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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Evolution du taux effectif en fonction du salaire et du capital\n",
      "\n",
      "Derni\u00e8re version du notebook : https://github.com/fmassot/openfisca-web-notebook/"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Libs \u00e0 importer\n",
      "%matplotlib inline\n",
      "import collections\n",
      "import json\n",
      "import requests\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from scipy import interpolate"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 344
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Simulation avec OpenFisca\n",
      "\n",
      "API : http://api.openfisca.fr/api/1/simulate\n",
      "\n",
      "\n",
      "On calcule avec OpenFisca les revenus et de cotisations pour une personne avec un salaire imposable sur [1000, 100 000] et un capital en action [0, 100 000].\n",
      "\n",
      "#### D\u00e9finition des simulations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# SIMULATIONS pour un revenu imposable allant de REVENU_MIN \u00e0 REVENU_MAX et un capital en action allant de REVENU_MIN \u00e0 REVENU_MAX\n",
      "OPENFISCA_API = \"http://api.openfisca.fr/api/1/simulate\"\n",
      "CAPITAL_MIN = 0\n",
      "CAPITAL_MAX = 100000\n",
      "SALI_MIN = 1000\n",
      "SALI_MAX = CAPITAL_MAX\n",
      "COUNT = 200\n",
      "\n",
      "simulation = dict(\n",
      "    decomposition = [\n",
      "        dict(code = 'salsuperbrut', title = 'Salaire super brut'),\n",
      "        dict(code = 'salnet', title = 'Salaire net'),\n",
      "        dict(code = 'sali', title = 'Salaire imposable'),\n",
      "        dict(code = 'revdisp', title = 'Revenu disponible'),\n",
      "        dict(code = 'cotsoc_noncontrib', title = 'Cotisations sociales non contributives'),\n",
      "        dict(code = 'cotsal_contrib', title = 'Cotisations sociales salariales contributives'),\n",
      "        dict(code = 'cotsal_noncontrib', title = 'Cotisations sociales salariales contributives'),\n",
      "        dict(code='cotpat_main_d_oeuvre', title='cotpat_main_d_oeuvre'),\n",
      "        dict(code = 'cotpat_contrib', title = 'Cotisations pa contributives'),\n",
      "        dict(code = 'cotpat_noncontrib', title = 'Cotisations pa non contributives'),\n",
      "        dict(code = 'irpp', title = 'irpp'),\n",
      "        dict(code = 'csg', title = 'csg'),\n",
      "        dict(code = 'crds', title = 'crds'),\n",
      "        dict(code = 'f2dc', title = 'f2dc'),\n",
      "        dict(code = 'rev_cap_bar', title = 'rev_cap_bar'),\n",
      "        ],\n",
      "    scenarios = [\n",
      "        dict(\n",
      "            axes = [\n",
      "                dict(\n",
      "                    count = COUNT,\n",
      "                    max = SALI_MAX,\n",
      "                    min = SALI_MIN,\n",
      "                    name = 'sali',\n",
      "                    ),\n",
      "                dict(\n",
      "                    count = COUNT+1,\n",
      "                    max = CAPITAL_MAX,\n",
      "                    min = CAPITAL_MIN,\n",
      "                    name = 'f2dc',\n",
      "                    )\n",
      "                ],\n",
      "            test_case = dict(\n",
      "                familles = [\n",
      "                    dict(\n",
      "                        parents = ['ind0'],\n",
      "                        ),\n",
      "                    ],\n",
      "                foyers_fiscaux = [\n",
      "                    dict(\n",
      "                        declarants = ['ind0'],\n",
      "                        ),\n",
      "                    ],\n",
      "                individus = [\n",
      "                    dict(\n",
      "                        activite = u'Actif occup\u00e9',\n",
      "                        birth = 1970,\n",
      "                        cadre = True,\n",
      "                        id = 'ind0',\n",
      "                        statmarit = u'C\u00e9libataire',\n",
      "                        ),\n",
      "                    ],\n",
      "                menages = [\n",
      "                    dict(\n",
      "                        personne_de_reference = 'ind0',\n",
      "                        ),\n",
      "                    ],\n",
      "                ),\n",
      "            year = 2013,\n",
      "            ),\n",
      "        ],\n",
      "    )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 345
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Appel de l'API OpenFisca"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "headers = {'Content-Type': 'application/json','User-Agent': 'OpenFisca-Notebook'}\n",
      "response = requests.post('http://api.openfisca.fr/api/1/simulate', data=json.dumps(simulation), headers=headers)\n",
      "variables = response.json(object_pairs_hook = collections.OrderedDict)['value']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 346
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Calcul du revenu total, du taux effectif, etc."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Cr\u00e9ation d'une dataframe pour stocker les r\u00e9sultats et ajouter des variables calcul\u00e9es.\n",
      "df = pd.DataFrame(dict([(var['code'], var['values']) for var in variables]))\n",
      "df['revenu_total'] = df['salsuperbrut']+df['f2dc']\n",
      "df['part_salaire'] = df['salsuperbrut']/df['revenu_total']\n",
      "df['part_capital'] = df['f2dc']/df['revenu_total']\n",
      "df['taux_effectif'] = 1 - df['revdisp']/df['revenu_total']\n",
      "df['taux_sali'] = 1 - df['sali']/df['revenu_total']\n",
      "df['taux_salnet'] = 1 - df['salnet']/df['revenu_total']\n",
      "df['taux_noncontrib'] = - (df['csg'] + df['crds'] + df['irpp'] + df['cotpat_noncontrib'] + df['cotsal_noncontrib'])/df['revenu_total']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 347
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[['revenu_total', 'taux_effectif', 'f2dc', 'salsuperbrut']].head(n=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>revenu_total</th>\n",
        "      <th>taux_effectif</th>\n",
        "      <th>f2dc</th>\n",
        "      <th>salsuperbrut</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>  1385.405151</td>\n",
        "      <td>-3.240972</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1385.405151</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>  2073.951416</td>\n",
        "      <td>-1.981557</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2073.951416</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>  2762.497803</td>\n",
        "      <td>-1.349955</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2762.497803</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>  3452.429443</td>\n",
        "      <td>-0.969774</td>\n",
        "      <td> 0</td>\n",
        "      <td>  3452.429443</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>  4140.976074</td>\n",
        "      <td>-0.716659</td>\n",
        "      <td> 0</td>\n",
        "      <td>  4140.976074</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>  4830.907715</td>\n",
        "      <td>-0.535406</td>\n",
        "      <td> 0</td>\n",
        "      <td>  4830.907715</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>  5519.454102</td>\n",
        "      <td>-0.399693</td>\n",
        "      <td> 0</td>\n",
        "      <td>  5519.454102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>  6209.385742</td>\n",
        "      <td>-0.293897</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6209.385742</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>  6897.932617</td>\n",
        "      <td>-0.209412</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6897.932617</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>  7587.862793</td>\n",
        "      <td>-0.140137</td>\n",
        "      <td> 0</td>\n",
        "      <td>  7587.862793</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>  8276.410156</td>\n",
        "      <td>-0.082516</td>\n",
        "      <td> 0</td>\n",
        "      <td>  8276.410156</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>  8966.340820</td>\n",
        "      <td>-0.033655</td>\n",
        "      <td> 0</td>\n",
        "      <td>  8966.340820</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>  9654.888672</td>\n",
        "      <td> 0.008145</td>\n",
        "      <td> 0</td>\n",
        "      <td>  9654.888672</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 10344.820312</td>\n",
        "      <td> 0.044449</td>\n",
        "      <td> 0</td>\n",
        "      <td> 10344.820312</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 11033.366211</td>\n",
        "      <td> 0.076153</td>\n",
        "      <td> 0</td>\n",
        "      <td> 11033.366211</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 11723.297852</td>\n",
        "      <td> 0.104185</td>\n",
        "      <td> 0</td>\n",
        "      <td> 11723.297852</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 12411.843750</td>\n",
        "      <td> 0.129054</td>\n",
        "      <td> 0</td>\n",
        "      <td> 12411.843750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 13101.776367</td>\n",
        "      <td> 0.151351</td>\n",
        "      <td> 0</td>\n",
        "      <td> 13101.776367</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 13790.322266</td>\n",
        "      <td> 0.171379</td>\n",
        "      <td> 0</td>\n",
        "      <td> 13790.322266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> 14480.252930</td>\n",
        "      <td> 0.189537</td>\n",
        "      <td> 0</td>\n",
        "      <td> 14480.252930</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>20 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 348,
       "text": [
        "    revenu_total  taux_effectif  f2dc  salsuperbrut\n",
        "0    1385.405151      -3.240972     0   1385.405151\n",
        "1    2073.951416      -1.981557     0   2073.951416\n",
        "2    2762.497803      -1.349955     0   2762.497803\n",
        "3    3452.429443      -0.969774     0   3452.429443\n",
        "4    4140.976074      -0.716659     0   4140.976074\n",
        "5    4830.907715      -0.535406     0   4830.907715\n",
        "6    5519.454102      -0.399693     0   5519.454102\n",
        "7    6209.385742      -0.293897     0   6209.385742\n",
        "8    6897.932617      -0.209412     0   6897.932617\n",
        "9    7587.862793      -0.140137     0   7587.862793\n",
        "10   8276.410156      -0.082516     0   8276.410156\n",
        "11   8966.340820      -0.033655     0   8966.340820\n",
        "12   9654.888672       0.008145     0   9654.888672\n",
        "13  10344.820312       0.044449     0  10344.820312\n",
        "14  11033.366211       0.076153     0  11033.366211\n",
        "15  11723.297852       0.104185     0  11723.297852\n",
        "16  12411.843750       0.129054     0  12411.843750\n",
        "17  13101.776367       0.151351     0  13101.776367\n",
        "18  13790.322266       0.171379     0  13790.322266\n",
        "19  14480.252930       0.189537     0  14480.252930\n",
        "\n",
        "[20 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 348
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Evolution du taux effectif en fonction du revenu sans capital"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "formatter = matplotlib.ticker.FuncFormatter(lambda x, p: \"{0:,}\".format(int(x)))\n",
      "ix_without_capital = np.arange(10,COUNT)\n",
      "fig, ax = plt.subplots(figsize=(12,6))\n",
      "ax.xaxis.set_major_formatter(formatter)\n",
      "ax.plot(df[['revenu_total']].ix[ix_without_capital], df[['taux_effectif']].ix[ix_without_capital], lw=2)\n",
      "ax.set_title(\"Evolution du taux effectif en fonction d'un revenu sans capital\")\n",
      "ax.set_xlabel(\"Revenu total sans capital\", fontsize=20)\n",
      "ax.set_ylabel(\"Taux effectif\", fontsize=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 349,
       "text": [
        "<matplotlib.text.Text at 0x11266e410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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v4NVBPjIyUpmZmY79zMxMRUVFObUJLNLd1r9/f/35z3/W0aNHFRISUuJ4U6ZM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\ngFjIExERERF1QP8HmAlJBS1P4BkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1117bea10>"
       ]
      }
     ],
     "prompt_number": 349
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Calcul du taux effectif fonction du [Revenu total, part capital]\n",
      "\n",
      "On veut une matrice du taux effectif o\u00f9 chaque ligne d\u00e9finit un revenu total fixe allant de 20 000 \u20ac \u00e0 200 000 \u20ac et chaque colonne d\u00e9finit la composition du revenu total en pourcentage de capital allant de 0% \u00e0 90%.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "revenu_total = np.arange(20000, CAPITAL_MAX*2, 5000)\n",
      "base_pct = np.array((np.arange(1, 0.05, -0.05), np.arange(0,0.95,0.05)))\n",
      "points = np.array([df['salsuperbrut'].tolist(), df['f2dc'].tolist()]).transpose()\n",
      "points_to_interpolate = np.vstack([base_pct.T * total for total in revenu_total])\n",
      "interpdata = interpolate.griddata(points, df['taux_effectif'], points_to_interpolate)\n",
      "results = np.zeros((base_pct.shape[1], base_pct.shape[1]))\n",
      "for ix in range(base_pct.shape[1]):\n",
      "    for iy in range(base_pct.shape[1]):\n",
      "        results[ix,iy] = interpdata[iy + base_pct.shape[1]*ix]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 350
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Evolution de la part du capital dans le revenu total\n",
      "base_pct[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 351,
       "text": [
        "array([ 0.  ,  0.05,  0.1 ,  0.15,  0.2 ,  0.25,  0.3 ,  0.35,  0.4 ,\n",
        "        0.45,  0.5 ,  0.55,  0.6 ,  0.65,  0.7 ,  0.75,  0.8 ,  0.85,  0.9 ])"
       ]
      }
     ],
     "prompt_number": 351
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Heatmap et contour [revenu total, part capital]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(22,7))\n",
      "axes[0].set_title('Heatmap')\n",
      "for ax in axes:\n",
      "    ax.set_xlabel(u'Revenu total en \u20ac', fontsize=15)\n",
      "    ax.set_ylabel(u'Part en capital du revenu total', fontsize=15)\n",
      "    ax.xaxis.set_major_formatter(formatter)\n",
      "im = axes[0].imshow(\n",
      "        results.T,\n",
      "        cmap=plt.cm.RdBu_r,\n",
      "        interpolation='none',\n",
      "        vmin=results.min(),\n",
      "        vmax=results.max(),\n",
      "        origin='lower',\n",
      "        extent=[revenu_total.min(), revenu_total.max(), base_pct[1].min(), base_pct[1].max()],\n",
      "        aspect='auto',\n",
      "    )\n",
      "cb = fig.colorbar(im, ax=axes[0])\n",
      "\n",
      "axes[1].set_title('Contour')\n",
      "cnt = axes[1].contour(\n",
      "        results.T,\n",
      "        cmap=plt.cm.RdBu_r,\n",
      "        vmin=results.min(),\n",
      "        vmax=results.max(),\n",
      "        origin='lower',\n",
      "        extent=[revenu_total.min(), revenu_total.max(), base_pct[1].min(), base_pct[1].max()],\n",
      "        aspect='auto',\n",
      "    )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HKLwENQC0WCywWq2+21arFSkpKR2O2b59Ox544AEAQP/+/ZGRkYEDBw6goKDg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\nRERERKRgHAAkIiIiIiIiIiJSsP8HjQ9YZrPe0ccAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112afbb50>"
       ]
      }
     ],
     "prompt_number": 352
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 352
    }
   ],
   "metadata": {}
  }
 ]
}