diff --git "a/ Apresentac\314\247a\314\203o_Projeto Final.pdf" "b/ Apresentac\314\247a\314\203o_Projeto Final.pdf" new file mode 100644 index 0000000..dbb3e7b Binary files /dev/null and "b/ Apresentac\314\247a\314\203o_Projeto Final.pdf" differ diff --git a/Link_do_Tableau b/Link_do_Tableau new file mode 100644 index 0000000..6664ecb --- /dev/null +++ b/Link_do_Tableau @@ -0,0 +1 @@ +https://public.tableau.com/app/profile/luana.queiros/viz/Projeto_Final_ENEM2023/Painel2 diff --git "a/Projeto_Final__An\303\241lise_Explorat\303\263ria_ENEM_2023.ipynb" "b/Projeto_Final__An\303\241lise_Explorat\303\263ria_ENEM_2023.ipynb" new file mode 100644 index 0000000..d7bb07f --- /dev/null +++ "b/Projeto_Final__An\303\241lise_Explorat\303\263ria_ENEM_2023.ipynb" @@ -0,0 +1,3550 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "Neste projeto trabalharemos com os microdados do ENEM do ano de 2023 disponiblizado pelo Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira - Inep.\n", + "\n", + "A base utilzada e o dicionário de dados estão disponíveis no link abaixo:\n", + "\n", + "https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem\n", + "\n", + "Serão analisados apenas os incritos do sexo feminino das regiões Norte e Sul do Brasil.\n" + ], + "metadata": { + "id": "JTpyb_IlRCg3" + } + }, + { + "cell_type": "markdown", + "source": [ + "O projeto tem como objetivo analisar se há diferenças significatvas entre as inscritas dos extremos do país." + ], + "metadata": { + "id": "f7MfNw53S0Ws" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Importação, filtragem e exportação dos dados" + ], + "metadata": { + "id": "hGmdiU5dS-C5" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "696WnyG2yKBJ" + }, + "outputs": [], + "source": [ + "#importando as bibliotecas que serão utilizadas\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib as plt\n", + "import zipfile" + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-jBGq5tolnfq", + "outputId": "d5f17412-433b-463c-ef7d-40bfd70f97f1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Acessando o drive com pasta onde contém o arquivo dos dados\n", + "\n", + "with zipfile.ZipFile('/content/drive/MyDrive/microdados_enem_2023.zip') as z:\n", + " print(*z.namelist(), sep=\"\\n\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "10N_RXeJosmx", + "outputId": "cddc85d3-75f3-4d63-d297-ce36f11a67ba" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "PROVAS E GABARITOS/\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_01_DIA_1_AZUL.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_02_DIA_1_AMARELO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_03_DIA_1_BRANCO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_04_DIA_1_ROSA_AMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_04_DIA_1_ROSA_SUPERAMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_05_DIA_2_AMARELO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_06_DIA_2_CINZA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_07_DIA_2_AZUL.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_08_DIA_2_ROSA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_08_DIA_2_ROSA_AMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_08_DIA_2_ROSA_SUPERAMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_09_DIA_1_LARANJA_BRAILE_LEDOR.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_09_DIA_1_LARANJA_LEITOR_TELA_DOSVOX_CH.txt\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_09_DIA_1_LARANJA_LEITOR_TELA_DOSVOX_LC.txt\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_09_DIA_1_LARANJA_LEITOR_TELA_NVDA_CH.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_09_DIA_1_LARANJA_LEITOR_TELA_NVDA_LC.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_10_DIA_1_VERDE_LIBRAS.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_10_DIA_1_VERDE_LIBRAS_AMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_10_DIA_1_VERDE_LIBRAS_SUPERAMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_11_DIA_2_LARANJA_BRAILE_LEDOR.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_11_DIA_2_LARANJA_LEITOR_TELA_DOSVOX_CN.txt\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_11_DIA_2_LARANJA_LEITOR_TELA_DOSVOX_MT.txt\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_11_DIA_2_LARANJA_LEITOR_TELA_NVDA_CN.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_11_DIA_2_LARANJA_LEITOR_TELA_NVDA_MT.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_12_DIA_2_VERDE_LIBRAS.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_12_DIA_2_VERDE_LIBRAS_AMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_CAD_12_DIA_2_VERDE_LIBRAS_SUPERAMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_01_DIA_1_AZUL.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_02_DIA_1_AMARELO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_03_DIA_1_BRANCO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_04_DIA_1_ROSA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_05_DIA_2_AMARELO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_06_DIA_2_CINZA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_07_DIA_2_AZUL.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_08_DIA_2_ROSA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_09_DIA_1_LARANJA_BRAILE_LEDOR.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_10_DIA_1_VERDE_LIBRAS.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_11_DIA_2_LARANJA_BRAILE_LEDOR.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P1_GAB_12_DIA_2_VERDE_LIBRAS.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_05_DIA_2_AMARELO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_06_DIA_2_CINZA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_07_DIA_2_AZUL.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_08_DIA_2_ROSA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_08_DIA_2_ROSA_AMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_08_DIA_2_ROSA_SUPERAMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_09_DIA_1_LARANJA_BRAILE_LEDOR.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_11_DIA_2_LARANJA_BRAILE_LEDOR.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_1_DIA_1_AZUL.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_2_DIA_1_AMARELO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_3_DIA_1_BRANCO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_4_DIA_1_ROSA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_4_DIA_1_ROSA_AMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_CAD_4_DIA_1_ROSA_SUPERAMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_05_DIA_2_AMARELO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_06_DIA_2_CINZA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_07_DIA_2_AZUL.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_08_DIA_2_ROSA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_08_DIA_2_ROSA_AMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_08_DIA_2_ROSA_SUPERAMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_09_DIA_1_LARANJA_BRAILE_LEDOR.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_11_DIA_2_LARANJA_BRAILE_LEDOR.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_1_DIA_1_AZUL.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_2_DIA_1_AMARELO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_3_DIA_1_BRANCO.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_4_DIA_1_ROSA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_4_DIA_1_ROSA_AMPLIADA.pdf\n", + "PROVAS E GABARITOS/ENEM_2023_P2_GAB_4_DIA_1_ROSA_SUPERAMPLIADA.pdf\n", + "DADOS/\n", + "DADOS/ITENS_PROVA_2023.csv\n", + "DADOS/MICRODADOS_ENEM_2023.csv\n", + "DICION╡RIO/\n", + "DICION╡RIO/Dicionário_Microdados_Enem_2023.ods\n", + "DICION╡RIO/Dicionário_Microdados_Enem_2023.xlsx\n", + "INPUTS/\n", + "INPUTS/INPUT_R_ITENS_PROVA_2023.R\n", + "INPUTS/INPUT_R_MICRODADOS_ENEM_2023.R\n", + "INPUTS/INPUT_SAS_ITENS_PROVA_2023.sas\n", + "INPUTS/INPUT_SAS_MICRODADOS_ENEM_2023.sas\n", + "INPUTS/INPUT_SPSS_ITENS_PROVA_2023.sps\n", + "INPUTS/INPUT_SPSS_MICRODADOS_ENEM_2023.sps\n", + "LEIA-ME E DOCUMENTOS TÉCNICOS/\n", + "LEIA-ME E DOCUMENTOS TÉCNICOS/Edital_Enem_2023_Impresso.pdf\n", + "LEIA-ME E DOCUMENTOS TÉCNICOS/enem_procedimentos_de_analise.pdf\n", + "LEIA-ME E DOCUMENTOS TÉCNICOS/entenda_a_sua_nota_no_enem_guia_do_participante (2).pdf\n", + "LEIA-ME E DOCUMENTOS TÉCNICOS/Leia_Me_Enem_2023.pdf\n", + "LEIA-ME E DOCUMENTOS TÉCNICOS/manual_de_redacao_do_enem_2023.pdf\n", + "LEIA-ME E DOCUMENTOS TÉCNICOS/matriz_referencia_enem.pdf\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "with zipfile.ZipFile('/content/drive/MyDrive/microdados_enem_2023.zip') as z:\n", + " with z.open('DADOS/MICRODADOS_ENEM_2023.csv') as f:\n", + " df = pd.read_csv(f, sep=';' , encoding='ISO-8859-1')" + ], + "metadata": { + "id": "d3c78C-Iq24T" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df.columns.values" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KjzYKjnt_Um5", + "outputId": "8dfc30c6-9bb6-4cc5-ea2b-045881c0ff6c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['NU_INSCRICAO', 'NU_ANO', 'TP_FAIXA_ETARIA', 'TP_SEXO',\n", + " 'TP_ESTADO_CIVIL', 'TP_COR_RACA', 'TP_NACIONALIDADE',\n", + " 'TP_ST_CONCLUSAO', 'TP_ANO_CONCLUIU', 'TP_ESCOLA', 'TP_ENSINO',\n", + " 'IN_TREINEIRO', 'CO_MUNICIPIO_ESC', 'NO_MUNICIPIO_ESC',\n", + " 'CO_UF_ESC', 'SG_UF_ESC', 'TP_DEPENDENCIA_ADM_ESC',\n", + " 'TP_LOCALIZACAO_ESC', 'TP_SIT_FUNC_ESC', 'CO_MUNICIPIO_PROVA',\n", + " 'NO_MUNICIPIO_PROVA', 'CO_UF_PROVA', 'SG_UF_PROVA',\n", + " 'TP_PRESENCA_CN', 'TP_PRESENCA_CH', 'TP_PRESENCA_LC',\n", + " 'TP_PRESENCA_MT', 'CO_PROVA_CN', 'CO_PROVA_CH', 'CO_PROVA_LC',\n", + " 'CO_PROVA_MT', 'NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n", + " 'NU_NOTA_MT', 'TX_RESPOSTAS_CN', 'TX_RESPOSTAS_CH',\n", + " 'TX_RESPOSTAS_LC', 'TX_RESPOSTAS_MT', 'TP_LINGUA',\n", + " 'TX_GABARITO_CN', 'TX_GABARITO_CH', 'TX_GABARITO_LC',\n", + " 'TX_GABARITO_MT', 'TP_STATUS_REDACAO', 'NU_NOTA_COMP1',\n", + " 'NU_NOTA_COMP2', 'NU_NOTA_COMP3', 'NU_NOTA_COMP4', 'NU_NOTA_COMP5',\n", + " 'NU_NOTA_REDACAO', 'Q001', 'Q002', 'Q003', 'Q004', 'Q005', 'Q006',\n", + " 'Q007', 'Q008', 'Q009', 'Q010', 'Q011', 'Q012', 'Q013', 'Q014',\n", + " 'Q015', 'Q016', 'Q017', 'Q018', 'Q019', 'Q020', 'Q021', 'Q022',\n", + " 'Q023', 'Q024', 'Q025'], dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Salvando na variável 'columns' apenas as colunas que serão utilizadas na análise.\n", + "\n", + "columns = ['NU_INSCRICAO', 'TP_FAIXA_ETARIA', 'TP_SEXO',\n", + " 'TP_COR_RACA', 'TP_ST_CONCLUSAO', 'TP_ESCOLA',\n", + " 'IN_TREINEIRO', 'NO_MUNICIPIO_PROVA', 'SG_UF_PROVA',\n", + " 'TP_PRESENCA_CN', 'TP_PRESENCA_CH', 'TP_PRESENCA_LC',\n", + " 'TP_PRESENCA_MT', 'NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n", + " 'NU_NOTA_MT', 'TP_LINGUA','TP_STATUS_REDACAO',\n", + " 'NU_NOTA_REDACAO', 'Q006']" + ], + "metadata": { + "id": "qet_L7h9CNNZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Lendo o arquivo apenas com as colunas desejadas, usando 'usecols' e salvando no dataframe 'df_enem'" + ], + "metadata": { + "id": "p9gx05hpGPzX" + } + }, + { + "cell_type": "code", + "source": [ + "with zipfile.ZipFile('/content/drive/MyDrive/microdados_enem_2023.zip') as z:\n", + " with z.open('DADOS/MICRODADOS_ENEM_2023.csv') as f:\n", + " df_enem = pd.read_csv(f, sep=';' , encoding='ISO-8859-1', usecols=columns)" + ], + "metadata": { + "id": "Pc1h0EaRE932" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "Visto que, para esta análise tomaremos como base apenas as regiões Norte e Sul do Brasil, faremos um filtro pelos estados de prova e salvaremos em um novo Dataframe." + ], + "metadata": { + "id": "GqgV5MyKG3B6" + } + }, + { + "cell_type": "code", + "source": [ + "# salvando as siglas dos estados da regão norte e sul em variáveis para usarmos no filtro\n", + "\n", + "estados_norte = ['AC', 'AP', 'AM', 'PA', 'RO', 'RR', 'TO']\n", + "estados_sul = ['PR', 'SC', 'RS']\n", + "estados_norte_sul = estados_norte + estados_sul" + ], + "metadata": { + "id": "wlWltlDSHF2d" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# gerando um novo dataframe apenas com os estados desejados\n", + "\n", + "df_norte_sul = df_enem[df_enem['SG_UF_PROVA'].isin(estados_norte_sul)]" + ], + "metadata": { + "id": "Ywkg8AKdHrK8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#Criaremos uma nova coluna chamada 'REGIAO' defininiremos inicialmente o valor como 'Norte'\n", + "\n", + "df_norte_sul['REGIAO'] = 'Norte'\n", + "\n", + "# Atualizando os valores da coluna 'REGIAO' para 'Sul' onde as siglas dos estados correspondem a região Sul\n", + "\n", + "df_norte_sul.loc[df_norte_sul['SG_UF_PROVA'].isin(estados_sul), 'REGIAO'] = 'Sul'" + ], + "metadata": { + "id": "B8iFEgTAIfW2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df_norte_sul" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 444 + }, + "id": "BPPwNOwqOL75", + "outputId": "d6b5c0ed-00a9-470f-e118-853518675497" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " NU_INSCRICAO TP_FAIXA_ETARIA TP_SEXO TP_COR_RACA TP_ST_CONCLUSAO \\\n", + "2 210061103945 6 F 1 1 \n", + "11 210060801595 3 M 3 2 \n", + "16 210058061534 4 F 1 2 \n", + "18 210058387328 11 M 1 1 \n", + "24 210059388088 3 F 1 1 \n", + "... ... ... ... ... ... \n", + "3933947 210061965965 9 M 1 1 \n", + "3933948 210061959674 3 M 1 2 \n", + "3933949 210061959675 6 F 3 1 \n", + "3933950 210061959676 12 M 1 1 \n", + "3933952 210061965966 3 F 3 2 \n", + "\n", + " TP_ESCOLA IN_TREINEIRO NO_MUNICIPIO_PROVA SG_UF_PROVA \\\n", + "2 1 0 Caxias do Sul RS \n", + "11 2 0 Ubiratã PR \n", + "16 2 0 Belém PA \n", + "18 1 0 Londrina PR \n", + "24 1 0 Maringá PR \n", + "... ... ... ... ... \n", + "3933947 1 0 Rio Grande RS \n", + "3933948 2 0 São Leopoldo RS \n", + "3933949 1 0 Igrejinha RS \n", + "3933950 1 0 Cachoeirinha RS \n", + "3933952 2 0 Florianópolis SC \n", + "\n", + " TP_PRESENCA_CN ... TP_PRESENCA_MT NU_NOTA_CN NU_NOTA_CH \\\n", + "2 1 ... 1 502.0 498.9 \n", + "11 1 ... 1 608.2 667.6 \n", + "16 1 ... 1 515.7 553.1 \n", + "18 1 ... 1 571.3 574.7 \n", + "24 1 ... 1 540.5 632.5 \n", + "... ... ... ... ... ... \n", + "3933947 0 ... 0 NaN NaN \n", + "3933948 1 ... 1 476.2 542.9 \n", + "3933949 0 ... 0 NaN NaN \n", + "3933950 1 ... 1 566.2 605.6 \n", + "3933952 0 ... 0 NaN NaN \n", + "\n", + " NU_NOTA_LC NU_NOTA_MT TP_LINGUA TP_STATUS_REDACAO \\\n", + "2 475.6 363.2 1 1.0 \n", + "11 607.9 691.9 0 1.0 \n", + "16 544.4 437.0 0 1.0 \n", + "18 613.3 719.7 0 1.0 \n", + "24 542.8 575.3 0 1.0 \n", + "... ... ... ... ... \n", + "3933947 NaN NaN 0 NaN \n", + "3933948 545.2 530.5 0 1.0 \n", + "3933949 NaN NaN 0 NaN \n", + "3933950 613.7 547.3 0 1.0 \n", + "3933952 NaN NaN 1 NaN \n", + "\n", + " NU_NOTA_REDACAO Q006 REGIAO \n", + "2 700.0 C Sul \n", + "11 780.0 D Sul \n", + "16 880.0 B Norte \n", + "18 560.0 D Sul \n", + "24 880.0 E Sul \n", + "... ... ... ... \n", + "3933947 NaN D Sul \n", + "3933948 600.0 F Sul \n", + "3933949 NaN F Sul \n", + "3933950 540.0 F Sul \n", + "3933952 NaN B Sul \n", + "\n", + "[871142 rows x 22 columns]" + ], + "text/html": [ + "\n", + "
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NU_INSCRICAOTP_FAIXA_ETARIATP_SEXOTP_COR_RACATP_ST_CONCLUSAOTP_ESCOLAIN_TREINEIRONO_MUNICIPIO_PROVASG_UF_PROVATP_PRESENCA_CN...TP_PRESENCA_MTNU_NOTA_CNNU_NOTA_CHNU_NOTA_LCNU_NOTA_MTTP_LINGUATP_STATUS_REDACAONU_NOTA_REDACAOQ006REGIAO
22100611039456F1110Caxias do SulRS1...1502.0498.9475.6363.211.0700.0CSul
112100608015953M3220UbiratãPR1...1608.2667.6607.9691.901.0780.0DSul
162100580615344F1220BelémPA1...1515.7553.1544.4437.001.0880.0BNorte
1821005838732811M1110LondrinaPR1...1571.3574.7613.3719.701.0560.0DSul
242100593880883F1110MaringáPR1...1540.5632.5542.8575.301.0880.0ESul
..................................................................
39339472100619659659M1110Rio GrandeRS0...0NaNNaNNaNNaN0NaNNaNDSul
39339482100619596743M1220São LeopoldoRS1...1476.2542.9545.2530.501.0600.0FSul
39339492100619596756F3110IgrejinhaRS0...0NaNNaNNaNNaN0NaNNaNFSul
393395021006195967612M1110CachoeirinhaRS1...1566.2605.6613.7547.301.0540.0FSul
39339522100619659663F3220FlorianópolisSC0...0NaNNaNNaNNaN1NaNNaNBSul
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871142 rows × 22 columns

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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df_norte_sul" + } + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Separando apenas os inscritos do sexo feminino das regiões Norte e Nordeste\n", + "\n", + "df_feminino_norte_sul = df_norte_sul[df_norte_sul['TP_SEXO'] == 'F']" + ], + "metadata": { + "id": "zGMVHxCLI4wU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#Exportando a base que será usada nas próximas análises\n", + "\n", + "df_feminino_norte_sul.to_csv('enem_feminino_norte_sul_2023.csv', index=False)" + ], + "metadata": { + "id": "LOE4kvadKCcv" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Importação, limpeza e tratamento dos dados" + ], + "metadata": { + "id": "JAfqgprELbt-" + } + }, + { + "cell_type": "markdown", + "source": [ + "Para facilitar, importaremos a base filtrada por sexo e região." + ], + "metadata": { + "id": "HRiXdpypLnR2" + } + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul = pd.read_csv('enem_feminino_norte_sul_2023.csv' , sep=',' , encoding='latin-1', on_bad_lines='warn')\n" + ], + "metadata": { + "id": "7146LSx9LmnB" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul.columns" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XA8clXqWOeV_", + "outputId": "039292c7-4edd-4d65-fb43-30d64029c809" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['NU_INSCRICAO', 'TP_FAIXA_ETARIA', 'TP_SEXO', 'TP_COR_RACA',\n", + " 'TP_ST_CONCLUSAO', 'TP_ESCOLA', 'IN_TREINEIRO', 'NO_MUNICIPIO_PROVA',\n", + " 'SG_UF_PROVA', 'TP_PRESENCA_CN', 'TP_PRESENCA_CH', 'TP_PRESENCA_LC',\n", + " 'TP_PRESENCA_MT', 'NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n", + " 'NU_NOTA_MT', 'TP_LINGUA', 'TP_STATUS_REDACAO', 'NU_NOTA_REDACAO',\n", + " 'Q006', 'REGIAO'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# RENOMEANDO ALGUMAS COLUNAS\n", + "enem_feminino_norte_sul = enem_feminino_norte_sul.rename(columns={\n", + "'IN_TREINEIRO': 'INSCRITOS_TREINEIRO' ,\n", + "'NO_MUNICIPIO_PROVA': 'MUNICIPIO_PROVA' ,\n", + "'TP_PRESENCA_CN': 'TP_PRESENCA_CIENCIAS_NATUREZA' ,\n", + "'TP_PRESENCA_CH': 'TP_PRESENCA_CIENCIAS_HUMANAS' ,\n", + "'TP_PRESENCA_LC': 'TP_PRESENCA_LIGUAGENS_CODIGOS' ,\n", + "'TP_PRESENCA_MT': 'TP_PRESENCA_MATEMATICA' ,\n", + "'NU_NOTA_CN': 'NOTA__CIENCIAS_NATUREZA' ,\n", + "'NU_NOTA_CH': 'NOTA_CIENCIAS_HUMANAS' ,\n", + "'NU_NOTA_LC': 'NOTA__LIGUAGENS_CODIGOS' ,\n", + "'NU_NOTA_MT': 'NOTA_MATEMATICA' ,\n", + "'TP_LINGUA': 'TP_LINGUA_ESTRANGEIRA',\n", + "'NU_NOTA_REDACAO': 'NOTA_REDACAO' ,\n", + "'Q006': 'RENDA_FAMILIAR' })" + ], + "metadata": { + "id": "_sxdE_JwOLsw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Nesta fase iremos substituir alguns valores da base, de acordo com o Dicionário de Dados disponiblizado também pelo Inep.\n", + "O objetivo dessa substituição é deixar a visualização e contagem dos dados mais clara.\n", + "\n", + "Segue abaixo um exemplo de legenda contida no dicionário:\n", + "![image.png](data:image/png;base64,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)" + ], + "metadata": { + "id": "tiClPcUlXzPe" + } + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul['TP_ESCOLA'] = enem_feminino_norte_sul['TP_ESCOLA'].replace({\n", + " 1: 'nao respondeu',\n", + " 2: 'publica',\n", + " 3: 'privada' })" + ], + "metadata": { + "id": "aGcDdTfcWzBZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul['INSCRITOS_TREINEIRO'] = enem_feminino_norte_sul['INSCRITOS_TREINEIRO'].replace({\n", + "0: 'Nao',\n", + "1: 'Sim' })" + ], + "metadata": { + "id": "BRmn37Y6fXWo" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul['TP_FAIXA_ETARIA'] = enem_feminino_norte_sul['TP_FAIXA_ETARIA'].replace({\n", + " 1: 'menor de 17',\n", + " 2: 17,\n", + " 3: 18,\n", + " 4: 19,\n", + " 5: 20,\n", + " 6: 21,\n", + " 7: 22,\n", + " 8: 23,\n", + " 9: 24,\n", + " 10: 25,\n", + " 11: '26-30',\n", + " 12: '31-35',\n", + " 13: '36-40',\n", + " 14: '41-45',\n", + " 15: '46-50',\n", + " 16: '51-55',\n", + " 17: '56-60',\n", + " 18: '61-65',\n", + " 19: '66-70',\n", + " 20: 'maior de 70' })" + ], + "metadata": { + "id": "2mykrqTNXdzr" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul['TP_COR_RACA'] = enem_feminino_norte_sul['TP_COR_RACA'].replace({\n", + " 0: 'Não declarado',\n", + " 1: 'Branca',\n", + " 2: 'Preta',\n", + " 3: 'Parda',\n", + " 4: 'Amarela',\n", + " 5: 'Indígena',\n", + " 6: 'Não dispõe da informação' })" + ], + "metadata": { + "id": "PKQNb5LnYddn" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul['TP_ST_CONCLUSAO'] = enem_feminino_norte_sul['TP_ST_CONCLUSAO'].replace({\n", + "1: 'Concluido' ,\n", + "2: 'A concluir em 2023',\n", + "3: 'A concluir após 2023' ,\n", + "4: 'Não cursando' })" + ], + "metadata": { + "id": "KYiWb3OmZVJk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "colunas_para_alterar = ['TP_PRESENCA_CIENCIAS_NATUREZA' ,'TP_PRESENCA_CIENCIAS_HUMANAS', 'TP_PRESENCA_LIGUAGENS_CODIGOS', 'TP_PRESENCA_MATEMATICA']" + ], + "metadata": { + "id": "2oxrIAfRZxvf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul[colunas_para_alterar] = enem_feminino_norte_sul[colunas_para_alterar].replace({\n", + " 0: 'Faltou' ,\n", + " 1: 'Presente' ,\n", + " 2: 'Eliminado' })" + ], + "metadata": { + "id": "wjQ1eJ_0b8Nq" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul['TP_LINGUA_ESTRANGEIRA'] = enem_feminino_norte_sul['TP_LINGUA_ESTRANGEIRA'].replace({\n", + "0: 'Ingles',\n", + "1: 'Espanhol' })" + ], + "metadata": { + "id": "HhFRZyV0gKY9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul['TP_STATUS_REDACAO'] = enem_feminino_norte_sul['TP_STATUS_REDACAO'].replace({\n", + "1: 'Sem problemas',\n", + "2: 'Anulada' ,\n", + "3: 'Cópia Texto Motivador' ,\n", + "4: 'Em Branco',\n", + "6: 'Fuga ao tema',\n", + "7: 'Não atendimento ao tipo textual',\n", + "8: 'Texto insuficiente',\n", + "9: 'Parte desconectada' })" + ], + "metadata": { + "id": "QOmRZiBIgQNy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul['RENDA_FAMILIAR'] = enem_feminino_norte_sul['RENDA_FAMILIAR'].replace({\n", + "'A': 'Nenhuma Renda',\n", + "'B': 'Até 1.320,00',\n", + "'C': '1.320,01 - 1.980,00',\n", + "'D': '1.980,01 - 2.640,00',\n", + "'E': '2.640,01 - 3.300,00',\n", + "'F': '3.300,01 - 3.960,00',\n", + "'G': '3.960,01 - 5.280,00',\n", + "'H': '5.280,01 - 6.600,00',\n", + "'I': '6.600,01 - 7.920,00',\n", + "'J': '7.920,01 - 9.240,00',\n", + "'K': '9.240,01 - 10.560,00',\n", + "'L': '10.560,01 - 11.880,00',\n", + "'M': '11.880,01 - 13.200,00',\n", + "'N': '13.200,01 - 15.840,00',\n", + "'O': '15.840,01 - 19.800,00',\n", + "'P': '19.800,01 - 26.400,00',\n", + "'Q': 'Acima de 26.400,00' })" + ], + "metadata": { + "id": "qJAa8gQQgfS3" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 617 + }, + "id": "cA8Dkr4iJqsf", + "outputId": "e29c790f-9a8e-4b32-a79b-2eb509c0ef6d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " NU_INSCRICAO TP_FAIXA_ETARIA TP_SEXO TP_COR_RACA TP_ST_CONCLUSAO \\\n", + "0 210061103945 21 F Branca Concluido \n", + "1 210058061534 19 F Branca A concluir em 2023 \n", + "2 210059388088 18 F Branca Concluido \n", + "3 210058061536 19 F Parda Concluido \n", + "4 210059980946 19 F Branca Concluido \n", + "... ... ... ... ... ... \n", + "536951 210061965960 20 F Branca Concluido \n", + "536952 210061965962 17 F Branca A concluir em 2023 \n", + "536953 210061965964 31-35 F Branca Concluido \n", + "536954 210061959675 21 F Parda Concluido \n", + "536955 210061965966 18 F Parda A concluir em 2023 \n", + "\n", + " TP_ESCOLA INSCRITOS_TREINEIRO MUNICIPIO_PROVA SG_UF_PROVA \\\n", + "0 nao respondeu Nao Caxias do Sul RS \n", + "1 publica Nao Belém PA \n", + "2 nao respondeu Nao Maringá PR \n", + "3 nao respondeu Nao Marituba PA \n", + "4 nao respondeu Nao Belém PA \n", + "... ... ... ... ... \n", + "536951 nao respondeu Nao Cachoeirinha RS \n", + "536952 privada Nao Capão da Canoa RS \n", + "536953 nao respondeu Nao Porto Alegre RS \n", + "536954 nao respondeu Nao Igrejinha RS \n", + "536955 publica Nao Florianópolis SC \n", + "\n", + " TP_PRESENCA_CIENCIAS_NATUREZA ... TP_PRESENCA_MATEMATICA \\\n", + "0 Presente ... Presente \n", + "1 Presente ... Presente \n", + "2 Presente ... Presente \n", + "3 Presente ... Presente \n", + "4 Presente ... Presente \n", + "... ... ... ... \n", + "536951 Presente ... Presente \n", + "536952 Presente ... Presente \n", + "536953 Presente ... Presente \n", + "536954 Faltou ... Faltou \n", + "536955 Faltou ... 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" + ] + }, + "metadata": {}, + "execution_count": 32 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Obs: Os dados nulos da coluna TP_STATUS_REDACAO indicam que o inscrito não fez a prova, uma vez que no dicionário de dados, há uma descrição para cada status de entrega/correção da redação, por isso iremos substituir os valores nulos pelo valor \"Faltou\"." + ], + "metadata": { + "id": "O0NgV8Gw_pKj" + } + }, + { + "cell_type": "code", + "source": [ + "enem_feminino_norte_sul['TP_STATUS_REDACAO'] = enem_feminino_norte_sul['TP_STATUS_REDACAO'].fillna('Faltou')\n" + ], + "metadata": { + "id": "0w0lDUXB_nZl" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "De acordo com a análise realizada, pudemos observar que os dados contabilizados nulos das colunas de Notas, correspondem aos números de faltantes + eliminados das colunas TP_PRESENÇA. Ressaltamos que a princípio manteramos os dados nulos, pois a elimação dos menos poderá acarretar uma distorção da nossa análise." + ], + "metadata": { + "id": "GJ2-5E1bk89v" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Questões Norteadoras**" + ], + "metadata": { + "id": "JFDywCcvl6Fd" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Identificar qual o percentual de inscritos do sexo feminino por região.\n", + "\n", + "Objetivo: Identifcar o percentual de inscrição feminina em cada região e compará-los entre as regiões(Norte x Sul)." + ], + "metadata": { + "id": "Enp3JyQfmCIC" + } + }, + { + "cell_type": "code", + "source": [ + "contagem_fem_por_regiao = enem_feminino_norte_sul.groupby('REGIAO')['NU_INSCRICAO'].count()\n", + "contagem_fem_por_regiao" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 178 + }, + "id": "YtF146m8lVx7", + "outputId": "a590d3b5-4c0c-49c9-ea3e-f00c14617fa7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "REGIAO\n", + "Norte 278371\n", + "Sul 258585\n", + "Name: NU_INSCRICAO, dtype: int64" + ], + "text/html": [ + "
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NU_INSCRICAO
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Norte278371
Sul258585
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" + ] + }, + "metadata": {}, + "execution_count": 35 + } + ] + }, + { + "cell_type": "code", + "source": [ + "total_insc_por_regiao = df_norte_sul.groupby('REGIAO')['NU_INSCRICAO'].count()\n", + "total_insc_por_regiao" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 178 + }, + "id": "4Et-SzKs6G9a", + "outputId": "5b1a2682-ab07-4f42-9dc6-81f45866cc30" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "REGIAO\n", + "Norte 453454\n", + "Sul 417688\n", + "Name: NU_INSCRICAO, dtype: int64" + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "execution_count": 36 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Encontrando o percentual de inscrições femininas por região\n", + "\n", + "percentual_feminino_por_regiao = (contagem_fem_por_regiao / total_insc_por_regiao) * 100\n", + "percentual_feminino_por_regiao.round(2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 178 + }, + "id": "QK5KBwX27Gwi", + "outputId": "c44e1494-afd7-4508-97df-c14d97c35a8d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "REGIAO\n", + "Norte 61.39\n", + "Sul 61.91\n", + "Name: NU_INSCRICAO, dtype: float64" + ], + "text/html": [ + "
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NU_INSCRICAO
REGIAO
Norte61.39
Sul61.91
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 38 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt" + ], + "metadata": { + "id": "VXZECJLAO0Zr" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "grafico_cor = ['#517EA6', '#F28A2E']\n", + "percentual_feminino_por_regiao.plot(kind='bar', color=grafico_cor)\n", + "plt.ylabel('Percentual de inscrição')\n", + "plt.title('Percentual de inscrição feminina por Região')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "id": "YYGvnjrFZT2w", + "outputId": "54855700-5782-439a-a6fb-18495722aa55" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Percentual de inscrição feminina por Região')" + ] + }, + "metadata": {}, + "execution_count": 41 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Isso indica que 61,39% das inscrições na região Norte são de mulheres e que 61,91% das inscrições na região Sul são de mulheres. Isso sugere uma participação feminina equilibrada entre as regiões Norte e Sul no ENEM 2023." + ], + "metadata": { + "id": "4bpKbKgj8Bx2" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Identificar o percentual de falta nas regiões Norte e Sul. Levando em consideração quem faltou em pelos menos uma das provas, incluindo Redação.\n", + "\n", + "Objetivo: comparar se há diferença discrepante entre as duas regiões." + ], + "metadata": { + "id": "2BDcB9-H-N8u" + } + }, + { + "cell_type": "code", + "source": [ + "copia_fem_norte_sul = enem_feminino_norte_sul.copy()" + ], + "metadata": { + "id": "blgXXJcqk2Bb" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Criando uma coluna que verifica se a pessoa faltou em pelo menos uma das provas ou redação\n", + "copia_fem_norte_sul['FALTOU_PELO_MENOS_UMA'] = copia_fem_norte_sul[[\n", + " 'TP_PRESENCA_CIENCIAS_NATUREZA',\n", + " 'TP_PRESENCA_CIENCIAS_HUMANAS',\n", + " 'TP_PRESENCA_LIGUAGENS_CODIGOS',\n", + " 'TP_PRESENCA_MATEMATICA',\n", + " 'TP_STATUS_REDACAO']].apply(lambda x: (x == 'Faltou').any(), axis=1)\n" + ], + "metadata": { + "id": "4HFfwldy-VAZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Calculando o numero de mulheres faltantes por região, comtando apenas as entradas que são True\n", + "\n", + "faltaram_por_regiao = copia_fem_norte_sul.groupby('REGIAO')['FALTOU_PELO_MENOS_UMA'].sum()\n", + "faltaram_por_regiao" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 178 + }, + "id": "qKlvdUPnGHzj", + "outputId": "76f5359f-83b5-4665-eb95-6415f597017d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "REGIAO\n", + "Norte 102606\n", + "Sul 79942\n", + "Name: FALTOU_PELO_MENOS_UMA, dtype: int64" + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "execution_count": 44 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Usaremos a variavel de contagem feminina criada anteriormente para calcular o percentual\n", + "\n", + "percentual_faltas_por_regiao = (faltaram_por_regiao / contagem_fem_por_regiao) * 100\n", + "percentual_faltas_por_regiao.round(2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 178 + }, + "id": "StrMepKHG8JI", + "outputId": "59be6bd0-d87e-461b-9da9-9c7ca79d565d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "REGIAO\n", + "Norte 36.86\n", + "Sul 30.92\n", + "dtype: float64" + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "execution_count": 45 + } + ] + }, + { + "cell_type": "code", + "source": [ + "grafico_cor = ['#517EA6', '#F28A2E']\n", + "percentual_faltas_por_regiao.plot(kind='bar', color=grafico_cor)\n", + "plt.ylabel('Percentual de Faltas')\n", + "plt.title('Percentual de Faltas por Região')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "id": "qzMIPUKcG6jy", + "outputId": "6f2fbd8c-9ff5-41e2-e650-4416b99f604c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Percentual de Faltas por Região')" + ] + }, + "metadata": {}, + "execution_count": 46 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Podemos verificar que o percentual de faltas entre as inscritas na região Norte foi um pouco superior a região Sul. A região Norte teve 36,86% de faltas, enquanto que a região Sul teve um percentual de 30,92% de inscritas faltantes." + ], + "metadata": { + "id": "IvE6DzPcMDsk" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Identificar qual a média geral de notas por tipo de escola que o participante estudou o ensino e comparar entre as regiões Norte e Sul." + ], + "metadata": { + "id": "-JkAmNCGLPNv" + } + }, + { + "cell_type": "markdown", + "source": [ + "Para esta análise levaremos em consideração apenas quem realizou todas a provas, dessa forma, faremos a limpeza dos dados nulos que constam nas colunas de nota." + ], + "metadata": { + "id": "8-ez_F8SLuS-" + } + }, + { + "cell_type": "code", + "source": [ + "df_notas_sem_null = copia_fem_norte_sul.dropna()" + ], + "metadata": { + "id": "DLDabdNKLttL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Criando uma nova coluna - 'SOMA_DE_NOTAS'\n", + "\n", + "df_notas_sem_null['SOMA_DE_NOTAS'] = df_notas_sem_null['NOTA__CIENCIAS_NATUREZA'] + df_notas_sem_null['NOTA_CIENCIAS_HUMANAS'] + df_notas_sem_null['NOTA__LIGUAGENS_CODIGOS'] + df_notas_sem_null['NOTA_MATEMATICA'] + df_notas_sem_null['NOTA_REDACAO']\n", + "df_notas_sem_null" + ], + "metadata": { + "id": "axCFHfE1LnpG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Agrupando por tipo de escola e região e calculando a média das notas\n", + "\n", + "media_por_tipo_escola_e_regiao = df_notas_sem_null.groupby(['TP_ESCOLA','REGIAO'])['SOMA_DE_NOTAS'].mean()" + ], + "metadata": { + "id": "DsJgmx1sMAed" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "media_por_tipo_escola_e_regiao.round(2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "WK_Xw-TgM-wI", + "outputId": "43a86868-55db-43da-9da2-f38e20ee5f7f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TP_ESCOLA REGIAO\n", + "nao respondeu Norte 2534.10\n", + " Sul 2788.12\n", + "privada Norte 3026.11\n", + " Sul 3114.49\n", + "publica Norte 2473.80\n", + " Sul 2652.37\n", + "Name: SOMA_DE_NOTAS, dtype: float64" + ], + "text/html": [ + "
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SOMA_DE_NOTAS
TP_ESCOLAREGIAO
nao respondeuNorte2534.10
Sul2788.12
privadaNorte3026.11
Sul3114.49
publicaNorte2473.80
Sul2652.37
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 88 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import seaborn as sns" + ], + "metadata": { + "id": "hp4NGyxeOwDG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#media_por_tipo_escola_e_regiao = media_por_tipo_escola_e_regiao.reset_index()\n", + "\n", + "# Plotar o gráfico\n", + "plt.figure(figsize=(7, 5))\n", + "sns.barplot(data=media_por_tipo_escola_e_regiao, x='TP_ESCOLA', y='SOMA_DE_NOTAS', hue='REGIAO')\n", + "\n", + "plt.title('Média de Notas por Tipo de Escola e Região', fontsize=12)\n", + "plt.ylabel('Média de Notas')\n", + "plt.xlabel('Tipo de Escola')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 502 + }, + "id": "MYuhenxDNAck", + "outputId": "1f050ee7-4c4f-44c9-c452-49fcba766977" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 0, 'Tipo de Escola')" + ] + }, + "metadata": {}, + "execution_count": 99 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Observou-se que as escolas privadas tiveram as maiores médias de notas em ambas as regiões. Já as escolas públicas apresentaram as menores médias, especialmente no Norte (2473,80). A região Sul, em geral, mostrou melhores resultados do que o Norte para todos os tipos de escola, indicando possíveis disparidades regionais e entre os setores público e privado." + ], + "metadata": { + "id": "kNJGTebLKGAk" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Identificar qual o top 100 de maiores notas entre todas as provas, incluindo Redação e visualizar em qual faixa de renda familiar está a maioria deste Top 100. Também verificar como o Top 100 está distribuído por região (Norte e Sul).\n", + "\n", + "Objetivo: visualizar se há uma possível relação entre as notas mais altas das inscritas x renda familiar e região." + ], + "metadata": { + "id": "HlYgFiLyIbsG" + } + }, + { + "cell_type": "code", + "source": [ + "# Ordenando o dataframe pelas maiores notas, em ordem decrescrente\n", + "df_ordenado = df_notas_sem_null.sort_values(by='SOMA_DE_NOTAS', ascending=False)\n", + "\n", + "# Separando as 100 maiores notas no dataframe 'top_100\"\n", + "top_100 = df_ordenado.head(100)" + ], + "metadata": { + "id": "WoxTBy48Z-J_" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Agrupar os 100 melhores por faixa de renda\n", + "\n", + "distribuicao_renda_top_100 = top_100.groupby('RENDA_FAMILIAR')['NU_INSCRICAO'].count()\n", + "\n", + "distribuicao_renda_top_100" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 586 + }, + "id": "s8zgFACmjjMz", + "outputId": "a54c4bba-fc38-450e-ef1d-3fae84fee327" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RENDA_FAMILIAR\n", + "1.320,01 - 1.980,00 1\n", + "10.560,01 - 11.880,00 3\n", + "11.880,01 - 13.200,00 7\n", + "13.200,01 - 15.840,00 11\n", + "15.840,01 - 19.800,00 6\n", + "19.800,01 - 26.400,00 10\n", + "2.640,01 - 3.300,00 1\n", + "3.300,01 - 3.960,00 3\n", + "3.960,01 - 5.280,00 10\n", + "5.280,01 - 6.600,00 5\n", + "6.600,01 - 7.920,00 9\n", + "7.920,01 - 9.240,00 12\n", + "9.240,01 - 10.560,00 4\n", + "Acima de 26.400,00 17\n", + "Até 1.320,00 1\n", + "Name: NU_INSCRICAO, dtype: int64" + ], + "text/html": [ + "
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NU_INSCRICAO
RENDA_FAMILIAR
1.320,01 - 1.980,001
10.560,01 - 11.880,003
11.880,01 - 13.200,007
13.200,01 - 15.840,0011
15.840,01 - 19.800,006
19.800,01 - 26.400,0010
2.640,01 - 3.300,001
3.300,01 - 3.960,003
3.960,01 - 5.280,0010
5.280,01 - 6.600,005
6.600,01 - 7.920,009
7.920,01 - 9.240,0012
9.240,01 - 10.560,004
Acima de 26.400,0017
Até 1.320,001
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 50 + } + ] + }, + { + "cell_type": "code", + "source": [ + "distribuicao_renda_top_100_ordenado = distribuicao_renda_top_100.sort_values(ascending=True)" + ], + "metadata": { + "id": "E2_VqZrzBfeU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "distribuicao_renda_top_100_ordenado.plot(kind='barh', color='#006400')\n", + "plt.ylabel('Valor da Renda Familiar em Real')\n", + "plt.title('Distribuição do Top 100 maiores notas x Faixa de Renda')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 469 + }, + "id": "bJc9pRP64HJr", + "outputId": "ec147030-7542-4c72-f6e0-d5fb50884c51" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Distribuição do Top 100 maiores notas x Faixa de Renda')" + ] + }, + "metadata": {}, + "execution_count": 52 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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r1k1rNO7zzz/H3t5e6yu9rHrw4AEHDx6ke/fuFC1aVGvfm3/NvzmCnJiYyD///EOJEiWwsLDg1KlT6r4dO3ZQrVo1atWqpaaZmprSq1cvbt68yYULF9JtS0p/Bg4cqJU+ePBgrddJSUn89ttvtGrVimLFiqnp9vb2dO7cmUOHDvHkyRMdep/aiRMnuH//Pv369dO6+c7b2xsXF5c0p2P0799f/X/KNICEhAR+//33LLXhbS9evMDAwCDNfUZGRupXwyn/GhoappnvzTwZadiwodYoUsooedu2bbXehynpb34V++b7JC4ujocPH1KjRg0URVFHqNOScu0DAwO10ocOHQqQ6rw7Ozvj5eWllbZ+/Xpq165NgQIFePjwobp5enqSlJTEwYMHgdc/w3FxcezevTvjE5GODh06aI3IpXx7k3Ie7t27R3h4OH5+flhaWqr5KlasSKNGjXT+uX3zXMbGxvLw4UPq1q3LjRs3iI2NVfsCsG3bNhITEzPVj3z58rFixQouXrxInTp12L59O7NmzUr1OZAeIyMjdu/erbV9++23qdqemfcBgJ2dHQsWLGD37t3Url2b8PBwvv/+e8zMzLTyvXmMx48fExsbS+3atbU+j9KS8jPep08frZ+rlK/737R+/XrKlCmDi4uL1nuqQYMGwOspRu8SFxeHtbU11tbWlChRgmHDhlGzZk1+/fVX9TN29+7dxMTE0KlTJ63j6Ovr4+7unuZx3h59r127ttbP4o4dO8iTJ4860gugr6/PgAEDUtX15rl8+fIlDx8+pHr16gDvPJ/ZSYJdIf4lz549y/Br3g4dOlCzZk169uyJra0tHTt2ZN26dZkKfAsVKpRu8JKWkiVLar3WaDSUKFEizfmymZXy4Vi+fPkM87148YKgoCB1LmTBggWxtrYmJiZG/cULcOvWLUqXLp2qfJkyZdT96bl16xZ6enqpVkJ4u74HDx7w/PnzdI+TnJycak6zrlLal1bdLi4uqdqvp6enFXADlCpVCiBbrg+8/kWUkJCQ5r6XL1+qv6hS/k1rLmHKMke6THt5O9hJCQDe/Hr3zfTHjx+raZGRkWqQlzKXsG7dugBa75O3pVz7EiVKaKXb2dlhYWGR6rw7OzunquPq1auEhoaqgUXKlrL6xv379wHo168fpUqVomnTphQuXJju3btnarmst89PSuCbch4yeg+VKVOGhw8fEhcX987j/Pnnn3h6eqpzfq2trdX5/Snnsm7durRt25YJEyZQsGBBWrZsyfLly3WeT1qzZk369u3LsWPH8PLyeudc3zfp6+vj6emptbm5uQFZfx+k6NixI97e3hw7dowvvviChg0bpsqzbds2qlevjpGREZaWllhbW7No0aJ31p9yfd7+XM2bN2+qn+WrV69y/vz5VO+plJ/xlPdURt78o2D58uWUKVOG+/fva/0sXr16FXg9veTtY/3222+pjmNkZKROd0lRoEABrZ/FW7duYW9vj6mpqVa+tN6Xjx49YtCgQep9BNbW1urPmC7XK7vInF0h/gW3b98mNjY21S/cNxkbG3Pw4EH27dvH9u3bCQ0NZe3atTRo0IDffvsNfX39dx4nO+bZvi29OVVJSUk6teldBgwYwPLlyxk8eDAeHh6Ym5uj0Wjo2LFjlke4hW7s7e1JSkri/v372NjYqOkJCQn8888/ODg4AGBpaYmhoSH37t1LVUdKWkrejKT3fkkvXfn/N+MlJSXRqFEjHj16xIgRI3BxccHExIQ7d+7g5+en0/tE17mBaf0MJScn06hRI4YPH55mmZQAxcbGhvDwcHbt2sXOnTvZuXMny5cvp1u3bqxcufKdx37XecgO169fp2HDhri4uDBz5kyKFCmCgYEBO3bsYNasWeq5THnozJEjR9i6dSu7du2ie/fufPvttxw5ciRVoPO2+Ph49u/frx7z+fPn6vz7rMqO98E///zDiRMnALhw4QLJycno6f3fuN8ff/xBixYtqFOnDgsXLsTe3p68efOyfPlyVq9e/V7tf1NycjIVKlRg5syZae5/+w/AtKT8UZDCy8sLFxcXevfuzZYtW9TjwOt5u3Z2dqnqyJNHOwzMjs/0N7Vv357Dhw/z5Zdf4urqiqmpKcnJyTRp0uRf/XyXYFeIf8GPP/4IkOrr0bfp6enRsGFDGjZsyMyZM5k6dSpfffUV+/btw9PTM9sn86f81Z9CURSuXbumtR5wgQIF0rzL9tatW6lGK96Usu/cuXMZtmHDhg34+vqqX1PC69HCt4/p6OjI5cuXU5W/dOmSuj89jo6OJCcnc/36da3Rh7frs7a2Jl++fOkeR09PT6dfQum1IeWYKV9VvtmOt9ufnJzMjRs31EAKUNfLfXMqwPtIWav5xIkTWjfonDhxguTkZHW/np4eFSpUUIOENx09epRixYq98+a093H27FmuXLnCypUr6datm5quy3SBlGt/9epV9VsAeH3DY0xMTIbvmxTFixfn2bNnWoFFegwMDPDx8cHHx4fk5GT69evHd999x9ixYzP8Y1cXb76H3nbp0iUKFiyIiYkJkH5wv3XrVuLj49myZYvWSHJ6X5tXr16d6tWrM2XKFFavXk2XLl1Ys2YNPXv2zLCt48aN4+LFi8yYMYMRI0YwcuRI5s6dq1M/0/M+74MUAQEBPH36lODgYEaNGsXs2bO1prj88ssvGBkZsWvXLq1pO8uXL39n3SnX5+rVq1o/44mJiURERGitE1y8eHH++usvGjZsmG2f6/b29gwZMoQJEyZw5MgRqlevrn6bZWNjo9P7VxeOjo7s2bOHZ8+eaf3R8/b78vHjx+zZs4cJEyZorc7x9u+df4NMYxAih+3du5dJkybh7OxMly5d0s336NGjVGkpwUbKV4cpv8iy66lIP/zwg9Y84g0bNnDv3j2aNm2qphUvXpwjR45ofd29bdu2d36db21tTZ06dfj++++JjIzU2vfmSJW+vn6qkat58+alWuqmWbNmHDt2jLCwMDUtLi6OxYsX4+TkRNmyZdNtS0p/3v5l+/YjifX19WncuDG//vqr1lSB6OhoVq9eTa1atVLN79NVlSpVsLGxISQkROur4J07d3Lx4sU07/KeP3+++n9FUZg/fz558+ZN86vXrGjQoAGWlpaploNatGgR+fLl02rT559/zvHjx7UC3suXL7N3717atWuXLe1JT8po05vvE0VR3rmkF6AG8W9f65QRtXfdXQ+vR6fCwsLYtWtXqn0xMTG8evUKINVySnp6euofjlldTupN9vb2uLq6snLlSq3PgHPnzvHbb79p/cGS3mdFWucyNjY2VTD3+PHjVD+Xb38epefo0aPMmDGDwYMHM3ToUL788kvmz5/PgQMHdOpnet7nfQCvP9/Wrl3L119/zciRI+nYsSNjxozReuiKvr4+Go1G6/Pn5s2bOq2OUKVKFaytrQkJCdH6vFyxYkWq69C+fXvu3LnDkiVLUtXz4sULnaajpGXAgAHky5ePr7/+Gng9wGJmZsbUqVPTnHv94MGDTB+jWbNmvHr1SutzIykpKdWTF9O6XpD6Z/HfICO7QmSjnTt3cunSJV69ekV0dDR79+5l9+7dODo6smXLlgyfCjZx4kQOHjyIt7c3jo6O3L9/n4ULF1K4cGH1pqzixYtjYWFBSEgI+fPnx8TEBHd39zTnGerC0tKSWrVq4e/vT3R0NLNnz6ZEiRJay6P17NmTDRs20KRJE9q3b8/169f56aefdHoS2Ny5c6lVqxafffYZvXr1wtnZmZs3b7J9+3b1kcfNmzfnxx9/xNzcnLJlyxIWFsbvv/+e6olcI0eO5Oeff6Zp06YMHDgQS0tLVq5cSUREBL/88ovWV5Fvc3V1pVOnTixcuJDY2Fhq1KjBnj17uHbtWqq8kydPVtc77tevH3ny5OG7774jPj6e6dOn63hmU8ubNy/Tpk3D39+funXr0qlTJ3XpMScnJ4YMGaKV38jIiNDQUHx9fXF3d2fnzp1s376d0aNHp5pT97Zbt26p3yakBKeTJ08GXo/KdO3aFXj9lf2kSZMICAigXbt2eHl58ccff/DTTz8xZcoUrZug+vXrx5IlS/D29mbYsGHkzZuXmTNnYmtrq97slVNcXFwoXrw4w4YN486dO5iZmfHLL79ozSNMT6VKlfD19WXx4sXExMRQt25djh07xsqVK2nVqhX169d/Zx1ffvklW7ZsoXnz5vj5+eHm5kZcXBxnz55lw4YN3Lx5k4IFC9KzZ08ePXpEgwYNKFy4MLdu3WLevHm4urpqjSq/j2+++YamTZvi4eFBjx491KXHzM3NGT9+vJovZY7rV199RceOHcmbNy8+Pj40btxYHX3u3bs3z549Y8mSJdjY2GhNU1m5ciULFy6kdevWFC9enKdPn7JkyRLMzMy0guq3vXz5El9fX0qWLKkuLzhhwgS2bt2Kv78/Z8+eVQPxzHqf98H9+/fp27cv9evXV2/8nD9/Pvv27cPPz49Dhw6hp6eHt7c3M2fOpEmTJnTu3Jn79++zYMECSpQowZkzZzI8Rt68eZk8eTK9e/emQYMGdOjQgYiICJYvX57qW7CuXbuybt06+vTpw759+6hZsyZJSUlcunSJdevWqWs9Z5aVlRX+/v4sXLiQixcvUqZMGRYtWkTXrl357LPP6NixI9bW1kRGRrJ9+3Zq1qyp9Ue1Lnx8fKhZsyYjR47k5s2blC1blo0bN6aag2tmZkadOnWYPn06iYmJFCpUiN9++42IiIhM9+u9/WvrPgiRi6UsPZayGRgYKHZ2dkqjRo2UOXPmaC2rlOLtpXH27NmjtGzZUnFwcFAMDAwUBwcHpVOnTqmWO/r111+VsmXLKnny5NFa5qVu3bpKuXLl0mxfekuP/fzzz8qoUaMUGxsbxdjYWPH29k5zeaBvv/1WKVSokGJoaKjUrFlTOXHihE5LjymKopw7d05p3bq1YmZmpgBK6dKllbFjx6r7Hz9+rPj7+ysFCxZUTE1NFS8vL+XSpUtpLnl2/fp15fPPP1csLCwUIyMjpVq1asq2bdvS7PPbXrx4oQwcOFCxsrJSTExMFB8fH+Xvv/9O8wlqp06dUry8vBRTU1MlX758Sv369ZXDhw/rdJwU6S0Tt3btWqVy5cqKoaGhYmlpqXTp0kW5ffu2Vp6UZYWuX7+uNG7cWMmXL59ia2urjBs3LsMl1lKkXN+0trSe4rR48WKldOnSioGBgVK8eHFl1qxZWsvDpfj777+Vzz//XDEzM1NMTU2V5s2bK1evXtXpfABKQECAVlrKe+abb75Js/1vPuHvwoULiqenp2JqaqoULFhQ+eKLL5S//vor1TlOa8mpxMREZcKECYqzs7OSN29epUiRIsqoUaOUly9fauVzdHRM9TS5FE+fPlVGjRqllChRQjEwMFAKFiyo1KhRQ5kxY4aSkJCgKIqibNiwQWncuLFiY2OjGBgYKEWLFlV69+6t3Lt3L8Nzk955SDlvb78/f//9d6VmzZqKsbGxYmZmpvj4+CgXLlxIVXbSpElKoUKFFD09Pa1lyLZs2aJUrFhRMTIyUpycnJRp06Yp33//vVaeU6dOKZ06dVKKFi2qGBoaKjY2Nkrz5s2VEydOZNiXIUOGKPr6+srRo0e10k+cOKHkyZNH6du3b4bl3/UEtay+D9q0aaPkz59fuXnzplZ9Kcu7TZs2TU1btmyZUrJkScXQ0FBxcXFRli9fnub7Kj0LFy5UnJ2dFUNDQ6VKlSrKwYMH03yCWkJCgjJt2jSlXLlyiqGhoVKgQAHFzc1NmTBhQqrl0N6W0Xm6fv26oq+vr/X5uW/fPsXLy0sxNzdXjIyMlOLFiyt+fn5a1zO9OtPq+z///KN07dpVMTMzU8zNzZWuXbsqp0+fTnUdbt++rbRu3VqxsLBQzM3NlXbt2il3797NsSdXpkejKNk4810IITLg6enJ8OHDady48Yduyn+an58fGzZs4NmzZx+6KUII8dGTObtCiH+Nj4+P1iOThRBCiJwmc3aFEDnu559/Ji4ujvXr12stcSWEEELkNBnZFULkuPPnz9O/f3/u3LnDsGHDPnRzhBBCfEJkzq4QQgghhMi1ZGRXCCGEEELkWhLsCiGEEEKIXEtuUBOfvOTkZO7evUv+/Pmz/XG8QgghhMgZiqLw9OlTHBwcMnywkAS74pN39+5dihQp8qGbIYQQQogs+PvvvylcuHC6+yXYFZ+8/PnzA69/WMzMzD5wa4QQQgihiydPnlCkSBH193h6JNgVn7yUqQtmZmYS7AohhBAfmXdNQZQb1IQQQgghRK4lwa4QQgghhMi1JNgVQgghhBC5lszZFeL/Mx9gDgYfuhVCCCFE7qEs+fAP6v2kR3adnJyYPXv2h24GAOPHj8fV1fVDN0MIIYQQIlf56IPdsLAw9PX18fb2znTZ48eP06tXrxxo1YcRHBxM1apVyZ8/PzY2NrRq1YrLly+nyhcWFkaDBg0wMTHBzMyMOnXq8OLFC52O8fXXX6PRaBg8eLBW+suXLwkICMDKygpTU1Patm1LdHS0Vp7IyEi8vb3Jly8fNjY2fPnll7x69SrD4z169IguXbpgZmaGhYUFPXr04NmzZ1p5zpw5Q+3atTEyMqJIkSJMnz5dp74IIYQQIvf76IPdZcuWMWDAAA4ePMjdu3czVdba2pp8+fLlUMv+fQcOHCAgIIAjR46we/duEhMTady4MXFxcWqesLAwmjRpQuPGjTl27BjHjx+nf//+GT55JMXx48f57rvvqFixYqp9Q4YMYevWraxfv54DBw5w9+5d2rRpo+5PSkrC29ubhIQEDh8+zMqVK1mxYgVBQUEZHrNLly6cP3+e3bt3s23bNg4ePKj1B8qTJ09o3Lgxjo6OnDx5km+++Ybx48ezePFiXU6ZEEIIIXK5jzrYffbsGWvXrqVv3754e3uzYsWKVHm2bt1K1apVMTIyomDBgrRu3Vrd9/Y0Bo1Gw3fffUfz5s3Jly8fZcqUISwsjGvXrlGvXj1MTEyoUaMG169fV8tcv36dli1bYmtri6mpKVWrVuX3339/Z9u//vprbG1tyZ8/Pz169ODly5ep8ixdupQyZcpgZGSEi4sLCxcuzLDO0NBQ/Pz8KFeuHJUqVWLFihVERkZy8uRJNc+QIUMYOHAgI0eOpFy5cpQuXZr27dtjaGiYYd3Pnj2jS5cuLFmyhAIFCmjti42NZdmyZcycOZMGDRrg5ubG8uXLOXz4MEeOHAHgt99+48KFC/z000+4urrStGlTJk2axIIFC0hISEjzmBcvXiQ0NJSlS5fi7u5OrVq1mDdvHmvWrFH/sFm1ahUJCQl8//33lCtXjo4dOzJw4EBmzpyZYX+EEEII8Wn4qIPddevW4eLiQunSpfnf//7H999/j6L830To7du307p1a5o1a8bp06fZs2cP1apVy7DOSZMm0a1bN8LDw3FxcaFz58707t2bUaNGceLECRRFoX///mr+Z8+e0axZM/bs2cPp06dp0qQJPj4+REZGZtju8ePHM3XqVE6cOIG9vX2qQHbVqlUEBQUxZcoULl68yNSpUxk7diwrV67U+fzExsYCYGlpCcD9+/c5evQoNjY21KhRA1tbW+rWrcuhQ4feWVdAQADe3t54enqm2nfy5EkSExO19rm4uFC0aFHCwsKA1yPKFSpUwNbWVs3j5eXFkydPOH/+fJrHDAsLw8LCgipVqqhpnp6e6OnpcfToUTVPnTp1MDD4vzvLvLy8uHz5Mo8fP06z3vj4eJ48eaK1CSGEECJ3+qhXY1i2bBn/+9//AGjSpAmxsbEcOHCAevXqATBlyhQ6duzIhAkT1DKVKlXKsE5/f3/at28PwIgRI/Dw8GDs2LF4eXkBMGjQIPz9/bXqe7POSZMmsWnTJrZs2aIVFL9p9uzZ9OjRgx49egAwefJkfv/9d63R3XHjxvHtt9+qUwGcnZ25cOEC3333Hb6+vu88N8nJyQwePJiaNWtSvnx5AG7cuAG8vhluxowZuLq68sMPP9CwYUPOnTtHyZIl06xrzZo1nDp1iuPHj6e5PyoqCgMDAywsLLTSbW1tiYqKUvO8Geim7E/Zl169NjY2Wml58uTB0tJSq15nZ+d06317FBpez21+8z0hhBBCiNzrox3ZvXz5MseOHaNTp07A6yCoQ4cOLFu2TM0THh5Ow4YNM1Xvm/NRU4KmChUqaKW9fPlSHQ189uwZw4YNo0yZMlhYWGBqasrFixczHNm9ePEi7u7uWmkeHh7q/+Pi4rh+/To9evTA1NRU3SZPnqw1hSIjAQEBnDt3jjVr1qhpycnJAPTu3Rt/f38qV67MrFmzKF26NN9//32a9fz9998MGjSIVatWYWRkpNOx/+tGjRpFbGysuv39998fuklCCCGEyCEf7cjusmXLePXqFQ4ODmqaoigYGhoyf/58zM3NMTY2znS9efPmVf+f8qzltNJSAsdhw4axe/duZsyYQYkSJTA2Nubzzz9Pdx6qLlJWG1iyZEmqoFhfX/+d5fv376/ezFW4cGE13d7eHoCyZctq5S9Tpky6wfnJkye5f/8+n332mZqWlJTEwYMHmT9/PvHx8djZ2ZGQkEBMTIzW6G50dDR2dnYA2NnZcezYMa26U1ZrSMnzNjs7O+7fv6+V9urVKx49eqRV79urPryrXkNDw3fOURZCCCFE7vBRjuy+evWKH374gW+//Zbw8HB1++uvv3BwcODnn38GXo/S7tmzJ0fb8ueff+Ln50fr1q2pUKECdnZ23Lx5M8MyZcqUUeecpki5kQtejx47ODhw48YNSpQoobW9/ZX9m1LmE2/atIm9e/emyuvk5ISDg0Oq5ciuXLmCo6NjmnU2bNiQs2fPap3nKlWq0KVLF8LDw9HX18fNzY28efNqnevLly8TGRmpjlh7eHhw9uxZreB19+7dmJmZpQq+U3h4eBATE6N1g93evXtJTk5W/wjw8PDg4MGDJCYmatVbunTpNKcwCCGEEOLT8lGO7G7bto3Hjx/To0cPzM3Ntfa1bduWZcuW0adPH8aNG0fDhg0pXrw4HTt25NWrV+zYsYMRI0ZkW1tKlizJxo0b8fHxQaPRMHbsWHXUNz2DBg3Cz8+PKlWqULNmTVatWsX58+cpVqyYmmfChAkMHDgQc3NzmjRpQnx8PCdOnODx48cEBgamWW9AQACrV6/m119/JX/+/Oq81pRRbo1Gw5dffsm4ceOoVKkSrq6urFy5kkuXLrFhwwa1noYNG9K6dWv69+9P/vz51Tm/KUxMTLCyslLTzc3N6dGjB4GBgVhaWmJmZsaAAQPw8PCgevXqADRu3JiyZcvStWtXpk+fTlRUFGPGjCEgICDdUdYyZcrQpEkTvvjiC0JCQkhMTKR///507NhRHdHv3LkzEyZMoEePHowYMYJz584xZ84cZs2aleE1EEIIIcSn4aMc2V22bBmenp6pAl14HeyeOHGCM2fOUK9ePdavX8+WLVtwdXWlQYMGqb5Kf18zZ86kQIEC1KhRAx8fH7y8vLS+8k9Lhw4dGDt2LMOHD8fNzY1bt27Rt29frTw9e/Zk6dKlLF++nAoVKlC3bl1WrFiR4cjuokWLiI2NpV69etjb26vb2rVr1TyDBw9m1KhRDBkyhEqVKrFnzx52795N8eLF1TzXr1/n4cOHmToPs2bNonnz5rRt25Y6depgZ2fHxo0b1f36+vps27YNfX19PDw8+N///ke3bt2YOHGimufmzZtoNBr279+vpq1atQoXFxcaNmxIs2bNqFWrltYauubm5vz2229ERETg5ubG0KFDCQoKylUPCxFCCCFE1mmUN9fqEuID2rdvH23atOHGjRv/6hSEJ0+eYG5uTmxsLGZmZv/acYUQQgiRdbr+/v4oR3ZF7rRjxw5Gjx4tc22FEEIIkW0+yjm7Inf65ptvPnQThBBCCJHLyMiuEEIIIYTItSTYFUIIIYQQuZYEu0IIIYQQIteSYFcIIYQQQuRaEuwKIYQQQohcS4JdIYQQQgiRa0mwK4QQQgghci0JdoUQQgghRK4lwa4QQgghhMi15AlqQvx/5gPMweBDt0IIIf77lCXKh26CEDqTkV0hhBBCCJFrSbArhBBCCCFyrVwR7Do5OaHRaFJtAQEB6ZZJTExk4sSJFC9eHCMjIypVqkRoaKhWnuDgYKpWrUr+/PmxsbGhVatWXL58WSvPy5cvCQgIwMrKClNTU9q2bUt0dHSG7VUUhaCgIOzt7TE2NsbT05OrV69q5ZkyZQo1atQgX758WFhYZO6EZECXfr9t//79tGzZEnt7e0xMTHB1dWXVqlXp5l+zZg0ajYZWrVpppevS77QsWLAAJycnjIyMcHd359ixY1r7s3INhBBCCPFpyBXB7vHjx7l375667d69G4B27dqlW2bMmDF89913zJs3jwsXLtCnTx9at27N6dOn1TwHDhwgICCAI0eOsHv3bhITE2ncuDFxcXFqniFDhrB161bWr1/PgQMHuHv3Lm3atMmwvdOnT2fu3LmEhIRw9OhRTExM8PLy4uXLl2qehIQE2rVrR9++fbN6WrLc77cdPnyYihUr8ssvv3DmzBn8/f3p1q0b27ZtS5X35s2bDBs2jNq1a6fap0u/37Z27VoCAwMZN24cp06dolKlSnh5eXH//n01T1augRBCCCE+DRpFUXLdLPPBgwezbds2rl69ikajSTOPg4MDX331ldbob9u2bTE2Nuann35Ks8yDBw+wsbHhwIED1KlTh9jYWKytrVm9ejWff/45AJcuXaJMmTKEhYVRvXr1VHUoioKDgwNDhw5l2LBhAMTGxmJra8uKFSvo2LGjVv4VK1YwePBgYmJisnIqsqXfafH29sbW1pbvv/9eTUtKSqJOnTp0796dP/74g5iYGDZv3gxkvt8p3N3dqVq1KvPnzwcgOTmZIkWKMGDAAEaOHJmla/C2J0+eYG5uDt2QG9SEEEIHcoOa+C9I+f0dGxuLmZlZuvlyxcjumxISEvjpp5/o3r17uoEuQHx8PEZGRlppxsbGHDp0KN0ysbGxAFhaWgJw8uRJEhMT8fT0VPO4uLhQtGhRwsLC0qwjIiKCqKgorTLm5ua4u7unWyY7ZaXfaYmNjVXPQ4qJEydiY2NDjx49UuXPSr8TEhI4efKkVhk9PT08PT3VMlm5BvHx8Tx58kRrE0IIIUTulOuC3c2bNxMTE4Ofn1+G+by8vJg5cyZXr14lOTmZ3bt3s3HjRu7du5dm/uTkZAYPHkzNmjUpX748AFFRURgYGKSaU2tra0tUVFSa9aSk29ra6lwmO2W232lZt24dx48fx9/fX007dOgQy5YtY8mSJWmWyUq/Hz58SFJSUoZlsnINgoODMTc3V7ciRYqk31khhBBCfNRyXbC7bNkymjZtioODQ4b55syZQ8mSJXFxccHAwID+/fvj7++Pnl7apyQgIIBz586xZs2anGj2e5s6dSqmpqbqFhkZmWa+zPb7bfv27cPf358lS5ZQrlw5AJ4+fUrXrl1ZsmQJBQsWzLY+5ZRRo0YRGxurbn///feHbpIQQgghckiueqjErVu3+P3339m4ceM781pbW7N582ZevnzJP//8g4ODAyNHjqRYsWKp8vbv359t27Zx8OBBChcurKbb2dmRkJBATEyM1shidHQ0dnZ2aR43JT06Ohp7e3utMq6urjr2NLU+ffrQvn179XV6wX5m+v22AwcO4OPjw6xZs+jWrZuafv36dW7evImPj4+alpycDECePHm4fPlylvpdsGBB9PX1U62s8Ob5zco1MDQ0xNDQ8J39FUIIIcTHL1eN7C5fvhwbGxu8vb11LmNkZEShQoV49eoVv/zyCy1btlT3KYpC//792bRpE3v37sXZ2VmrrJubG3nz5mXPnj1q2uXLl4mMjMTDwyPN4zk7O2NnZ6dV5smTJxw9ejTdMrqwtLSkRIkS6pYnT8Z/x2TU77Ts378fb29vpk2bRq9evbT2ubi4cPbsWcLDw9WtRYsW1K9fn/DwcIoUKZKlfhsYGODm5qZVJjk5mT179qhlsnINhBBCCPHpyDUju8nJySxfvhxfX980A71u3bpRqFAhgoODATh69Ch37tzB1dWVO3fuMH78eJKTkxk+fLhaJiAggNWrV/Prr7+SP39+dQ6oubk5xsbGmJub06NHDwIDA7G0tMTMzIwBAwbg4eGR7ioAGo2GwYMHM3nyZEqWLImzszNjx47FwcFBa13ayMhIHj16RGRkJElJSYSHhwNQokQJTE1Ns3yedOn3/Pnz2bRpkxpA7tu3j+bNmzNo0CDatm2rngcDAwMsLS0xMjJS5zGnSBllfTNdl36/LTAwEF9fX6pUqUK1atWYPXs2cXFx6nzhrFwDIYQQQnw6ck2w+/vvvxMZGUn37t3T3B8ZGak1L/Xly5eMGTOGGzduYGpqSrNmzfjxxx+1vgpftGgRAPXq1dOqa/ny5eoNcLNmzUJPT4+2bdsSHx+Pl5cXCxcu1Mrv5OSEn58f48ePB2D48OHExcXRq1cvYmJiqFWrFqGhoVqrJAQFBbFy5Ur1deXKlYHXgefb7ckMXfr98OFDrl+/rr5euXIlz58/Jzg4WP1jAaBu3brs379f52Pr0u969erh5OTEihUrAOjQoQMPHjwgKCiIqKgoXF1dCQ0N1bppTZdrIIQQQohPU65cZ/e/5Pnz51hZWbFz5873ClI/FY6OjkyYMOGdq2lkJ13X6RNCCCHEf8cnu87uf82+ffto0KCBBLo6OH/+PObm5lo3vwkhhBBCvA8Z2RWfPBnZFUIIIT4+MrIrhBBCCCE+eRLsCiGEEEKIXEuCXSGEEEIIkWtJsCuEEEIIIXItCXaFEEIIIUSuJcGuEEIIIYTItSTYFUIIIYQQuZYEu0IIIYQQIteSYFcIIYQQQuRaeT50A4T4rzAfYA4GH7oVQgiRecoSeRiqEOmRkV0hhBBCCJFrfdBg9+DBg/j4+ODg4IBGo2Hz5s2p8owfPx4XFxdMTEwoUKAAnp6eHD16NMN6g4ODqVq1Kvnz58fGxoZWrVpx+fJlrTwvX74kICAAKysrTE1Nadu2LdHR0Vp5IiMj8fb2Jl++fNjY2PDll1/y6tWrDI/96NEjunTpgpmZGRYWFvTo0YNnz55pHdfPz48KFSqQJ08eWrVqlfFJyoSNGzfSuHFjrKys0Gg0hIeHp8pTr149NBqN1tanT58M63327Bn9+/encOHCGBsbU7ZsWUJCQtLMqygKTZs2TfN65sT5BDhz5gy1a9fGyMiIIkWKMH369AzrFEIIIcSn44MGu3FxcVSqVIkFCxakm6dUqVLMnz+fs2fPcujQIZycnGjcuDEPHjxIt8yBAwcICAjgyJEj7N69m8TERBo3bkxcXJyaZ8iQIWzdupX169dz4MAB7t69S5s2bdT9SUlJeHt7k5CQwOHDh1m5ciUrVqwgKCgowz516dKF8+fPs3v3brZt28bBgwfp1auXVr3GxsYMHDgQT09PXU6TzuLi4qhVqxbTpk3LMN8XX3zBvXv31O1dwWFgYCChoaH89NNPXLx4kcGDB9O/f3+2bNmSKu/s2bPRaDSp0nPqfD558oTGjRvj6OjIyZMn+eabbxg/fjyLFy/OsF4hhBBCfBo0iqL8Jyb6aDQaNm3a9M6RzidPnmBubs7vv/9Ow4YNdar7wYMH2NjYcODAAerUqUNsbCzW1tasXr2azz//HIBLly5RpkwZwsLCqF69Ojt37qR58+bcvXsXW1tbAEJCQhgxYgQPHjzAwCD15M6LFy9StmxZjh8/TpUqVQAIDQ2lWbNm3L59GwcHB638fn5+xMTEpDmi/T5u3ryJs7Mzp0+fxtXVVWtfvXr1cHV1Zfbs2TrXV758eTp06MDYsWPVNDc3N5o2bcrkyZPVtPDwcJo3b86JEyewt7fXup45dT4XLVrEV199RVRUlFrHyJEj2bx5M5cuXdKpfynvKbohc3aFEB8lmbMrPkUpv79jY2MxMzNLN99HNWc3ISGBxYsXY25uTqVKlXQuFxsbC4ClpSUAJ0+eJDExUWtk1cXFhaJFixIWFgZAWFgYFSpUUAMzAC8vL548ecL58+fTPE5YWBgWFhZqYAbg6emJnp7eO6de/JtWrVpFwYIFKV++PKNGjeL58+cZ5q9RowZbtmzhzp07KIrCvn37uHLlCo0bN1bzPH/+nM6dO7NgwQLs7OxS1ZFT5zMsLIw6depoBcteXl5cvnyZx48fp1lvfHw8T5480dqEEEIIkTt9FKsxbNu2jY4dO/L8+XPs7e3ZvXs3BQsW1KlscnIygwcPpmbNmpQvXx5AHQW0sLDQymtra0tUVJSa583ALGV/yr60REVFYWNjo5WWJ08eLC0t0y3zb+vcuTOOjo44ODhw5swZRowYweXLl9m4cWO6ZebNm0evXr0oXLgwefLkQU9PjyVLllCnTh01z5AhQ6hRowYtW7ZMs46cOp9RUVE4OzunW2+BAgVS1RscHMyECRPS7a8QQgghco+PItitX78+4eHhPHz4kCVLltC+fXuOHj2aKhBKS0BAAOfOnePQoUP/QktzRtOmTfnjjz8AcHR0THckVBdvznetUKEC9vb2NGzYkOvXr1O8ePE0y8ybN48jR46wZcsWHB0dOXjwIAEBATg4OODp6cmWLVvYu3cvp0+fznK7/k2jRo0iMDBQff3kyROKFCnyAVskhBBCiJzyUQS7JiYmlChRghIlSlC9enVKlizJsmXLGDVqVIbl+vfvr97UVLhwYTXdzs6OhIQEYmJitEZ3o6Oj1a/g7ezsOHbsmFZ9Kas1pPU1fUr6/fv3tdJevXrFo0eP0i2ji6VLl/LixQsA8ubNm+V60uLu7g7AtWvX0gx2X7x4wejRo9m0aRPe3t4AVKxYkfDwcGbMmIGnpyd79+7l+vXrqUbK27ZtS+3atdm/f3+OnU87O7tUq2i8q15DQ0MMDQ3T3CeEEEKI3OWjmrObIjk5mfj4+HT3K4pC//792bRpE3v37k31Nbebmxt58+Zlz549atrly5eJjIzEw8MDAA8PD86ePasVbO3evRszMzPKli2b5nE9PDyIiYnh5MmTatrevXtJTk5Wg8qsKFSokBrsOzo6ZrmetKQsT2Zvb5/m/sTERBITE9HT036r6Ovrk5ycDLy+IezMmTOEh4erG8CsWbNYvnw5kHPn08PDg4MHD5KYmKhVb+nSpdOcwiCEEEKIT8sHHdl99uwZ165dU19HREQQHh6OpaUlRYsWJS4ujilTptCiRQvs7e15+PAhCxYs4M6dO7Rr104t17BhQ1q3bk3//v2B11MXVq9eza+//kr+/PnV+Z3m5uYYGxtjbm5Ojx49CAwMxNLSEjMzMwYMGICHhwfVq1cHoHHjxpQtW5auXbsyffp0oqKiGDNmDAEBAemOCpYpU4YmTZrwxRdfEBISQmJiIv3796djx45aKzFcuHCBhIQEHj16xNOnT9Xg8O2VEzLr0aNHREZGcvfuXQB1bWE7Ozvs7Oy4fv06q1evplmzZlhZWXHmzBmGDBlCnTp1qFixolqPi4sLwcHBtG7dGjMzM+rWrcuXX36JsbExjo6OHDhwgB9++IGZM2dq1f+2okWLqn9o5NT57Ny5MxMmTKBHjx6MGDGCc+fOMWfOHGbNmvVe51IIIYQQucMHDXZPnDhB/fr11dcp8yh9fX1ZsWIF+vr6XLp0iZUrV/Lw4UOsrKyoWrUqf/zxB+XKlVPLXb9+nYcPH6qvFy1aBLxeZutNy5cvx8/PD3g96qinp0fbtm2Jj4/Hy8uLhQsXqnn19fXZtm0bffv2xcPDAxMTE3x9fZk4caKaJ2WJr3379qnHWrVqFf3796dhw4Zq/XPnztVqR7Nmzbh165b6unLlysDrEen3sWXLFvz9/dXXHTt2BGDcuHGMHz8eAwMDfv/9d2bPnk1cXBxFihShbdu2jBkzRquey5cvqytYAKxZs4ZRo0bRpUsXHj16hKOjI1OmTHnnwyjelFPn09zcnN9++42AgADc3NwoWLAgQUFBWnOThRBCCPHp+s+ss/sx2rdvH23atOHGjRvylXk2+FDnU9d1+oQQQgjx35Er19n9r9mxYwejR4+WQDebyPkUQgghRHaTkV3xyZORXSGEEOLjIyO7QgghhBDikyfBrhBCCCGEyLUk2BVCCCGEELmWBLtCCCGEECLXkmBXCCGEEELkWjo9VKJy5cpoNBqdKjx16tR7NUgIIYQQQojsolOw26pVqxxuhhBCCCGEENlP1tkVnzxZZ1cIIYT4+Mg6u0IIIYQQ4pOn0zSGNyUlJTFr1izWrVtHZGQkCQkJWvsfPXqUbY0TQgghhBDifWQ62J0wYQJLly5l6NChjBkzhq+++oqbN2+yefNmgoKCcqKNQvwrzAeYg8GHboUQIrdQlsgsQSH+CzI9jWHVqlUsWbKEoUOHkidPHjp16sTSpUsJCgriyJEjOdFGIYQQQgghsiTTwW5UVBQVKlQAwNTUlNjYWACaN2/O9u3bs7d1Oli0aBEVK1bEzMwMMzMzPDw82LlzZ4ZlEhMTmThxIsWLF8fIyIhKlSoRGhqaKt+dO3f43//+h5WVFcbGxlSoUIETJ06o+xVFISgoCHt7e4yNjfH09OTq1avvbPOCBQtwcnLCyMgId3d3jh07prV/8eLF1KtXDzMzMzQaDTExMbqdjHfYv38/Go0m1RYVFZVhmZYtW2Jvb4+JiQmurq6sWrUqVb7Zs2dTunRpjI2NKVKkCEOGDOHly5eZ6nda1q9fj4uLC0ZGRlSoUIEdO3Zo7c/qNRBCCCHEpyHTwW7hwoW5d+8eAMWLF+e3334D4Pjx4xgaGmZv63Rsz9dff83Jkyc5ceIEDRo0oGXLlpw/fz7dMmPGjOG7775j3rx5XLhwgT59+tC6dWtOnz6t5nn8+DE1a9Ykb9687Ny5kwsXLvDtt99SoEABNc/06dOZO3cuISEhHD16FBMTE7y8vFIFeW9au3YtgYGBjBs3jlOnTlGpUiW8vLy4f/++muf58+c0adKE0aNHv+fZSdvly5e5d++eutnY2KSb9/Dhw1SsWJFffvmFM2fO4O/vT7du3di2bZuaZ/Xq1YwcOZJx48Zx8eJFli1bxtq1a7Xar0u/0zp2p06d6NGjB6dPn6ZVq1a0atWKc+fOqXmycg2EEEII8enI9NJjI0eOxMzMjNGjR7N27Vr+97//4eTkRGRkJEOGDOHrr7/OqbbqzNLSkm+++YYePXqkud/BwYGvvvqKgIAANa1t27YYGxvz008/Aa/7+eeff/LHH3+kWYeiKDg4ODB06FCGDRsGQGxsLLa2tqxYsYKOHTumWc7d3Z2qVasyf/58AJKTkylSpAgDBgxg5MiRWnn3799P/fr1efz4MRYWFpk6B2nJrvq8vb2xtbXl+++/B6B///5cvHiRPXv2qHmGDh3K0aNHOXToEJC5fqfo0KEDcXFxWoF19erVcXV1JSQkJMvX4G0pS5fQDZmzK4TINjJnV4iclWNLj3399dfqiF2HDh04ePAgffv2ZcOGDR880E1KSmLNmjXExcXh4eGRbr74+HiMjIy00oyNjdXADGDLli1UqVKFdu3aYWNjQ+XKlVmyZIm6PyIigqioKDw9PdU0c3Nz3N3dCQsLS/O4CQkJnDx5UquMnp4enp6e6ZbJCa6urtjb29OoUSP+/PPPTJePjY3F0tJSfV2jRg1OnjypTku4ceMGO3bsoFmzZkDW+x0WFqZVBsDLy0stk5VrAK+v/5MnT7Q2IYQQQuROmV6N4W0eHh4ZBpb/hrNnz+Lh4cHLly8xNTVl06ZNlC1bNt38Xl5ezJw5kzp16lC8eHH27NnDxo0bSUpKUvPcuHGDRYsWERgYyOjRozl+/DgDBw7EwMAAX19fdZ6rra2tVt22trbpzoF9+PAhSUlJaZa5dOlSVruvM3t7e0JCQqhSpQrx8fEsXbqUevXqcfToUT777DOd6li3bh3Hjx/nu+++U9M6d+7Mw4cPqVWrFoqi8OrVK/r06aP+UZTVfkdFRWV4frNyDQCCg4OZMGGCDr0VQgghxMcuSw+V+PHHH6lZsyYODg7cunULeH2D0q+//pqtjdNV6dKlCQ8P5+jRo/Tt2xdfX18uXLiQbv45c+ZQsmRJXFxcMDAwoH///vj7+6On93+nIzk5mc8++4ypU6dSuXJlevXqxRdffEFISMi/0aVMWbVqFaampuqW3tSL0qVL07t3b9zc3KhRowbff/89NWrUYNasWTodZ9++ffj7+7NkyRLKlSunpu/fv5+pU6eycOFCTp06xcaNG9m+fTuTJk3Klv5lt1GjRhEbG6tuf//994dukhBCCCFySKaD3ZTRzmbNmhETE6OOhlpYWDB79uzsbp9ODAwMKFGiBG5ubgQHB1OpUiXmzJmTbn5ra2s2b95MXFwct27d4tKlS5iamlKsWDE1j729farR4TJlyhAZGQmAnZ0dANHR0Vp5oqOj1X1vK1iwIPr6+pkqo4sWLVoQHh6ublWqVNG5bLVq1bh27do78x04cAAfHx9mzZpFt27dtPaNHTuWrl270rNnTypUqEDr1q2ZOnUqwcHBJCcnZ7nfdnZ2GZbJyjUAMDQ0VFfvSNmEEEIIkTtlOtidN28eS5Ys4auvvkJfX19Nr1KlCmfPns3WxmVVcnIy8fHx78xnZGREoUKFePXqFb/88gstW7ZU99WsWZPLly9r5b9y5QqOjo4AODs7Y2dnp3VT1pMnTzh69Gi60zoMDAxwc3PTKpOcnMyePXveaypI/vz5KVGihLoZGxvrXDY8PBx7e/sM8+zfvx9vb2+mTZtGr169Uu1//vy51qg4oL43FEXJcr89PDy0ygDs3r1bLZOVayCEEEKIT0um5+xGRERQuXLlVOmGhobExcVlS6MyY9SoUTRt2pSiRYvy9OlTVq9ezf79+9m1a5eap1u3bhQqVIjg4GAAjh49yp07d3B1deXOnTuMHz+e5ORkhg8frpYZMmQINWrUYOrUqbRv355jx46xePFiFi9eDIBGo2Hw4MFMnjyZkiVL4uzszNixY3FwcKBVq1bptjcwMBBfX1+qVKlCtWrVmD17NnFxcfj7+6t5oqKiiIqKUkdcz549S/78+SlatKjWjWGZNXv2bJydnSlXrhwvX75k6dKl7N27V10+DmD+/Pls2rRJDSD37dtH8+bNGTRoEG3btlXnwhoYGKht8fHxYebMmVSuXBl3d3euXbvG2LFj8fHxUYNeXfr9tkGDBlG3bl2+/fZbvL29WbNmDSdOnHjvayCEEEKIT0emg11nZ2fCw8PVEc4UoaGhlClTJtsapqv79+/TrVs37t27h7m5ORUrVmTXrl00atRIzRMZGak18vjy5UvGjBnDjRs3MDU1pVmzZvz4449ay3FVrVqVTZs2MWrUKCZOnIizszOzZ8+mS5cuap7hw4cTFxdHr169iImJoVatWoSGhmqt9FCvXj2cnJxYsWIF8HoFiwcPHhAUFERUVBSurq6EhoZq3WQVEhKidQNVnTp1AFi+fDl+fn5ZPlcJCQkMHTqUO3fukC9fPipWrMjvv/9O/fr11TwPHz7k+vXr6uuVK1fy/PlzgoOD1T8WAOrWrcv+/fuB1+sWazQaxowZw507d7C2tsbHx4cpU6ao+XXpt5+fHzdv3lTrrVGjBqtXr2bMmDGMHj2akiVLsnnzZsqXL6+W0eUaCCGEEOLTlel1dpcuXcr48eP59ttv6dGjB0uXLuX69esEBwezdOlSndc2/VQ4OjoyYcKE9wpSPxV169alfv36jB8//l89rq7r9AkhhBDiv0PX39+ZHtnt2bMnxsbGjBkzhufPn9O5c2ccHByYM2eOBLpvOX/+PObm5qlu6BKpxcbGcv369Q/yyGkhhBBC5F6ZHtl90/Pnz3n27Jn6uNk7d+5QqFChbGucEP8GGdkVQgghPj459gS1N+XLlw8bGxuioqIYMGAAJUuWfJ/qhBBCCCGEyFY6B7uPHz+mU6dOFCxYEAcHB+bOnUtycjJBQUEUK1aM48ePs3z58pxsqxBCCCGEEJmi85zdkSNHcvjwYfz8/Ni1axdDhgwhNDQUPT099u7dS/Xq1XOynUIIIYQQQmSaziO7O3fuZPny5cyYMYOtW7eiKAqurq5s27ZNAl0hhBBCCPGfpHOwe/fuXXUdXScnJ4yMjPjf//6XYw0TQgghhBDifekc7CqKQp48/zfrQV9fP1OPpRVCCCGEEOLfpvOcXUVRaNiwoRrwvnjxAh8fHwwMDLTynTp1KntbKIQQQgghRBbpHOyOGzdO63XLli2zvTFCCCGEEEJkp/d6qIQQuYE8VEIIIYT4+OTY44KFyK3MB5iDwbvzCSGELpQlMpYkxH/Bez1BTQghhBBCiP8yCXaFEEIIIUSu9UGD3YMHD+Lj44ODgwMajYbNmzenyhMdHY2fnx8ODg7ky5ePJk2acPXq1XfWPXv2bEqXLo2xsTFFihRhyJAhvHz5UivPggUL1DWD3d3dOXbsmNb+ly9fEhAQgJWVFaamprRt25bo6OgMj6soCkFBQdjb22NsbIynp2eq9k6ZMoUaNWqQL18+LCws3tkXXS1ZsoTatWtToEABChQogKenZ6o+AVy8eJEWLVpgbm6OiYkJVatWJTIyUqdjrFmzBo1GQ6tWrbTSden3o0eP6NKlC2ZmZlhYWNCjRw+ePXuW4fF0uQaRkZF4e3uTL18+bGxs+PLLL3n16pVO/RFCCCFE7vZBg924uDgqVarEggUL0tyvKAqtWrXixo0b/Prrr5w+fRpHR0c8PT2Ji4tLt97Vq1czcuRIxo0bx8WLF1m2bBlr165l9OjRap61a9cSGBjIuHHjOHXqFJUqVcLLy4v79++reYYMGcLWrVtZv349Bw4c4O7du7Rp0ybDPk2fPp25c+cSEhLC0aNHMTExwcvLSyvQTkhIoF27dvTt21fXU6WT/fv306lTJ/bt20dYWBhFihShcePG3LlzR81z/fp1atWqhYuLC/v37+fMmTOMHTsWIyOjd9Z/8+ZNhg0bRu3atVPt06XfXbp04fz58+zevZtt27Zx8OBBevXqleEx33UNkpKS8Pb2JiEhgcOHD7Ny5UpWrFhBUFCQLqdMCCGEELlcllZjOH78OPv27eP+/fskJydr7Zs5c2bWGqLRsGnTJq0RwytXrlC6dGnOnTtHuXLlAEhOTsbOzo6pU6fSs2fPNOvq378/Fy9eZM+ePWra0KFDOXr0KIcOHQLA3d2dqlWrMn/+fLXeIkWKMGDAAEaOHElsbCzW1tasXr2azz//HIBLly5RpkwZwsLC0nxEsqIoODg4MHToUIYNGwZAbGwstra2rFixgo4dO2rlX7FiBYMHDyYmJiZL5+xdkpKSKFCgAPPnz6dbt24AdOzYkbx58/Ljjz9muq46derQvXt3/vjjD2JiYtSReF36ffHiRcqWLcvx48epUqUKAKGhoTRr1ozbt2/j4OCQ6pi6XIOdO3fSvHlz7t69i62tLQAhISGMGDGCBw8epFoHOi0pd3PSDblBTQiRbeQGNSFylq6rMWR6ZHfq1Km4u7uzfPlyTpw4wenTp9UtPDz8fdqcSnx8PIDWqKOenh6GhoZq0JqWGjVqcPLkSfUr/Bs3brBjxw6aNWsGvB5ZPXnyJJ6enlr1enp6EhYWBsDJkydJTEzUyuPi4kLRokXVPG+LiIggKipKq4y5uTnu7u7plslJz58/JzExEUtLS+B1QL99+3ZKlSqFl5cXNjY2uLu7pzl95G0TJ07ExsaGHj16pNqnS7/DwsKwsLBQA10AT09P9PT0OHr0aJrH1OUahIWFUaFCBTXQBfDy8uLJkyecP38+zXrj4+N58uSJ1iaEEEKI3CnTS4/NmTOH77//Hj8/vxxojraUwGbUqFF89913mJiYMGvWLG7fvs29e/fSLde5c2cePnxIrVq1UBSFV69e0adPH3Uaw8OHD0lKStIKkABsbW25dOkSAFFRURgYGKSaU2tra0tUVFSax01JT6ve9MrkpBEjRuDg4KAGi/fv3+fZs2d8/fXXTJ48mWnTphEaGkqbNm3Yt28fdevWTbOeQ4cOsWzZsnT/mNGl31FRUdjY2Gjtz5MnD5aWlhmez3ddg6ioqDSP+2a73hYcHMyECRPS3CeEEEKI3CXTI7t6enrUrFkzJ9qSSt68edm4cSNXrlzB0tKSfPnysW/fPpo2bYqeXvpN379/P1OnTmXhwoWcOnWKjRs3sn37diZNmvSvtDu7TZ06FVNTU3XT5Wayr7/+mjVr1rBp0yZ1ZDxlyknLli0ZMmQIrq6ujBw5kubNmxMSEpJmPU+fPqVr164sWbKEggULZl+nPqBRo0YRGxurbn///feHbpIQQgghckimR3aHDBnCggULmD17dg40JzU3NzfCw8OJjY0lISEBa2tr3N3dtb4Of9vYsWPp2rWrOqe3QoUKxMXF0atXL7766isKFiyIvr5+qrv6o6OjsbOzA8DOzo6EhARiYmK0RhbfzPO2lPTo6Gjs7e21yri6umal+wD06dOH9u3bq6/Tmt/6phkzZvD111/z+++/U7FiRTW9YMGC5MmTh7Jly2rlL1OmTLrTQq5fv87Nmzfx8fFR01KC5jx58nD58mWd+m1nZ6d18x/Aq1evePToUYbn813XwM7OLtWKEynXNb16DQ0NMTQ0THOfEEIIIXKXTI/sDhs2jMuXL1O8eHF8fHxo06aN1pZTzM3Nsba25urVq5w4cYKWLVumm/f58+epRn719fWB1zdTGRgY4ObmpnUDW3JyMnv27MHDwwN4HWTnzZtXK8/ly5eJjIxU87zN2dkZOzs7rTJPnjzh6NGj6ZbRhaWlJSVKlFC3PHnS/xtl+vTpTJo0idDQ0FR/EBgYGFC1alUuX76slX7lyhUcHR3TrM/FxYWzZ88SHh6ubi1atKB+/fqEh4dTpEgRnfrt4eFBTEwMJ0+eVPPs3buX5ORk3N3d0zy2LtfAw8ODs2fPagXSu3fvxszMLFVQL4QQQohPT6ZHdgcOHMi+ffuoX78+VlZWaDSaLB/82bNnXLt2TX0dERFBeHg4lpaWFC1aFID169djbW1N0aJFOXv2LIMGDaJVq1Y0btxYLdetWzcKFSpEcHAwAD4+PsycOZPKlSvj7u7OtWvXGDt2LD4+PmrQGxgYiK+vL1WqVKFatWrMnj2buLg4/P39gdfBdY8ePQgMDMTS0hIzMzMGDBiAh4dHmisxwOsVJQYPHszkyZMpWbIkzs7OjB07FgcHB61VJiIjI3n06BGRkZEkJSWpc2FLlCiBqalpls/ntGnTCAoKYvXq1Tg5OalzVlOmPwB8+eWXdOjQgTp16lC/fn1CQ0PZunUr+/fvT/N8GhkZUb58ea3jpIyyvpn+rn6XKVOGJk2a8MUXXxASEkJiYiL9+/enY8eO6Y5U63INGjduTNmyZenatSvTp08nKiqKMWPGEBAQIKO3QgghhMh8sLty5Up++eUXvL293/vgJ06coH79+urrwMBAAHx9fVmxYgUA9+7dIzAwUP2KvFu3bowdO1arnsjISK2R3DFjxqDRaBgzZgx37tzB2toaHx8fpkyZoubp0KEDDx48ICgoiKioKFxdXQkNDdW62WnWrFno6enRtm1b4uPj8fLyYuHChVrHdnJyws/Pj/HjxwMwfPhwdcpETEwMtWrVIjQ0VGtFiaCgIFauXKm+rly5MgD79u2jXr16WTiTry1atIiEhAR1ma4U48aNU9vXunVrQkJCCA4OZuDAgZQuXZpffvmFWrVqqfnfPp+60KXfq1aton///jRs2FA9r3PnztWqR6PRsHz5cvUGyHddA319fbZt20bfvn3x8PDAxMQEX19fJk6cmKn2CyGEECJ3yvQ6u46OjuzatQsXF5ecatNH4/nz51hZWbFz5873ClLFaxEREZQqVYoLFy5QsmTJf+24uq7TJ4QQQoj/jhxbZ3f8+PGMGzeO58+fv1cDc4N9+/bRoEEDCXSzyY4dO+jVq9e/GugKIYQQInfL9Mhu5cqVuX79Ooqi4OTkRN68ebX2nzp1KlsbKEROk5FdIYQQ4uOj6+/vTM/ZffNGKyGEEEIIIf7LMj2yK0RuIyO7QgghxMcnx+bsAsTExLB06VJGjRrFo0ePgNfTF+7cuZO11gohhBBCCJEDMj2N4cyZM3h6emJubs7Nmzf54osvsLS0ZOPGjURGRvLDDz/kRDuFEEIIIYTItEyP7AYGBuLn58fVq1e11lBt1qwZBw8ezNbGCSGEEEII8T4yHeweP36c3r17p0ovVKiQ+sQuIYQQQggh/gsyHewaGhry5MmTVOlXrlzB2to6WxolhBBCCCFEdsh0sNuiRQsmTpxIYmIi8PrxrpGRkYwYMYK2bdtmewOFEEIIIYTIqkwHu99++y3Pnj3DxsaGFy9eULduXUqUKEH+/PmZMmVKTrRRCCGEEEKILMnyOrt//vknf/31F8+ePeOzzz7D09Mzu9smxL8iZZ0+ugEGH7o1QoicoiyRZeWFyE1y7AlqKWrWrEnNmjWzWlwIIYQQQogcl6WHSvzX3Llzh//9739YWVlhbGxMhQoVOHHiRIZl4uPj+eqrr3B0dMTQ0BAnJye+//57rTzr16/HxcUFIyMjKlSowI4dO7T2K4pCUFAQ9vb2GBsb4+npydWrV9/Z3gULFuDk5ISRkRHu7u4cO3ZMa//ixYupV68eZmZmaDQaYmJidDsR7+Dn54dGo0m1lStXLsNy69atw9XVlXz58uHo6Mg333yjtX/jxo00atQIa2trzMzM8PDwYNeuXanqeVe/05JT10AIIYQQn4aPPth9/PgxNWvWJG/evOzcuZMLFy7w7bffUqBAgQzLtW/fnj179rBs2TIuX77Mzz//TOnSpdX9hw8fplOnTvTo0YPTp0/TqlUrWrVqxblz59Q806dPZ+7cuYSEhHD06FFMTEzw8vLi5cuX6R537dq1BAYGMm7cOE6dOkWlSpXw8vLi/v37ap7nz5/TpEkTRo8e/R5nJrU5c+Zw7949dfv777+xtLSkXbt26ZbZuXMnXbp0oU+fPpw7d46FCxcya9Ys5s+fr+Y5ePAgjRo1YseOHZw8eZL69evj4+PD6dOnM9Xvt+XUNRBCCCHEpyPLc3b/K0aOHMmff/7JH3/8oXOZ0NBQOnbsyI0bN7C0tEwzT4cOHYiLi2Pbtm1qWvXq1XF1dSUkJARFUXBwcGDo0KEMGzYMgNjYWGxtbVmxYgUdO3ZMs153d3eqVq2qBovJyckUKVKEAQMGMHLkSK28+/fvp379+jx+/BgLCwud+6erzZs306ZNGyIiInB0dEwzT+fOnUlMTGT9+vVq2rx585g+fTqRkZFoNJo0y5UrV44OHToQFBQEZK7fKXLqGrxN5uwK8WmQObtC5C66ztnN1Mjuq1ev+OGHH4iOjn7vBmaXLVu2UKVKFdq1a4eNjQ2VK1dmyZIlOpWZPn06hQoVolSpUgwbNowXL16oecLCwlLddOfl5UVYWBgAERERREVFaeUxNzfH3d1dzfO2hIQETp48qVVGT08PT0/PdMvkpGXLluHp6ZluoAuvp3u8+aQ8AGNjY27fvs2tW7fSLJOcnMzTp0/VPySy2u+cuAYpfXry5InWJoQQQojcKVPBbp48eejTp89/6iviGzdusGjRIkqWLMmuXbvo27cvAwcOZOXKlRmWOXToEOfOnWPTpk3Mnj2bDRs20K9fPzVPVFQUtra2WuVsbW3Vp8Sl/JtRnrc9fPiQpKSkTJXJKXfv3mXnzp307Nkzw3xeXl5s3LiRPXv2kJyczJUrV/j2228BuHfvXpplZsyYwbNnz2jfvj2Q9X7nxDUACA4OxtzcXN2KFCmSbl4hhBBCfNwyPWe3WrVqhIeH50BTsiY5OZnPPvuMqVOnUrlyZXr16sUXX3xBSEhIhmU0Gg2rVq2iWrVqNGvWjJkzZ7Jy5Uqt0d2PxapVqzA1NVU3XaZ0rFy5EgsLC1q1apVhvi+++IL+/fvTvHlzDAwMqF69ujo9QE8v9dtn9erVTJgwgXXr1mFjY5Ol/uS0UaNGERsbq25///33h26SEEIIIXJIppce69evH4GBgfz999+4ublhYmKitb9ixYrZ1jhd2NvbU7ZsWa20MmXK8Msvv2RYplChQq/nab5RRlEUbt++TcmSJbGzs0s1XSM6Oho7OzsA9d/o6Gjs7e218ri6uqZ53IIFC6Kvr59hvVnRokUL3N3d1deFChXKML+iKHz//fd07doVA4OMJ6lqNBqmTZvG1KlTiYqKwtramj179gBQrFgxrbxr1qyhZ8+erF+/XmtqQVb7nRPXAF4/8trQ0DCDXgshhBAit8j0yG7Hjh2JiIhg4MCB1KxZE1dXVypXrqz++2+rWbMmly9f1kq7cuVKhvNQa9asyd27d3n27JlWGT09PQoXLgyAh4eHGtSl2L17Nx4eHgA4OztjZ2enlefJkyccPXpUzfM2AwMD3NzctMokJyezZ8+edMvoIn/+/JQoUULdjI2NM8x/4MABrl27Ro8ePXQ+hr6+PoUKFcLAwICff/4ZDw8PrK2t1f0///wz/v7+/Pzzz3h7e2uVzWq/c+IaCCGEEOLTkumR3YiIiJxoR5YNGTKEGjVqMHXqVNq3b8+xY8dYvHgxixcvVvOMGjWKO3fu8MMPPwCvVxiYNGkS/v7+TJgwgYcPH/Lll1/SvXt3NVAcNGgQdevW5dtvv8Xb25s1a9Zw4sQJtV6NRsPgwYOZPHkyJUuWxNnZmbFjx+Lg4JDh1IDAwEB8fX2pUqUK1apVY/bs2cTFxeHv76/miYqKIioqimvXrgFw9uxZ8ufPT9GiRdNdPSIzli1bhru7O+XLl0+1b/78+WzatEkNIB8+fMiGDRuoV68eL1++ZPny5axfv54DBw6oZVavXo2vry9z5szB3d1dnS9rbGysjp7r0u+35dQ1EEIIIcSnI9PBbkYjph9C1apV2bRpE6NGjWLixIk4Ozsze/ZsunTpoua5d+8ekZGR6mtTU1N2797NgAEDqFKlClZWVrRv357JkyereWrUqMHq1asZM2YMo0ePpmTJkmzevFkrQBw+fDhxcXH06tWLmJgYatWqRWhoqNbqBfXq1cPJyYkVK1YAr5fTevDgAUFBQURFReHq6kpoaKjWTVYhISFMmDBBfV2nTh0Ali9fjp+f33udr9jYWH755RfmzJmT5v6HDx9y/fp1rbSVK1cybNgwFEXBw8OD/fv3U61aNXX/4sWLefXqFQEBAQQEBKjpvr6+meq3n58fN2/eZP/+/UD2XQMhhBBCfLqyvM7uhQsXiIyMJCEhQSu9RYsW2dKw3MLR0ZEJEya8d5D6Kahbty7169dn/Pjx/+pxdV2nTwghhBD/Hbr+/s70yO6NGzdo3bo1Z8+eRaPRkBIrpzxcICkpKYtNzn3Onz+Pubk53bp1+9BN+c+LjY3l+vXrbN++/UM3RQghhBC5SKZvUBs0aBDOzs7cv3+ffPnycf78eQ4ePEiVKlXUr5/Fa+XKlePMmTNpLtEltJmbm3P79m1MTU0/dFOEEEIIkYtkemQ3LCyMvXv3UrBgQfT09NDT06NWrVoEBwczcOBATp8+nRPtFEIIIYQQItMyPeSYlJRE/vz5gdfrp969exd4PTf17SXAhBBCCCGE+JAyPbJbvnx5/vrrL5ydnXF3d2f69OkYGBiwePHiVA8ZEEIIIYQQ4kPKdLA7ZswY4uLiAJg4cSLNmzendu3aWFlZsXbt2mxvoBBCCCGEEFmV5aXH3vTo0SMKFCigrsggxMdElh4TQgghPj45tvRYWrLjqV5CCCGEEEJkN52C3TZt2rBixQrMzMxo06ZNhnk3btyYLQ0TQgghhBDifekU7Jqbm6tTFMzNzXO0QUIIIYQQQmSXbJmzK8THTObsCiGEEB8fXX9/y6O9hBBCCCFErqXTNIbKlSvrvNLCqVOn3qtBQnwo5gPMweBDt0KID0dZIl/0CSFyH52C3VatWuVwM4QQQgghhMh+Ok1jGDdunM5bZhw8eBAfHx8cHBzQaDRs3rw5VZ6NGzfSuHFjrKys0Gg0hIeH61T37NmzKV26NMbGxhQpUoQhQ4bw8uVLdX9SUhJjx47F2dkZY2NjihcvzqRJk3hzCrOiKAQFBWFvb4+xsTGenp5cvXr1ncdesGABTk5OGBkZ4e7uzrFjx7T2L168mHr16mFmZoZGoyEmJkanPr3LvXv36Ny5M6VKlUJPT4/BgwenyrNx40aqVKmChYUFJiYmuLq68uOPP2ZY78aNG2nUqBHW1taYmZnh4eHBrl27UuV7V79fvnxJQEAAVlZWmJqa0rZtW6KjozM8ti7X4NGjR3Tp0gUzMzMsLCzo0aMHz549y7BeIYQQQnwaPuic3bi4OCpVqsSCBQsyzFOrVi2mTZumc72rV69m5MiRjBs3josXL7Js2TLWrl3L6NGj1TzTpk1j0aJFzJ8/n4sXLzJt2jSmT5/OvHnz1DzTp09n7ty5hISEcPToUUxMTPDy8tIKmt+2du1aAgMDGTduHKdOnaJSpUp4eXlx//59Nc/z589p0qSJVnuyQ3x8PNbW1owZM4ZKlSqlmcfS0pKvvvqKsLAwzpw5g7+/P/7+/mkGrykOHjxIo0aN2LFjBydPnqR+/fr4+Phw+vRpNY8u/R4yZAhbt25l/fr1HDhwgLt3775zKTtdrkGXLl04f/48u3fvZtu2bRw8eJBevXrpetqEEEIIkYvptBqDpaUlV65coWDBgu98UtqjR4+y1hCNhk2bNqU7ZeLmzZs4Oztz+vRpXF1dM6yrf//+XLx4kT179qhpQ4cO5ejRoxw6dAiA5s2bY2try7Jly9Q8bdu2xdjYmJ9++glFUXBwcGDo0KEMGzYMgNjYWGxtbVmxYgUdO3ZM89ju7u5UrVqV+fPnA5CcnEyRIkUYMGAAI0eO1Mq7f/9+6tevz+PHj7GwsMiwT5lVr149XF1dmT179jvzfvbZZ3h7ezNp0iSd6y9XrhwdOnQgKCgIeHe/Y2Njsba2ZvXq1Xz++ecAXLp0iTJlyhAWFkb16tVTHUOXa3Dx4kXKli3L8ePHqVKlCgChoaE0a9aM27dv4+Dg8M6+pNzNSTdkzq74pMmcXSHExyRbn6A2a9Ys8ufPD6BT8PSh1ahRg59++oljx45RrVo1bty4wY4dO+jatatWnsWLF3PlyhVKlSrFX3/9xaFDh5g5cyYAERERREVF4enpqZYxNzfH3d2dsLCwNIPdhIQETp48yahRo9Q0PT09PD09CQsLy8EeZ42iKOzdu5fLly9nauQ8OTmZp0+fqk/O06XfJ0+eJDExUet8uri4ULRo0XSDXV2uQVhYGBYWFmqgC+Dp6Ymenh5Hjx6ldevWqeqNj48nPj5eff3kyROd+y6EEEKIj4tOwa6vr2+a//+v6ty5Mw8fPqRWrVooisKrV6/o06eP1rSBkSNH8uTJE1xcXNDX1ycpKYkpU6bQpUsXAKKiogCwtbXVqtvW1lbd97aHDx+SlJSUZplLly5lZxffS2xsLIUKFSI+Ph59fX0WLlxIo0aNdC4/Y8YMnj17Rvv27QHd+h0VFYWBgUGqEeyMzqcu1yAqKgobGxut/Xny5MHS0jLdeoODg5kwYYIOPRVCCCHExy7Lc3bv37/PuXPnOHPmjNb2X7B//36mTp3KwoULOXXqFBs3bmT79u1aX9OvW7eOVatWsXr1ak6dOsXKlSuZMWMGK1eu/IAtT9uqVaswNTVVtz/++OO96sufPz/h4eEcP36cKVOmEBgYyP79+3Uqu3r1aiZMmMC6detSBZkfi1GjRhEbG6tuf//994dukhBCCCFyiE4ju286efIkvr6+XLx4kben+2o0GpKSkrKtcVk1duxYunbtSs+ePQGoUKECcXFx9OrVi6+++go9PT2+/PJLRo4cqU5HqFChArdu3SI4OBhfX1/s7OwAiI6Oxt7eXq07Ojo63TnDBQsWRF9fP9UKA9HR0Wp9WdGiRQvc3d3V14UKFcpyXfB6ikGJEiUAcHV15eLFiwQHB1OvXr0My61Zs4aePXuyfv16rakFuvTbzs6OhIQEYmJitEZ3Mzo3ulwDOzs7rZvgAF69esWjR4/SrdfQ0BBDQ8MM+yqEEEKI3CHTI7vdu3enVKlSHD58mBs3bhAREaFuN27cyIk2Ztrz58/R09Pumr6+PoAaoKeXJzk5GQBnZ2fs7Oy0bnJ78uQJR48excPDI83jGhgY4ObmplUmOTmZPXv2pFtGF/nz56dEiRLqZmxsnOW60pKcnKw1hzUtP//8M/7+/vz88894e3tr7dOl325ubuTNm1crz+XLl4mMjEz33OhyDTw8PIiJieHkyZNqnr1795KcnKz1B4IQQgghPk2ZHtm9ceMGv/zyizoy+D6ePXvGtWvX1NcRERGEh4djaWlJ0aJFgderO0RGRnL37l3gdYAEr0f0UkbuunXrRqFChQgODgbAx8eHmTNnUrlyZdzd3bl27Rpjx47Fx8dHDXp9fHyYMmUKRYsWpVy5cpw+fZqZM2fSvXt34PUo9eDBg5k8eTIlS5bE2dmZsWPH4uDgkOFDNgIDA/H19aVKlSpUq1aN2bNnExcXh7+/v5onKiqKqKgote9nz54lf/78FC1aVL3pK6tS1iF+9uwZDx48IDw8HAMDA8qWLQu8nq9apUoVihcvTnx8PDt27ODHH39k0aJFah2jRo3izp07/PDDD8DrqQu+vr7MmTMHd3d3dS6ssbHx61UMdOi3ubk5PXr0IDAwEEtLS8zMzBgwYAAeHh5p3pwGul2DMmXK0KRJE7744gtCQkJITEykf//+dOzYUaeVGIQQQgiRu2U62G3YsCF//fVXtgS7J06coH79+urrwMBA4PVNcCtWrABgy5YtWoFiyrSDcePGMX78eAAiIyO1RmnHjBmDRqNhzJgx3LlzB2trazW4TTFv3jzGjh1Lv379uH//Pg4ODvTu3VtdSgtg+PDh6vSHmJgYatWqRWhoKEZGRmqeevXq4eTkpLa3Q4cOPHjwgKCgIKKionB1dSU0NFTrJquQkBCtG6Tq1KkDwPLly/Hz88vKqVRVrlxZ/f/JkydZvXo1jo6O3Lx5E3i9bnG/fv24ffs2xsbGuLi48NNPP9GhQwe13L1794iMjFRfL168mFevXhEQEEBAQICa/uZ10qXfs2bNQk9Pj7Zt2xIfH4+XlxcLFy7Uar+TkxN+fn7qtdXlGqxatYr+/fvTsGFDtf65c+e+13kUQgghRO6g0zq7b3r48CG+vr5Uq1aN8uXLkzdvXq39LVq0yNYG/tc5OjoyYcKE9w5SxeupJVZWVuzcufOd84ezk67r9AkhhBDivyNb19l9U1hYGH/++Sc7d+5Mte+/coPav+X8+fOYm5vTrVu3D92UXGHfvn00aNDgXw10hRBCCJG7ZXpk18nJiebNmzN27NhU658K8TGSkV0hhBDi46Pr7+9Mr8bwzz//MGTIEAl0hRBCCCHEf16mg902bdqwb9++nGiLEEIIIYQQ2SrTc3ZLlSrFqFGjOHToEBUqVEh1g9rAgQOzrXFCCCGEEEK8j0zP2XV2dk6/Mo3mP/NgCSF0JXN2hRBCiI9Pjq3GEBER8V4NE0IIIYQQ4t+S6Tm7QgghhBBCfCwyPbILcPv2bbZs2UJkZCQJCQla+2bOnJktDRNCCCGEEOJ9ZTrY3bNnDy1atKBYsWJcunSJ8uXLc/PmTRRF4bPPPsuJNgohhBBCCJElmZ7GMGrUKIYNG8bZs2cxMjLil19+4e+//6Zu3bq0a9cuJ9oohBBCCCFElmR6NYb8+fMTHh5O8eLFKVCgAIcOHaJcuXL89ddftGzZkps3b+ZQU4XIGSl3c9INMPjQrRFCm7IkUx/RQgjxycixJ6iZmJio83Tt7e25fv26uu/hw4dZaKoQQgghhBA5I9NzdqtXr86hQ4coU6YMzZo1Y+jQoZw9e5aNGzdSvXr1nGijEEIIIYQQWZLpkd2ZM2fi7u4OwIQJE2jYsCFr167FycmJZcuWZaqugwcP4uPjg4ODAxqNhs2bN6fK4+fnh0aj0dqaNGmSYb1JSUmMHTsWZ2dnjI2NKV68OJMmTSK9GRt9+vRBo9Ewe/ZsrfRHjx7RpUsXzMzMsLCwoEePHjx79izDY798+ZKAgACsrKwwNTWlbdu2REdHa+UZOHAgbm5uGBoa4urqmmF9maHL+YyOjsbPzw8HBwfy5ctHkyZNuHr16jvrnj17NqVLl8bY2JgiRYowZMgQXr58qZVnwYIFODk5YWRkhLu7O8eOHdPar8u5eZuiKAQFBWFvb4+xsTGenp6p2puV6ySEEEKIT4POwW63bt14+vQpxYoVo2LFivz1118YGBgQEhLCmTNn+OWXX3B0dMzUwePi4qhUqRILFizIMF+TJk24d++euv38888Z5p82bRqLFi1i/vz5XLx4kWnTpjF9+nTmzZuXKu+mTZs4cuQIDg4OqfZ16dKF8+fPs3v3brZt28bBgwfp1atXhsceMmQIW7duZf369Rw4cIC7d+/Spk2bVPm6d+9Ohw4dMqwrs951PhVFoVWrVty4cYNff/2V06dP4+joiKenJ3FxcenWu3r1akaOHMm4ceO4ePEiy5YtY+3atYwePVrNs3btWgIDAxk3bhynTp2iUqVKeHl5cf/+fTWPrufmTdOnT2fu3LmEhIRw9OhRTExM8PLy0gq0s3KdhBBCCPFp0PkGNX19fe7du4eNjQ0AZmZmhIeHU6xYsexpiEbDpk2baNWqlVa6n58fMTExaY5Spqd58+bY2tpqjTS3bdsWY2NjfvrpJzXtzp07uLu7s2vXLry9vRk8eDCDBw8G4OLFi5QtW5bjx49TpUoVAEJDQ2nWrBm3b99OMziOjY3F2tqa1atX8/nnnwNw6dIlypQpQ1hYWKppHuPHj2fz5s2Eh4fr3DddpXU+r1y5QunSpTl37hzlypUDIDk5GTs7O6ZOnUrPnj3TrKt///5cvHiRPXv2qGlDhw7l6NGjHDp0CAB3d3eqVq3K/Pnz1XqLFCnCgAEDGDlyZKbPDbwOzh0cHBg6dCjDhg0DXp9jW1tbVqxYQceOHbN0neLj44mPj1dfP3nyhCJFisgNauI/SW5QE0KItGX7DWpvx8SZXMThvezfvx8bGxtKly5N3759+eeffzLMX6NGDfbs2cOVK1cA+Ouvvzh06BBNmzZV8yQnJ9O1a1e+/PJLNfB7U1hYGBYWFmoABeDp6Ymenh5Hjx5N87gnT54kMTERT09PNc3FxYWiRYsSFhaWqT7nhJQAz8jISE3T09PD0NBQDVrTUqNGDU6ePKlOS7hx4wY7duygWbNmACQkJHDy5Emtfuvp6eHp6an2OyvnJiIigqioKK0y5ubmuLu7q2Wycp2Cg4MxNzdXtyJFiqTbdyGEEEJ83LL0BLV/U5MmTWjTpg3Ozs5cv36d0aNH07RpU8LCwtDX10+zzMiRI3ny5AkuLi7o6+uTlJTElClT6NKli5pn2rRp5MmTh4EDB6ZZR1RUlDqKnSJPnjxYWloSFRWVbhkDAwMsLCy00m1tbdMt829KCS5HjRrFd999h4mJCbNmzeL27dvcu3cv3XKdO3fm4cOH1KpVC0VRePXqFX369FGnMTx8+JCkpCRsbW21ytna2nLp0iUga+cmJT2telP2ZeU6jRo1isDAQPW1OrIrhBBCiFwnU8HuhQsX1ABCURQuXbqU6kagihUrZl/rgI4dO6r/r1ChAhUrVqR48eLs37+fhg0bpllm3bp1rFq1itWrV1OuXDnCw8MZPHgwDg4O+Pr6cvLkSebMmcOpU6fQaDTZ2t6c0KdPH63pF1m9+Spv3rxs3LiRHj16YGlpib6+Pp6enjRt2jTDkfr9+/czdepUFi5ciLu7O9euXWPQoEFMmjSJsWPHZqktH5KhoSGGhoYfuhlCCCGE+BdkKtht2LChVlDUvHlz4PX8UEVR0Gg0JCUlZW8L31KsWDEKFizItWvX0g12v/zyS0aOHKkGyhUqVODWrVsEBwfj6+vLH3/8wf379ylatKhaJikpiaFDhzJ79mxu3ryJnZ2d1s1VAK9eveLRo0fY2dmleVw7OzsSEhKIiYnRGsGMjo5Ot4wuJk6cqM5ZfV9ubm6Eh4cTGxtLQkIC1tbWuLu7a00DeNvYsWPp2rWrOqe3QoUKxMXF0atXL7766isKFiyIvr5+qpUV3ux3Vs5NSnp0dDT29vZaZVJWscjKdRJCCCHEp0PnObsRERHcuHGDiIiIVFtK+o0bN3KyrQDcvn2bf/75Ryv4edvz58/R09Pumr6+PsnJyQB07dqVM2fOEB4erm4ODg58+eWX7Nq1CwAPDw9iYmI4efKkWsfevXtJTk5Wl157m5ubG3nz5tW6kevy5ctERkbi4eGR5T7b2NhQokQJdcsO5ubmWFtbc/XqVU6cOEHLli3TzZve+YTXI/wGBga4ublp9Ts5OZk9e/ao/c7KuXF2dsbOzk6rzJMnTzh69KhaJivXSQghhBCfDp1HdjO7rJgunj17xrVr19TXERERhIeHY2lpSdGiRXn27BkTJkygbdu22NnZcf36dYYPH06JEiXw8vJSyzVs2JDWrVvTv39/AHx8fJgyZQpFixalXLlynD59mpkzZ9K9e3cArKyssLKy0mpL3rx5sbOzo3Tp0gCUKVOGJk2a8MUXXxASEkJiYiL9+/enY8eOad7hD68DyB49ehAYGIilpSVmZmYMGDAADw8PrdUGrl27xrNnz4iKiuLFixfqagxly5bFwCDrywG863wCrF+/Hmtra4oWLcrZs2cZNGgQrVq1onHjxmq5bt26UahQIYKDg9XzOXPmTCpXrqxOYxg7diw+Pj5q0BsYGIivry9VqlShWrVqzJ49m7i4OPz9/TN1bt6k0WgYPHgwkydPpmTJkjg7OzN27FgcHBzUVSaycp2EEEII8en4oDeonThxgvr166uvU24a8vX1ZcWKFejr63PmzBlWrlxJTEwMDg4ONG7cmEmTJmnNubx+/brWo4rnzZvH2LFj6devH/fv38fBwYHevXsTFBSUqfatWrWK/v3707BhQ/T09Gjbti1z587VyqPRaFi+fDl+fn4AzJo1S80bHx+Pl5cXCxcu1CrTs2dPDhw4oL6uXLky8Do4dXJyylQb3/Su8wlw7949AgMD1akB3bp1SzXvNjIyUmskd8yYMWg0GsaMGcOdO3ewtrZW/6BI0aFDBx48eEBQUBBRUVG4uroSGhqqdXOZLufGyckJPz8/xo8fD8Dw4cPVKRMxMTHUqlWL0NBQrRUldLlOQgghhPg06bzOrkgtIiKCUqVKceHCBUqWLPmhm/PRe/78OVZWVuzcuZN69er9a8fVdZ0+IYQQQvx3ZPs6uyK1HTt20KtXLwl0s8m+ffto0KDBvxroCiGEECJ3k5Fd8cmTkV0hhBDi4yMju0IIIYQQ4pOXpRvUNmzYwLp164iMjCQhIUFr36lTp7KlYUIIIYQQQryvTI/szp07F39/f2xtbTl9+jTVqlXDysqKGzdu0LRp05xooxBCCCGEEFmS6WB34cKFLF68mHnz5mFgYMDw4cPZvXs3AwcOJDY2NifaKIQQQgghRJZkOtiNjIykRo0aABgbG/P06VPg9VPJfv755+xtnRBCCCGEEO8h08GunZ0djx49AqBo0aIcOXIEeL3mrCzsIIQQQggh/ksyHew2aNCALVu2AODv78+QIUNo1KgRHTp0oHXr1tneQCGEEEIIIbIq0+vsJicnk5ycTJ48rxdyWLNmDYcPH6ZkyZL07t0bAwODHGmoEDlF1tkVQgghPj66/v6Wh0qIT54Eu0IIIcTHR9ff3zqts3vmzBmdD1yxYkWd8wrxX2I+wBzkiwnxDsoSGR8QQoiPiU7BrqurKxqNBkVR0Gg0GeZNSkrKloYJIYQQQgjxvnS6QS0iIoIbN24QERHBL7/8grOzMwsXLuT06dOcPn2ahQsXUrx4cX755Zecbm8q48ePR6PRaG0uLi4ZllmyZAm1a9emQIECFChQAE9PT44dO6aV59mzZ/Tv35/ChQtjbGxM2bJlCQkJ0crz8uVLAgICsLKywtTUlLZt2xIdHZ3hsRVFISgoCHt7e4yNjfH09OTq1ataeaZMmUKNGjXIly8fFhYWup8MHcTExBAQEIC9vT2GhoaUKlWKHTt2vLPNM2bMoFSpUhgaGlKoUCGmTJmilWf//v189tlnGBoaUqJECVasWJGqngULFuDk5ISRkRHu7u6pznla1q9fj4uLC0ZGRlSoUCFVW3U5n0IIIYT4dOkU7Do6Oqrb1KlTmTt3Lr1796ZixYpUrFiR3r17M3v2bCZNmpTT7U1TuXLluHfvnrodOnQow/z79++nU6dO7Nu3j7CwMIoUKULjxo25c+eOmicwMJDQ0FB++uknLl68yODBg+nfv7+6EgXAkCFD2Lp1K+vXr+fAgQPcvXuXNm3aZHjs6dOnM3fuXEJCQjh69CgmJiZ4eXnx8uVLNU9CQgLt2rWjb9++WTwjaUtISKBRo0bcvHmTDRs2cPnyZZYsWUKhQoUyLDdo0CCWLl3KjBkzuHTpElu2bKFatWrq/oiICLy9valfvz7h4eEMHjyYnj17smvXLjXP2rVrCQwMZNy4cZw6dYpKlSrh5eXF/fv30z3u4cOH6dSpEz169OD06dO0atWKVq1ace7cOTWPLudTCCGEEJ+uTN+gZmxszKlTpyhTpoxW+sWLF/nss8948eJFtjbwXcaPH8/mzZsJDw/Pch1JSUkUKFCA+fPn061bNwDKly9Phw4dGDt2rJrPzc2Npk2bMnnyZGJjY7G2tmb16tV8/vnnAFy6dIkyZcoQFhZG9erVUx1HURQcHBwYOnQow4YNAyA2NhZbW1tWrFhBx44dtfKvWLGCwYMHExMTk+W+vSkkJIRvvvmGS5cukTdvXp3KXLx4kYoVK3Lu3DlKly6dZp4RI0awfft2rSC0Y8eOxMTEEBoaCoC7uztVq1Zl/vz5wOtVPYoUKcKAAQMYOXJkmvV26NCBuLg4tm3bpqZVr14dV1dXQkJCMn0+05MywZ1uyJxd8U4yZ1cIIf4bdL1BLdPr7JYpU4bg4GASEhLUtISEBIKDg1MFwP+Wq1ev4uDgQLFixejSpQuRkZGZKv/8+XMSExOxtLRU02rUqMGWLVu4c+cOiqKwb98+rly5QuPGjQE4efIkiYmJeHp6qmVcXFwoWrQoYWFhaR4nIiKCqKgorTLm5ua4u7unWyY7bdmyBQ8PDwICArC1taV8+fJMnTo1w3nWW7dupVixYmzbtg1nZ2ecnJzo2bOn+mARgLCwMK0+AXh5eal9SkhI4OTJk1p59PT08PT0zLDf76o3q+czPj6eJ0+eaG1CCCGEyJ10ukHtTSEhIfj4+FC4cGF15YUzZ86g0WjYunVrtjfwXdzd3VmxYgWlS5fm3r17TJgwgdq1a3Pu3Dny58+vUx0jRozAwcFBK2iaN28evXr1onDhwuTJkwc9PT2WLFlCnTp1AIiKisLAwCDVnFpbW1uioqLSPE5Kuq2trc5lstONGzfYu3cvXbp0YceOHVy7do1+/fqRmJjIuHHj0i1z69Yt1q9fzw8//EBSUhJDhgzh888/Z+/evcDrfqXVpydPnvDixQseP35MUlJSmnkuXbqUbnvTqzflXGX1fAYHBzNhwoR09wshhBAi98h0sFutWjVu3LjBqlWr1EClQ4cOdO7cGRMTk2xv4Ls0bdpU/X/FihVxd3fH0dGRdevW0aNHj3eW//rrr1mzZg379+/HyMhITZ83bx5Hjhxhy5YtODo6cvDgQQICAlIFxf8FU6dOZerUqerrCxcuULRo0VT5kpOTsbGxYfHixejr6+Pm5sadO3f45ptv0g12k5OTiY+P54cffqBUqVIALFu2DDc3Ny5fvpzu1Ib/slGjRhEYGKi+fvLkCUWKFPmALRJCCCFETsl0sAtgYmJCr169srst2cLCwoJSpUpx7dq1d+adMWMGX3/9Nb///rvW+sAvXrxg9OjRbNq0CW9vb+B1IB0eHs6MGTPw9PTEzs6OhIQEYmJitEZ3o6OjsbOzS/N4KenR0dHY29trlXF1dc1Cb1/r06cP7du3V187ODikmc/e3p68efOir6+vppUpU4aoqCgSEhLSfPqdvb09efLkUQPdlDIAkZGRlC5dGjs7u1SrUERHR2NmZoaxsTH6+vro6+unmSe9cwWkW29KmayeT0NDQwwNDdPdL4QQQojcI9NzduH1HNnFixczefJkJk6cqLV9aM+ePeP69etawU9apk+fzqRJkwgNDaVKlSpa+xITE0lMTERPT/v06Ovrk5ycDLy+WS1v3rzs2bNH3X/58mUiIyPx8PBI85jOzs7Y2dlplXny5AlHjx5Nt4wuLC0tKVGihLqlPMr5bTVr1uTatWtqHwCuXLmCvb19uo95rlmzJq9eveL69etaZeD1Kh0AHh4eWn0C2L17t9onAwMD3NzctPIkJyezZ8+eDPv9rnpz6nwKIYQQIvfI9MjukiVL6Nu3LwULFsTOzk7rIRMajYagoKBsbeC7DBs2DB8fHxwdHbl79y7jxo1DX1+fTp06qXm6detGoUKFCA4OBmDatGkEBQWxevVqnJyc1PmdpqammJqaYmZmRt26dfnyyy8xNjbG0dGRAwcO8MMPPzBz5kzg9Y1QPXr0IDAwEEtLS8zMzBgwYAAeHh5prsQAr8/P4MGDmTx5MiVLlsTZ2ZmxY8fi4OBAq1at1HyRkZE8evSIyMhIkpKS1JUmSpQogampaZbPVd++fZk/fz6DBg1iwIABXL16lalTpzJw4EA1z/z589m0aZMaQHp6evLZZ5/RvXt3Zs+eTXJyMgEBATRq1Egd7e3Tpw/z589n+PDhdO/enb1797Ju3Tq2b9+u1hsYGIivry9VqlShWrVqzJ49m7i4OPz9/dNt76BBg6hbty7ffvst3t7erFmzhhMnTrB48eJMnU8hhBBCfLoyHexOnjyZKVOmMGLEiJxoT6bdvn2bTp068c8//2BtbU2tWrU4cuQI1tbWap7IyEitUdpFixaRkJCgLhmWYty4cYwfPx6ANWvWMGrUKLp06cKjR49wdHRkypQp9OnTR80/a9Ys9PT0aNu2LfHx8Xh5ebFw4UKtOp2cnPDz81PrHT58OHFxcfTq1YuYmBhq1apFaGio1nzhoKAgVq5cqb6uXLkyAPv27aNevXpZPldFihRh165dDBkyhIoVK1KoUCEGDRqkdS0fPnyoNYqrp6fH1q1bGTBgAHXq1MHExISmTZvy7bffqnmcnZ3Zvn07Q4YMYc6cORQuXJilS5fi5eWl5unQoQMPHjwgKCiIqKgoXF1dCQ0N1bq5zM/Pj5s3b7J//37g9YoYq1evZsyYMYwePZqSJUuyefNmypcvr5bR5XwKIYQQ4tOV6XV2zczMCA8Pp1ixYjnVplzj+fPnWFlZsXPnzvcKUj8VdevWpX79+uofBv8WXdfpE0IIIcR/R46ts9uuXTt+++2392rcp2Lfvn00aNBAAl0dxMbGcv36dfXhEEIIIYQQ2SHT0xhKlCjB2LFjOXLkCBUqVEj1JK43539+6ry9vdXVHETGzM3NuX379oduhhBCCCFymUxPY3B2dk6/Mo2GGzduvHejhPg3yTQGIYQQ4uOj6+/vTI/sRkREvFfDhBBCCCGE+LdkaZ1dgISEBC5fvsyrV6+ysz1CCCGEEEJkm0wHu8+fP6dHjx7ky5ePcuXKERkZCcCAAQP4+uuvs72BQgghhBBCZFWmg91Ro0bx119/sX//fq21TD09PVm7dm22Nk4IIYQQQoj3kek5u5s3b2bt2rVUr15d6+lp5cqV03oYgRBCCCGEEB9apkd2Hzx4gI2NTar0uLg4reBXCCGEEEKIDy3TwW6VKlXYvn27+jolwF26dCkeHh7Z1zIhhBBCCCHeU6anMUydOpWmTZty4cIFXr16xZw5c7hw4QKHDx/mwIEDOdFGIYQQQgghsiTTI7u1atUiPDycV69eUaFCBX777TdsbGwICwvDzc0tJ9oohBBCCCFElmT6CWoZ2bBhA59//nl2VSfEvyLlCSx0Aww+dGtEZilLsu0jTAghxEdE1yeoZWpk99WrV5w7d44rV65opf/6669UqlSJLl26ZK21QgghhBBC5ACdg91z585RokQJKlWqRJkyZWjTpg3R0dHUrVuX7t2707Rp0xxZeuzp06cMHjwYR0dHjI2NqVGjBsePH8+wzMaNG2nUqBHW1taYmZnh4eHBrl270s3/9ddfo9FoGDx4sFb6y5cvCQgIwMrKClNTU9q2bUt0dHSGx1YUhaCgIOzt7TE2NsbT05OrV69q5ZkyZQo1atQgX758WFhYZFhfZpw/f562bdvi5OSERqNh9uzZaeZbsGABTk5OGBkZ4e7uzrFjxzKsd8WKFWg0Gq3tzTWWU1y8eJEWLVpgbm6OiYkJVatWVR86Ajl3Ph89ekSXLl0wMzPDwsKCHj168OzZswzrFUIIIcSnQedgd8SIEZQoUYJff/2Vjh07snnzZurVq4ePjw+3b9/m66+/pnDhwtnewJ49e7J7925+/PFHzp49S+PGjfH09OTOnTvpljl48CCNGjVix44dnDx5kvr16+Pj48Pp06dT5T1+/DjfffcdFStWTLVvyJAhbN26lfXr1/+/9u4/rua7f/z4o9IvpVLTL5MihFEpEsY2XcuYzTTiatP8nK2QzMaFsg1hdjE/m10m25ixC9v8yLpC5iOh1ja/wtbk1ykuqxQqnff3D1/va2cqp5Q4nvfb7X277bzfr9fr/Xy9jp2evXq9X4eUlBQuXLjAwIEDq4x3/vz5LF68mPj4eNLS0rCysiI4OJgbN26oZUpLSxk0aBBvvPFGNUbi7q5du0aLFi2YO3cuzs7OFZb56quviI6OJjY2loyMDLy9vQkODiYvL6/Ktm1sbLh48aJ6nDlzRuf6r7/+So8ePfDy8mLPnj38/PPPzJgxQycprqvxDAsL4+jRoyQlJbF161b27t3LmDFj7jZcQgghhHgE6L1m19HRke+//x4fHx8KCgpo3Lgxa9as4dVXX62z4K5fv06jRo345ptv6Nevn3rez8+P5557jlmzZundVvv27QkNDSUmJkY9V1RURKdOnVi+fDmzZs3Cx8dHnQ0tKCigSZMmrFu3Tl2HfOLECdq2bUtqaipdu3a94x6KouDq6sqkSZN466231HacnJxISEhgyJAhOuUTEhKIiooiPz9f737oy93dnaioqDtmqwMCAujcuTNLly4FQKvV0qxZM8aNG8eUKVMqbEufOIcMGYKpqSmff/55hdfrajyPHz9Ou3btOHToEP7+/gAkJibSt29fzp07h6ura5XjBLJm92Ena3aFEOLRVOtrdi9fvqwmDrf/TF1RglKbbt68SXl5+R1/Mre0tGTfvn16t6PVarl69Sr29vY65yMiIujXrx9BQUF31ElPT6esrEznmpeXF25ubqSmplZ4n+zsbDQajU4dW1tbAgICKq1zP5WWlpKenq4Tn7GxMUFBQXeNr6ioiObNm9OsWTNefPFFjh49ql7TarVs27aN1q1bExwcjKOjIwEBAWzZskUtU1fjmZqaip2dnZrowq2vrjY2NiYtLa3CdktKSigsLNQ5hBBCCGGY9E52jYyMuHr1KoWFhRQUFGBkZMT169frNGlo1KgRgYGBvP/++1y4cIHy8nK++OILUlNTuXjxot7tLFiwgKKiIgYPHqyeW79+PRkZGcTFxVVYR6PRYGZmdseaWicnJzQaTaV1bpfRt879dPnyZcrLy6sdX5s2bfj000/55ptv+OKLL9BqtXTr1o1z584BkJeXR1FREXPnzqVPnz58//33vPTSSwwcOFDde7muxlOj0dzxjX4NGjTA3t6+0nbj4uKwtbVVj2bNmlXadyGEEEI83PROdhVFoXXr1jRu3Bh7e3uKiorw9fWlcePGNG7cGDs7Oxo3blzrAX7++ecoikLTpk0xNzdn8eLFDB06FGNj/UJft24d7777Lhs2bFCTorNnzzJhwgTWrl1b4YNWD5o5c+ZgbW2tHn9+6Ot+CAwMZNiwYfj4+NCrVy82bdpEkyZN+Pjjj4FbM7sAL774IhMnTsTHx4cpU6bw/PPPEx8ff19j1cfUqVMpKChQj7Nnz9Z3SEIIIYSoI3p/g9ru3bvrMo5KtWzZkpSUFIqLiyksLMTFxYXQ0FBatGhx17rr169n1KhRbNy4UedP4enp6eTl5dGpUyf1XHl5OXv37mXp0qWUlJTg7OxMaWkp+fn5OrORubm5lT78dft8bm4uLi4uOnV8fHyq2fP/GTt2rM6stD7rUCvy2GOPYWJicscOCFX1qSKmpqb4+vpy+vRptd0GDRrQrl07nXJt27ZVl5vU1Xg6Ozvf8XDdzZs3uXLlSqXtmpubY25urnd/hRBCCPHw0jvZ7dWrV13GcVdWVlZYWVnxxx9/sHPnTubPn19l+S+//JIRI0awfv16nYfbAHr37s0vv/yic2748OF4eXnxzjvvYGJigp+fH6ampiQnJxMSEgJAVlYWOTk5BAYGVnhPDw8PnJ2dSU5OVpOxwsJC0tLS7mnnBXt7+zvWG9eEmZkZfn5+JCcnM2DAAODWrGxycjKRkZF6t1NeXs4vv/xC37591XY7d+5MVlaWTrmTJ0/SvHlzgDobz8DAQPLz80lPT1e/wW/Xrl1otVoCAgL07pMQQgghDJPeyW592blzJ4qi0KZNG06fPs3kyZPx8vJi+PDhapmpU6dy/vx5PvvsM+DW0oXw8HA++ugjAgIC1LWblpaW2Nra0qhRI5544gmd+1hZWeHg4KCet7W1ZeTIkURHR2Nvb4+NjQ3jxo0jMDCw0gfzbu/VO2vWLFq1aoWHhwczZszA1dVVTS4BcnJyuHLlCjk5OZSXl5OZmQmAp6cn1tbWNR6r0tJSjh07pv73+fPnyczMxNraGk9PTwCio6MJDw/H39+fLl26sGjRIoqLi3XGc9iwYTRt2lRdz/zee+/RtWtXPD09yc/P54MPPuDMmTOMGjVKrTN58mRCQ0Pp2bMnTz/9NImJiXz33Xfs2bOnTsezbdu29OnTh9GjRxMfH09ZWRmRkZEMGTKkxjPgQgghhDAcD3yyW1BQwNSpUzl37hz29vaEhIQwe/ZsTE1N1TIXL17UWce6cuVKbt68SUREBBEREer58PBwEhIS9L73woULMTY2JiQkhJKSEoKDg1m+fLlOGXd3d1577TVmzpwJwNtvv01xcTFjxowhPz+fHj16kJiYqLM2OCYmhjVr1qivfX19gVtLRZ566im94/urCxcuqG3BrQfzFixYQK9evdSkMzQ0lEuXLhETE4NGo8HHx4fExESdh8BycnJ01kT/8ccfjB49Go1GQ+PGjfHz82P//v06yxZeeukl4uPjiYuLY/z48bRp04Z///vf9OjRo87Hc+3atURGRtK7d2+1/cWLF9d4HIUQQghhOPTeZ1fc6dq1azg4OLBjx457SlLFLfU1nvru0yeEEEKIB0et77Mr7rR7926eeeYZSXRriYynEEIIIWqbzOyKR57M7AohhBAPH31/ftdoze7hw4fZsGEDOTk5lJaW6lzbtGlTTZoUQgghhBCi1lV7GcP69evp1q0bx48fZ/PmzZSVlXH06FF27dqFra1tXcQohBBCCCFEjVQ72Z0zZw4LFy7ku+++w8zMjI8++ogTJ04wePBg3Nzc6iJGIYQQQgghaqTaye6vv/6qfkmDmZkZxcXFGBkZMXHiRFauXFnrAQohhBBCCFFT1U52GzduzNWrVwFo2rQpR44cASA/P59r167VbnRCCCGEEELcg2o/oNazZ0+SkpLo0KEDgwYNYsKECezatYukpCR69+5dFzEKIYQQQghRI9VOdpcuXcqNGzcAmDZtGqampuzfv5+QkBCmT59e6wEKIYQQQghRU7LPrnjkyT67QgghxMOnVvfZLSws1PvGkiwIIYQQQogHhV7Jrp2dHUZGRno1WF5efk8BCVFfbMfZgll9R/HwUz6RPxYJIYR4cOiV7O7evVv9799//50pU6bw2muvERgYCEBqaipr1qwhLi6ubqIUQgghhBCiBqq9Zrd3796MGjWKoUOH6pxft24dK1euZM+ePbUZnxB17vaaH4YhM7u1QGZ2hRBC3A/6rtmt9j67qamp+Pv733He39+fgwcPVre5WrFixQo6duyIjY0NNjY2BAYGsmPHjirrbNq0CX9/f+zs7LCyssLHx4fPP/9cp4yiKMTExODi4oKlpSVBQUGcOnVKp8yVK1cICwvDxsYGOzs7Ro4cSVFRUZX3vnHjBhERETg4OGBtbU1ISAi5ubk6ZcaPH4+fnx/m5ub4+PjoPxh3UZOxKisr47333qNly5ZYWFjg7e1NYmLiHeXOnz/PK6+8goODA5aWlnTo0IHDhw+r1/UZz4osW7YMd3d3LCwsCAgIuOPfmT7jKYQQQohHU7WT3WbNmvHJJ5/ccf5f//oXzZo1q5Wgquvxxx9n7ty5pKenc/jwYZ555hlefPFFjh49Wmkde3t7pk2bRmpqKj///DPDhw9n+PDh7Ny5Uy0zf/58Fi9eTHx8PGlpaVhZWREcHKxuvQYQFhbG0aNHSUpKYuvWrezdu5cxY8ZUGe/EiRP57rvv2LhxIykpKVy4cIGBAwfeUW7EiBGEhobWYEQqV5Oxmj59Oh9//DFLlizh2LFjjB07lpdeeokff/xRLfPHH3/QvXt3TE1N2bFjB8eOHePDDz+kcePGahl9xvOvvvrqK6Kjo4mNjSUjIwNvb2+Cg4PJy8tTy+g7nkIIIYR49FR7GcP27dsJCQnB09OTgIAAAA4ePMipU6f497//Td++fesk0Oqyt7fngw8+YOTIkXrX6dSpE/369eP9999HURRcXV2ZNGkSb731FgAFBQU4OTmRkJDAkCFDOH78OO3atePQoUPqbHdiYiJ9+/bl3LlzuLq63nGPgoICmjRpwrp163j55ZcBOHHiBG3btiU1NZWuXbvqlJ85cyZbtmwhMzOzhiNxd3cbK1dXV6ZNm0ZERIR6LiQkBEtLS7744gsApkyZwv/93//xww8/VNiGPuNZkYCAADp37szSpUsB0Gq1NGvWjHHjxjFlypRqjydASUkJJSUl6uvCwsJbv6jJMoZaIcsYhBBC3A91toyhb9++nDp1ihdeeIErV65w5coV+vfvz8mTJx+IRLe8vJz169dTXFysPkB3N4qikJycTFZWFj179gQgOzsbjUZDUFCQWs7W1paAgABSU1OBW0s67OzsdJZ1BAUFYWxsTFpaWoX3Sk9Pp6ysTKddLy8v3Nzc1HbvF33HqqSkBAsLC51zlpaW7Nu3T3397bff4u/vz6BBg3B0dMTX11fnLwD6jOdflZaWkp6erlPH2NiYoKAgtU5NxjMuLg5bW1v1qK+/SAghhBCi7lX7G9Tg1p/CZ8+eXdux3JNffvmFwMBAbty4gbW1NZs3b6Zdu3ZV1ikoKKBp06aUlJRgYmLC8uXL+dvf/gaARqMBwMnJSaeOk5OTek2j0eDo6KhzvUGDBtjb26tl/kqj0WBmZoadnV2l7da16o5VcHAw//znP+nZsyctW7YkOTmZTZs26Wwz99tvv7FixQqio6P5xz/+waFDhxg/fjxmZmaEh4frNZ5/dfnyZcrLyyusc+LECaBm4zl16lSio6PV1+rMrhBCCCEMTo2S3QdRmzZtyMzMpKCggK+//prw8HBSUlKqTOIaNWpEZmYmRUVFJCcnEx0dTYsWLXjqqafuX+C1ZOzYseqSAqDKh+SqO1YfffQRo0ePxsvLCyMjI1q2bMnw4cP59NNP1TJarRZ/f3/mzJkDgK+vL0eOHCE+Pp7w8PBa6mXtMDc3x9zcvL7DEEIIIcR9UO1lDA8qMzMzPD098fPzIy4uDm9vbz766KMq6xgbG+Pp6YmPjw+TJk3i5ZdfVvcKdnZ2Brjjqf7c3Fz1mrOzs86DUgA3b97kypUrapm/cnZ2prS0lPz8/ErbrYn33nuPzMxM9ahKdceqSZMmbNmyheLiYs6cOcOJEyewtramRYsWahkXF5c7kuW2bduSk5MD6Deef/XYY49hYmJy1/egLsZTCCGEEIbBYJLdv9JqtToPIVW3joeHB87OziQnJ6vXCwsLSUtLU9e3BgYGkp+fT3p6ulpm165daLVa9eG9v/Lz88PU1FSn3aysLHJycvReY1wRR0dHPD091aM69B0rCwsLmjZtys2bN/n3v//Niy++qF7r3r07WVlZOuVPnjxJ8+bNAf3G86/MzMzw8/PTqaPVaklOTlbr1NV4CiGEEMIwVGsZg6IonD17FkdHxzseWKpPU6dO5bnnnsPNzY2rV6+ybt069uzZo7ON2LBhw2jatKk6cxsXF4e/vz8tW7akpKSE7du38/nnn7NixQoAjIyMiIqKYtasWbRq1QoPDw9mzJiBq6srAwYMAG7NXPbp04fRo0cTHx9PWVkZkZGRDBkypMKdGODWQ1kjR44kOjoae3t7bGxsGDduHIGBgTo7B5w+fZqioiI0Gg3Xr19XZ2vbtWuHmVnNtwyoyVilpaVx/vx5fHx8OH/+PDNnzkSr1fL222+rdSZOnEi3bt2YM2cOgwcP5uDBg6xcuZKVK1fqPZ4ViY6OJjw8HH9/f7p06cKiRYsoLi5m+PDh1RpPIYQQQjyaqp3senp6cvToUVq1alVXMVVbXl4ew4YN4+LFi9ja2tKxY0d27typPmwGkJOTg7Hx/yayi4uLefPNNzl37hyWlpZ4eXnxxRdf6Oxr+/bbb1NcXMyYMWPIz8+nR48eJCYm6iT6a9euJTIykt69e2NsbExISAiLFy/Wic/IyIjVq1fz2muvAbBw4UK1bElJCcHBwSxfvlynzqhRo0hJSVFf+/r6Ard2NXB3d7+vY3Xjxg2mT5/Ob7/9hrW1NX379uXzzz/XeSisc+fObN68malTp/Lee+/h4eHBokWLCAsLq9Z4PvXUU7i7u5OQkABAaGgoly5dIiYmBo1Gg4+PD4mJiToPrekznkIIIYR4NFV7n9327duzatUqmTXTU3Z2Nq1bt+bYsWMP1C8ID6rmzZvz7rvvqr8Y3A/67tMnhBBCiAdHne2zO3fuXCZPnsyRI0fuKcBHxfbt2xkzZowkuno4evQotra2DBs2rL5DEUIIIYSBqPbMbuPGjbl27Ro3b97EzMwMS0tLnetXrlyp1QCFqGsysyuEEEI8fPT9+V3tfXYXLVp0L3EJIYQQQghx31Q72X3QviBACCGEEEKIytToG9TKy8vZsmULx48fB249tPbCCy9gYmJSq8EJIYQQQghxL6qd7J4+fZq+ffty/vx52rRpA9zas7ZZs2Zs27aNli1b1nqQQgghhBBC1ES1d2MYP348LVu25OzZs2RkZJCRkUFOTg4eHh6MHz++LmIUQgghhBCiRqo9s5uSksKBAwewt7dXzzk4ODB37ly6d+9eq8EJIYQQQghxL6o9s2tubs7Vq1fvOF9UVHRPX2MrhBBCCCFEbat2svv8888zZswY0tLSUBQFRVE4cOAAY8eO5YUXXqiLGIUQQgghhKiRaie7ixcvpmXLlgQGBmJhYYGFhQXdu3fH09OTjz76qC5iFEIIIYQQokaq/Q1qt506dYoTJ04A0LZtWzw9PWs1MCHul9vfwMIwQFbi3DPlkxp9pAghhBDVUmffoHZbq1ataNWqVU2rCyGEEEIIUef0WsYQHR2t91Ede/fupX///ri6umJkZMSWLVvuKKMoCjExMbi4uGBpaUlQUBCnTp2qst2ZM2diZGSkc3h5ed1RLjU1lWeeeQYrKytsbGzo2bMn169fV69fuXKFsLAwbGxssLOzY+TIkRQVFVV57xs3bhAREYGDgwPW1taEhISQm5urU2b8+PH4+flhbm6Oj49Ple1Vhz7juWnTJp599lkcHBwwMjIiMzNTr7YXLVpEmzZtsLS0pFmzZkycOJEbN26o18vLy5kxYwYeHh5YWlrSsmVL3n//ff78h4OavJcAy5Ytw93dHQsLCwICAjh48KDOdX3GXAghhBCPJr2S3R9//FGvQ9/E6bbi4mK8vb1ZtmxZpWXmz5/P4sWLiY+PJy0tDSsrK4KDg3USrYq0b9+eixcvqse+fft0rqemptKnTx+effZZDh48yKFDh4iMjMTY+H9DEhYWxtGjR0lKSmLr1q3s3buXMWPGVHnfiRMn8t1337Fx40ZSUlK4cOECAwcOvKPciBEjCA0NrbKt6tJnPIuLi+nRowfz5s3Tu91169YxZcoUYmNjOX78OKtWreKrr77iH//4h1pm3rx5rFixgqVLl3L8+HHmzZvH/PnzWbJkiVqmJu/lV199RXR0NLGxsWRkZODt7U1wcDB5eXlqGX3HXAghhBCPnhqv2a1tRkZGbN68mQEDBqjnFEXB1dWVSZMm8dZbbwFQUFCAk5MTCQkJDBkypMK2Zs6cyZYtW6pMvrt27crf/vY33n///QqvHz9+nHbt2nHo0CH8/f0BSExMpG/fvpw7dw5XV9c76hQUFNCkSRPWrVvHyy+/DMCJEydo27YtqampdO3atdpx1lRF4/lnv//+Ox4eHvz44493nV2OjIzk+PHjJCcnq+cmTZpEWlqa+kvE888/j5OTE6tWrVLLhISEYGlpyRdffFHj9zIgIIDOnTuzdOlSALRaLc2aNWPcuHFMmTKl2mNeEVmzW7tkza4QQoj7Qd81u9XejeF+ys7ORqPREBQUpJ6ztbUlICCA1NTUKuueOnUKV1dXWrRoQVhYGDk5Oeq1vLw80tLScHR0pFu3bjg5OdGrVy+d2d/U1FTs7OzURBcgKCgIY2Nj0tLSKrxneno6ZWVlOvF6eXnh5uZ213gfZN26dSM9PV1dPvDbb7+xfft2+vbtq1MmOTmZkydPAvDTTz+xb98+nnvuOaBm72VpaSnp6ek6dYyNjQkKClLr1GTMS0pKKCws1DmEEEIIYZhq9IDa4cOH2bBhAzk5OZSWlupc27RpU60EBqDRaABwcnLSOe/k5KReq0hAQAAJCQm0adOGixcv8u677/Lkk09y5MgRGjVqxG+//QbcmlldsGABPj4+fPbZZ/Tu3ZsjR47QqlUrNBoNjo6OOu02aNAAe3v7Su+t0WgwMzPDzs6uWvE+6P7+979z+fJlevTogaIo3Lx5k7Fjx+osY5gyZQqFhYV4eXlhYmJCeXk5s2fPJiwsDKjZe3n58mXKy8srrHN7J5CajHlcXBzvvvuu/gMghBBCiIdWtWd2169fT7du3Th+/DibN2+mrKyMo0ePsmvXrlt/Cn4APPfccwwaNIiOHTsSHBzM9u3byc/PZ8OGDcCtP4UDvP766wwfPhxfX18WLlxImzZt+PTTT+sz9AqNHTsWa2tr9bjf9uzZw5w5c1i+fDkZGRls2rSJbdu26SwB2bBhA2vXrmXdunVkZGSwZs0aFixYwJo1a+57vHczdepUCgoK1OPs2bP1HZIQQggh6ki1Z3bnzJnDwoULiYiIoFGjRnz00Ud4eHjw+uuv4+LiUqvBOTs7A5Cbm6vTdm5ubrV2MbCzs6N169acPn0aQG2rXbt2OuXatm2rLndwdnbWeQgK4ObNm1y5ckWNq6J4S0tLyc/P15lpzM3NrbSOPt577z11nWt9mDFjBq+++iqjRo0CoEOHDhQXFzNmzBimTZuGsbExkydPZsqUKera2w4dOnDmzBni4uIIDw+v0Xv52GOPYWJicsfOCn8ez5qMubm5Oebm5jUaCyGEEEI8XKo9s/vrr7/Sr18/AMzMzCguLsbIyIiJEyeycuXKWg3Ow8MDZ2dnnQejCgsLSUtLIzAwUO92ioqK+PXXX9Uky93dHVdXV7KysnTKnTx5kubNmwMQGBhIfn4+6enp6vVdu3ah1WoJCAio8D5+fn6YmprqxJuVlUVOTk614v0rR0dHPD091eN+u3btms4uFQAmJiYA6tZilZW5PYtek/fSzMwMPz8/nTparZbk5GS1Tl2NuRBCCCEMQ7Vndhs3bszVq1cBaNq0KUeOHKFDhw7k5+dz7dq1arVVVFSkzrbCrYeYMjMzsbe3x83NDSMjI6Kiopg1axatWrXCw8ODGTNm4OrqqrPLQO/evXnppZeIjIwE4K233qJ///40b96cCxcuEBsbi4mJCUOHDgVu7VQwefJkYmNj8fb2xsfHhzVr1nDixAm+/vpr4NYsb58+fRg9ejTx8fGUlZURGRnJkCFDKtyJAW49cDVy5Eiio6Oxt7fHxsaGcePGERgYqLMrwOnTpykqKkKj0XD9+nV1N4Z27dphZlbz7QDuNp5wa+/gnJwcLly4AKAm/M7OzupM6LBhw2jatClxcXEA9O/fn3/+85/4+voSEBDA6dOnmTFjBv3791eT3v79+zN79mzc3Nxo3749P/74I//85z8ZMWKEOub6vJd/FR0dTXh4OP7+/nTp0oVFixZRXFzM8OHDqzXmQgghhHg0VTvZ7dmzJ0lJSXTo0IFBgwYxYcIEdu3aRVJSEr17965WW4cPH+bpp59WX9/+Uorw8HASEhIAePvtt9U/mefn59OjRw8SExOxsLBQ6/36669cvnxZfX3u3DmGDh3Kf//7X5o0aUKPHj04cOAATZo0UctERUVx48YNJk6cyJUrV/D29iYpKYmWLVuqZdauXUtkZCS9e/fG2NiYkJAQFi9erNMHIyMjVq9ezWuvvQbAwoUL1bIlJSUEBwezfPlynTqjRo0iJSVFfe3r6wvcSk7d3d2rNYZ/ps94fvvtt2qiCKjLDmJjY5k5cyYAOTk5OrO006dPx8jIiOnTp3P+/HmaNGmiJre3LVmyhBkzZvDmm2+Sl5eHq6srr7/+OjExMWoZfd7Lp556Cnd3dzXe0NBQLl26RExMDBqNBh8fHxITE3UeWtNnzIUQQgjxaNJ7n90jR47wxBNPcOXKFW7cuIGrqytarZb58+ezf/9+WrVqxfTp02ncuHFdx/zAyM7OpnXr1hw7dky+OrmWNG/enHfffVf95eF+0HefPiGEEEI8OPT9+a33zG7Hjh3p3Lkzo0aNUmcDjY2NmTJlyr1H+5Davn07Y8aMkUS3lhw9ehRbW1uGDRtW36EIIYQQwkDoPbP7ww8/sHr1ar7++mu0Wi0hISGMGjWKJ598sq5jFKJOycyuEEII8fCp9W9Qe/LJJ/n000+5ePEiS5Ys4ffff6dXr160bt2aefPmPdRfmiCEEEIIIQxTtbces7KyYvjw4aSkpHDy5EkGDRrEsmXLcHNz44UXXqiLGIUQQgghhKgRvZcxVKa4uJi1a9cydepU8vPzKS8vr63YhLgvZBmDEEII8fCp9QfU/mrv3r18+umn/Pvf/8bY2JjBgwczcuTImjYnhBBCCCFEratWsnvhwgUSEhJISEjg9OnTdOvWjcWLFzN48GCsrKzqKkYhhBBCCCFqRO9k97nnnuM///kPjz32GMOGDWPEiBG0adOmLmMTQgghhBDinuid7JqamvL111/z/PPPq18RK4QQQgghxINM72T322+/rcs4hBBCCCGEqHXV3npMCCGEEEKIh4Uku0IIIYQQwmDVeOsx8WD5448/WLx4MWPGjMHFxaW+w3ko2Y6zBbP7cy/lk3va3loIIYQQepKZXQOgKArh4eFcv35dEl0hhBBCiD+RZPcBkZqaiomJCf369bvj2syZM/Hx8am07gcffICNjQ1xcXF3vc/s2bPp1q0bDRs2xM7OTq/YZs6ciZeXF1ZWVjRu3JigoCDS0tLU67///jsjR47Ew8MDS0tLWrZsSWxsLKWlpTrt/Pzzzzz55JNYWFjQrFkz5s+ff9d75+Tk0K9fPxo2bIijoyOTJ0/m5s2bOmX27NlDp06dMDc3x9PTk4SEBL36JYQQQgjDJ8nuA2LVqlWMGzeOvXv3cuHChWrVffvtt/niiy8wMjK6a9nS0lIGDRrEG2+8oXf7rVu3ZunSpfzyyy/s27cPd3d3nn32WS5dugTAiRMn0Gq1fPzxxxw9epSFCxcSHx/PP/7xD7WNwsJCnn32WZo3b056ejoffPABM2fOZOXKlZXet7y8nH79+lFaWsr+/ftZs2YNCQkJxMTEqGWys7Pp168fTz/9NJmZmURFRTFq1Ch27typd/+EEEIIYbiMFEWRxYP1rKioCBcXFw4fPkxsbCwdO3ZUE8WEhASGDx+uU3716tW89tpr5Ofn89Zbb/HNN99QUlKCv78/CxcuxNvb+673TEhIICoqivz8/GrHe/u7qP/zn//Qu3fvCst88MEHrFixgt9++w2AFStWMG3aNDQaDWZmtxbGTpkyhS1btnDixIkK29ixYwfPP/88Fy5cwMnJCYD4+HjeeecdLl26hJmZGe+88w7btm3jyJEjar0hQ4aQn59PYmJitfrDMGTNrhBCCPGQuP3zu6CgABsbm0rLyczuA2DDhg14eXnRpk0bXnnlFT799FNu/w4SGhrKpEmTaN++PRcvXuTixYuEhoYCMGjQIPLy8tixYwfp6el06tSJ3r17c+XKlTqLtbS0lJUrV2Jra1tlUl1QUIC9vb36OjU1lZ49e6qJLkBwcDBZWVn88ccfFbaRmppKhw4d1ET3dp3CwkKOHj2qlgkKCtKpFxwcTGpqaqWxlZSUUFhYqHMIIYQQwjBJsvsAWLVqFa+88goAffr0oaCggJSUFAAsLS2xtramQYMGODs74+zsjKWlJfv27ePgwYNs3LgRf39/WrVqxYIFC7Czs+Prr7+u9Ri3bt2KtbU1FhYWLFy4kKSkJB577LEKy54+fZolS5bw+uuvq+c0Go1O0gqorzUaTYXt6FOnsjKFhYVcv369wnbj4uKwtbVVj2bNmlXWbSGEEEI85CTZrWdZWVkcPHiQoUOHAtCgQQNCQ0NZtWpVlfV++uknioqKcHBwwNraWj2ys7P59ddfaz3O22ti9+/fT58+fRg8eDB5eXl3lDt//jx9+vRh0KBBjB49utbjqA1Tp06loKBAPc6ePVvfIQkhhBCijsg+u/Vs1apV3Lx5E1dXV/WcoiiYm5uzdOnSW2tJK3B7ne+ePXvuuKbvLgvVYWVlhaenJ56ennTt2pVWrVqxatUqpk6dqpa5cOECTz/9NN26dbvjwTNnZ2dyc3N1zt1+7ezsXOE9nZ2dOXjwYJV1KmvXxsYGS0vLCts1NzfH3Nz8bl0WQgghhAGQmd16dPPmTT777DM+/PBDMjMz1eOnn37C1dWVL7/8EgAzMzPKy8t16nbq1AmNRkODBg3UJPT2Udnygtqk1WopKSlRX58/f56nnnoKPz8/Vq9ejbGx7j+twMBA9u7dS1lZmXouKSmJNm3a0Lhx4wrvERgYyC+//KIzg5yUlISNjQ3t2rVTyyQnJ+vUS0pKIjAw8J77KIQQQoiHnyS79Wjr1q388ccfjBw5kieeeELnCAkJUZcyuLu7k52dTWZmJpcvX6akpISgoCACAwMZMGAA33//Pb///jv79+9n2rRpHD58uNJ75uTkkJmZSU5ODuXl5WqCXVRUpJbx8vJi8+bNABQXF/OPf/yDAwcOcObMGdLT0xkxYgTnz59n0KBBwP8SXTc3NxYsWMClS5fQaDQ6a3H//ve/Y2ZmxsiRIzl69ChfffUVH330EdHR0ZXG+uyzz9KuXTteffVVfvrpJ3bu3Mn06dOJiIhQZ2bHjh3Lb7/9xttvv82JEydYvnw5GzZsYOLEiTV/Y4QQQghhMCTZrUerVq0iKCiowqUKISEhHD58mJ9//pmQkBD69OnD008/TZMmTfjyyy8xMjJi+/bt9OzZk+HDh9O6dWuGDBnCmTNn7nhg689iYmLw9fUlNjaWoqIifH198fX11UmQs7KyKCgoAMDExIQTJ04QEhJC69at6d+/P//973/54YcfaN++PXBrJvX06dMkJyfz+OOP4+Lioh632dra8v3335OdnY2fnx+TJk0iJiaGMWPGqGX27NmDkZERv//+u3rvrVu3YmJiQmBgIK+88grDhg3jvffeU+t4eHiwbds2kpKS8Pb25sMPP+Rf//oXwcHBNXtThBBCCGFQZJ9d8cBYvXo1c+bM4dixY5iamt63++q7T58QQgghHhyyz6546Gzfvp05c+bc10RXCCGEEIZNdmMQD4yNGzfWdwhCCCGEMDAysyuEEEIIIQyWJLtCCCGEEMJgSbIrhBBCCCEMliS7QgghhBDCYEmyK4QQQgghDJYku0IIIYQQwmBJsiuEEEIIIQyWJLtCCCGEEMJgSbIrhBBCCCEMlnyDmhD/n+04WzC7P/dSPlHuz42EEEKIR5zM7AohhBBCCINlEMluXFwcnTt3plGjRjg6OjJgwACysrLuWi8/P5+IiAhcXFwwNzendevWbN++vcKyc+fOxcjIiKioKJ3zN27cICIiAgcHB6ytrQkJCSE3N7fK+yqKQkxMDC4uLlhaWhIUFMSpU6d0ysyePZtu3brRsGFD7Ozs7toXfW3atAl/f3/s7OywsrLCx8eHzz//vMo6+/bto3v37jg4OGBpaYmXlxcLFy68o9yyZctwd3fHwsKCgIAADh48qHO9rsbqypUrhIWFYWNjg52dHSNHjqSoqEjPERFCCCGEITOIZDclJYWIiAgOHDhAUlISZWVlPPvssxQXF1dap7S0lL/97W/8/vvvfP3112RlZfHJJ5/QtGnTO8oeOnSIjz/+mI4dO95xbeLEiXz33Xds3LiRlJQULly4wMCBA6uMd/78+SxevJj4+HjS0tKwsrIiODiYGzdu6MQ3aNAg3njjjWqMxN3Z29szbdo0UlNT+fnnnxk+fDjDhw9n586dldaxsrIiMjKSvXv3cvz4caZPn8706dNZuXKlWuarr74iOjqa2NhYMjIy8Pb2Jjg4mLy8PLVMXY1VWFgYR48eJSkpia1bt7J3717GjBlzD6MkhBBCCIOhGKC8vDwFUFJSUiots2LFCqVFixZKaWlplW1dvXpVadWqlZKUlKT06tVLmTBhgnotPz9fMTU1VTZu3KieO378uAIoqampFban1WoVZ2dn5YMPPtBpx9zcXPnyyy/vKL969WrF1ta2yhjvla+vrzJ9+vRq1XnppZeUV155RX3dpUsXJSIiQn1dXl6uuLq6KnFxcYqi1N1YHTt2TAGUQ4cOqWV27NihGBkZKefPn9erLwUFBQqgMAyFUffnEEIIIcS9uf3zu6CgoMpyBjGz+1cFBQXArVnMynz77bcEBgYSERGBk5MTTzzxBHPmzKG8vFynXEREBP369SMoKOiONtLT0ykrK9O55uXlhZubG6mpqRXeNzs7G41Go1PH1taWgICASuvUFUVRSE5OJisri549e+pd78cff2T//v306tULuDULnZ6ertMnY2NjgoKC1D7V1VilpqZiZ2eHv7+/WiYoKAhjY2PS0tIqbLekpITCwkKdQwghhBCGyeB2Y9BqtURFRdG9e3eeeOKJSsv99ttv7Nq1i7CwMLZv387p06d58803KSsrIzY2FoD169eTkZHBoUOHKmxDo9FgZmZ2x5paJycnNBpNpXVul9G3Tm0rKCigadOmlJSUYGJiwvLly/nb3/5213qPP/44ly5d4ubNm8ycOZNRo0YBcPnyZcrLyyvs04kTJ4C6GyuNRoOjo6PO9QYNGmBvb19pu3Fxcbz77rt37a8QQgghHn4GN7MbERHBkSNHWL9+fZXltFotjo6OrFy5Ej8/P0JDQ5k2bRrx8fEAnD17lgkTJrB27VosLCzuR+j3ZM6cOVhbW6tHTk5OpWUbNWpEZmYmhw4dYvbs2URHR7Nnz5673uOHH37g8OHDxMfHs2jRIr788sta7MH9M3XqVAoKCtTj7Nmz9R2SEEIIIeqIQc3sRkZGqg8oPf7441WWdXFxwdTUFBMTE/Vc27Zt0Wg06p/l8/Ly6NSpk3q9vLycvXv3snTpUkpKSnB2dqa0tJT8/HydGcvc3FycnZ0rvO/t87m5ubi4uOjU8fHxqUGvbxk7diyDBw9WX7u6ulZa1tjYGE9PTwB8fHw4fvw4cXFxPPXUU1Xew8PDA4AOHTqQm5vLzJkzGTp0KI899hgmJiZ37Kzw53Goq7FydnbWeQgO4ObNm1y5cqXSds3NzTE3N6+yr0IIIYQwDAYxs6soCpGRkWzevJldu3apSVlVunfvzunTp9Fqteq5kydP4uLigpmZGb179+aXX34hMzNTPfz9/QkLCyMzMxMTExP8/PwwNTUlOTlZbSMrK4ucnBwCAwMrvK+HhwfOzs46dQoLC0lLS6u0jj7s7e3x9PRUjwYN9P89RqvVUlJSUq37/bmOmZkZfn5+On3SarUkJyerfaqrsQoMDCQ/P5/09HS1zK5du9BqtQQEBFSrT0IIIYQwPAYxsxsREcG6dev45ptvaNSokbpW09bWFktLSwCGDRtG06ZNiYuLA+CNN95g6dKlTJgwgXHjxnHq1CnmzJnD+PHjgVt/6v/rml8rKyscHBzU87a2towcOZLo6Gjs7e2xsbFh3LhxBAYG0rVr1wpjvb1X76xZs2jVqhUeHh7MmDEDV1dXBgwYoJbLycnhypUr5OTkUF5eTmZmJgCenp5YW1vXeKzi4uLw9/enZcuWlJSUsH37dj7//HNWrFihlpk6dSrnz5/ns88+A27tn+vm5oaXlxcAe/fuZcGCBepYAURHRxMeHo6/vz9dunRh0aJFFBcXM3z48Dodq7Zt29KnTx9Gjx5NfHw8ZWVlREZGMmTIkCpnt4UQQgjxaDCIZPd2ovbXP8OvXr2a1157DbiVPBob/28iu1mzZuzcuZOJEyfSsWNHmjZtyoQJE3jnnXeqde+FCxdibGxMSEgIJSUlBAcHs3z5cp0y7u7uvPbaa8ycOROAt99+m+LiYsaMGUN+fj49evQgMTFRZ21wTEwMa9asUV/7+voCsHv37rsuN6hKcXExb775JufOnVO/IOKLL74gNDRULXPx4kWdNb9arZapU6eSnZ1NgwYNaNmyJfPmzeP1119Xy4SGhnLp0iViYmLQaDT4+PiQmJio83BZXY3V2rVriYyMpHfv3mr7ixcvrvEYCSGEEMJwGCmKotR3EIbs2rVrODg4sGPHjntKUh8F9TVWhYWF2NraUlBQgI2NzX27rxBCCCFqTt+f3waxZvdBtnv3bp555hlJdPUgYyWEEEKI2iYzu+KRJzO7QgghxMNHZnaFEEIIIcQjT5JdIYQQQghhsCTZFUIIIYQQBkuSXSGEEEIIYbAk2RVCCCGEEAZLkl0hhBBCCGGwJNkVQgghhBAGS5JdIYQQQghhsCTZFUIIIYQQBkuSXSGEEEIIYbAa1HcAQjwobMfZgtn9uZfyiXxLtxBCCHE/yMyuEEIIIYQwWA99srt371769++Pq6srRkZGbNmy5a519u3bR/fu3XFwcMDS0hIvLy8WLlyoUyYuLo7OnTvTqFEjHB0dGTBgAFlZWTplbty4QUREBA4ODlhbWxMSEkJubm6V91YUhZiYGFxcXLC0tCQoKIhTp07plJk9ezbdunWjYcOG2NnZ6TUO+jh69CghISG4u7tjZGTEokWL9Kq3YcMGfHx8aNiwIc2bN+eDDz64o8zatWvx9vamYcOGuLi4MGLECP773//qlNm4cSNeXl5YWFjQoUMHtm/fftd779mzh06dOmFubo6npycJCQl3lFm2bBnu7u5YWFgQEBDAwYMH9eqXEEIIIQzfQ5/sFhcX4+3tzbJly/SuY2VlRWRkJHv37uX48eNMnz6d6dOns3LlSrVMSkoKERERHDhwgKSkJMrKynj22WcpLi5Wy0ycOJHvvvuOjRs3kpKSwoULFxg4cGCV954/fz6LFy8mPj6etLQ0rKysCA4O5saNG2qZ0tJSBg0axBtvvFGNkbi7a9eu0aJFC+bOnYuzs7NedXbs2EFYWBhjx47lyJEjLF++nIULF7J06VK1zP/93/8xbNgwRo4cydGjR9m4cSMHDx5k9OjRapn9+/czdOhQRo4cyY8//siAAQMYMGAAR44cqfTe2dnZ9OvXj6effprMzEyioqIYNWoUO3fuVMt89dVXREdHExsbS0ZGBt7e3gQHB5OXl1eDERJCCCGEoTFSFMVgFg8aGRmxefNmBgwYUO26AwcOxMrKis8//7zC65cuXcLR0ZGUlBR69uxJQUEBTZo0Yd26dbz88ssAnDhxgrZt25KamkrXrl3vaENRFFxdXZk0aRJvvfUWAAUFBTg5OZGQkMCQIUN0yickJBAVFUV+fn61+3M37u7uREVFERUVVWW5v//975SVlbFx40b13JIlS5g/fz45OTkYGRmxYMECVqxYwa+//qpTZt68eZw7dw6A0NBQiouL2bp1q1qma9eu+Pj4EB8fX+G933nnHbZt26aTEA8ZMoT8/HwSExMBCAgIoHPnzmryrdVqadasGePGjWPKlCl6jUVhYSG2trYwDFmzK4QQQjwkbv/8LigowMbGptJyD/3Mbm348ccf2b9/P7169aq0TEFBAQD29vYApKenU1ZWRlBQkFrGy8sLNzc3UlNTK2wjOzsbjUajU8fW1paAgIBK69S3kpISLCwsdM5ZWlpy7tw5zpw5A0BgYCBnz55l+/btKIpCbm4uX3/9NX379lXrpKam6vQbIDg4uMp+361OaWkp6enpOmWMjY0JCgqqst2SkhIKCwt1DiGEEEIYpkc62X388ccxNzfH39+fiIgIRo0aVWE5rVZLVFQU3bt354knngBAo9FgZmZ2x5paJycnNBpNhe3cPu/k5KR3nfoWHBzMpk2bSE5ORqvVcvLkST788EMALl68CED37t1Zu3YtoaGhmJmZ4ezsjK2trc7SEo1GU+1+V1ansLCQ69evc/nyZcrLy6vdblxcHLa2turRrFkz/QZDCCGEEA+dRzrZ/eGHHzh8+DDx8fEsWrSIL7/8ssJyERERHDlyhPXr19/nCPUzZ84crK2t1SMnJ6fW2h49ejSRkZE8//zzmJmZ0bVrV3W5hbHxrX8+x44dY8KECcTExJCenk5iYiK///47Y8eOrbU4atPUqVMpKChQj7Nnz9Z3SEIIIYSoI4/0PrseHh4AdOjQgdzcXGbOnMnQoUN1ykRGRrJ161b27t3L448/rp53dnamtLSU/Px8ndnd3NzcSh/+un0+NzcXFxcXnTo+Pj417sfYsWMZPHiw+trV1bXGbf2VkZER8+bNY86cOWg0Gpo0aUJycjIALVq0AG7NlHbv3p3JkycD0LFjR6ysrHjyySeZNWsWLi4uODs737FTRVVjBVRax8bGBktLS0xMTDAxMal2u+bm5pibm+s/CEIIIYR4aD3SM7t/ptVqKSkpUV8rikJkZCSbN29m165damJ8m5+fH6ampmriB5CVlUVOTg6BgYEV3sPDwwNnZ2edOoWFhaSlpVVaRx/29vZ4enqqR4MGtf87jImJCU2bNsXMzIwvv/ySwMBAmjRpAtza5eH2LO+fy8OtcYRb63r/3G+ApKSkKvt9tzpmZmb4+fnplNFqtSQnJ9/TeAohhBDCcDz0M7tFRUWcPn1afZ2dnU1mZib29va4ubkBt/5sff78eT777DPg1r6sbm5ueHl5Abf26l2wYAHjx49X24mIiGDdunV88803NGrUSF0Damtri6WlJba2towcOZLo6Gjs7e2xsbFh3LhxBAYGVrgTA9yaJY2KimLWrFm0atUKDw8PZsyYgaurq84OEjk5OVy5coWcnBzKy8vJzMwEwNPTE2tr6xqPVWlpKceOHVP/+/z582RmZmJtbY2npycAS5cuZfPmzWoCefnyZb7++mueeuopbty4werVq9Wt1m7r378/o0ePZsWKFQQHB3Px4kWioqLo0qWLOss8YcIEevXqxYcffki/fv1Yv349hw8f1tnu7a/Gjh3L0qVLefvttxkxYgS7du1iw4YNbNu2TS0THR1NeHg4/v7+dOnShUWLFlFcXMzw4cNrPE5CCCGEMCDKQ2737t0KcMcRHh6ulgkPD1d69eqlvl68eLHSvn17pWHDhoqNjY3i6+urLF++XCkvL1fLVNQmoKxevVotc/36deXNN99UGjdurDRs2FB56aWXlIsXL+rE17x5cyU2NlZ9rdVqlRkzZihOTk6Kubm50rt3byUrK0unTnh4eIX33r179z2NVXZ2doXt/nlsYmNjlebNm6uvL126pHTt2lWxsrJSGjZsqPTu3Vs5cODAHW0vXrxYadeunWJpaam4uLgoYWFhyrlz53TKbNiwQWndurViZmamtG/fXtm2bZvO9b/eW1Fuvb8+Pj6KmZmZ0qJFC53xv23JkiWKm5ubYmZmpnTp0qXC+KpSUFBwayyGoTDq/hxCCCGEuDe3f34XFBRUWc6g9tl90Fy7dg0HBwd27NjBU089Vd/hPPDCw8MxMjKq8FvS6pK++/QJIYQQ4sGh78/vh34Zw4Ns9+7dPPPMM5Lo6kFRFPbs2cO+ffvqOxQhhBBCGBCZ2RWPPJnZFUIIIR4+8g1qQgghhBDikSfJrhBCCCGEMFiyZlc88m6v5CksLKznSIQQQgihr9s/t++2IleSXfHI++9//wtAs2bN6jkSIYQQQlTX1atXsbW1rfS6JLvikWdvbw/c+jKPqv5neRgVFhbSrFkzzp49a5AP30n/Hl6G3Dcw7P4Zct/AsPtnaH1TFIWrV6+qX2BVGUl2xSPv9lcd29raGsT//BWxsbEx2L6B9O9hZsh9A8PunyH3DQy7f4bUN30mqeQBNSGEEEIIYbAk2RVCCCGEEAZLkl3xyDM3Nyc2NhZzc/P6DqXWGXLfQPr3MDPkvoFh98+Q+waG3T9D7ltV5BvUhBBCCCGEwZKZXSGEEEIIYbAk2RVCCCGEEAZLkl0hhBBCCGGwJNkVQgghhBAGS5JdIYQQQghhsCTZFY+0ZcuW4e7ujoWFBQEBARw8eLC+Q6oVcXFxdO7cmUaNGuHo6MiAAQPIysqq77DqxNy5czEyMiIqKqq+Q6k158+f55VXXsHBwQFLS0s6dOjA4cOH6zusWlFeXs6MGTPw8PDA0tKSli1b8v777/Mwbgy0d+9e+vfvj6urK0ZGRmzZskXnuqIoxMTE4OLigqWlJUFBQZw6dap+gq2BqvpXVlbGO++8Q4cOHbCyssLV1ZVhw4Zx4cKF+gu4mu72/v3Z2LFjMTIyYtGiRfctvnuhT9+OHz/OCy+8gK2tLVZWVnTu3JmcnJz7H+x9IMmueGR99dVXREdHExsbS0ZGBt7e3gQHB5OXl1ffod2zlJQUIiIiOHDgAElJSZSVlfHss89SXFxc36HVqkOHDvHxxx/TsWPH+g6l1vzxxx90794dU1NTduzYwbFjx/jwww9p3LhxfYdWK+bNm8eKFStYunQpx48fZ968ecyfP58lS5bUd2jVVlxcjLe3N8uWLavw+vz581m8eDHx8fGkpaVhZWVFcHAwN27cuM+R1kxV/bt27RoZGRnMmDGDjIwMNm3aRFZWFi+88EI9RFozd3v/btu8eTMHDhzA1dX1PkV27+7Wt19//ZUePXrg5eXFnj17+Pnnn5kxYwYWFhb3OdL7RBHiEdWlSxclIiJCfV1eXq64uroqcXFx9RhV3cjLy1MAJSUlpb5DqTVXr15VWrVqpSQlJSm9evVSJkyYUN8h1Yp33nlH6dGjR32HUWf69eunjBgxQufcwIEDlbCwsHqKqHYAyubNm9XXWq1WcXZ2Vj744AP1XH5+vmJubq58+eWX9RDhvflr/ypy8OBBBVDOnDlzf4KqRZX179y5c0rTpk2VI0eOKM2bN1cWLlx432O7VxX1LTQ0VHnllVfqJ6B6IDO74pFUWlpKeno6QUFB6jljY2OCgoJITU2tx8jqRkFBAQD29vb1HEntiYiIoF+/fjrvoSH49ttv8ff3Z9CgQTg6OuLr68snn3xS32HVmm7dupGcnMzJkycB+Omnn9i3bx/PPfdcPUdWu7Kzs9FoNDr/Pm1tbQkICDDIzxi49TljZGSEnZ1dfYdSK7RaLa+++iqTJ0+mffv29R1OrdFqtWzbto3WrVsTHByMo6MjAQEBVS7jeNhJsiseSZcvX6a8vBwnJyed805OTmg0mnqKqm5otVqioqLo3r07TzzxRH2HUyvWr19PRkYGcXFx9R1Krfvtt99YsWIFrVq1YufOnbzxxhuMHz+eNWvW1HdotWLKlCkMGTIELy8vTE1N8fX1JSoqirCwsPoOrVbd/hx5FD5jAG7cuME777zD0KFDsbGxqe9wasW8efNo0KAB48ePr+9QalVeXh5FRUXMnTuXPn368P333/PSSy8xcOBAUlJS6ju8OtGgvgMQQtStiIgIjhw5wr59++o7lFpx9uxZJkyYQFJSkkGuL9Nqtfj7+zNnzhwAfH19OXLkCPHx8YSHh9dzdPduw4YNrF27lnXr1tG+fXsyMzOJiorC1dXVIPr3KCorK2Pw4MEoisKKFSvqO5xakZ6ezkcffURGRgZGRkb1HU6t0mq1ALz44otMnDgRAB8fH/bv3098fDy9evWqz/DqhMzsikfSY489homJCbm5uTrnc3NzcXZ2rqeoal9kZCRbt25l9+7dPP744/UdTq1IT08nLy+PTp060aBBAxo0aEBKSgqLFy+mQYMGlJeX13eI98TFxYV27drpnGvbtq3BPCU9efJkdXa3Q4cOvPrqq0ycONHgZulvf44Y+mfM7UT3zJkzJCUlGcys7g8//EBeXh5ubm7q58yZM2eYNGkS7u7u9R3ePXnsscdo0KCBQX/O/JUku+KRZGZmhp+fH8nJyeo5rVZLcnIygYGB9RhZ7VAUhcjISDZv3syuXbvw8PCo75BqTe/evfnll1/IzMxUD39/f8LCwsjMzMTExKS+Q7wn3bt3v2ObuJMnT9K8efN6iqh2Xbt2DWNj3R89JiYm6myTofDw8MDZ2VnnM6awsJC0tDSD+IyB/yW6p06d4j//+Q8ODg71HVKtefXVV/n55591PmdcXV2ZPHkyO3furO/w7omZmRmdO3c26M+Zv5JlDOKRFR0dTXh4OP7+/nTp0oVFixZRXFzM8OHD6zu0exYREcG6dev45ptvaNSokbpG0NbWFktLy3qO7t40atTojrXHVlZWODg4GMSa5IkTJ9KtWzfmzJnD4MGDOXjwICtXrmTlypX1HVqt6N+/P7Nnz8bNzY327dvz448/8s9//pMRI0bUd2jVVlRUxOnTp9XX2dnZZGZmYm9vj5ubG1FRUcyaNYtWrVrh4eHBjBkzcHV1ZcCAAfUXdDVU1T8XFxdefvllMjIy2Lp1K+Xl5ernjL29PWZmZvUVtt7u9v79NXk3NTXF2dmZNm3a3O9Qq+1ufZs8eTKhoaH07NmTp59+msTERL777jv27NlTf0HXpfreDkKI+rRkyRLFzc1NMTMzU7p06aIcOHCgvkOqFUCFx+rVq+s7tDphSFuPKYqifPfdd8oTTzyhmJubK15eXsrKlSvrO6RaU1hYqEyYMEFxc3NTLCwslBYtWijTpk1TSkpK6ju0atu9e3eF/5+Fh4crinJr+7EZM2YoTk5Oirm5udK7d28lKyurfoOuhqr6l52dXennzO7du+s7dL3c7f37q4dp6zF9+rZq1SrF09NTsbCwULy9vZUtW7bUX8B1zEhRHsKvrRFCCCGEEEIPsmZXCCGEEEIYLEl2hRBCCCGEwZJkVwghhBBCGCxJdoUQQgghhMGSZFcIIYQQQhgsSXaFEEIIIYTBkmRXCCGEEEIYLEl2hRBCCCGEwZJkVwghhBBCGCxJdoUQQgghhMGSZFcIIYQQQhis/wc1w1DEy2XnmgAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Agrupando os top 100 por região\n", + "top_100_por_regiao = top_100.groupby('REGIAO')['NU_INSCRICAO'].count()" + ], + "metadata": { + "id": "fm1Kb6vR5JUp" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "top_100_por_regiao" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 178 + }, + "id": "mMapNk7g7212", + "outputId": "798a2ccc-013a-4fc3-d7b2-97df1240ca50" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "REGIAO\n", + "Norte 7\n", + "Sul 93\n", + "Name: NU_INSCRICAO, dtype: int64" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NU_INSCRICAO
REGIAO
Norte7
Sul93
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 54 + } + ] + }, + { + "cell_type": "code", + "source": [ + "top_100_por_regiao.plot(kind='bar', color =grafico_cor)\n", + "plt.ylabel('Número de inscritas')\n", + "plt.title('Distribuição das melhores notas por Região')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "id": "1VQnF5Mh8ET5", + "outputId": "1282183f-67b3-4d5c-bdc7-0d72b4188c6c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Distribuição das melhores notas por Região')" + ] + }, + "metadata": {}, + "execution_count": 55 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+5LYv2fvz59fsrxw72X7++Wf17t1bQUFB8vX1VUhIiFq2bClJ1uPC09NTU6dO1caNGxUaGqoWLVpo2rRpSk5Otmtf3NzcbP4YS9I999wjSdb35MmTJxUeHp7jsySvfYmKirJr29kqVKigTz/9VJ988olef/11lS1bVufPn7cJtMeOHZNhGKpSpUqO3+0PP/xg/d1m11K5cmWbbbi7u9t1iuvixYsaOnSoQkND5e3trZCQEOv+ZL/m58+f17Vr1/L8/WZlZeU6bgf5Y4wMcjh9+rRSUlJyvKH/zNvbW9u3b9fWrVv10UcfadOmTVq+fLkeeOABbd682a6rBZwxruVWed20LzMz0ylXMDzzzDNasGCBhg0bpsaNGysgIEAWi0XdunVzyb0r7K2nRYsWOn78uNatW6fNmzfr7bff1owZMzRv3jz169fvttvJ67XLrd340yDOrKwslS5dWkuWLMl1eXuu+spr28Ytg0Xt5enpKTc32//h7K0z+wZ9u3fv1vr16/XJJ5+oT58+euWVV7R79275+vrmu21n70tuMjMz9eCDD+rixYt67rnnFB0dLR8fH/3yyy/q3bu3zXExbNgwPfLII1q7dq0++eQTjRs3TvHx8dqyZYvq1q3rtJrs5ehngo+Pj2JiYqzPmzZtqvvuu0/PP/+8Zs+eLemP363FYtHGjRtzff1v9zuzV9euXbVr1y6NGjVKderUka+vr7KysvTQQw+57L42fxcEGeTw7rvvSpJiY2Pznc/NzU2tW7dW69atNX36dL344ov697//ra1btyomJsbpdwK+9bSIYRj68ccfbe53U7JkSV2+fDnHsidPnszx3+OfZU/77rvv8q1h1apV6tWrl1555RVr2/Xr13NsMzIyUkeOHMmx/OHDh63T8xIZGamsrCwdP37c5j+33NZnbz2SFBQUpLi4OMXFxenq1atq0aKFJk6caFeQuVOVKlXSZ599pqZNmxZIcJX+77U8cuSIHnjgAZtpR44cyfe1vtM6GzVqpEaNGmnKlCl6//331aNHDy1btswpr6W9x05e769vv/1WR48e1aJFi2wGXed1hVqlSpU0cuRIjRw5UseOHVOdOnX0yiuv2Fy1mJusrCz99NNP1l4YSTp69KgkWXswIiMj9dlnn+nKlSs2vTL2vA/uxL333quePXvqjTfe0LPPPqvy5curUqVKMgxDUVFRNrXeKruWH3/8Uf/4xz+s7RkZGTpx4oTN58ytLl26pISEBE2aNEnjx4+3tt/6mRUSEqISJUrk+ft1c3PL0cuJ2+PUEmxs2bJF//3vfxUVFWW9LDU3Fy9ezNGWfVOu7G727Htz5PZH9U4sXrzYZtzOqlWr9Ouvv6pt27bWtkqVKmn37t26ceOGtW3Dhg237a4NCQlRixYt9M477+jnn3+2mfbn/5aLFSuW47/nOXPmKDMz06atXbt22rt3rxITE61taWlpevPNN1WhQoV8xwFk70/2f5TZcvsaBHvrufUSUF9fX1WuXNnuUyJ3qmvXrsrMzNR///vfHNMyMjKccmzUr19fpUuX1rx582z2Z+PGjfrhhx9yvdrrTuu8dOlSjtf71uP+r7L32Mnr/ZXd6/DnOg3D0KxZs2zmu3btmq5fv27TVqlSJfn5+dm9L6+++qrNNl599VUVL17cejfbdu3aKTMz02Y+SZoxY4YsFovNe9dZRo8erZs3b2r69OmSpEcffVTFihXTpEmTcvzuDMOwvjfq16+v4OBgvfXWW8rIyLDOs2TJktuOIcrtNZdyvmeLFSumNm3aaN26dTanxM+ePav3339fzZo1k7+/v0P7C3pk/tY2btyow4cPKyMjQ2fPntWWLVv06aefKjIyUh9++GG+N6KaPHmytm/frvbt2ysyMlLnzp3T66+/rnLlylkHKVaqVEmBgYGaN2+e/Pz85OPjo4YNGzp8HjxbUFCQmjVrpri4OJ09e1YzZ85U5cqVbS4R79evn1atWqWHHnpIXbt21fHjx/Xee+/lGG+Sm9mzZ6tZs2a67777NGDAAEVFRenEiRP66KOPrLeBf/jhh/Xuu+8qICBA1atXV2Jioj777DMFBwfbrGvMmDFaunSp2rZtqyFDhigoKEiLFi1SUlKSPvjggxynNv6sTp066t69u15//XWlpKSoSZMmSkhIyPXeKfbWU716dbVq1Ur16tVTUFCQ9u3bp1WrVtkM1iwILVu21FNPPaX4+HgdOHBAbdq0UfHixXXs2DGtXLlSs2bN0mOPPfaXtlG8eHFNnTpVcXFxatmypbp37269/LpChQoaPny40+pctGiRXn/9dXXu3FmVKlXSlStX9NZbb8nf31/t2rX7S/uRzd5jJ6/3V3R0tCpVqqRnn31Wv/zyi/z9/fXBBx/k+GOcffv8rl27qnr16nJ3d9eaNWt09uxZdevW7bZ1enl5adOmTerVq5caNmyojRs36qOPPtLzzz9vPRX3yCOP6B//+If+/e9/68SJE6pdu7Y2b96sdevWadiwYXa9Lx1VvXp1tWvXTm+//bbGjRunSpUq6YUXXtDYsWOtl1P7+fkpKSlJa9as0YABA/Tss8/Kw8NDEydO1DPPPKMHHnhAXbt21YkTJ7Rw4UJVqlQp3x5mf39/6xijmzdvqmzZstq8ebOSkpJyzPvCCy9Y78E1cOBAubu764033lB6erqmTZvm9Nfjb6GQr5JCEZB9SW32w8PDwwgLCzMefPBBY9asWTaXOGe79fLrhIQEo2PHjkZ4eLjh4eFhhIeHG927dzeOHj1qs9y6deuM6tWrG+7u7jaXK7Zs2dKoUaNGrvXldRnu0qVLjbFjxxqlS5c2vL29jfbt2+e4VNowDOOVV14xypYta3h6ehpNmzY19u3bZ9fl14ZhGN99953RuXNnw9/f35BkVK1a1Rg3bpx1+qVLl4y4uDijVKlShq+vrxEbG2scPnw418u+jx8/bjz22GNGYGCg4eXlZTRo0MDYsGFDrvt8q99//90YMmSIERwcbPj4+BiPPPKIcerUqRyXX9tbzwsvvGA0aNDACAwMNLy9vY3o6GhjypQpxo0bN/KtI69L9bOPh1svv+/Vq5fh4+OTYz1vvvmmUa9ePcPb29vw8/MzatWqZYwePdo4c+aMdZ68fu8rV660WVdev7vly5cbdevWNTw9PY2goCCjR48exunTp+2qz9469+/fb3Tv3t0oX7684enpaZQuXdp4+OGHjX379uW5zmyRkZG5XgJ/634bhv3HTl7vr++//96IiYkxfH19jVKlShn9+/c3Dh48aDPPhQsXjEGDBhnR0dGGj4+PERAQYDRs2NBYsWLFbfcl+3U8fvy40aZNG6NEiRJGaGioMWHChBy3Frhy5YoxfPhwIzw83ChevLhRpUoV4+WXX7a5nN8w/rj8etCgQbfd9p9ft7w+Qz7//PMc75UPPvjAaNasmeHj42P4+PgY0dHRxqBBg4wjR47YLDt79mwjMjLS8PT0NBo0aGDs3LnTqFevnvHQQw9Z58ntGDx9+rTRuXNnIzAw0AgICDAef/xx48yZMznqMIw/jqPY2FjD19fXKFGihPGPf/zD2LVrl937DlsWw3DiKDPgLhITE6PRo0erTZs2ri4FKFJ69+6tVatW6erVq64upcBlZWUpJCREjz76qN566y1Xl4NcMEYGyMMjjzxy2wGPAO4e169fzzHOZfHixbp48WKuX5uCooExMsAtli5dqrS0NK1cuVKlS5d2dTkACsnu3bs1fPhwPf744woODtb+/fs1f/581axZU48//riry0MeCDLALQ4dOqT//e9/KlOmDIPvgL+RChUqKCIiQrNnz9bFixcVFBSkJ598Ui+99JJDN+9E4WKMDAAAMC3GyAAAANO6608tZWVl6cyZM/Lz83P6nWYBAEDBMAxDV65cUXh4eL733rrrg8yZM2e45TMAACZ16tSpHF/0+md3fZDJ/n6PU6dOcetnAABMIjU1VRERETm+Pf1Wd32QyT6d5O/vT5ABAMBkbjcshMG+AADAtAgyAADAtAgyAADAtAgyAADAtAgyAADAtAgyAADAtAgyAADAtAgyAADAtAgyAADAtAgyAADAtAgyAADAtAgyAADAtAgyAADAtAgyAADAtAgyAADAtNxdXQAAwHFXXq7t6hJQiPxGHXR1CUUWPTIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0CDIAAMC0XBpkMjMzNW7cOEVFRcnb21uVKlXSf//7XxmGYZ3HMAyNHz9eZcqUkbe3t2JiYnTs2DEXVg0AAIoKlwaZqVOnau7cuXr11Vf1ww8/aOrUqZo2bZrmzJljnWfatGmaPXu25s2bpz179sjHx0exsbG6fv26CysHAABFgbsrN75r1y517NhR7du3lyRVqFBBS5cu1d69eyX90Rszc+ZM/ec//1HHjh0lSYsXL1ZoaKjWrl2rbt265Vhnenq60tPTrc9TU1MLYU8AAIAruLRHpkmTJkpISNDRo0clSQcPHtSOHTvUtm1bSVJSUpKSk5MVExNjXSYgIEANGzZUYmJiruuMj49XQECA9REREVHwOwIAAFzCpT0yY8aMUWpqqqKjo1WsWDFlZmZqypQp6tGjhyQpOTlZkhQaGmqzXGhoqHXarcaOHasRI0ZYn6emphJmAAC4S7k0yKxYsUJLlizR+++/rxo1aujAgQMaNmyYwsPD1atXrztap6enpzw9PZ1cKQAAKIpcGmRGjRqlMWPGWMe61KpVSydPnlR8fLx69eqlsLAwSdLZs2dVpkwZ63Jnz55VnTp1XFEyAAAoQlw6RubatWtyc7MtoVixYsrKypIkRUVFKSwsTAkJCdbpqamp2rNnjxo3blyotQIAgKLHpT0yjzzyiKZMmaLy5curRo0a+vrrrzV9+nT16dNHkmSxWDRs2DC98MILqlKliqKiojRu3DiFh4erU6dOriwdAAAUAS4NMnPmzNG4ceM0cOBAnTt3TuHh4Xrqqac0fvx46zyjR49WWlqaBgwYoMuXL6tZs2batGmTvLy8XFg5AAAoCizGn2+jexdKTU1VQECAUlJS5O/v7+pyAMAprrxc29UloBD5jTro6hIKnb1/v/muJQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoOB5lNmzZpx44d1uevvfaa6tSpoyeeeEKXLl1yanEAAAD5cTjIjBo1SqmpqZKkb7/9ViNHjlS7du2UlJSkESNGOL1AAACAvLg7ukBSUpKqV68uSfrggw/08MMP68UXX9T+/fvVrl07pxcIAACQF4d7ZDw8PHTt2jVJ0meffaY2bdpIkoKCgqw9NQAAAIXB4R6ZZs2aacSIEWratKn27t2r5cuXS5KOHj2qcuXKOb1AAACAvDjcI/Pqq6/K3d1dq1at0ty5c1W2bFlJ0saNG/XQQw85vUAAAIC8ONwjU758eW3YsCFH+4wZM5xSEAAAgL0cDjJ/dv36dd24ccOmzd/f/y8VBAAAYC+HTy2lpaVp8ODBKl26tHx8fFSyZEmbBwAAQGFxOMiMHj1aW7Zs0dy5c+Xp6am3335bkyZNUnh4uBYvXlwQNQIAAOTK4VNL69ev1+LFi9WqVSvFxcWpefPmqly5siIjI7VkyRL16NGjIOoEAADIweEemYsXL6pixYqS/hgPc/HiRUl/XJa9fft251YHAACQD4eDTMWKFZWUlCRJio6O1ooVKyT90VMTGBjo1OIAAADy43CQiYuL08GDByVJY8aM0WuvvSYvLy8NHz5co0aNcnqBAAAAeXF4jMzw4cOtP8fExOjw4cP66quvVLlyZd17771OLQ4AACA/DvfILF68WOnp6dbnkZGRevTRRxUdHc1VSwAAoFDd0amllJSUHO1XrlxRXFycU4oCAACwh8NBxjAMWSyWHO2nT59WQECAU4oCAACwh91jZOrWrSuLxSKLxaLWrVvL3f3/Fs3MzFRSUhJfGgkAAAqV3UGmU6dOkqQDBw4oNjZWvr6+1mkeHh6qUKGCunTp4vQCAQAA8mJ3kJkwYYIkqUKFCvrnP/8pLy+vAisKAADAHg6PkenVq5dTQ8wvv/yinj17Kjg4WN7e3qpVq5b27dtnnW4YhsaPH68yZcrI29tbMTExOnbsmNO2DwAAzMuuIBMUFKQLFy5IkkqWLKmgoKA8H464dOmSmjZtquLFi2vjxo36/vvv9corr9h8i/a0adM0e/ZszZs3T3v27JGPj49iY2N1/fp1h7YFAADuPnadWpoxY4b8/PwkSTNnznTaxqdOnaqIiAgtWLDA2hYVFWX92TAMzZw5U//5z3/UsWNHSX/cxyY0NFRr165Vt27dnFYLAAAwH7uCTK9evSRJGRkZslgsio2NVWho6F/e+IcffqjY2Fg9/vjj2rZtm8qWLauBAweqf//+kqSkpCQlJycrJibGukxAQIAaNmyoxMTEXINMenq6zQ37UlNT/3KdAACgaHJojIy7u7v+9a9/Oe20zk8//aS5c+eqSpUq+uSTT/T0009ryJAhWrRokSQpOTlZknKEptDQUOu0W8XHxysgIMD6iIiIcEqtAACg6HF4sG+DBg309ddfO2XjWVlZuu+++/Tiiy+qbt26GjBggPr376958+bd8TrHjh2rlJQU6+PUqVNOqRUAABQ9Dn9p5MCBAzVy5EidPn1a9erVk4+Pj810R744skyZMqpevbpNW7Vq1fTBBx9IksLCwiRJZ8+eVZkyZazznD17VnXq1Ml1nZ6envL09LS7BgAAYF4OB5nscSlDhgyxtlksFutXF2RmZtq9rqZNm+rIkSM2bUePHlVkZKSkPwb+hoWFKSEhwRpcUlNTtWfPHj399NOOlg4AAO4yDgeZpKQkp218+PDhatKkiV588UV17dpVe/fu1Ztvvqk333xT0h8BadiwYXrhhRdUpUoVRUVFady4cQoPD7feaRgAAPx9ORxksntLnOH+++/XmjVrNHbsWE2ePFlRUVGaOXOmevToYZ1n9OjRSktL04ABA3T58mU1a9ZMmzZt4s7CAABAFsMwDEcWiI+PV2hoqPr06WPT/s477+j8+fN67rnnnFrgX5WamqqAgAClpKTI39/f1eUAgFNcebm2q0tAIfIbddDVJRQ6e/9+O3zV0htvvKHo6Ogc7TVq1PhLVxsBAAA4yuEgk5ycbHMFUbaQkBD9+uuvTikKAADAHg4HmYiICO3cuTNH+86dOxUeHu6UogAAAOzh8GDf/v37a9iwYbp586YeeOABSVJCQoJGjx6tkSNHOr1AAACAvDgcZEaNGqXffvtNAwcO1I0bNyRJXl5eeu655zR27FinFwgAAJAXh4OMxWLR1KlTNW7cOP3www/y9vZWlSpVuJsuAAAodA6Pkcnm6+ur+++/X+XLl9fGjRv1ww8/OLMuAACA23I4yHTt2lWvvvqqJOn3339X/fr11bVrV917773W70gCAAAoDA4Hme3bt6t58+aSpDVr1sgwDF2+fFmzZ8/WCy+84PQCAQAA8uJwkElJSVFQUJAkadOmTerSpYtKlCih9u3b69ixY04vEAAAIC93dB+ZxMREpaWladOmTWrTpo0k6dKlS3z/EQAAKFQOX7U0bNgw9ejRQ76+voqMjFSrVq0k/XHKqVatWs6uDwAAIE8OB5mBAweqQYMGOnXqlB588EG5uf3RqVOxYkXGyAAAgELlcJCRpPr166t+/fo2be3bt3dKQQAAAPZyOMhkZmZq4cKFSkhI0Llz55SVlWUzfcuWLU4rDgAAID8OB5mhQ4dq4cKFat++vWrWrCmLxVIQdQEAANyWw0Fm2bJlWrFihdq1a1cQ9QAAANjN4cuvPTw8VLly5YKoBQAAwCEOB5mRI0dq1qxZMgyjIOoBAACwm8Onlnbs2KGtW7dq48aNqlGjhooXL24zffXq1U4rDgAAID8OB5nAwEB17ty5IGoBAABwiMNBZsGCBQVRBwAAgMMcHiMDAABQVNjVI3PfffcpISFBJUuWVN26dfO9d8z+/fudVhwAAEB+7AoyHTt2lKenpySpU6dOBVkPAACA3SzGXX4ddWpqqgICApSSkiJ/f39XlwMATnHl5dquLgGFyG/UQVeXUOjs/fvNGBkAAGBaBBkAAGBaBBkAAGBaBBkAAGBadxxkbty4oSNHjigjI8OZ9QAAANjN4SBz7do19e3bVyVKlFCNGjX0888/S5KeeeYZvfTSS04vEAAAIC8OB5mxY8fq4MGD+vzzz+Xl5WVtj4mJ0fLly51aHAAAQH4c/q6ltWvXavny5WrUqJHNHX5r1Kih48ePO7U4AACA/DjcI3P+/HmVLl06R3taWlq+X10AAADgbA4Hmfr16+ujjz6yPs8OL2+//bYaN27svMoAAABuw+FTSy+++KLatm2r77//XhkZGZo1a5a+//577dq1S9u2bSuIGgEAAHLlcI9Ms2bNdODAAWVkZKhWrVravHmzSpcurcTERNWrV68gagQAAMiVwz0yklSpUiW99dZbzq4FAADAIXYFmdTUVLtXyDdMAwCAwmJXkAkMDLT7iqTMzMy/VBAAAIC97AoyW7dutf584sQJjRkzRr1797ZepZSYmKhFixYpPj6+YKoEAADIhV1BpmXLltafJ0+erOnTp6t79+7Wtg4dOqhWrVp688031atXL+dXCQAAkAuHr1pKTExU/fr1c7TXr19fe/fudUpRAAAA9nA4yEREROR6xdLbb7+tiIgIpxQFAABgD4cvv54xY4a6dOmijRs3qmHDhpKkvXv36tixY/rggw+cXiAAAEBeHO6RadeunY4dO6YOHTro4sWLunjxoh555BEdPXpU7dq1K4gaAQAAcnVHN8QrV66cpkyZ4uxaAAAAHOJwjwwAAEBRQZABAACmRZABAACmRZABAACmdUeDfSXp/PnzOnLkiCSpatWqCgkJcVpRAAAA9nC4RyYtLU19+vRReHi4WrRooRYtWig8PFx9+/bVtWvXCqJGAACAXDkcZEaMGKFt27bpww8/1OXLl3X58mWtW7dO27Zt08iRIwuiRgAAgFw5fGrpgw8+0KpVq9SqVStrW7t27eTt7a2uXbtq7ty5zqwPAAAgTw73yFy7dk2hoaE52kuXLs2pJQAAUKgcDjKNGzfWhAkTdP36dWvb77//rkmTJqlx48ZOLQ4AACA/Dp9amjlzph566CGVK1dOtWvXliQdPHhQXl5e+uSTT5xeIAAAQF4cDjK1atXSsWPHtGTJEh0+fFiS1L17d/Xo0UPe3t5OLxAAACAvDgWZmzdvKjo6Whs2bFD//v0LqiYAAAC7ODRGpnjx4jZjYwAAAFzJ4cG+gwYN0tSpU5WRkVEQ9QAAANjN4TEyX375pRISErR582bVqlVLPj4+NtNXr17ttOIAAADy43CQCQwMVJcuXQqiFgAAAIc4HGQWLFhQEHXopZde0tixYzV06FDNnDlTknT9+nWNHDlSy5YtU3p6umJjY/X666/nekM+AADw9+PwGBlJysjI0GeffaY33nhDV65ckSSdOXNGV69evaMivvzyS73xxhu69957bdqHDx+u9evXa+XKldq2bZvOnDmjRx999I62AQAA7j4OB5mTJ0+qVq1a6tixowYNGqTz589LkqZOnapnn33W4QKuXr2qHj166K233lLJkiWt7SkpKZo/f76mT5+uBx54QPXq1dOCBQu0a9cu7d692+HtAACAu4/DQWbo0KGqX7++Ll26ZHMDvM6dOyshIcHhAgYNGqT27dsrJibGpv2rr77SzZs3bdqjo6NVvnx5JSYm5rm+9PR0paam2jwAAMDdyeExMl988YV27dolDw8Pm/YKFSrol19+cWhdy5Yt0/79+/Xll1/mmJacnCwPDw8FBgbatIeGhio5OTnPdcbHx2vSpEkO1QEAAMzJ4R6ZrKwsZWZm5mg/ffq0/Pz87F7PqVOnNHToUC1ZskReXl6OlpGnsWPHKiUlxfo4deqU09YNAACKFoeDTJs2baxXFUmSxWLR1atXNWHCBLVr187u9Xz11Vc6d+6c7rvvPrm7u8vd3V3btm3T7Nmz5e7urtDQUN24cUOXL1+2We7s2bMKCwvLc72enp7y9/e3eQAAgLuTw6eWXnnlFcXGxqp69eq6fv26nnjiCR07dkylSpXS0qVL7V5P69at9e2339q0xcXFKTo6Ws8995wiIiJUvHhxJSQkWO9bc+TIEf38889q3Lixo2UDAIC7kMNBply5cjp48KCWLVumb775RlevXlXfvn0d/vZrPz8/1axZ06bNx8dHwcHB1va+fftqxIgRCgoKkr+/v5555hk1btxYjRo1crRsAABwF3I4yEiSu7u7evbs6exacpgxY4bc3NzUpUsXmxviAQAASJLFMAzD0YXOnDmjHTt26Ny5c8rKyrKZNmTIEKcV5wypqakKCAhQSkoK42UA3DWuvFzb1SWgEPmNOujqEgqdvX+/He6RWbhwoZ566il5eHgoODhYFovFOs1isRS5IAMAAO5eDgeZcePGafz48Ro7dqzc3O7oGw4AAACcwuEkcu3aNXXr1o0QAwAAXM7hNNK3b1+tXLmyIGoBAABwiMOnluLj4/Xwww9r06ZNqlWrlooXL24zffr06U4rDgAAID93FGQ++eQTVa1aVZJyDPYFAAAoLHd0Z9933nlHvXv3LoByAAAA7OfwGBlPT081bdq0IGoBAABwiMNBZujQoZozZ05B1AIAAOAQh08t7d27V1u2bNGGDRtUo0aNHIN9V69e7bTiAAAA8uNwkAkMDNSjjz5aELUAAAA4xOEgs2DBgoKoAwAAwGHcnhcAAJiWwz0yUVFR+d4v5qeffvpLBQEAANjrtkFm1apVatSokcqVKydJGjZsmM30mzdv6uuvv9amTZs0atSoAikSAAAgN7cNMu7u7mrevLnWrl2r2rVra+jQobnO99prr2nfvn1OLxAAACAvtx0j06lTJy1fvly9evXKd762bdvqgw8+cFphAAAAt2PXYN8GDRpo+/bt+c6zatUqBQUFOaUoAAAAe9g92Nff31+SVLduXZvBvoZhKDk5WefPn9frr7/u/AoBAADy4PBVS506dbJ57ubmppCQELVq1UrR0dHOqgsAAOC2HA4yEyZMKIg6AAAAHMYN8QAAgGnZ3SPj5uaW743wJMlisSgjI+MvFwUAAGAPu4PMmjVr8pyWmJio2bNnKysryylFAQAA2MPuINOxY8ccbUeOHNGYMWO0fv169ejRQ5MnT3ZqcQAAAPm5ozEyZ86cUf/+/VWrVi1lZGTowIEDWrRokSIjI51dHwAAQJ4cCjIpKSl67rnnVLlyZR06dEgJCQlav369atasWVD1AQAA5MnuU0vTpk3T1KlTFRYWpqVLl+Z6qgkAAKAwWQzDMOyZ0c3NTd7e3oqJiVGxYsXynG/16tVOK84ZUlNTFRAQoJSUFOvdiQHA7K68XNvVJaAQ+Y066OoSCp29f7/t7pF58sknb3v5NQAAQGGyO8gsXLiwAMsAAABwHHf2BQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApkWQAQAApuXSIBMfH6/7779ffn5+Kl26tDp16qQjR47YzHP9+nUNGjRIwcHB8vX1VZcuXXT27FkXVQwAAIoSlwaZbdu2adCgQdq9e7c+/fRT3bx5U23atFFaWpp1nuHDh2v9+vVauXKltm3bpjNnzujRRx91YdUAAKCosBiGYbi6iGznz59X6dKltW3bNrVo0UIpKSkKCQnR+++/r8cee0ySdPjwYVWrVk2JiYlq1KhRjnWkp6crPT3d+jw1NVURERFKSUmRv79/oe0LABSkKy/XdnUJKER+ow66uoRCl5qaqoCAgNv+/S5SY2RSUlIkSUFBQZKkr776Sjdv3lRMTIx1nujoaJUvX16JiYm5riM+Pl4BAQHWR0RERMEXDgAAXKLIBJmsrCwNGzZMTZs2Vc2aNSVJycnJ8vDwUGBgoM28oaGhSk5OznU9Y8eOVUpKivVx6tSpgi4dAAC4iLurC8g2aNAgfffdd9qxY8dfWo+np6c8PT2dVBUAACjKikSPzODBg7VhwwZt3bpV5cqVs7aHhYXpxo0bunz5ss38Z8+eVVhYWCFXCQAAihqXBhnDMDR48GCtWbNGW7ZsUVRUlM30evXqqXjx4kpISLC2HTlyRD///LMaN25c2OUCAIAixqWnlgYNGqT3339f69atk5+fn3XcS0BAgLy9vRUQEKC+fftqxIgRCgoKkr+/v5555hk1btw41yuWAADA34tLg8zcuXMlSa1atbJpX7BggXr37i1JmjFjhtzc3NSlSxelp6crNjZWr7/+eiFXCgAAiiKXBhl7bmHj5eWl1157Ta+99lohVAQAAMykSAz2BQAAuBMEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFruri4ABefRCUtdXQIK0epJ3V1dAgAUOnpkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaZkiyLz22muqUKGCvLy81LBhQ+3du9fVJQEAgCKgyAeZ5cuXa8SIEZowYYL279+v2rVrKzY2VufOnXN1aQAAwMWKfJCZPn26+vfvr7i4OFWvXl3z5s1TiRIl9M4777i6NAAA4GLuri4gPzdu3NBXX32lsWPHWtvc3NwUExOjxMTEXJdJT09Xenq69XlKSookKTU1tWCLLYJupl9zdQkoRH/HY/zv7Mr1TFeXgEJk/A3f39mfaYZh5DtfkQ4yFy5cUGZmpkJDQ23aQ0NDdfjw4VyXiY+P16RJk3K0R0REFEiNQFERMLWfq0sAUFDGB7i6Ape5cuWKAgLy3v8iHWTuxNixYzVixAjr86ysLF28eFHBwcGyWCwurAyFITU1VRERETp16pT8/f1dXQ4AJ+L9/fdiGIauXLmi8PDwfOcr0kGmVKlSKlasmM6ePWvTfvbsWYWFheW6jKenpzw9PW3aAgMDC6pEFFH+/v580AF3Kd7ffx/59cRkK9KDfT08PFSvXj0lJCRY27KyspSQkKDGjRu7sDIAAFAUFOkeGUkaMWKEevXqpfr166tBgwaaOXOm0tLSFBcX5+rSAACAixX5IPPPf/5T58+f1/jx45WcnKw6depo06ZNOQYAA9IfpxYnTJiQ4/QiAPPj/Y3cWIzbXdcEAABQRBXpMTIAAAD5IcgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTKvL3kQEA/L048k3ufFUBuI8M7grvvvuu5s2bp6SkJCUmJioyMlIzZ85UVFSUOnbs6OryADjAzc3ttl/yaxiGLBaLMjMzC6kqFFX0yMD05s6dq/Hjx2vYsGGaMmWK9YMtMDBQM2fOJMgAJrN161ZXlwAToUcGple9enW9+OKL6tSpk/z8/HTw4EFVrFhR3333nVq1aqULFy64ukQAQAGhRwaml5SUpLp16+Zo9/T0VFpamgsqAuAs27dvz3d6ixYtCqkSFFUEGZheVFSUDhw4oMjISJv2TZs2qVq1ai6qCoAztGrVKkfbn8fPMEYGBBmY3ogRIzRo0CBdv35dhmFo7969Wrp0qeLj4/X222+7ujwAf8GlS5dsnt+8eVNff/21xo0bpylTprioKhQljJHBXWHJkiWaOHGijh8/LkkKDw/XpEmT1LdvXxdXBqAgbNu2TSNGjNBXX33l6lLgYgQZ3FWuXbumq1evqnTp0q4uBUABOnz4sOrXr6+rV6+6uhS4GEEGpvfAAw9o9erVCgwMtGlPTU1Vp06dtGXLFtcUBuAv++abb2yeG4ahX3/9VS+99JIyMjK0Y8cOF1WGooIgA9Nzc3NTcnJyjl6Yc+fOqWzZsrp586aLKgPwV2XfHO/WP1WNGjXSO++8o+joaBdVhqKCwb4wrT//p/b9998rOTnZ+jwzM1ObNm1S2bJlXVEaACdJSkqyee7m5qaQkBB5eXm5qCIUNfTIwLT+fBvz3A5jb29vzZkzR3369Cns0gD8RYmJifrtt9/08MMPW9sWL16sCRMmKC0tTZ06ddKcOXPk6enpwipRFNAjA9NKSkqSYRiqWLGi9u7dq5CQEOs0Dw8PlS5dWsWKFXNhhQDu1OTJk9WqVStrkPn222/Vt29f9e7dW9WqVdPLL7+s8PBwTZw40bWFwuUIMjCtyMhI3bx5U7169VJwcHCOG+IBMK8DBw7ov//9r/X5smXL1LBhQ7311luSpIiICE2YMIEgA7m5ugDgryhevLjWrFnj6jIAONmlS5cUGhpqfb5t2za1bdvW+vz+++/XqVOnXFEaihiCDEyvY8eOWrt2ravLAOBEoaGh1oG+N27c0P79+9WoUSPr9CtXrqh48eKuKg9FCKeWYHpVqlTR5MmTtXPnTtWrV08+Pj4204cMGeKiygDcqXbt2mnMmDGaOnWq1q5dqxIlSqh58+bW6d98840qVarkwgpRVHDVEkwvKioqz2kWi0U//fRTIVYDwBkuXLigRx99VDt27JCvr68WLVqkzp07W6e3bt1ajRo14vuWQJABABRdKSkp8vX1zXEF4sWLF+Xr6ysPDw8XVYaigiCDu0r24Zx9fxkAwN2Nwb64KyxevFi1atWSt7e3vL29de+99+rdd991dVkAgALGYF+Y3vTp0zVu3DgNHjxYTZs2lSTt2LFD//rXv3ThwgUNHz7cxRUCAAoKp5ZgelFRUZo0aZKefPJJm/ZFixZp4sSJOb6rBQBw9+DUEkzv119/VZMmTXK0N2nSRL/++qsLKgIAFBaCDEyvcuXKWrFiRY725cuXq0qVKi6oCABQWBgjA9ObNGmS/vnPf2r79u3WMTI7d+5UQkJCrgEHAHD3YIwM7gpfffWVpk+frsOHD0uSqlWrppEjR6pu3bourgwAUJAIMgAAwLQ4tQTTcnNzu+2N7ywWizIyMgqpIgBAYSPIwLTWrFmT57TExETNnj1bWVlZhVgRAKCwcWoJd5UjR45ozJgxWr9+vXr06KHJkycrMjLS1WUBAAoIl1/jrnDmzBn1799ftWrVUkZGhg4cOKBFixYRYgDgLkeQgamlpKToueeeU+XKlXXo0CElJCRo/fr1qlmzpqtLAwAUAsbIwLSmTZumqVOnKiwsTEuXLlXHjh1dXRIAoJAxRgam5ebmJm9vb8XExKhYsWJ5zrd69epCrAoAUJjokYFpPfnkk7e9/BoAcHejRwYAAJgWg30BAIBpEWQAAIBpEWQAAIBpEWQAAIBpEWQAAIBpEWQAFJjevXvLYrHIYrGoePHiioqK0ujRo3X9+nXrPNnTb30sW7bMOo9hGHrrrbfUuHFj+fv7y9fXVzVq1NDQoUP1448/WuebOHGi6tSpk6OO06dPy8PDI887PmdmZmrGjBmqVauWvLy8VLJkSbVt21Y7d+503osBoEAQZAAUqIceeki//vqrfvrpJ82YMUNvvPGGJkyYYDPPggUL9Ouvv9o8OnXqJOmPEPPEE09oyJAhateunTZv3qzvv/9e8+fPl5eXl1544YXb1rBw4UJ17dpVqamp2rNnj800wzDUrVs3TZ48WUOHDtUPP/ygzz//XBEREWrVqpXWrl3rrJcCQAHghngACpSnp6fCwsIkSREREYqJidGnn36qqVOnWucJDAy0znOr5cuXa9myZVq3bp06dOhgbS9fvrwaNWqk290KyzAMLViwQK+//rrKlSun+fPnq2HDhtbpK1as0KpVq/Thhx/qkUcesba/+eab+u2339SvXz89+OCD8vHxuaP9B1Cw6JEBUGi+++477dq1Sx4eHnYvs3TpUlWtWtUmxPzZ7e7uvHXrVl27dk0xMTHq2bOnli1bprS0NOv0999/X/fcc49NiMk2cuRI/fbbb/r000/trhdA4SLIAChQGzZskK+vr7y8vFSrVi2dO3dOo0aNspmne/fu8vX1tXn8/PPPkqSjR4+qatWqNvMPGzbMOl+5cuXy3f78+fPVrVs3FStWTDVr1lTFihW1cuVK6/SjR4+qWrVquS6b3X706FGH9xtA4SDIAChQ//jHP3TgwAHt2bNHvXr1UlxcnLp06WIzz4wZM3TgwAGbR3h4eJ7r/Pe//60DBw5o/Pjxunr1ap7zXb58WatXr1bPnj2tbT179tT8+fNt5uObWgDzYowMgALl4+OjypUrS5Leeecd1a5dW/Pnz1ffvn2t84SFhVnnuVWVKlV05MgRm7aQkBCFhISodOnS+W77/fff1/Xr123GxBiGoaysLB09elT33HOP7rnnHv3www+5Lp/dfs8999x+RwG4BD0yAAqNm5ubnn/+ef3nP//R77//btcy3bt315EjR7Ru3TqHtzd//nyNHDnSpqfn4MGDat68ud555x1JUrdu3XTs2DGtX78+x/KvvPKKgoOD9eCDDzq8bQCFgyADoFA9/vjjKlasmF577TVr2+XLl5WcnGzzyB6Q261bNz322GPWS6T37NmjEydOaNu2bVq+fLmKFSuW63YOHDig/fv3q1+/fqpZs6bNo3v37lq0aJEyMjLUrVs3de7cWb169dL8+fN14sQJffPNN3rqqaf04Ycf6u233+aKJaAII8gAKFTu7u4aPHiwpk2bZg0rcXFxKlOmjM1jzpw5kv64Kmn58uWaOXOmPv74Y7Vu3VpVq1ZVnz59FBERoR07duS6nfnz56t69eqKjo7OMa1z5846d+6cPv74Y1ksFq1YsULPP/+8ZsyYoapVq6p58+Y6efKkPv/8c+v9bAAUTRaDUW4AAMCk6JEBAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACm9f8B4yGri6eEtW0AAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Podemos observar que a faixa de renda familiar acima de R$26.400,00 contém o maior número de inscritas entre as 100 maiores notas, em compensação\n", + "existe uma representação mínima entre as menores faixas de renda familar.\n", + "Ao levar essa comparação para região e observar a quantidade de inscrita que cada região obteve entre as 100 melhores notas é gritante a diferença entre as regiões Norte e Sul." + ], + "metadata": { + "id": "chgDiQgXDCqy" + } + }, + { + "cell_type": "code", + "source": [ + "# EXPORTANDO A BASE FINAL\n", + "copia_fem_norte_sul.to_csv('base_final_enem_2023.csv', index=False)" + ], + "metadata": { + "id": "wYK1TD5aLr21" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/README.md b/README.md index 6f06bd0..c6d71ae 100644 --- a/README.md +++ b/README.md @@ -1,104 +1,41 @@ -

- logo reprograma -

+# Análise de dados Enem 2023 +![enem1](https://github.com/user-attachments/assets/0f22035f-d723-4833-9b95-4016bb4b3217) -# Tema da Aula +## Contexto -Turma Online 34 | Python | Semanas 17 e 18 | 2024 | [Daniele Junior](https://travatech.com.br?router=danijr) +Neste projeto será realizada a análise dos microdados do ENEM do ano de 2023, filtrando apenas pelas regiões Norte e Sul do país, analisaremos especificamente os dados dos inscritos do sexo feminino das regiões Norte e Sul do Brasil. O projeto tem como objetivo analisar se há diferenças entre as inscritas dos extremos do país. -### Instruções -Antes de começar, vamos organizar nosso setup. -* Fork esse repositório -* Clone o fork na sua máquina (Para isso basta abrir o seu terminal e digitar `git clone url-do-seu-repositorio-forkado`) -* Entre na pasta do seu repositório (Para isso basta abrir o seu terminal e digitar `cd nome-do-seu-repositorio-forkado`) -* [Add outras instruções caso necessário] +## Objetivo -### Resumo -O que veremos na aula de hoje? -* [Slide Semana 17](https://docs.google.com/presentation/d/1axo2Dlm0Hx35ahKdZW6s-UAdG61L41QXdete8ZcQV0w/edit?usp=sharing) -* Slide Semana 18 +O objetivo geral é analisar as possíveis diferenças entre as inscritas das regiões Norte e Sul para o ENEM 2023. -* [Escolhendo uma fonte de dados](#Escolhendoumafontededados) -* Análise exploratória -* Criando uma história com dados +Dentre os objetivos específicos temos: -## Conteúdo +* Identificar qual o percentual de inscritos do sexo feminino nas regiões Norte e Sul; +* Identificar o percentual de falta entre as inscritas nas regiões Norte e Sul; +* Identificar o percentual de inscritas por faixa de renda familiar; +* Identificar qual o top 100 de maiores notas entre todas as provas e visualizar em qual faixa de renda familiar está à maioria deste Top 100. Também verificar como o Top 100 está distribuído por região (Norte e Sul). -### O que é um projeto de análise de dados? -Nesse ponto vocês já aprenderam que ter dados não é a mesma coisa que ter informação. -**Dados:** são elementos brutos e não processados, como números, palavras, ou símbolos que precisam ser interpretados para se tornarem úteis. -**Informação:** é o resultado do processamento, organização e interpretação dos dados, fornecendo significado e contexto para tomar decisões ou entender situações. -Assim, dados são a matéria-prima da informação, que é o produto final após análise e interpretação dos dados. +Neste projeto usa-se análise exploratória de dados, a fim de obter informações da base de dados escolhida. -Por isso a importância de nós contarmos uma história estruturada a partir dos dados que conseguimos coletar. E é exatamente sobre isso, que se trata um projeto de análise de dados: **gerar informação útil a partir da construção de uma perspectiva contextualizada!** +## Base Escolhida +Microdados do ENEM do ano de 2023 +Disponibilizado pelo Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira - Inep. +(https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem) -Então aqui vão algumas perguntas gerais que devemos nos fazer ao iniciar um projeto como esse: - -- **Conteúdo** - - O que eu quero informar? -- **Público** - - Para quem eu estou contanto essa história? Com quem vou compartilhar essa informação? -- **Transformação** - - Por que essa informação é relevante? - -Ok, as perguntas são importantes, - -MAS POR ONDE COMEÇAR?! - -### Escolhendo uma fonte de dados - -#### O caminho comum -Se você já fez algum tipo de pesquisa acadêmica (TCC, Iniciação Científica, etc) você certamente está familiarizado com esse processo, pois tudo começa com a escolha de um TEMA, seguindo para a definição do PROBLEMA, que em seguida é desdobrado em PERGUNTAS, que irão guiar a COLETA DE DADOS. - -1. Delimitação do Tema -2. Definição do Problema -3. Desenvolvimento de Perguntas -4. Coleta de Dados - -#### O caminho que iremos seguir -Porque esse projeto é um exercício e encontrar os dados ideais para responder às nossas perguntas pode se tornar um trabalho extremamente complexo... - -Nós iremos fazer um caminho um pouco diferente e a partir de um tema de interesse, escolher uma base e então pensar quais perguntas podem ser respondidas a partir dela. - -O QUE TAMBÉM É SUPER VÁLIDO! E PODE RENDER DESCOBERTAS INCRÍVEIS! - - * **Escolha do tema** - - No primeiro momento você deve escolher qual assunto gostaria de abordar. Pense em um tema atual, relevante e até onde você vai aprofundar a análise. Lembre-se, não adianta abraçar o mundo sozinho, você precisa focar e entregar o melhor resultado possível, então trabalhe na delimitação do Tema! Quais são os recortes possíveis dentro do universo escolhido? - - #Dica: Dê prioridade para algo que você goste, se interesse, tenha afinidade ou conhecimento na área. - - * **Escolha da Base de Dados** - - [Algumas opções de Bases de Dados](#base-de-dados) - -* **Definindo nossas perguntas** +## Ferramentas utilizadas +* Tableau +* Python, com as bibliotecas: +- Pandas +- Matplotlib - O que eu quero tentar responder? VAMOS AO [BRAINSTORM](#material-da-aula)! - -*** - -### Material da aula +##Link da visualização do projeto no Tableau -* [Slides](https://docs.google.com/presentation/d/1axo2Dlm0Hx35ahKdZW6s-UAdG61L41QXdete8ZcQV0w/edit?usp=sharing) +https://public.tableau.com/app/profile/luana.queiros/viz/Projeto_Final_ENEM2023/Painel2 -### Links Úteis -- [Documentação Pandas](https://pandas.pydata.org/docs/user_guide/index.html#user-guide) -- [Introdução ao Pandas](https://medium.com/tech-grupozap/introdu%C3%A7%C3%A3o-a-biblioteca-pandas-89fa8ed4fa38) -- [Análise Exploratória de Dados I](https://escoladedados.org/tutoriais/analise-exploratoria-de-dados/) -- [Análise Exploratória de Dados II](https://www.alura.com.br/artigos/analise-exploratoria) -- [Storytelling com Dados](https://medium.com/resumos-resenhas/storytelling-com-dados-resumo-fd63ebe4f704) -- [Markdown Cheastsheet](https://www.ibm.com/docs/en/watson-studio-local/1.2.3?topic=notebooks-markdown-jupyter-cheatsheet) +![Reprograma-on34](https://github.com/user-attachments/assets/3942ca3d-6735-430c-aa05-5b540bea2863) - #### Base de Dados -- [Kaggle](https://www.kaggle.com/datasets) -- [IBGE](https://ces.ibge.gov.br/base-de-dados/links-base-de-dados.html) -- [Brasil.io](https://brasil.io/datasets/) -- [Gov.br](https://dados.gov.br/dados/conjuntos-dados) -- [Nosso Mundo em Dados](https://ourworldindata.org/charts) -

-Desenvolvido com :purple_heart: -

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