fiap_ano2_fase_03.ipynb

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          "text": [
            "        cod_pedido regiao_pais                              produto  valor  \\\n",
            "0                1       Norte         Biscoito  True Champion 300g     22   \n",
            "1                2       Norte         Biscoito  True Champion 300g     21   \n",
            "2                3       Norte         Biscoito  True Champion 300g     22   \n",
            "3                4       Norte         Biscoito  True Champion 300g     19   \n",
            "4                5       Norte         Biscoito  True Champion 300g     22   \n",
            "...            ...         ...                                  ...    ...   \n",
            "200781      200976     Sudeste  Bandana Disney Mickey Fábrica Petti  19,44   \n",
            "200782      200977     Sudeste  Bandana Disney Mickey Fábrica Petti  21,06   \n",
            "200783      200978         Sul  Bandana Disney Mickey Fábrica Petti  14,58   \n",
            "200784      200979         Sul  Bandana Disney Mickey Fábrica Petti   16,2   \n",
            "200785      200980         Sul  Bandana Disney Mickey Fábrica Petti  14,58   \n",
            "\n",
            "        quantidade valor_total_bruto        data             estado  \\\n",
            "0              2.0                44  13/06/2019               Acre   \n",
            "1              2.0                42  03/01/2019              Amapá   \n",
            "2              NaN                44  18/01/2019               Pará   \n",
            "3              4.0                88  19/08/2019           Rondônia   \n",
            "4              2.0                44  18/10/2019          Tocantins   \n",
            "...            ...               ...         ...                ...   \n",
            "200781         3.0             58,32  07/02/2022     Rio de Janeiro   \n",
            "200782         1.0             21,06  06/02/2022     Espírito Santo   \n",
            "200783         3.0             43,74  25/01/2022             Paraná   \n",
            "200784         2.0              32,4  27/03/2022    Santa  Catarina   \n",
            "200785         1.0             14,58  02/04/2022  Rio Grande do Sul   \n",
            "\n",
            "             formapagto centro_distribuicao responsavelpedido valor_comissao  \\\n",
            "0              Dinheiro          Rapid Pink            Carlos           0,44   \n",
            "1                   Pix          Rapid Pink       Maria Linda           0,42   \n",
            "2       Boleto Bancário          Rapid Pink             Julia           0,88   \n",
            "3       Boleto Bancário          Rapid Pink              Yuri           0,76   \n",
            "4        Cartão Crédito          Rapid Pink           Dolores           0,44   \n",
            "...                 ...                 ...               ...            ...   \n",
            "200781    Cartão Débito         Papa Léguas           Antonio           0,42   \n",
            "200782    Cartão Débito         Papa Léguas            Tereza           0,26   \n",
            "200783   Cartão Crédito           Tree True           Beatriz           0,48   \n",
            "200784              Pix           Tree True           Vitória           0,52   \n",
            "200785    Cartão Débito           Tree True            Vivian            0,2   \n",
            "\n",
            "       lucro_liquido categoriaprod  \n",
            "0               17,6   Alimentação  \n",
            "1               16,8   Alimentação  \n",
            "2               35,2   Alimentação  \n",
            "3               30,4   Alimentação  \n",
            "4               17,6   Alimentação  \n",
            "...              ...           ...  \n",
            "200781          14,7     Acessório  \n",
            "200782           9,1     Acessório  \n",
            "200783          16,8     Acessório  \n",
            "200784          18,2     Acessório  \n",
            "200785             7     Acessório  \n",
            "\n",
            "[200786 rows x 14 columns]\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.preprocessing import scale\n",
        "from sklearn.covariance import EmpiricalCovariance, MinCovDet\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "# lista com os arquivos a serem analisados\n",
        "arquivos = ['/content/vendas_linha_petshop_2019 - vendas_linha_petshop_2019.csv',\n",
        "            '/content/vendas_linha_petshop_2020 - vendas_linha_petshop_2020.csv',\n",
        "            '/content/vendas_linha_petshop_2021 - vendas_linha_petshop_2021.csv',\n",
        "            '/content/vendas_linha_petshop_2022 - vendas_linha_petshop_2022.csv']\n",
        "\n",
        "df_list = []\n",
        "\n",
        "for arquivo in arquivos:\n",
        "  df_list.append(pd.read_csv(arquivo))\n",
        "\n",
        "df = pd.concat(df_list, ignore_index=True)\n",
        "\n",
        "print(df)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.dtypes)"
      ],
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        },
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        "outputId": "275cd933-da10-4351-b313-7a09e7f4605f"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cod_pedido               int64\n",
            "regiao_pais             object\n",
            "produto                 object\n",
            "valor                   object\n",
            "quantidade             float64\n",
            "valor_total_bruto       object\n",
            "data                    object\n",
            "estado                  object\n",
            "formapagto              object\n",
            "centro_distribuicao     object\n",
            "responsavelpedido       object\n",
            "valor_comissao          object\n",
            "lucro_liquido           object\n",
            "categoriaprod           object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.3 Tratando os dados nas colunas de valores\n",
        "# corrigindo a coluna de data para o formato padrao YYYY-MM-DD\n",
        "df['data'] = pd.to_datetime(df['data'], format='%d/%m/%Y')\n",
        "df['ano'] = df['data'].dt.year\n",
        "df['mes'] = df['data'].dt.month\n",
        "df['ano-mes'] = pd.to_datetime(df['data']).dt.strftime('%Y-%m')\n",
        "\n",
        "# corrigindo tipo de dado object para float\n",
        "colunas_para_float = ['valor', 'valor_total_bruto', 'valor_comissao', 'lucro_liquido']\n",
        "for coluna in colunas_para_float:\n",
        "  df[coluna] = df[coluna].str.replace(',', '.').astype(float)"
      ],
      "metadata": {
        "id": "Oi6VhAVHuZ-_"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.dtypes)"
      ],
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        },
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      "execution_count": 135,
      "outputs": [
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          "text": [
            "cod_pedido                      int64\n",
            "regiao_pais                    object\n",
            "produto                        object\n",
            "valor                         float64\n",
            "quantidade                    float64\n",
            "valor_total_bruto             float64\n",
            "data                   datetime64[ns]\n",
            "estado                         object\n",
            "formapagto                     object\n",
            "centro_distribuicao            object\n",
            "responsavelpedido              object\n",
            "valor_comissao                float64\n",
            "lucro_liquido                 float64\n",
            "categoriaprod                  object\n",
            "ano                             int64\n",
            "mes                             int64\n",
            "ano-mes                        object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.1 Identificando dados missing\n",
        "missings_por_linha = pd.DataFrame({'cod_pedido':df['cod_pedido'].tolist(), 'n_missing':df.isna().sum(axis=1).tolist()})\n",
        "\n",
        "n_colunas = df.shape[1] - 1\n",
        "\n",
        "missings_por_linha.assign(perc_missings = missings_por_linha['n_missing'] / n_colunas).sort_values('perc_missings', ascending=False).head(10)"
      ],
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      "outputs": [
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              "       cod_pedido  n_missing  perc_missings\n",
              "30611       30612          3         0.1875\n",
              "24052       24053          3         0.1875\n",
              "1180         1181          3         0.1875\n",
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              "47342       47343          3         0.1875\n",
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              "40966       40967          3         0.1875\n",
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      "source": [
        "# 1.4.1 Identificando dados missing\n",
        "missings_por_coluna = df.isna().sum()\n",
        "\n",
        "missings_por_coluna = pd.DataFrame(missings_por_coluna, columns=['n'])\n",
        "\n",
        "n_linhas = df.shape[0]\n",
        "\n",
        "missings_por_coluna.assign(perc_missings = missings_por_coluna['n'] / n_linhas).sort_values('n', ascending=False)"
      ],
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      "execution_count": 137,
      "outputs": [
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          "data": {
            "text/plain": [
              "                         n  perc_missings\n",
              "valor                50283       0.250431\n",
              "valor_total_bruto    50283       0.250431\n",
              "quantidade             667       0.003322\n",
              "cod_pedido               0       0.000000\n",
              "responsavelpedido        0       0.000000\n",
              "mes                      0       0.000000\n",
              "ano                      0       0.000000\n",
              "categoriaprod            0       0.000000\n",
              "lucro_liquido            0       0.000000\n",
              "valor_comissao           0       0.000000\n",
              "formapagto               0       0.000000\n",
              "centro_distribuicao      0       0.000000\n",
              "regiao_pais              0       0.000000\n",
              "estado                   0       0.000000\n",
              "data                     0       0.000000\n",
              "produto                  0       0.000000\n",
              "ano-mes                  0       0.000000"
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              "      <th>lucro_liquido</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>valor_comissao</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>formapagto</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>centro_distribuicao</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>regiao_pais</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>estado</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>produto</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ano-mes</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.1 Tratando os dados missing\n",
        "# Aplicando a media a partir da coluna valor\n",
        "df['valor'] = df['valor'].fillna(df['valor_total_bruto'] / df['quantidade'])\n",
        "\n",
        "df_agrupado = df.groupby(['regiao_pais', 'produto', 'mes'])['valor'].mean()\n",
        "\n",
        "df = pd.merge(df, df_agrupado, on=['produto', 'regiao_pais', 'mes'], suffixes=('', '_media'))\n",
        "\n",
        "df['valor'] = df['valor'].fillna(df['valor_media'])\n",
        "\n",
        "df['quantidade'] = df['quantidade'].fillna(df['valor_total_bruto'] / df['valor'])\n",
        "\n",
        "df['valor_total_bruto'] = df['valor_total_bruto'].fillna(df['valor'] * df['quantidade'])\n",
        "\n",
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "7pIyetnJ-Dol"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uLbF6n-EUC2",
        "outputId": "28e954eb-b548-48f9-94c6-34a4ad36abc1"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        cod_pedido regiao_pais  \\\n",
            "0                1       Norte   \n",
            "1                7       Norte   \n",
            "3             4087       Norte   \n",
            "4             5284       Norte   \n",
            "5             5287       Norte   \n",
            "...            ...         ...   \n",
            "200781      135828         Sul   \n",
            "200782      138827         Sul   \n",
            "200783      141828         Sul   \n",
            "200784      147826         Sul   \n",
            "200785      149028         Sul   \n",
            "\n",
            "                                                  produto       valor  \\\n",
            "0                            Biscoito  True Champion 300g   25.287111   \n",
            "1                            Biscoito  True Champion 300g   25.287111   \n",
            "3                            Biscoito  True Champion 300g   25.287111   \n",
            "4                            Biscoito  True Champion 300g   25.287111   \n",
            "5                            Biscoito  True Champion 300g   25.287111   \n",
            "...                                                   ...         ...   \n",
            "200781  Antipulgas e Carrapatos MSD Bravecto para Pet ...  352.450000   \n",
            "200782  Antipulgas e Carrapatos MSD Bravecto para Pet ...  388.360000   \n",
            "200783  Antipulgas e Carrapatos MSD Bravecto para Pet ...  352.450000   \n",
            "200784  Antipulgas e Carrapatos MSD Bravecto para Pet ...  353.780000   \n",
            "200785  Antipulgas e Carrapatos MSD Bravecto para Pet ...  364.420000   \n",
            "\n",
            "        quantidade  valor_total_bruto       data             estado  \\\n",
            "0              2.0          50.574222 2019-06-13               Acre   \n",
            "1              3.0          75.861333 2019-06-09           Amazonas   \n",
            "3              4.0         101.148444 2019-06-26           Rondônia   \n",
            "4              2.0          50.574222 2019-06-08               Acre   \n",
            "5              4.0         101.148444 2019-06-24           Rondônia   \n",
            "...            ...                ...        ...                ...   \n",
            "200781        45.0       15860.250000 2021-05-15  Rio Grande do Sul   \n",
            "200782         1.0         388.360000 2021-05-21    Santa  Catarina   \n",
            "200783         2.0         704.900000 2021-05-02  Rio Grande do Sul   \n",
            "200784         3.0        1061.340000 2021-05-04             Paraná   \n",
            "200785         1.0         364.420000 2021-05-10  Rio Grande do Sul   \n",
            "\n",
            "             formapagto centro_distribuicao responsavelpedido  valor_comissao  \\\n",
            "0              Dinheiro          Rapid Pink            Carlos            0.44   \n",
            "1                   Pix          Rapid Pink            Silvia            0.63   \n",
            "3                   Pix          Rapid Pink              Yuri            1.52   \n",
            "4         Cartão Débito          Rapid Pink            Carlos            0.88   \n",
            "5              Dinheiro          Rapid Pink              Yuri            1.52   \n",
            "...                 ...                 ...               ...             ...   \n",
            "200781    Cartão Débito           Tree True            Vivian          357.75   \n",
            "200782    Cartão Débito           Tree True           Vitória            8.76   \n",
            "200783  Boleto Bancário           Tree True            Vivian           15.90   \n",
            "200784              Pix           Tree True           Beatriz           23.94   \n",
            "200785  Boleto Bancário           Tree True            Vivian            8.22   \n",
            "\n",
            "        lucro_liquido categoriaprod   ano  mes  ano-mes  valor_media  \n",
            "0               17.60   Alimentação  2019    6  2019-06    25.287111  \n",
            "1               25.20   Alimentação  2019    6  2019-06    25.287111  \n",
            "3               30.40   Alimentação  2019    6  2019-06    25.287111  \n",
            "4               17.60   Alimentação  2019    6  2019-06    25.287111  \n",
            "5               30.40   Alimentação  2019    6  2019-06    25.287111  \n",
            "...               ...           ...   ...  ...      ...          ...  \n",
            "200781        6558.75   Medicamento  2021    5  2021-05   342.918333  \n",
            "200782         160.60   Medicamento  2021    5  2021-05   342.918333  \n",
            "200783         291.50   Medicamento  2021    5  2021-05   342.918333  \n",
            "200784         438.90   Medicamento  2021    5  2021-05   342.918333  \n",
            "200785         150.70   Medicamento  2021    5  2021-05   342.918333  \n",
            "\n",
            "[200631 rows x 18 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.2 Analise dos outiliers nas colunas quantidade e valor, pelo metodo scatterplot\n",
        "df.plot.scatter(x='quantidade', y='valor')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "yG4ivu8oLHEN",
        "outputId": "7169c44b-ad3c-43e0-91ca-74365df58303"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='quantidade', ylabel='valor'>"
            ]
          },
          "metadata": {},
          "execution_count": 140
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.2 Analise dos outiliers na coluna quantidade pelo metodo scatterplot\n",
        "df.plot.scatter(x='quantidade', y='valor_total_bruto')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "-888RD-0P-9z",
        "outputId": "16a1d5d1-38bd-4465-d20c-12e6f72f9731"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='quantidade', ylabel='valor_total_bruto'>"
            ]
          },
          "metadata": {},
          "execution_count": 141
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.4.a Analisando o produto Biscoito  True Champion 300g\n",
        "# filtrando o df pelo produto escolhido\n",
        "df_filtrado = df.loc[df['produto'] == 'Biscoito  True Champion 300g']\n",
        "\n",
        "df_agrupado = df_filtrado.groupby([df['ano-mes']])['quantidade'].sum()\n",
        "\n",
        "plt.plot(df_agrupado.index, df_agrupado.values)\n",
        "\n",
        "plt.xlabel('Data')\n",
        "plt.ylabel('Quantidade')\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.title('Série de Quantidade por Mês e Ano')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "v3Wp2eDDlgs4",
        "outputId": "8e95f123-646a-441e-e987-ed7988ae9fb2"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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DAjfddBPUajWmTJkCrVaL2267zeE44Vi98cYb6Nmzp8Mxwt/UbbfdhmuvvRarVq3CunXr8MYbb+C1117DypUrMXr06Hrnv/vuu50aXd27d6/3swgp9nUz4QROnz5tc747Oy+Z5gMbRMxVSd++fQEAly5dAgC0bdsWRIT09HRJb1Jt27bF/v37MXz4cLerIguhiBMnToihLwDQ6/XIyclBjx49JNlPu3bt0KFDB6xevRpvvfWWw9DZJ598AgC49dZbxdeio6NRUlJiN/bs2bM2N5JvvvkGbdq0wcqVK23WNnfuXLfWaY2rnzEuLg7BwcEOayjVrYfzww8/QKvV4vvvv7fxUtUNN1p/L9afs7Cw0M6L1bZtW1RUVGDEiBEurdcRJ0+eBBHZfObjx48DgOixS01NdVjf5+jRozZr9pbg4GBMmDABy5cvx+jRoxEbG+twXNu2bQGYw3uufPakpCQ88sgjeOSRR1BQUIDevXvj1VdfdWoQxcXFITw8HEaj0aNjm5OTgz///BMzZ87E0KFDbbaZTCbcc889+Oyzz9yuZ5WamooNGzagvLzc5u9I6u+BaRpYQ8QENJs3b3boWRBCJR06dAAA3HLLLVAoFHjppZfsxhMRioqKPNr/bbfdhgsXLuCDDz6w21ZdXY3Kykqn7+3bty/i4uKwZMkSm4J4H3/8sZ0h4s1+ALNxcuXKFTz00EN2acTZ2dl47bXX0KtXL5sbVNu2bbF9+3abta1Zs8auTIHwy9r6uO7YsQPbtm2rd031IdSJcWSQ1d13VlYWVq9ejdzcXPH1I0eOYO3atQ2us7S0FMuWLbMZN2LECAQFBeGdd96xGfvvf//bbv+33XYbtm3bZrcvYe0Gg6He9QPAxYsXsWrVKvF5WVkZPvnkE/Ts2ROJiYkAgDFjxmDnzp02x7SyshJLly5FWlqa6AWUgqeeegpz587F888/73RMnz590LZtW7z55puoqKiw215YWAjA7E0UtFkC8fHxSE5OrrdchEKhEGsf/fXXX07nd4bgHZo9ezYmTZpk87jtttswdOhQj4o0jhkzBkajEYsXL7Z5fdGiRZDJZE4NPCYwYA8RE9A8+uijqKqqws0334yOHTtCp9Phzz//xJdffom0tDRRGN22bVu88sormDNnDs6cOYMJEyYgPDwcOTk5WLVqFaZPn46nnnrK7f3fc889+Oqrr/DQQw9h8+bNGDx4MIxGI44ePYqvvvoKa9euFb1VdQkKCsIrr7yCBx98ENdffz1uv/125OTkYNmyZXb6Cm/2AwB/+9vfsHv3bixcuBCHDx/GXXfdhejoaOzZswcfffQR4uLi8M0339gUZbz//vvxzTffYNSoUbjttttw6tQpLF++XPQOCNx4441YuXIlbr75ZowdOxY5OTlYsmQJOnfu7PBm6Qp9+vQBADz22GPIysqCQqHAHXfc4XDsSy+9hF9++QXXXnstHnnkERgMBrzzzjvo0qWLTZhw5MiRUKlUGDduHB588EFUVFTggw8+QHx8vOhJBCDWn5k/fz5uvPFGjBkzBnv37sXPP/9s5zF5+umn8f333+PGG2/E1KlT0adPH1RWVuLgwYP45ptvcObMGadeFoH27dvjvvvuw65du5CQkICPPvoI+fn5Noba3//+d3z++ecYPXo0HnvsMcTExOB///sfcnJy8O2337pdcb0+evToYeOddIRcLseHH36I0aNHo0uXLpg2bRpatmyJCxcuYPPmzYiIiMAPP/yA8vJytGrVCpMmTUKPHj0QFhaGDRs2YNeuXViwYEG9+/jXv/6FzZs3Y8CAAXjggQfQuXNnFBcXY8+ePdiwYUO97WZWrFiBnj17Og0l33TTTXj00UexZ88e9O7du+GDUsu4ceNw3XXX4R//+AfOnDmDHj16YN26dfjuu+8wa9Ysu78NJsBoktw2hpGIn3/+me69917q2LEjhYWFkUqlooyMDHr00UcpPz/fbvy3335L11xzDYWGhlJoaCh17NiRZsyYQceOHRPHDB06lLp06eJwf3VTzonM6fOvvfYadenShdRqNUVHR1OfPn3opZdeotLS0gY/w7vvvkvp6emkVqupb9++9Ntvv/lkP0RE33//PY0YMYKioqIIAAGgLl26OH3/ggULqGXLlqRWq2nw4MG0e/duu7WZTCb65z//SampqaRWq6lXr160Zs0amjJlCqWmporjhLT7N954w24/qJNKbzAY6NFHH6W4uDiSyWQ26fN1xxIR/frrr9SnTx9SqVTUpk0bWrJkiZgqXvfzd+/enTQaDaWlpdFrr71GH330EQGgnJwccZzRaKSXXnqJkpKSKDg4mIYNG0Z//fWXw1IE5eXlNGfOHMrIyCCVSkWxsbE0aNAgevPNN+3KP9QlNTWVxo4dS2vXrqXu3buTWq2mjh070tdff2039tSpUzRp0iSKiooijUZD/fv3pzVr1tiMEdLuHb3fGahNu68PR3WIiIj27t1Lt9xyC7Vo0YLUajWlpqbSbbfdRhs3biQiIq1WS08//TT16NGDwsPDKTQ0lHr06GFT26c+8vPzacaMGZSSkkJBQUGUmJhIw4cPp6VLlzp9T3Z2NgGg559/3umYM2fOEAB6/PHHicicdh8aGur0c1tTXl5Ojz/+OCUnJ1NQUBC1a9eO3njjDZvyA0xgIiPi8poM01y5//778d///hcffPCBX7ZduNpJS0tD165dsWbNmqZeCsM0ezhkxjDNmPfffx/5+fl4+OGHkZycjDFjxjT1khiGYZoENogYphmjUCjwww8/NPUyGIZhmhzOMmMYhmEYptnDGiKGYRiGYZo97CFiGIZhGKbZwwYRwzAMwzDNHhZVu4DJZMLFixcRHh7udtsEhmEYhmGaBiJCeXk5kpOTGyxgygaRC1y8eFGyJp0MwzAMwzQu586dQ6tWreodwwaRCwhN/M6dO4eIiIgmXg3DMAzDMK5QVlaGlJQUh02t68IGkQsIYbKIiAg2iBiGYRgmwHBF7sKiaoZhGIZhmj1sEDEMwzAM0+xhg4hhGIZhmGYPG0QMwzAMwzR72CBiGIZhGKbZwwYRwzAMwzDNHjaIGIZhGIZp9rBBxDAMwzBMs4cNIoZhGIZhmj1sEDEMwzAM0+xhg4hhGIZhmGYPG0QMwzAMwzR72CBiGIZhGKbZwwYRwzAMwzQSm48WoP+rG/D7icKmXgpTBzaIGIZhGKaR2HKsAAXlWvx+4nJTL4WpAxtEDMMwDNNIVOuNAICa2n8Z/4ENIoZhGIZpJGr0JgBAtY4NIn+DDSKGYRiGaSQEz1CNwdTEK2HqwgYRwzAMwzQSgiHEITP/gw0ihmEYhmkkanSsIfJX2CBiGIZhmEaixmA2hLR6Dpn5G2wQMQzDMEwjYdEQsYfI32CDiGEYhmEaCU6791/YIGIYhmGYRkJIu6/hkJnfwQYRwzAMwzQSNewh8lvYIGIYhmGYRkKr57R7f4UNIoZhGIZpBIwmgs5YaxBxYUa/gw0ihmEYhmkErL1COoMJJhM14WqYurBBxDAMwzCNQN0wmZa9RH5FkxpE8+fPR79+/RAeHo74+HhMmDABx44dsxkzbNgwyGQym8dDDz1kMyY3Nxdjx45FSEgI4uPj8fTTT8NgMNiM2bJlC3r37g21Wo2MjAx8/PHHvv54DMMwDCNSN0zGOiL/okkNol9//RUzZszA9u3bsX79euj1eowcORKVlZU24x544AFcunRJfLz++uviNqPRiLFjx0Kn0+HPP//E//73P3z88cd44YUXxDE5OTkYO3YsrrvuOuzbtw+zZs3C/fffj7Vr1zbaZ2UYhmGaN3U73FezQeRXKJty57/88ovN848//hjx8fHIzs7GkCFDxNdDQkKQmJjocI5169bh8OHD2LBhAxISEtCzZ0+8/PLLeOaZZ/Diiy9CpVJhyZIlSE9Px4IFCwAAnTp1wtatW7Fo0SJkZWX57gMyDMMwTC11PULsIfIv/EpDVFpaCgCIiYmxeX3FihWIjY1F165dMWfOHFRVVYnbtm3bhm7duiEhIUF8LSsrC2VlZTh06JA4ZsSIETZzZmVlYdu2bQ7XodVqUVZWZvNgGIZhGG/QGuoaRKwh8iea1ENkjclkwqxZszB48GB07dpVfP3OO+9EamoqkpOTceDAATzzzDM4duwYVq5cCQDIy8uzMYYAiM/z8vLqHVNWVobq6moEBwfbbJs/fz5eeuklyT8jwzAM03ypawBxPzP/wm8MohkzZuCvv/7C1q1bbV6fPn26+P9u3bohKSkJw4cPx6lTp9C2bVufrGXOnDl44oknxOdlZWVISUnxyb4YhmGY5kFdDRGHzPwLvwiZzZw5E2vWrMHmzZvRqlWrescOGDAAAHDy5EkAQGJiIvLz823GCM8F3ZGzMREREXbeIQBQq9WIiIiweTAMwzCMN9T1CGk5ZOZXNKlBRESYOXMmVq1ahU2bNiE9Pb3B9+zbtw8AkJSUBADIzMzEwYMHUVBQII5Zv349IiIi0LlzZ3HMxo0bbeZZv349MjMzJfokDMMwDFM/diEz9hD5FU1qEM2YMQPLly/HZ599hvDwcOTl5SEvLw/V1dUAgFOnTuHll19GdnY2zpw5g++//x6TJ0/GkCFD0L17dwDAyJEj0blzZ9xzzz3Yv38/1q5di+eeew4zZsyAWq0GADz00EM4ffo0Zs+ejaNHj+Ldd9/FV199hccff7zJPjvDMAzTvLDLMmMNkV/RpAbRe++9h9LSUgwbNgxJSUni48svvwQAqFQqbNiwASNHjkTHjh3x5JNPYuLEifjhhx/EORQKBdasWQOFQoHMzEzcfffdmDx5MubNmyeOSU9Px48//oj169ejR48eWLBgAT788ENOuWcYhmEaDfu0ew6Z+RNNKqomqr+PS0pKCn799dcG50lNTcVPP/1U75hhw4Zh7969bq2PYRiGYaSC6xD5N34hqmYYhmGYqx17DRF7iPwJNogYhmEYphFgD5F/wwYRwzAMwzQCdXuXsajav2CDiGEYhmEaASFEFqSQAeA6RP4GG0QMwzAM0wgIHqHIYBUA+8rVTNPCBhHDMAzDNALa2pBZdEgQAA6Z+RtsEDEMwzBMIyBoiKIEg4hF1X4FG0QMwzAM0wgIGiIhZMZp9/4FG0QMwzAM0wjU1A2ZsYfIr2CDiGEYhmEaASFkFh1a6yEysIfIn2CDiGEYhmEaAa0YMguqfc4eIn+CDSKGYRiGaQRqWFTt17BBxDAMwzCNgGgQsajaL2GDiGEYhmF8DBFZNERch8gvYYOIYRiGYXyM3kgwkfn/kRwy80vYIGIYhmEYH2PtDYoKsYTMiKiplsTUgQ0ihmEYhvExgjdIJgMiNErxdS2n3vsNbBAxDMMwjI+p0ZkNH41SAU2QQnydO977D8qGhzAMwzAM4w1CyEwTJEeQQg6FXAajySy0jkRQE6+uYQxGE97/7TRKqnQIDlJAo1IgOKj2oTIbecL/g4PMz+PC1WLNpUCADSKGYRiG8TFCyCy41jukUcpRqTMGjLB609ECvLH2mFvvUSnk+P7RweiYGOGjVUkLG0QMwzAM42OEmkNCuEwTpDAbRAGSen80rxwA0CkpAv3TolGlM6JabzboqvVGVOuMqNabzM91RhRX6qAzmrAvt4QNIoZhGIZhzAg1iNRWBhEQOMUZTxZUAABu6pGMh4e1bXD87G/246vd53G5QuvrpUkGi6oZhmEYxscIoTFNkNzm30AJmQkGUUZ8mEvjY8PUAIDLFTqfrUlq2CBiGIZhGB9jpyESPUT+bxCZTITTlz0ziArZQ8QwDMMwjIDWgYYICIyQ2YWSatToTVAp5EiJDnbpPbHhtR6icjaIGIZhGIappdpJyEwbAKLqk4Vm71B6bCiUCtfMhtgwczVu1hAxDMMwDCMiaoiUCpt/AyFkdqpWP9Q2PtTl98SxhohhGIZhmLqIafeqwAuZiYLqONf0Q4BFQ1RarYcuQNqTsEHEMAzDMD5GrFRd6xlSB1CW2UnRQ+S6QRQZHASlXAYAKKoMjLAZG0QMwzAM42OqdXU1RIHhISIiUUPkaoYZAMjlMrQQdETlgRE2Y4OIYRiGYXyMVuxlVkdD5Oei6uJKHUqq9JDJgLZuhMwA61pE7CFiGIZhGAYWT5ClDpH59it4jvwVIVzWKjpYNOZcJdBqEbFBxDAMwzA+xlnIzN/T7sVwmZveIYA9RAzDMAzD1EEIjanreIj8XUMkCqo9MYjCWUPEMAzDMIwVll5mgdW6w90eZtbEsYeIYRiGYRhr7DREAVKY8ZQXBhGHzBiGYRiGscGu273K/9PuK7UGXCytAcAGEcMwDMMwEmAXMlPWaoj8WFR9urASgLkvWVSIyu33ixqiAGnfwQYRwzAMw/gY+7R7//cQnSwsB+CZoBqweIiuVOlgMPrv5xRgg4hhGIZhfIzYuqNu2r0fa4i8EVQDQHSICnIZQGQu8OjvsEHEMAzDMD5GqEOkVtZNu/d/g8hTD5FCLkNMaOAUZ2SDiGEYhmF8CBFBW9vx3S7t3o87wXvrIQLM+iMgMHREbBAxDMMwjA/RWhk9warASLvXG004W1QFwDuDKC68NtOsnD1EDMMwDNOssTZ6hOwy65AZETXJuurjbFElDCZCqEqBpEiNx/MEUuo9G0QMwzAM40Oqaw0ipVwGpcJ82xVaeJgI0Bv9zyA6WWBOuW8bHwaZTObxPJaQGRtEDMMwDNOsEVLrrbvFCx4iwGIw+ROnvGjqao3FQ8QaIoZhGIZp1tQtyggAKoUcguPFH1PvxQwzL/RDAIfMGIZhGIappW7bDgCQyWRWwmr/yzTzNuVeILZWVF3IomqGYRiGad5UO/AQmZ/7Z/sOk4ksITOvPUScds8wDMMwDACtqCGyveVa2nf4l0F0qawGVTojlHIZUluEeDVXXG3IrLhSC6PJ/8Tj1rBBxDAMwzA+RDB4gu08RP4ZMjtVGy5Liw1FkMI7MyEmVAWZzJxNd6XKv71EbBAxDMMwjA+x9DFzZhD5l4dIrFDtpX4IAJQKOaJDAiP1ng0ihmEYhvEh1TqzB0joYybgr/3MTkqkHxIQdUTl7CFiGIZhmGaLoywzwKp9h5/1M5Oih5k1gZJ6zwYRwzAMw/gQIWRmryHyTw/RKYlS7gXYIGIYhmEYBjW6+jVE/lSY8UqlDkWV5tBW2/hQSeYUDKJCNogYhmEYpvkihMScp937T8hM0A+1jApGiEopyZyx4awhYhiGYZhmj6PWHebn/hcyOyVRyw5rYkM5ZMYwDMMwzR5nBpFaFFX7j0EkZcq9gOghYoOIYRiGYZov1Q663Vs/F9Ly/QGpU+4BFlUzDMMwDIN60u79sJeZ1Cn3gMUgKqrQweTH7TvYIGIYhmEYH9Jw6w7/MIiqdUZcKKkGALSNkybDDABa1BZmNJgIpdV6yeaVGjaIGIZhGMaHaJ2FzJRym+1NzanCChAB0SFBaFHr1ZECtVKBCI05Y82fw2ZsEDEMwzCMD6l2GjLzLw/RKR/ohwRiw/2/FhEbRAzDMAzjQ0QNkV0vM//KMjvlA/2QgEVY7b+1iJrUIJo/fz769euH8PBwxMfHY8KECTh27JjNmJqaGsyYMQMtWrRAWFgYJk6ciPz8fJsxubm5GDt2LEJCQhAfH4+nn34aBoPBZsyWLVvQu3dvqNVqZGRk4OOPP/b1x2MYhmEYS7d7lTMNkX+EzIQMM6ladlgTJxhE5ewhcsivv/6KGTNmYPv27Vi/fj30ej1GjhyJyspKcczjjz+OH374AV9//TV+/fVXXLx4Ebfccou43Wg0YuzYsdDpdPjzzz/xv//9Dx9//DFeeOEFcUxOTg7Gjh2L6667Dvv27cOsWbNw//33Y+3atY36eRmGYZjmh2Dw2HuI/Kswoy8yzATEjvd+HDKTpi63h/zyyy82zz/++GPEx8cjOzsbQ4YMQWlpKf773//is88+w/XXXw8AWLZsGTp16oTt27dj4MCBWLduHQ4fPowNGzYgISEBPXv2xMsvv4xnnnkGL774IlQqFZYsWYL09HQsWLAAANCpUyds3boVixYtQlZWVqN/boZhGKb5YOll5r8aIoPRhJzLZmeEb0Nm/msQ+ZWGqLS0FAAQExMDAMjOzoZer8eIESPEMR07dkTr1q2xbds2AMC2bdvQrVs3JCQkiGOysrJQVlaGQ4cOiWOs5xDGCHPURavVoqyszObBMAzDMJ4ghsycpt03fcgst7gKeiMhOEiB5MhgyecXRNWsIXIBk8mEWbNmYfDgwejatSsAIC8vDyqVClFRUTZjExISkJeXJ46xNoaE7cK2+saUlZWhurrabi3z589HZGSk+EhJSZHkMzIMwzDNC6OJoDeaixHa1yGqTbv3A1G1EC5rExcKuVwm+fyW4ozsIWqQGTNm4K+//sIXX3zR1EvBnDlzUFpaKj7OnTvX1EtiGIZhAhDrcJh9HSL/8RD5omWHNRYNkf96iJpUQyQwc+ZMrFmzBr/99htatWolvp6YmAidToeSkhIbL1F+fj4SExPFMTt37rSZT8hCsx5TNzMtPz8fERERCA62dw2q1Wqo1dIVpWIYhmGaJ9VWBpFa6b8aolMFtfohH2SYARYPUWGFFkQEmUx6L5S3NKmHiIgwc+ZMrFq1Cps2bUJ6errN9j59+iAoKAgbN24UXzt27Bhyc3ORmZkJAMjMzMTBgwdRUFAgjlm/fj0iIiLQuXNncYz1HMIYYQ6GYRiG8QWCsaNSyu1CUULIzGAi6I1N6yXytYcorlZDpDOYUK41NDC6aWhSg2jGjBlYvnw5PvvsM4SHhyMvLw95eXmiricyMhL33XcfnnjiCWzevBnZ2dmYNm0aMjMzMXDgQADAyJEj0blzZ9xzzz3Yv38/1q5di+eeew4zZswQvTwPPfQQTp8+jdmzZ+Po0aN499138dVXX+Hxxx9vss/OMAzDXP0I4bC6+iHANoTWlF4iIvJpUUbA/FnD1LXtO/y0FlGTGkTvvfceSktLMWzYMCQlJYmPL7/8UhyzaNEi3HjjjZg4cSKGDBmCxMRErFy5UtyuUCiwZs0aKBQKZGZm4u6778bkyZMxb948cUx6ejp+/PFHrF+/Hj169MCCBQvw4Ycfcso9wzAM41OcdboHbENoTakjyi/TokJrgEIuQ2oL6Zq61sXfdURNqiEiogbHaDQa/Oc//8F//vMfp2NSU1Px008/1TvPsGHDsHfvXrfXyDAMwzCeYjGI7D1EMpkMaqUcWoOpST1EQoZZakwIVErf+Uliw9Q4U1Tlt7WI/CbLjGEYhmGuNpxVqRYQDKWmTL0/WVAOAGjro3CZgL8XZ2SDiGEYhmF8hOghUjkziIT2HU0XMjtV6LsK1dbEhteGzFhDxDAMwzDNC7FKtZNQlD+k3os9zHyUci9gSb33Tw0RG0QMwzAM4yOqdc41RIAl+6wpPUS+TrkX4JAZwzAMwzRTagzO0+4BQN3EHqLSaj0Ka0NYrCFiGIZhGMYnaOtJuwcsobSaJhJVC+GypEiNWCfIV8QJGiI2iBiGYRimedFQyKypO94LBRnb+lg/BFh5iMpZQ8QwDMMwzQpRVO3UIBKyzJrIQ9RI+iHAYhBV642o9MP2HWwQMQzDMIyPEOsQNeghahqDSPQQNYJBFKpWiloqfwybsUHEMAzDMD6ivtYdgKVgo9bQNCEz0UPUCCEzwKoWERtEDMMwDNN8qK6ndYf59aYLmdXojThXXAWgcUJmgFUtIj/UEbFBxDAMwzA+Qiu27qi/MKMgvm5Mci5XwkRAZHCQ2HjV1/hz6j0bRAzDMAzjIwTPT7CT1h1iHaImSLsXK1THh0EmkzXKPtkgYhiGYZhmiOtZZo2vITopptyHNto+48JYQ8QwDMMwzQ4hFKZ21u1e2XRZZo2Zci8QG+6/tYjYIGIYhmEYH2FJu2+ouWvjeoiICEculgFoZIOIQ2YMwzAM0/wQQmbOepkJhpK2kTVEaw/l4/TlSoSoFOiVEt1o+2WDiGEYhmGaIdoGCjMGN0FhRoPRhDfXHQMA3H9NOqJDGyfDDICYzXa5gkNmDMMwDNNsaLgOUeOHzFbuuYCTBRWIDgnC/UPaNNp+AYuGqEJraLLq3M5gg4hhGIZhfERDlarVjVyYsUZvxKINxwEAM67LQIQmqFH2KxCuVkJVW5OpsNy/wmZsEDEMwzCMDyAiSx2ihjxEjaQhWr79LC6V1iA5UoO7B6Y2yj6tkclkiPNTHREbRAzDMAzjA/RGgonM/1c7M4iUjRcyK6vRY/HmkwCAWTe0dxrG8zX+qiNig4hhGIZhfEC1VRjMedp944XMPvjtNEqq9MiID8MtvVr6fH/O8NdMMzaIGIZh/BwiauolMB6grTVy5DJApai/DpHWxx6iwnItPvw9BwDw1MgOUDpZT2MgGkSsIWIYhmFcpVpnxA2LfsPTX+9v6qUwblJjlXLvrFeYYBDpjCYYTb4zfBdvOoFqvRE9U6KQ1SXBZ/txhdhw/2zfwQYRwzCMH3OioBwnCyrwy195Tb0Uxk0a6mNm3ma5DfsqbJZbVIXPduYCAJ4Z1bHRGrk6wxIyYw0RwzAM4yIVNQYAQLnW4FMPAiM9Qh8zjdL5rVZj1ePMVwbRwvXHoDcShrSPQ2bbFj7ZhzsIBlEhe4gYhmEYVynXGsT/C8YRExiINYhUzj1EcrlM1BfVGKTXER2+WIbv9l8EAMzO6iD5/J5w1YmqS0pK8OGHH2LOnDkoLi4GAOzZswcXLlyQbHEMwzDNHWsjqKxG34QrYdxFMHA0TjrdC/iyOOMba4+CCLixexK6toyUfH5PiBM0RH4mqlZ68qYDBw5gxIgRiIyMxJkzZ/DAAw8gJiYGK1euRG5uLj755BOp18kwDNMsKbcygkqr9UhpwrUw7iGGzJyk3AtoghQor5G+lcWO00XYfKwQSrkMT430D+8QYPEQldUYoDUYoW7AYGwsPPIQPfHEE5g6dSpOnDgBjUYjvj5mzBj89ttvki2OYRimuVOhZQ9RoKJ1QVRt3i54iKQLmRERXl9rbuB6e78UpMWGSja3t0QGB0EpNwu7i/xIWO2RQbRr1y48+OCDdq+3bNkSeXmcCcEwDCMV1hqismrWEAUSDbXtEAgWaxFJ5yHaeKQA2WevQBMkx2PD20k2rxTIZDK0CPO/1HuPDCK1Wo2ysjK7148fP464uDivF8UwDMOYYQ1R4GJdh6g+pO5nZjQRXl97FAAwbXA6EiI0Dbyj8fFHYbVHBtFNN92EefPmQa83/3HKZDLk5ubimWeewcSJEyVdIMMwTHPGJmRWzQZRICG07lA3pCGSuJ/Z6r0XcDy/AhEaJR4a0laSOaXGUq06wENmCxYsQEVFBeLj41FdXY2hQ4ciIyMD4eHhePXVV6VeI8MwTLPF1kMkfcjMZCKYuL6RTxDT7hvwEEmZZaY1GLFw/XEAwCPXZSAyJMjrOX2BP9Yi8ijLLDIyEuvXr8fWrVtx4MABVFRUoHfv3hgxYoTU62MYhmnWlNf4zkNkMhHGLd4KhVyG1Y8MhlzetBWMrzYEj09DGiIxZCaBh+izHbm4UFKNhAg1pmSmeT2fr/DH9h0eGUQC11xzDa655hqp1sIwDMPUodyHWWZFlTocumjWg5bXGPzWmxCoWDxEDafdW4/3lAqtAYs3nQQA/N/w9giupyBkUxPnh+07XDaI3n77bZcnfeyxxzxaDMMwDGNLhdZiBEmdZVZabVvjiA0iaRENogbq7AitPbwVVf944CKKKnVIjw3FrX1beTWXr/HHjvcuG0SLFi2yeV5YWIiqqipERUUBMFeuDgkJQXx8PBtEDMMwEuHLLDNrg4gz2KTHVQ2RVCGz/DKzcTGwTQyCFP7dmSugs8xycnLEx6uvvoqePXviyJEjKC4uRnFxMY4cOYLevXvj5Zdf9uV6GYZhmg1E5NMsM+v5OINNesS0+wZCVxqJRNXCdxih8X9Pnz9qiDwyIZ9//nm888476NDBUgq8Q4cOWLRoEZ577jnJFscwDNOc0RpM0BstGWDlEmeZsYfItwghsPq63QPSaYiE7zAiOAAMoloP0ZUqPfRG6ZvaeoJHBtGlS5dgMNj/YRqNRuTn53u9KIZhGMbeAJLai1NXQ8RIi6WXmashM289RObzJULjVb5UoxAdooKQ1Fhc6R/Cao8MouHDh+PBBx/Enj17xNeys7Px8MMPc+o9w/gBJhOhpMo/LjKM51iHywBzxplRwppBNh4ibgsiOUK3+4bS7tVKaXqZBZKHSCGXISa0thaRnwirPTKIPvroIyQmJqJv375Qq9VQq9Xo378/EhIS8OGHH0q9RoZh3OSpb/aj7ysbcLqwoqmXwniBIKhuEaqye00KOGTmW7Rui6q98xAJHsVA0BABQKyf9TPzyK8WFxeHn376CcePH8fRo+Z+KR07dkT79u0lXRzDMJ6x/1wJDCbC4UtlaBMX1tTLYTykvDblPjpUhSqdEdV6I8pqpEuP55CZb3G7DpFBKg+R/4fMACAuXI2jeeV+U4vIq6PWvn17NoIYxg8prQ1/8E0usBG8QeEaJSKClajWG1FarUeKRPOXcpaZT6l20UMULJmGyPwdhgeMh8gcMisKZA8RAJw/fx7ff/89cnNzodPZWncLFy70emEMw3gGEaG02vw3yQZRYCNoiMLUSkRogpBfppU0tFVaZR0yYw2R1Lje7d7sQdJ6YRARkfgdcsjMMzwyiDZu3IibbroJbdq0wdGjR9G1a1ecOXMGRITevXtLvUaGYdygWm8UU7XZIApsBIMoXKNElc58k5NS/MwhM9/ifusOz0NmVTqjKLgPlJBZrJ+17/BIVD1nzhw89dRTOHjwIDQaDb799lucO3cOQ4cOxa233ir1GhmGcYMSq1/91h4AJvAQRLJmD5H5Jieph4hDZj7DZCJoDe55iLxp3SGcF0q5rMGsNn/B36pVe2QQHTlyBJMnTwYAKJVKVFdXIywsDPPmzcNrr70m6QIZhnEP/tV/9WAxiILEVGopDRfOMvMdWiuBdEMGkVrpvYZIrEEUHASZTObxPI1JbPhVkHYfGhoq6oaSkpJw6tQpcdvly5elWRnDMB5h4yFigyigERq7hmmUoi5EKoNIZzCJol/zvKwhkhJr48b1StWeh8zKhQyzACjKKGDREPlHyMyjIzdw4EBs3boVnTp1wpgxY/Dkk0/i4MGDWLlyJQYOHCj1GhmGcQP2EF09VNRYKg8bTeabpVTi57rnRrXeCJ3BBFUDN2/GNYTwV5BCBmUDjVal6GUWSEUZBeJqQ2bFlVoYTQSFvGk9Wx4ZRAsXLkRFhbng20svvYSKigp8+eWXaNeuHWeYMUwTI2SYmf/PBlEgY51lJghmpfIQCedGmFop7qesRi/qOhjvENt2KBvW8wgeIq3BBJOJIPfAMBA8fOEB5CGKCVVBJgNMBFyp0jX5uefRkWvTpo34/9DQUCxZskSyBTEM4x3sIbp6EDVEGiWEhh1SaX2EcyMqJAgymXlfZdVsEEmFEP5SuyBwttYYaQ0mBKvcF0WLHqIASbkHAKVCjugQFYordbhcoW3yc499owxzlWGtISqvkbb3FdO42GaZSZt2L3iaIoODxLnZgJYOIWQWrGr4NmutMfI0bCZ8n4FkEAFWOqLyptcRuewhio6Odlm5Xlxc7PGCGIbxjro3tbJqPaKtemExgYN1HSJBX+ELD5GJgAsl1VycUULEGkQuhMyUCjmUchkMJvI49V4syhggNYgEYsPUOJ5f4Rep9y4fuX//+9/i/4uKivDKK68gKysLmZmZAIBt27Zh7dq1eP755yVfJMMwrlNSxyAqZYMoYLFoiIKglJu9CFJriCKDgyTXJzHWRRldC39pghSo0Bo8zjQLXA+R/9QictkgmjJlivj/iRMnYt68eZg5c6b42mOPPYbFixdjw4YNePzxx6VdJcMwLlP3psZhkMDFupeZkP0ldZZZZHAQDLWVzbkWkXRY2na4pkzRBMlRofU8ZCZ2ug+gLDPAYhAV+oFB5JGGaO3atRg1apTd66NGjcKGDRu8XhTDMJ5TUsUG0dWA1mCEzmi+qZrrEJl/v1ZoDTAYveuKDljOi4hgS9FHPlekw10PkbfFGQVjNpCyzAAgNtx/NEQeGUQtWrTAd999Z/f6d999hxYtWni9KIZhPEe4qQkeBb7JBSYVVp6gUJXSpoO5EErzhlIHomouzigdrjZ2FRAyyzhk1nR4ZEq+9NJLuP/++7FlyxYMGDAAALBjxw788ssv+OCDDyRdIMMw7lFSZf6l1TomBCcLKuw0RUxgIIRAQlUKKOQyKGp7VFXrjSirNiAqxDtdmLVBpDdwyExqqt3WEHnXz6wsQENmcX5kEHnkIZo6dSr++OMPREREYOXKlVi5ciUiIiKwdetWTJ06VeIlMgzjKiYTobzWe9A6JgQAC2UDFVFQbRUCETKIpDBcbDxEtfOyN1E6hNBXsKsaotqQmdbbtPsAzDID/MMg8vjIDRgwACtWrJByLQzDeEl5jQFUW3ZIMIj4JheYWNcgEojQBCG/TCuJkWtdh0hbG6Zh41k6tB5kmQGehcyIKCALMwIWDVFRhc7jKt1S4bJBVFZWhoiICPH/9SGMYximcSmpbdsRolKIBc9Kq/gmF4hYPESWG5zY8V5iD5FoEHEdIsmoMbinIfKmn1mN3gR9baZgoIXMWoSaPUQGEzV5iRC3CjNeunQJ8fHxiIqKclikkYggk8lgNHreoI5hGM+xvslFcuZQQCN0urfuXi78Xwrxs/W5InglyvlckQxLLzPXQmbqIM+zzIRO93KZWXMWSKiUckQGB6G0Wo/LFdomNYhc1hBt2rQJMTExAIDNmzdj06ZNdg/hdVf57bffMG7cOCQnJ0Mmk2H16tU226dOnQqZTGbzqJvuX1xcjLvuugsRERGIiorCfffdJzaeFThw4ACuvfZaaDQapKSk4PXXX3d5jQwTSAgp95HBQYisFd2WVDd9OivjPhWOQmYSeYj0RhOqam/YrCHyDWLavYsGiqAhEjxL7mBJuQ9yuaOEPyF4s5u6FpHLHqKhQ4eK/09PT0dKSordgScinDt3zuWdV1ZWokePHrj33ntxyy23OBwzatQoLFu2THyuVts2f7vrrrtw6dIlrF+/Hnq9HtOmTcP06dPx2WefATCH90aOHIkRI0ZgyZIlOHjwIO69915ERUVh+vTpLq+VYQIBxx4iDoMEImVONESA91ofa8MnXBMkGkdlNXrR0894hxgyc6F1B2AJmQmeJXcQ/sYDTVAtEBumxqnCSlyuaNofbx4dvfT0dDF8Zk1xcTHS09NdDpmNHj0ao0ePrneMWq1GYmKiw21HjhzBL7/8gl27dqFv374AgHfeeQdjxozBm2++ieTkZKxYsQI6nQ4fffQRVCoVunTpgn379mHhwoVsEDFXHUKKfVSIxSBioWxgUn+WmXdGrmAQCT3ShHNFbyTU6D3rts7Y4knrDsCztPtAFVQLxIbXZpqVN62HyKO0e2e/ICoqKqDRaLxelDVbtmxBfHw8OnTogIcffhhFRUXitm3btiEqKko0hgBgxIgRkMvl2LFjhzhmyJAhUKksccmsrCwcO3YMV65ccbhPrVaLsrIymwfDBAJlrCG6ahDbdvjQQyScIyG1tY6stzHeYTGIXG/dAUAUuLtDoBZlFPCXWkRueYieeOIJAIBMJsPzzz+PkJAQcZvRaMSOHTvQs2dPyRY3atQo3HLLLUhPT8epU6fw7LPPYvTo0di2bRsUCgXy8vLsvFRKpRIxMTHIy8sDAOTl5SE9Pd1mTEJCgrgtOjrabr/z58/HSy+9JNnnYJjGQijKaG0QCa0elAqPfv8wTYRjD5E0GqK6BpFMJkOERokrVXqU1eiRGCntD9vmiKUOkZsaIg9E1YHa6V5A0BAFlEG0d+9eAGYP0cGDB228LiqVCj169MBTTz0l2eLuuOMO8f/dunVD9+7d0bZtW2zZsgXDhw+XbD91mTNnjmj8AWYdUkpKis/2xzBSUSqGzFQ22UllNQbEcMf7gKJcbOxqlXYvUYuNsjoGkfD/K1V6DrFKhLutOzReZJkFuofIUpwxgDREmzdvBgBMmzYNb731VqPXG2rTpg1iY2Nx8uRJDB8+HImJiSgoKLAZYzAYUFxcLOqOEhMTkZ+fbzNGeO5Mm6RWq+3E2wwTCAhZZhHBQVAq5AhXK1GuNaCkSscGUYAhpN3bZplJU6m6rofIPDeHWKVEMGzUbobMPCnMGKid7gX8pVq1Rz70ZcuWNUnxxfPnz6OoqAhJSUkAgMzMTJSUlCA7O1scs2nTJphMJrHHWmZmJn777Tfo9ZY/8vXr16NDhw4Ow2UME8iIHqLaCyPf5AIXhyEzqTREVQ4MIo10RR8Z93uZqSUQVQdap3uBgBZVV1ZW4vnnn8egQYOQkZGBNm3a2DxcpaKiAvv27cO+ffsAADk5Odi3bx9yc3NRUVGBp59+Gtu3b8eZM2ewceNGjB8/HhkZGcjKygIAdOrUCaNGjcIDDzyAnTt34o8//sDMmTNxxx13IDk5GQBw5513QqVS4b777sOhQ4fw5Zdf4q233rIJiTHM1ULdX/4srA5cyh2JqkUNkTRZZnVDZgB3vJcKwdPjqoYouBmHzBIi1OicFIHOyZFNug6PzMn7778fv/76K+655x4kJSV5XLNi9+7duO6668TngpEyZcoUvPfeezhw4AD+97//oaSkBMnJyRg5ciRefvllm3DWihUrMHPmTAwfPhxyuRwTJ07E22+/LW6PjIzEunXrMGPGDPTp0wexsbF44YUXOOWeuSoptUq7B9ggCmTEwowOKlV7K5QXyjNE2ITMhCrYfK5IQWP2MgvUTvcCSZHB+On/rm3qZXhmEP3888/48ccfMXjwYK92PmzYMJDQidIBa9eubXCOmJgYsQijM7p3747ff//d7fUxTKBRUuXYQ8Q3ucCjXOu8UjVgNoqiQjzThTnUEGnYeJYSIfTlbtq9dx6iwAyZ+Qse/byIjo4W23gwDOMfaA1GUbcQFWy+UbKHKDDRGozQ1VY6ts4yC1LIEVJbNNGb0FZ9omrWEHmPwWhptup6pWrzOK0XrTsC1UPkL3hkEL388st44YUXUFVVJfV6GIbxEOEmJ5NZxJVC6KyEO94HFJVai5fA2kMESCN+dpR2H8EaIsmw7kfmatVvb+oQiVlmAaoh8hc88q8tWLAAp06dQkJCAtLS0hAUZPsl7NmzR5LFMQzjOtbCSnlt1WHOMgtMBP2QdQVpgYhgJfLKvAuDOg6ZcYNXqbA2atQudruXJGQWoIUZ/QWPjt6ECRMkXgbDMN5SX+YQ3+QCC8H7U9c7BEjjIeKQmW8RGrSqlXKXk448FVXX6I1imC2cPURe4ZFBNHfuXKnXwTCMlwhhMSFMBrBBFKg4qkEk4G1oS280id3tHabds0HkNVqDexlmgKWAY7Xe6LRfqCOEcJlMZluigXEfbm7EMFcJ7CG6enDU2FVACG15arhYnwsRjgozsobIa9ytQQTYGk/uCKutvYlyuWclcBgzHpmTRqMRixYtwldffYXc3FzodLb9R4qLiyVZHMMwrlM35d76/5x2H1gIHiJHIZAIL79TwSAKVytt9EnWbUFMJuKbqxe42+kesM1G0+pNLnuXAr0ooz/hkYfopZdewsKFC3H77bejtLQUTzzxBG655RbI5XK8+OKLEi+RYRhXcOQhErPM2CAKKBzVIBKwaIg88+SUOijKaD0vEVChYy+RN7jbtgMAghQyCDaoO+07Ar0ooz/hkUG0YsUKfPDBB3jyySehVCrxt7/9DR9++CFeeOEFbN++Xeo1MgzjAnWrVAMW46hKZ4Te6H59E6ZpcFSlWsDbitKODGfAfPMWMqLYo+gd7na6BwCZTOZRx/tyoQYRF2X0Go8Mory8PHTr1g0AEBYWhtLSUgDAjTfeiB9//FG61TEM4zKObnTWIRfWEQUO5T7MMitzYDiLc7PmTBI8CZmZx7ufaSZovthD5D0eGUStWrXCpUuXAABt27bFunXrAAC7du2y6TPGMEzjUVJl1vJZG0QKuUws0sg3ucDBoiGSPsvMmYcIsBJss7DaK2o8CJkBgEbpfi2iQO907094ZBDdfPPN2LhxIwDg0UcfxfPPP4927dph8uTJuPfeeyVdIMMwrmG50dn2t+JMs8BDDJn5wENU6kB8L8Cp99IgGkQutu0Q0KjcD5mxqFo6PDIp//Wvf4n/v/3229G6dWts27YN7dq1w7hx4yRbHMMwrlPi5Jd/ZHAQzl+pZoMogCivN8vMNxoi89yclSgFYtq9i207BMT2HR6k3XPIzHsk8bFlZmYiMzNTiqkYhvEQZ9oQ4Xkp9zMLGOoVVfsoy8x6bjaevcNzDZEHITNBQ8QhM6/x6Ah+8skn9W6fPHmyR4thGMYziMhhHSLr53yTCxxEDZGjkFnt91mhNcBgNEGpcO+mW5+HyBIyYw2RNwhp82p3Q2YeZJmxh0g6PDKI/u///s/muV6vR1VVFVQqFUJCQtggYphGpkpnhMFEAOw9RGwQBR71te6wFs9WaA2IClHZjamP+kNm3oXjGDPVOvfT7q3Ha93IMuNO99Lhkaj6ypUrNo+KigocO3YM11xzDT7//HOp18gwTAMI+qEghcyuXQCnUgce9aXdBynkCKnVpniSDVZ/lhlriKRA8BC507oDsAqZuVOYURRVc8jMWyTrZdauXTv861//svMeMQzjeyyZQyq7ppDsIQo8yuvJMgO8yzQrc0VUzVlmXuGxhkjJIbOmRNLmrkqlEhcvXpRySoZhXKCkWqhBZH8DZYPI92gNRsz74TC2nrjs9Vw6g0ls7umstow3oS2XNERch8grtB5UqgYAde14IeTmChZRNRtE3uKRj+3777+3eU5EuHTpEhYvXozBgwdLsjCGYVzHkmFmryeJqq1LxFlmvuO345fx0R85yD5bjGvaXePVXJVaizEitYdIbzShUmf2PtQXMmPj2Tuqvc0yczFkpjOYxH1FOPgxxLiHR0dwwoQJNs9lMhni4uJw/fXXY8GCBVKsi2EYN3DlVz/f5HzHpdJqAEBBudbruQRBdXCQwmkGmafVqq09Sg7T7q063jOe43GlajezzMqtvidnxjPjOh4dQZPJ7M4rLCyESqVCZGSkpItiGMY9hJT7KDaImoSCMrMhVFSpAxHZ6bjcobyeGkQCYosNNw0X4RwIVyuhkNuvMZILM0qC56073OtlVmalNXO3/AJjj9tHsKSkBDNmzEBsbCwSExMRExODxMREzJkzB1VVVb5YI8MwDVBfsT02iHxPQXkNAHMIo0Lrnf5G+NXvqAaRgKcVpes7TwBLyKxSZ4TB6LqOhbHFk2735vHmW7LWTQ8RZ5hJg1tHsbi4GJmZmbhw4QLuuusudOrUCQBw+PBhvPPOO1i/fj22bt2KAwcOYPv27Xjsscd8smiGYWwpcVKlGrAYRNV6I3QGE1RK/iUpNdahsuJKncOWG65SXw0iAU+rVdcXWgVsRdxlNQbEhLpX44gxY+ll5mG3exc1RELI1JvzjbHglkE0b948qFQqnDp1CgkJCXbbRo4ciXvuuQfr1q3D22+/LelCGYZxTn03unCNEjIZQGQeFxeubuzlXfUIITPAHDZLbRHq8VyiQVSvh8izLLOGDCKlQo4wtRIVWgPKqvVsEHmIYBC53ctMbN3hashM8Pixh0gK3DJfV69ejTfffNPOGAKAxMREvP766/j222/xxBNPYMqUKZItkmGY+hEyyBx5iORymVX2kK5R19VcsPYQFVV4d4wFDZGzlHvA82yw+moQWeZmYbW3CM1ZfS2q5k730uKWQXTp0iV06dLF6fauXbtCLpdj7ty5Xi+MYRjXaeiXP+uIfIfBaEJRpXXIzLtMM4uHqB6jxcMCig2dJ9Zz87niOdU6IWTmY4OIizJKilsGUWxsLM6cOeN0e05ODuLj471dE8MwbmIpzOg4xMEGke8ortSByPK8qNI7D1GFGx4id9PuxQbADjyJ3s7NmCEiUQOkUXmoIXI1ZMad7iXFrW8rKysL//jHP6DT2f/Ba7VaPP/88xg1apRki2MYxjVKnXS6F2CDyHfUrT3kfcjMeR8zAU/rBbnjIeKQmWfojCbRQHY/7d69wozsIZIWt0XVffv2Rbt27TBjxgx07NgRRIQjR47g3XffhVarxSeffOKrtTIM4wCjicRsowYNIq5WLTlCyr1AsZceonJ3sswkTrs3b+OO995g7d3xNGTmard77nQvLW4ZRK1atcK2bdvwyCOPYM6cOaBaM1gmk+GGG27A4sWL0bp1a58slGEYx1hXq3VmEAk3wBK+yUmOdYYZIF3IrH4PkW29IFeL8rnkIeL2HV4h6H/kMiBI4V6BTk9F1fWFVxnXcfsopqen4+eff8aVK1dw4sQJAEBGRgZiYmIkXxzDMA0j6EJCVAqnNYaE7DO+yUmPEDKLD1ejoFyLogppRNX13eSst5XXGBDtYnq8KwZRJIfMvEJMuQ9SuF2x3JJ2zyGzpsBjszI6Ohr9+/eXci0Mw3iAcJNz1LZDgDVEvkMImXVMikBBeaHXITNXDKIghRwhKgWqdEaU1ehdNohcSrvnjvde4WmVauv3VOuNLrWA4U730sIlaxkmwClxQRfCPap8hxAy65QUDsDSz8xTLCGz+m9ynmSDuRYyU9qMZdzD0z5mgEVzZCJAb2z4HOLCjNLCBhHTrNAbTTh8sQwmk+c3LH9D9BDVk0rNHiLfIYTMOiVGAPC+n1mZCxoiwP1MM73RhMra+jicZeY7qmsNInWQ+7dX6/e4kmnGhRmlhQ0iplmxeNNJjHn7d3yz53xTL0UySquEGkRsEDUFhbUGUesWIQiu9Qp4Ezar0LomlHU308x6XH11a9ib6B3WGiJ3USvlEKJkDemIDFYGLmuIpIENIqZZse1UEQBgb+6VJl6JdFg0RM51JMJNroTT7iWFiESDKD5cLfb+8jTTTG80iRqUhj1E7nlyhPMkTK2sNyvNkmXGGiJP8EZDJJPJoFYKHe/rT7239kJylpk0sEHENBuICEculQEAci5XNvFqpEPUhXDIrNEpqdJDZzTfuOLC1WgRVmsQeVicsdLqJldfHSLAqueYi4aLK/ohwPOij4wZrVCl2oOQmfl9rqXeC997cJACQS6WXWDqh48i02w4f6VaLHp35nJVE69GOkoaqFINWIwlrcHkckov0zCCfigqJAhqpQItaj1EnvYzEwrtaYLkDd7kPPUQNRReEc4jHZ8rHuFpHzMB4X0Nte9gQbX0sEHENBsO13qHACCvrEa8cAU6rvzyD1MpIa/VJrA2RDqElPv4cDUAICbU/K+nITNXGrsKuKshcqU8AwCEWp8r7CVyGzHLTOWZQRRc+76GRNUsqJYeNoiYZsPhi2U2z88UXR1hsxIXsszkchl3MfcBQsp9fLgGALwOmblSg0jAEtpyLWTmSg0iwHyuhHvYGoQBagy1GiIPPUSChqjBkBkXZZQcNoiYZsORS3UMoqtER+TqjY51RNJjXaUagFXIzDODyJXGrgKeeogaOk+sx7Cw2n0sdYi81RA1EDLjTveSwwYR02wQQmbJkeZf8zlXi4eoquEsM4AzzXyBEDKLixBCZt5lmZW7WIMI8FxDVJ/43jI3C6s9pdqLwozm97GHqKlgg4hpFpRW63H+SjUAYFTXJABXj4fI1V/+7CGSHouHqG7IzDNRdYULne4F3K1U7Y6HyF3vE2NBSJf3pA4R4EaWGXe6lxw2iJhmwdFa71DLqGD0SIkEcHVkmmkNRvEXaUO//Nkgkp7CsrohM/O/nobMhLYdrmiI3G3C6mqWGcAGkTd4HTITsswMDYXMuNO91LBBxDQLBP1Qp6RwpMeGAgBOXwUeIuEmJ5MB4Q2EWdggkh4xZBZuHzLzpJ+ZKKp2KWQm1CHynYbIVcE2Y8GbXmbm9wmFGTlk1tiwQcQ0CwT9UOekCKTVGkSXK7SiiDVQKbWqQSSX198Zmw0i6SmsK6quDZl52s9M1BC5ETKr1BlhMNbvTQAsAmmXQmZuGluMBe81RLUd7xsoC8Kd7qWHDSKmWXDkUjkAoFNSBCI0QWI20NmiwA6befSrn29yklCpNYi9pOIjzBqiEJXSq35m5S52ugdsQyXlLnhyXM1GBKzbd/C54i7etO6wfl+DdYi4MKPksEHEXPUYjCYcyzcbRJ2TzR3JBS9RoLfwcKVKtYCYZcY3OUkQBNUhKoVNVpg3mWZCY1dXPERKhRyhtUX8XNERuWU8h3DHe0/xVkOkFrPMXNMQsYdIOtggYq56Tl+uhM5gQqhKgZToEAAQdUSBnmnmzk1OKNzIv/qloaDMtkq1gDfFGd3REAFWqfcNZJoZjJYQnntZZqwhchfRIPK6dUf9HiLBK8gaIulgg4i56hEqVHdKihB1NoJBFOi1iErcCYOwhkhS6qbcC3jTz8ydLDPAynBpwJNjLY52pZCfEIbhc8V9BM9OsIetO1wuzCiEzDjLTDLYIGKueg5fshhEAmktri4PUX1tOwRYVC0tgkEkFGUU8KafWbnW9cKMgOviZ+E7D1MroXShM7qrhhZjT43X3e7lNvM4wmQiqzYv7CGSCjaImKseIeVe0A8BQFqsOXR2JtBF1VXmm6577Rj4JicFdRu7CngVMnMjywxw3XBxJ7RqPY4F+O4jhLrUnobMaj1E9aXdl2sNEKo6cB0i6WCDiLmqISKbkJmA4CEqrtSJqeuBiKWDef1tOwDLTU5nMDWoT2AaprCsoZCZ51lm4S5kmQGua4jcKcpoM2+NwaN6Ss0ZIV3e0yyzYBdCZoKhqlbKPd4PYw8bRMxVTWG5FkWVOshlQIeEcPH1ULVS/GUfyDoid375h6mVUNRqqLifmffUbewq4GmWmcFoEmvYuO4hcq3nmOU8cc/zZDSRWFqAcQ2hwrTnGqKGe5lxUUbfwAYRc1VzqDZclh4baneBSrsKMs1EUbULGiKZTMZhMwkRQ2Z1NESxYbUaIjf7mVVqLTdA1zVEroW23AmtAuabsqpWa8RhM9cxmQi6WoNIo/Q07b7hOkTc6d43sEHEXNVY9EORdtvSWwR+LSJPtSFsEHmPsyyzGA9DZuVaSxhE5eLN1KIhci1k5up5IpPJuOO9B2it+o95XJhR2XDIrJw9RD6BDSLmqsaiHwq32yZ6iAI5ZFblepYZwKn3UqE1GMWwo9OQWYV7/cwsWUOu/+p3N8vMVYMIsKpWzeFVl6m2CnN528us/pAZZ5j5AjaImKuaI1Y9zOoS6MUZiYg9RE2E0MNMpZDbGaNiPzOje/3MxAwzF8NlgO+yzABbYTXjGoIRo1LIRb2eu7hSh8hSpZpDZlLCBhFz1VKtM4rhsPoMopzLlQGZSVOpM8JgMq/blSwzgA0iqRBrEIWrIZPZ3vg87Wcm1iByy0PkXpaZRwYRnysuI6bce1iDCHAt7Z5F1b6BDSLmquVYfjlMBMSGqRBXJ6wBAKktzLWIymoMuBKAYQHhJqdSyF0uAidkGQkiW8YzCsosBpEjhLDZZTdqEZU3gofInRuo4H1g49l1vG3san6v+W+5uj6DiDvd+wQ2iJirFuv6Q3V/xQPmi1ZypFkQG4jC6hIhcygkyOHnc4TgSeKbnHcUOinKKBAb5r6wusKNTvcCrmuIXO9jZpmbq1W7S7WXjV0Bi6jaYCIYjI7DZtzp3jfw0WQ84o+Tl7H2UB6SIoOREhOM1jEhaB0Tgshg12/OvqY+/ZBAWmwoLpbWIOdyJfqkRjfW0iTBkzAIh8ykQcwwi6jfQ+ROPzOh0707uhDBQ1CpM8JgNDlty1HmxbnCDV5dRwhzBXvlIbK8t8ZgQpiD75Q73fsGNogYtyEiPPX1flwqrbHbFq5WIiUmxMZIalX7b5vY0EY1lg47aNlRl7TYUPx5qigghdVihhkbRI1OgZMq1QJCPzN3Qmbutu0AbDPSymsMiA51rCXzJsuMPUSuY+lj5rlBpLYquVCjNzoMoXKne9/ABhHjNhdKqnGptAZKuQzjeiTjXHEVcourUFCuRbnWgMOXykRjxJpJfVrhzVt7NMoaTSbCUQdNXesi1iIKwNR7b4SybBB5h7M+ZgKehMzcbewKAEqFHKEqBSp1RpTV6B0aRAarbDf3zhXWELmLqCHysI8ZAMjlMqiU8npb7AhGKvcxk5Ym1RD99ttvGDduHJKTkyGTybB69Wqb7USEF154AUlJSQgODsaIESNw4sQJmzHFxcW46667EBERgaioKNx3332oqKiwGXPgwAFce+210Gg0SElJweuvv+7rj3ZVk332CgCgS8tILLq9J755eBB2/mMEjr48ChueGIKPpvbFi+M6475r0nFD5wS0TwgDAPx44JJYxdXX5BZXoVJnhEopR5vabDJHBHK1aneqVAuwh0gaCitcDZn51kMENJxpZp02745HgRu8uo/Yx8zDth0CQpVrZ6n3ooaIQ2aS0qQGUWVlJXr06IH//Oc/Dre//vrrePvtt7FkyRLs2LEDoaGhyMrKQk2NJVRz11134dChQ1i/fj3WrFmD3377DdOnTxe3l5WVYeTIkUhNTUV2djbeeOMNvPjii1i6dKnPP9/VimAQ9Wltq7nRBCmQER+O6zsmYOrgdDx/Y2d8MLkvfvm/IYgJVaFab8TBCyWNskZBP9QhIdyprgIA0oWu9wGYes8aoqZDzDILcxYyE7LMXNcQWRq7umkQNRDaEr7rUJUCQfX8LTiflzVEriKGzDxs2yFgqUXkxEMkiuTZQyQlTXo0R48ejdGjRzvcRkT497//jeeeew7jx48HAHzyySdISEjA6tWrcccdd+DIkSP45ZdfsGvXLvTt2xcA8M4772DMmDF48803kZycjBUrVkCn0+Gjjz6CSqVCly5dsG/fPixcuNDGcGJcRzSIXBQhy+UyDGwTg58O5mHbqSL0SY3x5fIAWOmH6gmXAUBKTAjkMrMotbBC61QT4o8IlZLdMYiEIoKl1XoQkd8I4AMJo4lEQ8eZh0joZ+aWh8iDOkRAw5lmnhjO5nnZQ+QuUqTdA5bGsFoH/cxMJrK07mAPkaT4bdp9Tk4O8vLyMGLECPG1yMhIDBgwANu2bQMAbNu2DVFRUaIxBAAjRoyAXC7Hjh07xDFDhgyBSmWJrWdlZeHYsWO4cuWKw31rtVqUlZXZPBgzFVqD6H3pm+Z6VlZmmxYAgG2ni3yyrrrU17LDGrVSgZbRwQCAM5erfL4uKRFuVJ6IqvVGqrfOCeOcokotTATIZEALJyJmT0JmgoYo3I20e8B1D1FkiGvFOy3zupbSz1iokSDtHqi/n1mlzoDaeqwsqpYYvzWI8vLyAAAJCQk2ryckJIjb8vLyEB8fb7NdqVQiJibGZoyjOaz3UZf58+cjMjJSfKSkpHj/ga4S9p8rgYmAllHBSIhw3ZsysNYg2n3misNfPVJTX1PXuqS1CEwdUUm1pQ6Rq4SoFFDWthTgsJlnCOGyFqFqp+FYT/qZVdQaNFJriCweIvfmFYzncq0BRlNghZObihoJ0u6B+vuZCSFMlUJuk5HGeA8fTQfMmTMHpaWl4uPcuXNNvSS/YfcZ98JlAhnxYYgNU0NrMGFfbokPVmahpEqHi7UlATo24CECLC08TgeYQVQqeohc/+Uvk8lYR+QlhWKXe8fhMsCzfmYVHmSZAVaenIY8RG56E6wbh1awjsglLB4i7wwidT39zMqtijJyyFta/NYgSkxMBADk5+fbvJ6fny9uS0xMREFBgc12g8GA4uJimzGO5rDeR13UajUiIiJsHoyZ7FyzQeROuAww34gHtjFrh3wdNhP0QykxwS7F2JvKQ1StM6LIDdFtXTxpxwBYbowlAdiuxB8QU+6d6IcAz/qZVdS43+0eaFjr40lRRgBQKeXiZ2Dj2TUEA0bttYfIuaha8ARyp3vp8VuDKD09HYmJidi4caP4WllZGXbs2IHMzEwAQGZmJkpKSpCdnS2O2bRpE0wmEwYMGCCO+e2336DXW/6g169fjw4dOiA6OrAqEzc1JhNhb62gundr949dZttaHdEpHxtEgn4o0TVDVux634i1iK5U6jD6rd8w5PXNbmUiWSMYNHW7rTcE1yLyDktRRucGEeBePzOjiVBZm7Ltvoeo/mwwTz1E1u/h4oyuIZ2GqDZk5kBewJ3ufUeTGkQVFRXYt28f9u3bB8AspN63bx9yc3Mhk8kwa9YsvPLKK/j+++9x8OBBTJ48GcnJyZgwYQIAoFOnThg1ahQeeOAB7Ny5E3/88QdmzpyJO+64A8nJyQCAO++8EyqVCvfddx8OHTqEL7/8Em+99RaeeOKJJvrUgcvxgnKUaw0IUSnQMbHhUFRdBGH13twSp+mkUnDkUjmA+itUW5NmZRCZGkErYTQRHvtiL84UmWsl7fUghGg0kZim7e6NzjrTjHEfsW1HAxmJ7hRntA6rSZ5l5kE2oqtzM7ZUS6Yhch4y4073vqNJDaLdu3ejV69e6NWrFwDgiSeeQK9evfDCCy8AAGbPno1HH30U06dPR79+/VBRUYFffvkFGo3lQrRixQp07NgRw4cPx5gxY3DNNdfY1BiKjIzEunXrkJOTgz59+uDJJ5/ECy+8wCn3HiCk2/dqHVVvbR9npMeGIiFCDZ3RhD1nHWf4ScFhFypUW9MqOhgKuQw1ehPyy+3bkUjNm+uO4fcTl8XnRx1U9W4I6xuUuzc6LrjnHa6EzAD3+pkJBpFZKOvezdTlLDNPDCING8/uIFXafb2iau5j5jOa1Oc2bNiwejMwZDIZ5s2bh3nz5jkdExMTg88++6ze/XTv3h2///67x+tkzDgryOgqMpkMmW1aYPW+i9h2ugiDMmKlXB4AQGcw4WRBrYfIRYMoSCFHSnQwzhRVIedyJZIigyVfl8DPBy/hvS2nAAD902Kw80wxjuaVuz2Pp8X2AC7O6C0FLoiqAff6mXmqHwJczzLzxKPAITP30BokCpnVpyES+5hxyExq/FZDxPgfgkHU24uu8L7WEZ0sqIDeSAjXKNEq2nXDRtQR+bAW0Yn8cjz19X4AwP3XpGPm9RkAgCN57nuIhLYdUW7WlgHYIPIWsUq1pCEzz1LugYa9OF55iLjjvVuIrTu86GUGNCSqZg+Rr2CDiHGJwnItzhZVQSYDennoIQKAzDZmr9D+8yWo0kl/kT1iFS5zJyU1zcfC6rIaPaZ/mo1KnRGZbVrg76M7iiUBzlyudFtTJcWvfs4ycx8icintHnCvOKOgB3NXUA1Y6Xx8EjKrf27GFrF1hw97mXGne9/BBhHjEntq0+3bx4d7dGEVSIkJRsuoYOiNJNY0khJXW3bURfAQ5fgg9d5kIjzx5T7kXK5EcqQGi+/sBaVCjrgwNVqEqmAi4ER+RcMTWVFSZb7JulOlWoCzzDyntFoPndF8k4pzOcvMdQ2RRwZRraegSmeE3uhAhCuBh4jPFdeQots9YF2HyFHIjDvd+wo2iBiXkCJcBgj1iHzXxuOIhwaRUIvIFwbRO5tOYsORAqiUciy5pw9a1Pa5kslkopfI3bCZNze5KL7JeYygH4oMDmpQOOtOP7NyLzRE1u8pr5N6bzSR2BLEq7R7PldcQrK0e8EgMtSTZcYhM8lhg4hxCcEg6uulQQT4TkdERG5nmAkIHqLcoipJ2xRsOpqPf288DgB4ZUJXdG8VZbO9Y22tpKOX3BNWe1qDCOCbnDe4WoMIcC9kVuFFyEypkCO0NkRT9zu1fu5JiIU73ruHVJWq688yY1G1r2CD6Crm/JUqMePKG7QGIw6eLwXgfssORwgG0cELpS63NXCFvLIalFTpoZDL0C4hzK33JkcFQ6WQQ2c04WJJtSTryblcif/7Yh+IgLsHtsZtfe174gn1nI666SHyqtge1yHyGFdT7gH3+pmJjV09/NUf4SQbzJtsRPO8Spt5mPoRQmbe1iEKdiFkxh4i6WGD6CrlcoUWN76zFWPf3ur1Df6vC6XQGU1oEapCaosQr9fWMioYrWNCYDQRduUUez2fgFChum1cqNu/0BRyGVJiarveSyCsrtQa8OCnu1FeY0Cf1Gi8cGMXh+MET9aRS2UuNwEFLFlm7jR2FbDOMnNnn4zrRRkB9/qZiR4iD3UhoienTjaYN4Yz0HBbEMYW6TxE5vdrHRVm9CKhgqkfNoiuUl77+ShKqvTQGkxYtfeCV3OJ9YdSoyVrJpjpAx2RYBC5qx8SsKTee2cQERFmf3sAx/MrEBeuxrt39YbKSVfqjPgwyGXAlSq9mL3kClK0YzBYtYtgXMPVDDPAvX5mYtq9ByEzwHmmWYmXN8+Gij4yFvRGEwy14XbvNUSOW3cQWSrUs4dIetggugrZk3sFX2efF5+v3HPeK0+AtUEkFb7QEQnCZHf1QwKWTDPvahEt/e00fjxwCUq5DO/d1RsJEc69CZogBdrEmcN7R9wo0Ci0Y3Cn071AcJACQQqzYcuhEPcQPEQNZZgJCF6ihoozVmg9F1UD1h4ixyEzTz1EXLPKdazDW157iJSOQ2bVeqNodLGGSHrYILrKMJoIc787BAAY0y0RmiA5ThVW4kCtBshdiMinBtGhi6WSXWxFD5GLPczqIkUtoq0nLuO1X44CAOaO64y+aTENvkfUEbnRwsObG51MJkNkrSFVyrWI3KKgTNAQNRwyA4AWLgqrvalDBDSsIfI4ZFZraNXoTWIVZsYx1jWD1E48wq6idtLLTAiJKuQyr3VKjD1sEF1lfLnrHA5eKEW4RomXbuqKrC6JAMxeIk/ILa7C5QodVAo5uraMlGydCREatIkNhYmAnRLoiCq0BpwtNnt2PPYQtfAuZHahpBqPfr4HJgJu7dMKdw9Mdel9wnrdaeFRUm2+wXr+y5/Fsp7gTsgMcL2fmdcGkVBAsY6GyJvyDIDZYyVEyeum9DO2WKfceystcJZlZhFUKyWTLzAW2CC6irhSqcPra83eiSduaI+4cDVu6d0KAPD9/ovQOahp0RCCd6hrywiv3cB1GShh2OxYXhmIzDcqof6LuwgeotziKhgcFLhriMWbTuJKlR7dWkbi5QldXb5gCR6iIx54iDxJuwc4FOIp7ofMXOtnVuHjLDNPDSK5XCYaaSysrh9LHzPvr5PCHNV1DSIWVPsUNoiuIt5cdwwlVXp0SAjHPbXeicFtWyA+XI0rVXpsPlbg9py7fRAuE5CyQOPh2jo+nnqHACAxQgO1Ug6DiXD+inuZeWU1eqyuFa8/N7aTWxfFjrVrPlVY4ZLRWqM3iq50Ty+MXIvIfap0BtFwcdVD5GrIzJvmroDz71MIiXpTXZ473rtGtU6alHvAeZYZp9z7FjaIrhL+ulCKz3bmAgDmje8CZW3NEaVCjgm9WgLwLGy2RzSIGtbCuMvANuY5j1wqwxUXitfVh7f6IcD8a1isWO2mjmjVnguo1hvRPiEM/dPdO1bJkRqEa5TQGwmnLzfcwkO46cllQLiHIRaxn1m1d8e9OSEUZQwOUrgc2nK1OKM3rTsA5wUUS70ozyBg6XjPIbP6qJHSQ1SrQdIZTTaFYrkoo29hg+gqwGQivPDdXyACxvdMxoBaz4vALb3NBtGmowVuGR5lNXocyzd7XnqnRkm2XoH4cA0y4s0ZVjtyvPMSedqyoy5pseY6S+7oiIgIy7efBQDcNSDV7di+TCazElY3rCOybuwql3umI+CQmfuINYgi1C5/x5aQmXMNkclEFoPI0yyzYMdhLW9DZvXNzdgi6H28FVQDtkaVtZi9nD1EPoUNoquAb/ecx57cEoSqFHh2TCe77R0TI9A5KQJ6I2HNgYsuz7s3twREQOuYEJcK0XmCWI/ICx2R0URipWdvQmaAVaaZGwbRjpxinCioQIhKgZtrjU93EVp4uNLTTKgt40ljV4HIkNosM77JuYxYpdrFcBngWsisQmfxvHjvIXJsEHmjOeGQmWuIjV0lDJlZzwtYvHRsEPkGNogCnNJqvZjm/djwdk5r3gheom/3uF6kUcr+Zc4Q6xF5oSM6U1SJGr0JmiC5WEvIU9LFkJnrtYgE79D4ni09vlAJTV5d8hBJoAuxeIg4DOIqlj5mrv84cCVkJuiHghQyj70LlorS0laqtn4vF2esH0EALYWGSCGXibXCrDPNBC8dd7r3DWwQBTj/3nAclyt0aBMXimmD052OG9+zJRRyGfadK8GpwoZ1KoBFP+Rth/v6EITVx/Mr6g0r1IegH+qQGAGFhyEkAXerVReU12DtoTwA5n5lniI2eXXDQyR4eTyBQ2bu426GGWApzFhfPzPrDDNPU6mdeYi8TbsHnBtbjC1SdboXcFScURRVc5aZT2CDKIA5mleGT7aZvRMv3dTFaXsIwHwRH9o+DoBZANwQBqMJe3NrPURpvjOIYkJVon5mu4deIqn0Q4DFIDp/pcqljK+vdp2D3kjo1ToKXZI9r9PUofYY5JdpGxTgSvmrnw0i13GnsatAi1Dz2Pr6mXlbgwiw6HyqdEboa0tGGE0kNo2VIsuMPUT1o5Woj5mAo+KMoqiaPUQ+gQ2iJmbdoTzxQusORIQXvjsEo4kwumsirm0X1+B7hLDZqr0XYDLV38rjWH45KnVGhKuVaBcf7vb63GGglzqiw6JB5P0648LVCFUpYCLg3JX6w2ZGE+HznecAQCxz4ClhaiVax5gF3Q15iUqrzAaTVxoiwSCq4iwzVyl0o7GrQLBK0WA/M28zzOq+VzCwrEXQUoiq2XiuHyk1RAAQrLLvZ8YeIt/CBlETcrlCi0c/34tr/rUZf//2gMuhLMBcaHFnTjE0QXL8Y6y9kNoRIzolIFyjxIWSamxvIKtLCJf1bB3ldRiqIQQdkdceIi9S7gVkMhlSBR1RYf1hs81HC3ChpBrRIUEY0y3J6327mmnGHqKmwaIhcq/wZ0P9zLztdA+Yy2vULaAofLchKgWCFJ5f6rlmlWtUS+whchgyE0TyLKr2CWwQNSHFlTp0SY6AzmjCF7vOYcTCXzH9k93IPlt/K4sKrQH//OkIAGDGsAy0ig5xaX+aIAVu7G6+ca9sIGy2WxRUS19/qC4D01tAJgNOFVaKvaJc5WRBBfJrb1QdEr03iAArHVEDtYg+rRVT39o3RZKLYMck13REYpaZF7VlhPeW1Ri8avzbnPAkZAY0nGkmpFJ7WlNKQGzfUWNrEHljOJvn5TpEriC5hshBcUax0z17iHwCG0RNSPuEcKx8ZDC+eSgTIzolgAhYdzgfE9/bhknv/Yn1h/Mdhrbe2XQC+WVatI4JwQND2ri1T6GVx88HL6FK5/wC54uGrs6IDAkS9T/uZJvll9Vg2sc7AQD902O8CjlYI9QiyqlHWJ1bVIXfThQCAO4a4LmY2ppOgoeogZ5mUqRSCzdJo1UNHMY5OoMJV2qz+9wtQdFQPzNvaxAJ1BU/S2YQsYfIJaQOmTnqZyYYu5xl5hvYIPID+qbF4MMpfbHhiSG4vW8KVAo5dp+9ggc+2Y0bFv2KL3flisW5ThZU4KOtOQDM3dTd/ePrmxqN1jEhqNQZse5QvsMx+WU1OH+lGnKZOWTWGAj1iFwNm5VU6XDPf3fgXHE1UluEYPHfekm2FqFadX0eohU7z4IIGNI+TgyxeYvgITqWV25TnbYuJVXe1yHSBClEET6HzRqmsDYDMkghQ7SbnrmG+pmVe9m2Q6Cu+Fkqg4hDZq4hhsyUUhlEtSGz2ms/EVlVqmYPkS9gg8iPyIgPx2uTuuP3Z67DQ0PbIlyjxKnCSjzz7UFc+9pmvLvlJOZ+/xf0RsLwjvEY3inB7X3IZDKrmkSOW3kI3qGOiRGSeV0aItONRq+VWgOmfbwLx/MrEB+uxvL7BiDeSf0lT7Ck3jsWVdfojfhql1lMfbdE3iHAXAAzOEgBrcFUrzEmRSq19fvZIGoYIZQbF+Z6lWqBhkJmFlG1t54cxxoi7z1EllAch1edI2SZCWJob1ErbbPMtAYTdLUZhJxl5hvYIPJDEiI0+Pvojvjz79fjH2M6ITFCg4JyLV7/5Rj+OFkElUKOF8Z19nj+W3qZw2Z/nLyMvFJ7zU5jhssE+qXHQC4DzhRV4VKp88aqWoMRDy3Pxt7cEkQGB+HT+wYgJcY1DZWrCNWqL5ZW27irBX7+6xKuVOmRHKnB9R3jJduvQi5DexeE1RYNked1iADrTDM2iBpCrEHkgeHdUHFGbxu7CvjKQyTMqzeSTQo4Y4uUvczM85hvz9U687zWPQxDVWwQ+QI2iPyYcE0QHhjSBr/Nvg5v3toD7RPMfb8eG57hVZimdYsQ9EuLhomA7/bZi6ubwiCK0AShW0tzHR9nXiKjifD4l/vw+4nLCFEpsGxaP7F+j5S0CFUhXKMEEZBbbO8l+rS29tPf+rcWm+hKhUVH5FhYTUSSh0LYQ9QwlpR79wTVQMP9zKRIuwfsNURSeRJDVAox05TPFeeIGiIfhcws+iHPexgy9cMGUQCgUsoxqU8rrJ01BNvmXI+Z17fzek5BXP3tnvM2bvAavRGHLpYCaFyDCAAG1hM2IyL8Y9VB/HQwDyqFHEvv6YverX2zPplMJobN6gqrD18sw57cEijlMtzeP0XyfQup90eceIgqtAZRX+TtjS6KDSKXKfDGIGooy0wqg8hHWWYymYzbd7iA4MlRS5ZlJoiqzYZWKXe69zlsEAUQMpkMSZHBksw1plsSVEo5judX4NBFizfiwPlS6I2E+HA1WkVLsy9XERu9OhBW/+uXo/hi1znIZcBbd/TENe1ifboWQVhd1yBavsPsHcrqmuiThrcNpd4LNzmVUu51ei97iFynUGzsKn3ITEi7ly7LrI5B5EV5BnFuDXe8bwjBkyNFLzPA4mkStEnc6d73sEHUTIkMDsINnc2ibGtxtXW4zNO+Sp7SLy0GCrkM569U45xVqOq9Lafw/q+nAQDzb+mG0RIUQWwIR13vy2v0WL3XHGK8e4B3lamdIXiIzl+pdvhr3PpXv7ffTwQbRC4jFmV0swYR0HA/M1FD5LWHyLZekFQeIoDPFVeQPu3etjBjmURaM8Y5bBA1YybVhs2+33dR7H8kFIVs7HAZAISqlejeqlZHVOsl+nxnLl775SgA4NkxHXF7P+myuuoj3UEtolV7L6BKZ0RGfBgGtvFNwcqoEBWSIs1eiOMO6hGVSpByLyDcKEv4Jtcg3oXM6u9nZt3c1RucZZlJkaLNIbOGkbqXWd2QGVep9j1sEDVjrm0Xi9gwFYoqdfjteCGIqEkE1daI9YhOFeHHA5fw7KqDAICHh7XF9CFtG20ddWsRERGW11amvmtAa596z0QdkSODSMJf/Rwyc50CL0Jm1v3MihzUIpKidQfguywzm7m5471ThDpEkoXMnIiquQaR72CDqBmjVMgxvqe5JtHKPReQc7kSV6r0UCvlXnVu9wahHtH6I/mY9eVeEJmzuWZndWjUdQii6vwyLap0BuzMKcbx/AoEBylEQbqvEHVEl+x1RFK07RDggnuuYTSRWFQxzgMPEWAVNqujIzKZCBU632SZSRsy4wavDSF16w513ZCZ2OmeDSJfwQZRM0co0rj+SD42HS0AAPRoFSVWMW5s+qbGIEghQ3mNAXojYWz3JLwyoWuj65miQlSi0XHmchWW78gFAIzvmSzJDaY+BA/RsXo8RFL8ShQ+H9/k6qe4UgejiSCTAbFhntV+cpZpVqU3QpAVSVmHyGgisQK2lBoiNp6dI3m3+yDbwowWDxFriHwFG0TNnM5JEeiYGA6dwYR3Np0EAPRuonAZYA4vCOG6Ie3jsOi2nmINlMZGCJvtPluMX/66BAC4e6BvxNTWdEwUMs3K7US4lrYd3hVlBDhk5ipCuKxFqMrjulNCpllRnVpEQuaQUi6D2ssfIcKNskpntDG8JA2ZsYbIIUQkhrakT7u3LczIHiLfwQZRM8e6lYdwY2wq/ZDAKxO64YUbO2PJ3b2bzFMFAG1qw2bvbDoJvZHQMyUKXVv6PpTYJi4UQQoZKrQGnL9iW7WbNUSNj1il2osyC0JxxrohM2v9kLdeUOuQ27kr5izNEJUCQRIUD60bjmNs0RpMoqdP6rT7GoPZQ8Sd7n0PG0QMJvRsCWsnTFMbRBnxYbj3mnSENHF5eiH1XqhS3BjeIQAIUsiRES9UrLYNm5VWm2+oUmqISqv1MNXTTLa5U1jmeYaZgLOQWblWulRqpUIuGkVC2QqpwrtCHSI2nh2jtWppInXavVZft1I1h8x8BRtEDOIjNLi2XRwAs1dEcO83dwSDCDDfWG7s7vv6RwJiC486wmpf1JYhstyYGXssGWZeGERhjkNmoofIy8auAoLhIngWpTKIOO2+foRwmUIuk8QjB3DIrClgg4gBAEwdnAaZDBjdLbGpl+I3pFv1i7utbyvJfvm5Qsckxx4iQUMkRfVhTZBC1K2wWNY5Yg0iD4oyCsSEOgmZaaUpyiggGLmCh0iq8EoEG0T1ImaYSRji19iJqrl1h6/hI8sAAK7rEI/d/xjhdQf1q4m02BCoFHLoTSbc6aPK1M4QhNVH8nznIQLMobf8Mi1Kq/WQvjPb1YFYpdobDZGTkJlUNYgEBO+BoCGSLmRWG16tYoPIEWINIpV0P5rEbvfsIWo02CBiRAThJ2MmXBOE9+/pAxORWJeosRA8RGcuV6JaZxQvtFJWqgbMN0zBIGIcI23IzNYgEjwu3tYgEhC8B+eKfRMyK9caYDIRd1uvg+DFUUvU6d56rhq9ETV6I7S14moWVfsODpkxTD1c1zEewzslNPp+48LUaBGqgomAEwXmsJnBaBK1PlLf6Nggco40ITOLh8i6lIIQMpPaQ3SxRFqDSBDyEkEsJMlYkLooo3muWlG1wSQazjKZdOFVxh42iBjGD5HJZBYd0SWzQSRoCADpDaISDoU4hIis+ph5EzJz3M+sQuKGnYL3wFCbNSjVecJ6s/qpkbiPmXkuy+35crnZsximVrJ3zoewQcQwfkpdHZHgxQlTKz0uEFgX7mJeP2XVBuhqQxWetu0AzNqSEJV9PzPJRdV1DCspq6rzueKcGon7mAG2xpUQtmX9kG9hg4hh/JSOibYeopIq841Uypsch8zqx3IjUnr961+sVm0lrBZCoNJpiGzPDV+cK1yc0R6p23YA5npkQpV+wUvJNYh8CxtEDOOndBKavOaVgYgkzzADLC1A2CByTKGoH/I8XCbgKNPMkmUmbTaYgKQeotqbMafe2+MLDRFgSeMXzkMWVPsWNogYxk/JiA+DXAZcqdKjoFzrE4MosjYriXUhjrHoh7zPwBTbd1gVZ6yQ3EOkrPOcQ2aNgZAar5a4VpngcSoo45BZY8AGEcP4KZogBdrEhQEAjlwqE29EUrTtEIjkjvf1IkXKvYDDkJnE7Rh86SGyhMz4XKmLEDKTUkMEWAyiwgrBQ8QhM1/CBhHD+DGijiivXKxB5IubXEm1roGRzROxKKOPQ2ZSZ5kJSBsyE6pVs4aoLj4LmdXOJ5yH7CHyLWwQMYwfI+qILpWhpFq6th0CLKquH2lDZvb9zCQXVftSQ8ThVacIvcw0EhZmBKxCZqwhahTYIGIYP8bGQ+QTDRG3ZKgPIWTmTcq9QN1+ZkQkfWFGq5BKcJACKgl7a3HIzDk1OulbdwDWBpEl25HxHWwQMYwf07HWQ3SyoELMNBEyw6QgsnYuoSUDY4sURRkF6obMqnRGCEWrwyXqdm/taZLScAasQ2buGUTWlbmvVnyRdm+eT+h4X9u2g0NmPoUNIobxY5IjNQjXKGEwEfadKwHgGw8REVDO2hA7Csu8b9shULefmeAdUshlkmlPlAq5aBRJKb4HrDreu1iHqFpnxNRlO3Hdm1uw7VSRpGtxF4PRhE1H83GuuMon8wshM7WEHjnAPgTHomrfwgYRw/gxMplMDJv5IstMpZSLmTGsI7KlWmcUNT7ShMxs+5mVWzV2lcmka8cghFWk1puIHe9dOE/0RhMeXpGNLccKcaaoCnd9uB3vbDwBYyN7IQ1GE77efQ7DF/6Kez/ejZvf/UPs8yYlvmjd4Wg+9hD5FjaIGMbPEVp4CEgdCuFMM8cIug1NkFyS1hp1+5kJHjmpBNUCgiHkq/OkoZCZyUR4+uv92HKsEJogOUZ2ToCJgAXrj2Pqsp24bCUq9xV6owlf7T6H6xf8iqe/OYCzRWbP0OUKHR5eni0aMFJR7aO0e3UdzyGLqn0LG0QM4+cITV4FfHWjYw+RLdb6ISk8OHX7mYl9zCQWygpeBMk1RC5kmRERXv7xMFbvuwilXIb37uqDpZP74o1J3aEJkuP3E5cx5q3ffRZCEwyh4Qt+xexvDiC3uAotQlV4dkxHrHt8CKJDgrD/fCn+seovSbVN7CG6OuCAJMP4OXYeIom1IWwQOUasQSRBuEwgJlSFKl01iip1ktcgEvCVh0i4GVfqjNAbTQhy0GD4P5tPYtkfZwAAb97aA9d1jAcA3No3BT1SojBjxR6cKKjAXR9ux6wR7THjugyxX5c36I0mrNpzAe9sPoFzxeaQWItQFR4c2gZ3D0xFiMp8jBff2Rv3/HcHvt1zHt1aRmDq4HSv9w0AWp+17mANUWPCHiKG8XM6JFo8RAq5TLLO6AJcrdoxYpVqCQTVAtaZZlLXIBIQDKEoiQ0ia8PNkQD/sx25eHPdcQDACzd2xoReLW22t08Ix3czB+PWPq1gImDh+uOY8tFOMXvSE/RGE77adQ7XL9iC2d8ewLniasSGqfCPMZ3w+zPXYfqQtqIxBACDM2Lx7JhOAICXfzyC7ael8VT5OstMQOpzhbGFjy7D+DlhaiVax4Qgt7gKERppBbgAe4icIWXKvYB1P7Oq2to1UjV2FbhrYGuU1ehxU89kSecVMtgqtAaUVetFkTgA/HzwEp5bfRAAMPO6DNx7jWPPS4hKiTdu7YEBbVrg+dV/YevJyxjz9u94646eGNQ2tsE1EBEuldbgrwul+OtCKVbtuyB6hGLDVHhwSFvcNbC1jRFUl/uuScdfF0qxet9FzFixB98/eg1aRgW7cyjsqG6EkFmoSgGlA68cIx1sEDFMANAxMRy5xVWICpGuBpEAG0SOEUJmUmSYCVj3MzMYzRoWqX/1924djQ8m95V0ToEITa1BZCWs/vPkZfzfF/tgIuBv/VvjyZHtG5xnUp9W6NEqEo/UhtDu/nAH/m94e8y83hJCIyKcv1JtNn4uluLghTIculBq0wsOcN0QEpDJZJh/S3ccz6/A4UtleOjTbHz9UKZXxoyvW3cALKhuDNggYpgAoGNSBNYdzvfJRZGrVTtGysauAtYhM0E6I7WGyJdEBAfhYmmNaDwfOF+CBz7ZDZ3RhNFdE/HKhK4uezDb1YbQ5n53CF9nn8eiDcex80wRuraMxKELZfjrYilKHJyTCrkM7eLD0LVlJHq3jsaEXskuGULWBKsUWDq5D25a/AcOXijFs6sOYsGtPTz2vjaGqJoF1b4ncP4SGaYZM7BNDN7eCGTEhUk+d6B5iAxGE3bkFOOng5ew9lA+DCbzzXh8z5bonxYDuZci3RP55fhu30Xsyy0BIE1jVwHrfmZCmwepNWG+xLo446nCCkxdtguVOiMGtW2Bf9/R022BtBBCG9imBZ5b/Rf+OFmEP05adD1BChnaJ4SjW8tIdGkZiW4tI9ExMVwSw6NVdAgW39kL9/x3J1buuYBuLSMxzUORdY3BRxoiK1E1C6p9Dx9hhgkABrWNxS+zrkVai1DJ567PICIiaA0mVOuMqNbXPnRG1Fj9v1pf+1xnRLXeZPO8qnZsdGgQerSKQs+UKLSJC3P7xqk3mvDnqSL8fPAS1h7Kw5U6noPPd57D5zvPITlSg3E9kzG+R0t0Sgp3+Rd/XmkNvt9/Aav3XsThS2Xi67FhKnRNjqjnne5h3c8s0mQ+7lL1MWsMBC/F8fxy/POnIyiu1KFby0gsndwXai8am07s0wo9UiLx3pbT0ATJ0bVlJLomR6J9YphX8zbEoLZmkfXLaw7jlR+PoENiuEtaJmuMJoLO4Js6RBoVe4gak8D5S2SYZk7d9HupELLM9uRewYiFv9oaPHpLvy1vWY5cAGbNTLeWkeiREoWeKZHo3ioKSZH2tX50BhP+OHkZPx28hHWH820MtphQFbK6JGB01yQo5DJ8t+8Cfj6Yh4ulNXj/19N4/9fTaJ8QhvE9W2J8z2S0ig6xW09ptR6//HUJq/dexPacIvFzKuUyDOsQh/E9W2JEpwRJG3Zah8wEozCQMocEL8Xbm06ACGgTG4qPp/WT5DNkxIdjwW09vJ7HXe4dnGYWaO+9gJmf7cX3Mwc7PF+coTVYijxKn3ZvmS+QQquBCh9hhmnmtIkNhUxmTh0+WVDhdJxKIYcmSI5glQKaIAWCgxRiscHgINvXxOcqBTRKOS6UVGP/uVIcvFCKCq0B204XYZtVynNcuLrWgxSJVtEh+O14IdYfybdJ744NUyGrSyLGdEvCgPQYm4ybwRmxmDe+KzYfLcB3+y5i09ECHM+vwBtrj+GNtcfQLy0aN/VsiRs6JWBv7hWs3ncBm48WQmc0iXP0S4vG+J4tMbZbEqJDpRevA7b9zARvQiDd6Kx73yVEqPHJff3FzLlAxSyy7oYTBeX460IZHlqejW8eGuRS+IuIcOaypT9a3bpB3mKjIWJRtc/x67/EF198ES+99JLNax06dMDRo0cBADU1NXjyySfxxRdfQKvVIisrC++++y4SEhLE8bm5uXj44YexefNmhIWFYcqUKZg/fz6USr/+6AzTaKS2CMWPj16L/LIa0YgJtjZuao0aKVJ+DUYTThZWYP+5Euw7V4oD50twNK8cheVabDiSjw1H8m3Gx4erMbprIkZ3S0K/tJh6Q22aIAVGd0vC6G5Jdt6fXWeuYNeZK3h+9V827+mQEI7xvZJxUw/HXiSpse5nJhgXYRJ1um8MBA9XZHAQPr1vQKMcs8ZAE6TA+/f0xbh3tuKvC2WYs/IgFt5mL7IurtRh//kS7D9nfhw4b8l6C1EpvNavOVqXAIfMfI/fWwVdunTBhg0bxOfWhszjjz+OH3/8EV9//TUiIyMxc+ZM3HLLLfjjjz8AAEajEWPHjkViYiL+/PNPXLp0CZMnT0ZQUBD++c9/NvpnYRh/pXNyBDpLqJVxhlIhR8fECHRMjMDt/cyvVeuMOHypVDSQzlyuRO/UaIztloTeraM9uslEBgfh9n6tcXu/1sgrrcEP+y9i9b4LOHSxTNQZTejZEp2SfP+ZrbHuZ3ap1Fw/J5A0RJP6pCC/TIvb+6WgfUJ4w28IIFpGBeM/d/bG3f/dgVV7L6BDYjj6pkZj37kS7D9fiv3nSpBbXGX3viCFDJ2SInBHv9aSr8k27T5wzpNARUZSNnSRmBdffBGrV6/Gvn377LaVlpYiLi4On332GSZNmgQAOHr0KDp16oRt27Zh4MCB+Pnnn3HjjTfi4sWLotdoyZIleOaZZ1BYWAiVyjW3eFlZGSIjI1FaWoqIiMa9gDIMIx2lVXqEa5SS/5J3h84v/CIWZQSAjU8ORVsfZA8ynvHR1hzMW3PY6fY2caHo0SoKPVqZdXCdkiIkzy4TOJ5fjpGLfgMA/OuWbrijv/RG19WOO/dvvzc5T5w4geTkZGg0GmRmZmL+/Plo3bo1srOzodfrMWLECHFsx44d0bp1a9Eg2rZtG7p162YTQsvKysLDDz+MQ4cOoVevXg73qdVqodVaysmXlZU5HMcwTGAhdR84TxD6mQkEUtp9c2Da4DQczy/HF7vOIS5cjZ4p5uzIHq2i0K1VpOQ94urDNu2+6c/dqx2//kscMGAAPv74Y3To0AGXLl3CSy+9hGuvvRZ//fUX8vLyoFKpEBUVZfOehIQE5OXlAQDy8vJsjCFhu7DNGfPnz7fTLjEMw0hBizA1zl+xGESBFDJrDggi6zmjOyEiWPpWOe5gEzJjDZHP8eu/xNGjR4v/7969OwYMGIDU1FR89dVXCA72rvdMfcyZMwdPPPGE+LysrAwpKSk+2x/DMM2HFlYZbHKZ9LVrGO+RyWR+4U1UW50bgZSNGKgEVKe4qKgotG/fHidPnkRiYiJ0Oh1KSkpsxuTn5yMxMREAkJiYiPz8fLvtwjZnqNVqRERE2DwYhmGkwLopapi6aT0QjH/Dvcwal4AyiCoqKnDq1CkkJSWhT58+CAoKwsaNG8Xtx44dQ25uLjIzMwEAmZmZOHjwIAoKCsQx69evR0REBDp37tzo62cYhhFqEQFAOIdBmHpQKeSIDVMjOEghaZNhxjF+7YN76qmnMG7cOKSmpuLixYuYO3cuFAoF/va3vyEyMhL33XcfnnjiCcTExCAiIgKPPvooMjMzMXDgQADAyJEj0blzZ9xzzz14/fXXkZeXh+eeew4zZsyAWs0nF8MwjY91yIzDIEx9yGQyfP1QJmr0xoCqaB6o+PURPn/+PP72t7+hqKgIcXFxuOaaa7B9+3bExcUBABYtWgS5XI6JEyfaFGYUUCgUWLNmDR5++GFkZmYiNDQUU6ZMwbx585rqIzEM08wR+pkBgdW2g2ka0mOl71/IOMav6xD5C1yHiGEYqdh8rADTlu0CAAzrEIePp/Vv4hUxzNWLO/fvgNIQMQzDBDot6oiqGYbxD9ggYhiGaURiWEPEMH4JG0QMwzCNSAvWEDGMX8IGEcMwTCMSrFIgRGUuuMdp9wzjP7BBxDAM08gIYTP2EDGM/8AGEcMwTCPTIswcNuM+ZgzjP7BBxDAM08hcmxGL4CAFeqZENfVSGIaphesQuQDXIWIYRmp0BhNUSv5NyjC+hOsQMQzD+DlsDDGMf8F/kQzDMAzDNHvYIGIYhmEYptnDBhHDMAzDMM0eNogYhmEYhmn2sEHEMAzDMEyzhw0ihmEYhmGaPWwQMQzDMAzT7GGDiGEYhmGYZg8bRAzDMAzDNHvYIGIYhmEYptnDBhHDMAzDMM0eNogYhmEYhmn2sEHEMAzDMEyzR9nUCwgEiAgAUFZW1sQrYRiGYRjGVYT7tnAfrw82iFygvLwcAJCSktLEK2EYhmEYxl3Ky8sRGRlZ7xgZuWI2NXNMJhMuXryI8PBwyGQySecuKytDSkoKzp07h4iICEnH89yNO7c/rYXn9u+1BOrc/rQWntu/1+Lrz+kqRITy8nIkJydDLq9fJcQeIheQy+Vo1aqVT/cRERHh1kngznieu3Hn9qe18Nz+vZZAnduf1sJz+/dafP05XaEhz5AAi6oZhmEYhmn2sEHEMAzDMEyzhw2iJkatVmPu3LlQq9WSj+e5G3duf1oLz+3fawnUuf1pLTy3f6/F15/TF7CommEYhmGYZg97iBiGYRiGafawQcQwDMMwTLOHDSKGYRiGYZo9bBAxDMMwDNPsYYOIYRiGYZhmDxtEDMMwDMM0e7h1hx+Rn5+P999/Hy+88ILN6+fPn0dUVBTCwsJsXtfr9di2bRuGDBkCACgqKsKBAwfQo0cPxMTE4PLly/jvf/8LrVaLW2+9FZ06dWpwDW3atMHatWvRrl07p2OICFu2bMHJkyeRlJSErKwsBAUF2axXo9EgNjYWAPD7779jyZIlyM3NRWpqKmbMmIHMzEwAwIIFCzBp0iSkpqa6dpAArFmzBjt37kRWVhYGDx6MTZs24c0334TJZMItt9yC6dOni2Orq6vx+eefY+vWrbh06RLkcjnatGmDCRMmYPjw4XZzX758GR999BG2bduGvLw8AEBiYiIGDRqEqVOnIi4uzuV1njt3DnPnzsVHH30kriU7OxsxMTHo3Lmzzdiamhp89dVXmDx5sktzV1ZWIjs7W/zuPcFoNEKhUIjPd+zYAa1Wi8zMTJvv0xHTpk3Dq6++iuTk5Ab3o9frcebMGcTHx9dbQr+kpARff/21eJ7ceuutNuOzs7PRp08fFz6ZhYKCAvz111/o06cPIiMjkZ+fj//9738wmUwYO3YsunXrZjP+9OnTdufKDTfc4LSVwM6dO+3OlczMTPTv39/lNV65cgU//PCD3XdvMpkc9l4ymUw4f/48Wrdu3ShzExHOnDmDlJQUKJVK6HQ6rFq1ClqtFmPGjBH/zp1x/fXXY9myZS79jefk5IjXla5du4qva7VayOVy8bw8deoUPvroI/Fcue+++5Ceni6O//bbbzF69GiEhIQ0uE8A2L9/P7KzszFs2DC0adMGhw4dwn/+8x+YTCbcfPPNyMrKsnvPpk2b7M6Vm266ye7aqdPpsHr1aofXlPHjx0OlUrm0RsD7e0R9NPU1BXDvuuITiPEb9u3bR3K5XHx+8eJF6tevH8nlclIoFHTPPfdQeXm5uD0vL08cv2PHDoqMjCSZTEbR0dG0e/duSk9Pp3bt2lHbtm0pODiYsrOzxfe+9dZbDh8KhYLmzJkjPiciGj16NJWUlBARUVFREQ0YMIBkMhnFxcWRXC6njh07UkFBgTh3//796YcffiAiotWrV5NcLqebbrqJnnnmGbr55pspKChI3C6TyUihUNCIESPoiy++IK1WW+8xWrJkCSmVSurTpw9FRETQp59+SuHh4XT//ffTgw8+SMHBwfTvf/+biIhOnDhBqampFB8fTykpKSSTyWjs2LE0YMAAUigUdOutt5Jerxfn3rlzJ0VHR1PLli1pypQpNHv2bJo9ezZNmTKFWrVqRTExMbRr1y6Pvs9jx45RamoqyWQyksvlNGTIELp48aLD79LduXU6HT399NPUtm1b6tevH/33v/+1GVt37osXL9LgwYNJoVDQkCFDqLi4mMaOHUsymYxkMhm1b99eXNv+/fsdPoKCgmjVqlXic4HXXnuNqqqqiIjIYDDQk08+SSqViuRyOSmVSpo2bRrpdDoiIrr55pvp66+/JiKiv/76i2JjYykuLo4GDBhACQkJlJiYSIcPHxbnlslk1LZtW3r11VfpwoULDR6jzZs3U2hoKMlkMkpMTKR9+/ZRq1atqF27dtShQwdSq9W0du1aIiKqqKigSZMmicdALpdTYmIiKRQKCgsLo8WLF9vMnZ+fT9dccw3JZDJKTU2l/v37U//+/cXv+JprrqH8/Hy3v0siotLSUrr11ltJo9FQfHw8Pf/882QwGJx+n76c++jRo5SamkpyuZwyMjLo9OnT1KdPHwoNDaWQkBCKjY2l48ePExHRd9995/ChUCho8eLF4nOBhx9+WLyeVVVV0cSJE0kul4vH/7rrrhO3Dx06VDxXtm7dSmq1mrp3706333479erVi0JCQujPP/8U55bJZBQREUEPPPAAbd++vd5j9O2335JCoaAWLVpQWFgYrV+/nqKiomjEiBGUlZVFCoWCVqxYIY7Pz8+n/v37i+e0XC6nPn36iOfL008/LY49ceIEtWnThjQaDQ0dOpRuu+02uu2222jo0KGk0WgoIyODTpw44dJ36ej7dOce4e7c7lxX3LmmELl/XWks2CBqRJydBMLjyy+/tDkhJ0+eTAMGDKBdu3bR+vXrqU+fPtS3b18qLi4mIvMJKZPJiIhoxIgRdP/991NZWRm98cYb1KpVK7r//vvFuaZNm0YTJkwQn8tkMmrVqhWlpaXZPGQyGbVs2ZLS0tIoPT1dHCtc3B9++GHq3LkznT59moiIzp07R3369KGHHnpInDs0NFTcPmDAAPrXv/5lcxzeeecd6tWrlzj3smXLaPz48RQUFEQtWrSg//u//6ODBw86PIadO3empUuXEhHRpk2bSKPR0H/+8x9x+7Jly6hTp05EZDbkHnzwQTKZTERE9K9//YtGjx5NRETHjx+ntLQ0mjt3rvjeAQMG0PTp08Xx1phMJpo+fToNHDhQfM3ZDUB4LFq0SPw+J0yYQGPHjqXCwkI6ceIEjR07ltLT0+ns2bPid+mpQTR37lxKSEigN954g/7xj39QZGQkTZ8+XRxrfZ4QEd1zzz00aNAg+v777+n222+nQYMG0bXXXkvnz5+ns2fP0uDBg2nGjBlEROLNSbiwWT+sb14CcrlcPFfeeOMNio6Opo8++ogOHTpEy5cvp/j4eHrttdeIiCg6OpqOHDkifld33nmnaBDrdDq67777aOTIkeLcMpmMHnjgAYqPjyelUkljx46lVatW2dzQrbnmmmtoxowZVF5eTm+88Qa1bNlS/FxERE899RQNGjSIiIimT59OgwcPpoMHD9KJEydo0qRJNHv2bKqsrKT//ve/FBISYnNTnDhxImVmZtLRo0ft9nv06FEaNGgQTZo0iYjMRkh9j99//93mGD722GPUvn17+vrrr+mDDz6g1NRUGjt2rHhsrL9PX85NRDR+/Hi66aab6MCBAzRr1izq1KkTjR8/nnQ6HdXU1NC4cePo7rvvbvBcsT5nHJ0rc+bMoVatWtGmTZuosrKStm7dSm3btqW///3vREQUEREhGl5Dhw6lxx9/3OaYP/fcczR48GCbc2XevHnUq1cvkslk1KVLF1q0aBFdvnzZ7vvq3bs3vfLKK0RE9Pnnn1NUVBTNmzdP3P7mm29Sz549xee33347TZgwgUpLS6mmpoZmzpxJkydPJiKijRs3UosWLcQfZSNGjKDx48dTaWmp3X5LS0tp/PjxNue4L+8RDVHXIHLnuuLONYXI/etKY8EGUSPi7kmQnJxMO3bsEJ8LF6CePXtSUVGRzU00Ojpa/DWt0+lILpfbvDc7O5tatmwpPn/wwQepZ8+eNr/AiYiUSiUdOnTIbt3ChatDhw42v/KIiDZs2CAaT0REkZGRonUfHx9vZ+mfPHmSQkJC7ObOz8+n1157jTp27EhyuZz69etHS5cupbKyMvG9wcHBohFBRBQUFGRjPOXk5Ihzh4SEiBdRIiKtVktBQUHiRXH16tWUlpYmbtdoNOIN2hFHjhwhjUZjc1xcvQHEx8fTgQMHxPeaTCZ66KGHqHXr1nTq1Ck7gyg6OrreR0REhDg+IyND9LgRmX+VZmRk0NSpU8lkMtnNnZSURNu2bSMis8dPJpPRhg0bxO0bN26kNm3aEBFRjx49aOzYsXTkyBE6c+YMnTlzhnJyckipVNL69evF16yPifB99urVi95//32bY7h8+XLq0qULEZm/y5MnT4pr2rNnj83YY8eOUWRkpN3cer2evvnmGxozZgwpFApKSEig2bNn07Fjx2zeHxERIc6v1+tJqVTS3r17xe3Hjx8X54+NjaXdu3eL24qLi0mj0VBlZSURES1evNjmphgWFma3Xmt2795NYWFh4rrlcrnTR92/+9atW9PmzZvF54WFhdS/f38aOXIk1dTU2HyfvpybiCguLk48ZhUVFSSTyej3338Xt//xxx/UunVrIiIaNWoUjR071s4z5uiaIqxdGNu1a1f67LPPbLZ/99131L59eyIy/8gS/jYTEhJo3759NmNPnjwpHu+6c+/evZsefvhhioqKIrVaTbfeeiutW7dOHBsaGko5OTlEZP67DAoKsvlbPXXqlM3cERER9Ndff4nPKyoqKCgoSDR6Pv30U+rQoQMRmc9xZz/uiIgOHDhAwcHBNuv25T3C1WsKkXvXFXeuKUTuX1caCxZVNyIxMTH44IMPkJOTY/c4ffo01qxZYzO+tLQU0dHR4nO1Wo2VK1ciLS0N1113HQoKCsRtOp0OwcHBAICgoCCEhITYxPZjY2NRVFQkPl+yZAleeOEFZGVlYfHixQ2uXSaTATBrEtq2bWuzLSMjAxcvXhSfDx06FJ9//jkAoFevXtiyZYvN+M2bN6Nly5Z2+4iPj8fs2bNx5MgRbNmyBZ07d8bjjz+OpKQkcUyLFi1w9uxZAMDFixdhMBiQm5srbj979ixiYmIAAFFRUSgvLxe3VVVVwWAwiDH77t2749KlS+L2xMRE7Ny50+kx2LlzJxISEsTnSUlJWLlyJUwmk8PHnj17xLHV1dVQKi2SPZlMhvfeew/jxo3D0KFDcfz4cZt9abVa3HvvvVi0aJHDx5NPPimOvXDhgo3eIiMjA1u2bMGff/6Je+65B0aj0WbuK1euiMc/JiYGISEhNvqOjIwM8bjs3LkTGRkZmDhxIoqLi5Gamoq0tDQAQHJyMlJTU+20IcK5kpubi0GDBtlsGzRoEHJycgCYj/+mTZvEYy98rwJnz54Vz2lrlEolJk6ciB9//BFnz57FjBkz8M0336BTp042+geVSoWamhoA5r8Pk8kkPgfM34mgazAYDDY6obCwMBgMBlRWVgIARo4ciaNHj4rb1Wo1ysrK7NYmUF5eLvZkCg8Px/z587Fp0yaHj6VLl9q8t7Cw0OaYxsbGYsOGDSgvL8eYMWNQVVUlbvPl3ABQUVEh/j2FhoYiNDTU5u8xJSUF+fn5AICff/4Zw4cPR9++fe2uZc4QzpW8vDx0797dZluPHj1w7tw5AMCAAQPwww8/AADatm2L/fv324zdt2+fuM669OnTB++++y4uXbqEDz74AIWFhRg1apSoOQoPDxevjSUlJTAYDDbXyqKiIhttjlqtFtcNAHK5HEajEQaDAYD5HD9z5gwA8zVI+L8jzpw5g6ioKPG5L+8R7lxTAPeuK+5cUwDPriuNQqObYM2YkSNH0ssvv+x0+759+2zcm926daNvvvnGbpxer6cJEyZQ69atRQu9Y8eOtHHjRnHMmjVrRC0HEdH27dupVatWdnOdP3+err/+eho1ahRdunTJqYdozJgxdPPNN1N0dLTNrwZh7oSEBPH54cOHqUWLFjR58mR6+eWXKSwsjO6++2569dVXafLkyaRWq2nZsmVEZOs2d0RpaakYIiMimjFjBrVr145eeeUV6t+/P02ZMoU6duxIP//8M/3yyy/UrVs3uvfee4mIaMqUKTR06FA6cuQInT59WtQbCGzZsoVSUlLE54sXLya1Wk2PPfYYfffdd7R9+3bavn07fffdd/TYY49RcHCwTXhu3Lhx9Pzzzztdu/X32a9fP/rkk08cjpsxYwZFRUXZ/DobNGiQ6HZ3NrcwPj093ebXmMCFCxeoffv2dMMNN9h5Cax/VT7zzDNUVFRkM3dsbKzNXD/99BO1atWK/vnPf5LRaKz3V/+rr75Kb731FiUlJdGvv/5qs33//v0UHR1NROZzNCYmhpYtW0bLli2jtLQ0+vDDD+mPP/6gjz76iFJSUmz0GA2dKxs2bKA777xTfD5+/Hi68cYbaevWrTR9+nTq27cvjR07lioqKqiyspImTZpEo0aNIiKiG264wcal/8Ybb1BSUpL4fM+ePTbH5JFHHqHU1FRauXKlTTiktLSUVq5cSWlpaTRz5kwiIho2bJgYJnRE3b/7Dh060I8//mg3rry8nDIzM6lHjx7i9+nLuYmI2rZta+MRevfdd208ttnZ2ZSYmGgz1969e6lz5840ffp0qqysrPdcefDBB+nxxx+n+Ph4G6+NMLdwzP/880+KjIykuXPn0jvvvEOxsbH03HPP0YoVK+iFF16gqKgom+PQ0Lly4sQJevbZZ4mI6O6776YBAwbQ8uXLady4cZSVlUUDBw6kI0eO0NGjR2no0KFi+JPIrH2bOHEiVVRUkE6no1mzZlFGRoa4ffv27eIxef755yk6OpoWLlxI+/fvp7y8PMrLy6P9+/fTwoULKSYmxiZs78t7hDvXFCL3riueXFOIXL+uNBZsEDUiK1eupE8//dTp9uLiYvr444/F57Nnz7aJL1uj1+vppptuEk/IF198kT7//HOncz/77LN0yy23ONxmMpnon//8pygKrHtCTp061ebx5Zdf2mx/+umnKSsry+a1kydP0h133EHh4eGiyzcoKIgGDRpEq1atEsdZu7ZdoaKigh544AHq2rUrTZ8+nbRaLb3xxhukUqlIJpPRsGHDbEJwAwcOFN3MqampNmGOr7/+mt5++22b+b/44gsaMGAAKZVKcd1KpZIGDBhg97l/++03+vnnn+td65YtW4iI6J///KeoX3LEww8/bHOhe/XVV+nFF190Oj43N5emTp1KRET33XefaATW5fz585SRkWFzobvpppvqvTAuXryYrr/+ervX8/LyaPTo0XTttdc6vXClpqbaaNIWLVpks/3f//63jQ7rm2++oVatWtmFCTQaDc2aNctGH+TuuXL8+HFq164dyWQy6tSpE50/f55uuukmUiqVpFQqKS4uTkw0yM7OppiYGEpMTKTWrVuTSqWy+XtavHixqBMhMocmHnroIVEwrtFoSKPRkFwuJ5VKRQ8//DDV1NQQEdHSpUvFBAVH5OXl2XzXjz76qM0N2JqysjIaMGCA+H36cm4ic2j9gw8+cDr//PnzacyYMXavV1VV0YMPPkjt2rVzeE0hMmuBhg0bJj7q7ufll1+moUOHis///PNP8e/Z+tGyZUu789mdcyUvL49uuOEGCgsLo6ysLCopKaGZM2eK14127dqJoVcicwitbdu2pFQqKSgoiKKiomj9+vXi9mXLlonaJyKzdjEpKckmvCmTySgpKcnOmPXlPcKdawqRe9cVT68pRK5dVxoL7nbvxxgMBlRVVTlN+TUYDLhw4YJLrsWqqiooFArRje+I7OxsbN26FZMnT7ZxwzZEZWUlFAoFNBqN3TYiQkFBAUwmE2JjY11KvfSEmpoa6PV6hIeH2207ceIEtFotOnbsaBO2qg+9Xo/Lly8DgE/XLQVnz57F0aNHHaYGA+bQ4vr16zFlyhSX5tu5cydCQkJs3OXWvP3229i8eTPeeecdtGrVyq21bt++HWq1Gr169RJfMxqNyM7ORk5ODkwmE5KSktCnTx+77/LXX3/F4MGDXf4OBYqKitCiRQvx+caNG1FdXY3MzEyb1y9duoQ1a9ZAq9Xi+uuvtyuN4IiysjJkZ2fbpFP36dPH6d+sK1y5cgUXL15Ely5dHG4vLy/Hnj17MHTo0CafOycnBxqNxiaMZs3333+PzZs3Y86cOYiPj3drradPn4ZKpbI7xwoLC3H69GnxXBFCLdacPXsWrVu3tgltucvp06dRVVXl8LpRVVWFrVu3QqfTYeDAgQ2WHgDMx8r6PLEuE+ApUt4j6iLldaWhawrg3XVFKtggYhiGYRim2cOFGRuZw4cPY/HixQ6Luc2cOdPuV6k743lu7+euj1OnTuGBBx4QhcBSjnc01p2Cbu4Wf2sOc/t6LfXhrICet2MdjXe3mKgvxzfF3JmZmZg2bZrfrrs+6hZv9fV4f/r7kfLvTSrYQ9SI/Pzzz5gwYQJ69+6NrKwsMWMpPz8f69evR3Z2Nr777jvRRenOeJ7b+7kbYv/+/ejdu7dd1pYU4+uOPXnyJLKysnDx4kUMGDDAZu07duxAq1at8PPPPyMjI8Otsc1lbl+vxZffvTvjd+3ahaysLISEhGDEiBE26964cSOqqqqwdu1a9O3bFwB8Op7ndjzeV9+9u+P96e9H6r83qWCDqBHp0aMHxo8fj3nz5jnc/uKLL2LlypU4cOCA2+N5bu/nfvvttx2OE7hw4QLefPNN8WLkznh3577hhhsQGhqKTz75xE4fUFZWhsmTJ6O6uhpr1651a2xzmdvXaxHOGWccPXoUf/vb32A0Gt0a6+7cAwcORI8ePbBkyRI7vQwR4aGHHsKBAwewbds2APDpeJ7bfvz3339f9+uz4fTp03jyySfF796X4/3p78fd8Y1GY6u4mzMajcZhZVuBo0eP2hT+c2c8z+393DKZjJKTk+2qdwuP5ORkmwwcd8a7O7c7Bd3cLf7WHOb29VrcKaDnbrE9d8a7W0zUl+N5bvvx7lbv9uV4f/r7cXd8Y8GFGRuRtLQ0/Pjjj063//jjjzbZAO6M57m9nzs1NRWLFi1yWBQtJyfHbi53xrs7tzsF3dwt/tYc5vb1WtwpoOdusT13xrtbTNSX43lu+/HuFG/19Xh/+vtxd3xjwaLqRmTevHm48847sWXLFoex519++QWfffaZR+N5bu/n7tOnD7Kzs3Hbbbc5/P5kMhnIKsLsznh3577//9s7+5gqyz+MXwflwJCDbI4EjNeaEVESCBsHZjNpQqP5Ry2oKWkrh1JhTW21ubkwpXxrOLDFpoSBLRdmW2ImL6NYrYBEB4JNIzYDVDZRUt7k/v3xm2c7AeZz5L65D8/12djkPBefXfc4R78+5zz389pryM7OxpYtW7Bs2bJx3bdt24Y333zTcNYsbtld4uPj8ffff096OfO1a9ecfvf3mjWa37hxI9auXYumpqYJe5eUlGDXrl2On5WZp3t8/s7rfsWKFRP+Lif7O0VGXqfXj9G8MpSfkzI5DQ0NIjMz07H5m9VqFaGhoSIzM9Ppbs2u5Om+P3dra+td72Y/PDzsdH8dI3mjbiGMbehmJGsWt8wuRjbQM7rZntG8kc1EZefpds4b2bxVRV6X148reRXwQ9WEaI6RDd2Mbv5mBrfsLrpgdDNRmXm69Uan149Orzd+hmiaKSgowLVr16Tk6VbrltUlIiICSUlJGBsbQ3Bw8JRlzeKW3eUODQ0NGBoamvLsveY9PT0RFBSEuro6DA8P/6dTZp7uyTl8+LDjpsHTldfp9ePq600K03Jeijiw2WziwoULUvJ0q3Xr1IVuvbu4q1unLnTr3UX2OmXAM0TTjDD4jqWRPN1q3Tp1oVvvLu7q1qkL3Xp3kb1OGXAgIoQQQgiRd/KJ3AtdXV1idHRUSp5utW7ZXcrLy8XAwMCUZ83i1qmLTPePP/4oBgcH79ktM0+3WrfRvLs+x2XBq8ymia6uLnR3d8PDwwORkZGYN2/elOXpVuuW3QWA4wO1Xl5eU5o1i1unLrLXSQhxkWkdx0xIUVGRCA0Ndey7cOcrOTlZNDY23leebrVu2V1Onjwp0tPThb+/vyPr7+8v0tPTxQ8//OBy1ixunbrIXudktLW1iYiICC3ydKt1T5Q/ffq0yM/PF0VFReLKlStO2f7+frFmzRqnx4zkZbpVwYFIITt37hTBwcFi3759oqSkRDz66KPigw8+EFVVVWLVqlXCx8fHafM+I3m61bpldyktLRWzZ88WWVlZ4uDBg+L48ePi+PHj4uDBg+Kll14Snp6eoqyszHDWLG6dushe5904ffq0072vpjNPt1r3v/Pff/+9sFqt4rHHHhOhoaFi3rx5oqamxpHt6elxchvJy3SrhG+ZKSQiIgLFxcVIT08HAJw/fx52ux09PT2YPXs28vLycO7cOZw8edJwnm61btldFi5ciLy8POTm5k74XCouLsbevXvxxx9/GMqaxa1TF5nud955Z8LMHa5cuYKKigrH3dFl5ulW6zaat9vtWLp0KT788EMIIbBz507k5+fjyJEjSEtLQ29vL4KDgx1uI3mZbpVwIFLInDlz0NraivDwcAD/v8zQarWiq6sLQUFBaGlpQUpKCm7cuGE4T7dat+wu3t7eaGlpwSOPPDLhc6mjowOxsbG4deuWoaxZ3Dp1kemeNWsWYmNj4efnN2F2YGAAzc3Njn9YZObpVus2mp87dy6am5vx0EMPOY5XVFRg7dq1+PLLL5GQkOA0hBjJy3QrRe0JKXMTGxsrPvvsM8f31dXVwsfHR4yNjQkhhGhvbxc2m82lPN1q3bK7xMXFiU2bNonJ2Lx5s4iLizOcNYtbpy4y3QsXLrzrfc9+//13p7ceZObpVus2mg8ICJjws4qHDx8WPj4+Yv/+/U5uI3mZbpXwbvcKee+997By5UqcOnUK3t7eqKysxFtvvQWLxQIAqKurQ0xMjEt5utW6ZXfZvXs3MjIycOLECaSmpo67G/TFixfx3XffGc6axa1TF5nuxYsXo6mpCStXrsRE/Pvu6DLzdKt1G83HxsaitrYW8fHxTpmsrCwIIfDKK684PW4kL9OtEr5lppiqqip88cUXGBoawvLly/H66687jvX19QGA02XYRvJ0q3XL7tLZ2Yn9+/fjl19+cbr5YVJSEnJychxvvxnNmsWtUxdZ7p6eHgwNDSEsLAz3gsw83WrdRvNHjx5FfX099u7dO+HxiooKlJSUoLa21nBeplslHIgIIYQQYnp46w6NGB0dRVdXl5Q83WrdsrsQQgiZWjgQaURraysiIiKk5OlW656KLsXFxUhNTcWLL76I6upqp2NXr15FZGSkS1mzuHXq4q5unbrQrXcX2etUAQciQjSksLAQmzZtQlRUFLy8vPDss89ix44djuO3b9/GX3/9ZThrFrdOXdzVrVMXuvXuInudylB7UZu5efLJJ+/6FRUV5XSpoZE83WrdsrtER0eL8vJyx/cNDQ0iICBAbNmyRQjhvJOrkaxZ3Dp1cVe3Tl3o1ruL7HWqgpfdK6StrQ1ZWVmTvo3S3d2N8+fPu5SnW61bdpc///wTdrvd8b3dbkdNTQ1SU1MxMjKCDRs2uJQ1i1unLu7q1qkL3Xp3kb1OZSgfwUxMfHy8KC4unvT4vzfdMpKnW61bdpeQkBBRX18/Ltfa2irmz58vsrOzHXkjWbO4derirm6dutCtdxfZ61QFP0OkkOTkZHR0dEx63GazYcmSJS7l6Vbrlt0lJSUFlZWV43LR0dGorq5GVVWVS1mzuHXq4q5unbrQrXcX2etUhvIRjBDyn7S0tIgDBw5Mevzs2bNi69athrNmcevUxV3dOnWhW+8ustepCm7MSAghhBDTww9VTwO//vorfv7553Fb8icmJt53nm61bp260K13F3d169SFbr27yF6ndJSfkzIxvb29IiUlRVgsFhEWFiYSExNFYmKiCAsLExaLRaSkpIje3l6X8nSrdavokpycfM/ue82axa1TF3d169SFbr27yF6nKjgQKeT5558XSUlJor29fdyx9vZ2YbfbxQsvvOBSnm61bp260K13F3d169SFbr27yF6nKjgQKcTX11c0NzdPeryxsVH4+vq6lKdbrVunLnTr3cVd3Tp1oVvvLrLXqQpedq8QLy8vXL9+fdLjN27cgJeXl0t5utW6depCt95d3NWtUxe69e4ie53KUD6CmZj169eLsLAwUVlZKfr7+x2P9/f3i8rKShEeHi7eeOMNl/J0q3Xr1IVuvbu4q1unLnTr3UX2OlXBgUghg4ODIicnR1itVuHh4SG8vb2Ft7e38PDwEFarVaxbt04MDg66lKdbrVunLnTr3cVd3Tp1oVvvLrLXqQruQzQNXL9+HU1NTU6XGsbHx8PPz+++83SrdevUhW69u7irW6cudOvdRfY6ZcOBiBBCCCGmhx+qVsytW7fw008/oa2tbdyxwcFBlJWVuZynW61bpy50693FXd06daFb7y6y16kE5W/SmZiOjg7HxlMeHh5iyZIl4tKlS47jPT09Tnf4NZKnW61bpy50693FXd06daFb7y6y16kKniFSyLvvvouYmBhcvnwZHR0dsNlsSElJQVdX133n6Vbr1qkL3Xp3cVe3Tl3o1ruL7HUqQ/kIZmIeeOABcebMGcf3Y2NjIicnR4SGhooLFy6Mm4qN5OlW69apC916d3FXt05d6Na7i+x1qoIDkUJsNptoa2sb93hubq548MEHRX19vdOTwEiebrVunbrQrXcXd3Xr1IVuvbvIXqcqOBApJCEhQZSVlU14LDc3V/j7+zs9CYzk6Vbr1qkL3Xp3cVe3Tl3o1ruL7HWqggORQrZv3y7S09MnPb5u3TphsVhcytOt1q1TF7r17uKubp260K13F9nrVAX3ISKEEEKI6eFVZoQQQggxPRyICCGEEGJ6OBARQgghxPRwICKEEEKI6eFARAghhBDTw4GIEDIjWL16NSwWCywWCzw9PTF//nw888wzOHDgAMbGxu7ZU1paCn9/f3lFCSFawoGIEDJjSEtLQ3d3Nzo7O1FVVYWlS5ciLy8PGRkZGB0dne56hBCN4UBECJkxeHl5ITAwEAsWLEBcXBzef/99HDt2DFVVVSgtLQUA7NmzB48//jjmzJmDkJAQrF+/HgMDAwCAuro6rFmzBv39/Y6zTVu3bgUAHDp0CIsXL4bNZkNgYCBefvllXL58eZpWSgiZajgQEUJmNE8//TQWLVqEyspKAICHhwcKCwvR2tqKzz//HDU1Ndi8eTMAwG6345NPPoGfnx+6u7vR3d2NjRs3AgBGRkaQn5+PlpYWfPPNN+js7MTq1auna1mEkClm9nQXIIQQ2URFReHMmTMAgA0bNjgeDw8Px7Zt25CTk4Pi4mJYrVbMnTsXFosFgYGBTo5XX33V8efIyEgUFhYiISEBAwMD8PX1VbIOQog8eIaIEDLjEULAYrEAAE6dOoVly5ZhwYIFsNlsWLVqFfr6+nDz5s27OpqamvDcc88hNDQUNpsNTz31FACgq6tLen9CiHw4EBFCZjznzp1DREQEOjs7kZGRgSeeeAJff/01mpqaUFRUBAAYHh6e9Of/+ecfLF++HH5+figvL8dvv/2Go0eP/ufPEULcB75lRgiZ0dTU1ODs2bN4++230dTUhLGxMezevRseHv///+BXX33llLdarbh9+7bTY+3t7ejr60NBQQFCQkIAAI2NjWoWQAhRAs8QEUJmDENDQ+jp6cGlS5fQ3NyM7du3Y8WKFcjIyEB2djYefvhhjIyMYN++fbh48SIOHTqETz/91MkRHh6OgYEBVFdX4+rVq7h58yZCQ0NhtVodP/ftt98iPz9/mlZJCJEBByJCyIzhxIkTCAoKQnh4ONLS0lBbW4vCwkIcO3YMs2bNwqJFi7Bnzx589NFHiImJQXl5OXbs2OHksNvtyMnJQWZmJgICAvDxxx8jICAApaWlOHLkCKKjo1FQUIBdu3ZN0yoJITKwCCHEdJcghBBCCJlOeIaIEEIIIaaHAxEhhBBCTA8HIkIIIYSYHg5EhBBCCDE9HIgIIYQQYno4EBFCCCHE9HAgIoQQQojp4UBECCGEENPDgYgQQgghpocDESGEEEJMDwciQgghhJie/wG5L6VnnkmBjgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.4.b Analisando estacionariedade do produto Biscoito  True Champion 300g\n",
        "df_agrupado = df_agrupado.reset_index()\n",
        "\n",
        "teste_adf = adfuller(df_agrupado['quantidade'])\n",
        "\n",
        "print('Estatistica ADF: {:.4}'.format(teste_adf[0]))\n",
        "\n",
        "print('p-value: {:.4}'.format(teste_adf[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imNtoFvExCAP",
        "outputId": "ccc04670-8698-41db-c874-3b67c61011cb"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estatistica ADF: 0.04751\n",
            "p-value: 0.9623\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.4.c Analisando estacionariedade do produto Biscoito  True Champion 300g\n",
        "# identificado que a serie é não estacionaria, porque p-value é maior que 0,05\n",
        "# aplicando o teste de estacionariedade da série transformada\n",
        "teste_adf = adfuller(df_agrupado['quantidade'].diff().dropna())\n",
        "\n",
        "print('Estatistica ADF: {:.4}'.format(teste_adf[0]))\n",
        "\n",
        "print('p-value: {:.4}'.format(teste_adf[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MSM8hbEh2_EK",
        "outputId": "791cd282-02c5-4db7-bed4-31b9608e3d59"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estatistica ADF: -3.227\n",
            "p-value: 0.01847\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.4.d Analisando estacionariedade do produto Biscoito  True Champion 300g\n",
        "# com o p-value menor que 0,05 a serie se tornou estacionaria após a transformação com ordem de integração 1\n",
        "df_agrupado['quantidade'].diff().plot(legend=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "1FV8Gzau5p6n",
        "outputId": "8842c46f-29ab-4efa-c2ec-6f555c6d3c97"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 145
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.5 calculando media e mediana da quantidade de vendas por categoria\n",
        "df_agrupado_vendas = df.groupby('categoriaprod')['quantidade'].agg(['mean', 'median']).reset_index()\n",
        "df_agrupado_vendas.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wwrQaWEn6ru_",
        "outputId": "aba3a5e6-4aa1-4d66-acfb-756bf5aa3308"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             categoriaprod      mean  median\n",
              "0                Acessório  3.346225     1.0\n",
              "1              Alimentação  4.174317     2.0\n",
              "2  Bebedouros e Comedouros  3.034158     1.0\n",
              "3                Brinquedo  3.616952     1.0\n",
              "4        Higiene e Limpeza  3.384394     1.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb90f728-8dc6-45a2-833d-18d77fe6de91\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>categoriaprod</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acessório</td>\n",
              "      <td>3.346225</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alimentação</td>\n",
              "      <td>4.174317</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bebedouros e Comedouros</td>\n",
              "      <td>3.034158</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Brinquedo</td>\n",
              "      <td>3.616952</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Higiene e Limpeza</td>\n",
              "      <td>3.384394</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb90f728-8dc6-45a2-833d-18d77fe6de91')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.5 calculando o rankind de produtos mais vendidos\n",
        "df_ranking_vendas = df.groupby('produto')['quantidade'].sum()\n",
        "\n",
        "df_ranking_vendas = df_ranking_vendas.sort_values(ascending=False)\n",
        "\n",
        "df_ranking_vendas = df_ranking_vendas.reset_index()\n",
        "df_ranking_vendas['ranking_vendas'] = df_ranking_vendas.index + 1\n",
        "\n",
        "df_ranking_vendas.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "VurF-UUY8mfF",
        "outputId": "cd63eab2-f909-4043-a18d-b9e548fd12ca"
      },
      "execution_count": 147,
      "outputs": [
        {
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            "text/plain": [
              "                                             produto  quantidade  \\\n",
              "0                       Biscoito  True Champion 300g     38928.0   \n",
              "1  Shampoo vegano para cachorro e gato de camomil...     34673.0   \n",
              "2                            Bola Pet Vinil Big Blue     34506.0   \n",
              "3  Suplemento Alimentar Glutamina Mundo Animal Nu...     34502.0   \n",
              "4  Ração Úmida Royal Canin Lata Veterinary Cães A...     34396.0   \n",
              "5      Ração Royal Canin Exigent Gatos Adultos 1,5Kg     33766.0   \n",
              "6  Whiskas Petisco Temptations Anti Bola de Pelo 40g     33684.0   \n",
              "7  Pote Petisco Para Cachorro Bifinho Sabor Carne...     33380.0   \n",
              "8                Bandana Disney Mickey Fábrica Petti     33054.0   \n",
              "9  Ração Royal Canin Club Performance para Cães A...     32687.0   \n",
              "\n",
              "   ranking_vendas  \n",
              "0               1  \n",
              "1               2  \n",
              "2               3  \n",
              "3               4  \n",
              "4               5  \n",
              "5               6  \n",
              "6               7  \n",
              "7               8  \n",
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    {
      "cell_type": "code",
      "source": [
        "# 1.4.5 comparando media e mediana de vendas e lucro liquido\n",
        "df_agrupado_lucro = df.groupby('categoriaprod')['lucro_liquido'].agg(['mean', 'median']).reset_index()\n",
        "\n",
        "df_agrupado = pd.merge(df_agrupado_vendas, df_agrupado_lucro, on='categoriaprod', how='inner')\n",
        "df_agrupado.head()"
      ],
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        "id": "MqJRGdBV9NjJ",
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      },
      "execution_count": 148,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "             categoriaprod    mean_x  median_x      mean_y  median_y\n",
              "0                Acessório  3.346225       1.0   78.531947     23.40\n",
              "1              Alimentação  4.174317       2.0  156.919791     49.00\n",
              "2  Bebedouros e Comedouros  3.034158       1.0  419.874957    229.20\n",
              "3                Brinquedo  3.616952       1.0   31.981485     12.24\n",
              "4        Higiene e Limpeza  3.384394       1.0  102.773540     40.00"
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 148
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.4.5 comparando ranking de vendas e lucro liquido\n",
        "df_ranking_lucro = df.groupby('produto')['lucro_liquido'].sum()\n",
        "\n",
        "df_ranking_lucro = df_ranking_lucro.sort_values(ascending=False)\n",
        "\n",
        "df_ranking_lucro = df_ranking_lucro.reset_index()\n",
        "df_ranking_lucro['ranking_lucro'] = df_ranking_lucro.index + 1\n",
        "\n",
        "df_ranking = pd.merge(df_ranking_lucro, df_ranking_vendas, on='produto', how='inner')\n",
        "\n",
        "df_ranking.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "NKbHZ_Th9qQb",
        "outputId": "9d247ab0-8d00-4dd4-c95d-d3af613292b3"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             produto  lucro_liquido  \\\n",
              "0  Nutri Alimentador Inteligente Automático Câmer...     6520918.50   \n",
              "1  Antipulgas e Carrapatos MSD Bravecto para Pet ...     5001436.75   \n",
              "2  Ração Royal Canin Club Performance para Cães A...     2781729.50   \n",
              "3      Ração Royal Canin Exigent Gatos Adultos 1,5Kg     1625448.00   \n",
              "4       Cama Coração Coroa Pet Nest Almofada Lavável     1473807.00   \n",
              "5        Kit Banho e Tosa com Escova PetShop Cãopeon     1318294.20   \n",
              "6                      Vitamina E Granulado BigForce     1167909.75   \n",
              "7                         Cama média almofadada Pity      811968.60   \n",
              "8  Suplemento Alimentar Glutamina Mundo Animal Nu...      684433.80   \n",
              "9  Shampoo vegano para cachorro e gato de camomil...      664327.80   \n",
              "\n",
              "   ranking_lucro  quantidade  ranking_vendas  \n",
              "0              1     26131.0              19  \n",
              "1              2     32311.0              11  \n",
              "2              3     32687.0              10  \n",
              "3              4     33766.0               6  \n",
              "4              5     27372.0              17  \n",
              "5              6     25448.0              20  \n",
              "6              7     31928.0              13  \n",
              "7              8     24184.0              24  \n",
              "8              9     34502.0               4  \n",
              "9             10     34673.0               2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-722d3fdb-5ae3-4376-96e2-ba5420695a03\">\n",
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              "      <td>Antipulgas e Carrapatos MSD Bravecto para Pet ...</td>\n",
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              "      <td>Kit Banho e Tosa com Escova PetShop Cãopeon</td>\n",
              "      <td>1318294.20</td>\n",
              "      <td>6</td>\n",
              "      <td>25448.0</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Vitamina E Granulado BigForce</td>\n",
              "      <td>1167909.75</td>\n",
              "      <td>7</td>\n",
              "      <td>31928.0</td>\n",
              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Cama média almofadada Pity</td>\n",
              "      <td>811968.60</td>\n",
              "      <td>8</td>\n",
              "      <td>24184.0</td>\n",
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              "      <td>684433.80</td>\n",
              "      <td>9</td>\n",
              "      <td>34502.0</td>\n",
              "      <td>4</td>\n",
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              "      <th>9</th>\n",
              "      <td>Shampoo vegano para cachorro e gato de camomil...</td>\n",
              "      <td>664327.80</td>\n",
              "      <td>10</td>\n",
              "      <td>34673.0</td>\n",
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