def df_with_tax_rate(df):
target = df['salaire_imposable'].values + df['irpp'].values
varying = df['salaire_imposable'].values
df2 = pandas.DataFrame({
'marginal_rate': marginal_rate(target, varying),
'average_rate': average_rate(target, varying)[1:],
})
return pandas.concat([df[1:], df2], axis=1)
def df_with_tax_rate(df):
target = df['salaire_imposable'].values + df['irpp'].values
varying = df['salaire_imposable'].values
df2=pandas.DataFrame({
'marginal_rate': marginal_rate(target, varying),
'average_rate': average_rate(target, varying)[1:],
})
return pandas.concat([df[1:], df2], axis = 1)
def varying_survey_simulation(year = 2009, increment = 10, target = 'irpp', varying = 'rni', used_as_input_variables = None):
TaxBenefitSystem = openfisca_france_data.init_country()
tax_benefit_system = TaxBenefitSystem()
input_data_frame = get_input_data_frame(year)
# Simulation 1 : get varying and target
survey_scenario = SurveyScenario().init_from_data_frame(
input_data_frame = input_data_frame,
used_as_input_variables = used_as_input_variables,
year = year,
tax_benefit_system = tax_benefit_system
)
simulation = survey_scenario.new_simulation(debug = False)
output_data_frame = pandas.DataFrame(
dict([(name, simulation.calculate_add(name)) for name in [
target, varying, 'idfoy_original'
]]))
# Make input_data_frame_by_entity_key_plural from the previous input_data_frame and simulation
input_data_frames_by_entity_key_plural = \
from_input_df_to_entity_key_plural_df(input_data_frame, tax_benefit_system, simulation)
foyers = input_data_frames_by_entity_key_plural['idfoy']
foyers = pandas.merge(foyers, output_data_frame, on = 'idfoy_original')
# Incrementation of varying:
foyers[varying] = foyers[varying] + increment
# On remplace la nouvelle base dans le dictionnaire
input_data_frames_by_entity_key_plural['idfoy'] = foyers
# 2e simulation à partir de input_data_frame_by_entity_key_plural:
# TODO: fix used_as_input_variabels in the from_input_df_to_entity_key_plural_df() function
used_as_input_variables = used_as_input_variables + [varying]
TaxBenefitSystem = openfisca_france_data.init_country()
tax_benefit_system = TaxBenefitSystem()
survey_scenario = SurveyScenario().init_from_data_frame(
input_data_frame = None,
input_data_frames_by_entity_key_plural = input_data_frames_by_entity_key_plural,
used_as_input_variables = used_as_input_variables,
year = year,
tax_benefit_system = tax_benefit_system,
)
simulation = survey_scenario.new_simulation(debug = False)
output_data_frame2 = pandas.DataFrame(
dict([(name, simulation.calculate_add(name)) for name in [
target, varying, 'idfoy_original'
]]))
output_data_frame2.rename(columns = {varying: '{}_2'.format(varying),
target: '{}_2'.format(target)}, inplace = True)
merged = pandas.merge(output_data_frame, output_data_frame2, on = 'idfoy_original')
merged['marginal_rate'] = marginal_rate_survey(merged, '{}'.format(target), '{}_2'.format(target), 'rni', 'rni_2')
merged['average_rate'] = average_rate(target = merged[target], varying = merged[varying])
return merged
def make_result(year, count, sali, menage):
single_entity_kwargs = menage(year, sali)
label = single_entity_kwargs.pop("label")
single_entity_kwargs.update(
dict(
axes = [
dict(
count = count,
max = 1000,
min = 0,
name = 'loyer',
),
],
period = periods.period('year', year),
)
)
# print filename
# print single_entity_kwargs
scenario = base.tax_benefit_system.new_scenario().init_single_entity(**single_entity_kwargs)
simulation = scenario.new_simulation(debug = True)
T = average_rate(
target = simulation.calculate('al'),
varying = simulation.calculate('loyer'),
)
t = marginal_rate(
target = simulation.calculate('al'),
varying = simulation.calculate('loyer'),
)
df1 = pd.DataFrame({
"t": t,
})
df2 = pd.DataFrame({
"T": T,
"loyer": simulation.calculate('loyer'),
"al": simulation.calculate('al') / 12,
})
N = count - 1
df = pd.concat([df2[0:N], df1], axis = 1)
df["sal"] = sali
df["typmen"] = menage.__name__
df["typmensal"] = "{} et {} euros de salaire imposable".format(label, sali)
# df[u"Salaire imposable"] = sali
# df[u"Type de ménage"] = menage.__name__
return df
def test_average_tax_rate():
year = 2013
simulation = base.tax_benefit_system.new_scenario().init_single_entity(
axes=[
dict(
count=100,
name='salaire_imposable',
max=24000,
min=0,
),
],
period=year,
parent1=dict(age_en_mois=40 * 12 + 6),
).new_simulation()
assert (average_rate(
target=simulation.calculate('revenu_disponible'),
varying=simulation.calculate('revenu_disponible'),
) == 0).all()
def test_average_tax_rate():
year = 2013
simulation = base.tax_benefit_system.new_scenario().init_single_entity(
axes = [
dict(
count = 100,
name = 'salaire_imposable',
max = 24000,
min = 0,
),
],
period = year,
parent1 = dict(age_en_mois = 40 * 12 + 6),
).new_simulation() # Remove debug = True, because logging is too slow.
assert (average_rate(
target = simulation.calculate('revdisp'),
varying = simulation.calculate('revdisp'),
) == 0).all()
def test_average_tax_rate():
year = 2013
simulation = tax_benefit_system.new_scenario().init_single_entity(
axes = [
dict(
count = 100,
name = 'salaire_imposable',
max = 24000,
min = 0,
),
],
period = year,
parent1 = dict(age = 40),
).new_simulation()
assert (average_rate(
target = simulation.calculate('revenu_disponible', period = year),
varying = simulation.calculate('revenu_disponible', period = year),
) == 0).all()
def test_average_tax_rate():
year = 2013
simulation = base.tax_benefit_system.new_scenario().init_single_entity(
axes=[
dict(
count=100,
name='sal',
max=24000,
min=0,
),
],
period=year,
parent1=dict(agem=40 * 12 + 6),
).new_simulation() # Remove debug = True, because logging is too slow.
assert (average_rate(
target=simulation.calculate('revdisp'),
varying=simulation.calculate('revdisp'),
) == 0).all()
def test_average_tax_rate():
year = 2013
simulation = base.tax_benefit_system.new_scenario().init_single_entity(
axes = [
dict(
count = 100,
name = 'sali',
max = 24000,
min = 0,
),
],
period = year,
parent1 = dict(agem = 40 * 12 + 6),
).new_simulation(debug = True)
assert (average_rate(
target = simulation.calculate('revdisp'),
varying = simulation.calculate('revdisp'),
) == 0).all()
def draw_rates(simulation, axes, x_axis = None, y_axis = None, reference_simulation = None, legend = True):
assert x_axis is not None
assert y_axis is not None
from openfisca_core.rates import average_rate, marginal_rate
varying = simulation.calculate(x_axis)
target = simulation.calculate(y_axis)
avg_rate = average_rate(target, varying)
marg_rate = marginal_rate(target, varying)
print avg_rate
axes.hold(True)
import numpy as np
axes.set_xlim(np.amin(varying), np.amax(varying))
axes.set_ylabel(r"$\left(1 - \frac{RevDisponible}{RevInitial} \right)\ et\ \left(1 - \frac{d (RevDisponible)}{d (RevInitial)}\right)$")
axes.set_ylabel(r"$\left(1 - \frac{RevDisponible}{RevInitial} \right)\ et\ \left(1 - \frac{d (RevDisponible)}{d (RevInitial)}\right)$")
axes.plot(varying, 100*avg_rate, label = u"Taux moyen d'imposition", linewidth = 2)
axes.plot(varying[1:], 100*marg_rate, label = u"Taux marginal d'imposition", linewidth = 2)
axes.set_ylim(0, 100)
from matplotlib.ticker import FuncFormatter
axes.yaxis.set_major_formatter(FuncFormatter(percentFormatter))
if legend:
createLegend(axes)
def draw_rates(simulation, axes = None, x_axis = None, y_axis = None, reference_simulation = None, legend = True,
period = None):
if axes is None:
fig = plt.figure()
axes = fig.gca()
assert x_axis is not None
assert y_axis is not None
varying = simulation.calculate(x_axis, period = period)
target = simulation.calculate(y_axis, period = period)
avg_rate = average_rate(target, varying)
marg_rate = marginal_rate(target, varying)
axes.hold(True)
axes.set_xlim(np.amin(varying), np.amax(varying))
axes.set_ylabel(r"$\left(1 - \frac{RevDisponible}{RevInitial} \right)\ et\ \left(1 - \frac{d (RevDisponible)}{d (RevInitial)}\right)$")
axes.set_ylabel(r"$\left(1 - \frac{RevDisponible}{RevInitial} \right)\ et\ \left(1 - \frac{d (RevDisponible)}{d (RevInitial)}\right)$")
axes.plot(varying, 100 * avg_rate, label = u"Taux moyen d'imposition", linewidth = 2)
axes.plot(varying[1:], 100 * marg_rate, label = u"Taux marginal d'imposition", linewidth = 2)
axes.set_ylim(0, 100)
axes.yaxis.set_major_formatter(FuncFormatter(percent_formatter))
if legend:
create_legend(axes)
for year in [2008, 2014]:
simulation = base.tax_benefit_system.new_scenario().init_single_entity(
axes = [
dict(
count = count,
max = max,
min = min,
name = name,
),
],
parent1 = dict(
birth = datetime.date(year - 40, 1, 1),
),
menage = dict(statut_occupation = 4),
period = periods.period('year', year)
).new_simulation(debug = True, reference = True)
df = pandas.DataFrame(dict(
salaire_imposable = simulation.calculate('salaire_imposable'),
irpp = simulation.calculate('irpp')))
target = df['salaire_imposable'].values + df['irpp'].values
varying = df['salaire_imposable'].values
df2 = pandas.DataFrame({
'marginal_rate': marginal_rate(target, varying),
'average_rate': average_rate(target, varying)[1:],
})
result[year] = df2.marginal_rate
def test_average_rate():
'''Compute the average tax rate when the gross income is never zero'''
target = numpy.array([1, 2, 3])
result = average_rate(target, varying = 2)
expected = numpy.array([.5, 0, -.5])
numpy.testing.assert_equal(result, expected)
def test_average_rate():
'''Compute the average tax rate when the gross income is never zero'''
target = numpy.array([1, 2, 3])
result = average_rate(target, varying=2)
expected = numpy.array([.5, 0, -.5])
numpy.testing.assert_equal(result, expected)
def varying_survey_simulation(year=2009,
increment=10,
target='irpp',
varying='rni',
used_as_input_variables=None):
TaxBenefitSystem = openfisca_france_data.init_country()
tax_benefit_system = TaxBenefitSystem()
input_data_frame = get_input_data_frame(year)
# Simulation 1 : get varying and target
survey_scenario = SurveyScenario().init_from_data_frame(
input_data_frame=input_data_frame,
used_as_input_variables=used_as_input_variables,
year=year,
tax_benefit_system=tax_benefit_system)
simulation = survey_scenario.new_simulation(debug=False)
output_data_frame = pandas.DataFrame(
dict([(name, simulation.calculate_add(name))
for name in [target, varying, 'idfoy_original']]))
# Make input_data_frame_by_entity_key_plural from the previous input_data_frame and simulation
input_data_frames_by_entity_key_plural = \
from_input_df_to_entity_key_plural_df(input_data_frame, tax_benefit_system, simulation)
foyers = input_data_frames_by_entity_key_plural['idfoy']
foyers = pandas.merge(foyers, output_data_frame, on='idfoy_original')
# Incrementation of varying:
foyers[varying] = foyers[varying] + increment
# On remplace la nouvelle base dans le dictionnaire
input_data_frames_by_entity_key_plural['idfoy'] = foyers
# 2e simulation à partir de input_data_frame_by_entity_key_plural:
# TODO: fix used_as_input_variabels in the from_input_df_to_entity_key_plural_df() function
used_as_input_variables = used_as_input_variables + [varying]
TaxBenefitSystem = openfisca_france_data.init_country()
tax_benefit_system = TaxBenefitSystem()
survey_scenario = SurveyScenario().init_from_data_frame(
input_data_frame=None,
input_data_frames_by_entity_key_plural=
input_data_frames_by_entity_key_plural,
used_as_input_variables=used_as_input_variables,
year=year,
tax_benefit_system=tax_benefit_system,
)
simulation = survey_scenario.new_simulation(debug=False)
output_data_frame2 = pandas.DataFrame(
dict([(name, simulation.calculate_add(name))
for name in [target, varying, 'idfoy_original']]))
output_data_frame2.rename(columns={
varying: '{}_2'.format(varying),
target: '{}_2'.format(target)
},
inplace=True)
merged = pandas.merge(output_data_frame,
output_data_frame2,
on='idfoy_original')
merged['marginal_rate'] = marginal_rate_survey(merged, '{}'.format(target),
'{}_2'.format(target),
'rni', 'rni_2')
merged['average_rate'] = average_rate(target=merged[target],
varying=merged[varying])
return merged
plt.xlabel(u"Gross Wage")
plt.show()
#average rate
average_rate = income_tax / gross_wage
average_rate = np.nan_to_num(average_rate)
print(average_rate)
plt.plot(gross_wage, average_rate)
plt.ylabel("Averate Tax Rate")
plt.xlabel("Gross Wage")
plt.show()
#marginal tax rate
marginal_rate = (income_tax[:-1] - income_tax[1:]) / (taxable_income[:-1] -
taxable_income[1:])
plt.plot(gross_wage[:-1], marginal_rate)
plt.ylabel("Marginal Tax Rate")
plt.xlabel("Gross Wage")
plt.show()
from openfisca_core.rates import average_rate, marginal_rate
#CSG
csg = simulation.calculate_add('csg', period=2014)
1 - average_rate(-csg[1:], gross_wage[1:])
plt.ylim(0, 0.1)
plt.plot(gross_wage[1:], 1 - average_rate(-csg[1:], gross_wage[1:]))
plt.show()
def test_average_rate_when_varying_is_zero():
'''Compute the average tax rate when the varying gross income cross zero (yields infinity)'''
target = numpy.array([1, 2, 3])
result = average_rate(target, varying = 0)
assert numpy.isinf(result[0]).all()
def test_average_rate_when_varying_is_zero():
'''Compute the average tax rate when the varying gross income cross zero (yields infinity)'''
target = numpy.array([1, 2, 3])
result = average_rate(target, varying=0)
assert numpy.isinf(result[0]).all()