def test_parameters_get_default_start_year():
paramdata = Policy.default_data(metadata=True, start_year=2015)
# 1D data, has 2015 values
meta_II_em = paramdata['_II_em']
assert meta_II_em['start_year'] == 2015
assert meta_II_em['row_label'] == ['2015', '2016']
assert meta_II_em['value'] == [4000, 4050]
# 2D data, has 2015 values
meta_std_aged = paramdata['_STD_Aged']
assert meta_std_aged['start_year'] == 2015
assert meta_std_aged['row_label'] == ['2015', '2016']
assert meta_std_aged['value'] == [[1550, 1250, 1250, 1550, 1550, 1250],
[1550, 1250, 1250, 1550, 1550, 1250]]
# 1D data, doesn't have 2015 values, is CPI inflated
meta_amt_thd_marrieds = paramdata['_AMT_thd_MarriedS']
assert meta_amt_thd_marrieds['start_year'] == 2015
assert meta_amt_thd_marrieds['row_label'] == ['2015']
# Take the 2014 parameter value and multiply by inflation for that year
should_be = 41050 * (1.0 + Policy.default_inflation_rates()[2014])
meta_amt_thd_marrieds['value'] == should_be
# 1D data, doesn't have 2015 values, is not CPI inflated
meta_kt_c_age = paramdata['_KT_c_Age']
assert meta_kt_c_age['start_year'] == 2015
assert meta_kt_c_age['row_label'] == ['2015']
assert meta_kt_c_age['value'] == [24]
def test_parameters_get_default_start_year():
paramdata = Policy.default_data(metadata=True, start_year=2015)
# 1D data, has 2015 values
meta_II_em = paramdata['_II_em']
assert meta_II_em['start_year'] == 2015
assert meta_II_em['row_label'] == ['2015', '2016']
assert meta_II_em['value'] == [4000, 4050]
# 2D data, has 2015 values
meta_std_aged = paramdata['_STD_Aged']
assert meta_std_aged['start_year'] == 2015
assert meta_std_aged['row_label'] == ['2015', '2016']
assert meta_std_aged['value'] == [[1550, 1250, 1250, 1550, 1550, 1250],
[1550, 1250, 1250, 1550, 1550, 1250]]
# 1D data, doesn't have 2015 values, is CPI inflated
meta_amt_thd_marrieds = paramdata['_AMT_thd_MarriedS']
assert meta_amt_thd_marrieds['start_year'] == 2015
assert meta_amt_thd_marrieds['row_label'] == ['2015']
# Take the 2014 parameter value and multiply by inflation for that year
should_be = 41050 * (1.0 + Policy.default_inflation_rates()[2014])
meta_amt_thd_marrieds['value'] == should_be
# 1D data, doesn't have 2015 values, is not CPI inflated
meta_kt_c_age = paramdata['_KT_c_Age']
assert meta_kt_c_age['start_year'] == 2015
assert meta_kt_c_age['row_label'] == ['2015']
assert meta_kt_c_age['value'] == [24]
def test_parameters_get_default_start_year():
paramdata = Policy.default_data(metadata=True, start_year=2015)
# 1D data, has 2015 values
meta_II_em = paramdata['_II_em']
assert meta_II_em['start_year'] == 2015
assert meta_II_em['row_label'] == [str(cyr) for cyr in range(2015, 2027)]
assert meta_II_em['value'] == [4000, 4050, 4050] + [0] * 8 + [4883]
# 2D data, has 2015 values
meta_std_aged = paramdata['_STD_Aged']
assert meta_std_aged['start_year'] == 2015
assert meta_std_aged['row_label'] == ['2015', '2016', '2017']
assert meta_std_aged['value'] == [[1550, 1250, 1250, 1550, 1550],
[1550, 1250, 1250, 1550, 1550],
[1550, 1250, 1250, 1550, 1550]]
# 1D data, doesn't have 2015 values, is not CPI inflated
meta_kt_c_age = paramdata['_AMT_KT_c_Age']
assert meta_kt_c_age['start_year'] == 2015
assert meta_kt_c_age['row_label'] == ['2015']
assert meta_kt_c_age['value'] == [24]
def test_parameters_get_default_data():
paramdata = Policy.default_data(metadata=True)
# 1D data, has 2013+ values
meta_II_em = paramdata['_II_em']
assert meta_II_em['start_year'] == 2013
assert meta_II_em['row_label'] == [str(cyr) for cyr in range(2013, 2027)]
expval = [3900, 3950, 4000, 4050, 4050] + [0] * 8 + [4883]
assert meta_II_em['value'] == expval
# 2D data, has 2013+ values
meta_std_aged = paramdata['_STD_Aged']
assert meta_std_aged['start_year'] == 2013
explabels = ['2013', '2014', '2015', '2016', '2017']
assert meta_std_aged['row_label'] == explabels
assert meta_std_aged['value'] == [[1500, 1200, 1200, 1500, 1500],
[1550, 1200, 1200, 1550, 1550],
[1550, 1250, 1250, 1550, 1550],
[1550, 1250, 1250, 1550, 1550],
[1550, 1250, 1250, 1550, 1550]]
# 1D data, has only 2013 values because is not CPI inflated
meta_kt_c_age = paramdata['_AMT_KT_c_Age']
assert meta_kt_c_age['start_year'] == 2013
assert meta_kt_c_age['row_label'] == ['2013']
assert meta_kt_c_age['value'] == [24]
def test_parameters_get_default():
paramdata = Policy.default_data()
assert paramdata['_CDCC_ps'] == [15000]
def test_parameters_get_default():
paramdata = Policy.default_data()
assert paramdata['_CDCC_ps'] == [15000]
def fixture_allparams():
"""
Return metadata for current law parameters
"""
return Policy.default_data(metadata=True)
'_ID_prt': [0.03],
'_ID_crt': [0.8]
}
}
return _reform_xx
@pytest.fixture(scope='module', name='sorted_param_names')
def fixture_sorted_param_names(allparams):
"""
Fixture for storing a sorted parameter list
"""
return sorted(list(allparams.keys()))
NPARAMS = len(Policy.default_data())
BATCHSIZE = 10
BATCHES = int(np.floor(NPARAMS / BATCHSIZE)) + 1
@pytest.fixture(scope='module',
name='allparams_batch',
params=[i for i in range(0, BATCHES)])
def fixture_allparams_batch(request, allparams, sorted_param_names):
"""
Fixture for grouping Tax-Calculator parameters
Experiments indicated that there is some overhead when you run
`test_compatible_data` on each parameter individually. Suppose it takes X
amount of time to set up the test data for `test_compatible_data` and Y
amount of time to run `test_compatible_data` on each parameter wihtout
