"""
Tax-Calculator Input-Output class.
"""
# CODING-STYLE CHECKS:
# pycodestyle taxcalcio.py
# pylint --disable=locally-disabled taxcalcio.py
import os
import gc
import copy
import sqlite3
import numpy as np
import pandas as pd
import paramtools
from taxcalc.policy import Policy
from taxcalc.records import Records
from taxcalc.consumption import Consumption
from taxcalc.growdiff import GrowDiff
from taxcalc.growfactors import GrowFactors
from taxcalc.calculator import Calculator
from taxcalc.utils import (delete_file, write_graph_file,
add_quantile_table_row_variable,
unweighted_sum, weighted_sum)
[docs]
class TaxCalcIO():
"""
Constructor for the Tax-Calculator Input-Output class.
TaxCalcIO class constructor call must be followed by init() call.
Parameters
----------
input_data: string or Pandas DataFrame
string is name of INPUT file that is CSV formatted containing
variable names in the Records USABLE_READ_VARS set, or
Pandas DataFrame is INPUT data containing variable names in
the Records USABLE_READ_VARS set. INPUT vsrisbles not in the
Records USABLE_READ_VARS set can be present but are ignored.
tax_year: integer
calendar year for which taxes will be computed for INPUT.
baseline: None or string
None implies baseline policy is current-law policy, or
string is name of optional BASELINE file that is a JSON
reform file.
reform: None or string
None implies no policy reform (current-law policy), or
string is name of optional REFORM file(s).
assump: None or string
None implies economic assumptions are standard assumptions,
or string is name of optional ASSUMP file.
outdir: None or string
None implies output files written to current directory,
or string is name of optional output directory
Returns
-------
class instance: TaxCalcIO
"""
# pylint: disable=too-many-instance-attributes
def __init__(self, input_data, tax_year, baseline, reform, assump,
outdir=None):
# pylint: disable=too-many-arguments,too-many-locals
# pylint: disable=too-many-branches,too-many-statements
self.gf_reform = None
self.errmsg = ''
# check name and existence of INPUT file
inp = 'x'
self.puf_input_data = False
self.cps_input_data = False
self.tmd_input_data = False
self.tmd_weights = None
self.tmd_gfactor = None
if isinstance(input_data, str):
# 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 name does not end in .csv'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check existence of INPUT file
self.puf_input_data = input_data.endswith('puf.csv')
self.cps_input_data = input_data.endswith('cps.csv')
self.tmd_input_data = input_data.endswith('tmd.csv')
if not self.cps_input_data and not os.path.isfile(input_data):
msg = 'INPUT file could not be found'
self.errmsg += 'ERROR: {}\n'.format(msg)
# if tmd_input_data is True, construct weights and gfactor paths
if self.tmd_input_data: # pragma: no cover
tmd_dir = os.path.dirname(input_data)
if 'TMD_AREA' in os.environ:
area = os.environ['TMD_AREA']
wfile = f'{area}_tmd_weights.csv.gz'
inp = f'{fname[:-4]}_{area}-{str(tax_year)[2:]}'
else: # using national weights
wfile = 'tmd_weights.csv.gz'
self.tmd_weights = os.path.join(tmd_dir, wfile)
self.tmd_gfactor = os.path.join(tmd_dir, 'tmd_growfactors.csv')
if not os.path.isfile(self.tmd_weights):
msg = f'weights file {self.tmd_weights} could not be found'
self.errmsg += 'ERROR: {}\n'.format(msg)
if not os.path.isfile(self.tmd_gfactor):
msg = f'gfactor file {self.tmd_gfactor} could not be found'
self.errmsg += 'ERROR: {}\n'.format(msg)
elif isinstance(input_data, pd.DataFrame):
inp = 'df-{}'.format(str(tax_year)[2:])
else:
msg = 'INPUT is neither string nor Pandas DataFrame'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check name and existence of BASELINE file
bas = '-x'
if baseline is None:
bas = '-#'
elif isinstance(baseline, str):
# remove any leading directory path from BASELINE filename
fname = os.path.basename(baseline)
# check if fname ends with ".json"
if fname.endswith('.json'):
bas = '-{}'.format(fname[:-5])
else:
msg = 'BASELINE file name does not end in .json'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check existence of BASELINE file
if not os.path.isfile(baseline):
msg = 'BASELINE file could not be found'
self.errmsg += 'ERROR: {}\n'.format(msg)
else:
msg = 'TaxCalcIO.ctor: baseline is neither None nor str'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check name(s) and existence of REFORM file(s)
ref = '-x'
if reform is None:
self.specified_reform = False
ref = '-#'
elif isinstance(reform, str):
self.specified_reform = True
# split any compound reform into list of simple reforms
refnames = []
reforms = reform.split('+')
for rfm in reforms:
# remove any leading directory path from rfm filename
fname = os.path.basename(rfm)
# check if fname ends with ".json"
if not fname.endswith('.json'):
msg = '{} does not end in .json'.format(fname)
self.errmsg += 'ERROR: REFORM file name {}\n'.format(msg)
# check existence of REFORM file
if not os.path.isfile(rfm):
msg = '{} could not be found'.format(rfm)
self.errmsg += 'ERROR: REFORM file {}\n'.format(msg)
# add fname to list of refnames used in output file names
refnames.append(fname)
# create (possibly compound) reform name for output file names
ref = '-'
num_refnames = 0
for refname in refnames:
num_refnames += 1
if num_refnames > 1:
ref += '+'
ref += '{}'.format(refname[:-5])
else:
msg = 'TaxCalcIO.ctor: reform is neither None nor str'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check name and existence of ASSUMP file
asm = '-x'
if assump is None:
asm = '-#'
elif isinstance(assump, str):
# 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 name does not end in .json'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check existence of ASSUMP file
if not os.path.isfile(assump):
msg = 'ASSUMP file could not be found'
self.errmsg += 'ERROR: {}\n'.format(msg)
else:
msg = 'TaxCalcIO.ctor: assump is neither None nor str'
self.errmsg += 'ERROR: {}\n'.format(msg)
# check name and existence of OUTDIR
if outdir is None:
valid_outdir = True
elif isinstance(outdir, str):
# check existence of OUTDIR
if os.path.isdir(outdir):
valid_outdir = True
else:
valid_outdir = False
msg = 'OUTDIR could not be found'
self.errmsg += 'ERROR: {}\n'.format(msg)
else:
valid_outdir = False
msg = 'TaxCalcIO.ctor: outdir is neither None nor str'
self.errmsg += 'ERROR: {}\n'.format(msg)
# create OUTPUT file name and delete any existing output files
output_filename = '{}{}{}{}.csv'.format(inp, bas, ref, asm)
if outdir is None:
self._output_filename = output_filename
delete_old_files = True
elif valid_outdir:
self._output_filename = os.path.join(outdir, output_filename)
delete_old_files = True
else:
delete_old_files = False
if delete_old_files:
delete_file(self._output_filename)
delete_file(self._output_filename.replace('.csv', '.db'))
delete_file(self._output_filename.replace('.csv', '-doc.text'))
delete_file(self._output_filename.replace('.csv', '-tab.text'))
delete_file(self._output_filename.replace('.csv', '-atr.html'))
delete_file(self._output_filename.replace('.csv', '-mtr.html'))
delete_file(self._output_filename.replace('.csv', '-pch.html'))
# initialize variables whose values are set in init method
self.calc = None
self.calc_base = None
self.param_dict = None
self.policy_dicts = []
def init(self, input_data, tax_year, baseline, reform, assump,
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.
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 = ''
# instantiate base and reform GrowFactors objects
if self.tmd_input_data:
gfactors_base = GrowFactors(self.tmd_gfactor) # pragma: no cover
gfactors_ref = GrowFactors(self.tmd_gfactor) # pragma: no cover
else:
gfactors_base = GrowFactors()
gfactors_ref = GrowFactors()
# check tax_year validity
max_tax_year = gfactors_base.last_year
if tax_year > max_tax_year:
msg = f'TAXYEAR={tax_year} is greater than {max_tax_year}'
self.errmsg += f'ERROR: {msg}\n'
if self.puf_input_data:
min_tax_year = max( # pragma: no cover
Policy.JSON_START_YEAR, Records.PUFCSV_YEAR)
elif self.cps_input_data:
min_tax_year = max(
Policy.JSON_START_YEAR, Records.CPSCSV_YEAR)
elif self.tmd_input_data:
min_tax_year = max( # pragma: no cover
Policy.JSON_START_YEAR, Records.TMDCSV_YEAR)
else:
min_tax_year = Policy.JSON_START_YEAR
if tax_year < min_tax_year:
msg = f'TAXYEAR={tax_year} is less than {min_tax_year}'
self.errmsg += f'ERROR: {msg}\n'
# tax_year out of valid range means cannot proceed with calculations
if self.errmsg:
return
# 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 = []
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
# set last_b_year
last_b_year = max(tax_year, Policy.LAST_BUDGET_YEAR)
# create gdiff_baseline object
gdiff_baseline = GrowDiff(last_budget_year=last_b_year)
try:
gdiff_baseline.update_growdiff(paramdict['growdiff_baseline'])
except paramtools.ValidationError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# apply gdiff_baseline to gfactor_base
gdiff_baseline.apply_to(gfactors_base)
# specify gdiff_response object
gdiff_response = GrowDiff(last_budget_year=last_b_year)
try:
gdiff_response.update_growdiff(paramdict['growdiff_response'])
except paramtools.ValidationError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# apply gdiff_baseline and gdiff_response to gfactor_ref
gdiff_baseline.apply_to(gfactors_ref)
gdiff_response.apply_to(gfactors_ref)
self.gf_reform = copy.deepcopy(gfactors_ref)
# create Policy objects:
# ... the baseline Policy object
base = Policy(gfactors=gfactors_base, last_budget_year=last_b_year)
try:
base.implement_reform(basedict['policy'],
print_warnings=True,
raise_errors=False)
for _, errors in base.parameter_errors.items():
self.errmsg += "\n".join(errors)
except paramtools.ValidationError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# ... the reform Policy object
if self.specified_reform:
pol = Policy(gfactors=gfactors_ref, last_budget_year=last_b_year)
for poldict in policydicts:
try:
pol.implement_reform(poldict,
print_warnings=True,
raise_errors=False)
if self.errmsg:
self.errmsg += "\n"
for _, errors in pol.parameter_errors.items():
self.errmsg += "\n".join(errors)
except paramtools.ValidationError as valerr_msg:
self.errmsg += valerr_msg.__str__()
else:
pol = Policy(gfactors=gfactors_base, last_budget_year=last_b_year)
# create Consumption object
con = Consumption(last_budget_year=last_b_year)
try:
con.update_consumption(paramdict['consumption'])
except paramtools.ValidationError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# 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
)
elif self.tmd_input_data: # pragma: no cover
wghts = pd.read_csv(self.tmd_weights)
recs = Records(
data=pd.read_csv(input_data),
start_year=Records.TMDCSV_YEAR,
weights=wghts,
gfactors=gfactors_ref,
adjust_ratios=None,
exact_calculations=exact_calculations,
weights_scale=1.0,
)
recs_base = Records(
data=pd.read_csv(input_data),
start_year=Records.TMDCSV_YEAR,
weights=wghts,
gfactors=gfactors_base,
adjust_ratios=None,
exact_calculations=exact_calculations,
weights_scale=1.0,
)
else: # if not {cps|tmd}_input_data but aging_input_data: puf
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,
start_year=tax_year,
gfactors=None,
weights=None,
adjust_ratios=None,
exact_calculations=exact_calculations)
recs_base = copy.deepcopy(recs)
# create Calculator objects
self.calc = Calculator(policy=pol, records=recs,
verbose=True,
consumption=con,
sync_years=aging_input_data)
self.calc_base = Calculator(policy=base, records=recs_base,
verbose=False,
consumption=con,
sync_years=aging_input_data)
[docs]
def custom_dump_variables(self, tcdumpvars_str):
"""
Return set of variable names extracted from tcdumpvars_str, which
contains the contents of the tcdumpvars file in the current directory.
Also, builds self.errmsg if any custom variables are not valid.
"""
assert isinstance(tcdumpvars_str, str)
self.errmsg = ''
# change some common delimiter characters into spaces
dump_vars_str = tcdumpvars_str.replace(',', ' ')
dump_vars_str = dump_vars_str.replace(';', ' ')
dump_vars_str = dump_vars_str.replace('|', ' ')
# split dump_vars_str into a list of dump variables
dump_vars_list = dump_vars_str.split()
# check that all dump_vars_list items are valid
recs_vinfo = Records(data=None) # contains records VARINFO only
valid_set = recs_vinfo.USABLE_READ_VARS | recs_vinfo.CALCULATED_VARS
for var in dump_vars_list:
if var not in valid_set:
msg = 'invalid variable name in tcdumpvars file: {}'
msg = msg.format(var)
self.errmsg += 'ERROR: {}\n'.format(msg)
# add essential variables even if not on custom list
if 'RECID' not in dump_vars_list:
dump_vars_list.append('RECID')
if 'FLPDYR' not in dump_vars_list:
dump_vars_list.append('FLPDYR')
# convert list into a set and return
return set(dump_vars_list)
[docs]
def tax_year(self):
"""
Return calendar year for which TaxCalcIO calculations are being done.
"""
return self.calc.current_year
[docs]
def output_filepath(self):
"""
Return full path to output file named in TaxCalcIO constructor.
"""
dirpath = os.path.abspath(os.path.dirname(__file__))
return os.path.join(dirpath, self._output_filename)
[docs]
def analyze(self, writing_output_file=False,
output_tables=False,
output_graphs=False,
dump_varset=None,
output_dump=False,
output_sqldb=False):
"""
Conduct tax analysis.
Parameters
----------
writing_output_file: boolean
whether or not to generate and write output file
output_tables: boolean
whether or not to generate and write distributional tables
to a text file
output_graphs: boolean
whether or not to generate and write HTML graphs of average
and marginal tax rates by income percentile
dump_varset: set
custom set of variables to include in dump and sqldb output;
None implies include all variables in dump and sqldb output
output_dump: boolean
whether or not to replace standard output with all input and
calculated variables using their Tax-Calculator names
output_sqldb: boolean
whether or not to write SQLite3 database with two tables
(baseline and reform) each containing same output as written
by output_dump to a csv file
Returns
-------
Nothing
"""
# pylint: disable=too-many-arguments,too-many-branches,too-many-locals
if self.puf_input_data and self.calc.reform_warnings:
warn = 'PARAMETER VALUE WARNING(S): {}\n{}{}' # pragma: no cover
print( # pragma: no cover
warn.format('(read documentation for each parameter)',
self.calc.reform_warnings,
'CONTINUING WITH CALCULATIONS...')
)
calc_base_calculated = False
self.calc.calc_all()
if output_dump or output_sqldb:
# might need marginal tax rates
(mtr_paytax, mtr_inctax,
_) = self.calc.mtr(wrt_full_compensation=False,
calc_all_already_called=True)
self.calc_base.calc_all()
calc_base_calculated = True
(mtr_paytax_base, mtr_inctax_base,
_) = self.calc_base.mtr(wrt_full_compensation=False,
calc_all_already_called=True)
else:
# definitely do not need marginal tax rates
mtr_paytax = None
mtr_inctax = None
mtr_paytax_base = None
mtr_inctax_base = None
# extract output if writing_output_file
if writing_output_file:
self.write_output_file(output_dump, dump_varset,
mtr_paytax, mtr_inctax)
self.write_doc_file()
# optionally write --sqldb output to SQLite3 database
if output_sqldb:
self.write_sqldb_file(
dump_varset, mtr_paytax, mtr_inctax,
mtr_paytax_base, mtr_inctax_base
)
# optionally write --tables output to text file
if output_tables:
if not calc_base_calculated:
self.calc_base.calc_all()
calc_base_calculated = True
self.write_tables_file()
# optionally write --graphs output to HTML files
if output_graphs:
if not calc_base_calculated:
self.calc_base.calc_all()
calc_base_calculated = True
self.write_graph_files()
[docs]
def write_output_file(self, output_dump, dump_varset,
mtr_paytax, mtr_inctax):
"""
Write output to CSV-formatted file.
"""
if output_dump:
outdf = self.dump_output(
self.calc, dump_varset, mtr_inctax, mtr_paytax
)
column_order = sorted(outdf.columns)
# place RECID at start of column_order list
assert 'RECID' in column_order, 'RECID not in dump output list'
column_order.remove('RECID')
column_order.insert(0, 'RECID')
weight_vname = 's006'
else:
outdf = self.minimal_output()
column_order = outdf.columns
weight_vname = 'WEIGHT'
assert len(outdf.index) == self.calc.array_len
if self.tmd_input_data: # pragma: no cover
if weight_vname in outdf:
weights = outdf[weight_vname].round(5)
outdf = outdf.round(2)
if weight_vname in outdf:
outdf[weight_vname] = weights
outdf.to_csv(self._output_filename, columns=column_order,
index=False)
else:
outdf.to_csv(self._output_filename, columns=column_order,
index=False, float_format='%.2f')
del outdf
gc.collect()
[docs]
def write_doc_file(self):
"""
Write reform documentation to text file.
"""
if len(self.policy_dicts) <= 1:
doc = Calculator.reform_documentation(
self.param_dict, self.gf_reform
)
else:
doc = Calculator.reform_documentation(
self.param_dict, self.gf_reform, self.policy_dicts[1:]
)
doc_fname = self._output_filename.replace('.csv', '-doc.text')
with open(doc_fname, 'w', encoding='utf-8') as dfile:
dfile.write(doc)
[docs]
def write_sqldb_file(self, dump_varset, mtr_paytax, mtr_inctax,
mtr_paytax_base, mtr_inctax_base):
"""
Write dump output to SQLite3 database table dump.
"""
db_fname = self._output_filename.replace('.csv', '.db')
dbcon = sqlite3.connect(db_fname)
# write baseline table
outdf = self.dump_output(
self.calc_base, dump_varset, mtr_inctax_base, mtr_paytax_base
)
assert len(outdf.index) == self.calc.array_len
outdf.to_sql('baseline', dbcon, if_exists='replace', index=False)
# write reform table
outdf = self.dump_output(
self.calc, dump_varset, mtr_inctax, mtr_paytax
)
assert len(outdf.index) == self.calc.array_len
outdf.to_sql('reform', dbcon, if_exists='replace', index=False)
dbcon.close()
del outdf
gc.collect()
[docs]
def write_tables_file(self):
"""
Write tables to text file.
"""
# pylint: disable=too-many-locals
tab_fname = self._output_filename.replace('.csv', '-tab.text')
# skip tables if there are not some positive weights
if self.calc_base.total_weight() <= 0.:
with open(tab_fname, 'w', encoding='utf-8') as tfile:
msg = 'No tables because sum of weights is not positive\n'
tfile.write(msg)
return
# create list of results for nontax variables
# - weights don't change with reform
# - expanded_income may change, so always use baseline expanded income
nontax_vars = ['s006', 'expanded_income']
nontax = [self.calc_base.array(var) for var in nontax_vars]
# create list of results for tax variables from reform Calculator
tax_vars = ['iitax', 'payrolltax', 'lumpsum_tax', 'combined']
reform = [self.calc.array(var) for var in tax_vars]
# create DataFrame with tax distribution under reform
dist = nontax + reform # using expanded_income under baseline policy
all_vars = nontax_vars + tax_vars
distdf = pd.DataFrame(data=np.column_stack(dist), columns=all_vars)
# create DataFrame with tax differences (reform - baseline)
base = [self.calc_base.array(var) for var in tax_vars]
change = [(reform[idx] - base[idx]) for idx in range(0, len(tax_vars))]
diff = nontax + change # using expanded_income under baseline policy
diffdf = pd.DataFrame(data=np.column_stack(diff), columns=all_vars)
# write each kind of distributional table
with open(tab_fname, 'w', encoding='utf-8') as tfile:
TaxCalcIO.write_decile_table(distdf, tfile, tkind='Reform Totals')
tfile.write('\n')
TaxCalcIO.write_decile_table(diffdf, tfile, tkind='Differences')
# delete intermediate DataFrame objects
del distdf
del diffdf
gc.collect()
[docs]
@staticmethod
def write_decile_table(dfx, tfile, tkind='Totals'):
"""
Write to tfile the tkind decile table using dfx DataFrame.
"""
dfx = add_quantile_table_row_variable(dfx, 'expanded_income', 10,
decile_details=False,
pop_quantiles=False,
weight_by_income_measure=False)
gdfx = dfx.groupby('table_row', as_index=False, observed=True)
rtns_series = gdfx.apply(
unweighted_sum, 's006', include_groups=False
).values[:, 1]
xinc_series = gdfx.apply(
weighted_sum, 'expanded_income', include_groups=False
).values[:, 1]
itax_series = gdfx.apply(
weighted_sum, 'iitax', include_groups=False
).values[:, 1]
ptax_series = gdfx.apply(
weighted_sum, 'payrolltax', include_groups=False
).values[:, 1]
htax_series = gdfx.apply(
weighted_sum, 'lumpsum_tax', include_groups=False
).values[:, 1]
ctax_series = gdfx.apply(
weighted_sum, 'combined', include_groups=False
).values[:, 1]
# write decile table to text file
row = 'Weighted Tax {} by Baseline Expanded-Income Decile\n'
tfile.write(row.format(tkind))
rowfmt = '{}{}{}{}{}{}\n'
row = rowfmt.format(' Returns',
' ExpInc',
' IncTax',
' PayTax',
' LSTax',
' AllTax')
tfile.write(row)
row = rowfmt.format(' (#m)',
' ($b)',
' ($b)',
' ($b)',
' ($b)',
' ($b)')
tfile.write(row)
rowfmt = '{:9.2f}{:10.1f}{:10.1f}{:10.1f}{:10.1f}{:10.1f}\n'
for decile in range(0, 10):
row = '{:2d}'.format(decile)
row += rowfmt.format(rtns_series[decile] * 1e-6,
xinc_series[decile] * 1e-9,
itax_series[decile] * 1e-9,
ptax_series[decile] * 1e-9,
htax_series[decile] * 1e-9,
ctax_series[decile] * 1e-9)
tfile.write(row)
row = ' A'
row += rowfmt.format(rtns_series.sum() * 1e-6,
xinc_series.sum() * 1e-9,
itax_series.sum() * 1e-9,
ptax_series.sum() * 1e-9,
htax_series.sum() * 1e-9,
ctax_series.sum() * 1e-9)
tfile.write(row)
del gdfx
del rtns_series
del xinc_series
del itax_series
del ptax_series
del htax_series
del ctax_series
gc.collect()
[docs]
def write_graph_files(self):
"""
Write graphs to HTML files.
All graphs contain same number of filing units in each quantile.
"""
pos_wght_sum = self.calc.total_weight() > 0.0
fig = None
# percentage-aftertax-income-change graph
pch_fname = self._output_filename.replace('.csv', '-pch.html')
pch_title = 'PCH by Income Percentile'
if pos_wght_sum:
fig = self.calc_base.pch_graph(self.calc, pop_quantiles=False)
write_graph_file(fig, pch_fname, pch_title)
else:
reason = 'No graph because sum of weights is not positive'
TaxCalcIO.write_empty_graph_file(pch_fname, pch_title, reason)
# average-tax-rate graph
atr_fname = self._output_filename.replace('.csv', '-atr.html')
atr_title = 'ATR by Income Percentile'
if pos_wght_sum:
fig = self.calc_base.atr_graph(self.calc, pop_quantiles=False)
write_graph_file(fig, atr_fname, atr_title)
else:
reason = 'No graph because sum of weights is not positive'
TaxCalcIO.write_empty_graph_file(atr_fname, atr_title, reason)
# marginal-tax-rate graph
mtr_fname = self._output_filename.replace('.csv', '-mtr.html')
mtr_title = 'MTR by Income Percentile'
if pos_wght_sum:
fig = self.calc_base.mtr_graph(
self.calc,
alt_e00200p_text='Taxpayer Earnings',
pop_quantiles=False
)
write_graph_file(fig, mtr_fname, mtr_title)
else:
reason = 'No graph because sum of weights is not positive'
TaxCalcIO.write_empty_graph_file(mtr_fname, mtr_title, reason)
if fig:
del fig
gc.collect()
[docs]
@staticmethod
def write_empty_graph_file(fname, title, reason):
"""
Write HTML graph file with title but no graph for specified reason.
"""
txt = ('<html>\n'
'<head><title>{}</title></head>\n'
'<body><center<h1>{}</h1></center></body>\n'
'</html>\n').format(title, reason)
with open(fname, 'w', encoding='utf-8') as gfile:
gfile.write(txt)
[docs]
def minimal_output(self):
"""
Extract minimal output and return it as Pandas DataFrame.
"""
varlist = ['RECID', 'YEAR', 'WEIGHT', 'INCTAX', 'LSTAX', 'PAYTAX']
odict = {}
scalc = self.calc
odict['RECID'] = scalc.array('RECID') # id for tax filing unit
odict['YEAR'] = self.tax_year() # tax calculation year
odict['WEIGHT'] = scalc.array('s006') # sample weight
odict['INCTAX'] = scalc.array('iitax') # federal income taxes
odict['LSTAX'] = scalc.array('lumpsum_tax') # lump-sum tax
odict['PAYTAX'] = scalc.array('payrolltax') # payroll taxes (ee+er)
odf = pd.DataFrame(data=odict, columns=varlist)
return odf
[docs]
def dump_output(self, calcx, dump_varset, mtr_inctax, mtr_paytax):
"""
Extract dump output and return it as Pandas DataFrame.
"""
recs_vinfo = Records(data=None) # contains only Records VARINFO
if dump_varset is None:
varset = recs_vinfo.USABLE_READ_VARS | recs_vinfo.CALCULATED_VARS
else:
varset = dump_varset
# create and return dump output DataFrame
odf = pd.DataFrame()
for varname in varset:
vardata = calcx.array(varname)
if varname in recs_vinfo.INTEGER_VARS:
odf[varname] = vardata
else: # specify precision that can handle small TMD area weights
odf[varname] = vardata.round(5)
odf = odf.copy()
# specify mtr values in percentage terms
if 'mtr_inctax' in varset:
odf['mtr_inctax'] = (mtr_inctax * 100).round(2)
if 'mtr_paytax' in varset:
odf['mtr_paytax'] = (mtr_paytax * 100).round(2)
# specify tax calculation year
odf['FLPDYR'] = self.tax_year()
return odf
