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 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_marginal_tax_rate():
year = 2013
simulation = base.tax_benefit_system.new_scenario().init_single_entity(
axes = [
dict(
count = 10000,
name = 'sali',
max = 1000000,
min = 0,
),
],
period = year,
parent1 = dict(agem = 40 * 12 + 6),
).new_simulation(debug = True)
assert (marginal_rate(
target = simulation.calculate('revdisp'),
varying = simulation.calculate('revdisp'),
) == 0).all()
def test_marginal_tax_rate():
year = 2013
simulation = base.tax_benefit_system.new_scenario().init_single_entity(
axes=[
dict(
count=10000,
name='salaire_imposable',
max=1000000,
min=0,
),
],
period=year,
parent1=dict(age_en_mois=40 * 12 + 6),
).new_simulation()
assert (marginal_rate(
target=simulation.calculate('revenu_disponible'),
varying=simulation.calculate('revenu_disponible'),
) == 0).all()
def test_marginal_tax_rate():
year = 2013
simulation = base.tax_benefit_system.new_scenario().init_single_entity(
axes = [
dict(
count = 10000,
name = 'salaire_imposable',
max = 1000000,
min = 0,
),
],
period = year,
parent1 = dict(age_en_mois = 40 * 12 + 6),
).new_simulation() # Remove debug = True, because logging is too slow.
assert (marginal_rate(
target = simulation.calculate('revdisp'),
varying = simulation.calculate('revdisp'),
) == 0).all()
def test_marginal_tax_rate():
year = 2013
simulation = tax_benefit_system.new_scenario().init_single_entity(
axes = [
dict(
count = 10000,
name = 'salaire_imposable',
max = 1000000,
min = 0,
),
],
period = year,
parent1 = dict(age = 40),
).new_simulation()
assert (marginal_rate(
target = simulation.calculate('revenu_disponible', period = year),
varying = simulation.calculate('revenu_disponible', period = year),
) == 0).all()
def test_marginal_tax_rate():
year = 2013
simulation = base.tax_benefit_system.new_scenario().init_single_entity(
axes=[
dict(
count=10000,
name='sal',
max=1000000,
min=0,
),
],
period=year,
parent1=dict(agem=40 * 12 + 6),
).new_simulation() # Remove debug = True, because logging is too slow.
assert (marginal_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
