Violência_Escolar.ipynb

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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mariliabrito/gunviolence/blob/limpeza_inc_arm/Viol%C3%AAncia_Escolar.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "<a href=\"TAIL\"><img src=\"https://i.imgur.com/GRTE0OG.png\" width=\"150\" title=\"Tail Logo\" /></a>\n",
        "\n",
        "# <font color =\"#00b8ff\">Análise de Violência Escolar</font>\n",
        "## <font color =\"#00b8ff\">Trainees - 2022.2</font>"
      ],
      "metadata": {
        "id": "JFB8z8ya0tUW"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "# Isto está formatado como código\n",
        "```\n",
        "\n",
        "## <font color =\"#00b8ff\">1. Introdução</font>\n",
        "\n",
        "Fornecer um contexto do problema, trazer dados sobre violência escolar com armas, esclarecer os objetivos do trabalho, informar de onde pegamos o notebook e sua credibilidade, descrever as colunas do dataset, etc"
      ],
      "metadata": {
        "id": "MzAG-w2g1GOT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "WkR0Ljajf3C5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importando 4 abas do arquivo de excel\n",
        "# As abas dos datasets são: incidente, atirador, vítima, arma.\n",
        "\n",
        "df_incid = pd.read_excel(\"https://github.com/mariliabrito/gunviolence/blob/main/SSDB_Raw_Data_2022.xlsx?raw=true\", sheet_name=1, parse_dates=[\"Date\"]) #Rafael, Gisele e Dantas (Marília)\n",
        "df_shoot = pd.read_excel(\"https://github.com/mariliabrito/gunviolence/blob/main/SSDB_Raw_Data_2022.xlsx?raw=true\", sheet_name=2) #Hermano e Duda (Jonas)\n",
        "df_victm = pd.read_excel(\"https://github.com/mariliabrito/gunviolence/blob/main/SSDB_Raw_Data_2022.xlsx?raw=true\", sheet_name=3) #Hermano e Duda (Jonas)\n",
        "df_weapn = pd.read_excel(\"https://github.com/mariliabrito/gunviolence/blob/main/SSDB_Raw_Data_2022.xlsx?raw=true\", sheet_name=4) #Rafael, Gisele e Dantas (Marília)"
      ],
      "metadata": {
        "id": "hL6SophSf74n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_shoot.describe(include='all') #Esse comando inclui TODAS as colunas do referido dataset."
      ],
      "metadata": {
        "id": "Xzvo-OiF6Qvb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_weapn.describe(include='all') "
      ],
      "metadata": {
        "id": "RlZgGM7l6-pM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_victm.describe(include='all') "
      ],
      "metadata": {
        "id": "JV51kOpp6y1-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_incid.describe(include='all') "
      ],
      "metadata": {
        "id": "ZlwlIodr8nEl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_incid.head()"
      ],
      "metadata": {
        "id": "fYdmLyec0ysy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_incid.info()"
      ],
      "metadata": {
        "id": "LQMKAoJr1prm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_shoot.head()"
      ],
      "metadata": {
        "id": "g9NXI-Pl1C1m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_victm.head()"
      ],
      "metadata": {
        "id": "MZt3F1gu1Pt0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_weapn.head()"
      ],
      "metadata": {
        "id": "bq99WdxE1avQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color =\"#00b8ff\">2. Limpeza</font>\n",
        "\n",
        "Realizar limpeza e uniformização do dataset. Também é possível juntar datasets pela coluna incidentid."
      ],
      "metadata": {
        "id": "t5HwnisD1zbC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Removendo valores duplicados caso existam\n",
        "df_incid = df_incid.drop_duplicates()\n",
        "df_shoot = df_shoot.drop_duplicates()\n",
        "df_victm = df_victm.drop_duplicates()\n",
        "df_weapn = df_weapn.drop_duplicates()\n",
        "\n",
        "#Verificando\n",
        "print(df_incid.isnull().sum())\n",
        "print(\"=\"*27)\n",
        "print(df_shoot.isnull().sum())\n",
        "print(\"=\"*27)\n",
        "print(df_victm.isnull().sum())\n",
        "print(\"=\"*27)\n",
        "print(df_weapn.isnull().sum())\n"
      ],
      "metadata": {
        "id": "deAGEyDe1EdW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#O tratamento de variáveis de tipo String do dataset 'Incidentes':\n",
        "colunas_string=['Number_News','Media_Attention','School','School_Level','Location','Location_Type','During_School','First_Shot','Narrative','Situation','Targets','Accomplice','Hostages','Barricade','Officer_Involved','Bullied','Domestic_Violence','Gang_Related','Preplanned','Shots_Fired','Active_Shooter_FBI']\n",
        "\n",
        "df_incid[colunas_string] = df_incid[colunas_string].fillna(\"Unknown\")\n",
        "\n",
        "#O tratamento de variáveis de tipo numérico, substituindo os valores nulos por 0.\n",
        "df_incid['Reliability'] = df_incid['Reliability'].fillna(-1)\n",
        "df_incid.isnull().sum()\n",
        "\n",
        "#O tratamento da coluna de estações\n",
        "seasons_dict = {\n",
        "    1: 'Winter',\n",
        "    2: 'Winter',\n",
        "    3: 'Spring',\n",
        "    4: 'Spring',\n",
        "    5: 'Spring',\n",
        "    6: 'Summer',\n",
        "    7: 'Summer',\n",
        "    8: 'Summer',\n",
        "    9: 'Fall',\n",
        "    10: 'Fall',\n",
        "    11: 'Fall',\n",
        "    12: 'Winter'\n",
        "}\n",
        "\n",
        "df_incid['Quarter'] = pd.to_datetime(df_incid['Date']).dt.month.map(seasons_dict)\n",
        "\n",
        "def get_time_period(time_string):                             #Tentando preencher os períodos do dia com null a partir da hora do primeiro tiro\n",
        "    if ':' in time_string:\n",
        "        hour = int(time_string.split(':')[0])\n",
        "        if hour < 12 and hour < 6:\n",
        "          return 'Night'\n",
        "        if hour < 12:\n",
        "            return 'Morning'\n",
        "        elif hour < 18:\n",
        "            return 'Afternoon'\n",
        "        else:\n",
        "            return 'Night'\n",
        "    else:\n",
        "        if 'AM' in time_string:\n",
        "            return 'Morning'\n",
        "        else:\n",
        "            return 'Afternoon/Night'\n",
        "\n",
        "df_incid['Time_Period'].fillna(df_incid['First_Shot'].apply(get_time_period), inplace=True)\n",
        "\n",
        "df_incid = df_incid.fillna('Unknown') #preenchendo o restante com 'Unknown'"
      ],
      "metadata": {
        "id": "SJ3snxdZG-d9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Nomeando meses\n",
        "month_names = {\n",
        "    1: 'Janeiro',\n",
        "    2: 'Fevereiro',\n",
        "    3: 'Março',\n",
        "    4: 'Abril',\n",
        "    5: 'Maio',\n",
        "    6: 'Junho',\n",
        "    7: 'Julho',\n",
        "    8: 'Agosto',\n",
        "    9: 'Setembro',\n",
        "    10: 'Outubro',\n",
        "    11: 'Novembro',\n",
        "    12: 'Dezembro'\n",
        "}"
      ],
      "metadata": {
        "id": "3r2XlV6Q0Qlo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extraindo ano, mês e dia do dataset\n",
        "df_incid['Year'] = df_incid['Date'].dt.year\n",
        "df_incid['Month'] = pd.to_datetime(df_incid['Date']).dt.month.map(month_names)\n",
        "df_incid['Day'] = df_incid['Date'].dt.day"
      ],
      "metadata": {
        "id": "FFJ_kZfJ16oG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_incid['Time_Period'].value_counts()"
      ],
      "metadata": {
        "id": "d9xr5RvNzFwQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_incid['Quarter'].value_counts()"
      ],
      "metadata": {
        "id": "68Tb3oCyK6SL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_shoot.describe()"
      ],
      "metadata": {
        "id": "12g51O6oC3V1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_weapn.describe()"
      ],
      "metadata": {
        "id": "-E-mjxg7DJ-h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_weapn[\"weaponcaliber\"].value_counts()"
      ],
      "metadata": {
        "id": "I-9vYl9oxNa5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_weapn.loc[df_weapn[\"weaponcaliber\"] == '.22 caliber ',\"weaponcaliber\"] = '.22 caliber' #renomeando o .22 que estava com um espaço no final\n",
        "df_weapn.loc[df_weapn[\"weaponcaliber\"] == 'airsoft',\"weaponcaliber\"] = 'Airsoft' #renomeando os \"airsoft\" minúsculos\n",
        "df_weapn.loc[df_weapn[\"weaponcaliber\"] == 'Service weapon',\"weaponcaliber\"] = 'Service Weapon' "
      ],
      "metadata": {
        "id": "CjIFOraewZS4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_weapn[\"weaponcaliber\"].value_counts()"
      ],
      "metadata": {
        "id": "clT8jNdqw-E_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_weapn[\"weaponcaliber\"].unique()\n"
      ],
      "metadata": {
        "id": "V6Nf4QLYepUB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_shoot = df_shoot.fillna('Unknown') #Preenchendo as informações nulas da aba atirador\n",
        "df_weapn = df_weapn.fillna('Unknown') #Preenchendo as informações nulas da aba arma"
      ],
      "metadata": {
        "id": "RbfYtiGBotNU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_shoot.isnull().sum() "
      ],
      "metadata": {
        "id": "BeNSrcVrzB-N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Preenchendo os valores nulos da coluna de gênero com Unknown\n",
        "df_victm['gender'] = df_victm['gender'].fillna('Unknown') \n",
        "df_victm['schoolaffiliation'] = df_victm['schoolaffiliation'].fillna('Unknown')\n",
        "df_victm['race'] = df_victm['race'].fillna('Unknown')"
      ],
      "metadata": {
        "id": "fvNdo2VDuQKw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_shoot[\"age\"].unique()"
      ],
      "metadata": {
        "id": "zXnQCqIHTult"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Classificando as vítimas em Adulto, Adolescente e Criança\n",
        "def classify_age(age):\n",
        "    if isinstance(age, str):\n",
        "        if age.strip() == '':\n",
        "            return 'Unknown'\n",
        "        else:\n",
        "            return age\n",
        "    else:\n",
        "        if age < 0:\n",
        "            return 'Invalid'\n",
        "        elif age <= 12:\n",
        "            return 'Child'\n",
        "        elif age <= 20:\n",
        "            return 'Teen'\n",
        "        elif age >20:\n",
        "            return 'Adult'\n",
        "        else:\n",
        "          return 'Unknown'\n",
        "\n",
        "df_incid['age'] = pd.to_numeric(df_shoot['age'], errors='coerce')\n",
        "df_incid['ageclass'] = df_incid['age'].apply(classify_age)\n",
        "df_incid['ageclass'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ZlgUX0y-917",
        "outputId": "e895fbca-1cc4-441a-f42d-1f37ec8ccecb"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Teen       912\n",
              "Unknown    820\n",
              "Adult      281\n",
              "Child       56\n",
              "Name: ageclass, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Classificando as vítimas em Adulto, Adolescente e Criança\n",
        "def classify_age(age):\n",
        "    if isinstance(age, str):\n",
        "        if age.strip() == '':\n",
        "            return 'Unknown'\n",
        "        else:\n",
        "            return age\n",
        "    else:\n",
        "        if age < 0:\n",
        "            return 'Invalid'\n",
        "        elif age <= 12:\n",
        "            return 'Child'\n",
        "        elif age <= 19:\n",
        "            return 'Teen'\n",
        "        else:\n",
        "            return 'Adult'\n",
        "\n",
        "df_victm['age'] = pd.to_numeric(df_victm['age'], errors='coerce')\n",
        "df_victm['ageclass'] = df_victm['age'].apply(classify_age)\n",
        "\n",
        "df_victm"
      ],
      "metadata": {
        "id": "yunHPS2WZViT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "59d98aa2-3ab3-4986-a2c1-705c91d4edd9"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         incidentid     race   injury  gender schoolaffiliation   age  \\\n",
              "0     20220620ILGRC  Unknown  Wounded  Female       No Relation  34.0   \n",
              "1     20220620ILGRC  Unknown  Wounded    Male       No Relation  16.0   \n",
              "2     20220620ILGRC  Unknown  Wounded  Female       No Relation  17.0   \n",
              "3     20220609ALWAG    Black    Fatal    Male       No Relation  32.0   \n",
              "4     20220605INWEG  Unknown  Wounded    Male           Unknown  19.0   \n",
              "...             ...      ...      ...     ...               ...   ...   \n",
              "3094  19700323CADAL  Unknown  Wounded    Male           Student  16.0   \n",
              "3095  19700206OHJOC  Unknown  Wounded    Male           Student  18.0   \n",
              "3096  19700105DCUNW  Unknown     None    Male           Unknown  16.0   \n",
              "3097  19700105DCSOW  Unknown  Wounded    Male           Student   NaN   \n",
              "3098  19700105DCHIW  Unknown    Fatal    Male           Student  15.0   \n",
              "\n",
              "     ageclass  ageinterp  \n",
              "0       Adult       34.0  \n",
              "1        Teen       16.0  \n",
              "2        Teen       17.0  \n",
              "3       Adult       32.0  \n",
              "4        Teen       19.0  \n",
              "...       ...        ...  \n",
              "3094     Teen       16.0  \n",
              "3095     Teen       18.0  \n",
              "3096     Teen       16.0  \n",
              "3097    Adult       16.0  \n",
              "3098     Teen       15.0  \n",
              "\n",
              "[2489 rows x 8 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>incidentid</th>\n",
              "      <th>race</th>\n",
              "      <th>injury</th>\n",
              "      <th>gender</th>\n",
              "      <th>schoolaffiliation</th>\n",
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              "      <th>0</th>\n",
              "      <td>20220620ILGRC</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Wounded</td>\n",
              "      <td>Female</td>\n",
              "      <td>No Relation</td>\n",
              "      <td>34.0</td>\n",
              "      <td>Adult</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20220620ILGRC</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Wounded</td>\n",
              "      <td>Male</td>\n",
              "      <td>No Relation</td>\n",
              "      <td>16.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20220620ILGRC</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Wounded</td>\n",
              "      <td>Female</td>\n",
              "      <td>No Relation</td>\n",
              "      <td>17.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20220609ALWAG</td>\n",
              "      <td>Black</td>\n",
              "      <td>Fatal</td>\n",
              "      <td>Male</td>\n",
              "      <td>No Relation</td>\n",
              "      <td>32.0</td>\n",
              "      <td>Adult</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20220605INWEG</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Wounded</td>\n",
              "      <td>Male</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>19.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>19.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3094</th>\n",
              "      <td>19700323CADAL</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Wounded</td>\n",
              "      <td>Male</td>\n",
              "      <td>Student</td>\n",
              "      <td>16.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3095</th>\n",
              "      <td>19700206OHJOC</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Wounded</td>\n",
              "      <td>Male</td>\n",
              "      <td>Student</td>\n",
              "      <td>18.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3096</th>\n",
              "      <td>19700105DCUNW</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>None</td>\n",
              "      <td>Male</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>16.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3097</th>\n",
              "      <td>19700105DCSOW</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Wounded</td>\n",
              "      <td>Male</td>\n",
              "      <td>Student</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Adult</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3098</th>\n",
              "      <td>19700105DCHIW</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Fatal</td>\n",
              "      <td>Male</td>\n",
              "      <td>Student</td>\n",
              "      <td>15.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2489 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56a62111-dcf7-496a-a454-884af06f6092')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-56a62111-dcf7-496a-a454-884af06f6092 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-56a62111-dcf7-496a-a454-884af06f6092');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_victm['age'].hist() #analisando a distribuição "
      ],
      "metadata": {
        "id": "9y9RttxLh1Uq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "6e370c41-6cb7-4bd8-91ca-ec941b8f8fca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "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": [
        "nan_interpol = df_victm['age'].fillna(df_victm['age'].interpolate(method='nearest')) #verificando a coerência da interpolação das idades\n",
        "print(nan_interpol.hist())\n",
        "df_victm['ageinterp'] = df_victm['age'].fillna(df_victm['age'].interpolate(method='nearest'))"
      ],
      "metadata": {
        "id": "gTMt36MFvWZy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "ec5f8ffc-6352-415c-9ea7-b99af061fb99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Axes(0.125,0.11;0.775x0.77)\n"
          ]
        },
        {
          "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": [],
      "metadata": {
        "id": "nlfmVZXq74sb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificação final de valores null"
      ],
      "metadata": {
        "id": "-5015UBCB7DE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_incid.isnull().sum())\n",
        "print(\"=\"*27)\n",
        "print(df_shoot.isnull().sum())\n",
        "print(\"=\"*27)\n",
        "print(df_victm.isnull().sum()) #Na coluna ['age'] ainda existem valores null pq estes não foram substituídos, preferimos criar uma nova coluna com valores interpolados\n",
        "print(\"=\"*27)\n",
        "print(df_weapn.isnull().sum())"
      ],
      "metadata": {
        "id": "VwbfZbGtBLx4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "933abc76-c1ec-4baa-d7b8-d5f1d5c31029"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Incident_ID           0\n",
            "Sources               0\n",
            "Number_News           0\n",
            "Media_Attention       0\n",
            "Reliability           0\n",
            "Date                  0\n",
            "Quarter               0\n",
            "School                0\n",
            "City                  0\n",
            "State                 0\n",
            "School_Level          0\n",
            "Location              0\n",
            "Location_Type         0\n",
            "During_School         0\n",
            "Time_Period           0\n",
            "First_Shot            0\n",
            "Summary               0\n",
            "Narrative             0\n",
            "Situation             0\n",
            "Targets               0\n",
            "Accomplice            0\n",
            "Hostages              0\n",
            "Barricade             0\n",
            "Officer_Involved      0\n",
            "Bullied               0\n",
            "Domestic_Violence     0\n",
            "Gang_Related          0\n",
            "Preplanned            0\n",
            "Shots_Fired           0\n",
            "Active_Shooter_FBI    0\n",
            "Year                  0\n",
            "Month                 0\n",
            "Day                   0\n",
            "dtype: int64\n",
            "===========================\n",
            "incidentid           0\n",
            "age                  0\n",
            "gender               0\n",
            "race                 0\n",
            "schoolaffiliation    0\n",
            "shooteroutcome       0\n",
            "shooterdied          0\n",
            "injury               0\n",
            "chargesfiled         0\n",
            "verdict              0\n",
            "minorchargedadult    0\n",
            "criminalhistory      0\n",
            "dtype: int64\n",
            "===========================\n",
            "incidentid             0\n",
            "race                   0\n",
            "injury                 0\n",
            "gender                 0\n",
            "schoolaffiliation      0\n",
            "age                  836\n",
            "ageclass               0\n",
            "ageinterp              0\n",
            "dtype: int64\n",
            "===========================\n",
            "incidentid       0\n",
            "weaponcaliber    0\n",
            "weapondetails    0\n",
            "weapontype       0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color =\"#00b8ff\">3. Análise Descritiva</font>\n",
        "\n",
        "Realizar análise do dataset, mostrar série histórica, trazer visualizações, etc"
      ],
      "metadata": {
        "id": "fLzhT-DS2GdW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color =\"#00b8ff\">3.1 Análise de série histórica e geográfica - Duda e Gisele</font>\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "hNru_NnxuIDl"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ipAkEHQ23Dd_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color =\"#00b8ff\">3.2 Análise dos Incidentes - João Victor, Hermano e Raffael</font>"
      ],
      "metadata": {
        "id": "MBIeA3TfuTda"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_incid.head()"
      ],
      "metadata": {
        "id": "njEqeM3-3C97",
        "colab": {
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              "     Incident_ID                                            Sources  \\\n",
              "0  20220620ILGRC  https://www.cbsnews.com/chicago/news/woman-and...   \n",
              "1  20220613WAMAE  https://www.q13fox.com/news/deputies-arrest-15...   \n",
              "2  20220610ALBYB  https://www.actionnews5.com/2022/06/10/student...   \n",
              "3  20220609ALWAG  https://www.live5news.com/2022/06/09/potential...   \n",
              "4  20220608ARLIL  https://www.kark.com/crime/lrpd-school-bus-hit...   \n",
              "\n",
              "  Number_News Media_Attention  Reliability       Date Quarter  \\\n",
              "0          50        National            4 2022-06-20  Summer   \n",
              "1           5           Local            4 2022-06-13  Summer   \n",
              "2           3           Local            4 2022-06-10  Summer   \n",
              "3         100        National            4 2022-06-09  Summer   \n",
              "4           5        National            4 2022-06-08  Summer   \n",
              "\n",
              "                            School         City State  ... Officer_Involved  \\\n",
              "0     Gresham School of Excellence      Chicago    IL  ...               No   \n",
              "1              Mariner High School      Everett    WA  ...               No   \n",
              "2              Byhalia High School      Byhalia    MS  ...               No   \n",
              "3    Walnut Park Elementary School      Gadsden    AL  ...          Unknown   \n",
              "4  Little Rock School District Bus  Little Rock    AR  ...               No   \n",
              "\n",
              "   Bullied Domestic_Violence Gang_Related Preplanned Shots_Fired  \\\n",
              "0       No                No      Unknown         No         100   \n",
              "1       No                No      Unknown         No          99   \n",
              "2       No                No      Unknown         No           1   \n",
              "3  Unknown           Unknown      Unknown    Unknown     Unknown   \n",
              "4       No                No      Unknown         No          99   \n",
              "\n",
              "  Active_Shooter_FBI  Year  Month Day  \n",
              "0                 No  2022  Junho  20  \n",
              "1                 No  2022  Junho  13  \n",
              "2                 No  2022  Junho  10  \n",
              "3                 No  2022  Junho   9  \n",
              "4                 No  2022  Junho   8  \n",
              "\n",
              "[5 rows x 33 columns]"
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              "      <th>0</th>\n",
              "      <td>20220620ILGRC</td>\n",
              "      <td>https://www.cbsnews.com/chicago/news/woman-and...</td>\n",
              "      <td>50</td>\n",
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              "      <td>No</td>\n",
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              "      <td>20220613WAMAE</td>\n",
              "      <td>https://www.q13fox.com/news/deputies-arrest-15...</td>\n",
              "      <td>5</td>\n",
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              "      <td>2022-06-13</td>\n",
              "      <td>Summer</td>\n",
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              "      <td>Everett</td>\n",
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              "      <td>No</td>\n",
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              "      <td>https://www.actionnews5.com/2022/06/10/student...</td>\n",
              "      <td>3</td>\n",
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              "      <td>4</td>\n",
              "      <td>2022-06-10</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Byhalia High School</td>\n",
              "      <td>Byhalia</td>\n",
              "      <td>MS</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>2022</td>\n",
              "      <td>Junho</td>\n",
              "      <td>10</td>\n",
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              "      <th>3</th>\n",
              "      <td>20220609ALWAG</td>\n",
              "      <td>https://www.live5news.com/2022/06/09/potential...</td>\n",
              "      <td>100</td>\n",
              "      <td>National</td>\n",
              "      <td>4</td>\n",
              "      <td>2022-06-09</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Walnut Park Elementary School</td>\n",
              "      <td>Gadsden</td>\n",
              "      <td>AL</td>\n",
              "      <td>...</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>No</td>\n",
              "      <td>2022</td>\n",
              "      <td>Junho</td>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20220608ARLIL</td>\n",
              "      <td>https://www.kark.com/crime/lrpd-school-bus-hit...</td>\n",
              "      <td>5</td>\n",
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              "      <td>4</td>\n",
              "      <td>2022-06-08</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Little Rock School District Bus</td>\n",
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              "      <td>No</td>\n",
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              "      <td>Unknown</td>\n",
              "      <td>No</td>\n",
              "      <td>99</td>\n",
              "      <td>No</td>\n",
              "      <td>2022</td>\n",
              "      <td>Junho</td>\n",
              "      <td>8</td>\n",
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    {
      "cell_type": "code",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2069 entries, 0 to 2068\n",
            "Data columns (total 33 columns):\n",
            " #   Column              Non-Null Count  Dtype         \n",
            "---  ------              --------------  -----         \n",
            " 0   Incident_ID         2069 non-null   object        \n",
            " 1   Sources             2069 non-null   object        \n",
            " 2   Number_News         2069 non-null   object        \n",
            " 3   Media_Attention     2069 non-null   object        \n",
            " 4   Reliability         2069 non-null   int64         \n",
            " 5   Date                2069 non-null   datetime64[ns]\n",
            " 6   Quarter             2069 non-null   object        \n",
            " 7   School              2069 non-null   object        \n",
            " 8   City                2069 non-null   object        \n",
            " 9   State               2069 non-null   object        \n",
            " 10  School_Level        2069 non-null   object        \n",
            " 11  Location            2069 non-null   object        \n",
            " 12  Location_Type       2069 non-null   object        \n",
            " 13  During_School       2069 non-null   object        \n",
            " 14  Time_Period         2069 non-null   object        \n",
            " 15  First_Shot          2069 non-null   object        \n",
            " 16  Summary             2069 non-null   object        \n",
            " 17  Narrative           2069 non-null   object        \n",
            " 18  Situation           2069 non-null   object        \n",
            " 19  Targets             2069 non-null   object        \n",
            " 20  Accomplice          2069 non-null   object        \n",
            " 21  Hostages            2069 non-null   object        \n",
            " 22  Barricade           2069 non-null   object        \n",
            " 23  Officer_Involved    2069 non-null   object        \n",
            " 24  Bullied             2069 non-null   object        \n",
            " 25  Domestic_Violence   2069 non-null   object        \n",
            " 26  Gang_Related        2069 non-null   object        \n",
            " 27  Preplanned          2069 non-null   object        \n",
            " 28  Shots_Fired         2069 non-null   object        \n",
            " 29  Active_Shooter_FBI  2069 non-null   object        \n",
            " 30  Year                2069 non-null   int64         \n",
            " 31  Month               2069 non-null   object        \n",
            " 32  Day                 2069 non-null   int64         \n",
            "dtypes: datetime64[ns](1), int64(3), object(29)\n",
            "memory usage: 549.6+ KB\n"
          ]
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              "       Reliability         Year          Day\n",
              "count  2069.000000  2069.000000  2069.000000\n",
              "mean      2.766554  2005.714355    15.745771\n",
              "std       0.996184    15.432015     8.732714\n",
              "min       1.000000  1970.000000     1.000000\n",
              "25%       2.000000  1993.000000     9.000000\n",
              "50%       2.000000  2010.000000    15.000000\n",
              "75%       4.000000  2020.000000    23.000000\n",
              "max       5.000000  2022.000000    31.000000"
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              "      <td>2069.000000</td>\n",
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              "      <th>mean</th>\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cae8bcfc-5b92-40da-93eb-cdd9735bb163 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cae8bcfc-5b92-40da-93eb-cdd9735bb163');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_incid['Reliability'].value_counts()\n",
        "confiabilidade = df_incid['Reliability'].value_counts()\n",
        "plt.bar(confiabilidade.index, confiabilidade.values)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "zMa7j6Zc6nyE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "23faa8c1-fffd-457d-8561-00d2dc12bbfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "escola20 = df_incid['School'].value_counts().head(20)\n",
        "fig = plt.figure(figsize=(17, 5))\n",
        "plt.bar(escola20.index, escola20.values)\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "DZvpvpub6vYW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "a3b2c690-35d9-4775-dba5-2cd408e5f33d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x500 with 1 Axes>"
            ],
            "image/png": 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Pavr06SpYsGBAe58+fcIZyzE27se2jesL75b9xx9/6P7771d0dHRAu1fulm3bPC1J/fr1C3h8+vRpjRw5MsONfcaNGxfOWI6xbUxLGe8qnJKSosTExAx3WPbKXYUXLFgQ8Dg9PV1LlizRN998E9DeunXrcMZylG3HJuZq78/Vto1pyb4+tu3YJNnXx1kdBVUP+fPdwSWpTZs27gQJkwvv3GgD2/r4wjvA22D8+PEBj4sWLao33ngjoM3n83mmoGrjfmzbuL7wDd5dd93lUpLwsG2elqRNmzYFPL766qu1d+/egDYvnTFi25iWAu8qbIML92NJuu+++wIee60QY9uxibna+3O1bWNasq+PbTs2Sfb1cVbnM38+LQgAAAAAAAAA8Je4KRUAAAAAAAAABImCKgAAAAAAAAAEiYIqAAAAAAAAAASJgioAAAAAAAAABImCKnAJmjlzpk6dOpWh/fTp05o5c6YLiZzVrVs3nThxIkN7amqqunXr5kIiAAjeH3/84XYEx509e1YjRozQDz/84HYUOOTMmTMqW7astm3b5nYUOOTMmTOKjIzUN99843aUsLnw7tgAkNXxnivr8BljjNsh8O9NmjQp6Nf26dPHwSThd+jQIT3yyCNasmSJfv75Z104pNPS0lxK5pxs2bLpp59+UuHChQPaDx8+rMKFC3tum/9qe3/99VcVLVpUZ8+edSmZc2zrYxv349TUVI0ePdq/zenp6QHPe/FD3q5du7Rs2bJMt3fYsGEupXJGenq6Ro4cqSlTpujQoUPauXOnypQpo6FDhyohIUHdu3d3O2LI5c2bV19//bUSEhLcjhI2No1pSbrsssv02WefqXLlym5HCYv9+/fL5/OpePHikqS1a9dq9uzZqlKliu69916X0zmjTJkyev/991WzZk23o4RFRESErrvuOnXv3l233nqrcubM6XYkxyUlJWXa7vP5lDNnTpUsWVJRUVFhThVa7dq1C/q17733noNJ3GFDH/fr1y/o144bN87BJO6w8T1XVhTpdgCExvjx44N6nc/n81xBtUuXLkpOTtbQoUNVrFgx+Xw+tyM5zhiT6Xb+8MMPio2NdSGRM44fPy5jjIwxOnHiRMCb3LS0NH344YcZCo5e8VffdZ06dUo5cuQIcxrn2bgf9+jRQ8uXL1enTp2s2OZXXnlFPXv2VMGCBVW0aNGA7fX5fJ4rPj399NOaMWOGxowZo3vuucffXq1aNU2YMMGTBdXGjRtr+fLl1ry5t21MS1KvXr307LPP6tVXX1VkpPc/Rtx5552699571alTJx08eFDXX3+9qlatqlmzZungwYOe7OPHHntMQ4YM0RtvvKH8+fO7HcdxGzdu1Ouvv65+/fqpd+/e6tChg7p376569eq5Hc0xtWrV+tv3HNmzZ1eHDh308ssvX7IFZi99Hvpf2NDHmzZtCup1Xn1/bdt7rqyKM1RxycubN6+++OIL1apVy+0ojktMTJTP59OWLVtUtWrVgA8zaWlp2rdvn2688UbNnTvXxZShExER8bcHQZ/Pp+HDh+uxxx4LYypnnT/bvG/fvnrqqaeUJ08e/3NpaWlasWKFvvvuu6DfRFwqbNqPz8uXL58WLVqk+vXrux0lLEqVKqUHHnhAAwcOdDtKWJQrV04vv/yymjRporx582rLli0qU6aMtm/frquuukq//fab2xFDbsqUKRo+fLg6duyo2rVrKzo6OuD51q1bu5TMGbaNaUm6+eabtWTJEuXJk0fVq1fP0MdeO9MrLi5Oq1evVsWKFTVp0iS9/fbbWrlypRYvXqz777/fk1cSJCYmavfu3Tpz5oxKlSqVoY83btzoUjJnnT17VgsWLND06dP18ccfq0KFCurWrZs6deqkQoUKuR0vpObPn6+BAwdqwIAB/sLx2rVrNXbsWD3xxBM6e/asBg0apA4dOuj55593OS3+F/Sx99n2niur8v5Xy/C8EiVK/OXZfF7Ttm1bSdLmzZvVrFmzgGJbjhw5lJCQoFtuucWldKG3bNkyGWPUuHFj/ec//wk4UyJHjhwqVaqU4uPjXUwYeufPNjfGaMqUKcqWLZv/ufN9PGXKFLfiOcam/fi8uLg4K87+Oe+3335T+/bt3Y4RNgcOHFC5cuUytKenp+vMmTMuJHLeAw88ICnzS+t8Pp/nlu6wbUxL574I8tL7jH9y5swZ/2Wxn332mf8DaqVKlfTTTz+5Gc0x599r2iYyMlLt2rVTy5Yt9X//938aPHiwHnnkEQ0ZMkS33Xabnn32WRUrVsztmCExcuRITZw4Uc2aNfO3Va9eXcWLF9fQoUO1du1aRUdHq3///hTbLlH0sffZ9p4rq+IMVY/64YcftGDBAiUnJ+v06dMBz3ltDZHFixdr7Nixevnll6055X3GjBnq0KHDJXuJxsX6/vvvVbJkSc9espGZRo0a6b333lNcXJzbUcLCxv34zTff1Pz58zVjxgzlzp3b7TiO6969u+rWrav777/f7ShhUbt2bfXt21d33XVXwBmqI0aM0KeffqovvvjC7Yj4l2wb0za64oor1KhRI7Vs2VI33HCDVq9erZo1a2r16tW69dZbuSGIh6xfv16vvfaa5syZo+joaHXu3Fndu3fXDz/8oOHDh+v48eNau3at2zFDIleuXNq0aZMqVaoU0L59+3YlJibq999/13fffacqVaro5MmTLqX8d85f1RcML551bUMfX2j9+vWaO3dupvUPr109gayDM1Q9aMmSJWrdurX/0sJq1arpu+++kzFGl19+udvxQq5Dhw46efKkypYtq9y5cyt79uwBzx85csSlZM7p3Lmz2xHCatu2bdq/f7+uueYaSdKLL76oV155RVWqVNGLL77oyaLjsmXL3I4QVjbux2PHjtWePXtUpEgRJSQkZNhmr73BL1eunIYOHarVq1erevXqGbbXa+t7Dxs2TJ07d9aBAweUnp6u9957Tzt27NDMmTP1wQcfuB0PIWDbmD7v7Nmz+vzzz7Vnzx7deeedyps3r3788UfFxMQEXDnjBc8++6xuvvlmPffcc+rcubP/Rk0LFizw9BqbR48e1bvvvqs9e/ZowIAByp8/vzZu3KgiRYrosssuczteSI0bN06vv/66duzYoRYtWmjmzJlq0aKFIiIiJEmlS5fW9OnTPfVlb6VKlTR69GhNnTrVvy7/mTNnNHr0aH8B7sCBAypSpIibMf8VW8+0Ps+GPv6zOXPm6O6771azZs20ePFi3XDDDdq5c6cOHTqkm2++2e14jvvjjz+sOdEqq+EMVQ+qV6+emjdvruHDh/vPiilcuLA6duyoG2+8UT179nQ7YkjNmDHjb5/3SvExLi4u6G9avVZ8ql69up599lm1aNFCX3/9terUqaP+/ftr2bJlqlSpkl5//XW3I4ZcWlqapk+f/pd3gF+6dKlLyZxhy378Z8OHD//b55944okwJQmP0qVL/+VzPp/Pk2sRfvHFFxoxYoS2bNmilJQUXX755Ro2bJhuuOEGt6M5JjU1VcuXL8/0DBGvFRhtHNPff/+9brzxRiUnJ+vUqVPauXOnypQpo4ceekinTp3y5JI0aWlpOn78eMCXt999951y587tyRtjJiUlqWnTpoqNjdV3332nHTt2qEyZMnr88ceVnJysmTNnuh0xpMqXL69u3bqpS5cuf3lJ/+nTp/XWW2955r3IqlWr1Lp1a0VERKhGjRqSpK+//lppaWn64IMPdOWVV+qNN97QwYMHNWDAAJfT4n9hWx/XqFFD9913n3r16uWvf5QuXVr33XefihUr9o/vuS9FaWlpeuaZZzRlyhQdOnTIfzweOnSoEhISPHnz06yIgqoH5c2bV5s3b1bZsmUVFxenL7/8UlWrVtWWLVvUpk0bfffdd25HxP/gnwpOf+aVN3zn5cmTR998840SEhL05JNP6ptvvtG7776rjRs3qkWLFjp48KDbEUOud+/emj59ulq2bJnpHeDPr7UKXAqMMUpOTlbhwoWVK1cut+OExQ8//KDixYtn+tzq1at15ZVXhjmR8zZt2qQWLVro5MmTSk1NVf78+fXrr7/6C09eLDDapm3btsqbN6+mTZumAgUK+Jey+Pzzz3XPPfdo165dbkfEv9S0aVNdfvnlGjNmTMByJatWrdKdd97J5wiPOHHihGbNmqWdO3dKkipWrOg/4xzeYFMfR0dH69tvv1VCQoIKFCigzz//XNWrV9e2bdvUuHFjT655PWLECM2YMUMjRozQPffco2+++UZlypTR22+/rQkTJuirr75yO6IVuOTfg6Kjo/1nhRQrVkx79uxR1apVJUm//vqrm9Eck5aWpnnz5mnbtm2SpKpVq6p169YBN/S51HmtSHoxcuTI4V/f57PPPtPdd98tScqfP7+OHz/uZjTHzJkzR3PnzlWLFi3cjuKYi+m7mJgYB5O4a8OGDQFzV2JiosuJQs8Yo/Lly+vbb79V+fLl3Y4TFjfccIO+/PLLDDceW7lypVq2bKmjR4+6E8xBffv2VatWrTRlyhTFxsZq9erVyp49u+666y499NBDbsdz1PnzE7y+1vcXX3yhVatW+S8hPS8hIUEHDhxwKVVo2b724rp16/Tyyy9naL/ssss88wV2UlJS0K89f3af1+TNm9ea9Z/T0tI0fvz4v1xf02tX9p1nUx/HxcXpxIkTks7NVd98842qV6+uo0ePemaN2AvNnDlTU6dOVZMmTQL6uWbNmtq+fbuLyexCQdWDrrzySn355ZeqXLmyWrRoof79++vrr7/We++958kzYnbv3q0WLVrowIEDqlixoiRp1KhRKlGihBYtWqSyZcu6nNAZe/bs0euvv649e/Zo4sSJKly4sD766COVLFnSX0D3imuuuUb9+vVT/fr1tXbtWr399tuSpJ07d/7lGWCXuhw5cmR6h3AvyZcv3z9+aDXGePZOlT///LNuv/12ff7558qXL5+kc+vWNWrUSHPmzFGhQoXcDRhCERERKl++vA4fPmxNQfXKK6/UDTfcoGXLlvnPBlmxYoVatWqlJ5980t1wDtm8ebNefvllRUREKFu2bDp16pTKlCmjMWPGqHPnzmrXrp3bEUNu5syZeu655/xnZlaoUEEDBgxQp06dXE7mjPT09Ezn4x9++MEzZz3ZvvZiVFRUpl947ty50zPHpVq1asnn8wX1RYgX339I0q5du7Rs2bJMl5UaNmyYS6mcMXz4cL366qvq37+/Hn/8cT322GP67rvvNG/ePM9t65/Z1McNGjTQp59+qurVq6t9+/Z66KGHtHTpUn366adq0qSJ2/EcceDAgUw/K6anp+vMmTMuJLITl/x70N69e5WSkqIaNWooNTVV/fv316pVq1S+fHmNGzdOpUqVcjtiSLVo0ULGGM2aNct/JtDhw4d11113KSIiQosWLXI5YegtX75czZs3V/369bVixQpt27ZNZcqU0ejRo7V+/Xq9++67bkcMqeTkZD3wwAPav3+/+vTp418Tpm/fvkpLS9OkSZNcThh6Y8eO1d69ezV58mTPnvG0fPnyoF973XXXOZjEHR06dNDevXs1c+ZMVa5cWZK0detWde7cWeXKldNbb73lcsLQWrhwocaMGaOXXnpJ1apVczuO49LT03XrrbfqyJEj+uSTT/zrmT399NOePVuzUKFC/vcbFSpU0AsvvKBmzZpp+/btql27tlJTU92OGFLjxo3T0KFD1bt3b9WvX1+S9OWXX+rFF1/U008/rb59+7qcMPQ6dOig2NhYTZ06VXnz5lVSUpIKFSqkNm3aqGTJkp5c09w2PXr00OHDhzV37lzlz59fSUlJypYtm9q2basGDRpowoQJbkf8177//nv/75s2bdIjjzyiAQMG6KqrrpIkffXVVxo7dqzGjBnjyQL7K6+8op49e6pgwYIqWrRowPtMn8/nuTOvy5Ytq0mTJqlly5YBS+NNmjRJq1ev1uzZs92OGHK29fGRI0f0xx9/KD4+Xunp6RozZoz//cjjjz/uyRsY165dW3379tVdd90VsDzLiBEj9Omnn+qLL75wO6IdDDzl7NmzZvny5ea3335zO0rY5M6d2yQlJWVo37x5s4mOjnYhkfOuvPJKM3bsWGOMMXny5DF79uwxxhizZs0ac9lll7kZDSHStm1bExsba0qXLm1uuukmc/PNNwf84NIXExNj1q5dm6F9zZo1JjY2NvyBHJYvXz6TI0cOExERYXLmzGni4uICfrzo1KlTpmnTpubqq682efLkMS+88ILbkRx1/fXXm1mzZhljjOnRo4epV6+eefPNN02zZs1MvXr1XE4XegkJCWbGjBkZ2qdPn24SEhJcSOS8/fv3mypVqpjKlSubyMhIc+WVV5oCBQqYihUrmkOHDrkdDyFw9OhR07RpU5MvXz6TLVs2U6JECZM9e3bToEEDk5KS4na8kKtbt65ZtGhRhvZFixaZyy+/3IVEzitZsqQZPXq02zHCJnfu3Ob77783xhhTtGhRs2HDBmOMMXv27DExMTFuRnOMbX1so3nz5pnY2FgzevRokzt3bvPcc8+ZHj16mBw5cpjFixe7Hc8aXPLvMdmyZdMNN9ygbdu2+S8h9bqoqCj/mil/lpKSkmGNL6/4+uuvM/02tXDhwp5dJ/e8P/74I8PaR15cXzNfvny6+eab3Y4RVkePHtW0adMC1hPt1q2bYmNjXU7mjPT0dGXPnj1De/bs2TNcmuUFXjir6Z9kti7fk08+qTvuuEN33XWXGjRo4H+NF9fle+aZZ/zH45EjR+ruu+9Wz549Vb58eb322msupwu9n376SVdffXWG9quvvtqTN8CQpOLFi2vLli2aM2eOkpKSlJKSou7du6tjx46eueFcXFxc0FeGeHHtxdjYWH366af68ssv/X18+eWXq2nTpm5Hc8TXX3+t0qVLZ2gvXbq0tm7d6kIi5/32229q37692zHCpnjx4vrpp59UsmRJlS1bVosXL9bll1+udevWKSoqyu14jrChj48fP+7/DPhP92Xw4mfFNm3aaOHChRoxYoSio6M1bNgwXX755Vq4cKGuv/56t+NZg0v+PahOnTp69tlnPbteyIXuvvtubdy4UdOmTVO9evUkSWvWrNE999yj2rVra/r06e4GdEDx4sU1d+5cXX311QGn+L///vt65JFHtGfPHrcjhlRqaqoGDhyouXPn6vDhwxme9+r6VjZZv369mjVrply5cvn343Xr1un333/3v/H1mjZt2ujo0aN66623FB8fL+ncekgdO3ZUXFyc3n//fZcT4mJFREQErMsnKcM6fcbD6wLbplq1arrzzjs1ZMiQgPann35ab7/9tr7++muXkuHfmDFjRtCvtfmGoV5x+eWXq1q1anr11Vf9J2KcPn1aPXr00DfffOO5S6MlqXv37qpbt641NywaNGiQYmJiNGTIEL399tu66667lJCQoOTkZPXt21ejR492O2LI2dDH2bJl008//aTChQv7339dyMvvuX744Ye/vJfI6tWrPXnvnKyIgqoHffzxxxo8eLCeeuop1a5dW9HR0QHPe+0bmqNHj6pz585auHCh/2yvs2fPqnXr1nr99dc9eabuI488ojVr1uidd95RhQoVtHHjRh06dEh333237r77bj3xxBNuRwypXr16admyZXrqqafUqVMnvfjiizpw4IBefvlljR49Wh07dnQ7Iv6la6+9VuXKldMrr7yiyMhzF0+cPXtWPXr00N69e7VixQqXE4be/v371bp1a3377bcqUaKEv61atWpasGCBJ2+4lpaWpnnz5gWchdy6dWtly5bN5WSh8ed1+f6J19Yz/7Off/5ZO3bskCRVqlTJMzeyudB//vMfdejQQU2bNvWvobpy5UotWbJEc+fO9eRVBgsWLMi03efzKWfOnCpXrlymZ/vh0rJkyRItWbIk05vZeO1s87Vr16pVq1YyxvivHEhKSpLP59PChQv9X/J6yahRozRu3Di1bNlS1atXz3C1TJ8+fVxKFh5fffWVvvrqK5UvX16tWrVyO44jbOjj5cuXq379+oqMjPzH+zJ48V4MVapU0Zdffum/h8x5K1euVMuWLXX06FF3glmGgqoHRURE+H//8zc1Xv6GRpJ2797t/5BeuXJlT98h/fTp0+rVq5emT5+utLQ0RUZGKi0tTXfeeaemT5/umeLEeSVLltTMmTPVsGFDxcTEaOPGjSpXrpzeeOMNvfXWW/rwww/djuiId999V3PnzlVycnKGZQ68dsZErly5tGnTJlWqVCmgfevWrapTp45OnjzpUjJnGWP02Wefafv27ZLOzV1evaxy9+7datGihQ4cOKCKFStKknbs2KESJUpo0aJFKlu2rMsJ8W+dOHFCDzzwgObMmeN/r5EtWzZ16NBBL774oieX79iwYYPGjx8f8P6jf//+SkxMdDmZMzI7C1sKPPv6mmuu0bx58y7Zm4DYfhnp8OHDNWLECNWpU0fFihXLcNaXF6+eSE1N1axZswKOxXfeeWeGk1K84u++9PD5fNq7d28Y08AJ9LH3devWTUlJSVq2bJny5s0rSVqxYoVatWqlJ5980pM3xsyKKKh6kG3f0IwYMUKPPPKIcufOHdD++++/67nnntOwYcNcSua85ORkffPNN0pJSVFiYqLKly/vdiRH5MmTR1u3blXJkiVVvHhxvffee6pXr5727dun6tWrKyUlxe2IITdp0iQ99thj6tKli6ZOnaquXbtqz549WrdunXr16qWRI0e6HTGkihQpojfeeEM33HBDQPsnn3yiu+++W4cOHXIpmXNmzpypDh06ZFi/6/Tp05ozZ47uvvtul5I5o0WLFjLGaNasWf5v0w8fPqy77rpLERERWrRokcsJQ2vUqFEqUqSIunXrFtD+2muv6ZdfftHAgQNdSuacDh06aNOmTXrhhRcC7pb90EMPqVatWpozZ47LCfFvLVmyRI899phGjhzpP3Nv7dq1Gjp0qB5//HHFxsbqvvvu0xVXXKFp06a5nPZ/Y/tlpMWKFdOYMWPUqVMnt6MAIbNr1y4tW7Ys07OuvfxZ0SZHjx7V2rVrM+1jr72nls7di+HWW2/VkSNH9Mknn2jVqlVq3bq1nn76aT300ENux7MGBVUPSk5OVokSJTK8ATTGaP/+/SpZsqRLyZzx5ze+f3b48GEVLlzYk292v/zyS11zzTVuxwibGjVq6IUXXtB1112npk2bqlatWnr++ec1adIkjRkzRj/88IPbEUOuUqVKeuKJJ3THHXcErJM7bNgwHTlyRJMnT3Y7Ykj16dNH77//vp5//nn/TV5WrlypAQMG6JZbbvHkDY1sm7uio6O1evVqVa9ePaB9y5Ytql+/vue+GElISNDs2bMz3LRozZo1uv3227Vv3z6XkjknOjpan3zySYbj0xdffKEbb7xRqampLiVzxsaNG5U9e3b/mJ4/f75ef/11ValSRU8++aQnb4xZrVo1TZ06NcO4Xrlype699159++23+uyzz9StWzclJye7lPLfsf0y0gIFCmjt2rVWXTXwxhtv6OWXX9bevXv11VdfqVSpUho/frzKlCmjNm3auB0P/9Irr7yinj17qmDBgipatGjAZ2Sfz+e5q75stHDhQnXs2FEpKSmKiYnJ0MdevIGgdO4kjJYtW+rkyZNKSkrSqFGj1Lt3b7djWSXS7QAIvdKlS2f6If3IkSMqXbq05z6knz9L4EJbtmzJsKaIVzRu3FiXXXaZ/87RVapUcTuSo7p27aotW7bouuuu06BBg9SqVStNnjxZZ86c0bhx49yO54jk5GT/B9ZcuXL575zdqVMnXXnllZ4rqD7//PPy+Xy6++67dfbsWUnn7nbfs2dPT94sQPrrueuHH37w5KXRUVFR/nH8ZykpKZ4sPB08eFDFihXL0F6oUCHP3gG+QIECmY7d2NjYS/by779z3333adCgQapevbr27t2rDh06qF27dnrnnXd08uRJT34RtGfPnkwvc4+JifFfQlq+fHn9+uuv4Y4WMn8uknqxYPpPevToodmzZ2vo0KFuRwmLl156ScOGDdPDDz+sp59+2v85KS4uThMmTPBMQbVfv3566qmnFB0drX79+v3ta7323vrpp5/WyJEjPXllyJ/Z3Mf9+/dXt27d9Mwzz2S4atVLkpKSMrQ9+eST/ppAgwYN/K85vyY0nEVB1YP+6kN6SkqKcubM6UIiZ8TFxcnn88nn86lChQoB25yWlqaUlBTP3tnwxx9/1Jw5c/TWW29p9OjRqlGjhjp27Kg77rjDkzey+fMaME2bNtX27du1YcMGlStXzrMHi6JFi+rIkSMqVaqUSpYsqdWrV6tmzZrat29fhrXrvCBHjhyaOHGiRo0apT179kiSypYt68k3RYmJif65q0mTJv6bcEnn5q59+/bpxhtvdDGhM2666Sbde++9mjZtmv9S4TVr1uj+++9X69atXU4XeiVKlNDKlSszrGO2cuVKxcfHu5TKWY8//rj69eunN954Q0WLFpV0rrA8YMAATxZndu7cqVq1akmS3nnnHV133XWaPXu2Vq5cqdtvv92TBdXatWtrwIABmjlzpv9mY7/88oseffRR1a1bV9K5S2vP32jvUpXZh9bMePE9yB9//KGpU6fqs88+U40aNTLczMZrhZgXXnhBr7zyitq2bRvwBW6dOnX0yCOPuJgstDZt2qQzZ874f7fJb7/9pvbt27sdw3HB9nFmdYJL3YEDB9SnTx9Pfm74s1q1amVYx/z845dffllTp0719JI0WREFVQ85/02Uz+fT0KFDAyaUtLQ0rVmzxv/G3wsmTJggY4y6deum4cOHB5wVkyNHDiUkJPjXcPOaggULqnfv3urdu7f27dun2bNna8aMGRo8eLAaNGigpUuXuh3RUaVKlfL0HbKlc2chL1iwQImJieratav69u2rd999V+vXr1e7du3cjhdyx44dU1pamvLnzx9wSfiRI0cUGRnpqRt/tG3bVpK0efNmNWvWTHny5PE/d37uuuWWW1xK55xJkyapc+fOuuqqq/wf0M+ePavWrVtr4sSJLqcLvXvuuUcPP/ywzpw5o8aNG0s6t/7ko48+qv79+7ucLnTOf0Fw3q5du1SyZEn/8kLJycmKiorSL7/8ovvuu8+tmI4wxvjXafvss8900003STpXTL+Uz9D8O9OmTVObNm1UvHhxf9F0//79KlOmjObPny/p3Bf4jz/+uJsx/7ULP7SeH+MXfoj14gfWpKQk/+eFb775JuA5LxZi9u3bl+lN5KKiojy1TMmyZcsy/f1CmV1Jcqlr3769Fi9e7NkTbc4Lto+9qFmzZlq/fr3KlCnjdhRHeXG5qEsdBVUPOf9NlDFGX3/9dcAllDly5FDNmjU99U1r586dJZ1b4uDqq6/O8A26LUqXLq1BgwapZs2aGjp06D+u93UpadGihd566y1/sXz06NG6//77lS9fPknn1pq89tprtXXrVhdTOmPq1Kn+D+q9evVSgQIF/IuNe60oIUm33367WrVqpQceeCCgfe7cuVqwYIE+/PBDl5KF3hNPPCHp3BqbHTp08NSVA38nX758mj9/vnbt2qVt27bJ5/OpcuXKKleunNvRHDFgwAAdPnxYDzzwgE6fPi1JypkzpwYOHKjBgwe7nC50zn9BYKM6dero6aefVtOmTbV8+XK99NJLks594ClSpIjL6ZxRsWJFbd26VYsXL9bOnTv9bddff70iIiIkeWNM/PlDqzFG1apV04cffuj5L3Ml+woxpUuX1ubNmzP07ccff6zKlSu7lMoZ48eP/9s7f584cUI33nijVq5cGcZUzpg0aZL/93Llymno0KH+ddwv/MzYp0+fcMdDiLVs2VIDBgzQ1q1bM+1jr1wJdX6eOnPmjO677z4NHTo0w5VQCC9uSuVBXbt21cSJEz11RtffOX78eKbtPp9PUVFRnlyb77yVK1dq1qxZevfdd/XHH3+oTZs26tixo2cuF77wpj0xMTHavHmz/9vHQ4cOKT4+3nNniJw9e1bPPPOMunXr5sklHDKTP39+rVy5MsOHl+3bt6t+/fo6fPiwS8nghAvP+vKylJQUbdu2Tbly5VL58uUVFRXldiSESFJSkjp27Kjk5GT169fP/2XJgw8+qMOHD2v27NkuJ0So/PnmkPCWV199VU8++aTGjh2r7t2769VXX9WePXs0atQovfrqq7r99tvdjhgyuXLl0ssvv5zpHc9TUlLUrFkzHT58WNu3b3chXWgFW2Ty+Xz+9Z+9JDU1VaNHj9aSJUsyveu917b5/Bd6mfHq1QSxsbHavHkzBVWXcYaqB73++utuRwirfPny/e2H8uLFi6tLly564okn/nayvZQMHjxYc+bM0Y8//qjrr79eEydOVJs2bTy3bsyF3/fY8v1PZGSkxowZk+kbXq86deqU/2ZUf3bmzBn9/vvvLiRyxvm1n4PhxTuSTps2TePHj9euXbsknbt5zcMPP6wePXq4nMw5efLk8d+cypZi6unTpzP9AHd+GQCvqFGjhr7++usM7c8995yyZcvmQqLwWLJkyV9+SH/ttddcSoVQ+eOPP/TCCy9o2bJlmfax1+6I3qNHD+XKlUuPP/64Tp48qTvvvFPx8fGaOHGip4qpkvTGG2+oU6dOypcvX8AZe6mpqbrxxhv1yy+/eOZKN9svje7Ro4eWL1+uTp06qVixYp7/AvvCecoGbdu21bx58/72rHM4j4KqB9n2jdT06dP12GOPqUuXLv4bnaxdu1YzZszQ448/rl9++UXPP/+8oqKiNGTIEJfThsaKFSs0YMAA3XbbbSpYsKDbceCAJk2aaPny5UpISHA7SljUq1dPU6dO1QsvvBDQPmXKFNWuXdulVKHnxZvUBGvYsGEaN26cHnzwQf/61l999ZX69u2r5ORkjRgxwuWEoZWenq6nn35aY8eOVUpKiqRzZ7n1799fjz32mGe+4PuznTt3qnv37lq1alVAu203SPDyMh7Dhw/XiBEjVKdOHSs+pNuoe/fuWrx4sW699VbVq1fPij7u2LGjOnbsqJMnTyolJcV/ZZTX3HrrrTp69KjuuOMOLVq0SA0bNvQXUw8dOqTly5f7vwD0srS0NH399dcqVaqU4uLi3I7jiI8++kiLFi1S/fr13Y4Ch5QvX14jRozQypUrVbt2bUVHRwc8z1IW4cEl/x50xx13/O03Ug899JBLyZzRpEkT3XfffbrtttsC2ufOnauXX35ZS5Ys0RtvvKGRI0d64hIWm2TLlk0HDx7030k4b968SkpK8l/a4NVL/qVzhcThw4erY8eOmR4kvbIW0HkrV65U06ZNVbduXTVp0kTSubOg1q1bp8WLF+vaa691OSH+rUKFCmnSpEm64447AtrfeustPfjgg567ic/gwYM1bdo0DR8+3P+B5ssvv9STTz6pe+65RyNHjnQ5YejVr19fkZGRGjRoUKbvP2rWrOlSstCx/SzzYsWKacyYMerUqZPbUcLqwvcfXhYbG6sPP/yQQoyHjRkzRiNHjtT8+fM1bNgwHThwQMuXL/fsMlMPP/ywqlevru7duystLU0NGjTQV199pdy5c+uDDz5Qw4YN3Y4YcqVLl9aHH37ouXWA/45tV0/83fHIq0tZZEUUVD0oX758Vn0jlStXLiUlJal8+fIB7bt27VLNmjV18uRJ7du3T1WrVtXJkyddShl6b7zxhqZMmaJ9+/bpq6++UqlSpTRhwgSVLl1abdq0cTteSERERKh58+b+y2QXLlyoxo0b+4uLp06d0scff+zJgqqNawFt3rxZzz33nDZv3qxcuXKpRo0aGjx4cIZ9+1J2/Phx//rWf7X+83leWwc7X758WrduXYb+3Llzp+rVq6ejR4+6E8wh8fHxmjJlSoYvP+bPn68HHnhABw4ccCmZc6Kjo7VhwwZVqlTJ7SiOmTFjRtCvPX/zTC8pUKCA1q5dq7Jly7odxVGJiYkBhfOkpCRVqlQpw7r8Xrv8XZKqVKmiOXPmqEaNGm5HCYtDhw7pkUce8RdiLvxo7MX3W5I0aNAgPffcc0pISNDnn3+uEiVKuB3JMcWLF9e8efNUp04dzZs3T7169dKyZcv0xhtvaOnSpZ64CdeF3nzzTc2fP18zZszw3JJwmfmnqyfef/99l5LB67jk34Pi4uKUP39+t2OETYkSJTRt2jSNHj06oH3atGn+NweHDx/21CUdL730koYNG6aHH35YI0eO9L/Zy5cvnyZMmOCZguqFH0bvuuuuDK/x6jqjNq4FVKtWLc2aNcvtGI6Ki4vz32jtr9Z/9url0Z06ddJLL72kcePGBbRPnTpVHTt2dCmVc44cOZJpYbFSpUqePHNROleI8dqZxhfyYpH0YvTo0UOzZ8/W0KFD3Y7iqLZt2wY89sr7qmCMHTtWAwcO1JQpU/x3lPayLl26KDk5WUOHDvX8Mhbt2rULeJw9e3YVLFgww9WL7733XjhjOe7XX39V0aJFJUkffvih2rdvrwoVKqhbt26aOHGiy+lC58Ivgnbv3q0iRYooISEhw13vvfZl0JQpUzR9+nTrrp6A+yioetBTTz2lYcOGWfON1PPPP6/27dvro48+Ut26dSVJ69ev1/bt2/Xuu+9KktatW6cOHTq4GTOkXnjhBb3yyitq27ZtQCG5Tp06euSRR1xMFlq23WDNdunp6dq9e3eml+o0aNDApVShtXTpUv8XXsuWLXM5jfP69evn/93n8+nVV1/V4sWLdeWVV0qS1qxZo+TkZE9+MVKzZk1NnjxZkyZNCmifPHmyJy59z8yzzz6rR/+/vfuMiurs3gZ+zaBIERQLCCoIgg1BJcTHTrGgEI09RqNGMPbeEyvY0FjQQKKxmxgNsSQhdgVEAaNYAAuiiIIRbIhKE4F5P/hnXkfQmIThds5cv7WetZxz5sOVR8WZffa994wZWLJkCRwcHEp8gZNC17U2d5kDLxcWff/99zh27BgcHR1L/B6//sBEU82fP190BGGcnZ2Rl5cHGxsbGBgYlPg9ltoDoVOnTuHkyZNo3ry56ChqV6VKFZXXr4/gkSozMzNcuXIF5ubmOHToEL777jsAQE5OjqQWCL7+IEib5Ofno02bNqJjlLs7d+7g999/R0pKCvLz81XuSeXf4/cdj/xLUIsWLZCUlASFQqEVT6SAl5sc169fj8TERABAw4YNMXLkSMku9NHX10dCQgKsrKxgZGSE2NhY2NjY4Pr163B0dJTUVnRtk5ubi+PHj+Ojjz4C8HIO4/Pnz5X3dXR0sHDhQsktPTl9+jQGDhyI27dvlzhuJ8VuTW3h5ub2Tu+TyWQIDQ1Vc5rydeLECXh5ecHS0lJlCVdqaioOHDggybnAxaNKXu/wklLXtY6OjrLLXC6Xa1WXOfD2v9NS/HusjTp16oSUlBT4+PjAzMysxJ9xqXVpN2nSBDt27ECLFi1ERyE1WbBgAQICAmBubo6cnBwkJiaiUqVK2Lx5MzZs2IDo6GjREek/mjlzJipXriz50xOvOn78OHr06AEbGxskJCSgadOmuHXrFhQKBZycnPjvcTlhh6oEaePTKWtr6xJH/qXM2toaFy9eLHEU69ChQ1o1fFyKtm3bhv379ysLqoGBgbC3t4e+vj4AICEhARYWFpg8ebLImGVu1KhRcHZ2xv79+yV/5O5VeXl5iIuLK7UrVwqLx7ShC/dNXFxckJiYiKCgIOVCxN69e2PMmDGwsLAQnE49tOH3+9Uu89DQUK35WVVMG36PtV1UVBSio6Ml20n/uoCAAMyaNQvr16+XbCOGtluwYAEcHByQkpKCfv36KXcz6OjoYNasWYLTqUdqaipkMply0diZM2fw008/oUmTJhgxYoTgdGVPW05PvOrLL7/EtGnT4OvrCyMjI+zZswempqYYNGgQunbtKjqe1mCHKklCZmYmzpw5U2pRQopHSTdu3IgFCxZg5cqV8PHxwcaNG5GUlISlS5di48aNGDBggOiI9C+1b98eM2bMQPfu3QFApQMZeDlkPigoSHJP0w0NDREbGwtbW1vRUcrNoUOHMGTIkFJnTkq1u+1VT58+RWhoKBo1aiTpJUavu3PnDvz8/PD999+LjkL/UnJyslZseyft5OTkhG+//VY5mkXqTExMkJOTg4KCAq0YcaBtXrx4ga5du2LdunWSWnL6d9q3b48RI0Zg8ODBSE9PR4MGDdC0aVNcv34d48ePx7x580RHLFPaeHrCyMgIFy9eRP369WFiYoJTp07B3t4esbGx+Pjjj3Hr1i3REbUCO1QlKjMzE7t370ZSUhKmT5+OatWq4fz58zAzM0Pt2rVFxytTISEhGDRoELKysmBsbKzSLSKTySRZUB0+fDj09fUxZ84c5OTkYODAgbCwsMCaNWtYTNVwN27cgIODg/K1np6e8hgtALRs2RJjx44VEU2t/ve//+HGjRtaVVAdP348+vXrh3nz5sHMzEx0HLXr378/OnTogHHjxiE3NxfOzs7Ko0m7du1Cnz59REcsF48ePcKmTZskWVCNiIh4632pzEKuX78+rKys4ObmBnd3d7i6uiq7gKSod+/e2Lp1K4yNjUsstXmd1JbZaCN/f39MnToVixcvluws5FcFBASIjkBqVLFiRcTFxYmOUe4uXbqEli1bAgCCg4Ph4OCAyMhIHDlyBKNGjZJcQVUbT08YGhoq56aam5sjKSkJ9vb2ACD5BaHvExZUJSguLg6dOnVClSpVcOvWLXzxxReoVq0a9u7di5SUFGzfvl10xDI1depUeHt7Y8mSJVqxhKvYoEGDMGjQIOTk5CArKwumpqaiI1EZyMzMVJmZ+uDBA5X7RUVFKvelYvz48Zg6dSrS09NL/QLn6OgoKJn63Lt3D1OmTNGKYirwstg2e/ZsAMC+ffugUCiQmZmJbdu2YdGiRVpTUJUyV1fXEtdefcgpla7r0NBQhIeHIzw8HDt37kR+fj5sbGzg7u4ONzc3uLm5ServdZUqVZS/j68vtSHpKT4q2rFjR5XrUp0NLLWZsFTSZ599hk2bNmnVeLgXL14oRxscO3ZMOUaqUaNGSEtLExlNrW7cuIGkpCR06NAB+vr6yp9bUtSqVSucOnUKjRs3hqenJ6ZOnYr4+Hjs3btXa04YvA9YUJWgKVOm4PPPP8fy5cthZGSkvO7p6YmBAwcKTKYef/31FyZMmKBVxdTc3FwoFAoYGBjAwMAADx48QEBAAJo0aYIuXbqIjkf/QZ06dXDp0iU0bNiw1PtxcXGS7IQqLqZ5e3srr8lkMsl+gQOAvn37Ijw8HPXr1xcdpVw8efJEOXvy0KFD6NOnDwwMDODl5YXp06cLTkdl4fHjxyqvX7x4gQsXLmDu3LlYvHixoFRlz9XVVVk8zsvLQ1RUlLLAum3bNrx48QKNGjXC5cuXxQYtI1u2bAHwsqDm6+uLmjVrKud6a4Pjx4/j+PHjpY6V2rx5s6BU6qONnV5FRUW4ceNGqb/HUumsL/bixQuMHDkSc+fO1ZrRJQUFBdi8eTOOHTuGDz74AIaGhir3pThf097eHuvWrYOXlxeOHj2KhQsXAgDu3r2L6tWrC05X9h49eoT+/fsjLCwMMpkM169fh42NDXx8fGBiYoKVK1eKjljmVq1ahaysLACAr68vsrKy8PPPP8POzk6Sf6bfVyyoStDZs2exfv36Etdr166N9PR0AYnUy8PDAzExMcoZk9rg448/Ru/evTFq1ChkZmaiZcuW0NXVxcOHD7Fq1SqMHj1adET6lzw9PTFv3jx4eXlBT09P5V5ubi58fX3h5eUlKJ36JCcni45Q7gIDA9GvXz+cPHmy1K7cCRMmCEqmHnXr1kV0dDSqVauGQ4cOYdeuXQBeFuFe/7NOmqm07sXOnTtDV1cXU6ZMwblz5wSkUi89PT24u7ujXbt2cHNzw8GDB7F+/XrlIjIpUSgUsLW1xeXLl7VmFqGvry/8/Pzg7OysNQsTXVxcREcoV6dPn8bAgQNx+/ZtvL5aRIoPdCtWrIg9e/Zo1Tb0S5cuwcnJCQCQmJiock+qf6eXLVuGXr164euvv8bQoUOVS+Z+//135SgAKZk8eTIqVqyIlJQUlQXNn3zyCaZMmSK5gurTp0+RlJSE/Px8mJubo2bNmli3bp3oWFqJBVUJqlSpEp4+fVriemJiImrWrCkgkXoVdzdduXKl1KKEFDZlv+78+fNYvXo1AGD37t2oVasWLly4gD179mDevHksqGqwr776CsHBwWjYsCHGjRuHBg0aAACuXbuGwMBAFBQU4KuvvhKcsuxZWVmJjlDudu7ciSNHjkBPTw/h4eEl5j9LraA6adIkDBo0CJUrV4alpaWywy8iIkJlbrCm+7sZk5mZmeUT5D1iZmaGa9euiY5RpvLz83H69GmEhYUhPDwcf/75J+rWrYsOHTogMDBQkkUpuVwOOzs7PHr0SGsKquvWrcPWrVsxePBg0VHKjbbMQi42atQoODs7Y//+/VpTNO/Zsyd+/fVXTJ48WXSUcqFtXdcKhQI2NjZISUlBQUEBTExMlPdGjBghyVOdR44cweHDh0uc4rOzs8Pt27cFpVKPixcvwtPTE/fu3YNCoYCRkRGCg4Ph4eEhOppWkilefxRHGm/48OF49OgRgoODUa1aNcTFxUFHRwc9e/ZEhw4dJDd8/dWFPa+T4pNlADAwMEBCQgIsLS3Rv39/2NvbY/78+UhNTUXDhg2Rk5MjOiL9B8nJyRg9ejSOHj2q7JaQyWTo3Lkzvv32W8l0Y//+++/o1q0bKlasiN9///2t75Xig5FatWphwoQJmDVr1lt/jklJTEwMUlNT0blzZ1SuXBkAsH//flStWhVt27YVnK5sDBs27J3eV3yMWkpeX/yhUCiQlpYGf39/FBQU4NSpU4KSlS13d3f8+eefsLa2houLC9q3bw8XFxeYm5uLjqZ2ISEhWL58Ob777js0bdpUdBy1q169Os6cOaM1o1mA0j9XS3EWcjFDQ0PExsZq1VLMRYsWYeXKlejYsWOpR+Cl9kBX2xQVFUFPT0+rThMYGRnh/PnzsLOzg5GREWJjY2FjY4OYmBh4eHjg0aNHoiOWGQ8PD2RlZWHFihXQ09PDwoULER8fj+vXr4uOppVYUJWgJ0+eoG/fvoiJicGzZ89gYWGB9PR0tG7dGgcOHCjxjyZpHkdHRwwfPhy9evVC06ZNcejQIbRu3Rrnzp2Dl5eXJEc7aKOMjAzcuHEDAGBra6ucPykVcrkc6enpMDU11coHI9WqVcPZs2e16os68P83j9aoUUNwEiprcrlcOfv4Va1atcLmzZvRqFEjQcnKVsWKFWFubo6ePXvC1dUVLi4ukpxJVxoTExPk5OSgoKAAurq6JWapZmRkCEqmHjNnzkTlypW16nj0kydPVF6/Pgv59WVVms7d3R0zZsxQLuPSBm+bnSqTyXDz5s1yTFM+YmJiEBwcjJSUFOVm9GJ79+4VlEp97O3tsWnTJq1ZTuTp6YkPPvgACxcuhJGREeLi4mBlZYUBAwagqKgIu3fvFh2xzNSoUQNHjhxRjrHIzMxEtWrVkJmZCWNjY8HptA8LqhIWGRmJ2NhYZGVlwcnJCZ06dRIdicrI7t27MXDgQBQWFsLd3R1Hjx4FACxduhQRERE4ePCg4IRE9HcmT56MmjVrSnKEw+syMzMxe/Zs/Pzzz8rFRSYmJhgwYAAWLVqEqlWrig1IZeL1Y3VyuRw1a9aU3Izc7OxsnDx5EuHh4QgLC8PFixfRoEEDuLi4KAusUhyxBADbtm17632pbUyfOHEitm/fDkdHRzg6OpYYK6VNiz9OnDghyVnI+/btw5w5czB9+vRSR4c5OjoKSkZlZdeuXRgyZAg8PDxw5MgRdOnSBYmJibh37x569eolyRMj2naa4NKlS+jYsSOcnJwQGhqKHj164PLly8jIyEBkZKSkmhdebUgpVlxE1pZFc+8TFlRJI61duxYjRoyAnp4e1q5d+9b3SvXYSnp6OtLS0tCsWTNld9+ZM2dgbGwsmS4gojt37sDPzw/ff/+96ChlbsKECdi+fTuaNWsm6S/qGRkZaN26Nf766y8MGjRIuSzgypUr+Omnn1C3bl1ERUWpzPgi0iTPnj3DqVOnlPNUY2NjYWdnh0uXLomORv+Rm5vbG+/JZDKEhoaWYxqxEhIS4OzsrNwqLRVvGnGgUCgke0KmWH5+PpKTk1G/fn1UqCDd1SqOjo4YOXIkxo4dqzwObm1tjZEjR8Lc3By+vr6iI5Y5bTtNALzsrg8MDFRpKBs7dqzkxvHI5XKEhoaqnFxs06YNgoODVWbI8mFQ+WBBVUJCQ0Mxbtw4nD59ukS795MnT9CmTRusW7cO7du3F5Sw7FhbWyMmJgbVq1fXymMrr7pz5w4AlBjCTSQFsbGxcHJykuQXGm35oj5p0iQcP34cx44dg5mZmcq99PR0dOnSBR07dlQu2iPNVFRUhK1bt2Lv3r24desWZDIZrK2t0bdvXwwePFjSi16Kiopw9uxZhIWFISwsDKdOnUJeXp4kf269Ki8vr8TRWSkdNywsLERkZCQcHBy06oGPtsxCLvZ3C2ukuDQzJycH48ePV3acJyYmwsbGBuPHj0ft2rUxa9YswQnLlqGhIS5fvox69eqhevXqCA8Ph4ODA65evQp3d3ekpaWJjljmtO00QUpKCurWrVvqZ42UlBRYWloKSKUebxqvBGjPw6D3CQuqEtKjRw+4ubm9cWPj2rVrERYWhn379pVzMiprRUVFyoHyxZ0CRkZGmDp1KmbPnq01C25I+qRcUNUW9erVw/r169+4ffTQoUMYNWoUbt26Vb7BqMwoFAp0794dBw4cQLNmzdCoUSMoFApcvXoV8fHx6NGjB3799VfRMctMUVERYmJilEf+IyMjkZ2djdq1a8PNzU35PykWYrKzszFz5kwEBweXuuRDaj+r9fT0cPXqVa06Rqkts5C12cSJExEZGYmAgAB07doVcXFxsLGxwW+//YYFCxbgwoULoiOWqTp16uDgwYNwcHCAo6MjvvzyS3z66aeIjo5G165dS8wNJs2jo6ODtLQ0lWPwAPDo0SOYmppK6t+mv3sIVEyKn0HeR9Lt7ddCsbGxWLZs2Rvvd+nSBStWrCjHRKQus2fPxqZNm+Dv76/cjH3q1CksWLAAeXl5WLx4seCEREQvpaWlwd7e/o33mzZtykV6Gm7r1q2IiIjA8ePHS3Reh4aGomfPnti+fTuGDBkiKGHZqlq1KrKzs1GrVi24ublh9erVcHV1ldSMtjeZMWMGwsLC8N1332Hw4MEICgrCX3/9hfXr18Pf3190vDLXtGlT3Lx5U6sKqsnJySqvpToL+VU//PAD1q1bh+TkZERHR8PKygoBAQGwtrbGxx9/LDpemfv111/x888/o1WrViodffb29khKShKYTD06dOiAo0ePwsHBAf369cPEiRMRGhqKo0ePSm7J2quSkpKwZcsWJCUlYc2aNTA1NcXBgwdhaWn51s9lmqi4K/N1WVlZkvvZxULp+4UFVQm5d+9eiRl8r6pQoQIePHhQjonKR2FhIbZu3Yrjx4/j/v37KCoqUrkvlWOzr9q2bRs2btyIHj16KK85Ojqidu3aGDNmDAuqRBpCG7bO1qhRA7du3XrjWJLk5GSVOVCkeXbu3Imvvvqq1DEW7u7umDVrFnbs2CGZgurXX38NNzc3NGjQQHSUchcSEoLt27fD1dUVw4YNQ/v27WFrawsrKyvs2LEDgwYNEh2xTC1atAjTpk3DwoUL8cEHH8DQ0FDlvpRGHBTTti/r3333HebNm4dJkyZh8eLFyk62qlWrIiAgQJIF1QcPHpTo5ANedqBLcTxLYGAg8vLyALxsSqlYsSKioqLQp08fzJkzR3A69Thx4gS6deuGtm3bIiIiAosXL4apqSliY2OxadMmyWy9nzJlCoCXR93nzp0LAwMD5b3CwkL8+eefaN68uaB0pA1YUJWQ2rVr49KlS7C1tS31flxcnOSGMgMvj61s3boVXl5eaNq0qSQ/CLwuIyOj1CNXjRo1kuSQcZKu3r17v/V+ZmZm+QQR4O+2zkqFh4cHZs+ejaNHj0JXV1fl3vPnzzF37lx07dpVUDoqC3FxcVi+fPkb73fr1u1vF0hqkpEjR4qOIExGRgZsbGwAvCwmFn/maNeuHUaPHi0ymlp4enoCeDlW69XPl1KcURcdHY1Hjx7ho48+Ul7bvn075s+fj+zsbPTs2RPffPMNKlWqJDBl2fvmm2+wYcMG9OzZU6XL2tnZGdOmTROYTH2cnZ2xf/9+jB8/HgCUf7Y3btyI1q1bi4ymFq8+tJXL5ZKbEVuaWbNmYdGiRZgyZQqMjIyU193d3REYGCgwWdkqHk+hUCgQHx+v8jlTV1cXzZo1k+zfY3o/sKAqIZ6ensovpq+3tufm5mL+/PkqH5KkYteuXQgODlZ+6NUGzZo1Q2BgYIkvqIGBgWjWrJmgVET/XJUqVf72vlS62l63ZMkSrF69Wrl1ds2aNSpbZ6XCz88Pzs7OsLOzw9ixY1Xma3777bd4/vw5fvjhB9Ex6T/IyMgosXDsVWZmZnj8+HE5JiJ1sbGxQXJyMiwtLdGoUSMEBwejZcuWCAkJQdWqVUXHK3NhYWGiI5QbPz8/uLq6Kr8rxMfHw8fHB59//jkaN26Mr7/+GhYWFliwYIHYoGUsOTkZLVq0KHG9UqVKyM7OFpBI/ZYsWYJu3brhypUrKCgowJo1a3DlyhVERUXhxIkTouNRGYiPj8dPP/1U4rqpqSkePnwoIJF6FP+MHjZsGNasWSPJUwP0fmNBVULmzJmDvXv3okGDBhg3bhwaNmwIAEhISEBQUBAKCwsxe/ZswSnLnq6u7hu7cqVq+fLl8PLywrFjx5RPkqOjo5GamooDBw4ITkf07rZs2SI6gjBJSUnw8vIC8PLnWPFRu8mTJ8Pd3R2+vr6CE5aNOnXqIDo6GmPGjMGXX36pXHQik8nQuXNnBAYGom7duoJT0n9RWFiIChXe/JFSR0cHBQUF5ZiI1GXYsGGIjY2Fi4sLZs2ahe7duyMwMBAvXrzAqlWrRMcrcy4uLqIjlJuLFy9i4cKFyte7du3C//73P2zYsAEAULduXcyfP19yBVVra2tcvHixxKiDQ4cOoXHjxoJSqVe7du1w8eJF+Pv7w8HBAUeOHIGTkxOio6Ph4OAgOl6Z0dHReaf3SanTvFjVqlWRlpZWYv7zhQsXULt2bUGp1Eebv0+QWCyoSoiZmRmioqIwevToEl9aPTw8EBQU9NYOEk01depUrFmzBoGBgVpx3B94+QE/MTERQUFBSEhIAPDy6PSYMWNgYWEhOB0RvQsTExM8e/YMwP8f2eLg4IDMzEzk5OQITle2rK2tcfDgQTx+/BjXr18HANja2nJ2qkQoFAp8/vnnbzwK/Pz583JOROoyefJk5a87deqEhIQEnDt3Dra2tnB0dBSYTH1OnjyJ9evX4+bNm/jll19Qu3Zt/PDDD7C2tka7du1Exyszjx8/VvmeUDyDsdiHH36I1NRUEdHUasqUKRg7dizy8vKgUChw5swZ7Ny5E0uXLsXGjRtFx1Ob+vXrK4vlUqVQKGBlZYWhQ4eW2oUsZQMGDMDMmTPxyy+/QCaToaioCJGRkZg2bZokT35lZ2fD39//jTtVbt68KSiZ+syfPx/e3t5aN/f6fSNTFFfdSFIeP36MGzduQKFQwM7ODiYmJqIjlanX5y6GhoaiWrVqsLe3L7GYSyqLXYhIWgYOHAhnZ2dMmTIFCxcuxDfffIOPP/4YR48ehZOTE392kcYYNmzYO72PHSSkafbs2YPBgwdj0KBB+OGHH3DlyhXY2NggMDAQBw4ckNSpICsrK/zwww/o0KED8vPzUbVqVYSEhCi3oMfHx8PFxUWSs/p37NiBBQsWKDfcW1hYwNfXF0OHDn1r970mKyoqwo0bN0otPnXo0EFQqrIVExODTZs2YdeuXbC2toa3tzcGDRokue/FpcnPz8fYsWOxdetW5SmSwsJCDBw4EFu3bn3n7l1N8emnn+LEiRMYPHgwzM3NSzRZTZw4UVAy9WnevDkuXboEFxcX+Pj4oE+fPpKbca0JWFAljfSuX94A6X6By8zMxKZNm3D16lUAgL29Pby9vf92JiURvR8yMjKQl5cHCwsLFBUVYfny5YiKioKdnR3mzJmjFR/4iUgz5Obm4vjx48r5ml9++aVK57GOjg4WLlxYYoa/pmvRogUmT56MIUOGwMjICLGxsbCxscGFCxfQrVs3pKeni45YZkaPHo3Y2FgsW7YMv/76K7Zt24a7d+8ql7zs2LEDAQEBOHv2rOCkZSM4OBj9+/dXuZaTk4OsrCyYmpqioKAA/fv3l+TDzdOnT2PgwIG4ffs2Xi8FSG3ZGgDk5eVh9+7d2LJlC06fPo3u3bvDx8cHnTt3Fh1N7VJTUxEfH4+srCy0aNECdnZ2oiOpRdWqVbF//360bdtWdJRydeHCBWzZsgU7d+5EQUEBBgwYAG9vb3z44Yeio2kNFlSJNFBMTAw8PDygr6+Pli1bAgDOnj2L3Nxc5RwkIiIiorKwbt067N+/HyEhIQAAIyMj2NvbQ19fH8DLef0zZsxQGQkgBQYGBrhy5Qrq1aunUlC9efMmmjRpgry8PNERy8zDhw/Ru3dvnDp1CpUrV8a2bdvQq1cv5f2OHTuiVatWWLx4scCUZUdPTw8hISGlFtUKCwvRv39/REVFIS0tTUA69WrevDkaNGgAX1/fUrv5pNyckZycDB8fH5w4cQIPHjyQ3OihoqIifP311/j999+Rn5+Pjh07Yv78+cqf1VJlbW2NAwcOSHbu8d958eIFQkJCsGXLFhw+fBiNGjVSLhWU8t/n94E0zzCQVsnNzYVCoYCBgQEA4Pbt29i3bx+aNGmCLl26CE6nHpMnT0aPHj2wYcMG5VGkgoICDB8+HJMmTUJERITghET0Jk+fPn2n93FTKRG9L3bs2IEZM2aoXPvpp59gY2MDAPjxxx8RFBQkuYJqrVq1cOPGDdSrV0/l+qlTp5T/7VJRo0YNRERE4MmTJ6hcuXKJI8G//PILKleuLChd2Vu2bBl69+6NY8eO4X//+5/yenEx9dSpUwgNDRWYUH2uX7+O3bt3a9VS3zt37mDr1q3YunUrcnJyMH36dEl+zlq8eDEWLFiATp06QV9fH2vWrMH9+/exefNm0dHUauHChZg3bx62bdumrAloE4VCgRcvXiA/Px8KhQImJiYIDAzE3LlzsWHDBnzyySeiI0oWO1RJ43Xp0gW9e/fGqFGjkJmZiYYNG0JXVxcPHz7EqlWrMHr0aNERy5y+vj4uXLiARo0aqVy/cuUKnJ2dJbfQhkhK5HL5WxfoKRQKSR65IyLNZW5ujujoaGVhsWbNmjh79qzydWJiIj788EM8efJEXEg1WLp0KX788Uds3rwZnTt3xoEDB3D79m1MnjwZc+fOxfjx40VHpP9g/vz5CAwMREREBOzt7VFYWIhPPvkEERERCA0NRdOmTUVHVAt3d3fMmDEDXbt2FR1FrfLz87Fv3z5s2rQJJ0+eRLdu3eDt7Y1u3bpJboZoMTs7O0ybNg0jR44EABw7dgxeXl7Izc2FXC4XnE59WrRogaSkJCgUCtSrV6/ETpXz588LSqZe586dUx75r1SpEoYMGYLhw4crH5Z88803WLRoEe7duyc4qXSxQ5U03vnz57F69WoAwO7du1GrVi1cuHABe/bswbx58yRZUDU2NkZKSkqJgmpqaiqMjIwEpSKidxEWFqb8tUKhgKenJzZu3IjatWsLTEVE9GaZmZkqM1MfPHigcr+oqEjlvlTMmjULRUVF6NixI3JyctChQwdUqlQJ06ZNYzFVAnx9fZGRkYEuXbogLCwMc+bMwYkTJ3D8+HHJFVPj4uKUvx4/fjymTp2K9PR0ODg4lCg+OTo6lnc8tTA3N4eRkRGGDh2Kb7/9FqampgBeboR/lZQ6VVNSUuDp6al83alTJ8hkMty9exd16tQRmEy9evbsKTpCuXNwcEBCQgK6dOmCTZs2oXv37iUeFHz66aeSXMj1PmGHKmk8AwMDJCQkwNLSEv3794e9vT3mz5+P1NRUNGzYUJLdmhMmTMC+ffuwYsUKtGnTBgAQGRmJ6dOno0+fPggICBAbkIje2atz+YiI3kd2dnbw9/dHnz59Sr0fHByMr776Cjdu3CjnZOUjPz8fN27cQFZWFpo0aSKpo+8EfPbZZ9izZw8qV66M48ePS6ag+Kri0zFv+upffE9KJ2Re7cgs7WSQ1P57gZcLAtPT01GzZk3lNSMjI8TFxcHa2lpgMiprCxcuhLe3NxsyBGOHKmk8W1tb/Prrr+jVqxcOHz6snN91//59ST1xfNWKFSsgk8kwZMgQFBQUAAAqVqyI0aNHw9/fX3A6IiIikhJPT0/MmzcPXl5e0NPTU7mXm5sLX19feHl5CUqnPt7e3lizZg2MjIzQpEkT5fXs7GyMHz9e8nMJpWzKlCnKX5uYmEChUKB58+bYunWryvtWrVpVzsnUIzk5WXSEcvfqiSBtoVAo8Pnnn6NSpUrKa3l5eRg1ahQMDQ2V1/bu3SsinlplZmZi9+7dSEpKwvTp01GtWjWcP38eZmZmkis6vnjxAlu3bkXfvn0l99+madihShpv9+7dGDhwIAoLC9GxY0ccOXIEwMu5VxERETh48KDghOqTk5ODpKQkAED9+vVhYGCA3NxcyW9yJJISdqgS0fvu3r17aN68OXR1dTFu3Dg0aNAAAHDt2jUEBgaioKAAFy5cgJmZmeCkZUtHRwdpaWnKo8LFHj58iFq1aikfapPmcXNz+9v3yGQySS6mioiIQJs2bZSLbYsVFBQgKioKHTp0EJSM/qthw4a90/u2bNmi5iTlKy4uDp06dUKVKlVw69YtXLt2DTY2NpgzZw5SUlKwfft20RHLXO3atXHs2DE0btxYdBStxoIqSUJ6ejrS0tLQrFkz5fGOM2fOwNjYuMScUal6/vw5goKCsHz5cqSnp4uOQ0TviEexiEgTJCcnY/To0Th69Kjy2LBMJkPnzp3x7bffSuqh0NOnT5Wbkq9fv65yfLawsBAhISGYNWsW7t69KzAl0b/zpgcFjx49gqmpqaSOwJN26NSpE5ycnLB8+XKVRoWoqCgMHDgQt27dEh2xzC1ZsgSJiYnYuHFjiYcjVH74/zxJQq1atVCrVi2Vay1bthSURn2eP3+OBQsW4OjRo9DV1cWMGTPQs2dPbNmyBbNnz4aOjo5y5AERvZ969+6t8rq0o1iANI9jEZHmsra2xqFDh5CRkaGclWpra4tq1aoJTlb2qlatCplMBplMpuzGfZVMJoOvr6+AZET/XfHs0Nc9evSoxGcRIk1w9uxZrF+/vsT12rVrS7bR6OzZszh+/DiOHDkCBwcHfo8QhAVVkoSYmBgEBwcjJSUF+fn5Kvek9MNk3rx5WL9+PTp16oSoqCj069cPw4YNw+nTp7Fq1Sr069evxHY/Inq/VKlSReX1Z599JigJEdE/V61aNUk+tH5VWFgYFAoF3N3dsWfPHpWisa6uLqysrGBhYSEwIdE/V/xAVyaTlZizWVhYiLi4OOWyWyJNUqlSJTx9+rTE9cTERJUTBlJStWrVNy6KpPLDgippvF27dmHIkCHw8PDAkSNH0KVLFyQmJuLevXvo1auX6Hhl6pdffsH27dvRo0cPXLp0CY6OjigoKEBsbGypT5qJ6P0jtblVRERS4+LiAuDlmANLS0t+xiJJKH6gq1AoYGRkpLJzQVdXF61atcIXX3whKh7Rv9ajRw/4+fkhODgYwMuHBikpKZg5c6Yki44FBQVwc3NDly5dSpzSpfLFGaqk8RwdHTFy5EiMHTtWOTPF2toaI0eOhLm5uaSOZOnq6iI5OVm5zU9fXx9nzpyBg4OD4GRERERE0nLo0CFUrlwZ7dq1AwAEBQVhw4YNaNKkCYKCgmBiYiI4IdE/5+vri2nTpvF4P0nGkydP0LdvX8TExODZs2ewsLBAeno6WrdujQMHDkjyz7qBgQGuXr0KKysr0VG0GguqpPEMDQ1x+fJl1KtXD9WrV0d4eDgcHBxw9epVuLu7Iy0tTXTEMqOjo4P09HTl0QUusyEiIiJSDwcHByxbtgyenp6Ij4+Hs7Mzpk6dirCwMDRq1IgnDog0QHZ2Nvz9/XH8+HHcv38fRUVFKvdv3rwpKBmVtcjISMTGxiIrKwtOTk7o1KmT6Ehq4+rqikmTJqFnz56io2g1HvknjWdiYoJnz54BeDl4+tKlS3BwcEBmZiZycnIEpytbCoVCZeYRl9kQERERqUdycjKaNGkCANizZw+6d++OJUuW4Pz58/D09BScjujfsba2fusYC6kVGIcPH44TJ05g8ODBMDc35wgPCWvbti3atm0rOka5GDNmDKZOnYo7d+7ggw8+KFEPcHR0FJRMu7CgShqvQ4cOOHr0KBwcHNCvXz9MnDgRoaGhOHr0KDp27Cg6XpkaOnSoymsusyEiIiJSD11dXeXD+WPHjmHIkCEAXi7mKm0BCklH48aNkZiYiMLCQtFRytykSZNUXr948QIXLlzAoUOHMH36dDGh1OjgwYPYv3+/1hTatEloaCjGjRuH06dPw9jYWOXekydP0KZNG6xbtw7t27cXlFB9BgwYAACYMGGC8ppMJoNCoYBMJpPkz673EQuqpPECAwORl5cHAJg9ezYqVqyIqKgo9OnTB3PmzBGcrmzxaBkRERFR+WjXrh2mTJmCtm3b4syZM/j5558BvNwcXadOHcHp6L+YN28eZs2aBQMDAwDA48ePVWbiLl26FE+ePBEVT60mTpxY6vWgoCDExMSUcxr1MzExQbVq1UTHIDUICAjAF198UaKYCrxcwjZy5EisWrVKkgXV5ORk0REInKFKGmzo0KHo2LEjXF1dYWlpKToOEREREUlISkoKxowZg9TUVEyYMAE+Pj4AgMmTJ6OwsBBr164VnJD+LR0dHaSlpcHU1BQAYGxsjIsXL8LGxkZwMnFu3ryJ5s2bS677+scff8Rvv/2Gbdu2KQvoJA1WVlY4dOgQGjduXOr9hIQEdOnSBSkpKeWcjLQFO1RJY92+fRsjR45Efn4+6tWrBzc3N7i5ucHd3R3m5uai4xERERGRBrO0tMQff/xR4vrq1asFpKGy9HpPEXuMgN27d0uyk3PlypVISkqCmZkZ6tWrh4oVK6rcP3/+vKBk9F/du3evxO/nqypUqIAHDx6UY6Ly9cMPP2DdunVITk5GdHQ0rKysEBAQAGtra3z88cei42kFFlRJY4WHh+P58+eIiopCeHg4wsPD8eOPP+LFixews7NTFlf79esnOioRERERabC8vDzk5+erXCvtmCnR+65FixYqi5kUCgXS09Px4MEDfPvttwKTqQe3oEtX8UJqW1vbUu/HxcVJttHqu+++w7x58zBp0iQsXrxYOTO1atWqCAgIYEG1nPDIP0lKXl4eoqKicPDgQXz//ffIysriQGYiIiIi+seys7Mxc+ZMBAcH49GjRyXu8zOm5tLR0UFiYiJq1qwJhUKBunXr4tSpU6hXr57K+6RYNPf19VV5LZfLUbNmTbi6uqJRo0aCUhH9c+PHj0d4eDjOnj0LPT09lXu5ublo2bIl3NzcJDmepUmTJliyZAl69uwJIyMjxMbGwsbGBpcuXYKrqysePnwoOqJWYEGVJCE/Px/R0dEIDw9HWFgY/vzzT1hYWMDFxQWbN28WHY+IiIiINMzYsWMRFhaGhQsXYvDgwQgKCsJff/2F9evXw9/fH4MGDRIdkf4luVxeokuztNfaVDS/f/8+Nm7ciK+++kp0FLXIz8/H/fv3UVRUpHKduzg017179+Dk5AQdHR2MGzcODRs2BPBydmpQUBAKCwtx/vx5mJmZCU5a9vT19ZGQkAArKyuVgur169fh6OiI3Nxc0RG1Ao/8k8aKiIhQKaBaWlrCxcUFI0aMwI8//sjtq0RERET0r4WEhGD79u1wdXXFsGHD0L59e9ja2sLKygo7duxgQVWDhYWFiY7w3klLS8PcuXMlV1BNTEyEj48PoqKiVK5rY9FcaszMzBAVFYXRo0fjyy+/VM5Clslk8PDwQFBQkCSLqQBgbW2NixcvwsrKSuX625Z0UdljQZU0lqurKywtLTFz5kzs2rVLsj8siYiIiKj8ZWRkKLe+GxsbIyMjAwDQrl07jB49WmQ0+o9cXFxER6ByMmzYMFSoUAF//PEHzM3NVTqRSfNZWVnhwIEDePz4MW7cuAGFQgE7OzuYmJiIjqYWfn5+mDZtGqZMmYKxY8ciLy8PCoUCZ86cwc6dO7F06VJs3LhRdEytwSP/pLFmzZqF8PBwXLhwAQ0bNoSLiwtcXV3h4uKCGjVqiI5HRERERBrM0dER33zzDVxcXNCpUyc0b94cK1aswNq1a7F8+XLcuXNHdESiMhMbGwsnJyfJdWwaGhri3LlznA9LkqCjo4O0tDSYmppix44dWLBgAZKSkgAAFhYW8PX1hY+Pj+CU2oMFVdJ4WVlZOHnyJMLDw5UF1gYNGsDFxQVubm7o27ev6IhEREREpGFWr14NHR0dTJgwAceOHUP37t2hUCjw4sULrFq1ChMnThQdkajMSLWg+uGHH2L16tVo166d6ChE/5lcLkd6ejpMTU2V13JycpCVlaVyjcoHC6okORkZGVi1ahW++eYbZGVlSe5DARERERGVv9u3b+PcuXOwtbWFo6Oj6DhE/8iUKVPeev/Bgwf46aefJPHd6enTp8pfx8TEYM6cOViyZAkcHBxQsWJFlfcaGxuXdzyif00ul+PevXuoWbOm6CgEFlRJAoqKinD27Fllh2pkZCSysrJgaWkJNzc3bNmyRXREIiIiIiIiYdzc3N7pfVJY2CWXy1VmpRYvoHoVl1KRJpLL5ahSpcrfzgIunvlN6sWlVKSxli9friygPnv2DLVr14arqysCAgLg5uYGa2tr0RGJiIiISMN4enpi586dqFKlCgDA398fo0aNQtWqVQEAjx49Qvv27XHlyhWBKYn+GSkUSt+VNv23kvbx9fVV/vtEYrFDlTSWhYUFXF1d4ebmBjc3N9ja2oqOREREREQa7tWlH8DLI8EXL16EjY0NAODevXuwsLBgZ5sEZGdnw9/fH8ePH8f9+/dRVFSkcv/mzZuCkhERlVTaDFUShx2qpLHu3r0rOgIRERERSczr/SbsP5Gu4cOH48SJExg8eDDMzc3/9hgtaabMzExs2rQJV69eBQDY29vD29ubXX6kcfgz6v3CgioRERERERFpnYMHD2L//v1o27at6CikJjExMfDw8IC+vj5atmwJAFi1ahUWL16MI0eOwMnJSXBConfHB3zvFxZUiYiIiIiI/o9MJivRBcSuIGkyMTFBtWrVRMcgNZo8eTJ69OiBDRs2oEKFl+WPgoICDB8+HJMmTUJERITghETv7vWxJCQWZ6gSERERERH9H7lcjm7duqFSpUoAgJCQELi7u8PQ0BAA8Pz5cxw6dIgzVCXgxx9/xG+//YZt27bBwMBAdBxSA319fVy4cAGNGjVSuX7lyhU4OzsjJydHUDIi0nTsUCUiIiIiIvo/Q4cOVXn92WeflXjPkCFDyisOqdHKlSuRlJQEMzMz1KtXDxUrVlS5f/78eUHJ1CszMxNnzpwpdRGX1P5sGxsbIyUlpURBNTU1FUZGRoJSEZEUsKBKRERERET0f7Zs2SI6ApWTnj17io5Q7kJCQjBo0CBkZWXB2NhYZZyFTCaTXEH1k08+gY+PD1asWIE2bdoAACIjIzF9+nR8+umngtMRkSbjkX+SrK+++grp6enYvHmz6ChERERERETCNWjQAJ6enliyZIlWjDnIz8/H9OnTsW7dOhQUFEChUEBXVxejR4+Gv7+/crQHEdE/xYIqaazt27fjk08+eeM/gkOHDkVqaipCQ0PLORkREREREWmK/Pz8Uo+/W1paCkqkPoaGhoiPj4eNjY3oKOUqJycHSUlJAID69etrRTGZiNSLBVXSWDo6OkhLS4OpqanoKEREREREpGESExPh4+ODqKgolesKhQIymUySi8d69+6NAQMGoH///qKjqFXv3r3/9j0VKlRArVq10LlzZ3Tv3r0cUhGRlHCGKmksPgsgIiIiIqJ/a9iwYahQoQL++OMPmJubq8wTlSovLy9Mnz4dV65cgYODQ4lFXD169BCUrGxVqVLlb99TVFSE69evY+PGjZg2bRr8/PzKIRkRSQU7VEljyeVy3Lt3DzVr1hQdhYiIiIiINIyhoSHOnTtXYgO8lMnl8jfek2pX7t/5448/MGbMGKSkpIiOQkQahB2qpNE6duyIChXe/sf4/Pnz5ZSGiIiIiIg0RZMmTfDw4UPRMcrV63NiCWjXrh2cnZ1FxyAiDcOCKmk0Dw8PVK5cWXQMIiIiIiLSAE+fPlX+etmyZZgxYwaWLFlS6vF3Y2Pj8o5HAlStWhV79+4VHYOINAyP/JPGksvlSE9P51IqIiIiIiJ6J3K5XGVWavECqldJbSnV2rVrMWLECOjp6WHt2rVvfe+ECRPKKRURkWZjQZU0lo6ODtLS0lhQJSIiIiKid3LixIl3fq+Li4sak5Qfa2trxMTEoHr16rC2tn7j+2QyGW7evFmOyYiINBcLqqSx2KFKRERERERERETl7c0r/ojec8nJyahZs6boGEREREREpKEyMzOxcuVKDB8+HMOHD8fq1avx5MkT0bHUxs/PDzk5OSWu5+bmws/PT0AiIiLNxA5VIiIiIiIi0joxMTHw8PCAvr4+WrZsCQA4e/YscnNzceTIETg5OQlOWPbeNDbt0aNHMDU1lczcWCIidWNBlYiIiIiIiLRO+/btYWtriw0bNqBChQoAgIKCAgwfPhw3b95ERESE4IRlTy6X4969eyVO+oWGhuKTTz7BgwcPBCUjItIsLKgSERERERGR1tHX18eFCxfQqFEjletXrlyBs7NzqUfjNZWJiQlkMhmePHkCY2NjyGQy5b3CwkJkZWVh1KhRCAoKEpiSiEhzVBAdgOi/KCgowJIlS+Dt7Y06deqIjkNERERERBrC2NgYKSkpJQqqqampMDIyEpRKPQICAqBQKODt7Q1fX19UqVJFeU9XVxf16tVD69atBSYkItIs7FAljWdkZIT4+HjUq1dPdBQiIiIiItIQEyZMwL59+7BixQq0adMGABAZGYnp06ejT58+CAgIEBuwjBUUFGDHjh1wd3dH3bp1RcchItJo7FAljefu7o4TJ06woEpERERERO9sxYoVkMlkGDJkCAoKCqBQKKCrq4vRo0fD399fdLwyV6FCBYwePRpXr14VHYWISOOxQ5U03rp16+Dr64tBgwbhgw8+gKGhocr9Hj16CEpGRERERETvu5ycHCQlJQEA6tevDwMDA8GJ1MfV1RWTJk1Cz549RUchItJoLKiSxpPL5W+8J5PJUFhYWI5piIiIiIjofda7d++/fU+FChVQq1YtdO7cGd27dy+HVOUjODgYX375JSZPnlxqM4qjo6OgZEREmoUFVSIiIiIiItIaw4YN+9v3FBUV4f79+zhx4gSmTZsGPz+/ckimfqU1o8hkMigUCjajEBH9AyyokqTk5eVBT09PdAwiIiIiIpKAP/74A2PGjEFKSoroKGXi9u3bb71vZWVVTkmIiDQbl1KRxissLMSSJUuwbt063Lt3D4mJibCxscHcuXNRr149+Pj4iI5IREREREQaqF27dnB2dhYdo8ywYEpEVDbYoUoaz8/PD9u2bYOfnx+++OILXLp0CTY2Nvj5558REBCA6Oho0RGJiIiIiIjeG1euXEFKSgry8/NVrnOhLxHRu2FBlTSera0t1q9fj44dO8LIyAixsbGwsbFBQkICWrdujcePH4uOSEREREREJNzNmzfRq1cvxMfHK2enAi/nqALgDFUionf05vXoRBrir7/+gq2tbYnrRUVFePHihYBERERERERE75+JEyfC2toa9+/fh4GBAS5fvoyIiAg4OzsjPDxcdDwiIo3BgippvCZNmuDkyZMlru/evRstWrQQkIiIiIiIiOj9Ex0dDT8/P9SoUQNyuRxyuRzt2rXD0qVLMWHCBNHxiIg0BpdSkcabN28ehg4dir/++gtFRUXYu3cvrl27hu3bt+OPP/4QHY+IiIiIiOi9UFhYCCMjIwBAjRo1cPfuXTRs2BBWVla4du2a4HRERJqDHaqk8T7++GOEhITg2LFjMDQ0xLx583D16lWEhISgc+fOouMRERERERG9F5o2bYrY2FgAwP/+9z8sX74ckZGR8PPzg42NjeB0RESag0upSOPduXMHderUKfXe6dOn0apVq3JORERERERE9P45fPgwsrOz0bt3b9y4cQMfffQREhMTUb16dfz8889wd3cXHZGISCOwoEoar0mTJjh16hSqVaumcj0yMhJeXl7IzMwUE4yIiIiIiOg9l5GRARMTE8hkMtFRiIg0Bo/8k8Zr1aoVunTpgmfPnimvRUREwNPTE/PnzxeYjIiIiIiI6P1WrVo1FlOJiP4hdqiSxisqKkLfvn2RkZGBw4cPIyoqCj169MCiRYswceJE0fGIiIiIiIiE8vb2fqf3bd68Wc1JiIikgQVVkoT8/Hx4eXkhJycHcXFxWLp0KcaNGyc6FhERERERkXByuRxWVlZo0aIF3lYC2LdvXzmmIiLSXCyokkaKi4srce3Zs2f49NNP4eXlhdGjRyuvOzo6lmc0IiIiIiKi98rYsWOxc+dOWFlZYdiwYfjss89K7KAgIqJ3x4IqaSS5XA6ZTKbydPXV18W/lslkKCwsFBWTiIiIiIjovfD8+XPs3bsXmzdvRlRUFLy8vODj44MuXbpwhioR0T/EgipppNu3b7/ze62srNSYhIiIiIiISLPcvn0bW7duxfbt21FQUIDLly+jcuXKomMREWmMCqIDEP0bLJISERERERH9O6+e+OOJPiKif04uOgBRWUhKSsL48ePRqVMndOrUCRMmTEBSUpLoWERERERERO+F58+fY+fOnejcuTMaNGiA+Ph4BAYGIiUlhd2pRET/EDtUSeMdPnwYPXr0QPPmzdG2bVsAQGRkJOzt7RESEoLOnTsLTkhERERERCTOmDFjsGvXLtStWxfe3t7YuXMnatSoIToWEZHG4gxV0ngtWrSAh4cH/P39Va7PmjULR44cwfnz5wUlIyIiIiIiEk8ul8PS0hItWrR46wKqvXv3lmMqIiLNxYIqaTw9PT3Ex8fDzs5O5XpiYiIcHR2Rl5cnKBkREREREZF4n3/++VsLqcW2bNlSDmmIiDQfj/yTxqtZsyYuXrxYoqB68eJFmJqaCkpFRERERET0fti6davoCEREksKCKmm8L774AiNGjMDNmzfRpk0bAC9nqC5btgxTpkwRnI6IiIiIiIiIiKSER/5J4ykUCgQEBGDlypW4e/cuAMDCwgLTp0/HhAkT3uloCxERERERERER0btgQZUk5dmzZwAAIyMjwUmIiIiIiIiIiEiKeOSfJOPBgwe4du0aAKBRo0aoUaOG4ERERERERERERCQ1ctEBiP6r7OxseHt7w9zcHB06dECHDh1gbm4OHx8f5OTkiI5HREREREREREQSwoIqabwpU6bgxIkTCAkJQWZmJjIzM/Hbb7/hxIkTmDp1quh4REREREREREQkIZyhShqvRo0a2L17N1xdXVWuh4WFoX///njw4IGYYEREREREREREJDnsUCWNl5OTAzMzsxLXTU1NeeSfiIiIiIiIiIjKFDtUSeN17NgR1atXx/bt26GnpwcAyM3NxdChQ5GRkYFjx44JTkhERERERERERFLBgippvEuXLsHDwwPPnz9Hs2bNAACxsbHQ09PD4cOHYW9vLzghERERERERERFJBQuqJAk5OTnYsWMHEhISAACNGzfGoEGDoK+vLzgZERERERERERFJCQuqRERERERERERERO+ogugARP/Vo0ePUL16dQBAamoqNmzYgNzcXHTv3h0dOnQQnI6IiIiIiIiIiKSEHaqkseLj49G9e3ekpqbCzs4Ou3btQteuXZGdnQ25XI7s7Gzs3r0bPXv2FB2ViIiIiIiIiIgkQi46ANG/NWPGDDg4OCAiIgKurq746KOP4OXlhSdPnuDx48cYOXIk/P39RcckIiIiIiIiIiIJYYcqaawaNWogNDQUjo6OyMrKgrGxMc6ePYsPPvgAAJCQkIBWrVohMzNTbFAiIiIiIiIiIpIMdqiSxsrIyECtWrUAAJUrV4ahoSFMTEyU901MTPDs2TNR8YiIiIiIiIiISIJYUCWNJpPJ3vqaiIiIiIiIiIioLFUQHYDov/j8889RqVIlAEBeXh5GjRoFQ0NDAMDz589FRiMiIiIiIiIiIgniDFXSWMOGDXun923ZskXNSYiIiIiIiIiISFuwoEpERERERERERET0jjhDlYiIiIiIiIiIiOgdsaBKRERERERERERE9I5YUCUiIiIiIiIiIiJ6RyyoEhEREREREREREb0jFlSJiIiIiIiIiIiI3hELqkRERERERERERETviAVVIiIiIiIiIiIionfEgioRERERERERERHRO2JBlYiIiIiIiIiIiOgd/T+HJNXc/yiBwwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color =\"#00b8ff\">3.3 Análise do Violentador</font>"
      ],
      "metadata": {
        "id": "d2oHA-Cpucyt"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lO2fKoT93BaC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color =\"#00b8ff\">3.4 Análise das Vítimas</font>"
      ],
      "metadata": {
        "id": "F6CZcYaFuf9q"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eWXCaFeDAXOL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color =\"#00b8ff\">3.5 Análise das Armas</font>"
      ],
      "metadata": {
        "id": "IlzizcbcujXL"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jP0OgVVU3Eyg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color =\"#00b8ff\">3.6 Regressão Linear</font>"
      ],
      "metadata": {
        "id": "GCEY2NCaykWa"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rG2fgZ6E3FLt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}