def test_complete_stepp_analysis_basic(self):
'''
Basic test of the entire completeness analysis using a synthetic
test catalogue with in-built completeness periods
'''
parser0 = CsvCatalogueParser(INPUT_FILE_1)
self.catalogue = parser0.read_file()
self.config = {'magnitude_bin': 0.5,
'time_bin': 5.0,
'increment_lock': True,
'filename': None}
expected_completeness_table = np.array([[1990., 4.0],
[1962., 4.5],
[1959., 5.0],
[1906., 5.5],
[1906., 6.0],
[1904., 6.5],
[1904., 7.0]])
np.testing.assert_array_almost_equal(
expected_completeness_table,
self.process.completeness(self.catalogue, self.config))
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):
"""
Read the sample catalogue
"""
flnme = 'gardner_knopoff_test_catalogue.csv'
filename = os.path.join(self.BASE_DATA_PATH, flnme)
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
def setUp(self):
"""
Read the sample catalogue
"""
flnme = 'gardner_knopoff_test_catalogue.csv'
filename = os.path.join(self.BASE_DATA_PATH, flnme)
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
def setUp(self):
filename = os.path.join(BASE_DATA_PATH, 'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {'maximum_iterations': 1000,
'number_earthquakes': 100,
'number_samples': 51,
'tolerance': 0.05}
self.model = KijkoNonParametricGaussian()
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()
def setUp(self):
filename = os.path.join(BASE_DATA_PATH, 'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {'maximum_iterations': 1000,
'number_earthquakes': 100,
'number_samples': 51,
'tolerance': 0.05}
self.model = KijkoNonParametricGaussian()
def setUp(self):
filename = os.path.join(BASE_DATA_PATH, 'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {'b-value': 1.0,
'sigma-b': 0.05,
'input_mmin': 5.0,
'input_mmax': None,
'input_mmax_uncertainty': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolBayes()
def get_catalogue(catalogue_filename, force_csv=False):
"""
"""
ext = Path(catalogue_filename).suffix
path, name = os.path.split(catalogue_filename)
cat_pickle_filename = os.path.join(path, Path(name).stem+'.pkl')
if (ext == '.csv' or ext == '.hmtk') or (force_csv):
parser = CsvCatalogueParser(catalogue_filename)
catalogue = parser.read_file()
pickle.dump(catalogue, open(cat_pickle_filename, 'wb'))
elif ext == '.pkl' or ext == '.p':
catalogue = pickle.load(open(catalogue_filename, 'rb'))
elif ext == '.ndk':
parser = ParseNDKtoGCMT(catalogue_filename)
catalogue = parser.read_file()
pickle.dump(catalogue, open(cat_pickle_filename, 'wb'))
else:
raise ValueError('File with an unkown extension')
return catalogue
def setUp(self):
filename = os.path.join(BASE_DATA_PATH, 'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {'b-value': 1.0,
'sigma-b': 0.05,
'input_mmin': 5.0,
'input_mmax': None,
'input_mmax_uncertainty': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolBayes()
def load_catalogue(catalogue_fname):
ext = pathlib.Path(catalogue_fname).suffix
if ext == '.pkl' or ext == '.p':
#
# load pickle file
cat = pickle.load(open(catalogue_fname, 'rb'))
elif ext == '.csv' or ext == '.hmtk':
#
# load hmtk file
parser = CsvCatalogueParser(catalogue_fname)
cat = parser.read_file()
cat.sort_catalogue_chronologically()
return cat
def setUp(self):
'''
Set up test class
'''
filename = os.path.join(BASE_DATA_PATH, 'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {'b-value': 1.0,
'input_mmin': 5.0,
'input_mmax': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolFixedb()
def setUp(self):
'''
Set up test class
'''
filename = os.path.join(BASE_DATA_PATH, 'completeness_test_cat.csv')
parser0 = CsvCatalogueParser(filename)
self.catalogue = parser0.read_file()
self.config = {'b-value': 1.0,
'input_mmin': 5.0,
'input_mmax': None,
'tolerance': 0.001,
'maximum_iterations': 1000}
self.model = KijkoSellevolFixedb()
def test_convert_to_decimal_2(self):
fname = os.path.join(DATA_DIR, 'test_cat_02.csv')
parser = CsvCatalogueParser(fname)
cat = parser.read_file()
for lab in ['day', 'hour', 'minute', 'second']:
idx = numpy.isnan(cat.data[lab])
if lab == 'day':
cat.data[lab][idx] = 1
elif lab == 'second':
cat.data[lab][idx] = 0.0
else:
cat.data[lab][idx] = 0
computed = cat.get_decimal_time()
expected = numpy.array([2015., 1963.65205479, 1963.65217088,
1963.58082192, 1999.62793753])
numpy.testing.assert_almost_equal(computed, expected)
def test_convert_to_decimal_2(self):
fname = os.path.join(DATA_DIR, 'test_cat_02.csv')
parser = CsvCatalogueParser(fname)
cat = parser.read_file()
for lab in ['day', 'hour', 'minute', 'second']:
idx = numpy.isnan(cat.data[lab])
if lab == 'day':
cat.data[lab][idx] = 1
elif lab == 'second':
cat.data[lab][idx] = 0.0
else:
cat.data[lab][idx] = 0
computed = cat.get_decimal_time()
expected = numpy.array([2015., 1963.65205479, 1963.65217088,
1963.58082192, 1999.62793753])
numpy.testing.assert_almost_equal(computed, expected)
def _load_catalogue(catalogue_fname):
ext = pathlib.Path(catalogue_fname).suffix
if ext == '.pkl' or ext == '.p':
# load pickle file
cat = pickle.load(open(catalogue_fname, 'rb'))
elif ext in ['.csv', '.hmtk']:
file = open(catalogue_fname, "r")
nonempty_lines = [line.strip("\n") for line in file if line != "\n"]
line_count = len(nonempty_lines)
file.close()
if line_count < 2:
return None
#
# load hmtk file
parser = CsvCatalogueParser(catalogue_fname)
cat = parser.read_file()
return cat
def test_IDL_02(self):
# tests that the smoothing works accross the IDL
# set bounds crossing the IDL
lons = [-179.5, 179.5, 179.5, -179.5, -179.5]
lats = [-15.5, -15.5, -16.0, -16.0, -15.5]
mesh = setUpMesh(lons, lats)
# read in the test catalogue
cat_filename = TESTDIR / 'data/tools/idl_test_catalogue.csv'
catalogue_parser = CsvCatalogueParser(cat_filename)
cat = catalogue_parser.read_file()
# smooth the catalogue onto the mesh grid
smooth = Smoothing(cat, mesh, 20)
values = smooth.gaussian(50, 20)
# check that smoothed values sum to 1.0
self.assertAlmostEqual(sum(values), len(cat.data['depth']), 5)
# check that the Gaussian distribution works across IDL:
# assert that max %-difference is < 1
self.assertLess(check_symmetry(mesh, values), 1)
def test_convert_to_decimal_1(self):
fname = os.path.join(DATA_DIR, 'test_cat_01.csv')
parser = CsvCatalogueParser(fname)
cat = parser.read_file()
with self.assertRaises(ValueError):
_ = cat.get_decimal_time()
def test_convert_to_decimal_1(self):
fname = os.path.join(DATA_DIR, 'test_cat_01.csv')
parser = CsvCatalogueParser(fname)
cat = parser.read_file()
with self.assertRaises(ValueError):
_ = cat.get_decimal_time()