def test_basic_instantiation(self):
'''
Tests the basic instantiation of the SHIFT class
'''
# Instantiatiation with float
self.model = Shift(5.0)
np.testing.assert_array_almost_equal(self.model.target_magnitudes,
np.array([5.0]))
self.assertEqual(self.model.number_magnitudes, 1)
# Instantiation with a numpy array
self.model = Shift(np.arange(5., 8., 0.5))
np.testing.assert_array_almost_equal(self.model.target_magnitudes,
np.arange(5., 8., 0.5))
self.assertEqual(self.model.number_magnitudes, 6)
# Instantiation with list
self.model = Shift([5., 6., 7., 8.])
np.testing.assert_array_almost_equal(self.model.target_magnitudes,
np.array([5., 6., 7., 8.]))
self.assertEqual(self.model.number_magnitudes, 4)
# Otherwise raise an error
with self.assertRaises(ValueError) as ae:
self.model = Shift(None)
self.assertEqual(str(ae.exception),
'Minimum magnitudes must be float, list or array')
# Check regionalisation - assuming defaults
self.model = Shift(5.0)
for region in self.model.regionalisation.keys():
self.assertDictEqual(BIRD_GLOBAL_PARAMETERS[region],
self.model.regionalisation[region])
np.testing.assert_array_almost_equal(np.log10(self.model.base_rate),
np.array([-20.74610902]))
def test_calculate_activity_rate(self):
# Tests for the calculation of the activity rate. At this point
# this is really a circular test - an independent test would be
# helpful in future!
parser0 = ReadStrainCsv(STRAIN_FILE)
self.strain_model = parser0.read_data()
self.model = Shift([5.0])
self.model.calculate_activity_rate(self.strain_model)
expected_rate = np.array([[5.66232696e-14], [5.66232696e-14],
[5.66232696e-14], [5.66232696e-14],
[2.73091764e-12], [2.80389274e-12],
[2.88207458e-12], [6.11293721e-12],
[8.19834427e-12], [6.55082175e-12],
[7.90822653e-11], [7.85391610e-11],
[8.12633607e-11], [7.66785657e-11],
[4.07359524e-11], [2.16914046e-10],
[4.74341943e-10], [1.99907599e-10],
[3.55861556e-11], [1.69536101e-10],
[1.69884622e-10], [1.70233341e-10],
[5.06642764e-10]])
np.testing.assert_array_almost_equal(
np.log10(expected_rate),
np.log10(self.model.strain.seismicity_rate))
def test_continuum_seismicity(self):
# Tests the function hmtk.strain.shift.Shift.continuum_seismicity -
# the python implementation of the Subroutine Continuum Seismicity
# from the Fortran 90 code GSRM.f90
self.strain_model = GeodeticStrain()
# Define a simple strain model
test_data = {
'longitude': np.zeros(3, dtype=float),
'latitude': np.zeros(3, dtype=float),
'exx': np.array([1E-9, 1E-8, 1E-7]),
'eyy': np.array([5E-10, 5E-9, 5E-8]),
'exy': np.array([2E-9, 2E-8, 2E-7])
}
self.strain_model.get_secondary_strain_data(test_data)
self.model = Shift([5.66, 6.66])
threshold_moment = moment_function(np.array([5.66, 6.66]))
expected_rate = np.array([[-14.43624419, -22.48168502],
[-13.43624419, -21.48168502],
[-12.43624419, -20.48168502]])
np.testing.assert_array_almost_equal(
expected_rate,
np.log10(
self.model.continuum_seismicity(
threshold_moment, self.strain_model.data['e1h'],
self.strain_model.data['e2h'],
self.strain_model.data['err'],
BIRD_GLOBAL_PARAMETERS['OSRnor'])))
def test_reclassify_with_bird_data(self):
'''
Tests the re-classification from the Kreemer classification (C, O, S,
R and IPL) to the Bird & Liu (2007) classification:
Region Type Kreemer Code Bird Code
Intraplate IPL IPL
Subduction S SUB
Oceanic O OCB
Continental Transform C CTF
Continental Convergent C CCB
Continental Rift C CRB
Rigde (e1h & e2h > 0.) R OSRnor (Normal spreading)
Ridge (e1h == 0.) R OSRnor
Ridge ((e1h * e2h < 0) and
(e1h + e2h >= 0) R OSRnor/OTFmed
Ridge ((e1h * e2h < 0) and
(e1h + e2h < 0) R OCB/OTFmed
Ridge (any other) R OCB
'''
self.model = Shift(5.0)
self.strain_model.data = {
# IPL SUB OCB CCB CRB CTF CTF OSRn OSRn OSR1 OSR2 OCB
'err':
np.array(
[0., 0., 0., 1.0, -1.0, 0.1, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]),
'e1h':
np.array(
[0., 0., 0., 0.0, -1.0, 0.0, -1.0, 1.0, 0.0, -1.0, -1.0,
-1.0]),
'e2h':
np.array(
[0., 0., 0., 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 2.0, 0.5, -1.0]),
'region':
np.array(
['IPL', 'S', 'O', 'C', 'C', 'C', 'C', 'R', 'R', 'R', 'R', 'R'],
dtype='a13')
}
self.model.strain = self.strain_model
expected_regions = [
'IPL', 'SUB', 'OCB', 'CCB', 'CRB', 'CTF', 'CTF', 'OSRnor',
'OSRnor', 'OSR_special_1', 'OSR_special_2', 'OCB'
]
# Apply Bird Classification
self.model._reclassify_Bird_regions_with_data()
self.assertListEqual(expected_regions,
self.model.strain.data['region'].tolist())
