def test_weichert_factor(self):
'''
Tests the Weichert adjustment factor to compensate for time varying
completeness
'''
# Test 1: Comparison against the USGS Implementation
beta = 0.8 * np.log(10.)
end_year = 2006.
comp_table = np.array([[1933., 4.0],
[1900., 5.0],
[1850., 6.0],
[1850., 7.0]])
self.assertAlmostEqual(0.0124319686,
utils.get_weichert_factor(beta, comp_table[:, 1], comp_table[:, 0],
end_year)[0])
# Test 2: Single value of completeness
comp_table = np.array([[1960., 4.0]])
self.assertAlmostEqual(1.0,
utils.get_weichert_factor(beta, comp_table[:, 1], comp_table[:, 0],
end_year)[0])
def test_weichert_factor(self):
'''
Tests the Weichert adjustment factor to compensate for time varying
completeness
'''
# Test 1: Comparison against the USGS Implementation
beta = 0.8 * np.log(10.)
end_year = 2006.
comp_table = np.array([[1933., 4.0], [1900., 5.0], [1850., 6.0],
[1850., 7.0]])
self.assertAlmostEqual(
0.0124319686,
utils.get_weichert_factor(beta, comp_table[:, 1], comp_table[:, 0],
end_year)[0])
# Test 2: Single value of completeness
comp_table = np.array([[1960., 4.0]])
self.assertAlmostEqual(
1. / (2006. - 1960. + 1.),
utils.get_weichert_factor(beta, comp_table[:, 1], comp_table[:, 0],
end_year)[0])
def run_analysis(self,
catalogue,
config,
completeness_table=None,
smoothing_kernel=None):
'''
Runs an analysis of smoothed seismicity in the manner
originally implemented by Frankel (1995)
:param catalogue:
Instance of the hmtk.seismicity.catalogue.Catalogue class
catalogue.data dictionary containing the following -
'year' - numpy.ndarray vector of years
'longitude' - numpy.ndarray vector of longitudes
'latitude' - numpy.ndarray vector of latitudes
'depth' - numpy.ndarray vector of depths
:param dict config:
Configuration settings of the algorithm:
* 'Length_Limit' - Maximum number of bandwidths for use in
smoothing (Float)
* 'BandWidth' - Bandwidth (km) of the Smoothing Kernel (Float)
* 'increment' - Output incremental (True) or cumulative a-value
(False)
:param np.ndarray completeness_table:
Completeness of the catalogue assuming evenly spaced magnitudes
from most recent bin to oldest bin [year, magnitude]
:param smoothing_kernel:
Smoothing kernel as instance of :class:
hmtk.seismicity.smoothing.kernels.base.BaseSmoothingKernel
:returns:
Full smoothed seismicity data as np.ndarray, of the form
[Longitude, Latitude, Depth, Observed, Smoothed]
'''
self.catalogue = catalogue
if smoothing_kernel:
self.kernel = smoothing_kernel
else:
self.kernel = IsotropicGaussian()
# If no grid limits are specified then take from catalogue
if isinstance(self.grid_limits, list):
self.grid_limits = Grid.make_from_list(self.grid_limits)
assert self.grid_limits['xmax'] >= self.grid_limits['xmin']
assert self.grid_limits['xspc'] > 0.0
assert self.grid_limits['ymax'] >= self.grid_limits['ymin']
assert self.grid_limits['yspc'] > 0.0
elif isinstance(self.grid_limits, float):
self.grid_limits = Grid.make_from_catalogue(
self.catalogue, self.grid_limits,
config['Length_Limit'] * config['BandWidth'])
completeness_table, mag_inc = utils.get_even_magnitude_completeness(
completeness_table, self.catalogue)
end_year = self.catalogue.end_year
# Get Weichert factor
t_f, _ = utils.get_weichert_factor(self.beta, completeness_table[:, 1],
completeness_table[:, 0], end_year)
# Get the grid
self.create_3D_grid(self.catalogue, completeness_table, t_f, mag_inc)
if config['increment']:
# Get Hermann adjustment factors
fval, fival = utils.hermann_adjustment_factors(
self.bval, completeness_table[0, 1], config['increment'])
self.data[:, -1] = fval * fival * self.data[:, -1]
# Apply smoothing
smoothed_data, sum_data, sum_smooth = self.kernel.smooth_data(
self.data, config, self.use_3d)
print 'Smoothing Total Rate Comparison - ' \
'Observed: %.6e, Smoothed: %.6e' % (sum_data, sum_smooth)
self.data = np.column_stack([self.data, smoothed_data])
return self.data
def run_analysis(self, catalogue, config, completeness_table=None,
smoothing_kernel=None, end_year=None):
'''
Runs an analysis of smoothed seismicity in the manner
originally implemented by Frankel (1995)
:param catalogue:
Instance of the hmtk.seismicity.catalogue.Catalogue class
catalogue.data dictionary containing the following -
'year' - numpy.ndarray vector of years
'longitude' - numpy.ndarray vector of longitudes
'latitude' - numpy.ndarray vector of latitudes
'depth' - numpy.ndarray vector of depths
:param dict config:
Configuration settings of the algorithm:
* 'Length_Limit' - Maximum number of bandwidths for use in
smoothing (Float)
* 'BandWidth' - Bandwidth (km) of the Smoothing Kernel (Float)
* 'increment' - Output incremental (True) or cumulative a-value
(False)
:param np.ndarray completeness_table:
Completeness of the catalogue assuming evenly spaced magnitudes
from most recent bin to oldest bin [year, magnitude]
:param smoothing_kernel:
Smoothing kernel as instance of :class:
hmtk.seismicity.smoothing.kernels.base.BaseSmoothingKernel
:param float end_year:
Year considered as the final year for the analysis. If not given
the program will automatically take the last year in the catalogue.
:returns:
Full smoothed seismicity data as np.ndarray, of the form
[Longitude, Latitude, Depth, Observed, Smoothed]
'''
self.catalogue = catalogue
if smoothing_kernel:
self.kernel = smoothing_kernel
else:
self.kernel = IsotropicGaussian()
# If no grid limits are specified then take from catalogue
if not isinstance(self.grid_limits, dict):
self.get_grid_from_catalogue(config)
completeness_table, mag_inc = utils.get_even_magnitude_completeness(
completeness_table,
self.catalogue)
if not end_year:
end_year = np.max(self.catalogue.data['year'])
# Get Weichert factor
t_f, _ = utils.get_weichert_factor(self.beta,
completeness_table[:, 1],
completeness_table[:, 0],
end_year)
# Get the grid
self.create_3D_grid(self.catalogue, completeness_table, t_f, mag_inc)
if config['increment']:
# Get Hermann adjustment factors
fval, fival = utils.hermann_adjustment_factors(self.bval,
completeness_table[0, 1], config['increment'])
self.data[:, -1] = fval * fival * self.data[:, -1]
# Apply smoothing
smoothed_data, sum_data, sum_smooth = self.kernel.smooth_data(
self.data, config, self.use_3d)
print 'Smoothing Total Rate Comparison - ' \
'Observed: %.6e, Smoothed: %.6e' % (sum_data, sum_smooth)
self.data = np.column_stack([self.data, smoothed_data])
return self.data
