class TestGetDistributions(unittest.TestCase):
"""
Class to test the hmtk.seismicity.catalogue.Catalogue methods to
determine depth distribution, magnitude-depth distribution,
and magnitude-time distribution
"""
def setUp(self):
"""
"""
self.catalogue = Catalogue()
def test_depth_distribution_no_depth_error(self):
"""
Checks to ensure error is raised when no depths are found in catalogue
"""
depth_bins = np.arange(0., 60., 10.)
self.catalogue.data['depth'] = np.array([])
with self.assertRaises(ValueError) as ae:
self.catalogue.get_depth_distribution(depth_bins)
self.assertEqual(ae.exception.message, 'Depths missing in catalogue')
def test_depth_distribution_simple(self):
"""
Tests the calculation of the depth histogram with no uncertainties
"""
# Without normalisation
self.catalogue.data['depth'] = np.arange(5., 50., 5.)
depth_bins = np.arange(0., 60., 10.)
expected_array = np.array([1., 2., 2., 2., 2.])
np.testing.assert_array_almost_equal(
expected_array, self.catalogue.get_depth_distribution(depth_bins))
# With normalisation
np.testing.assert_array_almost_equal(
expected_array / np.sum(expected_array),
self.catalogue.get_depth_distribution(depth_bins,
normalisation=True))
def test_depth_distribution_uncertainties(self):
"""
Tests the depth distribution with uncertainties
"""
# Without normalisation
self.catalogue.data['depth'] = np.arange(5., 50., 5.)
self.catalogue.data['depthError'] = 3. * np.ones_like(
self.catalogue.data['depth'])
depth_bins = np.arange(-10., 70., 10.)
expected_array = np.array([0., 1.5, 2., 2., 2., 1.5, 0.])
hist_array = self.catalogue.get_depth_distribution(depth_bins,
bootstrap=1000)
array_diff = np.round(hist_array, 1) - expected_array
self.assertTrue(np.all(np.fabs(array_diff) < 0.2))
# With normalisation
expected_array = np.array([0., 0.16, 0.22, 0.22, 0.22, 0.16, 0.01])
hist_array = self.catalogue.get_depth_distribution(depth_bins,
normalisation=True,
bootstrap=1000)
array_diff = np.round(hist_array, 2) - expected_array
self.assertTrue(np.all(np.fabs(array_diff) < 0.03))
class TestGetDistributions(unittest.TestCase):
"""
Class to test the hmtk.seismicity.catalogue.Catalogue methods to
determine depth distribution, magnitude-depth distribution,
and magnitude-time distribution
"""
def setUp(self):
self.catalogue = Catalogue()
def test_depth_distribution_no_depth_error(self):
# ensure error is raised when no depths are found in catalogue
depth_bins = np.arange(0., 60., 10.)
self.catalogue.data['depth'] = np.array([])
with self.assertRaises(ValueError) as ae:
self.catalogue.get_depth_distribution(depth_bins)
self.assertEqual(str(ae.exception),
'Depths missing in catalogue')
def test_depth_distribution_simple(self):
# Tests the calculation of the depth histogram with no uncertainties
# Without normalisation
self.catalogue.data['depth'] = np.arange(5., 50., 5.)
depth_bins = np.arange(0., 60., 10.)
expected_array = np.array([1., 2., 2., 2., 2.])
np.testing.assert_array_almost_equal(
expected_array,
self.catalogue.get_depth_distribution(depth_bins))
# With normalisation
np.testing.assert_array_almost_equal(
expected_array / np.sum(expected_array),
self.catalogue.get_depth_distribution(depth_bins,
normalisation=True))
def test_depth_distribution_uncertainties(self):
# Tests the depth distribution with uncertainties
# Without normalisation
self.catalogue.data['depth'] = np.arange(5., 50., 5.)
self.catalogue.data['depthError'] = 3. * np.ones_like(
self.catalogue.data['depth'])
depth_bins = np.arange(-10., 70., 10.)
expected_array = np.array([0., 1.5, 2., 2., 2., 1.5, 0.])
hist_array = self.catalogue.get_depth_distribution(depth_bins,
bootstrap=1000)
array_diff = np.round(hist_array, 1) - expected_array
self.assertTrue(np.all(np.fabs(array_diff) < 0.2))
# With normalisation
expected_array = np.array([0., 0.16, 0.22, 0.22, 0.22, 0.16, 0.01])
hist_array = self.catalogue.get_depth_distribution(depth_bins,
normalisation=True,
bootstrap=1000)
array_diff = np.round(hist_array, 2) - expected_array
self.assertTrue(np.all(np.fabs(array_diff) < 0.03))
class TestMagnitudeDepthDistribution(unittest.TestCase):
"""
Tests the method for generating the magnitude depth distribution
"""
def setUp(self):
"""
"""
self.catalogue = Catalogue()
x, y = np.meshgrid(np.arange(5., 50., 10.), np.arange(5.5, 9.0, 1.))
nx, ny = np.shape(x)
self.catalogue.data['depth'] = (x.reshape([nx * ny, 1])).flatten()
self.catalogue.data['magnitude'] = (y.reshape([nx * ny, 1])).flatten()
def test_depth_distribution_no_depth_error(self):
"""
Checks to ensure error is raised when no depths are found in catalogue
"""
depth_bins = np.arange(0., 60., 10.)
self.catalogue.data['depth'] = np.array([])
with self.assertRaises(ValueError) as ae:
self.catalogue.get_depth_distribution(depth_bins)
self.assertEqual(ae.exception.message,
'Depths missing in catalogue')
def test_distribution_no_uncertainties(self):
"""
Tests the magnitude-depth distribution without uncertainties
"""
# Without normalisation
depth_bins = np.arange(0., 60., 10.)
mag_bins = np.arange(5., 10., 1.)
expected_array = np.ones([len(mag_bins) - 1, len(depth_bins) - 1],
dtype=float)
np.testing.assert_array_almost_equal(
expected_array,
self.catalogue.get_magnitude_depth_distribution(mag_bins,
depth_bins))
# With normalisation
np.testing.assert_array_almost_equal(
expected_array / np.sum(expected_array),
self.catalogue.get_magnitude_depth_distribution(mag_bins,
depth_bins, normalisation=True))
def test_mag_depth_distribution_uncertainties(self):
"""
Tests the magnitude depth distribution with uncertainties
"""
self.catalogue.data['depthError'] = 3.0 * np.ones_like(
self.catalogue.data['depth'])
self.catalogue.data['sigmaMagnitude'] = 0.1 * np.ones_like(
self.catalogue.data['sigmaMagnitude'])
# Extend depth bins to test that no negative depths are being returned
depth_bins = np.arange(-10., 70., 10.)
mag_bins = np.arange(5., 10., 1.)
expected_array = np.array([[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.]])
test_array = self.catalogue.get_magnitude_depth_distribution(
mag_bins, depth_bins)
array_diff = expected_array - np.round(test_array, 1)
self.assertTrue(np.all(np.fabs(array_diff) < 0.2))
# Check to make sure first columns is all zeros
np.testing.assert_array_almost_equal(test_array[:, 0],
np.zeros(4, dtype=float))
class TestMagnitudeDepthDistribution(unittest.TestCase):
"""
Tests the method for generating the magnitude depth distribution
"""
def setUp(self):
self.catalogue = Catalogue()
x, y = np.meshgrid(np.arange(5., 50., 10.), np.arange(5.5, 9.0, 1.))
nx, ny = np.shape(x)
self.catalogue.data['depth'] = (x.reshape([nx * ny, 1])).flatten()
self.catalogue.data['magnitude'] = (y.reshape([nx * ny, 1])).flatten()
def test_depth_distribution_no_depth_error(self):
# ensure error is raised when no depths are found in catalogue
depth_bins = np.arange(0., 60., 10.)
self.catalogue.data['depth'] = np.array([])
with self.assertRaises(ValueError) as ae:
self.catalogue.get_depth_distribution(depth_bins)
self.assertEqual(str(ae.exception),
'Depths missing in catalogue')
def test_distribution_no_uncertainties(self):
# Tests the magnitude-depth distribution without uncertainties
# Without normalisation
depth_bins = np.arange(0., 60., 10.)
mag_bins = np.arange(5., 10., 1.)
expected_array = np.ones([len(mag_bins) - 1, len(depth_bins) - 1],
dtype=float)
np.testing.assert_array_almost_equal(
expected_array,
self.catalogue.get_magnitude_depth_distribution(mag_bins,
depth_bins))
# With normalisation
np.testing.assert_array_almost_equal(
expected_array / np.sum(expected_array),
self.catalogue.get_magnitude_depth_distribution(
mag_bins, depth_bins, normalisation=True))
def test_depth_to_pmf(self):
# Tests the function to get depth pmf assuming a simple PMF can be
# extracted
self.catalogue.data["depth"] = np.array([2.5, 2.5, 7.5, 12.5, 12.5])
self.catalogue.data["depthError"] = np.array([0.1, 0.1, 0.1, 0.1, 0.1])
# Test case with good data
depth_bins = np.array([0.0, 5.0, 10.0, 15.0])
output_pmf = self.catalogue.get_depth_pmf(depth_bins)
self.assertTrue(isinstance(output_pmf, PMF))
expected_output = [(0.4, 2.5), (0.2, 7.5), (0.4, 12.5)]
for iloc, (prob, val) in enumerate(output_pmf.data):
self.assertAlmostEqual(prob, expected_output[iloc][0])
self.assertAlmostEqual(val, expected_output[iloc][1])
def test_depth_to_pmf_default(self):
# Tests the function to get depth pmf assuming no depths are found in
# catalogue - takes a default value
self.catalogue.data["depth"] = np.array([])
self.catalogue.data["depthError"] = np.array([])
depth_bins = np.array([0.0, 5.0, 10.0, 15.0])
output_pmf = self.catalogue.get_depth_pmf(depth_bins,
default_depth=10.0)
self.assertAlmostEqual(output_pmf.data[0][0], 1.0)
self.assertAlmostEqual(output_pmf.data[0][1], 10.0)
def test_mag_depth_distribution_uncertainties(self):
# Tests the magnitude depth distribution with uncertainties
self.catalogue.data['depthError'] = 3.0 * np.ones_like(
self.catalogue.data['depth'])
self.catalogue.data['sigmaMagnitude'] = 0.1 * np.ones_like(
self.catalogue.data['sigmaMagnitude'])
# Extend depth bins to test that no negative depths are being returned
depth_bins = np.arange(-10., 70., 10.)
mag_bins = np.arange(5., 10., 1.)
expected_array = np.array([[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.]])
test_array = self.catalogue.get_magnitude_depth_distribution(
mag_bins, depth_bins)
array_diff = expected_array - np.round(test_array, 1)
self.assertTrue(np.all(np.fabs(array_diff) < 0.2))
# Check to make sure first columns is all zeros
np.testing.assert_array_almost_equal(test_array[:, 0],
np.zeros(4, dtype=float))
class TestMagnitudeDepthDistribution(unittest.TestCase):
"""
Tests the method for generating the magnitude depth distribution
"""
def setUp(self):
self.catalogue = Catalogue()
x, y = np.meshgrid(np.arange(5., 50., 10.), np.arange(5.5, 9.0, 1.))
nx, ny = np.shape(x)
self.catalogue.data['depth'] = (x.reshape([nx * ny, 1])).flatten()
self.catalogue.data['magnitude'] = (y.reshape([nx * ny, 1])).flatten()
def test_depth_distribution_no_depth_error(self):
# ensure error is raised when no depths are found in catalogue
depth_bins = np.arange(0., 60., 10.)
self.catalogue.data['depth'] = np.array([])
with self.assertRaises(ValueError) as ae:
self.catalogue.get_depth_distribution(depth_bins)
self.assertEqual(str(ae.exception), 'Depths missing in catalogue')
def test_distribution_no_uncertainties(self):
# Tests the magnitude-depth distribution without uncertainties
# Without normalisation
depth_bins = np.arange(0., 60., 10.)
mag_bins = np.arange(5., 10., 1.)
expected_array = np.ones(
[len(mag_bins) - 1, len(depth_bins) - 1], dtype=float)
np.testing.assert_array_almost_equal(
expected_array,
self.catalogue.get_magnitude_depth_distribution(
mag_bins, depth_bins))
# With normalisation
np.testing.assert_array_almost_equal(
expected_array / np.sum(expected_array),
self.catalogue.get_magnitude_depth_distribution(
mag_bins, depth_bins, normalisation=True))
def test_depth_to_pmf(self):
# Tests the function to get depth pmf assuming a simple PMF can be
# extracted
self.catalogue.data["depth"] = np.array([2.5, 2.5, 7.5, 12.5, 12.5])
self.catalogue.data["depthError"] = np.array([0.1, 0.1, 0.1, 0.1, 0.1])
# Test case with good data
depth_bins = np.array([0.0, 5.0, 10.0, 15.0])
output_pmf = self.catalogue.get_depth_pmf(depth_bins)
self.assertTrue(isinstance(output_pmf, PMF))
expected_output = [(0.4, 2.5), (0.2, 7.5), (0.4, 12.5)]
for iloc, (prob, val) in enumerate(output_pmf.data):
self.assertAlmostEqual(prob, expected_output[iloc][0])
self.assertAlmostEqual(val, expected_output[iloc][1])
def test_depth_to_pmf_default(self):
# Tests the function to get depth pmf assuming no depths are found in
# catalogue - takes a default value
self.catalogue.data["depth"] = np.array([])
self.catalogue.data["depthError"] = np.array([])
depth_bins = np.array([0.0, 5.0, 10.0, 15.0])
output_pmf = self.catalogue.get_depth_pmf(depth_bins,
default_depth=10.0)
self.assertAlmostEqual(output_pmf.data[0][0], 1.0)
self.assertAlmostEqual(output_pmf.data[0][1], 10.0)
def test_mag_depth_distribution_uncertainties(self):
# Tests the magnitude depth distribution with uncertainties
self.catalogue.data['depthError'] = 3.0 * np.ones_like(
self.catalogue.data['depth'])
self.catalogue.data['sigmaMagnitude'] = 0.1 * np.ones_like(
self.catalogue.data['sigmaMagnitude'])
# Extend depth bins to test that no negative depths are being returned
depth_bins = np.arange(-10., 70., 10.)
mag_bins = np.arange(5., 10., 1.)
expected_array = np.array([[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.]])
test_array = self.catalogue.get_magnitude_depth_distribution(
mag_bins, depth_bins)
array_diff = expected_array - np.round(test_array, 1)
self.assertTrue(np.all(np.fabs(array_diff) < 0.2))
# Check to make sure first columns is all zeros
np.testing.assert_array_almost_equal(test_array[:, 0],
np.zeros(4, dtype=float))
