def test_controlled_execution_2(self):
"""
For a table representing the key properties of a known catalogue
verify the execution of the penalised MLE. The tables and expected
results are taken from:
Bommer, J. J., Strasser, F. O., Rathje, E., Rodriguez-Marek, A.,
Stafford, P. J. and Hatting, E. (2013), "Verification and Validation
Report for Calculations Performed for the Thyspunt PSHA and Compliance
with RD-0016", Council of Geoscience Report Number 2013-0032 (Rev. 0)
Test Case 2 - Table 4.2
"""
pen_mle = PenalizedMLE()
# Magnitudes from 4.0 to 8.0 in 0.5 increments
m_data = 4.0 - np.array([4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0])
count_data = np.array([11., 3., 1., 0., 0., 0., 0., 0.])
cum_count = np.array(
[np.sum(count_data[i:]) for i in range(len(count_data))])
yr_data = np.array([105., 129., 159., 199., 236., 262., 304., 346.])
test_config = {"area": 185657.0}
# Prior beta
betap = 0.85 * np.log(10.)
# Penalty weight
wbu = 16.0 / np.log(10.)
# a-prior and weight
wau = 0.0
apu = 1.0
bval, sigmab, rate, sigma_rate, rho = pen_mle._run_penalized_mle(
test_config, m_data, count_data, yr_data, cum_count, betap, betap,
wbu, wau)
self.assertAlmostEqual(bval, 0.9921, 4)
self.assertAlmostEqual(sigmab, 0.1335, 4)
self.assertAlmostEqual(rate, 0.1276, 4)
self.assertAlmostEqual(sigma_rate, 0.0331, 4)
self.assertAlmostEqual(rho, 0.0998, 4)
def test_controlled_execution_2(self):
"""
For a table representing the key properties of a known catalogue
verify the execution of the penalised MLE. The tables and expected
results are taken from:
Bommer, J. J., Strasser, F. O., Rathje, E., Rodriguez-Marek, A.,
Stafford, P. J. and Hatting, E. (2013), "Verification and Validation
Report for Calculations Performed for the Thyspunt PSHA and Compliance
with RD-0016", Council of Geoscience Report Number 2013-0032 (Rev. 0)
Test Case 2 - Table 4.2
"""
pen_mle = PenalizedMLE()
# Magnitudes from 4.0 to 8.0 in 0.5 increments
m_data = 4.0 - np.array([4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0])
count_data = np.array([11., 3., 1., 0., 0., 0., 0., 0.])
cum_count = np.array([np.sum(count_data[i:])
for i in range(len(count_data))])
yr_data = np.array([105., 129., 159., 199., 236., 262., 304., 346.])
test_config = {"area": 185657.0}
# Prior beta
betap = 0.85 * np.log(10.)
# Penalty weight
wbu = 16.0 / np.log(10.)
# a-prior and weight
wau = 0.0
apu = 1.0
bval, sigmab, rate, sigma_rate, rho = pen_mle._run_penalized_mle(
test_config, m_data, count_data, yr_data, cum_count, betap, betap,
wbu, wau)
self.assertAlmostEqual(bval, 0.9921, 4)
self.assertAlmostEqual(sigmab, 0.1335, 4)
self.assertAlmostEqual(rate, 0.1276, 4)
self.assertAlmostEqual(sigma_rate, 0.0331, 4)
self.assertAlmostEqual(rho, 0.0998, 4)
def test_end_to_end(self):
"""
Tests PenalizedMLE with bootstrapping and increased sample size
and asserts that the known b-value and (approx) rate
are within a narrow range (0.4 - 0.6 quantiles)
"""
poly1 = Polygon([
Point(20.0, 30.0),
Point(20.0, 40.0),
Point(30.0, 40.0),
Point(30.0, 30.0)
])
self.config = {
"b_prior": 0.9,
"reference_magnitude": 3.0,
"b_prior_weight": 25.0,
"a_prior": 0.0,
"a_prior_weight": 0.0,
"area": area_of_polygon(poly1),
"mmax": 8.0
}
sample_size = [5, 10, 20, 50, 100, 200, 500, 1000]
expected_b = 0.9
for sample in sample_size:
outputs = []
expected_rate = (float(sample) /
float(self.catalogue.get_number_events())) * 100.
for i in range(1000):
idx = np.arange(self.catalogue.get_number_events())
np.random.shuffle(idx)
idx = idx[:sample]
new_cat = deepcopy(self.catalogue)
new_cat.select_catalogue_events(np.sort(idx))
mle = PenalizedMLE()
bval, sigmab, rate, sigma_rate = mle.calculate(
new_cat, self.config, self.completeness)
outputs.append([bval, sigmab, rate, sigma_rate])
outputs = np.array(outputs)
mean_b = np.mean(outputs[:, 0])
mean_sigmab = np.mean(outputs[:, 1])
mean_rate = np.mean(outputs[:, 2])
mean_sigma_rate = np.mean(outputs[:, 3])
# Assert that b-value is in the expected range
l_b, u_b = (norm.ppf(0.4, loc=mean_b, scale=mean_sigmab),
norm.ppf(0.6, loc=mean_b, scale=mean_sigmab))
self.assertTrue((0.9 >= l_b) and (0.9 <= u_b))
# Assert that rate is in the expected range
l_b, u_b = (norm.ppf(0.4, loc=mean_rate, scale=mean_sigma_rate),
norm.ppf(0.6, loc=mean_rate, scale=mean_sigma_rate))
self.assertTrue((expected_rate >= l_b) and (expected_rate <= u_b))
def test_rate_counting(self):
"""
Tests the algorithm to determine the observed rates of events in
the different completeness bins
"""
pen_mle = PenalizedMLE()
self.config["mmax"] = 8.0
# Run counting algorithm
delta, kval, tval, lamda, cum_rate, cum_count, nmx, nmt = \
pen_mle._get_rate_counts(self.catalogue,
self.config,
self.completeness)
# Delta is the difference in magnitude from the minimum magnitude
# of completeness
np.testing.assert_array_almost_equal(
delta,
np.array([0., -1., -2., -3., -4., -5.]))
# K-value is the count of earthquakes in the completeness bin
np.testing.assert_array_almost_equal(
kval,
np.array([1749., 391., 72., 13., 1.]))
# T-val is the number of years for which each count applies
np.testing.assert_array_almost_equal(
tval,
np.array([20., 35., 50., 80., 100.]))
# lamda is the rate of events (count / number of years)
np.testing.assert_array_almost_equal(
lamda,
np.array([1749. / 20.,
391. / 35.,
72. / 50.,
13. / 80,
1. / 100.]))
# Cumulative rate
np.testing.assert_array_almost_equal(
cum_rate,
np.array([np.sum(lamda[i:]) for i in range(len(lamda))]))
# Cumulative count
np.testing.assert_array_almost_equal(
cum_count,
np.array([np.sum(kval[i:]) for i in range(len(kval))]))
# Numbers of magnitude and time bins
self.assertEqual(nmx, 5)
self.assertEqual(nmt, 5)
def test_end_to_end(self):
"""
Tests PenalizedMLE with bootstrapping and increased sample size
and asserts that the known b-value and (approx) rate
are within a narrow range (0.4 - 0.6 quantiles)
"""
poly1 = Polygon([Point(20.0, 30.0), Point(20.0, 40.0),
Point(30.0, 40.0), Point(30.0, 30.0)])
self.config = {"b_prior": 0.9, "reference_magnitude": 3.0,
"b_prior_weight": 25.0, "a_prior": 0.0,
"a_prior_weight": 0.0,
"area": area_of_polygon(poly1), "mmax": 8.0}
sample_size = [5, 10, 20, 50, 100, 200, 500, 1000]
expected_b = 0.9
for sample in sample_size:
outputs = []
expected_rate = (float(sample) /
float(self.catalogue.get_number_events())) * 100.
for i in range(1000):
idx = np.arange(self.catalogue.get_number_events())
np.random.shuffle(idx)
idx = idx[:sample]
new_cat = deepcopy(self.catalogue)
new_cat.select_catalogue_events(np.sort(idx))
mle = PenalizedMLE()
bval, sigmab, rate, sigma_rate = mle.calculate(
new_cat,
self.config,
self.completeness)
outputs.append([bval, sigmab, rate, sigma_rate])
outputs = np.array(outputs)
mean_b = np.mean(outputs[:, 0])
mean_sigmab = np.mean(outputs[:, 1])
mean_rate = np.mean(outputs[:, 2])
mean_sigma_rate = np.mean(outputs[:, 3])
# Assert that b-value is in the expected range
l_b, u_b = (norm.ppf(0.4, loc=mean_b, scale=mean_sigmab),
norm.ppf(0.6, loc=mean_b, scale=mean_sigmab))
self.assertTrue((0.9 >= l_b) and (0.9 <= u_b))
# Assert that rate is in the expected range
l_b, u_b = (norm.ppf(0.4, loc=mean_rate, scale=mean_sigma_rate),
norm.ppf(0.6, loc=mean_rate, scale=mean_sigma_rate))
self.assertTrue((expected_rate >= l_b) and (expected_rate <= u_b))
def test_rate_counting(self):
"""
Tests the algorithm to determine the observed rates of events in
the different completeness bins
"""
pen_mle = PenalizedMLE()
self.config["mmax"] = 8.0
# Run counting algorithm
delta, kval, tval, lamda, cum_rate, cum_count, nmx, nmt = \
pen_mle._get_rate_counts(self.catalogue,
self.config,
self.completeness)
# Delta is the difference in magnitude from the minimum magnitude
# of completeness
np.testing.assert_array_almost_equal(
delta,
np.array([0., -1., -2., -3., -4., -5.]))
# K-value is the count of earthquakes in the completeness bin
np.testing.assert_array_almost_equal(
kval,
np.array([1749., 391., 72., 13., 1.]))
# T-val is the number of years for which each count applies
np.testing.assert_array_almost_equal(
tval,
np.array([20., 35., 50., 80., 100.]))
# lamda is the rate of events (count / number of years)
np.testing.assert_array_almost_equal(
lamda,
np.array([1749. / 20.,
391. / 35.,
72. / 50.,
13. / 80,
1. / 100.]))
# Cumulative rate
np.testing.assert_array_almost_equal(
cum_rate,
np.array([np.sum(lamda[i:]) for i in range(len(lamda))]))
# Cumulative count
np.testing.assert_array_almost_equal(
cum_count,
np.array([np.sum(kval[i:]) for i in range(len(kval))]))
# Numbers of magnitude and time bins
self.assertEqual(nmx, 5)
self.assertEqual(nmt, 5)