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Merge pull request #1543 from martinholmer/dropq-return-dict
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Return a dictionary (not a tuple) from dropq run_nth_year_tax_calc_model
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@martinholmer
martinholmer authored Sep 7, 2017
2 parents 7e3458f + 22a0ce3 commit fb87028
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4 changes: 3 additions & 1 deletion RELEASES.md
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,9 @@ Release 0.11.0 on 2017-??-??
- Revise dropq distribution and difference tables used by TaxBrain
[[#1537](https://github.com/open-source-economics/Tax-Calculator/pull/1537)
by Anderson Frailey and Martin Holmer]
- Make dropq run_nth_year_tax_calc_model return a dictionary of results
[[#1543](https://github.com/open-source-economics/Tax-Calculator/pull/1543)
by Martin Holmer]

**New Features**
- None
Expand Down Expand Up @@ -43,7 +46,6 @@ Release 0.10.0 on 2017-08-28
[[#1524](https://github.com/open-source-economics/Tax-Calculator/pull/1524)
by Martin Holmer]


**Bug Fixes**
- None

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137 changes: 40 additions & 97 deletions taxcalc/dropq/dropq.py
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Expand Up @@ -17,26 +17,27 @@
import pandas as pd
from taxcalc.dropq.dropq_utils import (dropq_calculate,
random_seed,
dropq_summary)
from taxcalc import (results, TABLE_LABELS, proportional_change_gdp,
Growdiff, Growfactors, Policy)
dropq_summary,
AGGR_ROW_NAMES)
from taxcalc import (results, DIST_TABLE_LABELS,
proportional_change_gdp, Growdiff, Growfactors, Policy)


# specify constants
PLAN_COLUMN_TYPES = [float] * len(TABLE_LABELS)
DIST_COLUMN_TYPES = [float] * len(DIST_TABLE_LABELS)

DIFF_COLUMN_TYPES = [int, int, int, float, float, str, str, str, str]

DECILE_ROW_NAMES = ['perc0-10', 'perc10-20', 'perc20-30', 'perc30-40',
'perc40-50', 'perc50-60', 'perc60-70', 'perc70-80',
'perc80-90', 'perc90-100', 'all']
DEC_ROW_NAMES = ['perc0-10', 'perc10-20', 'perc20-30', 'perc30-40',
'perc40-50', 'perc50-60', 'perc60-70', 'perc70-80',
'perc80-90', 'perc90-100', 'all']

BIN_ROW_NAMES = ['less_than_10', 'ten_twenty', 'twenty_thirty', 'thirty_forty',
'forty_fifty', 'fifty_seventyfive', 'seventyfive_hundred',
'hundred_twohundred', 'twohundred_fivehundred',
'fivehundred_thousand', 'thousand_up', 'all']

TOTAL_ROW_NAMES = ['ind_tax', 'payroll_tax', 'combined_tax']
AGG_ROW_NAMES = AGGR_ROW_NAMES

GDP_ELAST_ROW_NAMES = ['gdp_elasticity']

Expand Down Expand Up @@ -107,26 +108,7 @@ def run_nth_year_tax_calc_model(year_n, start_year,
np.random.seed(seed) # pylint: disable=no-member

# construct dropq summary results from raw results
(dist1_dec, dist2_dec,
diff_itax_dec, diff_ptax_dec, diff_comb_dec,
dist1_bin, dist2_bin,
diff_itax_bin, diff_ptax_bin, diff_comb_bin,
aggr_itax_d, aggr_ptax_d, aggr_comb_d,
aggr_itax_1, aggr_ptax_1, aggr_comb_1,
aggr_itax_2, aggr_ptax_2, aggr_comb_2) = dropq_summary(rawres1,
rawres2,
mask)

# construct DataFrames containing aggregate tax totals
# ... for reform-minus-baseline difference
aggrd = [aggr_itax_d, aggr_ptax_d, aggr_comb_d]
aggr_d = pd.DataFrame(data=aggrd, index=TOTAL_ROW_NAMES)
# ... for baseline
aggr1 = [aggr_itax_1, aggr_ptax_1, aggr_comb_1]
aggr_1 = pd.DataFrame(data=aggr1, index=TOTAL_ROW_NAMES)
# ... for reform
aggr2 = [aggr_itax_2, aggr_ptax_2, aggr_comb_2]
aggr_2 = pd.DataFrame(data=aggr2, index=TOTAL_ROW_NAMES)
summ = dropq_summary(rawres1, rawres2, mask)

elapsed_time = time.time() - start_time
print('elapsed time for this run: ', elapsed_time)
Expand All @@ -140,78 +122,39 @@ def append_year(pdf):

# optionally return non-JSON results
if not return_json:
return (append_year(dist2_dec),
append_year(dist1_dec),
append_year(diff_itax_dec),
append_year(diff_ptax_dec),
append_year(diff_comb_dec),
append_year(dist2_bin),
append_year(dist1_bin),
append_year(diff_itax_bin),
append_year(diff_ptax_bin),
append_year(diff_comb_bin),
append_year(aggr_d),
append_year(aggr_1),
append_year(aggr_2))
res = dict()
for tbl in summ:
res[tbl] = append_year(summ[tbl])
return res

# optionally construct JSON results tables for year n
dec_row_names_n = [x + '_' + str(year_n) for x in DECILE_ROW_NAMES]
dist2_dec_table_n = create_json_table(dist2_dec,
row_names=dec_row_names_n,
column_types=PLAN_COLUMN_TYPES)
dist1_dec_table_n = create_json_table(dist1_dec,
row_names=dec_row_names_n,
column_types=PLAN_COLUMN_TYPES)
diff_itax_dec_table_n = create_json_table(diff_itax_dec,
row_names=dec_row_names_n,
column_types=DIFF_COLUMN_TYPES)
diff_ptax_dec_table_n = create_json_table(diff_ptax_dec,
row_names=dec_row_names_n,
column_types=DIFF_COLUMN_TYPES)
diff_comb_dec_table_n = create_json_table(diff_comb_dec,
row_names=dec_row_names_n,
column_types=DIFF_COLUMN_TYPES)
dec_row_names_n = [x + '_' + str(year_n) for x in DEC_ROW_NAMES]
bin_row_names_n = [x + '_' + str(year_n) for x in BIN_ROW_NAMES]
dist2_bin_table_n = create_json_table(dist2_bin,
row_names=bin_row_names_n,
column_types=PLAN_COLUMN_TYPES)
dist1_bin_table_n = create_json_table(dist1_bin,
row_names=bin_row_names_n,
column_types=PLAN_COLUMN_TYPES)
diff_itax_bin_table_n = create_json_table(diff_itax_bin,
row_names=bin_row_names_n,
column_types=DIFF_COLUMN_TYPES)
diff_ptax_bin_table_n = create_json_table(diff_ptax_bin,
row_names=bin_row_names_n,
column_types=DIFF_COLUMN_TYPES)
diff_comb_bin_table_n = create_json_table(diff_comb_bin,
row_names=bin_row_names_n,
column_types=DIFF_COLUMN_TYPES)
total_row_names_n = [x + '_' + str(year_n) for x in TOTAL_ROW_NAMES]
aggr_d_table_n = create_json_table(aggr_d,
row_names=total_row_names_n)
aggr_d_table_n = dict((k, v[0]) for k, v in aggr_d_table_n.items())
aggr_1_table_n = create_json_table(aggr_1,
row_names=total_row_names_n)
aggr_1_table_n = dict((k, v[0]) for k, v in aggr_1_table_n.items())
aggr_2_table_n = create_json_table(aggr_2,
row_names=total_row_names_n)
aggr_2_table_n = dict((k, v[0]) for k, v in aggr_2_table_n.items())

# return JSON results
return (dist2_dec_table_n,
dist1_dec_table_n,
diff_itax_dec_table_n,
diff_ptax_dec_table_n,
diff_comb_dec_table_n,
dist2_bin_table_n,
dist1_bin_table_n,
diff_itax_bin_table_n,
diff_ptax_bin_table_n,
diff_comb_bin_table_n,
aggr_d_table_n,
aggr_1_table_n,
aggr_2_table_n)
agg_row_names_n = [x + '_' + str(year_n) for x in AGG_ROW_NAMES]
info = dict()
for tbl in summ:
info[tbl] = {'row_names': [], 'col_types': []}
if 'dec' in tbl:
info[tbl]['row_names'] = dec_row_names_n
elif 'bin' in tbl:
info[tbl]['row_names'] = bin_row_names_n
else:
info[tbl]['row_names'] = agg_row_names_n
if 'dist' in tbl:
info[tbl]['col_types'] = DIST_COLUMN_TYPES
elif 'diff' in tbl:
info[tbl]['col_types'] = DIFF_COLUMN_TYPES
res = dict()
for tbl in summ:
if 'aggr' in tbl:
res_table = create_json_table(summ[tbl],
row_names=info[tbl]['row_names'])
res[tbl] = dict((k, v[0]) for k, v in res_table.items())
else:
res[tbl] = create_json_table(summ[tbl],
row_names=info[tbl]['row_names'],
column_types=info[tbl]['col_types'])
return res


def run_nth_year_gdp_elast_model(year_n, start_year,
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