class TestSyntheticCatalogues(unittest.TestCase):
'''
Tests the synthetic catalogue functions
'''
def setUp(self):
'''
'''
self.occur = AkiMaxLikelihood()
def test_generate_magnitudes(self):
'''
Tests the hmtk.seismicity.occurence.utils function
generate_trunc_gr_magnitudes
'''
bvals = []
# Generate set of synthetic catalogues
for _ in range(0, 100):
mags = rec_utils.generate_trunc_gr_magnitudes(1.0, 4.0, 8.0, 1000)
cat = Catalogue.make_from_dict({
'magnitude':
mags,
'year':
np.zeros(len(mags), dtype=int)
})
bvals.append(self.occur.calculate(cat)[0])
bvals = np.array(bvals)
self.assertAlmostEqual(np.mean(bvals), 1.0, 1)
def test_generate_synthetic_catalogues(self):
'''
Tests the hmtk.seismicity.occurence.utils function
generate_synthetic_magnitudes
'''
bvals = []
# Generate set of synthetic catalogues
for i in range(0, 100):
cat1 = rec_utils.generate_synthetic_magnitudes(
4.5, 1.0, 4.0, 8.0, 1000)
bvals.append(
self.occur.calculate(Catalogue.make_from_dict(cat1))[0])
bvals = np.array(bvals)
self.assertAlmostEqual(np.mean(bvals), 1.0, 1)
class TestSyntheticCatalogues(unittest.TestCase):
'''
Tests the synthetic catalogue functions
'''
def setUp(self):
'''
'''
self.occur = AkiMaxLikelihood()
def test_generate_magnitudes(self):
'''
Tests the hmtk.seismicity.occurence.utils function
generate_trunc_gr_magnitudes
'''
bvals = []
# Generate set of synthetic catalogues
for _ in range(0, 100):
mags = rec_utils.generate_trunc_gr_magnitudes(1.0, 4.0, 8.0, 1000)
cat = Catalogue.make_from_dict(
{'magnitude': mags,
'year': np.zeros(len(mags), dtype=int)})
bvals.append(self.occur.calculate(cat)[0])
bvals = np.array(bvals)
self.assertAlmostEqual(np.mean(bvals), 1.0, 1)
def test_generate_synthetic_catalogues(self):
'''
Tests the hmtk.seismicity.occurence.utils function
generate_synthetic_magnitudes
'''
bvals = []
# Generate set of synthetic catalogues
for i in range(0, 100):
cat1 = rec_utils.generate_synthetic_magnitudes(4.5, 1.0, 4.0, 8.0,
1000)
bvals.append(self.occur.calculate(
Catalogue.make_from_dict(cat1))[0])
bvals = np.array(bvals)
self.assertAlmostEqual(np.mean(bvals), 1.0, 1)
class AkiMaximumLikelihoodTestCase(unittest.TestCase):
def setUp(self):
"""
This generates a minimum data-set to be used for the regression.
"""
# Test A: Generates a data set assuming b=1 and N(m=4.0)=10.0 events
self.dmag = 0.1
mext = np.arange(4.0, 7.01, 0.1)
self.mval = mext[0:-1] + self.dmag / 2.0
self.bval = 1.0
self.numobs = np.flipud(
np.diff(np.flipud(10.0**(-self.bval * mext + 8.0))))
# Test B: Generate a completely artificial catalogue using the
# Gutenberg-Richter distribution defined above
numobs = np.around(self.numobs)
size = int(np.sum(self.numobs))
magnitude = np.zeros(size)
lidx = 0
for mag, nobs in zip(self.mval, numobs):
uidx = int(lidx + nobs)
magnitude[lidx:uidx] = mag + 0.01
lidx = uidx
year = np.ones(size) * 1999
self.catalogue = Catalogue.make_from_dict({
'magnitude': magnitude,
'year': year
})
# Create the seismicity occurrence calculator
self.aki_ml = AkiMaxLikelihood()
def test_aki_maximum_likelihood_A(self):
"""
Tests that the computed b value corresponds to the same value
used to generate the test data set
"""
bval, sigma_b = self.aki_ml._aki_ml(self.mval, self.numobs)
self.assertAlmostEqual(self.bval, bval, 2)
def test_aki_maximum_likelihood_B(self):
"""
Tests that the computed b value corresponds to the same value
used to generate the test data set
"""
bval, sigma_b = self.aki_ml.calculate(self.catalogue)
self.assertAlmostEqual(self.bval, bval, 2)
class AkiMaximumLikelihoodTestCase(unittest.TestCase):
def setUp(self):
"""
This generates a minimum data-set to be used for the regression.
"""
# Test A: Generates a data set assuming b=1 and N(m=4.0)=10.0 events
self.dmag = 0.1
mext = np.arange(4.0, 7.01, 0.1)
self.mval = mext[0:-1] + self.dmag / 2.0
self.bval = 1.0
self.numobs = np.flipud(
np.diff(np.flipud(10.0 ** (-self.bval * mext + 8.0))))
# Test B: Generate a completely artificial catalogue using the
# Gutenberg-Richter distribution defined above
numobs = np.around(self.numobs)
size = int(np.sum(self.numobs))
magnitude = np.zeros(size)
lidx = 0
for mag, nobs in zip(self.mval, numobs):
uidx = int(lidx+nobs)
magnitude[lidx:uidx] = mag + 0.01
lidx = uidx
year = np.ones(size) * 1999
self.catalogue = Catalogue.make_from_dict(
{'magnitude': magnitude, 'year': year})
# Create the seismicity occurrence calculator
self.aki_ml = AkiMaxLikelihood()
def test_aki_maximum_likelihood_A(self):
"""
Tests that the computed b value corresponds to the same value
used to generate the test data set
"""
bval, sigma_b = self.aki_ml._aki_ml(self.mval, self.numobs)
self.assertAlmostEqual(self.bval, bval, 2)
def test_aki_maximum_likelihood_B(self):
"""
Tests that the computed b value corresponds to the same value
used to generate the test data set
"""
bval, sigma_b = self.aki_ml.calculate(self.catalogue)
self.assertAlmostEqual(self.bval, bval, 2)
