656</td>\n",
       "      <td>184.766984</td>\n",
       "      <td>159.931764</td>\n",
       "      <td>128.225548</td>\n",
       "      <td>179.669702</td>\n",
       "      <td>204.352620</td>\n",
       "      <td>201.479427</td>\n",
       "      <td>161.859685</td>\n",
       "      <td>142.200368</td>\n",
       "      <td>197.578599</td>\n",
       "      <td>237.121667</td>\n",
       "      <td>254.410143</td>\n",
       "      <td>184.200151</td>\n",
       "      <td>214.120624</td>\n",
       "      <td>235.033210</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401w1</th>\n",
       "      <td>292.646522</td>\n",
       "      <td>321.763551</td>\n",
       "      <td>354.227141</td>\n",
       "      <td>301.006544</td>\n",
       "      <td>344.025603</td>\n",
       "      <td>400.484043</td>\n",
       "      <td>425.400089</td>\n",
       "      <td>407.467430</td>\n",
       "      <td>403.573255</td>\n",
       "      <td>332.490850</td>\n",
       "      <td>611.790835</td>\n",
       "      <td>775.843267</td>\n",
       "      <td>650.907932</td>\n",
       "      <td>573.104062</td>\n",
       "      <td>838.892383</td>\n",
       "      <td>730.917053</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454-1W</th>\n",
       "      <td>235.108410</td>\n",
       "      <td>347.593388</td>\n",
       "      <td>326.940999</td>\n",
       "      <td>219.137592</td>\n",
       "      <td>412.961801</td>\n",
       "      <td>399.310629</td>\n",
       "      <td>412.950320</td>\n",
       "      <td>365.466186</td>\n",
       "      <td>223.336290</td>\n",
       "      <td>342.198072</td>\n",
       "      <td>371.464419</td>\n",
       "      <td>427.214122</td>\n",
       "      <td>339.792059</td>\n",
       "      <td>282.602682</td>\n",
       "      <td>642.037408</td>\n",
       "      <td>611.360546</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276w1</th>\n",
       "      <td>195.349930</td>\n",
       "      <td>242.110267</td>\n",
       "      <td>201.530452</td>\n",
       "      <td>177.709275</td>\n",
       "      <td>198.604238</td>\n",
       "      <td>281.389807</td>\n",
       "      <td>285.985801</td>\n",
       "      <td>268.983147</td>\n",
       "      <td>170.169065</td>\n",
       "      <td>247.204193</td>\n",
       "      <td>297.960234</td>\n",
       "      <td>343.918227</td>\n",
       "      <td>309.583819</td>\n",
       "      <td>243.417762</td>\n",
       "      <td>329.706723</td>\n",
       "      <td>375.996766</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683w1</th>\n",
       "      <td>238.146513</td>\n",
       "      <td>293.028599</td>\n",
       "      <td>304.991956</td>\n",
       "      <td>254.541109</td>\n",
       "      <td>233.819904</td>\n",
       "      <td>302.735779</td>\n",
       "      <td>301.450432</td>\n",
       "      <td>292.408574</td>\n",
       "      <td>249.574750</td>\n",
       "      <td>235.345501</td>\n",
       "      <td>323.738077</td>\n",
       "      <td>293.757592</td>\n",
       "      <td>267.598501</td>\n",
       "      <td>257.392863</td>\n",
       "      <td>266.386492</td>\n",
       "      <td>267.924797</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192w</th>\n",
       "      <td>151.261104</td>\n",
       "      <td>238.474977</td>\n",
       "      <td>199.523384</td>\n",
       "      <td>182.219643</td>\n",
       "      <td>145.007560</td>\n",
       "      <td>250.203329</td>\n",
       "      <td>261.998542</td>\n",
       "      <td>244.237755</td>\n",
       "      <td>175.738927</td>\n",
       "      <td>224.449685</td>\n",
       "      <td>267.344203</td>\n",
       "      <td>358.037445</td>\n",
       "      <td>317.587117</td>\n",
       "      <td>255.502163</td>\n",
       "      <td>328.227142</td>\n",
       "      <td>339.569557</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r416w1</th>\n",
       "      <td>254.943664</td>\n",
       "      <td>352.264211</td>\n",
       "      <td>382.426187</td>\n",
       "      <td>290.794376</td>\n",
       "      <td>252.069121</td>\n",
       "      <td>307.631948</td>\n",
       "      <td>361.349543</td>\n",
       "      <td>424.552710</td>\n",
       "      <td>401.220855</td>\n",
       "      <td>242.379496</td>\n",
       "      <td>361.066138</td>\n",
       "      <td>383.726061</td>\n",
       "      <td>396.087708</td>\n",
       "      <td>414.232695</td>\n",
       "      <td>413.060421</td>\n",
       "      <td>483.415421</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586w1</th>\n",
       "      <td>272.676831</td>\n",
       "      <td>247.489215</td>\n",
       "      <td>276.003361</td>\n",
       "      <td>243.778790</td>\n",
       "      <td>231.989849</td>\n",
       "      <td>256.220956</td>\n",
       "      <td>265.188094</td>\n",
       "      <td>245.343548</td>\n",
       "      <td>230.019872</td>\n",
       "      <td>216.526965</td>\n",
       "      <td>241.492359</td>\n",
       "      <td>241.229797</td>\n",
       "      <td>225.820208</td>\n",
       "      <td>252.868198</td>\n",
       "      <td>234.142344</td>\n",
       "      <td>234.591283</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155w1</th>\n",
       "      <td>202.740320</td>\n",
       "      <td>244.850975</td>\n",
       "      <td>233.935872</td>\n",
       "      <td>182.759460</td>\n",
       "      <td>187.059685</td>\n",
       "      <td>199.511857</td>\n",
       "      <td>214.397249</td>\n",
       "      <td>197.603918</td>\n",
       "      <td>169.329819</td>\n",
       "      <td>152.818641</td>\n",
       "      <td>209.839059</td>\n",
       "      <td>232.239395</td>\n",
       "      <td>205.141151</td>\n",
       "      <td>155.996496</td>\n",
       "      <td>199.432020</td>\n",
       "      <td>227.479793</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>088w1</th>\n",
       "      <td>269.305514</td>\n",
       "      <td>332.407086</td>\n",
       "      <td>350.191853</td>\n",
       "      <td>301.477253</td>\n",
       "      <td>265.554741</td>\n",
       "      <td>286.413564</td>\n",
       "      <td>294.396764</td>\n",
       "      <td>314.602624</td>\n",
       "      <td>317.583328</td>\n",
       "      <td>290.625426</td>\n",
       "      <td>347.461970</td>\n",
       "      <td>388.176574</td>\n",
       "      <td>326.486996</td>\n",
       "      <td>290.390508</td>\n",
       "      <td>307.706076</td>\n",
       "      <td>288.323259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575w1</th>\n",
       "      <td>170.152280</td>\n",
       "      <td>279.777065</td>\n",
       "      <td>242.932027</td>\n",
       "      <td>173.217540</td>\n",
       "      <td>155.108012</td>\n",
       "      <td>218.686546</td>\n",
       "      <td>232.657583</td>\n",
       "      <td>220.663540</td>\n",
       "      <td>173.691340</td>\n",
       "      <td>166.997212</td>\n",
       "      <td>221.092523</td>\n",
       "      <td>252.668540</td>\n",
       "      <td>207.340418</td>\n",
       "      <td>161.949553</td>\n",
       "      <td>276.865313</td>\n",
       "      <td>280.286714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s425w1</th>\n",
       "      <td>250.422982</td>\n",
       "      <td>270.318286</td>\n",
       "      <td>283.844280</td>\n",
       "      <td>208.397945</td>\n",
       "      <td>224.158734</td>\n",
       "      <td>261.867770</td>\n",
       "      <td>286.176247</td>\n",
       "      <td>288.386216</td>\n",
       "      <td>271.238862</td>\n",
       "      <td>225.277260</td>\n",
       "      <td>240.873280</td>\n",
       "      <td>217.874120</td>\n",
       "      <td>248.514927</td>\n",
       "      <td>213.020714</td>\n",
       "      <td>234.881990</td>\n",
       "      <td>210.970145</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221w</th>\n",
       "      <td>419.996345</td>\n",
       "      <td>434.509292</td>\n",
       "      <td>403.317749</td>\n",
       "      <td>381.659124</td>\n",
       "      <td>401.405223</td>\n",
       "      <td>289.698764</td>\n",
       "      <td>265.922202</td>\n",
       "      <td>252.380602</td>\n",
       "      <td>352.466681</td>\n",
       "      <td>402.338480</td>\n",
       "      <td>332.502305</td>\n",
       "      <td>321.518884</td>\n",
       "      <td>299.118344</td>\n",
       "      <td>517.946504</td>\n",
       "      <td>551.889538</td>\n",
       "      <td>538.393368</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573w1</th>\n",
       "      <td>165.995478</td>\n",
       "      <td>193.638516</td>\n",
       "      <td>194.975543</td>\n",
       "      <td>155.340383</td>\n",
       "      <td>172.734042</td>\n",
       "      <td>200.131926</td>\n",
       "      <td>201.845221</td>\n",
       "      <td>204.631836</td>\n",
       "      <td>162.791081</td>\n",
       "      <td>174.770098</td>\n",
       "      <td>255.611551</td>\n",
       "      <td>231.804331</td>\n",
       "      <td>248.618193</td>\n",
       "      <td>161.065695</td>\n",
       "      <td>291.582685</td>\n",
       "      <td>245.640025</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509w1</th>\n",
       "      <td>358.070596</td>\n",
       "      <td>388.872357</td>\n",
       "      <td>346.710199</td>\n",
       "      <td>357.433391</td>\n",
       "      <td>415.485564</td>\n",
       "      <td>411.651081</td>\n",
       "      <td>404.350005</td>\n",
       "      <td>380.264781</td>\n",
       "      <td>353.232641</td>\n",
       "      <td>400.754036</td>\n",
       "      <td>393.384876</td>\n",
       "      <td>406.492947</td>\n",
       "      <td>573.757271</td>\n",
       "      <td>348.858135</td>\n",
       "      <td>469.813879</td>\n",
       "      <td>582.533385</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32w1</th>\n",
       "      <td>173.479577</td>\n",
       "      <td>226.826589</td>\n",
       "      <td>234.218458</td>\n",
       "      <td>230.691579</td>\n",
       "      <td>170.289599</td>\n",
       "      <td>229.840647</td>\n",
       "      <td>287.880493</td>\n",
       "      <td>259.756068</td>\n",
       "      <td>213.937382</td>\n",
       "      <td>164.451257</td>\n",
       "      <td>261.906745</td>\n",
       "      <td>296.724189</td>\n",
       "      <td>299.378267</td>\n",
       "      <td>247.626910</td>\n",
       "      <td>335.810254</td>\n",
       "      <td>380.445263</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429w1</th>\n",
       "      <td>233.371824</td>\n",
       "      <td>303.727030</td>\n",
       "      <td>291.172953</td>\n",
       "      <td>240.113432</td>\n",
       "      <td>227.826423</td>\n",
       "      <td>293.515936</td>\n",
       "      <td>312.506509</td>\n",
       "      <td>304.129389</td>\n",
       "      <td>228.253443</td>\n",
       "      <td>271.712146</td>\n",
       "      <td>323.456357</td>\n",
       "      <td>337.799293</td>\n",
       "      <td>348.639850</td>\n",
       "      <td>284.074047</td>\n",
       "      <td>465.557961</td>\n",
       "      <td>588.502148</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219w1</th>\n",
       "      <td>158.856345</td>\n",
       "      <td>214.475632</td>\n",
       "      <td>198.680917</td>\n",
       "      <td>156.417518</td>\n",
       "      <td>156.525914</td>\n",
       "      <td>222.993664</td>\n",
       "      <td>217.635980</td>\n",
       "      <td>204.032934</td>\n",
       "      <td>145.229116</td>\n",
       "      <td>163.719947</td>\n",
       "      <td>185.562177</td>\n",
       "      <td>203.664577</td>\n",
       "      <td>195.525564</td>\n",
       "      <td>176.923921</td>\n",
       "      <td>183.634263</td>\n",
       "      <td>186.703143</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517w1</th>\n",
       "      <td>201.781635</td>\n",
       "      <td>264.136863</td>\n",
       "      <td>240.825319</td>\n",
       "      <td>186.893382</td>\n",
       "      <td>188.095426</td>\n",
       "      <td>232.376887</td>\n",
       "      <td>251.435595</td>\n",
       "      <td>231.418672</td>\n",
       "      <td>162.527286</td>\n",
       "      <td>155.025951</td>\n",
       "      <td>218.180461</td>\n",
       "      <td>245.652474</td>\n",
       "      <td>221.920480</td>\n",
       "      <td>195.139316</td>\n",
       "      <td>400.495245</td>\n",
       "      <td>469.629279</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r229w1</th>\n",
       "      <td>279.718048</td>\n",
       "      <td>311.120536</td>\n",
       "      <td>304.679740</td>\n",
       "      <td>242.836413</td>\n",
       "      <td>253.011305</td>\n",
       "      <td>248.946277</td>\n",
       "      <td>288.170241</td>\n",
       "      <td>287.984125</td>\n",
       "      <td>298.649042</td>\n",
       "      <td>174.844439</td>\n",
       "      <td>232.628491</td>\n",
       "      <td>235.218483</td>\n",
       "      <td>251.440759</td>\n",
       "      <td>159.170262</td>\n",
       "      <td>304.895375</td>\n",
       "      <td>341.409238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342mw1</th>\n",
       "      <td>157.048276</td>\n",
       "      <td>246.308396</td>\n",
       "      <td>230.929523</td>\n",
       "      <td>118.699322</td>\n",
       "      <td>191.580831</td>\n",
       "      <td>234.092460</td>\n",
       "      <td>288.148120</td>\n",
       "      <td>241.515146</td>\n",
       "      <td>171.773750</td>\n",
       "      <td>220.412493</td>\n",
       "      <td>267.693225</td>\n",
       "      <td>293.964637</td>\n",
       "      <td>257.844263</td>\n",
       "      <td>189.330796</td>\n",
       "      <td>268.902727</td>\n",
       "      <td>252.201547</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548w</th>\n",
       "      <td>239.707562</td>\n",
       "      <td>305.247044</td>\n",
       "      <td>307.389962</td>\n",
       "      <td>243.413969</td>\n",
       "      <td>247.179206</td>\n",
       "      <td>325.639342</td>\n",
       "      <td>369.979986</td>\n",
       "      <td>336.941704</td>\n",
       "      <td>260.122845</td>\n",
       "      <td>287.362677</td>\n",
       "      <td>354.714112</td>\n",
       "      <td>386.119348</td>\n",
       "      <td>338.233396</td>\n",
       "      <td>265.796083</td>\n",
       "      <td>374.327833</td>\n",
       "      <td>344.948639</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S084-1W</th>\n",
       "      <td>240.188142</td>\n",
       "      <td>304.095737</td>\n",
       "      <td>321.239219</td>\n",
       "      <td>234.329483</td>\n",
       "      <td>213.242632</td>\n",
       "      <td>288.783016</td>\n",
       "      <td>350.168379</td>\n",
       "      <td>287.805812</td>\n",
       "      <td>192.164906</td>\n",
       "      <td>221.328238</td>\n",
       "      <td>281.979464</td>\n",
       "      <td>313.797641</td>\n",
       "      <td>253.614721</td>\n",
       "      <td>226.817178</td>\n",
       "      <td>307.570905</td>\n",
       "      <td>381.700414</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515w1</th>\n",
       "      <td>198.172872</td>\n",
       "      <td>226.705750</td>\n",
       "      <td>228.148816</td>\n",
       "      <td>219.683764</td>\n",
       "      <td>186.676105</td>\n",
       "      <td>216.192194</td>\n",
       "      <td>251.178447</td>\n",
       "      <td>225.242539</td>\n",
       "      <td>222.054242</td>\n",
       "      <td>232.283089</td>\n",
       "      <td>254.559398</td>\n",
       "      <td>265.448432</td>\n",
       "      <td>345.449779</td>\n",
       "      <td>276.374074</td>\n",
       "      <td>400.399749</td>\n",
       "      <td>438.443691</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510-1W</th>\n",
       "      <td>395.173736</td>\n",
       "      <td>481.122013</td>\n",
       "      <td>382.917365</td>\n",
       "      <td>350.212120</td>\n",
       "      <td>417.898810</td>\n",
       "      <td>491.661447</td>\n",
       "      <td>449.674197</td>\n",
       "      <td>454.461710</td>\n",
       "      <td>483.547336</td>\n",
       "      <td>465.984600</td>\n",
       "      <td>480.287259</td>\n",
       "      <td>461.720582</td>\n",
       "      <td>464.706132</td>\n",
       "      <td>381.368260</td>\n",
       "      <td>470.807168</td>\n",
       "      <td>497.704576</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508w1</th>\n",
       "      <td>665.004382</td>\n",
       "      <td>589.861611</td>\n",
       "      <td>517.989294</td>\n",
       "      <td>410.385378</td>\n",
       "      <td>543.790547</td>\n",
       "      <td>613.213655</td>\n",
       "      <td>508.394253</td>\n",
       "      <td>513.140716</td>\n",
       "      <td>391.902845</td>\n",
       "      <td>541.882001</td>\n",
       "      <td>606.322124</td>\n",
       "      <td>568.735523</td>\n",
       "      <td>589.573521</td>\n",
       "      <td>516.571139</td>\n",
       "      <td>642.458345</td>\n",
       "      <td>595.759113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33w1</th>\n",
       "      <td>133.813852</td>\n",
       "      <td>187.700915</td>\n",
       "      <td>214.031283</td>\n",
       "      <td>163.609798</td>\n",
       "      <td>143.845639</td>\n",
       "      <td>202.366174</td>\n",
       "      <td>233.904637</td>\n",
       "      <td>213.592914</td>\n",
       "      <td>182.788525</td>\n",
       "      <td>228.681643</td>\n",
       "      <td>206.899651</td>\n",
       "      <td>229.157174</td>\n",
       "      <td>210.288310</td>\n",
       "      <td>193.091059</td>\n",
       "      <td>295.865714</td>\n",
       "      <td>295.083444</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382w1</th>\n",
       "      <td>242.587595</td>\n",
       "      <td>230.059819</td>\n",
       "      <td>247.661365</td>\n",
       "      <td>230.707720</td>\n",
       "      <td>207.313741</td>\n",
       "      <td>249.410504</td>\n",
       "      <td>245.569738</td>\n",
       "      <td>247.023559</td>\n",
       "      <td>206.229312</td>\n",
       "      <td>161.192517</td>\n",
       "      <td>239.384986</td>\n",
       "      <td>268.091678</td>\n",
       "      <td>271.596783</td>\n",
       "      <td>251.919185</td>\n",
       "      <td>276.751926</td>\n",
       "      <td>301.223745</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103w</th>\n",
       "      <td>163.103329</td>\n",
       "      <td>191.815826</td>\n",
       "      <td>195.462292</td>\n",
       "      <td>155.599866</td>\n",
       "      <td>163.146695</td>\n",
       "      <td>220.531592</td>\n",
       "      <td>253.294411</td>\n",
       "      <td>214.071590</td>\n",
       "      <td>153.416366</td>\n",
       "      <td>183.092314</td>\n",
       "      <td>318.559745</td>\n",
       "      <td>333.569794</td>\n",
       "      <td>328.175471</td>\n",
       "      <td>225.947617</td>\n",
       "      <td>313.374033</td>\n",
       "      <td>396.105128</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307w1</th>\n",
       "      <td>249.677092</td>\n",
       "      <td>278.756457</td>\n",
       "      <td>250.845298</td>\n",
       "      <td>233.203164</td>\n",
       "      <td>244.705039</td>\n",
       "      <td>306.153452</td>\n",
       "      <td>303.798953</td>\n",
       "      <td>263.455901</td>\n",
       "      <td>236.327749</td>\n",
       "      <td>237.826767</td>\n",
       "      <td>355.031556</td>\n",
       "      <td>357.035163</td>\n",
       "      <td>338.611685</td>\n",
       "      <td>320.717762</td>\n",
       "      <td>362.815018</td>\n",
       "      <td>521.262616</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>022w1</th>\n",
       "      <td>293.180251</td>\n",
       "      <td>309.881497</td>\n",
       "      <td>308.534592</td>\n",
       "      <td>270.422913</td>\n",
       "      <td>272.894824</td>\n",
       "      <td>316.570740</td>\n",
       "      <td>352.379621</td>\n",
       "      <td>328.507380</td>\n",
       "      <td>278.392059</td>\n",
       "      <td>260.738461</td>\n",
       "      <td>336.340603</td>\n",
       "      <td>361.709217</td>\n",
       "      <td>368.099951</td>\n",
       "      <td>380.331913</td>\n",
       "      <td>506.696324</td>\n",
       "      <td>488.705591</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  F7          F3          F4          F8          T3  \\\n",
       "312w1     206.320397  281.263372  299.172999  257.392182  211.820132   \n",
       "642w1     366.396354  382.963122  418.888553  297.033108  282.734802   \n",
       "387-02w1  234.795503  287.451087  178.627186  193.460809  207.708783   \n",
       "s351w     235.326948  276.417191  303.862620  242.048113  224.213878   \n",
       "423w      271.413695  319.346132  293.445698  245.076982  251.858844   \n",
       "485w1     321.123158  366.058307  315.590077  230.039855  273.682033   \n",
       "314w1     188.503249  206.377499  210.450124  157.257440  197.954727   \n",
       "585w1     256.471974  187.546342  229.976701  251.165103  284.208555   \n",
       "113w1     311.777220  282.284711  243.702085  209.386413  230.519184   \n",
       "156w1     173.870172  221.301829  201.075190  151.966254  195.879945   \n",
       "s083w1    261.245302  307.953362  326.677720  226.410697  230.329172   \n",
       "387-03w1  284.816371  341.931017  306.802659  271.994405  270.272335   \n",
       "540w1     272.317395  367.474717  358.977936  263.742082  311.966984   \n",
       "719w1     114.822781  144.747385  156.666669  126.077710   93.408988   \n",
       "249w1     129.947992  201.243656  184.766984  159.931764  128.225548   \n",
       "401w1     292.646522  321.763551  354.227141  301.006544  344.025603   \n",
       "454-1W    235.108410  347.593388  326.940999  219.137592  412.961801   \n",
       "276w1     195.349930  242.110267  201.530452  177.709275  198.604238   \n",
       "683w1     238.146513  293.028599  304.991956  254.541109  233.819904   \n",
       "192w      151.261104  238.474977  199.523384  182.219643  145.007560   \n",
       "r416w1    254.943664  352.264211  382.426187  290.794376  252.069121   \n",
       "586w1     272.676831  247.489215  276.003361  243.778790  231.989849   \n",
       "155w1     202.740320  244.850975  233.935872  182.759460  187.059685   \n",
       "088w1     269.305514  332.407086  350.191853  301.477253  265.554741   \n",
       "575w1     170.152280  279.777065  242.932027  173.217540  155.108012   \n",
       "s425w1    250.422982  270.318286  283.844280  208.397945  224.158734   \n",
       "221w      419.996345  434.509292  403.317749  381.659124  401.405223   \n",
       "573w1     165.995478  193.638516  194.975543  155.340383  172.734042   \n",
       "509w1     358.070596  388.872357  346.710199  357.433391  415.485564   \n",
       "32w1      173.479577  226.826589  234.218458  230.691579  170.289599   \n",
       "429w1     233.371824  303.727030  291.172953  240.113432  227.826423   \n",
       "219w1     158.856345  214.475632  198.680917  156.417518  156.525914   \n",
       "517w1     201.781635  264.136863  240.825319  186.893382  188.095426   \n",
       "r229w1    279.718048  311.120536  304.679740  242.836413  253.011305   \n",
       "342mw1    157.048276  246.308396  230.929523  118.699322  191.580831   \n",
       "548w      239.707562  305.247044  307.389962  243.413969  247.179206   \n",
       "S084-1W   240.188142  304.095737  321.239219  234.329483  213.242632   \n",
       "515w1     198.172872  226.705750  228.148816  219.683764  186.676105   \n",
       "510-1W    395.173736  481.122013  382.917365  350.212120  417.898810   \n",
       "508w1     665.004382  589.861611  517.989294  410.385378  543.790547   \n",
       "33w1      133.813852  187.700915  214.031283  163.609798  143.845639   \n",
       "382w1     242.587595  230.059819  247.661365  230.707720  207.313741   \n",
       "103w      163.103329  191.815826  195.462292  155.599866  163.146695   \n",
       "307w1     249.677092  278.756457  250.845298  233.203164  244.705039   \n",
       "022w1     293.180251  309.881497  308.534592  270.422913  272.894824   \n",
       "\n",
       "                  C3          Cz          C4          T4          T5  \\\n",
       "312w1     294.820716  319.786544  345.369290  272.147546  276.660156   \n",
       "642w1     330.218452  328.949447  336.694488  316.450935  238.148698   \n",
       "387-02w1  281.326902  286.289586  265.439389  262.181173  202.033771   \n",
       "s351w     278.223172  293.151385  310.499721  301.374194  240.573027   \n",
       "423w      240.822275  274.157577  264.048354  225.329668  226.555383   \n",
       "485w1     379.195359  392.642043  379.276591  237.137714  218.613786   \n",
       "314w1     250.850112  257.437574  244.540417  166.504868  236.484505   \n",
       "585w1     328.489284  306.831605  330.613654  282.786000  281.657758   \n",
       "113w1     202.143195  215.298730  266.146725  219.864124  197.267254   \n",
       "156w1     270.835018  283.097083  276.803309  190.147621  179.189104   \n",
       "s083w1    363.336543  464.088941  405.804865  270.807081  230.348875   \n",
       "387-03w1  273.050238  278.117245  262.699012  219.752193  265.325355   \n",
       "540w1     385.186960  388.477966  351.545504  276.839435  346.381944   \n",
       "719w1     158.428544  157.664348  146.569405  116.064589  112.649072   \n",
       "249w1     179.669702  204.352620  201.479427  161.859685  142.200368   \n",
       "401w1     400.484043  425.400089  407.467430  403.573255  332.490850   \n",
       "454-1W    399.310629  412.950320  365.466186  223.336290  342.198072   \n",
       "276w1     281.389807  285.985801  268.983147  170.169065  247.204193   \n",
       "683w1     302.735779  301.450432  292.408574  249.574750  235.345501   \n",
       "192w      250.203329  261.998542  244.237755  175.738927  224.449685   \n",
       "r416w1    307.631948  361.349543  424.552710  401.220855  242.379496   \n",
       "586w1     256.220956  265.188094  245.343548  230.019872  216.526965   \n",
       "155w1     199.511857  214.397249  197.603918  169.329819  152.818641   \n",
       "088w1     286.413564  294.396764  314.602624  317.583328  290.625426   \n",
       "575w1     218.686546  232.657583  220.663540  173.691340  166.997212   \n",
       "s425w1    261.867770  286.176247  288.386216  271.238862  225.277260   \n",
       "221w      289.698764  265.922202  252.380602  352.466681  402.338480   \n",
       "573w1     200.131926  201.845221  204.631836  162.791081  174.770098   \n",
       "509w1     411.651081  404.350005  380.264781  353.232641  400.754036   \n",
       "32w1      229.840647  287.880493  259.756068  213.937382  164.451257   \n",
       "429w1     293.515936  312.506509  304.129389  228.253443  271.712146   \n",
       "219w1     222.993664  217.635980  204.032934  145.229116  163.719947   \n",
       "517w1     232.376887  251.435595  231.418672  162.527286  155.025951   \n",
       "r229w1    248.946277  288.170241  287.984125  298.649042  174.844439   \n",
       "342mw1    234.092460  288.148120  241.515146  171.773750  220.412493   \n",
       "548w      325.639342  369.979986  336.941704  260.122845  287.362677   \n",
       "S084-1W   288.783016  350.168379  287.805812  192.164906  221.328238   \n",
       "515w1     216.192194  251.178447  225.242539  222.054242  232.283089   \n",
       "510-1W    491.661447  449.674197  454.461710  483.547336  465.984600   \n",
       "508w1     613.213655  508.394253  513.140716  391.902845  541.882001   \n",
       "33w1      202.366174  233.904637  213.592914  182.788525  228.681643   \n",
       "382w1     249.410504  245.569738  247.023559  206.229312  161.192517   \n",
       "103w      220.531592  253.294411  214.071590  153.416366  183.092314   \n",
       "307w1     306.153452  303.798953  263.455901  236.327749  237.826767   \n",
       "022w1     316.570740  352.379621  328.507380  278.392059  260.738461   \n",
       "\n",
       "                  P3          Pz          P4          T6          01  \\\n",
       "312w1     449.150581  524.197324  523.097816  435.508664  603.983848   \n",
       "642w1     247.791669  248.684117  263.109678  262.007634  296.003741   \n",
       "387-02w1  284.241773  292.374388  285.597331  281.038923  293.338473   \n",
       "s351w     288.047160  288.309579  360.355677  326.735962  309.567799   \n",
       "423w      242.301031  260.228691  260.978638  231.488794  285.456193   \n",
       "485w1     365.679971  490.870199  497.869384  373.756691  468.536046   \n",
       "314w1     325.113289  350.156646  336.925900  232.581078  432.306539   \n",
       "585w1     285.789540  337.777720  296.233518  261.141142  425.521574   \n",
       "113w1     204.431280  225.165079  291.204492  237.693068  257.713474   \n",
       "156w1     360.347039  419.801158  408.060797  261.521094  469.821441   \n",
       "s083w1    382.420915  402.315447  353.052124  223.280934  394.495125   \n",
       "387-03w1  235.997730  247.531111  239.618544  240.494297  276.671087   \n",
       "540w1     366.205023  468.825188  389.886306  315.188745  522.919419   \n",
       "719w1     124.491471  153.789216  155.488478  143.695496  163.052439   \n",
       "249w1     197.578599  237.121667  254.410143  184.200151  214.120624   \n",
       "401w1     611.790835  775.843267  650.907932  573.104062  838.892383   \n",
       "454-1W    371.464419  427.214122  339.792059  282.602682  642.037408   \n",
       "276w1     297.960234  343.918227  309.583819  243.417762  329.706723   \n",
       "683w1     323.738077  293.757592  267.598501  257.392863  266.386492   \n",
       "192w      267.344203  358.037445  317.587117  255.502163  328.227142   \n",
       "r416w1    361.066138  383.726061  396.087708  414.232695  413.060421   \n",
       "586w1     241.492359  241.229797  225.820208  252.868198  234.142344   \n",
       "155w1     209.839059  232.239395  205.141151  155.996496  199.432020   \n",
       "088w1     347.461970  388.176574  326.486996  290.390508  307.706076   \n",
       "575w1     221.092523  252.668540  207.340418  161.949553  276.865313   \n",
       "s425w1    240.873280  217.874120  248.514927  213.020714  234.881990   \n",
       "221w      332.502305  321.518884  299.118344  517.946504  551.889538   \n",
       "573w1     255.611551  231.804331  248.618193  161.065695  291.582685   \n",
       "509w1     393.384876  406.492947  573.757271  348.858135  469.813879   \n",
       "32w1      261.906745  296.724189  299.378267  247.626910  335.810254   \n",
       "429w1     323.456357  337.799293  348.639850  284.074047  465.557961   \n",
       "219w1     185.562177  203.664577  195.525564  176.923921  183.634263   \n",
       "517w1     218.180461  245.652474  221.920480  195.139316  400.495245   \n",
       "r229w1    232.628491  235.218483  251.440759  159.170262  304.895375   \n",
       "342mw1    267.693225  293.964637  257.844263  189.330796  268.902727   \n",
       "548w      354.714112  386.119348  338.233396  265.796083  374.327833   \n",
       "S084-1W   281.979464  313.797641  253.614721  226.817178  307.570905   \n",
       "515w1     254.559398  265.448432  345.449779  276.374074  400.399749   \n",
       "510-1W    480.287259  461.720582  464.706132  381.368260  470.807168   \n",
       "508w1     606.322124  568.735523  589.573521  516.571139  642.458345   \n",
       "33w1      206.899651  229.157174  210.288310  193.091059  295.865714   \n",
       "382w1     239.384986  268.091678  271.596783  251.919185  276.751926   \n",
       "103w      318.559745  333.569794  328.175471  225.947617  313.374033   \n",
       "307w1     355.031556  357.035163  338.611685  320.717762  362.815018   \n",
       "022w1     336.340603  361.709217  368.099951  380.331913  506.696324   \n",
       "\n",
       "                  02  diagnosis  \n",
       "312w1     633.859315          1  \n",
       "642w1     254.928184          1  \n",
       "387-02w1  280.024243          1  \n",
       "s351w     414.304186          1  \n",
       "423w      283.472827          1  \n",
       "485w1     523.482060          1  \n",
       "314w1     371.034786          1  \n",
       "585w1     420.625659          1  \n",
       "113w1     343.016491          1  \n",
       "156w1     514.013210          1  \n",
       "s083w1    345.571714          1  \n",
       "387-03w1  355.116046          1  \n",
       "540w1     423.323232          1  \n",
       "719w1     166.015375          1  \n",
       "249w1     235.033210          1  \n",
       "401w1     730.917053          1  \n",
       "454-1W    611.360546          1  \n",
       "276w1     375.996766          1  \n",
       "683w1     267.924797          1  \n",
       "192w      339.569557          1  \n",
       "r416w1    483.415421          1  \n",
       "586w1     234.591283          1  \n",
       "155w1     227.479793          1  \n",
       "088w1     288.323259          1  \n",
       "575w1     280.286714          1  \n",
       "s425w1    210.970145          1  \n",
       "221w      538.393368          1  \n",
       "573w1     245.640025          1  \n",
       "509w1     582.533385          1  \n",
       "32w1      380.445263          1  \n",
       "429w1     588.502148          1  \n",
       "219w1     186.703143          1  \n",
       "517w1     469.629279          1  \n",
       "r229w1    341.409238          1  \n",
       "342mw1    252.201547          1  \n",
       "548w      344.948639          1  \n",
       "S084-1W   381.700414          1  \n",
       "515w1     438.443691          1  \n",
       "510-1W    497.704576          1  \n",
       "508w1     595.759113          1  \n",
       "33w1      295.083444          1  \n",
       "382w1     301.223745          1  \n",
       "103w      396.105128          1  \n",
       "307w1     521.262616          1  \n",
       "022w1     488.705591          1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_schizo_data = pd.DataFrame(schizo_averaged_values, columns = ['F7', 'F3', 'F4', 'F8', 'T3', 'C3', 'Cz', 'C4', 'T4', 'T5', 'P3', 'Pz', 'P4', 'T6', '01', '02'])\n",
    "reduced_schizo_data['diagnosis'] = 1\n",
    "reduced_schizo_data.index = schizo_filenames\n",
    "reduced_schizo_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a77abb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F7</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
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       "      <th>T3</th>\n",
       "      <th>C3</th>\n",
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       "      <th>Pz</th>\n",
       "      <th>P4</th>\n",
       "      <th>T6</th>\n",
       "      <th>01</th>\n",
       "      <th>02</th>\n",
       "      <th>diagnosis</th>\n",
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       "      <th>S42W1</th>\n",
       "      <td>234.691796</td>\n",
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       "      <td>366.827124</td>\n",
       "      <td>331.260919</td>\n",
       "      <td>210.572525</td>\n",
       "      <td>316.188987</td>\n",
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       "      <th>S31W</th>\n",
       "      <td>209.802646</td>\n",
       "      <td>310.155904</td>\n",
       "      <td>323.160348</td>\n",
       "      <td>227.777398</td>\n",
       "      <td>275.764142</td>\n",
       "      <td>304.740733</td>\n",
       "      <td>310.459188</td>\n",
       "      <td>285.944573</td>\n",
       "      <td>255.524574</td>\n",
       "      <td>419.788974</td>\n",
       "      <td>457.526612</td>\n",
       "      <td>517.576155</td>\n",
       "      <td>517.303068</td>\n",
       "      <td>475.087018</td>\n",
       "      <td>422.721320</td>\n",
       "      <td>486.114878</td>\n",
       "      <td>0</td>\n",
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       "      <td>352.218137</td>\n",
       "      <td>434.144626</td>\n",
       "      <td>355.861077</td>\n",
       "      <td>446.941460</td>\n",
       "      <td>369.187810</td>\n",
       "      <td>351.056225</td>\n",
       "      <td>401.587008</td>\n",
       "      <td>343.221223</td>\n",
       "      <td>281.817039</td>\n",
       "      <td>303.818939</td>\n",
       "      <td>392.647286</td>\n",
       "      <td>431.740410</td>\n",
       "      <td>393.273398</td>\n",
       "      <td>387.944044</td>\n",
       "      <td>542.842977</td>\n",
       "      <td>536.968509</td>\n",
       "      <td>0</td>\n",
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       "      <th>S50W</th>\n",
       "      <td>269.647852</td>\n",
       "      <td>338.634255</td>\n",
       "      <td>344.864874</td>\n",
       "      <td>234.133116</td>\n",
       "      <td>256.133755</td>\n",
       "      <td>368.086639</td>\n",
       "      <td>368.735112</td>\n",
       "      <td>355.779238</td>\n",
       "      <td>234.084414</td>\n",
       "      <td>258.421585</td>\n",
       "      <td>391.401720</td>\n",
       "      <td>409.314609</td>\n",
       "      <td>358.623829</td>\n",
       "      <td>293.393776</td>\n",
       "      <td>625.496439</td>\n",
       "      <td>629.259563</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>S165W1</th>\n",
       "      <td>205.958906</td>\n",
       "      <td>257.508954</td>\n",
       "      <td>265.755559</td>\n",
       "      <td>242.377389</td>\n",
       "      <td>203.218307</td>\n",
       "      <td>274.589074</td>\n",
       "      <td>278.940344</td>\n",
       "      <td>279.626380</td>\n",
       "      <td>257.992473</td>\n",
       "      <td>213.305772</td>\n",
       "      <td>249.756887</td>\n",
       "      <td>298.145780</td>\n",
       "      <td>280.551883</td>\n",
       "      <td>300.361348</td>\n",
       "      <td>294.697612</td>\n",
       "      <td>314.157604</td>\n",
       "      <td>0</td>\n",
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       "      <th>33w1</th>\n",
       "      <td>133.813852</td>\n",
       "      <td>187.700915</td>\n",
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       "      <td>143.845639</td>\n",
       "      <td>202.366174</td>\n",
       "      <td>233.904637</td>\n",
       "      <td>213.592914</td>\n",
       "      <td>182.788525</td>\n",
       "      <td>228.681643</td>\n",
       "      <td>206.899651</td>\n",
       "      <td>229.157174</td>\n",
       "      <td>210.288310</td>\n",
       "      <td>193.091059</td>\n",
       "      <td>295.865714</td>\n",
       "      <td>295.083444</td>\n",
       "      <td>1</td>\n",
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       "      <th>382w1</th>\n",
       "      <td>242.587595</td>\n",
       "      <td>230.059819</td>\n",
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       "      <td>230.707720</td>\n",
       "      <td>207.313741</td>\n",
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       "      <td>245.569738</td>\n",
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       "      <td>206.229312</td>\n",
       "      <td>161.192517</td>\n",
       "      <td>239.384986</td>\n",
       "      <td>268.091678</td>\n",
       "      <td>271.596783</td>\n",
       "      <td>251.919185</td>\n",
       "      <td>276.751926</td>\n",
       "      <td>301.223745</td>\n",
       "      <td>1</td>\n",
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       "      <th>103w</th>\n",
       "      <td>163.103329</td>\n",
       "      <td>191.815826</td>\n",
       "      <td>195.462292</td>\n",
       "      <td>155.599866</td>\n",
       "      <td>163.146695</td>\n",
       "      <td>220.531592</td>\n",
       "      <td>253.294411</td>\n",
       "      <td>214.071590</td>\n",
       "      <td>153.416366</td>\n",
       "      <td>183.092314</td>\n",
       "      <td>318.559745</td>\n",
       "      <td>333.569794</td>\n",
       "      <td>328.175471</td>\n",
       "      <td>225.947617</td>\n",
       "      <td>313.374033</td>\n",
       "      <td>396.105128</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>307w1</th>\n",
       "      <td>249.677092</td>\n",
       "      <td>278.756457</td>\n",
       "      <td>250.845298</td>\n",
       "      <td>233.203164</td>\n",
       "      <td>244.705039</td>\n",
       "      <td>306.153452</td>\n",
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       "      <td>357.035163</td>\n",
       "      <td>338.611685</td>\n",
       "      <td>320.717762</td>\n",
       "      <td>362.815018</td>\n",
       "      <td>521.262616</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>022w1</th>\n",
       "      <td>293.180251</td>\n",
       "      <td>309.881497</td>\n",
       "      <td>308.534592</td>\n",
       "      <td>270.422913</td>\n",
       "      <td>272.894824</td>\n",
       "      <td>316.570740</td>\n",
       "      <td>352.379621</td>\n",
       "      <td>328.507380</td>\n",
       "      <td>278.392059</td>\n",
       "      <td>260.738461</td>\n",
       "      <td>336.340603</td>\n",
       "      <td>361.709217</td>\n",
       "      <td>368.099951</td>\n",
       "      <td>380.331913</td>\n",
       "      <td>506.696324</td>\n",
       "      <td>488.705591</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                F7          F3          F4          F8          T3  \\\n",
       "S42W1   234.691796  288.619884  297.082391  201.988486  201.117730   \n",
       "S31W    209.802646  310.155904  323.160348  227.777398  275.764142   \n",
       "S167W1  352.218137  434.144626  355.861077  446.941460  369.187810   \n",
       "S50W    269.647852  338.634255  344.864874  234.133116  256.133755   \n",
       "S165W1  205.958906  257.508954  265.755559  242.377389  203.218307   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "33w1    133.813852  187.700915  214.031283  163.609798  143.845639   \n",
       "382w1   242.587595  230.059819  247.661365  230.707720  207.313741   \n",
       "103w    163.103329  191.815826  195.462292  155.599866  163.146695   \n",
       "307w1   249.677092  278.756457  250.845298  233.203164  244.705039   \n",
       "022w1   293.180251  309.881497  308.534592  270.422913  272.894824   \n",
       "\n",
       "                C3          Cz          C4          T4          T5  \\\n",
       "S42W1   308.864366  320.471932  314.160993  196.207816  212.271569   \n",
       "S31W    304.740733  310.459188  285.944573  255.524574  419.788974   \n",
       "S167W1  351.056225  401.587008  343.221223  281.817039  303.818939   \n",
       "S50W    368.086639  368.735112  355.779238  234.084414  258.421585   \n",
       "S165W1  274.589074  278.940344  279.626380  257.992473  213.305772   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "33w1    202.366174  233.904637  213.592914  182.788525  228.681643   \n",
       "382w1   249.410504  245.569738  247.023559  206.229312  161.192517   \n",
       "103w    220.531592  253.294411  214.071590  153.416366  183.092314   \n",
       "307w1   306.153452  303.798953  263.455901  236.327749  237.826767   \n",
       "022w1   316.570740  352.379621  328.507380  278.392059  260.738461   \n",
       "\n",
       "                P3          Pz          P4          T6          01  \\\n",
       "S42W1   386.414909  366.827124  331.260919  210.572525  316.188987   \n",
       "S31W    457.526612  517.576155  517.303068  475.087018  422.721320   \n",
       "S167W1  392.647286  431.740410  393.273398  387.944044  542.842977   \n",
       "S50W    391.401720  409.314609  358.623829  293.393776  625.496439   \n",
       "S165W1  249.756887  298.145780  280.551883  300.361348  294.697612   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "33w1    206.899651  229.157174  210.288310  193.091059  295.865714   \n",
       "382w1   239.384986  268.091678  271.596783  251.919185  276.751926   \n",
       "103w    318.559745  333.569794  328.175471  225.947617  313.374033   \n",
       "307w1   355.031556  357.035163  338.611685  320.717762  362.815018   \n",
       "022w1   336.340603  361.709217  368.099951  380.331913  506.696324   \n",
       "\n",
       "                02  diagnosis  \n",
       "S42W1   368.760953          0  \n",
       "S31W    486.114878          0  \n",
       "S167W1  536.968509          0  \n",
       "S50W    629.259563          0  \n",
       "S165W1  314.157604          0  \n",
       "...            ...        ...  \n",
       "33w1    295.083444          1  \n",
       "382w1   301.223745          1  \n",
       "103w    396.105128          1  \n",
       "307w1   521.262616          1  \n",
       "022w1   488.705591          1  \n",
       "\n",
       "[84 rows x 17 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data = reduced_normal_data\n",
    "combined_data = combined_data.append(reduced_schizo_data)\n",
    "#final_data = combined_data.sample(frac = 1)\n",
    "final_data = combined_data\n",
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ed1ff73f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = FINAL_data.values\n",
    "Y = FINAL_data['diagnosis']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ac1ab2c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5536505",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score 0.6153846153846154\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Users/shreyamantripragada/opt/anaconda3/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "index_list = []\n",
    "count = 0\n",
    "\n",
    "for index in final_data.index:\n",
    "    index_list.append([final_data.loc[index]])\n",
    "        \n",
    "classifier = LogisticRegression(random_state = 0, class_weight = \"balanced\") #look into parameters\n",
    "classifier.fit(X_train, Y_train)\n",
    "y_score = classifier.predict_proba(X_test)[:,1]\n",
    "false_positive_rate, true_positive_rate, threshold = metrics.roc_curve(Y_test, y_score)\n",
    "\n",
    "print('roc_auc_score', roc_auc_score(Y_test, y_score))\n",
    "\n",
    "plot_roc_auc_curve(false_positive_rate, true_positive_rate)\n",
    "\n",
    "#print('Logistic Regression', roc_auc_score(Y_test, classifier.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2ff76d8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "model = RandomForestRegressor()\n",
    "model.fit(X_train, Y_train)\n",
    "#names = \n",
    "\n",
    "#print sorted(zip(map(lambda x: round(x, 4), model.feature_importances_), names), \n",
    "             #reverse=True)\n",
    "\n",
    "#sel = SelectFromModel(RandomForestClassifier(n_estimators = 100))\n",
    "#sel.fit(X_train, Y_train)\n",
    "\n",
    "#selected_feat= X_train.columns[(sel.get_support())]\n",
    "#len(selected_feat)\n",
    "\n",
    "#print(selected_feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "219ff09b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                      fpr  \\\n",
      "classifiers                                                                 \n",
      "LogisticRegression      [0.0, 0.0, 0.0, 0.25, 0.25, 0.375, 0.375, 0.5,...   \n",
      "SVC                                                     [0.0, 0.625, 1.0]   \n",
      "KNeighborsClassifier                                     [0.0, 0.25, 1.0]   \n",
      "GaussianNB                                                [0.0, 0.0, 1.0]   \n",
      "RandomForestClassifier  [0.0, 0.0, 0.0, 0.25, 0.25, 0.25, 0.375, 0.375...   \n",
      "\n",
      "                                                                      tpr  \\\n",
      "classifiers                                                                 \n",
      "LogisticRegression      [0.0, 0.07692307692307693, 0.15384615384615385...   \n",
      "SVC                                        [0.0, 0.8461538461538461, 1.0]   \n",
      "KNeighborsClassifier                       [0.0, 0.3076923076923077, 1.0]   \n",
      "GaussianNB                                                [0.0, 1.0, 1.0]   \n",
      "RandomForestClassifier  [0.0, 0.15384615384615385, 0.3076923076923077,...   \n",
      "\n",
      "                             auc  \n",
      "classifiers                       \n",
      "LogisticRegression      0.615385  \n",
      "SVC                     0.610577  \n",
      "KNeighborsClassifier    0.528846  \n",
      "GaussianNB              1.000000  \n",
      "RandomForestClassifier  0.793269  \n"
     ]
    },
    {
     "data": {
      "image/png": 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lMkc2BxEVFot7KUda9K2MXx0PmVBGGLSGa4eNFvmpP+HKAWN7JrrY03Ip7BKj/hlBlZtr+c36BuENfsDNN/d1u6dGErsQIle4ez2SgA2XOLXTmFDGp2oRarYoRckKMqGMAGKjUnSxXyZ5F3tbKFYlQ13sqdFa8+PpH5m3byKj1TnqW4egS72IW4kWWfM+coAkdiGExWituXr2Lgf/vsT5IzextraiwlPFqNHcm8IlHCwdnrAw1/hb1L63B5Z9BYGbk3exN/vEGMWeiS72tFwNv8rIHSNR19az3PY6zkpDnZkovwGP/YUhJ0liF0LkuIT4BP49cIOA9RcJvhBGQQdb/Nv5Uq2JF/bOdpYOT1jK/S5200Qxs4IPmnaUgpo9jHvLfRuDTdZOOqS1ZuXZlUzZO4V4Hc+3pZ/FOWQXqtFycM0DK8ulIIldCJFjYqLiOL79Coc2XiL89j1cPArR5JUKVKjvia1d7h2MJLLR/S72+xPFhF0BFHj5s8ypD/sLPMVnb3bPthZzcGQwo3eMJvDyBl4q7Eu3pt/h5VgSEu6Bdd5cEF4SuxAi24XdNiaUOb7tMjHR8ZQo58oz3crjW80dJRPKPHnCrj1YIS1wM8RFgZ2j0cVefripi70oK2ftNMpnQ1LXWrM6cDUT90ykafx1VhW4im18FMrewzhfHk3qIIldCJGNblwM4+DfF/l3fzAa8KtdlJotvfHwkQllnihaw9VDpmT+J1wNMLa7eEPtXsbtaNnQxZ6Wm1E3GbNzDLsubWCqwz2a6EAo3AAaLgXrvH8pSBK7ECJL6QTNhaO3CFh/kcun72Jb0JpqzUwTyhSRCWWeGLFRELjFWFTl9DoIu4rRxV4Xnh1hLKziUTnHB6WtPb+W8bvGo2LD+MvxDi73rkGVT6DaaLDKH/MjSGIXQmSJuJh4Tu2+xqENl7hzLRJHtwI0fMGPyo1LUKCQ/Kl5IoRefXA72kNd7G0Tu9gt4U70HcbtGsdfF/6iapGqjG80Htfzc8GzufHIR+R/mxDisUSFxXBky2WObjEmlCnq7UTL1ypTto4H1tYyoUy+prXRrX5/opiHutjbGBPF5FAXe1o2XNzAmJ1jIOY2v7kXxLv+YGxcy0DNCRaNK7tIYhcih53cdZXwO/csHUaWCL0Zxek914mPTcC3WhFqtvCmRHlXmVDGgpbuvshvAZezrX47HU3Ve4eoc28XtaP3UDjhFgkozthW5IBTH/YXqM8lGx+4pOASwIFMn+v41VAqF8/8eIyQeyFM3DORPwL/oLOzOyNsb2IbdgtCjkMR/0zXm9tJYhciB0VHxLJh/glLh5FlrG2tqFDfk5rNS+HmKRPK5Aa/BVx+7ISYklv8LWrf202d6N1UvRdAAe4RpQpxqEAd9hd4ioMF6hJm7Zpl57uvcnFnOtbM3EIrW4O2MnrHaEKibzOvhA/+N/9EOZSBpquhcJ0sjjR3kcQuRA7SWgPQqEs5qjbJnStDZYSyUrL+eS5Uubgzywc2yHwF97vYTRPFEHzI2O7qDTX6QIU2FPJ5mvo2BaifFQFnobCYMKbsncLKsyvxc/VjSfnGFD8+Bnx7Qt1vwNbJ0iFmO0nsQliAslKyoInIXWIi4dwW41r5mb8ejGIvVQ+ajzKul3tUytVTq+64vIORO0ZyI+oGb1buwWu138dOKXCvA17PWzq8HCOJXQghnlShVx5MFHNuC8RFg50T+D1rJPJyrcDB3dJRPlJEbASf7/ucH0//SHlnb34oXQX3yzOg+utQoMgTldTBAoldKdUG+BKwBuZqrSel2O8GzAPKAtHAa1rrozkdpxBC5DsJCaZR7GuNx9UkXey1extzsfs0Apu8M0nL3mt7GbF9BFfCr/B+uXb0vrsWq0troMJ7YONo6fAsIkcTu1LKGpgBtASCgL1KqVVa6+NJin0CBGitOyulKprK56+bDIUQIqfERBr3lN+/vzz8Gigr8DJ1sVdoC0Ur5uou9tRExkby5YEvWXpyKd6Opfi9emd8Tn8GNvbQ5A8o2c7SIVpMTrfY6wFntdaBAEqpH4COQNLEXhmYCKC1PqmU8lVKFdNaX8/hWIUQIk8qHH8D9s1LpYu9eZIu9iKWDjPTDgYfZPg/w7kYdpEelXrwTq23KfTPi+BeHxosAvsSlg7RonI6sZfEdGejSRDwVIoyh4AXgH+UUvUAH8ALSJbYlVIDgAEA3t7e2RWvEELkfkm62Cfd+InScf/CasDVB+r0MZK5z9N5qos9NdFx0Uw/OJ2FxxdSwrEEyxsMpnLJ5mBrD42Wg7UDWMkqgTmd2FPr69EpXk8CvlRKBQBHgINA3EMHaT0bmA3g7++fsg4hhMjfYiKSzMX+V2IX+z2biixxeo0evQZC0Qp5ros9LYdvHGb49uGcCzlH13Iv8bGTNXb7X4cbXeDppWArCwvdl9OJPQgoleS1F3AlaQGtdSjQF0AZ01edMz2EEOLJFnL5wcC3c1uTd7FXaAt+LRm1+DQAPTwqWjjYrBETH8O3h75l3tF5eNh7MK/JROoGfg3n/4JSL0LdGZYOMdfJ6cS+FyinlCoNXAZeBl5JWkAp5QpEaq1jgP7AVlOyF0KIJ0tCAlw9+GCimGtHjO1uvlCnrzGK3bthnu9iT8vxW8cZ9s8wzt49S2e/znzs1wqH7S9BbAjUnQl+A/JNj0RWytHErrWOU0q9BazDuN1tntb6mFJqkGn/TKASsFApFY8xqK5fTsYohBAWFRNhjGK/P1FM+HVjFHupp6DFp8b18nzUxZ6a2IRY5hyew5zDc3Ar6MaM5jN4xusZiA2FIk9BjfHgWtXSYeZaOX4fu9Z6DbAmxbaZSZ7vBMrldFxCCGExIUFJJorZCvH3oICzaRR7W/BrkadHsWfE6TunGf7PcE7cPkH7Mu0ZWvllnE//D4rXM66jN/nN0iHmejLznBBC5LSEBLhy0DTwbW2SLvbSULcflG+dr7vYUxOXEMf3R7/nm0Pf4GznzLRm02ieEAwbGhs9FhX+m+8Xb8kqktiFECInxETAv5uMRJ6si72+0cVeoS24l8/XXexpCbwbyLB/hnH01lFa+7ZmWJ13cTs6CgLngXtDY9S7g4+lw8wzJLELIUR2eVQXe7mWYF/Y0lFaTHxCPIuOL+Lrg19jb2vP1CZTaePbBv7pBhd/hCrDoNposJJUlRHyaQkhRFZJSIArBx4k8+spu9jbgE9DsLa1bJy5wIXQCwz/ZzgBNwJ4ttSzjKg/HPcCpiVVq30KfgPB81nLBplHSWIXQojHcS/cNBe7aaKYiOAHXewtxxgtc/dyT2QXe2oSdALLTi5j2v5p2FrbMrHxRJ4rUR+1pz9YFYSnl4FLReMhMkUSuxBCZNTdS0kmitlm6mJ3STJRTIsnuos9LUFhQYzYPoJ91/fRuGRjRjccjUf4SVhbC6KvQ80plg4xX5DELoQQj3K/i/2UaRT7ddNK0oXLQN3+xih26WJPk9aaH0//yGf7PsNaWTOm4Rg6lWmPOjYejo0Fx7LQahcUrm3pUPMFSexCCJGae+EQuMm4Vn5mHUTcMLrYvRtAy7HG9XLpYn+kq+FXGbljJLuu7qJB8QZ82vBTijsWh6ircPpr8O0J/tPB1snSoeYbktiFEOK+ZF3sWyE+xuhiL9fCSOTSxW42rTUrz65kyt4pxOt4RtQfQZfyXVA3/gGHYlCoOLQ7DPYlLR1qviOJXQiRKUt3X+S3gMuWDiNL1I/aygvhy/CJM9abumpdgv0Fn+NAgfqctKtC/C0b2AnsPGXZQM1w/GoolYtbdqWz4MhgRu8YzbbL26jrWZcxDcfgZe8O+/4LZ2bAU3OhbD9J6tlEErsQIlN+C7icK5LI42oS+ReDQv7HRZvSLHLqz/6CT3HVptSjD8ylKhd3pmNNyyRMrTWrA1czcc9EYuNjGVJvCN0rdscq9BSsew7uHoYK7xnd7yLbSGIXQmRa5eLOLB/YwNJhZN6BRbDqf1CmKb7dl+FrW4helo4pj7oZdZOxO8ey8dJGahatybhG4/Bx9oELy2HXa2BjD03+gJLtLB1qvieJXQjxZNq/AH5/G8o2h5eXgG0hS0eUZ609v5bxu8YTGRvJB/4f0LNST6ytrI2dhUpA0UZQ/3uwL2HZQJ8QktiFEE+effNg9XtQrhV0XQS2BS0dUZ50J/oO43ePZ935dVQtUpXxjcZTxrUM3NwNN/6BSoPBozE0Wyt3D+QgSexCiCfLnjmw5gMo1xq6LQKbApaOKE/acHEDY3aOITQmlHdqv0OfKn2wUVZwfDIcGg72Xsa0sLaOktRzmCR2IcSTY/ds+PNDY5rXrgskqWdCyL0QJu6ZyB+Bf1CpcCXmtJpDebfyEHUNdr4K1/6GUi/CU3OMpC5ynCR2IcSTYde3sHYIVGwPL33/RK11nlW2Bm1l9I7R3Im+w39q/If+1ftja2VrTKn7V31jWti6M8FvgLTSLUgSuxAi/9s5A9Z9ApU6GEldpn7NkLCYMKbsncLKsyvxc/VjevPpVC5SGRLijQLWBaDmZHCpAq5VLRuskMQuhMjntn8Ff4+Ayp3gxbmS1DNox5UdjNoxiuDIYF6v9jqDagzCztoOwgPhn5eh4vvg+zL4dLN0qMJEErsQIv/653+wfjRUeQFemAPW8ifPXBGxEXy+73N+PP0jpV1Ks7jtYqoVrWbsPL8M9gwEZQ02cptgbmOV0ydUSrVRSp1SSp1VSg1JZb+LUup3pdQhpdQxpVTfnI5RCJEPbP3MSOpVX5KknkF7r+3lxVUv8tPpn+hTpQ8r2q8wknpchDHZzI5XwK06tAsAr46WDlekkKO/6Uopa2AG0BIIAvYqpVZprY8nKfYmcFxr3UEpVRQ4pZRaorWOyclYhRB52JapsGkcVOsKnb6VpG6myNhIvjzwJUtPLsXbyZsFbRdQy6PWgwLX1kPgfKgyHKqNAiv5XHOjnP6p1APOaq0DAZRSPwAdgaSJXQNOSikFOAK3gbgcjlMIkVdtngSbJ0KN7tBxBtyfAU2k62DwQYb/M5yLYRfpUakH79R+h0I2hUBrCDkKrtWM1vlzx8GloqXDFenI6cReEriU5HUQ8FSKMtOBVcAVwAnoprVOyJnwhBB5ltZGQt8yGWr2gOe/lqRuhui4aKYfnM7C4wsp4ViCea3nUdezrrHz3i2j6/3qOmh3BJzLSVLPA3I6sad2Y6NO8bo1EAA8C5QF/lZKbdNahyarSKkBwAAAb2/vrI9UCJF3aA0bx8G2z6BWL+jwFVjl+BCiPOfIjSMM2z6McyHn6Fq+K4P9B2Nva2/svL4FdvSAezeg5hRw8rNssMJsOZ3Yg4Ck6yF6YbTMk+oLTNJaa+CsUuocUBHYk7SQ1no2MBvA398/5ZcDIcSTQmvY8KkxAr52b2g/TZL6I8TExzDz0Ey+O/odHvYezGo5i4YlGj4ocORTODoGHMtCk51QuLblghUZltOJfS9QTilVGrgMvAy8kqLMRaA5sE0pVQyoAATmaJRCiLxBa1g/CrZ/Cf6vQbvPJak/wolbJxi2fRhn7pyhs19nPqz7IU52TskLxUcZa6b7Twdbp9QrErlWjiZ2rXWcUuotYB1gDczTWh9TSg0y7Z8JjAXmK6WOYHTdf6y1vpmTcQoh8gCt4a/hsHM61O0P7T6TaUzTEZsQy9zDc5l9eDZuBd2Y0XwGz3g986DApZVQoIixGluNCaDkC1JeleP3Kmit1wBrUmybmeT5FaBVTsclhMhDtDamiN31DdQbCG0nS1JPx+k7pxn+z3BO3D5B+zLtGVJvCC4FXIyd8dFwYDCc+QZKPm8kdknqeZrchCiEyFu0NhZz2T0TnnoD2kyUpJ6GuIQ4vj/6Pd8c+gZnO2emNZtGc+/mDwqEnIDtL8Pdw8bUsDUmWC5YkWUksQsh8g6tYc2HsHcONHgLWo2TpJ6GwLuBDN8+nCM3j9DatzXDnhqGW0G3BwXuHoF19cHGHpr8ASXbWS5YkaUksQsh8oaEBFjzAez7Dhq+DS3HSFJPRXxCPIuOL+Lrg19jb2vP1CZTaePb5kEBrY3PzaWK0Uov9wbYl7BcwCLLSWIXecLOX89y9sANS4fx2HSC3JmZKQkJ8Md7sH8+NHoPmo+SpJ6KC6EXGLF9BAeDD/JsqWcZ0WAE7oXcHxS4uRv2vQWNfwGHUlBjrOWCFdlGErvIEy4ev018bAIly7taOpTHVrK8Kz5Vi1g6jLwjIQFWvwMHFkLjD+DZ4ZLUU0jQCSw7uYxp+6dha23LxMYTea70c6j7n5NOgBNT4dBwsC8J924aiV3kS5LYRZ5R1NuJlq9VsXQYIiclxMOqtyFgMTzzETT7RJJ6CkFhQYzcMZK91/bSuGRjRjccjYe9x4MCUddgZy9jARfvLlBvNti5Wixekf0ksQshcqeEePjtTTi0DJoOhaYPrfL8RNNa8+PpH/ls32dYK2vGNBxDJ79OD1rp9x0dAze2Gwm9bH/5YvQEkMQuhMh9EuJh5RtweDk0GwZNPrJ0RLnKtYhrjNw+kp1Xd9KgeAM+bfgpxR2LPygQH2N0t9uXgJqToPxb4FLZcgGLHCWJXQiRu8THwcpBcORHeHYEPPOBpSPKNbTWrDy7kil7pxCv4xlRfwRdyndJ3koP+xe2d4eEGGizD2ydJak/YSSxCyFyj/g4+HUAHP0ZWow2RsALAIIjgxm9YzTbLm/Dv5g/Y58ei5eTV/JC55fCnkGgrKH+d2Alf+KfRPJTF0LkDvGx8HN/OL7SuEf96XcsHVGuoLVmdeBqJu6ZSGx8LEPqDaF7xe5YJZ32NS7CuI0tcD4UfRoaLgEHH4vFLCxLErsQwvLiY+Gn1+DEKmg1Hhq+ZemIcoWbUTcZu3MsGy9tpGbRmoxrNA4f51QStrKGO4eg6gioOlJa6k84+ekLISwrLgZ+6gsnV0PridDgP5aOKFdYe34t43eNJzI2kg/8P6BnpZ5YW1k/KKA1/PudcQubnQu02gXWdpYLWOQaktiFEJYTFwM/9oFTf0DbKfDUQEtHZHF3ou8wfvd41p1fR9UiVRnfaDxlXMskLxR9E3a/Bpd/h9gQqDRYkrpIJIldCGEZcfdgxatweq2xlnq91y0dkcVtuLiBMTvHEBoTytu13qZv1b7YpOxWv74ZdvQwbmerPQ0qvG2JUEUuJoldCJHzYqNhRS848xc89wXU7WfpiCwq5F4Ik/ZMYnXgaioVrsScVnMo71b+4YKBC2BXX3Dygya/Q+HaOR+syPUksQshclZsNCzvAWfXQ/tp4N/X0hFZ1NagrYzeMZo70Xf4T43/0L96f2ytbFMv7Nkcyv3HmHTG1jFnAxV5hiR2IUTOiY2CZd0hcDM8/zXUftXSEVlMWEwYU/dO5dezv+Ln6sf05tOpXCSViWQu/QoXlsPTS8HeC+pOz/lgRZ6SocSujOmNvIBSwCGtdUS2RCWEyH9iIuGH7hC4BTrOgFo9LB2Rxey4soNRO0YRHBnM69VeZ1CNQdilHPwWFwUHB8OZb6GwP8TcgQKyKqB4NLMTu1LqP8BwwBPQQF3ggFLqF2Cr1npatkQohMj7YiJg2ctwbht0+hZqdrd0RBYRGRvJ5/s+Z8XpFZR2Kc3itoupVrTawwVDjsP2l+HuEag4GGpMkFHvwmxmJXal1IfAWGAysAnYmGT3ZqA7MC2LYxNC5AcxEbC0G1zYDp1nQY1ulo7IIvZe28uI7SO4En6FPlX68GbNNyloU/DhggnxsO0FuHcbmq6BEm1zPliRp5nbYn8TGKm1nqKUsk6x7xSQyvDN1Cml2gBfAtbAXK31pBT7PwTu99HZAJWAolrr2+aeQwiRS9wLhyVd4NIu6DwbqnexdEQ5Liouimn7p7H05FK8nbxZ0HYBtTxqPVwwJgSsCxkt84ZLoVBx4yFEBpmb2D2B/WnsSwBS+dr5MNOXghlASyAI2KuUWqW1Pn6/jNZ6KjDVVL4D8J4kdSHyoHthsPglCNoLL86Fqi9aOqIcdzD4IMP/Gc7FsIv0qNSDt2u9jb2t/cMFb+4yVmTz6WaMeJfb2MRjMDexnwWaABtS2fcMcDyV7ampB5zVWgcCKKV+ADqmc3x3YJmZdQshcovoUFjyEgTtg5e+gyqdLR1RjroXf4/pB6ez4NgCSjiWYF7redT1rPtwQZ0Ax6fA4RHGiHevTjkeq8h/zE3s04BvlFIxwE+mbR5KqX7A+4C5U0aVBC4leR0EPJVaQaWUPdAGSHU1CKXUAGAAgLe3t5mnF0Jku+gQWPwiXDkIXeZD5ectHVGOOnLjCMO2D+NcyDm6lu/KYP/BqbfSo67Bzl5wbT14d4V6s8DONcfjFfmPWYldaz1XKeUGjAQ+NW1eA0QCo7XWS808n0qt+jTKdgC2p9UNr7WeDcwG8Pf3T6sOIUROiroLi1+Aq4egywKo1N7SEeWYmPgYZh6ayXdHv8PD3oNZLWfRsETDtA+IDobb+6HeHCjbD1Rqfx6FyDizb3fTWk9VSs0EGgDuwG1gp9Y6JAPnC8K4B/4+L+BKGmVfRrrhhcg7ou7Aos5w7Sh0XQQV21k6ohxz4tYJhm0fxpk7Z+js15kP636Ik53TwwXjYyBoJfh0Bbfq0PEC2KZSTojHYO7tbq8Cf2itbwF/pdhXGGivtV5oRlV7gXJKqdLAZYzk/Uoq53PBuKbf05z4hBAWFnkbFnWC4BPQbTFUaGPpiHJEbEIscw/PZfbh2bgVdGNG8xk84/VM6oXDzhoD5G7vA8cyUMRfkrrIFua22L/HaKnfSmVfadP+RyZ2rXWcUuotYB3G7W7ztNbHlFKDTPtnmop2Bv6Sme2EyAMib8PC5+HGaei2BMq3snREOeL0ndMM/2c4J26foH2Z9gypNwSXAi6pFz63BPYOAmUDjX82kroQ2cTcxJ7exZ8iQKi5J9Rar8G4Pp9028wUr+cD882tUwhhGU4JIbDgebh5GrovBb8Wlg4p28UlxDH/2HxmBMzA2c6ZaU2n0dynedoH7HsbTn8NRZ827k93kMG+InulmdiVUh0xbkW7b4RS6kaKYgWBxhhd7EKIJ4hT/F1G3B4CXIPuy8AvneSWTwTeDWT49uEcuXmE1r6tGfbUMNwKuqV/UNGnjdHuVUdCyrXVhcgG6f2WeQBJJzEuizFRTVIxGNfcx2VxXEKI3Cz8BiNvD6FY3FXotRzKNrN0RNkqPiGexScW89WBr7C3tWdqk6m08U1jHIHWcOorsC4I5QYak84IkYPSTOxa6znAHACl1CbgDa31yZwKTAiRS4UHw4IOFIu7yuTCnzIynyf1C6EXGLF9BAeDD/JsqWcZ0WAE7oXcUy8cfRN29YUrq8G7C/gNkNvYRI4z9z72/P0/VwhhnrBrsKADhAQxsfBYThSobumIsk2CTmDZyWVM2z8NW2tbJjSaQPsy7VFpJerrm2FHD7h3E+p8CeX/K0ldWERGlm11wrjmXp5U5obXWn+UhXEJIXKb0KtGUg+9Aj1/5sTa/Ju0gsKCGLljJHuv7aVxycaMbjgaD3uPtA8IPwcbW4BjWWiyGgqnssiLEDnE3PvYywLbAXvAAbgBFDYdfwcIASSxC5FfhV6B+e0h/Dr0/Bl8GgA7LR1VltNa8+PpH/ls32dYK2vGNBxDJ79OabfS4yLAxgEcS8PTP0DxNmDrmLNBC5GClZnl/gfsA4ph3PrWDiiEMYFMOCCjQ4TIr0Iuw/znjGvrPX8xJfX851rENQb+PZCxu8ZSs2hNfnn+FzqX65x2Ur/0K/zmC8HbjNfeL0lSF7mCuV3x9YD+wD3TazutdTywVCnljrG+ejqTIgsh8qS7l2BBe2MSml6/QqlUVijL47TWrDy7kil7pxCv4xlRfwRdyndJp5UeBQcHw5lvobA/FCqRswEL8QjmJvaCQKjWOkEpdRtI+pt8FKiR5ZEJISzr7kWj+z3qLvRaCV51LB1RlguODObTnZ+yNWgr/sX8Gfv0WLycvNI+IOQ4bH8Z7h6BSh9A9fFgbZdzAQthBnMT+2nAx/T8IDBIKbUGiAf6kfZCLkKIvOjOBaOlHh0Cr66EkrUtHVGW0lrzx7k/mLh7IjHxMQypN4TuFbtjpR5xdfLKGoi+Dk3/hBJPxnz4Iu8xN7H/ANQEFgEjMOZ6DwUSTHX0yYbYhBCWcPucMfr9Xhi8ugpK1LR0RFnqZtRNxu0ax4aLG6hZtCbjGo3Dx9kn7QNi7kLoSXCvDxXfh9K9oWDRHItXiIwy9z72L5I836WUqgq0xeii36i1PppN8QkhctLtQJjfAWIjoPcqKJ6/rrKtPb+W8bvGExkbyeA6g+lVuRfWVtZpH3BjJ+zoDvHR8Pw5sCkkSV3kepmauFhrfQmYDaAM3bTWy7M0MiFEzrr1r3FNPS4aev8OntUefUwecSf6DuN3j2fd+XVULVKV8Y3GU8a1TNoH6AQ4PhkOjwD7UvDMSiOpC5EHmHsfe1HgptZaJ9lWCOP6+nuALyCJXYi86uZZ45p6fIwpqVe1dERZZsPFDYzZOYbQmFDervU2fav2xSa9xVjiImHL83B9A3h3hXqzjEVchMgj0hwpopSyV0rNVkpFAteAO0qpD0z7BgLnga+As0DT7A9VCJEtbpw27lOPj4Xeq/NNUg+5F8LQbUN5d9O7FLMvxvL2y3m9+uvpJ3UA60Lg4AP15hiTzkhSF3lMer/hI4HewDzgEMao+E+UUvWBF4CNwFCttSzZKkRedeOU0f0O0OcP8Kho2XiyyNagrXy641NuR9/mjRpv8Hr117G1sk37gPgYODISSvcBl4pQ/7sci1WIrJZeYn8BGKO1Hn9/g1JqC7AGmKe17p/dwQkhslHwCWP0u7IyWupFy1s6oscWFhPG1L1T+fXsr/i5+vF186+pXKTyIw46a9ybfns/FHA3ErsQeVh6id0H2JJi2/3XC7InHCFEjrh+DBY8D1Y20Gc1uJezdESPbeeVnYzcMZLgyGBer/Y6g2oMwu5Rk8ecWwx73wArW2j8C5TqnDPBCpGN0kvstkBMim33X0dkTzhCiGx37SgsfN6YMa33anD3s3REjyUyNpLP933OitMrKO1SmsVtF1OtqBkj+s8thp29oGgjaLgEHLyzP1ghcsCjRsX/Vyl1Ncnr+5Mnv6OUup5ku9Zaf5y1oQkhstzVw7CwI9gWMka/Fylr6Ygey95rexmxfQRXwq/Qp0of3qz5JgVtHlpVOrmEWKOF7v2SMflMuUFGz4UQ+UR6v80XgUapbL8APJNimwYksQuRm10JgEWdwNYB+vwOhdO5jzuXi4qL4ssDX7LkxBK8nbxZ0HYBtTwesQa61nDqKzg7C1rvAltnqPBWzgQsRA5KM7FrrX2z44RKqTYYq8FZA3O11pNSKdMUmIZxOeCm1rpJdsQixBPjykGjpV7AxUjqbr6WjijTAoIDGPbPMC6GXaRHpR68Xett7G3t0z8o+ibs6gtXVkPJDpAQlzPBCmEBOdr/pJSyBmYALYEgYK9SapXW+niSMq7AN0AbrfVFpZRHTsYoRL5zeT8s7AyFXIxr6m7pzIuei92Lv8f0g9NZcGwBJRxLMK/1POp6mrGM7PVNsKMn3LsJdb6C8m9BWkuyCpEP5PSFpXrAWa11IIBS6gegI3A8SZlXgF+01hcBtNbBORyjEPlH0D5Y1BkKuRmj313z5gCxIzeOMGz7MM6FnKNr+a687/8+DrYOjz5QazgyBmydoOkf4FYz22MVwtJyOrGXBC4leR0EPJWiTHnAVim1GXACvtRaL0xZkVJqADAAwNs7b/6xEiJbXdoDi18E+yJGUndJZ53xXComPoaZh2Yy7+g8itoXZVbLWTQs0fDRB0ZcBOuCUNDDmD3O1hFszPgiIEQ+kNOJPbX+L53itQ1QB2gOFAJ2KqV2aa1PJztI69mYFqLx9/dPWYcQT7aLu42k7uhhjH53KWnpiDLsxK0TDNs+jDN3ztDZrzMf1v0QJzunRx946RfY1Q88W0DjH6FQsewPVohcJKcTexBQKslrL+BKKmVuaq0jgAil1FagBnAaIcSjXdgJS14CJ08jqTuXsHREGRKbEMvcw3OZfXg2bgXdmNF8Bs94pbwRJxVxUXDgfTg7Ewr7Q82HxuUK8UTIcGJXSimgOBCstc7o0NK9QDmlVGngMvAyxjX1pH4DpiulbAA7jK76/2U0TiGeSOe3w5IuRjLvs9pI7nnI6TunGf7PcE7cPkH7Mu0ZUm8ILgVcHn1g2L+wtROEHIVKH0L1ccYEPEI8gcxO7EqpdsAooCbGrWr1gANKqdnAVq314kfVobWOU0q9Bawz1TFPa31MKTXItH+m1vqEUmotcBhIwLgl7mgG35cQT55z22BpV3ApZbTUnfJOF3RcQhzzj81nRsAMnO2cmdZ0Gs19mptfgZ0rWNlB07VQonW2xSlEXmDueuyvYqzytgTjVrTvk+w+g7Eu+yMTO4DWeg3GQjJJt81M8XoqMNWc+oQQQOAWWNrNuD+99yrj2noeEXg3kOHbh3Pk5hFa+bRieP3huBV0e/SBMXfhxOdQbSQUKAJt9sltbEJgfot9GDBVaz3UdC960sR+DPggyyMTQpjn302w7GUoXBZe/Q0ci1o6IrPEJ8Sz+MRivjrwFfa29kxtMpU2vm3MO/jGDtjxCkReBs/mUKypJHUhTMxN7D7A32nsiwacsyYcIUSGnN0AP7wCRfyMpO7gbumIzHIh9AIjto/gYPBBni31LCMajMC9kBmxJ8TDiclweCTYe0PLf8A95R2zQjzZzE3sl4BawMZU9vkDZ7MsIiGEec6sN5J60fLQ6zdwKGLpiB4pQSew7OQypu2fhq21LRMaTaB9mfYoc1vb+9405nr37gb1ZoGdGQPrhHjCmJvYvwNGmVZ0W2nappRSzYGPgDHZEJsQIi2n/4LlPaBoRaOlbl/Y0hE9UlBYECN3jGTvtb00LtmY0Q1H42Fv5lgArY2u9nJvQJG6UOY16XoXIg3mJvbJGPefLwDiTdt2YIxsn6W1/iobYhNCpObUWljRCzwqw6srjeliczGtNT+e/pHP932OUooxDcfQya+Tea30+Bg4NBRiw+Cp2eBWw3gIIdJkVmLXWmvgTaXU/4BnAXfgNrAx5YxwQohsdPIPWNEbPKtBr1+hkKulI0rXtYhrjNw+kp1Xd1K/eH3GNBxDccfi5h0cegZ2dIfb+6Hcm6ATQFllb8BC5APm3u5mr7WO1FqfRa6nC2EZJ36HH/tA8ZrQ6xcomHuvL2utWXl2JVP2TiFexzOi/gi6lO9i/rX0c4th7xtgZQuNf4VSnbI1XiHyE3O74m8qpX4HfgDWaK3vZWNMQoiUjv8GP70GJWpBz59zdVIPjgzm052fsjVoK/7F/Bn79Fi8nDKwAE10MOz9D7jVgoZLwKHUo48RQiQyN7F/BHQBfgLClVKrMJL8ukxMKyuEyIijv8DP/cHLH3r8BAVz592lWmv+OPcHE3dPJCY+hiH1htC9YneszO0+DzsLjmWNFdla/gMulcEqp5ezECLvM+t/nNZ6uta6CcYAulFAWWAVEKyU+k4p1TIbYxTiyXXkJyOpl6pnaqnnzqR+M+om721+j6HbhlLGpQw/Pf8TPSr1MC+paw0np8EfVeDf74xtbtUlqQuRSRn6n6O1vgJMA6YppbyBrsB7QO+M1iWEeITDP8KvA8C7AbyyAgo4WjqiVK07v45xu8YRGRvJ4DqD6VW5F9ZW1uYdHH0DdvWFK39AyeehVOfsDVaIJ0CmkrFSyg/oZnoUx5jARghhhqW7L/JbwOV0yzSK3MCbIZ9zwq4qk6M/5N78IzkUnfmOX7+Ks9fvfLBlH1WLVGV8o/GUcS1jfgXXtxij3u/dgjpfQ/k35d50IbJARlZ388VooXfDWOHtOsY19ze01tuzIzgh8qPfAi5z/GoolYun3q3+TOTfvBHyBcfsajCl8GhiVMGcDdAMYVYBWHsvIlxF8Xatt+lbtS82Ge06T7gHtq7QdA241cyOMIV4Ipl7u9tujKljbwO/YCz6stl0f7sQIoMqF3dm+cAGD+84sAhWfQFlmlDt5WUssrPP+eDSEXIvhMl7JvN74O9UKlyJcY3GUd6tvPkVhJ+H4K1Q5lUo3graHZZr6UJkMXP/R53AGDT3t9Y6/lGFhRCZsH8B/P42lH0WXl4KtoUsHVEy24K2MXrHaG5H3+aNGm/wevXXsbWyNb+Ciz/B7v7GJDNeHcDOTZK6ENnA3Jnn+mRzHEI82fZ9D6vfBb+W0G0x2Oae7vewmDA+2/cZv5z5BT9XP75u/jWVi1Q2v4K4KDjwnrF4S5F68PQyI6kLIbJFmoldKdUO+EdrHWp6ni6t9ZosjUyIJ8XeufDHYCjXGrotApsClo4o0c4rOxm5YyTBkcH0r9afN2q8gZ21nfkVJMTB30/DnYNQ6SOoPhYycrwQIsPSa7GvBuoDe0zP06MxFoQRQmTEnjmw5gMo3xa6Lsg1ST0yNpLP933OitMrKO1SmsVtF1OtaLWMV2RlA34DwLGMcU1dCJHt0kvspYGrSZ4LIbLSrpmw9mOo8Bx0mQ82uaMlu/faXkZsH8GV8Cv0rtybt2q9RUGbDFwaiLkDuwdA6V7g9TyUG5R9wQohHpJmYtdaX0j6EriqtY5NWU4pZQOUyIbYhMi32oX/AmtnQ8X28NL3uSKpR8VF8eWBL1lyYgneTt4saLuAWh61MlbJjR2wvTtEXYFiTbMlTiFE+sxdA/EckNb/8Bqm/WZRSrVRSp1SSp1VSg1JZX9TpVSIUirA9Bhpbt1C5AXtw3+md9hsqNwx17TUA4ID6PJ7F5acWEKPSj34scOPGUvqCfFwdDysfwaUtTHXe/k3sy9gIUSazL3XJL3poAoCZq32ppSyBmYALYEgYK9SapXW+niKotu01u3NjE2IvOOfafQKm8OOgs/Q8MXvwDoDt4tlg3vx95h+cDoLji2ghGMJ5rWeR13Puhmv6OqfcHg4+LwMdWeCXe5dfU6I/C69UfHVMWaYu6+dUqpiimIFMWajO23m+eoBZ7XWgaZz/AB0BFImdiHyn22fw4YxbC/YhOmuH9HQwkn96M2jDPtnGIEhgXQt35X3/d/HwdYhY5VEXYNCnlDiOXh2AxRrJtPCCmFh6bXYO2NMSgPGNfa0usTPAQPNPF9Jks8rHwQ8lUq5BkqpQ8AV4AOt9TEz6xcid9oyFTaNg2pdmH6jDwnKcjeRxMTHMPPQTOYdnYd7IXdmtZxFwxINM1ZJ/D0IGAr/zoE2B8C5HHg+mz0BCyEyJL3EPgH4DKMbPhR4FtibokxMagPq0pHaV/mU09IeAHy01uGm++dXAuUeqkipAcAAAG9v7wyEIEQO2zwZNk+A6i9Dp29ImLPHYqGcuHWCYduHcebOGTr7debDuh/iZOeUsUpCz8D2l+HOASj/FjiUyp5ghRCZkt6o+FjgftI2d5DdowRhrOl+nxdGqzzpeUOTPF+jlPpGKeWutb6ZotxsYDaAv7+/zFkvch+tYfMk2DIJavaA578Gc5czzWKxCbHMPTyX2Ydn41bQjRnNZ/CM1zMZr+jcYtj7BljZwTMrwatjlscqhHg86V1jrwz8q7W+Z3qerlQGwKVmL1BOKVUauAy8DLyS4ryewHWttVZK1cP4UnHLjLqFyD20hk3jYetUqNUTOnwNVln1/Thjztw5w7B/hnHi9gnal2nPkHpDcCmQycFtN3dB4drQYLG01IXIpdLrij/Kg5nnjvJwl/l9CjNnntNaxyml3gLWmcrP01ofU0oNMu2fCbwEvKGUigOigJdlFTmRp2gNG8bAP19A7Veh/ZcWSepxCXHMPzafbwK+wcnOiWlNp9Hcp3nGK7q9H1BGQq/9OSgbi/U8CCEeLb3E3owHo9WbZdUJTXPKr0mxbWaS59OB6Vl1PiFylNawfhRs/xLq9IXnvrBIUg8MCWT4P8M5cvMIrXxaMbz+cNwKZnDhFa3h1DQI+BjcG0KLzWCdO6a8FUKkLb1r7FtSey6ESIPW8PcI2PE1+PeDdp/leFKPT4hn8YnFfHXgK+xt7ZnaZCptfNtkvKLoYNjVF66sMa6jP/Vd1gcrhMgWZk1Qo5TyABy01udMrxXwOlAZ2KC1/j37QhQiD9Aa1g2DXTOg3gBoOyXH7+e+EHqBEdtHcDD4IM1KNWNkg5G4F3LPeEVhZ+Hvxsac7/7Todx/5N50IfIQc2eemw+cBd42vf4U+MS07S2lVH+t9fwsj06IvEBrWDsUdn8LT70BbSbmaCJM0AksO7mMafunYWtty4RGE2hfpj0qszE4+EKJdlDhbXCrkaWxCiGyn7n9hLWBjQBKKSvgDeATrXVFYDzwbrZEJ0RupzX8+ZGR1Ou/meNJPSgsiP5/9WfSnknU9azLyo4r6VC2Q8aTevh52NoZoq4bS63W/06SuhB5lLktdhce3HJWBygMLDG93ggMzuK4hMj9EhKMtdT3fQcN/wstx+ZYUtda8+PpH/l83+copRjTcAyd/DplrpV+8SfY3R90AoQeh0LFsj5gIUSOMTexB2FcT98GPAec1FpfNu1zAaKzITYhEt2JiOVUSCQLZ+20dCgAKJ1Av9DptIxcw0qHriwLbAezd5l17PGroVQu7pzpc1+LuMbI7SPZeXUn9YvXZ0zDMRR3LJ7xiuIi4cB7cHY2FKkHTy8DxzKZjksIkTuYm9jnAVOUUi0wEvvQJPvqAyeyOjAhkrobFUNYQjxg+dutlE7g9ZCvaB61ll8cXma5U+8MtdQrF3emY82SGT6v1pqVZ1cyZe8U4nU8I+qPoEv5Lpm/ln7oEyOpV/oIaowDK8suSiOEyBpmJXat9USl1GWgLvBfjER/X2FgbjbEJkQyTgVsWD6wgWWDSEiA3/8L19bCMx/yQrNhvJAD3e/BkcGM2TmGLUFb8C/mz5inx1DKKRMzv2kNcWFg6wxVR0DJ9uDZIusDFkJYjLktdrTWC4GFqWwflKURCZFbJcTDb2/BoaXQZAg0HZLt19S11vxx7g8m7p5ITHwMQ+oNoXvF7lipTNwfH3MHdr8OkUHQchsUKCJJXYh8yOzErpSyAV4EGmG00m9jXHP/RWsdlz3hCZFLJMTDyv/A4R+g6SfQ9ONsP+XNqJuM2zWODRc3ULNoTcY1GoePs0/mKruxA7Z3h6grUGMCWHDZWCFE9srIBDV/AdWB88B1oAHwJnBIKdVKa30ju4IUwqLi42DlIDjyIzw7HJ75MNtPue78OsbvGk9EbASD6wymV+VeWGdmfvaEeDg+CY6MAgcfaLkd3OtlfcBCiFzD3Bb7F0AR4CmtdeKa7EqpusDPpv29sj48ISwsPg5+HQBHf4bmo6Dx+9l6ujvRd5iwewJrz6+lapGqjG80njKujzFSPSEazi8C765Q91uwy+SqbkKIPMPcxN4OeCtpUgfQWu9VSg0Fvs7yyISwtPhY+OV1OPYrtPgUGr2brafbeHEjY3aOISQmhLdrvU3fqn2xsTL7allyV/+Gog3BxgFa7gA7N5kWVognhLl/NQoAYWnsCwPssiYcIXKJ+Fj46TU4sQpajTMmoMkmIfdCmLxnMr8H/k6lwpWY3Wo25d3KZ66y+HvGamynvoRqn0K1kVCgcNYGLITI1cxN7LuAj5VSG7XWEfc3KqUcgI9N+4XIH+Ji4Ke+cHI1tJ4ADd7MtlNtC9rG6B2juR19mzdqvMHr1V/HNrP3k4eehu0vw52DUP6/UPmjrA1WCJEnmJvYBwObgEtKqb8wBs95AK0BBTTNluiEyGlxMfBjHzj1B7SZDPWz527OsJgwPtv3Gb+c+QU/Vz++bv41lYtUznyFl/+A7d3AqgA88xt4PZ91wQoh8hRzJ6gJUEqVAz7AmKSmOnAVmAl8obW+mX0hCpFD4u7Bit5w+k9jLfV6r2fLaXZe2cnIHSMJjgymf7X+vFHjDeysH/NqlnNF8GgG9b4Fe6+sCVQIkSc9MrErpYoAvsA1rfWQbI9ICEuIjYYVr8KZdfDc51C3f5afIjI2ki/2f8HyU8sp7VKaxW0XU61otcxXeHs/nFsMtb8Ap7LQ9PesC1YIkWelOX2VUspJKbUCCAb2ABeVUruUUmVzLDohckJsNCzvYST19tOyJanvvbaXF1a9wIpTK+hduTcr2q/IfFLXCXDiC/irAVz6CaKuZm2wQog8Lb0W+6dAW2AksB8oDXyCMU98k+wPTYgcEBsFP7wC/26CDl9Bnd5ZWn1UXBRfHfiKxScW4+3kzYK2C6jlUSvzFUYHw84+cPVP8OoET30no96FEMmkl9ifB4Zrrb+8v0EpdRTYrJRy0VqHZHt0QmSnmEj4oTsEboGO06FWzyytPiA4gOHbh3Mh9AKvVHyFd2q/g72tfeYr1Bo2tYaQE+A/Hcr9R+5NF0I8JL3E7gPsTbFtN8YoeB/gcGZOqJRqA3wJWANztdaT0ihXF+M2um5a658ycy4h0hQTCcu6wblt0OkbqPlKllV9L/4eMw7OYMHxBRR3KM681vOo61k38xUmxAJWYGUNtaeBnSu41ciiaIUQ+U16id0aiE2xLT7JvgxTSlkDM4CWQBCwVym1Smt9PJVyk4F1mTmPEOmKiYCl3eDCdug8C2p0y7Kqj948yrB/hhEYEkjX8l153/99HGwdMl9h+HnY8QoUbwvVRkAxuQomhEjfo0bFT1RK3U7y+n6/3xSl1J0k27XW2py/jvWAs1rrQACl1A9AR+B4inL/xZiD/jGaOUKk4l44LO0KF3dC59lQvUuWVBsTH8PMQzOZd3Qe7oXcmdVyFg1LNHy8Si/+aCyzioYK72RJnEKI/C+9xL4Vo2VeNMX2LabjUm43R0ngUpLXQcBTSQsopUoCnYFnSSexK6UGAAMAvL29MxGKeOLcC4MlXeDSHnhhDlR7KUuqPXHrBMO2D+PMnTN09uvMh3U/xMnOKfMVxkXCgffg7Gwo8hQ8vRQcH2MhGCHEEyXNxK61bpoN50ttpI9O8Xoa8LHWOl6lMzBIaz0bmA3g7++fsg4hkosOhSUvQdA+eOk7qNL5sauMTYhl7uG5zD48G7eCbsxoPoNnvJ55/FhDjkPg91D5Y6g+FjI7xawQ4omUyaWjMi0IKJXktRdwJUUZf+AHU1J3B9oppeK01itzJEKR/0SHwOIX4cpB6PI9VO742FWeuXOGYf8M48TtE7Qv054h9YbgUuAxlkTVGm5sB49GUMQfOpwFB+mJEkJkXE4n9r1AOaVUaeAy8DKQbDiy1rr0/edKqfnAaknqItOiQ2DRC3A1ALrMh0odHqu6uIQ45h+bzzcB3+Bk58S0ptNo7tP88WKMuQO7+8OlX6DlP1D0aUnqQohMy9HErrWOU0q9hTHa3RqYp7U+ppQaZNo/MyfjEblbdEQs1wJDuBYYQsEYTYxtBu/ZjroLizrDtSPQdSFUfO6x4gkMCWTEPyM4fPMwrXxaMbz+cNwKuj1WnQT/Y4x6j7oKtT4D9waPV58Q4omX0y12tNZrgDUptqWa0LXWfXIiJmF5OkFz+1oE1wNDuRoYwvXAEO5ciwRAWSmiCyiuF87Ar2vkbSOpBx+HbougQttMxxafEM/iE4v56sBX2NvaM7XJVNr4tsl0fYlOfGasne5QGlrtgCJyE4gQ4vHleGIXAiAmKo7r50MTW+TXz4VyLzIOgIIOtniWcaZCfU88S7vg4etMz/l7zK888jYs7Ag3TkK3JVC+VabjvBh6keHbh3Mw+CDNSjVjZIORuBdyz3R9ydi5gffLxopsts5ZU6cQ4omXocSujBFtXhgD4A5prSOyJSqRr2itCbkRZSTxf0O4FhjKrSvhxv0QCgoXd6BsHQ+Kl3HBs4wLLh6FSO+OiHRF3DKS+s3T8PIyKNciU9Uk6ASWnVzGtP3TsLW2ZUKjCbQv0z7zcd13+Q+IDQPfl6HMa8ZDpoUVQmQhsxO7Uuo/wHDAE+NPcl3ggFLqF2Cr1npatkQo8pzYmHhuXAjlqimJXwsMITrcmMTQrqA1xcq4UKZWaTzLOFOstAsFCmVRx1HETSOp3zoL3ZeBX+YGtV0Ov8zI7SPZc20PjUo2YnSD0RRzKPZ4scXfM7rdT31pDI7z6SYJXQiRLcz6i6qU+hAYizHN6yZgY5Ldm4HuGPefiyeM1pqw29HJro3fvBROQoIxtYBrMXt8qxXB09QadyvugJVVNiS08Buw8Hm4HQjdf4CyzTJchdaan878xGd7P0MpxZiGY+jk1+nxW+mhp2H7y3DnIJR/G2pNlqQuhMg25jaV3gRGaq2nmOZxT+oUUD5rwxK5VXxsAjcuhSXpVg8hIiQGABs7K4r5OlOrlTeeZVwoVsaZQo522R9UeDAs6AB3LsArK6BMxudTvxZxjVE7RrHjyg7qF6/PmIZjKO5Y/PFji7wMa2uDdUF4ZhV4Pd7tdkII8SjmJnZPjDXZU5MAFMyacERuExFyL9m18eCLoSTEGa1xZ/eClCjvRvGyRmu8SEkHrKytcjbAsOtGUg+5BD1+hNKNM3S41prf/v2NyXsmE6/jGVF/BF3Kd3n8VnpCvLEam31JqDEJSnUCe6/Hq1MIIcxgbmI/CzQBNqSy7xkeXsRF5EEJ8QncuhxhujZuPMJuRQNgbWNFUW8nqjcrhWcZZzzLuODgUsCyAYddg/ntIfQK9PgJfJ/O0OHBkcGM2TmGLUFb8C/mz5inx1DKqdSjD3yUW/tg56vQYKExi1yFtx6/TiGEMJO5iX0a8I1SKga4vza6h1KqH/A+8Ho2xCayWVR4TLJr49fPhxIXkwCAg4sdnmVdqN7MC88yLhQt5YS1bQ63xtMTesVoqYddg54/g4/5E7torfnj3B9M3D2RmPgYhtQbQveK3bFSj/n+dAKcnAaHhkDBYqZ11IUQImeZldi11nOVUm7ASOBT0+Y1QCQwWmu9NJviE1kkIUFz52rEg271c6HcvW5MAGNlpXAv5Ujlp0sYg9zKuuDoVuDxu6OzS8hlWNDeGDDX82fwrm/2obeibjF211g2XNxAzaI1Gfv0WHxdfB8/puhg2NkHrv4JXp3gqe+gQOHHr1cIITLI7PuMtNZTlVIzgYZAEeA2sFNrHZJdwYnMuxcVx/VzD5L49cAQYqLjASjkZEux0i5UalgczzLOFPVxxtYu5ZjIXCokyOh+j7gJvX6BUvXMPnTd+XWM3zWeiNgIBtcZTK/KvbC2yqL3/e88uL4R/GdAuTdk1LsQwmIydAOx1joMY553kYtorQkJjkp2bfz21QjQRn4pXMKRcvU8E6+NuxR9jAlgLMg97jp8PxCi7sCrK8HL36zj7kbfZfzu8aw9v5aqRaoyvtF4yrhmwfrmCbEQHgjOFaDSB0ZL3aXi49crhBCPwdz72P/zqDJa628ePxxhjth78QSff3Bt/FpgKNERxvXcAvY2FCvtjF8dD+OWM19n7LJqAhgLKhp3jZG3PwbbaCOpl6xj1nEbL25kzM4xhMSE8Hatt+lbtS82VlnweYSfg+2vQMR56HAabJ0kqQshcgVz/8JNT2efNv0riT0baK0JuxWd7Nr4zaBwtGkCGDdPe0rXcH8wAYynPSo7JoCxpDvnGXX7IwolRMKrq6FErUceEnIvhMl7JvN74O9UKlyJ2a1mU94ti6ZbuLAC9rwOKKg320jqQgiRS5g7eO6h4cJKKVegNfAxxsxzIgvExcZz42K4KYkbyTwy1DQBTAFrivk6U6eND8VKG93qBR1sLRxxNrsdCPM7UCghknFFJjLJjKS+LWgbo3eM5nb0bd6o8QavV38dW6ss+JziY2DfW/DvHChSH55eBo6+j1+vEEJkoUz3SWqt7wLLlVIuwCygaRbF9EQJv3Mv8br4tcAQblwKSzYBjFclN4qXcaFYGReKlLDABDCWdOtf45a22CjGFJnMBduy6RYPjwln6r6p/HLmF/xc/fi6+ddULlI56+KxsoV7wVB5KFT/1HgthBC5TFZcfD0HmDeK6QkXH5/AraDwZIPcwm/fA8Da1goPHydqPFsqsVvd3jkHpmPNrW6eNW5pi4+B3r9z4bewdIvvvLKTkTtGEhwZTP9q/XmjxhvYWWfB56c1nJ0NxVuBY2lo9LMxo5wQQuRSj5XYlVLFgcEYyV2kEBUWk6Q1Hkrw+VDiYo0JYBzdChgJvLmRxN1LOWJt8wS1xtNz84xxS1tCHPReDcUqAztTLRoZG8kX+79g+anllHYpzeK2i6lWtFrWxHHvtnEt/dIvUOkjY/EWSepCiFzO3FHxN3gwSO4+O8AJiAZeyOK48pyEBM3tKxHJFkcJuREFgJW1wr2UE1Ual6SY6ZYzp8IyvX6qbpwyut91AvRZDR6V0iy699peRmwfwZXwK/Su3Ju3ar1FQZss+lyD/4Edr0D0Naj1GVR8L2vqFUKIbPY4o+KjgSBgrdb6VtaFlDfci4zl2rnQxCR+/XwosUkmgPEs40LlxsZMbh7eTtjklQlgLCn4pJHUwWipe6R++1hUXBRfHfiKxScW4+3kzYK2C6jl8ehBdWYL+h22dQKH0tByhzHfuxBC5BGPTOxKKVtgPXBOa30l+0PKfXSC5s71yGTd6neuRgDGBDBFvByp8JRn4rVxZ/eCeXICGIu6ftxI6lY20Pt3KJr6rWkBwQEM3z6cC6EXeKXiK7xT+x3sbe2zJgZtmtGnWDOoOBiqDgdb56ypWwghcog5LfZ4YCPQDngiEnscNgSdvJ2YxK8FhnAvMg4wJoDxLONC+brF8CzjjIevM3YF8/4EMBZ17SgsfB6s7YyWurvfQ0USiOWGzW/0Xrue4g7Fmdd6HnU962ZdDJdXw4mp0PRPsHWEWlPSLBobG0tQUBDR0dFZd34hhEihYMGCeHl5YWubsTtwHpmRtNYJSqkzQLHMBpeUUqoN8CVgDczVWk9Ksb8jMBZjnfc44F2t9T9ZcW5zhCtnthZ6noRpAQC4FXegbK2iFCvjQvGyLrh65MMJYCzp2hFY8DzYFDSuqRd5+Ja2G5E3OG83iWirS3Qp14XB/oNxsHXImvPH34ODH8Hpr8CtJty7BTbp9wAEBQXh5OSEr6+v9MwIIbKF1ppbt24RFBRE6dKlM3SsuU3NYcBkpdQRrfWRDEdoopSyBmYALTGuz+9VSq3SWiddz30DsEprrZVS1YEVQI7N1Rmt7ElQNjTqUo4K9T3z/wQwlnT1ECzsCLYO0Od3KPzw/O3nQs4x6O9BxKhblIp5i5ENBmbd+UNPwfaX4U4AlH/bGPVu/ejBd9HR0ZLUhRDZSilFkSJFuHHjRoaPTTOxK6WeAQ5orcOB4RgrugUopS4D10kxSl5rbc4yW/WAs1rrQNM5fgA6AomJ3XS++xxSnienFPV2lKSena4chIWdoICTcU298MPfSA/fOMybG97ESlnhE/MBhbRP1saw9w2IvATPrAKvDhk6VJK6ECK7ZfbvTHot9k1AA2APcNT0eFwlgUtJXgcBT6UspJTqDEwEPIDnUqtIKTUAGADg7e2dBaGJHHN5PyzqDAVcjO53t4cT9tagrXyw5QPcC7kzq8UsPlgWlDXnjg01bqWzc4Wn5hmzx9mXzJq6hRAiF0gvsSd+VdBa982i86X29eOhFrnW+lfgV1OvwVigRSplZgOzAfz9/S3SqheZELQPFr0AhVyNpO768JeyX8/8yqc7P6VC4Qp80/wbihQqgvEd8DHd2gvbu4NbLWj8o8zzLoTIl3J6qrMgoFSS116kM9Jea70VKKuUcs/uwEQOuLTXaKnbu0GfPx5K6lpr5hyew8gdI3mq+FN83/p7U1J/TDoBTnwGfzWEhBio8M7j1/mEGzRoEG+99Vamj69SpQrLly/PwojyhgkTJtChQ8Yu+wiRUY9K7O2UUq+a8zDzfHuBckqp0kopO+BlYFXSAkopP2W6sKCUqo0xw90TNwFOvnNxtympF4E+a8C1VLLd8QnxTNwzka8OfsVzZZ5j+rPTs+b+9Ohg2PwcHPwQSnaAtgHg0ejx680DmjZtyrhx47Kl7pkzZzJ9enqrORvOnz+PUoqgoOQ9LseOHaNbt25mnUsphb29PY6OjhQpUoQWLVoQEBCQmbAt7pNPPuH333/P8fNGRkbi6upK2bJl0Tp5B+fo0aNp0eKhTtFUt69fv55WrVrh6uqKi4sLVapUYeTIkYSEhGQqptdeew03NzdcXV3p168fUVFR6R7z77//0rlzZ1xcXHBxcaF+/frExsYCEBUVRZcuXShXrhxWVlap/u77+vpSsGBBHB0dEx9HjmR6PHiu9ajEPhKYb8bje3NOprWOA94C1gEngBVa62NKqUFKqUGmYi8CR5VSARgj6LvplL+JIm+5sBMWvwCOHtB3Dbgkv6Z9L/4eH279kGUnl9G3Sl8mNJqArXUWDVzUCRB6Aup+C41/hgKFs6ZekaP++usvwsPDOX/+PEWLFqVTp07Zdq77iSI/+eGHHwC4cOEC69evz1Qd8+fPp0OHDrRq1YpTp04REhLC6tWrCQsL4/Dhwxmu75133uHkyZOcPHmS06dPc+LECd5///00y9+4cYPGjRtTo0YNLl68yO3bt5k+fTrW1sasnkopGjZsyOzZs6lXL+2x3HPnziU8PDzxUa1aFq0tkYs8KrE3w5gP/lEPs6fn0lqv0VqX11qX1VqPN22bqbWeaXo+WWtdRWtdU2vdICfvYRfZ4Px2WPwiOBU3ut+dSyTbHRYTxqC/B/H3hb/50P9D3vd/Hyv1mFeIEmLhzCxIiIdCntD+FJQbZMwqJwDjD3zHjh1xd3enVKlSvPvuu8laS6dPn6ZJkyY4OztTo0YNvvzyy2QjdPv06UP//v0B4xLKsGHDKFGiROL9/V9//TUANWrUAKBChQo4OjoyduxYwGg5LV68OLG+w4cP06ZNG4oWLUrhwoVp2bJlqnE7OTnRs2dPLly4wM2bNwGIi4tjwoQJlC9fHldXV55++mn279+feExsbCzvvfceHh4eeHp6MmXKFPz8/Jg/fz5gJCw/Pz+mTp2Kl5cXNWvWBODo0aO0bt0ad3d3vL29GTp0aGLSj4mJYcCAAXh4eODs7Ez58uX56aefAKOXonXr1ri6uuLm5kadOnU4deoU8HAr+NatW7z66qsUL14cT09Pevfuze3btxP3+/r6MmHCBJo3b46joyNVq1Zlx44dZv2Mk5o1axY9e/akbdu2zJo1K8PHh4eH8+677zJ06FA++OADihUzpjUpXbo0//vf/2jcuHGG6ouKimLx4sWMHTuWYsWK4eHhwdixY1mwYEGaEz998cUXeHt7M3r0aFxcXLC2tsbf3x8rK+PvRcGCBXnvvfdo1qwZBQs+2WtxPOo+9iitdUSORCLyn3PbYGlXcPEybmlz8ky2OzgymDfWv0FgSCCTG0+mXZl2j3/O8HOw/RW4tcsY7V6yPVgXePx60/Hp78c4fiU0W89xX+USzozqUOWx6oiLi+O5557j6aef5sKFC9y9e5dOnTrxwQcfMGPGDOLi4ujQoQOtW7dm7dq1XL16leeffz7N+v7++28WLFjA7t27KVWqFNevX+fy5csAHDp0iNKlS3Pq1Cm8vLxSPf7q1as0adKEjz76iJ9//hlbW1u2bt2aatm7d++yYMECPDw8cHV1BWDkyJFs2LCBtWvX4uPjw/z582ndujVnzpzBzc2NiRMn8ueff7Jr1y6KFy/Oxx9/zIULF5LVe/78ea5cucKZM2fQWhMcHEyTJk2YMGECv//+Ozdu3KBjx44UKlSIkSNHMn/+fPbu3cuJEycoUqQIly5dIizMWFr4k08+wdvbm1WrVmFjY8OxY8cSY02pR48e2Nracvy4ccdvz5496dWrF3/88UdimXnz5vHbb79RsWJFPvjgA3r37s2ZM2fS/HmkdOjQIfbs2cO3337LhQsX6NatG9euXcPT0/PRB5vs2LGDkJAQXnnllXTL/ec//2Hp0qVp7h8yZAhDhgzh1KlTREdHU6dOncR9tWvXJioqitOnT1O9evWHjt20aRPlypWjY8eObNu2DS8vLz7++GN69Ohh9vsAeP/993n77bfx9vbmjTfeYODALJwbI5eQdUJF9gjcAku6GAPk+vzxUFIPDAmk55qeBIUF8U3zb7ImqV9YAX/WNLreG60wkrp4yJ49ezhz5gxffPEFDg4OlCxZknHjxjFv3jy01uzatYvz588zefJkChUqRJkyZXjvvbRXt7OzsyM6Oppjx44RHR1NsWLFqF27ttnxLFq0CD8/P4YOHYqDgwN2dnYPXdtt27Ytzs7OuLm5sWvXLlauXImNjQ1aa77++mumTp1KmTJlsLa2pl+/fhQvXjwxOS5cuJCPPvqIMmXKUKhQISZPnpzYyrvP1taWSZMmUahQIezt7Vm4cCE1atRg4MCB2NnZUbJkSYYOHcrChQsT33N4eDjHjx8nLi6OUqVKUbly5cR9165dIzAwEGtra6pXr57Ywk3qypUrrFu3ji+++AI3Nzfc3Nz44osvWLNmDVevXk0sN3DgQKpUqYK1tTX9+/fn7NmzGbqmPWvWLGrUqEHt2rVp3749bm5ufP+9WVdPE92fJKVkyfRvDf3mm2+4e/dumo8hQ4YAJH4JcnFxSTz2/vPQ0NS/JN+8eZOlS5fSq1cvgoOD+fzzz+nXrx///GN+p+6CBQsIDAzk+vXrTJ06lU8++SRTPRi5nUxyLrJe4GZY+jK4+RotdceiyXYfunGINze8iY2yYX6b+VQqkvbSrGY7PAqOjoEi9eHpZTl6K9vjtqBz2qVLl/Dw8MDB4cG0vGXLliU6OpobN25w+fJlPDw8KFSoUOJ+H5+0Jwdq2rQpEyZMYNy4cXTt2pUGDRowfvx4/P3NWxXv/PnzlC+f+qI/9/355580atSIM2fO0L59e44ePUqDBg24efMm4eHhdOjQIdmlgvvz+QNcvnw5WfyFChWiaNHkv5PFixenQIEHPTvnzp1j+/btyVraWmvi440VHHv27Mn169d57733OHPmDM2bN0/s4p86dSpjx46lQ4cORERE8NJLLzFx4kQcHR2TnfPSJWNKj6TThZYtWzZxX/HixRNju+/+zywsLCxZUkxLREQES5YsSbwEYmtry6uvvsqcOXMYMmQISilsbW1THVcQGxubOEf5/c/r8uXL+Pk9vJZDRjk5OQEQEhKS+Bnf/7Li7Jz6lV0nJycaNGjASy+9BEDLli1p06YNq1atolEj8wbENmnSJPF5y5Ytef/991m8eHG+a7Wn2WLXWltprffkZDAiHzi7AZZ2M6aH7bP6oaS+5dIW+q/rj4udC4vaLcqapA5Qoi1UHgott8r96Y9QqlQpgoODiYyMTNwWGBhIwYIFcXd3p2TJkty4cSPZNfeLFy+mW+eAAQP4559/uHbtGjVq1OCFF14AeKhlnBpfX1+zu5bLlSvHzJkzee+997hy5Qru7u44ODiwfv36ZC3DiIiIxNZhyZIlk3W9R0VFPTRNZ8o4fXx8aNGiRbI6Q0JCCA83Jsa0sbHh448/Zt++fVy4cAF7e3tee+01wEiCX331FWfPnmX79u1s3ryZKVMeXlSoVCnjzpDz588nbgsMDEy273EtW7aM0NBQPv30Uzw9PfH09GTu3LmcO3eOv//+GzA+/8DAwIdGy589e5YyZYxpnhs2bIiLiwvLli1L93yDBg1KNuI85WPChAmAMeaiYMGCHDhwIPHYgwcPUqhQoTS/5NWsWTPVmdgeZxZIKyurh953fiBd8SLrnFkPy7pDkXJGS90h+fQDv575lXc2vYOfqx8L2y6klNNj/PHSGk5/AwHGH2/c60PNCcZMciJRXFwc0dHRyR716tXDz8+PwYMHExkZyZUrVxgxYgR9+/bFysqK+vXrJw4Wi46O5ty5c0ybNi3Nc+zdu5d//vmHe/fuUaBAAZycnLCxMToDixYtipWVVbqJu2fPnpw6dYrJkycTGRlJbGwsGzZsSLN8s2bNeOqppxgzZgxKKd555x0++OCDxHOEh4ezbt06rlwxpsjo1asXU6dO5dy5c0RHRzN06FASEhLS/dxeffVV9u3bx7x584iOjiYhIYHAwEDWrl0LwMaNG9m/fz+xsbEUKlQIBweHxPe8fPlyzp07h9YaFxcX7OzsEvclVaJECVq1asXgwYO5e/cud+7cYfDgwbRt2zZZKz09mzdvRimV7MtBUrNnz6ZHjx4cO3aMgIAAAgICOHHiBC1atEjsgm7Xrh337t1jzJgxREREEBsby2+//caqVat49VXjTmZHR0f+97//MXHiRP73v/8lfjG6cOECH3zwAdu2bQOM2yCTjjhP+fjkk08Ao9ekZ8+ejBw5kuDgYIKDgxk5ciSvvvpqmgPfBg4cmHgZJiEhgU2bNvHXX38lu0Pi3r17iT+v+7/793sjLly4wKZNm4iOjiY+Pp4tW7bwv//9z+zbLvMUrXWef9SpU0dnlX2vfqinD9ygL5++nWV1PhFOrdN6jLvW3zbSOuJWsl0JCQl61qFZuur8qnrgXwN1RExEhqvvOnOH7jpzh/Ei+pbWWzprvQStN7bVOj42K96B2Y4fP56j58usJk2aaIyZHZM9rl69qgMDA3X79u11kSJFdMmSJfV///tfHRHx4Ody4sQJ3bhxY+3o6KirV6+up0yZou3s7BL39+7dW/fr109rrfWGDRt0rVq1tKOjo3ZxcdGNGjXSu3fvTiw7fvx4XaxYMe3i4qLHjRuntdbax8dHL1q0KLHMgQMHdPPmzbWbm5t2c3PTLVu2TNwH6G3btiV7b9u2bdM2Njb6zJkzOjY2Vn/++ee6UqVK2snJSXt6eupOnTrpS5cuaa21vnfvnv7vf/+r3d3ddbFixfSkSZO0t7e3Xrp0qdZa6++//16XLVv2oc/v2LFjukOHDrpYsWLa2dlZV69eXc+YMUNrrfXSpUt1pUqVtIODg3Zzc9OtW7fWp0+f1lpr/fHHH+tSpUppe3t7XaxYMd2/f//Ez3bUqFG6efPmiecIDg7WPXr00MWKFdMeHh66Z8+e+saNG4n7U35O586d00Die1uwYIH28/PTMTExD8V/8OBBDehDhw49tG/dunXaxsZGX7lyRWut9aFDh3S7du20h4eHdnV11fXq1dO///77Q8f99ddfukWLFtrZ2Vk7OzvrypUr65EjR+qQkJCHyj5KRESE7tu3r3ZxcdEuLi76tdde05GRkYn7x48frytXrpzsmBUrVujy5ctre3t7XaVKFb1ixYpk+318fB76fe/du7fW2vh51qxZUzs6OmonJyddpUoV/fXXX2c47pyW3t8bYJ9OJScqnQ+6Ifz9/fW+ffuypK79vT9iV6E2dB5cixLl3LKkznzv1FpY0Qs8KkOvX8H+wb3i9yeeWX5qOR3KdODTpz/FNhOt6m6zdgKwvHMc7OgB0degxkSo+B487u1xGXTixAkqVcqiSwh5xKxZs/j88885ffq0pUN5bOHh4bi5ubFlyxYaNmxo6XAey6uvvkqHDh3o0qWLpUMR2SS9vzdKqf1a64cGs8jgOfF4Tq6BFa+CZ1UjqRd68GXoXvw9hmwdwvqL63mt6mu8W/vdx7oe5kAobO4CBT2g5Q4oYt7gLJFx27dvx9PTkzJlynDkyBGmTJlCz549LR1Wpty5c4fdu3fTvHlzIiMjee+99/Dx8aFu3bqWDu2x3R+lL0RSkthF5p1YDT/2geLVoecvxsIuJqExoby98W32X9/Px3U/pmflx0gK924DmgicoclvULgO2Jo9J5LIhIsXL9K9e3du3rxJ0aJF6dKlC0OHDrV0WJkSHx/P8OHD6dKlC7a2tvj7+/P7778njvgWIr+RxC4y5/hv8NNrUKIW9PwZCj649eZ6xHUGrR/E+dDzTHlmCm1Lt838eYJWwa6+PGPzH7bGtYNizbIgePEo3bt3p3v37pYOI0u4u7uTVZfqhMgLJLGLjDv2K/zUD7z8ocdPUPBB6znwbiAD1w8kLCaMmS1m8lTxpzJ3jvhoOPgRnP4a3Gpx5nbVLApeCCHyN7ndTWTM0Z+NpF6qnqml/iCpBwQH0OvPXsQlxDG/zfzMJ/XQU7CuvpHUK7wLrXZyVT+8brsQQoiHSYtdmO/IT/DL61CqPvT4EQo8mElr08VNfLj1QzwdPJnZYiZeTqnPC26WkOMQdRma/C7TwgohRAZJi12Y59ByI6l7N3woqf98+mfe3fwu5VzLsbDtwswl9dhQuPKn8bxUZ3j+X0nqQgiRCdJiF48WsAxWvgG+jeCV5WBnzFettWbW4VnMCJhBo5KN+LzJ59jb2me8/lt7YXt3iLoCHc8bt7PJqHchhMgUabGL9B1cbCT1Mk3glRWJST0+IZ5xu8YxI2AGz5d9nq+e/SrjSV0nwInP4K+Gxhrqz/5tJHUhhBCZJoldpO3AQvjtLSjbDLr/AHZG4o6Oi2bwlsGsOL2C/tX6M+7pcRmfTU4nwJYOcPBD8Hoe2gVA0aez/j08wQIDA+nSpQuenp44OjpSqlQpOnfuTExMDL/++iuOjo6Jy2cmtWDBAtzd3bl37x5grJf+xhtv4OPjg4ODA97e3nTt2pX9+/dnKB6tNeXLl8fZ2TlxMZX75s+fn+qqYalt37dvH506daJo0aI4OztTvnx53n333WRLnZorPj6eDz/8kKJFi+Lk5MSLL77IzZs30z0mODiY3r17U6RIEZydnalZs2bivPQA/fv3p0qVKtjY2NC/f/+Hjv/qq6946qmnsLe3z5KV0oRISRK7SN2+72HVf8GvOby8DGyNJTxD7oUw8O+BbLy4kSH1hvBO7XcyN5ucsjISed1vodFPYCfT92a1du3aUbx4cU6dOkVYWBg7d+6kdevWaK3p0KEDzs7OLF269KHjZs+eTe/evSlQoABXrlyhbt26XLp0iTVr1hAaGsrx48fp0KEDv/zyS4bi2bRpE4GBgVhZWT1ylbC0/P333zRq1IgKFSoQEBBAaGgoW7ZsoUiRImzZsiXD9U2aNInffvuN3bt3Jy7z2qtXrzTLR0dH07x5c+zs7Dh16hR3795lyZIlyZZkrV69Ol988QXPP/98qnWUKFGCjz76iGHDhmU4XiHMktoE8nntIYvAZLE9c7Ue5az14pe0jolK3Hw1/KrutLKTrrWwlv7z3J8Zrzc+RuuDQ7S++neGD022CIyF5YVFYG7evKkBfezYsTTLDBs2TNeuXTvZtqNHj2qllD558qTWWut+/frp8uXLp7rISEZ17dpVP/fcc/qtt97SKf/PprUQS8rtfn5+um/fvo8dy33e3t567ty5ia/Pnj2rAX3u3LlUy8+cOVN7eXmZ9XkkXSgnNWm9ZyGSyswiMDJ4TiS3Zw6s+QDKt4GuC8GmAAD/3v2XgX8PJCI2gpktZlKveL2M1Rt+zhggd2s3oMCzRdbHbil/DoFrR3LmXJ7VoO2kRxYrUqQIVapUoX///gwaNAh/f38qVaqUrHfl9ddfZ+LEiRw4cIDatWsDRmv9mWeeoUKFCgCsWbOG11577bGnX71x4wYrV65k2bJllClThunTp7N//37q1Kljdh2nT5/m7NmzfPvtt+mWmzRpEpMmpf0ZvfLKK3zzzTeEhIRw8eLFZDGULVsWZ2dnDh8+jK+v70PHbtq0icqVKzNw4EB+++03ihYtyoABA3j//ffNfh9CZLcc74pXSrVRSp1SSp1VSg1JZX8PpdRh02OHUqpGTsf4xNo9y0jqFdolS+oHgw/y6p+vEq/jmd9mfsaT+oXl8GdNCD0JjVYY66aLbLd582aaNm3KtGnTqFmzJsWKFWPs2LFo04qOPj4+tG7dmjlz5gBGN/OiRYsYOHBgYh03btygZMmSjx3L999/j4uLCx06dKBmzZrUqlWL2bNnZ6iO+2uAPyqeIUOGcPfu3TQf33zzDQChoaEAuLi4JDve1dU1cV9KN2/e5K+//qJGjRpcvXqVxYsXM2HCBJYsWZKh9yJEdsrRFrtSyhqYAbQEgoC9SqlVWuvjSYqdA5pore8opdoCs4FMTmEmzLbzG1g3FCq2h5e+Bxs7ADZe3MhHWz+iuENxZracSUnHDP6Rv7Yetr8M7g2g4VJw9M362C3NjBa0Jbi7uzNhwgQmTJhAZGQkK1as4PXXX6dkyZK89tprAAwYMIDevXvz2Wef8csvv2Btbc0LL7yQWEfRokW5fPnyY8WhtWbOnDn07NkzseXfr18/hgwZwueff46joyO2trbExsY+dGxsbGziMUWLFgXg8uXLWbJsrpOTEwAhISHJtt+9exdn59Rvt3RycqJkyZK88847APj7+9OzZ09+++03evTo8dgxCZEVcrrFXg84q7UO1FrHAD8AHZMW0Frv0FrfMb3cBTzGFGbCLDumG0m90vPQZX5iUv/x9I+8t/k9KrhVYGHbhRlL6nGRxr/FmkP9+dBiS/5M6nmEvb09ffr0oXr16gQEBCRu79ChA46OjixfvjzZoLn72rVrx08//ZRq0jXXhg0bOHv2LPPmzcPT0xNPT09GjRpFeHh44uA9X19frl69SmRkZLJjz549S5kyZQAoX748fn5+jxx4N2HCBBwdHdN8DBo0CDBa5t7e3hw4cCDx2MDAQEJDQ6levXqqddesWTPVwaKPsxyxEFktpxN7SeBSktdBpm1p6Qf8ma0RPem2fwl/DYPKneCleWBti9aabwO+ZczOMTxd4mnmtJqDW0EzR61rDadnwKoyEH4elIIyvSGjt8OJx3Lnzh2GDh3K0aNHiY2NJS4ujp9//pmjR4/SuHHjxHLW1tb069ePcePGsX37dgYMGJCsnk8//ZTw8HBeeuklTpw4QXx8PBERESxbtozhw4cDcP78eZRSbN68OdVY7l+3P3nyJAEBAQQEBHD06FH69u3LrFmzAKhXrx7lypXj3Xff5c6dO8THx7N161bmzp1Lnz59Euv65ptvWLJkCZ988kniLWbBwcFMnDiR5cuXA/DJJ58QHh6e5mPmzJmJ9Q0YMIDJkydz7tw5QkND+fjjj2ndunWq19cB+vTpw61bt5gxYwbx8fEcOnSIJUuWJOvliImJITo6mvj4eOLj44mOjiYmJiZxf1xcHNHR0cTGxqK1Jjo6mujo6Ef8RIXIgNRG1GXXA+gCzE3yuhfwdRplmwEngCJp7B8A7AP2eXt7Z2awYaqeqFHxWz83Rr+v6KN1XKzWWuu4+Dg9esdoXXV+VT1s2zAdE5+B0dDRt7Te0lHrJWi9sa3WUdezLFQZFZ8x4eHh+rXXXtPlypXTjo6O2tXVVdesWVPPmjXrobIXLlzQVlZWumnTpqnWdeXKFT1w4EDt5eWl7e3tdalSpXTXrl31gQMHtNZab9myRbu6uurbtx/+P3P9+nVta2urV61a9dC+kydPaqWU3rt3r9Za63PnzumuXbvqEiVKaGdnZ129enU9b968h47bu3ev7tixoy5cuLB2dHTUfn5++t1339VXr17N0GektdZxcXF68ODBukiRItrR0VF37txZ37hxI3H/4sWLtYODQ7JjNm3apGvWrKnt7e21n5+fnj59erL9TZo00UCyR5MmTRL3jxo16qH9xp9iIR6WmVHxSpsG0uQEpVQDYLTWurXp9VCM3+iJKcpVB34F2mqtTz+qXn9/f51V6y3v7/0Ruwq1ofPgWpQol4/vrd46FTaOg2pdoNNMsLYhOi6aj7d+zMZLG3m92uv8t9Z/ze9iDN4GO16B6OtQczJUeMe4Vz2LdJu1E4DlAxtkWZ2ZdeLEiSy5xptfjBw5EicnJz788ENLhyJEvpPe3xul1H6ttX/K7Tl9u9teoJxSqjRwGXgZeCVpAaWUN/AL0MucpC4yYfNk2DwBqneDTt+ClTUh90L478b/EhAcwNB6Q3ml0iuPriepwPlgVRBa7YTC5t/CJPK+MWPGWDoEIUQSOZrYtdZxSqm3gHWANTBPa31MKTXItH8mMBIoAnxjai3GpfaNRGTSpomwZRLUeAU6Tgcra65FXGPQ34O4GHaRz5p8RivfVubVFRkEcRHgXAH8vzKmibV1yt74hRBCpCvHJ6jRWq8B1qTYNjPJ8/7AwxMsi8ejNWyaAFunQM2e8PxXYGXN2TtnGbh+IJGxkcxqOYu6nnXNqy9oFezqC07ljFa6jUP2xi+EEMIsMlf8k0Br2DjWSOq1esHzX4OVNfuv7+fVta+itWZ+m/nmJfX4aNj3NmztCA4+0GChMfJdCCFEriBTyuZ3WsP60bB9GtTpA8/9D6ys2HBxAx9t+YgSjiWY1XIWJRxLPLquyCuwuR3cPQQV3oWak8C6wCMPE0IIkXMksednWsPfI2DH1+DfD9p9BlZWrDi1gvG7x1PVvSoznp2Ba0FX8+or4A6FSkCN8VDyuWwNXQghROZIV3x+pTX8NdxI6nVfh+c+RyvFjIAZjN01lsYlGzO31dxHJ/XYUNj3DsTcAWs7aLZGkroQQuRi0mLPj7SGtUNh97fw1CBoM4k4Hc+4XeP4+czPdPbrzMgGI7GxesSP/9ZeY573iAtQrAmUeiH98kIIISxOEnt+ozX8+THsmQX1/wOtJxAVH81HWz9i86XNDKg+gLdqvpX+xDM6AU58Doc+MbreW2yFog1z7C0IIYTIPOmKz08SEoxlV/fMggZvQesJhMSEMuCvAWy5tIVhTw0zbza5w6Mg4CPw6gjtAiSpi3RNmDCBDh06mF2+T58+9O+f9h2t8+fPx8/PLytCy1JNmzZl3Lhx2XqOQYMG8dZbbyW+DgwMpGHDhjg7O9O5c2e2bduGq6trtsYg8j5psadwJyoWCsHoVccIcbS2dDhmUzqBfqHTaRm5ht8curD03HPEzv6DC3ZfEatuUDJ2AL9u8eHXLTvTrMOKOBKwwVXVo7bNEDYe7wDHT+bgu0jb8auhVC6e+lKaInVNmzalRYsWiYu13Llzh44dO6K1ZtWqVRw6dIhmzZrRrFkzNm7cmHjc4sWLGT58OOfPnzfrPJ988kl2hJ/j1q9fz5QpU9izZw9aa7y8vHjxxRcZPHjwQ2u2Z5ekC9QATJo0iVKlSrF9+/bEL+R3797NkVgAxo0bx4gRI1iwYAGvvvpqsn1KKbZt20ajRo3S3R4aGsq4ceP49ddfuXLlCq6urtSsWZP333+f5s2bZzimtWvXMnjwYAIDAylbtixffPEFrVqlPamWr68v165dw8bmQbrbuXMn1apVA+Djjz9m9erVXLp0CUdHR5577jkmT55M4cKFAYiPj2fChAnMmzePGzduUKtWLWbMmJHmCoC5gbTYUwiNyvzylJaidAL9Q76mZeQafnXoxlKn14i2usy5ApOJU3fxjnkX54S0p3m1JpYeBWbwSaH3UcRzV7uzMfZ5IPfcn165uDMda2ZwLXiR6NKlSzRu3Bh3d3f+/vtv3NyMdRCsrKwICAhg9erVFo4w+8THx5OQkJBumfnz59OhQwdatWrFqVOnCAkJYfXq1YSFhXH48OEcivRhgYGBVKtWLcuWhc3I8rsJCQl89913FC5cOHEVvowKDw+nUaNGbNu2jaVLl3Lnzh3+/fdfBgwYwE8//ZTh+gIDA3nhhRcYOnQoISEhDB06lM6dOz/yS+jcuXOTrfB3P6mDscLh4sWLuXXrFocOHSIoKIi+ffsm7v/iiy9YvHgxGzZs4Pbt2zRu3JjWrVsTFhaW4fhzTGorw+S1R506dcxaJcccP7d/M2+t7hYfr/XKN41V2taP0TohQe+9ulc3WNJAP7v8WX3q9qn0jw/7V+u19YwV2XYP1DouOmfizsPywupuWhurjI0dO1YfOXJEe3l56TfffFPHx8cn7t+0aZO2trbWn3/+ua5UqZKOi4vTWmu9aNEi7ePjk1guNjZWjx8/XpcrV067uLjohg0b6n379iXuHzVqlG7evHni66tXr+r27dtrZ2dnXa5cOT137lwN6HPnzmmtte7du7fu2bOn7t+/v3ZxcdElSpTQM2fOTDz++++/12XLltWTJk3Snp6eumjRovr999/XMTEPVho8dOiQbtasmXZ1ddWlS5fWY8eOTYz/3LlzGtBz587VlSpV0nZ2dvrq1at62bJlumLFitrR0VF7eHjo3r17a621DgsL0y4uLvrTTz816/O8r0+fPtrLy0s7OjrqSpUq6SVLliTuu337tn7ppZd04cKFtbOzs65SpYreunWr1lrrAwcO6Kefflo7OztrNzc33aBBg8SV8Xr37q379euntda6evXq2srKStvZ2WkHBwc9d+7cxJ9ZUrNnz9ZVqlTRzs7OumbNmnrdunXJfjbNmjXTgwcP1h4eHrpNmzbpvsek1qxZo21sbPTq1as1oI8cOZJsP6C3bdv20HFJt48dO1YXLlxY37p1y+zzpmfkyJG6UaNGybY1atRIjx49Os1jfHx89KJFi8w+x+rVq7Wzs3Pi67p16+pp06Ylvo6JidG2trZ6wYIFGYg88zKzupt0xedlCfGw6r8QsASafAxNh/L3xfUM2TqEkk4lmdViFsUdi6d9/PkfYO9AQEGjH8H7pRwLPT+ZvGcyJ2/nzCWLioUr8nG9j80uv2vXLr744gs++OCDNLvM33rrLWbMmMGcOXMYNGjQQ/tHjhzJhg0bWLt2LT4+PsyfP5/WrVtz5syZxJZ/Uj169MDV1ZVLly4RHR1N165dHyrz008/sXz5cmbNmsXKlSvp1q0bbdq0wcfHB4ALFy5w8eJFAgMDuXLlCm3btsXd3T2xpdayZUveeust/vzzTwIDA3nuuecoUKBAshXmli5dysaNGylcuDCxsbH06tWLdevW8eyzzxIREcGBAwcA2LFjByEhIbzySsYWPmrUqBGfffYZrq6u/Pjjj7z66qvUrFmTypUrM3XqVCIjI7lw4QIODg6cOXMGW1tbAN58803atGnDli1bSEhIYP/+/djZ2T1U/6FDhx66nJJyzfvZs2czZcoUfv75Z6pVq8batWt54YUXCAgISBynsHXrVp577jkuXbpEXFyc2e9v1qxZtG3blueee44aNWowe/Zsvvrqqwx9RmvWrKFt27aJ3dqpuXjx4iO7te9ffjh06BB16iTvfaxduzaHDh1K9/j333+ft99+G29vb9544w0GDhyYZtkNGzYkiychIeH+UuGJtNYEBAQ8dHkit5Cu+LwqIR5W/sdI6k2HQrNPWH5qBYM3D6ZSkUosbLMw/aQeFwWHhoJLVWh3SJJ6PrVt2zZsbW3TTVp2dnZMmDCB0aNHP9S9qLXm66+/ZurUqZQpUwZra2v69etH8eLF+eOPPx6qKygoiI0bNzJ16lScnZ3x8PBgxIgRD5V79tlnef7557GysuKFF17A1dWVgICAxP1WVlZMnTqVQoUKUbZsWT766CO+//57AP744w/s7OwYPnw4BQoUoFKlSnz88cfMnTs32TlGjRqFp6cndnZ2KKWwtbXl5MmT3L59GwcHBxo3bgzAjRs3AChZMmOXevr160eRIkWwtrbm5Zdfpnr16omJ187Ojlu3bnHq1Cm01pQvX57SpUsn7rt48SKXLl3C1taW+vXr4+CQubUWvvrqK0aOHEmNGjWwsrKiXbt2NGvWjB9++CGxjLe3N4MHD8bOzg57e3uz6r1y5Qp//PEHr732GgCvvfYaixYtIioqKkPx3bhx45Gfq7e3N3fv3k33cV9YWNhD4x1cXV0JDQ1Ns/4FCxYQGBjI9evXmTp1Kp988kmalxZ+/vln5syZw5dffpm4rUOHDsyYMYMzZ84QHR3N8OHDiY+PT/ecliYt9rwoPg5WvgFHVkCz4ehnPmD6wa+ZfXg2Tb2aMqXJFArZFEr92LvHwMkPbApB841gXwoedT+7SFdGWtA57f333ycwMJDGjRuzfv16KlSokGq5bt26MW3aNKZMmZKszM2bNwkPD6dDhw7JrvPGxsYSFBT0UD2XL18GjD/W991vhSdVvHjyL50ODg7JvlR4eHgkS0K+vr6J57t06RK+vr7J4ilbtiyXLl1KVqevr2/ic3t7e9asWcMXX3zBsGHDKFOmDIMHD+aVV16haNGiibGbOxo/ISGB0aNHs3z5cq5du4ZSioiIiMQvCR9++CGxsbH07t2bq1ev0r59e6ZMmUKxYsX4/vvvGTt2LI0aNcLW1paePXsyatSoZIO7zHXu3DnefPNN3n777cRtcXFxeHl5pfo5mOv+tfX27dsD0LNnTz766COWL19Onz59ALCxsXnomv391/d7J4oWLZr4O5EVnJycCAkJSbbt7t27ODunPbC2SZMmic9btmzJ+++/z+LFix9qtf/4448MHDiQVatWUbt27cTtQ4YMISIiglatWhEREUHfvn2pVKkS7u7uWfSusp602POa+Dj4daCR1JuPJK7xe4zaMYrZh2fzYrkX+V+z/6We1LWGU9NhbR04arplx7G0JPV8ztraOnFg2DPPPJPuQLDPPvuML774ItkfYnd3dxwcHFi/fn2yFlRERARDhgx5qI77rbOLFy8mbkv63FzBwcFERkYmvj5//nxisipVqhQXLlxI1j0aGBhIqf+3d+ZxVRXvH3+PIMiOCoqCivtWYGguSYqmmApupZWZYpr602+5pLmkplmmaWm2CfG1lAyt1MJyzd3qq2KCuS8gSKKIioCAbPP741yul9WL4gVt3q/XeXnPmTkznzNHznNm5jnz1KmTr4xKlfI/3nx8fAgLCyMxMZGZM2cyZMgQzp8/z1NPPYWDgwOhoaFG6wsNDSU4OJh169Zx48YNkpKS8PT01GuysbHh/fff59ixYxw/fpx//vlHP01Qv359VqxYQVxcHGFhYQQHB7Nq1arSNZCOevXqsWLFinz3JjU1lS+//LLYdrgbubm5BAcHk5SUhJubGy4uLrRo0YKcnByCgoL0+dzd3Tl37ly+c/P2GzRoAECvXr3YsmULN27cKLa+2NhYbG1tS9zy8PT01E+h5HHkyBE8PT2Nvr5KlSoVGlr/+uuvGT16NBs3bqRLly750iwtLfnwww+Jjo4mISGBKVOmEBUVhY+Pj9F1mhpl2B8mcrJh/Wtw7EfoNof0DmOZsGsCG85tYIznGN7p8E7Rq8ndvg77+sPh18HlGWj6RuE8ikcWIQRffPEFQ4cOxcfHh4MHDxaZr2PHjjz77LMsXrw437njx49n8uTJnD17FtA8nbdu3cqlS5cKleHm5oaPjw/Tpk0jJSWFhISEe/r2Ozc3l2nTppGenk5UVBSLFy9m2LBhAPTu3ZuMjAzmz59PZmYmp0+fZuHChYwYMaLY8q5cucK6deu4efMmZmZm+m/BzczMsLW1ZcmSJXzwwQcsWbJE3+uOiYlh8uTJ7Nu3r1B5ycnJmJub4+zsTG5uLitWrMg3z7tx40ZOnjxJTk4Otra2VKlSRd8jX7lypb7tHB0dMTc3v6feOsDEiROZM2cOERERSClJT09n//79nDpVss+Hj4+PvuddkC1bthAXF8cff/xBRESEfvv111/5888/+fvvvwFtPYJFixZx9OhRpJTEx8czefJkevXqpR8FGT9+PLVr18bPz4/w8HCysrK4ffs2v/76K2PHjgW00R1Dj/WitjyGDh1KeHg4oaGhZGVlERoayuHDh/X/NwoSExPDrl27yMjIICcnhz179rBkyRJeeOEFfZ5ly5YxefJktm7dSseOHQuVcfnyZb3X/cWLFwkICKBDhw706NGjxDYuT5Rhf1jIyYJ1r8Lx9dB9HklthjNy20j2xu1lVvtZjGs1ruhPYhIPwGZPuLQJvD6Gzr9AFWfT61eUO4sWLWL8+PF069aNvXv3Fpln4cKFhYY6586dS9++fenbty/29vY0btyY5cuXF/sJ2XfffUdaWhpubm54e3szcOBAQOv5GEu9evVwdXWlfv36tGvXjmeffZa33noLAAcHB7Zt28Zvv/1GzZo16dGjB0OHDmXSpEnFlpebm8vnn3+Ou7s7dnZ2jBs3jpUrV+qHqYcPH87PP//Mpk2baNSoEQ4ODvTq1QsbG5sie4PDhg2jXbt2NGrUCFdXV06cOKGfswc4f/48/v7+2Nvb4+7ujpWVFQsWLABg586dtG7dGltbWzp06MDgwYN5+eWXjW4bQ1577TXeeusthg8fTtWqValbty7z5s2762dtsbGxxfY4AwMD6devH61bt8bFxUW/+fr60qFDB/389NSpU3n11VcZNGgQDg4OtG/fnrp16xISEqIvy87Ojv3799OxY0deeOEFHBwcaNCgAV9++WWRTpV3o2HDhqxfv5733nsPe3t7/ffxhtMNtra2rF69GoBbt24xadIknJ2dqVq1KuPGjWP27Nm8/vrr+vzjx48nOTmZLl26FDlKEBcXR/fu3bG2tqZNmza4u7sTFhZWZp8gPghEwSGJh5E2bdrI8PDwMilrvf9/iHcdQP83n6B248Iev+VCdqZm1E9uhB7zufR4f0ZvH82l1Ess7LSQbvW6FX/uzRPw+2Bo/1+oVvy37ArjOXnyJM2bNy9vGQ8NW7dupW/fvqSnp1foh+G/haioKPr27UtkZGSph+kVpqek540Q4rCUsk3B42qCtaKTnQk/BMDpX+HZBZxu0pX/2zSEjJwMgnyDaF2zCGOdFgcXvoMWb4FDC+h5BNQDVWEiIiMjEULw+OOPEx0dzcyZM3nhhReUUa8gNGjQQD+crng0Ua9rFZns2/D9UM2o91zEIfc2DN8yHCEEK59dWbRRj/sZNnnCsXchNVo7ph6oChNy/fp1BgwYgK2tLd7e3nh4eOT7fEihUDxYVI+9opJ9G9a+Ame3Qq/FbKtRj2nbR1PHrg6B3QNxsXHJnz8nA45MgTOfQVUv6LhG83pXKExMly5dCnlLKxQK06EMe0UkKwPWDoFz28FvCaG2VnywZzKtarTi066f4mBZICCFlLCrJyTshqYTodUHYGa8o5JCoVAoHh1MPhQvhHhWCHFaCHFOCFHoQ1ghRDMhxJ9CiNtCiMmm1lfuZKXDmsFwbjvSbynLKqUy/8B8OtfpTFD3oPxGXUptEwKaTdI83lt/rIy6QqFQ/IsxaY9dCGEGfA50B+KAQ0KIMCnlCYNs14E3gH6m1FYhyEzTjHrUbrL8P+HdjPP8dO4nnm/yPG+3ezv/N+qZN+HgaHBqB80mgpvx8bAVCoVC8ehi6qH4tsA5KWUUgBBiDdAX0Bt2KWUCkCCE6G1ibeVLZhqEvgjRe0nzX8rkGwfZ988+xnqOZYznmPwexYkH4PeXIC1WfcKmUCgUinyY2rC7AoYLOscB7e6lICHEKGAU5F+X+qEk8xZ89wJc2M8N/4/5z+XtHLt2jFntZzGoqcEiDjIXTi6CyJlg7Qrd9oLzU+WnW6FQKBQVDlPPsRf13dU9rZAjpQySUraRUrbJW77woeR2KqweBDG/80/vDxkau4HTN07zsc/H+Y06wI0jEDEd3PpBzwhl1BUPNba2tvz555/lLUOheOQwtWGPAwwjNbgBhRec/rdwOwVWD4TYPzj97DyGRIdyLeMaQd2DeKbuM3fypUZp/1ZrDT0Ogvf3YOFYLpIVDxeHDx/mueeeo0aNGtja2uLu7s5zzz3Hzp07y1saqampdOjQoUzKunDhAkIImjZtmi/m+P79+/NNY82ZMwdzc3P9sqG1atVi9OjRpQ5HCtqyrGZmZnTt2rVQWkBAACNHjjTq+Pfff4+3tzd2dnZUq1aNJ554gsWLF5OZmVlqTTNnzuSJJ57AwsKCbt1KWJHSgEWLFuHq6oqNjQ3dunUjKioqX/qWLVto2bIlVlZWPPbYY2zbti1f+rlz5+jWrRs2Nja4ubnx0UcflVq3omwxtWE/BDQWQtQXQlgALwJhJtZQMchIhm+fh4sHOOg7k4DzqzETZqx6dhVeNXUhA3My4chbsLEJJOjW9q7eRi04ozCK7du307FjRxo2bEh4eDgpKSn8/fffDB48mA0bNpS3vAfCtWvXWL58eYl5fHx89MFFwsPD+fPPP5k3b16p6woODsbR0ZFdu3Zx5syZe9I7d+5cRo0axYgRI4iNjeX69et8++23HD16lPj4+FKX17BhQ959911GjRplVP7Vq1ezaNEiNm7cyNWrV2nRogV9+vQhJycH0JafHTBgANOnT+fmzZtMnz6d/v3764Oi5OTk4O/vT/Pmzbl69SphYWEsXLiQtWvXllq7ogyRUpp0A3oBZ4DzwNu6Y2OAMbrfLmg9+2QgSffbvqQyW7duLcuKdX7j5Gejd8h/zlwvszILkX5Tyq+6STm3mty89135xKonZN8NfWV8avydPCnnpdzSVsrVSHlgtJRZtx6cHkWpOHHiRHlLMIqGDRvKESNG3DVfaGio9PDwkHZ2dtLFxUWOGjVKpqam6tPr1asnQ0JC9PvR0dESkBcvXpRSSrl9+3bZqlUraWdnJ6tXry6feeYZfd5PPvlEuru7S1tbW1m7dm05ffp0fRog9+3bJ6WU8uLFi7JHjx7SyclJ2tvbS29vbxkeHq7P+84778iuXbvK6dOnS2dnZ+ns7Cxnz55dSNMnn3winZyc5M2bN6WUUu7bt09qj7k75Rjqk1LKyZMnS39//7u2kyHZ2dnS1dVVLlq0SD722GPyzTffzJc+bNiwItve8Hh0dLQ0MzOTK1euLFXdxlDUdRZFp06d5MyZM/X7KSkp0srKSu7evVtKKeXs2bOlt7d3vnO8vb3lnDlzpJRS7ty5U1pZWcmUlBR9+syZM6WPj09ZXIZClvy8AcJlETbR5AvUSCk3AZsKHFtu8Psy2hD9o0nGTfj2Obh0hNVPj2Jh1A88UeMJlnVdducb9Zi1cOA1EGbg/SPUfa58NStK5PL8+dw+WXKYzLLCsnkzXGbMuGu+M2fOcP78eX0krpJwcHDgu+++o3nz5kRFRdGnTx/ee+89PvjgA6M0DR06lPfff5+AgAAyMzP5448/9BqmTZvGoUOHaNmyJUlJScWGE83NzWXs2LF069YNIQTTpk1jwIABnDt3jsqVKwOwd+9eBg4cyKVLlzh8+DDe3t74+vrmC7U5YMAAvv/+e+bPn6+PplYSMTExbN68maFDhxp1rXls3LiRK1eu8Morr2BmZsb8+fN5//33SxXBbtu2bUgpefHFF0vM5+fnx/79+4tN/+KLLxg8eLDR9RoSGRnJxIkT9fu2trY0btyYyMhIOnfuTGRkJK1b5//yxsvLSx+iNjIykiZNmuSLhubl5cXnn39+T3oUZYNaK96UpCdBSH/kpQiWtn+JBbG/0KVOFwK7B+ZfeCb9Mjg+Dr0ilFFX3BN5McVdXV31x8LCwnB0dMTBwYEqVaroj/fs2ZOWLVtSqVIlGjVqxNixY9mxY4fRdVlYWHD+/HmuXLmCpaUlXbp0AcDc3BwpJcePHyc1NRVHR0fat29fZBl169alT58+WFtbY2VlxXvvvUdsbKw+BjxAkyZNGDNmDObm5rRr145WrVpRVFTHjz76iGXLlhEbG1tkXXv27MHR0VEfUtXOzo5XX33V6OsFCAoKonfv3tSsWZNXXnmF5ORk1q9fX6oyrl69ipOTExYWFiXm++WXX0hKSip2u1ejDpCSkoKDQ/6VLB0dHUlOTi6TdEX5oJaUNRXpNyCkP1mXjzGntR9h8XsY2GQgb7d7G7NKZnAjEtIvQe2e0PQNaDIOKqnb8zBgTA/a1Dg5OQFaLOlmzZoB0KdPH5KSkti/f3++2OHbt2/n3Xff5dSpU9y+fZucnBxq1KhhdF0///wz8+fP5/HHH8fZ2ZlRo0YxYcIEGjRowOrVq/nyyy8ZOXIkHh4ezJ49G19f30JlJCYmMmnSJHbv3k1SUpI+nGjeCwpArVq18p1jY2NDSkpKobLatWuHv78/b7/9NqNHjy6U3rlzZ3777TdAM0wzZ86kY8eOHD161Kged0xMDFu3btX7KTg5OdGnTx8CAwN56aWXAKhcuXKRzm9ZWVlYWVkB4OzsTGJiIpmZmXc17g8KOzs7bt68me9YUlIS9vb2ZZKuKB9Uj90UpF2HVX1JSzjBG55dCEs8zLhW45jVfhZmohKc/gy2toMjkyE3R3OOU0ZdcR80adKEBg0asGbNmhLzZWZm0q9fP1588UViY2NJTk5m4cKFef4wgDY8e+vWLf3+pUv5P2Tx9PRk7dq1JCQkEBgYyPTp0/Ve9wMGDGD79u0kJiYyaNAg+vbtS1paWiEd06dPJz4+ngMHDpCcnMzFi9pyF4Y6SsOCBQv48ccf+euvv0rMZ2dnx2uvvcaZM2c4duyYUWV/9dVX5ObmMnLkSFxcXHBxcWHr1q3s2bOH06dPA+Du7l5kIJxz587RoEEDAHx9fRFC3NXRrGfPnnov/qK21atXG6W7KDw9PfO1UWpqKmfPnsXT07PIdIAjR47kSz9z5ky+/x+G6YryQRn2B03adVjVh+uJpxnRvA1/3DzDOx3e0VaTy7wO+/rD4dfBpRs8sxsqmZW3YsUjgBCCzz//nJCQEKZOncrFixeRUpKWlsaBAwf0+TIzM8nIyKBq1apYWVlx4sQJPvvss3xltWnThtDQUFJTU7l69Wo+D/LMzExWrlxJYmIiQgiqVq1KpUqVMDc35/Tp02zZsoW0tDQqV66Mg4MDQgh9b9yQ5ORkrK2tqVq1KqmpqUydOvW+rr9+/fqMHTv2rt7uaWlp/Pe//8XGxoaGDRsC8M033xQbOz47O5uvv/6aadOmcfToUSIiIoiIiODMmTM0a9aMoKAgAF544QX++usvgoODycjIICMjg8DAQI4fP87AgQMBzfjPmjWL8ePHs3LlSpKSkgA4deoUw4cPJyYmBoDNmzfrvfiL2l5++WW9vqysLDIyMsjOziY3N5eMjAxu375d7PWPGjWKwMBAjhw5Qnp6OjNnzqR+/fp4e3sDmv9EeHg4oaGhZGVlERoayuHDhxk2bBgAnTp1ol69esyYMYP09HQiIiIIDAwscqREYUKK8qh72LYK6xWfmijlF0/Ji+/XlL3XdJGtQ1rLnTE7tbT0BCk3uEkZWlnKk0ukzM29//oUJuFh8YqXUsqDBw/Kfv36SScnJ2ltbS3d3d1l//795Y4dO/R5AgMDpaurq7SxsZE+Pj5y7ty5sl69evr0ixcvyq5du0pbW1vZokUL+c033+i94m/fvi179uwpq1evLm1sbGT9+vXlokWLpJRSHj16VLZv317a29tLe3t76eXlJTdt2qQvFwOv+JMnT8r27dtLa2trWb9+fRkSEiLNzMzkrl27pJRFe3l37txZzps3T0pZ2FNfSimvX78uq1WrVsgr3szMTNrY2EgbGxvp6OgoO3XqJPfs2aPPM3fuXNm5c+ci23P9+vWySpUq8sqVK4XSAgMDZfXq1WVGRoaUUso9e/bIzp07y+rVq8vq1atLHx8f+fvvvxc6b82aNfKpp56SNjY2smrVqrJVq1Zy8eLF8vbt20VqKIlhw4ZJtEW/9JvhvRw9erR89tln852zcOFCWatWLWllZSW7du0qz507ly998+bNskWLFrJKlSqyRYsWcuvWrfnSz549K7t27SqtrKxkrVq19PdfUTbci1e8kPc41FWRaNOmjSzKieZeWO//H+JdB9D/zSeo3bjqvRd0KxFW9uFkSgxj67iTKeDzZz6nVY1Wd/JEzoI6/aGa133rVpiOkydP0rx58/KWoXhAdO7cmUWLFtG2bdvylqJQlPi8EUIcllK2KXhcTeQ+CFITYGUfDqT9w3jX2thZWPPfboE0qFwZdvYAr4/A8THwLP2iGAqF4sGyZ8+e8pagUNwXao69rEm5At/4seV2PGNqVqeWnRshPUNokHIUNntC4h93lohVKBQKhaKMUYa9LEm5DCv9+DYnkSnV7fFw9uSb7stxOTFPc5KzqQ/P/gVufcpbqUKhUCgeUZRhLyuS45Hf9OJjbrDQ0YZudbsR5BuEw4VVcPYLaDoRfP8A+8blrVShUCgUjzBqjr0sSL5E1je9mVP5FmHWVrzQZBDTPUdiZmYJTcdrgVtqdilvlQqFQqH4F6B67PfLzTjSvu7J65bphFlbMsljJG/nHMdsW3vITAIzC2XUFQqFQmEyVI/9fki6yLVVvRlnncVJCwuWeQymS+wnkBYLHvPA3K68FSoUCoXiX4Yy7PfKjRguhvgxxiaHqxZWrG/4JA1PzQJrV+i2F5yfKm+FCoVCofgXoobi74UbFzgR4scrtrnctLInyDeYhunnwa0f9IxQRl2hUCgU5YYy7KXlejR/fuvHcDtJB2szVnddQquaXtD5Z/D+Hiwcy1uhQlFu7N+/v9h11hV3CAgIYOTIkQ+0jvnz5+Pv76/fv379Oj169MDBwYHWrVsTGxuLra1toaA+iocfZdhLw7XzbPrOjzfscplmncYHuX9TL3allmZuo0VlUygqCD4+PlhaWmJra4uDgwOtWrXihx9+KG9Z9427uztVqlTJF+HsbsFeHhRCCPbv31/oeHh4OP369cPZ2Rl7e3uaNGnChAkTiI+PN5m2GTNmsHHjRv3+8uXLSU1N5dq1axw+fJi6deuSmppK7dq1TaLn22+/RQjBu+++WyjN3d2db7/99q7HMzMzmT9/Pi1btsTGxgYXFxe6dOnCjz/+eE+awsPDadu2LdbW1jRs2LBIDYYUjKxnaWmJmZkZiYmJgBZUaMKECbi5uWFnZ0fv3r2JjY3Vn79v3z68vLyoVq0aDg4OeHl5sX79+nvSXhLKsBvLtfOsWuPPp3bZrK1ymf450dBoDHgtKW9lCkWxzJo1S/8wDwgIYPDgwUWGE33YCA4OzhfhbNasWaUuIycnh9zc3DLXtn37dry9vWnatCkREREkJyezZ88eqlevXq7L1UZFRdG8eXPMzcvGtSorK6tU+YOCgqhWrRrBwcHk5OSUur6cnBx69+5NSEgIn376KYmJicTFxTFr1izWrVtX6vJu3rxJz549ee6557hx4wbLly9nzJgx/Pnnn8WeUzCy3vPPP0+PHj1wcnICYMqUKRw6dIi//vqLK1eu4OTkhJ+fn/7/WdOmTdmwYQPXrl0jKSmJpUuXMmTIEE6ePFlq/SVSVGSYh2170NHdchJOycVfNJdDV9aX6asry9zvHaWM+bHM6lQ8XDws0d0Mo59JKWVqaqoE5A8//KA/FhAQIN3c3KStra1s3ry5XL16tT5t165d0szMTK5Zs0Y2aNBA2tvby4EDB8rk5GR9njNnzsjOnTtLW1tb6eHhIZcsWZIvmtqtW7fkG2+8Id3c3GT16tVl3759ZUxMTD6NEydOlP369ZO2trayQYMG8rfffpPbt2+XLVu2lHZ2drJfv3756qxXr54MCQkp8pojIyNlly5dpKOjo6xfv76cN2+ezM7OllLeiQAXHBwsmzdvLi0sLGR8fLxMTEyUr776qnRzc5NOTk5y4MCB8vLly/oyP/nkE+nu7i5tbW1l7dq15fTp06WUUnp4eEhAWllZSRsbGzlixAgppZSNGjWSw4cPL/HeDBs2TJ9fSimnT58u69evL21sbGSDBg3kkiVL9Gm3b9+Wr732mnR2dpZ2dnaycePG+nsYHR0tfX19pYODg3R0dJReXl7y1KlTUsr8EfH8/Pykubm5NDc3lzY2NnL27NlFRsTbsGGD9PLykg4ODrJZs2by22+/1ad9/fXXsmHDhvLDDz+Urq6uskWLFiVeoyEnTpyQgNy4caM0NzeXGzduzJde3D01PB4SEiItLCzkmTNnjK63JFasWCHr1Kkjcw0iaw4ZMkQGBAQYdX5iYqK0tLSUP//8s/6Ys7Oz/Omnn/T7Fy5ckEC+6IF55OTkyH379klLS0sZFhZWbD33Et1NecXfhawrx5n900B+sTZjmGsvLCwTEU8sBJt65S1NUUHY9/0ZEi+mmqQupzq2PD2oSanPy8zM5MsvvwSgSZM753t7e7N48WIcHR354YcfGDp0KK1ataJFixaA1kvatm0bkZGR3Lp1C29vb5YtW8bbb79NdnY2/v7+PPPMM2zevJm4uLh8c7oAEydOJCIigv/97384Ojoyfvx4/P39+euvvzAzMwMgJCSEjRs38uOPPzJr1ixeeeUVvL292bt3r17jp59+yowZM0q8xps3b9K9e3f+85//sHnzZqKioujduzeWlpZMmTJFn++7775j586dVKtWDTMzM3x8fGjatCnHjh2jcuXKvP766wwePJgdO3Zw5swZpk2bxqFDh2jZsiVJSUmcOnUKgMjISIQQbNu2TR+//MyZM5w7d07f1sbSokUL9u/fT61atdi1axe9e/emefPm9OjRg2+++YZDhw5x8uRJqlevzsWLF0lJSQG04fa6desSFhaGubk5x48fx9HRsVD5GzduJCAgAHNzc4KDgwG4cOFCvjzbt29nxIgR/PTTT3Ts2JHw8HB69OhBnTp16NSpk/6cS5cucfbsWWQpIoMGBgby+OOP4+fnR69evQgKCsLPz69UbbRp0yaefPJJGjcuefXOoq7fkKNHj1K3bl0iIyPx8vLK5xPi5eVFSEiIUXq+/vprnJ2d6d27t/5YnmHNI6+nHhERoW/DPI23bt0iOzubTp064evra1SdxqKG4kvg1qUjLNjoTyebS0xoPJA3u35KJe81yqgrHhref/99HB0dsbKyYubMmQQHB+Ph4aFPHzFiBNWrV8fMzIwXX3wRDw8Pdu/ena+MBQsWYGtrS82aNenXrx95IZIPHDhAdHQ0ixYtwsrKisaNG/Pmm2/qz8vNzWXVqlW89957uLq6YmNjw9KlSzl58iQHDx7U5xs0aBDt27fHzMyMIUOGEB8fz5QpU6hWrRrVqlXDz8+PQ4cO5dM0evRoHB0d9dulS5f49ddfsbCwYObMmVhaWtK8eXOmTp2qN2R5vPPOO7i4uGBhYcGRI0c4fPgwn3/+OQ4ODlhbW/Phhx+yc+dO4uLiMDc3R0rJ8ePHSU1NxdHRkfbt2xfb3levXgXA1dW1VPdpyJAh1K5dGyEEXbt2pXfv3uzYsQMACwsLUlNTOXHiBNnZ2dSpU0f/4mVhYcHly5eJiorCzMwMDw8PatasWaq68/jkk08YP348Tz/9NJUqVaJt27YMGTKEVatW6fNUrlyZBQsWYGVlhbW1tVHlZmRkEBISwquvvgpo/+c2bdpEXFxcqfRdvXrVqHZNSkoqcatbty4AKSkpODg45DvX0dGR5OTku9YhpSQoKIiRI0fqX1AB/Pz8WLhwIfHx8aSkpDBr1iyEEIXKTEpKIjU1lQ0bNtCrV68ymx7Jw+Q9diHEs8AngBkQLKVcUCBd6NJ7AWlAgJTyL1PrvHn5GFvPD2SqVQI5le2xajlIOccpiuReetCm4u2332bmzJncuHGDESNGsHPnTkaMGAFohnfOnDmsXbuWy5cvI4Tg1q1beuMEYGZmhrOzs37fxsZG31uMi4ujRo0a+R7w9evX1/++evUqGRkZNGjQQH/M1taWGjVqcPHiRTp06ABArVq19Ol5ZRU8lldnHoGBgQwZMiTfsYsXL+Lu7p6vB9awYUMuXryYL5+7u7v+d3R0NLdv3y5kDKtUqUJsbCxPPfUUq1ev5ssvv2TkyJF4eHgwe/bsYntYeW31zz//FBtDuyiWLVvGV199RVxcHFJK0tPTGTx4MKAZ/StXrjBx4kTOnj3LM888w4cffkijRo1YtGgR8+bNw9/fn1u3bvH888/zwQcfYGtra3Tdhm2xa9cuPv74Y/2xnJwcnn76af1+rVq1sLS0LFW5P/zwA6mpqfr71atXL2rUqEFwcDBz5swBtBeGoubss7KyqFy5MqC1raEj2v1iZ2dXaNQiKSkJe3v7u567a9cuoqKiCn3ZsHTpUqZMmcKTTz6JlJLJkycTFhamn4M3xNLSkn79+tGrVy8cHR0ZPXr0fV2PISbtsQshzIDPgZ5AC+AlIUSLAtl6Ao112yigdGNaZUAV85uknxvIcIsrpDi0wqrPOXBoZmoZCkWZUbVqVYKDg9m0aRM///wzAKGhoQQHB7Nu3Tpu3LhBUlISnp6eRg+xurq6kpCQQFpamv5YdHS0/rezszOWlpb5jqWmppKQkECdOnXK6MruUKdOHWJiYvLpj4qKKlRXpUp3Hnv16tXDxsaG69ev5+vVpaen89RT2noUAwYMYPv27SQmJjJo0CD69u2rv+aCn/Y1adKERo0aERoaarTu33//nalTpxIYGEhiYiJJSUn4+/vrr8Pc3JypU6cSHh5OTEwM1tbW+t6vs7Mzy5Yt49y5c/z+++/s3r2bDz/8sBStdod69eoxZ86cfO2QkpLCpk2bimw7YwkMDCQnJ4fHHnsMFxcX3NzcuH79Ov/973/1TnTu7u6FnDpTU1O5cuWK/sWwV69eHDp06K7OnwU91wtueS8Hnp6eHDlyJN+5R44cwdPT867XtHz5cvz9/QuNIDg4OBAUFERcXBz//PMPvr6+pKSk0Llz52LLys7O5uzZs3etszSYeii+LXBOShklpcwE1gB9C+TpC6zS+Qb8D3AUQtQqWNCDwkzeoluzD3jc4ir/1B1F9d6HoUrhty2F4mGjWrVqTJo0iRkzZpCbm0tycjLm5uY4OzuTm5vLihUriIyMNLq89u3bU69ePaZNm0Z6ejrnz59nyZI7X4lUqlSJoUOHMmvWLC5dukRaWhpvvvkmzZo1o23btmV+fb179yYjI4P58+eTmZnJ6dOnWbhwoX6EoijatGlDq1atGD9+PNeuXQO0kYY1a9YAcPr0abZs2UJaWhqVK1fGwcEBIYTewLm4uBR6KH/xxResXr2aGTNm6L8RT0hI4IMPPmDt2rWFNCQnJ+tHRoQQ/Prrr2zevFmfvnPnTg4fPkxWVhZWVlbY2Njoh27Xrl1LdHQ0UkocHBywsLC452HdCRMmsHTpUvbt20dOTg6ZmZkcPnxYP/VSHAEBAfj4+BSZduLECX7//Xc2bNhARESEfjt48CCXL1/WvzQEBAQQFBSkr/vGjRuMHz+eli1b8sQTTwDw0ksv8fTTT9O3b192795NRkYGOTk57NmzRz+6AYU91wtueUPx/fv3Jy0tjUWLFpGZmcmOHTtYv349o0aNKvF6ExIS+OmnnxgzZkyhtOjoaOLj45FScurUKV599VUCAgL0ozfr1q3j77//Jjs7m4yMDL766it27txJjx49SqyztJjasLsChuNicbpjpc2DEGKUECJcCBFuOHR4v2Q7O/DnGV+i6y7H1TtQDb8rHinGjx9PfHw8q1atYtiwYbRr145GjRrh6urKiRMn8g273g1zc3PCwsKIjIykRo0aDBgwoNBDccmSJbRp04Ynn3ySunXrEh8fT1hYWL55ybLCwcGBbdu28dtvv1GzZk169OjB0KFDmTRpUrHnVKpUiZ9++onc3Fxat26NnZ0d7dq10/sZZGZmMnfuXGrVqoWjoyPLli1j3bp1VKlSBdB8GGbPnk3VqlX1Q6ndu3dn//79nDhxgscffxw7Ozs6duxIQkJCkT23Hj168Morr9C2bVucnJz48ccf6d+/vz79ypUrvPLKK1StWpVatWoRExNDYGAgoPUwO3fujK2tLS1btsTLy4vJkyffU/v5+voSFBTElClTcHJyolatWkycOJHU1JIdQ2NjY4s17IGBgXh5eeHv74+Li4t+8/DwYODAgfrrePnll5k/fz7jxo2jWrVqPPbYY6Snp/PLL7/oX1TMzMzYtGkTgwcPZuzYsVSrVg1XV1fmzp3LwIEDS329jo6ObNq0iR9++AEHBwdee+01li9frp8iAmjZsiXz58/Pd96KFStwc3Mrcjrm+PHjtG/fHhsbG3x9ffH19eWrr77Sp8fHxzNgwAAcHR2pXbs2K1asIDQ0lO7du5daf0mI0ng23ndlQgwEekgpR+r2XwHaSilfN8jzK/CBlHK/bn8H8JaU8nBx5bZp00be7a2yNMjcXMQ9DDkp/h2cPHmyVPOnCsWjSnp6Os2aNeP48eP3NK+vuDslPW+EEIellG0KHje181wcYDjh5QYUXM/QmDwPFGXUFQqF4u5YWVkRExNT3jIUBTC1BTsENBZC1BdCWAAvAmEF8oQBQ4VGe+CmlNJ06zAqFAqFQvEQY9Ieu5QyWwjxH2Ar2uduK6SUx4UQY3Tpy4FNaJ+6nUP73G24KTUqFAqFQvEwY/Lv2KWUm9CMt+Gx5Qa/JTDO1LoUCoVCoXgUUJPJCsU9YEqnU4VC8e/kXp8zyrArFKWkSpUqXLt2TRl3hULxwJBScu3aNf2nlaVBBYFRKEqJm5sbcXFxlOX6CQqFQlGQKlWq4ObmVurzlGFXKEpJ5cqV862JrlAoFBUJNRSvUCgUCsUjhDLsCoVCoVA8QijDrlAoFArFI4Qy7AqFQqFQPEIow65QKBQKxSOESaO7PSiEEFeBsoxE4AQklmF5DwKl8f6p6Pqg4mus6PpAaSwLKro+qPgaH4S+elJK54IHHwnDXtYIIcKLCoVXkVAa75+Krg8qvsaKrg+UxrKgouuDiq/RlPrUULxCoVAoFI8QyrArFAqFQvEIoQx70QSVtwAjUBrvn4quDyq+xoquD5TGsqCi64OKr9Fk+tQcu0KhUCgUjxCqx65QKBQKxSPEv9qwCyGeFUKcFkKcE0JMKyJdCCGW6dKPCiG8KqDGZkKIP4UQt4UQkyugvpd1bXdUCPGHEMKzAmrsq9MXIYQIF0J4VyR9BvmeFELkCCGeN6U+Xd13a0MfIcRNXRtGCCFmVzSNBjojhBDHhRB7KpI+IcQUg/Y7prvX1SqYRgchxEYhRKSuDYdXMH1VhRAbdH/PB4UQj5lY3wohRIIQ4lgx6aaxKVLKf+UGmAHngQaABRAJtCiQpxewGRBAe+BABdRYA3gSeB+YXAH1PQVU1f3uWUHb0JY701IewKmKpM8g305gE/B8BWxDH+AXU+q6B42OwAmgrm6/RkXSVyC/P7CzArbhDGCh7rczcB2wqED6FgHv6H43A3aYuA07AV7AsWLSTWJT/s099rbAOSlllJQyE1gD9C2Qpy+wSmr8D3AUQtSqSBqllAlSykNAlgl1lUbfH1LKG7rd/wGlDy784DWmSt1fHWADmNLxxJj/hwCvA+uABBNqy8NYjeWJMRoHA+ullLGg/e1UMH2GvASEmkTZHYzRKAE7IYRAeyG+DmRXIH0tgB0AUspTgLsQoqaJ9CGl3IvWJsVhEpvybzbsrsBFg/043bHS5nmQlHf9d6O0+kagva2aEqM0CiH6CyFOAb8Cr5pIGxihTwjhCvQHlptQlyHG3ucOuiHazUKIlqaRpscYjU2AqkKI3UKIw0KIoSZTV4q/FSGENfAs2oucKTFG42dAc+AS8DcwXkqZaxp5RumLBAYACCHaAvUwfWeiJEzyTP83G3ZRxLGCPTVj8jxIyrv+u2G0PiFEFzTDPvWBKiqi6iKOFdIopdwgpWwG9APmPWhRBhijbykwVUqZ8+DlFIkxGv9CW97SE/gU+OlBiyqAMRrNgdZAb6AHMEsI0eRBC9NRmr9lf+B3KWVJPb8HgTEaewARQG2gFfCZEML+wcrSY4y+BWgvbxFoo1xHMN2IgjGY5JluXtYFPkTEAXUM9t3Q3kJLm+dBUt713w2j9AkhPIBgoKeU8pqJtOVRqjaUUu4VQjQUQjhJKU2x7rQx+toAa7TRT5yAXkKIbCnlTybQB0ZolFImG/zeJIT4woRtaJRGXZ5EKeUt4JYQYi/gCZypIPryeBHTD8ODcRqHAwt0U1fnhBDRaHPZByuCPt3/w+GgOaoB0bqtomCaZ7opHQsq0ob2UhMF1OeOI0bLAnl6k9/R4WBF02iQdw6md54zpg3rAueApyrwfW7EHec5L+CfvP2KoK9A/m8wvfOcMW3oYtCGbYFYU7VhKTQ2R5t/NQesgWPAYxVFny6fA9ocrY0p73Ep2vBLYI7ud03d34pTBdLniM6ZD3gNbT7b1O3oTvHOcyaxKf/aHruUMlsI8R9gK5q35Qop5XEhxBhd+nI0D+ReaIYpDd2bYEXSKIRwAcIBeyBXCDEBzVM0ubhyTakPmA1UB77Q9TizpQkDNRip8TlgqBAiC0gHXpC6v8IKoq9cMVLj88D/CSGy0drwRVO1obEapZQnhRBbgKNALhAspSzys6Ty0KfL2h/YJrVRBZNipMZ5wDdCiL/RjNNUaaJRGSP1NQdWCSFy0L6AGGEKbXkIIULRvhBxEkLEAe8AlQ30mcSmqJXnFAqFQqF4hPg3O88pFAqFQvHIoQy7QqFQKBSPEMqwKxQKhULxCKEMu0KhUCgUjxDKsCsUCoVC8QihDLtCYSRCiDlCCFnE9puR57vr8vuZQOsFA32ZQohTQohZQgiLMqwjQFe+rW6/hq6N3Avk89HlM0mkrQL3Jl0IcVIIMVUIUerPe4UQbwkhfMpepULx4PjXfseuUNwjN9HW8S54rCLyHdryrpZAF7Rvah2Asgrv+yvQAe17XNAiDb4D7AYuGOT7S5fvfBnVawwfAT8CVoAf2lKjlYH3SlnOW2jro+8uS3EKxYNEGXaFonRkSy0q08NAvIHWPUIIN2CMEGJKWSweI6W8Clw1Il8yWmQ/U3LB4Np36YLSDKX0hl2heOhQQ/EKRRkghKglhFghhIjSDf+eEUK8d7ehbyFEH12ksVtCiBtCiANCiM4G6ZWEENOEEOeEELd15Q67R5mH0cLSOunK7qqrL0MIcUW3vrutQd2VhRCLhRCxurovCSE25F2T4VC8bvj9b92pu/KGwnX58g3FCyH2CCG+L6It8uoSuv0qQogPhRAXdfVHCiF63eO1R5J/jW6EEAuEEH8LIVKFEHFCiNW6lRzz0i+grZr4jsHQvo8urSzvi0JRpqgeu0JRSoqYq81BM5bXgUnADbQQoXMAZ2B0MeU0RBsu/gSYAlRBiz5WzSDbp8Aw4F20Ie3uwAohxDUp5S+llO4OZALXhRAtgC3AdrQldeugDVc34M5Uw3TgZWAaWiANF7TlMM2KKDtel3c1ME6ntTjWAB8JIWzylk7VGfOBwPcGowk/oq07/w7aMP4gIEwI0UZKGVHKa69L4WAgNYD5aEE4nIE3gZ1CiMelFkmvP7BLpyNYd84J3b9leV8UirLF1Avkq01tD+uGZqhlEVu3IvKaA4OBDO4EpXDX5ffT7T8PXCuhvkZoa5oPK3B8FXDoLlovoM0z5wU88UPzBfhRl74GOAuYGZwzSKevg27/F+CjEuoI0OW31e0/ptv3KZDPR3f8Md2+M1oozRcN8nTQ5Wmj239Gt9+5QFl7gR/ucu0SeEN37XbAS8Btw/qKOMcMLS62BDoZHE9EF/SkLO6L2tRmik0NxSsUpeMm8GSB7YDQmCCEOCGESAey0Hqvlmi9xaL4G3AQQqwUQvgKIWwKpD+DZkA2CCHM8za0CGWthBBF9ZwNmaTTcQvYiGYUx+nS2gIbZP4Y7+vQDK63bj8CCNB5hnvkDZHfL1Kbm98JvGBw+AXgvJQyXLffDbgM/F7EtRsTROgTtGtPRnMi/FxKucYwgxCipxDiDyHETbTrjtMl3S1G+/3eF4XigaKG4hWK0pFtYHz0CCEmAovRhrP3oA3HPwl8jjbEXggp5WkhRF+0oe5NQJYQYgMwXmf8nNB6ksV53dfijjEqim/RDNxtNGeylALnXimgJ0cIcY07UwHvoRmwscBC4B8hxCIp5Scl1Gksa9Ai/tkDqWjD8N8YpDuhDf1nFXFuThHHCrII+B7tK4AJwEQhxG9Syk0AQogngTBgA9o9S0Drrf+PYu5XAW33c18UigeKMuwKRdkwEG2I+O28A7p57BKRUv4K/CqEcECL1bwUbf72RbQ5+2ygI5qBLUjCXYq/UtRLiI54tDlmPbqeZnVdvUgpM9DC7s4WQjQGxgBLhRCnpZRb7nZtd2EDWmzvvkAMUBtYa5B+HS3Wd797LD8279qFEHvRRkcWCSE2Sykl2vz5VQxC9Aoh6hlZ9v3eF4XigaIMu0JRNlih9YwNednYk6WUN4HvdB7xHXSHd6L1DB2klNvLROUdDgD9hRAzDIbjB6A9E/YXoe+sEGIy2lB+nuNdQTJ1/96tx4uU8oYQYhvaEHwMcFJKedQgyw40Z7ZUKeUpI6+puLqyhBCz0Hrw/mg9dSsgK8+o6yjqfmVS+Hoe5H1RKO4bZdgVirJhO/CGEOIAmgf3y2hOVsUihBiNZsS3oHlmN0br+a8C/VD9cmCNEOJDIBzNyLQEmkgpR96H3veAI8BPQogvATe04fatUso/dfo2oH0idwRIR3P2M0ebqy+KWF2+Ybp566wSRgxA66GvQBvS/qxA2nZgK7BdCLEQOA7YA62AKlLK6aW6Ws1/4BTa1wdhuvInCCGWovkfPAUMKeK8U0BvIcQWtCmD0w/4vigU9095e++pTW0Py4bmFZ9YTJot8DXaMO11tM+j/MjvDe5Ofq/4Dmirt11C856PRjOulgblCrQ54uNoIwJX0ebwh95F6wVg8V3yPIPWc89AGz7+Ap2Huy59CprRugmk6PL2NUgPwMArXnfsZeAMWk9X6o75GLaDQV47tFXrJNC0CH2WwFzgnK68y2gvQb3vcl0S+E8Rx4eS3+v/LeAimnPhb2gvVvnORfv88H+6PHqP/3u9L2pTmyk2IeV9L0ClUCgUCoWigqA+d1MoFAqF4hFCGXaFQqFQKB4hlGFXKBQKheIRQhl2hUKhUCgeIZRhVygUCoXiEUIZdoVCoVAoHiGUYVcoFAqF4hFCGXaFQqFQKB4hlGFXKBQKheIR4v8BRvgPmROsjxQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## IGNORE THIS!!!\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "classifiers = [LogisticRegression(random_state=1234), SVC(kernel='linear'), KNeighborsClassifier(), GaussianNB(),\n",
    "               RandomForestClassifier(random_state=1234)]\n",
    "\n",
    "result_table = pd.DataFrame(columns=['classifiers', 'fpr','tpr','auc'])\n",
    "\n",
    "for index, cls in enumerate(classifiers):\n",
    "    model = cls.fit(X_train, Y_train)\n",
    "    \n",
    "    if index == 1:\n",
    "        yproba = model.predict(X_test)\n",
    "    else:\n",
    "        yproba = model.predict_proba(X_test)[::,1]\n",
    "    \n",
    "    fpr, tpr, _ = roc_curve(Y_test,  yproba)\n",
    "    auc = roc_auc_score(Y_test, yproba)\n",
    "    \n",
    "    result_table = result_table.append({'classifiers':cls.__class__.__name__, 'fpr':fpr, 'tpr':tpr,'auc':auc}, ignore_index=True)\n",
    "\n",
    "result_table.set_index('classifiers', inplace=True)\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "for i in result_table.index:\n",
    "    plt.plot(result_table.loc[i]['fpr'], \n",
    "             result_table.loc[i]['tpr'], \n",
    "             label=\"{}, AUC={:.3f}\".format(i, result_table.loc[i]['auc']))\n",
    "\n",
    "print (result_table)\n",
    "\n",
    "plt.plot([0,1], [0,1], color='orange', linestyle='--')\n",
    "\n",
    "plt.xticks(np.arange(0.0, 1.1, step=0.1))\n",
    "plt.xlabel(\"False Positive Rate\", fontsize=15)\n",
    "\n",
    "plt.yticks(np.arange(0.0, 1.1, step=0.1))\n",
    "plt.ylabel(\"True Positive Rate\", fontsize=15)\n",
    "\n",
    "plt.title('ROC Curve Analysis', fontweight='bold', fontsize=15)\n",
    "plt.legend(prop={'size':13}, loc='lower right')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "523c5b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()\n",
    "solvers = ['newton-cg', 'lbfgs', 'liblinear']\n",
    "penalty = ['l2']\n",
    "c_values = [100, 10, 1.0, 0.1, 0.01]\n",
    "\n",
    "grid = dict(solver=solvers,penalty=penalty,C=c_values)\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy',error_score=0)\n",
    "grid_result = grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ee83a258",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score(Y_test, y_score) (array([0.   , 0.   , 0.   , 0.25 , 0.25 , 0.375, 0.375, 0.5  , 0.5  ,\n",
      "       0.75 , 0.75 , 0.875, 0.875, 1.   ]), array([0.        , 0.07692308, 0.15384615, 0.15384615, 0.46153846,\n",
      "       0.46153846, 0.53846154, 0.53846154, 0.84615385, 0.84615385,\n",
      "       0.92307692, 0.92307692, 1.        , 1.        ]), array([1.99995586, 0.99995586, 0.99000412, 0.96029826, 0.79477011,\n",
      "       0.78507029, 0.67787439, 0.66055514, 0.5679729 , 0.49906652,\n",
      "       0.49566944, 0.2900975 , 0.22880206, 0.11846892]))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Users/shreyamantripragada/opt/anaconda3/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_score = grid_result.predict_proba(X_test)[:,1]\n",
    "false_positive_rate, true_positive_rate, threshold = roc_curve(Y_test, y_score)\n",
    "print ('roc_auc_score(Y_test, y_score)', roc_curve(Y_test, y_score))\n",
    "plot_roc_auc_curve(false_positive_rate, true_positive_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "455c1c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "classifier = LinearRegression() #look into parameters\n",
    "classifier.fit(X_train, Y_train)\n",
    "#y_score = classifier.predict_proba(X_test)[:,1]\n",
    "#false_positive_rate, true_positive_rate = roc_curve(Y_test, y_score)\n",
    "\n",
    "#print('roc_auc_score', roc_auc_score(Y_test, y_score))\n",
    "\n",
    "#plot_roc_auc_curve(false_positive_rate, true_positive_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ab0cac09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score 0.4951923076923077\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Users/shreyamantripragada/opt/anaconda3/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X_train, Y_train)\n",
    "y_score = classifier.predict_proba(X_test)[:,1]\n",
    "false_positive_rate, true_positive_rate, threshold = roc_curve(Y_test, y_score)\n",
    "\n",
    "print('roc_auc_score', roc_auc_score(Y_test, y_score))\n",
    "\n",
    "plot_roc_auc_curve(false_positive_rate, true_positive_rate)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "af69afa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression 0.5865384615384616\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using Logistic Regression Algorithm to the Training Set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, Y_train)\n",
    "print('Logistic Regression', roc_auc_score(Y_test, classifier.predict(X_test)))\n",
    "\n",
    "\n",
    "#Using KNeighborsClassifier Method of neighbors class to use Nearest Neighbor algorithm\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
    "classifier.fit(X_train, Y_train)\n",
    "\n",
    "#Using SVC method of svm class to use Support Vector Machine Algorithm\n",
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'linear', random_state = 0)\n",
    "classifier.fit(X_train, Y_train)\n",
    "\n",
    "#Using SVC method of svm class to use Kernel SVM Algorithm\n",
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
    "classifier.fit(X_train, Y_train)\n",
    "\n",
    "#Using GaussianNB method of naïve_bayes class to use Naïve Bayes Algorithm\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "classifier = GaussianNB()\n",
    "classifier.fit(X_train, Y_train)\n",
    "\n",
    "#Using DecisionTreeClassifier of tree class to use Decision Tree Algorithm\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X_train, Y_train)\n",
    "\n",
    "#Using RandomForestClassifier method of ensemble class to use Random Forest Classification algorithm\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "64a8a8b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>122874</th>\n",
       "      <th>122875</th>\n",
       "      <th>122876</th>\n",
       "      <th>122877</th>\n",
       "      <th>122878</th>\n",
       "      <th>122879</th>\n",
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       "      <th>S42W1</th>\n",
       "      <td>-154.12</td>\n",
       "      <td>-112.09</td>\n",
       "      <td>-154.12</td>\n",
       "      <td>-154.12</td>\n",
       "      <td>-14.01</td>\n",
       "      <td>-51.37</td>\n",
       "      <td>-233.51</td>\n",
       "      <td>-233.51</td>\n",
       "      <td>-112.09</td>\n",
       "      <td>-112.09</td>\n",
       "      <td>...</td>\n",
       "      <td>317.58</td>\n",
       "      <td>616.47</td>\n",
       "      <td>219.5</td>\n",
       "      <td>-233.51</td>\n",
       "      <td>-32.69</td>\n",
       "      <td>158.79</td>\n",
       "      <td>177.47</td>\n",
       "      <td>317.58</td>\n",
       "      <td>126.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S31W</th>\n",
       "      <td>308.45</td>\n",
       "      <td>248.96</td>\n",
       "      <td>328.28</td>\n",
       "      <td>429.63</td>\n",
       "      <td>469.29</td>\n",
       "      <td>429.63</td>\n",
       "      <td>508.94</td>\n",
       "      <td>627.92</td>\n",
       "      <td>887.9</td>\n",
       "      <td>808.58</td>\n",
       "      <td>...</td>\n",
       "      <td>-321.67</td>\n",
       "      <td>-420.82</td>\n",
       "      <td>-491.32</td>\n",
       "      <td>-581.65</td>\n",
       "      <td>-550.81</td>\n",
       "      <td>-451.66</td>\n",
       "      <td>-370.14</td>\n",
       "      <td>-121.18</td>\n",
       "      <td>-50.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S167W1</th>\n",
       "      <td>-91.45</td>\n",
       "      <td>-91.45</td>\n",
       "      <td>-150.39</td>\n",
       "      <td>-231.68</td>\n",
       "      <td>-191.03</td>\n",
       "      <td>-111.77</td>\n",
       "      <td>-30.48</td>\n",
       "      <td>-91.45</td>\n",
       "      <td>-270.29</td>\n",
       "      <td>-331.26</td>\n",
       "      <td>...</td>\n",
       "      <td>138.19</td>\n",
       "      <td>-50.81</td>\n",
       "      <td>-231.68</td>\n",
       "      <td>-591.38</td>\n",
       "      <td>-731.61</td>\n",
       "      <td>-731.61</td>\n",
       "      <td>-611.7</td>\n",
       "      <td>-270.29</td>\n",
       "      <td>-40.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S50W</th>\n",
       "      <td>248.66</td>\n",
       "      <td>229.26</td>\n",
       "      <td>229.26</td>\n",
       "      <td>308.62</td>\n",
       "      <td>449.71</td>\n",
       "      <td>409.15</td>\n",
       "      <td>128.74</td>\n",
       "      <td>29.98</td>\n",
       "      <td>149.9</td>\n",
       "      <td>209.86</td>\n",
       "      <td>...</td>\n",
       "      <td>-430.31</td>\n",
       "      <td>-1010.52</td>\n",
       "      <td>-1171.0</td>\n",
       "      <td>-1331.49</td>\n",
       "      <td>-1331.49</td>\n",
       "      <td>-1070.48</td>\n",
       "      <td>-751.28</td>\n",
       "      <td>98.76</td>\n",
       "      <td>619.01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S165W1</th>\n",
       "      <td>8.95</td>\n",
       "      <td>-10.07</td>\n",
       "      <td>-170.04</td>\n",
       "      <td>-250.59</td>\n",
       "      <td>-350.16</td>\n",
       "      <td>-330.02</td>\n",
       "      <td>-151.03</td>\n",
       "      <td>-50.34</td>\n",
       "      <td>8.95</td>\n",
       "      <td>-50.34</td>\n",
       "      <td>...</td>\n",
       "      <td>-130.89</td>\n",
       "      <td>-350.16</td>\n",
       "      <td>-390.43</td>\n",
       "      <td>-210.32</td>\n",
       "      <td>-30.21</td>\n",
       "      <td>219.27</td>\n",
       "      <td>279.68</td>\n",
       "      <td>199.13</td>\n",
       "      <td>99.57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122881 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0       1       2       3       4       5       6       7  \\\n",
       "S42W1   -154.12  -112.09 -154.12 -154.12  -14.01  -51.37 -233.51 -233.51   \n",
       "S31W     308.45   248.96  328.28  429.63  469.29  429.63  508.94  627.92   \n",
       "S167W1   -91.45   -91.45 -150.39 -231.68 -191.03 -111.77  -30.48  -91.45   \n",
       "S50W     248.66   229.26  229.26  308.62  449.71  409.15  128.74   29.98   \n",
       "S165W1     8.95   -10.07 -170.04 -250.59 -350.16 -330.02 -151.03  -50.34   \n",
       "\n",
       "             8       9  ...  122871   122872  122873   122874   122875  \\\n",
       "S42W1  -112.09 -112.09  ...  317.58   616.47   219.5  -233.51   -32.69   \n",
       "S31W     887.9  808.58  ... -321.67  -420.82 -491.32  -581.65  -550.81   \n",
       "S167W1 -270.29 -331.26  ...  138.19   -50.81 -231.68  -591.38  -731.61   \n",
       "S50W     149.9  209.86  ... -430.31 -1010.52 -1171.0 -1331.49 -1331.49   \n",
       "S165W1    8.95  -50.34  ... -130.89  -350.16 -390.43  -210.32   -30.21   \n",
       "\n",
       "         122876  122877  122878  122879 diagnosis  \n",
       "S42W1    158.79  177.47  317.58   126.1         0  \n",
       "S31W    -451.66 -370.14 -121.18  -50.67         0  \n",
       "S167W1  -731.61  -611.7 -270.29  -40.64         0  \n",
       "S50W   -1070.48 -751.28   98.76  619.01         0  \n",
       "S165W1   219.27  279.68  199.13   99.57         0  \n",
       "\n",
       "[5 rows x 122881 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FINAL_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "96fe58fb",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>S165W1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>33w1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>382w1</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>103w</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307w1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>022w1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        F7  F3  F4  F8  T3  C3  Cz  C4  T4  T5  P3  Pz  P4  T6  01  02  \\\n",
       "S42W1  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "S31W   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "S167W1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "S50W   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "S165W1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "...     ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..   \n",
       "33w1   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "382w1  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "103w   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "307w1  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "022w1  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "\n",
       "        diagnosis  \n",
       "S42W1         NaN  \n",
       "S31W          NaN  \n",
       "S167W1        NaN  \n",
       "S50W          NaN  \n",
       "S165W1        NaN  \n",
       "...           ...  \n",
       "33w1          NaN  \n",
       "382w1         NaN  \n",
       "103w          NaN  \n",
       "307w1         NaN  \n",
       "022w1         NaN  \n",
       "\n",
       "[84 rows x 17 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TEMP = FINAL_data\n",
    "#TEMP.loc['S42W1',0] = TEMP.loc['S42W1',0].replace(TEMP.loc['S42W1',0], \"1, 2, 3\")\n",
    "\n",
    "#TEMP.at['S42W1', 0] = np.asarray(added_list)\n",
    "#TEMP.at['S42W1', 0] = -154.12\n",
    "\n",
    "listed_DF = pd.DataFrame().reindex_like(final_data)\n",
    "#listed_DF.at['S42W1', 0] = -154.12\n",
    "\n",
    "listed_DF\n",
    "#DataFrame.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad')[source]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "0a3a77bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array content -154.12\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'list'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-159-530088b90373>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0;31m#print ('shape', np.asarray(added_list).shape)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m             \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'array content'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madded_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m             \u001b[0mlisted_DF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madded_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m             \u001b[0mcol_index\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0madded_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2165\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2167\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2116\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Not enough indexers for scalar access (setting)!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2118\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_value\u001b[0;34m(self, index, col, value, takeable)\u001b[0m\n\u001b[1;32m   3271\u001b[0m             \u001b[0mvalidate_numeric_casting\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3273\u001b[0;31m             \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3274\u001b[0m             \u001b[0;31m# Note: trying to use series._set_value breaks tests in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3275\u001b[0m             \u001b[0;31m#  tests.frame.indexing.test_indexing and tests.indexing.test_partial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
     ]
    }
   ],
   "source": [
    "#FINAL_data - original & final_data - condensed \n",
    "values_to_stop = [7679, 15359, 23039, 30719, 38399, 46079, 53759, 61439, 69119, 76799, 84479, 92159, 99839, 107519, 115199, 122879]\n",
    "added_list = []\n",
    "row_names = FINAL_data.index\n",
    "count = 0\n",
    "col_index = 0\n",
    "\n",
    "for rows in FINAL_data.index:\n",
    "    row_data = [float(x) for x in FINAL_data.loc[rows]]\n",
    "    #print ('patient')\n",
    "    for cols, value in enumerate(row_data):\n",
    "        if cols in values_to_stop and count < 84:\n",
    "            #print ('cols', cols, 'value', value)\n",
    "            #print ('shape', np.asarray(added_list).shape)\n",
    "            print ('array content', np.asarray(added_list)[0])\n",
    "            listed_DF.at[row_names[count], col_index] = list(added_list)\n",
    "            col_index += 1\n",
    "            added_list.clear()\n",
    "        else:\n",
    "            added_list.append(value)\n",
    "    count += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "ed8a6f2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>022w1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-72.01</td>\n",
       "      <td>108.01</td>\n",
       "      <td>309.63</td>\n",
       "      <td>226.82</td>\n",
       "      <td>298.83</td>\n",
       "      <td>248.43</td>\n",
       "      <td>86.41</td>\n",
       "      <td>-10.8</td>\n",
       "      <td>259.23</td>\n",
       "      <td>-223.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 1361 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        F7  F3  F4  F8  T3  C3  Cz  C4  T4  T5  ...   1334    1335    1336  \\\n",
       "S42W1  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ...    NaN     NaN     NaN   \n",
       "S31W   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ...    NaN     NaN     NaN   \n",
       "S167W1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ...    NaN     NaN     NaN   \n",
       "S50W   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ...    NaN     NaN     NaN   \n",
       "S165W1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ...    NaN     NaN     NaN   \n",
       "...     ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...    ...     ...     ...   \n",
       "33w1   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ...    NaN     NaN     NaN   \n",
       "382w1  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ...    NaN     NaN     NaN   \n",
       "103w   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ...    NaN     NaN     NaN   \n",
       "307w1  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ...    NaN     NaN     NaN   \n",
       "022w1  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... -72.01  108.01  309.63   \n",
       "\n",
       "          1337    1338    1339   1340  1341    1342    1343  \n",
       "S42W1      NaN     NaN     NaN    NaN   NaN     NaN     NaN  \n",
       "S31W       NaN     NaN     NaN    NaN   NaN     NaN     NaN  \n",
       "S167W1     NaN     NaN     NaN    NaN   NaN     NaN     NaN  \n",
       "S50W       NaN     NaN     NaN    NaN   NaN     NaN     NaN  \n",
       "S165W1     NaN     NaN     NaN    NaN   NaN     NaN     NaN  \n",
       "...        ...     ...     ...    ...   ...     ...     ...  \n",
       "33w1       NaN     NaN     NaN    NaN   NaN     NaN     NaN  \n",
       "382w1      NaN     NaN     NaN    NaN   NaN     NaN     NaN  \n",
       "103w       NaN     NaN     NaN    NaN   NaN     NaN     NaN  \n",
       "307w1      NaN     NaN     NaN    NaN   NaN     NaN     NaN  \n",
       "022w1   226.82  298.83  248.43  86.41 -10.8  259.23 -223.22  \n",
       "\n",
       "[84 rows x 1361 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listed_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d46c5953",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'temp_DF' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-19444d08b9d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0;31m#print ('brain_regions[col_count]', brain_regions[col_count])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;31m#print ('temp_list', temp_list)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m             \u001b[0mtemp_DF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow_count\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbrain_regions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol_count\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0mcol_count\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m             \u001b[0mtemp_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'temp_DF' is not defined"
     ]
    }
   ],
   "source": [
    "values_to_stop = [7679, 15359, 23039, 30719, 38399, 46079, 53759, 61439, 69119, 76799, 84479, 92159, 99839, 107519, 115199, 122879]\n",
    "brain_regions = ['F7', 'F3', 'F4', 'F8', 'T3', 'C3', 'Cz', 'C4', 'T4', 'T5', 'P3', 'Pz', 'P4', 'T6', '01', '02']\n",
    "row_names = final_data.index\n",
    "temp_list = []\n",
    "listed_DF = pd.DataFrame().reindex_like(final_data)\n",
    "listed_DF.head()\n",
    "row_count = 0\n",
    "col_count = 0\n",
    "\n",
    "for rows in FINAL_data.index:\n",
    "    row_data = [float(x) for x in FINAL_data.loc[rows]]\n",
    "    \n",
    "    for index, value in enumerate(row_data):\n",
    "        if index in values_to_stop:\n",
    "            \n",
    "            #print ('row_names[row_count]', row_names[row_count])\n",
    "            #print ('brain_regions[col_count]', brain_regions[col_count])\n",
    "            #print ('temp_list', temp_list)\n",
    "            temp_DF.at[row_names[row_count], brain_regions[col_count]] = temp_list\n",
    "            col_count += 1\n",
    "            temp_list = []\n",
    "        else:\n",
    "            temp_list.append(value)\n",
    "            \n",
    "    row_count += 1\n",
    "\n",
    "temp_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8c79f13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F7</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F8</th>\n",
       "      <th>T3</th>\n",
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       "      <th>P3</th>\n",
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       "      <th>P4</th>\n",
       "      <th>T6</th>\n",
       "      <th>01</th>\n",
       "      <th>02</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S42W1</th>\n",
       "      <td>234.691796</td>\n",
       "      <td>288.619884</td>\n",
       "      <td>297.082391</td>\n",
       "      <td>201.988486</td>\n",
       "      <td>201.117730</td>\n",
       "      <td>308.864366</td>\n",
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       "      <td>314.160993</td>\n",
       "      <td>196.207816</td>\n",
       "      <td>212.271569</td>\n",
       "      <td>386.414909</td>\n",
       "      <td>366.827124</td>\n",
       "      <td>331.260919</td>\n",
       "      <td>210.572525</td>\n",
       "      <td>316.188987</td>\n",
       "      <td>368.760953</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S31W</th>\n",
       "      <td>209.802646</td>\n",
       "      <td>310.155904</td>\n",
       "      <td>323.160348</td>\n",
       "      <td>227.777398</td>\n",
       "      <td>275.764142</td>\n",
       "      <td>304.740733</td>\n",
       "      <td>310.459188</td>\n",
       "      <td>285.944573</td>\n",
       "      <td>255.524574</td>\n",
       "      <td>419.788974</td>\n",
       "      <td>457.526612</td>\n",
       "      <td>517.576155</td>\n",
       "      <td>517.303068</td>\n",
       "      <td>475.087018</td>\n",
       "      <td>422.721320</td>\n",
       "      <td>486.114878</td>\n",
       "      <td>0</td>\n",
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       "      <th>S167W1</th>\n",
       "      <td>352.218137</td>\n",
       "      <td>434.144626</td>\n",
       "      <td>355.861077</td>\n",
       "      <td>446.941460</td>\n",
       "      <td>369.187810</td>\n",
       "      <td>351.056225</td>\n",
       "      <td>401.587008</td>\n",
       "      <td>343.221223</td>\n",
       "      <td>281.817039</td>\n",
       "      <td>303.818939</td>\n",
       "      <td>392.647286</td>\n",
       "      <td>431.740410</td>\n",
       "      <td>393.273398</td>\n",
       "      <td>387.944044</td>\n",
       "      <td>542.842977</td>\n",
       "      <td>536.968509</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S50W</th>\n",
       "      <td>269.647852</td>\n",
       "      <td>338.634255</td>\n",
       "      <td>344.864874</td>\n",
       "      <td>234.133116</td>\n",
       "      <td>256.133755</td>\n",
       "      <td>368.086639</td>\n",
       "      <td>368.735112</td>\n",
       "      <td>355.779238</td>\n",
       "      <td>234.084414</td>\n",
       "      <td>258.421585</td>\n",
       "      <td>391.401720</td>\n",
       "      <td>409.314609</td>\n",
       "      <td>358.623829</td>\n",
       "      <td>293.393776</td>\n",
       "      <td>625.496439</td>\n",
       "      <td>629.259563</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S165W1</th>\n",
       "      <td>205.958906</td>\n",
       "      <td>257.508954</td>\n",
       "      <td>265.755559</td>\n",
       "      <td>242.377389</td>\n",
       "      <td>203.218307</td>\n",
       "      <td>274.589074</td>\n",
       "      <td>278.940344</td>\n",
       "      <td>279.626380</td>\n",
       "      <td>257.992473</td>\n",
       "      <td>213.305772</td>\n",
       "      <td>249.756887</td>\n",
       "      <td>298.145780</td>\n",
       "      <td>280.551883</td>\n",
       "      <td>300.361348</td>\n",
       "      <td>294.697612</td>\n",
       "      <td>314.157604</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>33w1</th>\n",
       "      <td>133.813852</td>\n",
       "      <td>187.700915</td>\n",
       "      <td>214.031283</td>\n",
       "      <td>163.609798</td>\n",
       "      <td>143.845639</td>\n",
       "      <td>202.366174</td>\n",
       "      <td>233.904637</td>\n",
       "      <td>213.592914</td>\n",
       "      <td>182.788525</td>\n",
       "      <td>228.681643</td>\n",
       "      <td>206.899651</td>\n",
       "      <td>229.157174</td>\n",
       "      <td>210.288310</td>\n",
       "      <td>193.091059</td>\n",
       "      <td>295.865714</td>\n",
       "      <td>295.083444</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382w1</th>\n",
       "      <td>242.587595</td>\n",
       "      <td>230.059819</td>\n",
       "      <td>247.661365</td>\n",
       "      <td>230.707720</td>\n",
       "      <td>207.313741</td>\n",
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       "      <td>245.569738</td>\n",
       "      <td>247.023559</td>\n",
       "      <td>206.229312</td>\n",
       "      <td>161.192517</td>\n",
       "      <td>239.384986</td>\n",
       "      <td>268.091678</td>\n",
       "      <td>271.596783</td>\n",
       "      <td>251.919185</td>\n",
       "      <td>276.751926</td>\n",
       "      <td>301.223745</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>103w</th>\n",
       "      <td>163.103329</td>\n",
       "      <td>191.815826</td>\n",
       "      <td>195.462292</td>\n",
       "      <td>155.599866</td>\n",
       "      <td>163.146695</td>\n",
       "      <td>220.531592</td>\n",
       "      <td>253.294411</td>\n",
       "      <td>214.071590</td>\n",
       "      <td>153.416366</td>\n",
       "      <td>183.092314</td>\n",
       "      <td>318.559745</td>\n",
       "      <td>333.569794</td>\n",
       "      <td>328.175471</td>\n",
       "      <td>225.947617</td>\n",
       "      <td>313.374033</td>\n",
       "      <td>396.105128</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>307w1</th>\n",
       "      <td>249.677092</td>\n",
       "      <td>278.756457</td>\n",
       "      <td>250.845298</td>\n",
       "      <td>233.203164</td>\n",
       "      <td>244.705039</td>\n",
       "      <td>306.153452</td>\n",
       "      <td>303.798953</td>\n",
       "      <td>263.455901</td>\n",
       "      <td>236.327749</td>\n",
       "      <td>237.826767</td>\n",
       "      <td>355.031556</td>\n",
       "      <td>357.035163</td>\n",
       "      <td>338.611685</td>\n",
       "      <td>320.717762</td>\n",
       "      <td>362.815018</td>\n",
       "      <td>521.262616</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>022w1</th>\n",
       "      <td>293.180251</td>\n",
       "      <td>309.881497</td>\n",
       "      <td>308.534592</td>\n",
       "      <td>270.422913</td>\n",
       "      <td>272.894824</td>\n",
       "      <td>316.570740</td>\n",
       "      <td>352.379621</td>\n",
       "      <td>328.507380</td>\n",
       "      <td>278.392059</td>\n",
       "      <td>260.738461</td>\n",
       "      <td>336.340603</td>\n",
       "      <td>361.709217</td>\n",
       "      <td>368.099951</td>\n",
       "      <td>380.331913</td>\n",
       "      <td>506.696324</td>\n",
       "      <td>488.705591</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                F7          F3          F4          F8          T3  \\\n",
       "S42W1   234.691796  288.619884  297.082391  201.988486  201.117730   \n",
       "S31W    209.802646  310.155904  323.160348  227.777398  275.764142   \n",
       "S167W1  352.218137  434.144626  355.861077  446.941460  369.187810   \n",
       "S50W    269.647852  338.634255  344.864874  234.133116  256.133755   \n",
       "S165W1  205.958906  257.508954  265.755559  242.377389  203.218307   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "33w1    133.813852  187.700915  214.031283  163.609798  143.845639   \n",
       "382w1   242.587595  230.059819  247.661365  230.707720  207.313741   \n",
       "103w    163.103329  191.815826  195.462292  155.599866  163.146695   \n",
       "307w1   249.677092  278.756457  250.845298  233.203164  244.705039   \n",
       "022w1   293.180251  309.881497  308.534592  270.422913  272.894824   \n",
       "\n",
       "                C3          Cz          C4          T4          T5  \\\n",
       "S42W1   308.864366  320.471932  314.160993  196.207816  212.271569   \n",
       "S31W    304.740733  310.459188  285.944573  255.524574  419.788974   \n",
       "S167W1  351.056225  401.587008  343.221223  281.817039  303.818939   \n",
       "S50W    368.086639  368.735112  355.779238  234.084414  258.421585   \n",
       "S165W1  274.589074  278.940344  279.626380  257.992473  213.305772   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "33w1    202.366174  233.904637  213.592914  182.788525  228.681643   \n",
       "382w1   249.410504  245.569738  247.023559  206.229312  161.192517   \n",
       "103w    220.531592  253.294411  214.071590  153.416366  183.092314   \n",
       "307w1   306.153452  303.798953  263.455901  236.327749  237.826767   \n",
       "022w1   316.570740  352.379621  328.507380  278.392059  260.738461   \n",
       "\n",
       "                P3          Pz          P4          T6          01  \\\n",
       "S42W1   386.414909  366.827124  331.260919  210.572525  316.188987   \n",
       "S31W    457.526612  517.576155  517.303068  475.087018  422.721320   \n",
       "S167W1  392.647286  431.740410  393.273398  387.944044  542.842977   \n",
       "S50W    391.401720  409.314609  358.623829  293.393776  625.496439   \n",
       "S165W1  249.756887  298.145780  280.551883  300.361348  294.697612   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "33w1    206.899651  229.157174  210.288310  193.091059  295.865714   \n",
       "382w1   239.384986  268.091678  271.596783  251.919185  276.751926   \n",
       "103w    318.559745  333.569794  328.175471  225.947617  313.374033   \n",
       "307w1   355.031556  357.035163  338.611685  320.717762  362.815018   \n",
       "022w1   336.340603  361.709217  368.099951  380.331913  506.696324   \n",
       "\n",
       "                02  diagnosis  \n",
       "S42W1   368.760953          0  \n",
       "S31W    486.114878          0  \n",
       "S167W1  536.968509          0  \n",
       "S50W    629.259563          0  \n",
       "S165W1  314.157604          0  \n",
       "...            ...        ...  \n",
       "33w1    295.083444          1  \n",
       "382w1   301.223745          1  \n",
       "103w    396.105128          1  \n",
       "307w1   521.262616          1  \n",
       "022w1   488.705591          1  \n",
       "\n",
       "[84 rows x 17 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "dec73846",
   "metadata": {},
   "outputs": [
    {
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       "        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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>122871</th>\n",
       "      <th>122872</th>\n",
       "      <th>122873</th>\n",
       "      <th>122874</th>\n",
       "      <th>122875</th>\n",
       "      <th>122876</th>\n",
       "      <th>122877</th>\n",
       "      <th>122878</th>\n",
       "      <th>122879</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S42W1</th>\n",
       "      <td>-154.12</td>\n",
       "      <td>-112.09</td>\n",
       "      <td>-154.12</td>\n",
       "      <td>-154.12</td>\n",
       "      <td>-14.01</td>\n",
       "      <td>-51.37</td>\n",
       "      <td>-233.51</td>\n",
       "      <td>-233.51</td>\n",
       "      <td>-112.09</td>\n",
       "      <td>-112.09</td>\n",
       "      <td>...</td>\n",
       "      <td>317.58</td>\n",
       "      <td>616.47</td>\n",
       "      <td>219.5</td>\n",
       "      <td>-233.51</td>\n",
       "      <td>-32.69</td>\n",
       "      <td>158.79</td>\n",
       "      <td>177.47</td>\n",
       "      <td>317.58</td>\n",
       "      <td>126.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S31W</th>\n",
       "      <td>308.45</td>\n",
       "      <td>248.96</td>\n",
       "      <td>328.28</td>\n",
       "      <td>429.63</td>\n",
       "      <td>469.29</td>\n",
       "      <td>429.63</td>\n",
       "      <td>508.94</td>\n",
       "      <td>627.92</td>\n",
       "      <td>887.9</td>\n",
       "      <td>808.58</td>\n",
       "      <td>...</td>\n",
       "      <td>-321.67</td>\n",
       "      <td>-420.82</td>\n",
       "      <td>-491.32</td>\n",
       "      <td>-581.65</td>\n",
       "      <td>-550.81</td>\n",
       "      <td>-451.66</td>\n",
       "      <td>-370.14</td>\n",
       "      <td>-121.18</td>\n",
       "      <td>-50.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S167W1</th>\n",
       "      <td>-91.45</td>\n",
       "      <td>-91.45</td>\n",
       "      <td>-150.39</td>\n",
       "      <td>-231.68</td>\n",
       "      <td>-191.03</td>\n",
       "      <td>-111.77</td>\n",
       "      <td>-30.48</td>\n",
       "      <td>-91.45</td>\n",
       "      <td>-270.29</td>\n",
       "      <td>-331.26</td>\n",
       "      <td>...</td>\n",
       "      <td>138.19</td>\n",
       "      <td>-50.81</td>\n",
       "      <td>-231.68</td>\n",
       "      <td>-591.38</td>\n",
       "      <td>-731.61</td>\n",
       "      <td>-731.61</td>\n",
       "      <td>-611.7</td>\n",
       "      <td>-270.29</td>\n",
       "      <td>-40.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S50W</th>\n",
       "      <td>248.66</td>\n",
       "      <td>229.26</td>\n",
       "      <td>229.26</td>\n",
       "      <td>308.62</td>\n",
       "      <td>449.71</td>\n",
       "      <td>409.15</td>\n",
       "      <td>128.74</td>\n",
       "      <td>29.98</td>\n",
       "      <td>149.9</td>\n",
       "      <td>209.86</td>\n",
       "      <td>...</td>\n",
       "      <td>-430.31</td>\n",
       "      <td>-1010.52</td>\n",
       "      <td>-1171.0</td>\n",
       "      <td>-1331.49</td>\n",
       "      <td>-1331.49</td>\n",
       "      <td>-1070.48</td>\n",
       "      <td>-751.28</td>\n",
       "      <td>98.76</td>\n",
       "      <td>619.01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S165W1</th>\n",
       "      <td>8.95</td>\n",
       "      <td>-10.07</td>\n",
       "      <td>-170.04</td>\n",
       "      <td>-250.59</td>\n",
       "      <td>-350.16</td>\n",
       "      <td>-330.02</td>\n",
       "      <td>-151.03</td>\n",
       "      <td>-50.34</td>\n",
       "      <td>8.95</td>\n",
       "      <td>-50.34</td>\n",
       "      <td>...</td>\n",
       "      <td>-130.89</td>\n",
       "      <td>-350.16</td>\n",
       "      <td>-390.43</td>\n",
       "      <td>-210.32</td>\n",
       "      <td>-30.21</td>\n",
       "      <td>219.27</td>\n",
       "      <td>279.68</td>\n",
       "      <td>199.13</td>\n",
       "      <td>99.57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33w1</th>\n",
       "      <td>-11.41</td>\n",
       "      <td>-74.18</td>\n",
       "      <td>-171.18</td>\n",
       "      <td>-194.0</td>\n",
       "      <td>-171.18</td>\n",
       "      <td>-154.06</td>\n",
       "      <td>-91.29</td>\n",
       "      <td>-91.29</td>\n",
       "      <td>-74.18</td>\n",
       "      <td>-74.18</td>\n",
       "      <td>...</td>\n",
       "      <td>-450.77</td>\n",
       "      <td>-410.83</td>\n",
       "      <td>-353.77</td>\n",
       "      <td>-194.0</td>\n",
       "      <td>-85.59</td>\n",
       "      <td>39.94</td>\n",
       "      <td>108.41</td>\n",
       "      <td>79.88</td>\n",
       "      <td>45.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382w1</th>\n",
       "      <td>349.76</td>\n",
       "      <td>329.87</td>\n",
       "      <td>149.12</td>\n",
       "      <td>169.91</td>\n",
       "      <td>569.37</td>\n",
       "      <td>729.34</td>\n",
       "      <td>709.45</td>\n",
       "      <td>609.14</td>\n",
       "      <td>569.37</td>\n",
       "      <td>609.14</td>\n",
       "      <td>...</td>\n",
       "      <td>69.59</td>\n",
       "      <td>-210.58</td>\n",
       "      <td>-250.34</td>\n",
       "      <td>-170.81</td>\n",
       "      <td>-90.38</td>\n",
       "      <td>159.97</td>\n",
       "      <td>239.5</td>\n",
       "      <td>-30.73</td>\n",
       "      <td>-310.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103w</th>\n",
       "      <td>-130.55</td>\n",
       "      <td>-30.72</td>\n",
       "      <td>49.92</td>\n",
       "      <td>49.92</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-10.24</td>\n",
       "      <td>8.96</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-151.03</td>\n",
       "      <td>-270.06</td>\n",
       "      <td>...</td>\n",
       "      <td>419.81</td>\n",
       "      <td>319.98</td>\n",
       "      <td>139.51</td>\n",
       "      <td>-340.46</td>\n",
       "      <td>-550.37</td>\n",
       "      <td>-620.76</td>\n",
       "      <td>-481.25</td>\n",
       "      <td>-30.72</td>\n",
       "      <td>139.51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307w1</th>\n",
       "      <td>67.98</td>\n",
       "      <td>166.85</td>\n",
       "      <td>247.19</td>\n",
       "      <td>129.78</td>\n",
       "      <td>-173.03</td>\n",
       "      <td>-253.37</td>\n",
       "      <td>-253.37</td>\n",
       "      <td>-234.83</td>\n",
       "      <td>-191.57</td>\n",
       "      <td>-191.57</td>\n",
       "      <td>...</td>\n",
       "      <td>-185.39</td>\n",
       "      <td>-30.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-55.62</td>\n",
       "      <td>-12.36</td>\n",
       "      <td>154.49</td>\n",
       "      <td>259.55</td>\n",
       "      <td>438.76</td>\n",
       "      <td>574.72</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>022w1</th>\n",
       "      <td>108.01</td>\n",
       "      <td>208.82</td>\n",
       "      <td>388.84</td>\n",
       "      <td>446.45</td>\n",
       "      <td>446.45</td>\n",
       "      <td>349.24</td>\n",
       "      <td>86.41</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>86.41</td>\n",
       "      <td>...</td>\n",
       "      <td>-1231.33</td>\n",
       "      <td>-1342.95</td>\n",
       "      <td>-1213.33</td>\n",
       "      <td>-471.65</td>\n",
       "      <td>-10.8</td>\n",
       "      <td>669.67</td>\n",
       "      <td>716.48</td>\n",
       "      <td>388.84</td>\n",
       "      <td>79.21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 122881 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0       1       2       3       4       5       6       7  \\\n",
       "S42W1   -154.12  -112.09 -154.12 -154.12  -14.01  -51.37 -233.51 -233.51   \n",
       "S31W     308.45   248.96  328.28  429.63  469.29  429.63  508.94  627.92   \n",
       "S167W1   -91.45   -91.45 -150.39 -231.68 -191.03 -111.77  -30.48  -91.45   \n",
       "S50W     248.66   229.26  229.26  308.62  449.71  409.15  128.74   29.98   \n",
       "S165W1     8.95   -10.07 -170.04 -250.59 -350.16 -330.02 -151.03  -50.34   \n",
       "...          ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "33w1     -11.41   -74.18 -171.18  -194.0 -171.18 -154.06  -91.29  -91.29   \n",
       "382w1    349.76   329.87  149.12  169.91  569.37  729.34  709.45  609.14   \n",
       "103w    -130.55   -30.72   49.92   49.92    8.96  -10.24    8.96    8.96   \n",
       "307w1     67.98   166.85  247.19  129.78 -173.03 -253.37 -253.37 -234.83   \n",
       "022w1    108.01   208.82  388.84  446.45  446.45  349.24   86.41     7.2   \n",
       "\n",
       "             8       9  ...   122871   122872   122873   122874   122875  \\\n",
       "S42W1  -112.09 -112.09  ...   317.58   616.47    219.5  -233.51   -32.69   \n",
       "S31W     887.9  808.58  ...  -321.67  -420.82  -491.32  -581.65  -550.81   \n",
       "S167W1 -270.29 -331.26  ...   138.19   -50.81  -231.68  -591.38  -731.61   \n",
       "S50W     149.9  209.86  ...  -430.31 -1010.52  -1171.0 -1331.49 -1331.49   \n",
       "S165W1    8.95  -50.34  ...  -130.89  -350.16  -390.43  -210.32   -30.21   \n",
       "...        ...     ...  ...      ...      ...      ...      ...      ...   \n",
       "33w1    -74.18  -74.18  ...  -450.77  -410.83  -353.77   -194.0   -85.59   \n",
       "382w1   569.37  609.14  ...    69.59  -210.58  -250.34  -170.81   -90.38   \n",
       "103w   -151.03 -270.06  ...   419.81   319.98   139.51  -340.46  -550.37   \n",
       "307w1  -191.57 -191.57  ...  -185.39    -30.9      0.0   -55.62   -12.36   \n",
       "022w1      7.2   86.41  ... -1231.33 -1342.95 -1213.33  -471.65    -10.8   \n",
       "\n",
       "         122876  122877  122878  122879 diagnosis  \n",
       "S42W1    158.79  177.47  317.58   126.1         0  \n",
       "S31W    -451.66 -370.14 -121.18  -50.67         0  \n",
       "S167W1  -731.61  -611.7 -270.29  -40.64         0  \n",
       "S50W   -1070.48 -751.28   98.76  619.01         0  \n",
       "S165W1   219.27  279.68  199.13   99.57         0  \n",
       "...         ...     ...     ...     ...       ...  \n",
       "33w1      39.94  108.41   79.88   45.65         1  \n",
       "382w1    159.97   239.5  -30.73 -310.89         1  \n",
       "103w    -620.76 -481.25  -30.72  139.51         1  \n",
       "307w1    154.49  259.55  438.76  574.72         1  \n",
       "022w1    669.67  716.48  388.84   79.21         1  \n",
       "\n",
       "[84 rows x 122881 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FINAL_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37b4e803",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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