def test_get_observed_mmax(self):
'''
Tests the function to _get_observed_mmax from data
'''
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 4.3]),
'sigmaMagnitude': np.array([0.1, 0.2, 0.3, 0.2, 0.1])
}
# Test 1: Finds the mmax from the catalogue with defined sigma
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6)
self.assertAlmostEqual(mmax_sig, 0.3)
# Test 2: Finds the mmax from the catalogue with default sigma (0.2)
test_catalogue['sigmaMagnitude'] = None
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6)
self.assertAlmostEqual(mmax_sig, 0.2)
# Test 3: Finds the mmax from the input file
self.config['input_mmax'] = 8.5
self.config['input_mmax_uncertainty'] = 0.35
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 8.5)
self.assertAlmostEqual(mmax_sig, 0.35)
def test_get_observed_mmax(self):
'''
Tests the function to _get_observed_mmax from data
'''
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 4.3]),
'sigmaMagnitude': np.array([0.1, 0.2, 0.3, 0.2, 0.1])
}
# Test 1: Finds the mmax from the catalogue with defined sigma
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6)
self.assertAlmostEqual(mmax_sig, 0.3)
# Test 2: Finds the mmax from the catalogue with default sigma (0.2)
test_catalogue['sigmaMagnitude'] = None
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6)
self.assertAlmostEqual(mmax_sig, 0.2)
# Test 3: Finds the mmax from the input file
self.config['input_mmax'] = 8.5
self.config['input_mmax_uncertainty'] = 0.35
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 8.5)
self.assertAlmostEqual(mmax_sig, 0.35)
def _get_observed_mmax_error(self, test_catalogue, test_config):
"""
Tests the get_observed_mmax exceptions are raised
"""
with self.assertRaises(ValueError) as ae:
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertEqual(ae.exception.message,
'Input mmax uncertainty must be specified!')
def test_get_observed_mmax_good_data(self):
# Asserts that the observed Mmax and corresponding sigma MMax are
# returned when data are availavle
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 4.3]),
'sigmaMagnitude': np.array([0.1, 0.2, 0.3, 0.2, 0.1])
}
# Test 1: Finds the mmax from the catalogue with defined sigma
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6)
self.assertAlmostEqual(mmax_sig, 0.3)
def test_get_observed_mmax_good_data(self):
# Asserts that the observed Mmax and corresponding sigma MMax are
# returned when data are availavle
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 4.3]),
'sigmaMagnitude': np.array([0.1, 0.2, 0.3, 0.2, 0.1])
}
# Test 1: Finds the mmax from the catalogue with defined sigma
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6)
self.assertAlmostEqual(mmax_sig, 0.3)
def test_mmax_uncertainty_largest_in_catalogue(self):
# When largest mmax has a NaN sigmaMagnitude, take the largest
# sigmaMagnitude found in catalogue
self.config['input_mmax'] = None
self.config['input_mmax_uncertainty'] = None
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 4.3]),
'sigmaMagnitude': np.array([0.1, 0.4, np.nan, 0.2, 0.1])
}
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6)
self.assertAlmostEqual(mmax_sig, 0.4)
def test_observed_mmax_catalogue_uncertainty_config(self):
# Tests the case when the observed Mmax must be read from the catalogue
# but the uncertainty is specified in the config
self.config['input_mmax'] = None
self.config['input_mmax_uncertainty'] = 0.5
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 4.3]),
'sigmaMagnitude': np.array([])
}
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6)
self.assertAlmostEqual(mmax_sig, 0.5)
def test_mmax_uncertainty_largest_in_catalogue(self):
# When largest mmax has a NaN sigmaMagnitude, take the largest
# sigmaMagnitude found in catalogue
self.config['input_mmax'] = None
self.config['input_mmax_uncertainty'] = None
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 4.3]),
'sigmaMagnitude': np.array([0.1, 0.4, np.nan, 0.2, 0.1])
}
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6)
self.assertAlmostEqual(mmax_sig, 0.4)
def test_observed_mmax_catalogue_uncertainty_config(self):
# Tests the case when the observed Mmax must be read from the catalogue
# but the uncertainty is specified in the config
self.config['input_mmax'] = None
self.config['input_mmax_uncertainty'] = 0.5
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 4.3]),
'sigmaMagnitude': np.array([])
}
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 7.6)
self.assertAlmostEqual(mmax_sig, 0.5)
def test_get_observed_mmax_from_input(self):
# Tests that the input mmax and its uncertainty are returned when
# specified in the config
# Test 3: Finds the mmax from the input file
self.config['input_mmax'] = 8.5
self.config['input_mmax_uncertainty'] = 0.35
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 4.3]),
'sigmaMagnitude': None
}
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 8.5)
self.assertAlmostEqual(mmax_sig, 0.35)
def test_get_observed_mmax_from_input(self):
# Tests that the input mmax and its uncertainty are returned when
# specified in the config
# Test 3: Finds the mmax from the input file
self.config['input_mmax'] = 8.5
self.config['input_mmax_uncertainty'] = 0.35
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 4.3]),
'sigmaMagnitude': None
}
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertAlmostEqual(mmax, 8.5)
self.assertAlmostEqual(mmax_sig, 0.35)
def get_mmax(self, catalogue, config):
'''
Calculates Maximum magnitude
:param catalogue:
Earthquake catalogue as instance of :class:
hmtk.seismicity.catalogue.Catalogue
:param dict config:
Configuration file for algorithm, contains the attributes:
* 'b-value': b-value (positive float)
* 'input_mmin': Minimum magnitude for integral (if less than
minimum observed magnitude, will be overwritten by
minimum observed magnitude)
* 'tolerance': Tolerance of stabilising of iterator
* 'maximum_interations': Maximum number of iterations
:returns: **mmax** Maximum magnitude and **mmax_sig** corresponding
uncertainty
'''
config = check_config(config, catalogue.data)
obsmax, obsmaxsig = _get_observed_mmax(catalogue.data, config)
mmin = config['input_mmin']
beta = config['b-value'] * np.log(10.)
neq, mmin = _get_magnitude_vector_properties(catalogue.data, config)
mmax = np.copy(obsmax)
d_t = np.inf
iterator = 0
while d_t > config['tolerance']:
delta = quadrature(self._ks_intfunc,
mmin,
mmax,
args=(neq, mmax, mmin, beta))[0]
#print mmin, neq, delta, mmax
tmmax = obsmax + delta
d_t = np.abs(tmmax - mmax)
mmax = np.copy(tmmax)
iterator += 1
if iterator > config['maximum_iterations']:
print 'Kijko-Sellevol estimator reached maximum # of iterations'
d_t = -np.inf
return mmax.item(), np.sqrt(obsmaxsig**2. + delta**2.)
def get_mmax(self, catalogue, config):
'''
Calculates Maximum magnitude
:param catalogue:
Earthquake catalogue as instance of :class:
hmtk.seismicity.catalogue.Catalogue
:param dict config:
Configuration file for algorithm, contains the attributes:
* 'b-value': b-value (positive float)
* 'input_mmin': Minimum magnitude for integral (if less than
minimum observed magnitude, will be overwritten by
minimum observed magnitude)
* 'tolerance': Tolerance of stabilising of iterator
* 'maximum_interations': Maximum number of iterations
:returns: **mmax** Maximum magnitude and **mmax_sig** corresponding
uncertainty
'''
config = check_config(config, catalogue.data)
obsmax, obsmaxsig = _get_observed_mmax(catalogue.data, config)
mmin = config['input_mmin']
beta = config['b-value'] * np.log(10.)
neq, mmin = _get_magnitude_vector_properties(catalogue.data, config)
mmax = np.copy(obsmax)
d_t = np.inf
iterator = 0
while d_t > config['tolerance']:
delta = quadrature(self._ks_intfunc, mmin, mmax,
args=(neq, mmax, mmin, beta))[0]
#print mmin, neq, delta, mmax
tmmax = obsmax + delta
d_t = np.abs(tmmax - mmax)
mmax = np.copy(tmmax)
iterator += 1
if iterator > config['maximum_iterations']:
print ('Kijko-Sellevol estimator reached '
'maximum # of iterations')
d_t = -np.inf
return mmax.item(), np.sqrt(obsmaxsig ** 2. + delta ** 2.)
def get_mmax(self, catalogue, config):
'''Calculate maximum magnitude
:return: **mmax** Maximum magnitude and **mmax_sig** corresponding
uncertainty
:rtype: Float
'''
# Check configuration file
config = check_config(config, catalogue.data)
# Negative b-values will return nan - this simply skips the integral
if config['b-value'] <= 0.0:
return np.nan, np.nan
obsmax, obsmaxsig = _get_observed_mmax(catalogue.data, config)
beta = config['b-value'] * np.log(10.)
sigbeta = config['sigma-b'] * np.log(10.)
neq, mmin = _get_magnitude_vector_properties(catalogue.data, config)
pval = beta / (sigbeta ** 2.)
qval = (beta / sigbeta) ** 2.
mmax = np.copy(obsmax)
d_t = np.inf
iterator = 0
while d_t > config['tolerance']:
rval = pval / (pval + mmax - mmin)
ldelt = (1. / (1. - (rval ** qval))) ** neq
delta = ldelt * quadrature(self._ksb_intfunc, mmin, mmax,
args=(neq, mmin, pval, qval))[0]
tmmax = obsmax + delta
d_t = np.abs(tmmax - mmax)
mmax = np.copy(tmmax)
iterator += 1
if iterator > config['maximum_iterations']:
print 'Kijko-Sellevol-Bayes estimator reached'
print 'maximum # of iterations'
d_t = -np.inf
return mmax.item(), np.sqrt(obsmaxsig ** 2. + delta ** 2.)
def _get_observed_mmax_error(self, test_catalogue, test_config):
# Tests the get_observed_mmax exceptions are raised
with self.assertRaises(ValueError) as ae:
mmax, mmax_sig = _get_observed_mmax(test_catalogue, self.config)
self.assertEqual(str(ae.exception),
'Input mmax uncertainty must be specified!')
