def test_recurrence_mle_algorithm(self):
context = create_context('config_recurrence_mle.yml')
workflow = create_workflow(context.config)
run(workflow, context)
sm = context.sm_definitions[0]
self.assertAlmostEqual(
sm.rupture_rate_model.truncated_gutenberg_richter.b_value,
0.595256,
self.decimal_places)
self.assertAlmostEqual(
sm.recurrence_sigb,
0.024816,
self.decimal_places)
self.assertAlmostEqual(
sm.rupture_rate_model.truncated_gutenberg_richter.a_value,
158.109066,
self.decimal_places)
self.assertAlmostEqual(
sm.recurrence_siga_m,
49.997286,
self.decimal_places)
def test_recurrence_weichert_algorithm(self):
context = create_context('config_recurrence_weichert.yml')
workflow = create_workflow(context.config)
run(workflow, context)
sm = context.sm_definitions[0]
self.assertAlmostEqual(
sm.rupture_rate_model.truncated_gutenberg_richter.b_value,
0.569790,
self.decimal_places)
self.assertAlmostEqual(
sm.recurrence_sigb,
0.041210,
self.decimal_places)
self.assertAlmostEqual(
sm.rupture_rate_model.truncated_gutenberg_richter.a_value,
132.051268,
self.decimal_places)
self.assertAlmostEqual(
sm.recurrence_siga_m,
7.701386,
self.decimal_places)
def setUp(self):
self.context_prep = Context()
self.context_noprep = Context()
self.config_prep = {
'eq_catalog_file': 'tests/data/completeness_input_test.csv',
'source_model_file': 'tests/data/area_source_model_processing.xml',
'result_file': 'tests/data/output.xml',
'apply_processing_jobs': True,
'pprocessing_result_file': 'tests/data/preprocessed_catalogue.csv',
'completeness_table_file': 'tests/data/completeness_table.csv',
'preprocessing_jobs': None,
'Afteran': {
'time_dist_windows': 'Uhrhammer',
'time_window': 60.0
},
'GardnerKnopoff': {
'time_dist_windows': 'GardnerKnopoff',
'foreshock_time_window': 0.5
},
'Stepp': {'increment_lock': True,
'magnitude_windows': 0.2,
'sensitivity': 0.1,
'time_window': 1
},
'processing_jobs': ['Recurrence'],
'Recurrence': {'magnitude_window': 0.5,
'recurrence_algorithm': None,
'reference_magnitude': 2.0,
'time_window': 1.0
}
}
self.config_noprep = {
'eq_catalog_file': 'tests/data/preprocessed_catalogue.csv',
'source_model_file': 'tests/data/area_source_model_processing.xml',
'result_file': 'tests/data/output.xml',
'apply_processing_jobs': True,
'completeness_table_file': 'tests/data/completeness_table.csv',
'preprocessing_jobs': None,
'processing_jobs': ['Recurrence'],
'Recurrence': {'magnitude_window': 0.5,
'recurrence_algorithm': None,
'reference_magnitude': 2.0,
'time_window': 1.0
}
}
self.wnoprep = create_workflow(self.config_noprep)
def test_gardner_knopoff(self):
context = create_context('config_gardner_knopoff.yml')
workflow = create_workflow(context.config)
run(workflow, context)
expected_vcl = np.array([0, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 3, 3])
self.assertTrue(np.array_equal(self.expected_vmain_shock,
context.working_catalog))
self.assertTrue(np.array_equal(expected_vcl, context.vcl))
self.assertTrue(np.array_equal(self.expected_flag_vector,
context.flag_vector))
def test_gardner_knopoff(self):
context = create_context('config_gardner_knopoff.yml')
workflow = create_workflow(context.config)
run(workflow, context)
expected_vcl = np.array(
[0, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 3])
self.assertTrue(
np.array_equal(self.expected_vmain_shock, context.working_catalog))
self.assertTrue(np.array_equal(expected_vcl, context.vcl))
self.assertTrue(
np.array_equal(self.expected_flag_vector, context.flag_vector))
def test_maximum_magnitude_kijko_npg(self):
context = create_context('config_maxmag_kijko_npg.yml')
workflow = create_workflow(context.config)
run(workflow, context)
sm = context.sm_definitions[0]
self.assertAlmostEqual(6.44392,
sm.rupture_rate_model.truncated_gutenberg_richter.max_magnitude,
self.decimal_places)
self.assertAlmostEqual(0.10922, sm.max_mag_sigma, self.decimal_places)
def test_maximum_magnitude_cum_moment(self):
# It depends on a random seed so precision is relaxed
self.decimal_places = 1
context = create_context('config_maxmag_cum_moment.yml')
workflow = create_workflow(context.config)
run(workflow, context)
sm = context.sm_definitions[0]
self.assertAlmostEqual(6.6,
sm.rupture_rate_model.truncated_gutenberg_richter.max_magnitude,
self.decimal_places)
self.assertAlmostEqual(0.05, sm.max_mag_sigma, self.decimal_places)
def test_stepp(self):
context = create_context('config_stepp.yml')
workflow = create_workflow(context.config)
run(workflow, context)
filtered_eq_events = np.array(
[[1994., 4.0], [1994., 4.1], [1994., 4.2], [1994., 4.3],
[1994., 4.4], [1964., 4.5], [1964., 4.6], [1964., 4.7],
[1964., 4.8], [1964., 4.9], [1964., 5.0], [1964., 5.1],
[1964., 5.2], [1964., 5.3], [1964., 5.4], [1919., 5.5],
[1919., 5.6], [1919., 5.7], [1919., 5.8], [1919., 5.9],
[1919., 6.0], [1919., 6.1], [1919., 6.2], [1919., 6.3],
[1919., 6.4], [1919., 6.5], [1919., 6.6], [1919., 6.7],
[1919., 6.8], [1919., 6.9], [1919., 7.0], [1919., 7.1],
[1919., 7.2], [1919., 7.3]])
self.assertTrue(
np.allclose(filtered_eq_events, context.completeness_table))
def test_stepp(self):
context = create_context('config_stepp.yml')
workflow = create_workflow(context.config)
run(workflow, context)
filtered_eq_events = np.array([
[1994., 4.0], [1994., 4.1], [1994., 4.2],
[1994., 4.3], [1994., 4.4], [1964., 4.5],
[1964., 4.6], [1964., 4.7], [1964., 4.8],
[1964., 4.9], [1964., 5.0], [1964., 5.1],
[1964., 5.2], [1964., 5.3], [1964., 5.4],
[1919., 5.5], [1919., 5.6], [1919., 5.7],
[1919., 5.8], [1919., 5.9], [1919., 6.0],
[1919., 6.1], [1919., 6.2], [1919., 6.3],
[1919., 6.4], [1919., 6.5], [1919., 6.6],
[1919., 6.7], [1919., 6.8], [1919., 6.9],
[1919., 7.0], [1919., 7.1], [1919., 7.2],
[1919., 7.3]])
self.assertTrue(np.allclose(filtered_eq_events,
context.completeness_table))
def execute(self, config_prep):
wprep = create_workflow(config_prep)
self.run_config(wprep, self.wnoprep)
