train2_LSTM_best+holiday.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "train2 LSTM_best.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JO8_9CiK8znD"
      },
      "source": [
        "##### train2 LSTM_best.ipynb\r\n",
        "- layer3 b50 -> 2차_공휴일 feature.csv -> 01.18스태킹.csv\r\n",
        "\r\n",
        "- LSTM_best+holidy모델(최고성능)그대로 사용하고, train2만 추가.\r\n",
        "\r\n",
        "- train2_LSTM_best+holiday.ipynb 파일 수정해서 사용\r\n",
        "\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MEAXOVLFqTeD",
        "outputId": "bd366c07-bfa2-4c8c-f7e0-efe2c0a9ccdc"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHrGQAT_3z6X"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "\r\n",
        "from datetime import datetime\r\n",
        "from datetime import date, timedelta\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "\r\n",
        "from matplotlib import pyplot\r\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-OIOaFMW_55S"
      },
      "source": [
        "#### train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fH1TWRdi4eLu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "254a20bd-488a-49fb-d8f7-93910d2e271c"
      },
      "source": [
        "path = '/content/drive/MyDrive/Colab Notebooks/DACON CUP'\r\n",
        "\r\n",
        "\r\n",
        "# 1차:  2018년 9월 9일 ~ 2020년 11월 8일\r\n",
        "\r\n",
        "train1 = pd.read_csv(path + '/open_data/train.csv', encoding = 'euc-kr')\r\n",
        "train1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DateTime</th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-09-09 00:00:00</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>8</td>\n",
              "      <td>206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-09-09 01:00:00</td>\n",
              "      <td>20</td>\n",
              "      <td>19</td>\n",
              "      <td>9</td>\n",
              "      <td>259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-09-09 02:00:00</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-09-09 03:00:00</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-09-09 04:00:00</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              DateTime  사용자  세션  신규방문자  페이지뷰\n",
              "0  2018-09-09 00:00:00   19  19      8   206\n",
              "1  2018-09-09 01:00:00   20  19      9   259\n",
              "2  2018-09-09 02:00:00   12   9      1    48\n",
              "3  2018-09-09 03:00:00   10  10      2   102\n",
              "4  2018-09-09 04:00:00    6   5      3    18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YsCktWIeAIWq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6eb08af-d330-4fcd-9657-1f52d0e4f4b0"
      },
      "source": [
        "train1.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19008 entries, 0 to 19007\n",
            "Data columns (total 5 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   DateTime  19008 non-null  object\n",
            " 1   사용자       19008 non-null  int64 \n",
            " 2   세션        19008 non-null  int64 \n",
            " 3   신규방문자     19008 non-null  int64 \n",
            " 4   페이지뷰      19008 non-null  int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 742.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "FYoMls7F5ssA",
        "outputId": "13c0c2d6-37dd-457d-9b16-8df04f2b6deb"
      },
      "source": [
        "# 2차데이터 20년 11월 9일 ~ 12월 8일\r\n",
        "#720행\r\n",
        "\r\n",
        "train2 = pd.read_csv(path + '/open_data/2차_open_data/2차_train.csv', encoding='euc-kr')\r\n",
        "train2.head()\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DateTime</th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-11-09 00:00:00</td>\n",
              "      <td>134</td>\n",
              "      <td>147</td>\n",
              "      <td>33</td>\n",
              "      <td>4082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-11-09 01:00:00</td>\n",
              "      <td>88</td>\n",
              "      <td>77</td>\n",
              "      <td>22</td>\n",
              "      <td>2444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-11-09 02:00:00</td>\n",
              "      <td>54</td>\n",
              "      <td>48</td>\n",
              "      <td>12</td>\n",
              "      <td>1148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-11-09 03:00:00</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-11-09 04:00:00</td>\n",
              "      <td>31</td>\n",
              "      <td>34</td>\n",
              "      <td>7</td>\n",
              "      <td>711</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              DateTime  사용자   세션  신규방문자  페이지뷰\n",
              "0  2020-11-09 00:00:00  134  147     33  4082\n",
              "1  2020-11-09 01:00:00   88   77     22  2444\n",
              "2  2020-11-09 02:00:00   54   48     12  1148\n",
              "3  2020-11-09 03:00:00   35   31      3   557\n",
              "4  2020-11-09 04:00:00   31   34      7   711"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jhrKhrfB6UQ-",
        "outputId": "441278fe-aeee-41bb-e325-5deac3dbae85"
      },
      "source": [
        "train_df = pd.concat([train1, train2])\r\n",
        "train_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19728, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOujb_mO_676",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "a8ae5c04-b59a-408d-989c-300a5630a817"
      },
      "source": [
        "# date 기준으로.\r\n",
        "\r\n",
        "train_df['DateTime'] = pd.to_datetime(train_df.DateTime) #dtype: datetime으로 변경\r\n",
        "train_df['date'] = train_df.DateTime.dt.date\r\n",
        "train_df  = train_df.groupby('date').sum().reset_index()\r\n",
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-09-09</td>\n",
              "      <td>281</td>\n",
              "      <td>266</td>\n",
              "      <td>73</td>\n",
              "      <td>1826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-09-10</td>\n",
              "      <td>264</td>\n",
              "      <td>247</td>\n",
              "      <td>51</td>\n",
              "      <td>2092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-09-11</td>\n",
              "      <td>329</td>\n",
              "      <td>310</td>\n",
              "      <td>58</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-09-12</td>\n",
              "      <td>300</td>\n",
              "      <td>287</td>\n",
              "      <td>45</td>\n",
              "      <td>2595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-09-13</td>\n",
              "      <td>378</td>\n",
              "      <td>344</td>\n",
              "      <td>50</td>\n",
              "      <td>3845</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  사용자   세션  신규방문자  페이지뷰\n",
              "0  2018-09-09  281  266     73  1826\n",
              "1  2018-09-10  264  247     51  2092\n",
              "2  2018-09-11  329  310     58  1998\n",
              "3  2018-09-12  300  287     45  2595\n",
              "4  2018-09-13  378  344     50  3845"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CM7Hluyg9nrO"
      },
      "source": [
        "# date 기준 변수 만들기\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "def create_features(df):\r\n",
        "    \"\"\"\r\n",
        "    Creates time series features from datetime index\r\n",
        "    \"\"\"\r\n",
        "    # df['DateTime'] = df.index\r\n",
        "    # df['hour'] = df['date'].dt.hour\r\n",
        "    df['dayofweek'] = pd.to_datetime(df['date']).dt.dayofweek #요일\r\n",
        "    df['quarter'] = pd.to_datetime(df['date']).dt.quarter \r\n",
        "    df['month'] = pd.to_datetime(df['date']).dt.month\r\n",
        "    df['year'] = pd.to_datetime(df['date']).dt.year # 날짜의 해당 연도 시작에서부터의 일 수를 반환 \r\n",
        "    df['dayofyear'] = pd.to_datetime(df['date']).dt.dayofyear\r\n",
        "    df['dayofmonth'] = pd.to_datetime(df['date']).dt.day\r\n",
        "    df['weekofyear'] = pd.to_datetime(df['date']).dt.weekofyear\r\n",
        "    \r\n",
        "    X = df[['사용자','세션','신규방문자','페이지뷰','date','dayofweek','quarter','month','year' #]]\r\n",
        "           ,'dayofyear','dayofmonth','weekofyear']]\r\n",
        "    X.set_index('date', inplace = True)\r\n",
        "    X.index = pd.to_datetime(X.index)\r\n",
        "    \r\n",
        "    #X['date'] = pd.to_datetime(X['date']).dt.date\r\n",
        "    \r\n",
        "    return X"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "id": "am_esN9k-CjW",
        "outputId": "3b3e1b6a-1e3e-444c-f0a9-750fd463ef4f"
      },
      "source": [
        "train = create_features(train_df)\r\n",
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n"
          ],
          "name": "stderr"
        },
        {
          "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>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>dayofmonth</th>\n",
              "      <th>weekofyear</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-09-09</th>\n",
              "      <td>281</td>\n",
              "      <td>266</td>\n",
              "      <td>73</td>\n",
              "      <td>1826</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>252</td>\n",
              "      <td>9</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-10</th>\n",
              "      <td>264</td>\n",
              "      <td>247</td>\n",
              "      <td>51</td>\n",
              "      <td>2092</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>253</td>\n",
              "      <td>10</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-11</th>\n",
              "      <td>329</td>\n",
              "      <td>310</td>\n",
              "      <td>58</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>254</td>\n",
              "      <td>11</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-12</th>\n",
              "      <td>300</td>\n",
              "      <td>287</td>\n",
              "      <td>45</td>\n",
              "      <td>2595</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>255</td>\n",
              "      <td>12</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-13</th>\n",
              "      <td>378</td>\n",
              "      <td>344</td>\n",
              "      <td>50</td>\n",
              "      <td>3845</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>256</td>\n",
              "      <td>13</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            사용자   세션  신규방문자  페이지뷰  ...  year  dayofyear  dayofmonth  weekofyear\n",
              "date                               ...                                         \n",
              "2018-09-09  281  266     73  1826  ...  2018        252           9          36\n",
              "2018-09-10  264  247     51  2092  ...  2018        253          10          37\n",
              "2018-09-11  329  310     58  1998  ...  2018        254          11          37\n",
              "2018-09-12  300  287     45  2595  ...  2018        255          12          37\n",
              "2018-09-13  378  344     50  3845  ...  2018        256          13          37\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_9M4EXV_Rk3s",
        "outputId": "3100818e-05a0-43fa-9db2-2bababa9a21a"
      },
      "source": [
        "train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(822, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VsQqMW3Eo-dR"
      },
      "source": [
        "# len(train['dayofyear'].unique())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pyGdeI1CucRm"
      },
      "source": [
        "#### 공휴일"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "Qd9KmEOBucRt",
        "outputId": "3251fca5-d334-4b03-a696-a56fd6e3feae"
      },
      "source": [
        "path = '/content/drive/MyDrive/Colab Notebooks/DACON CUP'\r\n",
        "\r\n",
        "holidays = pd.read_csv(path + '/open_data/holidays.csv', encoding = 'euc-kr', index_col=0)\r\n",
        "holidays.set_index('date', inplace = True)\r\n",
        "holidays.index = pd.to_datetime(holidays.index)\r\n",
        "holidays.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>1월1일</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-15</th>\n",
              "      <td>설날</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-16</th>\n",
              "      <td>설날</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-17</th>\n",
              "      <td>설날</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-01</th>\n",
              "      <td>삼일절</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            name\n",
              "date            \n",
              "2018-01-01  1월1일\n",
              "2018-02-15    설날\n",
              "2018-02-16    설날\n",
              "2018-02-17    설날\n",
              "2018-03-01   삼일절"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGZ6AjkKOmRr"
      },
      "source": [
        "- 공휴일에 1 or 0을 넣어서 피쳐추가.\r\n",
        "    --> 결과가 좋지 않음"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "d9JIm_rlEy3Q",
        "outputId": "c6c685f8-e8f2-46f2-d439-8d9ad9fd1e2d"
      },
      "source": [
        "holidays['holiday']=1\r\n",
        "holidays.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>holiday</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>1월1일</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-15</th>\n",
              "      <td>설날</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-16</th>\n",
              "      <td>설날</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-17</th>\n",
              "      <td>설날</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-01</th>\n",
              "      <td>삼일절</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            name  holiday\n",
              "date                     \n",
              "2018-01-01  1월1일        1\n",
              "2018-02-15    설날        1\n",
              "2018-02-16    설날        1\n",
              "2018-02-17    설날        1\n",
              "2018-03-01   삼일절        1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nL9R1OFwucRy",
        "outputId": "d0805326-65b5-4c33-d859-571ef6a69e28"
      },
      "source": [
        "train.insert(4, 'holiday', holidays['holiday'])\r\n",
        "train = train.fillna(0)\r\n",
        "len(train[train['holiday']==1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "njl2o4_uucRz"
      },
      "source": [
        "# 페이지뷰: 홈페이지에 들어온 접속자가 둘러 본 페이지수. \r\n",
        "# PV/UV : 한 사용자가 둘러본 페이지 수.\r\n",
        "\r\n",
        "#train['PV/UV'] = train['페이지뷰']/train['사용자']\r\n",
        "#train['PV/UV'] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "kj-pJ4fnXYS_",
        "outputId": "a818e33c-ff17-45d8-f6a2-e5c44e788e25"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "      <th>holiday</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>dayofmonth</th>\n",
              "      <th>weekofyear</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-09-09</th>\n",
              "      <td>281</td>\n",
              "      <td>266</td>\n",
              "      <td>73</td>\n",
              "      <td>1826</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>252</td>\n",
              "      <td>9</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-10</th>\n",
              "      <td>264</td>\n",
              "      <td>247</td>\n",
              "      <td>51</td>\n",
              "      <td>2092</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>253</td>\n",
              "      <td>10</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-11</th>\n",
              "      <td>329</td>\n",
              "      <td>310</td>\n",
              "      <td>58</td>\n",
              "      <td>1998</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>254</td>\n",
              "      <td>11</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-12</th>\n",
              "      <td>300</td>\n",
              "      <td>287</td>\n",
              "      <td>45</td>\n",
              "      <td>2595</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>255</td>\n",
              "      <td>12</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-13</th>\n",
              "      <td>378</td>\n",
              "      <td>344</td>\n",
              "      <td>50</td>\n",
              "      <td>3845</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>256</td>\n",
              "      <td>13</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            사용자   세션  신규방문자  페이지뷰  ...  year  dayofyear  dayofmonth  weekofyear\n",
              "date                               ...                                         \n",
              "2018-09-09  281  266     73  1826  ...  2018        252           9          36\n",
              "2018-09-10  264  247     51  2092  ...  2018        253          10          37\n",
              "2018-09-11  329  310     58  1998  ...  2018        254          11          37\n",
              "2018-09-12  300  287     45  2595  ...  2018        255          12          37\n",
              "2018-09-13  378  344     50  3845  ...  2018        256          13          37\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "KVOKcTiSucR0",
        "outputId": "d2289d17-c187-4477-8786-37228360542a"
      },
      "source": [
        "for i, value in enumerate(train['holiday']):\r\n",
        "  if value==1:\r\n",
        "#    train['사용자'][i]= train['사용자'][i] - (train['사용자'][i]*0.1)\r\n",
        "#    train['세션'][i]= train['세션'][i] - (train['세션'][i]*0.1)\r\n",
        "#    train['신규방문자'][i]= train['신규방문자'][i] - (train['신규방문자'][i]*0.1)\r\n",
        "    train['페이지뷰'][i]= train['페이지뷰'][i] - (train['페이지뷰'][i]*0.1)  \r\n",
        "\r\n",
        "#train = train.drop(['holiday','PV/UV'],axis=1)\r\n",
        "train = train.drop(['holiday'],axis=1)\r\n",
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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"
          ],
          "name": "stderr"
        },
        {
          "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>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>dayofmonth</th>\n",
              "      <th>weekofyear</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-09-09</th>\n",
              "      <td>281</td>\n",
              "      <td>266</td>\n",
              "      <td>73</td>\n",
              "      <td>1826</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>252</td>\n",
              "      <td>9</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-10</th>\n",
              "      <td>264</td>\n",
              "      <td>247</td>\n",
              "      <td>51</td>\n",
              "      <td>2092</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>253</td>\n",
              "      <td>10</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-11</th>\n",
              "      <td>329</td>\n",
              "      <td>310</td>\n",
              "      <td>58</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>254</td>\n",
              "      <td>11</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-12</th>\n",
              "      <td>300</td>\n",
              "      <td>287</td>\n",
              "      <td>45</td>\n",
              "      <td>2595</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>255</td>\n",
              "      <td>12</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-13</th>\n",
              "      <td>378</td>\n",
              "      <td>344</td>\n",
              "      <td>50</td>\n",
              "      <td>3845</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>256</td>\n",
              "      <td>13</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            사용자   세션  신규방문자  페이지뷰  ...  year  dayofyear  dayofmonth  weekofyear\n",
              "date                               ...                                         \n",
              "2018-09-09  281  266     73  1826  ...  2018        252           9          36\n",
              "2018-09-10  264  247     51  2092  ...  2018        253          10          37\n",
              "2018-09-11  329  310     58  1998  ...  2018        254          11          37\n",
              "2018-09-12  300  287     45  2595  ...  2018        255          12          37\n",
              "2018-09-13  378  344     50  3845  ...  2018        256          13          37\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6j3eLly7ucR1"
      },
      "source": [
        "# train['사용자'][1]*0.01"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "PBSP-mqkujSw",
        "outputId": "ab06212e-5fd9-4a97-eb4e-9570a1967474"
      },
      "source": [
        "\"\"\"for i, value in enumerate(train['holiday']):\r\n",
        "  if value == '1월1일':\r\n",
        "    train['사용자'][i]= train['사용자'][i] - (train['사용자'][i]*0.5)\r\n",
        "    train['세션'][i]= train['세션'][i] - (train['세션'][i]*0.5)\r\n",
        "    train['신규방문자'][i]= train['신규방문자'][i] - (train['신규방문자'][i]*0.5)\r\n",
        "    train['페이지뷰'][i]= train['페이지뷰'][i] - (train['페이지뷰'][i]*0.5)  \r\n",
        "\r\n",
        "  # 공휴일이면 페이지뷰 감소\r\n",
        "  elif value!=0:\r\n",
        "#    train['사용자'][i]= train['사용자'][i] - (train['사용자'][i]*0.1)\r\n",
        "#    train['세션'][i]= train['세션'][i] - (train['세션'][i]*0.1)\r\n",
        "#    train['신규방문자'][i]= train['신규방문자'][i] - (train['신규방문자'][i]*0.1)\r\n",
        "    train['페이지뷰'][i]= train['페이지뷰'][i] - (train['페이지뷰'][i]*0.1)  \r\n",
        "\r\n",
        "#train = train.drop(['holiday','PV/UV'],axis=1)\r\n",
        "train = train.drop(['holiday'],axis=1)\r\n",
        "train.head()\"\"\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"for i, value in enumerate(train['holiday']):\\n  if value == '1월1일':\\n    train['사용자'][i]= train['사용자'][i] - (train['사용자'][i]*0.5)\\n    train['세션'][i]= train['세션'][i] - (train['세션'][i]*0.5)\\n    train['신규방문자'][i]= train['신규방문자'][i] - (train['신규방문자'][i]*0.5)\\n    train['페이지뷰'][i]= train['페이지뷰'][i] - (train['페이지뷰'][i]*0.5)  \\n\\n  # 공휴일이면 페이지뷰 감소\\n  elif value!=0:\\n#    train['사용자'][i]= train['사용자'][i] - (train['사용자'][i]*0.1)\\n#    train['세션'][i]= train['세션'][i] - (train['세션'][i]*0.1)\\n#    train['신규방문자'][i]= train['신규방문자'][i] - (train['신규방문자'][i]*0.1)\\n    train['페이지뷰'][i]= train['페이지뷰'][i] - (train['페이지뷰'][i]*0.1)  \\n\\n#train = train.drop(['holiday','PV/UV'],axis=1)\\ntrain = train.drop(['holiday'],axis=1)\\ntrain.head()\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Mn3-RqzjnDe"
      },
      "source": [
        "#### 범주형 인코딩\r\n",
        "\r\n",
        "- one-hot 인코딩: 카테고리가 별로 없는거\r\n",
        "- binary 인코딩: 카테고리가 많은 거"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ziFLhds4kax1",
        "outputId": "df23d0fe-fc9b-4c68-a2c4-67656ced1e32"
      },
      "source": [
        "pip install category_encoders"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: category_encoders in /usr/local/lib/python3.6/dist-packages (2.2.2)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.22.2.post1)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.19.5)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.10.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.4.1)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.5.1)\n",
            "Requirement already satisfied: pandas>=0.21.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.1.5)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.0.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.5.1->category_encoders) (1.15.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2.8.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHx-tnrbj1cc"
      },
      "source": [
        "import category_encoders as ce"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nzCaqAJd531",
        "outputId": "f53a2409-873c-4f69-f16d-a03c73c0f617"
      },
      "source": [
        "# bianry encoding\r\n",
        "\r\n",
        "# dayofyear\tdayofmonth\tweekofyear\r\n",
        "\r\n",
        "encoder = ce.BinaryEncoder(cols=['dayofyear'])\r\n",
        "train = encoder.fit_transform(train)\r\n",
        "\r\n",
        "encoder = ce.BinaryEncoder(cols=['dayofmonth'])\r\n",
        "train = encoder.fit_transform(train)\r\n",
        "\r\n",
        "encoder = ce.BinaryEncoder(cols=['weekofyear'])\r\n",
        "train = encoder.fit_transform(train)\r\n",
        "\r\n",
        "print(train.shape)\r\n",
        "# train.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(822, 31)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/category_encoders/utils.py:21: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  elif pd.api.types.is_categorical(cols):\n",
            "/usr/local/lib/python3.6/dist-packages/category_encoders/utils.py:21: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  elif pd.api.types.is_categorical(cols):\n",
            "/usr/local/lib/python3.6/dist-packages/category_encoders/utils.py:21: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  elif pd.api.types.is_categorical(cols):\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "ZFEkYwX-kxU9",
        "outputId": "88ed6131-372c-4f6f-b2c2-a68deda6b5cc"
      },
      "source": [
        "# one-hot encoding\r\n",
        "\r\n",
        "def dummy_data(data, columns):\r\n",
        "    for column in columns:\r\n",
        "        data = pd.concat([data, pd.get_dummies(data[column], prefix = column)], axis=1)\r\n",
        "        data = data.drop(column, axis=1)\r\n",
        "    return data\r\n",
        "\r\n",
        "\r\n",
        "dummy_columns = [\"dayofweek\", \"quarter\", \"month\", \"year\"]\r\n",
        "train = dummy_data(train,dummy_columns)\r\n",
        "\r\n",
        "print(train.shape)\r\n",
        "train.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(822, 53)\n"
          ],
          "name": "stdout"
        },
        {
          "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>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "      <th>dayofyear_0</th>\n",
              "      <th>dayofyear_1</th>\n",
              "      <th>dayofyear_2</th>\n",
              "      <th>dayofyear_3</th>\n",
              "      <th>dayofyear_4</th>\n",
              "      <th>dayofyear_5</th>\n",
              "      <th>dayofyear_6</th>\n",
              "      <th>dayofyear_7</th>\n",
              "      <th>dayofyear_8</th>\n",
              "      <th>dayofyear_9</th>\n",
              "      <th>dayofmonth_0</th>\n",
              "      <th>dayofmonth_1</th>\n",
              "      <th>dayofmonth_2</th>\n",
              "      <th>dayofmonth_3</th>\n",
              "      <th>dayofmonth_4</th>\n",
              "      <th>dayofmonth_5</th>\n",
              "      <th>weekofyear_0</th>\n",
              "      <th>weekofyear_1</th>\n",
              "      <th>weekofyear_2</th>\n",
              "      <th>weekofyear_3</th>\n",
              "      <th>weekofyear_4</th>\n",
              "      <th>weekofyear_5</th>\n",
              "      <th>weekofyear_6</th>\n",
              "      <th>dayofweek_0</th>\n",
              "      <th>dayofweek_1</th>\n",
              "      <th>dayofweek_2</th>\n",
              "      <th>dayofweek_3</th>\n",
              "      <th>dayofweek_4</th>\n",
              "      <th>dayofweek_5</th>\n",
              "      <th>dayofweek_6</th>\n",
              "      <th>quarter_1</th>\n",
              "      <th>quarter_2</th>\n",
              "      <th>quarter_3</th>\n",
              "      <th>quarter_4</th>\n",
              "      <th>month_1</th>\n",
              "      <th>month_2</th>\n",
              "      <th>month_3</th>\n",
              "      <th>month_4</th>\n",
              "      <th>month_5</th>\n",
              "      <th>month_6</th>\n",
              "      <th>month_7</th>\n",
              "      <th>month_8</th>\n",
              "      <th>month_9</th>\n",
              "      <th>month_10</th>\n",
              "      <th>month_11</th>\n",
              "      <th>month_12</th>\n",
              "      <th>year_2018</th>\n",
              "      <th>year_2019</th>\n",
              "      <th>year_2020</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-09-09</th>\n",
              "      <td>281</td>\n",
              "      <td>266</td>\n",
              "      <td>73</td>\n",
              "      <td>1826</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-10</th>\n",
              "      <td>264</td>\n",
              "      <td>247</td>\n",
              "      <td>51</td>\n",
              "      <td>2092</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-11</th>\n",
              "      <td>329</td>\n",
              "      <td>310</td>\n",
              "      <td>58</td>\n",
              "      <td>1998</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            사용자   세션  신규방문자  페이지뷰  ...  month_12  year_2018  year_2019  year_2020\n",
              "date                               ...                                           \n",
              "2018-09-09  281  266     73  1826  ...         0          1          0          0\n",
              "2018-09-10  264  247     51  2092  ...         0          1          0          0\n",
              "2018-09-11  329  310     58  1998  ...         0          1          0          0\n",
              "\n",
              "[3 rows x 53 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-nqMth6966w"
      },
      "source": [
        "#### 데이터 전처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "MB-KiRHF92gN",
        "outputId": "85589f31-8721-40d1-e430-837d47488cd6"
      },
      "source": [
        "# scaling\r\n",
        "\r\n",
        "mini = train.iloc[:,:4].min()\r\n",
        "size = train.iloc[:,:4].max() - train.iloc[:,:4].min()\r\n",
        "train.iloc[:,:4] = (train.iloc[:,:4] -  mini) / size\r\n",
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "      <th>dayofyear_0</th>\n",
              "      <th>dayofyear_1</th>\n",
              "      <th>dayofyear_2</th>\n",
              "      <th>dayofyear_3</th>\n",
              "      <th>dayofyear_4</th>\n",
              "      <th>dayofyear_5</th>\n",
              "      <th>dayofyear_6</th>\n",
              "      <th>dayofyear_7</th>\n",
              "      <th>dayofyear_8</th>\n",
              "      <th>dayofyear_9</th>\n",
              "      <th>dayofmonth_0</th>\n",
              "      <th>dayofmonth_1</th>\n",
              "      <th>dayofmonth_2</th>\n",
              "      <th>dayofmonth_3</th>\n",
              "      <th>dayofmonth_4</th>\n",
              "      <th>dayofmonth_5</th>\n",
              "      <th>weekofyear_0</th>\n",
              "      <th>weekofyear_1</th>\n",
              "      <th>weekofyear_2</th>\n",
              "      <th>weekofyear_3</th>\n",
              "      <th>weekofyear_4</th>\n",
              "      <th>weekofyear_5</th>\n",
              "      <th>weekofyear_6</th>\n",
              "      <th>dayofweek_0</th>\n",
              "      <th>dayofweek_1</th>\n",
              "      <th>dayofweek_2</th>\n",
              "      <th>dayofweek_3</th>\n",
              "      <th>dayofweek_4</th>\n",
              "      <th>dayofweek_5</th>\n",
              "      <th>dayofweek_6</th>\n",
              "      <th>quarter_1</th>\n",
              "      <th>quarter_2</th>\n",
              "      <th>quarter_3</th>\n",
              "      <th>quarter_4</th>\n",
              "      <th>month_1</th>\n",
              "      <th>month_2</th>\n",
              "      <th>month_3</th>\n",
              "      <th>month_4</th>\n",
              "      <th>month_5</th>\n",
              "      <th>month_6</th>\n",
              "      <th>month_7</th>\n",
              "      <th>month_8</th>\n",
              "      <th>month_9</th>\n",
              "      <th>month_10</th>\n",
              "      <th>month_11</th>\n",
              "      <th>month_12</th>\n",
              "      <th>year_2018</th>\n",
              "      <th>year_2019</th>\n",
              "      <th>year_2020</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-09-09</th>\n",
              "      <td>0.051689</td>\n",
              "      <td>0.049083</td>\n",
              "      <td>0.042604</td>\n",
              "      <td>0.011741</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-10</th>\n",
              "      <td>0.048551</td>\n",
              "      <td>0.045564</td>\n",
              "      <td>0.029586</td>\n",
              "      <td>0.013453</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-11</th>\n",
              "      <td>0.060550</td>\n",
              "      <td>0.057233</td>\n",
              "      <td>0.033728</td>\n",
              "      <td>0.012848</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-12</th>\n",
              "      <td>0.055197</td>\n",
              "      <td>0.052973</td>\n",
              "      <td>0.026036</td>\n",
              "      <td>0.016689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-13</th>\n",
              "      <td>0.069596</td>\n",
              "      <td>0.063530</td>\n",
              "      <td>0.028994</td>\n",
              "      <td>0.024731</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 사용자        세션     신규방문자  ...  year_2018  year_2019  year_2020\n",
              "date                                      ...                                 \n",
              "2018-09-09  0.051689  0.049083  0.042604  ...          1          0          0\n",
              "2018-09-10  0.048551  0.045564  0.029586  ...          1          0          0\n",
              "2018-09-11  0.060550  0.057233  0.033728  ...          1          0          0\n",
              "2018-09-12  0.055197  0.052973  0.026036  ...          1          0          0\n",
              "2018-09-13  0.069596  0.063530  0.028994  ...          1          0          0\n",
              "\n",
              "[5 rows x 53 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3B6jULuUW5Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8aa744f0-0635-4faa-c29a-24de5629cf95"
      },
      "source": [
        "# scaling\r\n",
        "\r\n",
        "input_window = 61\r\n",
        "output_window = 61 # 7\r\n",
        "\r\n",
        "window_x = np.zeros((train.shape[0] - (input_window + output_window), input_window, 53))\r\n",
        "window_y = np.zeros((train.shape[0] - (input_window + output_window), output_window, 4))\r\n",
        "\r\n",
        "for start in range(train.shape[0] - (input_window + output_window)):\r\n",
        "    end = start + input_window    \r\n",
        "    window_x[start,:, :] = train.iloc[start : end                , : ].values\r\n",
        "    window_y[start,:, :] = train.iloc[end   : end + output_window, :4 ].values\r\n",
        "\r\n",
        "\r\n",
        "print('window_x.shape: ', window_x.shape)\r\n",
        "print('window_y.shape: ', window_y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "window_x.shape:  (700, 61, 53)\n",
            "window_y.shape:  (700, 61, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wy1pDYOzVKDD"
      },
      "source": [
        "# window_x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dHc8qQZCDedw"
      },
      "source": [
        "- 시계열 데이터 임베딩 찾아서 적용해보기\r\n",
        "- 스케일링 다르게 해보기\r\n",
        "- dropout 적용해보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RMVmMfUBILC-"
      },
      "source": [
        "import tensorflow as tf\r\n",
        "from tensorflow import keras\r\n",
        "from tensorflow.keras import layers, Sequential\r\n",
        "\r\n",
        "from tensorflow.keras.layers import Dense, Conv1D, LSTM, Input, TimeDistributed\r\n",
        "from tensorflow.keras.models import Model\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJZU8NwSoRz5"
      },
      "source": [
        "optimizer = Adam, loss = mse, activation_function = 따로지정x"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKj7gc585ToK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "faa873b7-b3f9-4aa4-8578-3dd2b388cbfd"
      },
      "source": [
        "tf.random.set_seed(40)\r\n",
        "\r\n",
        "\r\n",
        "model = Sequential()\r\n",
        "model.add(LSTM(32, input_shape=(61, 53), return_sequences=True))  # (timestep, features)\r\n",
        "model.add(LSTM(32, return_sequences=True)) \r\n",
        "model.add(LSTM(32, return_sequences=True)) \r\n",
        "#model.add(LSTM(32, return_sequences=True)) \r\n",
        "#model.add(LSTM(32, return_sequences=True)) \r\n",
        "# model.add(LSTM(32, return_sequences=True)) \r\n",
        "model.add(TimeDistributed(Dense(4)))\r\n",
        "\r\n",
        "model.summary()\r\n",
        "\r\n",
        "opt = keras.optimizers.Adam(learning_rate=0.01)\r\n",
        "model.compile(loss='mean_squared_error', optimizer=opt)\r\n",
        "\r\n",
        "model.fit(window_x, window_y, epochs=100, batch_size=50)\r\n",
        "\r\n",
        "\r\n",
        "# layer3 b50 -> 1.2607e-04\r\n",
        "# layer3 b55 ->  1.0079e-04\r\n",
        "# layer3 b60 -> 1.3633e-04 추세는 비슷한데 값이 너무 낮음\r\n",
        "\r\n",
        "# layer3 b30 -> 5.9194e-05 XX\r\n",
        "# layer3 b40 -> 5.9194e-05 \r\n",
        "# layer3 b45 -> 6.3832e-05 괜춘.\r\n",
        "# layer3 b70 ->\r\n",
        "\r\n",
        "\r\n",
        "# layer 4 b50 -> 1.3527e-04 페이지뷰 너무 낮음\r\n",
        "# layer 4 b40 ->  9.5294e-05  xx\r\n",
        "# layer 4 b30 -> 추세를 잘 예측하지 못함\r\n",
        "\r\n",
        "\r\n",
        "# layer2 \r\n",
        "\r\n",
        "# lstm2층 - epochs100, batch_size=10"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_9 (LSTM)                (None, 61, 32)            11008     \n",
            "_________________________________________________________________\n",
            "lstm_10 (LSTM)               (None, 61, 32)            8320      \n",
            "_________________________________________________________________\n",
            "lstm_11 (LSTM)               (None, 61, 32)            8320      \n",
            "_________________________________________________________________\n",
            "time_distributed_3 (TimeDist (None, 61, 4)             132       \n",
            "=================================================================\n",
            "Total params: 27,780\n",
            "Trainable params: 27,780\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/100\n",
            "14/14 [==============================] - 5s 56ms/step - loss: 0.0436\n",
            "Epoch 2/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 0.0112\n",
            "Epoch 3/100\n",
            "14/14 [==============================] - 1s 56ms/step - loss: 0.0085\n",
            "Epoch 4/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 0.0073\n",
            "Epoch 5/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 0.0064\n",
            "Epoch 6/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 0.0055\n",
            "Epoch 7/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 0.0045\n",
            "Epoch 8/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 0.0039\n",
            "Epoch 9/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 0.0035\n",
            "Epoch 10/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 0.0034\n",
            "Epoch 11/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 0.0029\n",
            "Epoch 12/100\n",
            "14/14 [==============================] - 1s 57ms/step - loss: 0.0026\n",
            "Epoch 13/100\n",
            "14/14 [==============================] - 1s 57ms/step - loss: 0.0023\n",
            "Epoch 14/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 0.0021\n",
            "Epoch 15/100\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 0.0019\n",
            "Epoch 16/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 0.0018\n",
            "Epoch 17/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 0.0016\n",
            "Epoch 18/100\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 0.0014\n",
            "Epoch 19/100\n",
            "14/14 [==============================] - 1s 56ms/step - loss: 0.0013\n",
            "Epoch 20/100\n",
            "14/14 [==============================] - 1s 57ms/step - loss: 0.0012\n",
            "Epoch 21/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 0.0012\n",
            "Epoch 22/100\n",
            "14/14 [==============================] - 1s 57ms/step - loss: 0.0011\n",
            "Epoch 23/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 0.0010\n",
            "Epoch 24/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 8.7813e-04\n",
            "Epoch 25/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 7.9791e-04\n",
            "Epoch 26/100\n",
            "14/14 [==============================] - 1s 57ms/step - loss: 7.3549e-04\n",
            "Epoch 27/100\n",
            "14/14 [==============================] - 1s 57ms/step - loss: 6.8834e-04\n",
            "Epoch 28/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 6.8159e-04\n",
            "Epoch 29/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 6.0269e-04\n",
            "Epoch 30/100\n",
            "14/14 [==============================] - 1s 56ms/step - loss: 5.6324e-04\n",
            "Epoch 31/100\n",
            "14/14 [==============================] - 1s 57ms/step - loss: 5.6658e-04\n",
            "Epoch 32/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 5.2296e-04\n",
            "Epoch 33/100\n",
            "14/14 [==============================] - 1s 57ms/step - loss: 4.8611e-04\n",
            "Epoch 34/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 5.3483e-04\n",
            "Epoch 35/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 5.5414e-04\n",
            "Epoch 36/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 4.3234e-04\n",
            "Epoch 37/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 3.8763e-04\n",
            "Epoch 38/100\n",
            "14/14 [==============================] - 1s 56ms/step - loss: 3.6034e-04\n",
            "Epoch 39/100\n",
            "14/14 [==============================] - 1s 57ms/step - loss: 3.3455e-04\n",
            "Epoch 40/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 3.3514e-04\n",
            "Epoch 41/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 3.3761e-04\n",
            "Epoch 42/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 3.3991e-04\n",
            "Epoch 43/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 3.0050e-04\n",
            "Epoch 44/100\n",
            "14/14 [==============================] - 1s 63ms/step - loss: 3.0875e-04\n",
            "Epoch 45/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 3.0443e-04\n",
            "Epoch 46/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 2.7491e-04\n",
            "Epoch 47/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 2.5329e-04\n",
            "Epoch 48/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 2.6252e-04\n",
            "Epoch 49/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 2.7986e-04\n",
            "Epoch 50/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 4.1914e-04\n",
            "Epoch 51/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 2.7060e-04\n",
            "Epoch 52/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 2.5094e-04\n",
            "Epoch 53/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 2.2887e-04\n",
            "Epoch 54/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 2.2013e-04\n",
            "Epoch 55/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 2.1978e-04\n",
            "Epoch 56/100\n",
            "14/14 [==============================] - 1s 57ms/step - loss: 2.3421e-04\n",
            "Epoch 57/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 2.1953e-04\n",
            "Epoch 58/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 2.1194e-04\n",
            "Epoch 59/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 2.2164e-04\n",
            "Epoch 60/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 2.1350e-04\n",
            "Epoch 61/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 2.0322e-04\n",
            "Epoch 62/100\n",
            "14/14 [==============================] - 1s 57ms/step - loss: 1.8663e-04\n",
            "Epoch 63/100\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 2.2056e-04\n",
            "Epoch 64/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 2.4533e-04\n",
            "Epoch 65/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 3.6015e-04\n",
            "Epoch 66/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 2.7461e-04\n",
            "Epoch 67/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 2.9495e-04\n",
            "Epoch 68/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 2.3474e-04\n",
            "Epoch 69/100\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 1.7854e-04\n",
            "Epoch 70/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 1.5511e-04\n",
            "Epoch 71/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 1.5153e-04\n",
            "Epoch 72/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 1.4544e-04\n",
            "Epoch 73/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 1.5204e-04\n",
            "Epoch 74/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 1.3232e-04\n",
            "Epoch 75/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 1.2821e-04\n",
            "Epoch 76/100\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 1.3898e-04\n",
            "Epoch 77/100\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 1.2826e-04\n",
            "Epoch 78/100\n",
            "14/14 [==============================] - 1s 58ms/step - loss: 1.2939e-04\n",
            "Epoch 79/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 1.2026e-04\n",
            "Epoch 80/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 1.2118e-04\n",
            "Epoch 81/100\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 1.2975e-04\n",
            "Epoch 82/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 1.1680e-04\n",
            "Epoch 83/100\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 1.3297e-04\n",
            "Epoch 84/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 1.2292e-04\n",
            "Epoch 85/100\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 1.2571e-04\n",
            "Epoch 86/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 1.1242e-04\n",
            "Epoch 87/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 1.1258e-04\n",
            "Epoch 88/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 1.1171e-04\n",
            "Epoch 89/100\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 1.0343e-04\n",
            "Epoch 90/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 9.8354e-05\n",
            "Epoch 91/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 1.0535e-04\n",
            "Epoch 92/100\n",
            "14/14 [==============================] - 1s 66ms/step - loss: 9.7086e-05\n",
            "Epoch 93/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 1.0235e-04\n",
            "Epoch 94/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 1.0555e-04\n",
            "Epoch 95/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 1.1828e-04\n",
            "Epoch 96/100\n",
            "14/14 [==============================] - 1s 60ms/step - loss: 1.1794e-04\n",
            "Epoch 97/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 1.0543e-04\n",
            "Epoch 98/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 9.8751e-05\n",
            "Epoch 99/100\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 1.0161e-04\n",
            "Epoch 100/100\n",
            "14/14 [==============================] - 1s 63ms/step - loss: 1.2607e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff6d10fb198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dbInac4YQ8pV"
      },
      "source": [
        "## test : last_df"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hL8wR22W18Co",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb5bb176-9c36-46e1-b34f-6e20d38d2ba2"
      },
      "source": [
        "# train은 이미 위에서 minmax scaler적용되어있음.\r\n",
        "\r\n",
        "last_df = train.iloc[-61:,:].values[np.newaxis,...] # np.newaxis: 3D로 차원을 늘려줌. \r\n",
        "last_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 61, 53)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaMBTNjj2JW9"
      },
      "source": [
        "pred_last = model.predict(last_df)\r\n",
        "#pred_last"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HoED9_139dCn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a5313c4-f0ce-4f54-9e14-f061b2118a39"
      },
      "source": [
        "pred_last = pred_last.reshape(output_window,4)\r\n",
        "pred_last = pred_last * size[:4].values + mini[:4].values\r\n",
        "pred_last = pred_last.astype(int)\r\n",
        "pred_last\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  2186,   2147,    490,  53605],\n",
              "       [  3065,   3035,    761,  71873],\n",
              "       [  3682,   3573,    957, 101012],\n",
              "       [  2105,   2018,    484,  49675],\n",
              "       [  2007,   1956,    429,  47148],\n",
              "       [  3382,   3325,    816,  89366],\n",
              "       [  2937,   2868,    694,  77382],\n",
              "       [  3437,   3379,    817,  91721],\n",
              "       [  3312,   3262,    781,  80183],\n",
              "       [  3155,   3145,    787,  75434],\n",
              "       [  2046,   2065,    552,  45086],\n",
              "       [  2108,   2155,    537,  50554],\n",
              "       [  3697,   3743,   1040, 104187],\n",
              "       [  2632,   2612,    602,  66276],\n",
              "       [  2566,   2600,    636,  61461],\n",
              "       [  2961,   3017,    829,  68332],\n",
              "       [  2973,   3013,    837,  76922],\n",
              "       [  1563,   1586,    437,  33327],\n",
              "       [  1458,   1527,    405,  32977],\n",
              "       [  2815,   2907,    773,  73740],\n",
              "       [  2485,   2558,    688,  67564],\n",
              "       [  2073,   2181,    580,  55033],\n",
              "       [  2529,   2703,    724,  68668],\n",
              "       [  2051,   2215,    632,  56342],\n",
              "       [  1955,   2093,    575,  55401],\n",
              "       [  2107,   2210,    499,  61820],\n",
              "       [  3036,   3150,    669,  89636],\n",
              "       [  2717,   2768,    539,  77047],\n",
              "       [  3319,   3391,    675,  92410],\n",
              "       [  3367,   3416,    753,  84647],\n",
              "       [  2784,   2806,    655,  61985],\n",
              "       [  1842,   1900,    518,  34599],\n",
              "       [  1931,   2031,    531,  36082],\n",
              "       [  2823,   2938,    701,  59929],\n",
              "       [  3550,   3634,    911,  85662],\n",
              "       [  2805,   2839,    690,  68315],\n",
              "       [  2995,   3072,    781,  67580],\n",
              "       [  2788,   2869,    734,  64535],\n",
              "       [  2308,   2334,    588,  50506],\n",
              "       [  2126,   2169,    469,  44948],\n",
              "       [  3370,   3419,    693,  75400],\n",
              "       [  3215,   3167,    627,  68758],\n",
              "       [  3526,   3506,    787,  72539],\n",
              "       [  3431,   3377,    783,  63643],\n",
              "       [  3052,   2949,    629,  51439],\n",
              "       [  1792,   1736,    338,  26702],\n",
              "       [  1596,   1598,    343,  22629],\n",
              "       [  2687,   2667,    513,  45655],\n",
              "       [  2743,   2638,    504,  49852],\n",
              "       [  2584,   2468,    486,  49995],\n",
              "       [  2899,   2773,    556,  58068],\n",
              "       [  2986,   2847,    586,  67454],\n",
              "       [  2182,   1998,    433,  44620],\n",
              "       [  2427,   2275,    433,  55888],\n",
              "       [  3699,   3457,    604, 108424],\n",
              "       [  3014,   2717,    450,  87790],\n",
              "       [  3060,   2840,    559,  89032],\n",
              "       [  2752,   2545,    567,  70920],\n",
              "       [  3136,   2980,    659,  87423],\n",
              "       [  2273,   2157,    636,  56741],\n",
              "       [  2267,   2179,    517,  59700]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FY7lgRivG5-L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11e4c2a7-17f8-4b69-aa5e-3d7c3d430681"
      },
      "source": [
        "pred_last.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkxkXBgBJ4tg"
      },
      "source": [
        "## submit"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zlyp_qpF8zX8"
      },
      "source": [
        "- submit 파일로드"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rMOy5_lV1jAY",
        "outputId": "8dbd5e31-0a6a-4aca-8e77-5c7b22848527"
      },
      "source": [
        "submission =  pd.read_csv(path + '/open_data/submission.csv', encoding = 'euc-kr')\r\n",
        "submission= submission[30:]\r\n",
        "submission.reset_index(drop=True,inplace=True)\r\n",
        "submission.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(31, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VUwY_duJ4uA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "a8c2830b-78c6-470d-bb35-1cb53eb95094"
      },
      "source": [
        "pred_df = pd.DataFrame(pred_last)\r\n",
        "pred_df = pred_df[:31]\r\n",
        "pred_df.reset_index(drop=True, inplace = True)\r\n",
        "pred_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2186</td>\n",
              "      <td>2147</td>\n",
              "      <td>490</td>\n",
              "      <td>53605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3065</td>\n",
              "      <td>3035</td>\n",
              "      <td>761</td>\n",
              "      <td>71873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3682</td>\n",
              "      <td>3573</td>\n",
              "      <td>957</td>\n",
              "      <td>101012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2105</td>\n",
              "      <td>2018</td>\n",
              "      <td>484</td>\n",
              "      <td>49675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2007</td>\n",
              "      <td>1956</td>\n",
              "      <td>429</td>\n",
              "      <td>47148</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      0     1    2       3\n",
              "0  2186  2147  490   53605\n",
              "1  3065  3035  761   71873\n",
              "2  3682  3573  957  101012\n",
              "3  2105  2018  484   49675\n",
              "4  2007  1956  429   47148"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFdMfTtYJ4uA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4952eae5-b0bc-4ee8-b88d-d0bc3859ef76"
      },
      "source": [
        "\r\n",
        "for i in range(len(submission)):\r\n",
        "  submission['사용자'][i] = pred_df[0][i]\r\n",
        "  submission['세션'][i] = pred_df[1][i]\r\n",
        "  submission['신규방문자'][i] = pred_df[2][i]\r\n",
        "  submission['페이지뷰'][i] = pred_df[3][i]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/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",
            "\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/python3.6/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",
            "\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/python3.6/dist-packages/ipykernel_launcher.py:5: 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/python3.6/dist-packages/ipykernel_launcher.py:6: 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"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "OB5R30cBn27U",
        "outputId": "24e9803d-1138-4813-d77a-0aef1dacf374"
      },
      "source": [
        "submission.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DateTime</th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-09</td>\n",
              "      <td>2186</td>\n",
              "      <td>2147</td>\n",
              "      <td>490</td>\n",
              "      <td>53605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-10</td>\n",
              "      <td>3065</td>\n",
              "      <td>3035</td>\n",
              "      <td>761</td>\n",
              "      <td>71873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-12-11</td>\n",
              "      <td>3682</td>\n",
              "      <td>3573</td>\n",
              "      <td>957</td>\n",
              "      <td>101012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-12-12</td>\n",
              "      <td>2105</td>\n",
              "      <td>2018</td>\n",
              "      <td>484</td>\n",
              "      <td>49675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-12-13</td>\n",
              "      <td>2007</td>\n",
              "      <td>1956</td>\n",
              "      <td>429</td>\n",
              "      <td>47148</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     DateTime   사용자    세션  신규방문자    페이지뷰\n",
              "0  2020-12-09  2186  2147    490   53605\n",
              "1  2020-12-10  3065  3035    761   71873\n",
              "2  2020-12-11  3682  3573    957  101012\n",
              "3  2020-12-12  2105  2018    484   49675\n",
              "4  2020-12-13  2007  1956    429   47148"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UeUT41zKMnRF"
      },
      "source": [
        "submission.to_csv('2차_공휴일 feature.csv', index = False, encoding = 'euc-kr')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0ARyON_J5BK"
      },
      "source": [
        "#### 결과 제일 좋았던, 나빴던 파일이랑 predict를 비교"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EvX_gyeMV9R"
      },
      "source": [
        "submission.set_index('DateTime', inplace = True)\r\n",
        "submission.index = pd.to_datetime(submission.index)\r\n",
        "# submission.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZ0zZ_iTJiyX"
      },
      "source": [
        "path2 = \"/content/drive/MyDrive/Colab Notebooks/DACONCUP 주희\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "40zTUOVqVQ1k",
        "outputId": "613e5a0c-ebe8-4326-bbd1-337cbbd4d816"
      },
      "source": [
        "# 2차데이터 \r\n",
        "#20년 11월 9일 ~ 12월 8일\r\n",
        "\r\n",
        "\r\n",
        "train2 = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/DACON CUP/' + '/open_data/2차_open_data/2차_train.csv', encoding='euc-kr')\r\n",
        "\r\n",
        "# date 기준으로.\r\n",
        "train2['DateTime'] = pd.to_datetime(train2.DateTime) #dtype: datetime으로 변경\r\n",
        "train2['DateTime'] = train2.DateTime.dt.date\r\n",
        "train2  = train2.groupby('DateTime').sum().reset_index()\r\n",
        "\r\n",
        "train2.set_index('DateTime', inplace = True)\r\n",
        "train2.index = pd.to_datetime(train2.index)\r\n",
        "\r\n",
        "train2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DateTime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-11-09</th>\n",
              "      <td>4364</td>\n",
              "      <td>4340</td>\n",
              "      <td>1143</td>\n",
              "      <td>95408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-10</th>\n",
              "      <td>4370</td>\n",
              "      <td>4256</td>\n",
              "      <td>1137</td>\n",
              "      <td>99623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-11</th>\n",
              "      <td>4492</td>\n",
              "      <td>4449</td>\n",
              "      <td>1071</td>\n",
              "      <td>114438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-12</th>\n",
              "      <td>3998</td>\n",
              "      <td>3899</td>\n",
              "      <td>796</td>\n",
              "      <td>94730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-13</th>\n",
              "      <td>4130</td>\n",
              "      <td>3967</td>\n",
              "      <td>835</td>\n",
              "      <td>101484</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             사용자    세션  신규방문자    페이지뷰\n",
              "DateTime                             \n",
              "2020-11-09  4364  4340   1143   95408\n",
              "2020-11-10  4370  4256   1137   99623\n",
              "2020-11-11  4492  4449   1071  114438\n",
              "2020-11-12  3998  3899    796   94730\n",
              "2020-11-13  4130  3967    835  101484"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKzXETTdZjDM"
      },
      "source": [
        "best = pd.read_csv(path + '/submit_data/best공휴일+xgb_new.csv', encoding = 'euc-kr')\r\n",
        "best = best.iloc[30:]\r\n",
        "best.set_index('DateTime', inplace = True)\r\n",
        "best.index = pd.to_datetime(best.index)\r\n",
        "\r\n",
        "\r\n",
        "# lstm\r\n",
        "lstm_sub_best = pd.read_csv(path + '/submit_data/공휴일 feature.csv', encoding = 'euc-kr')\r\n",
        "#lstm_sub_best = pd.read_csv(path2 + '/best+holiday+1.1.csv', encoding = 'euc-kr')\r\n",
        "lstm_sub_best = lstm_sub_best.iloc[30:]\r\n",
        "lstm_sub_best.set_index('DateTime', inplace = True)\r\n",
        "lstm_sub_best.index = pd.to_datetime(lstm_sub_best.index)\r\n",
        "\r\n",
        "\r\n",
        "xgb_sub_best = pd.read_csv(path + '/submit_data/xgb_distribute_new.csv', encoding = 'euc-kr')\r\n",
        "xgb_sub_best = xgb_sub_best.iloc[30:]\r\n",
        "xgb_sub_best.set_index('DateTime', inplace = True)\r\n",
        "xgb_sub_best.index = pd.to_datetime(xgb_sub_best.index)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCT4at_iSIP5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "31816c28-f74e-4970-c9ea-2fa7343c383d"
      },
      "source": [
        "\"\"\"worst = pd.read_csv(path2+'/submit/seed40_add 날짜feature.csv', encoding = 'euc-kr')\r\n",
        "# worst = pd.read_csv(path2 + '/submit/lstm5 e150 b10.csv', encoding = 'euc-kr')\r\n",
        "worst.set_index('DateTime', inplace = True)\r\n",
        "worst.index = pd.to_datetime(best.index)\r\n",
        "#worst.head()\"\"\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"worst = pd.read_csv(path2+'/submit/seed40_add 날짜feature.csv', encoding = 'euc-kr')\\n# worst = pd.read_csv(path2 + '/submit/lstm5 e150 b10.csv', encoding = 'euc-kr')\\nworst.set_index('DateTime', inplace = True)\\nworst.index = pd.to_datetime(best.index)\\n#worst.head()\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qgpdgvAzKK9R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "50246ff0-bc90-48e5-f82c-68199d2cd3da"
      },
      "source": [
        "# plot expected vs preducted\r\n",
        "\r\n",
        "\r\n",
        "plt.figure(figsize = (25,7))\r\n",
        "#pyplot.plot(train2['사용자'], label='best')\r\n",
        "pyplot.plot(best['사용자'], label='best')\r\n",
        "pyplot.plot(lstm_sub_best['사용자'], label='lstm_sub_best')\r\n",
        "pyplot.plot(submission['사용자'], label='Predicted')\r\n",
        "#pyplot.plot(xgb_sub_best['사용자'], label='xgb_sub_best')\r\n",
        "\r\n",
        "#pyplot.plot(worst['사용자'], label = 'worst')\r\n",
        "pyplot.legend()\r\n",
        "pyplot.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1800x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBhRD3aLLPV4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "62dbd5dd-2498-4afc-b34d-f90307e8ee76"
      },
      "source": [
        "plt.figure(figsize = (25,7))\r\n",
        "pyplot.plot(best['신규방문자'], label='best')\r\n",
        "pyplot.plot(lstm_sub_best['신규방문자'], label='lstm_sub_best')\r\n",
        "pyplot.plot(submission['신규방문자'], label='Predicted')\r\n",
        "\r\n",
        "#pyplot.plot(xgb_sub_best['신규방문자'], label='xgb_sub_best')\r\n",
        "\r\n",
        "#pyplot.plot(worst['신규방문자'], label = 'worst')\r\n",
        "pyplot.legend()\r\n",
        "pyplot.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1800x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBs3Ig9sLHcw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "09fe5a2b-f53b-4045-dd69-d4941edd0b0f"
      },
      "source": [
        "\r\n",
        "plt.figure(figsize = (25,7))\r\n",
        "pyplot.plot(best['세션'], label='best')\r\n",
        "pyplot.plot(lstm_sub_best['세션'], label='lstm_sub_best')\r\n",
        "pyplot.plot(submission['세션'], label='Predicted')\r\n",
        "\r\n",
        "#pyplot.plot(xgb_sub_best['세션'], label='xgb_sub_best')\r\n",
        "\r\n",
        "#pyplot.plot(worst['세션'], label = 'worst')\r\n",
        "pyplot.legend()\r\n",
        "pyplot.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1800x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NV6qNNQoLPZT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "aabe4938-2b38-4816-8bb5-fa44b7e60075"
      },
      "source": [
        "\r\n",
        "plt.figure(figsize = (25,7))\r\n",
        "pyplot.plot(best['페이지뷰'], label='best')\r\n",
        "pyplot.plot(lstm_sub_best['페이지뷰'], label='lstm_sub_best')\r\n",
        "pyplot.plot(submission['페이지뷰'], label='Predicted')\r\n",
        "\r\n",
        "#pyplot.plot(xgb_sub_best['페이지뷰'], label='xgb_sub_best')\r\n",
        "#pyplot.plot(worst['페이지뷰'], label = 'worst')\r\n",
        "pyplot.legend()\r\n",
        "pyplot.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABbUAAAGbCAYAAADz1X0ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxU5dn/8c+Z7PtGIAuEhC0rOwkggnvAWqu2dd+oitqqdWmt9uda61r7WNeK1qXSR9RHq9VqEdxR2TchkIUtBEI2kpCEhIRkcn5/nCFlyUYyyWSS7/v1mtdJ7nOf+1xDbDNz5Z7rMkzTRERERERERERERETEHdhcHYCIiIiIiIiIiIiISGcpqS0iIiIiIiIiIiIibkNJbRERERERERERERFxG0pqi4iIiIiIiIiIiIjbUFJbRERERERERERERNyGp6sDcLZBgwaZ8fHxrg5DRERERERERERERNqxdu3afaZpRp7odf0uqR0fH8+aNWtcHYaIiIiIiIiIiIiItMMwjF1duU7lR0RERERERERERETEbSipLSIiIiIiIiIiIiJuQ0ltEREREREREREREXEb/a6mtoiIiIiIiIiIiAwMjY2N7Nmzh/r6eleHIu3w9fVl6NCheHl5OWU9JbVFRERERERERETELe3Zs4egoCDi4+MxDMPV4UgrTNOkvLycPXv2kJCQ4JQ1VX5ERERERERERERE3FJ9fT0RERFKaPdhhmEQERHh1N30SmqLiIiIiIiIiIiI21JCu+9z9s9ISW0RERERERERERERcRtKaouIiIiIiIiIiIh0UX5+Pmlpad1a4+uvv2bZsmVOiqj/U1JbRERERERERERExIWU1D4xSmqLiIiIiIiIiIiIdENTUxOXX345ycnJ/PznP6euro61a9dyyimnMHnyZGbPnk1RUREAzz77LCkpKYwbN45LLrmE/Px85s+fz1/+8hcmTJjAt99+6+Jn0/d5ujoAERERERERERERke76w783s2VvtVPXTIkJ5oFzUzucl5uby6uvvsqMGTO45ppreOGFF/jggw/48MMPiYyM5J133uGee+7htdde4/HHH2fnzp34+Piwf/9+QkNDufHGGwkMDOS3v/2tU+Pvr5TUFhEREREREREREemGYcOGMWPGDACuuOIKHn30UbKysjjrrLMAsNvtREdHAzBu3Dguv/xyzj//fM4//3yXxezOlNQWERERERERERERt9eZHdU9xTCMo74PCgoiNTWV5cuXHzf3k08+YenSpfz73//mkUceYdOmTb0VZr+hmtoiIiLu5uB+sDe6OgoRERERERFxKCgoaElgL1y4kGnTplFWVtYy1tjYyObNm2lubmb37t2cdtppPPHEE1RVVXHgwAGCgoKoqalx5VNwK0pqi4iIuJPaffDcJPj6MVdHIiIiIiIiIg6JiYm88MILJCcnU1lZyS233MJ7773HXXfdxfjx45kwYQLLli3DbrdzxRVXMHbsWCZOnMivf/1rQkNDOffcc/nggw/UKLKTVH5ERETEnXz1CNSVQ/53ro5EREREREREgPj4eHJyco4bnzBhAkuXLj1u/Lvvjn8/N2bMGDZu3Ngj8fVH2qktIiLiLoo3wdq/g1cAFP2gEiQiIiIiIiIyICmpLSIi4g5MEz79PfiGQuZD0FQPpdmujkpERERERESk1ympLSIi4g6yP4L8b+H0e2Dk6dZY4VrXxiQiIiIiIiLiAkpqi4iI9HWNB2HJvTA4FSbNhbAE8AuDvetcHZmIiIiIiIhIr1OjSBERkb5u+fOwvwCu+gg8HL+6YyZBoZLaIiIiIiIiMvBop7aIiEhfVr0Xvn0Kks+FEaf8dzx2klVT+1Cd62ITERERERERcYEOk9qGYbxmGEapYRhZR4xdaBjGZsMwmg3DmHLM/N8bhrHNMIxcwzBmHzE+xzG2zTCMu48YTzAMY6Vj/B3DMLwd4z6O77c5zsc74wmLiIi4lc8fhGY7nPXHo8djJ4Nph+KNLglLRERERERExFU6s1P778CcY8aygJ8CS48cNAwjBbgESHVc81fDMDwMw/AAXgDOBlKASx1zAZ4A/mKa5iigErjWMX4tUOkY/4tjnoiIyMCxexVsfAdOuhnCE44+FzPJOqpZpIiIiIiIiEsFBga2e/7RRx/tpUiOFh8fz759+zo1d+7cubz33nvdvmdvPdcOk9qmaS4FKo4ZyzZNM7eV6ecBb5um2WCa5k5gG5DheGwzTXOHaZqHgLeB8wzDMIDTgcP/Ym8A5x+x1huOr98DznDMFxER6f+am2HRXRAYBSffcfz5oCEQPFR1tUVERERERPo4VyW1XaG3nquzG0XGAiuO+H6PYwxg9zHjU4EIYL9pmk2tzI89fI1pmk2GYVQ55h/35wXDMK4HrgeIi4tzyhMRERFxqY1vw951cMFL4NPGX/1jJ2qntoiIiIiIyGGL7obiTc5dM2osnP14p6YWFRVx8cUXU11dTVNTEy+++CKffPIJBw8eZMKECaSmpvLII48wZ84cpk2bxrJly0hPT+cXv/gFDzzwAKWlpbz55ptkZGS0uv4333zDrbfeCoBhGCxdupS1a9fy5z//mY8//hiAm2++mSlTpjB37lwA/vSnP7Fo0SL8/PxYuHAho0aNajP+zz//nMcff5zq6mqeeuopfvzjH2O327n77rv5+uuvaWho4KabbuKGG27o1HN98803T+Af+sQ4O6ntEqZpvgy8DDBlyhTTxeGIiIh0T0ONVUs7dgqMvajteTGTIPvfUFcB/uG9Fp6IiIiIiIgcb+HChcyePZt77rkHu91OXV0dM2fO5Pnnn2fDhg0A5Ofns23bNt59911ee+010tPTWbhwId999x0fffQRjz76KP/6179aXf/Pf/4zL7zwAjNmzODAgQP4+vp2GFNISAibNm1iwYIF3HbbbS3J79bk5+ezatUqtm/fzmmnnca2bdtYsGABISEhrF69moaGBmbMmEFmZibvv/9+h8+1Jzk7qV0IDDvi+6GOMdoYLwdCDcPwdOzWPnL+4bX2GIbhCYQ45ouIiPRv3/4PHCiBSxaCrZ1KYbGTrePedTDqzN6JTUREREREpK/q5I7qnpKens4111xDY2Mj559/PhMmTGh1XkJCAmPHjgUgNTWVM844A8MwGDt2LPn5+W2uP2PGDO644w4uv/xyfvrTnzJ06NAOY7r00ktbjrfffnu7cy+66CJsNhujR49mxIgR5OTksGTJEjZu3NhSb7uqqoqtW7d2+rn2lM40ijwRHwGXGIbhYxhGAjAaWAWsBkYbhpFgGIY3VjPJj0zTNIGvgJ87rr8a+PCIta52fP1z4EvHfBERkf6rYgcsfwHGXwpDp7Q/N8bxoqFwfc/HJSIiIiIiIu2aNWsWS5cuJTY2lrlz57JgwYJW5/n4+LR8bbPZWr632Ww0NTW1eg3A3XffzSuvvMLBgweZMWMGOTk5eHp60tzc3DKnvr7+qGuObFHYUbvCY88bhoFpmjz33HNs2LCBDRs2sHPnTjIzMzv9XHtKh0ltwzDeApYDiYZh7DEM41rDMC4wDGMPMB34xDCMxQCmaW4G/g/YAnwK3GSapt2xC/tmYDGQDfyfYy7AXcAdhmFsw6qZ/apj/FUgwjF+B3C3c56yiIhIH7bkPrB5wRkPdDzXNwQGjVFdbRERERERkT5g165dDBkyhHnz5nHdddexbt06ALy8vGhsbOz2+tu3b2fs2LHcddddpKenk5OTw/Dhw9myZQsNDQ3s37+fL7744qhr3nnnnZbj9OnT213/3Xffpbm5me3bt7Njxw4SExOZPXs2L774Ykv8eXl51NbW9vhz7UiH5UdM07y0jVMftDH/EeCRVsb/A/ynlfEdwHHVz03TrAcu7Cg+ERGRfmP7V5DzMZxxPwRHd+6amEmw4yswTejgr+4iIiIiIiLSc77++muefPJJvLy8CAwMbNm9fP311zNu3DgmTZrEI48clzbttKeffpqvvvoKm81GamoqZ599Nj4+Plx00UWkpaWRkJDAxIkTj7qmsrKScePG4ePjw1tvvdXu+nFxcWRkZFBdXc38+fPx9fXluuuuIz8/n0mTJmGaJpGRkfzrX//q1HPtyUaRRn+r6DFlyhRzzZo1rg5DRETkxNibYP7J0FgHN60Cr44bfgCw8mVYdCfcvgVCYns2RhERERERkT4mOzub5ORkV4chndDaz8owjLWmaXZQe/N4zq6pLSIiIl2x9nUoy4bZj3Q+oQ0QO8k6qgSJiIiIiIiIDBAdlh8RERGRHlZXAV8+DAmzIOnHJ3btkDSrBvfedZDyk56JT0RERERERHrN66+/zjPPPHPU2IwZM3jhhRe6vfYjjzzCu+++e9TYhRdeyD333NPttXuTyo+IiIi42n/uhNWvwI3fwZDUE7/+pVPANxiu/rfzYxMREREREenDVH7Efaj8iIiISH9RsgVWvwpTrulaQhusEiR7N0Bzs3NjExEREREREemDlNQWERFxFdOET+8GnyA4rRsf9YqdDA3VUL7NebGJiIiIiIiI9FFKaouIiLhK7n9g5zdw2v8D//CurxPjaBa5d51z4hIRERERERHpw5TUFhERcYWmBlj8/yAyySo90h2RieAVAIVKaouIiIiIiPQ2Dw8PJkyYQFpaGhdeeCF1dXVdXmvu3Lm89957AFx33XVs2bKlzblff/01y5YtO+F7xMfHs2/fvi7H2BcoqS0iIuIKK/4Klfkw5zHw8OreWjYPiJkAhWudEpqIiIiIiIh0np+fHxs2bCArKwtvb2/mz59/1PmmpqYurfvKK6+QkpLS5vmuJrX7AyW1RUREeltNMSz9MyT+CEae7pw1YydB8SZoOuSc9UREREREROSEzZw5k23btvH1118zc+ZMfvKTn5CSkoLdbufOO+8kPT2dcePG8dJLLwFgmiY333wziYmJnHnmmZSWlrasdeqpp7JmzRoAPv30UyZNmsT48eM544wzyM/PZ/78+fzlL39hwoQJfPvtt5SVlfGzn/2M9PR00tPT+f777wEoLy8nMzOT1NRUrrvuOkzT7P1/GCfzdHUAIiIiA84XD1nlRzIfdt6aMZPA3gClmyFmovPWFRERERERcRNPrHqCnIocp66ZFJ7EXRl3dWpuU1MTixYtYs6cOQCsW7eOrKwsEhISePnllwkJCWH16tU0NDQwY8YMMjMzWb9+Pbm5uWzZsoWSkhJSUlK45pqjS1SWlZUxb948li5dSkJCAhUVFYSHh3PjjTcSGBjIb3/7WwAuu+wybr/9dk4++WQKCgqYPXs22dnZ/OEPf+Dkk0/m/vvv55NPPuHVV1916r+RKyipLSIi0psK18KGN2HGrRAx0nnrxjqaRRauU1JbRERERESkFx08eJAJEyYA1k7ta6+9lmXLlpGRkUFCQgIAS5YsYePGjS31squqqti6dStLly7l0ksvxcPDg5iYGE4//fhP865YsYJZs2a1rBUeHt5qHJ9//vlRNbirq6s5cOAAS5cu5f333wfgnHPOISwszHlP3kWU1BYREektpgmL7oKAwTDzt85dO3Q4+EdYSe30a527toiIiIiIOyrNhrcvg+gJMGY2jDoTAga5OirpQZ3dUe1sh2tqHysgIKDla9M0ee6555g9e/ZRc/7zn/84LY7m5mZWrFiBr6+v09bsq1RTW0REpLds/D/YsxrOfAB8g527tmFYJUj2rnPuuiIiIiIi7ir731CxE/K/gw9ugCdHwStnwjdPwt4N1qYTkV4ye/ZsXnzxRRobGwHIy8ujtraWWbNm8c4772C32ykqKuKrr7467tpp06axdOlSdu7cCUBFRQUAQUFB1NTUtMzLzMzkueeea/n+cKJ91qxZLFy4EIBFixZRWVnZM0+yFympLSIi0hsaDsDnD1i7RMZf1jP3iJ0MZTnWvUREREREBrqC5TA4BX6TC/O+glPvhmY7fPUwvHwK/E8SfHizlfxuqOl4PZFuuO6660hJSWHSpEmkpaVxww030NTUxAUXXMDo0aNJSUnhqquuYvr06cddGxkZycsvv8xPf/pTxo8fz8UXXwzAueeeywcffNDSKPLZZ59lzZo1jBs3jpSUFObPnw/AAw88wNKlS0lNTeX9998nLi6uV597TzD6Q7fLI02ZMsU83BVURESkz/jij/Dtn+GaJRA3tWfukbcYFl4Ec/8D8TN65h4iIiIiIu7A3gRPDIfxl8A5/3P0uQOlsO1z6/Xz9i+hoRpsXtZr6NGzrVIlzux/Iz0qOzub5ORkV4chndDaz8owjLWmaU450bVUU1tERKSnVebDsudg7IU9l9AGq/wIWCVIlNQWERERkYGsZBMcOgBxx+96JXAwTLjMetgboWAFbF0MeUtg8e+tR/hIK7k9+iwYPgM8fXr/OYhIm5TUFhER6WlL7gObB5z5h569T2AkhMRB4dqevY+IiIiISF9XsMI6tpbUPpKHFyTMtB6ZD1sbUvKWWEnu1a/Cir+CdyCMOBVGZ1qP4OgeDl5EOqKktoiISE/auRSyP4LT7oGQ2J6/X+wkKFSzyP6owd7Afd/fxw3jbmBkqD4OKyIiItKuXcsgNO7EX4OHxcPU663HoTrr9fzhXdw5H1tzosY5dnHPtl5/2zycHr6cGNM0MQzD1WFIO5xdAltJbRERkZ5ib4JPfw8hw+CkW3rnnrGTYMu/oHYfBAzqnXtKr8guz2bRzkUM8R/Cb6b8xtXhiIiIiPRdpmk1iRx5evfW8faHxDnWwzShdItVh3vrEvj2f2Dpk+AfAaPOssqUjDoD/MKc8xyk03x9fSkvLyciIkKJ7T7KNE3Ky8vx9fV12ppKaouIiPSUdW9ASRZc+Hfw8nPKkvvrDnHr2xu4YGIs509sZddJS13t9dYLa+k3cityAVhVvMrFkYiIiIj0cRU7oLas49IjJ8IwYEiq9Zh5B9RVWE0mDye5N74NhgcMmwpjMq1d3IOTreukRw0dOpQ9e/ZQVlbm6lCkHb6+vgwdOtRp6ympLSIi0hMOVsKXD1tNZVLOd8qSVQcbufLVVWwqrMLTZrSR1J4AGFZdbSW1+5W8yjzA2rFd1VBFiE+IiyMSERER6aN2LbOOw0/quXv4h8PYn1uPZjvsWWMlt7cuhs8ftB4hw6wa3GNmQ/xMa+e3OJ2XlxcJCQmuDkN6mZLaIiIiPeHrJ6zE9pzHnbI7o6a+kbmvryKnuJoxQwLJ2lvV+kSfIIhMVF3tfiivMo8ArwBqG2tZW7KW0+O6+XFaERERkf6qYDn4hcOgMb1zP5sHxE21HmfcB9V7rQR33hL44W1Y8yp4+kLCrP82mwwb3juxifRTSmqLiIg4W2kOrHoZJl8N0eO6vVxtQxO/eH01m/ZU8dfLJ1FQUcfDn2RTVtNAZJDP8RfETrZeRJumPu7YTzSbzWzdv5U58XP4eMfHrCpepaS2iIiISFsKllulR1z1Wjg4BibPtR5NDZD/nSPJ7ShVAhCZ9N9d3MOmgoeXa2IVcVM2Vwcg0mc1NVhN3kREToRpwuLfg3cgnH5ft5c7eMjONX9fzfrd+3n20olkpkaRGmOVndjc1m7tmIlWDcGq3d2+v/QNhQcKqW2sJW1QGhMHT1RdbREREZG21JRYNbWHO7Gednd4+lgNJM9+An69Hm5eA7MfhcAhsOJF+Ps58KeR8O5cqMx3dbQibkNJbZG2vP4j+PQuV0chIu4mb7HVMObUuyBgULeWqm+0M2/BGlbnV/DUReP50dhoAFJiggHYvLe69QtjJ1tHlSDpNw7X004MSyQjKoOtlVupqK9wcVQiIiIifVCBo562M5tEOothwKDRMP0muPoj+N0OuOgfkHKuVarkk9+4OkIRt6GktkhravdB4RrYtdzVkYiIO2k6ZO3SjhgN6fO6tVR9o50b/rGW77fv48mfj+e8Cf9tChni50VcuH/bO7WHpIGHt9UsUvqFvMo8DAxGho4kIzoDgNXFq10clYiIiEgfVLACvPwheryrI+mYbzCk/ATOewFm/Ra2fQ579BpepDOU1BZpTcEK67gvD+yNro1FRNzHyvnWRx3nPAae3l1e5lBTMze9uY5v8sp44qfj+NnkocfNSYsNJquwjZ3ant5WYnvv+i7HIH1LXkUeccFx+Hv5kxKRgr+nv5LaIiIiIq3ZtQyGTnG/GtUZ88AvDL553NWRiLgFJbVFWlPg2KHd3Aj7tro2FhFxDwdK4Zs/ObqZn9XlZRrtzdzy1jq+yCnlkQvSuCh9WKvzUmNCKKioo+pgG394i51sJbWb7V2ORfqOvMo8xoSNAcDL5sXkIZNZWbTSxVGJiIiI9DH11VCS1TdLj3TEJwim32w1ktQnLkU6pKS2SGsKloO/oxZu6RbXxiIi7uGLh6DpoNX0pYua7M3c9vYGFm8u4cFzU7h86vA256Y66mpvabOu9iQ4dEB/mOsH6hrr2F2zuyWpDZARlUF+dT6ldaUujExERESkj9mzCsxm90xqA2RcD76h1mYZEWmXktoixzpUC0U/wPhLwOYJJZtdHZGI9HV718P6/4WpN1qNX7rA3mzym3d/4JNNRdx7TjJzZyS0Oz81JgSg7braLc0itcvD3W3bvw0T8+iktqOu9qriVa4KS0RERKTv2bUcDA8Ymu7qSLrGN9jarZ33qUoJinRASW2RY+1ZA81NMOJUq9mbdmqLSHtMExbdDf4RMOvOLi3R3Gzyu/c28uGGvdw1J4nrZo7o8JrIIB+GBPuwua2d2hGjwTsI9q7rUkzSd+RW5gIcldRODEskyDtIdbVFREREjlSwHKLHgU+gqyPpuqmO3dpfP+HqSET6NCW1RY5VsAIwuHvPIl4PC4MSJbVFpB1Z/4TdK+CM+8Av9IQvb242+X8fbOKf6/Zwx1lj+OWpIzt9bVpMCFmFbezUttkgZgIUKqnt7vIq8gjwCiA2MLZlzMPmQfqQdNXVFhERETmsqcH6lGLcSa6OpHt8Q2D6TZC3CPZucHU0In2WktoixypYRmFUCp8UfManxkGoKrCaTYiIHOtQHXx2P0SNhYlXnvDlpmly/0dZvL16N7ecPopfn3FipUtSY4LZXnaAg4faaAYZOwmKN1kv8MVtHW4SaRjGUeMZ0RkUHiik8EChiyITERER6UP2boCmehjupvW0jzT1Biu5rdraIm3qMKltGMZrhmGUGoaRdcRYuGEYnxmGsdVxDHOMG4ZhPGsYxjbDMDYahjHpiGuudszfahjG1UeMTzYMY5PjmmcNxzu2tu4h0qPsTbB7NZ9FRAOwrbGKRoDSbJeGJSJ91PfPQHUhzHkCbB4ndKlpmjz08Rb+d0UBN54ykjvOGtPxRcdIjQ2h2YTs4raaRU6G5karA7y4JdM02Vq59ajSI4dlRDnqaheprraIiIgIBcus47Bpro3DGXxDYNqvIPcTKNro6mhE+qTO7NT+OzDnmLG7gS9M0xwNfOH4HuBsYLTjcT3wIlgJauABYCqQATxwRJL6RWDeEdfN6eAeIj2neCM01rKEWgwMDplN5Ht5QamaRYrIMfbvhu+fhtQLIH7GCV1qmiaPLcrh9e/zufbkBO6ak3jcLtzOSIt1NItsqwRJjONvyypB4raKaouoaaxpNak9KnQU4b7hqqstIiIiAlYp0YjREBjp6kicY+qN4BMC36i2tkhrOkxqm6a5FKg4Zvg84A3H128A5x8xvsC0rABCDcOIBmYDn5mmWWGaZiXwGTDHcS7YNM0VpmmawIJj1mrtHiI9p2AFez092FRXyI9H/BiAHP9g1dUWkeN9dr91POuhE7rMNE2eXJzLy0t3cPX04dx7TnKXEtoAMSG+hPp7td0sMmQoBEQqqe3G8irzAFpNahuGwZQhU1hZvBLrZZSIiIjIANXcbCW1+0PpkcP8QmHajZDzMRTrk5cix+pqTe0hpmkWOb4uBoY4vo4Fdh8xb49jrL3xPa2Mt3eP4xiGcb1hGGsMw1hTVlbWhacj4lCwjM8irP8E542bh4+HDzkhkVCqpLaIHGHXMtj8Psy4DULjTujSZ77Yyl+/3s5lU+N48CepXU5og5XUTIsJIWtvGzu1DcMqQVK4tsv3ENc6nNQeHdZ6vfWp0VMprSuloKagN8MSERER6VvKsqF+P8T1o6Q2wLRfgk+wdmuLtKLbjSIdO6x7dHtQR/cwTfNl0zSnmKY5JTKyn3zMRHqfaULBCpYEBpIcnkxCSAKjQ0eT6+MDJZut8yIizXZYdBcEx8KMW0/o0he+2sbTn2/lwslDefi8tG4ltA9LjQkmt7iGQ03NrU+ImQT78qChptv3kt6XW5HL0MChBHgFtHo+PSodgJVFK3szLBEREZG+pWC5dexvSW2/MKsMSfZHVl5CRFp0Nald4igdguNY6hgvBIYdMW+oY6y98aGtjLd3D5GeUb6dvQ0VbGw+QGZ8JgBJEUlkN9dh1u+HmmIXBygifcL6/7Xq75/1EHj7d/qyl77ZzpOLc7lgYiyP/2wcNlv3E9pgNYtstJtsLW0jaR07GTCtbvDidvIq81otPXJYfHA8g/0Gq662iIiIDGy7lkNQNITFuzoS55v2S/AO0m5tkWN0Nan9EXC14+urgQ+PGL/KsEwDqhwlRBYDmYZhhDkaRGYCix3nqg3DmGZY29WuOmat1u4h0jMKlvOZv5Wgmj18NgBJYUlUNzdQ7OGhZpEiAvVV8MVDVkf1tJ91+rLXvtvJY4tyOHd8DE/+fBweTkpoA6TFBAOwubCNutoxE62jSpC4nYNNBymoKSAxPLHNOYZhkB6dzqriVaqrLSIiIgOTaVo7teOmWeX3+hv/cJh6A2z5UP2+RI7QYVLbMIy3gOVAomEYewzDuBZ4HDjLMIytwJmO7wH+A+wAtgF/A34FYJpmBfBHYLXj8ZBjDMecVxzXbAcWOcbbuodIzyhYzpKgYJLDkxgWbH2w4HAiIcfHW788RAS++RPUlcPZj3f6BfM/lufz0MdbODstiqcuGo+nR7crfx0lPiKAAG8PNrdVVzsgwtqxslfNIt3Njv07aDab292pDTA1aioV9RVs37+9lyITERER6UOqdkN1IcSd5OpIes70m8A7EJb+ydWRiPQZnh1NME3z0jZOndHKXBO4qY11XiytcgYAACAASURBVANea2V8DZDWynh5a/cQ6SlFu5exMdiDW+Nnt4yNCRuDgUFOYBinqVmkyMC2byusnA8Tr/jv7ucOvLWqgPs+3MyZyUN45pKJeDk5oQ1gsxmkxASTtbeNndpg1dXeo/IU7ia3Mhegw6T24braq4pXMSpsVI/HJSIiItKn7HLU0x7ez+ppH8k/HDKuh+/+AqfkwOAkV0ck4nLOf3ct4o5qSljSuA+AzOGZLcP+Xv4MDx5OTkCImjKIDHSL7wFPPzjj/k5Nf3fNbv7fB5s4LTGSFy6fiLdnz/3KTY0JYcveauzNbZSfiJ1k7WA5oPYU7iSvMg8/Tz+GBg1td97QoKHEBsayqnhVL0UmIiIi0ocULAOfYBic4upIetb0m8HLX7u1RRyU1BYBq/RIgD/JQXHEBccddSopPIlcDxPKcsHe5KIARcSltn4GWxfDKb+DwMEdTv/X+kJ+98+NnDxqEC9eMRkfT48eDS81JpiDjXZ27qttfULsZOtYqBIk7iSvMo/RYaOxGR2/XEuPSmd18WqazeZeiExERESkDylYAcOmgq1nX3O7XEAEZMyDrPet/ITIAKektghQvPMrNvr6kDni3OPOJYUnUWivo8o8BBU7XBCdiLiUvRE+/T2Ej4SpN3Y4/eONe7nj/zYwLSGCl6+cgq9Xz7+4TosNAWi7rnb0eDBsqqvtRkzTJK8yr8PSI4dlRGVQfaia3Aq9wREREZEBpK4CynL6d+mRI510C3j5wdInXR2JiMspqS0CLCm2anBljvjRceeSwq1aVXne3lCqEiQiA86qv0H5Vpj9KHh6tzv106xibn17A1OGh/Pq3Cn4effObpFRgwPx9rSRVdhGUts7ACKToXBtr8Qj3VdSV0JVQ9UJJbUBlSARERGRgaXAUU+7PzeJPFLAIMdu7X9aPX9EBjAltUUaalhs30+SV+hxpUcAEsMTAcjx8YESNYsUGVBq98HXj8PIM2DM7Hanfr6lhFveWsf4oSG89ot0/L077MXsNF4eNpKigtjcXrPI2IlW+RGzjbrb0qfkVeYBkBiW2Kn5QwKGEB8cr6S2iIiIDCwFy8HDu9ON3PuFk34Nnr7arS0DnpLaMuAVb1tslR6JmXHU+L/WF7JqZwWD/AYR6RdJTmA4lCqpLTKgfPkwHDoAcx4Dw2hz2le5pfzqzXWkRAfz92syCPTpvYT2YakxIWQVVmG2lbSOnQwHK2D/rt4NTLrkcFJ7dNjoTl+THpXO2pK1NDWr/4OIiIgMELuWW69zvXxdHUnvCRgE6dfCpndh3zZXRyPiMkpqy4C3ZNtHAGSmXdUyVlbTwJ3v/cCzX1gf50kMTyTHxxtKVH5EZMAo2ghr/w4Z10Nk27tlv9u6jxv+sZYxUYEsuGYqwb5evRfjEVJjgqmub2JP5cHWJ8RMso4qQeIW8irziAmIIcg7qNPXZERlUNtYy5Zy/QFWREREBoBDtVC0AeIGSD3tI530a/Dw0W5tGdCU1JYBb0nlFpKaPRg+KKVl7O1VBTTaTbKLqjFNk6TwJHaYDRyqzLd+cYpI/2aaVnNIvzA49a42py3fXs51C1YzYlAA/7hmKiH+rkloQyeaRQ5JtV74FqpZpDvIq+h8k8jDpkRNAVRXW0RERAaIwrXQ3DQwk9qBgx27tf8Pyre7OhoRl1BSWwa04qoCfjAayAwa1TLWZG9m4aoCPGwG5bWHKDvQQFJ4Ek2YbPP2hNIcF0YsIr1iy4ew6zs4/V4rsd2KVTsruObvqxkW5s+b100lLKD9JpI9LSkqCA+bQVZhG3W1PbwgepyS2m6gwd5AfnU+Y8JPLKk9yG8Qo0JHsbp4dQ9FJiIiItKH7FoOGDAsw9WRuMZJv7bqiS/9s6sjEXEJJbVlQPts8z8AyBxxdsvY59klFFXVc/X0eACyi2pICk8CINfbW3W1Rfq7xoOw5D4YkgaT57Y6Ze2uSn7x+iqiQ315c95UIgJ9ejfGVvh6eTAqMrDtndpglSAp2gB21Vzuy3bs34HdtJ/wTm2wSpCsL11Po72xByITERER6UMKllmv2f1CXR2JawQNgSnXwMZ3oGKHq6MR6XVKasuAtnj3lyQ2HGL4mHNbxt5YtovYUD9uOm0kANlF1QwLGoa/pz85vn5Kaov0d8ueh6oCqzmkzeO40z/s3s/c11YRGeTDW/OmMTio7zSlSY0NJmtvGzu1wWqi01gH+3J7Lyg5YbmV1s+nq0ntg00H2bRvk7PDEhEREek77E2wezXETXN1JK4141brE5lL/8fVkYj0OiW1ZcAqri3mh/pSMk1fqx4VsLWkhuU7yrlsahwRgT7EhPiSXVSNzbBZzSIDgtUsUqQ/qyqE756C5J9AwqzjTmcVVnHlqysJDfBi4bxpDAnuOwltgNSYEMpqGiitrm99QuzhZpEqQdKX5VXm4evhS1xQ3AlfOyVqCgYGK4tX9kBkIiIiIn1E8UZorIXhA7Ce9pGComDyL+CHt6Bip6ujEelVSmrLgPVZ/hIAMgdNahn7x4pdeHvYuCR9GADJ0cHkFNUAkBiWSK7NpFk7tUX6r88fhGY7ZP7xuFPZRdVc8epKgny9WHjdNGJC/Xo/vg6kxQQDsLmt3drhI8EnBPYqqd2X5VXmMSp0FB6tfFKgIyE+ISSFJ6mutoiIiPRvBcut40BsEnmsGbeCzRO+1W5tGViU1JYBa8m2j0hsOET8iDMAqKlv5J9r9/DjcdEt9XGTo4PZXnaAhiY7SeFJ1GKnsKESDpS5MnQR6QmF66zu4SfdAmHxR53aWlLDFa+sxNfTg4XzpjIs3N81MXYgxZHUzipso662zQYxE6xO8dInmaZJXkXeCTeJPFJGVAYbSjdQ39TGjn0RERERd1ewHEKHQ3CMqyNxveBoqxfQD29BZb6roxHpNUpqy4BUXFvMhv25ZNbWtfxl94P1hdQesnPl9OEt85Kig2hqNtlacoCkCKtZZLa3F5SqBIlIv/PdU9Yu5hm3HjW8vewAl/5tJR42g7eun8bwiAAXBdixIF8v4iP8296pDVZd7ZLN0KiEZ1+07+A+Khsqu1RP+7CM6Awamxv5oewHJ0YmIiIi0keYJuxaDsNPcnUkfcfJt4Fh025tGVCU1JYB6fNdnwOQafpB+AhM02TB8l2MjQ1hwrD/dk5OjrZ2PeYU11gfBTds5Ph4Q4lKkIj0K2V5kP0xZMwD3+CW4fx9tVz2txWAycJ5U0kY1HcT2oelxoaQtbeNndpg1dVuboJiNRLsi/Iq84CuNYk8bNLgSXgYHqwsUl1tERER6YfKt0HdPpUeOVJwDEy6GjYshMpdro5GpFcoqS0D0uL8xYxpgvjYqWAYLN9ezrbSA1w1fTiGYbTMi48IwNfLRnZRNT4ePiSEjCDXL1A7tUX6m++fAU8fmHpjy9Duijou+9sKGu0mb143jVGDg1wYYOelxYSwp/Ig++sOtT4hxtFHQHW1+yRnJLUDvQNJjUhVXW0RERHpn3Yts45Kah/t5Nut3drfPeXqSER6hZLaMuAU1xazoWwDmTX7Wz6utGD5LkL9vTh3/NH1uDxsBolDgsgusj7KnxyerJ3aIv1N1R7Y+DZMugoCIwEo3H+QS15eQe0hO/977VQSo9wjoQ2Q6qirvaWtEiTBMRAYpbrafVReZR5D/IcQ4hPSrXUyojPI2pdFXWOdkyITERER6SMKVoD/IBg02tWR9C0hsTDxSlj/Juzf7epoRHqcktoy4LSUHqmtg7hp7N1/kM+yS7h4yjB8vTyOm58cHUx2UTWmaZIYnkgpdsrLc6G5ubdDF5GesOx5qy7fSbcAUFxVz6Uvr6C6vpH/vXZqS/NFd3E4qd1mCRLDsEqQFGqndl+UW5nbrV3ah6VHpdNkNrGuVD9nERER6WcKlkHcNOt1rRzt5Nuto3ZrywCgpLYMOEt2LWG0RyAJhi8MGcvClQU0myZXTBve6vzk6GAq6xoprWkgKdxqFplr2KFyZ2+GLSI9obYc1r0BYy+E0DhKq+u59G8rqKg9xIJrMhg7tHu7ZV0hItCH6BDfDppFToLyrXBwf+8FJh1qtDeyc/9OEsMTu73WxMET8bR5sqp4lRMiExEREekjqougMl+lR9oSOgwmXQnr/mF9IlWkH1NSWwaUktoS1peuZ/bBRhiaToNp8PbqAk5PHMywcP9WrzncLHJLUXVLUjvHxwtKVYJExO2tegka6+Dk26isPcRlr6ykpLqeN65JZ2JcmKuj67LUmBCyCttpFnm4rnbRht4JSDplR9UOmswmp+zU9vP0Y9ygcawqUlJbRERE+pGC5dZxuJLabTr5Duv43V9cG4dID1NSWwaUzwscpUdK8yFuOp9mFbPvwCGunN76Lm2gpZZudlE1IT4hRPtHkeOtutoibq+hBla+BInn0BSRyC1vraegvI7X5qYzeXi4q6PrlrTYYHbsq6W2oan1CTETraNKkPQpzmgSeaSM6AyyK7KpPtTOrn0RERERd1KwHLwCIGq8qyPpu0KHwcTLYd0CqCp0dTQiPUZJbRlQluQvYbR/NAmNjTB8Om8syyc+wp9ZoyPbvCbEz4vYUD+yi2oASIxIItcvQDu1Rdzd2r9D/X44+Xb+tDiX77bt4+Hz05g2IsLVkXVbakwIpgk5xW0kM/3DIXyEmkX2MXmVeXjbvBke3PYfWk9ERlQGzWYza4v1cxYREZF+YtdyGDoFPDxdHUnfdvIdYDZrt7b0a0pqy4BRUlvCutJ1ZHqEgc2TzbYxrCvYzxXThmOztd9gIjk6mJwiKzmUHJ5MvgccLN3cG2GLSE9oaoDlL0D8TD4sj+HlpTu4ctpwLkof5urInCIt1tEssrC9utqTYe/6XopIOiOvMo+RoSPxtDnnTdr4yPH4ePiorraIiIj0D/VVUJIFw09ydSR9X9hwmHCZ1T+oeq+roxHpEUpqy4DRUnpk/z6InsCC1aX4etm4cHLHSayU6CB27KulvtFOYngizcDWmt3QWN/DUYtIj/jhbagpIj/lBu7650bS48O478cpro7KaaKCfYkI8O64rnZ1IdQU915g0q68yjynlR4B8PbwZsLgCUpqi4iISP+wexVgqklkZ838jWO39tOujkSkRyipLQPGkvwljAoZyYjCjdTHZPDhD4VcMDGWEH+vDq9Nig7G3myyteTAf5tFenvCvtyeDltEnK3ZDt8/Q9OQcVzxpT+hft789fLJeHv2n1+JhmGQEhPM5r3t7dR2NItUXe0+ofxgOfsO7nNqUhusEiR5lXlU1lc6dV0RERGRXrdrGdg8rfIj0rGweBh/iVV2sbrI1dGIOF3/eQcv0o7SulLWl65ndlgq2A+xtH4U9Y3NXDktvlPXJ0dbH+XPLq4mJiCGIM8Acry91CxSxB1t+RAqtvNC408oPXCI+VdOJjLIx9VROV1abAh5JTU0NNlbnxA1DgwP1dXuIw43iUwMT3TquhlRGQCsLl7t1HVFREREel3BCogeD94Bro7Efcz8LTQ3wffPOG3J3IpcssuznbaeSFcpqS0Dwme7PsPEJLPJA4Dntw9iyvAwUmKCO3X98HB//Lw8yC6qxjAMkiKSyfXxAdXVFnEvpgnf/YVy3zie2ZvEw+enMWFYqKuj6hGpMcE0OT5h0ipvfxicAnu1U7svOJzUHh022qnrpg5Kxc/TTyVIRERExL01NVibMVR65MSEJ8D4S2Ht604pO1hRX8G1S67l0k8u5c3sNzFN0wlBinSNktoyICzJX8Ko0FGMKM6mNngkGys8ueqk+E5fb7MZJEYFke1oFpkYnkSetzf2YiW1RdzK9i+geCNP1MzmiukJXDSlfzSGbE1aTAhA+3W1YydZ5Uf0YtTl8irziPSLJNw33Knretm8mDxkspLaIiIi4t4K14G9QUntrpj1G7A3wvfPdnupJ1c/SW1jLelR6Ty+6nHu/f5e6pvUa0xcQ0lt6fcOlx7JHH4WFKxkdXMigwJ9mJMadULrJEcHk11Ug2maJEckU2/Argp95EakI1UNVSzbu8zVYQBQ+8WTFJvh7B76k37VGLI1ceH+BPl4krW3g6R2/X6o2NF7gUmrnN0k8kgZURnsrNpJWV1Zj6wvIiIi0uMKlltHJbVPXPgIGHcxrHkNakq6vMyyvcv4eMfHXJN2DS+d9RK/mvArPtr+EVd/ejXFtWo+L71PSW3p9w6XHpkdOBIaqviocjiXZQw74aZwKdFBVB1spLi6nsQwq+ZpTmMV1FX0RNgi/UKz2cxtX93GDZ/dwD/z/unSWKrzviegaAXveJ7Hs1dMxcujf/8KtNkMkjtsFjnZOu5d3ztBSasamxvZvn87Y8J7KKkdbdXV1m5tERERcVsFy2FQIgREuDoS9zTrt9ZO92Vd2619sOkgf1z+R4YHD+f6cddjM2z8cvwvee705yioLuDijy9WDxfpdf37Hb0IR5QeqSgAYK2ZxGVTh5/wOi3NIouqGRE6Ai/DgxxvbyhVs0iRtryV8xZrStYwNHAoD694mFVFrkmqNdmbyXv/ISrNQE6//M5+2RiyNWkxIWQXVdNkb259QmQyePqpWaSL7araRWNzY4/t1E4KSyLIO0hvNERERMQ9NduhYCXETXN1JO4rYiSMvQhWvwoHSk/48vk/zGfPgT3cP+1+fDz++17q1GGnsvCchQR7BzNvyTzV2ZZepaS29GtldWVW6ZH4TJryl1FCOKkpaUSF+J7wWolRQQBkF9XgZfNiVEgCOT5eUKKktkhr8qvyeXrt08yMnck7575DXHAct399O7uqd/V6LK++/wlT6lewd8yVjB0R2+v3d5W02GDqG5vZsa+29QkenhA9zqpRKC6TW5kL0GNJbQ+bh+pqi4iIiPsqzYaGKhh+kqsjcW+z7uzSbu3cilze2PwG5486v+UTgEdKCElg4TkLmTl0Zkud7QZ7g7OiFmmTktrSr7WUHonL5NCO71llH8OV0xO6tFaQrxfDwv3Y4mgWmTRoLLnePpglWc4MWaRfsDfbuff7e/H28ObBkx4k2DuY509/Hpth4+YvbqaqoZ06z0724YZCIjfO55DNl9QLftdr9+0LUh3NIje3W1d7MhT9APamXopKjpVXmYenzZOE4K79fuqMqVFT2V2zm6IDRT12DxEREZEeoXrazjFoFKT93LFbu3O9VuzNdv6w/A8Eewfzm8m/aXNekHcQz5z2DL8a76izvUh1tqXnKakt/dqSXVbpkQQ88K8vYVfAOKaNCO/yeslRweQcTmqHJ1HhYaOsVEltkWMt2LKAH8p+4PdTf89g/8EADAsextOnPc2eA3v4zde/obG5scfjyCqs4tn3Puc8j2V4pP8C/Lv+v393NDIyAB9PG1mF7dTVjpkETQehTI1vXSWvMo+RISPx8vDqsXukR6UDqqstIiIibmjXMgiKgdA4V0fi/mbdCY0HYflznZr+Tu47bNq3id9l/I5Q39B259oMG7+c8EuePe1Z8qvzufjji1lTvMYZUYu0qltJbcMwbjUMI8swjM2GYdzmGAs3DOMzwzC2Oo5hjnHDMIxnDcPYZhjGRsMwJh2xztWO+VsNw7j6iPHJhmFsclzzrGEYRnfilYGlrK6MdSXryByeSf76LwGIn3QW3fnPKDk6mJ37aqlvtJMUngRATtUOUM0okRbb92/n+fXPc0bcGZyTcM5R5yYPmcwD0x9gZfFKHlv5WI/WW6uoPcQN/1jLL70XYbN54HHSLT12r77K08NGUnRwBzu1Hb+OVVfbZfIq83qs9Mhho8NGE+YTpqS2iIiIuBfTtHZqD58OSgl1X+QYGPtzWPUK1Ja3O7W4tphn1j3DSTEnHfe+rj2nxZ12VJ3thdkLVWdbekSXk9qGYaQB84AMYDzwY8MwRgF3A1+Ypjka+MLxPcDZwGjH43rgRcc64cADwFTHWg8cToQ75sw74ro5XY1XBp7DpUcy4zPZu/FLakw/Tp15SrfWTI4OotmE3OKalgREjs0OVbudEbKI22tsbuSe7+4hwCuA+6bd1+ofkc4fdT6/SPsF7+a9y5vZb/ZIHE32Zm5euI7mAyX81PgSY/zFEDJwamkfKS0mmM2F1TQ3t/FCMnwE+IaqrraL7K/fT2ldaY8ntW2GjSlRU1hVvEpvKkRERMR97N8FNUUqPeJMs+6ExroOd2s/tvIxms1m7p127wlvDhwRMoKF5yzk5NiTeWzVY9z3/X2qsy1O152d2snAStM060zTbAK+AX4KnAe84ZjzBnC+4+vzgAWmZQUQahhGNDAb+Mw0zQrTNCuBz4A5jnPBpmmuMK13XwuOWEukQ0t2LWFkyEiCPYYyuHI9xSETCPTz6fjCdiRHBwOQXVRNoHcgcX6DyfHxVrNIEYdXN73K5vLN3DvtXiL8Itqcd9uk2zh92Ok8ueZJvt3zrdPjeGxRDsu2l/N60lps9kMw4zan38NdpMWGUNPQxO7KutYnGIa1W3uvktqukFeZB8CY8J5NaoNVV7u4tpjdNfpDrIiIiLiJXaqn7XSRiZD2U1j1N6iraHXKF7u+4MvdX3Lj+BsZFjSsS7cJ8g7imdOf4Zfjf8mH2z9UnW1xuu4ktbOAmYZhRBiG4Q/8CBgGDDFN83AXomJgiOPrWODId1F7HGPtje9pZfw4hmFcbxjGGsMw1pSVda7YvfRvh0uPzI6fzYfLNjLa2EN48qxurzsszJ8Abw9yimsASIxIIcfbC0o3d3ttEXeXU5HDSz+8xNkJZ5MZn9nuXJth47GZjzEmbAx3Lr2TrZVbnRbHB+v38Op3O7lh6iCSdr8DyefCoNFOW9/dpMZYf4zbvLeDutolW+BQG4lv6TEtSe0e3qkNkB6tutoiIiLiZgqWg28IDE7p0uWmaZJTkaNPqh1r1u/gUC0sf/64UwcOHeDRlY8yJmwMV6Ve1a3b2Awbv5rwK54+7WnV2Ran63JS2zTNbOAJYAnwKbABsB8zxwR6/P85TNN82TTNKaZpTomMjOzp24kb+Lzgc0xMTh92JrmrPwcgIuXUbq9rsxkkRgWx5XCzyMix7Pby4kDxpm6vLX1HzaEaveg5QY12q+xIqG8o90y9p1PX+Hv589zpz+Hn6cctX95C+cH2a7p1RlZhFXf/cxNTE8L5XcT30FANM+/o9rrubMyQIDxtBlmF7dXVngymHfT/Zb0urzKPcN9wBvkN6vF7JQQnEOkXyaoiJbVFRETETRQsh2HTwNa19NXHOz7mwn9fyBub3+h48kAyOAlSz4eVLx23W/uZdc9QdrCMB6c/iJfNOY3Mz4g7g4U/+m+d7bdy3tJ7bum2bjWKNE3zVdM0J5umOQuoBPKAEkfpEBzHUsf0Qqyd3IcNdYy1Nz60lXGRDi3Jt0qP7CwKYmR9Fs02L2snohMkRweTXVSNaZotzSLzylV+pL+oa6wj871MHlrxkH7JnoAXf3iRvMo8Hpz+ICE+IZ2+LiogiudOf459B/dx+9e3c8h+qMsxlB9o4IZ/rCUiwJsXLkrGY+WLMOI0iJnY5TX7A18vD0YPCSKrvZ3aahbpMrmVub2ySxvAMAzSo9JVV1tERETcQ+0+2JcHcdO6vMTHOz4G4Ol1T2uH8LFm/Q4OHYDlL7QM/VD2A+/kvsOlSZcyNnKsU283ItSqsz0jdgaPrnyU+5fdrzrb0i3dSmobhjHYcYzDqqe9EPgIuNox5WrgQ8fXHwFXGZZpQJWjTMliINMwjDBHg8hMYLHjXLVhGNMMqyL9VUesJdKmfQf3sbZkLZnxmSxYns/JXnkYsZPAy9cp6ydHB1NT38TeqvqWpHb2wWJo6noyTvqOTfs2caDxAO/lvcc7ue+4Ohy3kLUvi9eyXuO8kedxyrATb8aaNiiNh09+mPWl63lw2YNdSrZZjSHXU3aggflXTmbQtvegtnTA79I+LDUmmM2FVW3/2wZFQVCM6mr3sqbmJrbv305iWGKv3XNq9FTK68vZUbWj1+4pIiIi0iUFK6zj8JO6dHn5wXJWFq3k0qRLGRY0jDuX3klZnUrWthiSAinntezWbmxu5MFlDxLpH8ktE2/pkVsGeQfx7OnPcsO4G/jXtn8xd9Fc1dmWLutWUhv4p2EYW4B/AzeZprkfeBw4yzCMrcCZju8B/gPsALYBfwN+BWCaZgXwR2C14/GQYwzHnFcc12wHFnUzXhkAPtv1GSYmSUEzWLd9L8nswHBiU4mWZpF7q4n0iyTc059cLw8od15NYHGdDaUbACvx88SqJ/TX/A402Bu457t7GOQ3iLsy7uryOnPi5/CrCb/i3zv+zatZr57w9Y/+J4flO8p57IKxjIsOhO+ftUpqxM/sckz9SVpMMOW1hyipbmcnROwk7dTuZQU1BTTYGzrdJLLJ3szji3L4x4pdlFbXd+me6VGqqy0iIiJuomA5ePh0+ZOXS3YtwW7auXDMhTx16lPUNtZy59I7aWpucnKgbuyUu+BQDax4kTc2v8G2/f+fvfsOj6pM+zj+PZNJ75VUSEhCEhI6ofeiIKuIBQR7d3UFpSwiCAJipSpYX5dVBBWwrCJFOoSEnoSWCqQnpIf0Nuf9YxJACclMMjNJ4Plcl1dg5pzz3CCEmXue87sTmd9/PlYmVnpbUiEp+Fevf7F65GouFV1iyrYpnLoi3ocI2mtp/MhQWZa7yrLcQ5blvXWP5cmyPFqWZX9ZlsfUN6hltVdkWfaVZbmbLMsnb7jOf2RZ9qv7b/0Nj5+UZTmk7px/yeJeWUEDfyb9SWfbzhw8Z0Rf5SWM5Jpmf7LbkABXawBiMq8iSRKBtn7Empioh6wJ7V5kTiR+dn6sGrEKT2tPZh2cRWZJZtMn3qHWRq7lUtEllgxagrWJdYuu9VL3lxjvM541p9ewJ3mPxuf9fDqN/xy5zFODvHmwjyec/wUKk2HITJCkFtV0uwj2UEfCnM9oLFe7N+RfgvICA1UlaDskMjqtiM8PXuStX8/R/729PPDpEb48dJHkuvz7QQAAIABJREFUvFKN1/S08sTd0p0TWSeaVbMgCIIgCILBJIerN6ooTZt1+vZL2/Gz88Pf3h9/e3/eGvAWp66c4uPIj3VcaDvWIRiC7iPl5Jd8Hv0ZYzqOYVTHUQZZenTH0WyasAlrE2ue2/UcP8T+ICLyBK20dKe2ILQp9dEjIz3H8tPpdKa5ZQASePXT2RpWpko6OVoQk6XOpw3o0ItEE2Oqr4gBa+2dSlZxJvsMPV16Xrstqqq2ihn7Z1BeU97a5bU5kdmRfHP+Gx7u8jCDPFr+wZEkSSwZtITuTt15M+xNLmiQVX8uvYh5P6sHQ86fEASyDGGrwDkQAu5pcU23iyA3GyQJzqU3lqvdR/01I9IwRQnE58ejlJR0tu2s0fFRqYUAbHquPzPHdKGqVsW722MZ/tEBxq0+xOo98cRmXW30zcCNudoqWaWTX4cgCIIgCILOVZVCZjR0at5d1xklGUTlRHGPz/X3BPf63svkLpNZf249+1L26arSdk8e9m+W2JigVKl4o98bBl3b186XTRM2MchjEMuOLWNR+CKRsy1oTDS1hdvKnuQ9yMjIJd0oqaxhiEk8uHQFc3udrhPkakNsZjEAgY5dqZYkLl2J0ukaguElFiZSXF1MLxf17W0+tj58MOwDYvNjWRS+SHxqfIOy6jIWhC3A3cqdWX1n6ey6Zkoz1oxag62pLa/ufZXssuxbHvuXwZCP9sbYSAHxuyD7PAx+rdkT0m9HVqZKfJwsOdfYTm23nuqvIoLEYOIL4vG29cbEyESj46NSC3G3NWOQnxOvjvZn26tDOfzvkSyYEISNmTFr9iYwbvVhRiw/wHvbYzidUoBKdfP3rX5u/SiqLCKhQMRmCYIgCILQRqWdALkWOjZv88yOy+r02nE+4/7y+Nx+cwl2DGZB2AJSr6a2uMzbwbayJI6Zm/FaQTEdFM3bFd8SNiY2fDLqE17o/gK/JP7C0zuf5krpFYPXIbQ/4h2/cFv5M1kdPbL9NPRwt8I6J7JFk5JvJcjNhst5pZRV1RDoqB4WGSeGbrV79XnavZyvZ7YN8xzG9N7T2XF5B/89/99WqqztWXN6DSnFKSwdvBRLY0udXtvJ3Im1o9ZSXF3Mq/tebXCXfHWtilc2nSa3pJIvHu+Lk1Xdi6+wVWDrBd0e0mlNt4Ngd1suZDSyU9vcDhz9IV3s1DaU+IJ4jaNHAKJSC+jhZfeXx7wcLHhuaGc2vzSQ42+O4d1J3ejkaMl/jlzmgU/DGfj+Xt769RxHEnOprlXvzO7nqr576VjmMd39YgRBEARBEHQpOQL1XdehzTp9x+UddHfujpe1118eNzEyYcWIFUiSxOsHXqeipnlzSm4XBRUFfHjiQ7rb+jG5IEc9NLIVKCQFr/Z6lVUjVnGx8CJTtk3h9BUxxF5onGhqC7eN+uiRrjZDScgu4ZXgCqSqEp3madcLdLNGliEuq5hO1p0wk4yIqS2GikZ2QQptXlR2FI5mjnhae/7l8WdDnuVu77tZfXo1R9KPtFJ1bcfxzONsit3Eo0GPXhs6p2sBDgF8OOxDYvJimB82/6aYhHe3x3D0Uj7vPdCNbp7qvGiSwyH1KAx6FYyM9VJXexbibkN6YTkFpVW3PkgMizSYosoiMkszNW5q55VUkppfTs+/NbVv5GxtyrT+Hfn2mX6cXDCW1VN60svLnq2n0nj0/44RumwPszZHczZZwsvKS+RqC4IgCILQdqVEgGsImNlqferFwovEFcT9JXrkRh5WHrw39D3iCuJ499i7La20XVt+cjklVSUsGv4BioAJcHRdq/Y1xnQaw6YJm7AyseLZXc/yY+yP4o5p4ZZEU1u4bexJ3oNKVpGe7o+dhTEjzS+qn9DDTu2ubjYAxGQWY6QwooulB3EmJpAdo/O1BMOJzI6kp0tPpL8NF6zPeva382fOoTkkX01upQqvq1XJ/BadwbYzGQ3GC+hLaXUpC8MX0smmEzN6z9DrWiO8RjCzz0x2J+9mXdS6a4//dCqN9UeSeHqwNw/0vuEDiMMrwcIJej2u17raq5BrwyIb2a3t3htKsuBqhoGqunPVR39oPiRSnafdWFP7Rrbmxtzfy4PPH+/D6bfG8sXjfRgV4MLuC1k8/+1JktPdOZx2jJ8jUyiuqG7eL0IQBEEQBEEfaqvV8SPNjB7Zfnk7CknB3d533/KYYZ7DrsVd/Jzwc3MrbdeOZh7lt4u/8VTIU+rXpMP/rW5ot9Ju7Xr1OdsD3QfyzrF3eDvibapqG9mYI9yxRFNbuG38mfwnXlbeHIlRMqWvF8ZpR8G2I9h6Nn2yljztzbE2VRJbNywy0DGYWBMT5Cvndb6WYBi55bmklaRdy9P+OwtjC9aMWoORZMT0fdMpqSoxcIXXHb+cz31rw5j+fST/2hTJpM/CiUwpMMjay08uJ7M0k3cGv4O50lzv6z0Z/CST/Cbx5Zkv2XZpG2fSCpn3y1kGdHbgzXuCrh+YdRYSd8OAl8DEQu91tUfB7uoP4xrN1a4fFil2a+tdfEE8oL4rQRNRKYUopOsfTmjD3MSIu4NdWTmlJ6feGsuGZ/sR6hpKLeXM/t92+izdw9Prj/PD8RTySsRgHkEQBEEQWlnmGagua9YGNVmW2XF5B6GuoTiZOzV67Ms9XmaA2wCWHV1GTN6dtUGtoqaCpRFL8bL24sXuL6ofdO8JXcZDxDqoaGQjjAHU52w/3+15fk74WeRsCw0STW3htlAfPeJIKCoZHuvfEVKO6mWXNqh37ga6WROTqf5GH+DWl2IjBRmZIvOpvYrMVucI93TpectjPKw8WDF8BclXk5kXNu+mSAx9S80v45WNp5n8RQT5pVWseaQnKyf3ILOwnEmfhjNrczTZV/WXCXck/Qhb47fyZNcnG/190iVJknhrwFv07dCXhUcW8twPW3G2MmXdtLrBkPXCVoGJFYQ+Z5C62iM7CxM87Mw5l95IU9u1GyiUkC6+l+lbfEE8dqZ2OJs7a3R8VFoRXTpYY2mqbNG6xkYKhvo7s/zeBwF4anQNTw7qRGJOCW/8fJbQZXuY8kUE649cJr3w5jx7QRAEQRAEvUuJUH9tRpTo+bzzpBan3jJ65EZGCiM+GPYB9mb2zDwwk6tVrdvINaQvz3xJSnEKCwcuxExpdv2JEXOhohCOt+5ubVD//5neezqrRqwioTCBKdumXHvfLgggmtrCbWJv8l5UsorziT6MDHDBiywouaK3pjZAoKsNsZnFyLJMoIN6WGRsntip3V5FZUdhojChq0PXRo/r59aPOaFzOJB6gM+iPzNIbaWVNSzfFcfolQfZG3uF18d0Yd+sEUzs6cEDvT3ZN3sELw335bfodEYuP8AXBy9SVaPbhvvVqqssDF+Ir60vr/R6RafXboqxkTEfDlsBNfaU2f8f7zzkjqPVDVO58y7C+V+g7zNgbm/Q2tqbYHebxodFGptBh2CxU9sA6odE/j3uqCGyLBOdWkivjppFj2jCydwJX1tfMirOMX9CVw7NGcn26UP51yh/CsuqWfz7BQa/v4/71oaxbn8iidmtd3eKIAiCIAh3mJQIsPcBa1etT91+eTvGCmNGdxyt0fEOZg4sH76crNKsBmf53I4SChJYf2499/nexwC3v/VM3HtBl3Hq3dqVxa1T4N+M6TSGTfdswtLYkmd2PcPmuM0iZ1sARFNbuE38mfwnzqZe5BfY88TATupd2qCXIZH1gtxsKK6sIa2gHH97fxRAXGkGiG+u7VJUdhQhTiEYazBgcFrgNO73u5/Poz9nT/IevdWkUslsPZXGyOUHWLs/kXtCXNk/ewQzxvhjbmJ07TgrUyVvjA/kz9eHM6CzI+/tiOXu1YfYH5uts1o+OP4BeeV5LBuyDFMj06ZP0LF1ezIpvPwY5ibw8fl5f41/Cf9Yvbt4oGGb7e1RiIctl3JLG89Qdu8NGVGguv1f0LeWWlUtiYWJGudpX84tpai8mh6eumtqg/pDutPZp6murUaSJLq62zBzbBd2vT6M/bNH8Mb4QBSSxEe74hiz8iBjVh5k+a44zqUXiTcSgiAIgiDohyyrm9rNeC9fq6pl5+WdDPEYgq2p5pFtPV16Mjt0NgdSD7D+3Hqt121PVLKKxRGLsTKxYnbf2Q0fNHwulBfA8S8NW1wj/Oz92DRhEwPcBrD06FIWRywWOduCaGoL7V9ueS4nr5xEVdIdb0dLhvk7q/8RNLMDJ82ySpsjyM0agJjMq5grzfE2sSdGUQvFmXpbU9CPipoKLuRf0DhSoz4So7tTd94Me/PawDddOpWcz6RPjzB7SzRudub8/PIgVj/SCzfbW+dY+zhZ8vVToax/OhQJePq/J3h6/XEu5bRsh+X+lP38dvE3nu32LMFOwS26VnNsOZnKf8OTeCq0Px+PXsXlosvMPTyXWlUtFGdB1CboOa1ZOznuNCEe14fc3pJHH6gsgvxLBqrqzpNWkkZ5Tbn2QyJ1uFMboJ9rP8pryjmXd+6m53ycLHlpuC+/vjKYo/NGs2RiMC7Wpnx28CL/+CSMIR/sZ8nvFzh+OZ9aAw6rFQRBEAThNpebAGV5zbrr+tSVU+SU52gUPfJ30wKncbf33Xwc+TEnsk5ofX57sSVuC9E50cwJnYO92S3ucvXoDf53QfhaqGw7d+vZmtqydtRanu/2PD8l/CRytgXR1Bbav/rokZRUfx4b0AmFQlI3tTsOBIX+/ogHuFojSdebQwF2vsSZmsCVC3pbU9CPc7nnqFHV3HJIZENMjExYNXIVVsZWTN83naLKRnKKtZBeWM707yN58LMIsq5WsGpKD3755yB6d9Q8VmNkgAs7XxvG/HuCOJFUwN2rD/HejhhKKmu0rqewopDFEYsJsA/gpe4vaX1+S0WnFjL/13MM7OzIm/cEMtB9IPP6zeNQ2iFWnFqhvi1OVQODZxi8tvYo2F29Y+V8o8Mie6u/iggSvakfEtnFQbOmdlRKIZYmRvi7WOu0jr4d+iIhcSzzWKPHudqa8cRAbzY9P4CT88fw0UPdCXKz5rtjyUz+IoL+7+5h3s9nOBCXrfPoI0EQBEEQ7jAp4eqvHbXfqb398nbMleYM9xqu9bmSJLF40GI62XRizsE55JTlaH2Nti67LJvVp1fT360/93a+t/GDh78B5flw4ivDFKeh+pztlSNWklCYwCN/PCJytu9goqkttHt/Jv+JpeSGicqNh/t4QUkO5CXqNU8bwMJEibej5bVhkUGufclUKinKFI2g9iYqJwqAns7aDT90sXBh1chVXCm7wpyDc6hRad80rldWVcPK3fGMXnGAXeezmD7Kj/2zRzCpl6f6gxotmSgVPD+sM/tmD+f+nh58cfASI5cf4KdTaai02FW57NgyiqqKWDZkmUbRLLqUU1zJS9+dwtnKlLXTeqGsGww5JXAK0wKnseHCBrZe2AjBk8Chs0Fra69crE1xsjLlXHojudrOgWBsCRliWKS+xOXHoZAU+Nr6anR8VGoh3TxtMWrG94LG2JnZEeAQoNVuJHtLEx7u68X/PRnK6bfGsm5abwb6OvF7dCZPrT9Bn6W7mfFDJDvOZlJRXavTegVBEARBuAMkR4ClMzhq9jqpXnVtNbuTdzPSayTmylvf3doYS2NLVg5fSVlNGbMPzqZa1UhkXzv0/vH3qVZVs3DAwqbnunj2Ab8xEP5Jm9qtXW9sp7FsvGcj5krzaznbwp1HNLWFdi2vPI+TWSe5mteV+3t6Ymth3KJJydoKcrMmNkvdHArooN7lG5slPiVsb6Kyo/Cx9cHOTPtb+3s49+CtAW8RkRnB6lOrtT5fpZL5NTKdUcsP8vHeBMYEdWDvrOHMvCsACxNlYyfC7oWwd2mjOe4u1mZ89HAPfn1lMB525szaEs0Dn4UTnVrYZG27knaxM2kn/+zxTwIc9Bfl05DqWhWvbDpNfmkVXzze56+DIYE5oXMYbObOMjsLjgfdZdDa2jNJkgjxsGl8p7bCCNx6iJ3aehRfEI+3jfdfJ83fQmVNLRcyr9LDS7fRI/X6ufYjKjuKytpKrc+1MlUyobsbn0ztxam3xrD+qVDu6ebG4YRc/rnxNM9+c0KrD9EEQRAEQRDUd10PAA2Gad/oSMYRrlZdZULnCS1a3s/ej0UDF3E6+zRrTq1p0bXakv0p+9mdvJuXerxER5uOmp00/A11FMyJ/9Nvcc3kb+/P9xO+p79bf5YeXcrb4W+LnO07jGhqC+3a3pS9qFBRUdiNxwd2Uj+YchSUZuCm3a7b5ghytSE5v4zSyhoCHQIBiL16Ue/rCrqjklVE5URpvUv7RpP8JzE1cCrfXPiGbZe2aXxeZEoBD3wWzms/RuFsbcrWlwaydlpvPO0tmihaBb+/CkfWwOHlsH9Zk2v19LLj538OYvnDPUgrKGfiuiPM2RJNTnHDjazc8lzeOfoOIY4hPBPyjMa/Jl15Z5s6q/eDB7sT4nHzkBdlTRUfpSTQSTLl9TOfkFSUZPAa26tgdxsSsksa30Xr0Rsyz0Dt7bU7pa2IL4jXOE/7QsZVqmtleumxqV2lqiI6O7pF1zFVGjEy0IUPHurO8TdH89Y/unIkMY8NR5N1VKkgCIIgCLe9qxlQmNzs6BFbU1sGug1scRkTOk9gSsAUvrnwDXuS97T4eq2ttLqUZceW4Wfnx5PBT2p+olco+I5S79auKtVfgS1ga2rLulHreK7bc+qc7V1Pk12W3dplCQYimtpCu7YraReKGhd6uQZey4olJRw8+oLSRO/rB7rZIMsQm1WMg5kDLgoz4qoKoLb5MRSCYSUVJVFUWaRVnnZD5oTOIdQ1lLfD3+Z83vlGj80qquD1H6OY9Gk46YXlfPRQd/73ymD6ejs0vVB9QzvyOxg2B3o/CYc+gmNfNHmqQiHxUB9P9s8ezovDOvNrVDqjlh/gq0OX/pKDK8sySyOWUlZdxrIhy1AqGtkxrgdbTqbyTUQyzw7x4f5eHg0fFLkB69I8Pum/ECPJiFf3vaqzXPPbXYi7LbUqmbisxoZF9obaSrjS+J9lQXslVSWkl6Rr3NSOqruroqeX5rn62ujdoTcKScHxrOM6u6bSSMEzg70ZGeDMeztiuJzbNt8ECYIgCILQxiTX5Wl30q4xXVZdxoHUA4ztNFZnkYn/Dv033Zy68daRt0i+2r4/pP8k8hOyy7JZNHARxgotf3+GvwFluXDia/0UpwNGCiNm9J7BiuErSChIYMq2KURlR7V2WYIBiKa20G7lledxIusk5YUhPDHQW/1gZYl6d6Ge87TrBbmph3bV52oHWnoSY2wE+WK3dntRP1Sip0vLdvYbK4xZPnw5jmaOzNg3g9zy3JuOqaiu5eO9CYxcfoA/zmbyykhf9s8ewcN9vTTLzVap4PfpdQ3tf8PI+TBhJQT+A3bMhbNbNarV2syYefcEseu1YfT1tmfZ9hjGrTnEgTj1J9rbLm1jX+o+Xu31Kp3tDJtVXT8YcpCvI/PGBzZ8UG21ereA1wC8AieyauQq0krSmHVg1m2Xe6cP14dFNpKr7V43LFLkautcQmECgFZN7Q42prjaNh1V0hzWJtYEOwbrtKkN6qib9x/sjqnSiFmbo6gVMSSCIAiCIDQlJQJMrKBDN61OO5h2kPKacu7xuUdnpZgYmbBi+AqUCiWvH3id8ppynV3bkM7mnGVTzCYmB0xu3nvejv2h80gI/xiqynRfoA7d5X3XtZztp3c9zZb4La1dkqBnoqkttFt7U/Yio8Kqpg/jQ9zUD6adALlW6092m8vDzhwbM+X1XG2nYC4bG1OZKT4VbC+icqKwN7XH28a7xddyMHNgzag1FFUWqRusddENsizzW3QGo5YfYOXueEYGOrN35nDm3B2IlamGu6CvNbQ3qHdoj3xTnTNnpIQHv1ZnyP/yElzcp3G9nZ2tWP90P/7zVF9kGZ5af4InvvmTZUffo5dLLx7v+nhzfhuaLae4khc31A+G7H1tMORNzm6BolQYOhOAPh368PbAtzmWdYz3jr2H3EjGuABeDuZYmyk511iutr03mDuIXG09iM+PB9A4pz46tZCeeooeqRfqGsrZnLOUVev2jUoHGzOWTAzmdEoh/3f4kk6vLQiCIAjCbSjlKHiGqt/jaGH75e24WLjQ26W3Tstxs3Lj/aHvk1iQyDtH32l37zOqVdUsjliMs7kzM3rPaP6FRrwBpTlw8j+6K05PruVsu/ZnScSSdvn/TdCcaGoL7dZviTtQVTrxaM9+mCjr/iinHAVJAZ79DFKDJEkEutkQk6m+jT/IYxC1kkRixjGDrC+0XFR2FD1cejQ9/VlDgQ6BLB28lNPZp3nv+HucSSvk4c8jmP59JHYWJvzwwgA+fbQPXg5N5GbfSKWCbTNuaGjP/+vgFGMzmPo9OAfCD49p3YgcFdiBna8NZe64AE6XfkVJVQVetU9TUW24f/yralS8svE0heVVfPlEHxwsbxEfpFJB2GpwCQb/6wMiJ/pN5JmQZ9gSv4WNMRsNVHX7JEkSIe62nE9vpKktSeoIknQx+FbX4gvisTaxpoNFhyaPLSitIimvTG/RI/X6u/anRq65dueKLt3Xw51xwa6s+DOe+CuNRN4IgiAIgnBnKy9UR9910i5Pu6iyiLD0MMZ5j8NIYaTzsgZ7DOalHi/x28Xf+CnhJ51fX582XNhAXEEc8/rPw9rEuvkX6jgAfIarZzq18d3aUJezPXodUwOn8mPcj5zJPdPaJQl6IpraQruUV57HmdxT1JZ059EB3tefSAmHDiFgZmOwWoJcrYnNvIpKJRPorL5NKjbvgsHWF5ovvyKfpKtJLRoS2ZBxPuN4xP8JtsRv4cHvVpKUV8oHD3bj91eHMKCzo3YXq29on/4Whs6+uaFdz8wWHtsKlk7w3UOQm6DVMqZKIzp4nEGyjCXIdCobw8oYteIAv0SmGeST7Xf+uMDxJPVgyGv5+A2J2w65cTDk9Zt+H2b0nsEor1F8dPIjDqcd1nPF7Vuwuw0xWcVU16pufZBHH8iJabNDYdqruII4uth30eiDtKg0dZ52D69G/k7oQE+XnigVSp1HkID6Q5R3JoVgbaZk1uboxv/MCYIgCIJw50o9BshaR4nuSd5DjapGp9Ejf/di9xcZ5D6I9469x4V28l4/tTiVz6I+Y6TXSEZ3HN3yC454A0qz4dR/W34tAzBSGPFa79ewMrYSm55uY6KpLbRLuy7vQUamn/PI6zmjtdWQdhI6GiZ6pF6Qmw2lVbWkFpThYe2BJQpiSzMMWoPQPPXDI1o6JPJGFdW1rNufyMadwdSWBmDu9hurn7RlSmhHjDTJzb6RSgXbXqtraM+CUQsabmjXs3aFx38BhRFsmKSeHq6hjJIMPjzxIf1c+/Hj1Fn8/PIgOtiY8fqP0Tz4WThn6ppr+rD5ZCrfRiTz/FAfJva8xWBIAFmGsJXqaIzgSTc9rZAUvDf0PbrYd2HOoTkkFGjX2L+ThHjYUlWj4mJOya0Pcu8Nsgoyow1X2G1OJatIKEggwF7z6BFJgu6e+o0fsTC2oLtTd45n6r6pDeBkZcqySSGcTS/i0/1i5oQgCIIgCA1IiQCFMXj01eq0HZd30NG6I10du+qpMHWD9P2h7+Ng7sDMAzPb/IB6WZZ55+g7KCQFb/Z/Uzd3JXcaBN5D4chqqG4f+eIWxhbc73c/u5N2k12W3drlCHogmtpCu/TDhW2oKp14aeCQ6w9mnoHqMoPladcLclPvCo/JLEYhKQgwdSJWrhC7G9uBqOwojBXGBDsFt/hasiyz/WwmY1Ye5KNdcQz1d2HLA2vpaOPJ/PA5ZJVmaXfBaw3tb+oa2m813tCu5+gLj25V37634QEoy296KVnFwiMLkWWZJYOXoJAU9O5oz68vD+bDh7qTkl/GxHVHmLv1DLklldr9OpoQlVrIgl/OMdjPkbnjbjEYsl7SYXW0yqDpt8zZszC24JNRn2ChtODVfa+SV56n03pvFyEe6u9b59MbGRbpUZdJmC6GRepKekk6ZTVlWg2J7OJirXn2fguEuoZyIf8CxVX6iQgZF+LG/T3d+WRfAucai74RBEEQBOHOlBwB7j3BRPOYxpyyHI5nHWe8z3idxUneir2ZPSuGr+BK2RXeDHsTldx27z7bfnk74RnhTO89HVdLV91deMQbUHKl3ezWBpgaOJVauVYMjbxNiaa20O7kledxuSQaG1UfBvreEOWQEqH+auCd2l06WKOQICZT3RwKtPMlzsQY1ZX2cVvSnSwqJ4qujl0xNTJt0XXOpRcx5cujvLzxNFamSjY9158vHu9LsKsrH4/8mMraSmbsn0FFTYVmF1Sp4I/X1Q3tITM1b2jXc+8JUzdB/kX4/pEmc89+jPuRY1nHmB06Gw+r6zulFQqJyX292Dd7BM8N8eGn02mMXH6Ar8Mu6yRCILu4gpc2nMLFxpS1UxsZDFnv8EqwdIGejzZ6mKulKx+P+pjc8lxeP/A6VbVVLa71duPjZIW5sVHjwyKtXMDWSwyL1KH4AvWQSE2a2rIsG2RIZL3+bv1RySpOXdHf/+/F94XgaGXCrM3RVNbU6m0dQRAEQRDameoKyDitdfTIrqRdyMh6jR65UXfn7szpO4dDaYf4+uzXBllTW0WVRXx44kO6OXXjkYBHdHtx7yHQaYh6xlG1hu9tW1lHm44M9RzKlrgt4n3hbUg0tYV257/R20CSeShwwl8/jU2JAHsfdQSDAZmbGOHtZHm9qd2hD+UKBalp4QatQ9BOVW0V53PPtyhPO6e4krlbz3Dv2jASs0tYNimEP6YPZZCf07VjOtt15v2h7xOTF8PiiMVN51PXN7RP/Vfd0B69ULuGdj2fYfDAV5B6HLY+rY7naUDK1RRWnVrFYPfBPOT/UIPH2JgZM39CV3a+NoxeHe1Zuu0C49cc5nBCjvZ11fnLYMjH+2J/q8GQ9TIi4dJ+GPiyejBmE0KcQlg2ZBmR2ZG8Hf4Iuir/AAAgAElEQVS2mHj9N0YKiSA368Z3agO491K/wRB0Ij4/HgkJXzvfJo9NyS+joKyaHgZqand37o6JwkQvudr1bC2Mef/B7sRdKWb1HhEPJAiCIAhCnYzTUFsFHbUbErn98nYCHQLpbNdZT4XdbGrgVMZ7j2dt1FqOZh412LqaWnFyBUWVRSwauEgvgzPVu7Wz4OR/dH9tPXk08FHyKvLYlbSrtUsRdEw0tYV257eEHchVTrww4IboEVlWN7UNvEu7XpCbDbFZ6lu2A73UdcVkid2NbdmFvAtUqaqaladdWVPL5wcvMnL5AX46ncazg33YP3sEj/bv1GBu9givEbzS8xW2XdrGtxe+vfWFVSr4Y2ZdQ/v15je06wXfDxNWQPxO+G26+u/JDWpVtbx15C2UkpK3B73d5C17fi5WfPN0KP/3RF+qa1U8/vVxnv/2JCl52k/AXrrtAieSCvjwoR50dddgsOvhlWBqC32f1XiNu73v5pWer/D7pd/5+lzb3EnRmoLdbblQN+T2ljz6QEESlIoYF12IL4ink00nLIybvq02KlWdY2+ondqmRqb0cunFiawTel1nZIALj4R68cXBi5xOKdDrWoIgCIIgtBPJdRvCtNipnXo1lbO5ZxnvM15PRTVMkiTeHvQ23jbezD00lyulVwy6fmNOZJ3gl8RfeDL4SQIcNJvhojWfodB5BPy5AI5+dtN7zLZogPsAvG28+T72+9YuRdAx0dQW2pXEvCzyai8QaD0EazPj60/kJkBZnta3K+lKVzcbUvLLKK6oxtfeH6UMcVcvtUotgmYisyMB6OHSQ+NzZFlm1/ks7lp1iPd3xDKgswN/vj6MBf/oiq25caPnvtD9BcZ2GsvKUysJz2hgF79KBdtnwan1dQ3tRS1raNcLfRZGvAnRm2D3wr889V3Md5zOPs3cfnM1zlqTJIkxXTvw5+vD+Pe4AI4k5jJm1UE+2hVLaWWNRtfYfCKVDUeTeWFYZ+7r4d70CbkJEPO7+tdipkED/AYvdn+Re3zuYc3pNexO3q3Vube7EA8bSiprSM5v5EOJ+lztjEjDFHWbiy+Ix9/eX6NjI1MKMTc2oksHKz1XdV2oayix+bEUVuhvMCzA/AlBuNmaM3tzNOVVIoZEEARBEO54KRHgHAgWDhqfsiNpBwDjvQ3b1Ab1HJ9VI1ZRXlPOnENzqFY1fFesIVXWVrIkYgkeVh681OMl/S425TsIGA8734Bf/9nmB0cqJAXTgqZxNvcsZ3LOtHY5gg6JprbQrqwK/xlJknmx76S/PlGfp91Ju9uVdCXQ1RqAuKxiTIxM6GxkSWxl0wP6hNYTmR1JR+uOOJk7NX0w6sz0aV8d48UNpzBVKtjwbD/+78lQOjtr1nCSJIl3Br+Dr50vcw7OIfVq6vUn6xvaJ/8Dg1/TXUO73vB/Q+jzEP4xhH8CwKWiS3x8+mNGeI7gPt/7tL6kqdKIl0f4sX/2CCZ0c2Pd/ouMXnGQ/0WlNxr1EZlSwIJfzzHEz4l/363h7oEjq0FpCgNe1rpOSZJYMngJ3Z278+bhNzmfd17ra9yugt1tARof2ufWE5BEBIkOlFWXkVqcqtWQyG4etk1nzetQf7f+AJy8clKv61ibGfPRQ925lFvKh7ti9bqWIAiCIAhtnKpWHZmoxV3Xsiyz/dJ2ern0ws3KTY/F3Vpnu84sGbSEyOxIVp1a1So13OirM1+RdDWJhQMWYq401+9iptYweQOMnA/R38P68VCUpt81W+g+3/uwNLZkU+ym1i5F0CHR1BbajZpaFUcy92IiuzDG92+RESlHwcIJHP1apbYgN/Xu0Wu52laexBoBJdmtUo/QOFmWic6JpqdL03naeSWVvPnLWSZ8fJjYrKssnRjM9ulDGervrPW6FsYWrBm5BkmSmL5/OmXVZXUN7dnXG9pj3tZtQxvU1xv/AQRPgj8XUBO5kQVhCzA3NmfRoEUtmhTewcaMVVN68tM/B+JsbcqMH6KY/EVEg43S7OIKXvruFB1sTflkai/NmnVF6RD9I/R6HKy0/z0HdazCmpFrsDezZ/re6W3qFsHW1KWDNcZGEuczGsnVNrMBpy5iWKQOJBQmICNr1NSuqlFxIeMqPTsaJnqkXrBTMOZKc45lHtP7WoP8nHhqkDfrjyQRcVHE2wiCIAjCHevKeai8qtUGtfiCeC4WXTTYgMhbGeczjmmB09hwYQN/Jv3ZanVcLLzI1+e+ZkLnCQzyMNBGP4VCvXnqke8hNxG+HHE9RqYNsjS2ZJLfJHYl7SK3PLe1yxF0RDS1hXbjt7MJ1JgkMsRt9M1NuJRwdfSIrpuBGnKzNcPW3JiY+lxtp2BylUbkpka0Sj1C41KKU8ivyG+0qV1Vo+KrQ5cY8dEBNp9I5clB3hyYPZLHB3q3aOekl7UXHw37iEtFl5gf9iaqP2bBya9h8Az9NLTrKYxg0hfgM5z/HpzH2dyzLOi/QOOd6k3p08mB/70ymA8e7MalnFLuXRvGvJ/PkldSCah/P1/+7jRF5dV88ZgGgyHrRawFWQWDXm1RfU7mTnwy6hNKqkuYvn865TVt+xY5QzBRKujSwZrzGY3s1AZ1rnb66XaRl9eWxRfEA2iUbxiTeZWqWpXB8rTrGSuM6e3SW++52vX+PS4Ab0cL5myNpkTD+CJBEARBEG4z9XddaxEluuPyDowkI+7yvktPRWludt/ZdHfuzsLwhVwuumzw9VWyisURi7E0tmRO3zkGX5/Ae+D5fWBmC9/cCyf+r82+b3gk8BFqVDVsidvS2qUIOiKa2kK78dWp35AkFS/0vv+vT1zNVA8ya6UhkaCOOAhys76+U9tTPSwyLl00tdui+jztXs4ND4k8GJ/D3asPsWx7DH297dn52jAW3RuMrUXjudmaGug+kFl9ZrEnZS9fXtxa19BerP8PZZSmxI9bwjo7W+4qq2Cckb1OL69QSEwJ7ci+2SN4ZrAPW06mMnL5AdYfuczi389zMlmLwZAAZfnqoZndHgL7Ti2uL8AhgA+GfUBMXgzzw+ajklUtvmZ7F+Juy/mMq41GxuDRG0qz2/wthW1dfH48lsaWuFs2nSNfPySyh4Gb2gD93PpxseiiQXawWJgoWTG5BxmF5Sz7I0bv6wmCoD8V1bWs3hNPWoH2w6MFQbjDpUSAjSfYddTocFmW2Zm0kwFuA3Aw0zyDW1+MjYxZMXwFJgoTZh6Yqb4b14B+SviJyOxIZvWZhaO5o0HXvsa5i7qx7Tsa/pgFv0+HmsrWqaURnWw6MdRjKJvjN1Nd2/o56ELLiaa20C4kZpeQVBGBjdKNrk5Bf33yWp526zW1AQJdbYjLKkalkgnwUOeSxuSK/N62KCo7CmsTazrbdb7pubySSp797wkkCf77dCjrn+6Hn4uOB7XJMo9fiuTe4lLW2duxr8sIg9xlUK2qZsHxd7Exs2dBjRVsmgzZum8k2Zob89Y/urLztaH08LJj8e8X2HgshRc1HQxZ79gXUF2mHpypIyO8RjCr7yx2J+9mbeRanV23vQr2sCG/tIrMoopbH+RePyxS5Gq3RHxBPF3su2gU9xOdWoiztSnutmYGqOyv+rn2AzDYbu0+nRx4fmhnvj+ewsH4HIOsKQiC7v154Qqr9yQw+fMIknJLW7scQRDaC1mG5Ait3stH50STXpLOeB/DD4i8FVdLV94f9j4XCy+y9OjSxjeM6FBOWQ6rTq4i1DWU+/3ub/oEfTKzhak/wLA5cPpb+O8E9QbENmZa0DRyy3P5M7n14mIE3RFNbaFd+Dr8LEaWF/mH77gGokeOgrEFuHZvneLqdHWzoayqluT8MmxMbPBASVxZRqvWJDQsKjuKns49UUg3fwsMv5hHjUpmxcM9GBHgovvFZRm2z0Y69R8W+j9CsGMw88LmcbHwou7X+puvznxFTH4MCwctwv6xX0FpBhsegMLUpk9uBj8Xa759ph9fPdGXGaP9maPpYEiAyhI49jkE3AMuQU0fr4Unuj7BA/4P8NXZr/j94u86vXZ7o9GwSNcQUBiLXO0WkGX5WlNbE1GphfT0smtR3n1zBToEYm1sbZBc7Xqvj+2Cv4sVc7eeoahM7JoRhPbocHwO1qZKyqtrmfxFBInZxa1dkiAI7UHBZSjJ0jp6xERhwuiOo/VYmPYGuQ/inz3/ybZL29gSb5h4iw9OfEBlbSULByxsldeNN1EoYNQCmPwtXLkAXw5XDwFtQwa5D8LbxptNMWJg5O1ANLWFNq+ksoZtiX8iSSomdWlgEERKOHiGgpFuoiGaq35YZGx9BImpI7GqukGAQptRVFnExaKLt8zTDkvIxdpMSXdPPdz2X9fQ5sT/waBXMbtrGatHrsZcac70fdMpqmwi27gFLuRd4KszX/GPzv9QvwC07wSP/QRVpbBhEpTqZ1CbJEmM7dqB18d20S6L/PQ3UFEIQ2bqpaYF/RfQt0NfFoUvIio7SudrtBdBbtYoJBofFqk0VTe208VO7ebKLM2kpLpEo6Z2UVk1l3JLDZ6nXU+pUNKnQx+D7dQGMDM2YuXknuSUVLL4d3GHkyC0N7Isczghl2FdnPnhhYGoZJjyxVEuNPZviyAIAqg3qAF01Gy4YY2qhp1JOxnuNRwrEx3fTasDL3Z/kcEeg3n/+Puc1/Nd2wdTD7IraRcvdH8Bb1tvva6lta4T4bk96s2H6++BU9+0dkXXKCQFjwQ+wpncM5zNOdva5QgtJJraQpv3S2Q6NebRdDD3IMD+bzs9K4rU05JbMU+7nn8HKxQS13K1A2z9SFYaUZZzoZUrE24UnRMNQC+Xm/O0ZVkmLDGXQb6OGCl0/Em3LMP2Odca2oxdCpKEq6Urq0auIqM0g7mH51KrqtXtukBVbRXzw+bjYObAG/3euP6EawhM+wGKUmHTw+rd0W1BTSWEr4VOQ8ArVC9LGBsZs2rEKtws3ZixfwbpJel6WaetszBR0tnZSrNhkRlR4kO6ZqofEqlJUzs6TZ2n3VpNbVDnaqcUp5BVmmWwNbt52vLKSD9+jkxn13nDrSsIQsslZpeQdbWCof5OBLhas/nFARgbKZj61VHO1H1PEwRBaFByOJjZgXOgRocfzzpOfkV+m4oeuZFCUvD+kPdxMndi5oGZFFbo53tgWXUZy44tw9fWl2dCntHLGi3WoSu8sB98hqkztrfNhJqq1q4KgIm+E7E0tmRTrNit3d6JprbQpsmyzDdHz6G8VfRI6gmQVa2epw3qnWadna24kKm+3TLQtS+yJBGffLCVKxNuFJkdiVJSEuIUctNzSXllpBeWM8TfWbeLXmtofwUD/3WtoV2vl0sv5vefz5H0I6yJXKPbtYFPoz4lsTCRtwe9ja2p7V+f7DQIHlqvblhufrxtvNA48yMUZ8BQ3WVpN8TOzI5PRn9Ctaqaf+39FyVVbaSpb2Ah7jaN79QGda52VTHkJRimqNtMXH4cAP72/k0eG5VaiCRBd0/bJo/Vl/pc7eNZhr1d9F8j/Qh2t2H+L2fJK2l7w4UEQWjYoQT1YNkh/k4AdHa2YvOLA7E2U/LoV8c4mZTfmuUJgtCWpUSoo0cUmrWmdlzegaWxJUM9huq5sOazM7Nj5YiV5JTnMC9snl6G06+NWktmaSaLBi3CuJXvWG+UuT08ugUGvwYnv4Zv74OS7NauCisTKyb6TmRn0k6DDEcX9KdFTW1Jkl6XJOm8JEnnJEn6XpIkM0mSfCRJOiZJUqIkST9KkmRSd6xp3c8T6573vuE68+oej5Mk6e4bHh9X91iiJElv3FyBcLs7djmf5IrjIKm42/vumw9IiQDJCDz6Gr64BgS52RCbpW4OBXmPAiDuirhlvy2JzI4k0CEQc6X5Tc+FJaiHlA31c9LdgrIMO/59vaF91zsNDoV8qMtDTAmYwvpz69l+abvOlo/OiWb9+fU84P8AQz1v8eIv8B64dw1c3Ae//rN1d+OqaiFstToj31f/OXmdbTuzYvgKLhdd1ttO+bYu2N2WzKIKchtrInr0UX8VESTNEl8Qj5e1F5bGlk0eG5VaiJ+zFdZmrfcGxd/eHztTO45nGrapbaJUsGJyD66W1/DW/84ZbMiSIAgtczghh85OlnjaW1x7rKOjBZtfHIiTtSlP/Oc44RdF00AQhL8pyYG8RI3vuq6srWRv8l5GdxyNmdLww7S1EeIUwtzQuYSlh/HVma90eu3zeefZGLORyV0mN3j3cZujMIKxi+HBr9Ubqb4Y3iZm9UwNnEqNqsZg+eeCfjS7qS1JkgcwHegry3IIYAQ8AnwArJJl2Q8oAJ6tO+VZoKDu8VV1xyFJUte684KBccCnkiQZSZJkBKwDxgNdgal1xwp3kG8jkjCzO4enlReBDg3ckpQSAW49wLRt5GkFuVmTVlDO1YpqOtj5YKuCmCL9DwAUNFOtquZc7rlb5mkfTsjFw86cTo4WDT6vtfqG9vEvG21o15sbOpfeLr1ZFL6ImLyYFi9fXlPOgrAFdLDowJy+cxo/uPfjMOZtOLcVds1T194aYn6H/IswdGajv1e6NNB9IG/2f5NDaYdYcWqFQdZsS4I91PMAGt2t7eQPJlZt4gVoe6TpkEhZlolKLaRHK0aPgPrW2VDXUI5nHTd4YznQ1YbXxvqz/WwWv5/JNOjagiBor7KmlqOX8hjqf/OGAHc7c358YQAeduY8vf4EB+Jaf3eeIAhtSEqE+msnzfK0w9LCKK4u5h6fBuZstUGTAyYzofME1kWtIzwjXCfXrFHVsDh8MQ5mDszoM0Mn1zSYbg/Bs3+CkRL+Mx6iWjf6w9vWm8Eeg9kct5nqWjGovL1qafyIEjCXJEkJWACZwChga93z3wD31/14Yt3PqXt+tKTOkpgI/CDLcqUsy5eBRKBf3X+JsixfkmW5Cvih7ljhDpFVVMGu2EtI5onc7X3XzdEjNZXqBksbyNOuF+RaPyyyGEmSCDSyIq5K3HLZVsTmxVJZW9lgU7umVkVE3ZsynUyOlmXYMVfjhjaoc55XjliJnZkdM/bPIK+8ZcMbPz79MUlXk1gyeIlmg1QGvwYDXoFjn8PhVmjuyjKErQQHXwi6z6BLTw6YzKNBj7LhwgZe3P0ikdmRBl2/NQW7q2MuGs3VVhiBW0/IEDu1tVVeU05KcYpGTe20gnLyS6saz9OO2wnFV3RYYcP6ufYjszSTtJI0va/1dy8M7Uyvjna89es5sq9WGHx9QRA0dyq5gIpqFUNvEd3mYmPGDy8MwNfZiue/PSky8wVBuC4lApRm6teYGth+eTsOZg70d+uv58J0Q5IkFg5YiK+dL28cekMns0o2xmwkJj+Gef3mYWNio4MqDcytOzx/ADr2V98hvGMutGJD+dHAR8ktz2V38u5Wq+GaO/COYV1odlNbluV0YDmQgrqZXQScAgplWa6pOywN8Kj7sQeQWnduTd3xjjc+/rdzbvX4TSRJekGSpJOSJJ3Myclp7i9JaGM2HU9BYXkOmVtEj2REQU1Fm8jTrhfkpv6HpX5YZKCVBwmSiprK4tYsS6hT36hs6DatM+lFFFfUXMuDbJFrDe0v1E1iDRra9RzNHVk9cjX5FfnMOjiLalXz/pE/mXWSjTEbmRIwhQFuAzQ7SZLUtXafAvuWGn5K9cV9kBkNg2eom6gGNrvvbGb2mUlsfixP7HiCZ3c9y4msE7d9BIKtuTFeDuacT28iV9ujN2SdbRu56+3IxcKLqGSVRk3tyNQmhkRmRML3U2BdKJz+Vq93VFzL1TZwBAmA0kjB8od7UFFdy7yfz972fwcFoT07nJCLUiExwNfxlsc4Wpny/fMD6Opuy8sbT/N7dIYBKxQEoc1KiVDHiCpNmjy0tLqUg2kHGdtpLEqF0gDF6YaFsQUrR6yksrZS/d6uBQ3c9JJ01kWtY7jncMZ2GqvDKg3M0hEe++X6ZqoNk6C0dSKqBnsMppNNJzbGbmyV9QGoLIGIT2FND8g613p1tFMtiR+xR71z2gdwByxRx4cYnCzLX8qy3FeW5b7Ozjoe8Ca0iqoaFZuOpeDiGoeXdSPRIwBeGjbsDKCDjSn2FsbXcrUDnEKoVEgkiWGRbUJUThQeVh64WLjc9FxYQi6SBIN8W9jUlmXY+cb1hvbdy7SO0Qh2DGbxoMWcunKKD49/qHUJZdVlLDiyAA8rD2b2mandyQoFTFwHfmNh22sQs03r9ZstbBVYu0GPRwy35g2UCiVPhzzNjgd2MKfvHC4VXeKZXc/w1M6nCM8Iv60bayHutpxrbKc2qJvatVVwRbzY0kZ8QTwAAfYBTR4blVKIqVJBgKt1wwck7AYkcA6C316Fb+6FPP1EXPnY+uBk7mTwYZH1fJ2tmDsukL2x2Ww5Zfjd4oIgaOZwQg69O9ljZdp4k8nWwpjvnu1H7452zPghkq3i77Ug3NkqSyDzjMYb1Pal7KOytrLdRI/cyMfWhyWDl3Am50yzow5lWeado+8AML//fN3cWdyajJQw7l2Y9AWknYAvR6g3NxmYQlIwNXAqZ3LOcC7XwO9xiq/A3iWwqqs6/tOuIzRzQ9udrCXxI2OAy7Is58iyXA38DAwG7OriSAA8gfS6H6cDXgB1z9sCeTc+/rdzbvW4cAfYeT6L3PJ8SqQY7urUQPQIqJvajv5g1XY+yJAkiSA3Gy5kqndmB3oOASA2XTcZWkLzybJMZHbkLfO0wxJzCXa3wcGy6Z0CjSyibmgf+xwGvNyshna9CZ0n8FTwU/wQ9wM/J/ys1bkrT60koySDd4a8g4VxM/LBjYxh8jfq4YBbn4GkMO2voa3UE5B0WB3VojTV/3qNsDC24IngJ9jxwA7m9ZtHekk6L+5+kce2P8ahtEO3ZXM7xMOW5LwyrlY08kLq2rBIkautjfiCeMyV5nhYN3iz2V9EpxXSzcMWY6NbvDxL2A3uveDpHerhrpln4NOB6rggHd+6KUlSq+Vq13tqkDf9fRxY8vsF0gvLW6UGQRBuLa+kknPpVxmm4V1u1mbGfPNMPwb6OjJ7SzTfHU3Wc4WCILRZacdBroWOmm1Q23F5B66Wrrd8L9fW3e19N48FPcbGmI3svLxT6/N3Je0iLD2MV3u9ipuVmx4qbCU9HoFndqrfR399N5wx/NDGib4TsVBasCnGQBnfuQnw23RYHQKHV4LPcHhuLzy9Xf06X9BKS5raKcAASZIs6rKxRwMXgP3AQ3XHPAn8r+7Hv9X9nLrn98nqd0m/AY9IkmQqSZIP4A8cB04A/pIk+UiSZIJ6mORvLahXaEc2RCTh6pqIChV3ed918wEqFaQc1fgfQUMKdLUhLusqtSoZH68hmMgycbkXWrusO156STq55bn0cr75H4rSyhoiUwoY4teCD0hkGXbOu6Gh/W6LBx2+1vs1BrkPYunRpURlR2l0TkRGBD/G/chjXR+jT4c+zV/cxBKmbQZ7b/h+qjp2Qp/CVoGZHfR5suljDcRMaca0oGlsf2A7bw14i9zyXF7Z+wqP/PEIe1P2opJVrV2iznR1V0cnXWhsWKStF1g4qSMwBI3F5cfhb++PQmr8JVd1rYpz6UW3jh4py4f0k+A/Vn1HRZ+n4F/Hocvd6l0eX47Q+QcO/Vz7kVuey+Wrl3V6XU0pFBLLH+6BLMvM3XoGler2+0BJENqzsET17eK3ytNuiIWJkq+fDGVUoAsLfj3H12Gt8/1FEIRWlnIUJAV49mvy0IKKAiIyIhjvM77J11Nt2cw+M+nh3INF4Yu4VHRJ4/OKKot47/h7BDsGMy1wmh4rbCXuveCFA+q7Qn9+Dv5cALU1TZ2lM1YmVkz0m8jOpJ3klusxBiXlGPzwKKwNhegfoNdj8OopmLIBPPvqb93bXEsytY+hHvh4Gjhbd60vgbnATEmSElFnZn9dd8rXgGPd4zOBN+qucx7YjLohvhN4RZbl2rrc7X8Bu4AYYHPdscJt7kLGVU4kFeDYIRZPK0+CHIJuPig3DioK29SQyHpBbtZUVKtIyitFaWyGv6wkpkxkB7a2+jzthj7dP3Y5j+pamSF+zYweudbQ/gz6/1MnDW0AI4URHw77EDdLN14/8DpXShsfDldcVczC8IV423gzvdf0Fq+PhQM8/jOYWsOGByBfT288s2Mg7g/o/6J6rTbGxMiEyQGT2fbANpYMWkJxVTGv7X+Nh39/mF1Ju26L5nbItWGRjTS1JUm9WztdDIvUlCzLxBfEa5SnHZtZTGWNih63ampf3AeyCvzGXH/M2lX9QnjKRijLg/8bo/5eVFmik/r7u6oHMbVGrnY9LwcL5k/oSlhiLhuPiV2dgtCWHE7Ixc7CmBAPW63OMzM24vPH+jAu2JWl2y6wbn+inioUBKHNSg4H125g1vSww93Ju6mRa9pl9MiNjI2MWT58OWZKM2bun0lZdZlG5606tYqiyiIWDVyEUSvMHTIIK2d44n8Q+jyEfwIbH1Jv6DCQqYFTqVZVszV+q24vrFJB7B/qXej/uUt9B/SwOfD6efjHKnD01e16d6AWfcwly/IiWZYDZVkOkWX5cVmWK2VZviTLcj9Zlv1kWX5YluXKumMr6n7uV/f8pRuus0yWZV9ZlgNkWd5xw+PbZVnuUvfcspbUKrQfG44mYWZaTnrFGe7yvkX0SHJdnEcbGhJZr35YZGx9BImpE3GqstsysqA9icqOwsrYCj87v5ueC0vIw1SpoK+3vfYXlmXY9eb1hva493TS0K5na2rLxyM/pqy6jNf2v0ZlbeUtj/3oxEdkl2WzbMgyzJRmOirAEx7/RZ3vtWESlGTr5ro3OrIGjC2g34u6v7YOGSuMmeQ/id/u/413h7xLVW0Vsw/O5oH/PcAfl/6gth1PrHa2NsXF2pTz6RrkaufEghh+q5ErZVe4WnVVszzttCaGRCbuBXP76zEwNwr6B7xyDPo8DUc/VUeSJOxpSekAeFp74mrp2mq52vWm9vNiWBdn3t0eS1JuaavWIgiCmizLHKezG7kAACAASURBVE7IYbCfE0YK7V/3mCgVrJ3Wi4k93floVxwr/owTr5UF4U5RUwVpJzXeoLb98nZ8bH00ej3V1rlauvLBsA+4fPUyiyMWN/l979SVU/yU8BOPd32cIMcGNvvdToyMYcJyuG8tJB9R34VooMGJPrY+DHYfzOa4zVTrIte6ugJOfQPr+sEP0+BqBoz/EGZegFHz21SEbnvXfu/dEG5LRWXV/BqZQe/ADGrl2oajR0B9u5JVB7D3MWyBGvBzscJIIRGTqd7xGGjnR6FC4kpeXCtXdmeLzImku3P3Bj/dDkvMoZ+PA2bGWn7yXd/QPvqpXhra9fzs/Xh36LucyzvHkoglDb74OZR2iF8Sf+GZkGfo7txdtwU4B8C0LVByBb57ECoa2c2rrcIUOLsFej+pnoTdDigVSu71vZdfJ/7KR8M+QpIk3jj8BhP/N5H/Jf5PNy+EWkGIhybDIvsAcqsMcmmP6odEarJTOyqlECcrEzztzW9+UqWCxD3gOwputUPHzBb+sRKe3gnGZrDxQfjp+RZNk5ckiX6u/TiRdaJV70iQJIkPHuyG0khiztZoakUMiSC0uoTsEq5crdQ4T7shSiMFKyf3ZHJfTz7Zl8i722NEY1sQ7gRZZ6CmXKOmdlZpFqevnGa8z/j/Z++8w6Mq0/99n5n03nshjSSEJLSEXgQEBAHFjqjognUt6+quuutavtZV8aeuDVQUFUUFK6AUQUBCT0JIT0hI75X0zJzfHycB0mYCTGYmybmvi2vCOe+854Ekc97zeZ/n8wz+5ogdTPKcxANjHmBbzjY2pW/qc1yrqpXn4p7Dy9qL+6Lv02OEBmbcbVL/GFUrfHwlJP+gl8suD19OeVM5u85cRmJIU7XU6+atKPj5ITC1hOs+hofipapkM2vdBSwDyKK2jJHx7fF8mtpUCLaJ+Nj4MMppVO8D8+Kkm6AR3tgsTJUEuVqfF7U7fI3Tcn83ZFjDmvrWerKqs3q1Himtayaj9CxTL9Z6RBTht391CNr3Dpig3ckcvzncF30fP2X/xJepX3Y5V9tSy7MHnyXEMWTgFjy+MXDjBihLkXab25p1M+/BdwABpvxVN/PpEaVCyYKABWxespk3Z72JpYkl//7z3yz+fjGbMzbTpuPGfQPNaC87ssrO0tSqIePca5z0KjeL7BedonaIY4jWsQn51Yzxdej9ga3kJDSUQfCV2i/qPxnuPQAz/wnJ35/37btEoSjWI5aalhoyqzMv6f26wtPekueWRHA0t5pPZA9eGRmDsy+jHIBpF+Gn3RtKhcAry6K4bZI/6/bn8J8fk2X/fBmZoU5n1XU/RO1fc35FRBz01iPdWRW5ihk+M3j16KucLD/Z65iPkz4mpzaHf0/6N1amVnqO0MD4TJB8tt1Hw7d3SP1jBrgqdpr3NHxtfS+tYWRNPvz6FLw5WorVPUKyU7lnH0ReD0oT3QcsA8iitowRoVaLfHHoDGP8zUipOtG39UhNPtTmG6WfdifhnnbnRO2RAbMRRJG0UtmH1lCcLD+JiMgY156i9p8dTY4uyk/7nKD9boeg/YpeNljujb6X2b6zef3Y6xwqPnTu+MtHXqa6uZoXp76ImdJs4AIIuRKueR9y98OW1Ze/sDhbDic2QNRNks3JIEUhKJjrP5dvrv6Gd2a/g6O5I8/GPcui7xfxddrXGi1jjIlRXvaoRUgr0ZCJb+0MDn6yr3Y/yajKwMvaC1szzV7xtU1tZJc3EO3Tl/XITuk1eE7/LmxiDlc8BffuB+dg+P4eyT6oOrf/wXcQ6yE1cDK0BQnAtWO9uXKUO6/tSCerTLbAMUpUbfDtnZJnpMyQZn9mBUGu1ng79FJdcpEoFALPL41g9fQAPj90hie2nJQrMmRkhjJ5ceAUCLbuWoduy9lGhHME/nb+eghMfygEBS9Newl3K3f+/sffqW6u7nL+dO1p1iWt46qAq5juM91AURoYWw9Y+YtU0bv/Ddh4EzTVDNjlFIKCW8JuIaE8geTKfrbzKzkFW+6Gt8fA4Q8gdCHcs1+y7wycZZRJmEMNWdSWMRr2Z1WQW9lI1Mh82sV2zdYjYJR+2p2Ee9pRVNtMbWMbVo6B+KvUpF9Eh2MZ3RJfFo9CUPRqy3EgswInazNGeWpvUgJIgvaOf0uCduw9ehO0oWPxM/0lAuwDeOyPxyioL2D3md1sPb2Vu6Pu1o/PWtSNUiPM1J9g698vOfsTkG787S0w9WHdxWdABEFglu8sNi7ayAdzP8Ddyp0XD7/Iws0L+SLlC5rbdZTdPkCM9pZ+B05pahYJcrPIiyCjOoORTtqtR5IKJNuXMX59iNqZu8AzGmzcLi4At3C46zdY+DoUHJW8tg++c1Ed5T1tPPG19TUKUVsQBF66NhJrMyWPfpNIu2rwN2kdcqT+DMlb4Nh6Q0ciM4A0t6k4nFPJ9MvM0r4QQRB4amE4D80O5ptjBTz6TYL8Oy4jMxRRq6Xneb8pWofm1uaSWpXKVQFX6SEw/WNvbs8bM9+gsqmSJ/c/ea4/j1pU89zB57AwseAfMf8wcJQGxsQclrwtNVU8vRfWzYaytAG73DXB12BpYqk5W1sU4fQf8Pky+GAqpP4CsXfDwwlw3Trw1LEVqIxGZFF7iKBSi4O+VG/DwVxcbMwoVR3B28Zbs/WIma1UimKkhHlIWXmpJXUgCIQqbUht1V/3XpmuJJQlEOoYirVpVw8rURQ5kFXBlCBnFP1pctQpaMf9TxK0r3pV77uv1qbWvHXFW6hFNQ/+/iDPH3qecKdwVkWt0l8Qkx+AaX+D4+th78uXNkdzHRxZJzW4c9Uu+g0mBEFgqvdUNly1gY/mfYSfnR+vHn2VBZsX8OmpT/vd6VzfeDtY4mBlSoo2X22vcVCbJ2Xay/RJi6qF3Lrc/vlp50vZOVG9ZWo3VUPBkf5Zj/SGQgGxq6VGkgEzpc+wj+ZclC96rEcsx0uOG0UzVFdbc164JpKTBbW8vzfb0OHIdOfIWun19F5JuJAZkhw/U01zm5rpl+Gn3RuCIPDovFAenx/KjwlF/HVjPK3t8s+RjMyQoiIDmqr6laC2PWc7AgILRizQQ2CGIcIlgidin+DPoj9Ze1K6h36f+T0nyk7w2ITHcLHU7efsoGXCXXDHz1Kz+o/mSELyAGBrZsuSoCVsz9lOZVNl15Oqdji1GdbOhA1LJHvA2U/D305JVqQOfgMSk4xmZFF7iLAzpZQpr/zOcz8nc/xM1aATuPOrGvk9vYxlExw5UnK4b+sRkERt39i+m2UZAZ1Zv+d8ta19KBRU1LVoEYtkdE67up2TFSd79dPOLDtLWX1L/x7KugjadxtE0O7Ez86P12e8zuna09S31vPitBcxVZjqN4g5z8DYFfDHq5I4fbEc+wRaamHao7qPzUgQBIGJnhNZv2A96+evJ8QxhDeOv8GCzQv4KOkjzraeNXSIXRAEgQgvO04V9iNTG6BIztbWRHZNNipR1U9Ru4ZAV2vsLXv5PT69F0S1ZP9zOdj7wC1fwQ2fSh3Y114BO/8Drdo3WWI9YqlvqyetauAyYy6GRVGeLI724u3fM0nWtgkjoz+KT0prNM8x0FgBZf0s3ZUZdOzLLMdUKTApcGAaPD9wRTBPXz2KX5NLuPeL4zS3GX5DTUZGRkfk9c9PWxRFtuVsY7z7eNyttduUDGZuGHkDiwMX837i+/yc/TNvHH+D8e7juTb4WkOHZlz4T5Z8tl1GwqZbYc/LA7KBvjxsOW3qNjZnbpYOtDbA4bXwzjj47i7p74vfgkdOwYzHwMpJ5zHI9B9Z1B4iONuYEeljz5eH8rju/TimvPI7z/+cMmgE7i8On0EhCHh6ZdMutjPff37vA5uqpUZ1RuynDVImmbO1GWnFkudnmIuUVZ6RL3tM6puM6gya2psY6za2x7n9mR1+2trKZ3sI2v81uD/WFO8prJm5htdnvt6vJnQ6RxDg6rck37Btj8OpLf1/b1uz1GAzcBZ4jxuoCI2KCR4TWDdvHZ9f9TmjXUbz1om3mL95Pu8nvk9dqxYRWY+M9rInvaSeNk0l357RIChkCxItdDaJDHUM1ThOFEUS8msZ46vBesTCHrwnXH5QggAR18Jfj8CY5fDnW/D+FEk410Csp/H4anfy/JIIHKzM+Ps3ibS0D33BK7moljU70mls7b91jN45shZMreDaD6S/Z+8xbDwyA8aBzArG+TlibT5wja/+Mi2AF64Zze9pZaz67Jhx/+zLyMj0n7xDYO0meWprILUqldy6XBYGDq0Gkb0hCAJPT36aYMdgnjrwFM3tzfxn8n/6TvIbzth7w53bIXo5/PGKJG436/ZZKtAhkMmek9mU+hVtv78Ab0bA9sfBxh1u+hIeOArjV4KphU6vK3NpyKL2ECFmhBPrbp/A8afn8v9uGsNob3u+OHSG696PY+qrnQJ3tVEK3M1tKr45ms+V4e4cLtsjWY8492U9clh6NWI/bZBuTOGedpL9CBDmMw2AtIKDhgxrWBJfFg/Qa5PIA5nlBLhoaXIkirDzaUnQjlltFIJ2J3P85zDbb7bhAlCawPWfgN8kqUFGfwWMxI1wtnRIZ2n3xRi3Mbw39z2+XvQ1493H817Ce8z/bj7vxL9DTfPANT7pL6O87GhVqcks1ZBFbm4DrmFyprYWMqozsFBa4Gvrq3FcYU0TFWdbGNubqC2KkLULgmbrtmu6pSMs/Z9UxikIsGEp/PAANPZuk+Vi6UKgfSCHSw7rLobLxNHajFeWRZJWUs/buzMNHc6AUd3Qyr9/SGLxOwd4+/cs3vk9y9Ah9U5jFSR9S17E1axKeIPDHiFwWha1hyIVZ1tILqpjxkjd+Wn3xYpJ/rx+QzQHsytY+clR6pvbBvyaMjIyA8yZOOlZXsvz1Pac7ZgIJlzpd5mVaoMESxNL1sxcg7OFMw+OfZBAe82i/7DG1AKueU96Ls/4TbIjqdDh+qgym1vPNlHWXMHu4+9KCZV3/QardkrWmQpZRjUm5O/GEMPWwpRrxnrz0R0TOPb0XN68KZoIr06B+yBTX/2d//vFuATunxOLqG5s47oYRw4X9cN6RGF6vvzdiAnzsCW9pJ52lRoX30k4t6tI628XXRmdkVCWgLuVO542nl2Ot7arOZxTxbRgDdYjoiiV5x98RxK0F75mNIK20WBqCbd83VEGtkJ79q6qXcoO9RoHATP0E6MREuESwduz3+a7xd8x2Wsy606uY/7m+bx5/M2e/m16ZLS3PQCn+uOrXXj88hqFDnEyqjIIdghGqcUqKyFf2syI7k3ULkmCsyUQPHcgQpR+B+87KG0wJX4F78ZC0ne9fl9jPWI5UXqCNrXxiEpzwt25YbwP7+/NJj6v2tDh6JR2lZrP43KZ9fpevjqSz+2TR3B1lCcf788hp6LB0OH15MQGkhRqbmtK4XDxYX5ydIEzB6XKHJkhxZ9ZUpWbrv20++L68T68dfNYjudVs+LjI9Q2Gs9nkIyMzEVSWyD1ZdFSda0W1WzP2c4U7yk4WPRRyTYEGWE/gt037ObO0XcaOhTjRxBg4j1w+4/QWAnrrpAE7suh4Dh8czu8M55pyb/io7BgY+g0yb7Pb5Ju4pbRObKoPYSxszDl2rE+3QRuOz6PkwTuaR0C94m8akQDChOfHzpDiJsNZ5WJmq1HQBK1vcZKQpqRE+5pR0u7mtzKBrCwI0ytJL2x2NBhDTviy+J7tR6Jz6umsVXFtL4eys4J2m9DzCpZ0NaEpQOs2Cz5iX15g+ad8pQfoDoXpj8q/38CoU6hrJm1hi1LtjDTdyafJn/Kgs0L+O/R/1LeqP9GjAHO1libKUkp0uarPVZaQNbk6SewQYYoiqRXpzPSSbufdmJ+DWYmCsI87HqezNolvQ6UqA3S/XTuM3DPH5Lv9ua/wMaboCa/y7BYz1ia2ptIrjCuzdmnF4/Cw86Cv3+bOGR8dw+druTqdw7w9I/JRHjZse2h6Ty7JIL/XD0KMxMFL/ySYugQu6JW8UfCx9zl5YmlmS1RLlEk0gLtzZB/yNDRyeiYfRkVOFqZEuFlr7drLo724r1bx5FSVMst6w5RebZFb9eWkZHRIXkd9wQtonZ8WTyljaVcFXCVHoIyLrQlQ8h0I2C65LPtOEJav+577eKSbtRqSQxfvwg+mg3Ze2HaIygfSeKWcQ8SX51GSqWRrbtkuiCL2sOE8wJ3DMeensuaG6MZ1SFwL3vvIFNf+Z0XDCBwJ+TXcLKgltsm+7PjzA7N1iNtTVIW6CDZJQs/1yyyw1fbwpksVSNtKjnDRF+UNJRQ2ljaa5PIA1kVKAR6b3IkirDrmQsE7ddlAVYbdp6w4nvp68+vhbpeNnBEEQ68CS6hELpIv/EZOcGOwfx3xn/5YekPzBsxj42pG1mweQEvHX6JkoYSvcWhUEjWSacKtWRqd1bLFB4f+KAGIRVNFdS01PS7SeRoLzvMTHpZkmXtAo9IsPUYgCi74REJq3bD/Jcgdz+8NwkOfwhqSSie4C55ehuTrzZI65v/Xh/N6fIGXvst3dDhXBZFNU38deMJbl57iPrmdt6/dRxfrppIqIctAG52Fjw0J5jdaWXsSSszcLTn+fbAczxkIxJk7cUXC79gtt9szjRXUG1iptWvXWZwIYoi+zPLmRrsglKh33XR/AgP1t0+gezys9y89hBldXIVgIzMoOPMQTCzBffRGodtz9mOhdKC2b4GtFmUGTw4+En2IJE3wO8vSNnWLRqsFAHaWyH+S6m3zMYboToH5r0IjybD3GfB1oNrQq7B0sSSjakb9fGvkLlEZFF7GGJnYcqycV0F7nBPOz6Ly2XZeweZ9uoeXtyaQrweBO4NB3OxMTdh7mg7DhUdYp6/BuuRwhOgbgP/KQMak64IcrPGRCGQWtzhq20fTLsA2ZVpBo5s+HDOT7sPUTva1wF7S9Oeb9z9nGSRMeEvsqB9MbgEw4rvoKkKvrgOmrp5RGfuhNJTMO0R2YusDwLsA3hx2ov8fM3PXB10Nd+mf8vCLQt5Pu55Cs8W6iWG0d72pBTXabaocosApZnsq90HnU0itYnabSo1SYW1jPF17HmyuVbKaArWo5ekQgmTH4D7D4HvRNj+D/h4HpSm4GjhSKhjKEeKjUvUBpgW4sJtk/z55M8cDp82nH3PpdLcpuKd3ZnMfmMvO1NKeWRuCLsenclVkZ491kQrpwQQ6GLN87+k0NquoaGrHhBFkXfi3+H5nO+Z2gafLP4GF0sXol2jATjpFSE3ixxiZJSepay+hRnaGmwPELNC3Vi/MobCmiZuWnuIopomg8QhIyNzieTFgW+Mxj4hbeo2fsv9jVm+s7AytdLJZcvqmnl0UwIvbk1h4+E8DmZXUFLbbNBqdRkdY2YFy9ZKwnTaL/DxlVB1uue45lrpOf+taPjxfhAUcO2H8HAiTPkrmNueG2pnZseSoCVsz9lOVXPvfWdkDM/AtayWGRR0CtzLxvlQ29TGrpRStiYV8+nBXNbtz8HbwZKFkR4sjPRkjK+DTjvwVp5t4ZeTxdwc68vhkj9oF9uZN2Je32/Ii5NefSfqLIaBxNxESbCbzXlR2zMGqg+Tlr+PMLdIA0c3PIgvi8fSxJJQx9Aux2ub2kjMr+GvVwT3fFNltpRNPHYFLHpDFrQvFq+xcNMXkg3JVzfDbd+ftws6sAbsfGD09YaNcRDga+fLc1Oe456oe/jk1CdsydzC95nfszhoMasiV+Fn5zdg147wsqOxVUVOZQNBrja9DzIxA48o7R7qw5T0ailjWJuonV5ST3ObmmjfXsr4T+8FUQUhBmiQ5OgvWQolfQu/PgEfTodpfyPGbRzfZm2hRdWCudJc/3Fp4Imrwvgjo5zHvkvk14dnYG1u/EtcURTZkVLKC1tTyK9qYmGkB08tDMfHse+HeDMTBf9ZPIqV64+y/s8c7pkZpMeIz9OmbuPZg8/yU/ZPLKs/y9OR92FiIVWoRbhEYCKYkOjkycyE7VITSSsng8Qpo1v2Z0q2WH1at+mBKcEubLgrlpXrj3Ljh3F8tXoSvk66Eb5kZGQGkKZqKEuBiGUahx0qOkRNS41OrUe+OpLPlvhCzEwUXTaELU2V+DtbEehqzQhnawJcpD8jXKxxtjbTqfYhowcEQRKm3SPguzth7Sy4/hPJxq+uCA69D8c/hZY6qa/MkncgeI7G5/1bwm5hU/omNmdsZnXUar39U2T6j/Gv+GX0hr2lKdeN9+G68ZLAvTOllG3dBO5FUZ4sjPQk2sf+sj/kNx3Lp1Wl5rZJ/qxJehdvG28inCP6fkNeHLiGDaoHo3BPO+KypawxX9+pWJ56m7RSWQTSFwllCUS5RGGi6PpRF5ddiVqEqb01iUz6DhBg1lOyoH2pBF0h7ZR/dxd8e6ckchcclX6HF7wqCaIy/cLLxot/T/o3qyNXsz55Pd9lfMeP2T+yMGAhf5/wd1wsdS8sdPqkniqs7VvUBvAeJ5XtqVVShq/MOTKqM3C3csfeXLPnbGKBVM0wtrdM7cydYG4PPrEDEaJ2BAGiboSgOfDbU7DvNSa6B/KFVTsny08S4xFjmLj6wNrchNdviOamtXG8vD2VF64x7s3jrLJ6nvs5hf2ZFYx0t2HjqolM0dS4+AJmhboxN9yNt3dncu1Yb9zsLAY42q40tDXw6N5HOVh0kPutgrg37yDChLvOnbc0sWSk00gSVe2AKG3QjNYsYsgMDvZlVhDsZoOXg2F720wY4cSXqyZy+ydHuOGDODaunkigpvuVjIyM4ck7LL36a/bT3p6zHVszW6Z5T9PZpbcmFREb4MTXqydRXNdMTnkDOZUN5JQ3kFvZQFpxPTuSS2m/oErR1sLkvMjdTfDutdJXxngIugJW74Gvb5USrYLnSpVjogpGLYUpD0nPMf2ZyiGISZ6T2JS+iZWjV2KqkL/3xoYsasv0ir2lKdeP9+H6CwTurSeLWP9nDmv3nb5sgVulFvnyUB5Tgpxxc1BzqPgQt4Xf1vc8ahXkH4HR1+ngX6c/wj1t+T6+kOqGVhxdRjKytZ202l7KYGR0TkNbA+nV6ayO7Lmj+mdWBVZmSsb6dROSRFHKTBwxDey99RTpEGX0MqmR4LbH4OeHoaEMrJxh3O2GjmxQ4m7tzhOxT7AqchWfnvqUr9K+Qi2qeXXGqzq/Voi7DWZKBSlFdSwdo+H3wHs8HFkLFRngFq7zOAYzGdUZhDqFah2XkFeDk7UZvk7dBCJRhKzdEDhTY4muXrB2hmUfQtSNjP/lERSWIkf+eJ6YJZ9LTWKNiNgAJ/4yNYCPDuQwb5QHM0YaxiJBE3XNbby1K5PPDuZiZabk2cWjWDHJHxPlxVky/XvRKOa9uY9Xfk1jzY09LbYGivLGch7Y/QAZ1Rk8H/ME137/uLQ2s+4qyEe7RvND1g+0m9tjcnqPLGoPAZrbVBw+XcnyiQNXKXQxRPs68NXqSdz28WFu/PBQF/95GRkZIyTvIChMz/dl6YXm9mZ25+1m/oj5mCl1kwSTUVpPRulZnl8agUIh4O1gibeDZY+KkzaVmsLqJnIqGsipkMTunIoGjp+p5qfEoi69B52tzRjRIXZfmOU9wsUKKzNZYjMKnAJg1U748a+Qvh3Gr5Qs9pwCLnqq5WHLeWjPQ/ye9zvzR8zXfawyl4X8GyejlS4Cd2MbO1JK2JZUzCcHJIHbx9GSRZGSwB3VT4F7d2ophTVNPH11OHvy99Cu1mI9UpYilYlo6ZRsbIR5dDSLLKljSpALYUprtrZWIYqiXM40wCRVJKEW1Yx1G9vj3IGsCiYFOvdszFacCJWZUtmSzOUTuxoayuGPDuH1in9Jfmcyl4yLpQuPxTxGfVs9O3J30Kpq1dmivxNTpYJQD1tOFWlpFunVkeFQeFwWtS+gTdVGTk0OM31mah2bkF/T+8ZwWQrUFxnGeqQvgudge/8hRn23gCM16Tzw7kRY+BqMWmLoyLrw2PxQ9qSX8c/NJ/n1kRlGk02lVot8d7yA//6WRmVDKzfH+PHYvJE421yajcsIF2tWTQ/gvb3Z3DrRn/H+vWT765jTtae5b+d9VLdU887sd5hecAraGqTP+m5EuUbxVdpXZPlPICx7r7RRI697BjXHcqtpaVcbzE+7N0Z52bHpnkksX3eYm9fG8flfJjLaW3OFjIyMjIE4EyfZFJr2XenxR8EfNLY3sjBwoc4uu/VkMYIAC0ZrbrptqlRIQrWLNVd0O9fcpiK/qrGH4H0gq5zNJwq6jHW3Mz+X1X1hlrefsxXmJnJlo14xs4Yb1oOqDZSXvh6c4TMDbxtvNqZulEVtI0QWtWUuCnsrU26Y4MsNE3zPCdxbk4r5+EAOH16EwP35oTN42lswN9ydh/bs0G49cqbDT1tLuZKxEe7ZIWoX10uitrU3m1pzKThbgK+tr4GjG9rEl8UjIBDlGtXleEG1tCBZMcm/55tOfSdlEIQbl0gzqJn1pNQwMvXnXoUPmUtjjt8ctmRu4XDxYab7TNf5/KO97diWVKJ5A845GMztJF/tsSt0HsNg5XTtadrFdq1+2vXNbWSVn+XqKK+eJzN3Sq/BcwcgwsvAzJqY0Gv5PHkDTaI1lt/cBmFXSw117TwNHR0AFqZK3rhxDNe9f5D/+yWF12+INnRIxOdV8+xPySQW1DLOz4H1K2OJ9Ll84e2BK4LZfKKA535O5of7p6JQDJxoHF8Wz4O/P4hSULJ+/noinMJhy8PgE9NrCW9ns8hEZ2/CMnZLzZqcDeP/LaMb9meWY6oUmBhoXDaAwW62fHPPZG796DC3rDvEhrtie1biycjIGJa2JiiKh8n3axy2PWc7LpYuxLjrxuJMFEW2JhUzMcAJN9tLt+qyMFUS4m5LiHvPapCGlnZyKxvIrWgkt7KB0x2WJjuSS6lsaD03TiGAl4NlV7Hb1ZoAZ2t8HC0vumJL5iK4DEEbQKlQckvYLbx+7HXSqtIIcwrTXNZAjgAAIABJREFUUWAyukAWtWUumQsF7prGVnZ0eHB3EbijPFkU6Umk93mBO7v8LPszK3hs3kga2uuJK45jRfgKzZnLeXFg5w32g0sIdrU1x8XGnLTOZpEuo6Eol/TSeFnUHmASyhIIdgzG1qzr4uPPrAoApndvcqRWQ9JmSUQaRL7tRo8gwML/woKXZd9lHTLRcyLWptbszts9IKJ2hJc9Xx3Jp7Cmqe+mdQoFeI2RMrVlzpFRnQHQo0Ftd5IKahFFGOPXi4VH1i5wHw12vQjeBmaix0TWn1pP/NWvMCX3GOx9Bd6NhbnPwvg7pZ8LAzPG14H7Zgbxvz1ZzI/w4MpR7gaJo6y+mf/+ms53xwtwszXn/900hqVjvHRWqWVtbsJTC8N5+OsEvjtewI0xA7Ou2H1mN//c/088rD14f+770volcxdUZUsbl73gY+ODk4UTiUq4CeD0HlnUHuTsy6xgvL+jUZbWj3CxPpexveKjw3yyMoaJgc6GDktGRqaTwuOgbgO/KX0OqW+tZ3/Bfm4IvQGljp4ZMkrPklV2ljumjNbJfL1hbW5ChJf9uZ40F1Lb1EZuR3b3hRne38cXUt/cfm6ciULAz8mKES7WBLpYc8eUEXIDXCPjmuBreDfhXTambuT5qc8bOhyZCzC+VYnMoMTByowbJ/hy4wUC99aTxXy8P4cP/ziNr5MlCyMlgXvLiUJMlQI3xfixJ3+7ZD3ir8F6RBQlUdt/6qAsXQ33tCW1RBK1g32moCz8mbSCOOYGy9nAA4VKrSKxPJFFAYt6nNufWYGbrTkhbt0aCuUdlMr95/2fnqIcZsiCtk4xV5ozw3sGe/L38LT6aZ0t/juJ8JKqTE4V1vUtaoNkQRL3LrQ1g6l+m9UZKxnVGZgpzPCz0+w7G58vNYmM7p6x21wn3fMmG6cN0li3sZgIJhwpPc6UaX+TKlt+eQS2Pir1JFj8NrhqzlLXBw/NCWFXailPbkligr8jjtb6a1Db2q7ms4O5vLU7k5Z2FffNCuKBK4KxMdf9sntJtBefx53h1V/TmD/aQ+d2KxtTN/LKkVeIdI3kf7P/h6NFRwbskQ/B2k1quNQLgiAQ7RpNYk022PtJDZpiVuk0Nhn9UV7fQmpxHY/P194rwFD4OFp1ZGwf4o71R/jo9pgenrkyMjIGorPq2rfv5te783bTqm7lqoCrdHbZrSeLUAiwIEKz9chAYW9pSrSvA9G+XRMYRFGksqG1F8G7kQNZFexIKWXL/VNwuUSLMhndY29uz9WBV/Nj1o/8bfzfzq+HZAyO4dNpZIYcnQL3Z3fFcuzfc/nvdVEEutjw8f4clvzvTz49mMvCSE9cbc3ZkbsDL2svRrto2D2tOQP1xeA3SX//CB0S7mlHRulZ2lVqLDyjCWhrI60y2dBhDWmyarJoaGtgjFvX5llqtcjB7EqmBbv0zJRL+hZMrSFUdwspGZmBZI7/HKqaq4gvi9f53OGedigVAinafLW9x0uZN6WndB7DYCW9Kp0ghyBMFJoFzMT8GgJcrHGw6ia25vwB6nbjsx7pwMrUikjXSI6WHJUOOAfB7T/B0nehLBU+mAp//BfaWzVPNMCYmShYc+MYaptaefpH/f187k0vY8Fb+3hxWyoTA5zY8beZ/HNB2IAI2iCJx88uiaCqsZW3d2fqbF61qGbN8TW8fORlZvnO4qN5H51/gKvMlixyJtwJJn1vFkS7RpNXn0dVwBTI2Q+q9j7Hyhg3nVVuxuSn3Rse9hZ8ffdkRjhbc9dnR9mdWmrokGRkZEDarHcbpbEadnvOdrxtvIlyiepzzMUgiiK/nCxmUqAzrrbGJQ4LgoCLjTkTRjhxwwRf/rEgjPduHc/2h6fzzT2TKatv5i+fHqWxVb5vGhPLw5bTqm5lc+ZmQ4cicwGyqC0zoDhYmXFjTFeBe0m0Fw/ODqGutY644jjmjZinuRT3nJ923+VKxky4py2t7WpOVzSAvS+h7ZDWWGTosIY0CWUJAD2aRKYU11HV0Nozc6e9FZJ/gLBFUkMJGZlBwHTv6ZgpzNidt1vnc1uYKgl2teFUUZ3mgZ1euoUndB7DYCWjOkOrn7YoiiTk1zDGtw/rETNbo97IjfGIIbkymbOtZ6UDgiD5qv/1qOSxvedF+HAGlBh2s2OUlx0Pzwnhl5PF/HJyYO+7ZyobWPXZMVauP4oowvqVMXy8MoYAl4G/p4z2tueWWD8+O5hLZmn9Zc/Xqmrlyf1Psv7Uem4KvYk3Z72JpckFjb2OfixV34y/U+M8nb7aSS7+0FILxQmXHZuMYdiXWY6jlem5Kh5jxtXWnK9WTyLU3ZZ7vzjO9qRiQ4ckIzO8Uasg/wj49d0bq6KpgkPFh1gYsFBnFl2pxfWcrmhgUZRx9PzoL2N8HXjnlnEkFdby0FcJqNSioUOS6SDYMZiJHhPZlL6JdrW84WAsyKK2jN7oFLjfvmUswW427Mnbo916BKSdXQt7cA3XT6A65nyzyDoQBMLNnSlVt1DdXG3gyIYu8eXxuFi64G3j3eX4gY5Mo2nB3UTt7N3QXAOR1+srRBmZy8bK1IopXlPYnbcbUdT9gjfCy45ThVoyte28wcZd9tXuoKKpgsrmSkKdNJfoF9c2U1bf0lPUFkXJqzhw5mU3tRlIJnpMRCWqOFHWbTPDxk3qMr/8G2isgK1/N0yAF3DvzCCifex5+odTlNU363z+hpZ2XvstjSvX7CMuu4Inrgrj10emc0WYm86vpYnH5oViZabkuZ9TLuvzoL61nvt23ce2nG08PO5h/jXxX13tjVrOQvwXku2IluagES4RmAgmJJoqAUGyIJEZdIiiyP7MCqaFuGpsRlp4tpA5385hzbE1NLU36THCnjham/Hl6olEetvz16/i+SG+0KDxyMgMa0qSoLVeo6i9I3cHalGtW+uRpCKUCsFg1iOXw5Wj3Hl2SQS7Ukt57ufkAVnny1way8OXU9JQwp58eU1jLMiitozB2HGmH9YjIInavpOMovnUpRDkaoOpUiC1WMqeCnUIBiCtMs2QYQ1pEsoSGOs2tsdO/4HMCkLdbXGz6+b9m/QdWDpB0Gw9Rikjc/nM8Z9DcUMxKVUpOp87wtuesvoWzUKgIEi+2kVypjZAZrVk/6AtUzuh00+7u6hdngZ1BRBy5YDEpyui3aIxU5hxuPhw7wNGzofYeyD/ENQW6De4bpgoFbxxYzQNrSqe2nJKZw+GoijyY0Ihc974g3f3ZHN1lCe/PzaLe2cGYW6i/x4CTtZm/H1e6DkvzkuhpKGEO369gxOlJ3hp2kusilzVM2Pu5CYp6zr2Hq3zWZpYMtJpJIk1GeAZJTWLlBl0pJfWU17f0rPBdjf2F+ynrLGM9cnrWfbjMuKK4vQUYe/YWZjy+V8mEjPCkb99k8Cmo3kGjUdGZtiSd0h69e9b1N6es51gh2BCHEN0cklRFNl6spgpQc44D1Jf6tsnj+DuGYFsiDvDuv2nDR2OTAczfWbibePNl6lfGjoUmQ4Gp0ooM+ipa63jYNFBrvS/UnOJUUMFVGQYdRm2NkyVCoLdbKVMbSDMYwIA6cVHDBnWkKWssYzCs4WMce3qp93cpuJIbhVTu2dpt5yF9G0QcY1RZ0bKyPTGLJ9ZKAUlu8/o3oKks8w8WasFyXioyIRmLVndw4CM6gwArQ9lifk1mCkVhHvadj2RuVN6NVI/7U7MleZEu0Wf99XujdHLpNfkH/QTlAaC3Wz5x/xQdqWWsvnE5WdsJhfVcuOHcTz8dQKutuZsvm8ya24ag3v3DVM9c+tEP0Ldbfm/X1JoblNd1HszqzNZsW0FRWeLeG/ueywOWtxzkCjCkXXgEaWx2deFRLtGk1SRRHvATKn8vOXsRcUlY3j2Z0hVbtpE7RNlJ3CzdOOT+Z+gVCi5e+fd/OvAv6hprtFHmL1ibW7C+pWxTA9x5Z+bk9gQl2uwWGRkhi15B6WGwfY+vZ4uPFtIQnkCCwMW6uySyUV15FY2sihycFmPdOeJBWEsivLkpW1pA26jJtM/lAolN4fezPHS46RXpRs6HBlkUVvGQOzN3ytZj4zQZj3SubM7OP20Own3PC9qO3iNx6O9nbRSObNxIOjLT/tYbjWt7eqeD2Xp26GtESJv0FeIMjI6w8HCgQnuE9iVt0vnc4/qFLW1WZB4jwVEKJL9cjOqM3C1dMXJou9GSADx+TWM8rLrmdGbtVOy2urjwc+YiPWIJa0qjdqWPn4+nIMk8TN5i34D64M7pwYQO8KJ535Opqjm0qwRqhpa+df3SSx+5wDZ5Q28siySHx6Yynh/zd9vfWGiVPDMklEUVDexbl//s7qOlhzlju13oBbVfLrgUyZ79ZFNl7sfylNh4j1SlUY/iHaNpqm9iSz3kVJT2TN/9jsuGeNgX2Y5IW42eNpb9jlGFEVOlJ5gnPs4Yjxi2LxkM6sjV7Pt9DaW/LCEX07/YrDyeUszJetuH8/ccHf+82Mya/dlGyQOGZlhiShK/bE0JKhtz9kOwIKABTq77NakYpQKgfmD0HrkQhQKgTduiCZmhCOPbkrkSE6VoUOSAa4NuRYLpQUb0zYaOhQZZFFbxkD8lvsbntaeRLpEah6YFwdKc/Aaq3mckTPK046y+hYqz7aA+yjCWlpJq5XLiAaC+LJ4zJXmhDmFdTm+P6scU6VAbEA38SHpW7DzkSxuZGQGIXP855BTm8PpGt1+pthZmDLC2Up7prZXR7NI2YJEahLppNl6pF2lJqmgtqefdstZ6cEvxLiztDuJ9YhFRORYybG+B41eJvmtV+fqLa6+UCoEXrshinaVyD83n7woga1dpWZDXC5XvL6Xr4/mc8eUEex5bBY3x/qh1OAxbAimBLmwKNKTd/dm9Uu8/zXnV+7ZeQ+uVq58ufDLHvfOLhz+ULLqGn1dv+PpbBaZaCqAiQWc3tvv98oYnuY2FUdyqpge4qpxXFFDEaWNpYxzl+4H5kpzHhr3EJsWb8LH1ocn9z/Jfbvvo/CsYbytzU2UvL9iHIsipYzHt3dnyh61MjL6oOo0NJRptR6Jco3C19ZXJ5fstB6ZGuyCo7WZTuY0JBamStbdPgEfJ0tWbzhGVplc8WRo7M3tuTroarae3mrQaiQZCVnUltE7ndYj8/znae9unBcnlbabDE4vrE7CPKSMx7SSerB0JFSwIKethuZ23TetGu4klCUw2mU0pt2sRP7MqmCsnyPW5ibnDzZWSU0iRy8btJ7tMjKzfSUv+N15A2FBYs+pIi2Z2lZO4Bgw7JtFtqnbyK7J1uqnnVl2lqY2VU9RO2eflMkabNx+2p1EukRiaWLJ4ZI+fLUBIq6VXpO/109QWvB3tuapReHsz6zgy8P989eNy67k6ncO8J8fk4nwsmP7w9N5ZnEE9pbGa1f15EJJmH5pW6rGcZ8lf8bj+x4n0iWSDVdtwNNGQ5l2TZ5k1TX+DjDtO2O3O9423jhbOJNYmSI1CZObRQ4qjuZW0dKuZvpILdYjHdWH49zGdTk+0nEkn1/1OU/EPsGJ0hNc++O1bEjegEp9cfY4usBUqeCtm8ewbJw3a3ZmMO/Nfdy5/ghPfZ/E/37PZMuJAuKyKzlT2UBLu/7jk5EZkuR1eOv79V51nV2TTUZ1hk6tR04V1pFX1cjVg9x65EIcrMz47M5YTJUCK9cfoby+xdAhDXtuCbuFFlULmzM3GzqUYY+J9iEyMrql39YjrQ1QnAhTHtJPYANIp29qanEdU4NdCLfxRq0uJqsmS3ujTJl+09TeRFpVGitHr+xyvKqhleSiOh6d201sSvkB1O2y9YjMoMbd2p0o1yh25e1iddRqnc4d4W3H1qRiahvbsLfSIOJ5j4M8DeLmMCC3Npc2dVu/m0T2ELWzdoKZjST8DQJMlaaMdRur2VfbcQR4T4BTW2Da3/QWmyZWTPTjt1MlvLQtlRkhrvg5W/U6rrCmiZe2pbL1ZDHeDpZ8sGIc8yM8tG/GGwE+jlbcNzOYN3dlsGJSJZMCnbucV4tqXjv6Gl+kfsGV/lfy8vSXMVdqSR44+rH0OuGui4pFEASiXaNJLE+EoHmw8z9QVwx2Q0dsGMrsz6zATKlgYvcqt24cLz2Oraltr/0ElAolt4bfymzf2bxw+AVeO/Ya23K28dyU5wh1Ch2o0HvFRKng9eujGeluy7Hcaoprm0gsqKWqobXHWBcbc7wcLPC0t8DLwRIve0s8Hc5/7WprbnSVGjIyRseZOLB0BJfe10bbcrahEBTMHzFfZ5f8JakIE4XAvAh3nc1pDPg6WfHJyhhu+vAQf/nsKF/fPQkrM1nOMxQjHUcS6xHLpvRN3BFxByYK+XthKOT/eRm9syN3R/+sRwqOSYLjIPfTBnC2McfN1pyUDl/tUOcIKC8mtSJZFrV1yKmKU7SL7T38tP/MqkAUYVp3P+2k78AlFDy0/CzKyBg5c/3msub4GorOFuFl46WzeUd72QOQXFzLlCANmXre4+HUZqgvBduh9RDRXzqbRGoVtfNqcLAyxf9CMVUUIXMXBMwEk8FTKhvrEcv/O/H/qGiqwMWyj5+P0cvgt6egMlvy2TYwgiDw6vVRLHhzH499l8jXqyehuECYam5TsXbfad7bm4Uowt/mjuSemYFYmCo1zGp83DMzkG+O5fPsT8n88uA0TJRSNVKLqoUn9z/JzjM7WRG+gsdjHkchaKlUamuCExsgdCE4+F10LFGuUfye/ztV48fjBJIFyZhbLnoeGf2zL6OcCSMctQonJ8pOMMZtjMafJU8bT/43+3/8lvsbLx95mZt+uYk7Iu7gvuj7sDDRX5NVhULg3plBMPP8saZWFcW1TRTXNlNU00RRTTPFtU0U1TaTXd7AgcwKGlq7Zm+bKATc7Sw6hG9J8PZ2sJS+7hDCHa1MB8VGmIzMgJEXJ23W91IRK4oi23O2E+MR0/ca4iLptB6ZFuKCg9XgWU/1lygfB/63fCyrNxzjwY3xfHjb+HP3dxn9szxsOY/sfYS9+XuZ6z847AOHIrKoLaNXapprOFh0kFvCbumH9cghQACfGL3ENtCEe9qRVlwPgLdnDLYlO0gvPgphNxk4sqFDZ5PITg/PTv7MqsDWwoRIb/vzB2sLpIZVV/y73w2vZGSMlTl+c1hzfA2783Zz26jbdDZvxLlmkXWaRe0LfbVDr9LZ9QcTGdUZmChMCLAP0DgusaCGaB+HrvfAigyozYPpxpHN3F9iPWIBOFZyrO8GT6OukUTtU1tg5uN6jK5vvB0s+c/iUTz+3UnWH8zlL9MCEEWR35JLeWFrCgXVTSyK9OTJhWH4OPaeyW3sWJgqefrqcO794gRfHcnjtskjqG2p5aHfH+JE2Qkem/AYd0Tc0b/JTm2GpiqpQeQl0HlPPkkrs6xc4PQeWdQeBJTVNZNWUs8/F2jwWQeqmqvIqc1hSdASrXMKgsCCgAVM9prMG8fe4JNTn7DzzE6emfwMEz0n6ir0i8bSTEmgqw2Brja9nhdFkbrmdknovlD0rpFE8IT8Gn491UyrSt3lfRamivMZ3vaWeDpY4tWZ+d0hhnexxZORGUrUl0JVNoxf2evpUxWnyK/PZ3Wk7qoMEwtqKahu4pHu1blDiDnh7jy/dDT//uEUz/yUzAvXjJY3zwzETN+ZeFl7sTFtoyxqGxD5LiqjVz4+9TEqUcWykGXaB+cdBPcIsHTQPnYQEOZpS1x2JW0qNaYeEYS2tpJWmWLosIYU8WXxBNoHYm9+XrwWRZH9mRVMCXLuupN9qsP/KrL/Da9kZIwVPzs/QhxDdC5qO9uY42lvQbI2X23PKBCUUDh8Re306nSC7IMwVfRt09LQ0k5GaT3zIzy6nsjcKb0GD64FcbhzODamNhwpOdK3qG3vLWVpJRuPqA1w/Xgffj1Vwn9/TcPX0ZINcWc4kFVBqLstG1dP1LyJM0iYH+HB1GBnXt+RwYQggScOPkR+fT6vzXit7+9Xd0RRahDpGg4jpl9SHBEuEZgIJiRWJjErcKaUqS2K8oaykXMgqwKA6d2r3LoRXxoPwHj38f2e297cnuenPs+iwEU8F/ccq3as4prga3hswmNd1nDGgiAI2FuaYm9peq5PTnfUapGKhhaKOwTvwppmims6sr9rm9iXWU5ZfQvd+1PaWZh0iNznM7w7BW8ve+lrORNTZlByzk+7d1u1bTnbMFWYMsd/js4uufVkEaZKgStHDe2qwRWT/CmobuKDP7Ily7FZhq+EG46YKEy4Kewm3jz+JulV6Xq31JKRkEVtGb1R0lDCxtSNLA5cTJCDlg9eVTvkH4Uxy/UTnB4Y5WlHq0pNdvlZwlxCCWtrZ3NjESq1CqVicJU1GyNqUU1CeQLz/Lt6tedWNlJY08S93W/2Sd9KXq9OgXqMUkZm4JjrN5cPEj/QbAVxCUR42XGqqE7zIDNrcAsf1s0iM6sytWYaniyoRS3CGL/uftq7JCukS7B2MCQmChPGu4/nSMkRzQMjlsH2x6EsVfo5MQIEQeDlZZHM+3/7uPvz49hZmPDckghuneg3ZAQkQRB4ZnEECz/YxG2//h9mJio+vPJDYjwuogIu/zCUnIRFay5ZhLY0sWSk00jJVzvwCmlTuSwV3Edd0nwy+mF/ZgVO1maM8uxdxO3kRNkJzBRmRDhHXPQ1JnpOZMuSLXyQ+AGfJn/KvoJ9PBH7BAtGLBh0mYcKhYCbrQVuthZEd++Z0EGbSk1pXXOXTO8Ls7/j86qpbmzr8p7YACc23T1p0P1/yMiQdwhMLMEzuscplVrFb7m/Mc17GnZmmj9j+kun9ciMEFejbuasK/4xP5TCmiZe/TUNLwcLlo7xNnRIw5LrQq7j/YT3+SrtK56d8qyhwxmWDI1Vu8yg4IPEDxARuX/M/doHlyZBWwP4TRr4wPREeMdDQVpxPZhaEGbqSJOoIq8+z8CRDQ1O15ymvrWeMW5juhw/kFkOwLTgC0S+8nQoSYLI6/UZoozMgDLHbw4iInvz9+p03ggve7LLz9LY2q55oNdYyX6kexraMKC6uZqypjKtGRqdTSKjfS4QPFobJCukkCsHMsQBI9YjljN1ZyhpKOl70KilICgkCxIjws3OgneXj+OemYHseWwWd0wZMWQE7U4qVaewHbGWplaR/0x49+IEbYAja8HcHqIuzyot2jVa6nsRMEM6cHrPZc0nM7B0VrlNC3bp4jnfGydKTxDpGomZ8tL8ay1MLHhk/CNsunoTXtZe/GPfP3hg9wMUny2+pPmMGVOlAh9HK2IDnFg6xpv7ZgXx/NLRfHRHDNsenk78f+aR+vwCdv99Jl/8ZSJ3zwjkSE4Vf2SUGzp0GZmLJ+8g+EzotVfI8dLjlDeVszBgoc4uF59fQ1FtM4uihkcjYoVC4PUbopgY4MTj357k0OlKQ4c0LLE3t2dR4CK2nt5KbYuWylaZAWFordxljJbTtaf5Put7bgq9qX9NzM5oLlcajAS6WGOmVJDa0SwyrCNbPb0q3ZBhDRkSyiU/7e5NIg9kVeDtYMmIC5uyJX0nCSwR1+ozRBmZAWWk40h8bHzYlbdLp/OO9rZHFCG1oydAn3iPh6ZqqM7R6fUHA5nVmQCEOIZoHJeYX4O/sxVO1hc84OXsB1XroLMe6STWU/LVPlpytO9Btu7gP1WyIDGyTY+pwS48eVU4zjbmhg5F5/yc/TP377off3tvzMoe5pPfmxEv5v+/rhhSfoSxK8C8d6/h/hLtGk1TexNZYhM4h0C2LGobM2kl9VScbdFqPdLY1khqVSrj3MZd9jVDnUL5YuEX/CPmHxwrPcbSH5fyRcoXqNQq7W8eQliaKQlytWFaiAuPzQvFw86CtftOGzosGZmLo7lOSiDSYD1iaWLJTN+ZvZ6/FLaeLMZMqWDuELceuRBzEyVrb5uAn7MVd284RmaplrW6zIBwS9gtNKua2ZJpXMkbwwVZ1JbRC/+L/x8WSgtWR/WzEURenFSGbT90ymhMlApC3G1I6RC1Az3GYSKKpJafNHBkQ4P4snicLJzwsz1fvt+uUnMwu5LpIS7nyzZFUbIeCZgBth59zCYjM/gQBIG5/nM5XHyY+lbdLWrPNYvU5qvt3SFqFJ7Q2bUHC+nV0ubkSEfNjYkS8msY070sPWsnmFqB/5SBCm9AGek4Entze+0WJKOXQWWW9JArM6CIoshHSR/x1IGnGOc+js8XbuCfcydyJLeKn09eRPbr8fWgVkHMXy47ps5mkYlliRA4S6pOaG+57HllBob9HVVu00NcNY5LLE9EJaouyk9bE0qFkttG3cYPS39gvPt4Xj36Krdtv23YJoCYmSi4a9oIDmZXklQgZwDKDCIKjoKoBv+eonabqo2dZ3Yy2282liaWOrmcWi2yLamYGSNdsbMY+tYjF2JvZcr6lTGYmypZuf4oZXXNhg5p2BHqFMoE9wl8nfb1sNuINQYuWdQWBCFUEISEC/7UCYLwiCAIToIg7BQEIbPj1bFjvCAIwtuCIGQJgnBSEIRxF8x1R8f4TEEQ7rjg+HhBEJI63vO2IJuJDUqSK5LZeWYnKyNW4mThpP0NoiiJ2n6D8wFfE+GedueyHU3dIwlpbSO9o8GOzOWRUJZAtGt0F8/Bk4W11De3M/VC65HCE1ImaeQNBohSRmZgmeM3h3Z1O/sK9ulsTk97C5yszUgu1OKr7TYKTCyGpaidUZ2Bk4WTRi/zktpmSuqau1qPiKLUJDJgBpgMzkxhhaAgxj2GI8VaRO3wpVIz0WQ5i2UgUalVvHj4Rd468RZXBVzF+3Pfx9bMlhsm+BLpbc9LW1O1WwkBtLfCsfWSLY7z5Teg8rbxxtnCWfLVDroC2hol0UPGKNmfWcFIdxs87C00jjtRdgKFoDi3aaErvGy8eG/Oe7w6/VUKzxZy8y838/aJt2lRDb+NkFti/bA1N+HDfdmGDkVGpv/kxUn3fJ+elld/Fv1JXWudjq1HqimubebqYWI90h0LVApiAAAgAElEQVRfJys+uSOG6sZW7vrsKA0t/bjPy+iUW8NvpaihiL0Few0dyrDjkkVtURTTRVEcI4riGGA80Ah8DzwB7BZFMQTY3fF3gKuAkI4/dwPvAwiC4AQ8A0wEYoFnOoXwjjGrL3hfP1u1yxgTb514C0dzR26PuL1/b6g6DQ3lQ8pPu5NwTzsqzrZQXt8C7qMIbW0lrU4uKbxcKpoqyKvP62E98mdmBYJAV1E76VtQmkP4Yj1HKSMz8ES5RuFq6cruvN06m1MQhI5mkVqyxJSm4BEl+WoPMzKqMwh17J+fdpcmkZXZUHNm0FqPdBLjEUNRQxEF9QV9D7J2hsCZkq+2kVmQDBWa2pv4296/sSl9E3eOvpNXpr9yzudYqRB4dskoSuqaeW9PP8SxlB+hoQxi79FJbIIgEO0aLYnaI6ZJYodsQWKUNLepOJxTpTVLGyQ/7VDHUGzMLs+epjcEQWBh4EJ+XPojCwMXsi5pHdf9dJ1mq6MhiK2FKcsn+bEtqZi8ykZDhyMj0z/OxIFHJJjb9ji1LWcb9ub2TPbUnc3oz4nFmJkomBPuprM5BxuRPva8u3wcKUV1PLDxBO0qtaFDGlbM8p2Fh7UHG1M3GjqUYYeu7EfmANmiKJ4BlgKfdRz/DLim4+ulwAZR4hDgIAiCJzAf2CmKYpUoitXATmBBxzk7URQPiZIB4IYL5pIZJBwqPkRccRyro1ZjbWrdvzedOSi9DtJSbE2Ee0g39rSSOnAYQZhKoLK9kfJGuQHM5ZBYlgj09NPen1VBhJfdef9atUrKEgy5Eizs9R2mjMyAoxAUzPabzYHCAzS36678MMLLnozSelrbtSyQvcdDUQKohk+GSLu6neya7H5Zj5gqBUZ1NA0GJOsRGLRNIjuZ6DkRoB8WJNdJIv4w3PgYaKqbq1m1YxV78/fyZOyTPDr+URRC12X+eH8nlo31Zu2+05ypbNA84ZEPwSkIgmbrLMZot2jy6vOoQiU1D5ObRRolR3KqaG1Xa/XTblO1cbL8JOPcL99PWxMOFg68OO1F1l65FpVaxV2/3cUzB58ZVg257poagFIh8PEBORFGZhDQ3gqFx3p9lm9sa2Rv/l6u9L8SU6VubEI6rUeuCHXFdphZj3TnijA3Xrgmkr3p5Tz946mL66Mhc1mYKEy4OfRmjpQcOddrR0Y/6ErUvhn4quNrd1EUOw37SoBOp35vIP+C9xR0HNN0vKCX4z0QBOFuQRCOCYJwrLxcFgeNBVEUeev4W3hae3Jj6I39f2PeIbB0AhfNAsFgJLxDzEgtrgOFgjBrqWlmWlWaIcMa9CSUJ2CqMGWU86hzxxpa2onPq+6apZ27H86WytYjMkOaOX5zaGpv4mDRQZ3NOdrbjjaVSIa2BjTe46C9CcqHz2daXl0eLaoWRjppE7WrCfe0w8JUef5g5k6paZ7jiIENcoAJtA/E2cJZu6gdtggUplK2tozOyK/PP+c7vGbWGpaHL+9z7D+vCsNUKfDC1tS+Jyw8IVmDxN4NCt213+m0qDhZfhICr4CieKm5rIxRsT+zHDOlgokBzhrHpVSl0Kxq1kmTyP4w2WsyW5Zu4c7Rd/Jj1o8s/WEpv+b+OixEG3c7C64Z482mY/lUNbQaOhwZGc0UJ0B7c69NIvfm76WpvUmn1iPHzlRTVt/Coigvnc05mFk+0Y8HrgjiqyP5vLdXti3SJ9eFXIe50pyNaXK2tj657JWqIAhmwBLg2+7nOjKsB3ylIYriWlEUJ4iiOMHVVXupnIx+2J23m1OVp7h/zP2YKy/CKzTvoHQTHIIW6o7WZnjYWZzz1R7pMhqAdFnUviziy+KJcI44V2YNUqZRm0pkevAFnwlJ34KZLYycb4AoZWT0wwSPCdiZ2enUgiTCS6ps0N4ssqNZ2DDKxM2ozgA0N4lUqUWSCmq7NolsbYTcA4M+Sxskm4BYj1iOFh/VLDBZOkLwHEj+AdRyWawuSK5IZsW2FdS01LBu3jrm+mu2snG3s+Cvs0PYmVLKHxl9JIIcWQem1jDmFp3GGuEcgYlgIlmQBM6Smojl6M7/X0Y37M+sICbAEUszpcZxJ0qlz/mBztS+EEsTSx4d/yhfLfoKd2t3Hv/jcR78/UFKGkr0FoOhuHtGIM1taj6PO2PoUGRkNNNZdd2Llej2nO24WbnpdDNs68kizE0UzAkbvtYj3XlsXihLx3jx2m/pfB+vwRpORqc4WDiwKHARv2T/MqyqiQyNLtIvrgJOiKJY2vH30g7rEDpeyzqOFwK+F7zPp+OYpuM+vRyXGQS0q9t5O/5tguyDWBx4Ed7F9aWSp/YQ9NPuJNzTVsrUBmw9ovFpayO1LMHAUQ1eWlQtpFSm9LQeyazA3ETBhBEdFv1tzZDys+SlbaqbTtsyMsaIqcKUWb6z2JO/hzZ1m07m9HeywsbchOQiLc0inQIla5/C4zq57mAgozoDE8GEQPvAPsdklZ2loVXVVdTOPQCqFknkHQLEeMZQ1lRGbl2u5oERy6CuQG4SqAP2F+znzt/uxNLEkg1XbehxH+yLu6aNYISzFc/9f/bOOzyKcn3D92xJL6QXSIOEVAg19B49gAIK2LCi/tRjPWLDgh4rVtTjURALeizYKIICSiehQxJCQkIqpPdeN5ud3x+TAFFI3WQ3ydzXxRUyO/N9D5rM7vfO+z3P1vi/WwpVF0HcBqmgrWebLjOVGf72/lJRe9AY6SFz2j69ziHTNQoq6kjMq2yfn3ZBFF42Xq0G5HYXgQ6BfDf3O54c8yTH8o6xYPMCvkv4jkZdY49r6Sn8XKyZFeDM14fPUdfQd/+dMn2AjCPg4AtWLYvM5fXlROZEMtt7NkpF6w/N2kujTmRbXB4zA5yxNFXpZcy+gCAIvL14OOMH2/P0L7EcSi0ytKR+w5KAJdQ11rEpeZOhpfQb9FHUvoWL1iMAW4A7m/5+J/DrJcfvECTGA+VNNiV/AFcLgmDXFBB5NfBH02sVgiCMFwRBAO64ZCwZI2dr6lbSy9N5ZNQjHXvTyjgsfe2DftrNBLjZkFpYJS0knQMJ1DRwtriVbcAyrRJfFE+DroERziNaHI9MKWSst/3Frf4pO6G+HIYtNoBKGZmeJdwznEpNpd4CtRQKgSB3G+Ky2+g6EARwHynZF/QTkkqT8Lb1brFT5K/EZEoWC6GXFrVTdoHKHLwmd7fEHmGcq+Sr3ebPnP8cKaw3XrYg6QqbkjfxyJ5H8Lbx5tu537b6UOWvmKqUvDgviLTCav53+FzLF09+JT1sCbtPn3IvMNxpOHFFcWgFQQqMlMMijYqIZKnw0Zaftk7UEV0Q3WPWI5dDpVBxZ/CdbJy/kRHOI3jz2JvcseOOPu1let/UwZRUa/j5pNx5KWOk6HTSev4y1iO7zu9Cq9Pq1Xrk+LkSCivruWa4m97G7CuYqpR8evsYvB0suf+bk5zNa8NCUEYv+Nv7M9plND+c/aFPP2g1JrpU1BYEwRK4Crh0ZfImcJUgCMlAeNP3ANuANCAF+Ax4EEAUxRLgVeB4059Xmo7RdM7nTdekAtu7olemZ6hvrOeTU58w3HE4Mz06GDCUcURa5LsO7x5xRkCgm+RNm1JQBc7B+Gs0ZNQVUt3QRmiTzGWJLogGaFHULqioIym/ismXLspO/wyWTuAzraclysj0OBPcJ2CuMmf3eX1akNiQkFtJo64NV7GBoyE/Hhpq9Ta3MXO29Gy7QiJtzFT4OFwSmJyyE3ymgNqsmxX2DB7WHrhYuHA092jrJ5rZSJYr8Zuk8F6ZDiGKIqtjVvPioRcZ7zaedbPXdapTdmaACzP8nfhgVzIFlU2hso1aOPGlZA3i5K9X3c2EOoVSq62VCo9DZkBpOpSe65a5ZDpORHIhjlYmBLratHpeWlka5fXlPWo9ciUGWQ9iTfga3pj8BpkVmdy49UY+iv6I+sZ6Q0vTO2E+9oR6DODziLS234tlZAxBYSLUlV22qL09fTue1p4tMpC6yu+xuZipFcyUrUcui625mq/uDsNcrWTpumPkV+gvRF7myiwJWEJ2VTb7s/YbWkq/oEtFbVEUq0VRdBBFsfySY8WiKM4SRdFPFMXw5gK1KPGQKIpDRFEcJoriiUuu+VIURd+mP+suOX5CFMWQpmseFvtDEkgf4MfEH8mrzuOxUY8hdNQXO+OQtCVVdeWOt95OkJs1AIl5FWDlRIBgjshFX1aZjhFTGIO3jTf2ZvYXjkWmSJ1Gk5tDIusqIOkPCL4elPLWNJm+j5nKjMkDJ7Mncw86UT/exSHuttQ2NJJeVNX6ie6jQGyEvNN6mdeYKa8vJ686D3/71guAMZnlhHoMQKFoek8sTpWstnx7v592M4IgMM5tHCfyT7T9MxeyUArtPa+/MNP+gFan5eXDL/PJqU+YP2Q+H836CEu1ZdsXXoEV1wZRr23knR1npQNnf4eK7G7r0oaLYZGSr/YM6aDcrW0U6HQikSlFTPJ1vHivugJRBdJunNHOo3tCWpsIgsC8IfP49bpfmeMzh7Wxa1m8ZTEn8k60fXEvQhAEHpg6mPPFNfwRbzw+4pHZkdy14y5yq3INLUXG0FzYdd2yqF1QU8CxvGPMHTy34/WBK9CoE9kel8usABcsTOT13ZUYOMCcL+8aS1ltA0vXHaeqXmtoSX2emZ4zcbFw4fsEOTCyJ9BfpLmMDFClqeKz058x0X0iYW5hHbu4vlIqglzmyW5fwtvBElOV4oKvdsAAXwAS5bDIDiOKIqcKTl1YJDcTmVyEvaUJQW5NnUaJv0sp3MNuMIBKGRnDEO4ZTlFtEbGFsXoZL2Sg5K8bl92Gr3ZzWGQ/8NVu3ubeWqd2jUbL2bwKRv7VegTAr/VQv97GWNexlNSVkFKW0vqJQ2eD2kK2IOkAVZoqHt3zKBuSN3Df8Pt4bdJrqBXqLo052MmKuyf78PPJLGIyy+DoWhjgKf3/6SYGWg3EwcxBKmo7+oG1O6TJRW1jICGvgqIqTbv8tE/mn8TJ3IlB1oPaPLcnsTOz440pb/Bp+Kc06BpY+sdS/n3o31Ro2njf6kVcHeyKt4MFn+5PbT2Yt4coqCng2YhnOZl/kmX7lvXJDnmZDpBxGKxcwc6nxeE/zv2BiMgcnzl6m+poejFFVRrZeqQdhAy05ZNbR3E2v5IHv4uioVEO6+5OVAoVNwfczNG8o6SUtvGZWKbLyEVtGb3y9ZmvKasv49FRj3b84sxjIOr+9mS3r6FSKhjqYk1CruRr5ew8HLtGneyr3QnOVZyjtL60RTiWKEqdRhOHOFzsNDr9s7RQHzTWQEplZHqeqYOmolao2XV+l17GG+IkPZBr01fbxg2s3fqFr/bZUqnDtbWi9umscnTiX/y0k3dKoZr27fdB7g2EuUoPs9v01TaxhKH/gDNbJMsLmVaJKYhh8dbFHMw5yIrxK3hk5CN663R7ZKYfTtamfLnxNzgfCWPvBT0FeF0OQRAIdQqVitqCIFmQpB+QrWiMgPb6aYPUqT3SeaTefg71zcSBE9k4fyN3Bd/FppRNLNi8gD/P/WkUReCuolQI3DtlMKeyyjmaXtL2Bd2ITtTxQuQL1GnrWDZ6GXHFcaw8utKgmmQMzPnD4Dleur9fwvb07QTYB3Qo/6EtfovNxcJEyQx/2XqkPUz3d+b160I4kFTIC5vi+sT90JhZ5LcIE4UJ3yfK3drdjVzUltEbxbXFfB3/Nf/w/gfBDsEdHyDjCAiKflF4DHSzJiG3AlEUEVyC8dfUk6Cnbsr+RExBDECLonZyQRUFlfUXF2VVBZC2T+rSNtLFl4xMd2BlYsU4t3Hsytillw+uKqWCADcb4nPa0fE2cDTk9P2idnJpMgNMB+BkfuXOxlNZZQCMaC5qN9TCucg+ZT3SjLuVO4OsBrXtqw0QvBBqiuDcge4X1kvR6rR8HPMxd+6Q8te/nv01N/rfqNc5rExVPDsngPGFv9CoMIWRt+t1/MsR6hxKZmUmJXUlkgVJbSnknur2eWVaJzK5CH8Xa1xsWvf5z6nKIa86zyj8tFvDQm3BE2Oe4PtrvsfJ3Ikn9j/BY3sfI78639DSuszi0YNwsDRh7YE0g+r49sy3HM49zFNjn2JpyFLuHXYvG5I3sCFpg0F1yRiIskyoyAKviS0OZ1ZkcrrotF67tLWNOnbE5TEr0AVzk+57ENvXuDnMk0dm+vLjiUz+u0fuIO5O7MzsmDt4Lr+l/UZ5fRsNQTJdQi5qy+iNz05/hqZRw8MjHu7cABmHwXUYmFrrV5gREuhmQ3G1hsKqenAJIrC+gZSK8zToGgwtrVcRUxiDrakt3rbeF441dxpNavbTjt8s+fvK1iMy/ZBwz3Cyq7L15tkf7G5DfE5520Vy95FQnAK1ZXqZ11hJKk3C386/1W7FmMwyPOzNcbAylQ6cPwjaWikssQ/S7KvdZuK731VgYg1xsgXJ5cioyODO7Xey5tQarh18Lb/M+6VFILI+uc7fkkWqQ2wVJ1Oh6P7PYM2WYbGFsVIoJcgWJAamVtPIsXMl7erSPpkvWUuNdjEOP+22CHYI5vtrvmfZ6GUczjnMgl8X8EPiD3rLmzAEZmold070Zk9iAUn5lQbRcLbkLB9EfcB0j+ncMFT6jP3wiIeZ4DaB14++TlxRnEF0yRiQZj/tv1iJbkvfBsAcb/0VtY+klVBSreGaYbL1SEdZdtVQFo4cyHs7k9hwMsvQcvo0SwKWUKutZXPKZkNL6dPIRW0ZvZBdlc2PZ3/kOt/rWhQY241WA1knwHNi2+f2AQKaUuUTcivBKRB/TQMaUUt6ebqBlfUuoguiCXUKRSFcvJUdTCnCx9GSQXYW0oG4X8A5GJwDDaRSRsZwTPeYjkJQsCtDPxYkIe62VNRpySqtbf3EgU0dfDnRepnXGGnUNZJSloKfnV+r58VklBE66FLrkV2gMgPvyd2s0DCMdR1LpaaSxNI2ciLU5hAwFxK2Sp8BZADJQmtT8iYWb11MekU670x7h9cnv46ViVW3zak49S2m1LO2PpyPdid32zzNBDsEoxJUkgWJlRO4DJPDIg3MsXMlaLQ6pgxt2087uiAaK7UVfgNav/cZEyqFiqUhS9m4YCPDHIfx+tHXefHgi716+/3t470wVysN0q1dp63jmQPPYGtqy8sTX77wYFepUPL21LdxMnfi8X2PS7sxZPoP5w+BqQ24XNyxLYoi29O3M8p5FG5W+itA/346B0sTJdP9275nybREEATeXDSciUMceGZDLAdTigwtqc8S6BDIKOdRrE9c33azh0ynkYvaMnrhk5hPUApKHgh9oHMD5MVKnWue4/UrzEhpDjBMyK0AEwsCzCUvsLMlZw0pq1dRVldGenl6C+sRjVbHkbRiJjd3aZeeg8yjMGyxYUTKyBgYB3MHRjmP0puvdshA6d7Vpq+2e9PvZR8Oi8yszKRWW9uqn3ZBRR055XUXrUcAUnZKBW21eQ+o7Hku+GrntuGrDZIFSV2ZZBElQ3l9OU/sf4IXD71IiGMIG+dvZLZ394U2ApKX9bHPwHMiw0dPZt3Bc6QUVHXrlGYqM/zt/aWiNsDgadJ7taamW+eVuTIRSYWYqBSEedu3eW5UfhQjnEeg7Ebv9e7Cw9qDtVet5f+G/R+/pv7KL8m/GFpSp7GzNOHGMYP4NSabvPK6Hp171clVpJan8tqk17A3a/kzM8BsAO/PeJ+S2hKePvA0Wp2cm9BvyDgCHmEtchmSSpNILU/Vq/VIQ5P1SHiQC2bq3ncfMgZMVArW3D6aIU5WPPDNSRLz+k6YrrGxJHAJ2VXZHMiS7fa6C7moLdNlkkuT2Zq6lSUBS3C1dO3cIOcPSV89+3ZIZDO2Fmrcbc2kojbg7RCEmQiJJW10tslcIKZQ8tMe4XRxO3Z0Rik1msaL1iNxTZ5+IYt6Wp6MjNEQ7hVOSlkK5yvOd3msoS7WKBUCcTltFLXN7cB+SJ/u1G62dBlqf+WidkymZL8y0rOpqF2SLtmy9EE/7WacLJzwsfXhWN6xtk8eMhPMbCFetiA5mnuUhVsWsjdzL8tGL+Pzqz/v/GeqjpD8J5Sdh3H38eQ//DE3UfLy1vhu72ANdQolrihOKngNmQGNGsg41K1zylyZiOQiwrzt2/SmLasrI7U8tddYj1wOQRB4aMRDTHKfxMqjK4kvije0pE5z75TBNOpE1h3suZ2eB7IOsD5xPbcF3sakgZMue06QQxArJqzgaO5R/hP9nx7TJmNAakqgMOFva/nt6dtRCkqu9r5ab1MdTi2mtKZBth7pIjZmatYtHYuFqZKl646TW97GLkyZTjHTcybOFs5yYGQ3Ihe1ZbrMR9EfYaW24p5h93R+kIwjYD8YrF30J8zICXSzITFX8sFTuobgp9GQWNx7P1j3NNEF0agUKkIcQy4cO5hShEKACUMcpAOnfwGP8WDnZSCVMjKGZ6bHTAB2Z+zu8lhmaiV+zlbtD4vsw53aSaVJKAQFvgN8r3hOTGYZKoVAsLutdCClqWPeN7wHFBqOMNcwTuafbDsnQmUCAfMg4Tdo6NlOQ2NB06jhvRPvce+f92KhsuC7ud+xNGRpC1utbuXYWrB2h4BrcbQy5fHwoUQkF7EroaBbpw11CqVWW0tyabJkPac0kS1IDER+RR1n8yvb5acdVSAFAI9yNu6QyLZQKpSsnLISR3NHlu1bRlld78x/8LC3YO4wN74/mkFFXffn8hTVFrHi4Ar87Pz41+h/tXrudb7XcePQG1kXt46d53d2uzYZA5NxRPp6SVG72XpkvNv4v3X0d4XfY3OxMlUxtR12STKt4z7AnHV3hVFZp2XpuuNU9sB9pL+hVqi52f9mjuQeIbUs1dBy+iRyUVumS8QUxLA3cy9LQ5Zia2rbuUF0OilYop/4aTcT4GZNamEV9dpGcA7Cv76exOLEXu3v15PEFMQQZB+EmcrswrGIlCJCPQZga66G/HgoOCNbj8j0e9ys3Ah2CGb3+a4XtQFCBtoSl92OsMiBo6AyFypy9DKvsZFUmoS3jTemStMrnhOTWUaAm/XF7bEpu8DOGxyG9IxIAxHmGkaNtoYzxWfaPjnketBUXiz49yNSy1K5ddutfBX/FTf538RP834iyCGo5wQUJUPqHhhzNyjVANw+wQs/Zyte/e0MdQ3d5/8Y6iyFRZ4qPAUmFpL9nGxDYxCaA7an+LVdIIrKj8JEYdKioaC3Ymdmx6rpqyisLWR55PJeGxx5/9QhVNZrWX80o1vnEUWRFw++SJWmiremvNXqe18zz4Q9w3DH4bwQ+QJpZT3v/S3Tg2Qclh5ODry4i+NU4SlyqnP0bz0Sn8dVsvWI3ghyt+GTW0eRXFDFg99F0dDYO++FxsyioYswUZiwPnG9oaX0SeSitkynEUWRD6I+wMHMgVsDb+38QMXJUFvSb/y0mwl0s0GrE0nOrwKXYAI1DVRoq8mrzjO0NKOnobGB+OL4C4tigPLaBk5lll300z79CwhKCL7eQCplZIyHcK9wYoti9XJ/CXa3oahKQ0FlfesnNi9ssqO6PKcxklSa1Kqftk4nEptVftFPu6EO0g9I1iNNoVp9lbGuYwHYl7mv7ZN9poG5fb+yIBFFkfWJ67npt5soqCngo5kf8cL4FzBX9bDP+rG1UhFi9F0XDqmVCl6aF0xGSQ1fRHafpYG7pTuO5o6X+GrPgPw4qOreDnGZvxORXIijlSkBrtZtnhtVEEWIYwgmSpMeUNb9hDiGsDxsOQezD/Jp7KeGltMphg2yZeIQB748mI5G233FqB/O/kBEdgTLxixrMyC5GROlCe9Nfw8zlRmP7X2MKk33+vXLGJCMw+A+CtQXm422pW/DRGHCLM9ZepvmYEoR5bWy9Yi+mTrUiZULhxGRXMSzG0/LTXZ6xt7Mnjk+c9iSuoUKjexfrm/korZMpzmYc5CT+Se5P/R+LNQWnR+o2U/bq391agdeGhZp54N/U45KQkmCAVX1Ds6UnKG+sb5FSOSRtGJ0IlJRWxSlovaQGWDZ9nZaGZm+TvOCYk/Gni6PFTJQ2pUT35avtuswUKggp+8VtSs1lWRXZbda1E4trKKqXssIDzvpQMYhaKgBv77rp92MnZkdYa5hfH76c+7acRdHc49eeYGkVEPQfDi7o18EBRbVFvHQ7od44+gbjHEdw4b5G5juMb3nhdRVQMz3UlinVcsO3cl+jswOduW/e1K6zWNTEASGOw6/pKg9Xfoqd2v3KDqdSGRyEVP8HFEoWn/YVtNQQ0JxAqNcerf1yF+5YegNzBs8j9UxqzmYfdDQcjrF/dOGkF9Rz68x2d0yfkppCu+deI9JAyexJGBJh651tXTl3WnvklmZyYqDK+RiWV9EUyNlqFzSoKbVafnj3B9M85iGlYmV3qb6LTYXazMVU4bK6zt9c+MYDx6b5ccvJ7P4cHeyoeX0OZYELqFWW8vm5M2GltLnkIvaMp1CJ+r4MOpDBloNZLFfF+0dMo6ApZPkqd2P8HawxEytIDGvEpQq/Gx9EICzJWcNLc3oiSmQQiIvLWpHJhdhYaJkpKcdZB6D8gwYdoOhJMrIGBU+tj4Mth2sF1/tQDcbBAHistvoNFCbg3MQZJ3o8pzGRkpZCgD+9v5XPCe6KSRyhEeTNVfyLlCagvfkbtdnDHw862OWhy0nsyKTe/+8l7t23MXhnMOXL2gEL4SGakj+o+eF9iAHsg6waMsijuUd49mwZ1k9azWO5gZamJ/6ATRVEHbfZV9+/ppAdKLIym3dF2Ad6hxKZmUmxbXF4BYqBczKRe0e5UxuBcXVmnb5accWxaIVtb3eT/uvCILAigkr8LXz5ZmIZ8ip6n2WWVP9HAlwteaziDR0Ov0WjTWNGp6JeAZLtSWvTXoNoRM7jca6juXx0Y+zK2MXX8Z9qVd9MkZA9knQaVs0qB3LO0ZJXYlerUc0Wh1/xOdxdZArpirZeqQ7+Fe4H4tGDeKDXcn8dNWf8sEAACAASURBVCLT0HL6FEEOQYx0Hsn6xPU06rrP3q0/Ihe1ZTrFn+f+JLEkkYdHPoy6yYex02QckkIl+vh27L+iVAj4u1hLndqAhUsI3lqRxJLuW0D2FWIKYhhkNahFMSAypYhxPvaYqBRw+mdQmUHANQZUKSNjXMzynMXJ/JOU1pV2aRwrUxU+DpZtd2qDtFviXCSU9C0vzeaHj611ap/KLMPaTMVgx6YOpZSd0oLPxLInJBocM5UZtwbeyrZF23g27FmyqrK4b+d93LH9Dg7lHGpZ3PaeDJbOENc3LUhqtbW8duQ1Htr9EI7mjvxwzQ8sCVzSqeKQXtDpJOuRgaNh0OjLnuJhb8H904aw5VQOx9JLukVGqJNkIRZbGAsKpWRFk7pX2m0l0yM0+2lfsG5rhaj8KAQERjiP6G5ZPY65ypz3p79Po66RJ/Y9gaZRY2hJHUIQBO6bOpik/Cr2JenXwufDqA9JKk3ilYmvdOkh3B1BdzDbezb/if4Ph3MO61GhjMHJOAwI4BF24dC2tG1Yqi2ZMnCK3qaJTCmksk7LtcNl65HuQhAEVi4cxmRfR57beJoDSYWGltSnWBKwhKyqLCKzIw0tpU8hF7VlOkyDroGPoj9iqN1Q5vrM7dpg5dlQltEiKbk/EehmQ0JuhbS4dw4ioK6GxPYEa/VjRFEkuiC6xaIqq7SG9KJqJvs5QaMW4jeB/xwwbdsfUkamvxDuFU6j2Ng+n+M2CB5o23anNsD4ByULksj3uzynMZFUmoS1iTUuFi5XPCcms4zQQQOkLf2l56EoqV9Yj/wVU6UpSwKXsG3hNp4f9zy51bncv/N+bt9+OwezD0rvfwolBF8HyX9CfaWhJeuVxJJEbv7tZn48+yN3Bt3J+mvW42vna1hRaXulPJOw+1s97Z/ThuBua8ZLW+Jp1HP3J0CwQzAqQXXRgmTIDKjMkX5XZHqEiORCAlytcbYxa/PcqIIo/O39sTbpm5+tvGy8eG3ya8QVx/H28bcNLafDzAt1x93WjE/36+8h8qGcQ/zvzP+4yf8mpnlM69JYgiDw8sSXGWw7mKcPPN0rO+JlrsD5Q9LOPHPJbq2+sZ7dGbuZ5TkLM1Xb95b28ltsLjZmKia14yGcTOcxUSn45LZR+Dpb8eB3UZzJkT2g9cUsr1k4mzvzXcJ3hpbSp5CL2jIdZlPyJjIqM3hs1GMohC7+CGU0Pan36r9F7dKaBilwzSUIf42GnJo8yuvb0QHZT8mqzKK4rriF9cjBFKnTaIqfI6Tvg5oiCOmiLY6MTB8j0D4Qd0t3vViQBLvbkF1WS2l1G91s1q4w6g6IWQ9lfWcbY1JpEv52/lfstK3VNJKYV0los/VIyi7pq2//K2o3Y6o05eaAm9m2cBsrxq8gvyafB3Y9wG3bbiMiKwIx6HrQ1kne2n0AnahjXdw6bvn9Fqo0Vay9ai1Pjn3SOAL2jn0m2b4FX9fqaeYmSp6/JoiE3ArWH8vQuwwzlRn+9v4twyJB6taW6XZqNFpOnCttl/VIg66B2MLYPmc98ldmec5iachSfjz7I1tTtxpaTodQKxXcPdmHo+klxDTZX3WF0rpSXoh8gcG2g3lizBN6UAgWags+mPEBWp2Wx/c9Tn1jG4HTMsZPoxayjrdYy0dmRVLVUNX15rdLqNc2sjM+n38Eu0q7cmW6FRszNeuWjsXKVMXSr46RU9Y9+Rr9DbVCzY3+N3I49zBpZX1rF6shke8IMh2iVlvLmlNrGOU8Sj/biTKOgIkVuAzr+li9kOak+TO5FeAcTKCmAZAKJjKXJ7owGqBFp3ZEchHO1qb4OVtJAZGmtv2yI1JGpjUEQWCm50wO5RyiuqG6S2OFuEvF2jO57ejemPSY9PXgh12a01jQiTqSS5NbtR6JzymnUSdeDIlM2QUDPMHRr4dUGi8mShNu9L+R36//nRXjV1BYW8iDux/k1riPOGA/EDFug6Eldpm86jzu+/M+Vp1cxfRB09kwfwMT3I3k4X1JOiTtgNF3gcq0zdPnDnNl/GB73v3zLGU1+rdkCHUKJb44Hq1OC3ZeYOcjdZLLdDtH00vQNOqY4ufU5rmJxYnUamvbDoksPNvr7WMeHfkoY1zG8MrhV3rd5/GbwzyxNlOx9kBql8YRRZGXDr1EaX0pb019C3OVuZ4USh3xb0x+gzPFZ3j9yOtycGRvJ/+0lM9wya7rbenbsDezZ5zbOL1NE5FURGW9lmtk65Eew83WnHVLx1Jd38jSdcepqGswtKQ+weKhi1Er1Hyf+L2hpfQZ5KK2TIf4PuF7CmsLeWzUY/rxgsw4DIPGglLV9bF6IQFuNgCSr7a1K0MVFtL3xQmGlGXURBdEY622xneAtH1bpxM5lFrMZF9HBG0dJGyFoPntWqzLyPQ3wr3CadA1EJEd0aVxgt2le1dcdjt2lQzwgBG3QNT/oDKvS/MaA9mV2dRoa1otajd3yYV62IK2HtL2S13a/Sw7ojUuLW6/NOElSupLechWyS1VMexP/b3XFjr+OPcHi7YsIrYollcmvsKq6asYYDbA0LIucvxzye5lzN3tOl0QBP49P5iK2gZW7dR/gS/UKZRabS3JpcnSgWYf/kZ58dzdRCYXYaJSEOZj3+a5UQVRAIx2ubwHOyA9vPs4DKK/1ZdEg6BSqHhn2jtYm1izbN8yKjW9xxLJylTFbeO92BGXx7mizj+83pC8gb2Ze/nXqH8RYB+gR4USMzxncN/w+9iUsolfkn/R+/gyPUjzTrSmonZ1QzX7s/ZzlddVqBT6W9//fjoXW3O1bD3SwwS62bD6tlGkFlbxz29PotHqDC2p1+Ng7sAcnzlsSd3Sq95fjBm5qC3Tbsrry/ki7gumDpradqdGe6gtg/z4fuunDWBrrmbgAHMScitBEHB0DMJJVHC29KyhpRktMQUxDHcefsH65kxuBSXVGib7OUrdZ5oqGHaDgVXKyBgnI5xGYG9mz+7zXbMgsbM0YeAAc+La67M3+XHQaeHgf7o0rzHQ3LnXWlE7OrOMgQPMcbY2kx7eNlSDb3hPSexVqJVqFg9dzNbrt/Jy4FLKFAIPRy7n5t9vZm/G3l5T3K5uqOaFyBd4cv+TeNl48fO8n7ne73rDhUFeDk01RH8DgfPAxr3dlwW42nD7eC++PXL+Qri1vgh1lsIiW1iQaKog64Re55H5OxHJhYzzscdMrWzz3JP5J/G09mw9KLD5/r7/LelhXi/G0dyRd6e9S1ZlFisOrug19yGApRO9USkUfB7Zua3t6eXpvH38bca5jeP2oNv1rO4iD4Y+yCT3Saw8upLThae7bR6ZbkJbD388D3teA4/xYDsQgD0Ze6hvrNer9UhdQyM7z+QzO9gVtVIuX/U0U/yceHPRcA6mFLN8Y2yvuh8aK0sCl1CrrWVzymZDS+kTyHcFmXazLm4dVZoqHh35qH4GzDwGiP3WT7uZQDcbEpsXiS5BBNTXkViSaFhRRkqFpoLUslRGOF20Hols8tOe5OsoWY9YuYL3ZENJlJExapQKJTM9Z3Ig60CXvSyD3W2Iz2mn/7/9YOlh04kvobqoS/MamqTSJASEVsP+YjLKGOHR1J2bsguUJuAztYcU9k7UCjULxz7O1ioVrwguVNRX8OjeR7npt5vYnbHbqBdRpwpPsXjLYrambeX+4ffz9Zyv8bLxMrSsv3P6Z6grbzMg8nI8ftVQbM3V/HtLvF7/X7hbuuNo7nixqO0zFQSFbEHSzeSV15GUX9UuP22dqCO6ILpFlsnfBzwN6U07UsozpZ05vZxRLqNYNnoZuzN281X8V4aW026cbcy4fuRAfj6RRXFVx97nGxobWB6xHBOlCa9Per3r2UmtoFQoeWvqWzhbOPP4vscpri3utrlk9ExhEnweDof/C2Pugds3XXhpW/o23CzdWthEdpX9SYVU1Wu5NlS2HjEUi0cP4vHwoWyMyub9bti11d8Idggm1CmU9Ynr0Yly93tXkYvaMu2isKaQ7xK+Y+7gufjb++tn0IzDoFDBwFa2MvYDAt2sSSuqpq6hEZyDCKirJa0sFU2j/r0rezunCk4hIv4tJHKoixUu6jpI/hNCFklbq2VkZC5LuGc4NdoajuQc6dI4we62pBdVU12vbd8FU56QggAP/7dL8xqapNIkvGy8rugxWlhZT3ZZ7cWidvIuaUeSqVUPquylCALq4Ou5/lw0W/7xNa9OepWqhir+tfdf3LD1Bnaf321UH/61Oi2rY1Zz5/Y7ERFZ9491PDzyYdQKtaGl/R1RhKNrpQwTz/EdvnyAhQlP/sOfo+kl/H46V2+yBEEg1Cn0YlHbfAC4j5LDIruZiORCgHb5aaeXp1NWX9a69cjhj0FtCYs+A69JcOAd0NToS67BuD3odq7yuooPoj7geN5xQ8tpN/83dTD1Wh1fHz7foes+jvmYM8VneHnCy7hYunSTuovYmtry/vT3Kasv46kDT0ne+jLGiyjCya/g06lQngU3r4drV4GJZJ9ZWlfKkZwjzPaZrdcHIr/H5mJnoWbCYAe9jSnTcR6d5cuNYwbxnz0p/Hhc/+HR/Y1bA28lszKTyOxIQ0vp9chFbZl28Wnsp2h1Wh4a8ZD+Bs04DG6hYGKpvzF7IYFuNjTqRJLzq8AlGP96DVqxkZSyFENLMzqiC6JRCkqGOUrBonUNjRxLL2Gyr5Pkpd2ogWGLDKxSRsa4CXMNw1ptza6MXV0aJ2SgDaJI++0InIZC8HVw7DOoKenS3IbkbOlZ/OyuHPh4qslPe4TnAGnRV5ggB9d2hOCFoNOiPruN63yvY8t1W3h98uvUNdbxr31ScXvn+Z0GL25nVmZy1467+OTUJ8zxmcPP837WjzVbd3H+IBTEw7j7Ou3tfvNYT4LcbHjj9wRqNPorPoU6hZJZmXmxU3PwdMg+KXWVy3QLEclFOFqZXggsb42T+ScBrvzzXZEr7ZQbeRuY28GM56EqH058oU/JBkEQBF6Z+Aqe1p48tf8pCmoKDC2pXfg6WxEe6MI3h89Rq2ls1zXH847zZdyXLPJbxCyvWd0r8BICHQJ5ccKLHM87zodRfSNQuk9SUwI/3gZbHwOPMPjnIQhoaTGy8/xOtKJW79YjuxLymR3ihkq2HjEogiDw+vXDmOLnyHOb4th3tnfcD42VcK9wnMyd+D5BDozsKvKdQaZNMioy2JC0gcVDF+Nh7aGfQRvqpAVLP/bTbibw0rBI50ACNVI40tkS2Vf7r8QUxuBv74+FWuoIOHGulHqtjsl+DtK2avvBUoeXjIzMFVEr1Uz1mMq+zH1d6ooKGWgLtDMsspmpT0l+uUc/7fS8hqSmoYbMysw2QyKVCoEQd1tI3ikd9JWL2u3GLVS6l8dtBKTQtvlD5rN5wWbemPwGmkYNy/YtY9GWRfxx7o8eL26LosiW1C3csPUG0srSeGvKW6ycshJrk7aLgwbl6KdSwbELmRNKhcDLC4LJKa9jzb5UvUkLdZJ8tWMLY6UDQ2aA2CgFRsroHZ1OJDKliKl+ju3yfI8qiMLBzAFPa8/Ln3D8MykzYfwD0vfek2DITIh8H+p7fwiWlYkV709/nxptDU/tf4oGXe8IMb1/2mBKaxr4+WRmm+eW15fzbMSzeNp48vTYp3tAXUvmD5nPTf438VX8V/xx7o8en1+mDdIPwOpJkPQHXPUK3L4ZbP5uBbItfRs+tj742+lpVzew72wBNZpGrh0uW48YA2qlgk9uHcVQF2se+i6K2KwyQ0vqtagVam70v5GDOQdJL083tJxejVzUlmmT/8b8F7VSzf2hHfdgvCK5MVJXrVzUxsveAnO1koS8CjC1ZpCVO5YoSChJMLQ0o6JB10BcUVwL65HIlCLUSoHxTg3SB65hN3S6A01Gpj8R7hlOWX0ZUflRnR7D2doURyuT9odFArgEQ8C1cHQ11Ok3cK4nSC5LBmh1wXYqqwx/F2vMTZSSn7atBzjpb4HX5xEEyUbqXARUXewCUilUzBsyj80LNrNyykq0Oi1P7n+SRVsWsePcjh4pbpfXl/PUgad4PvJ5AuwD+GX+L8wdrL+OtG6jPAsSf4dRd4D68rY57WWstz0LRriz5kAamSX6sZcIcghCJaguWpAMCpOsLGQLkm6hOWB7ytC2/bQBovOjGeUy6vIFcE01HP8CAq+VHkY1M+MFqCmGI2v0pNqw+Nr58tKEl4gqiOKDkx8YWk67GONlxyjPAXwWkYa28cr3R1EUeeXwKxTXFvPmlDcvNI70NM+MfYZQp1BWHFxBSqm8W9UoaGyAXS/D1/Mli5F7d8Kkx0Dx9xJSXnUeJ/NPMsdnjl4Dkn+LzcXB0oRxPvZ6G1Oma1ibqVl311hszdVc9/FBHvou6sIuRZmOsXjoYtQKNesT1xtaSq9GLmrLtEpiSSLb07dzW+BtrSeed5Tzh6SvnfB17GsoFAL+rtYXtvArnIPwb5Q7tf9KUkkStdrav4REFjLS0w6LpC2ACCGLDSdQRqYXMdF9ImZKsy5ZkAiCQLC7LfEdKWoDTH1SshU4/lmn5zYUzfflofaX79TW6URiMssk6xGtBtL2g+8s+WFbRwleCKIOzvz6t5eUCiXXDr6WzQs289aUt9CJOp7a/xQLf13I9vTtNOrat9W+oxzPO86iLYvYfX43j416jC+u/gJ3K/dumUvvnPgSEKVALz3w7JxAVAqB134/o5fxzFRmBNgHXCxqq0ykbl85LLJbONDkpz3Jt+3P9XnVeeRU51zZTzvme6grgwkPtzw+aDT4XwOHPoLa0q5KNgquGXwNtwTcwv/O/I8/z/1paDltIggC900dQmZJLTvi86543pbULfx5/k8eGvkQIY4hPaiwJWqlmlXTV2GhsuDxfY9Tqen9Xf69muJU+OJqiFwlWQvdtx/crxwWuyN9B4BerUdqNY3sTihgdoirbD1iZLjamrH54Un839TBHEgqZMHHB7np08PsScxHpzPeYG9jw9Hckdnes/k15VeqNFWGltNrke8OMq3yYdSH2JjYcFfIXfodOOMIOA4FSz0WynsxgW42JORWIooiOAfhX13B2ZKzBvcMNSaiC6IBLqRpl1RriM+pYLKvo2Q94hYqefbKyMi0iYXagonuE9md0bXgvZCBNiTnV1Kv7UAh0X2kZMdx+GOpy68XkVSahJXaCnfLyxcz04qqqazTMmLQAMg8CppK2XqkM7gEgVMAxG+64ilKhZK5g+eycf5G3pn6DgBPH3ia67dcz+9pv+utuN3Q2MCqk6u45497MFeZ8+3cb7l32L0oe0sgcUOdFOw1dA7YeellSFdbMx6a4csf8fkXAge7ynCn4cQXx1+0RBo8HYpToKxt6wSZjhGRVESgmw3O1mZtnnvBT9v5MtZuOh0c+UQKfPcY9/fXZzwH9eVwqHeHA1/KU2OeYrjjcFYcXNErtotfFeTCYEdLPt2fJq0x/kJmRSZvHH2D0S6jWRq81AAKW+Js4cy7094lszKT5yOfl9dBhkAUpYdVn06FklS44WtY8N82w663pW8j2CEYLxv9vM8A7D1bQG1DI9fI1iNGibO1Gc/OCeTQszN5fm4gGSU13P3VCf7xwQF+OpHZsbVBP2ZJ4BJqtDX8mvr3Rg6Z9iEXtWWuyIm8E0RmR3LvsHuxMbHR38A6HWQeka1HLiHIzZry2gZyy+vAJZiA+nqqtdVkVWYZWprREF0QjZulG66WrgAcSi1CFGGWSxXkRMld2jIyHSTcK5yCmgLii+I7PUawuy1anUhSXge7C6Y+JW1NP7Gu03MbguTSZIbaDb3i1toWIZEpO0GhhsHTelJi3yF4obSrqyKn1dOUCiWzfWazccFG3p32LkpByfKI5Vz363VsTd3aJd/4tPI0bt12K+vi1rFo6CJ+vPZHgh2DOz2eQYjfKP2ujbtPr8PeM9kHLwcLXt56hoZWrA3aS6hTKLXaWpJKk6QDg2dIX+Vubb1So9Fy4nwJU/za11QSlR+Fldrq8jkCSduhJE3q0r7cPdE1RPo9PrIaqou6qNw4UCvVvDf9PUyVpjy+93FqGvRjwdNdKBUC904ZzOnscg6nFbd4rUHXwPKI5SgFJSsnrzSaB3VjXMfw5Jgn2Zu5ly9O9/6w0V5FbRlsuAc2/1NqFvrnISnguw3Sy9NJKElgjs8cvcr5PTYXRytTxvk46HVcGf1ibaaWOrafnsGqG0NRKgSe/iWWKW/tZfW+VMpre0cOgaEIcQxhuNNwvk/4Xn6Q10nkorbMZRFFkQ+jPsTZ3JlbAm7R7+CFCdLWc7mofYGAprDIxLwKcA4iQKORvi9JNKQso0EURWIKYi50aQNEJhdhbaYioPBPoMmDVUZGpt1MHTQVlaDqkgVJiHtTWGROB8IiATzHgc9UOPQfaKjt9Pw9iSiKJJUm4Wfnd8VzYjLLsDJVMcTJCpJ3SRZbpkYeIGishCwERIjf3K7TFYKCf3j/gw3zN/DetPdQK9U8F/lcp4rboijy09mfuGnrTeRW5/LhjA95acJLBvOa7TSiKAVEOvqDj34frpiplay4JoiUgir+d/h8l8cLdZbCIi9YkDgHgpUrpO3r8tgyFzmaVkJDo9j+onZBFKHOoZcveB7+GGw9IXD+lQeY/ixoa6XQyD6Cq6Urb019i7TyNP59+N+X7YA2JhaOGoijlQmf7k9rcXxt7Fpii2J5ccKLuFkZVyfsrYG3MtdnLh9Ff8Sh7EOGltM/OH8Y1kyR3nNnroA7t4LtoHZduj19OwICs71n601OjUbL7sR85g5zRamQLdx6A2qlgoWjBrH9sSl8fXcYfi5WvLUjkUlv7uG1386QU9Y7Pu8bgiUBS8iozOBg9kFDS+mVyEXtvkJZJiTvlN6Q8k5LnRNVhVKxoBMftvZn7SemMIZ/jvgnZqq2tyd2iGY/bS+5qN1MgKtU9EjIrQRHP4ZoQYUgF7WbyK3OpaC24IKftiiKRCQXMcHHHmXcz+A1CWwHGliljEzvwtbUljC3MHad39XpRbmHvTnWZirisjtY1AaY+jRU5UPUN52au6fJqc6hqqHq8h2LTcRkljFsoC3KyhwoiAc/2Xqk0zj6gcswqdO4AygEBVd7X80v837h/envY6Y047nI51iweQG/pvzaZnG7uLaYR/c8yqtHXmWUyyg2zt/ITM+ZXfmXGI6sE1Iwd9j/dYuv+6xAZ6YNdeKDnUkUVdV3aSx3S3cczR0vFrUFQbIgSdsn7fCT0QsHkgsxVSkY69126FpZXRkpZSmXtx7JjoLzB2H8A6BUXXkQp6Ew/GY4/jlU5HZBuXExwX0CD498mO3p2/nh7A+GltMqZmold030Zn9S4YX8nuiCaNbGrmX+kPnM9tFfIVJfCILASxNewtfOl6cjnia7KtvQkvoujVrYuxK+misFQN7zp5R90s7OfVEU2Z6+nTGuY3CxdNGbrN0JBdQ16LhmmHE9cJFpG0EQmDbUie/uHc9vj0xmZoAz6w6dY+rbe1n2Y8yF+5DMRa72uhpHc0e+jvu21WBfmcsjF7X7Cqm74bvFsG42rJkM/xkJ7/rC667wigO86QmrguC/YfDZTCnF+IdbYeP98Nsy2Pki7H8Hjqym8eTXfHjoVbzMnLhO7dpUJE/vUpG8BRlHwNoNBujPc6u3Y22mxsPenDO5FaBUY+o4FB9M5KJ2E81+2iOdpYCS88U1ZJfVMt+1CIqTYZhsPSIj0xlmec4iozKDlLKUTl0vhUXadDwsEsB7MniMh4MfSKGKRk5SiWSLcKWidl1DIwm5FU3WI03d777hPSWvbxJyPWQdh7KMDl+qEBSEe4Xz07yf+GDGB1ioLXjh4AvM3zyfTcmbaND9fTtsRFYEi7Ys4lDOIZ4Z+wyrw1fjZOGkj3+JYTj2KZjaQKied9w1IQgCL84LorahkXd2dC3cWhAEQp1COVVw6uLBITMk65T8011UKtNMRHIRYT72mKnbLlg1f/a6bFH78MdgYg0jb2970mlPg04LEe91VK5Rc++we5k6aCpvH3/74sMYI+W28V5YmCj57EAalZpKno14FjdLN54Ne9bQ0q6IhdqCD6Z/gE6n4/G9j1OnrTO0pL5H6TmpmL3/TRh2I9wfAYPGdGiIhJIEzlWc6xbrEWdrU8a04wGcjPESMtCW/9wykn1PTuf2CV7siM9jzocR3PHlMQ6mFBn9TpeeILOkhv8dzkKonMDR/ENsPWPc7yfGSCuP1mV6FQHXgnMQ1FdKwVuaKqivkkKq6qsuOVZ58bXqokter4JGqaiwzcqCFCdH3ikoQpVwzd/nEpRSWISJNZhYNv3dStpibWJ1yTHri6+ZWF58/fwhyXqkG7qGejOBrjYkNj+5dAkisOgIR0q6tkjsK0QXRGOhsriw7T8iRfJmnFy3T/KsDVpgQHUyMr2XGR4zeO3Ia+zO2N2qrUZrhLjb8s2R82gbdR1LpxcEmPYUfLsITn0Po+/q1Pw9RbPX75WK2vE5FWh1IiM8BkDcLrB2l96XZTpP8ELY/YoUGDnpsU4NoRAUzPKcxUyPmezN3MuaU2t48dCLrI1dy33D7+PaIdfSqGtk1clVrE9cj+8AX9ZevbbVjvxeQWW+tI187L1tBnx1hSFOVtw92YfPItJYMs6TUI8BnR4r1CmU3Rm7Ka4txsHc4aJlSupeyd9VpkvklteSUlDFTWM82nV+VEEUaoWaYU7DWr5QniX9To7/J5i1I3PH3kcqfp/8CiY9CgM8Oy7eCFEICt6Y/AY3/XYTT+x7gp/m/YS9mXEW4AZYmHDTWA++OXweneP35FXn8dXsr7Ay6b57gz7wtPFk5ZSVPLznYV478hqvTnr1ipkWMh3k9C/w2+PS3xd+DsNv6PAQOVU5vHXsLVSCiqs89bczrapey96zBdwS5ilbj/QRPOwteGleMI/N8uO7oxmsO3iOWz8/SrC7DfdNHcw1w9w6toboxYiiSFx2BTvP5PHnmXwS8yoBpvsZyAAAIABJREFUGOI6ATun05iaGndWgzEiF7X7CpaO0p+uoNWgqS3h4x23Eaiy4OoZn10shmuq/1Ikbz52aZG88JJi+sUi+WXxntQ1rX2QADcbdiXkU9fQiJlzEP7nd7DFTLi4uOvHxBTEMNxpOCqFdMuKTC5kkK0ptqlbwXcWWBjnIkJGxthxsnBihPMIdmfs5oHQBzo1RvBAG+q1OlILq/F37aB/9JBZ4D4SIlbBiNta38ZuYJJKk/Cw9riir3JMU0jkSHdL2LJPCleSF99dw95H+vmI29jponYzgiAw03MmMzxmsD9rP6tPrebFQy/yaeynmCpNSStP47bA2/jX6H9hqjTV0z/AgJz8CnQNUlG7m3lkpi8bo7L599Z4NjwwEUUnixChTlLhOrYwlhmeM8DGDZwCpbDIyf/Sp+R+SUSy1BAwZWj7/bRDHEP+/vtwdI30ddz97Z986lMQ8z3sfxsW/Lf91xk5tqa2rJq+itu33c4zB55hTfgaowlc/Cv3TPbhu7jN7M7azoOhD7bIqTFmpnlM44HQB1hzag3DnYZzo/+NhpbUu6mvhG1Pwan14DEOFq4FO+8ODaETdfx89mdWnVwFwMuTXmaAWecfaP6V3Qn51Gt1XDNcth7pawywMOGhGb7cM9mHzdHZrI1I47EfYnh7x1numezDTWM9sDQ13rVAZ9FodRxNL+bP+Hx2JeSTW16HQoAx3va8cE0gVwW54OVgCcwztNReSd/7iZHpPCoTfs7aTXZNPi+Gf4pi4PiujafVNBW/Lyl011dCY4O0pVSmBUFu1uhEOJtXSahL8IWwyLMlZ5k4cKKB1RmOKk0VyWXJ3D9cWjw16kQOpRbzkE8+Qno2XPWKgRXKyPRuZnnO4t0T75JZmYmHdfs6+C6lOSwyPqe840VtQZC8tX+4BU7/DCO6xyZBHySVJrXpp+1ma4ZzeSzUV4Cv7KetF0IWwZ8vQHEqOAzp8nCCIDDdYzrTBk0jIjuCNafWUFpXyqfhn/ad91qtBk58KdnfOPp2+3TWZmqWzwngyZ9PsSk6m0Wj2xcu9leCHIJQCSpOFZ6SitogfV48/oVkf6c216Pq/kdEchFO1qb4u7R9n67V1nKm6Ax3Bt/Z8oX6Sjj5tbRDriMd17YDYczdcGwtTH5cL7/LxkKQQxDPj3+elw69xCenPuGRkY8YWtJlEdSlWLj/irbOixv97jK0nA7xz9B/El8Uz8pjKxlqN7TXFOSNjqwTsOEeydJr2nLpYVMHmwmyKrN46dBLHMs7xni38bw88WXcrdz1KvP32FxcbEwZ7Wmn13FljAcztZKbwzy5cYwHuxMLWHsglVd+O8OHu5O5bbwnd070xtlaz7luPUxFXQP7zhay80w++xILqKzXYqZWMNXPiWVXDWVWoAv2liaGltkn6B89/jLtoqahhrWxawlzDWOCux5CHFUmUgftAE9wCQKPMKmr1n82qPpAF5SeCXSTtnAm5FaAcxD+TUXtxNL+7asdWxSLTtRdCImMzSqjsk7LbDES1Bbgr18PNxmZ/sYsz1kA7MnY06nrBztZYaZWEJfdyeAX/zlSIGDEu6Br7NwY3UyttpbzFedbLWqfyiyTrEdSdoJCBYOn9aDCPkzw9dLX+E16HVYQBKYOmsr313zP9kXb+05BGyBhC1TlQVgHOmm7yMKRAxnhMYA3dyRSVd96GOeVMFOZEWAf0NKfePAMaKyX8lhkOo1OJxKZXMgUP8d22TecLjyNVtQyyuUvftrR30oP7SY83HERU5ZJn//3vdnxa42chX4Lud73etbGruVA1gFDy/kbjbpGnot4DhOlQHXWjfx0PMfQkjqEQlCwcspKXC1ceWLfExTVFhlaUu9C1wgH3oUvrpb+ftc2mPFshwraOlHH9wnfs3DLQuKL4/n3hH+z9qq1ei9oV9Y1sC+pkLnD3Dq960em96BQCFwV5MLPD0xk44MTmTDYgU/2pTL5zb0s3xBLSkGVoSV2iNzyWr45fI7bvzjK6Fd38uj6aA6lFDFnmCuf3TGG6BVXs/aOMdwwxkMuaOsRuagtc4FvznxDSV0Jj416TPYrMwAedhZYmiglXyXbQdiqbXBXmPX7sMiYghgUgoLhTsMBiEwuQo0Wz7w/IeAaya9dRkam0wyyHkSAfQC7zu/q1PVKhUCgmw3xOeWdEyAIMPUJKE6BM5s7N0Y3k1qWioiIv53/ZV8vrqono6RGKmon75K29JrZ9rDKPortIOm/p56L2n2aY5+BnU+PBpUqFAIvzw+msLKej/Ykd3qcUOdQ4ovj0eqaCuNeE6XsjLS9elLaP4nPqaC0poGpfu0LPj1ZcBIBoWVHbKMWjnwi5eIMGt1xEVbOkmXJ6Z8h/0zHrzdynhv3HAH2ASyPWE5WZZah5bTgi7gviCqIYsWEF5js7c+XB9Op1xrnQ+QrYWtqywczPqBCU8FT+5+6bNivzGUoz4Kv58OeV6UdFg9EglfHmtcyKzK5+4+7WXlsJaNcRrF5wWYWDV3ULfWC3QkFaLQ6rpWtR/odozztWHP7aPY8MZ0bxgxiU3Q24av2c+/XJzhxrsTQ8i6LKIok5lXw0e5k5v83kgkr97Di13iySmu5e5IPvzwwgWPPh/P24lCuCnLB3MQ47al6O3JRWwaA0rpSvor/ipkeMy8UD2V6FoVCwN/VmjO5FVKRxzkQ/0b6fVE7uiAavwF+F8JsIlOKuNUxBUVdKQzreKiJjIzM35nlOYtThacorCns1PUh7racyalAp+tkinngAnD0lzqJdLrOjdGNnG0K7b1Sp/apLMlPe4xDPeSf7tFiYr8geCHkx0FhkqGVGD+5pyDzCIT9Hyh69mN+qMcAbhg9iC8j00kr7Fx3VahTKLXa2gvBrJhaSTv9UuWidlc4kCzd2yf5ttNPOz+KoXZDsTG5JAgy8TfJtmDCQ50XMvFRKTh+3xudH8NIMVOZsWq65DG8bN8y6hvrDaxI4nThaVbHrGaO9xyuHXwt900dTGFlPb9G965ubQB/e39enPAiJ/JP8P7J9w0tx/iJ3wyrJ0FONFy3GhZ/Cebt977WiTq+PfMtC7csJKkkiVcmvsLqWatxtXTtNsm/xebibmvGSA/ZeqS/4uNoyevXD+Pg8pk8OsuPk+dLWLzmMAs/OciOuDwaO7vW0BPaRh1H0op59bczTH1nL7M/iOC9nUkoFQJPz/Zn17Kp7HliGs/ODWSMt70cdtoDdOnTriAIAwRB+EUQhERBEBIEQZggCIK9IAg7BUFIbvpq13SuIAjCfwRBSBEEIVYQhFGXjHNn0/nJgiDcecnx0YIgnG665j+C3D7cbXxx+gtqtDVG6wPXXwh0syEhtwJRFMEliMDKMs6Vn6OmoX+m4DbqGoktjL3QKVRdryUqo5QbTI+AuT0MmWlghTIyfYNwz3BERPZmdq5wFOxuQ2W9loySTt6rFAqY8gQUnIGzv3dujG4kqTQJc5U5A60HXvb1mMxyFAKE1B6XDvjJftp6JWgBIED8RkMrMX6OrpWsuUbcapDpn54dgKlKyTt/nO3U9c1hkX+zIMmLhWrZcqCzRCQXEuRmg5N12/Z/Wp2WU4WnGOk8suULh/8r7QDwn9t5IRb2UlE8YatUaOtjeFh78MbkN0goSWDl0ZWGlkNNQw3LI5bjZOHECxNeQBAEJvs6EuRmw6cHUjv/INqAzBsyjyUBS/jmzDdsT99uaDnGiaYafn0Yfr5TClx+IAJGLOlQePW58nPcteMu3jr+FmFuYWxasInr/a7v1t3cFXUNHJCtR2SacLQyZdlVQzm4fCavLAimqErDA9+eJHzVfr47ep66hp7bbVKj0bIjLo9lP8Uw9vVd3Lz2CN8cOY+vkxVvXD+MY8/NYtODk3hwui++ztay60EP09UWjg+BHaIoBgChQAKwHNgtiqIfsLvpe4A5gF/Tn/uA1QCCINgDLwHjgDDgpeZCeNM5/3fJdbO7qFfmMuRV57E+cT3zBs/D1677A4Vkrkygmw2VdVqyy2olX+3aSkREUspSDC3NICSXJVOjrblQ1D6WXoKqsZaAsgipyKFUG1ihjEzfYMiAIXjbeHfagiRkYHNYZCd9tUEKBLTzgQPvgGhcC+2k0iT87PxQCJf/2BSTWcZQF2tM0/eAtRu4hPSwwj6OjRt4TYK4DUb3s2FUVBdL1g6hN3eoG0+fOFmbcu8UH7bH5RHbtIOhI7hZuuFk7tSyqN0cLp6+X08q+xfV9VpOni9lytD2dWknliRSq61ltMslFiOZxyDrOIx/EBRd3D49/p9gbgf/z959h0dVZg8c/94p6YX0nkB6AULoJYFAEAR0VXbF31qwi2tZFXVdd9V1d9W1oWJdu7IWdFUsoKuAtNAhCSEJ6YSEkN5Jn5n7++MGBAkwITOZZPJ+nidPyJ1775yQzGTm3POes8n6qrUBkoOSuXXMrXxZ8CVrCizbNumZvc9Q1lLGU4lPnay6lySJZbNCKapp5efcaovGd6EemPgACd4J/G3H3yhoMKLdkcGgtLzZ8zZ8faeyKqx0lzJU19ocy4A3Zyr97xPvg5t+6tNgVr1Bz4fZH/K7735HYWMhTyU+xatzXsXH0ceMQSvWZ1fRpTewSLQeEU7hYKNh6bSRbHogmdeuHo+znYa/rslixtM/8/LGAhpazfM4rmnpZPWeUm7+YC8J/1jP7R/tZ+OhapKjvHnjmvGkP3oR7984maunBOPtMrSHWg51fRt3ewpJklyBmcANALIsdwFdkiRdBiT37PYhsBl4CLgMWCXLsgzs6qny9uvZd70sy/U9510PXCxJ0mbARZblXT3bVwGXA+KSrIm9ceANZGTuGHeHpUMZ9k4Mi8ytaCHQO5boE8Mi63OHZVuY9GqliudEtVBqYS0Xa9NR69tF6xFBMCFJkpgTPIdV2ato6mzC1bZv/aAjfJzQqiWyjjVd+JsRtUap1v72LihYD5HzLuw8JibLMvkN+cwfOf+stx8oa2RRnCcUboKYS/tUDSUYafQVsO5+qMoGX3HRoFfpq5ShipNvs2gYNyeO4sMdJTz3Yx7/uXlKn46VJImxXmM5UH1KUts/QelRX7RJufgl9Mnuw3V062Xj+2lX7Qc4fUjkzleVn8G4q/sfkJ0rzLgHNjwOpbshuG+/I0PBnePuJLM2kyd3P0m0ezQxHjEDHsP6I+v5quArbh1zKxN9J55228Ixfjz7vzze2lrM3FjzJytNTavWsmLWCpasXcK9m+7l00s+Pb1VjkGvtKwq2Q5HtsORHdDe05PX3g3aG5R/a+whaBKEJMLIGRAwEbRDNDllMCiP043/AEcvuP5bGDWzT6cobirm0e2PklmTSXJQMo9NfQwvB+OeN0xh3cEKAkbYK/NJBOFX1CqJRWP9WDjGl13F9by1tYgX1ufzxuYilkwM5JakUILcHfp1H0U1x1mfU8X6nCrSShuQZQh0s+fqKcFcFOvDpJHuaNWig/Ngc8FJbWAUUAO8L0lSPLAfuAfwkWW5omefSuDEX8oAoOyU44/2bDvX9qO9bD+DJEm3oVR/ExwcfOHf0TBU3FTM14Vfc3X01SafXiz0XZSvMwCHKpqZOz0WP50eF5XNsO2rnV6djre9N/6Oyu9makEt/3LcAzYByqAiQRBMZm7wXN7Leo+tR7dyadilfTrWVqMmwtuZrPILHBZ5wtirYMszsPVZpYXHIEgOV7VV0dzVfNZ+2odrW2lq72aOUyl0NEG4aD1iFjGXwfcPKi1IRFL7THod7H0XRiaB98An0E7lbKfljuRwnvz+EDuL6pgW5tGn4+O94tlYupG69jo87D2UyuBRM6F4s1KpPwieF4aSrfm12GpUTAgxrkdtWlUagU6BeDt4KxsaSpR2ITPuUXqcm8Lk22Dna7DpCbj+O9OccxBRq9Q8k/QMS9YuYfnm5ay+ZHWfLxb3R2VrJY/veJw4jzj+MO4PZ9yuVau4OXEU/1ibQ1ppA+ODh17/Yi8HL15IfoGb/ncTf936MCujrkdVulNJZJfugs6e1yMjQiDyYiVpHTJdWRHWVt+T7N6u7L/5X4AMalsInNSz7wzl3zb9S5INiOYK+Pp25Tky5lK49GWl1Y+RdAYdq3JW8Vr6a9hr7Xk66WkWjlo4oC0Umtq62VZQw40zRonWDcI5SZLEtDAPpoV5kF/Vwltbi/lkTyn/2XWEBWP8WDYzlLGBxl0YMRhk0ssaexLZlRTVtAIwOsCFe1MiuSjWhxg/0U5ksOtPUlsDjAfulmV5tyRJK/ml1QgAsizLkiSZfZ2oLMtvAW8BTJw4UaxL7YNX01/FTm3HrWNvtXQoAuBkqyHEw4FDlc1gH4Hk7E+0ZHdySNlwk1GdwTjvcUiSRHVzB1VVx4i33w/j7xjwAViCYO3iPOPwcfBhw5ENfU5qg/ICcOOhamRZvvAXfxobSLxXqcg9vAVCky/sPCZ0YmDd+YZEjuvaB5J6UMRslZy8lMRm1lcw51GR2Py1/B+gqQwutnwfX4DrpoXwTmoxz/+Uxxe3T+vTc8KpfbXnBPfMzghNVhKrdUXgKVrl9cW2ghqmhHpgpz1/2xBZlkmvTicpMOmXjbv+DZLKtCsAbByVlTn/+zMUb4HQWaY79yDhYe/BilkruPF/N/JI6iOsnLPyrC2sTMkgG3gk9RG6Dd08M/MZtKreW/VdNSmIlRsLeGtLMf++bkKv+wxauk4oTyPhSCoPyCN4unwrb2d9y7LGZvAIh7jLYWSiksR2DTzzeEcPiP2N8gFKkrt0V0+Se1tPG7RnQKWFgAlKkntkIgRNUX53B5Pc7+GbO6G7HS5dCeOv79Pfx8KGQh7d/ihZdVnMDZ7LX6f+FU9741oVmdJPOZV062UWjRGtRwTjRfo48/yV8TwwL4r3dxzmk12lrMusYGqoO8tmhpEc5XXG64+Obj07imp7EtnV1B7vRKOSmBrqwdJpI5kb60PACHsLfUfChehPUvsocFSW5d09X3+BktSukiTJT5blip72IieadZUDQaccH9izrZxf2pWc2L65Z3tgL/sLJpJVm8X6I+v5Q/wfcLcz/mquYF4xvi7kVrQoX/jEEtVRwn8b8tEb9Kj728dwCKlsraSitYKlsUsBpfXIQvUe1LJOtB4RBDNQSSpSglP4suBL2rrbcND2rTppdIArn+87SmVzB36u/XgxOO5a2PKc8hGafOHnMZETSe0It4heb88obcTBRo1HxVYImmyxXsbDQtxi+O6PUJGhtKQQfrH7TXANgsgFlo4EADutmj+mRPDXNVlsyqtmTrTxLQ5iPWLRSJpfJbV7+moXbxJJ7T4ob2ynqKaV3082biXr4ebDNHQ2/NJPu70R0v8Do38HLiZe0TnhRtj+Mmx6UrlgZYUXqsZ5j+OBSQ/w9J6neS/rPW4Zc4vZ73NV9ip2V+7m79P/TohLyFn3c7TVcN3UEF7bXEhxzXFCvUxUhW8OXW1KT/cTrUSO7gVdBwBXe8eQ5e7Ha24SsYteJymi7xflcXCH6IXKByirrkp3QUmqcp+pL8G2FaDSKH97Qk5Jctu5nPvc5tLdDj89AnvfAd8x8Nv3wKv3i++90Rl0fJD9Aa9nvI6T1onnZj3H/JD5FqtIXXewgkA3e8YGDtyKBsF6+Lra8fCCGO6aHc7qPWW8t/0wN36wl0gfJ25NCmVWlBepBbX8lF3F1oIa2rr0ONlqSI7y4qJYH5KjvHG1F7O6hqoLTmrLslwpSVKZJElRsiznASlATs/H9cDTPZ+/6TnkW+AuSZJWowyFbOpJfP8IPHXKcMh5wMOyLNdLktQsSdJUYDewFHjlQuMVzrQybSVutm4nk4bC4BDt58yPOZW0delw8I4l5uAeOrQjONJ8hNARoZYOb8Bk1GQAnBwSmVpYy9XanciekUi+YywZmiBYrbkhc/kk9xO2H9vORSF9a6MR56+8scsub+5fUltrpyxz//Fh5c1ryPQLP5cJ5NfnE+AUgLONc6+3Z5Q1kuirR6o4oFQQC+YTcymsW65Ua4uk9i+qDynVhXMfV3rTDxJLJgbx5pZinvsxn+RIb1Qq45Ildho7ot2jTx8W6R4KI4KV5fWTxepCY6UW1ACQZGQ/7bSqNADGe/f00077ELqOwzQzzN3R2sGsB2HtfVC4QWk5ZYWujr6aA9UHeCX9FUZ7jmaq31Sz3dehukOsTF/J3OC5XBF+xXn3v376SN7aVsw7qYd56opB9Nq6s0Xpt36iRUh5Ghi6lRUDvmNg4k1KYjl4GpKjB4/p2in4/jr+vO9pVvuOI8g56Pz3cS52rhA5X/k4EU/ZbqVVSUmq0rt6+0tKPH7xPUnuJAieOjAXtiuz4MuboSYXpt0FKY+Bxtbow/Mb8nl0+6Pk1OUwL2Qef5nyF6XVk4U0tnWRWlDLzUmi9YjQP852Wm6dGcoNM0by3YFjvLW1mAe/yDx5u6+LHYvHB3BRrC9TQ92x1QyfgkFr1t9XvncDH0uSZAMUAzcCKuBzSZJuBo4AS3r2/R5YCBQCbT370pO8/iewt2e/f5wYGgncAXwA2KMMiBRDIk1k57Gd7KrYxZ8m/Qknm0F8ZX4YivFzQZYhr7KFBJ84ova2AyPIrc8dXknt6gzsNfZEuUcpg9rycxnPIaQxf7HKah5BGAwSvBMYYTuCjaUb+5zUjvFzQZIg61hT/wdPTbhBqYra+hxct6Z/5+qnvIa8s1Zpd+r05FQ0c2vUIWVdmpUmZQYNB3cImwPZX8NF/xB/C07Y85bSCzZhcBUpaNUqll8Uyb2fZbDuYAWXxhtf6RvvHc+X+V+iM+jQqDTKzzp0NmSvUfqHD6Lk/WC2raAWb2dbIn2Me62fVpWGu527UuGr71ZWAIxMUhJ35jDuWqUK9ud/Qvhcq3xMS5LE49MfJ68hj4e2PsRnl3yGr6Ovye+nXdfOQ9sewt3Wnb9N+5tRyUEvZ1t+Oz6QL/Yf5b65kXg5G58YNan2htMroysyQdYrLb38E5SLKiEzlKSx3ZmVvPYae15MfpGr1l3F8s3LWbVgFfYaE7YPsHVWfj/D5ypfd7VC2Z5fenLveUtJdCMpSfeRiUq8IdP71Nv6vGRZeUyuf0xJnl/7FYSnGH14t6Gbdw++y5uZb+Ji48KKWSuYN9LyQ7l/yq5CZ5C5dKyY7yWYhlatYvH4QK5ICGBrQS1Z5U0khnsyJsDV6AvswtDRr1eEsixnABN7uemMZ1dZlmXgzrOc5z3gvV627wPENCATk2WZlWkr8XP0Y0nUkvMfIAyoWD+l2vFQRQsJwbGM6u7GRlKTW5/LwtCFFo5u4KRXpzPaczRalZb8qhamtW9BpZVh9G8tHZogWC2NSsPsoNmsP7Kebn03WrXxS/EcbDSEeTmRVd7c/0BsHGD6XbDhcTi6HwIt0++zU99JSXMJc0Pm9np7zrFmuvUyE7r2g5MP+I4d4AiHobjFUHA7HN0HQZMsHY3ltTfCgdVKWy5Hy1Xanc2l8f68sbmIF9bns2C0Lxq1cT2F473i+fjQx+Q35BPrEatsDJutVA4fS1Na/QjnpDfIpBbWkhLtY3T1Y1p1GhN8Jij7Z38NzeVwyYvmC1JjA8kPK0PuctcqqzGskIPWgRdnv8jv1/6e+7fczwfzP+jT31djrNi3gsNNh3l73tuMsDO+WvjWpFGs3lvKqp0l3D8vyqQxndXxGijdoSSEj+yAqiyUQY02EDAREu9T+lgHTjZ6OGmQSxDPJD3DnRvv5J87/8mTiU+ar+rXxlF5PgrraYvU3a78TTrSU8m97z3Y9bpym3eckuQ+MXzS8QL7VR+vhq/vgML1yuDLy17r07ly63N5dPuj5NbnsmDUAh6e/DBudoNjQOjagxWEeDicXPEnCKYiSRKzIr2YFWncaiVhaBKT1oahDaUbyK7L5o5xd2CrttAVeeGsAt3scbLVkFvZDF5RaCU14RoXcutzLR3agGnrbiOvPo9xXj2tRwpquUy9gy6fBPAIs3B0gmDd5obM5Xj3cXZX7j7/zr8S5+9CzrEm0wQy6Rawd1OqtS2kqLEIg2wgyq33N/oZZY2oMOBTswPCUqyyynDQiV6oJD6yv7J0JINDxsfQ3QZTTDjEz4TUKon750VyuLaVL9OOGn3cqcMiTxo1C5CgaJOJo7RO2ceaaGzrZmakcYmvytZKyo+Xk+CdoFSE7nwFPCIg3MwrUMYuAc9I+PlJMOjNe18WFOoayt9n/J3Mmkye3/e8Sc+9uWwzn+V9xg1xN/S5vUmolxMXxfiwaucRWjt1Jo3rpOYKOPiF0mrm1cnwfDh8vhTSVoGDm3Jh44Z18OdSuOkHSHlUWZVjZEL7hKTAJP4w7g98V/wdq/NWm+d76Y3WHkYlQfKf4Ya1yvdx4w8w+xFlyHH6f5Tv97kweG0KrF0OWV9CS5Vx5y/YAG9Mh8NbYeHz8PvVRie0u/XdvJ7xOr9f+3tq2mp4afZLPDvz2UGT0K5v7WJ7YS2LxviJ1iOCIFwQsXZvmNEZdLyc9jKhrqFcGmqd1RBDnSRJRPs6c6iiWemP5hFOtB42N+Qhy/Kw+IOfVZuFXtYrb6yAwpw0blKVQMLtlg1MEIaBKX5TcNQ6suHIBhIDEvt07Gh/V77JOEZ9axfujjb9C8TWGabeoQwRq8gEv4Gvgj4xJDLSrffhSwfKGpntVIqqsxEieq/mFkzMzlVJsmWvgXlPgmoY12cYDLDnbQiaar72ECZwUawP8UEjWLmhgMvGBWCnPX8PSz9HP7zsvThQc4DfR/9e2ejgrnyfxZsg+SEzRz30bSuoBWBGuHHJr5P9tH3GK9WzFQeUKm1zP8ZUaiWp+cWNSr/8sdY7DPzikRdzoPoAHx36iHiveJOswKxtr+Wx7Y8R7R7N3Ql3X9A5ls0K46ecKj7fV8aNM0b1OyYajii/Q0dSlWrshsPKdhsnpYVI/P8pVcv+CUq1vgktG7uM7Npsnt3zLDHuMSd40vC4AAAgAElEQVRn8wwoja3SeiRkOvAg6LqUAcclqcpH5mew711lX4+InirunmruUweydnfAxr8rVd/esbD0W/CJNTqMnLocHt3+KPkN+VwSegl/nvxnXG0H1yDGH7Mr0RtkFo31s3QogiAMUcP4ncDw9G3Rt5Q0l/DHhD+iVonG+INVjJ8LuRUtyLIMPrFEHW+gvqOe6rZqS4c2INKr0wEY6zWWLp2BwPJ1GFBB3PmH3giC0D+2aluSApLYVLYJfR+r5k4OizRVtfbk28DWBbaZtqrNWHn1edip7c46dCqjrJErnHOUYVGhswc4umFs9GJoqYDSnZaOxLIKNyjJokE+OFGSJP40P4pjTR18srvU6GPiveI5UH3g9BvCZsPRvcrgNuGctubXEOfvgqeTcasy06rTcNA4KCtTdr4K9u4Q/3szR9kj9nLwGQ2bn1J6plux5ROXk+CdwOM7H6ewobBf5zLIBh5JfYQ2XRvPJD2DjfrCEsQTQtyYGOLGO9sOo9Mb+n4CXSek/Qe+ug1eHA0rxyotZQ6tBe8YmPcE3LoJHjoC134JScsheIrJE9oAKknFU0lP4efkx/LNy6ltrzX5ffSZxkZpmZS0HK77Svl/uOVnZTaEe6hyMeerW+CFGFg5Dr65E/Z/AO+kKAntycvg1p+NTmh36bt4Oe1lrl53NQ0dDbwy5xX+lfSvQZfQBliXWcEoT8eT7TcFQRD6SiS1h5FOfSevZ7zOGM8xzAmeY+lwhHOI8XOhpVPH0YZ28I4jprESUAaWDQfpNemEjwjH1daVjNIGFsip1HtPAWfTD9YRBOFMKSEp1HfUk1GT0afj4vyVN0wm6asNyiCkybdBzrdQPfAtmAoaCggfEd7rReCG1i5K6tqYrEuDwEmmHQYlnFvkxaCxFy1I9rwJTr4Qe5mlIzmvGeGeTA/z4LVNhUa3OIj3iufo8aPUtdf9sjF0Nhh0SvWncFbHO3WklTaQFGF8H9G06jTGeY9DU18CeT8oLaC0Jhy2dy4qFcz+K9QXw4FPB+Y+LUSr0vLczOew19hz3+b7aO1uveBzfZr7KduPbefBiQ/2e5j8sllhlDe2s+5gRd8P/v5B+PYuKNwIAeNhwbNw+3b402H4/acw/W5l+wANeHWxceGl2S9xvPs492++n25D94Dcr9HUGmVWyIx74JrP4aESuG0LzH9KuQhwaC18d49y8fbqz2Hhs0Y/FrNrs7lq7VW8ffBtLgm9hDWXrSE5KNms386FqjveyY4i0XpEEIT+EUntYWR17mqq2qq4d/y94g/HIBfj5wygtCDxiSWyS3kxNhz6ahtkA5nVmSeXCxakb2WUqgqHCf9n4cgEYfhICkjCRmXDhiMb+nScq4OWIHd7skxVqQ1KCxKtA2xbYbpzGkGWZfIa8ohyP0s/7aONeNCEd0uO+XvOCqezdYLIeZDzjdVXdZ5VXZFSqT3xJjDxwDlzeWB+FHWtXby//bBR+8d799JXO2iKckGjWPTVPpfdxXV062VmRhjXeqSps4nChkLGe49XKkPV2oFfARC1APzHw5ZnlMpfK+bj6MNzM5+jtKWUx7Y/pqzM7KP8hnxe2PcCswJnsSRqSb9jSon2JszLkbe2FvctnoINygDXaXfBg4WwZBVMWQa+oy3aHirSLZLHpz1OWnUaL+x7wWJxGEWlBv9xMO1O5SLAnw7DHbvg7v0QOd+oU3TqO3lp/0tc8/01NHc181rKazyR+MSgrM4+4X/ZlRhkROsRQRD6RSS1h4mWrhbeOfgO0/2nM9lPTIwf7KJ8nZEkOFTRAt6xOMoywTYjhkVSu6ixiJbulpP9tJ0Lv6YbDQ5jL7dwZIIwfDhqHZnuP52NpRv7/GY7zs+VnGMmqtQGcPSASTdB1hdKIm+A1LTX0NjZSIRbRK+3HyhrZJY6U/kiPGXA4hJ6xC2G1hqlZ+twtOdtUGlhwg2WjsRo44PdmBvjw5tbi2ls6zrv/jHuMWhUmtOT2lo7CJkmhkWex7aCWuy0KiaMNG4YXEZ1BjIy40dEQMYnyvBGJ28zR/krkgRzHoGmMmWAoJWb7DeZe8bfw09HfuKjQx/16dhOfScPbX0IZxtn/j797yYpVlKpJG6bGUr2sWa2F9ad/wCA9kb49m7wjII5jw66YckLQxdybcy1fHToI9YVr7N0OMZTqZSKbXvjHr8Hag6w5LslvJv1LpeFX8bXl33NzMCZZg6y/9ZlVhDq5Ui0r7OlQxEEYQgTSe1h4sPsD2nsbOSP4/9o6VAEIzjYaBjp4ahUao8IAa0j0ZLdsEhqn+inPc5rHM1tHUxt20yJe6LShkAQhAEzJ3gOFa0V5NTn9Om40QEuHK5tpaXDhMt9p90NahvYNnDVVucbEplR1sglDjng6AV+FhhENdxFzFOGjmUNwxYkncch42OIuxycfSwdTZ88MD+S45063txafN597TR2RLtFn57UBqUFSW0eNJWbKcqhb2tBDVNDPbDVGDc/Z3/1fjQqDWMO7wFdO0y908wRnkXYHAieDlufh+52y8QwgG6Mu5E5QXN4Yd8LJwd1GuOl/S9R2FjIE4lP4GHvYbJ4Lk8IwMvZlje3GnkB+X8Pw/EquOIN5YLTILR84nLGe4/n8R2Pk1dvXW0cO3QdrNi3gqU/LKVN18a/5/6bv0//O842gz9JXNPSya7iOi4RrUcEQegnkdQeBmrba1mVs4p5IfOI84izdDiCkWL8nDlU2dxztT6a6I4OylrKON513NKhmVVGdQbudu4EOQeRt/sHvKVG5DG/s3RYgjDsJAclo5bUbDyysU/HxQUoS11NWq3t7APjr4fM1dBwxHTnPYdzJbVlWeZgaR1TDBkQlmLRJdbDlo2D0q7g0LegH2T9Us3twKfQ2awMDxtion1duCzen/e3H6a6peO8+8d7x5Ndm316T9ywnqGsxZvNE+QQd7ShjeKaVhLDjWs9ApBWlcZo91js9r2nPKcZOZDO5E5Uax+vhL3vWiaGASRJEk8kPoG/kz8PbHnAqKGGqeWpfHToI66JuYbEgESTxmOrUXPD9JFsK6g9/8DnvB/gwCeQeB8ETDBpHKakVWlZkbwCFxsX7tt8H02dJmyPZkEZ1Rlc+d2VfJD9AYsjFrPmN2uYETDD0mEZ7UTrkUvi/S0diiAIQ5x4FzYMvJ35Nl36Lu5KuMvSoQh9EO3rwpG6NmWgkncsUcNkWGR6dToJ3glIkoR08AuOy/aMnLrY0mEJwrDjZufGRJ+JbCztY1LbX5lgn23KpDYoA5WQYPtLpj3vWeQ35OPr6NtrP8rS+jaCOvJx1DdBhOinbTFxi6G9AYq3WDqSgSPLSusR/wQInGjpaC7IvXMj0ellXvu58Lz7xnvF06HvOHmRCQDvOGWFhEhq9yq1QEmMzow0bkhkh66D7LpsEiQHpep2moWqtE8YOUOpxk99QVmVYOWcbZx5IfkFWrpa+NPWP6EznH1OQH1HPY+kPkL4iHDum3CfWeK5dkoIjjZq3j7Xaoq2emWQoc9omPWQWeIwJU97T1Ykr6CitYKHtz1MS1fLBfUxHwzade08u/dZlv6wlC59F29d9BZ/m/Y3nGycLB1an6zLPEaEtxORPoO/qlwQhMFNJLWtXPnxcj7P/5zLwy9nlOsoS4cj9EGMn5IYyq1sAZ84YlqUNynW3IKktr2Wo8ePKv20dZ1E12/igFMiNvaOlg5NEIallJAUipuKKW48f6uAE7yd7fB2tjXtsEgA1wBIuAbSP4LmY6Y9dy/y6vPO2XokWZ2BjKQslxcsIzwFbF0hexi1IDm8RWm9MXnZoOtfa6yRno4smRTEJ3tKKatvO+e+8V49wyKrT2lBolJBaLKS1B6iiSlz2lZQi4+LLRHexiW5DtYeRGfQMaHsAHjHDo7ntDmPQlsd7P63pSMZEFHuUTw67VH2Vu7llfRXet1HlmX+tv1vtHS18MzMZ7BV25olFlcHLf83OZjvMis42nCWx+cPf1J+Ppe/ARobs8RhauO8x/HQpIfYVr6N6Z9OZ/LHk1nw5QKW/rCU5ZuX89Tup3g7823WFKwhtTyVvPo86trrMMgGS4d+0v6q/fzu29/xn5z/sCRqCV9d9hXT/KdZOqw+q27uYPfhejEgUhAEk9BYOgDBvF7PeB21pOb2+NstHYrQRzF+ypXrQxXNTPCOxVNvwF3rZHX94E6VUZ0BKC886zLW4UErzRFiQKQgWMqcoDk8tfspNpZuJHREqNHHxfm7mLb9yAmJ90Haf2D7y7DgadOfv0eXvouSphKSg5J7vT29tJEr1JnKkmsHd7PFIZyHxhaiF8GhtXDJi8rX1m73W+DgAXFXWDqSfvnjnAi+2H+UlRsLeP7K+LPu5+foh5e9FwdqDnB1zNW/3BCaDAf/C1XZ4Dva7PEOFXqDTGphLRfF+hjdp3Z/1X4kJMZV5cOlrw6OiyWBEyBqIex4GSbdMizmqvwm7DdkVGfwXtZ7jPUaS0rw6QOI/5v/XzYf3cxDkx466wVXU7kpcRQf7ijhvdQSHrv0V61ocr5VHnvJfwG/sWaNw9SuirqKAKcAipuKqWmroaa9htr2WgobC9l1bBct3S1nHKOW1HjYeeDp4ImXvRee9p54OXjhaed5ctuJ7Vq11ixxt3W38XL6y3xy6BP8nfx5d967TPabbJb7Ggg/ZFUiy7BojEhqC4LQfyKpbcUKGgr4rug7boi7AV9HX0uHI/RRwAh7nO005FY2w5g4JCBaO8KqK7XTq9OxUdkQ4x5D5VdPIMsuhE5eZOmwBGHY8nH0YaznWDaWbuTWsbcafdzoAFe2FtTS0a3HTmvcoDKjuI2E+P+D/e9D0nJw8jbduU9xuOkwOllHlFtUr7cXlx5hjFSEFPFns9y/0AejFyt9XQs3QvRCS0djXg1HIP8H5eLOIB3KZixfVzuunxbCu6mHuX1WKOHevS9BlySJeK/43odFAhRvEkntUxwsb6KpvZukiL710w5Hi6u9F4y50ozR9dHsv8C/E2Hnq0qf7WHgockPkVOXwyOpjxBxSQTBLsEAFDcW89ze55jhP+P0iztmEjDCnkvj/Vm9t5R7UiJwdehJ1rbWwtr7wC9e+Rs8xEiSRFJgEkmBSb3e3qHroLa9ltr2Wmraa6hpqznt66q2KrJqs6jvqEfmzFUiI2xH4GnvqSS+7b3OSHqfSIg7ao1fgbq3ci+PbX+Mo8ePcnX01dwz/h4ctA4X/H8wGKzLrCDKx5kI0XpEEAQTEEltK/Zy+ss4aZ24eczNlg5FuACSJBHj68KhihZwHAOO3kTr4T+NhXTru81WDWBJGdUZjPYcjY2uE9/KzaxRp7DEz/qrcwRhMEsJSeHF/S9ScbwCPyfjqmri/F3RG2RyK1sYF2Tix3DicmVQ3s5X4aJ/mPbcPc41JLJLZ8CzajsqtQzhop+2xYUmg72b0oLE2pPa+94FJJhoHa/r/pAczie7S3lhfT6vX3P2QXPxXvFsKN1AbXstnvY9yVrXAPCMhKJNMP3uAYp48EstqAEwekikzqDjQHU6lzbXweQ7B9dqB98xyoqEXW/AlNvB0fhE/VBlq7ZlRfIKrlp7Ffdtvo+PFn6EWlLz0LaHsNfY888Z/0QlDUz30NtmhrImvZyPdh/hztnhSqufdcuhowmu/w6s8H2IncaOQOdAAp0Dz7mfzqCjvqP+l4T3KVXfNW011HbUsr9qPzXtNacPue1hr7E/rerby94LD3uPXxLgDp642LjwzsF3+CzvM4Kcg3h//vtM9B2acxROVdnUwd4j9dw317yrDQRBGD5EUttKZVRnsLlsM3cn3N3rkCthaIjxc+aL/UcxGGRUPrFEt1TRbdNNcVMxUe69VxAOVR26DnLqc1gauxTDobXYyF1UBl9i9PJZQRDMIyVYSWpvLN3ItbHXGnXML8Mim0yf1PYMVwYE7nkHZtxrlvYfefV52KhsTlbJnepQRTOJZNBp44atf4LJ71voI7UWYi6FrK+gux209paOyDy62yFtFcRcoiR0rYC7ow23JIWycmMBB482MSaw99er8d5Ke5LMmkzmBJ/S7zl0tvJ/ouscXMlYC9paUMvoABc8nIz7/8hryKNN38GELhkm3mTm6C5A8sOQ840yIHjeE5aOZkAEOAXwdNLT3LHhDp7Y9QTudu7k1ufy8uyX8XIwbvinKcT4uTAz0ov3t5dwc+Io7PK+Vn4WKY+BT+z5T2DFNCoN3g7eeDuce7WYLMs0dzWflvQ+UfVd26Z8zqvPI7U9ldbu1jOOl5C4LvY67k64G3uNdfxt+yGrAlmGhaL1iCAIJiKS2lZIlmVe3P8iHnYeXBtjXAJCGJxi/Fxo7dJT1tBGiHccURl7wc+D3Ppcq0tqZ9dlozPoSPBOoPXHF2k0eBE4ZpalwxKEYS/EJYQItwg2lG4wOqkd6GaPq72WrHIz9NUGSLofsr5QKvjm/NXkp89vyCdsRBga1ZkvkzJK61mkykQfepEysE6wvLjFSnKz4CeIvczS0ZieQQ/f3QPtDUrFqhW5JWkUH+4s4fmf8vjwpt57xMZ6xKJRaThQc+D0pHbYbNjzJpTthlEzBybgQex4p460Iw3cOtP4+QdppVsBGB++EBw9zBXahfOKgrFXwZ63Ydpd4Dw82ikmBiRye/ztvHHgDQCWRC5hdvDsAY9j2cxQrnlnNz/sOsAVO+5X5khMv2fA4xiqJEnC1dYVV1tXwt3Cz7lvW3cbde11StuTngT4WM+xjPEaM0DRDox1mRVE+zoTbuQgW0EQhPMR78asUGp5KmnVaSyLXzbke24NdzF+SrXjoYoW8IklpKMVe7WtVfbVTq9OByDeIRDH8m18a5hGYuTAVaQIgnB2KcEppFWlUddeZ9T+kiQxOsCF7GNN5gnIJ1apzt39prIU2sTyG/LPeuGwvmA3nlIz9jHzTX6/wgUamQQOnkq1trUxGODbuyHzM6VCMmS6pSMyKWc7LXckh7Elv4bdxb0/v9iqbYlxjzmzr3bIDJDUSgsSgV1FdegMct/6aRd8R0C3Dp8Zg7g/8qw/gUEH21ZYOpIBtWzsMmYHzSbaPZoHJj1gkRimh3kw2t8Zr81/Ru5qg8vfALWoiTMHB60DQS5BjPcZz/yR87km5hqrS2hXNLWz70gDl8b7WzoUQRCsiEhqWxmDbODl9JcJcArgdxG/s3Q4Qj9F+jijkpTl7njHogYi7LzJa8izdGgml1GdwUiXkbgVbUIl68lwvQgfl6E9CEsQrMXc4LnIyGwu22z0MXH+ruRWttCtN5gnqJkPQmcT7H7LpKetba+lrqOu137aAK7HtmJAQgpPMen9Cv2g1kDc5ZD/I3Qet3Q0pmMwwNp7IONjSP6LskLBCi2dNhIfF1ue/ykPWT5z+BoofbWza7NP709r5wKBk5RhkQLbCmqw16qZEOJm1P5yVxtpraVMsPEAzwgzR9cP7qGQcC3sex8aSy0dzYBRq9SsnL2S1YtWW6z1hCRJ/GNUNon63eTF3atUzgvCBfr+YCUgWo8IgmBaIqltZX4s+ZHc+lzuSrjLKgcJDjf2NmpGejoqSW2vaEAiWlIqtc/2xm8oMsgGMmoySPBOwJD5OXlyMIHRZx8aJQjCwIp0iyTQKZANpRuMPibO34UunYHCajMlGf3iIWI+7HrNpInMcw2JbGrrJr5jLzXOscNiaNmQErcYdO2Q/z9LR2IasgzfP6C0VZn5ICQ/ZOmIzMZOq+buORHsLWlgc35Nr/vEe8XToe84+fg8KWw2HMuAtvoBiHRw21ZQy9RQd2w1aqP2L9n3JvUqiYTwRWaOzARmPgiSBFufs3QkA0qSJNQq436eZtF8jITsf5GpiubRyiTLxSFYhXWZx4jzd2GUp6OlQxEEwYqIpLYV6TZ080r6K0S6RbJw1EJLhyOYSIyfC4cqm8HGAdxDie7ooKWrhWOtxywdmsmUNJXQ1NlEglMQqqN7+EY3rU/LZwVBMC9JkpgbMpddFbto6Wox6pg4f2XoW/YxM/XVBiXR0d4A+9412SkLGgqA3pPa2UUljJMK6Rw554zbBAsLngbOfpC9xtKR9J8sww8PKb/XM+6F2abvGz/YLJkYRLC7A8//mIfBcOZF+3gvZVjkgepftSAJnQ3IcHjrAEQ5eJXVt1Fc20pShJFt2wwG0g7+B4DxsVeZMTITcQ1UBlmmfwx1RZaOZniQZfj2j0i6LgqmPsPe0mb2lYiLR8KFKW9sJ620kUVjRZW2IAimJZLaVmRNwRrKWsr4Y8IfUUniR2stYnydKatvp6WjG7xjiG5Ulm5ZU1/tjJoMAMbVHgXge3k6k0cNwoFFgjCMpQSnoDPo2HrUuOTRKE9HHGzUZJWbqa82QNAkCE2GHa9Ad7tJTpnfkI+3vTdudmcu4W/K+hG1JOMxbghUNg43KhXEXg4F66HDjBdSzE2W4adHlAGI0+6CuY8rFapWzkaj4r6LIsg+1swPWZVn3O7r6IuXvdeZfbUDJoCN87BvQZJaWAvAzEgjCwKKNpLW3YC7xoFRrqPMGJkJJS4HtQ1secbSkQwP6R9B4XqY+zgLkhMZ4aDlza3Flo5KGKK+z6wAYJFoPSIIgomJzKeVaNe188aBN0jwTmBmoJgAb01ODIvMq2wBnzgi6kpQSSqrSmqnV6czwnYEI/N+IkcTg3dwFE62YhCNIAwmY73G4mnvycbSjUbtr1ZJxPq5sOdwPTpz9dUGmPknaK2B/R+a5HR59XlEuPfeX9bl6GaaJWccR002yX0JJjZ6Meg7Ie97S0dyYWQZNvwNdr4KU26HeU8Mi4T2Cb+JDyDSx4kV6/POeM6QJIl4r/gzk9pqDYxKGvbDIrcV1ODrYkeYl5NxB+x4hTQHRxL8piANld8xZx+YsgwyP4dq63kNPCg1lsH/HlaG8E6+DQcbDUunhrDhUJX5WooJVm3twQrGBLgS4iFajwiCYFoiqW0lPs/7nNr2Wu4df+/QeXEqGOVEUvvEsEg7g4FRDn5WldTOqM5gnEsYUnUOq9unkChajwjCoKOSVKQEp5BankqHrsOoY66cGEhORTN//upgry0FTGLkDAiZAdtXgq6zX6fqNnRT1FTUa+sR2aAnqnUvRc6TwZI9ToWzC5wErkGQ9aWlI+k7WYafn1B+jyfeDBc/PawS2qBcCLt/XhTFNa18lV5+xu3xXvGUHy+ntr329BtCZ0PjEagfnlWkeoNMakEtSRGexr0HqDxIdWkqR9US430mmj9AU5pxD9g4weanLB2J9ZJl+PYukA1w2avKKhhg6fSR2KhVvLNteD7OhAtXVt/GgTLRekQQBPMQSW0rcWXklTw781nG+4y3dCiCifm52uFqr+VQT6U2QJTWlbz6PAtHZhr1HfWUNJcwrrMTg6RmnX4KM8JFUlsQBqOU4BTade3sOLbDqP2vmhTMvXMj+GL/Uf6xNsd8A25nPgAtxyDj436dpqSpBJ1BR5Rb1Bm3VeXvxZNG2kJm9+s+BDOSJIi7HIp+HnqDA7c8A9ueh/FLYeHzwy6hfcK8WB/iA11ZuaGATp3+tNvivXv6av+6Wjus5zE5TKu1M4820tyhIynSyH7aO18nzVEpmJjgM8SGcju4w7Q7IecbqDhw/v2Fvtv3HhRvhnn/BLeRJzd7OtnyuwmBfJVWTnWLcRe2BQHg+4Oi9YggCOYjktpWwkHrwIJRCywdhmAGkiQR7eusVGq7h4LGjhg9VLRW0NjRaOnw+i2jWumnnXA0iwLHiXTZeRAf6GrhqARB6M1E34m42LgY3YIE4J6UCG5OHMUHO0p4cUOBeQILnQ0BEyH1RdB3X/Bp8hvygd6HRDZkKi0t3MeKv7WDWtxiMOggd62lIzHe1udh879g3DVwycqTlZHDkSRJPDg/mvLGdj7ZXXrabbEesWhUmjOT2h7h4BKoJOKGoW0FtUgSJBpTENBcAQf/y/6AOBw0DkS5n3kBb9CbdgfYjYCfn7R0JNan/jD89Kgyq2LiTWfcfEtSKN0GAx9sLxnoyIQhbN3BCuKDRhDk7mDpUARBsELD91WzIAwhMX4u5FW2YEAFXlFEtTQAkNcw9Ku1M2oy0Ehq4upL+bxzCtNCPdCoxVOTIAxGWpWW5KBkNpVtottgXPJYkiQeWRTDVRODeHljgXmWLksSzPoTNJZC5mcXfJq8hjw0Kg0jXUeecZvjkU1kyaGEh4b2I1DB7PwTlOrCrK8sHYlxtq+En/8JY/8PfvPKsE5onzAj3INpoR68tqmQ1k7dye22alti3GM4UP2rpLYkKUm4w1vBcHp193CwraCG0f6uuDvanH/nvW+DQUeajYp4r3g0qiE4v8TOVWlDUvAjlO2xdDTWw2CAb+5S2mv95tVeV4uM8nTk4jhfPtp1hOOnPDYF4WxK69rIPNrEJaJKWxAEMxGvnAVhCIj1c6GtS8+R+jbwjiO6tgTAKvpqZ1RnEKt2Qquy47OWsaKftiAMcinBKbR0tbCvcp/Rx0iSxFOLx7BojB9PrDvE6j2l5z+oryLmge9Y2LbighNb+Q35hLmGoVVpT7+hvYGA1ixynaagFRfdBjdJgtG/VRKcrbXn39+Sdr4G6x9T4r38ddGrvYckSTwwP4ra4118sKPktNviveLJqcs586Ja2GzoaIRjGQMX6CDQ0tFNemkjSca8dupqhX3v0Ry9gILmIyT4JJg/QHOZsgwcvZQ+9IJp7HkLjqTC/KdgRNBZd7ttZijNHTo+21s2gMEJQ9W6ntYjC8b4WjgSQRCslXhnJghDwIlhkbkVzeATi1tLFT72XkM+qd2l7yK7NpuEplpKvWZyHAfjls8KgmAx0/2nY6+x71MLElCGwL141ThmRXrx8JqDrM08ZtrAJAlmPqgMi7vAKt2C+oJel+PrCn5GjYGWwFn9jVIYCHGLQdYrfXcHq91vwY9/gdjL4Iq3REL7VyaEuDE3xpt/bymiqe2XBHa8Vzwd+ooEBqQAACAASURBVI6TrYJOGtXz2Cz+eQCjtLxdxfXoDDJJEUb00874BNobyIicjYzMBO8h1k/7VDaOkLgcDm9RLmAJ/VNXBBseVy4OJ1x7zl0Tgt2YPMqdd7cV0603DEx8wpC1NvMYCcEjCHQTrUcEQTAPkdQWhCEgwscJlYTSV9s7FoBoe58hn9TOqcuhy9BFwvFGfiCRgBH2jPJ0tHRYgiCcg53GjsSARDaWbsQg9+0NrY1Gxb+vncCkEHfuXZ3Bptxq0wYXfQl4xSgD9wx9i62ho4Hq9upe+2k3Z/2PJtkBr+gZpopUMCefOPCMhOw1lo6kd3vfhR8eVH5ff/suqIdgC4gBcP+8KFo6dLy5tejktnivnmGRv25B4uQFvmOgaPMARmh52wpqcLBRMz5kxLl3NBhg1+sQMJH9hlY0Kg1jvMYMTJDmMvEmcPZXemubawjxcGDQw9d/AI0NXLrSqCG1y2aGcqypg3WZFQMQoDBUHa5tJftYsxgQKQiCWYmktiAMAXZaNaFeTuRUtChv1oEoyY7DTYfp1HdaOLoLd2JIZLxswzuVYcwI90Ay4sW0IAiWlRKcQm17LZk1mX0+1t5GzTs3TCTaz5nbP9rP7uI60wWmUsHMB6AmF3K/69OhJyo/I9wiTr9BlrE7solthrHEh4iVJEOCJCnV2iWp0FJp6WhOt/9DWLccIi+G370Pau35jxmmYvxc+E28P+9vL6G6pQMAX0dfvO29zxwWCcrA2LLdSpuNYWJbQS1TQz2w1Zyn0j//B2UVy7Q7SatOI9YjFnuN/cAEaS5aO+X5vmwXFPZt5ZBwil2vK4+bBc+Ci79Rh8yO8ibC24l/bylCFhcUhLP4vqf1yEKR1BYEwYxEUlsQhogYPxelUtvJB+zdieloRy/rKWwotHRoFyy9ah9BOj1S4HzqOiQSjVk+KwiCxc0MnIlGpelzC5ITXOy0fHjjZALd7Ln5w31kHm00XXBxV4B7GGx9rk/VeyeS2mdUalcexKGzhn3aCQS6DfEk0HAyejEgD64WJBmfwHf3QPhcWLJKqYwUzum+iyLp0ht4fZNSrS1JEvHe8WdJaieDoRuO7BjQGC2lrL6Nw7WtxvXT3vkauAbTETGPrLqsod165FQJ18GIYGXYqkiu9l1NHmz8J0QtgrFXGX2YSiVx68xQcitb2FYwyGcXCBazNrOCCSFu+I8Qr50EQTAfkdQWhCEi2teZ8sZ2mjt14BNHVINSfTZUW5DIskxG5T4S2tvZZpcMwIwwD8sGJQiCUZxtnJnqN5UNRzZccJWWh5MtH98ylREOWq5/bw8FVS2mCU6lhqT7ofIg5P9o9GH5Dfl42Hngaf+rBFHhegCa/GeJlSRDiVcUeMdB1peWjkSR+Tl8fQeEzoKrPgKNraUjGhJGeTqyZGIgH+8+wtGGNkBpQVJ+vJza9l8l00Kmg9oWijZZINKBdyKZeN5+2uVpcGQ7TL2drIZcdAYd433GD0CEA0BjA8kPQ0UG5K6zdDRDi16ntB2xcYBLXjSq7cipLhvnj7ez7WntgQThhKKa4xyqEK1HBEEwP5HUFoQhIvbksMgW8I4loDofJ60Th+oPWTiyC1PaUkq9rpVx2LK6JoRYPxc8nMSbfEEYKuYGz+Xo8aNnDmzrA19XOz6+ZQoatYpr391NWX2baYIbu0Sp3tv6rNHVe3n1eb3209bl/USWYSSho0JNE5swcEZfoSyrbzpq2TiyvoQ1y2BkIvzfp6AVVWt9cfecCCRJYuWGAuCUvtq/rtbW2kPwVCgeLkntGvxd7QjzOs8skp2vgY0zJFxHWnUaAAneCQMQ4QAZswQ8ImDTk32epTCs7VgJ5fth0Qpw9unz4bYaNTcljmJ7YR1Z5U1mCFAYyr7PrECSROsRQRDMTyS1BWGIiOlJaivDImNQdbcS5RJCXn2ehSO7MOlHtwMwJmgO+8uajVs+KwjCoJEclIxKUrGhdEO/zhPi4chHN0+hU2fgmnd2U9Xc0f/g1FpIvE95w25Egktn0FHUWHRmUrujCXX5XrYYxjIu+DyD2ITBJ26x8tmSAyNzvoEvb4XgaXD1Z0pVpNAn/iPsuW5qCF+mHaWw+jgxHjFoVJreW5CEzYbqnMHXS93EdHoD2wtrSYrwOvcKkqajyu//hOvBzoW0qjTCR4Tjaus6cMGam1oDsx9Wfu7ZX1k6mqGhKhs2/QtiL/vlefICXD0lGCdbDW9uLTZhcII1WHewgkkh7vi62lk6FEEQrJxIagvCEOHjYoubg1ZJavcMi4zWjCCvIQ+DPPQqUzKKvsdZb6DZ83K69TKJIqktCEOKh70HCd4JF9xX+1RRvs58cONk6o53cu07u2lo7ep/gOOuAZcA2PLceXctbS6ly9BFlHvU6TcUb0aS9WzWj2NsoEhqDzkeYeAXD1kWSnTlroMvboLAiT0J7fNU1ApndUdyGPZaNS+uz8dWbUuMewwHqs8yLBKgeMvABjjAMsubaO7Qnf+10+43lc9TlqE36MmoyWCCj5X00z5V7BVKu6HN/1Laaghnp++GNbeDnSsseqHPbUdO5WKn5eopwXx/sMJ0K62EIa+wuoXcyhYWjRVV2oIgmF+/ktqSJJVIknRQkqQMSZL29WxzlyRpvSRJBT2f3Xq2S5IkvSxJUqEkSZmSJI0/5TzX9+xfIEnS9adsn9Bz/sKeY0UzS2HYkiSJaF8XDlW2gHcMAFF6mXZdO6XNpRaOru8y6nOIN6j4sT4AG42KSSPdLR2SIAh9NDd4LgUNBRxpPtLvc40LGsE710/iSH0b17+/h5aO7v6dUGMLM+6B0h1QknrW3Y53HWdVzioAItwiTr+xYD1tKkcaPMbhaq/tXzyCZcQthmNpUH94YO83/0f4/HrwGwfXfAG2zgN7/1bGw8mWmxNHse5gBVnlTcR7xZNTl0O34VfPE75jwd7d6luQbMuvRZJgRvg5ktqdLbD/Q6Uad0QweQ15tHa3WlfrkRNUKpjzV6grhMzVlo5mcNv2AlRmwqUvgWP/C0punDESCXg3dYCfY4VBa11mJZIEC0b7WjoUQRCGAVNUas+WZXmcLMsTe77+M7BRluUIYGPP1wALgIiej9uAN0BJggN/A6YAk4G/nUiE9+xz6ynHXWyCeAVhyIrxcyGvshm91glGBBNzvBGA3IahNSyyqS6fIrpJ8BxDamEdk0a6YadVWzosQRD6KCU4BcAk1doA08I8eOOa8eQca+aWD/fR0a3v3wnHLwVHb9h6ZrW23qDnv/n/ZdGaRXxZ8CW/jfjt6e1HZBm5cCM75LGMDRZDbIesuCuUzwPZgqRwA3x2LfiOhmu/BDuXgbtvK3bLzFBc7bU8/1Me8V7xdOg7yK//VU9/lUoZxlm0yeh++kPRtoIaxgS44u5oc/ad0j+CziaYdhcAaVVKP22rrNQGiFoI/gmw+RnQmWC1jzWqOKDMmhhzJcRcapJT+rnac9m4AFbvLeW7A8cueHi0YD3WZh5j8kh3vF1E6xFBEMzPHO1HLgM+7Pn3h8Dlp2xfJSt2ASMkSfID5gPrZVmul2W5AVgPXNxzm4ssy7tk5a/jqlPOJQjDUoyfMx3dBkrqWsE7jrDaw2hUmiHXV/tA+rsAhI78DXlVLSSGe1k4IkEQLoSfkx9xHnFsPGKapDZASowPK5bEs6eknjs+TqNL14/2Slp7mH43FG+Gsr0nN+84toMr117JP3b+g5EuI1m9aDWPT38clXTKy6KqbKSWY/zYNYZxQaL1yJDlFgKBkwau127RJvj0avCKgmu/Anvxu2MqLnZa/pAcxua8GuSOEAAyajLO3DF0NhyvhJqhdcHfWM0d3aSXNZ57FoleB7teV3q5BypJ7LTqNPwd/fF1tNLqSUmCOY9AUymkr7J0NIOPrgvW/AEcPGDBsyY99fJ5kUR4O3P3p+ksfW8PJbWtJj2/MHTkV7VQUH2cS0TrEUEQBkh/k9oy8JMkSfslSbqtZ5uPLMsVPf+uBE6MUw4Ayk459mjPtnNtP9rL9jNIknSbJEn7JEnaV1NT05/vRxAGtRPDInMrWsAnFm1tIWGuoRyqP2ThyPomvXQTahnq9MoCj8RzLZ8VBGFQSwlOIbM2k6rWKpOd87JxATx5+Rh+zq1m+ecZ6A39qPyaeJPSjmDrcxQ3FnPHhjtYtn4Z7d3tvJD8Ah9c/AFxnnFnHleoDMDcqh8rktpDXdxiqDwItYXmvZ/D2+DT34NHOCz9FhxEWy1Tu37aSLydbXl3Sz3e9t5nHxYJygUGK7SzqA69QSYp4hwFAblrobEUpt0JgCzLpFWlMd5n/NmPsQZhKUoif+vz0N1u6WgGly3PQHU2XPqyyZ+bAkbY8/WdM/j7b+LIKG1k3ktbWbmhgE5dP1dbCUPO2swKVBLMF61HBEEYIP1NaifKsjwepbXInZIkzTz1xp4Ka7OvQZJl+S1ZlifKsjzRy0tUfArWK9zbCbVKUoZFeseCrCfGwY9dx3Zx04838eaBN8mozkBnGMRDcuqKyOhqINrOk91Fx3Fz0BLnL5ZmC8JQlRKitCD5uexnk5736inBPLwgmrWZFTzy9cELX9Js60TD5Jt4sn4Pi7+9gvTqdO6fcD/fXP4NF4VcxFnHdRRuoMo+nAaNJ9G+4jlqSIu7HJDMW619ZAd8skSpDF/6jUhom4m9jZq7UyLYV9KIv30UmTWZZ+40Ihjcw5QVGlZoW0ENDjZqxge7nX2nna+B2yilJQdQ2lJKXUed9Se1T1Rrt1TAvvcsHc3gUb4fUl9UBihHmaebp1olcf30kWy8fxbz43x5cUM+C17axvbCWrPcnzD4yLLMusxjTBnlgbezaD0iCMLA6FdSW5bl8p7P1cAalJ7YVT2tQ+j5XN2zezkQdMrhgT3bzrU9sJftgjBs2WnVhHk5KkltH6WycJnLaK6NuZaWrhZezXiV6364jsTVidy58U5WZa8irz4Pg9yP5fsm1n3wC7JsbRgXMIPthbVMD/dEpRIzYAVhqAp1DSXUNdSkLUhOWDYrjDtnh/HpnjL+9UNunxPbXfouPsz+kEUV3/NfZyd+J7mxbvE6bhh9Azbqc/Si7WiG0p3sVCUw2t8FG405urUJA8bFX6nezDJTUrt0N3x8JbgGwvXfgZMosDCnqyYGEeRuT0m5F+XHy6lt7yVpFpqsDIi1wt7KqQW1TAv1OPvzUtkeOLoHpt4BKmVeycl+2t5W2k/7VCMTlZ//theg87ilo7G87g6l7YiTz/+3d9/xVdX3H8df39wsMggk3AzCDGEkjEBYAgoiCGpV0Kqlpa1abW2tdljrr/bX9au1v1+r1g5XXXXUuhduBFGQTQIBAgTCnhmsDEjI+P7+OAcIkLAybu7N+/l4nEduvud7zvnk5ku493O+9/OFyX9s9svFtw/nH18fwgvfGUGNtUx/ejE/fmU5haUVzX5t8a28glI2FpVzZYZKj4hIyznvd2nGmEhjTPTRx8AkYDUwA7jR7XYj8K77eAbwbeO4ADjolin5BJhkjOnoLhA5CfjE3VdijLnAONOovl3nXCJtVlpSeyepHZcKQSF0ObiLu4ffzetXvc7cr83loXEP8ZWeX2FryVYeWPYA1713HeNfG8/dX9zN6+tfZ3vJdt8t4mIt69a8RkVQEEnth1NQUqnSIyIBYEK3CSwrWMb+iv1Nfu67J/XlxlHdeXLuJh6dc3blI6y1zNo6i6nvTuXBZQ+SET+YN5Ou4FcblxN7cM+ZT7D5C6it5s2SNDJUeiQwDLgWitZCwZqmPe+OLPj3V52E0Y3vQVR8055fThEaHMRPJ/ZhZ4FT4bDBEiRV5bBj6an7/Ni2vYfYsvfQ6etpL3wEwjvAkOnHmrIKsugY1pGeMT1bIMpW4JJfw6FiWPJPX0fie5//EYrzYMo/WrTG/9g+Xj75yVh+PKE3H63aw4SHvuDFRVsbV05MWrUP3NIjl/VX6RERaTnBjTg2AXjb/dhuMPAfa+3HxpilwGvGmFuArcANbv8PgSuAfOAQcDOAtXafMeY+4Oirzt9ba/e5j28HngPaAR+5m0ib1i+xPe+u2MXBSojx9j3hDXrH8I5M6jGJST0mAbCnfA+Ldy8+tn2y5RMAkiKTGJk00tkSR+KNaKFZZXtWsbyiACI7cnB/MlCgpLZIAJjQfQJPrXqKz7d/zjW9r2nScxtj+O1V/SmtqObBmeuJCgvmpjENJ2Zy9+bywNIHyCrIIrVDKk9MfIIxyWPg0D7IegnmPQjXneFj6Rs+pSYkioUVqVynpHZgSJ8CH93jlCBJSG+ac+5aDi9eA5FxTkI7Wm/kW8qUwck89nkfCqyHFQUrmNBtwokdelwEJgg2zYEeY3wTZDOYl++sHXRRnwZet+3fAmvfgzE/htDIY83ZhdkMiR/ScLmlQNNlGPS5HOb/DYbd0nYXbN2+BBb8AzJvhNSJLX758BAPP720D1MGd+bX767m1++s5o1l27n/moEMSI5p8Xik+TilR3Yzulcn4qLCfB2OiLQh553UttZuAjLqad8LTKin3QI/bOBczwKnvMO01i4DBpxvjCKBKC0pGoC1e0q4ID4dts5vsG9iZCJTUqcwJXUK1lq2lGw5luD+bNtnvJP/DuCUDzia5B6WMIyYsGZ6obnqdVaEh5MckUjOFkuPuAi6xkY0z7VEpMWkx6aTFJnEZ9s+a/KkNkBQkOHP1w2irLKa3723hqjwEK4b2uWEPgXlBfx9+d+ZsXEGseGx/PqCX3Nt72sJDnJf6kTEwvBbYP7f4eJ7oVPv+i9mLeTPZmfsSKpLgxnS9TR1a8V/RMU7ZQlWvwXj/9upvdsYu1fCC1OhXQzc+D7E1LuWuTQTT5Dh7kkDuOvLzny2ZSk/G35Sh3YdIHmos1jkJb/ySYzNYd76YpI7tCOlU2T9HRY94STzR3zvWFPRoSK2l27na32/1kJRthLjfwn/vAgWPeY8bmuOHIK3vw/tu8Dk+30aSoo3in/fMpIZObu47/21XP3Il3x7VA9+NqkP0eEhPo1Nmsba3aVsKi7nu2NTfB2KiLQxKhIp4mfSk5wFy5y62ulQshMOn/kj/8YYesb0ZFq/aTw8/mHmfm0ur175KncNvYukyCTeyX+Hn8z5CWNfHcu096fxcNbDLNi1gMPVTbR6fG0tdvWbrIiMZlB8Jos27eXC0318VkT8hjGGCd0msGDXAsqrypvlGsGeIP7xjSFcmNqJe97I4ePVuwE4VHWIx1Y8xpVvX8lHmz/iOwO+wwfXfMANfW84ntA+atSdEBzu1FptSNE6KNnB4qBMYiND6Rrbrll+HvGB/tfCvo2wp57FBc9FQS68MAVCo5yEdoeuZz5Gmtzk/gl09PRmW/l6yo/UU683ZTzsyobDB1o+uGZQXVPL/I3FXNS7U/0zrg8fgOUvwoDrnDryrqzCLAAy4wN8kciTJQ2C9Kmw8DEo3+vraFreZ/c5f++mPAJh0b6OBmMMUwYnM/tn45g+sjvPL9zChIe+4IOVu31XFlGazAerduEJMkxW6RERaWFKaov4GW90GHGRoU5SO95ZLJLCted8Hk+Qh/S4dG4ecDNPXPoE86fN57nLnuO2QbcR5gnjhdwXuO3T2xjz8hhu/vhmnsh5guWFy6mqrTrxRLW1zpuFwrWweS6sesOZKTT7PphxJ7z8dXh6Ivwtg52H9lBENbHBfSg/UqPSIyIBZGL3iRypPcK8nfOa7RphwR7++a2hDO7agR+9nM0D8//NVW9fxeM5j3Nx14uZMXUGPx36U6JCo+o/QZQXht0MK1+FfZvr77PhUwDeLksjo0tM2/m4fluQdjUEBTduwcjCdfD81c7NkZveg47dmy4+OSfGGK4fcCGYKh75sp6/OykXg62FLc33N6kl5ew4SGlFNRf1bqD0SPbzcKQMRp34wdjsgmzaBbejX1y/FoiylRn/S6e2+vy/+jqSlrVlPix6HIZ/F1LG+TqaE8S0C+G+qQN45/YxeKPD+OF/srnpX0vZurd5bohL87PW8v7K3YzuFUds5GkW4RYRaQaNqaktIj5gjKFfUjTr9pTCxDSnsSAXuo9u1HlDPCEMTRjK0ISh3N7/Oxw6uJXsnQtYXJDF4v1reaxgGY+ueJQIghhKGCOP1DKyrIQ+JUUE2dp6AvVAZCeIjHe+dk9heXAt7F9M2cGuBJkqRvVSUlskUAz2DiY2PJbZW2dzWY/Lmu06kWHB/OgrHu6a9Sgv5G8nJTqNhy5/iMHxg8/uBKPvhKVPO0mOq/526v78T6nxprFwRzg/yVDpkYASGeckOnPfgom/O/cSJEXr4fmrnMT4je9BrD5m7Ws3DBjDM+vhlZXzuPPC8USE1nlr02U4hEQ6JUjSrvJdkE1k3oYijIExqXGn7qypgsX/hJ5jnRnKdWQXZDPIO4iQoDZY5sHbFwbeAEuecpL9baHufWUZvHu7c8Nt4u98HU2DMrp24N0fjuHFRVt5aOZ6Jj08lzvGp/K9cSmEBXt8HZ6cg9xdJWzde4jbL+7l61BEpA1SUlvED6UltufFRVupjupMcFjMmWdq19ZCxQEoLzq+lRWd+H3d9iOlRAAXuhvAgaAglkbFsDgqmsWhR5gXWgOxYXTs1IvhkV0Z2bEfIxOG0S0uHROdAOEdIOjED4OsWHgfUWW55G5px6AukcS0a4NvsEQClCfIw/iu4/lo80dU1lQS5mn6hYK2lWzjL1l/Yfa22cRHJ3Co4EY2be5PyAUNLxx5ivadYci3IPsFGPtziKlTm7uyFLYuZE+/m7DbIaOrFrIKOP2vdRI+O7Ohy9CzP27vRiehjXUS2p1Smy1EOXtJUUl0CO1EUdAmnluwhdsvrvN7CQ516qhvmuO7AJvQvA3FDEqOoUNEPTMh17zrlKO78uETmkuPlLJ+/3p+kPGDFoqyFbr4v2DV607ZqSv+7Otomt+s38L+rXDzhxDWwKeWWolgTxA3j+nJFQOT+P37a3jo0/W8vWInf5gygNH6NKffeH/lboKDDJPS28BNIxFpdZTUFvFDaUntqayuZcu+Q6TGp8G2RbDiP1BWWH+S+lAx1FafeiITBBFxEOl1ts6Zztco7/E2d6Z1h0gvl4ZGcKl76J7yPSzZs4TFuxezaPciZm57H7a9T2JkIiMTRx5beDI+Iv7Y5VYUrSA9bgCfry7lB+N0N18k0EzsPpE3N7zJ4t2LGdtlbJOd92DlQZ5c+ST/WfcfQoJCuHPInXw7/dvsLbNc//gCvv3MEl77/ih6ec/yDfyFP3E+qj//7ycmOTbPhdoqlgY7tWcHd+3QZD+DtBL9vgLvhzqztc82qb1vs5PQrq2Cmz4Ab5/mjVHOmjGG4UlD+KJyOU98vpHpI7ufeMO813jY8ImT5PPjUjEHD1exYvuB+l87WQsL/gFxvSH10hN2rShcgcWSmdDG6mnXFZsCQ74JWf9yPqkTyDXwN33ufBLpgh82+hOcLSmhfTiPfiOTG4YV8et3VvONpxdzzZBkfnlFGt7opr9BLk3HWssHq3YxJrUTHVV6RER8QEltET+UdmyxyFJSkzOdld3fcWfhBLc7npRunwxJg91EdbybpD5aEsQLEbEQdH4f8UuMTOTqXldzda+rsdaytWQri3cvZvGexXy+43Pe3fguAD1jejIicQSZ8Zls2L+BSzt/i5paq0UiRQLQyMSRRIVEMWvrrCZJalfVVvF63us8nvM4BysPcm3va7ljyB10auf8/UjuAP++dSTXP7GQbz29mNd/MJrkDmexsGOHbpAxzUlsX/QziE5w2vNnQWgUn5T2pGenyvpnRIp/a9cBek2A3Lfh0vtO+UTRKfZvdRLaVYecRSHj01omTjlrGd4MPt36KWVV+3lq7ibuntz3+M6U8c7XTZ/D0Bt9El9TWLhxLzW1lovqe+20dQHsXgFX/vWU8ZxdmE2wCWaQd9Cpx7UlY38OOS/D3Afg6r/7OprmUVEC794Bcakw4de+jua8jOvjZeZPx/LYnHwe/2Ijs9cWcM9l/fjGiG4EBWl9i9Zo1c6DbN93mB9d0tvXoYhIG6Wktogf6hUfSXCQYe3uEq665Fcw4KtOgjoyHkIjz71OaCMZY+gR04MeMT34Wr+vUWtryduX58zi3rOIGRtn8GreqwCUH+xKuxAPmd1Uq1Yk0IR4QhjXdRxzts+huraa4KDze5lhrWXujrk8uOxBtpRsYWTiSH4+/Of0je17St8UbxQv3DKCaU8u4ptPL+a120ad3cyuC+9yPuGy4O8w+X5ntuOGWdieY8naVM4YffQ5cA24FtZ/BDuWQLcLGu53cIeT0K4scUqOJA5ouRjlrGV4MwAYmV7Ks/M3c+PoHsf/Bnj7QnSSU4LEj5Pa8zYUERnqYUh9r50WPgLtYp0bdSfJLsgmPS6ddsFncbMvkHXoCkNvdmYxj/kxxAXgpwVn/sopQfOdmRDiv7/v8BAPd03qy5Qhyfz6ndX86p3VvJG1g/uvGUD/zioJ1hrU1lqWb9/PzNwC3l+5mxCPSo+IiO+cYXqKiLRGYcEeUuOjWLu7xElidxnmfLwyLKrFE9r1CTJBpMWlcdOAm3hi4hPMnzaf5y97nj+M+QN5W+IZmRJLaLD+/IgEogndJnCg8gDZBdnndXzevjy+9+n3uOOzOwB45JJHeGrSU/UmtI/q3zmG524ezp6DFXzrmcUcPFR15gvF9YIB18GyZ6F8LxSvh4PbOJh8MYWllWR00ZvngNX3cggOh9VvNtynZBc8dyUcPgDfegeSMlouPjknaXFpBAcF06fbXiqra3ns8/zjO41xFgfd9IWzvoifmrehmFG94k597VScD3kfwfBbT0lkVtZUsqp4VdsuPVLXRT8DTyh8EYB1tTfMcj55NPpO6Drc19E0iV7eKF66dSR//dpgduw/xFX/+JLfv7eGssp6yilKs6usrmFOXiH3vrWKEX+czVcfX8iz8zfTKz6Kx6cPb3ozVQAAIABJREFUJSZC6ySJiG8oqyTip9KS2rN2d6mvwzgrIZ4QMhMyGdZpEpuLD3OhZkCKBKwxnccQ5glj9rbZ53Rc8eFifrfgd9zw/g2s3beWX4z4BW9NeYtxXcdhzuJm3dDusTz57aFsKirnpueWUH42b3zH3g1Vh2HRo7DhUwCWhw0DYLA+TRK4wqKh96XO4nq1NafuL93jJLTLi+Fbb0GykoKtWZgnjPTYdLaWreX6oV14adE2dh44fLxDyng4vA/25PguyEbYurecbfsOcVFv76k7Fz8OnhAY8d1Tdq0uXk1VbRVD4oe0QJR+IDoBRn4PVr4Khet8HU3TOXwAZtwJ3n5w8S99HU2TMsYwdUgys++6mK+P6Ma/Fmxm4kNf8OGq3VhrfR1ewCutqOK9nF3c8Z9sht43i5v/tZQZK3YyMiWWv00bTNavL+WF74xgYnqCr0MVkTZMSW0RP5WWFM2ekgr2lx/xdShnbf6GYoD635iJSECICIlgTOcxzNo2i1p75pmRFdUVPLXyKb7y1ld4d+O7TE+bzgfXfMD0tOmEBJ3bzJ+Lenv5+9eHsHLHQb734jIqqupJWNbl7QvpU2Dxk87Cgd5+LNobQagniLSk6HO6tviZ/tdCWQFsnX9ie1mhU3KkdA988w3nk1DS6g3yDiJ3by63j+8JwN9mrT++M+Vi5+vGOS0eV2NtLCrjfz90ErCn1NM+tA+WvwSDbnDWTTnJ0U/LZMbrpswxY34CoVHw+f/6OpKm8/G9zt+yqY9DSLivo2kWMREh3H/NQN76wWhiI0O5/aVsbn5uKdv2HvJ1aAGnsLSC/yzexo3PLiHzvk+58+XlLNq0lysHJfGvm4aT9etLefQbmUwZnEz7cM3OFhHfU1JbxE/1S3QXi9xT4uNIzt68/GK80WH0SYjydSgi0owmdp9I4aFCcotzG+xjreXDTR9y9TtX8/flf2dU51G8M+Ud7hl+DzFh51/647IBifz5q4OYn7+XO19eTnXNGRLrY++GI6WwMwtSJ7J8+wHSOrcnLPj8FtEVP9FnMoREwOq3jreVF8PzVzu1tKe/fvp629KqZMRnUFlTSWntNr55QXfeyNrBxqIyZ2d0AsT3dxaL9APWWpZs3setzy9jwkNf8FleIbeNS6Fnp8gTOy57FqoPw6g76j1PVmEWvWJ60SG8QwtE7SciYmHU7bDmHWc8+Pts37yPIOc/cNFdbeITJUO6dWTGHWP4zZXpLN28j0sf/oJHPttAZfUZbmDLaW0uLuefX2zkq48vYOQfZ/PLt1exubicm0b34I3vj2LxLyfyf18dxPh+8YSH6LWRiLQuWihSxE+lJblJ7d2ljO7V+st51NZa5ucXM66P96xKCYiI/xrbZSzBJphZ22Yx0DvwlP0rClfwwLIHWFm0krTYNO6/8H6GJzZdHdCvDu1CWWU1v52Ryz1vrOTB6zMICmrg707iQOh7BeR9SE3KBFZ9eZCvDe/aZLFIKxUa6dTWXjsDrnjQWQzyhSmwfzN84zXoMcbXEco5GOwdDMCKohXcPv46Xlm6jb98up5Hv+Em+lIudhYJrDrcahfRq66p5ZPcAp6ct4mc7QfoGBHCjyb05tujutMp6qTFb6srYcmT0GsCxKedcq6a2hpyCnO4vOflLRS9Hxn1Q+eGwAtTnEVEe11yfIuI9XV0Z+/QPnjvx5AwAMbe4+toWkywJ4jvXNiTKwYm8fv3c3lw5nreXr6TP0wdyKhecb4Ozy9Ya1m9s4RPcvcwc80e1hc4NwAHJLfnpxP7MKl/An0TovV+TUT8gpLaIn7KGx1Gp6gwZ7HIVshay56SCjYWlpNfWErurhL2lR9RPW2RNiAmLIbhicOZvW02P8n8ybE3RjvLdvLXrL/y8ZaP8bbzct+Y+7gq5So8QU0/8+fG0T0orajiwZnriQwL5vdT+jf8Bu3S+6BDN9a3y+Bw1SIGd9XMxjah/7XOYpFr34Uv/wrFG+Abr0DKOF9HJucoMTKR+Ih4copymJ42nVsu7Mk/PsvnB+MOMiA5BnqNd2rnb10AqRN8He4JyiureX3Zdp6Zv5nt+w7TIy6C+6YO4LrMLrQLbeBv4+o3j5ecqMeGAxsoqyrTIpH1CY+BHyyA9R9D/mxY9wGseAkwzmznXhOcMZI8DDyt+K3yR/fAob0w/Q0IDvV1NC0uMSacx6YPZU5eIb95dzVff2oR1w5J5pdfSTv1JpBQVVPL0s37mLmmgJm5e9h1sIIgAyN6xvLbq9K5ND2BLh0jfB2miMg5a8X/U4vImaQlRbPOx+VHqmpq2br3EBuLysgvLGNjYRkbi8rYWFR+wgrl0eHBjOwZyyX9Tq37KCKBZ2L3idy36D42HthIYmQiT696mhfXvEiQCeL7Gd/n5v43ExHSvG+gfjg+lZKKap6cu4n27YL5+eR+9XfslAqX/4kVS7YBkKGkdtuQOhHC2sObt0JQMEx72ZmtKX4pw5vByqKVANx6UQovLNzKQzPz+NfNI6D7aPCEwqY5rSapXVhSwfMLt/DvRds4eLiKod078t9XOMklT0OfLAGnZMbCRyE+vcHxmlWQBcDQ+KHNEHkAiIqHzG87W20N7MyGjbOdJPe8B2HunyEsBlLGHk9yd+jm66iPWzMDVr3uLAyZNMjX0fjU+L7xfPrTcTzyWT7/nLuRWWsL+MXlaUwb3rXhT2i1EYeP1PDF+iJmrtnD7LWFHDxcRVhwEGP7ePnppX2YkJZAbGTbuyEiIoFFSW0RP5aW1J7nFmyhuqaWYE/zlsgvr6w+nrh2v+YXlrF17yGqa4/XJExsH05qfBRfzUwmNT6KXvFRpHqj8EaH6WNsIm3I+K7j+cOiP/DnpX8mb38e+yr2cVXKVfwo80ckRia2SAzGGO69vB+lFdU8Omcj0eEhfH9crwb7r9h2gA4RIfSI02ylNiEkHNKvhpxX4YYXofdEX0ckjZDhzeDTrZ9SfLiYTu068f1xvfjTx+tYumUfw3vEQteRsPFzX4fJhoJSnpq3iXeW76KqtpbJ6Yl8d2xPhnY/y9IXmz6HgtUw5VFo4HVVdkE2SZFJJEUlNV3ggSrIA12HO9vFv4DD+2HTF8eT3Gvfc/rF9XaS270mOOWJQiNPf97mUl4M7/8UkjKcWtpCeIiHuyf3ZeqQzvzqndX88u1VvJ61nfunDiS9c3tfh9ei9pcfYdbaAmauKWDehiIqqmqJaRfChLR4JqUnMrZPJyJClQISkcChv2gifiwtKZoj1bVsKi6nT0J0o89nraWorNIpGVJ0fNZ1fmEZuw9WHOvnCTJ0j4sg1RvF5P6J9PJGHUtgR4Xpz4qIgDfCy+D4wSzcvZDM+Ewem/AY/Tv1b/E4jDH8YeoAyiqr+b+P1hEdHsz0kd3r7Zuz4wAZXTroBlxbcvmfYezPoWMPX0cijZThzQAgpzCHCd0ncOPo7jw7fzMPfJzHq7ddgEm5GD67D8qKIMrborFZa1m4aS9Pzd3EnLwiwkOC+NrwrtxyYU96nLwA5JksfBQi42Hg9Q1eK7swm5FJI5sg8jaoXUfoP9XZrIWivOMJ7qznYPETzqz/bqOOJ7kT+jd4g6FJWQsf3OWsATD1PfCENP81/UhqfDQvf/cC3l6+k/s/WMtVj3zJTaN78NNL+wT0+5Md+w8xM7eAmWv2sGTzPmotdI4JZ9rwbkxKT2B4z1hCmnnyk4iIrwTuX3eRNuD4YpEl55TUrqm1bN936IQZ10cfl1QcLxkSGeqhV3wUF6TEOUlrbxSp8ZF0i40kNFgvjkTk9H4/+vfsKtvFqM6jfJoo9gQZ/nJDBuWV1fzqndVEhQUzZXDyCX3KK6tZX1DKpP4tM4tcWonQSN/NuJQmlRaXRnBQMDlFTlI7IjSYOy9J5Tfv5jJ3QzHjUsY7Se3NX8DA61okpuqaWj5YtZun5m1i9c4S4iJDuevSPnzzgu7n97H/wnWQ/ymM/xUE1183eHvpdooPF5MZr3rajWYMxPdztlE/dBYa3boANn7mJLk//Y2zRSU6pWBSJ0DKeIhspgULc9+CNe/ChN9CQnrzXMPPGWO4NrMLE/ol8KdP1vHMl5v5YOVufnd1OpP7JwbETWtrLXkFpczMLeCT3D3k7nJKUfZJiOKH41OZlJ7IgOT2AfGzioiciZLaIn4spVMUIR7D2t2lTBl86v6Kqppj9a3r1rveVFzOkeraY/06RYWRGh/J1YM7H5t1nRofRWL7cL0gEpHz1iOmBz1ievg6DABCPEE8Nj2TG59dwl2v5RAZGszE9IRj+1fuOEithSGqpy3il8I8YaTHppNTlHOsbdrwbjw5dxMPfLKOsbePwoR3gI1zmj2pXVZZzatLt/Psl5vZeeAwKd5I/vfagVwzJJnwkEYsjLvoUQhuB8O+02CXo/W0ldRuBiHtnMR16gSYfD8c3OkkuDfOhrwPIec/gIHOg52a/b0mQJdhTTOjurQAPviZs4Dl6B81/nwBLiYihD9eM5Drhnbhv99ezff/nc0l/eL5n6v70zXW/0qM1dRasrftZ2buHmauKWDr3kMYA5ndOnLv5f2Y1D+Rnuf6qQ8RkQCgpLaIHwsNDiI1Ppqc7QdYumXfscR1vjvreueBw1i33HWQgW6xEfTyRjGuj5deR2dee6OIidDHF0Uk8IWHeHj6xmF88+nF3P6fbJ67eTije3UCnNIjoEUiRfzZIO8gXl//OlU1VYR4QggNDuInE/tw9+s5fLymiMt7jnUWi7S2WcpF7DlYwXMLtvDS4q2UVlQzomcs/3N1fy7pF9/4RevKipz670Omn3YmcHZhNjFhMaR0SGnc9eTMYpIh81vOVlsDu5Y7M7g3zoZ5D8HcB5zFaHuOPV6qpGP95a9Oy1qnjnbVYZj6OHj0Fv5sZXbryHt3jOG5BVt4+NP1TPjLF3SOCSfEE0RosLt5Tvra0OM6bUePD2ugPdTj7qvbdrSvJ+is/h5UVNWwYGMxM3MLmLW2gOKyI4R6ghidGsdtY3sxMT2e+OjwFngWRURaL/2PKOLn0pPa82b2Dq5/YiEA4SFBpHSKIrNbR64f2vXYrOvucRGNmx0kIhIAosNDeO7mEXztyYV89/ll/PvWkQzp1pEV2w7QPS7i/EoCiEirkBGfwb/X/pu8/XkM6DQAgGuGJPPEFxt5cGYek8eOJ2jtDCjeAN4+TXbddXtKeGruZmbk7KSm1nL5wCS+e1EKg5vyJtmyZ6CmEi64/bTdsguyGRI/hCCjMnEtKsjjzMruMgwu/i84fMApdZM/25nNve59p19cqpPcTp0APS48u/JHK1+FvA9g0v1NOm7bimBPELdelMJXBiXx1NzN7C2v5Eh1rbPVOF/LKquPtVXVHN9XWaff0YlCTSHEY05IgJ+cQA8OMuTtKaX8SA1RYcGM7xfPpPQELu7rJTpck5FERI5SUlvEz/1oQioDktvTo1Mkqd4okju0a/xsIBGRANYxMpQXbxnJ9U8s5KZ/LeW120axYvsBRvSM9XVoItIIg71OLbacopxjSW1PkOFnl/bhBy9l88nhNC4H2PR5o5OD1lrm5+/lyXmbmLu+iIhQD9NHdueWC3s2fXmDqsOw5Cnocxl06t1gt+LDxWwr3cb1fepfRFJaULsOkD7F2ax1bqQcXXAy+wVY8k93wckLjie5Ewac+gmCkl3w0T3Q9QK44Ae++VkCRFJMO35z1fnVIrfWUlNrjyXBj1Q7Ce+qmtoT2uomyk9or6+tzvGV9bRdPTiZyf0TGNUrjrBgTUwSEamPktoifq57XCQ3j+np6zBERPxKQvtwXrp1JNc9sYCvP7WIfeVHmnZWpYi0uMTIROIj4skpzGF62vRj7ZcNSGRgcgx/WHCYyzp0x2yaAyO/d17XqKqp5f2Vu3hy7mbW7i7BGx3Gzyf3ZfrIbnSIaKZPeqx8DQ4Vw6g7TtstuyAbgMwE1dNuVYxxbqJ4+ziJ6aoK2LbQTXJ/BrN+62xRCc6Ck70mQK/xEBEHM34E1Udg6mPObHDxCWMMwR5DsCeI5vpnLiIi505JbREREWmTusZG8O9bRnLDP53yTYO7Kakt4u8yvBmsLF55Qpsxhrsn9+XGZ5ewIXUYfTbPhJqqc1rAr6SiileWbOPZL7ewp6SC3vFR/Pm6QUwZ3Ll5Z1HW1sLCRyFxkFOu4jSyC7NpF9yOtLi05otHGi8k3Ela9xoPk3BmY2/8zJnFvf5jyHkZME6pkr0b4PI/Q1wvX0ctIiLS6iipLSIiIm1W74RoXrxlJG9k7WBgcoyvwxGRRsrwZvDp1k8pPlxMp3adjrWP7d2JET1jeWZXT/5UWwo7s5zSD2ew88Bh/vXlZl5Zup2yympG94rjf786kHG9vU1f7s1aOLQPDm6DA9vh4HYoyIXiPLj2qTMubpldkM2gToMICVLNXb/SvjMM+aaz1dbArhXOLO6Nn0HiQBj+XV9HKCIi0iopqS0iIiJt2oDkGAYooS0SEDK8GQDkFOYwofuEY+3GGO6Z3JdbntjG/4UbzMY5p01qr955kKfmbeL9lbsBuHKQs/hjo/5W1NZC2Z7jCesD29yv249/rSo/8ZiQSOg9GdKnnvbUZUfKyNufx22Dbjv/+MT3gjzQZaizjbvH19GIiIi0akpqi4iIiIhIQEiPSyckKIScohOT2gDDesQytF8KuVtS6Jc/h+Dx956w31rLF+uLeHLuJhZs3EtkqIebR/fg5gt7ktyh3ZkvXn0ESnaelKjedjx5fXAn1FadeEy7jtChm1NqotclENMVOnR1v3Zz9p9hhjbAiqIV1NpahsQPOXOcIiIiIgFASW0REREREQkIoZ5Q0uLSyCnKqXf/zyb14fPH+pO+8wOoKIHw9lRW1zBjxS6enreZvIJSEtuHc+/l/Zg2ohsx7eqU8jhSfvpZ1qW7AVvnagaiE50EdfJQZ7Z1h64Q0+144josqkl+7uyCbDzGc2ymuoiIiEigU1JbREREREQCRoY3g9fyXqOqpoqQkxaD7N85hlk9LiZoxwx2r5zF24cG8dz8LRSWVjAs3vDM5FDGxh8ipHQmfLH9xPrWh/aeeKGgYGif7MyoTrm4zgxr92tMFwgOa5GfOasgi7TYNCJCIlrkeiIiIiK+pqS2iIiIiIgEjEHeQby45kXy9ucxoNOAU/ZfdeVUDj1+L3vf/x/62o68FbaPxKhigkvK4Ys6HYPbHU9Qdx58vCRIh27O4+hEpwayjx2pOcLq4tVM6zfN16GIiIiItBgltUVEREREJGAM9g4GIKcop96kdkpiHGuTLiOlcDapHSMI79T/+AzrownrDt0gIu6s6ln7Wu7eXI7UHiEzIdPXoYiIiIi0GCW1RUREREQkYCRGJhIfEU9OYQ7T06bX2yft+y+0cFTNJ6sgC4DMeCW1RUREpO0I8nUAIiIiIiIiTSnDm9HgYpGBJrsgm5SYFDqGd/R1KCIiIiItRkltEREREREJKBneDHaV76LoUJGvQ2lWNbU1rChcodIjIiIi0uYoqS0iIiIiIgElw5sBEPCztfMP5FNaVarSIyIiItLmKKktIiIiIiIBJT0unZCgkIBPah+rp62Z2iIiItLGNDqpbYzxGGOWG2Ped7/vaYxZbIzJN8a8aowJddvD3O/z3f096pzjXrc9zxgzuU77ZW5bvjHmF42NVUREREREAl+oJ5S0uLSAT2pnF2aTEJFA58jOvg5FREREpEU1xUztHwNr63z/J+Bha20qsB+4xW2/Bdjvtj/s9sMYkw5MA/oDlwGPuYlyD/AocDmQDnzd7SsiIiIiInJaGd4M1uxdQ1VNla9DaRbWWrILsslMyMQY4+twRERERFpUo5LaxpguwFeAp93vDXAJ8Ibb5Xlgqvt4ivs97v4Jbv8pwCvW2kpr7WYgHxjhbvnW2k3W2iPAK25fERERERGR08rwZlBZU0ne/jxfh9IsdpTtoOhwEUPjh/o6FBEREZEW19iZ2n8F7gFq3e/jgAPW2mr3+x1Asvs4GdgO4O4/6PY/1n7SMQ21n8IY8z1jzDJjzLKiosBe4VxERERERM4s0BeLzC7IBlRPW0RERNqm805qG2OuBAqttVlNGM95sdY+aa0dZq0d5vV6fR2OiIiIiIj4WGJkIgkRCeQUBmhSuzCb9qHt6dWhl69DEREREWlxwY04dgxwtTHmCiAcaA/8DehgjAl2Z2N3AXa6/XcCXYEdxphgIAbYW6f9qLrHNNQuIiIiIiJyWhnejICeqZ0Zn0mQaYplkkRERET8y3m/ArLW3mut7WKt7YGz0ONn1trpwBzgOrfbjcC77uMZ7ve4+z+z1lq3fZoxJswY0xPoDSwBlgK9jTE9jTGh7jVmnG+8IiIiIiLStgzyDmJX+S6KDgVWicLiw8VsKdmi0iMiIiLSZjXHbf3/Au4yxuTj1Mx+xm1/Bohz2+8CfgFgrc0FXgPWAB8DP7TW1rgzve8APgHWAq+5fUVERERERM4oUOtqLy9cDsCQ+CE+jkRERETENxpTfuQYa+3nwOfu403AiHr6VADXN3D8/cD99bR/CHzYFDGKiIiIiEjbkh6XTkhQCDlFOUzsPtHX4TSZ7IJswj3h9I/r7+tQRERERHxCBdhERERERCQghXpCSYtLC7iZ2lkFWQz0DiTEE+LrUERERER8QkltEREREREJWBneDHKLc6mqqfJ1KE2ivKqcvP15ZMarnraIiIi0XUpqi4iIiIhIwMrwZnCk9gjr9q3zdShNIqcwh1pbq0UiRUREpE1TUltERERERAJWoC0WmVWYhcd4GOwd7OtQRERERHxGSW0REREREQlYiZGJJEQkBExSO7sgm36x/YgIifB1KCIiIiI+o6S2iIiIiIgEtAxvBiuLVvo6jEY7UnOEVcWrVHpERERE2jwltUVEREREJKBleDPYVb6LokNFvg6lUdbsXUNlTaUWiRQREZE2T0ltEREREREJaBnxgVFXO6sgC4Ah8UN8HImIiIiIbympLSIiIiIiAS0tNo2QoBC/T2pnF2bTo30P4trF+ToUEREREZ9SUltERERERAJaqCeU9Lh0v05q19palhcuZ2jCUF+HIiIiIuJzSmqLiIiIiEjAG+QdRG5xLlU1Vb4O5bzkH8in9EipFokUERERQUltERERERFpAzK8GRypPcK6fet8Hcp5yS7IBtAikSIiIiIoqS0iIiIiIm1Ahte/F4vMLsgmPiKe5KhkX4ciIiIi4nNKaouIiIiISMBLjEwkISLBL5Pa1lqyCrMYGj8UY4yvwxERERHxOSW1RURERESkTcjwZvhlUntn2U4KDxWqnraIiIiIS0ltERERERFpEzK8Gewu303hoUJfh3JOsgudetpD4of4OBIRERGR1kFJbRERERERaRMy4v2zrnZ2QTbRodH07tjb16GIiIiItApKaouIiIiISJuQFptGSFAIOYX+ldTOKshiSPwQgozevomIiIiAktoiIiIiItJGhHpCSY9LZ2XxSl+Hctb2VexjS8kWMuNVT1tERETkKCW1RURERESkzcjwZpBbnEtVTZWvQzkrywuWAzA0YaiPIxERERFpPZTUFhERERGRNiPDm8GR2iOs27fO16GclazCLMI8YfSP6+/rUERERERaDSW1RURERESkzcjw+tdikdkF2QzsNJAQT4ivQxERERFpNYJ9HYCIiIiIiEhLSYhMIDEykX+t/hfzds4j3BNOWHCY89UTRrvgdoQFhxHmCTthX3hw+LG2Y4/raTPGNFmsh6oOsW7fOm4ZeEuTnVNEREQkECipLSIiIiIibcp3B36Xj7d8TNmRMopriqmorqCipoLKmkoqqyupqKk473OHecKOJbwbSoqfbdv20u3U2BotEikiIiJyEiW1RURERESkTbmh7w3c0PeGBvdba50Ed03lsYR3RXXFsbbD1YeP7avb52hS/Nj+mopjSfKK6gr2V+0/4fij+6pqG160MswTdqxkioiIiIg4lNQWERERERGpwxjjzLQODicmLKbZr1dTW3NCEvxwzWEqq50EekxYDFGhUc0eg4iIiIg/UVJbRERERETEhzxBHiKCIogIifB1KCIiIiJ+IcjXAYiIiIiIiIiIiIiInC0ltUVERERERERERETEbyipLSIiIiIiIiIiIiJ+Q0ltEREREREREREREfEbSmqLiIiIiIiIiIiIiN9QUltERERERERERERE/IaS2iIiIiIiIiIiIiLiN847qW2MCTfGLDHG5Bhjco0x/+O29zTGLDbG5BtjXjXGhLrtYe73+e7+HnXOda/bnmeMmVyn/TK3Ld8Y84vz/zFFREREREREREREJBA0ZqZ2JXCJtTYDGAxcZoy5APgT8LC1NhXYD9zi9r8F2O+2P+z2wxiTDkwD+gOXAY8ZYzzGGA/wKHA5kA583e0rIiIiIiIiIiIiIm3UeSe1raPM/TbE3SxwCfCG2/48MNV9PMX9Hnf/BGOMcdtfsdZWWms3A/nACHfLt9ZustYeAV5x+4qIiIiIiIiIiIhIG9WomtrujOoVQCHwKbAROGCtrXa77ACS3cfJwHYAd/9BIK5u+0nHNNReXxzfM8YsM8YsKyoqasyPJCIiIiIiIiIiIiKtWKOS2tbaGmvtYKALzszqfk0S1bnH8aS1dpi1dpjX6/VFCCIiIiIiIiIiIiLSAhqV1D7KWnsAmAOMAjoYY4LdXV2Ane7jnUBXAHd/DLC3bvtJxzTULiIiIiIiIiIiIiJt1HkntY0xXmNMB/dxO+BSYC1Ocvs6t9uNwLvu4xnu97j7P7PWWrd9mjEmzBjTE+gNLAGWAr2NMT2NMaE4i0nOON94RURERERERERERMT/BZ+5S4OSgOeNMR6c5Phr1tr3jTFrgFeMMX8AlgPPuP2fAV40xuQD+3CS1Fhrc40xrwFrgGrgh9baGgBjzB3AJ4AHeNZam9uIeEVERERERERERETEzxlnsnTgMMYUAVt9HYePdALbBY6NAAAIOElEQVSKfR2EyGlojIo/0DiV1k5jVFo7jVFp7TRG2y797qW10xiV1q45xmh3a+05L5IYcEnttswYs8xaO8zXcYg0RGNU/IHGqbR2GqPS2mmMSmunMdp26XcvrZ3GqLR2rWmMNslCkSIiIiIiIiIiIiIiLUFJbRERERERERERERHxG0pqB5YnfR2AyBlojIo/0DiV1k5jVFo7jVFp7TRG2y797qW10xiV1q7VjFHV1BYRERERERERERERv6GZ2iIiIiIiIiIiIiLiN5TUFhERERERERERERG/oaR2MzLGdDXGzDHGrDHG5Bpjfuy2xxpjPjXGbHC/dnTbpxtjVhpjVhljFhhjMuqc6zJjTJ4xJt8Y84vTXPNjY8wBY8z7J7W/5B6/2hjzrDEmpIHjexpjFrvXedUYE+q2dzfGzHbj+9wY06UpniPxLT8do3e417DGmE512qe4sa0wxiwzxlzY2OdHfC/AxujP3fG5wj1HjTEmtrHPkfiWn47RevsZY/oZYxYaYyqNMXc3xfMjvhdgY/RiY8zBOn9Lf9MUz5H4XoCN047GmLfd+JYYYwY0xXMUiJr49/6sMabQGLP6DNesd3yYBl6/1XN8T1P/++WxxphsY0y1Mea6xj430noE2Di9yRhTVOf/0Vsb+/yI7wXYGD233KO1VlszbUASkOk+jgbWA+nAn4FfuO2/AP7kPh4NdHQfXw4sdh97gI1AChAK5ADpDVxzAnAV8P5J7VcAxt1eBn7QwPGvAdPcx08c7Qe8DtzoPr4EeNHXz6+2NjtGhwA9gC1ApzrtURxfJ2AQsM7Xz682jdG6Y/SkPlcBn/n6+dXWZsdovf2AeGA4cD9wt6+fW20ao/WM0YtPPqe2wNgCbJw+APzWfdwPmO3r57e1bk31e3e/HwtkAqtPc70Gxwdn8frN7dfQ++UeOO9BXgCu8/Vzq03jtIFxehPwiK+fU20ao6cZo+eUe9RM7WZkrd1trc12H5cCa4FkYArwvNvteWCq22eBtXa/274IOHpHYgSQb63dZK09ArzinqO+a84GSutp/9C6gCV1zn2MMcbgDJo3To4N5x/EZ+7jOQ1dX/yLv41Rt99ya+2WetrL3GMBIgGtghsAAmmMnuTrOG+Axc/56Ritt5+1ttBauxSoOpfnQFq3QBqjErgCbJwee99krV0H9DDGJJzdM9G2NOHvHWvtXGDfGS7Z4Pg4m9dvp3u/bK3dYq1dCdSeIQbxM4E0TiUwBdgYPafco5LaLcQY0wPnjsViIMFau9vdtQeo70XOLcBH7uNkYHudfTvctvOJIwT4FvBxPbvjgAPW2up6rpMDXOs+vgaINsbEnU8M0jr5yRg907HXGGPWAR8A3zmf60vrFQhj1D0+ArgMePN8jpfWy9/GaGPHsvifABmjo4wxOcaYj4wx/c/n+tK6BcA4Pfa+yRgzAuiObsycUSN/72ersePjdO+XpQ0IkHH6Vbe0wxvGmK7nGJu0cgEwRs8p9xh8DheV82SMicJJXvzEWlvi3JRwWGutMcae1H88zsBqjprAjwFzrbXzzvG4u4FHjDE3AXOBnUBNE8cmPhIgYxRr7dvA28aYscB9wMSmDk58I1DGqOsqYL619kx3wMWP+OkYbexYFj8SIGM0G+hurS0zxlwBvAP0bob4xEcCZJz+H/A3Y8wKYBWwHL1vOq1W9nsXqVeAjNP3gJettZXGmNtwZshe4uOYpIkEyBg9p9yjZmo3M/fO/ZvAS9bat9zmAmNMkrs/CSis038Q8DQwxVq7123eCdS9g9YF2GmMGVmnwP/VZxHLbwEvcFedtk/c458G9gIdjDFHb3Z0ca+NtXaXtfZaa+0Q4L/dtgPn9GRIq+RnY/SsuB+ZSTndwgTiPwJwjE5DpUcCij+O0fr6SeAKlDFqrS2x1pa5jz8EQvR/feAIsHF6s7V2MPBtd/+ms34i2pgm+r03dO6udX7v36eB8XGGc5zV+2UJbIEyTq21e621lW7708DQM/3s4h8CaIyeU+5RM7WbkXFuizwDrLXW/qXOrhnAjTh38W8E3nX7dwPeAr5lrV1fp/9SoLcxpifOL3oa8A1rbS4w+CxjuRWYDEyw1h6r82WtnXxSvznAdTg1cerG1gnY5x57L/Ds2VxXWjd/HKOnOT4V2OjegcwEwnD+WIofC6Qx6p4jBhgHfPNsj5HWzR/HaEP9JDAF0hg1xiQCBe7/9SNwJujo//oAEGDjtANwyDp1Rm/FmcVdcjbXbmua8PdeL2vtdur83t0Eyinj4wznOKv3yxK4AmmcGmOS6pSjuBqn9rL4uQAbo+eWe7StYKXOQN1wpvBbYCWwwt2uwKkfMxvYAMwCYt3+TwP76/RdVudcV+CsYLoR+O/TXHMeUAQcxqlLM9ltr3aPPXru3zRwfArOQif5OKuOhrnt17nxrnfjDPP186utzY7RH7nHVQO7gKfd9v8Cct1jFwIX+vr51aYxWneMuvtuAl7x9fOqrc2P0Xr7AYnu+UqAA+7j9r5+jrVpjNYZo3fg/F+fg7Ow0WhfP7/aNE7rGaej3Ovn4SQNOvr6+W2tWxP/3l8GduMsdrwDuKWBa9Y7PjjN67eTjm/o/fJw9/hynJttub5+frVpnNYzTv+X4/+PzgH6+fr51aYxSiNyj8Y9SERERERERERERESk1VNNbRERERERERERERHxG0pqi4iIiIiIiIiIiIjfUFJbRERERERERERERPyGktoiIiIiIiIiIiIi4jeU1BYRERERERERERERv6GktoiIiIiIiIiIiIj4DSW1RURERERERERERMRv/D9Ry0wt5TF7+gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1800x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLF-l5iKM46Q"
      },
      "source": [
        "- 위에 잘 예측하지 못하는 부분들은 조금 튀는 값인 것 같음\r\n",
        "- 해당 일자에 어떤 특징(개최된 대회 등)을 파악\r\n",
        "- 이후 1) 제거 또는 2) 특징 적용  (추세파악과 관련없다면 제거하기)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYr8KEnDjgQ1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}