def setUp(self):
'''
Set up test class
'''
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,
'input_mmin': 5.0,
'input_mmax': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolFixedb()
def setUp(self):
'''
Set up test class
'''
parser0 = CsvCatalogueParser(TEST_CAT_1)
self.catalogue = parser0.read_file()
self.config = {'b-value': 1.0,
'input_mmin': 5.0,
'input_mmax': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolFixedb()
def setUp(self):
'''
Set up test class
'''
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,
'input_mmin': 5.0,
'input_mmax': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolFixedb()
# Plot magnitude time density
from hmtk.plotting.seismicity.catalogue_plots import plot_magnitude_time_density
magnitude_bin = 0.2
time_bin = 10.0
plot_magnitude_time_density(catalogue, magnitude_bin, time_bin)
# In[ ]:
mmax_config = {'b-value': 1.0,
'input_mmin': 4.5,
'input_mmax': None,
'input_mmax_uncertainty': 0.5}
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()
class TestKijkoSellevolFixedb(unittest.TestCase):
'''
Test suite for the Kijko & Sellevol fixed b-value estimator of Mmax
'''
def setUp(self):
'''
Set up test class
'''
parser0 = CsvCatalogueParser(TEST_CAT_1)
self.catalogue = parser0.read_file()
self.config = {'b-value': 1.0,
'input_mmin': 5.0,
'input_mmax': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolFixedb()
def test_integral_function(self):
'''
Tests the integral of the Kijko & Sellevol fixed-b estimator
define in Equation 6 of Kijko (2004)
'''
# Simple test case 1 - all good parameters
mmax = 8.5
mmin = 5.0
mval = 6.5
beta = np.log(10.)
neq = 100.
self.assertAlmostEqual(self.model._ks_intfunc(mval, neq, mmax, mmin,
beta), 0.04151379)
# Simple test case 2 - Mmin == Mmax (returns inf)
mmin = 6.0
mmax = 6.0
self.assertTrue(np.isinf(self.model._ks_intfunc(mval, neq, mmax, mmin,
beta)))
# Test case 3 - Mmin > MMax (raises value Error)
mmin = 6.1
with self.assertRaises(ValueError) as ae:
_ = self.model._ks_intfunc(mval, neq, mmax, mmin, beta)
self.assertEqual(ae.exception.message,
'Maximum magnitude smaller than minimum magnitude'
' in Kijko & Sellevol (Fixed-b) integral')
# Test case 4 - Number of earthquakes is 0
mmax = 8.5
mmin = 5.0
neq = 0.
self.assertAlmostEqual(1.0, self.model._ks_intfunc(mval, neq, mmax,
mmin, beta))
# Test case 5 - beta is negative
neq = 100.
self.assertAlmostEqual(0.0,
self.model._ks_intfunc(mval, neq, mmax, mmin, -0.5))
def test_get_mmin(self):
'''
Tests the main method to calculate Mmax
BEHAVIOUR NOTE 1: the estimator of mmax is dependent on the mmin
If mmin < mmin_observed then the integral will not reach stability
(or give rubbish) therefore if the mmin specified in the config
is less than mmin_obs it will be overwritten by mmin_observed
BEHAVIOUR NOTE 2: Negative or very small b-values (< 1E-16) will result
in immediate stability of the integral, thus giving mmax == mmax_obs.
If b-value == 0 then will give a divide by zero warning
'''
# Test good working case b = 1, mmin = 5.0
mmax, sigma_mmax = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6994981)
self.assertAlmostEqual(sigma_mmax, 0.31575163)
# Test case with mmin < min(magnitude) (b = 1)
# Gives very high value of mmax
self.config['input_mmin'] = 4.0
mmax_1, sigma_mmax_1 = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax_1, 8.35453605)
self.assertAlmostEqual(sigma_mmax_1, 0.959759906)
self.config['input_mmin'] = 3.0
mmax_2, sigma_mmax_2 = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax_1, mmax_2)
# Test case with b-value = 0
self.config['input_mmin'] = 5.0
self.config['b-value'] = 0.0
mmax_1, sigma_mmax_1 = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax_1, 7.4)
self.assertAlmostEqual(sigma_mmax_1, 0.1)
# With b < 0, results should be the same as b = 0.
self.config['b-value'] = 0.0
mmax_2, sigma_mmax_2 = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax_2, mmax_1)
self.assertAlmostEqual(sigma_mmax_2, sigma_mmax_1)
# Case where the maximum magnitude is overriden
self.config['input_mmax'] = 7.6
self.config['b-value'] = 1.0
self.config['input_mmax_uncertainty'] = 0.2
mmax_1, sigma_mmax_1 = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax_1, 8.1380422)
self.assertAlmostEqual(sigma_mmax_1, 0.57401164)
class TestKijkoSellevolFixedb(unittest.TestCase):
'''
Test suite for the Kijko & Sellevol fixed b-value estimator of Mmax
'''
def setUp(self):
'''
Set up test class
'''
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,
'input_mmin': 5.0,
'input_mmax': None,
'tolerance': 0.001,
'maximum_iterations': 1000
}
self.model = KijkoSellevolFixedb()
def test_integral_function(self):
# Tests the integral of the Kijko & Sellevol fixed-b estimator
# define in Equation 6 of Kijko (2004)
# Simple test case 1 - all good parameters
mmax = 8.5
mmin = 5.0
mval = 6.5
beta = np.log(10.)
neq = 100.
self.assertAlmostEqual(
self.model._ks_intfunc(mval, neq, mmax, mmin, beta), 0.04151379)
# Test case 4 - Number of earthquakes is 0
mmax = 8.5
mmin = 5.0
neq = 0.
self.assertAlmostEqual(
1.0, self.model._ks_intfunc(mval, neq, mmax, mmin, beta))
# Test case 5 - beta is negative
neq = 100.
self.assertAlmostEqual(
0.0, self.model._ks_intfunc(mval, neq, mmax, mmin, -0.5))
def test_get_mmin(self):
# Tests the main method to calculate Mmax
# BEHAVIOUR NOTE 1: the estimator of mmax is dependent on the mmin
# If mmin < mmin_observed then the integral will not reach stability
# (or give rubbish) therefore if the mmin specified in the config
# is less than mmin_obs it will be overwritten by mmin_observed
# BEHAVIOUR NOTE 2: Negative or very small b-values (< 1E-16) will
# result in immediate stability of the integral, thus giving
# mmax == mmax_obs.
# If b-value == 0 then will give a divide by zero warning
# Test good working case b = 1, mmin = 5.0
mmax, sigma_mmax = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6994981)
self.assertAlmostEqual(sigma_mmax, 0.31575163)
# Test case with mmin < min(magnitude) (b = 1)
# Gives very high value of mmax
self.config['input_mmin'] = 4.0
mmax_1, sigma_mmax_1 = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax_1, 8.35453605)
self.assertAlmostEqual(sigma_mmax_1, 0.959759906)
self.config['input_mmin'] = 3.0
mmax_2, sigma_mmax_2 = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax_1, mmax_2)
# Case where the maximum magnitude is overriden
# self.config['input_mmax'] = 7.6
# self.config['b-value'] = 1.0
# self.config['input_mmax_uncertainty'] = 0.2
# mmax_1, sigma_mmax_1 = self.model.get_mmax(self.catalogue, self.config)
# self.assertAlmostEqual(mmax_1, 8.1380422)
# self.assertAlmostEqual(sigma_mmax_1, 0.57401164)
def test_raise_runTimeWarning(self):
"""Test case with b-value = 0
"""
self.config['input_mmin'] = 5.0
self.config['b-value'] = 0.0
with warnings.catch_warnings(record=True) as cm:
self.model.get_mmax(self.catalogue, self.config)
assert len(cm) > 0
def test_raise_valueError(self):
# Simple test case 2 - Mmin == Mmax (returns inf)
mmin = 6.0
mmax = 6.0
mval = 6.5
beta = np.log(10.)
neq = 100.
with self.assertRaises(ValueError) as cm:
self.model._ks_intfunc(mval, neq, mmax, mmin, beta)
self.assertEqual(
str(cm.exception),
'Maximum magnitude smaller than minimum magnitude'
' in Kijko & Sellevol (Fixed-b) integral')
def test_raise_valueError_1(self):
# Test case 3 - Mmin > MMax (raises value Error)
mmin = 6.2
mmax = 6.0
mval = 6.5
beta = np.log(10.)
neq = 100.
with self.assertRaises(ValueError) as ae:
self.model._ks_intfunc(mval, neq, mmax, mmin, beta)
exception = ae.exception
self.assertEqual(
str(exception), 'Maximum magnitude smaller than minimum magnitude'
' in Kijko & Sellevol (Fixed-b) integral')
# Plot magnitude time density
from hmtk.plotting.seismicity.catalogue_plots import plot_magnitude_time_density
magnitude_bin = 0.2
time_bin = 10.0
plot_magnitude_time_density(catalogue, magnitude_bin, time_bin)
# In[ ]:
mmax_config = {
'b-value': 1.0,
'input_mmin': 4.5,
'input_mmax': None,
'input_mmax_uncertainty': 0.5
}
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
}
class TestKijkoSellevolFixedb(unittest.TestCase):
'''
Test suite for the Kijko & Sellevol fixed b-value estimator of Mmax
'''
def setUp(self):
'''
Set up test class
'''
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,
'input_mmin': 5.0,
'input_mmax': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolFixedb()
def test_integral_function(self):
# Tests the integral of the Kijko & Sellevol fixed-b estimator
# define in Equation 6 of Kijko (2004)
# Simple test case 1 - all good parameters
mmax = 8.5
mmin = 5.0
mval = 6.5
beta = np.log(10.)
neq = 100.
self.assertAlmostEqual(self.model._ks_intfunc(mval, neq, mmax, mmin,
beta), 0.04151379)
# Test case 4 - Number of earthquakes is 0
mmax = 8.5
mmin = 5.0
neq = 0.
self.assertAlmostEqual(1.0, self.model._ks_intfunc(mval, neq, mmax,
mmin, beta))
# Test case 5 - beta is negative
neq = 100.
self.assertAlmostEqual(
0.0, self.model._ks_intfunc(mval, neq, mmax, mmin, -0.5))
def test_get_mmin(self):
# Tests the main method to calculate Mmax
# BEHAVIOUR NOTE 1: the estimator of mmax is dependent on the mmin
# If mmin < mmin_observed then the integral will not reach stability
# (or give rubbish) therefore if the mmin specified in the config
# is less than mmin_obs it will be overwritten by mmin_observed
# BEHAVIOUR NOTE 2: Negative or very small b-values (< 1E-16) will
# result in immediate stability of the integral, thus giving
# mmax == mmax_obs.
# If b-value == 0 then will give a divide by zero warning
# Test good working case b = 1, mmin = 5.0
mmax, sigma_mmax = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6994981)
self.assertAlmostEqual(sigma_mmax, 0.31575163)
# Test case with mmin < min(magnitude) (b = 1)
# Gives very high value of mmax
self.config['input_mmin'] = 4.0
mmax_1, sigma_mmax_1 = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax_1, 8.35453605)
self.assertAlmostEqual(sigma_mmax_1, 0.959759906)
self.config['input_mmin'] = 3.0
mmax_2, sigma_mmax_2 = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(mmax_1, mmax_2)
# Case where the maximum magnitude is overriden
# self.config['input_mmax'] = 7.6
# self.config['b-value'] = 1.0
# self.config['input_mmax_uncertainty'] = 0.2
# mmax_1, sigma_mmax_1 = self.model.get_mmax(self.catalogue, self.config)
# self.assertAlmostEqual(mmax_1, 8.1380422)
# self.assertAlmostEqual(sigma_mmax_1, 0.57401164)
def test_raise_runTimeWarning(self):
"""Test case with b-value = 0
"""
self.config['input_mmin'] = 5.0
self.config['b-value'] = 0.0
with warnings.catch_warnings(record=True) as cm:
self.model.get_mmax(self.catalogue, self.config)
assert len(cm) > 0
def test_raise_valueError(self):
# Simple test case 2 - Mmin == Mmax (returns inf)
mmin = 6.0
mmax = 6.0
mval = 6.5
beta = np.log(10.)
neq = 100.
with self.assertRaises(ValueError) as cm:
self.model._ks_intfunc(mval, neq, mmax, mmin, beta)
self.assertEqual(str(cm.exception),
'Maximum magnitude smaller than minimum magnitude'
' in Kijko & Sellevol (Fixed-b) integral')
def test_raise_valueError_1(self):
# Test case 3 - Mmin > MMax (raises value Error)
mmin = 6.2
mmax = 6.0
mval = 6.5
beta = np.log(10.)
neq = 100.
with self.assertRaises(ValueError) as ae:
self.model._ks_intfunc(mval, neq, mmax, mmin, beta)
exception = ae.exception
self.assertEqual(str(exception),
'Maximum magnitude smaller than minimum magnitude'
' in Kijko & Sellevol (Fixed-b) integral')
