def test_coincidence(self, peaks):
"""Test the coincidence trigger."""
triggers = coin_trig(peaks, [('a', 'Z'), ('b', 'Z')],
samp_rate=10,
moveout=3,
min_trig=2,
trig_int=1)
assert triggers, [(0.45, 100)]
def test_coincidence(self):
"""Test the coincidence trigger."""
from eqcorrscan.utils.findpeaks import coin_trig
peaks = [[(0.5, 100), (0.3, 800), (0.3, 105)], [(0.4, 120),
(0.7, 850)]]
triggers = coin_trig(peaks, [('a', 'Z'), ('b', 'Z')],
samp_rate=10,
moveout=3,
min_trig=2,
trig_int=1)
self.assertEqual(triggers, [(0.45, 100)])
def _detect(detector,
st,
threshold,
trig_int,
moveout=0,
min_trig=0,
process=True,
extract_detections=False,
cores=1):
"""
Detect within continuous data using the subspace method.
Not to be called directly, use the detector.detect method.
:type detector: eqcorrscan.core.subspace.Detector
:param detector: Detector to use.
:type st: obspy.core.stream.Stream
:param st: Un-processed stream to detect within using the subspace \
detector
:type threshold: float
:param threshold: Threshold value for detections between 0-1
:type trig_int: float
:param trig_int: Minimum trigger interval in seconds.
:type moveout: float
:param moveout: Maximum allowable moveout window for non-multiplexed,
network detection. See note.
:type min_trig: int
:param min_trig: Minimum number of stations exceeding threshold for \
non-multiplexed, network detection. See note.
:type process: bool
:param process: Whether or not to process the stream according to the \
parameters defined by the detector. Default is to process the \
data (True).
:type extract_detections: bool
:param extract_detections: Whether to extract waveforms for each \
detection or not, if true will return detections and streams.
:return: list of detections
:rtype: list of eqcorrscan.core.match_filter.Detection
"""
detections = []
# First process the stream
if process:
Logger.info('Processing Stream')
stream, stachans = _subspace_process(
streams=[st.copy()],
lowcut=detector.lowcut,
highcut=detector.highcut,
filt_order=detector.filt_order,
sampling_rate=detector.sampling_rate,
multiplex=detector.multiplex,
stachans=detector.stachans,
parallel=True,
align=False,
shift_len=None,
reject=False,
cores=cores)
else:
# Check the sampling rate at the very least
for tr in st:
if not tr.stats.sampling_rate == detector.sampling_rate:
raise ValueError('Sampling rates do not match.')
stream = [st]
stachans = detector.stachans
outtic = time.clock()
# If multiplexed, how many samples do we increment by?
if detector.multiplex:
Nc = len(detector.stachans)
else:
Nc = 1
# Here do all ffts
fft_vars = _do_ffts(detector, stream, Nc)
Logger.info('Computing detection statistics')
Logger.info('Preallocating stats matrix')
stats = np.zeros(
(len(stream[0]), (len(stream[0][0]) // Nc) - (fft_vars[4] // Nc) + 1))
for det_freq, data_freq_sq, data_freq, i in zip(fft_vars[0], fft_vars[1],
fft_vars[2],
np.arange(len(stream[0]))):
# Calculate det_statistic in frequency domain
stats[i] = _det_stat_freq(det_freq, data_freq_sq, data_freq,
fft_vars[3], Nc, fft_vars[4], fft_vars[5])
Logger.info('Stats matrix is shape %s' % str(stats[i].shape))
trig_int_samples = detector.sampling_rate * trig_int
Logger.info('Finding peaks')
peaks = []
for i in range(len(stream[0])):
peaks.append(
findpeaks.find_peaks2_short(arr=stats[i],
thresh=threshold,
trig_int=trig_int_samples))
if not detector.multiplex:
# Conduct network coincidence triggering
peaks = findpeaks.coin_trig(peaks=peaks,
samp_rate=detector.sampling_rate,
moveout=moveout,
min_trig=min_trig,
stachans=stachans,
trig_int=trig_int)
else:
peaks = peaks[0]
if len(peaks) > 0:
for peak in peaks:
detecttime = st[0].stats.starttime + \
(peak[1] / detector.sampling_rate)
rid = ResourceIdentifier(id=detector.name + '_' + str(detecttime),
prefix='smi:local')
ev = Event(resource_id=rid)
cr_i = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
ev.creation_info = cr_i
# All detection info in Comments for lack of a better idea
thresh_str = 'threshold=' + str(threshold)
ccc_str = 'detect_val=' + str(peak[0])
used_chans = 'channels used: ' +\
' '.join([str(pair) for pair in detector.stachans])
ev.comments.append(Comment(text=thresh_str))
ev.comments.append(Comment(text=ccc_str))
ev.comments.append(Comment(text=used_chans))
for stachan in detector.stachans:
tr = st.select(station=stachan[0], channel=stachan[1])
if tr:
net_code = tr[0].stats.network
else:
net_code = ''
pick_tm = detecttime
wv_id = WaveformStreamID(network_code=net_code,
station_code=stachan[0],
channel_code=stachan[1])
ev.picks.append(Pick(time=pick_tm, waveform_id=wv_id))
detections.append(
Detection(template_name=detector.name,
detect_time=detecttime,
no_chans=len(detector.stachans),
detect_val=peak[0],
threshold=threshold,
typeofdet='subspace',
threshold_type='abs',
threshold_input=threshold,
chans=detector.stachans,
event=ev))
outtoc = time.clock()
Logger.info('Detection took %s seconds' % str(outtoc - outtic))
if extract_detections:
detection_streams = extract_from_stream(st, detections)
return detections, detection_streams
return detections
def _detect(detector,
st,
threshold,
trig_int,
moveout=0,
min_trig=0,
process=True,
extract_detections=False,
debug=0):
"""
Detect within continuous data using the subspace method.
Not to be called directly, use the detector.detect method.
:type detector: eqcorrscan.core.subspace.Detector
:param detector: Detector to use.
:type st: obspy.core.stream.Stream
:param st: Un-processed stream to detect within using the subspace \
detector
:type threshold: float
:param threshold: Threshold value for detections between 0-1
:type trig_int: float
:param trig_int: Minimum trigger interval in seconds.
:type moveout: float
:param moveout: Maximum allowable moveout window for non-multiplexed,
network detection. See note.
:type min_trig: int
:param min_trig: Minimum number of stations exceeding threshold for \
non-multiplexed, network detection. See note.
:type process: bool
:param process: Whether or not to process the stream according to the \
parameters defined by the detector. Default is to process the \
data (True).
:type extract_detections: bool
:param extract_detections: Whether to extract waveforms for each \
detection or not, if true will return detections and streams.
:type debug: int
:param debug: Debug output level from 0-5.
:return: list of detections
:rtype: list of eqcorrscan.core.match_filter.DETECTION
"""
from eqcorrscan.core import subspace_statistic
detections = []
# First process the stream
if process:
if debug > 0:
print('Processing Stream')
stream, stachans = _subspace_process(
streams=[st.copy()],
lowcut=detector.lowcut,
highcut=detector.highcut,
filt_order=detector.filt_order,
sampling_rate=detector.sampling_rate,
multiplex=detector.multiplex,
stachans=detector.stachans,
parallel=True,
align=False,
shift_len=None,
reject=False)
else:
# Check the sampling rate at the very least
for tr in st:
if not tr.stats.sampling_rate == detector.sampling_rate:
raise ValueError('Sampling rates do not match.')
stream = [st]
stachans = detector.stachans
outtic = time.clock()
if debug > 0:
print('Computing detection statistics')
stats = np.zeros(
(len(stream[0]), len(stream[0][0]) - len(detector.data[0][0]) + 1),
dtype=np.float32)
for det_channel, in_channel, i in zip(detector.data, stream[0],
np.arange(len(stream[0]))):
stats[i] = subspace_statistic.\
det_statistic(detector=det_channel.astype(np.float32),
data=in_channel.data.astype(np.float32))
if debug > 0:
print(stats[i].shape)
if debug > 3:
plt.plot(stats[i])
plt.show()
# Hard typing in Cython loop requires float32 type.
# statistics
if detector.multiplex:
trig_int_samples = (len(detector.stachans) * detector.sampling_rate *
trig_int)
else:
trig_int_samples = detector.sampling_rate * trig_int
if debug > 0:
print('Finding peaks')
peaks = []
for i in range(len(stream[0])):
peaks.append(
findpeaks.find_peaks2_short(arr=stats[i],
thresh=threshold,
trig_int=trig_int_samples,
debug=debug))
if not detector.multiplex:
# Conduct network coincidence triggering
peaks = findpeaks.coin_trig(peaks=peaks,
samp_rate=detector.sampling_rate,
moveout=moveout,
min_trig=min_trig,
stachans=stachans,
trig_int=trig_int)
else:
peaks = peaks[0]
if len(peaks) > 0:
for peak in peaks:
if detector.multiplex:
detecttime = st[0].stats.starttime + (
peak[1] /
(detector.sampling_rate * len(detector.stachans)))
else:
detecttime = st[0].stats.starttime + (peak[1] /
detector.sampling_rate)
rid = ResourceIdentifier(id=detector.name + '_' + str(detecttime),
prefix='smi:local')
ev = Event(resource_id=rid)
cr_i = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
ev.creation_info = cr_i
# All detection info in Comments for lack of a better idea
thresh_str = 'threshold=' + str(threshold)
ccc_str = 'detect_val=' + str(peak[0])
used_chans = 'channels used: ' +\
' '.join([str(pair) for pair in detector.stachans])
ev.comments.append(Comment(text=thresh_str))
ev.comments.append(Comment(text=ccc_str))
ev.comments.append(Comment(text=used_chans))
for stachan in detector.stachans:
tr = st.select(station=stachan[0], channel=stachan[1])
if tr:
net_code = tr[0].stats.network
else:
net_code = ''
pick_tm = detecttime
wv_id = WaveformStreamID(network_code=net_code,
station_code=stachan[0],
channel_code=stachan[1])
ev.picks.append(Pick(time=pick_tm, waveform_id=wv_id))
detections.append(
DETECTION(detector.name,
detecttime,
len(detector.stachans),
peak[0],
threshold,
'subspace',
detector.stachans,
event=ev))
outtoc = time.clock()
print('Detection took %s seconds' % str(outtoc - outtic))
if extract_detections:
detection_streams = extract_from_stream(st, detections)
return detections, detection_streams
return detections
