def setUp(self):
filename = os.path.join(BASE_DATA_PATH, 'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {'algorithm': None, 'number_bootstraps': None}
self.model = CumulativeMoment()
def setUp(self):
filename = os.path.join(BASE_DATA_PATH,'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {'algorithm': None,
'number_bootstraps': None}
self.model = CumulativeMoment()
def setUp(self):
test_data = np.genfromtxt(TEST_CAT_1, delimiter=',', skip_header=1)
parser0 = CsvCatalogueParser(TEST_CAT_1)
self.catalogue = parser0.read_file()
self.config = {'algorithm': None,
'number_bootstraps': None}
self.model = CumulativeMoment()
'input_mmin': 4.5,
'input_mmax': None,
'input_mmax_uncertainty': 0.5}
mmax_ksb = KijkoSellevolBayes()
mmax, mmax_sigma = mmax_ksb.get_mmax(catalogue, mmax_config)
print 'Mmax = %8.3f +/- %8.3f' %(mmax, mmax_sigma)
# In[ ]:
mmax_config = {'number_earthquakes': 100, # Selects the N largest earthquakes in the catalogue for analysis
'input_mmax': None,
'input_mmax_uncertainty': 0.5}
mmax_knpg = KijkoNonParametricGaussian()
mmax, mmax_sigma = mmax_knpg.get_mmax(catalogue, mmax_config)
print 'Mmax = %8.3f +/- %8.3f' %(mmax, mmax_sigma)
# In[ ]:
mmax_config = {'number_bootstraps': 1000} # Number of samples for the uncertainty analyis
mmax_cum_mo = CumulativeMoment()
mmax, mmax_sigma = mmax_cum_mo.get_mmax(catalogue, mmax_config)
print 'Mmax = %8.3f +/- %8.3f' %(mmax, mmax_sigma)
class TestCumulativeMoment(unittest.TestCase):
'''
Test suite for the
:class: hmtk.seismicity.max_magnitude.cumulative_moment_release
module
'''
def setUp(self):
test_data = np.genfromtxt(TEST_CAT_1, delimiter=',', skip_header=1)
parser0 = CsvCatalogueParser(TEST_CAT_1)
self.catalogue = parser0.read_file()
self.config = {'algorithm': None,
'number_bootstraps': None}
self.model = CumulativeMoment()
def test_check_config(self):
'''
Tests the configuration checker
'''
# Test 1: No bootstraps specified
self.config['number_bootstraps'] = None
fixed_config = self.model.check_config(self.config)
self.assertEqual(1, fixed_config['number_bootstraps'])
# Test 2: Invalid number of bootstraps specified
self.config['number_bootstraps'] = 0
fixed_config = self.model.check_config(self.config)
self.assertEqual(1, fixed_config['number_bootstraps'])
# Test 3: Valid number of bootstraps
self.config['number_bootstraps'] = 1000
fixed_config = self.model.check_config(self.config)
self.assertEqual(1000, fixed_config['number_bootstraps'])
def test_cumulative_moment(self):
'''
Tests the cumulative moment function
'''
# Test 1: Ordinary behaviour using the completeness_test_cat.csv
self.assertAlmostEqual(7.4847335589, self.model.cumulative_moment(
self.catalogue.data['year'],
self.catalogue.data['magnitude']))
# Test 2: If catalogue is less than or equal to 1 year duration
id0 = self.catalogue.data['year'].astype(int) == 1990
self.assertTrue(np.isinf(self.model.cumulative_moment(
self.catalogue.data['year'][id0],
self.catalogue.data['magnitude'][id0])))
def test_get_mmax_cumulative_moment(self):
'''
Tests the cumulative moment function sampled with uncertainty
'''
# Test 1: Case when no sigma is found on magnitude
self.catalogue.data['backup'] = np.copy(
self.catalogue.data['sigmaMagnitude'])
self.catalogue.data['sigmaMagnitude'] = None
mmax, sigma_mmax = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(7.4847335589, mmax)
self.assertAlmostEqual(0.0, sigma_mmax)
# Test 2: Case when one or no bootstraps are specified
self.catalogue.data['sigmaMagnitude'] = self.catalogue.data['backup']
self.config['number_bootstraps'] = 0
mmax, sigma_mmax = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(7.4847335589, mmax)
self.assertAlmostEqual(0.0, sigma_mmax)
# Test 3: Ordinary test case with uncertainty - seeded random generator
self.config['number_bootstraps'] = 1000
mmax, sigma_mmax = self.model.get_mmax(self.catalogue,
self.config,
seed=123456)
self.assertAlmostEqual(7.518906927, mmax)
self.assertAlmostEqual(0.058204597, sigma_mmax)
class TestCumulativeMoment(unittest.TestCase):
'''
Test suite for the
:class: hmtk.seismicity.max_magnitude.cumulative_moment_release
module
'''
def setUp(self):
filename = os.path.join(BASE_DATA_PATH, 'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {'algorithm': None, 'number_bootstraps': None}
self.model = CumulativeMoment()
def test_check_config(self):
# Tests the configuration checker
# Test 1: No bootstraps specified
self.config['number_bootstraps'] = None
fixed_config = self.model.check_config(self.config)
self.assertEqual(1, fixed_config['number_bootstraps'])
# Test 2: Invalid number of bootstraps specified
self.config['number_bootstraps'] = 0
fixed_config = self.model.check_config(self.config)
self.assertEqual(1, fixed_config['number_bootstraps'])
# Test 3: Valid number of bootstraps
self.config['number_bootstraps'] = 1000
fixed_config = self.model.check_config(self.config)
self.assertEqual(1000, fixed_config['number_bootstraps'])
def test_cumulative_moment(self):
# Tests the cumulative moment function
# Test 1: Ordinary behaviour using the completeness_test_cat.csv
self.assertAlmostEqual(
7.5,
self.model.cumulative_moment(self.catalogue.data['year'],
self.catalogue.data['magnitude']), 1)
# Test 2: If catalogue is less than or equal to 1 year duration
id0 = self.catalogue.data['year'].astype(int) == 1990
self.assertTrue(
np.isinf(
self.model.cumulative_moment(
self.catalogue.data['year'][id0],
self.catalogue.data['magnitude'][id0])))
def test_get_mmax_cumulative_moment(self):
# Tests the cumulative moment function sampled with uncertainty
# Test 1: Case when no sigma is found on magnitude
self.catalogue.data['backup'] = np.copy(
self.catalogue.data['sigmaMagnitude'])
self.catalogue.data['sigmaMagnitude'] = None
mmax, sigma_mmax = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(7.4847335589, mmax, 1)
self.assertAlmostEqual(0.0, sigma_mmax)
# Test 2: Case when one or no bootstraps are specified
self.catalogue.data['sigmaMagnitude'] = self.catalogue.data['backup']
self.config['number_bootstraps'] = 0
mmax, sigma_mmax = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(7.4847335589, mmax, 1)
self.assertAlmostEqual(0.0, sigma_mmax)
# Test 3: Ordinary test case with uncertainty - seeded random generator
self.config['number_bootstraps'] = 1000
# Can fix the seed (used for testing!)
np.random.seed(123456)
mmax, sigma_mmax = self.model.get_mmax(self.catalogue, self.config)
self.assertAlmostEqual(7.518906927, mmax)
self.assertAlmostEqual(0.058204597, sigma_mmax)
}
mmax_ksb = KijkoSellevolBayes()
mmax, mmax_sigma = mmax_ksb.get_mmax(catalogue, mmax_config)
print 'Mmax = %8.3f +/- %8.3f' % (mmax, mmax_sigma)
# In[ ]:
mmax_config = {
'number_earthquakes':
100, # Selects the N largest earthquakes in the catalogue for analysis
'input_mmax': None,
'input_mmax_uncertainty': 0.5
}
mmax_knpg = KijkoNonParametricGaussian()
mmax, mmax_sigma = mmax_knpg.get_mmax(catalogue, mmax_config)
print 'Mmax = %8.3f +/- %8.3f' % (mmax, mmax_sigma)
# In[ ]:
mmax_config = {
'number_bootstraps': 1000
} # Number of samples for the uncertainty analyis
mmax_cum_mo = CumulativeMoment()
mmax, mmax_sigma = mmax_cum_mo.get_mmax(catalogue, mmax_config)
print 'Mmax = %8.3f +/- %8.3f' % (mmax, mmax_sigma)