index.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dataclasses\n",
    "from dataclasses import dataclass\n",
    "from enum import Enum\n",
    "from pathlib import Path\n",
    "from typing import List\n",
    "\n",
    "from IPython.display import display, JSON\n",
    "from ipytree import Tree, Node\n",
    "import ipywidgets as widgets\n",
    "import plotly.graph_objects as go\n",
    "from ruamel.yaml import YAML\n",
    "from traitlets import Unicode\n",
    "\n",
    "from openfisca_core.simulation_builder import SimulationBuilder\n",
    "from openfisca_france import FranceTaxBenefitSystem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize tax and benefit system of France\n",
    "tax_benefit_system = FranceTaxBenefitSystem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "PERIOD = \"2019\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_case = {\n",
    "    \"individus\": {\n",
    "        \"Michel\": {\n",
    "            'date_naissance': {'ETERNITY': '1980-01-01'},\n",
    "        },\n",
    "    },\n",
    "    \"familles\": {\n",
    "        \"famille_1\": {\n",
    "            \"parents\": [\"Michel\"]\n",
    "        }\n",
    "    },\n",
    "    \"foyers_fiscaux\": {\n",
    "        \"foyer_fiscal_1\": {\n",
    "            \"declarants\": [\"Michel\"],\n",
    "        },\n",
    "    },\n",
    "    \"menages\": {\n",
    "        \"menage_1\": {\n",
    "            \"personne_de_reference\": [\"Michel\"],\n",
    "        }},\n",
    "    \"axes\": [[\n",
    "        {\n",
    "            \"name\": 'salaire_de_base',\n",
    "            \"count\": 101,\n",
    "            \"min\": 0,\n",
    "            \"max\": 100000,\n",
    "            \"period\": PERIOD,\n",
    "        },\n",
    "    ]],\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulation_builder = SimulationBuilder()\n",
    "simulation = simulation_builder.build_from_entities(tax_benefit_system, test_case)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0.     ,    999.99994,   1999.9999 ,   3000.     ,\n",
       "         3999.9998 ,   5000.     ,   6000.     ,   7000.0005 ,\n",
       "         7999.9995 ,   9000.     ,  10000.     ,  11000.001  ,\n",
       "        12000.     ,  12999.999  ,  14000.001  ,  15000.     ,\n",
       "        15999.999  ,  17000.002  ,  18000.     ,  19000.     ,\n",
       "        20000.     ,  21000.     ,  22000.002  ,  22999.998  ,\n",
       "        24000.     ,  25000.002  ,  25999.998  ,  27000.     ,\n",
       "        28000.002  ,  28999.998  ,  30000.     ,  31000.002  ,\n",
       "        31999.998  ,  33000.     ,  34000.004  ,  34999.996  ,\n",
       "        36000.     ,  37000.     ,  38000.     ,  39000.     ,\n",
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       "        48000.     ,  48999.996  ,  50000.004  ,  51000.     ,\n",
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       "        56000.004  ,  57000.     ,  57999.996  ,  59000.004  ,\n",
       "        60000.     ,  60999.996  ,  62000.004  ,  63000.     ,\n",
       "        63999.996  ,  65000.004  ,  66000.     ,  66999.99   ,\n",
       "        68000.01   ,  69000.     ,  69999.99   ,  71000.01   ,\n",
       "        72000.     ,  73000.     ,  74000.     ,  75000.     ,\n",
       "        76000.     ,  77000.     ,  78000.     ,  79000.     ,\n",
       "        80000.     ,  81000.     ,  82000.     ,  83000.     ,\n",
       "        84000.     ,  85000.     ,  86000.     ,  87000.     ,\n",
       "        88000.01   ,  88999.99   ,  90000.     ,  91000.01   ,\n",
       "        91999.99   ,  93000.     ,  94000.01   ,  94999.99   ,\n",
       "        96000.     ,  97000.01   ,  97999.99   ,  99000.     ,\n",
       "       100000.01   ], dtype=float32)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "salaire_de_base_arr = simulation.calculate_add(\"salaire_de_base\", PERIOD)\n",
    "display(salaire_de_base_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6842.9014,  7362.618 ,  7908.988 ,  8442.072 ,  8975.147 ,\n",
       "        9508.152 , 10041.235 , 10574.318 , 11107.323 , 11532.662 ,\n",
       "       12175.397 , 12818.047 , 13460.794 , 14125.117 , 14811.8   ,\n",
       "       15498.586 , 16185.369 , 16872.047 , 17558.836 , 18111.191 ,\n",
       "       18625.477 , 19007.844 , 19377.207 , 19746.492 , 20115.86  ,\n",
       "       20484.785 , 20839.098 , 21133.148 , 21513.95  , 22085.568 ,\n",
       "       22775.309 , 23464.043 , 24153.668 , 24842.402 , 25532.129 ,\n",
       "       26220.754 , 26910.488 , 27600.23  , 28199.229 , 28771.46  ,\n",
       "       29347.09  , 29930.705 , 30514.447 , 31099.186 , 31682.81  ,\n",
       "       32267.537 , 32851.266 , 33435.895 , 34019.625 , 34604.367 ,\n",
       "       35187.99  , 35771.727 , 36356.457 , 36940.07  , 37524.816 ,\n",
       "       38108.562 , 38692.176 , 39276.914 , 39860.645 , 40445.254 ,\n",
       "       41028.996 , 41613.742 , 42197.35  , 42781.098 , 43365.816 ,\n",
       "       43949.445 , 44534.176 , 45117.918 , 45701.535 , 46286.273 ,\n",
       "       46870.016 , 47454.63  , 48038.375 , 48623.117 , 49206.734 ,\n",
       "       49790.457 , 50375.19  , 50958.805 , 51543.555 , 52127.297 ,\n",
       "       52710.91  , 53295.656 , 53879.38  , 54463.99  , 55047.73  ,\n",
       "       55632.48  , 56216.098 , 56800.832 , 57384.574 , 57968.184 ,\n",
       "       58552.934 , 59136.66  , 59720.27  , 60305.01  , 60888.754 ,\n",
       "       61473.37  , 62057.105 , 62641.848 , 63225.473 , 63771.215 ,\n",
       "       64271.94  ], dtype=float32)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "revenu_disponible_arr = simulation.calculate(\"revenu_disponible\", PERIOD)\n",
    "display(revenu_disponible_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display \"Revenu disponible\" interactively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32ed959980c240da9b15c0cddf51918c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectionSlider(description='salaire_de_base', options=(0.0, 999.99994, 1999.9999, 3000.…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@widgets.interact(salaire_de_base=widgets.SelectionSlider(options=salaire_de_base_arr))\n",
    "def render(salaire_de_base):\n",
    "    revenu_disponible = dict(zip(salaire_de_base_arr, revenu_disponible_arr))[salaire_de_base]\n",
    "    return widgets.HBox([widgets.Label(f\"Revenu disponible : {revenu_disponible:.2f}\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot \"Revenu disponible\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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       "                    Plotly.newPlot(\n",
       "                        '95493aa6-b17e-42ca-b184-23f8b357a7c9',\n",
       "                        [{\"type\": \"scatter\", \"x\": [0.0, 999.9999389648438, 1999.9998779296875, 3000.0, 3999.999755859375, 5000.0, 6000.0, 7000.00048828125, 7999.99951171875, 9000.0, 10000.0, 11000.0009765625, 12000.0, 12999.9990234375, 14000.0009765625, 15000.0, 15999.9990234375, 17000.001953125, 18000.0, 19000.0, 20000.0, 21000.0, 22000.001953125, 22999.998046875, 24000.0, 25000.001953125, 25999.998046875, 27000.0, 28000.001953125, 28999.998046875, 30000.0, 31000.001953125, 31999.998046875, 33000.0, 34000.00390625, 34999.99609375, 36000.0, 37000.0, 38000.0, 39000.0, 40000.0, 41000.0, 42000.0, 43000.0, 44000.00390625, 45000.0, 45999.99609375, 47000.00390625, 48000.0, 48999.99609375, 50000.00390625, 51000.0, 51999.99609375, 53000.00390625, 54000.0, 54999.99609375, 56000.00390625, 57000.0, 57999.99609375, 59000.00390625, 60000.0, 60999.99609375, 62000.00390625, 63000.0, 63999.99609375, 65000.00390625, 66000.0, 66999.9921875, 68000.0078125, 69000.0, 69999.9921875, 71000.0078125, 72000.0, 73000.0, 74000.0, 75000.0, 76000.0, 77000.0, 78000.0, 79000.0, 80000.0, 81000.0, 82000.0, 83000.0, 84000.0, 85000.0, 86000.0, 87000.0, 88000.0078125, 88999.9921875, 90000.0, 91000.0078125, 91999.9921875, 93000.0, 94000.0078125, 94999.9921875, 96000.0, 97000.0078125, 97999.9921875, 99000.0, 100000.0078125], \"y\": [6842.9013671875, 7362.6181640625, 7908.98779296875, 8442.072265625, 8975.1474609375, 9508.15234375, 10041.2353515625, 10574.318359375, 11107.3232421875, 11532.662109375, 12175.3974609375, 12818.046875, 13460.7939453125, 14125.1171875, 14811.7998046875, 15498.5859375, 16185.369140625, 16872.046875, 17558.8359375, 18111.19140625, 18625.4765625, 19007.84375, 19377.20703125, 19746.4921875, 20115.859375, 20484.78515625, 20839.09765625, 21133.1484375, 21513.94921875, 22085.568359375, 22775.30859375, 23464.04296875, 24153.66796875, 24842.40234375, 25532.12890625, 26220.75390625, 26910.48828125, 27600.23046875, 28199.228515625, 28771.4609375, 29347.08984375, 29930.705078125, 30514.447265625, 31099.185546875, 31682.810546875, 32267.537109375, 32851.265625, 33435.89453125, 34019.625, 34604.3671875, 35187.98828125, 35771.7265625, 36356.45703125, 36940.0703125, 37524.81640625, 38108.5625, 38692.17578125, 39276.9140625, 39860.64453125, 40445.25390625, 41028.99609375, 41613.7421875, 42197.3515625, 42781.09765625, 43365.81640625, 43949.4453125, 44534.17578125, 45117.91796875, 45701.53515625, 46286.2734375, 46870.015625, 47454.62890625, 48038.375, 48623.1171875, 49206.734375, 49790.45703125, 50375.19140625, 50958.8046875, 51543.5546875, 52127.296875, 52710.91015625, 53295.65625, 53879.37890625, 54463.98828125, 55047.73046875, 55632.48046875, 56216.09765625, 56800.83203125, 57384.57421875, 57968.18359375, 58552.93359375, 59136.66015625, 59720.26953125, 60305.01171875, 60888.75390625, 61473.37109375, 62057.10546875, 62641.84765625, 63225.47265625, 63771.21484375, 64271.94140625]}],\n",
       "                        {\"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"title\": {\"text\": \"Salaire de Base\"}}, \"yaxis\": {\"title\": {\"text\": \"Revenu disponible\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('95493aa6-b17e-42ca-b184-23f8b357a7c9');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure(\n",
    "    data=go.Scatter(x=salaire_de_base_arr, y=revenu_disponible_arr),\n",
    "    layout=go.Layout(xaxis_title=\"Salaire de Base\", yaxis_title='Revenu disponible'),\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display interactive waterfall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "children": [
        {
         "children": [
          {
           "children": [
            {
             "children": [
              {
               "children": [
                {
                 "children": [
                  {
                   "children": [
                    {
                     "code": "cout_du_travail",
                     "short_name": "Coût du travail"
                    },
                    {
                     "code": "credit_impot_competitivite_emploi",
                     "short_name": "CICE"
                    },
                    {
                     "code": "aide_premier_salarie",
                     "short_name": "Aide premier salarié"
                    },
                    {
                     "code": "aide_embauche_pme",
                     "short_name": "Aide PME"
                    },
                    {
                     "code": "tehr",
                     "short_name": "Taxe exceptionnelle"
                    }
                   ],
                   "code": "salaire_super_brut",
                   "short_name": "Salaire super brut"
                  },
                  {
                   "code": "allegement_fillon",
                   "short_name": "Fillon"
                  }
                 ],
                 "code": "salaire_super_brut_hors_allegements",
                 "short_name": "Salaire super brut HA"
                },
                {
                 "code": "cotisations_employeur",
                 "short_name": "Cotisations employeur"
                }
               ],
               "code": "salaire_de_base",
               "short_name": "Salaire brut"
              },
              {
               "code": "cotisations_salariales",
               "short_name": "Cotisations salariales"
              },
              {
               "code": "csg_deductible_salaire",
               "short_name": "CSG déductible"
              }
             ],
             "code": "salaire_imposable",
             "short_name": "Salaire imposable"
            },
            {
             "code": "csg_imposable_salaire",
             "short_name": "CSG imposable"
            },
            {
             "code": "crds_salaire",
             "short_name": "CRDS"
            }
           ],
           "code": "salaire_net",
           "short_name": "Salaire net"
          }
         ],
         "code": "revenus_nets_du_travail",
         "short_name": "Revenus nets"
        },
        {
         "children": [
          {
           "children": [
            {
             "code": "rsa",
             "short_name": "RSA"
            },
            {
             "code": "ppa",
             "short_name": "PPA"
            }
           ],
           "code": "minima_sociaux",
           "short_name": "Minima sociaux"
          }
         ],
         "code": "prestations_sociales",
         "short_name": "Prestations sociales"
        },
        {
         "code": "ppe",
         "short_name": "PPE"
        },
        {
         "children": [
          {
           "code": "irpp",
           "short_name": "Impôt sur le revenu"
          }
         ],
         "code": "impots_directs",
         "short_name": "Impôts directs"
        }
       ],
       "code": "revenu_disponible",
       "short_name": "Revenu disponible"
      },
      "text/plain": [
       "<IPython.core.display.JSON object>"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "application/json": {
       "expanded": false,
       "root": "root"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yaml = YAML(typ='safe')\n",
    "decomposition = yaml.load(Path(\"./decomposition.yaml\"))\n",
    "JSON(decomposition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BarType(Enum):\n",
    "    VALUE = 1\n",
    "    SUB_TOTAL = 2\n",
    "    TOTAL = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Bar:\n",
    "    code: str\n",
    "    type: BarType\n",
    "    short_name: str = None\n",
    "    value: float = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bars(decomposition: dict) -> List[Bar]:\n",
    "    def visit(node):\n",
    "        children = node.get(\"children\")\n",
    "        if children:\n",
    "            for child in children:\n",
    "                yield from visit(child)\n",
    "            type_ = BarType.TOTAL if node[\"code\"] == decomposition[\"code\"] else BarType.SUB_TOTAL\n",
    "            yield Bar(code=node[\"code\"], short_name=node.get(\"short_name\"), type=type_)\n",
    "        else:\n",
    "            yield Bar(code=node[\"code\"], short_name=node.get(\"short_name\"), type=BarType.VALUE)\n",
    "\n",
    "    return list(visit(decomposition))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bar(code='cout_du_travail', type=<BarType.VALUE: 1>, short_name='Coût du travail', value=None),\n",
       " Bar(code='credit_impot_competitivite_emploi', type=<BarType.VALUE: 1>, short_name='CICE', value=None),\n",
       " Bar(code='aide_premier_salarie', type=<BarType.VALUE: 1>, short_name='Aide premier salarié', value=None),\n",
       " Bar(code='aide_embauche_pme', type=<BarType.VALUE: 1>, short_name='Aide PME', value=None),\n",
       " Bar(code='tehr', type=<BarType.VALUE: 1>, short_name='Taxe exceptionnelle', value=None),\n",
       " Bar(code='salaire_super_brut', type=<BarType.SUB_TOTAL: 2>, short_name='Salaire super brut', value=None),\n",
       " Bar(code='allegement_fillon', type=<BarType.VALUE: 1>, short_name='Fillon', value=None),\n",
       " Bar(code='salaire_super_brut_hors_allegements', type=<BarType.SUB_TOTAL: 2>, short_name='Salaire super brut HA', value=None),\n",
       " Bar(code='cotisations_employeur', type=<BarType.VALUE: 1>, short_name='Cotisations employeur', value=None),\n",
       " Bar(code='salaire_de_base', type=<BarType.SUB_TOTAL: 2>, short_name='Salaire brut', value=None),\n",
       " Bar(code='cotisations_salariales', type=<BarType.VALUE: 1>, short_name='Cotisations salariales', value=None),\n",
       " Bar(code='csg_deductible_salaire', type=<BarType.VALUE: 1>, short_name='CSG déductible', value=None),\n",
       " Bar(code='salaire_imposable', type=<BarType.SUB_TOTAL: 2>, short_name='Salaire imposable', value=None),\n",
       " Bar(code='csg_imposable_salaire', type=<BarType.VALUE: 1>, short_name='CSG imposable', value=None),\n",
       " Bar(code='crds_salaire', type=<BarType.VALUE: 1>, short_name='CRDS', value=None),\n",
       " Bar(code='salaire_net', type=<BarType.SUB_TOTAL: 2>, short_name='Salaire net', value=None),\n",
       " Bar(code='revenus_nets_du_travail', type=<BarType.SUB_TOTAL: 2>, short_name='Revenus nets', value=None),\n",
       " Bar(code='rsa', type=<BarType.VALUE: 1>, short_name='RSA', value=None),\n",
       " Bar(code='ppa', type=<BarType.VALUE: 1>, short_name='PPA', value=None),\n",
       " Bar(code='minima_sociaux', type=<BarType.SUB_TOTAL: 2>, short_name='Minima sociaux', value=None),\n",
       " Bar(code='prestations_sociales', type=<BarType.SUB_TOTAL: 2>, short_name='Prestations sociales', value=None),\n",
       " Bar(code='ppe', type=<BarType.VALUE: 1>, short_name='PPE', value=None),\n",
       " Bar(code='irpp', type=<BarType.VALUE: 1>, short_name='Impôt sur le revenu', value=None),\n",
       " Bar(code='impots_directs', type=<BarType.SUB_TOTAL: 2>, short_name='Impôts directs', value=None),\n",
       " Bar(code='revenu_disponible', type=<BarType.TOTAL: 3>, short_name='Revenu disponible', value=None)]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bars = get_bars(decomposition)\n",
    "display(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {\n",
    "    bar.code: simulation.calculate_add(bar.code, period=PERIOD)\n",
    "    for bar in bars\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def iter_displayed_bars(bars: List[Bar], results: dict, salaire_de_base: float, include_subtotals: bool, include_zero: bool):\n",
    "    for bar in bars:\n",
    "        if bar.type == BarType.SUB_TOTAL and not include_subtotals:\n",
    "            continue\n",
    "        value = dict(zip(salaire_de_base_arr, results[bar.code]))[salaire_de_base]\n",
    "        if include_zero or value != 0:\n",
    "            yield dataclasses.replace(bar, value=value)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BarNode(Node):\n",
    "    code = Unicode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_root_node(decomposition: dict, results: dict, salaire_de_base: float, include_subtotals: bool, include_zero: bool) -> Node:\n",
    "    def visit(node):\n",
    "        children = node.get(\"children\")\n",
    "        value = dict(zip(salaire_de_base_arr, results[node[\"code\"]]))[salaire_de_base]\n",
    "        if value == 0 and not include_zero:\n",
    "            return\n",
    "        # Cf https://fontawesome.com/icons?d=gallery&m=free\n",
    "        icon = \"folder\" if children else \"arrow-down\" if value < 0 else \"arrow-up\"\n",
    "        text_value = (\"{:.0f}\" if children else \"{:+.0f}\").format(value)\n",
    "        name = node.get(\"short_name\") or node[\"code\"]\n",
    "        node = BarNode(f\"{name} ({text_value})\", code=node[\"code\"], icon=icon)\n",
    "        if children:\n",
    "            node.nodes = list(filter(None, (visit(child) for child in children)))\n",
    "        return node\n",
    "\n",
    "    return visit(decomposition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_plot(*args):\n",
    "    tree.nodes = [get_root_node(decomposition, results, salaire_de_base=slider.value,\n",
    "                                include_subtotals=include_subtotals.value, include_zero=include_zero.value)]\n",
    "    displayed_bars = list(iter_displayed_bars(bars, results, salaire_de_base=slider.value,\n",
    "                                              include_subtotals=include_subtotals.value, include_zero=include_zero.value))\n",
    "    with fig.batch_update():\n",
    "        waterfall = fig.data[0]\n",
    "        waterfall.x = [bar.short_name or bar.code for bar in displayed_bars]\n",
    "        waterfall.y = [\n",
    "            bar.value if bar.type == BarType.VALUE else 0\n",
    "            for bar in displayed_bars\n",
    "        ]\n",
    "        waterfall.measure = [\n",
    "            \"relative\" if bar.type == BarType.VALUE else \"total\"\n",
    "            for bar in displayed_bars\n",
    "        ]\n",
    "#         waterfall.selectedpoints = [1, 3]\n",
    "        waterfall.customdata = [{\"code\": bar.code} for bar in displayed_bars]\n",
    "        waterfall.text = [\n",
    "            (\"{:+.0f}\" if bar.type == BarType.VALUE else \"{:.0f}\").format(bar.value)\n",
    "            for bar in displayed_bars\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cef87965d5244cebabe5262bac81b94b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Output(), HBox(children=(Label(value='Salaire de base'), SelectionSlider(options=(0.0, 999.9999…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "include_subtotals = widgets.Checkbox(description=\"Afficher les sous-totaux\")\n",
    "include_subtotals.observe(update_plot, 'value')\n",
    "\n",
    "include_zero = widgets.Checkbox(description=\"Afficher les valeurs 0\")\n",
    "include_zero.observe(update_plot, 'value')\n",
    "\n",
    "slider = widgets.SelectionSlider(options=salaire_de_base_arr)\n",
    "slider.observe(update_plot, 'value')\n",
    "\n",
    "waterfall = go.Waterfall(textposition=\"inside\")\n",
    "\n",
    "output = widgets.Output()\n",
    "\n",
    "def select_tree_node(tree, code):\n",
    "    def visit(node):\n",
    "        for child in node.nodes:\n",
    "            child.selected = child.code == code\n",
    "            if child.nodes:\n",
    "                select_tree_node(child, code)\n",
    "\n",
    "    visit(tree)\n",
    "        \n",
    "@output.capture()\n",
    "def on_hover(trace, points, selector):\n",
    "    code = trace.customdata[points.point_inds[0]][\"code\"]\n",
    "    select_tree_node(tree, code)\n",
    "\n",
    "layout = go.Layout()\n",
    "fig = go.FigureWidget(data=[waterfall], layout=layout)\n",
    "fig.data[0].on_hover(on_hover)\n",
    "\n",
    "tree = Tree()\n",
    "\n",
    "update_plot()\n",
    "\n",
    "widgets.VBox([\n",
    "    output,\n",
    "    widgets.HBox([widgets.Label('Salaire de base'), slider]),\n",
    "    widgets.HBox([include_subtotals, include_zero]),\n",
    "    widgets.HBox([tree, fig]),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display bareme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "            <script type=\"text/javascript\">\n",
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       "                    Plotly.newPlot(\n",
       "                        'e146dfd5-587c-4f7e-9e85-e04aff4c233b',\n",
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('e146dfd5-587c-4f7e-9e85-e04aff4c233b');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "areas = [\n",
    "    go.Scatter(\n",
    "        x=salaire_de_base_arr,\n",
    "        y=results[bar.code],\n",
    "        name=bar.short_name or bar.code,\n",
    "        text=bar.short_name or bar.code,\n",
    "        stackgroup='one',\n",
    "        fill='tozeroy',\n",
    "    #     fillcolor='darkviolet',\n",
    "        hoveron = 'points+fills', # select where hover is active\n",
    "        hoverinfo = 'text+x+y',\n",
    "    )\n",
    "    for bar in bars\n",
    "    if bar.type == BarType.VALUE and (results[bar.code] != 0).any()\n",
    "]\n",
    "lines = [\n",
    "    go.Scatter(\n",
    "        x=salaire_de_base_arr,\n",
    "        y=results[bar.code],\n",
    "        name=bar.short_name or bar.code,\n",
    "        text=bar.short_name or bar.code,\n",
    "        hoverinfo = 'text+x+y',\n",
    "    )\n",
    "    for bar in bars\n",
    "    if bar.type == BarType.TOTAL\n",
    "]\n",
    "fig = go.Figure(data=areas + lines)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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