def test_apply_criterion_track(self):
"""Test _apply_criterion function."""
criterion = list()
tmp_chg = {
'criteria': {
'basin': ['NA'],
'category': [1, 2, 3, 4, 5]
},
'year': 2100,
'change': 1.045,
'variable': 'intensity',
'function': np.multiply
}
criterion.append(tmp_chg)
scale = 0.75
# artificially increase the size of the hazard by repeating (tiling) the data:
ntiles = 8
tc = TropCyclone()
tc.intensity = np.zeros((4, 10))
tc.intensity[0, :] = np.arange(10)
tc.intensity[1, 5] = 10
tc.intensity[2, :] = np.arange(10, 20)
tc.intensity[3, 3] = 3
tc.intensity = np.tile(tc.intensity, (ntiles, 1))
tc.intensity = sparse.csr_matrix(tc.intensity)
tc.basin = ['NA'] * 4
tc.basin[3] = 'WP'
tc.basin = ntiles * tc.basin
tc.category = np.array(ntiles * [2, 0, 4, 1])
tc.event_id = np.arange(tc.intensity.shape[0])
tc_cc = tc._apply_criterion(criterion, scale)
for i_tile in range(ntiles):
offset = i_tile * 4
# no factor applied because of category 0
np.testing.assert_array_equal(
tc.intensity[offset + 1, :].toarray(),
tc_cc.intensity[offset + 1, :].toarray())
# no factor applied because of basin "WP"
np.testing.assert_array_equal(
tc.intensity[offset + 3, :].toarray(),
tc_cc.intensity[offset + 3, :].toarray())
# factor is applied to the remaining events
np.testing.assert_array_almost_equal(
tc.intensity[offset + 0, :].toarray() * 1.03375,
tc_cc.intensity[offset + 0, :].toarray())
np.testing.assert_array_almost_equal(
tc.intensity[offset + 2, :].toarray() * 1.03375,
tc_cc.intensity[offset + 2, :].toarray())
def test_apply_criterion_track(self):
"""Test _apply_criterion function."""
tc = TropCyclone()
tc.intensity = np.zeros((4, 10))
tc.intensity[0, :] = np.arange(10)
tc.intensity[1, 5] = 10
tc.intensity[2, :] = np.arange(10, 20)
tc.intensity[3, 3] = 3
tc.intensity = sparse.csr_matrix(tc.intensity)
tc.basin = ['NA'] * 4
tc.basin[3] = 'NO'
tc.category = np.array([2, 0, 4, 1])
tc.event_id = np.arange(4)
tc.frequency = np.ones(4) * 0.5
tc_cc = tc.set_climate_scenario_knu(ref_year=2050, rcp_scenario=45)
self.assertTrue(np.allclose(tc.intensity[1, :].toarray(), tc_cc.intensity[1, :].toarray()))
self.assertTrue(np.allclose(tc.intensity[3, :].toarray(), tc_cc.intensity[3, :].toarray()))
self.assertFalse(
np.allclose(tc.intensity[0, :].toarray(), tc_cc.intensity[0, :].toarray()))
self.assertFalse(
np.allclose(tc.intensity[2, :].toarray(), tc_cc.intensity[2, :].toarray()))
self.assertTrue(np.allclose(tc.frequency, tc_cc.frequency))
self.assertEqual(
tc_cc.tag.description,
'climate change scenario for year 2050 and RCP 45 from Knutson et al 2015.')
def test_apply_criterion_track(self):
""" Test _apply_criterion function. """
criterion = list()
tmp_chg = {'criteria': {'basin': ['NA'], 'category':[1, 2, 3, 4, 5]},
'year': 2100, 'change': 1.045, 'variable': 'intensity', 'function': np.multiply}
criterion.append(tmp_chg)
scale = 0.75
tc = TropCyclone()
tc.intensity = sparse.lil_matrix(np.zeros((4, 10)))
tc.intensity[0, :] = np.arange(10)
tc.intensity[1, 5] = 10
tc.intensity[2, :] = np.arange(10, 20)
tc.intensity[3, 3] = 3
tc.intensity = tc.intensity.tocsr()
tc.basin = ['NA'] * 4
tc.basin[3] = 'WP'
tc.category = np.array([2, 0, 4, 1])
tc.event_id = np.arange(4)
tc_cc = tc._apply_criterion(criterion, scale)
self.assertTrue(np.allclose(tc.intensity[1, :].todense(), tc_cc.intensity[1, :].todense()))
self.assertTrue(np.allclose(tc.intensity[3, :].todense(), tc_cc.intensity[3, :].todense()))
self.assertFalse(np.allclose(tc.intensity[0, :].todense(), tc_cc.intensity[0, :].todense()))
self.assertFalse(np.allclose(tc.intensity[2, :].todense(), tc_cc.intensity[2, :].todense()))
self.assertTrue(np.allclose(tc.intensity[0, :].todense()*1.03375, tc_cc.intensity[0, :].todense()))
self.assertTrue(np.allclose(tc.intensity[2, :].todense()*1.03375, tc_cc.intensity[2, :].todense()))
def test_two_criterion_track(self):
"""Test _apply_criterion function with two criteria"""
criterion = [
{'basin': 'NA', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1.045, 'variable': 'intensity'},
{'basin': 'WP', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1.025, 'variable': 'intensity'},
{'basin': 'WP', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1.025, 'variable': 'frequency'},
{'basin': 'NA', 'category': [0, 1, 2, 3, 4, 5],
'year': 2100, 'change': 0.7, 'variable': 'frequency'},
{'basin': 'NA', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'},
{'basin': 'NA', 'category': [3, 4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'},
{'basin': 'NA', 'category': [4, 5],
'year': 2100, 'change': 2, 'variable': 'frequency'}
]
scale = 0.75
tc = TropCyclone()
tc.intensity = np.zeros((4, 10))
tc.intensity[0, :] = np.arange(10)
tc.intensity[1, 5] = 10
tc.intensity[2, :] = np.arange(10, 20)
tc.intensity[3, 3] = 3
tc.intensity = sparse.csr_matrix(tc.intensity)
tc.frequency = np.ones(4) * 0.5
tc.basin = ['NA'] * 4
tc.basin[3] = 'WP'
tc.category = np.array([2, 0, 4, 1])
tc.event_id = np.arange(4)
tc_cc = tc._apply_knutson_criterion(criterion, scale)
self.assertTrue(np.allclose(tc.intensity[1, :].toarray(), tc_cc.intensity[1, :].toarray()))
self.assertFalse(
np.allclose(tc.intensity[3, :].toarray(), tc_cc.intensity[3, :].toarray()))
self.assertFalse(
np.allclose(tc.intensity[0, :].toarray(), tc_cc.intensity[0, :].toarray()))
self.assertFalse(
np.allclose(tc.intensity[2, :].toarray(), tc_cc.intensity[2, :].toarray()))
self.assertTrue(
np.allclose(tc.intensity[0, :].toarray() * 1.03375, tc_cc.intensity[0, :].toarray()))
self.assertTrue(
np.allclose(tc.intensity[2, :].toarray() * 1.03375, tc_cc.intensity[2, :].toarray()))
self.assertTrue(
np.allclose(tc.intensity[3, :].toarray() * 1.01875, tc_cc.intensity[3, :].toarray()))
res_frequency = np.ones(4) * 0.5
res_frequency[1] = 0.5 * (1 + (0.7 - 1) * scale)
res_frequency[2] = 0.5 * (1 + (2 - 1) * scale)
res_frequency[3] = 0.5 * (1 + (1.025 - 1) * scale)
self.assertTrue(np.allclose(tc_cc.frequency, res_frequency))
def test_negative_freq_error(self):
"""Test _apply_knutson_criterion with infeasible input."""
criterion = [{'basin': 'SP', 'category': [0, 1],
'year': 2100, 'change': 0.5,
'variable': 'frequency'}
]
tc = TropCyclone()
tc.frequency = np.ones(2)
tc.basin = ['SP', 'SP']
tc.category = np.array([0, 1])
with self.assertRaises(ValueError):
tc._apply_knutson_criterion(criterion, 3)