explo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "town = \"grenoble\"\n",
    "df_nodes = pd.read_csv(f\"{town}/nodes.csv\", sep=\";\")\n",
    "X = np.array(df_nodes['X'])\n",
    "Y = np.array(df_nodes['Y'])\n",
    "C = np.where(df_nodes['Type'] == 'distribution')[0]\n",
    "T = np.where(df_nodes['Type'] != 'distribution')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.5"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.size / C.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D = np.concatenate([[0], np.array(pd.read_csv(f\"{town}/distances.csv\", sep=\";\")).reshape(-1)])\n",
    "D = D.reshape((X.size, X.size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2a0a4a39ac8>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a0a4db4a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[T], Y[T])\n",
    "plt.scatter(X[C], Y[C])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}