Source code for taxcalc.records

Tax-Calculator tax-filing-unit Records class.
# pycodestyle
# pylint --disable=locally-disabled

import os
import numpy as np
import pandas as pd
from import Data
from taxcalc.growfactors import GrowFactors
from taxcalc.utils import read_egg_csv

[docs]class Records(Data): """ Records is a subclass of the abstract Data class, and therefore, inherits its methods (none of which are shown here). Constructor for the tax-filing-unit Records class. Parameters ---------- data: string or Pandas DataFrame string describes CSV file in which records data reside; DataFrame already contains records data; default value is the string 'puf.csv' NOTE: when using custom data, set this argument to a DataFrame. NOTE: to use your own data for a specific year with Tax-Calculator, be sure to read the documentation on creating your own data file and then construct a Records object like this: mydata = pd.read_csv(<mydata.csv>) myrec = Records(data=mydata, start_year=<mydata_year>, gfactors=None, weights=None) NOTE: data=None is allowed but the returned instance contains only the data variable information in the specified VARINFO file. start_year: integer specifies calendar year of the input data; default value is PUFCSV_YEAR. Note that if specifying your own data (see above NOTE) as being a custom data set, be sure to explicitly set start_year to the custom data's calendar year. gfactors: GrowFactors class instance or None containing record data growth (or extrapolation) factors. weights: string or Pandas DataFrame or None string describes CSV file in which weights reside; DataFrame already contains weights; None creates empty sample-weights DataFrame; default value is filename of the PUF weights. NOTE: when using custom weights, set this argument to a DataFrame. NOTE: assumes weights are integers that are 100 times the real weights. adjust_ratios: string or Pandas DataFrame or None string describes CSV file in which adjustment ratios reside; DataFrame already contains transposed/no-index adjustment ratios; None creates empty adjustment-ratios DataFrame; default value is filename of the PUF adjustment ratios. NOTE: when using custom ratios, set this argument to a DataFrame. NOTE: if specifying a DataFrame, set adjust_ratios to my_df defined as: my_df = pd.read_csv('<my_ratios.csv>', index_col=0).transpose() exact_calculations: boolean specifies whether or not exact tax calculations are done without any smoothing of stair-step provisions in income tax law; default value is false. Raises ------ ValueError: if data is not the appropriate type. if taxpayer and spouse variables do not add up to filing-unit total. if dividends is less than qualified dividends. if gfactors is not None or a GrowFactors class instance. if start_year is not an integer. if files cannot be found. Returns ------- class instance: Records Notes ----- Typical usage when using PUF input data is as follows:: recs = Records() which uses all the default parameters of the constructor, and therefore, imputed variables are generated to augment the data and initial-year grow factors are applied to the data. There are situations in which you need to specify the values of the Record constructor's arguments, but be sure you know exactly what you are doing when attempting this. Use Records.cps_constructor() to get a Records object instantiated with CPS input data. """ # suppress pylint warning about constructor having too many arguments: # pylint: disable=too-many-arguments # suppress pylint warnings about uppercase variable names: # pylint: disable=invalid-name # suppress pylint warnings about too many class instance attributes: # pylint: disable=too-many-instance-attributes PUFCSV_YEAR = 2011 CPSCSV_YEAR = 2014 PUF_WEIGHTS_FILENAME = 'puf_weights.csv.gz' PUF_RATIOS_FILENAME = 'puf_ratios.csv' CPS_WEIGHTS_FILENAME = 'cps_weights.csv.gz' CPS_RATIOS_FILENAME = None CODE_PATH = os.path.abspath(os.path.dirname(__file__)) VARINFO_FILE_NAME = 'records_variables.json' VARINFO_FILE_PATH = CODE_PATH def __init__(self, data='puf.csv', start_year=PUFCSV_YEAR, gfactors=GrowFactors(), weights=PUF_WEIGHTS_FILENAME, adjust_ratios=PUF_RATIOS_FILENAME, exact_calculations=False): # pylint: disable=no-member,too-many-branches if isinstance(weights, str): weights = os.path.join(Records.CODE_PATH, weights) super().__init__(data, start_year, gfactors, weights) if data is None: return # because there are no data # read adjustment ratios self.ADJ = None self._read_ratios(adjust_ratios) # specify exact value based on exact_calculations self.exact[:] = np.where(exact_calculations is True, 1, 0) # specify FLPDYR value based on start_year self.FLPDYR.fill(start_year) # check for valid MARS values if not np.all(np.logical_and(np.greater_equal(self.MARS, 1), np.less_equal(self.MARS, 5))): raise ValueError('not all MARS values in [1,5] range') # create variables derived from MARS, which is in MUST_READ_VARS self.num[:] = np.where(self.MARS == 2, 2, 1) self.sep[:] = np.where(self.MARS == 3, 2, 1) # check for valid EIC values if not np.all(np.logical_and(np.greater_equal(self.EIC, 0), np.less_equal(self.EIC, 3))): raise ValueError('not all EIC values in [0,3] range') # check that three sets of split-earnings variables have valid values msg = 'expression "{0} == {0}p + {0}s" is not true for every record' tol = 0.020001 # handles "%.2f" rounding errors if not np.allclose(self.e00200, (self.e00200p + self.e00200s), rtol=0.0, atol=tol): raise ValueError(msg.format('e00200')) if not np.allclose(self.e00900, (self.e00900p + self.e00900s), rtol=0.0, atol=tol): raise ValueError(msg.format('e00900')) if not np.allclose(self.e02100, (self.e02100p + self.e02100s), rtol=0.0, atol=tol): raise ValueError(msg.format('e02100')) # check that spouse income variables have valid values nospouse = self.MARS != 2 zeros = np.zeros_like(self.MARS[nospouse]) msg = '{} is not always zero for non-married filing unit' if not np.allclose(self.e00200s[nospouse], zeros): raise ValueError(msg.format('e00200s')) if not np.allclose(self.e00900s[nospouse], zeros): raise ValueError(msg.format('e00900s')) if not np.allclose(self.e02100s[nospouse], zeros): raise ValueError(msg.format('e02100s')) if not np.allclose(self.k1bx14s[nospouse], zeros): raise ValueError(msg.format('k1bx14s')) # check that ordinary dividends are no less than qualified dividends other_dividends = np.maximum(0., self.e00600 - self.e00650) if not np.allclose(self.e00600, self.e00650 + other_dividends, rtol=0.0, atol=tol): msg = 'expression "e00600 >= e00650" is not true for every record' raise ValueError(msg) del other_dividends # check that total pension income is no less than taxable pension inc nontaxable_pensions = np.maximum(0., self.e01500 - self.e01700) if not np.allclose(self.e01500, self.e01700 + nontaxable_pensions, rtol=0.0, atol=tol): msg = 'expression "e01500 >= e01700" is not true for every record' raise ValueError(msg) del nontaxable_pensions # check that PT_SSTB_income has valid value if not np.all(np.logical_and(np.greater_equal(self.PT_SSTB_income, 0), np.less_equal(self.PT_SSTB_income, 1))): raise ValueError('not all PT_SSTB_income values are 0 or 1')
[docs] @staticmethod def cps_constructor(data=None, gfactors=GrowFactors(), exact_calculations=False): """ Static method returns a Records object instantiated with CPS input data. This works in a analogous way to Records(), which returns a Records object instantiated with PUF input data. This is a convenience method that eliminates the need to specify all the details of the CPS input data just as the default values of the arguments of the Records class constructor eliminate the need to specify all the details of the PUF input data. """ if data is None: data = os.path.join(Records.CODE_PATH, 'cps.csv.gz') if gfactors is None: weights = None else: weights = os.path.join(Records.CODE_PATH, Records.CPS_WEIGHTS_FILENAME) return Records(data=data, start_year=Records.CPSCSV_YEAR, gfactors=gfactors, weights=weights, adjust_ratios=Records.CPS_RATIOS_FILENAME, exact_calculations=exact_calculations)
[docs] def increment_year(self): """ Add one to current year, and also does extrapolation, reweighting, adjusting for new current year. """ super().increment_year() self.FLPDYR.fill(self.current_year) # pylint: disable=no-member # apply variable adjustment ratios self._adjust(self.current_year)
[docs] @staticmethod def read_cps_data(): """ Return data in cps.csv.gz as a Pandas DataFrame. """ fname = os.path.join(Records.CODE_PATH, 'cps.csv.gz') if os.path.isfile(fname): cpsdf = pd.read_csv(fname) else: # find file in conda package cpsdf = read_egg_csv(fname) # pragma: no cover return cpsdf
# ----- begin private methods of Records class -----
[docs] def _extrapolate(self, year): """ Apply to variables the grow factor values for specified calendar year. """ # pylint: disable=too-many-statements,no-member # put values in local dictionary gfv = dict() for name in GrowFactors.VALID_NAMES: gfv[name] = self.gfactors.factor_value(name, year) # apply values to Records variables self.PT_binc_w2_wages *= gfv['AWAGE'] self.e00200 *= gfv['AWAGE'] self.e00200p *= gfv['AWAGE'] self.e00200s *= gfv['AWAGE'] self.pencon_p *= gfv['AWAGE'] self.pencon_s *= gfv['AWAGE'] self.e00300 *= gfv['AINTS'] self.e00400 *= gfv['AINTS'] self.e00600 *= gfv['ADIVS'] self.e00650 *= gfv['ADIVS'] self.e00700 *= gfv['ATXPY'] self.e00800 *= gfv['ATXPY'] self.e00900s[:] = np.where(self.e00900s >= 0, self.e00900s * gfv['ASCHCI'], self.e00900s * gfv['ASCHCL']) self.e00900p[:] = np.where(self.e00900p >= 0, self.e00900p * gfv['ASCHCI'], self.e00900p * gfv['ASCHCL']) self.e00900[:] = self.e00900p + self.e00900s self.e01100 *= gfv['ACGNS'] self.e01200 *= gfv['ACGNS'] self.e01400 *= gfv['ATXPY'] self.e01500 *= gfv['ATXPY'] self.e01700 *= gfv['ATXPY'] self.e02000[:] = np.where(self.e02000 >= 0, self.e02000 * gfv['ASCHEI'], self.e02000 * gfv['ASCHEL']) self.e02100 *= gfv['ASCHF'] self.e02100p *= gfv['ASCHF'] self.e02100s *= gfv['ASCHF'] self.e02300 *= gfv['AUCOMP'] self.e02400 *= gfv['ASOCSEC'] self.e03150 *= gfv['ATXPY'] self.e03210 *= gfv['ATXPY'] self.e03220 *= gfv['ATXPY'] self.e03230 *= gfv['ATXPY'] self.e03270 *= gfv['ACPIM'] self.e03240 *= gfv['ATXPY'] self.e03290 *= gfv['ACPIM'] self.e03300 *= gfv['ATXPY'] self.e03400 *= gfv['ATXPY'] self.e03500 *= gfv['ATXPY'] self.e07240 *= gfv['ATXPY'] self.e07260 *= gfv['ATXPY'] self.e07300 *= gfv['ABOOK'] self.e07400 *= gfv['ABOOK'] self.p08000 *= gfv['ATXPY'] self.e09700 *= gfv['ATXPY'] self.e09800 *= gfv['ATXPY'] self.e09900 *= gfv['ATXPY'] self.e11200 *= gfv['ATXPY'] # ITEMIZED DEDUCTIONS self.e17500 *= gfv['ACPIM'] self.e18400 *= gfv['ATXPY'] self.e18500 *= gfv['ATXPY'] self.e19200 *= gfv['AIPD'] self.e19800 *= gfv['ATXPY'] self.e20100 *= gfv['ATXPY'] self.e20400 *= gfv['ATXPY'] self.g20500 *= gfv['ATXPY'] # CAPITAL GAINS self.p22250 *= gfv['ACGNS'] self.p23250 *= gfv['ACGNS'] self.e24515 *= gfv['ACGNS'] self.e24518 *= gfv['ACGNS'] # SCHEDULE E self.e26270 *= gfv['ASCHEI'] self.e27200 *= gfv['ASCHEI'] self.k1bx14p *= gfv['ASCHEI'] self.k1bx14s *= gfv['ASCHEI'] # MISCELLANOUS SCHEDULES self.e07600 *= gfv['ATXPY'] self.e32800 *= gfv['ATXPY'] self.e58990 *= gfv['ATXPY'] self.e62900 *= gfv['ATXPY'] self.e87530 *= gfv['ATXPY'] self.e87521 *= gfv['ATXPY'] self.cmbtp *= gfv['ATXPY'] # BENEFITS self.other_ben *= gfv['ABENOTHER'] self.mcare_ben *= gfv['ABENMCARE'] self.mcaid_ben *= gfv['ABENMCAID'] self.ssi_ben *= gfv['ABENSSI'] self.snap_ben *= gfv['ABENSNAP'] self.wic_ben *= gfv['ABENWIC'] self.housing_ben *= gfv['ABENHOUSING'] self.tanf_ben *= gfv['ABENTANF'] self.vet_ben *= gfv['ABENVET'] # remove local dictionary del gfv
[docs] def _adjust(self, year): """ Adjust value of income variables to match SOI distributions Note: adjustment must leave variables as numpy.ndarray type """ # pylint: disable=no-member if self.ADJ.size > 0: # Interest income self.e00300 *= self.ADJ[f'INT{year}'].iloc[self.agi_bin].values
[docs] def _read_ratios(self, ratios): """ Read Records adjustment ratios from file or use specified transposed/no-index DataFrame as ratios or create empty DataFrame if None """ if ratios is None: setattr(self, 'ADJ', pd.DataFrame({'nothing': []})) return if isinstance(ratios, pd.DataFrame): assert 'INT2013' in ratios.columns # check for transposed assert is None # check for no-index ADJ = ratios elif isinstance(ratios, str): ratios_path = os.path.join(Records.CODE_PATH, ratios) if os.path.isfile(ratios_path): ADJ = pd.read_csv(ratios_path, index_col=0) else: # find file in conda package ADJ = read_egg_csv(os.path.basename(ratios_path), index_col=0) # pragma: no cover ADJ = ADJ.transpose() else: msg = 'ratios is neither None nor a Pandas DataFrame nor a string' raise ValueError(msg) assert isinstance(ADJ, pd.DataFrame) if != 'agi_bin': = 'agi_bin' self.ADJ = pd.DataFrame() setattr(self, 'ADJ', ADJ.astype(np.float32)) del ADJ