log = logging.getLogger(__name__) import sys logging.basicConfig(level=logging.INFO, stream=sys.stdout) # Liste des variables que l'on veut simuler simulated_variables = [ 'pondmen', 'depenses_diesel', 'depenses_essence', 'coicop12_7' ] # Calcul des dépenses agrégées des ménages dans les transports, dont les carburants, dont l'essence et le diesel depenses_transports_totales = dict() depenses_carburants_totales = dict() depenses_diesel_totales = dict() depenses_essence_totales = dict() for year in [2000, 2005, 2011]: data_simulation = simulate_df_calee_on_ticpe( simulated_variables=simulated_variables, year=year) depenses_diesel = (data_simulation['depenses_diesel'] * data_simulation['pondmen']).sum() depenses_essence = (data_simulation['depenses_essence'] * data_simulation['pondmen']).sum() depenses_carburants = depenses_diesel + depenses_essence depenses_transports = (data_simulation['coicop12_7'] * data_simulation['pondmen']).sum() depenses_transports_totales['en {}'.format( year)] = depenses_transports / 1000000 depenses_carburants_totales['en {}'.format( year)] = depenses_carburants / 1000000 depenses_diesel_totales['en {}'.format( year)] = depenses_diesel / 1000000 depenses_essence_totales['en {}'.format(
'niveau_vie_decile', 'ocde10', 'vag', 'poste_coicop_411', 'poste_coicop_412', 'poste_coicop_421' ] simulated_variables_with_e10 = simulated_variables_without_e10 + ['sp_e10_ticpe'] # First, obtain the fuel consumption of each individual: for year in [2005]: try: data_simulation = \ simulate_df_calee_on_ticpe(simulated_variables = simulated_variables_with_e10, year = year) except: data_simulation = \ simulate_df_calee_on_ticpe(simulated_variables = simulated_variables_without_e10, year = year) data_simulation['sp_e10_ticpe'] = 0 del simulated_variables_with_e10, simulated_variables_without_e10 liste_carburants_accise = get_accise_ticpe_majoree() value_accise_diesel = liste_carburants_accise['accise majoree diesel'].loc[u'{}'.format(year)] / 100 value_accise_sp = liste_carburants_accise['accise majoree sans plomb'].loc[u'{}'.format(year)] / 100 value_accise_super_plombe = \ liste_carburants_accise['accise majoree super plombe'].loc[u'{}'.format(year)] / 100 data_simulation['quantite_diesel'] = data_simulation['diesel_ticpe'] / (value_accise_diesel) data_simulation['quantite_sans_plomb'] = (data_simulation['sp95_ticpe'] + data_simulation['sp98_ticpe'] + data_simulation['sp_e10_ticpe']) / (value_accise_sp)
'super_plombe_ticpe', 'diesel_ticpe' ] simulated_variables_without_e10 = [ 'pondmen', 'sp95_ticpe', 'sp98_ticpe', 'super_plombe_ticpe', 'diesel_ticpe' ] # Calcul des contributions agrégées des ménages sur la TICPE, dont diesel et essence depenses_ticpe_totales = dict() depenses_ticpe_diesel = dict() depenses_ticpe_essence = dict() for year in [2000, 2005, 2011]: try: data_simulation = \ simulate_df_calee_on_ticpe(simulated_variables = simulated_variables_with_e10, year = year) depenses_diesel_ticpe = (data_simulation['diesel_ticpe'] * data_simulation['pondmen']).sum() depenses_sp95_ticpe = (data_simulation['sp95_ticpe'] * data_simulation['pondmen']).sum() depenses_sp98_ticpe = (data_simulation['sp98_ticpe'] * data_simulation['pondmen']).sum() depenses_super_plombe_ticpe = ( data_simulation['super_plombe_ticpe'] * data_simulation['pondmen']).sum() depenses_sp_e10_ticpe = (data_simulation['sp_e10_ticpe'] * data_simulation['pondmen']).sum() except: data_simulation = \ simulate_df_calee_on_ticpe(simulated_variables = simulated_variables_without_e10, year = year) depenses_diesel_ticpe = (data_simulation['diesel_ticpe'] *
log = logging.getLogger(__name__) import sys logging.basicConfig(level=logging.INFO, stream=sys.stdout) # Liste des variables que l'on veut simuler simulated_variables = ["pondmen", "depenses_diesel", "depenses_essence", "coicop12_7"] # Calcul des dépenses agrégées des ménages dans les transports, dont les carburants, dont l'essence et le diesel depenses_transports_totales = dict() depenses_carburants_totales = dict() depenses_diesel_totales = dict() depenses_essence_totales = dict() for year in [2000, 2005, 2011]: data_simulation = simulate_df_calee_on_ticpe(simulated_variables=simulated_variables, year=year) depenses_diesel = (data_simulation["depenses_diesel"] * data_simulation["pondmen"]).sum() depenses_essence = (data_simulation["depenses_essence"] * data_simulation["pondmen"]).sum() depenses_carburants = depenses_diesel + depenses_essence depenses_transports = (data_simulation["coicop12_7"] * data_simulation["pondmen"]).sum() depenses_transports_totales["en {}".format(year)] = depenses_transports / 1000000 depenses_carburants_totales["en {}".format(year)] = depenses_carburants / 1000000 depenses_diesel_totales["en {}".format(year)] = depenses_diesel / 1000000 depenses_essence_totales["en {}".format(year)] = depenses_essence / 1000000 # Enregistrement des dépenses agrégées dans des fichiers csv assets_directory = os.path.join(pkg_resources.get_distribution("openfisca_france_indirect_taxation").location) writer_transports = csv.writer( open(