def compute_and_save_bareme(disable_api = False):
csv_file = DIR + 'bareme_new.csv'
nb_enf_max = 4
nb_enf_max_14 = 0
rev_smic_max = 4
rev_smic_step = .5
temps_garde_range = ['classique', 'alternee_pension_non_decl', 'alternee_pension_decl']
disabled = None
if disable_api:
disabled = ['api'] + ["majo_rsa"]
else:
disabled = []
first = True
for uc_parameters in [ {'alpha' : 0, 'beta' : .5, 'gamma' : 1}, {'alpha' : 0.4, 'beta' : .7, 'gamma' : 1.4}]:
for nb_enf in range(1,nb_enf_max+1):
for ea in range(0,nb_enf_max_14+1):
e = nb_enf - ea
for temps_garde in temps_garde_range:
disabled += ['asf']
for rev_smic_chef in arange(0,rev_smic_max, rev_smic_step):
for rev_smic_part in arange(0,rev_smic_max, rev_smic_step):
df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, disabled=disabled)
print df
if first:
df_final = df
first = False
else:
df_final = concat([df_final, df], axis=0)
df_final.to_csv(csv_file)
def compute_and_save_opt_pension(criterium, disable_api = False):
if disable_api:
csv_file = DIR + 'opt_pension_no_api.csv'
else:
csv_file = DIR + 'opt_pension_new.csv'
nb_enf_max = 4
nb_enf_max_14 = 0
rev_smic_max = 4
rev_smic_step = .5
temps_garde_range = ['classique', 'alternee_pension_non_decl', 'alternee_pension_decl']
first = True
disabled = None
if disable_api:
disabled = ['api']
for uc_parameters in [ {'alpha' : 0, 'beta' : .5, 'gamma' : 1}, {'alpha' : 0.4, 'beta' : .7, 'gamma' : 1.4}]:
for nb_enf in range(1,nb_enf_max+1):
for ea in range(0,nb_enf_max_14+1):
e = nb_enf - ea
for temps_garde in temps_garde_range:
for rev_smic_chef in arange(0,rev_smic_max, rev_smic_step):
for rev_smic_part in arange(0,rev_smic_max, rev_smic_step):
opt_pension = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, criterium = criterium, disabled=disabled)
asf = get_asf(nb_enf, year=YEAR)
if opt_pension >= asf:
if disabled is not None:
disabled = disabled + ['asf']
else:
disabled = ['asf']
df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, pension = opt_pension, disabled = disabled)
else:
df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, pension = 0, disabled = disabled)
print df
if first:
df_final = df
first = False
else:
df_final = concat([df_final, df], axis=0)
df_final.to_csv(csv_file)
def optimal_pension(criterium, disable_api = False):
e = 2
ea = 0
rev_smic_chef = 3.5
rev_smic_part = 0
temps_garde ="alternee_pension_non_decl" # 'classique', 'alternee_pension_decl
uc_parameters = {'alpha' : 0.4, 'beta' : .7, 'gamma' : 1.4}
disabled = []
if disable_api:
disabled += ['api']
if criterium == "unaf":
print get_unaf(e,ea)
opt_pension_nv = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters,
criterium = "nivvie", disabled=disabled)
print "nivvie : ", opt_pension_nv
opt_pension_unaf = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters,
criterium = "unaf", disabled=disabled)
print "unaf : ", opt_pension_unaf
opt_pension = max(opt_pension_nv, opt_pension_unaf)
else:
opt_pension = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters,
criterium=criterium, disabled=disabled)
asf = get_asf(e+ea, year=YEAR)
print 'opt_pension :', opt_pension
print 'asf :', asf
if opt_pension >= asf:
df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, pension = opt_pension, disabled = disabled + ['asf'] )
else:
df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, pension = 0, disabled=disabled)
print df.to_string()
return df
def pension_according_to_bareme(disable_api=False):
e = 2
ea = 0
rev_smic_chef = 1.5
rev_smic_part = 1.5
disabled = None
if disable_api:
disabled = ['api']
else:
disabled = []
temps_garde = "alternee_pension_non_decl" #'alternee_pension_decl' # "alternee_pension_non_decl" "classique"
uc_parameters = {'alpha' : 0.4, 'beta' : 0.7, 'gamma' : 1.4}
if temps_garde == "alternee_pension_non_decl":
disabled += ['asf']
df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, pension = None, disabled=disabled)
print df.to_string()