def test_read_with_cent(self):
""" Test read_footprints while passing in a Centroids object """
var_names = copy.deepcopy(DEF_VAR_EXCEL)
var_names['sheet_name'] = 'fp_centroids-test'
var_names['col_name']['region_id'] = 'iso_n3'
test_centroids = Centroids()
test_centroids.read_excel(os.path.join(DATA_DIR,
'fp_centroids-test.xls'),
var_names=var_names)
storms = StormEurope()
storms.read_footprints(TEST_NCS, centroids=test_centroids)
self.assertEqual(storms.intensity.shape, (2, 9944))
self.assertEqual(
np.count_nonzero(~np.isnan(storms.centroids.region_id)), 6401)
def test_set_ssi(self):
""" Test set_ssi with both dawkins and wisc_gust methodology. """
storms = StormEurope()
storms.read_footprints(TEST_NCS)
storms.set_ssi(method='dawkins')
ssi_dawg = np.asarray([1.44573572e+09, 6.16173724e+08])
self.assertTrue(np.allclose(storms.ssi, ssi_dawg))
storms.set_ssi(method='wisc_gust')
ssi_gusty = np.asarray([1.42124571e+09, 5.86870673e+08])
self.assertTrue(np.allclose(storms.ssi, ssi_gusty))
storms.set_ssi(threshold=20, on_land=False)
ssi_special = np.asarray([2.96582030e+09, 1.23980294e+09])
self.assertTrue(np.allclose(storms.ssi, ssi_special))
def test_generate_prob_storms(self):
""" Test the probabilistic storm generator; calls _hist2prob as well as
Centroids.set_region_id() """
storms = StormEurope()
storms.read_footprints(TEST_NCS)
storms_prob = storms.generate_prob_storms()
self.assertEqual(
np.count_nonzero(storms.centroids.region_id), 6190
# here, we don't rasterise; we check if the centroids lie in a
# polygon. that is to say, it's not the majority of a raster pixel,
# but the centroid's location that is decisive
)
self.assertEqual(storms_prob.size, 60)
self.assertEqual(np.count_nonzero(storms_prob.orig), 2)
self.assertEqual(storms_prob.centroids.size, 3054)
self.assertIsInstance(storms_prob.intensity, sparse.csr.csr_matrix)
def test_read_with_ref(self):
""" Test read_footprints while passing in a reference raster. """
storms = StormEurope()
storms.read_footprints(TEST_NCS, ref_raster=TEST_NCS[1])
self.assertEqual(storms.tag.haz_type, 'WS')
self.assertEqual(storms.units, 'm/s')
self.assertEqual(storms.event_id.size, 2)
self.assertEqual(storms.date.size, 2)
self.assertEqual(dt.datetime.fromordinal(storms.date[0]).year, 1999)
self.assertEqual(dt.datetime.fromordinal(storms.date[0]).month, 12)
self.assertEqual(dt.datetime.fromordinal(storms.date[0]).day, 26)
self.assertEqual(storms.event_id[0], 1)
self.assertEqual(storms.event_name[0], 'Lothar')
self.assertTrue(isinstance(storms.intensity, sparse.csr.csr_matrix))
self.assertTrue(isinstance(storms.fraction, sparse.csr.csr_matrix))
self.assertEqual(storms.intensity.shape, (2, 9944))
self.assertEqual(storms.fraction.shape, (2, 9944))
def test_read_footprints(self):
""" Test read_footprints function, using two small test files"""
storms = StormEurope()
storms.read_footprints(TEST_NCS, description='test_description')
self.assertEqual(storms.tag.haz_type, 'WS')
self.assertEqual(storms.units, 'm/s')
self.assertEqual(storms.event_id.size, 2)
self.assertEqual(storms.date.size, 2)
self.assertEqual(dt.datetime.fromordinal(storms.date[0]).year, 1999)
self.assertEqual(dt.datetime.fromordinal(storms.date[0]).month, 12)
self.assertEqual(dt.datetime.fromordinal(storms.date[0]).day, 26)
self.assertEqual(storms.event_id[0], 1)
self.assertEqual(storms.event_name[0], 'Lothar')
self.assertIsInstance(storms.intensity, sparse.csr.csr_matrix)
self.assertIsInstance(storms.fraction, sparse.csr.csr_matrix)
self.assertEqual(storms.intensity.shape, (2, 9944))
self.assertEqual(storms.fraction.shape, (2, 9944))
def test_generate_prob_storms(self):
"""Test the probabilistic storm generator; calls _hist2prob as well as
Centroids.set_region_id()"""
storms = StormEurope()
storms.read_footprints(WS_DEMO_NC)
storms_prob = storms.generate_prob_storms()
self.assertEqual(
np.count_nonzero(storms.centroids.region_id), 6402
# here, we don't rasterise; we check if the centroids lie in a
# polygon. that is to say, it's not the majority of a raster pixel,
# but the centroid's location that is decisive
)
self.assertEqual(storms_prob.size, 60)
self.assertTrue(
np.allclose((1 / storms_prob.frequency).astype(int), 330))
self.assertAlmostEqual(storms.frequency.sum(),
storms_prob.frequency.sum())
self.assertEqual(np.count_nonzero(storms_prob.orig), 2)
self.assertEqual(storms_prob.centroids.size, 3054)
self.assertIsInstance(storms_prob.intensity, sparse.csr.csr_matrix)
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)
