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()