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_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
'BonusDeprec_15yr': 0.50,
'BonusDeprec_20yr': 0.50
}
# specify baseline and reform Calculator objects for 2019 calculations
assets = Assets()
baseline_parameters = Specification(year=cyr)
calc1 = Calculator(baseline_parameters, assets)
reform_parameters = Specification(year=cyr)
reform_parameters.update_specification(business_tax_reform)
calc2 = Calculator(reform_parameters, assets)
# do calculations by asset and by industry
baseln_assets_df = calc1.calc_by_asset()
reform_assets_df = calc2.calc_by_asset()
baseln_industry_df = calc1.calc_by_industry()
reform_industry_df = calc2.calc_by_industry()
# generate dataframes with reform-minus-baseline differences
diff_assets_df = diff_two_tables(reform_assets_df, baseln_assets_df)
diff_industry_df = diff_two_tables(reform_industry_df, baseln_industry_df)
# save dataframes to disk as csv files in this directory
baseln_industry_df.to_csv('baseline_byindustry.csv', float_format='%.5f')
reform_industry_df.to_csv('reform_byindustry.csv', float_format='%.5f')
baseln_assets_df.to_csv('baseline_byasset.csv', float_format='%.5f')
reform_assets_df.to_csv('reform_byasset.csv', float_format='%.5f')
diff_industry_df.to_csv('changed_byindustry.csv', float_format='%.5f')
diff_assets_df.to_csv('changed_byasset.csv', float_format='%.5f')
# create and show in browser a range plot
def test_example_output():
"""
Test that can produce expected output from code in ../../example.py script
"""
# execute code as found in ../../example.py script
cyr = 2019
# ... specify baseline and reform Calculator objects
assets = Assets()
dp = DepreciationParams()
baseline_parameters = Specification(year=cyr)
calc1 = Calculator(baseline_parameters, dp, assets)
reform_parameters = Specification(year=cyr)
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_specification(business_tax_adjustments)
calc2 = Calculator(reform_parameters, dp, assets)
# ... calculation by asset and by industry
baseline_assets_df = calc1.calc_by_asset()
reform_assets_df = calc2.calc_by_asset()
baseline_industry_df = calc1.calc_by_industry()
reform_industry_df = calc2.calc_by_industry()
diff_assets_df = ccc.utils.diff_two_tables(reform_assets_df,
baseline_assets_df)
diff_industry_df = ccc.utils.diff_two_tables(reform_industry_df,
baseline_industry_df)
# ... save calculated results as csv files in ccc/test directory
baseline_industry_df.to_csv(os.path.join(TDIR, 'baseline_byindustry.csv'),
float_format='%.5f')
reform_industry_df.to_csv(os.path.join(TDIR, 'reform_byindustry.csv'),
float_format='%.5f')
baseline_assets_df.to_csv(os.path.join(TDIR, 'baseline_byasset.csv'),
float_format='%.5f')
reform_assets_df.to_csv(os.path.join(TDIR, 'reform_byasset.csv'),
float_format='%.5f')
diff_industry_df.to_csv(os.path.join(TDIR, 'changed_byindustry.csv'),
float_format='%.5f')
diff_assets_df.to_csv(os.path.join(TDIR, 'changed_byasset.csv'),
float_format='%.5f')
# compare actual calculated results to expected results
failmsg = ''
expect_output_dir = os.path.join(TDIR, '..', '..', 'example_output')
for fname in [
'baseline_byasset', 'baseline_byindustry', 'reform_byasset',
'reform_byindustry', 'changed_byasset', 'changed_byindustry'
]:
actual_path = os.path.join(TDIR, fname + '.csv')
actual_df = pd.read_csv(actual_path)
expect_path = os.path.join(expect_output_dir, fname + '_expected.csv')
expect_df = pd.read_csv(expect_path)
try:
assert_frame_equal(actual_df, expect_df)
# cleanup actual results if it has same contents as expected file
os.remove(actual_path)
except AssertionError:
failmsg += 'ACTUAL-vs-EXPECT DIFFERENCES FOR {}\n'.format(fname)
if failmsg:
raise AssertionError('\n' + failmsg)
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()
reform_industry_df = calc2.calc_by_industry()
# Generate dataframes with differences
diff_assets_df = ccc.utils.diff_two_tables(
reform_assets_df, base_assets_df)
diff_industry_df = ccc.utils.diff_two_tables(
reform_industry_df, base_industry_df)
# Save dataframes to disk as csv files
base_industry_df.to_csv('baseline_byindustry.csv', encoding='utf-8')
reform_industry_df.to_csv('reform_byindustry.csv', encoding='utf-8')
base_assets_df.to_csv('baseline_byasset.csv', encoding='utf-8')
reform_assets_df.to_csv('reform_byasset.csv', encoding='utf-8')
diff_industry_df.to_csv('changed_byindustry.csv', encoding='utf-8')
diff_assets_df.to_csv('changed_byasset.csv', encoding='utf-8')
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):
# Test the run_ccc.run_ccc() function by reading in inputs and
# confirming that output variables have the same values.
test_df.sort_index(inplace=True)
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()
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):
# Test the run_ccc.run_ccc() function by reading in inputs and
# confirming that output variables have the same values.
test_df.sort_index(inplace=True)