def run_pension(context, yearleg, time_step='year', to_check=False, output='pension', cProfile=False):
''' run PensionSimulation after having converted the liam context in a PenionData
- note there is a selection '''
sali = context['longitudinal']['sali']
workstate = context['longitudinal']['workstate']
# calcul de la date de naissance au bon format
datesim = context['period']
age_year = context['agem'] // 12
age_month = context['agem'] % 12 + 1
naiss_year = datesim // 100 - age_year
naiss_month = datesim % 100 - age_month + 1
naiss = pd.Series(naiss_year * 100 + naiss_month)
naiss = naiss.map(lambda t: dt.date(t // 100, t % 100, 1))
info_ind = pd.DataFrame({'index':context['id'], 'agem': context['agem'],'naiss': naiss, 'sexe' : context['sexe'],
'nb_enf_all': context['nb_enf'], 'nb_pac': context['nb_pac'], 'nb_enf_RG': context['nb_enf_RG'],
'nb_enf_RSI': context['nb_enf_RSI'], 'nb_enf_FP': context['nb_enf_FP'], 'tauxprime': context['tauxprime']})
info_ind = info_ind.to_records(index=False)
# TODO: filter should be done in liam
if output == 'dates_taux_plein':
# But: déterminer les personnes partant à la retraite avec préselection des plus de 55 ans
#TODO: faire la préselection dans Liam
info_ind = info_ind[(info_ind['agem'] > 55 * 12)]
if output == 'pension':
info_ind = info_ind[context['to_be_retired']] #TODO: filter should be done in yaml
workstate = workstate.loc[workstate['id'].isin(info_ind.index), :].copy()
workstate.set_index('id', inplace=True)
workstate.sort_index(inplace=True)
sali = sali.loc[sali['id'].isin(info_ind.index), :].copy()
sali.set_index('id', inplace=True)
sali.sort_index(inplace=True)
sali.fillna(0, inplace=True)
yearleg = context['period'] // 100
if yearleg > 2009: #TODO: remove
yearleg = 2009
data = PensionData.from_arrays(workstate, sali, info_ind)
param = PensionParam(yearleg, data)
legislation = PensionLegislation(param)
simul_til = PensionSimulation(data, legislation)
if cProfile:
result_til_year = simul_til.profile_evaluate(yearleg, to_check=to_check, output=output)
else:
result_til_year = simul_til.evaluate(yearleg, to_check=to_check, output=output)
if output == 'dates_taux_plein':
# Renvoie un dictionnaire donnant la date de taux plein par régime (format numpy) et l'index associé
return result_til_year
elif output == 'pension':
result_to_liam = output_til_to_liam(output_til=result_til_year,
index_til=info_ind.index,
context_id=context['id'])
return result_to_liam.astype(float)
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)
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()
