def test_Policy_reform_makes_no_changes_before_year():
ppo = Policy(start_year=2013)
reform = {2015: {'_II_em': [4400], '_II_em_cpi': True}}
ppo.implement_reform(reform)
ppo.set_year(2015)
assert_array_equal(ppo._II_em[:3], np.array([3900, 3950, 4400]))
assert ppo.II_em == 4400
def test_convert(self):
values = {"II_brk2_0": [36000., 38000., 40000.],
"II_brk2_1": [72250., 74000.],
"II_brk2_2": [36500.]
}
ans = package_up_vars(values, first_budget_year=FBY)
pp = Policy(start_year=2013)
pp.set_year(FBY)
# irates are rates for 2015, 2016, and 2017
irates = pp.indexing_rates_for_update(param_name='II_brk2', calyear=FBY,
num_years_to_expand=3)
# User choices propagate through to all future years
# The user has specified part of the parameter up to 2017.
# So, we choose to fill in the propagated value, which is
# either inflated or not.
f2_2016 = int(36500 * (1.0 + irates[0]))
f3_2016 = int(50200 * (1.0 + irates[0]))
f4_2016 = int(74900 * (1.0 + irates[0]))
f5_2016 = int(37450 * (1.0 + irates[0]))
f1_2017 = int(74000 * (1.0 + irates[1]))
f2_2017 = int(f2_2016 * (1.0 + irates[1]))
exp = [[36000, 72250, 36500, 50200, 74900, 37450],
[38000, 74000, f2_2016, 50400, 75300, 37650],
[40000, f1_2017, f2_2017, None, None, None]]
assert ans['_II_brk2'] == exp
assert len(ans) == 1
def test_read_json_reform_file_and_implement_reform_b(reform_file):
"""
Test reading and translation of reform file into a reform dictionary
that is then used to call implement_reform method.
NOTE: implement_reform called when policy.current_year > policy.start_year
"""
reform = Policy.read_json_reform_file(reform_file.name)
policy = Policy()
policy.set_year(2015) # the only difference between this and prior test
policy.implement_reform(reform)
syr = policy.start_year
amt_tthd = policy._AMT_tthd
assert amt_tthd[2015 - syr] == 200000
assert amt_tthd[2016 - syr] > 200000
assert amt_tthd[2017 - syr] == 300000
assert amt_tthd[2018 - syr] > 300000
ii_em = policy._II_em
assert ii_em[2016 - syr] == 6000
assert ii_em[2017 - syr] == 6000
assert ii_em[2018 - syr] == 7500
assert ii_em[2019 - syr] > 7500
assert ii_em[2020 - syr] == 9000
assert ii_em[2021 - syr] > 9000
amt_em = policy._AMT_em
assert amt_em[2016 - syr, 0] > amt_em[2015 - syr, 0]
assert amt_em[2017 - syr, 0] > amt_em[2016 - syr, 0]
assert amt_em[2018 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2019 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2020 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2021 - syr, 0] > amt_em[2020 - syr, 0]
assert amt_em[2022 - syr, 0] > amt_em[2021 - syr, 0]
def test_variable_inflation_rate_with_reform():
syr = 2013
pol = Policy(start_year=syr)
assert pol._II_em[2013 - syr] == 3900
# implement reform in 2020 which is two years before the last year, 2022
reform = {
2018: {
'_II_em': [1000]
}, # to avoid divide-by-zero under TCJA
2020: {
'_II_em': [20000]
}
}
pol.implement_reform(reform)
pol.set_year(2020)
assert pol.current_year == 2020
# extract price inflation rates
pirates = pol.inflation_rates()
irate2018 = pirates[2018 - syr]
irate2020 = pirates[2020 - syr]
irate2021 = pirates[2021 - syr]
# check implied inflation rate between 2018 and 2019 (before the reform)
grate = float(pol._II_em[2019 - syr]) / float(pol._II_em[2018 - syr])
assert round(grate - 1.0, 5) == round(irate2018, 5)
# check implied inflation rate between 2020 and 2021 (after the reform)
grate = float(pol._II_em[2021 - syr]) / float(pol._II_em[2020 - syr])
assert round(grate - 1.0, 5) == round(irate2020, 5)
# check implied inflation rate between 2021 and 2022 (after the reform)
grate = float(pol._II_em[2022 - syr]) / float(pol._II_em[2021 - syr])
assert round(grate - 1.0, 5) == round(irate2021, 5)
def test_read_json_reform_file_and_implement_reform_b(reform_file):
"""
Test reading and translation of reform file into a reform dictionary
that is then used to call implement_reform method.
NOTE: implement_reform called when policy.current_year > policy.start_year
"""
reform = Policy.read_json_reform_file(reform_file.name)
policy = Policy()
policy.set_year(2015) # the only difference between this and prior test
policy.implement_reform(reform)
syr = policy.start_year
amt_tthd = policy._AMT_tthd
assert amt_tthd[2015 - syr] == 200000
assert amt_tthd[2016 - syr] > 200000
assert amt_tthd[2017 - syr] == 300000
assert amt_tthd[2018 - syr] > 300000
ii_em = policy._II_em
assert ii_em[2016 - syr] == 6000
assert ii_em[2017 - syr] == 6000
assert ii_em[2018 - syr] == 7500
assert ii_em[2019 - syr] > 7500
assert ii_em[2020 - syr] == 9000
assert ii_em[2021 - syr] > 9000
amt_em = policy._AMT_em
assert amt_em[2016 - syr, 0] > amt_em[2015 - syr, 0]
assert amt_em[2017 - syr, 0] > amt_em[2016 - syr, 0]
assert amt_em[2018 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2019 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2020 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2021 - syr, 0] > amt_em[2020 - syr, 0]
assert amt_em[2022 - syr, 0] > amt_em[2021 - syr, 0]
def test_Policy_reform_makes_no_changes_before_year():
ppo = Policy(start_year=2013)
reform = {2015: {'_II_em': [4400], '_II_em_cpi': True}}
ppo.implement_reform(reform)
ppo.set_year(2015)
assert_array_equal(ppo._II_em[:3], np.array([3900, 3950, 4400]))
assert ppo.II_em == 4400
def test_Policy_reform_after_start_year():
ppo = Policy(start_year=2013)
reform = {2015: {'_STD_Aged': [[1400, 1100, 1100, 1400, 1400, 1199]]}}
ppo.implement_reform(reform)
ppo.set_year(2015)
assert_array_equal(ppo.STD_Aged,
np.array([1400, 1100, 1100, 1400, 1400, 1199]))
def test_calculator_using_nonstd_input(rawinputfile):
"""
Test Calculator using non-standard input records.
"""
# check Calculator handling of raw, non-standard input data with no aging
pol = Policy()
pol.set_year(RAWINPUTFILE_YEAR) # set policy params to input data year
nonstd = Records(data=rawinputfile.name,
gfactors=None, # keeps raw data unchanged
weights=None,
start_year=RAWINPUTFILE_YEAR) # set raw input data year
assert nonstd.array_length == RAWINPUTFILE_FUNITS
calc = Calculator(policy=pol, records=nonstd,
sync_years=False) # keeps raw data unchanged
assert calc.current_year == RAWINPUTFILE_YEAR
calc.calc_all()
assert calc.weighted_total('e00200') == 0
assert calc.total_weight() == 0
varlist = ['RECID', 'MARS']
dframe = calc.dataframe(varlist)
assert isinstance(dframe, pd.DataFrame)
assert dframe.shape == (RAWINPUTFILE_FUNITS, len(varlist))
mars = calc.array('MARS')
assert isinstance(mars, np.ndarray)
assert mars.shape == (RAWINPUTFILE_FUNITS,)
exp_iitax = np.zeros((nonstd.array_length,))
assert np.allclose(calc.array('iitax'), exp_iitax)
mtr_ptax, _, _ = calc.mtr(wrt_full_compensation=False)
exp_mtr_ptax = np.zeros((nonstd.array_length,))
exp_mtr_ptax.fill(0.153)
assert np.allclose(mtr_ptax, exp_mtr_ptax)
def test_convert(self):
values = {"II_brk2_0": [36000., 38000., 40000.],
"II_brk2_1": [72250., 74000.],
"II_brk2_2": [36500.]
}
ans = package_up_vars(values, first_budget_year=FBY)
pp = Policy(start_year=2013)
pp.set_year(FBY)
# irates are rates for 2015, 2016, and 2017
irates = pp.indexing_rates_for_update(param_name='II_brk2', calyear=FBY,
num_years_to_expand=3)
# User choices propagate through to all future years
# The user has specified part of the parameter up to 2017.
# So, we choose to fill in the propagated value, which is
# either inflated or not.
f2_2016 = int(36500 * (1.0 + irates[0]))
f3_2016 = int(50200 * (1.0 + irates[0]))
f4_2016 = int(74900 * (1.0 + irates[0]))
f5_2016 = int(37450 * (1.0 + irates[0]))
f1_2017 = int(74000 * (1.0 + irates[1]))
f2_2017 = int(f2_2016 * (1.0 + irates[1]))
exp = [[36000, 72250, 36500, 50200, 74900],
[38000, 74000, f2_2016, 50400.0, 75300.0],
[40000, f1_2017, f2_2017, 50800.0, 75900.0]]
assert ans['_II_brk2'] == exp
assert len(ans) == 1
def test_round_trip_tcja_reform(tests_path):
"""
Check that current-law policy has the same policy parameter values in
a future year as does a compound reform that first implements the
reform specified in the 2017_law.json file and then implements the
reform specified in the TCJA.json file. This test checks that the
future-year parameter values for current-law policy (which incorporates
TCJA) are the same as future-year parameter values for the compound
round-trip reform. Doing this check ensures that the 2017_law.json
and TCJA.json reform files are specified in a consistent manner.
"""
# pylint: disable=too-many-locals
fyear = 2020
# create clp metadata dictionary for current-law policy in fyear
pol = Policy()
pol.set_year(fyear)
clp_mdata = pol.metadata()
# create rtr metadata dictionary for round-trip reform in fyear
pol = Policy()
reform_file = os.path.join(tests_path, '..', 'reforms', '2017_law.json')
with open(reform_file, 'r') as rfile:
rtext = rfile.read()
pol.implement_reform(Policy.read_json_reform(rtext))
# eventually activate: assert not clp.parameter_warnings
ctc_c_warning = 'CTC_c was redefined in release 1.0.0\n'
assert pol.parameter_warnings == ctc_c_warning
assert not pol.parameter_errors
reform_file = os.path.join(tests_path, '..', 'reforms', 'TCJA.json')
with open(reform_file, 'r') as rfile:
rtext = rfile.read()
pol.implement_reform(Policy.read_json_reform(rtext))
# eventually activate: assert not clp.parameter_warnings
assert pol.parameter_warnings == ctc_c_warning
assert not pol.parameter_errors
pol.set_year(fyear)
rtr_mdata = pol.metadata()
# compare fyear policy parameter values
assert clp_mdata.keys() == rtr_mdata.keys()
fail_dump = False
if fail_dump:
rtr_fails = open('fails_rtr', 'w')
clp_fails = open('fails_clp', 'w')
fail_params = list()
msg = '\nRound-trip-reform and current-law-policy param values differ for:'
for pname in clp_mdata.keys():
rtr_val = rtr_mdata[pname]['value']
clp_val = clp_mdata[pname]['value']
if not np.allclose(rtr_val, clp_val):
fail_params.append(pname)
msg += '\n {} in {} : rtr={} clp={}'.format(
pname, fyear, rtr_val, clp_val
)
if fail_dump:
rtr_fails.write('{} {} {}\n'.format(pname, fyear, rtr_val))
clp_fails.write('{} {} {}\n'.format(pname, fyear, clp_val))
if fail_dump:
rtr_fails.close()
clp_fails.close()
if fail_params:
raise ValueError(msg)
def test_calculator_using_nonstd_input(rawinputfile):
# check Calculator handling of raw, non-standard input data with no aging
pol = Policy()
pol.set_year(RAWINPUTFILE_YEAR) # set policy params to input data year
nonstd = Records(
data=rawinputfile.name,
gfactors=None, # keeps raw data unchanged
weights=None,
start_year=RAWINPUTFILE_YEAR) # set raw input data year
assert nonstd.dim == RAWINPUTFILE_FUNITS
calc = Calculator(policy=pol, records=nonstd,
sync_years=False) # keeps raw data unchanged
assert calc.current_year == RAWINPUTFILE_YEAR
calc.calc_all()
assert calc.weighted_total('e00200') == 0
assert calc.total_weight() == 0
varlist = ['RECID', 'MARS']
pdf = calc.dataframe(varlist)
assert isinstance(pdf, pd.DataFrame)
assert pdf.shape == (RAWINPUTFILE_FUNITS, len(varlist))
mars = calc.array('MARS')
assert isinstance(mars, np.ndarray)
assert mars.shape == (RAWINPUTFILE_FUNITS, )
exp_iitax = np.zeros((nonstd.dim, ))
assert np.allclose(calc.records.iitax, exp_iitax)
mtr_ptax, _, _ = calc.mtr(wrt_full_compensation=False)
exp_mtr_ptax = np.zeros((nonstd.dim, ))
exp_mtr_ptax.fill(0.153)
assert np.allclose(mtr_ptax, exp_mtr_ptax)
def test_variable_inflation_rate_with_reform():
syr = 2013
irates = {
2013: 0.04,
2014: 0.04,
2015: 0.04,
2016: 0.04,
2017: 0.04,
2018: 0.04,
2019: 0.04,
2020: 0.04,
2021: 0.08,
2022: 0.08
}
ppo = Policy(start_year=syr, num_years=10, inflation_rates=irates)
assert ppo._II_em[2013 - syr] == 3900
# implement reform in 2020 which is two years before the last year, 2022
reform = {2020: {'_II_em': [20000]}}
ppo.implement_reform(reform)
ppo.set_year(2020)
assert ppo.current_year == 2020
# check implied inflation rate between 2018 and 2019 (before the reform)
grate = float(ppo._II_em[2019 - syr]) / float(ppo._II_em[2018 - syr]) - 1.0
assert round(grate, 3) == round(0.04, 3)
# check implied inflation rate between 2020 and 2021 (after the reform)
grate = float(ppo._II_em[2021 - syr]) / float(ppo._II_em[2020 - syr]) - 1.0
assert round(grate, 3) == round(0.04, 3)
# check implied inflation rate between 2021 and 2022 (after the reform)
grate = float(ppo._II_em[2022 - syr]) / float(ppo._II_em[2021 - syr]) - 1.0
assert round(grate, 3) == round(0.08, 3)
def test_Policy_reform_after_start_year():
ppo = Policy(start_year=2013)
reform = {2015: {'_STD_Aged': [[1400, 1100, 1100, 1400, 1400, 1199]]}}
ppo.implement_reform(reform)
ppo.set_year(2015)
assert_array_equal(ppo.STD_Aged,
np.array([1400, 1100, 1100, 1400, 1400, 1199]))
def test_expand_2D_accept_None():
_II_brk2 = [[36000, 72250, 36500, 48600, 72500, 36250],
[38000, 74000, 36900, 49400, 73800, 36900],
[40000, 74900, 37450, 50200, 74900, 37450],
[41000, None, None, None, None, None]]
exp1 = 74900 * 1.02
exp2 = 37450 * 1.02
exp3 = 50200 * 1.02
exp4 = 74900 * 1.02
exp5 = 37450 * 1.02
exp = [[36000, 72250, 36500, 48600, 72500, 36250],
[38000, 74000, 36900, 49400, 73800, 36900],
[40000, 74900, 37450, 50200, 74900, 37450],
[41000, exp1, exp2, exp3, exp4, exp5]]
exp = np.array(exp).astype('i4', casting='unsafe')
res = Policy.expand_array(_II_brk2, inflate=True,
inflation_rates=[0.02] * 5,
num_years=4)
npt.assert_array_equal(res, exp)
user_mods = {2016: {u'_II_brk2': _II_brk2}}
pol = Policy(start_year=2013)
pol.implement_reform(user_mods)
pol.set_year(2019)
irates = Policy.default_inflation_rates()
# The 2019 policy should be the combination of the user-defined
# value and CPI-inflated values from 2018
exp_2019 = [41000.] + [(1 + irates[2018]) * i for i in _II_brk2[2][1:]]
exp_2019 = np.array(exp_2019)
npt.assert_array_equal(pol.II_brk2, exp_2019)
def test_expand_2D_accept_None():
_II_brk2 = [[36000, 72250, 36500, 48600, 72500, 36250],
[38000, 74000, 36900, 49400, 73800, 36900],
[40000, 74900, 37450, 50200, 74900, 37450],
[41000, None, None, None, None, None]]
exp1 = 74900 * 1.02
exp2 = 37450 * 1.02
exp3 = 50200 * 1.02
exp4 = 74900 * 1.02
exp5 = 37450 * 1.02
exp = [[36000, 72250, 36500, 48600, 72500, 36250],
[38000, 74000, 36900, 49400, 73800, 36900],
[40000, 74900, 37450, 50200, 74900, 37450],
[41000, exp1, exp2, exp3, exp4, exp5]]
exp = np.array(exp).astype('i4', casting='unsafe')
res = Policy.expand_array(_II_brk2,
inflate=True,
inflation_rates=[0.02] * 5,
num_years=4)
npt.assert_array_equal(res, exp)
syr = 2013
pol = Policy(start_year=syr)
irates = pol.inflation_rates()
reform = {2016: {u'_II_brk2': _II_brk2}}
pol.implement_reform(reform)
pol.set_year(2019)
# The 2019 policy should be the combination of the user-defined
# value and CPI-inflated values from 2018
exp_2019 = [41000.] + [(1.0 + irates[2018 - syr]) * i
for i in _II_brk2[2][1:]]
exp_2019 = np.array(exp_2019)
npt.assert_array_equal(pol.II_brk2, exp_2019)
def test_variable_inflation_rate_with_reform():
"""
Test indexing of policy parameters involved in a reform.
"""
pol = Policy()
syr = Policy.JSON_START_YEAR
assert pol._II_em[2013 - syr] == 3900
# implement reform in 2020 which is two years before the last year, 2022
reform = {
'II_em': {2018: 1000, # to avoid divide-by-zero under TCJA
2020: 20000}
}
pol.implement_reform(reform)
pol.set_year(2020)
assert pol.current_year == 2020
# extract price inflation rates
pirates = pol.inflation_rates()
irate2018 = pirates[2018 - syr]
irate2020 = pirates[2020 - syr]
irate2021 = pirates[2021 - syr]
# check implied inflation rate between 2018 and 2019 (before the reform)
grate = float(pol._II_em[2019 - syr]) / float(pol._II_em[2018 - syr])
assert round(grate - 1.0, 5) == round(irate2018, 5)
# check implied inflation rate between 2020 and 2021 (after the reform)
grate = float(pol._II_em[2021 - syr]) / float(pol._II_em[2020 - syr])
assert round(grate - 1.0, 5) == round(irate2020, 5)
# check implied inflation rate between 2021 and 2022 (after the reform)
grate = float(pol._II_em[2022 - syr]) / float(pol._II_em[2021 - syr])
assert round(grate - 1.0, 5) == round(irate2021, 5)
def test_index_offset_reform():
"""
Test a reform that includes both a change in CPI_offset and a change in
a variable's indexed status in the same year.
"""
reform1 = {'CTC_c-indexed': {2020: True}}
policy1 = Policy()
policy1.implement_reform(reform1)
offset = -0.005
reform2 = {'CTC_c-indexed': {2020: True}, 'CPI_offset': {2020: offset}}
policy2 = Policy()
policy2.implement_reform(reform2) # caused T-C crash before PR#2364
# extract from policy1 and policy2 the parameter values of CTC_c
pvalue1 = dict()
pvalue2 = dict()
for cyr in [2019, 2020, 2021]:
policy1.set_year(cyr)
pvalue1[cyr] = policy1.CTC_c
policy2.set_year(cyr)
pvalue2[cyr] = policy2.CTC_c
# check that pvalue1 and pvalue2 dictionaries contain the expected values
assert pvalue2[2019] == pvalue1[2019]
assert pvalue2[2020] == pvalue1[2020]
assert pvalue2[2020] == pvalue2[2019]
# ... indexing of CTC_c begins shows up first in 2021 parameter values
assert pvalue1[2021] > pvalue1[2020]
assert pvalue2[2021] > pvalue2[2020]
# ... calculate expected pvalue2[2021] from offset and pvalue1 values
indexrate1 = pvalue1[2021] / pvalue1[2020] - 1.
expindexrate = indexrate1 + offset
expvalue = round(pvalue2[2020] * (1. + expindexrate), 2)
# ... compare expected value with actual value of pvalue2 for 2021
assert np.allclose([expvalue], [pvalue2[2021]])
def test_variable_inflation_rate_with_reform():
"""
Test indexing of policy parameters involved in a reform.
"""
pol = Policy()
syr = Policy.JSON_START_YEAR
assert pol._II_em[2013 - syr] == 3900
# implement reform in 2020 which is two years before the last year, 2022
reform = {
'II_em': {
2018: 1000, # to avoid divide-by-zero under TCJA
2020: 20000
}
}
pol.implement_reform(reform)
pol.set_year(2020)
assert pol.current_year == 2020
# extract price inflation rates
pirates = pol.inflation_rates()
irate2018 = pirates[2018 - syr]
irate2020 = pirates[2020 - syr]
irate2021 = pirates[2021 - syr]
# check implied inflation rate between 2018 and 2019 (before the reform)
grate = float(pol._II_em[2019 - syr]) / float(pol._II_em[2018 - syr])
assert round(grate - 1.0, 5) == round(irate2018, 5)
# check implied inflation rate between 2020 and 2021 (after the reform)
grate = float(pol._II_em[2021 - syr]) / float(pol._II_em[2020 - syr])
assert round(grate - 1.0, 5) == round(irate2020, 5)
# check implied inflation rate between 2021 and 2022 (after the reform)
grate = float(pol._II_em[2022 - syr]) / float(pol._II_em[2021 - syr])
assert round(grate - 1.0, 5) == round(irate2021, 5)
def test_calculator_using_nonstd_input():
"""
Test Calculator using non-standard input records.
"""
# check Calculator handling of raw, non-standard input data with no aging
pol = Policy()
pol.set_year(RAWINPUT_YEAR) # set policy params to input data year
nonstd = Records(
data=pd.read_csv(StringIO(RAWINPUT_CONTENTS)),
start_year=RAWINPUT_YEAR, # set raw input data year
gfactors=None, # keeps raw data unchanged
weights=None)
assert nonstd.array_length == RAWINPUT_FUNITS
calc = Calculator(policy=pol, records=nonstd,
sync_years=False) # keeps raw data unchanged
assert calc.current_year == RAWINPUT_YEAR
calc.calc_all()
assert calc.weighted_total('e00200') == 0
assert calc.total_weight() == 0
varlist = ['RECID', 'MARS']
dframe = calc.dataframe(varlist)
assert isinstance(dframe, pd.DataFrame)
assert dframe.shape == (RAWINPUT_FUNITS, len(varlist))
mars = calc.array('MARS')
assert isinstance(mars, np.ndarray)
assert mars.shape == (RAWINPUT_FUNITS, )
exp_iitax = np.zeros((nonstd.array_length, ))
assert np.allclose(calc.array('iitax'), exp_iitax)
mtr_ptax, _, _ = calc.mtr(wrt_full_compensation=False)
exp_mtr_ptax = np.zeros((nonstd.array_length, ))
exp_mtr_ptax.fill(0.153)
assert np.allclose(mtr_ptax, exp_mtr_ptax)
def test_round_trip_tcja_reform(tests_path):
"""
Check that current-law policy has the same policy parameter values in
a future year as does a compound reform that first implements the
reform specified in the 2017_law.json file and then implements the
reform specified in the TCJA.json file. This test checks that the
future-year parameter values for current-law policy (which incorporates
TCJA) are the same as future-year parameter values for the compound
round-trip reform. Doing this check ensures that the 2017_law.json
and TCJA.json reform files are specified in a consistent manner.
"""
# pylint: disable=too-many-locals
fyear = 2020
# create clp metadata dictionary for current-law policy in fyear
pol = Policy()
pol.set_year(fyear)
clp_mdata = pol.metadata()
# create rtr metadata dictionary for round-trip reform in fyear
pol = Policy()
reform_file = os.path.join(tests_path, '..', 'reforms', '2017_law.json')
with open(reform_file, 'r') as rfile:
rtext = rfile.read()
pol.implement_reform(Policy.read_json_reform(rtext))
# eventually activate: assert not clp.parameter_warnings
ctc_c_warning = 'CTC_c was redefined in release 1.0.0\n'
assert pol.parameter_warnings == ctc_c_warning
assert not pol.parameter_errors
reform_file = os.path.join(tests_path, '..', 'reforms', 'TCJA.json')
with open(reform_file, 'r') as rfile:
rtext = rfile.read()
pol.implement_reform(Policy.read_json_reform(rtext))
# eventually activate: assert not clp.parameter_warnings
assert pol.parameter_warnings == ctc_c_warning
assert not pol.parameter_errors
pol.set_year(fyear)
rtr_mdata = pol.metadata()
# compare fyear policy parameter values
assert clp_mdata.keys() == rtr_mdata.keys()
fail_dump = False
if fail_dump:
rtr_fails = open('fails_rtr', 'w')
clp_fails = open('fails_clp', 'w')
fail_params = list()
msg = '\nRound-trip-reform and current-law-policy param values differ for:'
for pname in clp_mdata.keys():
rtr_val = rtr_mdata[pname]['value']
clp_val = clp_mdata[pname]['value']
if not np.allclose(rtr_val, clp_val):
fail_params.append(pname)
msg += '\n {} in {} : rtr={} clp={}'.format(
pname, fyear, rtr_val, clp_val)
if fail_dump:
rtr_fails.write('{} {} {}\n'.format(pname, fyear, rtr_val))
clp_fails.write('{} {} {}\n'.format(pname, fyear, clp_val))
if fail_dump:
rtr_fails.close()
clp_fails.close()
if fail_params:
raise ValueError(msg)
def test_2017_law_reform(tests_path):
"""
Check that policy parameter values in a future year under current-law
policy and under the reform specified in the 2017_law.json file are
sensible.
"""
# create pre metadata dictionary for 2017_law.json reform in fyear
pol = Policy()
reform_file = os.path.join(tests_path, '..', 'reforms', '2017_law.json')
with open(reform_file, 'r') as rfile:
rtext = rfile.read()
reform = Calculator.read_json_param_objects(rtext, None)
pol.implement_reform(reform['policy'])
# eventually activate: assert not clp.parameter_warnings
ctc_c_warning = 'CTC_c was redefined in release 1.0.0\n'
assert pol.parameter_warnings == ctc_c_warning
assert not pol.parameter_errors
pol.set_year(2018)
pre_mdata = pol.metadata()
# check some policy parameter values against expected values under 2017 law
pre_expect = {
# relation '<' implies asserting that actual < expect
# relation '>' implies asserting that actual > expect
# ... parameters not affected by TCJA and that are not indexed
'AMEDT_ec': {'relation': '=', 'value': 200000},
'SS_thd85': {'relation': '=', 'value': 34000},
# ... parameters not affected by TCJA and that are indexed
'STD_Dep': {'relation': '>', 'value': 1050},
'CG_brk2': {'relation': '>', 'value': 425800},
'AMT_CG_brk1': {'relation': '>', 'value': 38600},
'AMT_brk1': {'relation': '>', 'value': 191100},
'EITC_c': {'relation': '>', 'value': 519},
'EITC_ps': {'relation': '>', 'value': 8490},
'EITC_ps_MarriedJ': {'relation': '>', 'value': 5680},
'EITC_InvestIncome_c': {'relation': '>', 'value': 3500},
# ... parameters affected by TCJA and that are not indexed
'ID_Charity_crt_all': {'relation': '=', 'value': 0.5},
'II_rt3': {'relation': '=', 'value': 0.25},
# ... parameters affected by TCJA and that are indexed
'II_brk3': {'relation': '>', 'value': 91900},
'STD': {'relation': '<', 'value': 7000},
'II_em': {'relation': '>', 'value': 4050},
'AMT_em_pe': {'relation': '<', 'value': 260000}
}
assert isinstance(pre_expect, dict)
assert set(pre_expect.keys()).issubset(set(pre_mdata.keys()))
for name in pre_expect:
aval = pre_mdata[name]['value']
if isinstance(aval, list):
act = aval[0] # comparing only first item in a vector parameter
else:
act = aval
exp = pre_expect[name]['value']
if pre_expect[name]['relation'] == '<':
assert act < exp, '{} a={} !< e={}'.format(name, act, exp)
elif pre_expect[name]['relation'] == '>':
assert act > exp, '{} a={} !> e={}'.format(name, act, exp)
elif pre_expect[name]['relation'] == '=':
assert act == exp, '{} a={} != e={}'.format(name, act, exp)
def convert_defaults(pcl):
type_map = {
"real": "float",
"boolean": "bool",
"integer": "int",
"string": "str",
}
new_pcl = defaultdict(dict)
new_pcl["schema"] = POLICY_SCHEMA
LAST_YEAR = 2028
pol = Policy()
pol.set_year(2028)
for param, item in pcl.items():
values = []
pol_val = getattr(pol, f"_{param}").tolist()
min_year = min(item["value_yrs"])
if isinstance(pol_val[0], list):
for year in range(len(pol_val)):
if min_year + year > LAST_YEAR:
break
for dim1 in range(len(pol_val[0])):
values.append({
"year": min_year + year,
item["vi_name"]: item["vi_vals"][dim1],
"value": pol_val[year][dim1]
})
else:
for year in range(len(pol_val)):
if min_year + year > LAST_YEAR:
break
values.append({
"year": min_year + year,
"value": pol_val[year]
})
new_pcl[param]['value'] = values
new_pcl[param]['title'] = pcl[param]["long_name"]
new_pcl[param]['type'] = type_map[pcl[param]["value_type"]]
new_pcl[param]["validators"] = {"range": pcl[param]["valid_values"]}
# checkbox if indexable
if item["indexable"]:
if item["indexed"]:
new_pcl[param]["checkbox"] = True
else:
new_pcl[param]["checkbox"] = False
to_keep = list(POLICY_SCHEMA["additional_members"].keys()) + [
"description",
"notes",
]
for k in to_keep:
if k in pcl[param]:
new_pcl[param][k] = pcl[param][k]
return new_pcl
def test_Policy_reform_after_start_year():
ppo = Policy(start_year=2013)
reform = {2015: {'_STD_Aged': [[1400, 1100, 1100, 1400, 1400]]}}
ppo.implement_reform(reform)
ppo.set_year(2015)
assert_allclose(ppo.STD_Aged,
np.array([1400, 1100, 1100, 1400, 1400]),
atol=0.01, rtol=0.0)
def test_Policy_reform_with_default_cpi_flags():
ppo = Policy(start_year=2013)
reform = {2015: {'_II_em': [4300]}}
ppo.implement_reform(reform)
# '_II_em' has a default cpi_flag of True, so
# in 2016 its value should be greater than 4300
ppo.set_year(2016)
assert ppo.II_em > 4300
def test_Policy_reform_with_default_cpi_flags():
ppo = Policy(start_year=2013)
reform = {2015: {'_II_em': [4300]}}
ppo.implement_reform(reform)
# '_II_em' has a default cpi_flag of True, so
# in 2016 its value should be greater than 4300
ppo.set_year(2016)
assert ppo.II_em > 4300
def main(start_year, delay_years, scale_factor, no_indexing, each_separate):
"""
Highest-level logic of make_reforms.py script.
"""
# check validity of function arguments
if start_year < FIRST_YEAR:
msg = 'ERROR: --year {} sets year before {}\n'
sys.stderr.write(msg.format(start_year, FIRST_YEAR))
return 1
if scale_factor < 1.0:
msg = 'ERROR: --scale {} sets scale below one\n'
sys.stderr.write(msg.format(scale_factor))
return 1
if each_separate:
reform = PROVISIONS_EACH_SEPARATE
else:
reform = PROVISIONS_ALL_TOGETHER
# get current-law-policy parameters for start_year in a Policy object
clp = Policy()
clp.set_year(start_year)
# construct of set of Policy parameter names
policy_param_names = set(getattr(clp, '_vals').keys())
# remove names of policy parameters not currently used by TaxBrain
param_names = policy_param_names - PARAMS_NOT_USED_IN_TAXBRAIN
# add *_cpi parameter names if no_indexing is true
if no_indexing:
param_names = param_names | indexed_parameter_names(clp, param_names)
# remove "null" reform provisions
if scale_factor == 1.0:
excluded_names = set()
for name in param_names:
if not name.endswith('_cpi'):
excluded_names.add(name)
param_names = param_names - excluded_names
# write a JSON entry for each parameter
np.random.seed(seed=987654321)
if reform == PROVISIONS_ALL_TOGETHER:
write_all_together(param_names, clp,
start_year, delay_years,
scale_factor)
elif reform == PROVISIONS_EACH_SEPARATE:
write_each_separate(param_names, clp,
start_year, delay_years,
scale_factor)
else:
msg = 'ERROR: reform has illegal value {}\n'
sys.stderr.write(msg.format(reform))
return 1
# return no-error exit code
return 0
def main(start_year, delay_years, scale_factor, no_indexing, each_separate):
"""
Highest-level logic of make_reforms.py script.
"""
# check validity of function arguments
if start_year < FIRST_YEAR:
msg = 'ERROR: --year {} sets year before {}\n'
sys.stderr.write(msg.format(start_year, FIRST_YEAR))
return 1
if scale_factor < 1.0:
msg = 'ERROR: --scale {} sets scale below one\n'
sys.stderr.write(msg.format(scale_factor))
return 1
if each_separate:
reform = PROVISIONS_EACH_SEPARATE
else:
reform = PROVISIONS_ALL_TOGETHER
# get current-law-policy parameters for start_year in a Policy object
clp = Policy()
clp.set_year(start_year)
# construct of set of Policy parameter names
policy_param_names = set(getattr(clp, '_vals').keys())
# remove names of policy parameters not currently used by TaxBrain
param_names = policy_param_names - PARAMS_NOT_USED_IN_TAXBRAIN
# add *_cpi parameter names if no_indexing is true
if no_indexing:
param_names = param_names | indexed_parameter_names(clp, param_names)
# remove "null" reform provisions
if scale_factor == 1.0:
excluded_names = set()
for name in param_names:
if not name.endswith('_cpi'):
excluded_names.add(name)
param_names = param_names - excluded_names
# write a JSON entry for each parameter
np.random.seed(seed=987654321)
if reform == PROVISIONS_ALL_TOGETHER:
write_all_together(param_names, clp,
start_year, delay_years,
scale_factor)
elif reform == PROVISIONS_EACH_SEPARATE:
write_each_separate(param_names, clp,
start_year, delay_years,
scale_factor)
else:
msg = 'ERROR: reform has illegal value {}\n'
sys.stderr.write(msg.format(reform))
return 1
# return no-error exit code
return 0
def get_defaults(start_year, **kwargs):
pol = Policy()
pol.set_year(start_year)
pol_mdata = pol.metadata()
# serialize taxcalc params
seri = {}
for param, data in pol_mdata.items():
seri[param] = dict(data, **{"value": np.array(data["value"]).tolist()})
return {"policy": seri, "behavior": BEHV_PARAMS}
def test_Policy_reform_makes_no_changes_before_year():
ppo = Policy()
reform = {2015: {'_II_em': [4400], '_II_em_cpi': True}}
ppo.implement_reform(reform)
ppo.set_year(2015)
assert np.allclose(ppo._II_em[:3],
np.array([3900, 3950, 4400]),
atol=0.01,
rtol=0.0)
assert ppo.II_em == 4400
def test_reform_makes_no_changes_before_year():
"""
Test that implement_reform makes no changes before first reform year.
"""
ppo = Policy()
reform = {'II_em': {2015: 4400}, 'II_em-indexed': {2015: True}}
ppo.implement_reform(reform)
ppo.set_year(2015)
assert np.allclose(ppo._II_em[:3], np.array([3900, 3950, 4400]),
atol=0.01, rtol=0.0)
assert ppo.II_em == 4400
def test_correct_Policy_instantiation():
pol = Policy()
assert pol
pol.implement_reform({})
with pytest.raises(ValueError):
pol.implement_reform(list())
with pytest.raises(ValueError):
pol.implement_reform({2099: {'_II_em': [99000]}})
pol.set_year(2019)
with pytest.raises(ValueError):
pol.implement_reform({2018: {'_II_em': [99000]}})
def test_reform_with_default_indexed():
"""
Test that implement_reform indexes after first reform year.
"""
ppo = Policy()
reform = {'II_em': {2015: 4300}}
ppo.implement_reform(reform)
# II_em has a default indexing status of true, so
# in 2016 its value should be greater than 4300
ppo.set_year(2016)
assert ppo.II_em > 4300
def test_reform_with_default_indexed():
"""
Test that implement_reform indexes after first reform year.
"""
ppo = Policy()
reform = {'II_em': {2015: 4300}}
ppo.implement_reform(reform)
# II_em has a default indexing status of true, so
# in 2016 its value should be greater than 4300
ppo.set_year(2016)
assert ppo.II_em > 4300
def propagate_user_list(x, defaults, cpi, first_budget_year):
"""
Dispatch to either expand_1D or expand2D depending on the dimension of x
Parameters:
-----------
x : list from user to propagate forward in time. The first value is for
year 'first_budget_year'. The value at index i is the value for
budget year first_budget_year + i.
defaults: list of default values; our result must be at least this long
cpi: Bool
first_budget_year: int
Returns:
--------
list of length 'num_years'. if 'cpi'==True, the values will be inflated
based on the last value the user specified
"""
# x can't be empty
assert x
num_years = max(len(defaults), len(x))
is_rate = any([ i < 1.0 for i in x])
pp = Policy(start_year=2013)
pp.set_year(first_budget_year)
# irates are rates for 2015, 2016, and 2017
if cpi:
irates = pp.indexing_rates_for_update(param_name='II_brk2',
calyear=first_budget_year,
num_years_to_expand=num_years)
else:
irates = [0.0] * num_years
last_val = x[-1]
ans = [None] * num_years
for i in range(num_years):
if i < len(x):
if x[i] == '*':
ans[i] = defaults[i]
else:
ans[i] = x[i]
else:
newval = ans[i-1] * (1.0 + irates[i-1])
ans[i] = newval if is_rate else int(newval)
return ans
def test_convert_multiple_items(self):
values = {
"II_brk2_0": [36000., 38000., 40000., 41000],
"II_brk2_1": [72250., 74000.],
"II_brk2_2": [36500.]
}
values['II_em'] = [4000]
ans = package_up_vars(values, first_budget_year=FBY)
defaults = taxcalc.policy.Policy.default_data(start_year=FBY)
pp = Policy(start_year=2013)
pp.set_year(FBY)
# irates are rates for 2015, 2016, and 2017
irates = pp.indexing_rates_for_update(param_name='II_brk2',
calyear=FBY,
num_years_to_expand=4)
# User choices propagate through to all future years
# The user has specified part of the parameter up to 2017.
# So, we choose to fill in the propagated value, which is
# either inflated or not.
f2_2016 = int(36500 * (1.0 + irates[0]))
f3_2016 = int(50200 * (1.0 + irates[0]))
f4_2016 = int(74900 * (1.0 + irates[0]))
f5_2016 = int(37450 * (1.0 + irates[0]))
f1_2017 = int(74000 * (1.0 + irates[1]))
f2_2017 = int(f2_2016 * (1.0 + irates[1]))
f1_2018 = int(f1_2017 * (1.0 + irates[2]))
f2_2018 = int(f2_2017 * (1.0 + irates[2]))
exp = [[36000, 72250, 36500, 50200, 74900, 37450],
[38000, 74000, f2_2016, 50400, 75300, 37650],
[40000, 75687, f2_2017, 50800.0, 75900.0, 37950.0],
[41000, f1_2018, f2_2018, None, None, None]]
assert ans['_II_brk2'] == exp
# For scalar parameter values, we still have that all user
# choices propagate up through whatever is specified as
# a default. We know that _II_em is specified up to 2016, so
# package up vars needs to overwrite those default and return
# 2015 and 2016 values
exp_em = [4000, int(4000 * (1 + irates[0])), 4165]
assert ans['_II_em'] == exp_em
assert len(ans) == 2
def test_correct_Policy_instantiation():
pol = Policy()
assert pol
wrates = Policy.default_wage_growth_rates()
assert len(wrates) == Policy.DEFAULT_NUM_YEARS
pol.implement_reform({})
with pytest.raises(ValueError):
pol.implement_reform(list())
with pytest.raises(ValueError):
pol.implement_reform({2099: {'_II_em': [99000]}})
pol.set_year(2019)
with pytest.raises(ValueError):
pol.implement_reform({2018: {'_II_em': [99000]}})
def test_correct_Policy_instantiation():
pol = Policy()
assert pol
wrates = Policy.default_wage_growth_rates()
assert len(wrates) == Policy.DEFAULT_NUM_YEARS
pol.implement_reform({})
with pytest.raises(ValueError):
pol.implement_reform(list())
with pytest.raises(ValueError):
pol.implement_reform({2099: {'_II_em': [99000]}})
pol.set_year(2019)
with pytest.raises(ValueError):
pol.implement_reform({2018: {'_II_em': [99000]}})
def test_reform_makes_no_changes_before_year():
"""
Test that implement_reform makes no changes before first reform year.
"""
ppo = Policy()
reform = {'II_em': {2015: 4400}, 'II_em-indexed': {2015: True}}
ppo.implement_reform(reform)
ppo.set_year(2015)
assert np.allclose(ppo._II_em[:3],
np.array([3900, 3950, 4400]),
atol=0.01,
rtol=0.0)
assert ppo.II_em == 4400
def test_calc_all():
"""
Test calc_all method.
"""
cyr = 2016
pol = Policy()
pol.set_year(cyr)
nonstd = Records(data=pd.read_csv(StringIO(RAWINPUT_CONTENTS)),
start_year=cyr, gfactors=None, weights=None)
assert nonstd.array_length == RAWINPUT_FUNITS
calc = Calculator(policy=pol, records=nonstd,
sync_years=False) # keeps raw data unchanged
assert calc.current_year == cyr
def test_correct_Policy_instantiation():
pol = Policy()
assert pol
pol.implement_reform({})
with pytest.raises(ValueError):
pol.implement_reform(list())
with pytest.raises(ValueError):
pol.implement_reform({2099: {'_rate2': [0.07]}})
pol.set_year(2018)
with pytest.raises(ValueError):
pol.implement_reform({2017: {'_rate2': [0.07]}})
with pytest.raises(ValueError):
pol.implement_reform({2019: {'_rate2': [-0.10]}})
def test_calc_all():
"""
Test calc_all method.
"""
cyr = 2016
pol = Policy()
pol.set_year(cyr)
nonstd = Records(data=pd.read_csv(StringIO(RAWINPUT_CONTENTS)),
start_year=cyr, gfactors=None, weights=None)
assert nonstd.array_length == RAWINPUT_FUNITS
calc = Calculator(policy=pol, records=nonstd,
sync_years=False) # keeps raw data unchanged
assert calc.current_year == cyr
def test_calc_all(reform_file, rawinputfile):
cyr = 2016
pol = Policy()
param_dict = Calculator.read_json_param_objects(reform_file.name, None)
pol.implement_reform(param_dict['policy'])
pol.set_year(cyr)
nonstd = Records(data=rawinputfile.name, gfactors=None,
weights=None, start_year=cyr)
assert nonstd.array_length == RAWINPUTFILE_FUNITS
calc = Calculator(policy=pol, records=nonstd,
sync_years=False) # keeps raw data unchanged
assert calc.current_year == cyr
calc.calc_all()
def test_convert_multiple_items(self):
values = {"II_brk2_0": [36000., 38000., 40000., 41000],
"II_brk2_1": [72250., 74000.],
"II_brk2_2": [36500.]
}
values['II_em'] = [4000]
ans = package_up_vars(values, first_budget_year=FBY)
defaults = taxcalc.policy.Policy.default_data(start_year=FBY)
pp = Policy(start_year=2013)
pp.set_year(FBY)
# irates are rates for 2015, 2016, and 2017
irates = pp.indexing_rates_for_update(param_name='II_brk2', calyear=FBY,
num_years_to_expand=4)
# User choices propagate through to all future years
# The user has specified part of the parameter up to 2017.
# So, we choose to fill in the propagated value, which is
# either inflated or not.
f2_2016 = int(36500 * (1.0 + irates[0]))
f3_2016 = int(50200 * (1.0 + irates[0]))
f4_2016 = int(74900 * (1.0 + irates[0]))
f5_2016 = int(37450 * (1.0 + irates[0]))
f1_2017 = int(74000 * (1.0 + irates[1]))
f2_2017 = int(f2_2016 * (1.0 + irates[1]))
f1_2018 = int(f1_2017 * (1.0 + irates[2]))
f2_2018 = int(f2_2017 * (1.0 + irates[2]))
exp = [[36000, 72250, 36500, 50200, 74900, 37450],
[38000, 74000, f2_2016, 50400, 75300, 37650],
[40000, f1_2017, f2_2017, None, None, None],
[41000, f1_2018, f2_2018, None, None, None]]
assert ans['_II_brk2'] == exp
# For scalar parameter values, we still have that all user
# choices propagate up through whatever is specified as
# a default. We know that _II_em is specified up to 2016, so
# package up vars needs to overwrite those default and return
# 2015 and 2016 values
exp_em = [4000, int(4000 *(1 + irates[0]))]
assert ans['_II_em'] == exp_em
assert len(ans) == 2
def test_calc_all():
"""
Test calc_all method.
"""
cyr = 2016
pol = Policy()
param_dict = Calculator.read_json_param_objects(REFORM_JSON, None)
pol.implement_reform(param_dict['policy'])
pol.set_year(cyr)
nonstd = Records(data=pd.read_csv(StringIO(RAWINPUT_CONTENTS)),
start_year=cyr, gfactors=None, weights=None)
assert nonstd.array_length == RAWINPUT_FUNITS
calc = Calculator(policy=pol, records=nonstd,
sync_years=False) # keeps raw data unchanged
assert calc.current_year == cyr
assert calc.reform_warnings == ''
def test_calc_all(reform_file, rawinputfile):
"""
Test calc_all method.
"""
cyr = 2016
pol = Policy()
param_dict = Calculator.read_json_param_objects(reform_file.name, None)
pol.implement_reform(param_dict['policy'])
pol.set_year(cyr)
nonstd = Records(data=rawinputfile.name, gfactors=None,
weights=None, start_year=cyr)
assert nonstd.array_length == RAWINPUTFILE_FUNITS
calc = Calculator(policy=pol, records=nonstd,
sync_years=False) # keeps raw data unchanged
assert calc.current_year == cyr
assert calc.reform_warnings == ''
def test_correct_class_instantiation():
"""
Test correct instantiation of Policy class object.
"""
pol = Policy()
assert pol
pol.implement_reform({})
with pytest.raises(ValueError):
pol.implement_reform(list())
with pytest.raises(ValueError):
pol.implement_reform({2099: {'II_em': 99000}})
pol.set_year(2019)
with pytest.raises(ValueError):
pol.implement_reform({2018: {'II_em': 99000}})
with pytest.raises(ValueError):
pol.implement_reform({2020: {'II_em': -1000}})
def test_correct_class_instantiation():
"""
Test correct instantiation of Policy class object.
"""
pol = Policy()
assert pol
pol.implement_reform({})
with pytest.raises(ValueError):
pol.implement_reform(list())
with pytest.raises(ValueError):
pol.implement_reform({2099: {'II_em': 99000}})
pol.set_year(2019)
with pytest.raises(ValueError):
pol.implement_reform({2018: {'II_em': 99000}})
with pytest.raises(ValueError):
pol.implement_reform({2020: {'II_em': -1000}})
def run():
"""
Compute histograms for each marginal tax rate income type using
sample input from the 'puf.csv' file and writing output to stdout.
"""
if NEG_DIFF:
sys.stdout.write('MTR computed using NEGATIVE finite_diff.\n')
else:
sys.stdout.write('MTR computed using POSITIVE finite_diff.\n')
# create a Policy object (clp) containing current-law policy parameters
clp = Policy()
clp.set_year(TAX_YEAR)
# create a Records object (puf) containing puf.csv input records
puf = Records()
recid = puf.RECID # pylint: disable=no-member
# create a Calculator object using clp policy and puf records
calc = Calculator(policy=clp, records=puf)
# . . . specify FICA mtr histogram bin boundaries (or edges):
fica_bin_edges = [0.0, 0.02, 0.04, 0.06, 0.08,
0.10, 0.12, 0.14, 0.16, 0.18, 1.0]
# the bin boundaries above are arbitrary, so users
# may want to experiment with alternative boundaries
# . . . specify IIT mtr histogram bin boundaries (or edges):
iit_bin_edges = [-1.0, -0.30, -0.20, -0.10, 0.0,
0.10, 0.20, 0.30, 0.40, 0.50, 1.0]
# the bin boundaries above are arbitrary, so users
# may want to experiment with alternative boundaries
assert len(fica_bin_edges) == len(iit_bin_edges)
sys.stdout.write('{} = {}\n'.format('Total number of data records',
puf.dim))
sys.stdout.write('FICA mtr histogram bin edges:\n')
sys.stdout.write(' {}\n'.format(fica_bin_edges))
sys.stdout.write('IIT mtr histogram bin edges:\n')
sys.stdout.write(' {}\n'.format(iit_bin_edges))
inctype_header = 'FICA and IIT mtr histogram bin counts for'
# compute marginal tax rate (mtr) histograms for each mtr income type
for inctype in Calculator.MTR_VALID_INCOME_TYPES:
(mtr_fica, mtr_iit, _) = calc.mtr(income_type_str=inctype,
negative_finite_diff=NEG_DIFF,
wrt_full_compensation=False)
sys.stdout.write('{} {}:\n'.format(inctype_header, inctype))
write_mtr_bin_counts(mtr_fica, fica_bin_edges, recid)
write_mtr_bin_counts(mtr_iit, iit_bin_edges, recid)
def propagate_user_list(x, num_years, cpi, first_budget_year):
"""
Dispatch to either expand_1D or expand2D depending on the dimension of x
Parameters:
-----------
x : list from user to progagate forward in time. The first value is for
year 'first_budget_year'. The value at index i is the value for
budget year first_budget_year + i.
num_years: int
Number of budget years to expand
cpi: Bool
first_budget_year: int
Returns:
--------
list of length 'num_years'. if 'cpi'==True, the values will be inflated
based on the last value the user specified
"""
# x can't be empty
assert x
pp = Policy(start_year=2013)
pp.set_year(first_budget_year)
# irates are rates for 2015, 2016, and 2017
if cpi:
irates = pp.indexing_rates_for_update(param_name='II_brk2',
calyear=first_budget_year,
num_years_to_expand=num_years)
else:
irates = [0.0] * num_years
last_val = x[-1]
ans = [None] * num_years
for i in range(num_years):
if i < len(x):
ans[i] = x[i]
else:
ans[i] = int(ans[i-1] * (1.0 + irates[i-1]))
return ans
def test_Calculator_using_nonstd_input(rawinputfile):
# check Calculator handling of raw, non-standard input data with no aging
policy = Policy()
policy.set_year(RAWINPUTFILE_YEAR) # set policy params to input data year
nonpuf = Records(
data=rawinputfile.name,
start_year=RAWINPUTFILE_YEAR, # set raw input data year
consider_imputations=False) # keeps raw data unchanged
assert nonpuf.dim == RAWINPUTFILE_FUNITS
calc = Calculator(policy=policy, records=nonpuf,
sync_years=False) # keeps raw data unchanged
assert calc.current_year == RAWINPUTFILE_YEAR
calc.calc_all()
exp_iitax = np.zeros((nonpuf.dim, ))
assert_allclose(nonpuf._iitax, exp_iitax)
mtr_fica, _, _ = calc.mtr(wrt_full_compensation=False)
exp_mtr_fica = np.zeros((nonpuf.dim, ))
exp_mtr_fica.fill(0.153)
assert_allclose(mtr_fica, exp_mtr_fica)
def test_Calculator_using_nonstd_input(rawinputfile):
# check Calculator handling of raw, non-standard input data with no aging
policy = Policy()
policy.set_year(RAWINPUTFILE_YEAR) # set policy params to input data year
nonpuf = Records(data=rawinputfile.name,
start_year=RAWINPUTFILE_YEAR, # set raw input data year
consider_imputations=False) # keeps raw data unchanged
assert nonpuf.dim == RAWINPUTFILE_FUNITS
calc = Calculator(policy=policy,
records=nonpuf,
sync_years=False) # keeps raw data unchanged
assert calc.current_year == RAWINPUTFILE_YEAR
calc.calc_all()
exp_iitax = np.zeros((nonpuf.dim,))
assert_array_equal(nonpuf._iitax, exp_iitax)
mtr_fica, _, _ = calc.mtr(wrt_full_compensation=False)
exp_mtr_fica = np.zeros((nonpuf.dim,))
exp_mtr_fica.fill(0.153)
assert_array_equal(mtr_fica, exp_mtr_fica)
def test_expand_2d_accept_none_add_row():
# pylint doesn't like caps in var name, so pylint: disable=invalid-name
_II_brk2 = [[36000, 72250, 36500, 48600, 72500],
[38000, 74000, 36900, 49400, 73800],
[40000, 74900, 37450, 50200, 74900],
[41000, None, None, None, None],
[43000, None, None, None, None]]
exx = [0.0]
exx.append(74900 * 1.02) # exx[1]
exx.append(37450 * 1.02) # exx[2]
exx.append(50200 * 1.02) # exx[3]
exx.append(74900 * 1.02) # exx[4]
exx.append(0.0)
exx.append(exx[1] * 1.03) # exx[6]
exx.append(exx[2] * 1.03) # exx[7]
exx.append(exx[3] * 1.03) # exx[8]
exx.append(exx[4] * 1.03) # exx[9]
exp = [[36000, 72250, 36500, 48600, 72500],
[38000, 74000, 36900, 49400, 73800],
[40000, 74900, 37450, 50200, 74900],
[41000, exx[1], exx[2], exx[3], exx[4]],
[43000, exx[6], exx[7], exx[8], exx[9]]]
inflation_rates = [0.015, 0.02, 0.02, 0.03]
res = ParametersBase._expand_array(_II_brk2,
inflate=True,
inflation_rates=inflation_rates,
num_years=5)
assert_equal(res, exp)
user_mods = {2016: {'_II_brk2': _II_brk2}}
syr = 2013
pol = Policy(start_year=syr)
irates = pol.inflation_rates()
pol.implement_reform(user_mods)
pol.set_year(2020)
irates = pol.inflation_rates()
# The 2020 policy should be the combination of the user-defined
# value and CPI-inflated values from 2018
exp_2020 = [43000.] + [(1 + irates[2019 - syr]) *
(1 + irates[2018 - syr]) * i
for i in _II_brk2[2][1:]]
exp_2020 = np.array(exp_2020)
assert np.allclose(pol.II_brk2, exp_2020)
def test_expand_2D_accept_None_additional_row():
_II_brk2 = [[36000, 72250, 36500, 48600, 72500, 36250],
[38000, 74000, 36900, 49400, 73800, 36900],
[40000, 74900, 37450, 50200, 74900, 37450],
[41000, None, None, None, None, None],
[43000, None, None, None, None, None]]
exp1 = 74900 * 1.02
exp2 = 37450 * 1.02
exp3 = 50200 * 1.02
exp4 = 74900 * 1.02
exp5 = 37450 * 1.02
exp6 = exp1 * 1.03
exp7 = exp2 * 1.03
exp8 = exp3 * 1.03
exp9 = exp4 * 1.03
exp10 = exp5 * 1.03
exp = [[36000, 72250, 36500, 48600, 72500, 36250],
[38000, 74000, 36900, 49400, 73800, 36900],
[40000, 74900, 37450, 50200, 74900, 37450],
[41000, exp1, exp2, exp3, exp4, exp5],
[43000, exp6, exp7, exp8, exp9, exp10]]
inflation_rates = [0.015, 0.02, 0.02, 0.03]
res = Policy.expand_array(_II_brk2,
inflate=True,
inflation_rates=inflation_rates,
num_years=5)
npt.assert_array_equal(res, exp)
user_mods = {2016: {u'_II_brk2': _II_brk2}}
syr = 2013
pol = Policy(start_year=syr)
irates = pol.inflation_rates()
pol.implement_reform(user_mods)
pol.set_year(2020)
irates = pol.inflation_rates()
# The 2020 policy should be the combination of the user-defined
# value and CPI-inflated values from 2018
exp_2020 = [43000.] + [(1 + irates[2019 - syr]) *
(1 + irates[2018 - syr]) * i
for i in _II_brk2[2][1:]]
exp_2020 = np.array(exp_2020)
npt.assert_allclose(pol.II_brk2, exp_2020)
def test_Calculator_using_nonstd_input(rawinputfile):
# check Calculator handling of raw, non-standard input data with no aging
policy = Policy()
policy.set_year(RAWINPUTFILE_YEAR) # set policy params to input data year
nonpuf = Records(data=rawinputfile.name,
blowup_factors=None, # keeps raw data unchanged
weights=None,
start_year=RAWINPUTFILE_YEAR) # set raw input data year
assert nonpuf.dim == RAWINPUTFILE_FUNITS
calc = Calculator(policy=policy,
records=nonpuf,
sync_years=False) # keeps raw data unchanged
assert calc.current_year == RAWINPUTFILE_YEAR
calc.calc_all()
exp_iitax = np.zeros((nonpuf.dim,))
assert np.allclose(nonpuf._iitax, exp_iitax)
mtr_ptax, _, _ = calc.mtr(wrt_full_compensation=False)
exp_mtr_ptax = np.zeros((nonpuf.dim,))
exp_mtr_ptax.fill(0.153)
assert np.allclose(mtr_ptax, exp_mtr_ptax)
def test_variable_inflation_rate_with_reform():
syr = 2013
irates = {2013: 0.04, 2014: 0.04, 2015: 0.04, 2016: 0.04, 2017: 0.04,
2018: 0.04, 2019: 0.04, 2020: 0.04, 2021: 0.08, 2022: 0.08}
ppo = Policy(start_year=syr, num_years=10, inflation_rates=irates)
assert ppo._II_em[2013 - syr] == 3900
# implement reform in 2020 which is two years before the last year, 2022
reform = {2020: {'_II_em': [20000]}}
ppo.implement_reform(reform)
ppo.set_year(2020)
assert ppo.current_year == 2020
# check implied inflation rate between 2018 and 2019 (before the reform)
grate = float(ppo._II_em[2019 - syr]) / float(ppo._II_em[2018 - syr]) - 1.0
assert round(grate, 3) == round(0.04, 3)
# check implied inflation rate between 2020 and 2021 (after the reform)
grate = float(ppo._II_em[2021 - syr]) / float(ppo._II_em[2020 - syr]) - 1.0
assert round(grate, 3) == round(0.04, 3)
# check implied inflation rate between 2021 and 2022 (after the reform)
grate = float(ppo._II_em[2022 - syr]) / float(ppo._II_em[2021 - syr]) - 1.0
assert round(grate, 3) == round(0.08, 3)
def test_read_json_reform_file_and_implement_reform(reform_file,
assump_file,
set_year):
"""
Test reading and translation of reform file into a reform dictionary
that is then used to call implement_reform method and Calculate.calc_all()
NOTE: implement_reform called when policy.current_year == policy.start_year
"""
pol = Policy()
if set_year:
pol.set_year(2015)
param_dict = Calculator.read_json_param_objects(reform_file.name,
assump_file.name)
pol.implement_reform(param_dict['policy'])
syr = pol.start_year
# pylint: disable=protected-access,no-member
amt_brk1 = pol._AMT_brk1
assert amt_brk1[2015 - syr] == 200000
assert amt_brk1[2016 - syr] > 200000
assert amt_brk1[2017 - syr] == 300000
assert amt_brk1[2018 - syr] > 300000
ii_em = pol._II_em
assert ii_em[2016 - syr] == 6000
assert ii_em[2017 - syr] == 6000
assert ii_em[2018 - syr] == 7500
assert ii_em[2019 - syr] > 7500
assert ii_em[2020 - syr] == 9000
assert ii_em[2021 - syr] > 9000
amt_em = pol._AMT_em
assert amt_em[2016 - syr, 0] > amt_em[2015 - syr, 0]
assert amt_em[2017 - syr, 0] > amt_em[2016 - syr, 0]
assert amt_em[2018 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2019 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2020 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2021 - syr, 0] > amt_em[2020 - syr, 0]
assert amt_em[2022 - syr, 0] > amt_em[2021 - syr, 0]
add4aged = pol._ID_Medical_frt_add4aged
assert add4aged[2015 - syr] == -0.025
assert add4aged[2016 - syr] == -0.025
assert add4aged[2017 - syr] == 0.0
assert add4aged[2022 - syr] == 0.0
def test_expand_2D_accept_None_additional_row():
_II_brk2 = [
[36000, 72250, 36500, 48600, 72500, 36250],
[38000, 74000, 36900, 49400, 73800, 36900],
[40000, 74900, 37450, 50200, 74900, 37450],
[41000, None, None, None, None, None],
[43000, None, None, None, None, None],
]
exp1 = 74900 * 1.02
exp2 = 37450 * 1.02
exp3 = 50200 * 1.02
exp4 = 74900 * 1.02
exp5 = 37450 * 1.02
exp6 = exp1 * 1.03
exp7 = exp2 * 1.03
exp8 = exp3 * 1.03
exp9 = exp4 * 1.03
exp10 = exp5 * 1.03
exp = [
[36000, 72250, 36500, 48600, 72500, 36250],
[38000, 74000, 36900, 49400, 73800, 36900],
[40000, 74900, 37450, 50200, 74900, 37450],
[41000, exp1, exp2, exp3, exp4, exp5],
[43000, exp6, exp7, exp8, exp9, exp10],
]
inflation_rates = [0.015, 0.02, 0.02, 0.03]
res = Policy.expand_array(_II_brk2, inflate=True, inflation_rates=inflation_rates, num_years=5)
npt.assert_array_equal(res, exp)
user_mods = {2016: {u"_II_brk2": _II_brk2}}
pol = Policy(start_year=2013)
pol.implement_reform(user_mods)
pol.set_year(2020)
irates = Policy.default_inflation_rates()
# The 2020 policy should be the combination of the user-defined
# value and CPI-inflated values from 2018
exp_2020 = [43000.0] + [(1 + irates[2019]) * (1 + irates[2018]) * i for i in _II_brk2[2][1:]]
exp_2020 = np.array(exp_2020)
npt.assert_allclose(pol.II_brk2, exp_2020)
def test_convert_non_cpi_inflated(self):
values = {"FEI_ec_c": [100000.]}
ans = package_up_vars(values, first_budget_year=FBY)
defaults = taxcalc.policy.Policy.default_data(start_year=2015)
pp = Policy(start_year=2013)
pp.set_year(FBY)
# irates are rates for 2015, 2016, and 2017
irates = pp.indexing_rates_for_update(param_name='FEI_ec_c', calyear=FBY,
num_years_to_expand=2)
# User choices propagate through to all future years
# The user has specified the parameter just for 2015, but
# the defaults JSON file has values up to 2016. We should
# give back values up to 2016, with user choice propagating
f2_2016 = 100000
exp = [100000, f2_2016]
assert ans['_FEI_ec_c'] == exp
def test_read_json_reform_file_and_implement_reform(reform_file, set_year):
"""
Test reading and translation of reform file into a reform dictionary
that is then used to call implement_reform method.
NOTE: implement_reform called when policy.current_year == policy.start_year
"""
reform, rb, rg, rc = Calculator.read_json_reform_file(reform_file.name)
policy = Policy()
if set_year:
policy.set_year(2015)
policy.implement_reform(reform)
syr = policy.start_year
amt_brk1 = policy._AMT_brk1
assert amt_brk1[2015 - syr] == 200000
assert amt_brk1[2016 - syr] > 200000
assert amt_brk1[2017 - syr] == 300000
assert amt_brk1[2018 - syr] > 300000
ii_em = policy._II_em
assert ii_em[2016 - syr] == 6000
assert ii_em[2017 - syr] == 6000
assert ii_em[2018 - syr] == 7500
assert ii_em[2019 - syr] > 7500
assert ii_em[2020 - syr] == 9000
assert ii_em[2021 - syr] > 9000
amt_em = policy._AMT_em
assert amt_em[2016 - syr, 0] > amt_em[2015 - syr, 0]
assert amt_em[2017 - syr, 0] > amt_em[2016 - syr, 0]
assert amt_em[2018 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2019 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2020 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2021 - syr, 0] > amt_em[2020 - syr, 0]
assert amt_em[2022 - syr, 0] > amt_em[2021 - syr, 0]
add4aged = policy._ID_Medical_frt_add4aged
assert add4aged[2015 - syr] == -0.025
assert add4aged[2016 - syr] == -0.025
assert add4aged[2017 - syr] == 0.0
assert add4aged[2022 - syr] == 0.0
def test_read_json_param_and_implement_reform(set_year):
"""
Test reading and translation of reform file into a reform dictionary
that is then used to call implement_reform method.
NOTE: implement_reform called when policy.current_year == policy.start_year
"""
reform_json = """
// Example of JSON reform text suitable for the
// Calculator.read_json_param_objects() method.
// This JSON text can contain any number of trailing //-style comments,
// which will be removed before the contents are converted from JSON to
// a dictionary.
// The primary keys are policy parameters and secondary keys are years.
// Both the primary & secondary key values must be enclosed in quotes (").
// Boolean variables are specified as true or false with no quotes and all
// lowercase characters.
{
"policy": {
"AMT_brk1": // top of first AMT tax bracket
{"2015": 200000,
"2017": 300000
},
"EITC_c": // max EITC amount by number of qualifying kids (0,1,2,3+)
{"2016": [ 900, 5000, 8000, 9000],
"2019": [1200, 7000, 10000, 12000]
},
"II_em": // personal exemption amount (see indexing changes below)
{"2016": 6000,
"2018": 7500,
"2020": 9000
},
"II_em-indexed": // personal exemption amount indexing status
{"2016": false, // values in future years are same as this year value
"2018": true // vals in future years indexed with this year as base
},
"SS_Earnings_c": // Social Security (OASDI) maximum taxable earnings
{"2016": 300000,
"2018": 500000,
"2020": 700000
},
"AMT_em-indexed": // AMT exemption amount indexing status
{"2017": false, // values in future years are same as this year value
"2020": true // vals in future years indexed with this year as base
}
}
}
"""
policy = Policy()
if set_year:
policy.set_year(2015)
param_dict = Calculator.read_json_param_objects(reform_json, None)
policy.implement_reform(param_dict['policy'])
syr = policy.start_year
amt_brk1 = policy._AMT_brk1
assert amt_brk1[2015 - syr] == 200000
assert amt_brk1[2016 - syr] > 200000
assert amt_brk1[2017 - syr] == 300000
assert amt_brk1[2018 - syr] > 300000
ii_em = policy._II_em
assert ii_em[2016 - syr] == 6000
assert ii_em[2017 - syr] == 6000
assert ii_em[2018 - syr] == 7500
assert ii_em[2019 - syr] > 7500
assert ii_em[2020 - syr] == 9000
assert ii_em[2021 - syr] > 9000
amt_em = policy._AMT_em
assert amt_em[2016 - syr, 0] > amt_em[2015 - syr, 0]
assert amt_em[2017 - syr, 0] > amt_em[2016 - syr, 0]
assert amt_em[2018 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2019 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2020 - syr, 0] == amt_em[2017 - syr, 0]
assert amt_em[2021 - syr, 0] > amt_em[2020 - syr, 0]
assert amt_em[2022 - syr, 0] > amt_em[2021 - syr, 0]
add4aged = policy._ID_Medical_frt_add4aged
assert add4aged[2015 - syr] == -0.025
assert add4aged[2016 - syr] == -0.025
assert add4aged[2017 - syr] == 0.0
assert add4aged[2022 - syr] == 0.0
