def test_match_filter(samp_rate=20.0, debug=0):
"""
Function to test the capabilities of match_filter and just check that it\
is working! Uses synthetic templates and seeded, randomised data.
:type debug: int
:param debug: Debug level, higher the number the more output.
"""
from eqcorrscan.utils import pre_processing
from eqcorrscan.utils import EQcorrscan_plotting as plotting
from eqcorrscan.core import match_filter
from obspy import UTCDateTime
import string
# Generate a random dataset
templates, data, seeds = generate_synth_data(nsta=5, ntemplates=2,
nseeds=50,
samp_rate=samp_rate,
t_length=6.0, max_amp=5.0,
debug=debug)
# Notes to the user: If you use more templates you should ensure they are
# more different, e.g. set the data to have larger moveouts, otherwise
# similar templates will detect events seeded by another template.
# Test the pre_processing functions
for tr in data:
pre_processing.dayproc(tr=tr, lowcut=2.0, highcut=8.0, filt_order=3,
samp_rate=20.0, debug=0,
starttime=UTCDateTime(0))
if debug > 0:
data.plot()
# Filter the data and the templates
for template in templates:
pre_processing.shortproc(st=template, lowcut=2.0, highcut=8.0,
filt_order=3, samp_rate=20.0)
if debug > 0:
template.plot()
template_names = list(string.ascii_lowercase)[0:len(templates)]
detections = match_filter.match_filter(template_names=template_names,
template_list=templates,
st=data, threshold=8.0,
threshold_type='MAD',
trig_int=6.0,
plotvar=False,
plotdir='.',
cores=1)
# Compare the detections to the seeds
print('This test made ' + str(len(detections)) + ' detections')
ktrue = 0
kfalse = 0
for detection in detections:
print(detection.template_name)
i = template_names.index(detection.template_name)
t_seeds = seeds[i]
dtime_samples = int((detection.detect_time - UTCDateTime(0)) *
samp_rate)
if dtime_samples in t_seeds['time']:
j = list(t_seeds['time']).index(dtime_samples)
print('Detection at SNR of: ' + str(t_seeds['SNR'][j]))
ktrue += 1
else:
min_diff = min(abs(t_seeds['time'] - dtime_samples))
if min_diff < 10:
# If there is a match within ten samples then its good enough
j = list(abs(t_seeds['time'] - dtime_samples)).index(min_diff)
print('Detection at SNR of: ' + str(t_seeds['SNR'][j]))
ktrue += 1
else:
print('Detection at sample: ' + str(dtime_samples) +
' does not match anything in seed times:')
kfalse += 1
print('Minimum difference in samples is: ' + str(min_diff))
# Plot the detections
if debug > 3:
for i, template in enumerate(templates):
times = [d.detect_time.datetime for d in detections
if d.template_name == template_names[i]]
print(times)
plotting.detection_multiplot(data, template, times)
# Set an 'acceptable' ratio of positive to false detections
print(str(ktrue) + ' true detections and ' + str(kfalse) +
' false detections')
if kfalse / ktrue < 0.25:
return True
else:
return False
def test_match_filter(samp_rate=20.0, debug=0):
"""
Function to test the capabilities of match_filter and just check that it\
is working! Uses synthetic templates and seeded, randomised data.
:type debug: int
:param debug: Debug level, higher the number the more output.
"""
from eqcorrscan.utils import pre_processing
from eqcorrscan.utils import EQcorrscan_plotting as plotting
from eqcorrscan.core import match_filter
from obspy import UTCDateTime
import string
# Generate a random dataset
templates, data, seeds = generate_synth_data(nsta=5,
ntemplates=2,
nseeds=50,
samp_rate=samp_rate,
t_length=6.0,
max_amp=5.0,
debug=debug)
# Notes to the user: If you use more templates you should ensure they are
# more different, e.g. set the data to have larger moveouts, otherwise
# similar templates will detect events seeded by another template.
# Test the pre_processing functions
for tr in data:
pre_processing.dayproc(tr=tr,
lowcut=2.0,
highcut=8.0,
filt_order=3,
samp_rate=20.0,
debug=0,
starttime=UTCDateTime(0))
if debug > 0:
data.plot()
# Filter the data and the templates
for template in templates:
pre_processing.shortproc(st=template,
lowcut=2.0,
highcut=8.0,
filt_order=3,
samp_rate=20.0)
if debug > 0:
template.plot()
template_names = list(string.ascii_lowercase)[0:len(templates)]
detections = match_filter.match_filter(template_names=template_names,
template_list=templates,
st=data,
threshold=8.0,
threshold_type='MAD',
trig_int=6.0,
plotvar=False,
plotdir='.',
cores=1)
# Compare the detections to the seeds
print('This test made ' + str(len(detections)) + ' detections')
ktrue = 0
kfalse = 0
for detection in detections:
print(detection.template_name)
i = template_names.index(detection.template_name)
t_seeds = seeds[i]
dtime_samples = int(
(detection.detect_time - UTCDateTime(0)) * samp_rate)
if dtime_samples in t_seeds['time']:
j = list(t_seeds['time']).index(dtime_samples)
print('Detection at SNR of: ' + str(t_seeds['SNR'][j]))
ktrue += 1
else:
min_diff = min(abs(t_seeds['time'] - dtime_samples))
if min_diff < 10:
# If there is a match within ten samples then its good enough
j = list(abs(t_seeds['time'] - dtime_samples)).index(min_diff)
print('Detection at SNR of: ' + str(t_seeds['SNR'][j]))
ktrue += 1
else:
print('Detection at sample: ' + str(dtime_samples) +
' does not match anything in seed times:')
kfalse += 1
print 'Minimum difference in samples is: ' + str(min_diff)
# Plot the detections
if debug > 3:
for i, template in enumerate(templates):
times = [
d.detect_time.datetime for d in detections
if d.template_name == template_names[i]
]
print(times)
plotting.detection_multiplot(data, template, times)
# Set an 'acceptable' ratio of positive to false detections
print(
str(ktrue) + ' true detections and ' + str(kfalse) +
' false detections')
if kfalse / ktrue < 0.25:
return True
else:
return False
# Now lets try and work out how many unique events we have just to compare
# with the GeoNet catalog of 20 events on this day in this sequence
for master in detections:
keep = True
for slave in detections:
if not master == slave and\
abs(master.detect_time - slave.detect_time) <= 6.0:
# If the events are within 6s of each other then test which
# was the 'best' match, strongest detection
if not master.detect_val > slave.detect_val:
keep = False
break
if keep:
unique_detections.append(master)
print('We made a total of ' + str(len(unique_detections)) + ' detections')
for detection in unique_detections:
print('Detection at :' + str(detection.detect_time) + ' for template ' +
detection.template_name + ' with a cross-correlation sum of: ' +
str(detection.detect_val))
# We can plot these too
stplot = st.copy()
template = templates[template_names.index(detection.template_name)]
lags = sorted([tr.stats.starttime for tr in template])
maxlag = lags[-1] - lags[0]
stplot.trim(starttime=detection.detect_time - 10,
endtime=detection.detect_time + maxlag + 10)
plotting.detection_multiplot(stplot, template,
[detection.detect_time.datetime])
# Now lets try and work out how many unique events we have just to compare
# with the GeoNet catalog of 20 events on this day in this sequence
for master in detections:
keep = True
for slave in detections:
if not master == slave and\
abs(master.detect_time - slave.detect_time) <= 6.0:
# If the events are within 6s of each other then test which
# was the 'best' match, strongest detection
if not master.detect_val > slave.detect_val:
keep = False
break
if keep:
unique_detections.append(master)
print('We made a total of ' + str(len(unique_detections)) + ' detections')
for detection in unique_detections:
print('Detection at :' + str(detection.detect_time) + ' for template ' +
detection.template_name + ' with a cross-correlation sum of: ' +
str(detection.detect_val))
# We can plot these too
stplot = st.copy()
template = templates[template_names.index(detection.template_name)]
lags = sorted([tr.stats.starttime for tr in template])
maxlag = lags[-1] - lags[0]
stplot.trim(starttime=detection.detect_time - 10,
endtime=detection.detect_time + maxlag + 10)
plotting.detection_multiplot(stplot, template,
[detection.detect_time.datetime])
