def setUp(self):
filename = os.path.join(BASE_DATA_PATH,'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {'b-value': 1.0,
'sigma-b': 0.05,
'input_mmin': 5.0,
'input_mmax': None,
'input_mmax_uncertainty': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolBayes()
def setUp(self):
parser0 = CsvCatalogueParser(TEST_CAT_1)
self.catalogue = parser0.read_file()
self.config = {'b-value': 1.0,
'sigma-b': 0.05,
'input_mmin': 5.0,
'input_mmax': None,
'input_mmax_uncertainty': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolBayes()
def setUp(self):
filename = os.path.join(BASE_DATA_PATH,'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {'b-value': 1.0,
'sigma-b': 0.05,
'input_mmin': 5.0,
'input_mmax': None,
'input_mmax_uncertainty': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolBayes()
mmax_ks = KijkoSellevolFixedb()
mmax, mmax_sigma = mmax_ks.get_mmax(catalogue, mmax_config)
print 'Mmax = %8.3f +/- %8.3f' %(mmax, mmax_sigma)
# In[ ]:
mmax_config = {'b-value': 1.0,
'sigma-b': 0.05,
'input_mmin': 4.5,
'input_mmax': None,
'input_mmax_uncertainty': 0.5}
mmax_ksb = KijkoSellevolBayes()
mmax, mmax_sigma = mmax_ksb.get_mmax(catalogue, mmax_config)
print 'Mmax = %8.3f +/- %8.3f' %(mmax, mmax_sigma)
# In[ ]:
mmax_config = {'number_earthquakes': 100, # Selects the N largest earthquakes in the catalogue for analysis
'input_mmax': None,
'input_mmax_uncertainty': 0.5}
mmax_knpg = KijkoNonParametricGaussian()
mmax, mmax_sigma = mmax_knpg.get_mmax(catalogue, mmax_config)
print 'Mmax = %8.3f +/- %8.3f' %(mmax, mmax_sigma)
class TestKijkoSellevolBayes(unittest.TestCase):
'''
Test the hmtk.seismicity.max_magnitude.KijkoSellevolBayes module
'''
def setUp(self):
parser0 = CsvCatalogueParser(TEST_CAT_1)
self.catalogue = parser0.read_file()
self.config = {'b-value': 1.0,
'sigma-b': 0.05,
'input_mmin': 5.0,
'input_mmax': None,
'input_mmax_uncertainty': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolBayes()
def test_ksb_intfunc(self):
'''
Tests the integral function of the Kijko-Sellevol-Bayes estimator
of mmax
'''
neq = 100.
mval = 6.0
mmin = 5.0
# Good case b-value is 1.0, sigma-b is 0.05
pval, qval = self._get_pval_qval(1.0, 0.05)
self.assertAlmostEqual(
self.model._ksb_intfunc(mval, neq, mmin, pval, qval),
2.4676049E-5)
# Bad case b-value is 0.0, sigma-b is 0,05
pval0, qval0 = self._get_pval_qval(0.0, 0.05)
self.assertAlmostEqual(
self.model._ksb_intfunc(mval, neq, mmin, pval0, qval0),
0.0)
# Bad case neq = 0.
self.assertAlmostEqual(
self.model._ksb_intfunc(mval, 0., mmin, pval0, qval0),
1.0)
# Bad case mval < mmin
mmin = 6.0
mval = 5.0
self.assertAlmostEqual(
np.log10(self.model._ksb_intfunc(mval, neq, mmin, pval, qval)),
95.7451687)
def test_get_mmax(self):
'''
Tests the function to calculate mmax using the Kijko-Sellevol-Bayes
operator
'''
# Good case - b = 1., sigma_b = 0.05, mmin = 5.0
mmax, mmax_sigma = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6902450)
self.assertAlmostEqual(mmax_sigma, 0.30698886)
# Bad case 1 - input mmin < catalogue mmin
self.config['input_mmin'] = 3.5
mmax, mmax_sigma = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax, 8.2371167)
self.assertAlmostEqual(mmax_sigma, 0.84306841)
self.config['input_mmin'] = 4.0
mmax_check, _ = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax, mmax_check)
# Good case 1 - input mmax
self.config['input_mmin'] = 5.0
self.config['input_mmax'] = 7.8
self.config['input_mmax_uncertainty'] = 0.2
mmax, mmax_sigma = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax, 8.9427386)
self.assertAlmostEqual(mmax_sigma, 1.16010841)
# Bad case 1 - negative b-value (should return nan)
self.config = {'b-value': -0.5,
'sigma-b': 0.05,
'input_mmin': 5.0,
'input_mmax': None,
'input_mmax_uncertainty': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
mmax, mmax_sigma = self.model.get_mmax(self.catalogue, self.config)
self.assertTrue(np.isnan(mmax))
self.assertTrue(np.isnan(mmax_sigma))
def _get_pval_qval(self, bval, sigma_b):
'''
Get the p-value and q-value from b and sigma b
'''
beta = bval * np.log(10.)
sigma_beta = sigma_b * np.log(10.)
pval = beta / (sigma_beta ** 2.)
qval = (beta / sigma_beta) ** 2.
return pval, qval
class TestKijkoSellevolBayes(unittest.TestCase):
'''
Test the hmtk.seismicity.max_magnitude.KijkoSellevolBayes module
'''
def setUp(self):
filename = os.path.join(BASE_DATA_PATH, 'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {
'b-value': 1.0,
'sigma-b': 0.05,
'input_mmin': 5.0,
'input_mmax': None,
'input_mmax_uncertainty': None,
'tolerance': 0.001,
'maximum_iterations': 1000
}
self.model = KijkoSellevolBayes()
def test_ksb_intfunc(self):
# Tests the integral function of the Kijko-Sellevol-Bayes estimator
# of mmax
neq = 100.
mval = 6.0
mmin = 5.0
# Good case b-value is 1.0, sigma-b is 0.05
pval, qval = self._get_pval_qval(1.0, 0.05)
self.assertAlmostEqual(
self.model._ksb_intfunc(mval, neq, mmin, pval, qval), 2.4676049E-5)
# Bad case b-value is 0.0, sigma-b is 0,05
pval0, qval0 = self._get_pval_qval(0.0, 0.05)
self.assertAlmostEqual(
self.model._ksb_intfunc(mval, neq, mmin, pval0, qval0), 0.0)
# Bad case neq = 0.
self.assertAlmostEqual(
self.model._ksb_intfunc(mval, 0., mmin, pval0, qval0), 1.0)
# Bad case mval < mmin
mmin = 6.0
mval = 5.0
self.assertAlmostEqual(
np.log10(self.model._ksb_intfunc(mval, neq, mmin, pval, qval)),
95.7451687)
def test_get_mmax(self):
# Tests the function to calculate mmax using the Kijko-Sellevol-Bayes
# operator
# Good case - b = 1., sigma_b = 0.05, mmin = 5.0
mmax, mmax_sigma = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6902450)
self.assertAlmostEqual(mmax_sigma, 0.30698886)
# Bad case 1 - input mmin < catalogue mmin
self.config['input_mmin'] = 3.5
mmax, mmax_sigma = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax, 8.2371167)
self.assertAlmostEqual(mmax_sigma, 0.84306841)
self.config['input_mmin'] = 4.0
mmax_check, _ = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax, mmax_check)
# Good case 1 - input mmax
self.config['input_mmin'] = 5.0
self.config['input_mmax'] = 7.8
self.config['input_mmax_uncertainty'] = 0.2
mmax, mmax_sigma = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax, 8.9427386)
self.assertAlmostEqual(mmax_sigma, 1.16010841)
# Bad case 1 - negative b-value (should return nan)
self.config = {
'b-value': -0.5,
'sigma-b': 0.05,
'input_mmin': 5.0,
'input_mmax': None,
'input_mmax_uncertainty': None,
'tolerance': 0.001,
'maximum_iterations': 1000
}
mmax, mmax_sigma = self.model.get_mmax(self.catalogue, self.config)
self.assertTrue(np.isnan(mmax))
self.assertTrue(np.isnan(mmax_sigma))
def _get_pval_qval(self, bval, sigma_b):
'''
Get the p-value and q-value from b and sigma b
'''
beta = bval * np.log(10.)
sigma_beta = sigma_b * np.log(10.)
pval = beta / (sigma_beta**2.)
qval = (beta / sigma_beta)**2.
return pval, qval
mmax, mmax_sigma = mmax_ks.get_mmax(catalogue, mmax_config)
print 'Mmax = %8.3f +/- %8.3f' % (mmax, mmax_sigma)
# In[ ]:
mmax_config = {
'b-value': 1.0,
'sigma-b': 0.05,
'input_mmin': 4.5,
'input_mmax': None,
'input_mmax_uncertainty': 0.5
}
mmax_ksb = KijkoSellevolBayes()
mmax, mmax_sigma = mmax_ksb.get_mmax(catalogue, mmax_config)
print 'Mmax = %8.3f +/- %8.3f' % (mmax, mmax_sigma)
# In[ ]:
mmax_config = {
'number_earthquakes':
100, # Selects the N largest earthquakes in the catalogue for analysis
'input_mmax': None,
'input_mmax_uncertainty': 0.5
}
mmax_knpg = KijkoNonParametricGaussian()
