def test_multiyear_diagnostic_table(records_2009):
behv = Behavior()
calc = Calculator(policy=Policy(), records=records_2009, behavior=behv)
with pytest.raises(ValueError):
adt = multiyear_diagnostic_table(calc, 0)
with pytest.raises(ValueError):
adt = multiyear_diagnostic_table(calc, 20)
adt = multiyear_diagnostic_table(calc, 3)
assert isinstance(adt, pd.DataFrame)
behv.update_behavior({2013: {'_BE_sub': [0.3]}})
assert calc.behavior.has_response()
adt = multiyear_diagnostic_table(calc, 3)
assert isinstance(adt, pd.DataFrame)
def test_myr_diag_table_wo_behv(records_2009):
pol = Policy()
reform = {
2013: {
'_II_rt7': [0.33],
'_PT_rt7': [0.33],
}
}
pol.implement_reform(reform)
calc = Calculator(policy=pol, records=records_2009)
calc.calc_all()
liabilities_x = (calc.records.combined * calc.records.s006).sum()
adt = multiyear_diagnostic_table(calc, 1)
# extract combined liabilities as a float and
# adopt units of the raw calculator data in liabilities_x
liabilities_y = adt.iloc[19].tolist()[0] * 1000000000
assert_almost_equal(liabilities_x, liabilities_y, 2)
def test_myr_diag_table_wo_behv(cps_subsample):
reform = {
2013: {
'_II_rt7': [0.33],
'_PT_rt7': [0.33],
}
}
pol = Policy()
pol.implement_reform(reform)
calc = Calculator(policy=pol,
records=Records.cps_constructor(data=cps_subsample))
calc.calc_all()
liabilities_x = (calc.records.combined * calc.records.s006).sum()
adt = multiyear_diagnostic_table(calc, 1)
# extract combined liabilities as a float and
# adopt units of the raw calculator data in liabilities_x
liabilities_y = adt.iloc[19].tolist()[0] * 1e9
assert np.allclose(liabilities_x, liabilities_y, atol=0.01, rtol=0.0)
def test_myr_diag_table_w_behv(cps_subsample):
pol = Policy()
rec = Records.cps_constructor(data=cps_subsample)
year = rec.current_year
beh = Behavior()
calc = Calculator(policy=pol, records=rec, behavior=beh)
assert calc.current_year == year
reform = {year: {'_II_rt7': [0.33], '_PT_rt7': [0.33]}}
pol.implement_reform(reform)
reform_behav = {year: {'_BE_sub': [0.4], '_BE_cg': [-3.67]}}
beh.update_behavior(reform_behav)
calc_clp = calc.current_law_version()
calc_beh = Behavior.response(calc_clp, calc)
calc_beh.calc_all()
liabilities_x = (calc_beh.records.combined * calc_beh.records.s006).sum()
adt = multiyear_diagnostic_table(calc_beh, 1)
# extract combined liabilities as a float and
# adopt units of the raw calculator data in liabilities_x
liabilities_y = adt.iloc[19].tolist()[0] * 1e9
assert np.allclose(liabilities_x, liabilities_y, atol=0.01, rtol=0.0)
def test_multiyear_diagnostic_table(cps_subsample):
rec = Records.cps_constructor(data=cps_subsample)
pol = Policy()
beh = Behavior()
calc = Calculator(policy=pol, records=rec, behavior=beh)
with pytest.raises(ValueError):
multiyear_diagnostic_table(calc, 0)
with pytest.raises(ValueError):
multiyear_diagnostic_table(calc, 20)
adt = multiyear_diagnostic_table(calc, 3)
assert isinstance(adt, pd.DataFrame)
beh.update_behavior({2013: {'_BE_sub': [0.3]}})
calc = Calculator(policy=pol, records=rec, behavior=beh)
assert calc.behavior.has_response()
adt = multiyear_diagnostic_table(calc, 3)
assert isinstance(adt, pd.DataFrame)
def test_myr_diag_table_w_behv(records_2009):
pol = Policy()
behv = Behavior()
calc = Calculator(policy=pol, records=records_2009, behavior=behv)
assert calc.current_year == 2013
reform = {
2013: {
'_II_rt7': [0.33],
'_PT_rt7': [0.33],
}
}
pol.implement_reform(reform)
reform_be = {2013: {'_BE_sub': [0.4], '_BE_cg': [-3.67]}}
behv.update_behavior(reform_be)
calc_clp = calc.current_law_version()
calc_behv = Behavior.response(calc_clp, calc)
calc_behv.calc_all()
liabilities_x = (calc_behv.records.combined * calc_behv.records.s006).sum()
adt = multiyear_diagnostic_table(calc_behv, 1)
# extract combined liabilities as a float and
# adopt units of the raw calculator data in liabilities_x
liabilities_y = adt.iloc[19].tolist()[0] * 1000000000
assert_almost_equal(liabilities_x, liabilities_y, 2)
