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(
