projeto2_squadAM_oficial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  (Squad AM) Projeto Oficial \n",
    "\n",
    "### Trens e Metrôs de SP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando Bibliotecas\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importando o csv em um Pandas DataFrame\n",
    "dados = pd.read_csv('csvs/metroetrem_sp_comlinks.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 197 entries, 0 to 17\n",
      "Data columns (total 6 columns):\n",
      "num_lin        197 non-null int64\n",
      "nome_lin       197 non-null object\n",
      "long           197 non-null int64\n",
      "lat            197 non-null int64\n",
      "estacao_upp    197 non-null object\n",
      "link           197 non-null object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 10.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# visão geral do DF\n",
    "dados.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>num_lin</th>\n",
       "      <th>nome_lin</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>estacao_upp</th>\n",
       "      <th>link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>AMARELA</td>\n",
       "      <td>-46723768</td>\n",
       "      <td>-23586440</td>\n",
       "      <td>SAO PAULO MORUMBI</td>\n",
       "      <td>BUTANTA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>AMARELA</td>\n",
       "      <td>-46708157</td>\n",
       "      <td>-23571857</td>\n",
       "      <td>BUTANTA</td>\n",
       "      <td>PINHEIROS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>AMARELA</td>\n",
       "      <td>-46701610</td>\n",
       "      <td>-23567392</td>\n",
       "      <td>PINHEIROS</td>\n",
       "      <td>FARIA LIMA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>AMARELA</td>\n",
       "      <td>-46694102</td>\n",
       "      <td>-23567269</td>\n",
       "      <td>FARIA LIMA</td>\n",
       "      <td>FRADIQUE COUTINHO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>AMARELA</td>\n",
       "      <td>-46684259</td>\n",
       "      <td>-23566228</td>\n",
       "      <td>FRADIQUE COUTINHO</td>\n",
       "      <td>OSCAR FREIRE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_lin nome_lin      long       lat        estacao_upp               link\n",
       "0        4  AMARELA -46723768 -23586440  SAO PAULO MORUMBI            BUTANTA\n",
       "1        4  AMARELA -46708157 -23571857            BUTANTA          PINHEIROS\n",
       "2        4  AMARELA -46701610 -23567392          PINHEIROS         FARIA LIMA\n",
       "3        4  AMARELA -46694102 -23567269         FARIA LIMA  FRADIQUE COUTINHO\n",
       "4        4  AMARELA -46684259 -23566228  FRADIQUE COUTINHO       OSCAR FREIRE"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# primeiras cinco linhas do DF\n",
    "dados.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gerando os Grafos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "grafos_dict = dict()\n",
    "# dicionário para armazenar os grafos por linha de metro/trem\n",
    "\n",
    "for linha in dados['nome_lin'].unique():\n",
    "    # para cada linha (ex: amarela, verde, etc...)\n",
    "    \n",
    "    G = nx.Graph()\n",
    "    df_temp = dados[dados['nome_lin'] == linha]\n",
    "    # dataframe temporário exclusivo para cada linha de metro/trem\n",
    "    \n",
    "    for id, row in df_temp.iterrows():\n",
    "        G.add_node(row['estacao_upp'], location=(row['long'], row['lat']))\n",
    "        # add nodes com informação de longitude e latitude\n",
    "    \n",
    "    for id,row in df_temp.iterrows():\n",
    "        G.add_edge(row['estacao_upp'], row['link'], linha=row['nome_lin'])\n",
    "        # add vértices com informação do nome da linha\n",
    "    \n",
    "    grafos_dict[linha] = G\n",
    "    # adicionando cada grafo no dicionário (grafos_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['AMARELA', 'AZUL', 'CORAL', 'DIAMANTE', 'ESMERALDA', 'JADE', 'LILAS', 'PRATA', 'RUBI', 'SAFIRA', 'TURQUESA', 'VERDE', 'VERMELHA'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checando as chaves do dicionário pra ver se o processo deu certo\n",
    "grafos_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotando os Grafos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linha_target = 'AMARELA'\n",
    "# variável para deixar o código mais genérico\n",
    "\n",
    "nx.draw_networkx(grafos_dict[linha_target], node_color='yellow')\n",
    "# plotando o grafo\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [16, 8]\n",
    "# escolhendo o tamanho do grafo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotar o Grafo Completo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pesos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Função de Percurso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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