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Feature/move unittest #732

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Jun 13, 2023
Merged

Feature/move unittest #732

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cddfabd
move unittest to integration
NicolasColombi Jun 2, 2023
208a076
Revert "move unittest to integration"
NicolasColombi Jun 2, 2023
2205d2c
Merge branch 'develop' into feature/move_unittest
NicolasColombi Jun 2, 2023
71b0f85
Revert "Revert "move unittest to integration""
peanutfun Jun 13, 2023
744c8a4
Format climada/test/test_engine.py with black
peanutfun Jun 13, 2023
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45 changes: 0 additions & 45 deletions climada/engine/test/test_impact_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -98,18 +98,6 @@ def test_emdat_df_2020_load(self):
self.assertFalse(False in list(df['Disaster Subtype'] == 'Tropical cyclone'))
self.assertFalse('Flood' in list(df['Disaster Subtype']))

class TestGDPScaling(unittest.TestCase):
"""test scaling of impact values proportional to GDP"""
def test_scale_impact2refyear(self):
"""scale of impact values proportional to GDP"""
impact_scaled = im_d.scale_impact2refyear([10, 100, 1000, 100, 100],
[1999, 2005, 2015, 2000, 2000],
['CZE', 'CZE', 'MEX', 'MEX', 'CZE'],
reference_year=2015)
# scaled impact value might change if worldbank input data changes,
# check magnitude and adjust if test fails in the following line:
self.assertListEqual(impact_scaled, [28, 137, 1000, 165, 304])

class TestEmdatProcessing(unittest.TestCase):
def test_emdat_impact_event_2018(self):
"""test emdat_impact_event event impact data extraction, version 2018"""
Expand Down Expand Up @@ -183,20 +171,6 @@ def test_emdat_affected_yearlysum(self):
self.assertIn('USA', list(df['ISO']))
self.assertIn('BGD', list(df['ISO']))

def test_emdat_damage_yearlysum(self):
"""test emdat_impact_yearlysum yearly impact data extraction with scaling"""
df = im_d.emdat_impact_yearlysum(EMDAT_TEST_CSV, countries=['Bangladesh', 'USA'],
hazard='Flood', year_range=(2015, 2017),
reference_year=2000)

self.assertEqual(36, df.size)
self.assertAlmostEqual(df.impact.max(), 15150000000.0)
self.assertEqual(df.impact_scaled.min(), 10943000.0)
self.assertEqual(df["year"][5], 2017)
self.assertEqual(df["reference_year"].max(), 2000)
self.assertIn('USA', list(df['ISO']))
self.assertIn(50, list(df['region_id']))

def test_emdat_countries_by_hazard_2020_pass(self):
"""test to get list of countries impacted by tropical cyclones from 2000 to 2019"""
iso3_codes, country_names = im_d.emdat_countries_by_hazard(EMDAT_2020_CSV_DEMO,
Expand Down Expand Up @@ -241,24 +215,6 @@ def test_emdat_to_impact_all_countries_pass(self):
self.assertIn('SPI', countries2020)
self.assertNotIn('SPI', countries)

def test_emdat_to_impact_scale(self):
"""test import DR EM-DAT to Impact() for 1 country and ref.year (scaling)"""
impact_emdat = im_d.emdat_to_impact(EMDAT_TEST_CSV, 'DR',
year_range=[2010, 2016], countries=['USA'],
hazard_type_emdat='Drought',
reference_year=2016)[0]
self.assertEqual(5, impact_emdat.event_id.size)
self.assertEqual(4, impact_emdat.event_id[-1])
self.assertEqual(0, impact_emdat.event_id[0])
self.assertIn('2012-9235', impact_emdat.event_name)
self.assertEqual(1, len(impact_emdat.eai_exp))
self.assertAlmostEqual(impact_emdat.aai_agg, impact_emdat.eai_exp[0])
self.assertAlmostEqual(0.14285714, np.unique(impact_emdat.frequency)[0], places=3)
# scaled impact value might change if worldbank input data changes,
# check magnitude and adjust if test failes in the following 2 lines:
self.assertAlmostEqual(3.69, np.sum(impact_emdat.at_event * 1e-10), places=0)
self.assertAlmostEqual(5.28, impact_emdat.aai_agg * 1e-9, places=0)

def test_emdat_to_impact_fakedata(self):
"""test import TC EM-DAT to Impact() for all countries in CSV"""
impact_emdat, countries = im_d.emdat_to_impact(EMDAT_TEST_CSV_FAKE, 'FL',
Expand Down Expand Up @@ -301,7 +257,6 @@ def test_emdat_to_impact_2020format(self):
# Execute Tests
if __name__ == "__main__":
TESTS = unittest.TestLoader().loadTestsFromTestCase(TestEmdatImport)
TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestGDPScaling))
TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestEmdatProcessing))
TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestEmdatToImpact))
unittest.TextTestRunner(verbosity=2).run(TESTS)
114 changes: 2 additions & 112 deletions climada/engine/unsequa/test/test_unsequa.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,11 +37,10 @@
from climada.hazard import Hazard
from climada.engine.unsequa import InputVar, CalcImpact, UncOutput, CalcCostBenefit

from climada.util.constants import EXP_DEMO_H5, HAZ_DEMO_H5, ENT_DEMO_TODAY, ENT_DEMO_FUTURE
from climada.util.constants import TEST_UNC_OUTPUT_IMPACT, TEST_UNC_OUTPUT_COSTBEN
from climada.util.constants import (EXP_DEMO_H5, HAZ_DEMO_H5, ENT_DEMO_TODAY, ENT_DEMO_FUTURE,
TEST_UNC_OUTPUT_IMPACT, TEST_UNC_OUTPUT_COSTBEN)
from climada.util.api_client import Client


apiclient = Client()
ds = apiclient.get_dataset_info(name=TEST_UNC_OUTPUT_IMPACT, status='test_dataset')
_target_dir, [test_unc_output_impact] = apiclient.download_dataset(ds)
Expand Down Expand Up @@ -283,24 +282,6 @@ def test_plot_unc_imp(self):
self.assertIsNotNone(plt_map)
plt.close()

def test_plot_unc_cb(self):
"""Test all cost benefit plots"""
unc_output = UncOutput.from_hdf5(test_unc_output_costben)
plt_s = unc_output.plot_sample()
self.assertIsNotNone(plt_s)
plt.close()
plt_u = unc_output.plot_uncertainty()
self.assertIsNotNone(plt_u)
plt.close()
with self.assertRaises(ValueError):
unc_output.plot_rp_uncertainty()
plt_sens = unc_output.plot_sensitivity()
self.assertIsNotNone(plt_sens)
plt.close()
plt_sens_2 = unc_output.plot_sensitivity_second_order(salib_si='S1')
self.assertIsNotNone(plt_sens_2)
plt.close()

def test_save_load_pass(self):
"""Test save and load output data"""

Expand Down Expand Up @@ -680,58 +661,6 @@ def test_make_sample_pass(self):
np.array(['x_haz', 'EN', 'IFi', 'CO', 'EG', 'PAA', 'MDD'])
)


def test_calc_uncertainty_pass(self):
"""Test compute the uncertainty distribution for an impact"""

ent_iv, ent_fut_iv = make_costben_iv()
_, _, haz_iv = make_input_vars()
unc_calc = CalcCostBenefit(haz_iv, ent_iv)
unc_data = unc_calc.make_sample( N=2)
unc_data = unc_calc.uncertainty(unc_data)

self.assertEqual(unc_data.unit, ent_dem().exposures.value_unit)

self.assertEqual(
unc_data.tot_climate_risk_unc_df.size,
unc_data.n_samples
)
self.assertEqual(
unc_data.cost_ben_ratio_unc_df.size,
unc_data.n_samples * 4 #number of measures
)
self.assertEqual(
unc_data.imp_meas_present_unc_df.size,
0
)
self.assertEqual(
unc_data.imp_meas_future_unc_df.size,
unc_data.n_samples * 4 * 5 #All measures 4 and risks/benefits 5
)

unc_calc = CalcCostBenefit(haz_iv, ent_iv, haz_iv, ent_fut_iv)
unc_data = unc_calc.make_sample( N=2)
unc_data = unc_calc.uncertainty(unc_data)

self.assertEqual(unc_data.unit, ent_dem().exposures.value_unit)

self.assertEqual(
unc_data.tot_climate_risk_unc_df.size,
unc_data.n_samples
)
self.assertEqual(
unc_data.cost_ben_ratio_unc_df.size,
unc_data.n_samples * 4 #number of measures
)
self.assertEqual(
unc_data.imp_meas_present_unc_df.size,
unc_data.n_samples * 4 * 5 #All measures 4 and risks/benefits 5
)
self.assertEqual(
unc_data.imp_meas_future_unc_df.size,
unc_data.n_samples * 4 * 5 #All measures 4 and risks/benefits 5
)

def test_calc_uncertainty_pool_pass(self):
"""Test compute the uncertainty distribution for an impact"""

Expand Down Expand Up @@ -767,45 +696,6 @@ def test_calc_uncertainty_pool_pass(self):
unc_data.n_samples * 4 * 5 #All measures 4 and risks/benefits 5
)

def test_calc_sensitivity_pass(self):
"""Test compute sensitivity default"""

ent_iv, _ = make_costben_iv()
_, _, haz_iv = make_input_vars()
unc_calc = CalcCostBenefit(haz_iv, ent_iv)
unc_data = unc_calc.make_sample(N=4, sampling_kwargs={'calc_second_order': True})
unc_data = unc_calc.uncertainty(unc_data)

unc_data = unc_calc.sensitivity(
unc_data,
sensitivity_kwargs = {'calc_second_order': True}
)

self.assertEqual(unc_data.sensitivity_method, 'sobol')
self.assertTupleEqual(unc_data.sensitivity_kwargs,
tuple({'calc_second_order': 'True'}.items())
)

for name, attr in unc_data.__dict__.items():
if 'sens_df' in name:
if 'imp_meas_present' in name:
self.assertTrue(attr.empty)
else:
np.testing.assert_array_equal(
attr.param.unique(),
np.array(['x_haz', 'EN', 'IFi', 'CO'])
)

np.testing.assert_array_equal(
attr.si.unique(),
np.array(['S1', 'S1_conf', 'ST', 'ST_conf', 'S2', 'S2_conf'])
)

self.assertEqual(len(attr),
len(unc_data.param_labels) * (4 + 4 + 4)
)


if __name__ == "__main__":
TESTS = unittest.TestLoader().loadTestsFromTestCase(TestInputVar)
TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestOutput))
Expand Down
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