Script pour le TP sous format Jupyter

script_analyse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exemple de script pour l'analyse des résultats de NEDUM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importation des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outNedum = pd.read_csv('output_NEDUM.csv', sep = ',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exemples de courbes et de cartes\n",
    "### 1. Courbe des loyers en fonction de la distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pour fermer les autres figures\n",
    "plt.close('all')\n",
    "\n",
    "#coordonnées du centre de Paris\n",
    "X_centre_Paris=653750\n",
    "Y_centre_Paris=6862350\n",
    "outNedum['distance_centre'] = np.sqrt((outNedum['X']-X_centre_Paris) ** 2 + (outNedum['Y']-Y_centre_Paris) ** 2)/1000\n",
    "plt.scatter(outNedum['distance_centre'],outNedum['simul_2030_sc1_BAU_loyer'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Carte de la construction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pour fermer les autres figures\n",
    "plt.close('all')\n",
    "\n",
    "#'-logement' est en nombre de m2 construits par nombre de km2 au sol\n",
    "# Attention, pour avoir le Coefficient d'Occupation des Sols, il faut diviser par 10^6 \n",
    "def carto(x):\n",
    "    plt.figure()\n",
    "    plt.scatter(outNedum['X'], outNedum['Y'],20, x) #construction\n",
    "    plt.colorbar()\n",
    "    \n",
    "carto(outNedum['simul_2030_sc3_ZI_logement'] * outNedum['coeff_land'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quelques calculs\n",
    "### Calcul de la densité de population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Attention, il faut multiplier par 'coeff_land' qui représente la part du pixel qui est urbanisable\n",
    "sc1_densite_pop = (outNedum['simul_2030_sc1_BAU_logement'] * outNedum['coeff_land'] \n",
    "                   / outNedum['simul_2030_sc1_BAU_taille'] )#en ménages / km2\n",
    "sc1_densite_pop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcul des émissions totales de la ville\n",
    "Les variables `emissions` donnent la quantité d'émissions pour un ménage vivant dans le carreau (en gCO2 / an)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc1_emission_tot = (np.sum( sc1_densite_pop * outNedum['simul_2030_sc1_BAU_emissions']) \n",
    "                    / np.sum(sc1_densite_pop) /1000) #en moyenne par ménage en kgCO2\n",
    "sc1_emission_tot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcul de la surface urbanisée"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "borne_urba = 100000 #en dessous de cette densité de construction on considère qu'on n'est plus en ville \n",
    "sc1_surface_urba = np.sum(outNedum['coeff_land']*(outNedum['simul_2030_sc1_BAU_logement']>borne_urba)) #en km2\n",
    "sc1_surface_urba"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scénario 3: zone inondable\n",
    "`part_inond` représente la part de la surface constructible qui est située en zone inondable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "carto(outNedum['part_inond'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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