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')
