def transition(): levels = ['haut', 'bas'] taxes_list = ['tva', 'tipp', 'cot', 'irpp', 'impot', 'property'] payments_list = ['chomage', 'retraite', 'revsoc', 'maladie', 'educ'] year_length = 250 year_min = 1996 year_max = year_min+year_length-1 arrays=arange(year_min, year_min+60) record = DataFrame(index=arrays) simulation = Simulation() for param1 in levels: for param2 in levels: population_scenario = "projpop0760_FEC"+param1+"ESP"+param2+"MIG"+param1 simulation.load_population(population_filename, population_scenario) # Adding missing population data between 1996 and 2007 : 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, simulation.population]) simulation.load_profiles(profiles_filename) simulation.year_length = year_length r = 0.03 g = 0.01 n = 0.00 net_gov_wealth = -3217.7e+09 year_gov_spending = (1094)*1e+09 # Loading simulation's parameters : simulation.set_population_projection(year_length=year_length, method="stable") simulation.set_tax_projection(method="per_capita", rate=g) simulation.set_growth_rate(g) simulation.set_discount_rate(r) simulation.set_population_growth_rate(n) simulation.set_gov_wealth(net_gov_wealth) simulation.set_gov_spendings(year_gov_spending, default=True, compute=True) record[population_scenario] = NaN col_name2 = population_scenario+'_precision' record[col_name2] = NaN simulation.create_cohorts() simulation.cohorts.compute_net_transfers(name = 'net_transfers', taxes_list = taxes_list, payments_list = payments_list) simulation.create_present_values(typ='net_transfers') for year in range(year_min, year_min+60): #On tente de tronquer la df au fil du temps try: simulation.aggregate_pv = simulation.aggregate_pv.drop(labels=year-1, level='year') except: print 'except path' pass simulation.aggregate_pv = AccountingCohorts(simulation.aggregate_pv) # imbalance = simulation.compute_gen_imbalance(typ='net_transfers') ipl = simulation.compute_ipl(typ='net_transfers') # Calcul du résidut de l'IPL pour vérifier la convergence #(on se place tard dans la projection) precision_df = simulation.aggregate_pv print precision_df.head().to_string() year_min_ = array(list(precision_df.index.get_level_values(2))).min() year_max_ = array(list(precision_df.index.get_level_values(2))).max() - 1 # age_min = array(list(self.index.get_level_values(0))).min() age_max_ = array(list(precision_df.index.get_level_values(0))).max() print 'CALIBRATION CHECK : ', year_min_, year_max_ past_gen_dataframe = precision_df.xs(year_min_, level = 'year') past_gen_dataframe = past_gen_dataframe.cumsum() past_gen_transfer = past_gen_dataframe.get_value((age_max_, 1), 'net_transfers') # print ' past_gen_transfer = ', past_gen_transfer future_gen_dataframe = precision_df.xs(0, level = 'age') future_gen_dataframe = future_gen_dataframe.cumsum() future_gen_transfer = future_gen_dataframe.get_value((1, year_max_), 'net_transfers') # print ' future_gen_transfer =', future_gen_transfer #Note : do not forget to eliminate values counted twice last_ipl = past_gen_transfer + future_gen_transfer + net_gov_wealth - simulation.net_gov_spendings - past_gen_dataframe.get_value((0, 0), 'net_transfers') last_ipl = -last_ipl print last_ipl, ipl precision = (ipl - last_ipl)/ipl print 'precision = ', precision record.loc[year, population_scenario] = ipl record.loc[year, col_name2] = precision print record.head().to_string() xls = "C:/Users/Utilisateur/Documents/GitHub/ga/src/countries/france/sources/Carole_Bonnet/"+'ipl_evolution'+'.xlsx' print record.head(30).to_string() record.to_excel(xls, 'ipl')