def test_space_cluster(self):
"""Test the wrapper around dist_mat_km."""
from eqcorrscan.utils.clustering import space_cluster
from obspy.clients.fdsn import Client
from obspy import UTCDateTime
client = Client("IRIS")
starttime = UTCDateTime("2002-01-01")
endtime = UTCDateTime("2002-01-02")
cat = client.get_events(starttime=starttime, endtime=endtime,
minmagnitude=6, catalog="ISC")
groups = space_cluster(catalog=cat, d_thresh=1000, show=False)
self.assertEqual(len([ev for group in groups for ev in group]),
len(cat))
def get_test_data():
"""
Generate a set of waveforms from GeoNet for use in subspace testing
:return: List of cut templates with no filters applied
:rtype: list
"""
from obspy import UTCDateTime
from eqcorrscan.utils.catalog_utils import filter_picks
from eqcorrscan.utils.clustering import space_cluster
from obspy.clients.fdsn import Client
client = Client("GEONET")
cat = client.get_events(minlatitude=-40.98,
maxlatitude=-40.85,
minlongitude=175.4,
maxlongitude=175.5,
starttime=UTCDateTime(2016, 5, 11),
endtime=UTCDateTime(2016, 5, 13))
cat = filter_picks(catalog=cat, top_n_picks=5)
stachans = list(
set([(pick.waveform_id.station_code, pick.waveform_id.channel_code)
for event in cat for pick in event.picks]))
clusters = space_cluster(catalog=cat, d_thresh=2, show=False)
cluster = sorted(clusters, key=lambda c: len(c))[-1]
client = Client('GEONET')
design_set = []
bulk_info = []
for event in cluster:
t1 = event.origins[0].time + 5
t2 = t1 + 15.1
for station, channel in stachans:
bulk_info.append(('NZ', station, '*', channel[0:2] + '?', t1, t2))
st = client.get_waveforms_bulk(bulk=bulk_info)
for event in cluster:
t1 = event.origins[0].time + 5
t2 = t1 + 15
design_set.append(st.copy().trim(t1, t2))
t1 = UTCDateTime(2016, 5, 11, 19)
t2 = UTCDateTime(2016, 5, 11, 20)
bulk_info = [('NZ', stachan[0], '*', stachan[1][0:2] + '?', t1, t2)
for stachan in stachans]
st = client.get_waveforms_bulk(bulk_info)
st.merge().detrend('simple').trim(starttime=t1, endtime=t2)
return design_set, st
within these distance clustered groups. Finally, clustering based on
waveform cross correlation will be applied within these groups.
"""
import sys
sys.path.insert(0, '/home/chet/EQcorrscan')
from eqcorrscan.utils import clustering, plotting
from obspy import read_events
# Read in whole damn catalog
cat = read_events('/home/chet/data/mrp_data/sherburn_catalog/quake-ml/' +
'rotnga/final_cat/bbox_final_QML.xml')
# Cluster events by distance
groups = clustering.space_cluster(cat, d_thresh=2.0)
# Eliminate groups with size below a certain threshold
real_groups = [g for g in refined_groups if len(g) > 1]
group_lengths = [len(g) for g in real_groups]
# print('At corr_thresh: ' + str(corr_thresh))
print('Total number of groups: %d' % len(real_groups))
print('Total number of events: %d' % sum(group_lengths))
# Here we can write the groups (which are Catalogs) to qml/shapefile/xyz
for i, group_cat in enumerate(real_groups):
file_names = '/media/chet/hdd/seismic/NZ/catalogs/qml/space_groups/' +\
'nlloc_thresh_%.02f_group_%03d' % (d_thresh, i)
# Write shapefile first
group_cat.write(file_names + '.shp', format="SHAPEFILE")
# Now qml
def run_tutorial(plot=False, multiplex=True, return_streams=False):
"""
Run the tutorial.
:return: detections
"""
# We are going to use data from the GeoNet (New Zealand) catalogue. GeoNet
# do not implement the full FDSN system yet, so we have a hack to get
# around this. It is not strictly part of EQcorrscan, so we haven't
# included it here, but you can find it in the tutorials directory of the
# github repository
import obspy
if int(obspy.__version__.split('.')[0]) >= 1:
from obspy.clients.fdsn import Client
else:
from obspy.fdsn import Client
from eqcorrscan.tutorials.get_geonet_events import get_geonet_events
from obspy import UTCDateTime
from eqcorrscan.utils.catalog_utils import filter_picks
from eqcorrscan.utils.clustering import space_cluster
from eqcorrscan.core import subspace
cat = get_geonet_events(minlat=-40.98,
maxlat=-40.85,
minlon=175.4,
maxlon=175.5,
startdate=UTCDateTime(2016, 5, 1),
enddate=UTCDateTime(2016, 5, 20))
# This gives us a catalog of events - it takes a while to download all
# the information, so give it a bit!
# We will generate a five station, multi-channel detector.
cat = filter_picks(catalog=cat, top_n_picks=5)
stachans = list(
set([(pick.waveform_id.station_code, pick.waveform_id.channel_code)
for event in cat for pick in event.picks]))
# In this tutorial we will only work on one cluster, defined spatially.
# You can work on multiple clusters, or try to whole set.
clusters = space_cluster(catalog=cat, d_thresh=2, show=False)
# We will work on the largest cluster
cluster = sorted(clusters, key=lambda c: len(c))[-1]
# This cluster contains 32 events, we will now download a trim the
# waveforms. Note that each chanel must start at the same time and be the
# same length for multiplexing. If not multiplexing EQcorrscan will
# maintain the individual differences in time between channels and delay
# the detection statistics by that amount before stacking and detection.
client = Client('GEONET')
design_set = []
bulk_info = []
for event in cluster:
t1 = event.origins[0].time
t2 = t1 + 25
for station, channel in stachans:
bulk_info.append(('NZ', station, '*', channel[0:2] + '?', t1, t2))
st = client.get_waveforms_bulk(bulk=bulk_info)
for event in cluster:
t1 = event.origins[0].time
t2 = t1 + 25
design_set.append(st.copy().trim(t1, t2))
# Construction of the detector will process the traces, then align them,
# before multiplexing.
detector = subspace.Detector()
detector.construct(streams=design_set,
lowcut=2.0,
highcut=9.0,
filt_order=4,
sampling_rate=20,
multiplex=multiplex,
name='Wairarapa1',
align=True,
reject=0.2,
shift_len=6,
plot=plot).partition(9)
if plot:
detector.plot()
# We also want the continuous stream to detect in.
t1 = UTCDateTime(2016, 5, 11, 19)
t2 = UTCDateTime(2016, 5, 11, 20)
# We are going to look in a single hour just to minimize cost, but you can \
# run for much longer.
bulk_info = [('NZ', stachan[0], '*', stachan[1][0] + '?' + stachan[1][-1],
t1, t2) for stachan in detector.stachans]
st = client.get_waveforms_bulk(bulk_info)
st.merge().detrend('simple').trim(starttime=t1, endtime=t2)
# We set a very low threshold because the detector is not that great, we
# haven't aligned it particularly well - however, at this threshold we make
# two real detections.
detections, det_streams = detector.detect(st=st,
threshold=0.005,
trig_int=2,
extract_detections=True)
if return_streams:
return detections, det_streams
else:
return detections
def test_space_cluster(self):
"""Test the wrapper around dist_mat_km."""
groups = space_cluster(catalog=self.cat, d_thresh=1000, show=False)
self.assertEqual(len([ev for group in groups for ev in group]),
len(self.cat))
def run_tutorial(plot=False, multiplex=True, return_streams=False):
"""
Run the tutorial.
:return: detections
"""
client = Client("GEONET")
cat = client.get_events(minlatitude=-40.98,
maxlatitude=-40.85,
minlongitude=175.4,
maxlongitude=175.5,
starttime=UTCDateTime(2016, 5, 1),
endtime=UTCDateTime(2016, 5, 20))
print("Downloaded a catalog of %i events" % len(cat))
# This gives us a catalog of events - it takes a while to download all
# the information, so give it a bit!
# We will generate a five station, multi-channel detector.
cat = filter_picks(catalog=cat, top_n_picks=5)
stachans = list(
set([(pick.waveform_id.station_code, pick.waveform_id.channel_code)
for event in cat for pick in event.picks]))
# In this tutorial we will only work on one cluster, defined spatially.
# You can work on multiple clusters, or try to whole set.
clusters = space_cluster(catalog=cat, d_thresh=2, show=False)
# We will work on the largest cluster
cluster = sorted(clusters, key=lambda c: len(c))[-1]
# This cluster contains 32 events, we will now download and trim the
# waveforms. Note that each chanel must start at the same time and be the
# same length for multiplexing. If not multiplexing EQcorrscan will
# maintain the individual differences in time between channels and delay
# the detection statistics by that amount before stacking and detection.
client = Client('GEONET')
design_set = []
bulk_info = []
for event in cluster:
t1 = event.origins[0].time
t2 = t1 + 25.1 # Have to download extra data, otherwise GeoNet will
# trim wherever suits.
t1 -= 0.1
for station, channel in stachans:
bulk_info.append(('NZ', station, '*', channel[0:2] + '?', t1, t2))
print("Downloading data for %i events" % len(cluster))
st = client.get_waveforms_bulk(bulk=bulk_info)
for event in cluster:
t1 = event.origins[0].time
t2 = t1 + 25
design_set.append(st.copy().trim(t1, t2))
# Construction of the detector will process the traces, then align them,
# before multiplexing.
detector = subspace.Detector()
detector.construct(streams=design_set,
lowcut=2.0,
highcut=9.0,
filt_order=4,
sampling_rate=20,
multiplex=multiplex,
name='Wairarapa1',
align=True,
reject=0.2,
shift_len=6,
plot=plot).partition(9)
print("Constructed Detector")
if plot:
detector.plot()
# We also want the continuous stream to detect in.
t1 = UTCDateTime(2016, 5, 11, 19)
t2 = UTCDateTime(2016, 5, 11, 20)
# We are going to look in a single hour just to minimize cost, but you can
# run for much longer.
bulk_info = [('NZ', stachan[0], '*', stachan[1][0] + '?' + stachan[1][-1],
t1, t2) for stachan in detector.stachans]
print("Downloading continuous data")
st = client.get_waveforms_bulk(bulk_info)
st.merge().detrend('simple').trim(starttime=t1, endtime=t2)
# We set a very low threshold because the detector is not that great, we
# haven't aligned it particularly well - however, at this threshold we make
# two real detections.
print("Computing detections")
detections, det_streams = detector.detect(st=st,
threshold=0.3,
trig_int=2,
extract_detections=True)
if return_streams:
return detections, det_streams
else:
return detections
