def test_store_assets():
assets = Assets()
p = Specification()
dp = DepreciationParams()
calc1 = Calculator(p, dp, assets)
calc1.store_assets()
assert isinstance(calc1, Calculator)
def test_p_param_return_value():
assets = Assets()
p = Specification()
dp = DepreciationParams()
calc1 = Calculator(p, dp, assets)
obj = calc1.p_param('tau_int')
assert np.allclose(obj, np.array([0.31774924]))
def test_p_param_set_value():
assets = Assets()
p = Specification()
calc1 = Calculator(p, assets)
new_tau_int = np.array([0.396])
calc1.p_param('tau_int', new_tau_int)
assert np.allclose(calc1._Calculator__p.tau_int, new_tau_int)
def test_calc_by_asset():
'''
Test calc_by_asset method
'''
assets = Assets()
p = Specification()
calc = Calculator(p, assets)
asset_df = calc.calc_by_asset()
assert ('major_asset_group' in asset_df.keys())
def test_calc_by_industry(include_land, include_inventories):
'''
Test calc_by_industry method
'''
assets = Assets()
p = Specification()
calc = Calculator(p, assets)
ind_df = calc.calc_by_industry(include_land=include_land,
include_inventories=include_inventories)
assert ('major_industry' in ind_df.keys())
def test_calc_by_industry():
'''
Test difference_table method.
'''
yr = 2018
assets = Assets()
p = Specifications(year=yr)
calc1 = Calculator(p, assets)
assert calc1.current_year == yr
asset_df = calc1.calc_by_industry()
assert isinstance(asset_df, pd.DataFrame)
def test_calc_by_asset():
'''
Test calc_by_asset method.
'''
cyr = 2018
assets = Assets()
p = Specification(year=cyr)
calc1 = Calculator(p, assets)
assert calc1.current_year == cyr
asset_df = calc1.calc_by_asset()
assert isinstance(asset_df, pd.DataFrame)
def test_calc_base():
'''
Test calc_base method
'''
assets = Assets()
p = Specification()
calc = Calculator(p, assets)
calc.calc_base()
calc_base_df = calc._Calculator__assets.df
assert ('z_mix' in calc_base_df.keys())
assert ('rho_mix' in calc_base_df.keys())
def test_asset_share_table(include_land, include_inventories):
'''
Test asset_share_table method.
'''
cyr = 2018
assets = Assets()
p = Specification(year=cyr)
calc1 = Calculator(p, assets)
assert calc1.current_year == cyr
asset_df = calc1.asset_share_table(include_land=include_land,
include_inventories=include_inventories)
assert isinstance(asset_df, pd.DataFrame)
def test_bubble_widget():
'''
Test asset bubble plot method.
'''
assets = Assets()
p = Specification()
calc = Calculator(p, assets)
p2 = Specification(year=2026)
p2.update_specification({'CIT_rate': 0.25})
calc2 = Calculator(p2, assets)
fig = calc.bubble_widget(calc2)
assert fig
def test_range_plot():
'''
Test range_plot method.
'''
assets = Assets()
p = Specifications()
calc = Calculator(p, assets)
p2 = Specifications(year=2026)
p2.update_specifications({'CIT_rate': 0.25})
calc2 = Calculator(p2, assets)
fig = calc.range_plot(calc2)
assert fig
fig = calc.range_plot(calc2, output_variable='rho')
assert fig
def test_asset_bubble():
'''
Test asset bubble plot method.
'''
assets = Assets()
p = Specifications()
calc = Calculator(p, assets)
p2 = Specifications(year=2026)
p2.update_specifications({'CIT_rate': 0.25})
calc2 = Calculator(p2, assets)
fig = calc.asset_bubble(calc2)
assert fig
fig = calc.asset_bubble(calc2, output_variable='rho_mix')
assert fig
def test_grouped_bar():
'''
Test grouped_bar method.
'''
assets = Assets()
p = Specifications()
calc = Calculator(p, assets)
p2 = Specifications(year=2026)
p2.update_specifications({'CIT_rate': 0.25})
calc2 = Calculator(p2, assets)
fig = calc.grouped_bar(calc2)
assert fig
fig = calc.grouped_bar(calc2, output_variable='rho', group_by_asset=False)
assert fig
def test_summary_table():
'''
Test summary_table method.
'''
cyr = 2018
assets = Assets()
p = Specification(year=cyr)
calc1 = Calculator(p, assets)
assert calc1.current_year == cyr
p.update_specification({'CIT_rate': 0.38})
calc2 = Calculator(p, assets)
assert calc2.current_year == cyr
summary_df = calc1.summary_table(calc2)
assert isinstance(summary_df, pd.DataFrame)
def test_calc_other():
'''
Test calc_other method
'''
assets = Assets()
p = Specification()
calc = Calculator(p, assets)
df = calc.calc_by_asset()
calc_other_df = calc.calc_other(df)
assert ('ucc_mix' in calc_other_df.keys())
assert ('metr_mix' in calc_other_df.keys())
assert ('mettr_mix' in calc_other_df.keys())
assert ('tax_wedge_mix' in calc_other_df.keys())
assert ('eatr_mix' in calc_other_df.keys())
def run_model(meta_param_dict, adjustment):
'''
Initializes classes from CCC that compute the model under
different policies. Then calls function get output objects.
'''
# update MetaParams
meta_params = MetaParams()
meta_params.adjust(meta_param_dict)
# Get data chosen by user
if meta_params.data_source == "PUF":
data = retrieve_puf(AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)
else:
data = "cps"
# Get TC params adjustments
iit_mods = cs2tc.convert_policy_adjustment(
adjustment["Individual and Payroll Tax Parameters"])
filtered_ccc_params = {}
# filter out CCC params that will not change between baeline and
# reform runs (These are the Household Savings Behavior and
# Economic Assumptions)
constant_param_list = [
'omega_scg', 'omega_lcg', 'omega_xcg', 'alpha_c_e_ft', 'alpha_c_e_td',
'alpha_c_e_nt', 'alpha_c_d_ft', 'alpha_c_d_td', 'alpha_c_d_nt',
'alpha_nc_d_ft', 'alpha_nc_d_td', 'alpha_nc_d_nt', 'alpha_h_d_ft',
'alpha_h_d_td', 'alpha_h_d_nt', 'Y_td', 'Y_scg', 'Y_lcg', 'gamma',
'E_c', 'inflation_rate', 'nominal_interest_rate'
]
filtered_ccc_params = OrderedDict()
for k, v in adjustment['Business Tax Parameters'].items():
if k in constant_param_list:
filtered_ccc_params[k] = v
# Baseline CCC calculator
params = Specification(year=meta_params.year,
call_tc=False,
iit_reform={},
data=data)
params.update_specification(filtered_ccc_params)
assets = Assets()
dp = DepreciationParams()
calc1 = Calculator(params, dp, assets)
# Reform CCC calculator - includes TC adjustments
params2 = Specification(year=meta_params.year,
call_tc=True,
iit_reform=iit_mods,
data=data)
params2.update_specification(adjustment["Business Tax Parameters"])
calc2 = Calculator(params2, dp, assets)
comp_dict = comp_output(calc1, calc2)
return comp_dict
def test_summary_table():
'''
Test difference_table method.
'''
yr = 2018
assets = Assets()
p = Specifications(year=yr)
calc1 = Calculator(p, assets)
assert calc1.current_year == yr
reform = {'CIT_rate': 0.38}
p.update_specifications(reform)
calc2 = Calculator(p, assets)
assert calc2.current_year == yr
summary_df = calc1.summary_table(calc2)
assert isinstance(summary_df, pd.DataFrame)
def test_Caculator_exception1():
'''
Raise exception for not passing parameters object
'''
assets = Assets()
with pytest.raises(Exception):
assert Calculator(assets=assets)
def test_calc_all():
'''
Test calc_all method
'''
assets = Assets()
p = Specification()
calc = Calculator(p, assets)
calc.calc_all()
calc_all_df = calc._Calculator__assets.df
assert ('z_mix' in calc_all_df.keys())
assert ('rho_mix' in calc_all_df.keys())
assert ('ucc_mix' in calc_all_df.keys())
assert ('metr_mix' in calc_all_df.keys())
assert ('mettr_mix' in calc_all_df.keys())
assert ('tax_wedge_mix' in calc_all_df.keys())
assert ('eatr_mix' in calc_all_df.keys())
def test_Caculator_exception2():
'''
Raise exception for not passing assets object
'''
p = Specification()
with pytest.raises(Exception):
assert Calculator(p=p)
def test_Calculator_exception2():
'''
Raise exception for not passing depreciation parameters object
'''
p = Specification()
assets = Assets()
with pytest.raises(Exception):
assert Calculator(p=p, assets=assets)
def test_asset_bubble():
'''
Test asset bubble plot method.
'''
assets = Assets()
p = Specification()
dp = DepreciationParams()
calc = Calculator(p, dp, assets)
p2 = Specification(year=2026)
p2.update_specification({'CIT_rate': 0.25})
calc2 = Calculator(p2, dp, assets)
fig = calc.asset_bubble(calc2, include_title=True)
assert fig
fig = calc.asset_bubble(calc2,
output_variable='rho_mix',
include_title=True)
assert fig
def test_Calculator_exception3():
'''
Raise exception for not passing assets object
'''
p = Specification()
dp = DepreciationParams()
with pytest.raises(Exception):
assert Calculator(p=p, dp=dp)
def test_range_plot(corporate):
'''
Test range_plot method.
'''
assets = Assets()
p = Specification()
calc = Calculator(p, assets)
p2 = Specification(year=2026)
p2.update_specification({'CIT_rate': 0.25})
calc2 = Calculator(p2, assets)
fig = calc.range_plot(calc2, corporate=corporate, include_title=True)
assert fig
fig = calc.range_plot(calc2,
output_variable='rho',
corporate=corporate,
include_title=True)
assert fig
def test_industry_summary_table(include_land, include_inventories):
'''
Test industry_summary_table method.
'''
cyr = 2018
assets = Assets()
p = Specification(year=cyr)
calc1 = Calculator(p, assets)
assert calc1.current_year == cyr
p.update_specification({'CIT_rate': 0.38})
calc2 = Calculator(p, assets)
assert calc2.current_year == cyr
ind_df = calc1.industry_summary_table(
calc2,
include_land=include_land,
include_inventories=include_inventories)
assert isinstance(ind_df, pd.DataFrame)
def test_Calculator_exception1():
'''
Raise exception for not passing parameters object
'''
assets = Assets()
dp = DepreciationParams()
with pytest.raises(Exception):
assert Calculator(dp=dp, assets=assets)
def test_grouped_bar(corporate):
'''
Test grouped_bar method.
'''
assets = Assets()
p = Specification()
dp = DepreciationParams()
calc = Calculator(p, dp, assets)
p2 = Specification(year=2026)
p2.update_specification({'CIT_rate': 0.25})
calc2 = Calculator(p2, dp, assets)
fig = calc.grouped_bar(calc2, corporate=corporate)
assert fig
fig = calc.grouped_bar(calc2,
output_variable='rho',
corporate=corporate,
group_by_asset=False)
assert fig
def run_model(meta_param_dict, adjustment):
'''
Initiliazes classes from CCC that compute the model under
different policies. Then calls function get output objects.
'''
meta_params = MetaParams()
meta_params.adjust(meta_param_dict)
if meta_params.data_source == "PUF":
data = retrieve_puf(AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)
else:
data = "cps"
params = Specification(year=meta_params.year, call_tc=True, data=data)
params.adjust(adjustment["ccc"])
assets = Assets()
calc1 = Calculator(params, assets)
params2 = Specification(year=meta_params.year)
calc2 = Calculator(params2, assets)
comp_dict = comp_output(calc1, calc2)
return comp_dict
def test_restore_assets():
assets = Assets()
p = Specification()
calc1 = Calculator(p, assets)
calc1.store_assets()
calc1.restore_assets()
assert isinstance(calc1, Calculator)
def test_calc_by_methods():
"""
Test the Calculator calc_by_asset and calc_by_industry methods by
comparing actual and expect dataframes
"""
# execute Calculator calc_by methods to get actual results
p = Specification()
dp = DepreciationParams()
assets = Assets()
calc = Calculator(p, dp, assets)
actual_by_asset = calc.calc_by_asset()
actual_by_industry = calc.calc_by_industry()
# load expected results from the calc_by_ methods
expect_by_asset = pd.read_json(
os.path.join(TDIR, 'run_ccc_asset_output.json'))
expect_by_industry = pd.read_json(
os.path.join(TDIR, 'run_ccc_industry_output.json'))
# compare the actual and expect DataFrames
for actual_df, expect_df in zip([actual_by_asset, actual_by_industry],
[expect_by_asset, expect_by_industry]):
actual_df.sort_index(inplace=True)
actual_df.reset_index(inplace=True)
expect_df.sort_index(inplace=True)
expect_df.reset_index(inplace=True)
assert tuple(actual_df.columns) == tuple(expect_df.columns)
for col in expect_df.columns:
try:
example = getattr(actual_df, col).iloc[0]
can_diff = isinstance(example, numbers.Number)
if can_diff:
assert np.allclose(actual_df[col].values,
expect_df[col].values,
atol=1e-5)
else:
pass
except AttributeError:
pass
import pandas as pd
import numpy as np
import numbers
import subprocess
from pandas.testing import assert_frame_equal
from ccc.parameters import Specifications
from ccc.calculator import Calculator
from ccc.data import Assets
CUR_PATH = os.path.abspath(os.path.dirname(__file__))
# Load input values
p = Specifications()
assets = Assets()
p = Specifications()
calc1 = Calculator(p, assets)
# Compute results
test_by_asset = calc1.calc_by_asset()
test_by_industry = calc1.calc_by_industry()
# Load expected results
result_by_asset = pd.read_json(os.path.join(
CUR_PATH, 'run_ccc_asset_output.json'))
result_by_industry = pd.read_json(os.path.join(
CUR_PATH, 'run_ccc_industry_output.json'))
@pytest.mark.parametrize('test_df,expected_df',
[(test_by_asset, result_by_asset),
(test_by_industry, result_by_industry)],
ids=['by assset', 'by industry'])
def test_run_ccc(test_df, expected_df):
from ccc.calculator import Calculator
# Read in a reform to compare against
# Note that TCJA is current law baseline in TC 0.16+
# Thus to compare TCJA to 2017 law, we'll use 2017 law as the reform
reform_url = ('https://raw.githubusercontent.com/'
'PSLmodels/Tax-Calculator/master/taxcalc/'
'reforms/2017_law.json')
ref = taxcalc.Calculator.read_json_param_objects(reform_url, None)
iit_reform = ref['policy']
# Initialize Asset and Calculator Objects
assets = Assets()
# Baseline
baseline_parameters = Specifications(year=2018, call_tc=True, iit_reform={})
calc1 = Calculator(baseline_parameters, assets)
# Reform
reform_parameters = Specifications(year=2018, call_tc=True,
iit_reform=iit_reform)
business_tax_adjustments = {
'CIT_rate': 0.35, 'BonusDeprec_3yr': 0.50, 'BonusDeprec_5yr': 0.50,
'BonusDeprec_7yr': 0.50, 'BonusDeprec_10yr': 0.50,
'BonusDeprec_15yr': 0.50, 'BonusDeprec_20yr': 0.50}
reform_parameters.update_specifications(business_tax_adjustments)
calc2 = Calculator(reform_parameters, assets)
# Do calculations by asset
base_assets_df = calc1.calc_by_asset()
reform_assets_df = calc2.calc_by_asset()
# Do Calculations by Industry
base_industry_df = calc1.calc_by_industry()