openquake.hmtk.seismicity package#

Subpackages#

Submodules#

openquake.hmtk.seismicity.catalogue module#

Prototype of a ‘Catalogue’ class

class openquake.hmtk.seismicity.catalogue.Catalogue[source]#

Bases: object

General Catalogue Class

FLOAT_ATTRIBUTE_LIST = ['second', 'timeError', 'longitude', 'latitude', 'SemiMajor90', 'SemiMinor90', 'ErrorStrike', 'depth', 'depthError', 'magnitude', 'sigmaMagnitude']#

INT_ATTRIBUTE_LIST = ['year', 'month', 'day', 'hour', 'minute', 'flag']#

SORTED_ATTRIBUTE_LIST = ['eventID', 'Agency', 'year', 'month', 'day', 'hour', 'minute', 'second', 'timeError', 'longitude', 'latitude', 'SemiMajor90', 'SemiMinor90', 'ErrorStrike', 'depth', 'depthError', 'magnitude', 'sigmaMagnitude', 'magnitudeType']#

STRING_ATTRIBUTE_LIST = ['eventID', 'Agency', 'magnitudeType', 'comment']#

TOTAL_ATTRIBUTE_LIST = ['sigmaMagnitude', 'second', 'timeError', 'magnitudeType', 'depthError', 'depth', 'SemiMinor90', 'Agency', 'latitude', 'SemiMajor90', 'month', 'flag', 'day', 'minute', 'eventID', 'ErrorStrike', 'hour', 'comment', 'year', 'longitude', 'magnitude']#

add_event()[source]#

catalogue_mt_filter(mt_table, flag=None)[source]#

Filter the catalogue using a magnitude-time table. The table has two columns and n-rows.

Parameters:: mt_table (nump.ndarray) – Magnitude time table with n-rows where column 1 is year and column 2 is magnitude

concatenate(catalogue)[source]#

This method attaches one catalogue to the current one

Parameters:: catalogue – An instance of htmk.seismicity.catalogue.Catalogue

get_bounding_box()[source]#

Returns the bounding box of the catalogue

Returns:: (West, East, South, North)

get_decimal_time()[source]#: Returns the time of the catalogue as a decimal

get_depth_distribution(depth_bins, normalisation=False, bootstrap=None)[source]#

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

Parameters:

depth_bins (numpy.ndarray) – getBin edges for the depths
normalisation (bool) – Choose to normalise the results such that the total contributions sum to 1.0 (True) or not (False)
bootstrap (int) – Number of bootstrap samples

Returns:

Histogram of depth values

get_depth_pmf(depth_bins, default_depth=5.0, bootstrap=None)[source]#: Returns the depth distribution of the catalogue as a probability mass function

get_magnitude_depth_distribution(magnitude_bins, depth_bins, normalisation=False, bootstrap=None)[source]#

Returns a 2-D magnitude-depth histogram for the catalogue

Parameters:

magnitude_bins (numpy.ndarray) – Bin edges for the magnitudes
depth_bins (numpy.ndarray) – Bin edges for the depths
normalisation (bool) – Choose to normalise the results such that the total contributions sum to 1.0 (True) or not (False)
bootstrap (int) – Number of bootstrap samples

Returns:

2D histogram of events in magnitude-depth bins

get_magnitude_time_distribution(magnitude_bins, time_bins, normalisation=False, bootstrap=None)[source]#

Returns a 2-D histogram indicating the number of earthquakes in a set of time-magnitude bins. Time is in decimal years!

Parameters:

magnitude_bins (numpy.ndarray) – Bin edges for the magnitudes
time_bins (numpy.ndarray) – Bin edges for the times
normalisation (bool) – Choose to normalise the results such that the total contributions sum to 1.0 (True) or not (False)
bootstrap (int) – Number of bootstrap samples

Returns:

2D histogram of events in magnitude-year bins

get_number_events()[source]#

get_observed_mmax_sigma(default=None)[source]#

Returns:: the sigma for the maximum observed magnitude

hypocentres_as_mesh()[source]#: Render the hypocentres to a nhlib.geo.mesh.Mesh object

hypocentres_to_cartesian()[source]#: Render the hypocentres to a cartesian array

load_from_array(keys, data_array)[source]#

This loads the data contained in an array into the catalogue object

Parameters:: keys – A list of keys explaining the content of the columns in the array

load_to_array(keys)[source]#

This loads the data contained in the catalogue into a numpy array. The method works only for float data

Parameters:: keys – A list of keys to be uploaded into the array

classmethod make_from_dict(data)[source]#

purge_catalogue(flag_vector)[source]#

Purges present catalogue with invalid events defined by flag_vector

Parameters:: flag_vector (numpy.ndarray) – Boolean vector showing if events are selected (True) or not (False)

select_catalogue_events(id0)[source]#

Orders the events in the catalogue according to an indexing vector.

Parameters:: id0 (np.ndarray) – Pointer array indicating the locations of selected events

sort_catalogue_chronologically()[source]#: Sorts the catalogue into chronological order

update_end_year()[source]#: NOTE: To be called only when the catalogue is loaded (not when it is modified by declustering or completeness-based filtering)

update_start_year()[source]#: NOTE: To be called only when the catalogue is loaded (not when it is modified by declustering or completeness-based filtering)

write_catalogue(output_file, key_list=['eventID', 'Agency', 'year', 'month', 'day', 'hour', 'minute', 'second', 'timeError', 'longitude', 'latitude', 'SemiMajor90', 'SemiMinor90', 'ErrorStrike', 'depth', 'depthError', 'magnitude', 'sigmaMagnitude', 'magnitudeType'])[source]#

Writes the catalogue to file using HTMK format (CSV).

Parameters:

output_file – Name of the output file
key_list – Optional list of attribute keys to be exported

openquake.hmtk.seismicity.gcmt_catalogue module#

Implements sets of classes for mapping components of the focal mechanism

class openquake.hmtk.seismicity.gcmt_catalogue.GCMTCatalogue(start_year=None, end_year=None)[source]#

Bases: Catalogue

Class to hold a catalogue of moment tensors

FLOAT_ATTRIBUTE_LIST = ['second', 'timeError', 'longitude', 'latitude', 'SemiMajor90', 'SemiMinor90', 'ErrorStrike', 'depth', 'depthError', 'magnitude', 'sigmaMagnitude', 'moment', 'strike1', 'rake1', 'dip1', 'strike2', 'rake2', 'dip2', 'eigenvalue_b', 'azimuth_b', 'plunge_b', 'eigenvalue_p', 'azimuth_p', 'plunge_p', 'eigenvalue_t', 'azimuth_t', 'plunge_t', 'f_clvd', 'e_rel']#

INT_ATTRIBUTE_LIST = ['eventID', 'year', 'month', 'day', 'hour', 'minute', 'flag']#

STRING_ATTRIBUTE_LIST = ['Agency', 'magnitudeType', 'comment', 'centroidID']#

TOTAL_ATTRIBUTE_LIST = ['timeError', 'eigenvalue_p', 'e_rel', 'latitude', 'SemiMajor90', 'flag', 'dip1', 'day', 'centroidID', 'minute', 'plunge_p', 'eventID', 'azimuth_p', 'ErrorStrike', 'comment', 'magnitude', 'plunge_b', 'azimuth_t', 'rake2', 'sigmaMagnitude', 'second', 'eigenvalue_b', 'magnitudeType', 'depthError', 'depth', 'plunge_t', 'SemiMinor90', 'Agency', 'month', 'rake1', 'strike2', 'strike1', 'moment', 'f_clvd', 'hour', 'eigenvalue_t', 'dip2', 'year', 'longitude', 'azimuth_b']#

gcmt_to_simple_array(centroid_location=True)[source]#: Converts the GCMT catalogue to a simple array of [ID, year, month, day, hour, minute, second, long., lat., depth, Mw, strike1, dip1, rake1, strike2, dip2, rake2, b-plunge, b-azimuth, b-eigenvalue, p-plunge, p-azimuth, p-eigenvalue, t-plunge, t-azimuth, t-eigenvalue, moment, f_clvd, erel]

get_number_tensors()[source]#: Returns number of CMTs

select_catalogue_events(id0)[source]#

Orders the events in the catalogue according to an indexing vector

Parameters:: id0 (np.ndarray) – Pointer array indicating the locations of selected events

serialise_to_hmtk_csv(filename, centroid_location=True)[source]#: Serialise the catalogue to a simple csv format, designed for comptibility with the GEM Hazard Modeller’s Toolkit

class openquake.hmtk.seismicity.gcmt_catalogue.GCMTCentroid(reference_date, reference_time)[source]#

Bases: object

Representation of a GCMT centroid

class openquake.hmtk.seismicity.gcmt_catalogue.GCMTEvent[source]#

Bases: object

Class to represent full GCMT moment tensor in ndk format

get_f_clvd()[source]#

Returns the statistic f_clvd: the signed ratio of the sizes of the intermediate and largest principal moments:

f_clvd = -b_axis_eigenvalue / max(|t_axis_eigenvalue|,|p_axis_eigenvalue|)

get_relative_error()[source]#: Returns the relative error statistic (e_rel), defined by Frohlich & Davis (1999): e_rel = sqrt((U:U) / (M:M)) where M is the moment tensor, U is the uncertainty tensor and : is the tensor dot product

class openquake.hmtk.seismicity.gcmt_catalogue.GCMTHypocentre[source]#

Bases: object

Simple representation of the hypocentre

class openquake.hmtk.seismicity.gcmt_catalogue.GCMTMomentTensor(reference_frame=None)[source]#

Bases: object

Class to represent a moment tensor and its associated methods

eigendecompose(normalise=False)[source]#: Performs and eigendecomposition of the tensor and orders into descending eigenvalues

get_nodal_planes()[source]#: Returns the nodal planes by eigendecomposition of the moment tensor

get_principal_axes()[source]#: Uses the eigendecomposition to extract the principal axes from the moment tensor - returning an instance of the GCMTPrincipalAxes class

normalise_tensor()[source]#: Normalise the tensor by dividing it by its norm, defined such that np.sqrt(X:X)

class openquake.hmtk.seismicity.gcmt_catalogue.GCMTNodalPlanes[source]#

Bases: object

Class to represent the nodal plane distribution of the tensor Each nodal plane is represented as a dictionary of the form: {‘strike’:, ‘dip’:, ‘rake’:}

class openquake.hmtk.seismicity.gcmt_catalogue.GCMTPrincipalAxes[source]#

Bases: object

Class to represent the eigensystem of the tensor in terms of T-, B- and P- plunge and azimuth

get_azimuthal_projection(height=1.0)[source]#: Returns the azimuthal projection of the tensor according to the method of Frohlich (2001)

get_moment_tensor_from_principal_axes()[source]#: Retreives the moment tensor from the prinicpal axes

openquake.hmtk.seismicity.gcmt_catalogue.cmp(a, b)[source]#

openquake.hmtk.seismicity.gcmt_catalogue.cmp_mat(a, b)[source]#: Sorts two matrices returning a positive or zero value

openquake.hmtk.seismicity.gcmt_utils module#

Set of moment tensor utility functions

openquake.hmtk.seismicity.gcmt_utils.eigendecompose(tensor, normalise=False)[source]#: Performs and eigendecomposition of the tensor and orders into descending eigenvalues

openquake.hmtk.seismicity.gcmt_utils.get_azimuth_plunge(vect, degrees=True)[source]#: For a given vector in USE format, retrieve the azimuth and plunge

openquake.hmtk.seismicity.gcmt_utils.matrix_to_euler(rotmat)[source]#: Inverse of euler_to_matrix().

openquake.hmtk.seismicity.gcmt_utils.moment_magnitude_scalar(moment)[source]#: Uses Hanks & Kanamori formula for calculating moment magnitude from a scalar moment (Nm)

openquake.hmtk.seismicity.gcmt_utils.ned_to_use(tensor)[source]#: Converts a tensor in NED coordinate sytem to USE

openquake.hmtk.seismicity.gcmt_utils.normalise_tensor(tensor)[source]#: Normalise the tensor by dividing it by its norm, defined such that np.sqrt(X:X)

openquake.hmtk.seismicity.gcmt_utils.tensor_components_to_ned(mrr, mtt, mpp, mrt, mrp, mtp)[source]#

Converts components to North, East, Down definition:

NED = [[mtt, -mtp, mrt],
       [-mtp, mpp, -mrp],
       [mrt, -mtp, mrr]]

openquake.hmtk.seismicity.gcmt_utils.tensor_components_to_use(mrr, mtt, mpp, mrt, mrp, mtp)[source]#

Converts components to Up, South, East definition:

USE = [[mrr, mrt, mrp],
       [mtt, mtt, mtp],
       [mrp, mtp, mpp]]

openquake.hmtk.seismicity.gcmt_utils.tensor_to_6component(tensor, frame='USE')[source]#: Returns a tensor to six component vector [Mrr, Mtt, Mpp, Mrt, Mrp, Mtp]

openquake.hmtk.seismicity.gcmt_utils.unique_euler(alpha, beta, gamma)[source]#

Uniquify euler angle triplet. Put euler angles into ranges compatible with (dip,strike,-rake) in seismology: alpha (dip) : [0, pi/2] beta (strike) : [0, 2*pi) gamma (-rake) : [-pi, pi) If alpha is near to zero, beta is replaced by beta+gamma and gamma is set to zero, to prevent that additional ambiguity.

If alpha is near to pi/2, beta is put into the range [0,pi).

openquake.hmtk.seismicity.gcmt_utils.use_to_ned(tensor)[source]#: Converts a tensor in USE coordinate sytem to NED

openquake.hmtk.seismicity.selector module#

Class to implement set of functionalities for selecting events from and earthquake catalogue

class openquake.hmtk.seismicity.selector.CatalogueSelector(master_catalogue, create_copy=True)[source]#

Bases: object

Class to implement methods for selecting subsets of the catalogue according to various attribute criteria.

Attr catalogue:: The catalogue to which the selection is applied as instance of openquake.hmtk.seismicity.catalogue.Catalogue
Attr create_copy:: Boolean to indicate whether to create copy of the original catalogue before selecting {default = True}

cartesian_square_centred_on_point(point, distance, **kwargs)[source]#

Select earthquakes from within a square centered on a point

Parameters:

point – Centre point as instance of nhlib.geo.point.Point class
distance – Distance (km)

Returns:

Instance of openquake.hmtk.seismicity.catalogue.Catalogue class containing only selected events

circular_distance_from_point(point, distance, **kwargs)[source]#

Select earthquakes within a distance from a Point

Parameters:

point – Centre point as instance of nhlib.geo.point.Point class
distance (float) – Distance (km)

Returns:

Instance of openquake.hmtk.seismicity.catalogue.Catalogue containing only selected events

create_cluster_set(vcl)[source]#

For a given catalogue and list of cluster IDs this function splits the catalogue into a dictionary containing an individual catalogue of events within each cluster

Parameters:: vcl (numpy.ndarray) – Cluster ID list
Returns:: Dictionary of instances of the :class: openquake.hmtk.seismicity.catalogue.Catalogue, where each instance if the catalogue of each cluster

select_catalogue(valid_id)[source]#

Method to post-process the catalogue based on the selection options

Parameters:: valid_id (numpy.ndarray) – Boolean vector indicating whether each event is selected (True) or not (False)
Returns:: Catalogue of selected events as instance of openquake.hmtk.seismicity.catalogue.Catalogue class

within_bounding_box(limits)[source]#

Selects the earthquakes within a bounding box.

Parameters:

limits –

A list or a numpy array with four elements in the following order:

min x (longitude)
min y (latitude)
max x (longitude)
max y (latitude)

Returns:

Returns a :class:htmk.seismicity.catalogue.Catalogue` instance

within_depth_range(lower_depth=None, upper_depth=None)[source]#

Selects events within a specified depth range

Parameters:

lower_depth (float) – Lower depth for consideration
upper_depth (float) – Upper depth for consideration

Returns:

Instance of openquake.hmtk.seismicity.catalogue.Catalogue containing only selected events

within_joyner_boore_distance(surface, distance, **kwargs)[source]#

Select events within a Joyner-Boore distance of a fault

Parameters:

surface – Fault surface as instance of nhlib.geo.surface.base.SimpleFaultSurface or as instance of nhlib.geo.surface.ComplexFaultSurface
distance (float) – Rupture distance (km)

Returns:

Instance of openquake.hmtk.seismicity.catalogue.Catalogue containing only selected events

within_magnitude_range(lower_mag=None, upper_mag=None)[source]#

Parameters:

lower_mag (float) – Lower magnitude for consideration
upper_mag (float) – Upper magnitude for consideration

Returns:

Instance of openquake.hmtk.seismicity.catalogue.Catalogue class containing only selected events

within_polygon(polygon, distance=None, **kwargs)[source]#

Select earthquakes within polygon

Parameters:

polygon – Centre point as instance of nhlib.geo.polygon.Polygon class
distance (float) – Buffer distance (km) (can take negative values)

Returns:

Instance of openquake.hmtk.seismicity.catalogue.Catalogue containing only selected events

within_rupture_distance(surface, distance, **kwargs)[source]#

Select events within a rupture distance from a fault surface

Parameters:

surface – Fault surface as instance of nhlib.geo.surface.base.BaseSurface
distance (float) – Rupture distance (km)

Returns:

Instance of openquake.hmtk.seismicity.catalogue.Catalogue containing only selected events

within_time_period(start_time=None, end_time=None)[source]#

Select earthquakes occurring within a given time period

Parameters:

start_time – Earliest time (as datetime.datetime object)
end_time – Latest time (as datetime.datetime object)

Returns:

Instance of openquake.hmtk.seismicity.catalogue.Catalogue containing only selected events

openquake.hmtk.seismicity.utils module#

Utility functions for seismicity calculations

openquake.hmtk.seismicity.utils.TO_Q(lat)[source]#

openquake.hmtk.seismicity.utils.TO_Qm(lat)[source]#

openquake.hmtk.seismicity.utils.area_of_polygon(polygon)[source]#: Returns the area of an OpenQuake polygon in square kilometres

openquake.hmtk.seismicity.utils.bootstrap_histogram_1D(values, intervals, uncertainties=None, normalisation=False, number_bootstraps=None, boundaries=None, random_seed=42)[source]#

Bootstrap samples a set of vectors

Parameters:

values (numpy.ndarray) – The data values
intervals (numpy.ndarray) – The bin edges
uncertainties (numpy.ndarray) – The standard deviations of each observation
normalisation (bool) – If True then returns the histogram as a density function
number_bootstraps (int) – Number of bootstraps
boundaries (tuple) – (Lower, Upper) bounds on the data
random_seed – Seed used in the random number generator
returns – 1-D histogram of data

openquake.hmtk.seismicity.utils.bootstrap_histogram_2D(xvalues, yvalues, xbins, ybins, boundaries=[None, None], xsigma=None, ysigma=None, normalisation=False, number_bootstraps=None, random_seed=42)[source]#

Calculates a 2D histogram of data, allowing for normalisation and bootstrap sampling

Parameters:

xvalues (numpy.ndarray) – Data values of the first variable
yvalues (numpy.ndarray) – Data values of the second variable
xbins (numpy.ndarray) – Bin edges for the first variable
ybins (numpy.ndarray) – Bin edges for the second variable
boundaries (list) – List of (Lower, Upper) tuples corresponding to the bounds of the two data sets
xsigma (numpy.ndarray) – Error values (standard deviatons) on first variable
ysigma (numpy.ndarray) – Error values (standard deviatons) on second variable
normalisation (bool) – If True then returns the histogram as a density function
number_bootstraps (int) – Number of bootstraps
random_seed – Seed used in the random number generator
returns – 2-D histogram of data

openquake.hmtk.seismicity.utils.decimal_time(year, month, day, hour, minute, second)[source]#

Returns the full time as a decimal value

Parameters:

year – Year of events (integer numpy.ndarray)
month – Month of events (integer numpy.ndarray)
day – Days of event (integer numpy.ndarray)
hour – Hour of event (integer numpy.ndarray)
minute – Minute of event (integer numpy.ndarray)
second – Second of event (float numpy.ndarray)

Returns decimal_time:

Decimal representation of the time (as numpy.ndarray)

openquake.hmtk.seismicity.utils.decimal_year(year, month, day)[source]#

Allows to calculate the decimal year for a vector of dates (TODO this is legacy code kept to maintain comparability with previous declustering algorithms!)

Parameters:

year (numpy.ndarray) – year column from catalogue matrix
month (numpy.ndarray) – month column from catalogue matrix
day (numpy.ndarray) – day column from catalogue matrix

Returns:

decimal year column

Return type:

numpy.ndarray

openquake.hmtk.seismicity.utils.greg2julian(year, month, day, hour, minute, second)[source]#

Function to convert a date from Gregorian to Julian format

Parameters:

year – Year of events (integer numpy.ndarray)
month – Month of events (integer numpy.ndarray)
day – Days of event (integer numpy.ndarray)
hour – Hour of event (integer numpy.ndarray)
minute – Minute of event (integer numpy.ndarray)
second – Second of event (float numpy.ndarray)

Returns julian_time:

Julian representation of the time (as float numpy.ndarray)

openquake.hmtk.seismicity.utils.haversine(lon1, lat1, lon2, lat2, radians=False, earth_rad=6371.227)[source]#

Allows to calculate geographical distance using the haversine formula.

Parameters:

lon1 (numpy.ndarray) – longitude of the first set of locations
lat1 (numpy.ndarray) – latitude of the frist set of locations
lon2 (numpy.float64) – longitude of the second set of locations
lat2 (numpy.float64) – latitude of the second set of locations
radians (bool) – states if locations are given in terms of radians
earth_rad (float) – radius of the earth in km

Returns:

geographical distance in km

Return type:

numpy.ndarray

openquake.hmtk.seismicity.utils.hmtk_histogram_1D(values, intervals, offset=1e-10)[source]#

So, here’s the problem. We tend to refer to certain data (like magnitudes) rounded to the nearest 0.1 (or similar, i.e. 4.1, 5.7, 8.3 etc.). We also like our tables to fall on on the same interval, i.e. 3.1, 3.2, 3.3 etc. We usually assume that the counter should correspond to the low edge, i.e. 3.1 is in the group 3.1 to 3.2 (i.e. L <= M < U). Floating point precision can be a bitch! Because when we read in magnitudes from files 3.1 might be represented as 3.0999999999 or as 3.1000000000001 and this is seemingly random. Similarly, if np.arange() is used to generate the bin intervals then we see similar floating point problems emerging. As we are frequently encountering density plots with empty rows or columns where data should be but isn’t because it has been assigned to the wrong group.

Instead of using numpy’s own historgram function we use a slower numpy version that allows us to offset the intervals by a smaller amount and ensure that 3.0999999999, 3.0, and 3.10000000001 would fall in the group 3.1 - 3.2!

Parameters:

values (numpy.ndarray) – Values of data
intervals (numpy.ndarray) – Data bins
offset (float) – Small amount to offset the bins for floating point precision

Returns:

Count in each bin (as float)

openquake.hmtk.seismicity.utils.hmtk_histogram_2D(xvalues, yvalues, bins, x_offset=1e-10, y_offset=1e-10)[source]#

See the explanation for the 1D case - now applied to 2D.

Parameters:

xvalues (numpy.ndarray) – Values of x-data
yvalues (numpy.ndarray) – Values of y-data
bins (tuple) – Tuple containing bin intervals for x-data and y-data (as numpy arrays)
x_offset (float) – Small amount to offset the x-bins for floating point precision
y_offset (float) – Small amount to offset the y-bins for floating point precision

Returns:

Count in each bin (as float)

openquake.hmtk.seismicity.utils.leap_check(year)[source]#: Returns logical array indicating if year is a leap year

openquake.hmtk.seismicity.utils.lonlat_to_laea(lon, lat, lon0, lat0, f_e=0.0, f_n=0.0)[source]#

Converts vectors of longitude and latitude into Lambert Azimuthal Equal Area projection (km), with respect to an origin point

Parameters:

lon (numpy.ndarray) – Longitudes
lat (numpy.ndarray) – Latitude
lon0 (float) – Central longitude
lat0 (float) – Central latitude
f_e (float) – False easting (km)
f_e – False northing (km)

Returns:

easting (km)
northing (km)

openquake.hmtk.seismicity.utils.piecewise_linear_scalar(params, xval)[source]#

Piecewise linear function for a scalar variable xval (float).

Parameters:

params – Piecewise linear parameters (numpy.ndarray) in the following form: [slope_i,… slope_n, turning_point_i, …, turning_point_n, intercept] Length params === 2 * number_segments, e.g. [slope_1, slope_2, slope_3, turning_point1, turning_point_2, intercept]
xval – Value for evaluation of function (float)

Returns:

Piecewise linear function evaluated at point xval (float)

openquake.hmtk.seismicity.utils.sample_truncated_gaussian_vector(data, uncertainties, bounds=None)[source]#

Samples a Gaussian distribution subject to boundaries on the data

Parameters:

data (numpy.ndarray) – Vector of N data values
uncertainties (numpy.ndarray) – Vector of N data uncertainties
number_bootstraps (int) – Number of bootstrap samples
bounds (tuple) – (Lower, Upper) bound of data space

openquake.hmtk.seismicity package#

Subpackages#

Submodules#

openquake.hmtk.seismicity.catalogue module#

openquake.hmtk.seismicity.gcmt_catalogue module#

openquake.hmtk.seismicity.gcmt_utils module#

openquake.hmtk.seismicity.selector module#

openquake.hmtk.seismicity.utils module#

Module contents#