def test_weichert_full(self):
"""
Tests the Weichert function for the synthetic catalogue
"""
wchrt = Weichert()
bval, sigmab, rate, sigma_rate = wchrt.calculate(
self.catalogue, self.config, self.completeness)
self.assertAlmostEqual(bval, 0.890, 3)
self.assertAlmostEqual(sigmab, 0.015, 3)
self.assertAlmostEqual(rate, 100.1078, 4)
self.assertAlmostEqual(sigma_rate, 2.1218, 4)
def test_weichert_full(self):
"""
Tests the Weichert function for the synthetic catalogue
"""
wchrt = Weichert()
bval, sigmab, rate, sigma_rate = wchrt.calculate(self.catalogue,
self.config,
self.completeness)
self.assertAlmostEqual(bval, 0.890, 3)
self.assertAlmostEqual(sigmab, 0.015, 3)
self.assertAlmostEqual(rate, 100.1078, 4)
self.assertAlmostEqual(sigma_rate, 2.1218, 4)
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))))
# Compute the number of observations in the different magnitude
# intervals (according to completeness)
numobs[0:6] *= 10
numobs[6:13] *= 20
numobs[13:22] *= 50
numobs[22:] *= 100
# Define completeness window
compl = np.array([[1900, 1950, 1980, 1990], [6.34, 5.44, 4.74, 3.0]])
self.compl = np.flipud(compl.transpose())
# Compute the number of observations (i.e. earthquakes) in each
# magnitude bin
numobs = np.around(numobs)
magnitude = np.zeros((np.sum(numobs)))
year = np.zeros((np.sum(numobs))) * 1999
# Generate the catalogue
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))
lidx = uidx
# Fix the parameters that later will be used for the testing
self.catalogue = Catalogue.make_from_dict({
'magnitude': magnitude,
'year': year
})
self.wei = Weichert()
self.config = {'Average Type': 'Weighted'}
class WeichertTestCase(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))))
# Compute the number of observations in the different magnitude
# intervals (according to completeness)
numobs[0:6] *= 10
numobs[6:13] *= 20
numobs[13:22] *= 50
numobs[22:] *= 100
# Define completeness window
compl = np.array([[1900, 1950, 1980, 1990], [6.34, 5.44, 4.74, 3.0]])
self.compl = np.flipud(compl.transpose())
# Compute the number of observations (i.e. earthquakes) in each
# magnitude bin
numobs = np.around(numobs)
magnitude = np.zeros( (np.sum(numobs)) )
year = np.zeros( (np.sum(numobs)) ) * 1999
# Generate the catalogue
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))
lidx = uidx
# Fix the parameters that later will be used for the testing
self.catalogue = Catalogue.make_from_dict(
{'magnitude' : magnitude, 'year' : year})
self.wei = Weichert()
self.config = {'Average Type' : 'Weighted'}
def test_weichert(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.wei.calculate(self.catalogue,
self.config, self.compl)
self.assertAlmostEqual(self.bval, bval, 1)
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)
print 'B-value = %9.4f +/- %9.4f' %(bval, sigmab)
print 'Rate = %9.4f +/- %9.4f' %(seismicity_rate, sigma_rate)
# In[ ]:
# In[ ]:
# Set up the configuration parameters
bks_recurrence = KijkoSmit()
'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)
print 'B-value = %9.4f +/- %9.4f' % (bval, sigmab)
print 'Rate = %9.4f +/- %9.4f' % (seismicity_rate, sigma_rate)
# In[ ]:
# In[ ]:
# Set up the configuration parameters
bks_recurrence = KijkoSmit()
bval, sigmab, seismicity_rate, sigma_rate = bks_recurrence.calculate(
catalogue, recurrence_config, completeness_table)
