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_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))