class TestShift(unittest.TestCase):
'''
Test suite for the class hmtk.strain.shift.Shift
'''
def setUp(self):
'''
'''
self.model = None
self.strain_model = GeodeticStrain()
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(ae.exception.message,
'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_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())
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_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))
class TestGeodeticStrain(unittest.TestCase):
'''
Tests the :class: hmtk.strain.geodetic_strain.GeodeticStrain
'''
def setUp(self):
'''
'''
self.data = {}
self.model = GeodeticStrain()
def test_instantiation(self):
'''
Tests the basic instantiation of the class
'''
expected_dict = {'data': None,
'regions': None,
'seismicity_rate': None,
'regionalisation': None,
'target_magnitudes': None,
'data_variables': []}
self.assertDictEqual(expected_dict, self.model.__dict__)
def test_secondary_strain_data_input_error(self):
'''
Tests to ensure correct error are raised when
i) strain data is missing
ii) Strain data lacks critical attribute
'''
# No strain data
with self.assertRaises(ValueError) as ae:
self.model.get_secondary_strain_data()
self.assertEqual(ae.exception.message,
'Strain data not input or incorrectly formatted')
# Strain data missing critical attribute - e.g. exy
self.data = {'longitude': np.array([10., 20., 30.]),
'latitude': np.array([10., 20., 30.]),
'exx' : np.array([1E-9, 20E-9, 25E-9]),
'eyy' : np.array([1E-9, 20E-9, 25E-9])}
with self.assertRaises(ValueError) as ae:
self.model.get_secondary_strain_data(self.data)
self.assertEqual(ae.exception.message,
'Essential strain information exy missing!')
def test_secondary_strain_data_with_input(self):
'''
Test to verify correct calculation of
i)
i) Second Invarient
ii) err
iii) dilatation
iv) e1h & e2h
'''
self.data = {'longitude': np.array([10., 20., 30.]),
'latitude': np.array([10., 20., 30.]),
'exx': 1E-9 * np.array([100., 10.0, 1.0]),
'eyy': 1E-9 * np.array([50., 5.0, 0.5]),
'exy': 1E-9 * np.array([10., 1.0, 0.1])}
self.model.get_secondary_strain_data(self.data)
# Check that all expected keys are present
expected_keys = ['longitude', 'latitude','exx', 'eyy', 'exy',
'2nd_inv', 'dilatation', 'err', 'e1h', 'e2h']
for key in expected_keys:
self.assertTrue(key in self.model.data.keys())
# Test second invariant
np.testing.assert_array_almost_equal(
np.log10(self.model.data['2nd_inv']),
np.array([-6.94809814, -7.94809814, -8.94809814]))
# Test dilatation
np.testing.assert_array_almost_equal(
np.log10(self.model.data['dilatation']),
np.array([-6.82390874, -7.82390874, -8.82390874]))
# Test err
np.testing.assert_array_almost_equal(
self.model.data['dilatation'] + self.model.data['err'],
np.zeros(3, dtype=float),
14)
# Test e1h
np.testing.assert_array_almost_equal(
np.log10(self.model.data['e1h']),
np.array([-7.31808815, -8.31808815, -9.31808815]))
# Test e2h
np.testing.assert_array_almost_equal(
np.log10(self.model.data['e2h']),
np.array([-6.99171577, -7.99171577, -8.99171577]))
def test_get_number_observations(self):
'''
Tests the count of the number of observations
'''
self.data = {'longitude': np.array([10., 20., 30.]),
'latitude': np.array([10., 20., 30.]),
'exx' : np.array([1E-9, 20E-9, 25E-9]),
'eyy' : np.array([1E-9, 20E-9, 25E-9]),
'exy' : np.array([1E-9, 20E-9, 25E-9])}
self.model = GeodeticStrain()
# Test when no data is input (should equal 0)
self.assertEqual(self.model.get_number_observations(), 0)
# Test with data
self.model.data = self.data
self.assertEqual(self.model.get_number_observations(), 3)
class ReadStrainCsv(object):
'''
:class: hmtk.parsers.strain_csv_parser.ReadStrainCsv reads a strain
model (defined by :class: hmtk.strain.geodetic_strain.GeodeticStrain) from
a headed csv file
:param str filename:
Name of strain file in csv format
:param strain
Container for the strain data as instance of :class:
hmtk.strain.geodetic_strain.GeodeticStrain
'''
def __init__(self, strain_file):
'''
'''
self.filename = strain_file
self.strain = GeodeticStrain()
def read_data(self, scaling_factor=1E-9, strain_headers=None):
'''
Reads the data from the csv file
:param float scaling_factor:
Scaling factor used for all strain values (default 1E-9 for
nanostrain)
:param list strain_headers:
List of the variables in the file that correspond to strain
parameters
:returns:
strain - Strain model as an instance of the :class:
hmtk.strain.geodetic_strain.GeodeticStrain
'''
if strain_headers:
self.strain.data_variables = strain_headers
else:
self.strain.data_variables = STRAIN_VARIABLES
datafile = open(self.filename, 'rU')
reader = csv.DictReader(datafile)
self.strain.data = OrderedDict([(name, [])
for name in reader.fieldnames])
for row in reader:
for name in row.keys():
if 'region' in name.lower():
self.strain.data[name].append(row[name])
elif name in self.strain.data_variables:
self.strain.data[name].append(scaling_factor *
float(row[name]))
else:
self.strain.data[name].append(float(row[name]))
for key in self.strain.data.keys():
if 'region' in key:
self.strain.data[key] = np.array(self.strain.data[key],
dtype='S13')
else:
self.strain.data[key] = np.array(self.strain.data[key])
self._check_invalid_longitudes()
if 'region' not in self.strain.data:
print('No tectonic regionalisation found in input file!')
self.strain.data_variables = self.strain.data.keys()
# Update data with secondary data (i.e. 2nd invariant, e1h, e2h etc.
self.strain.get_secondary_strain_data()
return self.strain
def _check_invalid_longitudes(self):
'''
Checks to ensure that all longitudes are in the range -180. to 180
'''
idlon = self.strain.data['longitude'] > 180.
if np.any(idlon):
self.strain.data['longitude'][idlon] = \
self.strain.data['longitude'][idlon] - 360.
class ReadStrainCsv(object):
'''
:class: hmtk.parsers.strain_csv_parser.ReadStrainCsv reads a strain
model (defined by :class: hmtk.strain.geodetic_strain.GeodeticStrain) from
a headed csv file
:param str filename:
Name of strain file in csv format
:param strain
Container for the strain data as instance of :class:
hmtk.strain.geodetic_strain.GeodeticStrain
'''
def __init__(self, strain_file):
'''
'''
self.filename = strain_file
self.strain = GeodeticStrain()
def read_data(self, scaling_factor=1E-9, strain_headers=None):
'''
Reads the data from the csv file
:param float scaling_factor:
Scaling factor used for all strain values (default 1E-9 for
nanostrain)
:param list strain_headers:
List of the variables in the file that correspond to strain
parameters
:returns:
strain - Strain model as an instance of the :class:
hmtk.strain.geodetic_strain.GeodeticStrain
'''
if strain_headers:
self.strain.data_variables = strain_headers
else:
self.strain.data_variables = STRAIN_VARIABLES
datafile = open(self.filename, 'rU')
reader = csv.DictReader(datafile)
self.strain.data = OrderedDict([(name, [])
for name in reader.fieldnames])
for row in reader:
for name in row.keys():
if 'region' in name.lower():
self.strain.data[name].append(row[name])
elif name in self.strain.data_variables:
self.strain.data[name].append(
scaling_factor * float(row[name]))
else:
self.strain.data[name].append(float(row[name]))
for key in self.strain.data.keys():
if 'region' in key:
self.strain.data[key] = np.array(self.strain.data[key],
dtype='S13')
else:
self.strain.data[key] = np.array(self.strain.data[key])
self._check_invalid_longitudes()
if not 'region' in self.strain.data.keys():
print 'No tectonic regionalisation found in input file!'
self.strain.data_variables = self.strain.data.keys()
# Update data with secondary data (i.e. 2nd invariant, e1h, e2h etc.
self.strain.get_secondary_strain_data()
return self.strain
def _check_invalid_longitudes(self):
'''
Checks to ensure that all longitudes are in the range -180. to 180
'''
idlon = self.strain.data['longitude'] > 180.
if np.any(idlon):
self.strain.data['longitude'][idlon] = \
self.strain.data['longitude'][idlon] - 360.
class TestGeodeticStrain(unittest.TestCase):
'''
Tests the :class: hmtk.strain.geodetic_strain.GeodeticStrain
'''
def setUp(self):
'''
'''
self.data = {}
self.model = GeodeticStrain()
def test_instantiation(self):
'''
Tests the basic instantiation of the class
'''
expected_dict = {
'data': None,
'regions': None,
'seismicity_rate': None,
'regionalisation': None,
'target_magnitudes': None,
'data_variables': []
}
self.assertDictEqual(expected_dict, self.model.__dict__)
def test_secondary_strain_data_input_error(self):
'''
Tests to ensure correct error are raised when
i) strain data is missing
ii) Strain data lacks critical attribute
'''
# No strain data
with self.assertRaises(ValueError) as ae:
self.model.get_secondary_strain_data()
self.assertEqual(str(ae.exception),
'Strain data not input or incorrectly formatted')
# Strain data missing critical attribute - e.g. exy
self.data = {
'longitude': np.array([10., 20., 30.]),
'latitude': np.array([10., 20., 30.]),
'exx': np.array([1E-9, 20E-9, 25E-9]),
'eyy': np.array([1E-9, 20E-9, 25E-9])
}
with self.assertRaises(ValueError) as ae:
self.model.get_secondary_strain_data(self.data)
self.assertEqual(str(ae.exception),
'Essential strain information exy missing!')
def test_secondary_strain_data_with_input(self):
'''
Test to verify correct calculation of
i)
i) Second Invarient
ii) err
iii) dilatation
iv) e1h & e2h
'''
self.data = {
'longitude': np.array([10., 20., 30.]),
'latitude': np.array([10., 20., 30.]),
'exx': 1E-9 * np.array([100., 10.0, 1.0]),
'eyy': 1E-9 * np.array([50., 5.0, 0.5]),
'exy': 1E-9 * np.array([10., 1.0, 0.1])
}
self.model.get_secondary_strain_data(self.data)
# Check that all expected keys are present
expected_keys = [
'longitude', 'latitude', 'exx', 'eyy', 'exy', '2nd_inv',
'dilatation', 'err', 'e1h', 'e2h'
]
for key in expected_keys:
self.assertTrue(key in self.model.data.keys())
# Test second invariant
np.testing.assert_array_almost_equal(
np.log10(self.model.data['2nd_inv']),
np.array([-6.94809814, -7.94809814, -8.94809814]))
# Test dilatation
np.testing.assert_array_almost_equal(
np.log10(self.model.data['dilatation']),
np.array([-6.82390874, -7.82390874, -8.82390874]))
# Test err
np.testing.assert_array_almost_equal(
self.model.data['dilatation'] + self.model.data['err'],
np.zeros(3, dtype=float), 14)
# Test e1h
np.testing.assert_array_almost_equal(
np.log10(self.model.data['e1h']),
np.array([-7.31808815, -8.31808815, -9.31808815]))
# Test e2h
np.testing.assert_array_almost_equal(
np.log10(self.model.data['e2h']),
np.array([-6.99171577, -7.99171577, -8.99171577]))
def test_get_number_observations(self):
'''
Tests the count of the number of observations
'''
self.data = {
'longitude': np.array([10., 20., 30.]),
'latitude': np.array([10., 20., 30.]),
'exx': np.array([1E-9, 20E-9, 25E-9]),
'eyy': np.array([1E-9, 20E-9, 25E-9]),
'exy': np.array([1E-9, 20E-9, 25E-9])
}
self.model = GeodeticStrain()
# Test when no data is input (should equal 0)
self.assertEqual(self.model.get_number_observations(), 0)
# Test with data
self.model.data = self.data
self.assertEqual(self.model.get_number_observations(), 3)
class TestShift(unittest.TestCase):
'''
Test suite for the class hmtk.strain.shift.Shift
'''
def setUp(self):
self.model = None
self.strain_model = GeodeticStrain()
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_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 = [
b'IPL', b'SUB', b'OCB', b'CCB', b'CRB', b'CTF', b'CTF', b'OSRnor',
b'OSRnor', b'OSR_special_1', b'OSR_special_2', b'OCB'
]
# Apply Bird Classification
self.model._reclassify_Bird_regions_with_data()
self.assertListEqual(expected_regions,
self.model.strain.data['region'].tolist())
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_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))