def test_case(year, save=False):
country = 'france'
salaires_nets = 30000
nmen = 3
nmax = 1
for reforme in [False, True]:
simulation = ScenarioSimulation()
simulation.set_config(year=year,
reforme=reforme,
nmen=nmen,
maxrev=salaires_nets * nmax,
x_axis='sali')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj',
'part')
# simulation.scenario.addIndiv(2, datetime(2000,1,1).date(), 'pac', 'enf')
# simulation.scenario.addIndiv(3, datetime(2000,1,1).date(), 'pac', 'enf')
# Loyers set statut d'occupation
simulation.scenario.menage[0].update({"loyer": 2000})
simulation.scenario.menage[0].update({"so": 4})
simulation.set_param()
simulation.P.ir.autre.charge_loyer.active = 1
simulation.P.ir.autre.charge_loyer.plaf = 1000
simulation.P.ir.autre.charge_loyer.plaf_nbp = 0
print simulation.P
print type(simulation.P)
reduc = 0
print simulation.P.ir.bareme
print simulation.P.ir.bareme.nb
for i in range(2, simulation.P.ir.bareme.nb):
simulation.P.ir.bareme.setSeuil(
i, simulation.P.ir.bareme.seuils[i] * (1 - reduc))
print simulation.P.ir.bareme
print simulation.P.ir.bareme.nb
if simulation.reforme is True:
df = simulation.get_results_dataframe(difference=True)
else:
df = simulation.get_results_dataframe()
print df.to_string()
# Save example to excel
if save:
destination_dir = "c:/users/utilisateur/documents/"
if reforme:
fname = destination_dir + "Trannoy_reforme_new_diff.%s" % "xlsx"
print "Saving " + fname
df.to_excel(fname, sheet_name="difference")
else:
fname = destination_dir + "Trannoy_reforme_new.%s" % "xlsx"
print "Saving " + fname
df.to_excel(fname, sheet_name="Trannoy")
def test_case(year, save = False):
country = 'france'
salaires_nets = 30000
nmen = 3
nmax = 1
for reforme in [False, True]:
simulation = ScenarioSimulation()
simulation.set_config(year = year,
reforme = reforme,
nmen = nmen,
maxrev = salaires_nets * nmax,
x_axis = 'sali')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
# simulation.scenario.addIndiv(2, datetime(2000,1,1).date(), 'pac', 'enf')
# simulation.scenario.addIndiv(3, datetime(2000,1,1).date(), 'pac', 'enf')
# Loyers set statut d'occupation
simulation.scenario.menage[0].update({"loyer": 2000})
simulation.scenario.menage[0].update({"so": 4})
simulation.set_param()
simulation.P.ir.autre.charge_loyer.active = 1
simulation.P.ir.autre.charge_loyer.plaf = 1000
simulation.P.ir.autre.charge_loyer.plaf_nbp = 0
print simulation.P
print type(simulation.P)
reduc = 0
print simulation.P.ir.bareme
print len(simulation.P.ir.bareme.thresholds)
for i in range(2, len(simulation.P.ir.bareme.thresholds)):
simulation.P.ir.bareme.setSeuil(i, simulation.P.ir.bareme.thresholds[i] * (1 - reduc))
print simulation.P.ir.bareme
print len(simulation.P.ir.bareme.thresholds)
if simulation.reforme is True:
df = simulation.get_results_dataframe(difference = True)
else:
df = simulation.get_results_dataframe()
print df.to_string()
# Save example to excel
if save:
destination_dir = "c:/users/utilisateur/documents/"
if reforme:
fname = destination_dir + "Trannoy_reforme_new_diff.%s" % "xlsx"
print "Saving " + fname
df.to_excel(fname, sheet_name = "difference")
else:
fname = destination_dir + "Trannoy_reforme_new.%s" % "xlsx"
print "Saving " + fname
df.to_excel(fname, sheet_name = "Trannoy")
def case_study(year = 2013):
# Creating a case_study household with one individual whose taxable income (salaire imposable, sali
# varies from 0 to maxrev = 100000 in nmen = 3 steps
simulation = ScenarioSimulation()
simulation.set_config(year = year,
reforme = True,
nmen = 11,
maxrev = 100000,
x_axis = 'sali')
print simulation.
# Adding a husband/wife on the same tax sheet (ie foyer, as conj) and of course same family (as part)
simulation.scenario.addIndiv(1, datetime(1975, 1, 1).date(), 'conj', 'part')
# Adding 3 kids on the same tax sheet and family
simulation.scenario.addIndiv(2, datetime(2001, 1, 1).date(), 'pac', 'enf')
simulation.scenario.addIndiv(3, datetime(2002, 1, 1).date(), 'pac', 'enf')
simulation.scenario.addIndiv(4, datetime(2003, 1, 1).date(), 'pac', 'enf')
# Set legislative parameters
simulation.set_param()
# Some prestation can be disabled (herethe aefa prestation) by uncommenting the following line
# simulation.disable_prestations( ['aefa'])
# Performing a parameterical reform (inspect openifsca-country.param.param.xml)
# Lower the part in the quotient familial for the third child from 1 to .5
print 'default value for P.ir.quotient_familial.enf2 : %s \n' % simulation.P.ir.quotient_familial.enf2
simulation.P.ir.quotient_familial.enf2 = 0
print 'reform value for P.ir.quotient_familial.enf2 : %s \n' % simulation.P.ir.quotient_familial.enf2
# Compute the pandas dataframe of the household case_study
df = simulation.get_results_dataframe(default = True)
print df.to_string()
# Save example to excel
# destination_dir = "c:/users/utilisateur/documents/"
# fname = "Example_%s.%s" %(str(yr), "xls")
# df.to_excel(destination_dir = "c:/users/utilisateur/documents/" + fname)
df_reform = simulation.get_results_dataframe()
print df_reform.to_string()
# Many other variables are accessible
# Input variables
print 'list of input variables : %s \n' % simulation.input_var_list
# Getting the value of some input variables
print simulation.input_table.table['so']
# Output variables
print 'list of output variables : %s \n' % simulation.output_var_list
print simulation.output_table.table['ir_plaf_qf']
print simulation.output_table_default.table['ir_plaf_qf']
def test_case_reform(year):
"""
A test case with reform
"""
simulation = ScenarioSimulation()
simulation.set_config(year=year, reforme=True, nmen=1)
# Adding a husband/wife on the same tax sheet (foyer)
simulation.set_param()
test_case = simulation.scenario
sal_mensuel = 1000
for i in range(10):
test_case.indiv[0].update({"sal" + str(i): sal_mensuel * 12})
test_case.indiv[0].update({"nb_trim_val": 50})
test_case.indiv[0].update({"age": 54})
simulation.set_param()
print simulation.P_default
param = simulation.P
# param.pension.rsna.taux_ann_base = .03
param.pension.rsna.age_dep_anticip = 55
df = simulation.get_results_dataframe()
print df.to_string()
def test_af2():
for yr in range(2006, 2010):
print yr
simulation = ScenarioSimulation()
simulation.set_config(year = yr,
nmen = 2,
maxrev = 100000,
x_axis = 'sali')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1),
'conj', 'part')
simulation.scenario.addIndiv(2, datetime.date(1975, 2, 2),
'conj', 'part')
simulation.set_param()
# Adding children on the same tax sheet (foyer)
simulation.scenario.addIndiv(3, datetime.date(2000, 1, 1),
'pac', 'enf')
simulation.scenario.addIndiv(4, datetime.date(2000, 1, 1),
'pac', 'enf')
df = simulation.get_results_dataframe(index_by_code = True)
# print df.loc["af"][0]
# print af_2enf[yr]
# print type(df.loc["af"][0])
# print type(af_2enf[yr])
# print abs(df.loc["af"][0] - af_2enf[yr]) < 1e-3
assert abs(df.loc["af"][0] - af_2enf[yr]) < 1e-3
def get_couple_results_dataframe(sali_vous_maxrev=12 * 4000,
sali_conj=12 * 2000,
nmen=11):
# Creating a case_study household with one individual whose taxable income (salaire imposable, sali
# varies from 0 to maxrev = 100000 in nmen = 3 steps
simulation = ScenarioSimulation()
simulation.set_config(year=2013,
nmen=nmen,
maxrev=sali_vous_maxrev,
x_axis='sali')
# Adding a husband/wife on the same tax sheet (ie foyer, as conj) and of course same family (as part)
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
simulation.scenario.indiv[1]['sali'] = sali_conj
# Set legislative parameters
simulation.set_param()
df = simulation.get_results_dataframe()
df2 = df.transpose()[['Salaires imposables', u'Impôt sur le revenu']]
df2['Salaires imposables vous'] = df2['Salaires imposables'] - sali_conj
df2['Salaires imposables conj'] = sali_conj
return df2
def get_couple_results_dataframe(sali_vous_maxrev = 12 * 4000, sali_conj = 12 * 4000, nmen = 11):
# Creating a case_study household with one individual whose taxable income (salaire imposable, sali
# varies from 0 to maxrev = 100000 in nmen = 11 steps
simulation = ScenarioSimulation()
simulation.set_config(year = 2013,
nmen = nmen,
maxrev = sali_vous_maxrev,
x_axis = 'sali')
# Adding a husband/wife on the same tax sheet (ie foyer, as conj) and of course same family (as part)
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
simulation.scenario.indiv[1]['sali'] = sali_conj
# Set legislative parameters
simulation.set_param()
df = simulation.get_results_dataframe()
df2 = df.transpose()[[u'Salaires imposables', u'Impôt sur le revenu']]
df2[u'Salaires imposables vous'] = df2[u'Salaires imposables'] - sali_conj
df2[u'Salaires imposables conj'] = sali_conj
df2.rename(columns = {u'Impôt sur le revenu' : u'Impôt sur le revenu déclaration commune'},
inplace = True)
df2 = df2.drop(u'Salaires imposables', axis=1)
return df2
def test_case_reform(year):
"""
A test case with reform
"""
simulation = ScenarioSimulation()
simulation.set_config(year=year, reforme=True, nmen=1)
# Adding a husband/wife on the same tax sheet (foyer)
simulation.set_param()
test_case = simulation.scenario
sal_mensuel = 1000
for i in range(10):
test_case.indiv[0].update({"sal" + str(i): sal_mensuel * 12})
test_case.indiv[0].update({"nb_trim_val": 50})
test_case.indiv[0].update({"age": 54})
simulation.set_param()
print simulation.P_default
param = simulation.P
# param.pension.rsna.taux_ann_base = .03
param.pension.rsna.age_dep_anticip = 55
df = simulation.get_results_dataframe()
print df.to_string()
def case_study(year = 2013):
# Creating a case_study household with one individual whose taxable income (salaire imposable, sali
# varies from 0 to maxrev = 100000 in nmen = 3 steps
simulation = ScenarioSimulation()
simulation.set_config(year = 2013,
nmen = 11,
maxrev = 10000,
x_axis = 'sali')
print simulation.scenario
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
print simulation.scenario
simulation.set_param() # Va chercher la legislation par défaut
df = simulation.get_results_dataframe()
print df.to_string()
print simulation.input_table.table.to_string()
print simulation.output_table.table.to_string()
def get_couple_with_child_results_datatframe(sali_vous = 0, sali_conj = 0):
simulation = ScenarioSimulation()
simulation.set_config(year = 2013,
nmen = 1)
# Adding a husband/wife on the same tax sheet (ie foyer, as conj) and of course same family (as part)
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
# Adding 1 kids on the same tax sheet and family
simulation.scenario.addIndiv(2, datetime.date(1993, 1, 1), 'pac', 'enf')
# Changing the revenu of the parents
scenario = simulation.scenario
scenario.indiv[0]['sali'] = sali_vous
scenario.indiv[1]['sali'] = sali_conj
# Set the number of major kids in the
scenario.declar[0]['nbJ'] = 1
# Set legislative parameters
simulation.set_param()
# Compute the pandas dataframe of the household case_study
df = simulation.get_results_dataframe()
return df
def get_alloc(maxrev = 10000, conj = False, nbenf = 0, zone_apl = 1, loyer_mensuel = 500, nmen = 51):
# Creating a case_study household with one individual whose taxable income (salaire imposable, sali
# varies from 0 to maxrev = 100000 in nmen = 3 steps
simulation = ScenarioSimulation()
simulation.set_config(year = 2013,
nmen = 11,
maxrev = maxrev,
x_axis = 'sali')
scenario = simulation.scenario
if conj:
# Adding a husband/wife on the same tax sheet (ie foyer, as conj) and of course same family (as part)
scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
for i in range(nbenf):
# Adding 3 kids on the same tax sheet and family
scenario.addIndiv(2 + nbenf, datetime.date(2001, 1, 1), 'pac', 'enf')
# Add caracteristics of menage
scenario.menage[0]['loyer'] = loyer_mensuel
scenario.menage[0]['zone_apl'] = zone_apl
# Set legislative parameters
simulation.set_param()
# Compute the pandas dataframe of the household case_study
df = simulation.get_results_dataframe()
df2 = df.transpose()[['Salaires imposables', 'Prestations logement']]
return df2
def test_isf_celib():
"""
test pour un célibataire
"""
# Comparaison avec la fiche de paie IPP calculé avec une cotisation transport correspondant à Paris ((.026)
# alors qu'Openfisca la cacule pour Lyon (.0175)
tests_list = [
# Célibataires (pas de supplément familial de traitement
{
"year": 2011,
"input_vars": {
"sali": 50000,
"b1bc": 5000,
"b1ab": 2500000,
"b2nf": 7000,
},
"output_vars": {
"irpp": 7889,
"tot_impot": 2144 + 9389,
"isf_inv_pme": 3500,
}
},
]
passed = True
for test in tests_list:
year = test["year"]
simulation = ScenarioSimulation()
simulation.set_config(year=test["year"], nmen=1)
simulation.set_param()
test_case = simulation.scenario
for variable, value in test['input_vars'].iteritems():
if variable in ['zone_apl']:
test_case.menage[0].update({variable: value})
else:
test_case.indiv[0].update({variable: value})
df = simulation.get_results_dataframe(index_by_code=True)
for var, value in test['output_vars'].iteritems():
if var in df.columns:
val = df.loc[var][0]
else:
val = simulation.output_table.table[var][0]
test_assertion = abs(abs(val) - value) < 1
# expression = "Test failed for variable %s on year %i and case %s: \n OpenFisca value : %s \n Real value : %s \n" % (variable, year, test['input_vars'], abs(computed_value), value)
if not test_assertion:
print year
print var
print "OpenFisca :", val
print "Real value :", value
passed = False
assert passed, "Test failed for some variables"
def test_case(year):
simulation = ScenarioSimulation()
simulation.set_config(year=year, reforme=False, nmen=4, maxrev=25 * 9 * 12 * 3, x_axis="sal0")
# Adding a husband/wife on the same tax sheet (foyer)
simulation.set_param()
df = simulation.get_results_dataframe()
print df.to_string()
def case_study(year = 2013):
simulation = ScenarioSimulation() #on instancie on prends une instance de ScenarioSimulation, on prends un exemple de cette classe là
simulation.set_config(year = year,
nmen = 4, #nombre de ménages
maxrev = 10000,
x_axis = 'sali')#salaire imposable (une seule personne dans le ménage)
simulation.set_param()#prends les paramètres par défaut de la législation
df = simulation.get_results_dataframe() #renvoie la dataframe avec tout calculé
print df.to_string()
def test_nonsal_famille():
"""
test pour un couple de fonctionnaire
"""
tests_list = [
# Couple de microentrepreneur
{"year" : 2013,
"input_vars":
{
"birth": datetime.date(1940, 1, 1),
"activite": 3,
"rsti" : 12500,
},
"output_vars" :
{
"rst" : 12500,
}
},
]
passed = True
for test in tests_list:
year = test["year"]
simulation = ScenarioSimulation()
simulation.set_config(year = test["year"], nmen = 1)
simulation.set_param()
test_case = simulation.scenario
test_case.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
for variable, value in test['input_vars'].iteritems():
if variable in ['activite', 'birth', 'rsti']:
test_case.indiv[0].update({variable: value})
test_case.indiv[1].update({variable: value})
else:
print variable
assert False
print test_case
for variable, value in test['output_vars'].iteritems():
df = simulation.get_results_dataframe(index_by_code = True)
if variable in df.columns:
val = df.loc[variable][0]
else:
val = simulation.output_table.table[variable][0]
to_test = abs(val - value)
if True: # not (to_test < 1):
print "Il y a une différence : ", to_test
print "Pour le scénario", test['input_vars']
print year
print variable
print "OpenFisca :", val
print "Real value :", value , "\n \n"
print df.to_string()
print simulation.input_table.table.transpose().to_string()
assert passed, "Test failed for some variables"
def test_case(year):
country = 'france'
salaires_nets = 50000
nmen = 100 #nombre de ménages que je fait varier
for reforme in [False, True]: #fais tourner un fois pour faux et une fois pour vrai
#print reforme
simulation = ScenarioSimulation()
simulation.set_config(year = year,
reforme = reforme,
nmen = nmen,
maxrev = salaires_nets,
x_axis = 'sali')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
# simulation.scenario.addIndiv(2, datetime(2000,1,1).date(), 'pac', 'enf')
# simulation.scenario.addIndiv(3, datetime(2000,1,1).date(), 'pac', 'enf')
# Loyers set statut d'occupation
simulation.scenario.menage[0].update({"loyer": 2000}) #fixe les variables au niveau du ménage
simulation.scenario.menage[0].update({"so": 4}) #
simulation.set_param() #va chercher des params de la législation
simulation.P.ir.autre.charge_loyer.active = 1 #simulation.p est un attribu de simulation
simulation.P.ir.autre.charge_loyer.plaf = 1000
simulation.P.ir.autre.charge_loyer.plaf_nbp = 0
#print simulation.P #montre ce qui est écrit dans param.xml
reduc = 0
#print simulation.P.ir.bareme #change le barème de l'impot sur le revenu
#print simulation.P.ir.bareme.nb #nb de tranche du brarème de l'impot
for i in range(2, simulation.P.ir.bareme.nb):
simulation.P.ir.bareme.setSeuil(i, simulation.P.ir.bareme.seuils[i] * (1 - reduc)) #a partir de la 2nd tranche réduit les seuils des barèmes
#print simulation.P.ir.bareme #nouvea barème
#print simulation.P.ir.bareme.nb
if simulation.reforme is True:
df = simulation.get_results_dataframe(default = True , difference = False) # prends la différence(argument de la fonction getresultdataframe
return df
else:
df = simulation.get_results_dataframe()
def case_study(year=2013):
#simulation est une classe , Scenariosimulation est une instance, on a instancier "simulation"
#on a mainteant fait simulation.set_congif pour modifier les attributs. on lui a dit de prendre l'année 2013
#avec year=year. reforme = true. Nmen est le nombre de ménage. Maxrev est le salaire imposable de la première personne déclaré.
# Ici tu prends que deux menage et on fait varier les salaire. Simulation.set.param est la legislation par défaut.
simulation = ScenarioSimulation()
simulation.set_config(year=year, nmen=2, maxrev=500000, x_axis='sali')
simulation.set_param()
df = simulation.get_results_dataframe()
print df.to_string()
def test_nonsal_famille():
"""
test pour un couple de fonctionnaire
"""
tests_list = [
# Couple de microentrepreneur
{"year" : 2013,
"input_vars":
{
"ebic_impv" : 20000,
"ebic_impv_c" : 10000,
},
"output_vars" :
{
"rev_microsocial": (20000 + 10000) - (2820 + 1410),
"microsocial" : 200 + 100,
}
},
]
passed = True
for test in tests_list:
year = test["year"]
simulation = ScenarioSimulation()
simulation.set_config(year = test["year"], nmen = 1)
simulation.set_param()
test_case = simulation.scenario
test_case.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
for variable, value in test['input_vars'].iteritems():
if variable in ['ebic_impv', 'ebic_imps', 'ebnc_impo']:
test_case.indiv[0].update({variable: value})
elif variable in ['ebic_impv_c', 'ebic_imps_c', 'ebnc_impo_c']:
test_case.indiv[1].update({variable[:-2]: value})
else:
print variable
assert False
for variable, value in test['output_vars'].iteritems():
df = simulation.get_results_dataframe(index_by_code = True)
if variable in df.columns:
val = df.loc[variable][0]
else:
val = simulation.output_table.table[variable][0]
to_test = abs(val - value)
if not (to_test < 1):
print "Il y a une différence : ", to_test
print "Pour le scénario", test['input_vars']
print year
print variable
print "OpenFisca :", val
print "Real value :", value , "\n \n"
assert passed, "Test failed for some variables"
def test_case(year):
simulation = ScenarioSimulation()
simulation.set_config(year = year, reforme = False,
nmen = 3, maxrev = 100000, x_axis = 'sali')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.scenario.addIndiv(1, datetime(1975, 1, 1).date(), 'conj', 'part')
simulation.set_param()
# A the aefa prestation can be disabled by uncommenting the following line
# simulation.disable_prestations( ['aefa'])
df = simulation.get_results_dataframe()
print df.to_string()
def test_case(year):
simulation = ScenarioSimulation()
simulation.set_config(year = year, reforme=False, nmen = 3, maxrev = 12*400, x_axis = 'sali')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.scenario.addIndiv(1, datetime(1975,1,1).date(), 'conj', 'part')
simulation.scenario.addIndiv(2, datetime(2000,1,1).date(), 'pac', 'enf')
simulation.scenario.addIndiv(3, datetime(2000,1,1).date(), 'pac', 'enf')
simulation.set_param()
df = simulation.get_results_dataframe()
print df.to_string()
def check_test_case(year = 2013):
simulation = ScenarioSimulation()
simulation.set_config(year = year, nmen = 2, maxrev = 2000, reforme = False, x_axis = 'sali')
# simulation.scenario.indiv[0]['sali'] = 16207
# # Add husband/wife on the same tax sheet (foyer)
# simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
simulation.set_param()
# The aefa prestation can be disabled by uncommenting the following line:
# simulation.disable_prestations( ['aefa'])
df = simulation.get_results_dataframe()
print df.to_string().encode('utf-8')
def test_nonsal_famille():
"""
test pour un couple de fonctionnaire
"""
tests_list = [
# Couple de microentrepreneur
{
"year": 2013,
"input_vars": {
"ebic_impv": 20000,
"ebic_impv_c": 10000,
},
"output_vars": {
"rev_microsocial": (20000 + 10000) - (2820 + 1410),
"microsocial": 200 + 100,
}
},
]
passed = True
for test in tests_list:
year = test["year"]
simulation = ScenarioSimulation()
simulation.set_config(year=test["year"], nmen=1)
simulation.set_param()
test_case = simulation.scenario
test_case.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
for variable, value in test['input_vars'].iteritems():
if variable in ['ebic_impv', 'ebic_imps', 'ebnc_impo']:
test_case.indiv[0].update({variable: value})
elif variable in ['ebic_impv_c', 'ebic_imps_c', 'ebnc_impo_c']:
test_case.indiv[1].update({variable[:-2]: value})
else:
print variable
assert False
for variable, value in test['output_vars'].iteritems():
df = simulation.get_results_dataframe(index_by_code=True)
if variable in df.columns:
val = df.loc[variable][0]
else:
val = simulation.output_table.table[variable][0]
to_test = abs(val - value)
if not (to_test < 1):
print "Il y a une différence : ", to_test
print "Pour le scénario", test['input_vars']
print year
print variable
print "OpenFisca :", val
print "Real value :", value, "\n \n"
assert passed, "Test failed for some variables"
def test_case(year):
simulation = ScenarioSimulation()
simulation.set_config(year=year,
reforme=False,
nmen=4,
maxrev=25 * 9 * 12 * 3,
x_axis='sal0')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.set_param()
df = simulation.get_results_dataframe()
print df.to_string()
def case_study(year=2013):
simulation = ScenarioSimulation(
) #on instancie on prends une instance de ScenarioSimulation, on prends un exemple de cette classe là
simulation.set_config(
year=year,
nmen=500, #nombre de ménages
maxrev=100000,
x_axis='sali') #salaire imposable (une seule personne dans le ménage)
simulation.set_param() #prends les paramètres par défaut de la législation
df = simulation.get_results_dataframe(
) #renvoie la dataframe avec tout calculé
#print df.to_string()
return df
def test_case(year, save = False):
country = 'france'
salaires_nets = 1120.43*12
nmen = 20
nmax = 3
for reforme in [False, True]:
simulation = ScenarioSimulation()
simulation.set_config(year = year,
param_file = param_file,
reforme=reforme,
nmen = nmen,
maxrev = salaires_nets*nmax,
x_axis = 'sali')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.scenario.addIndiv(1, datetime.date(1975,1,1), 'conj', 'part')
# simulation.scenario.addIndiv(2, datetime(2000,1,1).date(), 'pac', 'enf')
# simulation.scenario.addIndiv(3, datetime(2000,1,1).date(), 'pac', 'enf')
# Loyers set statut d'occupation
simulation.scenario.menage[0].update({"loyer": 1120.43/3})
simulation.scenario.menage[0].update({"so": 4})
simulation.set_param()
simulation.P.ir.autre.charge_loyer.active = 1
simulation.P.ir.autre.charge_loyer.plaf = 1000
simulation.P.ir.autre.charge_loyer.plaf_nbp = 0
reduc = 0
print simulation.P.ir.bareme
print simulation.P.ir.bareme.nb
for i in range(2, simulation.P.ir.bareme.nb):
simulation.P.ir.bareme.setSeuil(i, simulation.P.ir.bareme.seuils[i]*(1-reduc) )
print simulation.P.ir.bareme
print simulation.P.ir.bareme.nb
df = simulation.get_results_dataframe()
print df.to_string()
#Save example to excel
if save:
destination_dir = "c:/users/utilisateur/documents/"
fname = "Trannoy_reforme.%s" %"xls"
if reforme:
df.to_excel(destination_dir + fname, sheet_name="difference")
else:
df.to_excel(destination_dir + fname, sheet_name="Trannoy")
def test_nonsal_celib():
"""
test pour un célibataire
"""
tests_list = [
{"year" : 2013,
"input_vars":
{
"activite": 3,
"rsti" : 12500,
},
"output_vars" :
{
"rst": 12500,
}
},
]
passed = True
for test in tests_list:
year = test["year"]
simulation = ScenarioSimulation()
simulation.set_config(year = test["year"], nmen = 1)
simulation.set_param()
test_case = simulation.scenario
for variable, value in test['input_vars'].iteritems():
if variable in ['rsti']:
test_case.indiv[0].update({variable: value})
else:
print variable
assert False
for variable, value in test['output_vars'].iteritems():
df = simulation.get_results_dataframe(index_by_code = True)
if variable in df.columns:
val = df.loc[variable][0]
else:
val = simulation.output_table.table[variable][0]
to_test = abs(val - value)
if True: # not (to_test < 1):
print "Il y a une différence : ", to_test
print "Pour le scénario", test['input_vars']
print year
print variable
print "OpenFisca :", val
print "Real value :", value , "\n \n"
print df.to_string()
assert passed, "Test failed for some variables"
def case_study(year = 2013) :
#simulation est une classe , Scenariosimulation est une instance, on a instancier "simulation"
#on a mainteant fait simulation.set_congif pour modifier les attributs. on lui a dit de prendre l'année 2013
#avec year=year. reforme = true. Nmen est le nombre de ménage. Maxrev est le salaire imposable de la première personne déclaré.
# Ici tu prends que deux menage et on fait varier les salaire. Simulation.set.param est la legislation par défaut.
simulation = ScenarioSimulation()
simulation.set_config(year = year,
nmen = 2,
maxrev = 500000,
x_axis= 'sali')
simulation.set_param()
df = simulation.get_results_dataframe()
print df.to_string()
def get_child_results_datatframe(pension_alimentaire=0):
simulation = ScenarioSimulation()
simulation.set_config(year=2013, nmen=1)
scenario = simulation.scenario
# Set the number of major kids in the
scenario.declar[0]['alr'] = pension_alimentaire
# Set legislative parameters
simulation.set_param()
# Compute the pandas dataframe of the household case_study
df = simulation.get_results_dataframe()
return df
def test_case_study(year = 2013, verbose = False):
'''
Tests that _salbrut which computes "salaire brut" from "imposable" yields an amount compatbe
with the one obtained from running openfisca satrting with a "salaire brut"
'''
for type_sal_category in ['prive_non_cadre', 'prive_cadre']: # , 'public_titulaire_etat']:
simulation = ScenarioSimulation()
maxrev = 24000
simulation.set_config(year = year, reforme = False, nmen = 11, maxrev = maxrev, x_axis = 'salbrut')
simulation.scenario.indiv[0]['salbrut'] = maxrev
simulation.scenario.indiv[0]['type_sal'] = CAT[type_sal_category]
if type_sal_category == 'public_titulaire_etat':
from openfisca_france.model.cotisations_sociales.travail import TAUX_DE_PRIME
simulation.scenario.indiv[0]['primes'] = TAUX_DE_PRIME * maxrev
simulation.set_param()
# The aefa prestation can be disabled by uncommenting the following line:
# simulation.disable_prestations( ['aefa'])
df = simulation.get_results_dataframe(index_by_code = True)
from openfisca_france.model.inversion_revenus import _salbrut
df_b2i = df.transpose()
if verbose:
print df_b2i.to_string()
sali = df_b2i['sal'].get_values()
hsup = simulation.input_table.table['hsup'].get_values()
type_sal = simulation.input_table.table['type_sal'].get_values()
primes = simulation.input_table.table['hsup'].get_values()
defaultP = simulation.P_default
from pandas import DataFrame
df_i2b = DataFrame({'sal': sali, 'salbrut' : _salbrut(sali, hsup, type_sal, defaultP) })
if verbose:
print df_i2b.to_string()
for var in ['sal', 'salbrut']:
passed = ((df_b2i[var] - df_i2b[var]).abs() < .01).all()
if (not passed) or type_sal_category in ['public_titulaire_etat']:
print (df_b2i / 12).to_string()
print (df_i2b / 12).to_string()
assert passed, "difference in %s for %s" % (var, type_sal_category)
def test_case_study(year = 2013, verbose = False):
'''
Tests that _salbrut which computes "salaire brut" from "imposable" yields an amount compatbe
with the one obtained from running openfisca satrting with a "salaire brut"
'''
for type_sal_category in ['prive_non_cadre', 'prive_cadre']: # , 'public_titulaire_etat']:
simulation = ScenarioSimulation()
maxrev = 50000
simulation.set_config(year = year, reforme = False, nmen = 11, maxrev = maxrev, x_axis = 'salbrut')
simulation.scenario.indiv[0]['salbrut'] = maxrev
simulation.scenario.indiv[0]['type_sal'] = CAT[type_sal_category]
if type_sal_category == 'public_titulaire_etat':
from openfisca_france.model.cotisations_sociales.travail import TAUX_DE_PRIME
simulation.scenario.indiv[0]['primes'] = TAUX_DE_PRIME * maxrev
simulation.set_param()
# The aefa prestation can be disabled by uncommenting the following line:
# simulation.disable_prestations( ['aefa'])
df = simulation.get_results_dataframe(index_by_code = True)
from openfisca_france.model.inversion_revenus import _salbrut_from_salnet
df_b2n = df.transpose()
if verbose:
print df_b2n.to_string()
salnet = df_b2n['salnet'].get_values()
hsup = simulation.input_table.table['hsup'].get_values()
type_sal = simulation.input_table.table['type_sal'].get_values()
primes = simulation.input_table.table['hsup'].get_values()
defaultP = simulation.P_default
from pandas import DataFrame
df_n2b = DataFrame({'salnet': salnet, 'salbrut' : _salbrut_from_salnet(salnet, hsup, type_sal, defaultP) })
if verbose:
print df_n2b.to_string()
for var in ['salnet', 'salbrut']:
passed = ((df_b2n[var] - df_n2b[var]).abs() < .01).all()
if (not passed) or type_sal_category in ['public_titulaire_etat']:
print (df_b2n / 12).to_string()
print (df_n2b / 12).to_string()
assert passed, "difference in %s for %s" % (var, type_sal_category)
def test_case(year):
simulation = ScenarioSimulation()
simulation.set_config(year = year, reforme=False, nmen = 2, maxrev = 19296, x_axis = 'sali')
simulation.scenario.addIndiv(1, datetime(2000,1,1).date(), 'pac', 'enf')
simulation.scenario.addIndiv(2, datetime(2000,1,1).date(), 'pac', 'enf')
# simulation.scenario.indiv[0]["alr"] = 2107
# simulation.scenario.indiv[0]["alr_decl"] = False
simulation.scenario.indiv[0]['caseT'] = True
simulation.set_param()
simulation.disable_prestations(["asf", "majo_rsa","api"])
# A the aefa prestation can be disabled by uncommenting the following line
# simulation.disable_prestations( ['aefa'])
df = simulation.get_results_dataframe()
print df.to_string()
def get_child_results_datatframe(pension_alimentaire = 0):
simulation = ScenarioSimulation()
simulation.set_config(year = 2013,
nmen = 1)
scenario = simulation.scenario
# Set the number of major kids in the
scenario.declar[0]['alr'] = pension_alimentaire
# Set legislative parameters
simulation.set_param()
# Compute the pandas dataframe of the household case_study
df = simulation.get_results_dataframe()
return df
def case_study(year = 2013):
simulation = ScenarioSimulation()
simulation.set_config(year = year,
nmen = 20,
maxrev = 20000,
x_axis = 'sali')
scenario = simulation.scenario
simulation.set_param() #legislation#
scenario = simulation.scenario
simulation.set_param()
df = simulation.get_results_dataframe(index_by_code=True)
print df.info()
print df.to_string()
def test_case_study(year = 2013, verbose = False):
for type_sal_category in ['prive_non_cadre', 'prive_cadre', 'public_titulaire_etat']:
simulation = ScenarioSimulation()
maxrev = 24000
simulation.set_config(year = year, reforme = False, nmen = 1, x_axis = 'salbrut')
# Add husband/wife on the same tax sheet (foyer).
# simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
simulation.scenario.indiv[0]['salbrut'] = maxrev
simulation.scenario.indiv[0]['type_sal'] = CAT[type_sal_category]
if type_sal_category == 'public_titulaire_etat':
from openfisca_france.model.cotisations_sociales.travail import TAUX_DE_PRIME
simulation.scenario.indiv[0]['primes'] = TAUX_DE_PRIME * maxrev
simulation.set_param()
# The aefa prestation can be disabled by uncommenting the following line:
# simulation.disable_prestations( ['aefa'])
df = simulation.get_results_dataframe(index_by_code = True)
from openfisca_france.model.cotisations_sociales.travail import _salbrut
df_b2i = df.transpose()
if verbose:
print df_b2i.to_string()
sali = df_b2i['sal'].get_values()
hsup = simulation.input_table.table['hsup'].get_values()
type_sal = simulation.input_table.table['type_sal'].get_values()
primes = simulation.input_table.table['hsup'].get_values()
defaultP = simulation.P_default
from pandas import DataFrame
df_i2b = DataFrame({'sal': sali, 'salbrut' : _salbrut(sali, hsup, type_sal, defaultP) })
if verbose:
print df_i2b.to_string()
for var in ['sal', 'salbrut']:
test = ((df_b2i[var] - df_i2b[var]).abs() < .01).all()
if (not test) or type_sal_category in ['public_titulaire_etat']:
print (df_b2i / 12).to_string()
print (df_i2b / 12).to_string()
assert test, "difference in %s for %s" % (var, type_sal_category)
def check_test_case(year=2013):
simulation = ScenarioSimulation()
simulation.set_config(year=year,
nmen=2,
maxrev=2000,
reforme=False,
x_axis='sali')
# simulation.scenario.indiv[0]['sali'] = 16207
# # Add husband/wife on the same tax sheet (foyer)
# simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
simulation.set_param()
# The aefa prestation can be disabled by uncommenting the following line:
# simulation.disable_prestations( ['aefa'])
df = simulation.get_results_dataframe()
print df.to_string().encode('utf-8')
def menage_avec_enfant(year = 2013):
simulation = ScenarioSimulation()
simulation.set_config(year = year,
nmen = 1)
simulation.scenario.addIndiv(1, datetime.date(1975,1,1), 'conj','part')
simulation.scenario.addIndiv(2, datetime.date(1990,1,1), 'pac', 'enf')
print simulation.scenario
print simulation.scenario.indiv[0]
simulation.scenario.indiv[0]['sali'] = 30000
simulation.set_param()
df = simulation.get_results_dataframe()
print df.to_string()
def test_case(year):
simulation = ScenarioSimulation()
simulation.set_config(year=year,
reforme=False,
nmen=11,
maxrev=12000,
x_axis='salaire_imposable')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
simulation.scenario.addIndiv(2, datetime.date(2000, 1, 1), 'pac', 'enf')
simulation.scenario.addIndiv(3, datetime.date(2000, 1, 1), 'pac', 'enf')
simulation.set_param()
simulation.P.impot_revenu.reforme.exemption.active = 1
df = simulation.get_results_dataframe()
print df.to_string()
def case_study(year = 2013):
simulation = ScenarioSimulation()
simulation.set_config(year = year,
nmen = 101,
maxrev = 20000,
x_axis = 'sali')
scenario = simulation.scenario
scenario.declar[0]['ppe_tp_sa']=True
simulation.set_param() #legislation#
df = simulation.get_results_dataframe(index_by_code=True)
# print df.info()
rsa_ppe = df.loc[['rsa','ppe','sal']]
print rsa_ppe.to_string()
rsa_ppe.to_excel('rsa.xls')
def test_case(year):
simulation = ScenarioSimulation()
simulation.set_config(year=year,
reforme=False,
nmen=3,
maxrev=12 * 400,
x_axis='sali')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.scenario.addIndiv(1,
datetime(1975, 1, 1).date(), 'conj', 'part')
simulation.scenario.addIndiv(2, datetime(2000, 1, 1).date(), 'pac', 'enf')
simulation.scenario.addIndiv(3, datetime(2000, 1, 1).date(), 'pac', 'enf')
simulation.set_param()
df = simulation.get_results_dataframe()
print df.to_string()
def test_case(year):
simulation = ScenarioSimulation()
simulation.set_config(year=year,
reforme=False,
nmen=3,
maxrev=1180 * 12,
x_axis='sali')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.scenario.addIndiv(1,
datetime(1975, 1, 1).date(), 'conj', 'part')
simulation.scenario.addIndiv(2, datetime(2000, 1, 1).date(), 'pac', 'enf')
simulation.scenario.addIndiv(3, datetime(2000, 1, 1).date(), 'pac', 'enf')
simulation.set_param()
# A the aefa prestation can be disabled by uncommenting the following line
# simulation.disable_prestations( ['aefa'])
df = simulation.get_results_dataframe()
print df.to_string()
def get_vous_results_dataframe(sali_single_maxrev=12 * 4000, nmen=11):
simulation = ScenarioSimulation()
simulation.set_config(year=2013,
nmen=nmen,
maxrev=sali_single_maxrev,
x_axis='sali')
# Set legislative parameters
simulation.set_param()
df = simulation.get_results_dataframe()
df2 = df.transpose()[[u'Salaires imposables', u'Impôt sur le revenu']]
df2.rename(columns={u'Salaires imposables': u'Salaires imposables vous'},
inplace=True)
df2.rename(columns={u'Impôt sur le revenu': u'Impôt sur le revenu vous'},
inplace=True)
return df2
def get_vous_results_dataframe(sali_single_maxrev = 12 * 4000, nmen = 11):
simulation = ScenarioSimulation()
simulation.set_config(year = 2013,
nmen = nmen,
maxrev = sali_single_maxrev,
x_axis = 'sali')
# Set legislative parameters
simulation.set_param()
df = simulation.get_results_dataframe()
df2 = df.transpose()[[u'Salaires imposables', u'Impôt sur le revenu']]
df2.rename(columns = {u'Salaires imposables' : u'Salaires imposables vous'}, inplace = True)
df2.rename(columns = {u'Impôt sur le revenu' : u'Impôt sur le revenu vous'}, inplace = True)
return df2
def case_study2(year=2013):
simulation = ScenarioSimulation() #on instancie on prends une instance de ScenarioSimulation, on prends un exemple de cette classe là
simulation.set_config(year = year,
nmen = 8, #nombre de ménages
maxrev = 10000,
x_axis = 'sali')#salaire imposable (une seule personne dans le ménage)
#print simulation.scenario
simulation.scenario.addIndiv(1, datetime.date(1975, 1 ,1), 'conj', 'part')
simulation.set_param()#prends les paramètres par défaut de la législation
df = simulation.get_results_dataframe(index_by_code=True) #renvoie la dataframe avec tout calculé
print df.to_string()
#print df.transpose()['logt']
a = df.loc['sal']
print a
def case_study2(year=2013):
simulation = ScenarioSimulation(
) #on instancie on prends une instance de ScenarioSimulation, on prends un exemple de cette classe là
simulation.set_config(
year=year,
nmen=8, #nombre de ménages
maxrev=10000,
x_axis='sali') #salaire imposable (une seule personne dans le ménage)
#print simulation.scenario
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
simulation.set_param() #prends les paramètres par défaut de la législation
df = simulation.get_results_dataframe(
index_by_code=True) #renvoie la dataframe avec tout calculé
print df.to_string()
#print df.transpose()['logt']
a = df.loc['sal']
print a
def test_af2b():
for yr in range(2006, 2010):
simulation = ScenarioSimulation()
simulation.set_config(year=yr, nmen=2, maxrev=100000, x_axis='sali')
# Adding a husband/wife on the same tax sheet (foyer)
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj',
'part')
simulation.scenario.addIndiv(2, datetime.date(1975, 2, 2), 'conj',
'part')
simulation.set_param()
# Adding children on the same tax sheet (foyer)
simulation.scenario.addIndiv(3, datetime.date(1992, 1, 1), 'pac',
'enf')
simulation.scenario.addIndiv(4, datetime.date(1990, 1, 1), 'pac',
'enf')
df = simulation.get_results_dataframe(index_by_code=True)
# print df.loc["af"][0]
# print af_2enf[yr]
# print type(df.loc["af"][0])
# print type(af_2enf[yr])
# print abs(df.loc["af"][0] - af_2enf[yr]) < 1e-3
assert abs(df.loc["af"][0] - af_2enfb[yr]) < 1e-3
def test_cho_rst(year = 2013, verbose = False):
'''
Tests that _chobrut which computes "chômage brut" from "imposable" yields an amount compatbe
with the one obtained from running openfisca satrting with a "chômage brut"
'''
remplacement = {'cho' : 'chobrut', 'rst': 'rstbrut'}
for var, varbrut in remplacement.iteritems():
simulation = ScenarioSimulation()
maxrev = 24000
simulation.set_config(year = year, reforme = False, nmen = 11, maxrev = maxrev, x_axis = varbrut)
simulation.set_param()
df = simulation.get_results_dataframe(index_by_code = True)
from openfisca_france.model.inversion_revenus import _chobrut
df_b2i = df.transpose()
if verbose:
print df_b2i.to_string()
vari = df_b2i[var].get_values()
csg_rempl = vari * 0 + 1
defaultP = simulation.P_default
from pandas import DataFrame
df_i2b = DataFrame({var: vari, varbrut : _chobrut(vari, csg_rempl, defaultP) })
if verbose:
print df_i2b.to_string()
for variable in [var, varbrut]:
passed = ((df_b2i[variable] - df_i2b[variable]).abs() < .01).all()
if (not passed):
print (df_b2i / 12).to_string()
print (df_i2b / 12).to_string()
assert passed, "difference in %s " % (var)
def get_couple_without_child_results_datatframe(sali_vous=0,
sali_conj=0,
pension_alimentaire=0):
simulation = ScenarioSimulation()
simulation.set_config(year=2013, nmen=1)
# Adding a husband/wife on the same tax sheet (ie foyer, as conj) and of course same family (as part)
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
# Changing the revenu of the parents
scenario = simulation.scenario
scenario.indiv[0]['sali'] = sali_vous
scenario.indiv[1]['sali'] = sali_conj
# Set the number of major kids in the
scenario.declar[0]['f6gi'] = pension_alimentaire
# Set legislative parameters
simulation.set_param()
# Compute the pandas dataframe of the household case_study
df = simulation.get_results_dataframe()
return df
def case_study(year=2013):
# Creating a case_study household with one individual whose taxable income (salaire imposable, sali
# varies from 0 to maxrev = 100000 in nmen = 3 steps
simulation = ScenarioSimulation()
simulation.set_config(year=2013, nmen=11, maxrev=10000, x_axis='sali')
print simulation.scenario
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
print simulation.scenario
simulation.set_param() # Va chercher la legislation par défaut
df = simulation.get_results_dataframe()
print df.to_string()
print simulation.input_table.table.to_string()
print simulation.output_table.table.to_string()
def test_irpp():
"""
test pour un célibataire pour un revenu de 20 000, 50 000 € et 150 000 €
et des revenus de différentes origines
"""
dico = {
# test pour un célibataire ayant un revenu salarial (1AJ)
"sali": [
{"year": 2012, "amount": 20000, "irpp": -1181},
{"year": 2011, "amount": 20000, "irpp": -1181},
{"year": 2010, "amount": 20000, "irpp": -1201},
]
}
for revenu, test_list in dico.iteritems():
for item in test_list:
year = item["year"]
amount = item["amount"]
irpp = item["irpp"]
simulation = ScenarioSimulation()
simulation.set_config(year=year, nmen=1)
simulation.set_param()
test_case = simulation.scenario
if revenu in ["rsti", "sali"]:
test_case.indiv[0].update({revenu: amount})
elif revenu in ["f2da", "f2dh", "f2dc", "f2ts", "f2tr", "f4ba", "f3vg", "f3vz"]:
test_case.declar[0].update({revenu: amount})
else:
assert False
df = simulation.get_results_dataframe(index_by_code=True)
if not abs(df.loc["irpp"][0] - irpp) < 1:
print year
print revenu
print amount
print "OpenFisca :", abs(df.loc["irpp"][0])
print "Real value :", irpp
assert abs(df.loc["irpp"][0] - irpp) < 1
def test_cotsoc_celib():
"""
test pour un célibataire
"""
# Comparaison avec la fiche de paie IPP calculé avec une cotisation transport correspondant à Paris ((.026)
# alors qu'Openfisca la cacule pour Lyon (.0175)
tests_list = [
# Célibataires (pas de supplément familial de traitement
{"year" : 2012,
"input_vars":
{
"type_sal" : CAT["public_titulaire_etat"],
"salbrut" : 12 * 2000,
"primes" : 12 * 500,
"zone_apl": 1,
},
"output_vars" :
{
"indemnite_residence": 60,
"cot_pat_pension_civile": 1371.80,
"cot_sal_pension_civile": 167.80,
"cot_sal_rafp": 20,
"cot_pat_rafp": 20,
"cotpat_transport": 2000 * 0.0175,
"cotpat" : 1371.80 + 6.6 + 20 + 194 + 108 + 2 + 8 + 2000 * 0.0175 + 6,
# pension, ati, rafp, maladie, famille, fnal1, fnal2, csa,
"cotsal" : 167.80 + 20 + 23.72,
# pension rafp
"salsuperbrut": 4328.40 + 2000 * (0.0175 - 0.026), # Correction transport
"csgsald" : 128.28,
"csgsali" : 60.36,
"crdssal": 12.58,
"salnet": 2147.26,
}
},
{"year" : 2012,
"input_vars":
{
"type_sal" : CAT["public_titulaire_territoriale"],
"salbrut" : 12 * 2000,
"primes" : 12 * 500,
"zone_apl": 1,
},
"output_vars" :
{
"indemnite_residence": 60,
"cot_pat_pension_civile": 546,
"cot_sal_pension_civile": 167.80,
"cot_sal_rafp": 20,
"cot_pat_rafp": 20,
"cotpat_transport": 2000 * 0.0175,
"cotpat" : 546 + 10 + 20 + 230 + 108 + 2 + 8 + 2000 * 0.0175 + 6,
# pension, ati, rafp, maladie, famille, fnal1, fnal2, csa,
"cotsal" : 167.80 + 20 + 23.72,
# pension rafp, fds
"csgsald" : 128.28,
"csgsali" : 60.36,
"crdssal": 12.58,
"salsuperbrut": 3542 + 2000 * (0.0175 - 0.026),
"salnet": 2147.26,
}
},
{"year" : 2012,
"input_vars":
{
"type_sal" : CAT["public_titulaire_hospitaliere"],
"salbrut" : 12 * 2000,
"primes" : 12 * 500,
"zone_apl": 1,
},
"output_vars" :
{
"indemnite_residence": 60,
"cot_pat_pension_civile": 546,
"cot_sal_pension_civile": 167.80,
"cot_sal_rafp": 20,
"cot_pat_rafp": 20,
"cotpat_transport": 2000 * 0.0175,
"cotpat" : 546 + 10 + 20 + 230 + 108 + 20 + 2 + 8 + 2000 * 0.0175 + 6,
# pension, ati, rafp, maladie, famille, feh, fnal1, fnal2, transport, csa
"cotpat_contrib": 546 + 20 + 20,
# pension, rafp, feh
"cotsal" : 167.80 + 20 + 23.72,
# pension, rafp, except de solidarité
"csgsald" : 128.28,
"csgsali" : 60.36,
"crdssal": 12.58,
"salsuperbrut": 3562 + 2000 * (0.0175 - 0.026), # second term is correction of transport
"salnet": 2147.26,
}
},
{"year" : 2011,
"input_vars":
{
"type_sal" : CAT["public_non_titulaire"],
"salbrut" : 12 * 2000,
"primes" : 12 * 500,
"zone_apl": 1,
},
"output_vars" :
{
"indemnite_residence": 60,
"cot_pat_pension_civile": 0,
"cot_sal_pension_civile": 0,
"cot_sal_rafp": 0,
"cot_pat_rafp": 0,
"cotpat_transport": 2560 * .0175,
"cotpat" : 212.48 + 40.96 + 90.24 + 327.68 + 138.24 + 2.56 + 10.24 + 2560 * 0.0175 + 7.68,
# viellesse plaf, deplaf, ircantec, maladie, famille, fnal1, fnal2, transport, csa,
"cotsal" : 170.24 + 2.56 + 58.24 + 19.20 + 23.16,
# viel_plaf viel_deplaf ircantecA maladie, cot excep de solidarite
"cotsal_contrib": 170.24 + 2.56 + 58.24,
# viel_plaf viel_deplaf ircantecA
"csgsald" : 128.28,
"csgsali" : 60.36,
"crdssal": 12.58,
"salsuperbrut": 3367.36 + 2000 * (0.0175 - 0.026),
"salnet": 2091.20,
}
},
]
passed = True
for test in tests_list:
year = test["year"]
simulation = ScenarioSimulation()
simulation.set_config(year = test["year"], nmen = 1)
simulation.set_param()
test_case = simulation.scenario
for variable, value in test['input_vars'].iteritems():
if variable in ['zone_apl']:
test_case.menage[0].update({ variable: value})
else:
test_case.indiv[0].update({ variable: value})
df = simulation.get_results_dataframe(index_by_code = True)
simulation.output_table.calculate_prestation(simulation.prestation_by_name['salnet'])
simulation.output_table.calculate_prestation(simulation.prestation_by_name['sal'])
for variable, value in test['output_vars'].iteritems():
computed_value = (simulation.output_table.table[variable] / 12).sum()
test_assertion = abs(abs(computed_value) - value) < 1
expression = "Test failed for variable %s on year %i and case %s: \n OpenFisca value : %s \n Real value : %s \n" % (variable, year, test['input_vars'], abs(computed_value), value)
if not test_assertion:
print expression
passed = False
assert passed, "Test failed for some variables"
def test_niches():
"""
test pour un célibataire pour un revenu de 20 000 €
et des revenus de différentes origines
"""
# test charges déductibles: pensions alimentaires "f6gi","f6gj","f6el","f6em","f6gp","f6gu".
tests_list = [
# Test pour un célibataire ayant un revenu salarial (1AJ) et pas de PA
{"year": 2012, "input_vars": {"sali": 50000}, "output_vars": {"irpp": -7934}},
{"year": 2012, "input_vars": {"sali": 20000}, "output_vars": {"irpp": -1181}},
{"year": 2011, "input_vars": {"sali": 20000}, "output_vars": {"irpp": -1181}},
# Test pour un célibataire ayant un revenu salarial (1AJ) et pas un PA de type 6GI
{"year": 2012, "input_vars": {"sali": 20000, "f6gi": 1800}, "output_vars": {"irpp": -819}},
{"year": 2011, "input_vars": {"sali": 20000, "f6gi": 1800}, "output_vars": {"irpp": -860}},
# Test pour un célibataire ayant un revenu salarial (1AJ) et pas un PA de type 6EL
{"year": 2012, "input_vars": {"sali": 20000, "f6el": 1800}, "output_vars": {"irpp": -913}},
{"year": 2011, "input_vars": {"sali": 20000, "f6el": 1800}, "output_vars": {"irpp": -929}},
# Test pour un célibataire ayant un revenu salarial (1AJ) et pas un PA de type 6gj
{"year": 2012, "input_vars": {"sali": 20000, "f6gj": 1800}, "output_vars": {"irpp": -819}},
{"year": 2011, "input_vars": {"sali": 20000, "f6gj": 1800}, "output_vars": {"irpp": -860}},
# Test pour un célibataire ayant un revenu salarial (1AJ) et pas un PA de type 6em
{"year": 2012, "input_vars": {"sali": 20000, "f6em": 1800}, "output_vars": {"irpp": -913}},
{"year": 2011, "input_vars": {"sali": 20000, "f6em": 1800}, "output_vars": {"irpp": -929}},
# Test pour un célibataire ayant un revenu salarial (1AJ) et pas un PA de type 6gp
{"year": 2012, "input_vars": {"sali": 20000, "f6gp": 1800}, "output_vars": {"irpp": -819}},
{"year": 2011, "input_vars": {"sali": 20000, "f6gp": 1800}, "output_vars": {"irpp": -860}},
# Test pour un célibataire ayant un revenu salarial (1AJ) et pas un PA de type 6gu
{"year": 2012, "input_vars": {"sali": 20000, "f6gu": 1800}, "output_vars": {"irpp": -913}},
{"year": 2011, "input_vars": {"sali": 20000, "f6gu": 1800}, "output_vars": {"irpp": -929}},
]
for test in tests_list:
year = test["year"]
simulation = ScenarioSimulation()
simulation.set_config(year=test["year"], nmen=1)
simulation.set_param()
test_case = simulation.scenario
for variable, value in test["input_vars"].iteritems():
if variable in ["sali"]:
test_case.indiv[0].update({variable: value})
else:
test_case.declar[0].update({variable: value})
df = simulation.get_results_dataframe(index_by_code=True)
passed = True
for variable, value in test["output_vars"].iteritems():
computed_value = (simulation.output_table.table[variable]).sum()
test_assertion = abs(abs(computed_value) - value) < 1
expression = "Test failed for variable %s on year %i : \n OpenFisca value : %s \n Real value : %s \n" % (
variable,
year,
abs(computed_value),
value,
)
if not test_assertion:
print expression
passed = True
assert passed, "Test failed for some variables"
def test_irpp():
"""
test pour un célibataire pour un revenu de 20 000, 50 000 € et 150 000 €
et des revenus de différentes origines
"""
dico = {
# test pour un célibataire ayant un revenu salarial (1AJ)
"sali": [
{
"year": 2010,
"amount": 20000,
"irpp": -1181
},
{
"year": 2011,
"amount": 20000,
"irpp": -1181
},
{
"year": 2012,
"amount": 20000,
"irpp": -1181
},
{
"year": 2010,
"amount": 50000,
"irpp": -7934
},
{
"year": 2011,
"amount": 50000,
"irpp": -7934
},
{
"year": 2012,
"amount": 50000,
"irpp": -7934
},
{
"year": 2010,
"amount": 150000,
"irpp": -42338
},
{
"year": 2011,
"amount": 150000,
"irpp": -42338
},
{
"year": 2012,
"amount": 150000,
"irpp": -43222
},
],
# test pour un retraité célibataire ayant une pension (1AS)
"rsti": [
{
"year": 2010,
"amount": 20000,
"irpp": -1181
},
{
"year": 2011,
"amount": 20000,
"irpp": -1181
},
{
"year": 2012,
"amount": 20000,
"irpp": -1181
},
{
"year": 2010,
"amount": 50000,
"irpp": -8336
},
{
"year": 2011,
"amount": 50000,
"irpp": -8336
},
{
"year": 2012,
"amount": 50000,
"irpp": -8336
},
{
"year": 2010,
"amount": 150000,
"irpp": -46642
},
{
"year": 2011,
"amount": 150000,
"irpp": -46642
},
{
"year": 2012,
"amount": 150000,
"irpp": -46642
},
],
# test sur un revenu des actions soumises à un prélèvement libératoire de 21 % (2DA)
"f2da": [
{
"year": 2010,
"amount": 20000,
"irpp": 0
},
{
"year": 2011,
"amount": 20000,
"irpp": 0
},
{
"year": 2012,
"amount": 20000,
"irpp": 0
},
{
"year": 2010,
"amount": 50000,
"irpp": 0
},
{
"year": 2011,
"amount": 50000,
"irpp": 0
},
{
"year": 2012,
"amount": 50000,
"irpp": 0
},
{
"year": 2010,
"amount": 150000,
"irpp": 0
},
{
"year": 2011,
"amount": 150000,
"irpp": 0
},
{
"year": 2012,
"amount": 150000,
"irpp": 0
},
],
# test sur un revenu (2DH) issu des produits d'assurance vie et de capitalisation soumis au prélèvement libératoire de 7.5 %
"f2dh": [
{
"year": 2010,
"amount": 20000,
"irpp": 345
},
{
"year": 2011,
"amount": 20000,
"irpp": 345
},
{
"year": 2012,
"amount": 20000,
"irpp": 345
},
{
"year": 2010,
"amount": 50000,
"irpp": 345
},
{
"year": 2011,
"amount": 50000,
"irpp": 345
},
{
"year": 2012,
"amount": 50000,
"irpp": 345
},
{
"year": 2010,
"amount": 150000,
"irpp": 345
},
{
"year": 2011,
"amount": 150000,
"irpp": 345
},
{
"year": 2012,
"amount": 150000,
"irpp": 345
},
],
# test sur un revenu des actions et parts (2DC)
"f2dc": [
{
"year": 2010,
"amount": 20000,
"irpp": 0
},
{
"year": 2011,
"amount": 20000,
"irpp": 0
},
{
"year": 2012,
"amount": 20000,
"irpp": -31
},
{
"year": 2010,
"amount": 50000,
"irpp": -2976
},
{
"year": 2011,
"amount": 50000,
"irpp": -2976
},
{
"year": 2012,
"amount": 50000,
"irpp": -3434
},
{
"year": 2010,
"amount": 150000,
"irpp": -22917
},
{
"year": 2011,
"amount": 150000,
"irpp": -22917
},
{
"year": 2012,
"amount": 150000,
"irpp": -23542
},
],
# test sur le revenu de valeurs mobilières (2TS)
"f2ts": [
{
"year": 2010,
"amount": 20000,
"irpp": -1461
},
{
"year": 2011,
"amount": 20000,
"irpp": -1461
},
{
"year": 2012,
"amount": 20000,
"irpp": -1461
},
{
"year": 2010,
"amount": 50000,
"irpp": -9434
},
{
"year": 2011,
"amount": 50000,
"irpp": -9434
},
{
"year": 2012,
"amount": 50000,
"irpp": -9434
},
{
"year": 2010,
"amount": 150000,
"irpp": -48142
},
{
"year": 2011,
"amount": 150000,
"irpp": -48142
},
{
"year": 2012,
"amount": 150000,
"irpp": -48142
},
],
# test sur les intérêts (2TR)
"f2tr": [
{
"year": 2010,
"amount": 20000,
"irpp": -1461
},
{
"year": 2011,
"amount": 20000,
"irpp": -1461
},
{
"year": 2012,
"amount": 20000,
"irpp": -1461
},
{
"year": 2010,
"amount": 50000,
"irpp": -9434
},
{
"year": 2011,
"amount": 50000,
"irpp": -9434
},
{
"year": 2012,
"amount": 50000,
"irpp": -9434
},
{
"year": 2010,
"amount": 150000,
"irpp": -48142
},
{
"year": 2011,
"amount": 150000,
"irpp": -48142
},
{
"year": 2012,
"amount": 150000,
"irpp": -48142
},
],
# test sur les revenus fonciers (4BA)
"f4ba": [
{
"year": 2010,
"amount": 20000,
"irpp": -1461
},
{
"year": 2011,
"amount": 20000,
"irpp": -1461
},
{
"year": 2012,
"amount": 20000,
"irpp": -1461
},
{
"year": 2010,
"amount": 50000,
"irpp": -9434
},
{
"year": 2011,
"amount": 50000,
"irpp": -9434
},
{
"year": 2012,
"amount": 50000,
"irpp": -9434
},
{
"year": 2010,
"amount": 150000,
"irpp": -48142
},
{
"year": 2011,
"amount": 150000,
"irpp": -48142
},
{
"year": 2012,
"amount": 150000,
"irpp": -48142
},
],
# test sur les plus-values mobilières (3VG)
"f3vg": [
{
"year": 2010,
"amount": 20000,
"irpp": -3600
},
{
"year": 2011,
"amount": 20000,
"irpp": -3800
},
{
"year": 2012,
"amount": 20000,
"irpp": -4800
},
{
"year": 2010,
"amount": 50000,
"irpp": -9000
},
{
"year": 2011,
"amount": 50000,
"irpp": -9500
},
{
"year": 2012,
"amount": 50000,
"irpp": -12000
},
{
"year": 2010,
"amount": 150000,
"irpp": -27000
},
{
"year": 2011,
"amount": 150000,
"irpp": -28500
},
{
"year": 2012,
"amount": 150000,
"irpp": -36000
},
],
# test sur les plus-values immobilières (3VZ)
"f3vz": [
{
"year": 2011,
"amount": 20000,
"irpp": 0
},
{
"year": 2012,
"amount": 20000,
"irpp": 0
},
{
"year": 2011,
"amount": 50000,
"irpp": 0
},
{
"year": 2012,
"amount": 50000,
"irpp": 0
},
{
"year": 2011,
"amount": 150000,
"irpp": 0
},
{
"year": 2012,
"amount": 150000,
"irpp": 0
},
],
}
for revenu, test_list in dico.iteritems():
for item in test_list:
year = item["year"]
amount = item["amount"]
irpp = item["irpp"]
simulation = ScenarioSimulation()
simulation.set_config(year=year, nmen=1)
simulation.set_param()
test_case = simulation.scenario
if revenu in ["rsti", "sali"]:
test_case.indiv[0].update({revenu: amount})
elif revenu in [
"f2da", "f2dh", "f2dc", "f2ts", "f2tr", "f4ba", "f3vg",
"f3vz"
]:
test_case.declar[0].update({revenu: amount})
else:
assert False
df = simulation.get_results_dataframe(index_by_code=True)
if not abs(df.loc["irpp"][0] - irpp) < 1:
print year
print revenu
print amount
print "OpenFisca :", abs(df.loc["irpp"][0])
print "Real value :", irpp
assert abs(df.loc["irpp"][0] - irpp
) < 1, "error in irpp for revenu %s in year %s \n" % (
revenu, year)
def test_case(year):
"""
Use to test individual test-case for debugging
"""
app = QApplication(sys.argv)
win = ApplicationWindow()
yr = year
simulation = ScenarioSimulation()
simulation.set_config(year=yr,
nmen=1,
reforme=False,
mode='castype',
decomp_file="decomp_contrib.xml")
simulation.set_param()
# Hack to get rid of missing parameters in 2012
if yr == 2012:
complete_2012_param(simulation.P)
test_case = simulation.scenario
# Changes in individualized caracteristics
#TRAITEMENTS, SALAIRES, PPE, PENSIONS ET RENTES
# salaires (case 1AJ)
test_case.indiv[0].update({"sali": 0})
# préretraites, chômage (case 1AP)
test_case.indiv[0].update({"choi": 0})
# pensions (case 1AS)
test_case.indiv[0].update({"rsti": 0})
# Changes in non-individualized items of the declaration
# REVENUS DES VALEURS ET CAPITAUX MOBILIERS
# intérêts
# f2ee intpfl (pdts de placement soumis aux prélèvements obligatoires autres que 2DA et 2DH
# f2tr intb (intérêts et autres revenus assimilés)
test_case.declar[0].update({"f2ts": 0})
test_case.declar[0].update({"f2tr": 0})
# dividendes
# f2da divplf (revenus des actions et parts soumis au prélèvement libératoire à 21 %
# f2dc divb (revenus des actions et parts ouvrant droit à abattement)
test_case.declar[0].update({"f2da": 0})
test_case.declar[0].update({"f2dh": 0})
test_case.declar[0].update({"f2dc": 0})
# REVENUS FONCIERS
# foncier f4ba (micro foncier f4be)
test_case.declar[0].update({"f4ba": 0})
#PLUS-VALUES DE CESSION DE VALEURS MOBILIERES, DROITS SOCIAUX ET GAINS ASSIMILéS
# plus-values TODO: F3VG
test_case.declar[0].update({"f3vg": 1000000})
test_case.declar[0].update({"f3vz": 0})
df = simulation.get_results_dataframe(index_by_code=True)
rev_cols = [
"salsuperbrut", "chobrut", "rstbrut", "fon", "rev_cap_bar",
"rev_cap_lib"
]
prelev_cols = [
"cotpat_noncontrib", "cotsal_noncontrib", "csgsald", "csgsali",
"crdssal", "cotpat_noncontrib", "cotsal_noncontrib", "csgsald",
"csgsali", "crdssal", "csgchod", "csgchoi", "crdscho", "csgrstd",
"csgrsti", "crdsrst", "prelsoc_cap_bar", "prelsoc_cap_lib",
"csg_cap_bar", "csg_cap_lib", "crds_cap_bar", "crds_cap_lib",
"imp_lib", "ppe", "irpp"
]
rev_df = df.loc[rev_cols]
rev = rev_df.sum(axis=0)
prelev_df = df.loc[prelev_cols]
prelev = prelev_df.sum(axis=0)
print -prelev / rev
print rev
# print prelev_df, rev, -prelev
ax = win.mplwidget.axes
if SHOW_OPENFISCA:
title = "Mon titre"
ax.set_title(title)
draw_simulation_waterfall(simulation, ax)
win.resize(1400, 700)
win.mplwidget.draw()
win.show()
sys.exit(app.exec_())
if EXPORT:
win.mplwidget.print_figure(DESTINATION_DIR + title + '.png')
del ax, simulation
def get_avg_tax_rate_dataframe(x_axis="sali", maxrev=50000, year=2006):
"""
Returns avgerage tax rate dataframe
Parameters
----------
x_axis : string
sali choi etc
maxrev : float
Maximal revenu
year : int, default 2006
year of the legislation
"""
simulation = ScenarioSimulation()
# Changes in individualized caracteristics
# salaires: sali
# retraites: choi
# intérêts: f2ee intpfl; f2tr intb
# dividendes: f2da divplf; f2dc divb
# foncier f4ba fon (micro foncier f4be)
simulation.set_config(year=year,
nmen=101,
x_axis=x_axis,
maxrev=maxrev,
reforme=False,
mode='bareme',
decomp_file="decomp_contrib.xml")
simulation.set_param()
if year == 2012:
complete_2012_param(simulation.P)
test_case = simulation.scenario
df = simulation.get_results_dataframe(index_by_code=True)
rev_cols = [
"salsuperbrut", "chobrut", "rstbrut", "fon", "rev_cap_bar",
"rev_cap_lib", "f3vz", "f3vg"
]
prelev_cols = [
"cotpat_noncontrib",
"cotsal_noncontrib",
"csgsald",
"csgsali",
"crdssal",
"cotpat_noncontrib",
"cotsal_noncontrib",
"csgsald",
"csgsali",
"crdssal",
"csgchod",
"csgchoi",
"crdscho",
"csgrstd",
"csgrsti",
"crdsrst",
"prelsoc_cap_bar",
"prelsoc_cap_lib",
"csg_cap_bar",
"csg_cap_lib",
"crds_cap_bar",
"crds_cap_lib",
"prelsoc_fon",
"csg_fon",
"crds_fon",
"prelsoc_pv_immo",
"csg_pv_immo",
"crds_pv_immo",
"prelsoc_pv_mo",
"csg_pv_mo",
"crds_pv_mo",
"imp_lib",
"ppe",
"irpp",
"ir_pv_immo",
]
# TODO: vérifier pour la ppe qu'il n'y ait pas de problème
# print df[100].to_string()
rev_df = df.loc[rev_cols]
rev = rev_df.sum(axis=0)
prelev_df = df.loc[prelev_cols]
prelev = prelev_df.sum(axis=0)
avg_rate = -prelev / rev
# Adding IS
if x_axis in ["f2dc", "f2tr"]:
rate_is = .3443
rev_before_is = rev / (1 - rate_is)
prelev = prelev - rate_is * rev_before_is
avg_rate = (-prelev) / rev_before_is
rev = rev_before_is
output_df = DataFrame({
"Revenus": rev,
"Prélèvements": prelev,
"Taux moyen d'imposition": avg_rate
})
output_df.set_index(keys=["Revenus"], inplace=True)
x_axis_long_name = simulation.io_column_by_name[x_axis].label
return output_df, x_axis_long_name
def test_irpp():
"""
test pour un célibataire pour un revenu de 20 000, 50 000 € et 150 000 €
et des revenus de différentes origines
"""
dico = {
# test pour un célibataire ayant un revenu salarial (1AJ)
"sali": [
{"year" : 2010, "amount": 20000, "irpp":-1181 },
{"year" : 2011, "amount": 20000, "irpp":-1181 },
{"year" : 2010, "amount": 50000, "irpp":-7934 },
{"year" : 2011, "amount": 50000, "irpp":-7934 },
{"year" : 2010, "amount": 150000, "irpp":-42338},
{"year" : 2011, "amount": 150000, "irpp":-42338}
],
# test pour un retraité célibataire ayant une pension (1AS)
"rsti": [
{"year" : 2010, "amount": 20000, "irpp":-1181 },
{"year" : 2011, "amount": 20000, "irpp":-1181 },
{"year" : 2010, "amount": 50000, "irpp":-8336 },
{"year" : 2011, "amount": 50000, "irpp":-8336 },
{"year" : 2010, "amount": 150000, "irpp":-46642 },
{"year" : 2011, "amount": 150000, "irpp":-46642 },
],
# test sur un revenu des actions soumises à un prélèvement libératoire de 21 % (2DA)
"f2da" :[
{"year" : 2010, "amount": 20000, "irpp":0},
{"year" : 2011, "amount": 20000, "irpp":0},
{"year" : 2010, "amount": 50000, "irpp":0},
{"year" : 2011, "amount": 50000, "irpp":0},
{"year" : 2010, "amount": 150000, "irpp":0},
{"year" : 2011, "amount": 150000, "irpp":0},
],
# test sur un revenu (2DH) issu des produits d'assurance vie et de capitalisation soumis au prélèvement libératoire de 7.5 %
"f2dh" :[
{"year" : 2010, "amount": 20000, "irpp":345},
{"year" : 2011, "amount": 20000, "irpp":345},
{"year" : 2010, "amount": 50000, "irpp":345},
{"year" : 2011, "amount": 50000, "irpp":345},
{"year" : 2010, "amount": 150000, "irpp":345},
{"year" : 2011, "amount": 150000, "irpp":345},
],
# test sur un revenu des actions et parts (2DC)
"f2dc" :[
{"year" : 2010, "amount": 20000, "irpp":0},
{"year" : 2011, "amount": 20000, "irpp":0},
{"year" : 2010, "amount": 50000, "irpp":-2976},
{"year" : 2011, "amount": 50000, "irpp":-2976},
{"year" : 2010, "amount": 150000, "irpp":-22917},
{"year" : 2011, "amount": 150000, "irpp":-22917},
],
# test sur le revenu de valeurs mobilières (2TS)
"f2ts" :[
{"year" : 2010, "amount": 20000, "irpp":-1461},
{"year" : 2011, "amount": 20000, "irpp":-1461},
{"year" : 2010, "amount": 50000, "irpp":-9434},
{"year" : 2011, "amount": 50000, "irpp":-9434},
{"year" : 2010, "amount": 150000, "irpp":-48142},
{"year" : 2011, "amount": 150000, "irpp":-48142},
],
# test sur les intérêts (2TR)
"f2tr" :[
{"year" : 2010, "amount": 20000, "irpp":-1461},
{"year" : 2011, "amount": 20000, "irpp":-1461},
{"year" : 2010, "amount": 50000, "irpp":-9434},
{"year" : 2011, "amount": 50000, "irpp":-9434},
{"year" : 2010, "amount": 150000, "irpp":-48142},
{"year" : 2011, "amount": 150000, "irpp":-48142},
],
# test sur les revenus fonciers (4BA)
"f4ba" :[
{"year" : 2010, "amount": 20000, "irpp":-1461},
{"year" : 2011, "amount": 20000, "irpp":-1461},
{"year" : 2010, "amount": 50000, "irpp":-9434},
{"year" : 2011, "amount": 50000, "irpp":-9434},
{"year" : 2010, "amount": 150000, "irpp":-48142},
{"year" : 2011, "amount": 150000, "irpp":-48142},
],
# test sur les plus-values mobilières (3VG)
"f3vg" :[
{"year" : 2010, "amount": 20000, "irpp":-3600},
{"year" : 2011, "amount": 20000, "irpp":-3800},
{"year" : 2010, "amount": 50000, "irpp":-9000},
{"year" : 2011, "amount": 50000, "irpp":-9500},
{"year" : 2010, "amount": 150000, "irpp":-27000},
{"year" : 2011, "amount": 150000, "irpp":-28500},
],
# test sur les plus-values immobilières (3VZ)
"f3vz" :[
{"year" : 2010, "amount": 20000, "irpp":0},
{"year" : 2011, "amount": 20000, "irpp":0},
{"year" : 2010, "amount": 50000, "irpp":0},
{"year" : 2011, "amount": 50000, "irpp":0},
{"year" : 2010, "amount": 150000, "irpp":0},
{"year" : 2011, "amount": 150000, "irpp":0},
],
}
for revenu, test_list in dico.iteritems():
for item in test_list:
year = item["year"]
amount = item["amount"]
irpp = item["irpp"]
simulation = ScenarioSimulation()
simulation.set_config(year = year, nmen = 1)
simulation.set_param()
test_case = simulation.scenario
if revenu in ["rsti", "sali"]:
test_case.indiv[0].update({revenu:amount})
elif revenu in ["f2da", "f2dh", "f2dc", "f2ts", "f2tr", "f4ba", "f3vg", "f3vz"]:
test_case.declar[0].update({revenu:amount})
else:
assert False
df = simulation.get_results_dataframe(index_by_code = True)
if not abs(df.loc["irpp"][0] - irpp) < 1:
print year
print revenu
print amount
print "OpenFisca :", abs(df.loc["irpp"][0])
print "Real value :", irpp
assert abs(df.loc["irpp"][0] - irpp) < 1
def test_cotsoc_famille():
"""
test pour un couple de fonctionnaire
"""
tests_list = [
# Famille avec 2 enfants
{"year" : 2012,
"input_vars":
{
"type_sal" : CAT["public_titulaire_etat"],
"salbrut" : 12 * 2000,
"primes" : 12 * 500,
"zone_apl": 1,
},
"output_vars" :
{
"cot_pat_pension_civile": 1371.80,
"cot_sal_pension_civile": 167.80,
"cot_sal_rafp": 20,
"cot_pat_rafp": 20,
"csgsald" : 131.94,
"csgsali" : 62.09,
"indemnite_residence": 60,
"supp_familial_traitement":73.04,
"crdssal": 12.93,
"cotpat_transport": 2000 * 0.0175,
"cotpat" : 1371.80 + 6.6 + 20 + 194 + 108 + 2 + 8 + 2000 * 0.0175 + 6,
# pension, ati, rafp, maladie, famille, fnal1, fnal2, csa,
"cotsal" : 167.80 + 20 + 24.45, # cot excep de solidarité
# pension rafp
"salsuperbrut": 4401.44 + 2000 * (.0175 - .026),
"salnet": 2213.83,
}
},
{"year" : 2012,
"input_vars":
{
"type_sal" : CAT["public_titulaire_etat"],
"salbrut" : 12 * 2000,
"primes" : 12 * 500,
"type_sal_c" : CAT["public_titulaire_etat"],
"salbrut_c" : 12 * 2000,
"primes_c" : 12 * 500,
"zone_apl": 2,
},
"output_vars" :
{
"cot_pat_pension_civile": 1371.80 * 2,
"cot_sal_pension_civile": 167.80 * 2,
"cot_sal_rafp": 20 * 2,
"cot_pat_rafp": 20 * 2,
"csgsald" : 131.94 * 2,
"csgsali" : 62.09 * 2,
"indemnite_residence": 240 * 2 / 12,
"supp_familial_traitement":73.04,
"crdssal": 12.93 * 2,
"cotpat_transport": 2000 * 0.0175 * 2,
"cotpat" : (1371.80 + 6.6 + 20 + 194 + 108 + 2 + 8 + 2000 * 0.0175 + 6) * 2,
# pension, ati, rafp, maladie, famille, fnal1, fnal2, csa,
"cotsal" : (167.80 + 20 + 24.45) * 2 , # cot excep de solidarité
# pension rafp
"salsuperbrut": (2000 + 500 + 20 + 1751.4) * 2 + 73.04,
"salnet": (2000 + 500 + 20 - 131.94 - 62.09 - 12.93 - (167.80 + 20 + 24.45)) * 2 + 73.04,
}
},
]
passed = True
for test in tests_list:
year = test["year"]
simulation = ScenarioSimulation()
simulation.set_config(year = test["year"], nmen = 1)
simulation.set_param()
test_case = simulation.scenario
test_case.addIndiv(1, datetime.date(1975, 1, 1), 'conj', 'part')
test_case.addIndiv(2, datetime.date(2000, 1, 1), 'pac', 'enf')
test_case.addIndiv(3, datetime.date(2009, 1, 1), 'pac', 'enf')
for variable, value in test['input_vars'].iteritems():
if variable in ['zone_apl']:
test_case.menage[0].update({ variable: value})
elif variable in ['type_sal', 'salbrut', 'primes']:
test_case.indiv[0].update({ variable: value})
elif variable in ['type_sal_c', 'salbrut_c', 'primes_c']:
test_case.indiv[1].update({ variable[:-2] : value})
else:
print "Variable non prise en charge : ", variable
pdb.set_trace()
df = simulation.get_results_dataframe(index_by_code = True)
simulation.output_table.calculate_prestation(simulation.prestation_by_name['salnet'])
simulation.output_table.calculate_prestation(simulation.prestation_by_name['sal'])
for variable, value in test['output_vars'].iteritems():
computed_value = (simulation.output_table.table[variable] / 12).sum()
test_assertion = abs(abs(computed_value) - value) < 2
expression = "Test failed for variable %s on year %i and case %s: \n OpenFisca value : %s \n Real value : %s \n" % (variable, year, test['input_vars'], abs(computed_value), value)
if not test_assertion:
print expression
passed = False
assert passed, "Test failed for some variables"
