def produce_agg_transfert_flux(simulation=simulation,
year_list=range(1996, 2050, 10),
year_min=1996):
tmp = simulation.cohorts.loc[:, ['net_transfers', 'pop', 'dsct']]
tmp['running_transfers'] = tmp['net_transfers']
tmp['net_transfers'] *= tmp['dsct'] * tmp['pop']
tmp_2 = simulation.cohorts_alt.loc[:, ['net_transfers', 'pop', 'dsct']]
tmp_2['running_transfers'] = tmp_2['net_transfers']
tmp_2['net_transfers'] *= tmp_2['dsct'] * tmp_2['pop']
for year in year_list:
flux_df = AccountingCohorts(tmp).extract_generation(
year=year, typ='net_transfers', age=0)
flux_df = flux_df.xs(0, level='sex')
flux_df.columns = [year]
print year
flux_df_alt = AccountingCohorts(tmp_2).extract_generation(
year=year, typ='net_transfers', age=0)
flux_df_alt = flux_df_alt.xs(0, level='sex')
flux_df_alt.columns = [str(year) + '_alt']
flux_df = concat([flux_df, flux_df_alt], axis=1) #, ignore_index=True)
flux_df[year] *= ((1 + simulation.discount_rate) /
(1 + simulation.growth_rate))**(year - year_min)
flux_df[str(year) +
'_alt'] *= ((1 + simulation.discount_rate_alt) /
(1 + simulation.growth_rate_alt))**(year -
year_min)
print flux_df.head()
flux_df.to_excel(str(xls) + str(year) + '_ESP_agg.xlsx', 'flux')
gc.collect()
def produce_agg_transfert_flux(simulation=simulation, year_list = range(1996, 2050, 10), year_min=1996):
print 'entering generation of aggregated flux of payments '
tmp = simulation.cohorts.loc[:, ['net_transfers', 'pop', 'dsct']]
tmp['running_transfers'] = tmp['net_transfers']
tmp['net_transfers'] *= tmp['dsct']*tmp['pop']
tmp_2 = simulation.cohorts_alt.loc[:, ['net_transfers', 'pop', 'dsct']]
tmp_2['running_transfers'] = tmp_2['net_transfers']
tmp_2['net_transfers'] *= tmp_2['dsct']*tmp_2['pop']
print type(str(str(xls)+'\agre_agg.xlsx'))
for year in year_list:
flux_df = AccountingCohorts(tmp).extract_generation(year=year, typ='net_transfers', age=0)
flux_df = flux_df.xs(0, level='sex')
flux_df.columns = [year]
print year
flux_df_alt = AccountingCohorts(tmp_2).extract_generation(year=year, typ='net_transfers', age=0)
flux_df_alt = flux_df_alt.xs(0, level='sex')
flux_df_alt.columns = [str(year)+'_alt']
flux_df = concat([flux_df, flux_df_alt], axis=1)#, ignore_index=True)
flux_df[year] *= ((1+simulation.discount_rate)/(1+simulation.growth_rate))**(year - year_min)
flux_df[str(year)+'_alt'] *= ((1+simulation.discount_rate_alt)/(1+simulation.growth_rate_alt))**(year - year_min)
print flux_df.head()
flux_df.to_excel(xls+'\_combine_agg_'+str(year)+'.xlsx', 'flux')
gc.collect()
def simple_scenario():
#Initialisation et entrée des paramètres de base :
simulation = Simulation()
year_length = 300
simulation.set_year_length(nb_year=year_length)
net_gov_wealth = -3217.7e+09
year_gov_spending = (1094)*1e+09
net_gov_wealth_alt = -3217.7e+09
year_gov_spending_alt = (1094)*1e+09
taxes_list = ['tva', 'tipp', 'cot', 'irpp', 'impot', 'property']
payments_list = ['chomage', 'retraite', 'revsoc', 'maladie', 'educ']
simulation.set_population_projection(year_length=simulation.year_length, method="exp_growth")
#On charge la population:
population_scenario = "projpop0760_FECbasESPbasMIGbas"
store_pop = HDFStore(os.path.join(SRC_PATH, 'countries', country, 'sources',
'Carole_Bonnet', 'pop_1996_2006.h5'))
corrected_pop = store_pop['population']
simulation.population = concat([corrected_pop.iloc[0:101, :], corrected_pop.iloc[1111:1212,:]]) #<- la première année TODO: c'est moche
simulation.load_population(population_filename, population_scenario, default=False)
simulation.population_alt = concat([corrected_pop, simulation.population_alt])
simulation.load_profiles(profiles_filename)
r = 0.03
g = 0.01
n = 0.00
r_alt = r
g_alt = g
n_alt = 0.00
#On crée le témoin :
simulation.set_tax_projection(method="aggregate", rate=g)
simulation.set_growth_rate(g)
simulation.set_discount_rate(r)
simulation.set_population_growth_rate(n)
simulation.create_cohorts()
simulation.set_gov_wealth(net_gov_wealth)
simulation.set_gov_spendings(year_gov_spending, default=True, compute=True)
simulation.cohorts.compute_net_transfers(name = 'net_transfers', taxes_list = taxes_list, payments_list = payments_list)
simulation.create_present_values('net_transfers', default=True)
#On crée le groupe test :
simulation.set_growth_rate(g_alt, default=False)
simulation.set_discount_rate(r_alt, default=False)
simulation.set_population_growth_rate(n_alt, default=False)
simulation.create_cohorts(default=False)
simulation.cohorts_alt.loc[[x>=2015 for x in simulation.cohorts_alt.index.get_level_values(2)], 'retraite'] *= (1/2)
simulation.set_gov_wealth(net_gov_wealth_alt, default=False)
simulation.set_gov_spendings(year_gov_spending_alt, default=False, compute=True)
simulation.cohorts_alt.compute_net_transfers(name = 'net_transfers', taxes_list = taxes_list, payments_list = payments_list)
simulation.create_present_values('net_transfers', default=False)
#Calcul de l'IPL et de sa décomposition
ipl_base = simulation.compute_ipl(typ='net_transfers')
ipl_alt = simulation.compute_ipl(typ='net_transfers', default=False, precision=False)
tmp = simulation.cohorts.loc[:, ['net_transfers', 'pop', 'dsct']]
tmp['running_transfers'] = tmp['net_transfers']
tmp['net_transfers'] *= tmp['dsct']
tmp_2 = simulation.cohorts_alt.loc[:, ['net_transfers', 'pop', 'dsct']]
tmp_2['running_transfers'] = tmp_2['net_transfers']
tmp_2['net_transfers'] *= tmp_2['dsct']
xls = "C:/Users/Utilisateur/Documents/GitHub/ga/src/countries/france/sources/Output_folder/"
for year in range(1996, 2007):
flux_df = AccountingCohorts(tmp).extract_generation(year=year, typ='net_transfers', age=0)
flux_df = flux_df.xs(0, level='sex')
print year
flux_df_alt = AccountingCohorts(tmp_2).extract_generation(year=year, typ='net_transfers', age=0)
flux_df_alt = flux_df_alt.xs(0, level='sex')
flux_df[year] = flux_df_alt['net_transfers']
flux_df.to_excel(str(xls)+str(year)+'_.xlsx', 'flux')
print ipl_base, ipl_alt
