def test_Forecast_calc_properties(self):
"""Test calc and propety functions from the Forecast class"""
#hazard
haz = StormEurope()
haz.read_cosmoe_file(
HAZ_DIR.joinpath('storm_europe_cosmoe_forecast_vmax_testfile.nc'),
run_datetime=dt.datetime(2018, 1, 1),
event_date=dt.datetime(2018, 1, 3))
#exposure
data = {}
data['latitude'] = haz.centroids.lat
data['longitude'] = haz.centroids.lon
data['value'] = np.ones_like(data['latitude']) * 100000
data['deductible'] = np.zeros_like(data['latitude'])
data[INDICATOR_IMPF + 'WS'] = np.ones_like(data['latitude'])
data['region_id'] = np.ones_like(data['latitude'], dtype=int) * 756
expo = Exposures(gpd.GeoDataFrame(data=data))
#vulnerability
#generate vulnerability
impact_function = ImpfStormEurope()
impact_function.set_welker()
impact_function_set = ImpactFuncSet()
impact_function_set.append(impact_function)
#create and calculate Forecast
forecast = Forecast({dt.datetime(2018, 1, 1): haz}, expo,
impact_function_set)
forecast.calc()
# test
self.assertEqual(len(forecast.run_datetime), 1)
self.assertEqual(forecast.run_datetime[0], dt.datetime(2018, 1, 1))
self.assertEqual(forecast.event_date, dt.datetime(2018, 1, 3))
self.assertEqual(forecast.lead_time().days, 2)
self.assertEqual(forecast.summary_str(),
'WS_NWP_run2018010100_event20180103_Switzerland')
self.assertAlmostEqual(forecast.ai_agg(), 26.347, places=1)
self.assertAlmostEqual(forecast.ei_exp()[1], 7.941, places=1)
self.assertEqual(len(forecast.hazard), 1)
self.assertIsInstance(forecast.hazard[0], StormEurope)
self.assertIsInstance(forecast.exposure, Exposures)
self.assertIsInstance(forecast.vulnerability, ImpactFuncSet)
def test_Forecast_init_raise(self):
"""Test calc and propety functions from the Forecast class"""
#hazard with several event dates
storms = StormEurope()
storms.read_footprints(WS_DEMO_NC, description='test_description')
#exposure
data = {}
data['latitude'] = np.array([1, 2, 3])
data['longitude'] = np.array([1, 2, 3])
data['value'] = np.ones_like(data['latitude']) * 100000
data['deductible'] = np.zeros_like(data['latitude'])
data[INDICATOR_IMPF + 'WS'] = np.ones_like(data['latitude'])
data['region_id'] = np.ones_like(data['latitude'], dtype=int) * 756
expo = Exposures(gpd.GeoDataFrame(data=data))
#vulnerability
#generate vulnerability
impact_function_set = ImpactFuncSet()
#create and calculate Forecast
with self.assertRaises(ValueError):
Forecast({dt.datetime(2018, 1, 1): storms}, expo,
impact_function_set)
def test_Forecast_plot(self):
"""Test cplotting functions from the Forecast class"""
#hazard
haz1 = StormEurope()
haz1.read_cosmoe_file(
HAZ_DIR.joinpath('storm_europe_cosmoe_forecast_vmax_testfile.nc'),
run_datetime=dt.datetime(2018, 1, 1),
event_date=dt.datetime(2018, 1, 3))
haz1.centroids.lat += 0.6
haz1.centroids.lon -= 1.2
haz2 = StormEurope()
haz2.read_cosmoe_file(
HAZ_DIR.joinpath('storm_europe_cosmoe_forecast_vmax_testfile.nc'),
run_datetime=dt.datetime(2018, 1, 1),
event_date=dt.datetime(2018, 1, 3))
haz2.centroids.lat += 0.6
haz2.centroids.lon -= 1.2
#exposure
data = {}
data['latitude'] = haz1.centroids.lat
data['longitude'] = haz1.centroids.lon
data['value'] = np.ones_like(data['latitude']) * 100000
data['deductible'] = np.zeros_like(data['latitude'])
data[INDICATOR_IMPF + 'WS'] = np.ones_like(data['latitude'])
data['region_id'] = np.ones_like(data['latitude'], dtype=int) * 756
expo = Exposures(gpd.GeoDataFrame(data=data))
#vulnerability
#generate vulnerability
impact_function = ImpfStormEurope()
impact_function.set_welker()
impact_function_set = ImpactFuncSet()
impact_function_set.append(impact_function)
#create and calculate Forecast
forecast = Forecast(
{
dt.datetime(2018, 1, 2): haz1,
dt.datetime(2017, 12, 31): haz2
}, expo, impact_function_set)
forecast.calc()
#test plotting functions
forecast.plot_imp_map(run_datetime=dt.datetime(2017, 12, 31),
save_fig=False,
close_fig=True)
forecast.plot_hist(run_datetime=dt.datetime(2017, 12, 31),
save_fig=False,
close_fig=True)
forecast.plot_exceedence_prob(run_datetime=dt.datetime(2017, 12, 31),
threshold=5000,
save_fig=False,
close_fig=True)
#create a file containing the polygons of Swiss cantons using natural earth
cantons_file = CONFIG.local_data.save_dir.dir() / 'CHE_cantons.shp'
adm1_shape_file = shapereader.natural_earth(
resolution='10m',
category='cultural',
name='admin_1_states_provinces')
if not cantons_file.exists():
with fiona.open(adm1_shape_file, 'r') as source:
with fiona.open(cantons_file, 'w', **source.meta) as sink:
for f in source:
if f['properties']['adm0_a3'] == 'CHE':
sink.write(f)
forecast.plot_warn_map(
str(cantons_file),
decision_level='polygon',
thresholds=[100000, 500000, 1000000, 5000000],
probability_aggregation='mean',
area_aggregation='sum',
title="Building damage warning",
explain_text="warn level based on aggregated damages",
save_fig=False,
close_fig=True)
forecast.plot_warn_map(
str(cantons_file),
decision_level='exposure_point',
thresholds=[1, 1000, 5000, 5000000],
probability_aggregation=0.2,
area_aggregation=0.2,
title="Building damage warning",
explain_text="warn level based on aggregated damages",
run_datetime=dt.datetime(2017, 12, 31),
save_fig=False,
close_fig=True)
forecast.plot_hexbin_ei_exposure()
plt.close()
with self.assertRaises(ValueError):
forecast.plot_warn_map(
str(cantons_file),
decision_level='test_fail',
probability_aggregation=0.2,
area_aggregation=0.2,
title="Building damage warning",
explain_text="warn level based on aggregated damages",
save_fig=False,
close_fig=True)
plt.close()
with self.assertRaises(ValueError):
forecast.plot_warn_map(
str(cantons_file),
decision_level='exposure_point',
probability_aggregation='test_fail',
area_aggregation=0.2,
title="Building damage warning",
explain_text="warn level based on aggregated damages",
save_fig=False,
close_fig=True)
plt.close()
with self.assertRaises(ValueError):
forecast.plot_warn_map(
str(cantons_file),
decision_level='exposure_point',
probability_aggregation=0.2,
area_aggregation='test_fail',
title="Building damage warning",
explain_text="warn level based on aggregated damages",
save_fig=False,
close_fig=True)
plt.close()