Merge pull request #142 from PeterDSteinberg/fix-conda-recipe-b

Fixes to conda.recipe

btax/btax_reform_run.py

@@ -3,7 +3,7 @@
 -------------------------------------------------------------------------------
 """
 # Import packages
-from btax.run_btax import run_btax, run_btax_with_baseline_delta, run_btax_to_json_tables
+from btax.run_btax import run_btax, run_btax_with_baseline_delta
 
 test_run = False # flag for test run (for Travis CI)
 start_year = 2016
@@ -15,7 +15,7 @@
 #          btax_depr_5yr_exp=40.,btax_depr_7yr_exp=40.,btax_depr_10yr_exp=40.,
 #          btax_depr_15yr_exp=40.,btax_depr_20yr_exp=40.)
 #2019 law
-run_btax_to_json_tables(test_run,start_year,iit_reform,btax_depr_3yr_exp=30.,
+run_btax_with_baseline_delta(test_run,start_year,iit_reform,btax_depr_3yr_exp=30.,
          btax_depr_5yr_exp=30.,btax_depr_7yr_exp=30.,btax_depr_10yr_exp=30.,
          btax_depr_15yr_exp=30.,btax_depr_20yr_exp=30.)
 #2020 law

btax/calc_final_outputs.py

@@ -13,7 +13,7 @@
 import sys
 import pandas as pd
 import numpy as np
-import parameters as param
+import btax.parameters as param
 from btax.util import get_paths
 
 globals().update(get_paths())

btax/check_asset_alloc.py

@@ -12,6 +12,7 @@
 Last updated 10/05/2016
 """
 # Packages:
+from __future__ import print_function
 import os.path
 import numpy as np
 import pandas as pd
@@ -113,8 +114,8 @@
 
 # compute value of land for owner occupied housing sector
 owner_occ_house_land = owner_occ_house_RE - owner_occ_house_FA
-print 'Owner Occ housing land: ', owner_occ_house_land
-print 'Owner Occ housing FAs: ', owner_occ_house_FA
+print('Owner Occ housing land: ', owner_occ_house_land)
+print('Owner Occ housing FAs: ', owner_occ_house_FA)
 # update amout of land for non-corporate sector
 noncorp_land -= owner_occ_house_land
 
@@ -128,22 +129,22 @@
 total_btax_INV = asset_data.loc[asset_data['Asset Type']=='Inventories','assets'].sum()
 total_btax_RES_FA = asset_data.loc[asset_data['Asset Type']=='Residential','assets'].sum()
 total_btax_FA = total_btax_assets - total_btax_LAND - total_btax_INV - total_btax_RES_FA
-print 'diff in FA: ', total_btax_RES_FA-total_bea_RES_FA
+print('diff in FA: ', total_btax_RES_FA-total_bea_RES_FA)
 
 
 '''
 Print percentage differences between control totals and BTax data
 '''
-print 'Diff in Non-residential Fixed Assets: ', (total_btax_FA-total_bea_FA)/total_bea_FA
-print 'Diff in Land: ', (total_btax_LAND-total_finacct_LAND)/total_finacct_LAND
-print 'Diff in Inventories: ', (total_btax_INV-total_bea_INV)/total_bea_INV
-print 'Diff in Residential Fixed Assets: ', (total_btax_RES_FA-total_bea_RES_FA)/total_bea_RES_FA
-
-print "amount non-res fixed assets: ", total_btax_FA
-print "amount of land: ", total_btax_LAND
-print "amount of inventories: ", total_btax_INV
-print "amount of res fixed assets: ", total_btax_RES_FA
-print "diff in res fixed assets: ", total_btax_RES_FA-total_bea_RES_FA
+print('Diff in Non-residential Fixed Assets: ', (total_btax_FA-total_bea_FA)/total_bea_FA)
+print('Diff in Land: ', (total_btax_LAND-total_finacct_LAND)/total_finacct_LAND)
+print('Diff in Inventories: ', (total_btax_INV-total_bea_INV)/total_bea_INV)
+print('Diff in Residential Fixed Assets: ', (total_btax_RES_FA-total_bea_RES_FA)/total_bea_RES_FA)
+
+print("amount non-res fixed assets: ", total_btax_FA)
+print("amount of land: ", total_btax_LAND)
+print("amount of inventories: ", total_btax_INV)
+print("amount of res fixed assets: ", total_btax_RES_FA)
+print("diff in res fixed assets: ", total_btax_RES_FA-total_bea_RES_FA)
 '''
 Do differences in fixed assets by industry (the place where BEA data has detail)
 '''

btax/check_output.py

@@ -9,9 +9,14 @@
 """
 
 import os.path
-import cPickle as pickle
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
+
 import pandas as pd
-from util import get_paths
+
+from btax.util import get_paths
 
 globals().update(get_paths())
 

btax/execute.py

@@ -11,7 +11,10 @@
 # Import packages
 from __future__ import unicode_literals
 from collections import namedtuple, defaultdict
-import cPickle as pickle
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
 from functools import partial
 import numpy as np
 import os.path

btax/format_output.py

@@ -12,8 +12,11 @@
 import sys
 import pandas as pd
 import numpy as np
-import cPickle as pickle
-from util import get_paths, read_from_egg
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
+from btax.util import get_paths, read_from_egg
 
 
 globals().update(get_paths())

btax/front_end_util.py

@@ -1,6 +1,7 @@
 from __future__ import unicode_literals
 from collections import defaultdict
 import os
+
 from btax.parameters import DEFAULT_ASSET_COLS, DEFAULT_INDUSTRY_COLS
 
 # Row labels, in order, including minor headings like "Durable goods"

btax/get_taxcalc_rates.py

@@ -16,19 +16,20 @@
 
 ------------------------------------------------------------------------
 '''
+from __future__ import print_function
+import copy
 
-import taxcalc
-from taxcalc import *
-import pandas as pd
 from pandas import DataFrame
-import numpy as np
-import copy
 import numba
+import numpy as np
+import pandas as pd
 import pickle
+import taxcalc
 
+from taxcalc import *
 
 def get_calculator(baseline, calculator_start_year, reform=None, data=None,
-weights=None, records_start_year=None):
+                   weights=None, records_start_year=None):
     '''
     --------------------------------------------------------------------
     This function creates the tax calculator object for the microsim
@@ -172,5 +173,5 @@ def get_rates(baseline=False, start_year=2017, reform={}):
     individual_rates = {'tau_nc':tau_nc,'tau_div':tau_div,'tau_int':tau_int,
                         'tau_scg':tau_scg,'tau_lcg':tau_lcg,'tau_td':tau_td,
                         'tau_h':tau_h}
-    print individual_rates
+    print(individual_rates)
     return individual_rates

btax/parameters.py

@@ -10,11 +10,11 @@
 import copy
 import json
 import os
-import pandas as pd
+
 import numpy as np
+import pandas as pd
 
-# from btax.util import read_from_egg, DEFAULT_START_YEAR
-from util import read_from_egg, DEFAULT_START_YEAR
+from btax.util import read_from_egg, DEFAULT_START_YEAR
 
 PARAMETER_START_YEAR = 2015 # first year for with tax parameters identified in btax_defaults.json
 DEFAULTS = json.loads(read_from_egg(os.path.join('param_defaults', 'btax_defaults.json')))
@@ -45,7 +45,7 @@ def translate_param_names(start_year=DEFAULT_START_YEAR,**user_mods):
             state = 'Economic'
         user_deprec_system[cl] = state
 
-    user_mods.update({k: v['value'][year] for k,v in defaults.iteritems()
+    user_mods.update({k: v['value'][year] for k,v in defaults.items()
                       if k not in user_mods})
 
     user_bonus_deprec = {cl: user_mods['btax_depr_{}yr_exp'.format(cl)]/100.

btax/pull_soi_corp.py

@@ -9,7 +9,9 @@
 
 """
 # Packages:
+from __future__ import print_function
 import os.path
+
 import numpy as np
 import pandas as pd
 # Directory names:
@@ -54,7 +56,7 @@ def load_corp_data():
         # put in dollars (data in 1000s)
         s_corp[columns]=s_corp[columns]*_CORP_FILE_FCTR
     except IOError:
-        print "IOError: S-Corp soi data file not found."
+        print("IOError: S-Corp soi data file not found.")
         raise
     # Opening the soi Total-corporate data file:
     try:
@@ -66,7 +68,7 @@ def load_corp_data():
         # put in dollars (data in 1000s)
         tot_corp[columns]=tot_corp[columns]*_CORP_FILE_FCTR
     except IOError:
-        print "IOError: total corp soi data file not found."
+        print("IOError: total corp soi data file not found.")
         raise
 
     # read in crosswalk for bea and soi industry codes
@@ -99,8 +101,8 @@ def load_corp_data():
         c_corp[var] = c_corp[var+'_x']-c_corp[var+'_y']
 
     # clean up data by dropping and renaming columns
-    c_corp.drop(map(lambda (x,y): x+y, zip(columns, ['_x']*len(columns))), axis=1, inplace=True)
-    c_corp.drop(map(lambda (x,y): x+y, zip(columns, ['_y']*len(columns))), axis=1, inplace=True)
+    c_corp.drop(map(lambda x,y: x+y, zip(columns, ['_x']*len(columns))), axis=1, inplace=True)
+    c_corp.drop(map(lambda x,y: x+y, zip(columns, ['_y']*len(columns))), axis=1, inplace=True)
 
     ## NOTE:
     # totals in s_corp match totals in SOI data
@@ -157,7 +159,7 @@ def calc_proportions(tot_corp, s_corp, columns):
 
     # clean up data by dropping and renaming columns
     s_corp.drop(['INDY_CD_y','_merge','sector_code']+columns, axis=1, inplace=True)
-    s_corp.drop(map(lambda (x,y): x+y, zip(columns, ['_ratio']*len(columns))), axis=1, inplace=True)
+    s_corp.drop(map(lambda x,y: x+y, zip(columns, ['_ratio']*len(columns))), axis=1, inplace=True)
     s_corp.rename(columns={"INDY_CD_x": "INDY_CD"},inplace=True)
     s_corp.columns = s_corp.columns.str.replace('_final', '')
 

btax/pull_soi_partner.py

@@ -9,11 +9,11 @@
 """
 # Packages:
 import os.path
+import re
+
 import numpy as np
 import pandas as pd
 import xlrd
-import re
-
 
 from btax.util import get_paths
 globals().update(get_paths())
@@ -151,7 +151,7 @@ def load_partner_data(entity_dfs):
         #part_data.ix[part_data['minor_code_alt']>99999, var+'_ratio'] = 1.
         part_data[var] = part_data[var]*part_data[var+'_ratio']
 
-    part_data.drop(map(lambda (x,y): x+y, zip(columns, ['_ratio']*len(columns))), axis=1, inplace=True)
+    part_data.drop(map(lambda x,y: x+y, zip(columns, ['_ratio']*len(columns))), axis=1, inplace=True)
     part_data.drop(['index','sector_code','major_code_x','minor_code',
                     'INDY_CD','major_code_y'],axis=1,inplace=True)
 

btax/pull_soi_proprietorship.py

@@ -10,10 +10,11 @@
 """
 
 import os.path
+import re
 import sys
+
 import numpy as np
 import pandas as pd
-import re
 
 from btax.util import get_paths
 import btax.pull_soi_partner as prt
@@ -154,7 +155,7 @@ def load_proprietorship_data(entity_dfs):
         nonfarm.ix[nonfarm['INDY_CD']>99999, var+'_ratio'] = 1.
         nonfarm[var] = nonfarm[var]*nonfarm[var+'_ratio']
 
-    nonfarm.drop(map(lambda (x,y): x+y, zip(columns, ['_ratio']*len(columns))), axis=1, inplace=True)
+    nonfarm.drop(map(lambda x,y: x+y, zip(columns, ['_ratio']*len(columns))), axis=1, inplace=True)
     nonfarm.drop(['index','sector_code','major_code_x','minor_code',
                     'major_code_y','_merge'],axis=1,inplace=True)
 

btax/read_bea.py

@@ -10,10 +10,12 @@
 """
 # Packages:
 import os.path
+import pickle
+
 import numpy as np
 import pandas as pd
 import xlrd
-import pickle
+
 from btax.util import get_paths
 
 # Directories:

btax/run_btax.py

@@ -11,13 +11,17 @@
 # Import packages
 from __future__ import unicode_literals
 from collections import namedtuple, defaultdict
-import cPickle as pickle
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
 from functools import partial
-import numpy as np
 import os.path
-import pandas as pd
 import sys
 
+import numpy as np
+import pandas as pd
+
 from btax.soi_processing import pull_soi_data
 from btax import calc_final_outputs
 from btax import check_output

btax/soi_processing.py

@@ -10,6 +10,7 @@
 # Import packages
 import os.path
 import sys
+
 import numpy as np
 import pandas as pd
 # Import custom modules