def setUp(self):
"""
This generates a catalogue to be used for the regression.
"""
# Generates a data set assuming b=1
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
numobs = np.flipud(np.diff(np.flipud(10.0**(-self.bval * mext + 7.0))))
# Define completeness window
numobs[0:6] *= 10
numobs[6:13] *= 20
numobs[13:22] *= 50
numobs[22:] *= 100
compl = np.array([[1900, 1950, 1980, 1990], [6.34, 5.44, 4.74, 3.0]])
print(compl)
self.compl = compl.transpose()
print('completeness')
print(self.compl)
print(self.compl.shape)
numobs = np.around(numobs)
print(numobs)
magnitude = np.zeros(int(np.sum(numobs)))
year = np.zeros(int(np.sum(numobs))) * 1999
lidx = 0
for mag, nobs in zip(self.mval, numobs):
uidx = int(lidx + nobs)
magnitude[lidx:uidx] = mag + 0.01
year_low = compl[0, np.min(np.nonzero(compl[1, :] < mag)[0])]
year[lidx:uidx] = (year_low + np.random.rand(uidx - lidx) *
(2000 - year_low))
print('%.2f %.0f %.0f' %
(mag, np.min(year[lidx:uidx]), np.max(year[lidx:uidx])))
lidx = uidx
self.catalogue = Catalogue.make_from_dict({
'magnitude': magnitude,
'year': year
})
self.b_ml = BMaxLikelihood()
self.config = {'Average Type': 'Weighted'}
def setUp(self):
"""
This generates a catalogue to be used for the regression.
"""
# Generates a data set assuming b=1
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
numobs = np.flipud(np.diff(np.flipud(10.0**(-self.bval*mext+7.0))))
# Define completeness window
numobs[0:6] *= 10
numobs[6:13] *= 20
numobs[13:22] *= 50
numobs[22:] *= 100
compl = np.array([[1900, 1950, 1980, 1990], [6.34, 5.44, 4.74, 3.0]])
print compl
self.compl = compl.transpose()
print 'completeness'
print self.compl
print self.compl.shape
numobs = np.around(numobs)
print numobs
magnitude = np.zeros((np.sum(numobs)))
year = np.zeros((np.sum(numobs))) * 1999
lidx = 0
for mag, nobs in zip(self.mval, numobs):
uidx = int(lidx+nobs)
magnitude[lidx:uidx] = mag + 0.01
year_low = compl[0,np.min(np.nonzero(compl[1,:] < mag)[0])]
year[lidx:uidx] = (year_low + np.random.rand(uidx-lidx) *
(2000-year_low))
print '%.2f %.0f %.0f' % (mag,np.min(year[lidx:uidx]),
np.max(year[lidx:uidx]))
lidx = uidx
self.catalogue = Catalogue.make_from_dict(
{'magnitude': magnitude, 'year': year})
self.b_ml = BMaxLikelihood()
self.config = {'Average Type' : 'Weighted'}
class BMaxLikelihoodTestCase(unittest.TestCase):
def setUp(self):
"""
This generates a catalogue to be used for the regression.
"""
# Generates a data set assuming b=1
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
numobs = np.flipud(np.diff(np.flipud(10.0**(-self.bval*mext+7.0))))
# Define completeness window
numobs[0:6] *= 10
numobs[6:13] *= 20
numobs[13:22] *= 50
numobs[22:] *= 100
compl = np.array([[1900, 1950, 1980, 1990], [6.34, 5.44, 4.74, 3.0]])
print compl
self.compl = compl.transpose()
print 'completeness'
print self.compl
print self.compl.shape
numobs = np.around(numobs)
print numobs
magnitude = np.zeros((np.sum(numobs)))
year = np.zeros((np.sum(numobs))) * 1999
lidx = 0
for mag, nobs in zip(self.mval, numobs):
uidx = int(lidx+nobs)
magnitude[lidx:uidx] = mag + 0.01
year_low = compl[0,np.min(np.nonzero(compl[1,:] < mag)[0])]
year[lidx:uidx] = (year_low + np.random.rand(uidx-lidx) *
(2000-year_low))
print '%.2f %.0f %.0f' % (mag,np.min(year[lidx:uidx]),
np.max(year[lidx:uidx]))
lidx = uidx
self.catalogue = Catalogue.make_from_dict(
{'magnitude': magnitude, 'year': year})
self.b_ml = BMaxLikelihood()
self.config = {'Average Type' : 'Weighted'}
def test_b_maximum_likelihood(self):
"""
Tests that the computed b value corresponds to the same value
used to generate the test data set
"""
bval, sigma_b, aval, sigma_a = self.b_ml.calculate(self.catalogue,
self.config, self.compl)
self.assertAlmostEqual(self.bval, bval, 1)
def test_b_maximum_likelihood_raise_error(self):
completeness_table = np.zeros((10,2))
catalogue = {'year': [1900]}
config = {'Average Type' : ['fake']}
self.assertRaises(ValueError, self.b_ml.calculate, catalogue,
config, completeness_table)
def test_b_maximum_likelihood_average_parameters_raise_error(self):
num = 4
gr_pars = np.zeros((10,num))
neq = np.zeros((num))
self.assertRaises(ValueError, self.b_ml._average_parameters,
gr_pars, neq)
def test_b_maximum_likelihood_average_parameters_use_harmonic(self):
num = 4
gr_pars = np.ones((num,10))
neq = np.ones((num))
self.b_ml._average_parameters(gr_pars, neq, average_type='Harmonic')
def test_b_maximum_likelihood_weighted_mean_raise_error(self):
num = 4
parameters = np.ones((num))
neq = np.ones((num+1))
self.assertRaises(ValueError, self.b_ml._weighted_mean,
parameters, neq)
def test_b_maximum_likelihood_harmonic_mean_raise_error(self):
num = 4
parameters = np.ones((num))
neq = np.ones((num+1))
self.assertRaises(ValueError, self.b_ml._harmonic_mean,
parameters, neq)
#print 'Magnitude N(OBS) N(CUM) Log10(Nc)'
#for row in earthquake_count:
# print '%6.2f %10.3f %10.3f %10.3f' %(row[0], row[1], row[2], row[3])
plot_observed_recurrence(catalogue, completeness_table, magnitude_bin, catalogue.end_year)
# In[ ]:
# Set up the configuration parameters
recurrence_config = {'reference_magnitude': 0.0,
'magnitude_interval': 0.2,
'Average Type': 'Weighted'}
bml_recurrence = BMaxLikelihood()
bval, sigmab, seismicity_rate, sigma_rate = bml_recurrence.calculate(catalogue,
recurrence_config,
completeness_table)
print 'B-value = %9.4f +/- %9.4f' %(bval, sigmab)
print 'Rate = %9.4f +/- %9.4f' %(seismicity_rate, sigma_rate)
# In[ ]:
# Set up the configuration parameters
bwc_recurrence = Weichert()
#for row in earthquake_count:
# print '%6.2f %10.3f %10.3f %10.3f' %(row[0], row[1], row[2], row[3])
plot_observed_recurrence(catalogue, completeness_table, magnitude_bin,
catalogue.end_year)
# In[ ]:
# Set up the configuration parameters
recurrence_config = {
'reference_magnitude': 0.0,
'magnitude_interval': 0.2,
'Average Type': 'Weighted'
}
bml_recurrence = BMaxLikelihood()
bval, sigmab, seismicity_rate, sigma_rate = bml_recurrence.calculate(
catalogue, recurrence_config, completeness_table)
print 'B-value = %9.4f +/- %9.4f' % (bval, sigmab)
print 'Rate = %9.4f +/- %9.4f' % (seismicity_rate, sigma_rate)
# In[ ]:
# Set up the configuration parameters
bwc_recurrence = Weichert()
bval, sigmab, seismicity_rate, sigma_rate = bwc_recurrence.calculate(
catalogue, recurrence_config, completeness_table)
