def test_read_eq_catalog(self):
expected_first_eq_entry = {
'eventID': 1,
'Agency': 'AAA',
'month': 1,
'depthError': 0.5,
'second': 13.0,
'SemiMajor90': 2.43,
'year': 2000,
'ErrorStrike': 298.0,
'timeError': 0.02,
'sigmamb': '',
'latitude': 44.368,
'sigmaMw': 0.355,
'sigmaMs': '',
'Mw': 1.71,
'Ms': '',
'Identifier': 20000102034913,
'day': 2,
'minute': 49,
'hour': 3,
'mb': '',
'SemiMinor90': 1.01,
'longitude': 7.282,
'depth': 9.3,
'ML': 1.7,
'sigmaML': 0.1
}
read_eq_catalog(self.context_jobs)
self.assertEqual(10, len(self.context_jobs.eq_catalog))
self.assertEqual(expected_first_eq_entry,
self.context_jobs.eq_catalog[0])
def test_stepp(self):
self.context.config['eq_catalog_file'] = get_data_path(
'completeness_input_test.csv', DATA_DIR)
self.context.config['Stepp']['time_window'] = 5
self.context.config['Stepp']['magnitude_windows'] = 0.1
self.context.config['Stepp']['sensitivity'] = 0.2
self.context.config['Stepp']['increment_lock'] = True
read_eq_catalog(self.context)
create_catalog_matrix(self.context)
filtered_eq_events = np.array([
[4.0, 1994.], [4.1, 1994.], [4.2, 1994.],
[4.3, 1994.], [4.4, 1994.], [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, 1964.],
[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, 1919.]])
stepp(self.context)
self.assertTrue(np.allclose(filtered_eq_events,
self.context.completeness_table))
gardner_knopoff(self.context)
stepp(self.context)
self.assertTrue(np.allclose(filtered_eq_events,
self.context.completeness_table))
def test_gardner_knopoff(self):
self.context.config['eq_catalog_file'] = get_data_path(
'declustering_input_test.csv', DATA_DIR)
self.context.config['GardnerKnopoff']['time_dist_windows'] = \
'GardnerKnopoff'
self.context.config['GardnerKnopoff']['foreshock_time_window'] = 0.5
read_eq_catalog(self.context)
create_catalog_matrix(self.context)
expected_vmain_shock = np.delete(
self.context.catalog_matrix, [4, 10, 19], 0)
expected_vcl = np.array([0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0,
0, 0, 0, 0, 6])
expected_flag_vector = np.array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 1])
gardner_knopoff(self.context)
self.assertTrue(np.array_equal(expected_vmain_shock,
self.context.catalog_matrix))
self.assertTrue(np.array_equal(expected_vcl, self.context.vcl))
self.assertTrue(np.array_equal(expected_flag_vector,
self.context.flag_vector))
def test_store_catalog_in_csv_after_preprocessing(self):
self.context_jobs.selected_eq_vector = np.array(
[0, 0, 0, 1, 1, 0, 1, 0, 1, 0])
read_eq_catalog(self.context_jobs)
self.context_jobs.catalog_matrix = self.context_jobs.eq_catalog
store_preprocessed_catalog(self.context_jobs)
self.assertTrue(filecmp.cmp(self.expected_preprocessed_catalogue,
self.context_jobs.config['pprocessing_result_file']))
def test_store_catalog_in_csv_after_preprocessing(self):
self.context_jobs.selected_eq_vector = np.array(
[0, 0, 0, 1, 1, 0, 1, 0, 1, 0])
read_eq_catalog(self.context_jobs)
self.context_jobs.catalog_matrix = self.context_jobs.eq_catalog
store_preprocessed_catalog(self.context_jobs)
self.assertTrue(
filecmp.cmp(self.expected_preprocessed_catalogue,
self.context_jobs.config['pprocessing_result_file']))
def test_read_eq_catalog(self):
expected_first_eq_entry = {'eventID': 1, 'Agency': 'AAA', 'month': 1,
'depthError': 0.5, 'second': 13.0, 'SemiMajor90': 2.43,
'year': 2000, 'ErrorStrike': 298.0, 'timeError': 0.02,
'sigmamb': '', 'latitude': 44.368, 'sigmaMw': 0.355,
'sigmaMs': '', 'Mw': 1.71, 'Ms': '',
'Identifier': 20000102034913, 'day': 2, 'minute': 49,
'hour': 3, 'mb': '', 'SemiMinor90': 1.01, 'longitude': 7.282,
'depth': 9.3, 'ML': 1.7, 'sigmaML': 0.1}
read_eq_catalog(self.context_jobs)
self.assertEqual(10, len(self.context_jobs.eq_catalog))
self.assertEqual(expected_first_eq_entry,
self.context_jobs.eq_catalog[0])
def test_parameters_gardner_knopoff(self):
self.context.config['eq_catalog_file'] = get_data_path(
'declustering_input_test.csv', DATA_DIR)
self.context.config['GardnerKnopoff']['time_dist_windows'] = \
'GardnerKnopoff'
self.context.config['GardnerKnopoff']['foreshock_time_window'] = 0.5
read_eq_catalog(self.context)
create_catalog_matrix(self.context)
def mock(data, time_dist_windows, foreshock_time_window):
self.assertEquals("GardnerKnopoff", time_dist_windows)
self.assertEquals(0.5, foreshock_time_window)
return None, None, None
self.context.map_sc['gardner_knopoff'] = mock
gardner_knopoff(self.context)
def test_parameters_stepp(self):
self.context.config['eq_catalog_file'] = get_data_path(
'completeness_input_test.csv', DATA_DIR)
self.context.config['Stepp']['time_window'] = 5
self.context.config['Stepp']['magnitude_windows'] = 0.1
self.context.config['Stepp']['sensitivity'] = 0.2
self.context.config['Stepp']['increment_lock'] = True
read_eq_catalog(self.context)
create_catalog_matrix(self.context)
def mock(year, mw, magnitude_windows, time_window, sensitivity, iloc):
self.assertEqual(time_window, 5)
self.assertEqual(magnitude_windows, 0.1)
self.assertEqual(sensitivity, 0.2)
self.assertTrue(iloc)
self.context.map_sc['stepp'] = mock
stepp(self.context)
