def test_update_and_apply_growdiff():
gdiff = GrowDiff()
# update GrowDiff instance
diffs = {
'AWAGE': {2014: 0.01,
2016: 0.02}
}
gdiff.update_growdiff(diffs)
expected_wage_diffs = [0.00, 0.01, 0.01, 0.02, 0.02]
extra_years = GrowDiff.DEFAULT_NUM_YEARS - len(expected_wage_diffs)
expected_wage_diffs.extend([0.02] * extra_years)
assert np.allclose(gdiff._AWAGE, expected_wage_diffs, atol=0.0, rtol=0.0)
# apply growdiff to GrowFactors instance
gf = GrowFactors()
syr = GrowDiff.JSON_START_YEAR
nyrs = GrowDiff.DEFAULT_NUM_YEARS
lyr = syr + nyrs - 1
pir_pre = gf.price_inflation_rates(syr, lyr)
wgr_pre = gf.wage_growth_rates(syr, lyr)
gfactors = GrowFactors()
gdiff.apply_to(gfactors)
pir_pst = gfactors.price_inflation_rates(syr, lyr)
wgr_pst = gfactors.wage_growth_rates(syr, lyr)
expected_wgr_pst = [wgr_pre[i] + expected_wage_diffs[i]
for i in range(0, nyrs)]
assert np.allclose(pir_pre, pir_pst, atol=0.0, rtol=0.0)
assert np.allclose(wgr_pst, expected_wgr_pst, atol=1.0e-9, rtol=0.0)
def test_response_json(tests_path):
"""
Check that each JSON file can be converted into dictionaries that
can be used to construct objects needed for a Calculator object.
"""
# pylint: disable=too-many-locals
responses_path = os.path.join(tests_path, '..', 'responses', '*.json')
for jpf in glob.glob(responses_path):
# read contents of jpf (JSON parameter filename)
jfile = open(jpf, 'r')
jpf_text = jfile.read()
# check that jpf_text can be used to construct objects
response_file = ('"consumption"' in jpf_text
and '"growdiff_baseline"' in jpf_text
and '"growdiff_response"' in jpf_text)
if response_file:
consumption = Consumption()
con_change = Consumption.read_json_update(jpf_text)
consumption.update_consumption(con_change)
del consumption
for topkey in ['growdiff_baseline', 'growdiff_response']:
growdiff = GrowDiff()
gdiff_change = GrowDiff.read_json_update(jpf_text, topkey)
growdiff.update_growdiff(gdiff_change)
del growdiff
else: # jpf_text is not a valid JSON response assumption file
print('test-failing-filename: ' + jpf)
assert False
def test_has_any_response():
start_year = GrowDiff.JSON_START_YEAR
gdiff = GrowDiff()
assert gdiff.current_year == start_year
assert gdiff.has_any_response() is False
gdiff.update_growdiff({'AWAGE': {2020: 0.01}})
assert gdiff.current_year == start_year
assert gdiff.has_any_response() is True
def reform_warnings_errors(user_mods, using_puf):
"""
The reform_warnings_errors function assumes user_mods is a dictionary
returned by the Calculator.read_json_param_objects() function.
This function returns a dictionary containing five STR:STR subdictionaries,
where the dictionary keys are: 'policy', 'consumption',
'growdiff_baseline' and 'growdiff_response'; and the subdictionaries are:
{'warnings': '<empty-or-message(s)>', 'errors': '<empty-or-message(s)>'}.
Note that non-policy parameters have no warnings, so the 'warnings'
string for the non-policy parameters is always empty.
"""
rtn_dict = {'policy': {'warnings': '', 'errors': ''},
'consumption': {'warnings': '', 'errors': ''},
'growdiff_baseline': {'warnings': '', 'errors': ''},
'growdiff_response': {'warnings': '', 'errors': ''}}
# create GrowDiff objects
gdiff_baseline = GrowDiff()
try:
gdiff_baseline.update_growdiff(user_mods['growdiff_baseline'])
except ValueError as valerr_msg:
rtn_dict['growdiff_baseline']['errors'] = valerr_msg.__str__()
gdiff_response = GrowDiff()
try:
gdiff_response.update_growdiff(user_mods['growdiff_response'])
except ValueError as valerr_msg:
rtn_dict['growdiff_response']['errors'] = valerr_msg.__str__()
# create Growfactors object
growfactors = GrowFactors()
gdiff_baseline.apply_to(growfactors)
gdiff_response.apply_to(growfactors)
# create Policy object
pol = Policy(gfactors=growfactors)
try:
pol.implement_reform(user_mods['policy'],
print_warnings=False,
raise_errors=False)
if using_puf:
rtn_dict['policy']['warnings'] = pol.parameter_warnings
rtn_dict['policy']['errors'] = pol.parameter_errors
except ValueError as valerr_msg:
rtn_dict['policy']['errors'] = valerr_msg.__str__()
# create Consumption object
consump = Consumption()
try:
consump.update_consumption(user_mods['consumption'])
except ValueError as valerr_msg:
rtn_dict['consumption']['errors'] = valerr_msg.__str__()
# return composite dictionary of warnings/errors
return rtn_dict
def test_response_json(tests_path):
"""
Check that each JSON file can be converted into dictionaries that
can be used to construct objects needed for a Calculator object.
"""
# pylint: disable=too-many-locals
responses_path = os.path.join(tests_path, '..', 'responses', '*.json')
for jpf in glob.glob(responses_path):
# read contents of jpf (JSON parameter filename)
jfile = open(jpf, 'r')
jpf_text = jfile.read()
# check that jpf_text can be used to construct objects
response_file = ('"consumption"' in jpf_text and
'"behavior"' in jpf_text and
'"growdiff_baseline"' in jpf_text and
'"growdiff_response"' in jpf_text and
'"growmodel"' in jpf_text)
if response_file:
# pylint: disable=protected-access
(con, beh, gdiff_base, gdiff_resp,
grow_model) = Calculator._read_json_econ_assump_text(jpf_text)
cons = Consumption()
cons.update_consumption(con)
behv = Behavior()
behv.update_behavior(beh)
growdiff_baseline = GrowDiff()
growdiff_baseline.update_growdiff(gdiff_base)
growdiff_response = GrowDiff()
growdiff_response.update_growdiff(gdiff_resp)
growmodel = GrowModel()
growmodel.update_growmodel(grow_model)
else: # jpf_text is not a valid JSON response assumption file
print('test-failing-filename: ' +
jpf)
assert False
def test_response_json(tests_path):
"""
Check that each JSON file can be converted into dictionaries that
can be used to construct objects needed for a Calculator object.
"""
# pylint: disable=too-many-locals
responses_path = os.path.join(tests_path, '..', 'responses', '*.json')
for jpf in glob.glob(responses_path):
# read contents of jpf (JSON parameter filename)
jfile = open(jpf, 'r')
jpf_text = jfile.read()
# check that jpf_text can be used to construct objects
response_file = ('"consumption"' in jpf_text and
'"growdiff_baseline"' in jpf_text and
'"growdiff_response"' in jpf_text)
if response_file:
# pylint: disable=protected-access
(con, gdiff_base,
gdiff_resp) = Calculator._read_json_econ_assump_text(jpf_text)
cons = Consumption()
cons.update_consumption(con)
growdiff_baseline = GrowDiff()
growdiff_baseline.update_growdiff(gdiff_base)
growdiff_response = GrowDiff()
growdiff_response.update_growdiff(gdiff_resp)
else: # jpf_text is not a valid JSON response assumption file
print('test-failing-filename: ' +
jpf)
assert False
def test_update_and_apply_growdiff():
gdiff = GrowDiff()
# update GrowDiff instance
diffs = {'AWAGE': {2014: 0.01, 2016: 0.02}}
gdiff.update_growdiff(diffs)
expected_wage_diffs = [0.00, 0.01, 0.01, 0.02, 0.02]
extra_years = GrowDiff.DEFAULT_NUM_YEARS - len(expected_wage_diffs)
expected_wage_diffs.extend([0.02] * extra_years)
assert np.allclose(gdiff._AWAGE, expected_wage_diffs, atol=0.0, rtol=0.0)
# apply growdiff to GrowFactors instance
gf = GrowFactors()
syr = GrowDiff.JSON_START_YEAR
nyrs = GrowDiff.DEFAULT_NUM_YEARS
lyr = syr + nyrs - 1
pir_pre = gf.price_inflation_rates(syr, lyr)
wgr_pre = gf.wage_growth_rates(syr, lyr)
gfactors = GrowFactors()
gdiff.apply_to(gfactors)
pir_pst = gfactors.price_inflation_rates(syr, lyr)
wgr_pst = gfactors.wage_growth_rates(syr, lyr)
expected_wgr_pst = [
wgr_pre[i] + expected_wage_diffs[i] for i in range(0, nyrs)
]
assert np.allclose(pir_pre, pir_pst, atol=0.0, rtol=0.0)
assert np.allclose(wgr_pst, expected_wgr_pst, atol=1.0e-9, rtol=0.0)
def test_has_any_response():
syr = 2014
gdiff = GrowDiff(start_year=syr)
assert gdiff.has_any_response() is False
gdiff.update_growdiff({2020: {'_AWAGE': [0.01]}})
assert gdiff.current_year == syr
assert gdiff.has_any_response() is True
def test_has_any_response():
start_year = GrowDiff.JSON_START_YEAR
gdiff = GrowDiff()
assert gdiff.current_year == start_year
assert gdiff.has_any_response() is False
gdiff.update_growdiff({'AWAGE': {2020: 0.01}})
assert gdiff.current_year == start_year
assert gdiff.has_any_response() is True
def test_incorrect_update_growdiff():
with pytest.raises(ValueError):
GrowDiff().update_growdiff([])
with pytest.raises(ValueError):
GrowDiff().update_growdiff({'xyz': {'_ABOOK': [0.02]}})
with pytest.raises(ValueError):
GrowDiff().update_growdiff({2012: {'_ABOOK': [0.02]}})
with pytest.raises(ValueError):
GrowDiff().update_growdiff({2052: {'_ABOOK': [0.02]}})
with pytest.raises(ValueError):
GrowDiff().update_growdiff({2014: {'_MPC_exxxxx': [0.02]}})
with pytest.raises(ValueError):
GrowDiff().update_growdiff({2014: {'_ABOOK': [-1.1]}})
def test_update_and_apply_growdiff():
syr = 2013
nyrs = 5
lyr = syr + nyrs - 1
gdiff = GrowDiff(start_year=syr, num_years=nyrs)
# update GrowDiff instance
diffs = {2014: {'_AWAGE': [0.01]}, 2016: {'_AWAGE': [0.02]}}
gdiff.update_growdiff(diffs)
expected_wage_diffs = [0.00, 0.01, 0.01, 0.02, 0.02]
assert_allclose(gdiff._AWAGE, expected_wage_diffs, atol=0.0, rtol=0.0)
# apply growdiff to GrowFactors instance
gf = GrowFactors()
pir_pre = gf.price_inflation_rates(syr, lyr)
wgr_pre = gf.wage_growth_rates(syr, lyr)
gfactors = GrowFactors()
gdiff.apply_to(gfactors)
pir_pst = gfactors.price_inflation_rates(syr, lyr)
wgr_pst = gfactors.wage_growth_rates(syr, lyr)
expected_wgr_pst = [
wgr_pre[i] + expected_wage_diffs[i] for i in range(0, nyrs)
]
assert_allclose(pir_pre, pir_pst, atol=0.0, rtol=0.0)
assert_allclose(wgr_pst, expected_wgr_pst, atol=1.0e-9, rtol=0.0)
def calculators(year_n, start_year,
use_puf_not_cps,
use_full_sample,
user_mods):
"""
This function assumes that the specified user_mods is a dictionary
returned by the Calculator.read_json_param_objects() function.
This function returns (calc1, calc2) where
calc1 is pre-reform Calculator object for year_n, and
calc2 is post-reform Calculator object for year_n.
Neither Calculator object has had the calc_all() method executed.
"""
# pylint: disable=too-many-locals,too-many-branches,too-many-statements
check_user_mods(user_mods)
# specify Consumption instance
consump = Consumption()
consump_assumptions = user_mods['consumption']
consump.update_consumption(consump_assumptions)
# specify growdiff_baseline and growdiff_response
growdiff_baseline = GrowDiff()
growdiff_response = GrowDiff()
growdiff_base_assumps = user_mods['growdiff_baseline']
growdiff_resp_assumps = user_mods['growdiff_response']
growdiff_baseline.update_growdiff(growdiff_base_assumps)
growdiff_response.update_growdiff(growdiff_resp_assumps)
# create pre-reform and post-reform GrowFactors instances
growfactors_pre = GrowFactors()
growdiff_baseline.apply_to(growfactors_pre)
growfactors_post = GrowFactors()
growdiff_baseline.apply_to(growfactors_post)
growdiff_response.apply_to(growfactors_post)
# create sample pd.DataFrame from specified input file and sampling scheme
tbi_path = os.path.abspath(os.path.dirname(__file__))
if use_puf_not_cps:
# first try TaxBrain deployment path
input_path = 'puf.csv.gz'
if not os.path.isfile(input_path):
# otherwise try local Tax-Calculator deployment path
input_path = os.path.join(tbi_path, '..', '..', 'puf.csv')
sampling_frac = 0.05
sampling_seed = 2222
else: # if using cps input not puf input
# first try Tax-Calculator code path
input_path = os.path.join(tbi_path, '..', 'cps.csv.gz')
if not os.path.isfile(input_path):
# otherwise read from taxcalc package "egg"
input_path = None # pragma: no cover
full_sample = read_egg_csv('cps.csv.gz') # pragma: no cover
sampling_frac = 0.03
sampling_seed = 180
if input_path:
full_sample = pd.read_csv(input_path)
if use_full_sample:
sample = full_sample
else:
sample = full_sample.sample(frac=sampling_frac,
random_state=sampling_seed)
# create pre-reform Calculator instance
if use_puf_not_cps:
recs1 = Records(data=sample,
gfactors=growfactors_pre)
else:
recs1 = Records.cps_constructor(data=sample,
gfactors=growfactors_pre)
policy1 = Policy(gfactors=growfactors_pre)
calc1 = Calculator(policy=policy1, records=recs1, consumption=consump)
while calc1.current_year < start_year:
calc1.increment_year()
assert calc1.current_year == start_year
# create post-reform Calculator instance
if use_puf_not_cps:
recs2 = Records(data=sample,
gfactors=growfactors_post)
else:
recs2 = Records.cps_constructor(data=sample,
gfactors=growfactors_post)
policy2 = Policy(gfactors=growfactors_post)
policy_reform = user_mods['policy']
policy2.implement_reform(policy_reform)
calc2 = Calculator(policy=policy2, records=recs2, consumption=consump)
while calc2.current_year < start_year:
calc2.increment_year()
assert calc2.current_year == start_year
# delete objects now embedded in calc1 and calc2
del sample
del full_sample
del consump
del growdiff_baseline
del growdiff_response
del growfactors_pre
del growfactors_post
del recs1
del recs2
del policy1
del policy2
# increment Calculator objects for year_n years
for _ in range(0, year_n):
calc1.increment_year()
calc2.increment_year()
# return Calculator objects
return (calc1, calc2)
def reform_warnings_errors(user_mods, using_puf):
"""
The reform_warnings_errors function assumes user_mods is a dictionary
returned by the Calculator.read_json_param_objects() function.
This function returns a dictionary containing five STR:STR subdictionaries,
where the dictionary keys are: 'policy', 'behavior', consumption',
'growdiff_baseline' and 'growdiff_response'; and the subdictionaries are:
{'warnings': '<empty-or-message(s)>', 'errors': '<empty-or-message(s)>'}.
Note that non-policy parameters have no warnings, so the 'warnings'
string for the non-policy parameters is always empty.
"""
rtn_dict = {'policy': {'warnings': '', 'errors': ''},
'behavior': {'warnings': '', 'errors': ''},
'consumption': {'warnings': '', 'errors': ''},
'growdiff_baseline': {'warnings': '', 'errors': ''},
'growdiff_response': {'warnings': '', 'errors': ''}}
# create GrowDiff objects
gdiff_baseline = GrowDiff()
try:
gdiff_baseline.update_growdiff(user_mods['growdiff_baseline'])
except ValueError as valerr_msg:
rtn_dict['growdiff_baseline']['errors'] = valerr_msg.__str__()
gdiff_response = GrowDiff()
try:
gdiff_response.update_growdiff(user_mods['growdiff_response'])
except ValueError as valerr_msg:
rtn_dict['growdiff_response']['errors'] = valerr_msg.__str__()
# create Growfactors object
growfactors = GrowFactors()
gdiff_baseline.apply_to(growfactors)
gdiff_response.apply_to(growfactors)
# create Policy object
pol = Policy(gfactors=growfactors)
try:
pol.implement_reform(user_mods['policy'],
print_warnings=False,
raise_errors=False)
if using_puf:
rtn_dict['policy']['warnings'] = pol.parameter_warnings
rtn_dict['policy']['errors'] = pol.parameter_errors
except ValueError as valerr_msg:
rtn_dict['policy']['errors'] = valerr_msg.__str__()
# create Behavior object
behv = Behavior()
try:
behv.update_behavior(user_mods['behavior'])
except ValueError as valerr_msg:
rtn_dict['behavior']['errors'] = valerr_msg.__str__()
# create Consumption object
consump = Consumption()
try:
consump.update_consumption(user_mods['consumption'])
except ValueError as valerr_msg:
rtn_dict['consumption']['errors'] = valerr_msg.__str__()
# return composite dictionary of warnings/errors
return rtn_dict
def calculator_objects(year_n, start_year,
use_puf_not_cps,
use_full_sample,
user_mods,
behavior_allowed):
"""
This function assumes that the specified user_mods is a dictionary
returned by the Calculator.read_json_param_objects() function.
This function returns (calc1, calc2) where
calc1 is pre-reform Calculator object calculated for year_n, and
calc2 is post-reform Calculator object calculated for year_n.
Set behavior_allowed to False when generating static results or
set behavior_allowed to True when generating dynamic results.
"""
# pylint: disable=too-many-arguments,too-many-locals
# pylint: disable=too-many-branches,too-many-statements
check_user_mods(user_mods)
# specify Consumption instance
consump = Consumption()
consump_assumptions = user_mods['consumption']
consump.update_consumption(consump_assumptions)
# specify growdiff_baseline and growdiff_response
growdiff_baseline = GrowDiff()
growdiff_response = GrowDiff()
growdiff_base_assumps = user_mods['growdiff_baseline']
growdiff_resp_assumps = user_mods['growdiff_response']
growdiff_baseline.update_growdiff(growdiff_base_assumps)
growdiff_response.update_growdiff(growdiff_resp_assumps)
# create pre-reform and post-reform GrowFactors instances
growfactors_pre = GrowFactors()
growdiff_baseline.apply_to(growfactors_pre)
growfactors_post = GrowFactors()
growdiff_baseline.apply_to(growfactors_post)
growdiff_response.apply_to(growfactors_post)
# create sample pd.DataFrame from specified input file and sampling scheme
tbi_path = os.path.abspath(os.path.dirname(__file__))
if use_puf_not_cps:
# first try TaxBrain deployment path
input_path = 'puf.csv.gz'
if not os.path.isfile(input_path):
# otherwise try local Tax-Calculator deployment path
input_path = os.path.join(tbi_path, '..', '..', 'puf.csv')
sampling_frac = 0.05
sampling_seed = 2222
else: # if using cps input not puf input
# first try Tax-Calculator code path
input_path = os.path.join(tbi_path, '..', 'cps.csv.gz')
if not os.path.isfile(input_path):
# otherwise read from taxcalc package "egg"
input_path = None # pragma: no cover
full_sample = read_egg_csv('cps.csv.gz') # pragma: no cover
sampling_frac = 0.03
sampling_seed = 180
if input_path:
full_sample = pd.read_csv(input_path)
if use_full_sample:
sample = full_sample
else:
sample = full_sample.sample(frac=sampling_frac,
random_state=sampling_seed)
# create pre-reform Calculator instance
if use_puf_not_cps:
recs1 = Records(data=sample,
gfactors=growfactors_pre)
else:
recs1 = Records.cps_constructor(data=sample,
gfactors=growfactors_pre)
policy1 = Policy(gfactors=growfactors_pre)
calc1 = Calculator(policy=policy1, records=recs1, consumption=consump)
while calc1.current_year < start_year:
calc1.increment_year()
calc1.calc_all()
assert calc1.current_year == start_year
# specify Behavior instance
behv = Behavior()
behavior_assumps = user_mods['behavior']
behv.update_behavior(behavior_assumps)
# always prevent both behavioral response and growdiff response
if behv.has_any_response() and growdiff_response.has_any_response():
msg = 'BOTH behavior AND growdiff_response HAVE RESPONSE'
raise ValueError(msg)
# optionally prevent behavioral response
if behv.has_any_response() and not behavior_allowed:
msg = 'A behavior RESPONSE IS NOT ALLOWED'
raise ValueError(msg)
# create post-reform Calculator instance
if use_puf_not_cps:
recs2 = Records(data=sample,
gfactors=growfactors_post)
else:
recs2 = Records.cps_constructor(data=sample,
gfactors=growfactors_post)
policy2 = Policy(gfactors=growfactors_post)
policy_reform = user_mods['policy']
policy2.implement_reform(policy_reform)
calc2 = Calculator(policy=policy2, records=recs2,
consumption=consump, behavior=behv)
while calc2.current_year < start_year:
calc2.increment_year()
assert calc2.current_year == start_year
# delete objects now embedded in calc1 and calc2
del sample
del full_sample
del consump
del growdiff_baseline
del growdiff_response
del growfactors_pre
del growfactors_post
del behv
del recs1
del recs2
del policy1
del policy2
# increment Calculator objects for year_n years and calculate
for _ in range(0, year_n):
calc1.increment_year()
calc2.increment_year()
calc1.calc_all()
if calc2.behavior_has_response():
calc2 = Behavior.response(calc1, calc2)
else:
calc2.calc_all()
# return calculated Calculator objects
return (calc1, calc2)
def test_incorrect_growdiff_ctor():
with pytest.raises(ValueError):
gdiff = GrowDiff(start_year=2000)
with pytest.raises(ValueError):
gdiff = GrowDiff(num_years=0)
def calculators(year_n, start_year,
use_puf_not_cps,
use_full_sample,
user_mods):
"""
This function assumes that the specified user_mods is a dictionary
returned by the Calculator.read_json_param_objects() function.
This function returns (calc1, calc2) where
calc1 is pre-reform Calculator object for year_n, and
calc2 is post-reform Calculator object for year_n.
Neither Calculator object has had the calc_all() method executed.
"""
# pylint: disable=too-many-locals,too-many-branches,too-many-statements
check_user_mods(user_mods)
# specify Consumption instance
consump = Consumption()
consump_assumptions = user_mods['consumption']
consump.update_consumption(consump_assumptions)
# specify growdiff_baseline and growdiff_response
growdiff_baseline = GrowDiff()
growdiff_response = GrowDiff()
growdiff_base_assumps = user_mods['growdiff_baseline']
growdiff_resp_assumps = user_mods['growdiff_response']
growdiff_baseline.update_growdiff(growdiff_base_assumps)
growdiff_response.update_growdiff(growdiff_resp_assumps)
# create pre-reform and post-reform GrowFactors instances
growfactors_pre = GrowFactors()
growdiff_baseline.apply_to(growfactors_pre)
growfactors_post = GrowFactors()
growdiff_baseline.apply_to(growfactors_post)
growdiff_response.apply_to(growfactors_post)
# create sample pd.DataFrame from specified input file and sampling scheme
tbi_path = os.path.abspath(os.path.dirname(__file__))
if use_puf_not_cps:
# first try TaxBrain deployment path
input_path = 'puf.csv.gz'
if not os.path.isfile(input_path):
# otherwise try local Tax-Calculator deployment path
input_path = os.path.join(tbi_path, '..', '..', 'puf.csv')
sampling_frac = 0.05
sampling_seed = 2222
else: # if using cps input not puf input
# first try Tax-Calculator code path
input_path = os.path.join(tbi_path, '..', 'cps.csv.gz')
if not os.path.isfile(input_path):
# otherwise read from taxcalc package "egg"
input_path = None # pragma: no cover
full_sample = read_egg_csv('cps.csv.gz') # pragma: no cover
sampling_frac = 0.03
sampling_seed = 180
if input_path:
full_sample = pd.read_csv(input_path)
if use_full_sample:
sample = full_sample
else:
sample = full_sample.sample(frac=sampling_frac,
random_state=sampling_seed)
# create pre-reform Calculator instance
if use_puf_not_cps:
recs1 = Records(data=sample,
gfactors=growfactors_pre)
else:
recs1 = Records.cps_constructor(data=sample,
gfactors=growfactors_pre)
policy1 = Policy(gfactors=growfactors_pre)
calc1 = Calculator(policy=policy1, records=recs1, consumption=consump)
while calc1.current_year < start_year:
calc1.increment_year()
assert calc1.current_year == start_year
# create post-reform Calculator instance
if use_puf_not_cps:
recs2 = Records(data=sample,
gfactors=growfactors_post)
else:
recs2 = Records.cps_constructor(data=sample,
gfactors=growfactors_post)
policy2 = Policy(gfactors=growfactors_post)
policy_reform = user_mods['policy']
policy2.implement_reform(policy_reform)
calc2 = Calculator(policy=policy2, records=recs2, consumption=consump)
while calc2.current_year < start_year:
calc2.increment_year()
assert calc2.current_year == start_year
# delete objects now embedded in calc1 and calc2
del sample
del full_sample
del consump
del growdiff_baseline
del growdiff_response
del growfactors_pre
del growfactors_post
del recs1
del recs2
del policy1
del policy2
# increment Calculator objects for year_n years
for _ in range(0, year_n):
calc1.increment_year()
calc2.increment_year()
# return Calculator objects
return (calc1, calc2)