def test_cho(year = 2014):
simulation = tax_benefit_system.new_scenario().init_single_entity(
axes = [dict(count = 11, max = 24000, min = 0, name = 'chobrut')],
period = year,
parent1 = dict(
birth = datetime.date(year - 40, 1, 1),
),
).new_simulation(debug = True)
brut = simulation.get_holder('chobrut').array
imposable = simulation.calculate('cho')
inversion_reform = inversion_revenus.build_reform(tax_benefit_system)
inverse_simulation = inversion_reform.new_scenario().init_single_entity(
axes = [dict(count = 11, max = 24000, min = 0, name = 'chobrut')],
period = year,
parent1 = dict(
birth = datetime.date(year - 40, 1, 1),
),
).new_simulation(debug = True)
inverse_simulation.get_holder('chobrut').delete_arrays()
inverse_simulation.get_or_new_holder('choi').array = imposable.copy()
new_brut = inverse_simulation.calculate('chobrut')
assert_near(new_brut, brut, error_margin = 1)
def net_plot(revenu, count = 11, max_revenu = 5000, min_revenu = 0):
year = 2014
period = periods.period("{}-01".format(year))
if revenu == 'chomage':
brut_name = 'chobrut'
net_name = 'chonet'
elif revenu == 'retraite':
brut_name = 'rstbrut'
net_name = 'rstnet'
elif revenu == 'salaire':
brut_name = 'salaire_de_base'
net_name = 'salaire_net'
else:
return
single_entity_kwargs = dict(
axes = [[
dict(count = count, max = max_revenu, min = min_revenu, name = brut_name)
]],
period = period,
parent1 = dict(
birth = datetime.date(year - 40, 1, 1),
),
)
simulation = tax_benefit_system.new_scenario().init_single_entity(
**single_entity_kwargs).new_simulation(debug = True)
smic_horaire = simulation.legislation_at(period.start).cotsoc.gen.smic_h_b
smic_mensuel = smic_horaire * 35 * 52 / 12
brut = simulation.get_holder(brut_name).array
simulation.get_or_new_holder('contrat_de_travail').array = brut < smic_mensuel # temps plein ou temps partiel
simulation.get_or_new_holder('heures_remunerees_volume').array = brut // smic_horaire # temps plein ou temps partiel
net = simulation.calculate(net_name)
inversion_reform = inversion_revenus.build_reform(tax_benefit_system)
inverse_simulation = inversion_reform.new_scenario().init_single_entity(
**single_entity_kwargs).new_simulation(debug = True)
inverse_simulation.get_holder(brut_name).delete_arrays()
inverse_simulation.get_or_new_holder(net_name).array = net.copy()
inverse_simulation.get_or_new_holder('contrat_de_travail').array = brut < smic_mensuel # temps plein ou temps partiel
inverse_simulation.get_or_new_holder('heures_remunerees_volume').array = (
(brut // smic_horaire) * (brut < smic_mensuel)
)
print inverse_simulation.get_or_new_holder('contrat_de_travail').array
print inverse_simulation.get_or_new_holder('heures_remunerees_volume').array
new_brut = inverse_simulation.calculate(brut_name)
pyplot.subplot(2, 1, 1)
pyplot.plot(brut, net, 'ro', label = "direct")
pyplot.plot(new_brut, net, 'db', label = "inversed")
pyplot.legend()
pyplot.subplot(2, 1, 2)
pyplot.plot(brut, new_brut - brut, 'r-')
pyplot.show()
assert_near(new_brut, brut, absolute_error_margin = 1)
def check_sal(type_sal, year = 2014):
period = periods.period("{}-01".format(year))
single_entity_kwargs = dict(
axes = [dict(count = 101, max = 2000, min = 40, name = 'salaire_de_base')], # TODO: min = 0
period = period,
parent1 = dict(
birth = datetime.date(year - 40, 1, 1),
type_sal = type_sal,
),
)
simulation = tax_benefit_system.new_scenario().init_single_entity(
**single_entity_kwargs
).new_simulation(debug = False)
brut = simulation.get_holder('salaire_de_base').array
smic_horaire = simulation.legislation_at(period.start).cotsoc.gen.smic_h_b
smic_mensuel = smic_horaire * 35 * 52 / 12
brut = simulation.get_holder('salaire_de_base').array
simulation.get_or_new_holder('contrat_de_travail').array = brut < smic_mensuel # temps plein ou temps partiel
simulation.get_or_new_holder('heures_remunerees_volume').array = brut // smic_horaire # temps plein ou partiel
imposable = simulation.calculate('salaire_imposable')
inversion_reform = inversion_revenus.build_reform(tax_benefit_system)
inverse_simulation = inversion_reform.new_scenario().init_single_entity(
**single_entity_kwargs).new_simulation(debug = True)
inverse_simulation.get_holder('salaire_de_base').delete_arrays()
inverse_simulation.get_or_new_holder('salaire_imposable_pour_inversion').array = imposable.copy()
inverse_simulation.get_or_new_holder('contrat_de_travail').array = brut < smic_mensuel # temps plein ou partiel
inverse_simulation.get_or_new_holder('heures_remunerees_volume').array = (
(brut // smic_horaire) * (brut < smic_mensuel)
)
new_brut = inverse_simulation.calculate('salaire_de_base')
assert_near(new_brut, brut, absolute_error_margin = 1)
def test_parametric_reform():
reform = ir_100_tranche_1(tax_benefit_system)
scenario = reform.new_scenario().init_single_entity(
axes=[
dict(
count=3,
name='salaire_imposable',
max=100000,
min=0,
),
],
period=simulation_period,
parent1=dict(date_naissance=datetime.date(simulation_year - 40, 1, 1)),
)
reference_simulation = scenario.new_simulation(reference=True)
assert_near(reference_simulation.calculate('impots_directs'),
[0, -7889.20019531, -23435.52929688],
absolute_error_margin=.01)
reform_simulation = scenario.new_simulation()
assert_near(
reform_simulation.calculate('impots_directs'),
[0, -13900.20019531, -29446.52929688],
absolute_error_margin=.0001,
)
def test_rst(year=2014):
simulation = tax_benefit_system.new_scenario().init_single_entity(
axes=[dict(count=11, max=24000, min=0, name='rstbrut')],
period=year,
parent1=dict(birth=datetime.date(year - 40, 1, 1), ),
).new_simulation(debug=True)
brut = simulation.get_holder('rstbrut').array
imposable = simulation.calculate('rst')
inverse_simulation = simulation.clone(debug=True)
inverse_simulation.get_holder('rstbrut').delete_arrays()
inverse_simulation.get_or_new_holder('rsti').array = imposable.copy()
new_brut = inverse_simulation.calculate('rstbrut')
assert_near(new_brut, brut, error_margin=1)
def brut_plot(revenu, count = 11, max_revenu = 5000, min_revenu = 0):
year = 2014
period = periods.period("{}-01".format(year))
if revenu == 'chomage':
brut_name = 'chobrut'
imposable_name = 'cho'
inversible_name = 'choi'
elif revenu == 'retraite':
brut_name = 'rstbrut'
imposable_name = 'rst'
inversible_name = 'rsti'
elif revenu == 'salaire':
brut_name = 'salaire_de_base'
imposable_name = 'sal'
inversible_name = 'sali'
else:
return
single_entity_kwargs = dict(
axes = [dict(count = count, max = max_revenu, min = min_revenu, name = brut_name)],
period = period,
parent1 = dict(
birth = datetime.date(year - 40, 1, 1),
),
)
simulation = tax_benefit_system.new_scenario().init_single_entity(
**single_entity_kwargs).new_simulation(debug = True)
brut = simulation.get_holder(brut_name).array
imposable = simulation.calculate(imposable_name)
inversion_reform = inversion_revenus.build_reform(tax_benefit_system)
inverse_simulation = inversion_reform.new_scenario().init_single_entity(
**single_entity_kwargs).new_simulation(debug = True)
inverse_simulation.get_holder(brut_name).delete_arrays()
inverse_simulation.get_or_new_holder(inversible_name).array = imposable.copy()
new_brut = inverse_simulation.calculate(brut_name)
pyplot.subplot(2, 1, 1)
pyplot.plot(brut, imposable, 'ro', label = "direct")
pyplot.plot(new_brut, imposable, 'db', label = "inversed")
pyplot.legend()
pyplot.subplot(2, 1, 2)
pyplot.plot(brut, new_brut - brut, 'r-')
pyplot.show()
assert_near(new_brut, brut, absolute_error_margin = 1)
def check_sal(type_sal, year = 2014):
simulation = tax_benefit_system.new_scenario().init_single_entity(
axes = [dict(count = 11, max = 24000, min = 0, name = 'salbrut')],
period = year,
parent1 = dict(
birth = datetime.date(year - 40, 1, 1),
type_sal = type_sal,
),
).new_simulation(debug = False)
brut = simulation.get_holder('salbrut').array
imposable = simulation.calculate('sal')
inverse_simulation = simulation.clone(debug = True)
inverse_simulation.get_holder('salbrut').delete_arrays()
inverse_simulation.get_or_new_holder('sali').array = imposable.copy()
new_brut = inverse_simulation.calculate('salbrut')
assert_near(new_brut, brut, error_margin = 1)
def test_parametric_reform():
def modify_legislation_json(reference_legislation_json_copy):
# FIXME update_legislation is deprecated.
reform_legislation_json = reforms.update_legislation(
legislation_json = reference_legislation_json_copy,
path = ('children', 'ir', 'children', 'bareme', 'brackets', 0, 'rate'),
period = simulation_period,
value = 1,
)
return reform_legislation_json
simulation_year = 2013
simulation_period = periods.period('year', simulation_year)
Reform = reforms.make_reform(
key = 'ir_100_tranche_1',
name = u"Imposition à 100% dès le premier euro et jusqu'à la fin de la 1ère tranche",
reference = tax_benefit_system,
)
reform = Reform()
reform.modify_legislation_json(modifier_function = modify_legislation_json)
scenario = reform.new_scenario().init_single_entity(
axes = [
dict(
count = 3,
name = 'salaire_imposable',
max = 100000,
min = 0,
),
],
period = simulation_period,
parent1 = dict(date_naissance = datetime.date(simulation_year - 40, 1, 1)),
)
reference_simulation = scenario.new_simulation(reference = True)
assert_near(reference_simulation.calculate('impo'), [0, -7889.20019531, -23435.52929688],
absolute_error_margin = .01)
reform_simulation = scenario.new_simulation()
assert_near(reform_simulation.calculate('impo'), [0, -13900.20019531, -29446.52929688],
absolute_error_margin = .0001)
def test_parametric_reform():
simulation_year = 2013
simulation_period = periods.period('year', simulation_year)
reference_legislation_json = tax_benefit_system.legislation_json
reform_legislation_json = reforms.update_legislation(
legislation_json = reference_legislation_json,
path = ('children', 'ir', 'children', 'bareme', 'brackets', 0, 'rate'),
period = simulation_period,
value = 1,
)
Reform = reforms.make_reform(
legislation_json = reform_legislation_json,
# name = u'IR_100_tranche_1',
name = u"Imposition à 100% dès le premier euro et jusqu'à la fin de la 1ère tranche",
reference = tax_benefit_system,
)
reform = Reform()
scenario = reform.new_scenario().init_single_entity(
axes = [
dict(
count = 3,
name = 'sal',
max = 100000,
min = 0,
),
],
period = simulation_period,
parent1 = dict(birth = datetime.date(simulation_year - 40, 1, 1)),
)
reference_simulation = scenario.new_simulation(debug = True, reference = True)
assert_near(reference_simulation.calculate('impo'), [0, -7889.20019531, -23435.52929688], error_margin = .01)
reform_simulation = scenario.new_simulation(debug = True)
assert_near(reform_simulation.calculate('impo'), [0, -13900.20019531, -29446.52929688], error_margin = .0001)
def test_rst(year = 2014):
period = periods.period("{}-01".format(year))
single_entity_kwargs = dict(
axes = [dict(count = 101, max = 2000, min = 0, name = 'rstbrut')],
period = period,
parent1 = dict(
birth = datetime.date(year - 40, 1, 1),
),
)
simulation = tax_benefit_system.new_scenario().init_single_entity(
**single_entity_kwargs
).new_simulation(debug = True)
brut = simulation.get_holder('rstbrut').array
imposable = simulation.calculate('rst')
inversion_reform = inversion_revenus.build_reform(tax_benefit_system)
inverse_simulation = inversion_reform.new_scenario().init_single_entity(
**single_entity_kwargs).new_simulation(debug = True)
inverse_simulation.get_holder('rstbrut').delete_arrays()
inverse_simulation.get_or_new_holder('rsti').array = imposable.copy()
new_brut = inverse_simulation.calculate('rstbrut')
assert_near(new_brut, brut, absolute_error_margin = 1)
def test_parametric_reform():
reform = ir_100_tranche_1(tax_benefit_system)
scenario = reform.new_scenario().init_single_entity(
axes = [
dict(
count = 3,
name = 'salaire_imposable',
max = 100000,
min = 0,
),
],
period = simulation_period,
parent1 = dict(date_naissance = datetime.date(simulation_year - 40, 1, 1)),
)
reference_simulation = scenario.new_simulation(reference = True)
assert_near(reference_simulation.calculate('impo'), [0, -7889.20019531, -23435.52929688],
absolute_error_margin = .01)
reform_simulation = scenario.new_simulation()
assert_near(reform_simulation.calculate('impo'), [0, -13900.20019531, -29446.52929688],
absolute_error_margin = .0001)
