def test_decay_values_andrew_pass(self):
""" Test _decay_values with central pressure function."""
tc_track = TCTracks()
tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL)
s_rel = False
land_geom = tc._calc_land_geom(tc_track.data)
tc._track_land_params(tc_track.data[0], land_geom)
v_lf, p_lf, x_val = tc._decay_values(tc_track.data[0], land_geom, s_rel)
ss_category = 6
s_cell_1 = 1*[1.0149413347244263]
s_cell_2 = 8*[1.047120451927185]
s_cell = s_cell_1 + s_cell_2
p_vs_lf_time_relative = [1.0149413020277482, 1.018848167539267, 1.037696335078534, \
1.0418848167539267, 1.043979057591623, 1.0450261780104713, \
1.0460732984293193, 1.0471204188481675, 1.0471204188481675]
self.assertEqual(list(p_lf.keys()), [ss_category])
self.assertEqual(p_lf[ss_category][0], array.array('f', s_cell))
self.assertEqual(p_lf[ss_category][1], array.array('f', p_vs_lf_time_relative))
v_vs_lf_time_relative = [0.8846153846153846, 0.6666666666666666, 0.4166666666666667, \
0.2916666666666667, 0.250000000000000, 0.250000000000000, \
0.20833333333333334, 0.16666666666666666, 0.16666666666666666]
self.assertEqual(list(v_lf.keys()), [ss_category])
self.assertEqual(v_lf[ss_category], array.array('f', v_vs_lf_time_relative))
x_val_ref = np.array([95.9512939453125, 53.624916076660156, 143.09530639648438,
225.0262908935547, 312.5832824707031, 427.43109130859375,
570.1857299804688, 750.3827514648438, 1020.5431518554688])
self.assertEqual(list(x_val.keys()), [ss_category])
self.assertTrue(np.allclose(x_val[ss_category], x_val_ref))
def test_calc_decay_no_landfall_pass(self):
""" Test _calc_land_decay with no historical tracks with landfall """
tc_track = TCTracks()
tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT)
land_geom = tc._calc_land_geom(tc_track.data)
tc._track_land_params(tc_track.data[0], land_geom)
with self.assertLogs('climada.hazard.tc_tracks', level='INFO') as cm:
tc_track._calc_land_decay(land_geom)
self.assertIn('No historical track with landfall.', cm.output[0])
def test_apply_decay_no_landfall_pass(self):
""" Test _apply_land_decay with no historical tracks with landfall """
tc_track = TCTracks()
tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT)
land_geom = tc._calc_land_geom(tc_track.data)
tc._track_land_params(tc_track.data[0], land_geom)
tc_track.data[0]['orig_event_flag']=False
tc_ref = tc_track.data[0].copy()
tc_track._apply_land_decay(dict(), dict(), land_geom)
self.assertTrue(np.array_equal(tc_track.data[0].max_sustained_wind.values, tc_ref.max_sustained_wind.values))
self.assertTrue(np.array_equal(tc_track.data[0].central_pressure.values, tc_ref.central_pressure.values))
self.assertTrue(np.array_equal(tc_track.data[0].environmental_pressure.values, tc_ref.environmental_pressure.values))
self.assertTrue(np.all(np.isnan(tc_track.data[0].dist_since_lf.values)))
def test_calc_land_decay_pass(self):
""" Test _calc_land_decay with environmental pressure function."""
tc_track = TCTracks()
tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL)
land_geom = tc._calc_land_geom(tc_track.data)
tc._track_land_params(tc_track.data[0], land_geom)
v_rel, p_rel = tc_track._calc_land_decay(land_geom)
self.assertEqual(7, len(v_rel))
for i, val in enumerate(v_rel.values()):
self.assertAlmostEqual(val, 0.0038894834)
self.assertTrue(i + 1 in v_rel.keys())
self.assertEqual(7, len(p_rel))
for i, val in enumerate(p_rel.values()):
self.assertAlmostEqual(val[0], 1.0598491)
self.assertAlmostEqual(val[1], 0.0041949237)
self.assertTrue(i + 1 in p_rel.keys())
def test_wrong_decay_pass(self):
""" Test decay not implemented when coefficient < 1 """
track = TCTracks()
track.read_ibtracs_netcdf(provider='usa', storm_id='1975178N28281')
track_gen = track.data[0]
track_gen['lat'] = np.array([28.20340431, 28.7915261 , 29.38642458, 29.97836984, 30.56844404,
31.16265292, 31.74820301, 32.34449825, 32.92261894, 33.47430891,
34.01492525, 34.56789399, 35.08810845, 35.55965893, 35.94835174,
36.29355848, 36.45379561, 36.32473812, 36.07552209, 35.92224784,
35.84144186, 35.78298537, 35.86090718, 36.02440372, 36.37555559,
37.06207765, 37.73197352, 37.97524273, 38.05560287, 38.21901208,
38.31486156, 38.30813367, 38.28481808, 38.28410366, 38.25894812,
38.20583372, 38.22741099, 38.39970022, 38.68367797, 39.08329904,
39.41434629, 39.424984 , 39.31327716, 39.30336335, 39.31714429,
39.27031932, 39.30848775, 39.48759833, 39.73326595, 39.96187967,
40.26954226, 40.76882202, 41.40398607, 41.93809726, 42.60395785,
43.57074792, 44.63816143, 45.61450458, 46.68528511, 47.89209365,
49.15580502])
track_gen['lon'] = np.array([-79.20514075, -79.25243311, -79.28393082, -79.32324646,
-79.36668585, -79.41495519, -79.45198688, -79.40580325,
-79.34965443, -79.36938122, -79.30294825, -79.06809546,
-78.70281969, -78.29418936, -77.82170609, -77.30034709,
-76.79004969, -76.37038827, -75.98641014, -75.58383356,
-75.18310414, -74.7974524 , -74.3797645 , -73.86393572,
-73.37910948, -73.01059003, -72.77051313, -72.68011328,
-72.66864779, -72.62579773, -72.56307717, -72.46607618,
-72.35871353, -72.31120649, -72.15537583, -71.75577051,
-71.25287498, -70.75527907, -70.34788946, -70.17518421,
-70.04446577, -69.76582749, -69.44372386, -69.15881376,
-68.84351922, -68.47890287, -68.04184565, -67.53541437,
-66.94008642, -66.25596075, -65.53496635, -64.83491802,
-64.12962685, -63.54118808, -62.72934383, -61.34915091,
-59.72580755, -58.24404252, -56.71972992, -55.0809336 ,
-53.31524758])
v_rel = {3: 0.002249541544102336, 1: 0.00046889526284203036, 4: 0.002649273787364977, 2: 0.0016426186150461349, 5: 0.00246400811445618, 7: 0.0030442198547309075, 6: 0.002346537842810565}
p_rel = {3: (1.028420239620591, 0.003174733355067952), 1: (1.0046803184177564, 0.0007997633912500546), 4: (1.0498749735343516, 0.0034665588904747515), 2: (1.0140127424090262, 0.002131858515233042), 5: (1.0619445995372885, 0.003467268426139696), 7: (1.0894914184297835, 0.004315034379018768), 6: (1.0714354641894077, 0.002783787561718677)}
track_gen.attrs['orig_event_flag'] = False
cp_ref = np.array([1012., 1012.])
land_geom = _calc_land_geom([track_gen])
track_res = _apply_decay_coeffs(track_gen, v_rel, p_rel, land_geom, True)
self.assertTrue(np.array_equal(cp_ref, track_res.central_pressure[9:11]))
def test_apply_decay_pass(self):
""" Test _apply_land_decay against MATLAB reference. """
v_rel = {
6: 0.0038950967656296597,
1: 0.0038950967656296597,
2: 0.0038950967656296597,
3: 0.0038950967656296597,
4: 0.0038950967656296597,
5: 0.0038950967656296597,
7: 0.0038950967656296597
}
p_rel = {
6: (1.0499941, 0.007978940084158488),
1: (1.0499941, 0.007978940084158488),
2: (1.0499941, 0.007978940084158488),
3: (1.0499941, 0.007978940084158488),
4: (1.0499941, 0.007978940084158488),
5: (1.0499941, 0.007978940084158488),
7: (1.0499941, 0.007978940084158488)
}
tc_track = TCTracks()
tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL)
tc_track.data[0]['orig_event_flag'] = False
land_geom = tc._calc_land_geom(tc_track.data)
tc._track_land_params(tc_track.data[0], land_geom)
tc_track._apply_land_decay(v_rel,
p_rel,
land_geom,
s_rel=True,
check_plot=False)
p_ref = np.array([
1.010000000000000, 1.009000000000000, 1.008000000000000,
1.006000000000000, 1.003000000000000, 1.002000000000000,
1.001000000000000, 1.000000000000000, 1.000000000000000,
1.001000000000000, 1.002000000000000, 1.005000000000000,
1.007000000000000, 1.010000000000000, 1.010000000000000,
1.010000000000000, 1.010000000000000, 1.010000000000000,
1.010000000000000, 1.007000000000000, 1.004000000000000,
1.000000000000000, 0.994000000000000, 0.981000000000000,
0.969000000000000, 0.961000000000000, 0.947000000000000,
0.933000000000000, 0.922000000000000, 0.930000000000000,
0.937000000000000, 0.951000000000000, 0.947000000000000,
0.943000000000000, 0.948000000000000, 0.946000000000000,
0.941000000000000, 0.937000000000000, 0.955000000000000,
0.9741457117, 0.99244068917, 1.00086729492, 1.00545853355,
1.00818354609, 1.00941850023, 1.00986192053, 1.00998400565
]) * 1e3
self.assertTrue(
np.allclose(p_ref, tc_track.data[0].central_pressure.values))
v_ref = np.array([
0.250000000000000, 0.300000000000000, 0.300000000000000,
0.350000000000000, 0.350000000000000, 0.400000000000000,
0.450000000000000, 0.450000000000000, 0.450000000000000,
0.450000000000000, 0.450000000000000, 0.450000000000000,
0.450000000000000, 0.400000000000000, 0.400000000000000,
0.400000000000000, 0.400000000000000, 0.450000000000000,
0.450000000000000, 0.500000000000000, 0.500000000000000,
0.550000000000000, 0.650000000000000, 0.800000000000000,
0.950000000000000, 1.100000000000000, 1.300000000000000,
1.450000000000000, 1.500000000000000, 1.250000000000000,
1.300000000000000, 1.150000000000000, 1.150000000000000,
1.150000000000000, 1.150000000000000, 1.200000000000000,
1.250000000000000, 1.250000000000000, 1.200000000000000,
0.9737967353, 0.687255951, 0.4994850556, 0.3551480462,
0.2270548036, 0.1302099557, 0.0645385918, 0.0225325851
]) * 1e2
self.assertTrue(
np.allclose(v_ref, tc_track.data[0].max_sustained_wind.values))
cat_ref = tc.set_category(tc_track.data[0].max_sustained_wind.values,
tc_track.data[0].max_sustained_wind_unit)
self.assertEqual(cat_ref, tc_track.data[0].category)