infnet_projeto_codigos.ipynb

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  "metadata": {
    "colab": {
      "name": "infnet_projeto_codigos.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMAnGrHL5E6B3H7u0OER2yo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/naokityokoyama/infnet/blob/main/infnet_projeto_codigos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QcIndghqYkH4",
        "outputId": "2709c3f7-d1c9-4370-8226-98841ea8f4a5"
      },
      "source": [
        "!pip install prophet\n",
        "import warnings"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting prophet\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f0/fa/c382f0ac5abe9f0a4df9d874a5e8843db035fe2f071b5c00a545b1e3c10b/prophet-1.0.1.tar.gz (65kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 9.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: Cython>=0.22 in /usr/local/lib/python3.7/dist-packages (from prophet) (0.29.23)\n",
            "Collecting cmdstanpy==0.9.68\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/b9/b4f0938b38dbe775c4def707f525ab816b9d0215ecd8ab2d76780f4514f8/cmdstanpy-0.9.68-py3-none-any.whl (49kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 7.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: pystan~=2.19.1.1 in /usr/local/lib/python3.7/dist-packages (from prophet) (2.19.1.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from prophet) (1.19.5)\n",
            "Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from prophet) (1.1.5)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from prophet) (3.2.2)\n",
            "Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.7/dist-packages (from prophet) (0.0.9)\n",
            "Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from prophet) (2.3.2)\n",
            "Requirement already satisfied: holidays>=0.10.2 in /usr/local/lib/python3.7/dist-packages (from prophet) (0.10.5.2)\n",
            "Requirement already satisfied: setuptools-git>=1.2 in /usr/local/lib/python3.7/dist-packages (from prophet) (1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from prophet) (2.8.1)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.7/dist-packages (from prophet) (4.41.1)\n",
            "Collecting ujson\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/17/4e/50e8e4cf5f00b537095711c2c86ac4d7191aed2b4fffd5a19f06898f6929/ujson-4.0.2-cp37-cp37m-manylinux1_x86_64.whl (179kB)\n",
            "\u001b[K     |████████████████████████████████| 184kB 35.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.4->prophet) (2018.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet) (0.10.0)\n",
            "Requirement already satisfied: ephem>=3.7.5.3 in /usr/local/lib/python3.7/dist-packages (from LunarCalendar>=0.0.9->prophet) (4.0.0.2)\n",
            "Requirement already satisfied: pymeeus<=1,>=0.3.13 in /usr/local/lib/python3.7/dist-packages (from convertdate>=2.1.2->prophet) (0.5.11)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->prophet) (1.15.0)\n",
            "Requirement already satisfied: hijri-converter in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->prophet) (2.1.2)\n",
            "Requirement already satisfied: korean-lunar-calendar in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->prophet) (0.2.1)\n",
            "Building wheels for collected packages: prophet\n",
            "  Building wheel for prophet (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for prophet: filename=prophet-1.0.1-cp37-none-any.whl size=6639693 sha256=7fb4ed992896b8ad75fa58ef92b3f624650840f3c7a2f0ec8cdc2f1a72540275\n",
            "  Stored in directory: /root/.cache/pip/wheels/92/1d/02/bc0a91bd3a84e060722075d2b0b68e875e37ec7dc49451d513\n",
            "Successfully built prophet\n",
            "\u001b[31mERROR: fbprophet 0.7.1 has requirement cmdstanpy==0.9.5, but you'll have cmdstanpy 0.9.68 which is incompatible.\u001b[0m\n",
            "Installing collected packages: ujson, cmdstanpy, prophet\n",
            "  Found existing installation: cmdstanpy 0.9.5\n",
            "    Uninstalling cmdstanpy-0.9.5:\n",
            "      Successfully uninstalled cmdstanpy-0.9.5\n",
            "Successfully installed cmdstanpy-0.9.68 prophet-1.0.1 ujson-4.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfzQqbZrgkcZ"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from fbprophet import Prophet\n",
        "from prophet.plot import plot_plotly, plot_components_plotly\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import numpy as np\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NudpX0fFgtS_"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/naokityokoyama/infnet/main/train.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5SCj5MMFgwat",
        "outputId": "45e2a751-94a8-4c3a-ad5b-4c9b3e58ca18"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Store           0\n",
              "Dept            0\n",
              "Date            0\n",
              "Weekly_Sales    0\n",
              "IsHoliday       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g13ca_FGOjXW"
      },
      "source": [
        "dados = df[df['Store']==1]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6kzLn0COzs2"
      },
      "source": [
        "dados = dados.drop(['Store',\t'Dept'], axis=1).groupby('Date').sum().reset_index()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1uxE8KeNPMac"
      },
      "source": [
        "mapa = {0:False, 71:True, 72:True, 73:True}\n",
        "dados.IsHoliday = dados.IsHoliday.map(mapa)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-IpbkMlU5OF",
        "outputId": "b20a3a89-1574-4d56-9919-4f5cb511f675"
      },
      "source": [
        "dados.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(143, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zMDyapmiTdLe"
      },
      "source": [
        "Convert Datetime"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3H1vKY3TbzJ"
      },
      "source": [
        "dados['Date'] = pd.to_datetime(dados['Date'])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_tTXpBW_Q1P8"
      },
      "source": [
        "test = dados.tail(12)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "eCW2v2LjSm-B",
        "outputId": "1b6519f4-e29f-4301-823f-29b50ad12728"
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>2012-08-31</td>\n",
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              "      <td>1661767.33</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "          Date  Weekly_Sales  IsHoliday\n",
              "131 2012-08-10    1592409.97      False\n",
              "132 2012-08-17    1597868.05      False\n",
              "133 2012-08-24    1494122.38      False\n",
              "134 2012-08-31    1582083.40      False\n",
              "135 2012-09-07    1661767.33       True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-TevwKBg4H7"
      },
      "source": [
        "treino = dados.iloc[0:131]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "WkC8LlmxUACs",
        "outputId": "200e7a67-4407-434e-a0aa-ae5c0c8e9073"
      },
      "source": [
        "treino.head()"
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      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Weekly_Sales</th>\n",
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              "      <th>0</th>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>1643690.90</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-02-12</td>\n",
              "      <td>1641957.44</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-02-19</td>\n",
              "      <td>1611968.17</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-02-26</td>\n",
              "      <td>1409727.59</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-03-05</td>\n",
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            "text/plain": [
              "        Date  Weekly_Sales  IsHoliday\n",
              "0 2010-02-05    1643690.90      False\n",
              "1 2010-02-12    1641957.44       True\n",
              "2 2010-02-19    1611968.17      False\n",
              "3 2010-02-26    1409727.59      False\n",
              "4 2010-03-05    1554806.68      False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDq0NqgUheAo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbec1519-7c10-41e2-d032-b2ad7d227b3a"
      },
      "source": [
        "test.info(), treino.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12 entries, 131 to 142\n",
            "Data columns (total 3 columns):\n",
            " #   Column        Non-Null Count  Dtype         \n",
            "---  ------        --------------  -----         \n",
            " 0   Date          12 non-null     datetime64[ns]\n",
            " 1   Weekly_Sales  12 non-null     float64       \n",
            " 2   IsHoliday     12 non-null     bool          \n",
            "dtypes: bool(1), datetime64[ns](1), float64(1)\n",
            "memory usage: 336.0 bytes\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 131 entries, 0 to 130\n",
            "Data columns (total 3 columns):\n",
            " #   Column        Non-Null Count  Dtype         \n",
            "---  ------        --------------  -----         \n",
            " 0   Date          131 non-null    datetime64[ns]\n",
            " 1   Weekly_Sales  131 non-null    float64       \n",
            " 2   IsHoliday     131 non-null    bool          \n",
            "dtypes: bool(1), datetime64[ns](1), float64(1)\n",
            "memory usage: 2.3 KB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "e1j0ukW2h0wh",
        "outputId": "36d75f0b-3b1e-4dae-b3ec-e6cb6ee42f09"
      },
      "source": [
        "plt.figure(figsize=(18,5))\n",
        "sns.lineplot(data=treino, x='Date', y='Weekly_Sales')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4b54aa8d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0TM0RNp8kuP"
      },
      "source": [
        "Tendencia"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "Wigr01Mz7wuY",
        "outputId": "3a55330c-cbf4-4e82-d62e-635804136e47"
      },
      "source": [
        "#media movel 52 semanas\n",
        "plt.figure(figsize=(18,5))\n",
        "sns.lineplot(data=treino, x='Date', y=treino.Weekly_Sales.rolling(52).mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4b54aa8150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9768uyX9OWVHKwkllFOVl09adoK2rj7buPtq6EgOPfbT+yb59rd1s2n/YMkI6wSwnJEmSJA1rLZ29rNzSyMrNB7l/y0H2t/UAMHd8Ce88axovnzuW5TMqKcj9YzHw8rljn33e0ZNg4962Z8uKp3a3cv+WRpKpeMzPzs4KlBXmUlqQQ2lhLhPKCrh08UTLCOkEs5yQJEmSNGxtO9jOG699kLbuBGWFuZw7p5rz5ozlZXOrmVhWOKhrFOfnUDu98nmTTHb3JXl632HW72klkYyUFuZQWpBLaWEupQW5/YVEYQ6FudmumCENgbSWEyGEG4DLgAMxxkVHOH4+cAewfWDX7THGawaO/Q3wPiACTwLvjjF2pzOvJEmSpOGjszfBh/5nDTnZWdz6wRWcXlNBdtaJKQoKcrNZOrWcpVPLT8j1JL00WWm+/veAi49xzv0xxqUD2zPFxGTgI0DtQKmRDVyZ1qSSJEmSho0YI5+5/Um2HGjnG1eexrLplSesmJA0/KS1nIgxrgSaX+Tbc4DCEEIOUATsOWHBJEmSJA1r//PwTu5Yu4dPXDSXc+dUZzqOpDRL98iJwVgRQlgXQvh1CGEhQIxxN/BlYBewF2iNMd6VyZCSJEmShsbjuw5xzS838Ir547j6/NmZjiNpCGS6nHgMmBZjXAL8P+BnACGECuD1wAxgElAcQnjHkS4QQnh/CGF1CGH1wYMHhyi2JEmSpHRoau/h6pseY3xpAV+7YilZ3sohjQoZLSdijG0xxvaB578CckMI1cCFwPYY48EYYx9wO3D2C1zjuhhjbYyxduzYsUc6RZIkSdJJIJmKfOzHa2nq6OXb7ziDsqLcTEeSNEQyWk6EECaEgXV5QgjLB/I00X87x1khhKKB468ENmYuqSRJkqR0+/o9m7l/SyPXvG4hiyaXZTqOpCGU7qVEbwbOB6pDCA3A54BcgBjjt4G3AB8KISSALuDKGGMEVoUQbqP/to8E8DhwXTqzSpIkScqce58+wDd+t5XLz5jCW5dNzXQcSUMs9HcBI0NtbW1cvXp1pmNIkiRJOg71zZ1c9v8eYFJ5IT+9+mwKcrMzHUlSGoQQ1sQYa490LNMTYkqSJEkaxbr7klx902OkYuTb7zjdYkIapdJ6W4ckSZIkHc3nf7GBJ3e3cv07a5lWVZzpOJIyxJETkiRJkjLi1tX13PzILq4+fxYXLRif6TiSMshyQpIkSdKQ27CnjX/42VOsmFnFxy+am+k4kjLMckKSJEnSkGrt6uNDN62hvCiXb7ztNHKy/bFEGu38W0CSJEnSkNm07zBv/a+H2H2oi2vffjpjx+RnOpKkYcAJMSVJkiSlXSoVufEPO/i3O5+mtCCH699ZS+30ykzHkjRMWE5IkiRJSqu9rV387a3reHBrExeeMo4vvvlUqkscMSHpjywnJEmSJKXNL5/Yw2dvf5K+ZORf37SYK5dNJYSQ6ViShhnLCUmSJEknXFt3H5+7Yz0/fXw3S6eW87W3LmVGdXGmY0kapiwnJEmSJJ1Qq+qa+Pgt69jX1s3HLpzDhy+Y7Yocko7KckKSJEnS87T3JLjl0Xp2NXdSU1lETWUR06qKmFpZREFu9gu+ryeR5Kt3b+a6lXVMqyzitg+u4LSaiiFMLulkZTkhSZIkCYD9bd3c+OAOblq1k8PdCQpzs+nqSz7vnAmlBdRUDRQWlUXUVBUxrar/do3P3v4kG/a28bblU/mHSxdQnO+PG5IGx78tJEmSpFFu8/7DXL+yjp+t3U0yFbl40QT+6mUzWTq1nOaOXnY2d7KrqZNdzZ3sbOpkV3MHKzcf5MDhnuddp6o4j+vfWctFC8Zn6JtIOllZTkiSJEmjUIyRh+uauW7lNu7ddJCC3CzetryG954749mREABVJflUleRz+hFuz+jqTbKrub+0OHi4h4sWjGfsGJcIlXT8LCckSZKkUSSRTHHn+n1ct7KOJxpaqSrO428unMtfrJhGZXHecV2rMC+beRPGMG/CmDSllTRaWE5IkiRJo0B3X5IfP1rPdx6oo765ixnVxXzhjYt48+lTjjrJpSQNBcsJSZIkaQRLpSK/eGIPX7pzE7tbujhjWgX/cOkCLjxlPNlZIdPxJAmwnJAkSZJGrFV1TfzLrzayrqGVBRNL+dJbTuWc2dWZjiVJf8ZyQpIkSRph6g6286+/fpq7N+xnYlkBX7l8CW88bTJZjpSQNEwddzkRQsgCSmKMbWnII0mSJOlFamrv4Ru/3cJNq3ZRkJvNJ189j/eeO8M5JSQNe4MqJ0IIPwQ+CCSBR4HSEMLXY4z/ns5wkiRJko6tuy/JDQ9u5z/v3UZnX5K3L6/hoxfOobrEZT0lnRwGO3JiQYyxLYRwFfBr4NPAGsByQpIkScqQVCpyx7rd/Pudm9jT2s2Fp4zn06+Zz+xxJZmOJknHZbDlRG4IIRd4A/DNGGNfCCGmMZckSZKkPxFjpK6xg9U7mnlk+yFWbW+i4VAXiyeX8ZUrlrJiVlWmI0rSizLYcuK/gB3AOmBlCGEa4JwTkiRJUholkinW72nj0R3NPLqjmdU7DtHU0QtAVXEetdMr+OSr5/HaUyc52aWkk9qgyokY4zeAbzxn184QwgXpiSRJkiSNTn3JFI9ub+aRgTLi8V0tdPYmAaipLOL8eeNYNr2CZTMqmVldTAgWEpJGhsFOiDke+BdgUozxNSGEBcAK4LvpDCdJkiSNBhv2tHHbmgbuWLubpo5eQoBTJpRy+RlTWDajkmXTKxlfWpDpmJKUNoO9reN7wI3A3w+83gz8GMsJSZIk6UVpau/hjrV7uG1NAxv2tpGbHbjwlPG88bTJnDWritKC3ExHlKQhM9hyojrGeEsI4TMAMcZECCGZxlySJEnSiNObSHHvpgP8ZE0Dv3v6AIlU5NQpZVzz+oW89tRJVBTnZTqiJGXEYMuJjhBCFRABQghnAa1pSyVJkiSNIOv3tA7ctrGH5o5eqkvyec+5M3jz6VOYN2FMpuNJUsYNtpz4OPBzYFYI4UFgLPCWtKWSJEmSRoDHdx3i87/YwNr6FvKys7hwwTjecsYUXj5nLDnZWZmOJ0nDxmBX63gshHAeMA8IwKYYY19ak0mSJEknqeaOXr5059P86NF6xpfm8/nXLeR1S7xtQ5JeyFHLiRDCm17g0NwQAjHG29OQSZIkSToppVKRHz1az5d+8zSHuxP81ctm8NEL51KSP9gBy5I0Oh3rb8nXHuVYBCwnJEmSJOCJhhb+8WdPsa6hlTNnVHLN6xc5n4QkDdJRy4kY47uHKogkSZJ0Mmrp7OXff7OJHz6yi+qSfP7jrUt5/dJJhBAyHU2SThqDHl8WQrgUWAgUPLMvxnhNOkJJkiRJw10qFbltTQNfvPNpWrv6ePfZM/jYRXMoLcjNdDRJOukMqpwIIXwbKAIuAL5D/0odj6QxlyRJkjRsPbW7lX+84yke39VC7bQKrnn9IhZMKs10LEk6aQ125MTZMcZTQwhPxBg/H0L4CvDrdAaTJEmShpueRJKv3LWZ79xfR0VRHl++fAlvPn2yt3BI0ks02HKia+CxM4QwCWgCJqYnkiRJkjT8bNjTxsdvWcvT+w7ztuU1fPo18ykr9BYOSToRBltO/DKEUA78O/AY/St1XJ+2VJIkSdIwkUxFrltZx1fv3kR5UR43vmsZF8wfl+lYkjSiDKqciDH+34GnPwkh/BIoiDG2pi+WJEmSlHn1zZ18/Ja1PLrjEK9ZNIEvvHExlcV5mY4lSSPOUcuJEMIyoD7GuG/g9TuBNwM7Qwj/FGNsHoKMkiRJ0pCKMXLr6gY+/4v1ZIXAV69YwhtPc24JSUqXY42c+C/gQoAQwsuBLwJ/DSwFrqN/1Q5JkiRpxGhs7+HTP3mSezbuZ8XMKr58xRImlxdmOpYkjWjHKieynzM64q3AdTHGn9B/e8fa9EaTJEmShtZd6/fxmduf5HBPgn+49BTec84MsrIcLSFJ6XbMciKEkBNjTACvBN5/HO+VJEmSTgrtPQmu+cV6blndwIKJpdx85VLmjh+T6ViSNGocq2C4Gfh9CKGR/uVE7wcIIcwGnBBTkiRJJ40YI4c6+9jV3Nm/NXU8+3zTvsO0dvXxfy6YxUdfOZe8nKxMx5WkUeWo5USM8QshhN8CE4G7Yoxx4FAW/XNPABBCqIgxHvrT94cQbgAuAw7EGBcd4fj5wB3A9oFdt8cYrxk4Vg58B1hE/9Kl74kxPnR8X0+SJEkjQTIV2bTvMB29CVKpSDJGUikGHiPJgX0xRpID+w9397Grqb982NnUSX1zJ4d7Es+77rgx+dRUFnHB/HFcdWYNZ0yrzNA3lKTR7Zi3ZsQYHz7Cvs1/suu3wOlHePv3gG8C3z/KR9wfY7zsCPu/DtwZY3xLCCEPKDpWVkmSJI0cHT0J7t/SyG837ud3Tx+gqaP3uK+Rl5PF1IpCaiqLWDa9gpqqYmoqi5hWVcTUiiIK87LTkFySdLxO1LwRR5wlKMa4MoQw/bgvFkIZ8HLgXQPX6QWO/79GkiRJOqnsa+3mt0/v554N+3lwWxO9iRSlBTlcMH8cF8wbR1VJHtkhkJUVyM4KZAXICs88f+4jFOfnMH5MgRNaStJJ4ESVE/HYp7ygFSGEdcAe4G9jjOuBGcBB4MYQwhJgDfDRGGPHS48qSZKk4SLGyIa9bdyz4QD3bNzPk7v7pzWrqSziL86axitPGcey6ZXkZjsHhCSNZJleceMxYFqMsT2EcAnwM2AO/blOB/46xrgqhPB14NPAP/7pBUII72dgFZGampohCy5JkqQXpzeRYtX2Ju7e0D9CYk9rNyHAaVPL+dTF87jolPHMHldCCI54kKTRIq23dRxLjLHtOc9/FUL4VgihGmgAGmKMqwYO30Z/OXGka1wHXAdQW1v7UkZwSJIkKU0Od/fx+80HuWv9fu7ddIDD3QkKcrN42ZyxfOyiubxi/jiqS/IzHVOSlCGDKidCCF8Bbhi45eJIXvliPjyEMAHYH2OMIYTl9K8C0jTwuj6EMC/GuGng+htezGdIkiQpMw60dXP3xv3ctX4/D21rojeZorI4j9csmsBFCyZw7uxqJ6SUJAGDHzmxEbguhJAD3AjcHGNsfeZgjLH5SG8KIdwMnA9UhxAagM8BuQPv+TbwFuBDIYQE0AVc+ZzlSv8auGlgpY464N3H+d0kSZI0xLYeaOeuDfu4a/1+1ta3ADCtqoi/PHsar1o4gdNrKsh2gkpJ0p8If+wCBnFyCPPoLwneBjwIXB9jvDdN2Y5bbW1tXL16daZjSJIkjSoxRh6ua+bae7fywNZGAJZMKeNVCydw0YLxzHH+CEkSEEJYE2OsPdKxQc85EULIBuYPbI3AOuDjIYQPxBivPCFJJUmSdNKIMXLvpgN883dbeWxXC2PH5PPp18znDUsnM6GsINPxJEknkcHOOfE14LXAb4F/iTE+MnDo30IIm9IVTpIkScNPMhX59VN7ufbebWzc28bk8kL+7xsWcfkZUyjIdQ4JSdLxG+zIiSeAf4gxdhzh2PITmEeSJEnDVF8yxU8f382379tGXWMHM8cW8+XLl/D6pZPIzc7KdDxJ0knsqOVECOH0gafrgHl/eq9gjPGx506MKUmSpJGnuy/Jjx+t57qVdexu6WLhpFK+ddXpvHrhBCe3lCSdEMcaOfGVoxyLwCtOYBZJkiQBW/Yf5oGtjeTnZFOcn01hbjbF+TkU5mVTnJdDUV72wJZDQW5W2iab7E2k+P5DO/j27+tobO+hdloF//zGRZw/d6wTXEqSTqijlhMxxgsAQghZMcbUc4+FEJzlSJIk6QRqONTJ1+7ewu2PNzDYBdVCgOK8HM6bO5ZPvGouM8eWnJAsv998kM//Yj11Bzs4d3Y1H37FaZw5o9JSQpKUFoOdc+I7wHueeRFCKAZ+DrwyHaEkSZJGk8b2Hq69dys3PbwLArzv3Bm8+5wZZIVAR2+Crt4knb3JZ5939CTo6kvS0ZOkqzdBY0cvP3t8N3eu38cVtVP52IVzGF/64n6PVN/cyTW/3MDdG/YzvaqIG9+1jAvmjzvB31iSpOcbbDmxO4TwrRjj1SGECuB/gevTmEuSJGnEO9zdx3fu38537q+jqy/JFbVT+cgr52VeGbUAACAASURBVDCpvPC4r/Xxi+byzd9t5aZVO/np4w286+wZfOi8WZQV5Q7q/V29Sf7zvq18e2UdOVmBT108j/eeO4P8HFffkCSlX4iDHDMYQvgSUAqcAXwxxviTdAZ7MWpra+Pq1aszHUOSJOmouvuS3LRqF9feu5Xmjl4uWTyBj180j9njXvotGfXNnXz17s38bO1uxuTncPUFs3nX2dNfcInPGCO/fmofX/jfjexu6eJ1SybxmUvmM7Hs+AsSSZKOJoSwJsZYe8RjRysnQghveu5L4B+BR4A7AWKMt5/AnC+Z5YQkSRrOEskUtz++m/+4ezN7Wrs5d3Y1n3z1PJZMLT/hn7VxbxtfuvNp7t10kPGl+XzswrlcfsYUcp6z5OeW/Yf5p1+s58GtTcyfMIbPv24hZ86sOuFZJEmCl1ZO3HiU68YY43uOcnzIWU5IkqThJpWKNBzq4vH6Q/y/321l64F2lkwp41MXz+ec2dVp//xVdU38251P89iuFmaOLeaTr5rH2bOr+fo9W/jvh3ZQnJfN3756Hm9fXvO84kKSpBPtRZcTJxvLCUmSlCmJZIqdzZ1s2d/O1gOH2XKgna0H2tl2sJ3uvv5Fz2aOLeZTr57HqxdOGNJVL2KM3LPxAF+682m2HGgnLzuLvlSKK5fV8MlXz6OyOG/IskiSRq+jlRODmhAzhDAX+E9gfIxxUQjhVOB1McZ/PoE5JUmSTgqpVGTV9mYermti60AJUdfYTl/yj7/0mVxeyOxxJayYWcWc8SXMHjeGJVPKMjI6IYTARQvG84r54/jp47t5uK6Jv1wxncVTyoY8iyRJRzKokRMhhN8DnwT+K8Z42sC+p2KMi9Kc77g4ckKSJKVLjJH1e9q4Y+1ufrFuL/vaugkBaiqLmDOuv3yYPa6EOeNKmDWuhJL8wS6KJknS6PCSR04ARTHGR/5k+GHiJSeTJEka5nY0dvDzdXu4Y+1uth3sIDc7cN7ccfz9pafwivnjKLaEkCTpJRvsf00bQwizgAgQQngLsDdtqSRJkjLowOFufrluL3es28O6+hYAzpxRyXvPnclrFk2gwjkaJEk6oQZbTvwf4DpgfghhN7AdeEfaUkmSJA2xva1d3L+5kV88sYcHtzaSirBwUimfvWQ+l506iUnlhZmOKEnSiDWociLGWAdcGEIoBrJijIfTG0uSJCm99rd183BdEw9ta+KhuiZ2NnUCMK2qiA9fMJvXLZ3E7HFjMpxSkqTRYbCrdYwH/gWYFGN8TQhhAbAixvjdtKaTJEk6QQ4c7ubhumYe2tbEqrom6ho7ABhTkMOZM6p454rpnDWzkgUTS4d0mU9JkjT42zq+B9wI/P3A683AjwHLCUmSNCzFGLl/SyN3bdjHQ9ua2Hawv4woyc9h+YxK3ra8hhWzqjhlYinZWZYRkiRl0mDLieoY4y0hhM8AxBgTIYRkGnNJkiS9KL2JFD9ft4frV9axaf9hivOyWTajkitqp3LWzCoWTiolJzsr0zElSdJzDLac6AghVPHH1TrOAlrTlkqSJOk4tXX3cfOqXdz44A72tXUzb/wYvnz5El67ZCL5OdmZjidJko7iqOVECOFjwB+ATwF3ADNDCA8CY4HL0x9PkiTp6Pa0dHHjg9u5+ZF62nsSnD2rii++eTHnzR3r3BGSJJ0kjjVyYgrwH8B84GngbmAlcHOMsTHN2SRJkl7Qhj1tXH9/Hb9Yt4cIXLp4Iu9/+UwWTS7LdDRJknScjlpOxBj/FiCEkAfUAmcD5wOfCSG0xBgXpD2hJEnSgBgjD2xt5LqVddy/pZGivGzeuWI67z5nOlMrizIdT5IkvUiDnXOiECgFyga2PcCT6QolSZL0p/a3dfP3P32SezYeYOyYfD518TyuWj6NsqLcTEeTJEkv0bHmnLgOWAgcBlbRP//EV2OMh4YgmyRJEjFGblvTwP/95QZ6Eik+e8l8/vLs6U5yKUnSCHKskRM1QD6wBdgNNAAt6Q4lSZIE/ZNdfvanT3LfpoMsm17Bv735VGaOLcl0LEmSdIIda86Ji0P/NNcL6Z9v4hPAohBCM/BQjPFzQ5BRkiSNMjFGfvRoPV/4340kU5F/eu0C3rliOllZrr4hSdJIdMw5J2KMEXgqhNACtA5slwHLAcsJSZJ0QtU3d/KZ25/kga2NrJhZxb+9+VRqqpzsUpKkkexYc058hP4RE2cDffTPOfEH4AacEFOSJJ1AqVTkplU7+eKvnwbgn9+wiLcvr3G0hCRJo8CxRk5MB24F/ibGuDf9cSRJ0mi0s6mDv/vJEzxc18zL5lTzr29azJQKR0tIkjRaHGvOiY8PVRBJkjS6pFKRDXvbuHvDfq5bWUdOVuCLb1rMW5dNpX/KK0mSNFocc84JSZKkE+Xg4R4e2HqQlZsbuX/LQRrbewF45fxx/PMbFzGxrDDDCSVJUiZYTkiSpLTpTaRYvbOZlZsbWbn5IBv2tgFQWZzHy+ZU8/I5Y3nZnGrGlRZkOKkkScokywlJknRCHe7u46eP7+b3mw7yUF0Tnb1JcrICp0+r4JOvnsfL54xl4aRSJ7qUJEnPspyQJEknzJqdh/jYjx+nvrmLaVVFvPn0Kbx87lhWzKqiJN//7ZAkSUfm/yVIkqSXLJFMce292/jG77YwsayAWz6wguUzKjMdS5IknSQsJyRJ0ktS39zJx368ljU7D/Gm0ybz+dcvZExBbqZjSZKkk4jlhCRJetF++ngD//iz9QTg61cu5fVLJ2c6kiRJOglZTkiSpOPW1t3HP/7sKe5Yu4dl0yv46hVLmVpZlOlYkiTpJGU5IUmSjsujO5r52I/Wsq+tm09cNJerL5hNtitvSJKkl8ByQpIkDUpfMsU3fruFa+/dypSKIm774ApOq6nIdCxJkjQCWE5IkqSjijFS19jBJ25Zx9r6Ft5yxhT+6XULXRpUkiSdMP5fhSRJAvpHRuxs6qTuYDvbDnYMPLZT19hBS2cfpQU5fPPtp3HZqZMyHVWSJI0waS0nQgg3AJcBB2KMi45w/HzgDmD7wK7bY4zXPOd4NrAa2B1jvCydWSVJGk0a23v43cYDbHtOEbGruZNEKj57zrgx+cwcW8yliycyc2wJlyyewMSywgymliRJI1W6R058D/gm8P2jnHP/UYqHjwIbgdITnEuSpFGpqzfJdx+o4z/v20ZHb5K87CymVxcxd/wYXrN4ArPGljBzbAkzxxZTWpCb6biSJGmUSGs5EWNcGUKY/mLeG0KYAlwKfAH4+AmMJUnSqJNKRX76+G6+fNcm9rZ2c9GC8XzswjnMGz+GnOysTMeTJEmj3HCYc2JFCGEdsAf42xjj+oH9/wF8ChiTsWSSJI0Af9jWyBf+dyPr97Rx6pQyvvbWpZw1syrTsSRJkp6V6XLiMWBajLE9hHAJ8DNgTgjhmXkq1gzMS/GCQgjvB94PUFNTk+68kiSdUL2JFHtauqg/1EnDoS7qmzupP9RFw6FO6pu7yMkKnD2rinNmV3PO7GomlBUM+tpbD7TzxV9v5J6NB5hcXsjXr1zKa0+dRFZWSOM3kiRJOn4hxnjss17KB/Tf1vHLI02IeYRzdwC1wCeAvwASQAH9c07cHmN8x9HeX1tbG1evXv0SE0uSlB5bD7Rz51N7qWvsoKG5v4DY19bNc+agJDsrMKm8gKkVRUytKKKjN8EftjXR3NELwJxxJZwzu5pzZ1dz5sxKxhxhXojG9h6+fs8WfvjILopys7n6gtm8+5zpFORmD9VXlSRJ+jMhhDUxxtojHcvoyIkQwgRgf4wxhhCWA1lAU4zxM8BnBs45n/7bPY5aTEiSNBwd7u7jl0/s5dbV9Ty2q4UQYEJpf/lw1swqplQWMbWikCkVRUytLGRCacGfzQGRSkU27mvjwa2NPLC1iR89uovv/WEH2VmBpVPLOWd2NS+bU838CWP4wcM7+da92+jqS3LVmTV89JVzqCrJz9C3lyRJGpx0LyV6M3A+UB1CaAA+B+QCxBi/DbwF+FAIIQF0AVfGdA/lkCQpzVKpyMPbm7htdQO/emov3X0pZo8r4bOXzOcNp01m3JjB35oBkJUVWDipjIWTynj/y2fRk0jy2M4WHth6kAe2NvHN323hG7/d8uz5F54ynk+/Zj6zx5Wc6K8mSZKUFmm/rWMoeVuHJCmTGg518pM1u7ntsXrqm7sYk5/DZUsmcUXtFJZOLSeE9Mz10NrZx0N1Taytb+G8uWNZMcvJLiVJ0vAzbG/rkCTpZNfRk+Cejfu5dXUDD25rJEY4e1YVn7hoHq9eOIHCvPTP81BWlMvFiyZw8aIJaf8sSZKkdLCckCTpGBLJFA2HuqhrbKfuYAfbG/u3uoMd7GvrBmByeSEfecUc3nLGFKZWFmU4sSRJ0snFckKSNOokkik6+5J09iTp6E3Q1ZukoydBZ2//69auPnY2dVJ3sIO6xnZ2NXWSeM6SGqUFOcwcW8LZs6uYWV3M6TUVnDWzyiU6JUmSXiTLCUnSSSfGSGdvkpauPlo6e2nt7Bt43kdL18DrgectnX20dvVxuDtBZ2+Cjt4kvYnUMT8jLyeL6VVFzB03hlcvnMCM6mJmjS1mRnUJFUW5aZs/QpIkaTSynJAkZVxfMkVLZx+HOntpau/tf+zo5VBHL81H2jp7j1ow5OVkUVGUS3lhHmVFuUytLGJMQQ7FeTkU5WVTlJdDcX7/Y//rbIrzcyjMy6Y4L4eSghwmlBaQ7UgISZKkIWE5IUkvUV8yRWtXH8lUJCcrkJOVRU52IDsrkJudRVZg2PyWvS+Zor07QXvPH7cAVJXkU12SR0l+zkvOmkpFWrr6aO7oobG9l5bOXpo7+ouHQwPFQktnH80dvc/ua+tOvOD1xhTkUFmcR2VxHhPLClg4qZTK4jwqivOoKMqlrDCP8qLc/m3geUFu+iehlCRJ0oljOSFJf6KzN0F9cxd7WrtoGfhB+lBn33Oe9/7xloGOPg73vPAP1s/IfaasyMoiO7u/tCge+G19cX4OJc8+9v/mvjg/p/83/QP7swL0JlL9W7L/sWdg++P+JD19/cc7ehIcfk4J8czrnmPczpCfk0V1ST7VY/IZW5LX/3yguKgek09lUR4dvUma2nto6uilsb2HpvZemjr6HxsHRj0kU0deprooL5uKojwqinOpKMqjprKIyuL+QqGiqL+AqBooHqqK8ygvyiMvJ+tF/XuUJEnSycNyQtKok0im2NvaTf2hTuqbO6lv7mJXc+ezrxvbe//sPSFAaUFu/60CRXlUleQxe1zJs7+tryjOJTsrkExF+pKRZCo18BhJJFMkUrF/S0YSqf4yoWNgEsb2ngT727oHnvfv6+pLDvr75GVnkZczsGVnkZ+b9bxbE0oK+suPZ7eB18+UH6kIjYd7aGx/ZusvHXa3dLOuoZWm9h5eoGugJD+HqpL+ImFqZRGn1ZRTVZzfv6+kv8zoH+XQXz44okGSJElHYjkhaUQ7cLibJ+pbWdfQwhMNrexo6mD3oa7nrbyQnRWYVF5ATWURF54ynqmVRUytLGJyeUH/b/mL8igtzB3S+QeSqUhHb/+Ih46eBKn4/BIi/zllRLpvGUmlIoc6+0dFNHf0PltIVBZbNkiSJOnEsJyQNGK09yR4sqG/iFhX319G7G7pAvoLiLnjx3DqlHIuO3UiUyuKqBkoISaWFZCTPbxuHcjOCpQW5FJakJvpKGRlBapK8qkqyc90FEmSJI1QlhOSTirJVKR5YK6DxvYedjR1DhQRLWw50E4cGBAxraqI06dV8O5zprN0ajkLJ5VRmOdv+SVJkqThyHJC0rCQTEUaDnWyvbGDA209NHb00Hj4jyVE08A8CM2dvc8WEM+oKs5jydRyLl08iSVTyzh1SjmVxXmZ+SKSJEmSjpvlhKQh1drVR93BduoOdlDX2M62A/2POxo76U0+fyWJ4rxsqsf0rxYxvbqIM6ZXUF3Sv4pE1cAqEpPKC5hcXjhsluqUJEmSdPwsJySdcK2dfdQf6uxfAaO5kx1NndQdbGfbwQ4a23uePS8nK1BTVcTM6hIumD+OWdUlzBhbzITSAqpL8r0NQ5IkSRolLCckHbfuviQNh/qX4Kw/1MmupmeW4ex/fbg78bzzK4pymTW2hFfMH8vMsSXMGlvCzLHF1FQWkTvMJqKUJEmSNPQsJyQNWn1zJ9feu5Xb1jQ8bynO/Jys/uU3KwqpnV7B1IoiplYWPrsk53BYcUKSJEnS8GU5IemYnltKZGUFrlw+ldpplf0FREURY8fkO+eDJEmSpBfNckLSC/rTUuIdZ03jg+fNYkJZQaajSZIkSRpBLCck/Zn65k6+dd9Wbl3dQFYIXHVmDR86f7alhCRJkqS0sJyQ9CxLCUmSJEmZYDkhjWIxRloGlv28+ZF6bl1d/2wp8cHzZzGxrDDTESVJkiSNApYT0gjW1ZtkT2sXe1u62dPSxZ7WLva0dLG3tZvdLf37u/qSAORlZ/H2M2v4kKWEJEmSpCFmOSGdxFKpyP7D3exs6mRXUye7mjvZ2dzJrqYO6g910dzR+2fvGTsmn0nlhcwbP4YL5o1jYlkBk8oLOWNaBeNLvX1DkiRJ0tCznJBOEk82tLJ6ZzM7mzqpHygh6ps76Umknj0nOyswubyQaVVFXDy5jMnlhUwqL2BiWSGTywsZX1pAXk5WBr+FJEmSJP05ywlpmEulItfeu5Wv3rOZGKEoL5uayiJmjS3mFfPHUVNZxLSqImoqi5hUXkhutuWDJEmSpJOL5YQ0jLV29fGJW9Zyz8YDvPG0yXzmkvmMLcknhJDpaJIkSZJ0wlhOSMPUpn2H+cAPVtNwqIvPv24h71wxzVJCkiRJ0ohkOSENQ79Yt4dP3fYEJQU5/Oj9Z1E7vTLTkSRJkiQpbSwnpGGkL5nii79+mu8+sJ3aaRV866rTGecKGpIkSZJGOMsJaZg4eLiHD//wMVZtb+ZdZ0/ns5ec4soakiRJkkYFywlpGHhs1yGu/p/HaOnq5WtvXcIbT5uS6UiSJEmSNGQsJ6QMijHyw0d28U8/X8+EsgJu/9A5LJhUmulYkiRJkjSkLCekDOnsTfC5O9Zz65oGzp83lv9461LKi/IyHUuSJEmShpzlhDTEdjV18oOHd/DjR+tp607wkVfO4WOvnENWlsuESpIkSRqdLCekIRBj5IGtjfz3H3bw26cPkBUCFy+awHvOmcEZ0yoyHU+SJEmSMspyQkqj9p4EP1nTwH8/tIO6gx1UFefx4Qtmc9WZ05hQ5hKhkiRJkgSWExpFUqnIjx6t57sP1HHGtAr+6mUzmTN+TFo+a9vBdn7w0E5uW9NAe0+CJVPK+OoVS7j01Ink52Sn5TMlSZIk6WRlOaFR4el9bXz29id5bFcLp0ws5efr9nDL6gYumDeWv3r5TFbMrCKElzbnQ3dfkpWbD/KDh3dy/5ZGcrMDly6eyF+ePZ3Tarx1Q5IkSZJeiOWERrTO3gRfv2cL33lgO2WFuXzl8iW86fTJHOrs438e3sl//2EHb79+FYsml/JXL5vJJYsnkpudNejrt3b1cd+mA9y1fj/3bTpAR2+ScWPy+ZsL5/K2M6cyboy3bkiSJEnSsYQYY6YznDC1tbVx9erVmY6hYeKeDfv53M/Xs7uliyuXTeXvLp5PRfHzl+rs7kvy08d3c/39ddQd7GByeSHvPmc6Vy6voST/yN3d3tYu7t6wn7vW7+fhuiYSqUh1ST4XLRjPqxaM55zZ1eTlDL7gkCRJkqTRIISwJsZYe8RjlhMaafa0dPH5X6znN+v3M3d8CV9442KWTa886ntSqcjvnj7AdffX8cj2ZsYU5PD2M2t499kzGF+az5YD7dy1fh93bdjPEw2tAMysLuZVCyfwqoXjWTql3KVAJUmSJOkoLCc0KiSSKb73hx187e7NJGPko6+cy3vPnXHcoxjW1rdw/f11/PrJvWSFwISyAhoOdQGwdGo5r1o4nlctmMDscSXp+BqSJEmSNCIdrZxwzgmNCGvrW/js7U+yYW8bF8wbyzWvX8TUyqIXda2lU8u59u2nU9/cyQ0Pbqe+uZMPnjeLixaMZ3ypc0hIkiRJ0olmOaGTWt3Bdv7zvm3c9lgD48bk8/+3d/9BVpX3Hcff312WX+4CIiwgruAPUEEDUrRpTNWx1qjRaqNWE5M4aWecOJM2zsSptpmOM7aZif2hSbQkcWqibR07nWBjorZWTYiJTY2RAGqICGJlZWVFgoDKr91v/7gHXQkg4u49Z/e+XzN37t1zzj3Pc85nR5/9cs5zFlw+j3OOn/y+n7wB0DF+NNefP7sfeilJkiRJ2heLExqUnn7pNb6+aBUPPN3F8OYm/viUI7j6zBm0jWwpu2uSJEmSpPfI4oQGjczk8dUbWLBoFY+ueIW2EcO46rSj+MwpRzCxbUTZ3ZMkSZIkHaABLU5ExLeA84DuzDx+D+tPB+4FVheL7snMGyKiA/hnYBKQwG2Z+dWB7Kuqa9eTNBYsWsniFzcyoXU4f372MXzyg9MY45USkiRJkjToDfSVE3cAt1IrNOzNjzPzvN2W7QS+kJmLI6INeDIiHsrMXw5QP1VBO3t6uW9ZF19ftIpn121m6rhR/PUFs7lkfgcjW5rL7p4kSZIkqZ8MaHEiMx+NiOkH8L0uoKv4vDkilgNTAYsTDWDbzh7+/eed3PboKtZseJMZ7a3c9EdzOH/OobQ0v7fHgkqSJEmSqq8Kc078TkQsBdYC12TmM31XFsWNE4HH69811dOOnl6+82QntzzyHGtf28rcjnH81UdnceZxk2hqev9P35AkSZIkVVPZxYnFwLTM3BIR5wLfBWbsWhkRrcBC4OrM3LSnHUTElcCVAIcffvjA91j9rqc3uXfJS3zl4ed4ccMbzO0Yx40Xf4APHz2hXx4JKkmSJEmqtlKLE30LDpn5QEQsiIgJmbk+IlqoFSbuysx79rGP24DbAObPn58D3mn1m97e5D+ffpmbH17Byu4tzJoyhtuvmM8Zx7ZblJAkSZKkBlJqcSIiJgPrMjMj4mSgCXg1an+Z3g4sz8ybyuyj+l9m8sjybv7hoRUs79rEjPZWFlw+j7NnT/b2DUmSJElqQAP9KNG7gdOBCRHRCVwPtABk5jeAi4GrImIn8CZwWVGo+DDwKeCpiFhS7O4vM/OBgeyvBlZm8pOV6/n7/17B0jUbmXbIaL5y6VzOn3MozRYlJEmSJKlhDfTTOj7+Lutvpfao0d2X/wTwr9Uhorc3eWzVem75wUp+tnoDU8eN4saLTuBj8w7z6RuSJEmSpNInxNQQtuqVLdyzuJP/WPwSa1/bysS2EdxwwWwuPamDEcOay+6eJEmSJKkiLE6oX218YzvfX9bFwic7WbJmI00Bp86cyHXnHsdZsyYxssWihCRJkiTpnSxO6H3b0dPLomdf4Z7FnTyyvJvtPb0cO7mNL557HBfMPZT2MSPL7qIkSZIkqcIsTuiAZCbPrN3EwsWdfG/JWl59fTuHHDScT35wGh+bN5XZh47xcaCSJEmSpP1icUL7ZcPr21m6ZiO/WLORpWs2srRzIxvf2MHw5ibOnNXORfMO49SZE53gUpIkSZL0nlmc0G/YuqOHZ9ZuYklRiFiyZiMvbngDgKaAmZPaOHv2ZOZNO5iPzJrM2NEtJfdYkiRJkjSYWZwQvb3J4hd/zQNPvcwTL2xgedcmdvYmAIeOHcmcjnFc/tuHM6djHCdMHctBI/y1kSRJkiT1H//KbFCZydLO17hv6Vruf6qLrte2MmJYE7817WCuPPVI5nSM48SOcU5mKUmSJEkacBYnSrZi3Wa27ejl6PZWRg0f2Mds7prE8vvL1nL/si46f/0mLc3BaTMncu3Zx3LmrEm0elWEJEmSJKnO/Eu0ZN/80fMsXNxJBHQcPJqZk1qZMamt9t7extHtrYxsOfCiRWby7LrN3Le0i/uWreWFV99gWFNwytET+PzvzeCs2ZMZO8o5IyRJkiRJ5bE4UbKrz5zBmce1s2LdFlZ0b+a5dZv50YpX2NFTm/OhKeDw8aPfKlgc3d7KsKYmtu7oYdvO3ne8117F5529bNvRw/PrX2dl9xaaAj501AQ+e9pRfGT2ZA4+aHjJRy5JkiRJUo3FiZJ1jB9Nx/jRnHPC28t29PTywvrXawWLdZt5rnszK9Zt4Ye/6n5rosrdRcCIYU2MbGlm5LBmRrbUPk8ZO5IrPjSdc46fzITWEXU6KkmSJEmS9p/FiQpqaW5ixqQ2Zkxq46NMeWv59p29vLjhdXqTtwoQI1pq78Obm4iIEnstSZIkSdKBsTgxiAwf1sTR7W1ld0OSJEmSpH7VVHYHJEmSJElSY7M4IUmSJEmSSmVxQpIkSZIklcrihCRJkiRJKpXFCUmSJEmSVCqLE5IkSZIkqVQWJyRJkiRJUqksTkiSJEmSpFJZnJAkSZIkSaWyOCFJkiRJkkoVmVl2H/pNRLwC/B8wAVhfcncanRlUj5lUi3lUl9lUj5lUi3lUh1lUj5lUj5lUy7TMnLinFUOqOLFLRPw8M+eX3Y9GZgbVYybVYh7VZTbVYybVYh7VYRbVYybVYyaDh7d1SJIkSZKkUlmckCRJkiRJpRqqxYnbyu6AzKCCzKRazKO6zKZ6zKRazKM6zKJ6zKR6zGSQGJJzTkiSJEmSpMFjqF45IUmSJEmSBonSixMR0RERP4yIX0bEMxHx+WL5+Ih4KCKeK94PLpYfGxE/jYhtEXHNbvv6VkR0R8TT79Lm2RHxbESsjIjr+iz/XLEsI2LCQBxvVVUsh9sjYmlELIuI70RE60Acc9VVLJM7ImJ1RCwpXnMH4pirrGJ5/LhPFmsj4rsDccyDRcWyOSMiFkfE0xFxZ0QMG4hjrrKS8tjjdhFxSdGH3oho2Jna+yuTve1nL2061tqDimXheIvKZdLw4y2oXCaOueopM0t9AVOAecXnNmAFMAv4W+C6Yvl1wI3F3qQtoAAABgBJREFU53bgJOBLwDW77etUYB7w9D7aawZWAUcCw4GlwKxi3YnAdOAFYELZ56aBcxjTZ7ubdrXfaK+KZXIHcHHZ58Q8annstt1C4NNlnx+zYRa1gv8aYGax3Q3An5R9foZ6HvvaDjgOOAZYBMwv+9wM9kz2tp89tOdYa3Bk4XirepncQYOPt6qWyW7bNfyYa6BfpV85kZldmbm4+LwZWA5MBS4A7iw2uxO4sNimOzOfAHbsYV+PAhvepcmTgZWZ+Xxmbgf+rWiLzPxFZr7wvg9qEKpYDpsAIiKAUUBDToxSpUxUzTwiYgxwBtDQVfwKZXMIsD0zVxTbPQRc9H6ObTAqIY+9bpeZyzPz2QM8lCGjvzLZx35251hrLyqWheMtqpWJaqqYiWOu+ii9ONFXREynVlF/HJiUmV3FqpeBSf3UzFRq/7K1Syd7/iVtWFXIISK+XbR3LHBLP7U5aFUhE+BLxaWfN0fEiH5qc1CqSB5Q+5/yI7sGmCo9m/XAsD63D1wMdPRTm4NSnfLQe9Bfmey2n9051toPVcjC8dY7VSETHG+9Q0UyAcdcdVGZ4kRxn9tC4OrdQ8/MpEGrufVWlRwy8zPAodQqnJfWo82qqkgmf0Ft4HISMB64tg5tVlJF8tjl48DddWyv0srOpmjjMuDmiPgZsBnoGcg2q6zsPPSb+iuTfe1H+6cqWTjeeltFMnG81UdFMtnFMVcdVKI4EREt1H5h7srMe4rF6yJiSrF+CtB9gPvu6DOJyWeBl3jnv2QdVixreFXLITN7qF1W1XCXRe9SlUyKy+IyM7cB36Z2+VvDqUoexfYTqOVw/4G0N9RUJZvM/Glm/m5mngw8Su3+1oZT5zy0H/orkz3tx7HWe1O1LBxvVScTx1tvq0omxfaOueqk9FnEi/vcbgeWZ+ZNfVZ9D7gC+HLxfu+B7D8z1wBvzXQbtZnTZ0TEEdR+6S4DPnFgvR86qpJD0Y+jMnNl8fkPgF8dSJuDXVUyKdZNycyuok8XAvucOX8oqlIehYuB+zJz64G0N5RUKZuIaM/M7uJS3GupTc7VUOqdh95df2Wyt/041tp/VcnC8dbbqpJJsa7hx1tQrUwKjrnqJUuekRP4MLVLcpYBS4rXudQmFnsEeA54GBhfbD+Z2n1Am4CNxecxxbq7gS5qk6F0spdZ0ov9r6A2K+sX+yz/s+J7O4G1wD+VfX4aLQdqV/M8BjxF7T/Id9FnNulGelUlk2L5D/pk8q9Aa9nnp5HzKNYtAs4u+7xU4VWlbIC/o3Z59LPULh8t/fw0SB573A74w+LnbcA64MGyz89gzmRv+9lLm461KpwFjrcql0mxvOHHW1XLpFi3CMdcdXlFccIlSZIkSZJKUYk5JyRJkiRJUuOyOCFJkiRJkkplcUKSJEmSJJXK4oQkSZIkSSqVxQlJkiRJklQqixOSJKmuIqInIpZExDMRsTQivhAR+xyTRMT0iPjEvraRJEmDl8UJSZJUb29m5tzMnA38PnAOcP27fGc6YHFCkqQhKjKz7D5IkqQGEhFbMrO1z89HAk8AE4BpwL8ABxWrP5eZ/xMR/wscB6wG7gS+BnwZOB0YAfxjZn6zbgchSZL6lcUJSZJUV7sXJ4plG4FjgM1Ab2ZujYgZwN2ZOT8iTgeuyczziu2vBNoz828iYgTwGHBJZq6u68FIkqR+MazsDkiSJPXRAtwaEXOBHmDmXrY7C/hARFxc/DwWmEHtygpJkjTIWJyQJEmlKm7r6AG6qc09sQ6YQ21urK17+xrwp5n5YF06KUmSBpQTYkqSpNJExETgG8CtWbvXdCzQlZm9wKeA5mLTzUBbn68+CFwVES3FfmZGxEFIkqRBySsnJElSvY2KiCXUbuHYSW0CzJuKdQuAhRHxaeC/gNeL5cuAnohYCtwBfJXaEzwWR0QArwAX1usAJElS/3JCTEmSJEmSVCpv65AkSZIkSaWyOCFJkiRJkkplcUKSJEmSJJXK4oQkSZIkSSqVxQlJkiRJklQqixOSJEmSJKlUFickSZIkSVKpLE5IkiRJkqRS/T8JU9gDuq2l8wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0lYzVIG8nNJ"
      },
      "source": [
        "Sazonalidade"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "uh3Cidr18oZI",
        "outputId": "123ae02c-0ee6-4fea-f1bc-e6f802f997ef"
      },
      "source": [
        "filtro = treino.Date <= '2010-12-31'\t \n",
        "treino[filtro].Weekly_Sales.diff().plot(figsize=(15,5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4b5452e2d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asIUCzaKW0qU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7bfa4e5-e654-4f8d-b529-442145f5a45a"
      },
      "source": [
        "treino[filtro].Weekly_Sales.diff()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            NaN\n",
              "1       -1733.46\n",
              "2      -29989.27\n",
              "3     -202240.58\n",
              "4      145079.09\n",
              "5     -115265.09\n",
              "6       32974.20\n",
              "7      -68085.87\n",
              "8      190538.36\n",
              "9      -49549.75\n",
              "10     -79360.25\n",
              "11     -74802.16\n",
              "12      33844.59\n",
              "13     178854.41\n",
              "14    -109703.62\n",
              "15     -94589.43\n",
              "16      32407.88\n",
              "17     183454.76\n",
              "18     -72963.62\n",
              "19     -39277.03\n",
              "20     -80572.46\n",
              "21      69706.54\n",
              "22      53656.04\n",
              "23     -97135.26\n",
              "24     -63873.72\n",
              "25     -13078.60\n",
              "26     233505.18\n",
              "27     -97254.02\n",
              "28       4842.73\n",
              "29     -63937.57\n",
              "30      91020.61\n",
              "31     -32702.84\n",
              "32     -77082.02\n",
              "33     -78587.64\n",
              "34     101538.47\n",
              "35      54910.43\n",
              "36     -48830.83\n",
              "37    -113955.10\n",
              "38      38755.22\n",
              "39     167450.06\n",
              "40     -57179.79\n",
              "41     -10695.31\n",
              "42     471839.93\n",
              "43    -407590.33\n",
              "44     134580.48\n",
              "45     208420.67\n",
              "46     496915.27\n",
              "47   -1020630.19\n",
              "Name: Weekly_Sales, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IyO9tKC_aMN"
      },
      "source": [
        "FB Prophet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyQmE24Xy32Z"
      },
      "source": [
        "X = pd.DataFrame(treino[['Date', 'Weekly_Sales']])\n",
        "X = X.rename(columns={'Date':'ds', 'Weekly_Sales':'y'})\n",
        "X['ds'] = pd.to_datetime(X['ds'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_00z2cbBoQj",
        "outputId": "bdcfc7ed-18ae-4fea-ce28-b00f27d505d3"
      },
      "source": [
        "m = Prophet(yearly_seasonality=True)\n",
        "m.add_country_holidays(country_name='US')\n",
        "m.fit(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fbprophet.forecaster.Prophet at 0x7f4b543f6190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17fbz9-FCbtO"
      },
      "source": [
        "future = m.make_future_dataframe(periods=12, freq='w')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "J2ei7vseCtg8",
        "outputId": "6f26dc83-e02d-4e7b-fba4-5753b6520138"
      },
      "source": [
        "future.tail(12)"
      ],
      "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>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>2012-08-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>2012-08-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>2012-08-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>2012-08-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>2012-09-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>2012-09-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>2012-09-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>2012-09-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>2012-09-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>2012-10-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>2012-10-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>2012-10-21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ds\n",
              "131 2012-08-05\n",
              "132 2012-08-12\n",
              "133 2012-08-19\n",
              "134 2012-08-26\n",
              "135 2012-09-02\n",
              "136 2012-09-09\n",
              "137 2012-09-16\n",
              "138 2012-09-23\n",
              "139 2012-09-30\n",
              "140 2012-10-07\n",
              "141 2012-10-14\n",
              "142 2012-10-21"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "uW4HAkJZX8-L",
        "outputId": "6c638bb4-8922-4245-de92-ae515c63c169"
      },
      "source": [
        "test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>2012-08-10</td>\n",
              "      <td>1592409.97</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>2012-08-17</td>\n",
              "      <td>1597868.05</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>2012-08-24</td>\n",
              "      <td>1494122.38</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>2012-08-31</td>\n",
              "      <td>1582083.40</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>2012-09-07</td>\n",
              "      <td>1661767.33</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>2012-09-14</td>\n",
              "      <td>1517428.87</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>2012-09-21</td>\n",
              "      <td>1506126.06</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>2012-09-28</td>\n",
              "      <td>1437059.26</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>2012-10-05</td>\n",
              "      <td>1670785.97</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>2012-10-12</td>\n",
              "      <td>1573072.81</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>2012-10-19</td>\n",
              "      <td>1508068.77</td>\n",
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              "      <th>142</th>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>1493659.74</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  Weekly_Sales  IsHoliday\n",
              "131 2012-08-10    1592409.97      False\n",
              "132 2012-08-17    1597868.05      False\n",
              "133 2012-08-24    1494122.38      False\n",
              "134 2012-08-31    1582083.40      False\n",
              "135 2012-09-07    1661767.33       True\n",
              "136 2012-09-14    1517428.87      False\n",
              "137 2012-09-21    1506126.06      False\n",
              "138 2012-09-28    1437059.26      False\n",
              "139 2012-10-05    1670785.97      False\n",
              "140 2012-10-12    1573072.81      False\n",
              "141 2012-10-19    1508068.77      False\n",
              "142 2012-10-26    1493659.74      False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YlUM1RBkC2Qr"
      },
      "source": [
        "forecast = m.predict(future)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "DG-Jn-THDOyT",
        "outputId": "cc8b9065-af1f-413e-e725-5e90cac99b3d"
      },
      "source": [
        "round(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]).tail(12)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>yhat</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>2012-08-05</td>\n",
              "      <td>1573707.0</td>\n",
              "      <td>1447842.0</td>\n",
              "      <td>1694387.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>2012-08-12</td>\n",
              "      <td>1605547.0</td>\n",
              "      <td>1472286.0</td>\n",
              "      <td>1729848.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>2012-08-19</td>\n",
              "      <td>1629796.0</td>\n",
              "      <td>1511914.0</td>\n",
              "      <td>1755913.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>2012-08-26</td>\n",
              "      <td>1634310.0</td>\n",
              "      <td>1505065.0</td>\n",
              "      <td>1765992.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>2012-09-02</td>\n",
              "      <td>1610275.0</td>\n",
              "      <td>1481542.0</td>\n",
              "      <td>1735544.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>2012-09-09</td>\n",
              "      <td>1566224.0</td>\n",
              "      <td>1434522.0</td>\n",
              "      <td>1682486.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>2012-09-16</td>\n",
              "      <td>1530485.0</td>\n",
              "      <td>1399265.0</td>\n",
              "      <td>1649046.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>2012-09-23</td>\n",
              "      <td>1528882.0</td>\n",
              "      <td>1398394.0</td>\n",
              "      <td>1647664.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>2012-09-30</td>\n",
              "      <td>1556627.0</td>\n",
              "      <td>1435026.0</td>\n",
              "      <td>1680109.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>2012-10-07</td>\n",
              "      <td>1579578.0</td>\n",
              "      <td>1459411.0</td>\n",
              "      <td>1703087.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>2012-10-14</td>\n",
              "      <td>1571284.0</td>\n",
              "      <td>1443737.0</td>\n",
              "      <td>1687147.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>2012-10-21</td>\n",
              "      <td>1546991.0</td>\n",
              "      <td>1415627.0</td>\n",
              "      <td>1667400.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ds       yhat  yhat_lower  yhat_upper\n",
              "131 2012-08-05  1573707.0   1447842.0   1694387.0\n",
              "132 2012-08-12  1605547.0   1472286.0   1729848.0\n",
              "133 2012-08-19  1629796.0   1511914.0   1755913.0\n",
              "134 2012-08-26  1634310.0   1505065.0   1765992.0\n",
              "135 2012-09-02  1610275.0   1481542.0   1735544.0\n",
              "136 2012-09-09  1566224.0   1434522.0   1682486.0\n",
              "137 2012-09-16  1530485.0   1399265.0   1649046.0\n",
              "138 2012-09-23  1528882.0   1398394.0   1647664.0\n",
              "139 2012-09-30  1556627.0   1435026.0   1680109.0\n",
              "140 2012-10-07  1579578.0   1459411.0   1703087.0\n",
              "141 2012-10-14  1571284.0   1443737.0   1687147.0\n",
              "142 2012-10-21  1546991.0   1415627.0   1667400.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TFBukbJL9juH",
        "outputId": "a84b21de-b02e-4c36-9203-a96ae980a673"
      },
      "source": [
        " m.plot_components(forecast)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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120J2YjRR6sIhQSLoA+CSJUvYvHkzSUlJ7N+//6LblJWV8eCDD+JyuUhMTOS9994DYOvWraxYsYL29nbuueceVq1aBcCRI0dYsGABDQ0NjB07lvXr19O7t/4mJiIiV8fV7uEPhxsoqXBSWu7kSOMZAIYlx3DfxAzsNjPXZ5pJi1cXDglOQR8AFy9eTFFREYsWLbro+qamJpYvX87WrVtJT0+ntrYWgPb2du6//362b9+O1Wpl3LhxzJ49m2HDhvHjH/+Yhx56iAULFrB06VJefPFFli1b5s+yRESkmzlxpo03D3inatl6oJbms24ie3q7cNxxXaqvC0dijN7WleAX9AHQbrdTVVV1yfWvvPIK8+bNIz09HYCkpCQAdu3aRU5ODjabDYAFCxbw+uuvk5ubyzvvvMMrr7wCwF133cXq1asVAEVE5AKf1Z+mtNxBSbmTP36tC8e0bAv2jke7Q5NiiI4M+l+nIufp9lfsoUOHcLlcTJs2jVOnTrFixQoWLVrE8ePHGThwoG87q9XKzp07aWhoID4+noiICN/y48ePX/TYxcXFFBcXA+BwOKiuru76gvykrq4u0EPwO9Uc+sKtXlDNna3dY/BxzWm2HW5i++FmKhvPApAV35v5wxKYZI1mfFosA+L6ENHDBJyhueEMzV02Ii/9nEOfv+vt9gHQ7XazZ88eduzYQWtrK5MmTWLixImdcuzCwkIKCwsByM/PJzU1tVOOGyxCrZ6roZpDX7jVC6r5WrWcc7PtYB0l5Q7e+LSW+tNt9OzowvGjkanYsyyMz0ggKcBdOPRzDn3+rLfbB0Cr1YrFYiE6Opro6Gjsdjv79u3DarVy9OhR33bHjh0jLS0Ni8VCU1MTbrebiIgI33IREQkfx5paKe2YquWdSm8XjrjICK7PSuiYkNnC8I4uHCKhqNsHwDlz5lBUVITb7aatrY2dO3fy0EMPMXToUCorKzly5AhpaWls3LiRV155BZPJxA033MCmTZtYsGAB69atY86cOYEuQ0REupBhGHx8rNkX+j457u3CMTC+D98flYrdZsaebSHL3JfICE3VIqEv6APgwoULKSsro76+HqvVypo1a3C5XAAsXbqU3NxcZsyYwciRI+nRowf33HMPw4cPB+DZZ5/l5ptvpr29nSVLlpCXlwfAk08+yYIFC/jJT37Cddddx9133x2w+kREpGucdbXzzmf1vn67x5vP0sMEI1Pi+PspmUy1WZiQEU9qXJS6cEjYCfoAuGHDhitus3LlSlauXHnB8pkzZzJz5swLlttsNnbt2tUp4xMRkeBRe+ocb3zqpKTcyfZDdZxu83bhmJgRz70T0inIMnOdtZ+6cEjYC/oAKCIicimGYVDhbPFN1fKnji4cyTG9mTk0yTdVS05/deEQ+ToFQBER6VZc7QbvVNZTWuENfZ83eLtw5CbFUDghHbvNwvVZCVjj+6oLh8glKACKiEjQa2p18eantZRWOHmjwsHJc+307mlifHoC80elUmDr6MIRHdipWkS6CwVAEREJSofrT/ve2v3j5424PQbmvr2YbI3mO8Os2G0WcpNjiFEXDpFvTP/XiIhIUGj3GOz68gQl5d7QV+FsAcBm6cvfjbVit5mZYjPT+2wz6VbN3ypyLRQARUQkYFrOudl+qI7SciebK5zUfdWFI60fj0y1UWAzMyE9geTYSN+j3erqkwEetUj3pwAoIiJ+dby51Tc3347Kes65PcRGRnB9ZgIFNjN2WyIjUmLpF6UuHCJdRQFQRES6lGEY7D1+kpJyByUVTj4+1gyAtV8fbhuRgt1mpsBmxmaJpo+mahHxCwVAERHpdGdd7bz7WT2lFU5Ky50caz6LCRiREkfR9ZnYbWYmZiSQ2i9KU7WIBIACoIiIdIq6lnO8UVFLaYWDtw56u3BE9erBxIwE7h6fToHNzOi0flii1YVDJNAUAEVE5FsxDIMDtS2UlDspLXfwwRfeLhxJMb25ZWgSBVlmpmabGdQ/hr699etGJJjo/0gREblqrnYP/3Ok0Rv6Khx8Vu/twjE0KYZ7v+rCkZmANT6KiJ49AjxaEbkUBUAREbms5lYXWw/UUlLuZMsBJ02tbnr3NJE/MJ7vjUylIMvbhaN/jLpwiHQXQR8AlyxZwubNm0lKSmL//v0XrC8rK2POnDlkZWUBMG/ePB5//HEOHjzI/Pnzfdt9/vnn/PSnP+XBBx9k9erVvPDCC/Tv3x+An/3sZ8ycOdM/BYmIdANHGs74eu2+d7gBt8cgIaoXBVkW7DZzRxeOWGL7BP2vERG5iKD/P3fx4sUUFRWxaNGiS25TUFDA5s2bz1s2ZMgQ9u7dC0B7eztpaWnMnTvXt/6hhx7iRz/6UdcMWkSkm/F4DHYdbaKk3EFpuZP9jlMA2Mx9+cGYNApsFqZkmck096V3hB7tinR3QR8A7XY7VVVV13SMHTt2kJ2dTUZGRucMSkQkBJw+5+btynpKyh1srnBS29JGTxNcl9aPh+027DYz4zMSSI6JpIemahEJKUEfAK/Ghx9+yKhRo0hNTeWpp54iLy/vvPUbN25k4cKF5y179tlnefnll8nPz+fpp58mISHhguMWFxdTXFwMgMPhoLq6uuuK8LO6urpAD8HvVHPoC7d64ZvX7Ghp4+3Pm9l2uJn3vzjJuXaD6F49GJ8azT2jzEyyxjCofzSxkRGAgdHSiKOla8b+bennHB7CrWZ/12syDMPw6xm/haqqKmbNmnXRzwCePHmSHj16EBMTw5YtW1ixYgWVlZW+9W1tbaSmplJeXk5ycjIATqeTxMRETCYTjz32GDU1Nbz00kuXHUN+fj67d+/u3MICqLq6mtTU1EAPw69Uc+gLt3rhyjUbhsG+6pO+t3Z3H/V24UiL6+P9LF+2hQKbmexu1IVDP+fwEG41X6zerswe3f4OYFxcnO/rmTNnsnz5curr60lMTATgzTffZMyYMb7wB5z39b333susWbP8N2ARkS52zt1O2WcNvtB3tOmrLhyxFF2fSYHNzMT0BNLi1YVDJFx1eQD87ne/e9lpAUpKSq7p+A6Hg+TkZEwmE7t27cLj8WCxWHzrN2zYcMHj35qaGlJSUgD4/e9/z/Dhw69pDCIigVbfco4tHVO1vHWglpa2dvpEeLtw/HDcQKZkmbkurR+JMZGBHqqIBIEuD4BfvWn73//93zgcDn7wgx8A3mD29Ttxl7Jw4ULKysqor6/HarWyZs0aXC4XAEuXLmXTpk0899xzREREEBUVxcaNG32B8/Tp02zfvp3nn3/+vGM++uij7N27F5PJRGZm5gXrRUSCnWEYHKxt4bcfOSg7eoQPqhrxGNA/ujc3DemP3eadrmVw/xiiI7v9wx4R6WRd/qfC1KlTAXjkkUfOe4793e9+l/z8/Cvuv2HDhsuuLyoqoqio6KLroqOjaWhouGD5+vXrr3heEZFg42738D9VHV04yp1U1p8GYEj/aO4en47dZub6LDMD1YVDRK7Ab38tPH36NJ9//jk2mw2AI0eOcPr0aX+dXkSkW2pudfHWwTpKyh1s+bSWE60uevU0Mc4az7wRAxgV7+FvRg0iSV04ROQb8FsA/Ld/+zemTZuGzWbDMAy++OILPXoVEbmIqsYzlJY7KSl38N7nDbjaDfr1iaAg6y9v7Q5LjiWuTy+qq6tJjtXn+kTkm/FbAJwxYwaVlZUcOHAAgKFDhxIZqT+0REQ8HoOPjjZRWuENfX+u8XbhyDJHsXB0GvZsMwVZFjLMUURGdI+pWkQkuPn1k8F79uyhqqoKt9vNvn37AC7b4k1EJFSdaXPz9qF6Siu8n+dztpyjpwlGp/XjQXsW9iwLEzISGBCrLhwi0vn8FgD/7u/+jsOHDzN69Gh69vT+DdZkMikAikjYqDl5ls0VTkrKnbx9qI6zbg/RvXsyOTMBuy2TgiwzI1PjSOjbO9BDFZEQ57cAuHv3bioqKvQhZREJG4Zh8OeaU5SUOygpd/LR0SYAUuMiuXX4AOw2M1NsFnISo4nqJl04RCQ0+C0ADh8+HIfD4ZuAWUQkFLW5PZQdru94icPJl02tmIC8AbEsn5yB3WZhYkY81vi+6sIhIgHjtwBYX1/PsGHDGD9+/Hkvf1xrJxARkUBrON3Glk+dlFY42XqgjlPn3ERG9GBiegKL8q1MyTIz1qouHCISPPwWAFevXu2vU4mIdLlDdS2U7HdSUuHgf454u3AkRvfmO4MSsdvM2G0WBifFEKMuHCIShPz2J9PUqVP54osvqKys5Dvf+Q5nzpyhvb3dX6cXEbkm7nYPH35xgpKO+fkO1Xknsh+cGM2S8QOx2yxMzjSTnhBFL3XhEJEg57cA+MILL1BcXExjYyOHDx/m+PHjLF26lB07dvhrCCIi38jJs94uHKXlTt741EnjGRcRPUzkW/vx6A3ZFGSZyR8YT3JspF5wE5FuxW8B8Fe/+hW7du1iwoQJAAwaNIja2lp/nV5E5Kp80XjGNzffu4frfV04pmSZKbB5J2TOGxBLv6hegR6qiMi35rcAGBkZSe/ef5nbyu1262/MIhJwHo/BnmPNlJQ7KK1wsq/6JAAZCVEsGJ3WMVWLmSxzX3XhEJGQ4dfPAP7sZz+jtbWV7du38+tf/5rvfve7/jq9iIhPq6udHZX13tBX7sRx6hw9TDA6NY4HC7IosJmZkJFASmwfdeEQkZDktwD45JNP8pvf/IYRI0bw/PPPM3PmTO65554r7rdkyRI2b95MUlIS+/fvv2B9WVkZc+bMISsrC4B58+bx+OOPA5CZmUlsbCw9e/YkIiKC3bt3A9DY2Mj8+fOpqqoiMzOTV199lYSEhE6sVkSCjaOjC0dphZPth+podXm7cEzKSGD55EymZJkZlRaHWV04RCQM+CUAtre3k5eXx4EDB7j33nu/0b6LFy+mqKjosi3jCgoK2Lx580XXvfvuuyQmJp63bO3atdx4442sWrWKtWvXsnbtWp588slvNC4RCW6GYbDfccp3l2/nl94uHCmxkczOG0BBx2f6BvWPURcOEQk7fgmAPXv2ZMiQIXz55Zekp6d/o33tdjtVVVWdOp7XX3+dsrIyAO666y6mTZumACgSAtraPbx9qI6Sciel5Q6qTrQCkJccy7LJGUzt6MKR1i+KCE3VIiJhzG+PgE+cOEFeXh7jx48nOjrat7wzOoF8+OGHjBo1itTUVJ566iny8vIAMJlM3HTTTZhMJu677z4KCwsBcDqdvpZ0AwYMwOl0XvS4xcXFFBcXA+BwOKiurr7msQaLurq6QA/B71RzaDrR6ubdqma2fdbMO0eaOO0yiOxpYsyAvswbnMyEtGjyBsQQ3yfC++LZ2WZqzzYHetidJhx+xn9NNYeHcKvZ3/X6LQCePXv2vMe0hmHw4x//+JqPO2bMGL744gtiYmLYsmULt956K5WVlQC8//77pKWlUVtby/Tp0xk6dCh2u/28/U0m0yXfRi4sLPSFxvz8fFJTU695vMEk1Oq5Gqo5NFTWtVBa4e21+/6RRto9Bpa+vbBnxDJ9mJUCm4WhYdSFIxR/xleimsNDuNXsz3r99qej2+1m6tSp5y1rbW295uPGxcX5vp45cybLly+nvr6exMRE0tLSAEhKSmLu3Lns2rULu91OcnIyNTU1pKSkUFNTQ1JS0jWPQ0S6TrvH4MOqRu+j3QonB2pbABiUGM3ifG/gm5JlptfZJtKtaQEerYhI8OvyAPjcc8/x61//ms8//5yRI0f6lp86dYrrr7/+mo/vcDhITk7GZDKxa9cuPB4PFouF06dP4/F4iI2N5fTp02zbts33dvDs2bNZt24dq1atYt26dcyZM+eaxyEinevUWTfbDtVSUu7kjQonDR1dOMZa+7FyWjZ224VdOKqrQ+fRrohIV+ryAHjHHXdwyy238L/+1/9i7dq1vuWxsbGYzeYr7r9w4ULKysqor6/HarWyZs0aXC4XAEuXLmXTpk0899xzREREEBUVxcaNGzGZTDidTubOnQt47z7ecccdzJgxA4BVq1Zx++238+KLL5KRkcGrr77aBZWLyDd19ERrx6NdB+9+1kBbu4d+fSKYnGlmqs1MgU1dOEREOkOXB8B+/frRr18/NmzY8K32v9J+RUVFFBUVXdHCJ+oAACAASURBVLDcZrOxb9++i+5jsVjUg1gkCHg8Bh8fb6a03Bv69nZ04UiPj+L2USnYbRam2BKwWaLVhUNEpBOFxyekRSRotLraeaejC8fmCifVJ71dOEalxrGiIIuCLG8XjtQ4deEQEekqCoAi0uWcp87xRsej3e2H6jnjaqdvr55MykzgvkkZTMmyMCo1Dku0unCIiPiDAqCIdDrDMCh3nPJN1bLzyxMYBgyIjeRvhyUx1WahIMtMTv9o+vbWH0MiIv6mP3lFpFO42j384XCDL/QdaTwDwLDkGO6bmIHdZmZSRgLWeHXhEBEJNAVAEfnWTpxp480DtZSWO3nzQC3NZ91E9uzB+PR4Fl6XSkGWmbED40mM7n3JCddFRMT/FABF5Bv5rP40peUOSsqd/LGjC4e5by+mZVuw2yzYs8OrC4eISHekP6FF5LLaPQZ/+uKEd6qWCgefOr1dOHIS+7JorJWpNjPX28xkJPSllx7tioh0CwqAInKBlnNuth2so7TCyeYKJ/Wn2+jZ0YXjR1NtFNgsjE8/vwuHiIh0HwqAIgLAsSZvF47Scic7Kutpa/cQFxnB9VkJ2G0WCmwWhqsLh4hISFAAFAlThmHwyfFmSjq6cHxy3NuFY2B8H74/KgW7zUyBzawuHCIiIUgBUCSMnHW18+5n9ZSUOymtcHK8+Sw9TDAyJY6/n5LJVJuFCRnxpMZFqQuHiEgIUwAUCXG1p87xxqfewLftYB2n29qJ6tWDSRkJ3DshnYIsM6Ot/TD3VRcOEZFwoQAoEmIMw+BTZwsl5Q42fXKUjx2nMQxIjunNzKFJFNjM2G1mBvWPURcOEZEwFfR/+i9ZsoTNmzeTlJTE/v37L1hfVlbGnDlzyMrKAmDevHk8/vjjHD16lEWLFuF0OjGZTBQWFrJixQoAVq9ezQsvvED//v0B+NnPfsbMmTP9V5RIJ3O1e3j/SCMlHfPzfd7g7cIx2BxJ4YR07DYLkzPVhUNERLyCPgAuXryYoqIiFi1adMltCgoK2Lx583nLIiIiePrppxkzZgynTp1i7NixTJ8+nWHDhgHw0EMP8aMf/ahLxy7SlZpaXbz5aS2lFU7ePOCkqdVN754mxqcnMH9UKgU2M+m9zzLMlq6pWkRE5DxBHwDtdjtVVVXfeL+UlBRSUlIAiI2NJTc3l+PHj/sCoEh39HnDae8LHOVO/vB5A26PQUJUL28Hjiwz9uxEcpP/0oWjurpa4U9ERC4Q9AHwanz44YeMGjWK1NRUnnrqKfLy8s5bX1VVxSeffMKECRN8y5599llefvll8vPzefrpp0lISLjguMXFxRQXFwPgcDiorq7u2kL8qK6uLtBD8LvuWHO7x+ATx2m2H25m++EmDjacBSCzX2++n5vAJGs049JiSImLoldPE3CGkw1nONmxf3es+VqEW72gmsOFag59/q632wfAMWPG8MUXXxATE8OWLVu49dZbqays9K1vaWnhtttu4xe/+AVxcXEALFu2jMceewyTycRjjz3GI488wksvvXTBsQsLCyksLAQgPz+f1NRU/xTlJ6FWz9XoDjWfPudm+6E6Ssq9XTjqOrpwjEmL45Hh3ke749MTGHCVXTi6Q82dKdzqBdUcLlRz6PNnvd0+AH4V6gBmzpzJ8uXLqa+vJzExEZfLxW233cadd97JvHnzfNslJyf7vr733nuZNWuWX8cs8teON7eyucJJSUcXjnNuD7GREUzOTMBuM2O3JTIiRV04RESkc3T7AOhwOEhOTsZkMrFr1y48Hg8WiwXDMLj77rvJzc3l4YcfPm+fmpoa3+cDf//73zN8+PBADF3CmGEY7D1+kpJyB6UVTvYcawbA2q8Pt404vwtHn17qwiEiIp0r6APgwoULKSsro76+HqvVypo1a3C5XAAsXbqUTZs28dxzzxEREUFUVBQbN27EZDLx/vvvs379ekaMGMHo0aOBv0z38uijj7J3715MJhOZmZk8//zzgSxRwsQ5dzvvftbgDX3lTo41n8UEjEiJo+j6TOw2MxMzEkjrpy4cIiLStYI+AG7YsOGy64uKiigqKrpg+ZQpUzAM46L7rF+/vlPGJnIldS3n2PJpLSXlDt76WheOiekJ3D0+nQKbmevUhUNERPws6AOgSHdiGAYHalsoLXdSUu7ggy9OYBiQFNObGUP6Y7dZmJqtLhwiIhJY+g0kco3cvi4cTkorHHxW7+3CMTQphns7unBcry4cIiISRBQARb6F5lYXWw/UUlLu5M0DtZxoddGrp4lxA+P53shUCrLMjEuPJzG6tyZiFhGRoKMAKHKVjjScobTC22v3vcN/6cIxJcuM3WZmaraF3ORYXxcOERGRYKXfVCKX4PEY7DraRGm5N/Ttd5wCwGbuy51j0rDbLBTYzGQk9KV3hB7tiohI96EAKPI1p8+5ebuynpJyB29U1OJsOUdPE1yX1o+H7DbfVC3JV9mFQ0REJBgpAErYq24+29GFw8GOynrOuj3E9O7J5My/PNodkRKnLhwiIhIyFAAl7BiGwb7qk5R2hL7dR71dONLi+jB3+ADs2Rbs6sIhIiIhTAFQwsI5dztlVc188CdvJ46jTV914Yjl/smZ2LPNTFIXDhERCRMKgBKy6lvOseVALaXlTrYeqKWlrZ0+ET2YmJHA4nEDsWdZGDNQXThERCT8KABKSDlY20JJx1u7H1Q14jGgf3RvbhrSn7GJPZg5OpvBSerCISIi4U2/BaVbc7d7+J+qxo7Wa04q608DMKR/NHePT8duMzMly4w1Popap4PU1PgAj1hERCTwFACl2zl51sXWA3WUVninavF14bDGM2/EAApsFsYNjKd/jLpwiIiIXIwCoHQLVY1nKO3otVt2uAFXu0G/PhEUZJmxZ1u8XTiSYonto0taRETkSoL+t+WSJUvYvHkzSUlJ7N+//4L1ZWVlzJkzh6ysLADmzZvH448/DsDWrVtZsWIF7e3t3HPPPaxatQqAI0eOsGDBAhoaGhg7dizr16+nd2+9CBBMPB6D3ceaKCn3TtXy5xpvF47MhCgWjk7Dnm3GbrOoC4eIiMi3EPQBcPHixRQVFbFo0aJLblNQUMDmzZvPW9be3s7999/P9u3bsVqtjBs3jtmzZzNs2DB+/OMf89BDD7FgwQKWLl3Kiy++yLJly7q6FLmCM21udlTWU1LuZHOFE8cpbxeO0Wn9eNCehT3LwsTMBAaoC4eIiMg1CfoAaLfbqaqq+sb77dq1i5ycHGw2GwALFizg9ddfJzc3l3feeYdXXnkFgLvuuovVq1crAAZIzUlvF47ScifbD9Vx1u0hundPJmcmcP/1mUy1WRiRGke8unCIiIh0mqAPgFfjww8/ZNSoUaSmpvLUU0+Rl5fH8ePHGThwoG8bq9XKzp07aWhoID4+noiICN/y48ePX/S4xcXFFBcXA+BwOKiuru76Yvykrq4uIOc1DINP61vZdriZtw838YnjDAADoiO4JTuOidZoJlvjsMb36Xi0e44zJ+o4c+Lazx2omgMp3GoOt3pBNYcL1Rz6/F1vtw+AY8aM4YsvviAmJoYtW7Zw6623UllZ2SnHLiwspLCwEID8/HxSU1M75bjBwl/1tLk9vHe4wTc/35dNrZiAvAGxLJ+cgd1mYXJmAqn9oujZxV04Qu1neDXCreZwqxdUc7hQzaHPn/V2+wAYFxfn+3rmzJksX76c+vp60tLSOHr0qG/dsWPHSEtLw2Kx0NTUhNvtJiIiwrdcOlfD6Ta2fOqktMLJ1gN1nDrnJjKiBxPS41mUb6XAZmasNR5LtF6+ERER8bduHwAdDgfJycmYTCZ27dqFx+PBYrEQHx9PZWUlR44cIS0tjY0bN/LKK69gMpm44YYb2LRpEwsWLGDdunXMmTMn0GWEhEN1LZTs907V8v4RbxeOxOjefGdQInabmWnZFnXhEBERCQJB/5t44cKFlJWVUV9fj9VqZc2aNbhcLgCWLl3Kpk2beO6554iIiCAqKoqNGzdiMpmIiIjg2Wef5eabb6a9vZ0lS5aQl5cHwJNPPsmCBQv4yU9+wnXXXcfdd98dyBK7LXe7hw+/OEFJuZPScgcH67xdOAYnRrNk/EDsNgtTsswMjI8ioqemahEREQkWQR8AN2zYcNn1RUVFFBUVXXTdzJkzmTlz5gXLbTYbu3bt6pTxhZuTZ11sO1hHSbmTLZ86aTjjIqKHiXxrPx69IZuCLDPj0xPUhUNERCSIBX0AlMD78sQZX6/ddw/X+7pwXJ9l7ni0m0jegFhiInU5iYiIdAf6jS0X8HgM9hxrprTC+9buvuqTAGQkRLFgdBp2mxl7tpnMhGh14RAREemGFAAFgFZXe0cXDgebK5zUnDxHDxOMTo3jwYIsCmxmJmWa1YVDREQkBCgAhjHHybO88WktJeUOth+qo9Xl7cIxKSOBZZMymZptYVRqHP3UhUNERCSkKACGEcMw2O84RWm5k017j/JJjfet3ZTYSGbnDaAgy8y0HAvZlmj69OoZ4NGKiIhIV1EADHFtbg9/+LzBN1VL1YlWAIZa+rBscgb2LAvXZyWQ1i+KHl3chUNERESCgwJgCGo808abn9ZSUu5k64FaTnZ04RifHs8PxlqZkmUmo3crQ20ZgR6qiIiIBIACYIiorGuhtMI7Vcv7Rxpp9xhY+vbib77WhWPI17pwVFdXB3jEIiIiEigKgN1Uu8fgw6pGX+g7UNsCwKDEaO7Kt2K3WbDb1IVDRERELqQA2I2cOutm26Haji4ctdSfbiOih4mx1n6snObtwjEhQ104RERE5PIUAIPc0ROtHXf5HLz7WT1t7QZxkd4uHFNtZqblJDIsOZbYPvpRioiIyNVRaggyHo/Bx8ebO1qvOdjb0YUjPT6K20elYrdZmJqjLhwiIiLy7SkABoF2j8HWA7WUVninaqnu6MIxKjWOB6ZkYbeZmZxlJjkmUlO1iIiIyDUL+ltIS5YsISkpieHDh192u48++oiIiAg2bdoEwLvvvsvo0aN9//Tp04fXXnsNgMWLF5OVleVbt3fv3i6v43J6mKDwd/tYv/sYucmxrLl5MO8sncw7yybzy7nDuW1UKilxfRT+REREpFME/R3AxYsXU1RUxKJFiy65TXt7Oz/+8Y+56aabfMtuuOEGX7BrbGwkJyfnvPU///nP+d73vtd1A/8GTCYT2++bSESPHqQnRKkLh4iIiHSpoL8DaLfbMZvNl93mmWee4bbbbiMpKemi6zdt2sQtt9xC3759u2KInWLYgDgGJ8Uo/ImIiEiXC/o7gFdy/Phxfv/73/Puu+/y0UcfXXSbjRs38vDDD5+37H//7//NT3/6U2688UbWrl1LZGTkBfsVFxdTXFwMgMPhCKnJk+vq6gI9BL9TzaEv3OoF1RwuVHPo83e93T4APvjggzz55JP06HHxm5k1NTX8+c9/5uabb/Yte+KJJxgwYABtbW0UFhby5JNP8vjjj1+wb2FhIYWFhQDk5+eTmpraNUUESKjVczVUc+gLt3pBNYcL1Rz6/Flvtw+Au3fvZsGCBQDU19ezZcsWIiIiuPXWWwF49dVXmTt3Lr169fLtk5KSAkBkZCQ//OEPeeqpp/w/cBEREZEA6fYB8MiRI76vFy9ezKxZs3zhD2DDhg088cQT5+1TU1NDSkoKhmHw2muvXfENYxEREZFQEvQBcOHChZSVlVFfX4/VamXNmjW4XC4Ali5detl9q6qqOHr0KFOnTj1v+Z133kldXR2GYTB69Gj+/d//vcvGLyIiIhJsTIZhGIEeRHeQmJhIZmZmoIfRaerq6ujfv3+gh+FXqjn0hVu9oJrDhWoOfRert6qqivr6+i45nwJgmMrPz2f37t2BHoZfqebQF271gmoOF6o59Pm73qCfB1BEREREOpcCoIiIiEiY6bl69erVgR6EBMbYsWMDPQS/U82hL9zqBdUcLlRz6PNnvfoMoIiIiEiY0SNgERERkTCjACgiIiISZhQAu4mjR49yww03MGzYMPLy8vjlL38JQGNjI9OnT2fQoEFMnz6dEydOAHDgwAEmTZpEZGTkBa3utm7dypAhQ8jJyWHt2rWXPOe6desYNGgQgwYNYt26dResnz17dpd2UQmmmjds2MCIESMYOXIkM2bM6LJ5mQJR84wZM4iPj2fWrFnnLb/zzjsZMmQIw4cPZ8mSJb4J2DtTZ9a7ZMkSkpKSrnhNXun78sADDxATE9NJFV4omGresWMHY8aMYfTo0UyZMoXPPvusk6v1CkTNl9pu5cqVDB06lJEjRzJ37lyampo6sdK/6KyaL3WciwmVa7szavbHtR2Iejv1ujakW6iurjb27NljGIZhnDx50hg0aJBRXl5urFy50njiiScMwzCMJ554wnj00UcNwzAMp9Np7Nq1y/iHf/gH4+c//7nvOG6327DZbMbhw4eNc+fOGSNHjjTKy8svOF9DQ4ORlZVlNDQ0GI2NjUZWVpbR2NjoW/9f//VfxsKFC428vLyQr9nlchn9+/c36urqDMMwjJUrVxr/+I//GBI1G4ZhvP3220ZJSYnxt3/7t+ctf+ONNwyPx2N4PB5jwYIFxq9//eugrdcwDOO9994z9uzZc9lr8krfl48++sj4wQ9+YERHR3d2qT7BVPOgQYOMiooKwzAM41e/+pVx1113dXa5hmH4v+bLbffWW28ZLpfLMAzDePTRR33n7GydVfOljvPXQuna7oya/XFt+7tew+jc61p3ALuJlJQUxowZA0BsbCy5ubkcP36c119/nbvuuguAu+66i9deew2ApKQkxo0bR69evc47zq5du8jJycFms9G7d28WLFjA66+/fsH53nrrLaZPn47ZbCYhIYHp06ezdetWAFpaWvjXf/1XfvKTn3RlyUFTs2EYGIbB6dOnMQyDkydPkpqaGhI1A9x4443ExsZesHzmzJmYTCZMJhPjx4/n2LFjnVkq0Hn1Atjtdsxm82XPd7nvS3t7OytXruRf/uVfOrPECwRTzSaTiZMnTwLQ3Nwc9Nc1XF3Nl9vupptuIiLC2wV14sSJXXJdQ+fVfKnj/LVQurY7o2Z/XNv+rhc697oO+l7AcqGqqio++eQTJkyYgNPpJCUlBYABAwbgdDovu+/x48cZOHCg77+tVis7d+68qu2+uiAfe+wxHnnkEfr27dsZ5VyVQNbcq1cvnnvuOUaMGEF0dDSDBg3iV7/6VSdVdmn+qPlquFwu1q9ff9nHEp3hWuq9Wpf7vjz77LPMnj3bd15/CHTNv/nNb5g5cyZRUVHExcXxpz/9qVPOeTn+qPlqvfTSS8yfP7/Lz9NZNX/9OH8tVK/tb1uzv69tf9R7ta72utYdwG6mpaWF2267jV/84hfExcWdt+6ruzVdae/evRw+fJi5c+d26Xm+LtA1u1wunnvuOT755BOqq6sZOXIkTzzxRJeeM9A1f93y5cux2+0UFBR02TkCXW91dTW/+93v+Pu///suPc/XBbpmgH/7t39jy5YtHDt2jB/+8Ic8/PDDXXq+YKj5K//8z/9MREQEd955Z5eep7NqvtxxLqc7X9vftmbw77UdDPV+5Ztc1wqA3YjL5eK2227jzjvvZN68eQAkJydTU1MDQE1NDUlJSZc9RlpaGkePHvX997Fjx0hLS2Pnzp2MHj2a0aNHU1JScsntPvzwQ3bv3k1mZiZTpkzh0KFDTJs2rfOL7RAMNe/duxeA7OxsTCYTt99+Ox988EFnl+rjz5qvZM2aNdTV1fGv//qv11DR5XVGvZdy9OhRX73//u//fsnvyyeffMJnn31GTk4OmZmZnDlzhpycnGsv7hKCoea6ujr27dvnu9Mwf/78oL+uL+Wva76S//t//y+bN2/mt7/9bZeGzs6q+WLHCfVr+1pq9ue17c96r+SbXtd6BNxNGIbB3XffTW5u7nl/k5k9ezbr1q1j1apVrFu3jjlz5lz2OOPGjaOyspIjR46QlpbGxo0beeWVV8jLy/MFHfC+xfQP//APvreXtm3bxhNPPIHZbGbZsmWA91b1rFmzKCsr6/yCCZ6az549S0VFBXV1dfTv35/t27eTm5sbEjVfzm9+8xveeustduzYQY8eXfN3xc6q91IGDhx4Xr1ut/uS3xeHw+HbLiYmpsveiA2WmhMSEmhububQoUMMHjy4W1zXl/LXNV/O1q1b+Zd/+Rfee++9Lv0YS2fVfKnjhPK1fa01++va9ne9l/OtrusrviYiQeGPf/yjARgjRowwRo0aZYwaNcp44403jPr6euNv/uZvjJycHOPGG280GhoaDMMwjJqaGiMtLc2IjY01+vXrZ6SlpRnNzc2GYXjf7hw0aJBhs9mMf/qnf7rkOV988UUjOzvbyM7ONl566aUL1h85cqRL3wIOppqfe+45Y+jQocaIESOMWbNmGfX19SFT85QpU4zExESjT58+RlpamrF161bDMAyjZ8+ehs1m841jzZo1QV3vggULjAEDBhgRERFGWlqa8Zvf/Oai57ya70tXvikZTDX/93//tzF8+HBj5MiRxtSpU43Dhw+HTM2X2i47O9uwWq2+cdx3331BXfOljnMxoXJtd0bN/ri2A1FvZ17XagUnIiIiEmb0GUARERGRMKMAKCIiIhJmFABFREREwowCoIiIiEiYUQAUERERCTMKgCIiXWT16tU89dRTgR6GiMgFFABFREREwowCoIhIJ/rnf/5nBg8ezJQpUzh48CAA/+f//B+GDRvGyJEjWbBgQYBHKCKiVnAiIp1mz549bNy4kb179+J2uxkzZgxjx45l7dq1HDlyhMjISJqamgI9TBER3QEUEeksf/zjH5k7dy59+/YlLi6O2bNnAzBy5EjuvPNO/vM//5OICP29W0QCTwFQRKSLvfHGG9x///18/PHHjBs3DrfbHeghiUiYUwAUEekkdrud1157jdbWVk6dOkVpaSkej4ejR49yww038OSTT9Lc3ExLS0ughyoiYU7PIkREOsmYMWOYP38+o0aNIikpiXHjxmEymfjBD35Ac3MzhmHwwAMPEB8fH+ihikiYMxmGYQR6ECIiIiLiP3oELCIiIhJmFABFREREwowCoIiIiEiYUQAUERERCTMKgCIiIiJhRgFQREREJMwoAIqIiIiEGQVAERERkTCjACgiIiISZhQARURERMKMAqCIiIhImFEAFBEREQkzAQ2ATU1NfO9732Po0KHk5uby4Ycf0tjYyPTp0xk0aBDTp0/nxIkTABiGwQMPPEBOTg4jR47k448/9h1n3bp1DBo0iEGDBrFu3Trf8j179jBixAhycnJ44IEHMAwD4JLnEBEREQkHAQ2AK1asYMaMGRw4cIB9+/aRm5vL2rVrufHGG6msrOTGG29k7dq1ALz55ptUVlZSWVlJcXExy5YtA7xhbs2aNezcuZNdu3axZs0aX6BbtmwZL7zwgm+/rVu3AlzyHCIiIiLhwGR8dVvMz5qbmxk9ejSff/45JpPJt3zIkCGUlZWRkpJCTU0N06ZN4+DBg9x3331MmzaNhQsXnrfdV/88//zzAL7tpk2bxg033MCBAwcA2LBhg2+7S53jchITE8nMzOyab0YAuFwuevXqFehh+JVqDn3hVi+o5nChmkPfxeqtqqqivr6+S84X0SVHvQpHjhyhf//+/PCHP2Tfvn2MHTuWX/7ylzidTlJSUgAYMGAATqcTgOPHjzNw4EDf/larlePHj192udVqvWA5cMlz/LXi4mKKi4sB6NOnDyUlJZ34HQisuro6+vfvH+hh+JVqDn3hVi+o5nChmkPfxeqdPXt2l50vYAHQ7Xbz8ccf88wzzzBhwgRWrFhxwaNYk8l03t3BrnC5cxQWFlJYWAhAfn4+qampXToWfwu1eq6Gag594VYvqOZwoZpDnz/rDdhnAK1WK1arlQkTJgDwve99j48//pjk5GRqamoAqKmpISkpCYC0tDSOHj3q2//YsWOkpaVddvmxY8cuWA5c8hwiIiIi4SBgAXDAgAEMHDjQ99m7HTt2MGzYMGbPnu17k3fdunXMmTMH8N4GffnllzEMgz/96U/069ePlJQUbr75ZrZt28aJEyc4ceIE27Zt4+abbyYlJYW4uDj+9Kc/YRgGL7/88nnHutg5RERERMJBwB4BAzzzzDPceeedtLW1YbPZ+I//+A88Hg+33347L774IhkZGbz66qsAzJw5ky1btpCTk0Pfvn35j//4DwDMZjOPPfYY48aNA+Dxxx/HbDYD8Otf/5rFixfT2trKLbfcwi233ALAqlWrLnoOERERkXAQ0AA4evRodu/efcHyHTt2XLDMZDLxq1/96qLHWbJkCUuWLLlgeX5+Pvv3779gucViueg5RERERMKBOoGICBWOU1z/zPs0n3EFeigiIuIHCoAiwkdHm/ig6gR/ONIQ6KGIiIgfKACKCGdc7QCc7fi3iIiENgVAEeFMmzf4nWsPSGMgERHxMwVAEfHdATzn8gR4JCIi4g8KgCLiuwPY1q5HwCIi4UABUERo7bgD6NIjYBGRsKAAKCK+R8AujwKgiEg4UAAUEd8jYHe7PgMoIhIOFABFRHcARUTCjAKgiPjuALp0B1BEJCwoAIqIXgIREQkzCoAi8pc7gB7dARQRCQcKgCLCad9LILoDKCISDhQAReQvL4EoAIqIhAUFQBHRI2ARkTCjACgiugMoIhJmFABFwpxhGJx1ee/8uXUHUEQkLCgAioQ5V7tBu2H4vhYRkdCnACgS5r56/AuaCFpEJFwoAIqEua9eAAG1ghMRCRcKgCJh7vw7gAqAIiLhQAFQJMy1fi0AuvUIWEQkLAQ8ALa3t3Pdddcxa9YsAI4cOcKECRPIyclh/vz5tLW1AXDu3Dnmz59PTk4OEyZMoKqqyneMJ554gpycHIYMGcJbb73lW75161aGDBlCTk4Oa9eu9S2/1DlEwpEeAYvI/2fvzgOjKs++j38nmSxAFpKwB3Ww0wAAIABJREFUZUEgTIAkEBASiBYXoClCbShiAaUSDTZP0ZY+6GvlcedtFbTVqhV8jKINtpXXpRI3AopLqwWiLCJEMGBis0IIWYCQbXLeP2JGYkiIMksy8/v8Ve7MnPu6Dqfh8l7OLZ7H5QXgY489RmxsrO3Pt99+O8uXL+fQoUOEhISwbt06ANatW0dISAiHDh1i+fLl3H777QDk5eWxYcMG9u/fT05ODjfddBNWqxWr1crNN9/Mpk2byMvL44UXXiAvL6/LPkQ8kTaBiIh4HpcWgMXFxbz55pvceOONQOv7yN59912uvvpqANLS0ti4cSMA2dnZpKWlAXD11VezdetWDMMgOzubhQsX4ufnx4gRI7BYLOTm5pKbm4vFYiE6OhpfX18WLlxIdnZ2l32IeKK2EUBvL5NGAEVEPITZlZ3/93//Nw899BAnTpwAoLKykv79+2M2t4YVFRVFSUkJACUlJQwdOhQAs9lMcHAwlZWVlJSUkJycbLvmmd9p+3xb+44dO7rs49syMzPJzMwEoLy8nNLSUnum71IVFRWuDsHplPPZFR+pAqCfjxf19Q29+jnX37FnUM6ewdNydna+LisA33jjDQYNGsSkSZN4//33XRVGlzIyMsjIyAAgMTGRiIgIF0dkX+6WT3co5478S1pHAIP7+GB4mRkyJBwvL5MzQnMI/R17BuXsGTwtZ2fm67IC8KOPPuK1117jrbfeor6+ntraWn7zm99QXV1Nc3MzZrOZ4uJiIiMjAYiMjKSoqIioqCiam5upqakhLCzM1t7mzO+crT0sLKzTPkQ8Udsu4GB/H5paDFoMAy96bwEoIiLn5rI1gKtWraK4uJjCwkI2bNjA9OnT+dvf/sa0adN4+eWXAcjKymLOnDkApKamkpWVBcDLL7/M9OnTMZlMpKamsmHDBhoaGigoKCA/P5/JkyeTlJREfn4+BQUFNDY2smHDBlJTUzGZTJ32IeKJ2jaBBPmbabK2oGWAIiLuz+W7gL/twQcf5JFHHsFisVBZWcmSJUsAWLJkCZWVlVgsFh555BHba13i4+OZP38+cXFxXHHFFaxZswZvb2/MZjNPPPEEM2fOJDY2lvnz5xMfH99lHyKeqG0TSLC/maaWb84FFhER9+XSTSBtLr/8ci6//HIAoqOjyc3N7fAZf39/XnrppbN+/8477+TOO+/s0D579mxmz57dob2zPkQ8UV2TFW8vE/18zTRbW2jREKCIiNvrcSOAIuJcdY1W/M1e+Jm9aLJqBFBExBOoABTxcKebzigAW7QGUETEE6gAFPFwdU1W/H28bSOALRoBFBFxeyoARTzcmVPAzVYDq4YARUTcngpAEQ9X12TFz+yFv9lbU8AiIh5CBaCIh2sdAfTG3+xFiwFN1hZXhyQiIg6mAlDEw9U1WvH3aZ0CBqhvtro4IhERcTQVgCIeru7rXcC+XxeADc0aARQRcXcqAEU8XOsIoDe+3ioARUQ8hQpAEQ9nGwH8ugCsb1IBKCLi7lQAini4tl3A34wAag2giIi7UwEo4uFON7bgb/bG12wCoF5TwCIibk8FoIgHa7K2YDUM/H2+GQFsVAEoIuL2VACKeLC6xtbpXn/zGZtA9B5AERG3pwJQxIPVNbUVgHoNjIiIJ1EBKOLB2kYA228CUQEoIuLuVACKeDDbCKCPF77erZtAGjUFLCLi9lQAiniwdmsAv54CbrIargxJREScQAWgiAc7feYawK+ngJs1Aigi4vZUAIp4sLYp4L6+3+wCbmrRCKCIiLtTASjiwdqmgPv6agpYRMSTqAAU8WBtI4ABvmbbJpCmFk0Bi4i4OxWAIh6sbQQwwO+MKWCNAIqIuD0VgCIe7LRtBNDHNgWsTSAiIu7PZQVgUVER06ZNIy4ujvj4eB577DEAjh8/TkpKCjExMaSkpFBVVQWAYRgsW7YMi8VCQkICu3btsl0rKyuLmJgYYmJiyMrKsrXv3LmTcePGYbFYWLZsGYZhdNmHiKexTQH7eWkTiIiIB/nOBWBVVRV79+49747NZjMPP/wweXl5bN++nTVr1pCXl8fq1auZMWMG+fn5zJgxg9WrVwOwadMm8vPzyc/PJzMzk6VLlwKtxdzKlSvZsWMHubm5rFy50lbQLV26lKefftr2vZycHIBO+xDxNHWNVry9TPTxMZ8xBawRQBERd9etAvDyyy+ntraW48ePM3HiRH7xi19wyy23nFfH4eHhTJw4EYDAwEBiY2MpKSkhOzubtLQ0ANLS0ti4cSMA2dnZLF68GJPJRHJyMtXV1ZSVlbF582ZSUlIIDQ0lJCSElJQUcnJyKCsro7a2luTkZEwmE4sXL253rbP1IeJp6pqs+Ju9MHuZ8GnbBKI1gCIibs/cnQ/V1NQQFBTEM888w+LFi1m5ciUJCQl2C6KwsJDdu3czZcoUjhw5Qnh4OABDhgzhyJEjAJSUlDB06FDbd6KioigpKemyPSoqqkM70Gkf35aZmUlmZiYA5eXllJaW2i1nV6uoqHB1CE6nnM/y8+oT+HrB8Ypymk/4YPYycfLkyV77rOvv2DMoZ8/gaTk7O99uFYDNzc2UlZXx4osvcv/999s1gJMnTzJv3jweffRRgoKC2v3MZDJhMpns2t+3ddVHRkYGGRkZACQmJhIREeHQWJzN3fLpDuXcnsmnnL6+PkRERBDk74Ov9x68/fr06vvUm2P/vpSzZ1DO7s+Z+XZrCviee+5h5syZWCwWkpKS+PLLL4mJiTnvzpuampg3bx6LFi3iqquuAmDw4MGUlZUBUFZWxqBBgwCIjIykqKjI9t3i4mIiIyO7bC8uLu7Q3lUfIp7mdFMLfj6tU8AAPt4mbQIREfEA3SoAp0+fzt69e1m7di0A0dHRvPLKK+fVsWEYLFmyhNjY2HbrCVNTU207ebOyspgzZ46tff369RiGwfbt2wkODiY8PJyZM2eyZcsWqqqqqKqqYsuWLcycOZPw8HCCgoLYvn07hmGwfv36dtc6Wx8inqausW0NYOuvAl9vL20CERHxAN2aAk5OTmbChAnccMMNzJo1yy7Tsh999BHPP/8848aNY8KECQA88MADrFixgvnz57Nu3TqGDRvGiy++CMDs2bN56623sFgs9O3bl+eeew6A0NBQ7r77bpKSkoDW0crQ0FAA1q5dy/XXX8/p06eZNWsWs2bNAui0DxFPc+YmEGgrADUCKCLi7rpVAH7xxRe88847PPvssyxbtoz58+dz/fXXM2rUqO/d8dSpU23v5fu2rVu3dmgzmUysWbPmrJ9PT08nPT29Q3tiYiL79u3r0B4WFnbWPkQ8TV1jM/5mb7zapoDNJpp1FJyIiNvr1hSwyWQiJSWFF154gaeffpqsrCwmT57MZZddxrZt2xwdo4g4yKlGK34+3/wa8NMIoIiIR+jWCGBlZSV//etfef755xk8eDB//vOfSU1NZc+ePfzsZz+joKDA0XGKiAOcbrLib/a2/VlTwCIinqFbBeBFF13Eddddx8aNG9u9Wy8xMZFf/vKXDgtORByrrrEFf/M3I4C+Zi+aNAUsIuL2ulUAHjx4sNONH7fffrtdAxIR52nbBNLG19uLZo0Aioi4vW4VgMeOHeOhhx5i//791NfX29rfffddhwUmIo53usmKv883U8B+Zi9q6ptcGJGIiDhDtzaBLFq0iDFjxlBQUMC9997L8OHDba9dEZHeqcnaQnOLgd+3RgCbrAYtehm0iIhb61YBWFlZyZIlS/Dx8eGyyy7j2Wef1eifSC9X12gFwN/nW2sArS20dPKKJhERcQ/dmgL28fEBIDw8nDfffJOIiAiOHz/u0MBExLFON31dALbbBdx6FJzVMLr3y0FERHqlbv2Ov+uuu6ipqeHhhx/m17/+NbW1tfzpT39ydGwi4kB1tgKw4yYQzQCLiLi3bhWAV155JQDBwcG89957Dg1IRJzDNgX8rdfANLe0aA2giIib67IA/PWvf93lub+PP/643QMSEeewjQD6dHwRtFVrAEVE3FqXm0ASExOZNGkS9fX17Nq1i5iYGGJiYtizZw+NjY3OilFEHOCsI4DerS+C1gCgiIh763IEMC0tDYAnn3ySDz/8ELO59eO//OUvueSSSxwfnYg4jG0TSLtdwKbW18BoBFBExK116zUwVVVV1NbW2v588uRJqqqqHBaUiDhe2xRwgO83/x3YtgnEqiFAERG31q1NICtWrODCCy9k2rRpGIbBP//5T+677z4HhyYijtQ2BdzX91trADUFLCLi9rpVAN5www3MmjWLHTt2APDggw8yZMgQhwYmIo5lGwH0O2ME0OxFiwGNzS2uCktERJygyyngAwcOALBr1y5KS0sZOnQoQ4cOpbS0lF27djklQBFxjLYRwEC/9iOAAPXNVpfEJCIiztHlCODDDz/M008/za233trhZyaTScfBifRiZx0B9G597VODRgBFRNxalwXg008/DaCXP4u4odNNLXiboI/5LCOATSoARUTcWZcF4D/+8Y8uv3zVVVfZNRgRcZ66Rit+Zm98vNufBALQYFUBKCLizrosAF9//fVOf2YymVQAivRidU1W/H28MHt/c9qP1gCKiHiGLgvA5557zllxiIiT1TVa8Td7YfbqWAA2agpYRMStdetF0DU1Ndxyyy0kJiaSmJjIrbfeSk1NjaNjExEHqmuy4m/2bl8Amr/eBKIpYBERt9atAjA9PZ3AwEBefPFFXnzxRYKCgrjhhhscHZuIONDptilgr/ZnAYN2AYuIuLtuFYCHDx9m5cqVREdHEx0dzb333suXX37p6NgcLicnh9GjR2OxWFi9erWrwxFxqtZNIGefAtYIoIiIe+tWAdinTx8+/PBD258/+ugj+vTp47CgnMFqtXLzzTezadMm8vLyeOGFF8jLy3N1WCJO07oG0BuvdlPAbSOA2gQiIuLOunUU3JNPPklaWppt3V9ISAhZWVkODczRcnNzsVgsREdHA7Bw4UKys7OJi4tzSTyXrfmIIycanNZfc3MzZvMBp/XXEyjn9gqO13Hx8NB2bbZNIM3fHAa8t7SWxX/f3St2BuvvuHcJ6+fLX6+9kBFh/dq17y2t5RcvfkpNfdNZv9ebc/6+lHPP84MRoaxbMMHVYXxv3SoAY2Nj+e1vf8vhw4eprq4mODiYjRs3kpCQ4Oj4HKakpIShQ4fa/hwVFWU767hNZmYmmZmZAJSXl1NaWuqweIb288JkNZ37g3bS3Ghg9nVefz2Bcm5vSN9+/GiYf7vnuqbqFADVVccpLfUD4K1PK/i0rJaLo/rh492z75/+jnuXHcXVLPzLDv6xcDQmU2sO1haDxX8/QGF1AxOGnH2mqTfn/H0p5x6osc6udUFFRYXdrtUd3SoA58yZQ//+/Zk4cSKRkZGOjqnHyMjIICMjA4DExEQiIiIc1tdfr3fctc+mtLTUofn0RMr53CqoAQ7gFxBs+57pQB0AT1+TRNyQQEeEaTf6O+5d/vDeIX77xudsLraSPuUCANZ8WMCnR+q4f9ZoVkyPabdEoU1vzvn7Us6ewZn5dqsALC4uJicnx9GxOFVkZCRFRUW2PxcXF3tUcStyNm1TwE3Wb6aAa+qb8PYyEeTv3dnXRL6X5ZdG81xuEf/z1ufMGxdOXbOVOzYdYPIF/bk+aehZiz8RsY9ubQK5+OKL+eyzzxwdi1MlJSWRn59PQUEBjY2NbNiwgdTUVFeHJeJStgKw5ZtdwDWnmwn088bfrAJQ7Mvs7cVzCydQcbKRX238jFuy91PfZGXFNAsRwb17o6FIT9flCOC4ceMwmUw0Nzfz3HPPER0djZ+fH4ZhYDKZ2Lt3r7PitDuz2cwTTzzBzJkzsVqtpKenEx8f7+qwRFzqbCOA1aebCPA123YIi9jTlGEh3DB5KM/lFmEAGckXMCt2kKvDEnF7XRaAb7zxhrPicInZs2cze/ZsV4ch0mO0FXnNZ7wHsKa+iQA/s604FLG3P/4kjo37ygnwNXPTxcPw99Fos4ijdVkADhs2zFlxiEgP4Pv1Lt+mljPXADYT4OutAlAcJqSvL7uWX8pXVXUkRAS7OhwRj9CtTSAi4hnONgVcdbqJQf18tSBfHGpYaF+GhfZ1dRgiHkP/SS8iNm1TwO03gbROAYuIiPtQASgiNm3nAjdbO04Bi4iI+1ABKCI2JpMJHy8TTV9vAmlpMTjZ0KwRQBERN6MCUETa8fH2sm0CqW1oxgAVgCIibkYFoIi042v+ZgSw5nQTAAF+mgIWEXEnKgBFpB1fby/bLuCa+mYAAnw1Aigi4k5UAIpIO77eXjR/PQVc/fUIYKBGAEVE3IoKQBFpp3UE8Osp4PrWAjDIz8eVIYmIiJ2pABSRdnzNJtsmkLYp4JB+KgBFRNyJCkARacfX28v2HsC2KeDQPioARUTciQpAEWnH1+xlOwmkbQo4tK+vK0MSERE7UwEoIu2cOQJYc7oZP28vAv21C1hExJ2oABSRdvy8248ABvh54+utXxUiIu5Ev9VFpB1fc+t7AFtaDKpPN9PPz6wCUETEzei3uoi00/YamBbDoKa+iUBfM75mk6vDEhERO1IBKCLttG4CMbB+XQBqClhExP3ot7qItNO2CaTFgKq6JgL8zJhMGgEUEXEnKgBFpB1fbxNN1hasLQY19c0E+mkHsIiIu1EBKCLttE0BtxgGtfXNBPjqHGAREXejAlBE2mnbBNLQ3EJdk5UAjQCKiLgdFYAi0o6vtxfNLQZVda2ngGgEUETE/bikALztttsYM2YMCQkJzJ07l+rqatvPVq1ahcViYfTo0WzevNnWnpOTw+jRo7FYLKxevdrWXlBQwJQpU7BYLCxYsIDGxkYAGhoaWLBgARaLhSlTplBYWHjOPkTkmxHA6q+PgdMIoIiI+3FJAZiSksK+ffvYu3cvo0aNYtWqVQDk5eWxYcMG9u/fT05ODjfddBNWqxWr1crNN9/Mpk2byMvL44UXXiAvLw+A22+/neXLl3Po0CFCQkJYt24dAOvWrSMkJIRDhw6xfPlybr/99i77EJFWvmYTTdYzRgBVAIqIuB2XFIA/+tGPMJtb/1FJTk6muLgYgOzsbBYuXIifnx8jRozAYrGQm5tLbm4uFouF6OhofH19WbhwIdnZ2RiGwbvvvsvVV18NQFpaGhs3brRdKy0tDYCrr76arVu3YhhGp32ISCtfby8M4LimgEVE3JbL1wA+++yzzJo1C4CSkhKGDh1q+1lUVBQlJSWdtldWVtK/f39bMdnW/u1rmc1mgoODqays7PRaItKq7aXPx041ANC/j48rwxEREQdw2NzOD3/4Q8rLyzu033///cyZM8f2v81mM4sWLXJUGOclMzOTzMxMAMrLyyktLXVxRPZTUVHh6hCcTjl3z+lTJwAoLK8EwOt0DaWlveNF0Po79gzK2TN4Ws7OztdhBeA777zT5c//8pe/8MYbb7B161bbKQORkZEUFRXZPlNcXExkZCTAWdvDwsKorq6mubkZs9nc7vNt14qKiqK5uZmamhrCwsK67OPbMjIyyMjIACAxMZGIiIjvcSd6LnfLpzuU87kNLGgEiqn38gdgxNAIIiL6OyAyx9DfsWdQzp7B03J2Zr4umQLOycnhoYce4rXXXqNv37629tTUVDZs2EBDQwMFBQXk5+czefJkkpKSyM/Pp6CggMbGRjZs2EBqaiomk4lp06bx8ssvA5CVlWUbXUxNTSUrKwuAl19+menTp2MymTrtQ0Ra+Xq3/gdZ2xRwaF9tAhERcTcu+c3+q1/9ioaGBlJSUoDWjSD/+7//S3x8PPPnzycuLg6z2cyaNWvw9m5dgP7EE08wc+ZMrFYr6enpxMfHA/Dggw+ycOFC7rrrLi688EKWLFkCwJIlS7juuuuwWCyEhoayYcMGgC77EJHWk0AAKuua6OvjTT9frQEUEXE3LikADx061OnP7rzzTu68884O7bNnz2b27Nkd2qOjo8+6i9ff35+XXnrpO/UhIt9sAjle10iAn7dtRFBERNyHy3cBi0jP0lYAVp5qIsDXbPuziIi4D/1mF5F22qaAq05/PQJo1q8JERF3o9/sItJO25TviQYrAX5mfDQCKCLidvSbXUTaOXPKN8BXO4BFRNyRCkARaaddAeinHfIiIu5IBaCItHPmmr9AP40Aioi4IxWAItKOpoBFRNyfCkARaefM9/5pClhExD2pABSRds6cAg7QFLCIiFtSASgi7Zw5Baw1gCIi7kkFoIi049NuDaCmgEVE3JEKQBFp58w1gP37+LgwEhERcRQVgCLSzplTwCF9VQCKiLgjFYAi0s6Zm0DC+vm6MBIREXEUFYAi0o7Zq3UK2ASEaApYRMQtaYufiLRjMpnw9Tbh6+2Fv1mbQERE3JFGAEWkAx9vLwL9zO2mg0VExH3ot7uIdODr7UWAn7ndjmAREXEfKgBFpANfbxMBft7tdgSLiIj70G93EenA1+xFgK8ZswpAERG3pN/uItLB8JC+jAjt6+owRETEQbQLWEQ6+ODmizl2ssHVYYiIiINoBFBEOjCZTAwM9Hd1GCIi4iAqAEVEREQ8jEsLwIcffhiTycSxY8cAMAyDZcuWYbFYSEhIYNeuXbbPZmVlERMTQ0xMDFlZWbb2nTt3Mm7cOCwWC8uWLcMwDACOHz9OSkoKMTExpKSkUFVVdc4+RERERDyBywrAoqIitmzZwgUXXGBr27RpE/n5+eTn55OZmcnSpUuB1mJu5cqV7Nixg9zcXFauXGkr6JYuXcrTTz9t+15OTg4Aq1evZsaMGeTn5zNjxgxWr17dZR8iIiIinsJlBeDy5ct56KGHMJm+edFsdnY2ixcvxmQykZycTHV1NWVlZWzevJmUlBRCQ0MJCQkhJSWFnJwcysrKqK2tJTk5GZPJxOLFi9m4caPtWmlpaQCkpaW1az9bHyIiIiKewiW7gLOzs4mMjGT8+PHt2ktKShg6dKjtz1FRUZSUlHTZHhUV1aEd4MiRI4SHhwMwZMgQjhw50mUfbZ89U2ZmJpmZmQCUl5dTWlp6vqn3GBUVFa4OwemUs/vztHxBOXsK5ez+nJ2vwwrAH/7wh5SXl3dov//++3nggQfYsmWLo7ruwGQytRtp7K6MjAwyMjIAGDBgAKmpqfYOzWUqKioYOHCgq8NwKuXs/jwtX1DOnkI5u7+z5VtYWOiw/hxWAL7zzjtnbf/ss88oKCiwjf4VFxczceJEcnNziYyMpKioyPbZ4uJiIiMjiYyM5P3332/XfvnllxMZGUlxcXGHzwMMHjyYsrIywsPDKSsrY9CgQQCd9nEubRtV3EViYiKffPKJq8NwKuXs/jwtX1DOnkI5uz9n5+v0NYDjxo3j6NGjFBYWUlhYSFRUFLt27WLIkCGkpqayfv16DMNg+/btBAcHEx4ezsyZM9myZQtVVVVUVVWxZcsWZs6cSXh4OEFBQWzfvh3DMFi/fj1z5swBIDU11bZbOCsrq1372foQERER8RQ96iSQ2bNn89Zbb2GxWOjbty/PPfccAKGhodx9990kJSUBcM899xAaGgrA2rVruf766zl9+jSzZs1i1qxZAKxYsYL58+ezbt06hg0bxosvvthlHyIiIiKewuUF4Jnz2yaTiTVr1pz1c+np6aSnp3doT0xMZN++fR3aw8LC2Lp1a4f2rvrwJG1rGz2JcnZ/npYvKGdPoZzdn7PzNRltb04WEREREY+go+BEREREPIwKQBEREREPowKwlygqKmLatGnExcURHx/PY489BnR+5vGBAwe46KKL8PPz449//GO7a+Xk5DB69GgsFovtiLyz6ez85TapqamMHTvWjlm215NyfuGFFxg3bhwJCQlcccUVDnstkCtyvuKKK+jfvz9XXnllu/ZFixYxevRoxo4dS3p6Ok1NTXbO1r75pqenM2jQoHM+k+e6L8uWLSMgIMBOGXbUk3LeunUrEydOZMKECUydOpVDhw7ZOdtWrsi5s8/ddtttjBkzhoSEBObOnUt1dbUdM/2GvXLu7Dpn4y7Ptj1ydsaz7Yp87fpcG9IrlJaWGjt37jQMwzBqa2uNmJgYY//+/cZtt91mrFq1yjAMw1i1apXx29/+1jAMwzhy5IiRm5tr3HHHHcYf/vAH23Wam5uN6Oho4/Dhw0ZDQ4ORkJBg7N+/v0N/lZWVxogRI4zKykrj+PHjxogRI4zjx4/bfv7KK68Y11xzjREfH+/2OTc1NRkDBw40KioqDMMwjNtuu82499573SJnwzCMd955x3jttdeMH//4x+3a33zzTaOlpcVoaWkxFi5caKxdu7bH5msYhvHBBx8YO3fu7PKZPNd9+fjjj42f//znRr9+/eydqk1PyjkmJsbIy8szDMMw1qxZY6Slpdk7XcMwnJ9zV5/bvHmz0dTUZBiGYfz2t7+19Wlv9sq5s+t8mzs92/bI2RnPtrPzNQz7PtcaAewlwsPDmThxIgCBgYHExsZSUlLS6ZnHgwYNIikpCR8fn3bXyc3NxWKxEB0dja+vLwsXLiQ7O7tDf52dvwxw8uRJHnnkEe666y5HptxjcjYMA8MwOHXqFIZhUFtbS0REhFvkDDBjxgwCAwM7tM+ePdt2is7kyZPbvXTdXuyVL8Cll15qez1UZ7q6L1arldtuu42HHnrInil20JNyNplM1NbWAlBTU9Pjn2voXs5dfe5HP/oRZnPrCzCSk5Md8lyD/XLu7Drf5k7Ptj1ydsaz7ex8wb7PtctfAyPfXWFhIbt372bKlCmdnnncmbOdhbxjx45ufa7tgbz77ru59dZb6du3rz3S6RZX5uzj48OTTz7JuHHj6NevHzExMU55lZAzcu6OpqYmnn/++S6nJezhfPLtrq7uyxNPPEFqaqpTXwzv6pyfeeYZZs+eTZ8+fWwv1Xc0Z+TcXc8++ywLFixweD/2yvnM63ybuz7b3zdnZz/bzsi3u7r7XGsEsJc5efKTcOJ7AAAgAElEQVQk8+bN49FHHyUoKKjdz77vmcffxZ49ezh8+DBz5851aD9ncnXOTU1NPPnkk+zevZvS0lISEhJYtWqVQ/t0dc5nuummm7j00ku55JJLHNaHq/MtLS3lpZde4te//rVD+zmTq3MG+NOf/sRbb71FcXExN9xwA7fccotD++sJObe5//77MZvNLFq0yKH92Cvnrq7Tld78bH/fnMG5z3ZPyLfNd3muVQD2Ik1NTcybN49FixZx1VVXAd+ceQy0O/O4M52dhbxjxw4mTJjAhAkTeO211zr93LZt2/jkk08YPnw4U6dO5YsvvuDyyy+3f7Jf6wk579mzB4CRI0diMpmYP38+//73v+2dqo0zcz6XlStXUlFRwSOPPHIeGXXNHvl2pqioyJbv//7v/3Z6X3bv3s2hQ4ewWCwMHz6curo6LBbL+SfXiZ6Qc0VFBZ9++qltpGHBggU9/rnuzLdzPpe//OUvvPHGG/ztb39zaNFpr5zPdh13f7bPJ2dnPtvOzPdcvutzrSngXsIwDJYsWUJsbGy7/5JpO/N4xYoV7c487kxSUhL5+fkUFBQQGRnJhg0b+Pvf/058fLyt0IHWXUx33HGHbffSli1bWLVqFaGhoSxduhRoHaq+8soref/99+2fMD0n5/r6evLy8qioqGDgwIG8/fbbxMbGukXOXXnmmWfYvHkzW7duxcvLMf+taK98OzN06NB2+TY3N3d6X8rLy22fCwgIcNiO2J6Sc0hICDU1NXzxxReMGjWqVzzXnfl2zl3JycnhoYce4oMPPnDoMhZ75dzZddz52T7fnJ31bDs73658r+f6nNtEpEf417/+ZQDGuHHjjPHjxxvjx4833nzzTePYsWPG9OnTDYvFYsyYMcOorKw0DMMwysrKjMjISCMwMNAIDg42IiMjjZqaGsMwWnd3xsTEGNHR0cbvf//7Tvtct26dMXLkSGPkyJHGs88+2+HnBQUFDt0F3JNyfvLJJ40xY8YY48aNM6688krj2LFjbpPz1KlTjQEDBhj+/v5GZGSkkZOTYxiGYXh7exvR0dG2OFauXNmj8124cKExZMgQw2w2G5GRkcYzzzxz1j67c18cuVOyJ+X8j3/8wxg7dqyRkJBgXHbZZcbhw4fdJufOPjdy5EgjKirKFsd//dd/9eicO7vO2bjLs22PnJ3xbLsiX3s+1zoKTkRERMTDaA2giIiIiIdRASgiIiLiYVQAioiIiHgYFYAiIiIiHkYFoIiIiIiHUQEoIuIg9913H3/84x9dHYaISAcqAEVEREQ8jApAERE7uv/++xk1ahRTp07l4MGDADz++OPExcWRkJDAwoULXRyhiIiOghMRsZudO3eyYcMG9uzZQ3NzMxMnTmTSpEmsXr2agoIC/Pz8qK6udnWYIiIaARQRsZd//etfzJ07l759+xIUFERqaioACQkJLFq0iL/+9a+YzfrvbhFxPRWAIiIO9uabb3LzzTeza9cukpKSaG5udnVIIuLhVACKiNjJpZdeysaNGzl9+jQnTpzg9ddfp6WlhaKiIqZNm8aDDz5ITU0NJ0+edHWoIuLhNBchImInEydOZMGCBYwfP55BgwaRlJSEyWTi5z//OTU1NRiGwbJly+jfv7+rQxURD2cyDMNwdRAiIiIi4jyaAhYRERHxMCoARURERDyMCkARERERD6MCUERERMTDqAAUERER8TAqAEVEREQ8jApAEREREQ+jAlBERETEw6gAFBEREfEwKgBFREREPIwKQBEREREPowJQRERExMOoABQRERHxMCoARURERDyM2dUB9BYDBgxg+PDhDu2jqakJHx8fh/bhznT/7EP30X50L+1L99N+dC/tw9H3sbCwkGPHjjnk2ioAu2n48OF88sknDu2jtLSUiIgIh/bhznT/7EP30X50L+1L99N+dC/tw9H3MTEx0WHX1hSwiIiIiIdRASgiIiLiYVQAioiIiHgYFYAiIiIiHkYFoIiIiIiH0S5gERER8Rj1TVYe+eBLXvmsjGOnGkmMCub3s8YQOzjQ1aE5lUYARURExCMUVNYx5bEPuXPTAVpaDMYMDGBr/jEmPPwB6z8pcnV4TqURQBEREXF7R040kPLUNipONvLonHh+PimKsH6+lNbU8+NndnDji58yLKQPl40c4OpQnUIjgCIiIuLWmq0tzH3uY0pq6nn8p/Hc/IPhhPXzBSAi2J93fnkRgwP8mL9+Jyfqm1wcrXOoABQRERG39sDWQ2z7qop7UkZxzcQozN7ty5+wfr5suG4iR0828ts3PndRlM7lsgKwvr6eyZMnM378eOLj47n33nsBKCgoYMqUKVgsFhYsWEBjYyMADQ0NLFiwAIvFwpQpUygsLLRda9WqVVgsFkaPHs3mzZtt7Tk5OYwePRqLxcLq1att7Z31ISIiIu7ls7Ja/u/bXzBrzEBuTL4AX/PZS58fjAjjqnFDeC63iC8rTzk5SudzWQHo5+fHu+++y6effsqePXvIyclh+/bt3H777SxfvpxDhw4REhLCunXrAFi3bh0hISEcOnSI5cuXc/vttwOQl5fHhg0b2L9/Pzk5Odx0001YrVasVis333wzmzZtIi8vjxdeeIG8vDyATvsQERER92EYBste3UeArze3XjaSgQF+XX7+jz+Jp6mlhd9t+cJJEbqOywpAk8lEQEAAAE1NTTQ1NWEymXj33Xe5+uqrAUhLS2Pjxo0AZGdnk5aWBsDVV1/N1q1bMQyD7OxsFi5ciJ+fHyNGjMBisZCbm0tubi4Wi4Xo6Gh8fX1ZuHAh2dnZGIbRaR8iIiLiPl7ZW8b7hytZevFwLhsZds7Pjwjry5Vxg3l1Xzm1p917LaBLdwFbrVYmTZrEoUOHuPnmmxk5ciT9+/fHbG4NKyoqipKSEgBKSkoYOnQoAGazmeDgYCorKykpKSE5Odl2zTO/0/b5tvYdO3ZQWVnZaR/flpmZSWZmJgDl5eWUlpba+Q60V1FR4dDruzvdP/vQfbQf3Uv70v20H0+4l80tBre/vp/hwb78dLgvR4+Ud+t7aWODeW3/Ee5/aw+/uSiyy8/25vvo0gLQ29ubPXv2UF1dzdy5czlw4IArw+kgIyODjIwMABITE4mIiHB4n87ow53p/tmH7qP96F7al+6n/bj7vXwu9z98WdXAH66MZUr8yG5/b264wZj3SskpqOPBeee+R731PvaIXcD9+/dn2rRpbNu2jerqapqbmwEoLi4mMrK1+o6MjKSoqPUljc3NzdTU1BAWFtau/czvdNYeFhbWaR8iIiLS+1lbDH7/Tj6xgwJYMOG7FWgmk4nrJkWxt6yWPSU1DorQ9VxWAFZUVFBdXQ3A6dOnefvtt4mNjWXatGm8/PLLAGRlZTFnzhwAUlNTycrKAuDll19m+vTpmEwmUlNT2bBhAw0NDRQUFJCfn8/kyZNJSkoiPz+fgoICGhsb2bBhA6mpqZhMpk77EBERkd7v1c/K+LKyjvTJQxka0vc7f/+aC1sHhtbt+I+9Q+sxXDYFXFZWRlpaGlarlZaWFubPn8+VV15JXFwcCxcu5K677uLCCy9kyZIlACxZsoTrrrsOi8VCaGgoGzZsACA+Pp758+cTFxeH2WxmzZo1eHt7A/DEE08wc+ZMrFYr6enpxMfHA/Dggw+etQ8RERHp3QzD4A/vHWZof3+uTvh+07MjwvqSNLQ/mw/23jV+52IyDMNwdRC9QWJiIp988olD+ygtLe21awl6At0/+9B9tB/dS/vS/bQfd76XH35ZySVr/s2KaSNZdWXc977Onz44zC2v5fHxby4h8YL+Z/2Mo++jI2uPHrEGUERERMQe/vj+YYL9zSyaFHVe1/lx3GAAXtrr2DeAuIoKQBEREXELB4+e5LX9R/jZ+HDihwSe17VGDQxgRGgfPiw4bqfoehYVgCIiIuIW/vTPL/HxNrFoYhQmk+m8r5caP4SdRTVUnGiwQ3Q9iwpAERER6fWOnmgg6+Mifhw7mB+MCLXLNa+MG0yDtYVXPiuzy/V6EhWAIiIi0uut/Xch9c0tLJoYiY+3fcqbqSNC8fX2cstpYBWAIiIi0qvVNTaz5qNCLokO5Yoxg+x2XX8fb6Zc0J9Piqpxt5emqAAUERGRXm39J8UcO9XIdROj6Odn31cc/3DUQL6oOMWXlafsel1XUwEoIiIivZa1xeCRD74kbnAAc8YOtvv1p1nCMIA38o7a/dqupAJQREREeq3X95eTf+wU102KYlCgv92vP/mC/vibvdj2VZXdr+1KKgBFRESk1/rj+4eJCPJjXkK4Q67vZ/ZmyrAQ9pTUuNU6QBWAIiIi0ittKzzOR4VVXHthJJYB/RzWzyUjQjl07BQlNfUO68PZVACKiIhIr/SH9w8T6GfmZ+Mj7PLi585cPDwEqwFb8485rA9nUwEoIiIivU5e+Qle/aycBRPCuTAq2KF9JQ8LAWDHf9xnHaAKQBEREel1Vr97CH+zFwsn2O/Fz50J6evL6IH92Fta69B+nEkFoIiIiPQqhcfr+PvuEq4aF263Y9/OZWp0KJ+VneB0Y7NT+nM0FYAiIiLSq/zhvcOYgJ9PisTfx9spfSZfEEJtQzOfFNU4pT9HUwEoIiIivUZ5bT3rcv/DT+IGc2l0mNP6nfj1OsOPCt3jXGCXFYBFRUVMmzaNuLg44uPjeeyxxwA4fvw4KSkpxMTEkJKSQlVV64JLwzBYtmwZFouFhIQEdu3aZbtWVlYWMTExxMTEkJWVZWvfuXMn48aNw2KxsGzZMtv7ezrrQ0RERHq2372dT3OLweJE+x/71pWxQ4Lw8TLx+ZGTTuvTkVxWAJrNZh5++GHy8vLYvn07a9asIS8vj9WrVzNjxgzy8/OZMWMGq1evBmDTpk3k5+eTn59PZmYmS5cuBVqLuZUrV7Jjxw5yc3NZuXKlraBbunQpTz/9tO17OTk5AJ32ISIiIj3XFxUneWr7V1w1dgg/HDXQqX37mr2IHxLIgQoVgOclPDyciRMnAhAYGEhsbCwlJSVkZ2eTlpYGQFpaGhs3bgQgOzubxYsXYzKZSE5Oprq6mrKyMjZv3kxKSgqhoaGEhISQkpJCTk4OZWVl1NbWkpycjMlkYvHixe2udbY+REREpOe6460D+Hl7ceOUCwhw4uhfm6QL+nPgyElO1jc5vW97c/7dO4vCwkJ2797NlClTOHLkCOHhrce5DBkyhCNHjgBQUlLC0KFDbd+JioqipKSky/aoqKgO7UCnfXxbZmYmmZmZAJSXl1NaWmrHrDuqqKhw6PXdne6ffeg+2o/upX3pftpPb7yXn5Se5JW9ZaQlhBHTt9Hh/yafTXQA1DY0895nh5kUGdQr72MblxeAJ0+eZN68eTz66KMEBQW1+5nJZHLom73P1UdGRgYZGRkAJCYmEhER4dBYAKf04c50/+xD99F+dC/tS/fTfnrTvTQMg4de+Yiwvj5kXDqGERc459Uv3zajuS+88x8+P2HmJ1/fv950H8/k0l3ATU1NzJs3j0WLFnHVVVcBMHjwYMrKygAoKytj0KBBAERGRlJUVGT7bnFxMZGRkV22FxcXd2jvqg8RERHpebI+LuajwiqWXjycKV+fyuEK48IDMXuZOHC0968DdFkBaBgGS5YsITY2lltuucXWnpqaatvJm5WVxZw5c2zt69evxzAMtm/fTnBwMOHh4cycOZMtW7ZQVVVFVVUVW7ZsYebMmYSHhxMUFMT27dsxDIP169e3u9bZ+hAREZGepeJkA7e+vp/xEUHcOOUCvL0cOzPYFX8fb2IHB7hFAeiyKeCPPvqI559/nnHjxjFhwgQAHnjgAVasWMH8+fNZt24dw4YN48UXXwRg9uzZvPXWW1gsFvr27ctzzz0HQGhoKHfffTdJSUkA3HPPPYSGtg4Nr127luuvv57Tp08za9YsZs2aBdBpHyIiItJzGIZBxkt7OdHQzJ0zLAwL7evqkEiM6s/GfeWcaujdG0FcVgBOnTrV9l6+b9u6dWuHNpPJxJo1a876+fT0dNLT0zu0JyYmsm/fvg7tYWFhZ+1DREREeo51O/7Dxn3l/PclI7gyfoirwwFgUlQwz31cxL6ykwz1dXU0359OAhEREZEe55Oian716j4mX9Cf31w6gj5OOvLtXNpOBPn3V737RBAVgCIiItKjFFefZu5zHxPax4f7rxjN8NB+rg7JZnxEEF4myDtywtWhnBeXvwZGREREpE3FyQZ+lLmdqtNNPP2zBGY4+cSPc+nra2bUwAAOHD3l6lDOiwpAERER6RGKq0/zo8ztfHmsjj/PHcvV4yMc/j7g7+P3s0ZTcbLR1WGcFxWAIiIi4nLbv6pi3l8+pqa+mT/PHcvipCh8vHvmSrV5Ca0vf3bFaST2ogJQnKK2vomXPi3j6MkGooL9mTsu3CXnOIqISM/SbG3hD+8f5t7NBxnUz49nfjaeeePDe2zx5y70L7A43N92FrP0lc840dBsawt45TMenzuWGyZf4MLIRETEld7NP8by7P3sLatlRswA7phhYZplQI+c9nU3KgDFof70wWFueS2PiZFBLLskmuRh/fm09ASr380n/f99ys7iGv48d6z+zy4i4kEOHDnBijc/J3v/ESKC/Hjwx7EsToxiSJC/q0PzGCoAxWG2flHBra/nMd0SxmNzxzJ2SBAAowcFctW4IVy/YQ9rPiok0M/Mqh/HujhaERFxtOLq09y3+Que+/g/+Ju9ufni4dwweSgXRgbj5cIj3jyRCkBxiKq6Rhb9bTfDQ/rwwOxYW/HXxuztxfprLqSu0crqdw8xKSqYq8dHuChaERFxpBP1zfz+nS94/F8FWA2DBeMjSJ88lEtHDsDXrLV+rqACUBzi/nfyOXqygeevvZApw0LO+hkvLxN/+/lEJjz8Ab946VOSh4UQ1b+PkyMVERFHeu/QMdI37OGrqtPMih3EL5OHkTJ6IP495GQPT6WyuwcwDIP7Nh/k9YNVrg7FLg4fO8XjHxbwk/jB/CR+cJef7ePjzcYbkqhrtHL9hj1OilBERBytsbmF32zcx/Qnt9FiwNPzE3jh5xP5ydghKv56ABWAPYDJZOKvO4v5+96jrg7FLh587xBemFh60TCC/H3O+fnYwYHcMSOGrfnH+NvOIidEKCIijnS8rpEfZW7n8X8VsHBCBNnpSSyZ0r1/E8Q5VAD2ELGDA/lPTSMtLYarQzkvFScbWP9JMT+OG8TllgHd/t7/zIhhZFhf7njrII3NVgdGKCIijlRxsoHpT27j34XH+f0Vo1k7bxwTIoNdHZZ8iwrAHmLskECKTzRy7FSDq0M5L0/++ysamlu49sLI7zTE72v24tGfjuU/1ad5YOshB0YoIiKOUlvfxI+e2s6Boyf5U2o8t1w+kpC+vq4OS85CBWAPETc4gOYW+LS01tWhfG/WFoOnthVy8bCQ73V4949jB5F8QX+e+LCA6rrefcaiiIinabK2cNVfPmFf+Qn+eGUcNyZfQB+t9euxXFoApqenM2jQIMaOHWtrO378OCkpKcTExJCSkkJVVevGCMMwWLZsGRaLhYSEBHbt2mX7TlZWFjExMcTExJCVlWVr37lzJ+PGjcNisbBs2TIMw+iyD1eKGxwIwJ5eXAC+80UFpbUNzBk7hP59vvs6D5PJxJ9+OpbKuib+560DDohQREQc5bdv5LE1/xh3zoghfcpQ/Mwq/noylxaA119/PTk5Oe3aVq9ezYwZM8jPz2fGjBmsXr0agE2bNpGfn09+fj6ZmZksXboUaC3mVq5cyY4dO8jNzWXlypW2gm7p0qU8/fTTtu+19dVZH640ZlAAAF9W1rk4ku8v65NigvzMzI4d9L2vkTwshJ/EDWb9J8UUV5+2Y3QiIuIor+wt5dF/FrBgQgS/vmQEfX31lrmezqUF4KWXXkpoaGi7tuzsbNLS0gBIS0tj48aNtvbFixdjMplITk6murqasrIyNm/eTEpKCqGhoYSEhJCSkkJOTg5lZWXU1taSnJyMyWRi8eLF7a51tj5cqZ+fmchAHwqO984CsLa+iVc/K2PmmIGM/rqY/b7+8JM46put/M+bn9spOhERcZTSmnoyXtpL3OAA7kqJIayf1vz1Bj2uRD9y5Ajh4eEADBkyhCNHjgBQUlLC0KFDbZ+LioqipKSky/aoqKgO7V318W2ZmZlkZmYCUF5eTmlpqR0z7WhEkDeHjtZSXFKCVy87G/fVz49T39zC1CFmKo6Un9e1AoHUUSG89Gkp/50YQnigX7e+V1FRcV79SivdR/vRvbQv3U/7see9THv1EKcamrltRiShLScpLT1pt2v3dL35mexxBeCZTCYTJgcXQl31kZGRQUZGBgCJiYlERDj2qLKx4cXk7j6KX/AABgZ0r+jpKd57u5SB/Xz5yaQYIsL6nff1HpwbxGsPvsdjO6tYf+3Ebn/P0X9HnkL30X50L+1L99N+7HEv38g7wjtf1vCbS0ZwzdRYj1z311ufyR63C3jw4MGUlZUBUFZWxqBBrevJIiMjKSr65iXBxcXFREZGdtleXFzcob2rPlxtVJg/jVaDvb1sI0hdYzObPj/K5ZYwLgjpa5drjhoYwM/GR/DSp2VaCygi0gPVN1n5zav7GBHah6UXD/PI4q8363EFYGpqqm0nb1ZWFnPmzLG1r1+/HsMw2L59O8HBwYSHhzNz5ky2bNlCVVUVVVVVbNmyhZkzZxIeHk5QUBDbt2/HMAzWr1/f7lpn68PVYsJaz8HtbTuBNx+soK7JynTLALy97Ddiu3LmaBqtLdypHcEiIj3OH98/zJfH6/g/l49k9KBAV4cj39E5C8Bbb72V/fv3O6Tza665hosuuoiDBw8SFRXFunXrWLFiBW+//TYxMTG88847rFixAoDZs2cTHR2NxWLhF7/4BWvXrgUgNDSUu+++m6SkJJKSkrjnnntsG0vWrl3LjTfeiMViYeTIkcyaNQug0z5cbVSYPwAFvWwn8Ov7jxDkZ2aaJcyu1x09KIB5CeG8+GkppTUaBRQR6Sm+Ol7HA1vzmREzgGsnRp37C9LjnHMNYGxsLBkZGTQ3N3PDDTdwzTXXEBxsnyNdXnjhhbO2b926tUObyWRizZo1Z/18eno66enpHdoTExPZt29fh/awsLCz9uFqAb7eRAb782Uv2gnc0mKw6cBRpgzrz4jQ81/7923/d+ZoXv60jDs3HeS5hRPsfn0REfnu7t18kJYWWH7JiO/13ldxvXOOAN5444189NFHrF+/nsLCQhISErj22mt57733nBGfx4kfHEjB8bpecybwp6W1lJ9o4AfDQ/E1239FwZjBgVyVEM7/21NCeW293a8vIiLfzRcVJ3l+ZzFXjw//Xqc+Sc/QrX+xrVYrBw4c4MCBAwwYMIDx48fzyCOPsHDhQkfH53Hih7QWgNWnm1wdSrdsOnAUgKkjQs/xye/vd1eMpr6phbs2aS2giIir/e7tL/Dx9iItMeo7nfkuPcs5C8Dly5czevRo3nrrLe644w527tzJ7bffzuuvv87u3budEaNHiRscQENzC/vKT7g6lG7JOXiUMYMCmBBpn2UBZxM7OJCfjh3C33eXcPREg8P6ERGRrh04coK/7yph/vgILrcMcHU4ch7OWQAmJCTw6aef8tRTTzF58uR2P8vNzXVYYJ6q7Uzg3SU1Lo7k3E41NLP9qyqmXNDf4W9+v3/2GI0Cioi42P99Ox8/sxfXJ0bh493jXiQi30Gnm0B27doFwPjx4zl48GCHn0+cONFum0HkG7GDW49RO1x5ysWRnNu2r6poshokRvV3eF+xgwOZM3Ywf9tVwu9njWFQN08HERER+zh49CQb9pSweFIUU6Pt+9YHcb5OC8Bbb7210y+ZTCbeffddhwTk6UL6+jI40K9XvArm/cOVeJvg4hEhTunv/lmxZO97n7tzDvDUz8Y7pU8REWn1+L8KMHuZuPbCSIds+hPn6rQAfO+992hpaWHbtm384Ac/cGZMHi9+cABfHq/DMAyHH4V3Pt4/dIzYwYGMGhjglP7ihgSSGj+Yv+4s4fdXjGGgRgFFRJzieF0jf/m4iCtGD+LSkRr9cwddlvBeXl786le/clYs8rWx4UEUHK+jtr7n7gQ+1dBMblE1k6KCCfBz3pHS98+O5XSTlbtytBZQRMRZntn+H+qarFxzYYR2/rqJc47hzpgxg1deeQXD6B3vpXMHcYMDON3UQt6Rk64OpVNt6/8mRTl3HWj8kEB+Et+6FvCI3gsoIuJwTdYW/vxhAYlRwVwZN8TV4YidnHPo5qmnnuKRRx7BbDbj7+9vm5asre1d59X2JmfuBL5ouOPer3c+2tb/XeKChcD3zxrDa/s/4LY3Pmf9tRc6vf9zqTzVyGdlteQfO0X16SaqTzfR3GLQz9dMgJ83EUH+RIf1ZWRYP4fvnhYROV+vflZOcU09t1wWTaC/82Z8xLHO+Td54kTveB+dO2krAPOP9dydwG3r/2IG2P/4t3MZGx7EdZOi+PvuEn5z6QgmOWEXclfqGpt554tjvPH5EXIOHKWouv3IpJcJvL1MNFk7jqIP7e9P8rAQpo4I5Yoxg4gZ0K9Hr/sUEc/z6D+/JCrYn2smRLo6FLGjbpXyVVVV5OfnU1//zT9sl156qcOC8nRh/XwZ0M+XL3voTuC29X/XXhhJPyeu/zvTw6lxZO8r579e2svH/32JS4qmg0dPsvbfhfwlt4jahmb6+XqTPCyEq8aFYwnrx+hBAUQG+xHSxweztxdNzS1UNzRRePw0X1ScoqCyjv1HTvDPL4/z0qdlwH4u6N+HueOGcO3ESJKG9lcxKCIuteOrKrZ9VcX/uTyaIcH+rg5H7Oic/3o/88wzPPbYYxQXFzNhwgS2b9/ORRddpNfAOFjc4AAKj/fMAtBV6//ONDDAj/tnj+HXr+7jqW1f8cuLhzut7/3lJ7hr0wE27ivH7GXihzEDuDJuMHAgg84AACAASURBVD8cNZDosL5dvhw1gj7EDQ5iduw3bdYWg0+Kqti4r5yPCqpY++9CHvtXAcNC+pCRPIxfJF/AwADteBYR53v0n1/Sz9ebn0+McnUoYmfnLAAfe+wxPv74Y5KTk3nvvfc4cOAAd9xxhzNi82hjhwSx/pMiTjU00c/Px9XhtPNB2/v/hjnn/X+dWXrxcJ7a9hV35xxkwfgIh/dXVlvPijc/5/mdxfT18eYXUy7gZ+PDuSQ67Lx2xXl7mZgyLJQpw1rXe5bWnObZ3CKy95Vz56YD3LflIFcnhPObS6KZ4uJ7LiKeo7j6NC/tLeOaCRGMCw9ydThiZ+fcBezv74+/f+uwb0NDA2PGjDnrySBiX3GDAzjZaOVARc9bB7jtqyosA/ox0knv/+uMt5eJZxdMoKqukUV/d9y51C0tBk/+u5Axq99jw+4Sfj4xkjeWTGbtvHGkjB5k91ciRAT34a6UUXy8/FK2L5vK3LFDyN53hOTHP+Sixz/kvUPHtCtfRBxuzUeFGIbBwgmReHlpOYq7OecIYFRUFNXV1fz0pz8lJSWFkJAQhg0b5ozYPFrckK93AhfXuHyTw5laWgw+/k81KaMG0L+P60cmky7oz4rpFu7feoj1kX6siLDvSOC+slr+6+W9/LuwiqShwfzP9Bhmxdq/6OvMlGEh/L/FiRw/1cgf3j/Muh3/YfqT25hyQX8emB3LNEuY1gmKiN3VNTbz1LavuHxkGDNGDXB1OOIA5xwBfPXVV+nfvz/33Xcfv/vd71iyZAkbN250RmwOl5OTw+jRo7FYLKxevdrV4bQTO6h1dO3g0Z71LsADR09S29DM2CE9ZzrgvpmjmTy0P/e9X8z2r47b5Zqnm6zc+dbnXPjIP9lffoL7fjSK19MnMzch3CUvQQ3t58uqH8dSeOcM7k6JoeD/t3efgVFVaQPH/1PSew8pJEAgpJMGhN5F0aB0VEQBWQVcRQVfu669rF3XxUVALHFlKZYVpUonBClCBIIkhCQEQkISUkiZOe+HyKyRGphJfX5fYGbunHPuycy9z5xaVMHgD7fS//0t7MkrafTyCCFat0/ScjhdWcOtsf6y8HMrdUWb+W3atIkFCxbQv39/kpKSyM3NtXS5LM5gMDBz5ky+//570tPT+eKLL0hPT2/qYpn4ONngYqsns5lNBNmefRqAGP/mEwDqdVq+mdodZxsdyfN3cLjg2oLmNYcKiH59PS+uOczwUC+W3ZXIk0O74OPc9DPg7G30/G14V7IeH8zjQzqz93gpsX/fwOTPd5EvC2MLIczAaFS8vTGTcB9HRkbKws+t1WW7gJ999lnS0tI4ePAgd911FzU1Ndx+++1s3ry5McpnMampqYSEhNCxY0cAJkyYwIoVKwgPD2/iktXRaDSE+zg1wwCwGEdrHd3bN59uaQBvJxs+H92ZMV9l0O/9LayfkUQXb6cGpXHsdCUPfbOfr/YcJ9DVlg9GRTIxLqBZdHX/mZ21nuev78oDfTsw++v9fL4rlyV7j/N/gzvx8IAQ7Fr4L/ayqloKyqqpNhipNhjRaTQ42+pxttXjaK2X8UgtVOnZGn76rZC0YyWm7S41Gg3ejtZE+DrRr6MHMX7OMqyhif14qIADJ8t4bngonrICQat12QBw2bJl7Nq1i7i4OAD8/PxaxeLQubm5BAYGmh4HBASwffv2esfMmzePefPmAZCfn09eXp5Fy1RQUFDvcUdnHd8eKuXosZxLLi3SmDYdPkmohw3GM0XklTevi7SXppzPR4cwcckhEt7cwLs3dGBop8sHqkUVtczfdZJ/pp3AoBSToz24I8aTUC9rKk4XUHG6EQp/DV4Z4MuUCGeeXJfNUysP8cHGTJ4ZGMCNXdyu6kb658+hJdUaFbuOl7Mrv5xfTlSQXlBBbmk1Z6qNl3yfk7UOLwc93g5WeDtY4WVvhZeD/vd/6/7vaW+Fp70e6wZ+d4xKYTCCQSlsdJprCkYasy6bqxqD4sffill+oIjVR0qoNii0GvCw0+Nso8OgFEWVtZRW1f3N/Z2suCPGizu6eeNsU/+HjNSn+VyqLl9ZlYGHnY5B7bQWv++1dC35M3nZANDa2hqN5n8XwfLy5jcr1VKmT5/O9OnTAUhISMDPzBMMLuSPeSR0PMtnv5yizMqFCN+GtWZZQnlVLQdO/cydiQEEBjTPFeFj/PzY2d6P6z/axp3Lf2NkhA9PDwsl9k9rFiql+OX4GeanZps2OR/c2ZP7+3RgSKhXi2tB8/ODTTEhfL0/nzlfp3PPt5kkBRXzwegouvk3fL1GS37Wq2uNrNifz5I9x/nx4EmKz9YC4O1oTRcvR2IC3PFysMbD3hprvQa9VovBqCivrqW82kB5tYHiszUUVdRQVFHN7hNnKawopbzacMH8bPVa9DoNVlotVrq665jBqDD8HujVGo0Yfw/4DEbFH+dXW+s0eDva4OdiSzc/Z+ICXEgKcieqndMVB4aNcd1ojmoMRj5Jy+GF1RlkFlXgYW/FqKh2DAzxoEeQG/7OtjjY6NFpNFTXGjh0qpyv95/g2/QTvLQpj3+kneTZ4aHM6t2hXotvW61PS7hQXabnn2F9Vin39gqiZ3gnaW2/Ai31M3nZAHDcuHH85S9/obi4mI8++oiPP/6Yu+++uzHKZlH+/v4cO3bM9DgnJwd//+YV1JybCLLzWHGzCAB35pRgUIrIZlCWSwnxdGDfnAE8tGI/H+84xor9J2jvakesvzPOtlZ1QUNeKbklZ9FpNVwf6sUdCQEM6eKFm33L3ps3OcKXEWE+vLQmg9fX/0b8mxu4MzGQl0eENfli0kcKy/loWzYfp2ZzsqwaTwdr+nXyoFeQG92D3IjwccLdvm7XlIY4W1MXFBaWV5FdXElO8VkKK2ooLK+mqKKaihojtUYjtUZF7e/b8em0GtMWfTqNBq1GU/d/LWh/f6zVQFmVgVMV1Zw4U8UXu/KYty0bqNvCb0x0OyYnBhLj13QLojdX36Wf4K/L93GksIIwb0devymckZE+dHR3uGBAYa3XEhfgSlyAK89cF8qajAIe/e5X7l++n0935rBkcgLt3eyb4Ezanrc3HsFGp2VMtJ8Ef63cFbUADhkyBGdnZw4ePMjf/vY3hg4d2hhls6jExEQyMjLIzMzE39+flJQUPv/886YuVj3n9gT+tZnMBD43ASSpg3sTl+TybPQ63hsdzZPDuvD2xky2Zp1mX/4ZyqsNONvoifJ14o74AAaFeNIz2A3HJtrSzhJ0Wg1PDO3CX3oGcf+KfSxKy+Hfe/J49rpQ7uvToVGHE9QYjHyz/wT/3HqUHw8VoNVAnw7uPDa4HTeF+xDkbo/uGm8ytlY6bK10eDhYN3jcZ0Ocralld14p/00/yZrDp3h3UxZvbsikf0cPHh/SmSFdPNv82LWc4kruX76Ppb/kE+xmx5vJ4Yzv5kc7F7sGpTO4sxfb7/fk7Y2ZPPH9AaJf/4klkxMIb9qlR1u9wvJqPknL4YYwb3oFy6Lzrd1l73onT57knXfeIS4ujilTpjBkyJDGKJfF6fV63nvvPa677joMBgNTpkwhIiKiqYtVT4CrLQ7WumYzEWR7djH+zramlsmWwMfJlhd/33fNYFRU1hjQasBWr2v1v269nGz4/PZ4HuxXzH3LfuGhr9N5e0Mmz18fyq1xAdcceF3K0aIKPtqezfzt2eSfqcLH0ZrpPdszIdaPPh08ms2Y1oawtdLTM8idnkHu/A3ILank1bWH+eznXIbN20afDu7MGxtNmE/zbiG3lCV78rj7qz1UVhuZ2SuYGX2CTT9ir4ZGo+GBfh0Z2sWT5Pk7uOFf23n7uiDubaHdbS3BP7ZkcbbWyIRYP1n6pQ3QqCvYUkApxY8//siCBQtIS0tj3LhxTJ06lU6dOjVGGZuFhIQE0tLSLJpHXl7eeWMJEt7cgE6jYfsDfS2a95UI+NsqIn2dWDm9Z1MX5YIuVH+ijlKKz3bm8NyqDA6dKifE054nhnRhQqwfNvr6F/qrrcdag5Hvfj3JP7ceZeXBk2iA3sHujIryZVw3f/xa6UbyldW1vLT2MG9tyKSixsB9fYJ56YYwbK10beIzWV5VywMr9vOv7dlE+Djx/PWhXB/mfd7n6loUVVQz8IOt7Msv5Y3kCO7v19FsabdVf/5snq0xEPT8akI8HfhmanfcW/hwmMZi6e+4JWOPK+r30mg0+Pr64uvri16v5/Tp04wZM4ahQ4fy6quvWqRgok6UrxPf/nqSGoOxSVtN8krOkltylgndWvfNrLXSaDTcnhDIrXEBfLg1izd+OsKdKbt56Ov9zOgVzG3xAYReRcuuUoqfc0r49OccPv85l5Nl1Xg5WDOte3tGRvgwsLMn9tatp3v9Quys69ZmnNErmHuW7OWtDZn899eTLL8rkdY+OnB3bgkTP/2ZgyfLuCsxkEcHh9DZAltEuttbs/WvvRny/kYeWLEfo1LM7t92GiAaw+KdOZwsq+Zv14VK8NdGXPbK/Pbbb/PJJ5/g6enJtGnTeO2117CyssJoNNK5c2cJAC0s3NeJhWk5ZBdVNOneu+fG/0W1a5vdW62FVqthRu8O3NsrmC9+zuUfW4/y/OoMnludQYSPIzdHtaOzo5EbnD0uOGmkxmDk4Mky9uSVsvbwKVYeOEleaRVWOg19O7gzIsyHEeHedPZ0bPVd7H/m62zL8ind+ffuPO5ZspfYNzbw3IAA5rTSFsDPduYw7d97cLLR88HoKCbFB+BgwbG09tZ6vhjThcnfZPHwN+l4OFhzR0Lg5d8oLstoVPx9/W909Xbk+q7eTV0c0Ugu+20tKipi6dKl5+3/q9Vq+fbbby1WMFHn3HiinbklTRsAHi1Gr9XQuwVMABGXp9FouDU+gFvjA0g/cYaPt2ez6tApXl6TgUEBy3/DwVqHl4M1rnZWVNYaKD1by6nyamp+n0nraK2jZ5Ab03q0Z3BnT+ICXFvVZJqrNa6bH72D3Ri1MI25q7M5XAbvj4pq8Ozm5qrWYOT/vvuVv/90hDh/Z14eEcbgzl6NEvBb6TT8d1oP+ry3mWn/3oOHvRUjwmWnimv1bfoJDhaU88L1obR3l9nWbcUV7QRyMWFhYWYtjDhfuE9d0PfL8TOM69Z05dh2tIguXg4EujZsNp9o/sJ9nHg9uW4CVG5JJctTMzhcruVkWTWnK6s5U2XA08Eaey8d7nZWhHg6EOLpQFyAC4GudjJY/AL8Xe3YfF9vJi3ayrxt2fx6ooxvpnbHpRnuKtMQRRXVTFi8k1WHTjE2ph3PD+9Kl0aeFGZvrWfNPUl0f3sT4xf/zJb7ehMtS/Fck9fW/0Y7JxtuiZJgui2Rn+vNXJCbPbZ6bZPOBDYYFWk5JYww88Bu0fz4u9hxS4Rnq5+40Bj0Oi1/H96BHiG+PPzNryS+tZGfZvaiXTPYU/pq7Dteys0LdpBdXMkTQzrzYP+OTbZuppu9NWvvSaLbGz9x4/xUdj/YD3cH2bLsaqw/fIpNmUU83L8joV4yxKctaR19Eq2YTquhi5cjmYVNFwDu/339vEhf5yYrgxAt1QP9OrHszgRySipJfHMjGQXNY13PhlixL5+kdzZRXFnDP0dH88TQzk2+aHqgmx3L70zkeGkVI/6VSq3h0tsHivMppXhy5UG8HKy5Nc6/zY3bbeskAGwBIn2dOFJUgcF42RV7LOLcBJD4AOlmEeJq3BThy5p7kiirrqXnO5tIO1bc1EW6IkopXlydwc0LdtDezY7Ft8YyOTGw2fQE9O3kwbu3RLItu5jpX+1t6uK0OKsOFbAps4gp3QOlG70NkgCwBYjwdSL/TBU5xZVNkv/2o8W42OolABTiGiQFu7P5vj5Y67QM+GALqw81703kK2sM3PbZLh7//gDDQ734ZGIs14f5NLtWont6BTO9Z3sW7DjGe5uONHVxWoxzrX++TjaMi/Gz6MLwonmSALAFCPt9IsjPuSVNkv+27NNE+Drh2cR7yQrR0kX4OpF6fx98HG0Y8a/tpOzKbeoiXVBuSSX93t9Myq5cZvUO5qNx0cQHujZ1sS7qvVFRJAW58eDX6Wz4rbCpi9MirD5SQmp2MdN6tCfGX37ct0UyCaQFOLed0t68Um6JateoeZ85W0t6/hnu7tlefiEKYQaBbvbsmN2XgR9s4bbPfuZUeTWz+nRo6mKZpGaf5uYFOyiprOX1m8KZ0qM9rs189rKVTss3U7sT8/pPjFmUxu6H+uHXwP2HLUkpxZ68Ur5NP8HOnBKOFVdSbTDi7WhDpK8Tg0I8ua6rV6N1rdcajLyyKQ9/F1smxErrX1slLYAtQCcPe6y0miaZCZyWU4yibhyiEMI83O2t2XJfH5KC3Lhv2T6eWnmAK9iV0+I+25lDv/e3oAE+Hh/DzD7BzT74O8fDwZrvpnXnTFUt13+0napaQ1MXCaNR8eWuXBLe2kjsGxt4auVBdueWYKPX4mpnRX5pFR9uPcrIBTvwffpH/u+7dArKqixern9uPcqvpyq5v2+Ha9qvWbRs0gLYAuh1WkK8HDjSBDOBtx2tmwDSI8it0fMWojVzsNGzbkYvRi9M47lVGXXBwJjoJhljV1Vr4KGv03l/cxZx/s68emM4gzp7otG0rJahGH8X/jUuhts/38UdX+wi5fb4JjuHn3OK+cuSvaQdK6GDux2PDOzEsFAvYvxccLOzMv2dT5dXs2xfPp/+nMOra3/jg81HeemGrtzbK9gin4WTZ6p4cuVBYn3tuS3Ov8X9jYX5SADYQkS3c2bjkUJqDcZG3VFg+9HTtHezo7OnQ6PlKURbYaXTsvyuRKb9ew8fbc/mZFkVKZPiG3Vx7czCCsZ+ksbOnBJuj/PnsSGdTTsQtUS3xQeQdqyYtzZmkhj4Gw8PCGnU/JVSvLL2ME+sPIirrZ5nhnXh9vgAOnrYXzDYcnOwZkqP9kzp0Z7tR4uYuXQfs5bt46Nt2Xx6WyyR7cy7/NbMpb9QVl3LrES/ZtVNLhqfdAG3EDF+zuSVVnHsdOPNBFZKsT27mEhfpxbTDSRES6PVapg/PobHBoewYv8Jery9kdySxvmeL//lOLFv/MShgnJevymct26ObNHB3zl/T45gQCcP/u+7A6w+2HizrUvP1jB6URqP/vcAg0I8WDI5nieHdqGTp8MVtbT1CHJnxwN9ee+WSLKLK4l/cwNvrP/NbMMDUnblsmTvcab3CGJgR9nWs62TALCFiPr9V+D2Rlw/7FhxJflnqoj0dZJuAiEsSKPR8MINYXx6ayyHT1UQ8/pPbLTgbNbiyhruStnNLQvT8Hex5dNbY7mvTwc8HJp2cWdz0Wo1LL8rkQAXW8Yt3klmYbnF8zxw4gw93t7E1/vymd2vI5/eGsuAkIbvkazRaJjZpwP75gwgMdCVh75JZ9A/tpJfevaayneooIzpX+0lyteJWX2CsdHL7b+tk09ACxHdru5X+Z680kbLc3t2XbApE0CEaBy3xQewaVYv7Kx0DPjHFuZ+k051rfl2uFBK8fW+fCJfW8/itGPclRhIyu3xJEf6Yt3KAgIXOytW3t2DWqNiwD+2cvKM5SZXLPvlON3f3sSJM1W8PyqKl0eE4e10bVv++bnYsnFWb164PpStWacJe3UdK/blX1VaheXV3LxgB1oNvDQijK6toJVXXLsm+cZ/9dVXREREoNVqSUtLq/faSy+9REhICKGhofzwww+m51euXEloaCghISG8/PLLpuczMzPp0aMHISEhjB8/nurqagCqqqoYP348ISEh9OjRg6ysrMvm0ZwFutrhbKPn8CnL/5I9Z/vR01jrNHRvLxNAhGgssQGu7JvTnxvDfXht/W/EvvET23+fjHUt9uaVMuyf2xi5YAc2ei0LJnTj7ZsjifJrvVs8dvVx4pupiZw4U0X/D7ZQerbGrOkbjIrH//sroxamEehqy+JbY5meFGS2YFqj0fDYkC5s+WtvvB1suHnBDqZ9uYeK6torTqO4soYR/9rOb6fKefXGMIZ39TZL2UTL1yQBYGRkJEuXLqVfv371nk9PTyclJYX9+/ezcuVKZsyYgcFgwGAwMHPmTL7//nvS09P54osvSE9PB+CRRx5h9uzZHD58GDc3N+bPnw/A/PnzcXNz4/Dhw8yePZtHHnnkknk0dxqNhghfp8YNALOL6ertiL9Ly9y8XoiWysXOmhVTurNoQjdOnKmi5zubuGl+Kr+eONPgtLYdPc2ExTuJfeMnUrOLebh/R76eksikhECcbFv/PMD+nTz5clIcGafKGfSPrZRVXXnwdCmF5dXc8NF2XlxzmJsjfVkyOZ4R4T4WGS4TF+DKnof7cUd8APNTs4l67SfWZpy67Pt+O1VOn/c2szOnhBev78odCYGy5p8waZIAMCwsjNDQ0POeX7FiBRMmTMDGxoYOHToQEhJCamoqqamphISE0LFjR6ytrZkwYQIrVqxAKcXatWsZM2YMAJMnT2b58uWmtCZPngzAmDFjWLNmDUqpi+bREsT6u5BxqpyzNea5gF1KjcFI2rFiInydcLRp/TcJIZqjOxIDOfzoYO7tFcS6w6eIeHU9Qz7cyidpxy7apVm36HAJL6/JoPtbG0l6ZxPfpZ/gtrgAlt2VwIsjwojwbb2tfhcyMrId/xobza7cEvq8u5miiuprSi/tWDEJb25g3W+neHxwCB+NjSbMx7J1amulZ9GtsfznzgQqawwM/nArAz/YwsoDJ6kx1B8mUFRRzYurM4h+/SeOFlXw7i2R3Ns7uFFnl4vmr1nd2XNzc+nZs6fpcUBAALm5dVslBQYG1nt++/btFBYW4urqil6vP+/43Nxc03v0ej0uLi4UFhZeMo/mLtrPifJqA78cP0OihbtlfzleytlaI1Ft7EYhRHPjam/FB6OjeXRQCM+vzmDF/hNM/mI3AIEutnTydMDVzgqjUhSUVXPwZBlFlXVdnV28HJg7sBMjI3xJCHRtdeP8GuLO7u2x1mu5M2U33f7+Eyvv7kF4A69vtQYjL689zLM/HsLD3oqPxkQzLtYfu0YMrEZFteP6UC+eXHmQhTuOcf1H23G21RPTzhl3eyvyz1SxK7eEaoOiX0d3HhkYwrBQr0ZdPky0DBYLAIcMGUJ+/vkDVl944QVGjhxpqWzNat68ecybNw+A/Px88vLyLJpfQcGllytoZ1X3q3Xtviz89ZZdJuKHvXVlCbattvh5m8vl6k9cGalH8zFnXeqAp3t78USSB+szS9h4tJTDRVWcKCnnaKERrUaDs42WJH97wrxsSWznQISPI652ejSas5w6eXUTCJqTa63PAb5aPh8Vwt3fHCH+jQ083s+fO2O90V5Bt+2+kxU8tjqbncfLGRDkxF+7e9PdX8fpghNc+wjNhnswwY17YpxZur+Qn46WklVcQV6xwsFKy82hrgzp4EyfIBdc7AycPHH+316+5+bRkuvRYgHg6tWrG/wef39/jh07Znqck5ODv78/wAWf9/DwoLi4mNraWvR6fb3jz6UVEBBAbW0tJSUleHh4XDKPP5s+fTrTp08HICEhAT8/vwafU0NdKo+B7rXAQbIrdRYvy8GfTuBub0XfyA74udpbNC9zaoy/UVsg9Wg+lqjLSQEBTOr7v8fVtUaMSmGl07b6MV7XWp9j/PzoHtqesZ/s5Ml1OaSkl/Dc9aHcFO5zXivZubVQ39mYScruXJxt9Dw/PJQpPdrTzrl5jI2eGxTI3N//X11rRKOpW2D8Ssj33Dxaaj02qy7g5ORkbr31Vh588EHy8vLIyMige/fuKKXIyMggMzMTf39/UlJS+Pzzz9FoNAwcOJAlS5YwYcIEFi1aZGpdTE5OZtGiRSQlJbFkyRIGDRqERqO5aB4tgZOtnvaudmQ0wkSQ7dl14/+8HZvHRU4IcXFtuWv3arR3s2frfX14f0smL685zKiFabjY6unb0YMQT3s0aMgpqWTb0dMcKz6Lg7WOSXEBTOkeSO8O7s22O1U+B6IhmiQAXLZsGffddx8FBQWMGDGCbt268cMPPxAREcG4ceMIDw9Hr9fz/vvvo9PVja147733uO666zAYDEyZMoWIiAgAXnnlFSZMmMATTzxBbGwsU6dOBWDq1KlMmjSJkJAQ3N3dSUlJAbhkHi1BjL8z+46XopSy2OLMxZU1HDhZxsAQ8y1nIIQQzYlWq+G+Ph35S48gFqYd49v0k/xyvNQ0u9bN3oqu3o5MSWzPdV29iA9o22MoReujUebaY6aVS0hIOG/NQnPLy8u7bFPyk98f4KU1GeQ8NRRfC3VBrDpYwLB523h/VCQzenewSB6WcCX1Jy5P6tF8pC7Ny9L1aTAqyqpqqao1otdpcLG1arVd6vLZNA9L16MlYw/5OdPCRPs5Y1CQmm25LeG2Z59GAyQEulosDyGEaG50Wg0udlZ4O9ngbm/daoM/IUACwBYn+vc9gX/OKbFYHluPnibY3Y5OHg4Wy0MIIYQQTUcCwBYmxNMBOysthwrKLJK+0ajYklVEjJ8LbnZWFslDCCGEEE1LAsAWRqfVENPOmV9PWiYA/PVkGcWVtXTzc0Yr3R9CCCFEqyQBYAuU2N6NgwVllFaad2NzgM2ZRQDEtOIN4oUQQoi2TgLAFijO34XKGiM7jpl/IsjmrCLc7a2I9ZcAUAghhGitJABsgeIDXYC6yRrmtimziJh2zvg4yQLQQgghRGslAWALFObtiI1ey68nzDsOML/0LEcKK4jxc8a2ETc3F0IIIUTjkgCwBdLrtES1c+LAyTNmTXdzVt34v24y/k8IIYRo1SQAbKGSgtz59WQZJRXVZktzU2YRNjot8e1lAWghhBCiNZMAsIXqGeRKZY2RzVnmGwe4OfM0Eb6OtHe1M1uaQgghhGh+JABsoXoGuQF1rXbmUFFdy67cEmL8XHC2lQWghRBCiNZMAsAWqoO7PR72VuzLN884wNTsYmqNStb/E0IIIdoACQBbKI1GQ88gYHT4BQAAGF9JREFUN345XorRqK45vZ9+K0QDJMr4PyGEEKLVkwCwBesV7E7W6UqOFJZfc1prDp+iq7cjYd6OZiiZEEIIIZozCQBbsF7BdeMAfzhUcE3plFfVsu3oaRIDXXGztzZH0YQQQgjRjEkA2IL1CHLDSqdh+zXuCLIps4gagyIxULp/hRBCiLagSQLAOXPm0LVrV6Kjo7nlllsoLv7fnrYvvfQSISEhhIaG8sMPP5ieX7lyJaGhoYSEhPDyyy+bns/MzKRHjx6EhIQwfvx4qqvr1sWrqqpi/PjxhISE0KNHD7Kysi6bR0tjZ6UjMdCVnTkl15TO2sOn0Gs1phZFIYQQQrRuTRIADh06lH379rF37166dOnCSy+9BEB6ejopKSns37+flStXMmPGDAwGAwaDgZkzZ/L999+Tnp7OF198QXp6OgCPPPIIs2fP5vDhw7i5uTF//nwA5s+fj5ubG4cPH2b27Nk88sgjl8yjpRoU4snBk2UcK6686jRWHSogqp0Tnb1k/J8QQgjRFjRJADhs2DD0ej0APXv2JCcnB4AVK1YwYcIEbGxs6NChAyEhIaSmppKamkpISAgdO3bE2tqaCRMmsGLFCpRSrF27ljFjxgAwefJkli9fbkpr8uTJAIwZM4Y1a9aglLpoHi1V/04eGBR8/+uJq3p/XslZduWW0jvYHSdbvZlLJ4QQQojmqMnv+B9//DHjx48HIDc3l549e5peCwgIIDc3F4DAwMB6z2/fvp3CwkJcXV1NweQfj8/NzTW9R6/X4+LiQmFh4SXz+LN58+Yxb948APLz88nLyzPXaV9QQUHDJ3ME2xjQa2FNeg43BjV8AkfKL6cAiHZVFj8/S7ua+hPnk3o0H6lL85L6NB+pS/NoyfVosQBwyJAh5Ofnn/f8Cy+8wMiRI03/1+v13HbbbZYqxjWZPn0606dPByAhIQE/Pz+L53k1efQOPsbOE5W0a9cOjUbToPduWZWHj6M1w2ND8HOzb3DezU1j/I3aAqlH85G6NC+pT/ORujSPllqPFgsAV69efcnXFy5cyLfffsuaNWtMQYu/vz/Hjh0zHZOTk4O/vz/ABZ/38PCguLiY2tpa9Hp9vePPpRUQEEBtbS0lJSV4eHhcMo+W6qYIHx7+Jp1dOSXENWAmb3WtkR8PFjCksyd+LrL/rxBCCNFWNMkYwJUrV/Lqq6/y9ddfY2//v1an5ORkUlJSqKqqIjMzk4yMDLp3705iYiIZGRlkZmZSXV1NSkoKycnJaDQaBg4cyJIlSwBYtGiRqXUxOTmZRYsWAbBkyRIGDRqERqO5aB4t2Q1h3gAs2Xu8Qe9bk1HAmapa+nZ0R6dtWMuhEEIIIVquJhkDOGvWLKqqqhg6dChQNxHkww8/JCIignHjxhEeHo5er+f9999Hp9MB8N5773HddddhMBiYMmUKERERALzyyitMmDCBJ554gtjYWKZOnQrA1KlTmTRpEiEhIbi7u5OSkgJwyTxaqq7ejgS62rIpq6hB7/v3nuM4WusY2sXLQiUTQgghRHOkUUpd+0aybUBCQgJpaWkWzSMvL++qxxLMWvoL/9qWTfZTQ/B2tLns8dW1Rryf/oG+Hdz5z52JWOtb/prg11J/4n+kHs1H6tK8pD7NR+rSPCxdj5aMPVr+XV8AMDHWnyqDkfnbs6/o+B8PFVBytpYhXbxaRfAnhBBCiCsnd/5WIinIjQAXW77df2XrAc7fno2rnZ6hodL9K4QQQrQ1EgC2Elqthtvi/NmefZrfTpVf8tic4kq+3p/PyAhfung6NFIJhRBCCNFcSADYikxODMSg4LV1hy953EfbslEKbonyRa+Tj4AQQgjR1sjdvxUJ83FiWKgX/95znLKq2gseU3q2hg+2ZJEU7MaATp6NXEIhhBBCNAcSALYyjw0O4XRlDa+svXAr4Ovrf+NUeTV/6RmEi51VI5dOCCGEEM2BBICtTL+OHiQFufHWhiMcLaqo91pmYQV/X3+EoZ09SY70baISCiGEEKKpSQDYymg0GhZN7EaNUTF+8U4qawwAlFXVMnJBKnqthll9OuAqrX9CCCFEm9UkO4EIy+rs5cgbyeHMXLqP+Dc2MDbGj89+ziGzqIJ3bo40bR0nhBBCiLZJWgBbqRm9O5ByexxFFdX8bdUhAN67JZLJiYEy81cIIYRo46QFsBUbH+vP6Oh25JacxUqnwc/FrqmLJIQQQohmQALAVk6v0xLkbt/UxRBCCCFEMyJ9gUIIIYQQbYwEgEIIIYQQbYwEgEIIIYQQbYwEgEIIIYQQbYwEgEIIIYQQbYxGKaWauhAtgaenJ8HBwRbNo6CgAC8vL4vm0ZpJ/ZmH1KP5SF2al9Sn+Uhdmoel6zErK4tTp05ZJG0JAJuRhIQE0tLSmroYLZbUn3lIPZqP1KV5SX2aj9SlebTkepQuYCGEEEKINkYCQCGEEEKINkb3zDPPPNPUhRD/Ex8f39RFaNGk/sxD6tF8pC7NS+rTfKQuzaOl1qOMARRCCCGEaGOkC1gIIYQQoo2RAFAIIYQQoo2RAPAqODo6NnURLmnKlCl4e3sTGRnZ1EW5LI1Gw+233256XFtbi5eXFzfeeKNZ0r+Sv9WBAwdISkrCxsaG119/3Sz5NgeWrNvCwkIGDhyIo6Mjs2bNuub0WorLfZ4GDBjQoCUhHn/8cQIDA5v9NeWPXnjhBSIiIoiOjqZbt25s3779qtJZv349W7ZsMVu5goODzbpe2vDhw3F1dTXbtehiNBoNDz30kOnx66+/TlMNzTfn57C5XSOa+3esKe7bEgC2ELW1tVd87J133snKlSstWBrzcXBwYN++fVRWVgKwatUq/P39G5RGQ+rmQtzd3XnnnXd4+OGHrymd5sYcdXsxtra2PPfcc60qYG4KN910E6mpqU1djCu2detWvv32W37++Wf27t3L6tWrCQwMvKq0zB0AXosLXUPmzJnD4sWLLZ63jY0NS5cutdhiv43lz3Uo14jmf9+WAPAqlZWVMXjwYOLi4oiKimLFihVA3ardYWFh3H333URERDBs2DDTDfiPrQOnTp0y7SySlZVF3759iYuLIy4uznRRXL9+PX379iU5OZnw8HCeeuop3nrrLVMZHn/8cd5+++3zytavXz/c3d0tefpmdcMNN/Ddd98B8MUXXzBx4kTTa6mpqSQlJREbG0uvXr04ePAgAAsXLiQ5OZlBgwYxePBgysrKuOuuu4iKiiI6Opr//Oc/pjQef/xxYmJi6NmzJydOnDgvf29vbxITE7GysrLwmTa+q6nbfv36sXv3btNxffr0Yc+ePfXSdXBwoE+fPtja2jbCWTQv69evr9cqNGvWLBYuXFjvmI8//pgHHnjA9Pijjz5i9uzZ56XVs2dP2rVrZ7Gymtvx48fx9PTExsYGqNshyc/PD4CdO3fSv39/4uPjue666zh+/DhQd927//776datG5GRkaSmppKVlcWHH37Im2++Sbdu3di4cSMFBQWMHj2axMREEhMT2bx5MwDPPPMMkydPpm/fvgQFBbF06VLmzp1LVFQUw4cPp6amxlS+V199laioKLp3787hw4cBLpnupEmT6N27N5MmTTrvXAcPHoyTk5PlKvN3er2e6dOn8+abb573WlZWFoMGDSI6OprBgweTnZ1NSUkJQUFBGI1GAMrLywkMDKSmpobffvuN4cOHEx8fT9++fTlw4ABQF1zce++99OzZk44dO7J+/XqmTJlCWFgYd955Z708Z8+eTUREBIMHD6agoADgkunec8899OjRg7lz59ZLpzleI+S+/SdKNJiDg4OqqalRJSUlSimlCgoKVKdOnZTRaFSZmZlKp9OpXbt2KaWUGjt2rFq8eLFSSqn+/furHTt2mN4TFBSklFKqvLxcVVZWKqWUOnTokIqPj1dKKbVu3Tplb2+vjhw5opRSKjMzU8XGxiqllDIYDKpjx47q1KlTFyxjZmamioiIsMDZm5eDg4Pas2ePGj16tKqsrFQxMTFq3bp1asSIEUoppUpKSlRNTY1SSqlVq1apUaNGKaWUWrBggfL391eFhYVKKaXmzp2r7r//flO6RUVFSimlAPX1118rpZSaM2eOeu655y5alqefflq99tpr5j/JJnK1dbtw4UJTXR48eND0ebyQBQsWqJkzZ1r4TJoPBweHenWolFIzZ85UCxYsUEr97zt+5swZ1bFjR1VdXa2UUiopKUnt3bv3kum2BGfOnFExMTGqc+fO6t5771Xr169XSilVXV2tkpKS1MmTJ5VSSqWkpKi77rpLKVVXJ9OmTVNKKfXTTz+Zrkt//r5NnDhRbdy4USml1NGjR1XXrl1Nx/Xu3VtVV1er3bt3Kzs7O/Xf//5XKaXUzTffrJYtW6aUUiooKEg9//zzSimlFi1aZPobXSrduLg4VVFRcdHz/fPf2hIcHBxUSUmJCgoKUsXFxeq1115TTz/9tFJKqRtvvFEtXLhQKaXU/Pnz1ciRI5VSSiUnJ6u1a9cqperqeurUqUoppQYNGqQOHTqklFJq27ZtauDAgUoppSZPnqzGjx+vjEajWr58uXJyclJ79+5VBoNBxcXFme5XgPr000+VUko9++yzpu/2pdIdMWKEqq2tvej5NZdrhNy3z6dv3HCz9VBK8dhjj7Fhwwa0Wi25ubmm1qUOHTrQrVs3oG59oKysrEumVVNTw6xZs9i9ezc6nY5Dhw6ZXuvevTsdOnQA6sa4eHh4sGvXLk6cOEFsbCweHh6WOcFGFB0dTVZWFl988QU33HBDvddKSkqYPHkyGRkZaDSaer/2hw4davrFtHr1alJSUkyvubm5AWBtbW1qrYmPj2fVqlWWPp1m5WrqduzYsTz33HO89tprfPzxx+e1EIjLc3R0ZNCgQXz77beEhYVRU1NDVFRUUxfrmjk6OrJz5042btzIunXrGD9+PC+//DIJCQns27ePoUOHAmAwGOq1bJ5ree7Xrx+lpaUUFxefl/bq1atJT083PS4tLaWsrAyA66+/HisrK6KiojAYDAwfPhyAqKioetfXc/lMnDjR1OJ6qXSTk5Oxs7O75nq5Vs7Oztxxxx2888479cqzdetWli5dCsCkSZNMrWzjx4/nyy+/ZODAgaSkpDBjxgzKysrYsmULY8eONb2/qqrK9P+bbroJjUZDVFQUPj4+ps9jREQEWVlZdOvWDa1Wy/jx4wG4/fbbGTVq1GXTHTt2LDqdzgK1Yn5y365PAsCr9Nlnn1FQUMDOnTuxsrIiODiYs2fPApi6RwB0Op2pKVmv15ua7c8dC/Dmm2/i4+PDnj17MBqN9ZrMHRwc6uU7bdo0Fi5cSH5+PlOmTLHY+TW25ORkHn74YdavX09hYaHp+SeffJKBAweybNkysrKyGDBggOm1P9fNhVhZWaHRaIC6v8W1jhdsiRpat/b29gwdOpQVK1bw73//m507dzZRyZunP36Pof53+Y+mTZvGiy++SNeuXbnrrrsaq3gWp9PpGDBgAAMGDCAqKopFixYRHx9PREQEW7duveB7zn0HL/YYwGg0sm3btgt2GZ67pmq12nrfaa1WW+87/cd0z/3/UuleyTWksTzwwAPExcVd0WclOTmZxx57jKKiInbu3MmgQYMoLy/H1dW13vCNP/pjHf7xHvXnOvwjjUaD0Wi8ZLrNqQ4vR+7b9ckYwKtUUlKCt7c3VlZWrFu3jqNHj172PcHBwaab6ZIlS+ql1a5dO7RaLYsXL8ZgMFw0jVtuuYWVK1eyY8cOrrvuums/kWZiypQpPP300+e1kpSUlJgmLvx5nNUfDR06lPfff9/0+PTp0xYpZ0t0NXU7bdo0/vrXv5KYmGhqTRV1goKCSE9Pp6qqiuLiYtasWXPB43r06MGxY8f4/PPP6429bMkOHjxIRkaG6fHu3bsJCgoiNDSUgoICUwBYU1PD/v37Tcd9+eWXAGzatAkXFxdcXFxwcnLizJkzpmOGDRvGu+++Wy/thjqXz5dffklSUpLZ0m0M7u7ujBs3jvnz55ue69Wrl6ln47PPPqNv375AXUtsYmIi999/PzfeeCM6nQ5nZ2c6dOjAV199BdS1dv157O7lGI1G073p888/p0+fPmZJt7mQ+3Z9EgA2UG1tLTY2Ntx2222kpaURFRXFJ598QteuXS/73ocffph//OMfxMbG1pvxNWPGDBYtWkRMTAwHDhy45C8qa2trBg4cyLhx4y7a7D5x4kSSkpI4ePAgAQEB9S4ozVVAQAB//etfz3t+7ty5PProo8TGxl6y9e6JJ57g9OnTREZGEhMTw7p166447/z8fAICAnjjjTd4/vnnCQgIoLS09KrOozm6mrqNj4/H2dn5kq0RwcHBPPjggyxcuJCAgIB63Wyt0bnvfmBgIOPGjSMyMpJx48YRGxt70feMGzeO3r17XzSInjt3LgEBAVRUVBAQENBky39cqbKyMiZPnkx4eDjR0dGkp6fzzDPPYG1tzZIlS3jkkUeIiYmhW7du9Wb42traEhsbyz333GO6Ht10000sW7bMNAnknXfeIS0tjejoaMLDw/nwww8bXL7Tp08THR3N22+/bZpUcbXp9u3bl7Fjx7JmzRoCAgL44YcfGlyehnrooYfq3RveffddFixYQHR0NIsXL643eWD8+PF8+umnpi5bqAsS58+fT0xMDBEREaZJDlfKwcGB1NRUIiMjWbt2LU899dQ1pdtcrhFy374w2Qqugfbs2cPdd9/dZEs3GI1G4uLi+Oqrr+jcuXOTlEG0fnl5eQwYMIADBw6g1crvRLi67/6NN97I7NmzGTx4sAVL1rwNGDCA119/nYSEhKYuimij5L59YXJlb4APP/yQiRMn8vzzzzdJ/unp6YSEhDB48OBm9SESrcsnn3xCjx49eOGFFyT4+11Dv/vFxcV06dIFOzu7Nh38CdHU5L59cdICKIQQQgjRxsjPeyGEEEKINkYCQCGEEEKINkYCQCGEEEKINkYCQCFEm6LT6ejWrRsRERHExMTw97//vd7CzpYwZ84cIiIimDNnjkXzEUKIKyWTQIQQbYqjo6NpK7CTJ09y66230rt3b5599lmL5eni4kJRUVGjbJlVW1uLXi+bPAkhLk1aAIUQbZa3tzfz5s3jvffeQylFVlYWffv2JS4ujri4ONNixnfccQfLly83ve+22247bzFcpRRz5swhMjKSqKgo064UycnJlJWVER8fb3oO6tYG69y5MwUFBabHISEhFBQUUFBQwOjRo0lMTCQxMZHNmzcDkJqaSlJSErGxsfTq1YuDBw8CdTu5JCcnM2jQIFl2RghxZZQQQrQhDg4O5z3n4uKi8vPzVXl5uaqsrFRKKXXo0CEVHx+vlFJq/fr1auTIkUoppYqLi1VwcLCqqampl8aSJUvUkCFDVG1trcrPz1eBgYEqLy/vonkqpdQzzzyj3nzzTaWUUj/88IMaNWqUUkqpiRMnqo0bNyqllDp69Kjq2rWrUkqpkpISU76rVq0yHb9gwQLl7++vCgsLr7JWhBBtjfQTCCHE72pqapg1axa7d+9Gp9Nx6NAhAPr378+MGTMoKCjgP//5D6NHjz6vm3XTpk1MnDgRnU6Hj48P/fv3Z8eOHSQnJ180vylTpjBy5EgeeOABPv74Y9PWe6tXr663bVZpaSllZWWUlJQwefJkMjIy0Gg01NTUmI4ZOnQo7u7u5qwOIUQrJgGgEKJNO3LkCDqdDm9vb5599ll8fHzYs2cPRqMRW1tb03F33HEHn376KSkpKSxYsMAseQcGBuLj48PatWtJTU3ls88+A+q6g7dt21Yvf4BZs2YxcOBAli1bRlZWFgMGDDC9dqm9SIUQ4s9kDKAQos0qKCjgnnvuYdasWWg0GkpKSmjXrh1arZbFixdjMBhMx95555289dZbAISHh5+XVt++ffnyyy8xGAwUFBSwYcMGunfvftkyTJs2jdtvv52xY8eaJokMGzaMd99913TM7t27ASgpKcHf3x+oG/cnhBBXSwJAIUSbUllZaVoGZsiQIQwbNoynn34agBkzZrBo0SJiYmI4cOBAvVY1Hx8fwsLCTN20f3bLLbcQHR1NTEwMgwYN4tVXX8XX1/ey5Tk3SeSP6b7zzjukpaURHR1NeHg4H374IQBz587l0UcfJTY2ltra2mupBiFEGyfLwAghxBWoqKggKiqKn3/+GRcXF7Olm5aWxuzZs9m4caPZ0hRCiMuRFkAhhLiM1atXExYWxn333WfW4O/ll19m9OjRvPTSS2ZLUwghroS0AAohhBBCtDHSAiiEEEII0cZIACiEEEII0cZIACiEEEII0cZIACiEEEII0cZIACiEEEII0cb8P4lplfnLlBHGAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "HFwwN2Ai9ybQ",
        "outputId": "69f73cb1-d117-4274-f01d-5d6a1843c9be"
      },
      "source": [
        "plot_plotly(m, forecast)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                    Plotly.newPlot(\n",
              "                        '4b81fe21-1834-47f4-9bb0-9254efa719b1',\n",
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      "source": [
        "fore = round(forecast['yhat']).tail(12)"
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      "outputs": []
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        "resultado_final_prophet_loja1 = pd.concat([test, fore], axis=1).drop('IsHoliday', axis=1)\n",
        "resultado_final_prophet_loja1['diff'] = resultado_final_prophet_loja1['Weekly_Sales'] -  resultado_final_prophet_loja1['yhat']\n",
        "resultado_final_prophet_loja1['%'] =  (resultado_final_prophet_loja1['diff']  / resultado_final_prophet_loja1['Weekly_Sales'])*100  "
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      "outputs": []
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              "      <th>Date</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>yhat</th>\n",
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              "      <th>131</th>\n",
              "      <td>2012-08-10</td>\n",
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              "      <td>51492.33</td>\n",
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              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>2012-09-14</td>\n",
              "      <td>1517428.87</td>\n",
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              "      <td>-48795.13</td>\n",
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              "      <th>137</th>\n",
              "      <td>2012-09-21</td>\n",
              "      <td>1506126.06</td>\n",
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              "      <td>-24358.94</td>\n",
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              "      <td>1571284.0</td>\n",
              "      <td>-63215.23</td>\n",
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              "    <tr>\n",
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              "          Date  Weekly_Sales       yhat       diff         %\n",
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              "132 2012-08-17    1597868.05  1605547.0   -7678.95 -0.480575\n",
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              "139 2012-10-05    1670785.97  1556627.0  114158.97  6.832651\n",
              "140 2012-10-12    1573072.81  1579578.0   -6505.19 -0.413534\n",
              "141 2012-10-19    1508068.77  1571284.0  -63215.23 -4.191800\n",
              "142 2012-10-26    1493659.74  1546991.0  -53331.26 -3.570509"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n73ZksRicqiq"
      },
      "source": [
        "RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aN3fpkp1csuU"
      },
      "source": [
        "base_treino = treino['Weekly_Sales'].values\n",
        "base_teste = test['Weekly_Sales'].values "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-znXQD07hD_n"
      },
      "source": [
        "min = MinMaxScaler()\n",
        "base_treino = base_treino.reshape(-1,1)\n",
        "base_treino = min.fit_transform(base_treino)\n",
        "base_treino = base_treino[:,0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ReUOKoFPhMRd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "20fcd044-768b-4223-f860-4035645e0564"
      },
      "source": [
        "previsores = []\n",
        "preco_real = []\n",
        "\n",
        "for i in range(12, treino.shape[0]):\n",
        "  previsores.append(base_treino[i-12:i])\n",
        "  preco_real.append(base_treino[i])\n",
        "\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-a70df1cbfe91>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpreco_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtreino\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m   \u001b[0mprevisores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_treino\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m   \u001b[0mpreco_real\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_treino\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'treino' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6I7hSFDeTBg"
      },
      "source": [
        "previsores, preco_real = np.array(previsores), np.array(preco_real)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPZ7Sii_fDMg"
      },
      "source": [
        "previsores = np.reshape(previsores, (previsores.shape[0], previsores.shape[1], 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9K9c7_MgjDV",
        "outputId": "dd3905e7-40de-4d40-fc60-e74f83c36c51"
      },
      "source": [
        "previsores.shape[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaeSL231fvVf"
      },
      "source": [
        "regressor = Sequential()\n",
        "\n",
        "regressor.add(LSTM(units=100, return_sequences=True, input_shape = (12,1)))\n",
        "regressor.add(Dropout(0.3))\n",
        "\n",
        "regressor.add(LSTM(units=50, return_sequences=True))\n",
        "regressor.add(Dropout(0.3))\n",
        "\n",
        "regressor.add(LSTM(units=50, return_sequences=False))\n",
        "regressor.add(Dropout(0.3))\n",
        "\n",
        "regressor.add(Dense(units=1, activation='linear'))\n",
        "\n",
        "regressor.compile(optimizer='adam', loss='mean_squared_error', metrics=['mean_absolute_error'])\n",
        "\n",
        "regressor.fit(previsores, preco_real, epochs=2000, batch_size=15)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_9VMb4gohOI"
      },
      "source": [
        "preco_real_test = test['Weekly_Sales'].values\n",
        "base_completa = pd.concat([treino['Weekly_Sales'], test['Weekly_Sales']], axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5tytQd-j9I6"
      },
      "source": [
        "entradas = base_completa[len(base_completa)-len(test)-12:].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12DKE-IolrK2"
      },
      "source": [
        "entradas = entradas.reshape(-1,1)\n",
        "entradas = min.fit_transform(entradas)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTE0AoSepLbx"
      },
      "source": [
        "entradas = entradas[:,0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKkS9MmmowSO"
      },
      "source": [
        "x_test = []\n",
        "for i in range(12, test.shape[0]+12):\n",
        "  x_test.append(entradas[i-12:i])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LTuDThmzpXfh"
      },
      "source": [
        "x_test = np.array(x_test)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p83bze0vpXnL"
      },
      "source": [
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLkv1-shr7Dt"
      },
      "source": [
        "previsoes = regressor.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGSFreKLr-na"
      },
      "source": [
        "previsoes = min.inverse_transform(previsoes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "WDce6F1KsC58",
        "outputId": "a9c7dfeb-c20f-471b-9faa-a58671faa0b6"
      },
      "source": [
        "plt.plot(preco_real_test, color='r', label='Preco Real')\n",
        "plt.plot(previsoes, color='b', label='Previsao')\n",
        "plt.title('Previsao venda Walmart')\n",
        "plt.ylabel('Venda Walmart')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-7277189bf492>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreco_real_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Preco Real'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprevisoes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Previsao'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Previsao venda Walmart'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Venda Walmart'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6f1tT7uht0Qd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}