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_EU(self):
"""test with demo data containing France and Germany"""
bbox = [-5, 42, 16, 55]
haz = RelativeCropyield()
haz.set_from_single_run(input_dir=INPUT_DIR,
yearrange=(2001, 2005),
bbox=bbox,
ag_model='lpjml',
cl_model='ipsl-cm5a-lr',
scenario='historical',
soc='2005soc',
co2='co2',
crop='whe',
irr='noirr',
fn_str_var=FN_STR_DEMO)
hist_mean = haz.calc_mean(yearrange_mean=(2001, 2005))
haz.set_rel_yield_to_int(hist_mean)
haz.centroids.set_region_id()
exp = CropProduction()
exp.set_from_single_run(input_dir=INPUT_DIR,
filename=FILENAME_LU,
hist_mean=FILENAME_MEAN,
bbox=bbox,
yearrange=(2001, 2005),
scenario='flexible',
unit='t',
crop='whe',
irr='firr')
exp.set_to_usd(INPUT_DIR)
exp.assign_centroids(haz, threshold=20)
if_cp = ImpactFuncSet()
if_def = IFRelativeCropyield()
if_def.set_relativeyield()
if_cp.append(if_def)
if_cp.check()
impact = Impact()
impact.calc(exp.loc[exp.region_id == 276],
if_cp,
haz.select(['2002']),
save_mat=True)
exp_manual = exp.value.loc[exp.region_id == 276].values
impact_manual = haz.select(event_names=['2002'],
reg_id=276).intensity.multiply(exp_manual)
dif = (impact_manual - impact.imp_mat).data
self.assertEqual(haz.tag.haz_type, 'RC')
self.assertEqual(haz.size, 5)
self.assertEqual(haz.centroids.size, 1092)
self.assertAlmostEqual(haz.intensity.mean(), -2.0489097e-08)
self.assertAlmostEqual(exp.value.max(), 53074789.755290434)
self.assertEqual(exp.latitude.values.size, 1092)
self.assertAlmostEqual(exp.value[3], 0.0)
self.assertAlmostEqual(exp.value[1077], 405026.6857207429)
self.assertAlmostEqual(impact.imp_mat.data[3], -176102.5359452465)
self.assertEqual(len(dif), 0)
def test_EU_nan(self):
"""Test whether setting the zeros in exp.value to NaN changes the impact"""
bbox = [0, 42, 10, 52]
haz = RelativeCropyield()
haz.set_from_isimip_netcdf(input_dir=INPUT_DIR,
yearrange=(2001, 2005),
bbox=bbox,
ag_model='lpjml',
cl_model='ipsl-cm5a-lr',
scenario='historical',
soc='2005soc',
co2='co2',
crop='whe',
irr='noirr',
fn_str_var=FN_STR_DEMO)
hist_mean = haz.calc_mean(yearrange_mean=(2001, 2005))
haz.set_rel_yield_to_int(hist_mean)
haz.centroids.set_region_id()
exp = CropProduction()
exp.set_from_isimip_netcdf(input_dir=INPUT_DIR,
filename=FILENAME_LU,
hist_mean=FILENAME_MEAN,
bbox=bbox,
yearrange=(2001, 2005),
scenario='flexible',
unit='t/y',
crop='whe',
irr='firr')
exp.assign_centroids(haz, threshold=20)
impf_cp = ImpactFuncSet()
impf_def = ImpfRelativeCropyield()
impf_def.set_relativeyield()
impf_cp.append(impf_def)
impf_cp.check()
impact = Impact()
impact.calc(exp, impf_cp, haz, save_mat=True)
exp_nan = CropProduction()
exp_nan.set_from_isimip_netcdf(input_dir=INPUT_DIR,
filename=FILENAME_LU,
hist_mean=FILENAME_MEAN,
bbox=[0, 42, 10, 52],
yearrange=(2001, 2005),
scenario='flexible',
unit='t/y',
crop='whe',
irr='firr')
exp_nan.gdf.value[exp_nan.gdf.value == 0] = np.nan
exp_nan.assign_centroids(haz, threshold=20)
impact_nan = Impact()
impact_nan.calc(exp_nan, impf_cp, haz, save_mat=True)
self.assertListEqual(list(impact.at_event), list(impact_nan.at_event))
self.assertAlmostEqual(12.056545220060798, impact_nan.aai_agg)
self.assertAlmostEqual(12.056545220060798, impact.aai_agg)
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)