def run_to_print(data, yearsim):
param = PensionParam(yearsim, data)
legislation = PensionLegislation(param)
simul_til = PensionSimulation(data, legislation)
simul_til.profile_evaluate(to_check=False)
def load_til_pensipp(pensipp_comparison_path,
years,
to_print=(None, None, True)):
result_pensipp = load_pensipp_result(pensipp_comparison_path, to_csv=False)
result_til = pd.DataFrame(columns=var_to_check_montant + var_to_check_taux,
index=result_pensipp.index)
result_til['yearliq'] = -1
for yearsim in years:
print(yearsim)
data_bounded = load_pensipp_data(pensipp_comparison_path, yearsim,
first_year_sal)
param = PensionParam(yearsim, data_bounded)
legislation = PensionLegislation(param)
simul_til = PensionSimulation(data_bounded, legislation)
simul_til.set_config()
vars_to_calculate = dict()
result_til_year = dict()
P = simul_til.legislation.P
for regime in ['FP', 'RG', 'RSI']:
trim_regime = simul_til.calculate('nb_trimesters', regime)
for varname in [
'coeff_proratisation', 'DA', 'decote', 'n_trim', 'salref',
'surcote', 'taux', 'pension'
]:
if varname == 'coeff_proratisation':
result_til_year['CP_' + regime] = simul_til.calculate(
varname, regime) * (trim_regime > 0)
elif varname == 'decote':
param_name = simul_til.get_regime(regime).param_name
taux_plein = reduce(getattr, param_name.split('.'),
P).plein.taux
calc = simul_til.calculate(varname, regime)
result_til_year[
varname + '_' +
regime] = taux_plein * calc * (trim_regime > 0)
else:
if varname != 'n_trim':
calc = simul_til.calculate(varname, regime)
result_til_year[varname + '_' +
regime] = calc * (trim_regime > 0)
else:
result_til_year[varname + '_' +
regime] = simul_til.calculate(
varname, regime)
for regime in ['agirc', 'arrco']:
for varname in ['coefficient_age', 'nb_points', 'pension']:
if varname == 'coefficient_age':
result_til_year['coeff_age_' +
regime] = simul_til.calculate(
varname, regime)
if varname == 'nombre_points':
result_til_year['nb_points_' +
regime] = simul_til.calculate(
varname, regime)
else:
result_til_year[varname + '_' +
regime] = simul_til.calculate(
varname, regime)
result_til_year['N_CP_RG'] = simul_til.calculate('N_CP', 'RG')
result_til_year['pension_tot'] = simul_til.calculate('pension', 'all')
result_til_year = pd.DataFrame(result_til_year,
index=data_bounded.info_ind['index'])
id_year_in_initial = [
ident for ident in result_til_year.index
if ident in result_til.index
]
assert (id_year_in_initial == result_til_year.index).all()
result_til.loc[result_til_year.index, :] = result_til_year
result_til.loc[result_til_year.index, 'yearliq'] = yearsim
to_compare = (result_til['yearliq'] != -1)
til_compare = result_til.loc[to_compare, :]
pensipp_compare = result_pensipp.loc[to_compare, :]
return til_compare, pensipp_compare, simul_til
sexe = data['sexi'][0] == 2
nb_enf = data['nbenf'][0]
return workstate, sali, sexe, nb_enf
agem = (2009-1954 + 0.5)*12
naiss = dt.date(1954, 6, 1)
info_ind['agem'] = agem
info_ind['naiss'] = naiss
info_ind['tauxprime'] = 0
for i in range(nb_scenarios):
work_i, sali_i, sexe, nb_enf = load_case(i+1) # Attention déclage dans la numérotaiton qui ne commence pas à zeros
sali[i,:] = sali_i
workstate[i,:] = work_i
info_ind.loc[i,['sexe','nb_enf','nb_pac',
'nb_enf_RG','nb_enf_RSI','nb_enf_FP']] = [sexe, nb_enf, nb_enf,
nb_enf, nb_enf, nb_enf]
#TODO: know why nbenf is often NaN and not 0.
info_ind.fillna(0, inplace=True)
data = PensionData.from_arrays(workstate, sali, info_ind, dates)
param = PensionParam(201001, data)
legislation = PensionLegislation(param)
simulation = PensionSimulation(data, legislation)
trim = simulation.profile_evaluate(output='trimesters_wages')
result_til_year = simulation.profile_evaluate(to_check=True)
pdb.set_trace()
