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# Copyright (c) 2010-2017, GEM Foundation, G. Weatherill, M. Pagani,
# D. Monelli.
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# The Hazard Modeller's Toolkit is free software: you can redistribute
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# Earthquake Model).
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# -*- coding: utf-8 -*-
"""
Prototype of a 'Catalogue' class
"""
import numpy as np
from openquake.hazardlib.pmf import PMF
from openquake.hazardlib.geo.mesh import Mesh
from openquake.hazardlib.geo.utils import spherical_to_cartesian
from openquake.hmtk.seismicity.utils import (decimal_time, bootstrap_histogram_1D,
bootstrap_histogram_2D)
[docs]class Catalogue(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']
STRING_ATTRIBUTE_LIST = ['eventID', 'Agency', 'magnitudeType', 'comment']
TOTAL_ATTRIBUTE_LIST = list(
(set(FLOAT_ATTRIBUTE_LIST).union(
set(INT_ATTRIBUTE_LIST))).union(
set(STRING_ATTRIBUTE_LIST)))
def __init__(self):
"""
Initialise the catalogue dictionary
"""
self.data = {}
self.end_year = None
self.start_year = None
self.processes = {'declustering': None,
'completeness': None,
'recurrence': None,
'Poisson Tests': None}
for attribute in self.TOTAL_ATTRIBUTE_LIST:
if attribute in self.FLOAT_ATTRIBUTE_LIST:
self.data[attribute] = np.array([], dtype=float)
elif attribute in self.INT_ATTRIBUTE_LIST:
self.data[attribute] = np.array([], dtype=int)
else:
self.data[attribute] = []
self.number_earthquakes = 0
[docs] def get_number_events(self):
return len(self.data['eventID'])
[docs] def add_event(self):
raise NotImplementedError
[docs] def write_catalogue(self, output_file, filetype):
raise NotImplementedError
[docs] def load_to_array(self, keys):
"""
This loads the data contained in the catalogue into a numpy array. The
method works only for float data
:param keys:
A list of keys to be uploaded into the array
:type list:
"""
# Preallocate the numpy array
data = np.empty((len(self.data[keys[0]]), len(keys)))
for i in range(0, len(self.data[keys[0]])):
for j, key in enumerate(keys):
data[i, j] = self.data[key][i]
return data
[docs] def load_from_array(self, keys, data_array):
"""
This loads the data contained in an array into the catalogue object
:param keys:
A list of keys explaining the content of the columns in the array
:type list:
"""
if len(keys) != np.shape(data_array)[1]:
raise ValueError('Key list does not match shape of array!')
for i, key in enumerate(keys):
if key in self.INT_ATTRIBUTE_LIST:
self.data[key] = data_array[:, i].astype(int)
else:
self.data[key] = data_array[:, i]
if key not in self.TOTAL_ATTRIBUTE_LIST:
print('Key %s not a recognised catalogue attribute' % key)
self.update_end_year()
@classmethod
[docs] def make_from_dict(cls, data):
cat = cls()
cat.data = data
cat.update_end_year()
return cat
[docs] def update_end_year(self):
"""
NOTE: To be called only when the catalogue is loaded (not when
it is modified by declustering or completeness-based filtering)
"""
self.end_year = np.max(self.data['year'])
[docs] def update_start_year(self):
"""
NOTE: To be called only when the catalogue is loaded (not when
it is modified by declustering or completeness-based filtering)
"""
self.start_year = np.min(self.data['year'])
[docs] def catalogue_mt_filter(self, mt_table, flag=None):
"""
Filter the catalogue using a magnitude-time table. The table has
two columns and n-rows.
:param nump.ndarray mt_table:
Magnitude time table with n-rows where column 1 is year and column
2 is magnitude
"""
if flag is None:
# No flag defined, therefore all events are initially valid
flag = np.ones(self.get_number_events(), dtype=bool)
for comp_val in mt_table:
id0 = np.logical_and(self.data['year'].astype(float) < comp_val[0],
self.data['magnitude'] < comp_val[1])
print(id0)
flag[id0] = False
if not np.all(flag):
self.purge_catalogue(flag)
[docs] def get_bounding_box(self):
"""
Returns the bounding box of the catalogue
:returns: (West, East, South, North)
"""
return (np.min(self.data["longitude"]),
np.max(self.data["longitude"]),
np.min(self.data["latitude"]),
np.max(self.data["latitude"]))
[docs] def get_observed_mmax_sigma(self, default=None):
"""
:returns: the sigma for the maximum observed magnitude
"""
if not isinstance(self.data['sigmaMagnitude'], np.ndarray):
obsmaxsig = default
else:
obsmaxsig = self.data['sigmaMagnitude'][
np.argmax(self.data['magnitude'])]
return obsmaxsig
[docs] def get_decimal_time(self):
'''
Returns the time of the catalogue as a decimal
'''
return decimal_time(self.data['year'],
self.data['month'],
self.data['day'],
self.data['hour'],
self.data['minute'],
self.data['second'])
[docs] def hypocentres_as_mesh(self):
'''
Render the hypocentres to a nhlib.geo.mesh.Mesh object
'''
return Mesh(self.data['longitude'],
self.data['latitude'],
self.data['depth'])
[docs] def hypocentres_to_cartesian(self):
'''
Render the hypocentres to a cartesian array
'''
return spherical_to_cartesian(self.data['longitude'],
self.data['latitude'],
self.data['depth'])
[docs] def sort_catalogue_chronologically(self):
'''
Sorts the catalogue into chronological order
'''
dec_time = self.get_decimal_time()
idx = np.argsort(dec_time)
if np.all((idx[1:] - idx[:-1]) > 0.):
# Catalogue was already in chronological order
return
self.select_catalogue_events(idx)
[docs] def purge_catalogue(self, flag_vector):
'''
Purges present catalogue with invalid events defined by flag_vector
:param numpy.ndarray flag_vector:
Boolean vector showing if events are selected (True) or not (False)
'''
id0 = np.where(flag_vector)[0]
self.select_catalogue_events(id0)
self.get_number_events()
[docs] def select_catalogue_events(self, id0):
'''
Orders the events in the catalogue according to an indexing vector.
:param np.ndarray id0:
Pointer array indicating the locations of selected events
'''
for key in self.data:
if isinstance(
self.data[key], np.ndarray) and len(self.data[key]) > 0:
# Dictionary element is numpy array - use logical indexing
self.data[key] = self.data[key][id0]
elif isinstance(
self.data[key], list) and len(self.data[key]) > 0:
# Dictionary element is list
self.data[key] = [self.data[key][iloc] for iloc in id0]
else:
continue
[docs] 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))
[docs] def get_depth_pmf(self, depth_bins, default_depth=5.0, bootstrap=None):
"""
Returns the depth distribution of the catalogue as a probability mass
function
"""
if len(self.data['depth']) == 0:
# If depth information is missing
return PMF([(1.0, default_depth)])
# Get the depth distribution
depth_hist = self.get_depth_distribution(depth_bins,
normalisation=True,
bootstrap=bootstrap)
# If the histogram does not sum to 1.0 then remove the difference
# from the lowest bin
depth_hist = np.around(depth_hist, 3)
while np.sum(depth_hist) > 1.0:
depth_hist[-1] -= (np.sum(depth_hist) - 1.0)
depth_hist = np.around(depth_hist, 3)
pmf_list = []
for iloc, prob in enumerate(depth_hist):
pmf_list.append((prob,
(depth_bins[iloc] + depth_bins[iloc + 1]) / 2.0))
return PMF(pmf_list)
[docs] 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)
[docs] 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)
[docs] def concatenate(self, catalogue):
"""
This method attaches one catalogue to the current one
:parameter catalogue:
An instance of :class:`htmk.seismicity.catalogue.Catalogue`
"""
atts = getattr(self, 'data')
attn = getattr(catalogue, 'data')
data = _merge_data(atts, attn)
if data is not None:
setattr(self, 'data', data)
for attrib in vars(self):
atts = getattr(self, attrib)
attn = getattr(catalogue, attrib)
if attrib is 'end_year':
setattr(self, attrib, max(atts, attn))
elif attrib is 'start_year':
setattr(self, attrib, min(atts, attn))
elif attrib is 'data':
pass
elif attrib is 'number_earthquakes':
setattr(self, attrib, atts+attn)
elif attrib is 'processes':
if atts != attn:
raise ValueError('The catalogues cannot be merged' +
' since the they have' +
' a different processing history')
else:
raise ValueError('unknown attribute: %s' % attrib)
self.sort_catalogue_chronologically()
def _merge_data(dat1, dat2):
"""
Merge two data dictionaries containing catalogue data
:parameter dictionary dat1:
Catalogue data dictionary
:parameter dictionary dat2:
Catalogue data dictionary
:returns:
A catalogue data dictionary containing the information originally
included in dat1 and dat2
"""
cnt = 0
for key in dat1:
flg1 = len(dat1[key]) > 0
flg2 = len(dat2[key]) > 0
if flg1 != flg2:
cnt += 1
if cnt:
raise Warning('Cannot merge catalogues with different' +
' attributes')
return None
else:
for key in dat1:
if isinstance(dat1[key], np.ndarray):
dat1[key] = np.concatenate((dat1[key], dat2[key]), axis=0)
elif isinstance(dat1[key], list):
dat1[key] += dat2[key]
else:
raise ValueError('Unknown type')
return dat1