def test_decimal_year(self):
'''Tests the function utils.decimal_year'''
self.year = np.array([1990., 1995., 2000.])
self.month = np.array([1., 6., 12.])
self.day = np.array([1., 30., 31.])
self.assertTrue(np.allclose(
utils.decimal_year(self.year, self.month, self.day),
np.array([1990., 1995.49315068, 2000.99726027])))
def test_decimal_year(self):
'''Tests the function utils.decimal_year'''
self.year = np.array([1990., 1995., 2000.])
self.month = np.array([1., 6., 12.])
self.day = np.array([1., 30., 31.])
self.assertTrue(
np.allclose(utils.decimal_year(self.year, self.month, self.day),
np.array([1990., 1995.49315068, 2000.99726027])))
def decluster(self, catalogue, config):
"""
catalogue_matrix, window_opt=TDW_GARDNERKNOPOFF, time_window=60.):
:param catalogue: a catalogue object
:type catalogue: Instance of the hmtk.seismicity.catalogue.Catalogue()
class
:keyword window_opt: method used in calculating distance and time
windows
:type window_opt: string
:keyword time_window: Length (in days) of moving time window
:type time_window: positive float
:returns: **vcl vector** indicating cluster number,
**flagvector** indicating which earthquakes belong to a
cluster
:rtype: numpy.ndarray
"""
# Convert time window from days to decimal years
time_window = config['time_window'] / 365.
# Pre-processing steps are the same as for Gardner & Knopoff
# Get relevent parameters
mag = catalogue.data['magnitude']
neq = np.shape(mag)[0] # Number of earthquakes
# Get decimal year (needed for time windows)
year_dec = decimal_year(catalogue.data['year'],
catalogue.data['month'],
catalogue.data['day'])
# Get space windows corresponding to each event
sw_space, _ = (
config['time_distance_window'].calc(catalogue.data['magnitude']))
# Pre-allocate cluster index vectors
vcl = np.zeros(neq, dtype=int)
flagvector = np.zeros(neq, dtype=int)
# Rank magnitudes into descending order
id0 = np.flipud(np.argsort(mag, kind='heapsort'))
iloc = 0
clust_index = 0
for imarker in id0:
# Earthquake not allocated to cluster - perform calculation
if vcl[imarker] == 0:
# Perform distance calculation
mdist = haversine(
catalogue.data['longitude'],
catalogue.data['latitude'],
catalogue.data['longitude'][imarker],
catalogue.data['latitude'][imarker]).flatten()
# Select earthquakes inside distance window, later than
# mainshock and not already assigned to a cluster
vsel1 = np.where(
np.logical_and(vcl==0,
np.logical_and(mdist <= sw_space[imarker],
year_dec > year_dec[imarker])))[0]
has_aftershocks = False
if len(vsel1) > 0:
# Earthquakes after event inside distance window
temp_vsel1, has_aftershocks = self._find_aftershocks(
vsel1,
year_dec,
time_window,
imarker,
neq)
if has_aftershocks:
flagvector[temp_vsel1] = 1
vcl[temp_vsel1] = clust_index + 1
# Select earthquakes inside distance window, earlier than
# mainshock and not already assigned to a cluster
has_foreshocks = False
vsel2 = np.where(
np.logical_and(vcl == 0,
np.logical_and(mdist <= sw_space[imarker],
year_dec < year_dec[imarker])))[0]
if len(vsel2) > 0:
# Earthquakes before event inside distance window
temp_vsel2, has_foreshocks = self._find_foreshocks(
vsel2,
year_dec,
time_window,
imarker,
neq)
if has_foreshocks:
flagvector[temp_vsel2] = -1
vcl[temp_vsel2] = clust_index + 1
if has_aftershocks or has_foreshocks:
# Assign mainshock to cluster
vcl[imarker] = clust_index + 1
clust_index += 1
iloc += 1
return vcl, flagvector
def decluster(self, catalogue, config):
"""
The configuration of this declustering algorithm requires two
objects:
- A time-distance window object (key is 'time_distance_window')
- A value in the interval [0,1] expressing the fraction of the
time window used for aftershocks (key is 'fs_time_prop')
:param catalogue:
Catalogue of earthquakes
:type catalogue: Dictionary
:param config:
Configuration parameters
:type config: Dictionary
:returns:
**vcl vector** indicating cluster number,
**flagvector** indicating which eq events belong to a cluster
:rtype: numpy.ndarray
"""
# Get relevant parameters
neq = len(catalogue.data['magnitude']) # Number of earthquakes
# Get decimal year (needed for time windows)
year_dec = decimal_year(catalogue.data['year'],
catalogue.data['month'], catalogue.data['day'])
# Get space and time windows corresponding to each event
sw_space, sw_time = (config['time_distance_window'].calc(
catalogue.data['magnitude']))
# Initial Position Identifier
eqid = np.arange(0, neq, 1)
# Pre-allocate cluster index vectors
vcl = np.zeros(neq, dtype=int)
# Sort magnitudes into descending order
id0 = np.flipud(
np.argsort(catalogue.data['magnitude'], kind='heapsort'))
longitude = catalogue.data['longitude'][id0]
latitude = catalogue.data['latitude'][id0]
sw_space = sw_space[id0]
sw_time = sw_time[id0]
year_dec = year_dec[id0]
eqid = eqid[id0]
flagvector = np.zeros(neq, dtype=int)
# Begin cluster identification
clust_index = 0
for i in range(0, neq - 1):
if vcl[i] == 0:
# Find Events inside both fore- and aftershock time windows
dt = year_dec - year_dec[i]
vsel = np.logical_and(
vcl == 0,
np.logical_and(
dt >= (-sw_time[i] * config['fs_time_prop']),
dt <= sw_time[i]))
# Of those events inside time window,
# find those inside distance window
vsel1 = haversine(longitude[vsel], latitude[vsel],
longitude[i], latitude[i]) <= sw_space[i]
vsel[vsel] = vsel1
temp_vsel = np.copy(vsel)
temp_vsel[i] = False
if any(temp_vsel):
# Allocate a cluster number
vcl[vsel] = clust_index + 1
flagvector[vsel] = 1
# For those events in the cluster before the main event,
# flagvector is equal to -1
temp_vsel[dt >= 0.0] = False
flagvector[temp_vsel] = -1
flagvector[i] = 0
clust_index += 1
# Re-sort the catalog_matrix into original order
id1 = np.argsort(eqid, kind='heapsort')
eqid = eqid[id1]
vcl = vcl[id1]
flagvector = flagvector[id1]
return vcl, flagvector
def decluster(self, catalogue, config):
"""
The configuration of this declustering algorithm requires two
objects:
- A time-distance window object (key is 'time_distance_window')
- A value in the interval [0,1] expressing the fraction of the
time window used for aftershocks (key is 'fs_time_prop')
:param catalogue:
Catalogue of earthquakes
:type catalogue: Dictionary
:param config:
Configuration parameters
:type config: Dictionary
:returns:
**vcl vector** indicating cluster number,
**flagvector** indicating which eq events belong to a cluster
:rtype: numpy.ndarray
"""
# Check declustering configuration
self._check_config(config)
# Get relevant parameters
neq = len(catalogue.data['magnitude']) # Number of earthquakes
# Get decimal year (needed for time windows)
year_dec = decimal_year(
catalogue.data['year'], catalogue.data['month'],
catalogue.data['day'])
# Get space and time windows corresponding to each event
sw_space, sw_time = (
config['time_distance_window'].calc(catalogue.data['magnitude']))
# Initial Position Identifier
eqid = np.arange(0, neq, 1)
# Pre-allocate cluster index vectors
vcl = np.zeros(neq, dtype=int)
# Sort magnitudes into descending order
id0 = np.flipud(np.argsort(catalogue.data['magnitude'],
kind='heapsort'))
longitude = catalogue.data['longitude'][id0]
latitude = catalogue.data['latitude'][id0]
sw_space = sw_space[id0]
sw_time = sw_time[id0]
year_dec = year_dec[id0]
eqid = eqid[id0]
flagvector = np.zeros(neq, dtype=int)
# Begin cluster identification
clust_index = 0
for i in range(0, neq - 1):
if vcl[i] == 0:
# Find Events inside both fore- and aftershock time windows
dt = year_dec - year_dec[i]
vsel = np.logical_and(
vcl == 0,
np.logical_and(
dt >= (-sw_time[i] * config['fs_time_prop']),
dt <= sw_time[i]))
# Of those events inside time window,
# find those inside distance window
vsel1 = haversine(longitude[vsel],
latitude[vsel],
longitude[i],
latitude[i]) <= sw_space[i]
vsel[vsel] = vsel1
temp_vsel = np.copy(vsel)
temp_vsel[i] = False
if any(temp_vsel):
# Allocate a cluster number
vcl[vsel] = clust_index + 1
flagvector[vsel] = 1
# For those events in the cluster before the main event,
# flagvector is equal to -1
temp_vsel[dt >= 0.0] = False
flagvector[temp_vsel] = -1
flagvector[i] = 0
clust_index += 1
# Re-sort the catalog_matrix into original order
id1 = np.argsort(eqid, kind='heapsort')
eqid = eqid[id1]
vcl = vcl[id1]
flagvector = flagvector[id1]
return vcl, flagvector
def decluster(self, catalogue, config):
"""
catalogue_matrix, window_opt=TDW_GARDNERKNOPOFF, time_window=60.):
:param catalogue: a catalogue object
:type catalogue: Instance of the hmtk.seismicity.catalogue.Catalogue()
class
:keyword window_opt: method used in calculating distance and time
windows
:type window_opt: string
:keyword time_window: Length (in days) of moving time window
:type time_window: positive float
:returns: **vcl vector** indicating cluster number,
**flagvector** indicating which earthquakes belong to a
cluster
:rtype: numpy.ndarray
"""
# Convert time window from days to decimal years
time_window = config['time_window'] / 365.
# Pre-processing steps are the same as for Gardner & Knopoff
# Get relevent parameters
mag = catalogue.data['magnitude']
neq = np.shape(mag)[0] # Number of earthquakes
# Get decimal year (needed for time windows)
year_dec = decimal_year(catalogue.data['year'],
catalogue.data['month'],
catalogue.data['day'])
# Get space windows corresponding to each event
sw_space, _ = (
config['time_distance_window'].calc(catalogue.data['magnitude']))
# Pre-allocate cluster index vectors
vcl = np.zeros(neq, dtype=int)
flagvector = np.zeros(neq, dtype=int)
# Rank magnitudes into descending order
id0 = np.flipud(np.argsort(mag, kind='heapsort'))
iloc = 0
clust_index = 0
for imarker in id0:
# Earthquake not allocated to cluster - perform calculation
if vcl[imarker] == 0:
# Perform distance calculation
mdist = haversine(
catalogue.data['longitude'],
catalogue.data['latitude'],
catalogue.data['longitude'][imarker],
catalogue.data['latitude'][imarker]).flatten()
# Select earthquakes inside distance window, later than
# mainshock and not already assigned to a cluster
vsel1 = np.where(
np.logical_and(vcl==0,
np.logical_and(mdist <= sw_space[imarker],
year_dec > year_dec[imarker])))[0]
has_aftershocks = False
if len(vsel1) > 0:
# Earthquakes after event inside distance window
temp_vsel1, has_aftershocks = self._find_aftershocks(
vsel1,
year_dec,
time_window,
imarker,
neq)
if has_aftershocks:
flagvector[temp_vsel1] = 1
vcl[temp_vsel1] = clust_index + 1
# Select earthquakes inside distance window, earlier than
# mainshock and not already assigned to a cluster
has_foreshocks = False
vsel2 = np.where(
np.logical_and(vcl == 0,
np.logical_and(mdist <= sw_space[imarker],
year_dec < year_dec[imarker])))[0]
if len(vsel2) > 0:
# Earthquakes before event inside distance window
temp_vsel2, has_foreshocks = self._find_foreshocks(
vsel2,
year_dec,
time_window,
imarker,
neq)
if has_foreshocks:
flagvector[temp_vsel2] = -1
vcl[temp_vsel2] = clust_index + 1
if has_aftershocks or has_foreshocks:
# Assign mainshock to cluster
vcl[imarker] = clust_index + 1
clust_index += 1
iloc += 1
return vcl, flagvector
