C02_fan_RUL.ipynb

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  "metadata": {
    "colab": {
      "name": "C02 fan RUL.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMASSb5pyNREXBaOJXAMtF4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LEMMAO/Predictive_Maintenance/blob/main/C02_fan_RUL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVWwx2zt5rFs"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "JdKBY7nm9oOy",
        "outputId": "752e6802-41f9-4813-c859-3301b4e8a33d"
      },
      "source": [
        "dataframe = pd.read_csv('/content/C02 sensors.csv', index_col=False, encoding='iso-8859-1', nrows=11739,\n",
        "                        warn_bad_lines=True, error_bad_lines=False)\n",
        "dataframe.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>TIME ZONE</th>\n",
              "      <th>EAU ACIDILE D10</th>\n",
              "      <th>VIBRATION PAL1 C02 (MM/S)</th>\n",
              "      <th>VIBRATION PAL1 C02 (MM/S).1</th>\n",
              "      <th>TEMP PALIER1 C02 (DEGC)</th>\n",
              "      <th>TEMP PALIER2 C02 (DEGC)</th>\n",
              "      <th>C02M (A)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5/29/2021</td>\n",
              "      <td>12:22:10</td>\n",
              "      <td>Morocco Standard Time</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5/29/2021</td>\n",
              "      <td>12:23:10</td>\n",
              "      <td>Morocco Standard Time</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5/29/2021</td>\n",
              "      <td>12:24:10</td>\n",
              "      <td>Morocco Standard Time</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5/29/2021</td>\n",
              "      <td>12:25:10</td>\n",
              "      <td>Morocco Standard Time</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5/29/2021</td>\n",
              "      <td>12:26:10</td>\n",
              "      <td>Morocco Standard Time</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        DATE      TIME  ... TEMP PALIER2 C02 (DEGC)  C02M (A)\n",
              "0  5/29/2021  12:22:10  ...                    49.0      31.0\n",
              "1  5/29/2021  12:23:10  ...                    49.0      31.0\n",
              "2  5/29/2021  12:24:10  ...                    49.0      31.0\n",
              "3  5/29/2021  12:25:10  ...                    49.0      31.0\n",
              "4  5/29/2021  12:26:10  ...                    49.0      31.0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gS-_lpDB95Yd",
        "outputId": "6b707da0-61d8-4193-8fb0-d2003cc5db03"
      },
      "source": [
        "dataframe.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 11736 entries, 0 to 11738\n",
            "Data columns (total 9 columns):\n",
            " #   Column                       Non-Null Count  Dtype  \n",
            "---  ------                       --------------  -----  \n",
            " 0   DATE                         11736 non-null  object \n",
            " 1   TIME                         11736 non-null  object \n",
            " 2   TIME ZONE                    11736 non-null  object \n",
            " 3   EAU ACIDILE D10              11736 non-null  float64\n",
            " 4   VIBRATION PAL1 C02 (MM/S)    11736 non-null  float64\n",
            " 5   VIBRATION PAL1 C02 (MM/S).1  11736 non-null  float64\n",
            " 6   TEMP PALIER1 C02 (DEGC)      11736 non-null  float64\n",
            " 7   TEMP PALIER2 C02 (DEGC)      11736 non-null  float64\n",
            " 8   C02M (A)                     11736 non-null  float64\n",
            "dtypes: float64(6), object(3)\n",
            "memory usage: 916.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "2TuNMQ5o-Hog",
        "outputId": "2062cde7-f001-44cf-d231-1b15c0adb00e"
      },
      "source": [
        "dataframe.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>EAU ACIDILE D10</th>\n",
              "      <th>VIBRATION PAL1 C02 (MM/S)</th>\n",
              "      <th>VIBRATION PAL1 C02 (MM/S).1</th>\n",
              "      <th>TEMP PALIER1 C02 (DEGC)</th>\n",
              "      <th>TEMP PALIER2 C02 (DEGC)</th>\n",
              "      <th>C02M (A)</th>\n",
              "      <th>rolling_avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11720.000000</td>\n",
              "      <td>11720.000000</td>\n",
              "      <td>11720.000000</td>\n",
              "      <td>11720.0</td>\n",
              "      <td>11720.000000</td>\n",
              "      <td>11720.000000</td>\n",
              "      <td>11720.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>44.322952</td>\n",
              "      <td>1.998720</td>\n",
              "      <td>3.751621</td>\n",
              "      <td>42.0</td>\n",
              "      <td>46.100000</td>\n",
              "      <td>29.382082</td>\n",
              "      <td>29.383089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>22.132190</td>\n",
              "      <td>0.049729</td>\n",
              "      <td>0.441855</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.925289</td>\n",
              "      <td>3.307350</td>\n",
              "      <td>3.273468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>42.0</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>42.0</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>29.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>49.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>42.0</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>42.0</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>89.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>42.0</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       EAU ACIDILE D10  VIBRATION PAL1 C02 (MM/S)  ...      C02M (A)   rolling_avg\n",
              "count     11720.000000               11720.000000  ...  11720.000000  11720.000000\n",
              "mean         44.322952                   1.998720  ...     29.382082     29.383089\n",
              "std          22.132190                   0.049729  ...      3.307350      3.273468\n",
              "min           0.000000                   0.000000  ...      0.000000      0.000000\n",
              "25%          30.000000                   2.000000  ...     29.000000     29.000000\n",
              "50%          49.000000                   2.000000  ...     30.000000     30.000000\n",
              "75%          60.000000                   2.000000  ...     31.000000     31.000000\n",
              "max          89.000000                   2.000000  ...     36.000000     36.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7FYxiUMB8YJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bATxeyxWBy8u"
      },
      "source": [
        "\n",
        "dataframe.dropna(axis=1, how=\"any\", thresh=None, subset=None, inplace=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ILmaGT0-1HU",
        "outputId": "28ee20c8-ad98-480d-b6f1-c8fbd7152b72"
      },
      "source": [
        "dataframe.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DATE                           0\n",
              "TIME                           0\n",
              "TIME ZONE                      0\n",
              "EAU ACIDILE D10                0\n",
              "VIBRATION PAL1 C02 (MM/S)      0\n",
              "VIBRATION PAL1 C02 (MM/S).1    0\n",
              "TEMP PALIER1 C02 (DEGC)        0\n",
              "TEMP PALIER2 C02 (DEGC)        0\n",
              "C02M (A)                       0\n",
              "rolling_avg                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 762
        },
        "id": "zPqA4STw_JtI",
        "outputId": "85a8780b-ce02-40ed-c863-fd1eecac5185"
      },
      "source": [
        "g,ax=plt.subplots(1,1, figsize=(30,20))\n",
        "dataframe['rolling_avg'] = dataframe['C02M (A)'].rolling(window = 5).mean()\n",
        "dataframe['rolling_avg'].plot(y='TIME', kind = \"line\", ax = ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5e4e0609d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
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2nZrT09YuVT7r6IdPntDR6YWWz7X/1Dn94Mnj+uOvP97wu+2Hz2rr3tM6f8Uivf6mDXrWxtUdja9cEpCNFeOBgBUAAACQQnbFr+1fZUSGFQAg3eLOZrF9YcYNWUCNun1LfN/XW198RXBeZRPxXE6uoPK+4+oLV+gjb76l5va5gqsNqxbrI2++Rb/yoqcF922l5JV/f67oRr6OJP3Oa6/WB994s9Ytn+pofGRYYZwQsAIAAABSyF6U2hXAxoiVlQCAVIt7wrVQGteAVdIjGD3dvCeu58vza8uvOY7NsOLNRTmQZPubhc0slJSvbDf230KbgFWxVN6mogLNNmAV9VqtOMawH8DYIGAFAAAApJAXZFiVf+ZCFQCQdi4ZVj0hqNKom0U8druoCVgFPawGOy6kU8H1I/tJzRVc5SrBJft7G5Bq/lzN90MF1695ro5RGhxjhIAVAAAAkEL2ktSWrDFG8ihbAwBIsbgnXOcKjSW40iqcncHhv1E370khCFiZ4DZ7fkUQAJK07+RckEEV5nq+8pXtxgauPn3/Xp05V9R9u05qZqGkI9PzemD3yeAxMwslSdKRiF5XNgs06rVaMRJ1FjA2skkPAAAAAED3vLoeVo4xXKgCAFLNizkB6qljM7ruopXxvsiQhAMyBFUaddPXq1hqLMNmM6wIBkIqB5lOnytE/s5mQ61dlpckferevZovuvrilgP6d8+5VI8cOKOH95/RQ+9+pVYuzunJozOSWvew6jbDyhjJH88EUkwgMqwAAACAFLLzMCacYcWEFQAgxeI+jmWd8ZkGCwdkugnOTIpuAk3FiDJshgwrVLiVjemWS8+L/L3dbq7dUA2GH5melyTds/OEHt5/RpJ0rpLhma2k7122ZknDcxUjsv06UV64xraK8TA+R2oAAABggtj5E7sC2Ig+CwCAdIu7h1Xczz9M4YDMGP1Zg9NVwKp5DyvKLcNuH4vz0YXKMk5jcGl2oXn2lP03KiRlSwLmsj1kWLGpYkwQsAIAAABSKCgJWPnZcQwrrAEAqRZ3SatxOk569LBqqZvMqOgeVpQERFnU9tHOXKHU9Hnsv1Fstl/3PawM2YAYGwSsAAAAgBSyl6ROqIcVkyoAgDSLe8LVHaMDZfitYqK6UTfviM1qCQcJbPVI3ltE9Thrp2WGVam8TUVtWX31sOrqEcDoImAFAAAApFCQYWV7WIlJFQBAusVdsm+M4lV1GVZj9IcNSDfvScuSgLy3Ey+qx1lYVObmgdPnJEk7j80Gt+0+PidJ+ovvPCFJ2nNiTte/+5v69U9vlSS9/xvb9OffLv8uqsxgO2yqGBcErAAAAIAUsmWTjO1hZQwrKwEAqRZ3cGCc+hGF3ysmqht1854EAatsY8CK9xZBX6lKwOrDb3qm3vzcy/SyTeslSQulaom/P3zdtU2fZ77YmHV1dqGkf3rooCTpb7//VM9jdLgOwBghYAUAAACkkF+5LLULMB0zXr05AACTx4u5h9U4Zct4lARsqZtzokLJZtCEe1iV/+W9RX0Pq9dcf6H+8PXX6YVXrpUkPeP85cF93/y8jfrjn76u5fNE6fcc3nAdgDFCwAoAAABIITtR5QQZVvFP9AEAEKfYM6zGaD7XJ8OqpW7eEpthFe5hZYKSgIMcFdIoavtopVnpwGKLgFWpzw3NiP0AxgcBKwAAACCF6ntYlUuBcKUKAEiXcODFjTk6EHePrGEiw6q1/ntYVZ6HiNXEi9o+WmkW2CqWvKZZUK2CWZ0wXAdgjGSTHgAAAACA7tnrXbsC2DGGVcAAgNQJH7vijruMU8ms8N/C8b9RLz2s8qEeVhnHZljx5k6q7YfP6sJViyK3j1aaBba+9OBBnbc0H/m7D31nR1/f43Jp8N4fD4wSMqwAAACAFLITVSZ0G5MqAIC0CWdVxZ0BFXcG1zDVBvrG5+8alG7OiQqu7WEVzrCiJOCke9UH/1Vv+r/3hnqc1U6jb7pghSTp1o3n1dzeLBHroX2n9V8/82Dk7/72X3f2OVpDwApjg4AVAAAAkEL2mtROqDiOumvYAADACPBqMoXoYdUpMqxa6yrDqhTVw6r8L4uBJpP9fv3owJlQhpWpuc/zrlijrb/3Cv3EDRtqbn/6+uU1P3/uV5/X8Pxf+rXn6xnnLxvYeI1hW8X4IGAFAAAApJC9KHVCPay4UAUApI0/xEyhcepHRIZVa72UBMyFAhJ2QRDv7WQqhb5grXpYRZX4q+9hdfm6pQ33WbUkryvPrw1srV8+1dNYpdqKC0DaEbACAAAAUsheR9sVwEassAYApE94sUVlXngor5V2HhlWLfldpJ1HBSQoCTjZiqGdUauAVZRspjZ8FPW4XMY0BLb6YehhhTFCwAoAAABIoaCHlS0JaAwVAQEAqeMOsSRg3D2yhin8XpEF1KibQFN0D6vyv+PU9wydK5aqn3vU9tGKDXZaUYGpfMZRLjO4vKjydQDbKsYDASsAAAAghezclL3UpXY96n132xHd9md36s7tR5MeysT63vaj+vm//bexKkMGDJofyqqKOzgwTofJ8N/CLqZRN0G8oEdROGBViVh9fvP+wQ4MqVAIZVg9evCMpOjAUxSnLg6VzRgtytU+NpdxGgJbG1Yt7mGkZeXrgJ4fDoyUbNIDAAAAANC9ag+r8sWuMYYV1qhx365T2nl8Vpv3nNJLrlqf9HAm0js+uUWzBVdzRVfLprj8BqLUZgrF/FpjNKPrDTEzLY26+aiLJVvyrRpAuGz1EknSQtEd6LiQDuHvVLYSgbr4vM4CSutXLNL1F63UZWuW6JbLzlMu4+gDb7hJb//UFknSjRev1KolOU1la4NY/+tnrtePDpzRpguWRz1tS0ZcB2B8cMYMAAAApJC9JnUq17oOtetRx64YL8TdFAZtUVIKaG6YgZfxKgkY/v/x+bv6UTth312GlTFSJpQak804etGVazW7UBrgCJEW4U2p6PpanMsEWXed+Op/eWHNz6+94UL91XeXa9vhs3rXj18tY4yu3bCy5j5XX7hCV1+4orcBm262eGC0URIQAAAASKH6DCvHGCasUKNQWTFu/8Xw2W9kkaAh0FQ4iBRHQCmcVTVOseOazLQExzFKaoN4nT+u4PrKZZygL6iVyzgs+phQ4X5QhZI30H5T+ezgnssyEjsCjA0CVgAAAEAK1U/EGGPGaiIO/bNBEoIlyeMzAJoLx6jiKGlVk8E1RgfKuN+3NOq1vGTR9SL7E+Uzjool3ttJVJth5Smf7X8K3QZEc3ZbG2DcyjGGeBXGBiUBAQAAgFSq62ElJqwm2cxCSXc8fkQlt7oNPHVsRpL0xJEZ3f3kcT3/6WuTGt7EmC+6+vZjR4KstrlCufdJfZab6/m64/Ej2rBqsYqup5svPW9oYzwzV9QPnjyu2zat1+J8Rp7n69uPH9F1F63URX00fAd6VVsSMI7nj36ttCm5nr7z+BEVXF8vv3p9zTHfG8GY+FyhpLt2HNdLrlqnqWxmKK/Za3nJxw9NK6raWy7r6ORcQbdv3q9sxui2Teu1fFFuEEPFiAtvPQ/sPlVTLrJfg3wuy5h079+AMAJWAAAAQArZCbigJKBDD6tJ9oUH9uk9X30s8nf37TqpN330Xj3ynldp6RSXgHH65qOH9V8/82DD7fUZVg/uO6W3fHxz8PPu97029rFZH/7+k/rb7+/UB95wo372lov16MFp/erHN+uWy87T7W97/tDGAVjhHm9x9HvzYi45OCyb95zSWz+xRZL0v3/uBt148argd6M4Uf2xu/fo/d/YFuxrhiEcuOvmLdlxdEbT8429qs5fPqVjZxf03z//kCTp937iGv3yC5/W7zCRMtuPnNXiXP9B15++eYMePzStNUunJEk3XFztYfXvnnNpX89dXrjW11MAI4OrFQAAACCF7OSUbbdAD6vJNlvJ5PnOb7y4pqzR8kVZfezfduuD39mh+aJLwCpmNqPq9rc9T+uWLdI3Hj2kP/nnbSrUlZSaXXCTGJ4k6dRsQVI5+0GSZiv/PnHkbGJjwmTzY86A6rVM3Kix+xdJmlsoxZ6Z1q8j0/OSpOn54tBes9cMK8dIP3HDhQ23/8/XXK3/8LyNKrieXv7n39d8Mbl9N4arvmrB215yRd/P+Z9fdLl+/lmXauXicpbepgtWaPsfvVqFkqel+f7Oz4wxNX23gDTjagUAAABIIT/IsCr/azSaE1YYDpvBc8W6pQ1N489fsahyHzaQuNnPYeOapVqzbEpXrFtWc3v9/ZJUqGwPdixxlCgCOhF3QCnuDK5hKYT2GwXXqwvIpPfvGqReA56u5wdBhLCMY3TpmiUqVd77NG8/6E79prRiUf9T6MaYhu1sKpsZSMlMY9IdkAfC+u8YBwAAAGDoqhlWJviX69TJVSh5ymVMQ7BKqjb3ru+jhMGz73Gu0pzdNmlvF7BKYhLUjiEIWEVsO8AwhDf/eEoChv8/vUfK8H6j6Pp1mWkJDGgE9fpZl4+hzadIbUCfgNXkssf1UWVkCFhhbIz2tw0AAABAJHtRaqeYHdNYvgSTo+g2n2zLZcpbSWEEsnrGnX2PbVnGIFhY994X6rLdksi4KlaCa7ZcoUOGFRISDgLEEVDyx6QkYHjRQaHk9Vz+btiGGeTp9bMuuF6wwCCKMUbGjPb7jMGq/6hbBTRHgeE6AGNktL9tAACMqLd/aov+/q6dSQ8DwAQqup7+0z/er637Tksq966SypMpTKRMrqLrN51MscGTUShDN+6KleBPri5gVV+O8dEDZ2p+fsentgxhdNJff+9Jfe6B/ZKkL209oNs+cKceO1geC/EqJCXugNK4lASszbDy6rKJEhhQh+z5yjD0GvwsH0Nb7wQzxqR6+0F/8qMesBKFQTE+6GEFAEAPvv7wIX394UP6lRddnvRQAEyY03NFfXfb0aCWvlO5fnaoXT/RCi0zrAhYDUvR9eSYavmoIFhYV44xWzcx+p3Hjw5lfH/6ze3B/+88PitJergSPKMkIJJCScDOhDMzi3U9rEYxs8IuqFmS678/T6d6CeK5ni/Xa77ow3IcAlaTxK8L/6QjwyrpUQCDMdrfNgAAAAA17ATVQmUC3IgMK5QDIvkmq8NzTfooYfDqSzPmsia4PWyU+onNLpQkURIQyYm7tF144jnN8YZw4Luxh9Xo/WF2vzfMcrS1gbvO3hM7znYBCTKsJktjScDRPkY6xjQE2YC0ImAFAAAApEjJqwtYVa6fjdI9EYf+FF2vaUPwoIdViQ0kbgXXqykb1KyHVX2JwCTNLriSqllhwLDVlnEb/PPXBHZSfKC0gZWl+YwKrjfyvbnseIe5WKKXDKtiXe/BZjKOkTuKbzRiUf9JNzvHGhXlHmtJjwIYjNH+tgEAAACo4QUBq/Iksy254xhD8foJRg+r0VAfOMw36WE1zIyDduYK5QwrSgIiKXFnCsWdwTUsdh++ZCqrYmn0e1jZ/dwwF0t4PQTx7P453yYg4Zh0BzzRnfoym6Pew0oyIxm4BnpBDysAAAAgRexK9IVibYaV44xW1gaGix5Wo6FY8mvKBtW/908ePaujZxe058RsIuOLsvvEnCRKAiI5cQeURj2w0ynbw2pRztGu47MjH4iz5yTDPPbUZutFvyd37TimS85boo1rl0rqoiQgGVYTLQ09rFi5hnFBwAoAgD64nk8JHQBDZUsC2pXLNsPKiB5Wk6zotuhhRcBqaBp6WGVqe1i9/M//NZFxdSLL+QwSUht4Gfzz15bOS+9xslgpOXp6rqhF2UxdNtHo/V2259Ywjz3htyHqLTlXcPXmj96nS1Yv1l3/4zZJ1Z6C7XoUZRwjDqOTo37zyY54Dyuj0SwNCvRitMPDAACMuJLHVQuA4bITVEGGVeV2KgJOtvpASVg+W+lhRQZe7Bp6WFVKTNkJ0bCnr1+mp/7kNXrpVet04cpFQxsjMGpqAlYxRKzCk7hpzpApljzlMka3XnaeMo6pK6WY3LiaSaaHVesgnl3ss+/kueC2oIdV25KAhpKAE6R+83FGvGyuYwzXARgbBKwAAOhDiq95AaSUW5dhZUI9rJhHmVzlUnRtSgJGBE0wWPWBw2Y9rKTy55JxjNYsmxqJiTAyNJGU2pJ9cfewGvjTD43tkbdkKhjB7SoAACAASURBVKuC6/XUr2mYgh5WQ1wsUfOeRPzejdgA7P6ZkoCoVftZj3oSsjEcxzE+CFgBANAHTgoBDFu1h5UrqXoBbcxolgTCcBRcr+nqcHs7JQHjV3T9ms+hVTlGW8LRaDjnE+0yA6ImcoFh8Lx4A0pxB8SGpeCWFybkM46Krjfyf1eQYTXExRLt3pPogFVnPazIsJosacuwoiQgxgkBKwAA+sDkDoBhCwJWpcYMKy5UJ1erkoD0sBqe8udQndTKOEaOiX7v7edSDjYPYWxtyhhzSoOkuLGXBBztXk+dsj2s8hlHxZI/+j2sKplLyZUEbP17q+B21sMqmyHDCqPLGDOS+wGgFwSsAADokj8mZUUApJOdLLE9cWyGlUMpkIlWdL2gV1U9Gxihh1X8CqXGwGEu4+ienSf0nD/5TsPtkmRk5A+h80RUWcIwFuEgKX6brJh+hTftNG/nNiCeyxoVXa/mmmQU/67Ne05JknYcndG5gjuU16wt/9hhhlXlfCrfriSgMSP5PiMe9Z/0yGdY0csWY4SAFQAAXaopNcFFC4Aha5ZhJdHDapIV3eY9rPJkWA1NMaI0Y9YxemjfGR2ZXqi5PZcdcoZVsM+Q/vIXbq753crFOSZikZi4e0yNy2Izm0mbyzgquF7NfqM0gn/Yolx1X3j07PxQXjOcSNp1D6smZXUtxzEsDJog9R/1iMerZETECuODgBUAAF0qha6EuGgBMGx2ssWWsDGhDCtKgUyuqMwey5Y5GmYfkUkVFTh0HBN8X8NylfTIYa2KtgHLP3r9dXrlNecHt+9+32v1ymvO55wGianJgIo5wyrNx8lCqXkPq3YZlEnwPOmS1YslVbPCY3/NNmUSo/ZznfawIsNqstRnPqchw4rjOMYFASsAALoUXrlHHXMAw1af2enQwwoqBzCbTbZlHCNjFBk0wWDV97CSyu9/lHB25DC+u4XQpGz9tpJxmIhFcsLHtTgCSuGJ5zRv5kXXUy5b/v4W3doeVqOWQev7vgqup6X5rKThHX98P/r/raj9XKc9rBzHaMTeZsSofvtpcigfGUYkWGF8ELACAKBLbs3KvQQHAmAi1Zf9sRfQrKycbEXXU77JZJsxJighhXhFBQ4zbVZll389vB5W+YzTEESj1BWSVFMSMIaIUs1isxRHrOx+Ppdx5Hq+Su7oBqzsucrSqXLAalgZYG6b8o+tMqza9rByOM+aZCOeYDW08sLAMBCwAgCgS+ELXS5aAAxbfWanUSjDKokBYSQUW5QElMoTccUSW0jcyhPKjSUBo9hMEqP4J5lOzRb0iXv2SIoue0WpKySptiRgHM8/HufuQQ+rbHmfMrNQDH43agsSTswUJElL8hlJ0uEzQ+ph1aYkYNTbRElARGnsYTXaEavydQDbJ8YDASsAALoUXvnJRQuAYXPrZvMMGVZQpXdSi4bxuYwZuRX446hYauxh1SzD6sF9pyUNp4fVp+/fq4/+YJek2rJXP3fLxeUxUhIQCWoXZBikNB8mC5UeeTmnvI+5+6kTwe9GbUHCPz10QJJ0/UUrJUkP7D45lNf1ahYWNv4+aj9n37tWx1CJTNRJk7YeVjLpLnkKhBGwAgCgS5QEBJCk+gwrewFt6GE1sWyvkFarw8s9TwhYxa3cY6Z1D6t3/+Q1kqSbLz1PUvk7HPck6LmCG/y/nZTd/b7X6s/ecGNoDLEOAWjKi7l6wdhkWFUyaV9y1TpJtd/rUdu/F0rl8bzz5c+QMc0zTQettvRg42cd9fl32sOKDKvJ0pBhlcwwOmY0hNUvwJAQsAIAoEtkWAFIUn1/D7vg0yHDamLZXiHNelhJoofVkEQFDp26q277+yA7UvEvgAl/9lF9WjIO5zRITk1JwBh2U7XPn97tvOh6ymdNsA9ZKHk1vxslhUrgKJcxWjaVDQJYcQu/Dx1nWHXYw8ohE3WijXqGVTlbm+0T44GAFQAAXXLHZJUmgHQqedEZVg4ZVhOrk/4b+awztKb3kyyqh1V9ScD635ezI+P9bMLlwqK2E8cxDdmbwLDEnQHljUl1hGoPKxuwqmZYjdqChPJYjYwx5R6KQxpf+HWiPuuo/Vw3Pay49ptcIx6vGsriF2BYCFgBANAlN+ayJQDQSv1+x6GH1cQL+m+0LAloVBzSCvdJVihFZVjVznLVlwyU4q/iE57EjSp7lTGmIXsTGJa4e1iFnzLNx0m7f7HfYZu1lHHMyC1IKIb2hcMsSVubYRVREjBiP2ffx7YBKzKsJkpDScARD1g5xpBfhbGRTXoAAMbPzEJJP/3XP9TJ2ULL+/3kjRv0np+6dkijAgbnyPR88P/dXLMcOH1Ov/B392h2oaSbL12lv//FZ8UwOmD0/Ok3t2nHkRn93X+4Nemh1Nh1fFav+z8/0PR8SZK0Zmk+kXGcqBwvv/L2F2i+6OrXP7NVJddXNmP0l2+8Wc+5fE3N/esnS6plxUY7w+r3vvyIdp+Y1cd/+Tk9Pf4v79ihj929W8sXZfW8K9bqW48eluMYHTu70Pazu+i8xfqDn7pWv/bJLcHE1InZgv7sDTfq5265uOa+5wqufvrDP9SxswuRz3XF+mX67Fueq3NFVz/+obu058ScpMbt503PuVS//MLL9dN/80OdmSu2/ftWLM7p9rc9X6u73A437zmpt31ii6RyFlUz+ayjO7Yd0XP/5A799Ztu1i2Xre7qddDee/7pUXl+4+eQrQtYLZ/KSZJWLi7/a4xii1j921Mn9M7PbtWp0DYYtZ1kyLBCgsKBhTjiGjYIluaFHWfmijp4Zl5Zx2gqk5EkPbT/jKRy1uZXHzqoD7zhxpbHgV78xbef0Cfu2dP142YWSlqcL48zn3X05QcP6o7Hj0qSnv/0tfqrX7h5YGM8PrOgn/ubu3V2vlRTJjHqk44KOG3ec0pS+x5W5UzUvoaKFKkvr5eGkoBp3b8B9QhYARi4w2fmtePojF749LXauHZJ5H3u3H5M9+46OeSRAYNxtjK5LXVXB3/nsRntPTmntcumdPdTJ+IYGjCS/vp7TyU9hEifvX9fEKySpBsuXqmLzls81DG4nvTp+/ZKkj6/eZ+etnaZjkwv6PU3bdCXHzyoRw9ONwSs6ksCmqAkYDwr0wfl4z1MeIXdt+ukTswWdGK2oAOn92nl4lwQVLp49RJdf9GKyMdtO3RWD+w5pa17T+vQmXm9/qYN+sGTxyVJv/n5hxoCVsfOLmjb4bN6/hVrdPm6pTW/+9GBad2366Rcz9eR6YUgWCVJi/MZveSqdZKkbz56RA/sOaVXXjunncdm9eJnrNMlq5tvW/tPndOd249p/6m5rgNWjx2c1tGzC/qFZ1+ql199ftP7/fptV+qbjx7R7Vv267FDZwlYxeCeneVj+0/duKHm9ne+/Bm6+6njWih6uvrCFbpt03r97muv1s8/6xJJlWBzTGN67NC0jkwv6A23XKxTc0VddcEyPeP85Q33syVFfd8P9inAsHihIFU8JQHL/2ZTHHDYd6p8vLlw5WKtXJLTb796kw6cntOapVP60YEz+u62ozpzrqh1y6cG+rr37TopxzF61bXNjy/N3HDRKknSb77qKt23q7x/vHfnyWBfOSh7Tsxp94k5vWzTel24apFOzxX1tYcPRZ4TRQXml06Vp0bb7fsyJjpDC+OpflMZ+YCVKAmI8UHACsDA2TT8Nz3nUv349RdG3ufXPrlZTxyZGeawgIEJl9zo5qLafjeecf4y3b+bgC0wan7pBU/Tjz1j3VBf0/X8IGAlVfcTv/Paa/TlBw9GltCpnywJ97Aa53mUcH+Oouvr8nXLdHymvC99zXUX6FdffEXk4/7hh7v0wJ5TKlVmRH/r1Zu0/9NbdXwmOhPcvs4bn31pQ+DhI99/Sg/tO62i6zd8NjdcvFJ/9PrrJUk7jsyo5FXv8x9fsFEvvWp907/t+08c053bj/VUMsk2tn/Xqzdp5ZJc0/u98toL9OynrdbtW/ZTGjAmJc/Xa66/QBvX1gY6X3P9hXpN3Tnxr7zo8uD/TYzBZvu8v/vaa1puH5lKFpjrlbM7gWGKuySgfX5nCP3i4mKPDzdcvFKS9LaXVI95n7lvr7677WgsZfeKrqcr1y8Ljm+9+KkbNwTH03d/5RF95aGDgxqepOp788svepqef8Va7To+WwlYNd7Xi3iLiq6nqyIC+fUoCThZ6j/pEY9XpWCAQOfoYQVg4DppWjrMOtbAoLVr5ttModJjZEk+q6Lrp/aCGRhX7XoXxCFTVyrMBhKWTpXL6EQdK+tXB9tnGPdSIEXX09JKeSFJWpSr/n+rz86+PzYzLdPmgt6+5/mISXv7OgXXC0oLRo0hmyn3A7ILHPJttq1qP5LuP7/gvCuiL1Lj6zg1j8FgFV2v7WcdJcaKgLUv0kIQsBrjfQhGV22PqfieP+uY1B4n7fEk6ngX57696Db25etHLuMMfNFE9bhdHqfd3UV91lH7uKLrd3QMLS8MSuf2g/6NejzIXlIwx4BxQMAKwMBVJ07aBKxY3YuUCl8MdrPKzj6uOhHNySQwSvIdTFbEze4nFmXL+4lCxH6ifr9jM6zMmDdbLrqelkxVC0RMhc4zWp1zOJUr+FLlvXTaXAG1Wnhjg1hF12uYGAzf3zHlfkCdLOIpP2/vk43FDpvFh+9DwCoexVJvE7vlDKsYBqTq87abaLP7kajsAyBu4SBCHEFTO4GbzTip3caDoEzE8c7eFse+veD6gw1YZZ2BXwPVH2vt/iw6wyoqYNXZYgMyrCZLfeBn9EsCNt/ugbQhYAVg4E7Olps6t2pamss4OnhmflhDAgYqvKp+dqHU4p5Vvu/r9i37JZUzrCQmDJPi+76+9vBBnZqNLgeGyZVEhlXYJ+7Zq+88flTGlIMsuYyJzrBq6GFV/reTHla+7+vrDx9K5fa/48hMTYZVeNIuKhvKisqwavUutQoyBRlWJU9f3nog8ndSpU+K5+v//WBX5XetJznsY4/PLLS8X6vxZp1OMqwqmVwsmIhFwfVbBk+bcYxpaO4+KPZ5220ddvMlwwpJCGet3LvzhE7PDfYYZQ+bmRRnWBWCY1OL7N8esnTbKbreQBf05DKOCq430CwQ+3fb98GeF0VmWEUEnAodLjZwHMM+coI0lARMZBSda7XdA2lDwArAwO2vNIRdNtW8Td7Z+XJQq9PJfmCUhFcF/o/bH+7oMbtPzOnO7cckSeevmKo8DwGrJGw/clbv+NRWvf8b25IeChK2YnHtcWr98kWJjOOS1YuD/3/s0HSwMrJZNnKzgFW5JGDr13rq2Ize/qkt+uN/fryvMfej10mqhZJX0xA9XxMgalES0NgMq/J7mXFM0AMkSv3EV5i97bGD0/rYv+2p+V04aJZxjE7MFHTHtqOSpPNXtN62VlV6Cx08fa7l/SLH6/rKZ5y2zeKl8nvRLBCK/vVaElAdfHd7Vc2war192JXjZA8gCeHNbsve0/rFf7h/wM9fWbCQ4oBVq2xaG1BKQ0lAe6wsDXBfU80+sxnn5dujXiG6JKAXmblWL2NMZIYWxlP9pjL6GVZlbKEYBwSsAAycPVBect6Spvd51sbVkqT5ojuEEQGDFb4Y3H+qs8nFuUI5OPuhN96kNcvKAasCE4aJmC+W3/fHDk0nPJLJM2o11ZfmqwGr977uWl2wMpmA1R2/8ZKan6+5cIWk5v0em5UE7KSZvN3+Hzlwptfh9q2XuR77d73m+guC23J1AaJm7PyCfd8cx+j3f+Ia3XzpKl130YqG+9dPfIXZ7JnpysIbSbo69HmFx3M2dJ8Nq6pBySh22+sk6BQ13nYZXGGUZY5Pt5+FZWRim2GyT9s+w8qWBByt/TQmQ/129/jBwZ6j2WfPOSa24HDcgp6IEYGVWHtY9VjqtJk4xlqfGW2CkoCNH3Z0ScDOyh5myLCaMCkrCRj0sEp2HMAgELACMHBBQ9gWq5SqdbY5miJ9euoxUtnWVyzKhXqgsP0nwU5aj/Ylx3gatZX74e/yysW5xMZRP/m0uFL2rlw2p33D8HDAqt1bPMgVzb3qZXW73V8uzlVLAtb0jGoRsLLvj32OjDEyxmjdsqmgr1Xta7XvYWUDf5K0fnl5EUL4vCfjGM13ERTKOX30sHK9rsrQNQuEon+9ZiIYo/hKAnbYw8oGrJiMRRLqjwvteg32+vyZTIozrDopV5uGHlY2YDXA8oWNPazKt0d91M0yrDpZbOAYk9oeaBiAEb94DAK15FhhDBCwAjBwreprWzT9Rpr1cjEYvpCqXqix/SeB/U5yRi1IG/4uJ92/KsweP/NNSrfVrw4OT0S3m4gbhe2/t4BVY7P5moymFrPx9jeuVy0JKJUDTFH7804mBW3WrCQtqQQYwz2kMo5T0++wHccxyjq9lerrNkjSLBCK/vi+3/Eq/XpG8a2Irvaw6qwkIBlWSEL9ZjfoTAabaZPmkm6d9LCK4zyrXOp0gD2ssoMPrtljWpBhVdnfRX3UUbcVOjyOZpzRW3yF+DSWBExmHJ0iwwrjZHSuzAGMDTtBk2uxNK7a9Dv5iTOgW72sCKzWnTehDEO2/yTwvidn1Pb54e/yaAWsymPJZ6MzYeqzpOwFqmPalxUbhUB5LxfS4SCSDTjl6zKamgkyrGxJQGMDgtHvb/3EV5i9bSbUgzOqPFMvc3vlzKfu35xCye+qb9JUk+0K/WlVrqudDr66PSPDCmlQX7qt1SKE3p6/8rypLglYWbgRmf0b32K4Tvs7dWoqjpKApdpFLUGGVcSeNbokYGf9BykJOFnqP+mRLwkoWwoz4YEAAzA6V+YAxsKp2YI+dMcOSa3L8+TJsEKK1W+3L/2zO9s+JlgVmXViLduB9sKf3yfv3aON7/q6Nr7r6zUZE4jHqO3zizUZVqN3EdqsdFv9ZEu1JGD77KX/9rkHJfXWK2lQesmwKoQDVpWxd9rDyiaWlNzaDKuMY7Tv5LmG9zOY+GoRsPr2Y0eC25ZUeqFlQwt1Wp0DtXLvzhNdP6bbycRck8w99KfYIvuhHaP2/efiZr9XZA8gCQ29GQecymCfPpdx0lsSsFQ9DtbLZW3p23gCVgMtCRjDWBuCeZXNJ2p3Ft7W/ubOp/QTf3WXiqXOsmOdFGfooXv1u4oRj1dVM6woCYgxQMAKwEAdOH1OkvTyq9e3vF8ctauBYamvc77r+GwHj6msvM44oYAt238SCqH9zu986ZHg/zv5HNGfUZskD4/n1o2rExyJ9E/veEHw/6fnipIqpdsijpP1q3vt3siY6MmZsFOz5ee+6vxlvQ+2T73M9YT3ofaCPOOEM6yaP9YG9EpBhpVq/q1fPBAEHrKNMxPZyr5/oTJx+M6XX6l33PZ0vfXFV+hnb7moer/QZOs7X35ly7/N8uVrRQ+91DrtvWHRwyoerUpJthNvhpUfvEYrNkBAfxYkof640HIRQk/P7wfPm9J4Vcs+0XEthuun1GkzcbQGqF8w4LSojRYOWL7/G9v0yIHpSi/I9tscGVaTpX4hyahnWLXq3QakTTbpAQAYL/Yk+U3Pvazl/eKoXQ0MS8H1tCiX0dIpE0wse57fcjVoZA8rtv9E8L4nZ9QWKSyESucsm0r2tPiGi1cF/3/NhSsklY+VnZQErGZYmbarKpNadRm+6O+ph1WpGkQKAlahiYNWkwj2NyXXl2Oq2WVXrCsH7epX9hdaBB7sbn6+6Grtsim98+XPkCS968c31dwvPNn6hlsvafWnBW64aNXweliN2HdxHBRaZD+0E2sPK1sSsE0PKztsJmORhPrjQmw9rByT2izCVj2s4loM10+p02aC4NoAj0MF15cx1WOvfYeie1g13thpDyvHpHf7Qf9GPF5VLQmY8DiAQSDDCsBA2QmlqTYnfPZEm4ljpJGtcx6+sCm2WZIcXvkXbP8j0EtmEtnPov5kvt1kHvo3aosURvUYZBOH8k1Kt9WXo7EX0Ma07s1hV0pLw8/wDE/w+D287VHZK+E1Ai17WNmJeM+vuV+znj2tAg92EnWh1LoJffh1Os1+Kvcs66VcYner35sFQtEfu3/rpp+YZYOocZQFtM/YNsMqKAnItoHha+hhNeCZqtoeVumczg2OgxF9ouNaDNdPqdNm4mgNYBdumNACHil6nxr1st30sKIk4ORI27WiPc6ndR8HhBGwAjBQrUoVhNHDCmlm65yHL2zaTTKGJ0DJMExWs/0O9b7jN2r7/FEbT71mpdvqM6zsBI0xrSe8w/upYe9/wmPut4eVFc5qzbTMsKouknEisrIaeliFyg/Ws4+ZL7otz3XC4+k0gNFrb6liqbOJtup46GEVh+o5cA89rGIs41PNsGotCOCyaSAB9dvdoDOsgh5WjtNTWdpRUHQ9ZR0TWdEhrsWg/ZQ6bSaO8oWFuuNgcHzvMMNqodRZhhUlASdL/Uc94EqlsWETxTggYAVgoDo9qbW//9KWA7GPqR937Tim6fli0sPAiLF1zrOh1YbtsqXCJTXsBdV80dNn79+rp47NxDdYNChUPov6nlWU+IjfqE2S7zg6mt89e6GZyzjB9hpWH2AJ92RqdZEafv+H/Vm4fQasooJINcGnFrMI9m5NM6w8X3tPzOkbjxzStx49rC9u2S8pekW5fUy7ya1wf61OJ/pyGUdPHDnb0X3DOu29EX6dUfsujoO+eljFWMbHLsYwbQIAGVP9PpwruPrs/Xt1eq4Qw4iARnGXBLTP7zjxZDIOQ6teUsFiuAFXb2hVIrdXcVSaaOjlWPnfL27dr0cPnqm5b9Q5iO939jc6xtDnb4LUL2Yc9R5WwXE+nbs4oAYBKwAD1aq2dpg9Ifzi1gM6MbMQ+7h6cXxmQW/+6H16x6e2Jj0UjBhb5/zUbHUip93kX1QPq817Tuq3b/+RfuOzD8Y3WDSwF8jNsioQn1GbJD9XcCWNTk36jWuWSJKee/kaSZXAQsSETv3q3nAJnFbBoCQDViU3HLDq/vFRwYDw3FKrkoD2/Sl6fm3fq1BJwF/9xGa99RNb9JaPbw4CmdmIySv78ELbgFX1/zud6Ds9V9R8cUg9rNjfDZzdRrMR5braqWZYxVASsMMMK/t98Hxf33rssH779h/p/961c+DjAaL4vl9zLB70cdkeG7OOk9oMmfJxJ/qNib2H1SADVjFUmqg/DtpTgkcOTOs//sP9Nfe1C2jqt7FOFn5kHPr8TZS6j3pUrheacYJ4Fdso0o+AFYCBshfr7U5q86ETwnNFN9Yx9cr+LdsPTyc8EowaW+f85265JLit3UVXtSRgtYfV9LmSJGn3ibmYRooowaRiZT913UUrkhzO2AsHBgfZYHtQXrZpvXb9r9cmPQxJ0p2/9VJt/6NX6/U3XySpfKyMCizVZwPaC1Sj1sGg8H5q0Kuw2wm/di+T8uF9qBVV3i+KfX9cz4ssI+h50uOHao/1/+ff3dzkucKl/lr1sApnWHU2w/Gcy1dL6v796bqHVZNAKPpjv5e99Hqxj4gnw6ryGm2GFc6wmqsE80/MkGGF4fD82v1rXBPD2Ux6exAVXU/5JqVoY+thZY+9PZQ6bSaO4Fqh5NcFrKrjPXa2dnGs1ySI31EPK2OoyDDB2mUqJ82Ojk0U44CAFYCB6rYkYPkxo3lErV64JzwQjBxbkmNRroseVhEZVrOFUnyDRFN2P2VX2y7OZZIcztgLr0QdtQyrkudr0Yh9/lPZ6nialW5rDFjZHlbNm4xLtUGqYWfYhP+OXi6kg31oNnpCqmWGVdDDqr4kYPnfqNXSS/LR20X48Z1kWOUypuMJDjtZ1u1kWKfN4oPXaRIIRX9sn7ZW5SmbibOHlX3StiUBQxmHwLC5vl/TH8a0zQnsTjXDyqS2v0urbNqMY+SYdPWwGuRY64N5rRaxeF70PrGjkoA2E5WIwERI26fc7joASBMCVgAGqliyDadb717CEz6jOmliD/O99NrAeLMlOWoDr52XBLQXVHYFM4bLTtQvlAZ/EY5Gbk2G1Wjt7xt6HoyYcsCq8RjketErgZ3gQjX6+cLPNewMm9qAVQ89rEqNGdyZiGypKNUMKz8yKytq4qnZfiE8odoyYFV57q4yn7K9rTpvVSYq8nUyzkBLMaHMbkfZngJWtodVDCUBO7xfeCKWDAIMm+f7sWZY2b5D7UrnjrJ2pWhzGSddPawGXhKws+3HBuXr79LJ3xgsaE3pNoTupO1jpoUVxgkzNAAGqtMeVuEVTaM2gWnZi3Uu2lHP9rAKr+Rrtx0XXU8ZxyjjVANdswtkWCXBXiAXCFgNhTfCGVbFNpM/SWsWWHA9T1O5xpXE9tDabDIuyR5WfQesXFturbFHhSS1ahtk35eS60X2vYo6zjcPWIUyrFoszrElAVtlfjV7zW6DSb30sKIk4ODZDKtWwdN24pgc8/3OJv/DJQFLI7avxvjz60sCDvj5gwyrjEltsKFc4aH5O5OPYTFCLD2s7LFugMehxh5WLTKsgqzT+nG13+qcFucNGD9p6wUVlBdO17CBSKN7hY5ID+07revf80196Ds7kh7KRDhXcLXxXV/Xxnd9XR/41vakh5MKD+47LUmayrQusRQ+IRy1CUzLnoieOVdMeCQYNbbsRHg7vvup4y0fc/+uU0F6vn0cGVbJqJ+ora6G5+w+DjUZViO0v3/iyFkdPDPfVUBh2PKZJj2s/NpSlvZPcIKAVfTz2cmhJfmMdhyd0VcfOjjQ8bYS7l/W7YX02fmi3v6pLZKkqXDJn5ryfi1KAppQScCIFfy/++VHGh7TScCqVSaNfXirSbOGx1TuevjMfMePkSoTdW0y28PKgVD2d4NmM6x62afE2RbDl9/R5L8N+m7ee0rv+epjkqSte0/HNzAgpJwBW/150L1i7B4v6zhBtlXaFNosTshlo8sI9yOOkoD5HrOJW6nv5dhqNxyUBKzbMzbrDxbWaqELxk/aAj9xZmsDw0bAKmV2HJ3R2fmSvvHo4aSHMhGOz1Qb0KBmTgAAIABJREFUdH7t4UMJjiQ97ATayiW5lvdbv3yRfqbSVH5Ue1iltVwE4mdX8YUvjNoFn1YszgYTl/SwSlb9xfx5S/MJjWQyhCeGRml/v/PYjCTpuZevSXgkzTXLhPE8X4tDPZZMkGHV+kLVbvtvffEVkqTHDk0PdLyt9JNhdWS6fD52/UUrddmaJcHtNT2sWkxu2t+UPK8myLVu2SJJ0j07TzQ8ptlq8tqSgs3H3EuGld0XdbtQplDqsodVk0Ao+mMzrLI9lBltV86zH+UMq/Zjst+hbYfOBrdduGrR4AcERPB8v6f+b52yi8byWUellEas6vs01ctlTFCef2CvWeqseko3YulhVXccbLVYJHjZhgwrSgIi3WLthwkMGQGrlOHicrhGaSV4WhRdT+evmOrovm989qXBY0YRK6fQTLHkd93DquD6uu6ilZKqF0RzC+UgF41Rh6vg+jWr2TesZEIuTu6IlgS0GSb2ezmKyqulG/cPJc+rzTQKMqw662H1zEvP09J8Zqhl4WoDVr099u0vvaJm4j0cpGo10Wnfl5Ln1wSQ1i2fCm4PB8IkKZeNfr5w6cFNF6xo+prVDKumd2mwYdViSd1/T9qViapX7o02Ot/FcWHPG7vJqrPsI+JYLOWrs/JqNtA2X6wuwGE7wbDEXxKw/G8+68jz03md1678axz79qDcfxdZvO3E1sMqdNxutRv2+ulhFer1h/GXtk/ZZg0ytYBxQMAqZbhoGC7e7+61K1UQZk9WRzUwSIYVmrEXjPmagFXr7SW88s/2siLDKhlF19PSfDb4eZR7GI2D8KTQKB1XbbBmkH0ZBs32sKoParteuayRVc2wKv/crodVLmNiKR3USqGPDKtmJYnCE1ItM6yCHla1JQHDQZ4loX1C1GtZNT2sWmw7NsOqm+DF0HpYDfmznxR2X5dt1VCtiTgbpXfaw8puq/OhQPagszWAZjy/tiTgoCNW9rAzlR18ds+w2AVzzaSuh9WgA1aZxvOiKPYcpH7v1snCD0oCTpa0LSqtnkuka9xAlNG9Qkck23tgdLstjJdiTb8FdvqdKLp+xye0QTmAEW38HT6H5vNHWMH2sAqt5GvXOLh+5V8+49DDKiFF19PSqWo5tUGuGkWjcHBikA22+xUEQZpk0owCO7FWqpsY8Xw/stScvanZISu8UjqXcVQc4oRLOKjf7TG1WcAq02EPKzsR73q1JafCz7c0X9t7s9m5TPh1Wm07dt6rm5KAwUKeLr4nnuer5Pkd9d6w8ply5h7nNoNlv6c9xKtCq6LjyLDyG3q1RLHbak2GVUpLpyF9yj2s4jse23ORqWx5X78wQucjnSpffzTvE52PsYdVN8eYdvLBHMBge1h1Ogdhg031+9tO/kZ7DkFJwMmQtk+53XUAkCbZ9ndBEv75R4e09+Rcw+22xv6R6Xl95PtP1fxuruBq5eKcfvaZF2nVEvpx9OruJ4/r4QNnJEkHT58Lbj90pvE970TGGL3+5ouCsjPDdHR6Xl958GDDCdVcwdWSfPOT3X48cfhsx6t87Unhp+/bq1dcc37TlVBffeigbr50lS4+b0nk76Xyiedn7t+rs/ODy1g5Ol3tYfaDJ4/rRVeuG9hz19t7Yk53PnFU/9+tl2hRpQ/Y97Yf1TMvOa9tP7CwB3af1AN7TkmSVi/J6w23XjzwpsXjrOh6+sz9+zS7UN6OfuzKdbpmQ2PJp6JbzpYKb+sP7T/dch9x4PQ5XXXB8uDnXMboXLH83ZyeL+lcwa3pSdPKnhOz+sYjhyNPoqOe56JVi/WTN27o6Lk7dfjMvP7poQNtS3u9bNN6rVyc02fv36dMxmjTBct126bzBzqWTvi+r88/sF8n5wrafvhsJcOq/B23n2N4V/nEkbNyPV9XX9i85Ne42XtiTsdnF/TMS89reb+5Qkl37TiuV117QUfPG16FuvvEbF9jbGbHkbO6Y9vRrh6zpbKvHOUMOxvA+MidT9UEVncfn9XyxY3HBjvZZyfmvvnoYe06Xn3Pdxwp9+3KVzJEHzlwpma/1e35QcYY/dRNG3T+itZlNXcfn9Uffu2x4GfPL+8Tv/bQwcj92OoleZ2cKwQ/23PiVgGrlr1PKr8qup6WmerlT3hyyh57o35X81Shl2k1OZbJdJ9hZZ/v0/ft1StbnBeFna0cr7rZju3f9oXN+/WGWy9p+L3v+/rcA/t0aq6xl9amC5brJVet7/i1Bm2+6OrO7cf06us62/80c/vm/ToW6lVrJC1flNMvPPsSPX7orHYen9FP3NDdcdN+70Ytw6rTmoB2W90fugY9dLrx+ue6DSv1wivXDnKESKEvbtmvfNbp+nvSjOfX7scHffUS7mElSafnCloZcRwdZdsPn9ULnt6872Y2Y/TNR49o856T2rr3tBblMnrjsy5RtnJ8eOTAGW3Ze6rmerPe1r2ndO+uk8HPjx0s97oc5LmS4xhlHaN7d53Ql7Yu1k/ffHFHj/N9X197+JDOnCtqZqH2mv/Y2Xldvm5p08d+7eGDwbZqT0vrJ/U7CXjZLO3pc0WtXx597rPt8LTu3H4s+Hm+6Ornbrm45VwGRlTKAj/B4peExwEMAgGrEfW5B/bVHOTqnZgt6H3/si3yd1NZR//+uZfFNbSx964v/igyWLhQ8pq+5+0UPU+/9pKn9zu0rn3m/n36828/MfTXfXWHk5jrlpWDeN/bfkz7T53TJasbT+LOnCvqv3x6q569cbU+99bnNX2uxw9N63e+9EhvA+7Amz96n3a/77WxPf8ffPVR3bHtqC45b4leumm9Ts4W9Ev/cL+ef8Uafeo/P7fj5/n9rzyqxw5NBz8/+2mrtXFt85N31Nqy55R+78vV7ejenSf0D7/07Ib7lfuFODWTUlv3ntbWvadbPn94gu3ydcv04L7q/X/45HG9/JrOAjkf+f5Offq+vR3d17pt03otnRrcYf8T9+zR//nek23v96P9Z7Q4n9EXNu+XJGUdoyf/5DUDG0endp+Y0/+4/eHg51dde76m50s6PVeIXFDwyr/41/LjYvzej5of+9PvSWr/N//+Vx7VFzbv1z//+osiA7r1wgGr2YV4sgo/dMcOfe3hQ10/bu2yvJYN8HsxaJetWSpjpA9EHMtff9MGLRRdTZ9rDCp4fjnz5tc+uaWhbM2SfEbrV0zp8nVLddeO43p4/5m+xjizUNJ/e8UzWt7nnZ99UE8enQmNz9c//nCX/u9duzp+namsow2raieHjDH6pRds1Kfu3atVLSYem2VYhScrn7Z2qX7w5HFJ0uJcRisWRT9fOADVqpyRndQ6PD3f9D717L7ozhbnRfXuq0wsTnWx+n3jmvJ5wW994WG9/Orzdd7S2oVuu0/M6bdv/1HkY1cvzWvL772i49catD/46mP69H179eW3v0A3XbKqp+c4Mj2v//75hyJ/t375lN71xYd1fKag2zatbygV2YrNsOomq65eHKuiO+1htX7FlJbkMzp4prrNHp6eb7j+WbM0r80JbgNI3tGz8/qNz5W/Q91+T5rxfb9mOx30gjt7KLT7/ft2ndRla9J1jTRfcnVyttD091esW6ZHDkzrZ//m34LbrtmwIliI9Ouf2aqdx2Z12ZqlevEzohdi/uHXHtOWuuuZ5YuyWr10sAuin7Z2qe5+6oTufuqEXnTlOq1d1n5x79cePqT/8umtzZ+zxef5jk9t1cs2na/F+UywuMDzfa1aktPpyuKMCzroaWsrNGzZc1pPX7888j4f+NYT+vZjR2pu++GTx/X5tz6/7fMDfbGlwSlZiTEwulfoE+4j//6WphcsuYxp6JXy1YcOBhNx4TIO6N65oqs33HKx3vu66ySVLzpzGaP5Yvfp9b58XfP73+zpsYNwrugq6xj96D2vCm67a8cxveXjmyVJj7/31bG8bqeTJuctzesvfv5G/bfPPqSFUvR2u1DZnu/fczLy99a5yv0++ou36vlXDG7V5xv+9m49cmC6/R37dPRseZWv/f7ackBPHZtp+pgo80VXr7n+Ar3ymgv0zs8+qPkm7yui2e3o0//5ufqzb21v+t0tlsp10sOTUp18nxblqt+N29/2fBVKnnafmNWPf+iurj6r+aKri1Yt1nd+48U1t7/p7+/Rlr2n9esvu1Jve/EVkqRP3rtHf/T1x7VQ8rR0gIme54rlTIzNv9t80upn/uZuzRddnT5XvbiuL202LOcq5Rc/9Mab9MprLtBU1pGv8sWqzV5GZ/ZUsqTqV7c2Ey4JGFe5n/miq00XLNeXfu0FXT0ulzHByuNR9KprL9Dj73115DnhopzTkOHohNI0ip4n1/P1X192pd5a2R9I5RXYuYyjj/3Ss2tKItl9xYUrF+m7//0lHY3vxvd+q6N917bDtcdRzyvvQ1YvzeuHv31bze9e99c/0BNHZvSqa8/XB3/+5uD2jGMasp4yxujdP3mtfuc1V7f8HO1WV3Q9ZUP77fAK8/e+7lr94euvk+/7cj2/6fOFe2C1ysC0D3/2xtVN71Nv1ZL250X17PnCjzWZfIzy2hsu1PGZa/Xuf3o08vOz+8u//IWb9Yqrqwsp3v+NbfrcA/s6fp047KssKjs73xio7ZT9+973M9frdTddpLPzRT37T+6QJJ2cK+j4TPmYtVD01E3RCrdSPq+XgJWJMcXK9/2OelitX75ID737lSq55T45Gafx+uf//XCX/vSb23XmXDF12SkYnPlCdbvo9nvSjOtFl7odFJth9bzL1wSvlzZG0nMvb55h9cGfv0kP7D6lA6EqMeG5oSOVYHSr+aJzRU8vvWqdPvymW4Lb7HnDIH3jnT+mz9y/V7/zpUc6nr86EcqK/fCbnqmX1mX7NqtU8a4f36T3/cs2LZTKVSjsZ+/55UUKt162Wh9+0zM7Kgl4a+WY3qpH0HzR1Y0Xr9Rn3vI8veD939XJ2YLu332q7XNj9KStFxR1dTBOCFiNqGYp2lZ96eIloV4c9cEsdKfoelqczzSc8HRaqqteuU9AMgGrYqncZyc89vBK8l7/pkFanCuPp9CkhnWnzVhtH6xlU9mB/l2tmrjHwf69vZ4cFVxPi3KZIJOGZtndsfvP5Yuymso6TfuIFCr9qOy1m2O6/z5lHFPe11T2993sJwqup6mc0/Ca9kJ/aWgfZle9xlHTvn7/Um8qG9382avLchgG+/fX7yMynNrHLjwpFFfPnILrayrXeOweB63OCev7g9uvlef7wf5s6VT0++JU9kGW3Vc4xnT8PuYzTkfHmfpApef7KpbK/SbqX8v2F1ncwedp/952QUf7+qUW+x4bMDDGKNui8Xr4b2l1jpDppZGR2p8X1WvW36udZS3OE6r7y9rPYHE+k9g57SDZv2Fpm3PGbv9We/dsLwGryr9xTI75vjrqYSWVt6PwLqf+/Xn6+mWSyiVkr7945cDGiHQJn9sNap/g+bX710GfndlTkanK4rG07cs8z5fnt+6zZIzR8kW1U3xRc0Ot/vZmcyGDlnFMpTx3b/NX3Vzz2+Od3W7DC6lcz9dU1um4R5fdv7fafAolLzgnHfLlDgYsbb2g7Lls2sYNRBndJaXoSvgiNW0nX6PGZk4MSi5jEmsyX3Qb/5bcABumDkK+0rC82Xbb6QnsQqiR/CANq6GqnaCwf2+px8BztbdSpXE7+4OuhCf+ci2CzfZ9thfW/awItdtsN/uJYslrWWc9/L0PtoUB74ei9i/1mgXsk2ji3uukLvoXnhjwYtqnlr8TzAqYUA8ru5Cj020+18P7V876b/99rg/s+H5lH5JtfE07jk7G3Wng2758yfMbgnzdCsehWu37e93VtDsvqlcI9m3d/WHBsSdqH91kf1k+LvqxBZ6HpVD39zV777o9h7IZVr0syAgSrOIqCTig3aMtJ7krpn6ESIfw/mlQ1xqe79fsXwe9ZtCef9jz50LKFvnac+e2595118LFiPP/dgGrYZ0r29fpZf6qq76NwetUMqu82oBVN/vsaonh1u9hJ/2wMPrSdrpjN+W0ZYYBUdiLjonwiQkBq/7Y3jSDks8ml2FViPhbRu3kqd2JaqfvnT0ZH/TfN+yPzv69vV782e0338cFwCQrhib+8lkn8mLWraxwDJcE7KfMWTW42PmJpc1uqmdXUIcDt/kWk5L9KFSyI1op7/+iVnYO/yS6foIyCqf28QhvenF99MOcYBll1QvV7oO0na4urn9MJ8eZ+l2k5/vlTNWIsdnbOhlPp/veIGDlen2XnAq/ZqvXtxlW3U4adDuBZ+/X7fmPDfBGvU6z/WX1MeneW9rx2+Bgswy9bv9OW/K2lwwruy3F8c6WM6wG49JKX7U9xwlYTbLwfmNQ+wPP92sWNwy6Yp+deE5rhlWw32qzr6/fb0cuHGuRwTvoxbut9LOoLh+x4KXp69iFIJXXCS9ILXn+/8/eeYdLbtV3/ytpNLfuvXd777v2em3v2utecAdXirHBBF4IJUBIAi+QlxYgL72ZBAIB3hdCyEtICCShGoPBZcHGHdzLVq/X2+72dtvMSOf9Q3OkI43KkXSkkeaez/Pss3NnNJJGOjrl176xsqCcDKuQa2gQZg4jg6nKTNlmO7QLLWHFU4mkBbmy7xBYzSCZUZEc0jSgJDHahPHd+563NT/ypG6YLXpSXXqxHntdCzem805gn9xl6WOIvnezBxzRnyzvIdXJuuXxXQCcxcXw0Ql863dbcc5nbseTO4/gf//0SSz54C/w+I7DvvuhiwzqsCjbYqzd1JiMBE1R8MzuVv0y1ggsomQkLX/1yPP8tc3v3XLAV4yalsboYhaadP8fbOociiLIacaiqgr+8Pwh/H6zWyNqDyPonheOgTLY0Sex+NN/fjD0c7pI38qpsccu6jPLsMpg7C4jbIYVHVd5rwudLwzE0KVRoODeLdEacN7oZatkoX8EMj1fHoMZr02NPuPeklNJcJUEDMuwSnicqHmRl7iZdN7j+Bozm/2lX4ZV0HdEMlpr4PzP3oH/+sOOTPbvOPlay0m9/78eb9mOFxq1n0zDqrmPDPpIAuI7Z0hCT1XDnIFubDswKmR/knLidlhlUxLQFGxxpcEDdF7sl3lUZOj5hpWsBVrHHr+xJGx8qbmcLdlC+8q4ms1AvDGPtqttzbU827QMk8Qar+l8JkyT1wqisrYb6pVaf5L8oPPdsmfCSySAdFh1DOsWTcVfv/gEAFKzJg104iGyrNCNZywAADzfhoUdO1mirJw1BR+4ahW+/adn5n4+fjjGD/92y7sI0puTR1qqRBRfvuk0XNwUMs/qHrITCmcR5bz36VufwfDRCfx24z78v/ueBwD82/3bffdFtZXyMip1GqxTQ4soEaRrir1oSdPrUtFyVoswiqm9VV8Xy/uuPBHvunwlrlg9237vghWWODMVkReFX//iZa1H3+KKkyxx5CNjYs+Fh6yyMDuR327cF/r53MEeAPzt3lUSMKOQP7+M4skIW1aMNxqbcsGKGXj3FSvx6etPiXXMqX3VWNsDlrEoKKM9iwwr1n/gdSZ8/63n4hfvupBrP4C3JGDwdvQ4cW0GUfMiL/aYFNPAGOqwCugvqzkFwxw4XsOuI+P44m0bArcJi26PwnHyOW3hLy9d3rJd3Kh/uo5I4qy0NayyKAkoMMMKsOYtIxMNgXuUlA1WY09UyWnTJK5sXNFl2WmXoWsKFKV8ayQ7gzNqTPc87Ek0rPKaKy+YamVsJimFzzPn++c3non1/+sSzBnoBgCM15saVsz40TDjOfRphlVYcAGbQf75G04FAJy1ZCr3MSTFoWyOH3sd0N7TkEiEIFf2HUK3ruGdl6/EtL5q6SZfRSILjZOXrp3n2nee+JVI0lQF77hkOS4/aXbAt/LFLl0XsNjhNdjUDROKkkyDI4yh3ir++iUn2MfIAvY38pYEjNJWckosyOlKHNg+4MTZUwC0GsVsQ17FKQl4wuz+VMedM9AdK9igYRKsnjfQ8v4p8wfx3hefgGmMAXlKt45XrpsvvP3ylGA7a8k0+/XsgS685cJlANrTLu17G6OEiMSfrphG61w0rKReAACmrBiJP6eZ0q3j3VecgHWL+I0qp8wfQIOjHXhvO2lmWPmN2Y6GVfSzyl8SMDgr6rzl03HyvEHvV7iOGV4SMFlfEzUv8uJkQ8XUsKKZXD79cVB/GTf7Kyl0/+ER7Mn7Ej8n36vOWOhzjLgaVk2HVZK5qF0SMKPxUeDQp1f4tOsknUs2GVbE1W8m1fMN2z9g9du65l/2u8gYnA7x1XOt9QHdzD+LNkrDKp+5Mg3ay0rD6rJVs7FkRh+m91ddx2HXdoZJYmlOapwZVnQsP2PxNJy5eKqsAlBSytVLOJTMzyaR+CJ7zQ6DV/xa4g81GIt0WLUz06XWKH7EuR4hLs4vOm79VlElT1iyvocu4eKmgSrqWBM+n7PaSnENyhILVsMq6L6zpZKosTJtacC4xp+4xvmqJl5Ljyejhf1c11S73nxb+sMMAhImO7xR1e6SgNmcS54GliJDbX0mIUyJ0+yui56wbzGJ1X78nkcaPc5XEjB+hlXakoAaZ0nASsIM3Kh5kRf7Pqvx+raw/tguJxmgg5q1hhXPb08zjvhlH/plqMX9nbwGZT/sb2SSYUWEZlhZxn45v5zMZKFhZZjEXRJQsMWV7k5RspkXZw3NOIvSyKNjfl+1AiC87KsfeWqC6iFailF4ZQ/Cj+Ne07Fz0bphxpoX0HE/rGJA3WOD0TVVVkEqKWVz/DhtuWQnLpH4IK02HYZcQKQjaVmVMJxo1PZkFIj8LVkQqWEVQ3Q8q+j6rO+hX5Ri1MTdL/KajaaXJQGTUXNdQ8X1HsUva8GrzxKXuH13XOO8ZVQW237rjehnjhVErra5XcYtjyaJhrdN5VESME+R8CLjaDURJksmu+uStG+xNaxCzo3nfvLamNjtkmY++e0rzMiVdFyIrWHVHA/iHi+sP64F6GJ5heuzgsewl85h1fr7/MaGuMewSwKm0LDKoockgNCArjIa+yViyUPDKiyDJQnElWFVviBfoznWRvX1tF/rberassFF9IoGBRxZ2c/5BbzS+QnvGpttEnHOsdVh5c6wijN+0oCESA0rZg2kV6SNrryUy/Hj6GG29zwkEhHIlX2HUc3AKDmZcESYxS7qAGDHofZoWIn8LVkQFa1LjSJR0S1ZRtfHLc8Tl5pr0Ufw8LaDeGDrwdDvbD9otacfPvwCHt5mbTvBRNPTifmP/rizdLWX28Vvnh7GgabOU1VTHa0Oz31nNazY8iJpiGv8iRv9qGsqDo5Yv40Qgu/d/zzu3bw/9nm2nENEeT1W0L5aabfDKjrDyu9RycrBUjSS9BO895HdLKuSgLUSBGjkQRoNqyTomppIv8TkMIrxnDdvJovCWcYv7r54Mqzi2jriZjEljYYP1bBi9Bz9vrP7yHjs48WBx7CXRjeHzaam+LW3uMeg40VUBoQfjlB67K9GQgi/c5eHakWuNyc7rINBmIYVIS6NQNHzL7o7uyRgxo530fBnWHkcVj7OxaA+Nqjvz4pqzHUBu13cwD0A2NbUo/aWBIzTZauqpYEW1j5rnnG5qimla2+ScpLlXEIiyRu5su8wrHRjORgmJQsNq2nNmsmHR+vC9slLnin9SYkyYGdtsOGhkqJcAQ9eDasb/899+Me7NreeBzObXjK9D3uPjeP9//U4bvrm/a7z62IcA/ds3o87n92byXl3EnuOjOOt330Y377nOVRUK1I9SPjecWyrWDi1F71VDW84b3Gq48fJUkgS/ThWt8TRDxyfwOa9x/GRnzyJ1/7TA4nOlcLzzE3t0+3Xbzx/SdszTgH/BW6YIa9uTo4xlS7iKWM1I3BbajRJomGV1a2vcWT8TQaoA8UwSSZzGi9VznKmXoco1dgKMzhxaVhxWpnYrURejjCHla0nFtNjFdexXzdIooyesP44qO3MbgrXP/rC4djHi4Pz28MNgknxyyDr7dJc+o9JjiEmw0p8J0kgviRg2bJTJGJh7Q2iMkdMQqApCq5bMxeAM9cQBZ2LKChnVRqjOR+NGveos8kJvLN+N10/WO+Fl+LPq8SyPd5xazYyDqsYTrWBHqs84rFxyx7TkmEV06OvKQq3hhUg+8wyUzbHT5ZzCYkkb+TKvsOQIrjpyMK4099VQbfenkeNR2Om3UTVrubXcMjutzpGp2zgXfSxkVmzB7owUXcLx7Ltl50kHxnL31laNiYajnGetqNADStG627OYDee/NiVePlp81MdP05pkiTRj6cvnAoAGG+YGK+LGSN4+pdZU7rt11efMjfzbMUwbANlzKjRyRJFPl53O6jCIkGjInS9sFGoWWV8Wk7cYmcU5wF9Jhum6coGzfJ4XA4rz98mIagZJqoVzXd7gO9Z5TUyqZxZUXEJLQnY/EiJ6Sqwy9HG0IhLMv8J64+DjJZrFgwCyN4QwqVhlSrDqnUc1TUVD3/4ClywYnqs82AxiRWpn6T8nq06kVmGlbh2r8tsgUlPViUBFUXBP752Hd50wRK7BJ4oXBpWJcwSpJc5KrOY9tuqokBV2HLz7gBJP/IIdGHRVPc5RsEGWMQJUuqquAMSWIdVI4HDSlWVUIeqN7BQOqzKS7l6CWfuWTZHm0TiR7Et2ZLYVEsYLVQkaowhWiTVNpUdKIOmh127OuD6xNKwyqh8AStinwV2xk6ldTLL/qYRJuOhZpBWbSWm/UaVapMEQxd6QRpWXq27tPpV1rH4+4gk0Y+s7oioMYJHN449R72iOOdRIg2ryZK17L0n4c5zGqHL1yeyi/os+9Gij3d5YGfNNIjddrO8LrzZoQ3PNiZXhhVHSUDeDCtO3am4iHR+UejYwttPNsyEGVYV/zGOfa9Fw0oNn7OJgsthlcLYHGSUVVXF1T6SaFglbROZa1gJ3J80vkoycVgxpdk0JdwhkARCCJSmQ1nXlNLN7xrNDKuoPibIUcLep6BKB0FGZxPTAAAgAElEQVR9f5ZY2W78FVXY78WBtccYnuyouPOCiqoEOlQNk7QEkmShJSzJh/I5fmRJQEnnIFf2HYZcQKSjZjsOxBog/BwReWA5cYrtuIjUsOLOsMpOw4pGpWYlZUPbXX9XxWUArqgKuhiH1WitYb+uG2aggVlnSgJK+GAXLnYpDc3fMOeUEhKodRejj/ArZRQFmy0mqi/i0Y1jI7p1zWmX7QisSBo1OlnG1JZMwjCHlZ1hFVw2kIU+XhVVaTESiMA0CRoJs0w6jSrjFM5Di4LX2e595k1CUG+QUAdyF1eGVfQ5Wttlk2HFq6EVh2rMftIwzUSaSaEaVgEBXKqqoKJmX82hFuIMp9ldacYR3sAPXqc8xUjjsLKNTBmUBBStYSUDJCc9E0y/H/c5CcIkzvOjaeLnCyZxHLdltJnQCtVxHFbss8rjZKTjeZ4lluME9rJOxrh9rVWJyGpT3qYV9+eGOVTZQFRKtaLIPrOklK20Hh3rswoQlEjyRK7sO4wyprcXiazS4Ns1KS5DxHmkhlWMrJPsSgJa/2dZygqwxHG9kWPsgmFkwmC+Y7YsEB1tJSWR8Woyw9YhjywJaIhfzMWJvEvST7GOImEOq5gZnBVGF6wtGaeGCVUJX+D6PeOTZYHpNRCHtRPaL/MaqWhJwIqmZOL4pzpjeYmEFxk/53SmGlaaZYSJGh+9hkezqbEVdm48Ec9JsqVEOpnUDC6to+nB97AkzrAK6Y/rhglNVXz3m0ekOF+GVfK+mTeLIG7/b5gElaSNItMyPmJzrCzNZLnenMywfYCoeZJhEjvQSVOycFg5pd9KqWHV7Byi1ngu7SQmIK7G4bCi9zXPSh16jKC9NP0+e8/NlBlWmqa07IPiFxBRRgeppJxIC5Ckk5Ar+w5DURT84flDLe+/8TsP4tSP3YZDI7U2nFV5yKp8TpxyXyLx1k8uItQgEjVxBoCP/OSJwHZcy9Rh1cywyijFauPwMQBWOznA/DaTuO/f3Zv22a9/v/lAS3YDa5xkM1syCAAvFe/8/iM481O/adHoYdm677j92nZYNY3fIxMGzv3MHbjmH+7Gw9sO4g3//KBrOxHE0bCqJXCY0cXttV+5B6//9oP2+9sPjMY4S+95kFh6UIqi2Of8qV88g5v+732Jj52EsD4i7BGZLEEgd23Y6/o7qD3+5b//EfdtPRC6jRdqdNI1NZN+1DawSA0rl3M6Lw0rAKHi4yyzB7oAWNHNtQCn9/SmzkSYUY5GmifRsBJRxpUS5ijq67JE3ucMdgduE7TPsHmRF8tJEv83hWW4143grC2TEPznwy/EPh4vhknwzu8/AgDYf9w93/uf//EIfr85Xv/j5cmdR/CFX20AED2Oxz3GA88dSHxeWfZeojOspGby5OX2p4ex4m9uxS2P77LfE9UWCHECCrQIjaBE+4czFlRL6EAwmgNf1BjGOpus0odU67h4GlbWseKsgZK3CU1RcO/m/QDQ0rbiavxpihI473HmpJ6SgCUrQSmxKFuiEm3LZTtvicSPYluyJbGpqIpvhPH6DftwbLyB4WPjbTir8pBV3WZrIpb/qJGlE0ckuhacJk8j5wHge/dvD2zHPHo6SVEzLglIDbir5kxxvX/C7CmuRUd/0/gFWAa9oIwIes//6tIVAICqFixqPxn4+WO7sP94DYdH64HbsKVNvBpWB0dq2HN0HE/vPorbntrDbCc2w4pfry1+9GNQ291xKLnDKskzxxrOH3juYOJjJyGq/Fjg90pm0EgKdSqdt2w6gOCSXL94fLf9mrfN0rIUuqZmUqIiD62msqAzTogsskFbjsept7R4ei8UBfjSTacBsNpEzTB9+7G/vHQFPnT1Klx0wszA/cVNYmG3F5lhFbavU+YP4u9fvRafvv6U2PuNY8BrGEkzrII1BcOcYNP7qhjqrfp+JgI2wGsKM+8BgJ8+yhrJk/UlP3lkp/066rrF7f+n9XUl7oecuWZGJQEF7q+M2SkSMTy9+ygaJsFTu47a74maJxmE2H21piogRGywoNnUsALc5eHKAr3MUQEKgRpWTN8aNMdLUnY8LfHWQNZ2X3/dutjHURRgWjMgJnVJQFUJ7KsbzXOstGRYlau9SSzKdtfsykClO3OJpBW5su8wTpwzJbRXleUbwrH1HrLIsGqXhlUJIs7DSov4CZr6bVs3SGbloJTmbrOqBUwn36fMH3S9f/lJs1wLhpGmhpWiWI68Fg0rj+bEK9fNd+1/shNa4oxpZ/T6Uf2UEUY7jG0CIvXh4mhYOUbo9I7ItBogcTM3KprKrTkjmrphcmeEseXN2pEd2w7qholpfVW89aKl9t883+GB9p3WAj/5OUadh3RYMVkzDTMXR55zvPAb2zAIXnn6AkxtOjoIIYFO79kD3Xj7xcvtDCU/ls/sB8A/LmttyLACgFeuW4Ap3Xrs/caZNzZMM1EZOk1VrPmEz3FMEpy9du7y6ZnOK9hApbD7m0ffHNfAWG+YWD1vINGx6OXOYqpJQIRrWMm55eSEbZ+0TYkyxLMl+2ifLTLLis00LGOJtgbNsIp4mNlxNa6GVR6BLl6qMcuiL57ei2tOnRv7OKvnDthZUalLAqoKGgHnTI/BOhZ5yydLikfZ7pmjYdXe85BIRCBX9h0GXeAGdawyGi4c2+gluG5zVwxjtEjiasy0i7CFr1+6vV87TmI85yXLqFfAKW/QV3U7ILwaVqNNDau+agW1RqvDyhF5bZa7qMQTbu90wq4Dey3pdaPPzuiE47Bi26PIzLVqDD0IJ/qRv70HbZnGyJBUN65dfVKcPoLtdspm0EgKvT5RuoLu7/C1H7qrakYZVknKZHYqdP5iaVjRbMzsM6yixhmTEGiqM57WDQKTJO8PqKOItxQh66QS2UySZDbxEMchYCTUsKJlWv3uHZuJkObcksA6osL6mKTnEKcHiusUS5PtbzusEn07HCvDSrCGlcwWmJSwDqS+qhVUIMp5zDrKaZ8tUsfKNImrJGDZApKoL78SMZdlx3zWMRdLwyrnDKs4mtVJ+1j2ON52lcRhFeRMpfvWmEASurbknbNIJEmhY33ZHG0SiR9yZd9h0Gya4Jq65ZqY5U1WUdrtiuKqx9SYaRdh18dvoeK3bZAWhgjUjCNV6O/p9USTVzXVN8Oqt6o1jZFMFLJJWtpvNYbheTIQnmHFlgR0O6xGaob/dgId23H6CMexnr69Jzb4EZJYI69dTgWeEqn0EWf7nclilKs1rPvJaiBFETfDqqIFl1BJQztEwotK3hpW1ZCyciwNk0BTnQzLiZTZX1pMYyabYSWyJKDIbC2WsMxzLw2TRBowgwgKlrAcjP77zNpZwbalsCC8POY2cY+RJngqSyMTgWANK02FYRKhzgRJOfAGeMUpXxqFyTjfKxk4rFgNK71NwaRpSJJhxZY+jKdhld98Ko4mHp2rJjuOageJeueiiRxWAW3TL8MqTjCYRJKKDINfJJK8Kb4lWxILOhh+8pan7QFxouEYW4ePSg2rMGikVRYlAe/fmq9eC2nqQ5Qh4lyvBGtY+Tlf/SZ7z+45lplzLusMKxrtRSMVKZqqYKzuPL/0UvR3VTB8dALP7D7m7IMpEeh1uDyx40gm5102qFFuw55j+Pr6zTjCaFpt3Tdiv3Y0rKzr9/W7Ntuf/frpYWY7sRpWB0Zq0RsC2HdsAoCYfirpwskun5rgmWMNvHkau0LLhnrWqW6H1eRYXNKoVVYDyYv3fj2z+2jLNn7YDitVQRaXU2SZzLJTZe5fLhpWzX0fG2+Ebkc1kagYNJ2bJjWK2eWieB1WTL8jtCSgSC8AQxwDXtIMK+s4/kZbtjSXl4qm4OBIDes37E10zCieY8ZjAPi3B7ajYZj4p7u3ut7/4cM7MNoM5Ll38358+faN+Modm/DN323B9+5/Hrc+sRt+PPz8Ie5zidP/mybBYzuO2G08LllnWMU1yIbBZnJKJhd1j+aryKBMq9+xXttBCQLXXmzmaJwydEWBnUuFwa5P2IxY1hlVC/jtdsZ6jgGvFVXlnk/GKe/tpaqp2H/cWkN51/RJNKyC5h9G07Go+TmspDxH6ShbohId68t23hKJH8W3ZEtisXBaLwDgu/c9j8d3HAYAPLL9sP05NXRK/EljhA1jpNaAYRKMM86HrKGOnjwnnEnR1eBFg2G2LoK8CyMajco6IESS9cBfN0zfyNdlM/uwee/x1g+a2z6x03FE1Q1iLz7opLi/23KA7Tw8JvaESwpdhH361mfwhV9twO3POM6nUUanil6/mVO6ALgzrNg+VKTDaqxuHZ8upMIYbp5DmL6LlzULBn3fT1oOJY4h/PrT52P5zD777yNjznP63P4Rv69kQr3B78BvMP3OZCmpSUs8Uu02v/IsXoMCb/uhi3pdUzPJHkhSJrNTYaN4abZHUgM6D9Qgs/3gaOh21KliZ1jV0xnF3nrRMgDAiln9XNu7SgIKvB5ZlQTU1RgaVgaJNGAGHicgO8IkCGw3u5pzijd+56FEx4zijmfdjrCP/ORJ3L1pPz71i2datr138wEAwCdueRpfvn0T/v43G/GZW5/FR37yJP7i3/7ou/99zeC90xYORZ5LHIP2Tx7dCQC489l0jrysNKxEoqsyW2Cy4q1IIDLj0jCJ3e/QvtWrNZQGqzSmhcjMsLygmklR4878qT2oVlS89pxFllxEc45E53W91UpgCb48tC+9HBqtoc55n9Poc480S7wfGqm1OJvizpM0JWaGlSzTX1pEj59Z47S6cp23ROJH8S3Zkli8ePVs+zU1BuTpJCk7WZUEfPlp8wE4JXDyoB0p/UmxopRiaFh5opPohPGcpdPEnxyYkoAZZYPUmqXV6ITo6lPm4LnPXoNLTpxlb3P2Eue3vWztPADAGFuqrmHai4wqk2G1btGQjLBpQp+J4+OWw4R9HtlrRK/fYI8euigUmbVw+sKpLecUSPNkF0zt4d7/rIFubPvctfjWG850vZ/UyBCnf/nSTafhjr++xDmXpiMQEKd7wEMczS22O+Ktq192rKjVcA0rNmMb4F/g0z66ogUv8NMgskxm2dGZEn1Jy3bGgTqMojKQHYcVzbBKN9+68uQ52Pa5azGtr8q1vaskoEAnU2aliFX+8pmpMqwCNKwIk+nQerxEh+LGMAmmdFfw3GevwWeuPxWAO9CBhbajOPPr/u4Krjx5Nn78F+dHbhvHuHiQM0s6CKc/zcRjJbQkoGo7E8TtU1IO2MwcmpUtyghPiNNXx9Up5MEkxG677SrXnwY6JkRlS84f6sGGT16FN5y3BNVKq4ZVX7O0vB9ZBe+Gcd6y6dzbJtXPBYB1i521lrdZCS0J6ONY5C2fLCkeZbOj0KYsK/ZKOoHI3l5RlG5FUR5UFOUxRVGeUhTl4833lyqK8oCiKJsVRfmBoih8K0ZJprCRHHRSwhokZVRHOFk5edoxSaEp53lGSCVFUxV7cueFR8Mqa2F5pyRgJru3S3FR/QJFaTUE93U5pa5o6UA2K4jVtGJ1XKoVcQvJstPabvzFh9kFRlhPILKfsMvrcBjdvJl0sY7jOeekfVIthYOAbdp59om1GNoibIZV2UrGJIU6zuk18us3vMECDc77Rxd7FVXNVMOqDCVws8aO4m2YmWo7Uni1Ehum6XFY0ZKA+dwzRvtcbEnAjDKswqK3vTRMExU12XUMKotlmMElATNM2ANgtaWuigpFUWyj6UjNXXKSXnbbEBswdvpdw4ZBUK1oXA73PAMWbHdVNv4qofeNDqUiy7VJyoE3w6qqKcKeE4MQu6+2116CM6xsDSsm86gsNJjgnygU5nd6Nax6uyocGlb5zaeqFZV7PllLEYjDBmRlWxKw9T5JDavyUrZhztHDbPOJSCQC4OmaJwBcRghZC+A0AFcpinIugM8D+BIhZAWAQwDekt1pSnhhF1/O5IQxvMm6uaHUMpqktWOSktVvyYKwqHseDausf6sTqZKRhhWHIb2XKf/W23ResaXqakarhhV9PZknx+xCN9xh5WzHa9TJIkqf516l0aXxfiethlXaZy5XJz5HVCZ9xCelhlXTuRGmYRUULBAFNWrqmpLJAqodBpai4tWwyvqa8PZdpmn1mbR/nci5jKMrwyprj4sA1Bh6b2kzrPyMzSbJzhkXBdtuafsYnXBnd9LPneC8cOMrS5zghTz7/2w1rIhtxBKBrS8kQ7gnHS6HVUUJ1MFLAqudR4NwhWtYNV9bmUflar/0eYuTDcSWPqT/WxlW4RpWeVZoiVNWsp4iEIcNyGopCRizf9RUJbBtUtuFprrX48DkWVNI2oc9l5AeK0kHECmAQayWTkVU9OY/AuAyAK9tvv//AHwMwDfEn6IkKRMNA4QQV/RQzTBaOq8stQ2iyKsj9fuN9NjsZ7WM6jbT/U3Uzdi/mef++O2zlsKonTeaqqJhEtfvUBQFhBAYAUZTdtuaXQovm7ZM74H3OotqvzxR8P1Vp7vubzqvaC1uwDL++ZVY6qqoODgSv911CmyWCH3+6JWYaDjXJaicUNhVE9l3eo1vrnPw3Ls0ej3ejCj2GsSh1hDTvyQ9PhDer/tRa5iBul/sQpUQ4nKU07G0THivDSHE7lODqBkmenTNzmjw+93eaOSaYcI0rRI79Bh+sBpWaY1PvuOd1LCysecbDQO1RnKtB+7jVfjmN1YWkGJnN9kaVjnNUVjni8gMq6zQVP4gmYaZXMOqoim+445JCFfwhncunXSO653T0X6Ito/jE+4Mq6qm2vMeQkhgJvlEw7R1+dj9e98Lotacb+axVsoyKlp0hpVdEpDjZEWOn3HuA+9x495b73iaxTnFJUn79DuXqHkC4J4H0CCXmiFmnWEymZ20jflV4PBbL3Ltn7CZR0rLebfTJsKDnbkTo7+nmWSsTai3WsFEY9y3b0sTFJeUaoUv24329dVKsvvUxcxX0jrbuTKsWA2r5vUcr+c3pkjEUK4VoLskINvWRPTRst1K8oZLsV1RFA3AHwCsAPA1AFsAHCaE0NXDDgDzMzlDSWL+6t8fwV/hEdd7X7trC7521xbXex+6ehXefvHyPE+teS6bcfNtGzI/ztlLpuGHf36e672xmoGLbr4L+45N4G0XLcPfXHMSntl9FF++fRMA8VGl3bqVEXPJF9cn+v6GT12Frorm+9lPHtmJ9/zw0cAFbpdefIdVpTnp+5Nv3Y/7tx7ETWcuxOzBbnzljk2+23/gv5/AB/77CQz16jht4RDWb9gHAIHXSBQPbTtkv35y5xHc8I17hemSLZzWg4Eeq0uePdBtv79kei+2HRhFf7fTXdOSgLuPjNvvXf53v7Vfs4aYLl3DU7uOYumHbhVynmXmHR4B9ptv2+DbB9HnFQB6dK3FUJYF9Jjff3A7PvWKU12fvecHj+Inj+5yvaepSqJ+qpt5RlQl+Brwwmv0Y5k72IPhoxMAgNf90wOJjrt2wSB++lcXtrx//dfvxaMvHA783hUnzQr8jHLNV+7BM7uP2n/T/qYsdFVU/PgvLsDqeQMAgH+9/3l88panccd7L8bNt23Azx7bFfjdy1fNsu/ph3/8JD784ycjj7fsb27FyfMGsP3gKO55/2UY7NVbtqGLpIqmpjISHB6t4ZIvrsfhUX89m6zHgDKgqQp0TbHnM0tn9GV6vO5me/ngj57AB38U/pzommqXcsu/JGCrlkSRiVMS0DBJYiecpiq489m92LZ/BEuYtsKWzvLC6icu/dCt6O+q4DfvvQhzB3vw3h8+hh8/spPr2P1dFdz2notw57N78dGfuPuaE2Zb2mh0bPwHz3zw1AWDuHfLAXz0J0+2fJdl7cd/7ft+WF8xb9D5fbc+sQdv/peH8J03nR3+YwB7LjpvsDtiS3+cDCvx5jFCwkscx4VmKUa10YmGgYu/sB57jo6HbsdDtaLih28/D6ctHIrc9qeP7sR7fvBoZCnvvqqG295zERZM7eU+j1d87fd4bMcRAMArT5+Pv7/pNK7v/cPtm/Cl2zcCCDd2J+GOv74Yy2f2c2//jfVb8PlfPRv4+WkLh7Dj0Bj2H58I3U9XRUVdV3HrE3uErTOozhB9DHYeHsPCac79uXfzfvzpdx5E3SA4Y/FUfPfNZ+Pim+/C/uN8GnJzm89nd0WDYRL7vBUFuPnGtbjxjAVCfkcWGHbmDv/T3K1r2Lp/xHV/Bnt07D8+gbM+fQd+9/5L0MsERf6sud7Icz5VUS3n4b2b9+P8FTN8tzFMgku/uB7bD47i1PmDiY7T1RxPrvnK3S2fxa2iEjZG07Li7H3qbtpjrvvqPQCAT19/Cl53zuJYx5S0B9bR01ct/jqDloj+k2/dD01V8LXXrsOOQ6P41C+eifzu685ZhKd2HfVdS/foGn7+zgtt7VqJJA+4HFaEEAPAaYqiDAH4MYBVvAdQFOVtAN4GAIsWLUpyjpKYfO8t5+B3m/ahl+lQZ/R3YXpfFRuGj7m2/fY9z2Hj8HHvLnJh4/AxDPXqeOP5S2J/t2EQ/ONdmwEA1546Fytn+3ec6zfsa/nNAHBwtIZ9x6xJ+IY91ucvHBwFALzjEvHOu4tPnIkPXr0K43UjeuMmw0fH8f0HXwAAHB1rYOYU/wFy095jUAD8zytWtnzWVdFw6apoA2270VQFDdO078WG4WM4POYsPGZN6cKfvWgpJuom/u43G+33D4/Wcc+m/Vg+sw83nLEALzl5dqbnyYq7P39gFBMNE68/dzGm96eX8Dtt4RAuXDEDX7ppLa4+Za79/rfecCae3n0U6xZNxWCPjqUz+nD+iun40NWrMFY3cMbiqXhq11G7bS2a1utyuPzlJSuwcpJPLKyFkFtA/th4A1O63UPgqjlTsGXfCK46ZY793ldfezoe3X4YAz06lkzvxRM7j2DRtF7X4k4E5yydBgAYq7U6QDcMH8fymX146dp59nvLZ/YninJaPW8AH71uNeYPdWOiYeK5/SOJz7lb1/CiE2bG/t7nb1iD9Rv2ggCx+kTKPZv248ldR3w/27DnGM5YPBUvWum/4L3ipOg+gnVWnbtsGs5tikFv3TdiO3veccnyRM66rNl/fALfu387th8ctR1WP3zoBdQaJnYcGsPG4WNYMasf162Z6/v9y1fNxlBvFV+4YQ12HRnz3WawR0dFU3HagiH8duNefGP9Fjy1y7pm+46P+zqs2GjTNAF+w0cncHi0jmvXzG3p1wZ7dKyaMyX5zjuIL910GjbvteZ26xZNzfRY0/u78LlXnhppkNYUBTeeuYDRsEqug5cEtgzgjWcsTL2/77757Ez1T1RV4TagmYQkLnN41Slz8PiOI9h5eMzlsLJKc/l/552XrUS3rqGqqdh+cBQ/fmQndh0ex9zBHjy75xiWzezDy5jxyo/tB0fxoz/uxK7DY9g0fAzduoo/Z4LnzlxsjYnnLpuOv7lmFUZrBo6ONTB7oAuaquC6NfPwu037sOuw1U+pioKzlkzDw9sO4vBYHRVNga6qvlovChS8/LTg8/vfL1uN81dMx6wp3fjkLU9zr5NmDXQBAH7w9vMitvQnew0rcS4r6iCNcrocHWtgz9FxXLZqFtYsSGZkBqz5/r/cuw3PHxjhclht2XscBMC7fdZGFKcNjsdyWFFnFQA8u6d1jRnExuFjmNZXxevOWYSv3mmtYU+dP4jLOQJp/Hjh4Bj++487AAA7Do3FclhtHD6GwR4db7pgif3e//ntFow3M19ZY+Xi6b24/vT5eGb3Udz21DAA4L0vPgEmIXjRypmoNUw88NyBRL/Bj2tOteYnc5qOpTHPPHHL/hHUDYJVc6Zg455jODhSw/7jNVx58mycNHcgcv/U2fGqM61xgGZ9f/XOzfa4WVTomBDHYfX2i5a5ggxm9HfhRStngIDgtqeGcWi07lrTUPuR31wuK168eja+eudmbN0/EuiwGq8b2H5wFC9aOcM1VsTh3KXOeAJY2YJfX28FcsftduNmWLFj2TfWb8GmNtnfJPGhd/lvr1uNs5vr9SKzduEgPnLtSTg6VsdX7tyMLfuOY5OPPfTyVbNwKjMu//cfd2Dj8DHftTS1S75waFQ6rCS5EsviRgg5rCjKXQDOAzCkKEqlmWW1AIBvOB0h5JsAvgkAZ555ZtkyKkvJhStn4MIAY93Vp7qNVD99dFfbaunWDRMz+7vw7itOiP3dhmHaDquXnzYPLzl5ju92ozUDG306aLZeP/39dHIRtchOQn9XJfbk6smdR2yHVVi0Zd0g6Na1RNexKFRUq742q7vG1rLu767gbRdZ1++OZ/e6FlINk+CkuQP4i0tWZHqOJ80dcJWcoe3mzRcuFRrBfv3p7si+lbOnYOVsywj7rsudhTebFfmilcFOg9XzBmzDtSQ+l544C5ee6BgTLudweCShW9ewdEafb39cN0ysmjsg5BnXVAVvuXBp6v2k4cQ5U3BiCseCYRI8EpBFVTdMnLtsmrD+8PXnLsG1TefO+g17bYfVuy5biZ4CRtlt3nsc37t/u295rLphomaYOHneYOT1efVZfAb9UxcM4vsPvoCR2ljzGAGL9+bbusZvhPeDPh+vOG0+Xrw62wCFMnPdGvHzmDBeczZ/QNreY5ZjK3cNK8ZwNCdhBgzLRQmc9XGIk2FFSPJyb+cvnwFgQ0ufYTClubxM66viA1dZsYv3btmPHz+y06WRctKc6PHqga0H8KM/7kS9YelvDnTrvt/pqWr2/M/Lq89s7afOWz499Lg89FYrePlpVuGQtQuHcPszw1zfqxsEy2b0ubJB4uDoTiT6eiiE0e4RAXWQRvXntF1cefJs3HRW8sDVFw6O4l/u3cbtJJ4wTFQ1NbQdPvjcQasNplgH00wKHmqGidkD3Xj3FSfYDqt1i4YSz1ce2nbQdlj56dBFncuM/qrr2P/2wHaM11szqlbM7Me7rzgBP3tsl+2wYtcjgJjnzgsNEvT+Nvr36Yum4rn9I3bfde2aebHW8JPdTjsAACAASURBVHMGu/FO5nf8093PFV5fqJEgw2rl7Cl49+zWOffVp8zFbU8Nt17fZuZanixq9plh159+dtmqWYnXEN7x5BeP77Zfx+13NVUJ7I/87lO37hz7e/dvL3xbkzA028ZLTp4dK7ihXXRVNPzZi5bBNAm+cufmwLZ25clzXOu9R7YfxuGxOuqGifOWTXeND0/tsuyScccaiSQtkWGNiqLMbGZWQVGUHgAvBvAMgLsA3Njc7E8B/DSrk5RkByvEmTe1BklcCoadAIRF5wb9vrqP48FIELWUJVXmd4UZLXj0j4qOlWFFXOLZ7D2quEr5tP7WPOpsVz1tqR2itJLOJqy/KoMWXV7ozbJy3n7RNC3tqTT9odf4xj7f7D0o6nNPz9FvQUH7VdHnzo5VQfMJtiRgGoeV7HfLj51h1Yyaz6tviyNSXwTUGCXD0ugT0WfJ22cQwqf1Zfc5zPyN5/mkc/eaYaZaD2SNd+4XRj31fLypM5FFSUBn90LQODOs6nafne7+0nEmKCii5bgNEtm30HYapH/GdRzO87G2tTQFNVWxsxfTXBf2u3HX8n5tNeh60e3yLqUadM/pb+2raq71Ytrza6dNhBczgcMqCN3Td1NqGcwTk54LS01QX8LCXsa4/a6mKoGarFR/m5Zm8xJnXJEUh7LpN6mqYgeF+w3VukcLTtdUTNQN37W0M9eT+SeSfOHp8ecCuEtRlMcBPATgN4SQWwB8AMB7FUXZDGA6gG9nd5qSrNA1ta0ZVklLwbADRtiCxPp9pFU43uV4sD5LUhc6S1wLkUZYhlX5HVYVVUHDIIzBg7iiljRmwucdXIF8NDC8z0o7RGklnU1Qf2wZForRLxWBoIVt3Uy+mKVDitdwxY5R7OuijBNeaP8Y5PjkMeLFPibTNoPmE2x5lDSyHdSoLvvd8tJSEjCne1nUZzYITeHPRrSyZ5L9viAjRFhJQBZvf8zrtGGPWzdMl+O7SOiayh1RbK1rkrezLDOsIFjDijozeTOs0j7nPAZt73Gj1pj2PmNEjJueASxOWVB2vWb/n6Lds2NvXKeb3zPnVz4TcM4x77Wm1xlOode8t6sCk1i61CLOr502EV7szB0BhvMgh2077Ao6hzGcfiZy/sfak5JkWMXRsGLRK6o0/JeILIJI8oLaQv2ygVucUhXFLsHq58wC4gdHSCRpiezxCSGPE0JOJ4SsIYScQgj5RPP9rYSQswkhKwghryKEhKtySgqJrqm2wyZvaKRZWsImVUEToBpTioYuVAyBk0ARsAuRb969BZv3HsPX12/GaK3h2k7UdWwnmqqi1jDtyWKtEZxh5Sz22Cy77H+/ZbRw2lE9Z2ObpPMJ6o9rRnGjz9uBnRHQsshOv5j1Gq7cWVXO66JG2dFz/OCPnsDf/XoDfv3UHvuzWtMwLLotsRGktYDgChqFqmtqi8EvDvQe56V7JBEPHc4nGmKMjHGPWxY0VWnRx1m/YS+eP9CqO5gme4Ze/5rh1omxHFYcmVL0+w2CrfuOY9eRca7nk37v73+zET97bFdhHYp6RcVIzcDX12+2DeNB3LvlQGBEPQ9ZXgECInTcomulPUfCl/90TEjvTLCOt2Ufn+7Ls3uORrZf6rDZcchfr9GPutlq3OflqV1Hmect/RqCnZ987a7NtjYzD3WfeWXQ1aLXPncnRvP+bPP0eXXDhKIA3br1+ahAh1XQHKYo2LYKEfaTkAy2vIOCbOdZiAPYXncLXPOzXYQ3uDmKSqjDilYVCHBYaWqmGpgSsdCmUcxZSjh1w8Q3f7cV925p1Rn09pm6pmJkwr/6AZsVL5HkiVzxT3KqMSIHRSPKcBaWeVDV/DtXOjnr0bUWDauiLJqn93XZr793/3Z8/OdP4wu/2oAHnjvo2q7WSJ6pVhQqquIS1fVqWGk+DitWIDaPRVRFUzwaVtJwKhFLUH/cCVmUIqFGJu9ir84EIiSl4Vl86i6HVTHGhjAGuh2R7K/euRlv+9c/2H/XG2az1IvYtnRwpOYcI7AkoPW/KA0r+TyUF6Ulwyqf56qoTuYgDozUWiK+3/idh3DxzetbN06RPWMbLT2GWpPwXbMqk9X53fueBwCcxKEvQu/7M7uPArA0iorIRN1qp1/41Qbc/1yrwYdimgRjdQPHJxqB20RBr3c2GlZijW2zB6w1yhM7j4RuZ1cjSGlkpnP+8Xq405BimASHRmuh28zst37DkbE693kElaeLe070HnelWEPMGnC0+DYOH8e373mO+7txyr4dG7fa9KJpvejWVdx4xoKIb4hhWq+lYXV83P1M0SAuusYfaT5zIspOFj17QGRwrW0j8cyl2yE1oChKZEnGLOZ/aooMKzVEZzLKpmQFKBa7rUkc6F0u2TQSgLOuPTxqjXMz+rtw05kLUdVULJ7u1uOqqKodGN/qzAqu4CGRZIlc8U9y9EobNawEZQ2EZ1j51+anv7lb1+zI76I5rHqqGp795FX23/uPW4scunim+EXJlQ1N83NYOb+TLVtBJ9h9Va3lvSyhujkUqaUiEU1Qf1zkckntIChz1l7MprhW3gVrkIZVUalWVFdGKoutYSU4I/X8FY7YelRJQC1lSUDZ75Yf2jwbRrHmXEXj3GXTuI0jloZVsusYVBbKNOOXBJxoGJg1pQuvP28J9/comZTBE8DahYP2a+/8m4Vev1eum5/4WPRyZ6JhRcQa206Zb12XqHMVZWTWVAXzh3q4xw+TABesmBG6zVCvDkWJl1nhXU/y6swB1v09b7k1Xk7pthxwF58wk/v7XgZ7dPzxoy+2/z46Hsfxxu+UOHPxVADAkhl9ePaTV+OLr1ob70QT0lPVMKVZ9o+FZgDReTHNsErrFC2DhpVIve3A8toGaUswZlRJxiw0rFji9roVLdhhVYsoXy01rMqFk2FVvvnqmgXOHOasJVPx8EeuwOdvXIONn74aq+YMuLbVVCcoJFDDSmYGSnKm+NYXSaa0VcNKUARPmCHXSXdvFRQFLIcVLU8kchIoCvb60PP0F0ct96Osq4pdaqWrYqXJsxFf7rJc1v3p7co3w0pTFVf2hV1WMkX5F4mEJag/rkkNKxdBi2wRmjhe45s7w6ocz7q3DBL9TXXD6ldFO97Y/QWVOHE0rNKVBIwyAkiKD22f9Pkt0pyrSFRjzM8Jp95U0HGA1v7UJITr3rD9ca3BH0BVliAMl54sR/R/mr4pSw0rguQ6Z344+k/hJ1sTWD47jkOBp1y6ldURrzR+i2M3xr2qG60akqJKJcYlTtm3dvbRuk/WU72ZHUav3UhARkDsY5WgTJshMNAjKFihXWuOqOufxfyPzfiPm/0flmFllygPGOfKoJcmaaWMGVYsUX0kG1To7QN4dOYkkiwox2pBkhlt17ASEOnNo2HVUhKwQR1Wqu2oMguWYQW4z8UWtPaZuHeChhVNQe7rqqBuENc9c+lV+WRY5VISUFVgMLXr64aJiqrYwtMSSVr8+mPTJGiY5c+iFEmYUDSQbjHrXa+y5XrKXv5zvG7CJOL7S3Z/QSVOTEKgKFY/KksCTm68DisenaTJCBXKpoRlgZhpSgIGOqz4sraceTaJlQ1clme4yu2wSq/VRC93mj4yCNEZVpqqQFOjHUgisyLiGHl5M4jiOIaB1qCMOBlWfueU1jGQ9LrWYziX24mfk5JeR3r+oxMCNawK7kSwg2sFPMyO09nPrtCeDKswm5SIPtYLO67G7XY1VbHvh5eouapXF1tSbLLIem4HUc8OOx/3zuWCbKoSSdYUf6YiyZS4E3WRjNaM3DSsWktHWX9bGVbWew2BdaGzgA4QLbotHZBhVWEiOnqrGmqGO8PKleVQ8dGwElziyg9vhlUnXHdJsfDrj6nAt2xrDkEZAWkWs04pJjdl07Dyg66njwvSefDizkDwX9SZhEBVLAd/mpKAIso+StoLnWLRthIkSj7ZoWWIqUHcq6/HQkBSlAT0nyebnFlbbJkYmvnAd9xy3He2rwmL/hfhTKcZUFmYxrLYJ0/Gk51pIGDcqcTIgOEtl65rSqysGu/vDTJYewkKPhLhZEmCVR64+OOonxNpok4dVtbzIirDqp02EV4M0wr+EREsWa0E2Ujas76NKpPn9LHixg52WI3bR2qqYpc29lKL0OjUK8V3jkocnJKA5YYnwypoW6lhJWkXxZ+pSDKlXfWab77tWew8PIbuiha9cQB0bd6tB+8juD5zM8OqotnGAPp/UTNmdhwaA+DzW0oSJReGxkzo+pqOKFbTSvfTsOrKV8PKyrBiHVakNAYXSTnwjyQVZ+zpFIJKEYlYzHqzGNi+J2ysKTK03/rBQy8AEG8oZgMGgjWsrGAQReE38PlRs52Ssu8tKzSCs0FLAhY0SKjd0OeqHhCsxEJSZVgpvvsnhC/7jT3POIZOb/TuIo/4d1Fg22eovkqEgZKLLEsCcmbMxYEnI8UJMkh/bFUBfr/5ANe2Nc6y83FLc3mN+7WGiTf/y0PR5+O5DqctHAKQfl6RtCpILYZzuafavrlP1ZNpCgA/emSn/RkAPLztEACrakoaLB3ZYmdSGCYRNmaGali1I8PKp/wji/MMZVMSMG7H67ULsERllkoNq3Jh3+USTlcXTnPmVlGVrdg5n7ft0vnD/Vv5xmCJRBSV6E0knUy1orZFPG/T8HEAwNsuXpZ4Hz98+3nYtn8EM/q7ArcJWojTiUQXWxKQUI2NYo1G77xsBb5652b7b2+6fM0wMVDV8z4toejMNe9tOqKoiC7gNtZX/TKsctGwUl2RVLUYpW8kEh6sEhH+5Uulgd6BLlZb6u5nsJhlr/tAt46/f/VazBvqEbb/PKACur1No5PofounZJZJCFTVMv7GEbj3UpcaVqWHDvf1ApZhLhK0jdcME926FmrcIgSJDSmWjk+r8cww+TKsXBpWMQydXUzA2idefjLOWjKN/6RzZDHjSAsvV9Xsm1L0r46xKJscK9FPGk9GisgyrhVNxbS+Kte2vGXn45aB8/u9dz67l/t79Ln+3A1rcP+WA5g5JXgNmyVhZd8uWDEdK2dNwfS+KgZ6dLxs7bycz87Bb17c31XBrIEu+1mjgabzU87NdE3FsfFGqn1kjcGpLchDoIaVYeZSuaT1fML7kyzmf6yuX9zs/4qmBmY+c5UElA6r8tBct4jUgcyLz9+wBvdu3o9Do/WYGVatv7WiKhjq4RuDJRJRSIfVJKddGlZ1w8SaBYNYPrM/8T7OWjItcoGrV4Kih6iGlWZrVzUKajx5zdmLXA6rTtSwYgdQmmHFRi25Nays12yGVX4aVkyGFWf0pkTCi1UiIiBrSDpHbYLKEohYzHp9Kd59vXLdgsT7bhcjTed/HiUBg7JAzGZUsKoIKgko+97S4tWwKtqcqyh49UWijOppDCl+xjNaxjMKGuRVMwjqjWTaJ284b0ns7+RFX5ezVM5cw6r5f3YZVmL3yaPDUhNoZF41ewp+e2Sca1tuDatKawZPGEnLeHnbx/yhHtxwhtj5RJxgkLAsmj+7cBkuXTVL1Gmlwsp6au2bzlw8ldGwaqBH11JnEOoxSk62C8MQ57DyK69NCGmvhlVIf5KFhhW7q7g6RWEO+7ph2jp/fng1KiXloIwFAfq7Knjp2nn47n3Px3JY+fUBq+ZOkY5WSe7IFf8kp10RHnmlmwdqWDUch5WdYUVLAhZsNPJGOPiJo5bdeMca43t9Sk+4dWSs1z06m2GVg4aVJjWsJNnit/gRKVjeKWShYUXxLlg74bqPNh1VNGtV9G9iMwqCFuAG1bBS4onUe5EOq/JDp1g0Y1k6rPzx6ouEGbcIIakMKX5Ge96SgIqi2GNXvU2R+VnCzi/DKlII0bCiJQET7yEYggwcVj7OBC8ijcw8x2OPy2N0r/pk8ITuN6FDI4+xK44BPCzorkjjq18GHF1/0fMcqRlC1oGl0LASmmHlDooArPkZIe3JYq9GlATMQsOKdXLGDRQIk9WI6n/K4ByVOJTdtUif9agMcFdJQJ9t42YkSyQiKM6MRNIWoiYHWRGndnYawuozA0B3RWU0rKzPilYS0DvhKUqtaZG4Mqy6WhM/2c/p/WEH3TxK81kZVs61lxpWEtFIDSs+orQJkzyXdNHqXbCWvW8FnAwrR5hcvI4JJbAkoEmgqopduidpWUCpYVV+lKaWGV30SoeVP95+LsxQnqIioH0srxGClvHk+76CWiOehlVZ4OnfADa4JPmdoFly2WRYEeHljGJpWAlxKGjcxjKrrBmHhlUMJxgQzynkOp8cyjvH+R0TIc7lIo2v3sBaKwPIWvfSko+jtYaQdaA1By+2adowBTqsPEER7Ot2VHWI0nXKIoCP1QOLe+fDgr4tDb3g+1SN2e9I2gsdk4vTM8bDdlhFZli1fse7H9luJXnTWasKSWx0TcFozcDvN+/P9bh5LWqD6jNv2nsMgLskIHVGqAUznnivU4uGVQeUpmNLGrIZVvT+dTETZ2pYjhpURaOp7gyrWgcaZiTtxW8iKDNKWqHXwls6RMRittVhVazxIA30t4l2frL9d7CGldWH0ui9pElW9WawS9rSP5L2oioKGtJhFQrte4aPjmPn4THc/OsNgdumLffml2XCWxIQsIybdcPERMKSgEXGVfI0xJi996hVqi7N77czrDLwWGWRYRVLw0qEQ6Gi4Nh4I/L60LJmPHMBERpWcb6XZYAdb8ZGUNk3Oq4WqQR11VM67fBo3Xq/4mRYPX9gVEwGXwmMsYZJhAXW+tlI2lnVIVLDKoNniA3KiJ9hZbXNfccm8M3fbXE9f/UInesytDWJAx1zyrr2oG0xqu9gHbh+69+qpuL+rQdTVcqQSOJSnBmJpC2cNHcAAPDFkIVwFuRVH9kv3d06fjPDSlftkoAi0+xF0q27S+T5aliVvARLUIZVb1PPKsqJ2BYNq4jJqEQSl0pz8cMaY/KIyi0btL8Lcu6leS7plV81ZwqmdFVKuTh560VLQz/PVMMqYAFulQQEaFeedLEjtQM7A1VxnJZaCZ+xPNh3bAIA8NbvPozv3rcNv3h8d+C2BOmyZ3RNaXl2DcJvnKHGtzpnVgvlzRcsxermOqSoaKqCKc15aZiBcf/xGgBgSree+Fi2hlXiPQRDiPjocB4dFtquRKz5JurWvnZH6Fg5Zc2if3FcwzH7nMwd7Ob+Xhb6O5S3XLi0eQy+30Gvj/dc/uxFSzG1V8eCoR7h55gUb+WBh7YdBGAFN07rqzLbiXCIFt+JYJj8gQRR6GprtQJ7Ht2GNUeUrnq9Id6ZtnbBkP365afNi/Vdui770u0b8Zlbn3UFf0c5zCuq1LAqE/ROlXW2Sp/nKHuaGqFhRe2kNPBfIsmD1tpbkknFdWvm4SeP7Iyc/Ium3shJw8on3R2wJnxLZ/RBU1XQKm8NgWn2ItFUBe++YiW+fPsmAP5G2rIb8FwOq6rTLXXrKo6MRX8/D8eRpqotGladFkksaS8aU5aOrkfziMotG1XNcuIHOqxSZVhZz/gbz1+C15y9KPF+2sn7rlwFTVXxlTs22e/NG+zGruY4L7otuUpmBQhmm00jC10MmQkzCGSgQGdgOUKkhhUPh0brtqE+CELAXb7PDz+jPSEEvDbLqqai1iDcukGUv33p6jin2Tae+PiVOP0Tvw41ZtM+bd4QvxOjBTvDKvkugiCA8BSrMA0XCh0TRKxT1i4cBBCdSRTHOVTVVIzVDe5zYH/vpatmYWSigcdeOMz9vSzWax+9bjX+8PyhUGO/+1yapaY9Y+nrzlmM152zWPj5pcGr9UPP/YIVMzBzSheznRgNq6LrCtUEzoFUVUFFVXwdVm3LsArVCfRvt2mY3t+FbZ+7NtF36Xx25yHLUDHRcPqRKLkGXVPQMIvd1iStlDW+qsL5PLszrFq/86fnL8ZvN+6LnJNKJCKRq35JW9KS40ZhJiVM60TXFGgq7Awr0ySliPTtRA0rdvLZ16X5vh9GHtknLRlWOTldJZMHO/uEsI5RqWHlRQ/KsKKGMQEZViUYCmLRy2SuCs+wYq53cElA4ioJmNQgW+uA8U7i9HWqUt4SK1kTp51bU5M0GVZqS4nVOCUBqR4unVt3IlFrJTsbOsX4Y2tYZZBjZWlYicXrTPCjbpjQVEWIYzpoTeclTlkzHqcbi3dbniwz9zll83z4lfWMPpfij6XerCfWoaIzXnoRv8Xqx4qd9RI3KCAKb/sV6WCOS5SuU9bPUFyozcivYkCUVrvmsSlIik0WQSR5wvvEsBlWfnMZ3jFYIhFJ8WcqkszhnWyLJGogF0WQhhXNStJUtVkagcAwo2u7FgE/3ZYyLDrCYCffvdX4htU8jPl0ckkzMHgFnSUSXvyyT0TqP3QKtoaVV88vxWLWtsvawrrFHwvi0FdlAwHE/jYeDSvDtHSL/JyycZCZrZ0BdYRU0qQFdTjxDPwklZPdrxSWacYpCagwDqvOvKd+Tj0WEcEl3nFINMI1rDhKqIl0YjpjvzjdLB6nm2vfnjbAq4FFnUlZjV96hLHfdS5tLPsWF6+GFTvPoxlCgJism7jOy3ZQaxhC+1hdU1ztv9bGNQevhpVekHkDHaP9MqWiyldXPLrYkmLjlAQsfp+ZBnbe6Tdu847BEolIitHjS9qKrqkYrTVyPeZ4PR+jU9UnEqBhmDg+0bAcVowAvGGakbVdi8Dxibr92hHOLf55h6EzBlRXhhVnG8lLwwpwIqk64bpLigU14rJrnzJFwuZFsDahgJKAWVkK20ySQABeeDSsTEKgqkwbT+GwKkp0rSQ5tB0UxO5USFjnQtTzklafqOpjqDWbunM8UENjrYM15nRNwUTDcJV9YqkJ0FfpVA0rUW3CWdOFHzOOQ0avOA6rhmFGZj14xzi/Z8f/nNJngIcRp1pKO8u+xYU+d5Sax/FHDaxCNKw0q/R7o8DG2LpBXGvmtHidzu3WsAp7tuuGiYqqFMZWQ9seXbOxXUdU+WpNVUGIVd1HUnxsbeliNL3MYCtN+a2ldc4xWCIRSfFnKpLMURRg+OgEntx5JJfj7T06jv3HJ3I5lh0J0Jzg1g0TKz78S9y/1RJtpX3xy792DwxSTA0rAJg1xamJf9tTw7b4diNAOLdssOcfZVid3hTZndHfFbqdaOickgprd3IksaQ90ObkKgnYcKJJJRZ0Eu2XOQuk6w+IE0ZXaga63RKlxyecoBTR/Va37gQZBC1ijGbJXbskYEJ7kOx3OwO6Ji5DGeZ2Qec6QLRmD0G67Bk/Y3eckoBWlklTw6pDs4EVRcEtj+/GiR/5Ff7rDztaPhdR+k5JWTI1DAIivPymNzvDD5FZsdRQvWHP0dDtnj8wGmu/W/ePoGGYOONTt+OCz90Zui37nMwd6I7U3aE8tctaY2dVyYM+gzy0s+xbEoaPTtjX3TvPE/msUOfBxTevF7dTwYieA3kzDEU43lOdS2iGVbFKQtO+nq7Z3I4/EvqsVzSanSUN/2Wi06esrpKAPs8aHcvzshlLJIB0WEkAXHHSLADAzsNjuRxv+KjlrDpp7kDmx6KRbHQSMVpzorTOXjrN7pif3HkUhllc8e/XnLUQ//ja0/G9t5wDANh2YARA55QLYwdFtnSVn5H+FafPx+dvOBU3nbXQfk90iSs/pvdbxqNDo9RhVayJs6T8+GWfSA2rVmi/0Jphld4I0yH+Krz6rIX4k7OdPvLYuJOZK7rfOmvJNHz6+lMwva8aaLijxu+0JQFrUjuwI6B9Xd5zrl+860Lc/t6Lcj1mUq48eY79mjXi+RlMLH2ilBpWHmO3SfjvD9XP6eQMSHYMfr45B2cR8dvpvc1Gw0r8uFZRVd9yWCwi9V6XTO/l2m6sudZbMDV6+/5mkNzxiQaOjNWx5+i4E03vA113feylq/GmC5c2y2lG3y/6LC2d0Re5bRKqMTKs2ln2LS6DPToAZ/3uXfeumNUPAFg0ja9thDFWt46Rlz0kCaKzWL3BCu3MvovKVrR+e3HGFzqPoU6nBqsFZpiohFxDzVO1RVIOitP6soF9vPz6gCUzepufdfqVkBSJ4s9UJJmzfKY12curbnPNsCaEWU3aWWzDZnMSwUYRrVkw6IruNUyzsNG+qqrgujXzcP7y6QCYjLGSRckF4dKw6grPsOrWNdx01qJMS1z5MWuKldFFJ5edXPpG0h6ckoA+GlayrdloqgJFaR2zagKy0eyqDwUdC3gZ6NbxxvOX2n+z7Ue087NaUfG6cxZj3lBP4DzCKgnoZB+kKglYAiObJBzqB8nbYXXyvEGsmDUl12MmRVEU/PnFy1s0XPzmqUIyrDzOZpPw62LpFatsV8PsXIfyCXOcduOXVSSi9J1dEjCLDCsiPjpcUxVE2VutPlvMgem8Pyqrizpkhnr1yH0unWmtRUeYgMbwsmTWZ687dzH6uyp2VkiYk4s9JzYjWSRx9JfKpGG1aLp1f5xKKXTda537ucusdfHJ87IPgi0CdcNEl8A5kNVu/DTC2qRhFfJsR5XZyxuaQUVLSLLOJ5OQUJuS/d0Ih7+kGGQxJheRKA2rroo1fsXRfZRI0lKcXl/SNpx6pDk5rHJ0slBhTrYkIAvbMRc5w4qiqoprUVIr0aIjjKAMK96JaR5tyRsNZU2cy33dJcXCMeY775UpEjYvFEXxzQigEe5JnE2Odghx/V1m2MUG20eK1D/wHi+onAstCaj4ZBHGQWoHdgZOhpXs18KoqAoMQlxGPL95amoNq0qrsZsQxCoJSDMgOtVhxfanfv2ciNJ3ToaVeAjSZeH5oalKpME1Cw2rqNJ3cfQs6bmNMmVzo7I8AMfgXOUs7ZV1gCFvaUKgXIFQVTvwtLnupYFJGY8dRc18EV3dw1uGz67q0Ib1bVS2YtFKQtMqPbRtsn2AYRK77J8fMsOqXNhrw5IHM0bBlgT0+610HsRbflYiEUFxen1J27DL5jXy6XzshUQOkyFVVVBRnYW4dxHCLsbNAmtYsbDpMvbNCgAAIABJREFU+2VadITBGgLY6EPe35Wrw4qpVV326y4pFna5NN8Mq+L3TXniV/5GxDPZSVF0bieV6vu+6OMFZ1hZ47GTRZjsGLLf7QwU22HV5hMpOKqqwDCJy6Do77BKp0/k9+waJgHvlJh1WHVq+Vr2d/n1c2JK31ENq2xKAoqOxNBUBQaH80hUm6DBFlEBlnHWRtQh4s6wisjy0FT7eeMN+hShcRaGXomhYVWitaP3+tYNExVVcRlWsyCvIN64iC67Wq14SgIWQMMqqP8rWil+mkFFSwEazMTWGj95Mqw6aNHRwdjVN9p7GpkTVWnKChjlz+aVSERQid5E0unQic9PH9uJwV7dVTc/KT97bBfu3rjPrj3Nsv3gaPO4+Uw6dE3F3Zv2o9Z4GofH6q7P2IUDXUwUHV1Tce+WA/jULU/j2HjDfq/MBBlTeSfleRhIKs1oPsMk+PcHtuPQaL30111SLFQ7w8pZwGzeexxA5xoBk6JrCu7bcgCf++WzePMFSzBzShe+/+AL3BkBQdgaVsUfCiJhM1SrAdlWoo/3yPbDAIBfPbkHD287aH+2Yc8xTOmu2A6KJBlWuw6P4aFth3DxCTOFnK+knVj3vyIzrEKhxoOJhmNM93VYpTyOrqnYdmAUIxMN9DXLMlPdOR6qmop9xyaa++qAztMHtt986LlD+NQtT7s+/+P2Q6mzV7PNsMpCw8rKAPzBQ9uxadiaq+w/PoG5Qz140wVLsGn4OG57ahinzBdTro3eg988PYz9zfYGAC9dOw+DPTr+/cHtME2CDcPHrO05MtPpPt/23Yft9259Yg+ePziC5TP78eozLS3Ix3ccxjO7j+I/HnrBdSHp93/26C685uxF9vsNw8Q31m/Bkea688FtBzN9NqqaiqPjdVe7PDbewKvOXIAzl0yz3/uvP+zAXRv2us69yNBz/OqdmzHUozevY/bn/dlbn4GuqVAU4FVnLsQJs1tLydYaJv79gefx+vOWZGI/ePSFw1i/YS/ecclypxSX4KAdXVOxafi43W6oRna7NKwAYKJh+pbOvOXxXVgoQKtMFPSe102fDKuIIGiaXf7xnz+NBVOt/nLWlO4Mz1aShk5aG4bBEwigayru2bQf9YbVZ3TrGt560TJfm69EIgLpsJJgqKeKFbP6cf/Wg9h1eFyIw+pd338EANBVUe0oEpa5g92YP9ST+jg8nLF4Kh7Zfghb9x13vb92wRDueGbY/vuFg6Pc0aTtZN2iITz43EFs229NKqf1VW3R2bKyYGoPZvR3YdmMPgz16lg8vRejNQNvu2gZbntqGNefPt/3e++4ZDl+/dQe9HVlUxOehdrV6g0Tf/PjJwBA2CJcIgEc4yRrzJ9oRjv2dcnhmmXdoqm4b+sBPP3bo5g/tQdXnDQLxycaGOhOeZ0ILfsg4CTbzFCvjr6qhpGagfe8+AR84L8fR1VTXWVXRXJ4tG633c/+8hnsODSGbsZgeOHKGalKAt7y+C4AwKq55dAgkgQzXpfRmTzQkkLs9eqr+vRxMcr3+THQbRkaHtx2EJeeOMvaJeEzXgDA2oWDWL9hL4Z6dZfWUydx6vxBW+9lx6FRfP/B7S3bXH7S7FTHsK92Rh4rRbANmmYAfuhHTzS1JRW7ZNv8oR789NGdAIDTF04VcryKqmDtgkE8u/sont19FAAwWjcwfGwCi6f14pu/22qPb0tn9GF6XzVynyubjoi9jAOMzvEB2A6rN//LQ9h/vAYA6NadC7mq2d7/4Y5NLofVhuFj+LvfbHStg9ctEnMd/Dhl/iC6NNVulzXDRN0g+MHDL2Db5661t/vbnz6JumFi1pQuLJpeHON/EMtn9mN6XxW/fGK3/d66xUP268tWzcItj+/CusXpr+21a+bhW3c/B8By7AFW5l2tYeLjLz+lZftv3b0VN9+2AXpTx1M07/vPx7Bp73G8aOUMnLHYcjqKzFgEgNMWDuE/dm939Wfzh3owdzB/5wl1+Gw7MIJVc9zr68OjNdQNYmczFQG78oqdYcWWBAyXmaB9ws8fs+a184d68D/OFd+GJGIRXVY3L646ZQ4++8tnccO6BaHbRWVYAZZd9Y/PW3ZVkwBjdQOr5w3gmlPnijpdicSFtIBJUK2ouP29F+N9//kYfr95f+r9sanc//f1Z+CS5uK3XXzvz84J/IxdWI/VjVJE+37nTWe3+xSEM2tKNx7+yBX2379936X2a3ah5eUDV63CB65alem5UWjbGG9GOr//qhNx/enhA79EEgdqcHQJ95oEswe6ShEJmyfffuNZODpex5qP/RoTdcM2kn3sZSen2q8dRVfSRQlLV0XDU5+4yv777vdflunxzlk6zQ4MqTVM3LBuPr5w41rXNtQIlKQkIL3H/+slJ6Y7UUnbeccly3HzbRvafRqFh44JYzUDF66YgTmD3bhvy4GW7UxCUjnZbzhjPv7598+5hLStDCu+77/touV420XLk59ACXj1WQvx6rMWZnqMtBp/YVgaVmLnERVVwUTDhEmA91y2Egum9eA9P3gMgNVf1xomLjlxJj75ilZjfxIURcFP/+pC13tXffl3qDUM1AwTvVX3mMfDGYun4m+vW41P3PI0ls7og6oAW/aN2J/TcpvUWQUAH2fmGeevmIHXnLXQzlqi0Gcpr3XwjWcswI1nOGuSL/1mI/7hjk0t29UaJv784uX4X1eWYxxdPW8Af/joiwM/v3DlDDz44SsCP4/DaQuHWtac53zmdjtwzAvNnqPVTkQzfHQcgOX8oIgpPerw0etW46PXrRa2vzScPG8QAHydUvR5evvFy3I9pzC8usNuDSsz1PhPvzt3sBu7j4y7xl5J8Sh7ufjF0/tC7WkUnkzRf32LY1fdtn8El3xxvWy/kkyRFjCJTZz612GwgplFL2PFnt9Y3ci8JrakvNCmMlbjF3OWSOJA+x92Yiy6/EcnQZ/BukFSazLYpZiI+28JP6xgdq3h327TlASk8xO/rG1JuaDPrhQcD4c+L+MNA7qmQFX8r1nacm/0frQ6rOSzlif0amdhHCNE/LimqQrGmtpPekV19fk1w8REwDggkmpz3Akac3gI03j005jxbmNpwLm3o89Su9YKVZ9yiKZJ0DCLpQNUdKiuUjth50t1w0xderSo6ExJQC+1Auqu0fGRwC/DKrwkIM2e7qI68lITqNDQe9zpU6K4dlA6dkqHlSRLitPrS9qOn4h9Eth98NQPbyfsxGesZkoBcEkgtN70eL25OJeNRSIYOk9kFz11g0jnaACsGHetQVzvJSULsfvJAiuYHeRoVVNkEHjF7iXlhRqmssgk6STovGOibj1Pmqr4XrO0zgi2L6UYJuSzljOZa1iJdlgpiu3Q0TW3w6reMIWXL/NDb65d64aZWCOK1Xj0Onr81sW+DiuPwY46sNq1Dva7FrbRv0MdHllQ9XFG5gU9Ktvnd3IQW9VnHKLQe1Ck9ZDmCTJkM8NMEp6tQj+j2mTSYVVsJstUlackIAsdZ9rt1Jd0NsXp9SVtx6rNLthhVaCJhR8VZkI/Xjds44BE4oVG9Y9Kh5UkI+x66Gw0ZQ4RymVFU62Mg7ph2pPlakpDzCRZk2QCNfo1TCvjzS/CO03JKyuCXhraOgFqxJUOq3Bocx+rG9ArlrPWLymNgKRyLuk+Ud4kRklAiRhoKdosAicIIcJL3Wqso0dTXMbkelNDKes+W9cs3ayxusFVzsh/H8EZVvVG673wjm16RcGE4XVYtTcjhD1uo3ku9JyKZPQvOn7OSEpeAU7sYfJwAreLaki2UbufJz+8FQMMptZ1wzS5NKyo81hmqJSDTo/hiTuGhjmZJRJRFKfXl7QdXVMxWjOw5IO/wIPPHUy8H9bL3qNnI+4uim7m/MbrBqQtTBIEzQwYp+VPZGORCMYuLzFJyn+IgGb1iFrMOiUB5TWPC5ulEWSopGuhJJXgrGdBTls7AdpWZEXAcKjxYLxuoKqpUBV/Jx8h6UoCOlGybIR4eEkjiXjKlmHFlmfVNdW1pqo1S/VmbWDWNRUPPHcQP/rjTgwfnUi0D7pWndZXbRm3/CLHvQ4DWqGEeDJhrPNrzzPErr+v//q9AJisrwIZ/YuOXgkO5v3lk3sAZFimuNmc3CUBO7eko1+mL2XzXksftUhTcxrkTJ1NDZf+MEJL6tLMqjkDPahqYiQ5JNnTCfrGYcSd84U9sxKJKDpzxJMkgp0A3fbUnsT7oRPis5ZMxQmz+1OfV5Ysm9GHN5y3GIA10ajIDCtJADQbj5YE9MsekEjSQBc37Lyvk8t/iKCqqag3iB0Bm9ph1fy/s5ck2UCv/XjdhBGgk6GlLAkon4XOQGpY8cGWItY1BZoSUBIQSNVp2VGyLg2rcIObRDxeLUWRZLFPtnyQrqk4c8lUfPaVpwKggQvZBxmIyDa55MRZ+NQrTsHnbzi1NcOqOSHzOudYdE0FId5yzu3NZrpmzVy89UVLAQBb9x13nZMcR/kJ07Aa6tUBAAM9eqbnQJuVYZLAuVUnQH9XzSerkbbdxdN7cz2nMGj/Rx1VDY+GVZgj87zl0/HZV56Kj71stbAKR5LsmCzl4uN2LY7DanJcH0l76MwRT5IIUQZ4uuB97TmLCh+lrigKbjxjgf239FdJgqCGmzHqsOrQBYOkfXjLSwDSSB+FXlE9JQHFaFgVfOgqJNQwOVprWH/7tFvFdsomKQko9dw6BTvDSjqsQnHGBOuaKYri/+yQdJG/fqWYTEJkP5gzzj3MoCQgxGcOs2XU9YqlYfUnZy/C1F69qS2Zj4YVJemxeqoa/se5i7Fi1hR0BWhYsRlL3qwp5/lpdVi1a/420K3jw9euxtsuWmY7PGqN9mZ9lRGqkRZG1s4Guiaw21SHVl2gJb3DNKymdGXrHIwD7f6odhWrYdUwCdQQh1W3ruFPzl6EBVN77XWMpLg41Tfaex5ZEzdIyc7OlyUtJRkiV/4SG1ET2HZP0uPCnqcsfyIJomKX5ilX+5aUBz9jft2QRvowaGRiekFm2fenhWpYjdaCnfpqigyCIF0sSfmg802pYRWO6slg0VTF99mxNKySH8evrItpEplhlTNZZliBEOGjHNvFVzV3W6XjctZ9NpvBJWIdG5RhxWbZeLPG7MwQZhuqfdXuMrZs9kZdUGDPZMIq9xj+QAZpXKWFHpUGUtXanLWXNWHlxYrorKMZVvWmdhWrYWWV1OXbD49TVNJeJkv1jbh2UEVRZIagJHM6c8STJEKUAb5WaodVOc5Zkj90EKcZVu1ehEo6D79yaVaGVadPkZOTmYZVxy9LxEOv/cgEzbBqvYa0H01eElDel06Ajp+GdFiFUmHae7ViaVj5ZVil1bCiATluDSvHwSzJh7JpWLkyrDT361qD5NJns/sXMS/3ziFqDQJCiMsZ1aph1ZoZ0m4NK4quqWiYBKZJpIZVAsKMsXT4yrocFvWDiCp9XVS4HFYF+u10fKbtgC0J2DBMbpmJarO/lBSXyaJvrCX4fdLhKsma4vT6krYjahKQPtI9X9jzlLYwSRCs+DnQ/kWopPNwjPnOe7WGLAkYBo1+rQsyDhHIkoBJcRxWwU59VWlt47zI8pidA513mXKNG4o7w0qBqgZrWKXJhlIUpdmXuiPEw0oaScRDAyWy0rASfTeDdJ2qFRUTDQONHPR22DVcd0UL2ZIPvwyrnYfHXPfET8OKbst+z3t+7YCe21jdKKTRv+hYztfwgSpI4yotNLPKKQnY2Q5HJ1OxtQOk96BI2YHeMZcNJomjASkzVMpDp8+Iksz5dI4sVIkkDcXp9SVth51UT+urJt5P2SbEbHq5LAkoCaLicVi1exEq6Tzo2oZd9NRyEC0vM7qmot4wGW0GURlWkrjYDqtQDSvr/0QaVkbnio1PNuh9lBlW4VQ8GSyq4u+wEqE3pWuKq7QViWFwk4jBybDKQsOKCI8OVwMcVrqmYKxmtLyfBbQELQD89UtOSL2/qqfkWN0w8Zunh+2/F0ztwZzBbtc2tsOKyZIoylqYOj22HRgpTNZXmQjTF3IyrHLWsOrQ+1e1n6NgDasirb29NiPWaG+YxJUhHYbMUCk+WYzJRSSJHZRWOpFIsqLS7hOQFAfWcdOtJ49Sq5dM1FVqWEl4UL0lAQs0aZZ0BrT/IZ6SgEVaoBUNveLRsErp3LPrlMuhIDbU0DfadFj5tVu/Ns5LvSGfhU5BaljxwTZ3atTy8/WKyJ7xGmZNQmRJwJyhl7uMGVaso0fXVDtwIes++/RFQ/jZY7sAANecOjf1/lpLAjolh5/8+JXo72o1ndCgIpeGVUGyYU6aOwAAaBjEXp/LcZSfMA0rGniTvcPK+t/WsOrQIDZqhypLSUBvQAd73kYMDUjpsCo+TknA9p5H1iQJUqp6gp0kEtEUp9eXtB12EpBm4LQjuEoyoZIOKwkPdGGeV9SoZPJBJ4ps9km9QUrj/G8HVUEaVpmK3U8SvCUB/YwqqUsCFkhwW5Ic2lbk8xYOazyoaiq0gAwrAKktKVaULBMhTvgNbhIxZKphRbLQsGLbpxPoqGuqnfmUvYaVv45W2v11MU4ox/nk/1t8NawKErzJlisUFdgzmQgr10bfz1zDypNh1akOxygNK1Uplp2mNcOKcVgRAt7bVK2ovmUQJcXBCWYsTvvLAtqm4/zMsCxUiUQEnTniSRLhclil8JQXMW07DPY85eJcEoStYWXX0ZZtRSIW22HlybCSztFgaGSiaA0rWRQwPrSdjmZUElA+C52DNJjywZYU0jUFiqKAEHeGIn2dtsdiNawIIbIkYFtInoEahZhW4oY12LLBBFVNxchEcxzI+FmvuhxW6X8fHWP6mplUdYM4zifV/7cEaVhVNbXtBs6qy/FWvCyVohOW/UIDdKM0rpJCewE6XaIlJzv1/tHAUF8NqwLO/yoBDitCCAyTQAvoL7xUm6XNJQVmkkRXac3xKs6oJTWsJFlTrJ5f0lbYSX8aT3nZJsTsAsc7+ZBIKLbDSmZYSTLCKZfmvFfERVqRqFasiXJN0LgzWco+ZIE3w8rPeEgXQ0kMslLDqnOQ95EP1mGkV1T7b/bxEdVnsZkEdJ/SYZUvWV5uIkDnzIsWpGFVUZgMq2yfddZRJsI5RLOlepql8euGiZphQlGCBemDHFbtzq4C2HMTN0+aTOiaGuiQoprG1DmbFXS+VLYKNnFRFMUVOMFSb5DCBUJ7+wPqaKMORo23JGAlOItPUhwmw3SI+ljjjKVSw0qSNcXq+SVthR1406Qml00UVFMVexAKWoxIJE6GlXRYSbKBdj+ukoAFMXoUFTvDSnDkqbzi8aHGhDANKzrGJi0JWDSDhSQZvVXLGLy6qa8i8cfrELDHCDbDqvm/krLXYjMJaAkqOSXOlyw1rNj9i8KlYeUpzUeN+Fn32T26WDluOofo1h0n1P1bD4TeE/qdWoOdu5FCOBbo9a8zWlyyQgQ/NCjKS61hYv/xGgDgP/+wAz9v6qhlgbckYCevCfQAPRyrJHT7nycWr0OKnjddw/F2fVLDqvhMlvwhuyRgjO9UQ8qmSiQiEDvLk5SapdP77NdpOp6JRrkiuBRFsSOoeKNhJJMP2jakhpUkKxxjPqMjYsqskjB0TbFF0TVVSV3f3i6vJceC2NDyZWPNqGM/44KfwZ0X6bztHGYPdOPrr1uHk+dJh1UYmsch4DdGOH1WumNZ82B3hLgM4soXOu6QDMxjWWtYsfOUqkvDKtv5yyUnzsT7rjwRcwa6heyPjluVZqh5wyCY2lvFlK5gkwl1ALk0rAqSHa8z51a2CihFQNcU3+wBGphD+dWTe/DStfOEHpt282bz8J2uYQVQB2GAw6pg8z/veoOOy/R/3pKAXv1ISfEgZHIEMlJbV5zseulwlWSNdFhJbHqqjmCuiJKAZdIoqDYdVpWCTYYkxYFOTKkxtpMXDJL2QCeIrDGyYRJZqjQEWoog7WI268j2yQBtp2O1YKOYXxvnpd4ohgFQIoZrTp3b7lMoPGwQlZVh5VMSsPl/2lGCFc42BTnBJPHIchwiIKmz8Ly4HVbu8pUNk7S8nwXduoa/vHSFsP3RMYb+NoMQ1A0TK2b3R37HVRKwUYyMYDv7K4NM9MmArqkwTKpJxFaiyc9A25ph1bn3L8h5UxQHMIvXYUX77UbMDKugMoiS4kBAJkUgIw1S4vS1Amg6rBpy8SzJjmL1/JK2IkzDqmQZVoCzoJL1+iVBKIqVvTFRpzXEZVuRiIUaJ2n3a5qW8D1vlN5khC70RC1mRRl/JyN22dR6sIaVY3BPqGFVokAYiSQtXocA/dOdYWX9n3b6ypZ1sSPE5Zw4V+jlzsRhlUGGFRtMw/bNrvKAJeuzqYaV7bAyCWoRwRJF1rCySwJKDatE+N1b6+/sDbQ005L2B7VJ4HAMytaoG8XTsArKsKIlAXltSrosqVZ4JluGVZy5n16RGlaSbClWzy9pK6wBfiJAYJQHOokrwkSdFzr5k5kMkjA0RZELPklmUL8UXfTQKD2Z+RkMjewSpW8kyvg7GaEllMKyUFWPUzYOUsNKMtnQPA4B5/lhNazElDF1a1hZ78kgrnyhGVBJMlCjIBA/rrHtw61h5f9+GaBze5VxWEWNPU4Wk0fDqgC/nXW4TIaScqKpBjmsUthJ4uLNsOpkDbKgkoC1hlGI54nFOz56HVa8NiVrHSMN/kVnMkyHNDvDiv/HVpul+SWSrChWzy9pK+xE4MmdRxLvp4wGfe8CRSLxgzUeSeemRDR2uTTTvehJq8vUyegVKzKx3hBjHHKMv6l3NenQqIZViHaJ1ykbh6JErEskeRGsYeVsI8rJzpZikiUB24OdYZXBvgkRXxKQDabRXQ4r/9dlwBvAaDmsSGiZe9upwRjtilLCjI6ZLg2rDnZ4iMa5fu6n0utUeWjbQeHHtjWsiPuYRWhXWRGUbfTEjiOFC95rKQnY/D/u2s3KUJEl1YrMZLk7dI7Zo2sRWzpIDStJ1nTuiCeJTUVVcPaSaQDSlaAq44SqWnEvUCQSP2j7qGrqpKhlLMkXzWOMbDSVlmW/FIxLw0qAEcY2/k6K4g9isTWsaElAHwNfmpKA9YIYACWSvFBbNKys18SvJGDKPouN8qZBE3Ke0yYy0bCC8JpG7FrRXb7SP9uqDNBxS1NYh1V4sASde7SUBCxAOUR6DrWGyVRAaf95lQV6/bwGWRqcu2xGHwCgvys7WfhJp2Hlo4ejV1QcGqm14YyCaS0JSP9vlgTkXLtJDav/z96dh0l2nXWe/527RFZVlkpSLSrJWiyVZEuWLWxLsi15BQw2S7PYTNPdbA3NYAzuBhrM4IHuZxgemmUeBrr7MZ4x63Q39MMYDDTT0DR4wWZ3e5GRsbAtyxJeZKm0lGrPiLj3zB83zo0bkRGZEZH33nMi4vt5Hj2VGZUVcZQRdznnPe/7hq8oCbhc17JFHNns6JYrL9G//ge3zvxvCFihaat7xcPcjDF62+vv1pc/50pl+V5KAuaKI7NUWQFuIrLTDjrAfT6WbQKO5eBOmZklw2pWdfWwcguz67KLrgnuczrMsJrew2reuY21NpgSS0BbRjNYzC4lAff2Wp1kWPLYPT23Ou0aZlg1E7Gq++2cdi9cnUst2zk7HWs6n1u76/3FpD5H3X5e9sPyqdrDypX7ZxPU7Mpyj2Mlr1zw71//g1v1qluPN7p+4DYodNcg4Og2oY2zVnrhDYc9jGi6bRlW4+XcZy4JSA+r0NkmLqAB2pfG+sPve7m+6rlPm/nfJJEp1y2AJqzuFQ8LKyLli594ikWl5Tqru12Cq3wTiL1zn48Qdk1i9YyXBJx30rOO3PWqtv5Gw3QFzGk8w2rS+xFXFgHn4e5J2FSCdRKPZ1jtVBJwj6812sOKzRI+lBsnGsqwqjtjbto1d6SH1ZKds93vyH32+wv3sAojI3i8hxUVIuYztYdVJdspndJ3aa/cp8md713QbJV7kHWm9HPKchtc24Z4Sg8rN4ebtQckGSpLYD3iVQuJI6OMkpZo0Ope8bCwIh178Qtntx/GTfo83EVo2caNdrmyH3xO0IRhScBBwCpzi4Z83qZJ40hZbnWxV891x91yMzGZXzxeEnDC++EWyuYPWLnFId4ZrI+RHlbJlJKAgz/r6GFFSUC/3G+7uR5W9Zp2zV3mHlaOu+/Kcrtrj8xJQY1uIBnBcWQUmUFJwD59IOc1DPiN9bDqD+9JOnvc6LubbSUBV7gHmetLOy63dluAyLfxqZkrTuQyoGftueU23i1SKhvtCezjF4wkNuUGW6AJzRXcxdLqJEZb/UyPn93SkYMb5eNZbvX4uS0d3dyYusvlQjfT6Yu9pdv9406zyzZutMtNXPicoAnj5Z7oYbU7N3F/6kKvloWYMluBmcncksHs/eSZLUmTFyrdZ/xiL9OZiz1dsi/d9XnPbvX1+acuTn1OYFWN9wUqs3BHMqwGwaUaelidPLsla+2wJCDXnlaVJQEby7Cq9zlnC1gt52fIDbvsYbVDkMD9Pz55vqtHz1zUkc0NPXZmS8cqc2ifImN0fjA/Dy1LJXTuvX3k9EVdvjm8Xzl5dnCfk0RKY6OLvUyPnrk413NvJLE2O7G2+rnOdfvb/t5tHBjPsFrl+6A0jvTgY+eV5Xbk+jP+fQjGA2jutN2fM8PKZaF+7qmLI+fL2BgdObihp873tJVlI//m0L5U+9J4wZFjXoRjposjM1KmGqgbAStsc6CT6LGzXd3x4+/QT3/dbfpHL7hOkvTG3/ywfudDn9W33P10/djXPGfbv+v2c939U+/UqfM9XXP5/raHvSeX7CsOhf0dLv6YbrPD5wTNicYyrOhhtTt3TN7z6VN66U1H9/x8ZT+YPT/T+hn/nE5aqHQT/B96+736obffqz/9X75I1x4+MPU5Hzl9US/96XeVu5cPcO7FGqkueB3oxNv6HEr1ZVhtdmL1Mqs3v+t+vfaOawavv7fnxHx+iCptAAAgAElEQVRc0LGJHla2gZJGmxuTz8eblfP0gc5yLTUc2l+M9+YrD+ndHzupLN+9h1UcGXWSSG99zwN663seKB+//emXNz7eWRgj/Ye/fFBZbsv5LmazuVH8vr7lV9438e8PdGId6CR69MyWXvhv3jnXcxsjHdxIdObi9mBVlduU8L5PPSFptTexbXYSffbUBf3gb31YP/v1zysfz22AJQGn9LCat6Suu699yU+9a+bXvvLQPv3VD79y5p/H3hQZymF9/kKRRBEZVmgUdy3Y5nUvP6ETxzb1I7/zEX321HC30GefvCBJ+typCxP/3YVuplPne/rK267SP3vpDa2MtS4/8Zrb9IGHntBX3HaV76EgYD/52tt0z6dP6barL/U9FKwgN7dx9339OctKrKPX3H619qVFOY0X7aEhc1mKiXvuhVUXUX7+G26fmKU2/tCjZy7uGLA6eWZLvczqm+66Tl9w9WX6ii/gGo31UT33Hz+0b1ufQ6m+c9brXn5Cv/Rnn9Lnnrowdw8O1GNY8rH+57aytWcOX3f4gN7yjbfrqkv3jTz+mtuv0f5OrMObGzq82an1NZt21w1H9JZvvF0vf+Yx/cJ7P1lmWO1UWcEYo1/91hfoU4+d0y/96QN68PHzkqQ3vuqZbQ17R9cePqAHHzsnSfrq5z7N82iWywtvOKyf/frn6nw32/Z3h/anuvn4JXr9K27UM44fnOu4/dypC3rLn3xyJFj14187uhn4QjfTv/mD+8oAyKUHUqWxWekKAD/46pv1+/c+vG2tKcvDKwloTFFu012OxzcbzhpYfO3t1+jAYMNI1b/63Y+UX3/PK5+hKy4pMjb/5GOP6h33PRpk1tmqspaSgNOQYYWmEbDCNscP7dM3vujp+t9/76Nj9bjzwZ+TT0ru7+86cVh3BLKrbFY3XXFQN11x0PcwELjnXH2pnkOwCg2Jx/r7DCc9q1v+Y68O7UvLLOA61JWtsI6qu1+/6JZju/6MJHX7O09y3H3FlzzruL7w5iv2OEJguYwv0LkA0sjCaE1lTK84tE9XX7Zf3b4tn5+AVctcwKqBp24iw8oYM3Gj36X7670utymKhv9PSRQps1a9GfpRveSmo3rJTUf1B/c+rAcfP69OEunpRzbbGPKunnfNZXrgZBGwuvvGI55Hs1zSONJrb79mx5+58tJi3WQe9z18Wm/5k0+W328kkb7prtHnGAasiu97/Vw3HlvttYrrj27qpTcdLXuhOqEGZ+LIKM9GSzdmc274mHa+rAasXvP8q3XD0eJ8cvpiT++471H1slxxRNWBNlhReWOaJDJlCwOgCayCYao0NmVTUWnY7LP6WNWwKTofKwCYlxnrT9LPKAnYtrKHFVOTPZl2HzC+AD+pufbI3w/uN+gbiHU0HuB1exfykZKA9ZUx7SSRellelhxkr0S7yutOAylWlhW3uUVRcQ3K8t0DVk6IvW5He4qFM651Nsv74G6X3Pm+l+Vlv6NVlsZm271hbsPcQFEdU5Pl3Ksltt25Zbf7Z9RrlTMb9yKOTLleATRh9a96WFg6mLg6ZcBqygWSgBUALG5YDmixshLYu3Lxl1/5nkz7zI4/vGvAajAJStdgkQYYN34cucWxkR5WZTbU3l/PLRS6hbcQFwhXmWkww0piI8a8YmN0sV9keqTJbL87Nwee1MPRl+rYQwqkrbPx92HSMT+eUTtLpt8qSONI3bHN0UWGlacB7WDkGu0yrObsYTWLzoSg8/jvCM2hXPx0RYaVLdcugLpREhBTpXE0Uv7PLRztGrBiYQkA5lYuRg4CVS7FPg5o4WPVuV6N/Mb3ZtpOxPHHdw9YsREG62tbhlW5gDm8N3fBpTp2/6ZxpD/828/ruiMHRl4P7Wiyl6K1lo0Yc4ojowvd+bJ8O4PgUEjXLDKswjMeAJ10aLrT/8cfOSOpKJEcUiC0KeMbpqUiCDR+PQxBdUzNZlhtP4afutDTkYMbtb0GprOyzAuniAep+LmV1uD0BA8IWGGqTjx6w+B2ckztYTXoRdHhbAUAc3MTn7IkIBlWrXELef/1bx6WJJ260PM4muX16mcf33HX5/gEftr9xPDvXcCKYwDrZ1oPq2p/6zr77qVxJGult77ngeL1ufa0ykwISNaFioDzi6NKhtWcJQFDCgyNZmfwKQjB4c3OyPff8bIT237GnX/ddaCX5Tq4sfpLd8X60+g5MMttkHOheCRgVfxZBqxq3CFwyb7h+54MjuHPnbqoEyve0ywUlNSdzn0e+zk91dCM1b/qYWHjNYQpCQgAzRkvCUj/Hn+obLCYt37znTv+/baSgLuUNHH3FRwDWEfjASP3bT6hJGAtPazGjrMA1wdXWplh1cBzW0up23nFUaSL3cUCViH1GhrJzghoXOtsI4l16f5UT13o6XUvP6E3vvrmbT9jjNG1h/eX54Nelq/FvdD4+lOWW2W5VScObzE8bqGH1W+87i4llff9piuKIJWrwoF2cPmczAWSs5yJM5qx+lc9LCyN6WEFAG0Z7p4vbvq6lFnFiqEkIDC78QUvY7YvDLi+e3VEI7aVqSLC0aqyh1UTJQFl6WE1pzhSJcNqiXtYVa6f6xDwWBazfFZiY8rzfa+/Pj2sJq0/zdpHrk3Va7Qdz7CqKWA1/p67Y3g8Cw3N4n5oMvc57xOwQkNW/6qHhRVNLyf0sJqyI9otroa0qwwAlsWwh1XxvTvnssCAVTE+gd81YDW4ByFoi3U0XlLIHT8jAY0aM6zGF8bqLGmE3bmAEhlWYUiiSBd7881tXVn8kAIL1YX+kMa17lxmwk7vSRQZZa7qQpavxb1Qsf60PWAV4lwontTDytYbsBr//07LgBUZVm1pokzvqigzrAigoiHhnfkRjPGmly4gNa3nhFtc5WYYAOY36FtaTnrILvHHNrJkiPH5Oz2sgOnGm8xPLAk4+LOuHlajr7/358QcxsoC18mKgNW8oki6sGBJwJDu2+hhFSZ3PO70WYmNUZ4Pqy6sw/vXSUZ7WIW8vhSZCRlWWc0ZVmOZZe4zQMCqPVw/p4sHxyUZVmhKeGd+BKNTqSFsrd29JCD9VgBgYdFYw/Uei/XesJmuGdGCJQG5rwCqWbiTeljt/Tox3tR+/HhFs5r8dRefE97PeSRRVCkJOGPAapABE9I1a6SHVUDjQmGnz0ocVUoCrmkPq5A37+2UYbXX6+c1l++XJB1Ik5HH3e+hu0sPWNTHWq6e0yRlSUA+j2hGsvuPYF1VawhnuS0nxbv2sAqwxjAAhG7Yw6r43k1GQpykrZrxxV7WaZuxLWC1y4Q75IUKoG2mzLAaPuayQZs4ZxGwapf7bTezYcJyXZtTZKoZVvP1sEoC2mg00sNqDUrKLZudPluRMZWqC+vTw6qfW+W5VRSZylwonGPKGQ1YDf4cfLHXc8B//Gcv1P2PntV1Rw6MPO6OYXpYtcfK0sNqCndO6vN5REMIWGGqNI50bqsvaXhRjCMzNWDVZWEJABbm5j3D3ZSDHlYsMGBFbC8JuHPAiqAtMDTsYTUpw6p+BKzaZcpNKw2UBGSH+NyKDKuepNkzplwPq5COnepCP9fS8OzUl2okw6qfr8X7V/ZoynNtRPEw0z7AuVA1YOWuy6402l57QJ44dlAnjh3c9jg9rPwI54weFnd92W0+BywqvDM/gpHGUdlfwp2EDnRi9TI7sb56ubi6BjdTAFA3Y4yMmVQSkHMqVsP4It6uAauydwFTRWA8C1ca9rBqYoGcHlbtKjOsGnhuenDML4rMMMNqxsXyEO/Xqgv9XEvDs9NnJoqMXOJCN8vXoopNJx7NIAq7h9Xwa7fRwP053oOyLvSwah9l4qfrEEBFw8I78yMYncRs61u12UkG308KWLG4CgB7UZT/KL6mh5U/TE6aMT6B7/V3/kX3Bk3GKcUBDAMO1R5WrvxQE9t/Q8oSWQfu193E9cdaW0ufs3WSREYXenP2sApwDkwPqzC543zHHlZmeI5fpx5W0rBkdMjrS9Vzqjttu+vzeE/IupQ9rAgQtIYNH9OVGX+7zOeARYV35kcwqj2s3J8HNuKR76tYXAWAvYnMcHceZVax6nbbkbcuJXCAWbgSQ5OqHDRx5x03tOCGydzip20gx4oFt/lVN1jM3MMqwLJlBKzCttN74koCZrlVbtfj/UuT0YyN7pKsL7nAoisJ2NSGDwIE7StuucL+/Pnijtd1DaC+9+Mn9WP/30e11c98D2Vl0cMKU6VxNNzdMrgoDjOstp+Uyl4TAd6sA8AyMMYoc3XQs2Z36WGIhTw/dg1YZQSssN5e8/yrdeLopqThAvpISUCXYNVESUDOi61qNsOK5bZ5VdfH5+1hFZLqQj9B6PDsFIiJBnOCkLOM6jaeQeTWokLMLqtedt15u8ywauhcEEdmx57yaIJlnjjFupeo/P63fViPnd3S1zzvaXrutZf5Hs5KImCFqUZ7WBVR4wOdIsPKBaeq3I1FiDcUALAMYmPKSU/ZuJcFBqyoWXpYrcMCDTDNz/2j55Vfu0tBXolouGycOq4S44ESSnGuDmst7+eckmj+zCT3c01kyS2KeXnYdtroG0dG3X6+NFlGdXCf125ZEnDQwyrADdHVc2o+1n+4yeMujQlYtW31j7zFrHsPq8fObkla3///NoR35kcwNpJhScCuy7DaKGKckxaZXBYWi0sAsJioUq8+y3MlEf17fAhnqWl1FfcYu/ew2ghwkQLwwV0LsmrAqsywqv/1Yq49rRpmWDVTEhDzqcSrZl4s7wR4vQpxoR9DO/awigYZVi7LaA3ey7LkXTYaAAp9fcllPrdRcajYVM4CeVvoazxduuYBK4fjsTlhn/nhVXX3RtnDquN6WG0/c/eyXJEhGwAAFhUZU056+rnlfIqVtbmRlIsw0xQlATkGAGlyDyv3VR2xpfHnaKoHByYre1g1sThmeT/nNZphNWMPqwAX1UMcE4Z2CkJFxijP7TDLaA3ey/ESY1v9cLPL3IiSyJRZle69ajLDqlPpM4/mWUvp+GnKEp5r3lNttw2YWNzqX/WwsLRyMXR/7tTDil4TALA3xgzLSmSZpX8VVtaBTkwPK2AOLuCQVw4bF7wyDRSsiTj0WlVmWDXw3FYsuM0rqtx/zd7DKryDJsSFfgyPx53uccoMqyXJMqqDy0zqjq1BhXhsufcwiYebDdt4r5LY6NxW1tjzY5SVbeQeaxV0kuXrYXX6Yk9nLvZ2/Jmnzvd0dqu/7fFeluvR0xe3P77LBkwsLrwzP4JRBKysrLXlTUNZEnBKD6sQbyYAYFnEkSkDVmRYYZUd3Eh272HVp4cV4LjFsbyhDKtnHr9k5HsyctrlfttNZFhZa1lum1N1w9CspdhCvGdjbh6m51x9qSRpc1C9ZpLIGGW51rKHVa8/GrAK8V6wDFhFUbl5pJflMg1XHMpy6f0PPdHY82MUGVbTLVtJwH6W6wt+9I/0/B/746nlly90Mz33x/5IL/7Jd277uzf8+gf1wp94pz4wdvwty///Mkp8DwDhcjfnvWyYin5ww5UEnJJhtQa1lQGgKUVJQBewypUEOEFbB030EMGozY1ktgwr7isASZUMqwk9rOrw+lfcqGsPH9Abf/PDksJcIFxl7v21DeRYkWE1v2rAdtZjwf2TkHbjr0Pfo2X0k6+9Tf/4BdfqpisOTv2ZONKgJGBxr7QOPT2r60/Fn833hNqrJDa6MEjY6OfNV8e47EBatulAO8I5o4dl2QJWrsRoP7fKcqtkwiaAM1vFwXz64vYMqw/+/ZOSpEdPb408Tg+r5oR75od31RrCbpfLgQ1XEnBCD6u+XYudPwDQFFPpYZWRYYUVti+Ndq353ctydbivACQNd2znI4dN8U0d2VD7O7Fe9ezj5fdkZrSrLAnYSIZVcX+B2VUXnZe5PDOB5zAdPbihVz7r+I7HZVkSsL9OPaxGF8C7LfSEWpQLTMeVzYZ5C3O3G45u0jOnRfympyt7WC3J57EaWJt2DFUfn7aBdTxAxfHYnPDO/AhG9YZh2MNq5wwrdnEBwOIiU0x2JKlPDyussFmaRtPDChhyl4NqhpULXtUVi6guCqYJ1582mTLDqn45JQHn5hadO3G01ME+rqHLKzJGeW4rJQFX/710m5/LHlb9cHtYOXFkyo0G/dwqbvh8Mcv9M+rDho/pxkt4hq4aaJqWFVX9f8ny3YNaxffL8f+/jMI988O7YcQ8Lw/oA52kfGxcl4UlANiTag+raanqwCpI42hiP8yqbkYPK8BxCybVCbRbJKurBFn1eOPY86SBFCsrUdNoTi5gtezVQ0Je6MfOygyrNQpYdcYyrIYlAcM7Dt0mEve+WGtbqY6RxoYF8hY1UaZ3Vbjjclk+j/1KoGnamGfLwhrPsFqO//9lRA8rTOVuGH7i9+8bNgYd9LB609v/Rr/3z1+q44f2lT9/z6dPabPDRwoAFhVVSgIWddBXf3IaAjbOtS9NIn32yQv60d/7W/2rr3zWSL+2z526oH/7jo/rwcfO6QXXH/Y4SiAc0YSScW4hpa5zWHWhjaoJ7TOmofJDNqy+SsvAHQp1lNv0KcSFfswmNkYPPX5eb/mTT0qSOmvwXrrgz//69nuVRJF+8r/93cjjQRmcG6rletsJWEV66PHzOnW+q8sOdBp9Lai4fq7+obcQd1z+7j2f030Pn/Y8mt2d62bl1z/yO/dqf7q9F9xTriGdpDf+5odHNq08drYrSfqN9/29/vqBx8vH3/b+T+uDDxX9rTaSWD/wqmfqiso6ORZHdAFTPefqS3X9kQP63Xs+Vzadu/HYQR0/tKFHTm/pzz7xmL7ujmvKn9+XxiMHOABgPsZoJMOKHlZYNW/68lv0iUfO6s7rL9dfP/CE/p+/eFDfdNd1uumKS8qfee/HT+pt7/+Mrr5sv172jKMeRwuEY7goNinDqj5f9uwrdWarxyY0D4wa6mEly4LbnE6eLZqqX7Jv9uPg9usu13OuPqQ3ffktTQ1rbvuSWC97xlHddeKI76FgTtHgnP/ej5/UrVcd0vVHNj2PqHlXXrpPN11xUPc/elbf+xsfkiR98S1XBF0i3VXDyK1VZq3ihjcbug3jH3joSb3yWcd3+WnslRUBq2mSyOiVt1yh+0+e1Yc+fcr3cObyd58/s+Pfp7HRRz731Mhj11y+X5958oJOXejpQ58+pacfOaCHHj+vMxf7+tCnT6mfWX321AXdfeMRfe3zr25y+GuDmQimuvVph/QL33KnXvVz79W5rSJgdXizo999w0t090++a1vqYz/L9aIT7IQGgEW5evWS1M/zoCdowCJe/4oby68vP9DR63/tA+r2J9cC/903vETHLtlodXxAqFymR7Wkvq25h5Uk/d/ffEd9T4a5GGMaKT9kLRUBF/WDX3bzzD+7uZHov/6LlzU4mvlFkdF/+vYX+R4GFlCdA/z2d79Y+yZkA6yafWms3/nuF+u2H/0jbfVzHdqX6Fe+9QW+hzWRe3eSymaSLLNqOhnsK7/gKr353fdThgzeGWP0y4Eenz585snzeulPv3tqfyzMj4AVduTSPM8P0ifTJCqnUdtrd9JrAgD2ouhhVXxNhhVWXWdK7fPuoGY4vTeAIReUyqs9rMq7cq4Vq6C5DCt2iC+KuS18iSpzgHWaD1SPuZBL07pzqsuoslZFhlXDJ9thn3l6K7XBWktJXcxkvAcf9o6AFXbkanae6xYZVp04KidS4xfJbpZzUw8Ae1AtCVj0sOIG2YcmFgyxXTrlxj7kJtuALzuWBORQWQlN9bBiwW1xzG3hSzXw0XQQJCTVYy7k4288w8q6HlZxs+9VuSjeZ1G8DWz4wKxcgJ1jsz4ErLAjd0E8vzXIsKoErMYXmM5c7KnT8AUaAFZZZIweP9uVtZYMqxaxkOdHp9wlOhawGtzok2EFDE0qCehwBlsNRmYkIFkXFtwWx3UIvlTnANEazQeq/98hB6yckR5WeQsZVoPNXFssireCkrqY1XAjJjtf6xL+FQBeuYPuXLevyBQ3EC7rqho5Pt/t62IvJzUZAPbgkyfP6i8feFz/6a8eUi/LCVh5cuWl+3wPYS2kyeQb+16Wy5j1KoED7KYsCTgxw4pjZSUYNZJixYLb/G48dlBS0ZcK8MFtUljne6E04M3Q7ro70sPKNr/Z8EBanJM+8NCTjb4OCsWGj3A/hwhHOmUjJhbHHRh25BaTznez8gCMIyNjRjOszlwsSgZee3h/+4MEgBXhFh/f9XePqp9ZbaTsK2nTs646pPsePq07n36576GshWllTbqDnphMEIGhYYbV9h5Wa7yeuVIailcNnpwPyTze+Oqb9YU3H9Md3A/AE5dctE7lAJ3LDqQ6db4XdIaVuxZvJPHgeynLmg9YXXoglSRdso+lXCAkZWIHAavahHsFQBDcQZfltlxYMsYojaORbKruYLHp2MGN9gcJACuoR1/A1rHo266delhRhgkY5RYt80pNwJweVivFmKLfVJ3c8/ERmc+hfam++Jbja53dAr9cGcBoDW+Hnn5kU9KwJ0yI3LXYjdGWGVbNj/nowQ6L4i0pekACuyvWyQ3HZo3CvQIgCNUFo7Ryw9CJo5ED0X0d8k0FACwTl2WC9pHZ0w63KWZbD6ssD7oMDODDpB5Ww2AEx8sqMDKqu4WVJagJLCW3SWEdM6xcX/SQ50FZmWHlAlYa9B9u/rXTsbU4NMdK7PjAzIpjkzY5dQn3CoAguCixNFpDeDxy7A7KkG8qAGCZkGXSvgZ63WMHZa3v/qSAFZ99oMoMDonRkoDuL1sfDhpgTP0lAd3zEdQElktcZlit37Hr7gFD3rzklsJcwCq3dhCwav7+lUXxFtEDEnNI42jbvBaLYzUAuxreMEQjj03KsGKBCQD2zlqyTNq0hptXg+Cysscn3d0+2YXAuIk9rFz2jI8BoXZG9W+cKLPw+JAAS8Wd86M1PHgnrT+FxpUEHOlhlVu1MXVLYqMP/f2Tzb8QZGWpvIGZFa1zCFjVJdwrAILhbhQ6YwGrrUrkuFsGrDiZA0Aden2yTLDaduxhRYlhYEQ8oSSgy59hMWU1GGNka86xGmZYAVgmLsNqHU/v57b6khR0pQm3eWQjrfSwyq2SFjKsTl/oa18aN/46AOZz47FNHdns+B7Gykh8DwDhm7TDpZOMpiG7tMeQbyoAYJl0MzvSOxDNo7hGu9wml0kBKzbAAKPcoiUZVqurmQyrwXPzIQGWSryGpQCdO66/XO9/6MmgN+65HlZujC7DqoV4lV504rA+9vkzzb8QZCkJiDn8v995t+8hrJRwrwAIRtn0MhnrYdWfUBKQxVUAqAU9rPxhYtKOsofVxIAVn32gypWFqgY0yuwZTlqroYH30ZKFByyldSwF6Lj5T8hrS64kYBINy/Vmtp0Mqw59clpjLfdYgC/hXgEQDHejQA8rAGgPWSbts3VvbceOypKA/bEeVhk9rIBxbrN9lk/KsOJasQqKDKuaSwJyWQOW0jrfBrl7wJCnQS7DKhkM0krq51ZRC5lxaWy2VSdAM6ws91iAJ2t8GcSsJpUEHG8m1+27lGhO5gBQB7JM2sPOOT/iyCiOtk+6e32yC4FxcWUXt1QErs51iz4fnMNWQ9HDqqnnbuiJATTCZVit46G7DPOffHDr6jKq8twqz20rQbY0jvTUhV7zLwQyrACPwr8SwDt3w1BdPOpMybDaCDhtGwBCd+LopqTixriXWSVLMGED9mLSLtFelo+UIQYwLOnmEqx+6O1/o2/71f9R/J2vQaFWxjTYw4pPCbBU1rmH1fnBZoxPPXbO80ime8H1l0uSLtmXSCrOtf3cKm6hJGC3n+t8N9OFbtb4awGAL6yEYVeuh1WnEozqJJF62XBGRUlAANi7X/ufXyRpuPiYrPFkFethPGNbIrsQmCYyw74Zv/WBz5SP059oNUTGlD2n6jLsYVXr0wJo2DoHrK66dL8k6fBmx/NIpvupr/sC/eH3vUxHDhZjzO0gw6qF29enHzkgSWWWNZpDVV3AH1YDsKthSUBTeczQwwoAava0y/brtqsvLRfw13my6hMLe+0Zz9iW6GEFTBNHpiwJWMU5azUUPazqfc5hhhWAZRKt8Yl9X1rcA152INyA1b401i1XHiqzV3NrlVlblghs0tGDG5JEH6sWFCUB1/dYBHxiNQC7mtrDql/pYZXZbT8DAJhfHJny/ErAql00p29fGkfq9Ud/8b2MHlbAJMaYsiTgyOPtDwUNMKb+3dzu+VhvA5YLc4Dl4M6tVkVvyaiF982tuY3fP6MJlnsswBNWA7CrNNnewypNxnpYDRZXWWACgL2JI1NuAohZYWoFvT386STbM6x6WT5ShhhAITKSnZhhxTlsNZgGMqwGJQG5zgFLhTnAcnCZcNZaZbltpZy7W58bL6mN+hUZVr5HAawnVgOwK9fDqpo9VZTwGc6oPvrw6eJnaJIOAHsSGzKssD7S2GybcHf7+UgZYgCFyBhlE1KsWExZDcX7WHcPq+pzA1gWbWTqYO9cwCq3gwyrFk62bn3u809dbPy1wPUT8IWAFXZVlgSsBKPGe1i5+6n9adzq2ABg1RQlAbPya7Sn7mb32F06oYdVL8spMQxMEFMScKU12cMKwHJZ59ugL731uO46cVj/8kue6Xsou3JTtbzFDKuDG6kk6dSFbuOvte64hAL+JL4HgPBN62FVXWDqZ1bXHt5PSRIA2KM4MmUGK7sr/eBa1p6iJODodLDIsFrjlRpgCmOKRbFJj2P5GdNAgMm65+ZDAiyTNjJ1QnXJvlS/8bq7fQ9jJu5tynOp31IPq6sv3y+pWINDs6y1lNQFPGE1ALtyi0adsYCVK1klFfVzWVwCgL2LomFJwDZ26WGInejtG7+fkKReZulhBUwQRWZiDytyrFaDkZkYkNwLlznMJwRYLlRZWA5uM4CVVW5b6mE1KAlID6vmWbEpCPCF1QDsqpNM6GE1tv/QkXEAACAASURBVCO6l+UjAS0AwGJiM5yA0HC5Hfya/ZnUw6ooCcibAoyLjFFGhtXKMqb+8kO2zLCq+YkBNIo5wHJwmXDWSv0sbyXQ6DZ1jZfURv2sZcMH4AslAbErdxEeLQlodKGXld9/6rFz7IYGgBrE0TDjhJKAWHVpHOnkmS397eeekjSY8OeWrG1ggogeVivNSDp1vlueD+tw+kK/fG4Ay4M5wHKo9rDKbTuZcW6juDu/o2EEjwEvCFhhV5fsK5o6Htw3/Li4xdSTZ7Z08syWPv7IWT3rqkNexgcAqySOhhlWlARsFxUB23doX6o//cRj+sp//2cjjx/c4BYVGBcZTSwJuM69TlbJ/k6sd9z3qN5x36O1P/eBDudUYJmQYbUc3PU3t1I/byfDal8aS5L+4N6H9V1feGPjr7fOmBsC/nDnil199xfdqBfdcFh3nThSPnbTFQclSU9d6Orxc1uSpNe/4oSX8QHAKokjU5Z4YHclVt3/9lW36quf97SRx5LI6O4bj0z5F8D6ioxRPqECEOuaq+EXv+VOfeLRs7U/bxobvfjGo7U/L4Dm0MNqSVQzrPJ2NpDsS2MdPdjRof0s5zbNWkuGMuAJZzjs6tC+VF90yxUjj126vyNJ6vZtubB6/ZHN1scGAKsmjqKy5wQZVu2alLmAZl1xaJ9e/ewrfQ8DWAqRUdnDyphKfyKWU1bCiWMHdeLYQd/DABAANq0th2EPK6vM2tbmbs+44hL1+sxb2sCmIMAPGgRgIZ2kOGv3slzdwYWSfhMAsHdx5aaYMk/t4LcMYBlEkVHuAlaVx7lUAMBqoSTgchj2sJKy3LYWaEyTqCwhj+ZYyzwR8IUIAxbiglO9LC8zrFwQCwCwuOpEh3IgAAAnMmaYVcViJgCsrIiVuqXgNhf2s+Li3FaGVScelpBHs7jfAvzgMoiFuIBVN8vV7ecjjwEAFled6FASsF0U1gAQssgUO7glMqwAYJWRYbUc3NvkgkdtbTZM44iAVQsss0PAGyIMWMgww2rYw4qAFQDsXXWiQ/369rE+ACBUkamUBKycq+hhBQCrxc0HyO4Im8uwKjeTtPR2pXFUbhxHcygJCPhDhAEL6biAVT8nYAUANar2rWJ3JQDAiaJhScAqLhUAsFrcpjX62YbNvTv9QcCqrblbkWFF9k/TrOUeC/CFCAMWkg76VfWyXN3BhbJDwAoA9iymh5U/zPsABCwyKjOsqlhMAYDV4gIfLLGEzQUWXYZVWwHGTmLUpSRg46wsWeyAJ1z+sJBqD6sywyrhRA4Ae0XAqn0s9gJYBpEx5aJYNW7FYgoArBY3B6DaQtjcVM1HSUB6WLWEQxDwgoAVFtKp9rDqUxIQAOpSnZhyWm0XCVYAQlb0sJr0ePtjAQA0x2Xq0M82bK7HWFb2l2yxJCA9rBo3qQwzgHawFIaFdBIXsBpmWCXcTAHAno1mWHGZbhtXMgChiiLJUhIQAFZemWHFGkvQXGAxy/PB9+28Lj2s2mHF3BDwhZUwLCSNhwGrbmbVSaLWdpMAwCobCVhxXgUADBQZVoOSgCN/w7UCAFaJq7LAXCBs7t3pZ233sIrUzfKJm1hQI8umIMAXAlZYSBoXZ+1uv8iw6lC3CgBqUQ1YkWDVLiZ9AEJmjNGkDdUspgDAanHlXykJGDYXoMptuz2sOoP1uP6kOsGojZWlTyjgCUthWEha7WGV5WUACwCwN9WdeQkRq5YUv3OmfABCFpspJQE9jAUA0JxsEIggwyps7u0ZdMlotYeVpLI9B5rDIQj4wUoYFlK9QBYBKz5KAFCHZKSHlceBrCnK2wIIVbUkYBXnLQBYLS5gRYZV2Hz2sJKkXp/tdk2i+AbgD0thWEgcGcWRKXpY9S0BKwCoSTQSsOLcCgAoRMZosCY2kmnFciYArBa3OYFllrC5qZorzddW+bg0KV64S4ZVo6zIsAJ8SXwPAMsrjY26gwyrTsKdFADUodrDijIgAAAnioa77qu4VADAarn1qkP66uc+Tf/ii2/yPRTswAWoyoy4lntYURKwWdbSwwrwhYAVFpbGkXp9elgBQJ2qQSoSrNpF2QcAIUuiSBeybNvjLKYAwGpJ4kj//p883/cwsIuo7GHlAlb0sFolZFgB/rAUhoV14ogeVgBQs2qGVULEqhVuImJFxApAuKLIlGWHqmcrFlMAAGif6yGZDXa9tXU9JmAFYNWxEoaFpYOAVTejhxUA1KUasCJe1T7WfQGEKomMckoCAgAQhDLDKnMBq3YzrLp9Nts1ieobgD8shWFhaVL0sOr2M3UIWAFALSJ6WAEAJogrGVZVbS2QAQCAoWgsw6q1HlYJPazaUJQE5B4L8IEoAxaWxpG6/Vy9zCpNOIkDQB0SSgICACaIzZQMKw9jAQBg3ZUBK3pYrSZruccCPGElDAuLjVFuLT2sAKBG1awq4lXtouwDgJDFsVE/LxanqucrNv8CANA+d/112c9t97DqErBqVJFh5XsUwHpiKQwLiyOjLLfq9glYAUBdRkoCtlVXYs253zIBKwAhi40pd3FXGfb/AgDQOhfMyHM/Pax6GZOXpnGHBfixa5TBGHOtMebdxpiPGmP+1hjzvYPHf9QY81ljzD2D/76i+eEiJJExyvIiDZkeVgBQj+rplIBV+9hFByBUSWTKPhlVnLcAAGjf9pKA7byuW3/r9cmwahKbGQF/khl+pi/pB6y1HzTGXCLpA8aYPx783c9Za3+mueEhZHHkSgJapTEzZQCoQ1ypAxizCgkAGIgjo2zCbmquFAAAtM8FrPpt97Aa9JCnh1WzrGxrWXMARu2aFmOtfdha+8HB12ck3Sfp6qYHhvBFRnrX3z2qz5++SElAAKhJNUhFhhUAwImnZFgRsQIAoH1uquYyrNq6HLv1t7/45OMtveJ6spZbLMCXuaIMxpjrJT1f0l8PHvrnxpi/Mcb8ijHm8in/5nXGmPcbY95/8uTJPQ0WYTlzsS9JuvLQPn3prcc9jwYAVkM1/s+OLgCA4/rHjqOHFQAAHriAlW23h9U1l++XJJ3b6rfyeuvKWsouA77MHLAyxhyU9HZJ32etPS3p/5J0o6TnSXpY0v856d9Za3/BWnuntfbOY8eO1TBkhMKlPb/+FTfqVc++0vNoAGA1VEsCoh1ucmkpVA4gYElkyvvvKhZTAABonysBmLfcw2ojiXXi6Ka6lARsATdZgA8zrYoZY1IVwapft9b+tiRZax+x1mbW2lzSL0p6YXPDRIj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            "text/plain": [
              "<Figure size 2160x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "_PP414mg_9Qq",
        "outputId": "1b727fe6-40db-475c-84d1-d05534919529"
      },
      "source": [
        "std_df= dataframe.groupby('TIME').std()\n",
        "std_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>EAU ACIDILE D10</th>\n",
              "      <th>VIBRATION PAL1 C02 (MM/S)</th>\n",
              "      <th>VIBRATION PAL1 C02 (MM/S).1</th>\n",
              "      <th>TEMP PALIER1 C02 (DEGC)</th>\n",
              "      <th>TEMP PALIER2 C02 (DEGC)</th>\n",
              "      <th>C02M (A)</th>\n",
              "      <th>rolling_avg</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TIME</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0:00:10</th>\n",
              "      <td>24.201535</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.462910</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.034129</td>\n",
              "      <td>1.685018</td>\n",
              "      <td>1.476482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:01:10</th>\n",
              "      <td>24.215919</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.462910</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.034129</td>\n",
              "      <td>1.685018</td>\n",
              "      <td>1.558158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:02:10</th>\n",
              "      <td>23.970220</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.034129</td>\n",
              "      <td>1.685018</td>\n",
              "      <td>1.594410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:03:10</th>\n",
              "      <td>24.106905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.462910</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.034129</td>\n",
              "      <td>1.125992</td>\n",
              "      <td>1.519163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:04:10</th>\n",
              "      <td>23.886263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.353553</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.034129</td>\n",
              "      <td>1.164965</td>\n",
              "      <td>1.442221</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         EAU ACIDILE D10  VIBRATION PAL1 C02 (MM/S)  ...  C02M (A)  rolling_avg\n",
              "TIME                                                 ...                       \n",
              "0:00:10        24.201535                        0.0  ...  1.685018     1.476482\n",
              "0:01:10        24.215919                        0.0  ...  1.685018     1.558158\n",
              "0:02:10        23.970220                        0.0  ...  1.685018     1.594410\n",
              "0:03:10        24.106905                        0.0  ...  1.125992     1.519163\n",
              "0:04:10        23.886263                        0.0  ...  1.164965     1.442221\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "_6Vhq2RtD6YB",
        "outputId": "a6639e9e-234c-451e-d407-c9c3677e5e4e"
      },
      "source": [
        "sns.scatterplot(x = dataframe['VIBRATION PAL1 C02.1 (MM/S).1'], y = dataframe['TIME'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2898\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'VIBRATION PAL1 C02.1 (MM/S).1'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-51-65c25f1701b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'VIBRATION PAL1 C02.1 (MM/S).1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TIME'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2904\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2905\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2906\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2907\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2908\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2900\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2902\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'VIBRATION PAL1 C02.1 (MM/S).1'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAKd9dY1Ckja",
        "outputId": "eba14610-faba-490f-c00a-52b490a4081a"
      },
      "source": [
        "std_df.sum(axis = 0) == 0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EAU ACIDILE D10                False\n",
              "VIBRATION PAL1 C02 (MM/S)      False\n",
              "VIBRATION PAL1 C02 (MM/S).1    False\n",
              "TEMP PALIER1 C02 (DEGC)         True\n",
              "TEMP PALIER2 C02 (DEGC)        False\n",
              "C02M (A)                       False\n",
              "rolling_avg                    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HP5Bl2-79n3C"
      },
      "source": [
        ""
      ]
    }
  ]
}