def get_magnitude_time_distribution(self, magnitude_bins, time_bins,
normalisation=False, bootstrap=None):
'''
Returns a 2-D histogram indicating the number of earthquakes in a
set of time-magnitude bins. Time is in decimal years!
:param numpy.ndarray magnitude_bins:
Bin edges for the magnitudes
:param numpy.ndarray time_bins:
Bin edges for the times
: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:
2D histogram of events in magnitude-year bins
'''
return bootstrap_histogram_2D(
self.get_decimal_time(),
self.data['magnitude'],
time_bins,
magnitude_bins,
xsigma=np.zeros(self.get_number_events()),
ysigma=self.data['sigmaMagnitude'],
normalisation=normalisation,
number_bootstraps=bootstrap)
def get_magnitude_time_distribution(self,
magnitude_bins,
time_bins,
normalisation=False,
bootstrap=None):
'''
Returns a 2-D histogram indicating the number of earthquakes in a
set of time-magnitude bins. Time is in decimal years!
:param numpy.ndarray magnitude_bins:
Bin edges for the magnitudes
:param numpy.ndarray time_bins:
Bin edges for the times
: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:
2D histogram of events in magnitude-year bins
'''
return bootstrap_histogram_2D(self.get_decimal_time(),
self.data['magnitude'],
time_bins,
magnitude_bins,
xsigma=np.zeros(
self.get_number_events()),
ysigma=self.data['sigmaMagnitude'],
normalisation=normalisation,
number_bootstraps=bootstrap)
def test_2D_bootstrap_with_uncertainty(self):
"""
Tests the bootstrap 1D histrogram function with uncertainties
"""
# Without normalisation
self.y_sigma = 0.1 * np.ones(len(self.y), dtype=float)
x_range = np.arange(0., 60., 10.)
y_range = np.arange(5., 10., 1.0)
expected_array = np.array([[1.5, 2.0, 2.0, 1.5],
[3.0, 4.0, 4.0, 3.0],
[3.0, 4.0, 4.0, 3.0],
[3.0, 4.0, 4.0, 3.0],
[3.0, 4.0, 4.0, 3.0]])
hist_values = utils.bootstrap_histogram_2D(
self.x,
self.y,
x_range,
y_range,
normalisation=False,
xsigma=self.x_sigma,
ysigma=self.y_sigma,
number_bootstraps=1000)
array_diff = expected_array - np.round(hist_values, 1)
print expected_array, hist_values, array_diff
self.assertTrue(np.all(np.fabs(array_diff) < 0.2))
# With normalisation
expected_array = np.array([[0.04, 0.05, 0.05, 0.04],
[0.05, 0.06, 0.06, 0.05],
[0.05, 0.06, 0.06, 0.05],
[0.05, 0.06, 0.06, 0.05],
[0.04, 0.05, 0.05, 0.04]])
hist_values = utils.bootstrap_histogram_2D(
self.x,
self.y,
x_range,
y_range,
normalisation=True,
xsigma=self.x_sigma,
ysigma=self.y_sigma,
number_bootstraps=1000)
array_diff = expected_array - hist_values
self.assertTrue(np.all(np.fabs(array_diff) < 0.02))
def test_2D_bootstrap_with_uncertainty(self):
"""
Tests the bootstrap 1D histrogram function with uncertainties
"""
# Without normalisation
self.y_sigma = 0.1 * np.ones(len(self.y), dtype=float)
x_range = np.arange(0., 60., 10.)
y_range = np.arange(5., 10., 1.0)
expected_array = np.array([[1.5, 2.0, 2.0, 1.5],
[3.0, 4.0, 4.0, 3.0],
[3.0, 4.0, 4.0, 3.0],
[3.0, 4.0, 4.0, 3.0],
[3.0, 4.0, 4.0, 3.0]])
hist_values = utils.bootstrap_histogram_2D(
self.x,
self.y,
x_range,
y_range,
normalisation=False,
xsigma=self.x_sigma,
ysigma=self.y_sigma,
number_bootstraps=1000)
array_diff = expected_array - np.round(hist_values, 1)
print expected_array, hist_values, array_diff
self.assertTrue(np.all(np.fabs(array_diff) < 0.2))
# With normalisation
expected_array = np.array([[0.04, 0.05, 0.05, 0.04],
[0.05, 0.06, 0.06, 0.05],
[0.05, 0.06, 0.06, 0.05],
[0.05, 0.06, 0.06, 0.05],
[0.04, 0.05, 0.05, 0.04]])
hist_values = utils.bootstrap_histogram_2D(
self.x,
self.y,
x_range,
y_range,
normalisation=True,
xsigma=self.x_sigma,
ysigma=self.y_sigma,
number_bootstraps=1000)
array_diff = expected_array - hist_values
self.assertTrue(np.all(np.fabs(array_diff) < 0.02))
def test_2D_bootstrap_no_uncertainty(self):
"""
Tests the bootstrap 1D histrogram function with no uncertainties
"""
# Without normalisation
x_range = np.arange(0., 60., 10.)
y_range = np.arange(5., 10., 1.0)
expected_array = np.array([[1., 2., 2., 2.],
[2., 4., 4., 4.],
[2., 4., 4., 4.],
[2., 4., 4., 4.],
[2., 4., 4., 4.]])
np.testing.assert_array_almost_equal(expected_array,
utils.bootstrap_histogram_2D(self.x, self.y, x_range, y_range))
# With normalisation
expected_array = expected_array / np.sum(expected_array)
np.testing.assert_array_almost_equal(expected_array,
utils.bootstrap_histogram_2D(self.x, self.y, x_range, y_range,
normalisation=True))
def test_2D_bootstrap_no_uncertainty(self):
"""
Tests the bootstrap 1D histrogram function with no uncertainties
"""
# Without normalisation
x_range = np.arange(0., 60., 10.)
y_range = np.arange(5., 10., 1.0)
expected_array = np.array([[1., 2., 2., 2.],
[2., 4., 4., 4.],
[2., 4., 4., 4.],
[2., 4., 4., 4.],
[2., 4., 4., 4.]])
np.testing.assert_array_almost_equal(expected_array,
utils.bootstrap_histogram_2D(self.x, self.y, x_range, y_range))
# With normalisation
expected_array = expected_array / np.sum(expected_array)
np.testing.assert_array_almost_equal(expected_array,
utils.bootstrap_histogram_2D(self.x, self.y, x_range, y_range,
normalisation=True))
def get_magnitude_depth_distribution(self,
magnitude_bins,
depth_bins,
normalisation=False,
bootstrap=None):
'''
Returns a 2-D magnitude-depth histogram for the catalogue
:param numpy.ndarray magnitude_bins:
Bin edges for the magnitudes
: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:
2D histogram of events in magnitude-depth bins
'''
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)
if len(self.data['sigmaMagnitude']) == 0:
self.data['sigmaMagnitude'] = np.zeros(self.get_number_events(),
dtype=float)
return bootstrap_histogram_2D(self.data['magnitude'],
self.data['depth'],
magnitude_bins,
depth_bins,
boundaries=[(0., None), (None, None)],
xsigma=self.data['sigmaMagnitude'],
ysigma=self.data['depthError'],
normalisation=normalisation,
number_bootstraps=bootstrap)
def get_magnitude_depth_distribution(self, magnitude_bins, depth_bins,
normalisation=False, bootstrap=None):
'''
Returns a 2-D magnitude-depth histogram for the catalogue
:param numpy.ndarray magnitude_bins:
Bin edges for the magnitudes
: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:
2D histogram of events in magnitude-depth bins
'''
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)
if len(self.data['sigmaMagnitude']) == 0:
self.data['sigmaMagnitude'] = np.zeros(self.get_number_events(),
dtype=float)
return bootstrap_histogram_2D(self.data['magnitude'],
self.data['depth'],
magnitude_bins,
depth_bins,
boundaries=[(0., None), (None, None)],
xsigma=self.data['sigmaMagnitude'],
ysigma=self.data['depthError'],
normalisation=normalisation,
number_bootstraps=bootstrap)
