class KijkoSmitTest(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]])
self.compl = np.flipud(compl.transpose())
numobs = np.around(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))
lidx = uidx
self.catalogue = Catalogue.make_from_dict({
'magnitude': magnitude,
'year': year
})
self.ks_ml = KijkoSmit()
self.config = {'Average Type': 'Weighted'}
def test_kijko_smit_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.ks_ml.calculate(
self.catalogue, self.config, self.compl)
print bval, sigma_b
self.assertAlmostEqual(self.bval, bval, 1)
def test_kijko_smit_set_reference_magnitude(self):
completeness_table = np.array([[1900, 1.0]])
catalogue = Catalogue.make_from_dict({
'magnitude': np.array([5.0, 6.0]),
'year': np.array([2000, 2000])
})
config = {'reference_magnitude': 0.0}
self.ks_ml.calculate(catalogue, config, completeness_table)
class KijkoSmitTest(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]])
self.compl = np.flipud(compl.transpose())
numobs = np.around(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))
lidx = uidx
self.catalogue = Catalogue.make_from_dict(
{'magnitude': magnitude, 'year': year})
self.ks_ml = KijkoSmit()
self.config = {'Average Type': 'Weighted'}
def test_kijko_smit_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.ks_ml.calculate(
self.catalogue, self.config, self.compl)
print(bval, sigma_b)
self.assertAlmostEqual(self.bval, bval, 1)
def test_kijko_smit_set_reference_magnitude(self):
completeness_table = np.array([[1900, 1.0]])
catalogue = Catalogue.make_from_dict(
{'magnitude': np.array([5.0, 6.0]),
'year': np.array([2000, 2000])})
config = {'reference_magnitude': 0.0}
self.ks_ml.calculate(catalogue, config, completeness_table)
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]])
self.compl = np.flipud(compl.transpose())
numobs = np.around(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))
lidx = uidx
self.catalogue = Catalogue.make_from_dict({
'magnitude': magnitude,
'year': year
})
self.ks_ml = KijkoSmit()
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]])
self.compl = np.flipud(compl.transpose())
numobs = np.around(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))
lidx = uidx
self.catalogue = Catalogue.make_from_dict(
{'magnitude': magnitude, 'year': year})
self.ks_ml = KijkoSmit()
self.config = {'Average Type': 'Weighted'}
# 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)
print 'B-value = %9.4f +/- %9.4f' % (bval, sigmab)
print 'Rate (M >= 4.0) = %9.4f +/- %9.4f' % (seismicity_rate, sigma_rate)
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)
print 'B-value = %9.4f +/- %9.4f' % (bval, sigmab)
print 'Rate (M >= 4.0) = %9.4f +/- %9.4f' % (seismicity_rate, sigma_rate)
