def __init__(self,
input_filename,
reform,
exact_calculations,
emulate_taxsim_2441_logic,
output_records):
"""
SimpleTaxIO class constructor.
"""
# pylint: disable=too-many-arguments
# check that input_filename is a string
if not isinstance(input_filename, str):
msg = 'SimpleTaxIO.ctor input_filename is not a string'
raise ValueError(msg)
# construct output_filename and delete old output file if it exists
# ... construct reform extension to output_filename
if reform is None:
ref = ''
self._using_reform_file = True
else: # if reform is not None
if isinstance(reform, str):
if reform.endswith('.json'):
ref = '-{}'.format(reform[:-5])
else:
ref = '-{}'.format(reform)
self._using_reform_file = True
elif isinstance(reform, dict):
ref = ''
self._using_reform_file = False
else:
msg = 'SimpleTaxIO.ctor reform is neither None, str, nor dict'
raise ValueError(msg)
# ... construct whole output_filename
self._using_input_file = True
self._output_filename = '{}.out-simtax{}'.format(input_filename, ref)
if os.path.isfile(self._output_filename):
os.remove(self._output_filename) # pragma: no cover
# check for existence of file named input_filename
if not os.path.isfile(input_filename):
msg = 'INPUT file named {} could not be found'
raise ValueError(msg.format(input_filename))
# read input file contents into self._input dictionary
self._read_input(input_filename)
self.policy = Policy()
# implement reform if reform is specified
if reform:
if self._using_reform_file:
param_dict = Calculator.read_json_param_objects(reform, None)
r_pol = param_dict['policy']
else:
r_pol = reform
self.policy.implement_reform(r_pol)
# validate input variable values
self._validate_input()
self.calc = self._calc_object(exact_calculations,
emulate_taxsim_2441_logic,
output_records)
def reform_documentation(params, policy_dicts=None):
"""
Generate reform documentation.
Parameters
----------
params: dict
dictionary is structured like dict returned from
the static Calculator method read_json_param_objects()
policy_dicts : list of dict or None
each dictionary in list is a params['policy'] dictionary
representing second and subsequent elements of a compound
reform; None implies no compound reform with the simple
reform characterized in the params['policy'] dictionary
Returns
-------
doc: String
the documentation for the policy reform specified in params
"""
# pylint: disable=too-many-statements,too-many-branches
# nested function used only in reform_documentation
def param_doc(years, change, base):
"""
Parameters
----------
years: list of change years
change: dictionary of parameter changes
base: Policy object with baseline values
syear: parameter start assessment year
Returns
-------
doc: String
"""
# pylint: disable=too-many-locals
# nested function used only in param_doc
def lines(text, num_indent_spaces, max_line_length=77):
"""
Return list of text lines, each one of which is no longer
than max_line_length, with the second and subsequent lines
being indented by the number of specified num_indent_spaces;
each line in the list ends with the '\n' character
"""
if len(text) < max_line_length:
# all text fits on one line
line = text + '\n'
return [line]
# all text does not fix on one line
first_line = True
line_list = list()
words = text.split()
while words:
if first_line:
line = ''
first_line = False
else:
line = ' ' * num_indent_spaces
while (words
and (len(words[0]) + len(line)) < max_line_length):
line += words.pop(0) + ' '
line = line[:-1] + '\n'
line_list.append(line)
return line_list
# begin main logic of param_doc
# pylint: disable=too-many-nested-blocks
assert len(years) == len(change.keys())
assert isinstance(base, Policy)
basex = copy.deepcopy(base)
basevals = getattr(basex, '_vals', None)
assert isinstance(basevals, dict)
doc = ''
for year in years:
# write year
basex.set_year(year)
doc += '{}:\n'.format(year)
# write info for each param in year
for param in sorted(change[year].keys()):
# ... write param:value line
pval = change[year][param]
if isinstance(pval, list):
pval = pval[0]
if basevals[param]['boolean_value']:
if isinstance(pval, list):
pval = [
True if item else False for item in pval
]
else:
pval = bool(pval)
doc += ' {} : {}\n'.format(param, pval)
# ... write optional param-index line
if isinstance(pval, list):
pval = basevals[param]['col_label']
pval = [str(item) for item in pval]
doc += ' ' * (4 + len(param)) + '{}\n'.format(pval)
# ... write name line
if param.endswith('_cpi'):
rootparam = param[:-4]
name = '{} inflation indexing status'.format(rootparam)
else:
name = basevals[param]['long_name']
for line in lines('name: ' + name, 6):
doc += ' ' + line
# ... write optional desc line
if not param.endswith('_cpi'):
desc = basevals[param]['description']
for line in lines('desc: ' + desc, 6):
doc += ' ' + line
# ... write baseline_value line
if param.endswith('_cpi'):
rootparam = param[:-4]
bval = basevals[rootparam].get('cpi_inflated', False)
else:
bval = getattr(basex, param[1:], None)
if isinstance(bval, np.ndarray):
bval = bval.tolist()
if basevals[param]['boolean_value']:
bval = [
True if item else False for item in bval
]
elif basevals[param]['boolean_value']:
bval = bool(bval)
doc += ' baseline_value: {}\n'.format(bval)
return doc
# begin main logic of reform_documentation
# create Policy object with pre-reform (i.e., baseline) values
clp = Policy()
# generate documentation text
doc = 'REFORM DOCUMENTATION\n'
doc += 'Policy Reform Parameter Values by Year:\n'
years = sorted(params['policy'].keys())
if years:
doc += param_doc(years, params['policy'], clp)
else:
doc += 'none: using current-law policy parameters\n'
if policy_dicts is not None:
assert isinstance(policy_dicts, list)
base = clp
base.implement_reform(params['policy'])
assert not base.parameter_errors
for policy_dict in policy_dicts:
assert isinstance(policy_dict, dict)
doc += 'Policy Reform Parameter Values by Year:\n'
years = sorted(policy_dict.keys())
doc += param_doc(years, policy_dict, base)
base.implement_reform(policy_dict)
assert not base.parameter_errors
return doc
def init(self, input_data, tax_year, reform, assump, growdiff_response,
aging_input_data, exact_calculations):
"""
TaxCalcIO class post-constructor method that completes initialization.
Parameters
----------
First four parameters are same as for TaxCalcIO constructor:
input_data, tax_year, reform, assump.
growdiff_response: Growdiff object or None
growdiff_response Growdiff object is used only by the
TaxCalcIO.growmodel_analysis method;
must be None in all other cases.
aging_input_data: boolean
whether or not to extrapolate Records data from data year to
tax_year.
exact_calculations: boolean
specifies whether or not exact tax calculations are done without
any smoothing of "stair-step" provisions in the tax law.
"""
# pylint: disable=too-many-arguments,too-many-locals
# pylint: disable=too-many-statements,too-many-branches
self.errmsg = ''
# get parameter dictionaries from --reform and --assump files
paramdict = Calculator.read_json_param_objects(reform, assump)
# create Behavior object
beh = Behavior()
beh.update_behavior(paramdict['behavior'])
self.behavior_has_any_response = beh.has_any_response()
# create gdiff_baseline object
gdiff_baseline = Growdiff()
gdiff_baseline.update_growdiff(paramdict['growdiff_baseline'])
# create Growfactors clp object that incorporates gdiff_baseline
gfactors_clp = Growfactors()
gdiff_baseline.apply_to(gfactors_clp)
# specify gdiff_response object
if growdiff_response is None:
gdiff_response = Growdiff()
gdiff_response.update_growdiff(paramdict['growdiff_response'])
elif isinstance(growdiff_response, Growdiff):
gdiff_response = growdiff_response
else:
gdiff_response = None
msg = 'TaxCalcIO.more_init: growdiff_response is neither None '
msg += 'nor a Growdiff object'
self.errmsg += 'ERROR: {}\n'.format(msg)
if gdiff_response is not None:
some_gdiff_response = gdiff_response.has_any_response()
if self.behavior_has_any_response and some_gdiff_response:
msg = 'ASSUMP file cannot specify any "behavior" when using '
msg += 'GrowModel or when ASSUMP file has "growdiff_response"'
self.errmsg += 'ERROR: {}\n'.format(msg)
# create Growfactors ref object that has both gdiff objects applied
gfactors_ref = Growfactors()
gdiff_baseline.apply_to(gfactors_ref)
if gdiff_response is not None:
gdiff_response.apply_to(gfactors_ref)
# create Policy objects
if self.specified_reform:
pol = Policy(gfactors=gfactors_ref)
try:
pol.implement_reform(paramdict['policy'])
self.errmsg += pol.reform_errors
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
else:
pol = Policy(gfactors=gfactors_clp)
clp = Policy(gfactors=gfactors_clp)
# check for valid tax_year value
if tax_year < pol.start_year:
msg = 'tax_year {} less than policy.start_year {}'
msg = msg.format(tax_year, pol.start_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
if tax_year > pol.end_year:
msg = 'tax_year {} greater than policy.end_year {}'
msg = msg.format(tax_year, pol.end_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# any errors imply cannot proceed with calculations
if self.errmsg:
return
# set policy to tax_year
pol.set_year(tax_year)
clp.set_year(tax_year)
# read input file contents into Records objects
if aging_input_data:
if self.cps_input_data:
recs = Records.cps_constructor(
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_clp = Records.cps_constructor(
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else: # if not cps_input_data
recs = Records(data=input_data,
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_clp = Records(data=input_data,
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else: # input_data are raw data that are not being aged
recs = Records(data=input_data,
gfactors=None,
exact_calculations=exact_calculations,
weights=None,
adjust_ratios=None,
start_year=tax_year)
recs_clp = copy.deepcopy(recs)
if tax_year < recs.data_year:
msg = 'tax_year {} less than records.data_year {}'
msg = msg.format(tax_year, recs.data_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# create Calculator objects
con = Consumption()
con.update_consumption(paramdict['consumption'])
self.calc = Calculator(policy=pol,
records=recs,
verbose=True,
consumption=con,
behavior=beh,
sync_years=aging_input_data)
self.calc_clp = Calculator(policy=clp,
records=recs_clp,
verbose=False,
consumption=con,
sync_years=aging_input_data)
# remember parameter dictionary for reform documentation
self.param_dict = paramdict
def init(self, input_data, tax_year, baseline, reform, assump,
growdiff_growmodel, aging_input_data, exact_calculations):
"""
TaxCalcIO class post-constructor method that completes initialization.
Parameters
----------
First five are same as the first five of the TaxCalcIO constructor:
input_data, tax_year, baseline, reform, assump.
growdiff_growmodel: GrowDiff object or None
growdiff_growmodel GrowDiff object is used only in the
TaxCalcIO.growmodel_analysis method.
aging_input_data: boolean
whether or not to extrapolate Records data from data year to
tax_year.
exact_calculations: boolean
specifies whether or not exact tax calculations are done without
any smoothing of "stair-step" provisions in the tax law.
"""
# pylint: disable=too-many-arguments,too-many-locals
# pylint: disable=too-many-statements,too-many-branches
self.errmsg = ''
# get policy parameter dictionary from --baseline file
basedict = Calculator.read_json_param_objects(baseline, None)
# get assumption sub-dictionaries
paramdict = Calculator.read_json_param_objects(None, assump)
# get policy parameter dictionaries from --reform file(s)
policydicts = list()
if self.specified_reform:
reforms = reform.split('+')
for ref in reforms:
pdict = Calculator.read_json_param_objects(ref, None)
policydicts.append(pdict['policy'])
paramdict['policy'] = policydicts[0]
# remember parameters for reform documentation
self.param_dict = paramdict
self.policy_dicts = policydicts
# create Behavior object
beh = Behavior()
try:
beh.update_behavior(paramdict['behavior'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
self.behavior_has_any_response = beh.has_any_response()
# create gdiff_baseline object
gdiff_baseline = GrowDiff()
try:
gdiff_baseline.update_growdiff(paramdict['growdiff_baseline'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# create GrowFactors base object that incorporates gdiff_baseline
gfactors_base = GrowFactors()
gdiff_baseline.apply_to(gfactors_base)
# specify gdiff_response object
gdiff_response = GrowDiff()
try:
gdiff_response.update_growdiff(paramdict['growdiff_response'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# create GrowFactors ref object that has all gdiff objects applied
gfactors_ref = GrowFactors()
gdiff_baseline.apply_to(gfactors_ref)
gdiff_response.apply_to(gfactors_ref)
if growdiff_growmodel:
growdiff_growmodel.apply_to(gfactors_ref)
# create Policy objects:
# ... the baseline Policy object
base = Policy(gfactors=gfactors_base)
try:
base.implement_reform(basedict['policy'],
print_warnings=False,
raise_errors=False)
self.errmsg += base.parameter_errors
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# ... the reform Policy object
if self.specified_reform:
pol = Policy(gfactors=gfactors_ref)
for poldict in policydicts:
try:
pol.implement_reform(poldict,
print_warnings=False,
raise_errors=False)
self.errmsg += pol.parameter_errors
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
else:
pol = Policy(gfactors=gfactors_base)
# create Consumption object
con = Consumption()
try:
con.update_consumption(paramdict['consumption'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# create GrowModel object
self.growmodel = GrowModel()
try:
self.growmodel.update_growmodel(paramdict['growmodel'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# check for valid tax_year value
if tax_year < pol.start_year:
msg = 'tax_year {} less than policy.start_year {}'
msg = msg.format(tax_year, pol.start_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
if tax_year > pol.end_year:
msg = 'tax_year {} greater than policy.end_year {}'
msg = msg.format(tax_year, pol.end_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# any errors imply cannot proceed with calculations
if self.errmsg:
return
# set policy to tax_year
pol.set_year(tax_year)
base.set_year(tax_year)
# read input file contents into Records objects
if aging_input_data:
if self.cps_input_data:
recs = Records.cps_constructor(
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_base = Records.cps_constructor(
gfactors=gfactors_base,
exact_calculations=exact_calculations)
else: # if not cps_input_data but aging_input_data
recs = Records(data=input_data,
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_base = Records(data=input_data,
gfactors=gfactors_base,
exact_calculations=exact_calculations)
else: # input_data are raw data that are not being aged
recs = Records(data=input_data,
gfactors=None,
exact_calculations=exact_calculations,
weights=None,
adjust_ratios=None,
start_year=tax_year)
recs_base = copy.deepcopy(recs)
if tax_year < recs.data_year:
msg = 'tax_year {} less than records.data_year {}'
msg = msg.format(tax_year, recs.data_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# create Calculator objects
self.calc = Calculator(policy=pol,
records=recs,
verbose=True,
consumption=con,
behavior=beh,
sync_years=aging_input_data)
self.calc_base = Calculator(policy=base,
records=recs_base,
verbose=False,
consumption=con,
sync_years=aging_input_data)
def generate_policy_revenues():
from taxcalc.growfactors import GrowFactors
from taxcalc.policy import Policy
from taxcalc.records import Records
from taxcalc.gstrecords import GSTRecords
from taxcalc.corprecords import CorpRecords
from taxcalc.parameters import ParametersBase
from taxcalc.calculator import Calculator
"""
for num in range(1, num_reforms):
block_selected_dict[num]['selected_item']= block_widget_dict[num][1].get()
block_selected_dict[num]['selected_value']= block_widget_dict[num][3].get()
block_selected_dict[num]['selected_year']= block_widget_dict[num][2].get()
print(block_selected_dict)
"""
f = open('reform.json')
block_selected_dict = json.load(f)
print("block_selected_dict from json",block_selected_dict)
#print(block_selected_dict)
# create Records object containing pit.csv and pit_weights.csv input data
#print("growfactors filename ", growfactors_filename)
#recs = Records(data=data_filename, weights=weights_filename, gfactors=GrowFactors(growfactors_filename=growfactors_filename))
#recs = Records(data=data_filename, weights=weights_filename, gfactors=GrowFactors(growfactors_filename=growfactors_filename))
#recs.increment_year1(3.0)
#grecs = GSTRecords()
f = open('global_vars.json')
vars = json.load(f)
print("data_filename: ", vars['cit_data_filename'])
print("weights_filename: ", vars['cit_weights_filename'])
print("growfactors_filename: ", vars['GROWFACTORS_FILENAME'])
print("policy_filename: ", vars['DEFAULTS_FILENAME'])
# create CorpRecords object using cross-section data
#crecs1 = CorpRecords(data='cit_cross.csv', weights='cit_cross_wgts1.csv')
crecs1 = CorpRecords(data=vars['cit_data_filename'], weights=vars['cit_weights_filename'], gfactors=GrowFactors(growfactors_filename=vars['GROWFACTORS_FILENAME']))
#crecs1 = CorpRecords(data=vars['cit_weights_filename'], weights=vars['cit_weights_filename'])
# Note: weights argument is optional
assert isinstance(crecs1, CorpRecords)
assert crecs1.current_year == 2017
# create Policy object containing current-law policy
pol = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
# specify Calculator objects for current-law policy
#calc1 = Calculator(policy=pol, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc1 = Calculator(policy=pol, corprecords=crecs1, verbose=False)
#calc1.increment_year1(3.8)
assert isinstance(calc1, Calculator)
assert calc1.current_year == 2017
np.seterr(divide='ignore', invalid='ignore')
pol2 = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
years, reform=read_reform_dict(block_selected_dict)
print("reform dictionary: ",reform)
#reform = Calculator.read_json_param_objects('app01_reform.json', None)
pol2.implement_reform(reform['policy'])
#calc2 = Calculator(policy=pol2, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc2 = Calculator(policy=pol2, corprecords=crecs1, verbose=False)
pit_adjustment_factor={}
revenue_dict_cit={}
revenue_amount_dict = {}
calc1.calc_all()
for year in range(2019, 2024):
cols = []
calc1.advance_to_year(year)
calc2.advance_to_year(year)
# NOTE: calc1 now contains a PRIVATE COPY of pol and a PRIVATE COPY of recs,
# so we can continue to use pol and recs in this script without any
# concern about side effects from Calculator method calls on calc1.
# Produce DataFrame of results using the calculator
# First run the calculator for the corporate income tax
calc1.calc_all()
print("***** Year ", year)
weighted_citax1 = calc1.weighted_total_cit('citax')
citax_collection_billions1 = weighted_citax1/10**9
citax_collection_str1 = '{0:.2f}'.format(citax_collection_billions1)
print("The CIT Collection in billions is: ", citax_collection_billions1)
# Produce DataFrame of results using cross-section
calc2.calc_all()
weighted_citax2 = calc2.weighted_total_cit('citax')
citax_collection_billions2 = weighted_citax2/10**9
citax_collection_str2 = '{0:.2f}'.format(citax_collection_billions2)
# This is the difference in the collection due to the reform
# This amount will now be allocated to dividends of PIT
citax_diff_collection_billions2 = (citax_collection_billions2-citax_collection_billions1)
citax_diff_collection_str2 = '{0:.2f}'.format(citax_diff_collection_billions2)
print("The CIT Collection after reform billions is: ", citax_collection_billions2)
print("The difference in CIT Collection in billions is: ", citax_diff_collection_billions2)
# Process of allocation of difference in CIT profits to PIT
# in the form of Dividends
# Dividends in this case is reported as Income from Other Sources
# TOTAL_INCOME_OS in the PIT form
# First get the unadjusted amounts
# Now calculate the adjusted amounts
# contribution to PIT Dividends
proportion_change_dividend = (weighted_citax1 - weighted_citax2)/weighted_citax1
new_dividend_proportion_of_old = (1 + proportion_change_dividend)
pit_adjustment_factor[year]=new_dividend_proportion_of_old
# Store Results
revenue_dict_cit[year]={}
revenue_dict_cit[year]['current_law']=citax_collection_str1
revenue_dict_cit[year]['reform']=citax_collection_str2
revenue_dict_cit[year]['difference']=citax_diff_collection_str2
print(revenue_dict_cit)
print("new_dividend_proportion_of_old ", pit_adjustment_factor)
# now update pit.csv with this proportion
# start a new round of simulation for pit
recs = Records(data=vars['pit_data_filename'], weights=vars['pit_weights_filename'], gfactors=GrowFactors(growfactors_filename=vars['GROWFACTORS_FILENAME']))
# create Policy object containing current-law policy
pol = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
# specify Calculator objects for current-law policy
#calc1 = Calculator(policy=pol, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc1 = Calculator(policy=pol, records=recs, verbose=False)
#calc1.increment_year1(3.8)
assert isinstance(calc1, Calculator)
assert calc1.current_year == 2017
np.seterr(divide='ignore', invalid='ignore')
pol2 = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
#years, reform=read_reform_dict(block_selected_dict)
#print("reform dictionary: ", reform)
#reform = Calculator.read_json_param_objects('app01_reform.json', None)
pol2.implement_reform(reform['policy'])
#calc2 = Calculator(policy=pol2, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc2 = Calculator(policy=pol2, records=recs, verbose=False)
total_revenue_text={}
reform_revenue_text={}
revenue_dict_pit={}
revenue_amount_dict = {}
num = 1
first_time = True
i=1
j=0
#rows = []
window = tk.Toplevel()
window.geometry("800x400+140+140")
display_table(window, revenue_dict_cit, revenue_dict_pit, header=True)
#for year in range(years[0], years[-1]+1):
for year in range(2019, 2024):
cols = []
calc1.advance_to_year(year)
calc2.advance_to_year(year)
# NOTE: calc1 now contains a PRIVATE COPY of pol and a PRIVATE COPY of recs,
# so we can continue to use pol and recs in this script without any
# concern about side effects from Calculator method calls on calc1.
# Produce DataFrame of results using the calculator
# First run the calculator for the corporate income tax
calc1.calc_all()
weighted_pitax1 = calc1.weighted_total_pit('pitax')
pitax_collection_billions1 = weighted_pitax1/10**9
pitax_collection_str1 = '{0:.2f}'.format(pitax_collection_billions1)
print('\n\n\n')
print(f'TAX COLLECTION FOR THE YEAR - {year} \n')
print("The PIT Collection in billions is: ", pitax_collection_billions1)
#total_revenue_text[year] = "PIT COLLECTION UNDER CURRENT LAW FOR THE YEAR - " + str(year)+" : "+str(pitax_collection_str1)+" bill"
# Produce DataFrame of results using cross-section
calc2.calc_all()
weighted_pitax2 = calc2.weighted_total_pit('pitax')
pitax_collection_billions2 = weighted_pitax2/10**9
pitax_collection_str2 = '{0:.2f}'.format(pitax_collection_billions2)
pitax_diff_collection_billions2 = (pitax_collection_billions2-pitax_collection_billions1)
pitax_diff_collection_str2 = '{0:.2f}'.format(pitax_diff_collection_billions2)
# Now calculate the adjusted amounts
# contribution to PIT Dividends
print("Total Income from Other Sources (bill) no adjustment is ", calc2.weighted_total_pit('TOTAL_INCOME_OS')/10**9 )
calc2.adjust_pit(pit_adjustment_factor[year])
print("Total Income from Other Sources (bill) after adjustment is ", calc2.weighted_total_pit('TOTAL_INCOME_OS')/10**9 )
calc2.calc_all()
weighted_pitax3 = calc2.weighted_total_pit('pitax')
pitax_collection_billions3 = weighted_pitax3/10**9
pitax_collection_str3 = '{0:.2f}'.format(pitax_collection_billions3)
pitax_diff_collection_billions3 = (pitax_collection_billions3-pitax_collection_billions1)
pitax_diff_collection_str3 = '{0:.2f}'.format(pitax_diff_collection_billions3)
pitax_diff_collection_billions4 = (pitax_collection_billions3-pitax_collection_billions2)
pitax_diff_collection_str4 = '{0:.2f}'.format(pitax_diff_collection_billions4)
#save the results
revenue_dict_pit[year]={}
revenue_dict_pit[year]['current_law']=pitax_collection_str1
revenue_dict_pit[year]['reform']={}
revenue_dict_pit[year]['reform']['unadjusted']=pitax_collection_str2
revenue_dict_pit[year]['reform']['adjusted']=pitax_collection_str3
revenue_dict_pit[year]['difference']=pitax_diff_collection_str3
print('\n\n\n')
print(f'TAX COLLECTION FOR THE YEAR UNDER REFORM - {year} \n')
print("The PIT Collection in billions is: ", pitax_collection_billions2)
print("The difference in PIT Collection in billions is: ", pitax_diff_collection_billions2)
print('****AFTER ADJUSTMENT \n\n\n')
print('TAX COLLECTION FOR THE YEAR UNDER REFORM WITH ADJUSTMENT \n')
print("The PIT Collection in billions after adjusting for the impact of CIT is: ", pitax_collection_billions3)
print("The difference in PIT Collection in billions after adjusting for the impact of CIT is: ", pitax_diff_collection_billions3)
print("The impact of adjustment is: ", pitax_diff_collection_billions4)
display_table(window, revenue_dict_cit, revenue_dict_pit, year=year, row=i)
i=i+1
#reverse the adjustment to obtain baseline
calc2.adjust_pit(1/pit_adjustment_factor[year])
display_table(window, revenue_dict_cit, revenue_dict_pit, footer=i)
"""
def reform_documentation(params):
"""
Generate reform documentation.
Parameters
----------
params: dict
compound dictionary structured as dict returned from
the static Calculator method read_json_param_objects()
Returns
-------
doc: String
the documentation for the policy reform specified in params
"""
# pylint: disable=too-many-statements,too-many-branches
# nested function used only in reform_documentation
def param_doc(years, change, base):
"""
Parameters
----------
years: list of change years
change: dictionary of parameter changes
base: Policy or Growdiff object with baseline values
syear: parameter start calendar year
Returns
-------
doc: String
"""
# nested function used only in param_doc
def lines(text, num_indent_spaces, max_line_length=77):
"""
Return list of text lines, each one of which is no longer
than max_line_length, with the second and subsequent lines
being indented by the number of specified num_indent_spaces;
each line in the list ends with the '\n' character
"""
if len(text) < max_line_length:
# all text fits on one line
line = text + '\n'
return [line]
# all text does not fix on one line
first_line = True
line_list = list()
words = text.split()
while words:
if first_line:
line = ''
first_line = False
else:
line = ' ' * num_indent_spaces
while (words
and (len(words[0]) + len(line)) < max_line_length):
line += words.pop(0) + ' '
line = line[:-1] + '\n'
line_list.append(line)
return line_list
# begin main logic of param_doc
# pylint: disable=too-many-nested-blocks
assert len(years) == len(change.keys())
basevals = getattr(base, '_vals', None)
assert isinstance(basevals, dict)
doc = ''
for year in years:
# write year
base.set_year(year)
doc += '{}:\n'.format(year)
# write info for each param in year
for param in sorted(change[year].keys()):
# ... write param:value line
pval = change[year][param]
if isinstance(pval, list):
pval = pval[0]
if basevals[param]['boolean_value']:
if isinstance(pval, list):
pval = [
True if item else False for item in pval
]
else:
pval = bool(pval)
doc += ' {} : {}\n'.format(param, pval)
# ... write optional param-index line
if isinstance(pval, list):
pval = basevals[param]['col_label']
pval = [str(item) for item in pval]
doc += ' ' * (4 + len(param)) + '{}\n'.format(pval)
# ... write name line
if param.endswith('_cpi'):
rootparam = param[:-4]
name = '{} inflation indexing status'.format(rootparam)
else:
name = basevals[param]['long_name']
for line in lines('name: ' + name, 6):
doc += ' ' + line
# ... write optional desc line
if not param.endswith('_cpi'):
desc = basevals[param]['description']
for line in lines('desc: ' + desc, 6):
doc += ' ' + line
# ... write baseline_value line
if isinstance(base, Policy):
if param.endswith('_cpi'):
rootparam = param[:-4]
bval = basevals[rootparam].get(
'cpi_inflated', False)
else:
bval = getattr(base, param[1:], None)
if isinstance(bval, np.ndarray):
# pylint: disable=no-member
bval = bval.tolist()
if basevals[param]['boolean_value']:
bval = [
True if item else False
for item in bval
]
elif basevals[param]['boolean_value']:
bval = bool(bval)
doc += ' baseline_value: {}\n'.format(bval)
else: # if base is Growdiff object
# all Growdiff parameters have zero as default value
doc += ' baseline_value: 0.0\n'
return doc
# begin main logic of reform_documentation
# create Policy object with pre-reform (i.e., baseline) values
# ... create gdiff_baseline object
gdb = Growdiff()
gdb.update_growdiff(params['growdiff_baseline'])
# ... create Growfactors clp object that incorporates gdiff_baseline
gfactors_clp = Growfactors()
gdb.apply_to(gfactors_clp)
# ... create Policy object containing pre-reform parameter values
clp = Policy(gfactors=gfactors_clp)
# generate documentation text
doc = 'REFORM DOCUMENTATION\n'
doc += 'Baseline Growth-Difference Assumption Values by Year:\n'
years = sorted(params['growdiff_baseline'].keys())
if years:
doc += param_doc(years, params['growdiff_baseline'], gdb)
else:
doc += 'none: using default baseline growth assumptions\n'
doc += 'Policy Reform Parameter Values by Year:\n'
years = sorted(params['policy'].keys())
if years:
doc += param_doc(years, params['policy'], clp)
else:
doc += 'none: using current-law policy parameters\n'
return doc
def generate_revenues():
from taxcalc.growfactors import GrowFactors
from taxcalc.policy import Policy
from taxcalc.records import Records
from taxcalc.gstrecords import GSTRecords
from taxcalc.corprecords import CorpRecords
from taxcalc.parameters import ParametersBase
from taxcalc.calculator import Calculator
"""
for num in range(1, num_reforms):
block_selected_dict[num]['selected_item']= block_widget_dict[num][1].get()
block_selected_dict[num]['selected_value']= block_widget_dict[num][3].get()
block_selected_dict[num]['selected_year']= block_widget_dict[num][2].get()
print(block_selected_dict)
"""
f = open('reform.json')
block_selected_dict = json.load(f)
print("block_selected_dict from json", block_selected_dict)
#print(block_selected_dict)
# create Records object containing pit.csv and pit_weights.csv input data
#print("growfactors filename ", growfactors_filename)
#recs = Records(data=data_filename, weights=weights_filename, gfactors=GrowFactors(growfactors_filename=growfactors_filename))
#recs = Records(data=data_filename, weights=weights_filename, gfactors=GrowFactors(growfactors_filename=growfactors_filename))
#recs.increment_year1(3.0)
#grecs = GSTRecords()
f = open('global_vars.json')
vars = json.load(f)
print("data_filename: ", vars['cit_data_filename'])
print("weights_filename: ", vars['cit_weights_filename'])
print("growfactors_filename: ", vars['GROWFACTORS_FILENAME'])
print("policy_filename: ", vars['DEFAULTS_FILENAME'])
# create CorpRecords object using cross-section data
#crecs1 = CorpRecords(data='cit_cross.csv', weights='cit_cross_wgts1.csv')
crecs1 = CorpRecords(
data=vars['cit_data_filename'],
weights=vars['cit_weights_filename'],
gfactors=GrowFactors(
growfactors_filename=vars['GROWFACTORS_FILENAME']))
#crecs1 = CorpRecords(data=vars['cit_weights_filename'], weights=vars['cit_weights_filename'])
# Note: weights argument is optional
assert isinstance(crecs1, CorpRecords)
assert crecs1.current_year == 2017
# create Policy object containing current-law policy
pol = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
# specify Calculator objects for current-law policy
#calc1 = Calculator(policy=pol, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc1 = Calculator(policy=pol, corprecords=crecs1, verbose=False)
#calc1.increment_year1(3.8)
assert isinstance(calc1, Calculator)
assert calc1.current_year == 2017
np.seterr(divide='ignore', invalid='ignore')
calc1.calc_all()
revenue_dict_cit = {}
for year in range(2019, 2024):
cols = []
calc1.advance_to_year(year)
# NOTE: calc1 now contains a PRIVATE COPY of pol and a PRIVATE COPY of recs,
# so we can continue to use pol and recs in this script without any
# concern about side effects from Calculator method calls on calc1.
# Produce DataFrame of results using the calculator
# First run the calculator for the corporate income tax
calc1.calc_all()
print("***** Year ", year)
weighted_citax1 = calc1.weighted_total_cit('citax')
citax_collection_billions1 = weighted_citax1 / 10**9
citax_collection_str1 = '{0:.2f}'.format(citax_collection_billions1)
print("The CIT Collection in billions is: ",
citax_collection_billions1)
# Store Results
revenue_dict_cit[year] = {}
revenue_dict_cit[year]['current_law'] = citax_collection_str1
# start a new round of simulation for pit
recs = Records(data=vars['pit_data_filename'],
weights=vars['pit_weights_filename'],
gfactors=GrowFactors(
growfactors_filename=vars['GROWFACTORS_FILENAME']))
# create Policy object containing current-law policy
pol = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
# specify Calculator objects for current-law policy
#calc1 = Calculator(policy=pol, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc1 = Calculator(policy=pol, records=recs, verbose=False)
#calc1.increment_year1(3.8)
assert isinstance(calc1, Calculator)
assert calc1.current_year == 2017
np.seterr(divide='ignore', invalid='ignore')
total_revenue_text = {}
reform_revenue_text = {}
revenue_dict_pit = {}
revenue_amount_dict = {}
num = 1
first_time = True
i = 1
j = 0
#rows = []
window = tk.Toplevel()
window.geometry("800x400+140+140")
display_table(window, revenue_dict_cit, revenue_dict_pit, header=True)
#for year in range(years[0], years[-1]+1):
for year in range(2019, 2024):
cols = []
calc1.advance_to_year(year)
# NOTE: calc1 now contains a PRIVATE COPY of pol and a PRIVATE COPY of recs,
# so we can continue to use pol and recs in this script without any
# concern about side effects from Calculator method calls on calc1.
# Produce DataFrame of results using the calculator
# First run the calculator for the corporate income tax
calc1.calc_all()
weighted_pitax1 = calc1.weighted_total_pit('pitax')
pitax_collection_billions1 = weighted_pitax1 / 10**9
pitax_collection_str1 = '{0:.2f}'.format(pitax_collection_billions1)
print('\n\n\n')
print(f'TAX COLLECTION FOR THE YEAR - {year} \n')
print("The PIT Collection in billions is: ",
pitax_collection_billions1)
#total_revenue_text[year] = "PIT COLLECTION UNDER CURRENT LAW FOR THE YEAR - " + str(year)+" : "+str(pitax_collection_str1)+" bill"
#save the results
revenue_dict_pit[year] = {}
revenue_dict_pit[year]['current_law'] = pitax_collection_str1
display_table(window,
revenue_dict_cit,
revenue_dict_pit,
year=year,
row=i)
i = i + 1
display_table(window, revenue_dict_cit, revenue_dict_pit, footer=i)
"""
def __init__(
self,
input_data,
tax_year,
reform,
assump,
growdiff_response, # =None in static analysis
aging_input_data,
exact_calculations):
"""
TaxCalcIO class constructor.
"""
# pylint: disable=too-many-arguments
# pylint: disable=too-many-locals
# pylint: disable=too-many-branches
# pylint: disable=too-many-statements
# check for existence of INPUT file
if isinstance(input_data, six.string_types):
# remove any leading directory path from INPUT filename
fname = os.path.basename(input_data)
# check if fname ends with ".csv"
if fname.endswith('.csv'):
inp = '{}-{}'.format(fname[:-4], str(tax_year)[2:])
else:
msg = 'INPUT file named {} does not end in .csv'
raise ValueError(msg.format(fname))
# check existence of INPUT file
if not os.path.isfile(input_data):
msg = 'INPUT file named {} could not be found'
raise ValueError(msg.format(input_data))
elif isinstance(input_data, pd.DataFrame):
inp = 'df-{}'.format(str(tax_year)[2:])
else:
msg = 'INPUT is neither string nor Pandas DataFrame'
raise ValueError(msg)
# construct output_filename and delete old output file if it exists
if reform is None:
self._reform = False
ref = ''
elif isinstance(reform, six.string_types):
self._reform = True
# remove any leading directory path from REFORM filename
fname = os.path.basename(reform)
# check if fname ends with ".json"
if fname.endswith('.json'):
ref = '-{}'.format(fname[:-5])
else:
msg = 'REFORM file named {} does not end in .json'
raise ValueError(msg.format(fname))
else:
msg = 'TaxCalcIO.ctor reform is neither None nor str'
raise ValueError(msg)
if assump is None:
asm = ''
elif isinstance(assump, six.string_types):
# remove any leading directory path from ASSUMP filename
fname = os.path.basename(assump)
# check if fname ends with ".json"
if fname.endswith('.json'):
asm = '-{}'.format(fname[:-5])
else:
msg = 'ASSUMP file named {} does not end in .json'
raise ValueError(msg.format(fname))
else:
msg = 'TaxCalcIO.ctor assump is neither None nor str'
raise ValueError(msg)
self._output_filename = '{}{}{}.csv'.format(inp, ref, asm)
delete_file(self._output_filename)
# get parameter dictionaries from --reform and --assump files
param_dict = Calculator.read_json_param_files(reform, assump)
# make sure no behavioral response is specified in --assump
beh = Behavior()
beh.update_behavior(param_dict['behavior'])
if beh.has_any_response():
msg = '--assump ASSUMP cannot assume any "behavior"'
raise ValueError(msg)
# make sure no growdiff_response is specified in --assump
gdiff_response = Growdiff()
gdiff_response.update_growdiff(param_dict['growdiff_response'])
if gdiff_response.has_any_response():
msg = '--assump ASSUMP cannot assume any "growdiff_response"'
raise ValueError(msg)
# create gdiff_baseline object
gdiff_baseline = Growdiff()
gdiff_baseline.update_growdiff(param_dict['growdiff_baseline'])
# create Growfactors clp object that incorporates gdiff_baseline
gfactors_clp = Growfactors()
gdiff_baseline.apply_to(gfactors_clp)
# specify gdiff_response object
if growdiff_response is None:
gdiff_response = Growdiff()
elif isinstance(growdiff_response, Growdiff):
gdiff_response = growdiff_response
else:
msg = 'TaxCalcIO.ctor growdiff_response is neither None nor {}'
raise ValueError(msg.format('a Growdiff object'))
# create Growfactors ref object that has both gdiff objects applied
gfactors_ref = Growfactors()
gdiff_baseline.apply_to(gfactors_ref)
gdiff_response.apply_to(gfactors_ref)
# create Policy object and implement reform if specified
if self._reform:
pol = Policy(gfactors=gfactors_ref)
pol.implement_reform(param_dict['policy'])
clp = Policy(gfactors=gfactors_clp)
else:
pol = Policy(gfactors=gfactors_clp)
# check for valid tax_year value
if tax_year < pol.start_year:
msg = 'tax_year {} less than policy.start_year {}'
raise ValueError(msg.format(tax_year, pol.start_year))
if tax_year > pol.end_year:
msg = 'tax_year {} greater than policy.end_year {}'
raise ValueError(msg.format(tax_year, pol.end_year))
# set policy to tax_year
pol.set_year(tax_year)
if self._reform:
clp.set_year(tax_year)
# read input file contents into Records object(s)
if aging_input_data:
if self._reform:
recs = Records(data=input_data,
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_clp = Records(data=input_data,
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else:
recs = Records(data=input_data,
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else: # input_data are raw data that are not being aged
recs = Records(data=input_data,
exact_calculations=exact_calculations,
gfactors=None,
adjust_ratios=None,
weights=None,
start_year=tax_year)
if self._reform:
recs_clp = copy.deepcopy(recs)
# create Calculator object(s)
con = Consumption()
con.update_consumption(param_dict['consumption'])
self._calc = Calculator(policy=pol,
records=recs,
verbose=True,
consumption=con,
sync_years=aging_input_data)
if self._reform:
self._calc_clp = Calculator(policy=clp,
records=recs_clp,
verbose=False,
consumption=con,
sync_years=aging_input_data)