def test_get_magnitude_vector_properties(self):
# Tests the function to retreive mmin and number of earthquakes if
# required for certain functions
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 3.8]),
'sigmaMagnitude': np.array([0.1, 0.2, 0.3, 0.2, 0.1])
}
self.config['input_mmin'] = 4.0
# Test 1: Finds the number of events from the catalogue with defined
# minimum magnitude
neq, mmin = _get_magnitude_vector_properties(test_catalogue,
self.config)
self.assertAlmostEqual(neq, 3.0)
self.assertAlmostEqual(mmin, 4.0)
# Test 2 Finds the number of events from the catalogue with an
# unspecified minimum magnitude
del self.config['input_mmin']
neq, mmin = _get_magnitude_vector_properties(test_catalogue,
self.config)
self.assertAlmostEqual(neq, 5.0)
self.assertAlmostEqual(mmin, 3.4)
def test_get_magnitude_vector_properties(self):
# Tests the function to retreive mmin and number of earthquakes if
# required for certain functions
test_catalogue = {
'magnitude': np.array([3.4, 4.5, 7.6, 5.4, 3.8]),
'sigmaMagnitude': np.array([0.1, 0.2, 0.3, 0.2, 0.1])
}
self.config['input_mmin'] = 4.0
# Test 1: Finds the number of events from the catalogue with defined
# minimum magnitude
neq, mmin = _get_magnitude_vector_properties(test_catalogue,
self.config)
self.assertAlmostEqual(neq, 3.0)
self.assertAlmostEqual(mmin, 4.0)
# Test 2 Finds the number of events from the catalogue with an
# unspecified minimum magnitude
del self.config['input_mmin']
neq, mmin = _get_magnitude_vector_properties(test_catalogue,
self.config)
self.assertAlmostEqual(neq, 5.0)
self.assertAlmostEqual(mmin, 3.4)
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.)
