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 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 get_brut_from_net(net,
type_sal=0,
hsup=0,
csg_rempl=0,
rev='sal',
year=2011):
from openfisca_core.simulations import ScenarioSimulation
import openfisca_france
openfisca_france.init_country()
simulation = ScenarioSimulation()
simulation.set_config(year=2011, nmen=2, x_axis="sali", maxrev=100)
simulation.set_param()
simulation.compute()
net = [net]
_defaultP = simulation.P
if rev == 'sal':
output = _salbrut_from_salnet(net, hsup, type_sal, _defaultP)
elif rev == 'cho':
output = _chobrut_from_chonet(net, csg_rempl, _defaultP)
elif rev == 'rst':
output = _rstbrut_from_rstnet(net, csg_rempl, _defaultP)
return output
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 run_simulation(apply_reform=False, reforme=False,
conj = False, kids = 0, sal_conj=False, year=2011):
simulation = ScenarioSimulation()
simulation.set_config(year = year, nmen = 1001,
x_axis = 'sali', maxrev = 10000, reforme = reforme,
mode ='bareme', same_rev_couple = False)
simulation.set_param()
if sal_conj:
conj = True
if conj:
simulation.scenario.addIndiv(1, datetime(1975,1,1).date(), 'vous', 'part')
if sal_conj:
simulation.scenario.indiv[1].update({'sali': simulation.P.cotsoc.gen.smig})
if kids > 0:
for n in range(1,kids+1):
simulation.scenario.addIndiv(n+1, datetime(2000,1,1).date(), 'pac', 'enf')
print simulation.scenario
if apply_reform:
simulation.P.ir.reforme.exemption.active = 1
simulation.P.cotsoc.gen.smig = 320
simulation.P_default.cotsoc.gen.smig = 320
return simulation
def menage_enfant_seulement(year = 2013):
simulation = ScenarioSimulation()
simulation.set_config(year = year,
nmen = 10,
maxrev = 0,
x_axis= 'sali')
print simulation.scenario
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_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 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):
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 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 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 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(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 menage_sans_enfant(year = 2013):
simulation = ScenarioSimulation()
simulation.set_config(year = year,
nmen = 10,
maxrev = 100000,
x_axis= 'sali')
print simulation.scenario
simulation.scenario.addIndiv(1, datetime.date(1975,1,1), 'conj','part')
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_bareme(x_axis="sali"):
"""
Use to test and debug bareme mode test-case
"""
yr = 2012
# Changes in individualized characteristics
# salaires: sali
# retraites: choi
# intérêts: f2ee intpfl; f2tr intb
# dividendes: f2da divplf; f2dc divb
# foncier f4ba fon (micro foncier f4be)
maxrev = 500000
year = 2012
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 = yr, nmen = 101, x_axis = x_axis, maxrev=maxrev,
reforme = False, mode ='bareme', 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
if SHOW_OPENFISCA:
app = QApplication(sys.argv)
win = ApplicationWindow()
ax = win.mplwidget.axes
title ="Barème openfisca"
ax.set_title(title)
graph_x_axis = simulation.get_varying_revenues(x_axis)
draw_simulation_bareme(simulation, ax, graph_x_axis = graph_x_axis, legend = True, position = 4)
win.resize(1400,700)
win.mplwidget.draw()
win.show()
if EXPORT:
win.mplwidget.print_figure(DESTINATION_DIR + title + '.png')
if ax:
del ax
del simulation
sys.exit(app.exec_())
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 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 couple_38ans_avec_3enfants(year = 2013):
simulation = ScenarioSimulation()
simulation.set_config(year=year,
nmen=1,
maxrev = 500000,
x_axis= 'sali',
)
print simulation.scenario
simulation.scenario.addIndiv(1, datetime(1975,1,1).date(), 'conj', 'part')
simulation.scenario.addIndiv(2, datetime(2000,1,1).date(), 'pac1', 'enf1')
simulation.scenario.addIndiv(3, datetime(2005,1,1).date(), 'pac2', 'enf2')
simulation.scenario.addIndiv(4, datetime(2010,1,1).date(), 'pac3', 'enf3')
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 complete_2012_param(P):
# Hack to get rid of missing parameters in 2012
dummy_simulation = ScenarioSimulation()
dummy_simulation.set_config(year = 2012-1, nmen = 1, reforme = False, mode ='castype',
decomp_file="decomp_contrib.xml")
dummy_simulation.set_param()
P.fam = dummy_simulation.P.fam
P.minim = dummy_simulation.P.minim
P.al = dummy_simulation.P.al
P.ir.crl = dummy_simulation.P.ir.crl
P.isf = dummy_simulation.P.isf
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):
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 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 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 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_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 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_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 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 = 2013,
nmen = 2,
maxrev = 10000,
x_axis = 'sali')
simulation.set_param()
df = simulation.get_results_datafram()
print df.to_string()
if_name_**'__main__'
case_study_()
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 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 complete_2012_param(P):
# Hack to get rid of missing parameters in 2012
dummy_simulation = ScenarioSimulation()
dummy_simulation.set_config(year=2012 - 1,
nmen=1,
reforme=False,
mode='castype',
decomp_file="decomp_contrib.xml")
dummy_simulation.set_param()
P.fam = dummy_simulation.P.fam
P.minim = dummy_simulation.P.minim
P.al = dummy_simulation.P.al
P.ir.crl = dummy_simulation.P.ir.crl
P.isf = dummy_simulation.P.isf
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 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=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 couple_38ans_avec_3enfants(year=2013):
simulation = ScenarioSimulation()
simulation.set_config(
year=year,
nmen=1,
maxrev=500000,
x_axis='sali',
)
print simulation.scenario
simulation.scenario.addIndiv(1,
datetime(1975, 1, 1).date(), 'conj', 'part')
simulation.scenario.addIndiv(2,
datetime(2000, 1, 1).date(), 'pac1', 'enf1')
simulation.scenario.addIndiv(3,
datetime(2005, 1, 1).date(), 'pac2', 'enf2')
simulation.scenario.addIndiv(4,
datetime(2010, 1, 1).date(), 'pac3', 'enf3')
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 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 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 run_simulation(apply_reform=False,
reforme=False,
conj=False,
kids=0,
sal_conj=False,
year=2011):
simulation = ScenarioSimulation()
simulation.set_config(year=year,
nmen=1001,
x_axis='salaire_imposable',
maxrev=10000,
reforme=reforme,
mode='bareme',
same_rev_couple=False)
simulation.set_param()
if sal_conj:
conj = True
if conj:
simulation.scenario.addIndiv(1, datetime.date(1975, 1, 1), 'vous',
'part')
if sal_conj:
simulation.scenario.indiv[1].update({
'salaire_imposable':
simulation.P.cotisations_sociales.gen.smig
})
if kids > 0:
for n in range(1, kids + 1):
simulation.scenario.addIndiv(n + 1, datetime.date(2000, 1, 1),
'pac', 'enf')
print simulation.scenario
if apply_reform:
simulation.P.impot_revenu.reforme.exemption.active = 1
simulation.P.cotisations_sociales.gen.smig = 320
simulation.P_default.cotisations_sociales.gen.smig = 320
return simulation
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 modify_parameters(simulation=None):
if simulation is None:
simulation = ScenarioSimulation()
simulation.set_config(year=2013)
simulation.set_param()
for _P in [simulation.P, simulation.P_default]:
sal = build_sal(_P)
pat = build_pat(_P)
_P.cotsoc.__dict__['cotisations_patronales'] = CompactNode()
_P.cotsoc.__dict__['cotisations_salariales'] = CompactNode()
for cotisation_name, bareme_dict in {
'cotisations_patronales': pat,
'cotisations_salariales': sal
}.iteritems():
for category in bareme_dict:
if category in CAT._nums:
_P.cotsoc.__dict__[cotisation_name].__dict__[
category] = bareme_dict[category]
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()