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_two_criterion_track(self):
"""Test _apply_criterion function with two criteria"""
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)
tmp_chg = {
'criteria': {
'basin': ['WP'],
'category': [1, 2, 3, 4, 5]
},
'year': 2100,
'change': 1.025,
'variable': 'intensity',
'function': np.multiply
}
criterion.append(tmp_chg)
tmp_chg = {
'criteria': {
'basin': ['WP'],
'category': [1, 2, 3, 4, 5]
},
'year': 2100,
'change': 1.025,
'variable': 'frequency',
'function': np.multiply
}
criterion.append(tmp_chg)
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_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[3] = 0.5 * 1.01875
self.assertTrue(np.allclose(tc_cc.frequency, res_frequency))