Internal_Standard_Checks.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02KF7vpWFGov",
        "outputId": "fe481b40-ea48-4567-8c54-77e771732982"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "negative_path = \"/content/drive/MyDrive/Control_Mg_Negative.csv\"\n",
        "positive_path = \"/content/drive/MyDrive/Control_Mg_Positive.csv\""
      ],
      "metadata": {
        "id": "LABLC7RgFW-D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy.stats import pearsonr\n",
        "from scipy.stats import rankdata\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from scipy.stats import ttest_ind\n",
        "from sklearn.impute import KNNImputer\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.model_selection import train_test_split,GridSearchCV\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix,roc_curve, RocCurveDisplay\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import svm\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "WGgbXwguFegs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "positive_data = pd.read_csv(positive_path)\n",
        "negative_data = pd.read_csv(negative_path)\n",
        "positive_data"
      ],
      "metadata": {
        "id": "BWJiwps7FghH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "positive_data = positive_data[positive_data['Sample'] != 'Compound75']\n",
        "positive_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "m5Tsk_6jFnd1",
        "outputId": "c83aa85e-4e8c-4a19-e3c1-d3407e358f19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Sample        S1        S2        S3        S4        S5  \\\n",
              "0              Label  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
              "1       Compound2078  7.206298  7.227946  7.366802  7.262313  7.285364   \n",
              "2       Compound4363  7.339353  7.345237  7.556745  7.465671  7.472435   \n",
              "3      Compound13589  6.643306  6.687743  6.465109  6.558714  6.760262   \n",
              "4       Compound5278  6.844170  6.682059  6.855306  6.786066  6.608705   \n",
              "...              ...       ...       ...       ...       ...       ...   \n",
              "11587  Compound15751  5.942907  5.812424  6.014887  5.973371  5.923199   \n",
              "11588  Compound15759  5.897462  5.956110  5.956139  5.909347  6.049577   \n",
              "11589  Compound15767  5.857506  5.922598  5.858036  5.907506  5.823600   \n",
              "11590  Compound15771  5.974184  5.815394  5.826349  5.945384  5.977391   \n",
              "11591  Compound15772  5.892866  5.846532  5.811023  5.825741  5.872908   \n",
              "\n",
              "             S6        S7        S8        S9  ...       S21       S22  \\\n",
              "0      1.000000  1.000000  1.000000  2.000000  ...  2.000000  2.000000   \n",
              "1      6.998687  7.132948  7.160038  7.390819  ...  7.315623  7.350978   \n",
              "2      7.174356  7.553920  7.135764  7.356667  ...  7.490783  6.872999   \n",
              "3      6.825837  6.609359  6.776504  6.503055  ...  6.635033  6.817662   \n",
              "4      6.962785  6.917193  6.859684  6.852445  ...  6.705961  7.050534   \n",
              "...         ...       ...       ...       ...  ...       ...       ...   \n",
              "11587  5.954896  5.984806  5.703635  5.837919  ...  5.921067  5.786371   \n",
              "11588  5.902071  5.928563  6.002281  5.963314  ...  5.923813  5.701904   \n",
              "11589  5.905061  6.008754  5.748952  5.955079  ...  5.871754  5.889129   \n",
              "11590  5.961304  5.615242  5.892542  5.894168  ...  5.811251  5.738694   \n",
              "11591  5.801443  5.805965  5.909493  5.857066  ...  5.849045  5.978340   \n",
              "\n",
              "            S23       S24       S25       S26       S27       S28       S29  \\\n",
              "0      2.000000  2.000000  2.000000  2.000000  2.000000  2.000000  2.000000   \n",
              "1      7.169137  7.067084  7.070416  7.081180  7.214982  7.140902  7.017724   \n",
              "2      7.397464  7.300424  7.600194  7.339486  7.181688  7.452621  7.474829   \n",
              "3      6.590528  6.496227  6.626065  6.712181  6.720424  6.837801  6.744136   \n",
              "4      6.804359  6.942128  6.721300  6.783166  6.629169  6.911243  6.798165   \n",
              "...         ...       ...       ...       ...       ...       ...       ...   \n",
              "11587  5.970959  6.012363  6.097851  5.910447  5.954089  5.794707  5.841617   \n",
              "11588  5.948298  5.934399  5.808896  5.880014  5.969594  5.950216  5.893222   \n",
              "11589  5.896064  6.004864  5.833496  5.881322  6.039688  6.070650  5.840757   \n",
              "11590  5.925044  6.011235  5.757122  5.852222  5.872955  5.984447  5.843946   \n",
              "11591  5.894769  5.837669  5.860348  5.809426  5.902778  5.917429  5.797798   \n",
              "\n",
              "            S30  \n",
              "0      2.000000  \n",
              "1      7.153893  \n",
              "2      7.069202  \n",
              "3      6.633435  \n",
              "4      6.971550  \n",
              "...         ...  \n",
              "11587  5.954547  \n",
              "11588  5.816031  \n",
              "11589  5.984992  \n",
              "11590  5.875227  \n",
              "11591  5.894428  \n",
              "\n",
              "[11591 rows x 31 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sample</th>\n",
              "      <th>S1</th>\n",
              "      <th>S2</th>\n",
              "      <th>S3</th>\n",
              "      <th>S4</th>\n",
              "      <th>S5</th>\n",
              "      <th>S6</th>\n",
              "      <th>S7</th>\n",
              "      <th>S8</th>\n",
              "      <th>S9</th>\n",
              "      <th>...</th>\n",
              "      <th>S21</th>\n",
              "      <th>S22</th>\n",
              "      <th>S23</th>\n",
              "      <th>S24</th>\n",
              "      <th>S25</th>\n",
              "      <th>S26</th>\n",
              "      <th>S27</th>\n",
              "      <th>S28</th>\n",
              "      <th>S29</th>\n",
              "      <th>S30</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Label</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Compound2078</td>\n",
              "      <td>7.206298</td>\n",
              "      <td>7.227946</td>\n",
              "      <td>7.366802</td>\n",
              "      <td>7.262313</td>\n",
              "      <td>7.285364</td>\n",
              "      <td>6.998687</td>\n",
              "      <td>7.132948</td>\n",
              "      <td>7.160038</td>\n",
              "      <td>7.390819</td>\n",
              "      <td>...</td>\n",
              "      <td>7.315623</td>\n",
              "      <td>7.350978</td>\n",
              "      <td>7.169137</td>\n",
              "      <td>7.067084</td>\n",
              "      <td>7.070416</td>\n",
              "      <td>7.081180</td>\n",
              "      <td>7.214982</td>\n",
              "      <td>7.140902</td>\n",
              "      <td>7.017724</td>\n",
              "      <td>7.153893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Compound4363</td>\n",
              "      <td>7.339353</td>\n",
              "      <td>7.345237</td>\n",
              "      <td>7.556745</td>\n",
              "      <td>7.465671</td>\n",
              "      <td>7.472435</td>\n",
              "      <td>7.174356</td>\n",
              "      <td>7.553920</td>\n",
              "      <td>7.135764</td>\n",
              "      <td>7.356667</td>\n",
              "      <td>...</td>\n",
              "      <td>7.490783</td>\n",
              "      <td>6.872999</td>\n",
              "      <td>7.397464</td>\n",
              "      <td>7.300424</td>\n",
              "      <td>7.600194</td>\n",
              "      <td>7.339486</td>\n",
              "      <td>7.181688</td>\n",
              "      <td>7.452621</td>\n",
              "      <td>7.474829</td>\n",
              "      <td>7.069202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Compound13589</td>\n",
              "      <td>6.643306</td>\n",
              "      <td>6.687743</td>\n",
              "      <td>6.465109</td>\n",
              "      <td>6.558714</td>\n",
              "      <td>6.760262</td>\n",
              "      <td>6.825837</td>\n",
              "      <td>6.609359</td>\n",
              "      <td>6.776504</td>\n",
              "      <td>6.503055</td>\n",
              "      <td>...</td>\n",
              "      <td>6.635033</td>\n",
              "      <td>6.817662</td>\n",
              "      <td>6.590528</td>\n",
              "      <td>6.496227</td>\n",
              "      <td>6.626065</td>\n",
              "      <td>6.712181</td>\n",
              "      <td>6.720424</td>\n",
              "      <td>6.837801</td>\n",
              "      <td>6.744136</td>\n",
              "      <td>6.633435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Compound5278</td>\n",
              "      <td>6.844170</td>\n",
              "      <td>6.682059</td>\n",
              "      <td>6.855306</td>\n",
              "      <td>6.786066</td>\n",
              "      <td>6.608705</td>\n",
              "      <td>6.962785</td>\n",
              "      <td>6.917193</td>\n",
              "      <td>6.859684</td>\n",
              "      <td>6.852445</td>\n",
              "      <td>...</td>\n",
              "      <td>6.705961</td>\n",
              "      <td>7.050534</td>\n",
              "      <td>6.804359</td>\n",
              "      <td>6.942128</td>\n",
              "      <td>6.721300</td>\n",
              "      <td>6.783166</td>\n",
              "      <td>6.629169</td>\n",
              "      <td>6.911243</td>\n",
              "      <td>6.798165</td>\n",
              "      <td>6.971550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <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>11587</th>\n",
              "      <td>Compound15751</td>\n",
              "      <td>5.942907</td>\n",
              "      <td>5.812424</td>\n",
              "      <td>6.014887</td>\n",
              "      <td>5.973371</td>\n",
              "      <td>5.923199</td>\n",
              "      <td>5.954896</td>\n",
              "      <td>5.984806</td>\n",
              "      <td>5.703635</td>\n",
              "      <td>5.837919</td>\n",
              "      <td>...</td>\n",
              "      <td>5.921067</td>\n",
              "      <td>5.786371</td>\n",
              "      <td>5.970959</td>\n",
              "      <td>6.012363</td>\n",
              "      <td>6.097851</td>\n",
              "      <td>5.910447</td>\n",
              "      <td>5.954089</td>\n",
              "      <td>5.794707</td>\n",
              "      <td>5.841617</td>\n",
              "      <td>5.954547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11588</th>\n",
              "      <td>Compound15759</td>\n",
              "      <td>5.897462</td>\n",
              "      <td>5.956110</td>\n",
              "      <td>5.956139</td>\n",
              "      <td>5.909347</td>\n",
              "      <td>6.049577</td>\n",
              "      <td>5.902071</td>\n",
              "      <td>5.928563</td>\n",
              "      <td>6.002281</td>\n",
              "      <td>5.963314</td>\n",
              "      <td>...</td>\n",
              "      <td>5.923813</td>\n",
              "      <td>5.701904</td>\n",
              "      <td>5.948298</td>\n",
              "      <td>5.934399</td>\n",
              "      <td>5.808896</td>\n",
              "      <td>5.880014</td>\n",
              "      <td>5.969594</td>\n",
              "      <td>5.950216</td>\n",
              "      <td>5.893222</td>\n",
              "      <td>5.816031</td>\n",
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              "    <tr>\n",
              "      <th>11589</th>\n",
              "      <td>Compound15767</td>\n",
              "      <td>5.857506</td>\n",
              "      <td>5.922598</td>\n",
              "      <td>5.858036</td>\n",
              "      <td>5.907506</td>\n",
              "      <td>5.823600</td>\n",
              "      <td>5.905061</td>\n",
              "      <td>6.008754</td>\n",
              "      <td>5.748952</td>\n",
              "      <td>5.955079</td>\n",
              "      <td>...</td>\n",
              "      <td>5.871754</td>\n",
              "      <td>5.889129</td>\n",
              "      <td>5.896064</td>\n",
              "      <td>6.004864</td>\n",
              "      <td>5.833496</td>\n",
              "      <td>5.881322</td>\n",
              "      <td>6.039688</td>\n",
              "      <td>6.070650</td>\n",
              "      <td>5.840757</td>\n",
              "      <td>5.984992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11590</th>\n",
              "      <td>Compound15771</td>\n",
              "      <td>5.974184</td>\n",
              "      <td>5.815394</td>\n",
              "      <td>5.826349</td>\n",
              "      <td>5.945384</td>\n",
              "      <td>5.977391</td>\n",
              "      <td>5.961304</td>\n",
              "      <td>5.615242</td>\n",
              "      <td>5.892542</td>\n",
              "      <td>5.894168</td>\n",
              "      <td>...</td>\n",
              "      <td>5.811251</td>\n",
              "      <td>5.738694</td>\n",
              "      <td>5.925044</td>\n",
              "      <td>6.011235</td>\n",
              "      <td>5.757122</td>\n",
              "      <td>5.852222</td>\n",
              "      <td>5.872955</td>\n",
              "      <td>5.984447</td>\n",
              "      <td>5.843946</td>\n",
              "      <td>5.875227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11591</th>\n",
              "      <td>Compound15772</td>\n",
              "      <td>5.892866</td>\n",
              "      <td>5.846532</td>\n",
              "      <td>5.811023</td>\n",
              "      <td>5.825741</td>\n",
              "      <td>5.872908</td>\n",
              "      <td>5.801443</td>\n",
              "      <td>5.805965</td>\n",
              "      <td>5.909493</td>\n",
              "      <td>5.857066</td>\n",
              "      <td>...</td>\n",
              "      <td>5.849045</td>\n",
              "      <td>5.978340</td>\n",
              "      <td>5.894769</td>\n",
              "      <td>5.837669</td>\n",
              "      <td>5.860348</td>\n",
              "      <td>5.809426</td>\n",
              "      <td>5.902778</td>\n",
              "      <td>5.917429</td>\n",
              "      <td>5.797798</td>\n",
              "      <td>5.894428</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>11591 rows × 31 columns</p>\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('positive_data');\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "positive_data"
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          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "negative_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "3KiEiRhUGfEv",
        "outputId": "0d06862b-1035-459c-fde6-5efb626c4264"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Sample        S1        S2        S3        S4        S5  \\\n",
              "0     Compound Annotation  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
              "1            Compound 468  7.475505  7.329756  6.811946  7.385248  7.626119   \n",
              "2           Compound 3382  6.989769  7.080236  6.889824  6.213607  6.722271   \n",
              "3           Compound 4793  6.819188  6.868873  6.843599  6.922629  6.833867   \n",
              "4           Compound 2186  6.858738  6.787733  6.856540  6.970408  6.947345   \n",
              "...                   ...       ...       ...       ...       ...       ...   \n",
              "5201        Compound 5919  6.245255  6.194300  6.165928  6.390953  6.402920   \n",
              "5202        Compound 5930  6.265979  6.269323  6.296698  6.347539  6.403661   \n",
              "5203        Compound 5964  6.242246  6.124306  6.177051  6.082200  6.317291   \n",
              "5204        Compound 5978  6.157839  6.032436  6.130037  6.045991  6.050399   \n",
              "5205        Compound 5980  5.683136  5.884875  5.765027  5.616814  5.886334   \n",
              "\n",
              "            S6        S7        S8        S9  ...       S19       S20  \\\n",
              "0     1.000000  1.000000  1.000000  2.000000  ...  2.000000  2.000000   \n",
              "1     7.590575  7.403692  7.287125  7.355329  ...  7.507965  7.435537   \n",
              "2     7.013836  7.060645  6.783460  7.001999  ...  7.101507  6.887513   \n",
              "3     6.737740  6.826248  6.793886  6.845979  ...  6.743371  6.835760   \n",
              "4     6.949742  7.394426  6.916902  6.692923  ...  6.995888  6.905149   \n",
              "...        ...       ...       ...       ...  ...       ...       ...   \n",
              "5201  6.258669  6.262649  6.370278  6.256610  ...  6.269349  6.284218   \n",
              "5202  6.362765  6.222485  6.277444  6.303384  ...  6.320016  6.411511   \n",
              "5203  6.257584  6.223415  6.282368  6.305714  ...  6.201274  6.219690   \n",
              "5204  6.133175  6.040674  6.042695  6.117320  ...  6.161811  6.124916   \n",
              "5205  5.653571  5.901391  5.905859  5.715543  ...  5.938979  5.725710   \n",
              "\n",
              "           S21       S22       S23       S24       S25       S26       S27  \\\n",
              "0     2.000000  2.000000  2.000000  2.000000  2.000000  2.000000  2.000000   \n",
              "1     7.330367  7.367307  7.418969  7.351389  7.670616  7.412378  7.425305   \n",
              "2     6.854224  7.062717  6.753966  6.863752  6.722227  6.822723  6.745978   \n",
              "3     6.805242  6.758864  6.780618  6.735840  6.859521  6.907125  6.843397   \n",
              "4     6.875869  6.914752  6.960787  6.756019  6.932341  6.792646  6.878472   \n",
              "...        ...       ...       ...       ...       ...       ...       ...   \n",
              "5201  6.166841  6.263467  6.387067  6.334254  6.145515  6.364408  6.246458   \n",
              "5202  6.379362  6.353872  6.367754  6.400277  6.367340  6.348874  6.343532   \n",
              "5203  6.144898  6.269480  6.191840  6.193191  6.295073  6.268855  6.180628   \n",
              "5204  6.129828  6.095715  6.096871  6.076834  6.058739  6.199614  5.997863   \n",
              "5205  5.471979  5.811799  5.761502  5.704077  5.918673  5.755731  5.892346   \n",
              "\n",
              "           S28  \n",
              "0     2.000000  \n",
              "1     7.367272  \n",
              "2     6.775755  \n",
              "3     6.905871  \n",
              "4     6.970429  \n",
              "...        ...  \n",
              "5201  6.258794  \n",
              "5202  6.375171  \n",
              "5203  6.218116  \n",
              "5204  5.999938  \n",
              "5205  5.779499  \n",
              "\n",
              "[5206 rows x 29 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sample</th>\n",
              "      <th>S1</th>\n",
              "      <th>S2</th>\n",
              "      <th>S3</th>\n",
              "      <th>S4</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Compound Annotation</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Compound 468</td>\n",
              "      <td>7.475505</td>\n",
              "      <td>7.329756</td>\n",
              "      <td>6.811946</td>\n",
              "      <td>7.385248</td>\n",
              "      <td>7.626119</td>\n",
              "      <td>7.590575</td>\n",
              "      <td>7.403692</td>\n",
              "      <td>7.287125</td>\n",
              "      <td>7.355329</td>\n",
              "      <td>...</td>\n",
              "      <td>7.507965</td>\n",
              "      <td>7.435537</td>\n",
              "      <td>7.330367</td>\n",
              "      <td>7.367307</td>\n",
              "      <td>7.418969</td>\n",
              "      <td>7.351389</td>\n",
              "      <td>7.670616</td>\n",
              "      <td>7.412378</td>\n",
              "      <td>7.425305</td>\n",
              "      <td>7.367272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Compound 3382</td>\n",
              "      <td>6.989769</td>\n",
              "      <td>7.080236</td>\n",
              "      <td>6.889824</td>\n",
              "      <td>6.213607</td>\n",
              "      <td>6.722271</td>\n",
              "      <td>7.013836</td>\n",
              "      <td>7.060645</td>\n",
              "      <td>6.783460</td>\n",
              "      <td>7.001999</td>\n",
              "      <td>...</td>\n",
              "      <td>7.101507</td>\n",
              "      <td>6.887513</td>\n",
              "      <td>6.854224</td>\n",
              "      <td>7.062717</td>\n",
              "      <td>6.753966</td>\n",
              "      <td>6.863752</td>\n",
              "      <td>6.722227</td>\n",
              "      <td>6.822723</td>\n",
              "      <td>6.745978</td>\n",
              "      <td>6.775755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Compound 4793</td>\n",
              "      <td>6.819188</td>\n",
              "      <td>6.868873</td>\n",
              "      <td>6.843599</td>\n",
              "      <td>6.922629</td>\n",
              "      <td>6.833867</td>\n",
              "      <td>6.737740</td>\n",
              "      <td>6.826248</td>\n",
              "      <td>6.793886</td>\n",
              "      <td>6.845979</td>\n",
              "      <td>...</td>\n",
              "      <td>6.743371</td>\n",
              "      <td>6.835760</td>\n",
              "      <td>6.805242</td>\n",
              "      <td>6.758864</td>\n",
              "      <td>6.780618</td>\n",
              "      <td>6.735840</td>\n",
              "      <td>6.859521</td>\n",
              "      <td>6.907125</td>\n",
              "      <td>6.843397</td>\n",
              "      <td>6.905871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Compound 2186</td>\n",
              "      <td>6.858738</td>\n",
              "      <td>6.787733</td>\n",
              "      <td>6.856540</td>\n",
              "      <td>6.970408</td>\n",
              "      <td>6.947345</td>\n",
              "      <td>6.949742</td>\n",
              "      <td>7.394426</td>\n",
              "      <td>6.916902</td>\n",
              "      <td>6.692923</td>\n",
              "      <td>...</td>\n",
              "      <td>6.995888</td>\n",
              "      <td>6.905149</td>\n",
              "      <td>6.875869</td>\n",
              "      <td>6.914752</td>\n",
              "      <td>6.960787</td>\n",
              "      <td>6.756019</td>\n",
              "      <td>6.932341</td>\n",
              "      <td>6.792646</td>\n",
              "      <td>6.878472</td>\n",
              "      <td>6.970429</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>5201</th>\n",
              "      <td>Compound 5919</td>\n",
              "      <td>6.245255</td>\n",
              "      <td>6.194300</td>\n",
              "      <td>6.165928</td>\n",
              "      <td>6.390953</td>\n",
              "      <td>6.402920</td>\n",
              "      <td>6.258669</td>\n",
              "      <td>6.262649</td>\n",
              "      <td>6.370278</td>\n",
              "      <td>6.256610</td>\n",
              "      <td>...</td>\n",
              "      <td>6.269349</td>\n",
              "      <td>6.284218</td>\n",
              "      <td>6.166841</td>\n",
              "      <td>6.263467</td>\n",
              "      <td>6.387067</td>\n",
              "      <td>6.334254</td>\n",
              "      <td>6.145515</td>\n",
              "      <td>6.364408</td>\n",
              "      <td>6.246458</td>\n",
              "      <td>6.258794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5202</th>\n",
              "      <td>Compound 5930</td>\n",
              "      <td>6.265979</td>\n",
              "      <td>6.269323</td>\n",
              "      <td>6.296698</td>\n",
              "      <td>6.347539</td>\n",
              "      <td>6.403661</td>\n",
              "      <td>6.362765</td>\n",
              "      <td>6.222485</td>\n",
              "      <td>6.277444</td>\n",
              "      <td>6.303384</td>\n",
              "      <td>...</td>\n",
              "      <td>6.320016</td>\n",
              "      <td>6.411511</td>\n",
              "      <td>6.379362</td>\n",
              "      <td>6.353872</td>\n",
              "      <td>6.367754</td>\n",
              "      <td>6.400277</td>\n",
              "      <td>6.367340</td>\n",
              "      <td>6.348874</td>\n",
              "      <td>6.343532</td>\n",
              "      <td>6.375171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5203</th>\n",
              "      <td>Compound 5964</td>\n",
              "      <td>6.242246</td>\n",
              "      <td>6.124306</td>\n",
              "      <td>6.177051</td>\n",
              "      <td>6.082200</td>\n",
              "      <td>6.317291</td>\n",
              "      <td>6.257584</td>\n",
              "      <td>6.223415</td>\n",
              "      <td>6.282368</td>\n",
              "      <td>6.305714</td>\n",
              "      <td>...</td>\n",
              "      <td>6.201274</td>\n",
              "      <td>6.219690</td>\n",
              "      <td>6.144898</td>\n",
              "      <td>6.269480</td>\n",
              "      <td>6.191840</td>\n",
              "      <td>6.193191</td>\n",
              "      <td>6.295073</td>\n",
              "      <td>6.268855</td>\n",
              "      <td>6.180628</td>\n",
              "      <td>6.218116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5204</th>\n",
              "      <td>Compound 5978</td>\n",
              "      <td>6.157839</td>\n",
              "      <td>6.032436</td>\n",
              "      <td>6.130037</td>\n",
              "      <td>6.045991</td>\n",
              "      <td>6.050399</td>\n",
              "      <td>6.133175</td>\n",
              "      <td>6.040674</td>\n",
              "      <td>6.042695</td>\n",
              "      <td>6.117320</td>\n",
              "      <td>...</td>\n",
              "      <td>6.161811</td>\n",
              "      <td>6.124916</td>\n",
              "      <td>6.129828</td>\n",
              "      <td>6.095715</td>\n",
              "      <td>6.096871</td>\n",
              "      <td>6.076834</td>\n",
              "      <td>6.058739</td>\n",
              "      <td>6.199614</td>\n",
              "      <td>5.997863</td>\n",
              "      <td>5.999938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5205</th>\n",
              "      <td>Compound 5980</td>\n",
              "      <td>5.683136</td>\n",
              "      <td>5.884875</td>\n",
              "      <td>5.765027</td>\n",
              "      <td>5.616814</td>\n",
              "      <td>5.886334</td>\n",
              "      <td>5.653571</td>\n",
              "      <td>5.901391</td>\n",
              "      <td>5.905859</td>\n",
              "      <td>5.715543</td>\n",
              "      <td>...</td>\n",
              "      <td>5.938979</td>\n",
              "      <td>5.725710</td>\n",
              "      <td>5.471979</td>\n",
              "      <td>5.811799</td>\n",
              "      <td>5.761502</td>\n",
              "      <td>5.704077</td>\n",
              "      <td>5.918673</td>\n",
              "      <td>5.755731</td>\n",
              "      <td>5.892346</td>\n",
              "      <td>5.779499</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5206 rows × 29 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c89ba72d-f6b2-4eaf-a1fe-f6bc44b9084b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9d06b712-4760-44ce-a392-0dadb93d4186\">\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('negative_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('negative_data');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "negative_data"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "positive_data = positive_data.drop(0,axis = 0)\n",
        "negative_data = negative_data.drop(0,axis = 0)"
      ],
      "metadata": {
        "id": "i0ScchxTGmt3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#samplewise statistics for positive data\n",
        "positive_data.iloc[:, 1:].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "0jv_JnDKG1XE",
        "outputId": "15a999e4-e267-4736-8fee-6d6ea8f5b313"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 S1            S2            S3            S4            S5  \\\n",
              "count  11590.000000  11590.000000  11590.000000  11590.000000  11590.000000   \n",
              "mean       6.996189      6.985335      6.985900      6.977418      6.979033   \n",
              "std        0.638724      0.643333      0.620399      0.661015      0.624308   \n",
              "min        5.368226      5.272065      5.415173      4.971511      4.577114   \n",
              "25%        6.679947      6.680167      6.676157      6.671195      6.670774   \n",
              "50%        6.945850      6.940959      6.937700      6.942052      6.929326   \n",
              "75%        7.331830      7.320515      7.306781      7.326782      7.300640   \n",
              "max       10.381825     10.282138     10.282857     10.218109     10.196735   \n",
              "\n",
              "                 S6            S7            S8            S9           S10  \\\n",
              "count  11590.000000  11590.000000  11590.000000  11590.000000  11590.000000   \n",
              "mean       6.970862      6.983763      6.964762      6.968063      6.976285   \n",
              "std        0.628079      0.600565      0.629786      0.647512      0.639617   \n",
              "min        5.394722      5.422743      5.293747      5.250955      5.022760   \n",
              "25%        6.666906      6.670927      6.672368      6.673108      6.672764   \n",
              "50%        6.930755      6.929528      6.933875      6.932632      6.936818   \n",
              "75%        7.304425      7.305409      7.287922      7.304510      7.317739   \n",
              "max       10.045613     10.218121     10.252226     10.128700     10.155481   \n",
              "\n",
              "       ...           S21           S22           S23           S24  \\\n",
              "count  ...  11590.000000  11590.000000  11590.000000  11590.000000   \n",
              "mean   ...      6.973523      6.970025      6.953668      6.974543   \n",
              "std    ...      0.638162      0.653236      0.640896      0.619910   \n",
              "min    ...      4.946556      5.297388      5.216584      5.405805   \n",
              "25%    ...      6.670571      6.677595      6.669463      6.672325   \n",
              "50%    ...      6.939904      6.934977      6.928000      6.929225   \n",
              "75%    ...      7.311619      7.303472      7.287448      7.300329   \n",
              "max    ...     10.393735     10.427216     10.259286     10.293810   \n",
              "\n",
              "                S25           S26           S27           S28           S29  \\\n",
              "count  11590.000000  11590.000000  11590.000000  11590.000000  11590.000000   \n",
              "mean       6.971951      6.964552      6.964708      6.975595      6.985603   \n",
              "std        0.625749      0.622947      0.636654      0.616378      0.656123   \n",
              "min        5.289029      5.220254      5.220683      5.224297      5.195807   \n",
              "25%        6.676029      6.672995      6.669989      6.671761      6.670072   \n",
              "50%        6.935615      6.927390      6.933957      6.937412      6.942473   \n",
              "75%        7.298465      7.291725      7.292796      7.304164      7.325411   \n",
              "max       10.060923     10.095066     10.162627      9.984048     10.335540   \n",
              "\n",
              "                S30  \n",
              "count  11590.000000  \n",
              "mean       6.968898  \n",
              "std        0.641925  \n",
              "min        5.156223  \n",
              "25%        6.669533  \n",
              "50%        6.931009  \n",
              "75%        7.300772  \n",
              "max       10.197142  \n",
              "\n",
              "[8 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70c56592-9316-42d6-808b-c16ef2b8b402\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>S1</th>\n",
              "      <th>S2</th>\n",
              "      <th>S3</th>\n",
              "      <th>S4</th>\n",
              "      <th>S5</th>\n",
              "      <th>S6</th>\n",
              "      <th>S7</th>\n",
              "      <th>S8</th>\n",
              "      <th>S9</th>\n",
              "      <th>S10</th>\n",
              "      <th>...</th>\n",
              "      <th>S21</th>\n",
              "      <th>S22</th>\n",
              "      <th>S23</th>\n",
              "      <th>S24</th>\n",
              "      <th>S25</th>\n",
              "      <th>S26</th>\n",
              "      <th>S27</th>\n",
              "      <th>S28</th>\n",
              "      <th>S29</th>\n",
              "      <th>S30</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "      <td>11590.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.996189</td>\n",
              "      <td>6.985335</td>\n",
              "      <td>6.985900</td>\n",
              "      <td>6.977418</td>\n",
              "      <td>6.979033</td>\n",
              "      <td>6.970862</td>\n",
              "      <td>6.983763</td>\n",
              "      <td>6.964762</td>\n",
              "      <td>6.968063</td>\n",
              "      <td>6.976285</td>\n",
              "      <td>...</td>\n",
              "      <td>6.973523</td>\n",
              "      <td>6.970025</td>\n",
              "      <td>6.953668</td>\n",
              "      <td>6.974543</td>\n",
              "      <td>6.971951</td>\n",
              "      <td>6.964552</td>\n",
              "      <td>6.964708</td>\n",
              "      <td>6.975595</td>\n",
              "      <td>6.985603</td>\n",
              "      <td>6.968898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.638724</td>\n",
              "      <td>0.643333</td>\n",
              "      <td>0.620399</td>\n",
              "      <td>0.661015</td>\n",
              "      <td>0.624308</td>\n",
              "      <td>0.628079</td>\n",
              "      <td>0.600565</td>\n",
              "      <td>0.629786</td>\n",
              "      <td>0.647512</td>\n",
              "      <td>0.639617</td>\n",
              "      <td>...</td>\n",
              "      <td>0.638162</td>\n",
              "      <td>0.653236</td>\n",
              "      <td>0.640896</td>\n",
              "      <td>0.619910</td>\n",
              "      <td>0.625749</td>\n",
              "      <td>0.622947</td>\n",
              "      <td>0.636654</td>\n",
              "      <td>0.616378</td>\n",
              "      <td>0.656123</td>\n",
              "      <td>0.641925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.368226</td>\n",
              "      <td>5.272065</td>\n",
              "      <td>5.415173</td>\n",
              "      <td>4.971511</td>\n",
              "      <td>4.577114</td>\n",
              "      <td>5.394722</td>\n",
              "      <td>5.422743</td>\n",
              "      <td>5.293747</td>\n",
              "      <td>5.250955</td>\n",
              "      <td>5.022760</td>\n",
              "      <td>...</td>\n",
              "      <td>4.946556</td>\n",
              "      <td>5.297388</td>\n",
              "      <td>5.216584</td>\n",
              "      <td>5.405805</td>\n",
              "      <td>5.289029</td>\n",
              "      <td>5.220254</td>\n",
              "      <td>5.220683</td>\n",
              "      <td>5.224297</td>\n",
              "      <td>5.195807</td>\n",
              "      <td>5.156223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.679947</td>\n",
              "      <td>6.680167</td>\n",
              "      <td>6.676157</td>\n",
              "      <td>6.671195</td>\n",
              "      <td>6.670774</td>\n",
              "      <td>6.666906</td>\n",
              "      <td>6.670927</td>\n",
              "      <td>6.672368</td>\n",
              "      <td>6.673108</td>\n",
              "      <td>6.672764</td>\n",
              "      <td>...</td>\n",
              "      <td>6.670571</td>\n",
              "      <td>6.677595</td>\n",
              "      <td>6.669463</td>\n",
              "      <td>6.672325</td>\n",
              "      <td>6.676029</td>\n",
              "      <td>6.672995</td>\n",
              "      <td>6.669989</td>\n",
              "      <td>6.671761</td>\n",
              "      <td>6.670072</td>\n",
              "      <td>6.669533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.945850</td>\n",
              "      <td>6.940959</td>\n",
              "      <td>6.937700</td>\n",
              "      <td>6.942052</td>\n",
              "      <td>6.929326</td>\n",
              "      <td>6.930755</td>\n",
              "      <td>6.929528</td>\n",
              "      <td>6.933875</td>\n",
              "      <td>6.932632</td>\n",
              "      <td>6.936818</td>\n",
              "      <td>...</td>\n",
              "      <td>6.939904</td>\n",
              "      <td>6.934977</td>\n",
              "      <td>6.928000</td>\n",
              "      <td>6.929225</td>\n",
              "      <td>6.935615</td>\n",
              "      <td>6.927390</td>\n",
              "      <td>6.933957</td>\n",
              "      <td>6.937412</td>\n",
              "      <td>6.942473</td>\n",
              "      <td>6.931009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.331830</td>\n",
              "      <td>7.320515</td>\n",
              "      <td>7.306781</td>\n",
              "      <td>7.326782</td>\n",
              "      <td>7.300640</td>\n",
              "      <td>7.304425</td>\n",
              "      <td>7.305409</td>\n",
              "      <td>7.287922</td>\n",
              "      <td>7.304510</td>\n",
              "      <td>7.317739</td>\n",
              "      <td>...</td>\n",
              "      <td>7.311619</td>\n",
              "      <td>7.303472</td>\n",
              "      <td>7.287448</td>\n",
              "      <td>7.300329</td>\n",
              "      <td>7.298465</td>\n",
              "      <td>7.291725</td>\n",
              "      <td>7.292796</td>\n",
              "      <td>7.304164</td>\n",
              "      <td>7.325411</td>\n",
              "      <td>7.300772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.381825</td>\n",
              "      <td>10.282138</td>\n",
              "      <td>10.282857</td>\n",
              "      <td>10.218109</td>\n",
              "      <td>10.196735</td>\n",
              "      <td>10.045613</td>\n",
              "      <td>10.218121</td>\n",
              "      <td>10.252226</td>\n",
              "      <td>10.128700</td>\n",
              "      <td>10.155481</td>\n",
              "      <td>...</td>\n",
              "      <td>10.393735</td>\n",
              "      <td>10.427216</td>\n",
              "      <td>10.259286</td>\n",
              "      <td>10.293810</td>\n",
              "      <td>10.060923</td>\n",
              "      <td>10.095066</td>\n",
              "      <td>10.162627</td>\n",
              "      <td>9.984048</td>\n",
              "      <td>10.335540</td>\n",
              "      <td>10.197142</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 30 columns</p>\n",
              "</div>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-34ccdf2f-072d-48ea-ae21-f49559110e48 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#samplewise statistics for negative data\n",
        "negative_data.iloc[:, 1:].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "pRLWSTosG5kQ",
        "outputId": "2a207a25-a279-4b76-e31f-80fa9fdc6401"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                S1           S2           S3           S4           S5  \\\n",
              "count  5205.000000  5205.000000  5205.000000  5205.000000  5205.000000   \n",
              "mean      7.088989     7.092271     7.078525     7.075488     7.075191   \n",
              "std       0.578480     0.568823     0.584924     0.587508     0.572077   \n",
              "min       5.530975     5.667683     5.443891     5.510296     5.533868   \n",
              "25%       6.758468     6.761539     6.755805     6.755341     6.759622   \n",
              "50%       6.991434     6.991715     6.989708     6.990831     6.976311   \n",
              "75%       7.371389     7.365837     7.357220     7.352454     7.347822   \n",
              "max      10.313452    10.517358    10.175624    10.251454    10.252820   \n",
              "\n",
              "                S6           S7           S8           S9          S10  ...  \\\n",
              "count  5205.000000  5205.000000  5205.000000  5205.000000  5205.000000  ...   \n",
              "mean      7.087294     7.089151     7.084638     7.077268     7.090120  ...   \n",
              "std       0.584182     0.578371     0.570221     0.569626     0.575367  ...   \n",
              "min       5.543956     5.565442     5.632375     5.595055     5.606844  ...   \n",
              "25%       6.764535     6.762915     6.758435     6.758332     6.758608  ...   \n",
              "50%       6.991497     6.990294     6.985199     6.988040     6.991822  ...   \n",
              "75%       7.370661     7.370892     7.363706     7.349550     7.365358  ...   \n",
              "max      10.341428    10.081599    10.442653    10.342311    10.305869  ...   \n",
              "\n",
              "               S19          S20          S21          S22          S23  \\\n",
              "count  5205.000000  5205.000000  5205.000000  5205.000000  5205.000000   \n",
              "mean      7.080864     7.067163     7.075454     7.088710     7.059228   \n",
              "std       0.555123     0.566367     0.597044     0.583146     0.562322   \n",
              "min       5.673660     5.514384     5.400301     5.573647     5.515034   \n",
              "25%       6.752571     6.752777     6.752158     6.759918     6.755757   \n",
              "50%       6.980287     6.975863     6.986959     6.991685     6.970714   \n",
              "75%       7.353290     7.349423     7.360592     7.372487     7.315471   \n",
              "max      10.420173    10.574945    10.312091    10.035146    10.478896   \n",
              "\n",
              "               S24          S25          S26          S27          S28  \n",
              "count  5205.000000  5205.000000  5205.000000  5205.000000  5205.000000  \n",
              "mean      7.069885     7.075978     7.078043     7.067329     7.072145  \n",
              "std       0.587103     0.555261     0.557836     0.552304     0.566090  \n",
              "min       5.253933     5.640595     5.612401     5.608310     5.577736  \n",
              "25%       6.756050     6.762455     6.757105     6.755924     6.754552  \n",
              "50%       6.980791     6.979874     6.981884     6.979500     6.979923  \n",
              "75%       7.344013     7.334904     7.339500     7.333296     7.360231  \n",
              "max      10.243467    10.366128    10.345576    10.522268    10.090550  \n",
              "\n",
              "[8 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dea82535-3d9d-42f3-8770-a58b6701851b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>S1</th>\n",
              "      <th>S2</th>\n",
              "      <th>S3</th>\n",
              "      <th>S4</th>\n",
              "      <th>S5</th>\n",
              "      <th>S6</th>\n",
              "      <th>S7</th>\n",
              "      <th>S8</th>\n",
              "      <th>S9</th>\n",
              "      <th>S10</th>\n",
              "      <th>...</th>\n",
              "      <th>S19</th>\n",
              "      <th>S20</th>\n",
              "      <th>S21</th>\n",
              "      <th>S22</th>\n",
              "      <th>S23</th>\n",
              "      <th>S24</th>\n",
              "      <th>S25</th>\n",
              "      <th>S26</th>\n",
              "      <th>S27</th>\n",
              "      <th>S28</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "      <td>5205.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.088989</td>\n",
              "      <td>7.092271</td>\n",
              "      <td>7.078525</td>\n",
              "      <td>7.075488</td>\n",
              "      <td>7.075191</td>\n",
              "      <td>7.087294</td>\n",
              "      <td>7.089151</td>\n",
              "      <td>7.084638</td>\n",
              "      <td>7.077268</td>\n",
              "      <td>7.090120</td>\n",
              "      <td>...</td>\n",
              "      <td>7.080864</td>\n",
              "      <td>7.067163</td>\n",
              "      <td>7.075454</td>\n",
              "      <td>7.088710</td>\n",
              "      <td>7.059228</td>\n",
              "      <td>7.069885</td>\n",
              "      <td>7.075978</td>\n",
              "      <td>7.078043</td>\n",
              "      <td>7.067329</td>\n",
              "      <td>7.072145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.578480</td>\n",
              "      <td>0.568823</td>\n",
              "      <td>0.584924</td>\n",
              "      <td>0.587508</td>\n",
              "      <td>0.572077</td>\n",
              "      <td>0.584182</td>\n",
              "      <td>0.578371</td>\n",
              "      <td>0.570221</td>\n",
              "      <td>0.569626</td>\n",
              "      <td>0.575367</td>\n",
              "      <td>...</td>\n",
              "      <td>0.555123</td>\n",
              "      <td>0.566367</td>\n",
              "      <td>0.597044</td>\n",
              "      <td>0.583146</td>\n",
              "      <td>0.562322</td>\n",
              "      <td>0.587103</td>\n",
              "      <td>0.555261</td>\n",
              "      <td>0.557836</td>\n",
              "      <td>0.552304</td>\n",
              "      <td>0.566090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.530975</td>\n",
              "      <td>5.667683</td>\n",
              "      <td>5.443891</td>\n",
              "      <td>5.510296</td>\n",
              "      <td>5.533868</td>\n",
              "      <td>5.543956</td>\n",
              "      <td>5.565442</td>\n",
              "      <td>5.632375</td>\n",
              "      <td>5.595055</td>\n",
              "      <td>5.606844</td>\n",
              "      <td>...</td>\n",
              "      <td>5.673660</td>\n",
              "      <td>5.514384</td>\n",
              "      <td>5.400301</td>\n",
              "      <td>5.573647</td>\n",
              "      <td>5.515034</td>\n",
              "      <td>5.253933</td>\n",
              "      <td>5.640595</td>\n",
              "      <td>5.612401</td>\n",
              "      <td>5.608310</td>\n",
              "      <td>5.577736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.758468</td>\n",
              "      <td>6.761539</td>\n",
              "      <td>6.755805</td>\n",
              "      <td>6.755341</td>\n",
              "      <td>6.759622</td>\n",
              "      <td>6.764535</td>\n",
              "      <td>6.762915</td>\n",
              "      <td>6.758435</td>\n",
              "      <td>6.758332</td>\n",
              "      <td>6.758608</td>\n",
              "      <td>...</td>\n",
              "      <td>6.752571</td>\n",
              "      <td>6.752777</td>\n",
              "      <td>6.752158</td>\n",
              "      <td>6.759918</td>\n",
              "      <td>6.755757</td>\n",
              "      <td>6.756050</td>\n",
              "      <td>6.762455</td>\n",
              "      <td>6.757105</td>\n",
              "      <td>6.755924</td>\n",
              "      <td>6.754552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.991434</td>\n",
              "      <td>6.991715</td>\n",
              "      <td>6.989708</td>\n",
              "      <td>6.990831</td>\n",
              "      <td>6.976311</td>\n",
              "      <td>6.991497</td>\n",
              "      <td>6.990294</td>\n",
              "      <td>6.985199</td>\n",
              "      <td>6.988040</td>\n",
              "      <td>6.991822</td>\n",
              "      <td>...</td>\n",
              "      <td>6.980287</td>\n",
              "      <td>6.975863</td>\n",
              "      <td>6.986959</td>\n",
              "      <td>6.991685</td>\n",
              "      <td>6.970714</td>\n",
              "      <td>6.980791</td>\n",
              "      <td>6.979874</td>\n",
              "      <td>6.981884</td>\n",
              "      <td>6.979500</td>\n",
              "      <td>6.979923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.371389</td>\n",
              "      <td>7.365837</td>\n",
              "      <td>7.357220</td>\n",
              "      <td>7.352454</td>\n",
              "      <td>7.347822</td>\n",
              "      <td>7.370661</td>\n",
              "      <td>7.370892</td>\n",
              "      <td>7.363706</td>\n",
              "      <td>7.349550</td>\n",
              "      <td>7.365358</td>\n",
              "      <td>...</td>\n",
              "      <td>7.353290</td>\n",
              "      <td>7.349423</td>\n",
              "      <td>7.360592</td>\n",
              "      <td>7.372487</td>\n",
              "      <td>7.315471</td>\n",
              "      <td>7.344013</td>\n",
              "      <td>7.334904</td>\n",
              "      <td>7.339500</td>\n",
              "      <td>7.333296</td>\n",
              "      <td>7.360231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.313452</td>\n",
              "      <td>10.517358</td>\n",
              "      <td>10.175624</td>\n",
              "      <td>10.251454</td>\n",
              "      <td>10.252820</td>\n",
              "      <td>10.341428</td>\n",
              "      <td>10.081599</td>\n",
              "      <td>10.442653</td>\n",
              "      <td>10.342311</td>\n",
              "      <td>10.305869</td>\n",
              "      <td>...</td>\n",
              "      <td>10.420173</td>\n",
              "      <td>10.574945</td>\n",
              "      <td>10.312091</td>\n",
              "      <td>10.035146</td>\n",
              "      <td>10.478896</td>\n",
              "      <td>10.243467</td>\n",
              "      <td>10.366128</td>\n",
              "      <td>10.345576</td>\n",
              "      <td>10.522268</td>\n",
              "      <td>10.090550</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 28 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dea82535-3d9d-42f3-8770-a58b6701851b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dea82535-3d9d-42f3-8770-a58b6701851b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f7c49c09-e2c2-49a2-9200-913920028aea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#so null values present is about 0\n",
        "print(positive_data.isna().sum().sum())\n",
        "print(negative_data.isna().sum().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2r6x141G73U",
        "outputId": "979d74fb-3b43-433a-9d84-5d823a8f97ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "data = pd.concat([positive_data,negative_data],axis = 0)\n",
        "data.reset_index(drop=True, inplace=True)\n"
      ],
      "metadata": {
        "id": "Yot9eAOrG9_Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#null values created\n",
        "data.isna().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29GF2090HAAg",
        "outputId": "823753ba-06a3-4713-df52-641a96223d4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10410"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "okk = data.isna()\n",
        "okk\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "QXO6T0j9HBWS",
        "outputId": "0ed3f54c-7f4a-4ca0-a5f6-7fa474169572"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Sample     S1     S2     S3     S4     S5     S6     S7     S8     S9  \\\n",
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              "\n",
              "       ...    S21    S22    S23    S24    S25    S26    S27    S28    S29  \\\n",
              "0      ...  False  False  False  False  False  False  False  False  False   \n",
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              "16790  ...  False  False  False  False  False  False  False  False   True   \n",
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              "16794  ...  False  False  False  False  False  False  False  False   True   \n",
              "\n",
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              "...      ...  \n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3f4770e9-76b3-4fc4-ac24-a7d5aeb294af button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3f4770e9-76b3-4fc4-ac24-a7d5aeb294af');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b3de1401-de8c-4919-bc05-4780efcadfc9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b2f4662d-cab7-4016-9505-93a93ea7ab66\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('okk')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b2f4662d-cab7-4016-9505-93a93ea7ab66 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('okk');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "okk"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_indices = []\n",
        "for i, row in okk.iterrows():\n",
        "    for j, value in row.items():\n",
        "        if value:  # If value is True (indicating missing value)\n",
        "            missing_indices.append((i, j))\n",
        "\n",
        "print(len(missing_indices))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DpNHNX-QHEve",
        "outputId": "a6f97083-586a-4419-8ef5-d872ae866ff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10410\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_1 = data.copy()\n",
        "imputer = KNNImputer(n_neighbors=18, weights='uniform', metric='nan_euclidean')\n",
        "okk = imputer.fit_transform(data_1.iloc[:,9:])\n",
        "data_1.iloc[:,9:] = pd.DataFrame(okk, columns = data_1.columns[9:])\n",
        "data_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "Kfu47pvoHHSc",
        "outputId": "4e223801-09c6-41d9-9cc1-d86b9d14114b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Sample        S1        S2        S3        S4        S5  \\\n",
              "0       Compound2078  7.206298  7.227946  7.366802  7.262313  7.285364   \n",
              "1       Compound4363  7.339353  7.345237  7.556745  7.465671  7.472435   \n",
              "2      Compound13589  6.643306  6.687743  6.465109  6.558714  6.760262   \n",
              "3       Compound5278  6.844170  6.682059  6.855306  6.786066  6.608705   \n",
              "4       Compound1640  7.889506  8.302098  8.159361  8.276149  8.310145   \n",
              "...              ...       ...       ...       ...       ...       ...   \n",
              "16790  Compound 5919  6.245255  6.194300  6.165928  6.390953  6.402920   \n",
              "16791  Compound 5930  6.265979  6.269323  6.296698  6.347539  6.403661   \n",
              "16792  Compound 5964  6.242246  6.124306  6.177051  6.082200  6.317291   \n",
              "16793  Compound 5978  6.157839  6.032436  6.130037  6.045991  6.050399   \n",
              "16794  Compound 5980  5.683136  5.884875  5.765027  5.616814  5.886334   \n",
              "\n",
              "             S6        S7        S8        S9  ...       S21       S22  \\\n",
              "0      6.998687  7.132948  7.160038  7.390819  ...  7.315623  7.350978   \n",
              "1      7.174356  7.553920  7.135764  7.356667  ...  7.490783  6.872999   \n",
              "2      6.825837  6.609359  6.776504  6.503055  ...  6.635033  6.817662   \n",
              "3      6.962785  6.917193  6.859684  6.852445  ...  6.705961  7.050534   \n",
              "4      7.928198  8.197410  7.913904  8.089097  ...  8.184827  8.134914   \n",
              "...         ...       ...       ...       ...  ...       ...       ...   \n",
              "16790  6.258669  6.262649  6.370278  6.256610  ...  6.166841  6.263467   \n",
              "16791  6.362765  6.222485  6.277444  6.303384  ...  6.379362  6.353872   \n",
              "16792  6.257584  6.223415  6.282368  6.305714  ...  6.144898  6.269480   \n",
              "16793  6.133175  6.040674  6.042695  6.117320  ...  6.129828  6.095715   \n",
              "16794  5.653571  5.901391  5.905859  5.715543  ...  5.471979  5.811799   \n",
              "\n",
              "            S23       S24       S25       S26       S27       S28       S29  \\\n",
              "0      7.169137  7.067084  7.070416  7.081180  7.214982  7.140902  7.017724   \n",
              "1      7.397464  7.300424  7.600194  7.339486  7.181688  7.452621  7.474829   \n",
              "2      6.590528  6.496227  6.626065  6.712181  6.720424  6.837801  6.744136   \n",
              "3      6.804359  6.942128  6.721300  6.783166  6.629169  6.911243  6.798165   \n",
              "4      7.995202  7.919273  8.186343  8.363670  8.159116  7.861378  8.238864   \n",
              "...         ...       ...       ...       ...       ...       ...       ...   \n",
              "16790  6.387067  6.334254  6.145515  6.364408  6.246458  6.258794  6.264900   \n",
              "16791  6.367754  6.400277  6.367340  6.348874  6.343532  6.375171  6.304681   \n",
              "16792  6.191840  6.193191  6.295073  6.268855  6.180628  6.218116  6.222105   \n",
              "16793  6.096871  6.076834  6.058739  6.199614  5.997863  5.999938  6.089855   \n",
              "16794  5.761502  5.704077  5.918673  5.755731  5.892346  5.779499  5.696160   \n",
              "\n",
              "            S30  \n",
              "0      7.153893  \n",
              "1      7.069202  \n",
              "2      6.633435  \n",
              "3      6.971550  \n",
              "4      7.689810  \n",
              "...         ...  \n",
              "16790  6.300099  \n",
              "16791  6.379278  \n",
              "16792  6.185317  \n",
              "16793  6.111005  \n",
              "16794  5.673221  \n",
              "\n",
              "[16795 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-430b7320-3875-4860-bfec-9cf4969ad801\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sample</th>\n",
              "      <th>S1</th>\n",
              "      <th>S2</th>\n",
              "      <th>S3</th>\n",
              "      <th>S4</th>\n",
              "      <th>S5</th>\n",
              "      <th>S6</th>\n",
              "      <th>S7</th>\n",
              "      <th>S8</th>\n",
              "      <th>S9</th>\n",
              "      <th>...</th>\n",
              "      <th>S21</th>\n",
              "      <th>S22</th>\n",
              "      <th>S23</th>\n",
              "      <th>S24</th>\n",
              "      <th>S25</th>\n",
              "      <th>S26</th>\n",
              "      <th>S27</th>\n",
              "      <th>S28</th>\n",
              "      <th>S29</th>\n",
              "      <th>S30</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Compound2078</td>\n",
              "      <td>7.206298</td>\n",
              "      <td>7.227946</td>\n",
              "      <td>7.366802</td>\n",
              "      <td>7.262313</td>\n",
              "      <td>7.285364</td>\n",
              "      <td>6.998687</td>\n",
              "      <td>7.132948</td>\n",
              "      <td>7.160038</td>\n",
              "      <td>7.390819</td>\n",
              "      <td>...</td>\n",
              "      <td>7.315623</td>\n",
              "      <td>7.350978</td>\n",
              "      <td>7.169137</td>\n",
              "      <td>7.067084</td>\n",
              "      <td>7.070416</td>\n",
              "      <td>7.081180</td>\n",
              "      <td>7.214982</td>\n",
              "      <td>7.140902</td>\n",
              "      <td>7.017724</td>\n",
              "      <td>7.153893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Compound4363</td>\n",
              "      <td>7.339353</td>\n",
              "      <td>7.345237</td>\n",
              "      <td>7.556745</td>\n",
              "      <td>7.465671</td>\n",
              "      <td>7.472435</td>\n",
              "      <td>7.174356</td>\n",
              "      <td>7.553920</td>\n",
              "      <td>7.135764</td>\n",
              "      <td>7.356667</td>\n",
              "      <td>...</td>\n",
              "      <td>7.490783</td>\n",
              "      <td>6.872999</td>\n",
              "      <td>7.397464</td>\n",
              "      <td>7.300424</td>\n",
              "      <td>7.600194</td>\n",
              "      <td>7.339486</td>\n",
              "      <td>7.181688</td>\n",
              "      <td>7.452621</td>\n",
              "      <td>7.474829</td>\n",
              "      <td>7.069202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Compound13589</td>\n",
              "      <td>6.643306</td>\n",
              "      <td>6.687743</td>\n",
              "      <td>6.465109</td>\n",
              "      <td>6.558714</td>\n",
              "      <td>6.760262</td>\n",
              "      <td>6.825837</td>\n",
              "      <td>6.609359</td>\n",
              "      <td>6.776504</td>\n",
              "      <td>6.503055</td>\n",
              "      <td>...</td>\n",
              "      <td>6.635033</td>\n",
              "      <td>6.817662</td>\n",
              "      <td>6.590528</td>\n",
              "      <td>6.496227</td>\n",
              "      <td>6.626065</td>\n",
              "      <td>6.712181</td>\n",
              "      <td>6.720424</td>\n",
              "      <td>6.837801</td>\n",
              "      <td>6.744136</td>\n",
              "      <td>6.633435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Compound5278</td>\n",
              "      <td>6.844170</td>\n",
              "      <td>6.682059</td>\n",
              "      <td>6.855306</td>\n",
              "      <td>6.786066</td>\n",
              "      <td>6.608705</td>\n",
              "      <td>6.962785</td>\n",
              "      <td>6.917193</td>\n",
              "      <td>6.859684</td>\n",
              "      <td>6.852445</td>\n",
              "      <td>...</td>\n",
              "      <td>6.705961</td>\n",
              "      <td>7.050534</td>\n",
              "      <td>6.804359</td>\n",
              "      <td>6.942128</td>\n",
              "      <td>6.721300</td>\n",
              "      <td>6.783166</td>\n",
              "      <td>6.629169</td>\n",
              "      <td>6.911243</td>\n",
              "      <td>6.798165</td>\n",
              "      <td>6.971550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Compound1640</td>\n",
              "      <td>7.889506</td>\n",
              "      <td>8.302098</td>\n",
              "      <td>8.159361</td>\n",
              "      <td>8.276149</td>\n",
              "      <td>8.310145</td>\n",
              "      <td>7.928198</td>\n",
              "      <td>8.197410</td>\n",
              "      <td>7.913904</td>\n",
              "      <td>8.089097</td>\n",
              "      <td>...</td>\n",
              "      <td>8.184827</td>\n",
              "      <td>8.134914</td>\n",
              "      <td>7.995202</td>\n",
              "      <td>7.919273</td>\n",
              "      <td>8.186343</td>\n",
              "      <td>8.363670</td>\n",
              "      <td>8.159116</td>\n",
              "      <td>7.861378</td>\n",
              "      <td>8.238864</td>\n",
              "      <td>7.689810</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>16790</th>\n",
              "      <td>Compound 5919</td>\n",
              "      <td>6.245255</td>\n",
              "      <td>6.194300</td>\n",
              "      <td>6.165928</td>\n",
              "      <td>6.390953</td>\n",
              "      <td>6.402920</td>\n",
              "      <td>6.258669</td>\n",
              "      <td>6.262649</td>\n",
              "      <td>6.370278</td>\n",
              "      <td>6.256610</td>\n",
              "      <td>...</td>\n",
              "      <td>6.166841</td>\n",
              "      <td>6.263467</td>\n",
              "      <td>6.387067</td>\n",
              "      <td>6.334254</td>\n",
              "      <td>6.145515</td>\n",
              "      <td>6.364408</td>\n",
              "      <td>6.246458</td>\n",
              "      <td>6.258794</td>\n",
              "      <td>6.264900</td>\n",
              "      <td>6.300099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16791</th>\n",
              "      <td>Compound 5930</td>\n",
              "      <td>6.265979</td>\n",
              "      <td>6.269323</td>\n",
              "      <td>6.296698</td>\n",
              "      <td>6.347539</td>\n",
              "      <td>6.403661</td>\n",
              "      <td>6.362765</td>\n",
              "      <td>6.222485</td>\n",
              "      <td>6.277444</td>\n",
              "      <td>6.303384</td>\n",
              "      <td>...</td>\n",
              "      <td>6.379362</td>\n",
              "      <td>6.353872</td>\n",
              "      <td>6.367754</td>\n",
              "      <td>6.400277</td>\n",
              "      <td>6.367340</td>\n",
              "      <td>6.348874</td>\n",
              "      <td>6.343532</td>\n",
              "      <td>6.375171</td>\n",
              "      <td>6.304681</td>\n",
              "      <td>6.379278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16792</th>\n",
              "      <td>Compound 5964</td>\n",
              "      <td>6.242246</td>\n",
              "      <td>6.124306</td>\n",
              "      <td>6.177051</td>\n",
              "      <td>6.082200</td>\n",
              "      <td>6.317291</td>\n",
              "      <td>6.257584</td>\n",
              "      <td>6.223415</td>\n",
              "      <td>6.282368</td>\n",
              "      <td>6.305714</td>\n",
              "      <td>...</td>\n",
              "      <td>6.144898</td>\n",
              "      <td>6.269480</td>\n",
              "      <td>6.191840</td>\n",
              "      <td>6.193191</td>\n",
              "      <td>6.295073</td>\n",
              "      <td>6.268855</td>\n",
              "      <td>6.180628</td>\n",
              "      <td>6.218116</td>\n",
              "      <td>6.222105</td>\n",
              "      <td>6.185317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16793</th>\n",
              "      <td>Compound 5978</td>\n",
              "      <td>6.157839</td>\n",
              "      <td>6.032436</td>\n",
              "      <td>6.130037</td>\n",
              "      <td>6.045991</td>\n",
              "      <td>6.050399</td>\n",
              "      <td>6.133175</td>\n",
              "      <td>6.040674</td>\n",
              "      <td>6.042695</td>\n",
              "      <td>6.117320</td>\n",
              "      <td>...</td>\n",
              "      <td>6.129828</td>\n",
              "      <td>6.095715</td>\n",
              "      <td>6.096871</td>\n",
              "      <td>6.076834</td>\n",
              "      <td>6.058739</td>\n",
              "      <td>6.199614</td>\n",
              "      <td>5.997863</td>\n",
              "      <td>5.999938</td>\n",
              "      <td>6.089855</td>\n",
              "      <td>6.111005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16794</th>\n",
              "      <td>Compound 5980</td>\n",
              "      <td>5.683136</td>\n",
              "      <td>5.884875</td>\n",
              "      <td>5.765027</td>\n",
              "      <td>5.616814</td>\n",
              "      <td>5.886334</td>\n",
              "      <td>5.653571</td>\n",
              "      <td>5.901391</td>\n",
              "      <td>5.905859</td>\n",
              "      <td>5.715543</td>\n",
              "      <td>...</td>\n",
              "      <td>5.471979</td>\n",
              "      <td>5.811799</td>\n",
              "      <td>5.761502</td>\n",
              "      <td>5.704077</td>\n",
              "      <td>5.918673</td>\n",
              "      <td>5.755731</td>\n",
              "      <td>5.892346</td>\n",
              "      <td>5.779499</td>\n",
              "      <td>5.696160</td>\n",
              "      <td>5.673221</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>16795 rows × 31 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "\n",
              "  <div id=\"id_f41164a7-5c4b-4041-a449-4579f4c0d8db\">\n",
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              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data_1')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    </button>\n",
              "    <script>\n",
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              "      const buttonEl =\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_1');\n",
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              "      })();\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_1"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_data = data_1.T\n",
        "positive_data = pd.read_csv(positive_path)\n",
        "okk = positive_data.T\n",
        "feature_data[16796] = okk[0]"
      ],
      "metadata": {
        "id": "To9WeADbHP9V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_data.columns = feature_data.iloc[0]\n",
        "feature_data = feature_data.drop('Sample')\n",
        "feature_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FUaYlYuiHWtx",
        "outputId": "b0b46d1e-a2a3-433a-88ed-f5bd41e77b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sample Compound2078 Compound4363 Compound13589 Compound5278 Compound1640  \\\n",
              "S1         7.206298     7.339353      6.643306      6.84417     7.889506   \n",
              "S2         7.227946     7.345237      6.687743     6.682059     8.302098   \n",
              "S3         7.366802     7.556745      6.465109     6.855306     8.159361   \n",
              "S4         7.262313     7.465671      6.558714     6.786066     8.276149   \n",
              "S5         7.285364     7.472435      6.760262     6.608705     8.310145   \n",
              "S6         6.998687     7.174356      6.825837     6.962785     7.928198   \n",
              "S7         7.132948      7.55392      6.609359     6.917193      8.19741   \n",
              "S8         7.160038     7.135764      6.776504     6.859684     7.913904   \n",
              "S9         7.390819     7.356667      6.503055     6.852445     8.089097   \n",
              "S10        7.154482     7.285962      6.491681     6.974768     8.222929   \n",
              "S11        7.785661     7.350227      6.431131     6.954796     8.001192   \n",
              "S12        7.335418     7.462065      6.721617       6.8953     8.134825   \n",
              "S13        7.122347     7.337117      6.663768     6.750573     8.147816   \n",
              "S14         7.28025      7.27385      6.742375     7.254883     8.359267   \n",
              "S15        7.091517     7.517887       6.67127     6.882377      8.05593   \n",
              "S16        7.076173     7.110656      6.590097     6.772618      8.16972   \n",
              "S17        7.373346     7.125399      6.669043     6.893784     8.021361   \n",
              "S18         7.35002     7.489748      6.415967      6.86708     8.194563   \n",
              "S19        7.325368     7.134707      6.542473     6.819134     8.403842   \n",
              "S20        6.820529      7.33514       6.73724     6.777429     8.336957   \n",
              "S21        7.315623     7.490783      6.635033     6.705961     8.184827   \n",
              "S22        7.350978     6.872999      6.817662     7.050534     8.134914   \n",
              "S23        7.169137     7.397464      6.590528     6.804359     7.995202   \n",
              "S24        7.067084     7.300424      6.496227     6.942128     7.919273   \n",
              "S25        7.070416     7.600194      6.626065       6.7213     8.186343   \n",
              "S26         7.08118     7.339486      6.712181     6.783166      8.36367   \n",
              "S27        7.214982     7.181688      6.720424     6.629169     8.159116   \n",
              "S28        7.140902     7.452621      6.837801     6.911243     7.861378   \n",
              "S29        7.017724     7.474829      6.744136     6.798165     8.238864   \n",
              "S30        7.153893     7.069202      6.633435      6.97155      7.68981   \n",
              "\n",
              "Sample Compound1644 Compound14195 Compound1725 Compound10230 Compound11327  \\\n",
              "S1         7.397869      6.556941     7.556308      6.956883      6.661771   \n",
              "S2         7.073804      6.475848     7.584376      6.931647      6.731832   \n",
              "S3         7.180634      6.600726     7.782393      6.828272      6.798851   \n",
              "S4         7.376412      6.666045     7.558829      6.820814      6.686754   \n",
              "S5         7.262928      6.538886      7.72045       6.89713       6.66785   \n",
              "S6         7.216227      6.623064     7.517319      6.672121       6.78445   \n",
              "S7          7.17679      6.693175     7.527826      6.900015      6.732847   \n",
              "S8          7.25132      6.717806     7.855232      6.777407      6.714273   \n",
              "S9         7.137726       6.66098     7.581239      6.793679      6.599669   \n",
              "S10        7.617274      6.645188     7.975537       6.75895      6.746146   \n",
              "S11        7.903491      6.580082      7.73839      6.840076      6.671649   \n",
              "S12        7.279289      6.411936     7.691858       6.88695      6.699031   \n",
              "S13        7.305167      6.755127     7.143603      6.843504      6.837627   \n",
              "S14         7.22396      6.801275     7.355699      6.874721      6.661794   \n",
              "S15        7.288213      6.593914     7.533735      6.825008      6.380943   \n",
              "S16        7.111338      6.086074     7.863201      6.996831      6.742119   \n",
              "S17        7.264206      6.620674     6.893817      6.770399      6.799157   \n",
              "S18        7.417917      6.679131     7.547795      6.785762      6.732438   \n",
              "S19          7.2769      6.486758     7.280937      6.849373      6.681532   \n",
              "S20        7.179195      6.357044     7.656134      6.932801      6.730333   \n",
              "S21        7.242447      6.721897     7.769412      6.927725      6.715717   \n",
              "S22        7.519532      6.614247     7.600361      6.797197      6.486627   \n",
              "S23        7.051129      6.621819     7.712697       6.83623      6.634136   \n",
              "S24        7.155662      6.615067     7.676115      6.459327      6.763523   \n",
              "S25        7.175008      6.704192     7.748435      6.620478      6.637309   \n",
              "S26        7.317933      6.566176     7.515413      6.324381      6.758207   \n",
              "S27        6.741294      6.622167     7.732847      6.845745       6.70448   \n",
              "S28        6.877592      6.636499     7.807067      6.661239      6.675724   \n",
              "S29        7.439263      6.511375       7.8027      6.441469      6.739582   \n",
              "S30         7.11166      6.764153     7.760972      6.966499      6.694234   \n",
              "\n",
              "Sample  ... Compound 5864 Compound 5869 Compound 5891 Compound 5900  \\\n",
              "S1      ...       6.45122      6.537409      6.293003      6.332792   \n",
              "S2      ...      6.342426      6.482791      6.407777      6.351458   \n",
              "S3      ...      6.365171      6.536428      6.473196      6.313976   \n",
              "S4      ...      6.350146      6.486427      6.437306      6.408881   \n",
              "S5      ...      6.412558      6.364361      6.449154      6.274385   \n",
              "S6      ...      6.411341       6.51427      6.419122      6.342484   \n",
              "S7      ...      6.383081      6.349379      6.435712      6.401976   \n",
              "S8      ...      6.418244       6.38073      6.447258      6.362343   \n",
              "S9      ...      6.393926      6.495857      6.373951      6.348692   \n",
              "S10     ...      6.409146      6.535594      6.457081      6.468728   \n",
              "S11     ...      6.380547      6.485251      6.495639      6.379915   \n",
              "S12     ...      6.403421      6.504234      6.224694      6.370397   \n",
              "S13     ...      6.385726      6.441894      6.392391      6.415585   \n",
              "S14     ...      6.240535      6.603532      6.478212      6.432465   \n",
              "S15     ...      6.310397      6.529021      6.464851      6.392824   \n",
              "S16     ...      6.446057      6.340946      6.444235      6.336243   \n",
              "S17     ...      6.236236      6.436395      6.304906      6.387386   \n",
              "S18     ...       6.37278      6.404316      6.359615       6.30203   \n",
              "S19     ...      6.304319      6.452923      6.419134       6.45814   \n",
              "S20     ...       6.37524       6.53084      6.422376      6.273041   \n",
              "S21     ...      6.473726      6.485233      6.252737       6.36044   \n",
              "S22     ...      6.321923      6.280885      6.472756      6.352057   \n",
              "S23     ...      6.517161      6.401942      6.561626      6.435342   \n",
              "S24     ...      6.374196      6.460165      6.432836      6.194422   \n",
              "S25     ...      6.388406      6.405947      6.388433      6.269856   \n",
              "S26     ...      6.331742      6.458608      6.341647      6.354497   \n",
              "S27     ...      6.358165      6.381432      6.374805      6.368389   \n",
              "S28     ...      6.475777      6.520364      6.456001      6.459965   \n",
              "S29     ...      6.369145      6.434197      6.374314      6.392561   \n",
              "S30     ...      6.382651      6.474437      6.396699      6.379918   \n",
              "\n",
              "Sample Compound 5919 Compound 5930 Compound 5964 Compound 5978 Compound 5980  \\\n",
              "S1          6.245255      6.265979      6.242246      6.157839      5.683136   \n",
              "S2            6.1943      6.269323      6.124306      6.032436      5.884875   \n",
              "S3          6.165928      6.296698      6.177051      6.130037      5.765027   \n",
              "S4          6.390953      6.347539        6.0822      6.045991      5.616814   \n",
              "S5           6.40292      6.403661      6.317291      6.050399      5.886334   \n",
              "S6          6.258669      6.362765      6.257584      6.133175      5.653571   \n",
              "S7          6.262649      6.222485      6.223415      6.040674      5.901391   \n",
              "S8          6.370278      6.277444      6.282368      6.042695      5.905859   \n",
              "S9           6.25661      6.303384      6.305714       6.11732      5.715543   \n",
              "S10         6.339981      6.297272      6.211835      6.018717      5.695217   \n",
              "S11         6.286293      6.355225      6.126946      6.069865       5.77149   \n",
              "S12          6.26132      6.251961      6.311069      6.033543      5.805155   \n",
              "S13         6.364807      6.287824      6.202221      6.033825      5.740337   \n",
              "S14         6.323632      6.198079       6.17375      6.029789      5.685554   \n",
              "S15         6.370338      6.391004      6.188228      6.174215      5.706898   \n",
              "S16         6.297884       6.27648      6.170679      6.078966      5.810594   \n",
              "S17         6.380778      6.412329      6.166273      6.144336       5.91354   \n",
              "S18          6.28435       6.33759      6.204087      6.085832      5.512236   \n",
              "S19         6.269349      6.320016      6.201274      6.161811      5.938979   \n",
              "S20         6.284218      6.411511       6.21969      6.124916       5.72571   \n",
              "S21         6.166841      6.379362      6.144898      6.129828      5.471979   \n",
              "S22         6.263467      6.353872       6.26948      6.095715      5.811799   \n",
              "S23         6.387067      6.367754       6.19184      6.096871      5.761502   \n",
              "S24         6.334254      6.400277      6.193191      6.076834      5.704077   \n",
              "S25         6.145515       6.36734      6.295073      6.058739      5.918673   \n",
              "S26         6.364408      6.348874      6.268855      6.199614      5.755731   \n",
              "S27         6.246458      6.343532      6.180628      5.997863      5.892346   \n",
              "S28         6.258794      6.375171      6.218116      5.999938      5.779499   \n",
              "S29           6.2649      6.304681      6.222105      6.089855       5.69616   \n",
              "S30         6.300099      6.379278      6.185317      6.111005      5.673221   \n",
              "\n",
              "Sample Label  \n",
              "S1       1.0  \n",
              "S2       1.0  \n",
              "S3       1.0  \n",
              "S4       1.0  \n",
              "S5       1.0  \n",
              "S6       1.0  \n",
              "S7       1.0  \n",
              "S8       1.0  \n",
              "S9       2.0  \n",
              "S10      2.0  \n",
              "S11      2.0  \n",
              "S12      2.0  \n",
              "S13      2.0  \n",
              "S14      2.0  \n",
              "S15      2.0  \n",
              "S16      2.0  \n",
              "S17      2.0  \n",
              "S18      2.0  \n",
              "S19      2.0  \n",
              "S20      2.0  \n",
              "S21      2.0  \n",
              "S22      2.0  \n",
              "S23      2.0  \n",
              "S24      2.0  \n",
              "S25      2.0  \n",
              "S26      2.0  \n",
              "S27      2.0  \n",
              "S28      2.0  \n",
              "S29      2.0  \n",
              "S30      2.0  \n",
              "\n",
              "[30 rows x 16796 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e8501d0b-f6b3-4e14-aded-490ee03e7a63\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Sample</th>\n",
              "      <th>Compound2078</th>\n",
              "      <th>Compound4363</th>\n",
              "      <th>Compound13589</th>\n",
              "      <th>Compound5278</th>\n",
              "      <th>Compound1640</th>\n",
              "      <th>Compound1644</th>\n",
              "      <th>Compound14195</th>\n",
              "      <th>Compound1725</th>\n",
              "      <th>Compound10230</th>\n",
              "      <th>Compound11327</th>\n",
              "      <th>...</th>\n",
              "      <th>Compound 5864</th>\n",
              "      <th>Compound 5869</th>\n",
              "      <th>Compound 5891</th>\n",
              "      <th>Compound 5900</th>\n",
              "      <th>Compound 5919</th>\n",
              "      <th>Compound 5930</th>\n",
              "      <th>Compound 5964</th>\n",
              "      <th>Compound 5978</th>\n",
              "      <th>Compound 5980</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>S1</th>\n",
              "      <td>7.206298</td>\n",
              "      <td>7.339353</td>\n",
              "      <td>6.643306</td>\n",
              "      <td>6.84417</td>\n",
              "      <td>7.889506</td>\n",
              "      <td>7.397869</td>\n",
              "      <td>6.556941</td>\n",
              "      <td>7.556308</td>\n",
              "      <td>6.956883</td>\n",
              "      <td>6.661771</td>\n",
              "      <td>...</td>\n",
              "      <td>6.45122</td>\n",
              "      <td>6.537409</td>\n",
              "      <td>6.293003</td>\n",
              "      <td>6.332792</td>\n",
              "      <td>6.245255</td>\n",
              "      <td>6.265979</td>\n",
              "      <td>6.242246</td>\n",
              "      <td>6.157839</td>\n",
              "      <td>5.683136</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S2</th>\n",
              "      <td>7.227946</td>\n",
              "      <td>7.345237</td>\n",
              "      <td>6.687743</td>\n",
              "      <td>6.682059</td>\n",
              "      <td>8.302098</td>\n",
              "      <td>7.073804</td>\n",
              "      <td>6.475848</td>\n",
              "      <td>7.584376</td>\n",
              "      <td>6.931647</td>\n",
              "      <td>6.731832</td>\n",
              "      <td>...</td>\n",
              "      <td>6.342426</td>\n",
              "      <td>6.482791</td>\n",
              "      <td>6.407777</td>\n",
              "      <td>6.351458</td>\n",
              "      <td>6.1943</td>\n",
              "      <td>6.269323</td>\n",
              "      <td>6.124306</td>\n",
              "      <td>6.032436</td>\n",
              "      <td>5.884875</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S3</th>\n",
              "      <td>7.366802</td>\n",
              "      <td>7.556745</td>\n",
              "      <td>6.465109</td>\n",
              "      <td>6.855306</td>\n",
              "      <td>8.159361</td>\n",
              "      <td>7.180634</td>\n",
              "      <td>6.600726</td>\n",
              "      <td>7.782393</td>\n",
              "      <td>6.828272</td>\n",
              "      <td>6.798851</td>\n",
              "      <td>...</td>\n",
              "      <td>6.365171</td>\n",
              "      <td>6.536428</td>\n",
              "      <td>6.473196</td>\n",
              "      <td>6.313976</td>\n",
              "      <td>6.165928</td>\n",
              "      <td>6.296698</td>\n",
              "      <td>6.177051</td>\n",
              "      <td>6.130037</td>\n",
              "      <td>5.765027</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S4</th>\n",
              "      <td>7.262313</td>\n",
              "      <td>7.465671</td>\n",
              "      <td>6.558714</td>\n",
              "      <td>6.786066</td>\n",
              "      <td>8.276149</td>\n",
              "      <td>7.376412</td>\n",
              "      <td>6.666045</td>\n",
              "      <td>7.558829</td>\n",
              "      <td>6.820814</td>\n",
              "      <td>6.686754</td>\n",
              "      <td>...</td>\n",
              "      <td>6.350146</td>\n",
              "      <td>6.486427</td>\n",
              "      <td>6.437306</td>\n",
              "      <td>6.408881</td>\n",
              "      <td>6.390953</td>\n",
              "      <td>6.347539</td>\n",
              "      <td>6.0822</td>\n",
              "      <td>6.045991</td>\n",
              "      <td>5.616814</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S5</th>\n",
              "      <td>7.285364</td>\n",
              "      <td>7.472435</td>\n",
              "      <td>6.760262</td>\n",
              "      <td>6.608705</td>\n",
              "      <td>8.310145</td>\n",
              "      <td>7.262928</td>\n",
              "      <td>6.538886</td>\n",
              "      <td>7.72045</td>\n",
              "      <td>6.89713</td>\n",
              "      <td>6.66785</td>\n",
              "      <td>...</td>\n",
              "      <td>6.412558</td>\n",
              "      <td>6.364361</td>\n",
              "      <td>6.449154</td>\n",
              "      <td>6.274385</td>\n",
              "      <td>6.40292</td>\n",
              "      <td>6.403661</td>\n",
              "      <td>6.317291</td>\n",
              "      <td>6.050399</td>\n",
              "      <td>5.886334</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S6</th>\n",
              "      <td>6.998687</td>\n",
              "      <td>7.174356</td>\n",
              "      <td>6.825837</td>\n",
              "      <td>6.962785</td>\n",
              "      <td>7.928198</td>\n",
              "      <td>7.216227</td>\n",
              "      <td>6.623064</td>\n",
              "      <td>7.517319</td>\n",
              "      <td>6.672121</td>\n",
              "      <td>6.78445</td>\n",
              "      <td>...</td>\n",
              "      <td>6.411341</td>\n",
              "      <td>6.51427</td>\n",
              "      <td>6.419122</td>\n",
              "      <td>6.342484</td>\n",
              "      <td>6.258669</td>\n",
              "      <td>6.362765</td>\n",
              "      <td>6.257584</td>\n",
              "      <td>6.133175</td>\n",
              "      <td>5.653571</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S7</th>\n",
              "      <td>7.132948</td>\n",
              "      <td>7.55392</td>\n",
              "      <td>6.609359</td>\n",
              "      <td>6.917193</td>\n",
              "      <td>8.19741</td>\n",
              "      <td>7.17679</td>\n",
              "      <td>6.693175</td>\n",
              "      <td>7.527826</td>\n",
              "      <td>6.900015</td>\n",
              "      <td>6.732847</td>\n",
              "      <td>...</td>\n",
              "      <td>6.383081</td>\n",
              "      <td>6.349379</td>\n",
              "      <td>6.435712</td>\n",
              "      <td>6.401976</td>\n",
              "      <td>6.262649</td>\n",
              "      <td>6.222485</td>\n",
              "      <td>6.223415</td>\n",
              "      <td>6.040674</td>\n",
              "      <td>5.901391</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S8</th>\n",
              "      <td>7.160038</td>\n",
              "      <td>7.135764</td>\n",
              "      <td>6.776504</td>\n",
              "      <td>6.859684</td>\n",
              "      <td>7.913904</td>\n",
              "      <td>7.25132</td>\n",
              "      <td>6.717806</td>\n",
              "      <td>7.855232</td>\n",
              "      <td>6.777407</td>\n",
              "      <td>6.714273</td>\n",
              "      <td>...</td>\n",
              "      <td>6.418244</td>\n",
              "      <td>6.38073</td>\n",
              "      <td>6.447258</td>\n",
              "      <td>6.362343</td>\n",
              "      <td>6.370278</td>\n",
              "      <td>6.277444</td>\n",
              "      <td>6.282368</td>\n",
              "      <td>6.042695</td>\n",
              "      <td>5.905859</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S9</th>\n",
              "      <td>7.390819</td>\n",
              "      <td>7.356667</td>\n",
              "      <td>6.503055</td>\n",
              "      <td>6.852445</td>\n",
              "      <td>8.089097</td>\n",
              "      <td>7.137726</td>\n",
              "      <td>6.66098</td>\n",
              "      <td>7.581239</td>\n",
              "      <td>6.793679</td>\n",
              "      <td>6.599669</td>\n",
              "      <td>...</td>\n",
              "      <td>6.393926</td>\n",
              "      <td>6.495857</td>\n",
              "      <td>6.373951</td>\n",
              "      <td>6.348692</td>\n",
              "      <td>6.25661</td>\n",
              "      <td>6.303384</td>\n",
              "      <td>6.305714</td>\n",
              "      <td>6.11732</td>\n",
              "      <td>5.715543</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S10</th>\n",
              "      <td>7.154482</td>\n",
              "      <td>7.285962</td>\n",
              "      <td>6.491681</td>\n",
              "      <td>6.974768</td>\n",
              "      <td>8.222929</td>\n",
              "      <td>7.617274</td>\n",
              "      <td>6.645188</td>\n",
              "      <td>7.975537</td>\n",
              "      <td>6.75895</td>\n",
              "      <td>6.746146</td>\n",
              "      <td>...</td>\n",
              "      <td>6.409146</td>\n",
              "      <td>6.535594</td>\n",
              "      <td>6.457081</td>\n",
              "      <td>6.468728</td>\n",
              "      <td>6.339981</td>\n",
              "      <td>6.297272</td>\n",
              "      <td>6.211835</td>\n",
              "      <td>6.018717</td>\n",
              "      <td>5.695217</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S11</th>\n",
              "      <td>7.785661</td>\n",
              "      <td>7.350227</td>\n",
              "      <td>6.431131</td>\n",
              "      <td>6.954796</td>\n",
              "      <td>8.001192</td>\n",
              "      <td>7.903491</td>\n",
              "      <td>6.580082</td>\n",
              "      <td>7.73839</td>\n",
              "      <td>6.840076</td>\n",
              "      <td>6.671649</td>\n",
              "      <td>...</td>\n",
              "      <td>6.380547</td>\n",
              "      <td>6.485251</td>\n",
              "      <td>6.495639</td>\n",
              "      <td>6.379915</td>\n",
              "      <td>6.286293</td>\n",
              "      <td>6.355225</td>\n",
              "      <td>6.126946</td>\n",
              "      <td>6.069865</td>\n",
              "      <td>5.77149</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S12</th>\n",
              "      <td>7.335418</td>\n",
              "      <td>7.462065</td>\n",
              "      <td>6.721617</td>\n",
              "      <td>6.8953</td>\n",
              "      <td>8.134825</td>\n",
              "      <td>7.279289</td>\n",
              "      <td>6.411936</td>\n",
              "      <td>7.691858</td>\n",
              "      <td>6.88695</td>\n",
              "      <td>6.699031</td>\n",
              "      <td>...</td>\n",
              "      <td>6.403421</td>\n",
              "      <td>6.504234</td>\n",
              "      <td>6.224694</td>\n",
              "      <td>6.370397</td>\n",
              "      <td>6.26132</td>\n",
              "      <td>6.251961</td>\n",
              "      <td>6.311069</td>\n",
              "      <td>6.033543</td>\n",
              "      <td>5.805155</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S13</th>\n",
              "      <td>7.122347</td>\n",
              "      <td>7.337117</td>\n",
              "      <td>6.663768</td>\n",
              "      <td>6.750573</td>\n",
              "      <td>8.147816</td>\n",
              "      <td>7.305167</td>\n",
              "      <td>6.755127</td>\n",
              "      <td>7.143603</td>\n",
              "      <td>6.843504</td>\n",
              "      <td>6.837627</td>\n",
              "      <td>...</td>\n",
              "      <td>6.385726</td>\n",
              "      <td>6.441894</td>\n",
              "      <td>6.392391</td>\n",
              "      <td>6.415585</td>\n",
              "      <td>6.364807</td>\n",
              "      <td>6.287824</td>\n",
              "      <td>6.202221</td>\n",
              "      <td>6.033825</td>\n",
              "      <td>5.740337</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S14</th>\n",
              "      <td>7.28025</td>\n",
              "      <td>7.27385</td>\n",
              "      <td>6.742375</td>\n",
              "      <td>7.254883</td>\n",
              "      <td>8.359267</td>\n",
              "      <td>7.22396</td>\n",
              "      <td>6.801275</td>\n",
              "      <td>7.355699</td>\n",
              "      <td>6.874721</td>\n",
              "      <td>6.661794</td>\n",
              "      <td>...</td>\n",
              "      <td>6.240535</td>\n",
              "      <td>6.603532</td>\n",
              "      <td>6.478212</td>\n",
              "      <td>6.432465</td>\n",
              "      <td>6.323632</td>\n",
              "      <td>6.198079</td>\n",
              "      <td>6.17375</td>\n",
              "      <td>6.029789</td>\n",
              "      <td>5.685554</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S15</th>\n",
              "      <td>7.091517</td>\n",
              "      <td>7.517887</td>\n",
              "      <td>6.67127</td>\n",
              "      <td>6.882377</td>\n",
              "      <td>8.05593</td>\n",
              "      <td>7.288213</td>\n",
              "      <td>6.593914</td>\n",
              "      <td>7.533735</td>\n",
              "      <td>6.825008</td>\n",
              "      <td>6.380943</td>\n",
              "      <td>...</td>\n",
              "      <td>6.310397</td>\n",
              "      <td>6.529021</td>\n",
              "      <td>6.464851</td>\n",
              "      <td>6.392824</td>\n",
              "      <td>6.370338</td>\n",
              "      <td>6.391004</td>\n",
              "      <td>6.188228</td>\n",
              "      <td>6.174215</td>\n",
              "      <td>5.706898</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S16</th>\n",
              "      <td>7.076173</td>\n",
              "      <td>7.110656</td>\n",
              "      <td>6.590097</td>\n",
              "      <td>6.772618</td>\n",
              "      <td>8.16972</td>\n",
              "      <td>7.111338</td>\n",
              "      <td>6.086074</td>\n",
              "      <td>7.863201</td>\n",
              "      <td>6.996831</td>\n",
              "      <td>6.742119</td>\n",
              "      <td>...</td>\n",
              "      <td>6.446057</td>\n",
              "      <td>6.340946</td>\n",
              "      <td>6.444235</td>\n",
              "      <td>6.336243</td>\n",
              "      <td>6.297884</td>\n",
              "      <td>6.27648</td>\n",
              "      <td>6.170679</td>\n",
              "      <td>6.078966</td>\n",
              "      <td>5.810594</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S17</th>\n",
              "      <td>7.373346</td>\n",
              "      <td>7.125399</td>\n",
              "      <td>6.669043</td>\n",
              "      <td>6.893784</td>\n",
              "      <td>8.021361</td>\n",
              "      <td>7.264206</td>\n",
              "      <td>6.620674</td>\n",
              "      <td>6.893817</td>\n",
              "      <td>6.770399</td>\n",
              "      <td>6.799157</td>\n",
              "      <td>...</td>\n",
              "      <td>6.236236</td>\n",
              "      <td>6.436395</td>\n",
              "      <td>6.304906</td>\n",
              "      <td>6.387386</td>\n",
              "      <td>6.380778</td>\n",
              "      <td>6.412329</td>\n",
              "      <td>6.166273</td>\n",
              "      <td>6.144336</td>\n",
              "      <td>5.91354</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S18</th>\n",
              "      <td>7.35002</td>\n",
              "      <td>7.489748</td>\n",
              "      <td>6.415967</td>\n",
              "      <td>6.86708</td>\n",
              "      <td>8.194563</td>\n",
              "      <td>7.417917</td>\n",
              "      <td>6.679131</td>\n",
              "      <td>7.547795</td>\n",
              "      <td>6.785762</td>\n",
              "      <td>6.732438</td>\n",
              "      <td>...</td>\n",
              "      <td>6.37278</td>\n",
              "      <td>6.404316</td>\n",
              "      <td>6.359615</td>\n",
              "      <td>6.30203</td>\n",
              "      <td>6.28435</td>\n",
              "      <td>6.33759</td>\n",
              "      <td>6.204087</td>\n",
              "      <td>6.085832</td>\n",
              "      <td>5.512236</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S19</th>\n",
              "      <td>7.325368</td>\n",
              "      <td>7.134707</td>\n",
              "      <td>6.542473</td>\n",
              "      <td>6.819134</td>\n",
              "      <td>8.403842</td>\n",
              "      <td>7.2769</td>\n",
              "      <td>6.486758</td>\n",
              "      <td>7.280937</td>\n",
              "      <td>6.849373</td>\n",
              "      <td>6.681532</td>\n",
              "      <td>...</td>\n",
              "      <td>6.304319</td>\n",
              "      <td>6.452923</td>\n",
              "      <td>6.419134</td>\n",
              "      <td>6.45814</td>\n",
              "      <td>6.269349</td>\n",
              "      <td>6.320016</td>\n",
              "      <td>6.201274</td>\n",
              "      <td>6.161811</td>\n",
              "      <td>5.938979</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S20</th>\n",
              "      <td>6.820529</td>\n",
              "      <td>7.33514</td>\n",
              "      <td>6.73724</td>\n",
              "      <td>6.777429</td>\n",
              "      <td>8.336957</td>\n",
              "      <td>7.179195</td>\n",
              "      <td>6.357044</td>\n",
              "      <td>7.656134</td>\n",
              "      <td>6.932801</td>\n",
              "      <td>6.730333</td>\n",
              "      <td>...</td>\n",
              "      <td>6.37524</td>\n",
              "      <td>6.53084</td>\n",
              "      <td>6.422376</td>\n",
              "      <td>6.273041</td>\n",
              "      <td>6.284218</td>\n",
              "      <td>6.411511</td>\n",
              "      <td>6.21969</td>\n",
              "      <td>6.124916</td>\n",
              "      <td>5.72571</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S21</th>\n",
              "      <td>7.315623</td>\n",
              "      <td>7.490783</td>\n",
              "      <td>6.635033</td>\n",
              "      <td>6.705961</td>\n",
              "      <td>8.184827</td>\n",
              "      <td>7.242447</td>\n",
              "      <td>6.721897</td>\n",
              "      <td>7.769412</td>\n",
              "      <td>6.927725</td>\n",
              "      <td>6.715717</td>\n",
              "      <td>...</td>\n",
              "      <td>6.473726</td>\n",
              "      <td>6.485233</td>\n",
              "      <td>6.252737</td>\n",
              "      <td>6.36044</td>\n",
              "      <td>6.166841</td>\n",
              "      <td>6.379362</td>\n",
              "      <td>6.144898</td>\n",
              "      <td>6.129828</td>\n",
              "      <td>5.471979</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S22</th>\n",
              "      <td>7.350978</td>\n",
              "      <td>6.872999</td>\n",
              "      <td>6.817662</td>\n",
              "      <td>7.050534</td>\n",
              "      <td>8.134914</td>\n",
              "      <td>7.519532</td>\n",
              "      <td>6.614247</td>\n",
              "      <td>7.600361</td>\n",
              "      <td>6.797197</td>\n",
              "      <td>6.486627</td>\n",
              "      <td>...</td>\n",
              "      <td>6.321923</td>\n",
              "      <td>6.280885</td>\n",
              "      <td>6.472756</td>\n",
              "      <td>6.352057</td>\n",
              "      <td>6.263467</td>\n",
              "      <td>6.353872</td>\n",
              "      <td>6.26948</td>\n",
              "      <td>6.095715</td>\n",
              "      <td>5.811799</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S23</th>\n",
              "      <td>7.169137</td>\n",
              "      <td>7.397464</td>\n",
              "      <td>6.590528</td>\n",
              "      <td>6.804359</td>\n",
              "      <td>7.995202</td>\n",
              "      <td>7.051129</td>\n",
              "      <td>6.621819</td>\n",
              "      <td>7.712697</td>\n",
              "      <td>6.83623</td>\n",
              "      <td>6.634136</td>\n",
              "      <td>...</td>\n",
              "      <td>6.517161</td>\n",
              "      <td>6.401942</td>\n",
              "      <td>6.561626</td>\n",
              "      <td>6.435342</td>\n",
              "      <td>6.387067</td>\n",
              "      <td>6.367754</td>\n",
              "      <td>6.19184</td>\n",
              "      <td>6.096871</td>\n",
              "      <td>5.761502</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S24</th>\n",
              "      <td>7.067084</td>\n",
              "      <td>7.300424</td>\n",
              "      <td>6.496227</td>\n",
              "      <td>6.942128</td>\n",
              "      <td>7.919273</td>\n",
              "      <td>7.155662</td>\n",
              "      <td>6.615067</td>\n",
              "      <td>7.676115</td>\n",
              "      <td>6.459327</td>\n",
              "      <td>6.763523</td>\n",
              "      <td>...</td>\n",
              "      <td>6.374196</td>\n",
              "      <td>6.460165</td>\n",
              "      <td>6.432836</td>\n",
              "      <td>6.194422</td>\n",
              "      <td>6.334254</td>\n",
              "      <td>6.400277</td>\n",
              "      <td>6.193191</td>\n",
              "      <td>6.076834</td>\n",
              "      <td>5.704077</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S25</th>\n",
              "      <td>7.070416</td>\n",
              "      <td>7.600194</td>\n",
              "      <td>6.626065</td>\n",
              "      <td>6.7213</td>\n",
              "      <td>8.186343</td>\n",
              "      <td>7.175008</td>\n",
              "      <td>6.704192</td>\n",
              "      <td>7.748435</td>\n",
              "      <td>6.620478</td>\n",
              "      <td>6.637309</td>\n",
              "      <td>...</td>\n",
              "      <td>6.388406</td>\n",
              "      <td>6.405947</td>\n",
              "      <td>6.388433</td>\n",
              "      <td>6.269856</td>\n",
              "      <td>6.145515</td>\n",
              "      <td>6.36734</td>\n",
              "      <td>6.295073</td>\n",
              "      <td>6.058739</td>\n",
              "      <td>5.918673</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S26</th>\n",
              "      <td>7.08118</td>\n",
              "      <td>7.339486</td>\n",
              "      <td>6.712181</td>\n",
              "      <td>6.783166</td>\n",
              "      <td>8.36367</td>\n",
              "      <td>7.317933</td>\n",
              "      <td>6.566176</td>\n",
              "      <td>7.515413</td>\n",
              "      <td>6.324381</td>\n",
              "      <td>6.758207</td>\n",
              "      <td>...</td>\n",
              "      <td>6.331742</td>\n",
              "      <td>6.458608</td>\n",
              "      <td>6.341647</td>\n",
              "      <td>6.354497</td>\n",
              "      <td>6.364408</td>\n",
              "      <td>6.348874</td>\n",
              "      <td>6.268855</td>\n",
              "      <td>6.199614</td>\n",
              "      <td>5.755731</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S27</th>\n",
              "      <td>7.214982</td>\n",
              "      <td>7.181688</td>\n",
              "      <td>6.720424</td>\n",
              "      <td>6.629169</td>\n",
              "      <td>8.159116</td>\n",
              "      <td>6.741294</td>\n",
              "      <td>6.622167</td>\n",
              "      <td>7.732847</td>\n",
              "      <td>6.845745</td>\n",
              "      <td>6.70448</td>\n",
              "      <td>...</td>\n",
              "      <td>6.358165</td>\n",
              "      <td>6.381432</td>\n",
              "      <td>6.374805</td>\n",
              "      <td>6.368389</td>\n",
              "      <td>6.246458</td>\n",
              "      <td>6.343532</td>\n",
              "      <td>6.180628</td>\n",
              "      <td>5.997863</td>\n",
              "      <td>5.892346</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S28</th>\n",
              "      <td>7.140902</td>\n",
              "      <td>7.452621</td>\n",
              "      <td>6.837801</td>\n",
              "      <td>6.911243</td>\n",
              "      <td>7.861378</td>\n",
              "      <td>6.877592</td>\n",
              "      <td>6.636499</td>\n",
              "      <td>7.807067</td>\n",
              "      <td>6.661239</td>\n",
              "      <td>6.675724</td>\n",
              "      <td>...</td>\n",
              "      <td>6.475777</td>\n",
              "      <td>6.520364</td>\n",
              "      <td>6.456001</td>\n",
              "      <td>6.459965</td>\n",
              "      <td>6.258794</td>\n",
              "      <td>6.375171</td>\n",
              "      <td>6.218116</td>\n",
              "      <td>5.999938</td>\n",
              "      <td>5.779499</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S29</th>\n",
              "      <td>7.017724</td>\n",
              "      <td>7.474829</td>\n",
              "      <td>6.744136</td>\n",
              "      <td>6.798165</td>\n",
              "      <td>8.238864</td>\n",
              "      <td>7.439263</td>\n",
              "      <td>6.511375</td>\n",
              "      <td>7.8027</td>\n",
              "      <td>6.441469</td>\n",
              "      <td>6.739582</td>\n",
              "      <td>...</td>\n",
              "      <td>6.369145</td>\n",
              "      <td>6.434197</td>\n",
              "      <td>6.374314</td>\n",
              "      <td>6.392561</td>\n",
              "      <td>6.2649</td>\n",
              "      <td>6.304681</td>\n",
              "      <td>6.222105</td>\n",
              "      <td>6.089855</td>\n",
              "      <td>5.69616</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S30</th>\n",
              "      <td>7.153893</td>\n",
              "      <td>7.069202</td>\n",
              "      <td>6.633435</td>\n",
              "      <td>6.97155</td>\n",
              "      <td>7.68981</td>\n",
              "      <td>7.11166</td>\n",
              "      <td>6.764153</td>\n",
              "      <td>7.760972</td>\n",
              "      <td>6.966499</td>\n",
              "      <td>6.694234</td>\n",
              "      <td>...</td>\n",
              "      <td>6.382651</td>\n",
              "      <td>6.474437</td>\n",
              "      <td>6.396699</td>\n",
              "      <td>6.379918</td>\n",
              "      <td>6.300099</td>\n",
              "      <td>6.379278</td>\n",
              "      <td>6.185317</td>\n",
              "      <td>6.111005</td>\n",
              "      <td>5.673221</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30 rows × 16796 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "  <div id=\"id_570324c5-1b04-4833-8d2f-9d1a188e6d01\">\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('feature_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    </button>\n",
              "    <script>\n",
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              "        document.querySelector('#id_570324c5-1b04-4833-8d2f-9d1a188e6d01 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('feature_data');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feature_data"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_data = feature_data.astype(float)\n",
        "feature_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EgqLfHkrKAEu",
        "outputId": "808d93a5-1aff-4915-c9e5-5ffc0eea4e2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 30 entries, S1 to S30\n",
            "Columns: 16796 entries, Compound2078 to Label\n",
            "dtypes: float64(16796)\n",
            "memory usage: 3.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import boxcox\n",
        "lambdas = []\n",
        "\n",
        "for i in feature_data.columns[:-1]:\n",
        "  feature_data[i],okk = boxcox(feature_data[i])\n",
        "  lambdas.append(okk)\n",
        "\n",
        "plt.plot(lambdas)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "DhGMIftZLRra",
        "outputId": "befa7759-3461-482d-c3b2-d2ba5ee3dc99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# #scaling of data on same scale for further analysis(Z-score normalization)\n",
        "# std_scale = MinMaxScaler()\n",
        "# feature_data[feature_data.columns[:-1]]= std_scale.fit_transform(feature_data[feature_data.columns[:-1]])\n",
        "# feature_data\n",
        "\n",
        "#scaling of data on same scale for further analysis(Z-score normalization)\n",
        "std_scale = StandardScaler()\n",
        "feature_data[feature_data.columns[:-1]]= std_scale.fit_transform(feature_data[feature_data.columns[:-1]])\n",
        "feature_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "10VYSePXHdMp",
        "outputId": "f8db1c71-b2a8-4c62-a22f-43d80ef95a6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sample  Compound2078  Compound4363  Compound13589  Compound5278  Compound1640  \\\n",
              "S1          0.054180     -0.100584      -0.072038      0.031804     -1.359991   \n",
              "S2          0.185447     -0.063551       0.326378     -1.445199      1.080558   \n",
              "S3          0.964752      1.528995      -1.480277      0.121679      0.081117   \n",
              "S4          0.388220      0.776949      -0.776755     -0.460003      0.884676   \n",
              "S5          0.520463      0.829147       1.021200     -2.231341      1.142657   \n",
              "S6         -1.358487     -1.006378       1.700056      0.922685     -1.184236   \n",
              "S7         -0.411969      1.503999      -0.363085      0.597063      0.329463   \n",
              "S8         -0.235863     -1.184963       1.184743      0.156637     -1.250253   \n",
              "S9          1.089264      0.009396      -1.204284      0.098720     -0.345795   \n",
              "S10        -0.271599     -0.420876      -1.288302      1.005000      0.503149   \n",
              "S11         2.779485     -0.031874      -1.717197      0.867057     -0.826355   \n",
              "S12         0.797660      0.749360       0.643871      0.433438     -0.072516   \n",
              "S13        -0.482172     -0.114560       0.108922     -0.780356      0.008205   \n",
              "S14         0.491378     -0.489738       0.844514      2.600053      1.536173   \n",
              "S15        -0.690613      1.194811       0.176328      0.334550     -0.533815   \n",
              "S16        -0.796768     -1.295130      -0.523270     -0.579542      0.147491   \n",
              "S17         0.998964     -1.231005       0.156259      0.421926     -0.721066   \n",
              "S18         0.876028      0.965320      -1.819907      0.215229      0.310441   \n",
              "S19         0.743074     -1.189698      -0.904368     -0.175313      1.915568   \n",
              "S20        -2.830675     -0.126881       0.794439     -0.536523      1.354332   \n",
              "S21         0.689639      0.973584      -0.144015     -1.206021      0.245947   \n",
              "S22         0.881135     -2.135740       1.612688      1.496065     -0.071969   \n",
              "S23        -0.177765      0.281041      -0.519719     -0.300911     -0.857078   \n",
              "S24        -0.860420     -0.336852      -1.254852      0.777624     -1.225609   \n",
              "S25        -0.837015      1.927708      -0.221270     -1.056681      0.255935   \n",
              "S26        -0.761946     -0.099753       0.554204     -0.485596      1.572681   \n",
              "S27         0.107171     -0.971135       0.632478     -2.003805      0.079558   \n",
              "S28        -0.359771      0.677833       1.829341      0.553071     -1.482036   \n",
              "S29        -1.216399      0.847750       0.861753     -0.354336      0.614608   \n",
              "S30        -0.275397     -1.467176      -0.157836      0.983026     -2.131839   \n",
              "\n",
              "Sample  Compound1644  Compound14195  Compound1725  Compound10230  \\\n",
              "S1          0.734368      -0.553593     -0.505212       1.459589   \n",
              "S2         -0.857874      -1.100562     -0.370098       1.116944   \n",
              "S3         -0.304521      -0.192816      0.813981      -0.001899   \n",
              "S4          0.636588       0.448993     -0.493366      -0.067507   \n",
              "S5          0.102119      -0.688137      0.395857       0.695941   \n",
              "S6         -0.126612       0.011772     -0.681381      -1.061151   \n",
              "S7         -0.323922       0.757405     -0.635181       0.729151   \n",
              "S8          0.045758       1.061314      1.370491      -0.415758   \n",
              "S9         -0.523221       0.394303     -0.385557      -0.291661   \n",
              "S10         1.678651       0.229339      2.467083      -0.547809   \n",
              "S11         2.773439      -0.369263      0.512078       0.105658   \n",
              "S12         0.181013      -1.440268      0.218414       0.581467   \n",
              "S13         0.304516       1.569212     -1.840367       0.137772   \n",
              "S14        -0.088371       2.285304     -1.286569       0.449429   \n",
              "S15         0.223781      -0.252333     -0.608791      -0.030840   \n",
              "S16        -0.660070      -2.386171      1.435983       2.069475   \n",
              "S17         0.108303      -0.010828     -2.252398      -0.466957   \n",
              "S18         0.824821       0.594435     -0.544793      -0.352971   \n",
              "S19         0.169534      -1.035111     -1.508600       0.193700   \n",
              "S20        -0.311778      -1.679910      0.009554       1.131951   \n",
              "S21         0.002458       1.114116      0.722366       1.066432   \n",
              "S22         1.270127      -0.070734     -0.289894      -0.263846   \n",
              "S23        -0.979202      -0.000019      0.346807       0.070108   \n",
              "S24        -0.431233      -0.063161      0.124660      -1.793669   \n",
              "S25        -0.332927       0.890411      0.578863      -1.296307   \n",
              "S26         0.364871      -0.481684     -0.689664      -2.028022   \n",
              "S27        -2.786692       0.003267      0.475757       0.158987   \n",
              "S28        -1.955704       0.142047      0.994264      -1.114557   \n",
              "S29         0.920172      -0.878580      0.961757      -1.832180   \n",
              "S30        -0.658390       1.701254      0.663957       1.598531   \n",
              "\n",
              "Sample  Compound11327  ...  Compound 5864  Compound 5869  Compound 5891  \\\n",
              "S1          -0.629294  ...       1.198207       1.178092      -1.528730   \n",
              "S2           0.318566  ...      -0.633603       0.297903      -0.113692   \n",
              "S3           1.570607  ...      -0.268681       1.161314       0.934590   \n",
              "S4          -0.326771  ...      -0.510781       0.353246       0.334744   \n",
              "S5          -0.558950  ...       0.521862      -1.279529       0.525862   \n",
              "S6           1.267452  ...       0.501035       0.792182       0.054024   \n",
              "S7           0.334731  ...       0.025234      -1.450762       0.309545   \n",
              "S8           0.050721  ...       0.619530      -1.085680       0.494829   \n",
              "S9          -1.242068  ...       0.206059       0.498880      -0.581774   \n",
              "S10          0.553931  ...       0.463530       1.147100       0.657473   \n",
              "S11         -0.513956  ...      -0.016702       0.335295       1.342789   \n",
              "S12         -0.164419  ...       0.366186       0.630840      -2.167605   \n",
              "S13          2.495671  ...       0.069138      -0.294707      -0.332388   \n",
              "S14         -0.629022  ...      -2.158811       2.395480       1.023530   \n",
              "S15         -2.383272  ...      -1.131996       1.035919       0.789454   \n",
              "S16          0.486096  ...       1.106205      -1.544626       0.445720   \n",
              "S17          1.577259  ...      -2.219400      -0.370338      -1.403384   \n",
              "S18          0.328215  ...      -0.144533      -0.793475      -0.766480   \n",
              "S19         -0.393004  ...      -1.224575      -0.140168       0.054206   \n",
              "S20          0.294820  ...      -0.104160       1.066519       0.103158   \n",
              "S21          0.071917  ...       1.605348       0.335026      -1.921109   \n",
              "S22         -1.979783  ...      -0.954701      -2.163409       0.926850   \n",
              "S23         -0.924542  ...       2.419998      -0.823593       2.708573   \n",
              "S24          0.861778  ...      -0.121305      -0.036615       0.264350   \n",
              "S25         -0.892584  ...       0.113743      -0.772682      -0.387066   \n",
              "S26          0.764919  ...      -0.801842      -0.059025      -0.987180   \n",
              "S27         -0.089287  ...      -0.382078      -1.077210      -0.570510   \n",
              "S28         -0.464772  ...       1.642961       0.891912       0.639358   \n",
              "S29          0.444017  ...      -0.203973      -0.400316      -0.576983   \n",
              "S30         -0.228976  ...       0.018105       0.172428      -0.272155   \n",
              "\n",
              "Sample  Compound 5900  Compound 5919  Compound 5930  Compound 5964  \\\n",
              "S1          -0.594721      -0.708391      -1.151307       0.539402   \n",
              "S2          -0.299673      -1.373940      -1.104287      -1.551825   \n",
              "S3          -0.875828      -1.721014      -0.693081      -0.605668   \n",
              "S4           0.718840       1.529269       0.209630      -2.320200   \n",
              "S5          -1.417511       1.737433       1.458325       1.825129   \n",
              "S6          -0.443589      -0.523742       0.519884       0.804961   \n",
              "S7           0.586933      -0.468183      -1.705423       0.211390   \n",
              "S8          -0.119813       1.178874      -0.987260       1.231034   \n",
              "S9          -0.344437      -0.552353      -0.585168       1.628998   \n",
              "S10          1.982945       0.685938      -0.683938       0.008594   \n",
              "S11          0.183220      -0.130542       0.363761      -1.504047   \n",
              "S12          0.017109      -0.486780      -1.341279       1.719822   \n",
              "S13          0.849551       1.088075      -0.831655      -0.160421   \n",
              "S14          1.190389       0.429753      -1.973921      -0.664358   \n",
              "S15          0.416225       1.179866       1.150547      -0.407452   \n",
              "S16         -0.541409       0.039775      -1.001355      -0.719012   \n",
              "S17          0.316979       1.355376       1.678711      -0.797545   \n",
              "S18         -1.046171      -0.158778       0.017390      -0.127567   \n",
              "S19          1.742686      -0.373822      -0.303056      -0.177095   \n",
              "S20         -1.434780      -0.160705       1.657573       0.146251   \n",
              "S21         -0.151675      -1.710104       0.881456      -1.180308   \n",
              "S22         -0.289916      -0.456709       0.336257       1.009933   \n",
              "S23          1.250220       1.462524       0.625935      -0.343570   \n",
              "S24         -2.331046       0.595432       1.374444      -0.319696   \n",
              "S25         -1.475420      -1.960839       0.617051       1.448015   \n",
              "S26         -0.250033       1.081492       0.236044       0.999189   \n",
              "S27         -0.017330      -0.691995       0.131246      -0.542135   \n",
              "S28          1.783577      -0.522009       0.787767       0.118701   \n",
              "S29          0.411387      -0.436589      -0.563868       0.188484   \n",
              "S30          0.183289       0.072687       0.879577      -0.459005   \n",
              "\n",
              "Sample  Compound 5978  Compound 5980  Label  \n",
              "S1           1.330264      -0.725607    1.0  \n",
              "S2          -1.016591       1.141513    1.0  \n",
              "S3           0.873848      -0.031110    1.0  \n",
              "S4          -0.724508      -1.231284    1.0  \n",
              "S5          -0.632145       1.157030    1.0  \n",
              "S6           0.926627      -0.957016    1.0  \n",
              "S7          -0.837863       1.319016    1.0  \n",
              "S8          -0.794680       1.367735    1.0  \n",
              "S9           0.653137      -0.460422    2.0  \n",
              "S10         -1.323067      -0.628184    2.0  \n",
              "S11         -0.235105       0.027212    2.0  \n",
              "S12         -0.992600       0.339793    2.0  \n",
              "S13         -0.986003      -0.249088    2.0  \n",
              "S14         -1.074767      -0.706247    2.0  \n",
              "S15          1.584562      -0.532373    2.0  \n",
              "S16         -0.055778       0.391693    2.0  \n",
              "S17          1.112552       1.452190    2.0  \n",
              "S18          0.076169      -1.935837    2.0  \n",
              "S19          1.393239       1.738388    2.0  \n",
              "S20          0.785684      -0.374670    2.0  \n",
              "S21          0.870249      -2.179393    2.0  \n",
              "S22          0.262694       0.403250    2.0  \n",
              "S23          0.283686      -0.062705    2.0  \n",
              "S24         -0.097161      -0.555656    2.0  \n",
              "S25         -0.459415       1.509142    2.0  \n",
              "S26          1.958811      -0.114083    2.0  \n",
              "S27         -1.810071       1.221300    2.0  \n",
              "S28         -1.760291       0.100235    2.0  \n",
              "S29          0.152339      -0.620508    2.0  \n",
              "S30          0.541582      -0.804313    2.0  \n",
              "\n",
              "[30 rows x 16796 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9937dbcf-7344-4611-bbfe-a6ea585350d1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Sample</th>\n",
              "      <th>Compound2078</th>\n",
              "      <th>Compound4363</th>\n",
              "      <th>Compound13589</th>\n",
              "      <th>Compound5278</th>\n",
              "      <th>Compound1640</th>\n",
              "      <th>Compound1644</th>\n",
              "      <th>Compound14195</th>\n",
              "      <th>Compound1725</th>\n",
              "      <th>Compound10230</th>\n",
              "      <th>Compound11327</th>\n",
              "      <th>...</th>\n",
              "      <th>Compound 5864</th>\n",
              "      <th>Compound 5869</th>\n",
              "      <th>Compound 5891</th>\n",
              "      <th>Compound 5900</th>\n",
              "      <th>Compound 5919</th>\n",
              "      <th>Compound 5930</th>\n",
              "      <th>Compound 5964</th>\n",
              "      <th>Compound 5978</th>\n",
              "      <th>Compound 5980</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>S1</th>\n",
              "      <td>0.054180</td>\n",
              "      <td>-0.100584</td>\n",
              "      <td>-0.072038</td>\n",
              "      <td>0.031804</td>\n",
              "      <td>-1.359991</td>\n",
              "      <td>0.734368</td>\n",
              "      <td>-0.553593</td>\n",
              "      <td>-0.505212</td>\n",
              "      <td>1.459589</td>\n",
              "      <td>-0.629294</td>\n",
              "      <td>...</td>\n",
              "      <td>1.198207</td>\n",
              "      <td>1.178092</td>\n",
              "      <td>-1.528730</td>\n",
              "      <td>-0.594721</td>\n",
              "      <td>-0.708391</td>\n",
              "      <td>-1.151307</td>\n",
              "      <td>0.539402</td>\n",
              "      <td>1.330264</td>\n",
              "      <td>-0.725607</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S2</th>\n",
              "      <td>0.185447</td>\n",
              "      <td>-0.063551</td>\n",
              "      <td>0.326378</td>\n",
              "      <td>-1.445199</td>\n",
              "      <td>1.080558</td>\n",
              "      <td>-0.857874</td>\n",
              "      <td>-1.100562</td>\n",
              "      <td>-0.370098</td>\n",
              "      <td>1.116944</td>\n",
              "      <td>0.318566</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.633603</td>\n",
              "      <td>0.297903</td>\n",
              "      <td>-0.113692</td>\n",
              "      <td>-0.299673</td>\n",
              "      <td>-1.373940</td>\n",
              "      <td>-1.104287</td>\n",
              "      <td>-1.551825</td>\n",
              "      <td>-1.016591</td>\n",
              "      <td>1.141513</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S3</th>\n",
              "      <td>0.964752</td>\n",
              "      <td>1.528995</td>\n",
              "      <td>-1.480277</td>\n",
              "      <td>0.121679</td>\n",
              "      <td>0.081117</td>\n",
              "      <td>-0.304521</td>\n",
              "      <td>-0.192816</td>\n",
              "      <td>0.813981</td>\n",
              "      <td>-0.001899</td>\n",
              "      <td>1.570607</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.268681</td>\n",
              "      <td>1.161314</td>\n",
              "      <td>0.934590</td>\n",
              "      <td>-0.875828</td>\n",
              "      <td>-1.721014</td>\n",
              "      <td>-0.693081</td>\n",
              "      <td>-0.605668</td>\n",
              "      <td>0.873848</td>\n",
              "      <td>-0.031110</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S4</th>\n",
              "      <td>0.388220</td>\n",
              "      <td>0.776949</td>\n",
              "      <td>-0.776755</td>\n",
              "      <td>-0.460003</td>\n",
              "      <td>0.884676</td>\n",
              "      <td>0.636588</td>\n",
              "      <td>0.448993</td>\n",
              "      <td>-0.493366</td>\n",
              "      <td>-0.067507</td>\n",
              "      <td>-0.326771</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.510781</td>\n",
              "      <td>0.353246</td>\n",
              "      <td>0.334744</td>\n",
              "      <td>0.718840</td>\n",
              "      <td>1.529269</td>\n",
              "      <td>0.209630</td>\n",
              "      <td>-2.320200</td>\n",
              "      <td>-0.724508</td>\n",
              "      <td>-1.231284</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S5</th>\n",
              "      <td>0.520463</td>\n",
              "      <td>0.829147</td>\n",
              "      <td>1.021200</td>\n",
              "      <td>-2.231341</td>\n",
              "      <td>1.142657</td>\n",
              "      <td>0.102119</td>\n",
              "      <td>-0.688137</td>\n",
              "      <td>0.395857</td>\n",
              "      <td>0.695941</td>\n",
              "      <td>-0.558950</td>\n",
              "      <td>...</td>\n",
              "      <td>0.521862</td>\n",
              "      <td>-1.279529</td>\n",
              "      <td>0.525862</td>\n",
              "      <td>-1.417511</td>\n",
              "      <td>1.737433</td>\n",
              "      <td>1.458325</td>\n",
              "      <td>1.825129</td>\n",
              "      <td>-0.632145</td>\n",
              "      <td>1.157030</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S6</th>\n",
              "      <td>-1.358487</td>\n",
              "      <td>-1.006378</td>\n",
              "      <td>1.700056</td>\n",
              "      <td>0.922685</td>\n",
              "      <td>-1.184236</td>\n",
              "      <td>-0.126612</td>\n",
              "      <td>0.011772</td>\n",
              "      <td>-0.681381</td>\n",
              "      <td>-1.061151</td>\n",
              "      <td>1.267452</td>\n",
              "      <td>...</td>\n",
              "      <td>0.501035</td>\n",
              "      <td>0.792182</td>\n",
              "      <td>0.054024</td>\n",
              "      <td>-0.443589</td>\n",
              "      <td>-0.523742</td>\n",
              "      <td>0.519884</td>\n",
              "      <td>0.804961</td>\n",
              "      <td>0.926627</td>\n",
              "      <td>-0.957016</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S7</th>\n",
              "      <td>-0.411969</td>\n",
              "      <td>1.503999</td>\n",
              "      <td>-0.363085</td>\n",
              "      <td>0.597063</td>\n",
              "      <td>0.329463</td>\n",
              "      <td>-0.323922</td>\n",
              "      <td>0.757405</td>\n",
              "      <td>-0.635181</td>\n",
              "      <td>0.729151</td>\n",
              "      <td>0.334731</td>\n",
              "      <td>...</td>\n",
              "      <td>0.025234</td>\n",
              "      <td>-1.450762</td>\n",
              "      <td>0.309545</td>\n",
              "      <td>0.586933</td>\n",
              "      <td>-0.468183</td>\n",
              "      <td>-1.705423</td>\n",
              "      <td>0.211390</td>\n",
              "      <td>-0.837863</td>\n",
              "      <td>1.319016</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S8</th>\n",
              "      <td>-0.235863</td>\n",
              "      <td>-1.184963</td>\n",
              "      <td>1.184743</td>\n",
              "      <td>0.156637</td>\n",
              "      <td>-1.250253</td>\n",
              "      <td>0.045758</td>\n",
              "      <td>1.061314</td>\n",
              "      <td>1.370491</td>\n",
              "      <td>-0.415758</td>\n",
              "      <td>0.050721</td>\n",
              "      <td>...</td>\n",
              "      <td>0.619530</td>\n",
              "      <td>-1.085680</td>\n",
              "      <td>0.494829</td>\n",
              "      <td>-0.119813</td>\n",
              "      <td>1.178874</td>\n",
              "      <td>-0.987260</td>\n",
              "      <td>1.231034</td>\n",
              "      <td>-0.794680</td>\n",
              "      <td>1.367735</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S9</th>\n",
              "      <td>1.089264</td>\n",
              "      <td>0.009396</td>\n",
              "      <td>-1.204284</td>\n",
              "      <td>0.098720</td>\n",
              "      <td>-0.345795</td>\n",
              "      <td>-0.523221</td>\n",
              "      <td>0.394303</td>\n",
              "      <td>-0.385557</td>\n",
              "      <td>-0.291661</td>\n",
              "      <td>-1.242068</td>\n",
              "      <td>...</td>\n",
              "      <td>0.206059</td>\n",
              "      <td>0.498880</td>\n",
              "      <td>-0.581774</td>\n",
              "      <td>-0.344437</td>\n",
              "      <td>-0.552353</td>\n",
              "      <td>-0.585168</td>\n",
              "      <td>1.628998</td>\n",
              "      <td>0.653137</td>\n",
              "      <td>-0.460422</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S10</th>\n",
              "      <td>-0.271599</td>\n",
              "      <td>-0.420876</td>\n",
              "      <td>-1.288302</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.503149</td>\n",
              "      <td>1.678651</td>\n",
              "      <td>0.229339</td>\n",
              "      <td>2.467083</td>\n",
              "      <td>-0.547809</td>\n",
              "      <td>0.553931</td>\n",
              "      <td>...</td>\n",
              "      <td>0.463530</td>\n",
              "      <td>1.147100</td>\n",
              "      <td>0.657473</td>\n",
              "      <td>1.982945</td>\n",
              "      <td>0.685938</td>\n",
              "      <td>-0.683938</td>\n",
              "      <td>0.008594</td>\n",
              "      <td>-1.323067</td>\n",
              "      <td>-0.628184</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S11</th>\n",
              "      <td>2.779485</td>\n",
              "      <td>-0.031874</td>\n",
              "      <td>-1.717197</td>\n",
              "      <td>0.867057</td>\n",
              "      <td>-0.826355</td>\n",
              "      <td>2.773439</td>\n",
              "      <td>-0.369263</td>\n",
              "      <td>0.512078</td>\n",
              "      <td>0.105658</td>\n",
              "      <td>-0.513956</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.016702</td>\n",
              "      <td>0.335295</td>\n",
              "      <td>1.342789</td>\n",
              "      <td>0.183220</td>\n",
              "      <td>-0.130542</td>\n",
              "      <td>0.363761</td>\n",
              "      <td>-1.504047</td>\n",
              "      <td>-0.235105</td>\n",
              "      <td>0.027212</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S12</th>\n",
              "      <td>0.797660</td>\n",
              "      <td>0.749360</td>\n",
              "      <td>0.643871</td>\n",
              "      <td>0.433438</td>\n",
              "      <td>-0.072516</td>\n",
              "      <td>0.181013</td>\n",
              "      <td>-1.440268</td>\n",
              "      <td>0.218414</td>\n",
              "      <td>0.581467</td>\n",
              "      <td>-0.164419</td>\n",
              "      <td>...</td>\n",
              "      <td>0.366186</td>\n",
              "      <td>0.630840</td>\n",
              "      <td>-2.167605</td>\n",
              "      <td>0.017109</td>\n",
              "      <td>-0.486780</td>\n",
              "      <td>-1.341279</td>\n",
              "      <td>1.719822</td>\n",
              "      <td>-0.992600</td>\n",
              "      <td>0.339793</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S13</th>\n",
              "      <td>-0.482172</td>\n",
              "      <td>-0.114560</td>\n",
              "      <td>0.108922</td>\n",
              "      <td>-0.780356</td>\n",
              "      <td>0.008205</td>\n",
              "      <td>0.304516</td>\n",
              "      <td>1.569212</td>\n",
              "      <td>-1.840367</td>\n",
              "      <td>0.137772</td>\n",
              "      <td>2.495671</td>\n",
              "      <td>...</td>\n",
              "      <td>0.069138</td>\n",
              "      <td>-0.294707</td>\n",
              "      <td>-0.332388</td>\n",
              "      <td>0.849551</td>\n",
              "      <td>1.088075</td>\n",
              "      <td>-0.831655</td>\n",
              "      <td>-0.160421</td>\n",
              "      <td>-0.986003</td>\n",
              "      <td>-0.249088</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S14</th>\n",
              "      <td>0.491378</td>\n",
              "      <td>-0.489738</td>\n",
              "      <td>0.844514</td>\n",
              "      <td>2.600053</td>\n",
              "      <td>1.536173</td>\n",
              "      <td>-0.088371</td>\n",
              "      <td>2.285304</td>\n",
              "      <td>-1.286569</td>\n",
              "      <td>0.449429</td>\n",
              "      <td>-0.629022</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.158811</td>\n",
              "      <td>2.395480</td>\n",
              "      <td>1.023530</td>\n",
              "      <td>1.190389</td>\n",
              "      <td>0.429753</td>\n",
              "      <td>-1.973921</td>\n",
              "      <td>-0.664358</td>\n",
              "      <td>-1.074767</td>\n",
              "      <td>-0.706247</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S15</th>\n",
              "      <td>-0.690613</td>\n",
              "      <td>1.194811</td>\n",
              "      <td>0.176328</td>\n",
              "      <td>0.334550</td>\n",
              "      <td>-0.533815</td>\n",
              "      <td>0.223781</td>\n",
              "      <td>-0.252333</td>\n",
              "      <td>-0.608791</td>\n",
              "      <td>-0.030840</td>\n",
              "      <td>-2.383272</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.131996</td>\n",
              "      <td>1.035919</td>\n",
              "      <td>0.789454</td>\n",
              "      <td>0.416225</td>\n",
              "      <td>1.179866</td>\n",
              "      <td>1.150547</td>\n",
              "      <td>-0.407452</td>\n",
              "      <td>1.584562</td>\n",
              "      <td>-0.532373</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S16</th>\n",
              "      <td>-0.796768</td>\n",
              "      <td>-1.295130</td>\n",
              "      <td>-0.523270</td>\n",
              "      <td>-0.579542</td>\n",
              "      <td>0.147491</td>\n",
              "      <td>-0.660070</td>\n",
              "      <td>-2.386171</td>\n",
              "      <td>1.435983</td>\n",
              "      <td>2.069475</td>\n",
              "      <td>0.486096</td>\n",
              "      <td>...</td>\n",
              "      <td>1.106205</td>\n",
              "      <td>-1.544626</td>\n",
              "      <td>0.445720</td>\n",
              "      <td>-0.541409</td>\n",
              "      <td>0.039775</td>\n",
              "      <td>-1.001355</td>\n",
              "      <td>-0.719012</td>\n",
              "      <td>-0.055778</td>\n",
              "      <td>0.391693</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S17</th>\n",
              "      <td>0.998964</td>\n",
              "      <td>-1.231005</td>\n",
              "      <td>0.156259</td>\n",
              "      <td>0.421926</td>\n",
              "      <td>-0.721066</td>\n",
              "      <td>0.108303</td>\n",
              "      <td>-0.010828</td>\n",
              "      <td>-2.252398</td>\n",
              "      <td>-0.466957</td>\n",
              "      <td>1.577259</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.219400</td>\n",
              "      <td>-0.370338</td>\n",
              "      <td>-1.403384</td>\n",
              "      <td>0.316979</td>\n",
              "      <td>1.355376</td>\n",
              "      <td>1.678711</td>\n",
              "      <td>-0.797545</td>\n",
              "      <td>1.112552</td>\n",
              "      <td>1.452190</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S18</th>\n",
              "      <td>0.876028</td>\n",
              "      <td>0.965320</td>\n",
              "      <td>-1.819907</td>\n",
              "      <td>0.215229</td>\n",
              "      <td>0.310441</td>\n",
              "      <td>0.824821</td>\n",
              "      <td>0.594435</td>\n",
              "      <td>-0.544793</td>\n",
              "      <td>-0.352971</td>\n",
              "      <td>0.328215</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.144533</td>\n",
              "      <td>-0.793475</td>\n",
              "      <td>-0.766480</td>\n",
              "      <td>-1.046171</td>\n",
              "      <td>-0.158778</td>\n",
              "      <td>0.017390</td>\n",
              "      <td>-0.127567</td>\n",
              "      <td>0.076169</td>\n",
              "      <td>-1.935837</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S19</th>\n",
              "      <td>0.743074</td>\n",
              "      <td>-1.189698</td>\n",
              "      <td>-0.904368</td>\n",
              "      <td>-0.175313</td>\n",
              "      <td>1.915568</td>\n",
              "      <td>0.169534</td>\n",
              "      <td>-1.035111</td>\n",
              "      <td>-1.508600</td>\n",
              "      <td>0.193700</td>\n",
              "      <td>-0.393004</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.224575</td>\n",
              "      <td>-0.140168</td>\n",
              "      <td>0.054206</td>\n",
              "      <td>1.742686</td>\n",
              "      <td>-0.373822</td>\n",
              "      <td>-0.303056</td>\n",
              "      <td>-0.177095</td>\n",
              "      <td>1.393239</td>\n",
              "      <td>1.738388</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S20</th>\n",
              "      <td>-2.830675</td>\n",
              "      <td>-0.126881</td>\n",
              "      <td>0.794439</td>\n",
              "      <td>-0.536523</td>\n",
              "      <td>1.354332</td>\n",
              "      <td>-0.311778</td>\n",
              "      <td>-1.679910</td>\n",
              "      <td>0.009554</td>\n",
              "      <td>1.131951</td>\n",
              "      <td>0.294820</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.104160</td>\n",
              "      <td>1.066519</td>\n",
              "      <td>0.103158</td>\n",
              "      <td>-1.434780</td>\n",
              "      <td>-0.160705</td>\n",
              "      <td>1.657573</td>\n",
              "      <td>0.146251</td>\n",
              "      <td>0.785684</td>\n",
              "      <td>-0.374670</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S21</th>\n",
              "      <td>0.689639</td>\n",
              "      <td>0.973584</td>\n",
              "      <td>-0.144015</td>\n",
              "      <td>-1.206021</td>\n",
              "      <td>0.245947</td>\n",
              "      <td>0.002458</td>\n",
              "      <td>1.114116</td>\n",
              "      <td>0.722366</td>\n",
              "      <td>1.066432</td>\n",
              "      <td>0.071917</td>\n",
              "      <td>...</td>\n",
              "      <td>1.605348</td>\n",
              "      <td>0.335026</td>\n",
              "      <td>-1.921109</td>\n",
              "      <td>-0.151675</td>\n",
              "      <td>-1.710104</td>\n",
              "      <td>0.881456</td>\n",
              "      <td>-1.180308</td>\n",
              "      <td>0.870249</td>\n",
              "      <td>-2.179393</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S22</th>\n",
              "      <td>0.881135</td>\n",
              "      <td>-2.135740</td>\n",
              "      <td>1.612688</td>\n",
              "      <td>1.496065</td>\n",
              "      <td>-0.071969</td>\n",
              "      <td>1.270127</td>\n",
              "      <td>-0.070734</td>\n",
              "      <td>-0.289894</td>\n",
              "      <td>-0.263846</td>\n",
              "      <td>-1.979783</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.954701</td>\n",
              "      <td>-2.163409</td>\n",
              "      <td>0.926850</td>\n",
              "      <td>-0.289916</td>\n",
              "      <td>-0.456709</td>\n",
              "      <td>0.336257</td>\n",
              "      <td>1.009933</td>\n",
              "      <td>0.262694</td>\n",
              "      <td>0.403250</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S23</th>\n",
              "      <td>-0.177765</td>\n",
              "      <td>0.281041</td>\n",
              "      <td>-0.519719</td>\n",
              "      <td>-0.300911</td>\n",
              "      <td>-0.857078</td>\n",
              "      <td>-0.979202</td>\n",
              "      <td>-0.000019</td>\n",
              "      <td>0.346807</td>\n",
              "      <td>0.070108</td>\n",
              "      <td>-0.924542</td>\n",
              "      <td>...</td>\n",
              "      <td>2.419998</td>\n",
              "      <td>-0.823593</td>\n",
              "      <td>2.708573</td>\n",
              "      <td>1.250220</td>\n",
              "      <td>1.462524</td>\n",
              "      <td>0.625935</td>\n",
              "      <td>-0.343570</td>\n",
              "      <td>0.283686</td>\n",
              "      <td>-0.062705</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S24</th>\n",
              "      <td>-0.860420</td>\n",
              "      <td>-0.336852</td>\n",
              "      <td>-1.254852</td>\n",
              "      <td>0.777624</td>\n",
              "      <td>-1.225609</td>\n",
              "      <td>-0.431233</td>\n",
              "      <td>-0.063161</td>\n",
              "      <td>0.124660</td>\n",
              "      <td>-1.793669</td>\n",
              "      <td>0.861778</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.121305</td>\n",
              "      <td>-0.036615</td>\n",
              "      <td>0.264350</td>\n",
              "      <td>-2.331046</td>\n",
              "      <td>0.595432</td>\n",
              "      <td>1.374444</td>\n",
              "      <td>-0.319696</td>\n",
              "      <td>-0.097161</td>\n",
              "      <td>-0.555656</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S25</th>\n",
              "      <td>-0.837015</td>\n",
              "      <td>1.927708</td>\n",
              "      <td>-0.221270</td>\n",
              "      <td>-1.056681</td>\n",
              "      <td>0.255935</td>\n",
              "      <td>-0.332927</td>\n",
              "      <td>0.890411</td>\n",
              "      <td>0.578863</td>\n",
              "      <td>-1.296307</td>\n",
              "      <td>-0.892584</td>\n",
              "      <td>...</td>\n",
              "      <td>0.113743</td>\n",
              "      <td>-0.772682</td>\n",
              "      <td>-0.387066</td>\n",
              "      <td>-1.475420</td>\n",
              "      <td>-1.960839</td>\n",
              "      <td>0.617051</td>\n",
              "      <td>1.448015</td>\n",
              "      <td>-0.459415</td>\n",
              "      <td>1.509142</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S26</th>\n",
              "      <td>-0.761946</td>\n",
              "      <td>-0.099753</td>\n",
              "      <td>0.554204</td>\n",
              "      <td>-0.485596</td>\n",
              "      <td>1.572681</td>\n",
              "      <td>0.364871</td>\n",
              "      <td>-0.481684</td>\n",
              "      <td>-0.689664</td>\n",
              "      <td>-2.028022</td>\n",
              "      <td>0.764919</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.801842</td>\n",
              "      <td>-0.059025</td>\n",
              "      <td>-0.987180</td>\n",
              "      <td>-0.250033</td>\n",
              "      <td>1.081492</td>\n",
              "      <td>0.236044</td>\n",
              "      <td>0.999189</td>\n",
              "      <td>1.958811</td>\n",
              "      <td>-0.114083</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S27</th>\n",
              "      <td>0.107171</td>\n",
              "      <td>-0.971135</td>\n",
              "      <td>0.632478</td>\n",
              "      <td>-2.003805</td>\n",
              "      <td>0.079558</td>\n",
              "      <td>-2.786692</td>\n",
              "      <td>0.003267</td>\n",
              "      <td>0.475757</td>\n",
              "      <td>0.158987</td>\n",
              "      <td>-0.089287</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.382078</td>\n",
              "      <td>-1.077210</td>\n",
              "      <td>-0.570510</td>\n",
              "      <td>-0.017330</td>\n",
              "      <td>-0.691995</td>\n",
              "      <td>0.131246</td>\n",
              "      <td>-0.542135</td>\n",
              "      <td>-1.810071</td>\n",
              "      <td>1.221300</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S28</th>\n",
              "      <td>-0.359771</td>\n",
              "      <td>0.677833</td>\n",
              "      <td>1.829341</td>\n",
              "      <td>0.553071</td>\n",
              "      <td>-1.482036</td>\n",
              "      <td>-1.955704</td>\n",
              "      <td>0.142047</td>\n",
              "      <td>0.994264</td>\n",
              "      <td>-1.114557</td>\n",
              "      <td>-0.464772</td>\n",
              "      <td>...</td>\n",
              "      <td>1.642961</td>\n",
              "      <td>0.891912</td>\n",
              "      <td>0.639358</td>\n",
              "      <td>1.783577</td>\n",
              "      <td>-0.522009</td>\n",
              "      <td>0.787767</td>\n",
              "      <td>0.118701</td>\n",
              "      <td>-1.760291</td>\n",
              "      <td>0.100235</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S29</th>\n",
              "      <td>-1.216399</td>\n",
              "      <td>0.847750</td>\n",
              "      <td>0.861753</td>\n",
              "      <td>-0.354336</td>\n",
              "      <td>0.614608</td>\n",
              "      <td>0.920172</td>\n",
              "      <td>-0.878580</td>\n",
              "      <td>0.961757</td>\n",
              "      <td>-1.832180</td>\n",
              "      <td>0.444017</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.203973</td>\n",
              "      <td>-0.400316</td>\n",
              "      <td>-0.576983</td>\n",
              "      <td>0.411387</td>\n",
              "      <td>-0.436589</td>\n",
              "      <td>-0.563868</td>\n",
              "      <td>0.188484</td>\n",
              "      <td>0.152339</td>\n",
              "      <td>-0.620508</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S30</th>\n",
              "      <td>-0.275397</td>\n",
              "      <td>-1.467176</td>\n",
              "      <td>-0.157836</td>\n",
              "      <td>0.983026</td>\n",
              "      <td>-2.131839</td>\n",
              "      <td>-0.658390</td>\n",
              "      <td>1.701254</td>\n",
              "      <td>0.663957</td>\n",
              "      <td>1.598531</td>\n",
              "      <td>-0.228976</td>\n",
              "      <td>...</td>\n",
              "      <td>0.018105</td>\n",
              "      <td>0.172428</td>\n",
              "      <td>-0.272155</td>\n",
              "      <td>0.183289</td>\n",
              "      <td>0.072687</td>\n",
              "      <td>0.879577</td>\n",
              "      <td>-0.459005</td>\n",
              "      <td>0.541582</td>\n",
              "      <td>-0.804313</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30 rows × 16796 columns</p>\n",
              "</div>\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    <script>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('feature_data');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feature_data"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_data['Label'] = feature_data['Label'].apply(lambda x: 1 if x == 1 else 0)\n",
        "feature_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IPdOgi9XJCTe",
        "outputId": "5933ec0a-6b26-4374-f88a-e86eafe86aae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sample  Compound2078  Compound4363  Compound13589  Compound5278  Compound1640  \\\n",
              "S1          0.054180     -0.100584      -0.072038      0.031804     -1.359991   \n",
              "S2          0.185447     -0.063551       0.326378     -1.445199      1.080558   \n",
              "S3          0.964752      1.528995      -1.480277      0.121679      0.081117   \n",
              "S4          0.388220      0.776949      -0.776755     -0.460003      0.884676   \n",
              "S5          0.520463      0.829147       1.021200     -2.231341      1.142657   \n",
              "S6         -1.358487     -1.006378       1.700056      0.922685     -1.184236   \n",
              "S7         -0.411969      1.503999      -0.363085      0.597063      0.329463   \n",
              "S8         -0.235863     -1.184963       1.184743      0.156637     -1.250253   \n",
              "S9          1.089264      0.009396      -1.204284      0.098720     -0.345795   \n",
              "S10        -0.271599     -0.420876      -1.288302      1.005000      0.503149   \n",
              "S11         2.779485     -0.031874      -1.717197      0.867057     -0.826355   \n",
              "S12         0.797660      0.749360       0.643871      0.433438     -0.072516   \n",
              "S13        -0.482172     -0.114560       0.108922     -0.780356      0.008205   \n",
              "S14         0.491378     -0.489738       0.844514      2.600053      1.536173   \n",
              "S15        -0.690613      1.194811       0.176328      0.334550     -0.533815   \n",
              "S16        -0.796768     -1.295130      -0.523270     -0.579542      0.147491   \n",
              "S17         0.998964     -1.231005       0.156259      0.421926     -0.721066   \n",
              "S18         0.876028      0.965320      -1.819907      0.215229      0.310441   \n",
              "S19         0.743074     -1.189698      -0.904368     -0.175313      1.915568   \n",
              "S20        -2.830675     -0.126881       0.794439     -0.536523      1.354332   \n",
              "S21         0.689639      0.973584      -0.144015     -1.206021      0.245947   \n",
              "S22         0.881135     -2.135740       1.612688      1.496065     -0.071969   \n",
              "S23        -0.177765      0.281041      -0.519719     -0.300911     -0.857078   \n",
              "S24        -0.860420     -0.336852      -1.254852      0.777624     -1.225609   \n",
              "S25        -0.837015      1.927708      -0.221270     -1.056681      0.255935   \n",
              "S26        -0.761946     -0.099753       0.554204     -0.485596      1.572681   \n",
              "S27         0.107171     -0.971135       0.632478     -2.003805      0.079558   \n",
              "S28        -0.359771      0.677833       1.829341      0.553071     -1.482036   \n",
              "S29        -1.216399      0.847750       0.861753     -0.354336      0.614608   \n",
              "S30        -0.275397     -1.467176      -0.157836      0.983026     -2.131839   \n",
              "\n",
              "Sample  Compound1644  Compound14195  Compound1725  Compound10230  \\\n",
              "S1          0.734368      -0.553593     -0.505212       1.459589   \n",
              "S2         -0.857874      -1.100562     -0.370098       1.116944   \n",
              "S3         -0.304521      -0.192816      0.813981      -0.001899   \n",
              "S4          0.636588       0.448993     -0.493366      -0.067507   \n",
              "S5          0.102119      -0.688137      0.395857       0.695941   \n",
              "S6         -0.126612       0.011772     -0.681381      -1.061151   \n",
              "S7         -0.323922       0.757405     -0.635181       0.729151   \n",
              "S8          0.045758       1.061314      1.370491      -0.415758   \n",
              "S9         -0.523221       0.394303     -0.385557      -0.291661   \n",
              "S10         1.678651       0.229339      2.467083      -0.547809   \n",
              "S11         2.773439      -0.369263      0.512078       0.105658   \n",
              "S12         0.181013      -1.440268      0.218414       0.581467   \n",
              "S13         0.304516       1.569212     -1.840367       0.137772   \n",
              "S14        -0.088371       2.285304     -1.286569       0.449429   \n",
              "S15         0.223781      -0.252333     -0.608791      -0.030840   \n",
              "S16        -0.660070      -2.386171      1.435983       2.069475   \n",
              "S17         0.108303      -0.010828     -2.252398      -0.466957   \n",
              "S18         0.824821       0.594435     -0.544793      -0.352971   \n",
              "S19         0.169534      -1.035111     -1.508600       0.193700   \n",
              "S20        -0.311778      -1.679910      0.009554       1.131951   \n",
              "S21         0.002458       1.114116      0.722366       1.066432   \n",
              "S22         1.270127      -0.070734     -0.289894      -0.263846   \n",
              "S23        -0.979202      -0.000019      0.346807       0.070108   \n",
              "S24        -0.431233      -0.063161      0.124660      -1.793669   \n",
              "S25        -0.332927       0.890411      0.578863      -1.296307   \n",
              "S26         0.364871      -0.481684     -0.689664      -2.028022   \n",
              "S27        -2.786692       0.003267      0.475757       0.158987   \n",
              "S28        -1.955704       0.142047      0.994264      -1.114557   \n",
              "S29         0.920172      -0.878580      0.961757      -1.832180   \n",
              "S30        -0.658390       1.701254      0.663957       1.598531   \n",
              "\n",
              "Sample  Compound11327  ...  Compound 5864  Compound 5869  Compound 5891  \\\n",
              "S1          -0.629294  ...       1.198207       1.178092      -1.528730   \n",
              "S2           0.318566  ...      -0.633603       0.297903      -0.113692   \n",
              "S3           1.570607  ...      -0.268681       1.161314       0.934590   \n",
              "S4          -0.326771  ...      -0.510781       0.353246       0.334744   \n",
              "S5          -0.558950  ...       0.521862      -1.279529       0.525862   \n",
              "S6           1.267452  ...       0.501035       0.792182       0.054024   \n",
              "S7           0.334731  ...       0.025234      -1.450762       0.309545   \n",
              "S8           0.050721  ...       0.619530      -1.085680       0.494829   \n",
              "S9          -1.242068  ...       0.206059       0.498880      -0.581774   \n",
              "S10          0.553931  ...       0.463530       1.147100       0.657473   \n",
              "S11         -0.513956  ...      -0.016702       0.335295       1.342789   \n",
              "S12         -0.164419  ...       0.366186       0.630840      -2.167605   \n",
              "S13          2.495671  ...       0.069138      -0.294707      -0.332388   \n",
              "S14         -0.629022  ...      -2.158811       2.395480       1.023530   \n",
              "S15         -2.383272  ...      -1.131996       1.035919       0.789454   \n",
              "S16          0.486096  ...       1.106205      -1.544626       0.445720   \n",
              "S17          1.577259  ...      -2.219400      -0.370338      -1.403384   \n",
              "S18          0.328215  ...      -0.144533      -0.793475      -0.766480   \n",
              "S19         -0.393004  ...      -1.224575      -0.140168       0.054206   \n",
              "S20          0.294820  ...      -0.104160       1.066519       0.103158   \n",
              "S21          0.071917  ...       1.605348       0.335026      -1.921109   \n",
              "S22         -1.979783  ...      -0.954701      -2.163409       0.926850   \n",
              "S23         -0.924542  ...       2.419998      -0.823593       2.708573   \n",
              "S24          0.861778  ...      -0.121305      -0.036615       0.264350   \n",
              "S25         -0.892584  ...       0.113743      -0.772682      -0.387066   \n",
              "S26          0.764919  ...      -0.801842      -0.059025      -0.987180   \n",
              "S27         -0.089287  ...      -0.382078      -1.077210      -0.570510   \n",
              "S28         -0.464772  ...       1.642961       0.891912       0.639358   \n",
              "S29          0.444017  ...      -0.203973      -0.400316      -0.576983   \n",
              "S30         -0.228976  ...       0.018105       0.172428      -0.272155   \n",
              "\n",
              "Sample  Compound 5900  Compound 5919  Compound 5930  Compound 5964  \\\n",
              "S1          -0.594721      -0.708391      -1.151307       0.539402   \n",
              "S2          -0.299673      -1.373940      -1.104287      -1.551825   \n",
              "S3          -0.875828      -1.721014      -0.693081      -0.605668   \n",
              "S4           0.718840       1.529269       0.209630      -2.320200   \n",
              "S5          -1.417511       1.737433       1.458325       1.825129   \n",
              "S6          -0.443589      -0.523742       0.519884       0.804961   \n",
              "S7           0.586933      -0.468183      -1.705423       0.211390   \n",
              "S8          -0.119813       1.178874      -0.987260       1.231034   \n",
              "S9          -0.344437      -0.552353      -0.585168       1.628998   \n",
              "S10          1.982945       0.685938      -0.683938       0.008594   \n",
              "S11          0.183220      -0.130542       0.363761      -1.504047   \n",
              "S12          0.017109      -0.486780      -1.341279       1.719822   \n",
              "S13          0.849551       1.088075      -0.831655      -0.160421   \n",
              "S14          1.190389       0.429753      -1.973921      -0.664358   \n",
              "S15          0.416225       1.179866       1.150547      -0.407452   \n",
              "S16         -0.541409       0.039775      -1.001355      -0.719012   \n",
              "S17          0.316979       1.355376       1.678711      -0.797545   \n",
              "S18         -1.046171      -0.158778       0.017390      -0.127567   \n",
              "S19          1.742686      -0.373822      -0.303056      -0.177095   \n",
              "S20         -1.434780      -0.160705       1.657573       0.146251   \n",
              "S21         -0.151675      -1.710104       0.881456      -1.180308   \n",
              "S22         -0.289916      -0.456709       0.336257       1.009933   \n",
              "S23          1.250220       1.462524       0.625935      -0.343570   \n",
              "S24         -2.331046       0.595432       1.374444      -0.319696   \n",
              "S25         -1.475420      -1.960839       0.617051       1.448015   \n",
              "S26         -0.250033       1.081492       0.236044       0.999189   \n",
              "S27         -0.017330      -0.691995       0.131246      -0.542135   \n",
              "S28          1.783577      -0.522009       0.787767       0.118701   \n",
              "S29          0.411387      -0.436589      -0.563868       0.188484   \n",
              "S30          0.183289       0.072687       0.879577      -0.459005   \n",
              "\n",
              "Sample  Compound 5978  Compound 5980  Label  \n",
              "S1           1.330264      -0.725607      1  \n",
              "S2          -1.016591       1.141513      1  \n",
              "S3           0.873848      -0.031110      1  \n",
              "S4          -0.724508      -1.231284      1  \n",
              "S5          -0.632145       1.157030      1  \n",
              "S6           0.926627      -0.957016      1  \n",
              "S7          -0.837863       1.319016      1  \n",
              "S8          -0.794680       1.367735      1  \n",
              "S9           0.653137      -0.460422      0  \n",
              "S10         -1.323067      -0.628184      0  \n",
              "S11         -0.235105       0.027212      0  \n",
              "S12         -0.992600       0.339793      0  \n",
              "S13         -0.986003      -0.249088      0  \n",
              "S14         -1.074767      -0.706247      0  \n",
              "S15          1.584562      -0.532373      0  \n",
              "S16         -0.055778       0.391693      0  \n",
              "S17          1.112552       1.452190      0  \n",
              "S18          0.076169      -1.935837      0  \n",
              "S19          1.393239       1.738388      0  \n",
              "S20          0.785684      -0.374670      0  \n",
              "S21          0.870249      -2.179393      0  \n",
              "S22          0.262694       0.403250      0  \n",
              "S23          0.283686      -0.062705      0  \n",
              "S24         -0.097161      -0.555656      0  \n",
              "S25         -0.459415       1.509142      0  \n",
              "S26          1.958811      -0.114083      0  \n",
              "S27         -1.810071       1.221300      0  \n",
              "S28         -1.760291       0.100235      0  \n",
              "S29          0.152339      -0.620508      0  \n",
              "S30          0.541582      -0.804313      0  \n",
              "\n",
              "[30 rows x 16796 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-802b362a-ee97-490f-96c8-3210c25eb42e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Sample</th>\n",
              "      <th>Compound2078</th>\n",
              "      <th>Compound4363</th>\n",
              "      <th>Compound13589</th>\n",
              "      <th>Compound5278</th>\n",
              "      <th>Compound1640</th>\n",
              "      <th>Compound1644</th>\n",
              "      <th>Compound14195</th>\n",
              "      <th>Compound1725</th>\n",
              "      <th>Compound10230</th>\n",
              "      <th>Compound11327</th>\n",
              "      <th>...</th>\n",
              "      <th>Compound 5864</th>\n",
              "      <th>Compound 5869</th>\n",
              "      <th>Compound 5891</th>\n",
              "      <th>Compound 5900</th>\n",
              "      <th>Compound 5919</th>\n",
              "      <th>Compound 5930</th>\n",
              "      <th>Compound 5964</th>\n",
              "      <th>Compound 5978</th>\n",
              "      <th>Compound 5980</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>S1</th>\n",
              "      <td>0.054180</td>\n",
              "      <td>-0.100584</td>\n",
              "      <td>-0.072038</td>\n",
              "      <td>0.031804</td>\n",
              "      <td>-1.359991</td>\n",
              "      <td>0.734368</td>\n",
              "      <td>-0.553593</td>\n",
              "      <td>-0.505212</td>\n",
              "      <td>1.459589</td>\n",
              "      <td>-0.629294</td>\n",
              "      <td>...</td>\n",
              "      <td>1.198207</td>\n",
              "      <td>1.178092</td>\n",
              "      <td>-1.528730</td>\n",
              "      <td>-0.594721</td>\n",
              "      <td>-0.708391</td>\n",
              "      <td>-1.151307</td>\n",
              "      <td>0.539402</td>\n",
              "      <td>1.330264</td>\n",
              "      <td>-0.725607</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S2</th>\n",
              "      <td>0.185447</td>\n",
              "      <td>-0.063551</td>\n",
              "      <td>0.326378</td>\n",
              "      <td>-1.445199</td>\n",
              "      <td>1.080558</td>\n",
              "      <td>-0.857874</td>\n",
              "      <td>-1.100562</td>\n",
              "      <td>-0.370098</td>\n",
              "      <td>1.116944</td>\n",
              "      <td>0.318566</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.633603</td>\n",
              "      <td>0.297903</td>\n",
              "      <td>-0.113692</td>\n",
              "      <td>-0.299673</td>\n",
              "      <td>-1.373940</td>\n",
              "      <td>-1.104287</td>\n",
              "      <td>-1.551825</td>\n",
              "      <td>-1.016591</td>\n",
              "      <td>1.141513</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S3</th>\n",
              "      <td>0.964752</td>\n",
              "      <td>1.528995</td>\n",
              "      <td>-1.480277</td>\n",
              "      <td>0.121679</td>\n",
              "      <td>0.081117</td>\n",
              "      <td>-0.304521</td>\n",
              "      <td>-0.192816</td>\n",
              "      <td>0.813981</td>\n",
              "      <td>-0.001899</td>\n",
              "      <td>1.570607</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.268681</td>\n",
              "      <td>1.161314</td>\n",
              "      <td>0.934590</td>\n",
              "      <td>-0.875828</td>\n",
              "      <td>-1.721014</td>\n",
              "      <td>-0.693081</td>\n",
              "      <td>-0.605668</td>\n",
              "      <td>0.873848</td>\n",
              "      <td>-0.031110</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S4</th>\n",
              "      <td>0.388220</td>\n",
              "      <td>0.776949</td>\n",
              "      <td>-0.776755</td>\n",
              "      <td>-0.460003</td>\n",
              "      <td>0.884676</td>\n",
              "      <td>0.636588</td>\n",
              "      <td>0.448993</td>\n",
              "      <td>-0.493366</td>\n",
              "      <td>-0.067507</td>\n",
              "      <td>-0.326771</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.510781</td>\n",
              "      <td>0.353246</td>\n",
              "      <td>0.334744</td>\n",
              "      <td>0.718840</td>\n",
              "      <td>1.529269</td>\n",
              "      <td>0.209630</td>\n",
              "      <td>-2.320200</td>\n",
              "      <td>-0.724508</td>\n",
              "      <td>-1.231284</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S5</th>\n",
              "      <td>0.520463</td>\n",
              "      <td>0.829147</td>\n",
              "      <td>1.021200</td>\n",
              "      <td>-2.231341</td>\n",
              "      <td>1.142657</td>\n",
              "      <td>0.102119</td>\n",
              "      <td>-0.688137</td>\n",
              "      <td>0.395857</td>\n",
              "      <td>0.695941</td>\n",
              "      <td>-0.558950</td>\n",
              "      <td>...</td>\n",
              "      <td>0.521862</td>\n",
              "      <td>-1.279529</td>\n",
              "      <td>0.525862</td>\n",
              "      <td>-1.417511</td>\n",
              "      <td>1.737433</td>\n",
              "      <td>1.458325</td>\n",
              "      <td>1.825129</td>\n",
              "      <td>-0.632145</td>\n",
              "      <td>1.157030</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S6</th>\n",
              "      <td>-1.358487</td>\n",
              "      <td>-1.006378</td>\n",
              "      <td>1.700056</td>\n",
              "      <td>0.922685</td>\n",
              "      <td>-1.184236</td>\n",
              "      <td>-0.126612</td>\n",
              "      <td>0.011772</td>\n",
              "      <td>-0.681381</td>\n",
              "      <td>-1.061151</td>\n",
              "      <td>1.267452</td>\n",
              "      <td>...</td>\n",
              "      <td>0.501035</td>\n",
              "      <td>0.792182</td>\n",
              "      <td>0.054024</td>\n",
              "      <td>-0.443589</td>\n",
              "      <td>-0.523742</td>\n",
              "      <td>0.519884</td>\n",
              "      <td>0.804961</td>\n",
              "      <td>0.926627</td>\n",
              "      <td>-0.957016</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S7</th>\n",
              "      <td>-0.411969</td>\n",
              "      <td>1.503999</td>\n",
              "      <td>-0.363085</td>\n",
              "      <td>0.597063</td>\n",
              "      <td>0.329463</td>\n",
              "      <td>-0.323922</td>\n",
              "      <td>0.757405</td>\n",
              "      <td>-0.635181</td>\n",
              "      <td>0.729151</td>\n",
              "      <td>0.334731</td>\n",
              "      <td>...</td>\n",
              "      <td>0.025234</td>\n",
              "      <td>-1.450762</td>\n",
              "      <td>0.309545</td>\n",
              "      <td>0.586933</td>\n",
              "      <td>-0.468183</td>\n",
              "      <td>-1.705423</td>\n",
              "      <td>0.211390</td>\n",
              "      <td>-0.837863</td>\n",
              "      <td>1.319016</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S8</th>\n",
              "      <td>-0.235863</td>\n",
              "      <td>-1.184963</td>\n",
              "      <td>1.184743</td>\n",
              "      <td>0.156637</td>\n",
              "      <td>-1.250253</td>\n",
              "      <td>0.045758</td>\n",
              "      <td>1.061314</td>\n",
              "      <td>1.370491</td>\n",
              "      <td>-0.415758</td>\n",
              "      <td>0.050721</td>\n",
              "      <td>...</td>\n",
              "      <td>0.619530</td>\n",
              "      <td>-1.085680</td>\n",
              "      <td>0.494829</td>\n",
              "      <td>-0.119813</td>\n",
              "      <td>1.178874</td>\n",
              "      <td>-0.987260</td>\n",
              "      <td>1.231034</td>\n",
              "      <td>-0.794680</td>\n",
              "      <td>1.367735</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S9</th>\n",
              "      <td>1.089264</td>\n",
              "      <td>0.009396</td>\n",
              "      <td>-1.204284</td>\n",
              "      <td>0.098720</td>\n",
              "      <td>-0.345795</td>\n",
              "      <td>-0.523221</td>\n",
              "      <td>0.394303</td>\n",
              "      <td>-0.385557</td>\n",
              "      <td>-0.291661</td>\n",
              "      <td>-1.242068</td>\n",
              "      <td>...</td>\n",
              "      <td>0.206059</td>\n",
              "      <td>0.498880</td>\n",
              "      <td>-0.581774</td>\n",
              "      <td>-0.344437</td>\n",
              "      <td>-0.552353</td>\n",
              "      <td>-0.585168</td>\n",
              "      <td>1.628998</td>\n",
              "      <td>0.653137</td>\n",
              "      <td>-0.460422</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S10</th>\n",
              "      <td>-0.271599</td>\n",
              "      <td>-0.420876</td>\n",
              "      <td>-1.288302</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.503149</td>\n",
              "      <td>1.678651</td>\n",
              "      <td>0.229339</td>\n",
              "      <td>2.467083</td>\n",
              "      <td>-0.547809</td>\n",
              "      <td>0.553931</td>\n",
              "      <td>...</td>\n",
              "      <td>0.463530</td>\n",
              "      <td>1.147100</td>\n",
              "      <td>0.657473</td>\n",
              "      <td>1.982945</td>\n",
              "      <td>0.685938</td>\n",
              "      <td>-0.683938</td>\n",
              "      <td>0.008594</td>\n",
              "      <td>-1.323067</td>\n",
              "      <td>-0.628184</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S11</th>\n",
              "      <td>2.779485</td>\n",
              "      <td>-0.031874</td>\n",
              "      <td>-1.717197</td>\n",
              "      <td>0.867057</td>\n",
              "      <td>-0.826355</td>\n",
              "      <td>2.773439</td>\n",
              "      <td>-0.369263</td>\n",
              "      <td>0.512078</td>\n",
              "      <td>0.105658</td>\n",
              "      <td>-0.513956</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.016702</td>\n",
              "      <td>0.335295</td>\n",
              "      <td>1.342789</td>\n",
              "      <td>0.183220</td>\n",
              "      <td>-0.130542</td>\n",
              "      <td>0.363761</td>\n",
              "      <td>-1.504047</td>\n",
              "      <td>-0.235105</td>\n",
              "      <td>0.027212</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S12</th>\n",
              "      <td>0.797660</td>\n",
              "      <td>0.749360</td>\n",
              "      <td>0.643871</td>\n",
              "      <td>0.433438</td>\n",
              "      <td>-0.072516</td>\n",
              "      <td>0.181013</td>\n",
              "      <td>-1.440268</td>\n",
              "      <td>0.218414</td>\n",
              "      <td>0.581467</td>\n",
              "      <td>-0.164419</td>\n",
              "      <td>...</td>\n",
              "      <td>0.366186</td>\n",
              "      <td>0.630840</td>\n",
              "      <td>-2.167605</td>\n",
              "      <td>0.017109</td>\n",
              "      <td>-0.486780</td>\n",
              "      <td>-1.341279</td>\n",
              "      <td>1.719822</td>\n",
              "      <td>-0.992600</td>\n",
              "      <td>0.339793</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S13</th>\n",
              "      <td>-0.482172</td>\n",
              "      <td>-0.114560</td>\n",
              "      <td>0.108922</td>\n",
              "      <td>-0.780356</td>\n",
              "      <td>0.008205</td>\n",
              "      <td>0.304516</td>\n",
              "      <td>1.569212</td>\n",
              "      <td>-1.840367</td>\n",
              "      <td>0.137772</td>\n",
              "      <td>2.495671</td>\n",
              "      <td>...</td>\n",
              "      <td>0.069138</td>\n",
              "      <td>-0.294707</td>\n",
              "      <td>-0.332388</td>\n",
              "      <td>0.849551</td>\n",
              "      <td>1.088075</td>\n",
              "      <td>-0.831655</td>\n",
              "      <td>-0.160421</td>\n",
              "      <td>-0.986003</td>\n",
              "      <td>-0.249088</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S14</th>\n",
              "      <td>0.491378</td>\n",
              "      <td>-0.489738</td>\n",
              "      <td>0.844514</td>\n",
              "      <td>2.600053</td>\n",
              "      <td>1.536173</td>\n",
              "      <td>-0.088371</td>\n",
              "      <td>2.285304</td>\n",
              "      <td>-1.286569</td>\n",
              "      <td>0.449429</td>\n",
              "      <td>-0.629022</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.158811</td>\n",
              "      <td>2.395480</td>\n",
              "      <td>1.023530</td>\n",
              "      <td>1.190389</td>\n",
              "      <td>0.429753</td>\n",
              "      <td>-1.973921</td>\n",
              "      <td>-0.664358</td>\n",
              "      <td>-1.074767</td>\n",
              "      <td>-0.706247</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S15</th>\n",
              "      <td>-0.690613</td>\n",
              "      <td>1.194811</td>\n",
              "      <td>0.176328</td>\n",
              "      <td>0.334550</td>\n",
              "      <td>-0.533815</td>\n",
              "      <td>0.223781</td>\n",
              "      <td>-0.252333</td>\n",
              "      <td>-0.608791</td>\n",
              "      <td>-0.030840</td>\n",
              "      <td>-2.383272</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.131996</td>\n",
              "      <td>1.035919</td>\n",
              "      <td>0.789454</td>\n",
              "      <td>0.416225</td>\n",
              "      <td>1.179866</td>\n",
              "      <td>1.150547</td>\n",
              "      <td>-0.407452</td>\n",
              "      <td>1.584562</td>\n",
              "      <td>-0.532373</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S16</th>\n",
              "      <td>-0.796768</td>\n",
              "      <td>-1.295130</td>\n",
              "      <td>-0.523270</td>\n",
              "      <td>-0.579542</td>\n",
              "      <td>0.147491</td>\n",
              "      <td>-0.660070</td>\n",
              "      <td>-2.386171</td>\n",
              "      <td>1.435983</td>\n",
              "      <td>2.069475</td>\n",
              "      <td>0.486096</td>\n",
              "      <td>...</td>\n",
              "      <td>1.106205</td>\n",
              "      <td>-1.544626</td>\n",
              "      <td>0.445720</td>\n",
              "      <td>-0.541409</td>\n",
              "      <td>0.039775</td>\n",
              "      <td>-1.001355</td>\n",
              "      <td>-0.719012</td>\n",
              "      <td>-0.055778</td>\n",
              "      <td>0.391693</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S17</th>\n",
              "      <td>0.998964</td>\n",
              "      <td>-1.231005</td>\n",
              "      <td>0.156259</td>\n",
              "      <td>0.421926</td>\n",
              "      <td>-0.721066</td>\n",
              "      <td>0.108303</td>\n",
              "      <td>-0.010828</td>\n",
              "      <td>-2.252398</td>\n",
              "      <td>-0.466957</td>\n",
              "      <td>1.577259</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.219400</td>\n",
              "      <td>-0.370338</td>\n",
              "      <td>-1.403384</td>\n",
              "      <td>0.316979</td>\n",
              "      <td>1.355376</td>\n",
              "      <td>1.678711</td>\n",
              "      <td>-0.797545</td>\n",
              "      <td>1.112552</td>\n",
              "      <td>1.452190</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S18</th>\n",
              "      <td>0.876028</td>\n",
              "      <td>0.965320</td>\n",
              "      <td>-1.819907</td>\n",
              "      <td>0.215229</td>\n",
              "      <td>0.310441</td>\n",
              "      <td>0.824821</td>\n",
              "      <td>0.594435</td>\n",
              "      <td>-0.544793</td>\n",
              "      <td>-0.352971</td>\n",
              "      <td>0.328215</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.144533</td>\n",
              "      <td>-0.793475</td>\n",
              "      <td>-0.766480</td>\n",
              "      <td>-1.046171</td>\n",
              "      <td>-0.158778</td>\n",
              "      <td>0.017390</td>\n",
              "      <td>-0.127567</td>\n",
              "      <td>0.076169</td>\n",
              "      <td>-1.935837</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S19</th>\n",
              "      <td>0.743074</td>\n",
              "      <td>-1.189698</td>\n",
              "      <td>-0.904368</td>\n",
              "      <td>-0.175313</td>\n",
              "      <td>1.915568</td>\n",
              "      <td>0.169534</td>\n",
              "      <td>-1.035111</td>\n",
              "      <td>-1.508600</td>\n",
              "      <td>0.193700</td>\n",
              "      <td>-0.393004</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.224575</td>\n",
              "      <td>-0.140168</td>\n",
              "      <td>0.054206</td>\n",
              "      <td>1.742686</td>\n",
              "      <td>-0.373822</td>\n",
              "      <td>-0.303056</td>\n",
              "      <td>-0.177095</td>\n",
              "      <td>1.393239</td>\n",
              "      <td>1.738388</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S20</th>\n",
              "      <td>-2.830675</td>\n",
              "      <td>-0.126881</td>\n",
              "      <td>0.794439</td>\n",
              "      <td>-0.536523</td>\n",
              "      <td>1.354332</td>\n",
              "      <td>-0.311778</td>\n",
              "      <td>-1.679910</td>\n",
              "      <td>0.009554</td>\n",
              "      <td>1.131951</td>\n",
              "      <td>0.294820</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.104160</td>\n",
              "      <td>1.066519</td>\n",
              "      <td>0.103158</td>\n",
              "      <td>-1.434780</td>\n",
              "      <td>-0.160705</td>\n",
              "      <td>1.657573</td>\n",
              "      <td>0.146251</td>\n",
              "      <td>0.785684</td>\n",
              "      <td>-0.374670</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S21</th>\n",
              "      <td>0.689639</td>\n",
              "      <td>0.973584</td>\n",
              "      <td>-0.144015</td>\n",
              "      <td>-1.206021</td>\n",
              "      <td>0.245947</td>\n",
              "      <td>0.002458</td>\n",
              "      <td>1.114116</td>\n",
              "      <td>0.722366</td>\n",
              "      <td>1.066432</td>\n",
              "      <td>0.071917</td>\n",
              "      <td>...</td>\n",
              "      <td>1.605348</td>\n",
              "      <td>0.335026</td>\n",
              "      <td>-1.921109</td>\n",
              "      <td>-0.151675</td>\n",
              "      <td>-1.710104</td>\n",
              "      <td>0.881456</td>\n",
              "      <td>-1.180308</td>\n",
              "      <td>0.870249</td>\n",
              "      <td>-2.179393</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S22</th>\n",
              "      <td>0.881135</td>\n",
              "      <td>-2.135740</td>\n",
              "      <td>1.612688</td>\n",
              "      <td>1.496065</td>\n",
              "      <td>-0.071969</td>\n",
              "      <td>1.270127</td>\n",
              "      <td>-0.070734</td>\n",
              "      <td>-0.289894</td>\n",
              "      <td>-0.263846</td>\n",
              "      <td>-1.979783</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.954701</td>\n",
              "      <td>-2.163409</td>\n",
              "      <td>0.926850</td>\n",
              "      <td>-0.289916</td>\n",
              "      <td>-0.456709</td>\n",
              "      <td>0.336257</td>\n",
              "      <td>1.009933</td>\n",
              "      <td>0.262694</td>\n",
              "      <td>0.403250</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S23</th>\n",
              "      <td>-0.177765</td>\n",
              "      <td>0.281041</td>\n",
              "      <td>-0.519719</td>\n",
              "      <td>-0.300911</td>\n",
              "      <td>-0.857078</td>\n",
              "      <td>-0.979202</td>\n",
              "      <td>-0.000019</td>\n",
              "      <td>0.346807</td>\n",
              "      <td>0.070108</td>\n",
              "      <td>-0.924542</td>\n",
              "      <td>...</td>\n",
              "      <td>2.419998</td>\n",
              "      <td>-0.823593</td>\n",
              "      <td>2.708573</td>\n",
              "      <td>1.250220</td>\n",
              "      <td>1.462524</td>\n",
              "      <td>0.625935</td>\n",
              "      <td>-0.343570</td>\n",
              "      <td>0.283686</td>\n",
              "      <td>-0.062705</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S24</th>\n",
              "      <td>-0.860420</td>\n",
              "      <td>-0.336852</td>\n",
              "      <td>-1.254852</td>\n",
              "      <td>0.777624</td>\n",
              "      <td>-1.225609</td>\n",
              "      <td>-0.431233</td>\n",
              "      <td>-0.063161</td>\n",
              "      <td>0.124660</td>\n",
              "      <td>-1.793669</td>\n",
              "      <td>0.861778</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.121305</td>\n",
              "      <td>-0.036615</td>\n",
              "      <td>0.264350</td>\n",
              "      <td>-2.331046</td>\n",
              "      <td>0.595432</td>\n",
              "      <td>1.374444</td>\n",
              "      <td>-0.319696</td>\n",
              "      <td>-0.097161</td>\n",
              "      <td>-0.555656</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S25</th>\n",
              "      <td>-0.837015</td>\n",
              "      <td>1.927708</td>\n",
              "      <td>-0.221270</td>\n",
              "      <td>-1.056681</td>\n",
              "      <td>0.255935</td>\n",
              "      <td>-0.332927</td>\n",
              "      <td>0.890411</td>\n",
              "      <td>0.578863</td>\n",
              "      <td>-1.296307</td>\n",
              "      <td>-0.892584</td>\n",
              "      <td>...</td>\n",
              "      <td>0.113743</td>\n",
              "      <td>-0.772682</td>\n",
              "      <td>-0.387066</td>\n",
              "      <td>-1.475420</td>\n",
              "      <td>-1.960839</td>\n",
              "      <td>0.617051</td>\n",
              "      <td>1.448015</td>\n",
              "      <td>-0.459415</td>\n",
              "      <td>1.509142</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S26</th>\n",
              "      <td>-0.761946</td>\n",
              "      <td>-0.099753</td>\n",
              "      <td>0.554204</td>\n",
              "      <td>-0.485596</td>\n",
              "      <td>1.572681</td>\n",
              "      <td>0.364871</td>\n",
              "      <td>-0.481684</td>\n",
              "      <td>-0.689664</td>\n",
              "      <td>-2.028022</td>\n",
              "      <td>0.764919</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.801842</td>\n",
              "      <td>-0.059025</td>\n",
              "      <td>-0.987180</td>\n",
              "      <td>-0.250033</td>\n",
              "      <td>1.081492</td>\n",
              "      <td>0.236044</td>\n",
              "      <td>0.999189</td>\n",
              "      <td>1.958811</td>\n",
              "      <td>-0.114083</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S27</th>\n",
              "      <td>0.107171</td>\n",
              "      <td>-0.971135</td>\n",
              "      <td>0.632478</td>\n",
              "      <td>-2.003805</td>\n",
              "      <td>0.079558</td>\n",
              "      <td>-2.786692</td>\n",
              "      <td>0.003267</td>\n",
              "      <td>0.475757</td>\n",
              "      <td>0.158987</td>\n",
              "      <td>-0.089287</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.382078</td>\n",
              "      <td>-1.077210</td>\n",
              "      <td>-0.570510</td>\n",
              "      <td>-0.017330</td>\n",
              "      <td>-0.691995</td>\n",
              "      <td>0.131246</td>\n",
              "      <td>-0.542135</td>\n",
              "      <td>-1.810071</td>\n",
              "      <td>1.221300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S28</th>\n",
              "      <td>-0.359771</td>\n",
              "      <td>0.677833</td>\n",
              "      <td>1.829341</td>\n",
              "      <td>0.553071</td>\n",
              "      <td>-1.482036</td>\n",
              "      <td>-1.955704</td>\n",
              "      <td>0.142047</td>\n",
              "      <td>0.994264</td>\n",
              "      <td>-1.114557</td>\n",
              "      <td>-0.464772</td>\n",
              "      <td>...</td>\n",
              "      <td>1.642961</td>\n",
              "      <td>0.891912</td>\n",
              "      <td>0.639358</td>\n",
              "      <td>1.783577</td>\n",
              "      <td>-0.522009</td>\n",
              "      <td>0.787767</td>\n",
              "      <td>0.118701</td>\n",
              "      <td>-1.760291</td>\n",
              "      <td>0.100235</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S29</th>\n",
              "      <td>-1.216399</td>\n",
              "      <td>0.847750</td>\n",
              "      <td>0.861753</td>\n",
              "      <td>-0.354336</td>\n",
              "      <td>0.614608</td>\n",
              "      <td>0.920172</td>\n",
              "      <td>-0.878580</td>\n",
              "      <td>0.961757</td>\n",
              "      <td>-1.832180</td>\n",
              "      <td>0.444017</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.203973</td>\n",
              "      <td>-0.400316</td>\n",
              "      <td>-0.576983</td>\n",
              "      <td>0.411387</td>\n",
              "      <td>-0.436589</td>\n",
              "      <td>-0.563868</td>\n",
              "      <td>0.188484</td>\n",
              "      <td>0.152339</td>\n",
              "      <td>-0.620508</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S30</th>\n",
              "      <td>-0.275397</td>\n",
              "      <td>-1.467176</td>\n",
              "      <td>-0.157836</td>\n",
              "      <td>0.983026</td>\n",
              "      <td>-2.131839</td>\n",
              "      <td>-0.658390</td>\n",
              "      <td>1.701254</td>\n",
              "      <td>0.663957</td>\n",
              "      <td>1.598531</td>\n",
              "      <td>-0.228976</td>\n",
              "      <td>...</td>\n",
              "      <td>0.018105</td>\n",
              "      <td>0.172428</td>\n",
              "      <td>-0.272155</td>\n",
              "      <td>0.183289</td>\n",
              "      <td>0.072687</td>\n",
              "      <td>0.879577</td>\n",
              "      <td>-0.459005</td>\n",
              "      <td>0.541582</td>\n",
              "      <td>-0.804313</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30 rows × 16796 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-802b362a-ee97-490f-96c8-3210c25eb42e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-802b362a-ee97-490f-96c8-3210c25eb42e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-802b362a-ee97-490f-96c8-3210c25eb42e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0a15534c-3b23-4d7a-8b3d-3004fee6140f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_86641487-8052-47fa-83a6-b7e3e22ab3f1\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('feature_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_86641487-8052-47fa-83a6-b7e3e22ab3f1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('feature_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feature_data"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "#lets see each feature importance using XGB Classifier\n",
        "import xgboost\n",
        "\n",
        "x_sample = feature_data.drop(columns = ['Label'])\n",
        "y_sample = feature_data['Label']\n",
        "\n",
        "model = xgboost.XGBClassifier()\n",
        "model.fit(x_sample,y_sample)\n",
        "\n",
        "feat_imp = pd.Series(model.feature_importances_, index=x_sample.columns)\n",
        "print(feat_imp.sort_values())\n",
        "feat_imp.nlargest(20).plot(kind='barh',figsize=(20,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 940
        },
        "id": "leU-RZ3FIM0r",
        "outputId": "c57d9684-31c6-476a-ac2c-48df86963c43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample\n",
            "Compound2078     0.000000\n",
            "Compound9771     0.000000\n",
            "Compound9785     0.000000\n",
            "Compound9825     0.000000\n",
            "Compound9851     0.000000\n",
            "                   ...   \n",
            "Compound 871     0.098062\n",
            "Compound 181     0.108587\n",
            "Compound 2667    0.130899\n",
            "Compound 195     0.171225\n",
            "Compound1247     0.459728\n",
            "Length: 16795, dtype: float32\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Sample'>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(x_sample,y_sample, test_size = 0.2, random_state = 123)\n",
        "oversample = SMOTE()\n",
        "X_train,y_train = oversample.fit_resample(X_train,y_train)\n",
        "sc = StandardScaler()\n",
        "\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)\n",
        "\n",
        "#using xgboost boosting algorithms\n",
        "model_1 = xgboost.XGBClassifier()\n",
        "model_1.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "m1BL1TdMI9js",
        "outputId": "4bd7abe5-d035-4d92-ad19-c8782115f7ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model_1.predict(X_test)\n",
        "metrics_1,cm_1 = test_results(y_pred, y_test)"
      ],
      "metadata": {
        "id": "jddrGbNTRdoM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "group_counts = y_train.value_counts()\n",
        "group1_count = group_counts[1]\n",
        "group2_count = group_counts[0]\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie([group1_count, group2_count], labels=['Group 1', 'Group 2'], autopct='%1.1f%%', startangle=140, colors=['#66c2a5', '#fc8d62'])\n",
        "plt.title('Distribution of Group 1 and 2 in traininig Samples')\n",
        "plt.axis('equal')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "AgBCFqQyJQ5R",
        "outputId": "d18b1838-7f8a-49e1-b50b-efa396959e2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "group_counts = y_test.value_counts()\n",
        "group1_count = group_counts[1]\n",
        "group2_count = group_counts[0]\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie([group1_count, group2_count], labels=['Group 1', 'Group 2'], autopct='%1.1f%%', startangle=140, colors=['#66c2a5', '#fc8d62'])\n",
        "plt.title('Distribution of Group 1 and 2 in testing Samples')\n",
        "plt.axis('equal')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "SIQ46XWWJT78",
        "outputId": "088e6587-4eb9-4048-b70c-0ac7a36bd5cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def test_results(y_pred, y_test):\n",
        "  results = {}\n",
        "  results['Accuracy'] = accuracy_score(y_test,y_pred)\n",
        "  results['Precision'] = precision_score(y_test,y_pred,average = 'weighted')\n",
        "  results['Recall'] = recall_score(y_test,y_pred,average = 'weighted')\n",
        "  results['F1_Score'] = f1_score(y_test,y_pred,average = 'weighted')\n",
        "\n",
        "  cm = confusion_matrix(y_test,y_pred)\n",
        "  return results,cm"
      ],
      "metadata": {
        "id": "mh4gSd0OJk9E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#using logistic regression algorithm\n",
        "model_2 = LogisticRegression()\n",
        "model_2.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Ff8nH1YWRhve",
        "outputId": "babedeea-ca1c-4e75-873f-5b3bc25892be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y_pred_2 = model_2.predict(X_test)\n",
        "metrics_2, cm_2 = test_results(y_pred_2, y_test)\n",
        "metrics_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1ZHTNG9RlPK",
        "outputId": "7cc273ac-eee9-4000-b6b8-ec25406c4510"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Accuracy': 0.6666666666666666,\n",
              " 'Precision': 0.4444444444444444,\n",
              " 'Recall': 0.6666666666666666,\n",
              " 'F1_Score': 0.5333333333333333}"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#using SVM algorithm using various kernels\n",
        "model_3 = svm.SVC(kernel = 'linear',probability = True)\n",
        "model_4 = svm.SVC(kernel = 'poly',probability = True)\n",
        "model_5 = svm.SVC(kernel = 'rbf',probability = True)\n",
        "\n",
        "model_3.fit(X_train,y_train)\n",
        "model_4.fit(X_train,y_train)\n",
        "model_5.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Du0QUaV6RpuH",
        "outputId": "0f060ca2-62a1-479f-b7e2-001a186b168f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(probability=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(probability=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_3 = model_3.predict(X_test)\n",
        "metrics_3, cm_3 = test_results(y_pred_3, y_test)\n",
        "\n",
        "y_pred_4 = model_4.predict(X_test)\n",
        "metrics_4, cm_4 = test_results(y_pred_4, y_test)\n",
        "\n",
        "y_pred_5 = model_3.predict(X_test)\n",
        "metrics_5, cm_5 = test_results(y_pred_5, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uISXmomWRteM",
        "outputId": "b26ed843-7ab6-4263-f25d-a7d6befd7bb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_6 = RandomForestClassifier(n_estimators = 1500, criterion = 'entropy', random_state = 42)\n",
        "model_6.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "y5YExqCsJWW9",
        "outputId": "018b7c79-0f65-437a-9c87-67be785838b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', n_estimators=1500, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=1500, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=1500, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_6 = model_6.predict(X_test)\n",
        "metrics_6,cm_6 = test_results(y_pred_6,y_test)\n",
        "metrics_6"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OucO5mX1JfpB",
        "outputId": "163c95f1-f6ab-46c7-d61d-738668c22b63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Accuracy': 0.8333333333333334,\n",
              " 'Precision': 0.8666666666666667,\n",
              " 'Recall': 0.8333333333333334,\n",
              " 'F1_Score': 0.8148148148148149}"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#using ANN for classification\n",
        "model_7 = tf.keras.Sequential([\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dropout(0.2),\n",
        "    layers.Dense(128, activation='relu'),\n",
        "    layers.Dropout(0.4),\n",
        "    layers.Dense(256, activation='relu'),\n",
        "    layers.Dropout(0.6),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Compile model\n",
        "model_7.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "E8CmNh8gRyup"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model_7.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "Zg7PRZPeR13s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_7 = model_7.predict(X_test)\n",
        "y_pred_7 = (y_pred_7 >= 0.5).astype(int)\n",
        "metrics_7,cm_7 = test_results(y_pred_7,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7eS_uzl-R5_O",
        "outputId": "341451c1-453a-4daf-edae-3e46d86dfd5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 1s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importance = model_6.feature_importances_\n",
        "okk = pd.DataFrame([x_sample.columns, feature_importance]).T\n",
        "okk = okk.sort_values(by = 1,ascending = False)\n",
        "plt.figure(figsize=(18, 15))\n",
        "sns.barplot(x = okk[1][:80], y = okk[0][:80], data = okk, palette='viridis')\n",
        "plt.title(\"Feature Importance from Random Forest (with estimator = 1500)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uDyQL6QrJatf",
        "outputId": "636af644-fc3f-409a-a12f-c92938398d6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-49-da2bf7d3d37b>:5: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x = okk[1][:80], y = okk[0][:80], data = okk, palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1500 with 1 Axes>"
            ],
            "image/png": 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31/jkk08aXVxcjHv27LGo96msfyU//yX38sUXX5jOKSwsNI4YMcLYsWNHY05OjtFo/H+fGz4+PsaMjAyza5R37A0dOrTUGLtZVFSU6XOxPPz9/c2+NyVeeOEF4x/+8AfT65deesn40ksvGdu3b29MSkoyGo1G46FDh8w+G4zG62Pgxp/rn3766Zafl+UdL2Up72doSkqK0cXFxfj111/ftr+OHTsaX3nllVLtJZ+LN/4e6N27t9HFxcWYnJxsavvXv/5ldHFxMXp5eRnT0tJM7Z9++qnZmDYa7/530OHDh40uLi7Gv/71r2btMTExRhcXF+Pu3btL5fp///d/t73/EiW/s+70r3fv3uXqr8TXX39d6n240YwZM4zx8fHGrVu3GtevX2+cOHGi0cXFxRgWFmYWN2jQIOOYMWNKnX/06FGz3zUl17vx/SwxdepUY8+ePUu1v/baa0YvL68K3ZeIiIil07JRIiIiIr+jWbNm8dFHH5n9g+t/yZqdnc2gQYO4fPmy6V+NGjXw9vY2LecB5hvtlvxFtbe3NwCHDh36TfIeOXKk2evt27dz7do1Bg4caJZvw4YNadmypVm+FXXjEi52dnY4OztTp04dBg4caGpv3bo1dnZ2nD17ttT5I0aMMPtL11GjRlGrVi2SkpKA6+91UVERY8aMMdu34dlnn6Vu3bqmuBI2NjYV2pTbxsbG1O/Vq1f55ZdfsLW1xdnZudTyKwCBgYFm6+yXLH9Tcm8pKSmkpqYyZsyYUrNZSmaSZGZmsmfPHgYOHGia3XP58mV++eUX/Pz8OHXqVKlliiri6aefNht3Bw8eJCMjg1GjRlG7dm1T+xNPPEHr1q355z//WaqPu/2+luXzzz+ne/fudO/eneHDh7Nnzx5CQkJMs0dK3Jh7yfvTpUsX8vLyOHHihFmsra2t2br1NjY2eHp6muX0f//3fzg6OjJgwABTW506dUr9Bb+lvE99+/Yt9blTsl5+yb0MHjzYFG9tbU1QUBC5ubn85z//MeurX79+pr+eh4qNPTs7O44ePcqpU6fKlXd5ZGZmljnLq3PnzqSkpJCbmwvADz/8gL+/P25ubvzwww/A9dkYVlZWphkVlVGe8VKW8n6GlvyF/r///e9yzSgor7Zt29KpUyfT65LfId26daNJkyal2m+8n7v9HVTyGXvzz+nzzz9vdrxEs2bNyr2HzdNPP11qrJf1LzY2tlz9lddbb73FSy+9RL9+/Xj66ad57733eO655/j666/58ccfTXH5+fll7qtS8vmQn59v9r+3ii05fiM7Ozvy8/OrdJyIiIhUNy0bJSIiIvI78vLyKnPD7pKHeTcv0VLixiUmMjMzWbRoEZs3byYjI8MsrmQN9KpWsmRViVOnTmE0GunXr1+Z8bVqVe7/ZtauXdvswShc3wS1cePGpgf1N7bfvEcFQMuWLc1eP/zwwzg6OpqWQDp37hxw/QHwjWxsbGjevLkprkSjRo0qtInvtWvXWLVqFatXryY1NdVsX4Wy1k+/8UEhYHoQW3JvJQ8NXVxcbnnNM2fOYDQaWbBgAQsWLCgzJiMjo9SSVeV18/e/5D0sa/mW1q1bmx4Ol6iK72tZ+vbty+jRoykqKuLAgQMsXbqU/Pz8UpuJHz16lPnz57Nnzx7Tfh0lbv6ZKSunevXq8fPPP5tep6Wl0bJly1JxN78flvI+NW7cmB49epR5rORebn7P2rRpY3YPJW4eCxUZe1OnTmXSpEn0798fFxcX/Pz8GDZsGG5ubuW6j1sx/v9L2d2oS5cuFBcX8+OPP9K4cWMyMjLo0qULx44d4/vvvweuFy/atm1b4X0NblSe8VKW8n6GNm/enPHjx/PRRx+xceNGunTpQp8+fRg6dCiPPPJIpfN+9NFHzV6X9NW4cWOz9pLfPTeOtbv9HZSWlkaNGjVKLW3l6OiInZ1dqc/gm8fc7TRv3rxSS879FsaPH89nn33Gt99+S8eOHYHrhZ+y9sYpKCgwHb/xf28VW9aG5SU/BzePRxERkXuZihciIiIiFqDkocM777yDo6NjqeM1a9Y0ff3nP/+Zffv2ERwcTPv27bG1teXatWuEhISU+RDvZrd6sHHz5sU3uvGvxuH6A3orKysMBoNZbiVsbW3vmEdZyurrdu3lud+7VdZDottZunQpCxYsYPjw4YSFhVGvXj1q1KjBW2+9VWa+Nz80LlGRe7t27Rpw/S+Xb/UXyndaA/92Kvoe3Oy3+r7e+FC+V69eNGjQgL/97W/4+vqaHgpnZ2czevRo6taty9SpU2nRogW1a9fm0KFDxMXFmd67O+X0e7DE8X+zm8dCRcZe165d2b59Ozt37mTXrl18/vnnrFy5kqioKLMZJxVRv379Mos4HTp0oHbt2vznP/+hSZMmODg44OzsTJcuXVi9ejWFhYX88MMPBAQEVOq6JSo7XiryGTp9+nSeeeYZ0/s2e/Zsli1bxmeffVaq2HC3eZdnrN3t76AS5X3IXpHPn19//dU02+Z2atasWapQWNVKCkRZWVmmNkdHxzJnwaWnpwPg5ORkigO4ePFimbElcTfKzs6mTp06d/15LSIiYklUvBARERGxACV/Kerg4HDLv5CG6w9Bdu/ezZQpU3jppZdM7WUtw3KrB0Mlm5Te/MDv5r+wvp0WLVpgNBpp1qzZbTdPrQ6nT5+mW7dupte//vor6enp+Pv7A/9vpsOJEyfM/kK3sLCQ1NTU277/N7rV+7t161Z8fX156623zNqzs7NNm9ZWREmO//3vf2+ZW0mMtbV1ufO/GyXv4cmTJ+nevbvZsZMnT5aaTfJ7GTFiBCtWrGD+/Pk8+eSTWFlZ8d1335n+Urxr166m2NTU1Epfp2nTpvz3v//FaDSajYOTJ0+axVnq+3Sjpk2b8vPPP3Pt2jWzQlrJclp3yrGiY69+/foMHz6c4cOH8+uvvzJ69Gji4+NNxYuK/tV469aty/xe2tjY4OXlxffff0+TJk1My7F17tyZwsJCvvzySy5dumQ2JsryW/0Ve0U/Q11dXXF1dWXSpEkkJyczatQoPvnkE8LDw3+T/G6lKn4HNW3alGvXrnH69GnTDB+AS5cukZ2dTdOmTSud3/Lly1m0aNEd45o2bco//vGPSl+nPEpmzd1YJHFzc2Pv3r2mTeNL7N+/H4D27dsD12fa1apVi4MHD/LUU0+Z4goLCzl8+LDZMnIlUlNTS80oFBERuddpzwsRERERC/D4449Tt25dli1bRlFRUanjly9fBm79V7ErV64s1VanTh2g9DIedevWpUGDBqalU0qsXr263Pn269ePmjVrsmjRolJ/aWs0Gvnll1/K3VdVW7Nmjdl7+Mknn1BcXGwqXvTo0QNra2s+/vhjs9w///xzcnJy6NWrV7muU6dOnTL/4rtmzZql3pOvv/660ntOeHh40KxZM1atWlXqeiXXcXBw4LHHHmPNmjVl/qVuyfipKh06dMDBwYFPP/3UbFmTpKQkjh8/zhNPPFGl1yuvWrVqMX78eI4fP87OnTuB/zez5cbvSWFhYYXG+838/f25ePEiW7ZsMbXl5eXx2WefmcVZ6vt0I39/f9LT09m8ebOprbi4mI8//hhbW9s7PtyvyNi7+XPh4YcfpkWLFmbvTcnnVnmXxOrYsSNHjx4tc3mdzp0789NPP7F3717Tvhb29va0adMGg8EA/L89Zm6lovmUV3k/Q69cuUJxcbHZcRcXF2rUqGF2z7a2tlWeY1mq4ndQyWfszeeU7AFV3s/gslTHnhdXrlwpNf6MRiPvvfcegGl/GYABAwZw9epV1qxZY2orLCxk3bp1eHt7m2ZrPPLII3Tv3p0vv/zSbKm7L774gtzcXLP9dkqkpKTg4+NTZfclIiJiCTTzQkRERMQC1K1blzfeeIO//OUvBAYG8tRTT2Fvb8+5c+dISkrCx8eHWbNmUbduXbp27coHH3xAUVERjRo1YteuXWX+5bGHhwcA8+bN46mnnsLa2prevXtja2vLs88+y/vvv89f//pXOnTowPfff1/qr8Zvp0WLFvz5z3/m3XffJS0tjYCAAB5++GFSU1PZsWMHzz33HMHBwVX2/lREUVER48aNY+DAgZw8eZLVq1fTuXNn+vbtC1x/eDlhwgQWLVpESEgIffr0McV5enoydOjQcl3Hw8ODTz75hCVLltCyZUvs7e3p3r07TzzxBIsXL2bGjBl06tSJ//73v2zcuLHS67DXqFGDN954gxdffJGnn36awMBAHB0dOXHiBMeOHePDDz8E4PXXX+ePf/wjQ4YM4bnnnqN58+ZcunSJH3/8kfPnz/Pll19W6vplsba2JiIighkzZjB69GgGDRpERkYGq1atomnTpowbN67KrlVRgYGBLFy4EIPBQEBAAJ06daJevXpMnz6doKAgrKys+OKLL+5qyaXnnnuOxMREXnnlFQ4dOoSjoyNffPFFqeVaLPl9KjFixAjWrFnD9OnTOXToEE2bNmXr1q0kJyczc+ZMs78Ov5Xyjr1Bgwbx2GOP4eHhQf369Tlw4ABbt25l9OjRpr5KPrdmz56Nn58fNWvWZNCgQbe8dt++fVmyZAnfffed2UNiuF6YWLp0Kf/73//MihRdunRhzZo1NG3a9I7LLrVo0QI7Ozs+/fRTHn74YWxtbfHy8rrrfRXK+xm6Z88e/va3vzFgwABatWrF1atX+eKLL6hZsyb9+/c39efh4cHu3bv56KOPcHJyolmzZqZNtKtSVfwOcnNz45lnnmHNmjVkZ2fTtWtXDhw4wPr16wkICDCbOVdRVb3nxZIlSwA4duwYcL14ULJXzaRJk4Drm5RPmzaNQYMG0aJFCwoKCti+fTvJycmMGDHC9D7A9Q3QBwwYwNy5c8nIyKBly5asX7+etLQ03nzzTbNrh4eHM3LkSIKCgnjuuec4f/48H330EX5+fqZifImDBw+SmZlp+j0nIiJyv1DxQkRERMRCDBkyBCcnJ95//30+/PBDCgsLadSoEV26dCEwMNAU9+677xIdHc3q1asxGo307NkTg8FQar15Ly8vwsLC+PTTT/nXv/7FtWvX2LlzJ7a2tkyePJnLly+zdetWvv76a/z9/fnggw9KLW1zOy+88AKtWrVixYoVLF68GLi+B0HPnj3p06dP1bwplTBr1iw2btzIwoULKSoqYtCgQbz66qtmS5hMmTIFe3t7EhISmDNnDvXq1eO5557j5ZdfxtraulzXmTx5MufOneODDz7g119/5bHHHqN79+5MnDiRvLw8Nm7cyObNm3F3d2fZsmW8++67lb6nxx9/nJUrV7J48WKWL1+O0WikefPmPPfcc6aYtm3bsnbtWhYtWsT69evJzMzE3t4ed3d3Jk+eXOlr30pgYCAPPfQQBoOBuLg4bG1tCQgIIDIy0rTpeHV46KGHTEsR7d27F19fX5YuXcrbb7/N/PnzsbOzY+jQoXTv3r3SBbY6deqwYsUKoqOjSUhI4KGHHmLIkCH4+/sTEhJiFmup71OJhx56iI8//pi4uDjWr1/PlStXcHZ2Zs6cOWafO7dT3rEXFBTEP/7xD3bt2kVhYSFNmjThz3/+s9n3oV+/fgQFBfHVV1/x5ZdfYjQab1u86NChA66urnz99delihedOnWiZs2aPPTQQ2abgpcUL+406wKuF6BiYmKYO3cub7zxBsXFxcyZM6dKHpCX5zPU1dUVPz8/vvnmGy5cuECdOnVwdXXFYDCYNoGG6/tizJo1i/nz55Ofn88zzzzzmxQvoGp+B82ePZtmzZqxfv16duzYQcOGDZkwYYLZUlSW4OZN6NeuXWv6uqR40aRJEzp37sz27du5dOkSNWrUoHXr1kRFRTFixIhSfb7zzjvMnz+fL7/8kqysLFxdXVm6dGmpWU4eHh589NFHxMXFMWfOHB5++GH+8Ic/8PLLL5fqc8uWLTRp0uSuCj8iIiKWyMpYHbu8iYiIiIhUsXXr1jFjxgw+//xzPD09qzsdEfmdbNiwgb/97W/885//tIiCkMjvqbCwkD59+hAaGsrYsWOrOx0REZEqpT0vRERERERE5J41dOhQmjRpQmJiYnWnIvK7W7t2LbVq1WLUqFHVnYqIiEiV07JRIiIiIiIics+qUaMGmzZtqu40RKrFqFGjVLgQEZH7lmZeiIiIiIiIiIiIiIiIRdGeFyIiIiIiIiIiIiIiYlE080JERERERERERERERCyKihciIiIiIiIiIiIiImJRtGG33PP27duH0WjE2tq6ulMRERERERERERERkVsoKirCysqKTp063TFWMy/knmc0Gk3/RKqb0WiksLBQ41EsgsajWBKNR7EkGo9iaTQmxZJoPIol0XgUS6LxWDUq8hxXMy/knmdtbU1hYSHt2rWjTp061Z2OPODy8vI4efIkzs7OGo9S7TQexZJoPIol0XgUS6MxKZZE41EsicajWBKNx6px4MCBcsdaGe/hUtHOnTtJTEzk4MGD5Obm4uTkhJ+fH+PHj8fZ2bm607M4w4YNo3379sTExJi1r1+/npUrV3L8+HFsbW3x9PRk0aJFPPTQQwB88MEHbNq0idTUVIqLi2nevDkjRozgT3/6E1ZWVmZ9Xbhwgblz55KUlERubi5NmzblxRdfZOjQoQCcPXuW2bNnc/jwYX755Rfq1auHj48P4eHhlf6elQx4T0/PSp0vIiIiIiIiIiIi8lu7evUaNWs+2IshVeRZ7j078yIuLg6DwUD//v2Jjo7G3t6eM2fOsHbtWsLDw9mwYUN1p3hPeO+99zAYDEycOJGOHTvyyy+/sHv3bq5evWqKycnJ4amnnqJdu3bUrl2b3bt3M3v2bK5cucLEiRNNcRcvXmTEiBE4OzsTHR1N3bp1OXr0KIWFhaaYX3/9lYYNG/Lyyy/z6KOPkp6ezrJlyxgzZgxffPEF9vb2lb6XN1+Yy+mfz1b6fBEREREREREREZHfQkvX5vz1/ZerO417yj1ZvEhKSsJgMDBp0iTCwsJM7V27dmX48OF888031ZjdvePEiRMsWrSIJUuW0KtXL1N7//79zeLCw8PNXvfo0YNz586xfv16s+JFbGwsjRs35oMPPqBmzZoAdO/e3excNzc33nzzTbO2Dh060L9/f3bt2sWQIUMqfT+nfz7L0Z9OVPp8EREREREREREREbEM9+QcleXLl9OwYUMmTZpU5vHevXsDUFBQwJw5c/Dz88PT05Nhw4axfft2s9jp06czePBgvv32W4YMGYKXlxejR48mNTWVzMxMwsLC8PHxISAggM2bN5udGxQUxIQJE9iwYQMBAQF4eXkRFBTEiRPmD9DLk0dJXzc6fPgwrq6u7N2719Tm6uqKwWAgPj6eHj164Ovry4wZM8jNzTU7Nzk5mcDAQDw9PRk8eDBJSUml3qd169bRrFkzs8JFeTVo0ICioiLT6ytXrvD111/zxz/+0VS4KK/69esDmPUnIiIiIiIiIiIiIg+ue654UVxcTHJyMt26dcPa2vq2sREREaxZs4aQkBAWL15M27ZtmTJlCjt37jSLS09PJyYmhhdffJG4uDjOnDlDREQE4eHhuLi4EB8fj4eHB5GRkaSlpZmde+jQIZYtW8a0adN4++23uXjxIiEhIWZLJZU3j/JKTEzk1KlTxMTEMHnyZDZu3MiSJUvM7ic4OBgbGxvmz59PcHAwUVFRXLhwwayf/fv34+LiwpIlS+jevTsdOnRg5MiR7N+/v8zrFhcXc+XKFf75z3+yYcMGxowZY/Y+FBUVUatWLUaPHo2Hhwc9e/YkNja2zKLEtWvXKCoqIjU1lejoaB599FGefPLJSr0fIiIiIiIiIiIiInJ/ueeWjcrMzKSwsJAmTZrcNu7IkSNs27aNqKgoRo4cCYC/vz9paWksXryYvn37mmKzsrJISEigXbt2wPW9G6KjowkNDWXy5MnA9Q1Etm/fzo4dOxg7dqzp3IyMDBISEmjVqhUA7u7uDBgwgHXr1jFy5MgK5VFejo6OvPvuu6a+UlJS2Lp1KxEREQCsXLkSKysrDAYDjzzyCACNGzdm3LhxZv2kp6dz8OBB/vvf//L6669Tp04dli5dyvPPP8+2bdtwcHAwxZ4+fZp+/fqZXr/44otm/V26dAmAV199leeee46XXnqJn376iYULF1KjRg2mTZtmdu2//OUvbNy4EYAWLVrw0UcfmXIVERERERERERERkQfbPTfzooSVldVtj//www8ADBgwwKx94MCBpKSkmC2z5OTkZCpcAKZCRI8ePUxtdnZ22Nvbc/78ebP+2rVrZ4oHaNmyJW5ubqbZCxXJo7xuzAugTZs2Znnt378fX19fs2JA9+7dTcszlTAajeTm5rJgwQIGDBhAr169eO+99zAajSQkJJjFPvroo3z++eesWrWKqVOnsmLFChYuXGg6fu3aNVNu06dPp1u3brzwwgsEBwezYsUK8vPzzfoLCwvj73//OwsXLsTR0ZHx48dz7ty5Cr8XIiIiIiIiIiIiInL/ueeKF/Xr16d27dp3fNCdlZWFtbV1qQf2DRs2xGg0kpOTY2qzs7MziylZjurmmQA2NjYUFBSYtd04O+HGtvT09ArnUV5l5XvjMlXp6ell5mVvb1+qn/r16+Pm5mZqq1+/Pu7u7hw7dsws1sbGBk9PT3x9fZk8eTLh4eEsXbrUdJ8lOXXr1s3svO7du1NYWMjp06fN2ps3b46Xlxf9+/fnww8/5OrVq3zwwQflfQtERERERERERERE5D52zxUvatWqhY+PD3v27KG4uPiWcfXq1aOoqIisrCyz9kuXLmFlZVVlSxRlZGSU2ebo6FihPGxsbErtDXHzOeXl6OhYZl6XL182e922bdtb9nFzkeZmHh4eXL161bQHyO36ulN/derUoU2bNqUKHCIiIiIiIiIiIiLyYLrnihcA48ePJz09naVLl5Z5PCkpic6dOwOwZcsWs2NbtmzB3d0dW1vbKsnl6NGjZg/dT58+zZEjR/D29gYodx6NGzfm5MmTGI1GU8yuXbsqlZOXlxd79+41m9Wxe/duMjMzzeJ69+5NZmYmhw8fNrX98ssvHDp0CA8Pj9teIzk5GSsrK5o1awZA06ZNcXFx4dtvvzWL+/bbb3nooYduW9y4cuUKP//8M82bNy/vLYqIiIiIiIiIiIjIfeye27AboFevXoSEhBAfH8+xY8cYNGgQDRo0IDU1lbVr15KTk8OGDRvo168fMTEx5Ofn4+zszJdffsm+fftYsmRJleXi4ODAxIkTmTp1KgALFiygUaNGBAYGAuDm5lauPPr378/nn39OdHQ0AQEBJCcns3Xr1krlNHbsWFavXk1oaCihoaFkZ2cTHx9faumqgIAAPD09mTp1KuHh4dSuXZv3338fGxsb/vjHPwKQk5NDaGgoQ4cOpWXLlhQXF7N3715WrVrFiBEjaNiwoam/8PBwJk2axJtvvskTTzzBgQMHWL58OcHBwaYiTXx8PDk5Ofj4+GBvb09aWhoff/wxhYWFZhuhV0ZLVxU/RERERERERERExPLo2WXF3ZPFC4DIyEg6depEYmIiM2fOJC8vDycnJ/z8/AgODgYgNjaWuXPnYjAYyMzMpHXr1ixcuJA+ffpUWR4eHh7069eP2NhY0tPT8fb2JioqChsbG1NMefLw9/cnMjKShIQE1q9fj7+/P1FRUYwbN67COTk5OWEwGJg9ezZhYWG0aNGCWbNmMW/ePLO4GjVq8P777zNnzhxmzZpFUVERXbp0ITEx0bTsVe3atXF2dmbFihVcuHCBhx56iBYtWhAVFcXTTz9t1l+fPn2YO3cuS5Ys4ZNPPsHJyYkpU6bwwgsvmGLc3d1ZsWIFX3zxBbm5uTRq1IiuXbuyYMGCu5558df3X76r80VERERERERERER+K1evXqNmzXtyMaRqYWW8cZ0iqZCgoCBsbW1ZtmxZdafyQDtw4ACFhYW4u7tTp06d6k5HHnB5eXmcPHkSZ2dnjUepdhqPYkk0HsWSaDyKpdGYFEui8SiWRONRLInGY9U4cOAAAJ6enneMVZlHREREREREREREROQOiouL0VyA3889PfNi586dJCYmcvDgQXJzc03LRo0fPx5nZ+ff/Pr32syLYcOG0b59e2JiYoDrm4t/+OGH7N+/n6NHj9K6dWs2bdpkds6VK1f46KOPSEpK4tSpU9jY2ODl5UV4eDiurq63vNakSZPYuXMnf/nLX0zLeMH1paXS0tLKPGfNmjV07NixwvdVkWqdiIiIiIiIiIiISGUUFxVTVFykmRd3oSLPcu/ZPS/i4uIwGAz079+f6Oho7O3tOXPmDGvXriU8PJwNGzb85jl8/PHHv/k1fktHjx4lKSkJb29vrl27VmbV8Ny5c6xZs4bhw4fz5z//mYKCApYvX86IESNYu3Ytbdq0KXVOUlIS+/fvL/OaixYtorCw0KwtLi6O48eP06FDh7u6nzcnLuDM0dS76kNERERERERERETkZi3aNeOvS8MoKi6q7lQeGPdk8SIpKQmDwcCkSZMICwsztXft2pXhw4fzzTffVGN2944+ffoQEBAAwPTp0zl48GCpmGbNmrF9+3azamK3bt3o06cPq1ev5rXXXjOLLyws5M033+Tll19m5syZpfpzd3c3e52bm8uhQ4d4+umnqVXr7objmaOpHP3p5F31ISIiIiIiIiIiIiLV757c82L58uU0bNiQSZMmlXm8d+/eABQUFDBnzhz8/Pzw9PRk2LBhbN++3Sx2+vTpDB48mG+//ZYhQ4bg5eXF6NGjSU1NJTMzk7CwMHx8fAgICGDz5s1m5wYFBTFhwgQ2bNhAQEAAXl5eBAUFceLECbO48uRR0teNDh8+jKurK3v37jW1ubq6YjAYiI+Pp0ePHvj6+jJjxgxyc3PNzk1OTiYwMBBPT08GDx5MUlJSqfepRo07f/ttbW1LTYN6+OGHadGiBRcvXiwV/+GHH2JnZ0dgYOAd+4brS3/l5uYyZMiQcsWLiIiIiIiIiIiIyP3vniteFBcXk5ycTLdu3bC2tr5tbEREBGvWrCEkJITFixfTtm1bpkyZws6dO83i0tPTiYmJ4cUXXyQuLo4zZ84QERFBeHg4Li4uxMfH4+HhQWRkZKn9Gg4dOsSyZcuYNm0ab7/9NhcvXiQkJMRsaaTy5lFeiYmJnDp1ipiYGCZPnszGjRtZsmSJ2f0EBwdjY2PD/PnzCQ4OJioqigsXLlTqejfLzs427ZFxo3PnzvH+++/z6quvYmVlVa6+Nm3aRNOmTfHx8amS3ERERERERERERETk3nfPLRuVmZlJYWEhTZo0uW3ckSNH2LZtG1FRUYwcORIAf39/0tLSWLx4MX379jXFZmVlkZCQQLt27QC4ePEi0dHRhIaGMnnyZOD6BiLbt29nx44djB071nRuRkYGCQkJtGrVCri+LNKAAQNYt24dI0eOrFAe5eXo6Mi7775r6islJYWtW7cSEREBwMqVK7GyssJgMPDII48A0LhxY8aNG1fha5UlNjYWKysrRo0aZdY+Z84cnnzyyXJvuv3LL7+wa9cunn/++SrJS0RERERERERERETuD/fczIsSd/rL/h9++AGAAQMGmLUPHDiQlJQUs2WWnJycTIULwFSI6NGjh6nNzs4Oe3t7zp8/b9Zfu3btTPEALVu2xM3NzbRhdUXyKK8b8wJo06aNWV779+/H19fXVLgA6N69O/Xr16/wtW62du1aPvvsM2bNmkXjxo1N7f/+97/597//zbRp08rd19dff01RURGDBw++67xERERERERERERE5P5xzxUv6tevT+3atTl37txt47KysrC2ti71wL5hw4YYjUZycnJMbXZ2dmYxJctR3fjwH8DGxoaCggKzNgcHh1LXdnBwID09vcJ5lFdZ+d64TFV6enqZednb21f4WjdKSkpi1qxZTJo0iWeeecbs2OzZsxkzZgx16tQhOzub7Oxs4Pp+HyVf32zTpk24urri4uJyV3mJiIiIiIiIiIiIyP3lnite1KpVCx8fH/bs2UNxcfEt4+rVq0dRURFZWVlm7ZcuXcLKyqpUYaKyMjIyymxzdHSsUB42NjYUFRWZxdx8Tnk5OjqWmdfly5cr1R/Ajz/+SFhYGE8//TRhYWGljp88eZKlS5fStWtX0z+ABQsW0LVr11JFn3PnzpGcnKxZFyIiIiIiIiIiIiJSyj1XvAAYP3486enpLF26tMzjSUlJdO7cGYAtW7aYHduyZQvu7u7Y2tpWSS5Hjx7l9OnTptenT5/myJEjeHt7A5Q7j8aNG3Py5EmMRqMpZteuXZXKycvLi71795rN6ti9ezeZmZmV6u/YsWNMmDCBbt26ERUVVWbMqlWrSv0DGDlyJKtWrSq1ufqmTZsAVLwQERERERERERERkVLuuQ27AXr16kVISAjx8fEcO3aMQYMG0aBBA1JTU1m7di05OTls2LCBfv36ERMTQ35+Ps7Oznz55Zfs27ePJUuWVFkuDg4OTJw4kalTpwLXZxo0atSIwMBAANzc3MqVR//+/fn888+Jjo4mICCA5ORktm7dWqmcxo4dy+rVqwkNDSU0NJTs7Gzi4+NLLV2Vl5dHUlISAGlpaVy5csVUZHnsscewt7cnIyOD4OBgateuzdixYzl48KDp/Lp169K2bVsAfH19y8ylRYsWZR7btGkTPj4+d9x4XUREREREREREREQePPdk8QIgMjKSTp06kZiYyMyZM8nLy8PJyQk/Pz+Cg4MBiI2NZe7cuRgMBjIzM2ndujULFy6kT58+VZaHh4cH/fr1IzY2lvT0dLy9vYmKisLGxsYUU548/P39iYyMJCEhgfXr1+Pv709UVBTjxo2rcE5OTk4YDAZmz55NWFgYLVq0YNasWcybN88sLiMjo9QSUCWvV61aha+vL8eOHTNtBn5zLo899hgff/xxhfM7duwYP//8M6+//nqFz72dFu2aVWl/IiIiIiIiIiIiIqBnj9XBynjjOkVSIUFBQdja2rJs2bLqTuWBduDAAQA8PT2rORMRERERERERERG5XxUXFVNUXESdOnWqO5V7VkWe5d6Te16I3KywsJC8vLzqTkOEvLw8UlJSNB7FImg8iiXReBRLovEolkZjUiyJxqNYEo1HsSR5eXmkHE5BcwF+P/fEslE7d+4kMTGRgwcPkpuba1oeavz48Tg7O1d3ehZp2LBhtG/fnpiYmFvGHDhwgNWrV/Pjjz9y8uRJevXqVWoWycWLF1mxYgW7du3izJkzPPLII3Tt2pWXX36Zpk2bmuJ++ukn5s2bx3//+1+ysrJo2LAhPXr0ICwsjEaNGpni4uPjWbRoUalc3njjDUaNGlUFdy4iIiIiIiIiIiJStYxGI0VFRdWdxgPF4osXcXFxGAwG+vfvT3R0NPb29pw5c4a1a9cSHh7Ohg0bqi23yuz3YEmSk5P5/vvv8fLyoqCgoMyYQ4cOsX37doYPH463tze//PIL7733Hs8++yybNm3C3t4egOzsbFq3bs2zzz6Lg4MDZ8+eZcmSJRw4cIC1a9ea7QHy0EMPsXLlSrPrNG/e/K7uxcbGRtO1xCLUqVMHd3f36k5DBNB4FMui8SiWRONRLI3GpFgSjUexJBqPUlFXr16jZk0tNnS/sOjiRVJSEgaDgUmTJpltLN21a1eGDx/ON998U43Z3fuCgoIYO3as6euydO7cma+//ppatf7fUPHx8eGJJ55gw4YNPP/88wD4+fnh5+dnivH19eXRRx/l+eef5+DBg/j4+JiO1ahRg44dO1b5/bw1OZ4zx9KqvF8RERERERERERGxbC3aNmXm4inVnYZUIYsuXixfvpyGDRsyadKkMo/37t3b9HVBQQFz587lq6++Iisri9atW/PSSy/x5JNPmmKmT5/OwYMHmTlzJnPmzOH06dN4eXkRExND3bp1ef311/nXv/6Fvb09L7/8Mk899ZTp3JLNuQcOHMiiRYu4ePEi3t7eREVF0bp16wrlUdZG34cPH+bpp59m1apV+Pr6AuDq6kpERAT5+fl88sknXL16lT59+vDaa69ha2trOjc5OZnZs2dz9OhRWrZsSWRkZLne3xo17lyFtLOzK9XWuHFj7O3tuXjx4m3PrV+/PsDvNp3qzLE0jh049btcS0RERERERERERER+OxY7h6a4uJjk5GS6deuGtbX1HeMjIiJYs2YNISEhLF68mLZt2zJlyhR27txpFpeenk5MTAwvvvgicXFxnDlzhoiICMLDw3FxcSE+Ph4PDw8iIyNJSzP/K/5Dhw6xbNkypk2bxttvv83FixcJCQmhsLCwwnmUV2JiIqdOnSImJobJkyezceNGlixZYnY/wcHB2NjYMH/+fIKDg4mKiuLChQuVul55nDx5koyMDNq0aVPq2NWrVyksLOT48ePExsbi4eFB586dzWLy8/Pp1q0b7u7uPPXUU3z22We/Wa4iIiIiIiIiIiIicu+x2JkXmZmZFBYW0qRJkzvGHjlyhG3bthEVFcXIkSMB8Pf3Jy0tjcWLF9O3b19TbFZWFgkJCbRr1w64viF1dHQ0oaGhTJ48GQBPT0+2b9/Ojh07TMsqAWRkZJCQkECrVq0AcHd3Z8CAAaxbt46RI0dWKI/ycnR05N133zX1lZKSwtatW4mIiABg5cqVWFlZYTAYeOSRR4DrMyPGjRtX4WuVh9FoZPbs2Tg5OTFo0KBSx0ePHk1ycjIAHTp04P333zdbcqpFixZERETg7u5OQUEBGzdu5LXXXiMnJ4fg4ODfJGcRERERERERERERubdY7MyLElZWVneM+eGHHwAYMGCAWfvAgQNJSUkhNzfX1Obk5GQqXACmQkSPHj1MbXZ2dtjb23P+/Hmz/tq1a2eKB2jZsiVubm7s37+/wnmU1415AbRp08Ysr/379+Pr62sqXAB0797dtGRTVYuPj2fPnj288847ZktXlXjzzTf57LPPiI2NpbCwkPHjx3PlyhXT8WHDhhEcHEz37t154oknePfdd+nfvz/vvffe77a8lIiIiIiIiIiIiIhYNostXtSvX5/atWtz7ty5O8ZmZWVhbW1d6oF9w4YNMRqN5OTkmNpu3sOhZEmqGx/+A9jY2FBQUGDW5uDgUOraDg4OpKenVziP8ior3xuXqUpPTy8zL3t7+wpf604+++wzFi9eTFRUFN27dy8zpnXr1nh7ezN06FA++ugjTp06xZo1a27b78CBA8nJyeHMmTNVnrOIiIiIiIiIiIiI3HsstnhRq1YtfHx82LNnD8XFxbeNrVevHkVFRWRlZZm1X7p0CSsrq1KFicrKyMgos83R0bFCedjY2JSaZXDzOeXl6OhYZl6XL1+uVH+3sn37dt544w2mTp3KH/7wh3Kd07BhQxo3bszp06erNBcRERERERERERERub9ZbPECYPz48aSnp7N06dIyjyclJQGYNoTesmWL2fEtW7bg7u5e5vJGlXH06FGzB/GnT5/myJEjeHt7VyiPxo0bc/LkSYxGoylm165dlcrJy8uLvXv3ms3q2L17N5mZmZXqryx79+7l5Zdf5tlnnzXtC1Ie//vf/zh37hzNmze/bdzmzZuxs7OjRYsWd5uqiIiIiIiIiIiIiNwHLHbDboBevXoREhJCfHw8x44dY9CgQTRo0IDU1FTWrl1LTk4OvXr1ws3NjX79+hETE0N+fj7Ozs58+eWX7Nu3jyVLllRZPg4ODkycOJGpU6cCsGDBAho1akRgYCBAufPo378/n3/+OdHR0QQEBJCcnMzWrVsrldPYsWNZvXo1oaGhhIaGkp2dTXx8fLn2vLh8+TLfffed6etff/3VVHjp1asXderU4fjx40yePJlWrVoxbNgwfvzxR9P59vb2poLDrFmzaNCgAZ6entStW5eTJ0/y0Ucf4eDgYDZTIzAwkKeffprWrVuTn5/Pxo0b2bZtGzNnzjQt4SUiIiIiIiIiIiIiDzaLLl4AREZG0qlTJxITE5k5cyZ5eXk4OTnh5+dHcHCwKS42Npa5c+diMBjIzMykdevWLFy4kD59+lRZLh4eHvTr14/Y2FjS09Px9vYmKioKGxubCuXh7+9PZGQkCQkJrF+/Hn9/f6Kiohg3blyFc3JycsJgMDB79mzCwsJo0aIFs2bNYt68eXc89+jRo4SFhZm1lbzeuXMnzZo1Y//+/eTk5JCTk8OoUaPMYp955hliYmKA6zNAPvvsM1avXk1hYSGPPvoo/v7+TJw4kQYNGpjOadGiBStWrDAtpeXi4kJsbCxDhw6t8L3frEXbpnfdh4iIiIiIiIiIiNx79Gzw/mNlvHHtIrmloKAgbG1tWbZsWXWnIjc5cOAAAJ6entWciYiIiIiIiIiIiFSXq1evUbPmb7NTQm5uLocPH6Z9+/ZVtk3Bg6giz3Ites8LkfIqLCwkLy+vutMQIS8vj5SUFI1HsQgaj2JJNB7Fkmg8iqXRmBRLovEolkTjUSrqtypcSPXQd1NERERERERERERERCzKPbFs1M6dO0lMTOTgwYPk5uaa9rwYP348zs7O1Z2eRRo2bBjt27c37UlRlk8//ZRt27bx888/k5eXR9u2bXnhhRcICAgoFXvhwgXmzp1LUlISubm5NG3alBdffLHUXhU//vgj8+fPZ//+/VhZWdG2bVuioqJo3749cH35rZJNwm82d+5cBg0aVOF71bJRIiIiIiIiIiIi96ffcimoitCyUVWjIs9yLX7D7ri4OAwGA/379yc6Ohp7e3vOnDnD2rVrCQ8PZ8OGDdWd4j1r6dKl+Pn5MWrUKGxtbdmyZQuTJ08mJiaGZ555xhR38eJFRowYgbOzM9HR0dStW5ejR49SWFho1t/u3bt54YUXGD58OKGhoRQXF/PTTz+ZTe17/fXXuXLlitl5K1euZNu2bXTv3v2u7idm6hLOHDt3V32IiIiIiIiIiIiIZWjRtgnTF06q7jSkmlh08SIpKQmDwcCkSZMICwsztXft2pXhw4fzzTffVGN2975169Zhb29vet2zZ0/S0tJYvny5WfEiNjaWxo0b88EHH1CzZk2AUoWG4uJi/vrXvzJmzBgiIyNN7b169TKLa9u2bak8pk2bRs+ePc1yqYwzx85x7OCpu+pDRERERERERERERKpf9c+3uY3ly5fTsGFDJk0qu7rWu3dv09cFBQXMmTMHPz8/PD09GTZsGNu3bzeLnz59OoMHD+bbb79lyJAheHl5MXr0aFJTU8nMzCQsLAwfHx8CAgLYvHmz2blBQUFMmDCBDRs2EBAQgJeXF0FBQZw4ccIsrjx5lPR1o8OHD+Pq6srevXtNba6urhgMBuLj4+nRowe+vr7MmDGD3Nxcs3OTk5MJDAzE09OTwYMHk5SUdId39rqyigXt27fn4sWLptdXrlzh66+/5o9//KOpcFGWb7/9lrS0NMaMGVOua9+Ye2pqKkOGDKnQeSIiIiIiIiIiIiJy/7LY4kVxcTHJycl069YNa2vrO8ZHRESwZs0aQkJCWLx4MW3btmXKlCns3LnTLC49PZ2YmBhefPFF4uLiOHPmDBEREYSHh+Pi4kJ8fDweHh5ERkaSlpZmdu6hQ4dYtmwZ06ZN4+233+bixYuEhISYLZ9U3jzKKzExkVOnThETE8PkyZPZuHEjS5YsMbuf4OBgbGxsmD9/PsHBwURFRXHhwoVKXe+HH36gdevWZvdcVFRErVq1GD16NB4eHvTs2ZPY2FiKiopMcfv376d+/focOHCA/v374+7uTv/+/e+4rNemTZuwtbWlb9++lcpXRERERERERERERO4/FrtsVGZmJoWFhTRp0uSOsUeOHGHbtm1ERUUxcuRIAPz9/UlLS2Px4sVmD8azsrJISEigXbt2wPX9HKKjowkNDWXy5MnA9c1Ctm/fzo4dOxg7dqzp3IyMDBISEmjVqhUA7u7uDBgwgHXr1jFy5MgK5VFejo6OvPvuu6a+UlJS2Lp1KxEREcD1/SKsrKwwGAw88sgjADRu3Jhx48ZV+FobN25k3759LF682NR26dIlAF599VWee+45XnrpJX766ScWLlxIjRo1mDZtGnC9iJKXl8fMmTOZOnUqbdq0YdOmTbzyyis4ODjw+OOPl7pecXExX3/9NX369NEmNyIiIiIiIiIiIiJiYrEzL0pYWVndMeaHH34AYMCAAWbtAwcOJCUlxWyZJScnJ1PhAjAVInr06GFqs7Ozw97envPnz5v1165dO1M8QMuWLXFzc2P//v0VzqO8bswLoE2bNmZ57d+/H19fX1PhAq7vR1G/fv0KXefIkSO8/vrrBAYGEhAQYGq/du2aKY/p06fTrVs3XnjhBYKDg1mxYgX5+fkAGI1GCgoKeOmllxg9ejTdu3fnzTffxMfHh6VLl5Z5zV27dnH58mUGDx5coVxFRERERERERERE5P5mscWL+vXrU7t2bc6dO3fH2KysLKytrUs9sG/YsCFGo5GcnBxTm52dnVlMyZJUNz78B7CxsaGgoMCszcHBodS1HRwcSE9Pr3Ae5VVWvjcuU5Wenl5mXhXZ/DotLY3Q0FC8vLz429/+Vub1u3XrZtbevXt3CgsLOX369B3jjh07VuZ1N23aRP369fHz8yt3riIiIiIiIiIiIiJy/7PY4kWtWrXw8fFhz549FBcX3za2Xr16FBUVkZWVZdZ+6dIlrKysShUmKisjI6PMNkdHxwrlYWNjY7ZfBFDqnPJydHQsM6/Lly+X6/zLly8THByMg4MDixYtKrW/SNu2bW97fkmB58bZLLeKuVF+fj47duxgwIAB5drTREREREREREREREQeHBZbvAAYP3486enpt1x2KCkpCYDOnTsDsGXLFrPjW7Zswd3dvcr2Uzh69KhppgHA6dOnOXLkCN7e3hXKo3Hjxpw8eRKj0WiK2bVrV6Vy8vLyYu/evWazOnbv3k1mZuYdz/31118JDQ2lqKiI999/n7p165aKadq0KS4uLnz77bdm7d9++y0PPfSQqbjh5+eHtbV1mXEeHh6l+v3HP/5Bbm4uQ4YMKc9tioiIiIiIiIiIiMgDxGI37Abo1asXISEhxMfHc+zYMQYNGkSDBg1ITU1l7dq15OTk0KtXL9zc3OjXrx8xMTHk5+fj7OzMl19+yb59+1iyZEmV5ePg4MDEiROZOnUqAAsWLKBRo0YEBgYClDuP/v378/nnnxMdHU1AQADJycls3bq1UjmNHTuW1atXExoaSmhoKNnZ2cTHx5drz4spU6Zw5MgR3nzzTc6dO2e2RFfHjh1NX4eHhzNp0iTefPNNnnjiCQ4cOMDy5csJDg42FWQaNmxIUFAQCxYswMrKijZt2vDVV1/x448/8sEHH5S69saNG2nSpImp4CMiIiIiIiIiIiIiUsKiixcAkZGRdOrUicTERGbOnEleXh5OTk74+fkRHBxsiouNjWXu3LkYDAYyMzNp3bo1CxcupE+fPlWWi4eHB/369SM2Npb09HS8vb2JiorCxsamQnn4+/sTGRlJQkIC69evx9/fn6ioKMaNG1fhnJycnDAYDMyePZuwsDBatGjBrFmzmDdv3h3PLZnt8corr5Q69vPPP5u+7tOnD3PnzmXJkiV88sknODk5MWXKFF544QWzc6ZNm4atrS0ffvghly9fpk2bNixevLjUnhZZWVn861//YuzYseXakL28WrRtUmV9iYiIiIiIiIiISPXS874Hm5XxxrWL5JaCgoKwtbVl2bJl1Z2K3OTAgQMAeHp6VnMmIiIiIiIiIiIiUpWuXr1GzZrVv/tBbm4uhw8fpn379lW2TcGDqCLPcqv/uy5SBYqKisjPz6/uNETIz8/nxIkTGo9iETQexZJoPIol0XgUS6MxKZZE41EsicajABZRuJDqYfHLRgHs3LmTxMREDh48SG5urmnZqPHjx+Ps7Fzd6VmkYcOG0b59e2JiYm4Zc+DAAVavXs2PP/7IyZMn6dWrV6mZJXv37mXMmDFlnu/s7Gy2Ofm+ffuIjY3l4MGD1K1bl4EDBxIREUGdOnUqdM3KsLa25qGHHrrrfkTu1kMPPUTr1q2rOw0RQONRLIvGo1gSjUexNBqTYkk0HsWSaDxaBkuZ+SAPHosvXsTFxWEwGOjfvz/R0dHY29tz5swZ1q5dS3h4OBs2bPhd8vj4449/l+v8npKTk/n+++/x8vKioKCgzBgPDw/WrFlj1nblyhVCQ0Px9/c3taWlpTFu3Di6dOlCfHw8Fy9eJC4ujvT0dBYuXFiha1ZWTPgyzh47d+dAERERERERERERuaPmbZswfd6E6k5DHlAWXbxISkrCYDAwadIkwsLCTO1du3Zl+PDhfPPNN9WY3b0vKCiIsWPHmr4uS926denYsaNZ27p167h27RqDBw82tS1btgw7Ozvee+890wbmdnZ2TJ06lZSUFNzd3ct9zco6e+wcxw6drtI+RUREREREREREROT3Z9HzfZYvX07Dhg2ZNGlSmcd79+5t+rqgoIA5c+bg5+eHp6cnw4YNY/v27Wbx06dPZ/DgwXz77bcMGTIELy8vRo8eTWpqKpmZmYSFheHj40NAQACbN282OzcoKIgJEyawYcMGAgIC8PLyIigoiBMnTpjFlSePkr5udPjwYVxdXdm7d6+pzdXVFYPBQHx8PD169MDX15cZM2aQm5trdm5ycjKBgYF4enoyePBgkpKS7vDOXlejRuW+/Zs2baJVq1Z4eXmZ5d+1a1dT4QLAz88PgH/84x93fU0REREREREREREReXBY7JPk4uJikpOT6datG9bW1neMj4iIYM2aNYSEhLB48WLatm3LlClT2Llzp1lceno6MTExvPjii8TFxXHmzBkiIiIIDw/HxcWF+Ph4PDw8iIyMJC0tzezcQ4cOsWzZMqZNm8bbb7/NxYsXCQkJobCwsMJ5lFdiYiKnTp0iJiaGyZMns3HjRpYsWWJ2P8HBwdjY2DB//nyCg4OJioriwoULlbrenVy6dIk9e/aYzbqA60WbGwsXcH0fCisrq1IFHhERERERERERERGR27HYZaMyMzMpLCykSZMmd4w9cuQI27ZtIyoqipEjRwLg7+9PWloaixcvpm/fvqbYrKwsEhISaNeuHQAXL14kOjqa0NBQJk+eDICnpyfbt29nx44dpiWOADIyMkhISKBVq1YAuLu7M2DAANatW8fIkSMrlEd5OTo68u6775r6SklJYevWrURERACwcuVKrKysMBgMPPLIIwA0btyYcePGVfha5bF582auXr1aqnjRqlUrDhw4gNFoxMrKCoCffvoJo9FIVlbWb5KLiIiIiIiIiIiIiNyfLHbmRYmSB+G388MPPwAwYMAAs/aBAweSkpJitsySk5OTqXABmAoRPXr0MLXZ2dlhb2/P+fPnzfpr166dKR6gZcuWuLm5sX///grnUV435gXQpk0bs7z279+Pr6+vqXAB0L17d+rXr1/ha5XHxo0b8fDwwNnZ2ax91KhRHDt2jHfffZfLly9z5MgRoqKiqFmz5m+Sh4iIiIiIiIiIiIjcvyy2eFG/fn1q167NuXPn7hiblZWFtbV1qQf2DRs2xGg0kpOTY2qzs7MziylZkurGh/8ANjY2FBQUmLU5ODiUuraDgwPp6ekVzqO8ysr3xmWq0tPTy8zL3t6+wte6kzNnzvDTTz8xdOjQUse6d+9OREQEH3/8Md27d+eZZ56hS5cuuLm54eTkVOW5iIiIiIiIiIiIiMj9y2KXjapVqxY+Pj7s2bOH4uJiatW6dar16tWjqKiIrKws6tWrZ2q/dOkSVlZWpQoTlZWRkVFmm5ubW4XysLGxoaioyKyfyi6t5OjoWGZely9frlR/t7Nx40Zq1KjBU089Vebx0NBQ/vSnP3H27FkcHR2xs7OjW7duPPfcc1Wei4iIiIiIiIiIiIjcvyx25gXA+PHjSU9PZ+nSpWUeT0pKAqBz584AbNmyxez4li1bcHd3x9bWtkryOXr0KKdPnza9Pn36NEeOHMHb27tCeTRu3JiTJ09iNBpNMbt27apUTl5eXuzdu9dsVsfu3bvJzMysVH+389VXX/HYY4/ddiaFra0trq6u2Nvbs2HDBoxGIwMHDqzyXERERERERERERETk/mWxMy8AevXqRUhICPHx8Rw7doxBgwbRoEEDUlNTWbt2LTk5OfTq1Qs3Nzf69etHTEwM+fn5ODs78+WXX7Jv3z6WLFlSZfk4ODgwceJEpk6dCsCCBQto1KgRgYGBAOXOo3///nz++edER0cTEBBAcnIyW7durVROY8eOZfXq1YSGhhIaGkp2djbx8fHl2vPi8uXLfPfdd6avf/31V1PhpVevXtSpU8cUm5KSwvHjxxk/fnyZfZ09e5YNGzbg5eUFwJ49e1i1ahVvvfWW2SyUilxTRERERERERERERB5MFl28AIiMjKRTp04kJiYyc+ZM8vLycHJyws/Pj+DgYFNcbGwsc+fOxWAwkJmZSevWrVm4cCF9+vSpslw8PDzo168fsbGxpKen4+3tTVRUFDY2NhXKw9/fn8jISBISEli/fj3+/v5ERUUxbty4Cufk5OSEwWBg9uzZhIWF0aJFC2bNmsW8efPueO7Ro0cJCwszayt5vXPnTpo1a2Zq37hxIzY2NvTv37/Mvqytrfnuu+9YuXIlRUVFuLm5sWjRInr37l3pa1ZU87ZNKn2uiIiIiIiIiIiImNPzNqlOVsYb1y6SWwoKCsLW1pZly5ZVdypykwMHDgDg6elZzZmIiIiIiIiIiIjcX65evUbNmha9+8DvIjc3l8OHD9O+ffsq26bgQVSRZ7kadXJfKCwsJC8vr7rTECEvL4+UlBSNR7EIGo9iSTQexZJoPIql0ZgUS6LxKJZE49EyqHAh1UUjT0RERERERERERERELMoDsWzUzp07SUxM5ODBg+Tm5pr2zBg/fjzOzs7VnZ7FGTZsGO3btycmJgaAr7/+mi+//JJDhw6RnZ1Ny5YtCQoKYvjw4VhZWZmd+/e//50PPviAc+fO4ezsTHh4uNm+F/Hx8SxatKjM644YMYK//e1vFc5Xy0aJiIiIiIiIiIhUjJaDqhgtG1U1KvIs1+I37L5bcXFxGAwG+vfvT3R0NPb29pw5c4a1a9cSHh7Ohg0bqjtFi7dixQqaNm3K9OnTadCgAd9++y2vvfYa58+f56WXXjLFffXVV7z22mtMnDiRbt26sXnzZl566SUSExPp2LEjAM8++yyPP/64Wf//+c9/iIuLw9/f/67yfHuagbPH/3dXfYiIiIiIiIiIiNzvmrd5lFfeDa3uNERu674uXiQlJWEwGJg0aRJhYWGm9q5duzJ8+HC++eabaszu3vHee+9hb29vet29e3cyMzP56KOPmDRpEjVqXK/QLly4kEGDBvHnP/8ZgG7duvHf//6XxYsXYzAYAGjcuDGNGzc26//TTz+lXr16d128OHv8fxxLOXNXfYiIiIiIiIiIiIhI9buv5wUtX76chg0bMmnSpDKPlyxnVFBQwJw5c/Dz88PT05Nhw4axfft2s9jp06czePBgvv32W4YMGYKXlxejR48mNTWVzMxMwsLC8PHxISAggM2bN5udGxQUxIQJE9iwYQMBAQF4eXkRFBTEiRMnzOLKk0dJXzc6fPgwrq6u7N2719Tm6uqKwWAgPj6eHj164Ovry4wZM8jNzTU7Nzk5mcDAQDw9PRk8eDBJSUml3qcbCxcl2rdvz5UrV0z9nT17llOnTjFw4ECzuKeeeordu3dTWFhYqo+Se96+fTv9+/fHxsamzBgRERERERERERERebDct8WL4uJikpOT6datG9bW1reNjYiIYM2aNYSEhLB48WLatm3LlClT2Llzp1lceno6MTExvPjii8TFxXHmzBkiIiIIDw/HxcWF+Ph4PDw8iIyMJC0tzezcQ4cOsWzZMqZNm8bbb7/NxYsXCQkJMXuoX948yisxMZFTp04RExPD5MmT2bhxI0uWLDG7n+DgYGxsbJg/fz7BwcFERUVx4cKFO/b9ww8/0KhRI+rWrQtgKsTcvIdImzZtKCoq4uzZs2X2880333DlyhUGDx5cqXsUERERERERERERkfvPfbtsVGZmJoWFhTRp0uS2cUeOHGHbtm1ERUUxcuRIAPz9/UlLS2Px4sX07dvXFJuVlUVCQgLt2rUD4OLFi0RHRxMaGsrkyZOB6xuNbN++nR07djB27FjTuRkZGSQkJNCqVSsA3N3dGTBgAOvWrWPkyJEVyqO8HB0deffdd019paSksHXrViIiIgBYuXIlVlZWGAwGHnnkEeD6sk7jxo27bb/ff/89mzdv5pVXXjF7bwDs7OzMYktelxy/2aZNm2jUqBFdu3at8P2JiIiIiIiIiIiIyP3pvp15UcLKyuq2x3/44QcABgwYYNY+cOBAUlJSzJZZcnJyMhUuAFMhokePHqY2Ozs77O3tOX/+vFl/7dq1M8UDtGzZEjc3N/bv31/hPMrrxrzg+iyIG/Pav38/vr6+psIFXN/Pon79+rfs8/z584SHh+Pr68uYMWMqnNONsrOzSUpKYtCgQaZ9M0RERERERERERERE7tsnxvXr16d27dqcO3futnFZWVlYW1uXemDfsGFDjEYjOTk5prabZxWULEd148N/ABsbGwoKCszaHBwcSl3bwcGB9PT0CudRXmXle+MyVenp6WXmVdYeF3C92BAaGkr9+vWJj483KzjUq1cPoFSe2dnZZsdvtHXrVgoLCxkyZEg570hEREREREREREREHgT3bfGiVq1a+Pj4sGfPHoqLi28ZV69ePYqKikota3Tp0iWsrKxKFSYqKyMjo8w2R0fHCuVhY2NDUVGRWcytlmS6E0dHxzLzunz5cqm2/Px8JkyYQE5ODh988EGp96V169YApTYhP3HiBNbW1jRv3rxUn5s2baJ169a4u7tXKn8RERERERERERERuT/dt8ULgPHjx5Oens7SpUvLPJ6UlETnzp0B2LJli9mxLVu24O7ujq2tbZXkcvToUU6fPm16ffr0aY4cOYK3tzdAufNo3LgxJ0+exGg0mmJ27dpVqZy8vLzYu3ev2WyJ3bt3k5mZaRZXXFzMn//8Z06cOMEHH3xAo0aNSvXVvHlzWrVqVSr/zZs30717d2xsbMzaL168yHfffaeNukVERERERERERESklPt2w26AXr16ERISQnx8PMeOHWPQoEE0aNCA1NRU1q5dS05ODhs2bKBfv37ExMSQn5+Ps7MzX375Jfv27WPJkiVVlouDgwMTJ05k6tSpACxYsIBGjRoRGBgIgJubW7ny6N+/P59//jnR0dEEBASQnJzM1q1bK5XT2LFjWb16NaGhoYSGhpKdnU18fHyppauioqL45ptvmD59OleuXOHHH380HXN3dzcVJqZMmUJERAQtWrTA19eXzZs389NPP5GQkFDq2ps3b+batWtVumRU8zaPVllfIiIiIiIiIiIi9ys9R5N7wX1dvACIjIykU6dOJCYmMnPmTPLy8nBycsLPz4/g4GAAYmNjmTt3LgaDgczMTFq3bs3ChQvp06dPleXh4eFBv379iI2NJT09HW9vb6KiosxmJJQnD39/fyIjI0lISGD9+vX4+/sTFRXFuHHjKpyTk5MTBoOB2bNnExYWRosWLZg1axbz5s0ziyuZ2RETE1Oqj507d9KsWTMABg8eTF5eHgaDgffffx9nZ2cWLVpEp06dSp23ceNGvLy8aNGiRYXzvpVX3g2tsr5ERERERERERETuZ1evXqNmzft6YR65x1kZb1x/SH4TQUFB2NrasmzZsupO5b504MABCgsLcXd3p06dOtWdjjzg8vLyOHnyJM7OzhqPUu00HsWSaDyKJdF4FEujMSmWRONRLInGo1iS3NxcDh8+TPv27atsq4EH0YEDBwDw9PS8Y6xKayIiIiIiIiIiImJxjEYjeXl56G+vRR5M9/SyUTt37iQxMZGDBw+Sm5trWg5q/PjxODs7V3d6FmfYsGG0b9/etPxTUFAQ3333XZmxc+fOZdCgQaXad+zYweTJk2nXrh2bNm0ytaemptK3b99S8d7e3nz22Wem1/Hx8SxatKhU3BtvvMGoUaMqfE8lbGxsVIEXi1CnTh3c3d2rOw0RQONRLIvGo1gSjUexNBqTYkk0Hi3PtavXqKGlfUTkAXTPFi/i4uIwGAz079+f6Oho7O3tOXPmDGvXriU8PJwNGzZUd4omH3/8cXWnUKbXX3+dK1eumLWtXLmSbdu20b1791Lx+fn5vPXWWzRs2PCWfb788sv4+vqaXj/88MOlYh566CFWrlxp1ta8efOKpl/KO39ZzpkT/7vrfkRERERERERELEGL1o/yl3eer+40RESqxT1ZvEhKSsJgMDBp0iTCwsJM7V27dmX48OF888031ZjdvaNt27al2qZNm0bPnj2xt7cvdWzZsmU0adKEZs2acfDgwTL7bNmyJR07drztdWvUqHHHmMo4c+J/HD98tsr7FREREREREREREZHf1z0552z58uU0bNiQSZMmlXm8d+/eABQUFDBnzhz8/Pzw9PRk2LBhbN++3Sx2+vTpDB48mG+//ZYhQ4bg5eXF6NGjSU1NJTMzk7CwMHx8fAgICGDz5s1m5wYFBTFhwgQ2bNhAQEAAXl5eBAUFceLECbO48uRR0teNDh8+jKurK3v37jW1ubq6YjAYiI+Pp0ePHvj6+jJjxgxyc3PNzk1OTiYwMBBPT08GDx5MUlLSHd/X5ORkUlNTGTJkSKljZ86c4aOPPuLVV1+9Yz8iIiIiIiIiIiIiInfjniteFBcXk5ycTLdu3bC2tr5tbEREBGvWrCEkJITFixfTtm1bpkyZws6dO83i0tPTiYmJ4cUXXyQuLo4zZ84QERFBeHg4Li4uxMfH4+HhQWRkJGlpaWbnHjp0iGXLljFt2jTefvttLl68SEhICIWFhRXOo7wSExM5deoUMTExTJ48mY0bN7JkyRKz+wkODsbGxob58+cTHBxMVFQUFy5cuG2/mzZtwtbWtsy9K958802GDRuGm5vbbft44403aN++Pd27d+fVV18lMzOzVEx+fj7dunXD3d2dp556ymxPDBERERERERERERGRe27ZqMzMTAoLC2nSpMlt444cOcK2bduIiopi5MiRAPj7+5OWlsbixYvNHtBnZWWRkJBAu3btALh48SLR0dGEhoYyefJkADw9Pdm+fTs7duxg7NixpnMzMjJISEigVatWALi7uzNgwADWrVvHyJEjK5RHeTk6OvLuu++a+kpJSWHr1q1EREQA1/etsLKywmAw8MgjjwDQuHFjxo0bd8s+i4uL+frrr+nTpw+2trZmx/7xj3+wb98+tmzZcsvzbWxsGDVqFH5+ftjZ2bF//36WLl3KwYMH+fvf/24qNLVo0YKIiAjc3d0pKChg48aNvPbaa+Tk5BAcHFzh90JERERERERERERE7j/3XPGihJWV1W2P//DDDwAMGDDArH3gwIHMmTOH3Nxc00N6JycnU+ECMBUievToYWqzs7PD3t6e8+fPm/XXrl07Uzxc3/PBzc2N/fv3M3LkyArlUV435gXQpk0bvvrqK9Pr/fv34+vraypcAHTv3p369evfss9du3Zx+fJlBg8ebNZeUFDAW2+9xZQpU8rcB6OEk5MTb7zxhun1Y489Rrt27ZgwYQLbt2/nqaeeAmDYsGFm5z3xxBMUFRXx3nvvMWbMmDvOphERERERERERERGR+989t2xU/fr1qV27NufOnbttXFZWFtbW1qUe2Dds2BCj0UhOTo6pzc7Oziym5AH6jQ//4frsgoKCArM2BweHUtd2cHAgPT29wnmUV1n53rhMVXp6epl53a74sGnTJurXr4+fn59Z+8qVK6lRowaDBg0iOzub7OxsioqKuHbtGtnZ2WbXvVmvXr2wtbXl0KFDt72fgQMHkpOTw5kzZ24bJyIiIiIiIiIiIiIPhntu5kWtWrXw8fFhz549FBcXU6tW2bdQr149ioqKyMrKol69eqb2S5cuYWVlVaowUVkZGRlltpXsDVHePGxsbCgqKjLrJysrq1I5OTo6lpnX5cuXy4zPz89nx44dDB06tNTMhxMnTnD69Gm6d+9e6ryuXbvyxhtvMGrUqErlKSIiIiIiIiIiIiJSlntu5gXA+PHjSU9PZ+nSpWUeT0pKonPnzgCl9mnYsmUL7u7uFV6q6VaOHj3K6dOnTa9Pnz7NkSNH8Pb2Bih3Ho0bN+bkyZMYjUZTzK5duyqVk5eXF3v37jWb1bF79+4yN8+G63ta5ObmMmTIkFLHQkNDWbVqldk/Pz8/mjZtyqpVq+jTp88t8/jmm2/Izc3F09Pztvlu3rwZOzs7WrRoUb4bFBEREREREREREZH72j038wKuL0cUEhJCfHw8x44dY9CgQTRo0IDU1FTWrl1LTk4OGzZsoF+/fsTExJCfn4+zszNffvkl+/btY8mSJVWWi4ODAxMnTmTq1KkALFiwgEaNGhEYGAiAm5tbufLo378/n3/+OdHR0QQEBJCcnMzWrVsrldPYsWNZvXo1oaGhhIaGkp2dTXx8/C33vNi4cSNNmjQxFVpu1KZNG9q0aWPWtn79ei5cuICvr6+pLSYmBisrKzp27IidnR0//fQTy5Yto0OHDgQEBJjiAgMDefrpp2ndujX5+fls3LiRbdu2MXPmTO13ISIiIiIiIiIiIiLAPVq8AIiMjKRTp04kJiYyc+ZM8vLycHJyws/Pj+DgYABiY2OZO3cuBoOBzMxMWrduzcKFC287W6CiPDw86NevH7GxsaSnp+Pt7U1UVBQ2NjammPLk4e/vT2RkJAkJCaxfvx5/f3+ioqIYN25chXNycnLCYDAwe/ZswsLCaNGiBbNmzWLevHmlYrOysvjXv/7F2LFj77gJ+u20adOGTz75hM8++4z8/HwaNWrEH/7wB6ZOnWq2tFeLFi1YsWKFadksFxcXYmNjGTp0aKWvbeq79aN33YeIiIiIiIiIiKXQsw4ReZBZGW9cp0gqJCgoCFtbW5YtW1bdqTzQDhw4AHDH5alERERERERERO41165eo0bNe3Ll97uWm5vL4cOHad++fZUtAS9SWRqPVaMiz3IfzE8+ue8UFhaSl5dX3WmIkJeXR0pKisajWASNR7EkGo9iSTQexdJoTIol0Xi0PA9q4UJERJ9+IiIiIiIiIiIiIiJiUR6I4sXOnTt5/vnneeyxx+jQoQN9+vRh1qxZnDx58q76/fjjj+/LJaOGDRvG9OnTzdq++eYbnnnmGTp06ECvXr1YuHAhV69eLXVuQUEBCxYsoE+fPnTo0IEnnniCt99++5bXWrFiBa6urkyYMOGucraxsaFOnTp31YdIVahTpw7u7u4aj2IRNB7Fkmg8iiXReBRLozEplkTjsXKuXb1W3SmIiNx37tkNu8srLi4Og8FA//79iY6Oxt7enjNnzrB27VrCw8PZsGFDdado8X788UcmTZrEoEGDePnllzl27Bjz588nLy+PV155xRR37do1Jk2axNmzZ3nppZdo1qwZ586du2WRKD09ncWLF+Pg4FAleb4zYwVnT5yvkr5ERERERERERMqjeevG/GXOuOpOQ0TkvnNfFy+SkpIwGAxMmjSJsLAwU3vXrl0ZPnw433zzTTVmd++Ij4+nffv2xMXFAfD4449jNBqZO3cuwcHBNGzYEIC1a9eyf/9+Nm/ejJOT0x37jY2NpU+fPpw7d65K8jx74jzHj6RWSV8iIiIiIiIiIiIiUn3u62Wjli9fTsOGDZk0aVKZx3v37g1cX+pozpw5+Pn54enpybBhw9i+fbtZ7PTp0xk8eDDffvstQ4YMwcvLi9GjR5OamkpmZiZhYWH4+PgQEBDA5s2bzc4NCgpiwoQJbNiwgYCAALy8vAgKCuLEiRNmceXJo6SvGx0+fBhXV1f27t1ranN1dcVgMBAfH0+PHj3w9fVlxowZ5Obmmp2bnJxMYGAgnp6eDB48mKSkpFLv0+HDh+nZs6dZm5+fH0VFRfz73/82tf39739nwIAB5SpcfP/99+zYsYNp06bdMVZEREREREREREREHiz3bfGiuLiY5ORkunXrhrW19W1jIyIiWLNmDSEhISxevJi2bdsyZcoUdu7caRaXnp5OTEwML774InFxcZw5c4aIiAjCw8NxcXEhPj4eDw8PIiMjSUtLMzv30KFDLFu2jGnTpvH2229z8eJFQkJCKCwsrHAe5ZWYmMipU6eIiYlh8uTJbNy4kSVLlpjdT3BwMDY2NsyfP5/g4GCioqK4cOGCWT8FBQXY2NiYtZW8Pn78OABFRUWkpKTQpEkT/vKXv9CxY0c6depEWFgY6enpZudevXqV6OhoJk6cWK5Ch4iIiIiIiIiIiIg8WO7bZaMyMzMpLCykSZMmt407cuQI27ZtIyoqipEjRwLg7+9PWloaixcvpm/fvqbYrKwsEhISaNeuHQAXL14kOjqa0NBQJk+eDICnpyfbt29nx44djB071nRuRkYGCQkJtGrVCgB3d3cGDBjAunXrGDlyZIXyKC9HR0feffddU18pKSls3bqViIgIAFauXImVlRUGg4FHHnkEgMaNGzNu3Dizflq2bMlPP/1k1vbjjz+a3hO4/n4XFRVhMBjo2rUrixYt4vLly8TGxjJlyhQ+/fRT07mrV68mLy+v1HVEREREREREREREROA+nnlRwsrK6rbHf/jhBwAGDBhg1j5w4EBSUlLMlllycnIyFS4AUyGiR48epjY7Ozvs7e05f9584+h27dqZ4uF6QcDNzY39+/dXOI/yujEvgDZt2pjltX//fnx9fU2FC4Du3btTv359s/P++Mc/8n//93+sXLmSzMxMvv/+e+bPn0/NmjVNMdeuXQPg4YcfZtGiRfj5+TF06FDefvtt9u3bx+7du4HrRZyFCxcyffr0UrM5RERERERERERERETgPi5e1K9fn9q1a99xM+isrCysra1LPbBv2LAhRqORnJwcU5udnZ1ZTMlyVDc+/IfrSyoVFBSYtTk4OJS6toODg2lJpYrkUV5l5XvjMlXp6ell5mVvb2/2OjAwkLFjx/LOO+/g6+vLuHHjGDlyJPXq1TMt+2RnZ4eVlRU+Pj5mRYnHHnuMmjVrcuzYMQAWLFiAq6srXbp0ITs7m+zsbIqLiykuLjZ9LSIiIiIiIiIiIiIPtvt22ahatWrh4+PDnj17KC4uplatsm+1Xr16FBUVkZWVRb169Uztly5dwsrKqlRhorIyMjLKbHNzc6tQHjY2NhQVFZn1U7J0U0U5OjqWmdfly5fNXteoUYOZM2cyZcoU0tLSaNKkCcXFxcybNw9vb28A6tSpQ9OmTW95rZJizsmTJ/nPf/5D165dS8V07doVg8GAv79/pe5HRERERERERERERO4P9+3MC4Dx48eTnp7O0qVLyzyelJRE586dAdiyZYvZsS1btuDu7o6trW2V5HL06FFOnz5ten369GmOHDlievhf3jwaN27MyZMnMRqNpphdu3ZVKicvLy/27t1rNqtj9+7dZGZmlhn/yCOP4Obmhp2dHR9//DHNmjUzW5qqd+/eJCcnm8062bNnD1evXsXDwwOAmTNnsmrVKrN/bm5udOzYkVWrVuHl5VWpexERERERERERERGR+8d9O/MCoFevXoSEhBAfH8+xY8cYNGgQDRo0IDU1lbVr15KTk8OGDRvo168fMTEx5Ofn4+zszJdffsm+fftYsmRJleXi4ODAxIkTmTp1KnB9+aRGjRoRGBgIgJubW7ny6N+/P59//jnR0dEEBASQnJzM1q1bK5XT2LFjWb16NaGhoYSGhpKdnU18fHyppat++uknvvvuO9q3b09+fj7/+Mc/+OKLLzAYDGb7XgQHB/PFF18wadIkxowZw+XLl3n33Xfp3Lkz3bp1A6B9+/al8rCzs8PW1hZfX99K3UeJ5q0b39X5IiIiIiIiIiIVpecRIiK/jfu6eAEQGRlJp06dSExMZObMmeTl5eHk5ISfnx/BwcEAxMbGMnfuXAwGA5mZmbRu3ZqFCxfSp0+fKsvDw8ODfv36ERsbS3p6Ot7e3kRFRZntD1GePPz9/YmMjCQhIYH169fj7+9PVFQU48aNq3BOTk5OGAwGZs+eTVhYGC1atGDWrFnMmzfPLM7a2ppt27axePFiALy9vfn444/p1KmTWdyjjz7KqlWreOutt5gyZQp16tShb9++TJ8+/Y4bp1eFv8wZ95tfQ0RERERERETkZteuXqNGzft6gRMRkd+dlfHG9YfkNxEUFIStrS3Lli2r7lTuSwcOHKCwsBB3d3fq1KlT3enIAy4vL4+TJ0/i7Oys8SjVTuNRLInGo1gSjUexNBqTYkk0HsWS5ObmcvjwYdq3b19lS7uLVJbGY9U4cOAAAJ6enneMVUlYREREREREREREREQsygNRvNi5cyfPP/88jz32GB06dKBPnz7MmjWLkydPVndqFmnYsGFMnz69zGO//vor/v7+uLq6mqpkJQoLC4mNjcXPzw8vLy/+8Ic/sHv37lJ9LF68mPHjx9OlS5cy+6kMGxsb/UWIWIQ6depoFpBYDI1HsSQaj2JJNB7F0mhMiiW5l8bjtavXqjsFERH5Dd33e17ExcVhMBjo378/0dHR2Nvbc+bMGdauXUt4eDgbNmz4zXP4+OOPf/Nr/F6WLFnC1atXyzz21ltv8cUXX/DnP/8ZZ2dn1q1bR2hoKGvWrMHDw8MUt2bNGlq0aEGPHj0qvdl4WWJfXcXZkxeqrD8RERERERERsUzNnRsROXtMdachIiK/ofu6eJGUlITBYGDSpEmEhYWZ2rt27crw4cP55ptvqjG7e8/x48dZvXo1r7zyCq+//rrZsQsXLvDZZ58xY8YMgoKCAHj88ccZOnQoixYt4r333jPF/vOf/6RGjRrs3bu3SosXZ09e4PiR1CrrT0RERERERERERESqx329bNTy5ctp2LAhkyZNKvN47969ASgoKGDOnDn4+fnh6enJsGHD2L59u1ns9OnTGTx4MN9++y1DhgzBy8uL0aNHk5qaSmZmJmFhYfj4+BAQEMDmzZvNzg0KCmLChAls2LCBgIAAvLy8CAoK4sSJE2Zx5cmjpK8bHT58GFdXV/bu3Wtqc3V1xWAwEB8fT48ePfD19WXGjBnk5uaanZucnExgYCCenp4MHjyYpKSkW76fs2fPZuTIkTg7O5c6duTIEa5evUrPnj1NbVZWVvj5+fHvf/+bwsJCU3uNGvf1sBMRERERERERERGRu3TfPkUuLi4mOTmZbt26YW1tfdvYiIgI1qxZQ0hICIsXL6Zt27ZMmTKFnTt3msWlp6cTExPDiy++SFxcHGfOnCEiIoLw8HBcXFyIj4/Hw8ODyMhI0tLSzM49dOgQy5YtY9q0abz99ttcvHiRkJAQs4f65c2jvBITEzl16hQxMTFMnjyZjRs3smTJErP7CQ4OxsbGhvnz5xMcHExUVBQXLpReemnLli3897//ZfLkyWVeq+Q+bGxszNptbGwoLCwkNVUzIkRERERERERERESkfO7bZaMyMzMpLCykSZMmt407cuQI27ZtIyoqipEjRwLg7+9PWloaixcvpm/fvqbYrKwsEhISaNeuHQAXL14kOjqa0NBQ00N9T09Ptm/fzo4dOxg7dqzp3IyMDBISEmjVqhUA7u7uDBgwgHXr1jFy5MgK5VFejo6OvPvuu6a+UlJS2Lp1KxEREQCsXLkSKysrDAYDjzzyCACNGzdm3LhxZv3k5eURExNDeHg4devWLfNaLVu2BOCnn36iWbNmpvYff/zR9N6JiIiIiIiIiIiIiJTHfTvzooSVldVtj//www8ADBgwwKx94MCBpKSkmC2z5OTkZCpcAKZCRI8ePUxtdnZ22Nvbc/78ebP+2rVrZ4qH6w/73dzc2L9/f4XzKK8b8wJo06aNWV779+/H19fXVLgA6N69O/Xr1zc777333sPBwYHhw4ff8louLi506dKFuLg49u3bxy+//MKHH37If/7zH+DO3wcRERERERERERERkRL3bfGifv361K5dm3Pnzt02LisrC2tr61IP7Bs2bIjRaCQnJ8fUZmdnZxZTshzVjQ//4fpSSQUFBWZtDg4Opa7t4OBAenp6hfMor7LyvXGZqvT09DLzsre3N32dlpbG8uXLmTp1Kjk5OWRnZ5sKKbm5ufz666+m2JiYGBo0aMDIkSPp1q0biYmJpv1GHB0dK5y/iIiIiIiIiIiIiDyY7ttlo2rVqoWPjw979uyhuLiYWrXKvtV69epRVFREVlYW9erVM7VfunQJKyurUoWJysrIyCizzc3NrUJ52NjYUFRUZNZPZZdkcnR0LDOvy5cvm75OTU2lqKiIF154oVTcmDFj8Pb25rPPPgOgefPmrF27ltTUVPLz83F2duajjz7C0dGRpk2bVipHEREREREREREREXnw3LczLwDGjx9Peno6S5cuLfN4UlISnTt3Bq5vSH2jLVu24O7ujq2tbZXkcvToUU6fPm16ffr0aY4cOYK3tzdAufNo3LgxJ0+exGg0mmJ27dpVqZy8vLzYu3ev2ayO3bt3k5mZaXrdvn17Vq1aZfZvxowZAERFRfH666+X6rdZs2a0bduWoqIiPv/8c5599tlK5SciIiIiIiIiIiIiD6b7duYFQK9evQgJCSE+Pp5jx44xaNAgGjRoQGpqKmvXriUnJ4cNGzbQr18/YmJiTLMFvvzyS/bt28eSJUuqLBcHBwcmTpzI1KlTAViwYAGNGjUiMDAQADc3t3Ll0b9/fz7//HOio6MJCAggOTmZrVu3ViqnsWPHsnr1akJDQwkNDSU7O5v4+Hizpavs7Ozw9fUt83wPDw88PDxMrxMSEqhbty6PPvooaWlpfPTRR9SuXZvQ0FCz87777jsuX77MsWPHANizZw9paWk0bdoUT0/PSt2LiIiIiIiIiIiIiNw/7uviBUBkZCSdOnUiMTGRmTNnkpeXh5OTE35+fgQHBwMQGxvL3LlzMRgMZGZm0rp1axYuXEifPn2qLA8PDw/69etHbGws6enpeHt7ExUVhY2NjSmmPHn4+/sTGRlJQkIC69evx9/fn6ioKMaNG1fhnJycnDAYDMyePZuwsDBatGjBrFmzmDdvXqXusbCwkEWLFnH+/Hnq169Pv379CAsLKzV7JT4+nu+++870Oi4uDoBnnnmGmJiYSl0boLlzo0qfKyIiIiIiIiL3Dj0DEBG5/1kZb1x/SH4TQUFB2NrasmzZsupO5b504MABAM3aEBEREREREXmAXLt6jRo17+sV0R94ubm5HD58mPbt21fZ0u4ilaXxWDUq8ixXn/ByXygsLCQvL6+60xAhLy+PlJQUjUexCBqPYkk0HsWSaDyKpdGYFEtyL41HFS5ERO5v9/Sn/M6dO3n++ed57LHH6NChA3369GHWrFmcPHmyulOzSMOGDWP69OlmbTt37uTZZ5+lU6dO+Pn5ERYWxtmzZ2/Zx44dO3B1dWXw4MFm7YWFhbzzzjv86U9/omPHjri6unL58uVS53/66ac8//zz9OzZEx8fH5577jl27NhRNTcoIiIiIiIiIiIiIveFe3bZqLi4OAwGA/3792fQoEHY29tz5swZ1q5dS25uLhs2bKjuFC3OsGHDaN++vWlfib179zJu3DiefvpphgwZQmZmJgsWLODatWts3LiRhx56yOz8/Px8nnrqKQoKCmjQoAGbNm0yHcvOzqZv3754enpiZWXFv//9b3bv3o29vb1ZH0888QR+fn706tULW1tbtmzZwmeffUZMTAzPPPNMpe5Ly0aJiIiIiIiI3Pu0DJTcTMv0iCXReKwaFXmWe09u2J2UlITBYGDSpEmEhYWZ2rt27crw4cP55ptvqjG7e8dXX31FkyZNeOutt7CysgLA3t6esWPHcvDgQbp06WIWv2zZMpo0aUKzZs04ePCg2TE7Ozu+++47rKysWLduHf/+97/LvOa6devMCho9e/YkLS2N5cuXV7p4USL2tQTOnrpwV32IiIiIiIiIyO+veatGREaPru40RETEgtyTxYvly5fTsGFDJk2aVObx3r17A1BQUMDcuXP56quvyMrKonXr1rz00ks8+eSTptjp06dz8OBBZs6cyZw5czh9+jReXl7ExMRQt25dXn/9df71r39hb2/Pyy+/zFNPPWU6t2Qj7oEDB7Jo0SIuXryIt7c3UVFRtG7d2hRXnjzK2tT78OHDPP3006xatQpfX18AXF1diYiIID8/n08++YSrV6/Sp08fXnvtNbOKX3JyMrNnz+bo0aO0bNmSyMjIUu9TcXExDz/8sKlwAfDII48AcPOEnDNnzvDRRx/x6aefsmLFijLf9xv7uZWbZ2IAtG/fnkOHDt3x3Ds5e+oCx39Ou+t+RERERERERERERKR63XNz8YqLi0lOTqZbt25YW1vfNjYiIoI1a9YQEhLC4sWLadu2LVOmTGHnzp1mcenp6cTExPDiiy8SFxfHmTNniIiIIDw8HBcXF+Lj4/Hw8CAyMpK0NPOH44cOHWLZsmVMmzaNt99+m4sXLxISEkJhYWGF8yivxMRETp06RUxMDJMnT2bjxo0sWbLE7H6Cg4OxsbFh/vz5BAcHExUVxYUL5rMSAgMDOX78OImJieTk5HD27Fnmzp2Lu7s7Pj4+ZrFvvvkmw4YNw83NrVI5384PP/xgVuwRERERERERERERkQfbPTfzIjMzk8LCQpo0aXLbuCNHjrBt2zaioqIYOXIkAP7+/qSlpbF48WL69u1ris3KyiIhIYF27doBcPHiRaKjowkNDWXy5MnA9TW4tm/fzo4dOxg7dqzp3IyMDBISEmjVqhUA7u7uDBgwgHXr1jFy5MgK5VFejo6OvPvuu6a+UlJS2Lp1KxEREQCsXLkSKysrDAaDaSZF48aNGTdunFk/Xbp0YdGiRUybNo2//e1vwPVZEB988AE1a9Y0xf3jH/9g3759bNmypcK53snGjRvZt28fixcvrvK+RUREREREREREROTedM/NvChxpyWKfvjhBwAGDBhg1j5w4EBSUlLIzc01tTk5OZkKF4CpENGjRw9Tm52dHfb29pw/f96sv3bt2pniAVq2bImbmxv79++vcB7ldWNeAG3atDHLa//+/fj6+poKFwDdu3enfv36ZuclJyfzl7/8heeee46VK1eaNut+4YUXyM/PB64vefXWW28xZcqUMpd8uhtHjhzh9ddfJzAwkICAgCrtW0RERERERERERETuXffczIv69etTu3Ztzp07d9u4rKwsrK2tSz2wb9iwIUajkZycHNMeEXZ2dmYxJctR3fjwH8DGxoaCggKzNgcHh1LXdnBwID09vcJ5lFdZ+d64TFV6ejotW7Ysdd7NxYfZs2fTrVs3pk+fbmrr2LEjTzzxBF988QUjRoxg5cqV1KhRg0GDBpGdnQ1AUVER165dIzs7m4ceeggbG5sK5Q+QlpZGaGgoXl5eplkfIiIiIiIiIiIiIiJwDxYvatWqhY+PD3v27KG4uJhatcq+hXr16lFUVERWVhb16tUztV+6dAkrK6tShYnKysjIKLOtZG+I8uZhY2NDUVGRWT9ZWVmVysnR0bHMvC5fvmz2+vjx46WWrWrcuDENGjTgzJkzAJw4cYLTp0/TvXv3Uv117dqVN954g1GjRlUov8uXLxMcHIyDgwOLFi26494lIiIiIiIiIiIiIvJguSeXjRo/fjzp6eksXbq0zONJSUl07twZoNQ+DVu2bMHd3b3Csx1u5ejRo5w+fdr0+vTp0xw5cgRvb2+AcufRuHFjTp48idFoNMXs2rWrUjl5eXmxd+9ecnJyTG27d+8mMzPTLK5JkyakpKSYtaWlpfHLL7/QtGlTAEJDQ1m1apXZPz8/P5o2bcqqVavo06dPhXL79ddfCQ0NpaioiPfff5+6detW6h5FRERERERERERE5P51z828AOjVqxchISHEx8dz7NgxBg0aRIMGDUhNTWXt2rXk5OSwYcMG+vXrR0xMDPn5+Tg7O/Pll1+yb98+lixZUmW5ODg4MHHiRKZOnQrAggULaNSoEYGBgQC4ubmVK4/+/fvz+eefEx0dTUBAAMnJyWzdurVSOY0dO5bVq1cTGhpKaGgo2dnZxMfHl1q6auTIkbz11lvMnj2bPn36kJmZyXvvvYeDgwMDBw4Eru+n0aZNG7Pz1q9fz4ULF/D19TVrT0pKIi8vj4MHDwLwzTff8PDDD9O2bVvatm0LwJQpUzhy5Ahvvvkm586dM1v+q2PHjpW6XxERERERERERERG5v9yTxQuAyMhIOnXqRGJiIjNnziQvLw8nJyf8/PwIDg4GIDY2lrlz52IwGMjMzKR169YsXLiwwrMFbsfDw4N+/foRGxtLeno63t7eREVFme0DUZ48/P39iYyMJCEhgfXr1+Pv709UVBTjxo2rcE5OTk4YDAZmz55NWFgYLVq0YNasWcybN88sbsyYMdjY2PDJJ5+wdu1aHn74YTp27Mj8+fNp0KBBha8bFRVFWlqa6fXMmTMBeOmll5gyZQrw/2aTvPLKK6XO//nnnyt8zRs1b9Xors4XERERERERkeqh/6YXEZGbWRlvXKdIKiQoKAhbW1uWLVtW3ak80A4cOACAp6dnNWciIiIiIiIiIpV17eo1atS8J1c4l99Ibm4uhw8fpn379lW2BLxIZWk8Vo2KPMvVbwS5LxQWFpKXl1fdaYiQl5dHSkqKxqNYBI1HsSQaj2JJNB7F0mhMiiWpzvGowoWIiNxIvxVERERERERERAQAo9FIXl4eWqhDRESq2z2750VZdu7cSWJiIgcPHiQ3N9e0B8b48eNxdnau8ut9/PHHVd7n723YsGG0b9+emJiY28b9/e9/Z9WqVZw9e5Z69erx+OOPEx4ejoODAwB79+5lzJgxZZ7r7OzMli1bALh8+TJLlixh//79HD58GGtra/bt23fX92FjY0OdOnXuuh+Ru1WnTh3c3d2rOw0RQONRLIvGo1gSjUexNBqT8nvRskwiInIvuW+KF3FxcRgMBvr37090dDT29vacOXOGtWvXEh4ezoYNG6o7xXvWhg0bePXVVwkODubxxx/n3LlzzJs3j2PHjvHpp58C1zcuX7Nmjdl5V65cITQ0FH9/f1PbhQsX2Lx5M15eXnTo0OGuN+m+UdwbiZw9dbHK+hMRERERERG5XzRv5UTEG3+q7jRERETK7b4oXiQlJWEwGJg06f9j786jqr7u/f8/UTlGr3Fgkjqg4IBCgGiqoCVYlYpGLS02jWmkaoGqGKU00Kq3MSWYil9wRIzmJDZGsDHXgUpCcLqW3m8c2oolKnojUSFilSP+QAzIoPz+8Of5eQIqICmDr8daXYuzP++9P+/P6V7L8Hmz9w4nIiLC3D5ixAimTZvGoUOHmjG71i8tLY2RI0fym9/8xqJ9yZIl/Otf/+I73/kOXbp04dlnn7W4vmvXLu7cucOUKVPMba6urhw+fBiAxMTEJi1efHWxkC+/KGiy8URERERERERERESkebSJtYKbN2/Gzs6O8PDwOq+PHTvW/HNFRQXLly/H19cXDw8PAgMD2b9/v0X8okWLmDJlCocPH2bq1Kl4enoyY8YMLl26RHFxMREREQwfPhx/f3/S09Mt+gYHBzNnzhxSU1Px9/fH09OT4OBgzp8/bxFXnzzujXW/M2fO4OrqyrFjx8xtrq6uGI1GEhMTGT16NN7e3ixevJiysjKLvllZWQQFBeHh4cGUKVPIzMx8xDd7V3V1NV26dLFoe/rppwEeugfmxx9/TP/+/fH09DS3tWvXJqaciIiIiIiIiIiIiHyLWv2b5OrqarKysvDx8cHa2vqR8VFRUWzfvp3Q0FCSkpIYOHAgCxYs4ODBgxZxJpOJuLg45s2bR0JCAvn5+URFRREZGcngwYNJTEzE3d2d6OhoCgos/9r/9OnTbNq0iddee40VK1ZQWFhIaGgolZWVDc6jvlJSUrh48SJxcXHMnz+ftLQ0NmzYYPE8ISEhGAwG1qxZQ0hICDExMVy9evWRY//kJz/hf/7nf8jIyODmzZucO3eOjRs3MnbsWHr16lVnn2vXrnH06FGLVRciIiIiIiIiIiIiIvXR6reNKi4uprKy8oEv0e939uxZ9u3bR0xMDNOnTwfAz8+PgoICkpKSGD9+vDm2pKSE5ORkBg0aBEBhYSGxsbGEhYUxf/58ADw8PNi/fz8HDhxg5syZ5r5FRUUkJyfTv39/ANzc3Jg4cSK7du1i+vTpDcqjvuzt7Vm5cqV5rJycHPbu3UtUVBQAW7ZswcrKCqPRaF414ejoyKxZsx459tSpUykvLycqKoqqqioARo8ezerVqx/YJz09ndu3b6t4ISIiIiIiIiIiIiIN1upXXtxjZWX1yJjjx48DMHHiRIv2SZMmkZOTY7HNkoODg7lwAZgLEaNHjza3de3aFRsbG65cuWIx3qBBg8zxAP369WPIkCFkZ2c3OI/6uj8vgAEDBljklZ2djbe3t7lwATBq1Ci6d+/+yLH37dtnXoWydetWVqxYQV5eHr/61a8euG1UWloa7u7uODs7N/hZREREREREREREROTJ1upXXnTv3p2OHTty+fLlR8aWlJRgbW1d64W9nZ0dNTU1lJaW0rlzZ+BuYeJ+97akuv/lP4DBYKCiosKizdbWtta9bW1tMZlMDc6jvurK9/5tqkwmE/369avVz8bG5qHj1tTU8MYbb/DTn/7UvOIEoG/fvvzsZz/js88+w9fX16JPfn4+n3/+OYsXL27QM4iIiIiIiIiIiIiIQBtYedGhQweGDx/O0aNHqa6ufmhst27dqKqqoqSkxKL92rVrWFlZ1SpMNFZRUVGdbfb29g3Kw2AwmLdpuuebferL3t6+zryuX7/+0H7Xr1/n+vXrDBkyxKLdzc0NuFuo+Ka0tDTatWvHCy+80KhcRUREREREREREROTJ1uqLFwCzZ8/GZDKxcePGOq9nZmYC8NxzzwGQkZFhcT0jIwM3N7cGr3Z4kHPnzpGXl2f+nJeXx9mzZ/Hy8mpQHo6Ojly4cMFia6bPPvusUTl5enpy7NgxSktLzW1HjhyhuLj4of1sbGzo1KkTOTk5Fu2nT58GoHfv3rX6fPLJJ4wcORIHB4dG5SoiIiIiIiIiIiIiT7ZWv20UwJgxYwgNDSUxMZHc3FwmT55Mjx49uHTpEjt37qS0tJQxY8YwZMgQJkyYQFxcHLdu3cLZ2Zk9e/Zw4sQJNmzY0GT52NraMnfuXBYuXAjA2rVr6dmzJ0FBQQD1ziMgIIAdO3YQGxuLv78/WVlZ7N27t1E5zZw5k23bthEWFkZYWBg3btwgMTHxkWdeWFlZ8dOf/pRt27bRpUsXRowYweXLl1m/fj2DBg1i1KhRFvE5OTl8+eWXzJ49+4Fj3iva5Obmcvv2bfNnDw+POoshIiIiIiIiIiIiIvJkaRPFC4Do6GiGDRtGSkoKS5Ysoby8HAcHB3x9fQkJCTHHxcfHs2rVKoxGI8XFxbi4uLBu3TrGjRvXZLm4u7szYcIE4uPjMZlMeHl5ERMTg8FgaFAefn5+REdHk5yczO7du/Hz8yMmJoZZs2Y1OCcHBweMRiPLli0jIiICJycnli5dyurVqx/ZNyoqChsbG/785z/z3nvv0aNHD7y9vYmMjLR4Jri7ZZTBYCAgIOCB40VERNT5efny5eYCT2P07a+VHiIiIiIiIiJ10e/MIiLS2ljV3L8nkTy24OBgOnfuzKZNm5o7lSfGyZMngbsrN0RERERERESkbndu36Fd+4fvIF5WVsaZM2cYOnRok22vLdJYmo/Skmg+No2GvMttE2deiFRWVlJeXt7caYhQXl5OTk6O5qO0CJqP0pJoPkpLovkoLY3mpPy7PKpwISIi0pK06m2jDh48SEpKCqdOnaKsrMy8TdTs2bNxdnZu7vRanMDAQIYOHUpcXBxw9yDx9957j+zsbM6dO4eLiwsff/xxrX7BwcH87W9/q9Wenp7OgAEDAEhMTGT9+vV13vell17izTfffGjc73//e15++eVGP5uIiIiIiIiIiIiItB2ttniRkJCA0WgkICCA2NhYbGxsyM/PZ+fOnURGRpKamtoseW3durVZ7tsY586dIzMzEy8vL+7cucPDdhAbPnw4v/3tby3a+vTpY/75xRdf5Pnnn7e4/ve//52EhAT8/Pws2p966im2bNli0da3b9/GPgYABoOBTp06PdYYIk2hU6dOuLm5NXcaIoDmo7Qsmo/Skmg+SkujOSlNrT7bQ4mIiLR0rbJ4kZmZidFoJDw83OLw5xEjRjBt2jQOHTrUjNm1HuPGjcPf3x+ARYsWcerUqQfGdu3alWefffaB1x0dHXF0dLRo+/DDD+nWrVut4kW7du0eOlZjJbz5J77KK2zycUVERERERERai779HIhaqp0NRESk9WuVxYvNmzdjZ2dHeHh4ndfHjh0LQEVFBatWreKTTz6hpKQEFxcXXn31VX7wgx+YY++9tF+yZAnLly8nLy8PT09P4uLi6NKlC2+88Qb/8z//g42NDb/+9a954YUXzH3vHc49adIk1q9fT2FhIV5eXsTExODi4mKOq08edR30febMGX70ox/xwQcf4O3tDYCrqytRUVHcunWLP/3pT9y+fZtx48bx+uuvWxwUk5WVxbJlyzh37hz9+vUjOjq61vfUrt2391cYFRUV7N+/nxdeeAGDwfCt3ed+X+UV8uUXl/8t9xIRERERERERERGRb0+rW0NYXV1NVlYWPj4+WFtbPzQ2KiqK7du3ExoaSlJSEgMHDmTBggUcPHjQIs5kMhEXF8e8efNISEggPz+fqKgoIiMjGTx4MImJibi7uxMdHU1BQYFF39OnT7Np0yZee+01VqxYQWFhIaGhoVRWVjY4j/pKSUnh4sWLxMXFMX/+fNLS0tiwYYPF84SEhGAwGFizZg0hISHExMRw9erVRt0P4G9/+xvPPvssHh4ezJgxg7///e8PjT906BA3b95kypQpta7dunULHx8f3NzceOGFF/joo48anZeIiIiIiIiIiIiItD2tbuVFcXExlZWV9OrV66FxZ8+eZd++fcTExDB9+nQA/Pz8KCgoICkpifHjx5tjS0pKSE5OZtCgQQAUFhYSGxtLWFgY8+fPB8DDw4P9+/dz4MABZs6cae5bVFREcnIy/fv3B8DNzY2JEyeya9cupk+f3qA86sve3p6VK1eax8rJyWHv3r1ERUUBsGXLFqysrDAajTz99NPA3W2dZs2a1eB7wd3tuAIDA+nfvz+FhYW89957zJ49m61btzJs2LA6+3z88cf07NmTESNGWLQ7OTkRFRWFm5sbFRUVpKWl8frrr1NaWkpISEij8hMRERERERERERGRtqXVrby4x8rK6qHXjx8/DsDEiRMt2idNmkROTg5lZWXmNgcHB3PhAjAXIkaPHm1u69q1KzY2Nly5csVivEGDBpnjAfr168eQIUPIzs5ucB71dX9eAAMGDLDIKzs7G29vb3PhAmDUqFF07969wfcCWLhwIT/5yU/47ne/ywsvvMDWrVtxcHCwWO1xvxs3bpCZmcnkyZNrbU0VGBhISEgIo0aN4vvf/z4rV64kICCAt99+m6qqqkblJyIiIiIiIiIiIiJtS6srXnTv3p2OHTty+fLDzzYoKSnB2tq61gt7Ozs7ampqKC0tNbd17drVIubedlT3v/wHMBgMVFRUWLTZ2trWuretrS0mk6nBedRXXfnev02VyWSqMy8bG5sG36sunTt3ZsyYMZw+fbrO63v37qWyspKpU6fWa7xJkyZRWlpKfn5+k+QnIiIiIiIiIiIiIq1bqytedOjQgeHDh3P06FGqq6sfGNetWzeqqqooKSmxaL927RpWVla1ChONVVRUVGebvb19g/IwGAy1Vh58s0992dvb15nX9evXGzVeQ3388ce4uLjg5ub2b7mfiIiIiIiIiIiIiLQtra54ATB79mxMJhMbN26s83pmZibPPfccABkZGRbXMjIycHNzo3Pnzk2Sy7lz58jLyzN/zsvL4+zZs3h5eQHUOw9HR0cuXLhATU2NOeazzz5rVE6enp4cO3bMYlXHkSNHKC4ubtR431RWVsZf/vIXPDw8al0rLCzkb3/7W50HdT9Ieno6Xbt2xcnJqUnyExEREREREREREZHWrdUd2A0wZswYQkNDSUxMJDc3l8mTJ9OjRw8uXbrEzp07KS0tJTU1lQkTJhAXF8etW7dwdnZmz549nDhx4oFnNTSGra0tc+fOZeHChQCsXbuWnj17EhQUBMCQIUPqlUdAQAA7duwgNjYWf39/srKy2Lt3b6NymjlzJtu2bSMsLIywsDBu3LhBYmJira2rysvLyczMBKCgoICbN2+aiywjR47ExsaGf/zjH7z77rv84Ac/oHfv3hQWFvLHP/4Rk8nE2rVra907PT2dO3fuPHDLqKCgIH70ox/h4uLCrVu3SEtLY9++fSxZssS8XZeIiIiIiIiIiIiIPNlaZfECIDo6mmHDhpGSksKSJUsoLy/HwcEBX19fQkJCAIiPj2fVqlUYjUaKi4txcXFh3bp1jBs3rsnycHd3Z8KECcTHx2MymfDy8iImJgaDwWCOqU8efn5+REdHk5yczO7du/Hz8yMmJoZZs2Y1OCcHBweMRiPLli0jIiICJycnli5dyurVqy3iioqKiIiIsGi79/mDDz7A29sbe3t7qqqqWL16NcXFxXTq1Ilhw4YRExODp6dnrXunpaXh6en5wFUUTk5OvP/+++ZtswYPHkx8fDw//OEPG/yc39S3n8NjjyEiIiIiIiLSmul3YxERaSusau7fp0gaJDg4mM6dO7Np06bmTuWJdvLkSYA6t7ESERERERERedLcuX2Hdu0bt1N4WVkZZ86cYejQoU225bZIY2k+Skui+dg0GvIut1WeeSHyTZWVlZSXlzd3GiKUl5eTk5Oj+SgtguajtCSaj9KSaD5KS6M5KU2tsYULERGRlkT/momIiIiIiIiIiIiISIvSas+8ADh48CApKSmcOnWKsrIy85kXs2fPxtnZ+Vu//9atW7/1ezSlwMBAhg4dSlxcHAB5eXm89957ZGdnc+7cOVxcXPj4449r9UtPT+fTTz8lOzubq1ev8pvf/MZ8rsj9vvjiC1auXEl2djbV1dW4urqyYMECfHx8zDGJiYmsX7++Vt/f//73vPzyy41+NoPBQKdOnRrdX6SpdOrUCTc3t+ZOQwTQfJSWRfNRWhLNR2lpNCelIR5nSygREZHWpNUWLxISEjAajQQEBBAbG4uNjQ35+fns3LmTyMhIUlNTmzvFFu/cuXNkZmbi5eXFnTt3eNDxJxkZGXz11Vd8//vfZ/v27XXGXL9+nVmzZtG3b1/eeustrK2t2bp1K2FhYezYsQNXV1dz7FNPPcWWLVss+vft2/exnyfhre18lW967HFEREREREREWqK+TvZE/edLzZ2GiIjIv0WrLF5kZmZiNBoJDw8nIiLC3D5ixAimTZvGoUOHmjG71mPcuHH4+/sDsGjRIk6dOlVn3Jo1a2jX7u5fdTyoeHHkyBGKior46KOP6NOnDwAjR45k5MiRHDhwwKJ40a5dO5599tkmfJK7vso38eW5y00+roiIiIiIiIiIiIj8e7XKdYabN2/Gzs6O8PDwOq+PHTsWgIqKCpYvX46vry8eHh4EBgayf/9+i9hFixYxZcoUDh8+zNSpU/H09GTGjBlcunSJ4uJiIiIiGD58OP7+/qSnp1v0DQ4OZs6cOaSmpuLv74+npyfBwcGcP3/eIq4+edwb635nzpzB1dWVY8eOmdtcXV0xGo0kJiYyevRovL29Wbx4MWVlZRZ9s7KyCAoKwsPDgylTppCZmVnre7pXkHiU+sRVVVUB8PTTT5vbOnbsiLW19QNXdIiIiIiIiIiIiIiI1KXVFS+qq6vJysrCx8cHa2vrh8ZGRUWxfft2QkNDSUpKYuDAgSxYsICDBw9axJlMJuLi4pg3bx4JCQnk5+cTFRVFZGQkgwcPJjExEXd3d6KjoykoKLDoe/r0aTZt2sRrr73GihUrKCwsJDQ0lMrKygbnUV8pKSlcvHiRuLg45s+fT1paGhs2bLB4npCQEAwGA2vWrCEkJISYmBiuXr3aqPvVx9ixY7GzsyMuLo7CwkKuX7/OypUrsbKyIjAw0CL21q1b+Pj44ObmxgsvvMBHH330reUlIiIiIiIiIiIiIq1Pq9s2qri4mMrKSnr16vXQuLNnz7Jv3z5iYmKYPn06AH5+fhQUFJCUlMT48ePNsSUlJSQnJzNo0CAACgsLiY2NJSwsjPnz5wPg4eHB/v37OXDgADNnzjT3LSoqIjk5mf79+wPg5ubGxIkT2bVrF9OnT29QHvVlb2/PypUrzWPl5OSwd+9eoqKiANiyZQtWVlYYjUbzSghHR0dmzZrV4HvVV7du3UhJSWHOnDk8//zzAHTv3h2j0WhxnoWTkxNRUVG4ublRUVFBWloar7/+OqWlpXUeAi4iIiIiIiIiIiIiT55Wt/LiHisrq4deP378OAATJ060aJ80aRI5OTkW2yw5ODiYCxeAuRAxevRoc1vXrl2xsbHhypUrFuMNGjTIHA/Qr18/hgwZQnZ2doPzqK/78wIYMGCARV7Z2dl4e3tbbOE0atQounfv3uB71VdRURGvvvoqTk5OvPPOO7z33nt4e3szb948vvzyS3NcYGAgISEhjBo1iu9///usXLmSgIAA3n77bfPWUyIiIiIiIiIiIiLyZGt1xYvu3bvTsWNHLl9++MHMJSUlWFtb13phb2dnR01NDaWlpea2rl27WsTc247q/pf/AAaDgYqKCos2W1vbWve2tbXFZDI1OI/6qivf+7epMplMdeZlY2PT4HvV17vvvktJSQlJSUmMGTMGX19fVq9eTffu3S22tKrLpEmTKC0tJT8//1vLT0RERERERERERERaj1ZXvOjQoQPDhw/n6NGjVFdXPzCuW7duVFVVUVJSYtF+7do1rKysahUmGquoqKjONnt7+wblYTAYaq08+Gaf+rK3t68zr+vXrzdqvPrIzc3FxcUFg8Fgbmvfvj2urq4qSoiIiIiIiIiIiIhIg7S64gXA7NmzMZlMbNy4sc7rmZmZPPfccwBkZGRYXMvIyMDNzY3OnTs3SS7nzp0jLy/P/DkvL4+zZ8/i5eUFUO88HB0duXDhAjU1NeaYzz77rFE5eXp6cuzYMYtVHUeOHKG4uLhR49VHr169+PLLLy1Wpty+fZuzZ8/Su3fvh/ZNT0+na9euODk5fWv5iYiIiIiIiIiIiEjr0eoO7AYYM2YMoaGhJCYmkpuby+TJk+nRoweXLl1i586dlJaWkpqayoQJE4iLi+PWrVs4OzuzZ88eTpw48chtjBrC1taWuXPnsnDhQgDWrl1Lz549CQoKAmDIkCH1yiMgIIAdO3YQGxuLv78/WVlZ7N27t1E5zZw5k23bthEWFkZYWBg3btwgMTGx1tZV5eXlZGZmAlBQUMDNmzfNRZaRI0eat5nKzc0lNzfX3O+LL74gIyODTp06MWbMGABefPFFduzYQXh4OK+88grt27dn+/bt5OXlsWzZMnPfoKAgfvSjH+Hi4sKtW7dIS0tj3759LFmyxLxdV2P1dbJ/rP4iIiIiIiIiLZl+7xURkSdJqyxeAERHRzNs2DBSUlJYsmQJ5eXlODg44OvrS0hICADx8fGsWrUKo9FIcXExLi4urFu3jnHjxjVZHu7u7kyYMIH4+HhMJhNeXl7ExMRYbJ9Unzz8/PyIjo4mOTmZ3bt34+fnR0xMDLNmzWpwTg4ODhiNRpYtW0ZERAROTk4sXbqU1atXW8QVFRURERFh0Xbv8wcffIC3tzcAn376KevXrzfHpKamkpqaSu/evfnv//5vAJ555hneffddNmzYwOLFi7lz5w4DBw7knXfeYcSIEea+Tk5OvP/+++ZtswYPHkx8fDw//OEPG/yc3xT1ny899hgiIiIiIiIiLdmd23do175VbqQhIiLSIFY19+9TJA0SHBxM586d2bRpU3On8kQ7efIklZWVuLm50alTp+ZOR55w5eXlXLhwAWdnZ81HaXaaj9KSaD5KS6L5KC2N5qS0JGVlZZw5c4ahQ4c22ZbbIo2l+SgtieZj0zh58iQAHh4ej4xVqV5ERERERERERERERFqUVl28OHjwIL/4xS8YOXIkzzzzDOPGjWPp0qVcuHChuVNrkQIDA1m0aFGd177++mv8/PxwdXU1V7/uSU9PZ8GCBebr7733Xp1jfPHFF8yZMwcfHx+++93v8sorr3D06FGLmN27d/PTn/6UkSNH4uHhQUBAAElJSVRWVj7WsxkMBv2FkrQInTp10iogaTE0H6Ul0XyUlkTzUVoazUm5353bd5o7BRERkRah1Z55kZCQgNFoJCAggNjYWGxsbMjPz2fnzp1ERkaSmpr6reewdevWb/0e/y4bNmzg9u3bdV7LyMjgq6++4vvf/z7bt2+vM+b69evMmjWLvn378tZbb2Ftbc3WrVsJCwtjx44duLq6AlBSUsLzzz/PL3/5S7p06cLnn3/O+vXruXLlCrGxsY/1DAnL/4uv8gsfawwRERERERGR5tLXyYGoxS82dxoiIiItQqssXmRmZmI0GgkPD7c4cHrEiBFMmzaNQ4cONWN2rc+XX37Jtm3b+O1vf8sbb7xR6/qaNWto1+7uIp0HFS+OHDlCUVERH330EX369AFg5MiRjBw5kgMHDpiLF988gNzHx4evv/6a999/n9///ve0b9++0c/xVX4hX+b+q9H9RURERERERERERKRlaJXbRm3evBk7OzvCw8PrvD527FgAKioqWL58Ob6+vnh4eBAYGMj+/fstYhctWsSUKVM4fPgwU6dOxdPTkxkzZnDp0iWKi4uJiIhg+PDh+Pv7k56ebtE3ODiYOXPmkJqair+/P56engQHB3P+/HmLuPrkcW+s+505cwZXV1eOHTtmbnN1dcVoNJKYmMjo0aPx9vZm8eLFlJWVWfTNysoiKCgIDw8PpkyZQmZm5gO/z2XLljF9+nScnZ3rvH6vcPEwVVVVADz99NPmto4dO2Jtbc2jzoTv3r071dXV3LmjpbEiIiIiIiIiIiIi0gqLF9XV1WRlZeHj44O1tfVDY6Oioti+fTuhoaEkJSUxcOBAFixYwMGDBy3iTCYTcXFxzJs3j4SEBPLz84mKiiIyMpLBgweTmJiIu7s70dHRFBQUWPQ9ffo0mzZt4rXXXmPFihUUFhYSGhpqcYZDffOor5SUFC5evEhcXBzz588nLS2NDRs2WDxPSEgIBoOBNWvWEBISQkxMDFevXq01VkZGBl988QXz589vVC73jB07Fjs7O+Li4igsLOT69eusXLkSKysrAgMDa8VXV1dTXl7OP/7xD7Zs2cLLL7/8yP8/RUREREREREREROTJ0Oq2jSouLqayspJevXo9NO7s2bPs27ePmJgYpk+fDoCfnx8FBQUkJSUxfvx4c2xJSQnJyckMGjQIgMLCQmJjYwkLCzO/1Pfw8GD//v0cOHCAmTNnmvsWFRWRnJxM//79AXBzc2PixIns2rWL6dOnNyiP+rK3t2flypXmsXJycti7dy9RUVEAbNmyBSsrK4xGo3klhKOjY60tm8rLy4mLiyMyMpIuXbo0OI/7devWjZSUFObMmcPzzz8P3F1RYTQa6du3r0VsdXU17u7u5s8//vGPWbJkyWPdX0RERERERERERETajla38uIeKyurh14/fvw4ABMnTrRonzRpEjk5ORbbLDk4OJgLF4C5EDF69GhzW9euXbGxseHKlSsW4w0aNMgcD9CvXz+GDBlCdnZ2g/Oor/vzAhgwYIBFXtnZ2Xh7e1ts4TRq1Ci6d+9u0e/tt9/G1taWadOmNTiHbyoqKuLVV1/FycmJd955h/feew9vb2/mzZvHl19+aRHboUMHduzYQUpKCosXL+bQoUMsXrz4sXMQERERERERERERkbah1a286N69Ox07duTy5csPjSspKcHa2rrWC3s7OztqamooLS2lc+fOwN3CxP3ubV90/8t/AIPBQEVFhUWbra1trXvb2tpiMpkanEd91ZXv/dtUmUwm+vXrV6ufjY2N+eeCggI2b95MUlISpaWlAOZCSllZGV9//TX/8R//Ue+c3n33XUpKSti1axcGgwG4WzCZPHkyGzZsMK8UucfDwwOA7373u/Tp04f58+czY8YMc7uIiIiIiIiIiIiIPLlaXfGiQ4cODB8+nKNHj1JdXU2HDnU/Qrdu3aiqqqKkpIRu3bqZ269du4aVlVWtwkRjFRUV1dk2ZMiQBuVhMBjMh17fU1JS0qic7O3t68zr+vXr5p8vXbpEVVUVv/zlL2vF/fznP8fLy4uPPvqo3vfMzc3FxcXFXLgAaN++Pa6uruTn5z+07zPPPANAfn6+ihciIiIiIiIiIiIi0jq3jZo9ezYmk4mNGzfWeT0zM5PnnnsOuHsg9f0yMjJwc3Nr8GqHBzl37hx5eXnmz3l5eZw9exYvLy+Aeufh6OjIhQsXqKmpMcd89tlnjcrJ09OTY8eOmVdUABw5coTi4mLz56FDh/LBBx9Y/O/e1k0xMTG88cYbDbpnr169+PLLLy1Wpty+fZuzZ8/Su3fvh/a9t7XWN8/GEBEREREREREREZEnU6tbeQEwZswYQkNDSUxMJDc3l8mTJ9OjRw8uXbrEzp07KS0tJTU1lQkTJhAXF8etW7dwdnZmz549nDhxgg0bNjRZLra2tsydO5eFCxcCsHbtWnr27ElQUBAAQ4YMqVceAQEB7Nixg9jYWPz9/cnKymLv3r2NymnmzJls27aNsLAwwsLCuHHjBomJiRZbV3Xt2hVvb+86+7u7u1scqJ2bm0tubq758xdffEFGRgadOnVizJgxALz44ovs2LGD8PBwXnnlFdq3b8/27dvJy8tj2bJl5r6vvPIKP/jBD3BxcaFdu3ZkZ2ezefNmnn/+eTw9PRv1vCIiIiIiIiIiIiLStrTK4gVAdHQ0w4YNIyUlhSVLllBeXo6DgwO+vr6EhIQAEB8fz6pVqzAajRQXF+Pi4sK6desYN25ck+Xh7u7OhAkTiI+Px2Qy4eXlRUxMjMX2SfXJw8/Pj+joaJKTk9m9ezd+fn7ExMQwa9asBufk4OCA0Whk2bJlRERE4OTkxNKlS1m9enWjnvHTTz9l/fr15s+pqamkpqbSu3dv/vu//xu4u/XTu+++y4YNG1i8eDF37txh4MCBvPPOO4wYMcLc95lnnuGjjz7i8uXLdOjQgT59+rBgwQJ+9rOfNSq3+/V1cnjsMURERERERESai36vFRER+f9Z1dy/T5E0SHBwMJ07d2bTpk3NncoT7eTJkwA6L0NERERERERavTu379CuffPt8l1WVsaZM2cYOnRok225LdJYmo/Skmg+No2GvMttlWdeiHxTZWUl5eXlzZ2GCOXl5eTk5Gg+Soug+SgtieajtCSaj9LSaE7K/ZqzcCEiItKS6F9EERERERERERERERFpUVrtmRcABw8eJCUlhVOnTlFWVmY+82L27Nk4Ozt/6/ffunXrt36PphQYGMjQoUOJi4sDIDMzE6PRSG5uLjdv3qRnz574+/vz6quv8vTTT5v7vfvuu3z88cdcunSJ6upq+vbty0svvcQrr7yClZUVAMeOHePnP/95nfd1dnYmIyMDgM8//5zVq1fzxRdfUFJSgp2dHaNHjyYiIoKePXs2+tkMBgOdOnVqdH+RptKpUyfc3NyaOw0RQPNRWhbNR2lJNB+lpdGcfHLduXOHdu30d6UiIiJ1abXFi4SEBIxGIwEBAcTGxmJjY0N+fj47d+4kMjKS1NTU5k6xxSsuLsbT05Pg4GC6d+/OuXPnSExM5Ny5c2zevNkcV1paygsvvMCgQYPo2LEjR44cYdmyZdy8eZO5c+cCdw8u3759u8X4N2/eJCwsDD8/P3PbjRs3cHFx4cUXX8TW1pavvvqKDRs2cPLkSXbu3Glx0HlDxf+fHXyVb2p0fxEREREREZF/l75O9kT/5ifNnYaIiEiL1SqLF/dWDISHhxMREWFuHzFiBNOmTePQoUPNmF3rERgYaPHZ29sbg8HA66+/ztWrV80rISIjIy3iRo8ezeXLl9m9e7e5eNGlSxeeffZZi7hdu3Zx584dpkyZYm7z9fXF19fX4p7f+c53+MUvfsGpU6cYPnx4o5/nq3wTX375r0b3FxEREREREREREZGWoVWuTdy8eTN2dnaEh4fXeX3s2LEAVFRUsHz5cnx9ffHw8CAwMJD9+/dbxC5atIgpU6Zw+PBhpk6diqenJzNmzODSpUsUFxcTERHB8OHD8ff3Jz093aJvcHAwc+bMITU1FX9/f/MqhvPnz1vE1SePe2Pd78yZM7i6unLs2DFzm6urK0ajkcTEREaPHo23tzeLFy+mrKzMom9WVhZBQUF4eHgwZcoUMjMz6/HNQvfu3QGoqqp6aFyPHj0eGfPxxx/Tv39/PD09m+SeIiIiIiIiIiIiIvJkaHXFi+rqarKysvDx8cHa2vqhsVFRUWzfvp3Q0FCSkpIYOHAgCxYs4ODBgxZxJpOJuLg45s2bR0JCAvn5+URFRREZGcngwYNJTEzE3d2d6OhoCgoKLPqePn2aTZs28dprr7FixQoKCwsJDQ2lsrKywXnUV0pKChcvXiQuLo758+eTlpbGhg0bLJ4nJCQEg8HAmjVrCAkJISYmhqtXr9Y53u3bt6moqOD06dMkJSUxbtw4+vTpUyuuurqamzdv8pe//IXU1NQHnnEBcO3aNY4ePWqx6uKb96ysrOTLL78kPj4ed3d3nnvuuQZ+EyIiIiIiIiIiIiLSFrW6baOKi4uprKykV69eD407e/Ys+/btIyYmhunTpwPg5+dHQUEBSUlJjB8/3hxbUlJCcnIygwYNAqCwsJDY2FjCwsKYP38+AB4eHuzfv58DBw4wc+ZMc9+ioiKSk5Pp378/AG5ubkycOJFdu3Yxffr0BuVRX/b29qxcudI8Vk5ODnv37iUqKgqALVu2YGVlhdFoNB+87ejoyKxZs+ocb+zYsebCxvPPP28e+355eXlMmDDB/HnevHkPHA8gPT2d27dvP7B4MWPGDLKysgB45plneOedd+jQodVNRxERERERERERERH5FrS6lRf3WFlZPfT68ePHAZg4caJF+6RJk8jJybHYZsnBwcFcuADMhYjRo0eb27p27YqNjQ1XrlyxGG/QoEHmeIB+/foxZMgQsrOzG5xHfd2fF8CAAQMs8srOzsbb29tcuAAYNWqUeXumb3rnnXf48MMPWbZsGefPn2fu3Lncvn3bIuY73/kOO3bs4IMPPmDhwoW8//77rFu37oE5pqWl4e7ujrOzc53X33rrLT766CPi4+OprKxk9uzZ3Lx581GPLiIiIiIiIiIiIiJPgFb3p+7du3enY8eOXL58+aFxJSUlWFtb13phb2dnR01NDaWlpXTu3Bm4W5i4373tqO5/+Q9gMBioqKiwaLO1ta11b1tbW0wmU4PzqK+68r1/myqTyUS/fv1q9bOxsalzvCFDhgAwbNgwizM57i+4GAwGPDw8gLuHbHfp0oUVK1bw8ssvY29vbzFefn4+n3/+OYsXL37gM7i4uADg5eXF6NGjGTt2LNu3byckJORhjy4iIiIiIiIiIiIiT4BWt/KiQ4cODB8+nKNHj1JdXf3AuG7dulFVVUVJSYlF+7Vr17CysqpVmGisoqKiOtvuvdCvbx4Gg6HWgdXf7FNf9vb2deZ1/fr1R/Z1dXXF2tqa/Pz8h8a5u7tz+/btWmeAwN1VF+3ateOFF16oV752dnY4OjqSl5dXr3gRERERERERERERadtaXfECYPbs2ZhMJjZu3Fjn9czMTPPhzxkZGRbXMjIycHNza/Bqhwc5d+6cxUv3vLw8zp49i5eXF0C983B0dOTChQvU1NSYYz777LNG5eTp6cmxY8coLS01tx05coTi4uJH9s3OzqaqqqrOA7vvl5WVhZWVVZ1xn3zyCSNHjsTBwaFe+f7rX//i8uXL9O3bt17xIiIiIiIiIiIiItK2tbptowDGjBlDaGgoiYmJ5ObmMnnyZHr06MGlS5fYuXMnpaWlpKamMmHCBOLi4rh16xbOzs7s2bOHEydOsGHDhibLxdbWlrlz57Jw4UIA1q5dS8+ePQkKCgLubslUnzwCAgLYsWMHsbGx+Pv7k5WVxd69exuV08yZM9m2bRthYWGEhYVx48YNEhMTa21d9eqrr/LMM8/g6urKU089xdmzZ3nvvfdwdXXF398fgNLSUsLCwvjhD39Iv379qK6u5tixY3zwwQe89NJL2NnZWYyZk5PDl19+yezZs+vMbenSpfTo0QMPDw+6dOnChQsX+OMf/4itrS0/+clPGvW89/R1sn90kIiIiIiIiEgLoN9hRUREHq5VFi8AoqOjGTZsGCkpKSxZsoTy8nIcHBzw9fU1n5sQHx/PqlWrMBqNFBcX4+Liwrp16xg3blyT5eHu7s6ECROIj4/HZDLh5eVFTEwMBoPBHFOfPPz8/IiOjiY5OZndu3fj5+dHTEwMs2bNanBODg4OGI1Gli1bRkREBE5OTixdupTVq1dbxHl6epKens4777xDTU0NvXv35sUXXyQkJMScf8eOHXF2dub999/n6tWrPPXUUzg5ORETE8OPfvSjWvdOS0vDYDAQEBBQZ26enp589NFHbNu2jcrKSr7zne/g5+fH3Llz6dGjR4Of9X7Rv3m84oeIiIiIiIjIv9OdO3do165VboohIiLyrbOquX+fImmQ4OBgOnfuzKZNm5o7lSfayZMnqaysxM3NjU6dOjV3OvKEKy8v58KFCzg7O2s+SrPTfJSWRPNRWhLNR2lpNCelJSkrK+PMmTMMHTq0ybbcFmkszUdpSTQfm8bJkycB8PDweGSsyvsiIiIiIiIiIiIiItKitNptoxri4MGDpKSkcOrUKcrKyszbS82ePRtnZ+fmTq/FCQwMZOjQocTFxQHw6aefsmfPHk6fPs2NGzfo168fwcHBTJs2DSsrKwBu3rzJH//4RzIzM7l48SIGgwFPT08iIyNxdXW1GL+0tJTly5dz4MABqqqqeP755/nd735X7wO+62IwGPQXStIidOrUCTc3t+ZOQwTQfJSWRfNRWhLNR2lpNCefTNoySkRE5OHafPEiISEBo9FIQEAAsbGx2NjYkJ+fz86dO4mMjCQ1NbXRY2/durXpEm3B3n//fXr37s2iRYvo0aMHhw8f5vXXX+fKlSu8+uqrAFy+fJnt27czbdo0fvWrX1FRUcHmzZt56aWX2LlzJwMGDDCP96tf/Yrc3Fx+//vf07FjR9asWUNYWBg7d+6kQ4fGT8n4hB189dW1x35eERERERERkW9T3752REfp3EYREZGHadPFi8zMTIxGI+Hh4URERJjbR4wYwbRp0zh06FAzZtd6vP3229jY2Jg/jxo1iuLiYv74xz8SHh5Ou3bt6NOnD/v377dY/eDj48O4cePYtm0br7/+OgAnTpzg//7f/8t7772Hr68vAM7Ozrzwwgvs27ePF154odF5fvXVNb788l+N7i8iIiIiIiIiIiIiLUObXp+4efNm7OzsCA8Pr/P62LFjAaioqGD58uX4+vri4eFBYGAg+/fvt4hdtGgRU6ZM4fDhw0ydOhVPT09mzJjBpUuXKC4uJiIiguHDh+Pv7096erpF3+DgYObMmUNqair+/v54enoSHBzM+fPnLeLqk8e9se535swZXF1dOXbsmLnN1dUVo9FIYmIio0ePxtvbm8WLF1NWVmbRNysri6CgIDw8PJgyZQqZmZm1vqf7Cxf3DB06lJs3b5rH69y5c61tm/7jP/4DJycnCgsLzW1//etf6dq1K9/73vfMbS4uLgwdOpS//vWvte4jIiIiIiIiIiIiIk+eNlu8qK6uJisrCx8fH6ytrR8aGxUVxfbt2wkNDSUpKYmBAweyYMECDh48aBFnMpmIi4tj3rx5JCQkkJ+fT1RUFJGRkQwePJjExETc3d2Jjo6moKDAou/p06fZtGkTr732GitWrKCwsJDQ0FAqKysbnEd9paSkcPHiReLi4pg/fz5paWls2LDB4nlCQkIwGAysWbOGkJAQYmJiuHr16iPHPn78OD179qRLly4PjLlx4wbnzp3DxcXF3Hb+/HmcnZ3NZ2Xc4+LiUquYIyIiIiIiIiIiIiJPpja7bVRxcTGVlZX06tXroXFnz55l3759xMTEMH36dAD8/PwoKCggKSmJ8ePHm2NLSkpITk5m0KBBABQWFhIbG0tYWBjz588HwMPDg/3793PgwAFmzpxp7ltUVERycjL9+/cHwM3NjYkTJ7Jr1y6mT5/eoDzqy97enpUrV5rHysnJYe/evURFRQGwZcsWrKysMBqNPP300wA4Ojoya9ash477j3/8g/T0dH77298+NC4+Ph4rKytefvllc9uNGzfM97pft27dOHXqVEMeT0RERERERERERETaqDa78uKeb/6F/zcdP34cgIkTJ1q0T5o0iZycHIttlhwcHMyFC8BciBg9erS5rWvXrtjY2HDlyhWL8QYNGmSOB+jXrx9DhgwhOzu7wXnU1/15AQwYMMAir+zsbLy9vS2KCaNGjaJ79+4PHPPKlStERkbi7e3Nz3/+8wfG7dy5k48++oilS5fi6OjY4NxFRERERERERERE5MnVZosX3bt3p2PHjly+fPmhcSUlJVhbW9d6YW9nZ0dNTQ2lpaXmtq5du1rE3NuO6psrCQwGAxUVFRZttra2te5ta2uLyWRqcB71VVe+929TZTKZ6syrrjMu4O6qibCwMLp3705iYiLt2tU9fTIzM1m6dCnh4eH8+Mc/rpXTzZs3a/UpKSmhW7duj3wmEREREREREREREWn72mzxokOHDgwfPpyjR49SXV39wLhu3bpRVVVFSUmJRfu1a9ewsrKqc4ujxigqKqqzzd7evkF5GAwGqqqqLGK+2ae+7O3t68zr+vXrtdpu3brFnDlzKC0t5d13333g9/LPf/6TiIgIfvSjHxEREVHruouLCxcuXKCmpsai/cKFCxZnY4iIiIiIiIiIiIjIk6vNFi8AZs+ejclkYuPGjXVez8zM5LnnngMgIyPD4lpGRgZubm507ty5SXI5d+4ceXl55s95eXmcPXsWLy8vgHrn4ejoWOvl/2effdaonDw9PTl27JjFqo4jR45QXFxsEVddXc2vfvUrzp8/z7vvvkvPnj3rHC83N5c5c+bg4+NDTExMnTF+fn6UlJRw5MgRc9uFCxfIycnBz8+vUc8hIiIiIiIiIiIiIm1Lmz2wG2DMmDGEhoaSmJhIbm4ukydPpkePHly6dImdO3dSWlpKamoqEyZMIC4ujlu3buHs7MyePXs4ceIEGzZsaLJcbG1tmTt3LgsXLgRg7dq19OzZk6CgIACGDBlSrzwCAgLYsWMHsbGx+Pv7k5WVxd69exuV08yZM9m2bRthYWGEhYVx48YNEhMTa21dFRMTw6FDh1i0aBE3b97kn//8p/mam5sbBoOBoqIiQkJC6NixIzNnzrQ4fLtLly4MHDgQgGHDhuHr68uSJUv47W9/S8eOHVm9ejWurq5MmDChUc8hIiIiIiIiIiIiIm1Lmy5eAERHRzNs2DBSUlJYsmQJ5eXlODg44OvrS0hICADx8fGsWrUKo9FIcXExLi4urFu3jnHjxjVZHu7u7kyYMIH4+HhMJhNeXl7ExMRgMBjMMfXJw8/Pj+joaJKTk9m9ezd+fn7ExMQwa9asBufk4OCA0Whk2bJlRERE4OTkxNKlS1m9erVF3L2VHXFxcbXGOHjwIH369CE3N9d8GPg3cxk5ciRbt241f16zZg3Lly9n6dKlVFdX4+vry+9+9zs6dHi86di3r91j9RcRERERERH5d9DvryIiIo9mVfPNwwekyQUHB9O5c2c2bdrU3Km0SSdPngTAw8OjmTMRERERERERqZ87d+7Qrl3L2827rKyMM2fOMHTo0CbbSluksTQfpSXRfGwaDXmX2/L+lRRphMrKSsrLy5s7DRHKy8vJycnRfJQWQfNRWhLNR2lJNB+lpdGcfDK1xMKFiIhIS6J/KUVEREREREREREREpEVp1WdeHDx4kJSUFE6dOkVZWZn5LIvZs2fj7Ozc3OmZ3X/eQ3MKDAxk6NCh5rMrMjMzMRqN5ObmcvPmTXr27Im/vz+vvvoqTz/9tEXfiooKNm7cyJ///GcKCwuxs7Nj0qRJ/Pa3vzXHjBs3joKCglr3/fzzz+nYsaP584kTJ4iPj+fUqVN06dKFSZMmERUVRadOnRr9bAaD4bH6izSVTp064ebm1txpiACaj9KyaD5KS6L5KC2N5mTb01K3hBIREWlNWm3xIiEhAaPRSEBAALGxsdjY2JCfn8/OnTuJjIwkNTW1uVNs8YqLi/H09CQ4OJju3btz7tw5EhMTOXfuHJs3bzbH3blzh/DwcL766iteffVV+vTpw+XLl7lw4UKtMQMCAvjFL35h0Xb/oeQFBQXMmjWL7373uyQmJlJYWEhCQgImk4l169Y91vP8n5U7+eqra481hoiIiIiIiMjj6NvXjt+8Nq250xAREWn1WmXx4t6KgfDwcCIiIsztI0aMYNq0aRw6dKgZs2s9AgMDLT57e3tjMBh4/fXXuXr1Kj179gRg586dZGdnk56ejoODw0PHtLOz49lnn33g9U2bNtG1a1fefvttc1Gja9euLFy4kJycnMf6a6OvvrrGl+f/1ej+IiIiIiIiIiIiItIytMo1jJs3b8bOzo7w8PA6r48dOxa4u9XR8uXL8fX1xcPDg8DAQPbv328Ru2jRIqZMmcLhw4eZOnUqnp6ezJgxg0uXLlFcXExERATDhw/H39+f9PR0i77BwcHMmTOH1NRU/P39zasYzp8/bxFXnzzujXW/M2fO4OrqyrFjx8xtrq6uGI1GEhMTGT16NN7e3ixevJiysjKLvllZWQQFBeHh4cGUKVPIzMysxzcL3bt3B6Cqqsrc9l//9V9MnDjxkYWL+jhz5gwjRoywWI3h6+sLwH//938/9vgiIiIiIiIiIiIi0vq1uuJFdXU1WVlZ+Pj4YG1t/dDYqKgotm/fTmhoKElJSQwcOJAFCxZw8OBBiziTyURcXBzz5s0jISGB/Px8oqKiiIyMZPDgwSQmJuLu7k50dHStMx1Onz7Npk2beO2111ixYgWFhYWEhoZSWVnZ4DzqKyUlhYsXLxIXF8f8+fNJS0tjw4YNFs8TEhKCwWBgzZo1hISEEBMTw9WrV+sc7/bt21RUVHD69GmSkpIYN24cffr0Ae4WMXJycujVqxe/+c1vePbZZxk2bBgRERGYTKZaY6WlpfHMM88wbNgwwsLC+N///V+L6xUVFRaFCwBra2usrKxqFX1ERERERERERERE5MnU6raNKi4uprKykl69ej007uzZs+zbt4+YmBimT58OgJ+fHwUFBSQlJTF+/HhzbElJCcnJyQwaNAiAwsJCYmNjCQsLY/78+QB4eHiwf/9+Dhw4wMyZM819i4qKSE5Opn///gC4ubkxceJEdu3axfTp0xuUR33Z29uzcuVK81g5OTns3buXqKgoALZs2YKVlRVGo9F88LajoyOzZs2qc7yxY8eaCxvPP/+8eWy4+31XVVVhNBoZMWIE69ev5/r168THx7NgwQI+/PBDc+y4cePw9PSkV69efPXVV2zcuJGf/exnpKam0rdvXwD69+/PyZMnqampwcrKCrh7oHdNTQ0lJSUN/i5EREREREREREREpO1pdSsv7rn34vtBjh8/DsDEiRMt2idNmkROTo7FNksODg7mwgVgLkSMHj3a3Na1a1dsbGy4cuWKxXiDBg0yxwP069ePIUOGkJ2d3eA86uv+vAAGDBhgkVd2djbe3t7mwgXAqFGjzFtCfdM777zDhx9+yLJlyzh//jxz587l9u3bwN3DugH+4z/+g/Xr1+Pr68sPf/hDVqxYwYkTJzhy5Ih5nN/97nf88Ic/5Lvf/S4//vGP2bp1KwDvvfeeOebll18mNzeXlStXcv36dc6ePUtMTAzt27dv8PcgIiIiIiIiIiIiIm1TqytedO/enY4dO3L58uWHxpWUlGBtbV3rhb2dnR01NTWUlpaa27p27WoRc287qvtf/gMYDAYqKios2mxtbWvd29bW1rylUkPyqK+68r1/myqTyVRnXjY2NnWON2TIEIYNG8aLL77Ihg0bOHbsmPlMjq5du2JlZcXw4cMttnsaOXIk7du3Jzc394F5Ojg48Nxzz3H69Glz26hRo4iKimLr1q2MGjWKH//4x3z3u99lyJAhTXKmhoiIiIiIiIiIiIi0fq1u26gOHTowfPhwjh49SnV1NR061P0I3bp1o6qqipKSErp162Zuv3btGlZWVrUKE41VVFRUZ9uQIUMalIfBYLA4JBto9DZK9vb2deZ1/fr1R/Z1dXXF2tqa/Px8ADp16kTv3r0fGP/NYk59hIWF8corr/DVV19hb29P165d8fHx4ac//WmDxxIRERERERERERGRtqfVrbwAmD17NiaTiY0bN9Z5PTMzk+eeew6AjIwMi2sZGRm4ubnRuXPnJsnl3Llz5OXlmT/n5eVx9uxZvLy8AOqdh6OjIxcuXKCmpsYc89lnnzUqJ09PT44dO2axquPIkSMUFxc/sm92djZVVVXmA7vh7pkYWVlZFoWKo0ePcvv2bdzd3R841tWrVzl+/DgeHh61rnXu3BlXV1dsbGxITU2lpqaGSZMm1fMJRURERERERERERKQta3UrLwDGjBlDaGgoiYmJ5ObmMnnyZHr06MGlS5fYuXMnpaWlpKamMmHCBOLi4rh16xbOzs7s2bOHEydOsGHDhibLxdbWlrlz57Jw4UIA1q5dS8+ePQkKCgLubslUnzwCAgLYsWMHsbGx+Pv7k5WVxd69exuV08yZM9m2bRthYWGEhYVx48YNEhMTa21d9eqrr/LMM8/g6urKU089xdmzZ3nvvfdwdXXF39/fHBcSEsKf//xnwsPD+fnPf87169dZuXIlzz33HD4+PgB8/PHHHDp0iDFjxuDg4MBXX33FO++8Q/v27Zk9e7Z5rK+++orU1FQ8PT2Bu0WQDz74gD/84Q8WK1Mao29fu8fqLyIiIiIiIvK49LupiIhI02iVxQuA6Ohohg0bRkpKCkuWLKG8vBwHBwd8fX0JCQkBID4+nlWrVmE0GikuLsbFxYV169Yxbty4JsvD3d2dCRMmEB8fj8lkwsvLi5iYGIvzIeqTh5+fH9HR0SQnJ7N79278/PyIiYlh1qxZDc7JwcEBo9HIsmXLiIiIwMnJiaVLl7J69WqLOE9PT9LT03nnnXeoqamhd+/evPjii4SEhFjk/53vfMdcYFiwYAGdOnVi/PjxLFq0yHxwep8+fSgsLOQPf/gDpaWlPP300/j4+LBw4UL69u1rHsva2pq//e1vbNmyhaqqKoYMGcL69esZO3Zsg5/zm37z2rTHHkNERERERETkcd25c4d27VrlZhciIiIthlXN/fsUSYMEBwfTuXNnNm3a1NypPNFOnjxJZWUlbm5udOrUqbnTkSdceXk5Fy5cwNnZWfNRmp3mo7Qkmo/Skmg+SkujOSktSVlZGWfOnGHo0KFNtuW2SGNpPkpLovnYNE6ePAlQ51ED36Q/AxARERERERERERERkRal1W4bBXDw4EFSUlI4deoUZWVl5m2jZs+ejbOzc3On1+IEBgYydOhQ4uLigLuHi7/33ntkZ2dz7tw5XFxc+Pjjj2v1Ky8vZ8OGDaSnp3Pt2jUcHR358Y9/TGhoKB063J1CiYmJrF+/vs77vvTSS7z55psAfPjhh+zbt4///d//pby8nIEDB/LLX/7S4oyNxjAYDPoLJWkROnXqhJubW3OnIQJoPkrLovkoLYnmo7Q0mpOtj7aFEhER+fa12uJFQkICRqORgIAAYmNjsbGxIT8/n507dxIZGUlqauq3nsPWrVu/9Xt8m86dO0dmZiZeXl7cuXOHB+0g9uabb7Jv3z5+/etfM2DAAP75z3+ybt06ysvLiYyMBODFF1/k+eeft+j397//nYSEBPz8/MxtGzduxNfXl5dffpnOnTuTkZHB/PnziYuL48c//vFjPc//Wb2L/EumxxpDRERERERE5GGc+tjzm8ig5k5DRESkzWuVxYvMzEyMRiPh4eFERESY20eMGMG0adM4dOhQM2bXeowbN8684mHRokWcOnWqVsydO3f49NNPCQkJ4ZVXXgHAx8eHCxcu8Mknn5iLF46Ojjg6Olr0/fDDD+nWrZtF8WLXrl3Y2NiYP3/ve9+joKCAzZs3P3bxIv+SiS/PX3msMURERERERERERESk+bXKNY6bN2/Gzs6O8PDwOq+PHTsWgIqKCpYvX46vry8eHh4EBgayf/9+i9hFixYxZcoUDh8+zNSpU/H09GTGjBlcunSJ4uJiIiIiGD58OP7+/qSnp1v0DQ4OZs6cOaSmpuLv74+npyfBwcGcP3/eIq4+edwb635nzpzB1dWVY8eOmdtcXV0xGo0kJiYyevRovL29Wbx4MWVlZRZ9s7KyCAoKwsPDgylTppCZmVnre6rPEteamhqqq6t5+umnLdqffvrpB67UuPfM+/fvJyAgAIPBYG6/v3Bxz9ChQyksLHxkLiIiIiIiIiIiIiLyZGh1xYvq6mqysrLw8fHB2tr6obFRUVFs376d0NBQkpKSGDhwIAsWLODgwYMWcSaTibi4OObNm0dCQgL5+flERUURGRnJ4MGDSUxMxN3dnejoaAoKCiz6nj59mk2bNvHaa6+xYsUKCgsLCQ0NpbKyssF51FdKSgoXL14kLi6O+fPnk5aWxoYNGyyeJyQkBIPBwJo1awgJCSEmJoarV682+F7t27cnKCiI5ORkPv/8c77++msOHz7Mn//8Z2bMmPHAfocOHeLmzZtMmTLlkfc4fvw4Li4uDc5NRERERERERERERNqmVrdtVHFxMZWVlfTq1euhcWfPnmXfvn3ExMQwffp0APz8/CgoKCApKYnx48ebY0tKSkhOTmbQoEEAFBYWEhsbS1hYGPPnzwfAw8OD/fv3c+DAAWbOnGnuW1RURHJyMv379wfAzc2NiRMnsmvXLqZPn96gPOrL3t6elStXmsfKyclh7969REVFAbBlyxasrKwwGo3mFROOjo7MmjWrwfcCeOONN3jjjTd48cUXzW1z5sxh9uzZD+zz8ccf07NnT0aMGPHQsdPS0jhx4gRJSUmNyk1ERERERERERERE2p5Wt/LiHisrq4deP378OAATJ060aJ80aRI5OTkW2yw5ODiYCxeAuRAxevRoc1vXrl2xsbHhyhXLMxUGDRpkjgfo168fQ4YMITs7u8F51Nf9eQEMGDDAIq/s7Gy8vb0ttnoaNWoU3bt3b/C94O7h6H/5y19YtmwZycnJREVF8cEHH/Duu+/WGX/jxg0yMzOZPHnyQ7emOnv2LG+88QZBQUHmszdERERERERERERERFrdyovu3bvTsWNHLl++/NC4kpISrK2ta72wt7Ozo6amhtLSUjp37gzcLUzc7952VN8858FgMFBRUWHRZmtrW+vetra2mEymBudRX3Xle/82VSaTiX79+tXqV9d5E4/yxRdfsHnzZt5++23GjRsH3D0Yvbq6mrVr1zJ9+nS6dOli0Wfv3r1UVlYyderUB45bUFBAWFgYnp6evPnmmw3OS0RERERERERERETarla38qJDhw4MHz6co0ePUl1d/cC4bt26UVVVRUlJiUX7tWvXsLKyqlWYaKyioqI62+zt7RuUh8FgoKqqyiLmm33qy97evs68rl+/3uCxcnNzgbuHat/Pzc2NysrKOs/R+Pjjj3FxccHNza3OMa9fv05ISAi2trasX7/+kWeXiIiIiIiIiIiIiMiTpdUVLwBmz56NyWRi48aNdV7PzMzkueeeAyAjI8PiWkZGBm5ubg1e7fAg586dIy8vz/w5Ly+Ps2fP4uXlBVDvPBwdHblw4QI1NTXmmM8++6xROXl6enLs2DFKS0vNbUeOHKG4uLjBY/Xu3Ru4ezD5/U6dOoWVlVWts0cKCwv529/+9sCDur/++mvCwsKoqqrinXfeqbVqQ0RERERERERERESk1W0bBTBmzBhCQ0NJTEwkNzeXyZMn06NHDy5dusTOnTspLS0lNTWVCRMmEBcXx61bt3B2dmbPnj2cOHGCDRs2NFkutra2zJ07l4ULFwKwdu1aevbsSVBQEABDhgypVx4BAQHs2LGD2NhY/P39ycrKYu/evY3KaebMmWzbto2wsDDCwsK4ceMGiYmJtbauKi8vJzMzE7i7jdPNmzfNRZaRI0diY2PDM888wzPPPMMbb7xBUVERTk5OfP7557zzzjtMmzaNTp06WYyZnp7OnTt3Hrhl1IIFCzh79ixvvfUWly9fttj+69lnn23U84qIiIiIiIiIiIhI29IqixcA0dHRDBs2jJSUFJYsWUJ5eTkODg74+voSEhICQHx8PKtWrcJoNFJcXIyLiwvr1q0zn93QFNzd3ZkwYQLx8fGYTCa8vLyIiYnBYDCYY+qTh5+fH9HR0SQnJ7N79278/PyIiYlh1qxZDc7JwcEBo9HIsmXLiIiIwMnJiaVLl7J69WqLuKKiIiIiIiza7n3+4IMP8Pb2pn379mzcuJG1a9eyadMmioqKcHR0JDQ0lLCwsFr3TktLw9PTEycnpzpzu7ea5Le//W2ta//7v//b4Ge9n1Mf+8fqLyIiIiIiIvIo+t1TRETk38Oq5v59iqRBgoOD6dy5M5s2bWruVJ5oJ0+eBMDDw6OZMxEREREREZEnwZ07d2jXrlXuxP1IZWVlnDlzhqFDhzbZltsijaX5KC2J5mPTaMi73Lb5L608cSorKykvL2/uNEQoLy8nJydH81FaBM1HaUk0H6Ul0XyUlkZzsvVpq4ULERGRlqTVbhtVl4MHD5KSksKpU6coKyszbyM1e/ZsnJ2dmzu9FikwMJChQ4cSFxf3wJiTJ0+ybds2/vnPf3LhwgXGjBlT52qT/+f/+X9YvXo1f/3rXykuLqZPnz688sorvPzyy+aY69evs2HDBrKzszlz5gzW1tacOHHiW3k2EREREREREREREWmd2kzxIiEhAaPRSEBAALGxsdjY2JCfn8/OnTuJjIwkNTW1ye+5devWJh+zJcrKyuIf//gHnp6eVFRUPDAuIiKC8+fP8+tf/5rvfOc7/PWvf+X3v/897du356c//SkAV69eJT09HU9PT5555pnHPufiHoPBUOvwcJHm0KlTJ9zc3Jo7DRFA81FaFs1HaUk0H6Wl0ZxsWW7fuUN7rawQERFpdm2ieJGZmYnRaCQ8PNziAOoRI0Ywbdo0Dh061IzZtX7BwcHMnDnT/HNdTCYTx44dY/ny5QQFBQEwatQoTp48ySeffGIuXri6unL48GEAEhMTm6x4ARC3dhdfXTI12XgiIiIiIiLyZOnbx55FEUHNnYaIiIjQRooXmzdvxs7OjvDw8Dqvjx071vxzRUUFq1at4pNPPqGkpAQXFxdeffVVfvCDH5hjFi1axKlTp1iyZAnLly8nLy8PT09P4uLi6NKlC2+88Qb/8z//g42NDb/+9a954YUXzH3vHeI9adIk1q9fT2FhIV5eXsTExODi4tKgPOo6EPzMmTP86Ec/4oMPPsDb2xu4WxCIiori1q1b/OlPf+L27duMGzeO119/3eLwmKysLJYtW8a5c+fo168f0dHR9fp+67OXZ3V1NQBPP/20RXuXLl0oKytr0FiN9dUlE7kXrnxr44uIiIiIiIiIiIjIv0erXwdZXV1NVlYWPj4+WFtbPzI+KiqK7du3ExoaSlJSEgMHDmTBggUcPHjQIs5kMhEXF8e8efNISEggPz+fqKgoIiMjGTx4MImJibi7uxMdHU1BQYFF39OnT7Np0yZee+01VqxYQWFhIaGhoVRWVjY4j/pKSUnh4sWLxMXFMX/+fNLS0tiwYYPF84SEhGAwGFizZg0hISHExMRw9erVRt3vm77zne/g6+vLxo0byc3N5ebNm6Snp/PZZ5/xyiuvNMk9REREREREREREROTJ0OpXXhQXF1NZWUmvXr0eGXv27Fn27dtHTEwM06dPB8DPz4+CggKSkpIYP368ObakpITk5GQGDRoEQGFhIbGxsYSFhTF//nwAPDw82L9/PwcOHDBvqwRQVFREcnIy/fv3B8DNzY2JEyeya9cupk+f3qA86sve3p6VK1eax8rJyWHv3r1ERUUBsGXLFqysrDAajebVEY6OjsyaNavB93qQxMREIiMjmTx5MgDt27fnd7/7HQEBAU12DxERERERERERERFp+1r9yot7rKysHhlz/PhxACZOnGjRPmnSJHJyciy2N3JwcDAXLgBzIWL06NHmtq5du2JjY8OVK5ZbFQ0aNMgcD9CvXz+GDBlCdnZ2g/Oor/vzAhgwYIBFXtnZ2Xh7e1ts6zRq1Ci6d+/e4HvVpaamhsWLF3Px4kVWrlzJBx98QFhYGH/4wx/45JNPmuQeIiIiIiIiIiIiIvJkaPUrL7p3707Hjh25fPnyI2NLSkqwtrau9cLezs6OmpoaSktLzWdEdO3a1SLm3pZU3zzTwWAwUFFRYdFma2tb6962traYTKYG51FfdeV7/zZVJpOJfv361epnY2PToPs8yF/+8hcyMjLYs2cPrq6uAHh7e1NUVERcXJx5NYaIiIiIiIiIiIiIyKO0+pUXHTp0YPjw4Rw9etR8aPSDdOvWjaqqKkpKSizar127hpWVVa3CRGMVFRXV2WZvb9+gPAwGA1VVVRYx3+xTX/b29nXmdf369UaN9025ubm0b9+ewYMHW7QPHTqUwsJCysvLm+Q+IiIiIiIiIiIiItL2tfriBcDs2bMxmUxs3LixzuuZmZkAPPfccwBkZGRYXM/IyMDNza3Bqx0e5Ny5c+Tl5Zk/5+XlcfbsWby8vBqUh6OjIxcuXKCmpsYc89lnnzUqJ09PT44dO0Zpaam57ciRIxQXFzdqvG/q3bs3t2/f5n//938t2k+fPo2trS2dOnVqkvuIiIiIiIiIiIiISNvX6reNAhgzZgyhoaEkJiaSm5vL5MmT6dGjB5cuXWLnzp2UlpYyZswYhgwZwoQJE4iLi+PWrVs4OzuzZ88eTpw4wYYNG5osH1tbW+bOncvChQsBWLt2LT179iQoKAig3nkEBASwY8cOYmNj8ff3Jysri7179zYqp5kzZ7Jt2zbCwsIICwvjxo0bJCYm1uvMi+vXr/O3v/3N/PPXX39tLryMGTOGTp064efnR69evVi4cCHz58/HwcGB//t//y+7d+9mwYIFFuPd65ubm8vt27fNnz08POjdu3ejnk9ERERERERERERE2o42UbwAiI6OZtiwYaSkpLBkyRLKy8txcHDA19eXkJAQc1x8fDyrVq3CaDRSXFyMi4sL69atY9y4cU2Wi7u7OxMmTCA+Ph6TyYSXlxcxMTEYDIYG5eHn50d0dDTJycns3r0bPz8/YmJimDVrVoNzcnBwwGg0smzZMiIiInBycmLp0qWsXr36kX3PnTtHRESERdu9zwcPHqRPnz506dKF999/n9WrV5OQkEBpaSl9+vRh0aJFzJgxo86+3/y8fPlyc4GnMfr2sW90XxERERERERH9XikiItJyWNXcvyeRPLbg4GA6d+7Mpk2bmjuVJ8bJkyeBuys3RERERERERB7H7Tt3aN+uTeyy3ShlZWWcOXOGoUOHNtn22iKNpfkoLYnmY9NoyLvcJ/dfY2lTKisrdSi4tAjl5eXk5ORoPkqLoPkoLYnmo7Qkmo/S0mhOtixPcuFCRESkJdG/yCIiIiIiIiIiIiIi0qK0mTMv4O75CykpKZw6dYqysjLzmRezZ8/G2dn535LD1q1b/y33aSqBgYEMHTqUuLi4h8bduXOHDz74gA8//JBLly7RrVs3fHx8WLlyJQCXLl1i/PjxdfY1GAzm5UCff/45f/rTn/jHP/5BYWEhPXv2JCAggHnz5j3WciuDwUCnTp0a3V+kqXTq1Ak3N7fmTkME0HyUlkXzUVoSzUdpaTQnm9aTvu2TiIhIW9FmihcJCQkYjUYCAgKIjY3FxsaG/Px8du7cSWRkJKmpqc2dYqu2dOlSDh06RHh4OIMGDcJkMnH8+HHzdQcHB7Zv327Rp6amhtDQUHx8fMxtn376KXl5eYSGhtK/f39yc3NZt24d2dnZfPDBB4+VY1ziLvILrj3WGCIiIiIiItJ6OfW2Y9GCoOZOQ0RERJpAmyheZGZmYjQaCQ8PJyIiwtw+YsQIpk2bxqFDh5oxu9bvyJEj7N69m127duHq6mpunzx5svlng8HAs88+a9Hv2LFj3Lx5kylTppjbwsLCsLGxMX/29vama9euREVFcerUKZ555plG55lfcI3cC1ca3V9EREREREREREREWoY2sY5y8+bN2NnZER4eXuf1sWPHmn+uqKhg+fLl+Pr64uHhQWBgIPv377eIX7RoEVOmTOHw4cNMnToVT09PZsyYwaVLlyguLiYiIoLhw4fj7+9Penq6Rd/g4GDmzJlDamoq/v7+eHp6EhwczPnz5y3i6pPHvbHud+bMGVxdXTl27Ji5zdXVFaPRSGJiIqNHj8bb25vFixdTVlZm0TcrK4ugoCA8PDyYMmUKmZmZj/hm7/roo48YOXKkReGiPj7++GO6dOnCuHHjzG33Fy7uubc8urCwsEHji4iIiIiIiIiIiEjb1OqLF9XV1WRlZeHj44O1tfUj46Oioti+fTuhoaEkJSUxcOBAFixYwMGDBy3iTCYTcXFxzJs3j4SEBPLz84mKiiIyMpLBgweTmJiIu7s70dHRFBQUWPQ9ffo0mzZt4rXXXmPFihUUFhYSGhpKZWVlg/Oor5SUFC5evEhcXBzz588nLS2NDRs2WDxPSEgIBoOBNWvWEBISQkxMDFevXn3k2NnZ2bi4uPDWW2/x3e9+F09PT0JCQrhw4cID+1RVVbFv3z5+8IMf0LFjx4eOf2/7KRcXl3o+rYiIiIiIiIiIiIi0Za1+26ji4mIqKyvp1avXI2PPnj3Lvn37iImJYfr06QD4+flRUFBAUlKSxYHTJSUlJCcnM2jQIODuqoDY2FjCwsKYP38+AB4eHuzfv58DBw4wc+ZMc9+ioiKSk5Pp378/cHdlwcSJE9m1axfTp09vUB71ZW9vbz4828/Pj5ycHPbu3UtUVBQAW7ZswcrKCqPRyNNPPw2Ao6Mjs2bNeuTYJpOJXbt2MXDgQBISEqiqqmL16tWEhITw6aef1lmc+Otf/0pxcbHFllF1uX79OomJiYwfP978fYmIiIiIiIiIiIjIk63Vr7y4x8rK6pEx9/7Cf+LEiRbtkyZNIicnx2KbJQcHB3PhAjC/WB89erS5rWvXrtjY2HDliuU5C4MGDbJ4Ed+vXz+GDBlCdnZ2g/Oor/vzAhgwYIBFXtnZ2Xh7e5sLFwCjRo2ie/fujxy7pqaG27dv8/bbb/P973+fH/zgB6xfv55//etfpKWl1dknLS0NOzs7Ro0a9cBxq6qq+PWvfw3A73//+0fmISIiIiIiIiIiIiJPhlZfvOjevTsdO3bk8uXLj4wtKSnB2tq61gt7Ozs7ampqKC0tNbd17drVIubellT3v/yHuwdVV1RUWLTZ2trWuretrS0mk6nBedRXXfnev02VyWSqM6+6zqCoa+yBAwdiZ2dnbnNxccHR0ZHc3Nxa8V9//TWHDh1i0qRJtG/fvs4xa2pqWLJkCZ9//jlGoxEHB4dH5iEiIiIiIiIiIiIiT4ZWX7zo0KEDw4cP5+jRo1RXVz80tlu3blRVVVFSUmLRfu3aNaysrGoVJhqrqKiozjZ7e/sG5WEwGKiqqrKI+Waf+rK3t68zr+vXrz+y78CBAx947ZuFG4D9+/dz69Ytpk6d+sB+K1as4NNPPyUpKYkhQ4Y8MgcREREREREREREReXK0+uIFwOzZszGZTGzcuLHO65mZmQA899xzAGRkZFhcz8jIwM3Njc6dOzdJPufOnSMvL8/8OS8vj7Nnz+Ll5dWgPBwdHblw4QI1NTXmmM8++6xROXl6enLs2DGLVR1HjhyhuLj4kX3Hjh1Lbm6ueeUIwJdffsmVK1dwd3evFf/xxx/j5ORkft5veuedd3j//feJi4t76LZSIiIiIiIiIiIiIvJkavUHdgOMGTOG0NBQEhMTyc3NZfLkyfTo0YNLly6xc+dOSktLGTNmDEOGDGHChAnExcVx69YtnJ2d2bNnDydOnGDDhg1Nlo+trS1z585l4cKFAKxdu5aePXsSFBQEUO88AgIC2LFjB7Gxsfj7+5OVlcXevXsbldPMmTPZtm0bYWFhhIWFcePGDRITE+t15sWLL77I1q1bmTNnDuHh4VRVVbF27VqcnJyYPHmyRez169c5cuQIYWFhdY6VlpbGypUr+eEPf0ifPn345z//ab7m5ORUr22sRERERERERERERKRtaxPFC4Do6GiGDRtGSkoKS5Ysoby8HAcHB3x9fQkJCTHHxcfHs2rVKoxGI8XFxbi4uLBu3TrGjRvXZLm4u7szYcIE4uPjMZlMeHl5ERMTg8FgaFAefn5+REdHk5yczO7du/Hz8yMmJoZZs2Y1OCcHBweMRiPLli0jIiICJycnli5dyurVqx/Zt0uXLmzZsoW33nqL6OhorKys8PX1ZcmSJXTq1Mki9tNPP6W6uvqBW0bdWzmyZ88e9uzZY3Ft+fLl5gJPYzj1tnt0kIiIiIiIiLRZ+r1QRESk7bCquX9PInlswcHBdO7cmU2bNjV3Kk+MkydPAuDh4dHMmYiIiIiIiEhzu33nDu3btYldsptFWVkZZ86cYejQoU22vbZIY2k+Skui+dg0GvIuV/+aS5tQVVXFrVu3mjsNEW7dusX58+c1H6VF0HyUlkTzUVoSzUdpaTQnm5YKFyIiIm1Dm/oX/eDBg/ziF79g5MiRPPPMM4wbN46lS5dy4cKF5k6txQoMDGTRokUPjUlMTMTV1bXW//70pz9ZxKWkpDBnzhx8fHxwdXWtdSD5/f7yl78wffp0nn32WUaMGEFwcDBXrlxp9HNYW1vz1FNPNbq/SFN56qmncHFx0XyUFkHzUVoSzUdpSTQfpaV50ubk7Tt3mjsFERERaQXazJkXCQkJGI1GAgICiI2NxcbGhvz8fHbu3ElkZCSpqan/ljy2bt36b7nPv9tTTz3Fli1bLNr69u1r8fnPf/4zcPcA9Yd933/+85/5z//8T37xi1/wq1/9iq+//pp//OMfVFRUPFaOy9fvIr/g2mONISIiIiIiIt8ep952LH618WcdioiIyJOjTRQvMjMzMRqNhIeHExERYW4fMWIE06ZN49ChQ82YXdvQrl07nn322YfGfPjhh7Rr145Lly49sHhRXFzMm2++yZIlS/jZz35mbh8/fvxj55hfcI3ci41fvSEiIiIiIiIiIiIiLUOb2DZq8+bN2NnZER4eXuf1sWPHmn+uqKhg+fLl+Pr64uHhQWBgIPv377eIX7RoEVOmTOHw4cNMnToVT09PZsyYwaVLlyguLiYiIoLhw4fj7+9Penq6Rd/g4GDmzJlDamoq/v7+eHp6EhwczPnz5y3i6pPHvbHud+bMGVxdXTl27Ji5zdXVFaPRSGJiIqNHj8bb25vFixdTVlZm0TcrK4ugoCA8PDyYMmUKmZmZj/hmG6ZdPfYV/fTTT7lz5w4/+clPmvTeIiIiIiIiIiIiItJ2tPriRXV1NVlZWfj4+GBtbf3I+KioKLZv305oaChJSUkMHDiQBQsWcPDgQYs4k8lEXFwc8+bNIyEhgfz8fKKiooiMjGTw4MEkJibi7u5OdHQ0BQUFFn1Pnz7Npk2beO2111ixYgWFhYWEhoZSWVnZ4DzqKyUlhYsXLxIXF8f8+fNJS0tjw4YNFs8TEhKCwWBgzZo1hISEEBMTw9WrV+s1/q1bt/Dx8cHNzY0XXniBjz76qFF5Zmdn4+zsTGpqKmPHjsXNzY3AwMAmL6SIiIiIiIiIiIiISOvV6reNKi4uprKykl69ej0y9uzZs+zbt4+YmBimT58OgJ+fHwUFBSQlJVlsXVRSUkJycjKDBg0CoLCwkNjYWMLCwpg/fz4AHh4e7N+/nwMHDjBz5kxz36KiIpKTk+nfvz8Abm5uTJw4kV27djF9+vQG5VFf9vb2rFy50jxWTk4Oe/fuJSoqCoAtW7ZgZWWF0Wjk6aefBsDR0ZFZs2Y9cmwnJyeioqJwc3OjoqKCtLQ0Xn/9dUpLSwkJCWlQniaTiQsXLrB27Vqio6Oxt7cnJSWF8PBwUlNTzd+3iIiIiIiIiIiIiDy5Wv3Ki3usrKweGXP8+HEAJk6caNE+adIkcnJyLLZZcnBwsHiRfq8QMXr0aHNb165dsbGx4coVy3MWBg0aZI4H6NevH0OGDCE7O7vBedTX/XkBDBgwwCKv7OxsvL29zYULgFGjRtG9e/dHjh0YGEhISAijRo3i+9//PitXriQgIIC3336bqqqqBuVZU1NDWVkZb775Jj/60Y/43ve+x9q1a+nZsydGo7FBY4mIiIiIiIiIiIhI29Tqixfdu3enY8eOXL58+ZGxJSUlWFtb13phb2dnR01NDaWlpea2rl27WsTc25Lq/pf/AAaDgYqKCos2W1vbWve2tbXFZDI1OI/6qivf+7epMplMdeZlY2PT4HvB3UJLaWkp+fn5jcrTx8fHItcRI0aQm5vbqFxEREREREREREREpG1p9cWLDh06MHz4cI4ePUp1dfVDY7t160ZVVRUlJSUW7deuXcPKyqpWYaKxioqK6myzt7dvUB4Gg6HWyoZv9qkve3v7OvO6fv16o8ZrrIEDBz7w2jeLQCIiIiIiIiIiIiLyZGr1xQuA2bNnYzKZ2LhxY53X7x0G/dxzzwGQkZFhcT0jIwM3Nzc6d+7cJPmcO3eOvLw88+e8vDzOnj2Ll5dXg/JwdHTkwoUL1NTUmGM+++yzRuXk6enJsWPHLFZ1HDlyhOLi4kaNl56eTteuXXFycmpQv7Fjx5rvfU9lZSV///vfcXd3b1QuIiIiIiIiIiIiItK2tPoDuwHGjBlDaGgoiYmJ5ObmMnnyZHr06MGlS5fYuXMnpaWljBkzhiFDhjBhwgTi4uK4desWzs7O7NmzhxMnTrBhw4Ymy8fW1pa5c+eycOFCAPOZDkFBQQD1ziMgIIAdO3YQGxuLv78/WVlZ7N27t1E5zZw5k23bthEWFkZYWBg3btwgMTGxXmdeBAUF8aMf/QgXFxdu3bpFWloa+/btY8mSJebttABOnjxJQUGBeTXHvTM+bGxsGDlyJADu7u4EBATw+uuvU1xcjL29Pdu2bePatWsNPvxbRERERERERERERNqmNlG8AIiOjmbYsGGkpKSwZMkSysvLcXBwwNfX1+KleHx8PKtWrcJoNFJcXIyLiwvr1q1j3LhxTZaLu7s7EyZMID4+HpPJhJeXFzExMRgMhgbl4efnR3R0NMnJyezevRs/Pz9iYmKYNWtWg3NycHDAaDSybNkyIiIicHJyYunSpaxevfqRfZ2cnHj//ffN21oNHjyY+Ph4fvjDH1rEpaSksHv3bvPnzZs3AzBy5Ei2bt1qbo+Li2PVqlWsXLmSmzdv4u7uzh//+EdcXV0b/FwWefa2e6z+IiIiIiIi8u3S720iIiJSX1Y19+9JJI8tODiYzp07s2nTpuZO5Ylx8uRJADw8PJo5ExEREREREXmU23fu0L5dm9jFuk0qKyvjzJkzDB06tMm21xZpLM1HaUk0H5tGQ97l6r8WpE2orKykvLy8udMQoby8nJycHM1HaRE0H6Ul0XyUlkTzUVqaJ21OqnAhIiIi9aH/YhARERERERERERERkRalVZ95cfDgQVJSUjh16hRlZWXmMy5mz56Ns7Nzs+R0/9kOLU1gYCBDhw4lLi4OgE8//ZQ9e/Zw+vRpbty4Qb9+/QgODmbatGlYWVlZ9P2v//ov3n33XS5fvoyzszORkZGMHTvWfD0xMZH169fXed+XXnqJN9980/z5zp07fPDBB3z44YdcunSJbt264ePjw8qVKxv9bAaDgU6dOjW6v0hT6dSpE25ubs2dhgig+Sgti+ajtCSaj9LStOU5qS2iREREpLFabfEiISEBo9FIQEAAsbGx2NjYkJ+fz86dO4mMjCQ1NbW5U2zx3n//fXr37s2iRYvo0aMHhw8f5vXXX+fKlSu8+uqr5rhPPvmE119/nblz5+Lj40N6ejqvvvoqKSkpPPvsswC8+OKLPP/88xbj//3vfychIQE/Pz+L9qVLl3Lo0CHCw8MZNGgQJpOJ48ePP/bz/CFpN/mXrz32OCIiIiIiIvL4nHrZsWT+j5s7DREREWmlWmXxIjMzE6PRSHh4OBEREeb2ESNGMG3aNA4dOtSM2bUeb7/9NjY2NubPo0aNori4mD/+8Y+Eh4fT7v/765h169YxefJkfvWrXwHg4+PDF198QVJSEkajEQBHR0ccHR0txv/www/p1q2bRfHiyJEj7N69m127duHq6mpunzx58mM/T/7la+RevPLY44iIiIiIiIiIiIhI82qVazc3b96MnZ0d4eHhdV6/t51RRUUFy5cvx9fXFw8PDwIDA9m/f79F7KJFi5gyZQqHDx9m6tSpeHp6MmPGDC5dukRxcTEREREMHz4cf39/0tPTLfoGBwczZ84cUlNT8ff3x9PTk+DgYM6fP28RV5887o11vzNnzuDq6sqxY8fMba6urhiNRhITExk9ejTe3t4sXryYsrIyi75ZWVkEBQXh4eHBlClTyMzMrPU93V+4uGfo0KHcvHnTPN5XX33FxYsXmTRpkkXcCy+8wJEjR6isrKw1xr1n3r9/PwEBARgMBnP7Rx99xMiRIy0KFyIiIiIiIiIiIiIi92t1xYvq6mqysrLw8fHB2tr6obFRUVFs376d0NBQkpKSGDhwIAsWLODgwYMWcSaTibi4OObNm0dCQgL5+flERUURGRnJ4MGDSUxMxN3dnejoaAoKCiz6nj59mk2bNvHaa6+xYsUKCgsLCQ0NtXipX9886islJYWLFy8SFxfH/PnzSUtLY8OGDRbPExISgsFgYM2aNYSEhBATE8PVq1cfOfbx48fp2bMnXbp0ATAXYr55hsiAAQOoqqriq6++qnOcQ4cOcfPmTaZMmWLRnp2djYuLC2+99Rbf/e538fT0JCQkhAsXLjToOxARERERERERERGRtqvVbRtVXFxMZWUlvXr1emjc2bNn2bdvHzExMUyfPh0APz8/CgoKSEpKYvz48ebYkpISkpOTGTRoEACFhYXExsYSFhbG/PnzAfDw8GD//v0cOHCAmTNnmvsWFRWRnJxM//79AXBzc2PixIns2rWL6dOnNyiP+rK3tzcfbu3n50dOTg579+4lKioKgC1btmBlZYXRaOTpp58G7m7rNGvWrIeO+49//IP09HR++9vfWnw3AF27drWIvff53vVv+vjjj+nZsycjRoywaDeZTOzatYuBAweSkJBAVVUVq1evJiQkhE8//ZSOHTvW81sQERERERERERERkbaq1a28uMfKyuqh1+8dAD1x4kSL9kmTJpGTk2OxzZKDg4O5cAGYCxGjR482t3Xt2hUbGxuuXLE8U2HQoEHmeIB+/foxZMgQsrOzG5xHfd2fF9xdBXF/XtnZ2Xh7e5sLF3D3PIvu3bs/cMwrV64QGRmJt7c3P//5zxuc0/1u3LhBZmYmkydPNp+bcU9NTQ23b9/m7bff5vvf/z4/+MEPWL9+Pf/6179IS0t7rPuKiIiIiIiIiIiISNvQ6ooX3bt3p2PHjly+fPmhcSUlJVhbW9d6YW9nZ0dNTQ2lpaXmtm+uKri3HdX9L/8BDAYDFRUVFm22tra17m1ra4vJZGpwHvVVV773b1NlMpnqzKuuMy7gbrEhLCyM7t27k5iYaFFw6NatG0CtPG/cuGFx/X579+6lsrKSqVOn1pn7wIEDsbOzM7e5uLjg6OhIbm5unfmJiIiIiIiIiIiIyJOl1RUvOnTowPDhwzl69CjV1dUPjOvWrRtVVVW1tjW6du0aVlZWtQoTjVVUVFRnm729fYPyMBgMVFVVWcQ8aEumR7G3t68zr+vXr9dqu3XrFnPmzKG0tJR333231vfi4uICUOsQ8vPnz2NtbU3fvn1rjfnxxx/j4uKCm5tbrWsDBw58YN7fLAyJiIiIiIiIiIiIyJOp1RUvAGbPno3JZGLjxo11Xs/MzOS5554DICMjw+JaRkYGbm5udO7cuUlyOXfuHHl5eebPeXl5nD17Fi8vL4B65+Ho6MiFCxeoqakxx3z22WeNysnT05Njx45ZrJY4cuQIxcXFFnHV1dX86le/4vz587z77rv07Nmz1lh9+/alf//+tfJPT09n1KhRGAwGi/bCwkL+9re/1Tqo+56xY8eSm5trXpkC8OWXX3LlyhXc3d0b+qgiIiIiIiIiIiIi0ga1ugO7AcaMGUNoaCiJiYnk5uYyefJkevTowaVLl9i5cyelpaWkpqYyYcIE4uLiuHXrFs7OzuzZs4cTJ06wYcOGJsvF1taWuXPnsnDhQgDWrl1Lz549CQoKAmDIkCH1yiMgIIAdO3YQGxuLv78/WVlZ7N27t1E5zZw5k23bthEWFkZYWBg3btwgMTGx1tZVMTExHDp0iEWLFnHz5k3++c9/mq+5ubmZCxMLFiwgKioKJycnvL29SU9P5/PPPyc5ObnWvdPT07lz506dW0YBvPjii2zdupU5c+YQHh5OVVUVa9euxcnJicmTJzfqee9x6mX36CARERERERH5t9DvaCIiIvI4WmXxAiA6Opphw4aRkpLCkiVLKC8vx8HBAV9fX0JCQgCIj49n1apVGI1GiouLcXFxYd26dYwbN67J8nB3d2fChAnEx8djMpnw8vIiJibGYkVCffLw8/MjOjqa5ORkdu/ejZ+fHzExMcyaNavBOTk4OGA0Glm2bBkRERE4OTmxdOlSVq9ebRF3b2VHXFxcrTEOHjxInz59AJgyZQrl5eUYjUbeeecdnJ2dWb9+PcOGDavVLy0tDU9PT5ycnOrMrUuXLmzZsoW33nqL6OhorKys8PX1ZcmSJXTq1KnBz3q/JfN//Fj9RUREREREpGndvnOH9u1a5aYPIiIi0sysau7fp0gaJDg4mM6dO7Np06bmTuWJdvLkSSorK3Fzc3vsAojI4yovL+fChQs4OztrPkqz03yUlkTzUVoSzUdpaTQnpSUpKyvjzJkzDB06tMm23BZpLM1HaUk0H5vGyZMnAfDw8HhkrP78QUREREREREREREREWpRWu21UXQ4ePEhKSgqnTp2irKzMvI3U7NmzcXZ2bu70WqTAwECGDh1a59ZRdbly5QqTJk2irKyMI0eOYGNjY3H96tWrrFq1iszMTMrKyujduzfz5s3jhz/8IQCJiYmsX7++zrFfeukl3nzzzUY9h8Fg0F8oSYvQqVMn3NzcmjsNEUDzUVoWzUdpSTQfpaVpqjmpLZpERESkLWkzxYuEhASMRiMBAQHExsZiY2NDfn4+O3fuJDIyktTU1Ca/59atW5t8zJYuLi6Ozp07U1ZWVutaYWEhL730Es7OzsTGxtKlSxfOnTtHZWWlOebFF1/k+eeft+j397//nYSEBPz8/B4rt7fe3k1+wbXHGkNERERERKQ1cuptx3/O0zmAIiIi0na0ieJFZmYmRqOR8PBwIiIizO0jRoxg2rRpHDp0qBmzazuOHDnCkSNHmDNnDitWrKh1PT4+HkdHR959913at28PwKhRoyxiHB0dcXR0tGj78MMP6dat22MXL/ILrnEu78pjjSEiIiIiIiIiIiIiza9NrCfdvHkzdnZ2hIeH13l97Nix5p8rKipYvnw5vr6+eHh4EBgYyP79+y3iFy1axJQpUzh8+DBTp07F09OTGTNmcOnSJYqLi4mIiGD48OH4+/uTnp5u0Tc4OJg5c+aQmpqKv78/np6eBAcHc/78eYu4+uRxb6z7nTlzBldXV44dO2Zuc3V1xWg0kpiYyOjRo/H29mbx4sW1VkdkZWURFBSEh4cHU6ZMITMz8xHf7P+vqqqK2NhYFixYQPfu3Wtdv3nzJp9++ik/+9nPzIWL+qioqGD//v0EBARgMBjq3U9ERERERERERERE2q5WX7yorq4mKysLHx8frK2tHxkfFRXF9u3bCQ0NJSkpiYEDB7JgwQIOHjxoEWcymYiLi2PevHkkJCSQn59PVFQUkZGRDB48mMTERNzd3YmOjqagoMCi7+nTp9m0aROvvfYaK1asoLCwkNDQUIvtk+qbR32lpKRw8eJF4uLimD9/PmlpaWzYsMHieUJCQjAYDKxZs4aQkBBiYmK4evVqvcb/4IMPaN++PS+//HKd10+fPk1VVRUdOnRgxowZuLu7873vfY/4+HiqqqoeOO6hQ4e4efMmU6ZMadgDi4iIiIiIiIiIiEib1eq3jSouLqayspJevXo9Mvbs2bPs27ePmJgYpk+fDoCfnx8FBQUkJSUxfvx4c2xJSQnJyckMGjQIuHueQ2xsLGFhYcyfPx8ADw8P9u/fz4EDB5g5c6a5b1FREcnJyfTv3x8ANzc3Jk6cyK5du5g+fXqD8qgve3t7Vq5caR4rJyeHvXv3EhUVBcCWLVuwsrLCaDTy9NNPA3e3cJo1a9Yjx7569SpJSUkkJSU9cFXFtWt3z5r43e9+x09/+lNeffVVPv/8c9atW0e7du147bXX6uz38ccf07NnT0aMGNHQRxYRERERERERERGRNqrVr7y4x8rK6pExx48fB2DixIkW7ZMmTSInJ8dimyUHBwdz4QIwFyJGjx5tbuvatSs2NjZcuWJ5zsKgQYPM8QD9+vVjyJAhZGdnNziP+ro/L4ABAwZY5JWdnY23t7e5cAF3z6Ooawuob/o//+f/8L3vfa/W+RX3u3PnjjmPRYsW4ePjwy9/+UtCQkJ4//33uXXrVq0+N27cIDMzk8mTJ9OuXZuZiiIiIiIiIiIiIiLymFr9G+Pu3bvTsWNHLl++/MjYkpISrK2ta72wt7Ozo6amhtLSUnNb165dLWLubUl1/8t/AIPBQEVFhUWbra1trXvb2tpiMpkanEd91ZXv/dtUmUymOvOysbF56LgnTpxg7969zJs3jxs3bnDjxg3Ky8sB+Prrr80/37u/j4+PRf9Ro0ZRWVlJXl5erbH37t1LZWUlU6dOrccTioiIiIiIiIiIiMiTotVvG9WhQweGDx/O0aNHqa6upkOHBz9St27dqKqqoqSkhG7dupnbr127hpWVVa3CRGMVFRXV2TZkyJAG5WEwGGqdF1FSUtKonOzt7evM6/r16w/td+HCBaqqqvjxj39c65q/vz8vvPACq1evZuDAgQ8d55sFHri7ZZSLiwtubm6PyF5EREREREREREREniStfuUFwOzZszGZTGzcuLHO65mZmQA899xzAGRkZFhcz8jIwM3Njc6dOzdJPufOnbNYaZCXl8fZs2fx8vJqUB6Ojo5cuHCBmpoac8xnn33WqJw8PT05duyYxaqOI0eOUFxc/NB+zz//PB988IHF/8LCwgBISkoyn//Ru3dvBg8ezOHDhy36Hz58mKeeeqpWcaOwsJC//e1vOqhbRERERERERERERGpp9SsvAMaMGUNoaCiJiYnk5uYyefJkevTowaVLl9i5cyelpaWMGTOGIUOGMGHCBOLi4rh16xbOzs7s2bOHEydOsGHDhibLx9bWlrlz57Jw4UIA1q5dS8+ePQkKCgKodx4BAQHs2LGD2NhY/P39ycrKYu/evY3KaebMmWzbto2wsDDCwsK4ceMGiYmJjzzzwt7eHnt7e4u2goICAIYPH26x7VRkZCTh4eG89dZbfP/73+fkyZNs3ryZkJCQWoWh9PR07ty5oy2jRERERERERERERKSWNlG8AIiOjmbYsGGkpKSwZMkSysvLcXBwwNfXl5CQEHNcfHw8q1atwmg0UlxcjIuLC+vWrWPcuHFNlou7uzsTJkwgPj4ek8mEl5cXMTExGAyGBuXh5+dHdHQ0ycnJ7N69Gz8/P2JiYpg1a1aDc3JwcMBoNLJs2TIiIiJwcnJi6dKlrF69uikeGYBx48axatUqNmzYwJ/+9CccHBxYsGABv/zlL2vFpqWl4enpiZOTU5Pd36m3XZONJSIiIiIi0pro9yERERFpa6xq7t+TSB5bcHAwnTt3ZtOmTc2dyhPj5MmTAHh4eDRzJiIiIiIiIs3n9p07tG/XJnaHlmZUVlbGmTNnGDp0aJNtry3SWJqP0pJoPjaNhrzL1X/VSJtQWVlJeXl5c6chQnl5OTk5OZqP0iJoPkpLovkoLYnmo7Q0TTUnVbgQERGRtkT/ZSMiIiIiIiIiIiIiIi1KiyxeHDx4kF/84heMHDmSZ555hnHjxrF06VIuXLjQ3Kk90tatW//tW0YFBgayaNEi8+e8vDyWLl1KYGAgbm5uTJky5ZFjHDhwAFdX10fGhoeH4+rqynvvvWfRvnv3bn76058ycuRIPDw8CAgIICkpicrKSou48vJyVq5cyfjx4/Hy8iIgIICNGzdSXV3dgCeuzWAw0KlTp8caQ6QpdOrUCTc3N81HaRE0H6Ul0XyUlkTzUVqahs7J23fufMsZiYiIiDS/Fndgd0JCAkajkYCAAGJjY7GxsSE/P5+dO3cSGRlJampqc6fY4p07d47MzEy8vLy4c+cOjzrW5NatW/zhD3/Azu7hB7xlZmaSnZ1d57WSkhKef/55fvnLX9KlSxc+//xz1q9fz5UrV4iNjTXHvfnmm+zbt49f//rXDBgwgH/+85+sW7eO8vJyIiMjG/6w93lr027yLl97rDFERERERERasn697PjPOT9u7jREREREvnUtqniRmZmJ0WgkPDyciIgIc/uIESOYNm0ahw4dasbsWo9x48bh7+8PwKJFizh16tRD4zdt2kSvXr3o06fPA2MrKyt56623+PWvf82SJUtqXZ81a5bFZx8fH77++mvef/99fv/739O+fXvu3LnDp59+SkhICK+88oo57sKFC3zyySePXbzIu3yNc3lXHmsMEREREREREREREWl+LWrbqM2bN2NnZ0d4eHid18eOHQtARUUFy5cvx9fXFw8PDwIDA9m/f79F7KJFi5gyZQqHDx9m6tSpeHp6MmPGDC5dukRxcTEREREMHz4cf39/0tPTLfoGBwczZ84cUlNT8ff3x9PTk+DgYM6fP28RV5887o11vzNnzuDq6sqxY8fMba6urhiNRhITExk9ejTe3t4sXryYsrIyi75ZWVkEBQXh4eHBlClTyMzMrPU9tWvAIW35+fn88Y9/5He/+91D49577z26du1KUFBQvcfu3r071dXV3Pn/ljTX1NRQXV3N008/bRH39NNPP3J1iIiIiIiIiIiIiIg8OVpM8aK6upqsrCx8fHywtrZ+aGxUVBTbt28nNDSUpKQkBg4cyIIFCzh48KBFnMlkIi4ujnnz5pGQkEB+fj5RUVFERkYyePBgEhMTcXd3Jzo6moKCAou+p0+fZtOmTbz22musWLGCwsJCQkNDLc5wqG8e9ZWSksLFixeJi4tj/vz5pKWlsWHDBovnCQkJwWAwsGbNGkJCQoiJieHq1auNuh/AW2+9RWBgIEOGDHlgzOXLl3nnnXf43e9+h5WV1UPHq66upry8nH/84x9s2bKFl19+2fz/Z/v27QkKCiI5OZnPP/+cr7/+msOHD/PnP/+ZGTNmNPoZRERERERERERERKRtaTHbRhUXF1NZWUmvXr0eGnf27Fn27dtHTEwM06dPB8DPz4+CggKSkpIYP368ObakpITk5GQGDRoEQGFhIbGxsYSFhf2/7P15WJXXvf//PzGyjTZBZJJqooIDgwGiVlFLsBqiGLUk2vxKPg1RC1TFgWOFE/XTaDmYisdZgpHsaKKCqakDVWMc6+G0TmnEUhVpNSoUrLLFMlhm5feHX/bHHVABaRnyelxXrou97vda9/veWX/g/WatxcyZMwHw8vLi8OHDHDlyhMmTJ5v75ufnk5SURK9evQDw9PQkMDCQXbt2ERwc3KA86svR0ZGVK1eax8rIyODgwYNERUUBsHnzZqysrDAajebVC87OzrW2bKqv3/3ud5w9e5YDBw48Mm7p0qW88sorvPjii4+Mq6qqon///ubPr7/+eq0tphYvXszixYt54403zG3Tpk1j6tSpDX8AEREREREREREREWmTWszKixqP+8v+M2fOABAYGGjRPnbsWDIyMiy2WXJycjIXLgBzIWL48OHmNhsbG+zs7Lhxw/KshL59+5rjAXr27Im7u7v5wOqG5FFfD+YF0Lt3b4u80tPT8fX1tdh2adiwYdja2jb4XuXl5fzqV79i9uzZ2NnZPTTuD3/4A3/4wx+YN2/eY8ds3749O3bsIDk5mQULFnDs2DEWLFhgEbNixQr+53/+hyVLlpCUlERUVBRbtmzho48+avAziIiIiIiIiIiIiEjb1GJWXtja2tKhQweuX7/+yLjCwkKsra1rvbB3cHCgurqa4uJiOnXqBNwvTDyoZvuib565YDAYKC8vt2izt7evdW97e3tMJlOD86ivuvJ9cJsqk8lEz549a/V7VPHhYTZv3ky7du0YN24c4+8MdgABAABJREFURUVFAFRWVnLv3j2Kiop4+umnMRgMLFmyhLfffpuOHTua4+B+8aOoqKhWzl5eXgB873vf47nnnmPmzJm89dZbeHl58de//pVNmzbxwQcfMGrUKOD+YexVVVWsXbuW4OBgnnnmmQY/i4iIiIiIiIiIiIi0LS1m5UX79u0ZOHAgp06doqqq6qFxnTt3prKyksLCQov2W7duYWVlVasw0Vj5+fl1tjk6OjYoD4PBQGVlpUXMN/vUl6OjY5153b59u8FjXblyhaysLIYNG8bgwYMZPHgw+/bt4+uvv2bw4MHs3LkTgKtXr7JhwwZzzODBgwFYu3YtgwcPrlX0edALL7wA3D8UHODy5csAeHh4WMR5enpSUVHxRGd3iIiIiIiIiIiIiEjb0WJWXgBMnTqVn/3sZ2zYsIFZs2bVup6amsqgQYMAOHDgAD/+8Y/N1w4cOICnp2eDVzs8zKVLl8jKyjKvdMjKyiIzM9N8z/rm4ezszIkTJ6iurjZviXX8+PFG5eTt7c2nn35KcXGxuThy8uRJCgoKGjxWeHg4r7/+ukXbhx9+yNWrV1m6dKl5y6wtW7bU6vv2228THBzMq6+++sjD1Wu21nr++ecB6N69O3D/MPTvfve75rjz589jZWX12PNOREREREREREREROTboUUVL0aMGEFYWBjx8fFcvnyZcePG0aVLF3Jycti5cyfFxcWkpKQwevRo4uLiKCsrw8XFhT179nD27FnWr1/fZLnY29szffp05syZA9xfadC1a1cmTpwIgLu7e73yGDNmDDt27CA2NpaAgADS0tI4ePBgo3KaPHky27ZtIzw8nPDwcIqKioiPj6+1dVVpaSmpqakA5ObmcufOHfOh3EOGDMHOzo7evXvTu3dvi367d+/m5s2b+Pr6mtse/PlBPXr0sLj2k5/8hFdeeQVXV1fatWtHeno6mzZt4qWXXsLb2xu4vxLjhRdeYPHixeTn59OjRw/+/Oc/8+GHHzJp0iQ6duzYqO+lRs9uDk/UX0REREREpKXTv3tERETk26JFFS8AoqOjGTBgAMnJySxcuJDS0lKcnJzw8/MjNDQUgOXLl7Nq1SqMRiMFBQW4urqybt068zkKTaF///6MHj2a5cuXYzKZ8PHxISYmBoPBYI6pTx7+/v5ER0eTlJTE7t278ff3JyYmhilTpjQ4JycnJ4xGI0uWLCEyMpIePXqwaNEiVq9ebRGXn59PZGSkRVvN5y1btjy0IPEkXnjhBT777DOuX79O+/btee6555g9ezb/5//8H3PMU089xYYNG1i7di2JiYnk5+fj7OxMWFgY4eHhT5zD/532+uODREREREREWrm79+7xVLsWswu0iIiIyL+EVXV1dXVzJ9HShISE0KlTJxITE5s7FamHc+fOUVFRgaen5xOv3hB5UqWlpVy9ehUXFxfNR2l2mo/Skmg+Skui+SgtjeaktCQlJSVcvHgRDw+PJtuaW6SxNB+lJdF8bBrnzp0DwMvL67Gx+lMNERERERERkWZUXV1NaWkp+ttCERERkf+nxW0bVZejR4+SnJzM+fPnKSkpMW8jNXXqVFxcXJo7vRYpKCgIDw8P4uLiHhpz7tw5tm3bxp/+9CeuXr3KiBEj6lxtUl1djdFoZNu2bdy+fRsPDw8WLFjAiy++aBH31VdfsXbtWjIzM2nXrh1eXl7MmzcPDw8Pc8xHH33Evn37yMnJoaqqiueff54f//jH/OQnPzEfaN4YBoNBf6EkLULHjh3x9PRs7jREAM1HaVk0H6Ul0XyUfxdt7yQiIiLSeC2+eLFixQqMRiNjxowhNjYWOzs7srOz2blzJ3PnziUlJaXJ77l169YmH7MlSktL46uvvsLb25vy8vKHxhmNRtatW0dUVBRubm4kJyfz05/+lN/+9rc8//zzAFy5coXQ0FCGDh3KypUrqaioIDExkSlTprBv3z4cHR0BKC4u5tVXX6Vv37506NCBkydPsmTJEu7cucP06dOf6HmWfLibrOu3nmgMERERERGRptCzmwO/+JnO5RMRERFprBZdvEhNTcVoNBIREWFxAPXgwYOZNGkSx44da8bsWr+QkBAmT55s/rku5eXlJCYm8tOf/tR8yPigQYMIDAxk48aN/PKXvwTgyJEjVFdXs3btWp5++mkA3NzcCAgI4Pjx47z22msAzJ0712L84cOHc/36dXbv3v3ExYus67e4lH3jicYQERERERERERERkebXotevbtq0CQcHByIiIuq8PnLkSPPP5eXlLF26FD8/P7y8vAgKCuLw4cMW8fPnz2f8+PGcOHGCCRMm4O3tzVtvvUVOTg4FBQVERkYycOBAAgIC2L9/v0XfkJAQpk2bRkpKCgEBAXh7exMSEsKVK1cs4uqTR81YD7p48SJubm6cPn3a3Obm5obRaCQ+Pp7hw4fj6+vLggULKCkpseiblpbGxIkT8fLyYvz48aSmpj7mm72vXT2WL6elpXHnzh3Gjh1rbjMYDLzyyiv87//+r7mtsrISg8FAhw4dzG3PPvtsvfLo0qULlZWV9YoVERERERERERERkbavxRYvqqqqSEtLY+jQoVhbWz82Pioqiu3btxMWFkZCQgJ9+vRh9uzZHD161CLOZDIRFxfHjBkzWLFiBdnZ2URFRTF37lz69etHfHw8/fv3Jzo6mtzcXIu+Fy5cIDExkXnz5rFs2TLy8vIICwujoqKiwXnUV3JyMteuXSMuLo6ZM2eyd+9e1q9fb/E8oaGhGAwG1qxZQ2hoKDExMdy8ebNR9/ummuKMq6urRXvv3r25fv06ZWVlAIwbN467d++yZs0a/vGPf3Dz5k2WLl3Kd7/7XV5++eVa41ZVVXHnzh3+53/+h5SUFN5+++0myVdEREREREREREREWr8Wu21UQUEBFRUVdOvW7bGxmZmZHDp0iJiYGIKDgwHw9/cnNzeXhIQEi5fnhYWFJCUl0bdvXwDy8vKIjY0lPDycmTNnAuDl5cXhw4c5cuSIeVslgPz8fJKSkujVqxcAnp6eBAYGsmvXLoKDgxuUR305OjqycuVK81gZGRkcPHiQqKgoADZv3oyVlRVGo9G80sHZ2dm8xdOTKioqqrWiAsDGxobq6moKCwt5+umn6dWrF5988gkRERFs2LABgO7du/Pxxx/XWoGRlZXF6NGjzZ9nzJjRZPmKiIiIiIiIiIiISOvXYlde1LCysnpszJkzZwAIDAy0aB87diwZGRkW2yw5OTmZCxeAuRAxfPhwc5uNjQ12dnbcuGF5fkLfvn3N8QA9e/bE3d2d9PT0BudRXw/mBfdXPDyYV3p6Or6+vhYFgmHDhmFra9vgez2Jq1evMnv2bL7//e/z8ccfs2HDBrp37054eDi3blkeov3d736XHTt2sGXLFubMmcMnn3zCunXr/q35ioiIiIiIiIiIiEjL1WJXXtja2tKhQweuX7/+2NjCwkKsra1rvbB3cHCgurqa4uJiOnXqBNwvTDyoZkuqb64OMBgMlJeXW7TZ29vXure9vT0mk6nBedRXXfk+uE2VyWSiZ8+etfrZ2dk16D6Pun9FRQXl5eUWqy+KioqwsrKic+fOAKxevRoHBwf++7//2xwzZMgQRo4cyZYtW/j5z39ubjcYDHh5eQHg6+vLM888w7Jly3jzzTdxdHRskrxFREREREREREREpPVqsSsv2rdvz8CBAzl16hRVVVWPjO3cuTOVlZUUFhZatN+6dQsrK6t6Hxz9OPn5+XW21bxwr28eBoOh1gHV3+xTX46OjnXmdfv27UaN9001Z11cvXrVov3KlSt069aNp59+GoDLly/j7u5uEfOd73yHHj16kJ2d/ch79O/fn7t379Y6Y0REREREREREREREvp1abPECYOrUqZhMJvMZCt+UmpoKwKBBgwA4cOCAxfUDBw7g6enZ4NUOD3Pp0iWysrLMn7OyssjMzMTHx6dBeTg7O3P16lWqq6vNMcePH29UTt7e3pw+fZri4mJz28mTJykoKGjUeN80cOBAnnnmGb744gtzW2VlJYcOHcLf39/c1q1bNy5evGjxTHfu3CErK4vu3bs/8h5paWlYWVnx3HPPNUnOIiIiIiIiIiIiItK6tdhtowBGjBhBWFgY8fHxXL58mXHjxtGlSxdycnLYuXMnxcXFjBgxAnd3d0aPHk1cXBxlZWW4uLiwZ88ezp49y/r165ssH3t7e6ZPn86cOXMAWLt2LV27dmXixIkA9c5jzJgx7Nixg9jYWAICAkhLS+PgwYONymny5Mls27aN8PBwwsPDKSoqIj4+vl5nXty+fZsvv/zS/PM///lPc+FlxIgRdOzYkQ4dOjBt2jTi4+Oxs7OjX79+fPrppxQUFBAaGmoeKzg4mJkzZxIVFUVQUBAVFRVs2rSJiooK3njjDQCKi4sJDw/nhz/8IT179qSqqorTp0+zZcsWfvzjH+Pg4NCo70BERERERERERERE2pYWXbwAiI6OZsCAASQnJ7Nw4UJKS0txcnLCz8/P4uX58uXLWbVqFUajkYKCAlxdXVm3bh2jRo1qslz69+/P6NGjWb58OSaTCR8fH2JiYjAYDA3Kw9/fn+joaJKSkti9ezf+/v7ExMQwZcqUBufk5OSE0WhkyZIlREZG0qNHDxYtWsTq1asf2/fSpUtERkZatNV8Pnr0qHklRHh4ONXV1WzatInbt2/j4eHBxo0bef755839AgICWLNmDRs3bmTu3LlYW1vj6enJli1bzIecd+jQARcXFz755BNu3rzJ008/TY8ePYiJieG1115r8LN/U89uKn6IiIiIiEjLoH+fiIiIiDwZq+oH9/mRhwoJCaFTp04kJiY2dyryDefOnQMwHwIuIiIiIiLSEty9d4+n2j1+t+aSkhIuXryIh4dHk217LNJYmo/Skmg+Skui+dg0GvIut0WfeSFSXxUVFZSWljZ3GiKUlpaSkZGh+SgtguajtCSaj9KSaD7Kv0t9ChciIiIiUrcWv21UUzh69CjJycmcP3+ekpIS87ZTU6dOxcXFpbnTa3GCgoLw8PAgLi4OuH8wutFo5PLly9y5c4euXbsSEBDArFmzePbZZ8397t69y6ZNm9i5cyd///vfcXBwYPTo0cyaNYvvfOc7AFy5coWkpCROnTpFbm4u9vb2vPTSS0RGRmJnZ9cszysiIiIiIiIiIiIiLUubL16sWLECo9HImDFjiI2Nxc7OjuzsbHbu3MncuXNJSUmp1zhbt2791ybaghUUFODt7U1ISAi2trZcunSJ+Ph4Ll26xKZNm8xxH3zwAR988AGRkZF4e3tz6dIlVq1aRV5eHitXrgTgxIkTfPXVV/z4xz/G3d2d69evs27dOr788kt++9vfWpwf0hAGg4GOHTs2yfOKPImOHTvi6enZ3GmIAJqP0rJoPkpLovko/wr13SJKREREROqnTRcvalYMREREWBxMPXjwYCZNmsSxY8eaMbvWIygoyOKzr68vBoOBd999l5s3b9K1a1cA9u3bx4QJE/jZz34GwNChQ/nHP/6B0WikqqqK9u3bM27cOH7yk59gZWVlHq9nz568+eabHDt2jDFjxjQ6z9iPdpP191uN7i8iIiIiItIYPb/rwLthrzd3GiIiIiJtSpsuXmzatAkHBwciIiLqvD5y5EgAysvLWbVqFZ9//jmFhYW4uroya9YsXnnlFXPs/PnzOX/+PAsXLmTp0qVkZWXh7e1NXFwczzzzDIsXL+b3v/89dnZ2/PznP+fVV18196057Hvs2LG8//775OXl4ePjQ0xMDK6urua4+uRR18HhFy9e5LXXXmPLli34+voC4ObmRlRUFGVlZXz66afcvXuXUaNG8e6771ocKJOWlsaSJUu4dOkSPXv2JDo6ul7fra2tLQCVlZXmtqqqKp555hmLuGeffZYHz4Tv0qVLrbFq/uotLy+vXvd+mKy/3+Kv2TeeaAwRERERERERERERaX5tdk1rVVUVaWlpDB06FGtr60fGRkVFsX37dsLCwkhISKBPnz7Mnj2bo0ePWsSZTCbi4uKYMWMGK1asIDs7m6ioKObOnUu/fv2Ij4+nf//+REdHk5uba9H3woULJCYmMm/ePJYtW0ZeXh5hYWFUVFQ0OI/6Sk5O5tq1a8TFxTFz5kz27t3L+vXrLZ4nNDQUg8HAmjVrCA0NJSYmhps3b9Y53t27dykvL+fChQskJCQwatQonnvuOfP1N954gz179nDy5En++c9/8uc//5mtW7cSHBxM+/YPr5OdOXMGgN69ezfqOUVERERERERERESkbWmzKy8KCgqoqKigW7duj4zLzMzk0KFDxMTEEBwcDIC/vz+5ubkkJCTw8ssvm2MLCwtJSkqib9++wP2VArGxsYSHhzNz5kwAvLy8OHz4MEeOHGHy5Mnmvvn5+SQlJdGrVy/g/mqDwMBAdu3aRXBwcIPyqC9HR0fzWRP+/v5kZGRw8OBBoqKiANi8eTNWVlYYjUbzwdvOzs5MmTKlzvFGjhxpLmy89NJL5rFrTJs2jYqKCqZOnWpebfHDH/6QhQsXPjTH8vJyli1bhqenJ8OGDWvwM4qIiIiIiIiIiIhI29NmV17UePBshbrU/NV/YGCgRfvYsWPJyMigpKTE3Obk5GQuXADmQsTw4cPNbTY2NtjZ2XHjhuX2RX379jXHw/1zHtzd3UlPT29wHvX1YF5wf2XDg3mlp6fj6+trLlwADBs2zLwl1Dd9+OGH/PrXv2bJkiVcuXKF6dOnc/fuXfP1pKQktmzZwoIFC0hKSmLx4sX87//+L7GxsQ/NcfHixeTk5LBs2bLH/r8SERERERERERERkW+HNrvywtbWlg4dOnD9+vVHxhUWFmJtbV3rhb2DgwPV1dUUFxebz4iwsbGxiKnZjurBl/8ABoOB8vJyizZ7e/ta97a3t8dkMjU4j/qqK98Ht6kymUz07NmzVj87O7s6x3N3dwdgwIABeHl5ERQUxOHDhwkMDOQf//gHy5Yt4z//8z8JCQkB7h+M/swzzxAdHc3bb7+Ni4uLxXirV69m7969bNiwgX79+jXo2URERERERERERESk7WqzKy/at2/PwIEDOXXqFFVVVQ+N69y5M5WVlRQWFlq037p1Cysrq1qFicbKz8+vs83R0bFBeRgMBotDsoFaferL0dGxzrxu37792L5ubm5YW1uTnZ0NwN/+9jcqKirw8PCwiKs5jLsmrsbWrVtJTEzkvffe46WXXmpU/iIiIiIiIiIiIiLSNrXZ4gXA1KlTMZlMbNiwoc7rqampDBo0CIADBw5YXDtw4ACenp4NXu3wMJcuXSIrK8v8OSsri8zMTHx8fADqnYezszNXr141nykBcPz48Ubl5O3tzenTpykuLja3nTx5koKCgsf2TU9Pp7Ky0nxgd83ZIhcuXLCIO3/+PIDFwd779u3jvffe4+c//zmvvfZao3IXERERERERERERkbarzW4bBTBixAjCwsKIj4/n8uXLjBs3ji5dupCTk8POnTspLi4mJSWF0aNHExcXR1lZGS4uLuzZs4ezZ8+yfv36JsvF3t6e6dOnM2fOHADWrl1L165dmThxInB/S6b65DFmzBh27NhBbGwsAQEBpKWlcfDgwUblNHnyZLZt20Z4eDjh4eEUFRURHx9fa+uqWbNm8cILL+Dm5sbTTz9NZmYmGzduxM3NjYCAAOD+9lYBAQGsXbuWu3fv4unpyeXLl4mPj2f48OH07t0bgC+//JL58+czdOhQhgwZwp/+9CfzfZydnXF2dm7Us4iIiIiIiIiIiIhI29GmixcA0dHRDBgwgOTkZBYuXEhpaSlOTk74+fkRGhoKwPLly1m1ahVGo5GCggJcXV1Zt24do0aNarI8+vfvz+jRo1m+fDkmkwkfHx9iYmIwGAzmmPrk4e/vT3R0NElJSezevRt/f39iYmKYMmVKg3NycnLCaDSyZMkSIiMj6dGjB4sWLWL16tUWcd7e3uzfv58PP/yQ6upqunfvzhtvvEFoaKhF/suWLSMhIYFPP/2Umzdv4ujoyIQJE5g9e7Y55vTp01RWVnLy5ElOnjxpcZ9Zs2ZZxDZUz+86NLqviIiIiIhIY+nfIiIiIiJNz6r6wf2H5F8iJCSETp06kZiY2NyptEnnzp0DwMvLq5kzERERERGRb6u79+7xVLvG7cxcUlLCxYsX8fDwaLKti0UaS/NRWhLNR2lJNB+bRkPe5bbpMy/k26OiooLS0tLmTkOE0tJSMjIyNB+lRdB8lJZE81FaEs1H+VdobOFCREREROqm365ERERERERERERERKRFabFnXhw9epTk5GTOnz9PSUmJ+ZyKqVOn4uLi0tzpNcjWrVv/5fcICgrCw8ODuLg4ALKysti4cSPp6elcunQJV1dX9u3b98gxjhw5wsyZM+nbt69FbE5ODi+//HKteB8fHz777DPz51//+tccOnSIv/zlL5SWltKnTx9+9rOfmQ/1rlFdXY3RaGTbtm3cvn0bDw8PFixYwIsvvtjo5zcYDHTs2LHR/UWaSseOHfH09GzuNEQAzUdpWTQfpSXRfPx2epJtnURERETk369FFi9WrFiB0WhkzJgxxMbGYmdnR3Z2Njt37mTu3LmkpKQ0d4ot3qVLl0hNTcXHx4d79+7xuKNNysrK+NWvfoWDw8MPmvv5z3+Or6+v+fN3vvMdi+sbNmzAz8+PN998k06dOnHgwAFmzpxJXFwcr7/+ujnOaDSybt06oqKicHNzIzk5mZ/+9Kf89re/5fnnn2/kE8N/bdxN1t9vNbq/iIiIiIi0TT2/68Ci0NcfHygiIiIiLUaLK16kpqZiNBqJiIggMjLS3D548GAmTZrEsWPHmjG71mPUqFHmFQ/z58/n/Pnzj4xPTEykW7duPPfccw+N7dmz5yNXR+zatQs7Ozvz5+9///vk5uayadMmc/GivLycxMREfvrTnzJlyhQABg0aRGBgIBs3buSXv/xl/R/yG7L+fou//u1Go/uLiIiIiIiIiIiISMvQ4tbMbtq0CQcHByIiIuq8PnLkSOD+S/ClS5fi5+eHl5cXQUFBHD582CJ2/vz5jB8/nhMnTjBhwgS8vb156623yMnJoaCggMjISAYOHEhAQAD79++36BsSEsK0adNISUkhICAAb29vQkJCuHLlikVcffKoGetBFy9exM3NjdOnT5vb3NzcMBqNxMfHM3z4cHx9fVmwYAElJSUWfdPS0pg4cSJeXl6MHz+e1NTUWt9TuwYsh87Ozubjjz/mF7/4Rb371OXBwkUNDw8P8vLyzJ/T0tK4c+cOY8eONbcZDAZeeeUV/vd///eJ7i8iIiIiIiIiIiIibUOLKl5UVVWRlpbG0KFDsba2fmRsVFQU27dvJywsjISEBPr06cPs2bM5evSoRZzJZCIuLo4ZM2awYsUKsrOziYqKYu7cufTr14/4+Hj69+9PdHQ0ubm5Fn0vXLhAYmIi8+bNY9myZeTl5REWFkZFRUWD86iv5ORkrl27RlxcHDNnzmTv3r2sX7/e4nlCQ0MxGAysWbOG0NBQYmJiuHnzZqPuB/Dee+8RFBSEu7v7I+N++ctf4uHhwbBhw/jFL35BQUHBY8c+c+YMrq6u5s81xZ8H2wB69+7N9evXKSsra/gDiIiIiIiIiIiIiEib0qK2jSooKKCiooJu3bo9Mi4zM5NDhw4RExNDcHAwAP7+/uTm5pKQkGBxuHRhYSFJSUn07dsXgLy8PGJjYwkPD2fmzJkAeHl5cfjwYY4cOcLkyZPNffPz80lKSqJXr14AeHp6EhgYyK5duwgODm5QHvXl6OjIypUrzWNlZGRw8OBBoqKiANi8eTNWVlYYjUaeffZZAJydnc1bMDXU7373O86ePcuBAwceGmMwGHjzzTfx8/PDxsaG9PR0NmzYwPnz5/nNb37z0ELT3r17OXv2LAkJCea2oqIiDAYDHTp0sIi1sbGhurqawsJCnn766UY9i4iIiIiIiIiIiIi0DS1q5UUNKyurR14/c+YMAIGBgRbtY8eOJSMjw2KbJScnJ3PhAjAXIoYPH25us7Gxwc7Ojhs3LM9L6Nu3rzke7p/54O7uTnp6eoPzqK8H84L7KxIezCs9PR1fX19z4QJg2LBh2NraNvhe5eXl/OpXv2L27Nl1bvlUw8nJiV/+8pcEBAQwZMgQwsPDWblyJRcvXqy1RVaNzMxMFi9ezMSJE81nb4iIiIiIiIiIiIiI1EeLKl7Y2trSoUMHrl+//si4wsJCrK2ta72wd3BwoLq6muLiYnObjY2NRUzNKoEHX/7D/dUF5eXlFm329va17m1vb4/JZGpwHvVVV74PblNlMpnqzOtRxYeH2bx5M+3atWPcuHEUFRVRVFREZWUl9+7do6ioyOK+3zRixAg6derEhQsXal3Lzc0lPDwcb29v/uu//svimo2NDRUVFbW+66KiIqysrOjcuXODn0NERERERERERERE2pYWVbxo3749AwcO5NSpU1RVVT00rnPnzlRWVlJYWGjRfuvWLaysrGoVJhorPz+/zjZHR8cG5WEwGKisrLSI+Waf+nJ0dKwzr9u3bzd4rCtXrpCVlcWwYcMYPHgwgwcPZt++fXz99dcMHjyYnTt3NnjM27dvExoair29Pe+//36tLaVqzrq4evVqrVy6deumLaNEREREREREREREpGUVLwCmTp2KyWRiw4YNdV5PTU1l0KBBALXOaThw4ACenp506tSpSXK5dOkSWVlZ5s9ZWVlkZmbi4+MDUO88nJ2duXr1KtXV1eaY48ePNyonb29vTp8+bbGq4+TJk/U6PPubwsPD2bJli8V/fn5+dO/enS1btjBq1KiH9j127BglJSV4eXmZ2/75z38SHh5OZWUlH374Ic8880ytfgMHDuSZZ57hiy++MLdVVlZy6NAh/P39G/wMIiIiIiIiIiIiItL2tKgDu+H+dkRhYWHEx8dz+fJlxo0bR5cuXcjJyWHnzp0UFxeTkpLC6NGjiYuLo6ysDBcXF/bs2cPZs2dZv359k+Vib2/P9OnTmTNnDgBr166la9euTJw4EQB3d/d65TFmzBh27NhBbGwsAQEBpKWlcfDgwUblNHnyZLZt20Z4eDjh4eEUFRURHx9fa+uq0tJSUlNTgfvbON25c8dcZBkyZAh2dnb07t2b3r17W/TbvXs3N2/exNfX19wWFxeHlZUVL774IjY2Nvz5z38mMTGRF154weI8i9mzZ5OZmcl7773H9evXLbb/evHFFwHo0KED06ZNIz4+Hjs7O/r168enn35KQUEBoaGhjfpORERERERERERERKRtaXHFC4Do6GgGDBhAcnIyCxcupLS0FCcnJ/z8/MwvuJcvX86qVaswGo0UFBTg6urKunXrHrlaoKH69+/P6NGjWb58OSaTCR8fH2JiYjAYDOaY+uTh7+9PdHQ0SUlJ7N69G39/f2JiYpgyZUqDc3JycsJoNLJkyRIiIyPp0aMHixYtYvXq1RZx+fn5REZGWrTVfN6yZYtFceJxevfuzaeffspnn31GWVkZXbt25Uc/+hFz5syhffv/N4VqVpO88847tcb4y1/+Yv45PDyc6upqNm3axO3bt/Hw8GDjxo08//zz9c6pLj2/6/BE/UVEREREpG3SvxVEREREWh+r6gf3MhKzkJAQOnXqRGJiYnOnIo9x7tw5AIstrERERERERB509949nmrX4nZOBqCkpISLFy/i4eHRZNsgizSW5qO0JJqP0pJoPjaNhrzLbZm/uYk0UEVFBaWlpc2dhgilpaVkZGRoPkqLoPkoLYnmo7Qkmo/fTi21cCEiIiIidWuR20Y1taNHj5KcnMz58+cpKSkxb0E1depUXFxcmju9FicoKAgPDw/i4uIA+OKLL9izZw8XLlygqKiInj17EhISwqRJk7CysrLoW1RUxLp16zhw4ACFhYV07dqV//N//g8//elPLeK+/vprVq1axZdffkllZSW9evUiOjqa73//+/+25xQRERERERERERGRlqnNFy9WrFiB0WhkzJgxxMbGYmdnR3Z2Njt37mTu3LmkpKTU2W/r1q3/3kRbsE8++YTu3bszf/58unTpwokTJ3j33Xe5ceMGs2bNMseVlJQQEhLCU089xcKFC7G3t+fatWvcuXPHYrxLly7x5ptv4ufnx/Lly7G2tubChQtP9JdvBoOBjh07Nrq/SFPp2LEjnp6ezZ2GCKD5KC2L5qO0JJqP3z4tecsoEREREalbmy5epKamYjQaiYiIsDi8evDgwUyaNIljx441Y3atxwcffICdnZ3587BhwygoKODjjz8mIiKCdv/fPwI+/PBD/vnPf7Jnzx7zvm91HQy+ePFi/Pz8WLNmjbmtKVZcxGzaTdaNW088joiIiIiItB09nR1Y/NPXmzsNEREREWmgNl282LRpEw4ODkRERNR5feTIkQCUl5ezatUqPv/8cwoLC3F1dWXWrFm88sor5tj58+dz/vx5Fi5cyNKlS8nKysLb25u4uDieeeYZFi9ezO9//3vs7Oz4+c9/zquvvmruW3P499ixY3n//ffJy8vDx8eHmJgYXF1dzXH1yaOug8QvXrzIa6+9xpYtW8zFAjc3N6KioigrK+PTTz/l7t27jBo1infffdfiQJm0tDSWLFnCpUuX6NmzJ9HR0bW+pwcLFzU8PDz47LPPKCkp4ZlnngFgx44d/OQnP3nkgTVff/01Z86cYdu2bQ+NaaysG7f4699uNPm4IiIiIiIiIiIiIvLv1WbXzVZVVZGWlsbQoUOxtrZ+ZGxUVBTbt28nLCyMhIQE+vTpw+zZszl69KhFnMlkIi4ujhkzZrBixQqys7OJiopi7ty59OvXj/j4ePr37090dDS5ubkWfS9cuEBiYiLz5s1j2bJl5OXlERYWRkVFRYPzqK/k5GSuXbtGXFwcM2fOZO/evaxfv97ieUJDQzEYDKxZs4bQ0FBiYmK4efPmY8c+c+YMXbt2NRcucnJyMJlMdOnShenTp/PCCy8wZMgQfvGLX/DPf/7T3C89PR24v8XU66+/jqenJz/4wQ/YuHFjo55RRERERERERERERNqeNrvyoqCggIqKCrp16/bIuMzMTA4dOkRMTAzBwcEA+Pv7k5ubS0JCAi+//LI5trCwkKSkJPr27QtAXl4esbGxhIeHM3PmTAC8vLw4fPgwR44cYfLkyea++fn5JCUl0atXLwA8PT0JDAxk165dBAcHNyiP+nJ0dGTlypXmsTIyMjh48CBRUVEAbN68GSsrK4xGI88++ywAzs7OTJky5ZHjfvXVV+zfv5933nnH3Hbr1v3tmpYtW8bo0aMxGo1cu3aNlStXUlJSwqpVqyzioqKimDJlCu+88w5/+MMfWL58Od/5znfMzy4iIiIiIiIiIiIi315tduVFDSsrq0deP3PmDACBgYEW7WPHjiUjI4OSkhJzm5OTk7lwAZgLEcOHDze32djYYGdnx40bltsX9e3b1xwP0LNnT9zd3c0rERqSR309mBdA7969LfJKT0/H19fXXLiA++dZ2NraPnTMGzduMHfuXHx9fXn77bfN7ffu3QPAxcWFZcuWMWzYMN58803eeecdPv/8c/72t79ZxL322mvMmDGDoUOHEhUVxfjx49mwYUODn1FERERERERERERE2p42W7ywtbWlQ4cOXL9+/ZFxhYWFWFtb13ph7+DgQHV1NcXFxeY2Gxsbi5ia7agefPkPYDAYKC8vt2izt7evdW97e3tMJlOD86ivuvJ9cJsqk8lUZ151nXEBUFRURHh4OLa2tsTHx5sP6gbo3LkzUPuA7qFDhwJw6dIli5xq2msMGzaMv//979y5c6dezyYiIiIiIiIiIiIibVebLV60b9+egQMHcurUKaqqqh4a17lzZyorKyksLLRov3XrFlZWVrUKE42Vn59fZ5ujo2OD8jAYDFRWVlrEfLNPfTk6OtaZ1+3bt2u1lZWVMW3aNIqLi/noo49qfS/PP/88BoPhofeqKeY8uHKlLg8WV0RERERERERERETk26nNFi8Apk6dislkeuh2RKmpqQwaNAiAAwcOWFw7cOAAnp6edOrUqUlyuXTpEllZWebPWVlZZGZm4uPjA1DvPJydnbl69SrV1dXmmOPHjzcqJ29vb06fPm2xquPkyZMUFBRYxFVVVfEf//EfXLlyhY8++oiuXbvWGstgMPD973+fkydPWrSfOHECgP79+wPw4osvYmtra25/MK5bt24PXfUhIiIiIiIiIiIiIt8ebfbAboARI0YQFhZGfHw8ly9fZty4cXTp0oWcnBx27txJcXExKSkpjB49mri4OMrKynBxcWHPnj2cPXuW9evXN1ku9vb2TJ8+nTlz5gCwdu1aunbtysSJEwFwd3evVx5jxoxhx44dxMbGEhAQQFpaGgcPHmxUTpMnT2bbtm2Eh4cTHh5OUVER8fHxtbauiomJ4dixY8yfP587d+7wpz/9yXzN09PTvOJi1qxZBAcHM2/ePF5//XWysrJYuXIlEyZMoEePHsD9ratmz57N0qVL6dy5MwMHDuT3v/89n3/+ObGxsY16DhERERERERERERFpW9p08QIgOjqaAQMGkJyczMKFCyktLcXJyQk/Pz9CQ0MBWL58OatWrcJoNFJQUICrqyvr1q1j1KhRTZZH//79GT16NMuXL8dkMuHj40NMTIzFVkv1ycPf35/o6GiSkpLYvXs3/v7+xMTEMGXKlAbn5OTkhNFoZMmSJURGRtKjRw8WLVrE6tWrLeJqVnbExcXVGuPo0aM899xzALzwwgsYjUZWrFjBjBkz6Ny5Mz/+8Y+ZO3euRZ+33nqL6upqNm/ezIYNG+jevTuxsbG88cYbDX6GB/V0dnii/iIiIiIi0vbo3wkiIiIirZNV9YP7D8m/REhICJ06dSIxMbG5U2mTzp07B4CXl1czZyIiIiIiIi3R3Xv3eKpdy901uaSkhIsXL+Lh4dFkWxeLNJbmo7Qkmo/Skmg+No2GvMttub+9iTRARUUFpaWlzZ2GCKWlpWRkZGg+Soug+SgtieajtCSaj98+LblwISIiIiJ1029wIiIiIiIiIiIiIiLSorT5My/g/rkMycnJnD9/npKSEvOZF1OnTsXFxeVffv+tW7f+y+/RlIKCgvDw8DCfcZGamorRaOTy5cvcuXOHrl27EhAQwKxZs3j22Wct+v7ud79jzZo1XL16lW7duvGzn/2MSZMmma/v2rWLBQsW1HlfPz8/Nm7c2KicDQYDHTt2bFRfkabUsWNHPD09mzsNEUDzUVoWzUdpSTQf246Wvh2UiIiIiDRemy9erFixAqPRyJgxY4iNjcXOzo7s7Gx27tzJ3LlzSUlJae4UW7yCggK8vb0JCQnB1taWS5cuER8fz6VLl9i0aZM57quvvmLWrFn86Ec/YuHChZw6dYr/+3//L9/5zncIDAwE4Ac/+AHbt2+3GP/atWu88847+Pv7P1GeMZ/s5tqNW080hoiIiIiItA69nB1YPOX15k5DRERERP5F2nTxombFQEREBJGRkeb2wYMHM2nSJI4dO9aM2bUeQUFBFp99fX0xGAy8++673Lx5k65duwLwwQcf4O3tzX/9138BMHToUP72t7+xbt06c/HCzs4OOzs7i/F+//vf89RTT/Hqq68+UZ7Xbtzir3+78URjiIiIiIiIiIiIiEjza9Prazdt2oSDgwMRERF1Xh85ciQA5eXlLF26FD8/P7y8vAgKCuLw4cMWsfPnz2f8+PGcOHGCCRMm4O3tzVtvvUVOTg4FBQVERkYycOBAAgIC2L9/v0XfkJAQpk2bRkpKCgEBAeZVDFeuXLGIq08eNWM96OLFi7i5uXH69Glzm5ubG0ajkfj4eIYPH46vry8LFiygpKTEom9aWhoTJ07Ey8uL8ePHk5qaWo9vFmxtbQGorKwE7h+Yffr0aXORosarr77K119/TU5OzkPH2rdvH0OHDsXR0bFe9xYRERERERERERGRtq3NFi+qqqpIS0tj6NChWFtbPzI2KiqK7du3ExYWRkJCAn369GH27NkcPXrUIs5kMhEXF8eMGTNYsWIF2dnZREVFMXfuXPr160d8fDz9+/cnOjqa3Nxci74XLlwgMTGRefPmsWzZMvLy8ggLC6OioqLBedRXcnIy165dIy4ujpkzZ7J3717Wr19v8TyhoaEYDAbWrFlDaGgoMTEx3Lx5s87x7t69S3l5ORcuXCAhIYFRo0bx3HPPAZCdnU1lZSWurq4WfXr37g1Qq1BT49y5c1y7do3x48c36hlFREREREREREREpO1ps9tGFRQUUFFRQbdu3R4Zl5mZyaFDh4iJiSE4OBgAf39/cnNzSUhI4OWXXzbHFhYWkpSURN++fQHIy8sjNjaW8PBwZs6cCYCXlxeHDx/myJEjTJ482dw3Pz+fpKQkevXqBYCnpyeBgYHs2rWL4ODgBuVRX46OjqxcudI8VkZGBgcPHiQqKgqAzZs3Y2VlhdFoNB+87ezszJQpU+ocb+TIkebCxksvvWQeu+a7AbCxsbHoU/O55vo37du3jw4dOjB69OgGP5+IiIiIiIiIiIiItE1tduVFDSsrq0deP3PmDECt7Y7Gjh1LRkaGxTZLTk5O5sIFYC5EDB8+3NxmY2ODnZ0dN25Ynr3Qt29fczxAz549cXd3Jz09vcF51NeDecH9VRAP5pWeno6vr6+5cAEwbNgw85ZQ3/Thhx/y61//miVLlnDlyhWmT5/O3bt3G5xXjXv37vH555/zgx/8gGeeeabR44iIiIiIiIiIiIhI29JmV17Y2trSoUMHrl+//si4wsJCrK2ta72wd3BwoLq6muLiYjp16gTUXlVQsx3Vgy//AQwGA+Xl5RZt9vb2te5tb2+PyWRqcB71VVe+D25TZTKZ6NmzZ61+3zxQu4a7uzsAAwYMsDiTIzAwkM6dOwNQXFxs0aeoqAjAfP1Bp0+fxmQyMWHChAY8lYiIiIiIiIiIiIi0dW125UX79u0ZOHAgp06doqqq6qFxnTt3prKysta2Rrdu3cLKyqpWYaKx8vPz62yrOaS6vnkYDAbzIdk1HrYl0+M4OjrWmdft27cf29fNzQ1ra2uys7MB6NGjB9bW1rXOtqj5/M2zMAD27t2LjY0NI0aMaEz6IiIiIiIiIiIiItJGtdniBcDUqVMxmUxs2LChzuupqakMGjQIgAMHDlhcO3DgAJ6eng1e7fAwly5dIisry/w5KyuLzMxMfHx8AOqdh7OzM1evXqW6utocc/z48Ubl5O3tzenTpy1WS5w8eZKCgoLH9k1PT6eystJ8YLfBYMDX15eDBw9axO3fv5/evXub42pUVFRw+PBhXnnlFQwGQ6PyFxEREREREREREZG2qc1uGwUwYsQIwsLCiI+P5/Lly4wbN44uXbqQk5PDzp07KS4uJiUlhdGjRxMXF0dZWRkuLi7s2bOHs2fPsn79+ibLxd7enunTpzNnzhwA1q5dS9euXZk4cSJwf0um+uQxZswYduzYQWxsLAEBAaSlpdUqGNTX5MmT2bZtG+Hh4YSHh1NUVER8fHytratmzZrFCy+8gJubG08//TSZmZls3LgRNzc3AgICzHEzZszg7bff5pe//CVjx47l9OnT7Nu3j9WrV9e6d2pqKkVFRU26ZVQvZ4cmG0tERERERFo2/f4vIiIi0ra16eIFQHR0NAMGDCA5OZmFCxdSWlqKk5MTfn5+hIaGArB8+XJWrVqF0WikoKAAV1dX1q1bx6hRo5osj/79+zN69GiWL1+OyWTCx8eHmJgYi1UH9cnD39+f6OhokpKS2L17N/7+/sTExDBlypQG5+Tk5ITRaGTJkiVERkbSo0cPFi1aVKvY4O3tzf79+/nwww+prq6me/fuvPHGG4SGhlrk/73vfY/4+HjWrFnDjh076NatG0uWLGHs2LG17r13714cHR3x9fVtcN4Ps3jK6002loiIiIiItHx3793jqXZtekMBERERkW8tq+oH9x+Sf4mQkBA6depEYmJic6fSJp07d46Kigo8PT3p2LFjc6cj33KlpaVcvXoVFxcXzUdpdpqP0pJoPkpLovkoLU1JSQkXL17Ew8OjybYuFmkszUdpSTQfpSXRfGwa586dA8DLy+uxsfoTFRERERERERERERERaVFa9bZRR48eJTk5mfPnz1NSUmLeDmrq1Km4uLg0d3otTlBQEB4eHsTFxQH3Dw3fuHEj6enpXLp0CVdXV/bt21erX0VFBWvXruW3v/0tRUVF9OvXj3nz5jFs2DBzzJUrV0hKSuLUqVPk5uZib2/PSy+9RGRkJHZ2dua4kJAQvvzyyzrzW7VqFePGjWvUsxkMBv3VnLQIHTt2xNPTs7nTEAE0H6Vl0XyUluTbNh+1tZKIiIiItEattnixYsUKjEYjY8aMITY2Fjs7O7Kzs9m5cydz584lJSWluVM027p1a3OnUKdLly6RmpqKj48P9+7d42E7iP3qV7/it7/9Lf/xH/+Bi4sLu3btIjw8nO3bt9O/f38ATpw4wVdffcWPf/xj3N3duX79OuvWrePLL7/kt7/9rflsjMWLF3Pnzh2L8Tdv3syhQ4csiiGN8cvNu7l249YTjSEiIiIi0pb0cnbgl5N1NpyIiIiItD6tsniRmpqK0WgkIiKCyMhIc/vgwYOZNGkSx44da8bsWo9Ro0YREBAAwPz58zl//nytmJs3b/LZZ5+xYMECQkJCAHjppZf44Q9/yPvvv88HH3wAwLhx4/jJT36ClZWVuW/Pnj158803OXbsGGPGjAGgT58+te4xb948vv/971us0GiMazdu8decG080hoiIiIiIiIiIiIg0v1a5dnjTpk04ODgQERFR5/WRI0cCUF5eztKlS/Hz88PLy4ugoCAOHz5sETt//nzGjx/PiRMnmDBhAt7e3rz11lvk5ORQUFBAZGQkAwcOJCAggP3791v0DQkJYdq0aaSkpBAQEIC3tzchISFcuXLFIq4+edSM9aCLFy/i5ubG6dOnzW1ubm4YjUbi4+MZPnw4vr6+LFiwgJKSEou+aWlpTJw4ES8vL8aPH09qamqt76ldPZaOZ2ZmcvfuXb7//e+b26ysrPDz8+MPf/gDFRUVAHTp0sWicAGYl+Ln5eU9dPy0tDRycnKYMGHCY3MRERERERERERERkW+HVle8qKqqIi0tjaFDh2Jtbf3I2KioKLZv305YWBgJCQn06dOH2bNnc/ToUYs4k8lEXFwcM2bMYMWKFWRnZxMVFcXcuXPp168f8fHx9O/fn+joaHJzcy36XrhwgcTERObNm8eyZcvIy8sjLCzM/FK/IXnUV3JyMteuXSMuLo6ZM2eyd+9e1q9fb/E8oaGhGAwG1qxZQ2hoKDExMdy8ebPB96p5jpptn2oYDAYqKirIycl5aN8zZ84A0Lt374fG7Nu3j06dOvHyyy83ODcRERERERERERERaZta3bZRBQUFVFRU0K1bt0fGZWZmcujQIWJiYggODgbA39+f3NxcEhISLF6WFxYWkpSURN++fYH7KwViY2MJDw9n5syZAHh5eXH48GGOHDnC5MmTzX3z8/NJSkqiV69ewP3VBoGBgezatYvg4OAG5VFfjo6OrFy50jxWRkYGBw8eJCoqCrh/hoSVlRVGo5Fnn30WAGdnZ6ZMmdLge/Xs2ROAP//5zzz33HPm9j/96U/A/e+uLuXl5SxbtgxPT8+HnmVRVVXFF198wahRo+jUqVODcxMRERERERERERGRtqnVrbyo8c0tir6p5q/+AwMDLdrHjh1LRkaGxTZLTk5O5sIFYC5EDB8+3NxmY2ODnZ0dN25YnqnQt29fczzcf9nv7u5Oenp6g/OorwfzgvsrGx7MKz09HV9fX3PhAmDYsGHY2to2+F79+vXje9/7HitWrODs2bP84x//YOPGjfzxj38EHv7/YfHixeTk5LBs2bKHxhw/fpzbt28zfvz4BuclIiIiIiIiIiIiIm1Xqyte2Nra0qFDB65fv/7IuMLCQqytrWu9sHdwcKC6upri4mJzm42NjUVMzXZUD778h/tbJZWXl1u02dvb17q3vb09JpOpwXnUV135PrhNlclkqjOvxh6IHRcXR5cuXQgODmbo0KEkJyebzxtxdHSsFb969Wr27t3L2rVr6dev30PH3bdvH7a2tvj5+TUqLxERERERERERERFpm1pd8aJ9+/YMHDiQU6dOUVVV9dC4zp07U1lZWWtbo1u3bmFlZVWrMNFY+fn5dbbVvNSvbx4Gg4HKykqLmIdtyfQ4jo6OdeZ1+/btRo33/PPPs3PnTo4ePcrnn3/O4cOHefrpp3F0dKR79+4WsVu3biUxMZH33nuPl1566aFjlpWVceTIEQIDAx97domIiIiIiIiIiIiIfLu0uuIFwNSpUzGZTGzYsKHO66mpqQwaNAiAAwcOWFw7cOAAnp6eTXbGwqVLl8jKyjJ/zsrKIjMzEx8fH4B65+Hs7MzVq1eprq42xxw/frxROXl7e3P69GmLVR0nT56koKCgUePVeO655+jTpw+VlZXs2LGDN954w+L6vn37eO+99/j5z3/Oa6+99sixfve731FSUsKECROeKCcRERERERERERERaXta3YHdACNGjCAsLIz4+HguX77MuHHj6NKlCzk5OezcuZPi4mJSUlIYPXo0cXFxlJWV4eLiwp49ezh79izr169vslzs7e2ZPn06c+bMAWDt2rV07dqViRMnAuDu7l6vPMaMGcOOHTuIjY0lICCAtLQ0Dh482KicJk+ezLZt2wgPDyc8PJyioiLi4+NrbV1VWlpKamoqALm5udy5c8dcZBkyZIh5m6mkpCSeeeYZvvvd75Kbm8vHH39Mhw4dCA8PN4/15ZdfMn/+fIYOHcqQIUPMB3rD/cKMs7Ozxb337t1Lt27dzMUdEREREREREREREZEarbJ4ARAdHc2AAQNITk5m4cKFlJaW4uTkhJ+fH6GhoQAsX76cVatWYTQaKSgowNXVlXXr1jFq1Kgmy6N///6MHj2a5cuXYzKZ8PHxISYmBoPBYI6pTx7+/v5ER0eTlJTE7t278ff3JyYmhilTpjQ4JycnJ4xGI0uWLCEyMpIePXqwaNEiVq9ebRGXn59PZGSkRVvN5y1btuDr6wtARUUF77//Pjdu3MDW1pbRo0cTGRlpsXrl9OnTVFZWcvLkSU6ePGkx5qxZs5g9e7b5c2FhIb///e+ZPHnyYw9eb4hezg5NNpaIiIiISFug35FFREREpLWyqn5wnyJpkJCQEDp16kRiYmJzp/Ktdu7cOQC8vLyaORMRERERkZbn7r17PNWuVe4Y/K1RUlLCxYsX8fDwaLItjkUaS/NRWhLNR2lJNB+bRkPe5eo3WGkTKioqKC0tbe40RCgtLSUjI0PzUVoEzUdpSTQfpSX5ts1HFS5EREREpDXSb7EiIiIiIiIiIiIiItKitNozL+py9OhRkpOTOX/+PCUlJeYzMKZOnYqLi0uT32/r1q1NPua/W1BQEB4eHsTFxT00ZtSoUeTm5tZ5bfv27bz44osAJCcn87//+7+kp6fzj3/8g7Vr1xIYGPjQce/du8ePfvQjLly48NjYxzEYDHTs2LHR/UWaSseOHfH09GzuNEQAzUdpWTQfpSX5NsxHbRUlIiIiIq1dmylerFixAqPRyJgxY4iNjcXOzo7s7Gx27tzJ3LlzSUlJae4UW63333+fiooKi7YVK1bw9ddf88ILL5jbfvvb3wIwYsSIen3fv/71r7l582aT5bl4y26u3bzVZOOJiIiIiLRGvbo6EPP2682dhoiIiIjIE2kTxYvU1FSMRiMRERFERkaa2wcPHsykSZM4duxYM2bX+n3zr9JKSkq4cOECr732Gu3b/78p9Otf/5p27dqRk5Pz2OLF7du3Wbt2Lf/5n//JwoULmyTPazdv8decG00yloiIiIiIiIiIiIg0nzaxjnjTpk04ODgQERFR5/WRI0eafy4vL2fp0qX4+fnh5eVFUFAQhw8ftoifP38+48eP58SJE0yYMAFvb2/eeustcnJyKCgoIDIykoEDBxIQEMD+/fst+oaEhDBt2jRSUlIICAjA29ubkJAQrly5YhFXnzxqxnrQxYsXcXNz4/Tp0+Y2Nzc3jEYj8fHxDB8+HF9fXxYsWEBJSYlF37S0NCZOnIiXlxfjx48nNTX1Md9s3Y4ePUpJSQkTJkywaG/XgGXpq1atwtfXF19f30blICIiIiIiIiIiIiJtV6svXlRVVZGWlsbQoUOxtrZ+bHxUVBTbt28nLCyMhIQE+vTpw+zZszl69KhFnMlkIi4ujhkzZrBixQqys7OJiopi7ty59OvXj/j4ePr37090dHSt8yAuXLhAYmIi8+bNY9myZeTl5REWFmax9VJ986iv5ORkrl27RlxcHDNnzmTv3r2sX7/e4nlCQ0MxGAysWbOG0NBQYmJiGrVt0759++jevTsDBw5sVK5//vOf2bdvH//5n//ZqP4iIiIiIiIiIiIi0ra1+m2jCgoKqKiooFu3bo+NzczM5NChQ8TExBAcHAyAv78/ubm5JCQk8PLLL5tjCwsLSUpKom/fvgDk5eURGxtLeHg4M2fOBMDLy4vDhw9z5MgRJk+ebO6bn59PUlISvXr1Au5vuxQYGMiuXbsIDg5uUB715ejoyMqVK81jZWRkcPDgQaKiogDYvHkzVlZWGI1Gnn32WQCcnZ2ZMmVKg+7zj3/8g+PHj/PTn/60wTnC/UO6Y2JimDp1Ks899xw5OTmNGkdERERERERERERE2q5Wv/KihpWV1WNjzpw5A0BgYKBF+9ixY8nIyLDYZsnJyclcuADMhYjhw4eb22xsbLCzs+PGDctzFvr27WuOB+jZsyfu7u6kp6c3OI/6ejAvgN69e1vklZ6ejq+vr7lwATBs2DBsbW0bdJ8vvviCyspKxo8f3+AcAX7zm99w69YtfvaznzWqv4iIiIiIiIiIiIi0fa2+eGFra0uHDh24fv36Y2MLCwuxtrau9cLewcGB6upqiouLzW02NjYWMTVbUj348h/AYDBQXl5u0WZvb1/r3vb29phMpgbnUV915fvgNlUmk6nOvOzs7Bp0n3379uHm5ka/fv0anOM///lPVq1axYwZM6isrKSoqIg7d+4AUFZWZv5ZRERERERERERERL7dWn3xon379gwcOJBTp05RVVX1yNjOnTtTWVlJYWGhRfutW7ewsrKqVZhorPz8/DrbHB0dG5SHwWCgsrLSIuabferL0dGxzrxu375d7zGuX79OWlpao1dd/OMf/6CgoIDFixczePBgBg8eTFBQEADvvPMOY8aMadS4IiIiIiIiIiIiItK2tPriBcDUqVMxmUxs2LChzuupqakADBo0CIADBw5YXD9w4ACenp506tSpSfK5dOkSWVlZ5s9ZWVlkZmbi4+PToDycnZ25evUq1dXV5pjjx483Kidvb29Onz5tsarj5MmTFBQU1HuMffv2ATS6eOHo6MiWLVss/lu1ahUAs2fPJj4+vlHjioiIiIiIiIiIiEjb0uoP7AYYMWIEYWFhxMfHc/nyZcaNG0eXLl3Iyclh586dFBcXM2LECNzd3Rk9ejRxcXGUlZXh4uLCnj17OHv2LOvXr2+yfOzt7Zk+fTpz5swBYO3atXTt2pWJEycC1DuPMWPGsGPHDmJjYwkICCAtLY2DBw82KqfJkyezbds2wsPDCQ8Pp6ioiPj4+AadebFv3z4GDhz40MPRz507R25urnk1R80ZH3Z2dgwZMoQOHTrg6+tr0afmwO4+ffowcODARjzZ/9Orq8MT9RcRERERaQv0e7GIiIiItAVtongBEB0dzYABA0hOTmbhwoWUlpbi5OSEn58foaGh5rjly5ezatUqjEYjBQUFuLq6sm7dOkaNGtVkufTv35/Ro0ezfPlyTCYTPj4+xMTEYDAYGpSHv78/0dHRJCUlsXv3bvz9/YmJiWHKlCkNzsnJyQmj0ciSJUuIjIykR48eLFq0iNWrV9er/+XLl/nLX/7C4sWLHxqTnJzM7t27zZ83bdoEwJAhQ9i6dWuDc26omLdf/5ffQ0RERESkNbh77x5PtWsTC+1FRERE5FvKqvrBPYnkiYWEhNCpUycSExObO5VvjXPnzlFRUYGnpycdO3Zs7nTkW660tJSrV6/i4uKi+SjNTvNRWhLNR2lJNB+lpSkpKeHixYt4eHg02XbGIo2l+SgtieajtCSaj03j3LlzAHh5eT02Vn+KIyIiIiIiIiIiIiIiLUqr3jbq6NGjJCcnc/78eUpKSszbRE2dOhUXF5fmTq/FCQoKwsPDg7i4OAC++OIL9uzZw4ULFygqKqJnz56EhIQwadIkrKyszP1CQkL48ssva423f/9+evfubf7817/+lZUrV5Kenk5VVRVubm7Mnj2boUOHWvS7d+8eW7Zs4de//jU5OTl07tyZoUOHsnLlykY/m8Fg0F/NSYvQsWNHPD09mzsNEUDzUVoWzUdpSdr6fNSWUSIiIiLSFrTa4sWKFSswGo2MGTOG2NhY7OzsyM7OZufOncydO5eUlJRmyevfcbZDU/nkk0/o3r078+fPp0uXLpw4cYJ3332XGzduMGvWLIvYgQMH8s4771i0Pffcc+afb9++zZQpU3j++ed57733sLa2ZuvWrYSHh7Njxw7c3NzMsYsWLeLYsWNERETQt29fTCYTZ86ceeLnWbx1N9fybj3xOCIiIiIirVUvJwdiQnQWnIiIiIi0fq2yeJGamorRaCQiIoLIyEhz++DBg5k0aRLHjh1rxuxajw8++AA7Ozvz52HDhlFQUMDHH39MREQE7R74ay0bGxtefPHFh4518uRJ8vPz+eyzz8xFjSFDhjBkyBCOHDliLl6cPHmS3bt3s2vXLouCxrhx4574ea7l3eIvOTeeeBwRERERERERERERaV6tci3xpk2bcHBwICIios7rI0eOBKC8vJylS5fi5+eHl5cXQUFBHD582CJ2/vz5jB8/nhMnTjBhwgS8vb156623yMnJoaCggMjISAYOHEhAQAD79++36BsSEsK0adNISUkhICAAb29vQkJCuHLlikVcffKoGetBFy9exM3NjdOnT5vb3NzcMBqNxMfHM3z4cHx9fVmwYAElJSUWfdPS0pg4cSJeXl6MHz+e1NTUWt/Tg4WLGh4eHty5c6fWeI9TWVkJwLPPPmtu69ChA9bW1jx4Jvxnn33GkCFDLAoXIiIiIiIiIiIiIiIPanXFi6qqKtLS0hg6dCjW1taPjI2KimL79u2EhYWRkJBAnz59mD17NkePHrWIM5lMxMXFMWPGDFasWEF2djZRUVHMnTuXfv36ER8fT//+/YmOjiY3N9ei74ULF0hMTGTevHksW7aMvLw8wsLCqKioaHAe9ZWcnMy1a9eIi4tj5syZ7N27l/Xr11s8T2hoKAaDgTVr1hAaGkpMTAw3b9587Nhnzpyha9euPPPMMxbtX375JS+++CJeXl689dZb/PGPf7S4PnLkSBwcHIiLiyMvL4/bt2+zcuVKrKysCAoKMselp6fj6urKe++9x/e+9z28vb0JDQ3l6tWrjfouRERERERERERERKTtaXXbRhUUFFBRUUG3bt0eGZeZmcmhQ4eIiYkhODgYAH9/f3Jzc0lISODll182xxYWFpKUlETfvn0ByMvLIzY2lvDwcGbOnAmAl5cXhw8f5siRI0yePNncNz8/n6SkJHr16gWAp6cngYGB7Nq1i+Dg4AblUV+Ojo7mw639/f3JyMjg4MGDREVFAbB582asrKwwGo3mlRDOzs5MmTLlkeN+9dVX7N+/v9bZFoMHDyYoKIhevXqRl5fHxo0bmTp1Klu3bmXAgAEAdO7cmeTkZKZNm8ZLL70EgK2tLUajkeeff948lslkYteuXfTp04cVK1ZQWVnJ6tWrCQ0N5YsvvqBDhw4N/j5EREREREREREREpG1pdSsvalhZWT3yes0B0IGBgRbtY8eOJSMjw2JbJCcnJ3PhAjAXIoYPH25us7Gxwc7Ojhs3LM9U6Nu3rzkeoGfPnri7u5Oent7gPOrrwbwAevfubZFXeno6vr6+Fls4DRs2DFtb24eOeePGDebOnYuvry9vv/22xbU5c+bwox/9iO9973u8+uqrbN26FScnJ4vVHvn5+cyaNYsePXrw4YcfsnHjRnx9fZkxYwZff/21Oa66upq7d+/ywQcf8IMf/IBXXnmF999/n7///e/s3bu3wd+FiIiIiIiIiIiIiLQ9ra54YWtrS4cOHbh+/foj4woLC7G2tq71wt7BwYHq6mqKi4vNbTY2NhYxNdtRPfjyH8BgMFBeXm7RZm9vX+ve9vb2mEymBudRX3Xl++A2VSaTqc686jrjAqCoqIjw8HBsbW2Jj4+3OKi7Lp06dWLEiBFcuHDB3PbRRx9RWFhIQkICI0aMwM/Pj9WrV2Nra2tR5LCxsaFPnz44ODiY21xdXXF2duby5cuPfnARERERERERERER+VZodcWL9u3bM3DgQE6dOkVVVdVD4zp37kxlZSWFhYUW7bdu3cLKyqpWYaKx8vPz62xzdHRsUB4Gg8F86HWNb/apL0dHxzrzun37dq22srIypk2bRnFxMR999FGjv5fLly/j6uqKwWAwtz311FO4ubmRnZ1tbuvTp89Dx/hmYUhEREREREREREREvp1aXfECYOrUqZhMJjZs2FDn9dTUVAYNGgTAgQMHLK4dOHAAT09POnXq1CS5XLp0iaysLPPnrKwsMjMz8fHxAah3Hs7Ozly9epXq6mpzzPHjxxuVk7e3N6dPn7ZY1XHy5EkKCgos4qqqqviP//gPrly5wkcffUTXrl3rNX5JSQn/8z//g5eXl7mtW7dufP311xYFiLt375KZmUn37t3NbSNHjuTy5cvmlSkAX3/9NTdu3KB///4NfVQRERERERERERERaYNa3YHdACNGjCAsLIz4+HguX77MuHHj6NKlCzk5OezcuZPi4mJSUlIYPXo0cXFxlJWV4eLiwp49ezh79qzFNkZPyt7enunTpzNnzhwA1q5dS9euXZk4cSIA7u7u9cpjzJgx7Nixg9jYWAICAkhLS+PgwYONymny5Mls27aN8PBwwsPDKSoqIj4+vtbWVTExMRw7doz58+dz584d/vSnP5mveXp6YjAY+Oqrr/joo4945ZVX6N69O3l5eXz88ceYTCbWrl1rjn/jjTfYsWMHERER/OQnP+Gpp55i+/btZGVlsWTJEou4rVu3Mm3aNCIiIqisrGTt2rX06NGDcePGNep5RURERERERERERKRtaZXFC4Do6GgGDBhAcnIyCxcupLS0FCcnJ/z8/AgNDQVg+fLlrFq1CqPRSEFBAa6urqxbt45Ro0Y1WR79+/dn9OjRLF++HJPJhI+PDzExMRbbJ9UnD39/f6Kjo0lKSmL37t34+/sTExPDlClTGpyTk5MTRqORJUuWEBkZSY8ePVi0aBGrV6+2iKtZ2REXF1drjKNHj/Lcc8/h6OhIZWUlq1evpqCggI4dOzJgwABiYmLw9vY2x7/wwgt89NFHrF+/ngULFnDv3j369OnDhx9+yODBg81xzzzzDJs3b+a9994jOjoaKysr/Pz8WLhwIR07dmzwsz6ol5PD44NERERERNow/U4sIiIiIm2FVfWD+xRJg4SEhNCpUycSExObO5VvtXPnzgFYbGMlIiIiIvJtdffePZ5q1yp3CP7WKikp4eLFi3h4eDTZFscijaX5KC2J5qO0JJqPTaMh73L1G620CRUVFZSWljZ3GiKUlpaSkZGh+SgtguajtCSaj9KStPX5qMKFiIiIiLQF+q1WRERERERERERERERalFZ75gXcP5chOTmZ8+fPU1JSYj7zYurUqbi4uPzL779169Z/+T2aUlBQEB4eHuYzLrKysti4cSPp6elcunQJV1dX9u3bV6tfSEgIX375Za32/fv307t3b/Pnr7/+mri4OP74xz9ibW3ND37wAxYsWICdnd1jxwJYtWpVow/tNhgMT3xmhkhT6NixI56ens2dhgig+Sgti+ajtCStYT5q6ycRERER+bZrtcWLFStWYDQaGTNmDLGxsdjZ2ZGdnc3OnTuZO3cuKSkpzZ1ii3fp0iVSU1Px8fHh3r17POr4k4EDB/LOO+9YtD333HPmn+/cucPkyZPp2rUrK1asoKysjFWrVjFt2jS2b99Ou//vH16LFy/mzp07FuNs3ryZQ4cOMWzYsCd6nkXJu7l289YTjSEiIiIi0tx6dXXgv37yenOnISIiIiLSrFpl8SI1NRWj0UhERASRkZHm9sGDBzNp0iSOHTvWjNm1HqNGjSIgIACA+fPnc/78+YfG2tjY8OKLLz70+rZt2yguLiYlJQUHBwcAevbsyY9+9COOHj3KK6+8AkCfPn1q9Z03bx7f//73LVZoNMa1m7f4S+6NJxpDRERERERERERERJpfq1yHvGnTJhwcHIiIiKjz+siRIwEoLy9n6dKl+Pn54eXlRVBQEIcPH7aInT9/PuPHj+fEiRNMmDABb29v3nrrLXJycigoKCAyMpKBAwcSEBDA/v37LfqGhIQwbdo0UlJSCAgIwNvbm5CQEK5cuWIRV588asZ60MWLF3Fzc+P06dPmNjc3N4xGI/Hx8QwfPhxfX18WLFhASUmJRd+0tDQmTpyIl5cX48ePJzU1tdb31K4Jl6FnZGTg7u5uLlzA/RPjbW1t+d3vfvfQfmlpaeTk5DBhwoQmy0VEREREREREREREWrdWV7yoqqoiLS2NoUOHYm1t/cjYqKgotm/fTlhYGAkJCfTp04fZs2dz9OhRiziTyURcXBwzZsxgxYoVZGdnExUVxdy5c+nXrx/x8fH079+f6OhocnNzLfpeuHCBxMRE5s2bx7Jly8jLyyMsLIyKiooG51FfycnJXLt2jbi4OGbOnMnevXtZv369xfOEhoZiMBhYs2YNoaGhxMTEcPPmzUbdD+DLL7/kxRdfxMvLi7feeos//vGPFtfLy8sxGAy1+hkMhlrFnAft27ePTp068fLLLzc6NxERERERERERERFpW1rdtlEFBQVUVFTQrVu3R8ZlZmZy6NAhYmJiCA4OBsDf35/c3FwSEhIsXpYXFhaSlJRE3759AcjLyyM2Npbw8HBmzpwJ3F9FcPjwYY4cOcLkyZPNffPz80lKSqJXr14AeHp6EhgYyK5duwgODm5QHvXl6OjIypUrzWNlZGRw8OBBoqKigPtnSFhZWWE0Gnn22WcBcHZ2ZsqUKQ2+F9zfjisoKIhevXqRl5fHxo0bmTp1Klu3bmXAgAEA9OrVi127dlFWVsbTTz8NwPXr1zGZTHTq1KnOcauqqvjiiy8YNWrUQ2NERERERERERERE5Nun1a28qGFlZfXI62fOnAEgMDDQon3s2LFkZGRYbLPk5ORkLlwA5kLE8OHDzW02NjbY2dlx44blmQp9+/Y1x8P9cx7c3d1JT09vcB719WBeAL1797bIKz09HV9fX3PhAmDYsGHY2to2+F4Ac+bM4Uc/+hHf+973ePXVV9m6dStOTk4Wqz3eeOMN7ty5w6JFi7h58yZZWVnMnz+fdu3aPfT/1fHjx7l9+zbjx49vVF4iIiIiIiIiIiIi0ja1uuKFra0tHTp04Pr164+MKywsxNrautYLewcHB6qrqykuLja32djYWMTUbEf14Mt/uL8FUnl5uUWbvb19rXvb29tjMpkanEd91ZXvg9tUmUymOvN60gOxa3Tq1IkRI0Zw4cIFc5urqyvvvfcex44dw9/fn9GjR9O5c2f8/f1xdHSsc5x9+/Zha2uLn59fk+QlIiIiIiIiIiIiIm1Dq9s2qn379gwcOJBTp05RVVVF+/Z1P0Lnzp2prKyksLCQzp07m9tv3bqFlZVVrcJEY+Xn59fZ5u7u3qA8DAYDlZWVFuMUFhY2KidHR8c687p9+3ajxquv1157jVdffZVr167RuXNnunbtyrhx4xg1alSt2LKyMo4cOcIPf/jDx55dIiIiIiIiIiIiIiLfLq1u5QXA1KlTMZlMbNiwoc7rqampDBo0CIADBw5YXDtw4ACenp5NdsbCpUuXyMrKMn/OysoiMzMTHx8fgHrn4ezszNWrV6murjbHHD9+vFE5eXt7c/r0aYtVHSdPnqSgoKBR431TSUkJ//M//4OXl1etawaDgX79+tG1a1dOnjzJtWvXeP3112vF/e53v6OkpIQJEyY0SU4iIiIiIiIiIiIi0na0upUXACNGjCAsLIz4+HguX77MuHHj6NKlCzk5OezcuZPi4mJSUlIYPXo0cXFxlJWV4eLiwp49ezh79qzFWQ1Pyt7enunTpzNnzhwA1q5dS9euXZk4cSIA7u7u9cpjzJgx7Nixg9jYWAICAkhLS+PgwYONymny5Mls27aN8PBwwsPDKSoqIj4+vtbWVaWlpaSmpgKQm5vLnTt3zEWWIUOGYGdnx1dffcVHH33EK6+8Qvfu3cnLy+Pjjz/GZDKxdu1a81glJSXEx8czePBgOnTowJ/+9Cc+/PBDZs2ahaura60c9+7dS7du3czFnabQq6tDk40lIiIiItJc9HutiIiIiEgrLV4AREdHM2DAAJKTk1m4cCGlpaU4OTnh5+dHaGgoAMuXL2fVqlUYjUYKCgpwdXVl3bp1dW5j1Fj9+/dn9OjRLF++HJPJhI+PDzExMRgMBnNMffLw9/cnOjqapKQkdu/ejb+/PzExMUyZMqXBOTk5OWE0GlmyZAmRkZH06NGDRYsWsXr1aou4/Px8IiMjLdpqPm/ZsgVfX18cHR2prKxk9erVFBQU0LFjRwYMGEBMTAze3t7mfu3ateOvf/0ru3btoqSkBFdXVxYvXmwu4jyosLCQ3//+90yePPmxB683xH/9pPYKDxERERGR1ujuvXs81a5VLpQXEREREWkSVtUP7lMkDRISEkKnTp1ITExs7lS+1c6dO0dFRQWenp507NixudORb7nS0lKuXr2Ki4uL5qM0O81HaUk0H6Ul0XyUlqakpISLFy/i4eHRZFscizSW5qO0JJqP0pJoPjaNc+fOAdR5JME36U95RERERERERERERESkRWlTxYujR4/y05/+lCFDhvDCCy8watQoFi1axNWrV5s7tRYrKCiI+fPnPzImPj4eNze3Wv99+umnFnHJyclMmzaNoUOH4ubmVuuQ8hpfffUVISEhDB48GF9fX8LCwrh48eITPYfBYNBfzUmL0LFjR60CkhZD81FaEs1HaUla6ny8e+9ec6cgIiIiItJitNozL75pxYoVGI1GxowZQ2xsLHZ2dmRnZ7Nz507mzp1LSkpKk99z69atTT5mS/X000+zefNmi7bnn3/e4vNvf/tb4P6B6g/7vq9cuUJoaChDhw5l5cqVVFRUkJiYyJQpU9i3bx+Ojo6NzvHdbbu4lner0f1FRERERJpLLycHYv9P7fPiRERERES+rdpE8SI1NRWj0UhERITFAdSDBw9m0qRJHDt2rBmzaxvatWvHiy+++MiYX//617Rr146cnJyHFi+OHDlCdXU1a9eu5emnnwbAzc2NgIAAjh8/zmuvvdboHK/l3eIvuTca3V9EREREREREREREWoY2sW3Upk2bcHBwICIios7rI0eONP9cXl7O0qVL8fPzw8vLi6CgIA4fPmwRP3/+fMaPH8+JEyeYMGEC3t7evPXWW+Tk5FBQUEBkZCQDBw4kICCA/fv3W/QNCQlh2rRppKSkEBAQgLe3NyEhIVy5csUirj551Iz1oIsXL+Lm5sbp06fNbW5ubhiNRuLj4xk+fDi+vr4sWLCAkpISi75paWlMnDgRLy8vxo8fT2pq6mO+2YZp1+7x06myshKDwUCHDh3Mbc8++2yT5iEiIiIiIiIiIiIirVurL15UVVWRlpbG0KFDsba2fmx8VFQU27dvJywsjISEBPr06cPs2bM5evSoRZzJZCIuLo4ZM2awYsUKsrOziYqKYu7cufTr14/4+Hj69+9PdHQ0ubm5Fn0vXLhAYmIi8+bNY9myZeTl5REWFkZFRUWD86iv5ORkrl27RlxcHDNnzmTv3r2sX7/e4nlCQ0MxGAysWbOG0NBQYmJiuHnzZr3GLysrY+jQoXh6evLqq6/y2WefNSrPcePGcffuXdasWcM//vEPbt68ydKlS/nud7/Lyy+/3KgxRURERERERERERKRtafXbRhUUFFBRUUG3bt0eG5uZmcmhQ4eIiYkhODgYAH9/f3Jzc0lISLB4eV5YWEhSUhJ9+/YFIC8vj9jYWMLDw5k5cyYAXl5eHD58mCNHjjB58mRz3/z8fJKSkujVqxcAnp6eBAYGsmvXLoKDgxuUR305OjqycuVK81gZGRkcPHiQqKgoADZv3oyVlRVGo9G80sHZ2ZkpU6Y8duwePXoQFRWFp6cn5eXl7N27l3fffZfi4mJCQ0MblGevXr345JNPiIiIYMOGDQB0796djz/+WCswRERERERERERERARoAysvalhZWT025syZMwAEBgZatI8dO5aMjAyLbZacnJzMhQvAXIgYPny4uc3GxgY7Oztu3LA8Z6Fv377meICePXvi7u5Oenp6g/OorwfzAujdu7dFXunp6fj6+loUCIYNG4atre1jxw4KCiI0NJRhw4bxgx/8gJUrVzJmzBg++OADKisrG5Tn1atXmT17Nt///vf5+OOP2bBhA927dyc8PJxbt3TYtoiIiIiIiIiIiIi0geKFra0tHTp04Pr164+NLSwsxNrautYLewcHB6qrqykuLja32djYWMTUbEn1zdUBBoOB8vJyizZ7e/ta97a3t8dkMjU4j/qqK98Ht6kymUx15mVnZ9fge8H9QktxcTHZ2dkN6rd69WocHBz47//+b4YPH87IkSPZsGEDRUVFbNmypVG5iIiIiIiIiIiIiEjb0uqLF+3bt2fgwIGcOnWKqqqqR8Z27tyZyspKCgsLLdpv3bqFlZVVk21blJ+fX2ebo6Njg/IwGAy1VjZ8s099OTo61pnX7du3GzVeY12+fBl3d3eLtu985zv06NGjwYUQEREREREREREREWmbWn3xAmDq1KmYTCbzGQrflJqaCsCgQYMAOHDggMX1AwcO4OnpSadOnZokn0uXLpGVlWX+nJWVRWZmJj4+Pg3Kw9nZmatXr1JdXW2OOX78eKNy8vb25vTp0xarOk6ePElBQUGjxtu/fz82Njb06NGjQf26devGxYsXLZ7pzp07ZGVl0b1790blIiIiIiIiIiIiIiJtS6s/sBtgxIgRhIWFER8fz+XLlxk3bhxdunQhJyeHnTt3UlxczIgRI3B3d2f06NHExcVRVlaGi4sLe/bs4ezZs6xfv77J8rG3t2f69OnMmTMHgLVr19K1a1cmTpwIUO88xowZw44dO4iNjSUgIIC0tDQOHjzYqJwmT57Mtm3bCA8PJzw8nKKiIuLj4+t15sXEiRN57bXXcHV1paysjL1793Lo0CEWLlxo3k4L4Ny5c+Tm5ppXc9Sc8WFnZ8eQIUMACA4OZubMmURFRREUFERFRQWbNm2ioqKCN954o1HPJiIiIiIiIiIiIiJtS5soXgBER0czYMAAkpOTWbhwIaWlpTg5OeHn50doaKg5bvny5axatQqj0UhBQQGurq6sW7eOUaNGNVku/fv3Z/To0SxfvhyTyYSPjw8xMTEYDIYG5eHv7090dDRJSUns3r0bf39/YmJimDJlSoNzcnJywmg0smTJEiIjI+nRoweLFi1i9erVj+3bo0cPPvnkE/O2Vv369WP58uX88Ic/tIhLTk5m9+7d5s+bNm0CYMiQIWzduhWAgIAA1qxZw8aNG5k7dy7W1tZ4enqyZcsWi0POG6OXk8MT9RcRERERaS76XVZERERExJJV9YP798gTCwkJoVOnTiQmJjZ3Kt8a586dA8DLy6uZMxERERERaby79+7xVLs2sbOvNFBJSQkXL17Ew8OjybYzFmkszUdpSTQfpSXRfGwaDXmXq9+MpU2oqKigtLS0udMQobS0lIyMDM1HaRE0H6Ul0XyUlqSlzkcVLkRERERE/p9vxW/HR48e5ac//SlDhgzhhRdeYNSoUSxatIirV682d2otUlBQEPPnzzd/3rVrF25ubrX+W7FiRa2+v/nNbxgzZgxeXl788Ic/5NixYxbXKyoq+O///m9+8pOf8OKLL+Lm5mY+I0NEREREREREREREBNrQmRcPs2LFCoxGI2PGjCE2NhY7Ozuys7PZuXMnc+fOJSUlpUnvV3O2Q1v00Ucf8eyzz5o/d+3a1eL6559/zrvvvsv06dMZOnQo+/fvZ9asWSQnJ/Piiy8CUFZWxm9+8xu8vLwYNGgQf/jDH5okN4PBQMeOHZtkLJEn0bFjRzw9PZs7DRFA81FaFs1HaUmaYj5qiycRERERkX+tNl28SE1NxWg0EhERQWRkpLl98ODBTJo0qdaqAHm0/v37Y2dn99Dr69atY9y4cfzHf/wHAEOHDuWvf/0rCQkJGI1GAGxsbPjyyy+xsrJi165dTVa8AHj3011czbvVZOOJiIiIiNTFxcmB2DcnNncaIiIiIiJtWpsuXmzatAkHBwciIiLqvD5y5EgAysvLWbVqFZ9//jmFhYW4uroya9YsXnnlFXPs/PnzOX/+PAsXLmTp0qVkZWXh7e1NXFwczzzzDIsXL+b3v/89dnZ2/PznP+fVV1819605xHvs2LG8//775OXl4ePjQ0xMDK6urua4+uRR14HgFy9e5LXXXmPLli34+voC4ObmRlRUFGVlZXz66afcvXuXUaNG8e6771ocKJOWlsaSJUu4dOkSPXv2JDo6ulHf9d/+9jeuXbtWq/+rr77Kf//3f1NRUYHBYADAysqqUfd4nKt5t/jL9Rv/krFFRERERERERERE5N+nza5zrqqqIi0tjaFDh2Jtbf3I2KioKLZv305YWBgJCQn06dOH2bNnc/ToUYs4k8lEXFwcM2bMYMWKFWRnZxMVFcXcuXPp168f8fHx9O/fn+joaHJzcy36XrhwgcTERObNm8eyZcvIy8sjLCyMioqKBudRX8nJyVy7do24uDhmzpzJ3r17Wb9+vcXzhIaGYjAYWLNmDaGhocTExHDz5s06xxs/fjweHh68/PLLJCYmcvfuXfO1K1euAODi4mLRp3fv3lRWVvK3v/2tUc8gIiIiIiIiIiIiIt8+bXblRUFBARUVFXTr1u2RcZmZmRw6dIiYmBiCg4MB8Pf3Jzc3l4SEBF5++WVzbGFhIUlJSfTt2xeAvLw8YmNjCQ8PZ+bMmQB4eXlx+PBhjhw5wuTJk8198/PzSUpKolevXgB4enoSGBjIrl27CA4OblAe9eXo6MjKlSvNY2VkZHDw4EGioqIA2Lx5M1ZWVhiNRvNZFs7OzkyZMqXWOLNnz8bHxwcrKyt+97vfsWbNGm7evMmiRYvM3w3c3xbqQTWfa66LiIiIiIiIiIiIiDxOm115UeNxWxSdOXMGgMDAQIv2sWPHkpGRQUlJibnNycnJXLgAzIWI4cOHm9tsbGyws7Pjxg3L7Yv69u1rjgfo2bMn7u7upKenNziP+nowL7i/CuLBvNLT0/H19bU4hHvYsGHY2tpa9HvppZeYNWsWL730En5+fixatIgpU6bw61//mry8vAbnJSIiIiIiIiIiIiLyKG22eGFra0uHDh24fv36I+MKCwuxtrau9cLewcGB6upqiouLzW3fXFVQsx3Vgy//AQwGA+Xl5RZt9vb2te5tb2+PyWRqcB71VVe+D25TZTKZ6szrUYdy1xg7dix3797l4sWLAHTu3BmgVp5FRUUW10VEREREREREREREHqfNFi/at2/PwIEDOXXqFFVVVQ+N69y5M5WVlbW2Nbp16xZWVla1ChONlZ+fX2ebo6Njg/IwGAxUVlZaxDR2SyZHR8c687p9+3aDx6o5eLzm7IsaV65cwdramueff75ROYqIiIiIiIiIiIjIt0+bLV4ATJ06FZPJxIYNG+q8npqayqBBgwA4cOCAxbUDBw7g6elJp06dmiSXS5cukZWVZf6clZVFZmYmPj4+APXOw9nZmatXr1JdXW2OOX78eKNy8vb25vTp0xarJU6ePElBQcFj++7fv5+nnnoKT09PAJ5//nl69epVK//9+/czbNgwDAZDo3IUERERERERERERkW+fNntgN8CIESMICwsjPj6ey5cvM27cOLp06UJOTg47d+6kuLiYlJQURo8eTVxcHGVlZbi4uLBnzx7Onj3L+vXrmywXe3t7pk+fzpw5cwBYu3YtXbt2ZeLEiQC4u7vXK48xY8awY8cOYmNjCQgIIC0tjYMHDzYqp8mTJ7Nt2zbCw8MJDw+nqKiI+Pj4WltXhYaG4uvri5ubGwBHjx7ls88+4+233zavHAGYPXs2UVFR9OjRA19fX/bv38+f//xnkpKSLMZLTU2ltLSU8+fPA3Ds2DG+853v0KdPH/r06dOoZxERERERERERERGRtqNNFy8AoqOjGTBgAMnJySxcuJDS0lKcnJzw8/MjNDQUgOXLl7Nq1SqMRiMFBQW4urqybt06Ro0a1WR59O/fn9GjR7N8+XJMJhM+Pj7ExMRYrEioTx7+/v5ER0eTlJTE7t278ff3JyYmhilTpjQ4JycnJ4xGI0uWLCEyMpIePXqwaNEiVq9ebRHn4uLCzp07uXHjBvfu3aNXr14sXLiQkJAQi7jx48dTWlqK0Wjkww8/xMXFhffff58BAwZYxMXExJCbm2v+vHDhQgBmzZrF7NmzG/wc5jydHBrdV0RERESkvvR7p4iIiIjIv55V9YP7D8m/REhICJ06dSIxMbG5U2mTzp07B4CXl1czZyIiIiIi3xZ3793jqXZtehde+TcqKSnh4sWLeHh4NNnWxSKNpfkoLYnmo7Qkmo9NoyHvcvXbtrQJFRUVlJaWNncaIpSWlpKRkaH5KC2C5qO0JJqP0pI0xXxU4UJERERE5F9Lv3GLiIiIiIiIiIiIiEiL0qbOvDh69CjJycmcP3+ekpIS89kWU6dOxcXFpdny2rp1a7Pd+3GCgoLw8PAgLi7uoTHnzp1j27Zt/OlPf+Lq1auMGDHisVtgffLJJyxdupQf/OAHFrHz589n9+7ddfaZN28eP/vZzxr1HAaDgY4dOzaqr0hT6tixI56ens2dhgig+Sgti+ajtCRPMh+1XZSIiIiIyL9HmylerFixAqPRyJgxY4iNjcXOzo7s7Gx27tzJ3LlzSUlJae4UW620tDS++uorvL29KS8vf2y8yWQiISEBe3v7WtciIiIIDg62aNu/fz+bN2/G39//ifL8xfZdXM279URjiIiIiIg8jIuTA0t+PLG50xARERER+VZoE8WL1NRUjEYjERERREZGmtsHDx7MpEmTOHbsWDNm1/qFhIQwefJk88+Ps3z5ckaNGsX169drXevRowc9evSwaFu5ciV9+vTB3d39ifK8mneLv1y/8URjiIiIiIiIiIiIiEjzaxPrnTdt2oSDgwMRERF1Xh85cqT55/LycpYuXYqfnx9eXl4EBQVx+PBhi/j58+czfvx4Tpw4wYQJE/D29uatt94iJyeHgoICIiMjGThwIAEBAezfv9+ib0hICNOmTSMlJYWAgAC8vb0JCQnhypUrFnH1yaNmrAddvHgRNzc3Tp8+bW5zc3PDaDQSHx/P8OHD8fX1ZcGCBZSUlFj0TUtLY+LEiXh5eTF+/HhSU1Mf883e164By+K/+uorjhw5wrx58+oVf/PmTb766ismTJhQ73uIiIiIiIiIiIiISNvW6osXVVVVpKWlMXToUKytrR8bHxUVxfbt2wkLCyMhIYE+ffowe/Zsjh49ahFnMpmIi4tjxowZrFixguzsbKKiopg7dy79+vUjPj6e/v37Ex0dTW5urkXfCxcukJiYyLx581i2bBl5eXmEhYVRUVHR4DzqKzk5mWvXrhEXF8fMmTPZu3cv69evt3ie0NBQDAYDa9asITQ0lJiYGG7evNmo+9Xl7t27xMbGMn36dJycnOrVZ9++fdy7d49x48Y1WR4iIiIiIiIiIiIi0rq1+m2jCgoKqKiooFu3bo+NzczM5NChQ8TExJjPXfD39yc3N5eEhARefvllc2xhYSFJSUn07dsXgLy8PGJjYwkPD2fmzJkAeHl5cfjwYY4cOWLeVgkgPz+fpKQkevXqBYCnpyeBgYHs2rWL4ODgBuVRX46OjqxcudI8VkZGBgcPHiQqKgqAzZs3Y2VlhdFo5NlnnwXA2dmZKVOmNPheD7Nt2zZKS0sbNOa+ffsYMGAAzz//fJPlISIiIiIiIiIiIiKtW6tfeVHDysrqsTFnzpwBIDAw0KJ97NixZGRkWGyz5OTkZC5cAOZCxPDhw81tNjY22NnZceOG5TkLffv2NccD9OzZE3d3d9LT0xucR309mBdA7969LfJKT0/H19fXXLgAGDZsGLa2tg2+V13y8/NZt24d8+fPx2Aw1KvP119/TUZGBuPHj2+SHERERERERERERESkbWj1xQtbW1s6dOhQ5+HQ31RYWIi1tXWtF/YODg5UV1dTXFxsbrOxsbGIqdmS6sGX/wAGg4Hy8nKLNnt7+1r3tre3x2QyNTiP+qor3we3qTKZTHXmZWdn1+B71WXt2rW4ubnxve99j6KiIoqKiqiqqqKqqsr88zft3buX9u3b8+qrrzZJDiIiIiIiIiIiIiLSNrT6baPat2/PwIEDOXXqFFVVVbRv//BH6ty5M5WVlRQWFtK5c2dz+61bt7CysqpVmGis/Pz8Otvc3d0blIfBYKCystJinMLCwkbl5OjoWGdet2/fbtR433T16lX++Mc/Mnjw4FrXBg8ejNFoxN/f36L9888/Z9iwYU1WQBERERERERERERGRtqHVr7wAmDp1KiaTiQ0bNtR5PTU1FYBBgwYBcODAAYvrBw4cwNPTk06dOjVJPpcuXSIrK8v8OSsri8zMTHx8fBqUh7OzM1evXqW6utocc/z48Ubl5O3tzenTpy1WdZw8eZKCgoJGjfdNCxcuZMuWLRb/ubu78+KLL7Jlyxa8vb0t4tPT08nOztaWUSIiIiIiIiIiIiJSS6tfeQEwYsQIwsLCiI+P5/Lly4wbN44uXbqQk5PDzp07KS4uZsSIEbi7uzN69Gji4uIoKyvDxcWFPXv2cPbsWdavX99k+djb2zN9+nTmzJkD3N9SqWvXrkycOBGg3nmMGTOGHTt2EBsbS0BAAGlpaRw8eLBROU2ePJlt27YRHh5OeHg4RUVFxMfH1+vMi9u3b/Pll1+af/7nP/9pLryMGDGCjh074uHhUaufjY0NnTp1wtfXt9a1vXv38vTTT/PKK6806nlEREREREREREREpO1qE8ULgOjoaAYMGEBycjILFy6ktLQUJycn/Pz8CA0NNcctX76cVatWYTQaKSgowNXVlXXr1jFq1Kgmy6V///6MHj2a5cuXYzKZ8PHxISYmxuIg6/rk4e/vT3R0NElJSezevRt/f39iYmKYMmVKg3NycnLCaDSyZMkSIiMj6dGjB4sWLWL16tWP7Xvp0iUiIyMt2mo+Hz16lOeee65Budy9e5cDBw4wcuRIvvOd7zSo76O4ODk02VgiIiIiIt+k3zdFRERERP59rKof3JNInlhISAidOnUiMTGxuVP51jh37hwAXl5ezZyJiIiIiLR1d+/d46l2bWL3XWlBSkpKuHjxIh4eHk22nbFIY2k+Skui+SgtieZj02jIu1z91i1tQmVlJWVlZc2dhghlZWVcuXJF81FaBM1HaUk0H6UleZL5qMKFiIiIiMi/R6veNuro0aMkJydz/vx5SkpKzNtETZ06FRcXl+ZOr8UJCgrCw8ODuLg4AL744gv27NnDhQsXKCoqomfPnoSEhDBp0iSsrKzqHOPIkSPMnDmTvn37sm/fPnN7RUUFa9asIT09nQsXLlBaWsrJkyexs7Oz6H/u3Dm2bdvGn/70J65evcqIESOaZJWKtbU1Tz/99BOPI/Kknn76aVxdXZs7DRFA81FaFs1HaUkaOh+12kJERERE5N+v1RYvVqxYgdFoZMyYMcTGxmJnZ0d2djY7d+5k7ty5pKSkNEteW7dubZb7NsYnn3xC9+7dmT9/Pl26dOHEiRO8++673Lhxg1mzZtWKLysr41e/+hUODrX3+i0rK+M3v/kNXl5eDBo0iD/84Q913jMtLY2vvvoKb29vysvLm/R5fvHZLq6abjXpmCIiIiLy7ebi6MCS/9/E5k5DRERERORbp1UWL1JTUzEajURERFgcJD148GAmTZrEsWPHmjG71uODDz6wWBkxbNgwCgoK+Pjjj4mIiKDdN/66LDExkW7duvHcc89x/vx5i2s2NjZ8+eWXWFlZsWvXrocWL0JCQpg8ebL556Z01XSLzOs3mnRMEREREREREREREfn3a5Vrnzdt2oSDgwMRERF1Xh85ciQA5eXlLF26FD8/P7y8vAgKCuLw4cMWsfPnz2f8+PGcOHGCCRMm4O3tzVtvvUVOTg4FBQVERkYycOBAAgIC2L9/v0XfkJAQpk2bRkpKCgEBAXh7exMSEsKVK1cs4uqTR81YD7p48SJubm6cPn3a3Obm5obRaCQ+Pp7hw4fj6+vLggULKCkpseiblpbGxIkT8fLyYvz48aSmptb6nr65pROAh4cHd+7cqTVednY2H3/8Mb/4xS9q9anxsK2mHvTNgoiIiIiIiIiIiIiIyDe1ujfJVVVVpKWlMXToUKytrR8ZGxUVxfbt2wkLCyMhIYE+ffowe/Zsjh49ahFnMpmIi4tjxowZrFixguzsbKKiopg7dy79+vUjPj6e/v37Ex0dTW5urkXfCxcukJiYyLx581i2bBl5eXmEhYVRUVHR4DzqKzk5mWvXrhEXF8fMmTPZu3cv69evt3ie0NBQDAYDa9asITQ0lJiYGG7evPnYsc+cOUPXrl155plnLNrfe+89goKCcHd3b1TOIiIiIiIiIiIiIiL11eq2jSooKKCiooJu3bo9Mi4zM5NDhw4RExNDcHAwAP7+/uTm5pKQkMDLL79sji0sLCQpKYm+ffsCkJeXR2xsLOHh4cycORMALy8vDh8+zJEjR8zbHgHk5+eTlJREr169APD09CQwMJBdu3YRHBzcoDzqy9HRkZUrV5rHysjI4ODBg0RFRQGwefNmrKysMBqNPPvsswA4OzszZcqUR4771VdfsX//ft555x2L9t/97necPXuWAwcONDhXEREREREREREREZGGanUrL2o8bouiM2fOABAYGGjRPnbsWDIyMiy2RXJycjIXLgBzIWL48OHmNhsbG+zs7Lhxw/JMhb59+5rjAXr27Im7uzvp6ekNzqO+HswLoHfv3hZ5paen4+vray5cwP3zLGxtbR865o0bN5g7dy6+vr68/fbb5vby8nJ+9atfMXv27Dq3mRIRERERERERERERaWqtrnhha2tLhw4duH79+iPjCgsLsba2rvXC3sHBgerqaoqLi81tNjY2FjE121E9+PIfwGAwUF5ebtFmb29f69729vaYTKYG51FfdeX74DZVJpOpzrweVnwoKioiPDwcW1tb4uPjLc6l2Lx5M+3atWPcuHEUFRVRVFREZWUl9+7do6ioyOK+IiIiIiIiIiIiIiJNodVtG9W+fXsGDhzIqVOnqKqqon37uh+hc+fOVFZWUlhYSOfOnc3tt27dwsrKqlZhorHy8/PrbKs5G6K+eRgMBiorKy3GKSwsbFROjo6OdeZ1+/btWm1lZWVMmzaN4uJitm/fXut7uXLlCllZWQwbNqxW38GDB/PLX/6SN998s1F5ioiIiIiIiIiIiIjUpdWtvACYOnUqJpOJDRs21Hk9NTWVQYMGAdQ6p+HAgQN4enrSqVOnJsnl0qVLZGVlmT9nZWXx/2fv3+OqLvP9//9ByTKpEDnpmKnggYMBimOoQzgSA5qajTZ7nNmRGrA1TImCT8Znsllf3CNu8Eg42ipLBRsbUUbNPLHdzGw120WRCs7WVEwcZYlxcEAOyu8PP66fK1ABmeHg8367ze3Wut6v9/V+vpnrD1wX13UdP34cPz8/gCbn6NWrF6dPn6a+vt5Sc+DAgRZl8vX15fDhw1arOg4dOkRpaalVXV1dHa+++iqnTp3ivffeo2fPng36ioqKYv369Vb/CwwM5LHHHmP9+vUEBwe3KKOIiIiIiIiIiIiIyO10uJUXAGPGjCEyMpLU1FROnjzJhAkT6NGjB+fOnSMzM5OKigqysrIIDQ0lKSmJq1ev4ubmxrZt2/jqq69YtWpVq2VxcnJi9uzZzJs3D4AVK1bQs2dPpkyZAoCnp2eTcoSFhbF582YSExMJCQkhNzeX3bt3tyjT9OnT2bhxI1FRUURFRVFeXk5qamqDrauMRiP79+9n/vz5XLlyha+//tpyzdvbG4PBwIABAxgwYIDVfVu3buXixYsEBARYtefk5FBVVcXRo0cB2L9/Pw8//DADBw5k4MCBwI3VH59//rnlv//+979bJnbGjBlDt27dWvTOIiIiIiIiIiIiItJ5dMjJC4D4+HiGDRtGRkYGCQkJVFVV4erqSmBgIBEREQAkJyezdOlSTCYTpaWluLu7s3LlylZdLTBkyBBCQ0NJTk7GbDbj5+eH0WjEYDBYapqSIygoiPj4eNLT09m6dStBQUEYjUZmzJjR7Eyurq6YTCYWLlxITEwMffv2ZcGCBSxbtsyq7ubKjqSkpAZ9ZGdn06dPn2Y912g0UlRUZPmckJAAwCuvvMLcuXOBGytVYmJirO67+bklz7yVm4tzi+8VEREREWmMfscUEREREWkbNvW37lMkzRIeHo6dnR1r1qxp6yj3tSNHjgDg4+PTxklEREREpDO6dv06Dz7QIXfclQ6isrKSgoICvLy8Wm2LY5GW0niU9kTjUdoTjcfW0ZzvcvUbuHQKNTU1VFVVtXUMEaqqqsjPz9d4lHZB41HaE41HaU+aOx41cSEiIiIi8s+n38JFRERERERERERERKRd6bBnXsCNMxIyMjI4evQolZWVljMvZs6ciZub2z/8+Rs2bPiHP6M1TZ48GS8vL8sZF59++inbtm3j2LFjlJeX069fP8LDw5k6dSo2NjZW9/7xj3/kvffe4/z587i5uREbG8vYsWMt17/55hs++ugjvvjiC4qLi+nZsydhYWG8/PLLVsuogoODrc7FuNWmTZsYOnRoi97NYDDosG9pF7p164a3t3dbxxABNB6lfdF4lPakqeNR20WJiIiIiLSdDjt5kZKSgslkIiwsjMTERBwdHTl79iyZmZnExsaSlZXV1hHbvQ8//JDHHnuM+fPn06NHDw4ePMhbb73FhQsXeOWVVyx1n3zyCW+99RazZ89m5MiR7Ny5k1deeYWMjAzLZMOnn35KYWEhkZGR9O/fn5MnT7Jy5Ury8vJYv369pa933nmHmpoaqxwpKSl8++23PPHEE/f0Pgmbt3DabL6nPkREREREANxcXPjd81PaOoaIiIiIyH2rQ05e5OTkYDKZiI6OJiYmxtI+YsQIpk6dyv79+9swXcfx+9//HkdHR8vnUaNGUVpaygcffEB0dDQP/L+/Mlu5ciUTJkzg1VdfBWDkyJH87//+L2lpaZhMJgCioqKs+goICMDe3p64uDiOHj1qmZj44V+4VVZWcuzYMZ577jm6dLm34XjabOb43y7cUx8iIiIiIiIiIiIi0vY65BrotWvX4uzsTHR0dKPXb25nVF1dzaJFiwgMDMTHx4fJkyezd+9eq9r58+czceJEDh48yKRJk/D19eWFF17g3LlzlJaWEhMTg7+/PyEhIezcudPq3vDwcGbNmkVWVhYhISH4+voSHh7OqVOnrOqakuNmX7cqKCjAw8ODw4cPW9o8PDwwmUykpqYyevRoAgICePPNN6msrLS6Nzc3lylTpuDj48PEiRPJyclp8HO6dbLhJi8vL65cuWLp77vvvuPMmTOMHz/equ6ZZ57h0KFDllUUjfV1c6KiuLi4wbWbsrOzqaysZNKkSbetEREREREREREREZH7S4ebvKirqyM3N5eRI0dia2t7x9q4uDg2bdpEZGQkaWlpDBw4kLlz55KdnW1VZzabSUpK4uWXXyYlJYWzZ88SFxdHbGwsgwcPJjU1lSFDhhAfH9/gvIZjx46xZs0aXn/9dRYvXkxxcTGRkZFWWyM1NUdTZWRkcObMGZKSkpgzZw7bt29n1apVVu8TERGBwWBg+fLlREREYDQauXjx4l37/vLLL+nZsyePPPIIgGUi5odniAwYMIDa2lq+++67O/YF4O7uftuaHTt28Nhjj+Hv73/XbCIiIiIiIiIiIiJyf+hw20aVlpZSU1ND796971h3/Phx9uzZg9FoZNq0aQAEBQVRVFREWloaTz/9tKW2rKyM9PR0Bg0aBNxYKZCYmEhUVBRz5swBwMfHh71797Jv3z6mT59uubekpIT09HT69+8P3FhtMG7cOLZs2cK0adOalaOpXFxcWLJkiaWv/Px8du/eTVxcHADr1q3DxsYGk8nEo48+CkCvXr2YMWPGHfv94osv2LlzJ2+88YbVzwbA3t7eqvbm55vXf+jy5cukpqby9NNPW342P/T9999z4MABXnrppTu/sIiIiIiIiIiIiIjcVzrcyoubbGxs7nj95l/9jxs3zqp9/Pjx5OfnW22z5Orqapm4ACxfto8ePdrSZm9vj6OjIxcuWJ+pMGjQIKsv5/v164enpyd5eXnNztFUt+aCG6sgbs2Vl5dHQECAZeICbpxn4eDgcNs+L1y4QGxsLAEBAbz44ovNznSr2tpaXnvtNQB++9vf3rbu008/pba2lokTJ97T80RERERERERERESkc+lwkxcODg507dqV8+fP37GurKwMW1vbBl/YOzs7U19fT0VFhaXth6sKbm5HdeuX/wAGg4Hq6mqrNicnpwbPdnJywmw2NztHUzWW99Ztqsxmc6O5GjuXAqC8vJyoqCgcHBxITU21HNQN0L17d4AGOcvLy62u31RfX09CQgLffPMNJpMJV1fX277Hjh078PDwYPDgwbetEREREREREREREZH7T4ebvOjSpQv+/v589tln1NXV3baue/fu1NbWNtjW6NKlS9jY2DSYmGipkpKSRttcXFyalcNgMFBbW2tVc7stme7GxcWl0VyXL19u0Hb16lVmzZpFRUUF7733XoOfy83zKn54CPmpU6ewtbXl8ccft2pfvHgxn376KWlpaXh6et424/nz58nNzdWqCxERERERERERERFpoMNNXgDMnDkTs9nM6tWrG72ek5PD8OHDAdi1a5fVtV27duHt7Y2dnV2rZDlx4gSFhYWWz4WFhRw/fhw/Pz+AJufo1asXp0+fpr6+3lJz4MCBFmXy9fXl8OHDVqslDh06RGlpqVVdXV0dr776KqdOneK9996jZ8+eDfp6/PHH6d+/f4P8O3fuZNSoURgMBkvbu+++y4cffkhSUhKjRo26Y8YdO3YAaPJCRERERERERERERBrocAd2A4wZM4bIyEhSU1M5efIkEyZMoEePHpw7d47MzEwqKirIysoiNDSUpKQkrl69ipubG9u2beOrr75i1apVrZbFycmJ2bNnM2/ePABWrFhBz549mTJlCgCenp5NyhEWFsbmzZtJTEwkJCSE3Nxcdu/e3aJM06dPZ+PGjURFRREVFUV5eTmpqakNtq4yGo3s37+f+fPnc+XKFb7++mvLNW9vb8vExNy5c4mLi6Nv374EBASwc+dOvvnmG9LT0y3127dvZ8mSJTz77LP06dPHqq++ffs22LJqx44d+Pv73/Xg9eZw+3+rXURERERE7pV+txQRERERaVsdcvICID4+nmHDhpGRkUFCQgJVVVW4uroSGBhIREQEAMnJySxduhSTyURpaSnu7u6sXLmS4ODgVssxZMgQQkNDSU5Oxmw24+fnh9FotFqR0JQcQUFBxMfHk56eztatWwkKCsJoNDJjxoxmZ3J1dcVkMrFw4UJiYmLo27cvCxYsYNmyZVZ1N1d2JCUlNegjOzubPn36ADdWR1RVVWEymXj33Xdxc3PjnXfeYdiwYQ362rZtG9u2bbPqa9GiRZbJHICTJ0/y17/+lbfffrvZ73Ynv3t+yt2LRERERESa6Nr16zz4QIdcrC4iIiIi0uHZ1N+6T5E0S3h4OHZ2dqxZs6ato9zXjhw5Qk1NDd7e3nTr1q2t48h9rqqqitOnT+Pm5qbxKG1O41HaE41HaU80HqW9qayspKCgAC8vr1bb4likpTQepT3ReJT2ROOxdRw5cgQAHx+fu9bqz4hEREREROS+Ul9fT1VVFfo7LhERERGR9qvDbhvVHNnZ2WRkZHD06FEqKyst20vNnDkTNze3to7X7kyePBkvLy/LdlI5OTmYTCZOnjzJlStX6NmzJyEhIbzyyis8+uijlvvee+89duzYwblz56irq+Pxxx/nl7/8Jf/6r/+KjY2NpS44OJiioqIGz/3mm2/o2rVrizIbDAb91Zy0C926dcPb27utY4gAGo/Svmg8SmvTlk4iIiIiIp1bp5+8SElJwWQyERYWRmJiIo6Ojpw9e5bMzExiY2PJyspqcd8bNmxovaDtWGlpKb6+voSHh+Pg4MCJEydITU3lxIkTrF271lJXUVHBM888w6BBg+jatSuHDh1i4cKFXLlyhdmzZ1v1GRYWxksvvWTVdus5IS2RkLmFU5fM99SHiIiIiLR/7s4u/G6qzjsTEREREenMOvXkxc0VA9HR0cTExFjaR4wYwdSpU9m/f38bpus4Jk+ebPU5ICAAg8HAW2+9xcWLF+nZsycAsbGxVnWjR4/m/PnzbN26tcHkhbOzM0OHDm3VnKcumTn+twut2qeIiIiIiIiIiIiI/PN16nXWa9euxdnZmejo6Eavjx07FoDq6moWLVpEYGAgPj4+TJ48mb1791rVzp8/n4kTJ3Lw4EEmTZqEr68vL7zwAufOnaO0tJSYmBj8/f0JCQlh586dVveGh4cza9YssrKyCAkJsaxiOHXqlFVdU3Lc7OtWBQUFeHh4cPjwYUubh4cHJpOJ1NRURo8eTUBAAG+++SaVlZVW9+bm5jJlyhR8fHyYOHEiOTk5TfjJgoODAwC1tbV3rOvRo8dda0REREREREREREREbtVpJy/q6urIzc1l5MiR2Nra3rE2Li6OTZs2ERkZSVpaGgMHDmTu3LlkZ2db1ZnNZpKSknj55ZdJSUnh7NmzxMXFERsby+DBg0lNTWXIkCHEx8c3ONPh2LFjrFmzhtdff53FixdTXFxMZGQkNTU1zc7RVBkZGZw5c4akpCTmzJnD9u3bWbVqldX7REREYDAYWL58ORERERiNRi5evNhof9euXaO6uppjx46RlpZGcHAwffr0aVBXV1fHlStX+K//+i+ysrJ48cUXG9Rs376dJ554gmHDhhEVFcVf//rXFr2jiIiIiIiIiIiIiHQ+nXbbqNLSUmpqaujdu/cd644fP86ePXswGo1MmzYNgKCgIIqKikhLS+Ppp5+21JaVlZGens6gQYMAKC4uJjExkaioKObMmQOAj48Pe/fuZd++fUyfPt1yb0lJCenp6fTv3x8Ab29vxo0bx5YtW5g2bVqzcjSVi4sLS5YssfSVn5/P7t27iYuLA2DdunXY2NhgMpksB2/36tWLGTNmNNrf2LFjLRMbTz31lKXvWxUWFhIaGmr5/PLLLzfoLzg4GF9fX3r37s13333H6tWr+fWvf01WVhaPP/54s99TRERERERERERERDqXTrvy4iYbG5s7Xv/yyy8BGDdunFX7+PHjyc/Pt9pmydXV1TJxAVgmIkaPHm1ps7e3x9HRkQsXrM9eGDRokKUeoF+/fnh6epKXl9fsHE11ay6AAQMGWOXKy8sjICDAMnEBMGrUKMuWUD/07rvv8oc//IGFCxdy6tQpZs+ezbVr16xqfvSjH7F582bWr1/PvHnz+PDDD1m5cqVVzW9+8xueffZZfvzjH/Pzn//ccvD5+++/3+x3FBEREREREREREZHOp9OuvHBwcKBr166cP3/+jnVlZWXY2to2+MLe2dmZ+vp6KioqsLOzA25MTNzq5nZUt375D2AwGKiurrZqc3JyavBsJycnzGZzs3M0VWN5b92mymw2069fvwb3OTo6Ntqfp6cnAMOGDbM6k+PWCReDwYCPjw9w42DvRx55hMWLF/OrX/0KFxeXRvt1dXVl+PDhHDt2rFnvJyIiIiIiIiIiIiKdU6ddedGlSxf8/f357LPPqKuru21d9+7dqa2tpayszKr90qVL2NjYNJiYaKmSkpJG225+od/UHAaDocEB2D+8p6lcXFwazXX58uW73uvh4YGtrS1nz569Y92QIUO4du1agzNARERERERERERERERup9NOXgDMnDkTs9nM6tWrG72ek5PD8OHDAdi1a5fVtV27duHt7d3s1Q63c+LECQoLCy2fCwsLOX78OH5+fgBNztGrVy9Onz5NfX29pebAgQMtyuTr68vhw4epqKiwtB06dIjS0tK73puXl0dtbW2jB3bfKjc3FxsbmzvWXbx4kS+//NKyYkNERERERERERERE7m+ddtsogDFjxhAZGUlqaionT55kwoQJ9OjRg3PnzpGZmUlFRQVZWVmEhoaSlJTE1atXcXNzY9u2bXz11VesWrWq1bI4OTkxe/Zs5s2bB8CKFSvo2bMnU6ZMAW5sydSUHGFhYWzevJnExERCQkLIzc1l9+7dLco0ffp0Nm7cSFRUFFFRUZSXl5Oamtpg66pXXnmFJ554Ag8PDx566CGOHz/O+++/j4eHByEhIQBUVFQQFRXFs88+S79+/airq+Pw4cOsX7+eX/7ylzg7OwOwY8cO9u/fz5gxY3B1deW7777j3Xff5cEHH2TmzJkteg8RERERERERERER6Vw69eQFQHx8PMOGDSMjI4OEhASqqqpwdXUlMDCQiIgIAJKTk1m6dCkmk4nS0lLc3d1ZuXIlwcHBrZZjyJAhhIaGkpycjNlsxs/PD6PRiMFgsNQ0JUdQUBDx8fGkp6ezdetWgoKCMBqNzJgxo9mZXF1dMZlMLFy4kJiYGPr27cuCBQtYtmyZVZ2vry87d+7k3Xffpb6+nscee4xf/OIXREREWPJ37doVNzc3PvzwQy5evMhDDz1E3759MRqNPPfcc5a++vTpQ3FxMb/73e+oqKjg0UcfZeTIkcybN4/HH3+82e9wK3fnxs/UEBEREZHORb/3iYiIiIh0fjb1t+4/JP8Q4eHh2NnZsWbNmraO0ikdOXIEQNtOiYiIiNxHrl2/zoMPtGwX3MrKSgoKCvDy8mq1bWJF7oXGpLQnGo/Snmg8Snui8dg6mvNdbqc+80LuHzU1NVRVVbV1DBGqqqrIz8/XeJR2QeNR2hONR2ltLZ24EBERERGRjkG/8YuIiIiIiIiIiIiISLvS6c+8AMjOziYjI4OjR49SWVlpOfNi5syZuLm5/cOfv2HDhn/4M1rT5MmT8fLyIikpydKWnZ3N6tWrOXnyJA8//DDDhw8nLi7Ock7FuXPnePrppxvtz2AwWJYDwY3DvRctWsS+ffuora3lqaee4je/+Q2urq4tzmwwGOjWrVuL7xdpLd26dcPb27utY4gAGo/Svmg8yq3uZcsnERERERG5P3T6yYuUlBRMJhNhYWEkJibi6OjI2bNnyczMJDY2lqysrLaO2O4dPnyYV155heeee47Y2FhKS0tZsWIFL730Etu3b+ehhx7C1dWVTZs2Wd1XX19PZGQkI0eOtGp/9dVXOXnyJL/97W/p2rUry5cvJyoqiszMTLp0afmQTNiyhVOXzC2+X0RERET+8dydXfjdlCltHUNERERERNq5Tj15kZOTg8lkIjo6mpiYGEv7iBEjmDp1Kvv372/DdB3HJ598Qu/evfnd736HjY0NAI6OjkyfPp2jR4/y4x//GIPBwNChQ63uO3z4MFeuXGHixImWtq+++or//u//5v333ycwMBAANzc3nnnmGfbs2cMzzzzT4pynLpk5fuFCi+8XERERERERERERkfahU6/VXrt2Lc7OzkRHRzd6fezYsQBUV1ezaNEiAgMD8fHxYfLkyezdu9eqdv78+UycOJGDBw8yadIkfH19eeGFFzh37hylpaXExMTg7+9PSEgIO3futLo3PDycWbNmkZWVRUhICL6+voSHh3Pq1CmruqbkuNnXrQoKCvDw8ODw4cOWNg8PD0wmE6mpqYwePZqAgADefPNNKisrre7Nzc1lypQp+Pj4MHHiRHJychr8nOrq6nj44YctExcAjz76KHBjdcXt7Nixg0ceeYTg4GBL25///Gfs7e35yU9+Ymlzd3fHy8uLP//5z7ftS0RERERERERERETuH5128qKuro7c3FxGjhyJra3tHWvj4uLYtGkTkZGRpKWlMXDgQObOnUt2drZVndlsJikpiZdffpmUlBTOnj1LXFwcsbGxDB48mNTUVIYMGUJ8fDxFRUVW9x47dow1a9bw+uuvs3jxYoqLi4mMjKSmpqbZOZoqIyODM2fOkJSUxJw5c9i+fTurVq2yep+IiAgMBgPLly8nIiICo9HIxYsXrfqZMmUK3377LRkZGVRUVPDdd9+xdOlSvL298ff3b/TZtbW17Nmzh5/97Gd07drV0n7q1Cnc3NysJkLgxgTGDydzREREREREREREROT+1Gm3jSotLaWmpobevXvfse748ePs2bMHo9HItGnTAAgKCqKoqIi0tDSrQ6jLyspIT09n0KBBABQXF5OYmEhUVBRz5swBwMfHh71797Jv3z6mT59uubekpIT09HT69+8PgLe3N+PGjWPLli1MmzatWTmaysXFhSVLllj6ys/PZ/fu3cTFxQGwbt06bGxsMJlMlpUUvXr1YsaMGVb9/PjHP+add97h9ddf5//7//4/ALy8vHjvvfd48MEHG332n//8Z0pLS622jAIoLy+3POtW3bt35+jRo81+RxERERERERERERHpfDrtyoubfvgX/j/05ZdfAjBu3Dir9vHjx5Ofn2+1zZKrq6tl4gKwTESMHj3a0mZvb4+joyMXfnD2wqBBgyz1AP369cPT05O8vLxm52iqW3MBDBgwwCpXXl4eAQEBVpMJo0aNwsHBweq+3Nxc/s//+T/8y7/8C+vWrWPFihVcv36df/u3f+Pq1auNPnv79u04OzszatSoZucWERERERERERERkftbp1154eDgQNeuXTl//vwd68rKyrC1tW3whb2zszP19fVUVFRgZ2cH3JiYuNXN7ah+uJLAYDBQXV1t1ebk5NTg2U5OTpjN5mbnaKrG8t66TZXZbKZfv34N7nN0dLT6vHDhQkaOHMn8+fMtbUOHDuWnP/0pf/rTn/jlL39pVf/3v/+d/fv384tf/KLBygx7e/sGEztw4/27d+/e9JcTERERERERERERkU6r06686NKlC/7+/nz22WfU1dXdtq579+7U1tZSVlZm1X7p0iVsbGwa3eKoJUpKShptc3FxaVYOg8FAbW2tVc0P72kqFxeXRnNdvnzZ6vO3336Lp6enVVuvXr3o0aMHZ8+ebXD/3r17uXr1KpMmTWpwzd3dndOnTzc46Pv06dO4u7u35DVEREREREREREREpJPptJMXADNnzsRsNrN69epGr+fk5DB8+HAAdu3aZXVt165deHt7N3u1w+2cOHGCwsJCy+fCwkKOHz+On58fQJNz9OrVq8GX/wcOHGhRJl9fXw4fPkxFRYWl7dChQ5SWllrV9e7dm/z8fKu2oqIivv/+ex577LEG/e7YsYO+ffta3u1WQUFBlJWVcejQIUvb6dOnyc/PJygoqEXvISIiIiIiIiIiIiKdS4u3jbp48SIFBQUUFxdz9epVHnroIVxdXfHy8qJnz56tmbHFxowZQ2RkJKmpqZw8eZIJEybQo0cPzp07R2ZmJhUVFWRlZREaGkpSUhJXr17Fzc2Nbdu28dVXX7Fq1apWy+Lk5MTs2bOZN28eACtWrKBnz55MmTIFAE9PzyblCAsLY/PmzSQmJhISEkJubi67d+9uUabp06ezceNGoqKiiIqKory8nNTU1AZbV02bNo3f/e53LFy4kODgYEpLS/n973+Pk5MT48ePt6q9fPkyhw4dIioqqtFnDhs2jMDAQBISEnjjjTfo2rUry5Ytw8PDg9DQ0Ba9x03uzi73dL+IiIiI/OPpdzYREREREWmKZk9e5ObmkpyczNdffw3QYPsfGxsb/Pz8iI+Pt6wmaEvx8fEMGzaMjIwMEhISqKqqwtXVlcDAQCIiIgBITk5m6dKlmEwmSktLcXd3Z+XKlQQHB7dajiFDhhAaGkpycjJmsxk/Pz+MRiMGg8FS05QcQUFBxMfHk56eztatWwkKCsJoNDJjxoxmZ3J1dcVkMrFw4UJiYmLo27cvCxYsYNmyZVZ1L774IgaDgY8++ojMzEwefvhhhg4dyvLly+nRo4dV7aeffkpdXV2jW0bdtHz5chYtWsSCBQuoq6sjMDCQ3/zmN3Tpcm9HsPzu/00EiYiIiEj7du36dR58oFMvAhcRERERkXtkU//D2Yc7OHjwIP/2b/9G7969ef755/Hx8cHV1RWDwUBNTQ3FxcXk5eWxdetWioqKePfddxk9evQ/Mn+HEB4ejp2dHWvWrGnrKJ3SkSNHqKmpwdvbm27durV1HLnPVVVVcfr0adzc3DQepc1pPEp7ovEo7UllZSUFBQV4eXm12jaxIvdCY1LaE41HaU80HqU90XhsHUeOHAHAx8fnrrXN+lP35cuX4+Pjw7p166xWDNw0YMAARo0axUsvvcSLL77I8uXLNXkhIiIiIiIiIiIiIiLN0qzJi7/+9a/85je/aXTi4lYGg4EpU6bw7//+7/cU7m6ys7PJyMjg6NGjVFZWWraDmjlzJm5ubv/QZ3dEkydPxsvLi6SkJODGoeHvv/8+eXl5nDhxAnd3d3bs2NHgvvDwcD7//PMG7Tt37mTAgAEAnDt3jqeffrpBjZ+fHx9//LHl85EjR9i4cSNff/01p0+fZsyYMa2yIsVgMOivOKVd6NatG97e3m0dQwTQeJT2ReNRbtKWUSIiIiIi0hTNmrywt7ensLCwSbWFhYXY29u3KFRTpKSkYDKZCAsLIzExEUdHR86ePUtmZiaxsbFkZWX9w57dXBs2bGjrCI06ceIEOTk5+Pn5cf369Qbnl9zK39+fN954w6qtT58+Depee+01AgICLJ8ffvhhq+u5ubl88cUX+Pr6Ul1dfY9vYO3NrExOXbrUqn2KiIiISOtxd3Zm0XNT2zqGiIiIiIh0AM2avHj22Wf58MMPcXZ25he/+EWDL6YB/v73v/Pxxx+zbt06pk+f3mpBb5WTk4PJZCI6OpqYmBhL+4gRI5g6dSr79+//hzy3swkODiYkJASA+fPnc/To0dvW2tvbM3To0Lv22a9fvzvWhYeHW8ZFeHh4s/LezalLlzh+4W+t2qeIiIiIiIiIiIiI/PM1a/IiJiaGv/3tbyQlJZGSkkL//v1xcXGxHNhtNps5c+YMdXV1jBs3zmpioTWtXbsWZ2dnoqOjG70+duxYAKqrq1m6dCmffPIJZWVluLu788orr/Czn/3MUnvzS/uEhAQWLVpEYWEhvr6+JCUl8cgjj/D222/zl7/8BUdHR1577TWeeeYZy703D+IeP34877zzDsXFxfj5+WE0GnF3d7fUNSVHY4d6FxQU8Nxzz7F+/XrLagYPDw/i4uK4evUqH330EdeuXSM4OJi33nrL6qCY3NxcFi5cyIkTJ+jXrx/x8fENfk4PtMFy/bZ4poiIiIiIiIiIiIh0LM36JtlgMLB06VI+/vhjXnzxRVxdXbl06RKnTp3i0qVLuLq68uKLL/Lxxx+zfPnyu56N0RJ1dXXk5uYycuRIbG1t71gbFxfHpk2biIyMJC0tjYEDBzJ37lyys7Ot6sxmM0lJSbz88sukpKRw9uxZ4uLiiI2NZfDgwaSmpjJkyBDi4+MpKiqyuvfYsWOsWbOG119/ncWLF1NcXExkZCQ1NTXNztFUGRkZnDlzhqSkJObMmcP27dtZtWqV1ftERERgMBhYvnw5ERERGI1GLl682KLnAXz++ecMHToUHx8fXnjhBf7nf/6n0brf/va3eHl5MWrUKH7zm99QWlra4meKiIiIiIiIiIiIyP2pWSsvbvL19cXX17e1szRJaWkpNTU19O7d+451x48fZ8+ePRiNRqZNmwZAUFAQRUVFpKWlWR0uXVZWRnp6OoMGDQKguLiYxMREoqKimDNnDgA+Pj7s3buXffv2WW2HVVJSQnp6Ov379wfA29ubcePGsWXLFqZNm9asHE3l4uLCkiVLLH3l5+eze/du4uLiAFi3bh02NjaYTCYeffRRAHr16sWMGTOa/Sy4sR3X5MmT6d+/P8XFxbz//vvMnDmTDRs2MGzYMODGxNavfvUrAgMDsbe3Jy8vj9WrV3P06FH++Mc/3nWiSURERERERERERETkpg67h4+Njc0dr3/55ZcAjBs3zqp9/Pjx5OfnU1lZaWlzdXW1TFwAlomI0aNHW9rs7e1xdHTkwoULVv0NGjTIUg83znzw9PQkLy+v2Tma6tZcAAMGDLDKlZeXR0BAgGXiAmDUqFE4ODg0+1kA8+bN4/nnn+fHP/4xzzzzDBs2bMDV1dVqtYerqyu//e1vCQkJ4cknnyQqKoolS5ZQUFDA3r17W/RcEREREREREREREbk/dbjJCwcHB7p27cr58+fvWFdWVoatrW2DL+ydnZ2pr6+noqLC0mZvb29Vc3OVwK1f/sON1QXV1dVWbU5OTg2e7eTkhNlsbnaOpmos763bVJnN5kZzOTo6NvtZjbGzs2PMmDEcO3bsjnVjxozBzs7urnUiIiIiIiIiIiIiIrfqcJMXXbp0wd/fn88++4y6urrb1nXv3p3a2lrKysqs2i9duoSNjU2DiYmWKikpabTNxcWlWTkMBgO1tbVWNT+8p6lcXFwazXX58uUW9SciIiIiIiIiIiIi8s/U4SYvAGbOnInZbGb16tWNXs/JyWH48OEA7Nq1y+rarl278Pb2xs7OrlWynDhxgsLCQsvnwsJCjh8/jp+fH0CTc/Tq1YvTp09TX19vqTlw4ECLMvn6+nL48GGrVR2HDh1qtcOzKysr+a//+i98fHzuWLd//34qKyvvWiciIiIiIiIiIiIicqsWHdjd1saMGUNkZCSpqamcPHmSCRMm0KNHD86dO0dmZiYVFRVkZWURGhpKUlISV69exc3NjW3btvHVV19ZndVwr5ycnJg9ezbz5s0DYMWKFfTs2ZMpU6YA4Onp2aQcYWFhbN68mcTEREJCQsjNzWX37t0tyjR9+nQ2btxIVFQUUVFRlJeXk5qa2mDrqqqqKnJycgAoKiriypUrlkmWJ598EkdHR7744gvee+89fvazn/HYY49RXFzMBx98gNlsZsWKFZa+kpKSsLGxYejQodjb2/PNN9+wZs0annjiCUJCQix1ly9f5vPPP7f899///nfLM8eMGUO3bt1a9M4iIiIiIiIiIiIi0nl0yMkLgPj4eIYNG0ZGRgYJCQlUVVXh6upKYGAgERERACQnJ7N06VJMJhOlpaW4u7uzcuVKgoODWy3HkCFDCA0NJTk5GbPZjJ+fH0ajEYPBYKlpSo6goCDi4+NJT09n69atBAUFYTQamTFjRrMzubq6YjKZWLhwITExMfTt25cFCxawbNkyq7qSkhJiYmKs2m5+Xr9+PQEBAbi4uFBbW8uyZcsoLS2lW7duDBs2DKPRiK+vr+W+AQMG8NFHH/Hxxx9z9epVevbsyfPPP8+8efPo0uX/P8xOnDhx22dmZ2fTp0+fZr/vTe7Ozi2+V0RERET+8fT7moiIiIiINJVN/a37FEmzhIeHY2dnx5o1a9o6yn3tyJEjANqeSkRERKQDuHb9Og8+0La711ZWVlJQUICXl1erbScrci80JqU90XiU9kTjUdoTjcfW0ZzvcjvkmRciP1RTU0NVVVVbxxChqqqK/Px8jUdpFzQepT3ReJSb2nriQkREREREOoYOu21Ua8vOziYjI4OjR49SWVlp2YJq5syZuLm5tXW8dmfy5Ml4eXmRlJQEwJYtW3jzzTcb1EVFRREXF2f5HB4ebjnz4lY7d+5kwIAB/7jAIiIiIiIiIiIiItJhaPICSElJwWQyERYWRmJiIo6Ojpw9e5bMzExiY2PJyspq9L4NGzb8c4N2AO+99x6PPvqo5XPPnj0b1Pj7+/PGG29Ytd3LWRcABoNBh31Lu9CtWze8vb3bOoYIoPEo7YvGo0D72DJKREREREQ6hvt+8iInJweTyUR0dLTVQdIjRoxg6tSp7N+/vw3TdTxDhgzB0dHxjjX29vYMHTq01Z/95rZMTl261Or9ioiIiMi9c3d2ZtGzU9s6hoiIiIiIdBD3/eTF2rVrcXZ2Jjo6utHrY8eOBaC6upqlS5fyySefUFZWhru7O6+88go/+9nPLLXz58/n6NGjJCQksGjRIgoLC/H19SUpKYlHHnmEt99+m7/85S84Ojry2muv8cwzz1juvXn49/jx43nnnXcoLi7Gz88Po9GIu7u7pa4pORo7SLygoIDnnnuO9evXExAQAICHhwdxcXFcvXqVjz76iGvXrhEcHMxbb71ldehMbm4uCxcu5MSJE/Tr14/4+Ph7/Kn/Y5y6dInjF//W1jFERERERERERERE5B7d12u26+rqyM3NZeTIkdja2t6xNi4ujk2bNhEZGUlaWhoDBw5k7ty5ZGdnW9WZzWaSkpJ4+eWXSUlJ4ezZs8TFxREbG8vgwYNJTU1lyJAhxMfHU1RUZHXvsWPHWLNmDa+//jqLFy+muLiYyMhIampqmp2jqTIyMjhz5gxJSUnMmTOH7du3s2rVKqv3iYiIwGAwsHz5ciIiIjAajVy8eLHR/iZOnIiXlxdPP/00a9as4dq1aw1qPv/8c4YOHYqPjw8vvPAC//M//9Oi7CIiIiIiIiIiIiLSOd3XKy9KS0upqamhd+/ed6w7fvw4e/bswWg0Mm3aNACCgoIoKioiLS2Np59+2lJbVlZGeno6gwYNAqC4uJjExESioqKYM2cOAD4+Puzdu5d9+/Yxffp0y70lJSWkp6fTv39/ALy9vRk3bhxbtmxh2rRpzcrRVC4uLixZssTSV35+Prt377Ycsr1u3TpsbGwwmUyWsyx69erFjBkzGvQzd+5c/Pz8sLGx4T//8z9Zvnw5Fy9eZMGCBZa6ESNGMHnyZPr3709xcTHvv/8+M2fOZMOGDQwbNqzZ+UVERERERERERESk87mvV17cZGNjc8frX375JQDjxo2zah8/fjz5+flUVlZa2lxdXS0TF4BlImL06NGWNnt7exwdHblw4YJVf4MGDbLUA/Tr1w9PT0/y8vKanaOpbs0FMGDAAKtceXl5BAQEWB3CPWrUKBwcHKzue+qpp3jllVd46qmnCAwMZMGCBcyYMYM//OEPFBcXW+rmzZvH888/z49//GOeeeYZNmzYgKurq9VqDxERERERERERERG5v93XkxcODg507dqV8+fP37GurKwMW1vbBl/YOzs7U19fT0VFhaXN3t7equbmdlS3fvkPYDAYqK6utmpzcnJq8GwnJyfMZnOzczRVY3lv3abKbDY3mutuh3LDjUmVa9euUVBQcNsaOzs7xowZw7Fjx5qRWkREREREREREREQ6s/t68qJLly74+/vz2WefUVdXd9u67t27U1tbS1lZmVX7pUuXsLGxaTAx0VIlJSWNtrm4uDQrh8FgoLa21qrmh/c0lYuLS6O5Ll++3KL+RERERERERERERETu5r6evACYOXMmZrOZ1atXN3o9JyeH4cOHA7Br1y6ra7t27cLb2xs7O7tWyXLixAkKCwstnwsLCzl+/Dh+fn4ATc7Rq1cvTp8+TX19vaXmwIEDLcrk6+vL4cOHrVZ1HDp0iNLS0rveu3PnTh588EG8vb1vW1NZWcl//dd/4ePj06J8IiIiIiIiIiIiItL53NcHdgOMGTOGyMhIUlNTOXnyJBMmTKBHjx6cO3eOzMxMKioqyMrKIjQ0lKSkJK5evYqbmxvbtm3jq6++atWzGpycnJg9ezbz5s0DYMWKFfTs2ZMpU6YA4Onp2aQcYWFhbN68mcTEREJCQsjNzWX37t0tyjR9+nQ2btxIVFQUUVFRlJeXk5qa2mDrqoiICAICAvDw8AAgOzubjz/+mBdffNGycuSLL77gvffe42c/+xmPPfYYxcXFfPDBB5jNZlasWNGifCIiIiIiIiIiIiLS+dz3kxcA8fHxDBs2jIyMDBISEqiqqsLV1ZXAwEAiIiIASE5OZunSpZhMJkpLS3F3d2flypUEBwe3Wo4hQ4YQGhpKcnIyZrMZPz8/jEYjBoPBUtOUHEFBQcTHx5Oens7WrVsJCgrCaDQyY8aMZmdydXXFZDKxcOFCYmJi6Nu3LwsWLGDZsmVWdW5ubmRmZnLhwgWuX79O//79SUhIIDw83FLj4uJCbW0ty5Yto7S0lG7dujFs2DCMRiO+vr7N/4H9gLuz8z33ISIiIiL/GPpdTUREREREmsOm/ta9haTNhIeHY2dnx5o1a9o6Sodz5MgRAG09JSIiItLOXbt+nQcfaPudaysrKykoKMDLy6vVtoAVuRcak9KeaDxKe6LxKO2JxmPraM53uW3/LweRVlBTU0NVVVVbxxChqqqK/Px8jUdpFzQepT3ReBSgXUxciIiIiIhIx6B/PYiIiIiIiIiIiIiISLvSoc+8yM7OJiMjg6NHj1JZWWk5p2LmzJm4ubm1dbxm2bBhwz/8GZMnT8bLy4ukpCQAPv30U7Zt28axY8coLy+nX79+hIeHM3XqVGxsbKzu/eMf/8h7773H+fPncXNzIzY2lrFjxzZ4xtdff83y5cvJy8vDxsaGgQMHYjQa8fLystTs37+flStXcuLECZycnJg6dSpz5szhwQcfbPG7GQwGunXr1uL7RVpLt27d8Pb2busYIoDGo7QvGo/SXraMEhERERGRjqHDTl6kpKRgMpkICwsjMTERR0dHzp49S2ZmJrGxsWRlZbV1xHbvww8/5LHHHmP+/Pn06NGDgwcP8tZbb3HhwgVeeeUVS90nn3zCW2+9xezZsxk5ciQ7d+7klVdeISMjg6FDh1rqDh06xL/9278xdepUoqKiqKur45tvvrHaHuLrr78mOjqaCRMm8Nprr3Hy5EmWL19OVVUVb7zxxj29z5vbMzlVcume+hARERGR1ufu5MyiSVPbOoaIiIiIiHQgHXLyIicnB5PJRHR0NDExMZb2ESNGMHXqVPbv39+G6TqO3//+9zg6Olo+jxo1itLSUj744AOio6N54P/9ZdzKlSuZMGECr776KgAjR47kf//3f0lLS8NkMgFQV1fH//2//5cXX3yR+Ph4S59jxoyxemZqaipeXl6kpKQA8NRTT1FfX8/SpUuJiIjA2dm5xe9zquQSxy/+rcX3i4iIiIiIiIiIiEj70CHXba9duxZnZ2eio6MbvX5zO6Pq6moWLVpEYGAgPj4+TJ48mb1791rVzp8/n4kTJ3Lw4EEmTZqEr68vL7zwAufOnaO0tJSYmBj8/f0JCQlh586dVveGh4cza9YssrKyCAkJwdfXl/DwcE6dOmVV15QcN/u6VUFBAR4eHhw+fNjS5uHhgclkIjU1ldGjRxMQEMCbb75JZWWl1b25ublMmTIFHx8fJk6cSE5OToOf060TFzd5eXlx5coVS3/fffcdZ86cYfz48VZ1zzzzDIcOHaKmpgaAgwcPUlRUxIsvvtigzx++009+8hOrtsDAQGpra/nv//7vO94rIiIiIiIiIiIiIveHDjd5UVdXR25uLiNHjsTW1vaOtXFxcWzatInIyEjS0tIYOHAgc+fOJTs726rObDaTlJTEyy+/TEpKCmfPniUuLo7Y2FgGDx5MamoqQ4YMIT4+nqKiIqt7jx07xpo1a3j99ddZvHgxxcXFREZGWr7Ub06OpsrIyODMmTMkJSUxZ84ctm/fzqpVq6zeJyIiAoPBwPLly4mIiMBoNHLx4sW79v3ll1/Ss2dPHnnkEQDLRMwPzxAZMGAAtbW1fPfddwDk5eXh4ODAkSNHCAsLw9vbm7CwsAbbd1VXV2MwGKzabn7+9ttvm/eDEBEREREREREREZFOqcNtG1VaWkpNTQ29e/e+Y93x48fZs2cPRqORadOmARAUFERRURFpaWk8/fTTltqysjLS09MZNGgQAMXFxSQmJhIVFcWcOXMA8PHxYe/evezbt4/p06db7i0pKSE9PZ3+/fsD4O3tzbhx49iyZQvTpk1rVo6mcnFxYcmSJZa+8vPz2b17N3FxcQCsW7cOGxsbTCYTjz76KAC9evVixowZd+z3iy++YOfOnVZnT5SVlQFgb29vVXvz883rZrOZqqoqEhISmDdvHgMGDGDHjh288cYbODk58dRTTwHQr18/vvnmG6u+vv76a6u+REREREREREREROT+1uFWXtxkY2Nzx+tffvklAOPGjbNqHz9+PPn5+VbbLLm6ulomLgDLRMTo0aMtbfb29jg6OnLhwgWr/gYNGmSphxtfznt6epKXl9fsHE11ay64sQri1lx5eXkEBARYJi7gxnkWDg4Ot+3zwoULxMbGEhAQcNetnxpTX19PdXU1r7zyCi+88AKjRo3i3//93/H392f16tWWul//+tf8+c9/Zt26dZSWlvLFF1+wfPlyHnzwwWY/U0REREREREREREQ6pw43eeHg4EDXrl05f/78HevKysqwtbVt8IW9s7Mz9fX1VFRUWNp+uKrg5nZUt375Dze2N6qurrZqc3JyavBsJycnzGZzs3M0VWN5b92mymw2N5qrsTMuAMrLy4mKisLBwYHU1FTLQd0A3bt3B2iQs7y83Or6zUwjR460qhs1ahQnT560fJ4yZQrTp0/nP/7jPwgICGDGjBlMmzaN7t274+rqeucXFxEREREREREREZH7QoebvOjSpQv+/v589tln1NXV3baue/fu1NbWNtiK6NKlS9jY2DSYmGipkpKSRttcXFyalcNgMFBbW2tV09JtlFxcXBrNdfny5QZtV69eZdasWVRUVPDee+81+Lm4u7sDNDiE/NSpU9ja2vL4448DWK1c+aFbJ3weeOABEhIS+Oyzz/jTn/7EwYMH+Zd/+RcuX76Mn59f019SRERERERERERERDqtDjd5ATBz5kzMZrPVdkS3ysnJYfjw4QDs2rXL6tquXbvw9vbGzs6uVbKcOHGCwsJCy+fCwkKOHz9u+SK+qTl69erF6dOnqa+vt9QcOHCgRZl8fX05fPiw1WqJQ4cOUVpaalVXV1fHq6++yqlTp3jvvffo2bNng74ef/xx+vfv3yD/zp07GTVqlOWw7cDAQGxtbTl48KBV3cGDBxkyZEiDfh999FE8PT2xt7dnw4YN9OnTp8F2WCIiIiIiIiIiIiJyf+pwB3YDjBkzhsjISFJTUzl58iQTJkygR48enDt3jszMTCoqKsjKyiI0NJSkpCSuXr2Km5sb27Zt46uvvmLVqlWtlsXJyYnZs2czb948AFasWEHPnj2ZMmUKAJ6enk3KERYWxubNm0lMTCQkJITc3Fx2797dokzTp09n48aNREVFERUVRXl5OampqQ22rjIajezfv5/58+dz5coVy8HZcOPg8ZsTE3PnziUuLo6+ffsSEBDAzp07+eabb0hPT7fUOzs7Ex4ezooVK7CxsWHAgAF88sknfP3117z33nuWum+++YbPP/8cLy8vrl69yn/+53/ypz/9CZPJpHMvRERERERERERERATooJMXAPHx8QwbNoyMjAwSEhKoqqrC1dWVwMBAIiIiAEhOTmbp0qWYTCZKS0txd3dn5cqVBAcHt1qOIUOGEBoaSnJyMmazGT8/P4xGo+WL/6bmCAoKIj4+nvT0dLZu3UpQUBBGo5EZM2Y0O5Orqysmk4mFCxcSExND3759WbBgAcuWLbOqu7myIykpqUEf2dnZ9OnTB4CJEydSVVWFyWTi3Xffxc3NjXfeeYdhw4ZZ3fP6669jZ2fH+++/z+XLlxkwYABpaWkEBgZaamxtbdmzZw9paWkA+Pn5sWHDhgZ9tYS7k/M99yEiIiIirU+/p4mIiIiISHPZ1N+6T5E0S3h4OHZ2dqxZs6ato9zXjhw5AoCPj08bJxERERGR27l2/ToPPtA+dq2trKykoKAALy+vVttOVuReaExKe6LxKO2JxqO0JxqPraM53+W2j389iNyjmpoaqqqq2jqGCFVVVeTn52s8Srug8SjticajtJeJCxERERER6Rg61b8gsrOzeemll3jyySd54oknCA4OZsGCBZw+fbqto7VbkydPZv78+XesSU1NxcPDo8H/Pvroo9ve8+GHH+Lh4cGsWbOs2r/55htmzpzJT37yE5544gl++tOfkpCQwMWLF1vlfURERERERERERESk4+uwZ178UEpKCiaTibCwMBITE3F0dOTs2bNkZmYSGxtLVlZWqz9zw4YNrd5ne/XQQw+xbt06q7bHH3+80Vqz2UxaWhpOTk4NrpWXl+Pu7s4vfvELnJyc+O6771i1ahVHjhwhMzPT6qyQ5jAYDHTr1q1F94q0pm7duuHt7d3WMUQAjUdpXzQe7z/taZsoERERERHpeDrF5EVOTg4mk4no6GhiYmIs7SNGjGDq1Kns37+/DdN1Dg888ABDhw5tUm1ycjLBwcGcP3++wbXAwECrA7wDAgL40Y9+xEsvvcTRo0fx9/dvccY3P9nMqZJLLb5fRERERFqHu5MziyY839YxRERERESkA+sUkxdr167F2dmZ6OjoRq+PHTvW8t/V1dUsXbqUTz75hLKyMtzd3XnllVf42c9+ZqmZP38+R48eJSEhgUWLFlFYWIivry9JSUk88sgjvP322/zlL3/B0dGR1157jWeeecZy781DvMePH88777xDcXExfn5+GI1G3N3dm5WjsQPBCwoKeO6551i/fj0BAQEAeHh4EBcXx9WrV/noo4+4du0awcHBvPXWW1aHx+Tm5rJw4UJOnDhBv379iI+Pv4efeuO++OIL9u3bx65du3j99debdI+DgwMAtbW19/TsUyWXOF78t3vqQ0RERERERERERETaXodfx11XV0dubi4jR47E1tb2rvVxcXFs2rSJyMhI0tLSGDhwIHPnziU7O9uqzmw2k5SUxMsvv0xKSgpnz54lLi6O2NhYBg8eTGpqKkOGDCE+Pp6ioiKre48dO8aaNWt4/fXXWbx4McXFxURGRlJTU9PsHE2VkZHBmTNnSEpKYs6cOWzfvp1Vq1ZZvU9ERAQGg4Hly5cTERGB0Whs8lkTV69eZeTIkXh7e/PMM8/w8ccfN6i5du0aiYmJzJ49G1dX1zv2d+3aNWpqavj2229JTk5myJAhDB8+vHkvLSIiIiIiIiIiIiKdUodfeVFaWkpNTQ29e/e+a+3x48fZs2cPRqORadOmARAUFERRURFpaWk8/fTTltqysjLS09MZNGgQAMXFxSQmJhIVFcWcOXMA8PHxYe/evezbt4/p06db7i0pKSE9PZ3+/fsD4O3tzbhx49iyZQvTpk1rVo6mcnFxYcmSJZa+8vPz2b17N3FxcQCsW7cOGxsbTCYTjz76KAC9evVixowZd+27b9++xMXF4e3tTXV1Ndu3b+ett96ioqKCiIgIS93GjRupqqpqUp8vvPACubm5ADzxxBO8++67dOnS4YejiIiIiIiIiIiIiLSCDr/y4iYbG5u71nz55ZcAjBs3zqp9/Pjx5OfnU1lZaWlzdXW1TFwAlomI0aNHW9rs7e1xdHTkwoULVv0NGjTIUg/Qr18/PD09ycvLa3aOpro1F8CAAQOscuXl5REQEGCZuAAYNWqUZcumO5k8eTIRERGMGjWKn/70pyxZsoSwsDB+//vfW7Z6KikpYeXKlcyfP79Jh27/+7//Ox9//DHJycnU1NQwc+ZMrly50sS3FREREREREREREZHOrMNPXjg4ONC1a9dGD4f+obKyMmxtbRt8Ye/s7Ex9fT0VFRWWNnt7e6uam1tS3frlP4DBYKC6utqqzcnJqcGznZycMJvNzc7RVI3lvXWbKrPZ3GguR0fHZj8Lbky0VFRUcPbsWQBWrFiBh4cHP/7xjykvL6e8vJy6ujrq6uos/30rd3d3/Pz8ePbZZ/nggw84c+YMmzZtalEWEREREREREREREelcOvw+PV26dMHf35/PPvuMurq6O2491L17d2praykrK6N79+6W9kuXLmFjY9NgYqKlSkpKGm3z9PRsVg6DwdDgEOuysrIWZXJxcWk01+XLl1vU3w+dPn2a//mf/2HEiBENro0YMQKTyURQUFCj9zo7O9OrVy8KCwtbJYuIiIiIiIiIiIiIdGwdfuUFwMyZMzGbzaxevbrR6zk5OQCWA6F37dpldX3Xrl14e3tjZ2fXKnlOnDhh9UV8YWEhx48fx8/Pr1k5evXqxenTp6mvr7fUHDhwoEWZfH19OXz4sNWqjkOHDlFaWtqi/nbu3Im9vT19+/YFICEhgfXr11v9z9PTk6FDh7J+/Xp8fX1v29ff/vY3zp8/z+OPP96iLCIiIiIiIiIiIiLSuXT4lRcAY8aMITIyktTUVE6ePMmECRPo0aMH586dIzMzk4qKCsaMGYOnpyehoaEkJSVx9epV3Nzc2LZtG1999RWrVq1qtTxOTk7Mnj2befPmATe2VOrZsydTpkwBaHKOsLAwNm/eTGJiIiEhIeTm5rJ79+4WZZo+fTobN24kKiqKqKgoysvLSU1NbdKZF1OmTOG5557D3d2dq1evsn37dvbs2UNCQoJlOy0vL68G99nb22NnZ0dAQIClbcGCBfTo0QMfHx8eeeQRTp8+zQcffICTkxPPP/98i95NRERERERERERERDqXTjF5ARAfH8+wYcPIyMggISGBqqoqXF1dCQwMJCIiwlKXnJzM0qVLMZlMlJaW4u7uzsqVKwkODm61LEOGDCE0NJTk5GTMZjN+fn4YjUarg6ybkiMoKIj4+HjS09PZunUrQUFBGI1GZsyY0exMrq6umEwmFi5cSExMDH379mXBggUsW7bsrvf27duXDz/80LKt1eDBg0lOTubZZ59tdg5fX18+/vhjNm7cSE1NDT/60Y8ICgpi9uzZ9OjRo9n93crdyfme7hcRERGR1qHfy0RERERE5F7Z1N+6J5Hcs/DwcOzs7FizZk1bR7lvHDlyBAAfH582TiIiIiIiN127fp0HH2ifu9RWVlZSUFCAl5dXq20dK3IvNCalPdF4lPZE41HaE43H1tGc73Lb578mRJqppqaGqqqqto4hQlVVFfn5+RqP0i5oPEp7ovF4/2mvExciIiIiItIx6F8UIiIiIiIiIiIiIiLSrnToMy+ys7PJyMjg6NGjVFZWWs64mDlzJm5ubm2SacOGDW3y3KaYPHkyXl5eJCUlAVBYWMj7779PXl4eJ06cwN3dnR07dljdc+XKFT744ANycnI4c+YMBoMBX19fYmNj8fDwsNSdO3eOp59+usEz/fz8+Pjjjy2fg4ODKSoqajTfpk2bGDp0aIvezWAw0K1btxbdK9KaunXrhre3d1vHEAE0HqV90Xhs39rzFk8iIiIiInJ/6rCTFykpKZhMJsLCwkhMTMTR0ZGzZ8+SmZlJbGwsWVlZbR2x3Ttx4gQ5OTn4+flx/fp1Gjv+5Pz582zatImpU6fy6quvUl1dzdq1a/nlL39JZmYmAwYMsKp/7bXXCAgIsHx++OGHra6/88471NTUWLWlpKTw7bff8sQTT9zT+7z56WZOXb50T32IiIiI3G/cHZ1ZNP75to4hIiIiIiJipUNOXuTk5GAymYiOjiYmJsbSPmLECKZOncr+/fvbMF3HERwcTEhICADz58/n6NGjDWr69OnD3r17rVY1jBw5kuDgYDZu3Mhbb71lVd+vX787rp744V9cVlZWcuzYMZ577jm6dLm34Xjq8iWOF//tnvoQERERERERERERkbbXIdeGr127FmdnZ6Kjoxu9PnbsWACqq6tZtGgRgYGB+Pj4MHnyZPbu3WtVO3/+fCZOnMjBgweZNGkSvr6+vPDCC5w7d47S0lJiYmLw9/cnJCSEnTt3Wt0bHh7OrFmzyMrKIiQkBF9fX8LDwzl16pRVXVNy3OzrVgUFBXh4eHD48GFLm4eHByaTidTUVEaPHk1AQABvvvkmlZWVVvfm5uYyZcoUfHx8mDhxIjk5OQ1+Tg80YWsAOzu7BtsxPfzww/Tt25fi4uK73n832dnZVFZWMmnSpHvuS0REREREREREREQ6hw43eVFXV0dubi4jR47E1tb2jrVxcXFs2rSJyMhI0tLSGDhwIHPnziU7O9uqzmw2k5SUxMsvv0xKSgpnz54lLi6O2NhYBg8eTGpqKkOGDCE+Pr7BeQ3Hjh1jzZo1vP766yxevJji4mIiIyOttkZqao6mysjI4MyZMyQlJTFnzhy2b9/OqlWrrN4nIiICg8HA8uXLiYiIwGg0cvHixRY974fKy8stZ2T80G9/+1u8vLwYNWoUv/nNbygtLb1jXzt27OCxxx7D39+/VbKJiIiIiIiIiIiISMfX4baNKi0tpaamht69e9+x7vjx4+zZswej0ci0adMACAoKoqioiLS0NKvDpcvKykhPT2fQoEEAFBcXk5iYSFRUFHPmzAHAx8eHvXv3sm/fPqZPn265t6SkhPT0dPr37w/c2BZp3LhxbNmyhWnTpjUrR1O5uLiwZMkSS1/5+fns3r2buLg4ANatW4eNjQ0mk4lHH30UgF69ejFjxoxmP6sxycnJ2NjY8Ktf/crSZjAY+NWvfkVgYCD29vbk5eWxevVqjh49yh//+MdGJ5q+//57Dhw4wEsvvdQquURERERERERERESkc+hwKy9usrGxueP1L7/8EoBx48ZZtY8fP578/HyrbZZcXV0tExeAZSJi9OjRljZ7e3scHR25cOGCVX+DBg2y1MONMx88PT3Jy8trdo6mujUXwIABA6xy5eXlERAQYJm4ABg1ahQODg7NftYPZWZm8vHHH7NgwQJ69eplaXd1deW3v/0tISEhPPnkk0RFRbFkyRIKCgoabJF106effkptbS0TJ06851wiIiIiIiIiIiIi0nl0uMkLBwcHunbtyvnz5+9YV1ZWhq2tbYMv7J2dnamvr6eiosLSZm9vb1Vzc5XArV/+w43VBdXV1VZtTk5ODZ7t5OSE2Wxudo6maizvrdtUmc3mRnM5Ojo2+1m3ysnJYcGCBURHR/Pzn//8rvVjxozBzs6OY8eONXp9x44deHh4MHjw4HvKJSIiIiIiIiIiIiKdS4ebvOjSpQv+/v589tln1NXV3baue/fu1NbWUlZWZtV+6dIlbGxsGkxMtFRJSUmjbS4uLs3KYTAYqK2ttar54T1N5eLi0miuy5cvt6g/gK+//pqYmBiee+45YmJiWtzPTefPnyc3N1erLkRERERERERERESkgQ43eQEwc+ZMzGYzq1evbvR6Tk4Ow4cPB2DXrl1W13bt2oW3tzd2dnatkuXEiRMUFhZaPhcWFnL8+HH8/PwAmpyjV69enD59mvr6ekvNgQMHWpTJ19eXw4cPW63qOHTo0F0Pz76dkydPMmvWLEaOHInRaGzyffv376eyshIfH58G13bs2AGgyQsRERERERERERERaaDDHdgNN7YjioyMJDU1lZMnTzJhwgR69OjBuXPnyMzMpKKigqysLEJDQ0lKSuLq1au4ubmxbds2vvrqK1atWtVqWZycnJg9ezbz5s0DYMWKFfTs2ZMpU6YA4Onp2aQcYWFhbN68mcTEREJCQsjNzWX37t0tyjR9+nQ2btxIVFQUUVFRlJeXk5qa2mDrqqqqKnJycgAoKiriypUrlkmWJ598EkdHR0pKSoiIiKBr165Mnz6do0ePWu5/5JFHGDhwIABJSUnY2NgwdOhQ7O3t+eabb1izZg1PPPEEISEhDTLu2LEDf3//ux683hzujs6t1peIiIjI/UK/Q4mIiIiISHvUIScvAOLj4xk2bBgZGRkkJCRQVVWFq6srgYGBREREAJCcnMzSpUsxmUyUlpbi7u7OypUrCQ4ObrUcQ4YMITQ0lOTkZMxmM35+fhiNRgwGg6WmKTmCgoKIj48nPT2drVu3EhQUhNFoZMaMGc3O5OrqislkYuHChcTExNC3b18WLFjAsmXLrOpKSkoabAF18/P69esJCAjg5MmTlsPAf5jlySefZMOGDcCNQ8M/+ugjPv74Y65evUrPnj15/vnnmTdvHl26WA+zkydP8te//pW333672e92J4vGP9+q/YmIiIjcL65dv86DD3TIRdkiIiIiItJJ2dTfuk+RNEt4eDh2dnasWbOmraPc144cOUJNTQ3e3t5069atrePIfa6qqorTp0/j5uam8ShtTuNR2hONR2lPKisrKSgowMvLq9W2kxW5FxqT0p5oPEp7ovEo7YnGY+s4cuQIQKNHDfyQ/rxKRERERERERERERETalQ67bRRAdnY2GRkZHD16lMrKSsu2UTNnzsTNza2t47U7kydPxsvLi6SkJAA+/fRTtm3bxrFjxygvL6dfv36Eh4czdepUbGxsALhy5QoffPABOTk5nDlzBoPBgK+vL7GxsXh4eFj6/uabb/joo4/44osvKC4upmfPnoSFhfHyyy9bzUQGBwdTVFTUaL5NmzYxdOjQFr2bwWDQX3FKu9CtWze8vb3bOoYIoPEo7YvG4z+XtoESEREREZGOrsNOXqSkpGAymQgLCyMxMRFHR0fOnj1LZmYmsbGxZGVl/cMz3DzvoaP68MMPeeyxx5g/fz49evTg4MGDvPXWW1y4cIFXXnkFgPPnz7Np0yamTp3Kq6++SnV1NWvXruWXv/wlmZmZDBgwALgxEVJYWEhkZCT9+/fn5MmTrFy5kry8PNavX2955jvvvENNTY1VjpSUFL799lueeOKJe3qfhF2bOXX50j31ISIiItLRuTs687txOgtMREREREQ6tg45eZGTk4PJZCI6OtrqwOkRI0YwdepU9u/f34bpOo7f//73ODo6Wj6PGjWK0tJSPvjgA6Kjo3nggQfo06cPe/futVrVMHLkSIKDg9m4cSNvvfUWAFFRUVZ9BQQEYG9vT1xcHEePHrVMTPzwLy4rKys5duwYzz33XIODvZvr1OVLHDf/7Z76EBEREREREREREZG21yHXkq9duxZnZ2eio6MbvT527FgAqqurWbRoEYGBgfj4+DB58mT27t1rVTt//nwmTpzIwYMHmTRpEr6+vrzwwgucO3eO0tJSYmJi8Pf3JyQkhJ07d1rdGx4ezqxZs8jKyiIkJARfX1/Cw8M5deqUVV1Tctzs61YFBQV4eHhw+PBhS5uHhwcmk4nU1FRGjx5NQEAAb775JpWVlVb35ubmMmXKFHx8fJg4cSI5OTkNfk63Tjbc5OXlxZUrVyz92dnZNdiO6eGHH6Zv374UFxffsa+bExW31v1QdnY2lZWVTJo06bY1IiIiIiIiIiIiInJ/6XCTF3V1deTm5jJy5EhsbW3vWBsXF8emTZuIjIwkLS2NgQMHMnfuXLKzs63qzGYzSUlJvPzyy6SkpHD27Fni4uKIjY1l8ODBpKamMmTIEOLj4xuc13Ds2DHWrFnD66+/zuLFiykuLiYyMtJqa6Sm5miqjIwMzpw5Q1JSEnPmzGH79u2sWrXK6n0iIiIwGAwsX76ciIgIjEYjFy9evGvfX375JT179uSRRx65bU15eTknTpzA3d39rn0Bd6zbsWMHjz32GP7+/nfNJiIiIiIiIiIiIiL3hw63bVRpaSk1NTX07t37jnXHjx9nz549GI1Gpk2bBkBQUBBFRUWkpaXx9NNPW2rLyspIT09n0KBBwI2VAomJiURFRTFnzhwAfHx82Lt3L/v27WP69OmWe0tKSkhPT6d///7AjdUG48aNY8uWLUybNq1ZOZrKxcWFJUuWWPrKz89n9+7dxMXFAbBu3TpsbGwwmUw8+uijAPTq1YsZM2bcsd8vvviCnTt38sYbb9yxLjk5GRsbG371q1/dtuby5cukpqby9NNPW342P/T9999z4MABXnrppTs+T0RERERERERERETuLx1u5cVNNjY2d7x+86/+x40bZ9U+fvx48vPzrbZZcnV1tUxcAJYv20ePHm1ps7e3x9HRkQsXLlj1N2jQIKsv5/v164enpyd5eXnNztFUt+YCGDBggFWuvLw8AgICLBMXcOM8CwcHh9v2eeHCBWJjYwkICODFF1+8bV1mZiYff/wxCxYsoFevXo3W1NbW8tprrwHw29/+9rZ9ffrpp9TW1jJx4sTb1oiIiIiIiIiIiIjI/afDTV44ODjQtWtXzp8/f8e6srIybG1tG3xh7+zsTH19PRUVFZY2e3t7q5qb21Hd+uU/gMFgoLq62qrNycmpwbOdnJwwm83NztFUjeW9dZsqs9ncaK7GzqWAG9tARUVF4eDgQGpqKg880PiwyMnJYcGCBURHR/Pzn/+80Zr6+noSEhL45ptvMJlMuLq63vY9duzYgYeHB4MHD75tjYiIiIiIiIiIiIjcfzrc5EWXLl3w9/fns88+o66u7rZ13bt3p7a2lrKyMqv2S5cuYWNj02BioqVKSkoabXNxcWlWDoPBQG1trVXND+9pKhcXl0ZzXb58uUHb1atXmTVrFhUVFbz33nu3/bl8/fXXxMTE8NxzzxETE3PbZy9evJhPP/2UtLQ0PD09b1t3/vx5cnNztepCRERERERERERERBrocJMXADNnzsRsNrN69epGr+fk5DB8+HAAdu3aZXVt165deHt7Y2dn1ypZTpw4QWFhoeVzYWEhx48fx8/PD6DJOXr16sXp06epr6+31Bw4cKBFmXx9fTl8+LDVqo5Dhw5RWlpqVVdXV8err77KqVOneO+99+jZs2ej/Z08eZJZs2YxcuRIjEbjbZ/77rvv8uGHH5KUlMSoUaPumHHHjh0AmrwQERERERERERERkQY63IHdAGPGjCEyMpLU1FROnjzJhAkT6NGjB+fOnSMzM5OKigqysrIIDQ0lKSmJq1ev4ubmxrZt2/jqq69YtWpVq2VxcnJi9uzZzJs3D4AVK1bQs2dPpkyZAoCnp2eTcoSFhbF582YSExMJCQkhNzeX3bt3tyjT9OnT2bhxI1FRUURFRVFeXk5qamqDrauMRiP79+9n/vz5XLlyha+//tpyzdvbG4PBQElJCREREXTt2pXp06dz9OhRS80jjzzCwIEDAdi+fTtLlizh2WefpU+fPlZ99e3bt8GWVTt27MDf3/+uB6+LiIiIiIiIiIiIyP2nQ05eAMTHxzNs2DAyMjJISEigqqoKV1dXAgMDiYiIACA5OZmlS5diMpkoLS3F3d2dlStXEhwc3Go5hgwZQmhoKMnJyZjNZvz8/DAajRgMBktNU3IEBQURHx9Peno6W7duJSgoCKPRyIwZM5qdydXVFZPJxMKFC4mJiaFv374sWLCAZcuWWdXdXNmRlJTUoI/s7Gz69OnDyZMnLYeB/zDLk08+yYYNG6z62rZtG9u2bbOqW7RokWUyB26s5PjrX//K22+/3ex3uxN3R+dW7U9ERESkI9LvRCIiIiIi0hnY1N+6T5E0S3h4OHZ2dqxZs6ato9zXjhw5AoCPj08bJxERERFpH65dv86DD3TIHWL/KSorKykoKMDLy6vVtpMVuRcak9KeaDxKe6LxKO2JxmPraM53ufoXjXQKNTU1VFVVtXUMEaqqqsjPz9d4lHZB41HaE43Hfy5NXIiIiIiISEenf9WIiIiIiIiIiIiIiEi70mHPvGht2dnZZGRkcPToUSorKy3nZ8ycORM3N7dG77l53sP9aPLkyXh5eTU4L2Pr1q2sW7eOb7/9Fjs7O3x8fHjnnXd46KGHAJg/fz5bt25t0J/JZCIoKKjFeQwGA926dWvx/SKtpVu3bnh7e7d1DBFA41HaF43H5tG2TyIiIiIicr/T5AWQkpKCyWQiLCyMxMREHB0dOXv2LJmZmcTGxpKVldXWETuE3//+95hMJmbPns3QoUP5/vvvOXToENeuXbOqe/zxx0lJSbFqGzBgwD0/P2HPZk5/b77nfkRERETaklsPF34X+nxbxxAREREREWlT9/3kRU5ODiaTiejoaGJiYiztI0aMYOrUqezfv78N03Ucp06d4p133mHVqlWMGTPG0h4WFtag9qGHHmLo0KGtnuH092aOm//W6v2KiIiIiIiIiIiIyD/Xfb8Wfe3atTg7OxMdHd3o9bFjxwJQXV3NokWLCAwMxMfHh8mTJ7N3716r2vnz5zNx4kQOHjzIpEmT8PX15YUXXuDcuXOUlpYSExODv78/ISEh7Ny50+re8PBwZs2aRVZWFiEhIfj6+hIeHs6pU6es6pqS42ZftyooKMDDw4PDhw9b2jw8PDCZTKSmpjJ69GgCAgJ48803qaystLo3NzeXKVOm4OPjw8SJE8nJyWnwc9qyZQt9+vSxmrgQEREREREREREREWmJ+3ryoq6ujtzcXEaOHImtre0da+Pi4ti0aRORkZGkpaUxcOBA5s6dS3Z2tlWd2WwmKSmJl19+mZSUFM6ePUtcXByxsbEMHjyY1NRUhgwZQnx8PEVFRVb3Hjt2jDVr1vD666+zePFiiouLiYyMpKamptk5miojI4MzZ86QlJTEnDlz2L59O6tWrbJ6n4iICAwGA8uXLyciIgKj0cjFixet+snLy2Pw4MGsWrWKUaNG8cQTTzBt2jTy8vIaPLOwsJDhw4fzxBNPMGXKFPbt29ei7CIiIiIiIiIiIiLSOd3X20aVlpZSU1ND796971h3/Phx9uzZg9FoZNq0aQAEBQVRVFREWloaTz/9tKW2rKyM9PR0Bg0aBEBxcTGJiYlERUUxZ84cAHx8fNi7dy/79u1j+vTplntLSkpIT0+nf//+AHh7ezNu3Di2bNnCtGnTmpWjqVxcXFiyZImlr/z8fHbv3k1cXBwA69atw8bGBpPJxKOPPgpAr169mDFjhlU/ZrOZo0eP8r//+7+8/fbbdOvWjdWrV/PSSy+xZ88enJycAPDy8sLHx4eBAwdSUVHBRx99xJw5c1ixYgXjxo1rdn4RERERERERERER6Xzu65UXN9nY2Nzx+pdffgnQ4Mv18ePHk5+fb7XNkqurq2XiArBMRIwePdrSZm9vj6OjIxcuXLDqb9CgQZZ6gH79+uHp6WlZvdCcHE11ay64cXD2rbny8vIICAiwTFwAjBo1CgcHB6v76uvrqaystExCjBkzht///vfU19eTnp5uqZs+fTr/+q//SkBAACEhIZhMJvz8/Fi5cmWzs4uIiIiIiIiIiIhI53RfT144ODjQtWtXzp8/f8e6srIybG1tG3xh7+zsTH19PRUVFZY2e3t7q5qb21Hd+uU/gMFgoLq62qrt5uqEH7aZzeZm52iqxvLeuk2V2WxuNJejo2ODfhwcHPD09LS0OTg44O3tzcmTJ2/7/AceeIDQ0FC+/fZbrl692uz8IiIiIiIiIiIiItL53NeTF126dMHf35/PPvuMurq629Z1796d2tpaysrKrNovXbqEjY1Ng4mJliopKWm0zcXFpVk5DAYDtbW1VjU/vKepXFxcGs11+fJlq88DBw68bR8/nKQREREREREREREREbmT+3ryAmDmzJmYzWZWr17d6PWcnByGDx8OwK5du6yu7dq1C29vb+zs7Foly4kTJygsLLR8Liws5Pjx4/j5+QE0OUevXr04ffo09fX1lpoDBw60KJOvry+HDx+2WtVx6NAhSktLrerGjh1LaWkpBQUFlrbvv/+eY8eOMWTIkNv2f/36dXbt2sWgQYN46KGHWpRRRERERERERERERDqX+/rAboAxY8YQGRlJamoqJ0+eZMKECfTo0YNz586RmZlJRUUFWVlZhIaGkpSUxNWrV3Fzc2Pbtm189dVXrFq1qtWyODk5MXv2bObNmwfAihUr6NmzJ1OmTAHA09OzSTnCwsLYvHkziYmJhISEkJuby+7du1uUafr06WzcuJGoqCiioqIoLy8nNTW1wdZVISEh+Pj4MG/ePGJjY+natSvvvvsuBoOBX//61wAUFRUxf/58JkyYQL9+/SgrK+Ojjz7i6NGjpKamtijfrdx6uNxzHyIiIiJtTb/TiIiIiIiIaPICgPj4eIYNG0ZGRgYJCQlUVVXh6upKYGAgERERACQnJ7N06VJMJhOlpaW4u7uzcuVKgoODWy3HkCFDCA0NJTk5GbPZjJ+fH0ajEYPBYKlpSo6goCDi4+NJT09n69atBAUFYTQamTFjRrMzubq6YjKZWLhwITExMfTt25cFCxawbNkyq7oHHniAd999l0WLFrFgwQJqa2v58Y9/TEZGhmXbq4cffphHHnmE3//+95SUlGBra8sTTzyByWTiqaeeatkP7Ra/C33+nvsQERERaQ+uXb/Ogw/c94ukRURERETkPmZTf+veQtJmwsPDsbOzY82aNW0dpcM5cuQINTU1eHt7061bt7aOI/e5qqoqTp8+jZubm8ajtDmNR2lPNB6lPamsrKSgoAAvL69W2wJW5F5oTEp7ovEo7YnGo7QnGo+t48iRIwD4+PjctVZ/ziUiIiIiIiIiIiIiIu1Ku528yM7O5qWXXuLJJ5/kiSeeIDg4mAULFnD69Om2jtYuTZ48mfnz51s+FxYWsmDBAiZPnoy3tzcTJ0687b1//OMfCQsLw8fHh2effZb9+/dbXa+pqeE//uM/+Nd//VeGDh2Kh4cHly9fvm1fkyZNYujQoYwZM4bf/OY3lJSUWNVkZGQwa9YsRo4ciYeHR4MDyFvCYDDorzilXejWrZtWAUm7ofEo7YnGY9Ncu369rSOIiIiIiIi0C+3yzIuUlBRMJhNhYWEkJibi6OjI2bNnyczMJDY2lqysrLaO2Oo2bNjQqv2dOHGCnJwc/Pz8uH79OrfbHeyTTz7hrbfeYvbs2YwcOZKdO3fyyiuvkJGRwdChQwG4evUqf/zjH/Hx8WH48OH893//d6N9ZWVl8Zvf/IaIiAieeuopzp8/z7Jlyzh58iR/+MMfLHV/+tOfgBuHpbfm/5e/2buZ09+bW60/ERERkX8mtx4uLPyZzvASERERERGBdjh5kZOTg8lkIjo6mpiYGEv7iBEjmDp1aoNVAdK44OBgQkJCAJg/fz5Hjx5ttG7lypVMmDCBV199FYCRI0fyv//7v6SlpWEymQCwt7fn888/x8bGhi1bttx28mL79u08+eST/J//83+s2hMSEvjb3/7Gj370IwD+8Ic/8MADD3Du3LlWnbw4/b2Z45f+1mr9iYiIiIiIiIiIiEjbaHfbRq1duxZnZ2eio6MbvT527FgAqqurWbRoEYGBgfj4+DB58mT27t1rVTt//nwmTpzIwYMHmTRpEr6+vrzwwgucO3eO0tJSYmJi8Pf3JyQkhJ07d1rdGx4ezqxZs8jKyiIkJARfX1/Cw8M5deqUVV1Tctzs61YFBQV4eHhw+PBhS5uHhwcmk4nU1FRGjx5NQEAAb775JpWVlVb35ubmMmXKFHx8fJg4cSI5OTkNfk4PPHD3/2u/++47zpw5w/jx463an3nmGQ4dOkRNTY2lzcbG5q791dXV8cgjj1i1PfroowBWKz+akk1ERERERERERERE7l/t6lvkuro6cnNzGTlyJLa2tnesjYuLY9OmTURGRpKWlsbAgQOZO3cu2dnZVnVms5mkpCRefvllUlJSOHv2LHFxccTGxjJ48GBSU1MZMmQI8fHxFBUVWd177Ngx1qxZw+uvv87ixYspLi4mMjLS6kv9puZoqoyMDM6cOUNSUhJz5sxh+/btrFq1yup9IiIiMBgMLF++nIiICIxGIxcvXmz2s25OxLi5uVm1DxgwgNraWr777rtm9ff888/zl7/8hV27dnHlyhVOnDjB6tWrGTt2LL179252PhERERERERERERG5P7WrbaNKS0upqam56xfdx48fZ8+ePRiNRqZNmwZAUFAQRUVFpKWl8fTTT1tqy8rKSE9PZ9CgQQAUFxeTmJhIVFQUc+bMAcDHx4e9e/eyb98+pk+fbrm3pKSE9PR0+vfvD4C3tzfjxo1jy5YtTJs2rVk5msrFxYUlS5ZY+srPz2f37t3ExcUBsG7dOmxsbDCZTJZVDb169WLGjBnNflZZWRlwY1uoW938fPN6U02aNImqqiri4uKora0FYPTo0SxbtqzZ2URERERERERERETk/tWuVl7cdLctir788ksAxo0bZ9U+fvx48vPzrbZZcnV1tUxcAJaJiNGjR1va7O3tcXR05MKFC1b9DRo0yFIP0K9fPzw9PcnLy2t2jqa6NRfcWAVxa668vDwCAgIsExcAo0aNwsHBodnPam179uyxrHLZsGEDixcvprCwkFdfffW2B4aLiIiIiIiIiIiIiPxQu1p54eDgQNeuXTl//vwd68rKyrC1tW3whb2zszP19fVUVFRgZ2cHNFxVcHM7qlu//AcwGAxUV1dbtTk5OTV4tpOTE2azudk5mqqxvLduU2U2m+nXr1+D+xwdHZv1HIDu3bsDUFFRgYuLi6W9vLzc6npT1NfX8/bbb/Mv//IvlhUtAI8//ji//vWvOXDgAIGBgc3OKCIiIiIiIiIiIiL3n3a18qJLly74+/vz2WefUVdXd9u67t27U1tb22Bbo0uXLmFjY9NgYqKlSkpKGm27+UV/U3MYDAbLNko3NXdLpptcXFwazXX58uVm9+Xu7g7Q4BDyU6dOYWtry+OPP97kvi5fvszly5fx9PS0avf29gbg7Nmzzc4nIiIiIiIiIiIiIvendjV5ATBz5kzMZjOrV69u9HpOTg7Dhw8HYNeuXVbXdu3ahbe3d7NXO9zOiRMnKCwstHwuLCzk+PHj+Pn5ATQ5R69evTh9+rTV1kkHDhxoUSZfX18OHz5MRUWFpe3QoUOUlpY2u6/HH3+c/v37N8i/c+dORo0ahcFgaHJfjo6OdOvWjfz8fKv2Y8eOAfDYY481O5+IiIiIiIiIiIiI3J/a1bZRAGPGjCEyMpLU1FROnjzJhAkT6NGjB+fOnSMzM5OKigqysrIIDQ0lKSmJq1ev4ubmxrZt2/jqq69YtWpVq2VxcnJi9uzZzJs3D4AVK1bQs2dPpkyZAoCnp2eTcoSFhbF582YSExMJCQkhNzeX3bt3tyjT9OnT2bhxI1FRUURFRVFeXk5qamqDrauqqqrIyckBoKioiCtXrlgmKZ588knLNlNz584lLi6Ovn37EhAQwM6dO/nmm29IT0+36i8nJ4eqqiqOHj0KwP79+3n44YcZOHAgAwcOxMbGhn/5l39h48aNPPLII4wYMYLz58/zzjvvMGjQIEaNGmXp68iRIxQVFVlWi9w8Q8TR0ZEnn3yyRT8XEREREREREREREek82t3kBUB8fDzDhg0jIyODhIQEqqqqcHV1JTAwkIiICACSk5NZunQpJpOJ0tJS3N3dWblyJcHBwa2WY8iQIYSGhpKcnIzZbMbPzw+j0Wi1IqEpOYKCgoiPjyc9PZ2tW7cSFBSE0WhkxowZzc7k6uqKyWRi4cKFxMTE0LdvXxYsWMCyZcus6kpKSoiJibFqu/l5/fr1BAQEADBx4kSqqqowmUy8++67uLm58c477zBs2DCre41GI0VFRZbPCQkJALzyyivMnTsXgLi4OBwdHfnTn/7E+++/T48ePQgICCA2NtbqZ5aRkcHWrVstn9euXQvcmFTZsGFDs38mN7n1cLl7kYiIiEg7pd9lRERERERE/v9s6m/dy0gswsPDsbOzY82aNW0dRe7iyJEjAPj4+LRxEhEREZF7c+36dR58oN3t7NrpVFZWUlBQgJeXV6ttOStyLzQmpT3ReJT2RONR2hONx9bRnO9y9S8j6RRqamqoqqpq6xgiVFVVkZ+fr/Eo7YLGo7QnGo9No4kLERERERGRG/SvIxERERERERERERERaVfa5ZkXrS07O5uMjAyOHj1KZWWl5fyMmTNn4ubm1ug993L2Qkc3efJkvLy8SEpKsrTt37+flStXcuLECZycnJg6dSpz5szhwQcfbLSPo0eP8otf/IKHHnqIr776ytK+ZcsW3nzzzUbvCQwM5P33329RZoPBQLdu3Vp0r0hr6tatG97e3m0dQwTQeJT2RePx9rRVlIiIiIiISEOdfvIiJSUFk8lEWFgYiYmJODo6cvbsWTIzM4mNjSUrK6utI7Z7X3/9NdHR0UyYMIHXXnuNkydPsnz5cqqqqnjjjTca1NfX11t+1pWVlVbXfvrTn7Jp0yartjNnzvDGG28QFBR0Tznfyv4jp0vN99SHiIiIyD+Tm4MLiU//oq1jiIiIiIiItDudevIiJycHk8lEdHQ0MTExlvYRI0YwdepU9u/f34bpOo7U1FS8vLxISUkB4KmnnqK+vp6lS5cSERGBs7OzVX1mZibff/89U6dObbCCxdHREUdHR6u2v/zlLzz44IM888wz95TzdKmZv1762z31ISIiIiIiIiIiIiJtr1OvT1+7di3Ozs5ER0c3en3s2LEAVFdXs2jRIgIDA/Hx8WHy5Mns3bvXqnb+/PlMnDiRgwcPMmnSJHx9fXnhhRc4d+4cpaWlxMTE4O/vT0hICDt37rS6Nzw8nFmzZpGVlUVISAi+vr6Eh4dz6tQpq7qm5LjZ160KCgrw8PDg8OHDljYPDw9MJhOpqamMHj2agIAA3nzzzQYrIXJzc5kyZQo+Pj5MnDiRnJycBj+ngoICfvKTn1i1BQYGUltby3//939btZeXl7NkyRLefPNNbG1tG/TVmB07djBy5EhcXFyaVC8iIiIiIiIiIiIinVunnbyoq6sjNzeXkSNH3vVL9Li4ODZt2kRkZCRpaWkMHDiQuXPnkp2dbVVnNptJSkri5ZdfJiUlhbNnzxIXF0dsbCyDBw8mNTWVIUOGEB8fT1FRkdW9x44dY82aNbz++ussXryY4uJiIiMjqampaXaOpsrIyODMmTMkJSUxZ84ctm/fzqpVq6zeJyIiAoPBwPLly4mIiMBoNHLx4kWrfqqrqzEYDFZtNz9/++23Vu3Lly9nyJAhlomhuzly5Ahnzpxh4sSJLXlFEREREREREREREemEOu22UaWlpdTU1NC7d+871h0/fpw9e/ZgNBqZNm0aAEFBQRQVFZGWlsbTTz9tqS0rKyM9PZ1BgwYBUFxcTGJiIlFRUcyZMwcAHx8f9u7dy759+5g+fbrl3pKSEtLT0+nfvz8A3t7ejBs3ji1btjBt2rRm5WgqFxcXlixZYukrPz+f3bt3ExcXB8C6deuwsbHBZDLx6KOPAtCrVy9mzJhh1U+/fv345ptvrNq+/vpry8/kpoKCAjZv3szWrVubnHHHjh107dqV0NDQ5r6eiIiIiIiIiIiIiHRSnXblxU02NjZ3vP7ll18CMG7cOKv28ePHk5+fb7XNkqurq2XiArBMRIwePdrSZm9vj6OjIxcuXLDqb9CgQZZ6uDEh4OnpSV5eXrNzNNWtuQAGDBhglSsvL4+AgADLxAXAqFGjcHBwsLrv17/+NX/+859Zt24dpaWlfPHFFyxfvpwHH3zQUlNfX4/RaOTXv/41AwYMaFK+69ev88knn/DTn/6URx55pNnvJyIiIiIiIiIiIiKdU6edvHBwcKBr166cP3/+jnVlZWXY2to2+MLe2dmZ+vp6KioqLG329vZWNTe3o7r1y3+4saVSdXW1VZuTk1ODZzs5OWE2m5udo6kay3vrNlVms7nRXD88UHvKlClMnz6d//iP/yAgIIAZM2Ywbdo0unfvjqurKwA7d+7k1KlThIeHU15eTnl5ueVncOt/3+rw4cOYzWYmTZrU7HcTERERERERERERkc6r024b1aVLF/z9/fnss8+oq6ujS5fGX7V79+7U1tZSVlZG9+7dLe2XLl3CxsamwcRES5WUlDTa5unp2awcBoOB2tpaq35u3bqpOVxcXBrNdfnyZavPDzzwAAkJCcydO5eioiJ69+5NXV0dy5Ytw8/PD4BTp05RVlZGcHBwg/5GjBhBVFSUZbuqm7Zv3469vT1jxoxpUX4RERERERERERER6Zw67coLgJkzZ2I2m1m9enWj13Nychg+fDgAu3btsrq2a9cuvL29sbOza5UsJ06coLCw0PK5sLCQ48ePW778b2qOXr16cfr0aerr6y01Bw4caFEmX19fDh8+bLWq49ChQ5SWljZa/+ijj+Lp6Ym9vT0bNmygT58+lq2pfv7zn7N+/Xqr//385z+na9eurF+/nl/+8pdWfdXU1LB3715+9rOfNTgMXERERERERERERETub5125QXAmDFjiIyMJDU1lZMnTzJhwgR69OjBuXPnyMzMpKKigqysLEJDQ0lKSuLq1au4ubmxbds2vvrqK1atWtVqWZycnJg9ezbz5s0DYMWKFfTs2ZMpU6YA4Onp2aQcYWFhbN68mcTEREJCQsjNzWX37t0tyjR9+nQ2btxIVFQUUVFRlJeXk5qa2mDrqm+++YbPP/8cLy8vrl69yn/+53/ypz/9CZPJZDn3ok+fPvTp08fqvs8//5wHH3yQgICABs/OycmhvLy8VbeMcnNwabW+RERERP4Z9PuLiIiIiIhI4zr15AVAfHw8w4YNIyMjg4SEBKqqqnB1dSUwMJCIiAgAkpOTWbp0KSaTidLSUtzd3Vm5cmWjWyC11JAhQwgNDSU5ORmz2Yyfnx9Go9Fq1UFTcgQFBREfH096ejpbt24lKCgIo9HIjBkzmp3J1dUVk8nEwoULiYmJoW/fvixYsIBly5ZZ1dna2rJnzx7S0tIA8PPzY8OGDQwbNqxlPwxubBnl4uLS6MRGSyU+/YtW60tERETkn+Xa9es8+ECnXhAtIiIiIiLSbDb1t+4/JP8Q4eHh2NnZsWbNmraO0ikdOXKEmpoavL296datW1vHkftcVVUVp0+fxs3NTeNR2pzGo7QnGo/SnlRWVlJQUICXl1erbRMrci80JqU90XiU9kTjUdoTjcfWceTIEQB8fHzuWqs/8RIRERERERERERERkXalU01eZGdn89JLL/Hkk0/yxBNPEBwczIIFCzh9+nRbR2u3Jk+ezPz58+9Yc+TIEd58803Gjx+Pp6cns2bNarQuIyODWbNmMXLkSDw8PBocPn7TF198QXh4OCNGjCAgIIDIyEgKCgru6T0MBoP+ilPahW7dumkVkLQbGo/Snmg83tgeSkRERERERJqm05x5kZKSgslkIiwsjMTERBwdHTl79iyZmZnExsaSlZXVZtk2bNjQZs9uDbm5uXzxxRf4+vpSXV1927o//elPwI2D0m/38z516hQRERGMHDmSJUuWUFNTw5o1a5gxYwY7duzAxaXlh1a+/V9/5ExpcYvvFxEREflH6e/givGnOp9LRERERESkqTrF5EVOTg4mk4no6GhiYmIs7SNGjGDq1Kns37+/DdN1fOHh4UyfPt3y37fzhz/8gQceeIBz587ddvJi37591NfXs2LFCh566CEAPDw8CAkJ4cCBAzz33HMtznmmtJi/lvytxfeLiIiIiIiIiIiISPvQKbaNWrt2Lc7OzkRHRzd6fezYsZb/rq6uZtGiRQQGBuLj48PkyZPZu3evVf38+fOZOHEiBw8eZNKkSfj6+vLCCy9w7tw5SktLiYmJwd/fn5CQEHbu3Gl1b3h4OLNmzSIrK4uQkBB8fX0JDw/n1KlTVnVNyXGzr1sVFBTg4eHB4cOHLW0eHh6YTCZSU1MZPXo0AQEBvPnmm1RWVlrdm5uby5QpU/Dx8WHixInk5OTc5Sd7wwMPNG2YNKWutrYWg8FA165dLW2PPvpok/oXERERERERERERkftDh5+8qKurIzc3l5EjR2Jra3vX+ri4ODZt2kRkZCRpaWkMHDiQuXPnkp2dbVVnNptJSkri5ZdfJiUlhbNnzxIXF0dsbCyDBw8mNTWVIUOGEB8fT1FRkdW9x44dY82aNbz++ussXryY4uJiIiMjqampaXaOpsrIyODMmTMkJSUxZ84ctm/fzqpVq6zeJyIiAoPBwPLly4mIiMBoNHLx4sUWPa+lJkyYwLVr11i+fDnff/89Fy9eZNGiRfzoRz/i6aef/qdmEREREREREREREZH2qcNvG1VaWkpNTQ29e/e+a+3x48fZs2cPRqORadOmARAUFERRURFpaWlWX56XlZWRnp7OoEGDACguLiYxMZGoqCjmzJkDgI+PD3v37mXfvn2WbZUASkpKSE9Pp3///gB4e3szbtw4tmzZwrRp05qVo6lcXFxYsmSJpa/8/Hx2795NXFwcAOvWrcPGxgaTyWRZ6dCrVy9mzJjR7Gfdi/79+/Phhx8SHR3N6tWrAXjsscf44IMPtAJDRERERERERERERIBOsPLiJhsbm7vWfPnllwCMGzfOqn38+PHk5+dbbbPk6upqmbgALBMRo0ePtrTZ29vj6OjIhQsXrPobNGiQpR6gX79+eHp6kpeX1+wcTXVrLoABAwZY5crLyyMgIMBqgmDUqFE4ODg0+1n34vTp08ydO5ef/OQnfPDBB6xevZrHHnuMqKgoLl269E/NIiIiIiIiIiIiIiLtU4efvHBwcKBr166cP3/+rrVlZWXY2to2+MLe2dmZ+vp6KioqLG329vZWNTe3pPrh6gCDwUB1dbVVm5OTU4NnOzk5YTabm52jqRrLe+s2VWazudFcjo6OzX7WvVi2bBnOzs78x3/8B6NHj2bs2LGsXr2a8vJy1q9f/0/NIiIiIiIiIiIiIiLtU4efvOjSpQv+/v589tln1NXV3bG2e/fu1NbWUlZWZtV+6dIlbGxsWm3bopKSkkbbXFxcmpXDYDBQW1trVfPDe5rKxcWl0VyXL19uUX8tdfLkSTw9Pa3aHn74Yfr27cvZs2f/qVlEREREREREREREpH3q8JMXADNnzsRsNlvOUPihnJwcAIYPHw7Arl27rK7v2rULb29v7OzsWiXPiRMnKCwstHwuLCzk+PHj+Pn5NStHr169OH36NPX19ZaaAwcOtCiTr68vhw8ftlrVcejQIUpLS1vUX0v17t2bgoICq3e6cuUKhYWFPPbYY//ULCIiIiIiIiIiIiLSPnX4A7sBxowZQ2RkJKmpqZw8eZIJEybQo0cPzp07R2ZmJhUVFYwZMwZPT09CQ0NJSkri6tWruLm5sW3bNr766itWrVrVanmcnJyYPXs28+bNA2DFihX07NmTKVOmADQ5R1hYGJs3byYxMZGQkBByc3PZvXt3izJNnz6djRs3EhUVRVRUFOXl5aSmpjbpzIvLly/z+eefW/7773//u2XiZcyYMXTr1g2AI0eOUFRUZFnNcfOMD0dHR5588kkApk2bxpw5c4iLi2Py5MnU1NSwdu1aampq+MUvftGidxMRERERERERERGRzqVTTF4AxMfHM2zYMDIyMkhISKCqqgpXV1cCAwOJiIiw1CUnJ7N06VJMJhOlpaW4u7uzcuVKgoODWy3LkCFDCA0NJTk5GbPZjJ+fH0ajEYPB0KwcQUFBxMfHk56eztatWwkKCsJoNDJjxoxmZ3J1dcVkMrFw4UJiYmLo27cvCxYsYNmyZXe998SJE8TExFi13fycnZ1Nnz59AMjIyGDr1q2WmrVr1wLw5JNPsmHDBgBCQkJYvnw577//PrGxsdja2uLt7c369eutDjlvif4Orvd0v4iIiMg/in5PERERERERaR6b+lv375F7Fh4ejp2dHWvWrGnrKPeNI0eOAODj49PGSURERERu79r16zz4QKfYtbXDq6yspKCgAC8vr1bbOlbkXmhMSnui8SjticajtCcaj62jOd/l6l9P0inU1NRQVVXV1jFEqKqqIj8/X+NR2gWNR2lPNB7RxIWIiIiIiEgzdJpto+DGFkYZGRkcPXqUyspKy7ZRM2fOxM3Nra3jtUuTJ0/Gy8uLpKSkO9ZlZmZiMpk4d+4cP/rRj3jxxRcJDw9vUFdeXs7KlSvZtWsXZWVl9OzZk1//+te89NJLlhoPD48G9zk7O7f4MHIRERERERERERER6Vw6zeRFSkoKJpOJsLAwEhMTcXR05OzZs2RmZhIbG0tWVtY/JcfNsx06k507d5KQkMCLL77IT3/6U7744gsWLVqEjY0NL7zwgqWusrKS8PBwHnzwQRISEnBycuLMmTNcuXKlQZ/h4eFMnDjR8tnW1vaeMhoMBsvB4SJtqVu3bnh7e7d1DBFA41Hal/Y+HrWlk4iIiIiISPvSKSYvcnJyMJlMREdHWx0sPWLECKZOncr+/fvbMF3Ht3LlSkJDQ/m///f/AvCTn/yE8vJyUlNT+eUvf2mZeHj33Xf5+9//zrZt2yz7vgUEBDTa54/+f+zde1jVVd7//ycq26AS5CSZqZAKQoCHEDXDUclD2tjYNEMHBh1gVDwQI0zq786+DDbhLR5R03Y6o4KTjRplOR7Hm5nx1CRGKvANUyHxVrYYiHFWfn94ub/uwARkhkOvx3V1Xez1ea/1eX+267rMz5u11iOP0K9fvybNM+7vf+F8UUGTjikiIiJtX097F94MfLG50xAREREREZE7tInixYYNG3ByciIyMrLO6yNGjDD/XFFRwdKlS/n0008pLi7G3d2dmTNn8swzz5hj5s6dy6lTp5g/fz5vv/02ubm5+Pr6kpCQwEMPPcSbb77JP/7xDxwcHPjtb3/Ls88+a+57+8DucePGsWrVKgoKCvDz8yMuLg53d/cG5VHX4d9ZWVk8//zzbNq0yVwY8PDwICYmhvLycv785z9z48YNRo4cyRtvvGFxeEx6ejoLFy4kJyeHHj16EBsbe8/vtqysjPPnzzN58mSL9mHDhpGcnMwXX3yBv78/ANu2beOVV15ptgNrzhcV8NXV/22We4uIiIiIiIiIiIhI02n1a+Orq6tJT09n8ODB9dp6KCYmhq1btxIeHs7q1avp1asXs2bN4sCBAxZxJpOJhIQEpk+fTmJiInl5ecTExBAdHU2fPn1ISkrC29ub2NhY8vPzLfqePn2adevWMWfOHBYtWkRBQQHh4eFUVlY2OI/6SklJ4fz58yQkJDBjxgx27tzJmjVrLJ4nLCwMg8HA8uXLCQsLIy4ujsuXL//guJWVldTU1GAwGCzab3/++uuvAbhw4QImk4nOnTszbdo0nnjiCQYNGsR//dd/8d1339Ua991338Xb25snn3yS1157jYsXLzbquUVERERERERERESk7Wn1Ky+KioqorKyka9eu94zNzs5m7969xMXFERwcDEBgYCD5+fmsXr2aUaNGmWOLi4tJTk6md+/eABQUFBAfH09ERAQzZswAwMfHh3379rF//35CQ0PNfQsLC0lOTqZnz54AeHl5MXbsWHbs2EFwcHCD8qgvZ2dnlixZYh4rMzOTPXv2EBMTA8DGjRuxsrLCaDTy8MMPA+Dq6lprRcX32dnZYW9vz5dffsmkSZPM7V988YX5ewK4cuUKAIsWLWL06NEYjUbOnz/PkiVLKC0tZenSpea+zz//PD/5yU9wcnLiq6++4p133uHll1/mo48+ws7OrsHPLiIiIiIiIiIiIiJtS6tfeXGblZXVPWOOHz8OwNixYy3ax40bR2ZmJqWlpeY2FxcXc+ECMBcihg4dam7r1KkTDg4OXLp0yWK83r17m+MBevTogaenJxkZGQ3Oo77uzAvg8ccft8grIyODgIAAc+ECYMiQIdjb299z7JdffpkdO3awc+dOiouLOXjwIJs2bQL+3/d+8+ZNANzc3Fi0aBFDhgzhpZde4vXXX+fTTz/lm2++MY+3aNEixo0bh7+/P6+88grvvfceBQUFfPDBBw1+bhERERERERERERFpe1r9ygt7e3s6duxYr22HiouLsba2rvXC3snJiZqaGkpKSsznNXTq1Mki5vaWVHe+/Idb2ydVVFRYtDk6Ota6t6OjIyaTqcF51Fdd+d65TZXJZKJHjx61+jk4ONxz7KlTp5KXl0dsbCw1NTXY2toSExPD73//e5ydnQHMKya+f0D34MGDAcjJyeGxxx6rc3xPT0/c3Nw4ffr0PXMRERERERERERERkbav1a+86NChAwMGDODo0aNUV1f/YKydnR1VVVXmrY5uu3LlClZWVrUKE41VWFhYZ9udL/rrk4fBYKCqqsoi5vt96svZ2bnOvK5evXrPvg888ABLlizh8OHDfPzxxxw6dAgfHx8A/Pz8AHjsscdqnYtxp+8XeERERERERERERERE7qbVFy8ApkyZgslkYu3atXVeT0tLA2DgwIEA7N692+L67t278fLyavBqh7vJyckhNzfX/Dk3N5fs7Gzzi/765uHq6sq5c+eoqakxxxw6dKhROfn6+nLs2DFKSkrMbUeOHKGoqKjeYzg4OODh4YGtrS0pKSk8+eSTuLu7A7cKLU899RRHjhyx6HP48GEAvL297zpuVlYW586dMxdEREREREREREREROTHrdVvGwUwfPhwwsPDSUpK4syZM4wfP57OnTtz4cIFtm/fTklJCcOHD8fT05PRo0eTkJBAeXk5bm5ufPzxx5w4cYI1a9Y0WT6Ojo5MmzaN2bNnA7BixQq6dOliPvC6vnmMGTOGbdu2ER8fT1BQEOnp6ezZs6dROYWGhrJlyxYiIiKIiIjg2rVrJCUl1evMi7S0NPLy8ujVqxfFxcXs3LmTY8eO8ec//9kibubMmQQHBzNnzhx+9rOfkZuby5IlS3juuefo3r07AOvXrycvL4+AgAAcHBzIyclh7dq1uLq68uKLLzbq2URERERERERERESkbWkTxQuA2NhY+vfvT0pKCvPnz6esrAwXFxeGDRtGWFiYOW7x4sUsXboUo9FIUVER7u7urFy5kpEjRzZZLt7e3owePZrFixdjMpnw8/MjLi7OYlul+uQRGBhIbGwsycnJfPjhhwQGBhIXF8fkyZMbnJOLiwtGo5GFCxcSFRVF9+7dWbBgAcuWLbtn3w4dOrBt2zZyc3Pp0KEDgwYNYuvWrTz++OMWcU888QRGo5HExESmT5+OnZ0dv/zlL4mOjjbHuLm5sXfvXv7617/y3Xff0blzZ4YPH85rr71W69yOhupp73Jf/UVEROTHSf8PISIiIiIi0vJY1dy5J5Hct5CQEGxtbVm3bl1zp/KjcfLkSQBtOyUiIiKNduPmTdq3axM7qko9lJaWkpWVRd++fZts61iR+6E5KS2J5qO0JJqP0pJoPjaNhrzL1b/QpE2orKykrKysudMQoaysjMzMTM1HaRE0H6UlaenzUYULERERERGRlkX/ShMRERERERERERERkRalzZx5AXDgwAFSUlI4deoUpaWl5jMvpkyZgpub238kh82bN/9H7tNUJk6cSN++fUlISLhrzIcffsif//xnzp8/T1lZGV27duWnP/0pERER5nM8rl+/zh//+EfS0tI4f/48BoMBX19foqOj8fDwsBjvq6++YsmSJWRkZFBdXY2Hh2PcLZYAAQAASURBVAezZs1i8ODBjX4Og8GAjY1No/uLNBUbGxu8vLyaOw0RQPNRWpaWMh+1PZSIiIiIiEjr0GaKF4mJiRiNRsaMGUN8fDwODg7k5eWxfft2oqOjSU1Nbe4UW63i4mKefvppfvOb3/DQQw/x5ZdfsmrVKi5dukR8fDwAFy9eZOvWrbzwwgu89tprVFRUsGHDBn75y1+yfft28+HeV69eZfLkyTz22GO89dZbWFtbs3nzZiIiIti2bVutQkdDxP/jA3KLTU3yzCIiItL29LBz5o2nf9HcaYiIiIiIiEg9tIniRVpaGkajkcjISKKioszt/v7+vPDCCxw8eLAZs2v9Jk+ebPF58ODBfPfdd/zpT3/i//yf/0P79u3p1q0b+/bts1j9MHjwYEaOHMmWLVt44403ADhy5AiFhYV88MEHdOvWDYBBgwYxaNAg9u/ff1/Fi9xiE19dvdjo/iIiIiIiIiIiIiLSMrSJNfMbNmzAycmJyMjIOq+PGDHC/HNFRQVvv/02w4YNw8fHh4kTJ7Jv3z6L+Llz5zJhwgQOHz7Mc889h6+vL6+++ioXLlygqKiIqKgoBgwYQFBQELt27bLoGxISwtSpU0lNTSUoKAhfX19CQkI4e/asRVx98rg91p2ysrLw8PDg2LFj5jYPDw+MRiNJSUkMHTqUgIAA5s2bR2lpqUXf9PR0Jk2ahI+PDxMmTCAtLe0e3+zd2dvbU11dzc2bNwGwtbWttW3Tgw8+SPfu3SkoKDC3VVVVAfDwww+b2zp27Ii1tTU1NTWNzkdERERERERERERE2o5WX7yorq4mPT2dwYMHY21tfc/4mJgYtm7dSnh4OKtXr6ZXr17MmjWLAwcOWMSZTCYSEhKYPn06iYmJ5OXlERMTQ3R0NH369CEpKQlvb29iY2PJz8+36Hv69GnWrVvHnDlzWLRoEQUFBYSHh1NZWdngPOorJSWF8+fPk5CQwIwZM9i5cydr1qyxeJ6wsDAMBgPLly8nLCyMuLg4Ll++XO97VFdXU1ZWxueff87GjRt56aWXfvA7v3btGjk5Obi7u5vbRowYgZOTEwkJCRQUFHD16lWWLFmClZUVEydObNSzi4iIiIiIiIiIiEjb0uq3jSoqKqKyspKuXbveMzY7O5u9e/cSFxdHcHAwAIGBgeTn57N69WpGjRplji0uLiY5OZnevXsDUFBQQHx8PBEREcyYMQMAHx8f9u3bx/79+wkNDTX3LSwsJDk5mZ49ewLg5eXF2LFj2bFjB8HBwQ3Ko76cnZ1ZsmSJeazMzEz27NlDTEwMABs3bsTKygqj0Whe9eDq6lprS6i7qa6uxtvb2/z5Zz/7GfPnz//BPosXL8bKyoqXXnrJ3GZnZ0dKSgpTp07l6aefBm6t4jAajTz22GP1fl4RERERERERERERabta/cqL26ysrO4Zc/z4cQDGjh1r0T5u3DgyMzMttllycXExFy4AcyFi6NCh5rZOnTrh4ODApUuXLMbr3bu3OR6gR48eeHp6kpGR0eA86uvOvAAef/xxi7wyMjIICAiw2K5pyJAh2Nvb12v8Dh06sG3bNlJSUpg3bx4HDx5k3rx5d43fvn07H3zwAQsWLMDV1dXcXlhYyMyZM+nevTvvvvsu69evJyAggOnTp/P111/X82lFREREREREREREpC1r9Ssv7O3t6dixIxcv3vug5uLiYqytrWu9sHdycqKmpoaSkhJsbW2BW4WJO93eHunOl/8ABoOBiooKizZHR8da93Z0dMRkMjU4j/qqK987t6kymUz06NGjVj8HB4d638PHxweAJ598km7dujFjxgxeffVVc/ttaWlpLFiwgMjISH72s59ZXHvvvfcoLi5mx44dGAwG4FYRZfz48axZs8a8ekREREREREREREREfrxa/cqLDh06MGDAAI4ePUp1dfUPxtrZ2VFVVUVxcbFF+5UrV7CysqpVmGiswsLCOtucnZ0blIfBYDAfcH3b9/vUl7Ozc515Xb16tVHjPfHEEwDk5eVZtH/xxRdERUXx/PPPExUVVavfmTNncHd3NxcuANq3b4+Hh0etsURERERERERERETkx6nVFy8ApkyZgslkYu3atXVeT0tLA2DgwIEA7N692+L67t278fLyavBqh7vJyckhNzfX/Dk3N5fs7Gz8/PwalIerqyvnzp2jpqbGHHPo0KFG5eTr68uxY8coKSkxtx05coSioqJGjXd766s7z6k4c+YMU6dOZfDgwcTFxdXZr2vXrnz99dcWq1Vu3LhBdnY2jz76aKNyEREREREREREREZG2pdVvGwUwfPhwwsPDSUpK4syZM4wfP57OnTtz4cIFtm/fTklJCcOHD8fT05PRo0eTkJBAeXk5bm5ufPzxx5w4cYI1a9Y0WT6Ojo5MmzaN2bNnA7BixQq6dOnCpEmTAOqdx5gxY9i2bRvx8fEEBQWRnp7Onj17GpVTaGgoW7ZsISIigoiICK5du0ZSUlK9zrx45ZVXeOaZZ3B3d6ddu3ZkZGSwYcMGnn76aXx9fYFbK0vCwsLo2LEjoaGhnDp1ytz/oYceolevXgC8+OKLbNu2jcjISF555RXat2/P1q1byc3NZeHChY16NhERERERERERERFpW9pE8QIgNjaW/v37k5KSwvz58ykrK8PFxYVhw4YRFhZmjlu8eDFLly7FaDRSVFSEu7s7K1euZOTIkU2Wi7e3N6NHj2bx4sWYTCb8/PyIi4uz2CqpPnkEBgYSGxtLcnIyH374IYGBgcTFxTF58uQG5+Ti4oLRaGThwoVERUXRvXt3FixYwLJly+7Z94knnuCDDz7g4sWLdOjQgW7dujFr1ixefvllc8yZM2fMB4R/P79BgwaxefNm81jvvfcea9asYd68edy8eZNevXrx7rvv4u/v3+DnulMPO+f76i8iIiJtm/5fQUREREREpPWwqrlzTyK5byEhIdja2rJu3brmTuVH4+TJkwC1Dg4XERER+b4bN2/Svl2b2DlV7kNpaSlZWVn07du3ybaOFbkfmpPSkmg+Skui+SgtieZj02jIu1z9y03ahKqqKsrLy5s7DRHKy8s5e/as5qO0CJqP0pK0lPmowoWIiIiIiEjr0Kb+9XbgwAF+/etfM2jQIJ544glGjhzJggULOHfuXHOn1mJNnDiRuXPn/mDM+++/z69//WueeuopBgwYwC9+8Qv2799fK+7bb79lwYIF/OQnP6Ffv35MmDCBP//5z3cd9+bNm0yaNAkPD49ah5c3lLW1NQ888MB9jSHSFB544AHc3d01H6VF0HyUlqQ55+ONmpv/8XuKiIiIiIjI/WkzZ14kJiZiNBoZM2YM8fHxODg4kJeXx/bt24mOjiY1NfU/ksftsx3akrVr1zJs2DBeeuklbG1t2b17NzNmzCAhIYGf/exn5rioqCjOnj3Lb3/7Wx555BH+/ve/83/+z/+hffv2/OIXv6g17vvvv8/ly5ebLM+3Dm0lr9jUZOOJiIhI69fdzpn/76lfNncaIiIiIiIi0kBtoniRlpaG0WgkMjKSqKgoc7u/vz8vvPACBw8ebMbsWr8dO3bg4OBg/vzUU0+Rn5/Phg0bzMULk8nEsWPHePvtt5k0aRIAQ4YM4eTJk3z66ae1ihdXr15lxYoV/O53v2P+/PlNkmdesYmcby82yVgiIiIiIiIiIiIi0nzaxLZRGzZswMnJicjIyDqvjxgxwvxzRUUFb7/9NsOGDcPHx4eJEyeyb98+i/i5c+cyYcIEDh8+zHPPPYevry+vvvoqFy5coKioiKioKAYMGEBQUBC7du2y6BsSEsLUqVNJTU0lKCgIX19fQkJCOHv2rEVcffK4PdadsrKy8PDw4NixY+Y2Dw8PjEYjSUlJDB06lICAAObNm0dpaalF3/T0dCZNmoSPjw8TJkwgLS3tHt/sLXcWLm7r27cvBQUF5s/V1dUAPPzwwxZxDz30EHWdCb906VICAgIICAioVw4iIiIiIiIiIiIi8uPR6osX1dXVpKenM3jwYKytre8ZHxMTw9atWwkPD2f16tX06tWLWbNmceDAAYs4k8lEQkIC06dPJzExkby8PGJiYoiOjqZPnz4kJSXh7e1NbGws+fn5Fn1Pnz7NunXrmDNnDosWLaKgoIDw8HAqKysbnEd9paSkcP78eRISEpgxYwY7d+5kzZo1Fs8TFhaGwWBg+fLlhIWFERcX1+htm44fP467u7v58yOPPMKwYcNYu3YtZ86c4fr16+zatYtDhw7xyiuvWPT98ssv+eSTT/jd737XqHuLiIiIiIiIiIiISNvW6reNKioqorKykq5du94zNjs7m7179xIXF0dwcDAAgYGB5Ofns3r1akaNGmWOLS4uJjk5md69ewNQUFBAfHw8ERERzJgxAwAfHx/27dvH/v37CQ0NNfctLCwkOTmZnj17AuDl5cXYsWPZsWMHwcHBDcqjvpydnVmyZIl5rMzMTPbs2UNMTAwAGzduxMrKCqPRaF4d4erqyuTJkxt8r507d3LixAlWr15t0Z6UlER0dDTjx48HoH379vzXf/0XY8aMMcfcvHmTuLg4pkyZQrdu3bhw4UKD7y8iIiIiIiIiIiIibVurX3lxm5WV1T1jjh8/DsDYsWMt2seNG0dmZqbFNksuLi7mwgVgLkQMHTrU3NapUyccHBy4dOmSxXi9e/c2xwP06NEDT09PMjIyGpxHfd2ZF8Djjz9ukVdGRgYBAQEW2zoNGTIEe3v7Bt0nOzubN998k0mTJhEUFGRur6mpYd68eZw/f54lS5awadMmIiIi+MMf/sCnn35qjvvLX/7ClStX+M1vftPAJxQRERERERERERGRH4tWv/LC3t6ejh07cvHivQ9qLi4uxtrautYLeycnJ2pqaigpKcHW1ha4VZi40+0tqb5/poPBYKCiosKizdHRsda9HR0dMZlMDc6jvurK985tqkwmEz169KjVr67zLO4mPz+fiIgIfH19+f3vf29x7X/+53/YvXs3H3/8MR4eHgAEBARQWFhIQkIC48eP57vvvmPp0qVER0dTVVVFVVUV169fB6C8vJzr16/z0EMP1TsfEREREREREREREWmbWv3Kiw4dOjBgwACOHj1qPjT6buzs7KiqqqK4uNii/cqVK1hZWdUqTDRWYWFhnW3Ozs4NysNgMFBVVWUR8/0+9eXs7FxnXlevXq1X/6tXrxIWFoajoyOrVq2qdb7ImTNnaN++PX369LFov32wd1lZGd9++y1FRUW8+eab+Pv74+/vz8SJEwF4/fXXLbaXEhEREREREREREZEfr1ZfvACYMmUKJpOJtWvX1nk9LS0NgIEDBwKwe/dui+u7d+/Gy8urwasd7iYnJ4fc3Fzz59zcXLKzs/Hz82tQHq6urpw7d46amhpzzKFDhxqVk6+vL8eOHaOkpMTcduTIEYqKiu7Z97vvviMiIoKqqirefffdOldHPProo9y4cYP/+3//r0X76dOncXR0xMbGBmdnZzZt2mTx39KlSwGYNWsWSUlJjXo2EREREREREREREWlbWv22UQDDhw8nPDycpKQkzpw5w/jx4+ncuTMXLlxg+/btlJSUMHz4cDw9PRk9ejQJCQmUl5fj5ubGxx9/zIkTJ1izZk2T5ePo6Mi0adOYPXs2ACtWrKBLly5MmjQJoN55jBkzhm3bthEfH09QUBDp6ens2bOnUTmFhoayZcsWIiIiiIiI4Nq1ayQlJdXrzItZs2aRnZ3NW2+9xcWLFy226OrXrx9w65Dwrl27Mnv2bGbMmIGLiwv//Oc/+fDDD5k1axYAHTt2JCAgwGLs2wd29+rViwEDBjTq2URERERERERERESkbWkTxQuA2NhY+vfvT0pKCvPnz6esrAwXFxeGDRtGWFiYOW7x4sUsXboUo9FIUVER7u7urFy5kpEjRzZZLt7e3owePZrFixdjMpnw8/MjLi4Og8HQoDwCAwOJjY0lOTmZDz/8kMDAQOLi4pg8eXKDc3JxccFoNLJw4UKioqLo3r07CxYsYNmyZffse3u1x+uvv17r2u2VFg899BB/+tOfWLZsGYmJiZSUlNCtWzfmzp3Lq6++2uB8G6O7nfN/5D4iIiLSeuj/D0RERERERFonq5o79ySS+xYSEoKtrS3r1q1r7lR+NE6ePAmAj49PM2ciIiIiLdGNmpu0t2oTu6VKEyktLSUrK4u+ffs22daxIvdDc1JaEs1HaUk0H6Ul0XxsGg15l6t/xUmbUFlZSVlZWXOnIUJZWRmZmZmaj9IiaD5KS9Kc81GFCxERERERkdZH/5ITEREREREREREREZEWpVWfeXHgwAFSUlI4deoUpaWl5jMupkyZgpubW7PktHnz5ma5b31MnDiRvn37kpCQAEBubi7r168nIyODnJwc3N3d+eSTT2r1q6ysZMWKFXz00Udcu3aNPn36MGfOHIYMGWIR5+HhUauvk5OT+cyM277++msWLlzIiRMnePDBB5k4cSKvvfaaxZkgDWUwGLCxsWl0f5GmYmNjg5eXV3OnIQJoPkrL8u+aj9oSSkREREREpG1qtcWLxMREjEYjY8aMIT4+HgcHB/Ly8ti+fTvR0dGkpqY2d4otXk5ODmlpafj5+XHz5k3udvzJH/7wBz766CNee+013Nzc2LFjBxEREWzduhVvb2+L2JCQECZMmGD+bG1tbXG9uLiY0NBQevbsSVJSEpcvXyYhIYHy8nIWLFhwX8+TcGQredcK7msMERERaT26d3Jh7pBfNncaIiIiIiIi8m/QKosXaWlpGI1GIiMjiYqKMrf7+/vzwgsvcPDgwWbMrvUYOXIkQUFBAMydO5dTp07Virl8+TIffPAB8+bNIyQkBICnn36an/70p6xatYp33nnHIv6RRx6hX79+d73n+++/z3fffceqVauwt7cH4MaNG8TFxTF16lS6dOnS6OfJu1bAmW8vNrq/iIiIiIiIiIiIiLQMrXKN/YYNG3ByciIyMrLO6yNGjACgoqKCt99+m2HDhuHj48PEiRPZt2+fRezcuXOZMGEChw8f5rnnnsPX15dXX32VCxcuUFRURFRUFAMGDCAoKIhdu3ZZ9A0JCWHq1KmkpqYSFBSEr68vISEhnD171iKuPnncHutOWVlZeHh4cOzYMXObh4cHRqORpKQkhg4dSkBAAPPmzaO0tNSib3p6OpMmTcLHx4cJEyaQlpZW63tq1+7ef/zZ2dncuHGDp556ytxmZWXFsGHD+Oc//0llZeU9x7jT3//+d4YMGWIuXACMGzeOmzdv1tpeSkRERERERERERER+nFpd8aK6upr09HQGDx5ca0ui74uJiWHr1q2Eh4ezevVqevXqxaxZszhw4IBFnMlkIiEhgenTp5OYmEheXh4xMTFER0fTp08fkpKS8Pb2JjY2lvz8fIu+p0+fZt26dcyZM4dFixZRUFBAeHi4xUv9+uZRXykpKZw/f56EhARmzJjBzp07WbNmjcXzhIWFYTAYWL58OWFhYcTFxXH58uUG3+v2c3z/PAqDwUBlZSUXLlywaH/33Xfx9vbmySef5LXXXuPiRcuVEGfPnsXd3d2irVOnTjg7O9cq+oiIiIiIiIiIiIjIj1Or2zaqqKiIyspKunbt+oNx2dnZ7N27l7i4OIKDgwEIDAwkPz+f1atXM2rUKHNscXExycnJ9O7dG4CCggLi4+OJiIhgxowZAPj4+LBv3z72799PaGiouW9hYSHJycn07NkTAC8vL8aOHcuOHTsIDg5uUB715ezszJIlS8xjZWZmsmfPHmJiYgDYuHEjVlZWGI1GHn74YQBcXV2ZPHlyg+/Vo0cPAL788ku6detmbv/iiy+AW9/dbc8//zw/+clPcHJy4quvvuKdd97h5Zdf5qOPPsLOzg6Aa9eu0alTp1r3sbOzsxhLRERERERERERERH68Wt3Ki9usrKx+8Prx48cBGDt2rEX7uHHjyMzMtNhmycXFxVy4AMyFiKFDh5rbOnXqhIODA5cuXbIYr3fv3uZ4uPWy39PTk4yMjAbnUV935gXw+OOPW+SVkZFBQECAuXAB1Nqqqb769OnDk08+SWJiIidOnODbb79l/fr1/Otf/wIs/xwWLVrEuHHj8Pf355VXXuG9996joKCADz74oMH3FREREREREREREZEfr1ZXvLC3t6djx461tiP6vuLiYqytrWu9sHdycqKmpoaSkhJz2/dXAtzejurOl/9wa6ukiooKizZHR8da93Z0dMRkMjU4j/qqK987t6kymUx15uXg4NDgewEkJCTQuXNngoODGTx4MCkpKebzRpydne/az9PTEzc3N06fPm2Re13PXFxcbF6dISIiIiIiIiIiIiI/bq2ueNGhQwcGDBjA0aNHqa6uvmucnZ0dVVVVtbYiunLlClZWVrUKE41VWFhYZ9vtl/r1zcNgMFBVVWUR09htlJydnevM6+rVq40a77HHHmP79u0cOHCATz/9lH379vHAAw/g7OzMo48+2qCx3N3da51tUVJSgslkqnUWhoiIiIiIiIiIiIj8OLW64gXAlClTMJlMrF27ts7raWlpDBw4EIDdu3dbXNu9ezdeXl7Y2to2SS45OTnk5uaaP+fm5pKdnY2fnx9AvfNwdXXl3Llz1NTUmGMOHTrUqJx8fX05duyYxQqHI0eOUFRU1KjxbuvWrRu9evWiqqqKbdu28eKLL/5gfFZWFufOncPHx8fcFhgYyOHDh7l27Zq5bffu3bRr146nnnrqvvITERERERERERERkbah1R3YDTB8+HDCw8NJSkrizJkzjB8/ns6dO3PhwgW2b99OSUkJqampjB49moSEBMrLy3Fzc+Pjjz/mxIkTrFmzpslycXR0ZNq0acyePRuAFStW0KVLFyZNmgTc2jqpPnmMGTOGbdu2ER8fT1BQEOnp6ezZs6dROYWGhrJlyxYiIiKIiIjg2rVrJCUl1dq6qqysjLS0NADy8/O5fv26ucgyaNAg8zZTycnJPPTQQzzyyCPk5+fzxz/+kY4dOxIREWEea/369eTl5REQEICDgwM5OTmsXbsWV1dXiyJHcHAwmzdvZsaMGUydOpXLly/z3//93wQHB9OlS5dGPe9t3Tu53Fd/ERERaV30d7+IiIiIiEjb1SqLFwCxsbH079+flJQU5s+fT1lZGS4uLgwbNoywsDAAFi9ezNKlSzEajRQVFeHu7s7KlSsZOXJkk+Xh7e3N6NGjWbx4MSaTCT8/P+Li4jAYDOaY+uQRGBhIbGwsycnJfPjhhwQGBhIXF8fkyZMbnJOLiwtGo5GFCxcSFRVF9+7dWbBgAcuWLbOIKywsJCoqyqLt9udNmzYREBAAQGVlJatWreLSpUvY29szevRooqKiLFavuLm5sXfvXv7617/y3Xff0blzZ4YPH85rr71mcUaHnZ0dGzduJD4+nhkzZvDggw/y85//nOjo6AY/5/fNHfLL+x5DREREWpcbNTdpb9UqFxOLiIiIiIjID7CquXOfImmQkJAQbG1tWbduXXOn8qN28uRJKisr8fLywsbGprnTkR+5srIyzp07h5ubm+ajNDvNR2lJNB+lJSktLSUrK4u+ffs22XayIvdDc1JaEs1HaUk0H6Ul0XxsGidPngSwOGrgbvRraiIiIiIiIiIiIiIi0qK0im2jDhw4QEpKCqdOnaK0tNS8PdSUKVNwc3Nr7vRapIkTJ9K3b18SEhLuGnPy5Em2bNnCF198wblz5xg+fHitVSTHjh3jV7/6VZ393dzcLA4i//zzz1mxYgXZ2dm0a9cOHx8f5syZQ9++fS36VVRUsHbtWj766CMKCgpwcnJi3LhxvP76641+XoPBoN/ilBbBxsYGLy+v5k5DBNB8lJalvvPxZs1N2mkbKBERERERkR+9Fl+8SExMxGg0MmbMGOLj43FwcCAvL4/t27cTHR1Nampqs+W2efPmZrt3U0hPT+fzzz/H19eXioqKOmO8vb3ZunWrRdv169eJiIggMDDQ3Hb27FnCwsIYPHgwS5YsobKyknXr1jF58mQ++eQTnJ2dAbh58yaRkZF88803zJw5k27dunHx4kXOnTt338+z+Nj7fHOt4L7HERERkebxWCcXYgOCmzsNERERERERaQFadPEiLS0No9FIZGSkxcHS/v7+vPDCCxw8eLAZs2v9QkJCCA0NNf9cl4ceeoh+/fpZtO3YsYObN28yYcIEc9v+/fupqalhxYoVPPDAAwB4eHgQFBTEoUOHeP755wHYvn07GRkZ7Nq1CxcXlyZ9nm+uFfB10cUmHVNERERERERERERE/vNa9Jr8DRs24OTkRGRkZJ3XR4wYYf65oqKCt99+m2HDhuHj48PEiRPZt2+fRfzcuXOZMGEChw8f5rnnnsPX15dXX32VCxcuUFRURFRUFAMGDCAoKIhdu3ZZ9A0JCWHq1KmkpqYSFBSEr68vISEhnD171iKuPnncHutOWVlZeHh4cOzYMXObh4cHRqORpKQkhg4dSkBAAPPmzaO0tNSib3p6OpMmTcLHx4cJEyaQlpZ2j2/2lnbtGvfH/8knn9CzZ098fX3NbVVVVRgMBjp27Ghue/jhh2v1/ctf/sLYsWObvHAhIiIiIiIiIiIiIm1Hiy1eVFdXk56ezuDBg7G2tr5nfExMDFu3biU8PJzVq1fTq1cvZs2axYEDByziTCYTCQkJTJ8+ncTERPLy8oiJiSE6Opo+ffqQlJSEt7c3sbGx5OfnW/Q9ffo069atY86cOSxatIiCggLCw8OprKxscB71lZKSwvnz50lISGDGjBns3LmTNWvWWDxPWFgYBoOB5cuXExYWRlxcHJcvX27U/e7lypUrHD161GLVBcD48eO5ceMGy5cv59tvv+Xy5cu8/fbbPPLII4waNQq4VeDIzMyka9eu/O53v6Nfv37079+fqKgoTCbTvyVfEREREREREREREWl9Wuy2UUVFRVRWVtK1a9d7xmZnZ7N3717i4uIIDr61T3JgYCD5+fmsXr3a/PIcoLi4mOTkZHr37g1AQUEB8fHxREREMGPGDAB8fHzYt28f+/fvN2+rBFBYWEhycjI9e/YEwMvLi7Fjx7Jjxw6Cg4MblEd9OTs7s2TJEvNYmZmZ7Nmzh5iYGAA2btyIlZUVRqPRvNLB1dWVyZMnN/he9bFr1y5u3LhRq3jRs2dP/vSnPxEZGcnatWsBePTRR/njH/9ozquoqIiqqiqMRiP+/v6sWrWKq1evsnjxYmbNmsX777//b8lZRERERERERERERFqXFrvy4jYrK6t7xhw/fhyAsWPHWrSPGzeOzMxMi22WXFxczIULwFyIGDp0qLmtU6dOODg4cOnSJYvxevfubY4H6NGjB56enmRkZDQ4j/q6My+Axx9/3CKvjIwMAgICLLZoGjJkCPb29g2+V33s3LkTb29v3NzcLNrPnTvHrFmzeOqpp/jjH//I2rVrefTRR4mIiODKlSvArcO6AR588EFWrVrFsGHD+OlPf8qiRYs4ceIER44c+bfkLCIiIiIiIiIiIiKtS4stXtjb29OxY0cuXrz3AczFxcVYW1vXemHv5ORETU0NJSUl5rZOnTpZxNzekur75zMYDAYqKios2hwdHWvd29HR0bzlUUPyqK+68r1zmyqTyVRnXg4ODg2+173k5eXx5Zdf8tOf/rTWtWXLluHk5MR///d/M3ToUEaMGMHatWu5du0amzZtAm49i5WVFQMGDMBgMJj7Dho0iPbt23PmzJkmz1lEREREREREREREWp8WW7zo0KEDAwYM4OjRo1RXV/9grJ2dHVVVVRQXF1u0X7lyBSsrqzoPjm6MwsLCOtucnZ0blIfBYKCqqsoi5vt96svZ2bnOvK5evdqo8X7Izp07adeuHc8++2yta2fOnMHT09Oi7cEHH6R79+7k5eUBYGNjw6OPPnrX8b9fLBIRERERERERERGRH6cWW7wAmDJlCiaTyXyGwvelpaUBMHDgQAB2795tcX337t14eXlha2vbJPnk5OSQm5tr/pybm0t2djZ+fn4NysPV1ZVz585RU1Njjjl06FCjcvL19eXYsWMWqzqOHDlCUVFRo8b7IZ9++imDBg3CxcWl1rWuXbuSlZVl8UzXr18nNzfXomAxYsQI0tPTLQoVR48e5caNG3h7ezd5ziIiIiIiIiIiIiLS+rTYA7sBhg8fTnh4OElJSZw5c4bx48fTuXNnLly4wPbt2ykpKWH48OF4enoyevRoEhISKC8vx83NjY8//pgTJ06wZs2aJsvH0dGRadOmMXv2bABWrFhBly5dmDRpEkC98xgzZgzbtm0jPj6eoKAg0tPT2bNnT6NyCg0NZcuWLURERBAREcG1a9dISkqq15kXV69e5bPPPjP//N1335kLL8OHD8fGxsYcm5mZyddff82UKVPqHCs4OJgZM2YQExPDxIkTqaysZMOGDVRWVvLiiy+a48LCwvjoo4+IjIzkV7/6FVevXmXJkiUMHDiQwYMHN+o7EBEREREREREREZG2pUUXLwBiY2Pp378/KSkpzJ8/n7KyMlxcXBg2bBhhYWHmuMWLF7N06VKMRiNFRUW4u7uzcuVKRo4c2WS5eHt7M3r0aBYvXozJZMLPz4+4uDiL8xvqk0dgYCCxsbEkJyfz4YcfEhgYSFxcHJMnT25wTi4uLhiNRhYuXEhUVBTdu3dnwYIFLFu27J59c3JyiIqKsmi7/fnAgQN069bN3L5z504MBgNjxoypc6ygoCCWL1/O+vXriY6OxtraGi8vLzZt2mRxyPkjjzzCpk2b+MMf/sCsWbOwsbFh1KhRzJ07t16Hs/+QxzrVXhEiIiIirYf+LhcREREREZHbrGru3OdH7iokJARbW1vWrVvX3KnI95w8eRIAHx+fZs5ERERE7tfNmpu0s2rRO5tKG1BaWkpWVhZ9+/Ztsi1mRe6H5qS0JJqP0pJoPkpLovnYNBryLlf/MpQ2obKykrKysuZOQ4SysjIyMzM1H6VF0HyUlqS+81GFCxEREREREQEVL0REREREREREREREpIVp1cWLAwcO8Otf/5pBgwbxxBNPMHLkSBYsWMC5c+ea/F6bN29u9VtGTZw4kblz59Zq//DDD3n++efx8fEhICCA8PBwysvLLWIqKipYsWIFI0eO5IknnuAnP/kJixYtuuu9/vSnP+Hh4cHUqVNrXTtx4gQvv/wyvr6+DB06lPj4+Pv+rWCDwWBxwLhIc7GxscHLy0vzUVoEzUdpSX5oPt6sudkMGYmIiIiIiEhL1uIP7L6bxMREjEYjY8aMIT4+HgcHB/Ly8ti+fTvR0dGkpqY2d4qtwjvvvIPRaGTatGn069ePb7/9liNHjnDjxg1zzM2bN4mMjOSbb75h5syZdOvWjYsXL961SGQymVi9ejWOjo61ruXn5zN58mSefPJJkpKSKCgoIDExEZPJxMqVK+/rWZb+6898U1JwX2OIiIjIf9ZjD7vwW/+XmjsNERERERERaWFaZfEiLS0No9FIZGQkUVFR5nZ/f39eeOEFDh482IzZtR5nz55l1apVrFmzhuHDh5vbx4wZYxG3fft2MjIy2LVrFy4uLvccd/HixYwcOZKLFy/WurZu3To6derEO++8g8FgAKBTp07Mnj2bzMxMvLy8Gv0835QUcLao9j1FREREREREREREpHVpldtGbdiwAScnJyIjI+u8PmLECODWVkdvv/02w4YNw8fHh4kTJ7Jv3z6L2Llz5zJhwgQOHz7Mc889h6+vL6+++ioXLlygqKiIqKgoBgwYQFBQELt27bLoGxISwtSpU0lNTSUoKAhfX19CQkI4e/asRVx98rg91p2ysrLw8PDg2LFj5jYPDw+MRiNJSUkMHTqUgIAA5s2bR2lpqUXf9PR0Jk2ahI+PDxMmTCAtLa3W97Rjxw66detmUbioy1/+8hfGjh1br8LF559/zv79+5kzZ06d17OysvD39zcXLgCGDRsGwN/+9rd7ji8iIiIiIiIiIiIibV+rK15UV1eTnp7O4MGDsba2/sHYmJgYtm7dSnh4OKtXr6ZXr17MmjWLAwcOWMSZTCYSEhKYPn06iYmJ5OXlERMTQ3R0NH369CEpKQlvb29iY2PJz8+36Hv69GnWrVvHnDlzWLRoEQUFBYSHh1NZWdngPOorJSWF8+fPk5CQwIwZM9i5cydr1qyxeJ6wsDAMBgPLly8nLCyMuLg4Ll++bDFORkYGffr0Yc2aNQwZMoQnnniC4OBgMjIyzDFVVVVkZmbStWtXfve739GvXz/69+9PVFQUJpPJYrwbN24QHx/PtGnT7lroqKiosChcAFhbW2NlZVWr6CMiIiIiIiIiIiIiP06tbtuooqIiKisr6dq16w/GZWdns3fvXuLi4ggODgYgMDCQ/Px8Vq9ezahRo8yxxcXFJCcn07t3bwAKCgqIj48nIiKCGTNmAODj48O+ffvYv38/oaGh5r6FhYUkJyfTs2dPALy8vBg7diw7duwgODi4QXnUl7OzM0uWLDGPlZmZyZ49e4iJiQFg48aNWFlZYTQaefjhhwFwdXVl8uTJFuOYTCZOnTrFV199xZtvvomNjQ1r167l17/+NXv37sXR0ZGioiKqqqowGo34+/uzatUqrl69yuLFi5k1axbvv/++ebwtW7ZQVlZW6z536tmzJydPnqSmpgYrKysAvvzyS2pqaiguLm7wdyEiIiIiIiIiIiIibU+rW3lx2+0X33dz/PhxAMaOHWvRPm7cODIzMy22WXJxcTEXLgBzIWLo0KHmtk6dOuHg4MClS5csxuvdu7c5HqBHjx54enqaVy80JI/6ujMvgMcff9wir4yMDAICAsyFC4AhQ4Zgb29v0a+mpobS0lJWrFjB2LFjGT58OO+88w41NTUkJycDtw7rBnjwwQdZtWoVw4YN46c//SmLFi3ixIkTHDlyBLhVxFm5ciVz586ttbLiTi+99BJnzpxhyZIlXL16lezsbOLi4mjfvn2DvwcRERERERERERERaZtaXfHC3t6ejh071nkY9J2Ki4uxtrau9cLeycmJmpoaSkpKzG2dOnWyiLm9HdWdL/8BDAYDFRUVFm2Ojo617u3o6GjeUqkhedRXXfneuU2VyWSqMy8HB4da49jb2+Pp6Wlus7e3x8vLizNnzphjrKysGDBggEVRYtCgQbRv394ct2LFCjw8PHjyySe5du0a165do7q6murqavPPcKuIEhMTw+bNmxkyZAg/+9nPePLJJ/H09KzXmRoiIiIiIiIiIiIi0va1um2jOnTowIABAzh69CjV1dV06FD3I9jZ2VFVVUVxcTF2dnbm9itXrmBlZVWrMNFYhYWFdbbdLgjUNw+DwUBVVZXFOI3dRsnZ2bnOvK5evWrxuVevXuTl5dU5xu0ijY2NDY8++uhd73U77ty5c/zrX//C39+/Voy/vz9Go5HAwEAAIiIieOWVV/jmm29wdnamU6dODB48mF/84hf1e0ARERERERERERERadNa3coLgClTpmAymVi7dm2d19PS0hg4cCAAu3fvtri2e/duvLy8sLW1bZJccnJyyM3NNX/Ozc0lOzsbPz8/gHrn4erqyrlz56ipqTHHHDp0qFE5+fr6cuzYMYtVHUeOHKGoqMgibsSIERQVFZGVlWVu+/bbbzl9+jTe3t4Wcenp6RarTo4ePcqNGzfMcfPnz2fTpk0W/3l6etKvXz82bdqEr6+vxb1tbW3x8PDAwcGB1NRUampqGDduXKOeV0RERERERERERETalla38gJg+PDhhIeHk5SUxJkzZxg/fjydO3fmwoULbN++nZKSElJTUxk9ejQJCQmUl5fj5ubGxx9/zIkTJ1izZk2T5eLo6Mi0adOYPXs2cGv7pC5dujBp0iQAPD0965XHmDFj2LZtG/Hx8QQFBZGens6ePXsalVNoaChbtmwhIiKCiIgIrl27RlJSUq2tq4KCgvDx8WH27NlER0fTsWNH3n33XQwGAy+//LI5LiwsjI8++ojIyEh+9atfcfXqVZYsWcLAgQMZPHgwAH379q2VR6dOnbC1tSUgIMDc9s0335CammouZhw9epRNmzbxhz/8wWJlSmM89rC2nRIREWlt9Pe3iIiIiIiI1KVVFi8AYmNj6d+/PykpKcyfP5+ysjJcXFwYNmwYYWFhACxevJilS5diNBopKirC3d2dlStXMnLkyCbLw9vbm9GjR7N48WJMJhN+fn7ExcVZnA9RnzwCAwOJjY0lOTmZDz/8kMDAQOLi4pg8eXKDc3JxccFoNLJw4UKioqLo3r07CxYsYNmyZRZx7dq149133+Xtt99mwYIFVFVV8eSTT5KSkoKzs7M57pFHHjEXGGbNmoWNjQ2jRo1i7ty59zw4/fusra357LPP2LhxI1VVVXh6erJq1SpGjBjR4Of8vt/6v3TfY4iIiMh/3s2am7SzapULgkVEREREROTfxKrmzn2KpEFCQkKwtbVl3bp1zZ3Kj9rJkyeprKzEy8sLGxub5k5HfuTKyso4d+4cbm5umo/S7DQfpSXRfJSWpLS0lKysLPr27dtk28mK3A/NSWlJNB+lJdF8lJZE87FpnDx5EgAfH597xupX3ERERETk366mpoaysjL0ezMiIiIiIiJSH6122yiAAwcOkJKSwqlTpygtLTVvGzVlyhTc3NyaO70WZ+LEifTt25eEhATg1uHi69evJyMjg5ycHNzd3fnkk09q9QsJCeGzzz6r1b5r1y4ef/xxACorK1m+fDkZGRmcPn2asrIyjhw5goODQ61+6enpLFq0iKysLBwdHXnppZeIiIho8BZUdzIYDPotTmkRbGxs8PLyau40RADNR/nP0/ZPIiIiIiIi0lRabfEiMTERo9HImDFjiI+Px8HBgby8PLZv3050dDSpqan/9hw2b978b7/Hv1NOTg5paWn4+flx8+bNH/xNyAEDBvD6669btHXr1s38c3l5OX/5y1/w8fFh4MCB/POf/6xznNzcXMLCwnjqqad47bXX+L//9/+SmJhI+/btzWeVNNbK41vIv15wX2OIiIhI4zz6kAuzB77c3GmIiIiIiIhIG9EqixdpaWkYjUYiIyOJiooyt/v7+/PCCy9w8ODBZsyu9Rg5ciRBQUEAzJ07l1OnTt01tlOnTvTr1+8Hr3/22WdYWVmxY8eOuxYv1q9fT+fOnVm6dCkGg4EhQ4Zw9epV1q5dS0hIiMVB5w2Vf72Ac8X5je4vIiIiIiIiIiIiIi1Dq1zXv2HDBpycnIiMjKzz+ogRIwCoqKjg7bffZtiwYfj4+DBx4kT27dtnETt37lwmTJjA4cOHee655/D19eXVV1/lwoULFBUVERUVxYABAwgKCmLXrl0WfUNCQpg6dSqpqakEBQXh6+tLSEgIZ8+etYirTx63x7pTVlYWHh4eHDt2zNzm4eGB0WgkKSmJoUOHEhAQwLx58ygtLbXom56ezqRJk/Dx8WHChAmkpaXV+p7atWvaP/76bPv097//nVGjRlkUKZ599lmuXbvGiRMnmjQfEREREREREREREWmdWl3xorq6mvT0dAYPHoy1tfUPxsbExLB161bCw8NZvXo1vXr1YtasWRw4cMAizmQykZCQwPTp00lMTCQvL4+YmBiio6Pp06cPSUlJeHt7ExsbS36+5W/2nz59mnXr1jFnzhwWLVpEQUEB4eHhVFZWNjiP+kpJSeH8+fMkJCQwY8YMdu7cyZo1ayyeJywsDIPBwPLlywkLCyMuLo7Lly836n4An332Gf369cPHx4dXX32Vf/3rXw0eo7S0lP/93//F3d3dot3d3R0rK6taRR8RERERERERERER+XFqddtGFRUVUVlZSdeuXX8wLjs7m7179xIXF0dwcDAAgYGB5Ofns3r1akaNGmWOLS4uJjk5md69ewNQUFBAfHw8ERERzJgxAwAfHx/27dvH/v37CQ0NNfctLCwkOTmZnj17AuDl5cXYsWPZsWMHwcHBDcqjvpydnVmyZIl5rMzMTPbs2UNMTAwAGzduxMrKCqPRyMMPPwyAq6srkydPbvC94NZ2XBMnTqRnz54UFBSwfv16pkyZwubNm+nfv3+9xykpKQFubTF1p9uHbRcXFzcqPxERERERERERERFpW1rdyovb7rVF0fHjxwEYO3asRfu4cePIzMy02GbJxcXFXLgAzIWIoUOHmts6deqEg4MDly5dshivd+/e5niAHj164OnpSUZGRoPzqK878wJ4/PHHLfLKyMggICDAXLgAGDJkCPb29g2+F8Ds2bP5+c9/zpNPPsmzzz7L5s2bcXFxsVjtISIiIiIiIiIiIiLSVFpd8cLe3p6OHTty8eLFH4wrLi7G2tq61gt7JycnampqzKsAoPZKgNvbUd358h9urRCoqKiwaHN0dKx1b0dHR0wmU4PzqK+68r1zmyqTyVRnXg4ODg2+V11sbW0ZPnw4p0+fblC/29/n95+5srKSsrIy7OzsmiQ/EREREREREREREWndWl3xokOHDgwYMICjR49SXV191zg7OzuqqqpqbUV05coVrKysahUmGquwsLDONmdn5wblYTAYqKqqsohp7DZKzs7OdeZ19erVRo3XVGxtbXnkkUdqnW1x7tw5ampqap2FISIiIiIiIiIiIiI/Tq2ueAEwZcoUTCYTa9eurfN6WloaAwcOBGD37t0W13bv3o2Xlxe2trZNkktOTg65ubnmz7m5uWRnZ+Pn5wdQ7zxcXV3NL/FvO3ToUKNy8vX15dixYxYrHI4cOUJRUVGjxvu+0tJS/ud//gcfH58G9w0MDOTAgQMWhZpdu3bRqVOnBp2fISIiIiIiIiIiIiJtV6s7sBtg+PDhhIeHk5SUxJkzZxg/fjydO3fmwoULbN++nZKSElJTUxk9ejQJCQmUl5fj5ubGxx9/zIkTJ5r0rAZHR0emTZvG7NmzAVixYgVdunRh0qRJAHh6etYrjzFjxrBt2zbi4+MJCgoiPT2dPXv2NCqn0NBQtmzZQkREBBEREVy7do2kpKRaW1eVlZWRlpYGQH5+PtevXzcXWQYNGoSDgwOff/457733Hs888wyPPvooBQUF/PGPf8RkMrFixQqL8dLS0igrK+PUqVMAHDx4kAcffJBevXrRq1cvAMLCwti5cydz5szhpZde4quvvmL9+vVER0djMBga9bwiIiIiIiIiIiIi0ra0yuIFQGxsLP379yclJYX58+dTVlaGi4sLw4YNIywsDIDFixezdOlSjEYjRUVFuLu7s3LlSkaOHNlkeXh7ezN69GgWL16MyWTCz8+PuLg4ixfx9ckjMDCQ2NhYkpOT+fDDDwkMDCQuLo7Jkyc3OCcXFxeMRiMLFy4kKiqK7t27s2DBApYtW2YRV1hYSFRUlEXb7c+bNm0iICAAZ2dnqqqqWLZsGUVFRdjY2NC/f3/i4uLw9fW16BsXF0d+fr758/z58wGYOXMms2bNAm4daL5+/XoSEhL4zW9+g4ODA7Nnz+bXv/51g5/z+x59yOW+xxAREZHG0d/DIiIiIiIi0pSsau7cp0gaJCQkBFtbW9atW9fcqfyonTx5EqBR21iJiIhI07lZc5N2VnXvSlpaWkpWVhZ9+/Ztsu07RRpL81FaGs1JaUk0H6Ul0XyUlkTzsWk05F1uqzzzQuT7KisrKSsra+40RCgrKyMzM1PzUVoEzUf5T7tb4UJERERERESkoVrttlENceDAAVJSUjh16hSlpaXm7aWmTJmCm5tbc6fX4kycOJG+ffuSkJBgbjt48CArV64kJycHR0dHXnjhBWbMmEH79u3NMR4eHncd8x//+AcuLi53jXNycmr0AeUiIiIiIiIiIiIi0ra0+eJFYmIiRqORMWPGEB8fj4ODA3l5eWzfvp3o6GhSU1MbPfbmzZubLtEW7IsvviAyMpLx48fz29/+ljNnzrB8+XLKysp4/fXXzXFbt26t1ff111/HxsbGXLi4LSQkhAkTJpg/W1tb31eOBoMBGxub+xpDpCnY2Njg5eXV3GmIAJqP8p/3Q9tGiYiIiIiIiDREmy5epKWlYTQaiYyMtDiY2t/fnxdeeIGDBw82Y3atR1JSEn379iUxMRGAp59+mpqaGpYuXUpYWBhOTk4A9OvXz6LfhQsXOH/+PLGxsbXGfOSRR2rF36/VJ1K4WFLQpGOKiIhI/XR92IUZ/V9p7jRERERERESkjWjTxYsNGzbg5OREZGRknddHjBgBQEVFBUuXLuXTTz+luLgYd3d3Zs6cyTPPPGOOnTt3LqdOnWL+/Pm8/fbb5Obm4uvrS0JCAg899BBvvvkm//jHP3BwcOC3v/0tzz77rLnv7YO9x40bx6pVqygoKMDPz4+4uDjc3d3NcfXJo65DwrOysnj++efZtGkTAQEBwK2tmWJiYigvL+fPf/4zN27cYOTIkbzxxhsWB8qkp6ezcOFCcnJy6NGjR52FhqysLF544QWLtmHDhrFo0SL++c9/8vzzz9f5/X7yySdYWVlZrLD4d7pYUsD5a/n/kXuJiIiIiIiIiIiIyL9Pm13XX11dTXp6OoMHD77nlkQxMTFs3bqV8PBwVq9eTa9evZg1axYHDhywiDOZTCQkJDB9+nQSExPJy8sjJiaG6Oho+vTpQ1JSEt7e3sTGxpKfb/kS/fTp06xbt445c+awaNEiCgoKCA8Pp7KyssF51FdKSgrnz58nISGBGTNmsHPnTtasWWPxPGFhYRgMBpYvX05YWBhxcXFcvnzZYpyKigoMBoNF2+3PX3/99V3v/+mnn+Lv74+rq2uta++++y7e3t48+eSTvPbaa1y8eLFRzygiIiIiIiIiIiIibU+bXXlRVFREZWUlXbt2/cG47Oxs9u7dS1xcHMHBwQAEBgaSn5/P6tWrGTVqlDm2uLiY5ORkevfuDUBBQQHx8fFEREQwY8YMAHx8fNi3bx/79+8nNDTU3LewsJDk5GR69uwJgJeXF2PHjmXHjh0EBwc3KI/6cnZ2ZsmSJeaxMjMz2bNnDzExMQBs3LgRKysrjEYjDz/8MACurq5MnjzZYpwePXrw5ZdfWrR98cUX5u+kLtnZ2Xz11Vf8/ve/r3Xt+eef5yc/+QlOTk589dVXvPPOO7z88st89NFH2NnZNfg5RURERERERERERKRtabMrL26zsrL6wevHjx8HYOzYsRbt48aNIzMzk9LSUnObi4uLuXABmAsRQ4cONbd16tQJBwcHLl26ZDFe7969zfFwqyDg6elJRkZGg/OorzvzAnj88cct8srIyCAgIMBcuAAYMmQI9vb2Fv1efvll/v73v7Nx40aKior4/PPPWb58Oe3bt7/rvXfu3Im1tTVjxoypdW3RokWMGzcOf39/XnnlFd577z0KCgr44IMPGvyMIiIiIiIiIiIiItL2tNnihb29PR07drzndkTFxcVYW1vXemHv5ORETU0NJSUl5rZOnTpZxNzejurOl/9wa0uliooKizZHR8da93Z0dMRkMjU4j/qqK987t6kymUx15uXg4GDxedKkSYSGhvLf//3fBAQEMHnyZIKDg7Gzs8PFxaVW/5qaGnbt2sXTTz9d63nq4unpiZubG6dPn67nk4mIiIiIiIiIiIhIW9ZmixcdOnRgwIABHD16lOrq6rvG2dnZUVVVVWv7oytXrmBlZVWrMNFYhYWFdbY5Ozs3KA+DwUBVVZVFzN22broXZ2fnOvO6evWqxed27doxf/58jh49ykcffcThw4f5xS9+wdWrV/Hz86vV//jx41y8eJHnnnuuUXmJiIiIiIiIiIiIyI9bmy1eAEyZMgWTycTatWvrvJ6WlsbAgQMB2L17t8W13bt34+Xlha2tbZPkkpOTQ25urvlzbm4u2dnZ5pf/9c3D1dWVc+fOUVNTY445dOhQo3Ly9fXl2LFjFqs6jhw5QlFRUZ3xDz/8MJ6ennTq1InNmzfTrVu3WltTwa0to2xtbRk5cmS98sjKyuLcuXP4+Pg06jlEREREREREREREpG1pswd2AwwfPpzw8HCSkpI4c+YM48ePp3Pnzly4cIHt27dTUlJCamoqo0ePJiEhgfLyctzc3Pj44485ceIEa9asabJcHB0dmTZtGrNnzwZgxYoVdOnShUmTJgG3tk6qTx5jxoxh27ZtxMfHExQURHp6Onv27GlUTqGhoWzZsoWIiAgiIiK4du0aSUlJtbZ6+vLLL/nss8/o27cv5eXl/O1vf+Ojjz7CaDTWOveiurqaPXv2EBQUxAMPPFDrnuvXrycvL4+AgAAcHBzIyclh7dq1uLq68uKLLzbqOURERERERERERESkbWnTxQuA2NhY+vfvT0pKCvPnz6esrAwXFxeGDRtGWFgYAIsXL2bp0qUYjUaKiopwd3dn5cqV9V45UB/e3t6MHj2axYsXYzKZ8PPzIy4uDoPBYI6pTx6BgYHExsaSnJzMhx9+SGBgIHFxcUyePLnBObm4uGA0Glm4cCFRUVF0796dBQsWsGzZMos4a2tr9u7dy+rVqwHw8/Nj8+bN9O/fv9aY//znP/n222+ZMGFCnfd0c3Nj7969/PWvf+W7776jc+fODB8+nNdee63WGR0N1fXh2udviIiIyH+G/h4WERERERGRpmRVc+f+Q/JvERISgq2tLevWrWvuVNqkkydPAmjbKRERkWZ2s+Ym7azq3pW0tLSUrKws+vbt22Tbcoo0luajtDSak9KSaD5KS6L5KC2J5mPTaMi73DZ95oX8eFRWVlJWVtbcaYhQVlZGZmam5qO0CJqP8p92t8KFiIiIiIiISEPpX5giIiIiIiIiIiIiItKitMgzLw4cOEBKSgqnTp2itLTUfEbFlClTcHNza+70Gmzz5s3/1vEnTpxI3759SUhIACA3N5f169eTkZFBTk4O7u7ufPLJJ3X2/ctf/sJ7773HxYsXcXNzIzo6mhEjRljEfP311yQkJPCvf/0La2trfvKTnzBv3jwcHBws4g4cOMDatWs5c+YMDz74IAMHDiQmJobHHnsMgAsXLjBq1Kg68zAYDOYlQ41hMBiwsbFpdH+RpmJjY4OXl1dzpyECaD7Kv8cPbQ0lIiIiIiIi0lRaXPEiMTERo9HImDFjiI+Px8HBgby8PLZv3050dDSpqanNnWKLl5OTQ1paGn5+fty8eZO7HWvy6aef8sYbbzBt2jQGDx7Mrl27mDlzJikpKfTr1w+A69evExoaSpcuXUhMTKS8vJylS5cydepUtm7dSrt2t15eHDt2jJkzZ/L8888THR1NUVERK1as4Ne//jU7d+7kgQcewMXFha1bt1rkUFNTQ3h4OIMHD77v516Xkcz/Xr983+OIiIhI3R55qAtT/V5t7jRERERERETkR6BFFS/S0tIwGo1ERkYSFRVlbvf39+eFF17g4MGDzZhd6zFy5EiCgoIAmDt3LqdOnaozbuXKlYwfP57XXnsNgMGDB/PVV1+xevVqjEYjAFu2bKGkpITU1FScnJwA6NGjBz//+c85cOAAzzzzDHCrENK1a1f+8Ic/YGVlBYCDgwOhoaGcOnWKJ598EoPBYC6K3Hbs2DGuX7/OhAkT7vu5//f6ZXKv5d/3OCIiIiIiIiIiIiLSvFrUmv8NGzbg5OREZGRknddvb2dUUVHB22+/zbBhw/Dx8WHixIns27fPInbu3LlMmDCBw4cP89xzz+Hr68urr77KhQsXKCoqIioqigEDBhAUFMSuXbss+oaEhDB16lRSU1MJCgrC19eXkJAQzp49axFXnzxuj3WnrKwsPDw8OHbsmLnNw8MDo9FIUlISQ4cOJSAggHnz5lFaWmrRNz09nUmTJuHj48OECRNIS0ur9T3dXg3xQ7755hvOnz/PuHHjLNqfffZZjhw5QmVlJQCZmZl4enqaCxdw6yR4e3t7/va3v5nbqqurefDBB82FC4CHH34Y4K4rPwA++eQTHnroIUaOHHnPnEVERERERERERETkx6HFFC+qq6tJT09n8ODBWFtb/2BsTEwMW7duJTw8nNWrV9OrVy9mzZrFgQMHLOJMJhMJCQlMnz6dxMRE8vLyiImJITo6mj59+pCUlIS3tzexsbHk51v+xv7p06dZt24dc+bMYdGiRRQUFBAeHm5+qd+QPOorJSWF8+fPk5CQwIwZM9i5cydr1qyxeJ6wsDAMBgPLly8nLCyMuLg4Ll9u+FZJtwsx3z9D5PHHH6eqqopvvvkGuFWgMRgMtfobDAaLYs6kSZP4+uuvSUlJoaSkhG+++YalS5fi5eXFgAED6syhqqqKvXv38swzz9CxY8cGP4OIiIiIiIiIiIiItE0tZtuooqIiKisr6dq16w/GZWdns3fvXuLi4ggODgYgMDCQ/Px8Vq9ebXEgdHFxMcnJyfTu3RuAgoIC4uPjiYiIYMaMGcCtVQT79u1j//79hIaGmvsWFhaSnJxMz549AfDy8mLs2LHs2LGD4ODgBuVRX87OzixZssQ8VmZmJnv27CEmJgaAjRs3YmVlhdFoNK9qcHV1ZfLkyQ2+V3FxMQCdOnWyaL/9+fb1nj17smPHDsrLy3nggQcAuHjxIiaTCVtbW3O/J598klWrVjFnzhx+//vfA9C3b1/ee+892rdvX2cOf//73ykqKmqSLaNEREREREREREREpO1oMSsvbrtz26G6HD9+HICxY8datI8bN47MzEyLbZZcXFzMhQvAXIgYOnSoua1Tp044ODhw6dIli/F69+5tjodb5zx4enqSkZHR4Dzq68684NYqiDvzysjIICAgwFy4ABgyZAj29vYNvld9vfjii1y/fp0FCxZw+fJlcnNzmTt3Lu3atbP4s0pPT+d3v/sdv/jFL9i4cSMrVqzg5s2b/OY3v6G8vLzOsXfu3ImTkxNDhgz5t+UvIiIiIiIiIiIiIq1Piyle2Nvb07FjRy5evPiDccXFxVhbW9d6Ye/k5ERNTQ0lJSXmtu+vKri9HdWdL//h1hZIFRUVFm2Ojo617u3o6IjJZGpwHvVVV753blNlMpnqzMvBwaHB97KzswOolee1a9csrru7u/PWW29x8OBBAgMDGT16NHZ2dgQGBuLs7Gzut3DhQgYPHszcuXMZPHgwY8eO5d133yUzM5OPPvqo1v2/++47Dh48yLhx4+66MkNEREREREREREREfpxaTPGiQ4cODBgwgKNHj1JdXX3XODs7O6qqqszbGt125coVrKysahUmGquwsLDOttsv7Oubh8FgoKqqyiLm+33qy9nZuc68rl692uCx3N3dAWodQn727Fmsra157LHHzG3PP/88hw4dYufOnfz9738nKSmJb775hn79+pljvv76azw9PS3GcnV1pXPnzuTl5dW6/759+ygvL+e5555rcO4iIiIiIiIiIiIi0ra1mOIFwJQpUzCZTKxdu7bO62lpaQwcOBCA3bt3W1zbvXs3Xl5eFucw3I+cnBxyc3PNn3Nzc8nOzsbPzw+g3nm4urpy7tw5ampqzDGHDh1qVE6+vr4cO3bMYrXEkSNHKCoqavBYjz32GD179qyV/65duxgyZEitQ7oNBgN9+vShS5cuHDlyhPPnz/Ozn/3MfL1r165kZmZa9MnPz+fbb7/l0UcfrXX/Tz75hO7du5u/TxERERERERERERGR21rMgd0Aw4cPJzw8nKSkJM6cOcP48ePp3LkzFy5cYPv27ZSUlJCamsro0aNJSEigvLwcNzc3Pv74Y06cOMGaNWuaLBdHR0emTZvG7NmzAVixYgVdunRh0qRJAHh6etYrjzFjxrBt2zbi4+MJCgoiPT2dPXv2NCqn0NBQtmzZQkREBBEREVy7do2kpKRaW1eVlZWRlpYG3CogXL9+3VykGDRokHmbqVmzZhETE0P37t0JCAhg165dfPnllyQnJ5vHKi0tJSkpCX9/fzp27MgXX3zBu+++y8yZM82rNwCCg4P5wx/+wMKFCxk5ciRFRUW88847ODo6Mm7cOIv8rl69ypEjR4iIiGjU9yAiIiIiIiIiIiIibVuLKl4AxMbG0r9/f1JSUpg/fz5lZWW4uLgwbNgwwsLCAFi8eDFLly7FaDRSVFSEu7s7K1euZOTIkU2Wh7e3N6NHj2bx4sWYTCb8/PyIi4uzWJFQnzwCAwOJjY0lOTmZDz/8kMDAQOLi4pg8eXKDc3JxccFoNLJw4UKioqLo3r07CxYsYNmyZRZxhYWFREVFWbTd/rxp0yYCAgIAmDBhAmVlZRiNRt59913c3NxYtWoV/fv3N/dr164dX331FTt27KC0tBR3d3fefPNNcxHntl/96lcYDAb+/Oc/s337dh588EH69evH8uXL6dy5s0XsX//6V6qrq5t8y6hHHurSpOOJiIiIJf1dKyIiIiIiIv8pVjV37mckAISEhGBra8u6deuaOxWph5MnTwLg4+PTzJmIiIi0fTdrbtLOquE7j5aWlpKVlUXfvn2bbJtPkcbSfJSWRnNSWhLNR2lJNB+lJdF8bBoNeZfbos68EGmsyspKysrKmjsNEcrKysjMzNR8lBZB81H+HRpTuBARERERERFpqFb9r88DBw7w61//mkGDBvHEE08wcuRIFixYwLlz55o7tRZp4sSJzJ071/w5NzeXBQsWMHHiRLy8vJgwYUKd/UJCQvDw8Kj139dff20R9/XXXxMREUG/fv3w9/cnNjaWq1ev3jWf7777jsDAQDw8PMwVNxERERERERERERGRFnfmRX0lJiZiNBoZM2YM8fHxODg4kJeXx/bt24mOjiY1NbXRY2/evLnpEm3BcnJySEtLw8/Pj5s3b/JDO4gNGDCA119/3aKtW7du5p+vX79OaGgoXbp0ITExkfLycpYuXcrUqVPZunUr7drVrpOtWbOGGzduNMmzGAwGbGxsmmQskfthY2ODl5dXc6chAmg+StNr7JZRIiIiIiIiIg3VKosXaWlpGI1GIiMjLQ6m9vf354UXXuDgwYPNmF3rMXLkSIKCggCYO3cup06dumtsp06d6Nev312vb9myhZKSElJTU3FycgKgR48e/PznP+fAgQM888wzFvFff/01W7Zs4fXXX+fNN9+8/4cB1n+5mf/9rqBJxhIRERFLjzzoQphvSHOnISIiIiIiIj8SrbJ4sWHDBpycnIiMjKzz+ogRIwCoqKhg6dKlfPrppxQXF+Pu7s7MmTMtXqTffmk/f/583n77bXJzc/H19SUhIYGHHnqIN998k3/84x84ODjw29/+lmeffdbc9/bB3uPGjWPVqlUUFBTg5+dHXFwc7u7u5rj65FHXIeFZWVk8//zzbNq0iYCAAAA8PDyIiYmhvLycP//5z9y4cYORI0fyxhtvWBwUk56ezsKFC8nJyaFHjx7ExsbW+p7qWg3RWJmZmXh6epoLF3Dr0BV7e3v+9re/1SpeLFy4kODgYNzc3Josh//9roBvSi402XgiIiIiIiIiIiIi0jxa3br/6upq0tPTGTx4MNbW1j8YGxMTw9atWwkPD2f16tX06tWLWbNmceDAAYs4k8lEQkIC06dPJzExkby8PGJiYoiOjqZPnz4kJSXh7e1NbGws+fn5Fn1Pnz7NunXrmDNnDosWLaKgoIDw8HAqKysbnEd9paSkcP78eRISEpgxYwY7d+5kzZo1Fs8TFhaGwWBg+fLlhIWFERcXx+XLlxt1P4DPPvuMfv364ePjw6uvvsq//vUvi+sVFRUYDIZa/QwGA2fPnrVo2717N1999RUzZsxodD4iIiIiIiIiIiIi0na1upUXRUVFVFZW0rVr1x+My87OZu/evcTFxREcHAxAYGAg+fn5rF69mlGjRplji4uLSU5Opnfv3gAUFBQQHx9PRESE+QW7j48P+/btY//+/YSGhpr7FhYWkpycTM+ePQHw8vJi7Nix7Nixg+Dg4AblUV/Ozs4sWbLEPFZmZiZ79uwhJiYGgI0bN2JlZYXRaOThhx8GwNXVlcmTJzf4XnBrO66JEyfSs2dPCgoKWL9+PVOmTGHz5s30798fgJ49e7Jjxw7Ky8t54IEHALh48SImk8liRUhZWRkJCQlER0fz0EMPNSofEREREREREREREWnbWt3Ki9usrKx+8Prx48cBGDt2rEX7uHHjyMzMpLS01Nzm4uJiLlwA5kLE0KFDzW2dOnXCwcGBS5cuWYzXu3dvczzcOufB09OTjIyMBudRX3fmBfD4449b5JWRkUFAQIC5cAEwZMgQ7O3tG3wvgNmzZ/Pzn/+cJ598kmeffZbNmzfj4uJisdrjxRdf5Pr16yxYsIDLly+Tm5vL3LlzadeuncWf1TvvvIOjoyMvvPBCo3IRERERERERERERkbav1RUv7O3t6dixIxcvXvzBuOLiYqytrWu9sHdycqKmpoaSkhJzW6dOnSxibm9HdefLf7i1BVJFRYVFm6OjY617Ozo6YjKZGpxHfdWV753bVJlMpjrzcnBwaPC96mJra8vw4cM5ffq0uc3d3Z233nqLgwcPEhgYyOjRo7GzsyMwMBBnZ2cA8vPz2bBhA7Nnz6akpIRr166ZizelpaV89913TZKfiIiIiIiIiIiIiLRurW7bqA4dOjBgwACOHj1KdXU1HTrU/Qh2dnZUVVVRXFyMnZ2duf3KlStYWVnVKkw0VmFhYZ1tnp6eDcrDYDBQVVVlMU5xcXGjcnJ2dq4zr6tXrzZqvPp6/vnnefbZZzl//jx2dnZ06dKF8ePHM3LkSAAuXLhAVVUVv/nNb2r1/dWvfoWfnx8ffPDBvzVHEREREREREREREWn5Wt3KC4ApU6ZgMplYu3ZtndfT0tIYOHAgcOtw6Dvt3r0bLy8vi3MY7kdOTg65ubnmz7m5uWRnZ+Pn5wdQ7zxcXV05d+4cNTU15phDhw41KidfX1+OHTtmsarjyJEjFBUVNWq87ystLeV//ud/8PHxqXXNYDDQp08funTpwpEjRzh//jw/+9nPAOjbty+bNm2y+G/evHkAxMXF8eabbzZJfiIiIiIiIiIiIiLSurW6lRcAw4cPJzw8nKSkJM6cOcP48ePp3LkzFy5cYPv27ZSUlJCamsro0aNJSEigvLwcNzc3Pv74Y06cOGFxVsP9cnR0ZNq0acyePRuAFStW0KVLFyZNmgSAp6dnvfIYM2YM27ZtIz4+nqCgINLT09mzZ0+jcgoNDWXLli1EREQQERHBtWvXSEpKqrV1VVlZGWlpacCtLZ2uX79uLrIMGjQIBwcHPv/8c9577z2eeeYZHn30UQoKCvjjH/+IyWRixYoV5rFKS0tJSkrC39+fjh078sUXX/Duu+8yc+ZM3N3dgVvbXQUEBNSZs7e3N97e3o16XhERERERERERERFpW1pl8QIgNjaW/v37k5KSwvz58ykrK8PFxYVhw4YRFhYGwOLFi1m6dClGo5GioiLc3d1ZuXKleRujpuDt7c3o0aNZvHgxJpMJPz8/4uLiMBgM5pj65BEYGEhsbCzJycl8+OGHBAYGEhcXx+TJkxuck4uLC0ajkYULFxIVFUX37t1ZsGABy5Yts4grLCwkKirKou32502bNhEQEICzszNVVVUsW7aMoqIibGxs6N+/P3Fxcfj6+pr7tWvXjq+++oodO3ZQWlqKu7s7b775prmI85/wyIMu/7F7iYiI/Njo71kRERERERH5T7KquXOfImmQkJAQbG1tWbduXXOn8qN28uRJgDq3sRIREZGmc7PmJu2sGrfraGlpKVlZWfTt27fJtu8UaSzNR2lpNCelJdF8lJZE81FaEs3HptGQd7mt8swLke+rrKykrKysudMQoaysjMzMTM1HaRE0H6WpNbZwISIiIiIiItJQ+heoiIiIiIiIiIiIiIi0KK32zIuGOHDgACkpKZw6dYrS0lLz2RhTpkzBzc2t0eNu3ry5CbNsOSZOnEjfvn1JSEgAIC0tDaPRyJkzZ7h+/TpdunQhKCiImTNn8vDDD5v7eXh43HXMf/zjH7i4uHD27FmSk5M5evQo+fn5ODo68vTTTxMVFYWDg0OjczYYDNjY2DS6v0hTsbGxwcvLq7nTEAE0H6Xp3M92USIiIiIiIiKN0eaLF4mJiRiNRsaMGUN8fDwODg7k5eWxfft2oqOjSU1Nbe4UW7yioiJ8fX0JCQnB3t6enJwckpKSyMnJYcOGDea4rVu31ur7+uuvY2Njg4vLrUM+Dx8+zOeff84vf/lLPD09uXjxIitXruSzzz7jo48+sjjovKE2ntrEpdLLje4vIiIitbnadiH0iV81dxoiIiIiIiLyI9Omixe3VwxERkYSFRVlbvf39+eFF17g4MGDzZhd6zFx4kSLzwEBARgMBt544w0uX75Mly5dAOjXr59F3IULFzh//jyxsbHmtvHjx/PKK69gZWVlbuvRowcvvfQSBw8eZMyYMY3O81LpZS6UXGh0fxERERERERERERFpGdr0+v8NGzbg5OREZGRknddHjBgBQEVFBW+//TbDhg3Dx8eHiRMnsm/fPovYuXPnMmHCBA4fPsxzzz2Hr68vr776KhcuXKCoqIioqCgGDBhAUFAQu3btsugbEhLC1KlTSU1NJSgoyLyK4ezZsxZx9cnj9lh3ysrKwsPDg2PHjpnbPDw8MBqNJCUlMXToUAICApg3bx6lpaUWfdPT05k0aRI+Pj5MmDCBtLS0enyzYG9vD0BVVdVdYz755BOsrKyYMGGCua1z584WhQvAvKVJQUFBve4tIiIiIiIiIiIiIm1bmy1eVFdXk56ezuDBg7G2tv7B2JiYGLZu3Up4eDirV6+mV69ezJo1iwMHDljEmUwmEhISmD59OomJieTl5RETE0N0dDR9+vQhKSkJb29vYmNjyc/Pt+h7+vRp1q1bx5w5c1i0aBEFBQWEh4dTWVnZ4DzqKyUlhfPnz5OQkMCMGTPYuXMna9assXiesLAwDAYDy5cvJywsjLi4OC5frnvrpRs3blBRUcHp06dZvXo1I0eOpFu3bne9/6effoq/vz+urq4/mOfx48cBePzxxxvxlCIiIiIiIiIiIiLS1rTZbaOKioqorKyka9euPxiXnZ3N3r17iYuLIzg4GIDAwEDy8/NZvXo1o0aNMscWFxeTnJxM7969gVsrBeLj44mIiGDGjBkA+Pj4sG/fPvbv309oaKi5b2FhIcnJyfTs2RO4tdpg7Nix7Nixg+Dg4AblUV/Ozs4sWbLEPFZmZiZ79uwhJiYGgI0bN2JlZYXRaDQfvO3q6srkyZPrHG/EiBHmwsbTTz9tHrsu2dnZfPXVV/z+97//wRwrKipYtGgRXl5eDBkypKGPKCIiIiIiIiIiIiJtUJtdeXHb97co+r7bv/U/duxYi/Zx48aRmZlpsc2Si4uLuXABmAsRQ4cONbd16tQJBwcHLl26ZDFe7969zfFw65wHT09PMjIyGpxHfd2ZF9xa2XBnXhkZGQQEBJgLFwBDhgwxbwn1fe+++y7vv/8+Cxcu5OzZs0ybNo0bN27UGbtz506sra3veYbFm2++yYULF1i0aNE9/6xERERERERERERE5Mehza68sLe3p2PHjly8ePEH44qLi7G2tq71wt7JyYmamhpKSkqwtbUFbhUm7nR7O6o7X/4DGAwGKioqLNocHR1r3dvR0RGTydTgPOqrrnzv3KbKZDLRo0ePWv0cHBzqHM/T0xOA/v37W5zJ8f2CS01NDbt27eLpp5++ayEEYNmyZezcuZO1a9fSp0+f+j6WiIiIiIiIiIiIiLRxbXblRYcOHRgwYABHjx6lurr6rnF2dnZUVVVRXFxs0X7lyhWsrKxqFSYaq7CwsM42Z2fnBuVhMBhqHZL9/T715ezsXGdeV69evWdfDw8PrK2tycvLq3Xt+PHjXLx4keeee+6u/Tdv3sy6det46623ePrppxuWuIiIiIiIiIiIiIi0aW22eAEwZcoUTCYTa9eurfN6WloaAwcOBGD37t0W13bv3o2Xl1eDVzvcTU5ODrm5uebPubm5ZGdn4+fnB1DvPFxdXTl37hw1NTXmmEOHDjUqJ19fX44dO0ZJSYm57ciRIxQVFd2zb0ZGBlVVVXUe2L1z505sbW0ZOXJknX0/+eQT3nrrLX7729/y/PPPNyp3EREREREREREREWm72uy2UQDDhw8nPDycpKQkzpw5w/jx4+ncuTMXLlxg+/btlJSUkJqayujRo0lISKC8vBw3Nzc+/vhjTpw4wZo1a5osF0dHR6ZNm8bs2bMBWLFiBV26dGHSpEnArS2Z6pPHmDFj2LZtG/Hx8QQFBZGens6ePXsalVNoaChbtmwhIiKCiIgIrl27RlJSUq2tnmbOnMkTTzyBh4cHDzzwANnZ2axfvx4PDw+CgoIsYqurq9mzZw9BQUE88MADte752WefMXfuXAYPHsygQYP44osvzNdcXV1xdXVt1LMAuNp2aXRfERERqZv+fhUREREREZHm0KaLFwCxsbH079+flJQU5s+fT1lZGS4uLgwbNoywsDAAFi9ezNKlSzEajRQVFeHu7s7KlSvvunKgMby9vRk9ejSLFy/GZDLh5+dHXFwcBoPBHFOfPAIDA4mNjSU5OZkPP/yQwMBA4uLimDx5coNzcnFxwWg0snDhQqKioujevTsLFixg2bJlFnG+vr7s2rWLd999l5qaGh599FFefPFFwsLCLPIH+Oc//8m3337LhAkT6rznsWPHqKqq4siRIxw5csTi2syZM5k1a1aDn+O20Cd+1ei+IiIicnc3a27SzqpNL9gVERERERGRFsaq5s79h+TfIiQkBFtbW9atW9fcqbRJJ0+epLKyEi8vL2xsbJo7HfmRKysr49y5c7i5uWk+SrPTfJSWpLS0lKysLPr27dtk23KKNJbmo7Q0mpPSkmg+Skui+SgtieZj0zh58iQAPj4+94zVr9CJiIiIiIiIiIiIiEiL0iqKFwcOHODXv/41gwYN4oknnmDkyJEsWLCAc+fONXdqLdbEiROZO3fuD8acPHmSefPmMW7cODw9PZk6dWqdcSUlJbzxxhsEBATg5+dHSEgIWVlZteK+/vprIiIi6NevH/7+/sTGxnL16lWLmL/+9a9Mnz6dwMBA+vXrx8SJE9m2bRv3uwDIYDDot4qlRbCxsdEqIGkxNB/lftysudncKYiIiIiIiMiPWIs/8yIxMRGj0ciYMWOIj4/HwcGBvLw8tm/fTnR0NKmpqc2d4j1t3ry5uVOoU3p6Op9//jm+vr5UVFTcNe63v/0tp06dIjY2FicnJ/70pz8RGhrKRx99xCOPPALA9evXCQ0NpUuXLiQmJlJeXs7SpUuZOnUqW7dupV27W3WyP/3pTzz66KPMnTuXzp07c/jwYd544w0uXbrEzJkz7+t5kjP/xOXvLt3XGCIiIgJdHnTlVa/JzZ2GiIiIiIiI/Ii16OJFWloaRqORyMhIoqKizO3+/v688MILHDx4sBmza/1CQkIIDQ01/1yXL774gr///e+888475oPDAwICGDVqFOvXr+e//uu/ANiyZQslJSWkpqbi5OQEQI8ePfj5z3/OgQMHeOaZZwB45513cHBwMI8/ZMgQioqK+OMf/0hkZKS5yNEYl7+7RP71C43uLyIiIiIiIiIiIiItQ4veNmrDhg04OTkRGRlZ5/URI0aYf66oqODtt99m2LBh+Pj4MHHiRPbt22cRP3fuXCZMmMDhw4d57rnn8PX15dVXX+XChQsUFRURFRXFgAEDCAoKYteuXRZ9Q0JCmDp1KqmpqQQFBeHr60tISAhnz561iKtPHrfHulNWVhYeHh4cO3bM3Obh4YHRaCQpKYmhQ4cSEBDAvHnzKC0tteibnp7OpEmT8PHxYcKECaSlpd3jm72lPoWCzMxMrKyseOqpp8xtNjY2PPnkkxbFo8zMTDw9Pc2FC7h16Iq9vT1/+9vfzG13Fi5u69u3L9evX6/1XCIiIiIiIiIiIiLy49RiixfV1dWkp6czePBgrK2t7xkfExPD1q1bCQ8PZ/Xq1fTq1YtZs2Zx4MABiziTyURCQgLTp08nMTGRvLw8YmJiiI6Opk+fPiQlJeHt7U1sbCz5+fkWfU+fPs26deuYM2cOixYtoqCggPDwcCorKxucR32lpKRw/vx5EhISmDFjBjt37mTNmjUWzxMWFobBYGD58uWEhYURFxfH5cuXG3W/76usrKRdu3a0b9/eot3a2pr8/HzKy8uBW0Ubg8FQq7/BYKhV4Pm+48eP06VLFx566KEmyVlEREREREREREREWrcWu21UUVERlZWVdO3a9Z6x2dnZ7N27l7i4OIKDgwEIDAwkPz+f1atXM2rUKHNscXExycnJ9O7dG4CCggLi4+OJiIhgxowZwK0VA/v27WP//v3mbZUACgsLSU5OpmfPngB4eXkxduxYduzYQXBwcIPyqC9nZ2eWLFliHiszM5M9e/YQExMDwMaNG7GyssJoNPLwww8D4OrqyuTJkxt8r7r06NGDGzdukJmZia+vLwA3b97k1KlT1NTUcO3aNR544AF69uzJjh07KC8v54EHHgDg4sWLmEwmbG1t7zr+559/zq5du3j99debJF8RERERERERERERaf1a7MqL26ysrO4Zc/z4cQDGjh1r0T5u3DgyMzMttiNycXExFy4AcyFi6NCh5rZOnTrh4ODApUuWhz/37t3bHA+3Xux7enqSkZHR4Dzq6868AB5//HGLvDIyMggICDAXLuDWORL29vYNvlddnnrqKbp3786bb77JV199RWFhIYsWLeKbb74B/t+fz4svvsj169dZsGABly9fJjc3l7lz59KuXbu7/hleunSJ6OhoAgIC+NWvftUk+YqIiIiIiIiIiIhI69diixf29vZ07NiRixcv3jO2uLgYa2vrWi/snZycqKmpoaSkxNzWqVMni5jbW1Ld+fIfbm13VFFRYdHm6OhY696Ojo6YTKYG51FfdeV75zZVJpOpzrzqOluiMQwGA8uWLaO0tJTnnnuOoUOHcvjwYUJDQy2e1d3dnbfeeouDBw8SGBjI6NGjsbOzIzAwEGdn51rjXrt2jYiICOzt7UlKSrqvg7pFREREREREREREpG1psdtGdejQgQEDBnD06FGqq6vp0OHuqdrZ2VFVVUVxcTF2dnbm9itXrmBlZVWrMNFYhYWFdbZ5eno2KA+DwUBVVZXFOMXFxY3KydnZuc68rl692qjx6vLEE0+we/ducnNzqampoWfPnvz+97/H29vb4jyS559/nmeffZbz589jZ2dHly5dGD9+PCNHjrQYr7y8nKlTp1JSUsLWrVub7M9HRERERERERERERNqGFv3r7lOmTMFkMrF27do6r6elpQEwcOBAAHbv3m1xfffu3Xh5ef3gmQsNkZOTQ25urvlzbm4u2dnZ+Pn5NSgPV1dXzp07R01NjTnm0KFDjcrJ19eXY8eOWazqOHLkCEVFRY0a726srKzo2bMnbm5ufPvtt+zatYsXX3yxVpzBYKBPnz506dKFI0eOcP78eX72s5+Zr1dXV/Paa69x9uxZ3nvvPbp06dKkeYqIiIiIiIiIiIhI69diV14ADB8+nPDwcJKSkjhz5gzjx4+nc+fOXLhwge3bt1NSUsLw4cPx9PRk9OjRJCQkUF5ejpubGx9//DEnTpxgzZo1TZaPo6Mj06ZNY/bs2QCsWLGCLl26MGnSJIB65zFmzBi2bdtGfHw8QUFBpKens2fPnkblFBoaypYtW4iIiCAiIoJr166RlJRUrzMvrl69ymeffWb++bvvvjMXXoYPH46NjQ0A77zzDj169MDR0ZFz586xbt06nnjiCfNzA5SWlpKUlIS/vz8dO3bkiy++4N1332XmzJm4u7ub4+Li4jh48CBz587l+vXrfPHFF+ZrXl5eGAyGRn0PIiIiIiIiIiIiItJ2tOjiBUBsbCz9+/cnJSWF+fPnU1ZWhouLC8OGDSMsLMwct3jxYpYuXYrRaKSoqAh3d3dWrlxZa8ui++Ht7c3o0aNZvHgxJpMJPz8/4uLiLF641yePwMBAYmNjSU5O5sMPPyQwMJC4uDgmT57c4JxcXFwwGo0sXLiQqKgounfvzoIFC1i2bNk9++bk5BAVFWXRdvvzgQMH6NatG3DrfIpFixZRWFiIi4sLP/3pT4mMjLQ4p6Jdu3Z89dVX7Nixg9LSUtzd3XnzzTctChzw/1aYJCQk1Mrnzns2RpcHXRvdV0RERP4f/Z0qIiIiIiIizc2q5s69i+SuQkJCsLW1Zd26dc2dinzPyZMnAfDx8WnmTERERNqOmzU3aWfVdDuMlpaWkpWVRd++fZtsS0+RxtJ8lJZGc1JaEs1HaUk0H6Ul0XxsGg15l9uiz7wQqa/KykrKysqaOw0RysrKyMzM1HyUFkHzUe5HUxYuRERERERERBpK/yoVEREREREREREREZEWpcWfefFDDhw4QEpKCqdOnaK0tNR8FsaUKVNwc3Nr0ntt3ry5ScdrDhMnTqRv377m8ybS0tIwGo2cOXOG69ev06VLF4KCgpg5cyYPP/ywRd+KigrWrl3LRx99REFBAU5OTowbN47XX3/dIu7y5cssXbqUtLQ0SktLefTRR5k+fTo//elPAUhKSmLVqlV15vfLX/6S3//+9416NoPBYD5gXKQ52djY4OXl1dxpiACaj9JwTb1VlIiIiIiIiEhjtdriRWJiIkajkTFjxhAfH4+DgwN5eXls376d6OhoUlNTmzvFFq+oqAhfX19CQkKwt7cnJyeHpKQkcnJy2LBhgznu5s2bREZG8s033zBz5ky6devGxYsXOXfunMV4BQUF/PKXv8TNzY34+HgeeughcnJyqKysNMe8+OKLPP300xb9/vWvf5GYmEhgYOB9Pc/7mRsoKP3f+xpDRETkx8rF9hGCvX7d3GmIiIiIiIiIAK20eHF7xUBkZCRRUVHmdn9/f1544QUOHjzYjNm1HhMnTrT4HBAQgMFg4I033uDy5ct06dIFgO3bt5ORkcGuXbtwcXG563iLFy/G1dWV9957j/bt2wMwZMgQixhXV1dcXV0t2t5//33s7Ozuu3hRUPq/XLz+zX2NISIiIiIiIiIiIiLNr1XuC7BhwwacnJyIjIys8/qIESOAW1sdvf322wwbNgwfHx8mTpzIvn37LGLnzp3LhAkTOHz4MM899xy+vr68+uqrXLhwgaKiIqKiohgwYABBQUHs2rXLom9ISAhTp04lNTWVoKAg8yqGs2fPWsTVJ4/bY90pKysLDw8Pjh07Zm7z8PDAaDSSlJTE0KFDCQgIYN68eZSWllr0TU9PZ9KkSfj4+DBhwgTS0tLq8c2Cvb09AFVVVea2v/zlL4wdO/YHCxfXr1/nr3/9Ky+//LK5cFEfFRUV7Nu3jzFjxmAwGOrdT0RERERERERERETarlZXvKiuriY9PZ3BgwdjbW39g7ExMTFs3bqV8PBwVq9eTa9evZg1axYHDhywiDOZTCQkJDB9+nQSExPJy8sjJiaG6Oho+vTpQ1JSEt7e3sTGxpKfn2/R9/Tp06xbt445c+awaNEiCgoKCA8Pt9gqqb551FdKSgrnz58nISGBGTNmsHPnTtasWWPxPGFhYRgMBpYvX05YWBhxcXFcvny5zvFu3LhBRUUFp0+fZvXq1YwcOZJu3boBt4oYmZmZdO3ald/97nf069eP/v37ExUVhclksvgeqqqq6NChA6+++ire3t489dRTLF682KIQ8n0HDx7k+vXrTJgwoVHfhYiIiIiIiIiIiIi0Pa1u26iioiIqKyvp2rXrD8ZlZ2ezd+9e4uLiCA4OBiAwMJD8/HxWr17NqFGjzLHFxcUkJyfTu3dv4NbZDfHx8URERDBjxgwAfHx82LdvH/v37yc0NNTct7CwkOTkZHr27AmAl5cXY8eOZceOHQQHBzcoj/pydnZmyZIl5rEyMzPZs2cPMTExAGzcuBErKyuMRqP54G1XV1cmT55c53gjRowwFzaefvpp89hw6/uuqqrCaDTi7+/PqlWruHr1KosXL2bWrFm8//77AFy5cgWA//qv/+IXv/gFM2fO5Msvv2TlypW0a9eOOXPm1HnvTz75hC5duuDv79/g70FERERERERERERE2qZWt/LiNisrqx+8fvz4cQDGjh1r0T5u3DgyMzMttllycXExFy4AcyFi6NCh5rZOnTrh4ODApUuXLMbr3bu3OR6gR48eeHp6kpGR0eA86uvOvAAef/xxi7wyMjIICAgwFy7g1tkTt7eE+r53332X999/n4ULF3L27FmmTZvGjRs3gFuHdQM8+OCDrFq1imHDhvHTn/6URYsWceLECY4cOWIRN3ToUObOncvgwYP5zW9+Q1hYGH/6058oLy+vdd9r166RlpbG+PHjadeu1U5FEREREREREREREWlire6Nsb29PR07duTixYs/GFdcXIy1tXWtF/ZOTk7U1NRQUlJibuvUqZNFzO3tqO58+Q9gMBioqKiwaHN0dKx1b0dHR/OWSg3Jo77qyvfObapMJlOdeTk4ONQ5nqenJ/379+fFF19kzZo1HDt2zHwmR6dOnbCysmLAgAEWZ1IMGjSI9u3bc+bMGYucBg8ebDH2kCFDqKysJDc3t9Z99+zZQ2VlJc8991x9HltEREREREREREREfiRaXfGiQ4cODBgwgKNHj1JdXX3XODs7O6qqqiguLrZov3LlClZWVrUKE41VWFhYZ5uzs3OD8jAYDLXOhvh+n/pydnauM6+rV6/es6+HhwfW1tbk5eUBYGNjw6OPPnrX+NvFnF69ev3guN8v+sCtLaPc3d3x8vK6Z14iIiIiIiIiIiIi8uPR6ooXAFOmTMFkMrF27do6r6elpTFw4EAAdu/ebXFt9+7deHl5YWtr2yS55OTkWKwqyM3NJTs7Gz8/P4B65+Hq6sq5c+eoqakxxxw6dKhROfn6+nLs2DGLVR1HjhyhqKjonn0zMjKoqqoyH9gNt87ESE9PtyhAHD16lBs3buDt7Q3Ao48+Sp8+fTh8+LDFeIcPH+aBBx6oVdwoKCjgs88+00HdIiIiIiIiIiIiIlJLqzuwG2D48OGEh4eTlJTEmTNnGD9+PJ07d+bChQts376dkpISUlNTGT16NAkJCZSXl+Pm5sbHH3/MiRMnWLNmTZPl4ujoyLRp05g9ezYAK1asoEuXLkyaNAm4tSVTffIYM2YM27ZtIz4+nqCgINLT09mzZ0+jcgoNDWXLli1EREQQERHBtWvXSEpKqrV11cyZM3niiSfw8PDggQceIDs7m/Xr1+Ph4UFQUJA5LiwsjI8++ojIyEh+9atfcfXqVZYsWcLAgQMttomKjo4mMjKSt956i5/85CecPHmSDRs2EBYWVqtYtGvXLm7evNmkW0a52D7SZGOJiIj82OjvUREREREREWlJWmXxAiA2Npb+/fuTkpLC/PnzKSsrw8XFhWHDhhEWFgbA4sWLWbp0KUajkaKiItzd3Vn5/7N351FVX/f+/58YOQZiFJm0alScmATUxKDWwFWpQ9Saq02/3iQEDRAVB0oCrXoTLRdTsc5BcTiJrQqmJg5EWy9OtbTXKTeSEBVscIKIVY4YBsOs/P7w57megAaQFtDXYy3X4uzPe+/P+3Pca7H8vN17v/8+w4YNa7A8PD09GTFiBEuWLMFkMuHj40N0dLTF+RC1ycPPz4+oqCgSEhLYtWsXfn5+REdHM3ny5Drn5OzsjNFoZOHChYSHh9OlSxfmz5/PihUrLOK8vb3Zu3cvGzZsoKqqik6dOvHyyy8THBxskf+PfvQjNm/ezG9+8xtmzZqFjY0Nw4cPZ86cORYHpw8bNozly5cTHx/PRx99hLOzM7NmzeLNN9+sluOePXvw9vamS5cudX6++5nk8UaDjSUiIvI4ul11mxZWzXJhroiIiIiIiDxirKru3adI6iQwMBBbW1vWr1/f2Kk81k6dOkV5eTkeHh7Y2Ng0djrymCspKeHixYu4uLhoPkqj03yUpqS4uJiMjAzc3d0bbPtOkfrSfJSmRnNSmhLNR2lKNB+lKdF8bBinTp0CwMvL6wdj9V/rRERERERERERERESkSXksiheHDh3ijTfe4Pnnn6dPnz4MGzaM+fPnc/HixcZOrUkaP348c+bMqda+a9cuXnrpJby8vPD19SUkJITS0lKLmD//+c/89Kc/xcvLi5EjR7Jjx45q41y7do1f/OIXPPvss/Tr149p06bxzTffPFTOBoNB/6tYmgQbGxutApImQ/NRHuR21e3GTkFERERERETkvprtmRe1tXTpUoxGIyNHjiQmJgZ7e3uys7PZsWMHERERJCUl1XvsLVu2NFyiTdzatWsxGo1MmzaNvn378u2333Ls2DFu3bpljvn888+ZOXMmP/vZz5g3bx7Hjx/nP//zP3nqqacYNWoUALdu3SIkJISSkhJiYmIwGAysXr2aoKAg9uzZw1NPPVXvHD85+wGm4n889LOKiIg86pxsf8TLbiGNnYaIiIiIiIjIfT3SxYuUlBSMRiNhYWGEh4eb2wcMGMDEiRM5fPhwI2bXfFy4cIHVq1cTHx+Pv7+/uX3kyJEWcWvXrsXb25v/+q//AmDgwIF88803vP/+++biRXJyMl9//TWffvopbm5uwJ39zQICAvjkk0/qdUD5Xabif/CPm9n17i8iIiIiIiIiIiIiTcMjvW3Uxo0bcXR0JCwsrMbrQ4cOBaCsrIxFixYxZMgQvLy8GD9+PAcOHLCInTNnDmPHjuXo0aOMGzcOb29vXnvtNS5fvkx+fj7h4eH079+fgIAA9u7da9E3MDCQqVOnkpSUREBAAN7e3gQGBnLhwgWLuNrkcXese2VkZODq6sqJEyfMba6urhiNRuLi4hg8eDC+vr7MnTuX4uJii76pqalMmDABLy8vxo4dS0pKSrXvaefOnXTu3NmicPF95eXlnDhxwlykuOvFF1/k/PnzXL58GYD09HScnJzMhQuA9u3b06tXL/785z/fd3wREREREREREREReXw8ssWLyspKUlNTGThwINbW1g+MjYyMZNu2bYSEhLBmzRp69uzJrFmzOHTokEWcyWQiNjaW6dOns3TpUrKzs4mMjCQiIoLevXsTFxeHp6cnUVFR5OTkWPQ9c+YM69ev5+2332bx4sXk5uYSEhJCeXl5nfOorcTERC5dukRsbCwzZsxgz549xMfHWzxPcHAwBoOBlStXEhwcTHR0NNeuXbMYJy0tjd69exMfH8+gQYPo06cPkyZNIi0tzRyTnZ1NRUUF3bt3t+jbo0cPAHOhpqysDIPBUC1Xg8FQrZgjIiIiIiIiIiIiIo+nR3bbqPz8fMrLy+nYseMD486ePcv+/fuJjo5m0qRJAPj5+ZGTk8OaNWsYPny4ObagoICEhAR69eoFQG5uLjExMYSGhjJjxgzgzhZIBw4c4ODBgwQFBZn75uXlkZCQQLdu3QDw8PBg1KhR7Ny5k0mTJtUpj9pycnJi2bJl5rHS09PZt28fkZGRAGzatAkrKyuMRiNPP/00AB06dKi2dZPJZOL06dN8/fXXLFiwABsbG9atW8cbb7zB/v37cXBwoKCgAIA2bdpY9L37+e71bt26cfXqVa5du0b79u0B+O677zh37ly1w79FRERERERERERE5PH0yK68uMvKyuqB10+ePAlQbbuj0aNHk56ebrHNkrOzs7lwAZgLEYMHDza3tWnTBnt7e65evWoxXq9evczxAF27dsXNzc28eqEuedTWvXnBnVUQ9+aVlpaGr6+vuXABMGjQIOzs7Cz6VVVVUVxczKpVqxg1ahT+/v6sXbuWqqoqEhIS6pTT2LFjeeqpp5g3bx7ffPMNV69e5Z133qG4uPgH/65ERERERERERERE5PHwyBYv7OzsaNWqFVeuXHlgXEFBAdbW1tVe2Ds6OlJVVUVRUZG57furCu5uR3Xvy3+4swVSWVmZRZuDg0O1ezs4OGAymeqcR23VlO+921SZTKYa87K3t682jp2dncU5FXZ2dnh4eHDu3DkA2rZtC1Atz8LCQovrdnZ2LF++nK+//pqAgAD8/f0xmUy89NJLODk51fkZRUREREREREREROTR88gWL1q2bEn//v05fvw4lZWV941r27YtFRUV5m2N7rp+/TpWVlbVChP1lZeXV2Pb3Rf2tc3DYDBQUVFhEfP9PrXl5ORUY143btyw+NyzZ8/7jnG3SNOlSxesra2rnVtx9/O9Z2G88MIL/OUvf2Hv3r0cPHiQhIQErl+/Tt++fev1HCIiIiIiIiIiIiLyaHlkixcAU6ZMwWQysW7duhqvp6Sk8OyzzwKQnJxscS05ORkPDw9sbW0bJJfMzEyysrLMn7Oysjh79iw+Pj4Atc6jQ4cOXLx4kaqqKnPMkSNH6pWTt7c3J06csFgtcezYMfLz8y3ihg4dSn5+PhkZGea2b7/9ljNnzuDp6QncKar4+vqyb98+i7579+6lR48edO7c2aL9iSeeoEePHjzzzDOcP3+eo0eP8vLLL9frOURERERERERERETk0fLIHtgN4O/vT0hICHFxcZw7d44xY8bQrl07Ll++zI4dOygqKiIpKYkRI0YQGxtLaWkpLi4u7N69my+++IL4+PgGy8XBwYFp06Yxe/ZsAFatWkX79u2ZMGECAG5ubrXKY+TIkWzfvp2YmBgCAgJITU2tVjCoraCgILZu3UpoaCihoaEUFhYSFxdXbeuqgIAAvLy8mD17NhEREbRq1YoNGzZgMBh45ZVXzHHTp0/n9ddf59e//jWjR4/mxIkT/PGPf2TFihUW4y1ZsoS+ffvSunVr/v73v7N27VpeeuklBg0aVK/nEBEREREREREREZFHyyNdvACIioqiX79+JCYmMm/ePEpKSnB2dmbIkCEEBwcDd16mL1++HKPRSH5+Pt27d+f9999n2LBhDZaHp6cnI0aMYMmSJZhMJnx8fIiOjsZgMJhjapOHn58fUVFRJCQksGvXLvz8/IiOjmby5Ml1zsnZ2Rmj0cjChQsJDw+nS5cuzJ8/v1qxoUWLFmzYsIFFixYxf/58KioqeO6550hMTLQ4p+K5554jLi6OlStXsn37djp27MjChQsZPXq0xXhXr17l17/+NQUFBXTu3Jlp06bx+uuv1zn/73Oy/dFDjyEiIvI40O9MERERERERaeqsqu7df0j+KQIDA7G1tWX9+vWNncoj6dSpUwB4eXk1ciYiIiLNx+2q27Sw+tftIFpcXExGRgbu7u4Nti2nSH1pPkpTozkpTYnmozQlmo/SlGg+Noy6vMt9pM+8kMdHeXk5JSUljZ2GCCUlJaSnp2s+SpOg+SgP8q8sXIiIiIiIiIjUlf7VKiIiIiIiIiIiIiIiTUqzPvPi0KFDJCYmcvr0aYqLi81nWUyZMgUXF5fGTs9sy5YtjZ0CAOPHj8fd3Z3Y2FgAUlJSMBqNnDt3jps3b9K+fXsCAgKYOXMmTz/9tLnfkSNH2LlzJ2lpaXzzzTe8+uqrzJ8//4H3eu+999i8eXO12KysLD788EPS0tLIzMyke/fu/PGPf3zoZzMYDNjY2Dz0OCIPy8bGBg8Pj8ZOQwTQfJTq/tVbRYmIiIiIiIjUV7MtXixduhSj0cjIkSOJiYnB3t6e7OxsduzYQUREBElJSY2dYpOXn5+Pt7c3gYGB2NnZkZmZSVxcHJmZmWzcuNEc97e//Y2zZ88yYMAACgoKfnDcv//97+zYsYPWrVtXu5aZmUlKSgo+Pj7cvn2bhjxyZefZ9Vwv/keDjSciIvIocbT9ERPcpjZ2GiIiIiIiIiK10iyLF3dXDISFhREeHm5uHzBgABMnTuTw4cONmF3zMX78eIvPvr6+GAwG3n33Xa5du0b79u0B+OUvf8mcOXMAOHHixA+OGxMTw+TJk2ssIA0bNoyAgAAA5syZw+nTpx/yKf7P9eJ/cPW7rAYbT0REREREREREREQaR7PcN2Djxo04OjoSFhZW4/WhQ4cCUFZWxqJFixgyZAheXl6MHz+eAwcOWMTOmTOHsWPHcvToUcaNG4e3tzevvfYaly9fJj8/n/DwcPr3709AQAB79+616BsYGMjUqVNJSkoiICDAvIrhwoULFnG1yePuWPfKyMjA1dXVomDg6uqK0WgkLi6OwYMH4+vry9y5cykuLrbom5qayoQJE/Dy8mLs2LGkpKTU4psFOzs7ACoqKsxtLVrUfprs3r2by5cvExoaWuP1uowlIiIiIiIiIiIiIo+nZvcmubKyktTUVAYOHIi1tfUDYyMjI9m2bRshISGsWbOGnj17MmvWLA4dOmQRZzKZiI2NZfr06SxdupTs7GwiIyOJiIigd+/exMXF4enpSVRUFDk5ORZ9z5w5w/r163n77bdZvHgxubm5hISEUF5eXuc8aisxMZFLly4RGxvLjBkz2LNnD/Hx8RbPExwcjMFgYOXKlQQHBxMdHc21a9dqHO/WrVuUlZVx5swZ1qxZw7Bhw+jcuXOd87p58ya//e1v+eUvf6nzJ0RERERERERERESk3prdtlH5+fmUl5fTsWPHB8adPXuW/fv3Ex0dzaRJkwDw8/MjJyeHNWvWMHz4cHNsQUEBCQkJ9OrVC4Dc3FxiYmIIDQ1lxowZAHh5eXHgwAEOHjxIUFCQuW9eXh4JCQl069YNAA8PD0aNGsXOnTuZNGlSnfKoLScnJ5YtW2YeKz09nX379hEZGQnApk2bsLKywmg0mg/e7tChA5MnT65xvKFDh5oLGy+88IJ57LpavXo1Xbt25cUXX6xXfxERERERERERERERaIYrL+6ysrJ64PWTJ08CMGrUKIv20aNHk56ebrHNkrOzs7lwAZgLEYMHDza3tWnTBnt7e65evWoxXq9evczxAF27dsXNzY20tLQ651Fb9+YF0KNHD4u80tLS8PX1NRcuAAYNGmTeEur7NmzYwB/+8AcWLlzIhQsXmDZtGrdu3apTTpmZmSQmJvLOO+/UqZ+IiIiIiIiIiIiIyPc1u5UXdnZ2tGrViitXrjwwrqCgAGtr62ov7B0dHamqqqKoqAhbW1vgTmHiXne3o7r35T+AwWCgrKzMos3BwaHavR0cHDCZTHXOo7ZqyvfebapMJhNdu3at1s/e3r7G8dzc3ADo16+fxZkc3y+4PEhsbCyjRo2iU6dOFBYWAnD79m0qKiooLCykdevWOu9CRERERERERERERGql2b1NbtmyJf379+f48eNUVlbeN65t27ZUVFRQUFBg0X79+nWsrKyqFSbqKy8vr8Y2JyenOuVhMBgsDskGqvWpLScnpxrzunHjxg/2dXV1xdramuzs7Drd8+LFi+zevZsBAwaY//zjH//g448/ZsCAAVy8eLFO44mIiIiIiIiIiIjI46vZFS8ApkyZgslkYt26dTVeT0lJ4dlnnwUgOTnZ4lpycjIeHh51Xu1wP5mZmWRlZZk/Z2VlcfbsWXx8fABqnUeHDh24ePEiVVVV5pgjR47UKydvb29OnDhBUVGRue3YsWPk5+f/YN+0tDQqKirqfGD38uXL2bx5s8UfR0dHAgIC2Lx58w+eUSIiIiIiIiIiIiIiclez2zYKwN/fn5CQEOLi4jh37hxjxoyhXbt2XL58mR07dlBUVERSUhIjRowgNjaW0tJSXFxc2L17N1988QXx8fENlouDgwPTpk1j9uzZAKxatYr27dszYcIE4M6WTLXJY+TIkWzfvp2YmBgCAgJITU1l37599copKCiIrVu3EhoaSmhoKIWFhcTFxVXbumrmzJn06dMHV1dXnnzySc6ePcuHH36Iq6srAQEB5ricnBxOnToFQElJCdnZ2eZizN2tpfr27Vstj1atWtG+fXt8fX3NbSUlJaSkpJjHvXnzpnms559//r5bW9WGo+2P6t1XRETkUaffkyIiIiIiItKcNMviBUBUVBT9+vUjMTGRefPmUVJSgrOzM0OGDCE4OBiAJUuWsHz5coxGI/n5+XTv3p3333+fYcOGNVgenp6ejBgxgiVLlmAymfDx8SE6OhqDwWCOqU0efn5+REVFkZCQwK5du/Dz8yM6OprJkyfXOSdnZ2eMRiMLFy4kPDycLl26MH/+fFasWGER5+3tzd69e9mwYQNVVVV06tSJl19+meDgYIv8T5w4wdy5c82f//a3v/G3v/0NgL///e91yi0vL4/w8HCLtrufN2/ebFHoqKsJblPr3VdERORxcLvqNi2smuXCWxEREREREXnMWFXdu0+R1ElgYCC2trasX7++sVN5rJ06dYry8nI8PDywsbFp7HTkMVdSUsLFixdxcXHRfJRGp/koTUlxcTEZGRm4u7s32PadIvWl+ShNjeakNCWaj9KUaD5KU6L52DDu7vDj5eX1g7H6r3ciIiIiIiIiIiIiItKkNIttow4dOkRiYiKnT5+muLjYvD3UlClTcHFxaez0mqTx48fj7u5ObGzsfWNOnTrF1q1b+fLLL7l48SL+/v41riIpKirit7/9Lfv376e0tBRvb2/mzZuHu7u7RdzXX3/NsmXLSEtLo7KyEldXV2bNmsXAgQPNMbt27eKjjz7i0qVLlJSU0LFjR376058SGhpqsVVVXRkMBv2vYmkSbGxs8PDwaOw0RADNx0eFtnoSERERERGRx1GTL14sXboUo9HIyJEjiYmJwd7enuzsbHbs2EFERARJSUmNltuWLVsa7d4NITU1lc8//xxvb2/KysruG/fWW29x+vRpoqKicHR05Pe//z1BQUF8+umn/OhHdw7/vHHjBpMnT+aZZ57hvffew9rami1bthAaGsr27dtxdXUFoKCggBdeeIE333yT1q1b89VXX7F69WquXr1KTEzMQz3P7r+vJa8456HGEBERaUocbDvxU9fpjZ2GiIiIiIiIyL9cky5epKSkYDQaCQsLszjkecCAAUycOJHDhw83YnbNX2BgIEFBQeafa/Lll1/y17/+lbVr15oPGPf19WX48OF8+OGHvPPOOwAcO3aMvLw8Pv74Yzp37gzA888/z/PPP8/BgwfNxYvvH0A+cOBAvvvuO37/+9/z61//mieeeKLez5NXnMO177Lq3V9EREREREREREREmoYmvQfBxo0bcXR0JCwsrMbrQ4cONf9cVlbGokWLGDJkCF5eXowfP54DBw5YxM+ZM4exY8dy9OhRxo0bh7e3N6+99hqXL18mPz+f8PBw+vfvT0BAAHv37rXoGxgYyNSpU0lKSiIgIABvb28CAwO5cOGCRVxt8rg71r0yMjJwdXXlxIkT5jZXV1eMRiNxcXEMHjwYX19f5s6dS3FxsUXf1NRUJkyYgJeXF2PHjiUlJeUHvtk7WrT44b/+9PR0rKys+PGPf2xus7Gx4bnnnrMoHlVUVADw9NNPm9tatWqFtbU1P3QmvJ2dHZWVldy+fbtWeYuIiIiIiIiIiIjIo63JFi8qKytJTU1l4MCBWFtb/2B8ZGQk27ZtIyQkhDVr1tCzZ09mzZrFoUOHLOJMJhOxsbFMnz6dpUuXkp2dTWRkJBEREfTu3Zu4uDg8PT2JiooiJ8dyC6IzZ86wfv163n77bRYvXkxubi4hISGUl5fXOY/aSkxM5NKlS8TGxjJjxgz27NlDfHy8xfMEBwdjMBhYuXIlwcHBREdHc+3atXrd7/vKy8tp0aJFtRUR1tbW5OTkUFpaCtwpJDk6OhIbG0tubi43btxg2bJlWFlZMX78+GrjVlZWUlJSwueff86mTZv4j//4j1r9PYuIiIiIiIiIiIjIo6/JbhuVn59PeXk5HTt2/MHYs2fPsn//fqKjo5k0aRIAfn5+5OTksGbNGoYPH26OLSgoICEhgV69egGQm5tLTEwMoaGhzJgxAwAvLy8OHDjAwYMHzdsqAeTl5ZGQkEC3bt0A8PDwYNSoUezcuZNJkybVKY/acnJyYtmyZeax0tPT2bdvH5GRkQBs2rQJKysrjEajedVDhw4dqm3PVF9du3bl1q1bpKen4+3tDcDt27c5ffo0VVVVFBYW8uSTT9K2bVsSExOZOnUqL7zwAnBnRYXRaOSZZ56xGLOyshJPT0/z53//939n3rx5DZKviIiIiIiIiIiIiDR/TXblxV1WVlY/GHPy5EkARo0aZdE+evRo0tPTLbZZcnZ2NhcuAHMhYvDgwea2Nm3aYG9vz9WrVy3G69Wrlzke7rzYd3NzIy0trc551Na9eQH06NHDIq+0tDR8fX0ttmsaNGgQdnZ2db5XTX784x/TpUsXFixYwNdff01eXh6LFy/mm2++Af7v7ycvL4+ZM2fSpUsXNmzYwIcffoivry/Tp0/n/PnzFmO2bNmS7du3k5iYyNy5czl8+DBz585tkHxFREREREREREREpPlrssULOzs7WrVqxZUrV34wtqCgAGtr62ov7B0dHamqqqKoqMjc1qZNG4uYu1sV3fvyH8BgMFBWVmbR5uDgUO3eDg4OmEymOudRWzXle+82VSaTqca87O3t63yvmhgMBlasWEFxcTHjxo1j8ODBHD16lKCgIItn/eCDDygoKGDNmjX4+/szZMgQVqxYgZ2dncU2V3d5eXnx3HPPMXnyZN577z2SkpI4depUg+QsIiIiIiIiIiIiIs1bky1etGzZkv79+3P8+HEqKysfGNu2bVsqKiooKCiwaL9+/TpWVlbVChP1lZeXV2Obk5NTnfIwGAzmA67v+n6f2nJycqoxrxs3btRrvJr06dOH5ORk9u3bR3JyMrt376a0tBRPT09z8efcuXN0794dg8Fg7vfEE0/g6upKdnb2D44P/GCciIiIiIiIiIiIiDwemmzxAmDKlCmYTCbWrVtX4/WUlBQAnn32WQCSk5MtricnJ+Ph4YGtrW2D5JOZmUlWVpb5c1ZWFmfPnsXHx6dOeXTo0IGLFy9SVVVljjly5Ei9cvL29ubEiRMWqzqOHTtGfn5+vca7HysrK7p164aLiwvffvste/fu5eWXXzZf79ixI+fPn7dYrXLr1i3Onj1Lp06dHjj23e22vn82hoiIiIiIiIiIiIg8nprsgd0A/v7+hISEEBcXx7lz5xgzZgzt2rXj8uXL7Nixg6KiIvz9/XFzc2PEiBHExsZSWlqKi4sLu3fv5osvvqhxy6L6cnBwYNq0acyePRuAVatW0b59eyZMmABQ6zxGjhzJ9u3biYmJISAggNTUVPbt21evnIKCgti6dSuhoaGEhoZSWFhIXFxcrc68uHHjBp999pn55++++85cePH398fGxgaAtWvX0rVrVxwcHLh48SLr16+nT58+5ucGePnll9m+fTthYWG8+uqrPPHEE2zbto2srCwWLlxojnv11Vf5yU9+Qvfu3WnRogVpaWls3LiRF154wXwguIiIiIiIiIiIiIg83pp08QIgKiqKfv36kZiYyLx58ygpKcHZ2ZkhQ4YQHBxsjluyZAnLly/HaDSSn59P9+7def/99xk2bFiD5eLp6cmIESNYsmQJJpMJHx8foqOjLbZKqk0efn5+REVFkZCQwK5du/Dz8yM6OprJkyfXOSdnZ2eMRiMLFy4kPDycLl26MH/+fFasWPGDfTMzMwkPD7dou/v50KFDdO7cGYDCwkIWL15MXl4ezs7O/PSnPyUsLIwWLf5v4U6fPn344IMPiI+PZ+7cudy+fZuePXuyYcMGBgwYYBH38ccfc+XKFVq2bEnnzp2ZNWsWr7zySp2f/fscbB+8wkNERKS50e82EREREREReVxZVd27d5HcV2BgILa2tqxfv76xU5HvuXvQt5eXVyNnIiIi0vBuV92mhVWT3umzVoqLi8nIyMDd3b3BtvQUqS/NR2lqNCelKdF8lKZE81GaEs3HhlGXd7nN/1/CIkB5eTklJSWNnYYIJSUlpKenaz5Kk6D5+Gh4FAoXIiIiIiIiInXV5LeNqotDhw6RmJjI6dOnKS4uNm8vNWXKFFxcXBo7vSZp/PjxuLu7Exsbe9+YU6dOsXXrVr788ksuXryIv79/jStQioqK+O1vf8v+/fspLS3F29ubefPm4e7ubo65fPkyw4cPr9bXx8eHjz/+uGEeSkRERERERERERESatUemeLF06VKMRiMjR44kJiYGe3t7srOz2bFjBxERESQlJT3U+Fu2bGmYRJuh1NRUPv/8c7y9vSkrK7tv3FtvvcXp06eJiorC0dGR3//+9wQFBfHpp5/yox/9qFqsr6+v+fNTTz31UDkaDAbzAeMijcnGxgYPD4/GTkME0Hxszh6VraJERERERERE6uuRKF6kpKRgNBoJCwuzOIB6wIABTJw4kcOHDzdids1fYGAgQUFB5p9r8uWXX/LXv/6VtWvXmg8n9/X1Zfjw4Xz44Ye88847FvFdu3alb9++DZrnn75ezY3inAYdU0RE5F/N3rYTY3rPbOw0RERERERERBrVI1G82LhxI46OjoSFhdV4fejQoeafy8rKWL58OX/6058oKCige/fuzJw5k5/85CfmmDlz5nD69GnmzZvHokWLyMrKwtvbm9jYWFq3bs2CBQv429/+hr29PW+99RYvvviiue/dg71Hjx7N6tWryc3NxcfHh+joaLp3716nPGo6JDwjI4OXXnqJzZs3m1cuuLq6EhkZSWlpKR999BG3bt1i2LBhvPvuuxaHx6SmprJw4UIyMzPp2rUrUVFRtfp+W7T44f/5mZ6ejpWVFT/+8Y/NbTY2Njz33HMcPny4WvHin+FGcQ653136p99HRERERERERERERP65mv1+BJWVlaSmpjJw4ECsra1/MD4yMpJt27YREhLCmjVr6NmzJ7NmzeLQoUMWcSaTidjYWKZPn87SpUvJzs4mMjKSiIgIevfuTVxcHJ6enkRFRZGTY/m//c+cOcP69et5++23Wbx4Mbm5uYSEhFBeXl7nPGorMTGRS5cuERsby4wZM9izZw/x8fEWzxMcHIzBYGDlypUEBwcTHR3NtWvX6nW/7ysvL6dFixY88cQTFu3W1tbk5ORQWlpq0f7rX/8ad3d3Bg0axDvvvEN+fn6D5CEiIiIiIiIiIiIizV+zX3mRn59PeXk5HTt2/MHYs2fPsn//fqKjo5k0aRIAfn5+5OTksGbNGouDpAsKCkhISKBXr14A5ObmEhMTQ2hoKDNmzADAy8uLAwcOcPDgQfO2SgB5eXkkJCTQrVs3ADw8PBg1ahQ7d+5k0qRJdcqjtpycnFi2bJl5rPT0dPbt20dkZCQAmzZtwsrKCqPRyNNPPw1Ahw4dmDx5cp3vVZOuXbty69Yt0tPT8fb2BuD27ducPn2aqqoqCgsLefLJJzEYDPzHf/wHQ4YMoU2bNqSlpbFu3TpOnz7NJ598UqsClIiIiIiIiIiIiIg82pr9you7rKysfjDm5MmTAIwaNcqiffTo0aSnp1NcXGxuc3Z2NhcuAHMhYvDgwea2Nm3aYG9vz9WrVy3G69Wrlzke7rzYd3NzIy0trc551Na9eQH06NHDIq+0tDR8fX3NhQuAQYMGYWdnV+d71eTHP/4xXbp0YcGCBXz99dfk5eWxePFivvnmG+D//n6cnZ359a9/TUBAAM8//zyhoaEsW7aMjIwMDhw40CC5iIiIiIiIiIiIiEjz1uyLF3Z2drRq1YorV678YGxBQQHW1tbVXtg7OjpSVVVFUVGRua1NmzYWMXdXBNz78h/AYDBQVlZm0ebg4FDt3g4ODphMpjrnUVs15XvvNlUmk6nGvOzt7et8r5oYDAZWrFhBcXEx48aNY/DgwRw9epSgoKAan/Ve/v7+2NracubMmQbJRURERERERERERESat2ZfvGjZsiX9+/fn+PHjVFZWPjC2bdu2VFRUUFBQYNF+/fp1rKysqhUm6isvL6/GNicnpzrlYTAYqKiosIj5fp/acnJyqjGvGzdu1Gu8mvTp04fk5GT27dtHcnIyu3fvprS0FE9PT20HJSIiIiIiIiIiIiK11uyLFwBTpkzBZDKxbt26Gq+npKQA8OyzzwKQnJxscT05ORkPDw9sbW0bJJ/MzEyysrLMn7Oysjh79iw+Pj51yqNDhw5cvHiRqqoqc8yRI0fqlZO3tzcnTpywWNVx7NixBj8o28rKim7duuHi4sK3337L3r17efnllx/Y5/DhwxQXF+Pl5dWguYiIiIiIiIiIiIhI89TsD+yGO9sOhYSEEBcXx7lz5xgzZgzt2rXj8uXL7Nixg6KiIvz9/XFzc2PEiBHExsZSWlqKi4sLu3fv5osvviA+Pr7B8nFwcGDatGnMnj0bgFWrVtG+fXsmTJgAUOs8Ro4cyfbt24mJiSEgIIDU1FT27dtXr5yCgoLYunUroaGhhIaGUlhYSFxcXK3OvLhx4wafffaZ+efvvvvOXHjx9/fHxsYGgLVr19K1a1ccHBy4ePEi69evp0+fPubnBoiNjcXKyoq+ffvSpk0bvvrqK3NcQEBAvZ5NRERERERERERERB4tj0TxAiAqKop+/fqRmJjIvHnzKCkpwdnZmSFDhhAcHGyOW7JkCcuXL8doNJKfn0/37t15//33GTZsWIPl4unpyYgRI1iyZAkmkwkfHx+io6MxGAx1ysPPz4+oqCgSEhLYtWsXfn5+REdHM3ny5Drn5OzsjNFoZOHChYSHh9OlSxfmz5/PihUrfrBvZmYm4eHhFm13Px86dIjOnTsDUFhYyOLFi8nLy8PZ2Zmf/vSnhIWF0aLF/y3w6dGjBx999BEff/wxpaWltG/fnp/97GfMnj2bli0fbjra23Z6qP4iIiJNgX6fiYiIiIiIiIBV1b17EslDCwwMxNbWlvXr1zd2Ko+NU6dOAWjbKREReWTcrrpNC6tHYndPs+LiYjIyMnB3d2+wrTpF6kvzUZoazUlpSjQfpSnRfJSmRPOxYdTlXe6j9a9ieWyVl5dTUlLS2GmIUFJSQnp6uuajNAmaj83Xo1a4EBEREREREakr/ctYRERERERERERERESalGZdvDh06BBvvPEGzz//PH369GHYsGHMnz+fixcvNlpOW7ZsabJbRo0fP545c+bUeO27777Dz88PV1dX89Kdmhw8eBBXV1fGjh1b7dr58+cJDQ2lb9++DBgwgKioKG7cuGERk5WVxfz58xk/fjweHh41jlMfBoPBfHC4SGOysbHBw8ND81GaBM3Hpu921e3GTkFERERERESkSWq2B3YvXboUo9HIyJEjiYmJwd7enuzsbHbs2EFERARJSUmNnWKzEh8fz61btx4YU1paym9+8xscHR2rXbt58yZBQUG0b9+epUuXUlpayvLly5k6dSrbtm0zH9qdmZlJSkoKPj4+3L59m4Y8cmXf16u4UZLTYOOJiIj8M9nbdGJk7/DGTkNERERERESkSWqWxYuUlBSMRiNhYWGEh//fP/oHDBjAxIkTOXz4cCNm1/ycP3+erVu38qtf/YoFCxbcN279+vV07NiRzp07c/r0aYtrW7dupaioiKSkJHNxo2vXrvzsZz/j0KFD/OQnPwFg2LBhBAQEADBnzpxq4zyMGyU5mL5rvFU3IiIiIiIiIiIiItIwmuW2URs3bsTR0ZGwsLAarw8dOhSAsrIyFi1axJAhQ/Dy8mL8+PEcOHDAInbOnDmMHTuWo0ePMm7cOLy9vXnttde4fPky+fn5hIeH079/fwICAti7d69F38DAQKZOnUpSUhIBAQF4e3sTGBjIhQsXLOJqk8fdse6VkZGBq6srJ06cMLe5urpiNBqJi4tj8ODB+Pr6MnfuXIqLiy36pqamMmHCBLy8vBg7diwpKSn3/T4XLlzIpEmTcHFxuW9MdnY2v/vd73jnnXdqvJ6eno6bm5vFqgwvLy/s7Oz485//bG67uwJDREREREREREREROR+mt2b5MrKSlJTUxk4cCDW1tYPjI2MjGTbtm2EhISwZs0aevbsyaxZszh06JBFnMlkIjY2lunTp7N06VKys7OJjIwkIiKC3r17ExcXh6enJ1FRUeTkWG5LdObMGdavX8/bb7/N4sWLyc3NJSQkhPLy8jrnUVuJiYlcunSJ2NhYZsyYwZ49e4iPj7d4nuDgYAwGAytXriQ4OJjo6GiuXbtWbazk5GS+/vprZsyY8cB7vvfee4wfPx43N7car5eVlWEwGKq1GwyGasUcEREREREREREREZEHaXbbRuXn51NeXk7Hjh0fGHf27Fn2799PdHQ0kyZNAsDPz4+cnBzWrFnD8OHDzbEFBQUkJCTQq1cvAHJzc4mJiSE0NNT8Ut/Ly4sDBw5w8OBBgoKCzH3z8vJISEigW7duAHh4eDBq1Ch27tzJpEmT6pRHbTk5ObFs2TLzWOnp6ezbt4/IyEgANm3ahJWVFUajkaeffhqADh06MHnyZItxSkpKiI2NJSIigtatW9/3fn/+85/54osvSE5Ovm9Mt27d2LlzJ6WlpTz55JMAXLlyBZPJhK2tbZ2fUUREREREREREREQeX81u5cVdVlZWD7x+8uRJAEaNGmXRPnr0aNLT0y22WXJ2djYXLgBzIWLw4MHmtjZt2mBvb8/Vq1ctxuvVq5c5Hu6c8+Dm5kZaWlqd86ite/MC6NGjh0VeaWlp+Pr6mgsXAIMGDcLOzs6i39q1a3FwcGDixIn3vVdZWRm/+c1vmDVrFvb29veNe/nll7l58ybz58/n2rVrZGVlMWfOHFq0aPGDf1ciIiIiIiIiIiIiIvdqdsULOzs7WrVqxZUrVx4YV1BQgLW1dbUX9o6OjlRVVVFUVGRua9OmjUXM3e2o7n35D3e2QCorK7Noc3BwqHZvBwcHTCZTnfOorZryvXebKpPJVGNe9xYfcnJy2LhxI7Nnz6aoqIjCwkJzIaW4uJjvvvsOuLOKo0WLFowZM4bCwkIKCwupqKjg9u3bFBYWmu/bvXt33nvvPQ4fPoyfnx8jRoygbdu2+Pn54eTkVOdnFBEREREREREREZHHV7PbNqply5b079+f48ePU1lZScuWNT9C27ZtqaiooKCggLZt25rbr1+/jpWVVbXCRH3l5eXV2Hb3bIja5mEwGKioqLAYp6CgoF45OTk51ZjXjRs3zD9fvnyZiooK3nzzzWpxr7/+Oj4+Pnz88cdcuHCBrKwsBg0aVC1uwIAB/PrXv+Y//uM/AHjppZd48cUXuXTpEm3btqV9+/aMGTOGYcOG1es5REREREREREREROTx1OyKFwBTpkzhzTffZN26dcycObPa9ZSUFJ599lngzoHU/+///T/zteTkZDw8PBrsHIbMzEyysrLo2rUrAFlZWZw9e9Z8z9rm0aFDB44ePUpVVZV5m6UjR47UKydvb28++ugjioqKzMWRY8eOkZ+fb45xd3dn8+bNFv0yMjJYtGgR0dHReHl5ARAaGsq///u/W8Rt2LCBixcvsmjRIosts+BOEaZ3797me166dKlafxERERERERERERGRB2mWxQt/f39CQkKIi4vj3LlzjBkzhnbt2nH58mV27NhBUVERSUlJjBgxgtjYWEpLS3FxcWH37t188cUXxMfHN1guDg4OTJs2jdmzZwOwatUq2rdvz4QJEwBwc3OrVR4jR45k+/btxMTEEBAQQGpqKvv27atXTkFBQWzdupXQ0FBCQ0MpLCwkLi7OYuuqNm3a4OvrW2N/T09PPD09gTvnafTo0cPi+q5du7h27ZpF/+LiYuLi4hgwYACtWrXiyy+/ZMOGDcycOZPu3bub40pKSkhJSQHubF118+ZN80Hgzz///APP1RARERERERERERGRx0OzLF4AREVF0a9fPxITE5k3bx4lJSU4OzszZMgQgoODAViyZAnLly/HaDSSn59P9+7def/99xt0GyNPT09GjBjBkiVLMJlM+Pj4EB0djcFgMMfUJg8/Pz+ioqJISEhg165d+Pn5ER0dzeTJk+uck7OzM0ajkYULFxIeHk6XLl2YP38+K1asaIhHrlGLFi34+uuv2blzJ8XFxXTv3p0FCxaYizh35eXlER4ebtF29/PmzZvvW1CpDXubTvXuKyIi8q+m31siIiIiIiIi92dVVVVV1dhJNFeBgYHY2tqyfv36xk7lsXbq1CkA81ZXIiIizcXtqtu0sGrR2Gn8SxQXF5ORkYG7u3uDbd8pUl+aj9LUaE5KU6L5KE2J5qM0JZqPDaMu73Ifj38tyyOvoqKC0tLSxk5DhNLSUi5cuKD5KE2C5mPT97gULkRERERERETqqtluGwVw6NAhEhMTOX36NMXFxeZto6ZMmYKLi0tjp9fkjB8/Hnd3d2JjY4E7B5sbjUbOnTvHzZs3ad++PQEBAcycOdN80DfAnDlz2LVrV7XxjEYjfn5+ABQVFTFv3jzOnDnD9evXsbW1pU+fPsyePRtvb29znxs3bhAfH09aWhoZGRlYW1vzxRdfPPSzWVtb8+STTz70OCIP68knn7Q450WkMWk+Nl2P04oLERERERERkfpotsWLpUuXYjQaGTlyJDExMdjb25Odnc2OHTuIiIggKSnpn57Dli1b/un3+GfKz8/H29ubwMBA7OzsyMzMJC4ujszMTDZu3GgR+8wzz7B06VKLtnsP8i4vL8dgMDB9+nQ6d+7MzZs32bRpE0FBQezcudNcTLp27Rp79+7F29ubPn368Pe//73BnudQ5nLySy432HgiIiL/DHY2nRne663GTkNERERERESkSWuWxYu7KwbCwsIsDn8eMGAAEydO5PDhw42YXfMxfvx4i8++vr4YDAbeffddrl27Rvv27c3XnnzySfr27XvfsRwcHFi2bJlF2+DBg/H19WXfvn1MmzYNAFdXV44ePQpAXFxcgxYv8ksuc/27Cw02noiIiIiIiIiIiIg0jma5X8HGjRtxdHQkLCysxutDhw4FoKysjEWLFjFkyBC8vLwYP348Bw4csIidM2cOY8eO5ejRo4wbNw5vb29ee+01Ll++TH5+PuHh4fTv35+AgAD27t1r0TcwMJCpU6eSlJREQECAeRXDhQuWL9Brk8fdse6VkZGBq6srJ06cMLe5urpiNBqJi4szFwfmzp1LcXGxRd/U1FQmTJiAl5cXY8eOJSUlpRbfLNjZ2QF3zpB4WLa2trRq1cpirBYtmuWUExEREREREREREZF/oWb3JrmyspLU1FQGDhyItbX1A2MjIyPZtm0bISEhrFmzhp49ezJr1iwOHTpkEWcymYiNjWX69OksXbqU7OxsIiMjiYiIoHfv3sTFxeHp6UlUVBQ5OTkWfc+cOcP69et5++23Wbx4Mbm5uYSEhFBeXl7nPGorMTGRS5cuERsby4wZM9izZw/x8fEWzxMcHIzBYGDlypUEBwcTHR3NtWvXahzv1q1blJWVcebMGdasWcOwYcPo3LmzRUxWVhbPPvssffr0YcKECRw8eLDGsW7fvk1lZSW5ubnExsbSokULXnrppXo9p4iIiIiIiIiIiIg8nprdtlH5+fmUl5fTsWPHB8adPXuW/fv3Ex0dzaRJkwDw8/MjJyeHNWvWMHz4cHNsQUEBCQkJ9OrVC4Dc3FxiYmIIDQ1lxowZAHh5eXHgwAEOHjxIUFCQuW9eXh4JCQl069YNAA8PD0aNGsXOnTuZNGlSnfKoLScnJ/MWTX5+fqSnp7Nv3z4iIyMB2LRpE1ZWVhiNRvPB2x06dGDy5Mk1jjd06FBzYeOFF16otv2Tu7s7Xl5e9OzZk6KiIj766CNmzJjBqlWrGDVqlEXsqlWrWLduHXBnK6kNGzbwzDPP1PkZRUREREREREREROTx1exWXtxlZWX1wOsnT54EqPZyffTo0aSnp1tss+Ts7GwuXADmQsTgwYPNbW3atMHe3p6rV69ajNerVy9zPEDXrl1xc3MjLS2tznnU1r15wZ2Ds+/NKy0tDV9fX3PhAmDQoEHmLaG+b8OGDfzhD39g4cKFXLhwgWnTpnHr1i3z9aCgIF599VV8fX0JCAjAaDTi4+PD+++/X22sV155he3bt7N27Vp8fHx48803OXPmTJ2fUUREREREREREREQeX82ueGFnZ0erVq24cuXKA+MKCgqwtrau9sLe0dGRqqoqioqKzG1t2rSxiLm7HdW9L/8BDAYDZWVlFm0ODg7V7u3g4IDJZKpzHrVVU773blNlMplqzMve3r7G8dzc3OjXrx8vv/wy8fHxnDhxotqZHPdq0aIFI0aM4Pz585SWllpca9++PV5eXgwbNow1a9bwzDPP1FjkEBERERERERERERG5n2ZXvGjZsiX9+/fn+PHjVFZW3jeubdu2VFRUUFBQYNF+/fp1rKysqhUm6isvL6/GNicnpzrlYTAYqh2S/f0+teXk5FRjXjdu3PjBvq6urlhbW5OdnV2ve9+rRYsWuLu7k5WV9dBjiYiIiIiIiIiIiMjjo9kVLwCmTJmCyWQyn63wfSkpKTz77LMAJCcnW1xLTk7Gw8MDW1vbBsklMzPT4uV8VlYWZ8+excfHB6DWeXTo0IGLFy9SVVVljjly5Ei9cvL29ubEiRMWqzqOHTtGfn7+D/ZNS0ujoqKi2oHd97p9+zbJycn06tWLJ5988r5xlZWVfPXVVzrzQkRERERERERERETqpNkd2A3g7+9PSEgIcXFxnDt3jjFjxtCuXTsuX77Mjh07KCoqIikpiREjRhAbG0tpaSkuLi7s3r2bL774gvj4+AbLxcHBgWnTpjF79mzgzoHV7du3Z8KECcCdLZlqk8fIkSPZvn07MTExBAQEkJqayr59++qVU1BQEFu3biU0NJTQ0FAKCwuJi4urtnXVzJkz6dOnD66urjz55JOcPXuWDz/8EFdXVwICAgDIyclhzpw5jBkzhq5du1JQUMBHH33E6dOniYuLM4+1bds2vvrqKwYPHoyTkxPXr1/nD3/4AxcvXmTBggUW971byDl37hy3bt0yf/by8qJTp071emYREREREREREREReXQ0y+IFQFRUFP369SMxMZF58+ZRUlKCs7MzQ4YMITg4GIAlS5awfPlyjEYj+fn5dO/enffff59hw4Y1WB6enp6MGDGCJUuWYDKZ8PHxITo6GoPBYI6pTR5+fn5ERUWRkJDArl278PPzIzo6msmTJ9c5J2dnZ4xGIwsXLiQ8PJwuXbowf/58VqxYYRHn7e3N3r172bBhA1VVVXTq1ImXX36Z4OBgc/5PPfUUrVu3Zu3ateTl5WFtbU2fPn0wGo288MIL5rF69uzJ/v37ee+99ygsLMTJyQkvLy+2b9+Om5ubxX3Dw8Nr/Lxo0SJz0ac+7Gzuv1pERESkqdDvKxEREREREZEfZlV17z5FUieBgYHY2tqyfv36xk7lsXbq1CngzsoNERGR5uB21W1aWDXL3Tvrrbi4mIyMDNzd3Rts+06R+tJ8lKZGc1KaEs1HaUo0H6Up0XxsGHV5l/t4/atZHlnl5eWUlJQ0dhoilJSUkJ6ervkoTYLmY9P1uBUuREREREREROpK/3IWEREREREREREREZEmpVkXLw4dOsQbb7zB888/T58+fRg2bBjz58/n4sWL/5L7b9mypVltGTV+/HjmzJlj0Xbo0CFefvll+vXrx5AhQwgPD+ebb76xiFm8eDFjxoyhX79+9O/fn4kTJ/KnP/3pgfd67733cHV15b/+67+qXTt8+DD//u//Tp8+ffD39+f999/n1q1bD/VsBoMBGxubhxpDpCHY2Njg4eGh+ShNguZj47lddbuxUxARERERERFp1prtgd1Lly7FaDQycuRIYmJisLe3Jzs7mx07dhAREUFSUlJjp9jknThxgpkzZ/LSSy8RERFBfn4+q1at4o033mDPnj08+eSTAHz33Xe8/PLLdO/eHSsrK/bt28dbb73F7du3GTduXLVx//73v7Njxw5at25d7dqXX35JWFgYY8aM4a233uLcuXOsXLmSkpISfvWrXz3U86RkLqGg5JsfDhQREfknamvzDP69oho7DREREREREZFmrVkWL1JSUjAajYSFhREeHm5uHzBgABMnTuTw4cONmF3z8ac//YmOHTvym9/8BisrKwDs7e0JCgri9OnTPPfccwDVVk+88MILnDt3jl27dtVYvIiJiWHy5Mk1FpDi4uJwd3dn6dKl5rGqqqpYvnw5wcHBODo61vt5Ckq+Ie+78/XuLyIiIiIiIiIiIiJNQ7PcNmrjxo04OjoSFhZW4/WhQ4cCUFZWxqJFixgyZAheXl6MHz+eAwcOWMTOmTOHsWPHcvToUcaNG4e3tzevvfYaly9fJj8/n/DwcPr3709AQAB79+616BsYGMjUqVNJSkoiICAAb29vAgMDuXDhgkVcbfK4O9a9MjIycHV15cSJE+Y2V1dXjEYjcXFxDB48GF9fX+bOnUtxcbFF39TUVCZMmICXlxdjx44lJSWl2vdUWVnJU089ZS5cADz99NMAVFVV1fjd3mVnZ0dFRUW19t27d3P58mVCQ0Nr7JeRkcGPf/xji7YhQ4ZQUVHB//zP/zzwniIiIiIiIiIiIiLyeGh2xYvKykpSU1MZOHAg1tbWD4yNjIxk27ZthISEsGbNGnr27MmsWbM4dOiQRZzJZCI2Npbp06ezdOlSsrOziYyMJCIigt69exMXF4enpydRUVHk5ORY9D1z5gzr16/n7bffZvHixeTm5hISEkJ5eXmd86itxMRELl26RGxsLDNmzGDPnj3Ex8dbPE9wcDAGg4GVK1cSHBxMdHQ0165dsxhnwoQJnD9/nsTERIqKivjmm29Yvnw5Hh4e9O/f3yK2qqqKyspKCgsLSUpK4siRI7z66qsWMTdv3uS3v/0tv/zlL++7v3pZWRkGg8Gi7e7n8+e1akJEREREREREREREmuG2Ufn5+ZSXl9OxY8cHxp09e5b9+/cTHR3NpEmTAPDz8yMnJ4c1a9YwfPhwc2xBQQEJCQn06tULgNzcXGJiYggNDWXGjBkAeHl5ceDAAQ4ePEhQUJC5b15eHgkJCXTr1g0ADw8PRo0axc6dO5k0aVKd8qgtJycnli1bZh4rPT2dffv2ERkZCcCmTZuwsrLCaDSaV1J06NCByZMnW4zz3HPPsXr1at5++23z1lDu7u588MEHPPHEExaxx44dY8qUKQC0bNmSd999l1GjRlnErF69mq5du/Liiy/eN/euXbvy1VdfWbR9+eWXwJ2/BxERERERERERERGRZrfy4q57tzqqycmTJwGqvWAfPXo06enpFtssOTs7mwsXgLkQMXjwYHNbmzZtsLe35+rVqxbj9erVyxwPd17Ou7m5kZaWVuc8auvevAB69OhhkVdaWhq+vr7mwgXAoEGDsLOzs+iXmprKL3/5S37+85+zadMmVq1axe3bt3nzzTcpLS21iPX29mb79u38/ve/5/XXX2fhwoV88skn5uuZmZkkJibyzjvvPDD3V155hb/+9a9s2rSJ/Px8Pv/8c1auXFmtWCIiIiIiIiIiIiIij69mt/LCzs6OVq1aceXKlQfGFRQUYG1tXe2FvaOjI1VVVRQVFWFrawvcKUzc6+52VPe+/Ic72xuVlZVZtDk4OFS7t4ODAyaTqc551FZN+d67TZXJZKJr167V+tnb21t8XrhwIQMHDmTOnDnmtr59+/Jv//ZvfPrpp/y///f/zO2tW7fGy8sLuFMIuXXrFrGxsUyYMIEnnniC2NhYRo0aRadOnSgsLATg9u3bVFRUUFhYSOvWrWnRogUTJkzg66+/5re//S2/+c1vsLa2ZubMmWzatAlnZ+c6fQ8iIiIiIiIiIiIi8mhqdisvWrZsSf/+/Tl+/DiVlZX3jWvbti0VFRXVtiK6fv06VlZW1QoT9ZWXl1djm5OTU53yMBgM1Q7Aru82Sk5OTjXmdePGDYvP58+fx83NzaKtQ4cOtGvXjuzs7Afew9PTk5s3b5rHvHjxIrt372bAgAHmP//4xz/4+OOPGTBgABcvXgSgRYsWzJs3j+PHj/Ppp59y9OhRfv7zn3Pjxg18fHzq9bwiIiIiIiIiIiIi8mhpdsULgClTpmAymVi3bl2N11NSUnj22WcBSE5OtriWnJyMh4dHnVc73E9mZiZZWVnmz1lZWZw9e9b8Ir62eXTo0IGLFy9SVVVljjly5Ei9cvL29ubEiRMUFRWZ244dO0Z+fr5FXMeOHUlPT7doy8nJ4dtvv6VTp04PvMfJkydp3bo17dq1A2D58uVs3rzZ4o+joyMBAQFs3ry52hklTz/9NG5ubrRp04YtW7bQuXPnatthiYiIiIiIiIiIiMjjqdltGwXg7+9PSEgIcXFxnDt3jjFjxtCuXTsuX77Mjh07KCoqIikpiREjRhAbG0tpaSkuLi7s3r2bL774gvj4+AbLxcHBgWnTpjF79mwAVq1aRfv27ZkwYQIAbm5utcpj5MiRbN++nZiYGAICAkhNTWXfvn31yikoKIitW7cSGhpKaGgohYWFxMXFVdu6atKkSfzmN79h4cKFDBs2jPz8fNauXYuDgwOjR48G7hx8vnTpUvOWUMXFxfzlL3/hk08+4a233qJlyztTqG/fvtXyaNWqFe3bt8fX19fc9tVXX/HZZ5/h7u5OaWkpf/7zn/n0008xGo0Pfe5FW5tnHqq/iIhIQ9DvIxEREREREZGH1yyLFwBRUVH069ePxMRE5s2bR0lJCc7OzgwZMoTg4GAAlixZwvLlyzEajeTn59O9e3fef/99hg0b1mB5eHp6MmLECJYsWYLJZMLHx4fo6GgMBoM5pjZ5+Pn5ERUVRUJCArt27cLPz4/o6GgmT55c55ycnZ0xGo0sXLiQ8PBwunTpwvz581mxYoVF3Ouvv47BYOCjjz5ix44dPPXUU/Tt25eVK1eaV1Q4OjrSpk0b4uPjMZlMPP3003Tv3p3Vq1cTEBBQ59ysra3Zv38/a9asAcDHx4ctW7bQr1+/Oo/1ff69oh56DBERkYZwu+o2Laya5QJXERERERERkSbBqurefYqkTgIDA7G1tWX9+vWNncpj7dSpU5SXl+Ph4YGNjU1jpyOPuZKSEi5evIiLi4vmozQ6zUdpSoqLi8nIyMDd3b3Btu8UqS/NR2lqNCelKdF8lKZE81GaEs3HhnHq1CkAvLy8fjBW/yVQRERERERERERERESalGZRvDh06BBvvPEGzz//PH369GHYsGHMnz+fixcvNnZqTdb48eOZM2fOA2NOnTrF3LlzGT16NG5ubkydOrXGuKKiIt599118fX3x8fEhMDCQjIyManFff/01U6dOZeDAgTz33HO8+uqrHD9+vFrcJ598wrhx4+jbty/+/v6888475OXl1e9B/38Gg0H/q1iaBBsbG60CkiZD87Fx3K661dgpiIiIiIiIiDR7Tf7Mi6VLl2I0Ghk5ciQxMTHY29uTnZ3Njh07iIiIICkpqdFy27JlS6PduyGkpqby+eef4+3tTVlZ2X3j3nrrLU6fPk1UVBSOjo78/ve/JygoiE8//ZQf/ehHANy4cYPJkyfzzDPP8N5772Ftbc2WLVsIDQ1l+/btuLq6ApCUlMQ777xDcHAwL7zwAleuXGHFihWcO3eOP/zhDw/1PP+TGUthyTcPNYaIiMjDaGPzDEN6Pfg/D4iIiIiIiIjID2vSxYuUlBSMRiNhYWGEh4eb2wcMGMDEiRM5fPhwI2bX/AUGBhIUFGT+uSZffvklf/3rX1m7dq35gHFfX1+GDx/Ohx9+yDvvvAPAsWPHyMvL4+OPP6Zz584APP/88zz//PMcPHjQXLzYs2cPzz//PL/85S8t7jNv3jz+8Y9/mIsh9VFY8g03is/Vu7+IiIiIiIiIiIiINA1NetuojRs34ujoSFhYWI3Xhw4dav65rKyMRYsWMWTIELy8vBg/fjwHDhywiJ8zZw5jx47l6NGjjBs3Dm9vb1577TUuX75Mfn4+4eHh9O/fn4CAAPbu3WvRNzAwkKlTp5KUlERAQADe3t4EBgZy4cIFi7ja5HF3rHtlZGTg6urKiRMnzG2urq4YjUbi4uIYPHgwvr6+zJ07l+LiYou+qampTJgwAS8vL8aOHUtKSsoPfLN3tGjxw3/96enpWFlZ8eMf/9jcZmNjw3PPPWdRPKqoqADg6aefNre1atUKa2tr7j0TvrKyktatW1vc424fnR0vIiIiIiIiIiIiItCEixeVlZWkpqYycOBArK2tfzA+MjKSbdu2ERISwpo1a+jZsyezZs3i0KFDFnEmk4nY2FimT5/O0qVLyc7OJjIykoiICHr37k1cXByenp5ERUWRk5Nj0ffMmTOsX7+et99+m8WLF5Obm0tISAjl5eV1zqO2EhMTuXTpErGxscyYMYM9e/YQHx9v8TzBwcEYDAZWrlxJcHAw0dHRXLt2rV73+77y8nJatGjBE088YdFubW1NTk4OpaWlwJ1CkqOjI7GxseTm5nLjxg2WLVuGlZUV48ePN/f72c9+xt/+9jeSk5O5efMmmZmZrFu3jqFDh9KxY8cGyVlEREREREREREREmrcmu21Ufn4+5eXltXqhffbsWfbv3090dDSTJk0CwM/Pj5ycHNasWcPw4cPNsQUFBSQkJNCrVy8AcnNziYmJITQ0lBkzZgDg5eXFgQMHOHjwoHlbJYC8vDwSEhLo1q0bAB4eHowaNYqdO3cyadKkOuVRW05OTixbtsw8Vnp6Ovv27SMyMhKATZs2YWVlhdFoNK9g6NChA5MnT67zvWrStWtXbt26RXp6Ot7e3gDcvn2b06dPU1VVRWFhIU8++SRt27YlMTGRqVOn8sILLwBgZ2eH0WjkmWeeMY83btw4SkpKiIyMNK/WGDx4MCtWrGiQfEVERERERERERESk+WuyKy/usrKy+sGYkydPAjBq1CiL9tGjR5Oenm6xzZKzs7O5cAGYCxGDBw82t7Vp0wZ7e3uuXr1qMV6vXr3M8XDnxb6bmxtpaWl1zqO27s0LoEePHhZ5paWl4evra7Fd06BBg7Czs6vzvWry4x//mC5durBgwQK+/vpr8vLyWLx4Md98c+dg7Lt/P3l5ecycOZMuXbqwYcMGPvzwQ3x9fZk+fTrnz583j7d//37zypctW7awePFisrKy+MUvfqFto0REREREREREREQEaMIrL+zs7GjVqhVXrlz5wdiCggKsra2rvbB3dHSkqqqKoqIibG1tgTuFiXvd3ZLq3pf/AAaDgbKyMos2BweHavd2cHDAZDLVOY/aqinfe7epMplMdO3atVo/e3v7Ot3nfgwGAytWrODtt99m3LhxAPTu3ZugoCC2bNliftYPPviAgoICdu7cicFgAO4UUcaMGUN8fDzLli2jqqqKBQsW8POf/9y8ygXgmWee4ZVXXuHIkSMMGTKkQfIWERERERERERERkearya68aNmyJf379+f48eNUVlY+MLZt27ZUVFRQUFBg0X79+nWsrKyqFSbqKy8vr8Y2JyenOuVhMBjMWybd9f0+teXk5FRjXjdu3KjXeDXp06cPycnJ7Nu3j+TkZHbv3k1paSmenp7m4s+5c+fo3r27uXAB8MQTT+Dq6kp2drY5pxs3buDm5mYxvoeHB4A5TkREREREREREREQeb022eAEwZcoUTCYT69atq/F6SkoKAM8++ywAycnJFteTk5Px8PCo82qH+8nMzCQrK8v8OSsri7Nnz+Lj41OnPDp06MDFixcttkk6cuRIvXLy9vbmxIkTFBUVmduOHTtGfn5+vca7HysrK7p164aLiwvffvste/fu5eWXXzZf79ixI+fPn7dYrXLr1i3Onj1Lp06dgDurQWxsbEhPT7cY+8yZMwDmOBERERERERERERF5vDXZbaMA/P39CQkJIS4ujnPnzjFmzBjatWvH5cuX2bFjB0VFRfj7++Pm5saIESOIjY2ltLQUFxcXdu/ezRdffEF8fHyD5ePg4MC0adOYPXs2AKtWraJ9+/ZMmDABoNZ5jBw5ku3btxMTE0NAQACpqans27evXjkFBQWxdetWQkNDCQ0NpbCwkLi4uFqdeXHjxg0+++wz88/fffedufDi7++PjY0NAGvXrqVr1644ODhw8eJF1q9fT58+fczPDfDyyy+zfft2wsLCePXVV3niiSfYtm0bWVlZLFy4ELhTAPn5z3/O1q1bad26NQMGDODKlSusXr2aXr16MWjQoHp9ByIiIiIiIiIiIiLyaGnSxQuAqKgo+vXrR2JiIvPmzaOkpARnZ2eGDBlCcHCwOW7JkiUsX74co9FIfn4+3bt35/3332fYsGENlounpycjRoxgyZIlmEwmfHx8iI6OttgqqTZ5+Pn5ERUVRUJCArt27cLPz4/o6GgmT55c55ycnZ0xGo0sXLiQ8PBwunTpwvz581mxYsUP9s3MzCQ8PNyi7e7nQ4cO0blzZwAKCwtZvHgxeXl5ODs789Of/pSwsDBatPi/hTt9+vThgw8+ID4+nrlz53L79m169uzJhg0bGDBggDkuMjISe3t7Pv30Uz788EPatWuHr68vERERFt9jfbSxeeah+ouIiDws/S4SERERERERaRhWVffuXST3FRgYiK2tLevXr2/sVOR7Tp06BYCXl1cjZyIiIgK3q27RwuqJxk6jySkuLiYjIwN3d/cG29JTpL40H6Wp0ZyUpkTzUZoSzUdpSjQfG0Zd3uU26TMvRGqrvLyckpKSxk5DhJKSEtLT0zUfpUnQfGwcKlyIiIiIiIiIPDwVL0REREREREREREREpElp8mde/KscOnSIxMRETp8+TXFxsflcjSlTpuDi4sKWLVsaO8UmZfz48bi7uxMbGwvAf//3f7N7927OnDlDYWEhXbt2JTAwkIkTJ2JlZWXut3jxYv76179y5coVrKyscHFx4Y033mDMmDEPlY/BYDAfMC7SmGxsbPDw8GjsNEQAzcd/taqqW1hp1YWIiIiIiIhIg1DxAli6dClGo5GRI0cSExODvb092dnZ7Nixg4iICJKSkho7xSbv97//PZ06dWLOnDm0a9eOo0eP8u6773L16lVmzpxpjvvuu+94+eWX6d69O1ZWVuzbt4+33nqL27dvM27cuIfK4XjmexSWZD/so4iIiNRZG5suDOz1n42dhoiIiIiIiMgj47EvXqSkpGA0GgkLCyM8PNzcPmDAACZOnMjhw4cbMbvmY+3atdjb25s/Dxo0iPz8fH73u98RFhZGixZ3dij7r//6L4t+L7zwAufOnWPXrl0PXbwoLMkmvzjzocYQERERERERERERkcb32J95sXHjRhwdHQkLC6vx+tChQwEoKytj0aJFDBkyBC8vL8aPH8+BAwcsYufMmcPYsWM5evQo48aNw9vbm9dee43Lly+Tn59PeHg4/fv3JyAggL1791r0DQwMZOrUqSQlJREQEIC3tzeBgYFcuHDBIq42edwd614ZGRm4urpy4sQJc5urqytGo5G4uDgGDx6Mr68vc+fOpbi42KJvamoqEyZMwMvLi7Fjx5KSklLte7q3cHGXu7s7N2/erDbe99nZ2VFRUfHAGBERERERERERERF5fDzWxYvKykpSU1MZOHAg1tbWD4yNjIxk27ZthISEsGbNGnr27MmsWbM4dOiQRZzJZCI2Npbp06ezdOlSsrOziYyMJCIigt69exMXF4enpydRUVHk5ORY9D1z5gzr16/n7bffZvHixeTm5hISEkJ5eXmd86itxMRELl26RGxsLDNmzGDPnj3Ex8dbPE9wcDAGg4GVK1cSHBxMdHQ0165d+8GxT548Sfv27WndurVFe1VVFZWVlRQWFpKUlMSRI0d49dVX65W/iIiIiIiIiIiIiDx6Hutto/Lz8ykvL6djx44PjDt79iz79+8nOjqaSZMmAeDn50dOTg5r1qxh+PDh5tiCggISEhLo1asXALm5ucTExBAaGsqMGTMA8PLy4sCBAxw8eJCgoCBz37y8PBISEujWrRsAHh4ejBo1ip07dzJp0qQ65VFbTk5OLFu2zDxWeno6+/btIzIyEoBNmzZhZWWF0Wjk6aefBqBDhw5Mnjz5geN+/vnn7N27l1/96lfVrh07dowpU6YA0LJlS959911GjRpV59xFRERERERERERE5NH0WK+8uMvKyuqB10+ePAlQ7QX76NGjSU9Pt9gWydnZ2Vy4AMyFiMGDB5vb2rRpg729PVevXrUYr1evXuZ4gK5du+Lm5kZaWlqd86ite/MC6NGjh0VeaWlp+Pr6mgsXcOc8Czs7u/uOefXqVSIiIvD19eX111+vdt3b25vt27fz+9//ntdff52FCxfyySef1Dl3EREREREREREREXk0PdYrL+zs7GjVqhVXrlx5YFxBQQHW1tbVXtg7OjpSVVVFUVERtra2wJ3CxL3ubkd178t/AIPBQFlZmUWbg4NDtXs7ODhgMpnqnEdt1ZTvvdtUmUwmunbtWq1fTWdcABQWFhIaGoqdnR1xcXHmg7rv1bp1a7y8vIA7hZBbt24RGxvLhAkTeOKJJ+qUv4iIiIiIiIiIiIg8eh7rlRctW7akf//+HD9+nMrKyvvGtW3bloqKCgoKCizar1+/jpWVVbXCRH3l5eXV2Obk5FSnPAwGQ7UDsL/fp7acnJxqzOvGjRvV2kpLS5k6dSpFRUV88MEHtf5ePD09uXnzZo1jioiIiIiIiIiIiMjj57EuXgBMmTIFk8nEunXraryekpLCs88+C0BycrLFteTkZDw8POq82uF+MjMzycrKMn/Oysri7Nmz+Pj4ANQ6jw4dOnDx4kWqqqrMMUeOHKlXTt7e3pw4cYKioiJz27Fjx8jPz7eIq6ys5Be/+AUXLlzggw8+oH379rW+x8mTJ2ndujXt2rWrV44iIiIiIiIiIiIi8mh5rLeNAvD39yckJIS4uDjOnTvHmDFjaNeuHZcvX2bHjh0UFRWRlJTEiBEjiI2NpbS0FBcXF3bv3s0XX3xBfHx8g+Xi4ODAtGnTmD17NgCrVq2iffv2TJgwAQA3N7da5TFy5Ei2b99OTEwMAQEBpKamsm/fvnrlFBQUxNatWwkNDSU0NJTCwkLi4uKqbV0VHR3N4cOHmTNnDjdv3uTLL780X/Pw8MBgMHD27FmWLl3KqFGj6NSpE8XFxfzlL3/hk08+4a233qJly4ebjm1sujxUfxERkfrS7yARERERERGRhvXYFy8AoqKi6NevH4mJicybN4+SkhKcnZ0ZMmQIwcHBACxZsoTly5djNBrJz8+ne/fuvP/++wwbNqzB8vD09GTEiBEsWbIEk8mEj48P0dHRGAwGc0xt8vDz8yMqKoqEhAR27dqFn58f0dHRTJ48uc45OTs7YzQaWbhwIeHh4XTp0oX58+ezYsUKi7i7KztiY2OrjXHo0CE6d+6Mo6Mjbdq0IT4+HpPJxNNPP0337t1ZvXo1AQEBdc7t+wb2+s+HHkNERKS+qqpuYWWls5tEREREREREGoJV1b17C0mjCQwMxNbWlvXr1zd2Ks3OqVOnKC8vx8PDAxsbm8ZORx5zJSUlXLx4ERcXF81HaXSaj9KUFBcXk5GRgbu7e4NtuSlSX5qP0tRoTkpTovkoTYnmozQlmo8N49SpUwB4eXn9YOxjf+aFiIiIiIiIiIiIiIg0Ldo26v936NAhEhMTOX36NMXFxeZto6ZMmYKLi0tjp9fkjB8/Hnd39xq3ifruu+8YPXo0165dY/v27RZVtMDAQD777LNqffbu3UuPHj3qnY/BYND/KpYmwcbGBg8Pj8ZOQwTQfPxX0XZRIiIiIiIiIg1PxQtg6dKlGI1GRo4cSUxMDPb29mRnZ7Njxw4iIiJISkr6p+ewZcuWf/o9/lXi4+O5devWfa/379+fX/3qVxZtnTt3fuj7/u+5GIpKsh56HBERkdp62qYrA3q+29hpiIiIiIiIiDxyHvviRUpKCkajkbCwMMLDw83tAwYMYOLEiRw+fLgRs2t+zp8/z9atW/nVr37FggULaoxp06YNffv2bfB7F5VkUVD8dYOPKyIiIiIiIiIiIiL/Wo/9mRcbN27E0dGRsLCwGq8PHToUgLKyMhYtWsSQIUPw8vJi/PjxHDhwwCJ2zpw5jB07lqNHjzJu3Di8vb157bXXuHz5Mvn5+YSHh9O/f38CAgLYu3evRd/AwECmTp1KUlISAQEBeHt7ExgYyIULFyziapPH3bHulZGRgaurKydOnDC3ubq6YjQaiYuLY/Dgwfj6+jJ37lyKi4st+qampjJhwgS8vLwYO3YsKSkp9/0+Fy5cyKRJk7TVloiIiIiIiIiIiIjU22NdvKisrCQ1NZWBAwdibW39wNjIyEi2bdtGSEgIa9asoWfPnsyaNYtDhw5ZxJlMJmJjY5k+fTpLly4lOzubyMhIIiIi6N27N3FxcXh6ehIVFUVOTo5F3zNnzrB+/XrefvttFi9eTG5uLiEhIZSXl9c5j9pKTEzk0qVLxMbGMmPGDPbs2UN8fLzF8wQHB2MwGFi5ciXBwcFER0dz7dq1amMlJyfz9ddfM2PGjAfe87PPPqNv3754eXnx2muv8b//+7/1yl1EREREREREREREHk2P9bZR+fn5lJeX07FjxwfGnT17lv379xMdHc2kSZMA8PPzIycnhzVr1jB8+HBzbEFBAQkJCfTq1QuA3NxcYmJiCA0NNb/U9/Ly4sCBAxw8eJCgoCBz37y8PBISEujWrRsAHh4ejBo1ip07dzJp0qQ65VFbTk5OLFu2zDxWeno6+/btIzIyEoBNmzZhZWWF0Wjk6aefBqBDhw5MnjzZYpySkhJiY2OJiIigdevW973fgAEDGD9+PN26dSM3N5cPP/yQKVOmsGXLFvr161fn/EVERERERERERETk0fNYr7y4y8rK6oHXT548CcCoUaMs2kePHk16errFNkvOzs7mwgVgLkQMHjzY3NamTRvs7e25evWqxXi9evUyxwN07doVNzc30tLS6pxHbd2bF0CPHj0s8kpLS8PX19dcuAAYNGgQdnZ2Fv3Wrl2Lg4MDEydOfOD9Zs+ezc9+9jOee+45XnzxRbZs2YKzs7PFag8RERERERERERERebw91sULOzs7WrVqxZUrVx4YV1BQgLW1dbUX9o6OjlRVVVFUVGRua9OmjUXM3e2o7n35D2AwGCgrK7Noc3BwqHZvBwcHTCZTnfOorZryvXebKpPJVGNe9vb25p9zcnLYuHEjs2fPpqioiMLCQnMhpbi4mO++++6+97e1tcXf358zZ87UOXcREREREREREREReTQ91ttGtWzZkv79+3P8+HEqKytp2bLmr6Nt27ZUVFRQUFBA27Ztze3Xr1/HysqqWmGivvLy8mpsc3Nzq1MeBoOBiooKi3EKCgrqlZOTk1ONed24ccP88+XLl6moqODNN9+sFvf666/j4+PDxx9/XK/7i4iIiIiIiIiIiMjj57FeeQEwZcoUTCYT69atq/F6SkoKzz77LHDnQOp7JScn4+Hhga2tbYPkkpmZSVZWlvlzVlYWZ8+excfHB6DWeXTo0IGLFy9SVVVljjly5Ei9cvL29ubEiRMWqzqOHTtGfn6++bO7uzubN2+2+DN37lwAoqOjWbBgwX3HLy4u5i9/+QteXl71yk9EREREREREREREHj2P9coLAH9/f0JCQoiLi+PcuXOMGTOGdu3acfnyZXbs2EFRURFJSUmMGDGC2NhYSktLcXFxYffu3XzxxRcNelaDg4MD06ZNY/bs2QCsWrWK9u3bM2HCBADc3NxqlcfIkSPZvn07MTExBAQEkJqayr59++qVU1BQEFu3biU0NJTQ0FAKCwuJi4uz2LqqTZs2+Pr61tjf09MTT09PAD7//HM++OADfvKTn9CpUydyc3P53e9+h8lkYtWqVfXKT0REREREREREREQePY998QIgKiqKfv36kZiYyLx58ygpKcHZ2ZkhQ4YQHBwMwJIlS1i+fDlGo5H8/Hy6d+/O+++/z7BhwxosD09PT0aMGMGSJUswmUz4+PgQHR2NwWAwx9QmDz8/P6KiokhISGDXrl34+fkRHR3N5MmT65yTs7MzRqORhQsXEh4eTpcuXZg/fz4rVqyo81hOTk5UVFSwYsUK8vPzsbGxoV+/fkRHR+Pt7V3n8b7vaZuuDz2GiIhIXeh3j4iIiIiIiMg/h1XVvXsLSaMJDAzE1taW9evXN3Yqzc6pU6cAtPWUiIg0iqqqW1hZPdHYaTR5xcXFZGRk4O7u3mBbborUl+ajNDWak9KUaD5KU6L5KE2J5mPDqMu73Mf+zAt5NJSXl1NSUtLYaYhQUlJCenq65qM0CZqP/xoqXIiIiIiIiIg0vMdi26hDhw6RmJjI6dOnKS4uNm8JNWXKFFxcXBo7vSZn/PjxuLu7Exsba9G+a9cuNm3axPnz57G1tcXLy4vVq1fz5JNPAnDr1i02btzIjh07+Mc//oGjoyMjRoxg5syZPPXUU+ZxXF1dq93T0dGx3oeKi4iIiIiIiIiIiMij5ZEvXixduhSj0cjIkSOJiYnB3t6e7OxsduzYQUREBElJSY2dIgBbtmxp7BQeaO3atRiNRqZNm0bfvn359ttvOXbsGLdu3bKIWbt2LeHh4Xh7e5OZmcny5cvJzc1l2bJlFuMFBgYyduxY82dra+uHys9gMGBjY/NQY4g0BBsbGzw8PBo7DRFA87GhaXsoERERERERkX+dR7p4kZKSgtFoJCwsjPDwcHP7gAEDmDhxIocPH27E7JqPCxcusHr1auLj4/H39ze3jxw50iLuj3/8I+PGjePNN98EYODAgXz77bcYjUYqKytp2fL/ptuPfvQj+vbt26B5fnHu19wsudSgY4qIiAC0tulGv56/buw0RERERERERB4bj3TxYuPGjTg6OhIWFlbj9aFDhwJQVlbG8uXL+dOf/kRBQQHdu3dn5syZ/OQnPzHHzpkzh9OnTzNv3jwWLVpEVlYW3t7exMbG0rp1axYsWMDf/vY37O3teeutt3jxxRfNfe8exj169GhWr15Nbm4uPj4+REdH0717d3NcbfKo6WDvjIwMXnrpJTZv3oyvry9wZ2umyMhISktL+eijj7h16xbDhg3j3XfftThQJjU1lYULF5KZmUnXrl2Jioqq9j3t3LmTzp07WxQualJZWUnr1q0t2p5++mn+VWfC3yy5RGHx1/+Se4mIiIiIiIiIiIjIP88je2B3ZWUlqampDBw48Ae3JIqMjGTbtm2EhISwZs0aevbsyaxZszh06JBFnMlkIjY2lunTp7N06VKys7OJjIwkIiKC3r17ExcXh6enJ1FRUeTk5Fj0PXPmDOvXr+ftt99m8eLF5ObmEhISQnl5eZ3zqK3ExEQuXbpEbGwsM2bMYM+ePcTHx1s8T3BwMAaDgZUrVxIcHEx0dDTXrl2zGCctLY3evXsTHx/PoEGD6NOnD5MmTSItLc0i7uWXX2b37t0cO3aM7777jq+++ootW7YwadIki1UXABs2bMDT05PnnnuOX/ziF1y5cqVezygiIiIiIiIiIiIij55HduVFfn4+5eXldOzY8YFxZ8+eZf/+/URHRzNp0iQA/Pz8yMnJYc2aNQwfPtwcW1BQQEJCAr169QIgNzeXmJgYQkNDmTFjBgBeXl4cOHCAgwcPEhQUZO6bl5dHQkIC3bp1A8DDw4NRo0axc+dOJk2aVKc8asvJycl81oSfnx/p6ens27ePyMhIADZt2oSVlRVGo5Gnn34agA4dOjB58mSLcUwmE6dPn+brr79mwYIF2NjYsG7dOt544w3279+Pg4MDAFOnTqW8vJwpU6aYV1v89Kc/Zd68eRbjvfTSS/zbv/0bjo6OfP3116xdu5ZXXnmFTz/9lLZt29b5OUVERERERERERETk0fLIrry4y8rK6oHXT548CcCoUaMs2kePHk16ejrFxcXmNmdnZ3PhAjAXIgYPHmxua9OmDfb29ly9etVivF69epnjAbp27Yqbm5t59UJd8qite/MC6NGjh0VeaWlp+Pr6mgsXAIMGDcLOzs6iX1VVFcXFxaxatYpRo0bh7+/P2rVrqaqqIiEhwRyXkJDA5s2bmTt3LgkJCSxYsIC//vWvxMTEWIy3ePFiRo8ezYABA3j11Vf54IMPyM3N5eOPP67zM4qIiIiIiIiIiIjIo+eRXXlhZ2dHq1atfnA7ooKCAqytrau9sHd0dKSqqoqioiLzGRFt2rSxiLm7HdW9L/8BDAYDZWVlFm13Vyd8v81kMtU5j9qqKd97t6kymUx07dq1Wj97e/tq49jZ2eHm5mZus7Ozw8PDg3PnzgHw7bffsnjxYn75y18SGBgI3DkYvXXr1kRFRfH666/j4uJSY55ubm64uLhw5syZOj2fiIiIiIiIiIiIiDyaHtmVFy1btqR///4cP36cysrK+8a1bduWiooKCgoKLNqvX7+OlZVVtcJEfeXl5dXY5uTkVKc8DAYDFRUVFjHf71NbTk5ONeZ148YNi889e/a87xh3izTffPMN5eXluLu7W1z38PAAIDs7u145ioiIiIiIiIiIiMjj55EtXgBMmTIFk8nEunXraryekpLCs88+C0BycrLFteTkZDw8POq82uF+MjMzycrKMn/Oysri7Nmz+Pj4ANQ6jw4dOnDx4kXzmRIAR44cqVdO3t7enDhxgqKiInPbsWPHyM/Pt4gbOnQo+fn5ZGRkmNu+/fZbzpw5g6enJ4D5bJHvr544ffo0AJ07d75vHhkZGVy8eBEvL696PYeIiIiIiIiIiIiIPFoe2W2jAPz9/QkJCSEuLo5z584xZswY2rVrx+XLl9mxYwdFRUUkJSUxYsQIYmNjKS0txcXFhd27d/PFF18QHx/fYLk4ODgwbdo0Zs+eDcCqVato3749EyZMAO5snVSbPEaOHMn27duJiYkhICCA1NRU9u3bV6+cgoKC2Lp1K6GhoYSGhlJYWEhcXFy1rasCAgLw8vJi9uzZRERE0KpVKzZs2IDBYOCVV14B7mxvFRAQwKpVq7h165Z5S6m4uDgGDx5Mjx49APjwww/Jzs7G19cXe3t7MjMzWbduHR06dODll1+u13OIiIiIiIiIiIiIyKPlkS5eAERFRdGvXz8SExOZN28eJSUlODs7M2TIEIKDgwFYsmQJy5cvx2g0kp+fT/fu3Xn//fcZNmxYg+Xh6enJiBEjWLJkCSaTCR8fH6KjozEYDOaY2uTh5+dHVFQUCQkJ7Nq1Cz8/P6Kjo5k8eXKdc3J2dsZoNLJw4ULCw8Pp0qUL8+fPZ8WKFRZxLVq0YMOGDSxatIj58+dTUVHBc889R2JionnbK7hzEPeaNWv46KOPuHbtGk5OTowbN45Zs2aZY1xcXNi/fz///d//zXfffUe7du3w9/fnF7/4RbUzOuqqtU23h+ovIiJyP/odIyIiIiIiIvKvZVV17/5D8k8RGBiIra0t69evb+xUHkmnTp0C0LZTIiLyT1VVdQsrqycaO41mq7i4mIyMDNzd3RtsW06R+tJ8lKZGc1KaEs1HaUo0H6Up0XxsGHV5l/tIn3khj4/y8nJKSkoaOw0RSkpKSE9P13yUJkHzsWGpcCEiIiIiIiLyr6PihYiIiIiIiIiIiIiINClN9syLQ4cOkZiYyOnTpykuLjafUzFlyhRcXFwaO7062bJlyz/9HuPHj8fd3Z3Y2FgAsrKy+PDDD0lLSyMzM5Pu3bvzxz/+8YFjHDx4kBkzZtCrVy+L2PLyclauXElaWhpnzpyhpKSEY8eOYW9vX22MTz75hM2bN/PNN9/Qtm1bXnjhBSIiInBwcADgxIkTvP766zXe38XFheTk5Ho9v8FgwMbGpl59RRqSjY0NHh4ejZ2GCKD52JC0ZZSIiIiIiIjIv1aTLF4sXboUo9HIyJEjiYmJwd7enuzsbHbs2EFERARJSUmNnWKTl5mZSUpKCj4+Pty+fZsfOtqktLSU3/zmNzg6OtZ47ZNPPsHLy4tnn32W//mf/6lxjKSkJN555x2Cg4N54YUXuHLlCitWrODcuXP84Q9/AO4cXL5t2zaLfjdv3iQ0NBQ/P796Pu0dp8+/y3cllx5qDBERke97yqYbfXrENHYaIiIiIiIiIo+VJle8SElJwWg0EhYWRnh4uLl9wIABTJw4kcOHDzdids3HsGHDCAgIAGDOnDmcPn36gfHr16+nY8eOdO7cuVpsmzZt+Oyzz7CysmLnzp33LV7s2bOH559/nl/+8pcW7fPmzeMf//gHP/rRj2jdujV9+/a1uL5z505u377N2LFj6/iUlr4ruURR8d8fagwRERERERERERERaXxN7syLjRs34ujoSFhYWI3Xhw4dCkBZWRmLFi1iyJAheHl5MX78eA4cOGARO2fOHMaOHcvRo0cZN24c3t7evPbaa1y+fJn8/HzCw8Pp378/AQEB7N2716JvYGAgU6dOJSkpiYCAALy9vQkMDOTChQsWcbXJ4+5Y98rIyMDV1ZUTJ06Y21xdXTEajcTFxTF48GB8fX2ZO3cuxcXFFn1TU1OZMGECXl5ejB07lpSUlGrfU4sWtf+rzc7O5ne/+x3vvPPOfWOsrKx+cJzKykpat25t0fb0008DPHDlxx//+Ee6deuGt7d3LTMWERERERERERERkUdZkypeVFZWkpqaysCBA7G2tn5gbGRkJNu2bSMkJIQ1a9bQs2dPZs2axaFDhyziTCYTsbGxTJ8+naVLl5KdnU1kZCQRERH07t2buLg4PD09iYqKIicnx6LvmTNnWL9+PW+//TaLFy8mNzeXkJAQysvL65xHbSUmJnLp0iViY2OZMWMGe/bsIT4+3uJ5goODMRgMrFy5kuDgYKKjo7l27Vq97gfw3nvvMX78eNzc3Oo9BsDPfvYz/va3v5GcnMzNmzfJzMxk3bp1DB06lI4dO9bY5/r16xw/fvyhV12IiIiIiIiIiIiIyKOjSW0blZ+fT3l5+X1fdN919uxZ9u/fT3R0NJMmTQLAz8+PnJwc1qxZw/Dhw82xBQUFJCQk0KtXLwByc3OJiYkhNDSUGTNmAODl5cWBAwc4ePAgQUFB5r55eXkkJCTQrVs3ADw8PBg1ahQ7d+5k0qRJdcqjtpycnFi2bJl5rPT0dPbt20dkZCQAmzZtwsrKCqPRaF7V0KFDByZPnlznewH8+c9/5osvvqj3Qdn3GjduHCUlJURGRlJRUQHA4MGDWbFixX377N27l1u3bql4ISIiIiIiIiIiIiJmTWrlxV0/tEXRyZMnARg1apRF++jRo0lPT7fYZsnZ2dlcuADMhYjBgweb29q0aYO9vT1Xr161GK9Xr17meICuXbvi5uZGWlpanfOorXvzAujRo4dFXmlpafj6+poLFwCDBg3Czs6uzvcqKyvjN7/5DbNmzcLe3r7O/b9v//795lUuW7ZsYfHixWRlZfGLX/zivttG7dmzB09PT1xcXB76/iIiIiIiIiIiIiLyaGhSKy/s7Oxo1aoVV65ceWBcQUEB1tbW1V7YOzo6UlVVRVFREba2tsCdwsS97m5Hde/LfwCDwUBZWZlFm4ODQ7V7Ozg4YDKZ6pxHbdWU773bVJlMJrp27VqtX32KD5s2baJFixaMGTOGwsJCACoqKrh9+zaFhYU8+eSTGAyGWo1VVVXFggUL+PnPf25e0QLwzDPP8Morr3DkyBGGDBli0Sc7O5uvvvqKuXPn1jl3EREREREREREREXl0NaniRcuWLenfvz/Hjx+nsrKSli1rTq9t27ZUVFRQUFBA27Ztze3Xr1/HysqqWmGivvLy8mpsu3s2RG3zMBgM5m2U7iooKKhXTk5OTjXmdePGjTqPdeHCBbKyshg0aFC1awMGDODXv/41//Ef/1GrsW7cuMGNGzeqnZvh4eEB3ClUfN+ePXto0aIFL774Yp1zFxEREREREREREZFHV5PbNmrKlCmYTCbWrVtX4/WUlBSeffZZgGrnNCQnJ+Ph4VHn1Q73k5mZSVZWlvlzVlYWZ8+excfHB6DWeXTo0IGLFy9abJ105MiReuXk7e3NiRMnKCoqMrcdO3aM/Pz8Oo8VGhrK5s2bLf4MGTKETp06sXnzZoYNG1brsezt7bGxsSE9Pd2i/cyZMwB06tSpWp8//elPPP/88zg7O9c5dxERERERERERERF5dDWplRcA/v7+hISEEBcXx7lz5xgzZgzt2rXj8uXL7Nixg6KiIpKSkhgxYgSxsbGUlpbi4uLC7t27+eKLL4iPj2+wXBwcHJg2bRqzZ88GYNWqVbRv354JEyYA4ObmVqs8Ro4cyfbt24mJiSEgIIDU1FT27dtXr5yCgoLYunUroaGhhIaGUlhYSFxcXLWtq0pKSkhJSQEgJyeHmzdvmosszz//PPb29vTo0YMePXpY9Nu1axfXrl3D19fXoj0lJYWSkhJOnz4NwOHDh3nqqafo2bMnPXv2xMrKip///Ods3bqV1q1bM2DAAK5cucLq1avp1atXtdUd6enpnD9/nilTptTrexARERERERERERGRR1eTK14AREVF0a9fPxITE5k3bx4lJSU4OzszZMgQgoODAViyZAnLly/HaDSSn59P9+7def/99+u0WuCHeHp6MmLECJYsWYLJZMLHx4fo6GiLcyBqk4efnx9RUVEkJCSwa9cu/Pz8iI6OZvLkyXXOydnZGaPRyMKFCwkPD6dLly7Mnz+fFStWWMTl5eURHh5u0Xb38+bNm6sVJ35IdHQ0OTk55s/z5s0DYObMmcyaNQuAyMhI7O3t+fTTT/nwww9p164dvr6+REREVDs7Y8+ePRgMBkaOHFmnPB7kKZtuDTaWiIjIXfr9IiIiIiIiIvKvZ1V1715GYhYYGIitrS3r169v7FTkB5w6dQoALy+vRs5EREQeVVVVt7CyeqKx02jWiouLycjIwN3dvcG2+BSpL81HaWo0J6Up0XyUpkTzUZoSzceGUZd3uU3uzAuR+igvL6ekpKSx0xChpKSE9PR0zUdpEjQfG44KFyIiIiIiIiL/Wk1y26jaOnToEImJiZw+fZri4mLz1lJTpkzBxcWlsdNrcsaPH4+7uzuxsbEA/Pd//ze7d+/mzJkzFBYW0rVrVwIDA5k4cSJWVlbmfoGBgXz22WfVxtu7d2+1MzO+/PJLVq5cSVpaGlZWVvTs2ZPo6Gjc3d0t4nbt2sWmTZs4f/48tra2eHl5sXr1ap588sl/wpOLiIiIiIiIiIiISHPSbIsXS5cuxWg0MnLkSGJiYrC3tyc7O5sdO3YQERFBUlLSQ42/ZcuWhkm0Cfv9739Pp06dmDNnDu3atePo0aO8++67XL16lZkzZ1rE9u/fn1/96lcWbZ07d7b4fOzYMd58800mTpxIaGgolZWVfPXVV9X+x+/atWsxGo1MmzaNvn378u2333Ls2DFu3bpV72cxGAzY2NjUu79IQ7GxscHDw6Ox0xABNB8flraKEhEREREREWk8zbJ4kZKSgtFoJCwszOJQ6gEDBjBx4kQOHz7ciNk1H2vXrsXe3t78edCgQeTn5/O73/2OsLAwWrT4v13F2rRpQ9++fe87VmVlJf/5n//J66+/TlRUlLnd39/fIu7ChQusXr2a+Ph4i2sNcXD32fPvUFxy8aHHERERsbVxwa3HwsZOQ0REREREROSx1SyLFxs3bsTR0ZGwsLAarw8dOhSAsrIyli9fzp/+9CcKCgro3r07M2fO5Cc/+Yk5ds6cOZw+fZp58+axaNEisrKy8Pb2JjY2ltatW7NgwQL+9re/YW9vz1tvvcWLL75o7nv3UO/Ro0ezevVqcnNz8fHxITo6mu7du5vjapNHTQeEZ2Rk8NJLL7F582Z8fX0BcHV1JTIyktLSUj766CNu3brFsGHDePfddy0OiklNTWXhwoVkZmbStWtXi4LCXfcWLu5yd3fn448/pri4mNatW//g38VdR48eJScnh9dff/2BcTt37qRz587VihoNobjkIjeL/97g44qIiIiIiIiIiIjIv1azO7C7srKS1NRUBg4ciLW19QNjIyMj2bZtGyEhIaxZs4aePXsya9YsDh06ZBFnMpmIjY1l+vTpLF26lOzsbCIjI4mIiKB3797ExcXh6elJVFQUOTk5Fn3PnDnD+vXrefvtt1m8eDG5ubmEhIRQXl5e5zxqKzExkUuXLhEbG8uMGTPYs2cP8fHxFs8THByMwWBg5cqVBAcHEx0dzbVr135w7JMnT9K+fftqhYvPPvuMvn374uXlxWuvvcb//u//WlxPS0vDzs6OU6dOMXLkSDw8PBg5cmS17bvS0tLo3bs38fHxDBo0iD59+jBp0iTS0tLq9V2IiIiIiIiIiIiIyKOn2a28yM/Pp7y8nI4dOz4w7uzZs+zfv5/o6GgmTZoEgJ+fHzk5OaxZs4bhw4ebYwsKCkhISKBXr14A5ObmEhMTQ2hoKDNmzADAy8uLAwcOcPDgQYKCgsx98/LySEhIoFu3bgB4eHgwatQodu7cyaRJk+qUR205OTmxbNky81jp6ens27ePyMhIADZ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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_list = {\n",
        "    \"XGBoost\" : pd.Series(metrics_1),\n",
        "    \"Logistic_Regression\" : pd.Series(metrics_2),\n",
        "    \"SVM_Linear\" : pd.Series(metrics_3),\n",
        "    \"SVM_Polynomial\" : pd.Series(metrics_4),\n",
        "    \"SVM_RBF\" : pd.Series(metrics_5),\n",
        "    \"Random_Forest\" : pd.Series(metrics_6),\n",
        "}\n",
        "\n",
        "pd.DataFrame(model_list).T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "Ua4R5IEhJt4L",
        "outputId": "31ae95a1-26cc-4151-eb0f-f5fd13c8bdcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Accuracy  Precision    Recall  F1_Score\n",
              "XGBoost              0.833333   0.866667  0.833333  0.814815\n",
              "Logistic_Regression  0.666667   0.444444  0.666667  0.533333\n",
              "SVM_Linear           0.666667   0.444444  0.666667  0.533333\n",
              "SVM_Polynomial       0.333333   0.111111  0.333333  0.166667\n",
              "SVM_RBF              0.666667   0.444444  0.666667  0.533333\n",
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              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18257418583505539,\n        \"min\": 0.3333333333333333,\n        \"max\": 0.8333333333333334,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8333333333333334,\n          0.6666666666666666,\n          0.3333333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2912439617700077,\n        \"min\": 0.1111111111111111,\n        \"max\": 0.8666666666666667,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8666666666666667,\n          0.4444444444444444,\n          0.1111111111111111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18257418583505539,\n        \"min\": 0.3333333333333333,\n        \"max\": 0.8333333333333334,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8333333333333334,\n          0.6666666666666666,\n          0.3333333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23936835795701564,\n        \"min\": 0.16666666666666666,\n        \"max\": 0.8148148148148149,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8148148148148149,\n          0.5333333333333333,\n          0.16666666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zB_nhwniSJzI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}