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 = 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 = {'magnitude' : magnitude, 'year' : year}
self.ks_ml = KijkoSmit()
self.config = {'Average Type' : 'Weighted'}
def test_kijko_smith_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)
self.assertAlmostEqual(self.bval, bval, 1)
def test_kijko_smith_set_reference_magnitude(self):
completeness_table = np.array([[1900, 1.0]])
catalogue = {'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 = 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 = {'magnitude' : magnitude, 'year' : year}
self.ks_ml = KijkoSmit()
self.config = {'Average Type' : 'Weighted'}
