def test_homemade_nbptr_function(
reform_config_base_2020, nbptr_parametres_par_defaut, various_cas_types
):
# Verifie que les resultats de nbptr et irpp sont les mêmes avec la fonction par defaut
period = "2020"
data = dataframe_from_cas_types_description(various_cas_types)
tbs_reforme_sans_nbptr = IncomeTaxReform(
FranceTaxBenefitSystem(), reform_config_base_2020, period
)
tbs_reforme_avec_nbptr = IncomeTaxReform(
FranceTaxBenefitSystem(),
{
"impot_revenu": {
**(reform_config_base_2020["impot_revenu"]),
**nbptr_parametres_par_defaut,
}
},
period,
)
sim_sans_nbptr, _ = simulation(period, data, tbs_reforme_sans_nbptr)
sim_avec_nbptr, _ = simulation(period, data, tbs_reforme_avec_nbptr)
print("sans", sim_sans_nbptr.calculate("nbptr", period))
print("avec", sim_avec_nbptr.calculate("nbptr", period))
assert array_equal(
sim_sans_nbptr.calculate("nbptr", period),
sim_avec_nbptr.calculate("nbptr", period),
)
assert array_equal(
sim_sans_nbptr.calculate("irpp", period),
sim_avec_nbptr.calculate("irpp", period),
)
def CompareOldNew(taux=None, isdecile=True, dictreform=None, castypedesc=None):
# if isdecile, we want the impact on the full population, while just a cas type on
# the isdecile=False
if isdecile:
data = DUMMY_DATA
reform = IncomeTaxReform(TBS, dictreform, PERIOD)
simulation_reform = simulation(PERIOD, data, reform)
return compare(PERIOD, {"apres": simulation_reform}, isdecile)
data, simulation_base, simulation_plf = (
(CAS_TYPE, simulation_base_castypes, simulation_plf_castypes)
if castypedesc is None else simulation_from_cas_types(castypedesc))
reform = IncomeTaxReform(TBS, dictreform, PERIOD)
simulation_reform = simulation(PERIOD, data, reform)
if dictreform is None:
assert taux is not None
dictreform = {
"impot_revenu": {
"bareme": {
"seuils": [0] + taux[:len(taux) // 2],
"taux": [0.0] + taux[len(taux) // 2:],
}
}
}
return compare(
PERIOD,
{
"avant": simulation_base,
"plf": simulation_plf,
"apres": simulation_reform
},
isdecile,
)
def CompareOldNew(taux=None, isdecile=True, dictreform=None, castypedesc=None):
# if isdecile, we want the impact on the full population, while just a cas type on
# the isdecile=False
if isdecile:
# On prend les données de la population entière
data = DUMMY_DATA
reform = IncomeTaxReform(TBS, dictreform, PERIOD)
# On ne crée qu'une simulation, le moteur de calcul ayant précalculé les autres.
simulation_reform = simulation(PERIOD, data, reform)
# On n'envoie à simuler que le "apres"
return compare(PERIOD, {"apres": simulation_reform}, isdecile)
# Pour les cas-types, on n'utilise pas de resultats précalculés
# donc on inclut aussi les simulations par défaut
data, default_sims = (
(CAS_TYPE, simulations_reformes_par_defaut_castypes)
if castypedesc is None
else simulation_from_cas_types(castypedesc)
)
reform = IncomeTaxReform(TBS, dictreform, PERIOD)
simulation_reform = simulation(PERIOD, data, reform)
default_sims["apres"] = simulation_reform
if dictreform is None:
assert taux is not None
dictreform = {
"impot_revenu": {
"bareme": {
"seuils": [0] + taux[: len(taux) // 2],
"taux": [0.0] + taux[len(taux) // 2 :],
}
}
}
return compare(PERIOD, default_sims, isdecile)
def test_zero_nbptr(reform_config_base_2020, nbptr_zero, various_cas_types):
# Verifie que les resultats de nbptr sont bien zero pour tout le monde si tous les param
# sont à zéro
period = "2020"
data = dataframe_from_cas_types_description(various_cas_types)
tbs_reforme_avec_nbptr = IncomeTaxReform(
FranceTaxBenefitSystem(),
{"impot_revenu": {**(reform_config_base_2020["impot_revenu"]), **nbptr_zero}},
period,
)
sim_avec_nbptr, _ = simulation(period, data, tbs_reforme_avec_nbptr)
resultats_nbptr = sim_avec_nbptr.calculate("nbptr", period)
assert not resultats_nbptr.any()
def test_deux_adultes_ancien_combattants_deux_enfants(reform_config_base_2020):
# données
foyer = {
"declarants": [
{
"ancienCombattant": True,
"invalide": False,
"parentIsole": False,
"retraite": False,
"veuf": False
},
{
"ancienCombattant": True,
"invalide": False,
"parentIsole": False,
"retraite": False,
"veuf": False
}
],
"personnesACharge": [
{
"chargePartagee": False,
"invalide": False
},
{
"chargePartagee": False,
"invalide": False
}
],
"residence": "metropole",
"revenuImposable": 120000
}
data = dataframe_from_cas_types_description([foyer])
period = "2020"
# loi française + réforme IR
tbs_reforme_impot_revenu = IncomeTaxReform(
FranceTaxBenefitSystem(), reform_config_base_2020, period
)
built_simulation, _dict_data_by_entity = simulation(
period, data, tbs_reforme_impot_revenu
)
nbptr = built_simulation.calculate("nbptr", period)
assert nbptr == [3.5]
avantx = dfv[bestsol][1]
apresx = dfv[bestsol + 1][1]
lambda_ = (val_brute - avanty) / (apresy - avanty)
return lambda_ * apresx + (1 - lambda_) * avantx
conversion_variables = {}
conversion_variables["salaire_de_base_to_salaire_imposable"] = scenar_values(
0, 12_000_000, "salaire_de_base", "salaire_imposable")
conversion_variables["retraite_brute_to_retraite_imposable"] = scenar_values(
0, 12_000_000, "retraite_brute", "retraite_imposable")
PERIOD = "2018"
TBS = FranceTaxBenefitSystem()
TBS_PLF = IncomeTaxReform(TBS, reformePLF, PERIOD)
CAS_TYPE = load_data("DCT.csv")
SIMCAT = partial(simulation, period=PERIOD, data=CAS_TYPE)
SIMCAT_BASE = SIMCAT(tbs=TBS)
if not version_beta_sans_simu_pop:
# Initialisation des données utilisées pour le calcul sur la population
DUMMY_DATA = load_data(data_path).sort_values(by="idfoy")
print(
"Dummy Data loaded",
len(DUMMY_DATA),
"lines",
len(DUMMY_DATA["idfoy"].unique()),
"foyers fiscaux",
)
# Resultats sur la population du code existant et du PLF. Ne change jamais donc pas besoin de fatiguer l'ordi à calculer
conversion_variables = {}
conversion_variables["salaire_de_base_to_salaire_imposable"] = scenar_values(
0, 12_000_000, "salaire_de_base", "salaire_imposable"
)
conversion_variables["retraite_brute_to_retraite_imposable"] = scenar_values(
0, 12_000_000, "retraite_brute", "retraite_imposable"
)
PERIOD = str(annee_de_calcul)
TBS = FranceTaxBenefitSystem()
TBS_DEFAULT = {"avant": TBS}
for nom_reforme in reformes_par_defaut:
TBS_DEFAULT[nom_reforme] = IncomeTaxReform(
TBS, reformes_par_defaut[nom_reforme], PERIOD
)
CAS_TYPE = load_data("DCT.csv")
DUMMY_DATA = (
CAS_TYPE
if version_beta_sans_simu_pop
else load_data(data_path).sort_values(by="idfoy")
)
# Initialisation des données utilisées pour le calcul sur la population
NB_FOYERS_FISCAUX_SIMULES = len(DUMMY_DATA["idfoy"].unique())
logging.info(
"Dummy Data loaded "
+ str(len(DUMMY_DATA))
+ " lines "
+ str(NB_FOYERS_FISCAUX_SIMULES)