Pollution_dynamics03_20.ipynb

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  "metadata": {
    "colab": {
      "name": "Pollution dynamics03-20",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1jR5_LdIZ1ysfaTzDw2BcKGpsDyrWZ0SB",
      "authorship_tag": "ABX9TyNwQOrW8T87JMXXdBi7nVS5",
      "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/ADv0rnik/Data-analysis-for-JER/blob/main/Pollution_dynamics03_20.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNlWeohWp4E0"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "% matplotlib inline"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJ4bhxLt7tuN",
        "outputId": "490fa9f7-c080-43c9-c4f0-b47b286fb10c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aREAwSE-MJKO"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "ysIbQx-Lym1A",
        "outputId": "896ff07b-efb1-4c04-cb43-cc73c018c690"
      },
      "source": [
        "#connect the data to dataframe\n",
        "path ='/content/drive/MyDrive/Colab Notebooks/Data for JER/activity2003_2020.csv'\n",
        "df = pd.read_csv(path, parse_dates=['Date'])\n",
        "df.head()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Conc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2003-01-01</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2003-01-02</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2003-01-03</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2003-01-04</td>\n",
              "      <td>46.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2003-01-05</td>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "        Date  Conc\n",
              "0 2003-01-01  22.0\n",
              "1 2003-01-02  18.0\n",
              "2 2003-01-03  22.0\n",
              "3 2003-01-04  46.0\n",
              "4 2003-01-05  23.0"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xAyPV28hzDmj",
        "outputId": "1c28f5d5-2714-4a56-c173-a641cf10b81e"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6487 entries, 0 to 6486\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype         \n",
            "---  ------  --------------  -----         \n",
            " 0   Date    6487 non-null   datetime64[ns]\n",
            " 1   Conc    6404 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(1)\n",
            "memory usage: 101.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "potuT0y-EUh9",
        "outputId": "18cfcb6f-864b-45f6-c479-41342aea8ef4"
      },
      "source": [
        "df['Week'] = df.Date.dt.isocalendar().week\n",
        "df['Month'] = df.Date.dt.month\n",
        "df['Year'] = df.Date.dt.year\n",
        "df"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Conc</th>\n",
              "      <th>Week</th>\n",
              "      <th>Month</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2003-01-01</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2003-01-02</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2003-01-03</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2003-01-04</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2003-01-05</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6482</th>\n",
              "      <td>2020-12-27</td>\n",
              "      <td>12.0</td>\n",
              "      <td>52</td>\n",
              "      <td>12</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6483</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>17.0</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6484</th>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>10.0</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6485</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>13.0</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6486</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>20.0</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6487 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date  Conc  Week  Month  Year\n",
              "0    2003-01-01  22.0     1      1  2003\n",
              "1    2003-01-02  18.0     1      1  2003\n",
              "2    2003-01-03  22.0     1      1  2003\n",
              "3    2003-01-04  46.0     1      1  2003\n",
              "4    2003-01-05  23.0     1      1  2003\n",
              "...         ...   ...   ...    ...   ...\n",
              "6482 2020-12-27  12.0    52     12  2020\n",
              "6483 2020-12-28  17.0    53     12  2020\n",
              "6484 2020-12-29  10.0    53     12  2020\n",
              "6485 2020-12-30  13.0    53     12  2020\n",
              "6486 2020-12-31  20.0    53     12  2020\n",
              "\n",
              "[6487 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5qxbrbdHCyP"
      },
      "source": [
        "#Adding dictionary for manipulating with month numbers\n",
        "m_dict = {1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun', 7:'Jul', 8:'Aug', 9:'Sep', 10:'Oct', 11:'Nov', 12:'Dec'}"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "w51RQBko-gH4",
        "outputId": "37529c2c-6cbf-4823-8cee-3e75cf575824"
      },
      "source": [
        "df1 = df.resample('M', on = 'Date').mean()\n",
        "df1.dropna(inplace = True)\n",
        "df1.reset_index('Date', inplace=True)\n",
        "df1['Month'] = df1.Date.dt.month\n",
        "df1['Mon'] = df1['Month'].replace(m_dict)\n",
        "df1['Week'] = df1.Date.dt.isocalendar().week\n",
        "df1['Year'] = df1.Date.dt.year\n",
        "df1['season'] = np.where(df1.Mon.isin(['Apr','May','Jun','Jul','Aug','Sep']), 'sum', 'win')\n",
        "df1"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Conc</th>\n",
              "      <th>Week</th>\n",
              "      <th>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Mon</th>\n",
              "      <th>season</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2003-01-31</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2003</td>\n",
              "      <td>Jan</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2003-02-28</td>\n",
              "      <td>12.592593</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>2003</td>\n",
              "      <td>Feb</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2003-03-31</td>\n",
              "      <td>17.333333</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>2003</td>\n",
              "      <td>Mar</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2003-04-30</td>\n",
              "      <td>15.071429</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>2003</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2003-05-31</td>\n",
              "      <td>18.275862</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>2020-08-31</td>\n",
              "      <td>16.967742</td>\n",
              "      <td>36</td>\n",
              "      <td>8</td>\n",
              "      <td>2020</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>2020-09-30</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>40</td>\n",
              "      <td>9</td>\n",
              "      <td>2020</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>2020-10-31</td>\n",
              "      <td>21.612903</td>\n",
              "      <td>44</td>\n",
              "      <td>10</td>\n",
              "      <td>2020</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>2020-11-30</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>49</td>\n",
              "      <td>11</td>\n",
              "      <td>2020</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>30.645161</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>213 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date       Conc  Week  Month  Year  Mon season\n",
              "0   2003-01-31  19.200000     5      1  2003  Jan    win\n",
              "1   2003-02-28  12.592593     9      2  2003  Feb    win\n",
              "2   2003-03-31  17.333333    14      3  2003  Mar    win\n",
              "3   2003-04-30  15.071429    18      4  2003  Apr    sum\n",
              "4   2003-05-31  18.275862    22      5  2003  May    sum\n",
              "..         ...        ...   ...    ...   ...  ...    ...\n",
              "208 2020-08-31  16.967742    36      8  2020  Aug    sum\n",
              "209 2020-09-30  19.200000    40      9  2020  Sep    sum\n",
              "210 2020-10-31  21.612903    44     10  2020  Oct    win\n",
              "211 2020-11-30  22.000000    49     11  2020  Nov    win\n",
              "212 2020-12-31  30.645161    53     12  2020  Dec    win\n",
              "\n",
              "[213 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "Rd4fSENgbgCF",
        "outputId": "f6790e9f-2f62-4a4e-fb22-f5d8b8251226"
      },
      "source": [
        "df1.drop('Month', axis=1, inplace=True)\n",
        "df1"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Conc</th>\n",
              "      <th>Week</th>\n",
              "      <th>Year</th>\n",
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              "      <th>season</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2003-01-31</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>Jan</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2003-02-28</td>\n",
              "      <td>12.592593</td>\n",
              "      <td>9</td>\n",
              "      <td>2003</td>\n",
              "      <td>Feb</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2003-03-31</td>\n",
              "      <td>17.333333</td>\n",
              "      <td>14</td>\n",
              "      <td>2003</td>\n",
              "      <td>Mar</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2003-04-30</td>\n",
              "      <td>15.071429</td>\n",
              "      <td>18</td>\n",
              "      <td>2003</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2003-05-31</td>\n",
              "      <td>18.275862</td>\n",
              "      <td>22</td>\n",
              "      <td>2003</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>2020-08-31</td>\n",
              "      <td>16.967742</td>\n",
              "      <td>36</td>\n",
              "      <td>2020</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>2020-09-30</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>40</td>\n",
              "      <td>2020</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>2020-10-31</td>\n",
              "      <td>21.612903</td>\n",
              "      <td>44</td>\n",
              "      <td>2020</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>2020-11-30</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>49</td>\n",
              "      <td>2020</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>30.645161</td>\n",
              "      <td>53</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>213 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date       Conc  Week  Year  Mon season\n",
              "0   2003-01-31  19.200000     5  2003  Jan    win\n",
              "1   2003-02-28  12.592593     9  2003  Feb    win\n",
              "2   2003-03-31  17.333333    14  2003  Mar    win\n",
              "3   2003-04-30  15.071429    18  2003  Apr    sum\n",
              "4   2003-05-31  18.275862    22  2003  May    sum\n",
              "..         ...        ...   ...   ...  ...    ...\n",
              "208 2020-08-31  16.967742    36  2020  Aug    sum\n",
              "209 2020-09-30  19.200000    40  2020  Sep    sum\n",
              "210 2020-10-31  21.612903    44  2020  Oct    win\n",
              "211 2020-11-30  22.000000    49  2020  Nov    win\n",
              "212 2020-12-31  30.645161    53  2020  Dec    win\n",
              "\n",
              "[213 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "8Wcyd7j5MOfE",
        "outputId": "8bb0d5ca-3e5d-415a-a5c6-cb987df6a671"
      },
      "source": [
        "sns.lineplot(data = df1, x = 'Date', y = 'Conc')"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb24ebd1e10>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ofsD6RrBoqi5",
        "outputId": "d809a314-786d-40a7-e3b8-c02dfabcfedc"
      },
      "source": [
        "df2 = df[df['Conc'] > 47]\n",
        "df2.dropna(inplace = True)\n",
        "df2['Mon'] = df2['Month'].replace(m_dict)\n",
        "df2['season'] = np.where(df2.Mon.isin(['Apr','May','Jun','Jul','Aug','Sep']), 'sum', 'win')\n",
        "df2.drop('Month',1, inplace=True)\n",
        "df_sum = df2[df2['season'] == 'sum']\n",
        "df_sum.head(50)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4174: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ]
        },
        {
          "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>Date</th>\n",
              "      <th>Conc</th>\n",
              "      <th>Week</th>\n",
              "      <th>Year</th>\n",
              "      <th>Mon</th>\n",
              "      <th>season</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>604</th>\n",
              "      <td>2004-08-27</td>\n",
              "      <td>62.0</td>\n",
              "      <td>35</td>\n",
              "      <td>2004</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1163</th>\n",
              "      <td>2006-05-06</td>\n",
              "      <td>48.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2006</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2715</th>\n",
              "      <td>2010-08-05</td>\n",
              "      <td>49.0</td>\n",
              "      <td>31</td>\n",
              "      <td>2010</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2716</th>\n",
              "      <td>2010-08-06</td>\n",
              "      <td>55.0</td>\n",
              "      <td>31</td>\n",
              "      <td>2010</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2727</th>\n",
              "      <td>2010-08-17</td>\n",
              "      <td>60.0</td>\n",
              "      <td>33</td>\n",
              "      <td>2010</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3312</th>\n",
              "      <td>2012-04-23</td>\n",
              "      <td>50.0</td>\n",
              "      <td>17</td>\n",
              "      <td>2012</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3316</th>\n",
              "      <td>2012-04-27</td>\n",
              "      <td>52.0</td>\n",
              "      <td>17</td>\n",
              "      <td>2012</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4031</th>\n",
              "      <td>2014-04-12</td>\n",
              "      <td>55.0</td>\n",
              "      <td>15</td>\n",
              "      <td>2014</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4041</th>\n",
              "      <td>2014-04-22</td>\n",
              "      <td>52.0</td>\n",
              "      <td>17</td>\n",
              "      <td>2014</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4049</th>\n",
              "      <td>2014-04-30</td>\n",
              "      <td>56.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2014</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4055</th>\n",
              "      <td>2014-05-06</td>\n",
              "      <td>49.0</td>\n",
              "      <td>19</td>\n",
              "      <td>2014</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4071</th>\n",
              "      <td>2014-05-22</td>\n",
              "      <td>52.0</td>\n",
              "      <td>21</td>\n",
              "      <td>2014</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4073</th>\n",
              "      <td>2014-05-24</td>\n",
              "      <td>52.0</td>\n",
              "      <td>21</td>\n",
              "      <td>2014</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4077</th>\n",
              "      <td>2014-05-28</td>\n",
              "      <td>58.0</td>\n",
              "      <td>22</td>\n",
              "      <td>2014</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4078</th>\n",
              "      <td>2014-05-29</td>\n",
              "      <td>54.0</td>\n",
              "      <td>22</td>\n",
              "      <td>2014</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4122</th>\n",
              "      <td>2014-07-12</td>\n",
              "      <td>48.0</td>\n",
              "      <td>28</td>\n",
              "      <td>2014</td>\n",
              "      <td>Jul</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4143</th>\n",
              "      <td>2014-08-02</td>\n",
              "      <td>76.0</td>\n",
              "      <td>31</td>\n",
              "      <td>2014</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4144</th>\n",
              "      <td>2014-08-03</td>\n",
              "      <td>77.0</td>\n",
              "      <td>31</td>\n",
              "      <td>2014</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4147</th>\n",
              "      <td>2014-08-06</td>\n",
              "      <td>62.0</td>\n",
              "      <td>32</td>\n",
              "      <td>2014</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4152</th>\n",
              "      <td>2014-08-11</td>\n",
              "      <td>48.0</td>\n",
              "      <td>33</td>\n",
              "      <td>2014</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4181</th>\n",
              "      <td>2014-09-09</td>\n",
              "      <td>63.0</td>\n",
              "      <td>37</td>\n",
              "      <td>2014</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4182</th>\n",
              "      <td>2014-09-10</td>\n",
              "      <td>55.0</td>\n",
              "      <td>37</td>\n",
              "      <td>2014</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4185</th>\n",
              "      <td>2014-09-13</td>\n",
              "      <td>52.0</td>\n",
              "      <td>37</td>\n",
              "      <td>2014</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4188</th>\n",
              "      <td>2014-09-16</td>\n",
              "      <td>59.0</td>\n",
              "      <td>38</td>\n",
              "      <td>2014</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4191</th>\n",
              "      <td>2014-09-19</td>\n",
              "      <td>59.0</td>\n",
              "      <td>38</td>\n",
              "      <td>2014</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4201</th>\n",
              "      <td>2014-09-29</td>\n",
              "      <td>53.0</td>\n",
              "      <td>40</td>\n",
              "      <td>2014</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4393</th>\n",
              "      <td>2015-04-09</td>\n",
              "      <td>48.0</td>\n",
              "      <td>15</td>\n",
              "      <td>2015</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4412</th>\n",
              "      <td>2015-04-28</td>\n",
              "      <td>102.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2015</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4433</th>\n",
              "      <td>2015-05-19</td>\n",
              "      <td>55.0</td>\n",
              "      <td>21</td>\n",
              "      <td>2015</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4443</th>\n",
              "      <td>2015-05-29</td>\n",
              "      <td>58.0</td>\n",
              "      <td>22</td>\n",
              "      <td>2015</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4457</th>\n",
              "      <td>2015-06-12</td>\n",
              "      <td>61.0</td>\n",
              "      <td>24</td>\n",
              "      <td>2015</td>\n",
              "      <td>Jun</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4514</th>\n",
              "      <td>2015-08-08</td>\n",
              "      <td>74.0</td>\n",
              "      <td>32</td>\n",
              "      <td>2015</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4517</th>\n",
              "      <td>2015-08-11</td>\n",
              "      <td>59.0</td>\n",
              "      <td>33</td>\n",
              "      <td>2015</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4562</th>\n",
              "      <td>2015-09-25</td>\n",
              "      <td>65.0</td>\n",
              "      <td>39</td>\n",
              "      <td>2015</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4760</th>\n",
              "      <td>2016-04-10</td>\n",
              "      <td>59.0</td>\n",
              "      <td>14</td>\n",
              "      <td>2016</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4763</th>\n",
              "      <td>2016-04-13</td>\n",
              "      <td>48.0</td>\n",
              "      <td>15</td>\n",
              "      <td>2016</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4782</th>\n",
              "      <td>2016-05-02</td>\n",
              "      <td>49.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2016</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4786</th>\n",
              "      <td>2016-05-06</td>\n",
              "      <td>51.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2016</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4868</th>\n",
              "      <td>2016-07-27</td>\n",
              "      <td>48.0</td>\n",
              "      <td>30</td>\n",
              "      <td>2016</td>\n",
              "      <td>Jul</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4869</th>\n",
              "      <td>2016-07-28</td>\n",
              "      <td>80.0</td>\n",
              "      <td>30</td>\n",
              "      <td>2016</td>\n",
              "      <td>Jul</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4872</th>\n",
              "      <td>2016-07-31</td>\n",
              "      <td>52.0</td>\n",
              "      <td>30</td>\n",
              "      <td>2016</td>\n",
              "      <td>Jul</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4889</th>\n",
              "      <td>2016-08-17</td>\n",
              "      <td>54.0</td>\n",
              "      <td>33</td>\n",
              "      <td>2016</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5177</th>\n",
              "      <td>2017-06-01</td>\n",
              "      <td>48.0</td>\n",
              "      <td>22</td>\n",
              "      <td>2017</td>\n",
              "      <td>Jun</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5251</th>\n",
              "      <td>2017-08-14</td>\n",
              "      <td>49.0</td>\n",
              "      <td>33</td>\n",
              "      <td>2017</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5256</th>\n",
              "      <td>2017-08-19</td>\n",
              "      <td>72.0</td>\n",
              "      <td>33</td>\n",
              "      <td>2017</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5258</th>\n",
              "      <td>2017-08-21</td>\n",
              "      <td>84.0</td>\n",
              "      <td>34</td>\n",
              "      <td>2017</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5497</th>\n",
              "      <td>2018-04-17</td>\n",
              "      <td>58.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2018</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5511</th>\n",
              "      <td>2018-05-01</td>\n",
              "      <td>53.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2018</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5512</th>\n",
              "      <td>2018-05-02</td>\n",
              "      <td>51.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2018</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5514</th>\n",
              "      <td>2018-05-04</td>\n",
              "      <td>57.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2018</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date   Conc  Week  Year  Mon season\n",
              "604  2004-08-27   62.0    35  2004  Aug    sum\n",
              "1163 2006-05-06   48.0    18  2006  May    sum\n",
              "2715 2010-08-05   49.0    31  2010  Aug    sum\n",
              "2716 2010-08-06   55.0    31  2010  Aug    sum\n",
              "2727 2010-08-17   60.0    33  2010  Aug    sum\n",
              "3312 2012-04-23   50.0    17  2012  Apr    sum\n",
              "3316 2012-04-27   52.0    17  2012  Apr    sum\n",
              "4031 2014-04-12   55.0    15  2014  Apr    sum\n",
              "4041 2014-04-22   52.0    17  2014  Apr    sum\n",
              "4049 2014-04-30   56.0    18  2014  Apr    sum\n",
              "4055 2014-05-06   49.0    19  2014  May    sum\n",
              "4071 2014-05-22   52.0    21  2014  May    sum\n",
              "4073 2014-05-24   52.0    21  2014  May    sum\n",
              "4077 2014-05-28   58.0    22  2014  May    sum\n",
              "4078 2014-05-29   54.0    22  2014  May    sum\n",
              "4122 2014-07-12   48.0    28  2014  Jul    sum\n",
              "4143 2014-08-02   76.0    31  2014  Aug    sum\n",
              "4144 2014-08-03   77.0    31  2014  Aug    sum\n",
              "4147 2014-08-06   62.0    32  2014  Aug    sum\n",
              "4152 2014-08-11   48.0    33  2014  Aug    sum\n",
              "4181 2014-09-09   63.0    37  2014  Sep    sum\n",
              "4182 2014-09-10   55.0    37  2014  Sep    sum\n",
              "4185 2014-09-13   52.0    37  2014  Sep    sum\n",
              "4188 2014-09-16   59.0    38  2014  Sep    sum\n",
              "4191 2014-09-19   59.0    38  2014  Sep    sum\n",
              "4201 2014-09-29   53.0    40  2014  Sep    sum\n",
              "4393 2015-04-09   48.0    15  2015  Apr    sum\n",
              "4412 2015-04-28  102.0    18  2015  Apr    sum\n",
              "4433 2015-05-19   55.0    21  2015  May    sum\n",
              "4443 2015-05-29   58.0    22  2015  May    sum\n",
              "4457 2015-06-12   61.0    24  2015  Jun    sum\n",
              "4514 2015-08-08   74.0    32  2015  Aug    sum\n",
              "4517 2015-08-11   59.0    33  2015  Aug    sum\n",
              "4562 2015-09-25   65.0    39  2015  Sep    sum\n",
              "4760 2016-04-10   59.0    14  2016  Apr    sum\n",
              "4763 2016-04-13   48.0    15  2016  Apr    sum\n",
              "4782 2016-05-02   49.0    18  2016  May    sum\n",
              "4786 2016-05-06   51.0    18  2016  May    sum\n",
              "4868 2016-07-27   48.0    30  2016  Jul    sum\n",
              "4869 2016-07-28   80.0    30  2016  Jul    sum\n",
              "4872 2016-07-31   52.0    30  2016  Jul    sum\n",
              "4889 2016-08-17   54.0    33  2016  Aug    sum\n",
              "5177 2017-06-01   48.0    22  2017  Jun    sum\n",
              "5251 2017-08-14   49.0    33  2017  Aug    sum\n",
              "5256 2017-08-19   72.0    33  2017  Aug    sum\n",
              "5258 2017-08-21   84.0    34  2017  Aug    sum\n",
              "5497 2018-04-17   58.0    16  2018  Apr    sum\n",
              "5511 2018-05-01   53.0    18  2018  May    sum\n",
              "5512 2018-05-02   51.0    18  2018  May    sum\n",
              "5514 2018-05-04   57.0    18  2018  May    sum"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3iGCSRl92MkT",
        "outputId": "ef728ffe-6c6a-4a48-dd10-32c05cfea4b7"
      },
      "source": [
        "df_sum.index[30]"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4457"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "M2N7GSUBfi73",
        "outputId": "325aac75-651e-434a-c6bb-50ac7282c0a8"
      },
      "source": [
        "df_win = df2[df2['season'] == 'win']\n",
        "df_win"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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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>Conc</th>\n",
              "      <th>Week</th>\n",
              "      <th>Year</th>\n",
              "      <th>Mon</th>\n",
              "      <th>season</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2003-01-08</td>\n",
              "      <td>63.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2003</td>\n",
              "      <td>Jan</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>2003-03-14</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11</td>\n",
              "      <td>2003</td>\n",
              "      <td>Mar</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>389</th>\n",
              "      <td>2004-01-25</td>\n",
              "      <td>73.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2004</td>\n",
              "      <td>Jan</td>\n",
              "      <td>win</td>\n",
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              "    <tr>\n",
              "      <th>948</th>\n",
              "      <td>2005-10-03</td>\n",
              "      <td>71.0</td>\n",
              "      <td>40</td>\n",
              "      <td>2005</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>981</th>\n",
              "      <td>2005-11-05</td>\n",
              "      <td>65.0</td>\n",
              "      <td>44</td>\n",
              "      <td>2005</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>6460</th>\n",
              "      <td>2020-12-05</td>\n",
              "      <td>69.0</td>\n",
              "      <td>49</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6465</th>\n",
              "      <td>2020-12-10</td>\n",
              "      <td>62.0</td>\n",
              "      <td>50</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6467</th>\n",
              "      <td>2020-12-12</td>\n",
              "      <td>51.0</td>\n",
              "      <td>50</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6468</th>\n",
              "      <td>2020-12-13</td>\n",
              "      <td>56.0</td>\n",
              "      <td>50</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6469</th>\n",
              "      <td>2020-12-14</td>\n",
              "      <td>51.0</td>\n",
              "      <td>51</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>126 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date  Conc  Week  Year  Mon season\n",
              "7    2003-01-08  63.0     2  2003  Jan    win\n",
              "72   2003-03-14  55.0    11  2003  Mar    win\n",
              "389  2004-01-25  73.0     4  2004  Jan    win\n",
              "948  2005-10-03  71.0    40  2005  Oct    win\n",
              "981  2005-11-05  65.0    44  2005  Nov    win\n",
              "...         ...   ...   ...   ...  ...    ...\n",
              "6460 2020-12-05  69.0    49  2020  Dec    win\n",
              "6465 2020-12-10  62.0    50  2020  Dec    win\n",
              "6467 2020-12-12  51.0    50  2020  Dec    win\n",
              "6468 2020-12-13  56.0    50  2020  Dec    win\n",
              "6469 2020-12-14  51.0    51  2020  Dec    win\n",
              "\n",
              "[126 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "1-yjpRURciZ-",
        "outputId": "2434cd0f-8825-4148-e7ff-14b91251f961"
      },
      "source": [
        "sns.scatterplot(x='Date', y = 'Conc', data=df_sum)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb24eb15c90>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "M9tdh9zoVFhC",
        "outputId": "a830ecfb-73f1-4fa2-b29c-0d40d3f0a07d"
      },
      "source": [
        "df2.tail(50)"
      ],
      "execution_count": 94,
      "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>Date</th>\n",
              "      <th>Conc</th>\n",
              "      <th>Week</th>\n",
              "      <th>Year</th>\n",
              "      <th>Mon</th>\n",
              "      <th>season</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5497</th>\n",
              "      <td>2018-04-17</td>\n",
              "      <td>58.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2018</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5511</th>\n",
              "      <td>2018-05-01</td>\n",
              "      <td>53.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2018</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5512</th>\n",
              "      <td>2018-05-02</td>\n",
              "      <td>51.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2018</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5514</th>\n",
              "      <td>2018-05-04</td>\n",
              "      <td>57.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2018</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5515</th>\n",
              "      <td>2018-05-05</td>\n",
              "      <td>50.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2018</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5521</th>\n",
              "      <td>2018-05-11</td>\n",
              "      <td>54.0</td>\n",
              "      <td>19</td>\n",
              "      <td>2018</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5524</th>\n",
              "      <td>2018-05-14</td>\n",
              "      <td>48.0</td>\n",
              "      <td>20</td>\n",
              "      <td>2018</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5568</th>\n",
              "      <td>2018-06-27</td>\n",
              "      <td>49.0</td>\n",
              "      <td>26</td>\n",
              "      <td>2018</td>\n",
              "      <td>Jun</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5630</th>\n",
              "      <td>2018-08-28</td>\n",
              "      <td>51.0</td>\n",
              "      <td>35</td>\n",
              "      <td>2018</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5634</th>\n",
              "      <td>2018-09-01</td>\n",
              "      <td>61.0</td>\n",
              "      <td>35</td>\n",
              "      <td>2018</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5636</th>\n",
              "      <td>2018-09-03</td>\n",
              "      <td>76.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2018</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5637</th>\n",
              "      <td>2018-09-04</td>\n",
              "      <td>84.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2018</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5638</th>\n",
              "      <td>2018-09-05</td>\n",
              "      <td>49.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2018</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5640</th>\n",
              "      <td>2018-09-07</td>\n",
              "      <td>61.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2018</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5641</th>\n",
              "      <td>2018-09-08</td>\n",
              "      <td>52.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2018</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5642</th>\n",
              "      <td>2018-09-09</td>\n",
              "      <td>83.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2018</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5643</th>\n",
              "      <td>2018-09-10</td>\n",
              "      <td>85.0</td>\n",
              "      <td>37</td>\n",
              "      <td>2018</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5644</th>\n",
              "      <td>2018-09-11</td>\n",
              "      <td>52.0</td>\n",
              "      <td>37</td>\n",
              "      <td>2018</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5654</th>\n",
              "      <td>2018-09-21</td>\n",
              "      <td>48.0</td>\n",
              "      <td>38</td>\n",
              "      <td>2018</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5675</th>\n",
              "      <td>2018-10-12</td>\n",
              "      <td>61.0</td>\n",
              "      <td>41</td>\n",
              "      <td>2018</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5679</th>\n",
              "      <td>2018-10-16</td>\n",
              "      <td>48.0</td>\n",
              "      <td>42</td>\n",
              "      <td>2018</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5680</th>\n",
              "      <td>2018-10-17</td>\n",
              "      <td>52.0</td>\n",
              "      <td>42</td>\n",
              "      <td>2018</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5681</th>\n",
              "      <td>2018-10-18</td>\n",
              "      <td>49.0</td>\n",
              "      <td>42</td>\n",
              "      <td>2018</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5682</th>\n",
              "      <td>2018-10-19</td>\n",
              "      <td>49.0</td>\n",
              "      <td>42</td>\n",
              "      <td>2018</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5683</th>\n",
              "      <td>2018-10-20</td>\n",
              "      <td>66.0</td>\n",
              "      <td>42</td>\n",
              "      <td>2018</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5700</th>\n",
              "      <td>2018-11-06</td>\n",
              "      <td>58.0</td>\n",
              "      <td>45</td>\n",
              "      <td>2018</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5708</th>\n",
              "      <td>2018-11-14</td>\n",
              "      <td>51.0</td>\n",
              "      <td>46</td>\n",
              "      <td>2018</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5711</th>\n",
              "      <td>2018-11-17</td>\n",
              "      <td>49.0</td>\n",
              "      <td>46</td>\n",
              "      <td>2018</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5712</th>\n",
              "      <td>2018-11-18</td>\n",
              "      <td>61.0</td>\n",
              "      <td>46</td>\n",
              "      <td>2018</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5719</th>\n",
              "      <td>2018-11-25</td>\n",
              "      <td>65.0</td>\n",
              "      <td>47</td>\n",
              "      <td>2018</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5764</th>\n",
              "      <td>2019-01-09</td>\n",
              "      <td>66.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2019</td>\n",
              "      <td>Jan</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5765</th>\n",
              "      <td>2019-01-10</td>\n",
              "      <td>63.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2019</td>\n",
              "      <td>Jan</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5788</th>\n",
              "      <td>2019-02-02</td>\n",
              "      <td>49.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2019</td>\n",
              "      <td>Feb</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5830</th>\n",
              "      <td>2019-03-16</td>\n",
              "      <td>64.0</td>\n",
              "      <td>11</td>\n",
              "      <td>2019</td>\n",
              "      <td>Mar</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6048</th>\n",
              "      <td>2019-10-20</td>\n",
              "      <td>51.0</td>\n",
              "      <td>42</td>\n",
              "      <td>2019</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6049</th>\n",
              "      <td>2019-10-21</td>\n",
              "      <td>50.0</td>\n",
              "      <td>43</td>\n",
              "      <td>2019</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6114</th>\n",
              "      <td>2019-12-25</td>\n",
              "      <td>58.0</td>\n",
              "      <td>52</td>\n",
              "      <td>2019</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6223</th>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>86.0</td>\n",
              "      <td>15</td>\n",
              "      <td>2020</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6227</th>\n",
              "      <td>2020-04-16</td>\n",
              "      <td>92.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2020</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6228</th>\n",
              "      <td>2020-04-17</td>\n",
              "      <td>92.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2020</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6256</th>\n",
              "      <td>2020-05-15</td>\n",
              "      <td>48.0</td>\n",
              "      <td>20</td>\n",
              "      <td>2020</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6442</th>\n",
              "      <td>2020-11-17</td>\n",
              "      <td>51.0</td>\n",
              "      <td>47</td>\n",
              "      <td>2020</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6457</th>\n",
              "      <td>2020-12-02</td>\n",
              "      <td>48.0</td>\n",
              "      <td>49</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6458</th>\n",
              "      <td>2020-12-03</td>\n",
              "      <td>67.0</td>\n",
              "      <td>49</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6459</th>\n",
              "      <td>2020-12-04</td>\n",
              "      <td>64.0</td>\n",
              "      <td>49</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6460</th>\n",
              "      <td>2020-12-05</td>\n",
              "      <td>69.0</td>\n",
              "      <td>49</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6465</th>\n",
              "      <td>2020-12-10</td>\n",
              "      <td>62.0</td>\n",
              "      <td>50</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6467</th>\n",
              "      <td>2020-12-12</td>\n",
              "      <td>51.0</td>\n",
              "      <td>50</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6468</th>\n",
              "      <td>2020-12-13</td>\n",
              "      <td>56.0</td>\n",
              "      <td>50</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6469</th>\n",
              "      <td>2020-12-14</td>\n",
              "      <td>51.0</td>\n",
              "      <td>51</td>\n",
              "      <td>2020</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date  Conc  Week  Year  Mon season\n",
              "5497 2018-04-17  58.0    16  2018  Apr    sum\n",
              "5511 2018-05-01  53.0    18  2018  May    sum\n",
              "5512 2018-05-02  51.0    18  2018  May    sum\n",
              "5514 2018-05-04  57.0    18  2018  May    sum\n",
              "5515 2018-05-05  50.0    18  2018  May    sum\n",
              "5521 2018-05-11  54.0    19  2018  May    sum\n",
              "5524 2018-05-14  48.0    20  2018  May    sum\n",
              "5568 2018-06-27  49.0    26  2018  Jun    sum\n",
              "5630 2018-08-28  51.0    35  2018  Aug    sum\n",
              "5634 2018-09-01  61.0    35  2018  Sep    sum\n",
              "5636 2018-09-03  76.0    36  2018  Sep    sum\n",
              "5637 2018-09-04  84.0    36  2018  Sep    sum\n",
              "5638 2018-09-05  49.0    36  2018  Sep    sum\n",
              "5640 2018-09-07  61.0    36  2018  Sep    sum\n",
              "5641 2018-09-08  52.0    36  2018  Sep    sum\n",
              "5642 2018-09-09  83.0    36  2018  Sep    sum\n",
              "5643 2018-09-10  85.0    37  2018  Sep    sum\n",
              "5644 2018-09-11  52.0    37  2018  Sep    sum\n",
              "5654 2018-09-21  48.0    38  2018  Sep    sum\n",
              "5675 2018-10-12  61.0    41  2018  Oct    win\n",
              "5679 2018-10-16  48.0    42  2018  Oct    win\n",
              "5680 2018-10-17  52.0    42  2018  Oct    win\n",
              "5681 2018-10-18  49.0    42  2018  Oct    win\n",
              "5682 2018-10-19  49.0    42  2018  Oct    win\n",
              "5683 2018-10-20  66.0    42  2018  Oct    win\n",
              "5700 2018-11-06  58.0    45  2018  Nov    win\n",
              "5708 2018-11-14  51.0    46  2018  Nov    win\n",
              "5711 2018-11-17  49.0    46  2018  Nov    win\n",
              "5712 2018-11-18  61.0    46  2018  Nov    win\n",
              "5719 2018-11-25  65.0    47  2018  Nov    win\n",
              "5764 2019-01-09  66.0     2  2019  Jan    win\n",
              "5765 2019-01-10  63.0     2  2019  Jan    win\n",
              "5788 2019-02-02  49.0     5  2019  Feb    win\n",
              "5830 2019-03-16  64.0    11  2019  Mar    win\n",
              "6048 2019-10-20  51.0    42  2019  Oct    win\n",
              "6049 2019-10-21  50.0    43  2019  Oct    win\n",
              "6114 2019-12-25  58.0    52  2019  Dec    win\n",
              "6223 2020-04-12  86.0    15  2020  Apr    sum\n",
              "6227 2020-04-16  92.0    16  2020  Apr    sum\n",
              "6228 2020-04-17  92.0    16  2020  Apr    sum\n",
              "6256 2020-05-15  48.0    20  2020  May    sum\n",
              "6442 2020-11-17  51.0    47  2020  Nov    win\n",
              "6457 2020-12-02  48.0    49  2020  Dec    win\n",
              "6458 2020-12-03  67.0    49  2020  Dec    win\n",
              "6459 2020-12-04  64.0    49  2020  Dec    win\n",
              "6460 2020-12-05  69.0    49  2020  Dec    win\n",
              "6465 2020-12-10  62.0    50  2020  Dec    win\n",
              "6467 2020-12-12  51.0    50  2020  Dec    win\n",
              "6468 2020-12-13  56.0    50  2020  Dec    win\n",
              "6469 2020-12-14  51.0    51  2020  Dec    win"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5A3CPoAifwi3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "outputId": "17410929-1c15-4f49-fe97-426bc633ec00"
      },
      "source": [
        "x = 'Date'\n",
        "y = 'Conc'\n",
        "s = chr(0x00b3)\n",
        "s1 = ' $10^{-5}$'\n",
        "cmap = 'seismic'\n",
        "fig, ax = plt.subplots(figsize = (18,8), dpi = 180)\n",
        "sns.lineplot(x, y, data= df1, color = 'black').axhline(31.6, linewidth = 1,\n",
        "                                                       color = 'r',\n",
        "                                                       linestyle = '--' )\n",
        "sns.scatterplot(x, y, data=df_sum,\n",
        "                s = 40,\n",
        "                marker = 'o',\n",
        "                fc = 'orangered',\n",
        "                ec = 'black' )\n",
        "sns.scatterplot(x, y, data=df_win,\n",
        "                s = 40,\n",
        "                marker = '^',\n",
        "                fc = 'slateblue',\n",
        "                ec = 'black' )\n",
        "sns.despine(ax=ax, top=True, right=True)\n",
        "ax.tick_params(axis = 'x', \n",
        "                direction='out',\n",
        "                labelsize = 12,\n",
        "                length = 6,\n",
        "                rotation = 35)\n",
        "ax.tick_params(axis = 'y', \n",
        "                direction='out',\n",
        "                labelsize = 12,\n",
        "                length = 6)\n",
        "ax.set_ylabel('Airborne activity, Bq per m'+s + s1,\n",
        "               fontsize = 14,\n",
        "               fontstyle='oblique',\n",
        "               fontweight = 'medium')\n",
        "ax.set_xlabel('Year',\n",
        "               fontsize = 14,\n",
        "               fontstyle='oblique',\n",
        "               fontweight = 'medium')\n",
        "ax.legend(labels=[\"Mean monthly activity\",\"95th percentile\", \"Summer\", \"Winter\"], fontsize = 14, loc = 2)\n",
        "ax.grid(axis='y', alpha = 0.5)\n",
        "date = df_sum.Date[df_sum.index[27]].strftime('%Y-%m-%d')\n",
        "date1 = df_sum.Date[df_sum.index[65]].strftime('%Y-%m-%d')\n",
        "date2 = df_sum.Date[df_sum.index[30]].strftime('%Y-%m-%d')\n",
        "plt.text(df_sum.Date[df_sum.index[27]], df_sum.Conc[df_sum.index[27]]+5,\n",
        "         str(date) +'\\n Fire in CEZ, \\n 14 939 ha',\n",
        "         fontdict=dict(color='black', size=14, style = 'oblique' ))\n",
        "plt.text(df_sum.Date[df_sum.index[65]], df_sum.Conc[df_sum.index[65]]+3,\n",
        "         str(date1) +'\\n Fire in CEZ, \\n 87 000 ha',\n",
        "         fontdict=dict(color='black', size=14, style = 'oblique'))\n",
        "plt.text(df_sum.Date[df_sum.index[30]], df_sum.Conc[df_sum.index[30]]+3,\n",
        "         str(date2) +'\\n Fire in CEZ, \\n 10 116 ha',\n",
        "         fontdict=dict(color='black', size=14, style = 'oblique'))\n",
        "#plt.savefig('/content/drive/MyDrive/Colab Notebooks/Data for JER/fig6.png', dpi=180) "
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(2015-06-12 00:00:00, 64.0, '2015-06-12\\n Fire in CEZ, \\n 10 116 ha')"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 3240x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AGDrDy2erZsQ",
        "outputId": "77ccdf73-9263-4e6d-f0c3-9faf2f4c5e96"
      },
      "source": [
        "df_sum.index[67]"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6228"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "vkHclnFcr6Y2",
        "outputId": "e74090df-6877-4fe6-b4a2-2beadac1d127"
      },
      "source": [
        "df_sum[df_sum['Year'] == 2015]"
      ],
      "execution_count": 97,
      "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>Date</th>\n",
              "      <th>Conc</th>\n",
              "      <th>Week</th>\n",
              "      <th>Year</th>\n",
              "      <th>Mon</th>\n",
              "      <th>season</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4393</th>\n",
              "      <td>2015-04-09</td>\n",
              "      <td>48.0</td>\n",
              "      <td>15</td>\n",
              "      <td>2015</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4412</th>\n",
              "      <td>2015-04-28</td>\n",
              "      <td>102.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2015</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4433</th>\n",
              "      <td>2015-05-19</td>\n",
              "      <td>55.0</td>\n",
              "      <td>21</td>\n",
              "      <td>2015</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4443</th>\n",
              "      <td>2015-05-29</td>\n",
              "      <td>58.0</td>\n",
              "      <td>22</td>\n",
              "      <td>2015</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4457</th>\n",
              "      <td>2015-06-12</td>\n",
              "      <td>61.0</td>\n",
              "      <td>24</td>\n",
              "      <td>2015</td>\n",
              "      <td>Jun</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4514</th>\n",
              "      <td>2015-08-08</td>\n",
              "      <td>74.0</td>\n",
              "      <td>32</td>\n",
              "      <td>2015</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4517</th>\n",
              "      <td>2015-08-11</td>\n",
              "      <td>59.0</td>\n",
              "      <td>33</td>\n",
              "      <td>2015</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4562</th>\n",
              "      <td>2015-09-25</td>\n",
              "      <td>65.0</td>\n",
              "      <td>39</td>\n",
              "      <td>2015</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date   Conc  Week  Year  Mon season\n",
              "4393 2015-04-09   48.0    15  2015  Apr    sum\n",
              "4412 2015-04-28  102.0    18  2015  Apr    sum\n",
              "4433 2015-05-19   55.0    21  2015  May    sum\n",
              "4443 2015-05-29   58.0    22  2015  May    sum\n",
              "4457 2015-06-12   61.0    24  2015  Jun    sum\n",
              "4514 2015-08-08   74.0    32  2015  Aug    sum\n",
              "4517 2015-08-11   59.0    33  2015  Aug    sum\n",
              "4562 2015-09-25   65.0    39  2015  Sep    sum"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-e0opQRNxpI"
      },
      "source": [
        "# **Mazyr data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xNeNTX8PNxYM",
        "outputId": "115745c6-a88b-47f6-b657-a0946a84a324"
      },
      "source": [
        "path1 = '/content/drive/MyDrive/Colab Notebooks/Data for JER/Airborne activity_Mazyr_2007_2018.csv'\n",
        "df_moz = pd.read_csv(path1, parse_dates=['Date'])\n",
        "df_moz.dropna(inplace = True)\n",
        "df_moz.head(50)"
      ],
      "execution_count": 98,
      "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>Date</th>\n",
              "      <th>Conc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2007-01-01</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2007-01-02</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2007-01-03</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2007-01-04</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2007-01-05</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2007-01-06</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2007-01-07</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2007-01-08</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2007-01-09</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2007-01-10</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2007-01-11</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2007-01-12</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2007-01-13</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2007-01-14</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2007-01-15</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2007-01-16</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2007-01-17</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2007-01-18</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2007-01-19</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2007-01-20</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2007-01-21</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2007-01-22</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2007-01-23</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2007-01-24</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2007-01-25</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2007-01-26</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2007-01-27</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2007-01-28</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2007-01-29</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2007-01-30</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2007-01-31</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2007-02-02</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2007-02-03</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2007-02-04</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2007-02-05</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2007-02-06</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2007-02-07</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2007-02-08</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2007-02-09</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2007-02-10</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2007-02-11</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2007-02-12</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2007-02-13</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2007-02-14</td>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2007-02-15</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2007-02-16</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2007-02-17</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2007-02-18</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>2007-02-19</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2007-02-20</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date  Conc\n",
              "0  2007-01-01   9.0\n",
              "1  2007-01-02   5.0\n",
              "2  2007-01-03  10.0\n",
              "3  2007-01-04  10.0\n",
              "4  2007-01-05  15.0\n",
              "5  2007-01-06   5.0\n",
              "6  2007-01-07   4.0\n",
              "7  2007-01-08   6.0\n",
              "8  2007-01-09  11.0\n",
              "9  2007-01-10   6.0\n",
              "10 2007-01-11  11.0\n",
              "11 2007-01-12   2.0\n",
              "12 2007-01-13   4.0\n",
              "13 2007-01-14   5.0\n",
              "14 2007-01-15   7.0\n",
              "15 2007-01-16   9.0\n",
              "16 2007-01-17   7.0\n",
              "17 2007-01-18  10.0\n",
              "18 2007-01-19  15.0\n",
              "19 2007-01-20   5.0\n",
              "20 2007-01-21   9.0\n",
              "21 2007-01-22  11.0\n",
              "22 2007-01-23  10.0\n",
              "23 2007-01-24   2.0\n",
              "24 2007-01-25  11.0\n",
              "25 2007-01-26   6.0\n",
              "26 2007-01-27   9.0\n",
              "27 2007-01-28  14.0\n",
              "28 2007-01-29   7.0\n",
              "29 2007-01-30   9.0\n",
              "30 2007-01-31  19.0\n",
              "32 2007-02-02   8.0\n",
              "33 2007-02-03  13.0\n",
              "34 2007-02-04   6.0\n",
              "35 2007-02-05   9.0\n",
              "36 2007-02-06   5.0\n",
              "37 2007-02-07   6.0\n",
              "38 2007-02-08   5.0\n",
              "39 2007-02-09   6.0\n",
              "40 2007-02-10  14.0\n",
              "41 2007-02-11  20.0\n",
              "42 2007-02-12   7.0\n",
              "43 2007-02-13  16.0\n",
              "44 2007-02-14  23.0\n",
              "45 2007-02-15  16.0\n",
              "46 2007-02-16  16.0\n",
              "47 2007-02-17   9.0\n",
              "48 2007-02-18  19.0\n",
              "49 2007-02-19  12.0\n",
              "50 2007-02-20  18.0"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "vAOlcwadPBjW",
        "outputId": "fc8bec36-bf5b-42f3-9b12-1759127bd619"
      },
      "source": [
        "df_moz['Week'] = df_moz.Date.dt.isocalendar().week\n",
        "df_moz['Month'] = df_moz.Date.dt.month\n",
        "df_moz['Year'] = df_moz.Date.dt.year\n",
        "df_moz"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Conc</th>\n",
              "      <th>Week</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2007-01-01</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2007-01-02</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2007-01-03</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2007-01-04</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2007-01-05</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4104</th>\n",
              "      <td>2018-12-27</td>\n",
              "      <td>16.0</td>\n",
              "      <td>52</td>\n",
              "      <td>12</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4105</th>\n",
              "      <td>2018-12-28</td>\n",
              "      <td>11.0</td>\n",
              "      <td>52</td>\n",
              "      <td>12</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4106</th>\n",
              "      <td>2018-12-29</td>\n",
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              "      <td>52</td>\n",
              "      <td>12</td>\n",
              "      <td>2018</td>\n",
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              "    <tr>\n",
              "      <th>4107</th>\n",
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              "      <td>12</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4108</th>\n",
              "      <td>2018-12-31</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4035 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date  Conc  Week  Month  Year\n",
              "0    2007-01-01   9.0     1      1  2007\n",
              "1    2007-01-02   5.0     1      1  2007\n",
              "2    2007-01-03  10.0     1      1  2007\n",
              "3    2007-01-04  10.0     1      1  2007\n",
              "4    2007-01-05  15.0     1      1  2007\n",
              "...         ...   ...   ...    ...   ...\n",
              "4104 2018-12-27  16.0    52     12  2018\n",
              "4105 2018-12-28  11.0    52     12  2018\n",
              "4106 2018-12-29   6.0    52     12  2018\n",
              "4107 2018-12-30  24.0    52     12  2018\n",
              "4108 2018-12-31   8.0     1     12  2018\n",
              "\n",
              "[4035 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "L2wiHdChPiDQ",
        "outputId": "ebb82042-3045-4516-9b60-84d1d8f821bf"
      },
      "source": [
        "df_moz1 = df_moz.resample('M', on = 'Date').mean()\n",
        "df_moz1.dropna(inplace = True)\n",
        "df_moz1.reset_index('Date', inplace=True)\n",
        "df_moz1['Month'] = df_moz1.Date.dt.month\n",
        "df_moz1['Mon'] = df_moz1['Month'].replace(m_dict)\n",
        "df_moz1['Week'] = df_moz1.Date.dt.isocalendar().week\n",
        "df_moz1['Year'] = df_moz1.Date.dt.year\n",
        "df_moz1['season'] = np.where(df_moz1.Mon.isin(['Apr','May','Jun','Jul','Aug','Sep']), 'sum', 'win')\n",
        "df_moz1.drop('Month', axis=1, inplace=True)\n",
        "df_moz1"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>Date</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>2007</td>\n",
              "      <td>Jan</td>\n",
              "      <td>win</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2007-02-28</td>\n",
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              "      <th>2</th>\n",
              "      <td>2007-03-31</td>\n",
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              "      <td>13</td>\n",
              "      <td>2007</td>\n",
              "      <td>Mar</td>\n",
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              "      <th>3</th>\n",
              "      <td>2007-04-30</td>\n",
              "      <td>16.366667</td>\n",
              "      <td>18</td>\n",
              "      <td>2007</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2007-05-31</td>\n",
              "      <td>20.709677</td>\n",
              "      <td>22</td>\n",
              "      <td>2007</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>2018-08-31</td>\n",
              "      <td>14.129032</td>\n",
              "      <td>35</td>\n",
              "      <td>2018</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>2018-09-30</td>\n",
              "      <td>19.966667</td>\n",
              "      <td>39</td>\n",
              "      <td>2018</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>2018-10-31</td>\n",
              "      <td>25.483871</td>\n",
              "      <td>44</td>\n",
              "      <td>2018</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>2018-11-30</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>48</td>\n",
              "      <td>2018</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>2018-12-31</td>\n",
              "      <td>16.580645</td>\n",
              "      <td>1</td>\n",
              "      <td>2018</td>\n",
              "      <td>Dec</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>135 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date       Conc  Week  Year  Mon season\n",
              "0   2007-01-31   8.483871     5  2007  Jan    win\n",
              "1   2007-02-28  14.111111     9  2007  Feb    win\n",
              "2   2007-03-31  17.344828    13  2007  Mar    win\n",
              "3   2007-04-30  16.366667    18  2007  Apr    sum\n",
              "4   2007-05-31  20.709677    22  2007  May    sum\n",
              "..         ...        ...   ...   ...  ...    ...\n",
              "130 2018-08-31  14.129032    35  2018  Aug    sum\n",
              "131 2018-09-30  19.966667    39  2018  Sep    sum\n",
              "132 2018-10-31  25.483871    44  2018  Oct    win\n",
              "133 2018-11-30  24.500000    48  2018  Nov    win\n",
              "134 2018-12-31  16.580645     1  2018  Dec    win\n",
              "\n",
              "[135 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ugFbGj5PQD5i",
        "outputId": "e4b68820-b7ba-45d5-d4b7-77fa9ef9a792"
      },
      "source": [
        "df_moz2 = df_moz[df_moz['Conc'] > 47]\n",
        "df_moz2.dropna(inplace = True)\n",
        "df_moz2['Mon'] = df_moz2['Month'].replace(m_dict)\n",
        "df_moz2['season'] = np.where(df_moz2.Mon.isin(['Apr','May','Jun','Jul','Aug','Sep']), 'sum', 'win')\n",
        "df_moz2.drop('Month',1, inplace=True)\n",
        "df_moz_sum = df_moz2[df_moz2['season'] == 'sum']\n",
        "df_moz_sum.head(50)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4174: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ]
        },
        {
          "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>Date</th>\n",
              "      <th>Conc</th>\n",
              "      <th>Week</th>\n",
              "      <th>Year</th>\n",
              "      <th>Mon</th>\n",
              "      <th>season</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>2007-04-21</td>\n",
              "      <td>51.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2007</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>2007-05-21</td>\n",
              "      <td>50.0</td>\n",
              "      <td>21</td>\n",
              "      <td>2007</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>2007-08-14</td>\n",
              "      <td>53.0</td>\n",
              "      <td>33</td>\n",
              "      <td>2007</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260</th>\n",
              "      <td>2007-09-18</td>\n",
              "      <td>68.0</td>\n",
              "      <td>38</td>\n",
              "      <td>2007</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>463</th>\n",
              "      <td>2008-04-08</td>\n",
              "      <td>48.0</td>\n",
              "      <td>15</td>\n",
              "      <td>2008</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>614</th>\n",
              "      <td>2008-09-06</td>\n",
              "      <td>55.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2008</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>830</th>\n",
              "      <td>2009-04-10</td>\n",
              "      <td>56.0</td>\n",
              "      <td>15</td>\n",
              "      <td>2009</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>838</th>\n",
              "      <td>2009-04-18</td>\n",
              "      <td>55.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2009</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>983</th>\n",
              "      <td>2009-09-10</td>\n",
              "      <td>64.0</td>\n",
              "      <td>37</td>\n",
              "      <td>2009</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1188</th>\n",
              "      <td>2010-04-03</td>\n",
              "      <td>67.0</td>\n",
              "      <td>13</td>\n",
              "      <td>2010</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1191</th>\n",
              "      <td>2010-04-06</td>\n",
              "      <td>52.0</td>\n",
              "      <td>14</td>\n",
              "      <td>2010</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1195</th>\n",
              "      <td>2010-04-10</td>\n",
              "      <td>68.0</td>\n",
              "      <td>14</td>\n",
              "      <td>2010</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1226</th>\n",
              "      <td>2010-05-11</td>\n",
              "      <td>50.0</td>\n",
              "      <td>19</td>\n",
              "      <td>2010</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>2010-06-09</td>\n",
              "      <td>57.0</td>\n",
              "      <td>23</td>\n",
              "      <td>2010</td>\n",
              "      <td>Jun</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1295</th>\n",
              "      <td>2010-07-19</td>\n",
              "      <td>53.0</td>\n",
              "      <td>29</td>\n",
              "      <td>2010</td>\n",
              "      <td>Jul</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1312</th>\n",
              "      <td>2010-08-05</td>\n",
              "      <td>51.0</td>\n",
              "      <td>31</td>\n",
              "      <td>2010</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1313</th>\n",
              "      <td>2010-08-06</td>\n",
              "      <td>52.0</td>\n",
              "      <td>31</td>\n",
              "      <td>2010</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1368</th>\n",
              "      <td>2010-09-30</td>\n",
              "      <td>48.0</td>\n",
              "      <td>39</td>\n",
              "      <td>2010</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1551</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>54.0</td>\n",
              "      <td>13</td>\n",
              "      <td>2011</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1553</th>\n",
              "      <td>2011-04-03</td>\n",
              "      <td>78.0</td>\n",
              "      <td>13</td>\n",
              "      <td>2011</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1593</th>\n",
              "      <td>2011-05-13</td>\n",
              "      <td>63.0</td>\n",
              "      <td>19</td>\n",
              "      <td>2011</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1632</th>\n",
              "      <td>2011-07-21</td>\n",
              "      <td>52.0</td>\n",
              "      <td>29</td>\n",
              "      <td>2011</td>\n",
              "      <td>Jul</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1825</th>\n",
              "      <td>2012-09-30</td>\n",
              "      <td>62.0</td>\n",
              "      <td>39</td>\n",
              "      <td>2012</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2164</th>\n",
              "      <td>2013-09-04</td>\n",
              "      <td>56.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2013</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2381</th>\n",
              "      <td>2014-04-09</td>\n",
              "      <td>53.0</td>\n",
              "      <td>15</td>\n",
              "      <td>2014</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2391</th>\n",
              "      <td>2014-04-19</td>\n",
              "      <td>69.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2014</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2409</th>\n",
              "      <td>2014-05-07</td>\n",
              "      <td>53.0</td>\n",
              "      <td>19</td>\n",
              "      <td>2014</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2410</th>\n",
              "      <td>2014-05-08</td>\n",
              "      <td>59.0</td>\n",
              "      <td>19</td>\n",
              "      <td>2014</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2414</th>\n",
              "      <td>2014-05-12</td>\n",
              "      <td>70.0</td>\n",
              "      <td>20</td>\n",
              "      <td>2014</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2435</th>\n",
              "      <td>2014-06-02</td>\n",
              "      <td>59.0</td>\n",
              "      <td>23</td>\n",
              "      <td>2014</td>\n",
              "      <td>Jun</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2450</th>\n",
              "      <td>2014-06-17</td>\n",
              "      <td>51.0</td>\n",
              "      <td>25</td>\n",
              "      <td>2014</td>\n",
              "      <td>Jun</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2498</th>\n",
              "      <td>2014-08-04</td>\n",
              "      <td>76.0</td>\n",
              "      <td>32</td>\n",
              "      <td>2014</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2526</th>\n",
              "      <td>2014-09-01</td>\n",
              "      <td>86.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2014</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2773</th>\n",
              "      <td>2015-05-06</td>\n",
              "      <td>53.0</td>\n",
              "      <td>19</td>\n",
              "      <td>2015</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2788</th>\n",
              "      <td>2015-05-21</td>\n",
              "      <td>75.0</td>\n",
              "      <td>21</td>\n",
              "      <td>2015</td>\n",
              "      <td>May</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2812</th>\n",
              "      <td>2015-06-14</td>\n",
              "      <td>67.0</td>\n",
              "      <td>24</td>\n",
              "      <td>2015</td>\n",
              "      <td>Jun</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2814</th>\n",
              "      <td>2015-06-16</td>\n",
              "      <td>56.0</td>\n",
              "      <td>25</td>\n",
              "      <td>2015</td>\n",
              "      <td>Jun</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2835</th>\n",
              "      <td>2015-07-07</td>\n",
              "      <td>57.0</td>\n",
              "      <td>28</td>\n",
              "      <td>2015</td>\n",
              "      <td>Jul</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2857</th>\n",
              "      <td>2015-07-29</td>\n",
              "      <td>52.0</td>\n",
              "      <td>31</td>\n",
              "      <td>2015</td>\n",
              "      <td>Jul</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2869</th>\n",
              "      <td>2015-08-10</td>\n",
              "      <td>52.0</td>\n",
              "      <td>33</td>\n",
              "      <td>2015</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2870</th>\n",
              "      <td>2015-08-11</td>\n",
              "      <td>97.0</td>\n",
              "      <td>33</td>\n",
              "      <td>2015</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2873</th>\n",
              "      <td>2015-08-14</td>\n",
              "      <td>61.0</td>\n",
              "      <td>33</td>\n",
              "      <td>2015</td>\n",
              "      <td>Aug</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2891</th>\n",
              "      <td>2015-09-01</td>\n",
              "      <td>50.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2015</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2892</th>\n",
              "      <td>2015-09-02</td>\n",
              "      <td>73.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2015</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2906</th>\n",
              "      <td>2015-09-16</td>\n",
              "      <td>48.0</td>\n",
              "      <td>38</td>\n",
              "      <td>2015</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2914</th>\n",
              "      <td>2015-09-24</td>\n",
              "      <td>75.0</td>\n",
              "      <td>39</td>\n",
              "      <td>2015</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2915</th>\n",
              "      <td>2015-09-25</td>\n",
              "      <td>55.0</td>\n",
              "      <td>39</td>\n",
              "      <td>2015</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2919</th>\n",
              "      <td>2015-09-29</td>\n",
              "      <td>56.0</td>\n",
              "      <td>40</td>\n",
              "      <td>2015</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2920</th>\n",
              "      <td>2015-09-30</td>\n",
              "      <td>76.0</td>\n",
              "      <td>40</td>\n",
              "      <td>2015</td>\n",
              "      <td>Sep</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3112</th>\n",
              "      <td>2016-04-09</td>\n",
              "      <td>60.0</td>\n",
              "      <td>14</td>\n",
              "      <td>2016</td>\n",
              "      <td>Apr</td>\n",
              "      <td>sum</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date  Conc  Week  Year  Mon season\n",
              "110  2007-04-21  51.0    16  2007  Apr    sum\n",
              "140  2007-05-21  50.0    21  2007  May    sum\n",
              "225  2007-08-14  53.0    33  2007  Aug    sum\n",
              "260  2007-09-18  68.0    38  2007  Sep    sum\n",
              "463  2008-04-08  48.0    15  2008  Apr    sum\n",
              "614  2008-09-06  55.0    36  2008  Sep    sum\n",
              "830  2009-04-10  56.0    15  2009  Apr    sum\n",
              "838  2009-04-18  55.0    16  2009  Apr    sum\n",
              "983  2009-09-10  64.0    37  2009  Sep    sum\n",
              "1188 2010-04-03  67.0    13  2010  Apr    sum\n",
              "1191 2010-04-06  52.0    14  2010  Apr    sum\n",
              "1195 2010-04-10  68.0    14  2010  Apr    sum\n",
              "1226 2010-05-11  50.0    19  2010  May    sum\n",
              "1255 2010-06-09  57.0    23  2010  Jun    sum\n",
              "1295 2010-07-19  53.0    29  2010  Jul    sum\n",
              "1312 2010-08-05  51.0    31  2010  Aug    sum\n",
              "1313 2010-08-06  52.0    31  2010  Aug    sum\n",
              "1368 2010-09-30  48.0    39  2010  Sep    sum\n",
              "1551 2011-04-01  54.0    13  2011  Apr    sum\n",
              "1553 2011-04-03  78.0    13  2011  Apr    sum\n",
              "1593 2011-05-13  63.0    19  2011  May    sum\n",
              "1632 2011-07-21  52.0    29  2011  Jul    sum\n",
              "1825 2012-09-30  62.0    39  2012  Sep    sum\n",
              "2164 2013-09-04  56.0    36  2013  Sep    sum\n",
              "2381 2014-04-09  53.0    15  2014  Apr    sum\n",
              "2391 2014-04-19  69.0    16  2014  Apr    sum\n",
              "2409 2014-05-07  53.0    19  2014  May    sum\n",
              "2410 2014-05-08  59.0    19  2014  May    sum\n",
              "2414 2014-05-12  70.0    20  2014  May    sum\n",
              "2435 2014-06-02  59.0    23  2014  Jun    sum\n",
              "2450 2014-06-17  51.0    25  2014  Jun    sum\n",
              "2498 2014-08-04  76.0    32  2014  Aug    sum\n",
              "2526 2014-09-01  86.0    36  2014  Sep    sum\n",
              "2773 2015-05-06  53.0    19  2015  May    sum\n",
              "2788 2015-05-21  75.0    21  2015  May    sum\n",
              "2812 2015-06-14  67.0    24  2015  Jun    sum\n",
              "2814 2015-06-16  56.0    25  2015  Jun    sum\n",
              "2835 2015-07-07  57.0    28  2015  Jul    sum\n",
              "2857 2015-07-29  52.0    31  2015  Jul    sum\n",
              "2869 2015-08-10  52.0    33  2015  Aug    sum\n",
              "2870 2015-08-11  97.0    33  2015  Aug    sum\n",
              "2873 2015-08-14  61.0    33  2015  Aug    sum\n",
              "2891 2015-09-01  50.0    36  2015  Sep    sum\n",
              "2892 2015-09-02  73.0    36  2015  Sep    sum\n",
              "2906 2015-09-16  48.0    38  2015  Sep    sum\n",
              "2914 2015-09-24  75.0    39  2015  Sep    sum\n",
              "2915 2015-09-25  55.0    39  2015  Sep    sum\n",
              "2919 2015-09-29  56.0    40  2015  Sep    sum\n",
              "2920 2015-09-30  76.0    40  2015  Sep    sum\n",
              "3112 2016-04-09  60.0    14  2016  Apr    sum"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "Q-9SM0_2SCAY",
        "outputId": "23bf2993-5b4f-44e9-ca8a-6b1dc08918b1"
      },
      "source": [
        "df_moz_win =df_moz2[df_moz2['season'] == 'win']\n",
        "df_moz_win"
      ],
      "execution_count": 102,
      "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>Date</th>\n",
              "      <th>Conc</th>\n",
              "      <th>Week</th>\n",
              "      <th>Year</th>\n",
              "      <th>Mon</th>\n",
              "      <th>season</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2007-03-20</td>\n",
              "      <td>63.0</td>\n",
              "      <td>12</td>\n",
              "      <td>2007</td>\n",
              "      <td>Mar</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>2007-03-23</td>\n",
              "      <td>60.0</td>\n",
              "      <td>12</td>\n",
              "      <td>2007</td>\n",
              "      <td>Mar</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>2007-10-04</td>\n",
              "      <td>69.0</td>\n",
              "      <td>40</td>\n",
              "      <td>2007</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>2007-10-05</td>\n",
              "      <td>53.0</td>\n",
              "      <td>40</td>\n",
              "      <td>2007</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>2007-10-11</td>\n",
              "      <td>58.0</td>\n",
              "      <td>41</td>\n",
              "      <td>2007</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4034</th>\n",
              "      <td>2018-10-18</td>\n",
              "      <td>69.0</td>\n",
              "      <td>42</td>\n",
              "      <td>2018</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4046</th>\n",
              "      <td>2018-10-30</td>\n",
              "      <td>91.0</td>\n",
              "      <td>44</td>\n",
              "      <td>2018</td>\n",
              "      <td>Oct</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4064</th>\n",
              "      <td>2018-11-17</td>\n",
              "      <td>53.0</td>\n",
              "      <td>46</td>\n",
              "      <td>2018</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4065</th>\n",
              "      <td>2018-11-18</td>\n",
              "      <td>78.0</td>\n",
              "      <td>46</td>\n",
              "      <td>2018</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4070</th>\n",
              "      <td>2018-11-23</td>\n",
              "      <td>76.0</td>\n",
              "      <td>47</td>\n",
              "      <td>2018</td>\n",
              "      <td>Nov</td>\n",
              "      <td>win</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>97 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date  Conc  Week  Year  Mon season\n",
              "78   2007-03-20  63.0    12  2007  Mar    win\n",
              "81   2007-03-23  60.0    12  2007  Mar    win\n",
              "276  2007-10-04  69.0    40  2007  Oct    win\n",
              "277  2007-10-05  53.0    40  2007  Oct    win\n",
              "283  2007-10-11  58.0    41  2007  Oct    win\n",
              "...         ...   ...   ...   ...  ...    ...\n",
              "4034 2018-10-18  69.0    42  2018  Oct    win\n",
              "4046 2018-10-30  91.0    44  2018  Oct    win\n",
              "4064 2018-11-17  53.0    46  2018  Nov    win\n",
              "4065 2018-11-18  78.0    46  2018  Nov    win\n",
              "4070 2018-11-23  76.0    47  2018  Nov    win\n",
              "\n",
              "[97 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "3oXpQQgXSJfm",
        "outputId": "442b3ea0-ff5c-4b47-c15b-55782f45702e"
      },
      "source": [
        "x1 = 'Date'\n",
        "y1 = 'Conc'\n",
        "s = chr(0x00b3)\n",
        "s1 = ' $10^{-5}$'\n",
        "cmap = 'seismic'\n",
        "fig, ax = plt.subplots(figsize = (18,8), dpi = 180)\n",
        "sns.lineplot(x1, y1, data= df_moz1, color = 'black').axhline(28.5, linewidth = 1,\n",
        "                                                       color = 'r',\n",
        "                                                       linestyle = '--' )\n",
        "sns.scatterplot(x1, y1, data=df_moz_sum,\n",
        "                s = 40,\n",
        "                marker = 'o',\n",
        "                fc = 'orangered',\n",
        "                ec = 'black' )\n",
        "sns.scatterplot(x1, y1, data=df_moz_win,\n",
        "                s = 40,\n",
        "                marker = '^',\n",
        "                fc = 'slateblue',\n",
        "                ec = 'black' )\n",
        "sns.despine(ax=ax, top=True, right=True)\n",
        "ax.tick_params(axis = 'x', \n",
        "                direction='out',\n",
        "                labelsize = 12,\n",
        "                length = 6,\n",
        "                rotation = 35)\n",
        "ax.tick_params(axis = 'y', \n",
        "                direction='out',\n",
        "                labelsize = 12,\n",
        "                length = 6)\n",
        "ax.set_ylabel('Airborne activity, Bq per m'+s + s1,\n",
        "               fontsize = 14,\n",
        "               fontstyle='oblique',\n",
        "               fontweight = 'medium')\n",
        "ax.set_xlabel('Year',\n",
        "               fontsize = 14,\n",
        "               fontstyle='oblique',\n",
        "               fontweight = 'medium')\n",
        "ax.legend(labels=[\"Mean monthly activity\",\"95th percentile\", \"Summer\", \"Winter\"], fontsize = 14, loc = 2)\n",
        "ax.grid(axis='y', alpha = 0.5)\n",
        "#plt.savefig('/content/drive/MyDrive/Colab Notebooks/Data for JER/fig6.png', dpi=180) "
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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IiGzHFkWeIwDchXpEe3UA6yRJOiVJUkcb3JuIiIiIiIiIiIiIiIjIJjQ7ebq5uaFatWoGXadZ5AlwZDupaRZ5lixZUvh60jeyXZZlEBERERFR0WP1Ik9ZllcDqAtgAYBMqIs9mwLYJ0nSdkmSAqydAxEREREREREREREREZE1ybIsFHnWqlULzs7OBl0bGBgoxCzypKysLOHrKSAgAE5OT1/abdSoEVq0aKHEkZGROH78uE1zJCIiIiIi27DJuHZZlpNlWZ4CoB6AzVAXekoAegC4IEnSCkmSnrFFLkRERERERERERERERESWdv/+fTx+/FiJDR3VDrCTJ+mKiYlBVlaWEueOatc0dOhQIV67dq3V8yIiIiIiItuzSZFnLlmW42RZ7gegPYBTUBd6ugAYDuCKJEnTJEkqYcuciIiIiIiIiIiIiIiIiMyl2XURAGrXrm3wtb6+vihR4ulLZBERERbLixyT5qh2QH+R54ABA+Du7q7EP//8M1JSKbbj2AAAIABJREFUUqyeGxERERER2ZZNizxzybJ8WJbllgAGAUiAutizNIA5UBd7DrZHXkRERERERERERERERESmMKfI09nZWSjii4qKEro4UvFjSJGnl5cXevfurcTJycnYsmWL1XMjIiIiIiLbskuRZy5ZltcD8Ie6uDMV6mLPqgC+kyTpjCRJL9ozPyIiIiIiIiIiIiIiIiJDmFPkCYgj27OysnT2o+LFkCJPgCPbiYiIiIiKA7sWeQKALMtpsizPhrrY88d/lyUAjQHsBVDGTqkRERERERERERERERERGSQ6OlqI/fz8jLo+MDBQiC9fvmx2TuS4NIs8JUlCnTp19D6vc+fOqFGjhhIfOHCABcJEREREREWM3Ys8c8myfFOW5YEAngdwFOpCT0nzOZIkGfe3YSIiIiIiIiIiIiIiIiIb0C6sM7bIU7OTJ8Aiz+JMlmWhyNPX1xceHh56n+vk5ITBgwcLa8HBwVbMjoiIiIiIbK3QFHnmkmX5lCzLbQEMABALdaGn/O/D/0iStFqSpBp5XU9ERERERERERERERERka5pFnlWrVs2zKC8vLPKkXPfu3cODBw+UOK9R7bn0FXnm5ORYIzUiIiIiIrKDQlfkmUuW5RAA9QBMBZAMdbGnC4ChACIlSfpakqTKdkyRiIiIiIiIiIiIiIiICOnp6UhISFBiY7t4AkDt2rXh6uqqxBERERbJjRyPZhdPoOAiz1q1aqFTp05KfOPGDfz1119WyY2IiIiIiGyv0BZ5AoAsy5myLM8HUBfAdwBUUBd7ugN4D8A1SZI+lySpgh3TJCIiIiIiIiIiIiIiomIsJiZGiGvXrm30Hq6urvD391fif/75h90YiyljizwBYOjQoUL8/fffWzQnIiIiIiKyn0Jd5JlLluW7siwPA9AcwN//LksAPACMBxBtr9yIiIiIiIiIiIiIiIioeNMc1Q6YVuQJiCPb09PTERsba05a5KBMKfLs3bs3ypQpo8S//vor7t+/b/HciIiIiIjI9hyiyDOXLMvnZFnuAqAXgCv/LksAStkvKyIiIiIiIiIiIiIiIirOoqPFfiSmjGsHgMDAQCG+fPmyyTmR4zKlyNPT0xMDBgxQ4szMTGzYsMHiuRERERERke05VJFnLlmWtwFoAOBDAA/tnA4REREREREREREREREVY9bo5AmwyLO4ioyMVD728vKCt7e3QddxZDsRERERUdHkkEWeACDLcrYsy4sB1AWw3N75EBERERERERERERERUfFkrSLPiIgIk3Mix5Seno6YmBglrlevHiRJMujali1bCl9D4eHhOHv2rMVzJCIiIiIi23LYIs9csiwnybL8gb3zICIiIiIiIiIiIiIiouJJs8izdOnSqFixokn7+Pv7w8np6ct37ORZ/Fy5cgWyLCuxIaPac0mShCFDhghr7OZJREREROT4HL7Ik4iIiIiIiIiIiIiIiMheVCqV0HnRz8/P4M6L2tzd3VGnTh0lvnz5slDwR0XfP//8I8TGFHkCwFtvvQVnZ2cl/umnn5CRkWGR3IiIiIiIyD5Y5ElERERERERERERERERkops3bwpFdKaOas8VGBiofJySkoL4+Hiz9iPHYm6RZ6VKldCzZ08lTkpKwo4dOyySG5Gpnjx5gkePHtk7DSIiIiKHxSJPIiIiIiIiIiIiIiIiIhNpjmoHzC/yrF+/vhBzZHvxYm6RJwCdke1r1641Kyd7S0tLw9ChQ9GsWTOsX7/e3umQEWRZxvz581G5cmVUqlQJkyZNQk5Ojr3TIiIiInI4LPIkIiIiIiIiIiIiIiIiMlF0dLQQ+/n5mbUfizyLN80iTxcXF9SqVcvoPV5++WU888wzSvz7778jISHBIvnZw7Rp0/D999/jzJkzGDRoEMLDw+2dEhkgKysL77zzDqZMmYLU1FRkZGRg4cKFePXVV9nVk4iIiMhILPIkIiIiIiIiIiIiIiIiMpG1O3lGRESYtR85DpVKJRR51qlTB66urkbv4+rqioEDBwr7/vDDDxbJ0dZiYmKwbNkyJZZlGR9++CFkWbZjVlSQR48e4eWXX9bbRXb37t1o1aoVrl69aofMiIiIiBwTizyJiIiIiIiIiIiIiIiITGTpIs969epBkiQlZifP4iMhIQGpqalKbMqo9lz6RrY7YmHkjBkzkJWVJazt378f27dvt1NGVJC4uDi0adMGf/31V57PiYiIQMuWLREaGmrDzIiIiIgcF4s8iYiIiIiIiIiIiIiIiEykWeTp7OwMHx8fs/bz9PSEr6+vEl++fNkhi/PIeJpdPAHzijzr16+P559/XomvXr2KI0eOmLyfPZw5cwY//fST3scmTJiAzMxMG2dEBTl9+jSCgoJw6dIlYX3o0KGYPXu2sPbgwQN07doV33zzjQ0zJCIiInJMLPIkIiIiIiIiIiIiIiIiMlF0dLTycc2aNU0ar60tMDBQ+fjhw4e4ffu22XtS4WfJIk9AXVinSd/o7MJs8uTJQqzZ4fbKlStYsWKFrVOifOzYsQPt27fX+X41b948rFmzBrNmzcLmzZvh6empPJaTk4P3338fI0eO1OnYWphkZmYiLi7O3mkQERFRMcYiTyIiIiIiIiIiIiIiIiITPHr0CElJSUps7qj2XPXr1xdijmwvHixd5Nm/f394eHgocUhICJKTk83a01b++usv/PHHH0pctmxZnSLVjz/+GPfv37d1aqTHsmXL8NprryE1NVVZc3Nzw4YNGzBt2jSlQLdPnz44cuSITsfjlStXomvXrkhMTLRp3oaIjY1FtWrV4Ovrq1M4TURERGQrLPIkIiIiIiIiIiIiIiIiMoHmqHbAekWeERERFtk3OzsbT548scheZHmRkZFCHBAQYNZ+ZcuWRZ8+fZQ4JSUFEyZMwK+//orTp08jMTERsiybdQ9rUKlUOl08p0yZgkGDBqFTp07K2oMHDzB37lxbp0cacnJyMG7cOIwePRoqlUpZ9/Lywl9//YU33nhD55omTZrg5MmTaN26tbC+f/9+tGzZUmfUu7198cUXSvHp999/X+jyIyIiouKBRZ5EREREREREREREREREJtAc1Q4Afn5+FtnXGp08Dxw4gIoVK6JixYr45ptvzN6PLE+zk2flypVRrlw5s/fU7jy4cuVK9O7dGy1atIC3tzdKlSqFwMBAvPTSSxgxYgQ++eQT/Pjjjzh06BDi4uKQnZ1tdg7GCgkJwenTp5W4WrVq+OCDDyBJEhYtWiSMbV+2bBmioqJsniMBqampeP3117FkyRJh3c/PD8eOHUO7du3yvLZSpUoIDQ3FkCFDhPWYmBgEBQVhx44dVsnZFGFhYUJ8+PBhO2VCRERExZlUGN+dRUWDJEkyAMgdOuT/xCZNAM0f/s+eBcaOLfgG+/eLcceOBV+zZIn6frnGjlXfLz+DB6v/yxUcrP4vPzzTUzyTGs+kxjOp8UxP8UxqPJMaz6TGMz3FM6nxTGo8kxrP9BTPpMYzqfFMajzTUzyTGs+kxjOp8UxP8UxqPJOamWdasGABNk6ejNwTNmjQAN4VK+q/zogzZefk4PDhwxgL4ByADh06YP/+/SafKWv1apw8eRKZWVkAACcnJ7Rp3RrOzs46ZwJQ5D5PAAr9mdJWrkTYyJFKXK5sWTTR3BMw6UwygNrXryMmJkZZ+zv/zABA+dpzdnZGtWrV8Hl2NhqrVKhWtSpKly5t0JlM+TxlnTyJ0+3bIz09XVkLCAhAlcqVlfifyEgE3r6txK+99hp+ffCg4EPxa0/NAmdK2rcPsb166XQGLlO6NBo2agQ3V1f1QgFnkgHcuHFD6Io8FsB5ScJnn32GiRMnQho3zm6fJ5Us49ChQ0LH2+BBgxCsuXch/jwVxa89nkkDz6TGM6nxTGo801M8k1phONP+/ZL+B4zjYolNiPJ14IBxz3/40PhrDL3Pw4difPZswddpf5OIjeWZcu/LM6nxTGo8kxrP9PS+PJMaz6TGM6nxTE/vyzOp8UxqPJMaz/T0vjyTGs+kxjOp8UxP71uEzhQfH4+qSUlwLkJnUu7LM6nxTGo8kxrP9PS+RehM165dQzkAyi7GjPHN5z4u/+5Z7t9Y6eRp4plcjx6FMBhZpQLy60ZXxD5Pyn0L8ZkehIdDWHn0qOA9DDiTBODrnTvRv39/pKSkqG+d/64Ann7t5eTk4Pr163gGQD0A0Ciu1GGBz9OO9evRW6PAEwAQGan+71/1AHh6eiI1NRUA8Ntvvxm2Ob/21Mw80+XLlzHnzTfxs1aBJwDgyRPg6NG8L9a6jwTA59//cpUDIMsyJk+ejIsXL2JdXBycDh3KPykrfZ6cAGi3Mxpx7Ji4UEg/T8p9i9DXnnJfnkmNZ1LjmdR4pqf35ZnUeCa1wn4mI7DIk6zPkE6emsqVK/gaU+6Tu3d+99bH11c35pl4Jk08k/5768Mz6d+XZ3qKZ9J/b314Jv378kxP8Uz6760Pz6R/X57pKZ5J/7314Zn078szPcUz6b+3PjyT/n2L0JmGDh2K77//Hl0qVMCuoCC4u7sXfD9NhfBMwn2LyOdJuC/PpMYz6b+3PjyT/n2L2Jmio6PxEMD+f+O2bdvCJbc7ZkEKuM+58+fx8N/uhPfu3UNiYiIqmnCm00lJ0FOKhapVqsDf318dFPHPk3LfQnymWACaQ8fr1K6N6tWr57+vgWfq0aMHbt68iUuXLiEuLg7Xp05FekYGMtLTkZ6ejvSMDOTk5AjXPNTa46zGx56enmjerNnTTrB5nMnYz9Pjx4/x9fr1KK/xcMOGDVGxQgWdyyZ17oxZs2Yp8alSpdC8eXNI+d3LAb/2VCoVwsPDkfxvgW6dOnVQvVo1ce/87q2PGWcKDQ1F79694fvokfJ9DwB8fHzg5+eX/68/kO99UlJTcfHCBTzUKPL98ccf0aNyZfRq1Qrubm5572ulz9OtW7cQGRUlrEVFRSEpKQkVcr8u+X1P/7314Zn078szPcUz6b+3PjyT/n15pqd4Jv331seeZzICx7WT1Sjj2vk1RkREREREREREpIiIiED9+vWVeOTIkfjmm2/smBEREZmqVq1aiI2NBQB4e3vj7t27Ftt77NixWLp0qRIfPHgQ7dq1M2qPxMRENGjQQG9e1apVQ3x8PCSpwLIssoHp06fjk08+UeK9e/eiW7duNrv/w4cPcf36dcTFxakLQbU+vnXrlvD8gQMHIjg42KJfPzNnzsTcuXOVuF27djhw4IDee6SkpCAgIAAJCQnKWnBwMAYNGmSxfAqDBQsWYPLkyUrs7u6Os2fPol69ejbPZd26dXjnnXeQnZ2trDk5OWH58uV49913LXKP+/fvo1+/fti3b5+wXq1aNWzbtg3Nmze3yH0M9f777+v9OX3nzp3o0aOHTXMhIiIih2WRH5idLLEJERERERERERERERnm1KlTQvzXX3/ZKRMiIjJHZmYmrl+/rsS1a9e26P6abwgANEa2G2HUqFF5Fp4mJCTg3LlzJuVGlvfPP/8Isa2L+MqVK4fGjRvjlVdewahRo7Bw4UJs2rQJx48fx82bN7F582bh+T/88AO+//57i93/9u3bWLRokbC2YMGCPItIS5YsiU8//VRYmzp1qjKSvii4ffs25s2bJ6xlZGRg6NChOp1XrUmWZcyePRuDBw8WCjxLlSqFnTt3WqzAEwDKly+PPXv2YNSoUcJ6QkIC2rVrh+3bt1vsXoY4ffq03vWj+Y2kJyIiIrICFnkSERERERERERER2dD58+eF+MqVK4iPj7dTNkREZKq4uDioVCol9vPzs+j+5hZ5btmyBZs2bVJiZ2dnDBw4UHjOrl27TE/QCAkJCXj11VfRpUuXPIumijvNIk8PDw/4+PjYMRtdffr0wQcffCCsvf/++7hw4YJF9v/444+RmpqqxL169UKrVq3yveZ///sfmjVrpsQ3b97E559/bpF8CoNp06YhOTlZZ/3YsWNYsmSJTXKQZRnvvfcePv74Y2G9atWqOHToELp3727xe7q6uuLrr7/GypUr4eLioqynpaVhyJAhePjwocXvqU92dnaehfDHjh2zSQ5EREREuVjkSURERERERERERGRD2kWeABAaGmqHTIiIyBzR0dFCbOlOnoGBgUJsTJHnvXv3MHLkSGFt0qRJmDVrlrC2c+dO0xM0wpAhQ7Bjxw6Ehobq5EXqYrIrV64osb+/P5ycCt/LuJ9//jmee+45JU5PT0ffvn3x5MkTs/aNiorC6tWrldjZ2RmfffZZgdc5OTnhyy+/FNYWLlwojHB3VOHh4fl2Sp0+fToiIyOtnsfatWuxcuVKYa1x48YICwtDkyZNrHrvESNG4M8//0SFChWUtfv37+PIkSNWvW+uiIgIpKen633sxIkTQldTIiIiImsrfH87ICIiIiIiIiIiIirC9HUEYpEnEZHjuXbtmhBbusizQoUKeOaZZ5TYmCLPUaNG4d69e0rcsGFDzJw5E35+fkLxaFhYWJ7j3C0lOjoaf/75pxKfOnUKGRkZVr2no4mNjUVmZqYS23pUu6Hc3NwQEhKCcuXKKWuRkZEYMWIEZFk2ed9p06YJ48fffvttBAQEGHRthw4d0KtXLyVOS0vDtGnTTM6lMJBlGWPHjhV+TUePHo0XX3xRidPT060+tv3ixYsYPXq0sNatWzccOnQI1atXt9p9NXXs2BHLly8X1k6cOGGTe2t3HdYsvE5JScHFixdtkgcRERERwCJPIiIiIiIiIiIiIpu5c+cO7ty5o7O+b98+s4ojiIjI9qxd5AmII9tv3ryJR48eFXjNL7/8gpCQECV2dnZGcHAw3N3dAQA9e/ZUHpNlGXv27LFgxrp++OEHIZZlGdevX7fqPR2N5qh2oPAWeQKAr68v1q1bJ6xt3LgRq1atMmm/sLAwbN68WYk9PT0xe/Zso/ZYsGABXF1dlXjdunU6BXqOZPPmzTh06JASly9fHrNnz8aaNWtQunRpZf3o0aP46quvrJJDSkoK+vXrh7S0NGWtZcuW2L59O8qUKWOVe+YlKChIiG1V5BkeHi7Er776qhBzZDsRERHZEos8iYiIiIiIiIiIiGzkwoULetcTEhKEMa1ERFT4aY9r9/Pzs/g9NIs8AfX44PzcvXsX7733nrA2efJkNG/eXIl79OghPL5r1y4zs8ybSqXSKQgEgJiYGKvd0xE5UpEnoC52+/DDD4W1MWPG4MyZM0btI8syJk6cKKyNGzcOVapUMWqfunXrYtSoUcLahx9+6JBvoElLS8OECROEtTlz5qB8+fKoUaMGvvjiC+GxqVOnWuVnyPfff1/4flO2bFn8/PPPcHNzs/i9ClKjRg2hq/HJkydt8rnVLhTW/t569OhRq+dARERElItFnkREREREREREREQ2om9Ue659+/bZMBMiIjKXZidPDw8PowvTDKFd5FnQyPb3338fiYmJStyoUSPMmDFDeE7r1q2Fcdu///67MCrckg4ePIjY2FiddX1rxZmjFXkCwGeffYZWrVopcUZGBvr27WtQt9lcu3fvxsGDB5W4QoUKOkWfhpoxYwbKly+vxAcOHMC2bdtM2sueFi9ejLi4OCWuX78+RowYocTDhg3DCy+8oMTp6ekYMmSIRce2r1u3Tqc4+/vvv0etWrUsdg9jSJKEli1bKnFSUpLVC8VzcnJw9uxZJa5duzY6dOiAEiVKKGvs5ElERES2xCJPIiIiIiIiIiIiIhs5f/58no+FhobaMBMiIjKHLMtCJ08/Pz9IkmTx+xhT5BkSEiKMvdYe057L1dUVL730khI/fvwYhw8ftlDGouDgYL3r7OQpioyMFGJ/f387ZWI4V1dXbNq0CRUqVFDWrl27hnfeecegLos5OTmYPHmysDZjxgyTR4F7eXlh1qxZwtqECROsVsBsDTdv3sSnn34qrC1evBguLi5KLEkSVq9ejVKlSilrR44cwbJlyyySQ0REhE7HytGjR6NXr14W2d9UmkWegPVHtkdGRiI1NVWJmzdvDjc3N6Er8rVr13D37l2r5kFERESUi0WeRERERERERERERDaiWeTp4uIiFDL8/fffUKlU9kiLiIiMdOfOHaSkpCixNUa1A0BgYKAQ51XkeefOHZ3CrKlTp6JZs2Z6n689sn3nzp1mZKlfcnKyUHSqiZ08RZqdPGvWrAlPT087ZmM4Hx8frF+/XljbvHmzQQWH69evx8WLF5W4Vq1aePfdd83KZ+TIkUKB7NWrV/HNN9+YtactTZ06Vfi+0rNnT3Tt2lXneb6+vvj888+FtSlTpuDq1atm3T81NRX9+vXTKW7Uvpc92LrIU3tUe+730tatWwvrx48ft2oeRERERLlY5ElERERERERERERkA1lZWUJxTmBgIDp16qTESUlJ+Xb6JCKiwkOziyegHuVrDZUqVYKXl5cS6yvylGUZ7733HpKSkpS1xo0bY/r06Xnu+9JLL8HJ6enLhLt27bJQxk9t3rxZKFjTxE6eTyUmJiIxMVGJHWFUu6bu3btjypQpwtqHH36IkydP5nlNWloaZsyYIazNmzdPp+ussVxdXXUKEufMmYP79++bta8tnDp1ShiR7uLigi+++CLP5w8fPhydO3dW4rS0NAwdOtSsNwyNGTNGKLwtU6YMNm3aZPbnxRJatGghxNYu8gwPDxfi3A6erVq1EtY5sp2IiIhshUWeRERERERERERERDYQGRkpjAxt3Lix8OI8AOzbt8/WaRERkQmuXbsmxNYq8pQkSRjZHhcXp1M4uWnTJmzdulWJXVxcsG7dOri5ueW5b8WKFYVipaioKERFRVkwc91R7ZpFpezk+ZT2qHZHK/IE1IWU7du3V+KsrCz069cPDx480Pv85cuX48aNG0rctGlTDBgwwCK5vPLKK8LPVw8ePMCcOXMssre1yLKMsWPHCmujR49GQEBAntc4OTlhzZo1KFmypLJ26NAhLF++3KQcNmzYgDVr1ghra9assdr3NmNVqFBByCU8PBzZ2dlWu592J8+mTZsC0C3yPHr0qNVyICL7un//PoKDg3WKvomI7IVFnkREREREREREREQ2oN2l89lnn0WXLl2ENRZ5EhE5Bu0iT2uNawcgFHkC4mjv27dv4/333xcenzZtGpo0aVLgvj179hRiS3bzjI6OxoEDB5S4Zs2aaNu2rRLfuXMHaWlpFrufI9P8fAKOWeTp4uKCjRs3wtvbW1mLjY3FkCFDIMuy8NwHDx7g008/FdYWLFggFAGbQ5IkLFq0CJIkKWvLly+3eBGzJW3atAlHjhxR4goVKmDmzJkFXlerVi0sXLhQWJs8ebLO96eCREVFYcSIEcLayJEj0bdvX6P2sTbNke1paWm4dOmSVe6jUqlw5swZJfb19UWFChUAAJUrV4avr6/y2MmTJ5GVlWWVPIjIfjIyMtC2bVsMGTIELVu2xO7du+2dEhERizyJiIiIiIiIiIiIbOHcuXNC3LhxY9SvXx+VKlVS1g4ePMgXiomIHICtxrUDukWeuSPbZVnGyJEjhVHUzz77LKZOnWrQvj169BBiSxZ5/vDDD0I8aNAgnUJYdvNUKwpFngBQtWpVbNiwQSiu3LZtGxYvXiw8b/78+UKHzxdeeAEvvviiRXNp0qQJhgwZosTZ2dmYOHGiRe9hKWlpaTq5zZ07F+XKlTPo+nfffRcdO3ZU4tTUVLz99tsGj21PT09Hv379kJycrKw1adIEX375pUHX25JmkSdgvZHtV65cEX49cke159Ls5pmWlqbzRi4icnx//vknIiIiAAA5OTkYOXIkUlNT7ZwVERV3LPIkIiIiIiIiIiIisgHtF4AbN24MSZKEkaIpKSlWe8GaiIgsR7NTniRJQmc3SwsMDBTi3CLPjRs34rffflPWDRnTrqlhw4aoUaOGEh84cACPHz82O1+VSoV169YJawMHDtT5NWKRp1pRKfIE1AWbM2bMENYmTZqEY8eOAQDi4+OxdOlS4fEFCxZYJZe5c+cKo8y3bduGv//+2yr3MscXX3yB+Ph4JW7YsCGGDRtm8PVOTk747rvv4OnpqawdOHAAK1asMOj6cePGCW9EKlWqFEJCQlCiRAmDc7AVWxV5ao9qb9asmRC3bt1aiHO/vomo6NB+48v169d1ulATEdkaizyJiIiIiIiIiIiIbECzyNPb2xuVK1cGAKHIEwBCQ0NtmhcRERlPs8jTx8cH7u7uVruXvk6et27dwqhRo4T1GTNm4NlnnzV4X0mShJHt2dnZ+OOPP8xLFuqu1JoFnO3bt0ft2rVRq1Yt4XkxMTFm36so0CzyLFu2rNDh2xHNnDlT+NkmOzsb/fv3R1JSEmbNmoWMjAzlsTfeeEOngM5SqlatikmTJglr48ePR05OjlXuZ4qEhATMnz9fWFuyZAlcXFyM2sfPz0+nWHbSpEk6HYe1hYSEYOXKlcLaqlWrULduXaPubytNmzaFs7OzEluryDM8PFyI8+vkCbDIk6iokWUZO3fu1Fn//PPPceXKFTtkRESkxiJPIiIiIiIiIiIiIitLTEzEzZs3lTi3iycAdOnSRXjuvn37bJobEREZJyUlBXfu3FFi7THklla9enWUKlVKiS9fvox3331XGHndtGlTTJkyxei9tUe26ytqMFZwcLAQDx48GADYyVOPjIwMoRAvICBAGHfuiJydnbFhwwblzSyAuoNnz549hQ6vrq6umDdvnlVz+fDDD1GtWjUlPnv2LNavX2/Vexpj8uTJwvjfV199VefnQkO99957aN++vRKnpKTkO7b92rVreOedd4S1YcOG4Y033jDp/rbg4eGBRo0aKfHFixeRkpJi8fsU1MmzcePG8PDwUOKjR49aPAcisp8LFy7gxo0bOuuZmZkYM2YMZFm2Q1ZERCzyJCIiIiIiIiIiIrI6faPac9WqVUsofDl27Jjwgj8RERUu2t3xateubdX7SZIkdPO8evUqtm/frsSurq6rxGNVAAAgAElEQVQIDg6Gq6ur0Xt36tRJKFbavXt3nkVhhkhOTsbmzZuV2NPTE6+//joAsJOnHlevXhV+vR15VLumSpUqYePGjXByevpS9PHjx4Wzjhw50uoF0p6envjss8+EtalTp+L27dtWva8hwsLC8OOPPyqxq6srvvjiC5P3c3Jywtq1a4Xfz/v378e3336r89yMjAz069cPT548UdYaNWqEpUuXmnx/W9Ec2a5SqXS6bppLe08fHx94e3sLz3F1dcVzzz2nxLGxsYXia4qILEN7VLumPXv2YMeOHTbMhojoKRZ5EhEREREREREREVmZdpGn9jhdza5NmZmZOHLkiE3yIiIi42mOagesX+QJ6I5s1zRz5kzhzQPG8PDwwAsvvKDE9+7dw8mTJ03aCwC2bNkidNZ7/fXXUbp0aQDq8dmahajs5CmOageKTpEnAHTs2BFz5szR+1jp0qUxffp0m+Tx5ptvCuO2b926haZNm+LAgQM2ub8+sixj7NixwtqYMWPMHpNeu3ZtnfHvEyZM0Pm9NmHCBKGQsWTJkggJCREKRAsrzSJPAGZ9v9InOjoajx8/VmLtUe25OLKdqOjS7mqu3fV4zJgxSEtLs2VKREQAWORJRERERETk8FJSUvDVV19h8eLF/AcmIiKiQurcuXNCrF2M07lzZyEODQ21ek5ERGQa7SJPa3cjBIDAwEC9682aNcOkSZPM2rtnz55CbM7Idu1R7YMGDVI+dnZ2Ro0aNZSYnTyLdpEnAEyZMgXdunXTWZ84caJOd0RrcXJywuLFi4W127dvo3PnzliwYIFZnWtNtWHDBhw/flyJvb29LVb0OmrUKLRr106Jc8e2544X3rp1K77++mvhmhUrVjjM1552keeJEycsun9Bo9pztW7dWohZ5ElUNCQmJgrfn/39/bF8+XL4+/sra7GxsViwYIE90iOiYo5FnkRERERERA7uo48+wpgxYzB+/HgMGDDA3ukQERGRHpqdPJ2dnXWKdbSLPPft22eTvIiIyHi2HtcO6O/kac6Ydk0vv/yyEJta5BkTE4P9+/crcY0aNdCxY0fhOb6+vsrHSUlJwrjo4igyMlKIHaXQzlBOTk5Yv349qlWrpqxVrlwZ48aNs2ke7dq1w8qVK+Hi4qKsqVQqTJ48Gb169cKDBw9slktKSopOYfa8efNQtmxZi+zv5OSE7777TujKGRoailWrViEmJgZDhw4Vnj9kyBC89dZbFrm3LdSvXx8lS5ZUYksXeWqPf8+rk2dQUJAQs8iTqGjYu3evUPzfo0cPuLm56RTHz58/X+fnQSIia2ORJxERERERkQPLycnBTz/9pMTbt2/H77//bseMiIiISFt2djYuXbqkxPXq1UOJEiWE51SuXBkNGjRQ4tOnT+Phw4c2y5GIiAxXWMa1z549G40aNTJ77+rVq6NJkyZKfPbsWSQkJBi9zw8//CDEgwYNgpOT+FJkrVq1hNiRRranpKQgOzvbontqdvJ0dna2ydeSrXl7e+P3339H+/bt0bJlS+zevVso0rOVESNG4ODBg6hevbqwvn37djRv3lynuM9aPv/8c+H3V+PGjfH2229b9B5169bFp59+Kqx99NFH6N27Nx49eqSs1a9fX6dwqbBzdnYWCi9jYmJw7949i+1vaCfPZ555Rvj9evLkSWRmZlosDyKyj127dglxbrfzrl27onfv3sp6RkYGxo4da9PciIhY5ElEREREROTALly4oNP55KOPPkJOTo6dMiIiIiJtV65cQUZGhhJrj2rPpdnNU6VS4cCBA1bPjYiIjKdZ5Onl5QUvLy+r39PX11fohtiiRQtMnDjRYvtrj2zXLnIoiEqlwrp164Q1zVHtuTQ7eQKOU+Q5e/ZslC5dGjVq1BDeuGEOWZaFIs/atWvDzc3NInsXNg0aNMCBAwcQFhaGpk2b2i2PVq1a4cyZM+jatauwHhMTg9atW2PVqlXKWHNriI+Px8KFC4W1JUuWwNnZ2eL3Gj16NNq0aaPEycnJOHv2rBJ7eHggJCTELgW35tIe2X7y5EmL7CvLslDsW7VqVVSuXDnP57dq1Ur5OCMjQ/j1JSLHk52djb179ypx6dKl0bZtWyVevHix0CV5x44dRv+8RERkDhZ5EhERERERObAjR47orF28eBHBwcG2T4aIiIj0OnfunBDnVeTZpUsXIebIdiLStnv3bsyePRsRERH2TqXYysnJEQoT/fz8bHJfJycnBAcHo2nTpujWrRt+/fVXYfS0uXr06CHExo5sP3ToEGJiYpS4Xbt2ertSanfy1LymsNq7dy8+/vhjyLKMW7duYcyYMRbZ99atW8KbNovaqPbCqmLFisr3UkmSlPWMjAyMGDECgwYNQkpKilXuPXnyZKSlpSlxr1690KlTJ6vcy9nZGWvXrtXpHp9r2bJlQhd5R/Lcc88JsaVGtsfGxuLBgwdKnNeo9lyaRZ4AR7YTObqjR48K0zS6desmvPmiRo0amDZtmnDNBx98gPT0dJvlSLYVFhaGzZs3C12wieyJRZ5EREREREQOTF+RJwDMmDEDycnJNs6GiIiI9Dl//rwQP/vss3qf16FDB2GsbWhoqFXzIiLHsnv3bvTo0QMff/wxOnbsiMTERHunVCzFx8cLI7ttOV77hRdeQHh4OPbu3aszctpczz33HLy9vZV43759QjFaQbTfaDh48GC9z3O0Tp6PHz/G8OHDhbV9+/bpvIHDFJpdPAEWedqSs7MzZs2ahb1796JChQrCY+vXr0dQUBAiIyMtes9jx45hw4YNSuzm5obPP//covfQ5u/vj08++URn/X//+x+GDBli1Xtbk7U6eRo6qj1X69athZhFnkSOTbsrp/YbYAD1BK06deoocXR0tNW/l5N9hISEICgoCH379kXdunWxevVqTk8ju2ORJxERWUVaWhr++OMPJCQk2DsVIiKiIi2vIs9bt25h0aJFNs6GiIiI9NEu8syrk2e5cuWEjkGXLl3C7du3rZobETmO+fPnKx/fvXsXa9eutWM2xZfmqHbAtkWe1uTs7IyXX35ZiVNTU7F//36Drk1OTsYvv/yixJ6enujbt6/e5zpakeekSZMQHx+vs7548WKz92aRp/117doVZ86cQVBQkLB+8eJFtGjRAiEhIRa5j0ql0ukAO3bsWJt8/xgzZowwtj0gIAArVqwQupg6mpo1awpF6SdOnIAsy2bvqzmqHSi4k2fDhg2Fcfcs8iRybNpdzLt3767zHHd3d3z11VfC2qefflrof54h4y1cuFD5+N69exg+fDhatGiBgwcP2jErKu5Y5ElERBb38OFDBAUFoVu3bqhTpw4uX75s75SIiIiKpPj4eFy/fl2Jq1WrJjy+cOFC3Lp1y9ZpERERkRbNbl/ly5dH1apV83xu586dhfjvv/+2Wl5E5DguX76MQ4cOCWurVq2CSqWyU0bFl3aRp63GtdtCz549hdjQke1btmwRxlv36dMHpUuX1vvcypUrw93dXYkL87j2/fv3Y+XKlXof27Bhg9l/32aRZ+Hg4+ODAwcO6BRhJicno3///hgzZgwyMzMN3i87OxuRkZHYunUr5s6diwEDBqBhw4ZCt8lKlSrpjPy1FmdnZ+zatQvjx4/HqFGjcPDgQZQqVcom97YWSZKEbp6JiYkWKbAytpOni4uLkMf169fZ+EQPWZZx48YNixTiEllLTEyM8Hp2y5YtUalSJb3P7d69O/7zn/8ocXp6OsaNG2f1HMl24uLidP5MAICzZ8+iQ4cO6N+/P+Li4uyQGRV3LPIkIiKLUqlUGDhwoNKlJD09HcuWLbNzVkREREWTdhfPIUOGoFevXkqcmpqKmTNn2jotIiIi0nD//n3cuHFDiZ999tl8Oyd16dJFiPft22e13IjIcaxatUpn7dq1a/weYQfR0dFCXFQ6eQLAiy++CBcXFyXeuXOnQUU5ho5qBwAnJyfUrFlTiQtr56uUlBS8/fbbwlrZsmWVj7OysrB8+XKz7qFd5BkQEGDWfmQ6Nzc3LFmyBCEhIToFkF999RXat28vvMkWAHJychAVFYVff/0V8+bNwxtvvIHGjRujZMmSqFevHvr06YOZM2di06ZNiIiIEK795JNPUKZMGaufK1fZsmWxaNEifP3113jmmWdsdl9r0h7ZfuLECbP2k2VZ6ORZqVKlfN+YlatVq1ZCzG6eoszMTLRq1Qo+Pj5o27YtsrKy7J0SkV6GjGrXtGTJEpQoUUKJf/vtN+zdu9cquZHt/fbbb/k+HhISgnr16mHmzJnCG52IrI1FnkREZFGfffYZduzYIayFhobaKRsiIqKiTbvIs02bNpg/f77wotzatWtx4cIFW6dGRERE/9L+czivUe252rRpAzc3NyXm36mJKC0tDevWrdP7WF5dBkm/Y8eOYdSoUVi1ahXS09NN2qOojmsH1IVg7du3V+Lr16/j0qVL+V4TExMjjHWvUaMGOnbsmO81tWrVUj5++PAhHj58aFK+1jRjxgyhoLdGjRo6RdUrVqxAamqqyffQLPJ85plnUL58eZP3Isvo27cvTp06hYYNGwrrYWFhaNasGaZPn44333wTTZo0QcmSJREQEIDevXtjxowZ+Pnnn3HhwoUCu342b94830JoMoylizzj4+ORmJioxM2aNTNopD2LPPP3008/ISwsDABw9OhR/Prrr3bOiEg/7SJP7e7m2nx9fTFlyhRhbfTo0cjIyLB4bmR7W7duFeJZs2ahSpUqwlp6ejrmzp2LgIAAbNiwgd2KySZY5ElERBbzxx9/YMaMGTrrkZGRHFFBRERkBZpFnpIkoVWrVvD398fIkSOVdZVKhYkTJ9ojPSIiIoI4qh0ouMjT09NTeLE4JiamUI+yJSLr27x5c55FcNu2bcPNmzdtnJFjunXrFl588UUsX74cI0aMQJ06dbBixQqjxjADYpGnq6srqlWrZulU7crYke0//PCDEA8aNAhOTvm//Ojr6yvEha2b5/Hjx7FkyRJhbdWqVWjevLnw63P//n2d8xsqOTkZ8fHxSswunoVHQEAAwsLCMHDgQGE9KSkJn3zyCTZs2IBz584ZVcjj7u6OZ599FsOHD8e2bdvg7Oxs6bSLnRYtWgixuUWe2mN5mzdvbtB1QUFBQswiT9GhQ4eEWPvvRkSFQUpKCv7++28lrlKlCpo2bVrgdRMnToSfn58SX716FYsWLbJKjmQ7d+7cEb53+fn5YdasWYiKisK0adPg7u4uPD8hIQFvvvkm2rZti5MnT9o6XSpmWORJREQWERcXh//+9795vktF84djIiIiMt+TJ0+Efxht1KiRMjpu5syZwtivvXv34o8//rB5jkRERAScP39eiJ999tkCr+ncubMQs5snUfH27bffCrHmi845OTn47rvvbJ2SQ9q5c6cwTjEhIQHvvfce/P39sXbtWmRnZxe4hyzLQpFnrVq1ilyxlvZ40vyKPFUqlU6XWe3COH00O3kCKFRvZkhPT8fQoUOFf+cePHgwunXrBgAYP3688PzFixdDpVIZfZ+oqCghrlevngnZkrV4enoiODgYq1at0inmyI+bmxsaNWqEAQMGYO7cudi6dSsiIyORnJyMs2fP4ttvvy1yheH2UrFiRaG4Kjw83KDv43nRHNUOGF7kWbFiRfj7+yvx6dOn2clPw9GjR4U4IiLCTpkQ5W3fvn3C79sePXoY1Mm3RIkSWLp0qbA2b948XL9+3eI5ku1s375d+Dmwd+/ekCQJpUqVwrx58xAREYE+ffroXHf06FG0bNkSQ4YMwa1bt2yZMhUjLPIkIiKzpaen4/XXX0dSUpKyVqpUKeE52qNsiIiIyDxhYWHCC0lt2rRRPq5YsSKmTZsmPH/ChAnIycmxWX5ERESkplnk6eTkhPr16xd4TZcuXYSYf6cmKr4uXbokdPD39fXF+vXrheesWrXKrMKW4kK7m1iuuLg4vP322wgMDMSPP/6Y79+b7t+/j8ePHytxURrVnsvf3x9169ZV4mPHjgn/7qvp0KFDQoFm27ZtUadOnQLvUZg7ec6dO1coQqpcuTK+/PJLJe7YsSOaNGmixFFRUdi9e7fR99Ec1Q6wyLMwkiQJw4YNw9GjR3UKk11dXdGwYUP0798fc+bMwZYtW/DPP/8gJSUF58+fx8aNGzF9+nT06tUL/v7+cHFxsdMpijbNke2pqam4fPmyyXtpd/Js1qyZwddqduHPzMzUKRgtrhITExEZGSmssciTCiPtUe3ab3jJT8+ePYUu32lpaTpvCCHH8uuvvwpxr169hLhWrVrYvHkzQkND9U5qCQ4Ohr+/PxYsWMCif7I4FnkSEZHZPvjgA5w6dUqJS5QogT///FN4h2toaGieXT6JiIjIeIcPHxZizSJPQP3nc40aNZT4/PnzJo+RIyIiItPk5OTg4sWLSuzv7w8PD48Cr2vZsiVKliypxPw7NVHxpd3Fc9iwYWjQoAE6dOigrN24cQN79uyxdWoOJ68iz1xXr17FW2+9hUaNGiEkJERvd0bNLp5A0SzyBMSR7SqVCnv37tX7vODgYCEePHiwQfsX1k6e4eHhWLBggbC2YsUKeHl5KbEkSTrFG5pFoIZikafjaNasGc6fP4+ffvoJmzdvRkREBFJSUnDhwgX8/PPPmDFjBnr37o2AgAAWc9qYZpEnYPrIdlmWhSLPihUrwsfHx+DrNYs8AY5sz3X8+HGdtatXryIrK8sO2RDpJ8uyUOTp5uaGF154wag9li5dKrwmvmXLFvz5558Wy5Fs59GjR/jrr7+UuHLlyggKCtL73E6dOiE8PBwrV65EhQoVhMeSk5MxefJkNGjQANu2bbNqzlS8sMiTiIjMsnbtWqxevVpYW7lyJYKCgoRik+vXryM6OtrW6RERERVZmt18AHXHFE0lSpTAZ599JqxNnz5dGE9IRERE1nX16lWkpaUpsSGj2gF1d6j27dsr8Z07d8zqTEREjik1NVV4o5aLiwuGDh0KAHj33XeF52oXg5IoPj5e6BbZsWNH7NixA02bNtV5bkREBPr374+mTZti27ZtQpG99r9vao4KLkoMGdmenJyMX375RYk9PDzQt29fg/YvjJ08s7KyMHToUKGTa//+/fHaa6/pPLd///6oUqWKEv/99984c+aMUffT7m7HIs/CrVSpUvjvf/+LPn36oF69enB1dbV3SgTLFXnevHkTd+/eVeJmzZoZNKo5F4s89dMe1Q4A2dnZuHr1qh2yIdLv3LlzSEhIUOKOHTvqTKssiJ+fHyZNmiSsjR49GpmZmRbJkWxn165dQiF6r1694OSUd1mds7MzRowYgStXrmDs2LE6b/a4du0aXnvtNSxbtsxqOVPxwiJPIiIyWXh4ON577z1hbcSIERg0aBAAoHPnzsJjoaGhNsuNiIioKMvOzhbeDV+tWjWha2euAQMGoEWLFkp88+ZNkzqMEBERkWk0R7UD0DvKKy8c2U5EISEhePTokRL/5z//QeXKlQGoX3D09vZWHtu9ezfi4uJsnqOj0O7i2b59e/Ts2ROnT5/Gli1b0LBhQ51rzp8/j9deew0tW7bEnj17IMtysenk2a5dO5QuXVqJ9+7di+zsbOE5W7duFd5E2KdPH5QpU8ag/b29veHp6anEhaGT54IFC3Du3DklrlixIr7++mu9z3Vzc8Po0aOFtcWLFxt1P81Onu7u7qhZs6ZR1xMR0LRpUzg7OyuxqUWe2qPamzdvbtT1DRo0EL5nsshTTV+RJ6DbyZjInrTfyGLMqHZNkydPFt7EEhkZafTPBmR/2qPae/fubdB1Xl5eWLx4MS5cuICXXnpJ53EWeZKlsMiTiIhMcv/+ffTp0wcZGRnK2nPPPYelS5cqMYs8iYiIrOPChQtITk5W4jZt2ujtMODk5IQvvvhCWFuwYAFu375t9RyJiIgIQrEIYFyRJ/9OTUSrVq0S4hEjRigfu7u7K109AfWoSe1pO/SUdpFnu3btAKhHb/fu3Rvnzp3Dxo0bERAQoHPtqVOn8PLLL6NNmzY6Y8uLapGnm5sbunXrpsQPHz7UKdYxdVQ7oP511yyEiI2NFTqm2tqlS5cwZ84cYe2rr74SCqm1jRgxQihU3bhxo9AJLD85OTmIiopSYn9/f6FQjYgM4+npKRTpX7x4EampqUbvEx4eLsTGFnk6Ozvj+eefV+KEhATEx8cbnUdRkpWVlWfRbUREhI2zIcqb5qh2wPQiTw8PDyxZskRYmzt3Lm7cuGFybmRbaWlp2L17txJ7eXmhQ4cORu1Rr1497NmzB7t27RK6vkdGRuL+/fsWy5WKLxZ5EhGR0VQqFd58801hjE7FihWxefNmuLu7K2stWrQQWtqHhoba9R/riIiIiorDhw8LcZs2bfJ8bocOHfCf//xHiVNSUjBr1iyr5UZERERPaXfyNHRce+5zy5cvr8T79+/X6aJGREXXhQsXhE5gfn5+Oh1+hw8fLsRr1qwRxgvSU5pFns7OzggKChIed3JywoABA3Dx4kUEBwfrHcN+7Ngxnb+L1apVyzoJFwI9e/YUYs1OV7Gxsfj777+V2MfHB506dTJqf80iz+TkZCQlJZmWqJlycnIwdOhQ4ffOq6++igEDBuR7Xfny5YXC1uzsbCxfvtyge16/fh3p6elKzFHtRKbTHNmek5ODM2fOGL2HdifPZs2aGb2H9sj2vLpYFhfnzp1DWlqa3sdY5EmFxb179xAWFqbE9erVM+sNPK+++iq6d++uxCkpKfjwww/NypFs548//hDeKPDKK6/A1dXVpL1efvll9O/fX1jT/FojMhWLPImIyGhz5swR3rXu5OSEjRs36oyJdXV1Fd7hcvfuXVy6dMlmeRIRERVVR44cEeL8ijwBdfdOza4ga9aswcWLF62SGxERET2lWeRZrlw5VK9e3eBrnZychIKZR48emfSiNRE5pm+//VaIhw0bBicn8SUdPz8/odvinTt3sG3bNpvk50iSkpKEf5Ns1qyZ8MZ0TS4uLhg0aBD++ecfrFq1Cj4+PnnuW6VKFaGTY1HTvXt3YWKEZpHnDz/8IDx30KBBOl+fBdEukNVsKGBLS5YsEbrNlS1bFitWrNA7LUPbmDFjhOetXLlSGGGfF+1RxSzyJDKdZpEnYNrIds1Onl5eXkIRuqG0izyL+8j2/IpcWeRJhcWePXuE5kSmdvHMJUkSli5dCjc3N2UtJCQE+/btM2tfsg1TR7XnhX8ukDWwyJOIiIyya9cufPzxx8LavHnz8MILL+h9PsfLERERWZ5mkWfJkiUL7AoWEBCAd999V4lVKhUmTpxotfyIiIhIPdo2Li5OiRs3bmxQwYgm7a59fHGIqHhISUnB+vXrldjFxQVDhgzR+1zNEe6AusiMRNrdN3NHtefH1dUVw4YNw5UrV7Bs2TJh3GKuOnXqWCzHwuiZZ54RiqciIiIQHR0NlUqlM6p90KBBRu+vXURljyLPK1euYPr06cLal19+iapVqxp0vb+/P1555RUlfvDggU4BrD4s8iSyHHOLPG/fvo2bN28qcbNmzYz+mR2ATofo4l7Mo13kqTkF8J9//rHp1L/FixejQ4cOWLZsmc3uSY5Be1S7dhdzU9StWxcfffSRsDZ69GhkZmaavTdZT1ZWFrZv367Enp6e6Nq1q1l7ssiTrIFFnkREZLDo6Gj873//E9ZeffVVTJo0Kc9rHLXI886dO5gxYwYWLlwotGYnIiKyt+vXr+PGjRtKHBQUBBcXlwKvmzVrFkqXLq3Ee/bswZ9//mmVHImIiEg9almTMaPacznq36mJyDybNm3C48ePlbhXr16oVKmS3uf27NlTKEjbt28foqKirJ6jI9Ec1Q4A7du3N/had3d3vP/++7h27RoWLVoEb29v5bF33nnHYjkWVtrFDrt27cLhw4cRExOjrLVt29akglftTp6ae9qCSqXCO++8I4xNf/HFF/MsqM7LuHHjhHjx4sVQqVT5XsMiTyLLqV+/vtBV2dgiT80ungDQvHlzk/Lw8vJCYGCgEp85c0b4/lLcaBZ5lixZEi+++KISp6SkCP+2aU2HDx/G+PHjcfDgQYwePRp//fWXTe5LhV9WVpYwtbJs2bIFTssy1NSpU4Vu8BEREfjqq68ssjdZx4EDB/DgwQMl7t69Ozw8PMza08fHB9WqVVPisLAw5OTkmLUnEYs8iYjIIGlpaejTpw8ePnyorNWpUwfr1q3LdxRP48aNUb58eSXev39/of8BRpZlvP7665g3bx4mTZqEadOm2TslIiIihbGj2nN5e3tj6tSpwtpHH31U6P9cJiIiclSao9oB9d+PjeXv7y+8KHD48GFkZGSYnRsRFW7ao9q1u3VqcnV11Sk2XLVqlVXyclTaRZ5t27Y1eg8PDw+MHz8eMTEx2LlzJ44dO4aBAwdaKsVCS3ts6c6dO3W6eA4ePNikve3dyXPlypU4ePCgEpcqVQqrV682uoNfhw4d0LRpUyW+cuWKTmcwbdpFnv7+/kbdk4iecnFxEQozo6OjkZiYaPD1p0+fFmJTizwBsWtbVlaWzt7FRXx8POLj45X4+eefR8OGDYXn2Gpk+x9//CHE7HhOuY4cOSK8qapbt25wdXW1yN4lS5bE4sWLhbWPP/4Yt27dssj+ZHmWHtWeS/PPhSdPnuDy5csW2ZeKLxZ5EhFRgWRZxsiRI3H27FllzcPDA1u3bkW5cuXyvdbJyQmdOnVS4kePHuHMmTNWy9USjh49KoxxCg4ORnZ2th0zIiIiekp71KAx7zAeM2aM8C7i8+fPC2MgiYiIyHLOnTsnxKYUeUqSJHTzTEtLw/Hjx83OjYgKr7NnzwpdyOrUqSP825o+77zzjvAm7ODg4GLdPUxTSkqK0KWtfv36qFChgsn7lSxZEj169NAZy1tUNWnSROgUu3//foSEhCixh4cH+vbta9Le9uzkGRcXpzOdav78+cV4M60AACAASURBVKhZs6bRe0mShPHjxwtrX375Zb7XaBZ5+vj4oFSpUkbfl4ieeu6554T45MmTBl+rXYjZrFkzk/PQHs2rPbK8uNAeSdy6dWuhyylguyJPzdc1AWD79u1GFQFT0bVz504h1n5ji7l69+4tdLBNTk7GlClTLHoPsgyVSiUUebq6ulrs64Ej28nSWORJREQFWrVqFdatWyesrV69Go0aNTLo+i5dugjxvn37LJabNXzzzTdC/PDhQ76IRkREhYZmJ08nJyejXlz08PDAp59+KqxNmzYNqampFsuPiIiI1DQ7eUqSpNO9xlCO9ndqIjKPdhfO4cOH5ztFB1AXiWmO1U5KSsKWLVuskp+jOX78uPDm7Xbt2tkxG8cjSZLwtZWZmYmUlBQl7t27N8qUKWPS3l5eXihdurQS26qTpyzLGD58OJKTk5W1du3aYeTIkSbv2a9fP51iWO0R0Lnu37+Pu3fvKjFHtROZr2XLlkJszMh2zd+rZcuWRe3atU3Og8U8atrFrfqKPLU7GluL9hvvsrKysGHDBpvcmwo3za7bkiShe/fuFt1fkiQsXboULi4uytq6deuK7feFwiwsLEzostqlSxeULVvWInvzzwWyNBZ5EhFRvk6cOIEPPvhAWBs1ahTefPNNg/fQ7DoCAKGhoRbJzRru3r2LX375RWd99+7ddsiGiIhI9PjxY1y4cEGJGzdubPQLav/973+FrgQ3b94ssMsIERERGUelUgl/ZtetWxeenp4m7eVIf6cmIvMkJyfjxx9/VGJXV1eDR2G/++67QsxxpGqa47gBFnmaQrPIU5upo9oBdfGDZjfP2NhYyLJs8n6GCg4OFsb3lihRAt99912BxdT5cXNzw+jRo4U17TGtuSIjI4U4ICDA5PsSkZp2kaehnTzv3bsnjBVv2rQpJEkyOY/AwEChMOjYsWM2+b5W2GgXeQYFBel8r7NFJ8/79+/j+vXrOuvff/+91e9Nhdu1a9eEQuPnn38e3t7eFr9PYGCgzmvso0ePhkqlsvi9yHRbt24VYkuNagfU3aHd3NyUmEWeZC4WeRIRUZ7u3buH119/HZmZmcpaq1atsGjRIqP28ff3F97JfOjQIWHPwuS7775DVlaWzrrmO7qIiIjs5fjx48I/Ahkzqj2Xk5MTvvjiC2Ft/vz5uH37ttn5ERERkdq1a9eETtmmjGrP5ePjg7p16ypxWFiY0H2MiIqOn3/+GU+ePFHi3r17G/yCc9euXeHr66vEhw8fxsWLFy2dosM5dOiQELdv395OmTiuzp07w93dXWfdx8cHnTp1Mmtvza/ZtLQ0ocOlNdy8eRPjxo0T1ubOnSv8OWuq4cOHC2/o+Pnnn5GQkKDzPO3udezkSWQ+X19fVKxYUYlPnDhhUHGldsfd5s2bm5WH9sSd27dvIy4uzqw9HU1qairOnDmjxPXr14eXlxfKlCmDatWqKev/Z+/M42O63j/+udn3CCFIiF0tQez7vlOtWotSfkVo0VK1k/pWq6po1fa19FtRtEqpPWhsUYlYI1Qi9i0hJJGErOf3R5rbe+5knbkzd5bn/Xrl9ZrnzJ1zniQzd+4953M+jyFEntLKClIuXbqkUcadsCzka75Kl2qXMn/+fHh5eYnx+fPnSWhsRDDGuFLtgiCgX79+ivVvb2/PGW7cuHEDz58/V6x/wvIgkSdBEASRL9nZ2Rg2bBi3i7FcuXLYsWMHt+OkOAiCwDmPvHr1CmFhYYrlqhTZ2dkFuhxcuXIFDx48MHBGBEEQBMEjLdUOaCfyBIBOnTrhzTffFOPU1FQEBgbqkhpBEARBEBLkC4q6iDwBvmR7VlaWhmiJIAjzYN26dVw8fvz4Yr/W2toa48aNK7Q/SyMjIwNnz54VY19fX1SqVEnFjEwTZ2dnDVdpABg5ciSsra116lvq5AkAt2/f1qm/wmCMYcKECUhKShLbmjdvriH61JbSpUtj9OjRYpyVlYUffvhB4zi5kyeJPAlCdwRB4Nw8nz59Wixx5fnz57lYKsTRFksvzRsREYGsrCwxbt26tfhYWrI9Pj5e70KnwoScJLKzbOQiz8Jcy3XF3d0dixcv5tpmzZqFxMREvY1JFJ/IyEjExsaKcdu2bTlRrhLIvxek9ycEUVJI5EkQBEHky/z583H06FExtra2xi+//MLttCsJplBe7sCBA/mWbsjj4MGDBsyGIAiCIDQ5ffo0F2sr8gSAr7/+mluQW79+Pa5du6Z1fwRBEARB/Itc5NmwYUOd+pPfUx87dkyn/giCMD4uXLiAiIgIMa5VqxY6duxYoj7GjBkDGxsbMd68eTNSU1OVStHkuHDhAl69eiXGVKpde/ITP4waNUrnfqVOnkBuyXZ9sWPHDvzxxx9ibGtri02bNuksVJUyZcoUrtTz2rVrNdy3ycmTIPSDvGR7eHh4ka+Rizx1dfIENMU88tLl5o5c1FqQyBPQv5tnYSLPLVu2ID09Xa/jE8ZJSkoKjh8/Lsbe3t46368XxciRIzWE6J9//rlexySKhz5Ltedh6eJ/QllI5EkQBEFocOLECXz55Zdc21dffVXiiWUpprAgtWbNGi6ePHkyF1PJdoIgCEJNsrKyOCfsSpUqoXLlylr3V6dOHc7pJycnB5999plOORIEQRAEkYvSTp7ycrjGeE9NEIRu/Pe//+XicePGcWKx4uDl5cUtTCYnJ2P79u2K5GeKnDx5kotJ5Kk98jKmbdq0UaTEuSGdPJcvX87F8+bNQ7169RQdo2bNmlyJz8TERPz000/cMVKRp4uLCypWrKhoDgRhqTRr1oyLiyPylJZrd3FxUeS81qJFC+7729LEPHJRa2EiT7noXWkuX74sPraxsUHPnj3F+Pnz59i7d69exyeMk6NHjyIjI0OM+/TpU+Jr7pJiZWWFlStXcm0rV64kwwUjQFqqHQD69++v+Bgk8iSUhESeBEEQBAdjDHPmzOHa3nnnHXz66ac69evr64vq1auL8dmzZ43KSSA2NhaHDh0S43LlymHx4sUoU6aM2Hb06FHa2UcQBEGoxuXLl7nvTl1cPPMIDAyEq6urGO/fv59EIwRBEAShANIFRTc3N/j6+urUn6enJ+cucunSJSQkJOjUJ0EQxsPLly/x888/i7GdnZ3WLokBAQFcvHbtWp1yM2VOnTrFxSTy1B5fX19MnDgRQK4D5pIlSxTp11BOnunp6ZxjX5UqVTBz5ky9jDV16lQuXrFiBbKzswEAmZmZXEnQN954Q+/CEoKwFEoq8kxISODOOf7+/rCy0l064e7ujrp164rx5cuXkZaWpnO/pgBjjBN5li5dGrVq1RJjuXOxPp08MzIyEBUVJcZ16tTRuEaiku2Wyb59+7hYvpFFXzRv3hyjR48W4+zsbEyePBmMMYOMT2hy8+ZNboNu48aNdZ67yQ8fHx+uUmp4eLh4bUgQJYVEngRBEATHsWPHEBoaKsYVKlTAjz/+qMhkk9TNMzMzkxtHbdatW8ddSH/wwQdwdHTkdvalpqZqTA4TBGF8WMqkGWF5yL83lRB5litXDjNmzODapk+fTpNLBEEQBKEDycnJnBNZgwYNFLmn7tKlCxeHhITo3CdBEMbBtm3buJLOAwcOhKenp1Z9dezYkRNUREREaJSjtQRycnK4eyhPT08qi60jq1atwvXr1xEfH885s+mCoUSeV69eRWZmphh36NABtra2ehmrXbt2aNy4sRjfvHlTFJTExsYiKytLfI7ekwShHGXLluXcgc+fP8993uRcvHiRi5Uo1Z6H9ByZlZWFiIgIxfo2Zm7evIlnz56JcevWrbn7IEOWa//777+5837Dhg3Ru3dvlCtXTmw7dOgQHj16pLccCOODMYYDBw6Isb29vcZ9tj756quv4ObmJsbHjh3TcJIkDIf8b6+PUu15SN08X758SS6uhNaQyJMgCIIQYYxhwYIFXNusWbO4C05dkJds//PPPxXpV1devXqFjRs3irGVlZVYvrZ3797csVSynSCMF8YYhg8fDldXV3Ts2JGcdwmz4/Tp01yshMgTAD755BNuJ+nFixdx9uxZRfo2JEePHsXEiRPxxx9/qJ0KQRAEYeFcvXqVi3Ut1Z6Hsd5TE4RamNPGpHXr1nFx3ryUNgiCgPHjxxfavyUQFRWFFy9eiHG7du3IMVEB3njjDZQqVUqx/tzd3eHh4SHG+irXLhdYNW3aVC/jALmfQbmb57JlywBoliYmkSdBKEvz5s3Fx2lpaYWKCOUbIKTibF2x1NK8hZVqBwAvLy/uO0SfIs9Lly5xcaNGjWBra4sRI0aIbTk5OQgKCtJbDoTxcfHiRTx+/FiMO3XqBGdnZ4ON7+XlpbEOP3XqVLx69cpgORD/opbIE7Cc7wVCeUjkSRAEQYgcPXqUuwmrUKECxo4dq1j/nTp14mJjWZDasWMHnj9/LsZ9+/YV7dh79OjBleiQ7vAiCMK4OHToELZu3YqcnBycOHGCE28ThKnDGONcaFxdXeHn56dI305OTpg1axbXtnPnTkX6NhQrV65Et27dsGbNGrz11ltYs2aN2ikRBEEQFoy0VDugnMizffv2sLa2FuNjx44p0i9BmBoPHjxAv3794OHhoeFKb4pERETgwoULYvzGG2+gffv2OvU5atQo2Nvbi/HWrVuRlJSkU5+mBpVqNx2kbp53795FTk6O4mPIRZ5KOvblx6BBg7jNlCdPnkRERASJPAlCz0hFnkDhJdvlIk8lzwtyMY9c/GiuFCXyFASBc/O8c+eO3sRt+Yk8AXDlsoHcku3mtHGIKBy1SrVL+eijj7jv/7t37+Kbb74xeB6WzqNHjzihZe3atTXchpWERJ6EUpDIkyAIggCQKx4JDAzk2mbNmgUHBwfFxvDy8kL9+vXF+Pz580hMTFSsf21ZvXo1F0+cOFF8XKZMGbRs2VKMo6OjcfPmTYPlRhBE8QkODuZiYxGSE4QS3L17lysf1LJlS9jY2CjW/8CBA7lNDb/99pvJTHAuXrwYkydP5to+/PBD/PbbbyplRJQUxhju3r3LlfQiCIIwZa5cucLFDRs2VKRfV1dXbuE6OjoaDx48UKRvgjAVIiIi0Lx5c+zduxdJSUlYsmSJyZdAzc/FU1fHyTJlymDw4MFinJqaip9//lmnPk2NkydPcjGJPI0XaXnljIwMzmFLKaRiLmtra8W+mwvCzs4OkyZN4tqWL19OIk+C0DMlEXlKN1g4OTmhdu3aiuVRq1YtzqX4r7/+Mpl5Nl2Qijytra3RrFkzjWOk5z3GGKKjo/WSi3zjXd55v379+lxeN27cILGVBSGv1qiGyNPOzg7ff/891/bVV1/h7t27Bs/Fktm9ezcX69PFE8h1i7azsxNjOu8Q2kIiT4IgCAKApotnxYoVFXXxzENaXi7PbU9Nzp8/j7CwMDGuXr06unXrxh0jL9lObp4EYZzIRZ2nTp2yiMkzwjKQungCypVqz8PLy4tzC7p7966Go4GxwRjDvHnzNFxI854bPnw4jh8/bvjEiBIzbtw4VKlSBWXLlkXNmjUxatQorFu3DpGRkcjOzlY7PYIgiBIjF3lKNzvqSpcuXbiYNjYRlsRvv/2G9u3bawjA5NfKpkRycjK2bdsmxvb29hg1apQifQcEBHDx2rVrLeYemTHGOXm6uLiIDl6E8SF18gRynd2U5PXr14iMjBTjevXqwcnJSdEx8mPcuHHcOL/++isnPrayskKNGjX0ngdBWBL+/v6c831BIs/ExETExsaKcaNGjbjX6YqVlRVnHvL06VPcunVLsf6NkcTERERFRYmxv79/vudauVOePkq2M8Y4J09vb294enqKcX5unoT5ExcXx50T6taty200MSTdunXD22+/LcavX7/G9OnTVcnFUtm1axcX61vkaW9vj8aNG4vxjRs3uCqjBFFcSORJEARBgDGGBQsWcG1Ku3jmIRV5AuovSMnLuU6YMIFzMgM0d3KRyJMgjI+nT59qLKbHx8cjJiZGpYwIQln0LfIEct08pRizEyZjDNOmTcMXX3zBtUsdjzIyMvDWW29p7NwnjItbt25hw4YNYnzz5k1s3rwZAQEBaNCgAUqXLo0ePXogMDAQwcHBFldmlCAI0yMnJ4e7Lq1evTpcXFwU659EnoQlwhjDokWLMGjQoHxLepry9d7WrVuRmpoqxoMGDULp0qUV6btVq1bw8/MT48jISItxjLl9+zZXCaF169aKVkIglEUusLh9+7ai/V+5cgVZWVli3LRpU0X7LwgPDw+MGTNGjLOysrjfrVq1arC3tzdILgRhKTg7O6NevXpiHBkZibS0NI3jLl68yMVKlmrPQ16q3Ny/g8PCwrjNJPLfPw9DiDwfPnzIiafk7s1Dhw7lzr+//PILdz1GmCcHDx7k4r59+6qUSS7ffvst9z7csWMHQkJCVMzIcnj+/DlnDlGpUiW9fA/IkZdsP3v2rN7HJMwPEnkSBEEQOHLkCHeDWbFiRXzwwQd6GatDhw6ciFLNBanExERs3bpVjO3t7fH+++9rHNewYUNUrFhRjI8fP043fARhZBR08yt17iAIU+b06dPiYysrK7Ro0ULxMd555x1OJGmsJdtzcnIQEBCA5cuXc+1t2rTBxYsXUb58ebEtOTkZPXv2VHyRkFCOc+fOFfp8cnIygoOD8fnnn6NHjx7w8PBAgwYNEBAQgM2bNyMmJsYo36cEQVgud+7cQUpKihgrXQ62ZcuW3IbMY8eO0XmQMGvS09MxcuRIzJ07t8BjTFXkyRjTKNU+fvx4xfoXBCFfN09LQD4XQKXajRt9O3lGRERwsaFEngAwZcoU7j5bCpVqJwj9IC3Znp2dzTk65iGvXiN1V1MKuZjH3EWe0kqBgLoiT/n/XO7m7eHhgf79+4vxy5cvNVz9CPNj3759XKxGqXYp1apV03DvnDx5MrcxhdAPe/fu5apH9e/fv8DrNSWxtO8FQj+QyJMgCMLCYYwhMDCQa9OXiycAlCpVirthvnr1KuLi4vQyVlH89NNPnAPE0KFDUaZMGY3jBEHgSranp6eTWwpBGBkFfSZJ5EmYA4mJibh69aoYN2rUCK6uroqPU6FCBbRt21aMY2NjjW7BPCsrC6NGjcJ///tfrr1Lly44fPgwGjZsiEOHDsHNzU187smTJ+jRoweePn1q6HSJYiBfWJE7qsthjCEyMhLr1q3DqFGjUKtWLZQrVw5vvfWW0QqTCYKwLOTu8g0aNFC0fwcHB+77+sGDB7h586aiYxCEsRAfH4/OnTtjy5YtXHvt2rVRqVIlMY6KijLJxdBz585xIoQ6deoo7tg/YsQIODs7i/Gvv/6KhIQERccwRkjkaVro28lTTZFnjRo18NZbb+X7XO3atQ2WB0FYElKRJ5B/yXb5XIQ+HNyaN2/OzXHIRZDmRnFFnlWqVOHcC//++2/Fc5GLPPPbeEcl2y2LjIwMBAcHi3GpUqUKfI8akpkzZ8LHx0eMr169ajGbstTE0KXa8yCRJ6EEJPIkCIKwcIKDg7mLCG9vb725eOYhL9kutUQ3FIwxrF69mmubOHFigcdLRZ4AlWwnCGODRJ6EOXP27FlOuKaPUu15GHPJ9oyMDAwdOlRjkb9Pnz7Yt2+fuHjdsGFD7NmzB3Z2duIxMTEx6N27N+esRhgH8oWVO3fu4K+//sKyZcswcOBAVKhQocg+nj17hj/++AODBg1Cly5dcO3aNX2lSxAEUSTyDRJKizwBzXvqY8eOKT4GQahNVFQUWrRooSEY6Nq1K/766y/umjg9PR3R0dGGTlFn8nPxVNpBxs3NDcOGDRPj9PR0bN68WdExjJGTJ0+Kj21tbTUEP4Rx4evry8X6dPK0sbGBn5+fov0XxdSpU/NtJydPgtAPxRF5XrhwQXzs4OCg4S6pBK6urqhfv74YX7lyxWznpbKzs7mywz4+PtyGHCnW1taoVauWGEdHR3OOekogvyeTO3kCuRvGpTmGhIRQJSAz5vTp03j58qUY9+zZEzY2NipmlIuzszOWLl3Ktc2bN4/MCvRISkoKJ/j19PTkNtLqEx8fH07UGxYWpvj5jzB/SORJEARhwRjaxTOPLl26cLEaC1J//vkntwDQpEkTNGvWrMDju3btCltbWzHev38/OUURhJFw//59xMTE5PvcrVu38OjRIwNnRBDKEhoaysX6FHnKd63u2LHDKL7vXr16hf79+2Pnzp1c+6BBg7Br1y6Na5eOHTti69at3CJ5REQEBgwYgIyMDIPkTBQNY4xbWPH29kalSpXQsmVLfPLJJ9ixYwcePnyIO3fuYOvWrfjoo4/QpEkTWFtbF9hnSEgIGjZsiBkzZpjt4glBEMaN3MlT6XLtgHHcUxOEPjl06BBatWqlIfQKCAjAgQMH4OHhofHZMjYH+qJISkrC9u3bxdjBwQEjR47Uy1jyEvBr1641imt8ffHkyRNujqBZs2ZwdHRUMSOiKFxcXFC2bFkxVlJkk5aWxm0C8/Pz0/vct5y2bdvm6xJIIk+C0A/16tXjzvtykWdycjK3NtSwYUO9ib2krm05OTk4d+6cXsZRm6tXr3JzMEU5JEpFtenp6YqLK6VOns7OzqhevbrGMdbW1hg1ahTX9tNPPymaB2E8GFupdimDBw9Ghw4dxDgxMRFz585VMSPz5tChQ3j9+rUYv/XWW4XONSuN9HshJSUFUVFRBhubMA9I5EkQBGHBBAcHc7vrvL298X//9396H7dNmzacYFKN0uf5uXgW5pbg6uqK9u3bi/H9+/fpwosgjISQkBAulpaiA3J3aRKEKWNIkaePjw830RAdHa36911KSgr69Omj4aI9cuRIbN26lXPslDJgwACsWrWKawsODsbo0aORk5Ojt3yJ4nPr1i0kJiaKcX4Ln4IgwNfXF++++y5WrlyJiIgIJCUlISQkBIsWLULfvn1RunRp7jVZWVlYsmQJ6tSpQyXcCYIwOFKRp4uLC6pUqaL4GI0bN4abm5sYh4SE0HcbYRYwxrBy5Ur06dOHc9qxsrLCd999h9WrV4vzSaYu8tyyZQvS0tLEePDgwfDw8NDLWPKNzdHR0apU1TEU8jkAKtVuGki/L+/fv4+srCxF+r18+TLnkGTIUu15CIKQr5sniTwJQj/Y2Nhw8wuxsbFISEgQY3kpb32Uas9DLnY019K8xS3VnofcOfX69euK5fLy5UvExsaKsZ+fX4ECrvfff5+L//e//9F9lZmyf/9+8bGVlRV69uypYjY8giDg+++/h5XVv9Kt9evXcxvjCeVQq1R7HlSyndAVEnkSBEFYKIwxLFiwgGszhIsnkCvAatmypRjHxsbi7t27eh83jwcPHmDPnj1iXKpUKQwdOrTI11HJdoIwTuRC8Y8++oiLqWQ7YcpkZmZyGzJ8fX25kh76wJhKticmJqJ79+4aYu6AgAD8+OOPRTotTJgwAfPnz+fatm7dik8//ZSEf0aAfLKyuAsrzs7O6NixI2bPno29e/ciLi4O3333HSd4AnKv+QYNGoSePXuaZAlXgiBMj5SUFI0FRelCjVLY2NigY8eOYpyQkKDhIEoQpkZmZiY+/PBDTJ48mVtcd3V1xb59+zB58mRuc26DBg2415vSZ4Axlm+pdn2Sn5unuSKfA5Bu2iaMF6nIMysrCw8fPlSkX2mpdkAdkSeQW4XC29tbjCtWrAhPT09VciEIS0BetU3qoHn+/HnuucaNG+stD0sR8+gq8vz7778VyyUyMpKb88uvVHse1atX564T7t69qzEHSZg+MTEx3Lxgy5Ytje47uEGDBpgwYYIYM8YwadIkmr9WmPT0dE7w6+rqqlEpRd9YyvcCoT9I5EkQBGGhHD58GGFhYWLs7e2NDz74wGDjd+7cmYsNeeO0fv16bgf3+++/DycnpyJfJxd5Si8ECYJQB8YYJ/J0cHDA1KlTucU/EnkSpsylS5fw6tUrMdani2ceAwYM4GK1RJ7Pnj1D586dNSY6pk2bhtWrVxdbNBMYGIhx48ZxbcuXL8fSpUsVy5XQDqUWVmxsbDB58mT8/fffGDFihMbzwcHB8PPzw5w5czjHLIIgCKW5evUqtwijj1LtecjvqdWokEEQSpGYmIg+ffpgzZo1XLuvry/OnDmDXr16abymYsWKKFOmjBibkpNnWFgYIiMjxbh+/foai31KM3ToUG5DzK5duxAXF6fXMdXi5MmT4mNBEIoUmhDGQdWqVbn4zp07ivRrLCJPW1tbbNq0Cfb29nBwcMCXX36pSh4EYSk0b96ci6UiT203nGpDjRo1ODHZX3/9ZZaiLanI09HRsVBhJaDpZKykk6fcqbWoe7LRo0dz8Y8//qhYLoRxIF/L7du3r0qZFM7ChQu5akVnzpzB1q1bVczI/Pjzzz+RnJwsxn369IG9vb1Bc/D39+cqk5HIkygpJPIkCIKwQBhjCAwM5Npmz55t0AsZtRakMjMzsX79eq4tICCgWK+tXbs2qlWrJsahoaFciVGCIAxPbGws7t+/L8Zt2rRBuXLluMmbK1eu0GeVMFkMWao9D19fX87xICoqStHJ1uLw+PFjdOjQARcvXuTaFyxYgG+++YYTcheFIAhYtWoV3n77ba79s88+w+bNmxXJl9AOuchT14WVChUqICgoCMePH0e9evW45zIyMvDll1+iTp062L17t1kuqhAEoT5yJ0G506CSyN0m9u3bp7exCEKfxMbGolWrVjhy5AjX3rp1a4SHh6N+/fr5vk4QBO6+79GjR3j27Jlec1WK/Fw8S3J9qw3Ozs4YOXKkGGdlZWHTpk16HVMNkpKSOMFvgwYNUKpUKRUzIoqL1MkT0I/I087OrsBziiHo3r07kpOT8fjxY4waNUq1PAjCEpCLPMPDw8XH0rkIOzs71K1bV295CILAVbVLSEhATEyM3sZTgydPnuDWrVti3KxZM9ja2hb6mlq1anHXPkrOO8o3/hQlOB04cCBc3BuyCAAAIABJREFUXFzEeOfOnUhKSlIsH0J95CLPPn36qJRJ4ZQuXRqLFi3i2qZPn46XL1+qlJH5oXapdgCwt7fnjA6io6ORkJBg8DwI04VEngRBEBZIfi6e//d//2fQHFq2bAlHR0cxPnbsmEEW2/fs2YPHjx+LcdeuXVG7du1ivVYQBM7NMzs7W2MRgiAIwyIXiOcJyNu1aye2McY0SsYQhKmghsgT0CzZvnPnToOMCwD37t1D+/btce3aNa59yZIlCAwM1GoB3MbGBlu3buXODQAwZswYHDhwQKd8Ce1gjHELKxUqVECFChUU6TtPILx06VJuoh7IfX/1798fffv25UoqEwRBKIF8QVGfIs969eqhfPnyYhwSEkJunoTJcfLkSbRo0UKjROfw4cNx7NgxlCtXrtDXy52ZTMHN89mzZ/jll1/E2NHRMV8ncn0gL9n+3//+l6t0Yw6cOXOGm1+UX/8TxovcyfP27ds695mSksIJhxo0aMA5J6mBnZ0dCY8JwgBUrVqVc/wODw8HYwwpKSncdYchzgvmXppX/vsUx0Hb0dGRO+9fv35dsfVBqZOnIAjw8/Mr9HgXFxcMHjxYjF+/fs1dqxGmDWOMc/L19vYu8j2hJmPHjuXucR4/fkzu3wqRnZ2NPXv2iLG9vX2+FSMMgfx7QarZIIiiIJEnQRCEhcEYw4IFC7g2Q7t4ArkTWtKJ1kePHiE6Olrv465evZqLJ06cWKLXy3d4Ucl2glCX4og8ASrZTpgmjDFO5Onm5mYw1xG1SrbfvHkT7dq1w82bN7n2VatWYfr06Tr17ejoiD/++IP7G2ZnZ2PQoEE0kaICd+7cwYsXL8RY6fJotra2mDZtGv7++28MGTJE4/kDBw6gXr16CAwMxKtXrxQdmyAIy0Xu5KnPxSNBEDBp0iSubdq0acjJydHbmAShFK9evcL69evRtWtXDdeSL774AkFBQXBwcCiyH7mQWv4ZNEZmzJjBXXsMGTLEYIKv+vXro23btmJ8584dBAYGmpXDufzen0SepoM+nDwvXrzIvb/VKtVOEIThEQSBc/OMj4/HvXv3cPnyZe68oM9S7XnIRY/mJvKUmysUR+QJAHXq1BEfJyUlIS4uTudcsrOzERkZKcY1a9aEs7Nzka+jku3mS1xcHOfM2qhRI7076OuCtbU1Vq5cybV9++23ZucArAahoaF4+vSpGHfv3l3DHMBQmLv4n9AvJPIkCIKwMA4dOsSVpvDx8TG4i2cehi7Zfv36dYSEhIixt7c33nzzzRL10aFDB86B9ODBg7SIRhAqwRjjzhuurq7ihL18Ief06dMGzY0glOD27duc+3SrVq1gbW1tkLGrV68Of39/Mb58+bLeJ5OioqLQrl073Lt3T2yzsrLCjz/+WOJNGQVRqlQpHDp0CJUrVxbb0tLS0KdPHw0HKUPz6NEjnDhxwuzclAriwoULXKyvhRVvb29s374dR48exRtvvME9l56ejs8//xz169enjTsEQegMY4wTmFWtWhVubm56HfPjjz+Gj4+PGF+6dAlbtmzR65gEoQ3Pnj3Dnj178Nlnn6F169Zwd3fHuHHjkJmZKR7j6OiIHTt2YM6cOcVeeDU1J8/Q0FCuRLqVlRU++eQTg+YQEBDAxV988QVGjRqF9PR0g+ahL0jkabr4+vpysRJOntLKAQCJPAnC0sivZLv8vCAtmasvmjVrxs3nmds8tVzkKRcvFYRU5AkoU7I9JiaG20xTVKn2PNq0aYOaNWuK8dmzZxUtIU+oh3y+Vz43aIy0a9cOw4YNE+PMzEyD3zOYI8ZQqj0PEnkSukAiT5UQBMFdEITBgiCsEQQhTBCEBEEQMgVBeCEIwmVBEFYLgtCshH32FAThF0EQ7gqC8FoQhHhBEEIFQfhEEISit6kQBGH2MMYQGBjItanh4pmHoUWea9as4eLx48fDxsamRH04OjpyecfHx2uIFAiCMAxRUVHczrv27duLn+ny5cujRo0a4nPh4eF4/fq1wXMkCF1Qq1R7HoYs2X7z5k107NgRT548EdvySqy///77io7l7e2Nw4cPc2W7EhIS0KNHDzx8+FDRsYpLREQEqlatio4dO+LNN980KzelgjD0wkqXLl1w+fJlLF68GE5OTtxzt27dQt++fTFgwACkpKToNQ+CIMyXu3fvIjk5WYz1Wao9DycnJ43SbbNnz0ZaWprexyaIgmCM4ebNm/jf//6HDz74AHXq1EHZsmXx9ttv45tvvsFff/3FiTsBoEKFCjh58qTG9WdR1K1bl5vXMWaRZ2ZmpobActKkSQY5V0gZPHgwWrRowbUFBQWhe/fuGq6qpsbr16+5je01atRAhQoVVMyIKAmOjo4oX768GCvh5BkREcHFJPIkCMuiWTN+mT08PNxgG06lODs7cxupo6KiNCrYmCrp6encubZWrVrw9PQs1mvlYjslRJXSUu2A5oagghAEQWP+kdw8zQNTFHkCwJIlSzgX2v379+PgwYMqZmTaMMbw+++/i7G1tXWJTaCUxMfHh9uwGxYWZjHGD4TukMhTBQRB+AxAHIBfAAQAaA6gNAAbAKUANAAwAUC4IAhBgiA4FdTXP/3ZC4KwDcBBAIMBVAZgD6AsgNYAlgG4LAiCYWeMCMLEiY+Px+HDh7lFGlPn4MGD3GRnpUqVMGbMGNXyady4Mdzd3cU4JCREb66YKSkp+Omnn8TYxsYGH3zwgVZ9Ucl2gjAOCirVnofUsSMjIwPnzp0zSF4EoRTGJvLUZ8n2jz/+GM+ePRNjOzs77Nq1K98y20rwxhtvYP/+/ZzY7969e+jZsydXQtxQLFy4EBkZGQByr9cswS1ALvI0xMKKnZ0dZsyYgevXr2PAgAEaz+/atQvjxo3Tex4EQZgn8jLRxV1Q1JXhw4dzi8YPHz7E8uXLDTI2QQC54sXw8HAsX74cAwYMQPny5VGzZk2MHj0aGzduLNItvVGjRggPD9dKfGVvb88t1F67dk1DQGosrFixAlevXhXjihUrYuHChQbPw9bWFsHBwejevTvXfvLkSbRq1cqkRSfh4eHiNTVALp6mSNWqVcXHDx480PnzLBUeOTg4oG7dujr1RxCEaSEXeZ47d46bi7C1tUX9+vUNksvbb7/NxfqcYzMkFy5c4L57i1uqHdCPk6d8w09xnTwBYOTIkbCy+lc6ExQUhKysLJ1zItTFVEWe3t7emDNnDtc2d+5cizAG0AcXLlzgqod16NCBM4BQA6mbZ0pKCqKiolTMhjAlSOSpDrWQK8IEgFsA/gtgInIFmuMB/AogT6o9AsDvgiAU9r/6CcDQfx4nAPgKwDAAkwHkqbmqAzgkCEIlhX4HgjBrYmNjUatWLfTs2RMtW7ZEUlKS2inpjLG5eAK5O2U6duwoxgkJCYiMjNTLWFu3buUEu/3799d6N3+vXr24+MCBAzrlRhCEdshFnl26dOHitm3bcrG8bBtBGDtSkae1tbWG446+qVWrFvz8/MT4/PnzipTMkxMaGsptmHB0dMS+ffv0vpu2RYsW+O2337iSWVevXsWQIUMMOmGWnJyM4OBgrs2YXaiUgDHGLax4eXmhYsWKBhu/cuXK+O2333Do0CGuHBcAbNu2DVu3bjVYLgRBmA9ykaeh3PmsrKzw7bffcm2LFy9GXFycQcYnLJeffvoJnTt3RqlSpdCiRQtMnToVu3btQnx8fJGvrV27NsaMGYOgoCCEhYVxLiYlRfpZy8jIwI0bN7TuS1/cu3dPY05uxYoVcHNzUyUfNzc37N+/H+PHj+faY2Ji0LJlS5MtI0ul2k2fKlWqiI9zcnJw//59rftKTk7mzgcNGzaEra2tLukRBGFilCtXjjuvnDt3DteuXRPj+vXrG2x9zJAbqQ2JtqXaAf2IPOVOniURefr4+HCbYJ48eYJDhw7pnBOhLqYq8gSAqVOnwtfXV4wvXLiA3bt3q5iR6WJMpdrzoJLthLaQyFMdGID9ADoyxqozxsYzxtYwxnYwxv7LGBsCoBOAvDpx3QGMyq8jQRDeApBnb3MPQGPG2GzG2DbG2EoArQDk+YlXQK6rJ0EQRfDVV1+Jws7r169rLJiYIgcPHuRc7CpVqoTRo0ermFEucue9Y8eOKT4GYwyrV6/m2iZOnKh1f1WqVOF2fp87d65YixgEQShHVlYWjh8/LsZlypThxGiA5oIOiTwJU+LFixec04+/vz9XosVQ6LtkO2MMM2fO5Npmz56Nbt26KTpOQfTq1QubNm3i2o4cOYKwsDCDjA/kOoKnp6dzbXKhkLlx7949rhxokyZNIAiCwfPo0aMHIiMjNdw7J06cyO3uJgiCKA5ygb4hSzB36tSJ2xyRkpKCBQsWGGx8wvIICgrC+++/j5CQEKSlpRV6rK2tLVq2bIlPP/0Uv//+O+Lj4/H3339j48aNGDFiBOzs7HTKRe6aa4ybZaZMmcL9nXr06FHi0vRKY2NjgzVr1mDp0qXcdVhCQgK6dOmCn3/+WcXstINEnqaP1MkTgE6bDOUlmalUO0FYJs2bNxcfp6WlcZXkDFFRJI/atWtrbKS+deuWwcbXF3KRZ0mcPD08PODl5SXGRbm/FwfpdaCnp2eJjV7ka6ZUst30kb6vypQpA09PTxWzKRn29vaYN28e17ZgwQK9VcQ0Z+QiT7m7shqQyJPQFhJ5qsNnjLG+jLETBR3AGDsFYJak6f0CDg2UPJ7AGONWohhjOQA+RK4AFAAGCoJgGO95gjBREhMTsW3bNq5t2bJlJu2CwRjTWOBR28UzD7nIU+7MpwRnz57lbu7q1KmDDh066NSntGQ7Y4x29BGEgbl48SLnstypUyeunAoA1KhRg5soOnPmDLKzs0EQpoD8pt7Qpdrz0LfTwMGDBzmnoHLlyuHjjz9WdIyiGDlyJObPn8+1GdLJMT/hrLmLPOULroZcWJFjb2+PlStXcu4OSUlJGDVqFE2aEgRRIqTnbicnJ1SvXt2g43/99decO/X69es5pyKCUIr4+PhCr9fc3d3Ru3dvLFq0CCdOnEBSUhL++usvfPPNN3j77bdRtmxZRfMxdpHn3r17Occde3t7/PDDD6pscJEjCAKmTZuGnTt3wtHRUWzPyMjAiBEjsHDhQpMpCZmVlcVVQqhQoYLBz8OE7kgd9wDgzp07WvclLdUOkMiTICwVqchTTuPGjQ2Yif43Uhsaxhgn8nRzc+PMUYqD1FXx4cOHXDW+khIXF4fHjx+LcaNGjUp8vdWvXz94eHiI8d69e/H06VOtcyLUJS0tDXfv3hVjU3LxzGPkyJGoVq2aGEdGRpqNE7ChuH79Oif2bdGiBby9vVXMKBd/f39uwyOJPIniQiJPFWCMvSjmoTskj/3kTwqCUBNA3kpUDGMs33rBjLFXANZLmgYXc3yCsEi2bNmi4UKQmpqKRYsWqZSR7hw4cICb2DIWF08AqFevHjfBf+LECWRmZio6Rn4unrpOpvfu3ZuLqWQ7QRgWuSBcLhgHchespM4dycnJZi+cIswH6QIloJ7Is27dulz5pLCwMMUcDnNycjB79myube7cuXBxcVGk/5IwadIk2NjYiPEvv/yCrKwsvY+bmpqa7zWEuZ+rpKXaAXVFngBgZ2eHn3/+GQ4ODmLb8ePHsWwZFcIgCKJ4pKWlISYmRoz9/Pw0NiDpmzp16nDOxDk5Ofjss88MmgNhGUyePBnPnz8X47Jly2L48OFYs2YNIiMj8fz5c+zfvx+zZ89G+/btOfGgPpCLPI3pOio1NRWTJk3i2ubMmYMaNWqolFH+9O/fHydOnED58uW59gULFmDUqFEarvPakpCQgFWrVqF58+awt7dH165dOXd3Xbh8+TJSUlLEuF27dkYhpCVKhlzkqYuTp/yeg0SeBGGZFCbyNPRchLmVbL9z5w6ePHkixq1atSrxPZC8ZLsubp7yjT7ya8Ti4ODggGHDholxZmamSbqbE7lER0dzsSmKPG1tbTVMnAIDA8nMpAT8/vvvXGwMpdqB3M1/0u+h6Ohoxe6NCPOGRJ7GzUvJ4/xmw3pIHh8uoi+pxVxPrTMiCDOHMYa1a9fm+9zatWt1mlhSC8YYAgMDubY5c+YYhYsnkCvCkoqzUlJSNCbhdOHp06f49ddfxdjZ2Rnvvfeezv22adMGbm5uYnz48GGDiEEIgsilOCJPQLM8m9QxkCCMGWMReQKak9Dy8iba8uuvv3ITsL6+vhplsw2Fp6cnevT49/YqPj4ex44d0/u4hw4dwqtXrzTaHzx4wIknzA35tZ6h3TPyo27duliyZAnXNnv2bKNzAyMIwjiJiori3O4MWapdSmBgIFxdXcV4//79Bvk+IyyHvXv34pdffhFjGxsbHDlyBFu2bEFAQADq169vcIGzl5cXt3nYmL67v/jiC849qFatWkYrvm7WrBnOnj2L+vX5ImBBQUHo3r271temmZmZ2Lt3LwYOHIgKFSrgo48+wrlz55CRkYFjx47hgw8+UMQtlEq1mwfycu1KOXk6OTmZpLCDIAjdady4cb7XJtbW1lz5dENQt25dzukyPDycu04wNXQp1Z6HXOR5/fp1rfORXwNKK7aUhPxKtpuKsznBIxcNm+q1wLBhw1C7dm0xvn79OrZv365iRqaFfC2jf//+KmWiibxk+9mzZ1XKhDAlSORp3EhnVPK7ypM+X5Qi6hKAPEl/XYG2sRJEvpw5cwZRUVH5PpeZmamxW8YU2L9/v9G6eObRpUsXLlayZPumTZuQkZEhxiNGjIC7u7vO/dra2qJ79+5inJiYSFbqBGEgMjIyuAWcihUrolatWvkeK1/YkS/8EIQxkpGRgfDwcDGuUqUKKlasqFo++nAayMzMxLx587i2zz//XNVNKNKd+oBhSrYX9reMjIzU+/hqwBjjRJ5ly5aFj4+Pihn9y4cffsiJfTMzMzF8+PB8hbgEQRBSlHCNUYJy5cph1qxZXNu0adPI5YNQhOTkZEycOJFr++yzz1R7v+chCAKXw5MnTxAfH69iRrlERUVh6dKlXNvq1auNZtN1fvj6+uL06dPcfBcAnDx5Ei1btsTNmzeL3delS5fwySefwMfHB/369cPOnTvzrdyze/dubNq0SefcSeRpHlSqVIlzYNVW5PnixQvu/erv789VbiAIwnJwdnZGvXr1NNrr1aund7fx/DCnku36EHnq4uR56dIlLtZW5Nm4cWNu096VK1dw8eJFrfMi1MNcRJ42NjYa+oTPP/+cjIeKwb1797h5aD8/P9SsWVPFjHjkIk/SGRDFgUSexo3UxmZ/Ps9L1Qx3CuuIMZYF4OE/oTMAb50yIwgzRe7iOWvWLG7ydcuWLSa14F6Qi6ednZ06CRWA3IFPKaeR7Oxsjf/phAkTFOkboJLtBKEWYWFhnOCmc+fOBZZha9CgAedmdOrUKdp5Sxg9Fy9e5N7jbdu2VTEbzcmP0NBQPHz4sJBXFM2mTZu4Rbe6detixIgROvWpK2+99RacnJzEeNeuXXoV971+/Rr79u0r8HlTuuYsCQ8ePMDTp0/FuEmTJkZTStPKygqbNm1C6dKlxbaoqCgNwRRBEIQceXlotZw8AeDjjz9GpUqVxPjy5csICgpSLR/CfJg5cyYePHggxrVq1dLYtKMWcqGp2m6ejDFMnDiRW3gdNmyYxiZnY8Td3R379u3D+PHjufaYmBi0bNmy0OoYcXFxWL58ORo1agR/f3+sWLGiWILbKVOmIDY2VuucGWOcyNPd3V3DkZQwDezt7eHt/e/SlbZVtS5cuMDFhi7JTBCEcZFfyXa1zguDBg3i4h07dqiShxJIRZ5WVlb5/p2LQl9OnnZ2dpzzYUkQBCFfN0/C9DAXkScADBkyhBOsx8TE0H1+MZCXajcmF08AaNmyJReTyJMoDrR1zUgRBKE1gLwriNcAludzWCnJ42fF6DYBQGXJax8Ucqw0F52UELrsuiEIQ/LixQuurLeTkxPeeecdPHnyRLyAZ4xhypQpWL16tVpploiQkBBuh0qFChXQqlUro/tcMsZQoUIFPH78GEBuOeXLly/r7G5w/Phxbse3v78/7O3tFfv9a9SowcW7du3CqFGjFOmbIIiCkZ6rgdzJoMI+1w0bNhQXop48eYLg4GD4+vrqNUeC0AX55EP16tVV/+7u1KkTYmJixHjt2rUYPny4Vn29fv0a8+fP59omTJjA9a8WnTt3FoWXKSkpWLduHXr27KmXsf7880+kpKSIcbVq1XDr1i0xPnnyJLp27aqXsdVEvpnH19dX9fe3nAULFmDKlCli/N1338HPzw9t2rRRMSuCIIwZeUktJe87teGjjz7CjBkzxHjGjBlo2LChKm5FxsyjR4/w8uVL1KxZ0+Alxk2NiIgIrFmzhmubO3euTqWUlURarh0Ajh49yomdDc3u3btx8uRJMXZ1dUVAQIDRXfMUxpQpU+Du7o5vvvlG3CiZkJCAzp0748svv0Tfvn0B5FYhCAkJwe7du3Hq1KlCnYPd3NzQu3dv9OvXD4sXLxYF8qmpqRg4cCCCgoK0clu8desWt4moUaNGRnFvQWiHl5eXKCh/9OgRrly5UmLDAvlG/IoVK5rU548gCGWpXLmyRpu3t7cq5wVra2tUrVpVFLGfPXsWISEhqFChgsFz0YXU1FRuo1utWrXw6NEjPHr0qET9MMbg5OSEtLQ0ALlCTW3+L69fv+ZeV6NGDZ02kDRv3hw2Njbihp2goCCMHTvW6Ax0iMKRCn9tbW013iemxrhx47j5ynnz5qFp06awtbVVMSvjZsuWLVzs7+9vdO+B8uXL48mTJwByvxOioqJgbW2tclaEPlBKaE6zV0aIIAjlAfyKf/8/8xhj+QkyXSSPXxeja6kNjWuBRxGEhbJ7926urHffvn3h4uKCsWPHwsXl349bSEiIxm5gY4QxhlWrVnFtAQEBRnkTIggCt1slIyNDo7SCNmzbto2L3333XZ37lFK2bFlu51R0dLQoVCUIQn/IF9FbtGhR6PHyndlS8TtBGCPy64zGjRurlMm/yEs2Hj58WOu+fv75Z87Rp0GDBkbjatSnTx8uLsxpU1eCg4O5OCAggHO0vHHjht7GVpOoqCguzq9smtp0794d77zzDtc2e/ZsvHjxQqWMCIIwZhhj3Dm7YsWKcHNzUzGj3PmMunXrinF8fDz+97//qZeQEfLbb7+hZ8+eePvtt/HFF1+onY5Rk56errFBZ8iQIWjWrJlKGWkid2pSc+HuxYsXWLJkCdc2ZcoUDSGqsZPnYvXdd9/BwcFBbM/MzMT06dOxdOlSLFy4EO3bt8fHH3+M48eP5yvwtLa2RocOHbB8+XKcPHkSCxYsgL+/P77++mtOeH7p0iWsX79eq1wjIiK4mFwbTRupkycArapIyO85yNmVICwbPz8/jTbptbIhEQRBY47tyJEjquSiC5GRkcjJyRFjbUujC4KAatWqifH9+/e5ddricvPmTe46RFchTenSpdGxY0cxTkpKwp9//qlTn4RhycnJ4RzBfX19tdpMZEx07dqVe28/fPhQwyyC+JfExERuncXHx0drh199Ij1/pqWlcdXPCCJfGGP0Y0Q/yC2lHgaA/fOzD4BQwLHRkuNqFKPvUMnxrQzwu7DctxhBGD85OTmsZs2aTPIZYRcuXBCfX7hwIfdc27ZtWU5OjooZF80ff/zB5Vy5cmWWnp6udloFsnnzZi7fuXPn6tTfrVu32D9OxAwA8/T0ZK9fv1Yo23+ZN28el/e6desUH4MgiH9JTU1ltra24meuWrVqRb7mxIkT3Od09OjRBsiUILQjJyeHeXl5ie9Xd3d3lp2drXZaLCcnh1WtWlXMSxAE9uTJkxL38+LFC+bh4cF9Jo8dO6aHjLUjIyODlSlTRszNzs6OPX/+XPFx0tPTmbu7uziOs7MzS0tL465HnZycjOJ/rzS9e/fm/v937txRO6V8SU5O5t7zANiAAQOM/h6AIAjDc+/ePe5c8eabb6qdEmOMsT///JPLy9nZmT1+/FjttIyCPXv2MCsrK+7vExERoXZaRsucOXO4v5W3tzdLTExUOy2O9PR07j6xQYMGquUyduxY7u/VtGlTlpWVpVo+ShAeHs7doxTnx8/Pj3377beFnnfWrVvHvcbGxoadO3euxPm99957XD+hoaG6/LqEysjnWg8fPlziPqTX8S4uLib/GSQIQjcyMjKYo6OjeF6wsrJiqampquVz6dIl7jzXpk0b1XLRlv/85z/c7xAUFKR1X/Lv8atXr5a4jw0bNnB9fPfdd1rnk4d8jbVXr14690kYjtu3b2vM6ZkDe/bs4X6vSpUq6WXt2xzYunUr97eaMmWK2inly7Jly7g8165dq3ZKhP5QRIdHTp5GhCAIDgD+AND8n6ZQAEMYYwWVS0+RPHYo4Bgp0ppML0ueIUGYLyEhIVwZn2bNmsHf31+MP/nkE27H/enTp3Hw4EGD5lgSGGMIDAzk2ubMmWOULp55dO7cmYt13RW3bt06SE+fH3zwgc7l3/ND7vi1f/9+xccgCOJfQkNDkZmZKcbyc0d+NG/enDv/nTp1Si+5EYQS3Lp1C3FxcWLcunVroygfKggCBg4cKMaMMa12Ci9dupRzQ+zatWuxPseGwtbWFoMGDRLjjIwM7Ny5U/Fx/vzzTyQlJYlx79694ejoiAYNGohtaWlpXPl2c4AxxrkplylTJt+yacaAq6srgoKCuM/fzp07sXnzZhWzIgjCGJGWKQTAncvVpFOnTnjzzTfFODU1FQsWLFAxI+MgLCwMQ4cO5ZyHAOCrr75SKSPj5sqVK/j666+5ttWrV8Pd3V2ljPLHzs4OderUEePr169r5QKlK2fOnOHcKK2srLB27VqTL7fXrFkzhIWFFenA7unpiSlTpuDChQu4fPkypk6divLlyxd4/NixY8Wy7wCQlZWFESNGiCVbi4v0Ht/BwQFNmzYt0esJ46Jq1apcLHWr6HpWAAAgAElEQVThKg4JCQncaxo3bmzyn0GCIHTD1tYWvXv3FuPOnTvDyclJtXwaNGiAGjVqiHFoaKhWrsVqcubMGS5u3bq11n1Jr+EA7RzZ5ZUBtXUWldKrVy94eXmJ8eHDh03u/2TJyN9HSpVJVps333yTu9a9f/8+NmzYoGJGxot8vV6+nm8stGrViov/+usvlTIhTAX1VwsJAIAgCHYAdgHIW+EMB9CbMZZayMsSJY89izFMmQJeSxAWz9q1a7k4ICCAi11cXDBv3jyubdasWRqLAsZCREQEZ0Hu6+uL999/X72EioG3tzdnkx4eHo6XL0uuR3/+/DkCAwPxww8/iG2CIGD8+PGK5CmnadOm8PT89xR89OhRpKen62UsgiA0BeDFEYc5ODhwpQRv3ryJJ0+eKJ6bORASEoL27dtj2LBhVJZYJU6fPs3Fbdq0USkTTaQiTyC3zGlJiIuLw4oVK7i2L7/8Uue8lGbYsGFcvHXrVsXHkP/t8v62cmGQXDhk6jx69IgTMTdp0oQrUW9stGnTBrNnz+baJk2aVOKFZoIgzJvLly9zccOGDVXKRJMlS5ZwwpYNGzZolLC1JGJiYtC3b1+8evVK47ldu3apWuLbGMnOzsb//d//ISsrS2wbPHgw+vXrp2JWBSO9jsrMzDT4/zMrKwsTJkzg2iZOnGg2pcN9fX0RGhqqUWLW1tYW/fv3x549e/Do0SOsWLEC/v7+xbrGEwQBGzZs4DbW37hxA9OnTy92Xvfv38edO3fEuEWLFka9yZ0omipVqnCx9P9bHKSbygCQ6JcgCADAihUrMGbMGIwYMUJjPdDQyDdSA7nXoqZCTk4OJ0Ly8vLSEOiXBLn47vr16yXuQ35PpsTGOxsbG4wcOVKMc3JyaOOvCWGuIk9BELBw4UKubdGiRfne41oy2dnZOHTokBg7Ozujffv2KmZUMP7+/tz9C4k8iaIgkacRIAiCLYAdAHr903QRQE/GWHIRL42WPK5SxBg2ALz/CVMB0FYTgviHJ0+ecE5U7u7uGDJkiMZx48aN4yaZrly5gu3btxsixRJz+PBhLv7kk09MYoJTKtbKysoqkdteXFwcZsyYAV9fX3z++edITf1XI9+7d2+NCUKlsLa2Rq9evcQ4LS0NJ0+e1MtYBEFoJ/IEgHbt2nGxXEhH5J6/Bg8ejFOnTmHbtm2YM2eO2ilZJKGhoVxsTCLPZs2aca6Lx48fx9OnT4v9+kWLFnHfzwMGDOAE2MZCmzZtNH5PJXfqZ2VlYffu3WLs4OAgOkr4+flxx5qbyFO6CQmASYge5s+fzy0Mv3z5Eu+99x6ys7NVzIogCGPCWJ08gdyFLOmGx5ycnBKJp8yJ+Ph49OrVC8+ePcv3ecYYlixZYuCsjJvvvvsOERERYuzh4YHvv/9exYwKRy6wli/265vvv/+eOx+UL18eX3zxhUFz0Dfu7u7Yt28fli5diiFDhmDlypV49OgRdu3ahX79+sHW1rbEfXp5eWHjxo1c2+rVq4tdQUk+d2isi7dE8dHVyVN63gJM456DIAj94+Pjg40bNyIoKAjVq1dXOx2uigxQ8o3UavL3338jMfFfL6nWrVvrtIFX7uRZUpFnTk4O5+RZpUoVlCpVSut8pIwePZqLf/zxRxRcgJUwJsxV5AkAPXv25NwfHz9+rLp43dgIDw9HQkKCGHft2lUv1T6VwN7enrtejY6O5nInCDkk8lSZf8SX2wDkbYGOBNCNMVYc66SrksdF3ak2ApC3df9aISXgCcLi2LRpE+dKMHLkSDg7O2scZ29vr7E7Zt68eaqUfyqK4OBgLpaWaDNmtCnZfv/+fUyePBlVqlTBkiVLkJKSwj1vbW2NmTNnKpqnHGmpD4BKthOEvkhKSuIm6+vVq8eVTCkMuciTSrZrcvDgQW7R+7fffjNax2pzRirytLGxQfPmzVXMhkfuNJCdnc2JFQvjzp073GSTlZUV/vOf/yieoxJYWVnh3XffFWPGGH755RfF+j9x4gQ3UdOzZ0+4uLgA0BQGRUZGKjauMSB31TGFBVdbW1ts2bIFjo6OYltoaKhG6VqCICwXqajL0dGRK71oDCxYsACurq5ifPDgQRw5ckTFjAxPamoq+vbti9jYWLHN0dERu3fv5hZ6goKCcP/+fTVSNDpu3bqFuXPncm3Lly8v9v2XGqgp8rx//z7mz5/PtS1fvtzoytorga2tLaZNm4bt27fjo48+4qrbaMubb76JcePGcW1jxowp1oYy+b29/N6fMD18fHw4F+qSOnnKRZ7k5EkQhDHi7+/PidpPnTplMpWnlCzVDgDVq1eHjY2NGJdU5Hnnzh2uKqCSlRXq1KmDFi1aiHFMTIzZbcg2V+QiT2klSVNHEASNefXFixdz5gqWjqmUas9DXrL97NmzKmVCmAIk8lQRQRCsAWwBMOCfpmsAujLGiivNllr19Sji2J6Sx4cKPIogLIzs7GysX7+eayusrPewYcNQv359Mb516xY2bNigt/y0ITk5mbPyrl69OqpVq6ZiRsWnY8eOXFyYyDM2NhZjx45F9erVsXLlSrx+/VrjmD59+uDs2bNo27at0qlydO/eHVZW/36lHjhwQK/jEYSlcuLECU50WFwXT0BzRzGJPDWRi9iePn2qsThC6Jfnz5/j2rVrYuzv7w8nJycVM9JE25LtCxYsQGZmphiPGjVKY6e+MaHPku07d+7k4gEDBoiPq1atym02MreJY7nIs3HjxiplUjJq166NZcuWcW0LFizQ+H0IgrA8Xr16hRs3bohx/fr1OWGKMVCuXDnMnj2ba/v0008txpE4KysLgwcPxrlz58Q2KysrbN++HW+99RbGjBnDHfvtt9+qkaZRwRjD+PHjuZJ/3bp148pVGiPyBX1DXkd9/PHH3KJq165d860SRBTMt99+y4nknzx5gnHjxhXpliW9t7e2ttZYICVMDxsbG1SqVEmMdXHydHNzM7rNFwRBEECuSEvq5skYM5mS7UqLPG1tbVGzZk0xvnHjRomMB+Qbexo1aqRTPnKkG8EBYO/evYr2T+gHqcizYsWK3MZHc6Bz586cg318fDxWrVqlYkbGhVzkKTdrMjbk9zBUsp0oDBJ5qoQgCFYANgHIm+25AaALYyy+uH0wxmKQW9odAGoKgtArv+MEQXAAMFbS9GvJMyYI8yQ4OJjbDdy2bVvUq1evwOOtra3x5Zdfcm0LFy40qt0xx48f55xJu3fvrmI2JcPT05OblL906ZKGJfm1a9cwYsQI1KpVCxs2bOAEI8C/LmMXLlzAvn37DLJbu3Tp0tyNbExMDGJiYvQ+LkFYGtqWageAUqVKcWWQL1++jOTkZMVyM3VSU1Oxb98+jXZyJjYs8pt3fW9S0IYWLVrA29tbjI8dO1Zk+ZCoqCgEBQWJsZ2dHQIDA/WVoiL4+flx14Tnz5/nRDzakp2dzU3a29raco7rVlZW3LkqNjZWw6XclJGKIj08PFClShX1kikh48eP53Z9Z2VlYfjw4UhLS1MxK4Ig1ObatWvcAqQxlWqXMmXKFE4wc+XKFWzevFnFjAwDYwwTJkzQ2Ij5ww8/oF+/3KJK06dP54S569evL7Cku6Xw008/4ejRo2Ls5OSEdevW6VSG0xCUK1eOcxo1lJPngQMHuOs7e3t7rF692uj/XsaGi4sLtmzZwn0ed+/ejR9//LHA1yQkJCAqKkqM/f39RYd8wrSR3ifEx8cX+5o7Pj6ec2Ru0qQJtzGfIAjCmNB2I7XaSEWednZ2imzglW4ET0tLK5G7vrRUO6C8yFNeKTG/OXTCuEhMTERcXJwYm1Op9jzyc/NcsmQJ52prqTx8+JA7LzRq1IhbzzBGSORJlAS6u1EBIXeGZx2AvO3PNwF0Zoxp48P+ueTxGkEQKsvGsgKwCkBe+2+MMWmZd4KwaKRlQwEgICCgyNf07duXE/TFxcXh+++/Vzw3bZGXajclkScAdOnSRXzMGMOJEycAABcuXMCAAQNQr149/Pzzzxo7+aytrfHee+8hKioKO3bsgL+/v0Hzlu8CIjdPglAeqchTEAR06NChRK+Xlm3LycnR2HVsyezbt49z6smDzmWG5fTp01zcpk0blTIpGCsrK855Mjs7G3/88Uehr5k7dy7nwDNx4kRUrly5kFeojyAIGm6e27Zt07nfM2fOcJOM3bp10yjjKRUIMca4hWtT5vHjx3j8+LEYN2nSxKTED4IgYOPGjShbtqzYduPGDUyfPl3FrAiCUBu5U6CxijwdHR01NqzOnTvXqDas6oP//Oc/GtVXZs2ahQkTJohx1apVMXToUDFOS0szqjkeQxMXF4epU6dybV988QVXTtSYkW4cjo+P13vZ07S0NHz00Udc28yZMzk3KqL4tGjRAnPnzuXapkyZglu3buV7vPz+iUq1mw/yzWDFLdkud9qnUu0EQRgzTZs2ha+vrxifOHEC8fHF9oJShWfPnnGboJs0aQIHBwed+5WL8EpSsl2+sUfJcu0AUK1aNdStW1eMw8PDubk9wviQb9Q3R5EnALRv3x5du3YV44SEBIu+l83j4MGDXGzsLp4A4O3tzW3MDQ8Pt5jqK0TJIZGnOiwC8ME/jzMBfAeguSAIbxfxo1GrkTG2B0BebU1fABcEQVgkCMJQQRA+BPAXgLy6Q48BTJX3QRCWyv3797kdV2XKlOFECwUhCAK++uorru3rr7/G8+fPFc9RG6QiT2tra3Tq1EnFbEqO3Jlv48aN6NOnD5o0aZJvuQpbW1uMGzcO0dHR2Lx5s2qlX0nkSRD6JT4+HpGRkWLcuHFjeHh4lKgP+YIPlWz/l19/zd/oPSIiQu8Lo8S/hIaGcrExijyBkjkNhIWFYffu3WLs4uKiUTLWWJGXY/r555+LLBdZFPK/lfxvCWgKhMylZPuFCxe4uEmTJiploj1eXl4aYqHVq1fTdR9BWDD6XlBUkmHDhnHn3kePHmHZsmUqZqRffvzxRyxYsIBre++997Bo0SKNY2fOnMnFK1eutFgHlEmTJuHFixdi3KxZM0yePFnFjEqG/DOobzfPRYsWcaWka9SoofF+IkrGnDlz0Lx5czFOSUnBe++9x1UtykN+Ty8tWUmYNnJheXFFntJS7YBp3nMQBGE55FWlyyMnJwe///67ihkVzdmzZ7lY11LtecjX9Uoi8pQ69rm5uemlaozUzZMxRtWvjBxpqXbAfEWeQG61USlLly5FYmKiStkYB/LPp7QykzEjdfNMSUnB1avk20fkD4k81UF6xWMLYCWA34vxU66A/kYB2P7P4zIAZgPYBuAHAHkzIrEAejLGiu9vThBmzoYNGzg3yNGjRxd7x1n79u3Rq1cvMU5KSsLXX3+teI4l5fbt21yZ8JYtW2q4Qxk77dq140ozHThwIN+Fc0dHR3E3/7p161CtWjVDpqlBgwYNOLv348ePm1V5VYJQm+PHj3NxSUq150Eiz/x5+fJloQKlQ4cOGTAbyyUjIwPnzp0T42rVqqF8+fIqZlQwrVu35nI7cuRIvpNHjDHMmjWLa5s6dSrnhGjMVK1alZusvnnzpsaiYUnIycnhNqxYW1uLpWKlmKvIU+6qY6oLrv369cO4ceO4tjFjxuDp06cqZUQQhBo8ePAAixcvxvbt27l2Pz8/lTIqGisrKyxdupRr+/rrrzmXZXPh0KFDGDt2LNfWtWtXbNiwIV8X6fr163MLt4mJiRqVXyyBPXv2YMeOHWJsY2ODDRs2cHM0xo5c5KnP66jr16/jm2++4dpWrVqliJuVJWNra4stW7bAyelfv4szZ87kO+8qv6dv27at3vMjDIO2Tp7y+zVy8iQIwtgxtZLt8spY+hJ5ykV6BfHixQvcvXtXjBs2bKiXqjF9+/bl4r179yo+BqEcliTybNWqFadXSExMxPLly1XMSF3S09Nx5MgRMS5TpgxatGihYkbFh0q2E8WFRJ5mAGMsnTH2LoBeAHYAuA8gHcAz5Dp5TgXQkDFmHquDBKEAWVlZGi488sXaopCXOvv+++/x8OFDnXPTBemFC2B6pdqB3J12zZo1K/B5V1dXzJw5E3fu3MGKFSvg4+NjwOwKRhAEzs0zIyODKy1NEIRuyD9P2og8K1asyAnCw8PDkZ6ernNups7evXvx+vVrMa5Xrx73PO1MNgwXLlzg/g/GvEBpbW2Nd955R4wzMzPzndw8evQoQkJCxLhMmTKYNm2aQXJUCnnJ9q1bt2rdV3h4OB48eCDGnTt3RpkyZTSOq1+/Phebq8izcePGKmWiO8uWLePKsMbFxWHs2LE6O70SBGHcpKamIigoCN26dUPlypUxa9Ysrkyfj48PSpcurWKGRdOxY0dug0FqaqqG22Vh5OTkIDIyEqtXr8a7776LKlWqoFSpUnB3d4ebmxtcXV3h4uICFxcXODs7w8nJCY6OjnBwcICDgwPs7e1hZ2cHW1tb2NjYwMbGBl5eXpg8eTJiY2MV+R3Pnz+PgQMHcqXNGjVqhJ07d8LOzq7A18k3pixbtoy7NjN3kpKSMHHiRK5t5syZGptPjB15vvpy8mSMYeLEicjMzBTbhgwZYpLzcMZIzZo1NRanAwMDOQFfamoq5xRfp04deHp6GixHQr/InTyljrmFIX2PlCpVSnVTAIIgiKJo3rw5t8YVEhKCZ8+eqZhR4chFnnJRkrbUrl2bi4vr5CmfM2vUqJEi+chp1aoVN4cXHBxsUfcKpoYliTwBTTfP5cuXIyEhQaVs1OXUqVNITU0V4549e5rMpsWWLVtyMYk8iYIgkacKMMY6MsYELX7uFNHvIcbYYMZYZcaYA2OsLGOsNWNsOWMstbDXEoSlsW/fPjx69EiMu3Tpwi3UFodGjRpxZTxfv36tcSFlaKSl2gHTFHkCuf8POR4eHvj8889x9+5dfPXVVyhXriBzY/WQW75T6U6CUA6pyNPGxkZrAZzUzTM9PV0nVz5zQV6q/ZtvvuFcoIODg7nFS0I/yF1ojLVUex5FOQ0wxjTKss+ePRtubm56z01JBg0axE0Ebd++nRONlISdO3dy8YABA/I9zsPDA5UqVRLjyMhIsxAPSkWepr7g6uzsjC1btnDvjT179mDjxo0qZkUQhD7IyclBSEgI3n//fXh5eWHkyJE4evRovudl6fyAMbNkyRLu/LVx48YCy4BlZmYiLCwMS5cuRb9+/eDp6YkGDRrgww8/xPbt23H37l0kJSUhOTkZL1++REpKClJTU5Gamoq0tDS8evUKr1+/Rnp6OtLT05GRkYHMzExkZWUhOzsb2dnZiI+Px8qVK1GzZk288847CA0N1fp77/bt2+jTpw+3qFO5cmXs37+/yGuQVq1aoUOHDmL85MkT/PTTT1rlYYrMmDGDmyerXbs25syZo2JG2vHGG29wYl59iTy3bNnCVZtwdXXFsmXL9DKWpTJ27FjONSsrKwsjRoxAWloagNxysdIS7vLKHYRpo42T5+PHj7nzWNOmTfXi5kYQBKEkVlZW3BxbdnY2du/erWJGBZOZmYnw8HAxrlq1KipUqKBI3y4uLtxcWHFFntJS7YD+RJ7W1tacyUtaWhq3sZ0wLqQiT2dnZ64KoznStGlTbjPny5cv8e2336qYkXrIDUukn1tjx9/fn7uXJZEnURAk8iQIwiKRl90KCAjQqp+FCxfCxsZGjDdu3Ijo6GidctOWrKwsHDt2TIxLlSplsiVpPvzwQ1HE6eXlhSVLluDu3buYP38+PDw8VM6uYLp06QJbW1sx3r9/v1mIMghCbe7fv4+YmBgxbtGiBVxcXLTqi0q28yQlJeHgwYNi7OnpiW7duqFHjx5iW3JyMkJDQ/+fvfOOiupqu/geqqCIICqgYAHB2DugRkVBsGHBFtFYgooYjT3W2OtrSaIiiLF3RUVRbNixoJEoNooV7AooUqTN94eL+825M9TpM89vrXetPIeZcx99x+Hec/bZWxntaQ1CoRA7d+5kxlTZyRP4/m9JNHb99OnT+PLlC1cfPnyYEVHXqFFDzB1KHahatSrc3d25+u3bt8yGfkkRCoWMyFNHRwe9e/cu9PWiLlQpKSlKd4uXlnfv3jF/hubNm6v9hmvr1q3xxx9/MGMTJ05EQkKCkjoiCEKWxMbGYs6cOahduzY6deqE7du3M8JBUVq2bImgoCAsW7ZMwV2WDUdHR2YNJD8/H9OmTQPw/5uVCxYsgJubGypVqgRnZ2dMmzYNx48fR0pKitz6EgqFOHLkCNq1awdnZ2ccOHCAEXAVx8ePH+Hp6cm4q1aqVAnh4eGwtrYu0Rx8N8+VK1eWqgdFc/z4cUydOhVbtmxBYmJimee5dOkSgoKCmLHNmzerZey4vr4+6tevz9WPHz+WeXpDSkqKmDv9kiVLSvw5I0qGQCDA5s2bmWeO2NhYTJ8+HQBw+fJl5vUk8tQsrK2tmTXWkjh58pMD1HVdnCAI7UNdItvv3r2LzMxMrpZVVHsBopHtHz9+LJGjKf9AT5MmTWTakyg9e/ZkaopsV01ycnKYtTlHR0fo6Gi+JIpvQvX333/jw4cPSupGeYiKPHV0dODp6anEbkqHoaEhWrRowdXx8fFa68hKFI3mf6MRBEHwePr0KU6fPs3VlpaW6NWrV5nmsre3x6hRo7g6Ly8Pc+fOlbrHsnD79m2kpqZydefOnRkBqjphZWWFZ8+eITo6Gi9evMC0adNgYmKi7LaKpUKFCozzR1JSUqGOKARBlBz+qdiyRLUXwBfOabvI89ixY8jOzuZqb29v6OnpiTkTU2S7fImMjERMTAxXN2zYkFnYVEX09PTQp08frv727Rv3OcnNzRVzfpo3b55aCgUA2US2R0dHMxuTP/74I6pVq1bo6/lRo+oe2S4apQmAWbBSZ2bNmsVE6aSnp2PIkCEqLQgiCKJwkpOTsXHjRjg7O6NevXpYsmQJXr58KfG11atXx4wZM/DgwQPcunULo0ePVpsIMOD772VRZ8tTp06hRYsWqFSpEjp16oT58+cjIiKCc8wrjPLly6NRo0Zo0qQJmjZtimbNmqF58+Zo0aIFWrRogZYtW6JVq1Zo3bo1nJyc4OzsDGdnZ7i4uKBNmzZwcnKS+PcWFRWFgQMHws7ODmvWrGEOkkgiIyMDXl5ezKFbQ0NDHDt2jBH8FUeXLl3QrFkzrn769CkOHjxY4vcriry8PEyaNAleXl5YvXo1fvnlF9ja2sLR0RHjxo3D0aNHmfWhosjKymLWtQDA399f5Q8cFYXo5n5ubm6JnaBKyqZNm5gN0+bNm6vlYSZ1oFq1amJO6Rs2bEB4eLjYs3z79u0V2RohZ3R1dWFra8vVJXHy5Ce1kMiTIAh1wcXFhTksEhERgeTkZCV2JBl+VLs8RZ5Aydw8RZ08dXV10aBBA5n2JIqHhwdzACEsLIxMXlSQp0+fMutymh7VXkCTJk0YwXh6ejpWrFihxI4UT3x8PGMW4+LiAnNzcyV2VHpcXFyY+saNG0rqhFBlSORJEITWsWnTJqYeOXIkc2NeWubOnQsjIyOuPnDggNjJYUWgKVHtBRgbG6Np06YwNDRUdiulgiLbCUL2iEa1A9KJPB0cHDinYOC7uK6s0cuaAD+qfcCAAQAAT09PxmWPvsvkS0BAAFP7+/urhcthYU4DO3bsQGxsLDfu4OCA4cOHK7I1mdK7d2/mXi8kJARZWVmlmoMf1c7/u+OjaSJP/r2xpog89fT0sGvXLpQvX54bu3nzJpYsWaLErgiCKA15eXk4fvw4+vXrBysrK/j7++PmzZsSX2tsbIwhQ4bg7NmzePHiBZYtW1YqAaEqUaVKFcyaNYsZu3PnDnJycop8X+XKldG7d2+sXr0aUVFRSE1Nxb179/Dff/8hOjoad+7cwb///ovbt2/j9u3buHXrFqKionDz5k3cuHED169fx/Xr13Ht2jVERkbixo0bePbsGaZNmwZTU1Ox6718+RJTpkxBjRo1MGXKFLx48ULsNXl5efDx8WGizAQCAXbt2lVqZz+BQCDm5rl8+XKV2rxNT0+Ht7c3/vzzT7GfxcXFISAgAH369EHlypXh7OyMuXPn4tKlS8zBLlEWLlzIbITVqFFDbVxpC4N/HyXryHa+a1NAQIBaibzVjZ49e4oJkUeOHMlseNra2jKCQEIzqF27Nvffnz59QlpaWpGv54s8NeWZgyAIzUdHRwfe3t5cnZubi9DQUCV2JBlFizxFI7clkZOTgwcPHnB1vXr15HrAvGLFiozJS2Jiotqv12ki/M+Ntog8AWD+/PnMnsKGDRvw5s0bJXakWPh7WOoU1V4AX+RJke2EJEjkSRCEVpGdnY0tW7ZwtUAgEFsoLC1WVlaYOHEiM8bfLFEEfJGnaLQooTj4N42BgYGIiIhQUjcEof4IhUJG5FmuXDnGNa20CAQCxpXm8+fPWuu4m5KSwjhbV61alXM/qVq1Klq1asX97OHDhyVyziBKz7t375gYpgoVKmDIkCFK7KjkdOzYkTkNe/LkSXz69Anz589nXrd48WK1dRcHABMTE3h5eXH158+fSyV8FgqFYlFboi6oktB0kWfz5s2V1InssbOzw19//cWMLVq0qFCRGEEQqkN+fj569uwJLy8vhISEFCqC69ixI7Zu3Yq3b99i586dcHNz0whB14QJE4oVRdnY2GDw4MEIDAzEgwcP8P79exw5cgSTJ09Gq1atZPL73cbGBitXrkRiYiL+/PNP1KpVS+w1aWlpWLNmDezs7DBo0CBERUUB+P479rfffsPRo0eZ169du7bYAxWF0bdvXzg4OHD1vXv3VObA09u3b9GxY8cSbfrn5+fj5s2bWLx4MTp27AgzMzN069YNa9aswb179yAUCvHff/9h5cqVzPs2btzIuLyqI/yYTlmKPFNSUpiNNgcHBzg5OclsfkIya9asgb29PVe/ffuWiYulqHbNhP/7oKg1CaFQyIg8K1eujJo1a8qpMxYzO44AACAASURBVIIgCNnDv3dVRTd5UZFnhQoV0LBhQ5nOX1onz8ePHzPPcE2bNpVpP5KgyHbVR5tFng0aNMCgQYO4OisrC8uXL1diR4qFn0bHN2VSB0jkSZQEEnkSBKFVHDlyhIlU6tq1q8QNhNIyffp0mJmZcfWZM2fEnOfkyefPn5kT7HXr1mVOOxOKo27durCzs+Pq58+fw83NDV26dFGYw+uDBw8wbdo0+Pj4YOHChTh69CiePXumUu4j8ub9+/fYsWOHWDwsoX48efIEiYmJXN2uXTupT+TyN4C0NbI9NDSUcWvq168fs1HPF62ryua2prF582bm/4eff/4ZJiYmSuyo5Ojr66N3795cnZWVhX79+jH/Zps3b864Eagr0kS2P3jwgImPdXFxQfXq1Yt8j4ODAwwMDLhak0SeFStWZO6VNIGRI0cy/xby8vIwZMgQfP36VYldEQRRHBcvXkR4eLjEn9WtWxeLFi3C8+fPceHCBQwfPlxtfj+XFCMjIwQFBTHpGY6Ojhg1ahR27NiB58+f4+XLl9i9ezfGjBmD+vXrQ0dHfkvJJiYm+O2335CQkIBDhw6JbW4A379f9+/fDycnJ/z444/w9/fHhg0bmNdMmTIFv/32W5n70NXVxfTp05kxVXC2vH//PpycnMSc6vr27Yt58+ahTZs2RYqPMzIyEB4ejilTpqBJkyawsrJCz549mVSDQYMGoUePHnL7MygKeYo8z5w5g/z8fK7u2rWrzOYmCqdChQrYtWtXoZ9xEnlqJvy17WfPnhX62levXuHdu3dc3bJlS7VIxyAIgiigbdu2sLS05Opz584hJSVFiR2xJCYmMut9Tk5OMj/QzRfjFSfyFI1qB8TvAeUB/16ZRJ6qhzaLPAFg3rx5zHN7YGAgkpKSlNiRYvj69SsuXbrE1TVq1BAzUVAHqlevDhsbG66OiorS6iRCQjIk8iQIQqsIDAxkaj8/P5nMW6lSJfz+++/M2MyZMxUmqrtw4QLzS17do9rVGYFAgDFjxoiNnz17Fi1btsTAgQOZKDRZUeB22K1bNzRs2BCrVq3Cnj17MG/ePPTp0wd16tSBqakp2rVrh3HjxiEoKAg3btzQSPFBSkoKnJycMGzYMLRo0QJubm64fPmystsiyogso9oL4G8AXb16Veo51ZH9+/czdUFUewH8k478k5CE9OTm5iIoKIgZ8/f3V1I3ZYPvNHDx4kWmXrp0qVwFIYrC09OTOdATFhaGz58/l+i9fBfPkjiL6enpMRHAjx8/xrdv30rYrWrx4cMHMeGvJnwmRBEIBAgODmY2ZBISEjB58mQldkUQRHGIpnwA35/r/fz8cO3aNcTGxmLOnDka7wLm6emJR48e4fz583j37h0eP36MTZs2YejQoUr7s+vq6sLb2xvXrl3DtWvX0L9/f4m/N65evSq2xjNw4EAxZ8qyMHToUOZARmRkpFIPhp05cwZt27bFy5cvmfHp06fj4MGDmD9/PiIjI5GcnIzQ0FCMHz9ezAmJz7t375jNPnNzczFnanXFwsIC1tbWXH337l2Zrc9pQgSguuLk5IQ5c+ZI/BmJPDWT0jh58gXwLVu2lENHBEEQ8kNXVxd9+/bl6pycHJUSEPLd5GQd1Q4AVapUYRKDSivyVISTZ506dZj1uqioKLx9+1bu1yVKjqjIUyAQoG7dukrsRvE4Ojpi6NChXJ2dnY2lS5cqsSPFcO7cOcbZt1u3bmp74Ef0wOvXr1+1NomQKBzN2lkhCIIogsePHzPCAxsbG5kuxo4fPx5WVlZcHRUVJRYZJi/4Ue0eHh4KuS4hmalTp2LLli0SXboOHDiA+vXrY+zYsXjz5o3U18rJycGePXvQokULdO7cuVAnGuB7xF1kZCQCAgLg5+cHFxcXVKxYEXXr1oW3tzcWLlyI0NBQtXf9LHDcKSAiIgIdOnRAx44dERERodZ/Nm1EHiLPJk2aoEKFClx95coVrftcfPr0CefOneNqKysrJsYe+C7EqlatGlefP3+eicQjpOfEiROM+K1Dhw5o0KCBEjsqPZ07d4apqanEn3Xo0EFjDp4YGBgw4sxv377hyJEjJXpvSEgIU4su2heF6GnjvLw8sZPo6gLfVbtFixZK6kS+WFhYiAnGgoODSxSpSxDazObNm1G3bl3069cP6enpCrvu58+fme/n8uXL4/nz59i4cSNcXFzUdjOgLNSuXRuurq6oWrWqslsRw8XFBQcOHEBCQgImTpzI3MPz6dChA7Zv3y6TgwQGBgaYMmUKM6YsN8/NmzejW7du+PLlCzemq6uLoKAgrFixgvnzVqxYEV5eXvj777/x8OFDJCYmYtu2bRgyZAhzEEESa9euVcnPQFkRvY/69OmTTNZf8vPzmTUXY2NjtG/fXup5iZIze/ZstG7dmhmzsLAoVtRMqCd8kWdRTp4k8iQIQhNQ5ch20ah2QD4iT4FAwPxOf/HiBTIyMgp9Pd+tXRFOnoB4ZDsZI6gOQqGQWT+tXbu21Klw6sjcuXMZB/zNmzfjxYsXpZ4nKysLT548QVRUlEo5C0tCkw7jUWQ7URwk8iQIQmvgO2X5+voWGWVVWoyNjTFv3jxmbPbs2cjNzZXZNQpDVOSpp6eHjh07yv2aROEIBAKMGDEC8fHxWLlyJeP8BXx3bgsMDIS9vT1mz55dYicwUb58+YI1a9bAzs4OPj4+iI6OLlOvQqEQCQkJOHz4MObNm4fevXujTp06qFSpEjp27Kh28cxxcXFYt26dxJ9dunQJbm5uaNeuHU6fPq11oj51JD8/nxF5mpiYyEQcpKenxyxEvX79usjNAk3kyJEjzO+nfv36if1O1NHRYeIHs7KycOHCBYX1qA0EBAQwtbq5eALfRRC9evWS+LNly5ZplEimLJHtsbGxzGnbli1bim1WFgY/UkZdI9tFo9oBzRV5At8jW8eNG8eM+fr6kqsDQRTC06dP4efnh4SEBISEhOCPP/5Q2LX379+PrKwsrh4wYEChhxYI5VO7dm2sXbsWSUlJWLVqFRNfBgANGjTA0aNHmeh5aRk1ahTjJBQeHi7mFiRP8vPzMXPmTIwaNYpJbjExMcHJkycxevToYueoUaMGhg0bhp07d+L169eIiYnB2rVr0a1bN5QvX5573eDBgxm3F01AHpHtd+7cwYcPH7i6c+fOWrlprEz09fWxa9cuGBsbc2MeHh4a9cxB/D/8uPainDy16ZmDIAjNpX379qhSpQpXnzlzpkx7R/KAL/J0dnaWy3X4BzdiY2Mlvk4oFDL35lZWVgo7sMQXeaqS46q28/79e6SmpnK1tkW1F2BnZ4cRI0ZwdU5ODhYtWsTVQqEQqampuH//Pk6fPo1//vkHCxcuxOjRo9G9e3c0adIEFhYWMDIygr29PZycnGBvby/2PaAqCIVCZi/bwMAAnTt3VmJH0kEiT6I4SORJEIRWkJmZie3bt3O1rq4ufvnlF5lfZ+TIkbC3t+fqR48eYefOnTK/jihPnjzBkydPuLpNmzYwMTGR6zWJkmFkZIRp06bh6dOnmDFjBoyMjJifZ2RkYOnSpahTpw5Wr17NbDIWRlJSEqZPnw4bGxtMmTKFcYArwMLCAvPmzcPdu3e5zdLevXuLLY4WxZcvX3Dp0iV0794dwcHBJX6fspk2bRojXJPk4nLt2jV4enrCyckJYWFhJPZUYR48eMBsonXo0AF6enoymZvvWqnM+EVlcODAAaYeOHCgxNfxI9vVTfitysTFxTGHNCwtLdG7d28ldlR2JMWPe3l5iS1IqDvt27dnXLojIiKKFe/xXTy9vb1LfD1NFXk2b95cSZ0ohpUrVzKLyB8/fsQvv/xC9xsEIYHdu3cz4rXNmzfj69evCrn21q1bmVp0A4RQXUxNTTFlyhQ8efIEe/fuRf/+/TFq1ChERESgUqVKMr1WhQoVMGHCBGZs+fLlMr1GYWRmZmLQoEFi17OxsUFkZGSZnNIFAgEaNmyIiRMn4sSJE0hOTsa1a9cQGRmJXbt2aZxIji/ylMV9FP9ZSPRAHKE46tati+PHj6N58+bo0qWLVsRPaivVqlVjxPuFiTyFQiHj5Fm1alXUqFFD3u0RBEHIHH5ke3Z2tkoICDMyMhiTkfr168v83rsAviivsMj2169f49OnT1ytiKj2ApydnWFhYcHVZ8+eLdHeHiF/+ClI2iryBIA5c+ZAX1+fq7dt24ZOnTrB0dERFSpUgJmZGRo1agRPT0/4+vpi3rx5CA4OxsmTJ3Hv3j3m3xcAJCcno2fPnoX+m1Qmd+/exatXr7i6Y8eORaaAqDrNmjVj7oFJ5EnwIZEnQRAqS1paGqKiovDt2zep5zp48CBjJe7l5SUxSlta9PX1sXjxYmZswYIFcnXzPHv2LFNrSiyqJlGpUiUsW7YMCQkJGD16tJhbXnJyMqZOnQoHBwds3bqV2egs4O7du/j5559Ru3Zt/O9//2Oi2gqoW7cuNm7ciBcvXmD+/Plo3Lgx+vbtiwULFuDIkSN4+vQpPn/+jKtXr2LDhg0YM2YMnJ2dGQcPSYwZM4YRSasqEREROHbsGFeXL18esbGxWL16NRM5XcCtW7fQs2dPtGjRAkeOHEF+fr4i2yVKgDyi2gv48ccfmVqbRJ4fPnxg/m6rV69eqBjP3d2dEdaeOHGChEoyIjAwkKlHjRoFAwMDJXUjHe7u7swBE4FAgCVLliixI/mgo6ODn376iavz8/Oxf//+It9z6NAhptZ2kaeJiQnq1q2rxG7kj7GxMXbv3s18d548eVLs3zxBaDtCoRC7d+9mxr58+YIdO3bI/dqPHj3CjRs3uNre3l7sABCh2ujr62PQoEE4cOAANm3aJPF5TxaMHz+eeV4+ePAgEhIS5HKtAj58+IDOnTuLxXO2aNECN2/eRKNGjWRyHQMDA7i4uKBNmzYaJ/AE5OPkSSJP1aFTp074999/cfr0adja2iq7HUJO6OjoMCkIhSWwvHz5Eh8/fuTqli1bauT3GkEQ2kH//v2Zmr+upAxu377N7HHKI6q9AL6TZ2GCMr7DvqKi2oHvYlzRKOiMjAxKv1IRSOT5/9SsWRO+vr5cnZeXhwsXLiAuLg4ZGRllmjM5ORkeHh6MoFIVOHHiBFPzjUvUDQMDA8aVPj4+nrnXJQgSeRIEoZI8f/4cDg4OcHJyQp06dbBz506pBFj8TVU/Pz9pWyyU/v37o1mzZlz94sULhIeHy+16oi5gAIk8VRlra2sEBQXhwYMHEl3PEhMTMXLkSDRu3BihoaEQCoU4c+YMunTpgqZNm2Lnzp0SBcNt27bFkSNH8OjRI/j5+TGxVXwqVqyItm3bwt/fH4GBgbh+/Tq+fPmC+Ph4xvVTNNpCKBRi5MiR2Lt3r2z+IuRAXl4eJk2axIzNmDED9vb2mDx5Mp49e4a///4b1tbWYu+Njo5G37590bRpUxw8eJDEniqEPEWeTk5OzElGbRJ5Hj58mBGT9+/fX6LrLfDdLUlU9PD8+XOxxRKi9GRkZDAOYrq6uiWK3FRVypUrh4kTJ3L1hAkT0LBhQyV2JD9KE9n+9OlTxumgcePGpRI4VqtWjfl9rI4iz0+fPuHFixdc3axZs0K/bzSJ5s2bY+HChczYlClT6PuTIESIjo6WGL23fv16uR8okeTiSWIQQhLm5uYYM2YMV+fn52PlypVyu97jx4/h7Ows5tTh5eWFS5cuwcrKSm7X1jQcHBwY9xNpRZ4fPnxAVFQUV9evX58RnxEEIR9E/519/vyZiWAtQNTFE/gu8iQIglBXOnTogMqVK3P1qVOnkJaWpsSOxKPaVVHkqUgnT4Ai21UVEnmyzJ49m3kmKilGRkaoW7cuOnbsiMGDBzN7zomJifD09JR4T6Ys+IfxREXY6grflEX0oDJBaP7uCkEQasmUKVO4+MvXr1/j559/houLS5l+id29e5dZIK9Tpw7c3Nxk1isfHR0dTJs2jRkLCgqSy7Vyc3MRERHB1ebm5hofgakJODo64uDBg4iKipIoWnv48CEntPTw8BBzawW+u6R5e3vj2rVruHr1Knr37i3mEFpSdHR0YG9vz7h+xsTEMA9A+fn5GDp0qFjsrKrwzz//ICYmhqsL4uwLMDIywvjx4/HkyRMEBARIdHqIiYnBgAED0LBhQ+zZs0eioyqhOHJzc3Hx4kWurly5ssxca4DvnwnRhf+4uDi8e/dOZvOrMiWNai+A/1DMPxlJlJ59+/YxCyG9evVS+zi5+fPn48yZMwgPD8eaNWuU3Y7caNq0KfP7MSoqqlBHL/7vTEkHPIpD1M3z7du3eP/+fannUCZ37txhatFTyJrO9OnTGZF8ZmYmhgwZguzsbCV2RRCqA9/Fs4BHjx4xz7iyJjc3Fzt37uRqHR0d/Pzzz3K7HqH+TJo0iTkctn37drx+/Vrm17l48SLatGmDp0+fMuMTJ07E4cOHi03gIFj09PTQoEEDro6NjZUqRvPMmTOMAJ1cPAlCMdSuXZupJbl5ksiTIAhNQk9PD3369OHqb9++ISwsTIkdfReaiiJPkWfNmjVhZGTE1YUdluUf4FG0yLNLly7MM0JYWBilX6kAJPJkqV69Ov78809mzNzcHI0bN0bXrl0xatQozJ8/H8HBwQgPD8e9e/eQnJyM9PR0xMXF4cKFC9i9ezcOHTrE7D/fv38fvXr1kur5SlZ8+vSJ0Y44ODjA3t5eiR3JBmdnZ6amyHZCFBJ5EgShcly/fh2HDx8WG4+KioKLiwt8fHyQlJRU4vn4AsvRo0fL3UGob9++sLCw4OqTJ08yLkayIioqiontdnNzK7PQj1A8rVq1wrlz53DmzBmJ4lxJ9utGRkYYN24c4uPjcejQoUIjlqWlatWqiIiIYG6G8/LyMGjQICYSXRX4/Pkz5syZw4ytWLGCWQwooFy5chg7dizi4+MRHBwstlgMfN9Y9vHxQf369ZW+gKLNREdHM99vrq6uMv/u5ke2R0ZGynR+VeTdu3eMeNbW1hZOTk5Fvocfb8E/GUmUDqFQiA0bNjBj/v7+SupGdujo6MDd3R2enp4a7dQoEAjE3DwLc7rmizxLE9VeAF/cLnqgQR0QjWoHtEvkqauri507d8LExIQb+/fff8UcPglCG8nLyysyJWDdunVyu/apU6e4A6UA4O7urvYHLQj5UqNGDUYInJ2dLfMDLTt27ECXLl2QkpLCjeno6GDdunVYu3YtrfOUEdHYzry8PDx8+LDMc2miOwxBqAN8x9znz5+LvYYv8tSmZw6CIDQTVYpsf/v2LS5fvszVtWrVgoODg9yup6OjA0dHR66Oi4uTmG4n6uRpZGSkcFFXxYoV0aFDB65OTEyU2jmekB5Rkae5uTmzT6+t+Pn5ITU1FfHx8cjIyMCnT59w9+5dnDx5Eps2bcK8efPg6+sLT09PNGrUCGZmZmJJJ127dsU///zDjF2+fBk+Pj5KN8s5deoUk86o7lHtBfD3/knkSYiiubtvBEGoJUKhUMwFk8+ePXvg4OCABQsWICMjo8jXfv36Fbt27eJqfX19jBgxQia9FoWhoSFGjhzJ1UKhEMHBwTK/DkW1qz8CgQDu7u64desW9u3bBzs7O4mvq1q1KhYtWoTExESsX7++0NfJEmtra5w/f54RQubm5qJ///5ipzeVydKlS/HhwweudnZ2xqBBg4p8j4GBAXx9fREbG4tt27ZJXASIi4uDl5cXtmzZIvOeieKRZ1R7AXyRpzZEtoeEhDAPvQMGDCg2nvSHH35AzZo1ufrKlSv4/Pmz3HrUdKKiohh3Q0dHR7l8vgn58dNPPzH17t27xU7rJyYm4ubNm1z9ww8/oH79+qW+lqiTJ6D+Ik9tc5yvVauWmKh72bJluHr1qpI6IgjV4NKlS3jz5g1Xt2/fHubm5lx9/PhxiU5dsoAf1S763E4QhTF9+nTmnjkwMBDJyclSzysUCjF//nwMGzYMOTk53Hj58uURGhqKX3/9VepraDP8+6iybrzn5eUxayAVKlRg3LoJgpAfxTl5CoVC5pnDysoK1tbWCumNIAhCXri6ujLPRydPnsTXr1+V0suRI0eYNa/+/fsXu5YsLaLuizk5OWJO91+/fmVSdRo3bqyUQ1EU2a5aZGRkMGZL9erVk/tnVV0wNTWFvb29RGOckjJs2DAsX76cGTt8+DAmTJigVBdbfuqcpog8q1evDhsbG66OiopSuqCWUB1I5EkQhEpx7NgxxkmtSpUq2LBhA6pWrcq8LjMzE/Pnz0e9evWwd+/eQm8g9u7di7S0NK729vYWm0tejB49mqk3b97MLNrLAr7I093dXabzE4pDR0cHAwcOxKNHjxAQEABLS0sA3x9EgoOD8eLFC8yZMweVK1dWaF82NjY4f/48czOZnZ2N3r1749y5cwrtRRJPnz4Vixv4888/S/zwpq+vj2HDhuHRo0fYtWuXWHyDUCjEL7/8go0bN8qsZ6JkKELk2bZtW6bWBpHn/v37mXrAgAHFvkcgEDAPx7m5uTh79qzMe9MWAgICmHrs2LG04KRm2Nvbo3Xr1lwdGxvLOAgAEHOlL4uLJyAuTrh3716Z5lEWohuu5cuXl6vbg6oyZMgQxoUjPz8fQ4cOZdyqCULb4Ee1//LLL/D19eVqoVAo9vtSFnz48IFJJTAzM4OXl5fMr0NoHg4ODujXrx9Xp6enY/369VLNGR8fjyFDhmDBggXMuLW1Na5cuYIePXpINT/BOnkCZRd53rp1ixH1uru7w8DAQKreCIIoGcU5eT579oxxQaaodoIgNAF9fX307t2bq7OyspSWrHTw4EGm5ruMyoMffviBqR89esTUMTExzJ4w/55PUZDIU7WIj49nPhfaHtUuD6ZPn44JEyYwYwEBAViyZIlS+pF0GI9v7KLOiLp5pqen4/79+0rshlAlSORJEITKkJubixkzZjBj8+bNg7+/P+Lj4zFt2jTo6+szP09MTMTgwYPRrl073Lp1i/mZUCgUE2b5+fnJp3kJ2NnZMc6a7969Q2hoqMzmT01NZRyi6tWrB1tbW5nNTygHfX19jB07FomJiUhMTMTDhw/h6+uLcuXKKa2nWrVq4fz588xJ+G/fvsHLywuXLl1SWl/A94eK7Oxsrvbx8Sk2eloSenp68PHxwf3797F//37UqVOH+bm/v7+YmJSQH9nZ2Yzg0traWi7CIDMzMzRs2JCro6OjmYMBmsbr16+Zv9fatWuXeAOEH0dIke1l4+PHj4zQ1tjYGMOGDVNiR0RZ8fHxYeo9e/YwNT9KS1QYUhrq168PHZ3/f2xXJ5FnSkoK47TTrFkzrYybFQgECAwMZO6jnj9/jt9++02JXRGE8sjKykJISAhXlytXDr1794a/vz/zfbd582akp6fL9Nq7d+9mov4GDx6s1OcsQr2YOXMmU//999+l/oy+fPkSq1atQosWLeDg4CB2/9C4cWPcvHkTzZo1k7pfQnYiT/6zT9euXcvcE0EQpaM4J09+VDuJPAmC0BT460jKiGx///49s/9Ts2ZNhXzP8kWeohHcAMQOWjdt2lTuPUmidu3aaNCgAVffunULb9++VUovhPjnhESeskcgEGDt2rVixiFz587F5s2bFd7PjRs3mMM+mnYYjyLbicIgkSdBECrDli1bmJuwunXrcm6YFStWxMqVK/Hw4UP06tVL7L3Xrl1D69atMWzYMLx+/RrA90We6Oho7jX16tVD+/bt5fynYOGLSgMDA2U29/nz55nIXYpq1yz09PRQo0YNlXF3s7e3x/nz51GtWjVuLDMzE927d8e1a9eU0tOlS5eYDWIjIyMsW7ZMqjl1dXUxYMAAREZGisXqTpo0CStWrJBqfqJk3Lx5E5mZmVzdqVMnuf1bED3Zl5+fr9EPSiEhIcxp1pJEtRfg6urKiCBOnjzJ/A4iSsbWrVvx7ds3rvbx8UGlSpWU2BFRVgYMGMCIkfbu3ctFprx584ZxprezsxNz5Cwp5cqVg6OjI1c/ePCAESipMnfu3GHqFi1aKKkT5WNubo7t27czY9u2bVPKJg1BKJuTJ0/i8+fPXO3l5YWKFSuiZs2ajKtmamqqmOOnNAiFQrGo9hEjRshsfkLzadasGTw8PLj606dPCA4OLvZ9b9++xfr169GuXTvUrFkT06ZNE/sdCXwXDl69ehU1atSQad/ajLm5OfP3ee/evTJFCZLIkyCUh4WFBYyNjbma7+RJIk+CIDSVzp07M2uGJ06cQEZGhkJ7OHLkCLP+269fP4XsVxXn5Mk/uKMskScg7ubJj44mFAeJPBWDjo4OduzYAVdXV2Z8zJgxCnez1dSo9gJI5EkUBok8CYJQCdLT0zFv3jxmbOnSpWLOnfb29jh69CjOnTvHuK8VsGPHDjg4OGDJkiX466+/mJ+NGTNG4YK5Hj16MK49ERERiIuLk8nc/Kh2EnkS8sbR0RERERGwsLDgxtLT09G1a1cxJ115k5eXh0mTJjFj06ZNY2LlpcHS0hIXL14Uc/6YMWMGFi1aJJNrEIXDj2rv3Lmz3K7Fj2/Q5Mh2flT7wIEDS/xeY2Nj5sH93bt3zEEKonjy8/PFHMb9/f2V1A0hLZaWlsx306tXr7jvjyNHjjACAmkXwUUFollZWUhISCjzXIpENKod0G6RJwC4ublh4sSJzNiYMWPw6tUrJXVEEMqBL9wcPHgw99/jx49nfrZu3boyCbIkER0dzbghN2rUCM2bN5fJ3IT2wHfzXL16NZMsUUCBANTNzQ3Vq1fH+PHjmQMgfCZMmIBjx47BxMRE5j1rO6L3UcnJyaX+vfv27VvmnqZx48YkxCUIBSIQCBg3z2fPnjH3BnyRp7Y/cxAEoTkYGBgwhjcZGRkIDw9XaA/KiGoHvhsAiR6s5os8RZ08BQIBGjVqpJC+JEGR7aoDiTwVh6GhIY4cOcLsn+bn52PgwIEKFSLyRZ6adhivWbNmMDQ05GoS4ieSAgAAIABJREFUeRIFkMiTIAiVYM2aNYyNvZOTE7y9vQt9fefOnREdHY2NGzeicuXKzM/S09MxZ84cZvOoXLly+Pnnn2XfeDHo6+vD19eXGdu0aZPU8wqFQpw+fZq5TocOHaSelyCKo0GDBjh37hzMzMy4sS9fvqBLly4KFXzt2LGDuZ61tTWmT58u02tUqVIF58+fF1sg/uOPPzBnzhyZbTgT4vBFnvxTgbJEW0SeSUlJzMayvb19qU85809C0snk0nHq1CkmWs7FxUWpJ80J6REVJgH/H9ku6jINoMh72pLAdwFVl8h2vsiTxFTAsmXLmCiv5ORkjBgxgpyRCa0hNTWVuX8wMzNjFuFdXV2ZfyP3799n4gGlYcuWLUw9cuRIlUlNINSH9u3bM24aSUlJ2LVrF4Dvz8U7d+5E9+7dYWlpidGjRyMiIqLQ7/iGDRti8eLFiI+Px19//QU9PT2F/Bm0DWkj20XX3gCgW7duUvdEEETpqFWrFvff6enp+PTpE4DvYgLRZw4bGxsmgYggCELdUWZk+4cPH3DhwgWutrGxQevWrRVybUNDQ9SpU4erHz16xO3F5OXlISYmhvuZvb09KlSooJC+JOHk5MSYopw9exZZWVlK60ebERV56uvrM4dECNljamqK8PBw5j4tMzMTPXr0EBNmy4OkpCRmjbxZs2aM6ZYmYGBgwOxPx8fH4+PHj0rsiFAVSORJEITSef/+PVauXMmMrVy5stgNFz09Pfj5+SEhIQGTJk0qckF84MCBMDc3l0m/pcXX15c5dbZ161apb/KfPHnCxNO0bdtWqQ8yhHbRpEkTnDlzBhUrVuTGUlNT4e7uzjxgy4uvX79i1qxZzNiyZctQvnx5mV/L3NwcERERcHZ2ZsaXLFmCadOmkdBTDmRkZDAn0uzs7FCzZk25Xa9GjRrMg+jNmzcluvGoO/yT16WJai+Av6HJjy0kiiYgIICpycVT/enTpw9zmvbQoUN49eoVLl68yI3Z2tpKHRmoCSJPY2NjOkGP7wfPdu/eDQMDA27s7NmzWL9+vRK7IgjFcfjwYXz79o2r+/fvz/x7EAgEEt08pSUrK4sT4gPf1xJ8fHyknpfQPgQCgZib5+LFi+Ht7Y2qVavi559/xsmTJ5Gbmyvx/fb29pgzZw5iYmIQExOD2bNnw97eXhGtay3Sijwpqp0glI/omg0A7vDkkydP8OXLF26cXDwJgtA03N3dmT2Y48ePIzMzUyHXPnr0qFKi2gsQjWxPS0vDmzdvAAAJCQlMbD3/Xk/R6OrqMmvmGRkZYgYWhPzJz89HbGwsV9etW5cO0SkAKysrnD59mhE6Jycnw9PTU+7JRfznNE2Lai+AH9l+48YNJXVCqBIk8iQIQuksXLgQX79+5eqePXuiffv2JX5/pUqVsGbNGty/f7/QX+J+fn5S91lWbGxs0KNHD65OTk4Wc3gqLRTVTiibli1b4vTp04y4+NOnT3Bzc5P7Ka3ly5czzr8tW7bEkCFD5HY9U1NTnDlzRszxcfXq1ZgwYQK5b8mYq1evIicnh6s7deok92uK/n+blZUl5j6nCRw4cICpSxPVXkDt2rWZBbaoqCh8+PBB6t60gWfPnjELDxYWFgqLOCLkh6mpKXOPl5KSgrFjxzK/F7y9vaVeBOfHTqmDyDM1NRVPnjzh6qZNm0JXV1eJHakOTZo0wdKlS5mx6dOn48GDB0rqiCAUh6jQEhB3RAaAIUOGoFKlSlx99OhRvHz5UqrrHjt2DCkpKVzds2dPVKlSRao5Ce2le/fuaNiwIVc/e/ZMTMAsio2NDaZOnYrbt28jLi4OixYtYt5PyBf+xn9p7qNyc3MZJ09TU1OxTTaCIOQP34mrwPiAH9Uu7eE6giAIVcPQ0BBeXl5cnZ6eLuYyLi/4hgF8V1F5I7oGDfx/ZLtoVDsAlUhJosh25ZOYmMgIoOmgueJwcHBAWFgYjI2NubGXL1/C09MTqampcrsuP2VOU0Werq6ucHd3xx9//IGTJ0+WSj9DaC4k8iQIQqnEx8cjKCiIq3V0dLB8+fIyzeXo6IiwsDCcOnWKeQBwc3ODk5OT1L1KA19kGhgYKNV8JPIkVAFnZ2eEh4czN+/v379H586dER8fL5drvnjxAqtXr2bG1q5dy7jlygMTExOEh4eLCQ7Xr18PPz8/EnrKEP5JV0WLPAHNi2x/8eIFc8LP0dFRTDRWUkQfloVCIU6dOiV1f9pAUFAQ4/zr6+vLOEAS6gvfCY6/kCttVDvw3Q1U1LlBEa7Z0hIdHc3U5KrDMmnSJLi6unL1t2/f4OPjU6hAiCA0gdevXzP3eTY2NmL3YABQvnx5jBw5kqvz8/OxceNGqa69detWph4xYoRU8xHajY6ODmbMmFHka6pWrYpff/0VV69exfPnz/G///0PLVq0UKj7EfEde3t7lCtXjqtL4+R5/fp1fP78mau7dOkCfX19mfZHEETx8J08SeRJEIQ2wRdX8sWX8uDjx4/Ms1v16tXFks7kTWEiT/69nCqIPPn3iGFhYZQAp2BEo9oBEnkqGicnJxw6dIg54H///n306tVL6mRTSWRlZeHcuXNcbWFhgVatWsn8OqpA9+7dcebMGSxYsABdu3Zl9ggI7YVEngRBKJVZs2YxMVYjR45E/fr1pZrTw8MDd+/eRUhICIKCgnDo0CGlL6R36dKFWZC6evUq7t+/X6a5cnJymAesypUro1mzZtK2SBBlol27djh+/DizafLmzRt06tSJi0+SJTNmzGAeCgYMGIB27drJ/DqSKF++PMLCwuDp6cmMBwcHY+TIkcjLy1NIH5oOX+QpKoKRF/zPkKaJPPmLfwMHDizz70V+ZDv/xCQhTlZWFv755x+uFggEGDNmjBI7ImRJ165dYWpqKvFnVlZWMnF7EggETGT78+fPGcGBKsJ3RCaRJ4uOjg62b9/OfHbu3r2LuXPnKrErgpAv+/btYza6fvrpp0IPao0bN465V9m0aVOZYwmTkpIYtxtLS0uKWyakZuDAgXBwcGDGzMzM4Ovri3PnzuHVq1dYt24d2rZtK/cDiUTR6OnpMc6pcXFxJf4+CQ8PZ2r+sxBBEIqB7+RZsN7IF3nSMwdBEJqIh4cHk6Z2/PhxuYimRAkNDWX2Ovr166fwe1q+SK8wJ09lx7UDQMWKFdGxY0euTkpKKtXBIkJ6RKPaARJ5KoOuXbsyeyAAcPnyZQwZMkTme6eXL19GRkYGV3t6elKCFKFV0CoTQRBK4+bNmzh06BBXGxkZYcGCBTKZW19fH3379sXo0aML3XhXJLq6uhg9ejQzJupgWhpu3ryJtLQ0rnZ3d6dNA0KpdOrUCUePHoWBgQE3lpSUBFdXV5kKPa9du4Z9+/ZxtaGhIVasWCGz+UuCkZERjh49KhbBsX37dgwdOpQRrROlJyYmhlmkb9CgAapVqyb369arVw8WFhZcHRkZWSZ31oyMDOzevRuTJ09GWFiYLFuUCn5U+4ABA8o8V7t27WBiYsLVp0+fps99MRw8eBAfP37k6u7du4s5kRDqS7ly5Qp16+zbt6/M7tFERZ4AynxYSFHwRZ7NmzdXUieqi42NjZi7/6pVqxAZGamkjghCvpQkqr2AOnXqMO7hycnJ2Lt3b5muu2PHDkZcOnToUOjp6ZVpLoIoQE9PD8ePH8fQoUPh6+uLsLAwvH37FsHBwejcuTN9xlQM0c3//Px8PHjwoETvO3nyJFPzD3wSBKEYJDl55uXl4c6dO8xrRNd1CIIgNIVy5coxexFpaWk4e/asXK+p7Kh2oGROnpUrV0b16tUV2ldhUGS7cuE7eTo6OiqpE+1m2LBhWLZsGTMWEhKCCRMmyNTdVlui2gmiMEgVRBCEUhAKhZg+fTozNnnyZFhbWyupI/kzYsQIZqF/x44dSE9PL/U8FNVOqCIeHh4ICQlhYilevHiBhg0bYsWKFcjOzpZq/vz8fEyaNIkZmzx5slKEUoaGhjh06JCYqGfv3r0YNGiQ1H9WbWbu3LnMw16vXr0Ucl2BQMC4eaakpJR4408oFCIyMhKjRo2CpaUlhgwZgrVr16Jnz56YM2eO0qNZnj59ilu3bnF1gwYN0KBBgzLPp6+vz/zeSU1NxfXr16XqUdMJCAhgan9/fyV1QsiLwoRKslwE54s87927J7O55YGoyNPIyEhscZ74zqBBg+Dj48PVQqEQa9euVWJHBCEfYmNjme+FBg0aiH2v8ZkwYQJTr1u3rtT3VUKhkKLaCbnh4OCAHTt2IDg4GN27d2cOPRKqBd/hqSTOSq9evWJe17x5c1haWsq8N4IgisfMzIyJpnz27Bni4uLw9etXboxcPAmC0GQUGdmenJyMiIgIrra2tkabNm3kdr3CMDU1hZWVFVc/fvwY79+/x+vXr7mxJk2aKD3FsQASeSoXEnmqDr///rvYek5AQACWLl0qk/mFQiEj8tTR0YGHh4dM5iYIdYFEngRBKIWwsDBcvnyZqy0sLMREn5qGpaUl+vTpw9VfvnxhXAlLCl/k6e7uLnVvBCELevTogf379zO2+BkZGZgxYwYaN24s1QnTPXv2ICoqiqurVauGmTNnStWvNBgYGGDfvn346aefmPGQkBD069cP3759U1Jn6ktUVBRCQ0O52sTEREzYK09+/PFHpi4usj0xMRFLly6Fo6Mj2rVrh82bNzMuywCwZMkS/Prrr2VyBZUV/EU/aVw8C+CfjOQ73BD/z507d3Djxg2urlOnDi06aCAdO3YU2/SvUqWK2PeKNChC5CkUCnHnzh1mwbwsfPnyBfHx8VzdpEkTcjQrgvXr18PMzIyrT5w4gS9fviixI4KQPZJcPIvbjHNzc2Mi1v77779SO91GRkYiISGBq52cnEh0ThBaCP8+qiQiT4pqJwjVQSAQMIe8nz9/LhbV3rJlSwV3RRAEoTi6du2K8uXLc3VoaCgTVSxLQkNDmdQmb29vpSUJij67vXnzhtlTBoCmTZsquqVCqVWrFho2bMjVt27dwps3b5TYkXYhKvK0srJSiYRPbUUgEGDt2rVi+1Bz5szB33//LfX8cXFxePLkCVe3adOGWVclCG2ARJ4EQSic3NxczJgxgxn7448/mBO5moqfnx9T8yMaiyM5OZlxZKtfvz5q1Kghk94IQhb06dMHe/fuhaGhITMeGxuLLl26oH///khMTCzVnOnp6WLfGUuWLGEio5WBnp4edu7cieHDhzPjx48fR+/evZGZmamcxtSUOXPmMPWkSZMUGrXFF2NdvXpV7DWZmZnYs2cPunTpgpo1a2L27NmMkEkSAQEBGDp0KHJycmTab0nZv38/U8tC5Nm1a1em5sdjEP/Pxo0bmXrs2LFKWxgl5Ieuri4GDRrEjPXp04c59CAtogvFgHxEnkOHDkWLFi1gZ2cn1b/r6OhopiZXnaKpVKkS4w6elZXFHHogCHVHKBRi9+7dzFhRUe0FCAQC/Prrr8zYunXrSnXtLVu2MPXIkSNL9X6CIDQDEnkShPpTu3Zt7r+zsrLEnldI5EkQhCZjZGTEHLr/8uWL3FJAVCGqvQDRQ38AxExzVEnkCXw3QRGF1swVw+fPnxlBLf9zQygeHR0d7NixA66ursz4b7/9htmzZ0uVfsc3HKGodkIboR1GgiAUzrZt2/Dw4UOutrOzw5gxY5TYkeJwdXWFg4MDV9++fVvs5HFRnD9/nnGEo6h2QhXp378/7t+/L3ET5NChQ6hXrx6WL19e4ljzVatW4dWrV1zdtGlTMWGlstDV1cU///yD0aNHM+OnTp1Cjx49kJ6erqTO1ItLly4xTq9mZmaYPHmyQnto1qwZcyL6ypUrEAqFEAqFuH79OsaMGQNLS0v4+Pjg7NmzEh9Ey5Urh59++gmdOnVixvfs2YM+ffooXPgbHx/PiK0aN24sk0UOS0tLRrQVExNTavG2NpCamsqIWgwNDSkiVoMZNWoUF9Oqo6MDX19fmc5vYmLCbGzGxMRItSDGJzo6mvu8ZmVlYciQIXjx4kWZ5hKNZAZI5FkS+M7gZXH7JwhVJSoqinFZaNu2LePGVRQ///wzc7ArJCQESUlJJXrv169fceDAAa42MjLCwIEDS9Y0QRAahZmZGWxtbbn63r17Rd5HZWdnM8+n5ubmaN26tVx7JAiiaPj3DmFhYUxNzxwEQWg6/v7+TL1ixQq8f/9eptdISUnBuXPnuNrS0hJt27aV6TVKAz+FgS+abNKkiSLbKRaKbJdMfn4+Jk+eDHt7e6kFfpKIjY1lahJ5qgaGhoY4cuSI2L/TpUuXYuTIkWU2ReF/D5DIk9BGSORJEIRCycjIwLx585ixJUuWcJvimo5AIBATtAYFBZX4/fyodhJ5EqqKvb09wsLCEBoaKrYQm5GRgZkzZ6JRo0Zin2k+SUlJWLlyJTO2du1ambqjSYuOjg4CAwMxfvx4Zvz8+fPo2rWr3KJTNAWhUIjZs2czY7///rvCIzX09PTg4uLC1UlJSZg5cyZ++OEHtGnTBps2bSo0PtfZ2RmBgYF48+YN9uzZg5MnT4qdcj5x4gQ8PT3x+fNnuf45RBEVNgCycfEsgC/ipsh2cbZt28YIewcNGoTKlSsrsSNCntSvXx8nT56Ev78/jh07hlatWsn8GqIuVGlpaWUWYUrin3/+YerU1FQMHjyYiegqKXyRZ/PmzaXqTRvo0KEDLC0tufrMmTP49OmTEjsiCNkhKaq9pJiYmDAHJPLy8kqchnHo0CHmwFXfvn0pso0gtBjRzcXU1NQiD6lFRkYiLS2Nqz08PFRqDYIgtBHRA28AmN/xdnZ2FNNJEITG06FDB8YpMi0tDQsWLJDpNUJDQxnhlbe3t1Lvgfgiz6ysLO6/DQwMVE7M5+TkxCSTnT17ltLe8H2NfO3atXjy5AmWLl2K06dPy3R+0ah2gESeqoSpqSnOnj0rdmBu27Zt6NWrV6lNctLS0nD58mWutrGxEUu/IghtgESeBEEolD///BOvX7/m6latWqF///5K7EjxDBs2jImy3rt3b4lEP0KhkBHEGRgYoH379nLpkSBkgUAggJeXFx4+fIg//vhDLMI9Li4OHh4e6NevH16+fClxjlmzZjEiyT59+qBjx47ybLtMCAQC/PXXX5g6dSozfuXKFbEYcnUgISEBfn5+mDVrltwXIk6dOoXIyEiurlatmlg0p6Jo164dU69YsULsJGgBVlZW+P333/Ho0SPO6bNSpUoAvp9S3LdvH3755RfmPZcvX0anTp3w4cMH+fwBeMhT5Mk/IUnxMyz5+fkICAhgxvgn7gnNo3PnztiwYYPcThDzo0ZlFdmemZmJXbt2iY1fu3YN8+fPL/V8oiJPQ0ND1K9fX5r2tAJdXV3mOzo3NxchISFK7IggZENubi7jTKunp1fq+5Fx48Yx9aZNm5jNvcLYunUrU5ObNkFoN6WJbOcfYKOodoJQPkW5gFNUO0EQ2sKKFSugo/P/0o6goCAxgZs0HDp0iKmVGdUOiIs8Ralfv77KmQfp6uoya4KZmZm4cOGCEjtSPvn5+WImLvyDoNJCIk/VpkqVKpwhjijh4eFwdXUt1V7Z2bNnGSF6t27dIBAIZNYrQagLJPIkCEJhfPjwAcuXL2fGVq5cyTyUaAOVK1dmNrbS09OZONfCiI+PZxyb2rVrx0QLE4SqYmRkhAULFuDBgwcSN0dCQkLwww8/YNmyZfj27Rs3HhUVhZ07d3K1vr4+/ve//ymk57IgEAiwcuVKMVfKv/76C1FRUUrqqvRERUWhdevWCAoKwrJlyzB06FC5XUsoFIqJYGfPnq2077Yff/yxyJ8bGBhgwIABCA8Px8uXL7F8+fJCFw10dXURHBwsJvy9c+cOfvzxR7nHmz9+/JgRgDVr1gx169aV2fwtW7ZkTiZHRESUSHChLZw/fx7x8fFc3bJlS4p4JKRGXiLPkJCQQg8cLV26FOfPny/xXGlpaYiLi+PqJk2aQF9fX+oetYFBgwYx9d69e5XUCUHIjvPnzzMRgl26dGHuH0qCg4MDsxnw4cMHsYMsfBISEhh3h5o1a8LV1bVU1yUIQrPgxwQWJfIMDw/n/lsgEMDDw0NufREEUTKKEnlSVDtBENpC/fr14evry9V5eXmYMWOGTOZOTU1lTGaqVatW7Fq5vLGyskLFihUl/qxp06YK7qZkUGQ7y/Hjx8VMNI4dO8bsA0oLiTxVn/LlyyM0NBTDhw9nxm/duoW2bdvi6dOnJZqHfxiPotoJbUW7lFUEQSiVxYsXM3FH3bt3V0lHPkXg5+fH1IGBgRAKhUW+h6LaCXXHzs6uyAj3WbNmoXHjxjhz5gyEQiEmTZrEvOa3336DnZ2dAjsuPQKBAIsXL8awYcO4sfz8fPj6+iI7O1uJnZWMy5cvw83NDSkpKdxYSEgIwsLC5HK9w4cP486dO1xta2uL0aNHy+VaJcHZ2VmiwLRVq1YICAjAmzdvsH//fnh6ekJPT6/Y+QqEv0uWLGHGY2Nj0bZtW0YIJWv44oeBAwfKdH5dXV1GcJGRkYFLly7J9BrqDLl4EvJAXiLPzZs3M7WoU4JQKISPjw8j0iqK//77j7mnpQ3XkuPs7IyaNWty9aVLl5gEBIJQR/iHGX18fMo0z/jx45l63bp1RT4/b9u2jamHDx+udYdLCYJg4Ys8C7uPevHiBR48eMDVrVq1QpUqVeTaG0EQxUNOngRBEN9ZsGABs34dGhqKK1euSD3vsWPHGIe8vn37KjWqHfi+tl6YYE9VRZ5dunRhHEbDwsKK3fvVVIRCIVasWCE2/vnzZ0RERMjsOqIiT2NjY9SoUUNmcxOyQ19fH1u2bBEzyYmPj0ebNm0QHR1d5PuFQiEj8jQ0NESnTp3k0itBqDq0wkkQhEJ48uQJNm7cyNU6Ojpirp7ahIuLCxo1asTVMTExuH79epHvIZEnoQmUNMLdxcUF165d48arVKmiVrHna9asQdWqVbk6JiZGpV1IAeD06dPw9PRkxPgF/Prrr8jIyJDp9fLy8jB37lxmTNJnQpEYGxtj/fr1MDU1hY2NDaZOnYr79+8jKioKY8eOhbm5eannFAgEmDVrFgICApjoiMTERLRr167Yh9eywhd59u/fX+bX4Dvz8k9SaitJSUkIDQ3lajMzM5mLbAntxM7ODkZGRlwtC5FnfHw8I9C2tbXFlStXGLHh27dvMXz4cOTn5xc7n2hUO0Aiz9IgEAgYN0+hUFisWyFBqDIZGRk4fPgwVxsbG6NXr15lmsvDw4NxJL99+zZu3rwp8bV5eXnYvn07MyZ6AIsgCO2Efx9VmJOnqIsnQFHtBKEqmJqawszMTOLPmjdvruBuCIIglIelpSWmT5/OjE2dOlVqIaGqRbUXUFhkO/8Aj6pgYmLCmBslJSXhv//+U15DSiQyMrLQfW/+562s5OTkICEhgasdHR3pgKcKU2CSs379emav7N27d+jQoQPOnTtX6Hujo6Px5s0brnZ1daW0U0JroW85giAUwuzZs5lTYMOGDUPDhg2V2JFyEQgEEt08CyM7OxsXLlzg6ipVqqjsQwxBlATRCHdJlvr8TduFCxfC1NRUUe1Jjbm5OdatW8eMLVy4UCw6QlU4evQovLy8kJmZKfHnL168wKJFi2R6zT179uDRo0dcXbduXZXYgB8+fDhSU1Px8uVL/O9//0ODBg1kMu/YsWOxa9cuxgH0w4cP6Nixo0xOW4vy4MEDMfeZOnXqyPQawHfBheiiyYkTJ7T2ZLIomzZtYsRwI0aMgLGxsRI7IjQFXV1d5v45Pj5eagH+li1bmHrkyJGoXLky9u7dyzg2hIeHY+3atcXOxxd50oZr6eBHtu/bt09JnRCE9ISFheHr169c3bt37zIvwOvo6GDcuHHMGP9eu4CIiAgkJSVxtaurK2rXrl2m6xIEoTno6uoyh63j4+ORnp4u9jr+wTUSeRKE6iDp97mDg4NarRcSBEHIgilTpsDKyoqro6KipDok+vnzZ5w+fZqrq1Spgvbt20vVo6xQN5EnAPTo0YOptTWyfeXKlYX+7OjRo4xmoKw8e/aMmYei2tWDcePG4cCBA4zrbVpaGrp164a9e/dKfM+JEyeYmp7TCG2GRJ4EQcidW7duYf/+/Vxdrlw5LFy4UIkdqQZDhgxhNrkOHDiAT58+SXztjRs3mA0yd3d3Oo1EaAQFEe7Hjh0rNHqpYcOG8PX1VWxjMqB///7w8vLi6uzsbIwaNapETmiKZPfu3ejXrx8TJ29hYYHVq1czr1u1ahUjHJSGnJwczJ8/nxlbsGBBiSLQ1ZnBgwfj6NGjKFeuHDf25csXeHh4yNQFk7+oN2DAAJnNLYqZmRnatGnD1U+ePEF8fLxcrqUuZGdnY9OmTcwY/1AHQUiDqDghPz8fDx8+LPNcOTk5TKSxQCDAiBEjAHx3nV+8eDHz+pkzZ+LWrVtFzikq8jQwMJCZUF5baNKkCbMgffPmTTx9+lSJHRFE2ZFVVHsBw4cPF3t+FnVxKIAvXi/4XiMIghAVAwiFQty/f5/5+bdv35joyCpVqpArOUGoEJLWDSmqnSAIbaR8+fJie6wzZ87Et2/fyjTf8ePHmb2Bvn37qsw6vSSRp62tbaHuzqpAz549mVqeIs/8/Hx8/foVb968QVxcHP79919cvHgRx48fx549exAUFIRVq1Zh3rx5mD9/vtxSxfg8fPiQ+XNXqlQJHh4eXJ2SksIYG5UVvqkKiTzVh379+uHMmTPMYZ2cnBwMHjwYa9asEXs9f/9MknkQQWgLpBAiCEKuCIVCseiAiRMnokaNGkrqSHWoWLEiBg8ezNXfvn0Ti5UrgKLaCU2nZ8+eePjwIebNmycW171mzRqVWVQoDQKBABs2bICJiQk3dvXqVTEBmDIJDg7G0KFDkZeXx41ZWVnh8uXLmDx5Mn766SduPDc3F2PHjpWJU+OWLVsY0UqjRo20Js7INbGcAAAgAElEQVS6e/fuOH36NPO5yMzMRK9evWTi2CYUCpmDFYB8otoL4D9M809UahtHjhzBu3fvuJofL0sQ0tK4cWOmjomJKfNcJ0+exNu3b7m6S5cusLW15erp06fDzc2Nq3NycjBo0CB8+fJF4nzp6enM4mrjxo2ZE9lE8fAj2wGIfacThDqQnJzMRB5bWFjA3d1dqjlNTU0xfPhwrs7NzUVQUBDzmpSUFBw9epSrTUxM4O3tLdV1CYLQHPiOT/fu3WPqy5cvMy7pnp6edMCaIFQISU6eJPIkCEJbGTFiBHOw9tmzZ9iwYUOZ5lLVqHZAsmivadOmSuik5NSqVYtJ4rl9+zZev35d5vmEQiEOHz6MXr16oWXLlnB0dIS1tTVMTEygp6cHExMTWFtbw9HRES1btoSrqyu8vLzg4+MDPz8/TJs2DQsXLsSCBQvQtm1b3L17VxZ/zCJZtWoVU48bN455ngdkE9lOIk/1pkOHDrhy5QqqV6/OjE+ZMgVTp07lDHM+fPjApD/Wq1dPLsl1BKEu0CoFQRByJTw8HBcvXuTqypUrY8aMGcprSMUYM2YMUwcGBkoUUPFFntJukBGEKmJkZIT58+fjwYMHGDx4MBwdHbFmzRq1/rzXqFEDK1asYMamT5/OREgqiz///BOjR49mvnNq1qyJK1eucCdk16xZg4oVK3I/v3LlSqFi9JKSlZUlFv2+aNEirdo8a9++PS5evAgLCwtuLDc3F4MHDxYTK5SGvLw8REZGIjY2lhtzdnZGzZo1peq3KPixGLJ0JFVHAgICmNrf319JnRCaCl/kyRcnlIbNmzczNd81W0dHBzt37kTVqlW5sadPn2LMmDES71f/++8/Zpycr8oGRbYTmsChQ4eYyLQBAwZAX19f6nl//fVXpg4KCmIcZ/bu3cu41wwaNAjGxsZSX5cgCM2Afx/F3+CmqHaCUG0kOXnSMwdBENqKrq6uWBz24sWLkZKSUqp5vnz5glOnTnG1hYUFOnbsKIsWZUKdOnXEDhCrclR7AXw3z7Kumd+/fx9ubm7w9vbGsWPH8O+//yIuLg5v3rzB169fS23IkZmZibFjx8o1be7Vq1fYtWsXVxsaGmL8+PHo3r07Y/By5MgR5ObmSnUtEnmqP40aNcK1a9fEXHtXr16NoUOHIjs7G6dOnWI+6+TiSWg72rObThCEwsnLy8Pvv//OjM2ZM4ex3tZ2WrRowZw4jo+PF7Oo//TpE27fvs3VDRs2hLW1tcJ6JAhFY2dnh927d+Px48eYNGmSstuRmjFjxqBdu3ZcnZaWBn9/f5k4YpYFoVCIxYsXi/3dOjg44MqVK7Czs+PGLC0tsXTpUuZ106ZNw6dPn8p8/Y0bN+LVq1dc3bp1aybWXlto3rw5rly5wjhbC4VC+Pn5Yfny5WKvz83NRWJiIq5fv46DBw9i7dq1mDJlCgYMGIA2bdrA1tYWhoaG+PHHH5n3ySuqvYBGjRoxf4ZLly4hLS1NrtdUVe7fv4/Lly9zta2tLS04EDJHNK4dKLvI89WrV8wCs4WFhcTvYktLS+zYsYMZ27dvH7Zu3Sr2WtGodoA2XMuKo6MjmjVrxtX37t3Dw4cPldgRQZQeWUe1F1CvXj3mANjbt28Z9w/+dxNFtRMEIUppRJ46OjqUokMQKgbfyVMgEDD3zQRBENpG165d0alTJ65OSUnBkiVLSjVHWFgYc1CuT58+KpWqpqenJ5aSpOpOnoD0ke0pKSn47bff0LRpU5w/f16WreH69esS1/VkxZ9//skc+hw+fDiqVasGExMTeHp6cuMfP37EpUuXpLqWqMhTIBBQopaaYmtri6tXr6JNmzbM+J49e9C9e3ccOHCAGafDeIS2QyJPgiDkxo4dO3D//n2urlWrFsaOHavEjlQTPz8/pg4MDGTqiIgIRgzm4eGhkL4IgpANOjo6CA4OZk6cHj9+XCZxFKVFKBRixowZmDt3LjPeuHFjXL58GTY2NmLv8fPzY8ToHz9+LLMj89evX7Fs2TJmbPHixRAIBGWaT92pV68erl69Krb4MHPmTPTt2xf9+vWDk5MTqlevDkNDQ9ja2qJNmzYYMGAAJk+ejDVr1uDgwYO4fv06EhMTkZeXJ3YNeUa1A98XT0SFjDk5OYiIiJDrNVWR//77T0xIMmbMGOjq6iqpI0JTsbCwYA773L17t0yHBrZv386c2h82bFih0eoeHh6YPn06MzZ+/Hg8evSIGeOLPJs3b17qvojv/PTTT0xNbp6EOpGYmMgceqhVqxZcXFxkNv/48eOZet26dQCAmJgY5nCko6MjnJ2dZXZdgiDUH1NTU8YJ8N69e9x91JMnTxAXF8f9zNnZGebm5opukSCIIuA7edarVw8mJibKaYYgCEIFEAgEWLVqFbO2vm7dOjx79qzEc6hyVHsBotHnANRC4N+6dWtUqVKFq8+ePYvMzMxi35eXl4fg4GA4ODjg77//FlvvNzQ0RNWqVWFnZ4cmTZqgXbt26Nq1KwYMGIBffvkFEydOxNy5c7Fy5Ups3LgRu3btQmhoKObPn8/M8/vvv0tl5FEYqampTFKZQCDA1KlTuZr/+ZJmj0woFDIiz1q1asHIyKjM8xHKxdzcHOfOnUOvXr2Y8XPnziEsLIyrTUxMGFMdgtBKhEIh/Y/+J5f//R979x0eRd21cfyeJBB66KFKkSpVkKo0EQgtqxQpIoKAFEVEkSaKSlFABHmUJqKoPIAU3VCjgvJKV5AuTZoQWkBQatq8fwTmySSBhGST3STfz3V5yfntlAOBzWbmzDmSzOi/YsiIrl27ZhYtWtS88/dAkvnf//7X3Wl5pKtXr5q5cuWy/px8fHzMs2fPWq/36tXL9ucYHBzsxmwBJNWYMWNs/5YLFixoXrx4MdXOHxkZaQ4YMMCWgySzVq1aCebx22+/mV5eXrb9NmzYcN85jB071naMRo0amVFRUUn9LaUb586dM6tXrx7na5Pc/zp16pQq+TudTtt5e/funSrn9QShoaFmv3794vz7yJQpk+17OeBKAQEBtr9vISEh97V/ZGSkWbp0adsx9u/ff899wsLCzNq1a9v2qVq1qnn9+nVrm0qVKtn+Ddy8eTNJvz+Y5vHjx21/1mXKlOH7JdKMCRMm2P7+jhw50qXHj4iIMEuVKmU7x7Zt28zBgwfb1t5//32XnhdA+uBwOGzvFceOHTNN0zT/85//2NbHjh3r3kQBxHHjxg0zb9681r/TF1980d0pAYBHePbZZ22fY7p06ZKo/f79918zS5Ys1n558+Y1w8LCUjjb+7dx40br2mv79u3dnU6i9ejRw/Z1WbFixT2337hxo1mzZs14r/PnypXLnDJlSpK/PpGRkWadOnVsx+zTp0+SjnUv77//vu0cHTp0sL1++fJlM3PmzLZ7ZBEREUk617lz52znatmypSt+C3Cz8PBw84UXXrjrPa+09B4AxMMldXh08gSQIqZPn24bx1uzZk116tTJjRl5ruzZs6t79+5WHBERoblz50qKLsT//vvvrdfiG8ULIG0YOnSo7anT8+fP255iTEkRERF6/vnnNX36dNt6w4YN9eOPPybYoaRmzZp68cUXbWv9+/e3jd1IyN9//61JkybZ1saNG5dhu3jGVLBgQf30009JfgLRy8tLRYsWVZ06ddS+fXsNGjRIc+fOTdGxKzE1bdrU1gFw1apVdx74SbciIiI0ffp0lS1bVjNnzrR1RJSi/737+/u7KTukd7FHjd7vyPaff/5ZR48eteL69eurYsWK99wnU6ZMWrBggXLlymU7753vY9evX7d19qxSpYp8fX3vKy/8T4kSJWwjio4cOaIdO3a4MSMg8f773//aYleNar/D29tbL730km3tww8/1Ndff23bJubP2ABwx91Gtscc1S4xAhDwRFmyZNHMmTNVtmxZNW/eXKNHj3Z3SgDgEcaOHWu7BrNgwQLblIO7WbFihW7evGnFTz31lDJlypQiOSZH/fr1dfLkSa1fv16LFi1ydzqJ1qZNG1t8t5HtISEh6t69ux599NE4U3IMw1CvXr10+PBhvfLKK0n++nh5eWnGjBny8vpfadCnn36qLVu2JOl48bl165amTp1qW4s9GcjPz0/Nmze34vPnz2vDhg1JOl/MLp5SdIdvpH0+Pj6aOXOm3nnnnXhf5+c0gHHtAFLIV199ZYsnTpxo+/AIu759+9ri2bNnKzIyUgcPHtRff/1lrTds2JB280AalTlzZn322We2osbPP/9cP/74Y4qeNywsTF27dtW8efNs6y1atNDq1attBTv3MmbMGBUuXNiK9+zZo48++ijReXzwwQe6cuWKFbds2VKPPvpoovdP73Lnzq3g4GC1bdvWtu7t7R3vmPZvvvnGGtN+69YtnTp1Slu2bNGSJUs0depU9ezZM9W+X2TPnl2NGze24pCQEOtmaXq0fv16q/D577//tr2WN29effLJJ3r33XfdlB0yguQWec6ZM8cW9+7dO1H7lS5dWrNnz7atTZ8+XcuWLdOuXbtsxc41a9a8r5wQV+yR7QsWLHBTJkDi7du3z/YZoFq1anrooYdcfp7nn39e2bJls+KFCxfqwoULVhwQEGD73AoAd1SrVs0W79q1Szdu3NBPP/1krRUuXFjVq1dP7dQAJELHjh116NAhBQcH28bgAkBG9sADD+iVV16xrQ0ZMiTBh/DTwqj2O4oWLaqGDRvK29vb3akkWvPmzW2NEVasWGH7mty6dUsTJ05U+fLl49xTl6Q6depo69atmjNnjgoWLJjsfB5++OE4D0z2799fERERyT62JH399dc6e/asFTdu3Fi1atWKs52rRrZT5Jl+GYaht956S7Nnz7bVlnh5eally5ZuzAzwDFRcAXC5kJAQ283m6tWr6/HHH3djRp6vcuXKtg5ux48f1/fff2/r4inJ9oQTgLSndu3aGjRokG2tb9++un79eoqc78aNG2rXrp0WL15sW3/qqafkdDptN8cT4ufnpylTptjWRo8erZMnTya47/nz5+MUhI4dOzbR584osmXLJqfTqe3bt2vr1q0KCQnRrVu3dOLECW3cuFGLFi3S5MmTNXjwYHXs2FF169ZVsWLF5OPj4+7U1bp1a1scuxNOevDXX3+pS5cuaty4cZyiOi8vL/Xv31+HDh3SgAEDeLAFKapKlSq2eM+ePYne99KlS1q2bJkV58yZUx07dkz0/p06dVKfPn1sa7169dK3335rW6PIM/k6duxoey9ZtGhRnK7BgKeZP3++LXZ1F887cufOrWefffaur/fs2TNFzgsg7Ytd5Ll79279/PPPti5WAQEBTJwAAABpyogRI5QvXz4rXr9+/V07R0rStWvXbNdv8+TJo6ZNm6ZojhlNzpw5bY0RTp8+rd9//11S9LXzKlWqaNiwYbp69aptP39/f82bN0+bNm2Kt0gyOd59910VKlTIinfu3Bln+ltSREVFxZniFruL5x2BgYG2+xlLly5N0vUuijzTvz59+ujbb79VoUKF5O3trXfeeYcHegFR5AkgBQQHB9tiWmcnTr9+/WzxzJkzKfIE0qExY8aoRIkSVnz06NEUGTF19epVtW7dWitXrrStP/PMM/rmm2+SNEb36aeftr0PXb9+PU7Ranzee+89Xbt2zYrbt2+vGjVq3Pf5MwLDMFSjRg3Vrl1bhQsXTjNPJ8f+Xh/7711advPmTY0bN04VKlTQwoUL47zeoEEDbd++XdOnT7ddTAVSSoUKFWwXQ++nk+f8+fN169YtK+7SpYty5MhxX+efOnWqrTPf5cuX41zI5T0++fz9/W0Pyp06dUobN250Y0bAvZmmaRvVbhiGOnfunGLni92B5I58+fLF6YwOAHeULl1a2bNnt+Jdu3Yxqh0AAKR5fn5+euutt2xrw4YNu2uXxpUrV+rGjRtW/OSTT3rkqPa0LvbPptOnT1ebNm3UunVrHT582PZapkyZ9Prrr+vQoUPq3r17ijQR8PPz0+TJk21ro0aN0pkzZ5J13OXLl+vgwYNWXLlyZQUEBMS7bZ48efTEE09Y8ZkzZ7R58+b7PidFnhlDYGCgTp48qdDQUI0aNcrd6QAegSJPAC63evVqW0zr7MRp3769rThkxYoVWrt2rRX7+/vH6dwEIO3JkSOHZs2aZVv78MMPtX37dped4/Lly2revLlt5JwU/eTbvHnzktz50TAMffLJJ7YC0e+++05BQUF33efUqVOaMWOG7RiMsk5/ypQpo3Llylnxli1bdPHiRTdmlHymaSooKEiVKlXSqFGj4nTcLVq0qP773/9q/fr1jHNEqsqcObMqVqxoxfv371d4eHiC+5mmqU8//dS2lthR7TFly5ZNixYtUpYsWeJ93cfHh8+sLhK7QC6+QnPAU2zatEknTpyw4oYNG6p48eIpdr7KlSurSZMmcda7detmG4kHADF5eXnZPqf8+eefto7k3t7eatasmTtSAwAASJZ+/fqpTJkyVnzgwAHNmTMn3m1jT/7y5FHtaVnsIs/PPvss3uYILVq00J49ezRx4kTlypUrRXPq0qWL7Wfpf//9V6+99lqSj2eapiZMmGBbGzp06D0747tiZHvMIs88efKoQIEC930MpA2ZMmVS7ty53Z0G4DEo8gTgUhEREfrhhx+s2M/PT3Xr1nVjRmlHlixZbGPloqKibOOimjdvzrgoIJ1o0aKFbcRkVFSUevXqlaginXu5efOmZs6cqerVq8d5+nHw4MGaNWtWsjtDlilTRiNHjrStDRw40NapM6YxY8bYusZ169bN1gEO6UfMjjdRUVFxOnunJQcPHlSrVq3kcDh09OhR22uZM2fWiBEjdODAAXXp0oXvzXCLqlWrWr8ODw+3PS1/N7/99ptttHuVKlX0yCOPJOn8lStX1tSpU+/62t0KQHF/2rVrZ+uksXjx4rt24QDcLWYXTynlRrXHNHDgwDhrjGoHkJCYI9tN09Tp06et+LHHHpOfn5870gIAAEiWzJkz6/3337etjR49Wv/++69tLfao9ty5c9s6K8J1SpQocc8HoUuXLq2goCCtXr1a5cuXT5Wc7jTyiHm9acGCBbamQ/dj48aNtntRxYsXT3Cqh8PhsN2nWrJkyX2NbL9x44aOHz9uxRUqVOAaPYAMgyJPAC61bds2Xb582YqbNWuW5I5xGdELL7xw19cY1Q6kLx9++KHy589vxbt27YozKiOxrl27pilTpqh06dLq37+/rYuSJL311luaPHmyy37QHTZsmK1r48mTJzVmzJg42/3555+aO3euFfv4+Ojtt992SQ7wPK1bt7bFaXFk++HDhzVo0CBVrlxZa9asifN6mzZttG/fPo0fP/6+R1wDrhSzyFNK3Mj22N0bevfunazvCy+88EK8nR5q1qyZ5GPCLk+ePLbxVhcuXNC6devcmBEQv/DwcH3zzTdWnClTJrVv3z7Fz9u2bVuVKlXKimvVqmUr3gKA+NzrfYJpRAAAIC1r166d6tevb8Xnz5/XpEmTbNusXr3aNrHI4XAwDSEFxe7mKUVPyRk/frz27duntm3bpnqBYsWKFTVkyBDb2osvvqiwsLD7PtbEiRNt8auvvmorII1P/vz5bd1ET506pW3btiX6nIcPH5ZpmlbMqHYAGQlFngBcKvao9pg3JZGwsmXL3vWJOZ6kA9KX/Pnz66OPPrKtvf322zp8+HCij3HlyhWNGzdOJUuW1KuvvqozZ87E2WbChAl65513XHqhwNfXV9OnT7etTZ48WXv37rWtvf3227aOY7169VLp0qVdlgc8S4MGDZQ9e3YrDgoK0oULF9yYUeKEhYXpm2++UdOmTVWuXDlNmzYtTqe8smXLauXKlVq+fLlt7BHgLvdb5Hnt2jUtWLDAin19fdWtW7dk5WAYhj799FOVKFHCtk6Rp2t16dLFFjOyHZ7o+++/V2hoqBW3atVKefPmTfHz+vj46JtvvlGtWrVUr149ffHFFyl+TgBp372KPGNOJwAAAEhrDMOIU9T5wQcf2DqXM6o9dfXs2VNZs2a14q5du+rgwYMaMWKEWyfhjBo1ynZN7+DBg/fdhGT//v1avny5FefJk0e9e/dO1L7JGdkec1S7RJEngIyFIk8ALhW761WLFi3clEna1a9fvzhr1apVU6FChdyQDYCU1KVLF9tNpFu3bqlPnz4JjqYIDQ21fggfNWqU7ab6HTVr1tSqVas0dOhQl+ctSU2bNlXXrl2tOCIiQv3797dy37dvn+bPn2+97uvrq1GjRqVILvAMvr6+atOmjRVfvXpVY8eOdWNG93b06FENHz5cxYsXV6dOneLtjpcjRw5NmDBBe/fu5YYvPMr9FnkuXrzYNp6rXbt2LinAyp07txYuXGhdrM6RI4fatWuX7OPif9q2bWu7GbBs2TLdunXLjRkBcbljVPsdjzzyiLZt26ZNmzbpoYceSrXzAki77jYys1ixYqpcuXIqZwMAAOBa9evXt01WuHHjht566y1J0vXr123Tl3LlyqVmzZqleo4ZSZkyZbRlyxZNmjRJ27Zt0/z581WsWDF3p6Vs2bJp2rRptrUxY8bYxqAn5IMPPrDFAwYMSPT0q6eeekpeXv8rVVqyZImtO+e9UOQJICOjyBOAy5w/f16//fabFVepUsUjPqimNYGBgXEKOhnVDqRPhmFoxowZtu6H69ev12effRbv9iEhIXrttddUokQJjRs3TleuXImzzWOPPaY1a9bo119/TfFRc5MnT5afn58Vb9iwQfPmzZMUPSI+5g/lAwYM4HtCBjBq1CjbxZkZM2bo6NGjbszILjw8XMuWLVOLFi304IMPasKECTp//nyc7by8vPTss8/q4MGDGjp0KCOL4HEKFy5sK9Lcs2fPPbePb1S7q9StW1fbt2/XJ598ot27d8vf399lx0Z04WxgYKAVX7lyJc6DdYA7Xb16Vd99950V58yZ0/bQBwB4mpw5c8Y7YaJVq1apPioTAAAgJbz//vvy8fGx4s8//1x79uzRmjVrdO3aNWvd4XDI19fXHSlmKFWrVtWQIUNUq1Ytd6diExgYaBsnf+PGDQ0aNChR+54+fVpff/21Ffv6+mrgwIGJPnfBggXVqFEjKz5x4oS2b9+eqH0PHjxoiynyBJCRUOQJwGW+//57W8yo9qTJlCmTevXqZVtL6UItAO7zwAMP6L333rOtvf766woJCbHi48ePq3///ipVqpQ+/PBDXb9+Pc5xmjdvrvXr1+uXX35RixYtUuXmVKFChTR+/Pg4uQcHB2vZsmXWWvbs2TV8+PAUzwfuV7lyZT333HNWHB4e7hEdXE+cOGF1v23fvn2czyx3FClSRG+99ZaOHz+uL7/8UkWKFEnlTIHEMQzD1s3z1KlTunTpUrzb/vHHH9q4caMVlypVSo0bN3ZpPhUrVtSAAQNUqlQplx4X0Tp37myLFyxY4KZMgLiCgoJsn03btWtn6z4LAJ4ovpHtXHsDAADpRZkyZTRgwAArNk1TQ4cOZVQ74vjoo49sY+ODgoIUFBSUqP3Cw8OtuEePHvf94HdSR7bH7OSZKVMmrkcCyFAo8gTgMrE7ylDkmXTDhg1T/fr15eXlpZ49e7r8RjwAzzJgwADVq1fPiq9cuaKXXnpJBw8eVI8ePVSmTBnNnDlTYWFhcfZ1OBzatm2bgoOD1bBhw9RMW5LUt29f2xOoFy9etD39KUmvvPKKChYsmNqpwU3eeecd24WhBQsWaMeOHameR0REhIKCgtS6dWuVKlVK48aN05kzZ+JsZxiGWrZsqe+++04nTpzQO++8o+LFi6d6vsD9ij2y/W7dPGN3h+7Vq5et4y48X0BAgHLlymXFy5cvt3XeANxp/vz5trhr165uygQAEi92kWemTJnUtGlTN2UDAADgem+++abtWsKaNWtsRXQ5c+ZkiiBUqlSpOE0aXn755Xgbjdxx+fJlzZw504oNw9Brr7123+d+6qmnbM1KEjOyPSoqylbkWaZMGWXKlOm+zw0AaRV3dgC4RFRUlIKDg604e/bseuyxx9yYUdqWM2dObdy4UVevXtXcuXMZFwWkc97e3vr0009tP4x+++23qlixoubNm6fIyEjb9l5eXurcubN2796t7777zq1jPry9vTVz5kxbwVDMJzhz586tIUOGuCM1uEnx4sX18ssv29ZSs5PrqVOn9Pbbb6tkyZJyOBxatWpVvBeHChUqpJEjR+ro0aNatWqVHA6HbYwR4OliF3nu3r07zjZhYWGaN2+eFXt5ealHjx4pnRpcLEuWLGrXrp0VX79+PVFdFYCUYpqmdu3apXfffdfWHdvf31+PP/64GzMDgMSJ/TmqYcOGypkzp5uyAQAAcL38+fNr5MiRtrWIiAjr14GBgbYH9ZFxDRkyROXKlbPiEydOxJngFtOsWbP077//WnH79u1VtmzZ+z5v4cKFbbUEf/75p3bt2nXPfU6fPm0rQC1fvvx9nxcA0jKKPAG4xPbt2xUaGmrFTZs2VebMmd2YUfrAmDsg46hUqVKciy6xC9N8fHz0/PPP68CBA1qwYIGqVKmSmineVY0aNfTSSy/F+9rrr7+u3Llzp3JGcLfhw4fbvu4//PCDfvjhhxQ/73fffady5crpnXfe0enTp+PdplmzZlqyZIlOnjypcePGqWTJkimeF5ASElPkuXz5cttn9FatWqlo0aIpnhtcL/bI9oULF7opE2RU4eHhWrdunQYNGqRSpUqpevXqGj16tO0mYefOnXlgAkCa0KBBA1tRJ12IAQBAevTyyy/fdWIRo9pxh6+vrz755BPb2sSJE3Xw4ME42966dUtTp061rQ0dOjTJ577fke0xu3hKUoUKFZJ8bgBIiyjyBOASjGoHgOQbMWKEKlasGGfd19dXL774oo4cOaLPPvssSU9FprQxY8aocOHCtrWCBQvG6eiIjCFPnjxxipaHDRumqKioFO7C/6EAACAASURBVDvnoUOH1K1bN924cSPOawUKFNDQoUN1+PBhff/992rfvj1jXJDmVapUydbtPb4izzlz5tji3r17p3heSBlNmzZV/vz5rXj16tX6+++/3ZgRMoJ//vlHixcvVrdu3VSwYEE1bdpU06ZN04kTJ+JsaxgGnYIBpBn58+fX4sWLFRgYqLFjx6pnz57uTgkAAMDlsmbNqnHjxsVZz5Ejh1q0aOGGjOCpnnjiCdsDxuHh4XrxxRfjNCL5+uuvdfbsWStu3LhxsibNxZxcI0mLFy++58h2ijwBZHQUeQJwCYo8ASD5fH199cUXXyhbtmySpOzZs2vIkCE6duyYPv74Y5UoUcLNGd5drly59NFHH9nWRo4cqRw5crgpI7jbwIEDbU+K//7771q0aFGKnOvWrVvq3Lmzrl27Zltv3LixFi5cqL/++ksTJkxQmTJlUuT8gDtky5bNVvS/d+9eRUZGWvHJkycVHBxsxYUKFVKrVq1SNUe4jo+Pjzp27GjF4eHh+vbbb92YEdKr06dPa8aMGQoICFCBAgX09NNPa/78+bp8+fJd96latarmz5+v6tWrp2KmAJA8LVq0kNPp1BtvvGF7cAYAACA9eeaZZ/Twww/b1tq0acMkQcQxefJkW7f7tWvX2q7nR0VFadKkSbZ9ktPFU5KKFSumevXqWfGhQ4e0d+/eu25PkSeAjI4iTwDJdunSJW3ZssWKy5cvr1KlSrkxIwBIu2rXrq39+/crKChIJ0+e1KRJk+J0yPRUHTp00CeffKI6depoxIgRGjhwoLtTghtlyZJF7777rm3tjTfe0K1bt1x+rmHDhun333+34hw5cmjHjh366aef1KlTJ/n6+rr8nIAnqFKlivXr69ev6+jRo1b8+eef2558f+655+hgm8Z16dLFFi9YsMBNmSA9MU1Te/bs0dixY1WrVi0VK1ZMAwYMUHBwsMLCwuLdx9vbW02aNNHUqVN19OhR7dq1K87fTwAAAACA+3l5eWny5Mm2NbqYIz5FihTRmDFjbGuDBw/WlStXJEnLly+3jXCvXLmyS5o+3c/I9thFnuXLl0/2+QEgLTHu1e4YSA7DMExJ92ypjfThm2++UadOnaz4lVde0ZQpU9yYEQAA8ASRkZGqXr267enbjz76SC+//LLLzrFixQq1bdvWtvb111/rmWeecdk5AE/17rvvavTo0Va8dOlStWvXTpGRkSpdurROnjxpvXbo0CFb50+kPVFRUSpRooROnTolKfpGTUhIiPz9/d2cGdKaiIgIbdiwQU6nU06nU8eOHUtwnxw5ciggIEAOh0OtWrVS3rx5UyFTAAAAAIArLFiwQAsXLlSrVq3Ut29fd6cDDxUREaFHHnlEu3btstYGDRqkqVOn6tFHH9WmTZus9S+//FLPPvtsss958uRJ2xS7hx56SPv27Yt326JFiyokJERS9NSiM2fOJPv8AJBKXDJChE6eAJKNUe0AACA+3t7eev/9921rY8aM0T///OOS458+fVo9evSwrT333HMUeCLDqFq1qi3evXu3pOhxSjELPBs1akSBZzrg5eVle7guKirqnt0NgJiuXr2qpUuXqnv37vL397c6cd6rwLNIkSLq16+fVq1apdDQUC1evFjdunWjwBMAAAAA0pguXbrI6XRS4Il78vHx0YwZM2xr//nPf/TJJ5/YCjyLFy+uzp07u+ScDzzwgGrXrm3F+/fv1/79++Ns988//1gFnhKj2gFkTBR5AkgW0zRtRZ5ZsmRRw4YN3ZgRAADwJK1atbJ9NggNDdWkSZOSfdzIyEh169ZNFy9etNbKlSunjz/+ONnHBtKKuxV5zpkzx7beu3fvVMsJKSv2BXRGtuNezpw5o9mzZ6t169bKnz+/OnTooK+++kqXLl266z6VK1fWG2+8oW3btumvv/7SjBkz1LJlS/n6+qZi5gAAAAAAwB3q1aunXr16WXFUVJReeukl2zavvvq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\n",
            "text/plain": [
              "<Figure size 3240x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}