{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "# The code was removed by Watson Studio for sharing."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "collapsed": true
            },
            "source": "# Dados Abertos e Ci\u00eancia de Dados: An\u00e1lise da Actividade Parlamentar da XIV Legislatura Portuguesa\n\n***Open Data and Data Science: Analysis of the Parliamentary Activity of the 14th Legislature of Portugal***\n\n---\n\n**Frederico Mu\u00f1oz** <frederico.munoz@pt.ibm.com>\n\n---\n\n\ud83d\udc49 **IBM Data Platform URL**: https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/0b23f01c-e55a-4fe4-a2ca-57f2e478bf3e/view?access_token=4cc13fba3f3967530650b11a2956ed27c5cec9042113237f97cb0e4c58d3905f\n\n\ud83d\udc49 **Jupyter Viewer**: https://nbviewer.jupyter.org/github/fsmunoz/pt-act-parlamentar/raw/master/Actividade%20Parlamentar%20da%20XIV%20Legislatura.ipynb\n\n\n\ud83d\udc49 *[How to cite this notebook](#How-to-cite-this-notebook)*"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "---\n\n**Abstract**: *The application of Data Science methods and analysis to voting records is a well-established practice; in this paper we describe the process of obtaining, cleaning, exploring and choosing adequate analytic methods for the purpose of determining the distance between parties in the Portuguese Parliament. Through the use of institutional Open Data resources we discuss the different approaches possible in terms of clustering and dimension reduction, using DBSCAN, Spectrum Clustering and Multidimensional Scaling to augment the information produced by distance and affinity matrices, and discuss the results of these methods in the available data in a time where the concepts of Left and Right are often hotly debated. A detailed explanation of how an euclidean distance matrix is obtained and how clustering and MDS work using test datasets is also included.*\n\n---"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Introdu\u00e7\u00e3o"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\ud83d\udc49 ***This notebook is currently only fully available in Portuguese; while a future translation is likely in the meantime one can make good use of the fact that the actual code blocks are in English to at least follow through those parts***\n\nO posicionamento absoluto e relativo dos v\u00e1rios partidos pol\u00edticos no Parlamento portugu\u00eas tem sido motivo de interesse redobrado nos \u00faltimos anos. A elei\u00e7\u00e3o de deputados de partidos sem anterior presen\u00e7a parlamentar tem alimentado o debate cujas implica\u00e7\u00f5es ideol\u00f3gicas foram v\u00edsiveis de forma bastante pr\u00e1tica na problem\u00e1tica em torno da escolha de lugares: partidos desagradados com o lugar atribu\u00eddo *(\u201cIniciativa Liberal Descontente Com Lugar Atribu\u00eddo a Deputado No Parlamento - TSF\u201d 2020)*, dificuldades gerais em termos de arruma\u00e7\u00e3o dos deputados *(Renascen\u00e7a 2019)*, quest\u00f5es de ordem mais ou menos pr\u00e1tica em torno de acessos (Almeida 2019), enfim, v\u00e1rias dimens\u00f5es para uma quest\u00e3o que acaba por revelar a import\u00e2ncia simb\u00f3lica do posicionamento absoluto e relativo de cada partido no hemiciclo.\n\nEsta quest\u00e3o n\u00e3o \u00e9 particularmente nova *(Louren\u00e7o 2020)*, colocando-se em maior ou em menor grau com a entrada de novos partidos e a consequente necessidade de tomada de posi\u00e7\u00e3o por parte do rec\u00e9m-chegado partido e a harmoniza\u00e7\u00e3o (poss\u00edvel) com os restantes, sendo que a sua posterior actividade parlamentar (nas suas diversas vertentes) poder\u00e1 ou n\u00e3o alinhar-se com a sua auto-identifica\u00e7\u00e3o (reflectida ou n\u00e3o nos lugares no hemiciclo).\n\nO ponto de partida para esta an\u00e1lise foi precisamente tentar descobrir se exclusivamente com base na actividade parlamentar, e em concreto no registo de vota\u00e7\u00f5es, \u00e9 poss\u00edvel estabelecer rela\u00e7\u00f5es de proximidade e dist\u00e2ncia que permitam um agrupamento que n\u00e3o dependa de classifica\u00e7\u00f5es a priori, e se sim, de que forma estes agrupamentos confirmam ou divergem da percep\u00e7\u00e3o existente?\n\nA utiliza\u00e7\u00e3o de dados abertos disponibilizados pelo Parlamento torna esta an\u00e1lise substancialmente mais simples, embora n\u00e3o sem a necessidade de tratamento e valida\u00e7\u00e3o dos dados; de um ponto de vista pr\u00e1tico este bloco de notas demonstra como aceder e transformar os dados de uma forma que pode ser \u00fatil para outras an\u00e1lises. No cen\u00e1rio nacional refer\u00eancia para a iniciativa http://hemiciclo.pt que, em linha com iniciativas europeias semelhantes, fornecesse um interface para um maior escrutinio da actividade parlamentar e um conjunto alargado de indicadores directos e indirectos do maior interesse *(Sapage 2020)*. O presente trabalho tem alguns pontos de contacto com esta iniciativa, dentro dos limites que o seu objectivo pedag\u00f3gico estabelece.\n\nA combina\u00e7\u00e3o de dados abertos com um bloco de notas Jupyter permite que o leitor tenha visibilidade dos v\u00e1rios passos e transforma\u00e7\u00f5es *(Randles et al. 2017)*, o que pode por vezes apresentar uma excessiva complexidade para quem n\u00e3o tenha familiaridade com programa\u00e7\u00e3o; tent\u00e1mos obviar esta limita\u00e7\u00e3o atrav\u00e9s da descri\u00e7\u00e3o das v\u00e1rias ac\u00e7\u00f5es de forma a que se possa seguir a l\u00f3gica e fruir dos resultados. Esta transpar\u00eancia assume uma dimens\u00e3o adicional tendo em conta a tem\u00e1tica que nos proposmos analisar, embora seja importante de forma tranversal (sobre a import\u00e2ncia da repetibilidade, rastreabilidade, acesso e o papel de blocos Jupyter no contexto de open science ver, entre outros, exemplos em ecologia *(Powers and Hampton 2019)* astronomia *(Wofford et al. 2019)*).\n\nA plataforma utilizada para o desenvolvimento deste trabalho \u00e9 a IBM Watson Data Platform (https://dataplatform.cloud.ibm.com/), que permite a utiliza\u00e7\u00e3o de *notebooks* Jupyter no contexto de uma gest\u00e3o integral do processo de _Data Science_.\n\n\ud83d\udc49 *NB: a visualiza\u00e7\u00e3o do conte\u00fado deste bloco de notas ter\u00e1 varia\u00e7\u00f5es conforme a forma que estiver a ser manipulado; na sua vers\u00e3o \"est\u00e1tica\" os diagramas e tabelas ser\u00e3o tamb\u00e9m eles est\u00e1ticos. \u00c9, contudo, feito para poder ser instanciado e executado de forma interactiva, sendo a forma mais r\u00e1pida a cria\u00e7\u00e3o de uma conta gratuita na IBM Watson Data Platform (com acesso ao Watson Studio) e importa\u00e7\u00e3o deste trabalho, ou a utiliza\u00e7\u00e3o de Binder como descrito na sec\u00e7\u00e3o [Execu\u00e7\u00e3o do bloco de notas](#Execu\u00e7\u00e3o-do-bloco-de-notas).*"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Metodologia"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Com base nos dados disponibilizados pela Assembleia da Rep\u00fablica em formato XML _(\u201cDados Abertos\u201d 2020)_ s\u00e3o criadas _dataframes_ (tabelas de duas dimens\u00f5es) com base no processamento e selec\u00e7\u00e3o de informa\u00e7\u00e3o relativa aos padr\u00f5es de vota\u00e7\u00e3o de cada partido (e/ou deputados n\u00e3o-inscritos).\n\nS\u00e3o fundamentalmente feitas as seguintes an\u00e1lises:\n\n1. Vista geral das vota\u00e7\u00f5es de cada partido, visualizado atrav\u00e9s de um _heatmap_\n2. Matriz de dist\u00e2ncia euclidiana entre todos os partidos e visualiza\u00e7\u00e3o de *clustering* hier\u00e1rquico atrav\u00e9s de um dendograma e m\u00e9todo de Ward.\n3. Identifica\u00e7\u00e3o de grupos (*clustering*) por DBSCAN e *Spectral Clustering*, com cria\u00e7\u00e3o de matriz de afinidade\n4. Redu\u00e7\u00e3o das dimens\u00f5es e visualiza\u00e7\u00e3o das dist\u00e2ncias e agrupamentos num espa\u00e7o cartesiano a duas e tr\u00eas dimens\u00f5es atrav\u00e9s de *Multidimensional Scaling* (MDS)\n\nA utilidade deste tipo de an\u00e1lise em ci\u00eancia pol\u00edtica \u00e9 reconhecida *(Figueiredo Filho et al. 2014)* e tem sido aplicada a v\u00e1rios registos de vota\u00e7\u00f5es; a an\u00e1lise presente tem como principal diferen\u00e7a o ser efectuado sobre as vota\u00e7\u00f5es de partidos e n\u00e3o, como \u00e9 mais comum na bibliografia consultada, a deputados individuais.\n\nO tratamento pr\u00e9vio dos dados em formato XML \u00e9 feito de forma a seleccionar as vota\u00e7\u00f5es de cada partido (ou deputado n\u00e3o inscrito), num processo com alguma complexidade que \u00e9 por isso detalhado em sec\u00e7\u00e3o pr\u00f3pria do Ap\u00eandice\n\nA informa\u00e7\u00e3o \u00e9 obtida a partir das listas publicadas de vota\u00e7\u00f5es relativas a\n\n- Actividades\n- Iniciativas\n\nOs dados utilizados s\u00e3o um subconjuntos dos disponibilizados, sendo que qualquer erro ou omiss\u00e3o nos dados originais ir\u00e1 ter imediato reflexo nos resultados das an\u00e1lises.\n\nEste trabalho n\u00e3o tem como objectivo principal apenas mostrar os resultados finais, mas tamb\u00e9m (ou fundamentalmente) todo o processo comum em *Data Science* para que se chegue at\u00e9 eles; \u00e9 por isso pleno de blocos de c\u00f3digo que tent\u00e1mos contextualizar com descri\u00e7\u00f5es que tornem a sua compreens\u00e3o dispens\u00e1vel para a quem n\u00e3o interessem esss detalhes, ao mesmo tempo que desvi\u00e1mos para sec\u00e7\u00f5es do Ap\u00eandice discuss\u00f5es mais extensas de v\u00e1rios passos. Requer, ainda assim, algum esfor\u00e7o em termos de seguir o caminho (por vezes pejado de desvios, atalhos e retrocessos) at\u00e9 aos diversos resultados apresentados.\n\n\ud83d\udc49 *NB: O processo de tratamento de dados n\u00e3o \u00e9 indiferente para o resultado final: *s\u00e3o feitas escolhas a v\u00e1rios n\u00edveis (desde a selec\u00e7\u00e3o dos dados considerados importantes aos algoritmos escolhidos) que t\u00eam impacto nos resultados, nem que seja por omiss\u00e3o*. Mais do que evit\u00e1-lo (o que n\u00e3o seria poss\u00edvel), opt\u00e1mos por identificar de forma clara as escolhas feitas e explicar as raz\u00f5es que levaram \u00e0 sua escolha: cada leitor poder\u00e1 assim determinar a razoabilidade de cada uma e, sobretudo, ensaiar novas formas que considere mais adequadas.*"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Instala\u00e7\u00e3o de pr\u00e9-requisitos\n\nComo primeiro passo definimos alguns valores que dever\u00e3o ser utilizados pelo bloco de notas Jupyter para exibir tabelas e diagramas e algumas bibliotecas essenciais que s\u00e3o importantes para poder executar o bloco de notas de forma independente (nomeadamente em ambientes Jupyter exteriores ao IBM Watson Data Platform como o Binder https://mybinder.org/ )."
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/javascript": "require.config({\n    paths: {\n        datatables: 'https://cdn.datatables.net/1.10.19/js/jquery.dataTables.min',\n    }\n});\n\n$('head').append('<link rel=\"stylesheet\" type=\"text/css\" \\\n                href = \"https://cdn.datatables.net/1.10.19/css/jquery.dataTables.min.css\" > ');\n\n$('head').append('<style> table td { text-overflow: ellipsis; overflow: hidden; } </style>');\n\n$('head').append(`<script>\nfunction eval_functions(map_or_text) {\n    if (typeof map_or_text === \"string\") {\n        if (map_or_text.startsWith(\"function\")) {\n            try {\n                // Note: parenthesis are required around the whole expression for eval to return a value!\n                // See https://stackoverflow.com/a/7399078/911298.\n                //\n                // eval(\"local_fun = \" + map_or_text) would fail because local_fun is not declared\n                // (using var, let or const would work, but it would only be declared in the local scope\n                // and therefore the value could not be retrieved).\n                const func = eval(\"(\" + map_or_text + \")\");\n                if (typeof func !== \"function\") {\n                    // Note: backquotes are super convenient!\n                    // https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Template_literals\n                    console.error(\"Evaluated expression \" + map_or_text + \" is not a function (type is \" + typeof func + \")\");\n                    return map_or_text;\n                }\n                // Return the function\n                return func;\n            } catch (e) {\n                // Make sure to print the error with a second argument to console.error().\n                console.error(\"itables was not able to parse \" + map_or_text, e);\n            }\n        }\n    } else if (typeof map_or_text === \"object\") {\n        if (map_or_text instanceof Array) {\n            // Note: \"var\" is now superseded by \"let\" and \"const\".\n            // https://medium.com/javascript-scene/javascript-es6-var-let-or-const-ba58b8dcde75\n            const result = [];\n            // Note: \"for of\" is the best way to iterate through an iterable.\n            // https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/for...of\n            for (const item of map_or_text) {\n                result.push(eval_functions(item));\n            }\n            return result;\n\n            // Alternatively, more functional approach in one line:\n            // return map_or_text.map(eval_functions);\n        } else {\n            const result = {};\n            // Object.keys() is safer than \"for in\" because otherwise you might have keys\n            // that aren't defined in the object itself.\n            //\n            // See https://stackoverflow.com/a/684692/911298.\n            for (const item of Object.keys(map_or_text)) {\n                result[item] = eval_functions(map_or_text[item]);\n            }\n            return result;\n        }\n    }\n\n    return map_or_text;\n}\n</` + 'script>');",
                        "text/plain": "<IPython.core.display.Javascript object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "!pip install -q  itables matplotlib pandas bs4 html5lib lxml seaborn sklearn pixiedust\n\n%matplotlib notebook\n\nfrom itables import show\nimport itables.options as opt\n\nopt.maxColumns=100\nopt.maxRows=2000\nopt.lengthMenu = [10, 20, 50, 100, 200, 500]\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Obten\u00e7\u00e3o, limpeza e tratamento dos dados\n\n\nEsta fase \u00e9 fundamental para toda a restante an\u00e1lise: \u00e9 onde obtemos os dados e os transformamos em informa\u00e7\u00e3o num formato que pode ser facilmente manipulado."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Obten\u00e7\u00e3o do ficheiro\n\nNa p\u00e1gina do Parlamento podem ser obtidos, para cada legislatura, ficheiros em formato JSON ou XML com a informa\u00e7\u00e3o relativa a cada \"\u00e1rea tem\u00e1tica\". Para a XIV Legislatura estamos interessados em duas dessas \u00e1reas:\n\n- **Iniciativas:** _\"...Da lista de tipo de Iniciativas entradas, constam Projetos e Propostas de Lei, Projetos e Propostas de Resolu\u00e7\u00e3o, Aprecia\u00e7\u00f5es Parlamentares, Inqu\u00e9ritos Parlamentares, Projetos de Revis\u00e3o Constitucional, Projetos de Delibera\u00e7\u00e3o, Projetos de Regimento, Ratifica\u00e7\u00f5es, sendo as mais usuais, projetos de lei que s\u00e3o apresentados por Deputados, Grupos Parlamentares ou um grupo de cidad\u00e3os, e as propostas de lei apresentadas pelo Governo e pelas Assembleias Legislativas das Regi\u00f5es Aut\u00f3nomas.\"_\n- **Actividades:** _\"...Consideram-se Atividades Parlamentares as seguintes atividades: Aprecia\u00e7\u00e3o de relat\u00f3rios entregues por entidades externas, Audi\u00eancias, Audi\u00e7\u00f5es, Cerim\u00f3nias, Conta Geral do Estado, Debates, Declara\u00e7\u00f5es Pol\u00edticas, Defesa Nacional, Desloca\u00e7\u00f5es no \u00e2mbito das Comiss\u00f5es, Desloca\u00e7\u00f5es do Presidente da Rep\u00fablica, Elei\u00e7\u00e3o e composi\u00e7\u00e3o para \u00f3rg\u00e3os externos, Eventos no \u00e2mbito de Comiss\u00f5es, Grandes Op\u00e7\u00f5es do Conceito Estrat\u00e9gico da Defesa Nacional, Interpela\u00e7\u00f5es ao Governo, Mo\u00e7\u00f5es, Or\u00e7amento e Conta de Ger\u00eancia da AR, Orienta\u00e7\u00e3o da Pol\u00edtica Or\u00e7amental, Perguntas ao Governo, Programa de Estabilidade e Crescimento, Programa do Governo, Relat\u00f3rios de Seguran\u00e7a Interna, Seguran\u00e7a Interna, Votos.\"_\n\nEstas \u00e1reas concentram a maioria (se n\u00e3o a totalidade) das vota\u00e7\u00f5es dos partidos, algo que tendo em conta o objectivo \u00e9 fundamental: para a an\u00e1lise proposta s\u00f3 interessam eventos onde seja poss\u00edvel extra\u00edr a informa\u00e7\u00e3o da vota\u00e7\u00e3o, e dentro destes (como veremos) os que sejam directamente imput\u00e1veis a partidos.\n\nComen\u00e7ando pelas **iniciativas parlamentares**, definimos o URL da vers\u00e3o em XML do ficheiro; para o processamento deste formato usaremos `Element.Tree`, uma das op\u00e7\u00f5es em Python para este fim que vem inclu\u00edda de base, pelo que o seguinte bloco descarrega o ficheiro e converte-o numa \"\u00e1rvore\" (onde os \"ramos\" e as \"folhas\" s\u00e3o subdivis\u00f5es da informa\u00e7\u00e3o dado o formato hier\u00e1rquico)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": "from urllib.request import urlopen\nimport xml.etree.ElementTree as ET\n\nini_url = 'http://app.parlamento.pt/webutils/docs/doc.xml?path=6148523063446f764c324679626d56304c3239775a57356b595852684c3052685a47397a51574a6c636e52766379394a626d6c6a6157463061585a68637939595356596c4d6a424d5a57647063327868644856795953394a626d6c6a6157463061585a686331684a566935346257773d&fich=IniciativasXIV.xml&Inline=true'\nini_tree = ET.parse(urlopen(ini_url))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Formato XML via Element.Tree, autoria das iniciativas e transforma\u00e7\u00f5es preliminares\n\nEsta \u00e1rvore pode ser visualizada abrindo o ficheiro original num editor de c\u00f3digo que o formate de forma adequada (nomeadamente atrav\u00e9s de indenta\u00e7\u00e3o dos blocos); trata-se de um ficheiro relativamente grande (~30 MB) e tem um conte\u00fado estruturado da forma habitual para o formato como pode ser observado na seguinte extrac\u00e7\u00e3o das primeiras linhas:"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<ArrayOfPt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut>\n <pt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut>\n  <iniNr>\n   28\n  </iniNr>\n  <iniTipo>\n   A\n  </iniTipo>\n  <iniDescTipo>\n   Aprecia\u00e7\u00e3o Parlamentar\n  </iniDescTipo>\n  <iniLeg>\n   XIV\n  </iniLeg>\n  <iniSel>\n   1\n  </iniSel>\n  <data/>\n </pt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut>\n</ArrayOfPt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut>\n"
                }
            ],
            "source": "## Import BeautifulSoup for the pretty-priting\nfrom bs4 import BeautifulSoup\n\n## Get a string with the XML from the root down\nxmlstr = ET.tostring(ini_tree.getroot(), encoding='utf8', method='xml')\n\n## Print the first 300 charaters; note that extra tags will be added to make the output \"valid\"\nprint(BeautifulSoup(xmlstr[0:300], \"xml\").prettify())"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O c\u00f3digo final \u00e9 o resultado final de um processo de estudo do formato (com tentativas, avan\u00e7os e recuos) mas, em termos gerais, o seguinte bloco diz-nos que:\n\n- A vari\u00e1vel `ini_tree` \u00e9 do tipo `xml.etree.ElementTree.ElementTree`\n- Na ra\u00edz da \u00e1rvore est\u00e1 um conjunto (`array`) de elementos\n- Esses elementos t\u00eam a etiqueta `pt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut`\n\nPor sua vez estes \u00faltimos elementos s\u00e3o constituidos por mais \"ramos\" e \"folhas\" que cont\u00e9m informa\u00e7\u00e3o sobre a iniciativa: quem a prop\u00f4s, a fases, as vota\u00e7\u00f5es e os seus resultados, etc."
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "<class 'xml.etree.ElementTree.ElementTree'>\n[<Element 'ArrayOfPt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut' at 0x7faa087dbc28>]\n<Element 'pt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut' at 0x7faa087dbb38>\n<Element 'pt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut' at 0x7faa08808e58>\n<Element 'pt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut' at 0x7faa08310b38>\n<Element 'pt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut' at 0x7faa08315868>\n<Element 'pt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut' at 0x7faa0831ad68>\n"
                }
            ],
            "source": "print(type(ini_tree))\nprint(ini_tree.findall(\".\"))\ncounter = 0\nfor initiative in ini_tree.findall('.')[0]:\n    ## Only provide an example of the first lines\n    if counter < 5:\n        print(initiative)\n    counter += 1"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O total de iniciativas \u00e9-nos dados pelo valor acumulado no bloco anterior:"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Total de iniciativas contidas na \u00e1rvore:  1466\n"
                }
            ],
            "source": "print(\"Total de iniciativas contidas na \u00e1rvore: \", counter)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Dada a import\u00e2ncia para o objectivo proposto \u00e9 \u00fatil descrever o elemento `pt_gov_ar_objectos_VotacaoOut` que cont\u00e9m a informa\u00e7\u00e3o sobre vota\u00e7\u00f5es; podemos come\u00e7ar por contar as entradas na \u00e1rvore:"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................."
                }
            ],
            "source": "counter = 0\nfor c in ini_tree.findall(\".//pt_gov_ar_objectos_VotacaoOut\"):\n    print('.', end='')\n    counter += 1"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Tal como anteriormente o acumulador indica-nos o total, neste caso de vota\u00e7\u00f5es:"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Total de vota\u00e7\u00f5es contidas na \u00e1rvore:  2057\n"
                }
            ],
            "source": "print(\"Total de vota\u00e7\u00f5es contidas na \u00e1rvore: \", counter)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O seguinte bloco encontra _apenas a primeira ocorr\u00eancia_, para simplificar a discuss\u00e3o dos dados apenas a um caso:"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "id             : 88184\nresultado      : Aprovado\ndescricao      : Texto Final apresentado pela Comiss\u00e3o de Administra\u00e7\u00e3o P\u00fablica, Moderniza\u00e7\u00e3o Administrativa, Descentraliza\u00e7\u00e3o e Poder Local relativo \u00e0s Aprecia\u00e7\u00f5es Parlamentares n.\u00bas 21/XIV/1.\u00aa (PSD); 22/XIV/1.\u00aa (BE); e 23/XIV/1.\u00aa (PCP)\nreuniao        : 76\ntipoReuniao    : RP\ndetalhe        : A Favor: <I>PS</I>, <I>PSD</I><BR>Contra: <I>BE</I>, <I>PCP</I>, <I>CDS-PP</I>, <I>PAN</I>, <I>PEV</I>, <I>CH</I>, Cristina Rodrigues (Ninsc), Joacine Katar Moreira (Ninsc)<BR>Absten\u00e7\u00e3o: <I>IL</I>\n"
                }
            ],
            "source": "## Get the first occurence of a \"VotacaoOut\" entry\nfor c in ini_tree.find(\".//pt_gov_ar_objectos_VotacaoOut\"):\n    print(\"{0:15}: {1}\".format(c.tag,c.text))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Note-se para j\u00e1 que existe informa\u00e7\u00e3o que n\u00e3o est\u00e1 presente (nomeadamente a autoria); a autoria da _iniciativa_ pode ser determinada facilmente mas n\u00e3o se relaciona de forma directa com a autoria da mat\u00e9ria em vota\u00e7\u00e3o, e existem iniciativas sem vota\u00e7\u00f5es associadas (cf. [Iniciativas e vota\u00e7\u00f5es](#Iniciativas-e-vota\u00e7\u00f5es))\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Vota\u00e7\u00f5es: de descri\u00e7\u00e3o textual a informa\u00e7\u00e3o estruturada\n\n\u00c9  importante notar que o o resultado da vota\u00e7\u00e3o est\u00e1 em \"texto livre\", o que nos remete para a diferen\u00e7a entre dados estruturados e n\u00e3o-estruturados  [DataStructureData2018]; de forma simples significa que antes de podermos utilizar os dados temos de os transformar pois o que temos no campo `detalhe` n\u00e3o pode ser usado sem ser convertido numa tabela com o par _partido/vota\u00e7\u00e3o_ "
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "\tid             : 88184\n\tresultado      : Aprovado\n\tdescricao      : Texto Final apresentado pela Comiss\u00e3o de Administra\u00e7\u00e3o P\u00fablica, Moderniza\u00e7\u00e3o Administrativa, Descentraliza\u00e7\u00e3o e Poder Local relativo \u00e0s Aprecia\u00e7\u00f5es Parlamentares n.\u00bas 21/XIV/1.\u00aa (PSD); 22/XIV/1.\u00aa (BE); e 23/XIV/1.\u00aa (PCP)\n\treuniao        : 76\n\ttipoReuniao    : RP\n\tdetalhe        : A Favor: <I>PS</I>, <I>PSD</I><BR>Contra: <I>BE</I>, <I>PCP</I>, <I>CDS-PP</I>, <I>PAN</I>, <I>PEV</I>, <I>CH</I>, Cristina Rodrigues (Ninsc), Joacine Katar Moreira (Ninsc)<BR>Absten\u00e7\u00e3o: <I>IL</I>\n"
                }
            ],
            "source": "## Find the first instance of VotacaoOut\nfor c in ini_tree.find(\".//pt_gov_ar_objectos_VotacaoOut\"):\n        print(\"\\t{0:15}: {1}\".format(c.tag, c.text))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O texto contido no `detalhe` tem informa\u00e7\u00e3o descritiva que cont\u00e9m formata\u00e7\u00e3o HTML. \u00c9 necess\u00e1rio processar o texto de forma a poder criar uma tabela que associe a cada partido (ou deputado n\u00e3o-inscrito) o seu sentido de voto. Este processamento seria simples se existisse sempre uma \u00fanica forma de descrever as vota\u00e7\u00f5es, o que n\u00e3o acontece."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Partidos e deputados na determina\u00e7\u00e3o do sentido de voto"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O tratamento e transforma\u00e7\u00e3o de dados \u00e9 um processo que por vezes parece estar sempre inacabado; seja 80% do tempo gasto _(\u201cCleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says\u201d 2020)_ ou menos _(Dodds 2020)_ tem sempre uma import\u00e2ncia fundamental. At\u00e9 agora cobrimos j\u00e1 v\u00e1rios aspectos que nos colocam muito pr\u00f3ximos da utiliza\u00e7\u00e3o dos dados de forma estruturada, mas ainda restam detalhes fundamentais.\n\nUm ponto importante a relembrar: a metodologia adoptada e os objectivos da an\u00e1lise prendem-se com o posionamento relativo de partidos pol\u00edticos, por via dos seus grupos parlamentares ou deputados individuais, e n\u00e3o com o perfil de vota\u00e7\u00e3o de cada deputado. O sistema parlamentar portugu\u00eas assenta na representatividade por via de elei\u00e7\u00e3o de deputados eleitos por partidos mas os deputados votam de forma individual. Com varia\u00e7\u00f5es dependendo dos partidos. A chamada \"disciplina partid\u00e1ria\" \u00e9 a regra, por raz\u00f5es que n\u00e3o s\u00e3o \u00fanicas do sistema pol\u00edtico portugu\u00eas _(Jackson 1968)_.\n\nEsta realidade est\u00e1 presente na forma como os votos s\u00e3o apresentados por partidos nos registos oficiais mas apresenta tamb\u00e9m excep\u00e7\u00f5es importantes:\n\n1. **Vota\u00e7\u00f5es nominais onde todos os votos s\u00e3o descritos de forma individual.**\n2. **Vota\u00e7\u00f5es onde existem votos de deputados diferentes do seu grupo** sendo apresentados de forma nominal.\n3. **Vota\u00e7\u00f5es onde existem votos de deputados diferentes do seu grupo***, apresentados como um n\u00famero total de deputados do grupo com esse sentido divergente.\n4. **Vota\u00e7\u00f5es onde os votos s\u00e3o indicados sem excep\u00e7\u00f5es**, sendo este o caso mais comum e tamb\u00e9m mais simples de cobrir.\n\nNos tr\u00eas primeiros casos n\u00e3o \u00e9 imediata a posi\u00e7\u00e3o de cada partido, e uma op\u00e7\u00e3o seria excluir estas vota\u00e7\u00f5es completamente. Outra op\u00e7\u00e3o, e a que escolhemos, foi o de **determinar o sentido de voto com base na maioria dos votos**.\n\nO reduzido n\u00famero de vota\u00e7\u00f5es por este m\u00e9todo tornam esta decis\u00e3o de impacto reduzido, mas n\u00e3o deixa de existir: olhando para as vota\u00e7\u00f5es relativas \u00e0 eutan\u00e1sia \u00e9 conhecida a forma diferenciada como foi votada. A sua elimina\u00e7\u00e3o iria, por exemplo, omitir dados que afastam a IL do CDS-PP e CHEGA, \u00e0 direita, e o PCP do PEV e BE, \u00e0 esquerda. _O reduzido n\u00famero de casos n\u00e3o deve remover a necessidade de se assumir a possibilidade do impacto ser relevante quando mais \u00e0 frente verificarmos que a dist\u00e2ncia relativa de partidos (em particular \u00e0 direita) \u00e9 muito semelhante_, o que torna qualquer observa\u00e7\u00e3o adicional pass\u00edvel de impactar o agrupamento e particularmnte o dendograma, raz\u00e3o pela qual consider\u00e1mos adequada a decis\u00e3o de extrapolar a decis\u00e3o da maioria dos deputados de um partido como indicador da posi\u00e7\u00e3o do partido.\n\nOs detalhes do processamento para cada tipo de vota\u00e7\u00e3o \u00e9 discutido em detalhe no ap\u00eandice [Processamento da descri\u00e7\u00e3o de votos](#Processamento-da-descri\u00e7\u00e3o-de-votos), com os respectivos exemplos. O seguinte bloco de c\u00f3digo \u00e9 o resultado final do tratamento dos v\u00e1rios casos:"
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [],
            "source": "from bs4 import BeautifulSoup\nimport re\n\n## Iteract through the existing dict\ndef party_from_votes (votes):\n    \"\"\"\n    Determines the position of a party based on the majority position by summing all the individual votes.\n    Argument is a dictionary returned by parse_voting()\n    Returns a dictionary with the majority position of each party\n    \"\"\"\n    party_vote = {}\n    for k, v in votes.items():\n        ## Erase the name of the MP and keep the party only\n        ## only when it's not from the \"Ninsc\" group - \n        ## these need to be differentiated by name\n        if re.match(\".*\\(Ninsc\\)\" , k) is None:\n            nk = re.sub(r\".*\\((.+?)\\).*\", r\"\\1\", k)\n        else:\n            nk = k\n        ## If it's the first entry for a key, create it\n        if nk not in party_vote:\n            party_vote[nk] = [0,0,0]\n        ## Add to a specific index in a list\n        if v == \"A Favor\":\n            party_vote[nk][0] += 1\n        elif v == \"Absten\u00e7\u00e3o\":\n            party_vote[nk][1] += 1\n        elif v == \"Contra\":\n            party_vote[nk][2] += 1\n    for k,v in party_vote.items():\n        party_vote[k]=[\"A Favor\", \"Absten\u00e7\u00e3o\", \"Contra\"][v.index(max(v))]\n    return party_vote\n\ndef parse_voting(v_str):\n    \"\"\"Parses the voting details in a string and returns a dict.\n    \n    Keyword arguments:\n    \n    v_str: a string with the description of the voting behaviour.\n    \"\"\"\n    ## Split by the HTML line break and put it in a dict\n    d = dict(x.split(':') for x in v_str.split('<BR>'))\n    ## Remove the HTML tags\n    for k, v in d.items():\n        ctext = BeautifulSoup(v, \"lxml\")\n        d[k] = ctext.get_text().strip().split(\",\")\n    ## Invert the dict to get a 1-to-1 mapping\n    ## and trim it\n    votes = {}\n    if len(v_str) < 1000:    # Naive approach but realistically speaking... works well enough.\n        for k, v in d.items():\n            for p in v:\n                if (p != ' ' and                                       # Bypass empty entries\n                    re.match(\"[0-9]+\", p.strip()) is None and           # Bypass quantified divergent voting patterns\n                    (re.match(\".*\\w +\\(.+\\)\", p.strip()) is None or     # Bypass individual votes...\n                     re.match(\".*\\(Ninsc\\)\" , p.strip()) is not None)): # ... except when coming from \"Ninsc\"\n                        #print(\"|\"+ p.strip() + \"|\" + \":\\t\" + k)\n                        votes[p.strip()] = k\n    else:  # This is a nominal vote since the size of the string is greater than 1000\n        for k, v in d.items():\n            for p in v:\n                if p != ' ':\n                    votes[p.strip()] = k\n        ## Call the auxiliary function to produce the party position based on the majority votes\n        votes = party_from_votes(votes)\n    return votes\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O resultado da vota\u00e7\u00e3o nominal relativa \u00e0 eutan\u00e1sia que foi dada como primeiro exemplo \u00e9 simplificado e apresenta a vota\u00e7\u00e3o maiorit\u00e1ria de cada partido como aquela que ser\u00e1 considerada (um tratamento semelhante, por simples omiss\u00e3o, \u00e9 dado \u00e0s restantes varia\u00e7\u00f5es):"
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "\tid             : 86004\n\tresultado      : Aprovado\n\treuniao        : 32\n\ttipoReuniao    : RP\n\tdetalhe        : {'PS': 'A Favor', 'PSD': 'Contra', 'BE': 'A Favor', 'PAN': 'A Favor', 'Joacine Katar Moreira (Ninsc)': 'A Favor', 'IL': 'A Favor', 'PCP': 'Contra', 'CDS-PP': 'Contra', 'CH': 'Contra', 'PEV': 'Absten\u00e7\u00e3o'}\n"
                }
            ],
            "source": "for v in ini_tree.find(\".//pt_gov_ar_objectos_VotacaoOut/[id='86004']\"):\n    if v.tag == \"detalhe\":\n        print(\"\\t{0:15}: {1}\".format(v.tag,parse_voting(v.text)))\n    else:\n        print(\"\\t{0:15}: {1}\".format(v.tag,v.text))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Deputados n\u00e3o-inscritos\n\nExiste um tratamento especial no c\u00f3digo para os deputados n\u00e3o-inscritos de forma a evitar que nestes casos seja removido o nome; recentemente foram aprovadas altera\u00e7\u00f5es que permitem a diferencia\u00e7\u00e3o de forma mais clara entre as duas deputadas n\u00e3o-inscritas _(Lusa 2020a)_  mas que n\u00e3o se aplicam retroactivamente aos dados existentes.\n\nEsta situa\u00e7\u00e3o leva a que seja necess\u00e1rio diferenciar pelo nome, caso contr\u00e1rio na seguinte vota\u00e7\u00e3o seriam contabilizados votos para um grupo/partido _\"Ninsc\"_, o que n\u00e3o faria sentido."
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "\tid             : 88184\n\tresultado      : Aprovado\n\tdescricao      : Texto Final apresentado pela Comiss\u00e3o de Administra\u00e7\u00e3o P\u00fablica, Moderniza\u00e7\u00e3o Administrativa, Descentraliza\u00e7\u00e3o e Poder Local relativo \u00e0s Aprecia\u00e7\u00f5es Parlamentares n.\u00bas 21/XIV/1.\u00aa (PSD); 22/XIV/1.\u00aa (BE); e 23/XIV/1.\u00aa (PCP)\n\treuniao        : 76\n\ttipoReuniao    : RP\n\tdetalhe        : {'PS': 'A Favor', 'PSD': 'A Favor', 'BE': 'Contra', 'PCP': 'Contra', 'CDS-PP': 'Contra', 'PAN': 'Contra', 'PEV': 'Contra', 'CH': 'Contra', 'Cristina Rodrigues (Ninsc)': 'Contra', 'Joacine Katar Moreira (Ninsc)': 'Contra', 'IL': 'Absten\u00e7\u00e3o'}\n"
                }
            ],
            "source": "## Example of vote where two MPs which are not registered with a party would\n## be grouped under a generic \"Unregistered\" group\nfor v in ini_tree.find(\".//pt_gov_ar_objectos_VotacaoOut/[id='88184']\"):\n    if v.tag == \"detalhe\":\n        print(\"\\t{0:15}: {1}\".format(v.tag,parse_voting(v.text)))\n    else:\n        print(\"\\t{0:15}: {1}\".format(v.tag,v.text))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Cria\u00e7\u00e3o do _dataframe_\n\nAp\u00f3s estes v\u00e1rios desvios (fundamentais para que se consiga produzir o resultado necess\u00e1rio) voltamos \u00e0 representa\u00e7\u00e3o em \u00e1rvore que resulta da interpreta\u00e7\u00e3o do formato XML da vota\u00e7\u00e3o e com o aux\u00edlio das v\u00e1rias fun\u00e7\u00f5es \u00e1 entretanto definidas criamos um modelo tabular de todas as vota\u00e7\u00f5es."
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ".........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................2050\n"
                }
            ],
            "source": "import collections\n\nroot = ini_tree\n\ncounter=0\n\n## We will build a dataframe from a list of dicts\n## Inspired by the approach of Chris Moffitt here https://pbpython.com/pandas-list-dict.html\ninit_list = []\n\nfor voting in ini_tree.findall(\".//pt_gov_ar_objectos_VotacaoOut\"):\n    votep = voting.find('./detalhe')\n    if votep is not None:\n        init_dict = collections.OrderedDict()\n        counter +=1                 \n        init_dict['id'] = voting.find('id').text\n        ## Add the \"I\" for Type to mark this as coming from \"Iniciativas\"\n        init_dict['Tipo'] = \"I\"\n        for c in voting:\n            if c.tag == \"detalhe\":\n                for party, vote in parse_voting(c.text).items():\n                    init_dict[party] = vote \n            elif c.tag == \"descricao\":\n                    init_dict[c.tag] = c.text\n            elif c.tag == \"ausencias\":\n                    init_dict[c.tag] = c.find(\"string\").text\n            else:\n                    init_dict[c.tag] = c.text\n        init_list.append(init_dict)\n    ## Provide progression feedback\n    print('.', end='')\n        \nprint(counter)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O processamento criou uma lista de dicion\u00e1rios que convertemos numa _dataframe_ Pandas. Note-se como v\u00e1rias vota\u00e7\u00f5es n\u00e3o t\u00eam o campo `descricao`: obter a informa\u00e7\u00e3o do t\u00edtulo da iniciativa seria uma op\u00e7\u00e3o possivel para permitir adicionar contexto a estes casos, mas como referido, n\u00e3o sendo tal necess\u00e1rio para os objectivos imediatos deste trabalho, optou-se por simplificar o c\u00f3digo e omitir essa informa\u00e7\u00e3o (ver [Informa\u00e7\u00e3o adicional da iniciativa](#Informa\u00e7\u00e3o-adicional-da-iniciativa)  para um exemplo)"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(2050, 20)\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>Tipo</th>\n      <th>resultado</th>\n      <th>descricao</th>\n      <th>reuniao</th>\n      <th>tipoReuniao</th>\n      <th>PS</th>\n      <th>PSD</th>\n      <th>BE</th>\n      <th>PCP</th>\n      <th>CDS-PP</th>\n      <th>PAN</th>\n      <th>PEV</th>\n      <th>CH</th>\n      <th>Cristina Rodrigues (Ninsc)</th>\n      <th>Joacine Katar Moreira (Ninsc)</th>\n      <th>IL</th>\n      <th>unanime</th>\n      <th>L</th>\n      <th>ausencias</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>88184</td>\n      <td>I</td>\n      <td>Aprovado</td>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Admin...</td>\n      <td>76</td>\n      <td>RP</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>88183</td>\n      <td>I</td>\n      <td>Aprovado</td>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Admin...</td>\n      <td>76</td>\n      <td>RP</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>88181</td>\n      <td>I</td>\n      <td>Aprovado</td>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Admin...</td>\n      <td>76</td>\n      <td>RP</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>87725</td>\n      <td>I</td>\n      <td>Aprovado</td>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...</td>\n      <td>68</td>\n      <td>RP</td>\n      <td>Contra</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>88346</td>\n      <td>I</td>\n      <td>Aprovado</td>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Assun...</td>\n      <td>76</td>\n      <td>RP</td>\n      <td>Contra</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "      id Tipo resultado                                          descricao  \\\n0  88184    I  Aprovado  Texto Final apresentado pela Comiss\u00e3o de Admin...   \n1  88183    I  Aprovado  Texto Final apresentado pela Comiss\u00e3o de Admin...   \n2  88181    I  Aprovado  Texto Final apresentado pela Comiss\u00e3o de Admin...   \n3  87725    I  Aprovado  Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...   \n4  88346    I  Aprovado  Texto Final apresentado pela Comiss\u00e3o de Assun...   \n\n  reuniao tipoReuniao       PS      PSD       BE      PCP   CDS-PP      PAN  \\\n0      76          RP  A Favor  A Favor   Contra   Contra   Contra   Contra   \n1      76          RP  A Favor  A Favor   Contra   Contra   Contra   Contra   \n2      76          RP  A Favor  A Favor   Contra   Contra   Contra   Contra   \n3      68          RP   Contra  A Favor  A Favor  A Favor  A Favor  A Favor   \n4      76          RP   Contra  A Favor  A Favor  A Favor  A Favor  A Favor   \n\n       PEV       CH Cristina Rodrigues (Ninsc) Joacine Katar Moreira (Ninsc)  \\\n0   Contra   Contra                     Contra                        Contra   \n1   Contra   Contra                     Contra                        Contra   \n2   Contra   Contra                     Contra                        Contra   \n3  A Favor  A Favor                    A Favor                       A Favor   \n4  A Favor  A Favor                    A Favor                       A Favor   \n\n          IL unanime    L ausencias  \n0  Absten\u00e7\u00e3o     NaN  NaN       NaN  \n1  Absten\u00e7\u00e3o     NaN  NaN       NaN  \n2  Absten\u00e7\u00e3o     NaN  NaN       NaN  \n3    A Favor     NaN  NaN       NaN  \n4    A Favor     NaN  NaN       NaN  "
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "import pandas as pd\n\nini_df = pd.DataFrame(init_list)\nprint(ini_df.shape)\nini_df.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "A tabela cont\u00e9m 1723 linhas (o n\u00famero de vota\u00e7\u00f5es que tinhamos j\u00e1 observado) e 20 colunas; cada \"caso\" equivale a uma vota\u00e7\u00e3o com os dados respectivos indicados pelas colunas, como pode ser observado tomando o primeiro caso como exemplo:"
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "id                                                                           88184\nTipo                                                                             I\nresultado                                                                 Aprovado\ndescricao                        Texto Final apresentado pela Comiss\u00e3o de Admin...\nreuniao                                                                         76\ntipoReuniao                                                                     RP\nPS                                                                         A Favor\nPSD                                                                        A Favor\nBE                                                                          Contra\nPCP                                                                         Contra\nCDS-PP                                                                      Contra\nPAN                                                                         Contra\nPEV                                                                         Contra\nCH                                                                          Contra\nCristina Rodrigues (Ninsc)                                                  Contra\nJoacine Katar Moreira (Ninsc)                                               Contra\nIL                                                                       Absten\u00e7\u00e3o\nunanime                                                                        NaN\nL                                                                              NaN\nausencias                                                                      NaN\nName: 0, dtype: object"
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "ini_df.loc[0]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Nem todos os campos est\u00e3o preenchidos em todas as vota\u00e7\u00f5es, o que \u00e9 normal visto nem todos os campos serem obrigat\u00f3rios."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### N\u00e3o-inscritos e a necessidade de processamento adicional\n\nJ\u00e1 anteriormente o tratamento dos \"n\u00e3o inscritos\" levou \u00e0 necessidade de pr\u00e9-processamento; agora que j\u00e1 temos a estrutura de dados num _dataframe_ \u00e9 tamb\u00e9m necess\u00e1rio proceder a alguns passos de p\u00f3s-processamento que surgem ap\u00f3s uma leitura supercicial da tabela acima.\n\nExistem dois casos que precisam ser analisados de forma individual; na sec\u00e7\u00e3o [Processamento adicional dos n\u00e3o-inscritos](#Processamento-adicional-dos-n\u00e3o-inscritos) do ap\u00eandice detalhamos a informa\u00e7\u00e3o que levou \u00e0 decis\u00e3o tomada e que \u00e9 a seguinte:\n\n- Agrupar os dados do Livre e da deputada Joacine Katar Moreira, por ser deputada \u00fanica e assim existir uma continuidade em termos do registo de vota\u00e7\u00e3o.\n- N\u00e3o includir a deputada Cristina Rodrigues na compara\u00e7\u00e3o global dado o limitado n\u00famero de vota\u00e7\u00f5es.\n\nO seguinte bloco cria uma nova coluna, `L/JKM`, composta da sobreposi\u00e7\u00e3o dos votos de ambos."
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>descricao</th>\n      <th>L</th>\n      <th>Joacine Katar Moreira (Ninsc)</th>\n      <th>L/JKM</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Admin...</td>\n      <td>NaN</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Admin...</td>\n      <td>NaN</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Admin...</td>\n      <td>NaN</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Assun...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Assun...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - al\u00ednea a) do n.\u00ba...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - n.\u00ba 2 do artigo 4.\u00ba</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - al\u00ednea c) do n.\u00ba...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - al\u00ednea c) do n....</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - n.\u00ba 4 ao artigo 4.\u00ba</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - artigo 5.\u00ba-A</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - artigo 5.\u00ba-B</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - artigo 6.\u00ba-A</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - artigo 13.\u00ba-A</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Requerimento oral, apresentado pelo PS, solici...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 7 ao artigo...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 8 ao artigo...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 4 do artigo...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 1 do artigo...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 1 do artig...</td>\n      <td>NaN</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 2 do artigo...</td>\n      <td>NaN</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 3 do artig...</td>\n      <td>NaN</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 4 do artigo...</td>\n      <td>NaN</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 5 ao artigo...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 6 ao artigo...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 8 ao artigo...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2020</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2021</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2022</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2023</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2024</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2025</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2026</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2027</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2028</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2029</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2030</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2031</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>2032</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2033</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2034</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2035</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2036</th>\n      <td>Requerimento, apresentado pela PS solicitando ...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2037</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>2038</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>2039</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>2040</th>\n      <td>Requerimento oral, apresentado pelo PS, solici...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2041</th>\n      <td>Requerimento, apresentado pela PS solicitando ...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2042</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2043</th>\n      <td>Proposta do PSD, de substitui\u00e7\u00e3o do artigo 1.\u00ba...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2044</th>\n      <td>Proposta do PSD, de substitui\u00e7\u00e3o do artigo 2.\u00ba...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2045</th>\n      <td>Proposta do PSD, de aditamento de um novo arti...</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2046</th>\n      <td>Artigo 3.\u00ba da proposta de lei</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2047</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2048</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2049</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n  </tbody>\n</table>\n<p>2050 rows \u00d7 4 columns</p>\n</div>",
                        "text/plain": "                                              descricao    L  \\\n0     Texto Final apresentado pela Comiss\u00e3o de Admin...  NaN   \n1     Texto Final apresentado pela Comiss\u00e3o de Admin...  NaN   \n2     Texto Final apresentado pela Comiss\u00e3o de Admin...  NaN   \n3     Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...  NaN   \n4     Texto Final apresentado pela Comiss\u00e3o de Assun...  NaN   \n5     Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...  NaN   \n6     Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...  NaN   \n7     Texto Final apresentado pela Comiss\u00e3o de Assun...  NaN   \n8     Proposta de altera\u00e7\u00e3o do BE - al\u00ednea a) do n.\u00ba...  NaN   \n9     Proposta de altera\u00e7\u00e3o do BE - n.\u00ba 2 do artigo 4.\u00ba  NaN   \n10    Proposta de altera\u00e7\u00e3o do BE - al\u00ednea c) do n.\u00ba...  NaN   \n11    Proposta de altera\u00e7\u00e3o do PAN - al\u00ednea c) do n....  NaN   \n12    Proposta de altera\u00e7\u00e3o do BE - n.\u00ba 4 ao artigo 4.\u00ba  NaN   \n13          Proposta de altera\u00e7\u00e3o do PAN - artigo 5.\u00ba-A  NaN   \n14          Proposta de altera\u00e7\u00e3o do PAN - artigo 5.\u00ba-B  NaN   \n15           Proposta de altera\u00e7\u00e3o do BE - artigo 6.\u00ba-A  NaN   \n16          Proposta de altera\u00e7\u00e3o do BE - artigo 13.\u00ba-A  NaN   \n17                                                  NaN  NaN   \n18    Requerimento oral, apresentado pelo PS, solici...  NaN   \n19    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 7 ao artigo...  NaN   \n20    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 8 ao artigo...  NaN   \n21    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 4 do artigo...  NaN   \n22    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 1 do artigo...  NaN   \n23     Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 1 do artig...  NaN   \n24    Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 2 do artigo...  NaN   \n25     Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 3 do artig...  NaN   \n26    Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 4 do artigo...  NaN   \n27    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 5 ao artigo...  NaN   \n28    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 6 ao artigo...  NaN   \n29    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 8 ao artigo...  NaN   \n...                                                 ...  ...   \n2020                                                NaN  NaN   \n2021                                                NaN  NaN   \n2022                                                NaN  NaN   \n2023                                                NaN  NaN   \n2024                                                NaN  NaN   \n2025                                                NaN  NaN   \n2026                                                NaN  NaN   \n2027                                                NaN  NaN   \n2028                                                NaN  NaN   \n2029                                                NaN  NaN   \n2030                                                NaN  NaN   \n2031                                                NaN  NaN   \n2032                                                NaN  NaN   \n2033                                                NaN  NaN   \n2034                                                NaN  NaN   \n2035                                                NaN  NaN   \n2036  Requerimento, apresentado pela PS solicitando ...  NaN   \n2037                                                NaN  NaN   \n2038                                                NaN  NaN   \n2039                                                NaN  NaN   \n2040  Requerimento oral, apresentado pelo PS, solici...  NaN   \n2041  Requerimento, apresentado pela PS solicitando ...  NaN   \n2042                                                NaN  NaN   \n2043  Proposta do PSD, de substitui\u00e7\u00e3o do artigo 1.\u00ba...  NaN   \n2044  Proposta do PSD, de substitui\u00e7\u00e3o do artigo 2.\u00ba...  NaN   \n2045  Proposta do PSD, de aditamento de um novo arti...  NaN   \n2046                      Artigo 3.\u00ba da proposta de lei  NaN   \n2047                                                NaN  NaN   \n2048                                                NaN  NaN   \n2049                                                NaN  NaN   \n\n     Joacine Katar Moreira (Ninsc)      L/JKM  \n0                           Contra     Contra  \n1                           Contra     Contra  \n2                           Contra     Contra  \n3                          A Favor    A Favor  \n4                          A Favor    A Favor  \n5                          A Favor    A Favor  \n6                          A Favor    A Favor  \n7                          A Favor    A Favor  \n8                          A Favor    A Favor  \n9                          A Favor    A Favor  \n10                         A Favor    A Favor  \n11                         A Favor    A Favor  \n12                         A Favor    A Favor  \n13                         A Favor    A Favor  \n14                         A Favor    A Favor  \n15                         A Favor    A Favor  \n16                         A Favor    A Favor  \n17                         A Favor    A Favor  \n18                         A Favor    A Favor  \n19                         A Favor    A Favor  \n20                         A Favor    A Favor  \n21                         A Favor    A Favor  \n22                         A Favor    A Favor  \n23                          Contra     Contra  \n24                          Contra     Contra  \n25                          Contra     Contra  \n26                          Contra     Contra  \n27                         A Favor    A Favor  \n28                         A Favor    A Favor  \n29                         A Favor    A Favor  \n...                            ...        ...  \n2020                       A Favor    A Favor  \n2021                       A Favor    A Favor  \n2022                       A Favor    A Favor  \n2023                       A Favor    A Favor  \n2024                       A Favor    A Favor  \n2025                       A Favor    A Favor  \n2026                       A Favor    A Favor  \n2027                       A Favor    A Favor  \n2028                       A Favor    A Favor  \n2029                       A Favor    A Favor  \n2030                       A Favor    A Favor  \n2031                     Absten\u00e7\u00e3o  Absten\u00e7\u00e3o  \n2032                       A Favor    A Favor  \n2033                       A Favor    A Favor  \n2034                       A Favor    A Favor  \n2035                       A Favor    A Favor  \n2036                       A Favor    A Favor  \n2037                     Absten\u00e7\u00e3o  Absten\u00e7\u00e3o  \n2038                     Absten\u00e7\u00e3o  Absten\u00e7\u00e3o  \n2039                     Absten\u00e7\u00e3o  Absten\u00e7\u00e3o  \n2040                       A Favor    A Favor  \n2041                       A Favor    A Favor  \n2042                       A Favor    A Favor  \n2043                       A Favor    A Favor  \n2044                       A Favor    A Favor  \n2045                       A Favor    A Favor  \n2046                       A Favor    A Favor  \n2047                       A Favor    A Favor  \n2048                       A Favor    A Favor  \n2049                     Absten\u00e7\u00e3o  Absten\u00e7\u00e3o  \n\n[2050 rows x 4 columns]"
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "## Copy Livre voting record to new aggregate columns...\nini_df[\"L/JKM\"] = ini_df[\"L\"]\n## ... and fill the NAs with JKM voting record.\nini_df[\"L/JKM\"] = ini_df[\"L/JKM\"].fillna(ini_df[\"Joacine Katar Moreira (Ninsc)\"])\nini_df[[\"descricao\",\"L\",\"Joacine Katar Moreira (Ninsc)\",\"L/JKM\"]]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Processo semelhante aplicamos \u00e0 deputada Cristina Rodrigues para podermos fazer uma an\u00e1lise espec\u00edfica:"
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>descricao</th>\n      <th>PAN</th>\n      <th>Cristina Rodrigues (Ninsc)</th>\n      <th>PAN/CR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Admin...</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Admin...</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Admin...</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Assun...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Assun...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - al\u00ednea a) do n.\u00ba...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - n.\u00ba 2 do artigo 4.\u00ba</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - al\u00ednea c) do n.\u00ba...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - al\u00ednea c) do n....</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - n.\u00ba 4 ao artigo 4.\u00ba</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - artigo 5.\u00ba-A</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - artigo 5.\u00ba-B</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - artigo 6.\u00ba-A</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Proposta de altera\u00e7\u00e3o do BE - artigo 13.\u00ba-A</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Requerimento oral, apresentado pelo PS, solici...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 7 ao artigo...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 8 ao artigo...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 4 do artigo...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 1 do artigo...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 1 do artig...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 2 do artigo...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 3 do artig...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 4 do artigo...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 5 ao artigo...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 6 ao artigo...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 8 ao artigo...</td>\n      <td>A Favor</td>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2020</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2021</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2022</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2023</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2024</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2025</th>\n      <td>NaN</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>2026</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2027</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2028</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2029</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2030</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2031</th>\n      <td>NaN</td>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>2032</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2033</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2034</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2035</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2036</th>\n      <td>Requerimento, apresentado pela PS solicitando ...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2037</th>\n      <td>NaN</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>2038</th>\n      <td>NaN</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>2039</th>\n      <td>NaN</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>2040</th>\n      <td>Requerimento oral, apresentado pelo PS, solici...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2041</th>\n      <td>Requerimento, apresentado pela PS solicitando ...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2042</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2043</th>\n      <td>Proposta do PSD, de substitui\u00e7\u00e3o do artigo 1.\u00ba...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2044</th>\n      <td>Proposta do PSD, de substitui\u00e7\u00e3o do artigo 2.\u00ba...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2045</th>\n      <td>Proposta do PSD, de aditamento de um novo arti...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2046</th>\n      <td>Artigo 3.\u00ba da proposta de lei</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2047</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2048</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2049</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n  </tbody>\n</table>\n<p>2050 rows \u00d7 4 columns</p>\n</div>",
                        "text/plain": "                                              descricao        PAN  \\\n0     Texto Final apresentado pela Comiss\u00e3o de Admin...     Contra   \n1     Texto Final apresentado pela Comiss\u00e3o de Admin...     Contra   \n2     Texto Final apresentado pela Comiss\u00e3o de Admin...     Contra   \n3     Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...    A Favor   \n4     Texto Final apresentado pela Comiss\u00e3o de Assun...    A Favor   \n5     Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...    A Favor   \n6     Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade...    A Favor   \n7     Texto Final apresentado pela Comiss\u00e3o de Assun...    A Favor   \n8     Proposta de altera\u00e7\u00e3o do BE - al\u00ednea a) do n.\u00ba...    A Favor   \n9     Proposta de altera\u00e7\u00e3o do BE - n.\u00ba 2 do artigo 4.\u00ba    A Favor   \n10    Proposta de altera\u00e7\u00e3o do BE - al\u00ednea c) do n.\u00ba...    A Favor   \n11    Proposta de altera\u00e7\u00e3o do PAN - al\u00ednea c) do n....    A Favor   \n12    Proposta de altera\u00e7\u00e3o do BE - n.\u00ba 4 ao artigo 4.\u00ba    A Favor   \n13          Proposta de altera\u00e7\u00e3o do PAN - artigo 5.\u00ba-A    A Favor   \n14          Proposta de altera\u00e7\u00e3o do PAN - artigo 5.\u00ba-B    A Favor   \n15           Proposta de altera\u00e7\u00e3o do BE - artigo 6.\u00ba-A    A Favor   \n16          Proposta de altera\u00e7\u00e3o do BE - artigo 13.\u00ba-A    A Favor   \n17                                                  NaN    A Favor   \n18    Requerimento oral, apresentado pelo PS, solici...    A Favor   \n19    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 7 ao artigo...    A Favor   \n20    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 8 ao artigo...    A Favor   \n21    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 4 do artigo...    A Favor   \n22    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 1 do artigo...    A Favor   \n23     Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 1 do artig...    A Favor   \n24    Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 2 do artigo...    A Favor   \n25     Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 3 do artig...    A Favor   \n26    Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 4 do artigo...    A Favor   \n27    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 5 ao artigo...    A Favor   \n28    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 6 ao artigo...    A Favor   \n29    Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 8 ao artigo...    A Favor   \n...                                                 ...        ...   \n2020                                                NaN    A Favor   \n2021                                                NaN    A Favor   \n2022                                                NaN    A Favor   \n2023                                                NaN    A Favor   \n2024                                                NaN    A Favor   \n2025                                                NaN  Absten\u00e7\u00e3o   \n2026                                                NaN    A Favor   \n2027                                                NaN    A Favor   \n2028                                                NaN    A Favor   \n2029                                                NaN    A Favor   \n2030                                                NaN    A Favor   \n2031                                                NaN     Contra   \n2032                                                NaN    A Favor   \n2033                                                NaN    A Favor   \n2034                                                NaN    A Favor   \n2035                                                NaN    A Favor   \n2036  Requerimento, apresentado pela PS solicitando ...    A Favor   \n2037                                                NaN  Absten\u00e7\u00e3o   \n2038                                                NaN  Absten\u00e7\u00e3o   \n2039                                                NaN  Absten\u00e7\u00e3o   \n2040  Requerimento oral, apresentado pelo PS, solici...    A Favor   \n2041  Requerimento, apresentado pela PS solicitando ...    A Favor   \n2042                                                NaN    A Favor   \n2043  Proposta do PSD, de substitui\u00e7\u00e3o do artigo 1.\u00ba...    A Favor   \n2044  Proposta do PSD, de substitui\u00e7\u00e3o do artigo 2.\u00ba...    A Favor   \n2045  Proposta do PSD, de aditamento de um novo arti...    A Favor   \n2046                      Artigo 3.\u00ba da proposta de lei    A Favor   \n2047                                                NaN    A Favor   \n2048                                                NaN    A Favor   \n2049                                                NaN    A Favor   \n\n     Cristina Rodrigues (Ninsc)     PAN/CR  \n0                        Contra     Contra  \n1                        Contra     Contra  \n2                        Contra     Contra  \n3                       A Favor    A Favor  \n4                       A Favor    A Favor  \n5                       A Favor    A Favor  \n6                       A Favor    A Favor  \n7                       A Favor    A Favor  \n8                           NaN    A Favor  \n9                           NaN    A Favor  \n10                          NaN    A Favor  \n11                          NaN    A Favor  \n12                          NaN    A Favor  \n13                          NaN    A Favor  \n14                          NaN    A Favor  \n15                          NaN    A Favor  \n16                          NaN    A Favor  \n17                          NaN    A Favor  \n18                          NaN    A Favor  \n19                          NaN    A Favor  \n20                          NaN    A Favor  \n21                          NaN    A Favor  \n22                          NaN    A Favor  \n23                          NaN    A Favor  \n24                          NaN    A Favor  \n25                          NaN    A Favor  \n26                          NaN    A Favor  \n27                          NaN    A Favor  \n28                          NaN    A Favor  \n29                          NaN    A Favor  \n...                         ...        ...  \n2020                    A Favor    A Favor  \n2021                    A Favor    A Favor  \n2022                    A Favor    A Favor  \n2023                    A Favor    A Favor  \n2024                    A Favor    A Favor  \n2025                  Absten\u00e7\u00e3o  Absten\u00e7\u00e3o  \n2026                    A Favor    A Favor  \n2027                    A Favor    A Favor  \n2028                    A Favor    A Favor  \n2029                    A Favor    A Favor  \n2030                    A Favor    A Favor  \n2031                  Absten\u00e7\u00e3o  Absten\u00e7\u00e3o  \n2032                    A Favor    A Favor  \n2033                    A Favor    A Favor  \n2034                    A Favor    A Favor  \n2035                    A Favor    A Favor  \n2036                    A Favor    A Favor  \n2037                  Absten\u00e7\u00e3o  Absten\u00e7\u00e3o  \n2038                  Absten\u00e7\u00e3o  Absten\u00e7\u00e3o  \n2039                  Absten\u00e7\u00e3o  Absten\u00e7\u00e3o  \n2040                    A Favor    A Favor  \n2041                    A Favor    A Favor  \n2042                    A Favor    A Favor  \n2043                    A Favor    A Favor  \n2044                    A Favor    A Favor  \n2045                    A Favor    A Favor  \n2046                    A Favor    A Favor  \n2047                    A Favor    A Favor  \n2048                    A Favor    A Favor  \n2049                    A Favor    A Favor  \n\n[2050 rows x 4 columns]"
                    },
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "## Copy PAN voting record to new aggregate columns...\nini_df[\"PAN/CR\"] = ini_df[\"PAN\"]\n## ... and update/replace with CR voting where it exists\nini_df[\"PAN/CR\"].update(ini_df[\"Cristina Rodrigues (Ninsc)\"])\nini_df[[\"descricao\",\"PAN\",\"Cristina Rodrigues (Ninsc)\",\"PAN/CR\"]]\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Processamento de Actividades"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "At\u00e9 agora trabalh\u00e1mos sobre _iniciativas_; existe uma fonte adicional de informa\u00e7\u00e3o (contida num ficheiro separado e com uma estrutura semelhante mas nao id\u00eantica) relativa \u00e0s _Actividades Parlamentares_, onde tamb\u00e9m se inclu\u00e9m vota\u00e7\u00f5es.\n\nToda a discuss\u00e3o e c\u00f3digo de processamento feito pode ser aplicado pelo que n\u00e3o iremos repetir as considera\u00e7\u00f5es j\u00e1 feitas; o processo de obten\u00e7\u00e3o do XML \u00e9 id\u00eantico:"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [],
            "source": "act_url = 'http://app.parlamento.pt/webutils/docs/doc.xml?path=6148523063446f764c324679626d56304c3239775a57356b595852684c3052685a47397a51574a6c636e52766379394264476c32615752685a47567a4c31684a566955794d45786c5a326c7a6247463064584a684c30463061585a705a47466b5a584e595356597565473173&fich=AtividadesXIV.xml&Inline=true'\nact_tree = ET.parse(urlopen(act_url))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O processamento tamb\u00e9m o \u00e9, com a diferen\u00e7a de que adicionamos um campo `descricao` que \u00e9 na verdade obtido a partir do assunto da actividade."
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ".......................................................................................................................................................................................................................................................247\n"
                }
            ],
            "source": "import re\nimport collections\n\nroot = act_tree\n\ncounter=0\n\n## We will build a dataframe from a list of dicts\n## Inspired by the approach of Chris Moffitt here https://pbpython.com/pandas-list-dict.html\nact_list = []\n\ndef get_toplevel_desc (vid, tree):\n    \"\"\"\n    Gets the top-level title from a voting id\n    \"\"\"\n    for c in tree.find(\".//pt_gov_ar_objectos_VotacaoOut/[id='\"+ vid +\"']/../..\"):\n        if c.tag == \"assunto\":\n            return c.text\n\nfor voting in act_tree.findall(\".//pt_gov_ar_objectos_VotacaoOut\"):\n    act_dict = collections.OrderedDict()\n    counter +=1\n    votep = voting.find('./detalhe')\n    if votep is not None:\n        act_dict['id'] = voting.find('id').text\n        ## Add the \"A\" for Type to mark this as coming from \"Iniciativas\"\n        act_dict['Tipo'] = \"A\"\n        for c in voting:\n            if c.tag == \"id\":\n                act_dict['descricao'] = get_toplevel_desc(c.text, act_tree)\n            if c.tag == \"detalhe\":\n                for party, vote in parse_voting(c.text).items():\n                    act_dict[party] = vote \n            elif c.tag == \"ausencias\":\n                    act_dict[c.tag] = c.find(\"string\").text\n            else:\n                    act_dict[c.tag] = c.text\n        act_list.append(act_dict)\n    ## Provide progression feedback\n    print('.', end='')\n\nprint(counter)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Criamos tamb\u00e9m um _dataframe_ com base na informa\u00e7\u00e3o recolhida:"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(226, 21)\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>Tipo</th>\n      <th>descricao</th>\n      <th>resultado</th>\n      <th>reuniao</th>\n      <th>unanime</th>\n      <th>PS</th>\n      <th>PSD</th>\n      <th>BE</th>\n      <th>PCP</th>\n      <th>...</th>\n      <th>PAN</th>\n      <th>PEV</th>\n      <th>CH</th>\n      <th>IL</th>\n      <th>Cristina Rodrigues (Ninsc)</th>\n      <th>Joacine Katar Moreira (Ninsc)</th>\n      <th>data</th>\n      <th>ausencias</th>\n      <th>publicacao</th>\n      <th>L</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>88959</td>\n      <td>A</td>\n      <td>De condena\u00e7\u00e3o pela destrui\u00e7\u00e3o de anta na Herda...</td>\n      <td>Aprovado</td>\n      <td>15</td>\n      <td>unanime</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>2020-10-16</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>88957</td>\n      <td>A</td>\n      <td>De pesar pelo falecimento de Fernando Alberto ...</td>\n      <td>Aprovado</td>\n      <td>15</td>\n      <td>unanime</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>2020-10-16</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>88956</td>\n      <td>A</td>\n      <td>De pesar pelo falecimento de Augusto Boucinha</td>\n      <td>Aprovado</td>\n      <td>15</td>\n      <td>unanime</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>2020-10-16</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>88954</td>\n      <td>A</td>\n      <td>De pesar pelo falecimento de Augusto Cymbron</td>\n      <td>Aprovado</td>\n      <td>15</td>\n      <td>unanime</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>2020-10-16</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>88863</td>\n      <td>A</td>\n      <td>De pesar pela morte de Quino</td>\n      <td>Aprovado</td>\n      <td>12</td>\n      <td>unanime</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>Aus\u00eancia</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>2020-10-09</td>\n      <td>CH</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 21 columns</p>\n</div>",
                        "text/plain": "      id Tipo                                          descricao resultado  \\\n0  88959    A  De condena\u00e7\u00e3o pela destrui\u00e7\u00e3o de anta na Herda...  Aprovado   \n1  88957    A  De pesar pelo falecimento de Fernando Alberto ...  Aprovado   \n2  88956    A      De pesar pelo falecimento de Augusto Boucinha  Aprovado   \n3  88954    A       De pesar pelo falecimento de Augusto Cymbron  Aprovado   \n4  88863    A                       De pesar pela morte de Quino  Aprovado   \n\n  reuniao  unanime       PS      PSD       BE      PCP  ...      PAN      PEV  \\\n0      15  unanime  A Favor  A Favor  A Favor  A Favor  ...  A Favor  A Favor   \n1      15  unanime  A Favor  A Favor  A Favor  A Favor  ...  A Favor  A Favor   \n2      15  unanime  A Favor  A Favor  A Favor  A Favor  ...  A Favor  A Favor   \n3      15  unanime  A Favor  A Favor  A Favor  A Favor  ...  A Favor  A Favor   \n4      12  unanime  A Favor  A Favor  A Favor  A Favor  ...  A Favor  A Favor   \n\n         CH       IL Cristina Rodrigues (Ninsc) Joacine Katar Moreira (Ninsc)  \\\n0   A Favor  A Favor                    A Favor                       A Favor   \n1   A Favor  A Favor                    A Favor                       A Favor   \n2   A Favor  A Favor                    A Favor                       A Favor   \n3   A Favor  A Favor                    A Favor                       A Favor   \n4  Aus\u00eancia  A Favor                    A Favor                       A Favor   \n\n         data ausencias publicacao    L  \n0  2020-10-16       NaN        NaN  NaN  \n1  2020-10-16       NaN        NaN  NaN  \n2  2020-10-16       NaN        NaN  NaN  \n3  2020-10-16       NaN        NaN  NaN  \n4  2020-10-09        CH        NaN  NaN  \n\n[5 rows x 21 columns]"
                    },
                    "execution_count": 20,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "act_df = pd.DataFrame(act_list)\nprint(act_df.shape)\n\nact_df.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\u00c9 necess\u00e1rio aplicar o mesmo princ\u00edpio relativo \u00e0 agrega\u00e7\u00e3o de votos do Livre e Joacine Katar Moreira, PAN e Cristina Rodrigues."
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>descricao</th>\n      <th>PAN</th>\n      <th>Cristina Rodrigues (Ninsc)</th>\n      <th>PAN/CR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>De condena\u00e7\u00e3o pela destrui\u00e7\u00e3o de anta na Herda...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>De pesar pelo falecimento de Fernando Alberto ...</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>De pesar pelo falecimento de Augusto Boucinha</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>De pesar pelo falecimento de Augusto Cymbron</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>De pesar pela morte de Quino</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                                           descricao      PAN  \\\n0  De condena\u00e7\u00e3o pela destrui\u00e7\u00e3o de anta na Herda...  A Favor   \n1  De pesar pelo falecimento de Fernando Alberto ...  A Favor   \n2      De pesar pelo falecimento de Augusto Boucinha  A Favor   \n3       De pesar pelo falecimento de Augusto Cymbron  A Favor   \n4                       De pesar pela morte de Quino  A Favor   \n\n  Cristina Rodrigues (Ninsc)   PAN/CR  \n0                    A Favor  A Favor  \n1                    A Favor  A Favor  \n2                    A Favor  A Favor  \n3                    A Favor  A Favor  \n4                    A Favor  A Favor  "
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "## Copy Livre voting record to new aggregate columns...\nact_df[\"L/JKM\"] = act_df[\"L\"]\n## ... and fill the NAs with JKM voting record.\nact_df[\"L/JKM\"] = act_df[\"L/JKM\"].fillna(act_df[\"Joacine Katar Moreira (Ninsc)\"])\n\n## Copy PAN voting record to new aggregate columns...\nact_df[\"PAN/CR\"] = act_df[\"PAN\"]\n## ... and update/replace with CR voting where it exists\nact_df[\"PAN/CR\"].update(act_df[\"Cristina Rodrigues (Ninsc)\"])\nact_df[[\"descricao\",\"PAN\",\"Cristina Rodrigues (Ninsc)\",\"PAN/CR\"]].head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Agrega\u00e7\u00e3o das vota\u00e7\u00f5es\n\nNeste momento temos dois _dataframes_, um para as Iniciativas e outro para as Actividades; durante o processo de constru\u00e7\u00e3o de ambos foi adicionada uma coluna `Tipo` que permite identificar, se necess\u00e1rio, a origem da vota\u00e7\u00e3o.\n\nNo presente trabalho consideramos todas as vota\u00e7\u00f5es de forma id\u00eantica pelo que nos resta construir um _dataframe_ \u00fanico que seja a jun\u00e7\u00e3o dos existentes, tendo em considera\u00e7\u00e3o que as colunas de ambos n\u00e3o s\u00e3o exactamente as mesmas."
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Index(['BE', 'CDS-PP', 'CH', 'Cristina Rodrigues (Ninsc)', 'IL',\n       'Joacine Katar Moreira (Ninsc)', 'L', 'L/JKM', 'PAN', 'PAN/CR', 'PCP',\n       'PEV', 'PS', 'PSD', 'Tipo', 'ausencias', 'descricao', 'id', 'resultado',\n       'reuniao', 'tipoReuniao', 'unanime'],\n      dtype='object')\nIndex(['BE', 'CDS-PP', 'CH', 'Cristina Rodrigues (Ninsc)', 'IL',\n       'Joacine Katar Moreira (Ninsc)', 'L', 'L/JKM', 'PAN', 'PAN/CR', 'PCP',\n       'PEV', 'PS', 'PSD', 'Tipo', 'ausencias', 'data', 'descricao', 'id',\n       'publicacao', 'resultado', 'reuniao', 'unanime'],\n      dtype='object')\n"
                }
            ],
            "source": "print(ini_df.sort_index(axis=1).columns)\nprint(act_df.sort_index(axis=1).columns)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "A concatena\u00e7\u00e3o dos dados \u00e9 feita atrav\u00e9s da remo\u00e7\u00e3o das colunas diferentes."
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [],
            "source": "votes = pd.concat([ini_df.drop([\"tipoReuniao\"],axis=1),act_df.drop([\"data\",\"publicacao\"],axis=1)], sort=True)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Este \u00faltimo passo de processamento d\u00e1 origem \u00e0 tabela final que ir\u00e1 ser usada para as v\u00e1rias an\u00e1lises posteriores; o seu formato formato tabular pode servir de base para an\u00e1lises com outras ferramentas (ver [Convers\u00e3o do _dataframe_ em CSV](#Convers\u00e3o-do-_dataframe_em-CSV))."
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(2276, 21)\n"
                },
                {
                    "data": {
                        "text/plain": "Index(['BE', 'CDS-PP', 'CH', 'Cristina Rodrigues (Ninsc)', 'IL',\n       'Joacine Katar Moreira (Ninsc)', 'L', 'L/JKM', 'PAN', 'PAN/CR', 'PCP',\n       'PEV', 'PS', 'PSD', 'Tipo', 'ausencias', 'descricao', 'id', 'resultado',\n       'reuniao', 'unanime'],\n      dtype='object')"
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "print(votes.shape)\nvotes.columns"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Explora\u00e7\u00e3o e visualiza\u00e7\u00e3o dos dados"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Tendo a nossa fonte de dados devidamente constru\u00edda podemos fazer algumas an\u00e1lises explorat\u00f3rias com base em visualiza\u00e7\u00f5es.\n\nPara come\u00e7ar seria interessante podermos ter uma representa\u00e7\u00e3o de alto-n\u00edvel que apresentasse as vota\u00e7\u00f5es de forma global. Para este fim um mapa t\u00e9rmico (_heatmap_) \u00e9 uma op\u00e7\u00e3o.\n\nCome\u00e7amos por criar um novo _dataframe_ que contenha apenas o sentido de voto dos partidos que vamos analisar, removendo os campos adicionais. Esta ac\u00e7\u00e3o poderia ser feita tamb\u00e9m por remo\u00e7\u00e3o de colunas: optamos pela especifica\u00e7\u00e3o dos campos tamb\u00e9m porque nos permite colocar os v\u00e1rios partidos em \"ordem\", consoante a sua posi\u00e7\u00e3o no parlamento _(\u201cEst\u00e3o Distribu\u00eddas (para J\u00e1) as Cadeiras No Parlamento - DN\u201d 2020)_, o que facilita a localiza\u00e7\u00e3o e interpreta\u00e7\u00e3o dos dados (e inclusivamente a maior ou menor pertin\u00eancia dessa dimens\u00e3o)"
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BE</th>\n      <th>PCP</th>\n      <th>PEV</th>\n      <th>L/JKM</th>\n      <th>PS</th>\n      <th>PAN</th>\n      <th>PSD</th>\n      <th>IL</th>\n      <th>CDS-PP</th>\n      <th>CH</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>A Favor</td>\n      <td>Contra</td>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>A Favor</td>\n      <td>Contra</td>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>Contra</td>\n      <td>A Favor</td>\n      <td>Contra</td>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Contra</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>Contra</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>Contra</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n      <td>A Favor</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "        BE      PCP      PEV    L/JKM       PS      PAN      PSD         IL  \\\n0   Contra   Contra   Contra   Contra  A Favor   Contra  A Favor  Absten\u00e7\u00e3o   \n1   Contra   Contra   Contra   Contra  A Favor   Contra  A Favor  Absten\u00e7\u00e3o   \n2   Contra   Contra   Contra   Contra  A Favor   Contra  A Favor  Absten\u00e7\u00e3o   \n3  A Favor  A Favor  A Favor  A Favor   Contra  A Favor  A Favor    A Favor   \n4  A Favor  A Favor  A Favor  A Favor   Contra  A Favor  A Favor    A Favor   \n\n    CDS-PP       CH  \n0   Contra   Contra  \n1   Contra   Contra  \n2   Contra   Contra  \n3  A Favor  A Favor  \n4  A Favor  A Favor  "
                    },
                    "execution_count": 25,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "votes_hm = votes[['BE', 'PCP', 'PEV', 'L/JKM', 'PS', 'PAN','PSD','IL','CDS-PP', 'CH']]\nvotes_hm.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Este mapa apresenta os votos seguindo o seguinte esquema de cores:\n\n- A Favor: verde\n- Absten\u00e7\u00e3o: amarelo\n- Contra: vermelho\n- Aus\u00eancia/NA: preto"
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "data": {
                        "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox \u2265 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
                        "text/plain": "<IPython.core.display.Javascript object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
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\" width=\"800\">",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport seaborn as sns\n\nvotes_hmn = votes_hm.replace([\"A Favor\", \"Contra\", \"Absten\u00e7\u00e3o\", \"Aus\u00eancia\"], [1,-1,0,2]).fillna(0)\n\nvoting_palette = [\"#FB6962\",\"#FCFC99\",\"#79DE79\", \"black\"]\n\nfig = plt.figure(figsize=(8,8))\nsns.heatmap(votes_hmn,\n            square=False,\n            yticklabels = False,\n            cbar=False,\n            cmap=sns.color_palette(voting_palette),\n           )\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Existem alguns pontos interessantes que podem ser observados desde j\u00e1. Uma an\u00e1lise da origem das iniciativas e actividades a vota\u00e7\u00e3o poder\u00e1 confirmar ou desmentir estas observa\u00e7\u00f5es:\n\n1. **Uma maior tend\u00eancia para votos \"A Favor\" \u00e0 esquerda**\n\nPode relacionar-se com uma maior produ\u00e7\u00e3o de propostas a vota\u00e7\u00e3o.\n\n2. **Uma not\u00f3ria diferencia\u00e7\u00e3o do PS na maior quantidade de votos Contra**\n\nEnquanto partido que suporta o Governo, esta tend\u00eancia pode relacionar-se com a anterior e representar o voto contra de propostas de outros partidos.\n\n3. **Algum acompanhamento do PSD nos votos contra do PS**\n\nPodem representar converg\u00eancias em propostas apresentadas pelo pr\u00f3prio ou em mat\u00e9rias consideradas estrat\u00e9gicas.\n\n4. **Uma maior tend\u00eancia para absten\u00e7\u00f5es \u00e0 direita**\n\nMais uma vez pode estar relacionada com a autoria das propostas ou representar uma maior abertura a outras propostas ao usar mais a Absten\u00e7\u00e3o do que a vota\u00e7\u00e3o contra.\n\n5. **Aus\u00eancias vis\u00edveis no Livre/Joacine Katar Moreira e Chega**\n\nS\u00e3o dois deputados \u00fanicos pelo que \u00e9 compreens\u00edvel quando comparado com grupos parlamentares, embora se note que a IL tem tamb\u00e9m um deputado \u00fanico e n\u00e3o regista comportamente id\u00eantico.\n\n6. **O PAN parece estar \"deslocado\"**\n\nObservando o alinhamento de cores o PAN estaria mais pr\u00f3ximo de qualquer dos partidos de ambos os lados dos  seus vizinhos imediatos (PS e PSD), sendo que o comportamente em termos de absten\u00e7\u00e3o o parece aproximar mais do lado esquerdo (L/JKM).\n\n7. **Aparente maior heterogeneidade \u00e0 direita e homogeneidade \u00e0 esquerda**\n\nPode ser artefacto visual dado pelo maior n\u00famero de absten\u00e7\u00f5es mas parece existir uma maior varia\u00e7\u00e3o crom\u00e1tica \u00e0 direita do que \u00e0 esquerda.\n\n\nEstes s\u00e3o apenas algumas das impress\u00f5es que uma explora\u00e7\u00e3o visual inicial proporciona; para algumas delas podemos encontrar novas visualiza\u00e7\u00f5es que forne\u00e7am informa\u00e7\u00e3o adicional. O seguinte diagrama de barras mostra o sentido de voto de cada partido ordenado em ordem descrecente de votos contra."
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "data": {
                        "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox \u2265 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
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\" width=\"640\">",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "from matplotlib.colors import ListedColormap\n\nvotes_against = votes_hm.apply(pd.Series.value_counts).sort_values(by='Contra', ascending=False, axis=1).T\nvotes_against.plot(kind='bar', stacked=True, colormap=ListedColormap(sns.color_palette(\"RdBu_r\", 4)),linewidth=0)\nplt.legend(loc='center left', bbox_to_anchor=(0.7, 0.15))\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Quem vota com quem: identifica\u00e7\u00e3o de vota\u00e7\u00f5es id\u00eanticas\n\nCom estes dados podemos tentar obter uma resposta mais clara do que o \"mapa t\u00e9rmico\" anterior nos apresenta como sendo semelhan\u00e7as e diferen\u00e7as no registo de vota\u00e7\u00e3o.\n\nUma das quest\u00f5es que se coloca (e normalmente coloca-se com maior \u00eanfase sempre que h\u00e1 uma vota\u00e7\u00e3o que \u00e9 apontada como sendo \"at\u00edpica\", com base na percep\u00e7\u00e3o geral do que \u00e9 o comportamente de voto habitual de cada partido) \u00e9 saber \"quem vota com quem\". Estes dados podem ser obtidos atrav\u00e9s da identifica\u00e7\u00e3o, para cada partido, da quantidade de vota\u00e7\u00f5es onde cada outro votou da mesma forma e cria\u00e7\u00e3o de uma tabela com os resultados:"
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Total voting instances:  2276\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BE</th>\n      <th>PCP</th>\n      <th>PEV</th>\n      <th>L/JKM</th>\n      <th>PS</th>\n      <th>PAN</th>\n      <th>PSD</th>\n      <th>IL</th>\n      <th>CDS-PP</th>\n      <th>CH</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>BE</th>\n      <td>2276</td>\n      <td>1833</td>\n      <td>1882</td>\n      <td>1884</td>\n      <td>1107</td>\n      <td>1710</td>\n      <td>1010</td>\n      <td>1109</td>\n      <td>1013</td>\n      <td>1008</td>\n    </tr>\n    <tr>\n      <th>PCP</th>\n      <td>1833</td>\n      <td>2276</td>\n      <td>2162</td>\n      <td>1679</td>\n      <td>1079</td>\n      <td>1461</td>\n      <td>984</td>\n      <td>1082</td>\n      <td>1009</td>\n      <td>990</td>\n    </tr>\n    <tr>\n      <th>PEV</th>\n      <td>1882</td>\n      <td>2162</td>\n      <td>2276</td>\n      <td>1730</td>\n      <td>1073</td>\n      <td>1532</td>\n      <td>970</td>\n      <td>1097</td>\n      <td>973</td>\n      <td>973</td>\n    </tr>\n    <tr>\n      <th>L/JKM</th>\n      <td>1884</td>\n      <td>1679</td>\n      <td>1730</td>\n      <td>2276</td>\n      <td>1006</td>\n      <td>1613</td>\n      <td>930</td>\n      <td>1080</td>\n      <td>941</td>\n      <td>938</td>\n    </tr>\n    <tr>\n      <th>PS</th>\n      <td>1107</td>\n      <td>1079</td>\n      <td>1073</td>\n      <td>1006</td>\n      <td>2276</td>\n      <td>1123</td>\n      <td>1535</td>\n      <td>1122</td>\n      <td>1175</td>\n      <td>830</td>\n    </tr>\n    <tr>\n      <th>PAN</th>\n      <td>1710</td>\n      <td>1461</td>\n      <td>1532</td>\n      <td>1613</td>\n      <td>1123</td>\n      <td>2276</td>\n      <td>1141</td>\n      <td>1277</td>\n      <td>1115</td>\n      <td>1073</td>\n    </tr>\n    <tr>\n      <th>PSD</th>\n      <td>1010</td>\n      <td>984</td>\n      <td>970</td>\n      <td>930</td>\n      <td>1535</td>\n      <td>1141</td>\n      <td>2276</td>\n      <td>1331</td>\n      <td>1573</td>\n      <td>1211</td>\n    </tr>\n    <tr>\n      <th>IL</th>\n      <td>1109</td>\n      <td>1082</td>\n      <td>1097</td>\n      <td>1080</td>\n      <td>1122</td>\n      <td>1277</td>\n      <td>1331</td>\n      <td>2276</td>\n      <td>1423</td>\n      <td>1328</td>\n    </tr>\n    <tr>\n      <th>CDS-PP</th>\n      <td>1013</td>\n      <td>1009</td>\n      <td>973</td>\n      <td>941</td>\n      <td>1175</td>\n      <td>1115</td>\n      <td>1573</td>\n      <td>1423</td>\n      <td>2276</td>\n      <td>1396</td>\n    </tr>\n    <tr>\n      <th>CH</th>\n      <td>1008</td>\n      <td>990</td>\n      <td>973</td>\n      <td>938</td>\n      <td>830</td>\n      <td>1073</td>\n      <td>1211</td>\n      <td>1328</td>\n      <td>1396</td>\n      <td>2276</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "          BE   PCP   PEV  L/JKM    PS   PAN   PSD    IL  CDS-PP    CH\nBE      2276  1833  1882   1884  1107  1710  1010  1109    1013  1008\nPCP     1833  2276  2162   1679  1079  1461   984  1082    1009   990\nPEV     1882  2162  2276   1730  1073  1532   970  1097     973   973\nL/JKM   1884  1679  1730   2276  1006  1613   930  1080     941   938\nPS      1107  1079  1073   1006  2276  1123  1535  1122    1175   830\nPAN     1710  1461  1532   1613  1123  2276  1141  1277    1115  1073\nPSD     1010   984   970    930  1535  1141  2276  1331    1573  1211\nIL      1109  1082  1097   1080  1122  1277  1331  2276    1423  1328\nCDS-PP  1013  1009   973    941  1175  1115  1573  1423    2276  1396\nCH      1008   990   973    938   830  1073  1211  1328    1396  2276"
                    },
                    "execution_count": 28,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "import numpy as np\npv_list = []\nprint(\"Total voting instances: \", votes_hm.shape[0])\n\n## Not necessarily the most straightforard way (check .crosstab or .pivot_table, possibly with pandas.melt and/or groupby)\n## but follows the same approach as before in using a list of dicts\nfor party in votes_hm.columns:\n    pv_dict = collections.OrderedDict()\n    for column in votes_hmn:\n        pv_dict[column]=votes_hmn[votes_hmn[party] == votes_hmn[column]].shape[0]\n    pv_list.append(pv_dict)\n\npv = pd.DataFrame(pv_list,index=votes_hm.columns)\npv"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Com estes dados podemos usar um \"mapa t\u00e9rmico\" (_heatmap_), uma visualiza\u00e7\u00e3o especialmente adequada para os casos onde temos valores que em magnitude em duas dimens\u00f5es; esta visualiza\u00e7\u00e3o vai fundamentalmente facilitar a interpreta\u00e7\u00e3o das proximidades e dist\u00e2ncias ao combinar a informa\u00e7\u00e3o num\u00e9rica j\u00e1 obtida com uma categoriza\u00e7\u00e3 crom\u00e1tica que depende do valor: neste caso, quantas mais vota\u00e7\u00f5es com vota\u00e7\u00f5es iguais, mais escuro."
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox \u2265 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
                        "text/plain": "<IPython.core.display.Javascript object>"
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                    "output_type": "display_data"
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\" width=\"800\">",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "fig = plt.figure(figsize=(8,8))\nax = fig.add_subplot()\n\nsns.heatmap(\n    pv,\n    cmap=sns.color_palette(\"mako_r\"),\n    linewidth=1,\n    annot = True,\n    square =True,\n    fmt=\"d\",\n    cbar_kws={\"shrink\": 0.8})\nplt.title('Portuguese Parliament 14th Legislature, identical voting count')\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Esta visualiza\u00e7\u00e3o j\u00e1 nos fornece pistas mais concretas em termos de quem votam mais vezes com quem, mas tem uma limita\u00e7\u00e3o que n\u00e3o deve ser ignorada: a valoriza\u00e7\u00e3o do sentido de voto n\u00e3o est\u00e1 a ser tido em conta, para a tabela acima \u00e9 exactamente igual que um partido vote a Favor e que o outro vote Contra ou se Abstenha. Esta forma bin\u00e1ria de considerar \"proximidade\" descarta informa\u00e7\u00e3o relevante ao n\u00e3o considerar que, mesmo nos casos onde existem diferentes sentidos de voto, existem diferen\u00e7as importantes.\n\nO ideal seria, portanto, conseguir determinar n\u00e3o s\u00f3 a quantidade de vezes que os partidos votam da mesma forma, mas tamb\u00e9m quantificar de forma diferente quando n\u00e3o o fazem. Isto para todos os partidos, e para todas as vota\u00e7\u00f5es. E \u00e9 precisamente isso que iremos fazer atrav\u00e9s da **cria\u00e7\u00e3o de uma matriz de dist\u00e2ncias assente na dist\u00e2ncia num\u00e9rica entre os sentidos de voto**."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Matriz de dist\u00e2ncia entre os partidos"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Com base nos hist\u00f3rico de vota\u00e7\u00f5es de cada partido produzimos uma matriz de dist\u00e2ncias entre eles; uma matriz de dist\u00e2ncias \u00e9 uma matriz quadradra $n\\times n$ (onde _n_ \u00e9 o n\u00famero de partidos) e onde a dist\u00e2ncia entre _p_ e _q_ \u00e9 o valor de $ d_{pq} $.\n\n$ \n\\begin{equation}\nD= \\begin{bmatrix} d_{11} & d_{12} & \\cdots & d_{1 n} \\\\  d_{21} & d_{22} & \\cdots & d_{2 n} \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ d_{31} & d_{32} & \\cdots & d_{n n}   \\end{bmatrix}_{\\ n\\times n}  \n\\end{equation}\n$\n\n\nA dist\u00e2ncia \u00e9 obtida atrav\u00e9s da compara\u00e7\u00e3o de todas as observa\u00e7\u00f5es de cada par usando uma determinada m\u00e9trica de dist\u00e2ncia, sendo a dist\u00e2ncia euclideana bastante comum em termos gerais e tamb\u00e9m dentro de estudos sobre o mesmo dom\u00ednio tem\u00e1tico _(Krilavi\u010dius and \u017dilinskas 2008)_: cada elemento da matriz representa $ d\\left( p,q\\right)   = \\sqrt {\\sum _{i=1}^{n}  \\left( q_{i}-p_{i}\\right)^2 }$, equivalente, para dois pontos $P,Q $ , \u00e0 mais gen\u00e9rica dist\u00e2ncia de Minkowski   $ D\\left(P,Q\\right)=\\left(\\sum _{i=1}^{n}|x_{i}-y_{i}|^{p}\\right)^{\\frac {1}{p}} $ para $ p = 1$, mas note-se que a diagonal da matrix ir\u00e1 representar a dist\u00e2ncia entre um partido e ele pr\u00f3prio, logo $ d_{11} = d_{22} = \\dots = d_{nn} = 0 $.\n\nNa sec\u00e7\u00e3o  [Dist\u00e2ncias e matrizes](Dist\u00e2ncias_e_matrizes) coloc\u00e1mos uma discuss\u00e3o mais detalhada (mas passo-a-passo e destinada a quem n\u00e3o tenha necessariamente presente a matem\u00e1tica utilizada) sobre dist\u00e2ncias, _clustering_ e como s\u00e3o calculdadas, para quem tenha interesse numa compreens\u00e3o mais quantitativa da mat\u00e9ria.\n\nA convers\u00e3o de votos em representa\u00e7\u00f5es n\u00famericas pode ser feita de v\u00e1rias formas _(Hix, Noury, and Roland 2006)_; adoptamos a abordagem de Krilavi\u010dius & \u017dilinskas (2008) no j\u00e1 citado  trabalho relativo \u00e0s vota\u00e7\u00f5es no parlamento lituano por nos parecer apropriada \u00e0 realidade portuguesa:\n\n* A favor: 1\n* Contra: -1\n* Absten\u00e7\u00e3o: 0\n* Aus\u00eancia: 0\n\nEste ponto \u00e9 (mais um) dos que de forma relativamente opaca - pois raramente os detalhes t\u00eam a mesma projec\u00e7\u00e3o que os resultado finais - podem influenciar os resultados; cremos que em particular a equipara\u00e7\u00e3o entre _absten\u00e7\u00e3o_ e _aus\u00eancia_ merece alguma reflex\u00e3o: consider\u00e1mos que uma aus\u00eancia em determinada vota\u00e7\u00e3o tem um peso equivalente \u00e0 absten\u00e7\u00e3o, embora uma de forma passiva e outra activa.\n\nPara obtermos a matriz de dist\u00e2ncia usamos a fun\u00e7\u00e3o `pdist` e constru\u00edmos um _dataframe_ que \u00e9 uma matriz sim\u00e9trica das dist\u00e2ncias entre os partidos."
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BE</th>\n      <th>PCP</th>\n      <th>PEV</th>\n      <th>L/JKM</th>\n      <th>PS</th>\n      <th>PAN</th>\n      <th>PSD</th>\n      <th>IL</th>\n      <th>CDS-PP</th>\n      <th>CH</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>BE</th>\n      <td>0.000000</td>\n      <td>27.422618</td>\n      <td>24.698178</td>\n      <td>22.090722</td>\n      <td>61.180062</td>\n      <td>33.166248</td>\n      <td>59.874870</td>\n      <td>50.734604</td>\n      <td>54.881691</td>\n      <td>47.927028</td>\n    </tr>\n    <tr>\n      <th>PCP</th>\n      <td>27.422618</td>\n      <td>0.000000</td>\n      <td>13.416408</td>\n      <td>29.698485</td>\n      <td>58.094750</td>\n      <td>40.570926</td>\n      <td>56.964901</td>\n      <td>51.088159</td>\n      <td>52.744668</td>\n      <td>47.085029</td>\n    </tr>\n    <tr>\n      <th>PEV</th>\n      <td>24.698178</td>\n      <td>13.416408</td>\n      <td>0.000000</td>\n      <td>27.495454</td>\n      <td>59.067758</td>\n      <td>38.574603</td>\n      <td>58.129167</td>\n      <td>51.029403</td>\n      <td>54.092513</td>\n      <td>47.717921</td>\n    </tr>\n    <tr>\n      <th>L/JKM</th>\n      <td>22.090722</td>\n      <td>29.698485</td>\n      <td>27.495454</td>\n      <td>0.000000</td>\n      <td>60.621778</td>\n      <td>32.984845</td>\n      <td>58.506410</td>\n      <td>48.826222</td>\n      <td>53.684262</td>\n      <td>46.593991</td>\n    </tr>\n    <tr>\n      <th>PS</th>\n      <td>61.180062</td>\n      <td>58.094750</td>\n      <td>59.067758</td>\n      <td>60.621778</td>\n      <td>0.000000</td>\n      <td>60.605280</td>\n      <td>42.000000</td>\n      <td>52.981129</td>\n      <td>49.020404</td>\n      <td>54.899909</td>\n    </tr>\n    <tr>\n      <th>PAN</th>\n      <td>33.166248</td>\n      <td>40.570926</td>\n      <td>38.574603</td>\n      <td>32.984845</td>\n      <td>60.605280</td>\n      <td>0.000000</td>\n      <td>55.163394</td>\n      <td>45.166359</td>\n      <td>50.734604</td>\n      <td>44.799554</td>\n    </tr>\n    <tr>\n      <th>PSD</th>\n      <td>59.874870</td>\n      <td>56.964901</td>\n      <td>58.129167</td>\n      <td>58.506410</td>\n      <td>42.000000</td>\n      <td>55.163394</td>\n      <td>0.000000</td>\n      <td>43.023250</td>\n      <td>34.132096</td>\n      <td>42.213742</td>\n    </tr>\n    <tr>\n      <th>IL</th>\n      <td>50.734604</td>\n      <td>51.088159</td>\n      <td>51.029403</td>\n      <td>48.826222</td>\n      <td>52.981129</td>\n      <td>45.166359</td>\n      <td>43.023250</td>\n      <td>0.000000</td>\n      <td>35.637059</td>\n      <td>36.482873</td>\n    </tr>\n    <tr>\n      <th>CDS-PP</th>\n      <td>54.881691</td>\n      <td>52.744668</td>\n      <td>54.092513</td>\n      <td>53.684262</td>\n      <td>49.020404</td>\n      <td>50.734604</td>\n      <td>34.132096</td>\n      <td>35.637059</td>\n      <td>0.000000</td>\n      <td>33.181320</td>\n    </tr>\n    <tr>\n      <th>CH</th>\n      <td>47.927028</td>\n      <td>47.085029</td>\n      <td>47.717921</td>\n      <td>46.593991</td>\n      <td>54.899909</td>\n      <td>44.799554</td>\n      <td>42.213742</td>\n      <td>36.482873</td>\n      <td>33.181320</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "               BE        PCP        PEV      L/JKM         PS        PAN  \\\nBE       0.000000  27.422618  24.698178  22.090722  61.180062  33.166248   \nPCP     27.422618   0.000000  13.416408  29.698485  58.094750  40.570926   \nPEV     24.698178  13.416408   0.000000  27.495454  59.067758  38.574603   \nL/JKM   22.090722  29.698485  27.495454   0.000000  60.621778  32.984845   \nPS      61.180062  58.094750  59.067758  60.621778   0.000000  60.605280   \nPAN     33.166248  40.570926  38.574603  32.984845  60.605280   0.000000   \nPSD     59.874870  56.964901  58.129167  58.506410  42.000000  55.163394   \nIL      50.734604  51.088159  51.029403  48.826222  52.981129  45.166359   \nCDS-PP  54.881691  52.744668  54.092513  53.684262  49.020404  50.734604   \nCH      47.927028  47.085029  47.717921  46.593991  54.899909  44.799554   \n\n              PSD         IL     CDS-PP         CH  \nBE      59.874870  50.734604  54.881691  47.927028  \nPCP     56.964901  51.088159  52.744668  47.085029  \nPEV     58.129167  51.029403  54.092513  47.717921  \nL/JKM   58.506410  48.826222  53.684262  46.593991  \nPS      42.000000  52.981129  49.020404  54.899909  \nPAN     55.163394  45.166359  50.734604  44.799554  \nPSD      0.000000  43.023250  34.132096  42.213742  \nIL      43.023250   0.000000  35.637059  36.482873  \nCDS-PP  34.132096  35.637059   0.000000  33.181320  \nCH      42.213742  36.482873  33.181320   0.000000  "
                    },
                    "execution_count": 31,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "from scipy.spatial.distance import squareform\nfrom scipy.spatial.distance import pdist\nimport scipy.spatial as sp, scipy.cluster.hierarchy as hc\nfrom itables import show\n\nvotes_hmn = votes_hm.replace([\"A Favor\", \"Contra\", \"Absten\u00e7\u00e3o\", \"Aus\u00eancia\"], [1,-1,0,0]).fillna(0)\n\n## Transpose the dataframe used for the heatmap\nvotes_t = votes_hmn.transpose()\n\n## Determine the Eucledian pairwise distance\n## (\"euclidean\" is actually the default option)\npwdist = pdist(votes_t, metric='euclidean')\n\n## Create a square dataframe with the pairwise distances: the distance matrix\ndistmat = pd.DataFrame(\n    squareform(pwdist), # pass a symmetric distance matrix\n    columns = votes_t.index,\n    index = votes_t.index\n)\n#show(distmat, scrollY=\"200px\", scrollCollapse=True, paging=False)\n\n## Normalise by scaling between 0-1, using dataframe max value to keep the symmetry.\n## This is essentially a cosmetic step to \n#distmat=((distmat-distmat.min().min())/(distmat.max().max()-distmat.min().min()))*1\ndistmat"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O mapa t\u00e9rmico \u00e9 constru\u00eddo directamente a partir da matriz de dist\u00e2ncia, fornencendo uma forma bastante mais intuitiva de visualizar as dist\u00e2ncias:"
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
                        "text/plain": "<IPython.core.display.Javascript object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
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\" width=\"800\">",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "## Display the heatmap of the distance matrix\n\nfig = plt.figure(figsize=(8,8))\nax = fig.add_subplot()\n\nsns.heatmap(\n    distmat,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    annot = True,\n    square =True,\n    cbar_kws={\"shrink\": 0.8})\nplt.title('Portuguese Parliament 14th Legislature, Distance Matrix')\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "A ordem dos \u00edndice \u00e9 a que foi determinada anteriormente e baseada na localiza\u00e7\u00e3o dos partidos e deputados no hemiciclo; deixando considera\u00e7\u00f5es adicionais para depois note-se que duas das impress\u00f5es iniciais obtidas pela observa\u00e7\u00e3o do mapa t\u00e9rmico das vota\u00e7\u00f5es) parecem confirmar-se:\n\n1. O PAN parece de facto deslocado (restando saber para qual dos lados).\n2. O quadrante superior esquerdo exibe dist\u00e2ncias menores que o inferior direito, demonstrando uma maior proximidade entre si dos partidos \u00e0 esquerda do que \u00e0 direita.\n\nCom base nas dist\u00e2ncias procedemos ao agrupamento usando o m\u00e9todo de Ward _(Carvalho et al. 2009)_  de forma a obtermos uma \u00e1rvore de proximidade hier\u00e1rquica: um dendograma, neste caso associado ao mapa t\u00e9rmica com as linhas e colunas devidamente reordenadas de forma a permitirem visualizar em simult\u00e2neo as dist\u00e2ncias e o agrupamentos (ver [Dist\u00e2ncias e matrizes](Dist\u00e2ncias_e_matrizes) para uma explica\u00e7\u00e3o mais aprofundanda do tema)"
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "data": {
                        "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox \u2265 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
                        "text/plain": "<IPython.core.display.Javascript object>"
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                    "output_type": "display_data"
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\" width=\"800\">",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "## Perform hierarchical linkage on the distance matrix using Ward's method.\ndistmat_link = hc.linkage(pwdist, method=\"ward\", optimal_ordering=True )\n\nsns.clustermap(\n    distmat,\n    annot = True,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    #standard_scale=1,\n    row_linkage=distmat_link,\n    col_linkage=distmat_link,\n    figsize=(8,8)).fig.suptitle('Portuguese Parliament 14th Legislature, Clustermap')\n\nplt.show()\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Uma visualiza\u00e7\u00e3o apenas do dendograma \u00e9 tamb\u00e9m poss\u00edvel; a altura das linhas e os pontos de deriva\u00e7\u00e3o n\u00e3o s\u00e3o aleat\u00f3rios, representam as dist\u00e2ncias determinadas pelo m\u00e9todo de agrupamento:"
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "data": {
                        "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox \u2265 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
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\" width=\"800\">",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "\nfrom scipy.cluster.hierarchy import dendrogram\nfig = plt.figure(figsize=(8,5))\ndendrogram(distmat_link, labels=votes_hmn.columns)\n\nplt.title(\"Portuguese Parliament 14th Legislature, Dendogram\")\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\ud83d\udc49 *Estamos a trabalhar sobre o total de vota\u00e7\u00f5es, inclu\u00edndo Actividade e Iniciativas; uma quest\u00e3o que se pode colocar \u00e9 se existem diferen\u00e7as, algo que discutimos em [Diferen\u00e7as entre Actividades e Iniciativas](#Diferen\u00e7as-entre-Actividades-e-Iniciativas).*"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Identificar grupos e reduzir dimens\u00f5es\n\nJ\u00e1 conseguimos determinar as dist\u00e2ncias e os agrupamentos, mas as possibilidades de visualiza\u00e7\u00e3o n\u00e3o se esgotam por a\u00ed.\n\n### _Clustering_ de observa\u00e7\u00f5es: DBSCAN e _Spectrum Scaling_\n\nUma forma diferente de determinar agrupamentos \u00e9 atrav\u00e9s de m\u00e9todos de _clustering_, que procuram determinar agrupamentos de pontos com base em mecanismos espec\u00edficos de cada um dos algoritmos.\n\nVamos demonstrar dois deles, e como passo preliminar vamos transformar a nossa matriz de dist\u00e2ncias: ao contr\u00e1rio do dendograma anterior estes m\u00e9todos utilizam uma _matriz de afinidade_, onde valores mais altos significam uma maior semelhan\u00e7a (e, consequentemente, para uma matriz sim\u00e9trica a diagonal passa de 0 para 1).\n\nComo passo preliminar normalizamos as dist\u00e2ncias no intervalo $ [0,1] $:"
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BE</th>\n      <th>PCP</th>\n      <th>PEV</th>\n      <th>L/JKM</th>\n      <th>PS</th>\n      <th>PAN</th>\n      <th>PSD</th>\n      <th>IL</th>\n      <th>CDS-PP</th>\n      <th>CH</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>BE</th>\n      <td>0.000000</td>\n      <td>0.448228</td>\n      <td>0.403697</td>\n      <td>0.361077</td>\n      <td>1.000000</td>\n      <td>0.542109</td>\n      <td>0.978666</td>\n      <td>0.829267</td>\n      <td>0.897052</td>\n      <td>0.783377</td>\n    </tr>\n    <tr>\n      <th>PCP</th>\n      <td>0.448228</td>\n      <td>0.000000</td>\n      <td>0.219294</td>\n      <td>0.485428</td>\n      <td>0.949570</td>\n      <td>0.663140</td>\n      <td>0.931102</td>\n      <td>0.835046</td>\n      <td>0.862122</td>\n      <td>0.769614</td>\n    </tr>\n    <tr>\n      <th>PEV</th>\n      <td>0.403697</td>\n      <td>0.219294</td>\n      <td>0.000000</td>\n      <td>0.449419</td>\n      <td>0.965474</td>\n      <td>0.630509</td>\n      <td>0.950133</td>\n      <td>0.834086</td>\n      <td>0.884153</td>\n      <td>0.779959</td>\n    </tr>\n    <tr>\n      <th>L/JKM</th>\n      <td>0.361077</td>\n      <td>0.485428</td>\n      <td>0.449419</td>\n      <td>0.000000</td>\n      <td>0.990875</td>\n      <td>0.539144</td>\n      <td>0.956299</td>\n      <td>0.798074</td>\n      <td>0.877480</td>\n      <td>0.761588</td>\n    </tr>\n    <tr>\n      <th>PS</th>\n      <td>1.000000</td>\n      <td>0.949570</td>\n      <td>0.965474</td>\n      <td>0.990875</td>\n      <td>0.000000</td>\n      <td>0.990605</td>\n      <td>0.686498</td>\n      <td>0.865987</td>\n      <td>0.801248</td>\n      <td>0.897350</td>\n    </tr>\n    <tr>\n      <th>PAN</th>\n      <td>0.542109</td>\n      <td>0.663140</td>\n      <td>0.630509</td>\n      <td>0.539144</td>\n      <td>0.990605</td>\n      <td>0.000000</td>\n      <td>0.901656</td>\n      <td>0.738253</td>\n      <td>0.829267</td>\n      <td>0.732257</td>\n    </tr>\n    <tr>\n      <th>PSD</th>\n      <td>0.978666</td>\n      <td>0.931102</td>\n      <td>0.950133</td>\n      <td>0.956299</td>\n      <td>0.686498</td>\n      <td>0.901656</td>\n      <td>0.000000</td>\n      <td>0.703223</td>\n      <td>0.557896</td>\n      <td>0.689992</td>\n    </tr>\n    <tr>\n      <th>IL</th>\n      <td>0.829267</td>\n      <td>0.835046</td>\n      <td>0.834086</td>\n      <td>0.798074</td>\n      <td>0.865987</td>\n      <td>0.738253</td>\n      <td>0.703223</td>\n      <td>0.000000</td>\n      <td>0.582495</td>\n      <td>0.596320</td>\n    </tr>\n    <tr>\n      <th>CDS-PP</th>\n      <td>0.897052</td>\n      <td>0.862122</td>\n      <td>0.884153</td>\n      <td>0.877480</td>\n      <td>0.801248</td>\n      <td>0.829267</td>\n      <td>0.557896</td>\n      <td>0.582495</td>\n      <td>0.000000</td>\n      <td>0.542355</td>\n    </tr>\n    <tr>\n      <th>CH</th>\n      <td>0.783377</td>\n      <td>0.769614</td>\n      <td>0.779959</td>\n      <td>0.761588</td>\n      <td>0.897350</td>\n      <td>0.732257</td>\n      <td>0.689992</td>\n      <td>0.596320</td>\n      <td>0.542355</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "              BE       PCP       PEV     L/JKM        PS       PAN       PSD  \\\nBE      0.000000  0.448228  0.403697  0.361077  1.000000  0.542109  0.978666   \nPCP     0.448228  0.000000  0.219294  0.485428  0.949570  0.663140  0.931102   \nPEV     0.403697  0.219294  0.000000  0.449419  0.965474  0.630509  0.950133   \nL/JKM   0.361077  0.485428  0.449419  0.000000  0.990875  0.539144  0.956299   \nPS      1.000000  0.949570  0.965474  0.990875  0.000000  0.990605  0.686498   \nPAN     0.542109  0.663140  0.630509  0.539144  0.990605  0.000000  0.901656   \nPSD     0.978666  0.931102  0.950133  0.956299  0.686498  0.901656  0.000000   \nIL      0.829267  0.835046  0.834086  0.798074  0.865987  0.738253  0.703223   \nCDS-PP  0.897052  0.862122  0.884153  0.877480  0.801248  0.829267  0.557896   \nCH      0.783377  0.769614  0.779959  0.761588  0.897350  0.732257  0.689992   \n\n              IL    CDS-PP        CH  \nBE      0.829267  0.897052  0.783377  \nPCP     0.835046  0.862122  0.769614  \nPEV     0.834086  0.884153  0.779959  \nL/JKM   0.798074  0.877480  0.761588  \nPS      0.865987  0.801248  0.897350  \nPAN     0.738253  0.829267  0.732257  \nPSD     0.703223  0.557896  0.689992  \nIL      0.000000  0.582495  0.596320  \nCDS-PP  0.582495  0.000000  0.542355  \nCH      0.596320  0.542355  0.000000  "
                    },
                    "execution_count": 35,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "import numpy as np\n\ndistmat_mm=((distmat-distmat.min().min())/(distmat.max().max()-distmat.min().min()))*1\npd.DataFrame(distmat_mm, distmat.index, distmat.columns)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "... e subtraimos \u00e0 unidade todos os valores da matriz, uma forma simples de obter a matriz de afinidade (mas n\u00e3o a \u00fanica, nem a que garantidamente resulta sempre melhor). O resultado \u00e9 uma matriz cujos valores indicam a _semelhan\u00e7a_ entre os pares, ao inv\u00e9s da _dist\u00e2ncia_."
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BE</th>\n      <th>PCP</th>\n      <th>PEV</th>\n      <th>L/JKM</th>\n      <th>PS</th>\n      <th>PAN</th>\n      <th>PSD</th>\n      <th>IL</th>\n      <th>CDS-PP</th>\n      <th>CH</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>BE</th>\n      <td>1.000000</td>\n      <td>0.551772</td>\n      <td>0.596303</td>\n      <td>0.638923</td>\n      <td>0.000000</td>\n      <td>0.457891</td>\n      <td>0.021334</td>\n      <td>0.170733</td>\n      <td>0.102948</td>\n      <td>0.216623</td>\n    </tr>\n    <tr>\n      <th>PCP</th>\n      <td>0.551772</td>\n      <td>1.000000</td>\n      <td>0.780706</td>\n      <td>0.514572</td>\n      <td>0.050430</td>\n      <td>0.336860</td>\n      <td>0.068898</td>\n      <td>0.164954</td>\n      <td>0.137878</td>\n      <td>0.230386</td>\n    </tr>\n    <tr>\n      <th>PEV</th>\n      <td>0.596303</td>\n      <td>0.780706</td>\n      <td>1.000000</td>\n      <td>0.550581</td>\n      <td>0.034526</td>\n      <td>0.369491</td>\n      <td>0.049867</td>\n      <td>0.165914</td>\n      <td>0.115847</td>\n      <td>0.220041</td>\n    </tr>\n    <tr>\n      <th>L/JKM</th>\n      <td>0.638923</td>\n      <td>0.514572</td>\n      <td>0.550581</td>\n      <td>1.000000</td>\n      <td>0.009125</td>\n      <td>0.460856</td>\n      <td>0.043701</td>\n      <td>0.201926</td>\n      <td>0.122520</td>\n      <td>0.238412</td>\n    </tr>\n    <tr>\n      <th>PS</th>\n      <td>0.000000</td>\n      <td>0.050430</td>\n      <td>0.034526</td>\n      <td>0.009125</td>\n      <td>1.000000</td>\n      <td>0.009395</td>\n      <td>0.313502</td>\n      <td>0.134013</td>\n      <td>0.198752</td>\n      <td>0.102650</td>\n    </tr>\n    <tr>\n      <th>PAN</th>\n      <td>0.457891</td>\n      <td>0.336860</td>\n      <td>0.369491</td>\n      <td>0.460856</td>\n      <td>0.009395</td>\n      <td>1.000000</td>\n      <td>0.098344</td>\n      <td>0.261747</td>\n      <td>0.170733</td>\n      <td>0.267743</td>\n    </tr>\n    <tr>\n      <th>PSD</th>\n      <td>0.021334</td>\n      <td>0.068898</td>\n      <td>0.049867</td>\n      <td>0.043701</td>\n      <td>0.313502</td>\n      <td>0.098344</td>\n      <td>1.000000</td>\n      <td>0.296777</td>\n      <td>0.442104</td>\n      <td>0.310008</td>\n    </tr>\n    <tr>\n      <th>IL</th>\n      <td>0.170733</td>\n      <td>0.164954</td>\n      <td>0.165914</td>\n      <td>0.201926</td>\n      <td>0.134013</td>\n      <td>0.261747</td>\n      <td>0.296777</td>\n      <td>1.000000</td>\n      <td>0.417505</td>\n      <td>0.403680</td>\n    </tr>\n    <tr>\n      <th>CDS-PP</th>\n      <td>0.102948</td>\n      <td>0.137878</td>\n      <td>0.115847</td>\n      <td>0.122520</td>\n      <td>0.198752</td>\n      <td>0.170733</td>\n      <td>0.442104</td>\n      <td>0.417505</td>\n      <td>1.000000</td>\n      <td>0.457645</td>\n    </tr>\n    <tr>\n      <th>CH</th>\n      <td>0.216623</td>\n      <td>0.230386</td>\n      <td>0.220041</td>\n      <td>0.238412</td>\n      <td>0.102650</td>\n      <td>0.267743</td>\n      <td>0.310008</td>\n      <td>0.403680</td>\n      <td>0.457645</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "              BE       PCP       PEV     L/JKM        PS       PAN       PSD  \\\nBE      1.000000  0.551772  0.596303  0.638923  0.000000  0.457891  0.021334   \nPCP     0.551772  1.000000  0.780706  0.514572  0.050430  0.336860  0.068898   \nPEV     0.596303  0.780706  1.000000  0.550581  0.034526  0.369491  0.049867   \nL/JKM   0.638923  0.514572  0.550581  1.000000  0.009125  0.460856  0.043701   \nPS      0.000000  0.050430  0.034526  0.009125  1.000000  0.009395  0.313502   \nPAN     0.457891  0.336860  0.369491  0.460856  0.009395  1.000000  0.098344   \nPSD     0.021334  0.068898  0.049867  0.043701  0.313502  0.098344  1.000000   \nIL      0.170733  0.164954  0.165914  0.201926  0.134013  0.261747  0.296777   \nCDS-PP  0.102948  0.137878  0.115847  0.122520  0.198752  0.170733  0.442104   \nCH      0.216623  0.230386  0.220041  0.238412  0.102650  0.267743  0.310008   \n\n              IL    CDS-PP        CH  \nBE      0.170733  0.102948  0.216623  \nPCP     0.164954  0.137878  0.230386  \nPEV     0.165914  0.115847  0.220041  \nL/JKM   0.201926  0.122520  0.238412  \nPS      0.134013  0.198752  0.102650  \nPAN     0.261747  0.170733  0.267743  \nPSD     0.296777  0.442104  0.310008  \nIL      1.000000  0.417505  0.403680  \nCDS-PP  0.417505  1.000000  0.457645  \nCH      0.403680  0.457645  1.000000  "
                    },
                    "execution_count": 36,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "affinmat_mm = pd.DataFrame(1-distmat_mm, distmat.index, distmat.columns)\naffinmat_mm "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "A matriz de afinididade pode agora ser visualizada de forma semelhante ao mapa t\u00e9rmico de dist\u00e2ncia:"
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "data": {
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' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
                        "text/plain": "<IPython.core.display.Javascript object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
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\" width=\"800\">",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "sns.set(style=\"white\")\n\n## Make the top triangle\nmask = np.triu(np.ones_like(affinmat_mm, dtype=np.bool))\nfig = plt.figure(figsize=(8,8))\nax = fig.add_subplot()\nplt.title('Portuguese Parliament 14th Legislature, Affinity Matrix')\n\n## Display the heatmap of the affinity matrix, masking the top triangle\n\nsns.heatmap(\n    affinmat_mm,\n    cmap=sns.color_palette(\"Greens\"),\n    linewidth=1,\n    annot = False,\n    square =True,\n    cbar_kws={\"shrink\": .8},\n    mask=mask,linewidths=.5)\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Existem v\u00e1rios m\u00e9todos de proceder \u00e0 identifica\u00e7\u00e3o de _clusters_; um deles \u00e9 o DBSCAN (_Density-Based Spatial Clustering of Applications with Noise_), um algoritmo que, entre outras caracter\u00edsticas, n\u00e3o necessita de ser inicializado com um n\u00famero pr\u00e9-determinado de grupos, procedendo \u00e0 sua identifica\u00e7\u00e3o atrav\u00e9s da densidade dos pontos _(\u201cDBSCAN: Macroscopic Investigation Python\u201d 2018)_.\n\nNote-se que estes m\u00e9todos s\u00e3o normalmente utilizados com um maior n\u00famero de observa\u00e7\u00f5es; seria, por exemplo, um m\u00e9todo que atrav\u00e9s do registo dos votos individuais de cada deputado identificaria os partidos. Aqui temos um n\u00famero reduzido de observa\u00e7\u00f5es (pois estamos a utilizar uma matriz de semelhan\u00e7a entre $10\\times10$, o que tamb\u00e9m explica o reduzido n\u00famero de _clusters_ identificados - dois, alinhados com a primeira divis\u00e3o do dendograma:"
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'BE': 0,\n 'PCP': 0,\n 'PEV': 0,\n 'L/JKM': 0,\n 'PS': 1,\n 'PAN': 0,\n 'PSD': 1,\n 'IL': 1,\n 'CDS-PP': 1,\n 'CH': 1}"
                    },
                    "execution_count": 38,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "from sklearn.cluster import DBSCAN\n\ndbscan_labels = DBSCAN(eps=1.1).fit(affinmat_mm)\ndbscan_labels.labels_\ndbscan_dict = dict(zip(distmat_mm,dbscan_labels.labels_))\ndbscan_dict"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Outra abordagem para efectuar a identifica\u00e7\u00e3o de grupos  passa pela utiliza\u00e7\u00e3o de _Spectral Clustering_, uma forma de _clustering_ que utiliza os valores-pr\u00f3prios e vectores-pr\u00f3prios de matrizes como forma de determina\u00e7\u00e3o dos grupos. Este m\u00e9todo necessita que seja determinado _a priori_ o n\u00famero de _clusters_; com base na an\u00e1lise anterior (dendograma) observ\u00e1mos que temos 4 grupos constituidos por 2 partidos, e nesse sentido usar 4 permite-nos aferir se a distribui\u00e7\u00e3o dos restantes est\u00e1 alinhada com a separa\u00e7\u00e3o proposta pelo modelo anterior."
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "{'BE': 1, 'PCP': 3, 'PEV': 3, 'L/JKM': 1, 'PS': 0, 'PAN': 1, 'PSD': 0, 'IL': 2, 'CDS-PP': 2, 'CH': 2}\n"
                }
            ],
            "source": "from sklearn.cluster import SpectralClustering\nsc = SpectralClustering(4, affinity=\"precomputed\",random_state=2020).fit_predict(affinmat_mm)\nsc_dict = dict(zip(distmat,sc))\n\nprint(sc_dict)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O agrupamento est\u00e1 em linha com o que observ\u00e1mos no dendograma - mas tal n\u00e3o era garantido ou necess\u00e1rio pois diferentes formas de agrega\u00e7\u00e3o podem ter resultados diferentes que devem ser fonte de an\u00e1lise e podem fornecer dados adicionais interessantes. A utiliza\u00e7\u00e3o de _Spectrum Scaling_ permite-nos aqui determinar um n\u00famero de grupos maior."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### _Multidimensional scaling_ \n\nAt\u00e9 agora temos conseguido extrair informa\u00e7\u00e3o interessante dos dados de vota\u00e7\u00e3o:\n\n1. O mapa t\u00e9rmico de vota\u00e7\u00e3o permite-nos uma primeira vis\u00e3o do comportamente de todos os partidos. \n2. A matriz de dist\u00e2ncias fornece-nos uma forma de comparar as dist\u00e2ncias entre os diferentes partidos atrav\u00e9s de um mapa t\u00e9rmico.\n3. O dendograma identifica de forma hier\u00e1rquica agrupamentos.\n4. Atrav\u00e9s de DBSCAN e _Spectrum Clustering_ identificamos \"blocos\" com base na matriz de afinidade.\n\nN\u00e3o temos ainda uma forma de visualizar a dist\u00e2ncia relativa de cada partido em rela\u00e7\u00e3o aos outros com base nas dist\u00e2ncias/semelhan\u00e7as: temos algo pr\u00f3ximo com base no dendograma mas existem outras formas de visualiza\u00e7\u00e3o interessantes.\n\nUma das formas \u00e9 o _multidimensional scaling_ que permite visualizar a dist\u00e2ncia ao projectar em 2 ou 3 dimens\u00f5es (tamb\u00e9m conhecidas como _dimens\u00f5es visualizavies_) conjuntos multidimensionais, mantendo a dist\u00e2ncia relativa _(\u201cGraphical Representation of Proximity Measures for Multidimensional Data \u00ab\u202fThe Mathematica Journal\u201d 2020)_.\n\nComo \u00e9 habitual temos em Python, atrav\u00e9s da biblioteca `scikit-learn` (que j\u00e1 us\u00e1mos para DBSCAN e _Spectrum Clustering_), uma implementa\u00e7\u00e3o que podemos usar sem grande dificuldade _(\u201c2.2. Manifold Learning \u2014 Scikit-Learn 0.23.2 Documentation\u201d 2020)_."
        },
        {
            "cell_type": "code",
            "execution_count": 40,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array([[ 0.32947197,  0.36768563],\n       [-0.00198288,  0.42950115],\n       [ 0.10197222,  0.44174205],\n       [ 0.39394375,  0.21823126],\n       [-0.68817252,  0.15327307],\n       [ 0.49485406, -0.08254548],\n       [-0.5243696 , -0.28781799],\n       [ 0.15152887, -0.51072806],\n       [-0.23299015, -0.4774982 ],\n       [-0.02425571, -0.25184343]])"
                    },
                    "execution_count": 40,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "from sklearn.manifold import MDS\n\nmds = MDS(n_components=2, dissimilarity='precomputed',random_state=2020, n_init=100, max_iter=1000)\n\n## We use the normalised distance matrix but results would\n## be similar with the original one, just with a different scale/axis\nresults = mds.fit(distmat_mm.values)\ncoords = results.embedding_\ncoords"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O resultado \u00e9 um um conjunto de pontos definidos por coordenadas, neste caso 2 ($x,y$) dado termos especificado que queriamos reduzir para `n_components = 2`. Com estas coordenadas podemos facilmente criar um gr\u00e1fico de dispers\u00e3o:"
        },
        {
            "cell_type": "code",
            "execution_count": 41,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox \u2265 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
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\" width=\"800\">",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "## Graphic options\nsns.set()\nsns.set_style(\"ticks\")\n\nfig, ax = plt.subplots(figsize=(8,8))\n\nplt.title('Portuguese Parliament Voting Records Analysis, 14th Legislature', fontsize=14)\n\nfor label, x, y in zip(distmat_mm.columns, coords[:, 0], coords[:, 1]):\n    ax.scatter(x, y, s=250)\n    ax.axis('equal')\n    ax.annotate(label,xy = (x-0.02, y+0.025))\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Atrav\u00e9s de MDS podemos agora ter uma ideia mais clara do que, visualmente, representam as dist\u00e2ncias que calcul\u00e1mos, reduzidas para duas dimens\u00f5es. Isto d\u00e1-nos a possibilidade de definirmos agrupamentos da forma que consideremos mais adequada.\n\nEm todo o caso, e visto que temos os resultados de DBSCAN e de  _Spectrum Clustering_, podemos adicionar tamb\u00e9m esta informa\u00e7\u00e3o, complementando o MDS com a informa\u00e7\u00e3o anterior ao definirmos as cores dos marcadores de cada partido consoante o grupo previamente identificado.\n\nPara o caso de DBSCAN:"
        },
        {
            "cell_type": "code",
            "execution_count": 42,
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "data": {
                        "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox \u2265 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
                        "text/plain": "<IPython.core.display.Javascript object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
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                }
            ],
            "source": "from sklearn.manifold import MDS\nimport random\n\nsns.set()\nsns.set_style(\"ticks\")\n\n\nfig, ax = plt.subplots(figsize=(8,8))\n\nfig.suptitle('Portuguese Parliament Voting Records Analysis, 14th Legislature', fontsize=14)\nax.set_title('MDS with DBSCAN clusters (2D)')\n\nfor label, x, y in zip(distmat_mm.columns, coords[:, 0], coords[:, 1]):\n    ax.scatter(x, y, c = \"C\"+str(dbscan_dict[label]), s=250)\n    ax.axis('equal')\n    ax.annotate(label,xy = (x-0.02, y+0.025))\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "e para _Spectrum Clustering_, com um maior n\u00famero de _clusters_:"
        },
        {
            "cell_type": "code",
            "execution_count": 43,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox \u2265 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
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\" width=\"800\">",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "from sklearn.manifold import MDS\nimport random\n\nsns.set()\nsns.set_style(\"ticks\")\n\nfig, ax = plt.subplots(figsize=(8,8))\nfig.suptitle('Portuguese Parliament Voting Records Analysis, 14th Legislature', fontsize=14)\nax.set_title('MDS with Spectrum Scaling clusters (2D)')\n\n\nfor label, x, y in zip(distmat_mm.columns, coords[:, 0], coords[:, 1]):\n    ax.scatter(x, y, c = \"C\"+str(sc_dict[label]), s=250)\n    ax.axis('equal')\n    ax.annotate(label,xy = (x-0.02, y+0.025))\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\u00c9 interessante reparar que ambos os m\u00e9todos apresentam resultados compat\u00edveis (os resultados de _Spectrum Clustering_ s\u00e3o essencialmente uma divis\u00e3o dentro dos grupos identificados por DBSCAN) e ambos fazem visualmente sentido quando consideradas as dist\u00e2ncias do diagrama de MDS."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### MDS em 3 dimens\u00f5es: hiperplanos ideol\u00f3gicos\n\nUs\u00e1mos MDS a duas dimens\u00f5es, mas podemos faz\u00ea-lo tamb\u00e9m a 3 dimens\u00f5es. Esta visualiza\u00e7\u00e3o ser\u00e1, provavelmente, a que mais interesse despertar\u00e1: n\u00e3o por ser qualitativamente melhor que a anterior (adiciona de facto mais uma dimens\u00e3o mas \u00e9 discut\u00edvel que seja mais compreens\u00edvel), mas porque quem queira usar os dados apresentados at\u00e9 agora para justificar as suas pr\u00f3prias posi\u00e7\u00f5es est\u00e1 de certa forma constrangido pela forma directa como as tabelas e os gr\u00e1ficos s\u00e3o exibidos: \u00e9 sempre poss\u00edvel (e desej\u00e1vel) tecer considera\u00e7\u00f5es v\u00e1rias sobre o que foi apresentado mas essas considera\u00e7\u00f5es est\u00e3o limitadas pela bidimensionalidade dos diagramas.\n\nTal n\u00e3o acontece com a visualiza\u00e7\u00e3o do MDS em 3D: na forma interactiva que apresentamos permite a manipula\u00e7\u00e3o (nos v\u00e1rios sentidos da palavra) do cubo de forma a construir seja qual for o \"compasso\", \"quadrante\" ou esquema similar que seja desejado: ajustando os v\u00e1rios eixos h\u00e1 sempre forma de se poder \"provar\" que determinado partido est\u00e1 \"no extremo\" ou que outro \"est\u00e1 pr\u00f3ximo\", que um \u00e9 \"do centro\" e os outros \"marginais\": tal como estrelas numa constela\u00e7\u00e3o pode-se construir artificialmente uma vis\u00e3o a duas dimens\u00f5es que n\u00e3o reflecte a verdadeira dist\u00e2ncia.\n\nNesta esp\u00e9cie de pacote \"fa\u00e7a o seu pr\u00f3prio compasso pol\u00edtico\" cada um passa a poder escolher o hiperplano ideol\u00f3gico que mais lhe conv\u00e9m, com a vantagem de poder ser apresentado como \"baseado em dados e an\u00e1lises quantitativas\" (mesmo que, ao omitir a terceira dimens\u00e3o, esteja de facto a representar a informa\u00e7\u00e3o _pior_ do que a vers\u00e3o a duas dimens\u00f5es).\n\nDito isto, a vers\u00e3o interactiva (e at\u00e9 certas visualiza\u00e7\u00e3o fixas de certos \u00e2ngulos que permitem uma maior no\u00e7\u00e3o da terceira dimens\u00e3o) \u00e9 \u00fatil pois permite uma maior interactividade e adiciona mais uma dimens\u00e3o, logo potencialmente mais informa\u00e7\u00e3o.\n\nPara podernos anotar o gr\u00e1fico definimos algums fun\u00e7\u00f5es auxiliares _(\u201cPython - Matplotlib: Annotating a 3D Scatter Plot - Stack Overflow\u201d 2020)_"
        },
        {
            "cell_type": "code",
            "execution_count": 44,
            "metadata": {},
            "outputs": [],
            "source": "## From https://stackoverflow.com/questions/10374930/matplotlib-annotating-a-3d-scatter-plot\n\nfrom mpl_toolkits.mplot3d.proj3d import proj_transform\nfrom matplotlib.text import Annotation\n\nclass Annotation3D(Annotation):\n    '''Annotate the point xyz with text s'''\n\n    def __init__(self, s, xyz, *args, **kwargs):\n        Annotation.__init__(self,s, xy=(0,0), *args, **kwargs)\n        self._verts3d = xyz        \n\n    def draw(self, renderer):\n        xs3d, ys3d, zs3d = self._verts3d\n        xs, ys, zs = proj_transform(xs3d, ys3d, zs3d, renderer.M)\n        self.xy=(xs,ys)\n        Annotation.draw(self, renderer)\n        \ndef annotate3D(ax, s, *args, **kwargs):\n    '''add anotation text s to to Axes3d ax'''\n\n    tag = Annotation3D(s, *args, **kwargs)\n    ax.add_artist(tag)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O processo \u00e9 semelhante aos anteriores, com a defini\u00e7\u00e3o de 3 dimens\u00f5es via o par\u00e2metro `n_components` e alguns ajustes na contru\u00e7\u00e3o do gr\u00e1fico:"
        },
        {
            "cell_type": "code",
            "execution_count": 45,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox \u2265 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
                        "text/plain": "<IPython.core.display.Javascript object>"
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                    "output_type": "display_data"
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/p1FQKKkOhwWSD/rjXONkkRA4s4lzhL6wf5Bu1E+5NlPyxIQ6o7uXTZenH3MT/ANM7ixplBAYKlBVlOpFgERkGrxHCEgSUHoLDJ8qzkU2TTQSuAegBY2GkyMVQDNRmhONGGYzYRNJVqqJCC0D8EQFyc0JSZY8E0Ovc0339xrNwchIAgoCCpJmUbQTCI4omFC62QEBA11SCqiONimmZSZyOqBgGS5YkWD0Ok7wo0FoSNoIHBkFTSBaAQR0sw6ZQQEzVA005bVV4SAJLUh7dtxMUkIuRAG3OgQQMJipBmh3QR6rCH0i3kcDV5EwGSecyibFcQICIIsh0JY7He4ByCYUQYhIMcff7x3W4oKwhzMHNBRl0DaSP8Zd4SUsECKEcRCBcMgBASNKeQtXPusO4RIXEM40ClY8xCq0T7iehEWtNpYJ8G5CAGJBqFD1llnCFLM0/Hjx498BldHNOdYcKOxbTxkgipYQ0LRLtImhDM07WlCGCSPOcVYM+ZhQeCm3+ynaG0pJjSyl+IeGHWPQHsNUYyLvUAQMuEwJCBoRQeNzUMYs5gjBCrLjIcWHKVDVOC3fuSdA2n7C5YIyFjUMhlHQGxvjK5tNNkQa/ZGc+WxcSVRAyQzrSQRENYu41RnAoY8BITxwZLFeWZEswoCEifUGhZmSUrDrQgBQelBX5nj4XlM/TauoRWENQjRiyOArBHqyUtA4vbqaL+KxoDY3ozgb0qorP20LAGJ2ztp/wknnOBdYEOlUtgvSDSkkn1apVoERECqxXNEUAqrDdPwosVDoKTgU8+GyIHNQowWE5Dw2cWVgZLmDsTvqyQg1r6k9IDmXz8IAWHhswHg7oOwEi0seuJlELrYQPg7GiyED1wOEIjYuKKWINMU0XYEuWhB2GJTxk0CDXlaSUrDi4CGewJjEwpYCDkc5mi/EMQQKMPC4WdaWSMgaZmaihCQMt/mIGIsw2IHSRzRNY17eGgwDmiKsYLEFYRWyIqlujSSEZd5yrRzCG6QBsogBAThmYPYLB1maUiyjJgbCm2MpqNFK4+GGJcpO5CyDsw0CwhzEwE8WiDXCGxmLcM9CDIe1WTae5Cioi5YceOEewTjisAfFsglJBNXi2gcE89hVYRoWGwBSgPGDiskLkhpxcgxexfkIFogEhAw3HZQ1qQJjbxrQnNSnANzAa1r6IIF0WTeMd5Y6dhTGOeokJfWD5QZCEfsB+zZViyuIqoMsX7knQPUx14COUSwBXPcoVCIWGFvxHpmJSkInbbgtgVRMmIA9ghaKL2MEDO32ZuZ94wDxBtsIK/REicYopghDgetMgor5g5Wl7wWpdSJE/wyDwFBkYIyD622xVZWQUDY06IxQig8ONPDvT6pL3kJCBYDsEfBE0cGmRvIDGa9xPoGqcarAFeyaOHsw8qZRUCwiEM+cEmiPuqnHSjNoumlkwgI6422Md9Q3uHSFxbmCOc5JWs/LUtAohZl+765X6IAYV+PFtYailaUM5BXleoQEAGpDktfU1oMSPRTpk2N0zLzrAlpoVYrDEKPuvnwTpUEJKt9Zt4dhICg8TQtb9pQhFmmcM9BwOenCfcIDgTjmnBkB1/W8IbavqRns0hf+B6bJ5pIDgu0qhx0HPC4TNBe/oR4GQFJ8xfOS0DKfjsuKUBaLE/cHAN/DvO0EiZDKBorNAgBoU0E1lpQOak6bW5DnHDpCwsHEeOE1tbcr8LfozDAbcf837MOzKwg9LgLv/gGJBsrW9JeELbJtOxFLCChYoH4Dkg+WkCEKVx0zK2C72Cds6DktDG2vcqeT7JshnXY+goJbfh7hGiC7iF8WGdMaEzK1GXzE+WGpUIP62O/RbkSEhCsSLh5QqKwulBYtwjq+IbHZVCL4mAaZcbbXBZ5xjKtIXQh3FvcS9adC9E5QF1ot5mXKD8gg8S/oPxA2GSPjJKcJAJicxKXMZQftg/xPC6TYSHujvgeCBqEh8L3wSUkjHGCIeQItzDmE3jzb9rL3EP5EE0wkrVfJ/0+i4CwVukn6ySMXauCgMStubhg6KS25yUgzMswxiypPtvTbM+M2+N417K1ZREQnoWYsq7Yj1DoUJh77JW4j0eTcoR1srdgUUMZB/mEuLC2eIeYj2gcStZ+WpaAhAqtuP0+a+4lnQdZ7+n3yQiIgFQ8O4oQkLwWkNAVxQ7r0P0l7EIWAUEbgvbdfHzDd3GTQTNlWbDKWEDMZxKtJoJ4WEwrGgq85grF4R8GsOYZFg50tKJsYAgvaLbRQqK9teDH0HqUp864Z4oQELS9tCVO+DHf6zgCEkcCrC15CUiV3y5KQOw29mjgchLmTRMQ0+BjsYI4mxtZnNk9zQKCAIUlDk0YAhkl68CsgoDUYQGJs2yaUgFLVpjhyFwlo0GuSeNbpQUEIReMoxaQpDsfylhAwn5gRcR6yR6JgBS1KsT1mX0fP/LQGhH3XBjnVZSAYP3FooQFh/0yjAMxQp2XgEAkcBcjwBbiaUJdnNXP+oGyBzwQxNhfIW1gZJd8Zl3MhyBK/BR7NJY9XF/RzOfJFJW1d2cRkLTsSmHdYfKFQS4irIOAmEUD90xIXVZBScK+PKgFJPwO89uSSDAHiO8hnoZ4VUrcXmeufWEcl9Vpab9DwpK1n6JoZN7EyUAHHXSQz7iWlAUr7kJJcx/O4y6Xhbl+XwwBEZBieGU+XYSAsKGjxULTiBAdjQExbVdcDEgSATHffQ5hDuhoYUNCAxeXahfTaqilthgQzJJhcCl1shHhj8pmFArUJuhh1kSoDouRo1DYZtGjSUPo42fZYv1GCOeQNp9oEzjL1st7RQiIxe1w0IY+z7gw4G5H2sS6CEiV3y5KQGyuIljEuWdE8S9KQDDbIzCj0YbcFS0ESlqcBMI87leWPSbqZpMWA4ImHi1xGAOCwIrwmWTJrIKA1BEDEkdAEExxtSGzHDhZPI/FdLBfccBnlapiQP71r3/59uC+Z+5oWYJ70RiQpL6ABXsoQila7rRi7iwoceJcSYk/QPBG+YNbDiWrH1ELCEIXwleYNcjaZEJeXgLCe5ZCnPFEgCTrFesyT7G4HYQ3rF6ULAIS1oubGi4tENuou1+e70efySIgELS4GDvOMfrCGcc842y0YGNIKMHHcWdZVn/rICB8E/dAkgWErnNpeFURA5JUv+17uGOhdKPE7XW48UJy4wR8O7NCApK1n5rLZpzF1KylRQgIZJ7xyhPnVGZu6p1kBERAKp4dRQgIn7Y4inAj5/9N+8JhRvBlNAtWEgExs2qSa5Et+FAIhmiQgYvDICQg4SYb3nPB/4cuGWFdJnigWcMaYpkt0JRgEUGwCQkI32ZTxcRLnED07gPcF9B8Wpo/DhI0OtGARss+EwYEkq4XIoKWG//jsEAIqCvP5YFFCIhZOfB3ZfOPCgj8uy4CUuW3ixIQtKKQEA4jyGA04w0+wIy9pZUtSkDQtlI38wPNW5li2n3cACAScUHP1ItvPeQaFwPmOXERFOYqlgEEJ+qyW8QJ/mZ+Jrk6VEFA+D7zmbUQzYKF/7QlQSjrghXiiUsa+0c0LsncvNizEMwslsbeRUOPe4Wt+bQsWMwH8MUNg3kBGWJNIgzaHEE4RKjFx579CVcPSpbgDtmEECKU5smChUsJBBfLVlh4H6VMSH7i5h3txjWGvY99ORqPZu8QS4ECwgSxrH5ECYhZOaJKFe4Lwr+deViEgOBahW8/KXIRanG9QoALC/sn/WeswmLxiaY8Cs+KcA4iKLMuwlgB9nTIOliEbi1GYJLu7Elb81kEJOndNBcs8wDg3ELojZairkBJbcjrgsX7kEW0/JxnCMxRpSXCO7KCubZZLMogWbBQfjBmYUpw2kJGMfYI4tVICkOJ2+tMOUi72cOsmLcE/w7HPGs/tfei44L7pGUSLUJA2IuwsqII5myO9pPYRuZqnpT9Zc6l0fyOCEjFo1+UgGCWZlGyceDTzCS3e0DwlSVA24RvmprlgkWgGAcUhwoad1yqENYtz7YJimxcCFn8xLcalxK+izY4tI5wMHOoWSAv6f0Q0LDY2D0gbIohcWATYDMg/gEzP+QClwz+jhYz6m7EIYfwiskYjaMF9PFtDl00kJYBBxMq5IxvEwNDe/EtRusJWUOwNXICpghK9As3AeIUEI7QqrJ5gj3B6FmlCAGhLYwnhwCHBG3nW/w/fSNLVF0EpMpvFyUgYGhaYOYSY40gxphywGIRQlNqqVSLEhDqt1z0CE2MNRgz15MEvui4QoItDTC/4yCzdJXRZy07C0SK4EhiAOweEOqgr6EAjkCFQIevMxpd2oa1huDbqggIefjRHEOEsODYPSDMYdYMGOfxU84SeHBtRIBn7Egda7FmZNNBkOEwxi2QNcVaQ/ijbdTLOjRhlXWPYMx+wdqze0AYB/Yc3PUsHgKSgSsHOLMvsW5Y57j8YDVgzUTvAUlywWIsEXyxqiL4sg4hkdRHHxA0CCq1GBDwY4/DJYj5QNwPz0Em2CPi3EnD+WJ7aiiIxe0p5hpm7m1FCQjPs7eACYoTCBN7GfMSAsfeWoSA2Jpi/+UcID4lTrGDuw/fY17ze7sfgbUBQbc4qTiBnLHjHUg/QjGCLN8BfwgZArIVXHSYB0nZiKKY0g7ORwpCImcE42z3+ZDmmTmVVtIICG0FV+L5CPqmn6x5ztLwHpCqYkDYK+K8Fmg/Y81agpTTJ6xpnPGQO/rM+QymKCjCxCpGSllznNGMA+cq69ruAWEMGAtK3JwEV2K5iGviTOd7rHnOdPYKLAiQ5SQCwhylfp5F0KefkDssTBB8FJ9R0pm2n2KZtIuXWXPsRXhiELdHH1mPRQgI7WatQ6ZRXphrIlZL2s4+xfzHyqhSLQIiINXiWSgI3T7NIYcvJAcJmig2NyY8gno0RWwWAaFODgU0Emw6CCtRqwYaODSmCAYc9AgzLD7bhKLuWQjybFIIDbZBIUyyMUE+ooIchwEaYjY5tJtYQyAYbJhJN6GjPeQwYfNg0SNssOGzYSKQ2MaMYMEhxUYLVmzICKCYzyEbUc07frNodNlg0OQgGNIOMs/Q7/AW1qSpUISAUAcbFlppNJMIQBzCbODgSjBnXQSkym+XISB8HyGP/tlt7swvNKgcUAjn5tJThoAwX8lkg6adg5Wxz7oJPTqm5vqBQJzlbsLBiNCIoGo3oXMwYjmJZn/hAKRtkE0EFtqW9yb0PEHo1g+ETwQ0LDjMZaxCWCexWLA2+H8T6pPmcxYB4T0TlqNuZaxtLFCsfcYD7T8CCVZJ1hKCQYgN48S6xp0L5QwEg/mAxQBlQvgsAgSadTBEWAlvQg81vVmCu/Wb+liHYJZ2Ezp7BPMIgQhsIF6QKPpEYDlCXloxy1CW4Mw+zzqAoCO0IswyD5OIVFwQOqSOvRghnnbj7ojVgjYi3BUlIGYxT3IfZK1xnjCv7OZ19mXahjU9vDMhjoBQP/MS0oqQzDjSZtrJfhBmD+T8QThGuZUnS1Z4l0Xc+ECYLIlD0vhlZVSj35xl7AEWgB+9Cb0qApI2x6IXuaLgQ7Fh7WK+givrCsVjeA7anAFXuwmdtceeRd/C+Mu4tcV5jCWR9YQswP7GmocUMQfChAtJWbBoJ/MWRQXf4PylDdQXvY0dHNL2U37PWc5+y1nLXosyhPnHPkN/ihIQ+yZeGChRsIqwjzLXUZyAaVw2zYrFx1FXnQjIqBvy6jrMwYewgJUiS+ip7quqSQgIgRABDnQ0iZABhD0VIZAXAZJ0YOGI3lGS9/2qnoNMIehhIQ0v5K2qftUzNQIoHVFG5o3bE4ZCoGoERECqRrSH9WFpwGoQFm5HxxQcd+N7DyFQl4TA0BHAJQSLQtQfH6saCR7yZGsaeifUgNYggJYXqxUudrj0DLNgHUYg5ieuUyrVIRB3fqPlxxWQdMpYU1SEwDAQEAEZBuod+yaaKXyj8f/E7QNfd8yxWD3QnNnFih3rlporBDqFAC48JDbA7xl3C9wrsUDiYoSrAC5l3JOhIgTSEMAVhzmD5hvXKLK+hbFRQhOKtt0AACAASURBVK9fCODaxlnNGY4rIrEzWEpxfyPWLSnupF8oqDdtREAEpI2j0rI2kaIOzRT+ufiA418KKcGHM5oxomVNV3OEQG8QICiSzDfEKqDVxCKCZZKgSdZi3KWJvem8OlIZAharwXwhnon4PJX+IkAMJBYuYrDIJkgMErGVZJUL4zf6i4B61lYEREDaOjJqlxAQAkJACAgBISAEhIAQ6CECIiA9HFR1SQgIASEgBISAEBACQkAItBUBEZC2jozaJQSEgBAQAkJACAgBISAEeoiACEgPB1VdEgJCQAgIASEgBISAEBACbUVABKStI6N2CQEhIASEgBAQAkJACAiBHiIgAtLDQVWXhIAQEAJCQAgIASEgBIRAWxEQAWnryKhdQkAICAEhIASEgBAQAkKghwiIgPRwUNUlISAEhIAQEAJCQAgIASHQVgREQNo6MmqXEBACQkAICAEhIASEgBDoIQIiID0cVHVJCAgBISAEhIAQEAJCQAi0FQERkLaOjNolBISAEBACQkAICAEhIAR6iIAISA8HVV0SAkJACAgBISAEhIAQEAJtRUAEpK0jo3YJASEgBISAEBACQkAICIEeIiAC0sNBVZeEgBAQAkJACAgBISAEhEBbERABaevIqF1CQAgIASEgBISAEBACQqCHCIiA9HBQ1SUhIASEgBAQAkJACAgBIdBWBERA2joyapcQEAJCQAgIASEgBISAEOghAiIgPRxUdUkICAEhIASEgBAQAkJACLQVARGQto6M2iUEhIAQEAJCQAgIASEgBHqIgAhIDwdVXRICQkAICAEhIASEgBAQAm1FQASkrSOjdgkBISAEhIAQEAJCQAgIgR4iIALSw0FVl4SAEBACQkAICAEhIASEQFsREAFp68ioXUJACAgBISAEhIAQEAJCoIcIiID0cFDVJSEgBISAEBACQkAICAEh0FYEREDaOjJqlxAQAkJACAgBISAEhIAQ6CECIiA9HFR1SQgIASEgBISAEBACQkAItBUBEZC2jozaJQSEgBAQAkJACAgBISAEeoiACEgPB1VdEgJCQAgIASEgBISAEBACbUVABKStI6N2CQEhIASEgBAQAkJACAiBHiIgAtLDQVWXhIAQEAJCQAgIASEgBIRAWxEQAWnryKhdQkAICAEhIASEgBAQAkKghwiIgPRwUNUlISAEhIAQEAJCQAgIASHQVgREQNo6MmqXEBACQkAICAEhIASEgBDoIQIiID0cVHVJCAgBISAEhIAQEAJCQAi0FQERkLaOjNolBISAEBACQkAICAEhIAR6iIAISA8HVV0SAkJACAgBISAEhIAQEAJtRUAEpK0jo3YJASEgBISAEBACQkAICIEeIiAC0sNBVZeEgBAQAkJACAgBISAEhEBbERABaevIqF1CQAgIASEgBISAEBACQqCHCIiA9HBQ1SUhIASEgBAQAkJACAgBIdBWBERA2joyapcQEAJCQAgIASEgBISAEOghAiIgPRxUdUkICAEhIASEgBAQAkJACLQVARGQto6M2iUEhIAQEAJCQAgIASEgBHqIgAhIDwdVXRICQkAICAEhIASEgBAQAm1FQASkrSOjdgkBISAEhIAQEAJCQAgIgR4iIALSw0FVl4SAEBACQkAICAEhIASEQFsREAFp68ioXUJACAgBISAEhIAQEAJCoIcIiID0cFDVJSEgBISAEBACQkAICAEh0FYEREDaOjJqlxAQAkJACAgBISAEhIAQ6CECIiA9HFR1SQgIASEgBISAEBACQkAItBUBEZC2jozaJQSEgBAQAkJACAgBISAEeoiACEgPB1VdEgJCQAgIASEgBISAEBACbUVABKStI6N2CQEhIASEgBAQAkJACAiBHiIgAtLDQVWXhIAQEAJCQAgIASEgBIRAWxEQAWnryKhdQkAICAEhIASEgBAQAkKghwiIgPRwUNUlISAEhIAQEAJCQAgIASHQVgREQNo6MmqXEBACQkAICAEhIASEgBDoIQIiID0cVHVJCAgBISAEhIAQEAJCQAi0FQERkLaOjNolBISAEBACQkAICAEhIAR6iIAISA8HVV0SAkJACAgBISAEhIAQEAJtRUAEpK0jo3YJASEgBISAEBACQkAICIEeIiAC0sNBVZeEgBAQAkJACAgBISAEhEBbERABaevIqF1CQAgIASEgBISAEBACQqCHCIiA9HBQ1SUhIASEgBAQAkJACAgBIdBWBERA2joyapcQEAJCQAgIASEgBISAEOghAiIgPRxUdUkICAEhIASEgBAQAkJACLQVARGQto6M2iUEhIAQEAJCQAgIASEgBHqIgAhIDwdVXRICQkAICAEhIASEgBAQAm1FQASkrSOjdgkBISAEhIAQEAJCQAgIgR4iIALSw0FVl4SAEBACQkAICAEhIASEQFsREAFp68ioXUJACAgBISAEhIAQEAJCoIcIiID0cFDVJSEgBISAEBACQkAICAEh0FYEREDaOjJqlxAQAkJACAgBISAEhIAQ6CECIiA9HFR1SQgIASEgBISAEBACQkAItBUBEZC2jozaJQSEgBAQAkJACAgBISAEeoiACEgPB1VdEgJCQAgIASEgBISAEBACbUVABKStI6N2CQEhIASEgBAQAkJACAiBHiIgAtLDQVWXhIAQEAJCQAgIASEgBIRAWxEQAWnryKhdQkAICAEhIASEgBAQAkKghwiIgPRwUNUlISAEhIAQEAJCQAgIASHQVgREQNo6MmqXEBACQkAICAEhIASEgBDoIQIiID0cVHVJCAgBISAEhIAQEAJCQAi0FQERkLaOjNolBISAEBACQkAICAEhIAR6iIAISA8HVV0SAkJACAgBISAEhIAQEAJtRUAEpK0jo3YJASEgBISAEBACQkAICIEeIiAC0sNBVZeEgBAQAkJACAgBISAEhEBbERABaevIqF1CQAgIASEgBISAEBACQqCHCIiA9HBQ1SUhIASEgBAQAkJACAgBIdBWBERA2joyapcQEAJCQAgIASEgBISAEOghAiIgPRxUdUkICAEhIASEgBAQAkJACLQVARGQto6M2iUEMhA4++yz3Ve/+lX/1E9/+lO3+uqrT/HGm2++6TbZZBN3//33u9VWW8397Gc/G/n9MsssM/L3aaed1s0+++xu4YUXdiuttJLbaqut/M9oefjhh91xxx3nrr76asffZ555Zrfgggu6FVdc0X3hC1/w71dd9t57b3fttde6P/3pTyNVH3vssW6ppZZyG2+88RSfMzzOPPNMt8IKK5RqyuWXX+5OPvlkd+edd7pnnnnGzT333G7cuHFunXXWcZ/5zGdK1VnlS0l9r/IbddR10003uR//+Mfu1ltvdU888YSbc8453WKLLeZWXnllxxjXUWw+/PGPf3SLLrqo/0TcfKrj22Xr/MEPfuCOPvpod8cdd5StIvW9Sy+91P397393X/ziF2upf5BK//Of/7j3vve9fv+xtXbbbbe5ww8/3K/Hp556yu85rMdtttnGve9975vicx//+Mf9XkGZZppp3CyzzOLGjBnjll12Wbf55pv7vZC9zgrf22yzzdzHPvYx98lPfnKQputdISAESiAgAlICNL0iBNqAgAlYs802m9too43coYceOkWz/vKXv7jtttvO8fvllltuKgKy6aabuk996lMOovLCCy+4f/7zn+6cc87xwg+H+de+9rWR+h555BG35ZZbesFx++2390IA79x1113uwgsvdPvuu68nOVUXyBPfmTRp0kjVCK20/dvf/vYUnxuUgPziF79w3/jGN3zd73nPe9xcc83lidbf/vY3d/PNNzvqH3ZJ6vuw25X2/UsuucR97nOf8/PjQx/6kJt//vnd448/7m655RZ3wQUXuMsuu6yW5scRkLj5VMvHS1ZaNwE58MAD3c9//vPaCE7JbvvXfvKTn3gFx8UXX+xmnXVW/3/sYcyRt7/97V7Z8fLLL7vf/OY3/v923XVX9/nPf37kk+xZrNfvfe97/v949sEHH/T1MQdXWWUVB4GfY445Rt759a9/7Q455BD3+9//3s0zzzyDNF/vCgEhUBABEZCCgOlxIdAWBEzA+uAHP+gP5SuvvNJbMqzsueee7oEHHvACPIdr1AKy7bbbuv3222+K7rz++uvu61//ujvrrLPc/vvv7zWNlKOOOsr98Ic/9Ic5mutoeeONN6bQLtaJUV0EZIMNNnCLLLKIO/XUU4favzTsihCQV155xc0000xeGzzMgob50Ucf9UR1+umnn6Ipdc6bOAIyTBzyfLurBARhH4tD2fLaa6+5DTfc0Fs19thjj8xqILKPPfaYJxZWICBPP/20O//886d6n/1sn3328RaPI444YuT3//73v711E6XKZz/72czv6gEhIASqQ0AEpDosVZMQaBQBE7BOOeUUf3jijvWRj3zEt+H55593a6+9trdioFnMS0B4F8F1/fXX95YT3FcoaE6xEPz1r38d0U7m7SwECO337rvv7nbccUf/Gu4U73jHO/w30HKaYPrNb37TCxC4eSE4R11mQtcx+765lxke9Bdh93e/+51/ZNVVV/WkCg1qWkG4p9+4fGQV2gGBmzBhgnfZeuihh9ziiy/uXdHe/e53T/E62n4ES4Ql+r3AAgt4NxPGLBTIEYZwU0K7i+YWLTD177bbbu5tb3uby9P3E0880b//5z//2QtjuNtAJKNubDQwTti1fmExoy1olHF3g6jiakf9p512mq8bN7eDDjrILbHEEqlwYU2iL7/85S+zYPW/h0xDAnG7oYArJAaiTYFo83vcuWjHQgst5NZcc033pS99yc0777wj38jrgmV9futb3+o18P/61798n8AdUhoWCPiRRx7pJk+e7OcTFsbnnnsut9sU1h4wxPqD0P2Wt7zFWxZ32mkn/5mkMdl5552ncptCYGfumyUQEoCiAG0+cw5CgOsZVk7GgLWExj9azEUNSyhjyzjRP8gruKLICJUOJugzrw477DCHmxRtYd2wbo855hg/drSH8WCefPe7300lKBdddJHvH3OX+ZZVwAvrq+1PPJ9GQPg9bl3gzzsoGqxg9bT/HzZZz+q3fi8E+oSACEifRlN9GVUIhC5HWDcQGn71q195DCALHPrENEBKihAQ3ocsIAzgM46Ad95553lBBFKDvzTCemhtyQL+wx/+sH8e4Yvy29/+1gtECN0IPQjYFHy1EbpNSxklIDfeeKP7xCc+4eNdzP2CehFaDA+EJYgEfxCgcU2bOHGij5NJK2hBEdQhBu985zvd0ksv7aabbrrYVxBaiXmBQNEOhD36Ad4IqO9617v8ewiCCM74niM0IUzj0vWjH/3IC4W4f1AQRhEUIXgItWussYbDGkXsBHhAavL0HaGYfhMfgwDIT9zjihAQhDNI0g477OBJIPgRD4OgjEUN7TMEF8F3vvnmc+eee26qlQUSzLxEQMTHH3e6GWaYIRZXsEOAxV+fPuMug2vgs88+611ubG7zfcac30P+IIGvvvqqJy9WdxECQp/pC2MAWTrhhBPcDTfc4EmsCd8IqYwhrjzMQcbspJNO8jEttCErbgMMIMIQYtYD32PN0j+EecogBASSyDqFiBH3wPhDBOgPBA73M9yTIChnnHHGCP6Mx4wzzujbBkFhnFAOgDlWTwgWY0w8BYXfUy9zHoUC64T5zXpg/YIPllPcNbF8sSaYA/w7qWCdgKBfddVVsY9gKeMPbUG5cPDBB/v2msLF2pVkAeH3p59+useZfTGMH2EvgmyCXRzJz9rb9HshIATKISACUg43vSUEho5ASEBeeuklL7hiPUAg2Hrrrf1PBFwE3aIEBEHl+OOP94IjmmG0oxze/BtBAMF0/Pjx3n0BgcSCfJNAgVAgJF533XVe2EEgIa4ENwqETTS8CCvrrruu16oj5FLigoazXLAQfkygow6ESYToK664wscfJBUENCwYpnkn4JVvQUZoTyg0I6jwe7SpJphBGMCan2h0KQiF5rOOttsKgut3vvOdEY0vsTdf+cpXHBYg0/THtTOr75AE6g1LUuB1krALRrTf/PDR+oMLQi0CqmmJsTQhCGYJbgiFvA+5ooDj8ssv77XmWJEgcRTIDfE3CLHmx5+1yJiX4M08wloBeSEeilKEgDCGCOZGqiEVzG0EUwuIZk3x/2DDHKa8+OKLvh8QtDQCwnPMbeYNMRhJmvZBCAjkDoILaUgqSTEgkFtIEXMFIm6FNcqYQGBQQFAs2BvLKxYSK+C3yy67eLIC4S9SGHPWB2s1rrCOjDQxfyAs5h5qz2dZQCCQn/70p92Xv/xl/9PKfffd5/cgLCEf/ehHizRbzwoBITAAAiIgA4CnV4XAMBEICQgCHYcowtf73/9+t8UWW3hBB21kGQKCwI4wYATE+ommF6sILiTXX3+94/BGE4q7TloQugXEI7Si3UdoQ2CAdOAmhPXAhPAwzqQMAaHdCI9W0MCiqcW1BDeitAK5QlCmvfQRwoQLGS5JWJVwS6EgSCLwEtQaFhMgzXKE0ImGmcxGYbn33nu9ht/ibPB7/8Mf/uCtHGGmnmhbswhIKIDbu0UJCPMF1xoraOmx6KD9xzJmBUKHlQQMoq5KcRgTyI+LDrhikYGYYHkgaxmuOgiYCJpoqulnUnnyySe9lQmMIR6MmRVwNMJQhIAwFt///ven+CTWPtbTAQcc4CD4WOkQxMPkDLyA6yPfSiMghhW4gm9SGYSAIJRDdrHOMP+Z65DksCQRENyncD/DvY3kC2GBJIKxWVdZt/TVMk7ZsxB4iAREFXLA3hMXLxbXd54F6yh5tmdxi2PccWEkIx5rGSLB/LOSRUCYL8yNKAFhfRPkTqIErEcqQkAINIOACEgzOOsrQqByBKJZn3DrwRULjSWuDGgkKWUICJpfXBNMkE5qPM8g9CGgI0gmFVytICgIR8Q/QJbQnKNhRTuOwI+gh9sLBMRKGQISTcNr5CcuVXHWoCB44sJEPxGOEcYoEBAsFVgswmKZtEwLDC646iQVNMb0H60zFoCw73HvZBGQKGGkjqIEJJqcgHgUhMO99tprCoHPcA1dzrLwtN+TAhUrB1p0hEjqZv5iKcMVJymlM4IwBBvigesb7mkQYCwhWKnCWIkiBCQuIUMYY8E8XW+99byAiqAaFrMWphEQc2E0pUASToMQEOYq5Ju5ajEckCiwHTt2rP9kEgExN7mkdkEkbG4i6EMEcNGMFpQStIG5QXt4j+dZ92kFBQr7Au3LUyDurHOUCxb3k0VAbG1GXbDYm4hTwf0OK6SKEBACzSAgAtIMzvqKEKgcgSgBwZqA/z8ldB0pSkAIQkfYwmcbrXxWwe0HgYd4hbSCoIkPNwIkmnq0wggpEBMELwR8NOmhEDJsAkJ/br/9du8zjnsGbhqUvBYQBECeTdKsEmtB3EZVFpC4O1DAFa0xeIclThi1gOwwO1odBIR2EMeB5hsrEe5+eSwgNhbEnzCPrJgbTV0EJM0CYsHddVlAEI6Ju4pmh0rLiIarGC5HWFyIk7GEDEkEhOcYAwiSuZeFc4X/s/iILEGf93CLw9KFQgSrDNalaHKGsH4sNvSHIPo8xbJahVbNrHbhdgVhYS2E7pDEabFOQ+tZnjboGSEgBAZDQARkMPz0thAYGgJx916gjb3nnnu8oIxwSylCQMI0vGEsBhpnqy/ssPnA5yEraEYRRCAcxBnYvSVon3EVQUBGA06qTCtxBIQAdHzPw3SaPJ90D0heC0hSH9H04noU3juQFgOCxcOIG5plrEjE5kRdW0Iczf3sW9/6lo/fiSto/3FfI0A4b9+pB/c4XGwQvixeBa0v8wLBPRSc6yAgSbha3AH9pd8QHSxjtAstdVyhrbgXRgVaXHeIq6mLgNCWQWNAIPXERiCUF4kBwf2N2A7G0QqubJASSFj0PpwQN2J0cHsEayxFPEssFsqC0D0Lt0PcppgnuFGllSxBP3zXSKZZuZLqxUKCciIuS1fcO1h1LPV4HguIEZaoeyF1Y3VFuRDnvji0zV0fFgKjAAERkFEwyOpiPxHIe/FeEgEJLyKESNhFhGiZEW7slnXQQ3PKQY1wghCF8ILAaOlQEXQ+8IEPpAJN2lTcLCgIjFhOKMRHYAFBKEOwCi8EiyMgCEB33323d3+CyBDETED8oAQEYoSAj0YegY+sSghqCGz0F+HISFhaFqxQiEP4JlMPsSO0mwscEf7BDg01bmdkGbMsWGBsWb5wK+L79A0hFMsUWlziR3gPywlB00suuWRi38EX1y5IHRpmYmHoF0IwfvW0o24CQnA0fcS6RV/oF6lbIQzMO2I+TLtuWbDsMki096RbJV4EdzVct5iDEGVIIaSOlMPELoBLnQQkmgWLNpDVDWsDWLJu0grucRBS5hjuYmTBIm6C98ziFOeChWsauNhljuDBuiMmgixnRkBwCcQCCpbgwhphLjLnwJhiawScmOfEG/E8Fg7LogURIVMXhAXrAOQEVzcL+k4iILg4XXPNNb4NuNAxzxD8cQUFJ6wMSYXAeQgALlzhfSJkumKOYwWCPDMPsObgZhYlNdGLCFkvzH0SRTBHWN9gGc3ex/pGGULb0zJ19fMUUa+EwPAQEAEZHvb6shAYCIFBCYh9HCGEjEe4JSCkQhJWWmmlKdqGIExcA8II/vBoNhFyiHHg4Ee7m1UQPLFcIEQgzNm9HKaBpK7obeNxBASBDQEcIZZUo9F7QMrGgOAChBWGehEqIQUIUlgcEP4Qoq2YpYBUsAgwpPvF3524BATusOAvj3CFEISbHISJ4GvcTgjstmxTCGwEAmNxIdif5yB7xIgggNGef/zjHz52gkxdCFgERiPcYWFBWItzwaItWGEQRnGVg7QRc0K7IH91ExCERYRAXHIgZJAI2oCQS/8hUGHBGmT3gJAGmfgF5piRVwRrLCZo9blHhTnFPEHwrZOA0MbwHhD6gOacPhHjEQ3KjlsPjAOWQLBgPTAPIOKWlSmOgEBYISDMC9YOWemIS2KuhfeA4EZF7BdCN+uC9UXsDmmljdRTF8oExoO6aIPdA0J7IQysA5QRWNwg3Mwx8CdOg5JEQBgP+sYchbjYPTbMNeJp0gpEDOsXczS0gNIe9gTGnD2HOlkTWKPCVLrWrnAMeBaSRxIISCv1xyV4wAIL0Y0mlMjaz/R7ISAEBkNABGQw/PS2EBACoxCBOFelKmGw9LKQDv6OdYi/o3VHiOL/+IOQaL/nGQRy/iC481MXq1U5KlPXBZmCQCDsY9FRKY8ARIk5npSKt3zNyW8a8YHEr7XWWnV8QnUKASGQgIAIiKaGEBACQqAgAnUSEAgFgi1kgwKJMAKCgBanxQ0JCe/wbywkaIFxaYGQ8EeEpOBARx4n1S2CKtYPrGS4HZGqGfKBpUylPAJY9YhpAVOsPE0U3Eyx6GLFVBECQqBZBERAmsVbXxMCQqAHCNRBQMyiAfnAsgHRCAmDkZK0e0IMWt4nVgBBGZ936uE9LnEzC0m0/h4MS+1dIBEBN9njvgaWuPfgRkY8hcrgCODmiTtUlsvW4F9y3tpCYD8uX8TJqAgBIdAsAiIgzeKtrwkBISAEpkIgyeoRPliEgFAffvy4BhFYG7WQQD7iCImGRggIASEgBIRAEwiIgDSBsr4hBISAEIhBIMvqUQUBiab/zYofMQuJ3LU0ZYWAEBACQqAuBERA6kJW9QoBISAEUhDIY/Wog4BEmxRHSLCOhAHtih/RVBYCQkAICIEqERABqRJN1SUEhIAQyECgiNWjLAHhPYJ6SaM699xzFxoTax9xJGYFUfxIIQj1sBAQAkJACGQgIAKiKSIEhIAQaAiBolaPYRCQ8JtmHbGf/A5SgkWEIGzLrpUnML4hiPUZISAEhIAQ6AACIiAdGCQ1UQgIge4jQFpdy3BlWamK9KpIEPogFpC0NkXdtXg2dNdS/EiREdWzQkAICIHRi4AIyOgde/VcCAiBBhBAaCflp10qWDb9bVECQhYs0vAWdcEqAkl4GaJdiBglJGX7W6QdelYICAEhIAS6hYAISLfGS60VAkKgQwgQR2HEwS4ULJtdykhMXncnCMiYMWPcPPPM0whiEBAKfeYPxdy1LKDdbmcvi0EjHdFHhIAQEAJCoHYEREBqh1gfEAJCYLQhUJXVI8St7QQkOsaKHxlts179FQJCQAjkR0AEJD9WelIICAEhkImAWT1CK0AVGv+uEZAkQgIu5q4FLlhFwoD2KrDKHCQ9IASEgBAQAkNFQARkqPDr40JACPQFAYRqCzTn71XHPhQlIHfddZebb775GnPBKjqOSfEjkBELZq8aw6Jt1PNCQAgIASFQDwIiIPXgqlqFgBAYRQjUZfUIISxDQOadd17Hn7aX0F3rueeec/xZdNFFPYmLEpK290XtEwJCQAgIgWwERECyMdITQkAICIFYBMzqATmAhNSpsTfrSt4gdCwgXSEgIbhPP/20e/LJJ9348eM9plF3LbOOWEC7pqYQEAJCQAh0DwERkO6NmVosBIRACxBAOIZ4QAwoluWqrqaNNgKy1FJLjUAZvX/E7lGx7Fp2IaLiR+qafapXCAgBIVAtAiIg1eKp2oSAEOg5Ak1aPUIoixKQu+++28d/dMEFK84CEhKQ6JQK40fsd3HuWiIkPV+M6p4QEAKdRUAEpLNDp4YLASHQNAJNWz2sf9wl8q9//ctbXGabbTY366yzuplmmslbXZIKBIRLCAlE71IxF6w0AhL2J4wfscxjkBGsIqGFJK/rWpewUluFgBAQAl1FQASkqyOndgsBIdAYAqZxhwiEMQl1N4BvPfvss558IEDPOOOM7uWXX/ZtQMCGiNgfgrXDMloISJx1JGohid7ODnayjtQ9e1W/EBACQiAZAREQzQ4hIASEQAoCCLN2mzmP1R3rYU3B2gHxICPUXHPN5RZYYIGRQHdIyEsvveRefPFF9+qrr/pXICchIZk8eXInLSBPk/n3rgAAIABJREFUPfWU409eC0jW5E1K9xvezl5n8oCs9un3QkAICIHRiIAIyGgcdfVZCAiBTARMcH300Uf9s8RSNKU1h3RAPmjDW97yFk9AICQQIbT3YSE2xMgIP3nOyiyzzOLGjBnjZp555sbanglsxgNVE5Dwc+BJwVUrvCgydNey7FpNjfWgeOl9ISAEhEAXERAB6eKoqc1CQAjUikBo9bjvvvu80L/44ovX+k0qh0w88sgjjjiIOeaYw5MPc61KIiBRARuSAhF5/PHHPYGhoOE36wgxJFF3rdo7VuADdRKQaDPC+BHDCuLBeIe3syt+pMAA6lEhIASEQA4EREBygKRHhIAQGB0IhLEedq/H/fff7zu/xBJL1AoC7lQPPvigJyELLbSQz2AVauHzEJCwgffcc48nMbPPPrt31YKUvPLKK/4RhGsICWQEK0nUqlJrRzMqb5KAJBGSuPtHQkIi68gwZ4i+LQSEQB8QEAHpwyiqD0JACAyMQFKsBxaQOgkIwi5uXly+ByngBnDiOaKlKAEhBgQCgguWFXPXgozwB2sJBRcty641bHetYRKQOEJiZMSSDyjd78BLTRUIASEgBJwIiCaBEBACoxqBOKtHqOHGAoIQOnbs2MpxIpgcq8e///1vt+CCC/qUuUna9TIEBOvH/PPPn9huvmtkhJ9m9cEqYoQEzX+TGv82EZAQuLzxI3LXqnyZqEIhIAR6iIAISA8HVV0SAkIgHwIIlQj2Frgdl+EKAoLlYNy4cfkqzfEU3yVG47HHHvPWB6we/EwrdRCQqICNi5YREsgRhaBsIyNYaOp212orAYmzjoQZtoyk8X+MJcTNAtpzTAk9IgSEgBAYVQiIgIyq4VZnhYAQMATsZnG0/giPSZrrBx54wBOUqggIaXOxeiDgY53gTx6ted0EJDozwMXICDEkobuWBbRjKanaOtIVAhJHSBhbCCvJAyAh4f0jEDfdP6L9RwgIASHwXwREQDQThIAQGFUIhFYP/p51B4S5SI0fP34gnPgWwjVZrtCOY/VAkM9bIEy4TOW1QNx7773ecpHmgpX32zxn2bUsoN2IWzS71qCEpKsEBIwgIMQMLbbYYp6AKH6kyAzTs0JACIwmBERARtNoq69CYJQjgEBolwqau1WWwAwBQbBccsklS6MHcXjooYd8NiruEyHLVR6rR/jBMgQEcsAFhlUXyBSY2P0jobtWSEjykqWwfX0hIFiHrITpfu3+EcY/LqC96rFSfUJACAiBNiIgAtLGUVGbhIAQqBSBolaP8OMQB2IjyhAQvvvMM8+4hx9+2AubWD0IDC9T2kRAou1HqIaEmHUEwkWZaaaZRtL9mktSVt+7TECYJ7hgcWdMWkyPEZK4dL92Q7vctbJmin4vBIRAlxEQAeny6KntQkAIZCJgVo/w5ussq0dYKTeSo+lfaqmlMr8VPkDMBu9yqzk3mRMXUMYiYHXSfqwOeevABasuC0gWEOauZTEkkCcwD7NrkWo4bhxGAwGJ4hdHSBQ/kjXL9HshIAS6jIAISJdHT20XAkIgEQGEOgRfiIClly1CPKziMgQE0oHlhALxgIAMWooSEGIR0MKT3neYJXTXgpBgKeH/IFJhdi00/5Q+EBAurcT6U7ZYdi2Ls6Ee3T9SFk29JwSEQBsREAFp46ioTUJACAyEwKBWj/DjuE+98MILbumll85sE4SH53G74hJAyAcB51WUrhKQaN/NXcusI1h1KFhEICRg+Pzzz+fCuwpcq6wDckXWtEEJSNimMH7E7iKBSEPgwtvZi8YUVdlv1SUEhIAQKIqACEhRxPS8EBACrUWgKqtH2EGyVmHRmDBhQmq/iX8gYB0BmiDzeeaZp9IUtQjuxFbkFTTbYgHJmixYqMJ0v+BHsWB2fmJNKGO9yvp21b+vg4BE2xh11+L35q4VEpIu4FU1/qpPCAiB7iAgAtKdsVJLhYAQSEEAAR1h1gTYuEsFywAIAXn22WfdMsssE/s633300Ufdk08+6YVmAs3R5lddihIQgqER3IftglUEB7ugEbyJFwndtcLsWuauVaTuJp5tgoDEEZK4dL8WzM7PrFTTTWCjbwgBISAEQgREQDQfhIAQ6DQCodWDv1dFPAwUyAUuVXEEBIHT7glB0J9vvvlq09SXISAQIawxXSphDAh9JrOUZdcK3bVCC0leq1DdOGDJYT6MHTu2FhKa1X5z0QK3MOkC7lohIal6jWS1S78XAkJACEQREAHRnBACQqCzCNRl9QgBeeyxx3xg9MSJE0f+2zT1/I5Ab6weaWlXqwB4NBKQKG5Yt+zuEX5i8aKE2bWG6a41bAISZx2xgHb7XTR+BGIid60qVqjqEAJCoAgCIiBF0NKzQkAItAIBE6pI91qH1SONgKCFR8uN9YNbxvnThAZeBGTKqce4M/5mHUH4t5vtsY5Yhq2qkgDkmfhtIyBphCRcN5AQxY/kGWE9IwSEQFUIiIBUhaTqEQJCoBEETPCsOtYjqfGPP/64e+KJJ7wFBEsIMSEIa1g9EHSbKkUJCNmYaGeXXbCKYMu8gBRaQDuuWxQwMDKCpSTvPSpFvm3PGgEZN25cZdnPyrQj7ztG5MNMWyQ64LJMLElgpfiRvGjqOSEgBIogIAJSBC09KwSEwNAQCK0eg9zrUbQDkA/iQCAbaNvnnXdeL9Q3YfUI21qGgKDZXnjhhYt2eajPV3UPiLlrmctW6K5l8SO4zVXpfsT84P6XrhCQcKDtvhaSFzBn7Ob6uPtHhjpB9HEhIAR6gYAISC+GUZ0QAv1GoGmrh6HJd3G3IisTwjxWD7TDwygiIOVRt/ljZARLiZHYMJh90OxlXSYgoIt7IembF1tsMU9AQguJBa5bMLsFtldJ4MqPsN4UAkKgawiIgHRtxNReITCKEBiW1QOI0ZhzCzp3gFCWXXbZWt13soZVBCQLofy/Z17homWEJHTXCglJUXctLqxkzowfP94T1q4VcMACsvjii0+VVCF6/wjEw+4foa9gxR8Rkq6NutorBIaDgAjIcHDXV4WAEMhAYFhWD5oF6cCVhsKN5qThXX755Yc6ZkUJCJYbBERuY+9SqcoFq0ifcdfCKmIB7QS3U7ACWEB7HnetrhOQIveYhNYRwzrOXUuEpMhM1LNCYPQgIAIyesZaPRUCnUEAgRAhsMlYD8Dhuw8//LAnHBAPhPfnn3/ea7VFQJqZPsMgINGeRbNr2TwM0/0S3B4VrkcTAQkxC4PY7f4RyEh4/4gFtDczi/QVISAE2o6ACEjbR0jtEwKjCAEEGVyfcANB41znxX5RWBEesXpAQgjCnXvuub2A+fTTT/v/X2655YbqXiILyHAWQuiuhcsWVgIKbkdhul8EbCMgSy655FDd9coiVVUa4ai7Fu0J3bXMZUvWkbIjpfeEQPcREAHp/hiqB0KgFwggYKN5hgDcfffd3gLRRAYnvkuWqyeffNILlASah8HIRkAmTZrUeOarcGCLEhBIEwKeXLCqXR6Mg6X65SdpaymkrUWwxo2rqzEgdaURjqb7jcaPgJvS/VY7T1WbEGg7AiIgbR8htU8I9BwBs3pg+bCL5CAgkIG6hWe02cRKIEQuuOCCsRYX3LF4posEhKmzyCKLdGoGtcEFqwhgkGYjJJAPCAoCdjS7Vhe0/U1k8WKNU8DJ3LXsdnbLsGW3s3cBsyJzRc8KASHwPwREQDQbhIAQGBoCodXD0nzy86677nL429clPCMEccHgY4895l29sHrwM66QgpdL/bqWBcuC6OvCsK5J0zUCEuJA8gIuqsR1EFJCVinmmrlrGSlpa4asYcSwhPEjRk6MkIS3szd9705d81v1CgEh8F8EREA0E4SAEGgcgTirR6jtxAKCSwvEoOrCXQdYNLB+zD///G6BBRZIje0QAal6BNLr6wMBWWqppbxLEQTbbmfHuhC6axkZgWi3RbhuQwxLXLpf9gZIW0hIZB1pdl3qa0KgagREQKpGVPUJASGQioBZPUL3i6gwcc8993hhgwvRqioINsR5EO9B3ZAbhMCsglaboPiJEycO9W6HojEgZO6iz7KAZI1wdb83C4gRkGjNuBna3SP8JN6JuQ8JsYB24o+GJVyT8Y0scG0Kok+KH2ENWzC74keqm8OqSQg0hYAISFNI6ztCYJQjgCCBwIUQlpVed/LkyT6LEBeiVVHQPOOShBYa9xjiPfJqnRHKuB16mWWW8cRlWKUMAeGdOqxIdWLQBwvI0ksvnUkiWA/MS7t7BEsJ/8e8D7NrNemulUWg6hz3PHVnxY+YhSTv2s7zTT0jBIRAPQiIgNSDq2oVAkIgQCCP1SME7N577/UEYVACgsBCEDlaXepDGJ999tkLjY0RkAkTJkyRHatQJRU8LAJSAYg1V4G7Hha2PAQk2pTQXQvrCK6CFCwis802mycldbtrtZ2ARDGLc9dCyUFhnds9JMOyKNU83VS9EOg0AiIgnR4+NV4ItBuBIlaPsCdYHChLLLFE6Q5iacHqAYHgTg9S+iKQFC34xUOIRECKIlfu+S5bQAYhIFG0zF3LXLbMXYtkCUZIiJOqUriusv3lRn+wt9hvSJuNq+W4ceN8ZeH9I6x//lSJ2WAt1ttCYPQiIAIyesdePRcCtSKARhchyjSSluUqz0eJueD9sWPH5nl8qmfQ5FoWKFL5zjXXXKXq4SVcZHAJQ6uNwDesUtQCgtUH/KuMo2mi730gIJDVKou5a4X3j4TuWhbQPqiLYNcJCJiH80fxI1XOQtUlBKpFQASkWjxVmxAY9QiEVg/+XoR4GHikvUV4Ni1mXlAhOwjeuF1xkSEB2IP60IuA5EW/mue6TECYd6R2rpqARJGFjJLi16wjobtWeP9I0ViIptpfzUyJryVp/oTpfi0BBvjwJxrQXmf7VLcQEAL/RUAERDNBCAiByhAYxOoRNsIuB+RG6bwFVymsHpAQ3K1wu6rC1QIhj6xcZDZKuiskbxsHeY5+QYbyut3IAjII2uXeHZYAz9wIs2tB3imWXQtSwtzNWg+4Lz3xxBPe2tfVgvsV40Amr7SSlu7XLkSUu1ZXZ4Ha3QUEREC6MEpqoxBoOQLm6sCt0GWtHmEXIRJoeLOECN6B9BD4i+CBoEWgOYG7VRWyE3EvCW1BoBtGAQusQmi682ZJ4kI8xqOLLlgIwnnGfhhjkfZNBF8uuBymAM/6Y9wtuxbEhP9D02/WEWJI4ty1LH4Cst3VAoHCBbOI8oK+JrlrhbezK91vV2eF2t1GBERA2jgqapMQ6BACJvCUifVI6iZ3WCA4ZQlCkAOzlpBalxS7WVreotAOk4CEd5dAquaZZ56R1K3mdoNFxNK2QpCs/xAQ0rwOmkmsKF6DPo8LTVcJSBstCMwhCKwREv5OgYCE8wZi22X3N5t3EECsoUXdN6Pz1ggJCg5bU3HuWlXvN4OuH70vBLqCgAhIV0ZK7RQCLUMgtHpk3etRtOkQEASmJE0y38bXHmED1xKsHnW5RyGw3XXXXV6jmufiwqJ9TXoeLTbkyu4u4dZ2XGvMrz/pUjvTckPgeGaQTGJV9aVIPSIgRdAq/qy5a1lAO/OMYi5aEFusT10VrNkX6FvZBBZxiIbxI3YXCfhA2sLb2YvG3BQfPb0hBPqDgAhIf8ZSPRECjSFQh9UjbDzxC6TPjQvmhRAgmPMToXyBBRaoVVhCIPvnP//pNaq4rjRRyEYECUPIsbtL7C6VOMGQ8aCdFgeA1YbCs3POOadvN9aRMmmIm+hv+I2uExBcAbMsd01jmvY9rGRGRiC75q7FfLF0vwjZXSEkuGOyN9RJvKPxI+Br6X5DQtIVzNo0H9WW0YOACMjoGWv1VAgMjECdVo+wcbgPIYRz+7iV0B2JQx7BvAmLRJMEJMziBXEghbBl8TICYsQibTB5FgJD2xGMTMttQckIlnmD2QeeNAUr6DIB6boLExZF4idI4AApMSJr7lpmXWszkR2G66HiRwoucj0uBJQFS3NACAiBvAjUbfUI24EWE3/6iRMn+v9GS0tgurkjEe/RlLsD377zzju9S0fRW9TzYstz9A3LTlIWL7sHJG9a41ATbFpuvoFQSV0WzG5xAIOmKy7S17Rn82Yxqup7VdbTdQJCADeWR4ufYC4yX8yyFrprGRkJ446qxLJsXcPO/mYuWqwxS/dr7lphQHvedVwWB70nBNqOgCwgbR8htU8ItAABBBGEj6pjPZK6hh83whwWEDILIVQgMHOvR50kIK49RkBw6eBukaoLmKJ55k9aFq9BCEjYZgSkUKiMC2YnHqApghfFUwSk6hmWv76sAG72gDDdrwVoR7NrDdP1CMsf7cJC2oYSumtZe6LxIxCTYWLWBpzUhtGHgAjI6Btz9VgI5EaAw5NAZv6Yb3gTB6UJ5LgKoZHFJYS7PYbh+oHQdccdd3if8qoJCMI/6XXxWSeWhZiWJHyrIiDRwbdgdhMsIZu0YVhCZZcJSJfbzrwoEsBtcUeWXcvctRCmzarGz6bXLJZSCsqKNpa4+0cg+9GA9ib22TbiozaNHgREQEbPWKunQqAQAhZzYAJpky4DuCJh+TCrB/EQwyoI6LfffrtPZ1tVOxBCsPDgr45/PXd1ZN0xUpSAFBEmDVvahcUHoZI/ECP+D6HSApLrFiq7LMR3ue1GQCASZQK4mZ8WzM5P5hElTBPdhGWNvQOBnvipLpSk+JHo7ewiJF0YTbWxCAIiIEXQ0rNCYBQgMCyrB9CGQdj8mxiQYccmGAGBJMw111wDzwDqQ0jiroJ5553XLbTQQrncnZogINHOhUIlhKSJYPYuC/FdbjtjX2UGKXPXMlJiiowwuxZ321QtWGNRZM/AYtq1Eqb7ve+++7zCg7t/4u4f6Vrf1F4hEEVABERzQggIgREEzOoRBk9WLSAkwY1AjvsEggoHLxaQ5ZdffuijQ3tuu+02b6UYlICQYchcRPBRL+LSNQwCEgXfbtgOg9kRjsw6ws9BCWOXhfiyt3APfZL/fwPqyiAVpom27Fr8HxbO0LI26NyhG/fff7+D2EDsu1xI/c3FqrifsvbBy6zQFswOfoof6fIoj+62i4CM7vFX74WAR2CYVg8OVzSvCJ4II/huI+AiqC+33HKVa0iLDjnt+8c//uGDWhEGyhTqIJCezF6QDvpYVNgyIS6vK1wZF6wifUsKZkf4Yxz5U8blRgSkyChU+2xdBCTaStZDmAgh6q6Fmx+WkjKJELAcMO/IlNfVwtqCgBAXFu45SfEjISGBlDSlNOoqvmp3OxAQAWnHOKgVQmBoCCAMoJkPtWlNHWBoQ3FHQrOOwIDGj28jqENAJk2aVEoIqRJMIyCQBtwhipawj7iFUEcZfIsSkKyMRkX7kfU8liILSOZn2WB2EZAspOv7/bBS2CYlQgjvrcnrrnXvvff6IHiE964WIyDsiWlW1zB+xPoa565VZr/pKnZqd3cQEAHpzlippUKgUgQ4vBASOfxvvfVW7zMNAWii8G009AjJCBkI92gtreB+BTFZdtllG8+iE+0/bQWfogSE9+gf/aSPWFAIyC1bLECc9/MIFE0TkLBfYTB76HKTJ5i9ywRkmJiXnVfhexAQ9oRhprC1uWNZ2bCUmLtWmJktyYI4efJkn6qbjHJdLSg97rrrLr8n53XTDONHzIXWsmuFFpIyVqWu4qh2txsBEZB2j49aJwRqQSAa60GWJzSGY8aMqeV7YaVkVoJcpKWe5RZ0gknbEIRuBISsOgSN5ymk16WPCE8IQmCbhzSk1d0lAhLtR1IwO6TTYgD4OxiJgOSZYfU807Y7NOglc4e9wqxrdm8NFhFL9xu6a0FAENqb2MvqGQXnlUL33HOPz+RV9t6jqLsWbYV8hJchyl2rrhFUvXkQEAHJg5KeEQI9QSC0eoSXChJkzYFdp9aQbyNcEu+B8ICWNSn1LC5hBJNyESHpKIddbrnlFi8MZBEQ+hhenEjgOkJSFaUoAYneal1FG6qqw4LZLUOSzUXIiMUHLLXUUlV9rrF6um4BafsdGgxk6K7F/OHfEFcjs6S3xm2pzr2s7gnF+oBIYXVlTVRRktL9hoQEgjKooqSKtqqO0YGACMjoGGf1Ugh4wY7DGhcLShjMjAUE4bouv2mCTLEIIDDg5oVvc5orAJcPEkw6YcIET1aGXXDBIqtOmosa2KJBhjzVcXFiGQJCW8aPHz9s+FK/T79Mw42WO9Rwm3WkbEBy0x2vO/C/7v5AQNgXunSHRpTMWrYoS4SAAqANSowiY8d+SSwLSpqqFBjh98GIwpkQZjy0OEAjJXkTXhTpm54VAoaACIjmghDoOQKh1SNM5Rh2m5u+EZqrzhwTtQig0cvjUkBKXg7gthAQsmCluahBmBDe6C/C26DpeuOmZF8JSLSvWBGwIuFGEwaz2/0RCJYIlG3U1HadgHTtEr/o3GGNEDuBNcSILc8wX8J0v22Pg4CEo4Dh8tMwNq6uoyqMHzFywvqK3s7edtzqwkf11oOACEg9uKpWIdAKBDhM0BDGWT3CBt55551e4Kvy8i4sAgjlCOdFLQIInrggLL300gMFblc1CEkEJEwhDLGCYNWlbR0tBCSMARkkmL2qsS9ST9cJSJcv8bNxIn0t7qRkm2PfMzc/9hT2JIpl18K6YLFHRca57meJHWMsuJF+kMQVZdsZjR8J7x9hf4OYKH6kLLp6TxYQzQEh0GMEzN8X8hHGeiR1mUMbDWFVrhcEkeOOREEo52LBIgWhgSBM4gCa0ABmtS0uRgYhAY0x7hK4Z+HCVqdWvigB6Wowd1q7iwSzZ41pHb+v8ibxOtqXVWfXCQhrJO7+DPptypgwu5btjZZdi59tcPm01N1jx45tRXuS4kcgI7hrQUYUP5K1uvT7KAKygGhOCIGeIZDX6hF2++677/aCPmRhkILGkVSe5kJT5sI9vo9wT5uWXHLJxED1QdpZ9N0wRgZ8CfBG2412Ej/tJkiSCMjUo5YUzG7ZkZr2/+86Aen6LeJGQLLuzzBCEmbX4u8UhOqQkCBcN13sItZx48bVZlEt26fQXSsaP4K1+/zzz3fbbbddK/btsn3Ue80gIALSDM76ihCoHYGiVo+wQVgbLDNV2YYSt4FFgEMJVy7crspaBBAG8OUmgLqOIMyifTQCQp9wK0NAwM2DuJCm/KJFQNJHLQxmR4NsAqXdzD7I7dp550vXCUjXbxG3+zOwSBa1uqI8QfFh6X4htxSUC0Zom3LXQpBHkYMCZhgEKO98NyJnZ89pp53mjjrqKHfSSSe5tdZaq0g1enYUIiACMgoHXV3uHwJlrB4hCsRbYEonbWzRwqH/yCOPONJf4saF1WNQNwaCMHGlQANYVRrKov0KnydIH+ED4QSBIG8w/SDfjL4rAlIMTfP/Z8zigtnN3aYsSY5rDesAlzyCh7tYuk5AGHMsp0Uu8EsapyTrmiVDMOtalfPH2kL2OuYSLqhNKTiqmK/HHnusO+GEE9zPfvYzt9pqq1VRperoMQIiID0eXHWt/wgMYvUI0SHjFAddUcHJfJU5rKuMgzACgg90nqxZdY40Qg0WELAmuxVxMsPQShYlIBBC/nTtPo06YlfSgtlDd61Bx7XrBIR9ADzqSsdd5zql7iou8Itro1nXLKAdSwkFpU2V88e+TQwd1jSScNRBcOoahyOOOMKdeuqp7swzz3QrrLBCXZ9RvT1BQASkJwOpbow+BAa1eoSIofmkPgT+PIVniYEgZSoaQeIgqszWghaZzFxkgSE717AKbmW4XEGwIEJ58amjvSIg1aFqlx2auw3zjRJ3M3uRr/aBgGBx7OolfnVc4Bc3/mEyBEiJzR/2wPDumrLkgRg69lYISJfKIYcc4s466yx3wQUXdE7x0SWc+9JWEZC+jKT6MaoQQCufN8NVHmAIPqVOXJ6yCr71xHrwE00pwkrZgzbpW/QNtycsMkV9ubPan+f3CBgQLILN0XCiWa06TXGedoTPiIAURSz/81UFs0NAqKuMK2P+1tb3JK6YEO2uEpC6L/BL26/CdL/sH+yJ0exaeffJrlov999/f08+/vznP1eWUbG+2a6ah42ACMiwR0DfFwIFEEAIRRjmD3+vKvWhpZNNuzWb7+Eeg2uABaxj/aij0D/cnhDk6rjUL63NRrBwA7PLBwmIrzJNcRnMihKQp59+2o+XXLCKoT1IMDuBw8zdrhIQklFA+Emw0MViF/iBf117UxYuzB/aYel+2U/4P3PXMlLCv5NKHW6IWe2u4vd77723u/jii93VV1/t05KrCIE0BERAND+EQEcQQKtmlwraxVB5NWpZXcTNiIOSrCtxBc0iJIVDdb755vM3ptcZHIk1hrs3miQgCAloHtFiRwkWBATBoap7UrLGI+735nLHzzzjDgHBgtM1N462CV9hMDvzH4IB/mEwMvOF/xMBKTOzq3uHPQxrblM3iOdpeZK7H+5aYXatcD9l3RKInqYQyvPtpp/Zdddd3ZVXXuluuummRlKTN90/fa9aBERAqsVTtQmByhGoy+oRNpRLAxGuotpyvo0gi1DeZPYnDm1uHyfbFDca110gdpAwYj7iCFZV96QM0g8RkEHQq+Zds0KFl9mF2m2IOkSkqxYQ5jmpplkDXSzDvkE8D2YQWJs//ITgGqE1QkIQOr8bZsxZnr5En9lpp53cDTfc4JVHeZQkZb6hd/qDgAhIf8ZSPekhAmb1CC98qmNjR3NL7vkJEyaMoIhQDjHh/xFKSG05aJagvEOEUHfrrbc2QkDs1nZwhfDEBb0jmNmlg3n7UPVzIiBVIzp4fWnB7E3fHTF4b5y/ewfXma66z7TtBvGsMUnKzmYWblxAmUdp7lpZ32jy91xASBzRjTfe2ORn9a2OIiAC0tGBU7P7jQAHkwWaVxnrkYQaFg4E8WWWWcY/YkI5f0coH0Yg+C233OJdnuoShsJb2+kf30o66Ku4qHHQGVuUgHQ1k07bXLCKjBtuimi4cclCGIag4FoclUceAAAgAElEQVQzrJvZi7SdZ7tOQNp8g3iesTBCSwYsFECseUqTl2nmaWfSMx/+8Ie9G+s111wzSDV6d5QgIAIySgZa3ewOAk1ZPUJECCzH1Yp4AaweEJAsobxuRLGAcLdIHe4gCIcPPPCAJ3l5bm0f5KLGqnAqQ0DI5BVatapqS531dJmAsHYYJ0h7WjC7ERLiSOqMpSo6Tlz+SQB6E26PRduW53lcKBkDYie6YjWI65fFErE3hdm1ou5azCMss3VYxfPgHX1miy228P9FFiwVIZCFgAhIFkL6vRBoCAGzeqBBNc1pUwcLGjf+IAzx7TxCed2wEANiWaiq+lbZ+0tEQKoagex6ukxAiCOiQECipUgwezZK9TwBASEFLy6XXSxGQEim0ZS7aB04xc2jMP7ISAn/Rz/DdL8zzDBDHU3KVecmm2zi586FF16Y63k9NLoREAEZ3eOv3rcEAYR+iAdCCsV8gJtoHt/mBmQONVLNIjxh8h92IZARbWxVdxKQGhMXGQJVi95fUvam+CoxlAWkSjTrqSuNgIRfzApmt8vsmhaiufyTtdFVAkK8GtaDrhMQ9imUQWlZ99i3yfplAe3sbxT27pCQNGlhW3fddf1dUmeffXY9C0y19goBEZBeDac60zUEhmn1ACsL2rSbfJdbbrnWmPMhILhfIRANUiyTF4IJ2kFubeeALlK4KZ7CzezDKkUJCG50uNbhVteUJa0KbPpqAUnDxnz/TZgMb2ZvKpid+YUFhBTbTd+9U8W8oQ5S1xLPRja/JgXvqtpv9eAeigsZlui8Jc7Cxru4+dkcqtNdi/mz+uqru7e97W3u5z//ed5m67lRjIAIyCgefHV9uAgM2+phblccUGR+Il6gTQSEiwjxRUcgKluwKqGVRjNKXcSUlNEqi4CUHYHi73WZgOTRXOdBhADkMFVrGMxuwmTVrjZGQFgjw0g6kQeXrGe6Srqj/eIuEywZjEWZYsoKgvJDdy1LiFDHHMICs9Zaa7m1117bnXjiiWWarXdGGQIiIKNswNXd4SMQWj3sUrkmNdR20zc/zRWJjEkI6pMmTWqN5vCOO+7wmtiyhzCkA4GQMmgmLwQChMBh5uWXBWT4azerBVURkPA7Fswe3qzN783VBnetKoLZmd9kweoyAWEf62Lihei8wuUTkjCo9dfqZQ7hempkhL2fAokNEyKUUc7YNyB/G220kXvnO9/pjj766Kylot8LAScCokkgBBpEwITIYcR68G20y7jlRG/65vDA7L/sssuWshDUASH+6Fhmirgh0A4EKdwwSAc5++yze/IxqLYYbLCm4N88rGJxA5aWOasd5o4iF6wspKr7PQQEIa7onC3SgrRgdhMm7Wb2ovVy3w1tj7sLp0hdw3qWTH7cIs6c73Ih6QV7V1Xxb1EsbA4ZIcHiRpl55pl9HCDziL8XUYyx577nPe9xZMI69NBDuwy/2t4QAiIgDQGtz4xuBBAaEYzJ0sKdEsQSsNE3VfApRzjiwIm76RthFS3/xIkTW5O+En90DuEiwpzdhMyBiiaXO0SKHKJJ4zFsAsL8QbBCu4uAy9wxQSHJ172r/vBddsEq47s/yB5QZTA7QmnXCQhKB/4QA9LlwhmBGxxJOJoonA9GRqL311hAe1ZiEqw2W2+9teMukAMPPLCJZusbHUdABKTjA6jmtx+B0OrBRo+bA648CNd1FwvARjuF4EoAdhzxwV2JOAcuIhzUWlBVn8CJwy8tE4x9ywR0rDto7hZbbDGfH7+qAnlj7LhfoOkCmeL7+HOTnYi+IiSY1hL3GyMkoeZbBKTpkXLeilg0eLjKVg4SzI6FD8GX9dbE3lRlv62uLpPXEA+IIGu9jjuQsnAPXf7YZ1DqUMxdywhJ1F2LmL2Pfexj7hOf+ITbZ599sj6j3wsBuWBpDgiBuhAwqweCogWRcsgT24AFpG43B77LpVx5ArCxzKDBwnWhSsF9EGw5hCETcXcqhPWG1h1cFvhTdQYcCABBlqT3bLJAIojNwYoDDoyNzSX6DSnhD0IC8w3h1ywjaLSxmHQtI1CXhUgICIJa2bilqueWBbNb/EhaMDvP4vrDPGvSOltln7ESsmaGoSiosh8oXyAfbbgQkn2E/cUC2kN3LciIWeFQ/nzmM59xn/3sZ91uu+02MBx8b7PNNvMuw2eeeaZbYYUVRur89a9/7Y477ji/N3KWfuELX/DPqnQLAVlAujVeam1HEEiK9YCAoClafPHFa800Q0wH5IOSJwCbzR7hA2EVob8NBW2sxarEtQeMCTolvW6adaeKvnDQEbjZFAEJ41ggqowhfQzJbNgv03wbITEhgWcQZNBol4kLqAK7onUgRDJ/m8K6aPvSnh80e1GVbYnWlRTMDmGCcEBuEfa6TEDI7IcyZZixWlWMYZvvY4lmaPv+97/vrr32Wr/PoPhZbbXV3H777TfwXTLEkZxzzjne9TQkIL/73e/crrvu6skOWbcuvvhid+qpp7oTTjjBZ+BS6Q4CIiDdGSu1tAMIxFk9whgEBEVu+MYVqo7LvtBWQTwQ4PAhxp0CrXhWQcOFxQGhD5eeNpS028chcvQTbSc4EicySAaXrP7yLbTITfiWQ3TQpGPhCONYjNSaFjutzbyLLzz4MP94Nxo7UideWXim/b7LWuw2E5Ao5mEgMsSVNUWBiEB6ISVdIa3WNyx+rNNhZqsbZO7zbpfuY6Gtf/3rX90vfvEL/xPyR2HPWX755T1BWGedddwqq6xSCBbOIuJJvvKVr7j9999/CgKCpWPChAnuyCOPHKlzhx128Jb+X/7yl4W+o4eHi4AIyHDx19d7hECeDFc8c+utt3otY9XmdTZ/XIUQUCEepLDNG4CN0IvZH9eFopf01TWESbePWz/B0vpZVxusXggIQlqd2XXoD6SBeB0EP+JYQmtUEQJCu+1WaMYU97G4S+4sdqTOC8qKjk0fCcg3vvENd/7553soIH4Qyw022MDttNNOnvD/8Y9/dD/96U+9GyTjzO/XXHPNSlxZ8uDPN5nfzHPmHPMlJK2WXautpNX6iAWHvWyYF4bmwTvtma6mQ2Z+M89Jw4vi44YbbhiJU8Ndav31188Nzac+9Skfj8g722233QgBQTGz8cYb+zS/fMcKLllf/epX3VVXXeUTj6h0AwERkG6Mk1rZcgTQJpp7DEJ/WgwCBAQBo6oAw9BdB4EScpOVsSQKJwIHWadwXWiL/3f08j/6iYBBjABtxIrUVMA8bl4Qn7oISHhhIgco8yM6h8oSEKxaoeDIPDVXLUhJ2wTNLhMQ5iwCfPTyTAQzyCWuKYz1jTfe6A466CCftnTDDTd0u+yyi/djX3fddb3SAOsfbi177bVXYzsfewDttwQOdm8Ec8VuZoeoGmktmqa1iY5A3mkrLq5dLZaNrGvJAM466yx3yCGHuMMOO8zPa/aW66+/3sc8fuQjH8kd84iLFVm0LrroIq+sCwnIpZde6l2vfvvb307hovn3v//dffCDH/Q3sBe1tnR1nvSh3SIgfRhF9WFoCCC8IVDwx+5nyLI64ILFBVNVpFhkk8fqMWjaWQ5t/I6bys6VZ8DCy/9CtySEO8hbFs55vpH3GQQbtHqY/qsuWCqIMWH+pMXrFCUgECY02lECErafOi3ANBQ0B7kPYFB8+kpAGGeEMyvf/OY33eWXX+422WQTv/bQEg+zsMZYcwjv0TiwIsHsw+wDigL2YkhUV0tXkwEg/B9++OHumGOO8RcSlinsRbhY7bzzzt4F6y9/+csUBOS8885ze+65p7viiiumuCMF4sw6GuTbZdqrdwZDQARkMPz09ihGAI08hwUaK4Rh+5MFCUHoaLkHueWWbxNwyR9cOLAGDJK9in6gqao7OD4Lm/D3CEMIE8Sy2OWJUbekIvUN8iwEhLga3AKqKkUtOmUJCC5YeeKA6Fc0wJQ2Yj0xFxx+5q2rLE6jhYAQZIu29+Mf/7jX3P7oRz9qJMYoaVyMgOC+lLaXZAWzW5rWqjPR5ZlPEG7mLPthVwvKIFzx6ENb3GHzYEkQ+LHHHutOOukkH/tRphDQfuWVV7pf/epX3gKcREB4JlTggdemm27q1xAWRZVuICAC0o1xUitbhEAZq0fYfAR9Aqejbhp5u4iggNWDn5AY0s4Oag2w7FwI+MSOtKGg1UIrj0ARd3lik22EAJFxqyoCgrsL/sz8zGvRaYKAhJiaoGnuWrSVUrcbTpcJCIIQQmNUuYALVmgBueWWW3wmH8sYtPfee3vBi2QKBO+uscYaXhNc1JVykDVhl3hmEZDoN+KC2dmPUIwMcjN7mb5gSaRkpe4uU3dT74SucG1JCJKn78RlnHLKKe700093K6+8cp5XpniGsYNE/PCHPxx5n8B20voSH8W6wKVLLliFoW3tCyIgrR0aNayNCJjVg5+UvFaPsC9lbvjmfQRCuw3b0tNWdUDVnZ2r6FhibYBk0ec2uIWRXQcffm6KH6TQn6efftqnDiZ+BcJXZAzN4pZHu2wuWEUsIGl9g6TaXQBGDGmHCZnEBlRhHekrAbnwwgs9oUBgB8v11lvPB85a0CzzHQHr5ptvdn/60598HNDJJ588hTvU66+/4Z549lX3n9dedzNMP50bM9dMbrrpph1kSo68a+6crLeyxMdIss2TpmOMwNDuzKkElCFUUpYIDqGpU3wSi94ZZ5zhzj333FL7pFk7kvqx4oorehdGBaEPe6Sr+74ISHVYqqYeI8DBaoHmeWM9kuAocsO31RFetofpGQ1rHiE075DUmZ0rbxt4DowRzrE2IKDTx7oCv4u0qwoCEqYOJgMa2u6iY1iEgCAEolUksUDVwfpZ1hFICcSqjGWurwSEOQThgKRhtUwja4zbVltt5fbdd1+34cabuUtvfNhdc+tj7r5Hnnevvf7myNSdfrpp3BILzeHWWG4Bt95KC7vZZ52hyLSe4tkqCEj04yg2sNSaFS0azD7IPInrKAQEl0HWVldLHePQBBYkVYB8/OEPfyiVBIAYu9tuu22KpvJvAtsPOOAAfxHhcsst5y2DKIKIN7GiNLxNjHD13xABqR5T1dgzBKqweoSQkOMcN5Y8fsqmMScGoe7L9nALIfPKsNIYIqQgQEBCECA4iNEGNnH3RtaUJdYGwXjZZZfNejT29/QNl6tBUwe3hYBEOwm5sjS//GQMQ+sIgmZeEtRlAkL2Ki59hGCEJeqClTWJmCfrb7Ch22jr3d1dT8/uXn/9Tfc/2jH129P49L7TuC3XGeu2WGcJN30Jq0idhNVanGRFg6xadq288yQOQ9YYxK7LBKSJcciaf2V+D1n+/e9/7xMrDBLfGH47GgPC77AkctM6Kazf8Y53jKSw1kWEZUZtuO+IgAwXf329xQiY1YND0y5/K6PRjXYx7YK98FmETTShuNKgMcclo848/KQ8JB6hiuxcRYYVbNEOI3giqELMcAGpO/VtkTaa69ukSZOKvOYJB32DwIR9K1RJ8HBbCUjYH/qMH7tpvdGAUxhTEzLTrCOjjYCQ/QqMCNxFcCZW5NTTz3G3P7+4m3GOhQpPlcUWmM3tuc2KbszcMxd61wTfqlz2sj4ezhNTNvCO3cxeJpi9SxdBJuGTJ3tdFrbD+P0ee+zhSJNL3AYkvIoSR0Col3s/CHjnfCRmiaxZWEZUuoWACEi3xkutbQgBhGKIB5pcSplYj6SmEqhKfWmXZREDQUYXnsMqQSaougvpgdHcRrW3dX7XgrERwIz8GMmrM/Vt0T6VISC4m6CRxYpTVbKAIgTEXDnqcMEqgh9rKIwdsaxxYexIqPUebQSEuA9ucEYBQJzR3GMWcfOtvL17c7pZ3JtpZo+EQZh22mncXLPN4A7YYZVCJKTqmKEic4Rnk25mD60jWTezJ93DUrQtw3weVyT2Piy/RV00h9lu7rGBMDCPq4gFG2Zf9O1mEBABaQZnfaUjCNRl9Qi7H95vEYWFQxjiAQGBdEA+mtrM8bcl21RV5vO0IQdnu/U7KRi76sxTg0xBLj9EKMAHOU8hhoVxxGJFoHlV6TTLEJBBgorz9LXIM0lab7OOgBPECSsAmviuFaybc8wxR2kr4muvv+H2Pe4699ATL7k33ijBPv4fMEjIImNmdd/aadXc7lhgjtUx7d6YpsYjTzB7XEropCxkTbW7iu+w97P3EftWhcW9ijblqYPbyzlDcOVVEQJ5EBAByYOSnhkVCNRp9QgBRCuOIBkVsOxCOtoB8SAdbpMH0KDpgfNOktC1LOnWb+qqIvA7b5uynoMsQShIBZlWQgJJqmVcaqp0m7NLL/NoRrsQzGpab3PXCi2OWOIQMstmZMoa0zp+f88993jFQVk3xrMvmezOvGRyZU3bev1xbqv1x+WqzwhIGzXvYTA78zqaEtqC2SEgcTE4uQBoyUMoL9j76rj0tM4ubrPNNp7AYtFTEQJ5EBAByYOSnuk1AmjbLNCcv1fpbhUHHH6ruByhaaTwbbTrCLn4yBMDMUggZtnB4jZmhCdiTeoquBdYrn76ibY4qXAIY3koG/hdZR+MgGABSSKFCEaQS4RoCCQEpOrSNwIS4sPaw22NcWd98G+KxQSwNnDHyUO+qsY9b32DEJAXXvqP+/xhV0yR5Srvd5OeI0vWMXusnSs7Vpdcf5KSHjBnmCNYcbtEXMPxI003+14bkm8UmX9kbWPdEoSuIgTyICACkgclPdNbBMzUX0esRxJoaNIRVjlgTEuNVQDBH4tAk1aPsI1l7yfJMznAF5LF4Qrp4KKwLNeyMnEXedpS5hnaDXGKIyDMIYLMEZwRfnC5qkv4KeKCZfcJtMkFKw/2FgNCjBR9MOsIQqddcBdmTBrWeonrCxnuIJ64MhYtF1x1vzvtortSs10VrZPsWNtuupTbfM3FM1/tqutP6NaH4G4F4mpxRvxsM3ENB4c+YAUxBVXmwLXkgc0339zfWUMaXhUhkAcBEZA8KOmZ3iEQWj2qzHCVBygEcQ57XKwQthBasQaQmneYhftJaEvVtwiHJAuXJDJ65REawQZf6LxxF3ViZwSELFihIBPez4LLEJrXPH0r29YyBKTozdZl21bVe3FB6GYdsWB2iAn/1zYhcxAC8vXjr3d3P/RcVTCO1LPkInO6gz69Sma9XSUgYcfYwyCACMI2V1gzFAtmh4yw19a5TjPBTnmgq0kYNtxwQ+9yet555w3Sfb07ihAQARlFg62u/heBYVg9QuzRpCPQUqrKjlTF2Ba5nyTP96KWgaIkC00gPsVZcRd52jLoM2gkuaMkJCAIbIwlhASrB1r5ustoJSBRXFEahPeOgEtoHbHYkaaFzLIEhBvOtz/40krdrwyzGaaf1p28z3qOwPS00tXYg7BPWHGJv0HJEe71cTezh9aRLGts3es6rB9rKhnJyF7XpcKdHOyP3IauIgTyICACkgclPdMLBIZp9bDD0LT6/BsTO1q5thT81y0j1aBtIkgUgR1NdVnLQJ64i0Hbmfd9yAbxHcSjINRCjCCRxMxgMaoy0DytTSIgU6MTZkxC0DTrCEKluWo15YIDAUH4LXqZ56NPvex2O+rqvNOx8HOH77KmW3De9L0GAoLwS/alrhbi2FDqJMVf5Q1mb5q4hnhj9SWWIi1Ne9vGBxfb1Vdf3a2xxhruJz/5Sduap/a0FAERkJYOjJpVLQLDtnogkKMtR2uLUMTPNrgWhSiTQQZt/uKLZ/uLJ40OOCPIIKAPmoI2ye2p2pmRrzYjIMRTEMMDEbBA8yaFlSIEBCGGlM99cMHKN0r/fQohM4wdibrgsP6y7pMo8r3wWVyAIB9FCciDj73g9jrm2rKfzXzvu59fzS26QPrlcKw3FCRdJSDsPVhAuE8I99Y8JS6YnfUcWkfqiudKah8uurh2DrIP5+l7lc9A/Ndbbz23wQYb+AsCVYRAHgREQPKgpGc6jQDaGYSQpmM9AI1DkYOdQwWBHDckBMO2uBaFA8slXpSymjcOc4RzsulUkYI2zu1pWBPRCAjfx78cl6thxOyUISAIMrS5K6VqH3iEudBdizWJdSQUMquyYJUlIG2wgGBx5E/Xsi/ZvGZ/B3+SeZS5uDXtZvYmg9k5G9hL2WO6Uliz73rXu/xt5EcccURXmq12DhkBEZAhD4A+Xx8CHCiWtpSv1J1eN9qT8L4L3DI4GBF00jIq1YdGds1oyyFrZXyP7Q4TMLc7TLK/mP5E6PZUlYBYpk2MI+QM4sg4Emg5rIw6IiBlRvB/74TWEUgJ5IRSVYAyAjAZsCwGIW9r2xADAvlgb+pa9iXDmL0LFzjWZ1p67/xj8vqIJY250lQwO0oc5inKqq4U3G233HJL94EPfMAdfPDBXWm22jlkBERAhjwA+nw9CLCBI1zwE+LRtMCI8MxBwreJEQgPRNPsE08wTME6inzSBYlpIxTeYcIFYPS1qjtMsKRAiiZOnJiZsreeWeS8NcfuLUHA4XKwpl0ywr4VISC4/UGcRrsFJG1ugKel+UXIhECzJsPYkSJrNBoEXWReDjsLVlfTvxrGKJuIY0MBwl5UdYmzpDE3qg5mZ7/h3KAfXSnM+49+9KNu2223dfvtt19Xmq12DhkBEZAhD4A+Xy0CZvXgTgYCKhFem/TR5xDEhA4BwQ2AQySaYaUNgnUc6mixEFrzakDxs+cdDuY67jDBqoIAvcwyy1RGavLOtpBYQR5xKYOgDZuAFLmI0AgIrhxtSnaQNQZVu2Blfc9+z94Rxo6YdQT3NSMkWelbByEgw74HZFi45x2frOcgk5MnT/ZKkLoz0oVzJXozuxES1lyZs4d9hjMDS05Xys033+y23357t+OOO7o999yzK81WO4eMgAjIkAdAn68OAbvNHE01rgRN3yFhbki0w9yQ4g4gE6yHLcxGkQ8vSEwbFQ5fuyQQgQxXgTpiDEhFSWB80zjhaoUQEBIrhAyEGwJ0hxH7EWp5ISF5LHoiIIPtLQi0YewI6zrUeCPkRq0jEBCyviVlYUprUW03oX95bTf7LDNkgtEXAsJ+BAloslQZzI7VFysrSp2ulOuuu8597nOfc1/84hfdzjvv3JVmq51DRkAEZMgDoM8PjkAY68HfEc5wc0q6uXrwL05ZQ1E3JFw+2iDMRnHAcoPQn5YFJ7x4j3z7pLzMIwyXwbxpnJg7+MGTMAABAMuBEaum25KElywgZWbS4O8kabyZH6bx5u8QkLQ0sFktOfuSye7MSyZnPZb791uvP85ttX6++yS6ev+EgcHehMJiGAQkHBALZjfyilWNkvfSTKy+zCWyeXWlXHbZZW733Xd3e+21l9thhx260my1c8gIiIAMeQD0+cEQMKsHPykWaN5UAHN4y3deNyTewVeZbDN1WA7KIorgjXsYFoe4AqnDSoLWFzeHOvysw+82iROCPYQV6xQpVBnLkFg12Za08StCQEwgkwtW2RWR/B7jEMaOWIY9fuKyhxWkzOV2r73+htv3uOvcQ0+85N54483SDefSwUXGzOq+tdOqbvrpps1VDwSEPpFquoulrRa/uEszwTcp8QEkCusac6gr5aKLLnL77LOP23///d0222zTlWarnUNGQARkyAOgz5dDAC0TrlYIAnHpdeuOH+CbxJngtsBBUuSWb1x8yJYzfvz4xl0F0tDGZQ3XNeJmwgLOEA9IHfn1cS8rEphbboSdd3+BqNV9YWOYwQtiFZfCEy0mGXbqbksWVpZSOo/VqS0a4aw+RX/fNVcg9iLWNMI7FjQruOpZ7AiKhrzxAE8884rb/8Tr3bMvkjq8OAmBfMw124zuwB3f7uabK3/6ZfYz1lxXCYjde9P2pAtZwewQQfZZLMxdKeeee6476KCD3Le//W33/ve/vyvNVjuHjIAIyJAHQJ8vjkCS1SOsqc74AQ46C9jG3YKDIq9wQRvR1OGqQbrbuoMli6CLAEImHLJzWQFHLAMIvnbxXpE6B3nWhP66iBrzCNJFnxkHSGRSBi8RkEFGsti7XSMg1ju7CA/NNQTdLCSmIDFXLeZalnUEEnLoaTe5Bx57sRh4zrnFFpjN7bnNim7M3PnJBx/p4g3cITisUWK3unTxprn2mbsWZwOF+YMihLlSNpi98MQZ4IUzzjjDHXrooe7II4/094GoCIE8CIiA5EFJz7QCgSyrR9jIOlxmwuBrYgQQWMtkFyK49Y477vAHZRX56qsaHAssnzRpkrcqmYUHwYm+Np1+1ixFdRA1DnqEFX7ia83dDWkksi1WK1lAqprt1dcTdxO3WUdMwGQeUVhLZh1JEjBxxzrv8vvcOZff615//U2XZguZxgut07gt1xnrtlhnidxuVyEKEBDWQ5du4I7u+SiGsOA0vVdVNZuw6BMfSPv5O+vdbmY3AouSpIjCq6q2pdVzyimnuKOPPtodf/zxbt11123ik/pGDxAQAenBII6GLiAQ24ZMf7MuFaxaYORgxhKAIDFo8DX9uP322/1BX+bG3rrGG0sAcSC4GQ1i4amqfWYpQqCoKt7EbqYn4J6DPG98RFusVkUIiKUlHXZQbtH50HULSNpN3IxfGDvCv3GnQ7gMBcwQM7JjXXbTw+7qWx5z9z/6gvvPa/+Nd6PMMP20bvEFZ3drLr+AW3elhXNlu0oaD9Y+7kFdJSDgyh6NwqKqu4iKzt0qnrdMarhhse+kBbNDXptwh83q17HHHutOOOEEd+qpp7pVV10163H9/v/YOw9osYpq708KqCAIgvqwICIlhCpIkxARiATpJUgLgrQnVWqAhFAlEOlBapQW6UVKqIbeEghICSUCPvHh+3wiqAhPJeVbvwM7TE5OmZkzp8y9M2vddSH3lJk9c2b2f+//3jtKIJFABCBxIXRaArrXg/8uAx4yGF/KqyisHM5s9ChzVWlTgKkXX3wxeZZLus66JgwAgmKOjKt4eHz1D2Vo+vTpiUXTBwAB+BHLQqC9bUXzrgAQxgCwMFE6mqyL4GvOeU6oAITvmtiuIgCiy0myJQBN7hcAACAASURBVAkgyfKOEDuix/sQE/Lnv/5TfTBjppqvfz/1uUX4O/6P6o1vn/UFKA+xQRfl+4ayWUZx6+r4srxo0teiYHYBr2V1auoa91lnnaV++ctfqhtuuEGtvPLKdb0mPreHSSACkB42oT1pOGzGUvk5Qct9+hi7nn3QnHgGFjUONhRWFAsTxa9sDhjXtGnTkkxSPLcLjbGSfQVFOysLVBt99DGH0m+UPChXyF5qtNiMyTcYsnm3fm0EIK6Sq/8+WwCS7hHeELF2s151+o3Qteq07IcOQEgmwRjw4PrYp+tfMfO+wWYN6cHsxL+k69QASpoCYmPGjFE33nijuuOOO4wL2bYh3/jObkkgApBuzUfsjSYBNlQUYhvgIbdzeL/00kuJNQ9Xtm0j5ax4AwAKvmM1ACAAGmIP2m5kt8JyiHKOzAlC78IB7oOqxpiIZSGzTJVYFgEgbcftRADS9teS/35RHqlgXXW/KKoloceOmGRDM5UYewBjwDMbYsOziaea9OY+5dKkLDi3yLZnu4bygtnxiOh1auqSC+l3J06cqO6///7EwBNblICJBCIAMZFSvKYVCcgh7BJw5+plQMEDeKCUE5/BZlqHFQkKlmTQakW4SiUWVsYK2GKs0JxQQkjDW8eYbcdZFUQCGvB6YB1E1mQncllL9NunN8ZWDvr1WHkB5cxVmTLBWiaNMWvYB4WtSr9t7g2VguWqPJrIRveO4CVhbtPByVUDr/H20jC4hNjYswmkp5Cq63fe9rh90SalMrt41OoOZh8xYoSaNGmSeuKJJzrj1W97LuP7yyUQAUi5jOIVLUpA0hK6dMHWyyD1ILAC1p1yFu8M3g8U4zYaBxPKOQcT1jZiUaCaUYV3+eWX70QQZ5VYGb1oIl4wrIBVmg9vTJX3A6hRrlDOaShYUsgMi3hWZpwIQKpI3P7eOgGI3hvWAuBaYkf0StusBQloLwOo6RGGDkD45vF25hVStZ/R5u+oo3ZPer0Qa8S/YWTS10sVr/dBBx2kHnvsMfXss892qrhu8zMY32gjgQhAbKQVr21cAlUAiKmXAcUB1z1F+LAUYwGsk2uNEEnDi9JPCtgmm05JShdQrLN2issYXbxYdRVNFADiSulzGb/co3ty8OIQmCyF71A+kRPrNU3NiQCkitTt720KgKR7pgcnA0rEO1IGUNPPIfMdoCVUCg37NwAdD0iorYlq7rJexDuC14XGenENZt9nn33UM888k9CeQ/U+hbpmQu53BCAhz14v6DvKFwqWSyPVLQHVRV4GDmwOXg5tPAEEhTexgZLdCZ4472yqcbgx1jxKErIgBz0HONzhtpsAEBQi5rGs6V4d3x6sqnSwsr7n/R1aCZZprJOAH2g2ehasPOUTRQKFAoUsUrBcpW93XxcAnyTu0L0jurVbLN5Z3hH2BtZZk3uSnYSLr6YKPT/EgITapJp7k8UU2U/09aIHswsgKaPk7rbbbsnZ8Zvf/CZU0cd+tyCBCEBaEHp8pbkEqgAQlHxiGwj2Tje90F7aE2DeO/crSdfJ5t6EtTFd+yKvLoQUbySLjEuBRXdp5N/5wgsvJApRUbA+4yPIHPoF/RZF3Wd/qtDBXPrB+4jPwarLGsYrh3JYFISeR81BeQDshlJVGdAEHZJ6DiG1LgCQrH2O71q3dgt9T5RLQC3/BiWTtRIqACGNODQs9q9QG/PUZjHFvGB2vXBmOjU0st5hhx0S2T/++OOhij72uwUJRADSgtDjK80lUAWA5Cn5eADY5Hm2BII34fXQR02mE7wMdWecQSnCgo5CV5ZKGLnQL/LoV42ZMJ/h4iuJ44GmRvHHrMYcMpcc3NCTmM865lIASBOpk7GCogwytrRXziYLlvDJAWX8t170TuhaZZZNX/No85wIQGykZXdtOnZE947wDaFchgpAQk1eoM9g14opSuHMrGB2zi/xmOMBoZEFy6U9/PDD6qKLLkrq6EAFZs/feOON1QEHHDBXRrkHH3xQUXOEcwrD4u6776522WUXl1fGezoggQhAOjAJsQv5EqgCQNJKPocthxSW8rYL7bFxo/zVWfQL0IFyTkNxLqu63pVie/pqII4HYMFPugk9CToJcqxaILLoO3SJR7H9rnVPFeuTMaEQ6g2FgG/CJGBUj0kgton5TRe9Q4kQMMK76gBvtnIIFYD4ymBkKy/X6wHVGB1kTeixALImxDvi+o4m78MLivIamudMl1GXiynqHlYACaDhjDPOSPYMDD+sn/PPPz8pRGhr2Lj99tuTuMhVVlklOaeoBj9u3Di14oorql/84heJiIgx2XXXXdVWW22lttxyS/X0008n15x44olq2LBhTS61+C5PEogAxJMg42PqkQCbGgelS9OVfJQvPAFsnFjT2TBts8S49CHvHor+8f4ll1zS52OTZyEvgurhQ0O9AXyYHAhdKbanCyQrW1gePcm7ILUH2saj2PYFsMD6LKvSbgNAiupSiGVTlE+uZT2K4slvE5BjO06T6yMAMZGS/2tkT2Le2SdZ8/y3rAm8om2tCZPRAkBYz0sttZTJ5Z28RoophlDLBPA6YcKEJPsVnmo5pwEQ6667rlp//fXV0KFDnWviXHfdderYY49VDz30UOIR2WuvvZL0+Ndff/2cuePveF24ps3zvJOLKYBORQASwCT15i5WASCklOUQZUOET48SDuWpTku56Vy98cYbyYbt+7DkUIC+g9xsg+q7UutCl2E6kUCV8ZnOTd51JvEoLu/Qg+cBi0WFM10ACFSFIu8X34hk1UKBk8xzeERE+cRT0pR3JAIQl1VU/R4ACCAD44x4R4R6g3GC1taaMBkdnm3663tPNXm3r2tCrGXC/rHmmmsmwf9rrbWWeuSRRxTnGw1PxU9/+lMn8dxzzz3qwAMPVPfdd1/iAV999dXV4YcfntCupE2ZMkUNHz48qcK+0korOb0n3tSeBCIAaU/28c0GEqgKQDiQUNrIooQVpSsWPEACfH5fdAE9EBslAfqObSartmtdZC0HSVeMUkSQKbUwGJfL+AyWW+EltnVlyt4nlEDGZBo8j2IIQDBZx+IBKQMg6X6yDsQzwvcjWXGasoSHSqWpo4ZD2Rry+XcdgKSfq2dKSntHJJjdZE367G/6WXxHAGkySIXaQqxlwn603nrrJR6P8ePHJ6IHgEyePFmtuuqqVnVZOKvZf4gFOeaYYxIj2oUXXpj8/2abbaYuueQSNXjw4DnTi5cfb8vYsWMTalZsYUkgApCw5qvX9ZbNiE3JtrGRQ2lByeNAgorUpUZsBhu3j4wt6UBsrEUu7mhJNYuXiBolXWhkMtPjF9qkz5nWlTGRG+uaNQDnmzEBjk08DE0AEL3/khVHAIlYwvUaE77jBCIAMVlB/q+Bssq3lhVvZbImxDsCIGkjngjaKeuzDlqrf2lnPzHEWiactQSMDxkyRJ133nmVRAW4AEjSADTnnntu4pWbOnWq2nnnndW1116rVltttTnvYB8lTmTkyJFKAuErdSDe3KgEIgBpVNzxZbYSsAUgXP/HP/4x4dKLYtTFwlT0EUtilZz1KIds/tDLsD5WpZc1menJdB1AwQIYyfhQkNpqvgAIoAPwwfwxZzbg2AaA8HyCOW09IEXyzbKES0XlohoTNnMWAYiNtPxdCwBhLeZlnMt7E2tCqFq6x0w8I/w2iUGrOhL2Qfb/OhN7VO1j2f1Y9AEhPgxTZe/y9XeA3+abb16JbiV9Yb9nDeHxIKAdMHnppZcm9UUAIMSF4FWRJgBk1KhRCRUrtrAkEAFIWPPV63prA0AI4BOvB65b3PEAkeWWW65zcuOwpL+ufdOBFt4KxluVAlF3oLXNJOiB9FT55kBuQokp6iMB8ShnZRbivGfoVehR1gEfjM2mIResvCYeLgEgeFeK4kps3q9fm5VFSWpM6FmUbJ8fKgBpooq1rSxtrn/99deTWCFbAKK/Iy+eqIlsaxh1WJN1pza3kanttSGmEoa6t/3226vvf//7SUYqX42Yu+22206dc845iaEuUrB8SbY7z4kApDtzEXuSIQETAIKFHCsMliMs5ATyotjhyuXfBgwY0DnZ0je8F8svv7x133QLOoUMfSqXvuMcrAenVAIcpQ6GWNe7oFSkA+JtxgZoYEwE0UtdExPKVfodXQIgacUz7R3h73yHAkagbZkApwhAbFaWv2tJW44xo6jop+3bJJ5IPCSSbU28I6wNX4YFjE809v9QW4hrH8MM3geCw48++mhvoudcJ6Xvj3/84+TZMQjdm2g786AIQDozFbEjWRKQoLQ86cBNh87CQZfO+sRmjkVphRVW6JxwydhCULVN3zi8AS7c52pBLxOEL5pR2Xuy/o71FAoCYFLqYKBUNFGw0aS/rgBE6pXgoYIeUqXIY1cBSBZQEqWTb5TvE8ClK5553p8QlTDGH7oHBABCsVISdtTRyrwjrA1Aqgswp7+cAwBcjDKhthAzeVGPY5999lH/+Z//qQ455BBvon/qqaeSIoMUHvze976XpOGFNUAciLTRo0cnWbJiGl5vYm/0QRGANCru+DJbCeQBEBQxNmsABgcXVq901icUdZRZgtS61qQg4sCBA426pnsFsKBjpXQ9qItemFV3w6iDFS/SK7ajABG3gDIBLQRFtQu8bsnIhfxNmk4jg9rCGq1Kk7MBIPSRIH4yiLWZVEAvYAYYwQtEA2Tq3hFZz6EDEHjr6QKSJuul7Wvg3fPt1QVA0uPjm9djR9jr+eb12BEbiiIeRrwpoVZyRz4hZvKiDshBBx2kDj30ULXvvvs6LWMqnpNGF0YA3w7GHjJqcc7dcMMNyV4hhQi32WYbtcUWWySFCAlSj4UInUTeiZsiAOnENMRO5EkgC4CgwGDtgtaCcgVnOUsZh36FBR0AUoeyXmXWTMERypukn82rjl2lH+l72fixgpoq2T7ercfupCu2ExiL0t6FzDYAEOhugKOypgNG23osRc8OEYCkx8M3rXtH0oonsuPvvlJUl82Vr7/Tb9KPhgpASFjAXsr333Rjn8ODJNnWkGURSM3qH7JnjzT5Ppsen+n7QgykxwNx5JFHJmlzf/CDH5gOda7rLr74YnXHHXck3w9rgXOArFp77rlnQquW9uCDD6ozzzxT4a1jnvfYY4/ESxJbmBKIACTMees1vdYBiNS6wEIqtJwiSyPUF6xi0JyqWp59C9wEHMGpB2hxKGMJAhSYcOir9NVGya7yHu41oZQR4Ah47EJuf7wJeDKKFBzJTEZAbB2AsScAEH3d5CmezDmKMB6SNlK6uqztCEBcpJZ9D/s++54AVf6/jMJH4VnWSpPGE38j/vBJIQbST5w4UR133HHq5JNPVsOGDfMtkvi8HiyBCEB68OT2hKGhcKGIYx1DGcf7YVoLggxYWFQIQvcV6OhLpgSgMx4oWFmgQuIG+BsB2E2ln8UKyrvqpjEwn4BDfhdRylAqaF0AIGWyQUlCgWDuUJ6RoW/AaAtA6DNZu9qkYNl8M9ByoE3ynaNwShFEPXaka8YEGR99Zk2zVm2LgNrIqK5ru0DXyxqbDlIBJGkKn8SOsNdLJfe6ZFT3c0MMpKcK+ZgxY9QZZ5yRpOONLUrAVAIRgJhKKl7XigRQ6uDFopTAB0YZNw3iJVsUFnRS3WKN7lLLA0e6Eou1nYDKJsETPHDkW1cgJ8oE3h+oBhLbQeBpXkOpQAldaqmlWp8+lHks8lmyEeUTBZq/16XwuwCQtmg1rhMmMSDMOV4FoeUAVml1FkF07TP3hQxA6k7ZXEWu6XuzKHxCsRUPSNf2e9PxY5QCYNdtADLtj8l1EyZMUGeffXZSt2OjjTYyuSVeEyWQSCACkLgQOi0B4h8AEQRGYim3sX5iLSOImRziXQsKJe4B6z5BdxJoKRm9OGAlvW7TsStwa+vKOqXXLjH1EGBR5r4uxANkgTM9Roc1BkCu0/rdmwBIes6ziiCyH0ggO799e5xsNscIQGyk5edaPcEBZwX/T3NJ/+ynR9WeEmIcC8HiF154YVIw8Fvf+lY1AcS7e5UEIgDpVdMd3mBRuKC0mHo99BFiPUVpXHrppZ3ur1NagA0CrKnSzmGpZ/RCiW3LgldX1inGC5hAQbCpXSLJBpjDthvgDJAhdQYARvQPT1tTMTq9GYDo848cxDuC/AEntHSa3yYBvAAQPDdtfb+u3wjyZK8kvgnPa4iN75O+4yETr5mkf9a9Zuy3Ta4LG1mGGMcybtw4dfnllyfpcVdbbTWb4cZre7kEIgDp5Qug68MXC5dLP6FsQJtBIWgqhsK0n+KdIWMO4ANlqkqBOtP3ll3nO+uUXv0b5dAWXMGJRjZUQm+76d4hvRgkY1pooYUa6Z4tAEGpBBy1kdnIVSAuaXjJiCdKJ0CAdYeiqad0rds7wjcNIA0ZgED9aWotu66PvPvSaYR174jEjrSxLmzGibc/tDiWn/70pwn4uOWWWzpZ9NdG/vHaZiUQAUiz8o5vs5RAFQCCVZSsTgSFdu1QRUlCocUSZxILYSk258t9Bn3r1b9Jl0wwtK3lkaBulAdodG038Q5h3UZJrqsYZNE4XQBIk7UdfMyRCwDR34uMXIsgVul/yAAE2if7UcgApCyNsL4umCvOB/Yj8Y6g+PNt2+5RVdZM+l4MQJxVxG2F0k466aQEfNx7772dSJceitxiP2MMSFwDHZdAFQDCoUphPYrYUb+hK42DDzoShyCHDf2r2zprOnZfQd9k+QI8VK3+TbA63gaoam03FDRAFevKFVBVHUMEIHYStC2CaPf0ua8WAELsik0BvSrv9HUvVCUANvTIrnmLTcdok/GNdaHHFInXjIQfElMEIGl6X2aPIYEFXstQ2siRI9Xdd9+tHnnkkcTIFFuUgKkEogfEVFLxutYkINlvbDvAITNt2rSEs98VCgrZryTVIops17wzVYO+9SxegD4UGpvEAek5BoAQsE8mszabZC3DOgrFBiWljRYBSDWp52VQ0pVOV/AABYxvOwKQanPkcrdk8cIw4JKBju8KECI0PokpajrjWtPV6F1knb6HCugPPfSQmjp1arDg1Ycc4jPsJRABiL3M4h0NS8AVgNDNF154IaEVtG1RQvEhlTApaPF6EO/BYUMMSJeCPuGwI2+XmAsswAAYyeLlogiklxYyIwkB2cLaaCgm9OHtt99OgBRW0TZrktgCkBAtqlUpWKbrJK8IIlnMBJDYFEEMGYCgcEP/wVjTFrg2nbes63ynEcbTKTQ+fvN8vCN6TFEVw0reWMtoZFVkVNe9++23n5oyZUpi7GsyZXxd44nPbU4CEYA0J+v4JkcJcBhIekXbR7z44osJXaZNTq1QMzjkAUN4Y1AkoYcRwOxDUbeVS971WHCxBNrEXEiFeoLpsRhCKfOVBYgaMIA2ikk23fRiiWQHwhPTdlX2CEDqWwVSfVsqcPP/UHB070iRgiUAhIxtoSli7LEEQNvUWapvJuyfXGcWL907wl6OrGh67AigtWrsiG8QZS9Ftzv22GMP9fLLLyfGvtiiBGwkEAGIjbTita1IoAoAYWMkCBcQ0nRLK+Z6jYgu0sOQj23MBXOD14SDGf4vcq56EOvzBKghv/8KK6zQ6PRJsUQUSQAVyobPAH3XwUQA4io5u/v4PrOKIOIREUCSVjqJVSLuKQIQO1n7uLrJIPqyejR4SVy8I3WCKB8yznvGzjvvnHiJn3zyyTpfE5/dAyUQAUgPnNSeNqQqAGT69OkJxQkLdpMN6zmKOd6EPMW8K/QwXS4cJMQ7mMRcQI3CY4KVGCW9DuoGdJy33nqrMQCCIgMII4gezxQeK1EmfAXoV1mHEYBUkZ77vQRpS3wAYJt50IsgonTyrYcKQNivANgCtt0l1c6dbQXRA1T12BHxjhQB1TwJNQmifM7Stttum4D1hx9+2Odj47N6gQQiAOkFkxz6ELE4ceC7NDi1ZHVBkWyicSBhPUeRL8sA1QV6WFomUJ5QvotiLpgLlHTGCbiDN+5i8TOZD8AHXpCBAweaXF7pGikkhzJD8HyaGlc1QL9S5z662RaAkNmIOWqTgmg77qZiQGz7JdfnKZ3QDlFA8XTiMfPpCXTtq+l9AkCISUN5Dq11JYaFfuixIwJU9QKZeXtlWyCq6lxvuummSWzMPffcU/VR8f5eJoEIQHrZhIc43CoAJF29us7xc4DgESBWgDgPvC5FijkxIATHt0EPy5MDyj4B13kxF6Kk6/EsdSpa0K8AcyuuuGJtU4dCyZh5D7QarMD8TrcIQGqbgrke3HUAkpaCUHLwCErCjLbTudrOFBZsPHyhApAuxrDoQBVQImsDgCeARE9y0MUxmKyjDTfcMDHw3XrrrSaXx2uiBOZIIAKQuBg6L4EqAITMLsLjr3OggA7AB4cOHgGTzFYUScTKTkasrjSUP37SHgfGBRjAQ1KkpPseB8AAWstKK63k+9HJ83TQCBhkLvJy/0OpQ0mA499Wix6QtiRf/l72ADyDKGNCy0kXu4Om6CtBQ3mPzK8QwwIZ3rLAt/mT2rkyBApZFo1PT3LAOcUeExoNbt11100MRFRDjy1KwEYCEYDYSCte24oEqgCQugOH9TStUL0AH6Z1BNqKTymaRChPgAzd44D8ORjhwEPlwWPTVIEuaF4AO/rj29PCePBqmILGKimKfX04tgAkxMrKoXlAZG4FgJBBTr4PAKvEjkixO/YHCWSHqtXUt1S0BgWAUOOmiwCp7PsJzYOTl+SAcWK8ooaSTQroMvnU9XdA1TrrrKMAIZdddlldr4nP7aESiACkh05sTxoWmxwBei4NWgH3UhzMd+PQFqs4dCuybdkoydQBwRVPvEFXWtrjIIUTGRfc9qarJBOPgozxyPhS1PTsZMgfi6MJaAQIoei41EjxNb8uAIQ5C6lCcagAhG8FGp8OQPR5Z+4kPgBQwr7Gd6XHB5isQ19rSX+OpAoPFYCE7sFhLbDXsf+yz7FW+K2vjS6mdibz2wYbbKCgYV1wwQV1LM34zB4sgQhAevDk9pShVQEgddBmUGAlOBq6Aoq5S+Bmk/EppmtBPA6kvcUTwoFI4UQ8O20cgPDq8VLQHx+B7nhzeB4KF54cFHNT0AgVjPtsaqSYyt30ughATCXV/HVlAETvEXtI2jvC3/E+6N4R07VZdbQhF1Fk7KEDKMYgcwAIxGgmYBWjB42zRgBJV5IccA4OHTpUfe9731NnnXVW1WUY7+9lEogApJdNeIjDrQJAfCuNet2LqnQkMhRh8cQC35UmHgcOO8bq4tnxORaUOrxYBMVXBUC6N8clbTBrCSVh2WWX9TlEq2dFAGIlrkYvFgDC+rAFDrrCyRqTIohNWcBDByCh95+FmldHJq9AJmtD1kdbnjMMfFtvvbXabrvt1CmnnNLo9xZfFr4EIgAJfw57/AiqABCbuhZFgsRiiTUeJRRLPF6PqnUvqDyMm53MM11ojBGaESBEAs1dPDs+xyIAhLTArocsSjveHILoq3hzbIs0+pSDPItvAcuoaeVl1hhKSpcyrZXJJVQKFvsD68wFgKS9IwRVS+yIbgEX74jv+ICQiygWKe9la61Lf5cYIiieed5e9uistaF7zlgbvuiqZfIhzf1OO+2kdt11V3XssceWXR7/HiUwlwQiAIkLovMSwAKE4uXSUAigFeWllTV5Ju8HeKBgEBxIzIYPOhAB8hwouNzbbno2KPqCEtWFbDgcysiJwoguwbEc1lCu+E2GKzJd2VqnZW58gVnXuZaxoJCmi+DlrccIQFylbX+fLwCSfrNuAQeUSHyAgBF+V92PBIAUKb/2EmnuDhPlvbneuL3JhsInb8jynDUZV/Tcc8+pH/7wh2qvvfZSRxxxhNvA4129VgIRgPTaqQ9n4FUASNVK2hzMuJk59LOK01WRYhfqSoj1kDEChgikR2auCn8VeWTdi/xRol0AEZ4cgKOkYYY3XaUBQFAyi4o0Vnl+0b1SdZ6xMEdiBZXKy4wNRZSAczxFArIiAKlrRuZ9bl0ARH9TXvYkl8rb+nNDV+BdlPfmVobZm9ivqMPk6kET74jEjhCYT8Nwo8eO+PSOTJkyRe23337qoIMOUvvvv7/ZQONVUQIfSSACkLgUOi+BKgDEtZAdgIPDgCA7FDuCsF0s8EXCbTutq05NYozQylBoSd3qovDXsZCw+Nr2h3EBPDjQfXqsTKrE+5aBnuaZsRCTw/cAwOBHiuAhJxQPlBA9zStrmLmNFCzfMzPv80SBBLw31bJqS5h6x/Q+hq7AV4m/aWquyt6Dp57zxleMme4dYW/Qs64JIKl6pj344IPqsMMOU0ceeaTac889y4YY/x4lMJcEIgCJC6LzEkAJQ9FyaS51JKC4CDioStsp6rPvAHkb+eRRkzioCI4n01Pb8R+Mx7Y/eqV28Vi5Uq7S8iyrEm8jf5Nr9TmiuN2iiy6aAAxAogAQ/TlZaV75O14TqGcAkaqB/Cb9rnpNqDEgbQAQXdZ65W0Aado7JkUQs76H0BX4qt6DqmvWx/1kHOSnjix7eVnXxFghAe223pG7775bjRw5Uh1//PFJLEhsUQI2EogAxEZa8dpWJFAFgEhWJ5M0rnq1byxDeASq0naKBNZGUDNjBJTxbsnApY8RBZ70wHDB6xy76UIy7Q/j4vCGJlVXpXYACB411lLdTadc6ZWRJQtWFgBJK6OSsY1/lzo6Vak6dY+b54cMQOi7Lwt2VVlneccAoBI7gtIpCmcT9LGq4ym6n32+S7J3GSt7C+Noos5QXk0aoXKyRnQqZ954brnlFnXSSSepU089VW2zzTYuw4739GIJRADSiyc/lKFXASCmaVz1at9Yi/F82FqDbOXZNKUHFzxeF2SCNR2renqMeH8okLj00ksnvOG2m0l/9HERHwFNqY65qxpPZCLLNOUqnfDAFIDIuwjgB3SwpvEmEVPDb54jVB3iRnRl1KSfdV4TKgDxTaHxKWPmGzAvmbXY7wCxDYYexwAAIABJREFUonACUAHwTdLHfI6vy7I3HWdb6x7jjQ5WWSf8Wx5Y1cdzzTXXqNNPP12de+65apNNNjEdarwuSiCRQAQgcSF0XgJVAIgEMRcFVUt6XQ5kYj1I1dpEa5LSg+JB0DuyZIzEE2Q1aD+kViQzV9NVz4v6QyX7rLTHjEuSBDCuhRdeuLapk+KTVGWvo2VRrtJ0GVcAAqCWplN1+D7SyqgEstcxRpNntqWImfSt6JpQlOC0wgkglbbIIosk3xnApA4QX1XGeffXSV+qq8/p53IeMBdtZ0UU74gEs+v7gx47wt502WWXqfPOO09dcsklavDgwdaiuvPOO9Vtt92mpk2bliT4wNsLlWvHHXeca/0Ra0KhQ7zzGJh23313tcsuu1i/L97QLQlEANKt+Yi9yZCA8FddhFMUQ4DVDyoSbm8UV6zNTXLk61ZokRey42BDqePwgFZWFHgIbWf69Onqq1/9amNArGhepT9pQMS4GBNjMxmXy9pJ31PnfOVRrtJ9sAUgFHFkvjm085pekVuU0bYqctPHCEB8rFbzZ0jCDTyj7H96sLLQtVxr8Jj3otqVTdKXqvU0/2484nh82Xu71PT9QbwjZ555piJzGsV4n3rqKTV+/Hi11lprWXd7hx12SM7dIUOGJF7ayZMnq4svvljttttuasSIEcnznnnmmaTOyFZbbaW23HJL9fTTT6tx48apE088UQ0bNsz6nfGG7kggApDuzEXsSY4EqgAQiSFIU4rEcg4IgYqE5c9XsLLpRLpm6DJ9PgcHXg9kQBakz33uc6VjRPl4+eWXk+KIdXoTbMaQBkQ6XY4xMbYm5q6O+SqjXKXlJKk2y2JA5D4TAKK/I68it8+aE2VzDwDh+2zbElzWz/TfQ7bC471hfRN/kFY4GWebgNRkHjAOAKDY50NtxK+xt+EF6GoTKt9PfvITNWnSpDmxZbAGvv3tb6v1119fDRo0KAEmJo1vBtqs3saMGaOuvvrqBNiw7qgxgoHm+uuvn3MZRQ/vv/9+9dBDDwXlqTORSW+6JgKQ3jTbgY61CgARSpFQeGw9AnWKzCVDl2l/pAYGPH8ONNN4DhTQl156Kbknj6Zl2gcf16UBEVY3KFco4HhzmqSJcVgSQ7PSSiv5GFpSy0OKJEqWqzIgVTcA0QeWV3NCD1RFQSjrs62wIgCxlVj16/PAUx4gFSpOVzKrheo102eOvQUFn30thIaR4LjjjlMPP/zwnPpR0u/11lsv8Yq40Ph+9atfJd4PnothcPXVV1eHH354QruSRv2R4cOHqxtvvNHbfhyCzHtaHyMA6Wkz2kPHg7Lm0rAovfLKK4lbG2UJ5dXGI+DyTtN7JEMXMQUuG3XWe6pWbecAfPHFF5M4EQLV224CQDiUoSdg6QR08P9N0uWQgwCQFVdcsbLSbUq5SstfB+Mmij8ABzkBcKq2oqxKzImvuIEIQKrOlP39JhQmAb8SyM73SCPrnHjISHhgsi7te1h8R1fiJ6qM680330xuZ+8NpQFAJk6cmHgjOFcBDXglOIcAIC7UPbwb99xzj3rssceSGlCbbbbZPDEm7MXrrruuGjt2bELNii1MCUQAEua89bpeuwIQseijTKPwsyGivJp6BOoUtGmGLtM+wN9H4WTMrlXbUTIICOT+tGvctB8+r5P5Y95QgIllgCvchpLjw2NlS7nqEgDR+6JnVSKQPR03ACBxBYihAhATJd7nt+HzWfQdUGxDYWLOJVAZUCKZ1dhbJbMaHtgmWlfjJ2zGjnEMQxR7byiNAoT33XefeuKJJ7wYrJ5//vkkAJ2q6lRYnzp1qtp5553Vtddeq1ZbbbU5YmHtYQiiBgnxIrGFKYEIQMKct17Xa3jJKMe2TYKYuS8v9aztM31dD52INKnLL7+8k6VI+qEHZGOFhj5VpcItAEQUfV9jdX2OKP0oMnix2gSO0hdXj5UL5aqrAETvV16RM1fLeAQgrl+L+31VYyiErgcYtS2C6N7rj+8kfoK9nti1UBvGIwwtRQkjuja2gw46KPFUPPfcc4knrErjuyconYx9V155ZSILASDXXXedWnXVVecBIKNGjUqoWLGFKYEIQMKct17XaxcAgocBtzZWdKz5XbMsSYpgCpe5bt5SbA5LpK+AbChYBHabBhLWsRixppKhDKWfxqHcZn/oQxXKnMTk4BXQCwvayq5NCpZpX/nehKajW8b1AnhFlvFQ6TQhe0B8x1DYFEE0XVdF17FXYBXvcgB32TilZo+eMrvsnrb/vvfee6vf/OY3SdxgFa80xjiABEaaq666ao43hZpUkYLV9izX9/4IQOqTbXyyRwnYABCd5kJ2DhR9NvW2Fdi0OIpSBJuITuIIcNtz8GbVyTB5TvoaDhNkBaBpo8EtxxrInBO7QHBmFyhhyJt+UQndlFpSlXKVlr8tAGmb1qHXHNEt43oQc9pbFyoAqepFaONbk3f6BiD6WPgG+KbFOwI4oelrwKTqdpF8QgvgzhrLf/3XfyUywfgTSoP+RL9JlevaAB177rln8hyoVnoMDGdADEJ3lWz374sApPtzFHuoVKKMmlCwCIRDSeSQk8xCBKHjAenaxp6XIrhswnXvAKly2bBNFeKyZ/N30vC2IS/mF48H1kyUUkAVQa1doYQJABkwYIBRfIMPylXoACTdf75LUUSlpgDKJzEDUgAPZbgLBdlMvhX9mpABSJOgL6uuBGtAPGQuyQxCDOBOry8CrvkO2jL82K53rqcOB/vi448/7nJ74rU64IADkpS7EyZMUOyt6UYaXjwkgBNpo0ePTmJPYhpeJ7F35qYIQDozFbEjRRJAcUHxzmt6HARKK8qr0JoAIKSU7Rq3Np0i2GQFZAGsKq7vrHe2IS9oOygR0OYAP8yVZAbrAiUMOdkkDfBFueppAEQfj1RcFkDCGmDOAdP8jZgf10B2k2/J9zUhA5C2grhlDQhlz7UIYtuePh9riSrfpJ0lyUYobYsttki+WcCASwNIACyOOOII9c1vfnOuRyyzzDIJIJNChNtss43ifRQiPPfcc2MhQheBd+yeCEA6NiGxO9kSKAIgZXEQv/3tb5ONzEcqUp/zY1N1HIAFxxxFAWClAyyffeJZFP6DutaUvPTsXXhz0vVH2qaEiXxNkgbo3inGAXXMp3cqNAqW6drUU7wC3gAjNIwJWMb5fuuoOWLaP5Pr6qQxmby/yjVtARC9z3nJDEyKIPpMN11FjlXuJd4B40sXsg+ajoMK5vT3jjvuML1lrus23HDDxPCU1a644gq19tprJ3968MEHFdXXAWkYp/bYYw+1yy67OL0z3tQdCUQA0p25iD0pkEAWAOHAQlmBsoOSR3rdrDgINi0Uma7lVzetOq5X/iY2AyqZr7ohWSLnIISLXHfQPvOH1Rjlpyh7V1uUsLRsBIAst9xymVnG6qBcpftgC0BCpKZAB8IijmIjlnHGjTdED2Sv8xtw2YxDBiBdzCJlUwTxjTfeSL7Jrnm5bdYRhjL29y7UXzLtN5XPv/71rycFAWOLErCVQAQgthKL17ciAZR1sYrSAf6fwENoMbitsdbnWZrh1kr2oVY6n/NSk6rjksmrycrfTQA25g+rJQomhy5JAvKoZFDCmOO2s8NI1rIsAKJTrkgFCuCto/UWAKLHgOg1R1gvAHLWSjqIuQ552zwzdACCXLuaRaqsCCLGAYwYoQIQxgcAwbjEXhdCo89rrbWWWmONNdQvf/nLELoc+9gxCUQA0rEJid3JloAOQFAE4fyyAWKlT1N20k8guwYKC5zyLjUp+pdVdTydyYtrmuLDv/7664k1EY9SHU3mj2fzDug1Ra2NmJSs/qD8Amb1tMl1U67S/eiNAESXAeNPB7Lzd9arBLK3VY071PolyC+0NLZZqZ7Z42UNAE6b2i997JHsI3ieAVAkFgmhkdls0KBBavDgwUml8tiiBGwlEAGIrcTi9a1IAACC4gFdh1gIqBgor2RPKWu459ngl1pqqbJLG/17XtVxPQ0tBxJUFN+B5kUDrctjxHiZP2hXKArMn4mS0HRMSp5sBIAQHImS2wTlygcAQe51gck6PhibjExC0wHU4jWRQHaJG0ER9RmDUzRem37XIbcqzww5jS3rW/Ys8ZQgC4kfYi0QN9fkHmo7F6xbPM948om/C6Hh9d14443Vd7/7XTVu3LgQuhz72DEJRADSsQmJ3cmWgPDvCdyGikOmENMDBW8J9y299NKdE6+e4YnD8+2331bwsfU0tE13ug6PEfKHckUWL6nJYjp/UBNQIuqOSSmTs163BZCI0gaAqpNyVRWAhKhYuiryUo1b4kYAiDSoOQJIqtabiACk7Ctp5+96CluMVbIG+GYxPgFCTQthtjEC+oznmT2uzCPcRv+y3sk5tfnmm6utttpKjR07tivdiv0ISAIRgAQ0Wb25qyiv5BuX2hA2skAJ4yDCct21JhmeCDwkYBiglU5D23SfqciLMufLYyQFE1ECmD+s0jatqaD4sj5J3RYoEsTm1JHlqqwPthSs3gRA0rLLqsZdtd5E0fx0IZNU2frJ+3uIyQr0saC8812mi83mFcIUUAoo6UJ2NdYqIAqqra+Csq5rwfQ+DFXbb7+9+v73v5+kxI0tSsBWAhGA2EosXt+KBNigsRK5ZL6B34xiT/Bw1xoZnjhwsNhxWHIAtc0BBuwh66997WuVxKXHsVQpmAg1AQpF2zQiKUSI50aKXJp6cSoJUrtZYiD4bfLu3gxAdJnn1ZsQqzi/TeiAPRWAhF5Hw7SGRhYo7UJ2Nbx1GH4w0ACOQmh476mEvvvuu6ujjz46hC7HPnZMAhGAdGxCYneyJQBHFqXYpWGZhK+6/PLLu9xe2z0oRQAQftvEtNTWoY8e7IOypsexVFXW6w6KN5En6wcrsYDEtlJl2gKQ0IKLmQtXCpbJPHKNeJGkACJrlQbIlSBml5iBkD0goQMQlxoaednV0t4R03VV5TrWILGKTdI5q/SXeykIuM8++6gf/ehH6sc//nHVx8X7e6EEIgDphZMe4pCrABCy0xD4vMIKK3Rm6BLAzMHDgUd8iolFu4kBoGjTL/K72zaUu3feeSfJquMrjqWuoHiTselZrggOxZOGZ6gtmkQEICazZncNhg0omhLIrscMAEigDJp4XgEgfNcokaG10Av5+aihQZyaxI5At+Rbq5Oyp68R3scckKkR8BtCe/TRR9XBBx+sDj30ULXvvvuG0OXYx45JIAKQjk1I7E62BFAKcJ+7NLJmETC34oorutzu9R5dQZeAWBScLhVJdI2ZASRyL1QlPAR4PkwUtzIBwzXmOU0rduksV8wTllZiY9oKFI0ApGy1VPt7VswAhoF0IHvWW7pYzM9UGii/7Eeh1tEgU57PGhp5lL26as8AgPFCsbdguAmhTZo0SY0YMUKNHDkyoWLFFiVgK4EIQGwlFq9vRQJVAAiZpVCMASBtehn04omioKNcc/B3qQCYS8wMByhKDCAEMFVWm8VmEcGNpqXruIwaNSrxSJxzzjlK/2+bZ+ddm1VYEAspik4EID4knP+MuilYNr3XreKscRoKosSOAExkTwkZgIRcSVyK+JFdz+e+I+tEKHu6d6RoHdisL7kW7xtnFJ7wqrFILu93uef2229Xxx9/vDr55JPVsGHDXB4R7+nlEogApJcvgFCGXwWAoExiXRo4cKAXi7yLzDi8UNAZh66gt2XdLxoDihReDJOYGQ5n8TChjAGkfFvw8uq41AFAigoLCgABCLWVq9/WAxKiUtwlAKJ/J6wNUUL5rdccAZCgRPJvXTImmO5VgHzqZqDEh9aaLuIntWdkLcg60L0jtiACQwqGH2ivTdWtqTrPN9xwgzr11FPVGWeckaTjjS1KwFYCEYDYSixe34oE9PSjth0gZSpK7IABAxq3LtFvFCriUDigyOSkK+h51n3bMfq8nv7iNUJeRQ2PDsAOxYv0l1AgfFCu0u/My8rlG4CUFRaEAkhV9ghAfK62eZ/VVQCi91QK3kkgu9QcYf2TRrsr6V1NZypkANJmET9ZBwJG9IQG4iUD2JV53jmjMBaQKr6OPdR0HdhcN2HCBHX22Wer888/X2200UY2t8ZrowQSCUQAEhdCEBKoAkBQEvA0kIbXt3W+SHh68T2U88997nPzHES+Ut76nESToH1kCviQKtt1egSQEcp/upCkTwAilCuhw6E0pBuAi6xlxKK0lSo5ekB8rnR/z2JtkLyBdcoc8SPpXYkXwjvYZcWS/REDCftUaK1LRfwkoYEAErwzzLue7jnLw4HHmSQGyy67bClY6cr8XHLJJeqiiy5Sl112mVp33XW70q3Yj4AkEAFIQJPVm7taBYDoFayzFMs65CoKbVnxPR8pb333n4xhWKGhrKWb7tFpKnUwMsLCnM7K5QOAFFGu0mPH0krhSCg2dXDNTebRBYAgu3T8jMm72romBA9Ilmyg0LBGqGZNViOUUIA6SikW8LoCmH3Mk15J3MfzmnxGV4v48a3iEREwIl4yziABJJLumcyB7LsAkFDauHHj1OWXX66uvfZatdpqq4XS7djPDkkgApAOTUbsSr4EqgAQqWCNAlt3kSc9E5RJpWzXjFN1rhViOlCmVlpppbleg0cHMACggysO7aqMWuCjn1iWmcN0JXsXADJz1kz1l/97R/175gdKzZyt3vvzu2rGBzMSpXGRRRYpHE+IACTE2hShApCsoo95AcwonjYUHR/fUdEzQgYg7Et4cKC3AvK62gCiAkbYQ/V0z6wTwGpIAGTs2LHquuuuU7feeqtRvGBX5yX2qz0JRADSnuzjmy0lIBYky9sS6zl54uuu36BnghKFtqyvKPpdO3iysobBUQYIQCfAA9DkQY9ix8GdPpxNAcg//v2eeviNKeqJN59Rb/ztj2rGrI8LWvbr008tufAX1bpfWV2tv+Ra6tPzL5g7ZSgMVP9F0QGstNFsPSARgDQ3SyZV5wGxeiC7roQCSPiu2ghCptgntEKMCqG1EKuIZ6V7Ru7pIohNGHhc5/ukk05St9xyi/r1r38dZOIF13HH+/xJIAIQf7KMT6pZAq4ApO7gYQ4T4iaw3NpmgrLJOFWzeOc8HjoAYEMoWPQRUIKCQgavphWkPJBWBkBmzJqpbpt+r7p1+r1q5qxZaraanSvCPqqP6te3r9pyuSFqi+WGqP59+81zrQAQZNB2JXThlpetiQhAyiTk7+8mAER/m1B0JJAdSz5Np2o1FbP22muvJaB6scUW8yeQhp4kACSkKuJp0fCdkgmL8wNDlh5DJMC0azFExxxzjLrnnnsUBQlDBK4NLc/4mgIJRAASl0cwEuCAZmO2bRI8XAd3X6clEWROEKeN1co045TtmKtcT0Akgd8EfaNUccBToIzsPjZjq9IH/V4AEB4YkgjoLQ1AACpHHHFEcslf//l3deVzN6q3Zv1Nzb/IvAHlRX37ysJLqEPX2UctvsCic13G2ps2bVoCwtoCIFjQ4YoDAgluLlNKQqQzhdhnFgqgneZaVBRDicSN6JW4mWeUUL3miK9vS54TMgAJsYp4ev70NY9xQWKIWA+sC70YpmRY870GbJ9HBfSHHnpITZ06tbXCrLZ9jtd3SwIRgHRrPmJvCiTgCkDqos6gqOu0JA4G24bnhJ+sgG/bZ/m6XgAIh55khao7dqao71gHCepP1yVJAxC4yOm26Gr/oZbcdt5g+qL39e3TV33mE59WowcfMg8IeeGFF5J4EcBY0w0gSDpp8QSaBDaHqMyH2GcfAERfT3olbqkvAtisUmuiaL2++uqryZpuY11X/Y4EgIRURTw95jxPpVAu9dgR7mVf1othlhkiqso46/4f/ehH6sknn0xoqU17xesYT3xm8xKIAKR5mcc3OkrAFYCI5dqX4qhnToKWxHNtC0+JCKSIH1Xau9CwsKPkcuAxNuId2jjcdFnYeImgXY1+4HT15rt/UrNmz3IWKSDkSwt9QZ24weFz0bEAIEsssUTjVBUBu6wz3s+ahqrBPKGA0SSwGYu5ZNcJUZkPsc++AYi+cPNqTWRlU3Jd8AAQ6FdtefZc+819fAckxwgZgJgWDNWBKd9+mxnW9thjj6Qu0vPPP19l+uK9vVgCEYD04skPbei4otmAXRpWGuhRVbmqYm2jLyiCHNhVaElZAd8u4/Nxjz42lB6CvlFkfTU8FuKlQJEmkxYFrPbbb7/EsnvvvfeqSy+9NMlowzwj3/XWW0/94Ac/SGhH06dPV8cee2zSHcmtT3rZwYMHq1122SWpTn7zy3epm16+y1eX1bYDhqptBgyd8zwoWNDRmuLKIwesowBVsqohE/5NjwHJCmyWGhQoKAB3EjCE0kIFICjBrEsMEnW2rGxKMt+u8QIk6WBvDBGAoIjjiWaN4xkIsUkKZww+pi0vwxpxQ7p3pMr5VNSXnXbaKdmb8ILEFiXgIoEIQFykFu9pRQJVAAj1G1AaXQttsdmjBLLhopQTT+JDOYdahOICBastT0N6bMSyEANC2lufdVMAIMiQ7CkoUXCHTzjhBLXFFluoIUOGJEDkwAMPVBtssEEC6sjMM3nyZLX33nsnAf4oSaeddloCYugzQZu/+c1v1M9//vOk/sIFP79QnTh1nMIL4qsRjD5u6IlzsmP5ArIm/ZNCltQSkBgc7pPvIGu9SHYdCWzmWhoKicQSuHrrTPrs45oIQMylmDXfJtS89BsA9+yNbWV3Mx/xvFey1olVI2at62s7b5xV44d4LnugeEUBZfy/XgQRI49P+WyzzTaJcYM4kNiiBFwkEAGIi9TiPa1IoAoAwVXM4YrV3bbxXkACmzpWQg5qX2BB4i0GDBjg9XAwHaNUcEaZB6AhH2IMCEr1XTdFj9mQ/h1//PHJAbbJJpskHg7ARLoJTQ2aCLnnybqiN/6+7bbbqqVWXlrN3niRwmxXpnKR68iOtdNKW6lNl9kg+aemAAjzIRZ1Pe2xcMJNsmBxLYAZBQ2rKECGJtQdAAn/XpeF1FbWcn0EIK6SU4lCqAey8ySxiDPfzH3WfIcMQPhW8CCwX4Uai+Dbeya0PTFESNwYRjMxRAhN03W1DR06NDFskAkrtigBFwlEAOIitXhPKxKoAkCwnrPxQmGxaVL/gkMb9zjP8Nk4PH//+98nAdZN0wdQVPB0cFgxNihMNBRVlH0sij7rfWQBkFNPPVXdcccdCc3qiiuuUOPHj5+n3ocURgQUZQEQ+oxn5NobrlMDjx6k+vTt43OK1NKLLqlO+PahyTPxpAFC8RLV0fRK86w15kW3WtoAEPpHggOUEOgpOnWHuddTffIuEg34AtZVZBMqAOFbkhidKuP3dW+ZRRzlEYWddcD+iPEBml9ojT2aGAo8tl1Yvy7yq3vt8O3r3hEf9We+853vJBnfqAUSW5SAiwQiAHGRWrynFQmwiXKoujSUV6x/piky2aCl/gWKOfeJIkg8wiWXXJJY7lGWyBwDgNh1113VOuuso7AMQQmgYWXCs0BV8WHDhqm11157ru7fdttt6he/+EXyLhQBiXs4/PDDc4d5/vnnqwsvvDD5OwcuyvC3vvUtdfDBB8/JYrPKKqvMuR8QQYDmXnvtpTbeeOPkPfQb5RQlBCVXBz91FW5MAxCCF/fff/9EJlCxSKH78MMPJxx6+r/uuuuqzTbbLFGgkSeg6Kc//ek8HhAGSuzIWWedpQYeOUjN9+n5XZZI7j3z9e2vxm8xVhGY/vLLLycydqXyFXWM9Y0iAjjg+cxr2lpdBYDo75ZUn2IhlWBW4Y7z2yddw2ZCIgCxkZbZtVJzRLIpiUUc0Mn+AMgPFYDgRcbTR8xa17x5ZrOjEiMU55OLh970HXKdryKI7M8kT7n22mttuxCvjxJIJBABSFwIwUigCgAhnkBSypYNGA8AiiB0hnT9C7i6WOsBJaQhpDYF/XrsscfUDTfckMQnAEDgx2633XYJX597Jk6cqG666aZE4d5nn32SLjzxxBPJM3bYYYeEQgRYkbiHo48+uhCAELANCAKQoRQfd9xxaoUVVlAXXHBBch8KPLEWBHHjZbnssssSSxUUJwJNsYblKbmMG0oGAd7iFSmTmcnfASDIAUoI/UZuWNH4dwnqRu5TpkxRzz33XFJhF0A2bty4REHCSnv66afPBUAkI9nlV1+hrrniarXiiEGq/4J+AQhjO2PIKPX5BRdPZI38fCsKujcKylWep80WgACWsRDjzcprEswqYESnagldo0mqVgQgJl9TtWuk5gjrTgrf4Q3he5eaI6F4E4ijY82k6wRVk1Czd5N4AyBYh2GjbCRZa6EsqQF7N8Y2DF8Yf2KLEnCRQAQgLlKL97QigSoAhA2eA5VquUWKGFmp8EagcKEIpoOwCZRGOQdopOlJKHqkrgWAkJVp+PDhc73qZz/7WQIabr755oQSA20ISg/eDpt4Czwg999/v7r++uvnPP/iiy9W/Dughj4DQM4++2y14YYbJtdwyABGiLWgb4wtr26JVI5HVozHVwNooCjwmwMOC38R7Qxe9JZbbpnIh/GkAYjUxQAwAUDuufsetdLRg+eiYM2e3UfN/tcnlZrVT6m+M1WfT/xT9eljX8xyzIYj1JcXXiJJO+kaS5QlR0kAwJpjPTEvZVQ8xmsSA8L7TABIul9C1QKQiHIqdQeEqlWnpTlUAEL6avYNjBYhNb733/3ud8n6Y211yRtmIsd33nknWed4QEJtyJ9vqy5qp6lcTIogAlTJmEiyEM4XMXqZvkOuw+uDQezZZ59N9naMJLfffvs8j+G8vOiiixJDHkYxjHibbrqp7evi9R2UQAQgHZyU2KVsCVQBICgHbK5QkbKaHowNxQYlIm0BxNVPylcyNUFnymt5AETuhyr1wx/+MNl8iXs46qijkueaFjLMAiA8B+/A448/njxHByCSynXrrbdOAMjo0aML6TV4JwBGvivHZ8WAFK11lHNAE7Jea6215gIgWD3ZVRMUAAAgAElEQVShZYlivMP3d1D9vrZAUnRw9oz51Iw/f0nNfPs/1Oz3F1Jqdr+PX9NnpuqzwLuq32f/n+r/uTdVn/4fZokqa+IBAYDAk/ehZCJnDlWAK3EleFVMFHsURslwU9ZvFwCiPzOvIJ5O1fId+BsBSNms+v07awmKKtRH5lUPZBdvmM/gZb+9VwqjET/EgITa8Hxj7KmaJt73+GUtSL2hSZMmJR4PKMd4pfmN8ctk30r3DQ83XvpVV101AcDs92kActdddyXUYlgDnAXcM2HChCRWcNCgQb6HG5/XsAQiAGlY4PF17hIQ2o7LE7Cms5lmUVGw9PJ3NkBiPfKs/sQs4D0g1oD6FbYAhOuhHHEvyjjW5cMOOyyhFKHQrrbaanPiHrCk5rU0AGHzBhTR76uuuiq5TQAImzaHG/QwPCZ4YdZff/1CEdZVOb4IgDAmlB36Bu0K2hhjufvuu5MYGRqeJ0AW1jAACIovngOAHHO36M7LqJl/W1bN+OPXlZotgehZAekfeUD6zFb9v/ia6r/E66pP33yviB4DQh+Qc1UAIjVXAL7E4Nh4mmwACEoCssLDVrXpBfH4ZvQ4AknzW7RuTd8fAYippPxcxxpkjwCApKl/WTVm+O50ANo2VcvnGvcjUfunhFCJnnMBb8WZZ56ZUFHZD2js1xjQvv3tbyfnl2niEt2LixGOIq9pAIKnA2rdOeecM0eoe+65Z3I+XHfddfaCjnd0SgIRgHRqOmJniiRQBYBgLUfh161kbIAoO1iJs4Kx030hLoFAc53alNXfPA8I1+K2pubFyJEjk1s5/B988MFEkUa5lbiHK6+8MlEc8VpIwxNATQyUdaxOWCUZA8Dqm9/8ZhIHIhQzAIjw9lFYUSy4l/gVaTNnzVR/+b931L9nfqDm7zefWuxTi6p+fT/MikPBPcCYz8JkRQCEuI9rrrkmOYRQKFDISU1Mn6FW4K6nTz/5yU+S7mNxY0y45Dn4hm6+nTrm8kfUv/5B4USbLFizVZ9PvavmX+5p1fcTH6apTTc9C5ZrNjV5JrKFMkLaUOaP+bJV2tsCIGm5ZHHH8UhJ3AgBzi6W0VABSJOBxD5PCqFg8b0XeWGzgpeZX+ZZAIntWvYxjqpePh99qPqM0ApBUsPpgAMOSIwnnFPsaTT2NM4m4kNsWhYAIR6QpCnnnXdecmZKg5JFjCRxl7AVYgtXAhGAhDt3va7nVQAICh9WW+EJY71lg+M31BeCoMuUpaoULDZqlOVDDjlE7b777sn8pelOEvcATWrzzTefk02La6H+8AMAIff6ueeem9DECFzUD36e+Y1vfCOheREkSLyJcIv/8e/31MNvTFFPvPmMeuNvf1QzZs2Ys4769+2vlvzMF9U6X/qGWvy9hdTXv7J0JzZ45o0YHuYnKzbnz3/9P3XMBU+ov/7jX+ojo5zltzFLqfn+rT4x8Il5QEi6DgiWSix8LtWuAYsAYdYBwA7LoYv1uCsARBeyULUkkF0vggYgQWamVC0yGuEhyqNLWk5uY5eHDkBQJk2t1wg1Tc8BoDRVhVufVD3VdGOT7fFFkgY5pEKQGOM4XzAQcZ5hOMKQxr/z/2THsmlZAITnQb0iTbvuweUdZJT85S9/mRjeYgtXAhGAhDt3va7nKDlS2dl28Cg1KH64c8UCLVmxsOCZNrJWYa1yDUKHu0o2KvFUCN1JvA0S9wClaqeddsrsVlYMiFwo1J7tt99enXzyyUkQN43q4LdNv1fdOv1eNXPWrMJifSjdfVUfNWTJQer7q22lqAbeVkM+xO+g2JKhh7gUXWmfMXOWOvJnj6n//t/31KxZ9sHlH49rlurzqX+oT6z4+Fx0LMZ+3tCT1ILzL5Bc6gpA9IB5wEsVz1IXAYi+PvSUr8wbiioN5dbEUs63Ch0P71ZILVQAwvwA8G0BSBqASorfrCrcUnOkjvkM1WMmsgixDsvkyZOTYPCDDjoo+V21ZQEQzlhSsz/yyCNzBefznX33u99NDHFFVOiqfYr31y+BCEDql3F8gycJVAEgWMn4wRpL0K+rBRoPxW677ZZ4IsiIBaDB2kvwN5xUwIWehheKFfdIGl4JQEckEvfAwY/FCMVa4h7IrY7nIqtlARAOMcnghRuctL5CFXvr/XfUmU9crP7w9/+xnomvLLyEOnSdfdTiCyxqfW/VG/R0yIwP+lw6K9n1k15V1056teqrPrp/tur/pVfVfF96bc7zth0wVG0zYOic/7etJ8ONeM4INifzF8AzPQbbzrOm+DHxnnQhQFeoWoARALJuKc+qzh0qAGkzlartGtKvBxyj1AHubYwxee/Mq8LNuq8jrXOo60XkJ0kA8Ij6THteZU2U3Yt3gvjFESNGJJ6Qqq0IgBAjqQfn852RTIXsW5Llser74/3tSCACkHbkHt/qIIEqAATqC8oYShvehioVfwEyUoiQ/wbMDBw4MEm7u+aaa85ViBAvC5snMRm4jcnmJE3iHp555pkEFOlxD6uvvnquhNIARM/gBZUMShkB7QCQVdb5hjrhobPU3//1DzVr9ixrqVN87zOf+LQaPfiQRkEIXirmDEoH1AToculUxe++/2+195j71YyZVTwfKZH0maU++Y37Vb/5ZqovLfQFdeIGh8/lAQKAAPAAjWVNso8R08J6w/NhSkMqenZoAEQfCzJJW8rTQc18UyF6QCIAyV61WWmdy+pMlH1b+t+Jn8OLU5Ri3eZ5TV9blASg6b6Yvo/sVMT0HX/88bmeetNncV2kYNlIq+dcGwFIz5nLHj8SFwCiV/1GQHgs2giULJocUt4CUlxywKPM4WFBNukMXtCuRj9wunrz3T85gQ/pMyAkSxmvY8HpcRLU20Bpx0KL4k8GM52jftsjv1NX3PGK8gg/lFKz1fxLvqIWX+odddzgH6vFUp4fsgWxfsoACAoRoAlFOl3MsqrcbChYXfCA5I1XqFoSN4LMiPMRkGZSE6WqLH3eHyoAYY1Cc/ThnSuTZ16dCZ2eV1YHJ/0OAAjfBOslxGaaBKBLY/vVr36VUHypZaUnSnHtYwxCd5Vc2PdFABL2/PWq3kvFZtNBixII7QPvAl4GPBUm1BXTd/i4zqW6NrLAUgz/Oa+A3c0v36VuevkuH11MnpGmI3l78EcP0ilXepwE/07sBZQ0PUvPUec/rl7977957sZs9cmF31dnH7RhpseHlMdYb4uUHVJEAgpZZ1xnE9hrMpieAkDSY5WgZkATtBQa3iaJG4HCU5YowkR+dV3TlWJytuOTuDFibpB3U032c/GI0Q+XOSfBCOulzCjQ1Lhs3+MjBsf2nVWvv/rqq9UZZ5yRJEKBDlW1FaXhJRsiqe+lxTS8VaXdnfsjAOnOXMSelEjABoBIoTqsqSiBuLmx8rGZoUB2qdnWlkABRcHl4MZrAkUprZiR7erAu0Ynwee+GgHZ44aeqD49/4K+HjnnOTrlKl2BHg8Igf9kRZI6BTNnzlK7HP9rRRC67zZf/75qwvFDVL++86bzRclkTWXRPXRvG/1EIapjrdkAkBCrREsWLOiE4h3Bci5ULcmq1TVDQgQg1b7EspojeZnUoGqyPkIFIE16oKrN0Md3U4yQmlIkVSmrK5X3TgAnsSQ0MlrhMQaI0KAqk2L3zjvvTLJq7bvvvklGRwohUnQ3FiL0NZPtPicCkHblH99uIQETAMIhxoFE4K/Ou8cqTaBlFylYNrUlxLoO4ODATRcOE3He+er96uoXbi3MdmUh+uTSdEpa2/uzrs+iXKUVSyyEgDQstBKk+ae331f7n/6Qjy5kPuNnhw9WX/jsh5mv9AbNhv6lAQgAlwMUUAggBBjWZa3vDQBEjwHR608ASBg/sq1C26lj4YQKQKiPhEEDgN8VemoWPY85I0heD2Tn30jwQIOCGmJrywNVRVYEgFMAtkoqXNZcXhYrQMbaa6+ddJG6HxdeeGEyz5wB1B+hQGFs4UsgApDw57DXjKAMgHCQogQCQqDwEEMgDcUQBYE6IE3SDEwmxySzkh7QjBLOYVtkXT/uwTPV6++8YfJ6q2v0onxWN2ZcnEe5Sl+KwvnKK6/MBUD+8Kd31SHnPFq1C7n3n3XweuorX1honr8DYml6iljWFuuO9Yn3Jg8U+upsbwMgabkBSMUzotN2RDHl+64L/BXNIfsL36aescfXnNf5HAEgUBxt4y/q7Jf+7KxMavQVeh5rgL0wVADSRQBYNq9UQydj40033WRd86Ps2fHvvUcCEYD0nrnuESOFjpNuejwEFjKUwLQlj0MKRT+dSakLQikLbGbMWItQ2E2KJlLhfK/bR8xVZNDXOOfr21+N32KsIjC9SiuiXKWfi3eBOBm8DsTy0NrygOgAhHVHhiuCYPPicKrIKO/e3g5AdLlk0XZ8ZliymT++Y9ZnaAAEAI11ucsARJ8HKXpJvwGiEi8kABRQUgf10WYt2FwbmvwZ25gxY9SNN96YUKRIDhJblICLBCIAcZFavKc1CWD9RPGTxv+jnGNFyouH4FqJI0gHMrc2EO3FRXEFEssigc8mefr/97231GH3nlzb0M4YMkp9fsHFnZ5vQrlKPzhdLZ6/txUDQhwRYwDkorSR2ACFE2DYlNXdBoCwfkhUAPUwlOZa10GoWuId0alaopzWqZiGCkCQF7RVFMk65VPH+mPOMQrw7fGDkYZGwgJJXtCWR8x0vNBqCaTHOOYjTbfpe6tcN3r06KRCOTEcZPmLLUrARQIRgLhILd7TmgR0ACIF3iTbkJ4hKd3BrDiC1gaRenEWrUdX1G1rSPz33/9HHX3fabUNb8yGI9SXF17C+vmmlKv0g6VaPDEvOq2unixYSi3z5c+oU/dbN3N8UK0As/QJzwx9Eq+MtUAcb4gApFxweoYlFOy0YgogwUvqEzTiYWV9EjwfUgsZgCBnvQK91BzBq4BRiu+0LY+Y6RrAiIEXlUKrXUuskDeGI488Ut13332Kiuj6nmw65nhdlAASiAAkroOgJACQwCKOxQgaD8of8R5llrssGk9XBo5VnTFJ5fO0os4Gb6ModdEDYkO5Ss8LyuS0adMSjjdFH6XVUQeEvFe7fW+A2mLQUvMsD/qBlRs6HxZWKGFtBO3aFCLsTR6Qou9ZV0xRTplL9gzxjOBZrKr8hQpAQrTA63OdV38lzyOmB7J3IeYFQxpeP+ITbfb5Ns+vAw88UD3++OPq+eefb2UPbHPs8d3+JBABiD9Zxic1IAE2axR2rMBLLLFEopCabNp5VvQGulz6CihkACsACDUQsIZBG4Dm4xIw36UYEBfKVR4AAWiSmlFaHZXQ+/fro8Yfs6H69Kfmm6sb+jhQXKE0VVVYSxdGzgU2ACRE5caVgmUqTymGJ1Qt5MlcEscjgMSFChM6AAnJAq/PtWn2Makzw7xL8gIMCHjOmfe26sxgJKCmEwAklLb33nurZ599Vr344otG528o44r9bFYCEYA0K+/4tgoSwKKFxUVqe9go52JFTyuxFbrj7VZiCaALMB7c8SjZ8GqrKLhdyILlSrnKEiweEGSSprdcP+lVde2kV73Nxfc3WkYN22iZuZ4H5QrQiwKD9ZS1BF+7rRYBiD/JC1VLwIhO1dLTvZoYOQAgGER0kOyvp/U9KUQKkC4Nl9gbPM7suVIEkf9nv5W4EX67gFCXWcLoxA8AMJQ2fPjwhPr2zDPPhNLl2M8OSiACkA5OSuxSvgSgC2CBdlHOsdZQo6FrWWpQblECGJOvmIK264BUoVxlzX7e3FGI8MifPab++3/fU7NmfZycwPYb6tu3j/ry5xdUY/f/lurf7+MMXxJnxJqDckXWK5TUCEBsJWx+fd0ekKKeCFULQIKCCkCRdK8AEgBoHhh59dVXE/ARGgAJ0Uumz2FVz5PUHBEwIpkWmWsBJL7jhfT+s6fgBWlzTzH/Oj+8ctiwYcmZ9dhjj9neGq+PEpgjgQhA4mIISgJQr6BQuLSXXnopsaADQrrQOPjeeuuthP+LUoML3ldMQV2V0M8bepJacP55C/SJPH1QrvIACFnO+Em3P//1/9QxFzyh/vbev51ACOBjkU/Pr075z3XU4ot8Knm8XndFTwJAtiAU0zatlVhr+Q5MQHiIymWbAERfW1npXous5BGAtLOr+pa71ByRQHYdhAJIfMQL6ZLiDECZDymd7eabb554iAhEjy1KwFUCEYC4Si7e14oEqgAQitkR0E3K1LYb4yD2g0MO7jlWtxVWWMFrt25++S5108t3eXvmtgOGqm0GDM19nk/KVfolZeAREDLmiqnqjf/3D+vxLvkfn1ZH77aG+txH4AOqFdmuGA+0LyzaYvUm+QHW8Tb52jYAJER6TVcAiL6QUEL5RoWqpVvJhaqFJxMDh54owXoxtnBDiIkKdDH99re/Tbzadcg9K16IvUCPFypLgFI2pcR/cA5QiT6UNmTIkGStT5w4MZQux352UAIRgHRwUmKX8iUARUIKT9nKiYMKZYHg9TYbNDLABwcZlCsCIjmEBg4c6LVbM2bNVKMfOF29+e6f1KzZbl4jOkTRwS8t9AV14gaHq/59+2X20TflKv0SChGiYBSBR+hYNz/wurrxgdfUzJmzVREhi2xX/fr1Udtt8HW1zQZLz6FdydxIamcUDb1FAOJ1iWY+rIsAJN3RLCs512AdRzEromrVL0G7N/DtYoVvE1Tb9Xjuq6dPn554tetOB5uX2pnYPQlkd6k5EsJ6T8/PoEGDEi8wxQhjixJwlUAEIK6Si/e1IoEqAASuMJlOSOfaRuMA47DhsAcIAT6wnkk17RVXXNF7t956/x114kNnqb/96x9OIATw8ZlPLKSOG/xjtdgCH6fAlY7WRblKC8LGe0V2rAee/qN69Ln/Ub//f++qD2Z8DL7m699XffU/FlLrrbKE+s4aX5qT7Yq5oWAfQFCfm3Q/yFCGV6HNwn7RA+L9M6n0QKFqQc/DqMBaEqoWawkQ21RAs8tAQgYgyBrDEoYJqJJNNr5DiRvhN+uAedYD2U1okuwpeF2JMQuhIfM111wz+ZkwYUIIXY597KgEIgDp6MTEbmVLoAoAIVsKMRYo/k03ofXg7ZBsTkLrIQMKygsAxCTbjm3fASFnPnGx+sPf/8f2VvWVhZdQh66zj1o8A3zUSblKdxQrJzVfbKvuzpw1W7311/9T//5gppp/vn5JjEe/vvg/Pm6sKShXKBFYUokzyZsHlAXiKpZffnlrWfq6gf5igTdRaiMFy5fUy58jVCCMHKwl6Fp89zRAiFjJu1B7Qh9NiFmYdAMIMSDsC00XBNVlKDVHBJCk5525z4vvw6vKN03a9RAa+z4ekMGDB6tLLrkkhC7HPnZUAhGAdHRiYreyJYDVic3apVGwCotU05YmyaQk6YPTtB442FCyoGCZWMxcxg4d67bp96pbp9+rZs6apYoISn1Un4R2te6iq6k9B+2cSbuqm3KVHmNd9DkUBsAHCgQKABbrooYHi/kKBYCEWGQuREoKayaLCgRQlLgRkhfQUEQlbqSt2hNpAML3HFIWpjQAgVa70EILuWyNtdwjNUfYXzA6sb9IzREJZBcjB2nYaW155m0FwP638cYbq0022USde+65trfH66ME5kggApC4GIKSQBUAQpAobvKmgv14l1Rs1zMppQUOQEEJJgjdxKpdZcLIjvXwG0+qJ958Wv3hb39UH8z6GMzN17e/+spnvqjW+dLqauWFllV//fM783hldMoVMRkc/HWBJn2cWDkBbtRx8dFQCIT6xnMBHyaWaWhaWIwHDBjgoxtOz7DxgEQA4iRip5vKYhH4dsRCDihxpew4da7gphDTwMpwOA+g1rIvlBkPfMvN9HlC0RMgmq45wv7Pvu9rbzPtl+t1eIHJgrXVVlupsWPHuj4m3hcloCIAiYsgKAlUASAo+Vgkm0h3qNOTyiq2oyRS1AmruokS7GvCCEx/6/231b9nfqDm7zefWnyBzyaeDxoWUSxzulemScpVeow+43dYQ4wNehLZc+CPm1LfACAobL4zltnMKfOAhRXreVmLAKRMQv7+XgZA9DdJ7QlRSplP1iDB6+IdaWovCDENrMgSMA61tssAJD3vZFATip5kU8OIg0GHua+z5oiP1Y68d9hhB7XjjjuqE044wccj4zN6qQQiAOmlEx/qsKsAEJTOuovIoVigvOP54CDBsl6mKKKEQA8jsNlXHZCq8yu0MPHKNE25SvffV/wOdAiAKIqLS9FHgtRR2NoCIFhLoevplA6UljwqD2uL+CLoNXV716quObk/RApW1WBooewwX6xRWtXsSqbzwXoGqH7ta18zvaUz12FQ+t3vfpfQl6A2hdbYh/DM0zjbWEckJpFAdryzTXiYbeRGUdjddttN7bHHHuqoo46yuTVeGyUwlwQiAIkLIigJ4M7m0HFpdadQ1S3r1I4gMNLk8IAbjoJNWsMysOIybpd78A5wMAKKsPoDSJqkXGUBECzCroGaOjBEsSMOyAXsoawhD98pk8vmiP5DfcD7AtcdxQRFFUsq606y70jWJVl3AkDw+lWtV1DWR19/740ARJddUXal9Pz6kDmgmnUSIgABuGG8wZiQjq3zIZsmnkH/6TveWPmm+a4556TmiACSprxiReOeOnWq2nfffdV+++2nDj744CZEFN/RQyUQAUgPndieOqwqAKTOAGJABJZ1lAescTYpITl0oBhhpYaC0YUmtDCUdKx0UBzqzrNfNG6snCjZLgkEfMattAFAUERYW6wx6Hxk+2FOkIdO5UGJ1JUW4cSz7iMAqferEg+I72xMenal9PxKVq2qwBJAzdpqKjbO50xAYYK+imGiK3un7fjY2/hWyb4njXnXExikvWLMfVsJDB599NEEeBx22GFqn332sR1uvD5KYI4EIgCJiyEoCVQBIHXw9zkoUEpR8jgAOQhtLevQwgiyxgLZBRqBbm1nLF/96lcTOkibDSsh1kD6YtNQUPDkYCkFRFWtllxnzZascUmWLv7G2mJ9FAWhM04UVX5YV9LwyAFcbNemjax9XRuKB+T4449Xt99+ezJswCAy3mCDDdRBBx00lzK8//77qyeffFL9/Oc/VyuvvPJcYpJnHHDAAWr33Xef87cHHnhAHX744eqpp56a63qZX8muxB/5NiVuxKUQXsgAhDXO941hoiveY9vvAO833yYekLyGYQuQKDFD6QQGTdaamTRpkhoxYoQaOXJkQsWKLUrAVQIRgLhKLt7XigSkGq3Ly31br1EE4eNzKNgGM+v9R6kggBULZNuZXHRvAX3Ect4FagNWTubexkor6Y+xEPtSUKRmy0orreSyBI3vKcrSZZoFi+voL/Q5KZAnqUCL4kaMO1nThSEBEOQ7evToxFp91113qfHjxycZgo4++uhEOtDmCNjdcsstE0A4atSoeQDIvffemwDDW265ZU4tizwAot8sVC32H5RTvl2JH2B+MYiYUEBDkXfWcpOYri4YSVw/B7zfeJcXW2wxo0eI1zNdayadwMA0sYbRS7WLAN0A55/85Cdq++23t709Xh8lMEcCEYDExRCUBKoAEJ8F/zj0JRgY/nGVHPQoL1T6Rklus5iWZLmiPxyGcMO7QgvDyonCZcJTRxFDqcJbUZT+2GXh+1xDRdZOAscBUMwDtB5dmUAOgFaToHKUFJIvoKDplI6iuBEXufi8JxSFGCUMquIZZ5yRKP94MakMPXnyZHX33XcnIrn44ouTGAWoKj/4wQ8SkKJThXiGpOFef/3153DqTQCILnOhaomFPE3Fw3OWR9UKrRK3Pm6AF/swhokQvHtZ34kUsHT1zjLXkt4ZebAWiBXRA9l9gpHrr79enXbaacm6B2zHFiXgKoEIQFwlF+9rRQJVAIiPgn+8H8oCyjkbPOCjamAgyuBLL72UUGxsYkd8TQBjQjYovRziACH+DYWqKx4QyVxVBkCk4jxgCsUdWozPw1fSE9dVtV4oYygVebFELgAEuck6LYsbKVJWfa25oueECECkHgXK2X333aegqSBnPB/QVagcPXz4cDVs2LDk36QJiEGRwzty0003JWmhbQFIGozoWbWEigdFSeJG9FSvIQMQAdj6+m5ijfp8h0365rL3Ss0RASR4QfGC4cUWQFI1ZujKK69U55xzjrrgggvUhhtuWNal+PcogVwJRAASF0dQEqgCQCSzE0XkXDZhDnWsbViZUBKgXflQbjk0SG2IwulqBXOdRBQnsoOls1yhCGOZ60pcCnJH/kU1XLBGcx0HLmCuDupYVn0UV9mn7xPKmGT7yuO0VwUg6fdmxY3wbmg8UpfA1xhNnhMqAMHrcfrpp6u1115bjRkzRj3xxBPq2GOPVXfeeWey31x11VUJMCEWJA1AsCaT1pTvDUpXFQCSljFKqCik/JZUrxI3wrpjTblmmDOZ07quCTHLWxosss9ynvg2PjHPUnOEeU8DUQCJS8zQJZdcoi666CJ12WWXqXXXXbeuqY3P7QUSiACkF0xyTxuiFG+yHZdkdnKptyHKIbQX38otB8W0adOSIGks9k01nXKVznLVpbgU5FFUw0X3SqFU4ZVyAZgmcvfhRUu/h/6jdBOjBAUPIFpEr7IBILYUFVFWJa5A6BwCRprIvBMSAAFc4E1gTvBa4ekAdPAdEweCQeHII49MphxK4Pe+9z117bXXzoll0mlcTz/9tPrRj36krr766iSwOisI3WSNFl2DsYO4CaFqMd801htZmFBKTah9Vfvh6/4QC23qYxfqnu/saVnyZa4lkF1ihphrAaKmNUfOPfdcdcUVV6jrrrtOrbrqqr6mMj6nF0ogApBeOOmhD9kVgGAFIuXhsssua5zViQMCigLcfxPl0FW2ABAOIdNARNf3cF8W5Sqd5UriUogdqBLfUqWf+r3Qwzg0qZWiNw5V6FnM7ec///lEifLhlcrrt/D1pUBj1fHp/Zf5L+s/a5JvwERRtAUgaeVIz7zTVNxISAAEOqYEnKMMQznD/LMAACAASURBVF9EoWOdbLrppknGsnT8DpmDyJRF0wEI/096U8DzFltskQlAZs+aqWa9+xc1e8YHqk//+VTfhRZTffr2c1qG4k3GA0o/WVc0YlSEqoU3rmw9Or3c0014tdmf2RdMAu49vdbbY9qq5J6X3jkdyJ410LFjxybg49Zbb1XLL7+8N1nEB/U+CUQA0vvmPPgRY51nA7VttvU28BBA6UHZo/4C1sy6DmNiQKB06bngbcdncn0e5Sp9Lwfjyy+/3FpcSro/WUUkJUUtawGvVBMZxKrS+PRxodxLFWRJsWsyh00BEL0vTcaNhARAJAg9rUhec801ih8oWXojHS/UlYkTJyZAIw1AiLvaeeed1S677KLg2pOGd9Y//6H++eIj6l+/naJmvPWGUjM/9FokrV9/1X/xJdUnll1LfXLgINX3k582WUJzXYN3kQYNSDIrpYOZJatWXfufdac/ugGgx3rBqNS1vpmMqSuV3CVmiPln7mmSMQ8wCjAR+Z544okJ+Pj1r3/tjbaHYfDkk09WFDnkXZtttlkCwENNrWwy9/EapSIAiasgOAm4AhDTuIa0hwDlsO6NEGUf2gZW/LpaEeUq/U4JjIfO1GYBQukXVk6Uf+hzRSlq65KdPNcHAKH/eNQYk9SOsUlkAADhGzCx+EqaUt9ZguqMG+kJAAQQAT/+wAMPnGtJouANGTJEnXLKKUnNkDQA4WJiQIgVYb968GfHqvefvE2pWTPxXRYs7z5K9e2nFlhzC7XANzdTffr1N/4UBIBA/ZOmBzND12I/YL1JIHNXqFpQIvFChQpAuljJXWqOSNwQ/0+DGsi8YzR57LHHFAUJi2qXmC5A9lSSMEADpro6eyMxVGSFSwN402fG68KQQAQgYcxT7KUmAVcAInENRbQiNltJgYrHA8+HiaJXdYJIw0sQIjQc382EcpV+p8SltBEYnzV+qWIP1QKvFJbnKrVXXGUscURQD2xAg67YofBJil2szrbrywWA1FknwXfcSIgARLdko6CR8eryyy9XZEtLt0MOOST5p7POOisTgODt227bbdW/P/hA3bmbfb2Zfot9WX1my0NUv4XM6kpI4gYUwKwmwcwSNyIUWJ2u01YKXJJCEDsFAAmxdb2Su3g+kTOFMgEHNPasTTbZRG288cZJ3FMVIxWpqs8///wke5zEQN52222JB+SOO+5IUsHH1jMlEAFIz5zXHj0qDnvhK9sMVGhFefU2xFrMdemgbJv3uFxLJhRoDgAen82UcpUHQJoOjM8bO0ophx9xD8wPnpk2aqZUSWSgV2UH2Lke2l0DIPqcieVclFWXuJFQAIg+bp9Umpnv/kX99bqT1Kz3/67U7A/jMqxan76q7wILq0V2ONYIhBBDBR3MdO/JqjuRR9ex6rfDxewJ/KRjwxwe1cotIRVSZP+aMmVKUtsGyrA09uRvfOMbCUDBu2fbdt111yTOkLS+0jAWrrHGGgqw/sMf/tD2kfH6QCQQAUggExW7+bEEXAGIpLtN04qE0oPiA9WKv6eDsuuWP9xvLIo6DaLqO20oV1nvajIwvmiszA9WZZR/5gcA2ZbFFcWawnK2mdSgGWBp9lGVvcsARJ9HPW4EOgdKBTxysu1I5p2sbGW9GYDMnjlDvXPN8Wrm2390Ax8yAX36qn6f/aJadMfjS+lYtgCkDHAKVYs5Zq5NkiW47nWAD6zzoVrJqySJcJVZ1ftIF43Hnpo1pIvmhwxua665ZpIdy7ZBVdxuu+0Sj4feiANZbbXVkorrsfVMCUQA0jPntUePyhWAZKW7xZqOYohiCaWHGAxbSowPYb/++uuJUg34qdoYJ4cyVA4pLOgCqKhNgjx88Hxdx4SyDSUOrjdt4MCBrcyP9N82k5qeIthXFjUbAAIIBbzVScEynVuJG0GGWH5pWfVGQgQgvrj8702+Rb0/+WZTkZZet8Da26gF196q8DrWB/tEVfqnAE4JZEcmNCmCByBxoS0WdZ7UxuwNoQKQEAsp7rTTTkncDd4QaRiHMCZgRLNt0BTJ/rbPPvvMdSvvISvkeeedZ/vIeH0gEogAJJCJit38WAKuAIQn6FZ9QAfgg4MTxb/NdLNkAcFSiHW/SvMZw4KbnQOgzsD4orHqlCWUdw45AEibTQAIlI+yxAR6il3fhStNg9AFgLCuyvrbpFyL4kYYG984gfOhNB8AhGxXfxn/Y6VmaVmuqgqgb3+12F5nF2bH+v3vf5+sDdaoz8YcChVPz6wk3i8fNWWI/2BfoIBjiE0KKQKg6vQU+ZTN1ltvnXyfDz30kJfH5gGQHXfcMckKOW7cOC/viQ/pngQiAOnenMQelUgA5UUyc9gKS5RqQMef//znJKsH4MO3Zc62XygBNCzVrk2UTV8xLGTmIhDft2JiMj4p/CiUJRR/skZlBfWaPM/XNShSeKvKAAjXQW3BW+E7RbCLB6RrAESfj6y4Ef4O6BQaTxteSZs14wOAvP/0Xeq9R64tyXZl0yuu7aMWXH9HtcA3Nsm9sS4Aor9Qz6yE0s2co3DrWbVc5pg9nOeFCkBCrGMydOjQ5Lu8++67bRdj5vWRguVFjEE+JAKQIKetd3e6CgBBqYaHjgWnicJ1pjMFDYJD2uUg1SlXUK1QeF0oV+m+Tp8+PVECq1IzTGXAdSgmUHCgVpAVjCB4FBW43lCxACBt5vsvqyWjz4XEq/gGtz0NgOjrA/mRJUwKLZrGjdissTquFQDCt+dCQ6FP71x7oprxp9e9d6//F5ZWi35/dO5ziWmCJtWUpzMvNsikCF56EFCBAPshecv0MYRYx4T00RjtbrnlFi9rNQahexFjkA+JACTIaevdnXYFIFLDAUsbngasb11pUMFQYpZeemmrLvmkXKVfXFdmrrwBMn68BnhyAD2kZBSwQUwLimlXAAjzhNKmNz1ehb4zBherbtkCsAEgXU/zmTVWPF30m2+0KG6E75fYhTYBqfS/qpypcP7WBfvOXWSwbCGY/r3ffGrxH12k+vTtm3kHAARZ1l0Etei7l7gRiQ3CgCLV2PnvvDkOMV5Il4PUMSGpRShtnXXWUSuvvHJSaNNHI6sWGbBIw4vHnUahzkMPPTSm4fUh4A4/IwKQDk9O7Fq2BGwBCAobSg1WdBRCrPo+gr19zg/KNYevTTpJ35Sr9HjIzIWSnVcfwOf44XFLPQKsyGnlnoOav7cdhI7MkUsagKAo48VCEUVecpD6lJE8qzcAEOSZjoeSuBEpkIYlHe+SxBTo1ZrrkHvRM6sCkJl/+1/19uVH1tbtz/5grOr3mewip8SfIcO2AIg+aAwqMr/8FqqWzDH7gg7q2dez1kptgvT8YAwreHtt9n3PXbB6HN8gAORb3/qWuvTSS63uzbtYChGSAZJChMjj1FNPTeqLxEKEXkTc2YdEANLZqYkdy5MAhxQboUlDMcCqzm8s0iiyWNS6BkDIWAWX2aSgVl2Uq7Q8X3vttdplpWeJQslgXrJSs0JVYB5XWGGFVoM1WUd4hqDKiQcN8CSpTJuIteitAERfn1mVun3EFJjsKVnXCABxnf8Zf3lTvfPLka6vL71v0V1+ovov9nGlc/0GYpowyrSZ7S5rAOwNGGXEOwJtVtI4i3eEGBDOAowWIbbQsnhxRkHB2mijjZLigb4aIPjkk09WU6dOTRIiUBmdtLxdSpzha6zxOR9LIAKQuBqCk4ApAJFUtCi0ws2GboAFrWq2Kd9Cw5KHkk2F7aJWJ+Uq/V4UEyzMdR3uNlmihD43YMCATIDiez7ynicABM45ShAcdJQgMqgBnprIZGMDQHzEJjQlW3mPrVU7K/1rWlHNArU+x1U121ibHpCuApD0/GTR8djL+eYoolhE1fI51z6fRRYv9jZb6q3PPtg8i/4ShE6NjjPPPNPm1nhtlMA8EogAJC6K4CRQBkD06t9Um+ZwEsUQmgwKXNeCFlFkoYihYOe1uilX6ff6Sg2cNR6smngNUB5NskRJBXIAmu+gbpsPACWI4Hz6DMDFIth0MgMXAAI4StPabMbd5LW2ACRLUU3HFEi9kbriRqoCkDZjQPB0sk+ScjuUJlQtwL9kRARkimcEOl4d8Ve+5RNaFi/27G222UZtv/32sUCg78XQC58XAUgvnPTQh4wChjs+q+GyZ5PMS0XL37i3axYnDiKsS1CM0q0pylX6vT5SA2eNBdoBSiYKMYq8CaAQAGJbgdz3WhcAIoC2jfoxEYCYzyrKqdSiAJTUFTfio+BjW1mwiGkiaQI/oTXiwvB2AaAkdkSoWnqK37o9YK5yCy2IHuPLzjvvrIYPH65GjRrlOux4X5RAIoEIQOJCCE4CWQAExQIPAoptUSpagr1RFrpWOVeU8nSdiyYpV+mF4NtbxFhQGAAT8M2pL2KawQglEvocMTI+Ugy7LnqAIkoD2ZfwovG76RYBiJvEs+JGsJJLgDMKq6vVXAwfVSrOt1UHJGQAIrFXeLlpnAMYCcQDxl5Pq9sD5rYiVXJehRRE/+yzz6o999wzqVp+2GGHuQ473hclEAFIXANhSiANQPB2ACxQbKERoNjmKRI2wd5NSierzkXTlKv0eH16i1DQpNYJXgOCXm2aTQVym+eaXsuaY+1Au6LhuaFOSRvNBoBgDYZK15soWCZz4jtuxAcAqa8S+jmq7yfzU46TVAGDQJ2Z20zmxOUa9hSMAHm1irIyp+EN0bNqmRpBXPpXdg97SkhB9JMnT1b777+/Ovjgg5OMVbFFCVSRQPSAVJFevLcVCegARI8lII1fmWKL9ZpMWGXB3k0PTOpckGaWA1EC6H0WFrQdE94Kgq6reIvS9DGC/128BqYVyG3HaHK9nmIXRQelITQAwrfRpbo3RXJvwyqsW82lFoWN1VwASFWv2HuTb1HvT77ZZFkaXbPA2tuoBdfeqvBaAAgpeKExhdZsqrhzbjBPQslD8cdQBRVUAEkTSSR0GVNclX51LStj3jp44IEHkuxURx11lNpjjz1CWy6xvx2TQAQgHZuQ2J1yCaDUohhDhyF42yaWgOuhO2XFWpS/ub4rJM0sMQ6AJP4fiyTUAldaSNXecjii+LvmqNcL81UdS1kF8qpjzbtfr08i4Omll16KAKQugav2aSkS4CyKqkncCN8JgL0qAJk9c4Z655rj1cy3/6jU7FnuUu7TV/X77BfVojser/r061/4HHj9JFIIEYC4VnGXM0SoWpwnNILXJZCd2LS6vSNS+6iJWkvui+njO++6664k9uOEE05QO+64o49Hxmf0YglEANKLJz/UoXNYkDqSQ982AxGB3oAQPA1dapJmlkMPBYgDqW2FoApdjTmCHoFlGet71bHkFQCsaw5RUATg6vVJAFUvvvhiYrGsOibXvrtQsKIHxE3apnEjvgAIvZz57l/UX687Sc16/+9uIKRPX9V3gc+oRXYYpfotVJzZinWOBwTaaluUQreZ+fAuX0UU8YYI4GQuTUBnlX7LvekYFh/PrPMZN998c5L96rTTTlNbb711na+Kz+4FEogApBdMck8botRiQEm3pZVkxVq0LR8OO2gneGagXGFpbzPQWuRBnwBGeGVsGt4bYnKkhoiPYlJZBQBt+mRzrR4snwa4AkBQ6NvizNsAEBQrwDrfCkAqhNYGBctELkVxI6x1qJ0UqDTJ6lb2PkDI3249S838y3+XXTrP3/st9mX1mS0PKQUf3CgABGphGX3VuiMN3AAAoQaPzyKKeaBTz6rli6pVFsPSgAitXnH11VerM844Q40bN05997vftbo3XhwlkJZABCBxTQQnAXGfu7jHURJwe+MBaYvapAtcz3LFv5MeuCv1GmzjZTi4uQcghTUVpdfXQS3pb6G41KlIS7C88LJRbvTG2ps2bVri1YkApJ6to6sAJD3arLgRDAesT36IdXLZo+Q90LHef2qiev/J25SaNRO4UCDwPkr17acWWHMLtcA3NyulXcmDWOdkwQoVgNRdw0TOGvGO6FQtiRtxiWkT+dvEsNTztdk99dJLL1U/+9nP1Pjx49X6669vd3O8OkogJYEIQOKSCFICchDYdl5iLYgB8aUc2/ZBrtezXGHBgxpGwDc85C40m3gZlDHoBIwJZYaaAlWUr/T4yeb0yiuvKNKcpkGBL1kR+E/ci3ih8hSLF154IQFXbdVNiB4QXzPu7zl4CqW2DSBWKDx6YTzX74HsWP986VH1r+mT1Yy3/qDUTK0GUr/5VP/Fv6I+sdza6pMrDCrMdpU1WgwgKPHEmtX1XfmT8rxPou8YApr6FtmH9CKXzDP7hMwz3l6befZFIatTxvqzzz//fPWLX/xCXXXVVWqNNdZo6rXxPT1UAhGA9NCJ7enDQuFl87dtXaionVVYkH/DEtklDwgxEPyUxcvogdpkh6rDgwOV6OWXX07oab6pIij0KI/Q84jrAFwUeccAIChsbVWOjgDE9quv/3qUUmiHULAwbEi2JSznKPmsJ1FSq9QbmT1rlpr17ltq9owPVJ/+86m+Cy2u+vTt6zzA0AEIeybfYRveSKFqiXdE5llP8Vtm5IIeyX7mk0LmvBgMbjzzzDMT8HHTTTepdM0qg9vjJVECc0kgApC4IIKUgCsAkXoSbRW00ylXWO3wFqCcNEUxspnsvOKI8gxAE14SQIoeqG3zDtNrkVsd2ad0zw2gAkWmzIIJBYt5CwGAhKhghkLB0tcuSijeMwwIetVtncLDNaw31hdeTqFqtVmlO8QYIV3uXalhkhUfRD8xxuhZtdL7Xd0UMtP91fS6U045JQEfZMMCbMcWJVBFAhGAVJFevLc1CbgCkLbSuSIoqRXAoU8MgZ51pgmKke1kScD+SiutNM+tjAHKFYCODDpY8MoUd9v369cLAPGZfQqFkDEAAG08N2TBIji9Lasl9EOohCg2ZQHPEYBUWXXm9+YBkPQT6o4bMe/xh1dKocqQsqTpBhAASBdTCCNXPasWfYaqJd4RoWqFVoV+9OjR6o477lAPPvhgbvFH2zUYr++9EogApPfOfdAjdwUgTadzRchZlKt0lqu6LPxVJlmKI+Jq18GFXvwRxb3OoHDpv8/sU8wH6ZgJmHfx3ABAKNzGT9NNQBPrhVYW9BwBSDMzJACEGK4y2o30KK/eiI+4EdNR9wQA0vUUwswzqX0FkLCXsUaYZ2KH2EfaoJCZrhH9uiOOOELdf//9asqUKUGmbXYZc7ynPglEAFKfbOOTa5QABycbuW1rmuqUR7lK97sL9SXSfUpnDENxF1qWTfFH2znKul6yT1UN/tZT7HLwYz219dxABcP70SQA0UETigvvZy3rSg3ekLTyKlmOQgoyDpGCRRwUdXNsAIi+zvUq3b7jRoq+P9YQxfzwLNYRu+Xj2897RogZvISqxRzzwzlG06uxl3k165Rp2bMPPPBA9fjjj6vnn38+8ejEFiVQRQIRgFSRXry3NQm4ApA6g5nTwiiiXKWv9aVg+5wQyRg2YMCAREknfTGKFsovVkdbxb1q36oGf+tZx1C4XIPZCYYnfgfw0kQDNBHgjLVUgA/rWOTP2hELqyivYmEFkKAYh5RmtTcCEH0dNRk3IgAET2ZXsu+ZflMhevfSoBMKFtnH+J45L2h4NcWQwH83vc8WyX/vvfdWzz33XJKKvEv9Ml0z8bpuSSACkG7NR+yNoQRcAUgTngYTylXWMNsObk73Saqzk/qWAFsO/CqKu+HU5l5WRT54c1DiOdBRtqoUegSAQJkAhNXdpKI8613ihiQLFgpAWgnIC4ZlvPQZxcaUIlT32PKe39sBSFouEk8AuNSVVB/1Rlhf1KIIEYCEHkCfBlBCyYPiyo9QtfSsWm3Xrho+fLiieOLTTz/d1vYQ39uDJBABSA+azN40FFcAUrenwZRylTVXbcYWZPVHUhaj5JbVxmhi7bkEf9um2DUZB/VISNdbNwBB/gTJkyWJ9MNSUb4IgKT7L14faB1C99AzMHWR7hEiAJE6IMsss0ytBU59x43I+tDXl8k30IVrQo5fQX5F/eecklTOgBGuZR/WqVptZE/bfvvtE+rYo48+2oUlEPsQuAQiAAl8Antr97F+SSCurQyqWNKL3mVDucp6TtPUnqKxoORi6eKwIVsX1ve2rW+2sRcc2ozBd3FEAAgygdZUR0P5ILUxKY6hZ+B10r0WNgCEZ5EpCLCE90O451C2aGVB7HWMr+yZEYCUSejDv+txIyip7Im29UYEgODlrOIVNOux36tCjl9BEqb95xvWs2r59oLZzMrmm2+eGEQmTZpkc1u8NkogUwIRgMSFEaQEqgAQW0W2TECulKv0c+tWbMvGIX8X2o9kGsOqK9Z302fUcZ0NQJNsUVgNse76DLCdPn16Ej9SBwDRg+SJMSHQPU2zcgUgetrnLLoH3hCh9dhWdPY53xGA2EuzLG4kL2WzGE1CBCAh08eYYdf+614wDAnsB4ACPQFFXcaijTfeOIlDmzhxov0ijXdECaQkEAFIXBJBSqAKAPFJoalCuUoLHks1CiAZi9pqBJ4TK4EyigIMBYjMPl0IUDWZt3S2KLjtvqkKdc2TaZC8WL5ZI3hGipQN3QOiAxB9fWVlYJIgdtYj4K0uhSZrnYcIQPhuSOtMgdMuBOeaxo2gwJJcYqmllgouq1HI9DHWvQ/vkySgwAOG0UWSUwBG5Mfn/jdo0KBkjd9www1tHVHxvT1IAhGA9KDJ7E1DQfFns3VpWLChtlRV9KtSrtJ9JyMKij50p6abHiuBokq6W5QY+kTFWw6ztluZ5yGdLaquTF3IBKUcGflqxBCgCAL88Nhk0WFQNvgRD4iehhqAID96n0wASPp6FCM9TSgKtVhXmwhijwDE16r68Dl5FnPAJcopNXFCBCAhe2+YF9/gj29dL3TJd0zDmymB7KTOdQXI7DdrrbWWWnPNNdWECRP8LtL4tF4pgQhAeuW0hz/oKgCkqgLpi3KVnoXXX389sULC+W+ycWjh6UjHSkARwNqPctJEscGyMRd5Hky9B2XvMPl71fWTVviJ9SDmA1oX4DMrS5UADwEdKBH8G98B/ybghGfrQIT/Bri5VIsWhUbACOuBVnfNgghATFah2zVZcSM8CWDJ+mNuu54lTUbuW4F3k6j7XXgt8DZj4KkjGQQGOsmoxW++Z6kVxHwz1zZghD0WD8jgwYPVJZdc4j7weGeUwEcSiAAkLoUgJVAFgFRR9H1SrtKC/93vfpcc/ljAm2pkWsLyjqIKXUmPlcADAu0Jfjgeo7YbAISDM+150Gljed4Dn31/7bXXEqtiVU8Vawngh4KPtwZudZZCIECD3wIw0uNBscwCIzyPdeWj2jLrQageEsTuy7qqjydEAEKaZ4Dkcsst53Op1fos1hP9Bvxi+MAQ0QTA9DWouhV4X/3Mew7fPenNl156ae800az9Qc+qJQkLdGNCGfB855131JAhQ9Qmm2yizj333LrFE5/fCyQQAUgvmOSeOMQqAITKvyjctoq+b8pVel7Ix09D4a+7oXyI5R3vBl6XNFe4yaKNJuNNU9QYA8oq1dmbzNQFAIEiVcVThTWR+QY48Jw8gKd7OVizJhZLnik/ULtQMAE3vEOeUTWmIy8drI8g9ghATL4GP9foSjBrjf9Hse9yljQZeZMKvB9pz/0UjD8UCa07dXO675KwQIwJ4tmE/itUy6wq5/R1iy22UFtvvbU67bTT6hBJfGYvk0AEIL1swnvKcFGwpK6B7ZhQ/NiEoRaZtLooV+l3kzIWxQ6XfJ0NYIHlnQOoyPJOX8gYhmckL4C5zn6mn60r/sw9Y0BRIpaHyuQmyrmP/lbxoPF+PDZ4nYpqq+jxHqx1U/Ah45NUvoAzgAcAhCZeFP47L27EVkb0Tyqxs6ZYN1hTBYyg2NgAnghAbGfA/XpRgkk0oVvAi+JGmFfmtKnvLW90AkDSfXeXRrN3diVxAeeBDjyFqiVxIzLX7Hs77LCD2nHHHdUJJ5zgTVjUFLnpppvUs88+m+zpu+yyixo9evQ8z2fPx/Ny8803K9btKqusokaOHKkGDBjgrS/xQc1KIAKQZuUd3+ZJAlUACJscm66Jol8n5SotCpRSaBC45OtqKIiMn4blvSi2Q6rGQzWiinbbjQMQDjNgQ8YAOGo6QB5KE94i3m3TTD02WfEeNsqeBOMz18R+CDiTWBGdqiX99wVG6Hs6iF2vTWESYxAiAIGeQjA3GYJCankARB+DZFoSJTVdb8RkTuuQSVseBF9j6SJtT4wJ4h1hr3jkkUeSuh/QC++55x610047qaOPPtqXGNSYMWPUww8/rFZddVX161//OvGyZAGQE088Uf3qV79SRx11VEJ/HT9+fGIgu+222xKKaWzhSSACkPDmLPb4oyJcrh4QAv9QkrCcFbW6KVfpd5v2y2UBoERgDUe5Q2FAeS4LfOQeijYSc4ES23YDgHBAMnemY6ijzy6xOrrXifohiy22mHO8R9GYkA1AFjkxb2UAsyiI3cZrkdWnsiB2ycKUvjcCkDpWbfYzbSu4C31HwEhTiQmyem/b9+akavamroNWmetbb71VnXPOOUndEhoekU033VR95zvfUeutt15lA5B4eHn2hhtuqDbYYIN5AAgprnkfHg88JDTW4EYbbaSGDRumDj/8cDOhx6s6JYEIQDo1HbEzphIQ5cb0ev06uKxsXnnWyqYoV+m+l/XLZazcoxe3g4pjk562rqrxtmNhDASho8jbjsH2XWXX28YQAWSh13HQAvzyQIFLvIfeVxQyglolk1oWjztvbEVB7L7iRrJqU0gQOzKR/kYAUrYC/f29Kg1Ir9CdjhvBMwnN0MZ7ZzOyqn23eVcd17799tsKEFJmCKvj3bbPxLBx5ZVXJj/sB5yfNIxY66+/vjr11FO90HTzAMiNN96ojjnmGDV58mS1yCKLzOk+npgXXngh8YLEFp4EIgAJb85ijz/iskvWFluBoOBweC2//PLz3Nok5Sr9cvqFEukzk44ov4wLTGTsLwAAIABJREFUyhWpNm3aiy++mNB4JIbA5l5f10pldn6jsBK02WazSRaAggEooN+AjyxQ4DveA89HFe+FHsQutC3kjSLpC4xkBbEjG4AI8wzQbCIZg691hDLJT9tr03Y8PpV4mVNJ/SoVuiWWwDbta9lYukhhKuuz/ncoe+z3dVJubfpTdi00rBEjRiReiDXWWEPdd9996v7770+y7EGN8vG95gGQsWPHqltuuUURL6I3aFhnn322eu655yrteWVjj3+vRwIRgNQj1/jUmiVQxQNC9ieUhXTwWtOUq7SIcDOjsPoIqkt7ccj4ZWMRl77BsQV8tMWx1VPsEneBEmyaPKCuJWiSxAD549FinWGxywMFPuM9mKM8aperLPLiRnyCET2IHcuq1Doh8YFUYq/Liu4ql/R9oQKQupT4JuJGuk5hKltbZKdjvZvEIpY9q4m/42Ug+PyUU05R22233ZxXMte+vs88ADJq1Cj11FNPqbvuumuuoV5//fWKv02dOrUTtaqamIee9I7/z955gOtVVHt/nZJIE0RCwMsFlR4IJQgkIL1JD03pSKSYAKH3EiQiXBBC74HQJApCIIBEigiREJpcSkIIXcyHQHIRBERJ+Z7f6IqTnXfvd5fZ7X1nnuc8h3B2mVkze2b911r/tTwAaaXZbKOxZAEgWJ4AIauttpqRGM9CgcADQcgAlupGlajzFi8HEn3r06dPplehwGF1R7mAuwHnIK1FfMqUKYaATthWkY05AZAhDy3Qx5jiJg/Is68aThUGhDRDF4AW2Ydl6LJDrlShT9JvPAXwPZAJpMy8i0UWRWKHC8XYkEmQ8ExYT9q1nES2Sa+tMwBh38mTPJ8Xb6SuMte1xRlE2FrZBpW4ax1ln/S7I0aMkB122CH0NpIDMLZmrZFHOAqAADIeeOCBeR57++23yxlnnOEBSDNhV/TvHoBUdGJ8t5pLQElxza+c9woOLpTZ1Vdf3VhbUXhwhWdV1pP2I3g9yjZKN/1K2zRcifA0lFI7XjbNMylEiCUaRbqoFkbYLiJLWJwxRmVRQ6EAoNCiMnS54HvgYSEGm9C6NN6tOGONusbmjajXguuzZtRSDgjyYx3bldgBJXbxtGDtmqxjSnt/neL57TGW4UVwxRshqQZGljpwKBqtq7pxneB/QEa/6qqrDFk8rJFSN06WLMK2gsY2H4KVdgeq530egNRz3nyvReZm5UgqDA5dQAeub36rBbnsWhc2MErj0rbDlVDe4B1kbRC/saxTa6OIFqXAA0AAWGUrHAAQlKhg7DbzByhA7oS8Ncoy5oLvAVDlh/oeWfkeruY0jMSeBoygmCHfYJrjuCR2V2NK8py6KsNlexGy8EbqxqEIrif2Cs6epOm8k6xLl9ded911cs0118hNN90kAwYMcPnouc+KIqHDPZk4caInoeci+XIe6gFIOXL3b3UgAayjdmG1uI/EaoYyi5JfZshVsL8KjAgNSxJmgvKH0oYyAYhCKbWLisWVS6PrqD6OxZln5tk0DI5DOSzFLmCRsKayib5BTwzyp9/MX5QXzQXfA88dHoE8+B6u5jdrRq0wAGL3D8VVPSOAVmSrJHYAc57ZlxrJyQOQ7KtHeSNag8IOvyP0jh97X6sbhyIoIb5lvhU8mHVoFAG8+eabhbAnanbk0Zql4SXcijokNNYJ1/s0vHnMRDHP9ACkGDn7t+QggTQABMUFEjFKSxQ5OIfuNn0kHgys67il4wIIZMA9pEmM4hs0fXnIBVQfx6JPOFdeTcPgGD8kasbRyAPEgc2hk2e8epwx2p4YrPKEXCF/QFpYwca68T3iyCHONWkyasUBIPa7eYdmXlISO6FZdiX2NB7FOOPTa+pqja8qcFLeiIIRu94IQIS5BfDXiUMRXE8YVGh57q1J1nCza8lEBfiAjO4yUyNyeOmll8zrIbkTgrzHHnuYf2+77bZzu0UhQjJhUYiQvfaGG24wKXjvu+++0pKkNJOZ/3u0BDwA8SukthJICkA0yxVKIwccimwZZPMwgcNDQZklC1ac2HbIfijDeEtw4+M5cN0o/qd1JVw/m+cl4azgZWDMLg+/NGPSgpEAJa3ITshVmPyz8j0YM+BLq6+XwfdII6fgPVEkdgC3EvGTAhD7Pcia71y9I0WR2OsKQOrSb/ZsBSNab0RTQqPAF+3xcvE9sHfwTRcV3pq1z4ADwAfVyl2GjUVxRuAgauO8xwszZswYcw7ghSEsy0XWyKyy8fenk4AHIOnk5u+qgAQ4lGzya1iXglmuCF9h84dLQFXXqjSUJorcoWBHKZmMhywjhCDAA+AAjgNY0owzTdXvuO8BcAGg6DsKfDPOSlT9lrjvdHEdAIQDEOUW0EHfG8nfJd8Di6/L0DoXcsjyjCgwglKMbJFrlmZnX+LbQoHJi8ReF0U+KM869hsvNiCE/Y91QuP7U8+I63ojWdZg1L0Ym9jni0zwkWUsEMsfeughmTBhgvFU++YlkFUCHoBklaC/vzQJxAEgHFbBLFcoInAbIBHn4TVIKxAsfCj8UZ4ZO0MUqXGp0ZFneAmAiOe7KDKlcmmUYjdOyBkZwuDvNCogmVbmSe9DcSYsDc9NVLiYS74Hc5z3PCeVg+vrlTdCKBtAEwI/6zsNiT2sb5pRi+8MLwkNA4SGajVKGhB3nCjygNK61HTQcdWZR4FHlDOAb6ORx6sRbyTufBZxHaHAGF2KTnGedmzHHHOMjB8/Xv74xz8asOebl0BWCXgAklWC/v7SJNAMgIQVFkQRmTp1qsm/nnfthCTCob8ot2GeGRQnDfmBuFhE35vVvEgyPq7NAqDCCkgm7UPa61k3yvfA4hrm+lfwAfjNWt8DrwdernZoKJEYC5AtVmF+qxeJ8bssfsg6tEN6eA9hPGpFTxrSU1dFnn4jh7rUorC/gyCJW2tD2WmbuR4jk85rFpDp+hvEuEPfevfu7frRuTxv8ODBphggxWmTJEnJpTP+oS0hAQ9AWmIa23MQYQCkWWFBlA8K7BHiQZG7qjSsv408M4wHsiiW4bAMUXmNIarmRdJ3aopdxkMMcVIA5apQY9J+cz1KDbLg4EWZYSyNuCgu+R6AzCpxlNLILc49+r0CMJEtIYXqEVPPiII6zXrnEowoiZ05Rhnn30lJ7HUFIHUrhmevp2Yk7ka8kSwgM85aTnIN/DrOHzw4dWiDBg0yhrsXX3yxDt31fayBBDwAqcEk+S42lgBAAiuz3RqFXAWtNVyDFQcFL2uhPpdzQ1gPdTdszwx9hSdBeAcHFe76PEOuguNxUfwPpZGMNc1qZDSTZbCCfbPrXfzdBn8oxwAn+kHGLjsUzAXfA5CJIttqfI+oedAU0poBDX5W2PoO4424BCOaClat6Hx/7B8apoUBoJH1t66eBMIaMXy4DLF08d3FeYYm4IiTIlx5I0GQWSZvBG83509d+BR77bWX2fueeuqpONPjr/ESaCoBD0CaishfUFUJBAFIWMhVsP8oGZMmTTKW1rC0qWWMGYsdWT9QBgi7YTyE/HB4ApbK8NZoxqe0xf9QGgmVgLuRtdK8eoGyVIpPMq92emAb/AW5KOr1sK3zSUCiykhBZqvzPXQO+H5RIlGAyQSUpBBoFIndFW8kjMSuSqtdl6KunoS6AxA8ZUmzSIVlSgNcAjSD9UaS7BlJrsXbzZ7ITx3aLrvsYkJoH3vssTp01/exBhLwAKQGk+S7GO0BCYZcEVrVLFUpAIQ48ypZnzQ0DLChxe0IGYgznrzWSJbaG3h0CFviN2Avq7dJK8X37ds3r+HOfa6mBwYU0ndbOba5KFn5HvBKUMJ5TzvxPQAdrA0Nx8uSjU7BiF1zRCfSFRjheUpix4pO/2lKYme98P/qxqUgrJNxZc04lvsH2eAFrB84HVmySJXJG8HbjbGhSkawqHmkJgeGsXHjxpUx3f6dLSgBD0BacFLbZUgo7ByewSxXcQhyhGCx+RPyUZWGAjV58mTD84BjkNVj4GJcaWtvJE2xG6evCkDwgCTxMMR5tn0NngjN0d8oPTAABG8MJHS8U8yb1iRI8i6bdN0ufA/kw9oA2AKuGbdrYnBYJXaXYIS9x67EzrhYk3yzWNExgOS5RpOss6hr+b4Zi8u6Dq761uw5eaSxLYo3AvABgBBSm8Tz10wmef59s802M+vk7rvvzvM1/tltJAEPQNposlttqCiKpK1FCQxaqZuNFRI6lqcqpUBUEjqKiwuPQTMZxPl70tS3wRolKJhxUuzG6Qs8EsDmaqutlksWFvpOLD8AA0tfWN8VgJAuOQ34UF4J7wqSruPIoa7XMG5iyPkhnJDQmTjGgizjDQMjLnkj7D8AKq3WzTsBVcobIdVqVcFInQFI3mls8+SNsEYIwcJ7U0ZobZpvqn///rLmmmvKL3/5yzS3+3u8BOaTgAcgflHUVgLKLcAq0yzkKjhIsnmw8Wdx37sUHCRclGsOJjwzVelXktS3GtOPZTiPGiXwSAhX6tOnjzNQo3Nok/3xipEas5HSaIMUPFWsIcBK3EKQNt+D8L8o0rXL9VX2s+xxM2bGXrRSbodoadiWei7Ug5UWECmXgr3IrsTOugKA22TntO/IYw6DqWzzeEdezywyja1r3gjrAhI6ILwOabbZ2wcMGCAbbrihjBo1Kq8p9c9tMwl4ANJmE95Kw2VTxGWe5kDH+oQCGSeDSp4y0yxAhBfhiic8BeW3KqFhcVPfKmGe8aRJsRtHxoA0QqMIfYqr8Md5rs33iCL7K9+DNYf3jR9C5WhwARSMhIUUKd+D36y7ulg+48gw6hrkxbwxbjx7VVC4okjsAIak9VsakblZL3g1NVQLOfDcRiT2rDJOe3+zVLZpn1vEfXi/8TIVvVeG8UaSFLXk7CINL/tA0nTkRcg2+A72us0331y23HJLufLKK8vogn9nC0rAA5AWnNR2GRJKBId6msbmj9cEhbOshkKGYoaSgseD+HGyYPG7KsWp4qS+BTxpit003qi48gecEfftEoAoVwXQAN8jrO4GSgdKA79tvodyATigqSHB3wm5QcnmR5+nfA+UW9Yc17RDA6BputSqjttFRq04ZG6ArhY/VBK7nXnJNRcmzvqqMwCpSh0NmzeCIYY9gLNFQ/AaFbVk78eDwzfBGqh6wxC13XbbyY477igXXnhh1bvr+1cTCXgAUpOJ8t2cXwJZAAjWM5TBsrK/oLCqYobSrocQAARPSFVCsCBbAy4aZZ6yU+zCp8k7ph+ZEfdN/Y2syprNVcETYRe/s1ea1vcgZCIIPoIrkmtQMOkngAP5oIjgrUERZ45dcmKqvicQMsfaYdzI16XXKs+xpyGxxwEgdp/DyM6qtBZFYk9SSyNPmad5dhXraDTjjeAlwYABGGUvY+/PkgEujdzS3IPhZ7fddpM99thDfvazn6V5hL/HS2A+CXgA4hdFbSWQBYCw+dOKLsAVJGkHFTMyo6CEJM1tn9ckKvE7mHkKCx6HEgcpYQRFpJJEqcdqSAXypJwfWz4oCXiemnFVstT3YG3yfDg06qVDAdcwLZSOojkQea2R4HN1jeMZI/UyYLquY40CI4xJQ7UAIMxzmmxSCly1SB7yK4rEDgBJU0ujqLUU9Z6q19GI4o3gFcG40yjLXhVkG+wDhrF9991XDjjgADnttNOq2EXfpxpKwAOQGk6a7/K/JJAFgKCAEj7z7W9/uzBx8j7ei5U8jKRdFW6KCkWJ33bmKdt7wwFalAUPueG5IvtUWKhUs8kk9MUu7hjGR7Dre6RJsWvzPQCTgA/ljbAOUPo0TAtOQF0V9KC8UaYJ69E1DjBtlbEpGNG1YReeJESFec3qUeUdeMsUjCiJXT0jakFvts7j/l3TTVfF4BG331xXpzoaYbwRDddkfrN6dZPILum1L7zwghx00EFy6KGHynHHHZf0dn+9l0BDCXgA4hdGbSWgm3qaAWSt8J30nShkHPY0wnDCiIdV4KbYY1PiN5mnUMQ1TS39x9rrKsVuHHmimCGfFVdcMRWHgrFg8W1W3FE9H2lS7DIO5pr1hbyCvAclJsM9AZBoEgXkCSDhd5qkCnHkl/c1eMOQL4o44wZYtWqzeSPMIaFm6knImlFLZdaIxM6zkav+ZP3+8qilUdSck8kQrlzWAqdF9dd+D98/XjMAJUaROLyRMvqp75w4caIcccQRcvTRR8uQIUPK7Ip/dwtJwAOQFprMdhtKFgCSpcJ3EjlrzQcOG2LhUdqjLF1Y+LGWpwnlSNKvuNcq8RuvA0oWllnN0lW0ZRuCJ3HfK6ywQiKvC3NAliII9fBrCHtrpOQn4Xs0kh/3E7LGu1AQCU2L4j1wPUq7ekb4b2Rqg5GsCmbcec56nV1UMc9EBFn76fp+5gzDAoCEUDNCA9UzwrtcFT8Ms6AriZ01k4ZjU1cAgjzqVsjPXnt8L5xByy+/vFkjmpyA36wlvvu8vF5pvoHf//73cvzxx8vJJ58sgwYNSvMIf4+XwHwS8ADEL4raSiALAAEQoFzDJ8ir2bUlqO1B2FUzpb0sbkqYDJT4DWjiYMSyXVYaVS3UyKEdN3OMHfaGghhWfyIL3wPZIRsAGmtKs5g1m+ugzAnbQt48Q7MkAWQ0VCuNgpnX2tbnIje4HnBd2qmoIuNHWcTjowYDwIeLjFpx5gyvi6b3BZjTCOexSexxnpN3Mb84fUhzTR0L+dnj5Dtnv8CYYhsZbN4I66sqqZsfeOABOeOMM2T48OGy5557ppkyf4+XgAcgfg20jgSyAJAkBfbSSEzrYgBCompLBJ+NRZLD9Vvf+laa1zq/h0MSsiRhSxD2s5C/s3ZOAQi8nTjhPXFrk2Tle6AkoIhiDSeWHi9L1tao1gigS8FIFeLFtYYNoW3tVFSRucXThRGjGehKk1Er6dppRGLnO9V6I1GV2Iss5pd0XFHX162QX3AsGoKFZznMUBHm9UpSb8SVzMeMGWOyX51//vkycOBAV4/1z2lzCXgPSJsvgLoPH6UvTYtT3yLNczUMB8W9Gdeg0fMJ50D5xMpfZlOLPooWLYnXIa9+M9eEXQDOmhXvot+EODSbA9vzoVmNkvQ/iu+R5DlR18atNeLqfXGeo1XvAYWuQFec95Z9jZ3hC4J9HK+m9jkMjGgmraTFDxvJwiaxaypoO5wHEGsrvHUFIHUr5BecK5J7YASLAiDBe3QfYF7j1htx9b2MHj3a1P+4/PLLZeutt3b1WP+cNpeAByBtvgDqPvy0ACSqvkVamdh1MQjDIeQnKaG4aHJ8o7HaBRIJHYN4npR3kVaGUffRL4ineGKisldhmWZ+IafCw2jG9+CdScGHzfcoss4Fll8UEK01ouRV5EGK30ZFz1zPBaADoMy761LHwIUM+L75PjV9c5YMXzzL/rEzarkksaOoaqgWCqyS2AHweEgIwSqjmnjW+cBIA18OPlccb2jW97m+HwMJRjAASJrG2imSNzJq1Ci54oorZOTIkbLxxhun6bK/x0tgPgl4AOIXRa0lgFJqkz7jDiasvkXc+4PXAYQIn6I/HIppM7MURY4PGyfKCsoligopRVHMSQ1cBQ8ISgf56OkXynYjCyFzQLYsrPKAwEbhDS74HoAcQo/S8j3SrjP7PlVClMTOvwnN0jCtPGqNEDqiniVCC6sQCuZCls2ewdpT7yTfdzMPXLPnBefRzrxm72d4L1x4RhqF8+i3wTrBWFJFjlGYHBWA1KWSeHAc8Kb4IaNf1lYEbwTwAQi57bbb5Dvf+U7WLvv7vQSMBDwA8Quh1hJIC0Ds9LJZMw3xLCyjKGNYhIm5TtuKIMc36huHmJ1il4MdhSRJ2FPaMce9DwvulClTGgIQQAfgg8YchFlFbUUvTX2PPPgecccfdR3jQgaa3lfrR7iqNcLzsdjyA/jLu+q9C5m4egZeBAXlwbTKrt6hzymaxM58KuBJQ2J3Pf64z2PfJ3ysrh445M636jrU1gaaeEg0mYXyRtgX0/D4RowYYcAHXBBqQvnmJeBCAh6AuJCif0ZpEkgLQDS70yqrrJLaiqskXCxZEI8J98kKZkjhindm1VVXLUymdrauJZdc0qTZVetonLCnojpKP1955RWjdNhEb+QP5wYFCu9ImFVeQ144pJOGXDFGFHzI5tyLIlpUAcak8lWLqHpGtNaIDUaShAZqaCHPY32EZRJL2s86XK8eH9aWgvIi+23zRvhvba7S+5LWGkCJUor3kzWuYX2aUauIsL6kMsUwQvhYXSqJB8eHsQd5510IV3kjgJHg3AJGohIU2H2GgA74GDduXO59TroW/PX1lYAHIPWdO99zEUPYtg/muEJh88eClraqts2TIHwhLNwnbn/0Og4mrGMU/iuiaWVwLR4XDG2K8joU0T/7Hczz5MmTjSJIiJtNlCceP8wqj0Klng+elxR8cC+kUa3lQghOXcJV6HuWWiMaeqShhWWlYC56rSE3eER8j1Xx+ERl1NI1nQRYIlMACN8O+xetEYmdtW7XpEiaXjqPudN9Cz4YAKluDQI6gKDIbIcasqncEbveCGAELlvY+iEFL6l4H3/8cZN4wTcvARcS8ADEhRT9M0qTQFoAkqWqNpZgLOFs1ljj49akiCOkvLJzNXo3SjWhYxzgjKPRQR7mdYgzFtfXoBROmjTJcGxQiAi50ixMqkAF3+mS75E065Hr8bt4ng1GkB3KJOsXJTtYzE49Po0qurvoS1WfYacXJgkDP1VQuoNgnG9TU0inJbHD7+LbafT92NwCDDaNSOxJAY+rOdewuLoCEDzdfH/0v4ymc6tgpFm9EYoQUozw6aefdpJmnLV7ww03yGOPPWY4hvybmlxUW99ggw3mE8n1118vv/jFL4xBgOtOPPFE6d+/fxmi8+90KAEPQBwK0z+qeAmkBSBpitppCk42QSzBeVjCCSeC5Lv66qvnpvTYoWNRmaLUImp7HYqf4f+8UQEIIUDwbmiEYIQBQBd8D81KhpcrbWKBMmUW9e6oWiO61ovM8FUFOdUxvXCWjFqktQZgAa6jmnrSNKMWHjEFr5pRq0ivoIJjPAhpOA1lrzVCRllrGH7KbsobUTCivBHCs8aPH2/CLgm/euaZZ+Sll15yIm/etemmm8ouu+wi3/3ud41HmXf85je/kauuuko233zzuWIBfFx00UVyzDHHGP7JHXfcIQ8//LD5TQi1b/WVgAcg9Z073/MMIVjKbYhTUwJB2xW1cUHnZRV1nZ0ruEhQOm3PQbNUorbXoZmSkveC1L7wHhRjwEeY0mMrZWnI5nXhe7iSOetbOSMoBzT4TFjGAdt1DHNJKhu8Q5DNWTt1za6UlMQeF4AEZcn+qQqrVmLPSnROMl9afwcORR0zsWFk0nWWZNxFXKu8EUJOf/jDHxrvBPss+8Fpp50mW2yxhQElWZqmE7e5fOzvu+++u0kgcsstt5jHs8423HBD+cEPfmC8HjTu3WmnnQz4AJj4Vl8JeABS37nzPf83MGBDStqScBs47FBMaCgmLlNwBvvtMjtX8NmaYhfLZZTnIHjfyy+/bPgVeB7KanaNFcAHikdYit0sfA/Gp1WuUajKIB6XJWO+Izw+rHdCspAja4bfABAlsVeRlJxVZoxTM9kx53W0qgdlEAeMEP5C0oks3j32UgUjRZHYmS+UeLJIFel5ybrO9H7WGg0vepUb6wPuBz/wVrSttdZasuWWW8oee+zh9Fw49dRT5bnnnpPf/va35lUTJ040ICiYfYuCiIRwcW3VwiOrPJ9V65sHIFWbEd+fRBLg8EsDQOJwG5SIquRj3OV5W9vIuoOHgixYrg5WTaFK3DHWJcaR5NnwLtTrk2hyHF2MFQyZaNFJFKZGFrisfA/uZ67hxqCQEXbVLocbsoXXpMkINI1xGbVGHC2b2I9RwMmYUQizZrKL/eKCLwyS2NkD+a7w5rLeXfA5GhXIy4PEjreOMCYKpNZxvpRDSObEOjSABsYxOBqPPPKI4YKwV+y4446mQrqLxtrZYYcdjHHpyiuvNI+E9zF8+HB54YUX5klvDyA6+uijDYeEfdq3ekrAA5B6zpvv9b8lkBaANAstslPTckCjgBehjGp6YIh2LqywatUG2KQdB6lvuTer2z3Nog0WRiRzGZ4YQIjdXPI9mOuyw83SyCrtPWr9R1EEnIatO2SMpVtDtTQ0Qz0jeKaK+EbSjjN4n/Jc4F21QoKBJHJRwElIJkqwGla06GGS4oc/+clP5L777pv7esJqiNUfOnSoAXSsLxTLRo30rt/73veSdN1cy36GsYBCfi6AU+IOZLwB4Md3VhflGaDBGoF7QWMPwDuBocwVj+Wmm26Sc88914RfrbfeeuY98EEAI3BP7DZhwgQZNGiQ3HPPPYWmrM847f72gAQ8APFLotYSQAkChKRpWPY5AIKhRcQ0c0DwbMIxGlXdTvO+OPeg4L311lup0wPb72iWYjdOf7iG4n9a8TvuPVmvs71PtteGvqiyqO/IyvdgvrFI0qpc3yOrTIP3I2OUb0IrCCtMUsdGs+jYtUawRPMcAAlzVmXFkG+bEB6UYwBnWBY11zKvwvPYFzSkVAGn/Q0lzagFAGEdDRs2zAyP9MUojvBL7r//fvP/1l13XSG8pm/fvgbEatYlDBvInnWTxJOBNR6PLmnU6wR6df6pYQLJuy4pbbfaaitjgLKBZnAtsxfYYVpha72RkQOPyo9+9CM54IAD5nI9FICwll588cV5HvfEE0+Y68eOHeuJ6FXYVFL2wQOQlILzt1VDAlkACNmd2FTVss/BSzgGrn3i3OFJuPBCJJGUpgcmtCBLoTtNsUv/GUcWEvGrr75qQjSKOixRhoiRRskIem3oCxZWgCPzlZXvofU9UAaw1uYdYpdkLeR5rZ1qFgDON5BWkdMMSVqFXTMkKRhJqlzmOW6erbVN+K0pnfN+Z1Wer94u9gXAdnC9h/FGojwjABCUTzsU5/nnn5dDDjlEHnroIWMwAIBccMEFstlmmxn4Q9gNAAAgAElEQVRRsEY0o1awWjfrpdl3yHeLsounuI4NTy4ew6Ant6pjIVMVpG8yT4W1u+66S0455ZSmQ7j77rvnqXOFUWm//faTjTbayJDK7X1IQ7AAIPYZ5kOwmoq5Fhd4AFKLafKdDJNAFgBiW/ZtkjMWORTcMiy4adID27JRHgNWSBR1FKys45g6darxAhURLmDzPVCQ7CwpjFP7AhjSOgj8/zTFBbXqvIKrrHKqy1ead6rZRrVG8IjgGQnWGilaZlo/Qmv4ZAHmRfc96/uU68IcxNkX4pDYkWMQgGBEufjii03a1jvvvNPsP0EAYo9Fsy4BSJgfTXqgxQ8BS0FwzFiomYQHpI4NLzfjKyOsNam8WAfrr7+++dHsVEmfEXY9kQb77LOPSSYwcuTI+Qx+noTuStLVfI4HINWcF9+rmBLIAkBUmcVCx0ao1Z6zZISJ2e3Qy1DeCF0IpgfmsL3uuutMJVosf4AkLFJYjgYMGCDbbrut7L333rLxxhubCrtkreIa3NePPvpopOWqWZ/pDwpk3oRJLfBIKAZeG7wSwaZ9wXLIwZgmxa4q4Cg7gKp24nto+A1KHqEQWbxszdYNf4+qNQIgaWbpjvOOuNfgoSHsinXVTtnNmGtqF2GUyJJcwSax89/azj77bJO1SL3FfFd4LgEhcARoABDAXhDkjx492syFNvZz9i/ACKFavIc1orVGWK+AEUK++IEDUsdGBXrmoszMgnHlxnxyrmyyySbmDHLVWJOcWcztrbfe2jC7pKbh3WuvvYRiiDTWyM4772y8Xz4Nr6vZKOc5HoCUI3f/VkcS4IBCyUnTSDGIsssGyyGHQtZI6U3z7LT3MBbCjKiQi4JGIxyJVIT8e8iQIWbjRYmGiPfrX//axMFus802hswJ2dNOsQuBLysA4bDUEKW044q6z87SxWHEPITFgzNnkKUBDvxOGjakfA/eWddaD2nnQBVwFMFG4Tdpnxv3vka1RlhXeNdY23mFO+r6AsTzLsB5u3i72B8JKWXuAe0YJZJ+M43m1wYjZClCmTzhhBPMswEP7EtPPvmkQCxG3gCQk08+eb7q1fzNzsg3Z/Ysmf23GTJn5pciXd3yRdeC8tnnfzfPRPFkX8AYogkR6gpAoirQx/2eiroOb9PWW29tjFyXXHKJk9diCNlzzz2N4e/nP/+5Aax2W3vttef+UwsRHnvssXMLERLa5wsROpmKUh/iAUip4vcvzyqBtACE+1D0OdQI80lCwM3a56j7tT4JSriGHx122GEm9AigEaz6DU8C0EKhJqxCpEm0D3QXAOTNN980yqFtqXQlAztLF+EIKElR9T2UG8I1KCIolHE5Bsr3KEsBdyWzpM+pogKuhcjweuVZa8RWwFFy8iogmnROirjeDrVjf8srmcYZZ5xhAM75558/Dy8LowiWa4wmhO/YHBB7/LO/+FS+mPwH+cdrT8vM6X8SmWUlFenqlu5ey8lXVlpf5Nvrymcz/1WbRo1OGqbFXpCExF6E/KPekbYAZBn9xmvI2ULV8vPOO89JF0j6QR2RsMbZrI39CxACHwQjAgY4wC6ef9/qLQEPQOo9f23f+zQABLcuWWCwhmOBhfDtwiroYjIYD+R4lH1c9AAMXN+ktDz44IPneYWtvB9zzDEmg8j+++8/zzUuAAjxypqi1cUY9RmEm2EBQ5mIyjYWrO/B9SghKD1Y0hSMaDrYoCLC/cr3UAJ7O1nAUSBQ9AF4hHxUZa3rOmDNM58KRjTshvlEaeYbTdPnohRwl9+Eq2fpHqeZ/IKGC1fv4Tlkv2LuCIfRbFrIHgUTxfXII4801awBKJDQ9dubM2umfP7s/fL5M/eKzKaY7JyIbnWIdHbJQuvtJAutu4O8/+F0s2YwjCiJnTFqqFaRoX1JZcl+BADJWgAy6XvTXo8HHG8F4VLwfXzzEnAlAQ9AXEnSP6cUCSQFIMoz4BDkkEJZJdypKk3rk2CxJFyC/Of77ruvOdxti5GtvEMo5YAgDCJYYBBlHYAVlb2k2djJ2IK8CO1y1QAPWMHoL/IPIwPb4KMR0Vw5BjwPQElTwrMdwkZcudb3SKPMuhp3kc+xsz2xnlQeRfYh6btc1Rrh+8DIwP5QBNcl6TjzvF6J9uxtUXVdXPUBAAK/5KyzzjKP5Fv81a9+ZfYcOGj9+vUzoVennXbaXKt1598/lo7xN0rHx39J3I2uJf5bZn33APlMehqunE1i5zunsZ9EkdgTv9ThDaxJQrAII83LK+Wwu/Lyyy/LgQceaNLennTSSS4f7Z/V5hLwAKTNF0Ddh4/CgrWvWdOiYyjpKGIo7cRGc3hRebVKza5PQvpBiOYQOrfYYgvTTbwihCIBoDTFLvG58D8GDhw4z1Buu+02ee655+YDILNmz5IZf/9I/jnrS+nZ1UOWWHBx6ersaigGctYjPw77rC04D3g+wkInktb3UI4BCpAqIgo28gxBySqTPO5n/FptGRmXzW1KM0a71ghzyvzGqTWiqWaV11Vla3gauUTdUwbRHgBy7733zu0WBgD2CpRW6kfQACHally4h4zYbnn52gLd0t3ZkVwEHZ0yZ4FF5IuNDpLl+qw1z/14fCCvK4mdNaQkdgBJWm9a8k6G30Ef8SrAf6mDUeDZZ5+VwYMHy+GHH268Wb55CbiSgAcgriTpn1OKBOIAEBQXrKEcTFjBNQ4cBQ1LKR6CKjVCsHDP0087BOuggw4y1X81xa7NWwGA4CmJCsH69J+fyfg/PS0Tpz0vf/r4/8nM2f+Jte7u7JblFvsvGbBMP9l4ufVlkZ4LzxWJFmXMCtTs6vKML6z2BHOq4INOJE2xyz1kyCHsCs+NZuwhg46G9bSyUgrXBXBNSApAO+gVq9Jaj9sX1gShNlr4EKMDc2sXPgSc6LwnLawYtx9Vvc4uKllloj1hVzNuGyYzZ0wTmfOfTFpJ5ToHEPLVJaX3/j+Tjq7uhrfz3QPEFZAoiV3DtPg+ygjF5DyCV8f+TV+q3v7whz/I0UcfbbJQUdvFNy8BVxLwAMSVJP1zSpFAMwDC4aNVf7EE2xs+sfEcUFXLpPLKK6+YWH0tUgWJk5jhSy+91CjTWr0dxRyLJwpHJAD5/aOyz/CDZOzUh2TW7NkyJyLWukM6pKuzU3ZeeWvZaeWtpbuzy1jSUfjI1Z622VXZCQsJs/wF+R5JFQT1sKCI2gULVXFlPXBNEdmX0soq7X021yVLutW07y/yvka1RgBahJ21+tiDcrbnPWtRybzn8NOJY+TTJ+9y8hoYIwv331UW7j+v17fRwxXAavFDrcSOt0YBSVEkdt4Nr64uWfgefvhhk8GMZAN4433zEnAlAQ9AXEnSP6c0CaCMNDqUyZiBFRxLF0pv0OqNNwEFvmrVdIOVxwEfeD84KMmItcYaa5jsXaS5vP322+Wee+4JBSAXXXOJPNtzqnQu3iPx/Cy76Dfk2AGHyj8++txYn9N6ihqFjIUpCYwrS30PQtOi+B5h2Zc0FWxdC9NpQgL18lHbpF24LqxN5t0OxeSb16QErezt4lth7CjWVa9pQ7arD64ZKmJ5XhNvSsEbOrtliYMvls4F4nsS1GilYETPDyWx510sk/cR1loXbhLhdfB7zjnnHNl9990zT5l/gJeASsADEL8Wai+BIACxQ30IYyLsqpEyRkE/LOVaLKsqgiDlLsoTCgX9I5wGJWvcuHGCOxweCwrmaqutZkKu1ltvvYYAZPrnH8mJD5wt/+yYKR0pYq07Ozplsa8sIj9eZR/5yqzuxJ4itcxq/QXCgVzxPYJzhXzw1KCQ8R4sm81ao+xLZNWxwUgdlHjWP2MntAOrapyxN5NNXf4O6GDsWJWZdzxbtreLcWjoXZ61RsqQF2NW7yRjr3o4z2fPPSB/e3x0k2xXSSXZIQtvvJcs1O97SW+cez1y1DAt5Y6xjmwSe+qHN7hRvcFhxVZdvsvFszBykcEMHuJ2223n4pH+GV4CRgIegPiFUHsJoISg7NLIAKOchajUrlyLYgwIQZGvUiNDCkoTCjLeA8IqACNJlOGZs2fJsN9fINP+9r7MzhBrDQjpvcASctC3d5dVV/5XVeM4TVOgqmU2LP2rC74HMgKkAR4aebri9BdZo4So8sq/eZ5a0atAXm00DiVca5rkvIr5xZFh0dfYRHvmPei9Um8XXk4NveManVP+O8k3VfT4ot6nWfC4pgoFVOPIZsboM+XLv7wZ59JE13T1/rZ87QdnOOFzFEFi1yxlUdn/Egkg54tvueUWU4Dw6quvls033zznt/nHt5MEPABpp9lu0bECQFAYqdiKIopigXWpmTIGmZswrNVXX71SkiHkSgttYdnUgoRJOjlmyji5a8q4JLdEXrtJr/XkkI32jfU8BYGaAjXMMuuS7+GSeKupYFFctQIzYTyquAIOy1ZcbdJxuxGuWYQKOgGGGBqaEe3DvF1VB5iNPjjAFJ6POmX5osL5+5cfPG+RwVi7SYyLurpl0YMul47OTgNC9CfGnZGXKIldM2rZJHa+OUK20uwDCpxJ6lGH8MBrr71W+Ln55pvnq2SfVcb+/vaWgAcg7T3/LTF6jQEn+w+1M/AWxCEvA1iInwaApDlI8hAeSi8eHPoP6TtN+lSyXQ0dN0zwgrhqXR2dcvl2P50nO1ajZzMHyLQZCFTwkZbvYXMeIOsz73nMIf1EYWBe8I7wXpRdzaZVBhhBZgBn9Y6FZRNzNfdVeg7zgeeSn7RFJVWxVG+XPafMa1rFsgg5aYYzwuyiQhqL6EuSd8z86wcyfdRxSW5JdO3X9v8f6fhqr7mV2LnZJRjRlNAaqoWBiOcriZ01E5fEDqAhAQr7ezPgnEgIOV1M8hPAB3Vd1lxzzZze4h/bjhLwAKQdZ72FxowyQdYoQhI4kMmAE7ehwJEhq0+fPrEPj7jPTnqdzZfgIEOxTVt344HXH5XRL4+NzHaVtH9cv0/fXWS7FTdreCv91xTBzAEpJhuBQK6zw664JilwSMP3SDPe4D2qhCgY0boUCkaKUFzt6t7UEUjjHXMhizKewbeOhxP5u6rqrgBTwYhdawSvWlmpWhutPQVedczy9eX0P8uMW07Jbdkssf+50qPXfxtPuCay0L2Gl7LH6F4TxzgV1dFGJHaez56tvJEoYMFaYx2T1CMuaMlNcDEefN555xnwARm9aglbYnTfX1JhCXgAUuHJ8V2LJwGteZDUW8BBQDaSVVZZpVRXuF2nBO+NxqunBSBnPjZC3vzoT/GEl+Cq5RdfTs7a9Nj57qD/eG3wFKAUh3kjXPA9tNAa4XWE3jQLs0swvESXKhhRxRWLqBbJQ3HFMpoUWDXrAMDLTimNwtMuzQZeeRWVbFZrBOUyq/KaZr7oF3scBhNXwCtNP7Lck7cHpNegC6X7a73n6aJdyJT/Vp6gSzDCC/n2NaMW4ae0KBI7exjGGtK/l7Geks4jGbAAH4888ojZc33zEnAlAQ9AXEnSP6c0CXAAaLG5JJ3g0Hj77beNVacsRRalHeWdwxEyKUoOSiZjSlN3gwrnB9930jxFBpPIJOraHp3dMnKn8wViuja7//BuwjIwueB7kP0L3o5LvocL2diKK8qFhmcovwCZZFU0FHgR2oYSUIfYcRey5Rm2x6uo1KVq5VZvFx5WFFfmUue1COu1ZvRDsa2zxytfDkgPWeqIkYYDEtYUgNjeEb2WeVRQknXNauIDzhb2RtYRZ4t6Rvh+AZIkP1lppZWcGymy9r/R/dQAoRbIhAkTTEIU37wEXEnAAxBXkvTPKU0CaQEIBwQVabFEJfWeZB2skoixhPFulHdVKuFQpK278cFn0+W4h87O2r3Q+y/c+nTpvXAv83c4NMQyB/sfvLkufA8XQmOsWiQP5TVYsTupFd3mPFQNeLmQV7NnaJavsgnXzKN6u9TKrWCEOc0DEPJOjBF4fwBehIPVueWVBavH0svLEnufFVs0UWDEJW/EJrGzjvk3oVn8sL/XBYBQBZ30788//3zt12DsReIvLEQCHoAUImb/kjwlwAGN5Slp4xAg5S2ehiIPdw4iFPcw0nyWCu1//uQ9OeV35yUVRezrz93iJPmvRZYyIQTUKKEeCZbZvPgeKPMoYcxvHWodBAVpgxG1oqOwYkXnd5QVXdcJim9dQ29iL6zAhQAvAC6FRJETc5/Vi5S2L8H7NOSGeSFckua61oimamXMjVIMuxpLkc/Jqw7IVzfdWxZeJ319ijDeiEswoiGbABEME5p8w67EXpX1HVwTgwcPlmeffdZwLavaxyLXsX+XOwl4AOJOlv5JJUkgLQBBISTlLVyLoop4qUIdRZrPUqE9bw/I/2x+svxjxuem3oryPRpNeyvxPVwtayzaGtID+I0K6UHJ1VA8OA8AlnZpmpABAAKfiCxnrvk0rmQJMLYLH9J3rTWCx4rwm6R953maSQ7wUYdMSXHkmVcl9N4/vlw6F2heeDRWHwsgsZNMAOMTyQQAJOwLrBGtxA4oqdKcH3jggeacfPHFF+OI0F/jJRBbAh6AxBaVv7CqEkgLQFDyXn31VaEgVBEKHson+fs5XKKq4GL15YCCHJ+05ckB6e7slhNW+pHhgND/MK+RS74H8xKWUSupbKp0PWtPwUgwpIf1AekYayN8j6LDA8uUk51emYQMeNjq0lzUGsGryPffquv+04lj5NMn73I2pYtssJssMmBXZ8+zH6SeETWmuCKxw2UDeFAHhAYA0fS+uhdoRi3ASFn8RJXFXnvtZdJeP/XUU7nI2T+0fSXgAUj7zn3LjDwtAEHZwa2MlTHPdKYcXJAOOXhQLFAqo0JvuBayNemB07S8smB94ytLyuBV9o6sNu6C70EIGgd0u4QdAUbUig4viQb4gPCJlbRK1tA06zHuPTbngW8kLKFB3OeVeZ3G/9vFLLV+TKNaI7pHAECq7vXJItc5s2bKjNuGycwZ00TmzE7/qI5O6V5iGVlin+HS0dWd/jkx7wzjjaTJqMX+znfeKMshZ5mCkTASe1KPWswhhl42cOBAEwL72GOPZX2Uv99LYB4JeADiF0TtJcDmyMadtHHoT5o0ycSX52Vp1fShKNRLLbWU9OrVq2lIBtYmDqnVVlst6ZDM9XnVAdnhvzeTH6yzc658DzxEyKyOfI9Uk/Xvm+ywIy1uqGCEfxPOg+KaB9k5S79d3avVoVuJ86CyCas1YldhJ+wSEMoeAQBp5Tbrk+ky45fDZfbnH6cDIR2d0rnwYrLEXmdK11eLz8qUlcSOh4sQTDzvUY33KBjht5LYNaNWUUVQt912W7P3jBs3rpWXpR9bCRLwAKQEoftXupVAWgBCLwAghHrkkV4Qdzopdjk4NMVunJHj/SAEp2/fvnEun++aXCqhS6dcuOXpssRX51eOXPA9UL7wfKBgl1nfI5XAM95khx3ZCihATEmreZGdM3bdye2kJWW9o1ABPFvZ49MoZbMKEeCB168diL6AkI/uGSEzp7+beA1191pWFh94rHQt+q9sfGU2BSN2zRHtTxiJHbCJt48w1rjNJrGzJ7A38HwFI3kWzNx0000NWBozZkzc7vrrvARiScADkFhi8hdVWQJZAMjkyZMNyRXPhKumGXxQqojfB3wkieOFfAsJdfXVV2/qLQnr85gp4+SuKe4sVht9fR05YL3vGyXRbnbIVZpc+naaWaxsENuLqK/gaq6zPodkBOr1iQo70voChPRooUrWllrRIT7XrTH3WtuFEEjmvujwkjJlpoUlmVtAF6F4mpgAj1ezLGll9j3ru03I2ft/kc+fuVcWfHOCyGzCseZEPLZDpLNLFum/syy83k6FhF2lGWOcjFoYWrgubVE/TfWtxQ+DJHbX66Z///6y1lpryejRo9OIxN/jJRAqAQ9A/OKovQTYzDm807QpU6bMjblOc3/wnmYpduO8AwI6SikckLTK+MzZs2TY7y+QaX97X2ZniLWGcP6NhXvL3kttLyutsOI8xHPP94gzm+HXaI0LlM8kIFXJzsovcJF5KdtIkt/dzimGkRYhZ2Q5s+c+qtYIQLNVPEOsV63sjvHnawv1lC9e+YN88epE+fLDd0VmWXt5Vw/pseSyssAqA2TB1TZxlu0q+YpNfkcYGCG8luYquQbrRsEIoJamJPasNWo4VzfYYAPZcMMNZdSoUcmF4O/wEogyK8zR1A5eTF4CNZVAFgAydepUE19PGFbWxkFAyBVWbQ6XtLwSFEues+qqq2ZSOqZ//pEMf/wi+fgfn6YCIYCPxb7yVTllwGEy488fmqwtEIPZMuywK0IBklqubct/O6aZhWyMIoKCwPjTAs2ozEusazwjSecm63fQ7H7CR1C+9Tuhn+3U7JCzsIQUjRITuK41UobMWa8YV/Dise6DyT/mkAb3k+kyZ+Y/paO7pwmziqpwXsYY0rxTQ7QACoRg8d0T9puGxB71fg3bRL5KYmcP0FCtpGmhCY3dfPPNZcstt5Qrr7wyzdD9PV4CoRLwHhC/OGovgSwAhEKExM9yGGZpbNQcrCjjxPYGQ5WSPJtnvfPOO7LyyisnCt1q9A5AyIiJ18q7n7yXpAvm2mUX/YYcO+BQWbR7YQGokbUFAKKHKddwgCZVcJXvgUUXBayO4UOJhfnvG5AdCghKKAoIMf9J5Rf2biWtakYt/g2nRgnshGy5elfa8WvYEfcz91m+k7R9KOs+QDv8LsLOkoScqVKphQ95Th3D7xR4Yqipe5azNGsIwxLhV6x5zptgel+eiSEiTShro/40IrGzH2jxwzgkdowk22+/vey4445y4YUXphm2v8dLwAMQvwZaVwJZAMgbb7xhFOAs8bgoFGzUcVLsxpkFrFdvvfWWrLTSSk6Uc8Kx7p36kIyd+pDMmj1b5kTEWndwCHZ2yc4rby07rby1dHd2mfA26qVo7Q/kneaQtBWwrJb/OHKs2jWaEQ0lHL5D3qmfWUcKRpRnoGAkjvLhWn52ogFCzlo1o1cjubH2AZ6EV8I3i5MNr9FzmEedV6zpfItYte2MWmWDzEb9Vu8w/Y2qgeR6zVXlefD6mH++P8CHzlHWjFpxx6eZ2DRUi3UE2FEwEkZixxO/2267yfe//305++yz477OX+clEEsC3gMSS0z+oipLgM2VAy5NQ9HXOOyk99spdolldmXNxnX+5ptvygorrODUQkx2rPF/ekYmTvujvPvx/5MvZ/8ndXF3R5cst9gyMuC/15FNlltfFu650FxxME64MmQo4sBKE3LFgUfcN0poFgUs6RxV5foyLf92GlissApGVGlF+chTadWkDKQfBXiyjtoh05OuPTvLGcCT2i4umu3xQrG0QeYll1wiDzzwgHkN+xvZ1bbYYgsZMmSIoAzvsMMODbtw0003yf/+7//KddddJw8++OB8+w9hc1tttZUceuihsv/++8caBtkACblD4U3CdYr18IpfZBtdCMllHqK+tTgk9qxD1kxsrBnArJLYFYzwW0NC2ff3228/OeCAA+S0007L+uq5999www0yduzYuQk4WBd77rmn7LvvvvPJ5/rrr5df/OIXxnNIVMCJJ54oEON9q78EPACp/xy2/QiyABBCnWjNcrIHhWyn2MV74rKSOsoqoWHLL798aLXxrJP+5cwvZdJbr8hHn3wsS/VaUlZZdiXj+Qg2JZpzEHEooUBiuU+itNoF5tqN74E8NfRCPW1lWv41nSdAkH4BLplXBSMoHy7BiG35dx1ylvUbKOJ+vIco3/zOM+woWGvk8ssvN/N7/PHHm3AtPJg//elPTSjNoEGDDAC5+uqrjZHDbnzbrI3vfe97cuaZZ5rr7fab3/zG/P/f/va3seqVoOQSmkofGH+rEOnjrB2T6euDDwS+VxqjSxQY0dBXF0C+EYn97bfflmeeecYAxksvvVR+/OMfy7HHHhtn2LGuASCz1+DlZz988sknDeg9+uijZfDgwXOfAfi46KKL5JhjjjF1se644w55+OGHze9VVlkl1rv8RdWVgAcg1Z0b37OYEsgCQFAOUMIgWMdtWBCJ5UWhJJwgSYrdOO/Ayvjaa68ZzgUKv+vG83Gtq1IURgK2ieaAIg3n4b64Squd6and+B52imFkjPXbhcLgaj2oJRRFlbllXumfDUay9Dcvy7+r8ef9HNvrxT5RFNeJeT399NONpwOlUecVq/Ozzz4reDl22mkn+eUvfxmqxB133HEGwKAU2g1FlPVxwQUXNBUf4WZ4PdvR62Vn+iLBSdqEJCpkBSNB3kheJHbWym233WZeD2jkfMTrtdlmm5kwsjwaa+7ll1824JYGMCL71g9+8APj9aCxp7B2AR8AE9/qLQEPQOo9f773ZI/PEIKFdY6NDm9Ds8YhwIHKwc6BkpdCSX8gfeOVcelZYXwoFYyZQyUqFlsPOjZ8m++hSqta0MPASLvzPeqWZpb5ApgqGGENaqEz1iBKZBIwYnu98rT8N/tmy/q7Am+ME4y/aK/XsGHDDKgcMWKEmVf+++KLL5bHH39czjnnHDnyyCMNuOjXr1/DDGzjx4+Xo446Su69914TMkfD6IJHBIv4RhttFCpa+9sn3AwF3KVXraw5jftevn3qOLEGkJ1rhT2MN+ISjDCHL774ogGprBnWkIIRwp8AAbvssovTeYVj8rvf/c780CZOnCg//OEPTQFEvB/a8O4BkJ577jmn7487v/46dxLwAMSdLP2TSpSAbpBJu8ChCudixRVXjLxVFSolEFO1OK+mnAsAgqvDiwMlLllew6446KL4HmFgBGUVeRGm1u5hN3UMOVNAr2CEbwvlhnnVAnlRYITvCZDbjjH/7AlKOC7T8q8ARK3EWJaHDh0q6667rhx44IEmrl9Tsio44DfAg3nD8ED2I5RMeCO0q666Su6++24hDCssbbQrsn1ee2vez0VueNU5JwCeeXiw7THkTWKHR3TGGWeYtcN6IfzphRdeMF0AmMBpydI469hfCPciXPCII44w65MG72P48EjQbKUAACAASURBVOHmfYTwaaNPhGo99thjTtLnZ+m/vzebBDwAySY/f3dFJJAWgJCZBEULcltYw5LFoeIixW4ccXGIvfLKK+YAc0FY5XkohFhBo8jyKA922FUSsrmCEcIu+KGh0KCw8uOaWxBHjmVco8o3smP+7IOzjP64eKda0Fk/KFZR1bo17AaOUFiNCxd9quIzFOSTajcO4TjPMQBAAAoojXz/KHqEz0AkZg7hgJBWlcQZEJExFtDwBGsI3jXXXCP333+/+WHOuYefww8/fJ6uz5k1S/45fYbM+scX8sH/fSSf9+iW/1pmGSd7V54ycv1s5fsga7gTRaeYzgOM3HXXXcZj9vOf/1x23nlnIzKSSWDM6tu3byYRwr/cZptt5j4DoAuw0AbgpfbISy+9NM97JkyYYHhM99xzj6mV5Vt9JeABSH3nzvfckgAWdxSApI3NFItlo43M9hpgySqKRMl7J02aZNz3WWOHUTbge+ihGBbSZQMPBQ9JwyY07ATlG8sY7wbcaZiWWtBbFYzYyjdz14qEW63WzbzaYIS5BajwLSWpcZH0e63q9RqeiVwA+XhIk34/LscGAIEAfeqpp5p1CNDQMDC8vgAJmwPSqNYI6xmwAWGYew877DCj9KFcz/z0U/m/x8bLR09OlL+/8yeZM/M/GfU6urtlwW8uJ4tvMEC+vunG0p0Dj82lrFw8S9MMs48WyfeJ6ruLjFqjR482QJWwp6233jr0dRgntMJ7VJ/sLGjIjOQIGG3wgBASeNBBB5nwQBoAhB9Cwez2xBNPyI9+9COTRcsT0V2s3vKe4QFIebL3b3YogbQABEvO9OnTpU+fPvP0xvYacHijVBSpUBAyAceEEKa0jWJ3xCKjPEQdimF8j7jv5X6t7A24QPnWEA2bW9CqYIQxKpBtp5h3rdbNvKoFnbXGmgXotiIAa/RN2PVdCLlzFTYZ9/trdF0wBMu+phEAsf/O3ocxAaXypJNOMp4MjAr8+8rLLpOPH3xY3r97rOD5kCijD0VKu7pkqV12lqUH7iQAk1ZsauRhz2OfLZrvE0emYWCkGW8ErgVeCLJRRfF+8JSccsopTbtCCF/wrNWbeBfJDQit4szVECwAiJ3AwYdgNRVzbS7wAKQ2U+U7GiWBtACEcAnCsFZfffW5j4/rNchzRiZPnmxAD+kbkzZViAFWKEM2IAg+Ky7fI6wPtuW3Gd+jERjRrEt1DdOyMz3h9cmTG5R0HRRxPSCE8D6+GRRVvkMsmjTCsDScp4pKmQv5KDeMdVBG2E3YGOIAkEZpeJkvW9m77777TPpe2uEHHCBrTn1D5nzwYWLRLbDssrLCicdKzxT7WeKXFXgD4Wusf0LdmP86gG72bPtHIwcagZErrrhCRo0aZTJifec738lVsk899ZSpN/KrX/1K1l57bU9Cz1Xa1Xi4ByDVmAffi4wSQBFiU03aCBnBSwAAYQPOO8Vu3P7BAUGhB4QkaXZxRBTisIrLWfge2h+UL80iltTyG5Z1CQWoLmCEkCPGj/KpRRqTzFXdr7XTzKJ8Kd+FNagpm1HQaMTD69y2ChipMtk+DgBptP7OPfdc2Xbbbef+iTkm9Gbxri75WZ++MvOTv4mk2Gels1N6LLaYrDx8WMuAENY4Zwdru658pyjeCB4dkhgQqhfMRJXH3oWnhQxrf/jDH8y5pWl499prL0NQp7HXwkWBs+nT8OYxC8U+0wOQYuXt35aTBNICEMKUIJjDAdGiUVhyUaiTpB11PSxiY+lHkiwjdnFEFMKwDCzq9bAtX0nDy1AsOXxdkK3rCEbs+ibtVt2Zta7KV7PiiigMNhhhrgEqgEwAiesaOq6/w7DnaXHJdiiwB79jyqnD5Itp09KBDxViZ6cssMwysuo5w2sfjqV8L9Zw2WeFqzUfBCP8++CDDzYJUfCEUTTQRWM/OOSQQwyQINU8BgtS7t5yyy2y++67y1lnnTX3NVqIkHo2WojwoYce8oUIXUxEBZ7hAUgFJsF3IbsE0gIQFAlI2igSWLThXUD8TqqQZx/BvE+gDgiHG/2J0zgQAQTNiiPmyfeI089m11QdjNh8FwAeykdYStJmY63j3+3xJ1W+bG4BAI5nsV4VjBRVqC+L3O3xV7G4ZJaxhd373p1j5C+/vsvZo5feYzf5xu67Onte0Q8ibBdjVSvzvQAfZL7C+4GHh8xThFS6aHg2zjzzTFPHA+4cZy/cGTwdpH2291O+N0AIfBBCivF8nHDCCTJgwAAXXfHPKFkCHoCUPAH+9W4kkBaAQEJnE9TCfK422ayjev31182Gj4Ib1TTvPoci2YcIBQrz3Ljke8B1yJuYXzUwwqEMXwivWTO+S9b5r+L9utYAu1nHjyyV6Mxv/o03xAYjZRsBgnNgJxvIOv4qzm+jPpHt6uUhQ+fJcpW175DR+151We2yYzH/6iVv5fnHI0H9DYpQkmUKAAAp3DcvAdcS8ADEtUT980qRAJsmFta4jcMEiwrgg/btb3/b1KqoSnvjjTeMdZjY4rDGmAkfIxwKT0lY6k8XfA/NcY/1incBdopsUWBEK3XnqbDamY7KGH+Rsm70Ls0KB++B8buoT6PvAXywhjVUS8GIEtixkOY5t3Fka1e3prJ31vTYcd5ZhWs+uP8BmfaL0dHZrlJ0dPHdd5FlBu5cyYxRjYbD/vPee+8Z40MrJ5tgnyeb1SOPPCJrrrmmSY3r8ltPsVT8LS0sAQ9AWnhy22loSQCIZi4i/ApFAuI5Bbiq4v1g3t566y3jlYFf0KjZfA/c12HgyfZ6oMTpT5K14ZLvkeS9YdcqGEFhZQ4BRZpNC6UVWbjk79hkawBh0QXGXMgsyzPsTE+MP8/vhLllvTGveEb4ViGtKxhB9kWDEQXf/MbDmHd16yxz5freV08/Uz5/403Xj5WOZf5LvnLQgSb8Rue2qiF4NvjEI1208cW58EMeiHGB8CbCrQhxIgNWO631ouTs3/MfCXgA4ldDS0ggLgCxU+yiTHHovfbaa/Ktb32rUpvt22+/PbfyenCCNFMXh3cUAdrmeySpaq7v437ehZcIpbOKxfXyBiNKNm5Gtm6Jj6jBIBR8EpddFNleMziNGDHCpPRlDgCbgBFAOQorhcgossc3AKH18ccfN///2WefneffDImwGYro8Xey7PAsCvExJqqF25nmCMncbrvtzLuoAI5XES8jzc701arzbY+LOh8vHHiw0/ArfX5Hjx7yrUsulE8/+8wATeUD2WCkaKDZaE7V84fBh/OiVRVyvjGqkD///POy+eabm+KTVQWE7fDttcsYPQBpl5lu8XFyUABCopoStYk118J8WDXJOEU2jrAq4WWIDmI8ljeAkTblIFD0D88NoTBRfA/kwcGeBnzYfIci+B4uZOwSjGiIntZSiZK1i75X8Rl2Zfci04w2SiHLfKAEoihRpJNrCA8hDfLJJ59sipfBHwkCEMDDkCFDzHcEqRYPihbiI5Rqjz32MNWXtVEM7Y477jBcH36rBwbw0Srpg+Outb+9+2d5/cTmxeXiPi943WoXXyhfWaq32eeCIXhle73oK/sn+zBnBPOfp+cvrQxd3Md5csQRR5hsVwDz8847r+3Wugs5+mckl4AHIMll5u+ooASiAAgHHFb8RkRt7mPj5YCpkmsdxYkDEG4KTcNAUMKiMnUp34NxpQUfWlxOs4JVSS5Jlh79R2G1w7SwYGqdkTDwxnpBScVSDvkSwmkVrLFJxp7lWtYNngC+lzIy/UTVsGBc1157rUyZMsVU6SZ9509+8hMBOBCbD3fqyCOPNB4RYvYPP/xwU0CNYnoKIBSAkGL0wQcflHvuuWeuuHbddVdT9wJwgxUY4BJVyDOLnKt8L56nd577o3xx5TW5dXPV88+VBZedl+OmIXjKB2Ifw1OlNWQAAUV8i4QdAj7oTyt7vvAOAtDffPNN2XPPPc235DJ8NbfF4x/cEhLwAKQlptEPIgyA2Io7Fs8gUZsDZtKkSUbJqBKxFMsuCvQKK6xgwlA4DGl4bsIsccr34DctDd9Di6txbyvxHeKCETven3jvKnnFivjK7Xh3QpPCEhvk2ZdmAGTfffeVffbZx1hrn3nmGTn00EPlzjvvNJb0F154Qc4++2yjSF1wwQUmnAoPia1UKQC59dZbjeWXUK9+/fqZ8JPjjjvOZAAaOnSoqQC91lprFaLw5inPpM/WsMOen/9dPv75iKS3x75ePSBhN6jXS8EI32aeXC/th3K+2APZb+taq6bZRGDkGjx4sDG24AWE/1EEuGvWL//39pGAByDtM9ctPdJGAAS3vsZvRynuhHTgVcDSXZXGoUD/6ROWXEJHosJA2oHv4WpuwsAIMc9Y/bG4Ar60srer91b9ObbnC0BeFviKAiBYbAEeDz/8sPFY2iFXeLeefPJJ4/WAKwKRFoDB/9dMaSiwCkCocUCqUTgIABZqE/D39ddf3/BLKL6GHNqpEY6Dtxgv4dK9e8tLPzo0Nw7IWjeOlI7OzljibZQFD2XZnlsX9XgwwHBm4C3jzGAdtWLDU4jnA0/nUUcdZf7bg49WnOlqj8kDkGrPj+9dTAlg/USBonFYoUgSx01GJBT3qINk8uTJhojaq1evmG/L/zKUJGLwGRdWaLw3zep7MO40Xg+b74EXiFCWdjmMFIwga+UQoayi3LrOppX/qkn/BkL78LrRyg45iQIgt99+u1AJmRApWpDzof8mjOqJJ54w3hD4XcyzKqwAjr333tsUWUNp/eEPfyh33XWXKYKG94NvAGI7JHS7Ds+c2bNk1iczZM7Mf0pHd0/pWnQJ6ejsSi/0Ct1ph93ZnK+8smAttMLyssrZ/6l4nVQUzKd6RvBYMLd8rwo00wAHnkcxV4w9RXKeko496/V4/AHppBQ+7bTT5IADDsj6SH+/l0AqCXgAkkps/qaqSUABiJ1iF0ARR5kmnlwP3SqMCyCFhQqFOCo0zDXfA5DTbjnf7eJyKC94QVBEUHAAfHE4I1VYM1n6oIpXVTJ9RQEQFKcNN9xQCMOKAiCQ0uFwPPDAAybzFTUNNJsWFm6svhdffLEJscL6CxBBkb355puNBZyqzACQpb++qPx90nj5YupE+fLDP4nMshJddHVLjyWXkwVWHiALrr6xdC6wSJZpKO1eu8YFhhjbE5xLHZCODllmv72l9/bbORkz+6WCETwYNMJUNaNWnOQBKOMYffjeo4q5OulwiQ+h+jhrH4PDz372M9ltt91K7I1/dbtLwAOQdl8BLTJ+AAiHEMqD5uuPS56eOnXqv0IOll66dGkQdqXkRzqz2mqrNeyT53tknyoFq8g8WFxMPSOtDEZYQ4TcENaEsoa1vwoE1DAAgnJJilD4HlqgM8wDAgkdZfKcc84xoVSXXXaZrLvuumbRvPPOO8bbAfeDbx6PCnwPyOv777+/Af/77r2XjD3vGOmc/KjIbAqc/otX1bh1iHR2ySL9d5aF19tJOrrqE7ZjfwONalzkVwn9culexH3hV4w2Ckb4rmlaa4Q9vhGfQ8POOC8IxW1V7y8eQThOzPmFF14o2267bfZN1D/BSyCDBDwAySA8f2t1JIAFi0weGrubJIc5dUBQVjh8ymqqDML3IJQACx5hZI0AiF1cME2KXcZIfQ9C1DTcIE3IQlmycvFeAAYhRxzGWDzDCjnyrjAwYvMKXPSpyGfYVm8s3mT7qoriBQDhOzj++OPnEQmGAjwUpMfVFgVAmB/GSVpRMl3hEYHfoRyQ66+/3qx/viH1eOEJmf6n16XriZvl24svIB0JJ6W717Ky+MBjpWvR6oRzhg0BZR2DDRmfAHRh38B7d46Rv/z6roSSCL986T12k2/svquz54U9iG+bcDsASaNaI4AR0myzz1btG3AtHDhTVDhnnweMb7LJJq5f4Z/nJZBYAh6AJBaZv6GKEsDrQfwuIQRJyYhYPLGSlUU41cxDgCgOQqyyWOVQwvr27TuPuG3PR1q+ByRTOA/txvdQQaKMsFa00nySLDetAEZQPBm/pnSO6yks6rsHgEAODzbI5xgJCMPS9vTTT8uPf/xjw/cAtDcqRMi1559/vowZM0YuuugiQy7mWXhH1lhjjbmeH+TyyXvvyGf3nC8d//hMujuTwg9Sz3VK50KLyRJ7Das0CEmSZnbOzJky5dRh8sW0aSKzZ6dfBp2dssAyy8iq5wyXjoLJ3eyxNhjh3wBPfrf6Pjh27Fg566yzzPdx9dVXy3rrrZd+Dv2dXgIOJeABiENh+keVJwEUcw7VNO2tt96aq4ymuT/LPaoIoNgCgJSDgYcCJXH11Vc3lmmb78H70oAPBWnEurcr30NDjvB4EXKSFKzac61EWLgFNmekyp4R+onVG8ULq3ddiqthzd5yyy3l8ssvnweUjxs3zhDHJ0yYEOsz5Dsi5Ix1ECywOWfWTJlx2zCZOWOayJwMinZHp3QvsYwssc/wSoZjATxZA6x9wFgcjsQ/p0+XqcOGy5cff5wOhHR2So/FFpOVf3qm9Cw52yBrifEjBwUhGCOUM1JUrZFYCzbjRbfddpspwMm5gscvaNDK+Hh/u5dAJgl4AJJJfP7mqkggCwAhJpxGtpwiGxY5DkIOQRQBwkG04aEgRKhPnz7m71pYMC34IH4eQENrpfoecefLzvSVR7hFEIwAEFWhAexUgVtBTDxrKo3nJ66c87oOwAD3g+KByBbgzrdDliuUKzwbzZpd4yTI+eHeTyeOkU+fdBdqtMgGu8kiA/IPNWo2bvvvmnAAjy/ZzpIAcEDIG+ePkC/efTfJK821Cyy7rKxw4rHSs+RMg+yjfAOADww+fJv8N3LBkIAXDJloet+6ZsLjPBw5cqRceeWVJrwSjtNKK62UeN78DV4CeUrAA5A8peufXagEUALTNEjfHExadTzNM5Lcw+FA7DGhUBx0AIIgB4PDkH6tssoqRuHSkIE0cfrtzvdAqUDpwPNDCE/eIUdVBCO6BlCoWqGy96OPPiqnn366yWJ1xhlnNOVvKd9BPY3BGiezv/hUPrhmqMhsK8tVko+60bWd3dL7x5dVJjsWRg3COrNkeiIc6y/33Cvv3z1W5syaRc7zcCl1dEhHV5cstcvOsvTAnQoPuwp2zOa8AL6C3j/2ZfYIJbEDcjEc8M1AYOd3EsCWdfmkvZ9xkOENvhTf+k033WTApm9eAlWTgAcgVZsR35/UEkgLQFBOOWyWX3751O+Oe2PcNMEcgnhmqITOoZeGbM5BBNEcxQMrMWFXaQBM3LFV8Tq7vkUZnp9GYERT+xbhGbFDjtp1DdhhZyhitqdR1+xnzz0gf3t8dJNsV0lXeId8ddO9ZeF13KSbTfp2vV4NHhg9XK0BsmP93+Pj5aMJE+Xvf3pX5vy7BhPv7OjRQxZcbllZfMMB8vVNNskl21VSWRB+qoYmvM1xiozmUWskab+TXo+hCm4T3kLOjhtuuKES2R2TjsNf3x4S8ACkPea5LUYJiOCwTdrIikOI0oorrpj01kTXc6BxCDZLE8wYCJd5++23TV0KrG9Y7ePEamuH1OqPAg7wgGjZbg0vEnNblfoWRYORYMgRa6DdAKhd2RrwEfYNzRh9pnz5lzedfiLsRB1LLCd/WKifqbmgjfpE/fr1M/UY8k58YRshCMUh/ND1Gpgze7b888PpMhuPQc+e0nPJXrErnDsVeMjDdN9l3ICPJEkn9JGcLeoZYU+lJa01kvdYOVdI4AAviuyJcD7gOfnmJVBVCXgAUtWZ8f1KLIG0AAQvAcrqyiuvnPidcW/QGiWEWkVZ4OwUu3BE6BdghP+P5RYwQvhIFBhRqz/31IloHFeWza6zLb7Ii7CrKnAw7H6zVplbJbBrlW7668IzgjKiNXEg2wdDjprJsBX+TlY5Qo6aVbamwvn7lx88b5FBRwKY09kl9/TsLzffcotRCAnjYb8BkKAIazV2R6+b5zE2AOUbaLciowhDCffKe0pixAmbk0a1RlhjyvlKA3Cyzj8g68QTTxTq36yzzjpyzTXXmLPCNy+BKkvAA5Aqz47vWyIJpAUgcDGIkV911VUTvS/OxSjDH3744dxibwCCsDjisPoems8eZRVQYlvfOGRs/gjhVig4mlbYxYEbZ5xVuQalC68HgC8vi6/rsboGI3bYGVb/OOEmrsdU5vP4jqjtwHcXp7jczL9+INNHHZdbl59Zeks574qRJhyGb5nv9ZlnnjHZie66665cuGc274k9B1Dbbo29kvDaNIT7uLKy92Y1FOFxVTDCf7v2OAX7xnuPPfZYISX1RhttZOp81CW7XVw5++taUwIegLTmvLblqNICEBQV4qPJOOWyacYVlGHqk0QVe0Nx1p8ovgfP1IwtWumXwwYgguKJ1RdLJ1l+qmb1dynbRs9qBat/VjBih50BPtqtwKRdYJFQJ36aKYBfTv+zzLjllNyW50vLbiPDL7nWWKc14xLV16lGDQgh1TYKK96RZn2N00nNEMZeEcZ5ifOcOl/DPoghIgvhPun4G9UawQCkXmuAkIv5tfvFOIcOHSovvfSSbLPNNmZNleGBSSorf72XABLwAMSvg5aRAAooh0DShrUUrwGKgKtGNhX4HlgisUCGucOz1PfQUAAOIY1L5vAh7pf31SFjiyt5E+uPtRPQhbxbweqvYATAyXqKCtOyrf5VDTtzNddhz9EEDwDzJCFHeXtAXlhuOzn74qtk/Pjxput4XE844QTzm7As5pa9i7WrlvO06V9139HU3u2ojJKyGdnG8X7ltSY5h9iTlDeini+XtUYwmlGUc+rUqbLLLruYsL52MzjkNX/+ucVIwAOQYuTs31KABNICkGDRv6xdxQqNMoz1C74HbvhGza5qbqwBpK3sSFZ9WcNtOPDwfKCAcPDR7GxLrQxGNMUsniBIva14CEeBEcZNcT2AaFyrf9Y1XrX7be8XABQFPm7LkwMiXT3k6W8OlDN/8hPDRdFUr3hbTzvtNGP04P8Rw284QX/9q/xj+nTpnDVbFl5sUVls2WXlq4stFsubqXVeAB3t6v1CKecnWGQy7lrI4zrml32a+QWQuKg1gndnyJAhxsi13377mbXUbh7vPObKP7NYCXgAUqy8/dtylEBaAGIX/cuiqGvKU0K6sEJH1VsI43skEY/yPYJZnpCDhmlx8AFqNJc9FrhWOaiQofJ3XKUXTSL/sq4NghHtB1muCPVrlfmNK19AN4R71jmKdxjgj3peHlmweF+PpZeXJxb+jpx//vlCVWrmhkxUdipgk9L2sfHy0ZMT5e/v/EmotTG3dXVJx9JLyUL91pYlt9hMFgsJrdSQI75zAFi7rQF7L6gy9yus1ogWPoyTgIL07IMHDzZee0AI2dSSGq7iflv+Oi+BPCXgAUie0vXPLlQCWJZwdSdtWvQPEnpa6znvRQmC+Aj/Iir2PC7fI2wc9mFLmAFpdsMUDsCIZltqFsaTVG5lXm+H27RrmmFNL4os8LYBTDRMS0M9Wl0R5XubNm2aiXvPYvXPsw7II3/+0nA9NARLv5tERf24qatLujf+riz2va1lscUXn5stjZAjPGBlhhyVuRewH+IRYJ+r015AvzW9r50NDxCp32/QIPbqq6+asCvCho8//ng55JBDyhS9f7eXQCYJeACSSXz+5ipJIC0AQYmh5gZpeNPETKPYY5UCWGB9DEt5moXvoXJmjChchFkBdJLUdmhEcKavWuW3LsoqijchbijeeJmShNtUab1m6YuG22h6UdatrczYYLNVwYiG3rkgGudRCX22dMjSQ66S+x58ZD4A8s/p0+WN80fIF+++m3gZdC69lPTYcw/p/NrXjMEEIwP7APtBu1nC2XPZC9gPSTdd59SzjWqNvP766/LCCy/Ipptuava5Y445xni3zzzzTNl7770Trx1/g5dAlSTgAUiVZsP3JZME0gIQDq8333zTFCJMSl4m9IEDkLCPqCJXLvgeGmrCs7Iq3kEwYhNgUeiqqsioxdtWvDMtmhrerIo3CklYmF8UGGF+s4Qali0yTW2NFdil4v3pxDHy6ZN3ORkeRQh7rrOjLLHpnjJ27Nh5AAjgY+qws+TLjz8RSZE0Qzo7pXvRRWXBQwbJlwsuOLe/tuU8rSfXyeALegj7PV5n1npS3k9BXUz9GkAle90VV1xhqprTMDLw/w844AADROwwvtQv8jd6CZQoAQ9AShS+f7VbCaQFIPAk3njjDVlhhRVib+oaBgXhkdAHFMEwD4ILvocWVcujqvdcAuwnn5jDXMGIekaqAEaQoYaaoEBj7ayzEp1m5SvHCDkkUbwbgRHlBNUNjNh1XpJ6AJvJfM6smTLjtmEyc8Y0kTnJs+nNfX5Hp3QvsYwssc9w6ejqnue1hF1NOXWYfDFtWjrwoU/r7JSOXr1k+Z+cIQt9dZG5nC9NQFG1Kt3NZJ/07yjiELBbPdUw6/3mm282IIQwO75lGoayTTbZRAYOHChbbbVVUvH5670EKiEBD0AqMQ2+Ey4kwGEECEnaUMBfe+01UxAsTjiPWt4IgyHmGFJpmJLugu/BwYPS2YzvkXTcwevnycbzySfG2oaSr2FaKDVlgBFkCOESEIaso+qpZJVBVe9nbRPnrhwjsvykaXUGI3ZxvbzCbWZ9Ml1m/HK4zP7843QgpKNTOhdeTJbY60zp+uoS803Re3eOkb/82o2XhYcvvcdu8o3dd537nqgq3RgUWqEwqXKfsiQdSPPtlHHPuHHj5PTTTzfeD7whGB4efPBBoY4M4Vm03//+9ybttG9eAnWTgAcgdZsx399QCaQFICjakPu++c1vhvI39KV4S7C8oRRDeg2rMIwyr+CDe9Ok2EWZQOkE6Li29jZbRna2FgiSCkZQYvjB/V8EGLGVTg5ZQFi7NTvFLJ42V1Wt6wRGiiyuBwj56J4RMnN6cn5Gd69lZfGBx0rXor3mW6Zku3p5yNB5s1xlXMwd3d3S96rLpLtBpXMtWgpnQKt0YznXwnhpW9FTCQAAIABJREFU+G4Zu5v5dvZfwq40BLMVAFWYUH7961/LOeecY7736667Tvr16zfPpYQNkwVwgw02yCxX/wAvgTIk4AFIGVL378xFAmkBCPe98sorBlBEKbjE3gMIOMS5NuwAd8X3gFsCiMnK98gqbAUjmk0LUIACoJ6RvMCI1jih/8R4t2PMsypceRdYbJQtrSphWlpkEm9c1HeXdZ3b9xOO9dkz98qnT40VmU1mPVgdYa1DpLNLFum/syy83k7zhV3pXR/c/4BM+8VokTlRz0o4io4OWWa/vaX39ttF3sgeh/cMMMJvvmnCORWMpEldnLCnmS/XxAv0lXXQyiGYN910k1x88cXG43vDDTcIGRp98xJoNQl4ANJqM9rG40FZR5FK2rhv8uTJRtHHxR1sHNbvvfeeCYOi3gThH3nyPVD0AToAHBTvKlkq7aJa9FMr/KpnBHDmwjOiMsiD85J0fZR1vcoAmbIOiiIWVwmMqAwAn8igaKWT7Fh/nzxevnh1onz54bsis6z9pauH9FhyWVlglQGy4GqbSOcC0cUPXz39TPn8jTedL6eFVlheVjn7rNjPZb+zwQj/Zo+xwYiLbzh2h2JcqOuAMNBWrnPC/nrllVfKyJEjTXjvqFGjZPnll48hIX+Jl0D9JOABSP3mzPc4RAJpAQib/qRJkwywCMbWa/gLlmgOBP6eJ9+DIoZk90EZIOSoyqlxkRvWaa3wq/UoFIwAHpIqMjxTqxnXQQZ5fIzIgDWgBS3LXAcKRrCcB4ta5klgt5MOVGUdzJk9WwjPmjPzn9LR3dOEWXV0dsZaAnNmzZIXDjzYafiVvrijRw9Z68aRsftid5g9E88C88uPghFN3ezKoBBLSCEXadY31gF7dNI9Jcu7i7wX2V9wwQUyevRok1HxxhtvNEYx37wEWlUCHoC06sy24bjSAhBEhQeEKtIUENSGcg3fg4bLP4yg7oLvYRfWox9RQKeKU9sIjKhVFcUhToiHneGoytWM85Q/MsDbBqhjLUYVtMyzH42eXRQYYS2RdOCvf/1ryyQd+Mf7H8jko4/LbcpWu/hC+cpSvTM9H7kDRlh7eEjUoKChlkWDERuIJ8n6lkkIJd2MrIcPH25SNlOPirAr9kDfvARaWQIegLTy7LbZ2LIAkClTphilH+WfRrgViiCHLtaoMLJjkO+RxmNBfY+q8D1cLBlbkVGrKgBEFZlGYMQmWmPlDCvm6KJ/VX1GnQj3eYERLSynGeYahURWdf6i+vX3d/8sU048Jbeur3r+ubLgsv/t7PmNDApF8L50AHbK6aoBcWdC/veD+JZOPfVUefjhh2WNNdYw4VeE+vrmJdDqEvAApNVnuI3Gx6GledKTDpssWBDQASAAD9z+ABLCrtqV75FUho2u1xAPtarybyW/4hnBS6Ik47yJ1i7Gk9czSC1Kdh/NrlYnwj0KFECTObbDtDSMJy5vI69sX3nNWZLn1sEDEjYem/fFPAOU80zPzfvgwLGe2H9bBYQ2ki973wknnCATJkyQ/v37m1S77Wh8SfIt+WtbRwIegLTOXLb9SLIAEOqAoPShCOKRwAofdfC5qO+hfA8OnChie6tMrE1+RZFhvrCqotBoZrGiiNZVkqlWd8fLRqhfnVOLBsEIcoYr0gyM8M0BwGjIgPXQSq2qHJCkMlYwopwRTc/NHLsoXMoeMW3aNBMCBv+BZ7ZqQ4ZHHXWUPP/887LZZpvJpZdeGitUtVXl4cfVfhLwAKT95rxlR5wFgEydOnVurQtCrsi20qi55nu0K9fB5ryonAGAmomnzkp4kg9MCbbwi1C44noLkryjrGvjghFNr9oKACxK1lXJguVqPTSqFYQXU8EmazpJSCp7AiAUMEqmK1f1blyN1+VzCPEdOnSo4R5uv/32cv7559fa8OBSNv5Z7SMBD0DaZ65bfqRpAIhm2yHsCuVvpZVWCk136oLvgYcFvgdWf5dF5eo0uTb4IMQC0IHFk5ALLZimYIS/taJXhLVEETEASKsTbFmbjcAICipzS4X7VgRgwW+yzDogee8PrGf2Ng3FIxQW8KHeL35HgRENv2NfxANWpxDEpLL94IMPZMiQIUIhwe9///ty1llntZThIak8/PXtKwEPQNp37lty5ByCcZtmXSLbDtZXuAnf+ta3Qj0fKM7cw0GaJhUkhzOxzbyravU94sos63U216FRgUWt3qxghPfhjVLPSCuAkSAAa+UY90brRQnsWIFROGla9BDreSt5gezx51cJ/XLpXiS6BknW7zbp/TYY4b/ZL20wYs8xYIVsg4AYvM9xMuYl7U9Vrsf4NHjwYBNmNmjQIDnppJNSnSVVGY/vh5dAFgl4AJJFev7eykkgLgDRQ4/rUYRReAEXQQDCoWiHXaUBH3Zti3bhezRaGMp1AETEqWiNcqqx5nhGWkFRZd2hhKCEt6sHzE41TKVnlFGtM9IKcxy1Kb535xj5y6/vcrZvLr3HbvKN3Xd19rw8HsSa11pBhFcBRgCc7IUYY1DGWQNR2Qbz6FfRz3zjjTeM5wPu35FHHimHHXaYBx9FT4J/X6Uk4AFIpabDdyarBDjsUPijGoowscaACQ493P0ohdxrV511xffA68E725XvYReVwwoK4T6plVvBCIoMmWPqqKiSIUrXHQCslS29Yd8f3h++NWTBOrBJxmFhWur9Srpmsu4ledw/Z+ZMmXLqMPli2jSR2bPTv6KzUxZYZhlZ9Zzh0tHdnf45Bd8ZTN/M69mHSbVLFsJW8HA2Eilcj8MPP9zUtjnllFPkwAMPLFjy/nVeAtWTgAcg1ZsT36MMEogCIFrYiiJnWOBQAvXAAySg2K644orm7Z7vkWESrFuxdiNv4vyxdgPC0oSv2b0JS/uKoppnde4sEmH8WleG8LtWVbSiZMS3CQADhCCDsEQPPKNVwQj7yrRXXpEPL75M5LPP04GQzk7psdhisvJPz5SeSyyRZVmWdi8eL4AoabgBlgBSWquFWzKmP/7xj8bjwRjPPvts2X333UuTu3+xl0CVJOABSJVmw/clswTCAIimd0QRxNq21FJLzaMIoxxyKFKFVsGHC75H3HCjzAOv4AOKKKynFlU8IxreoSlBmxFfixCZHX4HQPrGN76RKDNQEX0s4h229wevI4pn3KZghO+zrt4vxmqnmF3yK1+RD68eKV/8O/VwXFlw3QLLLisrnHis9OzVK8ltlbkWLwD7rR2O2ijcshWy4j3xxBNy3HHHGdB9wQUXyHbbbVeZefAd8RIoWwIegJQ9A/79TiWAssJBbzd4HpAcNe4eV3+wkZGIg5EsWFrjIyvfI224kVOBlPQwFE4snDSs3UVktQF8ahYeBSOaErQMMGJzHdo1/I75Z06I86e2R1bvTxQYYY6r6llCwcb7w16kKWYJx/rLPffK+3ePFeqESFToaEeHdHR1yVK77CxLD9ypVmFX9hY0Y8YMIQsUlb7JgNfIG4qs7Kx43M/+od9yEvBa0vZnXktlc8KtWJPU+Nh0003L7I5/t5dA5STgAUjlpsR3KIsEggAE5YeDn0MAy2tYgTMORQ7HFVZYwRyK+pOkL1i5lO+Bl4WfrOFGSd5flWvxRiAHOA4oW2XU9FDiK31B6dOUoBqmlfe82N6fINehKvNURD/IdAW4zyP5Ql3AiB161ijFLNmx/u/x8fLRhIny9z+9K3O+/HLu1HT06CELLresLL7hAPn6JptULttV3DWEJ5A9lvWQZG9kTw2m6GYPVzBSVR7V2LFjTXpdgNM111wj6623XlxR+eu8BNpGAh6AtM1Ut8dAFYBw4JFthEOPwwpFOIzEanNDUJZRUvGSJDnc7PoeKJy8s91aVcONmBuACD9an4D5cVG5udEc433B+4MHpNVrGoStcbvOyde//nXp3bt3rmAcwKeZluwwLVVUy/KMJA09mzN7tvzzw+kymzoaPXtKzyV7SUdnZ623EtYCIVeEvxL6ynpI0/ieFIzwm+eyR+u3jGckb8NCnH6PHj3aFBbEyzNy5EhZY4014tyW+poHHnhA7r33Xpk0aZKRMXvO3nvvLXvttdc84Z6PPfaYXHTRRUI2LrxPEOH33Xff1O/1N3oJZJWAByBZJejvr5QEACD8oADi/SD0JUr5Ub4HljYUR1ViOOw43FBS+Yly+2t9D5QcgE4S4FIp4WXojNZUUZlDOK+CMhAckj3HgBFAqSowEGCz9llTDbd6Ve+opWJzHbIonGmXY1XAiK4F9g474UXacdXxPnstYJhpFP6aZlwKRthvkDP/Rs76LbMHZ/2Wk/aLs+T666+XK664wpw7o0aNMiG9ebcf/OAHJqPc1ltvbRJ9PPXUU3LttdfKAQccYOqM0J5//nnZb7/9ZODAgbLzzjsbYvxll10mw4cPN8UQffMSKEMCHoCUIXX/ztwkwGH09ttvGxACGLDTfNovjarvwWFG3QkFI1yL25/DU3PX8yz1nOBpaWe+h13boi7eH63crJ4R1ktWMKLhRu28FmyuA3VOyvYEhqVvztszgiWaMESy7bEPRVUBz20zLPnBdspl5b3k0SXdr7VmEP9WTzbzzN6dNxhhP7nkkkvkpptuMvV9brzxRhPyW0Rj3wl6lc4991zBE/Pss88aYHbwwQcb78gdd9wxt0tnnHGGPProo/L444+35fosYm78O6Il4AGIXyEtJYGpU6eaMJuoirpJ6ntwmCmxGXBDw1LOwQZI4f8liWluKWGLmKxEeJtQsOpa24L1oJ4RAAlKK94staYSxx2lwBQdblTVNWRXua9i6FkRYMQ2SmCwIOtZ3spvFdcDstbEH6yFqJTLLvuP/NmXFYwAgviWtZZMs285TV84I8455xy58847TR0pPB+EOJXZ7r77buP9GD9+vAkFW2eddeT444+fp/7I008/Lfvvv7/pd9++fcvsrn93m0rAA5A2nfhWHTYx1xxCUXwPDiUODRSDJJZJ7uNgI1uW5q3Huob1qar1J/Kc548++sjU+EC5wOpXVpy9yzGydphb9X4pGNFQvKA1lTVBhieUHpSOxRdf3GV3avMsxg8QrUvoWR5gxAai7WyUwAAE+EAegI+wxB95L27ej4FEDUgKRtT75SLkEs/pmWeeKfAw+vTpIzfccENqjotLeeDdePDBB2XChAny1ltvyQ477CDXXXedbLLJJnNfg+dkgw02MHwVQrN88xIoWgIegBQtcf++XCWAYsFB06i5qO+hcd0AHJRSDjgUVsBMlepP5ClkW9GKSqeZZx+KeLYNRgAkrCsN7WDumXOUbtYcISaE27Rj03AjFLqoZA9VlU0jMJK0IJ7NgWpnIIonEfDB/gj4qErKXP2WFYww5xpyCSDh203qqcLjh5cBcjceBrJdhYX8Frn2X3rpJUNAp/L6YYcdJs8995zss88+8qtf/UrWXnvtuV1BBquvvrqcdtpphi/im5dA0RLwAKRoifv35SoBlEQ2VrtF8T3idsYOreCwwuKvXhYthocipilf88yyFLfPeVzXrhZ/taaqZ0RBLh40eC+Az6QKTB7zU+QzWzHcKA0YYS2Q6hvluwq8lyLXgP0ujDHIAZBOCGxVPaJ2yCWAhP2b71g9I+zvzTzjePyOPfZYIYzpu9/9rlx++eWFhZlFzS98REjpJH+45ZZbzFwoALn99ttlrbXWmg+AnH766SYUyzcvgaIl4AFI0RL378tVAkEAkoTvEdYx27pJlhEynIQpm8GUr2plIx48j/jjXIUZeLgd44+i1a4Wf63kzNzq+sLSq2Fa7ZAFzU6t2qrhRnHACMorFn/2Hbw/RXEdivzu47xLi02yx9XJC2Yno2AMmqYbgwKApFEBU4wQQ4cOlRdffNFknhoxYkQlPD30HyDBPn3bbbfNDQd9/fXXfQhWnEXsrylcAh6AFC5y/8I8JWADEDvkKinfQ/uoGZ74naSgnH2w4RlpxiXIUyYunq2hZ1g1qxRa4WJscZ/BnE6fPt38ACi1kjOysTPwxE3fHPe9VbvO9oK5TK1atXHa/VEwwjxj/abBbVCFNSrpRZXH5aJvGoKXR7FJF/2L+wy+by1gyjyjyHNuYHD43//9X9l4442NZ4HQJpKd7LLLLvKzn/2sEp4e+nrQQQeZDJCEWmEgss8wT0KPuwr8dUVKwAOQIqXt35W7BPBWYJV0wfdA0YBgjDseq15aMmUUlyBpwcPcBRh4AX2HrEhBx3ZOL8u6opga1k88YI3qnISlb9YMPFWJh8+yhvi2CLPRNNft6AUDjMyYMcN8F9qSckayzEGV7kUO7A2tmPELpR4gArGclLY0vmX2gO9973umqF9YspMi54j1eMQRR5iUu7feequsuuqq872eNLyMBXCibdiwYfK73/3Op+EtcrL8u+aRgAcgfkG0lASwzmLFQhnkB/CQNDbfVrqDfI+swlIuAVZDDoQkBQ+zvjvp/fSNLFf0tVnoWdJn1+l6u7ZFXC9Yo6rNAFgN0yI2u24NjgPgg++primXXcgcBZQaH4QbkWZX076qZ6QdwAj7GHwDAEir7w18/6SqBYSQdEL5X3hAt9lmG9l+++2lX79+LpZWqmcAJAAWJ5xwgqy77rrzPGPFFVc0hiMtRLjrrrvKTjvtZAoRXnrppb4QYSqJ+5tcScADEFeS9M+phAQOOeQQc0Bgodp8883N5puk2ZbuvA/WuAUPk/Tf1bUcuhy2KJ0oWa4qGLvqX1HPUaWb9+EFQ+lM2uxaMiipKG88Rz0jdQAjhJmxHggva9eq3sy7Fptk7gCjtnGjUZiW1gzi+qqSspOuZ9YvhglCk3r37m0ASCs3wq3IJgXYOuqoo0zmqN/+9rfyyCOPGBnQIHyvv/76pYhhiy22MJ76Ru3mm2+W/v37mz+RrQu+yhtvvGHCRwcNGiT77rtvKX32L/USQAIegPh10FISOPTQQ81GS0NZIm53u+22k4022qgpUTAt38OFANVijrdBldSyLKmkFUbZzKJ0u5BJ2c/AQ4Wl22VtC8AxyjxWdLuwpYKRKiqpWu8FME9sebMMQWXPWx7vR+km1EirTqN4R3lWWxWM2Ak5MEyQhruVGyltCW9iL8DTQDpbbYQhPvPMM/Lyyy8bRb4dwxFbee792PKXgAcg+cvYv6FgCUDEu//++80P1h4ayhOWom233VbWW2+9+ayRKIMom1n5Hi6GqgUPUVI1rIPDDS9E3gUPNbwE8IbFvw7WeRcyt5+BsonS/f777xt556V0h82zpgMtG4zYSjcFFiHgJg1ndD03ZTwPObA38G0gAwqPJmmAEQWddpiWpuoue57jjoX1imECA0U7pBsGXBx99NGGjP4///M/svPOO8cVlb/OS8BLIIYEPACJISR/ST0lgOLw6quvytixY02lWpQIGgoE6RMBIyuttJKpZEtM7NVXXy3LL798pUIlUF5QfPjJs+ChneEJazzWzXa1dGt4CeukmaXb1ZcRVplbPSNFk11tS3capduVXMp+jmb8osZFXP5PVJ+jwAiApKqAX3lQeIkJwWv1dMN40U888UQzlZDNt9pqq7KXon+/l0DLScADkJabUj+gRhJAwQZk3HfffQaMYOGmEUJAHG/fvn1l1KhRlQIfwXFowUPACNwELZ6Fkpqmkq8+31Y2wzI8tcOqstPLlhle0gh0Mr/Mc94eMObZ5v+0g6U7bG3nnfGrLmDErnVCuuG02QDrsoeMGzdOKM5H1rorrrjCFBr0zUvAS8C9BDwAcS9T/8SKS4CD//rrr5fLLrvMpBOFkIfVG+8HfBEI7Fj5qtzCCh6ipGKdjBsqo7wX5ICFFytsOzbkQIYn1gahZ1WJ52ZeiD/P2wOmc44cKKwHKOUbSEO6b4X1o0U3MVwgh7yV7kbheFVIVIAcWA/sJ4CPVkglHbU+yXZFbQ+A/rXXXivUz/DNS8BLIB8JeACSj1z9UysqARQKPB0///nPjbfjjDPOMJZl+CK43TlwaXhECNEizSJegao2u+AhSioKK+PSdK8oTmFghLASYrrxpLRzWtW6yCHoAWNeUZTUM5I1ZE7lQLhXuxab5Du35YDSXXRYVFXACCGfgA+XSRiquo/SLzJGEW5F6CW1P/r06VPl7vq+eQnUXgIegNR+Cv0AkkjgySeflAMPPNCQSS+//HJZc801596Opfmhhx4yYVoTJ0406XxR6sitDhiBxF7ldLRRBQ9RUm0rrmY2wltCmE1diLBJ5jrOtYTf4f3C2lwnOWjFZkAnoJl1qmAE701SMGLXtsADVDTnJM5cFXENewApTflWAGFly6EsMKJpl6sihzznnn3zqquukuuuu854wzFQ4Q33zUvASyBfCXgAkq98/dMrJgEUDNzsO+64o/Tq1Su0d+R8hyuCZwTuCA1L4IYbbmjAyKabblrp8BQteKgEdi14SIiVKq/wXzhw44ZrVWwqM3XHLqRW9yrOGo7H2lYwohmWmnGD7KKb7Zx8gMWkoBzZEY6YFMRlWpAxbi4KjJAKnIQdeWaAizHcQi5h/V9wwQVy2223mRCzG2+80RgifPMS8BLIXwIegOQvY/+GmksAi6im9Z0yZYoZDRbzzTbbzICRDTbYoPAwjSQi5ZDFoqnV1xVMkVoVpbPoEJMkfc/jWpt0T5YrQi5aBYQFuUFRiQrsgnJ5F93MYx5dPdPOAFeXdMN5gREttFh3UB5nbSDDn/70p3LPPfeYbIiEXbEf+OYl4CVQjAQ8AClGzv4tLSIB6orce++9BpAQH03jsCZNI2AE0mLVLKf0UUm1HLooWfy77IKHZSwJeBTwXhh/K2d4asQNIpxIPSPUecHKzRooM+NXGWvAfidyeu+99ww4r2sGOAUjeMC0uGVSArsNwopMP13W/LMPnHrqqfLwww/LGmusYcKv2Bd98xLwEihOAh6AFCdr/6YWkgAHNlVy4Yv85je/kQ8//NCMDgsaNUbIprXaaqtVwrKOUoIXB08H8f2ayabMgodlLAVSF5PpioYc2iXDE2uVsWs4Hpm+tLWaByjJusITBhgFhBFyVWV+V9xxpQEjrA+KbhKCVlcQFlc+XEeSgRNOOEEmTJgg66+/vlx55ZVtm/0vidz8tV4CriXgAYhrifrntZ0EOPSpmgsYefDBB401lUZMMSl98YyUQWq04/uJ50bJCiPVFlXwsKzFoeTidq7wjuzJbAQIQ/nGU8fajZs1ray5y+O9dmE9PGF8H63WbDCi3k5At3rBMEjYVd7bwRPGPkB1c3h98PhIxc6e4JuXgJdA8RLwAKR4mfs3trAEIHiPHz/ehGj97ne/MwofbZVVVjFABEDCQZ93Q8EkuxNgKGl8f6OChyhoWIibkZrzHlfS59sgrKrk4qRjSnu97QkjwxPAg/WpnhEUVpRSVVCjUjin7UMV7mvHWifMLfPPXCsYYX7ZJ5AHIAw+WCs3PDxHHHGETJ482XioScXebvy3Vp5fP7b6ScADkPrNme9xTSSAqx8QgmcEUKKhL/369TNAhFAt4q1dN7uSNWAnS2iJq4KHrscY53meZP0fKZFuGK4DABJlM+gJs7OmYSVWMKL1ZLAStwJRXz1A7VzrhLnFMEHYKACEFvSMxPm+6nTNBx98IEOGDJE333xT9thjDxk+fHjpKZbrJD/fVy+BPCTgAUgeUvXP9BIISAAFkPAswAjhWhz8KEH9+/c3npHNN9/cSRgIChZx7TSXPIcsBQ/LWAwoWcgBENgOoSVhMrbTDcdNu8w9WMmxlgNGWKvwhmwwUsacZn2n1rYATKkHKOsz63g/Hk4SaPCNAEY1/DIsTKuOY7T7zD4wePBgw4MbNGiQnHTSSS0Bpus+L77/XgIegPg14CVQsAQgfGqNkRdffNG8HaVoo402MqEB/E4Tl6zF5PLmOSipGSsq7wxay+2ChwWL1ryOkBJ4DqpgYfVvxwZwwOvBHKUlmzcCI6wvwAihWmnWaRlzoR6gdqhtESVfPJqAD7xZgDB7/sLCtBR41jFcCY8Hng88IEceeaQcdthhHnyU8QH6d3oJNJCAByB+WXgJlCiBd955x3hF4IyQ4peGwkzVdTwjZGlpVqXcTqFZdDG5sIKHqrRoxq2iRIwVF0tnO4fYIGv1AOERI/mAi/h+AI16RvAmaHHLsuY6zppifVJUlHCjuB6gOM+t4zUafsZ+AviIAhStAEbgehx++OEC+DzllFPkwAMPrOO0+T57CbSsBDwAadmp9QOrmwQocqhghBoNNDgicEUAI2uttdZ81ju7qF7ZKTS14KGG7vBvvCFFWVDVyr3QQguZ8LOwjF91WxdJ+2t7gJAD8nDdWHdKauZ30XMdZzx2etlevXoJP63AY4kz9uA1AEdCkTT8LMm3EQVG8IIVbWSIM/7nn39ehg4dapIsUGwQ3kfeDWPS9ddfLy+88IK89tprJvMh+3mwjRkzRq655hpjKPnmN79pQBKeb9+8BNpNAh6AtNuM+/FWXgIoThygHF6EalGdmIYle5tttjFgZOWVVzbE9ksvvdRY9kgpiTJQldZIQUUR1tCdZl6dJONIw3NI8vw6XatWblLskga6COWQuYYrooXwmI+khfBcy9gG5ksvvXRbF5nT0EwF5lkKpdYBjFDf47jjjjPcFjJdbb/99q6XV8PnUdQQsIOh6K233jKgPAhAxo0bJ0cddZQceuih8t3vftcUQrz11ltl5MiRJvTWNy+BdpKAByDtNNt+rLWTAIfoxIkTTfV1DiutdIxyCc8BRf7aa6+Vtddeu7JjCyt4qGAkiTU2OEhb0UzLc6is4BJ2TBVNvE54PlyCvLhdsRVUXasovprat4g+0Qe+DYovtnK1+zhzQupZ0nHzrWHAcOkBqiIYeeSRR0y4FSAL48xmm20WR0xOrtHaOjzs5JNPlpdffnk+AIKnA+PRJZdcMvedBx10kAHvt99+u5N++Id4CdRFAh6A1GWmfD/bXgIQSB966CG58MILhRAtFDusbLj68YrgHUEJr3JelPzsAAAgAElEQVQDUHHYoiyToQqFCGIwChK//397ZwJtV1Xf/93Wii3tohA0SFeLQCgJRIYkGAhhCEl0rQIBFyQEQsOkDGGmhDnQMBhBQGVIwiABBBWQIXQtZZB5MiEkBFQIg4kskUGKuqqtdlr/9dn979eblzfce9+555779ue31ltR3rlnn/3Z5723v/s3NXI6SzUfwkrgkvtGEy8ZxQ2q1OukJ+HZKi9YeudrKzyR55BLt/vuP/O1uS8bbrhhGDp0aKHio/t4SYzUesFS+GVZYVoc0sydOzeGfS5cuDDmz7XLehIgiOJJkyaFa665JobVJiMkC9GE56YVZdnbxcBxJdAfAQVIf4T8vgQqQuCDDz6IjbQIzxo5cmSYMmVKDMPiDxcbADbvo0ePjmJk4sSJA+r/UcaUe2t4mMRIX6e1teWG2Wi2u/JWGbx6GqM2z4HNCwK0yFPuoubVXXhyX4otFOEFS8+Ix4NNXk8VnoqaRyfcp925L+0QI9/5znfCpZdeGgsN3HDDDWG77bZr61L1JECeeOKJGHr1ve99L2y55ZZdz0clxKlTp4bbb789jBkzpq3P7eASKJOAAqRM2o4lgSYJsKnYZ599whtvvBH222+/GGucSmhy+k2uCPHGy5cvjyMQ6jJu3LiY3Eh+SNVPgkmcxitCaV/+N2FZKWyHU/PaTTWnrCRwtrrccJNLVdrHCPmAA6FOnZTnkPpOsI54wbBaL1gzIXkpyZrKToQnlhHqVdpCNzAQvyfwjvKzVIV3ojcxkn62i8hRIn/i2muvDRThuOmmm2KIU7utJwFy//33h9mzZ4enn346PmsyktfxXs+fPz8eHGkSyIWAAiSXlXaeHU/g8ssvj031DjnkkF5Pudl8UNIXMUJVLQzxgQjBM4IoqXI9/94aHqYNCxtWSqpWKdSoHS8Wm3hO+wk/I9+DDXwnGl6wFJKHVwuhWesZqSckrzbJuqcu753IpZlnRpASkogYg0MRpZebeY7ePtNb5bRmxQi/K8iluOWWW2J+C/8iPqtgfQmQZ555JlZkS7ZmzZrwuc99LixYsCCWX9ckkAsBBUguK+08syNAX5FU1pdTNmyDDTaIf+TwjIwaNarSpWprGx6ySWXTjRFuxeku/1Yx3KjVLxqiA/EBn8EUftY9JK+e/KCU+9KKJOtWr2OR969NvOedqHrzzYGKET4/b9688N3vfjfmwC1atCj+TqiKGYJVlZXwOapMQAFS5dXx2SRQAAE2qqkiC/HHdAXGCAPA9Y8Y2WabbSq7mU+bK07ICcdiA85/I3yDjSeiqohQjgJQt/wWtaFG/TWTa/nDtHCAFJKHd4P1Roykk3I21/x/3mMEyJAhQ+K7nKMYZQkQbghSBHonJt73JEYIr+ytwSXzvOCCC2IuxfDhw2PYFe9Alcwk9Cqths9SVQIKkKqujM/VNAH+IBFvSzhC+qN80EEHhRkzZqyzSaFxFMl/hPUQO3zGGWeEsWPHNj121T/IH/vnn38+ekYefPDBmHOBsXEhDIAwrdoEyXbPh80n64jgSE31ahsepo7cZTY8bBcT1ooQOzbgOYUadRcjhGWRJ8LGm6T7qm0+y3w/YPPWW29FbxjhRykvrMxnKHKsnsQIxQXon4HHduedd47ig4TuHXfcMZYgr1qoGTz6KsOLaPrqV7/ahc0yvEW+Qd6rkwgoQDpptXzWuggQF8wmbauttor5Ds8991ysjHLKKaeEY489tuseiA/+EJx66qnRA3DXXXfFXhv8u/XWW9c1VidfxAaOhEjKVz766KOxazCGEEOIIEiIrW6XpdN+EooRSD15OXrasJDzgleEE/PBkIzM5pIKaHwxL/KAcj3tJweIxPsUjocY6e4Zadf7Wva4bMwRH4gxxEeVc7uaYZN+tqn0d95558Vb8DuA31v8jiLsqkrik9+fCCOMQy28UggRjJLAVKmjWAh/b4455piYj0ffkltvvdVGhM28IH6m4wkoQDp+CZ1APQTojEsYEqf+GCeH/AGYNm1a9HpgnLLvu+++UXzUnlDVc/9Ov4Y/nogQPCP8weePPEaDQ4QIoVpl1qj/9a9/Hd55552GTvtb2fCwXeuL+IAD3g/CjNhw5So+UqgR/+INY+NNiBZf/Le+Kqe1a/1aNW4S52zIEeeDQWj3xYrqfhThYFNf2+CSRoMclpDX1m4Bhqe2typWiIzkWafvB31KENKbbbZZLK1OGKwmgdwIKEByW/FM53vxxRfHDTZfGN3FDzvssMAfA7wfyWgSRQjXCy+8kO1Gj83uQw89FMXI0qVLAyeRbO44xeMPJX/0OXVuhbHhTrH91PQnsbSZDXeRDQ9bMc967omgYpPCZhNPFN6PXK028b57qFEqVpCqaSUxknII8Ig18w5VlXUqQ00+FEKsnkphVZ1LPc/1L//yL2HWrFnhtddeiwdERx55ZGzISljWT3/603gLPNt4FjQJSKBzCChAOmetfNIGCbAJZeNCzsPpp58eT5oOP/zweBdc5BdeeGFYuXLlWk3scJETqoUrvUpVVRqcemGXIwZI9qS0Lw2zMOLMd9111yhGdtttt8Lizmv7WtC5mQ7ORWwce6qulMJ2KF9bxBiFAf//N+p+2l/1qkZFz7/2foRdcbqcQvH6OulOYiR5RvgdwOfSene6GEmewVzKUOP9O+644wJV/Cg/PmfOnC7BxVq//vrr0WNLmfFhw4a18jX03hKQQMEEFCAFA/V21SCQmjulp+GPGMIiGTXXafz08ssvr/XAdBU/4ogjwuLFi2OFFe3/CBBvnsr60hARY2M8YcKEGAZBiEGzoSC1G25O+1vlYekpoTmdlHdveNiutSccjlATTrYJr+n0xOKBcEyn/QiHFHZV7/3YoMIyiRE8Sryfab07rYwzngAOBAbiGayXXRWu43c4no133303dhA/7bTTKnlYUAVWPoMEOpGAAqQTVy2zZ2YTkkrH9jX12kRlNpqrVq2KnZbxgJCETrWRk046Kd4CAcJXOtVP96VJFC5+qmjlkIje7KsEW8QIX1RmwsgRmTRpUhQj22+/fd2hIWwSOeHGyuxrQRJv9xyCdoft1HZ5zyG2v6/371e/+lXcfBZx2o8Y4XcB6w1jxAielLTeiLwqesLgUxuWSAM7vqr6rM3+Pun+OcKtCLtCdJG/hwDRJCCBwUVAATK41nNQzuaee+4JZ599dr9zu++++8KIESN6vI68DpIYCa0imTeFYCFAak+YDcHqF/NaF7A5evHFF6MQgR0bBoxKTanHCBVretswpS7WrEG7Ntw9he20Y3OaNtyEhVFmd7DH9vf2prEelMXmXSIMj3C8IjfcPYmR1FMGsYNnpCpWW4QADmUWgmgXA7zShMsiFAm5ony6JgEJDD4CCpDBt6bOqAcCS5YsCTNnzgx33HFHrOxkEnrxrwnx9nCmrC9Joqlazeabbx69InyRQIxxGs1aUACAzTZhV1XYcPe1OW1Vw8PaE+5WbLiLX+nW3bF2w02PDzbcRYqP7k/OeCT5J88IeUhJjOAdaWf4W21OVC5FCPBWEypL7h6dzvfbb7/WvWzeWQISaCsBBUhb8Tt4WQTI97jqqqti3wtCGFIZ3unTp8cEdYywjClTpsQa87mV4S16HeD7+OOPR88IXifCnTAEB4nrVNlavXp1LIHMGrRyk9ns3HranBbd8JBNJiFsnPbmcsLd23rAglC8dlX9YvwkRlKDy746cjf7XtXzOX4XwYLwRHJf8IoNdnvyySfj7wPWgd+/kydPHuxTdn4SyJqAAiTr5R98k2cj98UvfjEKCWqscyqPt+Ob3/xmOOCAA8LcuXO7Jp0aEZLcmBoRcnKfSyPCslafzRwNHhEjJPmzsWeTgdA78MAD40aDxNoqW28ND1MOQTPJ97ybbDIRZ3iBWpV4X2Wu6dlgQeI9whUW7d5wp/Xm9wlfvLNFi8/e1gUWFHygMANhiRRHGOxGSV2aDRL6eO2114bx48cP9ik7PwlkT0ABkv0rMLgAsIG54IILYh+P9957L24aCPvhlH3//feP/SySsalAhJAPQpdpNsSzZ88OO++88+CCUpHZ4PXA28QasSZUucHYvO+yyy5dPUaoeFRl43QaUUW/FE7MMaqBIUYQEbXvWG/zgAGbTDa6bDKrPudWrkctC96LKuVgMO++xCfrXWQDvMSC303d+520cg3aeW9y/OjThOi8/vrrw6hRo9r5OI4tAQmUREABUhJoh5FAzgTwQLHJYKNOeAV1+6nxT38RPCOvvPJKxMPmc/fdd49ihF4jRW7uWsG/p4aHzJF8ETZUPeW1pL4WCJXaym2teL6q3zOVHO4UFkl8kjOC+EQoIB4H4glLa4QnDFHaKSyKeLf4vXDllVfGYgMUCqltClvE/b2HBCRQXQIKkOqujU8mgUFDgA7GbNgWLlwYPU3djY7GqcfImjVr4rfZ1O21115RjIwePbouz0I7gdXT8DBV/Wqmr0U759aKsfEiEYKG6CTPoZkwtlY8V733RIyk7uvJE0a4VPKENTIfRCkhaCTAt6saXL3zLuI6hBu/C/B4kPu0aNGisOWWWxZxa+8hAQl0CAEFSIcslI8pgU4mwGaTzRVffRkbkx//+MdRjNCBnTA6jMIBlPWlktbIkSMrmbReO6+eGh4yd065i+hr0cnvAs+eOnoPlpLDyROGIGk0LG8gzRY78T3gZ/yKK66Ioa+EmSE+EKCaBCSQFwEFSF7r7Wwl0DEEiL1ftmxZFCMPPvhg3LRibFY+97nPRc9IJ5yaIjqodEVpUYwQm3Y3PGzXS8Dmk/4e9PkYrB29u4flwbq3HKEkxHIRpXiNLrroorB48eIwbNiwKD4ot6xJQAL5EVCA5LfmzlgCHUeA8Ca61NNj5NFHH419RDDCuRAjfFE9qWrGhuvtt9+Op+KbbLJJDDdK3dfZqLaj4WG7GCE+6GzOpjuXjt68tylMi3wXyk0nMYKXjOIXg1WIdX/PYHHuuefGHkF4MW+88caY+6FJQAJ5ElCA5LnuzrpiBNiokoRJz4w33ngj9iRhc01HYCpEdbdUvYuTZK6jfv7YsWMrNqvWPA4bucceeyx6RugdwMYG22677aJXhLK+Q4YMac3gDdyV5yKun3/x2rDxTNZXw8N2N8BrYIp1X1rbVO+Tn/xk5csu1z2xBi7sniPERwnLQ4zhAalCI84GptPQpfzMUmGQQ4SddtopLFiwIOuy0w3B82IJDFICCpBBurBOq7MIcEJOZShKBVP9iQTWe++9N+ZB8Md6woQJXRNK/UtOPfXUrv4l9NnIsX8JpXA5UUWM0IWdjS4hTmxyECNwa0d/DcKuEB8YScV9lZbtqeEhDfCopIUYqXolsP5+0hDTsLDfSYhVs5IXCEEKG7jgGeE95au36mn9ca7q9/EA0d18+fLlscIdDWFzLjtd1XXyuSRQNgEFSNnEHU8CPRBI5T3ZdNaektM8kY0K5Sqx1MF92rRp0euB8VmqTG299dZZd3DHG4Rgo7TvypUrIxtOmBF0iBE6sJfRY4KEe8KuEA6Ij0YERCsaHrbzB45Tf0rL8o7m3u+ktut9rReop4IFiBDEJz/7newZ+dWvfhW9uD/5yU/iz+BXvvKVhn4e2vnuOrYEJNBaAgqQ1vL17hIYEIFzzjknNlUkCRujq/thhx0WvSO1NfOvueaaGMLFtZym5m6cuKeyvq+//nrEwWZuzz33jJW0aDbZSJnUenmmpGLGIuxqIJvHIhoe1vvcrbgueYF4HxEfeHVyNdaSksOEIvXV9Z5CBSlHCGHC+4NXJImRTvrZfv/998Nxxx0XKLHNQQrJ5/U06cz1HXHeEsiNgAIktxV3vh1DgBPTvffeO2y++eZh/vz58bkpXXnhhRfGE/7a0/zvf//7McyBHBKSnbX/I/Daa6/F5HU8I3gmMJJfJ02aFMXIDjvsMCChwP0IrcEDQ4WnViQV99bwMPWcGIjQacW7QkghG+5c+lr0xZC1QxAjKBBi9AqpxxBwiBFCmPgsm/ckRrhHlcUIP2fHHntsfAcOP/zwcNZZZ7XteVevXh2boHI4Q+gXv1NPP/30Uryh9ayz10ggVwIKkFxX3nlXnsAtt9wS5s2bF8OvyGnAyAdBjLz88strPf+zzz4bjjjiiFjecvjw4ZWfWzseEJGAcEOMPPDAA7ECEYZgo4oWYoQwtkY3dghFurqzWaSk6EYbbdTwPRrhkSorkf9Smz9QlZAdnouyw3iBOO3P+dS7NgSNnhfNhADy3tZ6RrhnlcUIHg88H3hATjzxxHD88ce39Oehr58dfib32WefsOmmm4ZZs2aFDz/8MP5OJRzz8ssvb+THzmslIIGCCShACgbq7SSQCHByyR/h/oxT0e4N+pYuXRqOPPLIMHPmzK5cjyRAECEvvfTSWrelugzX33///XETrfVNgJAYktYRIySxs1bYpz71qShE+Npss836xVgbWsMmBxFQpvWUP9DOkB08QLzz5DKR59ComCuTXavHQjSQ/wIDxEd/TTjreR7ESPKMsLnGu0IoYVpzTvjbyfyVV16JgoPcj7PPPjt6P9ppdFrnwIbS3RwMYPzM4wEhX6wT+gi1k59jS6CVBBQgraTrvbMmcM8998Q/wv3ZfffdF0aMGNF12auvvhoOPfTQMH78+JhUXruhSCFYCJDamHpDsPqj3Pv32cQTukbOyOOPPx43eBg5NqnHyNChQ9e5AZ8jtAYRQr5HvaE1zT9p359kw4v3gY1pOiUvq+EhG2OEByfMlED++Mc/3taNcKsY13tf+tTwblCAAPHRinwjmJNTksK0khhJa463pUwxsmLFinDSSSfFHj2EiU6dOrVeXC27jt+jiDMObZLxczt69OhAFUEObTQJSKA9BBQg7eHuqBLokQAnpoccckjYYostYqOu7qemJqG39sUhd4GSxogRwtrY1LGJGzVqVPSKkDdCjse3v/3t6G0688wzo1Ap4nS7qJn1dkqeyvoiXIvcmNaGoCHU0klzUfPptPtQBY3cBwQA3s0yQtBqxQiCBFGcmlyyAW+1GHnuuefCaaedFn9eLrvssphnUQWjhxIJ8Hg8ao3nI/frkksuqcJj+gwSyJKAAiTLZXfSVSRAEvPBBx8c+wDcdttt8d/ulsrwTp8+veuPKpuNKVOmxIaEeEy0Yghwmk/1McQICaxs8thMwplQE4TI3XffXekNd9qY4hkhzIx3BbGUTskHWpmq3SFoxax0cXdJ+S/87JL/0o7iAD01uUxiJDW5LFKAPvLII9HTy1y//vWvr9WzqDiyzd1p2223DSeffHI4+uij17oBv2fx1FE9UJOABNpDQAHSHu6OKoG1CBD2c9BBB8WYcWrl0x251jitS5YaEXLiyOk7DQjJY8ixEWFZrxHN4wiVgz0nzIgPxCA9Rv7+7/8+jBs3rlJekJ64FN3wsLbTeyPVncpas7LHQbC+9957lcp/6WnNixSg5FPMnTs3eljItaC8dZWsNwHCAQ5hgldffXWVHtdnkUBWBBQgWS23k60qAUI2Jk6c2OvjrVq1qut7bCrYCJMPQiUnTuRnz55duT/+VWXdzHOxuaSKDnHu22+/ffjMZz4TRd+aNWvi7Qhz2WuvvWKztTFjxpQSdtPMPNJnBtrwMCVYcz9yHAbqSRnIXNr9WX4e+Tnki/AzKqEV6WEoan48J+FheML44h1g3ZI3rNEwwu985zvh0ksvjYKLcNHtttuuqEct7D6GYBWG0htJoHACCpDCkXpDCUhgMBHA07HffvvFhmr8S08BNmts6OjwzCkwRQDwkmB4ryZPnhxzRj796U9XcjNauz6p4SGeHTaoGCV0U4+R7jkMtQnWjXZ6H0zvBXPhHWDdaUCJ8CCspxMM8UG+U0pgZx54MVI1rf7ECAcghC/xrtMAtaqV90xC74S30WfMlYACJNeVd94SkEBdBBAgRx11VAyzorlaT6fbbOjIE0GMPPTQQ7EMKUYeQCrrO2zYsLrGa+dFiJHUiRuhgZHPkMQIAoUeH5R7pfJXGQnW7eTR19isOSzwJlBymLC8TrSevGGIkeQZIX8kGULlqquuCjfffHPsrcG/9ZSrbhcXyvBSAYsyvDQfxWhISviqZXjbtSqOK4H/JaAA8U2QgAQkUCABciOooIUYIUE3beS32mqrroaHCJOqW2p4iCCh3GuyVN2pFaVlq84kPR+bdsImWdt29H9pFSfmhaBizfGQ8A5cd911UYwgpJ988slYeGHzzTcPixYtisKrypYaEfLzRgglfWq+/OUvxxLnNiKs8sr5bDkQUIDksMrOUQISaAsBigs89thjsZIWmze8KRjx8mzoCNXqXnCgLQ/ax6C1YUZ4PPCSUPGonQ0P28mIUrP0+GAt8QIRrjYYjXUmtAzv389+9rM4xdSB/aKLLoo5a53gAVu9enUMm8RDiXimMzpleZvpSj8Y19k5SaBdBBQg7SLvuBIYRAToxE7jxZUrV8bN2YwZM8L555+/zgw5USWE4957740nrWzEzz333DB8+PBBRKPnqXAaS+I6YoR+Lpw2s5HfaaedYvL6hAkTSu+k3h90xMc777wTGxymHAcS0JkL/63shof9PW+rv898qVTH5pz8F0LRBrshoill+/bbb8dmkzDAqCJFo85p06ZVNgdksK+N85NAJxNQgHTy6vnsEqgIgXnz5oWnnnoqVoiikd++++7bowChQzLlbM8666yYH0H1HHpqEK7EhiYXo+cLievEo7/44otx2iT+UtYXz8juu+/e9hPaFGZEKA5hRlQ7qrXeGh62qxN3q9+dVPmLHCAqf/WXqN3q5ynj/oSYkS+xZMmSQEUpDg9effXV+O7SI4eQJn5un3766TIexzEkIIFBREABMogW06lIoF0E0mk+41OOds8991xHgNAjgVN+PB54SDCSmgnlmDp16jrdits1l7LHxWNEQiyekddeey0OT1jPHnvsET0jY8eOjV2ty7RGw4xa3fCwzLn3NBY5MKwTeS+5VP7Cy3XiiSeGl156Kf6Mfu1rX1tLdPGOPP/88/G/jR49ut1L5PgSkECHEVCAdNiC+bgSqDqB3gQIyavnnHNOPE2trRhEF+Uf/ehH0QuSu73++uuRA54RkpwxWE2aNCl6RnbccceWd9cmt4EwI0QlJ/2Nxsr31vCw2X4T7X4n8AAhPlLyfSfkPQyUGZ4NkrYRxFOmTAl4OHMuOjBQnn5eAhJYl4ACxLdCAhIolEBvAuSyyy4LixcvDuSL1BphWJyuctJKToT2v/0l4IEYeeCBBwIhW9jQoUO7KmmRN1N0w7t00s8mm5P+gYYZpX4T5Ivg7WJe5E0kMVL1TS1eAHIf8EiRcJ7D+0lfE8pNk3h+8MEHR09mDvP2944EJFAuAQVIubwdTQKDnkBvAuS8884Ly5YtixvqWrvrrrsC36NKDT0ntLUJkPC8dOnSrh4jJO9j9F/AK0KYVhG9GBAIeF046WezXbQ4qC3xmhoe/vmf/3mXGKmaZ4FeLmzGEUvkwBQt9qr4niM6jjvuuFh44Itf/GL4x3/8xyzmXcW18JkkMNgJKEAG+wo7Pwk0QYBNLhVv+rOeTsn7EiCIDBJYa+3OO+8Mc+bMUYD0BzuEWPqVcr7ki1DelwpF2IgRI6JnhK9NNtmkjjutfQnlVtl0IgApDtDqE+/+Gh62evy+AOGl+eCDD+IXzevwOuUgPgj/Q3wQfkXi+THHHNPwe+QHJCABCdRLQAFSLymvk0BGBCipS25Gf0ZFKza/tWYIVn/Uivk+uQl0eCZMi7A2koLZKJMngmeEvJHU/bm3EWs32+SaIF7K3mzz3Kn7OiFgjI8QouoWoU9lihF4UCwB7wfVnYYMGVI6j2LejsbuQg7WCSecENeBw4BUJKKxu3i1BCQggfoJKEDqZ+WVEpBAHQT6SkKnAhY9MExCrwNkA5ewYaYsKp4RwtzYSBPSROlUxAjVxwh3qrXaBoNV2Wzj4UlihLK3ZTY8hMcvfvGLOD5CrD/x1sDyVPpSKlmdcsopAd5f+tKXwv7771/p5/XhJCCBwUFAATI41tFZSKAyBPorw8sJK8mtGKf4XJ9zGd6iF468Bcr6UkmLk21svfXWi71FyBeh1wjhVnSDphEkpVZrBWHRz9Ps/VLDQwQBwgRBlZLXSWQv0lNT2/OEEDTGycHo3TN79uxY8ezKK68Mn/3sZ3OYtnOUgAQqQEABUoFF8BEk0OkEqBT08ssvx2nMnTs3bLvttuHAAw+M/58T+GQ0IqQSFo0ISey96aab4iaZk/ucGhGWtd6rV6+OQgS+/G+MsCY27ySCn3rqqWHmzJllPU5T47S64SH5KJTZJZ+GnCb45GAUg6D4Az1mrr322jB+/Pgcpu0cJSCBihBQgFRkIXwMCXQygb5yRlatWtU1NU6y6aZ87733BhLd6ZxOWBYlZbXWEWATT8f5BQsWhIcffjiGaG288cbx38mTJ0eRiDekSK9CK2aTGh6mMC3EA6WCk2cET08j9p//+Z9RfJCHgvjAs5KD8fN30UUXRbF1/fXX20gwh0V3jhKoGAEFSMUWxMeRgAQk0AoCbDo58aa87vHHHx/7W5A3Qv4IRugRVbQQI1tttVUrHqHQew604WFquMh9aLjYqHgpdDIl3uy2224LV1xxRcxxwQO5zTbblDi6Q0lAAhL4XwIKEN8ECUhAAoOcAOE2J598csz1uO6668IOO+wQZ8zJ/7PPPhtDtH7wgx/EnBxs2LBhXWKEniBVt9TwEM8InrXahod/+Zd/GcOMaq224SLio/v3qz7fZp4PJqw9X5QWXrRoUdhyyy2buZWfkYAEJDBgAgqQASP0BhKQgASqTeCJJ54IN998cyyxusUWW/T4sORAPP7441GMcD0eAuzTn/509IqQoEzYVtWtv4aHzJOGi4RuEXZVdMPFKvJBfOD1uP322+OceRc6QVhWkaXPJAEJFENAAVyULZEAABtPSURBVFIMR+8iAQlIYNAQwJOARwQxQtlkci0oiTtmzJhYSYvKZZ1QKYrnxiPym9/8Jvzbv/1b1/oQboXnIwfxAYOLL7440LMHzxZhV3hANAlIQALtJKAAaSd9x5aABCRQcQJ0BKd7PdW0VqxYEZ+WkCXK+eIZobxvJyRv0+H7/fffj0IKL0lqeIiQovFhmQ0Py1pykuzJ+3nooYfCyJEjw4033phNf5OyGDuOBCTQHAEFSHPc/JQEJDCICFCillPiF154IW6m995779gn42Mf+9ggmuXAp0LoEj1G8Iyk6mY0ONxjjz2iZ2TnnXeuXD4F4UeIj1/+8pcxB4Ymg2zMk2ek7IaHA1+F+u5AnssZZ5wRnn766ei5WrhwYSAfRpOABCRQBQIKkCqsgs8gAQm0jQDhRvvss0/sSzJr1qzw4Ycfhnnz5oXddtstXH755W17rqoP/MYbb4R//ud/jp4RStliG2ywQZg0aVL0jIwaNartXgXEB14P1pT8Fb66lxruqeEhG3XmUnTDw7LWlB4vFB1Yvnx59FBR+roTvFRl8XEcCUig/QQUIO1fA59AAhJoIwH6IMyfPz88+uijYaONNopPwsYaDwin/VYK6ntx2OTThBJmhGrhacA+8YlPdFXSGjFiROk9RnguOr6T/0HOQ1rb3mbD9YgRrkeUUiGMHJHUYwRvWNX7pDA3yiqfcMIJ4Sc/+Unkj4gm4V6TgAQkUCUCCpAqrYbPIgEJlE7g0EMPjaEpNOlLRgWo0aNHx07hRx55ZOnP1KkDkvC8dOnSGKJF3gEbeYyEb0K08Ix86lOfavn0yPGgzwmeADxbeDMasZ4aHpL3ghjhXlXtGYK3By/em2++GQ444IDYbPBP/uRPGpl64dc+88wzgUalK1eujJ6yGTNmhPPPP3+dcQiLq21SSmNMm5QWvhzeUAKVIaAAqcxS+CASkEA7COyyyy5xs4bHo9bIA6FfxiWXXNKOx+r4MRFxTz31VBQjeJcof4vR9T41PCQfo2hDBLHRZTxKzZJgPhAbaMPDgYzdyGcRXMcee2wsMTxz5sxwzjnnVMJjQzgj78H2228fK6vtu+++PQqQCy+8MFbqOuuss2JTTBLmX3nllehZ+/jHP94ICq+VgAQ6gIACpAMWyUeUgARaR2DbbbeN8fJHH330WoMcfPDBYciQIeGaa65p3eCZ3JkSuI888kgUIyRFE95EONOOO+4YvSITJ07sN0SqHlTc96233opJ5vS7IEG+SOup4SGhWXhFemp4WOTYfd2LIgqIDzwgdLk/8cQTKyE+eGaYpQpjlG/ec8891xEg7733XpgwYUL0eOAhwfBe8V5MnTp1ncOBsrg6jgQk0DoCCpDWsfXOEpBABxDoTYBMnz49nrxeffXVHTCLznlEchQIz0KMPP/887FrOWFCY8eOjWFabETXX3/9hieExwXxwf0I+Wp1mFRfDQ8RI2X1GHn11Vdj2BVczzzzzEqHDPYmQO6+++7osVmyZEmsVJbs7LPPDj/60Y+iF0STgAQGFwEFyOBaT2cjAQk0SMAQrAaBFXj5u+++29VjhER2DOFABTLEyPjx4+tKoCbcCvGBkEF8kK9RpqWGh+S8/O53v4tDE/qVeoy0Kg/jxRdfjN4OxiSEadq0aWVOu+GxehMgl112WVi8eHEgX6TWCMP62te+Fl566aW2V1RreLJ+QAIS6JOAAsQXRAISyJqASejVWP41a9bEkr54Rn760592beLZtBKmtdNOO/XoVSC8i5wPKj0RdlWW56E3aoSBIUT4ohdHqxoe0qGeIgmMd+mll8ZS0lW33gQIzRKXLVsWHnjggbWmcNddd8VGivTnGWguT9XZ+HwSyI2AAiS3FXe+EpDAWgQow0sFLBKlN9xww/g9NsKnnXaaZXjb8K4QQkVYUSrr+4tf/CI+BWV0J0+eHMUICc3YddddFys+UXaWhPNWeRqaxUAuCkKE0r5FNjzkXSVZm9wKPARs7MsyGjiSa9KfIQa7l//tS4AgMijjXGt33nlnmDNnjgKkP9h+XwIdSEAB0oGL5iNLQALFEUiNCKm8Qyw9XbO//OUvx/AfGxEWx7mZO5FnsWLFiihGOB0nzwH75Cc/GXt7EIJEn5Y77rijcuKj+3x7a3hImBbJ8vX2GMFD9E//9E8xVA3hTPf5Mo2SuuRm9GdUtKL/S60ZgtUfNb8vgXwIKEDyWWtnKgEJ9EKAKkIXX3xxPGmlqhHhLJTl5X9r1SBAqNFzzz0XcwVoEEneBZ3NCc2hZDKlfTl1r7qlhofJM9JIw0M8ApS1perWDTfc0OUJqvqc0/P1lYROBSzCykxC75TV9DklMDACCpCB8fPTEpCABCRQEgFKs1Jmlo3q3/3d38WwKxKX8S5gI0eOjCFan/3sZzuid0RfDQ9TJa2UUP+Nb3wjloRGdN10001h6623Lol6ccP0V4aXcCvKX2Mk1nO9ZXiL4++dJFAlAgqQKq2GzyIBCUhAAj0SoMwum1PKsuLtIDyOHANyEh5++OGYvI4wwTNCbsSYMWOiGGET22gn9HYsAWKEhHryRZgTYoOu8oRYEaJFCBpd3RctWlRKN/miGNAgMVU4mzt3bqDs9YEHHhhvz/oko4oX3i1yW5gnIou1Zl1tRFjUangfCVSHgAKkOmvhk0hAAhKQQC8E3nnnnVia9/Of/3ysjNRTwjn5OyQyU0Rg+fLl8U54EMaNGxc3u3vssUf4sz/7s8ozJvcFwbFw4cJY4QsjRwTPDpt3SkeXXWq4WWh95YysWrWq67YIzKuuuirce++9UYBRaICwrOHDhzc7tJ+TgAQqTEABUuHF8dEkIAEJSOD/CFBVqt6NNyfvCBG+qKqFIT7oxI0YqfomntwQks1p2jhkyJDo1UkVwajWRt4LZXgtT+tPiAQk0IkEFCCduGo+swQkIAEJ1E2AUr1U0kKM0LAQIyxr0qRJUYyMGjWqUo3u8AYQivTYY4+FHXbYIVAqmmpZNOQjJAkvzy9/+ctw8803RyGlSUACEug0AgqQTlsxn1cCEsiawM9+9rNAQvLKlSvD66+/HrbYYou4Ke1uhLLQJwNPwGabbRaTtwlhytnIsyAfAV5U0mITj33iE5+I4U2IkW222abukritYEkeCD1olixZEsXFtddeG9Zff/21hiLPBW9IJ1T9agUj7ykBCXQ+AQVI56+hM5CABDIi8IMf/CBcdNFFMUae8sFsqrsLEPIHTj755HD00UeHXXfdNfCZ2267Ldx4442xv4kWYrL6888/H9kR5kTyN/a3f/u3MckdMYK4K9MozXvSSSdFcUny/Ne//vV1mvmV+TyOJQEJSKBVBBQgrSLrfSUgAQm0gAAJyuQDYITppEpBtUPh6aBMLRvYZEcddVRM7qWXhLY2AUKennrqqRiiRZfxf//3f48XUOoWIYIgoflhK+3DDz8Mxx13XHjttdfClClTYr+Pj3zkI60c0ntLQAISaBsBBUjb0DuwBCQggYER6EmAUDWJ3AbKuE6ePLlrAEKy6GD97LPPho022mhgAw/iTxMChQjBM4IoIRkc23HHHaMQgWnR/N59990oPtasWROmT58eLrjggkrlpAzi5XZqEpBAmwgoQNoE3mElIAEJDJRATwLkiSeeiKFX5DhsueWWXUOQwExTt9tvvz32yND6J/DrX/86hmchRujJQbgb5X/Hjh3b1WOke35G/3dd+wqS4o899thAmeEvfOEL4fTTT29rDkqjz+/1EpCABJohoABphpqfkYAEJFABAj0JkPvvvz/Mnj07PP3002s1cCN5nUTr+fPnh4kTJ1bg6TvrEd57772uHiOIOWy99daLOTWEvPEv/78Ro4jArFmzwgcffBBL6iJENAlIQAI5EFCA5LDKzlECEhiUBPoSIM8880zYeOONu+ZNeA8hRAsWLIgJzlrzBBBzeEXIGaHEL4YnBK7kjHzmM5/pN3+D3J0TTjghJr/PmTMnHHrooc0/kJ+UgAQk0GEEFCAdtmA+rgQkIIFEwBCs9r4LhGTRzZseI4S8pUaB5IiQK4IYoVoZXcxrbdmyZeGUU04Jv//978Mll1wSu7trEpCABHIioADJabWdqwQkMKgImIReneVEjKxYsaKrUSBVrbBNN920q8cIlckIjSNEjmpmV1xxRfRKaRKQgARyI6AAyW3Fna8EJDBoCPRVhnf48OHhq1/9atdcLcNb3rJTOeuHP/xh9IzQg+W3v/1tHHzo0KGx+eFHP/rRWKVst912K++hHEkCEpBAhQgoQCq0GD6KBCQggf4I0KOCSlcYFa0ou4sQwcg9IPzn+9//fkxqPuaYY8K4cePCI488Em699VYbEfYHtwXf/8Mf/hAef/zxmC/y8MMPx0parNvo0aNbMJq3lIAEJNAZBBQgnbFOPqUEJCCBSODnP/95r1WsEBmUiMXo+7Fw4cLw9ttvh8022ywmPFOtSWsfAcr6IiBb3dSwfTN0ZAlIQAL1EVCA1MfJqyQgAQlIQAISkIAEJCCBAggoQAqA6C0kIAEJSEACEpCABCQggfoIKEDq4+RVEpCABCQgAQlIQAISkEABBBQgBUD0FhKQgAQkIAEJSEACEpBAfQQUIPVx8ioJSEACEpCABCQgAQlIoAACCpACIHoLCUhAAhKQgAQkIAEJSKA+AgqQ+jh5lQQkIAEJFESAPiU06fvxj38cfvOb34S/+Zu/CQcffHCYPn16+OM//uOuUeh3QjPFN998M2yyySbh8MMPDzNmzCjoKQbvbf77v/873HTTTbFfzBtvvBH4/3RhpxTzLrvsss7Ev/GNb8TeJDRJ5Lozzjijq5zz4KXkzCQggXYSUIC0k75jS0ACEsiQwLRp08Kmm24aJk+eHIYMGRKWLFkSrr/++jBz5sxw5plnRiIrVqwIhx56aNhvv/3ClClTwvLly8PVV18dLrzwwjB16tQMqdU/5d/97ndhjz32CPvvv3/Yddddw0c+8pHYF+Z73/teWLBgQZgwYULXzRAfiDwaV26zzTbhrrvuit3b+Xfrrbeuf1CvlIAEJNAAAQVIA7C8VAISkIAEBk7gww8/jB3ba23evHnh29/+dli2bFn46Ec/Gr7whS9E7wgb4WRz5swJjz32WHjyySfX8pQM/IkG1x3wePz2t78NG2ywQdfE6MB+wAEHhPXXXz9885vfjP/9P/7jP8K4ceMCghCvB8Zn99133yg+ECaaBCQggVYQUIC0gqr3lIAEJCCBhgjcd9990fvx1FNPhb/6q78Ko0aNCqeffnoMu0q2dOnS8A//8A/h7rvvDiNHjmzo/l4cwjnnnBNeeOGF8OCDD0YcP/zhD8Nhhx0WvSN4P5Jdc801MYSLa//oj/5IdBKQgAQKJ6AAKRypN5SABCQggUYJ4N146KGHwrPPPhtWr14d9t5773DDDTeE3XffvetWeE7IYbjssstiaJZWP4H/+Z//iUw333zzMH/+/PhB8j4IaVu5cmX42Mc+1nUzcnROOeWUmENC7o0mAQlIoGgCCpCiiXo/CUhAAhJoiMDLL78cE9CPP/74MGvWrHjyfsghh4Q77rgj7LDDDl33+q//+q+w7bbbhnPPPTfmi2j1E7jlllsCYW6EX+20007xg+SDIEbgX2uIwCOOOCIsXrw4DB8+vP5BvFICEpBAnQQUIHWC8jIJSEACEiieAJWXyEEYOnRo3Bz/6Z/+aZcAufPOO8P222+/jgA577zzYihWTvav//qv4f333+93ylQUI4em1ghdO/LII6NoS7keSYAgQl566aW1rn/mmWfi9ffff7+J6P0S9wIJSKAZAgqQZqj5GQlIQAISGDABNtUIiT/84Q/hW9/6Vthwww3jPSkdawjW2njvueeecPbZZ/fLnFyaESNGdF336quvxmpi48ePj0nltTkdKQQLAbLeeut1fcYQrH4xe4EEJDBAAgqQAQL04xKQgAQk0DgBRMdRRx0V1qxZE0Ot/vqv/7rrJlRnMgm9cabdP/HWW2/FULYtttgi3Hjjjet4RkxCHzhj7yABCTRHQAHSHDc/JQEJSEACTRIgl4OmeJTcve2223rMM6AMLx4SxEmy888/Pzz66KOW4a2DO6FtNHf8i7/4i8iYf7tbKsNL/g0VxzDK8NJ3hYaEluGtA7SXSEACTRFQgDSFzQ9JQAISkECzBBASCIvZs2eHMWPGrHWbYcOGxc1yakT4+c9/PvaloBHhVVddZSPCOqD//ve/DwcddFDAA/KVr3wlbLzxxmt9qjaxPzUiPO2007oaET788MM2IqyDs5dIQALNE1CANM/OT0pAAhKQQBME9tprr/D222/3+Mlbb701jB07Nn6PMrBXXnllePPNN2M5WCozzZgxo4kR8/rIz3/+8zBx4sReJ71q1aqu79GgEBFCPsgHH3wQPR8Iw5133jkvaM5WAhIolYACpFTcDiYBCUhAAhKQgAQkIIG8CShA8l5/Zy8BCUhAAhKQgAQkIIFSCShASsXtYBKQgAQkIAEJSEACEsibgAIk7/V39hKQgAQkIAEJSEACEiiVgAKkVNwOJgEJSEACEpCABCQggbwJKEDyXn9nLwEJSEACEpCABCQggVIJKEBKxe1gEpCABCQgAQlIQAISyJuAAiTv9Xf2EpCABCQgAQlIQAISKJWAAqRU3A4mAQlIQAISkIAEJCCBvAkoQPJef2cvAQlIQAISkIAEJCCBUgkoQErF7WASkIAEJCABCUhAAhLIm4ACJO/1d/YSkIAEJCABCUhAAhIolYACpFTcDiYBCUhAAhKQgAQkIIG8CShA8l5/Zy8BCUhAAhKQgAQkIIFSCShASsXtYBKQgAQkIAEJSEACEsibgAIk7/V39hKQgAQkIAEJSEACEiiVgAKkVNwOJgEJSEACEpCABCQggbwJKEDyXn9nLwEJSEACEpCABCQggVIJKEBKxe1gEpCABCQgAQlIQAISyJuAAiTv9Xf2EpCABCQgAQlIQAISKJWAAqRU3A4mAQlIQAISkIAEJCCBvAkoQPJef2cvAQlIQAISkIAEJCCBUgkoQErF7WASkIAEJCABCUhAAhLIm4ACJO/1d/YSkIAEJCABCUhAAhIolYACpFTcDiYBCUhAAhKQgAQkIIG8CShA8l5/Zy8BCUhAAhKQgAQkIIFSCShASsXtYBKQgAQkIAEJSEACEsibgAIk7/V39hKQgAQkIAEJSEACEiiVgAKkVNwOJgEJSEACEpCABCQggbwJKEDyXn9nLwEJSEACEpCABCQggVIJKEBKxe1gEpCABCQgAQlIQAISyJuAAiTv9Xf2EpCABCQgAQlIQAISKJWAAqRU3A4mAQlIQAISkIAEJCCBvAkoQPJef2cvAQlIQAISkIAEJCCBUgkoQErF7WASkIAEJCABCUhAAhLIm4ACJO/1d/YSkIAEJCABCUhAAhIolYACpFTcDiYBCUhAAhKQgAQkIIG8CShA8l5/Zy8BCUhAAhKQgAQkIIFSCShASsXtYBKQgAQkIAEJSEACEsibgAIk7/V39hKQgAQkIAEJSEACEiiVgAKkVNwOJgEJSEACEpCABCQggbwJKEDyXn9nLwEJSEACEpCABCQggVIJKEBKxe1gEpCABCQgAQlIQAISyJuAAiTv9Xf2EpCABCQgAQlIQAISKJWAAqRU3A4mAQlIQAISkIAEJCCBvAkoQPJef2cvAQlIQAISkIAEJCCBUgkoQErF7WASkIAEJCABCUhAAhLIm4ACJO/1d/YSkIAEJCABCUhAAhIolYACpFTcDiYBCUhAAhKQgAQkIIG8CShA8l5/Zy8BCUhAAhKQgAQkIIFSCShASsXtYBKQgAQkIAEJSEACEsibgAIk7/V39hKQgAQkIAEJSEACEiiVgAKkVNwOJgEJSEACEpCABCQggbwJKEDyXn9nLwEJSEACEpCABCQggVIJKEBKxe1gEpCABCQgAQlIQAISyJuAAiTv9Xf2EpCABCQgAQlIQAISKJWAAqRU3A4mAQlIQAISkIAEJCCBvAkoQPJef2cvAQlIQAISkIAEJCCBUgkoQErF7WASkIAEJCABCUhAAhLIm4ACJO/1d/YSkIAEJCABCUhAAhIolYACpFTcDiYBCUhAAhKQgAQkIIG8CShA8l5/Zy8BCUhAAhKQgAQkIIFSCShASsXtYBKQgAQkIAEJSEACEsibgAIk7/V39hKQgAQkIAEJSEACEiiVgAKkVNwOJgEJSEACEpCABCQggbwJKEDyXn9nLwEJSEACEpCABCQggVIJKEBKxe1gEpCABCQgAQlIQAISyJuAAiTv9Xf2EpCABCQgAQlIQAISKJWAAqRU3A4mAQlIQAISkIAEJCCBvAkoQPJef2cvAQlIQAISkIAEJCCBUgkoQErF7WASkIAEJCABCUhAAhLIm4ACJO/1d/YSkIAEJCABCUhAAhIolYACpFTcDiYBCUhAAhKQgAQkIIG8CShA8l5/Zy8BCUhAAhKQgAQkIIFSCShASsXtYBKQgAQkIAEJSEACEsibgAIk7/V39hKQgAQkIAEJSEACEiiVgAKkVNwOJgEJSEACEpCABCQggbwJKEDyXn9nLwEJSEACEpCABCQggVIJKEBKxe1gEpCABCQgAQlIQAISyJuAAiTv9Xf2EpCABCQgAQlIQAISKJWAAqRU3A4mAQlIQAISkIAEJCCBvAkoQPJef2cvAQlIQAISkIAEJCCBUgkoQErF7WASkIAEJCABCUhAAhLIm4ACJO/1d/YSkIAEJCABCUhAAhIolYACpFTcDiYBCUhAAhKQgAQkIIG8CShA8l5/Zy8BCUhAAhKQgAQkIIFSCShASsXtYBKQgAQkIAEJSEACEsibgAIk7/V39hKQgAQkIAEJSEACEiiVgAKkVNwOJgEJSEACEpCABCQggbwJKEDyXn9nLwEJSEACEpCABCQggVIJKEBKxe1gEpCABCQgAQlIQAISyJuAAiTv9Xf2EpCABCQgAQlIQAISKJWAAqRU3A4mAQlIQAISkIAEJCCBvAkoQPJef2cvAQlIQAISkIAEJCCBUgkoQErF7WASkIAEJCABCUhAAhLIm4ACJO/1d/YSkIAEJCABCUhAAhIolYACpFTcDiYBCUhAAhKQgAQkIIG8CShA8l5/Zy8BCUhAAhKQgAQkIIFSCShASsXtYBKQgAQkIAEJSEACEsibgAIk7/V39hKQgAQkIAEJSEACEiiVgAKkVNwOJgEJSEACEpCABCQggbwJKEDyXn9nLwEJSEACEpCABCQggVIJKEBKxe1gEpCABCQgAQlIQAISyJuAAiTv9Xf2EpCABCQgAQlIQAISKJWAAqRU3A4mAQlIQAISkIAEJCCBvAn8P6qvqjRrSXQGAAAAAElFTkSuQmCC\" width=\"800\">",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "from sklearn.manifold import MDS\nimport mpl_toolkits.mplot3d\nimport random\n\nmds = MDS(n_components=3, dissimilarity='precomputed',random_state=1234, n_init=100, max_iter=1000)\nresults = mds.fit(distmat.values)\nparties = distmat.columns\ncoords = results.embedding_\n\nsns.set()\nsns.set_style(\"ticks\")\n\nfig = plt.figure(figsize=(8,8))\nax = fig.add_subplot(111, projection='3d')\n\nfig.suptitle('Portuguese Parliament Voting Records Analysis, 14th Legislature', fontsize=14)\nax.set_title('MDS with Spectrum Scaling clusters (3D)')\n\nfor label, x, y, z in zip(parties, coords[:, 0], coords[:, 1], coords[:, 2]):\n    #print(label,pmds_colors[label])\n    ax.scatter(x, y, z, c=\"C\"+str(sc_dict[label]),s=250)\n    annotate3D(ax, s=str(label), xyz=[x,y,z], fontsize=10, xytext=(-3,3),\n               textcoords='offset points', ha='right',va='bottom')  \nplt.show()\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Conclus\u00e3o\n\n\nExiste uma prolifera\u00e7\u00e3o de recursos e question\u00e1rios assentes em \"quadrantes\", \"compassos\" e outros termos que pretendem posicionar o utilizador em termos ideol\u00f3gicos, com metodologias diferentes (e com resultados diferentes) e de diversos graus de complexidade e sofistica\u00e7\u00e3o _(Wall, Krouwel, and Kleinnijenhuis 2012)_. Este trabalho n\u00e3o pretende ser uma \"revela\u00e7\u00e3o\" ou emprestar uma base \"cient\u00edfica\" aos posicionamentos de cada um atrav\u00e9s de uma \"matematiza\u00e7\u00e3o\" do conhecimento enquanto valorizador qualitativo, lembrando Ortega y Gasset e a forma cr\u00edtica como referia a tend\u00eancia de todas disciplinas se quererem ao exemplo da f\u00edsica _(Ortega y Gasset 1995)_.\n\nNesta conclus\u00e3o iremos consciente e explicitamente abandonar pretens\u00f5es de completa neutralidade e distanciamento, sem que isso signifique a execu\u00e7\u00e3o de um determinado programa pol\u00edtico pessoal: trata-se t\u00e3o s\u00f3 do resultado natural da an\u00e1lise dos dados sob um prisma mais qualitativo.\n\nOs resultados obtidos s\u00e3o, de certa forma, razoavelmente modestos em termos de novidades:\n\n* Existe uma primeira divis\u00e3o entre os partidos que se enquadra nos conceitos de Esquerda e Direita.\n* Nesta divis\u00e3o o posicionamento do Partido Socialista ser\u00e1, talvez, um dos pontos mais sens\u00edveis.\n* O PAN aparece agrupado \u00e0 esquerda (n\u00e3o obstante ser o primeiro a ser diferenciado dos restantes).\n* \u00c0 direita o agrupamento entre CDP-PP, IL e CHEGA \u00e9 bastante pr\u00f3ximo, sendo que o m\u00e9todo de agrupamento utilizado diferencia a IL primeiro que os restantes (mas outros m\u00e9todos t\u00eam, empiricamente falando, outros resultados).\n* \u00c0 esquerda a CDU (PCP/PEV) e o BE+Livre/JKM s\u00e3o diferenciados.\n* As vota\u00e7\u00f5es da deputada n\u00e3o-inscrita Cristina Rodrigues s\u00e3o aproximadas,mas n\u00e3o id\u00eanticas, \u00e0s do PAN.\n\nH\u00e1 que ter em conta o facto do Partido Socialista ser o partido de sustenta\u00e7\u00e3o do Governo, algo que determina de v\u00e1rias formas a forma como as vota\u00e7\u00f5es ocorrem; de que forma \u00e9 que este facto influencia o seu posicionamento n\u00e3o \u00e9 poss\u00edvel quantificar mas, intutitivamente, n\u00e3o ser\u00e1 irrelevante. Outro factor (embora n\u00e3o abordado de forma directa neste trabalho) \u00e9 a aparente maior produ\u00e7\u00e3o em termos de propostas dos partidos \u00e0 esquerda, o que leva a m maior n\u00famero de rejei\u00e7\u00f5es do partido que sustenta o Governo e, logo, um maior dist\u00e2nciamento.\n\n**\u00c0 direita**, e pondo de lado a j\u00e1 referida inclus\u00e3o do PS que pode ter as raz\u00f5es apontadas como factor relevante, o PSD aparece com um posicionamento pouco surpreendente no sentido em que se enquadra no \"centro-direita\" que se esperaria, sem preju\u00edzo do posicionamento mais amplo que em diferentes fases possa considerar adequadas _(Lourinho 2020)_.\n\nA dist\u00e2ncia entre CDS-PP, CHEGA e IL \u00e9 bastante pequena (note-se que embora os partidos \u00e0 esquerda sejam genericamente mais pr\u00f3ximos exibem dist\u00e2ncias entre si _maiores_ do que estes tr\u00eas); este resultado ser\u00e1 talvez mais interessante para a IL no sentido em ser, como referido anteriormente, quem mais frequentemente recusa ser classificado em termos de \"esquerda e direita\" _(\u201cIniciativa Liberal Descontente Com Lugar Atribu\u00eddo a Deputado No Parlamento - TSF\u201d 2020)_: em termos de vota\u00e7\u00f5es esta pretens\u00e3o n\u00e3o tem aparente materializa\u00e7\u00e3o quantitativa, sempre dentro dos contragimentos explicitamente identificados.\n\n**\u00c0 esquerda**, o resultado n\u00e3o nos parece ser grandemente surpreendente: apesar das diferen\u00e7as em v\u00e1rias vota\u00e7\u00f5es o PCP e o PEV (este mais pr\u00f3ximo dos restantes partidos agrupados \u00e0 esquerda) aparecem como um bloco diferenci\u00e1vel do BE e Livre/JKM. No caso  do PCP e PEV a proximidade n\u00e3o \u00e9 estranha considerando serem partidos pertencentes a uma mesma coliga\u00e7\u00e3o eleitoral _(Lusa 2020b)_, mas tamb\u00e9m no caso do BE e Livre/JKM h\u00e1 que considerar a proximidade hist\u00f3rica existente _(ZAP 2019)_.\n\nA posi\u00e7\u00e3o do PAN ser\u00e1, aqui, provavelmente a mais interessamente no sentido em que este se classifica como \"nem de esquerda ou direita\" _(\u201cExpresso | A Hist\u00f3ria Do Partido Que Diz Que N\u00e3o \u00c9 de Direita Nem de Esquerda: O PAN\u201d 2020)_: aparece \"\u00e0 esquerda\" (estamos sempre aqui a mapear os resultados com uma realidade pre-existente) do Partido Socialista, e embora sendo por pouco n\u00e3o deixa de ser relevante.\n\nOutras leituras podem ser feitas (e dadas as v\u00e1rias dimens\u00f5es dos dados, leituras aparentemente opostas podem coexistir) tanto com os resultados apresentados como atrav\u00e9s da alter\u00e7\u00e3o da forma de tratamento dos dados.\n\nEm termos de _Data Science_, este trabalho poderia (e poder\u00e1) ter uma continuidade, nomeadamente dentro do que s\u00e3o as possibilidade do _Watson Studio_, componente da plataforma \"IBM Cloud Pak for Data\" _(\u201cIBM Cloud Pak for Data\u201d 2020)_: execu\u00e7\u00e3o di\u00e1ria da extra\u00e7\u00e3o de dados, valida\u00e7\u00e3o dos mesmos, actualiza\u00e7\u00e3o dos resultados de forma autom\u00e1tica e tamb\u00e9m a possibilidade de cria\u00e7\u00e3o de modelos preditivos automaticamente actualizados e testados atrav\u00e9s de _Watson Machine Learning_ _(\u201cWatson Machine Learning - Overview | IBM\u201d 2020)_; estas possibilidades da plataforma IBM Watson s\u00e3o transversais a qualquer projecto de _Data Science_, sendo o presente trabalho apenas um exemplo que utiliza uma pequena parte das funcionalidades disponibilizadas. Em concreto, e em diferentes fases de execu\u00e7\u00e3o, podemos referir as seguintes linhas de trabalho:\n\n* Actualiza\u00e7\u00e3o semana, autom\u00e1tica, dos dados, com visualiza\u00e7\u00e3o da evolu\u00e7\u00e3o das dist\u00e2ncias ao longo da XV legislatura (utiliza\u00e7\u00e3o da funcionalidade de cria\u00e7\u00e3o de _jobs_ em IBM Watson Studio)\n* Compara\u00e7\u00e3o dos resultados obtidos com os de outros estudos que utilizam crit\u00e9rios assentes na classifica\u00e7\u00e3o de posi\u00e7\u00f5es de forma qualitativa.\n* Incorpora\u00e7\u00e3o de mais dados: quem produz mais propostas, quem vota mais a favor ou contra as propostas de determinado partido.\n* Classifica\u00e7\u00e3o autom\u00e1tica das propostas em termos de conceitos chaves, atrav\u00e9s de an\u00e1lise textual, para poss\u00edvel cria\u00e7\u00e3o de diferentes \u00e1reas tem\u00e1ticas (\"Pol\u00edtica externa\", \"Trabalho\", etc.)\n* Cruzamento da informa\u00e7\u00e3o dos deputados para an\u00e1lise de escolaridade, profiss\u00e3o e outros dados.\n* Quantidade de propostas, inclu\u00edndo propostas de altera\u00e7\u00e3o, de cada partido.\n\nPara finalizar, e considerando sempre a necessidade de analise cr\u00edtica dos dados apresentados e a sua correc\u00e7\u00e3o, recordamos quando um (excelente) professor (de uma mat\u00e9ria bastante relevante para a an\u00e1lise efectuada) utilizou, numa das suas aulas, uma cita\u00e7\u00e3o que se mant\u00e9m fundamental: _\"Se n\u00e3o temo o erro, \u00e9 porque estou sempre disposto a corrigi-lo\"_ _(\u201cFrase do matem\u00e1tico portugu\u00eas Bento de Jesus Cara\u00e7a\u201d, SPM 2020)_. Tamb\u00e9m para este trabalho estamos necessariamente abertos a correc\u00e7\u00f5es e sugest\u00f5es fundamentadas."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Bibliografia\n\n* \u201c2.2. Manifold Learning \u2014 Scikit-Learn 0.23.2 Documentation.\u201d 2020. Accessed September 2. https://scikit-learn.org/stable/modules/manifold.html#multidimensional-scaling.\n* Almeida, Joana. 2019. \u201cCDS explica contesta\u00e7\u00e3o ao lugar do Chega no Parlamento: \u2018Andr\u00e9 Ventura pode cansar-se e dizer \u201colhe, basta, chega.\u201d\u2019\u201d O Jornal Econ\u00f3mico. October 23. https://jornaleconomico.sapo.pt/noticias/cds-explica-contestacao-ao-lugar-do-chega-no-parlamento-andre-ventura-pode-cansar-se-e-dizer-olhe-basta-chega-504766.\n* Carvalho, Alexandre Xavier Ywata, Pedro Henrique Melo Albuquerque, Gilberto Rezende de Almeida Junior, and Rafael Dantas Guimaraes. 2009. \u201cSpatial Hierarchical Clustering.\u201d Revista Brasileira de Biometria 27 (3): 411\u201342.\n* \u201cCleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says.\u201d 2020. Accessed August 20. https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#8e17b266f637.\n* \u201cDados Abertos.\u201d 2020. Accessed August 17. https://www.parlamento.pt/Cidadania/Paginas/DadosAbertos.aspx.\n* \u201cDBSCAN: Macroscopic Investigation Python.\u201d 2018. DataCamp Community. August 3. https://www.datacamp.com/community/tutorials/dbscan-macroscopic-investigation-python.\n* Dodds, Leigh. 2020. \u201cDo Data Scientists Spend 80% of Their Time Cleaning Data? Turns Out, No?\u201d Lost Boy. January 31. https://blog.ldodds.com/2020/01/31/do-data-scientists-spend-80-of-their-time-cleaning-data-turns-out-no/.\n* \u201cEst\u00e3o Distribu\u00eddas (para J\u00e1) as Cadeiras No Parlamento - DN.\u201d 2020. Accessed August 25. https://www.dn.pt/poder/nova-legislatura-estao-distribuidas-para-ja-as-cadeiras-no-parlamento-11412255.html.\n* \u201cExpresso | A Hist\u00f3ria Do Partido Que Diz Que N\u00e3o \u00c9 de Direita Nem de Esquerda: O PAN.\u201d 2020. Accessed September 20. https://expresso.pt/europeias-2019/2019-05-27-A-historia-do-partido-que-diz-que-nao-e-de-direita-nem-de-esquerda-o-PAN.\n* Figueiredo Filho, Dalson Britto, Enivaldo Carvalho da Rocha, Jos\u00e9 Alexandre da Silva J\u00fanior, Ranulfo Paranhos, Mariana Batista da Silva, and B\u00e1rbara Sofia F\u00e9lix Duarte. 2014. \u201cCluster Analysis for Political Scientists.\u201d Applied Mathematics 2014.\n* \u201cFrase do matem\u00e1tico portugu\u00eas Bento de Jesus Cara\u00e7a.\u201d 2020. Sociedade Portuguesa de Matem\u00e1tica. Accessed September 20. https://www.spm.pt/news/4730.\n* \u201cGraphical Representation of Proximity Measures for Multidimensional Data \u00ab\u202fThe Mathematica Journal.\u201d 2020. Accessed September 2. https://www.mathematica-journal.com/2015/09/30/graphical-representation-of-proximity-measures-for-multidimensional-data/.\n* Hix, Simon, Abdul Noury, and G\u00e9rard Roland. 2006. \u201cDimensions of Politics in the European Parliament.\u201d American Journal of Political Science 50 (2): 494\u2013511. https://www.jstor.org/stable/3694286.\n* \u201cIBM Cloud Pak for Data.\u201d 2020. Accessed September 20. https://dataplatform.cloud.ibm.com/home2?context=cpdaas.\n* \u201cIniciativa Liberal Descontente Com Lugar Atribu\u00eddo a Deputado No Parlamento - TSF.\u201d 2020. Accessed August 17. https://www.tsf.pt/portugal/politica/iniciativa-liberal-descontente-com-lugar-atribuido-a-deputado-no-parlamento-11412664.html.\n* Jackson, Robert J. 1968. Rebels and Whips: An Analysis of Dissension, Discipline and Cohesion in British Political Parties. Macmillan.\n* \u201cJoacine Katar Moreira demite-se do Livre - DN.\u201d 2020. Accessed August 20. https://www.dn.pt/poder/joacine-katar-moreira-demite-se-do-livre-11783401.html.\n* Krilavi\u010dius, Tomas, and Antanas \u017dilinskas. 2008. \u201cOn Structural Analysis of Parliamentarian Voting Data.\u201d Informatica 19 (3): 377\u201390.\n* Louren\u00e7o, Eunice. 2020. \u201cGuerra de lugares.\u201d P\u00daBLICO. Accessed August 17. https://www.publico.pt/1999/10/26/jornal/guerra-de-lugares-125525.\n* Lourinho, Jos\u00e9 Carlos. 2020. \u201cRui Rio: \u2018Tanto sou do centro-direita como do centro-esquerda.\u2019\u201d O Jornal Econ\u00f3mico. January 14. https://jornaleconomico.sapo.pt/noticias/rui-rio-tanto-sou-do-centro-direita-como-do-centro-esquerda-535068.\n* Lusa. 2020a. \u201cQuadro de tempos e vota\u00e7\u00f5es da AR j\u00e1 distingue as duas deputadas n\u00e3o-inscritas.\u201d P\u00daBLICO. Accessed August 23. https://www.publico.pt/2020/06/26/politica/noticia/quadro-tempos-votacoes-ar-ja-distingue-duas-deputadas-naoinscritas-1922017.\n* \u2014\u2014\u2014. 2020b. \u201cPCP e PEV confirmam converg\u00eancia e priorizam creches, sal\u00e1rios, ambiente e ferrovia.\u201d P\u00daBLICO. Accessed September 20. https://www.publico.pt/2019/10/24/politica/noticia/pcp-pev-confirmam-convergencia-priorizam-creches-salarios-ambiente-ferrovia-1891225.\n* Morse, J. N. 1980. \u201cReducing the Size of the Nondominated Set: Pruning by Clustering.\u201d Computers & Operations Research 7 (1): 55\u201366. doi:10.1016/0305-0548(80)90014-3.\n* \u201c\u2018N\u2019-Dimensional Euclidean Distance.\u201d 2020. Accessed September 5. https://hlab.stanford.edu/brian/euclidean_distance_in.html.\n* Ortega y Gasset, Jos\u00e9. 1995. \u00bf Qu\u00e9 Es Filosof\u00eda?. Espasa Calpe.\n* Powers, Stephen M., and Stephanie E. Hampton. 2019. \u201cOpen Science, Reproducibility, and Transparency in Ecology.\u201d Ecological Applications 29 (1): e01822.\n* \u201cPython - Matplotlib: Annotating a 3D Scatter Plot - Stack Overflow.\u201d 2020. Accessed September 4. https://stackoverflow.com/questions/10374930/matplotlib-annotating-a-3d-scatter-plot.\n* Randles, Bernadette M., Irene V. Pasquetto, Milena S. Golshan, and Christine L. Borgman. 2017. \u201cUsing the Jupyter Notebook as a Tool for Open Science: An Empirical Study.\u201d In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 1\u20132. IEEE.\n* Renascen\u00e7a. 2019. \u201cUma nova porta para o Chega? Perceba porque \u00e9 t\u00e3o dif\u00edcil sentar os deputados - Renascen\u00e7a.\u201d R\u00e1dio Renascen\u00e7a. October 22. https://rr.sapo.pt/2019/10/22/legislativas-2019/uma-nova-porta-para-o-chega-perceba-porque-e-tao-dificil-sentar-os-deputados/noticia/169040/.\n* Sapage, S\u00f3nia. 2020. \u201c\u2018Hemiciclo.pt\u2019 \u00e9 uma nova vers\u00e3o digital do Parlamento.\u201d P\u00daBLICO. Accessed September 21. https://www.publico.pt/2017/09/04/politica/noticia/hemiciclo-uma-nova-versao-digital-do-parlamento-1784304.\n* SAPO. 2020. \u201cMais uma sa\u00edda do PAN. Cristina Rodrigues deixa partido e passa a deputada n\u00e3o inscrita.\u201d SAPO 24. Accessed August 20. https://24.sapo.pt/atualidade/artigos/mais-uma-saida-do-pan-cristina-rodrigues-deixa-partido-e-passa-a-deputada-nao-inscrita.\n* \u201cSpectral Clustering. Foundation and Application | by William Fleshman | Towards Data Science.\u201d 2020. Accessed September 1. https://towardsdatascience.com/spectral-clustering-aba2640c0d5b.\n* \u201cVota\u00e7\u00e3o dos cinco projetos da eutan\u00e1sia ser\u00e1 nominal - JN.\u201d 2020. Accessed August 20. https://www.jn.pt/nacional/votacao-dos-cinco-projetos-da-eutanasia-sera-nominal--11838428.html.\n* Wall, Matthew, Andr\u00e9 Krouwel, and Jan Kleinnijenhuis. 2012. \u201cPolitical Matchmakers: How Do the Decision Rules Employed by Vote Advice Application Sites Influence Their Advice?\u201d In Elecdem Conference, Florence, 28\u201330.\n* \u201cWatson Machine Learning - Overview | IBM.\u201d 2020. Accessed September 20. https://www.ibm.com/cloud/machine-learning.\n* Wofford, Morgan F., Bernadette M. Boscoe, Christine L. Borgman, Irene V. Pasquetto, and Milena S. Golshan. 2019. \u201cJupyter Notebooks as Discovery Mechanisms for Open Science: Citation Practices in the Astronomy Community.\u201d Computing in Science & Engineering 22 (1): 5\u201315.\n* ZAP. 2019. \u201cO Livre procurou o Bloco. E \u00e9 mais o que os une do que o que os separa.\u201d ZAP. October 24. https://zap.aeiou.pt/livre-procurou-bloco-287820."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "----\n## Ap\u00eandice"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### How to cite this notebook\n\n*MU\u00d1OZ, Frederico Serrano - Dados abertos e Ci\u00eancia de Dados: An\u00e1lise da Actividade Parlamentar da XIV Legislatura do Parlamento Portugu\u00eas [Online]  WWW: : https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/0b23f01c-e55a-4fe4-a2ca-57f2e478bf3e/view?access_token=4cc13fba3f3967530650b11a2956ed27c5cec9042113237f97cb0e4c58d3905f*\n\n---\n\n**BibTeX**\n```latex\n@report{munozDadosAbertosCiencia2020,\n  title = {Dados abertos e Ci\u00eancia de Dados: An\u00e1lise da Actividade Parlamentar da XIV Legislatura do Parlamento Portugu\u00eas},\n  author = {Mu\u00f1oz, Frederico Serrano},\n  date = {2020},\n  url = {https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/0b23f01c-e55a-4fe4-a2ca-57f2e478bf3e/view?access_token=4cc13fba3f3967530650b11a2956ed27c5cec9042113237f97cb0e4c58d3905f},\n  abstract = {The application of Data Science methods and analysis to voting records is a well-established practice; in this paper we describe the process of obtaining. cleaning, exploring and choosing adequate analytic methods for the purpose of determining the distance between parties in the Portuguese Parliament. Through the use of institutional Open Data resources we discuss the different approaches possible in terms of clustering and dimension reduction, using DBSCAN, Spectrum Clustering and Multidimensional Scaling to augment the information produced by distance and affinity matrices. A detailed explanation of how an euclidean distance matrix is obtained and how clustering and MDS work using test datasets is also included},\n  langid = {por, en},\n  type = {Jupyter Notebook}\n}\n```"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Execu\u00e7\u00e3o do bloco de notas"
        },
        {
            "attachments": {
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                    "image/png": 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"
                }
            },
            "cell_type": "markdown",
            "metadata": {},
            "source": "Este bloco de notas pode ser executado na plataforma IBM Data Platform atrav\u00e9s da cria\u00e7\u00e3o de um novo *Notebook* (https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/creating-notebooks.html?audience=wdp) e importa\u00e7\u00e3o do URL (https://github.com/fsmunoz/pt-act-parlamentar/raw/master/Actividade%20Parlamentar%20da%20XIV%20Legislatura.ipynb).\n\n\u00c9 tamb\u00e9m poss\u00edvel utilizar o ambiente de execu\u00e7\u00e3o _Binder_; a forma mais directa \u00e9 atrav\u00e9s da integra\u00e7\u00e3o disponibilizada no visualizador do projecto Jupyter e que para este projecto tem a seguinte liga\u00e7\u00e3o:\n\nhttps://nbviewer.jupyter.org/github/fsmunoz/pt-act-parlamentar/raw/master/Actividade%20Parlamentar%20da%20XIV%20Legislatura.ipynb\n\nNo canto superior direito o \u00edcone do Binder (tr\u00eas c\u00edrculos) permite iniciar um ambiente dedicado onde a interac\u00e7\u00e3o passa a ser poss\u00edvel.\n![binder.png](attachment:binder.png)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Iniciativas e vota\u00e7\u00f5es"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "H\u00e1 uma **diferen\u00e7a entre a primeira vota\u00e7\u00e3o e a primeira iniciativa** (e, em termos gerais, entre iniciativas e vota\u00e7\u00f5es): existem iniciativas que por raz\u00f5es v\u00e1rias n\u00e3o t\u00eam vota\u00e7\u00f5es associadas. Assim, note-se os detalhes da primeira iniciativa e como o tema \u00e9 diferente do apresentados para a primeira iniciativa com vota\u00e7\u00e3o mostrada anteriormente:"
        },
        {
            "cell_type": "code",
            "execution_count": 46,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "iniNr          : 28\niniTipo        : A\niniDescTipo    : Aprecia\u00e7\u00e3o Parlamentar\niniLeg         : XIV\niniSel         : 1\ndataInicioleg  : 2019-10-25\ndataFimleg     : 2020-09-14\niniTitulo      : Decreto-Lei n.\u00ba 29/2020, de 29 de junho, que \"Cria um programa de apoio ao emparcelamento rural simples, designado \u00abEmparcelar para Ordenar\u00bb\"\niniTextoSubst  : NAO\niniLinkTexto   : http://app.parlamento.pt/webutils/docs/doc.pdf?path=6148523063446f764c324679595842774f6a63334e7a637664326c756157357059326c6864476c3259584d7657456c574c33526c6548527663793977595841794f433159535659755a47396a&fich=pap28-XIV.doc&Inline=true\niniEventos     : None\niniAutorDeputados: None\niniAutorOutros : None\niniId          : 45156\niniAutorGruposParlamentares: None\n"
                }
            ],
            "source": "for c in ini_tree.find(\"pt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut\"):\n        print(\"{0:15}: {1}\".format(c.tag,c.text))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Esta iniciativa n\u00e3o tem vota\u00e7\u00f5es associadas pelo que n\u00e3o ir\u00e1 ser inclu\u00edda na tabela que lista as vota\u00e7\u00f5es, mas deveria ser inclu\u00edda numa tabela com objectivos diferentes do presente trabalho, focada por exemplo nas iniciativas parlamentares; aproveitemo-la, por\u00e9m, para demonstrar como extrair a informa\u00e7\u00e3o da autoria da iniciativa: os campos onde aparece `None`s\u00e3o quase sempre campos que cont\u00e9m informa\u00e7\u00e3o com v\u00e1rios n\u00edveis e que \u00e9 preciso extrair de forma especial. Para a autoria existe a sec\u00e7\u00e3o `iniAutorGruposParlamentares` que cont\u00e9m um objecto `pt_gov_ar_objectos_AutoresGruposParlamentaresOut` com o nome do grupo parlamentar como conte\u00fado de `GP`:"
        },
        {
            "cell_type": "code",
            "execution_count": 47,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "iniNr                         : 28\niniTipo                       : A\niniDescTipo                   : Aprecia\u00e7\u00e3o Parlamentar\niniLeg                        : XIV\niniSel                        : 1\ndataInicioleg                 : 2019-10-25\ndataFimleg                    : 2020-09-14\niniTitulo                     : Decreto-Lei n.\u00ba 29/2020, de 29 de junho, que \"Cria um programa de apoio ao emparcelamento rural simples, designado \u00abEmparcelar para Ordenar\u00bb\"\niniTextoSubst                 : NAO\niniLinkTexto                  : http://app.parlamento.pt/webutils/docs/doc.pdf?path=6148523063446f764c324679595842774f6a63334e7a637664326c756157357059326c6864476c3259584d7657456c574c33526c6548527663793977595841794f433159535659755a47396a&fich=pap28-XIV.doc&Inline=true\niniEventos                    : None\niniAutorDeputados             : None\niniAutorOutros                : None\niniId                         : 45156\niniAutorGruposParlamentares   : PCP\n"
                }
            ],
            "source": "for c in ini_tree.find(\"pt_gov_ar_objectos_iniciativas_DetalhePesquisaIniciativasOut\"):\n    if c.tag == \"iniAutorGruposParlamentares\":\n        print(\"{0:30}: {1}\".format(c.tag,c.find('pt_gov_ar_objectos_AutoresGruposParlamentaresOut/GP').text))\n    else:\n        print(\"{0:30}: {1}\".format(c.tag,c.text))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "A **autoria das iniciativas** onde se integram as vota\u00e7\u00f5es pode ser registada, mas \u00e9 importante ter claro que esta autoria **\u00e9 independente da autoria da mat\u00e9ria em vota\u00e7\u00e3o**: uma iniciativa de um partido pode integrar vota\u00e7\u00f5es propostas por outros. Esta situa\u00e7\u00e3o n\u00e3o impacta a possiblidade de an\u00e1lise a que nos propomos mas abre \u00e1reas de explora\u00e7\u00e3o adicional para o futuro. Tendo em conta o objectivo deste trabalho optou-se por utilizar apenas a informa\u00e7\u00e3o das vota\u00e7\u00f5es de forma a tornar o c\u00f3digo mais simples e focado, deixando para um segundo trabalho (em curso) uma contextualiza\u00e7\u00e3o mais ampla."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Processamento da descri\u00e7\u00e3o de votos"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "**Come\u00e7emos pela primeira** usando a vota\u00e7\u00e3o `86004` que representa o primeira caso ao ser uma vota\u00e7\u00e3o nominal, uma das cinco relativas aos projectos relativos \u00e0 eutan\u00e1sia <cite data-cite=\"1084038/C56FTGBX\"></cite>:"
        },
        {
            "cell_type": "code",
            "execution_count": 48,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "\tid             : 86004\n\tresultado      : Aprovado\n\treuniao        : 32\n\ttipoReuniao    : RP\n\tdetalhe        : {'Bruno Arag\u00e3o (PS)': 'A Favor', 'Ana Maria Silva (PS)': 'A Favor', 'Joana Bento (PS)': 'A Favor', 'Jo\u00e3o Gouveia (PS)': 'A Favor', 'Ana Passos (PS)': 'A Favor', 'Francisco Pereira Oliveira (PS)': 'A Favor', 'Sara Velez (PS)': 'A Favor', 'Rita Borges Madeira (PS)': 'A Favor', 'Diogo Le\u00e3o (PS)': 'A Favor', 'Jo\u00e3o Miguel Nicolau (PS)': 'A Favor', 'Alexandra Tavares de Moura (PS)': 'A Favor', 'Fernando Anast\u00e1cio (PS)': 'A Favor', 'Fernando Paulo Ferreira (PS)': 'A Favor', 'Vera Braz (PS)': 'A Favor', 'Paulo Marques (PS)': 'A Favor', 'Eduardo Barroco de Melo (PS)': 'A Favor', 'Manuel dos Santos Afonso (PS)': 'A Favor', 'Mara Coelho (PS)': 'A Favor', 'Sofia Ara\u00fajo (PS)': 'A Favor', 'Fernando Jos\u00e9 (PS)': 'A Favor', 'Clarisse Campos (PS)': 'A Favor', 'Cl\u00e1udia Santos (PS)': 'A Favor', 'Filipe Neto Brand\u00e3o (PS)': 'A Favor', 'Porf\u00edrio Silva (PS)': 'A Favor', 'Susana Correia (PS)': 'A Favor', 'Hugo Oliveira (PS)': 'A Favor', 'Joana S\u00e1 Pereira (PS)': 'A Favor', 'Telma Guerreiro (PS)': 'A Favor', 'S\u00f3nia Fertuzinhos (PS)': 'A Favor', 'Maria Begonha (PS)': 'A Favor', 'Hugo Pires (PS)': 'A Favor', 'Palmira Maciel (PS)': 'A Favor', 'Lu\u00eds Soares (PS)': 'A Favor', 'Nuno S\u00e1 (PS)': 'A Favor', 'Pedro Coimbra (PS)': 'A Favor', 'Cristina Jesus (PS)': 'A Favor', 'Tiago Estev\u00e3o Martins (PS)': 'A Favor', 'Lu\u00eds Capoulas Santos (PS)': 'A Favor', 'Norberto Patinho (PS)': 'A Favor', 'Maria Joaquina Matos (PS)': 'A Favor', 'Lu\u00eds Gra\u00e7a (PS)': 'A Favor', 'Santinho Pacheco (PS)': 'A Favor', 'Elza Pais (PS)': 'A Favor', 'Jo\u00e3o Paulo Pedrosa (PS)': 'A Favor', 'Edite Estrela (PS)': 'A Favor', 'Eduardo Ferro Rodrigues (PS)': 'A Favor', 'Maria da Luz Rosinha (PS)': 'A Favor', 'Marcos Perestrello (PS)': 'A Favor', 'S\u00e9rgio Sousa Pinto (PS)': 'A Favor', 'Pedro Delgado Alves (PS)': 'A Favor', 'Jorge Lac\u00e3o (PS)': 'A Favor', 'Isabel Alves Moreira (PS)': 'A Favor', 'Ricardo Le\u00e3o (PS)': 'A Favor', 'Miguel Matos (PS)': 'A Favor', 'Lu\u00eds Moreira Testa (PS)': 'A Favor', 'Alexandre Quintanilha (PS)': 'A Favor', 'Ros\u00e1rio Gamb\u00f4a (PS)': 'A Favor', 'Cristina Mendes da Silva (PS)': 'A Favor', 'Tiago Barbosa Ribeiro (PS)': 'A Favor', 'Isabel Oneto (PS)': 'A Favor', 'Bacelar de Vasconcelos (PS)': 'A Favor', 'Joana Lima (PS)': 'A Favor', 'Constan\u00e7a Urbano de Sousa (PS)': 'A Favor', 'Jos\u00e9 Magalh\u00e3es (PS)': 'A Favor', 'Hugo Carvalho (PS)': 'A Favor', 'Carla Sousa (PS)': 'A Favor', 'Ant\u00f3nio Gameiro (PS)': 'A Favor', 'Hugo Costa (PS)': 'A Favor', 'Eur\u00eddice Pereira (PS)': 'A Favor', 'Catarina Marcelino (PS)': 'A Favor', 'Maria Ant\u00f3nia de Almeida Santos (PS)': 'A Favor', 'Filipe Pacheco (PS)': 'A Favor', 'Marina Gon\u00e7alves (PS)': 'A Favor', 'Anabela Rodrigues (PS)': 'A Favor', 'Francisco Rocha (PS)': 'A Favor', 'Jos\u00e9 Rui Cruz (PS)': 'A Favor', 'Jo\u00e3o Azevedo Castro (PS)': 'A Favor', 'Olavo C\u00e2mara (PS)': 'A Favor', 'Paulo Pisco (PS)': 'A Favor', 'Andr\u00e9 Coelho Lima (PSD)': 'A Favor', 'Ant\u00f3nio Mal\u00f3 de Abreu (PSD)': 'A Favor', 'Crist\u00f3v\u00e3o Norte (PSD)': 'A Favor', 'Isabel Meireles (PSD)': 'A Favor', 'Lina Lopes (PSD)': 'A Favor', 'Hugo Martins de Carvalho (PSD)': 'A Favor', 'Rui Rio (PSD)': 'A Favor', 'Catarina Rocha Ferreira (PSD)': 'A Favor', 'Sofia Matos (PSD)': 'A Favor', 'Duarte Marques (PSD)': 'A Favor', 'Mois\u00e9s Ferreira (BE)': 'A Favor', 'Nelson Peralta (BE)': 'A Favor', 'Jos\u00e9 Maria Cardoso (BE)': 'A Favor', 'Alexandra Vieira (BE)': 'A Favor', 'Jos\u00e9 Manuel Pureza (BE)': 'A Favor', 'Jo\u00e3o Vasconcelos (BE)': 'A Favor', 'Ricardo Vicente (BE)': 'A Favor', 'Mariana Mort\u00e1gua (BE)': 'A Favor', 'Pedro Filipe Soares (BE)': 'A Favor', 'Beatriz Gomes Dias (BE)': 'A Favor', 'Jorge Costa (BE)': 'A Favor', 'Isabel Pires (BE)': 'A Favor', 'Catarina Martins (BE)': 'A Favor', 'Jos\u00e9 Moura Soeiro (BE)': 'A Favor', 'Lu\u00eds Monteiro (BE)': 'A Favor', 'Maria Manuel Rola (BE)': 'A Favor', 'Fab\u00edola Cardoso (BE)': 'A Favor', 'Joana Mort\u00e1gua (BE)': 'A Favor', 'Sandra Cunha (BE)': 'A Favor', 'Andr\u00e9 Silva (PAN)': 'A Favor', 'In\u00eas de Sousa Real (PAN)': 'A Favor', 'Bebiana Cunha (PAN)': 'A Favor', 'Cristina Rodrigues (PAN)': 'A Favor', 'Joacine Katar Moreira (Ninsc)': 'A Favor', 'Jo\u00e3o Cotrim de Figueiredo (IL)': 'A Favor', 'C\u00e9lia Paz (PS)': 'Contra', 'Cristina Sousa (PS)': 'Contra', 'Maria da Gra\u00e7a Reis (PS)': 'Contra', 'Paulo Neves (PSD)': 'Contra', 'Bruno Dias (PCP)': 'Contra', 'Jo\u00e3o Gon\u00e7alves Pereira (CDS-PP)': 'Contra', 'Raul Miguel Castro (PS)': 'Contra', 'Pedro Cegonho (PS)': 'Contra', 'Romualda Fernandes (PS)': 'Contra', 'Ana Paula Vitorino (PS)': 'Contra', 'Jos\u00e9 Lu\u00eds Carneiro (PS)': 'Contra', 'Ascenso Sim\u00f5es (PS)': 'Contra', 'Ant\u00f3nio Topa (PSD)': 'Contra', 'Helga Correia (PSD)': 'Contra', 'Bruno Coimbra (PSD)': 'Contra', 'Andr\u00e9 Neves (PSD)': 'Contra', 'Carla Madureira (PSD)': 'Contra', 'Firmino Marques (PSD)': 'Contra', 'Clara Marques Mendes (PSD)': 'Contra', 'Carlos Eduardo Reis (PSD)': 'Contra', 'Jorge Paulo Oliveira (PSD)': 'Contra', 'Maria Gabriela Fonseca (PSD)': 'Contra', 'Em\u00eddio Guerreiro (PSD)': 'Contra', 'Isabel Lopes (PSD)': 'Contra', 'Cl\u00e1udia Andr\u00e9 (PSD)': 'Contra', 'Paulo Leit\u00e3o (PSD)': 'Contra', 'Rui Cristina (PSD)': 'Contra', 'Of\u00e9lia Ramos (PSD)': 'Contra', 'Carlos Peixoto (PSD)': 'Contra', 'Hugo Patr\u00edcio Oliveira (PSD)': 'Contra', 'Olga Silvestre (PSD)': 'Contra', 'Jo\u00e3o Gomes Marques (PSD)': 'Contra', 'Filipa Roseta (PSD)': 'Contra', 'Jos\u00e9 Silvano (PSD)': 'Contra', 'Lu\u00eds Marques Guedes (PSD)': 'Contra', 'Sandra Pereira (PSD)': 'Contra', 'Ricardo Baptista Leite (PSD)': 'Contra', 'Carlos Silva (PSD)': 'Contra', 'Alexandre Po\u00e7o (PSD)': 'Contra', 'Alberto Machado (PSD)': 'Contra', 'Jos\u00e9 Cancela Moura (PSD)': 'Contra', 'Maria Germana Rocha (PSD)': 'Contra', 'Afonso Oliveira (PSD)': 'Contra', '\u00c1lvaro Almeida (PSD)': 'Contra', 'Alberto Fonseca (PSD)': 'Contra', 'Paulo Rios de Oliveira (PSD)': 'Contra', 'Carla Barros (PSD)': 'Contra', 'Hugo Carneiro (PSD)': 'Contra', 'Ant\u00f3nio Cunha (PSD)': 'Contra', 'M\u00e1rcia Passos (PSD)': 'Contra', 'Isaura Morais (PSD)': 'Contra', 'Jo\u00e3o Moura (PSD)': 'Contra', 'Nuno Miguel Carvalho (PSD)': 'Contra', 'Fernando Negr\u00e3o (PSD)': 'Contra', 'Fernanda Velez (PSD)': 'Contra', 'Jorge Salgueiro Mendes (PSD)': 'Contra', 'Eduardo Teixeira (PSD)': 'Contra', 'Lu\u00eds Leite Ramos (PSD)': 'Contra', 'Cl\u00e1udia Bento (PSD)': 'Contra', 'Artur Soveral Andrade (PSD)': 'Contra', 'Fernando Ruas (PSD)': 'Contra', 'Pedro Alves (PSD)': 'Contra', 'Carla Borges (PSD)': 'Contra', 'Ant\u00f3nio Lima Costa (PSD)': 'Contra', 'Paulo Moniz (PSD)': 'Contra', 'Ant\u00f3nio Ventura (PSD)': 'Contra', 'S\u00e9rgio Marques (PSD)': 'Contra', 'Sara Madruga da Costa (PSD)': 'Contra', 'Carlos Alberto Gon\u00e7alves (PSD)': 'Contra', 'Jos\u00e9 Ces\u00e1rio (PSD)': 'Contra', 'Jo\u00e3o Dias (PCP)': 'Contra', 'Jo\u00e3o Oliveira (PCP)': 'Contra', 'Jer\u00f3nimo de Sousa (PCP)': 'Contra', 'Alma Rivera (PCP)': 'Contra', 'Duarte Alves (PCP)': 'Contra', 'Diana Ferreira (PCP)': 'Contra', 'Ana Mesquita (PCP)': 'Contra', 'Ant\u00f3nio Filipe (PCP)': 'Contra', 'Paula Santos (PCP)': 'Contra', 'Jo\u00e3o Pinho de Almeida (CDS-PP)': 'Contra', 'Telmo Correia (CDS-PP)': 'Contra', 'Ana Rita Bessa (CDS-PP)': 'Contra', 'Cec\u00edlia Meireles (CDS-PP)': 'Contra', 'Andr\u00e9 Ventura (CH)': 'Contra', 'Carlos Br\u00e1s (PS)': 'Absten\u00e7\u00e3o', 'Jos\u00e9 Manuel Carpinteira (PS)': 'Absten\u00e7\u00e3o', 'Paulo Porto (PS)': 'Absten\u00e7\u00e3o', 'Pedro do Carmo (PS)': 'Absten\u00e7\u00e3o', 'Joaquim Barreto (PS)': 'Absten\u00e7\u00e3o', 'Jorge Gomes (PS)': 'Absten\u00e7\u00e3o', 'Hortense Martins (PS)': 'Absten\u00e7\u00e3o', 'Nuno Fazenda (PS)': 'Absten\u00e7\u00e3o', 'Jo\u00e3o Ata\u00edde (PS)': 'Absten\u00e7\u00e3o', 'Ricardo Pinheiro (PS)': 'Absten\u00e7\u00e3o', 'Jo\u00e3o Paulo Correia (PS)': 'Absten\u00e7\u00e3o', 'Pedro Sousa (PS)': 'Absten\u00e7\u00e3o', 'Ana Catarina Mendon\u00e7a Mendes (PS)': 'Absten\u00e7\u00e3o', 'Andr\u00e9 Pinotes Batista (PS)': 'Absten\u00e7\u00e3o', 'Jo\u00e3o Azevedo (PS)': 'Absten\u00e7\u00e3o', 'Isabel Rodrigues (PS)': 'Absten\u00e7\u00e3o', 'Lara Martinho (PS)': 'Absten\u00e7\u00e3o', 'Marta Freitas (PS)': 'Absten\u00e7\u00e3o', 'Ana Miguel dos Santos (PSD)': 'Absten\u00e7\u00e3o', 'Rui Silva (PSD)': 'Absten\u00e7\u00e3o', 'Ad\u00e3o Silva (PSD)': 'Absten\u00e7\u00e3o', 'Pedro Pinto (PSD)': 'Absten\u00e7\u00e3o', 'Mariana Silva (PEV)': 'Absten\u00e7\u00e3o', 'Jos\u00e9 Lu\u00eds Ferreira (PEV)': 'Absten\u00e7\u00e3o'}\n"
                }
            ],
            "source": "from bs4 import BeautifulSoup\n\ndef parse_voting1(v_str):\n    \"\"\"Parses the voting details in a string and returns a dict.\n    \n    Keyword arguments:\n    \n    v_str: a string with the description of the voting behaviour.\n    \"\"\"\n    ## Split by the HTML line break and put it in a dict\n    d = dict(x.split(':') for x in v_str.split('<BR>'))\n    ## Remove the HTML tags\n    for k, v in d.items():\n        ctext = BeautifulSoup(v, \"lxml\")\n        d[k] = ctext.get_text().strip().split(\",\")\n    ## Invert the dict to get a 1-to-1 mapping\n    ## and trim it\n    votes = {}\n    for k, v in d.items():\n        for p in v:\n            ## Sometimes there are empty entries (\"boo, , bar, baz\"), bypass them\n            if p != ' ':\n                votes[p.strip()] = k\n    return votes\n\nfor v in ini_tree.find(\".//pt_gov_ar_objectos_VotacaoOut/[id='86004']\"):\n    if v.tag == \"detalhe\":\n        vote_86004 = parse_voting1(v.text)\n        print(\"\\t{0:15}: {1}\".format(v.tag,parse_voting1(v.text)))\n    else:\n        print(\"\\t{0:15}: {1}\".format(v.tag,v.text))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "A divis\u00e3o dos votos por partido para esta vota\u00e7\u00e3o em espec\u00edfico \u00e9 a seguinte para esta vota\u00e7\u00e3o:"
        },
        {
            "cell_type": "code",
            "execution_count": 49,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Bruno Arag\u00e3o (PS) A Favor\nAna Maria Silva (PS) A Favor\nJoana Bento (PS) A Favor\nJo\u00e3o Gouveia (PS) A Favor\nAna Passos (PS) A Favor\nFrancisco Pereira Oliveira (PS) A Favor\nSara Velez (PS) A Favor\nRita Borges Madeira (PS) A Favor\nDiogo Le\u00e3o (PS) A Favor\nJo\u00e3o Miguel Nicolau (PS) A Favor\nAlexandra Tavares de Moura (PS) A Favor\nFernando Anast\u00e1cio (PS) A Favor\nFernando Paulo Ferreira (PS) A Favor\nVera Braz (PS) A Favor\nPaulo Marques (PS) A Favor\nEduardo Barroco de Melo (PS) A Favor\nManuel dos Santos Afonso (PS) A Favor\nMara Coelho (PS) A Favor\nSofia Ara\u00fajo (PS) A Favor\nFernando Jos\u00e9 (PS) A Favor\nClarisse Campos (PS) A Favor\nCl\u00e1udia Santos (PS) A Favor\nFilipe Neto Brand\u00e3o (PS) A Favor\nPorf\u00edrio Silva (PS) A Favor\nSusana Correia (PS) A Favor\nHugo Oliveira (PS) A Favor\nJoana S\u00e1 Pereira (PS) A Favor\nTelma Guerreiro (PS) A Favor\nS\u00f3nia Fertuzinhos (PS) A Favor\nMaria Begonha (PS) A Favor\nHugo Pires (PS) A Favor\nPalmira Maciel (PS) A Favor\nLu\u00eds Soares (PS) A Favor\nNuno S\u00e1 (PS) A Favor\nPedro Coimbra (PS) A Favor\nCristina Jesus (PS) A Favor\nTiago Estev\u00e3o Martins (PS) A Favor\nLu\u00eds Capoulas Santos (PS) A Favor\nNorberto Patinho (PS) A Favor\nMaria Joaquina Matos (PS) A Favor\nLu\u00eds Gra\u00e7a (PS) A Favor\nSantinho Pacheco (PS) A Favor\nElza Pais (PS) A Favor\nJo\u00e3o Paulo Pedrosa (PS) A Favor\nEdite Estrela (PS) A Favor\nEduardo Ferro Rodrigues (PS) A Favor\nMaria da Luz Rosinha (PS) A Favor\nMarcos Perestrello (PS) A Favor\nS\u00e9rgio Sousa Pinto (PS) A Favor\nPedro Delgado Alves (PS) A Favor\nJorge Lac\u00e3o (PS) A Favor\nIsabel Alves Moreira (PS) A Favor\nRicardo Le\u00e3o (PS) A Favor\nMiguel Matos (PS) A Favor\nLu\u00eds Moreira Testa (PS) A Favor\nAlexandre Quintanilha (PS) A Favor\nRos\u00e1rio Gamb\u00f4a (PS) A Favor\nCristina Mendes da Silva (PS) A Favor\nTiago Barbosa Ribeiro (PS) A Favor\nIsabel Oneto (PS) A Favor\nBacelar de Vasconcelos (PS) A Favor\nJoana Lima (PS) A Favor\nConstan\u00e7a Urbano de Sousa (PS) A Favor\nJos\u00e9 Magalh\u00e3es (PS) A Favor\nHugo Carvalho (PS) A Favor\nCarla Sousa (PS) A Favor\nAnt\u00f3nio Gameiro (PS) A Favor\nHugo Costa (PS) A Favor\nEur\u00eddice Pereira (PS) A Favor\nCatarina Marcelino (PS) A Favor\nMaria Ant\u00f3nia de Almeida Santos (PS) A Favor\nFilipe Pacheco (PS) A Favor\nMarina Gon\u00e7alves (PS) A Favor\nAnabela Rodrigues (PS) A Favor\nFrancisco Rocha (PS) A Favor\nJos\u00e9 Rui Cruz (PS) A Favor\nJo\u00e3o Azevedo Castro (PS) A Favor\nOlavo C\u00e2mara (PS) A Favor\nPaulo Pisco (PS) A Favor\nAndr\u00e9 Coelho Lima (PSD) A Favor\nAnt\u00f3nio Mal\u00f3 de Abreu (PSD) A Favor\nCrist\u00f3v\u00e3o Norte (PSD) A Favor\nIsabel Meireles (PSD) A Favor\nLina Lopes (PSD) A Favor\nHugo Martins de Carvalho (PSD) A Favor\nRui Rio (PSD) A Favor\nCatarina Rocha Ferreira (PSD) A Favor\nSofia Matos (PSD) A Favor\nDuarte Marques (PSD) A Favor\nMois\u00e9s Ferreira (BE) A Favor\nNelson Peralta (BE) A Favor\nJos\u00e9 Maria Cardoso (BE) A Favor\nAlexandra Vieira (BE) A Favor\nJos\u00e9 Manuel Pureza (BE) A Favor\nJo\u00e3o Vasconcelos (BE) A Favor\nRicardo Vicente (BE) A Favor\nMariana Mort\u00e1gua (BE) A Favor\nPedro Filipe Soares (BE) A Favor\nBeatriz Gomes Dias (BE) A Favor\nJorge Costa (BE) A Favor\nIsabel Pires (BE) A Favor\nCatarina Martins (BE) A Favor\nJos\u00e9 Moura Soeiro (BE) A Favor\nLu\u00eds Monteiro (BE) A Favor\nMaria Manuel Rola (BE) A Favor\nFab\u00edola Cardoso (BE) A Favor\nJoana Mort\u00e1gua (BE) A Favor\nSandra Cunha (BE) A Favor\nAndr\u00e9 Silva (PAN) A Favor\nIn\u00eas de Sousa Real (PAN) A Favor\nBebiana Cunha (PAN) A Favor\nCristina Rodrigues (PAN) A Favor\nJoacine Katar Moreira (Ninsc) A Favor\nJo\u00e3o Cotrim de Figueiredo (IL) A Favor\nC\u00e9lia Paz (PS) Contra\nCristina Sousa (PS) Contra\nMaria da Gra\u00e7a Reis (PS) Contra\nPaulo Neves (PSD) Contra\nBruno Dias (PCP) Contra\nJo\u00e3o Gon\u00e7alves Pereira (CDS-PP) Contra\nRaul Miguel Castro (PS) Contra\nPedro Cegonho (PS) Contra\nRomualda Fernandes (PS) Contra\nAna Paula Vitorino (PS) Contra\nJos\u00e9 Lu\u00eds Carneiro (PS) Contra\nAscenso Sim\u00f5es (PS) Contra\nAnt\u00f3nio Topa (PSD) Contra\nHelga Correia (PSD) Contra\nBruno Coimbra (PSD) Contra\nAndr\u00e9 Neves (PSD) Contra\nCarla Madureira (PSD) Contra\nFirmino Marques (PSD) Contra\nClara Marques Mendes (PSD) Contra\nCarlos Eduardo Reis (PSD) Contra\nJorge Paulo Oliveira (PSD) Contra\nMaria Gabriela Fonseca (PSD) Contra\nEm\u00eddio Guerreiro (PSD) Contra\nIsabel Lopes (PSD) Contra\nCl\u00e1udia Andr\u00e9 (PSD) Contra\nPaulo Leit\u00e3o (PSD) Contra\nRui Cristina (PSD) Contra\nOf\u00e9lia Ramos (PSD) Contra\nCarlos Peixoto (PSD) Contra\nHugo Patr\u00edcio Oliveira (PSD) Contra\nOlga Silvestre (PSD) Contra\nJo\u00e3o Gomes Marques (PSD) Contra\nFilipa Roseta (PSD) Contra\nJos\u00e9 Silvano (PSD) Contra\nLu\u00eds Marques Guedes (PSD) Contra\nSandra Pereira (PSD) Contra\nRicardo Baptista Leite (PSD) Contra\nCarlos Silva (PSD) Contra\nAlexandre Po\u00e7o (PSD) Contra\nAlberto Machado (PSD) Contra\nJos\u00e9 Cancela Moura (PSD) Contra\nMaria Germana Rocha (PSD) Contra\nAfonso Oliveira (PSD) Contra\n\u00c1lvaro Almeida (PSD) Contra\nAlberto Fonseca (PSD) Contra\nPaulo Rios de Oliveira (PSD) Contra\nCarla Barros (PSD) Contra\nHugo Carneiro (PSD) Contra\nAnt\u00f3nio Cunha (PSD) Contra\nM\u00e1rcia Passos (PSD) Contra\nIsaura Morais (PSD) Contra\nJo\u00e3o Moura (PSD) Contra\nNuno Miguel Carvalho (PSD) Contra\nFernando Negr\u00e3o (PSD) Contra\nFernanda Velez (PSD) Contra\nJorge Salgueiro Mendes (PSD) Contra\nEduardo Teixeira (PSD) Contra\nLu\u00eds Leite Ramos (PSD) Contra\nCl\u00e1udia Bento (PSD) Contra\nArtur Soveral Andrade (PSD) Contra\nFernando Ruas (PSD) Contra\nPedro Alves (PSD) Contra\nCarla Borges (PSD) Contra\nAnt\u00f3nio Lima Costa (PSD) Contra\nPaulo Moniz (PSD) Contra\nAnt\u00f3nio Ventura (PSD) Contra\nS\u00e9rgio Marques (PSD) Contra\nSara Madruga da Costa (PSD) Contra\nCarlos Alberto Gon\u00e7alves (PSD) Contra\nJos\u00e9 Ces\u00e1rio (PSD) Contra\nJo\u00e3o Dias (PCP) Contra\nJo\u00e3o Oliveira (PCP) Contra\nJer\u00f3nimo de Sousa (PCP) Contra\nAlma Rivera (PCP) Contra\nDuarte Alves (PCP) Contra\nDiana Ferreira (PCP) Contra\nAna Mesquita (PCP) Contra\nAnt\u00f3nio Filipe (PCP) Contra\nPaula Santos (PCP) Contra\nJo\u00e3o Pinho de Almeida (CDS-PP) Contra\nTelmo Correia (CDS-PP) Contra\nAna Rita Bessa (CDS-PP) Contra\nCec\u00edlia Meireles (CDS-PP) Contra\nAndr\u00e9 Ventura (CH) Contra\nCarlos Br\u00e1s (PS) Absten\u00e7\u00e3o\nJos\u00e9 Manuel Carpinteira (PS) Absten\u00e7\u00e3o\nPaulo Porto (PS) Absten\u00e7\u00e3o\nPedro do Carmo (PS) Absten\u00e7\u00e3o\nJoaquim Barreto (PS) Absten\u00e7\u00e3o\nJorge Gomes (PS) Absten\u00e7\u00e3o\nHortense Martins (PS) Absten\u00e7\u00e3o\nNuno Fazenda (PS) Absten\u00e7\u00e3o\nJo\u00e3o Ata\u00edde (PS) Absten\u00e7\u00e3o\nRicardo Pinheiro (PS) Absten\u00e7\u00e3o\nJo\u00e3o Paulo Correia (PS) Absten\u00e7\u00e3o\nPedro Sousa (PS) Absten\u00e7\u00e3o\nAna Catarina Mendon\u00e7a Mendes (PS) Absten\u00e7\u00e3o\nAndr\u00e9 Pinotes Batista (PS) Absten\u00e7\u00e3o\nJo\u00e3o Azevedo (PS) Absten\u00e7\u00e3o\nIsabel Rodrigues (PS) Absten\u00e7\u00e3o\nLara Martinho (PS) Absten\u00e7\u00e3o\nMarta Freitas (PS) Absten\u00e7\u00e3o\nAna Miguel dos Santos (PSD) Absten\u00e7\u00e3o\nRui Silva (PSD) Absten\u00e7\u00e3o\nAd\u00e3o Silva (PSD) Absten\u00e7\u00e3o\nPedro Pinto (PSD) Absten\u00e7\u00e3o\nMariana Silva (PEV) Absten\u00e7\u00e3o\nJos\u00e9 Lu\u00eds Ferreira (PEV) Absten\u00e7\u00e3o\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A Favor</th>\n      <th>Absten\u00e7\u00e3o</th>\n      <th>Contra</th>\n      <th>Total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>PS</th>\n      <td>79</td>\n      <td>18</td>\n      <td>9</td>\n      <td>106</td>\n    </tr>\n    <tr>\n      <th>PSD</th>\n      <td>10</td>\n      <td>4</td>\n      <td>59</td>\n      <td>73</td>\n    </tr>\n    <tr>\n      <th>BE</th>\n      <td>19</td>\n      <td>0</td>\n      <td>0</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>PAN</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Ninsc</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>IL</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>PCP</th>\n      <td>0</td>\n      <td>0</td>\n      <td>10</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>CDS-PP</th>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>CH</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>PEV</th>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "        A Favor  Absten\u00e7\u00e3o  Contra  Total\nPS           79         18       9    106\nPSD          10          4      59     73\nBE           19          0       0     19\nPAN           4          0       0      4\nNinsc         1          0       0      1\nIL            1          0       0      1\nPCP           0          0      10     10\nCDS-PP        0          0       5      5\nCH            0          0       1      1\nPEV           0          2       0      2"
                    },
                    "execution_count": 49,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "import pandas as pd\nimport re\n## Initialise an empty dict\nvote_e = {}\n\n## Iteract through the existing dict with the nominal voting results\nfor k, v in vote_86004.items():\n    print(k,v)\n    ## Erase the name of the MP and keep the party only\n    nk = re.sub(r\".*\\((.+?)\\).*\", r\"\\1\", k)\n    ## If it's the first entry for a key, create it\n    if nk not in vote_e:\n        vote_e[nk] = [0,0,0]\n    ## Add to a specific index in a list\n    if v == \"A Favor\":\n        vote_e[nk][0] += 1\n    elif v == \"Absten\u00e7\u00e3o\":\n        vote_e[nk][1] += 1\n    elif v == \"Contra\":\n        vote_e[nk][2] += 1\n\n## Convert dict to pandas dataframe, add a total columns and display it\nvote_e_df = pd.DataFrame.from_dict(vote_e, orient='index', columns=['A Favor', 'Absten\u00e7\u00e3o', 'Contra'])\nvote_e_df['Total'] = vote_e_df.sum(axis=1)\nvote_e_df"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Um gr\u00e1fico de barras torna claro que a divis\u00e3o ocorreu apenas nos dois maiores grupos parlamentares."
        },
        {
            "cell_type": "code",
            "execution_count": 50,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox \u2265 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
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\" width=\"640\">",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fa90e70b7f0>"
                    },
                    "execution_count": 50,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "## Plot a stacked bar plot using the dataframe\nimport seaborn as sns\n\nlegend_colors = [\"#79DE79\", \"#FCFC99\",\"#FB6962\"]\nsns.set(style=\"whitegrid\")\n\n## Plot sorted by total number of votes\nvote_e_df.sort_values('Total', ascending=False).drop('Total',axis=1).plot(kind=\"bar\", stacked=True, color=sns.color_palette(legend_colors))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O segundo caso diz respeito a **vota\u00e7\u00f5es onde a posi\u00e7\u00e3o partid\u00e1ria \u00e9 indicada e apenas as excep\u00e7\u00f5es inclu\u00eddas**; nestes casos existem uma determinada posi\u00e7\u00e3o maiorit\u00e1ria \u00e0 qual se registam os desvios. N\u00e3o existe forma, na verdade, de determinar se estamos perante uma posi\u00e7\u00e3o mais ou menos \"oficial\" que a determinada pelo caso anterior, at\u00e9 porque isso implicaria saber se foi relaxada ou n\u00e3o a disciplina de voto, algo que n\u00e3o \u00e9 poss\u00edvel obter dos dados em si.\n\nTamb\u00e9m estes casos s\u00e3o excepcionais e quando existem s\u00e3o quase sempre com um n\u00famero reduzido de excep\u00e7\u00f5es; atentemos \u00e0 vota\u00e7\u00e3o `87724`, a primeira que apresenta uma situa\u00e7\u00e3o deste tipo:"
        },
        {
            "cell_type": "code",
            "execution_count": 51,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "\tid             : 87724\n\tresultado      : Aprovado\n\tdescricao      : Texto Final apresentado pela Comiss\u00e3o de Sa\u00fade relativo \u00e0s Aprecia\u00e7\u00f5es Parlamentares n.\u00bas 12/XIV/1.\u00aa (BE); 13/XIV/1.\u00aa (PCP) e 20/XIV/1.\u00aa (PSD)\n\treuniao        : 68\n\ttipoReuniao    : RP\n\tdetalhe        : {'PSD': 'A Favor', 'BE': 'A Favor', 'PCP': 'A Favor', 'CDS-PP': 'A Favor', 'PAN': 'A Favor', 'PEV': 'A Favor', 'CH': 'A Favor', 'IL': 'A Favor', 'Cristina Rodrigues (Ninsc)': 'A Favor', 'Joacine Katar Moreira (Ninsc)': 'A Favor', 'PS': 'Contra'}\n"
                }
            ],
            "source": "for v in ini_tree.find(\".//pt_gov_ar_objectos_VotacaoOut/[id='87724']\"):\n    if v.tag == \"detalhe\":\n        print(\"\\t{0:15}: {1}\".format(v.tag,parse_voting(v.text)))\n    else:\n        print(\"\\t{0:15}: {1}\".format(v.tag,v.text))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Para al\u00e9m das deputadas n\u00e3o-inscritas temos a indica\u00e7\u00e3o do sentido de votos dos partidos e tamb\u00e9m a de dois deputados do PS que votaram de forma diferente do seu grupo parlamentar.\n\nComo considerar esta situa\u00e7\u00e3o? Em coer\u00eancia com a op\u00e7\u00e3o anterior consideramos a posi\u00e7\u00e3o indicada pelo partido como sendo aquela que determina o seu posicionamento, n\u00e3o considerando as excep\u00e7\u00f5es. A justifica\u00e7\u00e3o \u00e9 semelhante \u00e0 anterior embora neste caso tenha menos impacto: acontece sobretudo nos grupos parlamentares de maior dimens\u00e3o (PS e PSD) e, como referido, de forma muito espor\u00e1dica e com um reduzido n\u00famero de deputados com vota\u00e7\u00f5es desviantes.\n\nA vota\u00e7\u00e3o 87946 apresenta-nos o terceiro caso, uma variante adicional quando o n\u00famero de votos em sentido divergente \u00e9 maior:"
        },
        {
            "cell_type": "code",
            "execution_count": 52,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "\tid             : 87946\n\tresultado      : Rejeitado\n\treuniao        : 75\n\ttipoReuniao    : RP\n\tausencias      : None\n\tdetalhe        : {'BE': 'A Favor', 'PAN': 'A Favor', 'PEV': 'A Favor', 'IL': 'A Favor', 'Cristina Rodrigues (Ninsc)': 'A Favor', 'PS': 'Contra', 'PSD': 'Contra', 'PCP': 'Contra', 'CDS-PP': 'Contra', 'CH': 'Contra', 'Joacine Katar Moreira (Ninsc)': 'Aus\u00eancia'}\n"
                }
            ],
            "source": "for v in ini_tree.find(\".//pt_gov_ar_objectos_VotacaoOut/[id='87946']\"):\n    if v.tag == \"detalhe\":\n        print(\"\\t{0:15}: {1}\".format(v.tag,parse_voting(v.text)))\n    else:\n        print(\"\\t{0:15}: {1}\".format(v.tag,v.text))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Aqui temos um indicador num\u00e9rico como prefixo e que indica quantidade de deputados de um determinado grupo parlamentar (neste caso do PS) que votaram de forma divergente. Tamb\u00e9m aqui usamos o mesmo princ\u00edpio e retemos a vota\u00e7\u00e3o indicada como sendo a do partido e por isso descartam-se estas indica\u00e7\u00f5es. "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Informa\u00e7\u00e3o adicional da iniciativa"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Para obter informa\u00e7\u00e3o adicional das iniciativas onde se incluem as vota\u00e7\u00f5es podem-se usar v\u00e1rias abordagens, desde o processamento de todas as iniciativas de forma recursiva \u00e0 procura a partir do identificador da vota\u00e7\u00e3o; o seguinte bloco exemplifica a segunda aproxima\u00e7\u00e3o para a primeira vota\u00e7\u00e3o encontrada:"
        },
        {
            "cell_type": "code",
            "execution_count": 53,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(1, 7)\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>iniDesc</th>\n      <th>id</th>\n      <th>resultado</th>\n      <th>descricao</th>\n      <th>reuniao</th>\n      <th>tipoReuniao</th>\n      <th>detalhe</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Decreto-Lei n.\u00ba 27/2020 de 17 de junho (Altera...</td>\n      <td>88184</td>\n      <td>Aprovado</td>\n      <td>Texto Final apresentado pela Comiss\u00e3o de Admin...</td>\n      <td>76</td>\n      <td>RP</td>\n      <td>A Favor: &lt;I&gt;PS&lt;/I&gt;, &lt;I&gt;PSD&lt;/I&gt;&lt;BR&gt;Contra: &lt;I&gt;B...</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                                             iniDesc     id resultado  \\\n0  Decreto-Lei n.\u00ba 27/2020 de 17 de junho (Altera...  88184  Aprovado   \n\n                                           descricao reuniao tipoReuniao  \\\n0  Texto Final apresentado pela Comiss\u00e3o de Admin...      76          RP   \n\n                                             detalhe  \n0  A Favor: <I>PS</I>, <I>PSD</I><BR>Contra: <I>B...  "
                    },
                    "execution_count": 53,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "import re\nimport collections\n\nroot = ini_tree\ninit_list = []\ninit_dict = collections.OrderedDict()\n\ndef get_initiative_desc (vid, tree):\n    \"\"\"\n    Gets the initiative title from a voting id\n    \"\"\"\n    for c in ini_tree.find(\".//pt_gov_ar_objectos_VotacaoOut/[id='\"+ vid +\"']/../../../..\"):\n        if c.tag == \"iniTitulo\":\n            return c.text\n\nfor c in ini_tree.find(\".//pt_gov_ar_objectos_VotacaoOut\"):\n    if c.tag == \"id\":\n        init_dict[\"iniDesc\"] = get_initiative_desc(c.text, ini_tree)\n    init_dict[c.tag] = c.text\ninit_list.append(init_dict)\n\nini_df_extra = pd.DataFrame(init_list)\nprint(ini_df_extra.shape)\nini_df_extra"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Esta abordagem n\u00e3o \u00e9 usada para as Iniciativas mas ser\u00e1 aplicada \u00e0s Actividades, essencialmente porque neste \u00faltimo caso h\u00e1 uma rela\u00e7\u00e3o mais directa entre a mat\u00e9ria a vota\u00e7\u00e3o e a actividade parlamentar (de tal forma que a vota\u00e7\u00e3o n\u00e3o inclui uma descri\u00e7\u00e3o pr\u00f3pria)."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Processamento adicional dos n\u00e3o-inscritos\n\nExistem dois casos que precisam ser analisados de forma individual."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\n#### Joacine Katar Moreira e o Livre\n\nCome\u00e7ando por **Joacine Katar Moreira**, a deputada eleita pelo Livre desvinculou-se  pouco tempo ap\u00f3s a elei\u00e7\u00e3o *(\u201cJoacine Katar Moreira demite-se do Livre - DN\u201d 2020)* e sendo deputada \u00fanica a situa\u00e7\u00e3o criada em termos de registo de vota\u00e7\u00e3o \u00e9 de complementaridade: ou h\u00e1 registo de vota\u00e7\u00e3o do Livre ou h\u00e1 registo de vota\u00e7\u00e3o da deputada Joacine Katar Moreira."
        },
        {
            "cell_type": "code",
            "execution_count": 54,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Livre:  138\nJKM:  1865\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>L</th>\n      <th>Joacine Katar Moreira (Ninsc)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>A Favor</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "     L Joacine Katar Moreira (Ninsc)\n0  NaN                        Contra\n1  NaN                        Contra\n2  NaN                        Contra\n3  NaN                       A Favor\n4  NaN                       A Favor"
                    },
                    "execution_count": 54,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "print(\"Livre: \", ini_df[\"L\"].dropna().shape[0])\nprint(\"JKM: \", ini_df[\"Joacine Katar Moreira (Ninsc)\"].dropna().shape[0])\nini_df[[\"L\",\"Joacine Katar Moreira (Ninsc)\"]].head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Cristina Rodrigues e o PAN\n\nUm caso diferente \u00e9 o da deputada Cristina Rodrigues, eleita por Set\u00fabal pelo PAN e que se desvinculou j\u00e1 perto do final da legislatura *(SAPO 2020)*: n\u00e3o s\u00f3 porque o n\u00famero de vota\u00e7\u00f5es efectadas enquanto deputada n\u00e3o-inscrita \u00e9 bastante menor, como o facto de n\u00e3o ser deputada \u00fanica faz com que existam vota\u00e7\u00f5es onde a deputada e o seu anterior grupo parlamentar ambos votam, e como se pode verificar na tabela seguinte , v\u00e1rias vezes de forma diferente:"
        },
        {
            "cell_type": "code",
            "execution_count": 55,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "PAN:  2038\nCR:  730\n"
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/ipykernel/__main__.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n  app.launch_new_instance()\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PAN</th>\n      <th>Cristina Rodrigues (Ninsc)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>105</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>115</th>\n      <td>A Favor</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>134</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>139</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>150</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>166</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>167</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>171</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>178</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>191</th>\n      <td>Contra</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>201</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>221</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>222</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>223</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>267</th>\n      <td>Contra</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>303</th>\n      <td>A Favor</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>304</th>\n      <td>A Favor</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>305</th>\n      <td>A Favor</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>306</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>308</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>349</th>\n      <td>Contra</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>490</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>505</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>513</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>518</th>\n      <td>Contra</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1353</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1354</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1355</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1356</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1362</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1364</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1365</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1366</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1367</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1368</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1369</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1373</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1376</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>1377</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>1378</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>1398</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1399</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1405</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>1426</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1928</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1936</th>\n      <td>A Favor</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n    <tr>\n      <th>1965</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>1968</th>\n      <td>Contra</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>1975</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>1983</th>\n      <td>Contra</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2003</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>Contra</td>\n    </tr>\n    <tr>\n      <th>2007</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2009</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2010</th>\n      <td>Absten\u00e7\u00e3o</td>\n      <td>A Favor</td>\n    </tr>\n    <tr>\n      <th>2031</th>\n      <td>Contra</td>\n      <td>Absten\u00e7\u00e3o</td>\n    </tr>\n  </tbody>\n</table>\n<p>107 rows \u00d7 2 columns</p>\n</div>",
                        "text/plain": "            PAN Cristina Rodrigues (Ninsc)\n105   Absten\u00e7\u00e3o                    A Favor\n108     A Favor                  Absten\u00e7\u00e3o\n109     A Favor                  Absten\u00e7\u00e3o\n115     A Favor                     Contra\n134     A Favor                  Absten\u00e7\u00e3o\n137   Absten\u00e7\u00e3o                    A Favor\n139   Absten\u00e7\u00e3o                    A Favor\n146   Absten\u00e7\u00e3o                    A Favor\n150      Contra                  Absten\u00e7\u00e3o\n166      Contra                  Absten\u00e7\u00e3o\n167   Absten\u00e7\u00e3o                    A Favor\n171      Contra                  Absten\u00e7\u00e3o\n178   Absten\u00e7\u00e3o                    A Favor\n191      Contra                    A Favor\n201     A Favor                  Absten\u00e7\u00e3o\n213     A Favor                  Absten\u00e7\u00e3o\n221      Contra                  Absten\u00e7\u00e3o\n222      Contra                  Absten\u00e7\u00e3o\n223      Contra                  Absten\u00e7\u00e3o\n267      Contra                    A Favor\n303     A Favor                     Contra\n304     A Favor                     Contra\n305     A Favor                     Contra\n306     A Favor                  Absten\u00e7\u00e3o\n308     A Favor                  Absten\u00e7\u00e3o\n349      Contra                    A Favor\n490   Absten\u00e7\u00e3o                    A Favor\n505   Absten\u00e7\u00e3o                    A Favor\n513   Absten\u00e7\u00e3o                    A Favor\n518      Contra                    A Favor\n...         ...                        ...\n1353    A Favor                  Absten\u00e7\u00e3o\n1354    A Favor                  Absten\u00e7\u00e3o\n1355    A Favor                  Absten\u00e7\u00e3o\n1356    A Favor                  Absten\u00e7\u00e3o\n1362     Contra                  Absten\u00e7\u00e3o\n1364    A Favor                  Absten\u00e7\u00e3o\n1365    A Favor                  Absten\u00e7\u00e3o\n1366     Contra                  Absten\u00e7\u00e3o\n1367     Contra                  Absten\u00e7\u00e3o\n1368     Contra                  Absten\u00e7\u00e3o\n1369     Contra                  Absten\u00e7\u00e3o\n1373     Contra                  Absten\u00e7\u00e3o\n1376  Absten\u00e7\u00e3o                    A Favor\n1377  Absten\u00e7\u00e3o                    A Favor\n1378  Absten\u00e7\u00e3o                    A Favor\n1398    A Favor                  Absten\u00e7\u00e3o\n1399     Contra                  Absten\u00e7\u00e3o\n1405  Absten\u00e7\u00e3o                    A Favor\n1426    A Favor                  Absten\u00e7\u00e3o\n1928    A Favor                  Absten\u00e7\u00e3o\n1936    A Favor                  Absten\u00e7\u00e3o\n1965  Absten\u00e7\u00e3o                    A Favor\n1968     Contra                    A Favor\n1975  Absten\u00e7\u00e3o                    A Favor\n1983     Contra                    A Favor\n2003  Absten\u00e7\u00e3o                     Contra\n2007  Absten\u00e7\u00e3o                    A Favor\n2009  Absten\u00e7\u00e3o                    A Favor\n2010  Absten\u00e7\u00e3o                    A Favor\n2031     Contra                  Absten\u00e7\u00e3o\n\n[107 rows x 2 columns]"
                    },
                    "execution_count": 55,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "print(\"PAN: \", ini_df[\"PAN\"].dropna().shape[0])\nprint(\"CR: \", ini_df[\"Cristina Rodrigues (Ninsc)\"].dropna().shape[0])\nini_df[[\"PAN\",\"Cristina Rodrigues (Ninsc)\"]].dropna()[ini_df[\"PAN\"] != ini_df[\"Cristina Rodrigues (Ninsc)\"]]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Convers\u00e3o do _dataframe_ em CSV\n\nExistem v\u00e1rias formas de se obter o resultado deste processamento; a que apresentamos utiliza `PixieDust` _(\u201cWelcome to PixieDust \u2014 PixieDust Documentation\u201d 2020)_, uma biblioteca que permite trabalhar directamente com os dados sem se ter neessidade de modificar o c\u00f3digo, inclu\u00edndo visualiza\u00e7\u00f5es, filtragem e e tamb\u00e9m descarregar em v\u00e1rios formatos."
        },
        {
            "cell_type": "code",
            "execution_count": 56,
            "metadata": {
                "pixiedust": {
                    "displayParams": {
                        "dynamicfilter": "pd_count",
                        "handlerId": "tableView",
                        "keyFields": "resultado",
                        "orientation": "horizontal",
                        "rendererId": "brunel",
                        "table_showrows": "All",
                        "timeseries": "false"
                    }
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>\n        <div class=\"pd_save is-viewer-good\" style=\"padding-right:10px;text-align: center;line-height:initial !important;font-size: xx-large;font-weight: 500;color: coral;\">\n            \n        </div>\n    <div id=\"chartFigure134428a9\" class=\"pd_save is-viewer-good\" style=\"overflow-x:auto\">\n            <style type=\"text/css\" class=\"pd_save\">\n    .df-table-wrapper .panel-heading {\n      border-radius: 0;\n      padding: 0px;\n    }\n    .df-table-wrapper .panel-heading:hover {\n      border-color: #008571;\n    }\n    .df-table-wrapper .panel-title a {\n      background-color: #f9f9fb;\n      color: #333333;\n      display: block;\n      outline: none;\n      padding: 10px 15px;\n      text-decoration: none;\n    }\n    .df-table-wrapper .panel-title a:hover {\n      background-color: #337ab7;\n      border-color: #2e6da4;\n      color: #ffffff;\n      display: block;\n      padding: 10px 15px;\n      text-decoration: none;\n    }\n    .df-table-wrapper {\n      font-size: small;\n      font-weight: 300;\n      letter-spacing: 0.5px;\n      line-height: normal;\n      height: inherit;\n      overflow: auto;\n    }\n    .df-table-search {\n      margin: 0 0 20px 0;\n    }\n    .df-table-search-count {\n      display: inline-block;\n      margin: 0 0 20px 0;\n    }\n    .df-table-container {\n      max-height: 50vh;\n      max-width: 100%;\n      overflow-x: auto;\n      position: relative;\n    }\n    .df-table-wrapper table {\n      border: 0 none #ffffff;\n      border-collapse: collapse;\n      margin: 0;\n      min-width: 100%;\n      padding: 0;\n      table-layout: fixed;\n      height: inherit;\n      overflow: auto;\n    }\n    .df-table-wrapper tr.hidden {\n      display: none;\n    }\n    .df-table-wrapper tr:nth-child(even) {\n      background-color: #f9f9fb;\n    }\n    .df-table-wrapper tr.even {\n      background-color: #f9f9fb;\n    }\n    .df-table-wrapper tr.odd {\n      background-color: #ffffff;\n    }\n    .df-table-wrapper td + td {\n      border-left: 1px solid #e0e0e0;\n    }\n  \n    .df-table-wrapper thead,\n    .fixed-header {\n      font-weight: 600;\n    }\n    .df-table-wrapper tr,\n    .fixed-row {\n      border: 0 none #ffffff;\n      margin: 0;\n      padding: 0;\n    }\n    .df-table-wrapper th,\n    .df-table-wrapper td,\n    .fixed-cell {\n      border: 0 none #ffffff;\n      margin: 0;\n      min-width: 50px;\n      padding: 5px 20px 5px 10px;\n      text-align: left;\n      word-wrap: break-word;\n    }\n    .df-table-wrapper th {\n      padding-bottom: 0;\n      padding-top: 0;\n    }\n    .df-table-wrapper th div {\n      max-height: 1px;\n      visibility: hidden;\n    }\n  \n    .df-schema-field {\n      margin-left: 10px;\n    }\n  \n    .fixed-header-container {\n      overflow: hidden;\n      position: relative;\n    }\n    .fixed-header {\n      border-bottom: 2px solid #000;\n      display: table;\n      position: relative;\n    }\n    .fixed-row {\n      display: table-row;\n    }\n    .fixed-cell {\n      display: table-cell;\n    }\n  </style>\n  \n  \n  \n  <div class=\"df-table-wrapper df-table-wrapper-134428a9 panel-group pd_save\">\n    <!-- dataframe schema -->\n    \n    <div class=\"panel panel-default\">\n      <div class=\"panel-heading\">\n        <h4 class=\"panel-title\" style=\"margin: 0px;\">\n          <a data-toggle=\"collapse\" href=\"#df-schema-134428a9\" data-parent=\"#df-table-wrapper-134428a9\">Schema</a>\n        </h4>\n      </div>\n      <div id=\"df-schema-134428a9\" class=\"panel-collapse collapse\">\n        <div class=\"panel-body\" style=\"font-family: monospace;\">\n          <div class=\"df-schema-fields\">\n            <div>Field types:</div>\n            \n              <div class=\"df-schema-field\"><strong>BE: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>CDS-PP: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>CH: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>Cristina Rodrigues (Ninsc): </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>IL: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>Joacine Katar Moreira (Ninsc): </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>L: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>L/JKM: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>PAN: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>PAN/CR: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>PCP: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>PEV: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>PS: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>PSD: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>Tipo: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>ausencias: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>descricao: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>id: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>resultado: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>reuniao: </strong> object</div>\n            \n              <div class=\"df-schema-field\"><strong>unanime: </strong> object</div>\n            \n          </div>\n        </div>\n      </div>\n    </div>\n    \n    <!-- dataframe table -->\n    <div class=\"panel panel-default\">\n      \n      <div class=\"panel-heading\">\n        <h4 class=\"panel-title\" style=\"margin: 0px;\">\n          <a data-toggle=\"collapse\" href=\"#df-table-134428a9\" data-parent=\"#df-table-wrapper-134428a9\"> Table</a>\n        </h4>\n      </div>\n      \n      <div id=\"df-table-134428a9\" class=\"panel-collapse collapse in\">\n        <div class=\"panel-body\">\n          \n          <input type=\"text\" class=\"df-table-search form-control input-sm\" placeholder=\"Search table\">\n          \n          <div>\n            \n            <span class=\"df-table-search-count\">Showing 100 of 2276 rows</span>\n            \n          </div>\n          <!-- fixed header for when dataframe table scrolls -->\n          <div class=\"fixed-header-container\">\n            <div class=\"fixed-header\">\n              <div class=\"fixed-row\">\n                \n                <div class=\"fixed-cell\">BE</div>\n                \n                <div class=\"fixed-cell\">CDS-PP</div>\n                \n                <div class=\"fixed-cell\">CH</div>\n                \n                <div class=\"fixed-cell\">Cristina Rodrigues (Ninsc)</div>\n                \n                <div class=\"fixed-cell\">IL</div>\n                \n                <div class=\"fixed-cell\">Joacine Katar Moreira (Ninsc)</div>\n                \n                <div class=\"fixed-cell\">L</div>\n                \n                <div class=\"fixed-cell\">L/JKM</div>\n                \n                <div class=\"fixed-cell\">PAN</div>\n                \n                <div class=\"fixed-cell\">PAN/CR</div>\n                \n                <div class=\"fixed-cell\">PCP</div>\n                \n                <div class=\"fixed-cell\">PEV</div>\n                \n                <div class=\"fixed-cell\">PS</div>\n                \n                <div class=\"fixed-cell\">PSD</div>\n                \n                <div class=\"fixed-cell\">Tipo</div>\n                \n                <div class=\"fixed-cell\">ausencias</div>\n                \n                <div class=\"fixed-cell\">descricao</div>\n                \n                <div class=\"fixed-cell\">id</div>\n                \n                <div class=\"fixed-cell\">resultado</div>\n                \n                <div class=\"fixed-cell\">reuniao</div>\n                \n                <div class=\"fixed-cell\">unanime</div>\n                \n              </div>\n            </div>\n          </div>\n          <div class=\"df-table-container\">\n            <table class=\"df-table\">\n              <thead>\n                <tr>\n                  \n                  <th><div>BE</div></th>\n                  \n                  <th><div>CDS-PP</div></th>\n                  \n                  <th><div>CH</div></th>\n                  \n                  <th><div>Cristina Rodrigues (Ninsc)</div></th>\n                  \n                  <th><div>IL</div></th>\n                  \n                  <th><div>Joacine Katar Moreira (Ninsc)</div></th>\n                  \n                  <th><div>L</div></th>\n                  \n                  <th><div>L/JKM</div></th>\n                  \n                  <th><div>PAN</div></th>\n                  \n                  <th><div>PAN/CR</div></th>\n                  \n                  <th><div>PCP</div></th>\n                  \n                  <th><div>PEV</div></th>\n                  \n                  <th><div>PS</div></th>\n                  \n                  <th><div>PSD</div></th>\n                  \n                  <th><div>Tipo</div></th>\n                  \n                  <th><div>ausencias</div></th>\n                  \n                  <th><div>descricao</div></th>\n                  \n                  <th><div>id</div></th>\n                  \n                  <th><div>resultado</div></th>\n                  \n                  <th><div>reuniao</div></th>\n                  \n                  <th><div>unanime</div></th>\n                  \n                </tr>\n              </thead>\n              <tbody>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto Final apresentado pela Comiss\u00e3o de Or\u00e7amento e Finan\u00e7as relativo \u00e0 Proposta de Lei n.\u00ba 33/XIV/1.\u00aa (GOV)</td>\n                  \n                  <td>87794</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>71</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Ponto 2</td>\n                  \n                  <td>88272</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>87599</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>65</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Corpo do artigo 2.\u00ba</td>\n                  \n                  <td>86986</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>51</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Requerimento oral, apresentado pelo PS, solicitando a dispensa de reda\u00e7\u00e3o final e do prazo para apresenta\u00e7\u00e3o de reclama\u00e7\u00f5es contra inexatid\u00f5es relativamente \u00e0 Proposta de Lei n.\u00ba 18/XIV/1.\u00aa (GOV), \u00e0 Proposta de Lei n.\u00ba 20/XIV/1.\u00aa (GOV), \u00e0 Proposta de Lei n.\u00ba 21/XIV/1.\u00aa (GOV),  ao Projeto de Lei n.\u00ba 285/XIV/1.\u00aa (PCP)  e ao Projeto de Lei n.\u00ba 292/XIV/1.\u00aa (PCP)</td>\n                  \n                  <td>86400</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>87405</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>60</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto de Substitui\u00e7\u00e3o apresentado pela Comiss\u00e3o de Defesa Nacional relativo \u00e0 Proposta de Lei n.\u00ba 3/XIV/1.\u00aa (GOV) e aos Projetos de Lei n.\u00bas 27/XIV/1.\u00aa (CDS-PP), 57/XIV/1.\u00aa (PAN), 121/XIV/1.\u00aa (PCP), 180/XIV/1.\u00aa (BE) e 193/XIV/1.\u00aa (PSD)</td>\n                  \n                  <td>88337</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Vota\u00e7\u00e3o da assun\u00e7\u00e3o pelo Plen\u00e1rio das vota\u00e7\u00f5es indici\u00e1rias realizadas na especialidade em sede de Comiss\u00e3o - Texto Final apresentado pela Comiss\u00e3o de Assuntos Constitucionais, Direitos, Liberdades e Garantias relativo ao Projeto de Lei n.\u00ba 117/XIV/1.\u00aa (PAN) e Projeto de Lei n.\u00ba 118/XIV/1.\u00aa (PCP)</td>\n                  \n                  <td>88180</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>86052</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>35</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de altera\u00e7\u00e3o do CDS-PP, de aditamento de um n.\u00ba 6 ao artigo 2.\u00ba da proposta de lei</td>\n                  \n                  <td>86703</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>CH</td>\n                  \n                  <td>Requerimento, apresentado pelo PCP solicitando a baixa \u00e0 Comiss\u00e3o Competente, sem vota\u00e7\u00e3o, por 60 dias, do Projeto de Lei n.\u00ba 200/XIV/1.\u00aa (PCP)</td>\n                  \n                  <td>86130</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>38</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>85945</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>29</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>De pesar pela morte em servi\u00e7o dos bombeiros Filipe Andr\u00e9 Azinheiro Pedrosa e Jos\u00e9 Augusto Dias Fernandes</td>\n                  \n                  <td>88157</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Requerimento, apresentado pelo PSD, de avoca\u00e7\u00e3o pelo Plen\u00e1rio da vota\u00e7\u00e3o na especialidade de artigos do Texto de Substitui\u00e7\u00e3o apresentado pela Comiss\u00e3o de Economia, Inova\u00e7\u00e3o, Obras P\u00fablicas e Habita\u00e7\u00e3o relativo \u00e0 Proposta de Lei n.\u00ba 41/XIV/1.\u00aa (GOV)  - Requerimento, apresentado pelo PS, de avoca\u00e7\u00e3o pelo Plen\u00e1rio da vota\u00e7\u00e3o na especialidade do artigo 70.\u00ba do Decreto-Lei n.\u00ba 18/2008, de 29 de janeiro, constante no artigo 19.\u00ba do Texto de Substitui\u00e7\u00e3o apresentado pela Comiss\u00e3o de Economia, Inova\u00e7\u00e3o, Obras P\u00fablicas e Habita\u00e7\u00e3o relativo \u00e0 Proposta de Lei n.\u00ba 41/XIV/1.\u00aa (GOV)</td>\n                  \n                  <td>88967</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>15</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>85605</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>17</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td> Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 3 do artigo 24.\u00ba</td>\n                  \n                  <td>86781</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto Final apresentado pela Comiss\u00e3o de Ambiente, Energia e Ordenamento do Territ\u00f3rio relativo aos Projetos de Resolu\u00e7\u00e3o n.\u00bas 52/XIV/1.\u00aa (BE); 58/XIV/1.\u00aa (PEV) e 130/XIV/1.\u00aa (PSD).</td>\n                  \n                  <td>85869</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>26</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Vota\u00e7\u00e3o da proposta apresentada pelo PCP, de aditamento de um novo n.\u00ba 2 ao artigo 21.\u00ba do Decreto-Lei n.\u00ba 10-A/2020, de 13 de mar\u00e7o</td>\n                  \n                  <td>86231</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>42</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>85370</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>7</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Requerimento, apresentado pelo PEV solicitando a baixa \u00e0 Comiss\u00e3o de Administra\u00e7\u00e3o P\u00fablica, Moderniza\u00e7\u00e3o Administrativa, Descentraliza\u00e7\u00e3o e Poder Local, sem vota\u00e7\u00e3o, por 30 dias, dos Projetos de Lei n.\u00ba 398/XIV/1.\u00aa (PEV) e 399/XIV/1.\u00aa (PEV)</td>\n                  \n                  <td>87750</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>60</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta apresentada pelo PCP, de elimina\u00e7\u00e3o do artigo 4.\u00ba da proposta de lei</td>\n                  \n                  <td>86371</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de aditamento do PAN de um artigo 2.\u00ba-A ao texto final</td>\n                  \n                  <td>87289</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>57</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Requerimento, apresentado pelo BE, de avoca\u00e7\u00e3o pelo Plen\u00e1rio da vota\u00e7\u00e3o na especialidade das propostas de altera\u00e7\u00e3o relativas ao Texto Final apresentado pela Comiss\u00e3o de Assuntos Constitucionais, Direitos, Liberdades e Garantias relativo ao Projeto de Lei n.\u00ba 117/XIV/1.\u00aa (PAN) e Projeto de Lei n.\u00ba 118/XIV/1.\u00aa (PCP)</td>\n                  \n                  <td>88193</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>CH</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>88883</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>12</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta apresentada pelo PSD, de aditamento de um artigo 6.\u00ba \u00e0 proposta de lei</td>\n                  \n                  <td>86361</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 2 ao artigo 21.\u00ba</td>\n                  \n                  <td>86754</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>De pesar pela morte de Dame Vera Lynn</td>\n                  \n                  <td>87668</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>68</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>De congratula\u00e7\u00e3o pelo reconhecimento internacional ao setor do Turismo em Portugal</td>\n                  \n                  <td>85581</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>17</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>I</td>\n                  \n                  <td>JOACINE KATAR MOREIRA (Ninsc)</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>87966</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>75</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>CH</td>\n                  \n                  <td>Requerimento oral, apresentado pelo PS, solicitando a dispensa de reda\u00e7\u00e3o final e do prazo para apresenta\u00e7\u00e3o de reclama\u00e7\u00f5es contra inexatid\u00f5es relativamente do Texto Final apresentado pela Comiss\u00e3o de Assuntos Constitucionais, Direitos, Liberdades e Garantias relativo aos Projetos de Lei n.\u00bas 505/XIV/1.\u00aa (PSD) e 549/XIV/2.\u00aa (PS); e do Texto Final apresentado pela Comiss\u00e3o de Assuntos Constitucionais, Direitos, Liberdades e Garantias relativo ao Projeto de Lei n.\u00ba 547/XIV/2.\u00aa (PS)</td>\n                  \n                  <td>89075</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>17</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>85924</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>29</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>N.\u00ba 5 do artigo 5.\u00ba da proposta de lei</td>\n                  \n                  <td>86407</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>86136</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>38</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de altera\u00e7\u00e3o da NINSC, de substitui\u00e7\u00e3o do n.\u00ba 2 do artigo 4.\u00ba da proposta de lei</td>\n                  \n                  <td>86529</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de altera\u00e7\u00e3o do PSD, de elimina\u00e7\u00e3o do n.\u00ba 8 do artigo 2.\u00ba</td>\n                  \n                  <td>86934</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>49</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td> Proposta de altera\u00e7\u00e3o do PAN - n.\u00ba 3 do artigo 26.\u00ba</td>\n                  \n                  <td>86797</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Requerimento, apresentado pelo PCP a requerer a vota\u00e7\u00e3o na generalidade, especialidade e vota\u00e7\u00e3o final global do Projeto de Lei n.\u00ba 285/XIV/1.\u00aa (PCP)</td>\n                  \n                  <td>86323</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>CH</td>\n                  \n                  <td>Texto Final apresentado pela Comiss\u00e3o de Assuntos Constitucionais, Direitos, Liberdades e Garantias relativo \u00e0 Proposta de Lei n.\u00ba 2/XIV/1.\u00aa (GOV)</td>\n                  \n                  <td>86171</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>38</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Requerimento, apresentado pelo PAN solicitando a baixa \u00e0 Comiss\u00e3o de Assuntos Constitucionais, Direitos, Liberdades e Garantias, sem vota\u00e7\u00e3o, por 60 dias, do Projeto de Lei n.\u00ba 52/XIV/1.\u00aa (PAN)</td>\n                  \n                  <td>85592</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>17</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de altera\u00e7\u00e3o do PS, de aditamento de um artigo 5.\u00ba-D \u00e0 proposta de lei</td>\n                  \n                  <td>86732</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto Final apresentado pela Comiss\u00e3o de Educa\u00e7\u00e3o, Ci\u00eancia, Juventude e Desporto relativo aos Projeto de Resolu\u00e7\u00e3o n.\u00bas 105/XIV/1.\u00aa (BE), 173/XIV/1.\u00aa (PCP) e 207/XIV/1.\u00aa (PAN)</td>\n                  \n                  <td>87442</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>60</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Artigo 4.\u00ba da proposta de lei</td>\n                  \n                  <td>86448</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de altera\u00e7\u00e3o do PCP \u2013 Artigo 8.\u00ba do texto de substitui\u00e7\u00e3o</td>\n                  \n                  <td>88313</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>85720</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>20</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>87403</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>60</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>CH</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>89133</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>17</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de altera\u00e7\u00e3o do PCP - n.\u00ba 7 ao artigo 23.\u00ba</td>\n                  \n                  <td>86773</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>88312</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>De preocupa\u00e7\u00e3o pela Inexist\u00eancia de conclus\u00f5es relativamente ao desaparecimento do empres\u00e1rio Am\u00e9rico Sebasti\u00e3o, em Mo\u00e7ambique</td>\n                  \n                  <td>85454</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>13</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto de Substitui\u00e7\u00e3o apresentado pela Comiss\u00e3o de Or\u00e7amento e Finan\u00e7as relativo aos Projetos de Lei n.\u00bas 410/XIV/1.\u00aa (CDS-PP) e 441/XIV/1.\u00aa (PS) - Vota\u00e7\u00e3o da assun\u00e7\u00e3o pelo Plen\u00e1rio das vota\u00e7\u00f5es indici\u00e1rias realizadas na especialidade em sede de Comiss\u00e3o</td>\n                  \n                  <td>88226</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de altera\u00e7\u00e3o do BE \u2013 aditamento de um n.\u00ba 5 ao artigo 11.\u00ba da Lei n.\u00ba 4-C/2020, de 6 de abril</td>\n                  \n                  <td>87206</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>55</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A</td>\n                  \n                  <td>CH</td>\n                  \n                  <td>De pesar pelo falecimento de Fernando Tavares Loureiro</td>\n                  \n                  <td>85904</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>29</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta apresentada pela Ninsc, de emenda do n.\u00ba 5 do artigo 5.\u00ba da proposta de lei</td>\n                  \n                  <td>86397</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Requerimento, apresentado pelo BE solicitando a baixa \u00e0 Comiss\u00e3o de Assuntos Constitucionais, Direitos, Liberdades e Garantias, sem vota\u00e7\u00e3o, por 60 dias, do Projeto de Lei n.\u00ba 114/XIV/1.\u00aa (BE)</td>\n                  \n                  <td>85596</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>17</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>87682</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>68</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto Final apresentado pela Comiss\u00e3o de Assuntos Constitucionais, Direitos, Liberdades e Garantias relativo ao Projeto de Lei n.\u00ba 99/XIV/1.\u00aa (PSD)</td>\n                  \n                  <td>87312</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>57</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta apresentada pelo BE, de aditamento de um n.\u00ba 6 ao artigo 5.\u00ba da proposta de lei</td>\n                  \n                  <td>86410</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto Final apresentado pela Comiss\u00e3o de Ambiente, Energia e Ordenamento do Territ\u00f3rio relativo aos Projetos de Resolu\u00e7\u00e3o n.\u00bas 52/XIV/1.\u00aa (BE); 58/XIV/1.\u00aa (PEV) e 130/XIV/1.\u00aa (PSD).</td>\n                  \n                  <td>85870</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>26</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>86636</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto Final apresentado pela Comiss\u00e3o de Educa\u00e7\u00e3o, Ci\u00eancia, Juventude e Desporto relativo aos Projeto de Resolu\u00e7\u00e3o n.\u00bas 105/XIV/1.\u00aa (BE), 173/XIV/1.\u00aa (PCP) e 207/XIV/1.\u00aa (PAN)</td>\n                  \n                  <td>87445</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>60</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>CH</td>\n                  \n                  <td>Requerimento, apresentado pela PS solicitando a baixa \u00e0 Comiss\u00e3o de Trabalho e Seguran\u00e7a Social, sem vota\u00e7\u00e3o, por 30 dias, da Proposta de Lei n.\u00ba 57/XIV/2.\u00aa (GOV) e da Proposta de Lei n.\u00ba 59/XIV/2.\u00aa (GOV)</td>\n                  \n                  <td>89126</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>17</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>85717</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>20</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto Final apresentado pela Comiss\u00e3o de Assuntos Constitucionais, Direitos, Liberdades e Garantias relativo ao Projeto de Lei n.\u00ba 459/XIV/1.\u00aa (PSD)</td>\n                  \n                  <td>88395</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto de Substitui\u00e7\u00e3o apresentado pela Comiss\u00e3o de Trabalho e Seguran\u00e7a Social relativo aos Projetos de Resolu\u00e7\u00e3o n.\u00bas 393/XIV/1.\u00aa (BE), 403/XIV/1.\u00aa (PSD) e 413/XIV/1.\u00aa (CDS-PP)</td>\n                  \n                  <td>88207</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>85990</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>32</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>87786</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>70</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta apresentada pelo PAN, de emenda do n.\u00ba 3 do artigo 5.\u00ba da proposta de lei</td>\n                  \n                  <td>86386</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta apresentada pelo PEV, de emenda ao artigo 8.\u00ba da proposta de lei</td>\n                  \n                  <td>86427</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Vota\u00e7\u00e3o da proposta apresentada pelo CH, de emenda do corpo do artigo 6.\u00ba da proposta de lei</td>\n                  \n                  <td>86267</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>42</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de altera\u00e7\u00e3o do PAN \u2013 aditamento de um n.\u00ba 2 ao artigo 8.\u00ba-A da Lei n.\u00ba 1-A/2020, de 19 de mar\u00e7o, constante do artigo 3.\u00ba</td>\n                  \n                  <td>86988</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>51</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Ponto 2</td>\n                  \n                  <td>85939</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>29</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>86827</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>46</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta apresentada pelo PCP, de emenda das al\u00edneas e) e f) do n.\u00ba 3 do artigo 2.\u00ba da proposta de lei</td>\n                  \n                  <td>86414</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Requerimento, apresentado pelo PCP solicitando a baixa \u00e0 Comiss\u00e3o de Trabalho e Seguran\u00e7a Social, sem vota\u00e7\u00e3o, por 30 dias, do Projeto de Lei n.\u00ba 427/XIV/1.\u00aa (PCP)</td>\n                  \n                  <td>87479</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>62</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>87487</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>62</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>N.\u00ba 2 do artigo 4.\u00ba da proposta de lei</td>\n                  \n                  <td>86533</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Requerimento, apresentado pelo PCP a requerer a vota\u00e7\u00e3o na generalidade, especialidade e vota\u00e7\u00e3o final global  do Projeto de Lei n.\u00ba 292/XIV/1.\u00aa (PCP)</td>\n                  \n                  <td>86331</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>44</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>De sauda\u00e7\u00e3o pelo 1.\u00ba de Dezembro de 1640</td>\n                  \n                  <td>85559</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>17</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>De condena\u00e7\u00e3o da repress\u00e3o contra as manifesta\u00e7\u00f5es populares no Chile</td>\n                  \n                  <td>85349</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>7</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Requerimento oral, apresentado pelo PS, solicitando a dispensa de reda\u00e7\u00e3o final e do prazo para apresenta\u00e7\u00e3o de reclama\u00e7\u00f5es contra inexatid\u00f5es relativamente \u00e0 Proposta de Lei n.\u00ba 22/XIV/1.\u00aa (GOV), \u00e0 Proposta de Lei n.\u00ba 23/XIV/1.\u00aa (GOV), \u00e0 Aprecia\u00e7\u00e3o Parlamentar n.\u00ba 9/XIV/1.\u00aa (PCP); \u00e0 Aprecia\u00e7\u00e3o Parlamentar n.\u00ba 10/XIV/1.\u00aa (BE);  ao Projeto de Lei n.\u00ba 258/XIV/1.\u00aa (PEV);  ao Projeto de Lei n.\u00ba 265/XIV/1.\u00aa (PEV); ao Projeto de Lei n.\u00ba 269/XIV/1.\u00aa (PEV); ao Projeto de Lei n.\u00ba 282/XIV/1.\u00aa (BE); ao Projeto de Lei n.\u00ba 284/XIV/1.\u00aa (BE); ao Projeto de Lei n.\u00ba 297/XIV/1.\u00aa (PCP); ao Projeto de Lei n.\u00ba 309/XIV/1.\u00aa (PAN); ao Projeto de Lei n.\u00ba 326/XIV/1.\u00aa (PAN) e ao Projeto de Lei n.\u00ba 328/XIV/1.\u00aa (BE)</td>\n                  \n                  <td>86695</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto Final apresentado pela Comiss\u00e3o de Ambiente, Energia e Ordenamento do Territ\u00f3rio relativo aos Projetos de Resolu\u00e7\u00e3o n.\u00bas 264/XIV/1.\u00aa (BE); 287/XIV/1.\u00aa (PAN); 320/XIV/1.\u00aa (PCP); 410/XIV/1.\u00aa (PSD) e 474/XIV/1.\u00aa (PS).</td>\n                  \n                  <td>87729</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>68</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>88540</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>3</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Artigo 2.\u00ba</td>\n                  \n                  <td>86657</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Vota\u00e7\u00e3o da proposta apresentada pelo PAN, de aditamento do artigo 7.\u00ba-H</td>\n                  \n                  <td>86305</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>42</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de altera\u00e7\u00e3o do PSD, de emenda do n.\u00ba 3 do artigo 4.\u00ba da proposta de lei</td>\n                  \n                  <td>86537</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Texto Final apresentado pela Comiss\u00e3o de Assuntos Constitucionais, Direitos, Liberdades e Garantias relativo aos Projetos de Regimento n.\u00bas 1/XIV/1.\u00aa (IL), 2/XIV/1.\u00aa (CH); 3/XIV/1.\u00aa (PS); 4/XIV/1.\u00aa (PSD); 5/XIV/1.\u00aa (CDS-PP); 6/XIV/1.\u00aa (PAN); 7/XIV/1.\u00aa (IL) e 8/XIV/1.\u00aa (PSD)</td>\n                  \n                  <td>88373</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>87581</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>65</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Requerimentos, apresentados pelo BE e PAN, de avoca\u00e7\u00e3o pelo Plen\u00e1rio da vota\u00e7\u00e3o na especialidade das propostas de altera\u00e7\u00e3o relativas aos Projetos de Regimento n.\u00bas 1/XIV/1.\u00aa (IL); 2/XIV/1.\u00aa (CH); 3/XIV/1.\u00aa (PS); 4/XIV/1.\u00aa (PSD); 5/XIV/1.\u00aa (CDS-PP); 6/XIV/1.\u00aa (PAN); 7/XIV/1.\u00aa (IL) e 8/XIV/1.\u00aa (PSD)</td>\n                  \n                  <td>88380</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>88780</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>9</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Proposta de altera\u00e7\u00e3o do PAN, de aditamento de um novo n.\u00ba 1 ao artigo 4.\u00ba da proposta de lei</td>\n                  \n                  <td>86716</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Ponto 1</td>\n                  \n                  <td>85938</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>29</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>De pesar pelo assassinato de quatro cidad\u00e3os portugueses na Venezuela</td>\n                  \n                  <td>85338</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>7</td>\n                  \n                  <td>unanime</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>88286</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>JOACINE KATAR MOREIRA (Ninsc)</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>87954</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>75</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>\u2022\tAvoca\u00e7\u00e3o requerida pelo BE e PAN - Proposta de altera\u00e7\u00e3o do PS \u2013 Artigo 224.\u00ba do Regimento da Assembleia da Rep\u00fablica n.\u00ba 1/2007, de 20 de agosto, constante do artigo 2.\u00ba do texto final</td>\n                  \n                  <td>88278</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>76</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Restantes normas do artigo 4.\u00ba</td>\n                  \n                  <td>86680</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Aus\u00eancia</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>CH</td>\n                  \n                  <td>Proposta do PS, de emenda do artigo 8.\u00ba</td>\n                  \n                  <td>89099</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>17</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>86603</td>\n                  \n                  <td>Rejeitado</td>\n                  \n                  <td>45</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>86026</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>34</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n                <tr>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Contra</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>Absten\u00e7\u00e3o</td>\n                  \n                  <td>A Favor</td>\n                  \n                  <td>I</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>nan</td>\n                  \n                  <td>86041</td>\n                  \n                  <td>Aprovado</td>\n                  \n                  <td>34</td>\n                  \n                  <td>nan</td>\n                  \n                </tr>\n                \n              </tbody>\n            </table>\n          </div>\n        </div>\n      </div>\n    </div>\n  </div>\n  \n  <script class=\"pd_save\">\n    $(function() {\n      var tableWrapper = $('.df-table-wrapper-134428a9');\n      var fixedHeader = $('.fixed-header', tableWrapper);\n      var tableContainer = $('.df-table-container', tableWrapper);\n      var table = $('.df-table', tableContainer);\n      var rows = $('tbody > tr', table);\n      var total = 100;\n  \n      fixedHeader\n        .css('width', table.width())\n        .find('.fixed-cell')\n        .each(function(i, e) {\n          $(this).css('width', $('.df-table-wrapper-134428a9 th:nth-child(' + (i+1) + ')').css('width'));\n        });\n  \n      tableContainer.scroll(function() {\n        fixedHeader.css({ left: table.position().left });\n      });\n  \n      rows.on(\"click\", function(e){\n          // var txt = e.delegateTarget.innerText;\n          var splits = $(e.delegateTarget).text().trim().replace(/[\\n\\r]/g, '\\t').split(\"\\t\").map(function(item){return item.trim()}).filter(function(item){return item!=\"\"});\n          // var splits = txt.split(\"\\t\");\n          var len = splits.length;\n          var hdrs = $(fixedHeader).find(\".fixed-cell\");\n          // Add all cells in the selected row as a map to be consumed by the target as needed\n          var payload = {type:\"select\", targetDivId: \"\" };\n          for (var i = 0; i < len; i++) {\n            payload[hdrs[i].innerHTML] = splits[i];\n          }\n  \n          //simple selection highlighting, client adds \"selected\" class\n          $(this).addClass(\"selected\").siblings().removeClass(\"selected\");\n          $(document).trigger('pd_event', payload);\n      });\n  \n      $('.df-table-search', tableWrapper).keyup(function() {\n        var val = '^(?=.*\\\\b' + 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'even' : 'odd'));\n          }\n        });\n        $('.df-table-search-count', tableWrapper).html('Showing ' + index + ' of ' + total + ' rows');\n      });\n    });\n  \n    $(\".df-table-wrapper td:contains('http://')\").each(function(){var tc = this.textContent; $(this).wrapInner(\"<a target='_blank' href='\" + tc + \"'/>\");});\n    $(\".df-table-wrapper td:contains('https://')\").each(function(){var tc = this.textContent; $(this).wrapInner(\"<a target='_blank' href='\" + tc + \"'/>\");});\n  </script>\n  \n        </div>",
                        "text/plain": "<IPython.core.display.HTML object>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "from pixiedust.display import *\ndisplay(votes)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Dist\u00e2ncias e matrizes\n\nA matriz de dist\u00e2ncia \u00e9 feita atrav\u00e9s da utiliza\u00e7\u00e3o da fun\u00e7\u00e3o `pdist`; como j\u00e1 mencionado a dist\u00e2ncia euclidiana entre dois pontos $p$ e $q$ \u00e9 $ d\\left( p,q\\right) = \\sqrt {\\sum _{i=1}^{n}  \\left( q_{i}-p_{i}\\right)^2 }$, para $n$ dimens\u00f5es <cite data-cite=\"1084038/MQZ8U9X4\"></cite>. De forma a facilitar a intui\u00e7\u00e3o de como esta dist\u00e2ncia \u00e9 aplicada ao registo de vota\u00e7\u00f5es vamos, passo a passo, analisar um caso simplificado (o que nos serve tamb\u00e9m de forma de valida\u00e7\u00e3o indirecta do processo).\n\nVamos criar um cen\u00e1rio com tr\u00eas partidos, com nomes que reflectem o seu perfil de vota\u00e7\u00e3o:\n\n* O Partido Favor\u00e1vel vota a favor.\n* O Partido Abstencionista abstem-se (pelo menos at\u00e9 certo ponto).\n* O Partido do Contra vota contra.\n\n### Uma vota\u00e7\u00e3o, $ n = 1 $\n\nCome\u00e7emos por uma \u00fanica vota\u00e7\u00e3o, onde cada um dos partidos segue a sua linha program\u00e1tica:"
        },
        {
            "cell_type": "code",
            "execution_count": 57,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>v1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>-1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   v1\nF   1\nA   0\nC  -1"
                    },
                    "execution_count": 57,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "v1=[[1],[0],[-1]]\nv1_df = pd.DataFrame(v1, columns=[\"v1\"], index=[\"F\",\"A\", \"C\"])\nv1_df"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Com uma \u00fanica vota\u00e7\u00e3o o n\u00famero de dimens\u00f5es \u00e9 de $n = 1$ e \u00e9 bastante intuitivo que a dist\u00e2ncia entre eles \u00e9 a  _norma_:$ d\\left( x,y\\right)   = | x - y |$; entre 1 e 0 a dist\u00e2ncia \u00e9 1, entre 1 e -1 a dist\u00e2ncia \u00e9 2, etc. Para $ n = 1 $ a dist\u00e2ncia euclidiana \u00e9 equivalente \u00e0 norma, pois $ \\sqrt{\\left( q - p\\right)^2} = | q - p | $, como pode ser observado quando medidas a dist\u00e2ncia entre eles:"
        },
        {
            "cell_type": "code",
            "execution_count": 58,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Distance from F and A = 1.0\nDistance from F and C = 2.0\nDistance from A and C = 1.0\n"
                }
            ],
            "source": "import math\n## We could also sue the array directly, e.g.\n## print(\"Distance from\" , a[0][0] , \"and\" , a[1][0],\"=\",math.sqrt((a[0][0]-a[1][0]) ** 2))\n\nprint(\"Distance from\", v1_df.loc[\"F\"].name, \"and\", v1_df.loc[\"A\"].name, \"=\", \n      math.sqrt((v1_df.loc[\"F\"][\"v1\"]-v1_df.loc[\"A\"][\"v1\"]) ** 2))\nprint(\"Distance from\", v1_df.loc[\"F\"].name, \"and\", v1_df.loc[\"C\"].name, \"=\", \n      math.sqrt((v1_df.loc[\"F\"][\"v1\"]-v1_df.loc[\"C\"][\"v1\"]) ** 2))\nprint(\"Distance from\", v1_df.loc[\"A\"].name, \"and\", v1_df.loc[\"C\"].name, \"=\", \n      math.sqrt((v1_df.loc[\"A\"][\"v1\"]-v1_df.loc[\"C\"][\"v1\"]) ** 2))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Temos a dist\u00e2ncia entre os tr\u00eas poss\u00edveis pares: a dist\u00e2ncia entre _F_ e _A_ \u00e9 id\u00eantica \u00e0 dist\u00e2ncia entre _A_ e _F_. Isto \u00e9 importante porque ajuda a explicar a diferen\u00e7a entre a forma \"condensada\" e forma \"quadrada\". A fun\u00e7\u00e3o `pdist` retorna a dist\u00e2ncia entre os v\u00e1rios pares na forma _condensada_:"
        },
        {
            "cell_type": "code",
            "execution_count": 59,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array([1., 2., 1.])"
                    },
                    "execution_count": 59,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "pdist(v1_df)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Estes s\u00e3o os mesmos valores que obtivemos de forma manual, e \u00e9 isso que a fun\u00e7\u00e3o faz: calcula uma determinada dist\u00e2ncia (euclidiana, neste caso e por omiss\u00e3o) entre todos os pares poss\u00edveis, sem que seja necess\u00e1rio especificarmos todas as combina\u00e7\u00f5es poss\u00edveis.\n\nEste formato condensado n\u00e3o \u00e9 o que permite uma leitura mais imediata, e para tal existe a fun\u00e7\u00e3o `squareform` que apresenta os mesmos resultados mas numa matriz sim\u00e9trica:"
        },
        {
            "cell_type": "code",
            "execution_count": 60,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array([[0., 1., 2.],\n       [1., 0., 1.],\n       [2., 1., 0.]])"
                    },
                    "execution_count": 60,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "squareform(pdist(v1_df))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Em formato tabular torna-se ainda mais claro... e isto \u00e9 exactmante a matriz de dist\u00e2ncia usada para o mapa t\u00e9rmico"
        },
        {
            "cell_type": "code",
            "execution_count": 61,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>F</th>\n      <th>A</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "     F    A    C\nF  0.0  1.0  2.0\nA  1.0  0.0  1.0\nC  2.0  1.0  0.0"
                    },
                    "execution_count": 61,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "v1_distmat=pd.DataFrame(squareform(pdist(v1)), columns=v1_df.index, index=v1_df.index)\nv1_distmat"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Geometricamente temos pontos numa recta: uma an\u00e1lise das dist\u00e2ncias entre os tr\u00eas partidos com base no hist\u00f3rico de vota\u00e7\u00e3o seria simples de visualizar com base na posi\u00e7\u00e3o desses pontos:"
        },
        {
            "cell_type": "code",
            "execution_count": 62,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "fig, ax = plt.subplots()\nax.axhline(y=0,c=\"gold\",zorder=-1)\n\nfor x in v1_df[\"v1\"]:\n    ax.scatter(x,y=0)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Com base na matriz de dist\u00e2ncia contruimos o mapa t\u00e9rmico de forma muito simples:"
        },
        {
            "cell_type": "code",
            "execution_count": 63,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 288x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "plt.figure(figsize=(4,4))\nsns.heatmap(\n    v1_distmat,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    annot = True,\n    square =True,\n)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "...e o agrupamento com base nessa matriz:"
        },
        {
            "cell_type": "code",
            "execution_count": 64,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<seaborn.matrix.ClusterGrid at 0x7faa07a027b8>"
                    },
                    "execution_count": 64,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 288x288 with 4 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "v1_distmat_link = hc.linkage(pdist(v1_df))\n\nsns.clustermap(\n    v1_distmat,\n    annot = True,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    row_linkage=v1_distmat_link,\n    col_linkage=v1_distmat_link,\n    figsize=(4,4)\n)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Duas vota\u00e7\u00f5es, $ n = 2 $\n\nUsando a mesma abordagem (agora sem necessidade de explica\u00e7\u00f5es adicionais) adicionamos mais uma vota\u00e7\u00e3o, sempre em linha com o perfil fixo de votos de cada um: cada partido passa a ter dois votos, logo temos duas dimens\u00f5es:"
        },
        {
            "cell_type": "code",
            "execution_count": 65,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>v1</th>\n      <th>v2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F</th>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>-1</td>\n      <td>-1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   v1  v2\nF   1   1\nA   0   0\nC  -1  -1"
                    },
                    "execution_count": 65,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "v2=[[1,1],[0,0],[-1,-1]]\nv2_df = pd.DataFrame(v2, columns=[\"v1\",\"v2\"], index=[\"F\",\"A\", \"C\"])\nv2_df"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "A dist\u00e2ncia euclidiana \u00e9 agora feita de forma mais gen\u00e9rica: a ra\u00edz quadrada da soma do quadrado das diferen\u00e7as: para a primeira diferen\u00e7a isto significa, passo a passo e para $q=F$ e $p=A$:\n\n$ d\\left(q,p\\right)   = \\sqrt {\\sum _{i=1}^{n}  \\left( q_{i}-p_{i}\\right)^2 } = \\sqrt{\\left( q_{1}-p_{1}\\right)^2  + \\left( q_{2}-p_{2}\\right)^2 } = \\sqrt{\\left( 1 - 0\\right)^2 + \\left( 1 - 0\\right)^2} = \\sqrt{ 1^2  + 1^2 } = \\sqrt{1+1} = \\sqrt{2} \\approx  1.4142135623730951 $\n\nE de facto:"
        },
        {
            "cell_type": "code",
            "execution_count": 66,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "1.4142135623730951\n2.8284271247461903\n1.4142135623730951\n"
                }
            ],
            "source": "print(math.sqrt(sum((px - qx) ** 2.0 for px, qx in zip(v2_df.loc[\"F\"],v2_df.loc[\"A\"]))))\nprint(math.sqrt(sum((px - qx) ** 2.0 for px, qx in zip(v2_df.loc[\"F\"],v2_df.loc[\"C\"]))))\nprint(math.sqrt(sum((px - qx) ** 2.0 for px, qx in zip(v2_df.loc[\"A\"],v2_df.loc[\"C\"]))))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Tal como antes \u00e9 id\u00eantico ao resultado de `pdist`, tanto na sua forma condensada como quadrada:"
        },
        {
            "cell_type": "code",
            "execution_count": 67,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "pdist:\n [1.41421356 2.82842712 1.41421356] \n\nsquareform:\n [[0.         1.41421356 2.82842712]\n [1.41421356 0.         1.41421356]\n [2.82842712 1.41421356 0.        ]]\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>F</th>\n      <th>A</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F</th>\n      <td>0.000000</td>\n      <td>1.414214</td>\n      <td>2.828427</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>1.414214</td>\n      <td>0.000000</td>\n      <td>1.414214</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>2.828427</td>\n      <td>1.414214</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "          F         A         C\nF  0.000000  1.414214  2.828427\nA  1.414214  0.000000  1.414214\nC  2.828427  1.414214  0.000000"
                    },
                    "execution_count": 67,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "print(\"pdist:\\n\", pdist(v2),\"\\n\")\nprint(\"squareform:\\n\",squareform(pdist(v2)))\nv2_distmat=pd.DataFrame(squareform(pdist(v2)), columns=v2_df.index, index=v2_df.index)\nv2_distmat\n\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Com duas vota\u00e7\u00f5es temos $ n = 2 $ e conseguimos ver os pontos num espa\u00e7o cartesiano em $ \\mathbb{R}^2 $ (um plano)."
        },
        {
            "cell_type": "code",
            "execution_count": 68,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "fig, ax = plt.subplots()\nfor x,y in zip(v2_df[\"v1\"], v2_df[\"v2\"]):\n    ax.scatter(x,y)\nfig.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "A matriz de dist\u00e2ncia seria neste caso, como no anterior, desnecess\u00e1ria (em $ \\mathbb{R}^2 $ as dist\u00e2ncias entre os partidos s\u00e3o \u00f3bvias por facilmente visualiz\u00e1veis); a forma de a construir \u00e9 id\u00eantica:"
        },
        {
            "cell_type": "code",
            "execution_count": 69,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 288x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "plt.figure(figsize=(4,4))\n\nsns.heatmap(\n    v2_distmat,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    annot = True,\n    square =True,\n)\nplt.show()\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "... e o mapa t\u00e9rmico correspondente:"
        },
        {
            "cell_type": "code",
            "execution_count": 70,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<seaborn.matrix.ClusterGrid at 0x7faa074e52b0>"
                    },
                    "execution_count": 70,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 288x288 with 4 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "v2_distmat_link = hc.linkage(pdist(v2_df))\n\nsns.clustermap(\n    v2_distmat,\n    annot = True,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    row_linkage=v2_distmat_link,\n    col_linkage=v2_distmat_link,\n    figsize=(4,4)\n)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Tr\u00eas vota\u00e7\u00f5es, $ n = 3 $\n\nEste \u00e9 o \u00faltimo caso onde a visualiza\u00e7\u00e3o pode ser feita de forma directa. Consideremos:"
        },
        {
            "cell_type": "code",
            "execution_count": 71,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>v1</th>\n      <th>v2</th>\n      <th>v3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   v1  v2  v3\nF   1   1   1\nA   0   0   0\nC  -1  -1  -1"
                    },
                    "execution_count": 71,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "v3=[[1,1,1],[0,0,0],[-1,-1,-1]]\nv3_df = pd.DataFrame(v3, columns=[\"v1\",\"v2\", \"v3\"], index=[\"F\",\"A\", \"C\"])\nv3_df"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "A dist\u00e2ncia \u00e9 calculada da mesma forma:"
        },
        {
            "cell_type": "code",
            "execution_count": 72,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "1.7320508075688772\n3.4641016151377544\n1.7320508075688772\n"
                }
            ],
            "source": "print(math.sqrt(sum((px - qx) ** 2.0 for px, qx in zip(v3_df.loc[\"F\"],v3_df.loc[\"A\"]))))\nprint(math.sqrt(sum((px - qx) ** 2.0 for px, qx in zip(v3_df.loc[\"F\"],v3_df.loc[\"C\"]))))\nprint(math.sqrt(sum((px - qx) ** 2.0 for px, qx in zip(v3_df.loc[\"A\"],v3_df.loc[\"C\"]))))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "... bem como a matriz de dist\u00e2ncia:"
        },
        {
            "cell_type": "code",
            "execution_count": 73,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "pdist:\n [1.73205081 3.46410162 1.73205081] \n\nsquareform:\n [[0.         1.73205081 3.46410162]\n [1.73205081 0.         1.73205081]\n [3.46410162 1.73205081 0.        ]]\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>F</th>\n      <th>A</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F</th>\n      <td>0.000000</td>\n      <td>1.732051</td>\n      <td>3.464102</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>1.732051</td>\n      <td>0.000000</td>\n      <td>1.732051</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>3.464102</td>\n      <td>1.732051</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "          F         A         C\nF  0.000000  1.732051  3.464102\nA  1.732051  0.000000  1.732051\nC  3.464102  1.732051  0.000000"
                    },
                    "execution_count": 73,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "print(\"pdist:\\n\", pdist(v3),\"\\n\")\nprint(\"squareform:\\n\",squareform(pdist(v3)))\nv3_distmat=pd.DataFrame(squareform(pdist(v3)), columns=v3_df.index, index=v3_df.index)\nv3_distmat"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Estamos agora em $ \\mathbb{R}^3 $, e para visualizar podemos usar uma projec\u00e7\u00e3o tridimensional:"
        },
        {
            "cell_type": "code",
            "execution_count": 74,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "fig, ax = plt.subplots()\nax = fig.add_subplot(111,projection='3d')\n\nfor x,y,z in zip(v3_df[\"v1\"], v3_df[\"v2\"], v3_df[\"v3\"]):\n    ax.scatter(x,y,z)\nfig.show()"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": 75,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAADzCAYAAACPKgMhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFKVJREFUeJzt3XuQlNWZx/FvTzsoFxXEiAwBXIR5TETRwQgalyxekJAlQSJuDCZGNKXWRsStipesIUiyQhZiEYVVc1EMSzSLsmjVipRRiYkhRrmokPAgF4E4gpQwIhBuPb1/dEPNGXp6emC63+6e36dqKv2e9/SZxzTzm3Pe7nlPLJlMIiJySEXUBYhIcVEoiEhAoSAiAYWCiAQUCiISUCiISEChICIBhYKIBBQKIhJQKIhIQKEgIoHjoi5ApJwl1y7N+Y+LYn0HxvJZS640UxCRgEJBRAIKBREJKBREJKBQEJGAQkFEAgoFEQkoFEQkoFAQkYBCQUQCCgURCSgURCSgUBCRgEJBRAIKBREJKBREJKBQEJGAQkFEAgoFEQkU+h6NOd+vTqSIFcW9FPOl4DdundelW6G/ZcGN2bEVgMTkcRFXUhjxiY8BkFy7NOJK8i/Wd2DUJeSdlg8iElAoiEhAoSAiAYWCiAS0Q5RIiTKzBcA/APXALuA2d1/RqE8ceBAYTurdv6nu/ots42qmIFK6rnf3Ae5+PjAdeCxDn7FAX6AfcBEwyczOyDaoZgoiRcLMOgOdM5yqc/e6xo3u/nGDw5NJzRga+xfg5+5eD2xLzy7GANOaqkOhIFI8JgA/yNB+HzAp0xPM7BfAMFIfqBqeoUsvYGOD401Az2xFKBREiscMYHaG9iNmCYe4+00AZvYNUr/9RxxrEQoFkSKRXiI0GQDNPHeOmf3MzLq6+0cNTm0CegNvpI8bzxyOoFAQKUFm1gno4u6b08cjge3pr4bmAd82s/lAV2AUMCTb2AoFkdLUEZhnZh2BBKkwGOnuSTN7Hpjo7m8Cc4BBwLvp50129/XZBlYoiJQgd98KDG7i3IgGjxPArS0ZW59TEJGAQkFEAlo+iORR/colOfeNF8m9GjRTEJGAQkFEAgoFEQkoFEQkoFAQkYBCQUQCCgURCZT15xS6XTaU86f8iFg8zvo5c/EZD0VdUquIjbyBWPUA2L2T+kcmHnn+ouHEzkl/AraiAk6ton767bB3d4ErPXb79u/nursms//AQRKJBMM+P4jx110d9Jn/4u+Y9tiv6db1FADGjhzGmCuHRlFuWSjfUKiooGbaVF696hr21NZy+cuLqF24iE98TdSVHbPkW6+RfOMlKkbdlPn8khdILnkhdVA9gIpBw0oyEADaVVYy+/576dj+BA4cPMjY797HkAsGcN5Z/YJ+XxwymIm33hBRleWlbJcPpwysYdf6DezeuJHkgQNsnr+AHiMy3ZimBG1aA3/P7Yc8dvYgkitfz3NB+ROLxejY/gQADh5McDCRIFbeu7ZFrmxnCu27n86e92sPH++praXrwJoIK4rAce2I9e1P/cK5UVdyTBKJer56+7+z6YMtfP1LwxhwVt8j+rz42hu8uXI1Z/Tozj3f/gbdP9U1gkrLQ9aZgpn9pMHjK/JfTuuJxY78bZJsY9vbxqoHwOa1Jbt0OCQer2DBzCksfmImb69Zx5r3Ngfnhw6q4aXHf8pzs37Mxef15+4HHo6o0vLQ3PKh4dWaH+ezkNa2p/YDOvSoOnzcoaqKvVu2RFhR4cX6l/bSobGTOnXkwnM/w++XvhW0dznpRNpVVgIw5spLWbV2QxTllY3mQiHWxOOit2PZcjqd2YcOvXoRq6yk5+hR1C5cFHVZhXN8e+hdTdKXR13JMdn+8U527krNdPbu28+SFSvp07Mq6PPh9h2HH7/8+lLO7NmjoDWWm+auKRxvZp8hFQgNHwPg7n/JZ3HHIplIsPzOexjyzFPE4nE2zH2Snas96rJaRWz0zcR6G3ToRMWE6SQXPwvxOADJpYtTfc6qIbluFRzYH2Glx27b9jrufuBhEvX1JJNJhl8ymKEX1vDgnHn079eHSwcPZM5zi3jl9aXE43FO7tSJKXfcHHXZJS2WzLLQNrP3SG01lUnS3fu08Psl53Xp1sKnlJ4xO7YCkJg8LuJKCiM+MbUxUXLt0ogryb9Y6p4HOc+aEwtm5nwlKz7qO0UxG886U3D3MwpUh4gUibL9nIKIHB2FgogEFAoiElAoiEhAoSAiAYWCiAQUCiISUCiISEChICKBsr2fgkg5M7OupLaZPxPYB6wFbnb3bU30/yfgJeB2d5+ZbWzNFERKUxL4T3c3dz8XWAdMzdTRzE4kdeuDhbkMrJmCSJEws85A5wyn6ty9rmGDu28HFjdo+hNwaxNDPwBMA/45lzo0UxApHhOADRm+JmR7kplVkAqE5zKc+yLQ2d2fzrUIzRREiscMYHaG9roMbQ09BOwCgmsF6ZnHVKBFt1JUKIgUifQSobkACJjZdKAfMNLd6xud7g90B/5sZgCnAiPN7BR3n9zUmAoFkRJlZv8BDAS+5O77Gp939z8ApzXoPxt4U+8+iJQhMzsb+B5QBfzRzFaY2f+mz60ws6qsA2ShmYJICXL3VTRxWzh3P6+J9m/lMrZCQSSf3l6We99R+SujJbR8EJGAQkFEAgoFEQkoFEQkUPALjYc2SmkLDm2S0lakN0qREqeZgogECj5TaAtbqR2aIbSFLfKgbW2T1xZmf5opiEhAoSAiAYWCiAQUCiISUCiISEChICIBhYKIBBQKIhJQKIhIQKEgIgGFgogEFAoiElAoiEhAoSAiAYWCiAQUCiISUCiISEChICIBhYKIBBQKIhJQKIhIQBvMipQgM5sOfBU4AzjH3Vdm6HMa8DjQE2gHvAyMd/eD2cbWTEGkNC0AhgAbs/T5HvBXdz8XOAcYCIxubmDNFESKhJl1BjpnOFXn7nUNG9z9D+nnZBsyCZxoZhXA8aRmC+83V0fZhEJs5A3EqgfA7p3UPzLxyPMXDSd2zuDUQUUFnFpF/fTbYe/uAleaH90uG8r5U35ELB5n/Zy5+IyHoi6p1bSh13YC8IMM7fcBk45ivB8CzwAfAB2Bme7+WnNPOqpQMLNLDiVVsUi+9RrJN16iYtRNmc8veYHkkhdSB9UDqBg0rBT/0WRWUUHNtKm8etU17Kmt5fKXF1G7cBGf+JqoK2sVbei1nQHMztBel6EtF2OAt4HLgBOBhWZ2tbs/ne1JOYeCmXUHrgfGATGg31EWmh+b1sDJXXPqGjt7EMmVr+e5oMI5ZWANu9ZvYPfG1PJy8/wF9BgxnNVlEgpt5bVNLxGONgAyuQ0Y5+71wMdm9iwwFDj6UDCz44AvAzcCg9P9r3T3P7VKyVE4rh2xvv2pXzg36kpaTfvup7Pn/drDx3tqa+k6sCbCiiJShq/tMdoADAf+bGbtgMuB+c09qcl3H8zsAWAzcAswF/g0sL2kAwFSa9PNa0t1eplRLBY7oi2ZjKCQiJXja9sUM3vQzP5G6ufyt2a2Kt3+vJldkO42AfhHM3sHWAGsAX7e3NjZZgq3An8Eprj7K+lvWPL/1GL9S3d62ZQ9tR/QoUfV4eMOVVXs3bIlwoqiUYyv7d5lq3Pu27EF47r7eGB8hvYRDR6vA65owbBA9s8pdCc11ZhuZuvNbBKl/m7F8e2hdzVJXx51Ja1qx7LldDqzDx169SJWWUnP0aOoXbgo6rIKq0xf2yg0+UOevugxC5hlZgNIXWBsb2avAnPd/dEC1ZiT2OibifU26NCJignTSS5+FuJxAJJLF6f6nFVDct0qOLA/wkpbXzKRYPmd9zDkmaeIxeNsmPskO1d71GW1mrb82kYhlmzB4tPMKoGrgG81nKa0QDIxedxRPK20xCc+BsC8Lt0irqQwxuzYCkAbem2PvIjThN2jLs75B6zjgj/mPG4+tWg54O4HgP9Jf4lIGdLfPohIQKEgIgGFgogEFAoiElAoiEhAoSAiAYWCiAQUCiISUCiISEChICIBhYKIBBQKIhJQKIhIQKEgIgGFgogEFAoiElAoiEhAoSAiAYWCiAQUCiISUCiISEChICIBhYKIBFq0GUwrKPm9KEXQZjAiUmzMbDrwVeAM4Bx3X5mhz/eBrwEH01/fc/dmNxkteCgk1y4t9LcsuFjfgUDb2EYN2tY2eYe2yCsCC4CfAr/P0ufPwE/cfU96P9jfmVl3d/97toE1UxApEmbWGeic4VRdesPnw9z9D+nnNDleo1nB26SWPV2Bv2WrQxcaRYrHBGBDhq8JrTD2N4F17p41EEAzBZFiMgOYnaG9LkNbzszsC8APgSty6a9QECkS6SXCMQVAY2Z2EfDfwFfc3XN5jpYPImXKzD4H/Aa42t2X5fo8zRRE8mjjO1ty7vvZFoxrZg8Co4HTgd+a2UfufraZPQ9MdPc3gf8C2gOPNrgg+Q13fyfb2AoFkRLk7uOB8RnaRzR4/LmjGVvLBxEJKBREJKBQEJGAQkFEAgoFEQkoFEQkoFAQkYBCQUQCCgURCSgURCSgUBCRgEJBRAIKBREJKBREJKBQEJGAQkFEAmVxk5V9+/dz3V2T2X/gIIlEgmGfH8T4664O+sx/8XdMe+zXdOt6CgBjRw5jzJVDoyj3mMVG3kCsegDs3kn9IxOPPH/RcGLnDE4dVFTAqVXUT78d9u4ucKX50+2yoZw/5UfE4nHWz5mLz3go6pLKRlmEQrvKSmbffy8d25/AgYMHGfvd+xhywQDOO6tf0O+LQwYz8dYbIqqy9STfeo3kGy9RMeqmzOeXvEByyQupg+oBVAwaVlaBQEUFNdOm8upV17CntpbLX15E7cJFfOJroq6sLGRdPphZ3Mw6ZGjvYGbx/JXVMrFYjI7tTwDg4MEEBxMJYrlv91d6Nq2Bv+f2Qx47exDJla/nuaDCOmVgDbvWb2D3xo0kDxxg8/wF9BgxPOqyykZz1xSmAl/P0H4TMKX1yzl6iUQ9o75zD58fewsXn3cOA87qe0SfF197gy//612Mv38GH2z7KIIqC+y4dsT69if51/Laqq9999PZ837t4eM9tbW07356hBWVl+ZCYQTweIb2WelzRSMer2DBzCksfmImb69Zx5r3Ngfnhw6q4aXHf8pzs37Mxef15+4HHo6o0sKJVQ+AzWvLa+lAambYWGE3Ty9vzYVCvbsnGjem2+rzU9KxOalTRy489zP8fulbQXuXk06kXWUlAGOuvJRVazdEUV5BxfqX39IBYE/tB3ToUXX4uENVFXu35H4rdcmuuVBo18Q1hU7A8fkpqeW2f7yTnbtSvw337tvPkhUr6dOzKujz4fYdhx+//PpSzuzZo6A1Ftzx7aF3NUlfHnUlrW7HsuV0OrMPHXr1IlZZSc/Ro6hd2OwO65Kj5t59+A3whJnd6O47AczsZOBRYF6+i8vVtu113P3AwyTq60kmkwy/ZDBDL6zhwTnz6N+vD5cOHsic5xbxyutLicfjnNypE1PuuDnqso9abPTNxHobdOhExYTpJBc/C/HUdd/k0sWpPmfVkFy3Cg7sj7DS/EgmEiy/8x6GPPMUsXicDXOfZOfqnHZEkxzEklkWY2Z2HKkNL78CvJtu7gc8B1zv7gdb+P2SybXlddErk1jfgQAkJo+LuJLCiE98DIB5XbpFXEn+jdmxFcj9ra2/nNkn56sdn123vijeMss6U0j/0F9nZn2B80n9n7HM3dcWojgRKbycPryUDgEFgUgboL99EJGAQkFEAmXxtw8ibZGZVQNPAF2Bj4Bvuvu7GfpdA3yf1DXBJHC5u29talzNFERK1yPALHevJvUp40cbdzCzC4BJwBXu3h+4BPg426CaKYgUCTPrDHTOcKrO3esa9T0NqAGuSDc9Ccw0s0+5+7YGXe8Aprv7FgB3zxoIoJmCSDGZAGzI8DUhQ9+ewPuH/gwh/b+16faGPgv0MbNXzWyZmd1rZlk/D6GZgkjxmEHqw4KN1WVoy9VxwLmkZhTtgBeATcCvsj1BRPJk1fbc/0LVd2ytI/cA2Az0MLO4uyfS9zepSrc3tBF42t33AfvM7FngQrKEgpYPIiXI3T8EVgDXppuuBZY3up4A8GtgmJnFzKwSuAx4iywUCiKl6xbgNjNbA9yWPsbMnk+/6wDwFPAh8BdSIbIK+GW2QbV8EClR7r4aGJShfUSDx/XAv6W/cqKZgogEFAoiElAoiEhAoSAiAYWCiAQUCiISUCiISEChICIBhYKIBBQKIhJQKIhIIOtmMHmgbUClHOS8acu8Lt1y/jc/ZsfW4t8MJg+K4j9aRJqm5YOIBBQKIhJQKIhIQKEgIgGFgogEFAoiElAoiEhAoSAiAYWCiAQUCiISKNt9H8zsPWBv+gvgFXe/I7KC8szMugAfAI+4e6YNSctGeqej7wNfAw6Q+uX2PHC3ux+IsrZyULahkHa1u6+MuogCGQssAa41szvdfX/UBeXR40B7YKC7f5IOiRuA40mFhByDcg+FtmQc8F3gHuDLwNPRlpMfZtYPuAr4tLt/ApCeHfws0sLKSLmHwtNmdmj5cJe7L4q0mjwxswHAKcDLwOmkAqIsQwE4H3jX3XdEXUi5KvdQaCvLhxuBX7l70szmAw+ZWQ93fz/qwvJAf36fZ3r3ocSZWTvg68C49MXVvwKVwPURlpVPy4B+6QurkgflPlNoC0YBq939kkMNZnYR8Cvg/siqyhN3f9fMngMeNbMb0xca48B3gF+6+66ISywYM6sGngC6Ah8B33T3dxv1iQMPAsNJ3flsqrv/Itu4mimUvhuAuQ0b3H0JUGFmQ6IpKe+uB94FlprZSuAdoCewL9KqCu8RYJa7VwOzgEcz9BkL9AX6ARcBk8zsjGyDFvoejSJtSkvu0XjvaZ27AJ0znKpz97qGDWZ2GrAG6OruifSM4COgn7tva9Dv/4DH3f3p9PFMYKO7T2uqDi0fRPKoJTdjvddsEvCDDKfuAyY1ausJvO/uCYB0MNSm27c16NcL2NjgeFO6T5MUCiLFYwYwO0N7XYa2vFEoiBSJ9BIh1wDYDPQws3iD5UNVur2hTUBv4I30ceOZwxF0oVGkBLn7h8AK4Np007XA8obXE9LmAd82swoz+xSpd6ueyTa2QkGkdN0C3GZma4Db0seY2fNmdkG6zxxgPal3a/4ETHb39dkG1bsPIhLQTEFEAgoFEQkoFEQkoFAQkYBCQUQCCgURCSgURCTw/yYMC9+KOE1NAAAAAElFTkSuQmCC\n",
                        "text/plain": "<Figure size 288x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "plt.figure(figsize=(4,4))\n\nsns.heatmap(\n    v3_distmat,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    annot = True,\n    square =True,\n)\nplt.show()\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": 76,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<seaborn.matrix.ClusterGrid at 0x7fa9e16cbbe0>"
                    },
                    "execution_count": 76,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAEBCAYAAAB18mC6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAE8JJREFUeJzt3XuQFeWdxvFvz+HOqFxUhBMuizA/EokTh6xgYrHBJEiIJIjgrmLWhWAZKhdJ7cbgxkVkrcCWxLDRlMakCJEQ3cULulsC5Y2YuIQ1Axoh4UXkujOgVmBEwYHhTO8ffSDDzKszzJzuHuY8n6qpnO5+T/fvGM9jv/12nzcIwxARkcZK0i5ARNonhYOIeCkcRMRL4SAiXgoHEfFSOIiIl8JBRLwUDiLipXAQES+Fg4h4KRxExEvhICJenVrzpnB7ZbNPawXDRgWt2beItA+tCodCM7NVwF8B9cB7wDedc680apMBfgRMAEJgkXPuZ0nXKlIs2kU4ADc6594BMLMvA0uBikZtpgPDgOFAX2CTmT3rnNuVZKGtkc1m5wLd0q4jIZeT/L9Xu/J/SautqqpalMJxExHb/4lm1gvo5dlU45yrabjiRDDknUN0BtHY3wI/dc7VA2/nzzamAXcXqOQ4dauqqpqfdhFJyGaz84vps6ZdQ5ziTPg5wB2e9XcC8xuvNLOfAeOBgKjr0NggYHeD5T3AwDZXKSJecYbDEmCZZ32NZx3OuVkAZvYVorOBibFVJiLNalU41G9e32ybfNfBGwTNvG+5mT1oZn2dc39usGkPMBh4Ob/c+ExCRAoo9QuSZjaI6AwjCxwlGq2oAQ40aroSuMnM/gw8CxwCRiVXqUhxaQ83QXUHLgCOEV2IzAIbnHOhmT1tZp/Mt1sO7AVWA7XAaufcjjQKFikGqY9WOOcc8LEG77sGmJ3fNrFBu5yZhcANwFXA72MqXUSI98xhDrDT8zfng95gZiVEwfCUZ9sXgF7OuUdjqVZETtFuRivy7iW65nBfw5X5s5BFwOcLVZyIfLjWhcMfNjbb5HRHK8xsMdHdj5PyNzo1NBLoD/yvmQGcC0wysz7OuQUtPYaItFx7uCCJmf0O+DrRmcHwxtudc78lCogtRKMUufzr7ydYpkhRST0czOwiYDRQDdQBj5rZE/ltr5jZgHzTfwb+5Jy7GFhFdJ/DlBRKFikK7WG0YgvRLdOY2S5gqnNuc37bJxq8LwTOyl+0/DrwG6AqluJFpH2NVjTjX4EyYB+wH1jrnHupAHWKiEec4bCE6DcaGv8taeX+pgF/ILowmQXGmtnUAtQpIh6t6lbUbtzabJvWPlvxIb4JzMyPZLxjZk8C4wDd9yASg9SfrYCTw5jXED1YNQzY7GnWCfhvM9tHNFoB8NNkKhQpPqmPVuQN5i+/lPQzM9sC0OjZikXAa0Q1nwNcRPS8hYjEIPXRCgDn3LT8e3YBVzUYrWj4bMUy8ndcmllA1GXpRXRHpYgU2Jk0WtHQ3wNvOOf+rwD7EhGPVp057H5tfwv23Lk1z1Y0y8z+hmhYU89ZiMQotm5FDKMVmNllwC+BL+cf9RaRmLSLC5JmVmZm64nuX/ilmTV5vsLM/hr4L6Jfi3rIzF4zs34JlypSNNpFOADPAUZ0G/UQYBM0Ga1YRnQBshY4TnQ79UeSLlSkWKQ+WmFm5wNnAX3zv/aUAf5sZuc1HK0AXgF+4JxbGlfNIvIX7WG0YiBQ5ZzLQfRzcERPaDaek+JjwFAze9HMNprZ7fkhTRGJQavOHLYcONx8o/N7FXq0ohNwMdEoRRdgDdHP1T/Uyv2JyIdoD6MVe4GsmWUadCsG5Nc3tBt41Dl3FDiaf7biUhQOIrFI/dkK59xbZuaArflflw6Arc65txs1fRiYZ2Z3EF2MrAX+PdlqRYpHexmtOBEKNPjfxqMVnYCuRPNbHCd6HuPZJIsUKSbtZbRiBM2PVlwL/NOJn6Y3s/uAqZwZs2yLnHFaFQ7TDr7Z7CjB7Wbzadks201GK8zsxGhFw66FZtkWSVB7m7dCRNqJM2m0QrNsiyQo9QuSzrm3iO5+vC6/6jpgk2e04sQs2yVmdh4wGXgsuUpFikvq4ZD3NeCbZraN6LcivwZNRiuWAzuA14HfAQs0y7ZIfFK/zwHAObeVaGKbxutPmWWb/OzbIhK/9nLmICLtjMJBRLwUDiLipXAQEa8gDMO0axCRdqhdjFYkKFzZu+P/7OS0g28CEG6vTLmS+AXDRgGQWzAz5Uril5m3FBo8mBg3dStExEvhICJeCgcR8VI4iIiXwkFEvBQOIuKlcBARL4WDiHgpHETES+EgIl4KBxHxUjiIiJfCQUS8FA4i4qVwEBEvhYOIeCkcRMRL4SAiXgoHEfFSOIiIl8JBRLwUDiLipXAQEa9im7ciUf0+O45LFt5FkMmwY/kK3JJ70y6pII4eO8YN313Asbrj5HI5xn96NN+6YeopbR5/5tfcvfRX9OvbB4Dpk8Yz7cpxaZTbJsGkGQRl5XD4EPUPzGu6/bIJBB8fEy2UlMC5A6hffAvUHk640sJTOMSlpISKuxfx4tXXcqS6ms89v5bq1Wt5121Lu7I269K5M8u+fzs9u3ej7vhxpn/nTsZ+spxPjBh+SrsvjB3DvNkzUqqyMMJXXyJ8+TlKJs/yb1+/hnD9mmihrJyS0ePbRTCYWQbo6pw70mh9D+Cocy7X3D7UrYhJn1EVvLdjJ4d37yasq2Pv46vITpyQdlkFEQQBPbt3A+D48RzHczmC5CZiStaebfB+y77swUWjCTdviLmgFlsEXO9ZPwtY2JId6MwhJt37X8CRquqTy0eqq+k7qiLFigorl6vnmlu+x559+7n+i+MpHzGsSZtnXnqZ32/eypBsf2676Sv0P69vCpUmpFMXgmEjqV+9ItbDmFkvoJdnU41zrqbB8kRgrqfdj4FXgVubO1abwiGbzc4FurVlH0mpqqqan+TxgqDpf0k70pzFmUwJq+5byKH3DvONu37Itl17KRsy8OT2caMruOozn6JL58488vSzzL3nfn6x8PYUK45XUFYOe7cn0aWYA9zhWX8nML/Bcr2v6+Ccy5lZfUsO1NYzh25Jf+nOFEeq99EjO+Dkco8BA6jdvz/FiuJxdmlPLr34o/ym8tVTwqH32WedfD3tyitY/POH0ygvMcHIxLoUS4BlnvU1jZa7mFkPzzWHUqBrSw6kbkVMDm7cROmFQ+kxaBDv79vHwCmT2XDT7LTLKogD7xyiUybD2aU9qT16jPWvbGbW1EmntHnrwEHO79MbgOc3VHLhwGwapSaja3cYXEb4xIOxHyrfdWgcBD7/AfzCzL7qnDsEYGbnAD8BVrbkWAqHmIS5HJtuvY2xjz1CkMmwc8XDHNrq0i6rIN4+UMPce+4nV19PGIZMuHwM4y6t4EfLVzJy+FCuGDOK5U+t5YUNlWQyGc4pLWXht29Ou+xWCabcTDDYoEcpJXMWE657EjIZAMLKdVGbERWEb2yBumMpVtrEAqIzjCozez2/bjjwFKd2Pz5QELahI5zNZuefYd2KcGXvfmnXELtpB98EINxemXIl8QuGjQIgt2BmypXELzNvKXB6w0JmNgy4JP++jc657S19r84cRDqwfBi0OBAa0n0OIuKlcBARL4WDiHgpHETES+EgIl4KBxHxUjiIiJfCQUS8FA4i4qVwEBEvhYOIeCkcRMRL4SAiXgoHEfFSOIiIl8JBRLwUDiLipXAQES+Fg4h4KRxExEvhICJeCgcR8Sq6eSvSLkCkjRKbzlxnDiLiVXST2hTTLFDFNLtXEc14lRidOYiIl8JBRLwUDiLipXAQES+Fg4h4KRxExEvhICJeCgcR8VI4iIiXwkFEvBQOIuKlcBARL4WDiHgpHETES+EgIl4KBxHxUjiIiJfCQUS8FA4i4qVwEBEvhYOIeCkcRMSr6H6aPg5Hjx3jhu8u4FjdcXK5HOM/PZpv3TD1lDaPP/Nr7l76K/r17QPA9EnjmXbluDTKjUW/z47jkoV3EWQy7Fi+Arfk3rRLKohg0gyCsnI4fIj6B+Y13X7ZBIKPj4kWSkrg3AHUL74Fag8nXGnhKRwKoEvnziz7/u307N6NuuPHmf6dOxn7yXI+MWL4Ke2+MHYM82bPSKnKGJWUUHH3Il68+lqOVFfzuefXUr16Le+6bWlX1mbhqy8RvvwcJZNn+bevX0O4fk20UFZOyejxHSIYQN2KggiCgJ7duwFw/HiO47kcQXKzlqWuz6gK3tuxk8O7dxPW1bH38VVkJ05Iu6zC2LMN3m/Zlz24aDTh5g0xF5QcnTkUSC5XzzW3fI89+/Zz/RfHUz5iWJM2z7z0Mr/fvJUh2f7cdtNX6H9e3xQqLbzu/S/gSFX1yeUj1dX0HVWRYkUp6NSFYNhI6levSLuSglE4FEgmU8Kq+xZy6L3DfOOuH7Jt117Khgw8uX3c6Aqu+syn6NK5M488/Sxz77mfXyy8PcWKCycImp4ltWF+5jNSUFYOe7fH3qUws15AL8+mGudcTYN2P3DO/WP+9eedc8+c7rHaGg6XZ7PZ+W3cRyKSmg387NKeXHrxR/lN5aunhEPvs886+XralVew+OcPJ1FOIo5U76NHdsDJ5R4DBlC7f3+KFSUvGJlYl2IOcIdn/Z3A/AbLDa92/xuQeDh0SupL154deOcQnTIZzi7tSe3RY6x/ZTOzpk46pc1bBw5yfp/eADy/oZILB2bTKDUWBzduovTCofQYNIj39+1j4JTJbLhpdtplJadrdxhcRvjEg0kcbQmwzLO+ptFy8AGvW0zdigJ4+0ANc++5n1x9PWEYMuHyMYy7tIIfLV/JyOFDuWLMKJY/tZYXNlSSyWQ4p7SUhd++Oe2yCybM5dh0622MfewRgkyGnSse5tBWl3ZZBRFMuZlgsEGPUkrmLCZc9yRkMgCEleuiNiMqCN/YAnXHYq8n33VoHAQ+Xc3so0TB0PD1if38sbkdBGEbOofZbHZdVVXVZ1q9g+SF4fbKtGuIXTBsFAAre/dLuZL4TTv4JgC5BTNTriR+mXlLoYVnAWa2C/igL3fonBva3D505iDSATnnhrR1H7rPQUS8FA4i4qVwEBEvhYOIeCkcRMRL4SAiXgoHEfFSOIiIl8JBRLwUDiLipXAQES+Fg4h4KRxExEvhICJeCgcR8VI4iIiXwkFEvBQOIuKlcBARL4WDiHgpHETES+EgIl4KBxHxKrpJbdIuQKSNWjW1XWu09cxhVyGKEJH2p60zXu0qRBFJKqJp04rqsxbT1H9J0TUHEfFSOIiIl8JBRLwUDiLipXAQES+Fg4h4KRxExEvhICJeCgcR8VI4iIiXwkFEvBQOIuKlcBARL4WDiHgpHETES+EgIl4KBxHxUjiIiJfCQUS8FA4i4qVwEBEvhYOIeLX1p+klL5g0g6CsHA4fov6BeU23XzaB4ONjooWSEjh3APWLb4HawwlX2nbF9Fl9+n12HJcsvIsgk2HH8hW4JfemXVIsFA4FEr76EuHLz1EyeZZ/+/o1hOvXRAtl5ZSMHn/GflmK6bM2UVJCxd2LePHqazlSXc3nnl9L9eq1vOu2pV1Zi5jZ5c6537akrcKhUPZsg3P6tqhpcNFows0bYi4oRsX0WRvpM6qC93bs5PDu3QDsfXwV2YkT2NqOw8HM+gM3AjOJptMb3pL3KRyS1qkLwbCR1K9ekXYl8euAn7V7/ws4UlV9cvlIdTV9R1WkWJGfmXUCvgR8FRhD9F2/0jn3u5buQ+GQsKCsHPZu7zin2R+iI37WIGg6j20b5qI+bWbWC+jl2VTjnKvJt7kHuA54DVgGTAX+eDrBAG0Ph9psNju/jftIRFVV1fy0awAIRnas0+wP0xE/65HqffTIDji53GPAAGr370+yhDnAHZ71dwLz869nA/8DLHTOvQBgZqcdYW0Kh6qqqkVteX/R6dodBpcRPvFg2pXEr4N+1oMbN1F64VB6DBrE+/v2MXDKZDbcNDvJEpYQnQ00VtPgdX9gOrDYzHoDD9GK77q6FQUSTLmZYLBBj1JK5iwmXPckZDIAhJXrojYjKgjf2AJ1x1KstO2K6bM2FuZybLr1NsY+9ghBJsPOFQ9zaKtL7Pj5rkNNC9r8GPixmZUTXYjsbmYvAiuccz9pybGCMMkOU/rCYpqWvpg+68re/VKuJH7TDr4J0WjDaTOzzsDVwD845ya25D06cxApAs65OuA/838totunRcRL4SAiXgoHEfFSOIiIl8JBRLwUDiLipXAQES+Fg4h4KRxExEvhICJeCgcR8VI4iIiXwkFEvBQOIuKlcBARL4WDiHgpHETES+EgIl4KBxHxUjiIiJfCQUS8FA4i4lV081akXYBIG7Vq3orWKLZ5KxL7BytyplO3QkS8FA4i4qVwEBEvhYOIeCkcRMRL4SAiXgoHEfFSOIiIl8JBRLyK7Q7JxJhZZ+BfgL8D6oiC+GlgrnOuLs3a4mBmu4Da/B/AC865b6dWUIzMrDewD3jAOTcn7XrionCIz8+B7sAo59y7+bCYAXQlCouOaKpzbnPaRSRgOrAeuM7MbnXOHUu7oDgoHGJgZsOBq4GPOOfeBcifLTyYamFSKDOB7wC3AV8CHk23nHgoHOJxCfC6c+5g2oUk7FEzO9Gt+K5zbm2q1cTAzMqBPsDzwAVEQaFwkBYr1qc/i6Fb8VXgIedcaGaPA/eaWdY5V5V2YYWm0Yp4bASG5y9cSQdhZl2A64GZ+QuwfwI6AzemWFZsFA4xcM69DjwF/MTMzgIws4yZ3WJmpelWJ20wGdjqnPuIc26Ic24IMJ7oQnOHo3CIz43A60ClmW0GXgMGAkdTrUraYgawouEK59x6oMTMxqZTUnyK7WfiRKSFdOYgIl4KBxHxUjiIiJfCQUS8FA4i4qVwEBEvhYOIeCkcRMTr/wEAD9sQwne+dgAAAABJRU5ErkJggg==\n",
                        "text/plain": "<Figure size 288x288 with 4 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "v3_distmat_link = hc.linkage(pdist(v3_df))\n\nsns.clustermap(\n    v3_distmat,\n    annot = True,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    row_linkage=v3_distmat_link,\n    col_linkage=v3_distmat_link,\n    figsize=(4,4)\n)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Mais de tr\u00eas vota\u00e7\u00f5es, $ n > 3 $\n\nA partir daqui coloca-se a quest\u00e3o fundamental: a impossibilidade de visualizar de forma directa a dist\u00e2ncia para al\u00e9m das tr\u00eas dimens\u00f5es. A dist\u00e2ncia existe e segue exactamente os mesmo passos, simplesmente n\u00e3o \u00e9 pass\u00edvel de visualiza\u00e7\u00e3o, raz\u00e3o pela qual \u00e9 necess\u00e1rio (agora sim) depender de formas que \"reduzam\" as dimens\u00f5es e as tornem visualiz\u00e1veis.\n\nAt\u00e9 agora os agrupamentos t\u00eam sido sempre iguais pois a dist\u00e2ncia \u00e9 sempre linear; vamos neste \u00faltimo caso assumir que o Partido Abstencionista teve uma mudan\u00e7a de posi\u00e7\u00e3o e passou a votar por vezes a favor e contra, embora mais contra que a favor:"
        },
        {
            "cell_type": "code",
            "execution_count": 77,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>v1</th>\n      <th>v2</th>\n      <th>v3</th>\n      <th>v4</th>\n      <th>v5</th>\n      <th>v6</th>\n      <th>v7</th>\n      <th>v8</th>\n      <th>v9</th>\n      <th>v10</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   v1  v2  v3  v4  v5  v6  v7  v8  v9  v10\nF   1   1   1   1   1   1   1   1   1    1\nA   0   0   0   1   0  -1   0  -1  -1   -1\nC  -1  -1  -1  -1  -1  -1  -1  -1  -1   -1"
                    },
                    "execution_count": 77,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "v4=[[1,1,1,1,1,1,1,1,1,1],[0,0,0,1,0,-1,0,-1,-1,-1],[-1,-1,-1,-1,-1,-1,-1,-1,-1,-1]]\nv4_df = pd.DataFrame(v4, columns=[\"v1\",\"v2\", \"v3\",\"v4\",\"v5\",\"v6\",\"v7\",\"v8\",\"v9\",\"v10\"], index=[\"F\",\"A\", \"C\"])\nv4_df"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "A dist\u00e2ncia calculdada \"manualmente\":"
        },
        {
            "cell_type": "code",
            "execution_count": 78,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "4.58257569495584\n6.324555320336759\n3.0\n"
                }
            ],
            "source": "print(math.sqrt(sum((px - qx) ** 2.0 for px, qx in zip(v4_df.loc[\"F\"],v4_df.loc[\"A\"]))))\nprint(math.sqrt(sum((px - qx) ** 2.0 for px, qx in zip(v4_df.loc[\"F\"],v4_df.loc[\"C\"]))))\nprint(math.sqrt(sum((px - qx) ** 2.0 for px, qx in zip(v4_df.loc[\"A\"],v4_df.loc[\"C\"]))))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "... e o c\u00e1lculo via `pdist` e a  matriz de dist\u00e2ncia:"
        },
        {
            "cell_type": "code",
            "execution_count": 79,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "pdist:\n [4.58257569 6.32455532 3.        ] \n\nsquareform:\n [[0.         4.58257569 6.32455532]\n [4.58257569 0.         3.        ]\n [6.32455532 3.         0.        ]]\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>F</th>\n      <th>A</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F</th>\n      <td>0.000000</td>\n      <td>4.582576</td>\n      <td>6.324555</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>4.582576</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>6.324555</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "          F         A         C\nF  0.000000  4.582576  6.324555\nA  4.582576  0.000000  3.000000\nC  6.324555  3.000000  0.000000"
                    },
                    "execution_count": 79,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "print(\"pdist:\\n\", pdist(v4),\"\\n\")\nprint(\"squareform:\\n\",squareform(pdist(v4)))\nv4_distmat=pd.DataFrame(squareform(pdist(v4)), columns=v4_df.index, index=v4_df.index)\nv4_distmat"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O mapa t\u00e9rmico:"
        },
        {
            "cell_type": "code",
            "execution_count": 80,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 288x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "plt.figure(figsize=(4,4))\n\nsns.heatmap(\n    v4_distmat,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    annot = True,\n    square =True,\n)\nplt.show()\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "... e o dendograma, j\u00e1 apresentando um agrupamento com base na maior aproxima\u00e7\u00e3o com base no registo de vota\u00e7\u00e3o:"
        },
        {
            "cell_type": "code",
            "execution_count": 81,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<seaborn.matrix.ClusterGrid at 0x7fa8df99e828>"
                    },
                    "execution_count": 81,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 288x288 with 4 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "v4_distmat_link = hc.linkage(pdist(v4_df), method=\"ward\")\n\nsns.clustermap(\n    v4_distmat,\n    annot = True,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    row_linkage=v4_distmat_link,\n    col_linkage=v4_distmat_link,\n    figsize=(4,4)\n)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### M\u00e9todos de _clustering_"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Da mesma forma que podemos especificar diferentes m\u00e9tricas de dist\u00e2ncia, podemos tamb\u00e9m especificar diferentes m\u00e9todos de agrega\u00e7\u00e3o, que com base na dist\u00e2ncia entre os v\u00e1rios pontos v\u00e3o determinar de que forma podem ser agrupados: de que forma se determina a liga\u00e7\u00e3o entre eles.\n\nO valor por omiss\u00e3o usado pela fun\u00e7\u00e3o `linkage` o da liga\u00e7\u00e3o `simple`, determinada pela dist\u00e2ncia m\u00ednima entre os pontos mais pr\u00f3ximos  $ \\min \\,\\{\\,d(a,b):a\\in A,\\,b\\in B\\,\\} $; uma alternativa bastante comum e que determina os _cluster_ com base na dist\u00e2ncia m\u00e9dia \u00e9 o `average` (tamb\u00e9m conhecido por UPGMA), e outra \u00e9 o m\u00e9todo de Ward [14WardMethod].\n\nPodemos ver a diferen\u00e7a entre os tr\u00eas com o seguinte exemplo; adicionamos um partido (\"B\") normalmente se abstem mas vota duas vezes de forma diferente, ambas em sentido oposto ao partido \"A\"."
        },
        {
            "cell_type": "code",
            "execution_count": 82,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>F</th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F</th>\n      <td>0.000000</td>\n      <td>4.582576</td>\n      <td>3.464102</td>\n      <td>6.324555</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>4.582576</td>\n      <td>0.000000</td>\n      <td>3.316625</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>3.464102</td>\n      <td>3.316625</td>\n      <td>0.000000</td>\n      <td>3.464102</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>6.324555</td>\n      <td>3.000000</td>\n      <td>3.464102</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "          F         A         B         C\nF  0.000000  4.582576  3.464102  6.324555\nA  4.582576  0.000000  3.316625  3.000000\nB  3.464102  3.316625  0.000000  3.464102\nC  6.324555  3.000000  3.464102  0.000000"
                    },
                    "execution_count": 82,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "v5=[[1,1,1,1,1,1,1,1,1,1],[0,0,0,1,0,-1,0,-1,-1,-1],[0,0,0,-1,0,0,0,0,0,1],[-1,-1,-1,-1,-1,-1,-1,-1,-1,-1]]\n\nv5_df = pd.DataFrame(v5, columns=[\"v1\",\"v2\", \"v3\",\"v4\",\"v5\",\"v6\",\"v7\",\"v8\",\"v9\",\"v10\"], index=[\"F\",\"A\",\"B\" ,\"C\"])\nv5_distmat=pd.DataFrame(squareform(pdist(v5)), columns=v5_df.index, index=v5_df.index)\nv5_distmat"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Usando o m\u00e9todo simples obtemos o seguinte dendograma:"
        },
        {
            "cell_type": "code",
            "execution_count": 83,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<seaborn.matrix.ClusterGrid at 0x7fa8df7d0128>"
                    },
                    "execution_count": 83,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 288x288 with 4 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "v5_distmat_link = hc.linkage(pdist(v5_df))\n\nsns.clustermap(\n    v5_distmat,\n    annot = True,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    row_linkage=v5_distmat_link,\n    col_linkage=v5_distmat_link,\n    figsize=(4,4)\n)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Usando UPGMA n\u00e3o h\u00e1 altera\u00e7\u00f5es (neste caso muito simples, porque o mais prov\u00e1vel \u00e9 existirem quando aplicadas a dados reais com maior n\u00famero de observa\u00e7\u00f5es e dimens\u00f5es); os agrupamentos tendem a ser feitos de forma recursiva, por separa\u00e7\u00e3o de elementos individuais at\u00e9 \u00e0 identifica\u00e7\u00e3o de um par final:"
        },
        {
            "cell_type": "code",
            "execution_count": 84,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<seaborn.matrix.ClusterGrid at 0x7fa8df6cdac8>"
                    },
                    "execution_count": 84,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 288x288 with 4 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "v5_distmat_link = hc.linkage(pdist(v5_df), method=\"average\")\n\nsns.clustermap(\n    v5_distmat,\n    annot = True,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    row_linkage=v5_distmat_link,\n    col_linkage=v5_distmat_link,\n    figsize=(4,4)\n)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Com o m\u00e9todo de Ward o resultado n\u00e3o \u00e9 muito diferente mas tende a agrupar em _clusters_ de n\u00famero e dimens\u00f5es iguais e \u00e9 por isso uma escolha muito popular *(Morse 1980)*; neste caso cria dois grupos de dois elementos, agrupando o novo partido pela considera\u00e7\u00e3o que faz das dest\u00e2ncias entre todos:"
        },
        {
            "cell_type": "code",
            "execution_count": 85,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<seaborn.matrix.ClusterGrid at 0x7fa8df5d0ba8>"
                    },
                    "execution_count": 85,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 288x288 with 4 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "v5_distmat_link = hc.linkage(pdist(v5_df), method=\"ward\")\n\nsns.clustermap(\n    v5_distmat,\n    annot = True,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    row_linkage=v5_distmat_link,\n    col_linkage=v5_distmat_link,\n    figsize=(4,4)\n)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Um exemplo simples de MDS\n\nConsideremos uma vota\u00e7\u00e3o como a seguinte, onde os 4 partidos t\u00eam um perfil de vota\u00e7\u00e3o simples que torna simples adivinhar os agrupamentos: entre o que vota sempre a favor e o que vota sempre contra temos partidos que metade das vezes se abst\u00eam e a outra metade votam de forma diferente; a matriz de dist\u00e2ncia correspondente:"
        },
        {
            "cell_type": "code",
            "execution_count": 86,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>F</th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F</th>\n      <td>0.000000</td>\n      <td>2.236068</td>\n      <td>5.000000</td>\n      <td>6.324555</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>2.236068</td>\n      <td>0.000000</td>\n      <td>3.162278</td>\n      <td>5.000000</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>5.000000</td>\n      <td>3.162278</td>\n      <td>0.000000</td>\n      <td>2.236068</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>6.324555</td>\n      <td>5.000000</td>\n      <td>2.236068</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "          F         A         B         C\nF  0.000000  2.236068  5.000000  6.324555\nA  2.236068  0.000000  3.162278  5.000000\nB  5.000000  3.162278  0.000000  2.236068\nC  6.324555  5.000000  2.236068  0.000000"
                    },
                    "execution_count": 86,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "v6=[[1,1,1,1,1,1,1,1,1,1],[1,0,1,0,1,0,1,0,1,0],[0,-1,0,-1,0,-1,0,-1,0,-1],[-1,-1,-1,-1,-1,-1,-1,-1,-1,-1]]\n\nv6_df = pd.DataFrame(v6, columns=[\"v1\",\"v2\", \"v3\",\"v4\",\"v5\",\"v6\",\"v7\",\"v8\",\"v9\",\"v10\"], index=[\"F\",\"A\",\"B\" ,\"C\"])\nv6_distmat=pd.DataFrame(squareform(pdist(v6)), columns=v6_df.index, index=v6_df.index)\nv6_distmat"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Com estas dist\u00e2ncias conseguimos, atrav\u00e9s de MDS, reduzir o n\u00famero de dimens\u00f5es de forma a podermos visualizar o resultado:"
        },
        {
            "cell_type": "code",
            "execution_count": 87,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x576 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "from sklearn.manifold import MDS\nimport random\n\nmds = MDS(n_components=2, dissimilarity='precomputed',random_state=2020, n_init=100, max_iter=1000)\n\nresults = mds.fit(v6_distmat.values)\ncoords = results.embedding_\nfig, ax = plt.subplots(figsize=(8,8))\n\nfor label, x, y in zip(v6_distmat.columns, coords[:, 0], coords[:, 1]):\n    ax.scatter(x, y, s=250)\n    ax.axis('equal')\n    ax.annotate(\n        label,\n        xy = (x+0.1, y),\n    )\nplt.show()\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Observe-se que n\u00e3o temos agrupamentos: MDS n\u00e3o \u00e9 um m\u00e9todo de _clustering_ mas sim de redu\u00e7\u00e3o das dimens\u00f5es de forma a manter as dist\u00e2ncias relativas entre os v\u00e1rios pontos, o que nos permite identificar visualmente poss\u00edveis grupos (neste caso parece claro que $\\{C,B\\}$ e $\\{A,F\\}$ s\u00e3o grupos que se destacam pela proximidade edos seus elementos e dist\u00e2ncia entre si)."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Spectrum Scaling\n\nEste m\u00e9todo \u00e9, como referido, uma das formas de se poder proceder \u00e0 identifica\u00e7\u00e3o autom\u00e1tica de _clusters_, tendo como base uma matriz de afinidade <cite data-cite=\"1084038/HX4BMSIN\"></cite>"
        },
        {
            "cell_type": "code",
            "execution_count": 88,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>F</th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>F</th>\n      <td>1.000000</td>\n      <td>0.646447</td>\n      <td>0.209431</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>0.646447</td>\n      <td>1.000000</td>\n      <td>0.500000</td>\n      <td>0.209431</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>0.209431</td>\n      <td>0.500000</td>\n      <td>1.000000</td>\n      <td>0.646447</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>0.000000</td>\n      <td>0.209431</td>\n      <td>0.646447</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "          F         A         B         C\nF  1.000000  0.646447  0.209431  0.000000\nA  0.646447  1.000000  0.500000  0.209431\nB  0.209431  0.500000  1.000000  0.646447\nC  0.000000  0.209431  0.646447  1.000000"
                    },
                    "execution_count": 88,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "v6_distmat_mm=((v6_distmat-v6_distmat.min().min())/(v6_distmat.max().max()-v6_distmat.min().min()))*1\n\npd.DataFrame(1-v6_distmat_mm, v6_distmat.index, v6_distmat.columns)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Um par\u00e2metros fundamental \u00e9 o n\u00famero de _clusters_ a identificar; neste caso indicamos dois:"
        },
        {
            "cell_type": "code",
            "execution_count": 89,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "{'F': 1, 'A': 1, 'B': 0, 'C': 0}\n"
                }
            ],
            "source": "v6_sc = SpectralClustering(2, affinity=\"precomputed\",random_state=2020).fit_predict(1 - v6_distmat_mm)\nv6_sc_dict = dict(zip(v6_distmat,v6_sc))\nprint(v6_sc_dict)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "O MDS correspondente:"
        },
        {
            "cell_type": "code",
            "execution_count": 90,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x432 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "fig, ax = plt.subplots(figsize=(6,6))\n\nfor label, x, y in zip(v6_distmat.columns, coords[:, 0], coords[:, 1]):\n    ax.scatter(x, y, c = \"C\"+str(v6_sc_dict[label]), s=250)\n    ax.axis('equal')\n    ax.annotate(\n        label,xy = (x+0.3, y),)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## DBSCAN\n\nEste m\u00e9todo dispensa a inicializa\u00e7\u00e3o com o n\u00famero de grupos <cite data-cite=\"1084038/PF2IFXA5\"></cite>:"
        },
        {
            "cell_type": "code",
            "execution_count": 91,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'F': 0, 'A': 0, 'B': 1, 'C': 1}"
                    },
                    "execution_count": 91,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "from sklearn.cluster import DBSCAN\n\ndbscan_labels = DBSCAN(min_samples=2,eps=0.7).fit((1 - v6_distmat_mm))\ndbscan_labels.labels_\nv6_dbscan_dict = dict(zip(v6_distmat_mm,dbscan_labels.labels_))\nv6_dbscan_dict"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Neste caso espec\u00edfico o resultado \u00e9 id\u00eantico:"
        },
        {
            "cell_type": "code",
            "execution_count": 92,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 432x432 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "fig, ax = plt.subplots(figsize=(6,6))\n\nfor label, x, y in zip(v6_distmat.columns, coords[:, 0], coords[:, 1]):\n    ax.scatter(x, y, c = \"C\"+str(v6_dbscan_dict[label]), s=250)\n    ax.axis('equal')\n    ax.annotate(\n        label,xy = (x+0.3, y),)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Deputada Cristina Rodrigues e o PAN\n\n\nComo referido opt\u00e1mos por n\u00e3o incluir a deputa\u00e7\u00e3o n\u00e3o-inscrita Cristina Rodrigues na an\u00e1lise principal, essencialmente pela mais reduzida quantidade de vota\u00e7\u00f5es. Fazemos aqui, contudo, duas an\u00e1lises de detalhes de forma a permitir uma leitura espec\u00edfica desta situa\u00e7\u00e3o.\n\n### Compara\u00e7\u00e3o com todas as vota\u00e7\u00f5es, assumindo votos do PAN\n\nNeste caso assumimos que os votos da deputada s\u00e3o os mesmos do PAN antes da sua sa\u00edda (condensamos todos os passos necess\u00e1rio para a constru\u00e7\u00e3o da matriz de dist\u00e2ncia e dendograma num \u00fanico bloco, seguindo-se as restantes an\u00e1lises sem explica\u00e7\u00f5es adicionais por redundantes):"
        },
        {
            "cell_type": "code",
            "execution_count": 93,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x576 with 4 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "cr_votes_hm = votes[['BE', 'PCP', 'PEV', 'L/JKM', 'PS', 'PAN','PAN/CR','PSD','IL','CDS-PP', 'CH']]\ncr_votes_hmn = cr_votes_hm.replace([\"A Favor\", \"Contra\", \"Absten\u00e7\u00e3o\", \"Aus\u00eancia\"], [1,-1,0,0]).fillna(0)\n\n\n## Transpose the dataframe used for the heatmap\ncr_votes_t = cr_votes_hmn.transpose()\n\n## Determine the Eucledian pairwise distance\n## (\"euclidean\" is actually the default option)\ncr_pwdist = pdist(cr_votes_t, metric='euclidean')\n\n## Create a square dataframe with the pairwise distances:\n## the distance matrix\ncr_distmat = pd.DataFrame(\n    squareform(cr_pwdist), # pass a symmetric distance matrix\n    columns = cr_votes_t.index,\n    index = cr_votes_t.index\n)\n## Perform hierarchical linkage on the distance matrix using Ward's method.\ncr_distmat_link = hc.linkage(cr_pwdist, method=\"ward\", optimal_ordering=True )\n#plt.figure(figsize=(8,8))\n\nsns.clustermap(\n    cr_distmat,\n    annot = True,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    #standard_scale=1,\n    row_linkage=cr_distmat_link,\n    col_linkage=cr_distmat_link,\n    figsize=(8,8)\n)\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Spectrum Clustering\n\nAplicando _Spectrum Clustering_ aos dados obtemos a seguinte classifica\u00e7\u00e3o de _clusters_:"
        },
        {
            "cell_type": "code",
            "execution_count": 94,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'BE': 4,\n 'PCP': 3,\n 'PEV': 3,\n 'L/JKM': 4,\n 'PS': 1,\n 'PAN': 0,\n 'PAN/CR': 0,\n 'PSD': 1,\n 'IL': 2,\n 'CDS-PP': 2,\n 'CH': 2}"
                    },
                    "execution_count": 94,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "cr_distmat_mm=((cr_distmat-cr_distmat.min().min())/(cr_distmat.max().max()-cr_distmat.min().min()))*1\n\ncr_affinmat_mm = pd.DataFrame(1-cr_distmat_mm, cr_distmat.index, cr_distmat.columns)\ncr_sc = SpectralClustering(5, affinity=\"precomputed\",random_state=2020).fit_predict(cr_affinmat_mm)\ncr_sc_dict = dict(zip(cr_distmat,cr_sc))\ncr_sc_dict "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### _Multi Dimension Scaling_\n\nO gr\u00e1fico de MDS com a inclus\u00e3o da deputada Cristina Rodrigues \u00e9 o seguinte:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 95,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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iBLWAxJSfsW7uCE7O18Zx5EDtzsXfivF/VymRe/waHlbXoqamCgM8wnEq5zr+7yolLpxVYc+ePfD09DR0qe1OWvYl1ArqRtvUCmrsUF7SU0X0pBgORiAoKAheXl7w8fFBQEAAFi5ciPv37+O1115Dv3794OPjo/lJSEhAUVERnnvuOVy7dk2yr5kzZ2pmzG2P7ty6j+9STqKmRoD6P+8ZsOvkgN6uA6BWi6ipEfBK9HgcOpiNhQsXGrja9ufQyRv13u/QEEEt4uBJ459upL3jaSUjkZycjICAABQVFSEuLk4zOdrixYsbfGBr0KBB2LFjR70Lh3fv3kV2dja2bdumt7r17cfsKxCa+Mt14qj3MGBQT7i7u+upqo6jqlq7C/SVWrYjw+HIwcj8dXK0x5kwYQJ27NhRb9n333+Pp59+ul1/KeadvKkZMTyOWi0i7+RNPVXUsVhaaPf3ppWW7chwGA5G5q+Toz1OSEgISktLceLECc2ynTt3ttsHtx55qOVfpNq2o+YZ7usGuYn0DrA/k5vIMMLXTU8V0ZNiOBiJhiZHA4APPvgAfn5+mp+PP/4YAGBpaYnQ0FDN6CE/Px9nz55FeHi4wfqgD+Za/kWqbTtqnshhT8FU3vjXiqncBBFDn9JTRfSk+BtiJJKSkhAQECBZ/u677zZ4zQEAIiMjkZCQgHfffRc7duzAkCFD0K1bt9Yu1aD6+boi9/i1Rk8tmZjI0M/XVY9VdRyK7jZYEDsQiSk/o1ZQ17s4LTeRwVRuggWxA3kbqxHgyKEd8/PzQ5cuXZCVlYWdO3e2i+mlmzJoWB/Im/jLVS43wYtD++ipoo7Hz8MJ6+aOQOigXrC2NIVMBlhbmiJ0UC+smzsCfh7tc7LC9oYjh3ZuwoQJWLVqFSoqKhAUFGToclqdfXcbvBTri+9STkIQ1PVGECYmMsjlJngp1hf2/Mu1VSm62yAhqj8SovobuhR6Qhw5GLlly5bVe84hKiqq3vqIiAgUFBRgzJgxMDc3N1CV+vW0hyPemDsUAwb1hMV//nK1sDTFgEE98cbcoXjaw9HQJRK1eRw5GIFHUzdUFqpQsGMnSg4pIVRVYaalJRxmvgmXiPGwUjg3uG2PHj1w/vx5fZbbJth3t8HYqH4YG8U5kIieBMPBSJSe/AXnV66CurYWEAQAgFBZCdW+TBQfOIRn589FV98BBq6SiNoLnlYyApWFqrpgqK7WBIOGIEBdXY3zK1ehslBlmAKJqN1hOBiBgh0760YMjVDX1qJg5y49VURE7R3DwQiUHFJKRwx/JQgoOZStn4KIqN1jOBgB4S8vm3lsu0rt2hERNYXhYATklpbatbPSrh0RUVMYDkbAYfhQ4D9vLHssuRwOwx//onYiouZgOBgBl4jxMDFt/K5jE1NTuIxv35PqEZH+MByMgJXCGc/OnwsTCwvpCEIuh4mFBZ6dP/exD8IRETUXH4IzEl19B8B7zWoU7NyFkkPZECqrILeyhMPwYXAZH85gICKdYjgYESuFM/q+EY++b8QbuhQiaud4WomIiCQYDkREJMFwICIiCYYDERFJMByIiEiC4UBERBIMByIikuBzDm1AUFAQbt26BblcDisrKwwbNgzvvvsubGxscPjwYSQnJ+PcuXOwsLDAU089hSlTpiA4OBjbt2/HP//5T1haWkImk6FHjx6YPXs2RowYYeguEZGR48ihjUhOTkZubi7S0tKQl5eHjRs34ocffsCsWbMwYcIEKJVKHDt2DG+99RYOHjyo2c7b2xu5ubk4ceIEXnrpJcyePRt37941YE+IqD1gOLQxTk5OCAwMxO+//47ExETMmDEDkyZNgq2tLUxMTPDCCy/ggw8+kGxnYmKCiRMnoqqqCtevXzdA5UTUnjAc2pjCwkIolUpYWlqisLAQo0eP1mq72tpapKamwtraGr17927dIomo3eM1hzZi5syZkMvlsLW1xbBhwxAeHo69e/fC0dGx0e1+/fVX+Pn5QS6Xo1evXkhKSoKtra2eqiai9kpn4XD16lUsWLAAd+/eRZcuXbBy5UrJX7BJSUnYvXs35HI5TE1NMWfOHAQGBuqqBKOWlJSEgIAAzefLly8DAIqLi9GjR4/Hbte/f3988803rV4fEXUsOjut9N577yE6Ohp79+5FdHQ0Fi9eLGnj5eWF7777Djt37sTy5csxZ84cVGn5fuSOpk+fPlAoFNi3b5+hSyGiDkgn4XD79m2cO3cOYWFhAICwsDCcO3cOd+7cqdcuMDAQVlZWAAB3d3eIosg7ax5DJpNhwYIF2LBhA7Zt24aKigqo1WqcOHECixYtMnR5RNTO6eS0UmFhIZycnCD/z1vK5HI5HB0dUVhYCHt7+wa3SU9PR8+ePeHsLH1JTXl5OcrLy+stU6lUuijVqISGhsLa2hrJycn44IMPYGFhgaeffhpxcXGGLo2I2jmDXJD+6aefsGbNGnz66acNrk9JScH69ev1XJXhHDhwAJWFKlxO3oSSQ0oIVVWQW1rCYfhQDIwYj6Fff93gdlFRUYiKitJztUTUEegkHBQKBYqKiiAIAuRyOQRBQHFxMRQKhaRtbm4u5s2bhw0bNqBPnz4N7i82NhaRkZH1lqlUKsTExOii3Dan9OQvOL9yFdS1tYAgAACEykqo9mWi+MAhPDt/Lrr6DjBwlUTUkejkmkO3bt3g4eGBjIwMAEBGRgY8PDwkp5ROnz6NOXPmYO3atXj++ecfuz87Ozu4ubnV+2no9FN7UFmoqguG6mpNMGgIAtTV1Ti/chUqCzveaTUiMhyd3a20ZMkSfPnllxg9ejS+/PJLLF26FAAQHx+PvLw8AMDSpUtRVVWFxYsXIyIiAhEREbhw4YKuSjBKBTt21o0YGqGurUXBzl16qoiISIfXHPr27YvU1FTJ8s2bN2v+edu2bbo6XLtRckgpHTH8lSCg5FA2+r4Rr5+iiKjD4/QZBiZo+ZyHUMnnQYhIfxgOBia3tNSunZV27YiIdIHhYGAOw4cC/3k+5LHkcjgMH6afgoiIwHAwOJeI8TAxbfzSj4mpKVzGh+upIiIihoPBWSmc8ez8uTCxsJCOIORymFhY4Nn5c2GlaJ+38hJR28Qpu9uArr4D4L1mNQp27kLJoWwIlVWQW1nCYfgwuIwPZzAQkd4xHNoIK4Uz+r4Rz9tViahN4GklIiKSYDgQEZEEw4GIiCQYDkREJMFwICIiCYYDERFJMByIiEiC4UBERBIMByIikmA4EBGRBMOBiIgkGA5ERCTBcCAiIgmGAxERSTAciIhIguFAREQSDAciIpJgOBARkQTDgYiIJBgOREQkwXAgIiIJhgMREUkwHIiISILhQEREEgwHIiKSYDgQEZEEw4GIiCQYDkREJMFwICIiCYYDERFJMByIiEiC4UBERBIMByIikmA4EBGRBMOBiIgkGA5ERCTBcCAiIgmdhcPVq1cxefJkjB49GpMnT0Z+fr6kjSAIWLp0KUaOHImQkBCkpqbq6vBERKRDOguH9957D9HR0di7dy+io6OxePFiSZtdu3bh2rVr2LdvH7Zu3Yp169bhxo0buiqBiIh0RCfhcPv2bZw7dw5hYWEAgLCwMJw7dw537typ12737t2YNGkSTExMYG9vj5EjR+KHH37QRQlERKRDprrYSWFhIZycnCCXywEAcrkcjo6OKCwshL29fb12Li4ums8KhQIqlUqyv/LycpSXl9db1lA7IiJqHToJB11LSUnB+vXrDV0GEVGHpZNwUCgUKCoqgiAIkMvlEAQBxcXFUCgUknYFBQXw8vICIB1JPBIbG4vIyMh6y1QqFWJiYnRRLhERNUEn1xy6desGDw8PZGRkAAAyMjLg4eFR75QSAISGhiI1NRVqtRp37txBZmYmRo8eLdmfnZ0d3Nzc6v04OzvrolQiItKCzu5WWrJkCb788kuMHj0aX375JZYuXQoAiI+PR15eHgAgIiICbm5uGDVqFF5++WXMnDkTPXr00FUJRESkIzq75tC3b98Gn1vYvHmz5p/lcrkmNIiIqO3iE9JERCTBcCAiIgmGAxERSTAciIhIguFAREQSDAciIpJgOBARkQTDgYiIJBgOREQkwXAgIiIJhgMREUkwHIiISILhQEREEgwHIiKSYDgQEZEEw4GIiCQYDkREJMFwICIiCYYDERFJMByIiEiC4UBERBIMByIikmA4EBHpUFBQEI4dO4bt27fj1VdfNXQ5T4zhQEREEgwHIiKSYDgQEZEEw4GIiCQYDkREJMFwICIiCVNDF0BE1F6Joojq6up6yywsLAxUTfMwHIiIWklubi68vLzqLTt79ixMTdv+V2/br5CIyIgcOHAAAKCqKMG8nktw+I+fUFVbBUtTSwT2egG3qkrh3MnBwFU2jeFARKRjuYVnsProZtSqBQiiAACorK1C1pWjyM4/jrcHx8NH4WngKhvHC9JERDqkqijB6qObUS081ATDI4IooFp4iNVHN0NVUWKgCrXDcCAi0qGM85moVQuNtqlVC/j+QpaeKnoyDAciIh06/MdPkhHDXwmiAGV+jp4qejIMByIiHaqqrdKyXXXTjQyI4UBEpEOWppZatmvbzzswHIiIdCiw1wuQy+SNtpHL5Bja219PFT0ZhgMRkQ6FPTsSpiaNh4OpiRzj3IP1VNGTYTgQkcSuXbsQFRUFHx8fDBkyBNOmTcOJEyewbt06zJ07V9Le3d0df/zxhwEqbXucOzng7cHxsJCbS0YQcpkcFnJzvD04vs0/CMeH4Iions8++wybNm3C0qVLMWTIEJiZmeHw4cPIysqCtbW1ocszCj4KT/wr9F18fyELyvwcVNVWw9LUAkN7+2Oce3CbDwaA4UBEf3Lv3j2sXbsWy5cvx6hRozTLg4KCEBQUhHXr1hmwOuPi3MkBcb6vIM73FUOX8kR4WomINHJzc1FdXY2QkBBDl0IGxpEDEWncvXsXXbt2bXTW0B9++AGHDh3SX1FkEC0Oh8rKSixcuBBnz56FXC7H/PnzMWLECEm7zMxMbNiwAQ8fPoQoipg4cSKmTp3a0sMTkQ516dIFpaWlqK2tfWxAhIaGYtWqVfWWubu766M80qMWh8OWLVtgY2OD/fv3Iz8/HzExMdi3bx9sbGzqtXNwcMDGjRvh5OSEe/fuISoqCl5eXvDz82tpCUSkIz4+PrCwsEBmZiZCQ0MNXQ4ZUIuvOezZswevvFJ3waV3797w9PSEUqmUtOvfvz+cnJwAALa2tujbty9u3rzZ0sMTkQ7Z2trirbfewrJly5CZmYnKykrU1NQgOzsbH374oaHLIz1q8cihoKAArq6ums8KhQIqlarRbS5fvoxTp05h6dKlDa4vLy9HeXl5vWVN7ZOIdGPKlCno1q0bNmzYgLlz58LGxgbPP/88EhIScPToUUOXR3rSZDhERkaioKCgwXXHjh1r9gGLi4sxY8YMLF68WDOS+KuUlBSsX7++2fsmIt14IWgQil3ua95iVm1qiV/UFzBpyisN3qN/4cIFA1RJranJcEhLS2t0vYuLC27evAl7e3sAQGFhIfz9G54z5Pbt25gyZQqmTZuGsWPHPnafsbGxiIyMrLdMpVIhJiamqXKJqIXaw1vMqOVafM0hNDQUW7duBQDk5+cjLy8PgYGBknalpaWYMmUKYmJiMGnSpEb3aWdnBzc3t3o/zs7OLS2ViJrQXt5iRi3X4nCIi4tDeXk5QkJC8MYbb2DZsmXo1KkTAGDNmjX45ptvAACbNm1Cfn4+tm7dioiICERERGDbtm0tPTwR6VB7eYsZtZxMFEXR0EVo48aNGwgODkZWVhbc3NwMXQ5RuxS7bQ4qtXhZjZWpJVImfqSHiqilnvS7k9NnEJFGe3mLGbUcw4GINNrLW8yo5RgORKTRXt5iRi3HcCAijfbyFjNqOYYDEWm0l7eYUctxym4iqqc9vMWMWo7hQEQSxv4WM2o5nlYiIiIJhgMREUkwHIiISILhQEREEgwHIiKSYDgQEZEEw4GIiCQYDkREJMFwICIiCYYDERFJMByIiEiC4UBERBIMByIikmA4EBGRBMOBiIgkGA5ERCTBcCAiIgmGAxERSTAciIhIguFAREQSDAciIpJgOBARkQTDgYiIJBgOREQkwXDQsCS9AAAVzElEQVQgIiIJhgMREUkwHIiISILhQEREEgwHIiKSYDgQEZEEw4GIiCQYDkREJMFwICIiCYYDERFJMByIiEiC4UBERBIMByIikmA4EBGRRIvDobKyErNnz0ZISAhCQ0Nx8ODBRttXV1dj7NixiIqKaumhiYiolbQ4HLZs2QIbGxvs378fycnJePfdd3H//v3Htv/oo4/g7e3d0sMSEVEranE47NmzB6+88goAoHfv3vD09IRSqWyw7YkTJ5Cfn4+IiIiWHpaIiFqRaUt3UFBQAFdXV81nhUIBlUolaffgwQMsX74cGzduRH5+fqP7LC8vR3l5eb1lDe2TiIhaR5PhEBkZiYKCggbXHTt2TOsDffjhh4iOjoaTk1OT4ZCSkoL169drvW8iItKtJsMhLS2t0fUuLi64efMm7O3tAQCFhYXw9/eXtDt58iSUSiU2bNiA6upqlJWVITw8HLt27ZK0jY2NRWRkZL1lKpUKMTExTZVLREQ60OLTSqGhodi6dSv69euH/Px85OXl4d///rek3Z9DICcnBytXrsT27dsb3KednR3s7OxaWhoRET2hFl+QjouLQ3l5OUJCQvDGG29g2bJl6NSpEwBgzZo1+Oabb1pcJBER6VeLRw7W1tZYu3Ztg+tmzZrV4HJ/f//HjhqIiMjw+IQ0ERFJMByIiEiixaeViIj+bNeuXfjss89w9epV2NjY4Nlnn0VCQgJ+/PFHJCcnw9zcHADg6OiIwYMHIyEhAY6Ojprtk5OT8b//+78oLS2Fra0tBgwYgI8//rjBY23fvh3//Oc/YWlpCZlMhh49emD27NkYMWIEcnJyEBsbCysrK83xpk+fjokTJ7b+v4R2gOFARDrz2WefYdOmTVi6dCmGDBkCMzMzHD58GFlZWbC2tsaYMWOwatUq1NTUID8/H+vWrUNUVBS2b98OR0dHpKWlYceOHfj888/Rs2dPlJSU4MCBA40e09vbG9988w3UajW++uorzJ49G9nZ2QDqAkGpVEIURWRlZeGtt95C//798dRTT+njX4dR42klItKJe/fuYe3atVi8eDFGjRoFa2trmJmZISgoCPPnz6/X1szMDE8//TQ++ugj2Nvb47PPPgMA5OXlYciQIejZsycAwMHBAZMnT9bq+CYmJpg4cSKqqqpw/fr1eutkMhlGjhwJOzs7XLp0SQe9bf8YDkSkE7m5uaiurkZISIjW28jlcgQHB+PEiRMAgP79+2PHjh345JNPkJeXB0EQtN5XbW0tUlNTYW1tjd69e9dbp1arsX//fty7dw/PPPOM1vvsyHhaiYh04u7du+jatStMTZv3teLo6IiysjIAQEREBGQyGbZv347169fDwsICcXFxmD59+mO3//XXX+Hn5we5XI5evXohKSkJtra2AIDi4mL4+fnBxMQECoUCH374Ifr06fPknexAGA5EpBNdunRBaWkpamtrmxUQRUVF6Ny5s+bz+PHjMX78eNTU1CAzMxPz5s2Dh4cHrKysEB8fD6Bu2p7vv/8eQN1o43EP2z665kDNx9NKRKQTPj4+sLCwQGZmptbbqNVqHDx4EH5+fpJ1ZmZmGDNmDJ555hlcvHgRfn5+yM3NRW5uriYYqPVw5EBEOmFra4u33noLy5Ytg6mpKQYPHgxTU1McO3YMOTk5mltKAaCmpgbXrl3DunXrcOvWLbz++usA6m5Ntbe3h5+fH6ytrXH48GFcunQJXl5eBupVx8VwICKdmTJlCrp164YNGzZg7ty5sLGxwfPPP4+EhAQcPXoUe/bsQVZWFkRRhKOjIwICArB9+3Y4OTkBADp16oTk5GRcvnwZgiDA1dUVS5YsaXBkQa1LJoqiaOgitHHjxg0EBwcjKysLbm5uhi6HiMgoPOl3J0cORKRTqooSZJzPxOE/fkJVbRUsTS0R2OsFhD07Es6dHAxdHmmJ4UBEOpNbeAarj25GrVqAINY9o1BZW4WsK0eRnX8cbw+Oh4/C08BVkjZ4t1IDgoKC4OXlBR8fHwQEBGDhwoW4f/8+Ll68iKlTp2LgwIHw8/NDVFSU5jF9oo5OVVGC1Uc3o1p4qAmGRwRRQLXwEKuPboaqosRAFVJzcOTwGMnJyQgICEBRURHi4uKwceNG7NmzB6+++iqSk5MB1D3qbySXbIhaXcb5TNSqG3+iuVYt4PsLWYjzfUVPVdGT4sihCU5OTggMDMTFixdx48YNvPzyyzA3N4e5uTl8fX15FwXRfxz+4yfJiOGvBFGAMj9HTxVRSzAcmlBYWAilUgkPDw/06tUL8+bNQ2ZmJm7dumXo0ojalKraKi3bVbdyJaQLDIfHmDlzJvz8/BAdHY2BAwciISEBX3zxBVxdXZGYmIghQ4YgJiYG+fn5hi6VqE2wNLXUsp1FK1dCusBrDo+RlJSEgICAesucnZ2xePFiAHUjikWLFmH+/PnYunWrIUokalMCe72ArCtHGz21JJfJMbS3vx6roifFkcMTUigUiImJwe+//27oUojahLBnR8LURN5oG1MTOca5B+upImoJhoOWysrKsHbtWvzxxx9Qq9W4c+cOtm3bBm9vb0OXRtQmOHdywNuD42EhN4dcVj8k5DI5LOTmeHtwPB+EMxI8raQlMzMz3Lx5E1OmTEFpaSmsra3h7++PRYsWGbo0ojbDR+GJf4W+i+8vZEGZn4Oq2mpYmlpgaG9/jHMPZjAYEYZDAxp6Z621tTVWrlxpgGqIjItzJwfE+b7CZxmMHMOhETWlKpQd34l7Z5QQH1ZBZm4JW8+h6PzieJh1dTZ0eURErYbh8BgPLv2Cou2rIAq1wH+e+hQfVqL8VCbu5R2CU9RcWD81wMBVEhG1Dl6QbkBNqaouGGqqNcGgoRYg1lSjaPsq1JSqDFMgEVErYzg0oOz4zroRQyNEoRZlObv0VBERkX4xHBpw74xSOmL4K7WAe2c4IysRtU8MhwaID7WbI0as1q4dEZGxYTg0QGau3RwxMgvt2hERGRuGQwNsPYcCTUwDABM5bD2H6acgIiI9Yzg0oPOL4yGTN36Xr0xuis7+4XqqiIhIvxgODTDr6gynqLmQmVlIRxAmcsjMLOAUNZcPwhFRu8WH4B7D+qkBcItfjbKcXbh3JhtidRVkFpaw9RyGzv7hDAYiatcYDo0w6+qM7qHx6B4ab+hSiIj0iqeViIhIguFAREQSPK1EHVZQUBBu3boFuVwOKysrDBs2DO+++y4KCgqwYsUK5OXlQRRF9OzZE7NmzcKwYcOQk5OD2NhYWFlZAQBsbW3h4+ODuLg4eHl5GbhHRLrDcKAOLTk5GQEBASgqKkJcXBw2btyIPXv24NVXX0VycjIAaELiEUdHRyiVSoiiiKKiImzduhUxMTHYtGkTBg0aZKiuEOkUw4EIgJOTEwIDA3Hx4kXcuHEDL7/8MszNzQEAvr6+DW4jk8ng7OyMWbNmoaysDP/617+wfft2fZZN1Gp4zYEIQGFhIZRKJTw8PNCrVy/MmzcPmZmZuHXrllbbh4SE4Ny5c3jw4EErV0qkHwwH6tBmzpwJPz8/REdHY+DAgUhISMAXX3wBV1dXJCYmYsiQIYiJiUF+fn6j+3F0dIQoirh3755+CidqZTytRB1aUlISAgIC6i1zdnbG4sWLAdSNKBYtWoT58+dj69atj91PcXExZDIZbG1tW7VeIn1p8cihsrISs2fPRkhICEJDQ3Hw4MHHtv3tt98QExODsWPHYuzYscjO5vsQqG1TKBSIiYnB77//3mi7/fv347nnnoO1tbWeKiNqXS0eOWzZsgU2NjbYv38/8vPzERMTg3379sHGxqZeuwcPHuAf//gH/v3vf8Pb2xu1tbUcglObU1ZWhpSUFERERKBHjx64e/cutm3bBm9vb0lbURRRXFyM1NRUpKamYuPGjQaomKh1tDgc9uzZg8TERABA79694enpCaVSiTFjxtRrl5GRAV9fX80vmampKbp27drSwxPplJmZGW7evIkpU6agtLQU1tbW8Pf3x6JFizRtiouL4ePjA1EU0alTJwwYMAD/8z//02CAEBmrFodDQUEBXF1dNZ8VCgVUKpWk3aVLl2Bqaor4+HgUFxfj+eefx/z589G5c2dJ2/LycpSXl9db1tA+iVriwIEDqClV4daeTbh3RgnxYRVk5paYN3woOi+Y0+Dkiv7+/jh//rwBqiXSrybDITIyEgUFBQ2uO3bsmNYHEgQBx48fx7fffovu3btjxYoVSExMxIoVKyRtU1JSsH79eq33TfQkHlz6BUXbV0EUajXvDBcfVqL8VCbu5R2CU9RcWD81wMBVEhlGk+GQlpbW6HoXFxfcvHkT9vb2AOru7vD392+wnb+/PxwdHQEA4eHheOeddxrcZ2xsLCIjI+stU6lUiImJaapcIq3UlKrqgqGmWrpSLUBUCyjavgpu8as5PTt1SC2+Wyk0NFRzi19+fj7y8vIQGBgoaTdmzBjk5eWhoqICAKBUKuHu7t7gPu3s7ODm5lbvx9mZv6CkO2XHd9aNGBohCrUoy9mlp4qI2pYWh0NcXBzKy8sREhKCN954A8uWLUOnTp0AAGvWrME333wDoG7kMG3aNLzyyisIDw/H2bNnsXDhwpYenuiJ3Duj1JxKeiy1gHtneLs1dUwtviBtbW2NtWvXNrhu1qxZ9T5PmDABEyZMaOkhiVpMfFilXbtq7doRtTecPoM6JJm5pXbtLLRrR9TeMByoQ7L1HAqYyBtvZCKHrecw/RRE1MYwHKhD6vzieMjkjZ9VlclN0dk/XE8VEbUtDAfqkMy6OsMpai5kZhbSEYSJHDIzCzhFzeVtrNRhcVZW6rCsnxoAt/jVKMvZhXtnsiFWV0FmYQlbz2Ho7B/OYKAOjeFAHZpZV2d0D41H99B4Q5dC1KbwtBIREUkwHIiISILhQEREEgwHIiKSYDgQEZEEw4GIiCQYDkREJMFwICIiCYYDERFJMByIiEiC4UBERBJGM7eSINS90lGlUhm4EiIi4/HoO/PRd6i2jCYcSkpKAAAxMTEGroSIyPiUlJSgV69eWreXiaIotmI9OlNVVYUzZ85AEAT8/e9/x1dffQVn5/YzpbJKpUJMTAz7ZQTaY58A9suYNKdPgiCgpKQEnp6esLTU/rW3RjNysLS0hJ+fH27cuAEAcHZ2hpubm4Gr0j32y3i0xz4B7Jcx0bZPzRkxPMIL0kREJMFwICIiCYYDERFJyJcsWbLE0EU0l4WFBfz9/WFhYWHoUnSK/TIe7bFPAPtlTFq7T0ZztxIREekPTysREZEEw4GIiCSMIhwqKysxe/ZshISEIDQ0FAcPHnxs299++w0xMTEYO3Ysxo4di+zsbD1W2jzN6RcAVFdXY+zYsYiKitJThU9G235lZmYiKioKYWFhGDduHD799FM9V9q0q1evYvLkyRg9ejQmT56M/Px8SRtBELB06VKMHDkSISEhSE1N1X+hzaBNn5KSkjBu3DiMHz8eUVFROHz4sP4LbSZt+vXIlStX0L9/f6xcuVJ/BT4hbfu1e/duhIeHIywsDOHh4bh161bLDiwagXXr1onvvPOOKIqiePXqVTEgIECsqKiQtLt//74YFBQk5ubmiqIoijU1NeKdO3f0WmtzaNuvR1asWCEuXLhQjIyM1FeJT0Tbfp06dUpUqVSiKIpieXm5OHLkSPHnn3/Wa61Nee2118T09HRRFEUxPT1dfO211yRt0tLSxKlTp4qCIIi3b98WAwMDxevXr+u7VK1p0yelUik+ePBAFEVR/O2330RfX1+xsrJSr3U2lzb9EkVRrK2tFf/2t7+Jb7/9tpiYmKjPEp+INv06ffq0OGbMGLG4uFgUxbrfp6qqqhYd1yhGDnv27MErr7wCAOjduzc8PT2hVCol7TIyMuDr6wtvb28AgKmpKbp27arXWptD234BwIkTJ5Cfn4+IiAh9lvhEtO1X//794eTkBACwtbVF3759cfPmTb3W2pjbt2/j3LlzCAsLAwCEhYXh3LlzuHPnTr12u3fvxqRJk2BiYgJ7e3uMHDkSP/zwgyFKbpK2fQoMDISVlRUAwN3dHaIo4u7du3qvV1va9gsANm3ahOHDh6N37956rrL5tO3X559/jqlTp8LBwQFA3e9TS+9iMopwKCgogKurq+azQqFocHbWS5cuwdTUFPHx8YiIiMA777yDsrIyfZbaLNr268GDB1i+fDmWLl2qz/KemLb9+rPLly/j1KlTePHFF1u7PK0VFhbCyckJcrkcACCXy+Ho6IjCwkJJOxcXF81nbfprKNr26c/S09PRs2fPNj0vkbb9On/+PI4cOYLXX3/dAFU2n7b9unz5Mq5fv46YmBhERkZiw4YNEFt4I2qbmFspMjISBQUFDa47duyY1vsRBAHHjx/Ht99+i+7du2PFihVITEzEihUrdFVqs+iqXx9++CGio6Ph5OTU6HlUfdFVvx4pLi7GjBkzsHjxYs1IgtqGn376CWvWrGmT14Oaq6amBosWLcKKFSs0X7bthSAIuHDhAj777DM8fPgQ06ZNg4uLCyZMmPDE+2wT4ZCWltboehcXF9y8eRP29vYA6tLU39+/wXb+/v5wdHQEAISHh+Odd97RfcFa0lW/Tp48CaVSiQ0bNqC6uhplZWUIDw/Hrl27WqXupuiqX0DdsHnKlCmYNm0axo4dq/NaW0KhUKCoqAiCIEAul0MQBBQXF0OhUEjaFRQUwMvLC4B0JNGWaNsnAMjNzcW8efOwYcMG9OnTxwDVak+bfpWUlODatWuYPn06AKC8vByiKKKiogLvv/++oUpvlLb/vVxcXBAaGgpzc3OYm5sjODgYp0+fblE4GMVppdDQUGzduhUAkJ+fj7y8PAQGBkrajRkzBnl5eaioqAAAKJVKuLu767XW5tC2X7t27cKBAwdw4MABrF69Gs8884zBgkEb2vartLQUU6ZMQUxMDCZNmqTvMpvUrVs3eHh4ICMjA0DdNS0PDw9N6D0SGhqK1NRUqNVq3LlzB5mZmRg9erQhSm6Stn06ffo05syZg7Vr1+L55583RKnNok2/XFxckJOTo/ldio2Nxcsvv9xmgwHQ/r9XWFgYjhw5AlEUUVNTg+PHj+PZZ59t2cFbdDlbT+7fvy+++eab4siRI8VRo0aJ+/fv16z7+OOPxa+//lrzOS0tTRw3bpwYFhYmJiQkiCUlJYYoWSvN6dcjx48fb/N3K2nbr8TERLFfv37i+PHjNT/fffedocpu0KVLl8SXXnpJHDVqlPjSSy+Jly9fFkVRFKdNmyaePn1aFMW6u18WL14sBgcHi8HBweK3335ryJKbpE2foqKiRH9//3r/bc6fP2/IspukTb/+bO3atUZxt5I2/RIEQVy+fLkYGhoqjh07Vly+fLkoCEKLjsvpM4iISMIoTisREZF+MRyIiEiC4UBERBIMByIikmA4EBGRBMOBiIgkGA5ERCTBcCAiIon/B9vSg2jYpS5SAAAAAElFTkSuQmCC\n",
                        "text/plain": "<Figure size 432x432 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "cr_mds = MDS(n_components=2, dissimilarity='precomputed',random_state=2020, n_init=100, max_iter=1000)\ncr_results = mds.fit(cr_distmat_mm.values)\ncr_coords = cr_results.embedding_\n\nsns.set()\nsns.set_style(\"ticks\")\n\n\nfig, ax = plt.subplots(figsize=(6,6))\n\n#fig.suptitle('Portuguese Parliament Voting Records Analysis, 14th Legislature\\nMDS with Spectrum Clustering clusters, inc. Cristina Rodrigues (2D)', fontsize=14)\n#plt.title('MDS with Spectrum Clustering clusters, inc. Cristina Rodrigues (2D)')\n\nfor label, x, y in zip(cr_distmat_mm.columns, cr_coords[:, 0], cr_coords[:, 1]):\n    ax.scatter(x, y, c = \"C\"+str(cr_sc_dict[label]), s=100)\n    ax.axis('equal')\n    if label==\"PAN/CR\":\n        ax.annotate(label,xy = (x-0.10, y+0.020))\n    else:\n        ax.annotate(label,xy = (x-0.02, y+0.020))\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Como podemos observar as diferen\u00e7as s\u00e3o bastante pequenas, o que tendo em conta o (relativamente) pequeno n\u00famero de vota\u00e7\u00f5es feitas de forma individual n\u00e3o \u00e9 de estranhar, em especial quando existem tamb\u00e9m muitos casos onde s\u00e3o id\u00eanticos, e onde s\u00e3o diferentes normalmente a diferen\u00e7a \u00e9 a m\u00ednima (i.e. s\u00e3o raras situa\u00e7\u00f5es de votos A Favor/Contra, que t\u00eam maior dist\u00e2ncia):"
        },
        {
            "cell_type": "code",
            "execution_count": 96,
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Voted the same: 647\nVoted differently: 108\n"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x432 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "cr_pan_comp = votes[[ 'PAN','PAN/CR','Cristina Rodrigues (Ninsc)']]\ncr_pan_comp = cr_pan_comp.dropna().replace([\"A Favor\", \"Contra\", \"Absten\u00e7\u00e3o\", \"Aus\u00eancia\"], [1,-1,0,0])\nvoting_palette = [\"#FB6962\",\"#FCFC99\",\"#79DE79\", \"black\"]\ncr_pan_diff = cr_pan_comp[[\"PAN\",\"Cristina Rodrigues (Ninsc)\"]].dropna()[cr_pan_comp[\"PAN\"] != cr_pan_comp[\"Cristina Rodrigues (Ninsc)\"]]\ncr_pan_same= cr_pan_comp[[\"PAN\",\"Cristina Rodrigues (Ninsc)\"]].dropna()[cr_pan_comp[\"PAN\"] == cr_pan_comp[\"Cristina Rodrigues (Ninsc)\"]]\n\nprint(\"Voted the same:\", cr_pan_same.shape[0])\nprint(\"Voted differently:\",cr_pan_diff.shape[0])\n\nfig, ax = plt.subplots(figsize=(6,6))\nsns.set(color_codes=True)\n\n## Build a heatmap of the vote when they diverged\n#fig.suptitle('Portuguese Parliament Voting Records Analysis, 14th Legislature', fontsize=14)\n#ax.set_title('PAN and Cristina Rodrigues voting record (only where they diverge)')\nsns.heatmap(cr_pan_diff.astype(float) , cbar=False,yticklabels = True, cmap=sns.color_palette(palette=[\"#FB6962\",\"#FCFC99\",\"#79DE79\"]), linewidth=1)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Diferen\u00e7as entre Actividades e Iniciativas\n\nA combina\u00e7\u00e3o de votos em Actividades e Iniciativas resulta na matriz de dist\u00e2ncia que ser\u00e1 usada para as restantes an\u00e1lises; recuperando o *clustermap* para refer\u00eancia:"
        },
        {
            "cell_type": "code",
            "execution_count": 97,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<Figure size 576x576 with 0 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x576 with 4 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "## Display the heatmap of the distance matrix\n\nfig = plt.figure(figsize=(8,8))\nax = fig.add_subplot()\n\n## Perform hierarchical linkage on the distance matrix using Ward's method.\ndistmat_link = hc.linkage(pwdist, method=\"ward\", optimal_ordering=True )\n\nsns.clustermap(\n    distmat,\n    annot = True,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    #standard_scale=1,\n    row_linkage=distmat_link,\n    col_linkage=distmat_link,\n    figsize=(8,8)).fig.suptitle('Portuguese Parliament 14th Legislature, Clustermap')\n\nplt.show()\n\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "e tamb\u00e9m o MDS correspondente:"
        },
        {
            "cell_type": "code",
            "execution_count": 98,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x576 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "from sklearn.manifold import MDS\n\nmds = MDS(n_components=2, dissimilarity='precomputed',random_state=2020, n_init=100, max_iter=1000)\n\n## We use the normalised distance matrix but results would\n## be similar with the original one, just with a different scale/axis\nresults = mds.fit(distmat_mm.values)\ncoords = results.embedding_\ncoords\n\n## Graphic options\nsns.set()\nsns.set_style(\"ticks\")\n\nfig, ax = plt.subplots(figsize=(8,8))\n\nplt.title('Portuguese Parliament Voting Records Analysis, 14th Legislature', fontsize=14)\n\nfor label, x, y in zip(distmat_mm.columns, coords[:, 0], coords[:, 1]):\n    ax.scatter(x, y, s=250)\n    ax.axis('equal')\n    ax.annotate(label,xy = (x-0.02, y+0.025))\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Uma quest\u00e3o que pode ser colocada \u00e9 se existem diferen\u00e7as entre as vota\u00e7\u00f5es em Iniciativas e Actividades: as Actividades s\u00e3o em menor n\u00famero (pelo que o seu peso ser\u00e1 sempre menor quando somado \u00e0s iniciativas) mas inclui votos sobre tem\u00e1ticas que podem ter uma dimens\u00e3o diferente: votos de pesar, votos de condena\u00e7\u00e3o e outro tipo de actividades que t\u00eam desde logo uma poss\u00edvel dimens\u00e3o internacional mais vincada e s\u00e3o formas de demonstra\u00e7\u00e3o de posi\u00e7\u00f5es ideol\u00f3gicas sobre acontecimentos nacionais e internacionais.\n\nRefira-se que todas as vota\u00e7\u00f5es s\u00e3o vota\u00e7\u00f5es pol\u00edticas: o votar contra ou a favor de um voto de pesar ou condena\u00e7\u00e3o demonstra uma avalia\u00e7\u00e3o pol\u00edtica sobre a pessoa, o acontecimento e/ou o pr\u00f3prio texto em vota\u00e7\u00e3o, texto esse que inclui ele pr\u00f3prio considera\u00e7\u00f5es pol\u00edticas e ideol\u00f3gicas. Existem muitas vota\u00e7\u00f5es por unanimidade nos casos de votos de pesar e congratula\u00e7\u00e3o, mas existem tamb\u00e9m muitos outros com vota\u00e7\u00f5es divididas, n\u00e3o sendo incomum vota\u00e7\u00f5es sobre o mesmo tema com vota\u00e7\u00f5es opostas, dada a forma como um mesmo acontecimento \u00e9 descrito e apresentado.\n\nO seguinte bloco apresentea o _clustermap_ para cada uma das fontes de vota\u00e7\u00e3o:"
        },
        {
            "cell_type": "code",
            "execution_count": 99,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<Figure size 576x576 with 0 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x576 with 4 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x576 with 4 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "votes_ini_hm = ini_df[['BE', 'PCP', 'PEV', 'L/JKM', 'PS', 'PAN','PSD','IL','CDS-PP', 'CH']]\nvotes_ini_hmn = votes_ini_hm.replace([\"A Favor\", \"Contra\", \"Absten\u00e7\u00e3o\", \"Aus\u00eancia\"], [1,-1,0,0]).fillna(0)\n\nvotes_act_hm = act_df[['BE', 'PCP', 'PEV', 'L/JKM', 'PS', 'PAN','PSD','IL','CDS-PP', 'CH']]\nvotes_act_hmn = votes_act_hm.replace([\"A Favor\", \"Contra\", \"Absten\u00e7\u00e3o\", \"Aus\u00eancia\"], [1,-1,0,0]).fillna(0)\n\n## Transpose the dataframe used for the heatmap\nvotes_ini_t = votes_ini_hmn.transpose()\nvotes_act_t = votes_act_hmn.transpose()\n\n## Determine the Eucledian pairwise distance\n## (\"euclidean\" is actually the default option)\npwdist_ini = pdist(votes_ini_t, metric='euclidean')\npwdist_act = pdist(votes_act_t, metric='euclidean')\n\n## Create the distance matrix\ndistmat_ini = pd.DataFrame(\n    squareform(pwdist_ini), # pass a symmetric distance matrix\n    columns = votes_ini_t.index,\n    index = votes_ini_t.index\n)\n\ndistmat_act = pd.DataFrame(\n    squareform(pwdist_act), # pass a symmetric distance matrix\n    columns = votes_act_t.index,\n    index = votes_act_t.index\n)\n\n## Display the heatmap of the distance matrix\n\nfig = plt.figure(figsize=(8,8))\nax = fig.add_subplot()\n\n\n## Perform hierarchical linkage on the distance matrix using Ward's method.\ndistmat_ini_link = hc.linkage(pwdist_ini, method=\"ward\", optimal_ordering=True )\ndistmat_act_link = hc.linkage(pwdist_act, method=\"ward\", optimal_ordering=True )\n\nsns.clustermap(\n    distmat_ini,\n    annot = True,\n    cmap=sns.color_palette(\"Purples_r\"),\n    linewidth=1,\n    #standard_scale=1,\n    row_linkage=distmat_ini_link,\n    col_linkage=distmat_ini_link,\n    figsize=(8,8)).fig.suptitle('Portuguese Parliament 14th Legislature, Clustermap (Initiatives Only)')\n\nsns.clustermap(\n    distmat_act,\n    annot = True,\n    cmap=sns.color_palette(\"Blues_r\"),\n    linewidth=1,\n    #standard_scale=1,\n    row_linkage=distmat_act_link,\n    col_linkage=distmat_act_link,\n    figsize=(8,8)).fig.suptitle('Portuguese Parliament 14th Legislature, Clustermap (Activities Only)')\n\n\nplt.show()\n\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Existem diferen\u00e7as, em particular na an\u00e1lise que apenas inclui as Atividades: em particular o PS aparence agora agrupado \"\u00e0 esquerda\" (separando-se antes do PAN, sendo o restante semelhante), e isto implica a rearruma\u00e7\u00e3o \"\u00e0 direita\", com a cria\u00e7\u00e3o de dois grupos (CDS-PP e CH, PSD e IL).\n\nEstas diferen\u00e7as podem tamb\u00e9m ser vistas no MDS de cada tipo de vota\u00e7\u00e3o:"
        },
        {
            "cell_type": "code",
            "execution_count": 100,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x576 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x576 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "from sklearn.manifold import MDS\nimport numpy as np\nmds_act = MDS(n_components=2, dissimilarity='precomputed',random_state=2020, n_init=100, max_iter=1000)\nmds_ini = MDS(n_components=2, dissimilarity='precomputed',random_state=2020, n_init=100, max_iter=1000)\n\ndistmat_act_mm=((distmat_act-distmat_act.min().min())/(distmat_act.max().max()-distmat_act.min().min()))*1\ndistmat_ini_mm=((distmat_ini-distmat_ini.min().min())/(distmat_ini.max().max()-distmat_ini.min().min()))*1\n\n## We use the normalised distance matrix but results would\n## be similar with the original one, just with a different scale/axis\nresults_act = mds_act.fit(distmat_act_mm.values)\ncoords_act = results_act.embedding_\n\nresults_ini = mds_ini.fit(distmat_ini_mm.values)\ncoords_ini= results_ini.embedding_\n\n## Graphic options\nsns.set()\nsns.set_style(\"ticks\")\n\nfig, ax = plt.subplots(figsize=(8,8))\n\nplt.title('Portuguese Parliament Voting Records Analysis, 14th Legislature (Initiatives)', fontsize=14)\n\nfor label, x, y in zip(distmat_ini_mm.columns, coords_ini[:, 0], coords_ini[:, 1]):\n    ax.scatter(x, y, s=250)\n    ax.axis('equal')\n    ax.annotate(label,xy = (x-0.02, y+0.025))\nplt.show()\n\nfig, ax = plt.subplots(figsize=(8,8))\n\nplt.title('Portuguese Parliament Voting Records Analysis, 14th Legislature (Activities)', fontsize=14)\n\nfor label, x, y in zip(distmat_act_mm.columns, coords_act[:, 0], coords_act[:, 1]):\n    ax.scatter(x, y, s=250)\n    ax.axis('equal')\n    ax.annotate(label,xy = (x-0.02, y+0.025))\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Se o \"mapa\" das Iniciativas \u00e9 muito semelhante ao global, j\u00e1 o das Actividades apresenta diferen\u00e7as (como seria de esperar tendo em conta o mapa de dist\u00e2ncias e dendograma), e quando comparado com o global:\n\n- IL mais pr\u00f3xima do PSD do que dos restantes\n- PCP+PEV mais distante de BE/L(JKM)/PAN\n- PS mais distante de PSD e mais pr\u00f3ximo dos partidos \"\u00e0 esquerda\".\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Utiliza\u00e7\u00e3o apenas das vota\u00e7\u00f5es da deputada Cristina Rodrigues enquanto deputada n\u00e3o-inscrita\n\nSe reduzirmos a an\u00e1lise apenas \u00e0s vota\u00e7\u00f5es onde a deputada Cristina Rodrigues participou como deputda n\u00e3o inscrita temos o seguinte resultado, que n\u00e3o altera de forma substancial o que j\u00e1 foi apresentado:"
        },
        {
            "cell_type": "code",
            "execution_count": 101,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x432 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "cr_only_votes_hm = votes[['BE', 'PCP', 'PEV', 'L/JKM', 'PS', 'PAN',\"Cristina Rodrigues (Ninsc)\",'PSD','IL','CDS-PP', 'CH']]\ncr_only_votes_hmn = cr_only_votes_hm.replace([\"A Favor\", \"Contra\", \"Absten\u00e7\u00e3o\", \"Aus\u00eancia\"], [1,-1,0,0]).dropna()\n\n## Transpose the dataframe used for the heatmap\ncr_only_votes_t = cr_only_votes_hmn.transpose()\n\n## Determine the Eucledian pairwise distance\n## (\"euclidean\" is actually the default option)\ncr_only_pwdist = pdist(cr_only_votes_t, metric='euclidean')\n\n## Create a square dataframe with the pairwise distances:\n## the distance matrix\ncr_only_distmat = pd.DataFrame(\n    squareform(cr_only_pwdist), # pass a symmetric distance matrix\n    columns = cr_only_votes_t.index,\n    index = cr_only_votes_t.index\n)\ncr_only_distmat_link = hc.linkage(cr_only_pwdist, method=\"ward\", optimal_ordering=True )\n\n## Normalise\ncr_only_distmat_mm=((cr_only_distmat-cr_only_distmat.min().min())/(cr_only_distmat.max().max()-cr_only_distmat.min().min()))*1\n\n\n## SC\n\ncr_only_affinmat_mm = pd.DataFrame(1-cr_only_distmat_mm, cr_only_distmat.index, cr_only_distmat.columns)\ncr_only_sc = SpectralClustering(5, affinity=\"precomputed\",random_state=2020).fit_predict(cr_only_affinmat_mm)\ncr_only_sc_dict = dict(zip(cr_only_distmat,cr_only_sc))\ncr_only_sc_dict \n\n## MDS\ncr_only_mds = MDS(n_components=2, dissimilarity='precomputed',random_state=2020, n_init=100, max_iter=1000)\ncr_only_results = mds.fit(cr_only_distmat_mm.values)\ncr_only_coords = cr_only_results.embedding_\n\n## Plot it\nsns.set()\nsns.set_style(\"ticks\")\n\nfig, ax = plt.subplots(figsize=(6,6))\n\nfig.suptitle('Portuguese Parliament Voting Records Analysis, 14th Legislature', fontsize=14)\nax.set_title('MDS with Spectrum Clustering clusters, inc. Cristina Rodrigues (2D)')\n\nfor label, x, y in zip(cr_only_distmat_mm.columns, cr_only_coords[:, 0], cr_only_coords[:, 1]):\n    ax.scatter(x, y, c = \"C\"+str(cr_only_sc_dict[label]), s=100)\n    #ax.scatter(x, y, s=100)\n    ax.axis('equal')\n    if label==\"PAN/CR\":\n        ax.annotate(label,xy = (x-0.10, y+0.020))\n    else:\n        ax.annotate(label,xy = (x-0.02, y+0.020))\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "... e a respectiva matriz de dist\u00e2ncia:"
        },
        {
            "cell_type": "code",
            "execution_count": 102,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x576 with 4 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "sns.clustermap(\n    cr_only_distmat,\n    annot = True,\n    cmap=sns.color_palette(\"Reds_r\"),\n    linewidth=1,\n    #standard_scale=1,\n    row_linkage=cr_only_distmat_link,\n    col_linkage=cr_only_distmat_link,\n    figsize=(8,8)\n)\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "---\n***2020, Frederico Mu\u00f1oz***"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        }
    ],
    "metadata": {
        "celltoolbar": "Raw Cell Format",
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                    "language": "pt-pt",
                    "note": "Section: Legislativas 2019",
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                        "year": 2020
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                    "note": "Section: Destaque",
                    "title": "O Livre procurou o Bloco. E \u00e9 mais o que os une do que o que os separa",
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                },
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                    "author": [
                        {
                            "family": "Krilavi\u010dius",
                            "given": "Tomas"
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                        {
                            "family": "\u017dilinskas",
                            "given": "Antanas"
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                    "title": "On structural analysis of parliamentarian voting data",
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                            "family": "Wall",
                            "given": "Matthew"
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                        {
                            "family": "Krouwel",
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                    "title": "Political Matchmakers: How do the Decision Rules Employed by Vote Advice Application Sites Influence Their Advice?",
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                    "URL": "https://www.dn.pt/poder/nova-legislatura-estao-distribuidas-para-ja-as-cadeiras-no-parlamento-11412255.html",
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                        "day": 25,
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                    "URL": "https://www.parlamento.pt/Cidadania/Paginas/DadosAbertos.aspx",
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                        "day": 17,
                        "month": 8,
                        "year": 2020
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                    "id": "1084038/FCD25MJX",
                    "title": "Dados abertos",
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                        "day": 1,
                        "month": 9,
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                    "title": "Spectral Clustering. Foundation and Application | by William Fleshman | Towards Data Science",
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                        "day": 5,
                        "month": 9,
                        "year": 2020
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                        "day": 3,
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                    "container-title": "DataCamp Community",
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                        "day": 3,
                        "month": 8,
                        "year": 2018
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                    "title": "DBSCAN: Macroscopic Investigation Python",
                    "title-short": "DBSCAN",
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                },
                "1084038/PHFRITJQ": {
                    "author": [
                        {
                            "family": "Wofford",
                            "given": "Morgan F."
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                        {
                            "family": "Boscoe",
                            "given": "Bernadette M."
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                        {
                            "family": "Borgman",
                            "given": "Christine L."
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                        {
                            "family": "Pasquetto",
                            "given": "Irene V."
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                        {
                            "family": "Golshan",
                            "given": "Milena S."
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                    "id": "1084038/PHFRITJQ",
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                    "issued": {
                        "year": 2019
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                    "note": "ISBN: 1521-9615\nPublisher: IEEE",
                    "page": "5-15",
                    "page-first": "5",
                    "title": "Jupyter notebooks as discovery mechanisms for open science: Citation practices in the astronomy community",
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                    "volume": "22"
                },
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                    "URL": "https://expresso.pt/europeias-2019/2019-05-27-A-historia-do-partido-que-diz-que-nao-e-de-direita-nem-de-esquerda-o-PAN",
                    "accessed": {
                        "day": 20,
                        "month": 9,
                        "year": 2020
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                    "id": "1084038/PIPADUKP",
                    "title": "Expresso | A hist\u00f3ria do partido que diz que n\u00e3o \u00e9 de direita nem de esquerda: o PAN",
                    "type": "article"
                },
                "1084038/PKVBVL6G": {
                    "URL": "https://www.publico.pt/2019/10/24/politica/noticia/pcp-pev-confirmam-convergencia-priorizam-creches-salarios-ambiente-ferrovia-1891225",
                    "abstract": "O aumento do sal\u00e1rio m\u00ednimo nacional para 850 euros ser\u00e1 uma das iniciativas da bancada do PCP na Assembleia da Rep\u00fablica.",
                    "accessed": {
                        "day": 20,
                        "month": 9,
                        "year": 2020
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                    "author": [
                        {
                            "family": "Lusa",
                            "given": ""
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                    ],
                    "container-title": "P\u00daBLICO",
                    "id": "1084038/PKVBVL6G",
                    "language": "pt",
                    "title": "PCP e PEV confirmam converg\u00eancia e priorizam creches, sal\u00e1rios, ambiente e ferrovia",
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                },
                "1084038/Q73WGWS2": {
                    "author": [
                        {
                            "family": "Jackson",
                            "given": "Robert J."
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                    ],
                    "issued": {
                        "date-parts": [
                            [
                                1968
                            ]
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                    "publisher": "Macmillan",
                    "title": "Rebels and whips: An analysis of dissension, discipline and cohesion in British political parties",
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                        "day": 4,
                        "month": 9,
                        "year": 2020
                    },
                    "id": "1084038/SCM5KUDM",
                    "title": "python - Matplotlib: Annotating a 3D scatter plot - Stack Overflow",
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                },
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                    "URL": "https://pixiedust.github.io/pixiedust/",
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                        "day": 5,
                        "month": 9,
                        "year": 2020
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                    "id": "1084038/TFZA6PDD",
                    "title": "Welcome to PixieDust \u2014 PixieDust Documentation",
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                "1084038/U8ZVZX2Z": {
                    "URL": "https://blog.ldodds.com/2020/01/31/do-data-scientists-spend-80-of-their-time-cleaning-data-turns-out-no/",
                    "abstract": "It\u2019s hard to read an article about data science or really anything that involves creating something useful from data these days without tripping over this factoid, or some variant of it: Data\u2026",
                    "accessed": {
                        "date-parts": [
                            [
                                2020,
                                8,
                                20
                            ]
                        ]
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                    "author": [
                        {
                            "family": "Dodds",
                            "given": "Leigh"
                        }
                    ],
                    "container-title": "Lost Boy",
                    "issued": {
                        "date-parts": [
                            [
                                2020,
                                1,
                                31
                            ]
                        ]
                    },
                    "language": "en",
                    "shortTitle": "Do data scientists spend 80% of their time cleaning data?",
                    "title": "Do data scientists spend 80% of their time cleaning data? Turns out, no?",
                    "type": "webpage"
                },
                "1084038/V4KQQ4DV": {
                    "URL": "https://www.publico.pt/2020/06/26/politica/noticia/quadro-tempos-votacoes-ar-ja-distingue-duas-deputadas-naoinscritas-1922017",
                    "abstract": "A Assembleia da Rep\u00fablica tem agora duas deputadas n\u00e3o-inscritas: Joacine Katar Moreira, eleita pelo Livre, e Cristina Rodrigues, eleita pelo PAN. Enquanto o Livre perdeu a sua representa\u00e7\u00e3o parlamentar, o PAN continua com tr\u00eas deputados.",
                    "accessed": {
                        "date-parts": [
                            [
                                2020,
                                8,
                                23
                            ]
                        ]
                    },
                    "author": [
                        {
                            "family": "Lusa",
                            "given": ""
                        }
                    ],
                    "container-title": "P\u00daBLICO",
                    "language": "pt",
                    "title": "Quadro de tempos e vota\u00e7\u00f5es da AR j\u00e1 distingue as duas deputadas n\u00e3o-inscritas",
                    "type": "webpage"
                },
                "1084038/VE79JPSR": {
                    "author": [
                        {
                            "family": "Figueiredo Filho",
                            "given": "Dalson Britto"
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                        {
                            "family": "da Rocha",
                            "given": "Enivaldo Carvalho"
                        },
                        {
                            "family": "da Silva J\u00fanior",
                            "given": "Jos\u00e9 Alexandre"
                        },
                        {
                            "family": "Paranhos",
                            "given": "Ranulfo"
                        },
                        {
                            "family": "da Silva",
                            "given": "Mariana Batista"
                        },
                        {
                            "family": "Duarte",
                            "given": "B\u00e1rbara Sofia F\u00e9lix"
                        }
                    ],
                    "container-title": "Applied Mathematics",
                    "id": "1084038/VE79JPSR",
                    "issued": {
                        "year": 2014
                    },
                    "note": "ISBN: 2152-7393\nPublisher: Scientific Research Publishing",
                    "title": "Cluster analysis for political scientists",
                    "type": "article-journal",
                    "volume": "2014"
                },
                "1084038/VHQVIPYJ": {
                    "URL": "https://dataplatform.cloud.ibm.com/home2?context=cpdaas",
                    "accessed": {
                        "day": 20,
                        "month": 9,
                        "year": 2020
                    },
                    "id": "1084038/VHQVIPYJ",
                    "title": "IBM Cloud Pak for Data",
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                        {
                            "family": "Pinto",
                            "given": "Alba Milagro"
                        }
                    ],
                    "container-title": "Brocar: Cuadernos de investigaci\u00f3n hist\u00f3rica",
                    "id": "1084038/VMMSZY72",
                    "issue": "33",
                    "issued": {
                        "year": 2009
                    },
                    "note": "ISBN: 1885-8309\nPublisher: Universidad de La Rioja",
                    "page": "195-224",
                    "page-first": "195",
                    "title": "Ortega y Gasset y la cr\u00edtica de la raz\u00f3n cient\u00edfica",
                    "type": "article-journal"
                },
                "1084038/W4VE9F5F": {
                    "URL": "https://www.jstor.org/stable/3694286",
                    "abstract": "We investigate the dimensionality of politics in the European Parliament by applying a scaling method to all roll-call votes between 1979 and 2001 in the European Parliament. Contrary to most existing studies using these methods, we are able to interpret the substantive content of the observed dimensions using exogenous measures of national party policy positions. We find that the main dimension of politics in the European Union's only elected institution is the classic left-right dimension found in domestic politics. A second dimension is also present, although to a lesser extent, which captures government-opposition conflicts as well as national and European party positions on European integration.",
                    "accessed": {
                        "day": 28,
                        "month": 8,
                        "year": 2020
                    },
                    "archive": "JSTOR",
                    "author": [
                        {
                            "family": "Hix",
                            "given": "Simon"
                        },
                        {
                            "family": "Noury",
                            "given": "Abdul"
                        },
                        {
                            "family": "Roland",
                            "given": "G\u00e9rard"
                        }
                    ],
                    "container-title": "American Journal of Political Science",
                    "id": "1084038/W4VE9F5F",
                    "issue": "2",
                    "issued": {
                        "year": 2006
                    },
                    "note": "Publisher: [Midwest Political Science Association, Wiley]",
                    "page": "494-511",
                    "page-first": "494",
                    "title": "Dimensions of Politics in the European Parliament",
                    "type": "article-journal",
                    "volume": "50"
                },
                "1084038/XJ2HB922": {
                    "author": [
                        {
                            "family": "Carvalho",
                            "given": "Alexandre Xavier Ywata"
                        },
                        {
                            "family": "Albuquerque",
                            "given": "Pedro Henrique Melo"
                        },
                        {
                            "family": "de Almeida Junior",
                            "given": "Gilberto Rezende"
                        },
                        {
                            "family": "Guimaraes",
                            "given": "Rafael Dantas"
                        }
                    ],
                    "container-title": "Revista Brasileira de Biometria",
                    "id": "1084038/XJ2HB922",
                    "issue": "3",
                    "issued": {
                        "year": 2009
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                    "page": "411-442",
                    "page-first": "411",
                    "title": "Spatial hierarchical clustering",
                    "type": "article-journal",
                    "volume": "27"
                },
                "1084038/Z5UKFKL2": {
                    "URL": "https://jornaleconomico.sapo.pt/noticias/cds-explica-contestacao-ao-lugar-do-chega-no-parlamento-andre-ventura-pode-cansar-se-e-dizer-olhe-basta-chega-504766",
                    "abstract": "Os democratas-crist\u00e3os dizem que o acesso ao lugar onde vai ficar sentado Andr\u00e9 Ventura \u00e9 \"muito estreito\"\u00a0e vai exigir que os centristas\u00a0saiam do lugar para o deixar passar. Telmo Correia argumenta que Andr\u00e9 Ventura \"pode cansar-se e dizer 'olhe, basta, chega'\" aos deputados do CDS.",
                    "accessed": {
                        "day": 17,
                        "month": 8,
                        "year": 2020
                    },
                    "author": [
                        {
                            "family": "Almeida",
                            "given": "Joana"
                        }
                    ],
                    "container-title": "O Jornal Econ\u00f3mico",
                    "id": "1084038/Z5UKFKL2",
                    "issued": {
                        "day": 23,
                        "month": 10,
                        "year": 2019
                    },
                    "language": "pt-PT",
                    "note": "Section: Pol\u00edtica",
                    "shortTitle": "CDS explica contesta\u00e7\u00e3o ao lugar do Chega no Parlamento",
                    "title": "CDS explica contesta\u00e7\u00e3o ao lugar do Chega no Parlamento: \"Andr\u00e9 Ventura pode cansar-se e dizer 'olhe, basta, chega'\"",
                    "title-short": "CDS explica contesta\u00e7\u00e3o ao lugar do Chega no Parlamento",
                    "type": "webpage"
                },
                "1084038/ZF7SWZB7": {
                    "URL": "https://www.publico.pt/1999/10/26/jornal/guerra-de-lugares-125525",
                    "abstract": "O Bloco de Esquerda conseguiu marcar a agenda parlamentar assim que a Assembleia da Rep\u00fablica tomou posse - ou melhor, enquanto a AR tomava posse. Ontem, os seus dois deputados - Francisco Lou\u00e7\u00e3 e Lu\u00eds Fazenda - estiveram toda a sess\u00e3o da manh\u00e3 em p",
                    "accessed": {
                        "day": 17,
                        "month": 8,
                        "year": 2020
                    },
                    "author": [
                        {
                            "family": "Louren\u00e7o",
                            "given": "Eunice"
                        }
                    ],
                    "container-title": "P\u00daBLICO",
                    "id": "1084038/ZF7SWZB7",
                    "language": "pt",
                    "title": "Guerra de lugares",
                    "type": "webpage"
                }
            }
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