python_parser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     Source  Target        Type   Id  Label  timeset  Weight\n",
       "0         0      12  Undirected    0    NaN      NaN    32.8\n",
       "1        12       1  Undirected    1    NaN      NaN    17.0\n",
       "2        12      13  Undirected    2    NaN      NaN    15.3\n",
       "3         0      13  Undirected    3    NaN      NaN    38.0\n",
       "4         2      13  Undirected    4    NaN      NaN    18.8\n",
       "..      ...     ...         ...  ...    ...      ...     ...\n",
       "331      95     111  Undirected  339    NaN      NaN     8.0\n",
       "332     121     111  Undirected  340    NaN      NaN     8.0\n",
       "333      86      96  Undirected  341    NaN      NaN     8.0\n",
       "334     110      96  Undirected  342    NaN      NaN     8.0\n",
       "335     110     122  Undirected  343    NaN      NaN     8.0\n",
       "\n",
       "[336 rows x 7 columns]"
      ],
      "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>Source</th>\n      <th>Target</th>\n      <th>Type</th>\n      <th>Id</th>\n      <th>Label</th>\n      <th>timeset</th>\n      <th>Weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>12</td>\n      <td>Undirected</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>32.8</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>12</td>\n      <td>1</td>\n      <td>Undirected</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>17.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>12</td>\n      <td>13</td>\n      <td>Undirected</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>15.3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>13</td>\n      <td>Undirected</td>\n      <td>3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>38.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>13</td>\n      <td>Undirected</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>18.8</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>331</th>\n      <td>95</td>\n      <td>111</td>\n      <td>Undirected</td>\n      <td>339</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>332</th>\n      <td>121</td>\n      <td>111</td>\n      <td>Undirected</td>\n      <td>340</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>333</th>\n      <td>86</td>\n      <td>96</td>\n      <td>Undirected</td>\n      <td>341</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>334</th>\n      <td>110</td>\n      <td>96</td>\n      <td>Undirected</td>\n      <td>342</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>335</th>\n      <td>110</td>\n      <td>122</td>\n      <td>Undirected</td>\n      <td>343</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>336 rows × 7 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv('edges_completo.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    " lista_edges = [\"{{source}} {{target}} {'weight': {{weight}}}\".replace(\"{{source}}\", str(v[0])).replace(\"{{target}}\", str(v[1])).replace(\"{{weight}}\", str(v[6])) for v in df.values.tolist()]\n",
    " lista_edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
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       " (43, 92, {'weight': 3.0})]"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "G = nx.parse_edgelist(lista_edges, nodetype=int)\n",
    "list(G.edges(data=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "nx.draw(G, node_color='r', edge_color='b')   # default spring_layout\n",
    "# plt.subplot(122)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Id                     Label\n",
       "0      0     sbc_alfa1_entrada_sul\n",
       "1      1   sbc_delta_entrada_leste\n",
       "2      2    sbc_beta_entrada_oeste\n",
       "3      3      sbc_ru_entrada norte\n",
       "4      4     sbc_delta_entrada_sul\n",
       "..   ...                       ...\n",
       "120  120     laboratorio_alfa1_305\n",
       "121  121  escada_alfa1_esquerda_3a\n",
       "122  122   escada_alfa1_direita_3a\n",
       "123  123     laboratorio_alfa1_306\n",
       "124  124     laboratorio_alfa1_304\n",
       "\n",
       "[125 rows x 2 columns]"
      ],
      "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>Label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>sbc_alfa1_entrada_sul</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>sbc_delta_entrada_leste</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>sbc_beta_entrada_oeste</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>sbc_ru_entrada norte</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>sbc_delta_entrada_sul</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>120</th>\n      <td>120</td>\n      <td>laboratorio_alfa1_305</td>\n    </tr>\n    <tr>\n      <th>121</th>\n      <td>121</td>\n      <td>escada_alfa1_esquerda_3a</td>\n    </tr>\n    <tr>\n      <th>122</th>\n      <td>122</td>\n      <td>escada_alfa1_direita_3a</td>\n    </tr>\n    <tr>\n      <th>123</th>\n      <td>123</td>\n      <td>laboratorio_alfa1_306</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>124</td>\n      <td>laboratorio_alfa1_304</td>\n    </tr>\n  </tbody>\n</table>\n<p>125 rows × 2 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "nodes = pd.read_csv('nodes_completo.csv')\n",
    "nodes = nodes[['Id','Label']]\n",
    "nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['laboratorio_alfa1_303',\n",
       " 'escada_alfa1_direita_3a',\n",
       " 'escada_alfa1_direita_2a',\n",
       " 'escada_alfa1_direita_1a',\n",
       " 'escada_alfa1_direita_terreo',\n",
       " 'sbc_alfa1_entrada_sul',\n",
       " 'sbc_tranferencia_1',\n",
       " 'sbc_transferencia_3',\n",
       " 'sbc_transferencia_10',\n",
       " 'sbc_transferencia_11',\n",
       " 'sbc_transferencia_12',\n",
       " 'sbc_alfa2_entrada_sul',\n",
       " 'sbc_alfa2_entrada_norte']"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "depara_temp = nodes.to_dict()\n",
    "string_to_index = {y:x for x,y in depara_temp[\"Label\"].items()}\n",
    "index_to_string = {x:y for x,y in depara_temp[\"Label\"].items()}\n",
    "\n",
    "def caminho(node1, node2):\n",
    "    id_inicio = string_to_index[node1]\n",
    "    id_fim = string_to_index[node2]\n",
    "    path = nx.shortest_path(G, source=id_inicio, target=id_fim)\n",
    "    path_string = [index_to_string[index] for index in path]\n",
    "    return path_string\n",
    "\n",
    "caminho('laboratorio_alfa1_303', 'sbc_alfa2_entrada_norte')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "298.7"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "def distancia(node1, node2):\n",
    "    id_inicio = string_to_index[node1]\n",
    "    id_fim = string_to_index[node2]\n",
    "\n",
    "\n",
    "    distancia = nx.dijkstra_path_length(G, source=id_inicio, target=id_fim)\n",
    "    \n",
    "    return distancia\n",
    "\n",
    "distancia('laboratorio_alfa1_303', 'sbc_alfa2_entrada_norte')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.6 64-bit (conda)",
   "metadata": {
    "interpreter": {
     "hash": "37eb5e3bb9792a6a35ae4cc37ddd8d91a257666e8cc598648863c6d41734e62c"
    }
   }
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}