def test_corrections():
years = [2006, 2007, 2008, 2009]
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_sans_cga_imposables=[{"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}],
)
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)[0].loc[year]
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 test_run_through():
years = [2006, 2007, 2008, 2009]
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)
def test_run_through():
years = [2006, 2007, 2008, 2009]
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)
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'
# 'ba',
# 'bic',
# 'bnc',
# '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_sans_cga_exoneres',
# 'benefices_agricoles_reels_sans_cga_imposables',
# 'benefices_agricoles_reels_sans_cga_deficits',
# ]])
])
return data_frame_by_irpp_table_name
def test_corrections():
years = [2006, 2007, 2008, 2009]
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_sans_cga_imposables=[
{
'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
}],
)
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)[0].loc[year]
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',
# TODO
# 'revenus_d_activite_non_salariee'
# 'ba',
# 'bic',
# 'bnc',
# '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'
]]),
# 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_sans_cga_exoneres',
# 'benefices_agricoles_reels_sans_cga_imposables',
# 'benefices_agricoles_reels_sans_cga_deficits',
# ]])
])
return data_frame_by_irpp_table_name
