def setUp(self):
"""
Read the sample catalogue
"""
flnme = 'afteran_test_catalogue.csv'
filename = os.path.join(self.BASE_DATA_PATH, flnme)
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
self.dec = Afteran()
def setUp(self):
"""
Read the sample catalogue
"""
flnme = 'afteran_test_catalogue.csv'
filename = os.path.join(self.BASE_DATA_PATH, flnme)
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
self.dec = Afteran()
class AfteranTestCase(unittest.TestCase):
"""
Unit tests for the Afteran declustering algorithm class.
"""
BASE_DATA_PATH = os.path.join(os.path.dirname(__file__), 'data')
def setUp(self):
"""
Read the sample catalogue
"""
flnme = 'afteran_test_catalogue.csv'
filename = os.path.join(self.BASE_DATA_PATH, flnme)
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
self.dec = Afteran()
def test_dec_afteran(self):
"""
Testing the Afteran algorithm
"""
config = {
'time_distance_window': GardnerKnopoffWindow(),
'time_window': 60.}
# Instantiate the declusterer and process the sample catalogue
# self.dec = Afteran()
print(dir(self.dec))
vcl, flagvector = self.dec.decluster(self.cat, config)
print('vcl:', vcl)
print('flagvector:', flagvector, self.cat.data['flag'])
self.assertTrue(np.allclose(flagvector, self.cat.data['flag']))
def test_find_aftershocks(self):
'''
Tests the find aftershocks function
'''
# Test when aftershocks are in array
year_dec = np.array([0.10, 0.20, 0.5, 0.60, 0.80, 1.2])
vsel = np.array([3, 4, 5])
expected_result = (np.array([False, False, False, True, True, False]),
True)
model_result = self.dec._find_aftershocks(vsel, year_dec, 0.25, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertTrue(model_result[1])
# Test when no aftershocks are found - reduce window to < 0.1
expected_result = (
np.array([False, False, False, False, False, False]), False)
model_result = self.dec._find_aftershocks(vsel, year_dec, 0.09, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertFalse(model_result[1])
def test_find_foreshocks(self):
'''
Tests the find_foreshocks function
'''
# Test when aftershocks are in array
year_dec = np.array([0.10, 0.40, 0.5, 0.60, 0.80, 1.2])
vsel = np.array([0, 1])
expected_result = (
np.array([False, True, False, False, False, False]), True)
model_result = self.dec._find_foreshocks(vsel, year_dec, 0.25, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertTrue(model_result[1])
# Test when no aftershocks are found - reduce window to < 0.1
expected_result = (
np.array([False, False, False, False, False, False]), False)
model_result = self.dec._find_foreshocks(vsel, year_dec, 0.09, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertFalse(model_result[1])
class AfteranTestCase(unittest.TestCase):
"""
Unit tests for the Afteran declustering algorithm class.
"""
BASE_DATA_PATH = os.path.join(os.path.dirname(__file__), 'data')
def setUp(self):
"""
Read the sample catalogue
"""
flnme = 'afteran_test_catalogue.csv'
filename = os.path.join(self.BASE_DATA_PATH, flnme)
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
self.dec = Afteran()
def test_dec_afteran(self):
"""
Testing the Afteran algorithm
"""
config = {
'time_distance_window': GardnerKnopoffWindow(),
'time_window': 60.}
# Instantiate the declusterer and process the sample catalogue
# self.dec = Afteran()
print(dir(self.dec))
vcl, flagvector = self.dec.decluster(self.cat, config)
print('vcl:', vcl)
print('flagvector:', flagvector, self.cat.data['flag'])
self.assertTrue(np.allclose(flagvector, self.cat.data['flag']))
def test_find_aftershocks(self):
'''
Tests the find aftershocks function
'''
# Test when aftershocks are in array
year_dec = np.array([0.10, 0.20, 0.5, 0.60, 0.80, 1.2])
vsel = np.array([3, 4, 5])
expected_result = (np.array([False, False, False, True, True, False]),
True)
model_result = self.dec._find_aftershocks(vsel, year_dec, 0.25, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertTrue(model_result[1])
# Test when no aftershocks are found - reduce window to < 0.1
expected_result = (
np.array([False, False, False, False, False, False]), False)
model_result = self.dec._find_aftershocks(vsel, year_dec, 0.09, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertFalse(model_result[1])
def test_find_foreshocks(self):
'''
Tests the find_foreshocks function
'''
# Test when aftershocks are in array
year_dec = np.array([0.10, 0.40, 0.5, 0.60, 0.80, 1.2])
vsel = np.array([0, 1])
expected_result = (
np.array([False, True, False, False, False, False]), True)
model_result = self.dec._find_foreshocks(vsel, year_dec, 0.25, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertTrue(model_result[1])
# Test when no aftershocks are found - reduce window to < 0.1
expected_result = (
np.array([False, False, False, False, False, False]), False)
model_result = self.dec._find_foreshocks(vsel, year_dec, 0.09, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertFalse(model_result[1])