def test_1D_bootstrap_no_uncertainty(self):
"""
Tests the bootstrap 1D histrogram function with no uncertainties
"""
# Without normalisation
x_range = np.arange(0., 60., 10.)
expected_array = np.array([7., 14., 14., 14., 14.])
np.testing.assert_array_almost_equal(expected_array,
utils.bootstrap_histogram_1D(self.x, x_range))
# Now with normalisaton
expected_array = expected_array / np.sum(expected_array)
np.testing.assert_array_almost_equal(expected_array,
utils.bootstrap_histogram_1D(self.x, x_range, normalisation=True))
def test_1D_bootstrap_no_uncertainty(self):
"""
Tests the bootstrap 1D histrogram function with no uncertainties
"""
# Without normalisation
x_range = np.arange(0., 60., 10.)
expected_array = np.array([7., 14., 14., 14., 14.])
np.testing.assert_array_almost_equal(expected_array,
utils.bootstrap_histogram_1D(self.x, x_range))
# Now with normalisaton
expected_array = expected_array / np.sum(expected_array)
np.testing.assert_array_almost_equal(expected_array,
utils.bootstrap_histogram_1D(self.x, x_range, normalisation=True))
def get_depth_distribution(self, depth_bins, normalisation=False,
bootstrap=None):
'''
Gets the depth distribution of the earthquake catalogue to return a
single histogram. Depths may be normalised. If uncertainties are found
in the catalogue the distrbution may be bootstrap sampled
:param numpy.ndarray depth_bins:
Bin edges for the depths
:param bool normalisation:
Choose to normalise the results such that the total contributions
sum to 1.0 (True) or not (False)
:param int bootstrap:
Number of bootstrap samples
:returns:
Histogram of depth values
'''
if len(self.data['depth']) == 0:
# If depth information is missing
raise ValueError('Depths missing in catalogue')
if len(self.data['depthError']) == 0:
self.data['depthError'] = np.zeros(self.get_number_events(),
dtype=float)
return bootstrap_histogram_1D(self.data['depth'],
depth_bins,
self.data['depthError'],
normalisation=normalisation,
number_bootstraps=bootstrap,
boundaries=(0., None))
def magnitude_distribution(
catalogue, magnitude_bins_nr, normalisation, bootstrap):
bins = numpy.linspace(catalogue.data['magnitude'].min(),
catalogue.data['magnitude'].max(),
magnitude_bins_nr + 1)
return bins, bootstrap_histogram_1D(
catalogue.data['magnitude'],
bins,
catalogue.data.get('sigmaMagnitude', numpy.zeros(
catalogue.get_number_events(), dtype=float)),
normalisation=normalisation,
number_bootstraps=bootstrap,
boundaries=(0., None))
def test_1D_bootstrap_with_uncertainty(self):
"""
Tests the bootstrap 1D histrogram function with uncertainties
"""
self.x_sigma = 1.0 * np.ones(len(self.x), dtype=float)
expected_array = np.array([0.17, 0.22, 0.22, 0.22, 0.17])
x_range = np.arange(0., 60., 10.)
hist_values = utils.bootstrap_histogram_1D(self.x,
x_range,
uncertainties=self.x_sigma,
number_bootstraps=1000,
normalisation=True)
np.testing.assert_array_almost_equal(np.round(hist_values, 2),
expected_array)
def test_1D_bootstrap_with_uncertainty(self):
"""
Tests the bootstrap 1D histrogram function with uncertainties
"""
self.x_sigma = 1.0 * np.ones(len(self.x), dtype=float)
expected_array = np.array([0.17, 0.22, 0.22, 0.22, 0.17])
x_range = np.arange(0., 60., 10.)
hist_values = utils.bootstrap_histogram_1D(
self.x,
x_range,
uncertainties=self.x_sigma,
number_bootstraps=1000,
normalisation=True)
np.testing.assert_array_almost_equal(np.round(hist_values, 2),
expected_array)
def get_depth_distribution(self,
depth_bins,
normalisation=False,
bootstrap=None):
'''
Gets the depth distribution of the earthquake catalogue to return a
single histogram. Depths may be normalised. If uncertainties are found
in the catalogue the distrbution may be bootstrap sampled
:param numpy.ndarray depth_bins:
getBin edges for the depths
:param bool normalisation:
Choose to normalise the results such that the total contributions
sum to 1.0 (True) or not (False)
:param int bootstrap:
Number of bootstrap samples
:returns:
Histogram of depth values
'''
if len(self.data['depth']) == 0:
# If depth information is missing
raise ValueError('Depths missing in catalogue')
if len(self.data['depthError']) == 0:
self.data['depthError'] = np.zeros(self.get_number_events(),
dtype=float)
return bootstrap_histogram_1D(self.data['depth'],
depth_bins,
self.data['depthError'],
normalisation=normalisation,
number_bootstraps=bootstrap,
boundaries=(0., None))
