exploracaoTratamentoDados.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <b style=\"color:#f87730\"> 🧰 Importando bibliotecas e o DataFrame. </b>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como boa prática no nosso código, devemos começar importando as bibliotecas que serão utilizadas \n",
    "\n",
    "ao longo do código. Enquanto trabalhamos no projeto, podemos sentir a necessidade de \"chamar\" outras bibliotecas.\n",
    "\n",
    "Se for o caso, devemos voltar aqui e adicioná-las abaixo, para que o fluxo de código se mantenha limpo e organizado,\n",
    "\n",
    "e para uma fácil identificação das ferramentas utilizadas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Após isso, vamos ler o(s) conjunto(s) de dados que será utilizado, a partir da função **READ** do pandas.\n",
    "\n",
    "O read conta com algumas variantes, a depender do tipo de arquivo lido. \n",
    "\n",
    "Neste caso, **read_csv** pois nossos arquivos estão no formato .csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ao rodar o código sem parâmetros (como o encoding), iremos nos deparar com um erro de decodificação de um caráctere.\n",
    "# Pesquisando, podemos encontrar sua causa (caso não fosse conhecida) e sua solução.\n",
    "# Nesse caso, apenas trocar o encoding nos dará o resultado desejado, e conseguiremos ler o arquivo:\n",
    "\n",
    "data = pd.read_csv('Data/forest_fires.csv', encoding='latin-1')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definindo o estilo visual padrão dos gráficos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')\n",
    "plt.style.use('seaborn-bright')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🌎 **Visão geral e Tratamento dos dados iniciais**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🔷 Início"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nosso **DataFrame (DF)** tem poucas colunas, e fazendo sua chamada conseguimos visualizá-las:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>month</th>\n",
       "      <th>number</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1998-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1999-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2002-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year state    month  number        date\n",
       "0  1998  Acre  Janeiro     0.0  1998-01-01\n",
       "1  1999  Acre  Janeiro     0.0  1999-01-01\n",
       "2  2000  Acre  Janeiro     0.0  2000-01-01\n",
       "3  2001  Acre  Janeiro     0.0  2001-01-01\n",
       "4  2002  Acre  Janeiro     0.0  2002-01-01"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caso não fosse o caso, poderíamos ver todas a partir da propriedade **COLUMNS**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'month', 'number', 'date'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O shape nos retorna uma **TUPLA** com o número de linhas e colunas do nosso DF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6454, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🔷 Tipos"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outra propriedade importante, a **DTYPES**, nos permite ver os nomes das colunas e os tipos de dados que elas armazenam.\n",
    "\n",
    "É de extrema importância saber qual o tipo em que estão armazenados os nossos dados, para pensarmos na melhor\n",
    "\n",
    "solução para os problemas que envolverão essas entradas em questões como **performance** e **tratamento dos dados**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year        int64\n",
       "state      object\n",
       "month      object\n",
       "number    float64\n",
       "date       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um exemplo claro disso é a coluna **date**, que atualmente está como object (string). Caso fosse utilizar essa coluna,\n",
    "\n",
    " ela seria mantida como **TIMESTAMP/DATETIME**, sendo transformada utilizando a função (to_datetime):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   1998-01-01\n",
       "1   1999-01-01\n",
       "2   2000-01-01\n",
       "3   2001-01-01\n",
       "4   2002-01-01\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['date'] = pd.to_datetime(data.date)\n",
    "data['date'].head(5)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Porém, se olharmos com um pouco mais de cuidado para essa coluna, vamos perceber sua redundância:\n",
    "\n",
    "todos os meses nessa coluna estão como 01, inutilizando o seu propósito. Os dados podem ter sido\n",
    "\n",
    "compilados de forma errada antes de serem enviados para o Kaggle ou para a própria fonte primária\n",
    "\n",
    "de dados, que não está mais disponível de forma oficial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    6454\n",
       "Name: date, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.date.dt.month.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com isso, opto por remover esta coluna do nosso DF, já que suas informações já existem nas colunas \"year\" e \"month\":\n",
    "\n",
    "> ❗ ATENÇÃO\n",
    ">\n",
    ">   Para a alteração ser atribuída e não apenas visualizada, devemos atribuir a operação (drop) a uma variável,\n",
    ">\n",
    ">   neste caso, o próprio DF. Pode ser feito como na atribuição abaixo, ou usando o parâmetro **INPLACE**, que apesar de funcionar,\n",
    ">\n",
    ">   é atualmente uma prática desencorajada pela própria documentação do Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(\"date\", axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continuando a troca dos tipos de dados, percebemos que a coluna \"number\" está no formato **FLOAT**, que serve para números decimais.\n",
    "\n",
    "Porém, podemos passar essa coluna para **INT**, já que esperamos apenas valores inteiros (não existe \"um incêndio e meio\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year       int64\n",
       "state     object\n",
       "month     object\n",
       "number     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.number = data.number.astype('int64')\n",
    "\n",
    "# Visualizando a alteração:\n",
    "\n",
    "data.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🔷 Duplicados"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, verificaremos se existem **dados duplicados**. Na linha de código abaixo,\n",
    "\n",
    "selecionamos dentro do conjunto de dados aqueles dados que são duplicados,\n",
    "\n",
    "e com o parâmetro Keep setado p/ False, podemos ter uma visão de todas entradas repetidas.\n",
    "\n",
    "> ❗ Atenção\n",
    ">\n",
    "> O keep=False servirá apenas p/ a visualização, devemos lembrar de retirar o parâmetro\n",
    ">\n",
    "> ao realizar a remoção dos duplicados, caso contrário nenhum dos N registros repetidos\n",
    ">\n",
    "> permanecerá nos nossos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>month</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>2017</td>\n",
       "      <td>Alagoas</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>2017</td>\n",
       "      <td>Alagoas</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Fevereiro</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Março</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4841</th>\n",
       "      <td>1998</td>\n",
       "      <td>Rio</td>\n",
       "      <td>Abril</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>1998</td>\n",
       "      <td>Rio</td>\n",
       "      <td>Maio</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4864</th>\n",
       "      <td>2001</td>\n",
       "      <td>Rio</td>\n",
       "      <td>Maio</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4910</th>\n",
       "      <td>2007</td>\n",
       "      <td>Rio</td>\n",
       "      <td>Julho</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4932</th>\n",
       "      <td>2009</td>\n",
       "      <td>Rio</td>\n",
       "      <td>Agosto</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year        state      month  number\n",
       "258   2017      Alagoas    Janeiro      38\n",
       "259   2017      Alagoas    Janeiro      38\n",
       "2391  1998  Mato Grosso    Janeiro       0\n",
       "2411  1998  Mato Grosso  Fevereiro       0\n",
       "2431  1998  Mato Grosso      Março       0\n",
       "...    ...          ...        ...     ...\n",
       "4841  1998          Rio      Abril       0\n",
       "4861  1998          Rio       Maio       0\n",
       "4864  2001          Rio       Maio       4\n",
       "4910  2007          Rio      Julho       7\n",
       "4932  2009          Rio     Agosto       1\n",
       "\n",
       "[61 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.duplicated(keep=False)]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ao trabalhar com esse conjunto de dados, notei uma grande quantidade de dados repetida. Por enquanto vamos apenas visualizar esses dados.\n",
    "\n",
    "Explico mais abaixo (na seção **Anomalias nos Dados**) as causas e o que será feito para contornar a situação. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>2391</th>\n",
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       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Fevereiro</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Março</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2451</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Abril</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2471</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Maio</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2630</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Fevereiro</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2670</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Março</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2690</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Abril</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2710</th>\n",
       "      <td>1998</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Maio</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year        state      month  number\n",
       "2391  1998  Mato Grosso    Janeiro       0\n",
       "2411  1998  Mato Grosso  Fevereiro       0\n",
       "2431  1998  Mato Grosso      Março       0\n",
       "2451  1998  Mato Grosso      Abril       0\n",
       "2471  1998  Mato Grosso       Maio       0\n",
       "2630  1998  Mato Grosso    Janeiro       0\n",
       "2650  1998  Mato Grosso  Fevereiro       0\n",
       "2670  1998  Mato Grosso      Março       0\n",
       "2690  1998  Mato Grosso      Abril       0\n",
       "2710  1998  Mato Grosso       Maio       0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separando por estados podemos ver claramente uma grande quantidade de dados que se repetem\n",
    "\n",
    "data[data.duplicated(keep=False)].query('state == \"Mato Grosso\"')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outro ponto importante que devemos estar atentos são valores nulos e faltantes. Podemos determinar a sua quantidade da seguinte maneira:\n",
    "\n",
    "Usamos o método .isnull(), que determinará uma variável booleana ao registro (False se o registro estiver preenchido, True caso esteja faltando)\n",
    "\n",
    "Após isso, usamos outro método, o .sum() para realizar a contagem geral do total de registros faltantes. Caso exista algum registro faltante devemos observar\n",
    "\n",
    "um número diferente de 0, e tomar as devidas providências para que nossas análises não sejam contaminadas ou atrapalhadas por ele."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year      0\n",
       "state     0\n",
       "month     0\n",
       "number    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No conjunto de dados trabalhado, observamos que todos os valores existem e não há registros nulos, logo não será necessário nenhuma alteração aqui:\n",
    "\n",
    "data.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🔠 Corrigindo erros ortográficos"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui, identifico quais os estados estão com nomes reduzidos ou incorretos para facilitar a identificação dos mesmos pelo PBI.\n",
    "\n",
    "Em seguida, crio um **Dicionário** contendo como chave os nomes incorretos e como valor os nomes certos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Acre', 'Alagoas', 'Amapa', 'Amazonas', 'Bahia', 'Ceara',\n",
       "       'Distrito Federal', 'Espirito Santo', 'Goias', 'Maranhao',\n",
       "       'Mato Grosso', 'Minas Gerais', 'Pará', 'Paraiba', 'Pernambuco',\n",
       "       'Piau', 'Rio', 'Rondonia', 'Roraima', 'Santa Catarina',\n",
       "       'Sao Paulo', 'Sergipe', 'Tocantins'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.state.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrigidos = {'Amapa':'Amapá', 'Ceara':'Ceará', 'Espirito Santo':'Espírito Santo',\n",
    "'Goias':'Goiás', 'Maranhao':'Maranhão', 'Paraiba':'Paraíba', 'Piau':'Piauí',\n",
    "'Rio':'Rio de Janeiro', 'Rondonia':'Rondônia', 'Sao Paulo':'São Paulo'}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Após realizar esse trabalho manual, utilizo o método **REPLACE** do Pandas para substituir as chaves pelos valores corretos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.replace(corrigidos.keys(), corrigidos.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Acre', 'Alagoas', 'Amapá', 'Amazonas', 'Bahia', 'Ceará',\n",
       "       'Distrito Federal', 'Espírito Santo', 'Goiás', 'Maranhão',\n",
       "       'Mato Grosso', 'Minas Gerais', 'Pará', 'Paraíba', 'Pernambuco',\n",
       "       'Piauí', 'Rio de Janeiro', 'Rondônia', 'Roraima', 'Santa Catarina',\n",
       "       'São Paulo', 'Sergipe', 'Tocantins'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Com isso, chamamos novamente o UNIQUE para ver se os dados foram substituídos corretamente.\n",
    "\n",
    "data.state.unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É importante atentar-se à falta de alguns estados no conjunto de dados. São eles:\n",
    "\n",
    "> _Rio Grande do Norte; Rio Grande do Sul; Paraná e Mato Grosso do Sul._\n",
    "\n",
    "Falaremos sobre isso na seção adiante"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 📣 <b style=\"color:red\">ANOMALIAS NOS DADOS</a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ➿ Contexto"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ao separar os dados por estados verificamos a seguinte situação:\n",
    "\n",
    "Os estados de Mato Grosso e Paraíba contam com 2x o número de registros normal, e Rio conta com 3x este número."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Acre</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alagoas</th>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amapá</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amazonas</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahia</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceará</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distrito Federal</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Espírito Santo</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goiás</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maranhão</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mato Grosso</th>\n",
       "      <td>478</td>\n",
       "      <td>478</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minas Gerais</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paraíba</th>\n",
       "      <td>478</td>\n",
       "      <td>478</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pará</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pernambuco</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Piauí</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rio de Janeiro</th>\n",
       "      <td>717</td>\n",
       "      <td>717</td>\n",
       "      <td>717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rondônia</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roraima</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santa Catarina</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergipe</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>São Paulo</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tocantins</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  year  month  number\n",
       "state                                \n",
       "Acre               239    239     239\n",
       "Alagoas            240    240     240\n",
       "Amapá              239    239     239\n",
       "Amazonas           239    239     239\n",
       "Bahia              239    239     239\n",
       "Ceará              239    239     239\n",
       "Distrito Federal   239    239     239\n",
       "Espírito Santo     239    239     239\n",
       "Goiás              239    239     239\n",
       "Maranhão           239    239     239\n",
       "Mato Grosso        478    478     478\n",
       "Minas Gerais       239    239     239\n",
       "Paraíba            478    478     478\n",
       "Pará               239    239     239\n",
       "Pernambuco         239    239     239\n",
       "Piauí              239    239     239\n",
       "Rio de Janeiro     717    717     717\n",
       "Rondônia           239    239     239\n",
       "Roraima            239    239     239\n",
       "Santa Catarina     239    239     239\n",
       "Sergipe            239    239     239\n",
       "São Paulo          239    239     239\n",
       "Tocantins          239    239     239"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('state').count()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os dados fornecidos para cumprir o documento foram fornecidos através do Kaggle, compilados e enviados por um usuário contém algumas inconsistências.\n",
    "\n",
    "Como visto acima, existem estados faltantes: _Rio Grande do Norte, Rio Grande do Sul, Paraná, Mato Grosso do Sul_. Muito provavelmente houve um erro\n",
    "\n",
    "ao parsear e separar os dados, transformando-os em um dataset:\n",
    "\n",
    "<li>\"Rio\" -> Rio de Janeiro, Rio Grande do Norte e Rio Grande do Sul</li>\n",
    "<li>\"Paraíba\" -> Paraíba e Paraná</li>\n",
    "<li>\"Mato Grosso\" -> Mato Grosso e Mato Grosso do Sul</li>\n",
    "\n",
    "Os estados acima estão com informações **UNIDAS**. Em todos os casos, creio que o REGEX estava configurado de maneira incorreta:\n",
    "\n",
    "<li>apenas para \"Rio\" e acabou apagando o restante do nome do estado.</li>\n",
    "<li>possivelmente o \"á\" no final do nome Paraná fez com que o nome do estado acabasse sendo totalmente apagado,\n",
    "\n",
    "e posteriormente preenchido como \"Paraíba\" através do método fillna(), já que estão sequencialmente posicionados.</li>\n",
    "<li>semelhante ao \"Rio\", o REGEX desconsiderou parte da string</li>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🧨 **Implicações**:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Desta forma, os dados fornecidos estão **CRITICAMENTE** comprometidos, de forma que não é possível determinar com exatidão as informações dos seus\n",
    "\n",
    "locais originais. Caso continue a utilizá-los, estados estarão com **anomalias** em seus números, e gráficos no PBI ficarão vazios."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **⭐ Fonte Primária**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No Kaggle disponibilizado para o desafio podemos encontrar a fonte primária de onde o autor retirou os dados:\n",
    "\n",
    "  > https://dados.gov.br/dataset/sistema-nacional-de-informacoes-florestais-snif\n",
    "\n",
    "  A página entretanto está fora do ar, com o código de erro 404, citando que a página pode não existir mais. E de fato ela foi desativada em 2021.\n",
    "  \n",
    "  Porém isso não vai impedir nosso acesso a esses dados. Utilizando a ferramenta \"Wayback Machine\" conseguimos ver instâncias do site salvas em momentos do passado.\n",
    "\n",
    "  Nem todas as \"capturas\" do site são 100% funcionais, e podemos encontrar algumas mais detalhadas e outras com apenas a frações do conjunto do site.\n",
    "\n",
    "  Buscando um pouco, percebemos que em 2018 houve um grande movimento de capturas do site, de forma que encontramos versões totalmente funcionais,\n",
    "\n",
    "  onde até os links de download funcionam, apesar do mirror original não existir mais. Com isso, conseguimos chegar à seguinte captura da página:\n",
    "\n",
    "  > https://web.archive.org/web/20181223043304/http://dados.gov.br/dataset/sistema-nacional-de-informacoes-florestais-snif/resource/949310f1-05bc-4f56-a63f-aef67aac6164\n",
    "\n",
    "Através dela, podemos baixar o conjunto de dados original:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>\"Ano</th>\n",
       "      <th>Estado</th>\n",
       "      <th>Mês</th>\n",
       "      <th>Número</th>\n",
       "      <th>Período\"</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>01/01/1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>01/01/1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>01/01/2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>01/01/2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>01/01/2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6449</th>\n",
       "      <td>2012</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>128</td>\n",
       "      <td>01/01/2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6450</th>\n",
       "      <td>2013</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>85</td>\n",
       "      <td>01/01/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6451</th>\n",
       "      <td>2014</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>223</td>\n",
       "      <td>01/01/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6452</th>\n",
       "      <td>2015</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>373</td>\n",
       "      <td>01/01/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6453</th>\n",
       "      <td>2016</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>119</td>\n",
       "      <td>01/01/2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6454 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      \"Ano     Estado       Mês Número    Período\"\n",
       "0     1998       Acre   Janeiro      0  01/01/1998\n",
       "1     1999       Acre   Janeiro      0  01/01/1999\n",
       "2     2000       Acre   Janeiro      0  01/01/2000\n",
       "3     2001       Acre   Janeiro      0  01/01/2001\n",
       "4     2002       Acre   Janeiro      0  01/01/2002\n",
       "...    ...        ...       ...    ...         ...\n",
       "6449  2012  Tocantins  Dezembro    128  01/01/2012\n",
       "6450  2013  Tocantins  Dezembro     85  01/01/2013\n",
       "6451  2014  Tocantins  Dezembro    223  01/01/2014\n",
       "6452  2015  Tocantins  Dezembro    373  01/01/2015\n",
       "6453  2016  Tocantins  Dezembro    119  01/01/2016\n",
       "\n",
       "[6454 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Abrindo o csv com parâmtros do ENCODING novamente, depois para o SEPARADOR que é diferente do padrão, usando a ENGINE do python para Parsear o doc,\n",
    "# e forçando o tipo das colunas como str para remover o \".\" dos milhares sem que os dados se percam ou assumam valores indevidos.\n",
    "\n",
    "dados_originais = pd.read_csv('Data/rf_incendiosflorestais_focoscalor_estados_1998-2017.csv', encoding='latin-1', sep=r'\\t', engine='python', dtype=str)\n",
    "dados_originais = dados_originais.replace('\\\"', '', regex=True)\n",
    "dados_originais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Ano        object\n",
       "Estado      object\n",
       "Mês         object\n",
       "Número      object\n",
       "Período\"    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_originais.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <b style='color:green'>**♻**</b> **Limpeza e criação de novas colunas**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora que temos o dataset original de sua fonte primária, percebemos que ele tem uma formatação muito mais concisa.\n",
    "\n",
    "Não é necessário trocar os nomes dos estados, pois estão todos corretos. Além disso, temos as 27 unidades da federação\n",
    "\n",
    "brasileira, onde antes só tínhamos 23."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Acre', 'Alagoas', 'Amapá', 'Amazonas', 'Bahia', 'Ceará',\n",
       "       'Distrito Federal', 'Espírito Santo', 'Goiás', 'Maranhão',\n",
       "       'Mato Grosso', 'Mato Grosso do Sul', 'Minas Gerais', 'Pará',\n",
       "       'Paraíba', 'Paraná', 'Pernambuco', 'Piauí', 'Rio de Janeiro',\n",
       "       'Rio Grande do Norte', 'Rio Grande do Sul', 'Rondônia', 'Roraima',\n",
       "       'Santa Catarina', 'São Paulo', 'Sergipe', 'Tocantins'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(dados_originais.Estado.unique()))\n",
    "\n",
    "dados_originais.Estado.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>\"Ano</th>\n",
       "      <th>Estado</th>\n",
       "      <th>Mês</th>\n",
       "      <th>Número</th>\n",
       "      <th>Período\"</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>01/01/1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>01/01/1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>01/01/2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>01/01/2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>01/01/2002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   \"Ano Estado      Mês Número    Período\"\n",
       "0  1998   Acre  Janeiro      0  01/01/1998\n",
       "1  1999   Acre  Janeiro      0  01/01/1999\n",
       "2  2000   Acre  Janeiro      0  01/01/2000\n",
       "3  2001   Acre  Janeiro      0  01/01/2001\n",
       "4  2002   Acre  Janeiro      0  01/01/2002"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_originais.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Porém, mesmo nos dados originais, o Período continua com todos os registros do mês '01'.\n",
    "\n",
    "Novamente removeremos a coluna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_originais = dados_originais.drop('Período\"', axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, substituo o nome das colunas que serão utilizadas, a fim de remover as acentuações que podem atrapalhar as buscas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ano</th>\n",
       "      <th>Estado</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Numero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ano Estado      Mes Numero\n",
       "0  1998   Acre  Janeiro      0\n",
       "1  1999   Acre  Janeiro      0\n",
       "2  2000   Acre  Janeiro      0\n",
       "3  2001   Acre  Janeiro      0\n",
       "4  2002   Acre  Janeiro      0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "novas_colunas = {'\"Ano':'Ano','Mês':'Mes', 'Número':'Numero'}\n",
    "\n",
    "dados_originais = dados_originais.rename(columns=novas_colunas)\n",
    "dados_originais.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, nossa tarefa é REMOVER o . dos milhares da coluna de Números para transformá-la em int,\n",
    "\n",
    "pois se a definirmos como numérica neste momento, ela erroneamente transformará em floats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# O método apply nos deixa aplicar uma função para cada uma de nossas entradas, sendo esta função\n",
    "# uma função fantasma para remover o '.'\n",
    "\n",
    "dados_originais['Numero'] = dados_originais.Numero.apply(lambda x: x.replace('.', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Passando para numérica (int):\n",
    "\n",
    "dados_originais.Numero = pd.to_numeric(dados_originais.Numero)\n",
    "\n",
    "# Convertendo também a coluna Ano:\n",
    "\n",
    "dados_originais.Ano = pd.to_numeric(dados_originais.Ano)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ano        int64\n",
       "Estado    object\n",
       "Mes       object\n",
       "Numero     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agora temos o DF com os tipos de dados corretos, limpo e com todos os nomes corretos\n",
    "\n",
    "dados_originais.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por último, verificando os valores vemos que quase todos os estados tem 239 registros, menos Alagoas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alagoas                240\n",
       "Acre                   239\n",
       "Paraíba                239\n",
       "Sergipe                239\n",
       "São Paulo              239\n",
       "Santa Catarina         239\n",
       "Roraima                239\n",
       "Rondônia               239\n",
       "Rio Grande do Sul      239\n",
       "Rio Grande do Norte    239\n",
       "Rio de Janeiro         239\n",
       "Piauí                  239\n",
       "Pernambuco             239\n",
       "Paraná                 239\n",
       "Pará                   239\n",
       "Minas Gerais           239\n",
       "Mato Grosso do Sul     239\n",
       "Mato Grosso            239\n",
       "Maranhão               239\n",
       "Goiás                  239\n",
       "Espírito Santo         239\n",
       "Distrito Federal       239\n",
       "Ceará                  239\n",
       "Bahia                  239\n",
       "Amazonas               239\n",
       "Amapá                  239\n",
       "Tocantins              239\n",
       "Name: Estado, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_originais.Estado.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verificando os valores duplicados, encontramos um registro repetido no estado:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ano</th>\n",
       "      <th>Estado</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Numero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>2017</td>\n",
       "      <td>Alagoas</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>2017</td>\n",
       "      <td>Alagoas</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Ano   Estado      Mes  Numero\n",
       "258  2017  Alagoas  Janeiro      38\n",
       "259  2017  Alagoas  Janeiro      38"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_originais[dados_originais.duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_originais = dados_originais.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "239    27\n",
       "Name: Estado, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Podemos verificar que todos os estados (27) possuem 239 registros:\n",
    "\n",
    "dados_originais.Estado.value_counts().value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dessa forma, aqui termina a limpeza dos dados."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📅 Criando colunas de trimestres e meses inteiros"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conforme dito acima, a coluna Período não continha informações relevantes, já que somente representava o ano. \n",
    "\n",
    "Porém ainda precisamos dos valores numéricos para ordenar os meses. Além disso, também dividirei em trimestres\n",
    "\n",
    "os meses, para gerar gráficos no Power BI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicionario_meses = {'Janeiro':1, 'Fevereiro':2,\n",
    "                      'Março':3, 'Abril':4,\n",
    "                      'Maio':5, 'Junho':6,\n",
    "                      'Julho':7, 'Agosto':8,\n",
    "                      'Setembro':9, 'Outubro':10,\n",
    "                      'Novembro':11, 'Dezembro':12}\n",
    "\n",
    "def nomeMes_Numeral(mes):\n",
    "  # Transforma o nome do mês em seu correspondente numérico.\n",
    "  \n",
    "\n",
    "  return dicionario_meses.get(mes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mes_Trimestre(mesNum):\n",
    "  # Classifica o trimestre de acordo com mês.\n",
    "  \n",
    "  # Essa classificação poderia ser feita através de uma função no PBI\n",
    "  # mas optei por enviar o conjunto de dados totalmente funcional.\n",
    "\n",
    "  return 'Q1' if 12/mesNum >= 4 else 'Q2' if 12/mesNum >= 2 else 'Q3' if 12/mesNum >=1.33 else 'Q4'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicionario_regiao = {'Acre':'N', 'Amapá':'N', 'Amazonas':'N', 'Pará':'N', 'Rondônia':'N', 'Roraima':'N', 'Tocantins':'N',\n",
    "                    'Alagoas':'NE', 'Bahia':'NE', 'Ceará':'NE', 'Maranhão':'NE', 'Paraíba':'NE', 'Pernambuco':'NE', 'Piauí':'NE', \n",
    "                    'Rio Grande do Norte':'NE', 'Sergipe':'NE', 'Espírito Santo':'SE', 'Minas Gerais':'SE', 'Rio de Janeiro':'SE',\n",
    "                    'São Paulo':'SE','Goiás':'CO', 'Mato Grosso':'CO', 'Mato Grosso do Sul':'CO','Distrito Federal':'CO', \n",
    "                    'Paraná':'S', 'Santa Catarina':'S', 'Rio Grande do Sul':'S'}\n",
    "\n",
    "dados_originais['Regiao'] = dados_originais.Estado.apply(lambda x: dicionario_regiao.get(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_originais['Mes_int'] = dados_originais.Mes.apply(lambda x: nomeMes_Numeral(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_originais['Trimestre'] = dados_originais.Mes_int.apply(lambda x: mes_Trimestre(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ano</th>\n",
       "      <th>Estado</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Numero</th>\n",
       "      <th>Regiao</th>\n",
       "      <th>Mes_int</th>\n",
       "      <th>Trimestre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6449</th>\n",
       "      <td>2012</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>128</td>\n",
       "      <td>N</td>\n",
       "      <td>12</td>\n",
       "      <td>Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6450</th>\n",
       "      <td>2013</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>85</td>\n",
       "      <td>N</td>\n",
       "      <td>12</td>\n",
       "      <td>Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6451</th>\n",
       "      <td>2014</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>223</td>\n",
       "      <td>N</td>\n",
       "      <td>12</td>\n",
       "      <td>Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6452</th>\n",
       "      <td>2015</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>373</td>\n",
       "      <td>N</td>\n",
       "      <td>12</td>\n",
       "      <td>Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6453</th>\n",
       "      <td>2016</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>119</td>\n",
       "      <td>N</td>\n",
       "      <td>12</td>\n",
       "      <td>Q4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6453 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Ano     Estado       Mes  Numero Regiao  Mes_int Trimestre\n",
       "0     1998       Acre   Janeiro       0      N        1        Q1\n",
       "1     1999       Acre   Janeiro       0      N        1        Q1\n",
       "2     2000       Acre   Janeiro       0      N        1        Q1\n",
       "3     2001       Acre   Janeiro       0      N        1        Q1\n",
       "4     2002       Acre   Janeiro       0      N        1        Q1\n",
       "...    ...        ...       ...     ...    ...      ...       ...\n",
       "6449  2012  Tocantins  Dezembro     128      N       12        Q4\n",
       "6450  2013  Tocantins  Dezembro      85      N       12        Q4\n",
       "6451  2014  Tocantins  Dezembro     223      N       12        Q4\n",
       "6452  2015  Tocantins  Dezembro     373      N       12        Q4\n",
       "6453  2016  Tocantins  Dezembro     119      N       12        Q4\n",
       "\n",
       "[6453 rows x 7 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_originais"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **🔍 Visualizando os dados limpos**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, com o conjunto de dados original, completo e limpo, vamos realizar algumas visualizações e descrições dos dados,\n",
    "\n",
    "a começar com o método **DESCRIBE**, para observar algumas das características das colunas numéricas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ano</th>\n",
       "      <th>Numero</th>\n",
       "      <th>Mes_int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6453.000000</td>\n",
       "      <td>6453.000000</td>\n",
       "      <td>6453.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.460251</td>\n",
       "      <td>567.641717</td>\n",
       "      <td>6.476987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.745872</td>\n",
       "      <td>1648.487298</td>\n",
       "      <td>3.441114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>25963.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Ano        Numero      Mes_int\n",
       "count  6453.000000   6453.000000  6453.000000\n",
       "mean   2007.460251    567.641717     6.476987\n",
       "std       5.745872   1648.487298     3.441114\n",
       "min    1998.000000      0.000000     1.000000\n",
       "25%    2002.000000      9.000000     3.000000\n",
       "50%    2007.000000     55.000000     6.000000\n",
       "75%    2012.000000    284.000000     9.000000\n",
       "max    2017.000000  25963.000000    12.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_originais.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir do describe, podemos ter uma visão das características gerais do DF:\n",
    "\n",
    "1. Count - **Contagem de registros** (linhas) na coluna;\n",
    "\n",
    "2. Mean - **Média da coluna**;\n",
    "\n",
    "3. Std - **Desvio padrão da coluna** (podemos observar por exemplo que no **YEAR** o STD é baixo,\n",
    "\n",
    "    pois os dados se concentram num período de apenas 19 anos de diferença, enquanto no NUMERO\n",
    "    \n",
    "    temos um STD muito mais alto, pois os dados variam entre 0 e 25963 na coluna;\n",
    "\n",
    "4. Min - **Valor mínimo** encontrado na coluna, menor registro;\n",
    "\n",
    "5. 25% - **Primeiro Quartil** ou **Q1** (25% dos valores se encontram abaixo daqui);\n",
    "\n",
    "6. 50% - **Segundo Quartil** ou **Q2** (50% dos valores estão abaixo daqui);\n",
    "\n",
    "7. 75% - **Terceiro Quartil** ou **Q3** (75% dos valores estão abaixo daqui);\n",
    "\n",
    "8. Max - **Valor máximo** encontrado na coluna, maior registro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ano        2009.0\n",
       "Numero       99.0\n",
       "Mes_int       8.0\n",
       "Name: 0.6, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Além dos quartis padrão vistos acima, podemos obter os dados que se encontram antes de uma porcentagem X%,\n",
    "# utilizando o método QUANTILE, e dentro dele passando a porcentagem que desejamos como argumento: nesse caso, 60%\n",
    "\n",
    "dados_originais.quantile(0.60)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Também podemos gerar gráficos que nos ajudam na visualização da distribuição dos dados, como o **BOXPLOT**.\n",
    "\n",
    "Nesse caso, podemos ver os três quartis citados acima na **BARRA** do gráfico, sendo sua parte inferior o Q1,\n",
    "\n",
    "a linha verde o Q2 e sua parte superior o Q3. Além disso, percebemos também uma linha que segue até um valor\n",
    "\n",
    "que se aproxima de 700. Esses dados são considerados \"normais\" dentro da distribuição. Os pontos pretos são os **OUTLIERS**,\n",
    "\n",
    "dados que não estão dentro da distribuição esperada da maioria dos dados. Porém, isso não quer dizer que devemos sempre\n",
    "\n",
    "eliminar estes dados, ou que eles são um problema. É esperado que em um país com grandes dimensões e peculiaridades\n",
    "\n",
    "(vegetação, cultura, clima) como o Brasil existam áreas muito mais propensas ao fenômeno, que terão um elevado número de casos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nestes dados os outliers (bolinhas pretas) vão até próximas do valor de 700 começam próximas ao valor de 700.\n",
    "# Para melhor visualização, limito os valores até 750 no gráfico da direita\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10,10))\n",
    "\n",
    "\n",
    "dados_originais.Numero.plot(kind='box', ylim=(0), ax=ax[0], ylabel='Número de incêndios', title='Outlier sem Ylim', )\n",
    "\n",
    "\n",
    "dados_originais.Numero.plot(kind='box',  ylim=(0,750), title='Outlier com Ylim', yticks=[i for i in range(0,751,50)])\n",
    "\n",
    "plt.axhline(y=dados_originais.Numero.median(), color='green', label='Mediana')\n",
    "plt.axhline(y=dados_originais.Numero.mean(), color='blue', label='Media')\n",
    "plt.axhline(y=dados_originais.Numero.mode()[0], color='red', label='Moda', marker='o')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outro método para verificarmos essa distribuição dos dados é o **DISTPLOT**, da biblioteca **SEABORN**:\n",
    "\n",
    "Notamos que ela não é uma Distribuição Padrão (Gaussiana/Bell Shaped), e sim uma distribuição <b style=\"color:#1c87d9\">**RIGHT SKEWED**</b>,\n",
    "\n",
    "com seus dados se concentrando na parte esquerda que se confirmam com o padrão da **moda** (0), seguido da **mediana** (55) e por último, a **média** (567.64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 570.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aqui é realizado o plot do gráfico. Em seguida, limito os valores do eixo X, caso contrário\n",
    "# o gráfico torna-se ilegível. Depois, rotulo os labels e o título do gráfico.  \n",
    "# Então é realizado o plot da linha da mediana, e por último a função de legenda para a linha.\n",
    "\n",
    "\n",
    "sns.displot(dados_originais, x='Numero', binwidth=25, hue='Regiao')\n",
    "\n",
    "plt.xlim(0,1000)\n",
    "\n",
    "plt.ylabel('Contagem')\n",
    "plt.xlabel('Incêndios')\n",
    "plt.title('Distribuição do número de incêndios')\n",
    "\n",
    "plt.axvline(x=dados_originais.Numero.median(), label='Mediana')\n",
    "\n",
    "plt.legend()\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moda: 0;\n",
      "Mediana: 55.0;\n",
      "Média: 567.6417170308383\n"
     ]
    }
   ],
   "source": [
    "print(f'Moda: {dados_originais.Numero.mode()[0]};\\nMediana: {dados_originais.Numero.median()};\\nMédia: {dados_originais.Numero.mean()}')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____________"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **📊 Plots**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Após realizar a limpeza e visualização da distribuição dos nossos dados, partimos para o plot\n",
    "\n",
    "de gráficos que nos forneçam mais informações sobre as ocorrências dos incêndios."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Série número de incêndios por estado"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definindo um colormap para os dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = sns.color_palette('YlOrRd', as_cmap=True)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui é realizado o agrupamento da coluna de números de acordo com os estados, de forma a calcular o total somado de cada estado.\n",
    "\n",
    "Após isso, é realizado a classificação ordenada e é atribuído o colormap que selecionamos para os dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  background-color: #fec05b;\n",
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       "#T_f7ae9_row8_col0, #T_f7ae9_row9_col0 {\n",
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       "  background-color: #fff1ab;\n",
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       "#T_f7ae9_row20_col0, #T_f7ae9_row21_col0 {\n",
       "  background-color: #fffdc8;\n",
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       "  background-color: #fffec9;\n",
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       "#T_f7ae9_row24_col0, #T_f7ae9_row25_col0, #T_f7ae9_row26_col0 {\n",
       "  background-color: #ffffcc;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f7ae9\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f7ae9_level0_col0\" class=\"col_heading level0 col0\" >Numero</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Estado</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row0\" class=\"row_heading level0 row0\" >Mato Grosso</th>\n",
       "      <td id=\"T_f7ae9_row0_col0\" class=\"data row0 col0\" >723629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row1\" class=\"row_heading level0 row1\" >Pará</th>\n",
       "      <td id=\"T_f7ae9_row1_col0\" class=\"data row1 col0\" >638042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row2\" class=\"row_heading level0 row2\" >Maranhão</th>\n",
       "      <td id=\"T_f7ae9_row2_col0\" class=\"data row2 col0\" >405879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row3\" class=\"row_heading level0 row3\" >Tocantins</th>\n",
       "      <td id=\"T_f7ae9_row3_col0\" class=\"data row3 col0\" >248378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row4\" class=\"row_heading level0 row4\" >Bahia</th>\n",
       "      <td id=\"T_f7ae9_row4_col0\" class=\"data row4 col0\" >239777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row5\" class=\"row_heading level0 row5\" >Rondônia</th>\n",
       "      <td id=\"T_f7ae9_row5_col0\" class=\"data row5 col0\" >224510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row6\" class=\"row_heading level0 row6\" >Piauí</th>\n",
       "      <td id=\"T_f7ae9_row6_col0\" class=\"data row6 col0\" >182406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row7\" class=\"row_heading level0 row7\" >Minas Gerais</th>\n",
       "      <td id=\"T_f7ae9_row7_col0\" class=\"data row7 col0\" >181589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row8\" class=\"row_heading level0 row8\" >Mato Grosso do Sul</th>\n",
       "      <td id=\"T_f7ae9_row8_col0\" class=\"data row8 col0\" >119897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row9\" class=\"row_heading level0 row9\" >Amazonas</th>\n",
       "      <td id=\"T_f7ae9_row9_col0\" class=\"data row9 col0\" >118691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row10\" class=\"row_heading level0 row10\" >Goiás</th>\n",
       "      <td id=\"T_f7ae9_row10_col0\" class=\"data row10 col0\" >115138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row11\" class=\"row_heading level0 row11\" >Ceará</th>\n",
       "      <td id=\"T_f7ae9_row11_col0\" class=\"data row11 col0\" >85436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row12\" class=\"row_heading level0 row12\" >Acre</th>\n",
       "      <td id=\"T_f7ae9_row12_col0\" class=\"data row12 col0\" >73439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row13\" class=\"row_heading level0 row13\" >São Paulo</th>\n",
       "      <td id=\"T_f7ae9_row13_col0\" class=\"data row13 col0\" >62308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row14\" class=\"row_heading level0 row14\" >Paraná</th>\n",
       "      <td id=\"T_f7ae9_row14_col0\" class=\"data row14 col0\" >54552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row15\" class=\"row_heading level0 row15\" >Rio Grande do Sul</th>\n",
       "      <td id=\"T_f7ae9_row15_col0\" class=\"data row15 col0\" >29891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row16\" class=\"row_heading level0 row16\" >Roraima</th>\n",
       "      <td id=\"T_f7ae9_row16_col0\" class=\"data row16 col0\" >29454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row17\" class=\"row_heading level0 row17\" >Santa Catarina</th>\n",
       "      <td id=\"T_f7ae9_row17_col0\" class=\"data row17 col0\" >27209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row18\" class=\"row_heading level0 row18\" >Amapá</th>\n",
       "      <td id=\"T_f7ae9_row18_col0\" class=\"data row18 col0\" >26403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row19\" class=\"row_heading level0 row19\" >Pernambuco</th>\n",
       "      <td id=\"T_f7ae9_row19_col0\" class=\"data row19 col0\" >24498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row20\" class=\"row_heading level0 row20\" >Paraíba</th>\n",
       "      <td id=\"T_f7ae9_row20_col0\" class=\"data row20 col0\" >12787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row21\" class=\"row_heading level0 row21\" >Rio de Janeiro</th>\n",
       "      <td id=\"T_f7ae9_row21_col0\" class=\"data row21 col0\" >11703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row22\" class=\"row_heading level0 row22\" >Rio Grande do Norte</th>\n",
       "      <td id=\"T_f7ae9_row22_col0\" class=\"data row22 col0\" >9426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row23\" class=\"row_heading level0 row23\" >Espírito Santo</th>\n",
       "      <td id=\"T_f7ae9_row23_col0\" class=\"data row23 col0\" >6546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row24\" class=\"row_heading level0 row24\" >Alagoas</th>\n",
       "      <td id=\"T_f7ae9_row24_col0\" class=\"data row24 col0\" >4606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row25\" class=\"row_heading level0 row25\" >Distrito Federal</th>\n",
       "      <td id=\"T_f7ae9_row25_col0\" class=\"data row25 col0\" >3561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f7ae9_level0_row26\" class=\"row_heading level0 row26\" >Sergipe</th>\n",
       "      <td id=\"T_f7ae9_row26_col0\" class=\"data row26 col0\" >3237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29de1c95130>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incendios_por_estado = dados_originais[['Estado', 'Numero']].groupby(by='Estado').sum().sort_values(by='Numero', ascending=False)\n",
    "incendios_por_estado.style.background_gradient(cmap=cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Soma total dos incêndios por estado"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesse caso para plotar o gráfico de barras foi utilizado o seu índice (estados) e o número de incêndios.\n",
    "\n",
    "Rotaciono os labels dos estados de forma vertical e atribuo novos nomes aos eixos, para em seguida plotar o gráfico:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=incendios_por_estado, x=incendios_por_estado.index, y='Numero', palette='YlOrRd_r')\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Total de incêndios florestais por estado (1997-2017)')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Ocorrências')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Média de incêndios por estado"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para realizar o plot a seguir, os dados usados são novamente dos Números e Estados,\n",
    "\n",
    " mas dessa vez calculando a sua média. Notamos que as posições ocupadas pelos estados são as mesmas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "medias_mais_recorrentes = dados_originais.groupby('Estado').Numero.mean().sort_values(ascending=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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th9v1pZCQENjb20uBDwCaNWsm12t04cIFyGQyNGzYMNPfJzY2FhEREdl+vR9td0hICEqVKiV3ullHRweHDh1Cly5dsvVeyVChQgW5QJAxljMjkGRn2792+fJlODg4yNXV1NRE69atcfPmTbx//x41a9ZEUlIS2rRpg1mzZuHq1auoX78+3Nzcvht2AaBjx45yPzdr1gyxsbGIiorC5cuXoampmek0c8ZY0y9nNTE1Nf3hafOLFy/Czs4OBQoUkH6PhQoVQo0aNaShHT96H2alQ4cOWLZsGT59+oQ7d+7g0KFDCAwMRFpaGj59+gQAuHr1KvLnzw97e3vpeRlDoTKEhoYiJSUl0xmhGjVqwNjY+LuzuERHR6NUqVLf3f6s6OnpYeXKlUhLS0OXLl1ga2sLPz8/6SBIR0cHAODl5YV27dph0qRJsLOzQ8eOHWFjY4Nq1apJj/nS+vXr4ejoKI2HzZCWlib3Ps44+/Clly9fom/fvnj58iWCgoKk9973poL70fvsWw4dOoTY2NjvXgBqbGyMV69e5dq4efp3YQ8vET6HUTc3N8TExEBbWxsXLlzAiBEjMj3uzZs3APDN3tYXL15IF6Jl9N5lMDAw+G4bHj9+DE9PT1y4cAH58+eHqakpzMzMAPx47tCsek8KFiz4zcdnbIeXlxe8vLwyrX/58qXcz19+MWb0hGW0qVWrVkhPT8eGDRuwcOFCzJ8/H8bGxhgzZozUy7p7927Mnj0bz58/h66uLqpUqYICBQp8d5u+9vbt20y/U01NTbllb968gRAC1atXz7LGy5cvsxwP+S3f2+43b95AX1//m8/Nznslq9f58rUyxolmZ9u/9vbtWxQvXjzT8uLFi0MIgcTERNjY2GDp0qVYvXo1Vq1ahaVLl6J48eIYPHjwd4MiALkLrABIv4u3b99K7f06kGd8BjIOvIDPvXw/8ubNG+zfvz/LsdgZn7fsvA+/9vHjR3h7e2PXrl1ITU1FqVKlYGNjA01NTenv/PbtW+jq6mYKZl9+njN6kb/1+/5ye7+WmJiYZfDMDgsLC+zZswcvXrxAUlISypYti4sXLwKAdAD822+/wc/PDx4eHnj27BmMjIzw22+/Ydu2bZl6Ru/cuYOHDx9meeagX79+csHdzs5O7tqFu3fvYvDgwXj//j2WL18OKysraV2hQoUQFxeXqeb79++zvCAvOw4dOoSKFStK+8isZOwD3717p/DvmNQHAy8RgAYNGuC3337DwYMHUbBgQZQqVQrm5uaZHpcxY8OaNWuy/KI2MjJCQkICAEhjCzNkBKCspKenY+DAgcifPz+2bduGKlWqQFNTE/fv35eGIahSxnaMGzcuy2mavp4m6EfatGmDNm3a4N27dzh79iyWLVuGsWPHwtbWFtHR0Rg/fjx69+4NFxcXKSjNnDkz0wVU36Orq5vpS1MIIXdRUOHChVGwYEEEBwdnWUPRGTWyUrhw4Swvurt27RqKFi2arfdKdmVn279WtGjRLENGbGwsgP87ILO3t5eGfVy8eBHBwcHw8fGBlZXVd8dwvn79Wu4UdMb7XV9fH0WLFsXr16+RlpYmF3ozDqS+F9SzUrhwYdStWzfLi9q+HKLyvffh1wEdAHx9fXHo0CHMnTsXdevWlQLSl1NZFStWLMtt+fLznPF5iYuLg6mpqdxrxMbGyp1p+dqXZ15y4vXr1zh58iQaNmwot223bt2CTCaTDuxOnDiBIkWKwNbWVhpG8urVK8TExKBq1apyNU+ePAkdHZ1MF7kBnw+Ovxx+8OV7+uLFi3B1dUXhwoWxfv166XUymJiY4OzZs0hPT5e7uO3Ro0coX758jrf906dPOHv27A+n93v79i1kMhl0dXVz/BqkfjikgQifp3Rq0qQJDh06hAMHDnyzVy5jDOrr169hYWEh/YuPj8e8efPw5s0blCtXDiVLlsw088P35vR9/fo1oqKi0LlzZ1hYWEhf4qdPnwaALK8IV4apqSn09fXx5MkTue0wNDTErFmzcOvWrWzXGjFiBFxdXQF8DiYtW7bE0KFDkZqaipcvX+L69etIT0/HsGHDpC/mtLQ06VR0dretTp06OH36tNzpyTNnzkinnoHPvU4fPnyAEEJuu+7du4cFCxbITbavrBo1aiA6OlpumERycjKGDRuGbdu2Zeu9kl3Z2fav1axZEydOnJCbLSMtLQ379u2DhYUFtLS0MGPGDHTq1AlCCOjo6MDBwQHjx48HgEwXOn3t6NGjcj8fPHgQxsbGKFOmDOzs7JCamprpM5AxRdTXY+N/xM7ODvfv30eVKlWk36O5uTlWr16NI0eOAPjx+xCAXNgCPg8VqVWrFpo0aSKF3Zs3byI+Pl56X9apUwepqaly25uSkiI37MfKygpaWlqZxotevXoVz549++YZB+Dzaffnz5/n6PcBfD7gcXd3x+HDh6Vl79+/x5YtW1CzZk0phG/atAkzZ86Ue+6aNWuQL1++TMOuQkNDUbVq1SzPvpiamsq9jzOC/a1btzB48GCULFkSmzdvzhR2gc9j19+/f48zZ85Iy+Lj43H16lXUq1cvx9t+7949JCUl/fB9FBMTg+LFi/9wyAz9N7CHl+j/a9WqFQYNGgQNDQ1MmjQpy8dUrlwZ7dq1w+TJk/H06VOYm5sjKioKc+bMQalSpVCuXDnIZDKMGTMGo0ePxqRJk9CiRQuEhoZi48aN33xtfX19GBsbY/369fj9999RpEgRnDlzRuqpVPUYtHz58mHkyJHw9PSUvvgSEhKwcOFCvHjxAtWqVct2rdq1a2PKlCmYMWMGGjRogISEBAQFBaFcuXIwMzOTxrxOmzYNnTp1wtu3b7F+/XrcuXMHwOcxsVkNyfiaq6srjh49ChcXF/z555+Ij4/H3Llz5ca1NmzYEDVr1sTQoUMxdOhQlC9fHuHh4QgMDIS9vb1K5z12cnLC2rVrMWTIEAwfPhzFihVDcHAwPn36hJ49e6J06dI/fK9kV3a2/Wtubm44ffo0+vTpI509WLduHaKjo7F8+XIAn/92q1atwoQJE9CuXTt8+vQJy5cvh66uLmrXrv3dNq1atQra2tqwtrbG4cOHceLECcyaNQvA5zMmtWrVwqRJk/DixQuYmZnh8uXLWLZsGTp27JjjG3wMHToU3bt3x6BBg9CjRw9oa2tj8+bNOHr0qDS/7I/eh8DnMxu3bt3C5cuXYWlpCUtLSxw4cAAbN25E+fLlcefOHSxatAgymUz6zNWpUwf169fHpEmT8OrVKxgbGyM4OBjx8fHSMA5dXV0MHDgQCxYsQP78+eHg4IAnT55g3rx5qFChQqbxzl+qV68eNmzY8MOLBL+mp6eH1q1bY+7cuShQoAD09PSwZMkSvHz5Uvo7AJDOrPj5+cHR0REXLlzAkiVLMGDAgEwXid27d09u6rDs8PDwQGpqKoYNG4bnz5/LhXc9PT2UKVMGNWvWhJ2dHcaOHYuxY8dCV1cX8+fPR+HChdGjR48cvV5GOwH8sHf42rVrcmOv6b+NgZfo/6tbty6KFCmCkiVLfndHOn36dCxZsgSbNm1CTEwM9PX10apVK4wYMUI65dmmTRtoaGhg4cKF2LVrFypVqoRp06Z9dyqphQsXwtfXFxMmTICWlhYqVKiARYsWwc/PD1evXv3hmMqc6tKlC3777TcsX74cmzdvRsGCBVG9enUEBAR89xTs17p3745Pnz5h06ZN2LBhAwoUKIA6depg7NixyJ8/P2rVqgVPT0+sWrUKBw8eRPHixVGrVi0EBQXB1dUVISEhchcAfUu5cuWwbt06/PXXXxg5ciT09fWlaaIyaGhoYOnSpZg3bx6WLFmCV69ewdDQEH/88YfU+6cqhQoVwrp16zBz5kx4e3sjPT0d1tbWCA4Oln5/2XmvZEd2tv1rFStWxIYNGzB79my4u7tDJpPB0tISwcHBUu9zw4YNERAQgJUrV0oXqtna2iI4OPiHp4EnTpyIHTt2YMmSJTA1NUVgYCCaN28OANJMFIGBgVi9ejXi4+NRqlQpjBo16rtz7X6LmZkZ1q9fjzlz5mDcuHEQQqBSpUpYsGABGjduDODH70MA6N+/P/z8/ODi4iIF/U+fPmHu3LlISUlBqVKlMGTIENy/fx/Hjx+XhjEEBQUhICAAgYGBSE5ORqtWrdC1a1ccO3ZMauOwYcNQvHhxrFu3Dps3b4auri5atGiBESNGfHc8fbNmzbBgwQKEh4fLjXvNDi8vLwQEBCAgIABJSUmwtrbGmjVr5IZj1a9fH7NmzcKiRYuwefNmGBkZYdKkSVnuT169evXNG+1kJTo6WjobNHz48EzrO3bsKL1Hg4KC8Ndff2HmzJlIT09H9erVMXfu3BwPnwIgDdX53nNfvnyJO3fu4H//+1+O65N6kokfXQ1DRET0/126dAl9+vRBcHAwatWqldfNUQuDBw9GsWLFMH369LxuitpYsGABjhw5gh07dig8EwSpF47hJSIiykMjR47E4cOHfzhumrLn/fv32LhxI0aNGsWwSxIGXiIiojxUuXJlDBo0SO4mMKS4pUuXwtHRUbpRCBHAIQ1EREREpObYw0tEREREao2Bl4iIiIjUGgMvEREREak1Bl4iIiIiUmu88cQ3xMbm/N7mRERERPTzGBgUztbj2MNLRERERGqNgZeIiIiI1BoDLxERERGpNQZeIiIiIlJrDLxEREREpNYYeImIiIhIrTHwEhEREZFaY+AlIiIiIrXGwEtEREREao2Bl4iIiIjUGgMvEREREak1Bl4iIiIiUmsMvERERESk1hh4iYiIiEitMfASERERkVpj4CUiIiIitcbAS0RERERqjYGXiIiIiNRangbeR48ewcXFBTY2NmjUqBGWL18urYuOjka/fv1gbW2NVq1a4ezZs3LPPX/+PNq0aQMrKyv06dMH0dHRcutXr14Ne3t72NjYYOLEiUhKSvop20REREREvxaZEELkxQunp6ejZcuWsLCwgJubGx49eoRRo0Zh6tSpaNOmDdq3b49KlSphyJAhOHr0KBYtWoT9+/fDyMgIz549Q+vWrTFs2DDY29tjwYIFiIyMxO7duyGTyXDo0CF4eHjA398f+vr6cHd3R61ateDp6Znt9sXGvpP+v8DMXiXb7HrnjErqEBERERFgYFA4W4/Lsx7euLg4VKlSBVOnTkW5cuXQsGFD1KlTByEhIbh48SKio6Mxbdo0lC9fHoMGDYK1tTW2b98OANi6dSvMzc3Rv39/VKxYEdOnT8fTp09x+fJlAEBwcDD69u0LBwcHWFpawsvLC9u3b2cvLxEREdF/UJ4F3hIlSmDu3LkoVKgQhBAICQnBlStXYGdnh7CwMFStWhUFCxaUHm9ra4vQ0FAAQFhYGGrUqCGt09HRQbVq1RAaGoq0tDTcuHFDbr21tTU+ffqEO3fu/LTtIyIiIqJfg2ZeNwAAHB0d8ezZMzg4OKB58+bw8/NDiRIl5B6jr6+PmJgYAEBsbOw31yckJCA5OVluvaamJnR1daXnZ4eGhgwaGjIltiozTU1eI0hERET0s/0SgTcwMBBxcXGYOnUqpk+fjqSkJGhpack9RktLCykpKQDw3fUfP36Ufv7W87NDT+83yGSqDbzFiv2m0npERERE9GO/ROC1sLAAACQnJ2PMmDHo1KlTpvG2KSkpKFCgAABAW1s7U3hNSUlBkSJFoK2tLf389XodHZ1styk+/r3Ke3hfv36v0npERERE/2XZ7UzMs8AbFxeH0NBQNGnSRFpWoUIFfPr0CQYGBnjw4EGmx2cMUzA0NERcXFym9VWqVIGuri60tbURFxeH8uXLAwBSU1Px5s0bGBgYZLt96ekC6emqncAiNTVdpfWIiIiI6MfybFDpkydP4ObmhhcvXkjLbt68CT09Pdja2uKff/6RhicAQEhICKysrAAAVlZWCAkJkdYlJSXh1q1bsLKygoaGBiwsLOTWh4aGQlNTE2ZmZj9hy4iIiIjoV5JngdfCwgLVqlXDxIkTcf/+fZw6dQr+/v4YPHgw7OzsULJkSbi7uyMiIgJLly5FeHg4OnfuDADo1KkTrl27hqVLlyIiIgLu7u4oVaoUatWqBQDo2bMnVqxYgaNHjyI8PBxTp05F165dczSkgYiIiIjUQ57deAIAXrx4AW9vb1y4cAE6Ojro1asXBg0aBJlMhkePHsHDwwNhYWEoW7YsJk6ciLp160rPPXXqFPz8/BATEwMbGxt4e3ujdOnS0vqlS5di9erVSElJQbNmzTBlyhRpfG928MYTRERERL+27N54Ik8D76+MgZeIiIjo1/bL32mNiIiIiOhnYOAlIiIiIrXGwEtEREREao2Bl4iIiIjUGgMvEREREak1Bl4iIiIiUmsMvERERESk1hh4iYiIiEitMfASERERkVpj4CUiIiIitcbAS0RERERqjYGXiIiIiNQaAy8RERERqTUGXiIiIiJSawy8RERERKTWGHiJiIiISK0x8BIRERGRWmPgJSIiIiK1xsBLRERERGqNgZeIiIiI1BoDLxERERGpNQZeIiIiIlJrDLxEREREpNYYeImIiIhIrTHwEhEREZFaY+AlIiIiIrXGwEtEREREao2Bl4iIiIjUGgMvEREREak1Bl4iIiIiUmsMvERERESk1hh4iYiIiEitMfASERERkVpj4CUiIiIitcbAS0RERERqjYGXiIiIiNQaAy8RERERqTXNvG7Af926qg1VUqfXrVMqqUNERESkbtjDS0RERERqjYGXiIiIiNQaAy8RERERqTUGXiIiIiJSawy8RERERKTWGHiJiIiISK0x8BIRERGRWmPgJSIiIiK1xsBLRERERGqNgZeIiIiI1BoDLxERERGpNQZeIiIiIlJrDLxEREREpNYYeImIiIhIrTHwEhEREZFaY+AlIiIiIrWWp4H3xYsXGD58OOzs7GBvb4/p06cjOTkZAODj44PKlSvL/Vu3bp303L1796JJkyawsrKCq6sr4uPjpXVCCAQEBKB27dqws7PDzJkzkZ6e/tO3j4iIiIjynmZevbAQAsOHD0eRIkWwfv16vH37FhMnToSGhgbGjx+PyMhIjB49Gh07dpSeU6hQIQBAeHg4PDw84OXlBTMzM/j6+sLd3R1LliwBAKxatQp79+5FUFAQUlNTMXbsWOjr68PFxSVPtpWIiIiI8k6e9fA+ePAAoaGhmD59OipWrIgaNWpg+PDh2Lt3LwAgMjISVatWhYGBgfRPR0cHALBu3Tq0bNkSHTp0gJmZGWbOnIlTp04hOjoaABAcHIzhw4ejRo0aqF27NsaMGYP169fn1aYSERERUR7Ks8BrYGCA5cuXo3jx4nLLExMTkZiYiBcvXqBcuXJZPjcsLAw1atSQfi5ZsiSMjIwQFhaGFy9e4Pnz56hZs6a03tbWFk+fPsXLly9zZVuIiIiI6NeVZ4G3SJEisLe3l35OT0/HunXrULt2bURGRkImk2Hx4sVo0KAB2rVrhx07dkiPffnyJUqUKCFXT19fHzExMYiNjQUAufUZoTomJiY3N4mIiIiIfkF5Nob3a/7+/rh16xa2bduGf/75BzKZDKampujVqxeuXLmCyZMno1ChQmjatCk+fvwILS0tuedraWkhJSUFHz9+lH7+ch0ApKSkZLs9GhoyaGjIVLBl/0dTM/eOL3KzNhEREdG/2S8ReP39/bFmzRrMmTMHlSpVQsWKFeHg4ABdXV0AgJmZGR4+fIiNGzeiadOm0NbWzhReU1JSoKOjIxdutbW1pf8DkMYAZ4ee3m+QyVQbeIsV+02l9X5WbSIiIqJ/szwPvN7e3ti4cSP8/f3RvHlzAIBMJpPCbgZTU1NcvHgRAGBoaIi4uDi59XFxcTAwMIChoSEAIDY2FqVKlZL+D3weN5xd8fHvVd7D+/r1e5XW+1m1iYiIiH5F2e3wy9PAGxQUhE2bNmH27Nlo0aKFtHzevHm4fv06Vq9eLS27c+cOTE1NAQBWVlYICQmBk5MTAOD58+d4/vw5rKysYGhoCCMjI4SEhEiBNyQkBEZGRpnG/X5PerpAerpQwVb+n9TU3JsLODdrExEREf2b5VngjYyMxMKFCzFw4EDY2tpKvbAA4ODggKVLl2LFihVo2rQpzp49i507dyI4OBgA0KNHD/Tu3RvW1tawsLCAr68vGjVqhNKlS0vrAwIC8PvvvwMAZs2ahf79+//8jSQiIiKiPJdngffYsWNIS0vDokWLsGjRIrl1d+/exbx58xAYGIh58+bB2NgYs2bNgo2NDQDAxsYG06ZNQ2BgIN6+fYt69erB29tber6LiwtevXoFNzc35MuXD507d0a/fv1+5uYRERER0S9CJoRQ7Xl7NREb+076/wIz++88Mvtc75zJtGxd1YYqqd3r1imV1CEiIiL6tzAwKJytx3EuKyIiIiJSawy8RERERKTWGHiJiIiISK0x8BIRERGRWmPgJSIiIiK1xsBLRERERGqNgZeIiIiI1BoDLxERERGpNQZeIiIiIlJrDLxEREREpNYYeImIiIhIrTHwEhEREZFaY+AlIiIiIrXGwEtEREREao2Bl4iIiIjUGgMvEREREak1Bl4iIiIiUmsMvERERESk1hh4iYiIiEitMfASERERkVpj4CUiIiIitcbAS0RERERqjYGXiIiIiNQaAy8RERERqTUGXiIiIiJSawy8RERERKTWGHiJiIiISK0x8BIRERGRWmPgJSIiIiK1xsBLRERERGqNgZeIiIiI1BoDLxERERGpNQZeIiIiIlJrDLxEREREpNYYeImIiIhIrTHwEhEREZFaY+AlIiIiIrXGwEtEREREao2Bl4iIiIjUGgMvEREREak1Bl4iIiIiUmsMvERERESk1hh4iYiIiEitMfASERERkVpj4CUiIiIitcbAS0RERERqjYGXiIiIiNQaAy8RERERqTUGXiIiIiJSawy8RERERKTWGHiJiIiISK0x8BIRERGRWmPgJSIiIiK1xsBLRERERGqNgZeIiIiI1FqeBt4XL15g+PDhsLOzg729PaZPn47k5GQAQHR0NPr16wdra2u0atUKZ8+elXvu+fPn0aZNG1hZWaFPnz6Ijo6WW7969WrY29vDxsYGEydORFJS0k/bLiIiIiL6deRZ4BVCYPjw4UhKSsL69esxZ84cnDhxAnPnzoUQAq6urihevDi2b9+O9u3bw83NDc+ePQMAPHv2DK6urnBycsK2bdugp6eHoUOHQggBADh06BCCgoIwbdo0rFmzBmFhYfD398+rTSUiIiKiPJRngffBgwcIDQ3F9OnTUbFiRdSoUQPDhw/H3r17cfHiRURHR2PatGkoX748Bg0aBGtra2zfvh0AsHXrVpibm6N///6oWLEipk+fjqdPn+Ly5csAgODgYPTt2xcODg6wtLSEl5cXtm/fzl5eIiIiov+gPAu8BgYGWL58OYoXLy63PDExEWFhYahatSoKFiwoLbe1tUVoaCgAICwsDDVq1JDW6ejooFq1aggNDUVaWhpu3Lght97a2hqfPn3CnTt3cnejiIiIiOiXo5lXL1ykSBHY29tLP6enp2PdunWoXbs2YmNjUaJECbnH6+vrIyYmBgC+uz4hIQHJycly6zU1NaGrqys9Pzs0NGTQ0JApsmnfpKmZe8cXuVmbiIiI6N8szwLv1/z9/XHr1i1s27YNq1evhpaWltx6LS0tpKSkAACSkpK+uf7jx4/Sz996fnbo6f0GmUy1gbdYsd9UWu9n1SYiIiL6N/slAq+/vz/WrFmDOXPmoFKlStDW1sabN2/kHpOSkoICBQoAALS1tTOF15SUFBQpUgTa2trSz1+v19HRyXab4uPfq7yH9/Xr9yqt97NqExEREf2Kstvhl+eB19vbGxs3boS/vz+aN28OADA0NMT9+/flHhcXFycNUzA0NERcXFym9VWqVIGuri60tbURFxeH8uXLAwBSU1Px5s0bGBgYZLtd6ekC6elCmU3LJDU1XaX1flZtIiIion+zPB34GRQUhE2bNmH27Nlo3bq1tNzKygr//POPNDwBAEJCQmBlZSWtDwkJkdYlJSXh1q1bsLKygoaGBiwsLOTWh4aGQlNTE2ZmZj9hq4iIiIjoV5JngTcyMhILFy7EgAEDYGtri9jYWOmfnZ0dSpYsCXd3d0RERGDp0qUIDw9H586dAQCdOnXCtWvXsHTpUkRERMDd3R2lSpVCrVq1AAA9e/bEihUrcPToUYSHh2Pq1Kno2rVrjoY0EBEREZF6yLMhDceOHUNaWhoWLVqERYsWya27e/cuFi5cCA8PDzg5OaFs2bJYsGABjIyMAAClSpXC/Pnz4efnhwULFsDGxgYLFiyQLjJr3bo1nj59Ck9PT6SkpKBZs2YYO3bsT99GIiIiIsp7MpFxezKSExv7Tvr/AjP77zwy+1zvnMm0bF3Vhiqp3evWKZXUISIiIvq3MDAonK3HcfJWIiIiIlJrDLxEREREpNYYeImIiIhIrTHwEhEREZFaY+AlIiIiIrXGwEtEREREao2Bl4iIiIjUGgMvEREREak1Bl4iIiIiUmsKB949e/YgJiYGALBw4UK0adMGnp6eSE5OVlnjiIiIiIiUpVDgXbhwITw8PPDs2TOEhIQgMDAQNjY2uHTpEgICAlTdRiIiIiIihSkUeLdv344ZM2agevXqOHToEKytreHt7Q1fX18cPHhQ1W0kIiIiIlKYQoH35cuXsLGxAQCcP38e9evXBwCULFkSCQkJqmsdEREREZGSNBV50u+//46oqCgkJyfj/v37qFevHgDg6tWr+P3331XaQCIiIiIiZSgUeLt3744RI0ZAS0sLlStXho2NDdavX4+ZM2di+PDhqm4jEREREZHCFAq8Li4uMDExQXR0NNq1awcAKFKkCCZPnozOnTurtIFERERERMpQKPACgKOjo9zPbdu2VboxRERERESqplDgTU5OxubNm3Hv3j2kpaVJy1NSUnDz5k0cOnRIZQ0kIiIiIlKGQoHXx8cHO3fuRNWqVXHjxg3Y2Njg0aNHePXqFfr166fiJhIRERERKU6hacmOHTuG6dOnY/PmzTA2Noa3tzdOnDiBxo0b49OnT6puIxERERGRwhQKvAkJCahevToAoEKFCrh16xby58+PQYMG4cSJEyptIBERERGRMhQKvHp6enj16hUAoFy5crh37x4AoFixYoiLi1Nd64iIiIiIlKRQ4G3QoAG8vLwQEREBW1tb7N27Fzdu3MD69et54wkiIiIi+qUoFHjHjRuHEiVK4PLly2jcuDHKly+PLl26YO3atbzxBBERERH9UhSapaFIkSJYuHCh9PPSpUtx+/ZtFC9eHCVKlFBZ44iIiIiIlJXtwHvlyhXY2NhAU1MTV65cyfIx79+/x6NHj1CzZk2VNZCIiIiISBnZDry9e/fGuXPnoK+vj969e3/zcTKZDLdv31ZJ44iIiIiIlJXtwHvs2DHo6elJ/yciIiIi+jfI9kVrxsbGkMlk0v+fPn2KR48ewdjYGMbGxli3bh2eP38OY2PjXGssEREREVFOKTRLw759+9C/f3+5oQtPnz5Fv379cPToUZU1joiIiIhIWQoF3sWLF2PChAlwcXGRlgUGBmLcuHGYP3++yhpHRERERKQshQLv48eP0bBhw0zLHRwc8PDhQ2XbRERERESkMgoF3pIlS2Y5Ndn169dhYGCgdKOIiIiIiFRFoRtP9OjRA97e3nj8+DGsrKwAADdu3MCaNWswdOhQlTaQiIiIiEgZCgXevn37IiUlBcHBwVi8eDEAoESJEhg5ciR69eql0gaS4vaYO6ikTtubJ1RSh4iIiCgvKBR4AWDAgAEYMGAAXr9+jfz586NQoUKqbBcRERERkUooHHiTkpJw7949fPr0CUIIuXW8tTARERER/SoUCrzHjh3DhAkTkJiYmCns8tbCRERERPQrUSjwBgQEoE6dOhg6dCgKFy6s6jYREREREamMQoH3yZMnWLJkCcqUKaPq9hARERERqZRC8/CWK1cOMTExqm4LEREREZHKKdTDO2bMGHh7e2PkyJEwNTWFlpaW3HojIyOVNI6IiIiISFkKBd6hQ4ciLS0NQ4cOhUwmk5YLIXjRGhERERH9UhQKvKtWrVJ1O4iIiIiIcoVCgdfOzk76f0pKSqYhDUREREREvwqFLloDgI0bN8LR0RHW1taIjo7GlClTsHDhQlW2jYiIiIhIaTkKvJcuXcKLFy+wZ88ezJo1Cx07dkT+/PkBAOXLl8fixYuxcuXKXGkoEREREZEish14V6xYgQEDBiA1NRUrVqyAh4cHhg0bBg2NzyX69OkDT09PbN68OdcaS0RERESUU9kOvAEBAZg/fz6MjY3x8OFD1KhRI9NjatWqhefPn6u0gUREREREysh24LWzs4OHhwfi4+NRvHhxREVFZXrM9evXUaJECZU2kIiIiIhIGdkOvKtXr0a3bt2QmJiIbt26Ydq0aTh27BgA4MGDB9i4cSN8fX3h5OSUa40lIiIiIsqpbE9LJpPJMGzYMADAgAED8O7dO4waNQrJyckYNGgQNDU10b17dwwePDjXGktERERElFMKzcN79epVDBs2DEOGDMH9+/chhICpqSkKFSqk6vYRERERESlFoXl4hw0bhnv37kFHRwcWFhawtLRk2CUiIiKiX5JCgVdPTw/v3r1TdVuIiIiIiFROoSENDRo0wKBBg9CwYUOULVsW2tracuvd3NxyVC8lJQVOTk6YPHkyatWqBQDw8fHB2rVr5R43efJk9OrVCwCwd+9ezJ07F7Gxsahfvz68vb2hp6cHABBCYNasWdi2bRvS09PRuXNnjBkzRpozmIiIiIj+OxQKvIcOHYK+vj5u3ryJmzdvyq2TyWQ5CrzJyckYPXo0IiIi5JZHRkZi9OjR6Nixo7QsY9hEeHg4PDw84OXlBTMzM/j6+sLd3R1LliwBAKxatQp79+5FUFAQUlNTMXbsWOjr68PFxUWRzSUiIiKifzGFAu+qVatQtmxZpV/8/v37GD16NIQQmdZFRkbCxcUFBgYGmdatW7cOLVu2RIcOHQAAM2fOhIODA6Kjo1G6dGkEBwdj+PDh0s0xxowZg3nz5jHwEhEREf0HKXSOv1evXggPD1f6xS9fvoxatWpluh1xYmIiXrx4gXLlymX5vLCwMLk7vZUsWRJGRkYICwvDixcv8Pz5c9SsWVNab2tri6dPn+Lly5dKt5mIiIiI/l0U6uHNnz8/NDUVeqqcnj17Zrk8MjISMpkMixcvxunTp6Grq4s//vhDGt7w8uXLTHd009fXR0xMDGJjYwFAbn3x4sUBADExMdm+E5yGhgwaGrIcb9P3aGrm3hjif2ttIiIiotymUGrt2LEj/vzzT7Rv3x5ly5ZFgQIF5NZnDDVQ1IMHDyCTyWBqaopevXrhypUrmDx5MgoVKoSmTZvi48eP0NLSknuOlpYWUlJS8PHjR+nnL9cBny+Oyy49vd8gk6k28BYr9ptK66lDbSIiIqLcplDgXbBgAYDPY3m/JpPJlA68HTp0gIODA3R1dQEAZmZmePjwITZu3IimTZtCW1s7U3hNSUmBjo6OXLjNmD0i47E6OjrZbkN8/HuV9/C+fv1epfXUoTYRERGRorLbKadQ4L1z544iT8s2mUwmhd0MpqamuHjxIgDA0NAQcXFxcuvj4uJgYGAAQ0NDAEBsbCxKlSol/R9AlhfAfUt6ukB6euaL6ZSRmpqu0nrqUJuIiIgotyk1ODMyMhIHDhzA0aNHERUVpao2Yd68eejXr5/csjt37sDU1BQAYGVlhZCQEGnd8+fP8fz5c1hZWcHQ0BBGRkZy60NCQmBkZJTt8btEREREpD4U6uHNmDv36NGj0jKZTAYHBwfMnTs30/janHJwcMDSpUuxYsUKNG3aFGfPnsXOnTsRHBwMAOjRowd69+4Na2trWFhYwNfXF40aNULp0qWl9QEBAfj9998BALNmzUL//v2VahMRERER/TspFHjnzJmD8PBwLFiwAHZ2dkhPT8eVK1fg4+OD+fPnY/To0Uo1ytLSEvPmzUNgYCDmzZsHY2NjzJo1CzY2NgAAGxsbTJs2DYGBgXj79i3q1asHb29v6fkuLi549eoV3NzckC9fPnTu3DlTjzERERER/TfIRFZ3ffiBjFv5Ojg4yC0/ceIEvLy8cPLkSVW1L8/Exr6T/r/AzF4lNV3vnMm0bF3Vhiqp3evWqUzL9pg7ZPHInGt784RK6hARERGpkoFB4Ww9TqExvO/fv5fG037JxMQE8fHxipQkIiIiIsoVCgXeSpUq4eDBg5mWHzhwACYmJko3ioiIiIhIVRQawztkyBAMHToUt2/fRvXq1QF8ngnhyJEjmDVrlkobSERERESkDIUCb6NGjTBv3jwsW7YMJ0+ehBAClStXxty5c9GsWTNVt5GIiIiISGEKBV4AaNKkCWxtbaGnpwcACA8PR7Vq1VTWMCIiIiIiVVBoDO/jx4/RokULLF++XFo2cOBAtG/fHs+fP1dZ44iIiIiIlKVQ4PXz80PZsmXl5rbdv38/SpYsienTp6uqbURERERESlMo8F69ehUTJkyQu1Wvnp4exo0bh4sXL6qscUREREREylIo8GpqaiIhISHT8qSkJChwHwsiIiIiolyjUOBt0KABfHx88PjxY2lZdHQ0pk+fDnt71dyVjIiIiIhIFRSapWH8+PH4448/0Lx5cxQpUgQAkJCQgGrVqsHd3V2lDSQiIiIiUoZCgVdfXx9BQUHYvXs3fvvtN+TLlw8VKlRAnTp1IJPJVN1GIiIiIiKF5SjwCiGwfPlyrFu3Di9fvpSWGxgYoFevXqhTp47KG0hEREREpIwcBV43NzecPn0a7du3R506dVCsWDG8ffsWly5dwqJFixAWFoYFCxbkVluJiIiIiHIs24F3+/btuHz5MrZs2YIqVarIrWvZsiWcnZ3Ru3dv/P3333ByclJ5Q4mIiIiIFJHtWRq2bt0KNze3TGE3Q8WKFTFs2DBs27ZNZY0jIiIiIlJWtgNvZGTkD6ccs7e3R0REhNKNIiIiIiJSlWwH3tTUVGhq/ngEBGdpICIiIqJfSbYDb4UKFXD27NnvPubs2bOoWLGi0o0iIiIiIlKVbAfejh07IigoCI8ePcpyfWRkJIKCgtC1a1eVNY6IiIiISFnZnqWhe/fuOHnyJDp16gQnJydUr14durq6SExMxJUrV7B161Y0bNgQ7du3z832EhERERHlSLYDr4aGBhYuXIjFixdj3bp1CA4OltYZGBjA1dUV/fv3z5VGEhEREREpKkc3ntDU1ISbmxtcXV0RFRWFt2/folixYihTpgw0NLI9OoKIiIiI6KfJUeDNIJPJYGpqquq2EBERERGpHLtliYiIiEitMfASERERkVpj4CUiIiIitcbAS0RERERqjYGXiIiIiNQaAy8RERERqTUGXiIiIiJSawy8RERERKTWGHiJiIiISK0x8BIRERGRWmPgJSIiIiK1xsBLRERERGqNgZeIiIiI1BoDLxERERGpNQZeIiIiIlJrDLxEREREpNYYeImIiIhIrTHwEhEREZFaY+AlIiIiIrXGwEtEREREao2Bl4iIiIjUGgMvEREREak1Bl4iIiIiUmsMvERERESk1hh4iYiIiEitMfASERERkVpj4CUiIiIitcbAS0RERERqjYGXiIiIiNQaAy8RERERqTXNvG4AAKSkpMDJyQmTJ09GrVq1AADR0dGYPHkyQkNDYWRkhIkTJ6J+/frSc86fPw8/Pz9ER0fDysoKvr6+KF26tLR+9erVWLFiBRITE9GyZUtMnjwZOjo6P33b1NFJG0eV1Wp0/bjKahERERFlJc97eJOTkzFq1ChERERIy4QQcHV1RfHixbF9+3a0b98ebm5uePbsGQDg2bNncHV1hZOTE7Zt2wY9PT0MHToUQggAwKFDhxAUFIRp06ZhzZo1CAsLg7+/f55sHxERERHlrTwNvPfv30fXrl3x+PFjueUXL15EdHQ0pk2bhvLly2PQoEGwtrbG9u3bAQBbt26Fubk5+vfvj4oVK2L69Ol4+vQpLl++DAAIDg5G37594eDgAEtLS3h5eWH79u1ISkr66dtIRERERHkrTwPv5cuXUatWLWzevFlueVhYGKpWrYqCBQtKy2xtbREaGiqtr1GjhrROR0cH1apVQ2hoKNLS0nDjxg259dbW1vj06RPu3LmTuxtERERERL+cPB3D27NnzyyXx8bGokSJEnLL9PX1ERMT88P1CQkJSE5OlluvqakJXV1d6flERERE9N/xS1y09rWkpCRoaWnJLdPS0kJKSsoP13/8+FH6+VvPzw4NDRk0NGSKNP+bNDVzr0NdXWqfr9lIZbXrXjmpslpERET07/VLBl5tbW28efNGbllKSgoKFCggrf86vKakpKBIkSLQ1taWfv56fU5madDT+w0ymWoDb7Fiv6m0HmvnXW0iIiL69/glA6+hoSHu378vtywuLk4apmBoaIi4uLhM66tUqQJdXV1oa2sjLi4O5cuXBwCkpqbizZs3MDAwyHYb4uPfq7yH9/Xr9yqtx9p5V5uIiIjyXnY7t37JwGtlZYWlS5fi48ePUq9uSEgIbG1tpfUhISHS45OSknDr1i24ublBQ0MDFhYWCAkJkeb0DQ0NhaamJszMzLLdhvR0gfR0ocKtAlJT01Vaj7XzrjYRERH9e+T5PLxZsbOzQ8mSJeHu7o6IiAgsXboU4eHh6Ny5MwCgU6dOuHbtGpYuXYqIiAi4u7ujVKlSUsDt2bMnVqxYgaNHjyI8PBxTp05F165deeMJIiIiov+gXzLw5suXDwsXLkRsbCycnJywe/duLFiwAEZGRgCAUqVKYf78+di+fTs6d+6MN2/eYMGCBdKY29atW2PQoEHw9PRE//79YWlpibFjx+blJhERERFRHvllhjTcvXtX7ueyZcti3bp133x8w4YN0bBhw2+uHzhwIAYOHKiy9hERERHRv9Mv2cNLRERERKQqDLxEREREpNZ+mSENRLntHwdHldWqduK4ymoRERFR7mIPLxERERGpNQZeIiIiIlJrDLxEREREpNYYeImIiIhIrTHwEhEREZFaY+AlIiIiIrXGwEtEREREao2Bl4iIiIjUGgMvEREREak1Bl4iIiIiUmsMvERERESk1hh4iYiIiEitMfASERERkVpj4CUiIiIitcbAS0RERERqjYGXiIiIiNQaAy8RERERqTUGXiIiIiJSawy8RERERKTWGHiJiIiISK0x8BIRERGRWmPgJSIiIiK1ppnXDSBSBw9bNVZZrXL7j6msFhERETHwEv3ynndtrrJaJbccUlktIiKifwsOaSAiIiIitcbAS0RERERqjYGXiIiIiNQaAy8RERERqTUGXiIiIiJSawy8RERERKTWGHiJiIiISK0x8BIRERGRWmPgJSIiIiK1xsBLRERERGqNgZeIiIiI1BoDLxERERGpNQZeIiIiIlJrDLxEREREpNYYeImIiIhIrTHwEhEREZFaY+AlIiIiIrXGwEtEREREao2Bl4iIiIjUGgMvEREREak1Bl4iIiIiUmsMvERERESk1hh4iYiIiEitMfASERERkVpj4CUiIiIitcbAS0RERERqjYGXiIiIiNQaAy8RERERqbVfOvAeOXIElStXlvs3fPhwAMCtW7fQpUsXWFlZoVOnTrh586bcc/fu3YsmTZrAysoKrq6uiI+Pz4tNICIiIqI89ksH3vv378PBwQFnz56V/vn4+ODDhw8YOHAgatSogb///hs2NjYYNGgQPnz4AAAIDw+Hh4cH3NzcsHnzZiQkJMDd3T2Pt4aIiIiI8sIvHXgjIyNRqVIlGBgYSP+KFCmC/fv3Q1tbG+PGjUP58uXh4eGB3377DQcPHgQArFu3Di1btkSHDh1gZmaGmTNn4tSpU4iOjs7jLSIiIiKin+2XD7zlypXLtDwsLAy2traQyWQAAJlMhurVqyM0NFRaX6NGDenxJUuWhJGREcLCwn5Gs4mIiIjoF6KZ1w34FiEEoqKicPbsWSxZsgRpaWlo0aIFhg8fjtjYWFSoUEHu8fr6+oiIiAAAvHz5EiVKlMi0PiYmJtuvr6Ehg4aGTPkN+YKmZu4dX7A2a+d1bSIiol/VLxt4nz17hqSkJGhpaWHu3Ll48uQJfHx88PHjR2n5l7S0tJCSkgIA+Pjx43fXZ4ee3m9SD7KqFCv2m0rrsfZ/o7YqB+J8Xftu24Yqq115zymV1SIiIlKlXzbwGhsb49KlSyhatChkMhmqVKmC9PR0jB07FnZ2dpnCa0pKCgoUKAAA0NbWznK9jo5Otl8/Pv69ynt4X79+r9J6rM3av3LtN67tVFZbd8FuldUiIiL1kd0OqF828AKArq6u3M/ly5dHcnIyDAwMEBcXJ7cuLi5OGsZgaGiY5XoDA4Nsv3Z6ukB6ulCs4d+Qmpqu0nqszdqsTURE9GO/bOA9c+YMxowZg5MnT0o9s7dv34auri5sbW2xbNkyCCEgk8kghMC1a9cwePBgAICVlRVCQkLg5OQEAHj+/DmeP38OKyurPNseIlKdj5M6q6xWAZ9tKqtFRES/pl/2ChYbGxtoa2tj0qRJePDgAU6dOoWZM2fizz//RIsWLZCQkABfX1/cv38fvr6+SEpKQsuWLQEAPXr0wK5du7B161bcuXMH48aNQ6NGjVC6dOk83ioiIiIi+tl+2cBbqFAhrFixAvHx8ejUqRM8PDzQrVs3/PnnnyhUqBCWLFki9eKGhYVh6dKlKFiwIIDPYXnatGlYsGABevTogaJFi2L69Ol5vEVERERElBd+2SENAFCxYkWsWrUqy3WWlpbYsWPHN5/r5OQkDWkgIiIiov+uX7aHl4iIiIhIFRh4iYiIiEitMfASERERkVpj4CUiIiIitcbAS0RERERqjYGXiIiIiNQaAy8RERERqTUGXiIiIiJSawy8RERERKTWGHiJiIiISK0x8BIRERGRWmPgJSIiIiK1ppnXDSAi+pUkz+yhslra4zaqrBYRESmOPbxEREREpNYYeImIiIhIrTHwEhEREZFaY+AlIiIiIrXGwEtEREREao2Bl4iIiIjUGqclIyL6SVIW91ZJHa3Ba1VSh4jov4I9vERERESk1hh4iYiIiEitMfASERERkVpj4CUiIiIitcbAS0RERERqjbM0EBGpgU8b+6ukTv4eK1VSh4joV8IeXiIiIiJSawy8RERERKTWGHiJiIiISK1xDC8REX1X6s6BKqmj2WGpSuoQEeUUe3iJiIiISK0x8BIRERGRWmPgJSIiIiK1xsBLRERERGqNgZeIiIiI1BoDLxERERGpNQZeIiIiIlJrDLxEREREpNYYeImIiIhIrTHwEhEREZFaY+AlIiIiIrXGwEtEREREao2Bl4iIiIjUmmZeN4CIiP670g4OVUmdfC0WZq59arhqajcMVEkdIso77OElIiIiIrXGHl4iIqIcEpfHqaSOzG5m5oU3pqqkNixUVIdIDTDwEhER/UfIIrMI2AoQ5VUT+Il+Fg5pICIiIiK1xsBLRERERGqNgZeIiIiI1BoDLxERERGpNQZeIiIiIlJrDLxEREREpNY4LRkREREpLV9M5rvdKSLt98x338v/arVKan/S76eSOvTvo7Y9vMnJyZg4cSJq1KiB+vXrY+XKlXndJCIiIiLKA2rbwztz5kzcvHkTa9aswbNnzzB+/HgYGRmhRYsWed00IiIi+kVov9uqkjrJhbuopA7lDrUMvB8+fMDWrVuxbNkyVKtWDdWqVUNERATWr1/PwEtEREQ/RYHkfSqp81G7tUrq/JepZeC9c+cOUlNTYWNjIy2ztbXF4sWLkZ6eDg0NtR3JQURERP8BBcVJldT5IGukkjq/OrUMvLGxsShWrBi0tLSkZcWLF0dycjLevHkDPT29PGwdERER0a+rkOYVldRJTK2ZaVlhnVsqqf0uqWqOHq+WgTcpKUku7AKQfk5JSclWDQ0NGTQ0ZCptl6Zm7vUsszZrs7ZqaifnYu3s7X1yXhcAPuVi7dRcrJ32L63N33fm2iIXa6vKT6+toh1KlrVV9CZUq9/3d8iEEKp6j/4yDhw4AB8fH5w7d05aFhkZiVatWuHSpUvQ1dXNu8YRERER0U+lloNZDQ0N8fr1a6Sm/t9xcmxsLAoUKIAiRYrkYcuIiIiI6GdTy8BbpUoVaGpqIjQ0VFoWEhICCwsLXrBGRERE9B+jlulPR0cHHTp0wNSpUxEeHo6jR49i5cqV6NOnT143jYiIiIh+MrUcwwt8vnBt6tSpOHz4MAoVKgQXFxf069cvr5tFRERERD+Z2gZeIiIiIiJATYc0EBERERFlYOAlIiIiIrXGwEtEREREao2Bl4iIiIjUGgMv0b9ccnIywsPD8e7du7xuChER0S9JM68b8G9169YtrFixAg8ePEBaWhpMTEzg7OwMOzu7vG5atiUmJiItLQ1FixZVee3k5GTcvXsXJiYmKFy4sMrr/5fdv38fEydOxIQJE1ChQgV069YNUVFR0NHRwaJFi1C7du0c1evduzdkMlm2HhscHKxIkwF8fr8tXrwYTk5OKFeuHCZMmIDDhw+jatWq8Pf3h7GxscK1/81CQkKwZs0aPHr0CIsXL8aePXtgbGyM1q1bK13737Kfcnd3z/Zjp0+fnostUb2UlBTcvn0bVlZWuVL/5cuXKFGiRK7U/jc4d+4cIiMjkZ6eDhMTE9StWxf58+dXum5ufi5fvnyJ9evXIzIyUvpcdunSBSYmJkrXPn36NKpVqwZ9fX1s27ZN2scOHToUWlpaOaq1c+fObD+2Q4cOOWtoHmDgVcCRI0cwcuRINGvWDE5OTkhLS0NoaCj69++PuXPnokmTJnndxO9as2YNli9fjri4OACAnp4eevToATc3N4VrqjqEfS0pKQmbN2/G/fv3kZaWJi1PSUnBrVu3cODAAaXqf8unT59UsvNUJS8vL5QuXRomJibYtm0b3r17h7Nnz2L79u2YMWMGduzYkaN6tWrVyqWWyvPy8sKdO3fQqVMn7NmzB4cPH4afnx8OHjwILy8vLF26VOnXUOXOPjuUDTOHDx+Gu7s7unbtipMnTyI1NRWampqYMGEC3r59i549eyrctn/7furf5tq1a/Dy8sL9+/eRnp4uty5fvny4efOmwrUfPHiAgIAAuf2fEAIpKSmIj4/HrVu3lGr7v1FMTAyGDh2KqKgomJiYIC0tDY8ePYKRkRFWrVoFQ0NDhWvn5ufy6tWrGDBgACpXrgxra2ukpaXh6tWrWL9+PVauXAlbW1uFay9YsADLly/H6tWrERkZCU9PT3Tp0gVHjhzB27dvMWXKlBzVCwwMzNbjZDKZSgJvYmIiHj9+jAoVKiAlJQWFChVSuqYcQTnWunVrsWrVqkzLV61aJdq1a6d0/fT0dHHkyBGxcOFCMX/+fOnfrFmzhIuLi1K1g4KCRO3atcXGjRvFnTt3xK1bt8S6detE7dq1xZIlSxSu26tXLzFq1CgRHx8vVqxYIerVqydiY2PF4sWLRYcOHZRqsxBCjBo1StSpU0eMGjVKVK1aVYwePVp07txZmJmZifnz5ytVOzY2Vvj4+Ih+/fqJ3r17i969e4tevXqJbt26iRo1auS4njK/x+ywtLQUjx8/FkII0aNHDzF16lQhhBBPnjwRlpaWufrayqhZs6a4ffu2EEKIIUOGiBEjRgghhIiKihLW1tZK1w8KChLW1tYiNDRUXLp0SVSpUkV4enqKVq1aSb8jRYWEhIh27dqJqlWrCjMzM7l/1apVU7hu27Ztxe7du4UQQlhbW0t/1927d4smTZoo1ebc3k99S3JysggNDc21+sr68OGDWLVqlfDw8BATJkyQ/o0aNUq0aNFC4bodO3YUQ4YMEadPnxY2NjbiyJEj0r513759Oa63fft26f89e/YU3bp1E5s2bRKWlpZi3bp1YsaMGcLKykr8/fffCrf5a1FRUeLo0aPi8OHDIiIiQmV1v+XFixcKP3fw4MHizz//FG/evJGWxcfHCxcXFzFs2DCl2pWbn8tOnTqJgICATMv9/f1Ft27dlKrdoEEDcebMGSGEEBMnThR9+vQRQggRHh4u7OzslKqdmz5+/Cg8PDxElSpVRJUqVcTjx4+Fq6ur6N+/v9zfV1ns4VVAdHQ0HBwcMi13cHDA7NmzFar56tUr6OvrAwC8vb2xbds2VK1aFeHh4bCxscHjx48RFxeHHj16KNX2LVu2wNfXF46OjtKyKlWqwNDQEL6+vhg4cKBCdcPDw7F3714UK1YMR48eRdOmTVG8eHG0adMGCxcuVKrNwOeeu3nz5qFu3bqIiIhAv379YG5ujr/++gsRERFK1Z44cSIeP36MZs2aYeXKlfjjjz/w+PFjHDlyBBMmTMhxvWXLluHixYtYtmwZJk2a9N3HKnJ6tnDhwoiLi4OmpiZCQ0MxaNAgAMDt27el95CifnRqWZnTyUII5M+fHx8/fsSFCxek3oa3b9+iYMGCCtfNsGXLFsyfPx9WVlbw8PBAzZo14eXlhRs3buDPP//Mce/G/PnzMWzYMACAj48PjI2NMWbMGPzvf//DzJkz8eLFCwQFBWHy5MkKt/nRo0ewtrbOtNzS0hIvXrxQuC6QO/upL+VWj2ZQUNB31ytzJgoAJk2ahAsXLqBOnTo4ePAgWrZsiUePHuHmzZtwdXVVuG5ERAT8/f1Rvnx5VKtWDfnz54ezszP09fWxbNkytGrVKkf1PD09cf78ecyYMQM3btzA5s2bUaVKFezcuROmpqZwdnaWzvJ07NhR4XYDQEJCAtzd3XHs2DEULVoUaWlpeP/+PWrWrIkFCxYoNSwtt3qnL168iM2bN8sNyStWrBjGjBkDZ2dnhdsL5O7nMiIiAgEBAZmWd+7cGWvXrlWq9tu3b2FqagohBE6ePIkBAwYAAAoVKiR3ZlRR8fHxiIqKkj7vGX/HW7duKZwdAMDf3x/379/Hjh070L17dwDAsGHD4O7uDh8fH/j7+yvddoAXrSmkfPnyOH36dKblp06dUngcYvv27XH27FkAwP79+xEQEIBNmzahTJkymDp1Kk6cOIHWrVvj06dPSrU9MTER5cqVy7TcxMQE8fHxCtfNCGHPnz9HaGgoGjVqBEA1IQz4PCY4o90VK1aUvky7deuGq1evKlX7ypUrmD59OkaNGoXKlSujUaNGmDdvHkaMGJHl3/lHTpw4gYYNGyr9t/oWJycnDBkyBN26dUOpUqVQv359bNy4EWPHjkWfPn1U+lqpqamIiorC/v37oaenp1St2rVrY/LkyXBzc4OGhgaaNGmCCxcuwN3dXe4ATFFf7+wzwp6iO/sNGzagV69e+PTpEyIiIjB69GjY29vLhZkpU6ZgxYoVCre5QoUKOHPmTKblO3bsQIUKFRSuC+TOfmr+/PnS/zMOAhYvXgwdHR3Mnz8fkyZNgq6uLmbOnKlwuy9duiT37/z589iyZQuWL1+O6OhohetmOH36NAICAjBr1iyUL18e/fr1w9atW9G3b1+lDp51dHSQL18+AICpqSnu3r0L4HNIioqKynG9Y8eOwdjYGMnJydDU1JRCp6mpKW7fvg0AqFu3rvQ6yvDx8UFMTAz279+PS5cu4erVq9izZw8+fPig9JjpyZMnIz4+Hi4uLoiLi0P//v3RokULJCYmwtfXV+G6RYsWxdu3bzMtT0hIUHoYWm5+Lo2NjREeHp5peVhYGIoXL65UbTMzM6xYsQJBQUGIj49H06ZN8eLFC8yePTvLAJ8TW7ZsQYMGDeDs7Iw+ffqgd+/e6NOnDwYMGIBjx44pVfvw4cPw8PBA5cqVpWWVK1eGt7e3Qt/B36SyvuL/kOPHj4tq1aqJ0aNHi+DgYBEcHCxGjRolqlWrptCpKyGEWL58uXB0dBTv3r0T1apVE0+fPhVCCDFs2DCxbds2IYQQ9+7dE/b29kq13cXFRXh4eIi0tDRpWWpqqpg4caLo1auXwnVnzZolatWqJezt7UXTpk1Famqq2LBhg7C2ts7ytGpOdejQQWzdulUIIcSiRYvEmDFjhBBChIWFCVtbW6VqW1lZSb/vcePGieDgYCGEEI8fP/5lTwMdPnxYrFq1SsTGxgohhDh58qQ4fvx4rr3esmXLxNixY5WqkZCQILy9vcWQIUPEhQsXhBCfT6//9ddfIikpSek2duvWTUybNk0EBgYKMzMz8eTJExETEyPc3NwUGgqUnJwstmzZIpKSkkTNmjVFVFSUEEIIT09PadjK06dPhY2NjcJtvnLlirC1tRXDhg0T1apVExMnThTOzs7CyspKnD9/XuG6QuTOfqp27drC2dlZpKSkCHNzc3H//n0hxOchTSdPnhRCCHHgwAHh5OSkVNuz4uvrK/z8/JSuY2FhIX3eR40aJTZu3CiEEOLBgweibt26CtcdNmyYcHNzEzExMWLHjh2iffv24vXr12Lt2rWiYcOGSrW5f//+YurUqSIpKUls2LBBODs7i/T0dHHw4EGl2pzB1tZWhIeHZ1oeFham9D7QwsJC3Lp1SwghRPfu3aX39ZYtW0TPnj0Vrjtz5kzRrFkzcf78efHu3Tvx7t07cfbsWdGsWTPh5eWlVJtz83O5fft2YW1tLebMmSOOHTsmjh07JmbPni1sbGzE2rVrlap9+/Zt0a5dO2FrayuWLVsmhBDCx8dHdOzYURqWoSgHBwcxf/58kZycLOrVqyeePXsmIiIiRMeOHaXXUlT16tWlITRfDiEJDw9X+vv9Swy8Cjp//rxwc3MTLVu2FB06dBAjR44UYWFhStVMS0sT6enpokWLFuLIkSNCCCECAwOFp6enEEKIu3fvKj3W8f79+6JWrVrCwcFBuLm5CTc3N+Hg4CDq1asnja9UVG6GsKNHj4pq1aqJzZs3i2fPngkbGxsxaNAg0ahRI2ksqKJ69OghFi5cKIT4HMAGDRokhPj8N/5VA+/PFh0drZJxtrkpN3f2uRlmYmNjxdy5c8WwYcPE0KFDhb+/vxTIlKXq/dTPOAj4lsePHys0pv5ruXXwHBMTI3r37i2Cg4NFSkqKcHZ2FpUrVxZVq1YVW7ZsUarNERERokmTJmLFihXi/fv3okWLFqJGjRqiatWqIigoSKnaQghRt25dcfPmzUzLVRE4bGxsRHR0tBDi87jSFStWCCE+X3OgTO3k5GQxfvx4UaVKFbnx9BkHBsrKzc/l9u3bRceOHYWlpaWoWbOm6Nq1q9i/f79Kan8tOTlZJXWqVasm/R0HDhwotffKlSuiWbNmStUeM2aMGDp0qHj37p0UeB8/fiy6du0qRo0apXTbM8iEEEJ1/cX/XfHx8ShWrFi2p3f6nq1bt8LX1xd+fn6oXLkynJyc0LlzZ1y/fh16enpYvny5UvVfv36NPXv24MGDB9DW1oaJiQnatm2L3377Tem256bo6Gikp6ejbNmyuHPnDnbt2oVixYqhd+/e0NHRUbhuSEgIBg8eDFdXV7Rv3x5t27ZFsWLF8OzZM7Rr1y7HYz+/ZGZm9t33RMapyR9p3Lgxtm3bhmLFisHR0fG7NZU9vfS1Dx8+YPHixdi7dy+OHz+ucJ28mGkjJSVFJbMzvHjxAmPHjkXTpk3RvXt3/PHHH7h69Sry5cuHqVOnokuXLgrVHTp0KEaPHo3y5csr3cafbfjw4RBCSGNiV69ejdWrV2Pv3r1Yvnw5Tp48qdLXW7duHRYvXiwN/VLUsWPH8L///Q+enp6wt7dH69atYWdnh7t378La2hpz5sxRSXuFELh//z6KFCmi1IwBX9b7+PEjdHR08OHDB1y+fBm6urpKn6oGPg87uHv3LgICAlCmTBkAwMOHDzF+/HiUKlUKs2bNUri2i4sLypQpg/Hjx2PHjh3Yt28f1q5di8OHD2PatGk4d+6cQnWvXr0KCwsLJCcn4+HDh9DS0kKZMmVUcj1AUFAQXFxcMn2vJCYmIigoSKFrOzJcuXIFNjY20NSUv4QqJSUFp0+fVnr2lNu3byMiIiLLcbZeXl4K123QoAGCgoJgaWmJGTNmIH/+/Bg1ahSio6PRtm1bhIaGKlz73bt3mDhxIo4dO4b09HQUKVIE7969Q/369eHv7w9dXV2Fa3+JgVcBL168wF9//YWBAwfC1NQULi4uCAkJwe+//45FixbBzMxM6de4cuUKChYsiGrVquHMmTPYunUrdHV1MWzYMBgYGKhgKzJTZj7Hp0+fYu7cubhx4wZSU1Px9dtK1SFM1RITE/Hx40cUL14cL168wNGjR6Grq4uWLVtCQ0Pxoe6XL1+W+zktLQ2PHz/GqlWrMGLECLRo0SJbdXbs2IHWrVtDS0vrh9OOKXMBy7cCura2Nnx8fNCmTRuFa48ePRrnz59H3bp15S4WunHjBtzc3JS+GAnIvZ19xpertra2VFcVYaZ27drYvHkzypYtq3CNL/3M+Wxz6yAgqwO69+/f4+3btxg/fjz69eunVLuB3Dt4TkxMxP3797PcB9asWVOpNr958wb79u3DgwcPIJPJULlyZbRs2VIlUzclJCTA1dUVV65ckS4Ce/v2LRo0aICZM2cqFTju37+PIUOGoEePHujevTs6deqEuLg4fPjwAUOHDlX4QsFatWphzZo1Kvm+BT5fXPfq1SsAQJ8+fTB//vxMc9Tfu3cP/v7+SoW7KlWq4Ny5c5muifjnn3/Qo0ePLMf3ZldQUBCCgoJQvHhxvHr1CoaGhoiLi0NaWhqaNm2a7WnGsjJ9+nScPn0avr6++PjxI8aNG4fJkyfjxIkTuH37Nnbt2qVw7QzR0dGIjIxEamoqTExMVN4RwMCrgCFDhuDDhw/466+/cPLkScyZMwfLli3D7t27cefOHaxfv16p+qru9Xny5AlKlSoFIPeumHV2dsbr16/Ro0ePLHfAioSwvOzVzG2XLl3C9OnTczSxd3YoO2/w1wFdJpMhf/78qFChgtJfrDVr1pRm2mjXrh38/PykmTaeP3+OefPm5bjmx48fUaBAAQC5u7NX9ZdrhsDAQJw8eRLdu3eHkZGRFKgz5DQk5eUNHFR1EPD1AV3Ge9Dc3FxlBwa5YdeuXZg6dSqSkpIyrZPJZNk+m5OV69evY+DAgShatCiqVq2KtLQ03L59G8nJyVi1ahUqVaqkTNMld+/eRWRkpHTmz9TUVCV1c6N3ulevXmjXrh26du2qkjZevHjxhwdTOjo66NOnD0aOHJmj2hs2bMC0adMgk8kghPjmd1ndunWVugjW3t4ebm5u6NatGxwdHbFmzRoULVoUI0eORJUqVTBmzBiFa3/69AlLlixBlSpV0LhxY8yZMwebN2+Grq4upk+fDhsbG4VrA5kP6MzMzNCiRQuVzsXLwKsAGxsb/P333zAxMYGLiwtKlCiB6dOnIzo6Gm3atEFYWJhS9VXd65MxzcemTZvg7OyMtLQ0dOzYEX5+fhg3bhyePn2KDRs2YMqUKQr3DlpYWKjkCtYv5WavZpUqVXD27Fno6+urbNhBTkRERKBTp04KHc3HxcVhyZIlmQ5aPn36hMjISFy5ckVl7Xz9+jUKFSqkkptvWFpa4uDBgzAyMsLo0aNRs2ZNdO/eHVFRUejVq5dCpzZbt26NOXPmoFKlSrm6s1f1l2uG7wVoZUPSz5CbPZq55erVq/Dx8cGDBw+ynElF0d95o0aN0KxZMwwfPlzlE+Y7OTmhRo0acHd3l/ZV6enp8PHxwZ07d7Bhwwal6qekpGDu3LkwNjaWpvRycnJC3bp18b///U/pz78QQrojWv78+WFqaqr0zYhcXV1x/Phx6Ovro1SpUpmGLilzV0hHR0ds374dxYoVU6qNX7py5QrS09PRt2/fTL3HMpkMOjo6qFSpklJDsMzNzXH48GEYGRnB1dUVzZs3R7t27XDz5k0MHz5cqSFpy5cvR5s2bfD7778rXONbftYBHefhVYC2tjaSk5Px9u1bXLp0SRrf9OTJE5Xcprdnz54YOXKkynp9Zs2ahS5duiAlJSXX5nMsV66cUtOaZeXLtig7z+TXMsIQoNyO8Uey6sF9//49tm3bpnDvhqrnDQY+35Vry5Yt8PHxgaGhIR49eoT//e9/uHv3LrS0tODs7IyxY8cqNUa9fPnyOH/+PDp37oyKFSsiJCQE3bt3x7t375CcnKxQzVq1aqFv3744duwYXr9+DXt7ewCfD2iuX7+Odu3aYeTIkRg+fLhSgbdo0aKYMmUKAgMDVfrleufOHYXb9CO5PZ+tqns0w8PD8ffff2P48OHQ09NDfHw8Jk+ejPPnz0NPTw/9+/dXen5VAPDw8EDFihUxatQo6eyAKrx58wZ9+vRR/d2hAERGRmLWrFlynz8NDQ307t1bJftGHx8fhISEYNq0adKyoUOHYu7cufj48eMP5xP/nrt378LNzQ2vXr1CuXLlIITAw4cPYWJiIn2eFFGlShVUqVJF4XZ9T9GiRfHy5UuVBt6M7+1jx47ByMhIJdf7fM3Q0BDR0dEwMjJC+fLlcevWLbRr1w6FChVS+vt58eLFaN68uYpaKs/b2xsdO3bM8oBu6tSpSh/QZWDgVUCTJk0wYsQIFChQAEWLFkWjRo2wf/9++Pn5qWTnk3GjBk9Pz0zrFPkiGTVqFDw9PaGlpZXlfI516tRB3bp1MWPGDIXbPGDAAEyaNAl//PEHypYtm6lHQNnennfv3mHZsmW4c+cOkpOTM/Um5TRw2NnZZfl/Vfv6NHrG6VkLCwuMGDFCoZpXrlzBypUrYWNjg3PnzqFRo0awtbXF0qVLcfr06RzPxbtv3z6MHz8eHTt2lILcyJEj8fz5cyxduhSFCxfG5MmToa+vDxcXF4XaDHwOWP/73/+Qnp6O9u3bo3Xr1hg8eDDu3r0rBdWc8vT0xMiRI6GtrZ2rO/vc+nLNzZ61S5cuyf2clpaGJ0+eICEhQSVfXHPmzEGXLl1U0qN5/vx5DBw4EHZ2dkhNTQXweb8VFhaG8ePHo3DhwggICECBAgXQqVMnpV7r5cuXWLx4MUxMTJSq8zUHBwccPnwY/fv3V2ldAKhTpw527tyZ6VT6qVOnlO4pBT7Pg7pq1Sq593iTJk1gaGiIQYMGKRV4p0yZAisrK3h5eUkXRickJGDixImYPHkyVq1apVBdVYz5/5bY2FhpTmVVK1GiBLZt2/bN612UGWrUpUsXjBo1Cn5+fmjSpAn69euHEiVK4Pz580oPx2rTpg0WLVqEgQMHwsjISKW3as/tA7oMDLwKmDp1KtatW4enT5+iW7du0NbWRkpKCgYPHqySHojc6PXZu3cv6tevDxsbG6xYsQLjx4+Hubk59u3bhz/++AM3b97M1JOcE+PGjQOALC8MUsWp2XHjxuGff/5By5YtlbrrT1Zy84I7ZU4hfYsQQhojWaFCBdy6dQu2trZo2bKlQuO/Vq9eDXd3d+m9Gx4ejlu3bmHkyJFSEB01ahT++usvpQJv48aNceDAAaSnp6NkyZLYsGEDdu3aherVq6N3794K1814P+Tmzj63vlxzs2ftW3dt8vPzU0nvkip7NBctWoTBgwdLv+eIiAhcvHgRAwcOlIZkyWQyLFmyROnA27ZtW+zbt0/lf1NDQ0PMmTMHBw4cyPKgX5kgU6pUKaxYsQJnzpxB9erVoampidu3b+Py5ctwdHSUG7utyOsIIbI8y5IxVEoZt27dwvTp0+VmASpSpAhGjhwJJyenHNVyd3eHh4cHChUqlKt3hezQoQP+/PNPtGvXDsbGxpm+Gzt06KBwbQ8PDxw+fBj29vYqPxswePBg/P777yhQoAAsLS3h7u6OTZs2QVdXF35+fkrVPn36NJ49e/bN4YXKfMfn9gFdBgZeBWhqasoNbk9OToapqSlMTExUdpoiNTUVr169ynRh2e3bt3N8i8q1a9fi4sWL0NLSgru7O4YMGYLSpUuje/fuCA4Ohp2dnXTFrKJy89QsAFy4cAHBwcGwtLRUee1x48bh9evXcHZ2zpXTkaq+HWPVqlWxa9cuDBkyRLrit3fv3njy5IlC7YuIiECDBg2kn8+cOQOZTIbGjRtLyypWrIhnz54pVP9LpUuXlv5vZmam0ovAMnb2Ojo6Kt/Z59aUarnZs/YtvXv3hpOTU44ucMuKKns0b968CW9vb+nn06dPQyaTyfVEm5ub4+HDh0q/1p9//onOnTvj77//hrGxcaZ9tqLDU96+favULCbf8/79e7Rt21b6PwAYGRkpFby+1Lx5c0yePBlTpkxB1apVAXzep/v4+KBp06ZK1bayssKFCxcy9ahfu3Yt14YkKGv//v3Q0NDA3r17M62TyWRK/d6PHDmCBQsWoF69ekq08Nsy2paYmIhmzZopPFvK1/766y+V1MlKbh/QZWDgVcD9+/cxceJETJgwARUqVEC3bt0QFRUFHR0dLFq0SOkjkqNHj2Ly5Ml48+ZNpnUGBgY5DrxaWlpSoKlQoQIOHz4sXTG7fft2ha+YffbsGUqWLAmZTPbDMGRkZJSj2l8zMDDItVNM4eHhKr/gLsOWLVswbdo0pKamSlfoAp93mpaWlgoF3tGjR2Pw4MHQ0dFB+/btsXz5crRt21aaNzintLS05Hp3zp07h5IlS8r9Pl6+fIkiRYrkuPaXfsbUdV9+EXXp0kVlO/tJkyZ9d0o1ReVmz9q3nDp1SqmzORlU2aP55WcDgDRut1q1atKyd+/eqWTM7ZgxY6Cnp4cmTZqodAyvqme9+Fm1gf/rOe3bt690YK6hoYEOHTpg4sSJOa735fjxsmXLws/PD5cvX4alpSU0NDRw79497N27F7169cpR3S9/D7n5O8mNM3MZChcurJJ5mb9lzZo1WL58OeLi4gAAenp66NGjh9JnNDKG/iUmJuLx48eoUKECUlJSVNJJlNsHdBkYeBXg5eWF0qVLo1y5cti2bRvevXuHs2fPYvv27ZgxY8YPZxT4kVmzZqFp06bo168fevTogaVLl+LNmzfw9vZWqhc2g0wmQ1JSEt68eQMhhHQF5LNnz3IUTB0dHXHu3Dno6+tL04Z9+aX15RQsipzu+DJEOzs7Y9KkSRg3bhxKlSqVKfwqE6hz44K7DIsXL8bgwYMxcOBAODo6YuvWrXj//j3GjRuncM9JlSpVcOLECXz8+BHFihXD9u3b5eYNzqnatWtj48aNmDx5MsLCwnD9+vVMvXYrVqyAra2tQu3NkBs96T/rFOfp06elKdUiIiLQr18/aUq1iIgIhet+r2dN2QnofzSfrbJU2aNpY2ODgwcPYsiQIXj8+DEuXbqEzp07yz1m48aNsLCwUPq17t69i7///lsl0z5+eYOC3LxIUAiBY8eOISIiIsszDMrejEhHRwezZ89GQkICHj16hPz586NUqVIKf06/Hj9uY2ODV69e4cSJE9IyKysr3Lx5U6l2h4SEYM2aNXj06BEWL16MPXv2wNjYGK1bt1aqLvD5IH/9+vWIjIxEWloaTE1N0aVLF5QrV06pukOGDIGvry8mTZqEsmXLZroBhTIWLFiAdevW4X//+x9sbGyQnp6Oa9euISgoCFpaWgp1sGRISUnBtGnT8PfffwMADh06hBkzZiApKQmzZ89W6oL93D6gy8DAq4Dw8HDs3bsXenp6OHr0KJo2bYrixYujTZs20gVnyoiOjsaSJUtQpkwZmJubIzY2Fk2aNIGGhgZmzpyZ43FPX7pw4QLGjRsnHf1lUCSYHjt2TLqKNTfmwf3yCzsjSP/xxx9yX+LKBOoMuXnB3cuXL9GhQwdoaWmhWrVqCA0NRcuWLTFx4kR4eHjgzz//zHHNNm3aICgoSApIhoaGSo0dHzNmDPr06YO9e/fi/fv3KF++PAYPHgwAOHDgAJYtW4YnT55g48aNCr8GkLs96bktOTlZ+qKrWLEibt68CXNzc3Tr1i3HvVRf+rpnTQgBTU1NdOjQQeEJ+TMMGzZM7mdVz2eryi+pkSNHol+/fjh8+DCePn0KXV1dDBkyBMDnfda6detw+vRprFmzRunXsrW1RWRkpEoC76VLl9CnTx/o6OhkCnlfUnaom7e3N7Zt24aqVasiPDwcNjY2ePz4MeLi4tCjRw+lagPINJXhx48f5fapOd0Hzp8/X2V3x/qWw4cPw93dHV27dsXJkyeRmpoKTU1NTJgwAW/fvkXPnj0Vrn316lUMGDAAlStXhrW1NdLS0nDlyhWsW7cOK1euVOrgf9myZXj58uU3DxaV+S7bsmULfH194ejoKC2rUqUKDA0N4evrq1TgnTlzJu7fv48dO3ZI4+qHDRsGd3d3+Pj4wN/fP0f1ftbB4pcYeBVQuHBhxMXFQVNTE6GhoRg0aBCAz29UfX19pesXKVJEmurHxMQEd+7cQZMmTWBqaqrwOM0MXl5eqFWrFv7880+lL/4yNjaW/u/u7o6goKBMp73j4+Px559/SkeFOfGzbiaRmxfcZUyvVKpUKWlWjJYtW8LQ0BAvXrxQqKaGhoZKT3eXKVMGBw8exPnz56GhoYG6detKV+AmJiaievXqmDdvntz4W0XkRk/6zzrFmRtTqgGZe9bS0tLw8OFD7NmzB02aNME///yjcO3vXd2s7A1KANX2OmZcQHv48GFoaGigZcuW0p2obty4gfT0dAQHBys9uT0A1K9fHxMnTsThw4dRunTpTGeLcvLl+uWFgd26dUO9evVUOpVVhv379yMgIADNmjVDixYtMHXqVJiYmGDChAkq2Rd866JRLS0tGBgY5Hhf3L59e/j7+8POzi7XhrsFBQVh6tSpaNu2LTZt2gQA6N+/PwwMDBAYGKhU4P3rr7/Qq1cvjB49Wm55QEAA/P39pddTtHZuSUxMzLIH2sTEROl97+HDh7FgwQJUrlxZWla5cmV4e3srNI7/Zx0sfomBVwFOTk4YMmQItLS0UKpUKdSvXx8bN27EzJkz8b///U/p+g0bNoSXlxemTZuGWrVqYebMmXBwcMChQ4cUvvVvhmfPnmHZsmVKhxfg82nejBsnXLlyBYsXL850H/NHjx7h6dOnCtX/MlB/S8aFfNl57Lfk5gV3LVu2xPjx4+Hr6wt7e3uMGzcO1apVw4kTJxTuZWvUqBH++OMPODg4wNjYONP0MIocDRcoUECuVyCDqsbAArk/dR3wefz78uXL8eDBA6SlpcHExAS9evVSeixYbkyp9qWIiAjs3LkTBw8eRGJiIsqXL6/Q2Mkv5fYNSlTd62hoaJhl8FKmVyorJ06cQJUqVfDixYtMB53KfLl6eXlhy5YtuRJ4ExMTYW5uDgCoVKkSwsPDUbFiRQwaNEipmVMyfL0PzLgFure3tzS2Mie6du2KMWPG4MCBA7ky3A34/N2S1XUnlpaWCncmZIiIiEBAQECm5Z07d/7m7CfZ9b1pMF++fKlUbRsbG6xcuRLTpk2DhoYGgM9/y5UrVyp9wff79++zvO12enq63AFvdn35e1T2d5pdDLwKGDVqFCwsLPD06VO0adMG+fLlg5GREWbPng0HBwel63t4eMDX1xc3b95E+/btcejQIXTu3BkFCxbM8WmDr9WqVQshISEqCbwmJiZYvnw5hBAQQuDatWtyIUYmk6FgwYLw9fVV+rWuXbsGLy8v3L9/X7qoIkO+fPmUHgv28eNH7N69W268VqtWrZQ+LTdmzBgULlwYr1+/RuPGjdGpUydMmTJFqZkD7t69i2rVquHly5eZdpC5MZm5quT21HWbNm3CjBkz0KtXLwwcOFAav+bl5YVPnz4pFd5zY0q1p0+fYufOndi1axeio6NRpEgRJCYmYtasWTm+MDUruXGDki/ldq9jbsmtL9datWphz549GDx4sErnKAU+z25y69YtGBkZoWLFiggPD0enTp0ghMC7d+9U+lrA531qxt9y4MCBOZ4L1dXVFQMGDED+/Plz7UxdhQoVcObMmUw9uaoYNmVsbIzw8PBMvaVhYWEoXry4UrUfPHiAgICATAeiKSkpiI+Px61btxSunTG95Pnz56ULPv/55x+kpKQoPc7b0dERc+bMkZuvPzo6Gj4+PmjYsKFStb81pCFjGFaJEiVgb2+v9Bl03lpYCQ8fPkRkZCTS09NhYmKi9Ids165dOHLkCPLnz4/GjRvLjfFJTEyEtra2Qqchv3wzvXjxArt27UKzZs1QpkwZ6Sgwg6JjZb68eCg3ODk54ffff0ePHj3wv//9DzNnzsSLFy8QFBSEyZMnKxQQEhMToaGhgSdPnuDPP/9Evnz5YG5ujrS0NGknsXbt2n/lmNP/oiZNmsDNzS1Tb+6OHTuwePFiHDp0SOnXUMUVytu3b8fOnTtx9epVlChRAo6OjmjWrBlq1qwJKysr7Nq1SyXvuYzeHhsbG3Tq1AkTJ06UblBy+fJlpb8Av7yN6fDhw9GwYUN06tQJERERcHFxwenTp5XeBlXZuXMnWrVqBS0trSzvfvglRc8G9OjRA9evX4eGhgb09PQyzYShTPDbunUr/Pz84Ovri8qVK8PJyQmdO3fG9evXoaenp/Tf8lsuXLgAV1dXXLt2LVfqK+Pq1asYPHgw6tati+PHj6N9+/Z49OgRbt68iUWLFqFOnToK1/7777/h7e2Nvn37Sj2jYWFhWLt2LUaNGqXUuH1nZ2ekpaWhY8eO8PPzw7hx4/D06VNs2LABU6ZMUfpGC69fv8aePXvw4MEDaGtrw8TEBG3btpWbB1kR7969w8SJE3Hs2DGkp6ejSJEiePfuHerXrw9/f3+lOodGjx6N/fv34/fff4e5uTmEELh9+zaePXsGa2trvHv3DjExMVi+fLnCdygF2MOrkISEBLi7u+P48eMoUqQI0tLS8P79e9SsWRMLFixQaGzsmjVrMHPmTNSpUwepqalwd3fHvXv3MGrUKABQKkh+PT7GyspK5afzMsZPxsbGZjnllLLTkkVERMDf3x/ly5dHtWrVkD9/fjg7O0NfXx/Lli3LceC9evUqxo8fjxUrVsDX1xf16tWDt7e3dMVsamoqJk2aBD8/P6xcuTJHtX/GYPzo6Ghs2LABjx49wtSpU3H69GmYmJgoPZNCbsutnnQAePXqVZY7QxsbGzx//lyp2snJyfD29lbJFcoeHh4oW7YsZsyYodA0ctml6huUfO1n9zoqIzAwEA0bNoSWllamux9+SZk5Vrt27YquXbsq2MLvy5gdoGDBgihfvjyCgoKwdetWmJubZ7o4URFZzXDy/v17nD9/Hi1atFCqdm7NMFGjRg0cOHBAuu3smzdvYG1tjZkzZyr9fZNxYfi6deuwatUqKTj6+voqNBPOl27cuIHNmzejSpUq2LlzJ0xNTeHs7AwTExNs27ZN6cBbrFixHN9tMzsKFy6M+fPnIzo6GpGRkUhNTYWJiYlKLv4EPg8XmTp1qjSmPj09Hb6+vvjw4QOmT5+OxYsX46+//lJq/DQDrwJ8fHwQExODffv2wdTUFMDnuXknTJiA6dOnK3SqetOmTfD19ZV2thlXoI4cOVLp09Q/Y3zMuXPnMHnyZClYZIzPUsUsCsDni3syPgimpqa4e/cuGjZsCEtLS0RFReW4Xr9+/eDh4YFy5cohNDQUU6ZMkZseRlNTEwMGDMg0NVJ25PZg/CtXrmDgwIGwt7fHmTNnkJycjAcPHmDq1KmYPXs2mjVrplDdLyUlJeHRo0dIT09HmTJlVNJzf+/evUw96UeOHMH8+fNV0pOe8QXy9S2bVXGK09/fX2VXKPv5+WHfvn1wd3fH9OnT0ahRIzRp0gT169dXqo1fU/UNSr7Wv39/jBkzBn5+fmjVqhWcnJygqamJ69evq+zAKzExEWlpaUpNeQTIz6uaW3Os/ugiQWV9Ocbd3t5eJWPHv0dXVxfjx49H+/btlaqTmzNMGBgYqOS6maw4OTkpNSPSt2hqakqdYhkXMtepUwd169aVGy6gCDMzs29+r+TPnx8GBgZo2bJltm9bntUFh/ny5ZOmMv3yMcocZBw/fhx///233AWkGhoa6NWrF5ycnDB9+nS0bt0aixcvVvg1AAZehRw/fhyrVq2Swi7wuQfF09MTAwYMUKhmdHS03CkYR0dHJCUl4eXLlyqfpDrjtE9WO2FFezemTZsGS0tLLFq0KFeGNdSuXRuzZs3CpEmTYGNjg9WrV6Nr165SL3tOWVpaYtWqVWjRogUMDAzw+PFjub8nADx+/Fih00Br166VhqcUL1480/AUZfn7+2P06NHo1auXdNX6uHHjUKJECQQGBioVeD99+gR/f39s2LABqampAD7voNu2bQsvLy+lxiaquif9a2PHjkW/fv1w6dIlWFlZAQBCQ0Nx+/ZtLFmyRKnaqrxCOeOLND4+HgcOHMD+/fvh5uaGAgUKID09HZcuXcryor6cUvUNSr6Wm72OuTV5fgZV3/0QyN2LBCMjIzF79mw8ePAAKSkpmdYrO042N2c4ya2x3gkJCVi5cuU3b2ST0zvm/Wioy5eUuQjWxsYGK1aswPjx46XZSf744w/cvHlT6RvCTJ06FUFBQRg2bBisra0hhMDNmzcxf/58dOrUCZUqVcKCBQsghMDYsWN/WC+ruby/RZlOreLFi+Pq1auZ7sYXEhIinf2Li4tTOlsw8CpAW1s709hX4HOPnSJXKwKQ5hDMoKmpCW1t7Sx3bspYvnw5AgICULRo0UxhTpnTeRnja1RxMVxWPDw8MHbsWBw+fBjdu3fHtm3bULt2beTLlw9Tp07Ncb3169dj9erV+PDhA7p3745Jkybhf//7n9x4rcDAQIUudPrR8BRl3bt3L8uLBBo3bozZs2crVXvGjBk4deoUFi1aJE1cfv36dfj4+GDOnDlK3bBA1T3pX7OxscHff/+NrVu3SuPX7OzsMGfOHJQsWVKp2qq+Qhn4HOKcnZ3h7OyMmJgY7N27F/v374e3tzfmz5+P9u3bK3X7X1tbW5XeoORrQ4cOxejRo6VTmqrqdczNyfOB3Ln7IZC7FwmOHj0aBQoUQJ8+fVR6d7gvXblyBZs2bUJkZCTy588PU1NT9OvXT+nb/+bWDBPjxo3DjRs30LZtW5V0snxvqAvweR+QkJAAQLnA6+7ujiFDhqB06dLo3r07goODYWdnhw8fPih9Y6kVK1bAz89P7lbxZmZmKFmyJKZNm4bhw4fD0NAQw4YNy1bg/fJA6uTJk1i7di3c3d1hYWEBLS0t/PPPP/jrr7+UHsozbNgweHh4ICQkBBYWFhBC4J9//sG+ffvg6emJqKgojB8/XvkbigjKsUmTJokuXbqIR48eScuioqJE165dxahRoxSqWblyZREXFye3zNraWjx+/Fiptn6tTp06Yvny5SqtKYQQ/fv3Fzt27FB53W9JT08X9+7dEzExMSqpFRgYKOzs7ETlypVF5cqVRb169cSyZctEWlpajuu1aNFC7ndx6NAhUb16dZGenq50W4UQolWrVmLfvn1CCPn3SHBwsGjRooVStWvVqiUuXryYafmFCxdEvXr1lKrduHFjceLEiUzLjx8/rlTtJ0+eCG9vb5GcnCyEEKJ169bCwcFBODg4CEdHRzF+/HiFa2cYM2aMGDp0qHj37p30O3/8+LFSn/lviYqKEvPnzxctW7ZU6Pnv3r0TR48eFadOnRLv3r1Tadu+VKtWLfHw4UOV123QoIE4duxYpuVHjhwRjRo1Urq+g4ODmD9/vkhOThb16tUTz549ExEREaJjx45i2bJlCte1trYW165dE0II4eTkJK5evSqEEGLJkiXCxcVFqTZbWVmJ+/fvK1Xje9auXSvMzc3FhAkTxNq1a8WaNWvE6NGjhbm5udi7d69StVu0aCGOHDkihBAiMDBQeHp6CiGEuHv3rrC2tla4roWFhQgLC1OqbdmRlpYm1q5dK2rUqCGaNWsmzp49q3TN9PR08eHDByGEEO/fvxcnTpwQ169fV7qutbW1uHv3bqbld+7cEVZWVkIIIaKjoxX6vTds2FCEhoZmWh4eHq70d4MQQly9elWMGjVKtGnTRrRv316MHj1a+p2EhYWJNWvWiNTUVKVegz28Chg7dixcXV3RvHlz6XR6QkIC7O3tMXnyZIXrHjhwQO5INT09HUeOHJEmYM+gzNFlcnKySsZ4fq1mzZrw8vLCyZMnszwdq6pTkQCkU8Hp6elo3Lix0vVkMhmGDRuGYcOG4dWrV9DW1laqxyC3h6eMGDECEyZMwI0bN5CWloadO3fiyZMn2LdvH2bOnKlUbSFEllO/6OnpSfc4V5Sqe9KBz2Pnu3fvDktLSyQkJKB48eJ4+vQpXF1doa+vj5iYGAQFBaFZs2ZZzjOcXZ6enpg4cSLs7OyQnp6OTp06qeQzn5Vy5crBzc1Noc9MWFgYBg4ciLdv3wL4/HebM2cOatWqpdI2AkDPnj0xcuRIdO/eHUZGRplOxyo6r3JuTp4P5M7dD4HcvUiwQYMGCAkJUdkFQl9btmwZvL29M3231KhRA7Nnz1aqZy23xnobGhpmeaZVlW7cuIGpU6ciMjISAwYMwIABA1Qy5ZxMJoOOjg5iY2Nx7do16OnpKTX7QIZ69eph2rRpmDFjhjQ3/dOnT+Hr64vatWsjLS0N27dvlxuDm13v37+Xhrl9KTExUekx6j4+PujTpw9mzZqV5XpLS0ul5xEGOKRBIW/evMHatWtx584duak/vh4DmhNGRkaZxjDq6+tj3bp1csuUGXYAAG3btsWGDRswbtw4lc7Zeu7cOZibm+PVq1d49eqV3DpFXycpKQkzZ87E/v37AXy+e0/v3r3RvXt3JCUlQQiBgIAALF++PMdfrrk5Xiu3h6c0bdoUpUuXxsqVK1GxYkUcO3YMJiYmWL9+vTR2VVG1a9dGQEAAAgICpNCfkJCA2bNnKx2aXFxckJSUhICAACmQFS9eHP369VPoTj3A59OQTZs2zTQGsXnz5tLwmmfPnmHjxo0KB95Hjx7ByMhI7grl5ORkVKhQIdcCiKLmz5+PunXrwsPDQ7oVuaenp0qmZPtaxm3UPT09M61T5kLV3Jw8H8idux8CuXuR4IQJE9CxY0fs2bMHxsbGmfapyo7BTUxMhIWFRablNWrUUPpCqtwa6z1u3DhMnToVw4cPz7KTRZmLqDLmw968eTPq1q2L3bt3o0yZMgrX+/TpE2bMmIHt27djx44dKFeuHE6dOiVdcJcvXz6UL18ey5cvV+ialAw+Pj4YOXIkGjduDF1dXQghkJCQgPr168Pb2xunTp3Cxo0bpc9uTrRr1w7jxo3DiBEjYGZmBiEEbty4gcDAQOlCXkXt3r0bffv2VapGdnAeXgXUq1cPS5YskcYl/ZuMGTMGBw8eRLFixVCqVKlMO4mcDvTPTZMnT0Z4eDgGDRqEAgUKYN26dbh9+zbq168PPz8/yGQyTJs2DVFRUTmeieLr8PP8+XNoaWmhdOnSyJ8/Px49eoTk5GSYmZlh+/btOaptZmaGc+fOyfWU2tjYYPfu3bk2xllVXrx4gT59+uDly5fSBQRRUVEoXbo0Fi1apNQd7b70ZU96eno6YmJiFPqCql27NlauXImqVatKy77+XYeHh2PAgAHfnTEjK0II+Pr6YsOGDVi9erXcHZKGDh2KEydOoG/fvhg/fvwvc8OP6tWrY8eOHdJd/F6/fo26devi4sWLSs908LNERkbC2dkZBQsWzHLyfDMzM6XqT58+HadPn4avry8+fvyIcePGYfLkyThx4gRu376NXbt2KVQ3JCQEgwcPhqurK9q3b4+2bduiWLFiePbsGdq2bavQtQYZXFxccOvWLdSuXTvLMbzKBt45c+bg7t27mDlzphS4kpOTMX78eBgaGio1lvzrsd6q8uX74MvPn1ByZqBdu3Zh5syZ0NTUhLu7u9LTsgGfx6Vv2bIFo0ePRvPmzSGTyeDo6AhdXV2sX78eOjo6GDNmDIoXL57lAWRORUVF4d69e8iXLx8qVKggnTH5+PEjtLW1FdpfpaamIjAwENu2bZPOtBQvXhzOzs4YPHiwUvvAhQsX4vr16+jXr1+WZ4uUnWYuA3t4FVC8ePFMvZj/FuXKlcPgwYNzpfbt27cRERGR5ZXPWd1d60eOHTuGxYsXS7061tbWqFu3Lnr16iUF9f79+ys0b+GXUxMtWrQIN27cgJ+fn3RFaGJiIjw9PRW+q05uDE9JTEzEtm3b0Lt3b+TLlw8DBw7Ex48fpfU1atTA8OHDFWpvBkNDQ+zduxenT5+WO3tRr149pU8fNmnSBP3790fPnj3lDgbi4+PRuHFjhb6gkpKSMt3KdeHChXK34NbT01Oodz04OBj79+/HggULMt0OdOHChTh+/Djc3d1RpkyZTHd7yisfPnyQe98VK1YM2traePfuXa4E3tTUVLx69SrTHaNu376t8N3iypcvjwMHDshNnl+vXj2VTJ4PfP/uh8oEx+9dJKjsnfOuXr2KjRs3yh3YKevLK/CFEHj27BkaNGiA0qVLQ0NDA48fP0ZycrLSF61du3ZN7oyXqqj6Dm6RkZGYOnUqrl+/jt69e2PYsGEoWLCgSmrv3r0bU6ZMkTpaTp06hbi4OIwYMUL6XPbp0wcjRoxQOvCmpqaiYMGCUo+9EAJRUVFKfSaBz2cqR40ahVGjRkmB9+vvM0VlXDB45swZAJB7X6piWtMMDLwKqFq1KoYOHQoLCwsYGxtnGtOTm1O8KEuVY2m/FBQUhKCgIOlgwNDQEHFxcUhLS0PTpk0VqhkfH4/ff/9d+llPTw86OjpyAadQoUJyoU8RK1aswObNm+VuflCoUCG4ubmhc+fOmDhxYo7q5cbwlOfPn6NHjx4oWLCgdPr1ypUr6NSpE3R1dRETE4NFixahbt26qFGjRo7a+7WMO/2pYnz0l548eYJ58+YhJCQE3t7ecl8mip5oMjIywt27d+VmYfj6Dkv//POP1OOZE1u2bMHkyZO/ebtwR0dHjBkzBsHBwb9M4M3KlzMRqNLRo0cxefJkvHnzJtM6AwMDpb5cc2vyfODzLCqdO3eW9i0jR47EyJEjla7bp08faR8IfD54dHZ2Rnx8PDp16iTdtEQRFStWlGYIUBVV3LAiO3JrrPe3zjZlHHDl9GxU+/btkZqaCkNDQ/zzzz/f7RjK6ZnQZ8+eyfVIX7hwAbL/1969x8Wcvv8Df41KCkX5FEUkpSQUkUOLDixZh4T6+CGHFSFaFSl0RgesilVWkrMk1pJSFBJFSjJUijbKIWvUoprevz/69P42Jm3NoZnqfj4eHg+9Z/buWjUz9/u+r/u6GAyOajt9+vShU714JczXJMDd8Cg6OlogDY+E1X76W2TCyyNhdkgSps+fP+P06dMctSKB/+t6c+XKFZ7GPX36NDw9PbFgwQKYmJggMjIS8vLycHR05Cv3qWEh6nqC3j7u3r07cnNzubbc7t+/z9MdrDAK2+/btw/a2trYv38/x0rrkiVL6K17FouFo0ePtnjC21Sx8m/xc6fNYDBw5MgReHt7Y+7cudi3bx80NTXpx3gxdepU7NixA6NGjWr0oGFlZSVCQkJ4ynsvKSn515xRIyMj+Pr6tnhsYWEwGFz/lsJKtwgKCoK5uTlsbW1hY2ODsLAw/P333/D29m5xeaWW1Pvk98Pxt99+w9SpU/kao15KSgqys7MB1JX1+u2337hWBV+8eIGSkhK+vo+NjQ1cXFxgaWmJvn37cq2Y8vL7zW9Hr+YSVq73gwcP4Onpifz8fHpXsZ6EhARycnJaNJ6dnZ3QXisKCgp4+/YtvTWfnJwMHR0d/Oc//6Gf8+zZM46veSHI1+S3hNnwqP7mJC8vD0VFRRg/fjzev3+Pvn37CvRnQia8PBDnFdx/4+7ujtTUVIwbNw5xcXGYNm0aXrx4gUePHvG1+vvhwwe6BqeOjg4yMzMxc+ZMODo6wsHBAU5OTjyNm5mZybEVS1EUsrOzUVpaCgB83xEDdW90bm5uuHv3LnR0dOhk/CtXrojNz/rmzZsICQlpMq1g0aJFPHUdarha8ejRI0RERNA7GFJSUsjNzUVISAjfK24URUFJSQlRUVHw9/fH/Pnzm1xBbQ47Oztcv34d06dPx7Jly2BgYIAePXqAxWIhMzMTkZGR6NWrF08HIhQVFVFSUtLkSlFpaalA2iILCkVRGD9+PNe1xj6M+N0mLC4uxsGDB6GmpoahQ4fi7du3MDMzow/LtaRLVcPVxpcvXyIyMhI2NjYcv4PHjh0TyMGWGTNm4MCBA1i5ciVUVFT4OnWvrq6OQ4cOgaIoUBSFBw8ecJyLYDAYkJWV5fumKDQ0FJKSkrh48SLXY/VVJ/ghzMYWTCaTn9C+y8fHB6qqqnBycsL69evh7++PsrIyhISE8FQ5RZgr3lOmTEFgYCD9+VtYWMgR4/v377F7926+KskAgn1NfkuYDY8+fvyI9evX4969ewDqWrf7+vqiuLgYYWFhAjs7Qia8LVBbW4vExER6i97DwwNfv36lHzcwMOC5vFJrSUlJwa+//opx48YhLy8Ptra2GDp0KHbu3Im8vDyex1VWVkZxcTFUVFSgoaGB3NxczJw5E926deOrlFBjk/CNGzdyfM3vHaC1tTVUVVURHR2NkydPAqjbQjx8+DDf6QGCwmKxONI7gLpmHA1XoFVUVHgqHdYwP3Xbtm3YtWsXx6RJW1sbqqqqcHV1ha2tbcuD/5/6n5OEhARcXV0xYsQIuLm5IS0tjecxZWRkcPLkSYSEhCAsLAzl5eX0Fn6PHj0wd+5crFu3jqccQnNzcwQHB+Pw4cONdj2rqalBSEiIwFsC86M1D53Kycnh8+fPAOomfkwmE2ZmZhg4cGCLKxM0XG20tLSEr68vR3MMU1NT6OjoYO/evXyvVKWkpODVq1c4f/58o4+35EagX79+9L+5q6sr3NzchNJp8ttdo69fvyIhIQHnz5/nq7JEPWE3thBGrndeXh4CAgKgoaEBXV1dSElJYeHChVBUVER4eDjf2/eCtGHDBmzatAmzZ88Gg8HA3LlzsXDhQgB1Ow779+/HoEGD+D6DIcjX5LeE2fDIx8cHMjIySEtLo7+Hn58fnJ2d4ePjgwMHDvA1fj0y4W2mjx8/YunSpXj16hV0dXWhoqKCCxcuwNjYGF27dsWbN2/g6ekJPT09vk8RC9PXr1/pE5uamprIycnB0KFDsWDBAvy///f/eB533rx5+OWXX+Dn5wczMzPY2tpCSUkJqampPP97CGtloDGt0ZueH0pKSigqKuKY9H7bnSw/P5/v06xv3rxptA6vjIwM3zmE3+aRTps2DVpaWnyvrMjKysLFxQXOzs54+fIlPnz4ADk5OfTv37/RlJjmsre3h5WVFSwtLbFo0SIMHToU3bt3x8ePH/H48WMcO3YMlZWVfNc+FqRvD9cJ08SJE+Hp6QkvLy+MGTMG/v7+mDx5Mq5evcpxaLClCgsLG60T2q9fP75TAwBg586dfI/RmB07dqCmpgZlZWUCndg1dP/+fcTGxiIuLg4VFRXQ0NBo8RmDxhQVFeHcuXNCKbMnrLxSGRkZ+vU9cOBAPH36FBMnTsSwYcNQWFjIT8gC17VrV4SEhKCiogIAOG6KDAwMEBQUhMmTJ/N9uE9Yr0mgLu3g0aNHXJWGbty4wfcK7M2bNxEVFcVRkk1BQQGurq58lzxriEx4myk4OBidO3fGtWvXOH5ZnZ2d6V8AW1tbRERE8F23UJg0NDSQmpoKKysraGpq4v79+7C2tsanT584VqtbatWqVejduzdkZGQwbNgwuLq64tSpU+jRowf8/PwE+H8geNXV1YiNjf1uT3ZxSGswMTFBUFAQTp061egkjs1mIywsjO8SOpMmTcKWLVvg7u7OUWvRx8eH73a0jd3AaGhoIDo6usX5do1hMBjo378/TwfUGiMnJ4czZ84gMDAQO3fupFdOKIpC9+7dMX36dKxbt47nSh5tnZubG3x9fZGTk4NZs2bh6tWrsLKygqysLAICAnged+TIkfDz84Ofnx/dyKG4uBg+Pj4CuSmtvykoKipCQUEBamtroa6ujkGDBvE1bmJiItzd3QU+sSspKUFsbCwuXLiA4uJiyMnJ0XViBbWKKczGFsLKKzUyMkJQUBDc3d2hr6+PI0eOYP78+UhKSuKrlq0wNbb6L8ibVGG9JgHhNjwC0Oj8o7y8XKAVPkgd3mYyMTGBn58fjIyM6Gvf1vu8efMm3NzckJKSIqow/1ViYiLWr1+Pbdu2wdjYGBYWFhg9ejSePn2KESNGYM+ePTyP/f79e7BYLLp+6+XLl2FoaMh3Ir6wubi4ID4+HsbGxo2+IYnDhLe8vBxWVlaQl5eHvb09Ro4cCXl5eTpXtf5DJDo6mq8t1YqKCmzfvh1xcXH0QRBJSUnMmjULW7du5Tph3RIURSExMRF5eXmNHpg8dOgQz2MLW1VVFYqLi8FisdCjRw+oqanxtXrcll24cAEJCQl0NY8ZM2bQj1VUVEBaWrrRFJDmevPmDRwcHJCVlQV5eXm6eP7YsWOxZ88evsursVgsuLq6IjExEfLy8mCz2aisrIShoSFCQ0PRvXt3nsadNm0aDA0Nvzuxa2n+5Llz5xAbG4uMjAwoKSnBxMQEU6ZMgaGhIYYPH44LFy7wPUmv9+rVK8yZMwdaWloCb2wxdOhQXL58GWpqali+fDlsbGxgZmaGmzdvwt/fH3/88QdP45aVlcHZ2Rnm5uawtrbG0qVLkZGRAQkJCXh4eAgsvbCiogJsNrvN1LH+liBek/WYTCYOHz6MgoICsNlsqKurw9bWlu+GRz4+Pnj8+DG8vLywYMECnDx5Eh8+fMD27dsxfvx4gdQmBsiEt9n09PSQkJDAsaUcHByMxYsX0y+EV69e4ccff6RP7Yqr4uJi1NbWon///mAymbhw4QJ69uyJRYsWQUZGhqcx79y5gzVr1sDW1pbOQ7K2tkZBQQF+++03vsuWCJO+vj5CQkK4DvuIm7KyMvj5+SExMZFjwtipUyeYm5tj69atjaYj8KKiooLeFlRXVxdIXqKXlxeio6MxZMgQZGdnQ19fHy9fvsS7d+9gY2MjsDc1QngiIyPh7++PsWPHQlJSErdv38bSpUvxyy+/CPx75efnIz8/H0Bd+pWgVh9dXFxQUFCAgIAAujtmfn4+Nm/eDC0tLZ53pAQ9sdPW1kb//v2xZs0arqpAurq6Ap3wCrOxxbhx4xAREYHBgwfDx8cHPXr0wNq1a1FSUoIZM2YgMzOTn9BpFEUhPz8fcnJyAmnhHhkZiUOHDuHdu3cA6rbYbWxshFbak1fC7BpaW1sr9PbNQN2Cwu7du3H8+HFUV1eDwWBAQkICVlZW2Lx5s+DyyimiWSZOnEhlZmY2+Zy0tDTK1NS0dQLi0Zw5cygmkynwcWfNmkUdPHiQ6/pvv/1GWVpaCvz7CZKxsTGVl5cn6jCa7ePHj9Tt27epS5cuUSkpKdT79+9FHVKzjBkzhrp69SpFURQ1depU6tmzZ1R1dTW1ceNGyt3dXcTRtV///PMP9eTJE+rx48fUp0+f+Brrxx9/pM6fP09/ffXqVcrAwICqra3lM8rWM3LkSCo7O5vrelZWFjV69Giexx07diz93urt7U0FBwdTFEVRf/31FzVixIgWj3fu3Dlq2bJl1JAhQygjIyNq8+bN1LVr16gvX75QQ4YMEeh71rBhw6jHjx8LbLyGNm/eTNnY2FB5eXlUfHw8ZWZmRuXk5FBBQUHUlClTWjRWSUnJd/+UlpZSX758EUjMISEhlJGREXXy5EmKyWRSubm51LFjxygjI6NGP+dEafLkyc36o6ur2+KxtbW1qXfv3nFcu3nzpsD+nSmKot6+fUvV1NRQFEVRnz9/pu7du0cdOnSIOnbsGFVZWSmw70NRFEVyeJtpwoQJCA0NRXh4+HefExkZyXdZEWF78+aNULZii4qKGs0fnTZtGk99u1vT6tWr4evrC3d3d/Tv318oXYEESU5ODuPGjRN1GC1WUVFBt+PW0tJCdnY2NDU1YWdnh+XLl4s4uvanuroaAQEBOHHiBGpqagDUpaf89NNP8PT05KkcV3FxMUdjDxMTE3z+/Blv3rwRyKpaa5CWlm501YrBYHDsnLSUoA8MWVpawtLSEuXl5bhy5QouX76MtWvXokuXLqitrcXdu3fRv39/gWxVC6OxRT1B5pV+W6+Z+l8nrvq/d+rUic4B56eN+5kzZ+Dr68vxea6jowNlZWX4+vpi5cqVPI9d7/bt2xw55OPGjePpZ9lU3Xd+q3lQjSQAODg44MKFC3z9+wJ1NdI3btyI5ORkXLp0ie6w6Obmht69e0NaWhqHDx/G8ePHuSoU8Uq8P9nFiJ2dHaysrLBq1So4ODhwtHh8+vQp9u/fj+zsbHh5eYkwyn83e/ZsrFixAjNnzoSqqipXTiav9RwHDhyIK1euwM7OjuN6UlISX40nWkN4eDjevHnDkYvYkKDaGnZ0/fr1Q25uLlRUVKCpqYns7GzMnTsXFEXh06dPog6v3dm1axeSk5Nx4MAB6Ovro7a2FpmZmfDx8cGePXuwadOmFo9ZU1PDcUMoKSkJaWlpnlo3i4qJiQk8PT0RGBhIvzcVFRXB29u70bJLzSWsA0MKCgpYuHAhFi5ciNLSUly6dAmXL1+Gt7c3goODMWvWLLi6uvI8PiCcxhbfy/UODAyEh4cHT3mlTdUDrn8fCQ0Nhbu7OyIjI1scc72Kigq6mlFD6urqfJXZBOpqd9vb26OwsBDq6upgs9l48eIFVFRUEBERIZAbR2FV8wB474r5reDgYJSUlODYsWMYOHAg/vnnH/j4+GDYsGGIioqClJQUtm/fjsDAQAQGBgrke5Ic3hZgMplwd3dHTk4OZGRkICcnBxaLhS9fvtC1bIVxylWQmlqBZjAYPBcYT0lJgb29PQwMDKCrqwug7kYgIyMDwcHBfH2QCFt9sevvac1ST+3Z2bNn4efnB19fXwwePBiWlpawsrJCZmYmFBQUxPrQWltkZGSEX3/9FWPGjOG4npaWBicnJ9y6davFY2pra+P27dscueLfHt4VdywWC2vWrEFGRgZ9mp/FYsHY2Bj+/v4CbSQiyAND3yoqKqInv5cvX+ZrLEF/LrRmrve3iouLMXPmTL5yg1esWIHevXvDy8uL3g1gs9nYtm0bXr58iaioKJ7HXr16NWpqahAYGEif//nw4QOcnZ0hKyuLffv28TRuY9U8WCwWX9U8hPl6/7YQQHx8PBwcHBAUFAQLCwsAQFZWFuzs7Piq1d4QWeFtAW1tbURHR+PZs2fIysqi630OHz4cOjo6og6vWYTR9haoK2tz/vx5nDt3Ds+fP4ekpCS0tbXh6ekp9h+EwipT1Fbl5ubi999/x/Pnz+mTuAsXLuR74j9v3jwMGDAAsrKy0NDQQEhICM6ePYuhQ4cKtctRR0VRVKOHGBUUFHhqUFLvypUrHIcYa2trkZCQwNWGm5/uX58+fcLFixdRWFgIe3t7ZGVlQUNDg+/dooqKCkhJSSEqKgpMJhPPnz+HtLQ0fTDTz8+vxSWWqqqqcOHCBUydOpWjHFb9KhU/3a2aMmDAAKxdu1Ygh6gE/blw6tQp+Pr60r8D8fHxcHV1haOjo9Da99ZLSkriu+asq6srFi5ciNTUVHoB5/Hjx6iqquL7xjwtLQ2nT5/mqPrQs2dPODk50c0oWuLfqnk0VtO6uRprVS4ob9++5Xg9p6amQkJCgqORT69evehykIJAJrw80NLS4uuXSNTKy8tRWFhIl52i/lccPTc3l6/cJE1NTaxfvx4vXrxAbW0t1NTUhNJ1SNCEVaZImFgsFqSlpSEtLQ0mk4lbt25BV1eXI7+SFwkJCXB0dMSUKVNgaWkJNpuNhw8fYtmyZdi7dy/MzMz4Gt/Q0JD+u7g3+2jrjIyM6O3A+tchi8XC7t27uVZ9m0tFRQWHDx/muKaoqIhjx45xXGMwGDxPeJ89e4YlS5agT58+9N/j4+MRFxeHgwcP8nTjVVpais2bN+Pu3bsA6m7Q/f39oa2tDTabjSNHjmD//v0tzt//+PEjVqxYgfz8fAwePBjDhg2jH3v37h2OHz+O2NhYhIeHi+X7SEOC/FwQRa53bm4uFi1ahNraWr7rwtbnk/7xxx/0TdH48ePx008/oWvXrnyNLS8vj48fP3JdZ7FYPO0EuLm5oX///ti1axdXNQ9+URQFHx8fjtTH+rMB3/47tLSSR8PurBRFITk5GcOHD+e4EcjMzESfPn34+59ogKQ0dDBnzpyBl5cXampq6BasQN0H1LBhw3D69Gmexm14QKb+4IeEhARfB2Rai7DKFAnLtWvX4OTkhP3790NVVRWWlpbo3bs3Xr16Rfc659WMGTNgZWXF1UL4yJEjOH/+PC5cuMDz2AUFBdi9ezeeP3/eaM4nr+k0ROPKysqwePFivHnzhq6NXVhYiH79+uHAgQMC608vaIsXL8aoUaPg4ODAsX0aEBCAu3fvIjo6usVj2tvbIy8vDw4ODpCSkkJYWBi0tLTg6OiI1atXg8lkwsrKCo6OjujZs2ezx/Xx8aHrYDe2ml5WVoYVK1ZgwoQJPOVMtxZBfy6IIvXln3/+wb1796Cnpyew8ozCEBAQgGvXrsHDwwN6enoA6rbuvby8eKo5GxMTgz///BNpaWmQk5PDpEmTYGZmhgkTJsDAwICv8nUtyQ1v6YQ3ODgYCQkJWL9+PdLS0hAVFYXAwEA615vJZMLe3h4zZ87Ehg0bWjT2dwm05gMh9iZPnkwFBwdTX79+pcaPH0+9evWKysvLo+bMmUOFh4fzPK63tzdlZmZGpaSkUJ8+faI+fvxI3bhxgzIzM6N27twpwP8DwRNWmSJhsbCwoH7//XeKoigqICCAsrCwoCiKopKSkqjJkyfzNfawYcOooqIirutFRUWUnp4eX2PPmjWLWrBgAXXixAkqJiaG6w8heFVVVdS1a9eosLAwKjIykkpJSaHYbLaow2rSiBEjqBcvXtB/f/nyJUVRFPXy5Utq+PDhPI05evRoKjU1lf76xYsXlIGBATVr1ixq5syZjb7+m2Py5MnUvXv3mnzOjRs3KBMTE57Gby2C/lwYPHgwVzmrhj9LcTR58mTKxMSkWX/48fXrV2rTpk2Ujo4Opa2tTWlra1O6urqUh4cH9fnzZ57Hff/+PXXs2DHqv//9L6WtrU2NGDGC0tbWpo4dO0ZVVVXxFbMwVFdXU35+ftTo0aOpMWPGUKGhofRjO3fupAYPHkzZ2dkJtAQaSWnoYN68eYPZs2ejc+fO0NXVxcOHDzFt2jRs2bIFbm5uWLFiBU/jXrp0ieuAzMSJEyEtLQ0nJyexXt0QVpkiYXn58iXd5jcxMZEuB6epqcn3CWINDQ2kpKRg0aJFHNeTk5P5XhEsKirCuXPnxP5gZ3tSf0Le1NSUvlZVVYUnT57w3R1JWBQUFFBYWMiVr/vgwQOeV+5YLBbH752amhqqq6uhqqqKvXv38nyorLy8/F+3XDU0NPD+/Xuexm8twvhcaI1cb0FqeI7g5cuXiIyMhI2NDfT09CAlJYXc3FwcO3YMS5Ys4ev7dO7cGTt37sSWLVtQVFSEzp07Q01NDbKysigvL+e5yUJrVPMQJElJSbi6ujYa0+zZs/HTTz9xVMMSyPcU6GgdTFs85KSgoIDy8nL07dsXAwcOxJMnTzBt2jQoKyvzVKevHiWkAzKtQVhlioRFRUUFd+/ehbKyMgoLC+kT1n/88UejpXRaYt26dVi3bh2ysrLoCdHDhw9x9epVvvPifvjhB9y/f59MeIXo7t27GDFiBKSlpfHgwQN4enoiPz+fzsusJyEhgZycHBFF2bSff/4Z7u7uWLVqFSiKQlpaGs6fP4/IyEg4OjryNCZFUVz1xyUkJLBu3Tq+Kij0798fOTk56Nu373efk5OTI7A6osIi6M+F1sj1FrQ5c+bQf7e0tISvry+9sAAApqam0NHRwd69e2Fvb8/z99HR0cHt27ehoKDAkfMtyM5zvXv3xooVK7BixQqOah7iNOFtyuDBg4UyLpnw8qAtHnKqN23aNGzatAm+vr4wNjaGi4sLdHV1cf36dfTv379FY6Wnp0NfXx+SkpJCOSDTWpydnbFmzRqOU9b1ZYq2bt0q4ui4OTg4wMXFBWw2G5MmTYKenh527dqFU6dOISQkhK+xJ0+ejPDwcJw4cQInT56kT7CfOHGC482ZF5s3b8acOXPwxx9/QFVVlev0Lz/tS4k6Z86cgbu7O+Li4uDj4wNVVVU4OTlh/fr18Pf3R1lZGUJCQsTy97qetbU1lJSU8Pvvv6NLly7w9/eHuro6vL29eS6v9D38HkCytLREQEAAhg4d2uik96+//kJgYKDYTOq+R5CfC4DwqgG1lsLCwkYPpvfr1w8lJSUtHi82NhYxMTEA6m6+1qxZw3Wj9ebNG/znP//hLeAmCLKaR1tHJrw88PHxQWlpKS5fvsx1yGnHjh1id8ipIScnJ3Tv3h0fPnyAqakp5s6di+3bt6NHjx4tjnvx4sW4desWFBUVsWXLFixevBjGxsaNHpARV8IoUyRs06dPh5GREcrKyuhyePPmzcPy5cvRq1cvvscfO3Ysxwnr8vLyFh3k+Z6tW7eiU6dO6NWrl9BLE3VUP//8M/3BlpeXh4CAAGhoaEBXVxdSUlJYuHAhFBUVER4eLvDJoyCZmJgIvGtlY1vs8fHxXDtTLZmcLl68GBkZGZg+fTrMzc2hp6eHbt26gcVi4fHjx7h27RpGjx4tkM5cwiTIz4XWVFxcjBMnTuDFixfw8PBASkoKBgwYgFGjRvE1bn23Nj8/P7qqRHFxMXx8fHiqLGNubo6//voLQF3d9xEjRnDdbMnKysLc3JyvuImmkSoNPBg1ahQiIiLoE5b1srOz8fPPP9Olb9q7b0/iVldXIyUlhWPSOH78+EbzY0Xte2WK6lfsG5YpEsefZ01NDd6/f0/nGFP/KyH05MkTviYyZWVl2LlzJ1auXImBAwdi+fLluH//Pnr37o0DBw5AW1ub57GHDx+OkydPCjwvi/g/06dPx9atWzF27FiMHj0aZ86cwYABA7B9+3aoqqpi5cqVePXqFWbMmIEHDx6IOlxaS3YmeFmpau7kmdfmOxcvXkRsbCyYTCZYLBZ69OiBoUOHYubMmWJ9Y9GWpaenY+XKlTA2Nsb169dx+fJlnDx5EkePHsXu3bsxZcoUnsd+8+YNHBwckJWVBXl5eVAUBRaLhbFjx2LPnj0cpbNa6vz587CwsBDrykXfI6wa7a2FrPDyoK0dchLmh0nDlbrGDsiIKy8vL5SUlMDf358uU7Rjx45GyxSJm2vXrmHr1q34+++/uR77z3/+w9cHrIeHB/755x/06NEDMTExePbsGU6dOoWLFy/C29sbx48f53lsTU1NsFgsnv974t+pq6sjKCgIhw8fhpGREYKCguDu7g59fX0cOXIE8+fPR1JSEkeDBHHQ8KaytrYW9+/fh5KSEnR0dCAlJQUmk4nXr1/jhx9+4Gl8YW+xz5w5U+A1UFvbnTt38OjRI1RXV3O1jxXH7fCAgAC6DKO+vj6AuhKTSkpK2LdvH18TXiUlJZw6dQr5+fnIz88HUPf+xev5g9jYWEyfPh2dO3cGg8FosjOeuKa/CLtGe2sgE14efO+Qk4+Pj1gecgoJCUGnTp2go6ODrl27frcXNi/bzHPnzm3WCq641Vi9f/8+9u7dS2/dDxkyBHPmzAGTyQRFUTh9+jTXCr64CAoKgrm5OWxtbWFjY4OwsDD8/fff8Pb25uswBVDXBSgmJgZ9+vTBtWvXYGpqiuHDh0NBQYGuj8grGxsbuLi4wNLSEn379uUq8i+ub/RtSWhoKAoKCiArKws3Nzc4OzsjPj4e1tbWiI6OhpGRESQkJODh4SHqUDk0bNXq7e0NDQ0NbNu2jf4doSgKO3fuxLt370QVYru2c+dOHD16FNra2lxb7eKafvTs2bNGP29NTU2xe/dugXyPQYMGCeQw+r59+zBx4kR07ty5ydbB4nSI71u//vornJycGq3RHhwcTCa87VVTh5zc3d1FHB237du349q1a3j48CEMDQ3pVdhvy8PwYunSpWJ9SO97hFWmqDUUFxfj4MGDUFNTw9ChQ/H27VuYmZmhU6dO8Pf356uVqbS0NL5+/YqPHz/i7t27CAoKAlB3+IafbTygbjImKSmJixcvcj1WXxaJ4F/977WysjKOHj1KX4+KikJ+fj7k5OSE1u1KEGJiYhATE8NxQ8RgMGBtbc1xkp4QnHPnzmHnzp1tapVaVVUVjx494mpkcePGDbFrqtJwh2HXrl0YNmwYR/eytqC4uBiTJ0/muj558mSB3WAIG5nw8kBOTq7RQ071B9jEjY2NDWxsbFBRUYHk5GQkJCQgICAAWlpaMDMzg7m5OU9vEAwGAxYWFmLd1eZ7hFWmqDXIycnR/cXV1dXBZDJhZmaGgQMH0gcjeGVmZoYNGzagS5cukJeXx6RJk3D58mX4+fnxPdn4dlv569evSEhIwPnz5/kqiUdwe/fuHXr27En/jufm5iItLQ0KCgp8bfW2BiUlJdy8eZM+/FovPj5eaF26OjoJCQm+q7C0tg0bNmDz5s149OgR2Gw2YmNj8ddff+HPP/8Uu4PGDa1duxZHjx4VWuktYRFmjfbWQia8PDA1NcW5c+egra3NcYinrKwMs2fPxp07d0QY3fd169YNFhYWsLCwQFVVFe7cuYPExERYW1ujV69eMDMzw5o1a5o9Xns878hvmaLWMHHiRHh6esLLywtjxoyBv78/Jk+ejKtXr0JJSYmvsT08PHDs2DGUlJRgwYIFkJaWRlVVFVatWoWFCxcKJP779+8jNjYWcXFxqKiogIaGBrZs2SKQsTu6yspKbNy4EcnJybh06RI0NDQQExMDd3d3KCsro0uXLggODsbx48fFtjask5MTHB0dcf36dfr99dGjR8jJyRHrii9t2cKFCxEcHAxvb2/IysqKOpxmMTc3R79+/XD48GFoamoiMTER6urqOH78uNg2VQHqcoGzsrLa3IRXmDXaWwup0tBMcXFxSE5OBlB3ynL69OlcWxIlJSV4/vw5bt26JYoQW6z+cEhiYiLOnj1LJ6E3l6urK9zc3DhK/bQV2tracHd354h9+/btcHBw4KtMUWuoqKiAr68vxowZg1mzZsHZ2Rl//vknZGVlERAQINByTl+/fsXTp0+hrq7OV+pKSUkJYmNjceHCBRQXF0NOTg4sFgtBQUHkFLsA7dy5E7dv34aHhwcMDAzw+fNnGBsbQ1NTE1FRUZCSksL27dtRWVmJwMBAUYf7Xfn5+YiJiUFBQQGAuknC/PnzubqviZsnT54gLy+PbvRRXz0lNzcXnp6eIo7u+xYtWoTMzEy6gdC3u1zidgajNXz69AkXL15EYWEh7O3tkZWVBQ0NDb5/B9esWYOkpCQoKiqib9++XNUaGqYhiZs7d+7gxIkTKCgooHe2bW1t28zuAJnwNlN5eTkCAgIA1E14p02bxtUCUFZWFrNmzRLrH35lZSVu3ryJpKQkpKSkAAAmTZoEExMTTJgwoc3c3fNL2GWKWltFRQWkpaX5TsfIz8/Hli1bsHnzZgwaNAgLFixAYWEhZGRkcODAARgZGbVovHPnziE2NhYZGRlQUlKCiYkJpkyZAkNDQwwfPhwXLlxoEx0K2woTExP4+fnRP6f4+Hg4ODggKCgIFhYWAICsrCzY2dkhLS1NlKG2OyEhIQgJCUGvXr3w/v17KCsr4927d2Cz2TA3N2/ysJKonT9/vsnHxSV3uiWdwvhpZPPs2TMsWbIEffr0wbNnz3DlyhXs378fcXFxOHjwIF9luP6tapI4VsQAOCtNNPTPP/8gOjoaixcvFlFkzUdSGppJQUGBfgGpqqpi2bJlbWZyWFpaisTERCQlJSE9PR3KysowMTHBvn37MHLkSK5c1o6grXcCevHiBXJyclBdXc31GD8r0p6enujXrx8GDBiA6OhofPr0Cbdu3cK5c+ewa9euf/1g/Jabmxv69++PXbt2takDMW3V27dvOVagUlNTISEhgQkTJtDXevXqReeAiyMWi4XDhw/j0aNHqKmp4UqdEtcVsNOnT8PT0xMLFiyAiYkJIiMjIS8vD0dHR7FfmU5ISMDGjRvbVNvvz58/Iy4uDnp6etDT04OUlBRyc3Px4MEDvnflfHx8YGNjAwcHB7rk2Y4dO6CgoAB/f39ER0e3aLyMjAy6GYa4TmgbU15eji9fvgCou9nQ1NTkakLEZDIRGBhIJrzt1dq1a1FeXo4nT540unUlbl11Jk+eDElJSRgaGmLTpk0cLRO/LT5vaGjY2uERLXTo0CEEBgZCXl6+0RJC/LzZZ2dn49KlS1BQUMC1a9dgbm6OXr16YcaMGdi/f3+Lx/Pz88Off/4JV1dX7NixA5MmTYKZmRnHBIwQHGVlZRQXF0NFRQUURSE5ORnDhw/nqLCRmZmJPn36iDDKprm4uODRo0f46aef2lS61IcPH+guXDo6OsjMzMTMmTPh6OgIBwcHODk5iTjC73vw4AFXmUBx1HDVdsOGDY22zD106BDf52gePXoEHx8fruvW1tY81SJfsmQJ7O3tsWbNGsTGxjb5XHFKobt37x42bNhAl6azsrICUDffYTAY9M1oW1nMEP/fcDF05swZeHl5oaamhuOHzmAwMGzYMLGb8FIUherqaqSmpiI1NfW7z2MwGHjy5EkrRkbw4vDhw3B2dsby5csFPnb37t3x7t07SEpK4uHDh7CzswNQl5vISzUOS0tLWFpaory8HFeuXMHly5exdu1adOnSBbW1tbh79y769+8v9pUx2opZs2bB19cX69evR1paGl6/fo2NGzfSjzOZTOzevVusP6BSU1Nx7NgxsU4Na0zDmw0NDQ3k5uZi5syZ6NatG8rLy0UdXpP++9//wtHREdbW1lBRUeE6nyKOCyE3btzAhg0buK6bmpoiODiYr7EVFBRQWFjItTL/4MEDnt4HIyMjERERgYqKijZVh/fHH39EUlISamtrYWZmhrNnz3KUM2UwGJCRkRFI6/lWQREtNnnyZCo4OJj6+vUrNX78eOrVq1dUXl4eNWfOHCo8PFzU4RHtnIGBAfXy5UuhjB0UFESNGTOGMjY2pszNzamamhrqxIkT1IgRI6iIiAiBfI/Xr19T4eHh1Jw5c6jBgwdTY8aMofz8/AQydkdXXV1N+fn5UaNHj6bGjBlDhYaG0o/t3LmTGjx4MGVnZ0d9+fJFhFE2zczMjHr06JGow2ixAwcOUOPGjaNu3LhBZWVlUfr6+tTvv/9OLV++nFqwYIGow2vS4MGDv/tHW1tb1OE1avbs2VRgYCDHtdraWsrb25uaN28eX2OfPHmSmjBhAnXs2DFq+PDh1JkzZ6hff/2VMjAwoKKiovgamxAdcmiNB0OHDkVcXBz69u0LOzs7zJ49G9OmTUNGRgbc3Nxw9epVUYdItGMeHh6QkZGBi4uLULogJSQkoKSkBDNmzECvXr2QnJyM2traRouO86uoqAiXLl3C5cuXm2y3SfDv6dOnYLPZGDJkiKhDaVJCQgIOHjwIBweHRlf/VVRURBTZv4uNjYWKigpGjx6Ns2fP4tSpU+jRowfc3d256goT/MnIyMCqVaugqKhIl/h6/Pgxvnz5gkOHDkFHR4ev8ZOSkvD777+joKAAbDabrkggqKoyFRUVKCoqgoSEBAYMGAAZGRmBjCtIixcvRkhICOTk5P41R1dcc+sbIhNeHvzwww8ICQnBsGHDsGvXLkhJSeGXX35BcXExfvrppxaV9iKIlnJyckJcXBx69uyJvn37ck0IBPHGU1RUhIKCAtTW1kJdXZ1UUiBaTcPa5g1v6Kj/5Q2Ka9pVWz/FzmazcfPmTRQVFcHS0hKFhYUYOHCgWHfSrE+Vali+zsLCgu6AKo7++ecfbNu2DXFxcaipqQEAdO7cGXPmzIG7u7tYpXeFhIRg+fLlkJGRabPVJRoiE14e7NixAykpKfD19cWXL1/g4uKCrVu34vr163jy5AkuXLgg6hCJdkyYbzwsFguurq5ISkqCnJwc2Gw2KisrYWhoiNDQULH+8CPah5KSkiYfF6euTg1PsZuamiI6OrrRU+wbNmxAdna2KEJsltevX2PZsmX4+PEjPn78iLi4OPj7+yMzMxOHDh3iuAlpr/7tfbUhft5jnZyc8PTpU7i7u2Po0KGora3Fo0eP6Nrq27Zt43lsYfLx8cHixYvFvuJIU8iElwfV1dU4ePAgdHR0YGpqij179uD06dPo0aMH/Pz8YGBgIOoQCYInLi4uKCgoQEBAAN0qOz8/H5s3b4aWlhb8/PxEHCHRUVVVVeHJkydi1UUrLi6O4xT7t6gGp9jFuRvV6tWr0atXL3h4eGDUqFG4ePEievfuDTc3N7x+/RpRUVGiDlHoGrbMrW/KpKSkBB0dHUhJSYHJZOL169f44Ycf+Or4Z2BggKioKOjq6nJcz8rKws8//4x79+7xPLYwjR49GjExMejbt6+oQ+EZqdLAAykpKY47PEdHRzg6OoowIqK9a9jV7t+Kr/NTcD0pKQkRERH0ZBcABg0ahG3btuHnn3/meVyCaK4HDx7A09MT+fn5dNnHehISEsjJyRFRZNzayyn2jIwMnDlzhqMmu5SUFOzt7cWm6YSwNZzUe3t7Q0NDA9u2baPLtVEUhZ07d+Ldu3d8fR9FRUW8f/+e63pVVZVYl+GztbWFp6cnbG1tG63kIc659fXIhLeZWmu7gyBESVpaGp06deK6zmAwwGazRRAR0dH4+PhAVVUVTk5OWL9+Pfz9/VFWVoaQkBBs3bpV1OFxqf+gZzKZIo6Ed126dMH79++5DtYVFhaK9SRMWGJiYhATE8NRm5jBYMDa2prvGwA7Ozu4ubnBzs4O+vr6kJSUxJMnT7Bv3z7MmTMH6enp9HPFqRxcfTm1mzdvAvi//Hpxz61viEx4mykkJASdOnWCjo4OunbtytX9p54wTs0TRMNVW35WcP+NiYkJPD09ERgYSOdqFRUVwcfHBxMnThTa9yWIenl5eQgICICGhgZ0dXUhJSWFhQsXQlFREeHh4QI7JS9obbVDHFDXUGHbtm1wcXEBUDfRvXfvHvbs2YN58+aJOLqm3b59m+OA7bhx4/g++KWkpISbN29y3QDEx8ejX79+fI3t7u4OAI02tggNDUVoaCgA8auLn5iYKOoQ+EYmvM20fft2XLt2DQ8fPoShoSFMTU1hamrKsX1FEMLSWjsMzs7OWLNmDaZOnUqfdGaxWDA2NhbL1TWi/ZGRkaG31gcOHIinT59i4sSJGDZsGAoLC0Uc3fe11Q5xALBmzRrIycnBw8MDnz9/xsqVK6GoqAhbW1uhNLgRhNLSUtjb26OwsBDq6upgs9l48eIFVFRUEBERAWVlZZ7HdnJygqOjI65fv04f2Hv06BFycnL4yt8F2u5OgKurK12irKHy8nKsWLECMTExIoqs+cihtRaqqKhAcnIyEhISkJqaCi0tLZiZmcHc3FysTg8T7Yu2tnazdxj4WUl6+fIl1NTUwGQy8fz5c0hLS0NdXZ0jp5cghMnBwQEURcHd3R137tzBkSNHcOTIEVy6dAmHDh3CjRs3RB1io4YNG9bmOsRduHABCQkJkJKSgqmpKWbMmIF//vkHbDZb7CuyrF69GjU1NXSbdaCuvbOzszNkZWWb7GjWHPn5+YiJieEoeTZ//nyBVCkoKCiAkpISunfvjps3byIpKQlDhgwRu9X0lJQUurpIaGgoli5dCllZWY7nvHjxAikpKbh7964oQmwRMuHlQ1VVFe7cuYPExERcv34dvXr1gpmZGdasWSPq0Ih25uTJk62ywzB+/HgcPHgQQ4cOFei4BNFcZWVlcHZ2hrm5OaytrbF06VJkZGRAQkICHh4eYjcpqGdubo49e/a0mddOZGQk/P39MXbsWEhKSuL27dtYunQpfvnlF1GH1iz6+vo4ffo0tLS0OK4zmUwsXLgQ9+/fF1FkTTt9+jS8vLwQERGBbt26Yf78+TAyMgKTycS8efOwfv16UYdIKy4uhpubGyiKQnp6OkaMGMGRLsJgMCArKwsrKyuYmZmJMNLmIRNePtWXL0lMTMTZs2fBZrNJ4wlCaIS9wzBr1iz88ssvJF+XEJmMjAzo6enRp8ApikJ+fj7k5OT42qYWtrbWIW7atGl0p1CgLj/V1dUVGRkZbeIsyqRJkxAQEMB1sOvevXtwcHBAWloaz2MLMx/b3Nwc69evx4wZM+Dt7Y3s7GycPXsW6enpcHR0xK1bt3geW5gaVgpqq0gOLw8qKyvpbYiUlBQAdS++HTt2YMKECSKOjmjPunXrBgsLC1hYWHDsMFhbWwtkh2HIkCGwt7eHnp4eVFVVubpGCfPAHEEAdfmkkZGRdO4kg8GApqamiKP6d+vWrQMArFy5sk10iCsuLsbYsWPpr01MTPD582e8efNGrG8s6llYWMDd3R0eHh7Q09MDUFfL1svLi++DjcLMxy4rK8PIkSMBANevX8eCBQsAAL1790ZlZaVAv5cg7dixAwUFBaAoSuxTMb6HTHibqbS0FImJiUhKSkJ6ejqUlZVhYmKCffv2YeTIkRz1CwmiNXTu3BnGxsaQlZWFrKwszp49i/DwcL5TambOnCmgCAmi5TQ1NZGdnd3munu1tVPsNTU1HGW3JCUlIS0tjaqqKhFG1Xzr16/H+/fvsXz5cnoFVkJCAvPmzaOrTfAqNTVVaPnYAwcOxB9//AEFBQW8evUKZmZmqK6uxuHDh8X6d/7bVIzVq1fDyMgICQkJePXqlVilYnwPSWloJh0dHUhKStL5k9/mDTUkTrXziPbnezsMJiYmmDBhAtehAoJoS9asWYOkpCQoKiqib9++XLsM4lzeqzHi2CEOqDsIe/v2bSgqKtLX9PX1cfHiRb5Lb7UmFouFoqIidO7cGWpqagJ5/xNmPvadO3ewYcMGfPz4Ef/973+xbds2eHl5IT4+Hr/99pvY5oC31VSMhsgKbzNRFIXq6mqkpqYiNTX1u88Tx60rou0T9g5DbW0tEhMTYW5uDgDw8PDA169f6ccNDAzazLYV0bbp6OhAR0dH1GG0WFvqEFfvypUrHFv2tbW1SEhI4DoMW5/nK2rp6el0s4aGDRoA4OvXr3j8+DH9NT8LTy4uLvDw8BBKPvbYsWNx584dfPr0ia4uYW9vD1dXV77rBwtTW03FaIis8BJEGyDMHYaPHz9i6dKlePXqFWJiYqCiogJ9fX0YGxuja9euePPmDe7evYvo6Gix3nIjCFGytLRE7969YWNj02iHOHFrmGFiYtKs5zEYDLFJ12i4Kt3UexG/C08NxxZEPnbDifr3fP78GZGRkVi1alXLA24Fs2fPxvTp06GgoAB3d3f8+eefUFNTg5+fH5hMJk6ePCnqEP8VmfASRBvQ3IkmL2/GPj4+yMnJwaFDh+jVnm+3Nm1tbaGsrIxdu3a1LHCCaKaKigpER0dj0aJFkJCQwMqVK/Hlyxf68VGjRsHBwUGEETZNT08PsbGx0NDQwKJFi7BixQpMnDgRcXFxCA8Px7lz50QdItFMJSUlTT7e0oo4Ojo6uHXrFkf6yE8//YSwsDD06dMHAPDu3TsYGxuL7Q5xW03FaIikNBBEGyDM7jxJSUnw8/Nr8jTy8uXL4ebmJrQYiI7t9evXsLGxgaysLKZNmwZlZWWkp6dj7ty56NGjB0pLS3HgwAGMGzcOo0aNEnW4jWqrHeLaooqKCkhISEBGRobrsbdv3yIgIAD+/v48j/+9CW19PnZLJ7yNrSv+9ddfqKmp4Sk+UWirqRgNkQkvQXRwb9++xYABAziuLVu2jKOFpIaGBv7+++/WDYzoMPbt2wdtbW3s378fnTp1oq8vWbKE3mVgsVg4evSo2E54jYyMEBQUBHd3d+jr6+PIkSOYP38+kpKSuNqxErwpLS3F5s2b6a5eP/zwA/z9/SEvLw82m40jR44gNDSU7wlYW8zHFoamcqa/1RYO65MJL0F0cIqKiigtLUXv3r3pa/U1ResVFxdDSUmptUMjOoibN28iJCSEY7L7rUWLFol16SM3Nzc4OzsjPj4e1tbWiI6OhpGREd0hjuCfl5cXSkpK4O/vDykpKYSFhWHHjh1wdHTE6tWrwWQyYWVlBUdHR76+j4+PD1RVVeHk5NRoPnZHsWjRIjpnetGiRd99Xls5rE8mvATRwU2YMAGhoaEIDw//7nMiIyObfciFIFqKxWJx3HABdRPIhtUCVFRUxPo0uLKyMkfJtKioqDbRIa4tuX//Pvbu3Us3zBgyZAjmzJkDJpMJiqJw+vRpugkFP/Ly8hAQEAANDQ3o6upCSkoKCxcuhKKiIsLDw8XuAKKwNEylE2ZaXWshE16C6ODs7OxgZWWFVatWwcHBAUOGDKEfe/r0Kfbv34/s7Gx4eXmJMEqiPVNSUkJRURHHpNfKyorjOfn5+WLXnrfeu3fv0LNnTzqHNzc3F2lpaVBQUMCUKVNEHF37wWKxoKGhQX+tpqaG6upqqKqqYu/evQLLJRVGPva/lYD79OkT/4ELUXFxMfLz81FZWYnu3btDU1NTbF+P30MmvATRwfXr1w+RkZFwd3eHpaUlZGRkICcnBxaLhS9fvmDo0KGIjIxEr169RB0q0U6ZmJggKCgIp06darSmNJvNRlhYGH788UcRRPd9lZWV2LhxI5KTk3Hp0iVoaGggJiYG7u7uUFZWRpcuXRAcHIzjx49zrWATLUdRFNfvh4SEBNatWyfQg1OCzsdWUVHB4cOHOa4pKiri2LFjHNfqKzaIkzt37mDHjh3Iy8vjOHzHYDCgq6uLzZs3i21e/bdIWTKCIGjPnj1DVlYWPnz4ADk5OQwfPrxNNgEg2pby8nJYWVlBXl4e9vb2GDlyJOTl5cFisZCZmYmwsDD8/fffiI6ObrKaSGvbuXMnbt++DQ8PDxgYGODz588wNjaGpqYmoqKiICUlhe3bt6OyshKBgYGiDrfNa63ucGVlZXB2doa5uTmsra2xdOlSZGRk0PnYHaUJz61bt2BnZwcLCwvMnz8fgwYNQvfu3VFRUQEmk4lz587hypUrOHr0KPT19UUd7r8iK7wEQdC0tLSabGpBEMKgoKCAkydPws/PD46OjmCz2fRjnTp1grm5OUJDQ8VqsgsA8fHx8PPzoztQ3bp1C5WVlVi0aBG94mhpaQk7OztRhtmuNJYaEB8fzzEJBvjrDldcXIzw8HBIS0sD6Lj52KGhobC1tYWzszPHdXl5eYwZMwZjxoyBvLw8Dhw4gLCwMBFF2XxkhZcgCIIQGywWCzk5OfQug66uLlerW3Ghp6eHq1ev0rmMHh4eOHv2LFJTU+lapSUlJZg+fTqysrJEGWq70Frd4caMGYPIyMgO31lSX18fZ8+exaBBg777nGfPnmHZsmW4detWK0bGG7LCSxAEQYgNOTk5jBs3TtRhNIuysjKKi4uhoqICiqKQnJyM4cOH05NdAMjMzBTL3My2KCkpqVW+j6amJrKzszv8hPfLly8cv8uN6dmzJ8rLy1spIv6QCS9BEARB8GDWrFnw9fXF+vXrkZaWhtevX2Pjxo3040wmE7t378bMmTNFGCXRUvLy8ti+fTv27duHvn37onPnzhyPNyw/155RFNVkbWygbjW9rSQKkAkvQRBcioqKUFBQgNraWqirqze5pUUQHdXq1atRUVGBLVu2gMFgwMHBATNmzAAA7Nq1CxEREZg0aRJWr14t4kiJltDR0SGHdf/n25zpb4l7ObWGSA4vQRA0FosFV1dXJCYm0u06KysrYWhoiNDQUHTv3l3UIRJEm/D06VOw2WyOutYE0Za0pNlQa6Wb8INMeAmCoLm4uKCgoAABAQEYOHAggLqC/5s3b4aWlhb8/PxEHCHRUdy+fZtjl2HcuHECrbVKEN+qqKhAdHQ0Fi1aBAkJCaxcuRJfvnyhHx81ahQcHBxEGCHBDzLhJQiCNmrUKERERHC158zOzsbPP/+Mu3fviigyoqMoLS2Fvb09CgsLoa6uDjabjRcvXkBFRQUREREdqiwU0Xpev34NGxsbyMrK0r9n+vr6mDt3Lnr06IHS0lKcO3cOUVFRbabRAsGJ5PASBEGTlpZu9JACg8HgqI1KEMLi6ekJRUVFRERE0CfEP3z4AGdnZ/j6+mLfvn0ijpBoj/bt2wdtbW3s37+f4z1wyZIldFMLFouFo0ePkglvG9X08TuCIDoUExMTeHp64uXLl/S1oqIi+Pj4YOLEiSKMjOgo0tLS4OzszFEOqWfPnnBycsLt27dFGBnRnt28eROrVq1qsirBokWLkJGR0YpREYJEVngJgqA5OztjzZo1mDp1Kt0znsViwdjYGO7u7iKOjugI5OXl8fHjR67rLBaL5PASQsNisdC7d2+Oa25ubhxNT1RUVFBZWdnaoRECQia8BEHQ5OTkEBUVBSaTiefPn0NaWhrq6ur0ATaCEDYLCwu4u7vDw8ODziXPysqCl5cXpk+fLuLoiPZKSUkJRUVFHJNeKysrjufk5+fTXfWItodMeAmCoJmamuLcuXPQ1tbm6DJUVlaG2bNn486dOyKMjugI1q9fj/fv32P58uV0QXsJCQnMmzcPLi4uIo6OaK9MTEwQFBSEU6dOQUJCgutxNpuNsLAw/PjjjyKIjhAEUqWBIDq4uLg4JCcnAwDOnz+P6dOnQ1pamuM5JSUleP78eZvol060DywWC0VFRejcuTPU1NQgKysr6pCIdqy8vBxWVlaQl5eHvb09Ro4cCXl5ebBYLGRmZiIsLAx///03oqOjm2zEQIgvssJLEB3c6NGj6QkvgEbbRGpqasLJyak1wyI6kPT0dOjr60NSUhLp6ekcj339+hWPHz+mvzY0NGzt8IgOQEFBASdPnoSfnx8cHR05qtJ06tQJ5ubmCA0NJZPdNoys8BIEQQsJCcGyZcvIahrRqrS1tXH79m0oKipypNJ8i8Fg4MmTJ60YGdERsVgs5OTk4MOHD5CTk4Ouri7H4TWibSITXoIgOJSXl6OwsBC1tbUA6lZ8q6qqkJubi5UrV4o4OoIgCIJoOTLhJQiCdubMGXh5eaGmpgYMBoNOb2AwGBg2bBhOnz4t4giJ9qyiogISEhKQkZHheuzt27cICAiAv7+/CCIjCKKtI40nCIKg/fbbb1i1ahWys7OhqKiI69ev49KlS9DR0YG5ubmowyPaqdLSUtja2sLQ0BAGBgaws7Oja/Gy2Wz8/vvvmDp1KkeuOUEQREuQCS9BELQ3b95g9uzZ6Ny5M3R1dfHw4UMMGjQIW7ZswdmzZ0UdHtFOeXl5oaSkBP7+/tizZw/evn2LHTt2oKysDPPmzUNQUBBmzJiBuLg4UYdKEEQbRao0EARBU1BQQHl5Ofr27YuBAwfiyZMnmDZtGpSVlVFWVibq8Ih26v79+9i7dy/Gjh0LABgyZAjmzJkDJpMJiqJw+vRpugkFQbSW27dvo6CgALW1tVBXV8e4ceNIt782jEx4CYKgTZs2DZs2bYKvry+MjY3h4uICXV1dXL9+Hf379xd1eEQ7xWKxoKGhQX+tpqaG6upqqKqqYu/evWSSQbSq0tJS2Nvbo7CwEOrq6mCz2Xjx4gVUVFQQEREBZWVlUYdI8IBMeAmCoDk5OaF79+748OEDTE1NMXfuXGzfvh09evSAn5+fqMMj2imKori6W0lISGDdunVksku0Ok9PTygqKiIiIgLy8vIAgA8fPsDZ2Rm+vr7Yt2+fiCMkeEGqNBAEQRAi1bAObz19fX1cvHgR/fr1E2FkREekr6+P06dPQ0tLi+M6k8nEwoULcf/+fRFFRvCDrPASRAcXEhLS7OeuXbtWiJEQHdmVK1c4uljV1tYiPj6eYxIMALNnz27lyIiORl5enq4S0hCLxSI7Dm0YWeEliA5OW1sbnTp1go6ODrp27dpoa2Ggrhbv0aNHWzk6oiMwMTFp1vMYDAYSExOFHA3R0QUEBODatWvw8PCgD0tmZWXBy8sL48ePx7Zt20QcIcELMuEliA7u5MmTuHbtGh4+fAhDQ0OYmprC1NSUtNIkCKJDqqqqwrZt23Dx4kV6AUBCQgLz5s3Dpk2b0KVLFxFHSPCCTHgJggBQ1+UqOTkZCQkJSE1NhZaWFszMzGBubg5VVVVRh0cQBNGqWCwWioqK0LlzZ6ipqUFWVlbUIRF8IBNegiC4VFVV4c6dO0hMTMT169fRq1cvmJmZYc2aNaIOjSAIQuDS09Ohr68PSUlJpKenN/lcQ0PDVoqKECQy4SUIolG1tbW4f/8+EhMTcfbsWbDZbDx8+FDUYREEQQhcw0oh2tra330eg8HAkydPWjEyQlDIhJcgCFplZSVu3ryJpKQkpKSkAAAmTZoEExMTTJgwgWzpEQRBEG0SmfASRAdXWlqKxMREJCUlIT09HcrKyjAxMYGpqSlGjhzJ1RCAIAiivaqoqICEhARkZGS4Hnv79i0CAgLg7+8vgsgIfpEJL0F0cDo6OpCUlKQrNHxbbL0hkrtGEER7VFpais2bN+Pu3bsAgB9++AH+/v6Ql5cHm83GkSNHEBoaCikpKfo5RNtCJrwE0cE1la/WEMldIwiivbK3t0deXh4cHBwgJSWFsLAwaGlpwdHREatXrwaTyYSVlRUcHR3Rs2dPUYdL8IBMeAmCIAiC6NDGjBmDvXv3YuzYsQCAly9fYs6cOejXrx8oioKPjw/dhIJom0hrYYIgCIIgOjQWiwUNDQ36azU1NVRXV0NVVRV79+4lLYXbgU6iDoAgCIIgCEKUKIriOqArISGBdevWkcluO0EmvARBEARBEI3o2rWrqEMgBISkNBAEQRAE0eFduXIF3bp1o7+ura1FfHw8FBUVOZ43e/bsVo6MEARyaI0gCIIgiA7NxMSkWc9jMBhITEwUcjSEMJAJL0EQBEEQBNGukRxegiAIgiAIol0jE16CIAiCIAiiXSMTXoIgCIIgCKJdIxNegiAIgiAIol0jE16CIAiCIAiiXSMTXoIgCIIgCKJdIxNegiAIgiAIol0jE16CIAiCIAiiXSMTXoIgCIIgCKJd+/9gPYOT0n9P0AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=medias_mais_recorrentes, x=medias_mais_recorrentes.Estado, y=medias_mais_recorrentes.Numero, palette='YlOrRd_r')\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Média mensal de incêndios por estado (1997-2017)')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Ocorrências')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anos onde ocorreram mais queimadas"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por último, vamos plotar uma linha temporal do número de queimadas ao decorrer dos anos.\n",
    "\n",
    "Para isso, vamos agrupar a soma dos dados por Ano."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ano\n",
       "1998    123881\n",
       "1999    134596\n",
       "2000    101528\n",
       "2001    145567\n",
       "2002    235792\n",
       "2003    235242\n",
       "2004    270384\n",
       "2005    240764\n",
       "2006    136890\n",
       "2007    231252\n",
       "2008    123249\n",
       "2009    123211\n",
       "2010    249274\n",
       "2011    133080\n",
       "2012    193791\n",
       "2013    115184\n",
       "2014    183660\n",
       "2015    236305\n",
       "2016    188295\n",
       "2017    261047\n",
       "Name: Numero, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anos_mais_queimadas = dados_originais.groupby('Ano').Numero.sum()\n",
    "anos_mais_queimadas"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dessa forma conseguimos plotar um gráfico de linhas onde conseguimos identificar quais anos ficaram abaixo da média,\n",
    "\n",
    "e podemos ver a tendência de **alta** nos dados, apesar de serem afetados de forma irregular pelo tempo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= sns.lineplot(anos_mais_queimadas, x=anos_mais_queimadas.index, y=anos_mais_queimadas, label='Ocorrências')\n",
    "\n",
    "plt.axhline(anos_mais_queimadas.mean(), color='black', linestyle=':', label='Média')\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.xticks(range(1997,2018,1),rotation=90)\n",
    "plt.title('Total de queimadas por ano')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora que fizemos alguns gráficos simples para entender nossos dados de forma geral,\n",
    "vamos usar o Power BI para gerar um dashboard mais completo."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 📤 Exportando dados em formato .csv"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui, a partir do método **to_csv** do Pandas, exportamos nossos dados para um novo arquivo, para ser usado no PBI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_originais.to_csv('Data/dados_corretos.csv', encoding='utf-16', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "11938c6bc6919ae2720b4d5011047913343b08a43b18698fd82dedb0d4417594"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}