Create translation.py

translation.py

@@ -0,0 +1,380 @@
+import pandas as pd
+import math
+import numpy as np
+
+output_filename = 'translationoutput.csv' 
+input_filename = 'puf.csv'
+x = pd.read_csv(input_filename)
+
+names = x.columns.values
+
+y = {}
+
+for n in names:
+	y[n] = np.array(x[n])
+
+AGIR1 = y['AGIR1']
+DSI = y['DSI']
+EFI = y['EFI']
+EIC = y['EIC']
+ELECT = y['ELECT']
+FDED = y['FDED']
+FLPDYR = y['FLPDYR']
+FLPDMO = y['FLPDMO']
+F2441 = y['F2441']
+F3800 = y['F3800']
+F6251 = y['F6251']
+F8582 = y['F8582']
+F8606 = y['F8606']
+IE = y['IE']
+MARS = y['MARS']
+MIdR = y['MIdR']
+N20 = y['N20']
+N24 = y['N24']
+N25 = y['N25']
+PREP = y['PREP']
+SCHB = y['SCHB']
+SCHCF = y['SCHCF']
+SCHE = y['SCHE']
+STATE = y['STATE']
+TFORM = y['TFORM']
+TXST = y['TXST']
+XFPT = y['XFPT']
+XFST = y['XFST']
+XOCAH = y['XOCAH']
+XOCAWH = y['XOCAWH']
+XOODEP = y['XOODEP']
+XOPAR = y['XOPAR']
+XTOT = y['XTOT']
+E00200 = y['E00200']
+E00300 = y['E00300']
+E00400 = y['E00400']
+E00600 = y['E00600']
+E00650 = y['E00650']
+E00700 = y['E00700']
+E00800 = y['E00800']
+E00900 = y['E00900']
+E01000 = y['E01000']
+E01100 = y['E01100']
+E01200 = y['E01200']
+E01400 = y['E01400']
+E01500 = y['E01500']
+E01700 = y['E01700']
+E02000 = y['E02000']
+E02100 = y['E02100']
+E02300 = y['E02300']
+E02400 = y['E02400']
+E02500 = y['E02500']
+E03150 = y['E03150']
+E03210 = y['E03210']
+E03220 = y['E03220']
+E03230 = y['E03230']
+E03260 = y['E03260']
+E03270 = y['E03270']
+E03240 = y['E03240']
+E03290 = y['E03290']
+E03300 = y['E03300']
+E03400 = y['E03400']
+E03500 = y['E03500']
+E00100 = y['E00100']
+P04470 = y['P04470']
+E04250 = y['E04250']
+E04600 = y['E04600']
+E04800 = y['E04800']
+E05100 = y['E05100']
+E05200 = y['E05200']
+E05800 = y['E05800']
+E06000 = y['E06000']
+E06200 = y['E06200']
+E06300 = y['E06300']
+E09600 = y['E09600']
+E07180 = y['E07180']
+E07200 = y['E07200']
+E07220 = y['E07220']
+E07230 = y['E07230']
+E07240 = y['E07240']
+E07260 = y['E07260']
+E07300 = y['E07300']
+E07400 = y['E07400']
+E07600 = y['E07600']
+P08000 = y['P08000']
+E07150 = y['E07150']
+E06500 = y['E06500']
+E08800 = y['E08800']
+E09400 = y['E09400']
+E09700 = y['E09700']
+E09800 = y['E09800']
+E09900 = y['E09900']
+E10300 = y['E10300']
+E10700 = y['E10700']
+E10900 = y['E10900']
+E59560 = y['E59560']
+E59680 = y['E59680']
+E59700 = y['E59700']
+E59720 = y['E59720']
+E11550 = y['E11550']
+E11070 = y['E11070']
+E11100 = y['E11100']
+E11200 = y['E11200']
+E11300 = y['E11300']
+E11400 = y['E11400']
+E11570 = y['E11570']
+E11580 = y['E11580']
+E11581 = y['E11581']
+E11582 = y['E11582']
+E11583 = y['E11583']
+E10605 = y['E10605']
+E11900 = y['E11900']
+E12000 = y['E12000']
+E12200 = y['E12200']
+E17500 = y['E17500']
+E18425 = y['E18425']
+E18450 = y['E18450']
+E18500 = y['E18500']
+E19200 = y['E19200']
+E19550 = y['E19550']
+E19800 = y['E19800']
+E20100 = y['E20100']
+E19700 = y['E19700']
+E20550 = y['E20550']
+E20600 = y['E20600']
+E20400 = y['E20400']
+E20800 = y['E20800']
+E20500 = y['E20500']
+E21040 = y['E21040']
+P22250 = y['P22250']
+E22320 = y['E22320']
+E22370 = y['E22370']
+P23250 = y['P23250']
+E24515 = y['E24515']
+E24516 = y['E24516']
+E24518 = y['E24518']
+E24535 = y['E24535']
+E24560 = y['E24560']
+E24598 = y['E24598']
+E24615 = y['E24615']
+E24570 = y['E24570']
+P25350 = y['P25350']
+E25370 = y['E25370']
+E25380 = y['E25380']
+P25470 = y['P25470']
+P25700 = y['P25700']
+E25820 = y['E25820']
+E25850 = y['E25850']
+E25860 = y['E25860']
+E25940 = y['E25940']
+E25980 = y['E25980']
+E25920 = y['E25920']
+E25960 = y['E25960']
+E26110 = y['E26110']
+E26170 = y['E26170']
+E26190 = y['E26190']
+E26160 = y['E26160']
+E26180 = y['E26180']
+E26270 = y['E26270']
+E26100 = y['E26100']
+E26390 = y['E26390']
+E26400 = y['E26400']
+E27200 = y['E27200']
+E30400 = y['E30400']
+E30500 = y['E30500']
+E32800 = y['E32800']
+E33000 = y['E33000']
+E53240 = y['E53240']
+E53280 = y['E53280']
+E53410 = y['E53410']
+E53300 = y['E53300']
+E53317 = y['E53317']
+E53458 = y['E53458']
+E58950 = y['E58950']
+E58990 = y['E58990']
+P60100 = y['P60100']
+P61850 = y['P61850']
+E60000 = y['E60000']
+E62100 = y['E62100']
+E62900 = y['E62900']
+E62720 = y['E62720']
+E62730 = y['E62730']
+E62740 = y['E62740']
+P65300 = y['P65300']
+P65400 = y['P65400']
+E68000 = y['E68000']
+E82200 = y['E82200']
+T27800 = y['T27800']
+S27860 = y['S27860']
+P27895 = y['P27895']
+E87500 = y['E87500']
+E87510 = y['E87510']
+E87520 = y['E87520']
+E87530 = y['E87530']
+E87540 = y['E87540']
+E87550 = y['E87550']
+RECID = y['RECID']
+S006 = y['S006']
+S008 = y['S008']
+S009 =  y['S009']
+WSAMP = y['WSAMP']
+TXRT = y['TXRT']
+
+_adctcrt = np.array([0.15]) #Rate for additional ctc
+
+_aged = np.array([[1500],[1200]]) #Extra std. ded. for aged
+
+_almdep = np.array([6950]) #Child AMT Exclusion base
+
+_almsp = np.array([179500]) #AMT bracket
+
+_amex = np.array([3900]) #Personal Exemption
+
+_amtage = np.array([24]) #Age for full AMT exclusion
+
+_amtsep = np.array([232500]) #AMT Exclusion
+
+_almsep = np.array([39275]) #Extra alminc for married sep
+
+_agcmax = np.array([15000]) #??
+
+_cgrate1 = np.array([0.10]) #Initial rate on long term gains
+
+_cgrate2 = np.array([0.20]) #Normal rate on long term gains
+
+_chmax = np.array([1000]) #Max Child Tax Credit per child 
+
+_crmax = np.array([[487],[3250],[5372],[6044]]) #Max earned income credit
+
+_dcmax = np.array([3000]) #Max dependent care expenses 
+
+_dylim = np.array([3300]) #Limits for Disqualified Income
+
+_ealim = np.array([3000]) #Max earn ACTC
+
+_edphhs = np.array([63]) #End of educ phaseout - singles
+
+_edphhm = np.array([126]) #End of educ phaseout - married 
+
+_feimax = np.array([97600]) #Maximum foreign earned income exclusion
+
+#_hopelm = np.array([1200])
+
+_joint = np.array([0]) #Extra to ymax for joint
+
+_learn = np.array([10000]) #Expense limit for the LLC
+
+_pcmax = np.array([35]) #Maximum Percentage for f2441
+
+_phase = np.array([172250]) #Phase out for itemized
+
+_rtbase = np.array([[0.0765], [0.3400], [0.4000], [0.4000]]) #EIC base rate
+
+_rtless = np.array([[0.0765], [0.1598], [0.2106], [0.2106]]) #EIC _phaseout rate
+
+_ssmax = np.array([115800]) #SS Maximum taxable earnings
+
+_ymax = np.array([[7970], [17530], [17530], [17530]]) #Start of EIC _phaseout
+
+_rt1 = np.array([0.1]) #10% rate
+
+_rt2 = np.array([0.15]) #15% rate
+
+_rt3 = np.array([0.25]) #25% rate
+
+_rt4 = np.array([0.28]) #28% rate
+
+_rt5 = np.array([0.33]) #33% rate
+
+_rt6 = np.array([0.35]) #35% rate
+
+_rt7 = np.array([0.396]) #39.6% rate
+
+
+
+def Comp(): 
+
+	# Factors for joint or separate filing #
+
+	_sep = np.where(np.logical_or(MARS == 3, MARS == 6), 2, 1)
+	_txp = np.where(np.logical_or(MARS== 2, MARS == 5), 2, 1)
+
+
+	# Form 2555 #
+
+	_feided = np.zeros((139651,))
+	#= np.maximum(E35300_0, E35600_0, + E35910_0) 
+
+
+	# Adjustments #
+
+	C02900 = E03210 + E03260 + E03270 + E03300 + E03400 + E03500 + E03220 + E03230 + E03240 + E03290
+	# + x03150 + e03600 + e03280 + e03900 + e04000 + e03700 
+	X02900 = C02900
+
+
+	# Capital Gains #
+
+	C23650 = np.zeros((139651,))
+	#= C23250 + E22250 + E23660 
+	C01000 = np.maximum(-3000/_sep, C23650)
+
+	C02700 = np.zeros((139651,))
+	#= np.minimum(_feided, _feimax[2013-FLPDYR] * F2555) - F2555 missing
+
+	_ymod1 = E00200 + E00300 + E00600 + E00700 + E00800 + E00900 + C01000 + E01100 + E01200 + E01400 + E01700 + E02000 + E02100 + E02300 
+	# E02800 + E02610 + E02600 - E02540
+	_ymod2 = E00400 + E02400/2 - C02900
+	_ymod3 = E03210 + E03230 + E03240
+	# + E02615
+	_ymod = _ymod1 + _ymod2 + _ymod3
+
+
+	# Taxation of Social Security Benefits # 
+
+	C02500 = np.zeros((139651,))
+	#= np.where(np.logical_or(SSIND != 0, 3 <= MARS <= 6), E02500, np.minimum(0.85 * (_ymod - _ssb85[MARS]) + 0.50 * np.minimum(E02400, _ssb85[MARS] - _ssb50[MARS]), 0.85 * E02400))
+	#= np.where(_ymod < _ssb50[MARS], 0, np.minimum(0.85 * (_ymod - _ssb85[MARS]) + 0.50 * np.minimum(E02400, _ssb85[MARS] - _ssb50[MARS]), 0.85 * E02400))
+	#= np.where(np.logical_and(_ymod > _ssb50[MARS], _ymod < _ssb85[MARS]), 0.5 * np.minimum(_ymod - _ssb50[MARS], E02400), np.minimum(0.85 * (_ymod - _ssb85[MARS]) + 0.50 * np.minimum(E02400, _ssb85[MARS] - _ssb50[MARS]), 0.85 * E02400)) 
+
+
+	# Gross Income #
+
+	C02650 = _ymod1 + C02500 - C02700 
+	#+ E02615
+
+
+	# Adjusted Gross Income #
+
+	C00100 = C02650 - C02900
+	_agierr = E00100 - C00100
+	#if _fixup >= 1 then C00100 = C00100 + _agierr
+
+	_posagi = np.maximum(C00100, 0)
+	_ywossbe = E00100 - E02500
+	_ywossbc = C00100 - C02500
+
+
+	# Personal Exemptions (_phaseout smoothed) #
+
+	#if _exact: What is _exact??? 
+
+	_dispc = 0
+
+	C04600 = _prexmp * (1-_dispc)
+
+
+	# Itemized Deductions #
+
+	# Medical #
+	C17750 = 0.75 * _posagi 
+	C17000 = np.maximum(0, E17500 - C17750)
+	xx = 2
+
+	# State and Local Income Tax, or Sales Tax #
+
+
+
+
+
+
+
+
+
+