def test_run_through():
years = [2006, 2007, 2008, 2009, 2010, 2011]
df = get_denombrements_fiscaux_data_frame(years=years)
index_by_variable_name = create_index_by_variable_name(
formula_by_variable_name, level_2_formula_by_variable_name)
variable_name = 'interets_imposes_au_prelevement_liberatoire'
get_or_construct_value(df,
variable_name,
index_by_variable_name,
years=years)
variable_name = 'dividendes_imposes_au_prelevement_liberatoire'
get_or_construct_value(df,
variable_name,
index_by_variable_name,
years=years)
variable_name = 'revenus_imposes_au_prelevement_liberatoire'
get_or_construct_value(df,
variable_name,
index_by_variable_name,
years=years,
fill_value=0)
variable_name = 'assurances_vie_imposees_au_prelevement_liberatoire'
get_or_construct_value(df,
variable_name,
index_by_variable_name,
years=years)
variable_name = 'f2da'
get_or_construct_value(df,
variable_name,
index_by_variable_name,
years=years)
variable_name = u'f5he'
get_or_construct_value(df,
variable_name,
index_by_variable_name,
years=range(2010, 2012))
variable_name = u'f5jr'
get_or_construct_value(df,
variable_name,
index_by_variable_name,
years=range(2007, 2012),
fill_value=0)
variable_name = 'plus_values_professionnelles_regime_normal'
get_or_construct_value(df,
variable_name,
index_by_variable_name,
years=range(2007, 2012),
fill_value=0)
def test_run_through():
years = [2006, 2007, 2008, 2009, 2010, 2011]
df = get_denombrements_fiscaux_data_frame(years = years)
index_by_variable_name = create_index_by_variable_name(formula_by_variable_name, level_2_formula_by_variable_name)
variable_name = 'interets_imposes_au_prelevement_liberatoire'
get_or_construct_value(df, variable_name, index_by_variable_name, years = years)
variable_name = 'dividendes_imposes_au_prelevement_liberatoire'
get_or_construct_value(df, variable_name, index_by_variable_name, years = years)
variable_name = 'revenus_imposes_au_prelevement_liberatoire'
get_or_construct_value(df, variable_name, index_by_variable_name, years = years, fill_value = 0)
variable_name = 'assurances_vie_imposees_au_prelevement_liberatoire'
get_or_construct_value(df, variable_name, index_by_variable_name, years = years)
variable_name = 'f2da'
get_or_construct_value(df, variable_name, index_by_variable_name, years = years)
variable_name = u'f5he'
get_or_construct_value(df, variable_name, index_by_variable_name, years = range(2010, 2012))
variable_name = u'f5jr'
get_or_construct_value(df, variable_name, index_by_variable_name, years = range(2007, 2012), fill_value = 0)
variable_name = 'plus_values_professionnelles_regime_normal'
get_or_construct_value(df, variable_name, index_by_variable_name, years = range(2007, 2012), fill_value = 0)
def test_corrections():
years = range(2006, 2013)
df = get_denombrements_fiscaux_data_frame(years=years)
index_by_variable_name = create_index_by_variable_name(
formula_by_variable_name, level_2_formula_by_variable_name)
test_by_variable = dict(
# Correction of f5io in 2008 in Agrégats IPP
benefices_agricoles_forfait_imposables=[
{
'year': 2006,
'target': 896512850
},
{
'year': 2008,
'target': 883970587
},
],
benefices_agricoles_reels_imposables=[
{
'year': 2006,
'target': 5150417953
},
{
'year': 2008,
'target': 6515953706
},
],
benefices_agricoles_reels_imposables_sans_cga=[
{
'year': 2006,
'target': 165830038
},
],
benefices_agricoles_reels_deficits=[
{
'year': 2006,
'target': 519217942
},
],
benefices_agricoles_reels_sans_cga_deficits=[
{
'year': 2006,
'target': 208934263
},
],
deficits_industriels_commerciaux_professionnels=[
{
'year': 2006,
'target': 1427052021
},
],
deficits_industriels_commerciaux_non_professionnels=[
{
'year': 2006,
'target': 301194784
},
],
plus_values_mobilieres_stock_options=[
{
'year': 2008,
'target': 228873359
},
# {'year': 2010, 'target': 690459289}, TODO: check dénombremenst DGFIP vs IPP
],
revenus_imposes_au_bareme=[
{
'year': 2010,
'target': 18907148239
},
],
plus_values_mobilieres_regime_normal=[
{
'year': 2010,
'target': 5393808406
},
],
plus_values_professionnelles_regime_normal=[
{
'year': 2011,
'target': 1101248065
},
{
'year': 2010,
'target': 1083102431
},
],
# Inversion 2ch et 2gr dans dénombrements fiscax (Agrégats IPP et en ligne)
assurances_vie_imposees_au_bareme=[
{
'year': 2009,
'target': 1063726777
},
],
# Test revenus exonérés
benefices_agricoles_forfait_exoneres=[
{
'year': 2011,
'target': (5826752 + 466632 + 467)
},
],
benefices_agricoles_reels_exoneres=[
{
'year': 2011,
'target': (64880784 + 15074222 + 3733)
},
],
benefices_agricoles_reels_exoneres_sans_cga=[
{
'year': 2011,
'target': (10214497 + 2275427 + 171787)
},
],
)
def assert_value_construction(variable_name, test):
year = test['year']
target = test['target']
value = get_or_construct_value(df,
variable_name,
index_by_variable_name,
years=years,
fill_value=0)[0].loc[year]
if year >= 2009:
assert all(
value == target), "{} for {}: got {} instead of {}".format(
variable_name, year, value.values, target)
for variable_name, tests in test_by_variable.iteritems():
for test in tests:
yield assert_value_construction, variable_name, test
def build_irpp_tables(years=None, fill_value=numpy.NaN):
assert years is not None
assert isinstance(years, list)
raw_data = get_denombrements_fiscaux_data_frame(years=years, fill_value=0)
aggregates = build_aggregates(
raw_data,
formula_by_variable_name,
level_2_formula_by_variable_name=level_2_formula_by_variable_name,
years=years,
fill_value=fill_value,
)
data_frame_by_irpp_table_name = collections.OrderedDict([
# 1. Tableau IRPP1: Les revenus figurant dans les déclarations de revenus
(
'irpp_1',
aggregates[[
'salaires',
'salaires_imposables',
'heures_supplementaires',
# TODO
'revenus_d_activite_non_salariee',
'benefices_agricoles',
'benefices_industriels_commerciaux',
'benefices_non_commerciaux',
'revenus_activite_non_salariee_exoneres',
'revenus_de_remplacement',
'pensions_de_retraite',
'allocations_chomage',
'revenus_fonciers',
'revenus_fonciers_regime_normal',
'revenus_fonciers_micro_foncier',
'revenus_financiers',
'frais_reels',
'pensions_alimentaires_percues',
]]),
# 2. Tableau IRPP2: Détails des revenus financiers (intérêts, dividendes, plus-values) figurant dans les
# déclations de revenus (imposition au barème, imposition au prélèvement forfaitaire libératoire (PL) et
# plus-values)
('irpp_2', aggregates[[
'revenus_imposes_au_bareme', 'dividendes_imposes_au_bareme',
'interet_imposes_au_bareme', 'assurances_vie_imposees_au_bareme',
'revenus_imposes_au_prelevement_liberatoire',
'dividendes_imposes_au_prelevement_liberatoire',
'interets_imposes_au_prelevement_liberatoire',
'assurances_vie_imposees_au_prelevement_liberatoire',
'plus_values', 'revenus_financiers',
'revenus_financiers_hors_plus_values'
]]),
# 3. Tableau IRPP3: Plus-values mobilières et professionnelles
('irpp_3', aggregates[[
'plus_values',
'plus_values_mobilieres',
'plus_values_mobilieres_regime_normal',
'plus_values_mobilieres_stock_options',
'plus_values_mobilieres_retraite_dirigeant',
'plus_values_professionnelles',
'plus_values_professionnelles_regime_normal',
'plus_values_professionnelles_retraite_dirigeant',
]]),
(
'irpp_4',
aggregates[[
'revenus_d_activite_non_salariee',
'benefices_agricoles',
'benefices_agricoles_bruts',
'deficits_agricoles',
'benefices_industriels_commerciaux',
'benefices_industriels_commerciaux_bruts',
'deficits_industriels_commerciaux',
# 'bnc',
'revenus_activite_non_salariee_exoneres',
]]),
# ('irpp_5_a', aggregates[[
# 'benefices_agricoles',
# 'benefices_agricoles_forfait_exoneres',
# 'benefices_agricoles_forfait_imposables',
# 'benefices_agricoles_reels_exoneres',
# 'benefices_agricoles_reels_imposables',
# 'benefices_agricoles_reels_deficits',
# 'benefices_agricoles_reels_exoneres_sans_cga',
# 'benefices_agricoles_reels_imposables_sans_cga',
# 'benefices_agricoles_reels_sans_cga_deficits',
# ]])
])
return data_frame_by_irpp_table_name
def test_corrections():
years = range(2006, 2013)
df = get_denombrements_fiscaux_data_frame(years = years)
index_by_variable_name = create_index_by_variable_name(formula_by_variable_name, level_2_formula_by_variable_name)
test_by_variable = dict(
# Correction of f5io in 2008 in Agrégats IPP
benefices_agricoles_forfait_imposables = [
{'year': 2006, 'target': 896512850},
{'year': 2008, 'target': 883970587},
],
benefices_agricoles_reels_imposables = [
{'year': 2006, 'target': 5150417953},
{'year': 2008, 'target': 6515953706},
],
benefices_agricoles_reels_imposables_sans_cga = [
{'year': 2006, 'target': 165830038},
],
benefices_agricoles_reels_deficits = [
{'year': 2006, 'target': 519217942},
],
benefices_agricoles_reels_sans_cga_deficits = [
{'year': 2006, 'target': 208934263},
],
deficits_industriels_commerciaux_professionnels = [
{'year': 2006, 'target': 1427052021},
],
deficits_industriels_commerciaux_non_professionnels = [
{'year': 2006, 'target': 301194784},
],
plus_values_mobilieres_stock_options = [
{'year': 2008, 'target': 228873359},
# {'year': 2010, 'target': 690459289}, TODO: check dénombremenst DGFIP vs IPP
],
revenus_imposes_au_bareme = [
{'year': 2010, 'target': 18907148239},
],
plus_values_mobilieres_regime_normal = [
{'year': 2010, 'target': 5393808406},
],
plus_values_professionnelles_regime_normal = [
{'year': 2011, 'target': 1101248065},
{'year': 2010, 'target': 1083102431},
],
# Inversion 2ch et 2gr dans dénombrements fiscax (Agrégats IPP et en ligne)
assurances_vie_imposees_au_bareme = [
{'year': 2009, 'target': 1063726777},
],
# Test revenus exonérés
benefices_agricoles_forfait_exoneres = [
{'year': 2011, 'target': (5826752 + 466632 + 467)},
],
benefices_agricoles_reels_exoneres = [
{'year': 2011, 'target': (64880784 + 15074222 + 3733)},
],
benefices_agricoles_reels_exoneres_sans_cga = [
{'year': 2011, 'target': (10214497 + 2275427 + 171787)},
],
)
def assert_value_construction(variable_name, test):
year = test['year']
target = test['target']
value = get_or_construct_value(
df, variable_name, index_by_variable_name, years = years, fill_value = 0)[0].loc[year]
if year >= 2009:
assert all(value == target), "{} for {}: got {} instead of {}".format(
variable_name, year, value.values, target)
for variable_name, tests in test_by_variable.iteritems():
for test in tests:
yield assert_value_construction, variable_name, test
def build_irpp_tables(years = None, fill_value = numpy.NaN):
assert years is not None
assert isinstance(years, list)
raw_data = get_denombrements_fiscaux_data_frame(years = years, fill_value = 0)
aggregates = build_aggregates(
raw_data,
formula_by_variable_name,
level_2_formula_by_variable_name = level_2_formula_by_variable_name,
years = years,
fill_value = fill_value,
)
data_frame_by_irpp_table_name = collections.OrderedDict([
# 1. Tableau IRPP1: Les revenus figurant dans les déclarations de revenus
('irpp_1', aggregates[[
'salaires',
'salaires_imposables',
'heures_supplementaires',
# TODO
'revenus_d_activite_non_salariee',
'benefices_agricoles',
'benefices_industriels_commerciaux',
'benefices_non_commerciaux',
'revenus_activite_non_salariee_exoneres',
'revenus_de_remplacement',
'pensions_de_retraite',
'allocations_chomage',
'revenus_fonciers',
'revenus_fonciers_regime_normal',
'revenus_fonciers_micro_foncier',
'revenus_financiers',
'frais_reels',
'pensions_alimentaires_percues',
]]),
# 2. Tableau IRPP2: Détails des revenus financiers (intérêts, dividendes, plus-values) figurant dans les
# déclations de revenus (imposition au barème, imposition au prélèvement forfaitaire libératoire (PL) et
# plus-values)
('irpp_2', aggregates[[
'revenus_imposes_au_bareme',
'dividendes_imposes_au_bareme',
'interet_imposes_au_bareme',
'assurances_vie_imposees_au_bareme',
'revenus_imposes_au_prelevement_liberatoire',
'dividendes_imposes_au_prelevement_liberatoire',
'interets_imposes_au_prelevement_liberatoire',
'assurances_vie_imposees_au_prelevement_liberatoire',
'plus_values',
'revenus_financiers',
'revenus_financiers_hors_plus_values'
]]),
# 3. Tableau IRPP3: Plus-values mobilières et professionnelles
('irpp_3', aggregates[[
'plus_values',
'plus_values_mobilieres',
'plus_values_mobilieres_regime_normal',
'plus_values_mobilieres_stock_options',
'plus_values_mobilieres_retraite_dirigeant',
'plus_values_professionnelles',
'plus_values_professionnelles_regime_normal',
'plus_values_professionnelles_retraite_dirigeant',
]]),
('irpp_4', aggregates[[
'revenus_d_activite_non_salariee',
'benefices_agricoles',
'benefices_agricoles_bruts',
'deficits_agricoles',
'benefices_industriels_commerciaux',
'benefices_industriels_commerciaux_bruts',
'deficits_industriels_commerciaux',
# 'bnc',
'revenus_activite_non_salariee_exoneres',
]]),
# ('irpp_5_a', aggregates[[
# 'benefices_agricoles',
# 'benefices_agricoles_forfait_exoneres',
# 'benefices_agricoles_forfait_imposables',
# 'benefices_agricoles_reels_exoneres',
# 'benefices_agricoles_reels_imposables',
# 'benefices_agricoles_reels_deficits',
# 'benefices_agricoles_reels_exoneres_sans_cga',
# 'benefices_agricoles_reels_imposables_sans_cga',
# 'benefices_agricoles_reels_sans_cga_deficits',
# ]])
])
return data_frame_by_irpp_table_name
