Colin Maudry
ded5e66ccc
Wraps temp file operations in try/except to ensure the temporary database file is always cleaned up, even if an exception occurs during write_snapshot or verify_integrity. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
198 lines
7.1 KiB
Plaintext
198 lines
7.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c5b66458e29113e9",
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"metadata": {},
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"outputs": [],
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"source": [
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"import polars as pl\n",
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"\n",
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"pl.Config(\n",
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" fmt_str_lengths=120,\n",
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" fmt_table_cell_list_len=50,\n",
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" set_tbl_rows=100,\n",
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" tbl_cols=-1,\n",
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")\n",
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"\n",
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"for methode_dividendes in (\"flat\", \"progressif\"):\n",
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" df: pl.DataFrame = pl.DataFrame()\n",
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"\n",
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" for i in range(0, 11):\n",
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" benefice_reel = 3000\n",
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" ca = 50000\n",
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" depenses_pro = 5000\n",
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" remuneration_plus_is = ca - depenses_pro - benefice_reel\n",
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" is_minimal = benefice_reel * 0.15\n",
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"\n",
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" salaires_brut = (remuneration_plus_is - is_minimal) * i / 10\n",
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"\n",
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" def taux_dividendes():\n",
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" taux = {}\n",
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" if methode_dividendes == \"flat\":\n",
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" taux = {\"retenue_source\": 0.7, \"abattement\": 0}\n",
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" elif methode_dividendes == \"progressif\":\n",
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" taux = {\"retenue_source\": 0.828, \"abattement\": 0.6}\n",
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" return taux\n",
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"\n",
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" def calcul_cout_dividendes():\n",
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" return remuneration_plus_is - salaires_brut - is_minimal\n",
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"\n",
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" def calcul_dividendes_bruts():\n",
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" return calcul_cout_dividendes() / 1.15\n",
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"\n",
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" def calcul_cout_remuneration():\n",
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" return calcul_dividendes_bruts() * 1.15 + salaires_brut\n",
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"\n",
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" def calcul_dividendes_net():\n",
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" dividendes_bruts = calcul_dividendes_bruts()\n",
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" dividendes_net = dividendes_bruts * taux_dividendes()[\"retenue_source\"]\n",
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" return dividendes_net\n",
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"\n",
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" def calcul_salaires_net():\n",
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" return salaires_brut * 0.563\n",
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"\n",
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" def calcul_revenu_imposable():\n",
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" return (calcul_salaires_net() * 0.9) + (\n",
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" calcul_dividendes_net() * taux_dividendes()[\"abattement\"]\n",
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" )\n",
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"\n",
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" def calcul_revenu_net():\n",
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" return calcul_salaires_net() + calcul_dividendes_net()\n",
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"\n",
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" def calcul_benefice():\n",
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" return ca - depenses_pro - salaires_brut\n",
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"\n",
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" def calcul_is():\n",
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" return (calcul_benefice() - is_minimal) * 0.15\n",
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"\n",
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" def calcul_ir(revenu):\n",
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" tranches_ir = [[11488, 0], [29315, 0.11], [83283, 0.3]]\n",
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" revenu_restant = revenu\n",
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" ir = 0\n",
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" for tranche in tranches_ir:\n",
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" if revenu_restant > 0:\n",
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" ir_tranche = min(revenu_restant, tranche[0]) * tranche[1]\n",
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" ir = +ir_tranche\n",
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" revenu_restant = revenu_restant - tranche[0]\n",
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"\n",
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" return ir\n",
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"\n",
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" def calcul_revenu_ae():\n",
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" apres_cotisations = calcul_cout_remuneration() * 0.76 - depenses_pro\n",
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" impots = calcul_ir(apres_cotisations * 0.66)\n",
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" return apres_cotisations - impots\n",
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"\n",
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" def fmt(value):\n",
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" return str(int(value))\n",
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"\n",
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" # print(salaires_brut)\n",
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" # print(calcul_cout_dividendes())\n",
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" # print(calcul_dividendes_bruts())\n",
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" # print(calcul_is())\n",
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" # print(\"\")\n",
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" # print(\"\")\n",
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" #\n",
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" # print(salaires_brut + calcul_dividendes_bruts() + calcul_is(), \" == \", remuneration_plus_is)\n",
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"\n",
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" # assert salaires_brut + calcul_dividendes_bruts() + calcul_is() == remuneration_plus_is\n",
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"\n",
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" row: dict = {\n",
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" \"Salaires bruts\": [fmt(salaires_brut / 12)],\n",
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" \"Dividendes bruts\": [fmt(calcul_cout_dividendes() / 12)],\n",
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" \"Benefice\": [fmt(calcul_benefice())],\n",
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" \"Benefice réél\": [\n",
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" fmt(calcul_benefice() - calcul_is() - calcul_dividendes_bruts())\n",
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" ],\n",
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" \"IS\": [fmt(calcul_is())],\n",
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" \"IR\": [fmt(calcul_ir(calcul_revenu_imposable()) / 12)],\n",
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" \"IS + IR\": [fmt(calcul_is() + calcul_ir(calcul_revenu_imposable()))],\n",
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" \"Salaires net\": [fmt(calcul_salaires_net() / 12)],\n",
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" \"Dividendes net\": [fmt(calcul_dividendes_net() / 12)],\n",
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" \"Revenu net ap. impôts\": [\n",
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" fmt((calcul_revenu_net() - calcul_ir(calcul_revenu_imposable())) / 12)\n",
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" ],\n",
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" \"Revenue AE ap. impôts\": [fmt(calcul_revenu_ae() / 12)],\n",
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" }\n",
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"\n",
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" df = pl.concat([df, pl.from_dict(row)])\n",
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"\n",
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" print(df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "initial_id",
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from dash import ALL, Dash, Input, Output, Patch, State, callback, dcc, html\n",
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"\n",
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"app = Dash()\n",
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"\n",
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"app.layout = html.Div(\n",
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" [\n",
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" html.Button(\"Add Filter\", id=\"add-filter-btn\", n_clicks=0),\n",
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" html.Div(id=\"dropdown-container-div\", children=[]),\n",
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" html.Div(id=\"dropdown-container-output-div\"),\n",
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" ]\n",
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")\n",
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"\n",
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"\n",
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"@callback(\n",
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" Output(\"dropdown-container-div\", \"children\"), Input(\"add-filter-btn\", \"n_clicks\")\n",
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")\n",
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"def display_dropdowns(n_clicks):\n",
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" patched_children = Patch()\n",
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" new_dropdown = dcc.Dropdown(\n",
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" [\"NYC\", \"MTL\", \"LA\", \"TOKYO\"],\n",
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" id={\"type\": \"city-filter-dropdown\", \"index\": n_clicks},\n",
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" )\n",
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" patched_children.append(new_dropdown)\n",
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" return patched_children\n",
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"\n",
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"\n",
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"@callback(\n",
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" Output(\"dropdown-container-output-div\", \"children\"),\n",
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" Input({\"type\": \"city-filter-dropdown\", \"index\": ALL}, \"value\"),\n",
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" State({\"type\": \"city-dynamic-dropdown\", \"index\": ALL}, \"id\"),\n",
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")\n",
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"def display_output(values, ids):\n",
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" return html.Div(\n",
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" [html.Div(f\"Dropdown {i + 1} = {value}\") for (i, value) in enumerate(values)]\n",
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" + [html.P(ids)],\n",
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" )\n",
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"\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" app.run(debug=True)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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