test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import date  # module nécessaire pour la définition des dates, dont notamment les dates de naissances\n",
    "import openfisca_france    # module décrivant le système socio-fiscal français\n",
    "import openfisca_core   \n",
    "import numpy\n",
    "\n",
    "from __future__ import division\n",
    "\n",
    "get_ipython().magic(u'matplotlib inline')\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "TaxBenefitSystem = openfisca_france.init_country()  # Initialisation de la classe décrivant le système socio-fiscal français\n",
    "tax_benefit_system = TaxBenefitSystem()  # Création d'une instance du système socio-fiscal français"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "simulation = tax_benefit_system.new_scenario().init_single_entity(parent1 = dict(age = 30), period = 2014).new_simulation()\n",
    "from openfisca_core import periods\n",
    "\n",
    "smic_by_year = dict()\n",
    "for year in range(2005, 2017):\n",
    "   instant = periods.period(year).start\n",
    "   smic_by_year[year] = simulation.legislation_at(instant).cotsoc.gen.smic_h_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.61"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = 2015\n",
    "smic_by_year[year]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "year = 2015\n",
    "count = 1650\n",
    "temps_plein = 1650\n",
    "def create_scenario(nb_adultes = 1, parent1_25ans = 1, parent2_25ans = 1, nb_enfants = 0, union_legale = 1):\n",
    "    enfants_base_list = [dict(birth = date(2014 - age, 1, 1)) for age in range(5, 15)]\n",
    "    scenario_kwargs = dict(\n",
    "    axes = [[\n",
    "            dict(\n",
    "                count = count,\n",
    "                min = 0,\n",
    "                max = temps_plein,\n",
    "                name = 'ppe_du_sa',\n",
    "                period = year -1,\n",
    "                ),\n",
    "            dict(\n",
    "                count = count,\n",
    "                min = 0,\n",
    "                max = temps_plein,\n",
    "                name = 'ppe_du_sa',\n",
    "                period = year,\n",
    "                ),\n",
    "            dict(\n",
    "                count = count,\n",
    "                min = 0,\n",
    "                max = smic_by_year[year] * temps_plein,\n",
    "                name = 'salaire_de_base',\n",
    "                period = year - 1,\n",
    "                ),\n",
    "            dict(\n",
    "                count = count,\n",
    "                min = 0,\n",
    "                max = smic_by_year[year] * temps_plein,\n",
    "                name = 'salaire_de_base',\n",
    "                period = year,\n",
    "                ),\n",
    "            dict(\n",
    "                count = count,\n",
    "                min = 0,\n",
    "                max = smic_by_year[year] * temps_plein,\n",
    "                name = 'salaire_de_base',\n",
    "                period = year + 1,\n",
    "                ),\n",
    "            dict(\n",
    "                count = count,\n",
    "                index = 1,\n",
    "                min = 0,\n",
    "                max = temps_plein,\n",
    "                name = 'ppe_du_sa',\n",
    "                period = year -1,\n",
    "                ) if nb_adultes == 2 else None,\n",
    "            dict(\n",
    "                count = count,\n",
    "                index = 1,\n",
    "                min = 0,\n",
    "                max = temps_plein,\n",
    "                name = 'ppe_du_sa',\n",
    "                period = year,\n",
    "                ) if nb_adultes == 2 else None,\n",
    "            dict(\n",
    "                count = count,\n",
    "                index = 1,\n",
    "                min = 0,\n",
    "                max = smic_by_year[year] * temps_plein,\n",
    "                name = 'salaire_de_base',\n",
    "                period = year - 1,\n",
    "                ) if nb_adultes == 2 else None,\n",
    "            dict(\n",
    "                count = count,\n",
    "                index = 1,\n",
    "                min = 0,\n",
    "                max = smic_by_year[year] * temps_plein,\n",
    "                name = 'salaire_de_base',\n",
    "                period = year,\n",
    "                ) if nb_adultes == 2 else None,\n",
    "            dict(\n",
    "                count = count,\n",
    "                index = 1,\n",
    "                min = 0,\n",
    "                max = smic_by_year[year] * temps_plein,\n",
    "                name = 'salaire_de_base',\n",
    "                period = year + 1,\n",
    "                ) if nb_adultes == 2 else None,\n",
    "                ]],\n",
    "    period = year,\n",
    "    parent1 = dict(\n",
    "    birth = date(1987, 1, 1) if parent1_25ans == 1 else date(1993, 1, 1),\n",
    "    statmarit = 1 if union_legale == 1 else 2,\n",
    "            ),\n",
    "    parent2 = dict(\n",
    "    birth = date(1987, 1, 1)if parent2_25ans == 1 else date(1993, 1, 1),\n",
    "    statmarit = 1 if union_legale == 1 else 2,\n",
    "    salaire_de_base = smic_by_year[year] * temps_plein,\n",
    "            ) if nb_adultes == 2 else None,\n",
    "    enfants = enfants_base_list[:nb_enfants],\n",
    "        )\n",
    "    \n",
    "    scenario = tax_benefit_system.new_scenario().init_single_entity(**scenario_kwargs)\n",
    "    return scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scenario = create_scenario(nb_adultes = 2, parent1_25ans = 1, parent2_25ans = 1, nb_enfants = 0, union_legale = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5471.4140625   5625.21240234  5624.65136719 ...,  5086.50341797\n",
      "  5086.22265625  5085.94238281]\n",
      "[ 0.  0.  0. ...,  0.  0.  0.]\n",
      "[  0.00000000e+00   0.00000000e+00   9.61582756e+00 ...,   1.58468838e+04\n",
      "   1.58565000e+04   1.58565000e+04]\n"
     ]
    }
   ],
   "source": [
    "simulation = scenario.new_simulation()\n",
    "\n",
    "print simulation.calculate_add('rsa', period='2015')\n",
    "print simulation.calculate('ppe','2015')\n",
    "print simulation.calculate_add('salaire_de_base','2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rsa_2015 = simulation.calculate_add('rsa', period= year)\n",
    "ppe_2015 = simulation.calculate('ppe', year - 1)\n",
    "ppa_2016 = simulation.calculate_add('ppa', period= 2016)\n",
    "rsa_socle_2016 = simulation.calculate_add('rsa', period= 2016)\n",
    "total_aides_2015 = rsa_2015 + ppe_2015\n",
    "total_aides_2016 = ppa_2016 + rsa_socle_2016\n",
    "\n",
    "salaire_brut = simulation.calculate_add('salaire_de_base',year)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa596b70950>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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TTz9lw4YNptsc9dEtjSdQSs0ACrXWK27lNDfTuHTGGRERQURExC1cWghxuxgKDBz/23H+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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa596a7b7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Figure1 = plt.figure()\n",
    "plt.plot(rsa_2015, label = \"RSA 2015\")\n",
    "plt.plot(ppe_2015, label = \"PPE 2015\")\n",
    "plt.plot(ppa_2016, label = \"PPA 2016\")\n",
    "plt.plot(rsa_socle_2016, label = \"RSA socle 2016\")\n",
    "plt.plot(salaire_brut, label = \"Salaire brut\")\n",
    "plt.plot(total_aides_2015, label = \"Aides 2015\")\n",
    "plt.plot(total_aides_2016, label = \"Aides 2016\")\n",
    "\n",
    "\n",
    "plt.legend(loc = 2, shadow=True, fancybox=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x111716dd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zYSafJ3xOSmoKMfW8sRCX1bmMyBKRJz9JkClpkOzIF0nDrFlG6dJkWMLDoZDu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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111606950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rsa_2015, label = \"RSA 2015\")\n",
    "plt.plot(ppa_2016, label = \"PPA 2016\")\n",
    "plt.plot(rsa_socle_2016, label = \"RSA socle 2016\")\n",
    "\n",
    "plt.legend(loc = 2, shadow=True, fancybox=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1117e3d10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uAPfccw9Dhw6lY8eOANxyyy3cd999B61r586dXHHFFbRr126f5RISEg7osLZl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\n5iIiIhXc/wfVhHtPmwXzNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111748a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(total_aides_2015, label = \"Aides 2015\")\n",
    "plt.plot(total_aides_2016, label = \"Aides 2016\")\n",
    "\n",
    "\n",
    "plt.legend(loc = 2, shadow=True, fancybox=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}