def run_survey_simulation(year = None):
assert year is not None
input_data_frame = get_input_data_frame(year)
TaxBenefitSystem = openfisca_france_indirect_taxation.init_country()
tax_benefit_system = TaxBenefitSystem()
survey_scenario = SurveyScenario().init_from_data_frame(
input_data_frame = input_data_frame,
tax_benefit_system = tax_benefit_system,
year = year,
)
simulation = survey_scenario.new_simulation()
simulation.calculate('tva_taux_plein')
return DataFrame(
dict([
(name, simulation.calculate(name)) for name in [
'tva_taux_plein',
'tva_taux_intermediaire',
'tva_taux_reduit',
'tva_taux_super_reduit',
'ident_men',
'pondmen',
'decuc',
'poste_coicop_611',
'categorie_fiscale_7',
'depenses_carburants',
]
])
)
def test_survey_simulation(year = 2005):
input_data_frame = get_input_data_frame(year)
TaxBenefitSystem = openfisca_france_indirect_taxation.init_country()
tax_benefit_system = TaxBenefitSystem()
survey_scenario = SurveyScenario().init_from_data_frame(
input_data_frame = input_data_frame,
tax_benefit_system = tax_benefit_system,
year = year,
)
simulation = survey_scenario.new_simulation()
simulation.calculate('tva_taux_plein')
return DataFrame(
dict([
(name, simulation.calculate(name)) for name in [
'tva_taux_plein',
'tva_taux_intermediaire',
'tva_taux_reduit',
'tva_taux_super_reduit',
'ident_men',
'pondmen',
'decuc',
'poste_coicop_611',
'categorie_fiscale_7',
'depenses_carburants',
]
])
)
def simulate_df(var_to_be_simulated, year):
"""
Construction de la DataFrame à partir de laquelle sera faite l'analyse des données
"""
input_data_frame = get_input_data_frame(year)
TaxBenefitSystem = openfisca_france_indirect_taxation.init_country()
tax_benefit_system = TaxBenefitSystem()
survey_scenario = SurveyScenario().init_from_data_frame(
input_data_frame=input_data_frame, tax_benefit_system=tax_benefit_system, year=year
)
simulation = survey_scenario.new_simulation()
return DataFrame(dict([(name, simulation.calculate(name)) for name in var_to_be_simulated]))
def test():
# inflators_by_year = get_inflators_by_year()
# Liste des coicop agrégées en 12 postes
simulated_variables = ['coicop12_{}'.format(coicop12_index) for coicop12_index in range(1, 13)]
year = 2011
elasticities = get_elasticities(year)
inflation_kwargs = None # dict(inflator_by_variable = inflators_by_year[year])
survey_scenario = SurveyScenario.create(
elasticities = elasticities,
inflation_kwargs = inflation_kwargs,
year = year,
)
coicop12_1 = survey_scenario.simulation.calculate('coicop12_1')
elasticite_1 = survey_scenario.simulation.calculate('elas_exp_1')
print elasticite_1
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values = [values], columns = ['niveau_vie_decile'])
])
df = pivot_table.T
df['depenses_tot'] = df[['coicop12_{}'.format(i) for i in range(1, 13)]].sum(axis = 1)
for i in range(1, 13):
df['part_coicop12_{}'.format(i)] = \
df['coicop12_{}'.format(i)] / df['depenses_tot']
graph_builder_bar(df[['part_coicop12_{}'.format(i) for i in range(1, 13)]])
return toto
def simulate(simulated_variables, year):
'''
Construction de la DataFrame à partir de laquelle sera faite l'analyse des données
'''
input_data_frame = get_input_data_frame(year)
TaxBenefitSystem = openfisca_france_indirect_taxation.init_country()
tax_benefit_system = TaxBenefitSystem()
survey_scenario = SurveyScenario().init_from_data_frame(
input_data_frame=input_data_frame,
tax_benefit_system=tax_benefit_system,
year=year,
)
simulation = survey_scenario.new_simulation()
return DataFrame(
dict([(name, simulation.calculate(name))
for name in simulated_variables]))
def simulate_df_calee_on_ticpe(simulated_variables, year):
'''
Construction de la DataFrame à partir de laquelle sera faite l'analyse des données
'''
input_data_frame = get_input_data_frame(year)
input_data_frame_calee = build_df_calee_on_ticpe(input_data_frame, year, year)
TaxBenefitSystem = openfisca_france_indirect_taxation.init_country()
tax_benefit_system = TaxBenefitSystem()
survey_scenario = SurveyScenario().init_from_data_frame(
input_data_frame = input_data_frame_calee,
tax_benefit_system = tax_benefit_system,
year = year,
)
simulation = survey_scenario.new_simulation()
return DataFrame(
dict([
(name, simulation.calculate(name)) for name in simulated_variables
])
)
def simulate_df(year = 2005):
'''
Construction de la DataFrame à partir de laquelle sera faite l'analyse des données
'''
input_data_frame = get_input_data_frame(year)
TaxBenefitSystem = openfisca_france_indirect_taxation.init_country()
tax_benefit_system = TaxBenefitSystem()
survey_scenario = SurveyScenario().init_from_data_frame(
input_data_frame = input_data_frame,
tax_benefit_system = tax_benefit_system,
year = year,
)
simulation = survey_scenario.new_simulation()
return pandas.DataFrame(
dict([
(name, simulation.calculate(name)) for name in [
'montant_tva_taux_plein',
'consommation_tva_taux_plein',
'categorie_fiscale_11',
'montant_tva_taux_intermediaire',
'consommation_tva_taux_intermediaire',
'montant_tva_taux_reduit',
'montant_tva_taux_super_reduit',
'montant_tva_total',
'ident_men',
'pondmen',
'decuc',
'age',
'revtot',
'rev_disponible',
'ocde10',
'niveau_de_vie',
'consommation_totale',
'decile',
]
])
)
def create_survey_scenario(year=None):
assert year is not None
input_data_frame = get_input_data_frame(year)
TaxBenefitSystem = openfisca_france_indirect_taxation.init_country()
tax_benefit_system = TaxBenefitSystem()
survey_scenario = SurveyScenario().init_from_data_frame(
input_data_frame=input_data_frame,
tax_benefit_system=tax_benefit_system,
year=year,
)
return survey_scenario
def test():
# inflators_by_year = get_inflators_by_year()
# Liste des coicop agrégées en 12 postes
simulated_variables = [
'coicop12_{}'.format(coicop12_index)
for coicop12_index in range(1, 13)
]
year = 2011
elasticities = get_elasticities(year)
inflation_kwargs = None # dict(inflator_by_variable = inflators_by_year[year])
survey_scenario = SurveyScenario.create(
elasticities=elasticities,
inflation_kwargs=inflation_kwargs,
year=year,
)
coicop12_1 = survey_scenario.simulation.calculate('coicop12_1')
elasticite_1 = survey_scenario.simulation.calculate('elas_exp_1')
print elasticite_1
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values=[values],
columns=['niveau_vie_decile'])
])
df = pivot_table.T
df['depenses_tot'] = df[['coicop12_{}'.format(i)
for i in range(1, 13)]].sum(axis=1)
for i in range(1, 13):
df['part_coicop12_{}'.format(i)] = \
df['coicop12_{}'.format(i)] / df['depenses_tot']
graph_builder_bar(df[['part_coicop12_{}'.format(i) for i in range(1, 13)]])
return toto
import pandas
import seaborn
# Import de modules spécifiques à Openfisca
from openfisca_france_indirect_taxation.examples.utils_example import graph_builder_bar
from openfisca_france_indirect_taxation.surveys import SurveyScenario
# Import d'une nouvelle palette de couleurs
seaborn.set_palette(seaborn.color_palette("Set2", 12))
if __name__ == '__main__':
# Sélection des variables utilisées pour la simulation
simulated_variables = ['depenses_carburants', 'depenses_essence', 'depenses_diesel', 'revtot']
for year in [2000, 2005, 2011]:
survey_scenario = SurveyScenario.create(year = year)
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values = [values], columns = ['strate'])
])
df = pivot_table.T
# Réalisation de graphiques
for element in simulated_variables:
if element == 'revtot':
continue
df['part_{}_revtot'.format(element)] = \
df['{}'.format(element)] / df['revtot']
graph_builder_bar(df[['part_{}_revtot'.format(element)]])
'vin_droit_d_accise',
'biere_droit_d_accise',
'alcools_forts_droit_d_accise',
'cigarette_droit_d_accise',
'cigares_droit_d_accise',
'tabac_a_rouler_droit_d_accise',
'assurance_transport_taxe',
'assurance_sante_taxe',
'autres_assurances_taxe',
'revtot',
'rev_disponible',
'somme_coicop12',
'taxes_indirectes_total'
]
for year in [2000, 2005, 2011]:
survey_scenario = SurveyScenario.create(year = year)
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values = [values], columns = ['niveau_vie_decile'])
])
taxe_indirectes = pivot_table.T
taxe_indirectes['TVA'] = taxe_indirectes['tva_total']
taxe_indirectes['TICPE'] = taxe_indirectes['ticpe_totale']
taxe_indirectes[u'Taxes alcools'] = (
taxe_indirectes['vin_droit_d_accise'] +
taxe_indirectes['biere_droit_d_accise'] +
taxe_indirectes['alcools_forts_droit_d_accise']
).copy()
seaborn.set_palette(seaborn.color_palette("Set2", 12))
if __name__ == '__main__':
inflators_by_year = get_inflators_by_year()
# Liste des coicop agrégées en 12 postes
simulated_variables = ['coicop12_{}'.format(coicop12_index) for coicop12_index in range(1, 13)]
year = 2011
elasticities = get_elasticities(year)
inflation_kwargs = dict(inflator_by_variable = inflators_by_year[year])
survey_scenario = SurveyScenario.create(
elasticities = elasticities,
inflation_kwargs = inflation_kwargs,
year = year,
)
coicop12_1 = survey_scenario.simulation.calculate('coicop12_1')
elasticite_1 = survey_scenario.simulation.calculate('elas_exp_1')
# boum
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values = [values], columns = ['niveau_vie_decile'])
])
df = pivot_table.T
df['depenses_tot'] = df[['coicop12_{}'.format(i) for i in range(1, 13)]].sum(axis = 1)
if __name__ == '__main__':
inflators_by_year = get_inflators_by_year()
# Liste des coicop agrégées en 12 postes
simulated_variables = [
'coicop12_{}'.format(coicop12_index)
for coicop12_index in range(1, 13)
]
year = 2011
elasticities = get_elasticities(year)
inflation_kwargs = dict(inflator_by_variable=inflators_by_year[year])
survey_scenario = SurveyScenario.create(
elasticities=elasticities,
inflation_kwargs=inflation_kwargs,
year=year,
)
coicop12_1 = survey_scenario.simulation.calculate('coicop12_1')
elasticite_1 = survey_scenario.simulation.calculate('elas_exp_1')
# boum
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values=[values],
columns=['niveau_vie_decile'])
])
df = pivot_table.T
df['depenses_tot'] = df[['coicop12_{}'.format(i)
elasticities = get_elasticities_aidsills(data_year, True)
inflation_kwargs = dict(inflator_by_variable = inflators_by_year[year])
# Homogeneous + SL :
#elasticities['elas_price_1_1'] = -0.466
#elasticities['elas_price_2_2'] = -0.214
# Homogeneous, no SL :
#elasticities['elas_price_1_1'] = -0.440
#elasticities['elas_price_2_2'] = -0.139
survey_scenario = SurveyScenario.create(
#elasticities = elasticities,
inflation_kwargs = inflation_kwargs,
reform_key = 'officielle_2018_in_2016',
year = year,
data_year = data_year
)
simulated_variables = [
'quantites_gaz_liquefie',
'quantites_electricite_selon_compteur',
'quantites_gaz_final',
'quantites_essence',
'quantites_diesel',
'quantites_combustibles_liquides',
'depenses_carburants_corrigees',
'depenses_combustibles_liquides',
'depenses_combustibles_solides',
'depenses_diesel_corrigees',
