def _change_all_exposures(self, exposures):
"""Change exposures to provided exposures_set.
Parameters:
exposures (Exposures): exposures instance
Returns:
Exposures
"""
if isinstance(self.exposures_set,
str) and self.exposures_set == NULL_STR:
return exposures
if isinstance(self.exposures_set, str):
LOGGER.debug('Setting new exposures %s', self.exposures_set)
new_exp = Exposures()
new_exp.read_hdf5(self.exposures_set)
new_exp.check()
elif isinstance(self.exposures_set, Exposures):
LOGGER.debug('Setting new exposures. ')
new_exp = copy.deepcopy(self.exposures_set)
new_exp.check()
else:
LOGGER.error('Wrong input exposures.')
raise ValueError
if not np.array_equal(np.unique(exposures.latitude.values),
np.unique(new_exp.latitude.values)) or \
not np.array_equal(np.unique(exposures.longitude.values),
np.unique(new_exp.longitude.values)):
LOGGER.warning('Exposures locations have changed.')
return new_exp
def test_calc_sector_total_impact(self):
"""Test running total impact calculations."""
sup = SupplyChain()
sup.read_wiod16(year='test',
range_rows=(5, 117),
range_cols=(4, 116),
col_iso3=2,
col_sectors=1)
# Tropical cyclone over Florida and Caribbean
hazard = Hazard('TC')
hazard.read_mat(HAZ_TEST_MAT)
# Read demo entity values
# Set the entity default file to the demo one
exp = Exposures()
exp.read_hdf5(EXP_DEMO_H5)
exp.check()
exp.gdf.region_id = 840 #assign right id for USA
exp.assign_centroids(hazard)
impf_tc = IFTropCyclone()
impf_tc.set_emanuel_usa()
impf_set = ImpactFuncSet()
impf_set.append(impf_tc)
impf_set.check()
sup.calc_sector_direct_impact(hazard, exp, impf_set)
sup.calc_indirect_impact(io_approach='ghosh')
sup.calc_total_impact()
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.total_impact.shape)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.total_aai_agg.shape)
def test_change_exposures_if_pass(self):
"""Test _change_exposures_if"""
meas = Measure()
meas.imp_fun_map = '1to3'
meas.haz_type = 'TC'
imp_set = ImpactFuncSet()
imp_tc = ImpactFunc()
imp_tc.haz_type = 'TC'
imp_tc.id = 1
imp_tc.intensity = np.arange(10, 100, 10)
imp_tc.mdd = np.arange(10, 100, 10)
imp_tc.paa = np.arange(10, 100, 10)
imp_set.append(imp_tc)
imp_tc = ImpactFunc()
imp_tc.haz_type = 'TC'
imp_tc.id = 3
imp_tc.intensity = np.arange(10, 100, 10)
imp_tc.mdd = np.arange(10, 100, 10) * 2
imp_tc.paa = np.arange(10, 100, 10) * 2
exp = Exposures()
exp.read_hdf5(EXP_DEMO_H5)
new_exp = meas._change_exposures_if(exp)
self.assertEqual(new_exp.ref_year, exp.ref_year)
self.assertEqual(new_exp.value_unit, exp.value_unit)
self.assertEqual(new_exp.tag.file_name, exp.tag.file_name)
self.assertEqual(new_exp.tag.description, exp.tag.description)
self.assertTrue(np.array_equal(new_exp.gdf.value.values, exp.gdf.value.values))
self.assertTrue(np.array_equal(new_exp.gdf.latitude.values, exp.gdf.latitude.values))
self.assertTrue(np.array_equal(new_exp.gdf.longitude.values, exp.gdf.longitude.values))
self.assertTrue(np.array_equal(exp.gdf[INDICATOR_IF + 'TC'].values, np.ones(new_exp.gdf.shape[0])))
self.assertTrue(np.array_equal(new_exp.gdf[INDICATOR_IF + 'TC'].values, np.ones(new_exp.gdf.shape[0]) * 3))
def test_change_all_exposures_pass(self):
"""Test _change_all_exposures method"""
meas = Measure()
meas.exposures_set = EXP_DEMO_H5
ref_exp = Exposures()
ref_exp.read_hdf5(EXP_DEMO_H5)
exposures = Exposures()
exposures.gdf['latitude'] = np.ones(10)
exposures.gdf['longitude'] = np.ones(10)
new_exp = meas._change_all_exposures(exposures)
self.assertEqual(new_exp.ref_year, ref_exp.ref_year)
self.assertEqual(new_exp.value_unit, ref_exp.value_unit)
self.assertEqual(new_exp.tag.file_name, ref_exp.tag.file_name)
self.assertEqual(new_exp.tag.description, ref_exp.tag.description)
self.assertTrue(
np.array_equal(new_exp.gdf.value.values, ref_exp.gdf.value.values))
self.assertTrue(
np.array_equal(new_exp.gdf.latitude.values,
ref_exp.gdf.latitude.values))
self.assertTrue(
np.array_equal(new_exp.gdf.longitude.values,
ref_exp.gdf.longitude.values))
def test_io_hdf5_pass(self):
"""write and read hdf5"""
exp_df = Exposures(pd.read_excel(ENT_TEMPLATE_XLS))
exp_df.set_geometry_points()
exp_df.check()
# set metadata
exp_df.ref_year = 2020
exp_df.tag = Tag(ENT_TEMPLATE_XLS, 'ENT_TEMPLATE_XLS')
exp_df.value_unit = 'XSD'
file_name = DATA_DIR.joinpath('test_hdf5_exp.h5')
exp_df.write_hdf5(file_name)
exp_read = Exposures()
exp_read.read_hdf5(file_name)
self.assertEqual(exp_df.ref_year, exp_read.ref_year)
self.assertEqual(exp_df.value_unit, exp_read.value_unit)
self.assertEqual(exp_df.crs, exp_read.crs)
self.assertEqual(exp_df.tag.file_name, exp_read.tag.file_name)
self.assertEqual(exp_df.tag.description, exp_read.tag.description)
self.assertTrue(
np.array_equal(exp_df.gdf.latitude.values,
exp_read.gdf.latitude.values))
self.assertTrue(
np.array_equal(exp_df.gdf.longitude.values,
exp_read.gdf.longitude.values))
self.assertTrue(
np.array_equal(exp_df.gdf.value.values, exp_read.gdf.value.values))
self.assertTrue(
np.array_equal(exp_df.gdf.deductible.values,
exp_read.gdf.deductible.values))
self.assertTrue(
np.array_equal(exp_df.gdf.cover.values, exp_read.gdf.cover.values))
self.assertTrue(
np.array_equal(exp_df.gdf.region_id.values,
exp_read.gdf.region_id.values))
self.assertTrue(
np.array_equal(exp_df.gdf.category_id.values,
exp_read.gdf.category_id.values))
self.assertTrue(
np.array_equal(exp_df.gdf.if_TC.values, exp_read.gdf.if_TC.values))
self.assertTrue(
np.array_equal(exp_df.gdf.centr_TC.values,
exp_read.gdf.centr_TC.values))
self.assertTrue(
np.array_equal(exp_df.gdf.if_FL.values, exp_read.gdf.if_FL.values))
self.assertTrue(
np.array_equal(exp_df.gdf.centr_FL.values,
exp_read.gdf.centr_FL.values))
for point_df, point_read in zip(exp_df.gdf.geometry.values,
exp_read.gdf.geometry.values):
self.assertEqual(point_df.x, point_read.x)
self.assertEqual(point_df.y, point_read.y)
def _change_all_exposures(self, exposures):
"""
Change exposures to provided exposures_set.
Parameters
----------
exposures : climada.entity.Exposures
exposures instance
Returns
-------
new_exp : climada.entity.Exposures()
Exposures
"""
if isinstance(self.exposures_set,
str) and self.exposures_set == NULL_STR:
return exposures
if isinstance(self.exposures_set, (str, Path)):
LOGGER.debug('Setting new exposures %s', self.exposures_set)
new_exp = Exposures()
new_exp.read_hdf5(self.exposures_set)
new_exp.check()
elif isinstance(self.exposures_set, Exposures):
LOGGER.debug('Setting new exposures. ')
new_exp = self.exposures_set.copy(deep=True)
new_exp.check()
else:
raise ValueError(
f'{self.exposures_set} is neither a string nor an Exposures object'
)
if not np.array_equal(np.unique(exposures.gdf.latitude.values),
np.unique(new_exp.gdf.latitude.values)) or \
not np.array_equal(np.unique(exposures.gdf.longitude.values),
np.unique(new_exp.gdf.longitude.values)):
LOGGER.warning('Exposures locations have changed.')
return new_exp
def _change_all_exposures(self, exposures):
"""Change exposures to provided exposures_set.
Parameters:
exposures (Exposures): exposures instance
Returns:
Exposures
"""
if self.exposures_set == NULL_STR:
return exposures
LOGGER.debug('Setting new exposures %s', self.exposures_set)
new_exp = Exposures()
new_exp.read_hdf5(self.exposures_set)
new_exp.check()
if exposures.shape[0] != new_exp.shape[0] or \
not np.array_equal(exposures.latitude.values, new_exp.latitude.values) or \
not np.array_equal(exposures.longitude.values, new_exp.longitude.values):
LOGGER.warning('Exposures locations have changed.')
return new_exp
def calc_sector_direct_impact(self):
"""Test running direct impact calculations."""
sup = SupplyChain()
sup.read_wiod16(year='test',
range_rows=(5, 117),
range_cols=(4, 116),
col_iso3=2,
col_sectors=1)
# Tropical cyclone over Florida and Caribbean
hazard = Hazard('TC')
hazard.read_mat(HAZ_TEST_MAT)
# Read demo entity values
# Set the entity default file to the demo one
exp = Exposures()
exp.read_hdf5(EXP_DEMO_H5)
exp.check()
exp.gdf.region_id = 840 #assign right id for USA
exp.assign_centroids(hazard)
impf_tc = IFTropCyclone()
impf_tc.set_emanuel_usa()
impf_set = ImpactFuncSet()
impf_set.append(impf_tc)
impf_set.check()
subsecs = list(range(10)) + list(range(15, 25))
sup.calc_sector_direct_impact(hazard,
exp,
impf_set,
selected_subsec=subsecs)
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.direct_impact.shape)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.direct_aai_agg.shape)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual(sup.reg_dir_imp[0], 'USA')
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, subsecs].sum(),
places=3)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[subsecs].sum(),
places=3)
sup.calc_sector_direct_impact(hazard,
exp,
impf_set,
selected_subsec='manufacturing')
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.direct_impact.shape)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.direct_aai_agg.shape)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual(sup.reg_dir_imp[0], 'USA')
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, range(4, 23)].sum(),
places=3)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[range(4, 23)].sum(),
places=3)
sup.calc_sector_direct_impact(hazard,
exp,
impf_set,
selected_subsec='agriculture')
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.direct_impact.shape)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.direct_aai_agg.shape)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, range(0, 1)].sum(),
places=3)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[range(0, 1)].sum(),
places=3)
sup.calc_sector_direct_impact(hazard,
exp,
impf_set,
selected_subsec='mining')
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.direct_impact.shape)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.direct_aai_agg.shape)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, range(3, 4)].sum(),
places=3)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[range(3, 4)].sum(),
places=3)
sup.calc_sector_direct_impact(hazard,
exp,
impf_set,
selected_subsec='service')
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.direct_impact.shape)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.direct_aai_agg.shape)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, range(26, 56)].sum(),
places=3)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[range(26, 56)].sum(),
places=3)
