Init memo for APL evolution according to base ressources

a47b8423 · sandcha · 26ee54d5 · a47b8423
Commit a47b8423 authored 3 years ago by sandcha
--- a/notebooks/memos/memo_aides_logement.ipynb
+++ b/notebooks/memos/memo_aides_logement.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Aides logement\n",
+    "\n",
+    "...\n",
+    "\n",
+    "## Références\n",
+    "\n",
+    "* service-public : [ALS](https://www.service-public.fr/particuliers/vosdroits/F1280)\n",
+    "* openfisca-france : [ALS](https://legislation.fr.openfisca.org/als)\n",
+    "\n",
+    "## Institutions concernées\n",
+    "\n",
+    "## Publications & Aggrégats\n",
+    "\n",
+    "* ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Les 100 valeurs :\n",
+      "apl :  [2055.36    2042.7599  2030.1597  2017.68    2005.1998  1992.7202\n",
+      " 1980.2401  1967.88    1955.3998  1943.1597  1930.8002  1918.56\n",
+      " 1906.1998  1894.0797  1881.7202  1869.6002  1857.4801  1845.3602\n",
+      " 1833.2401  1821.2401  1809.12    1797.2401  1785.2401  1773.3602\n",
+      " 1761.3602  1749.6002  1737.7202  1725.9598  1714.0797  1702.44\n",
+      " 1690.56    1678.9203  1667.2798  1652.7599  1636.32    1620.12\n",
+      " 1604.0402  1587.9598  1571.7599  1555.9203  1540.0797  1524.12\n",
+      " 1508.5199  1492.8002  1477.1998  1458.2401  1435.0797  1412.04\n",
+      " 1389.12    1366.32    1343.7599  1321.4401  1299.12    1277.1602\n",
+      " 1255.0797  1233.3602  1211.7599  1190.28    1169.0399  1147.9202\n",
+      " 1127.0399  1106.28    1085.6399  1065.12    1044.84    1024.0798\n",
+      " 1001.16     978.4799   955.92017  933.4799   911.4001   889.44\n",
+      "  867.5999   846.12006  824.6399   803.4001   782.5201   761.75995\n",
+      "  741.12006  720.84     700.6801   680.64     660.84     641.27997\n",
+      "  621.84     602.76     583.8      564.84     546.2399   527.87994\n",
+      "  507.12003  486.12003  465.48007  444.84006  424.68005  404.76004\n",
+      "  384.96008  365.51993  346.31995  327.24   ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This script needs\n",
+    "# pip install matplotlib\n",
+    "# pip install seaborn==0.11.2\n",
+    "# pip install openfisca-france >= 80.2.0\n",
+    "\n",
+    "from openfisca_france import FranceTaxBenefitSystem\n",
+    "from openfisca_france.scenarios import init_single_entity\n",
+    "\n",
+    "\n",
+    "tax_benefit_system = FranceTaxBenefitSystem()\n",
+    "current_period = 2022\n",
+    "STEPS_COUNT = 100\n",
+    "\n",
+    "scenario = init_single_entity(\n",
+    "    tax_benefit_system.new_scenario(),\n",
+    "    # Axe declaration\n",
+    "    axes=[\n",
+    "        [\n",
+    "            dict(  #  in a dictionary\n",
+    "                count=STEPS_COUNT,  # 'count' indicates the number of steps\n",
+    "                min=0,\n",
+    "                max=100000,\n",
+    "                name=\"aide_logement_base_ressources\",  # the variable that will evolve 'count' times between 'min' and 'max' values\n",
+    "            ),\n",
+    "        ],\n",
+    "    ],\n",
+    "    period=current_period,\n",
+    "    menage = dict(\n",
+    "        loyer=2106,\n",
+    "        statut_occupation_logement=\"locataire_foyer\", # openfisca_france.model.base.TypesStatutOccupationLogement.locataire_foyer\n",
+    "        logement_conventionne=True\n",
+    "    ),\n",
+    "    parent1=dict(\n",
+    "        date_naissance=\"1980-01-01\",\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "simulation = scenario.new_simulation()\n",
+    "\n",
+    "aide_logement_base_ressources = simulation.calculate_add(\"aide_logement_base_ressources\", current_period)\n",
+    "apl = simulation.calculate_add(\"apl\", current_period)\n",
+    "\n",
+    "print(f\"Les {STEPS_COUNT} valeurs :\")\n",
+    "print(\"apl : \", apl)\n",
+    "\n",
+    "# print(simulation.menage.get_holder('statut_occupation_logement').get_array('2022-01'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"darkgrid\")\n",
+    "sns.lineplot(x=aide_logement_base_ressources, y=apl)\n",
+    "\n",
+    "plt.xlabel(\"base de ressources\")\n",
+    "plt.ylabel(\"APL\")\n",
+    "# plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "e37ffc178c1572129188c9b315d1f1e12bae309a221bd084075c1ff803967aa4"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
+%% Cell type:markdown id: tags:
+
+# Aides logement
+
+...
+
+## Références
+
+* service-public : [ALS](https://www.service-public.fr/particuliers/vosdroits/F1280)
+* openfisca-france : [ALS](https://legislation.fr.openfisca.org/als)
+
+## Institutions concernées
+
+## Publications & Aggrégats
+
+* ...
+
+%% Cell type:code id: tags:
+
+``` python
+# This script needs
+# pip install matplotlib
+# pip install seaborn==0.11.2
+# pip install openfisca-france >= 80.2.0
+
+from openfisca_france import FranceTaxBenefitSystem
+from openfisca_france.scenarios import init_single_entity
+
+
+tax_benefit_system = FranceTaxBenefitSystem()
+current_period = 2022
+STEPS_COUNT = 100
+
+scenario = init_single_entity(
+    tax_benefit_system.new_scenario(),
+    # Axe declaration
+    axes=[
+        [
+            dict(  #  in a dictionary
+                count=STEPS_COUNT,  # 'count' indicates the number of steps
+                min=0,
+                max=100000,
+                name="aide_logement_base_ressources",  # the variable that will evolve 'count' times between 'min' and 'max' values
+            ),
+        ],
+    ],
+    period=current_period,
+    menage = dict(
+        loyer=2106,
+        statut_occupation_logement="locataire_foyer", # openfisca_france.model.base.TypesStatutOccupationLogement.locataire_foyer
+        logement_conventionne=True
+    ),
+    parent1=dict(
+        date_naissance="1980-01-01",
+    ),
+)
+
+simulation = scenario.new_simulation()
+
+aide_logement_base_ressources = simulation.calculate_add("aide_logement_base_ressources", current_period)
+apl = simulation.calculate_add("apl", current_period)
+
+print(f"Les {STEPS_COUNT} valeurs :")
+print("apl : ", apl)
+
+# print(simulation.menage.get_holder('statut_occupation_logement').get_array('2022-01'))
+```
+
+%% Output
+
+    Les 100 valeurs :
+    apl :  [2055.36    2042.7599  2030.1597  2017.68    2005.1998  1992.7202
+     1980.2401  1967.88    1955.3998  1943.1597  1930.8002  1918.56
+     1906.1998  1894.0797  1881.7202  1869.6002  1857.4801  1845.3602
+     1833.2401  1821.2401  1809.12    1797.2401  1785.2401  1773.3602
+     1761.3602  1749.6002  1737.7202  1725.9598  1714.0797  1702.44
+     1690.56    1678.9203  1667.2798  1652.7599  1636.32    1620.12
+     1604.0402  1587.9598  1571.7599  1555.9203  1540.0797  1524.12
+     1508.5199  1492.8002  1477.1998  1458.2401  1435.0797  1412.04
+     1389.12    1366.32    1343.7599  1321.4401  1299.12    1277.1602
+     1255.0797  1233.3602  1211.7599  1190.28    1169.0399  1147.9202
+     1127.0399  1106.28    1085.6399  1065.12    1044.84    1024.0798
+     1001.16     978.4799   955.92017  933.4799   911.4001   889.44
+      867.5999   846.12006  824.6399   803.4001   782.5201   761.75995
+      741.12006  720.84     700.6801   680.64     660.84     641.27997
+      621.84     602.76     583.8      564.84     546.2399   527.87994
+      507.12003  486.12003  465.48007  444.84006  424.68005  404.76004
+      384.96008  365.51993  346.31995  327.24   ]
+
+%% Cell type:code id: tags:
+
+``` python
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.set_theme(style="darkgrid")
+sns.lineplot(x=aide_logement_base_ressources, y=apl)
+
+plt.xlabel("base de ressources")
+plt.ylabel("APL")
+# plt.legend()
+
+plt.show()
+```
+
+%% Output
+
+
+
+%% Cell type:code id: tags:
+
+``` python
+```