User contributed plug-ins for Pyrocko's seismic waveform browser Snuffler.
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from pyrocko.gui.snuffling import Snuffling, Param, Switch, NoViewerSet, Choice
from pyrocko.gui.pile_viewer import Marker, EventMarker, PhaseMarker
from pyrocko import io, trace, model
from collections import defaultdict
class CorrsearchSnuffling(Snuffling):
'''
<html>
<head>
<style type="text/css">
body { margin-left:10px };
</style>
</head>
<body>
<h2 align="center">Cross Correlation Pattern Matching</h2>
<h3 align="left">Usage</h3>
<p>Select master signal using extended markers. Only one master signal per
channel can be used.</p>
<p>
<b>Set parameters:</b><br />
<b>&middot; Downsample to [Hz]</b> - Reduce the number of samples to
improve speed<br />
<b>&middot; Apply to full dataset</b> - Work on the entire data set
(By default, processing is limited to data shown in viewer)
<br />
<b>&middot; Normalization</b> - Normalize traces before cross
correlation. <i>Gliding</i>: time varying normalization (using
moving sum); <i>Normal</i>: normalizes by product of energy of
both traces<br />
<b>&middot; Threshold</b> - Peak detection threshold applied to
correlation trace<br />
</p>
Hit <b>run</b>.<br>
</body>
</html>
'''
def setup(self):
'''Customization of the snuffling.'''
self.set_name('Cross Correlation Search')
self.add_parameter(
Param('Downsample to [Hz]', 'downsample',
None, 0.1, 200., high_is_none=True))
self.add_parameter(Param('t search', 'tsearch', 1., 0.1, 100.))
self.add_parameter(Switch('Apply to full dataset', 'apply_to_all', False))
self.add_parameter(Choice('Normalization', 'normalization', 'Off',
('Off', 'Normal', 'Gliding')))
self.add_parameter(Param('Threshold', 'threshold', 0.5, 0., 1.))
self.add_parameter(Switch('Use FFT', 'use_fft', True))
self.set_live_update(False)
def call(self):
self.cleanup()
try:
viewer = self.get_viewer()
lowpass = viewer.lowpass
highpass = viewer.highpass
markers = viewer.selected_markers()
if not markers:
return
if len(markers) != 1:
return
marker = markers[0]
master_tmin, master_tmax = marker.tmin, marker.tmax
if master_tmin >= master_tmax:
return
except NoViewerSet:
viewer = None
master_tmin, master_tmax = self.master_tmin, self.master_tmax
tpad = 0.
if highpass:
tpad = 1. / highpass
pile = self.get_pile()
masters = {}
def preprocess(_tr):
if self.downsample:
_tr.downsample_to(1./self.downsample)
if highpass:
_tr.highpass(4, highpass)
if lowpass:
_tr.lowpass(4, lowpass)
for tr in pile.all(tmin=master_tmin, tmax=master_tmax, tpad=tpad):
for m in markers:
if m.match_nslc(tr.nslc_id):
preprocess(tr)
tr.chop(tr.wmin, tr.wmax)
masters[tr.nslc_id] = tr
break
if self.apply_to_all:
tmin, tmax = pile.get_tmin()+tpad, pile.get_tmax()
else:
tmin, tmax = self.get_viewer().get_time_range()
normalization = {'Off': None, 'Normal': 'normal', 'Gliding': 'gliding'}[self.normalization]
for traces in pile.chopper(tmin=tmin, tmax=tmax, want_incomplete=True):
sccs = defaultdict()
sccn = defaultdict()
for b in traces:
nslc = b.nslc_id
if nslc in masters:
a = masters[nslc]
preprocess(b)
c = trace.correlate(
a, b, mode='valid',
normalization=normalization,
use_fft=self.use_fft)
c.shift(-c.tmin + b.tmin)
c.meta = {'tabu': True}
scc = sccs.get(nslc, None)
if not scc:
scc = c.copy()
scc.meta = {'tabu': True}
scc.wmin = b.wmin
scc.wmax = b.wmax
scc.set_codes(location=scc.location+'_SUM')
sccn[nslc] = 1
else:
sccn[nslc] += 1
sccs[nslc] = scc
for nslc_id, scc in sccs.items():
scc.ydata /= sccn[nslc_id]
scc.chop(scc.wmin, scc.wmax)
markers = []
for t, a in zip(*scc.peaks(self.threshold, tsearch=self.tsearch)):
m = PhaseMarker(tmin=t, tmax=t, phasename='%1.3f' % a, kind=3, nslc_ids=(nslc_id,))
markers.append(m)
if viewer:
self.add_traces([scc])
self.add_markers(markers)
else:
io.save([scc], self.out_path, format='from_extension')
def __snufflings__():
'''Returns a list of snufflings to be exported by this module.'''
return [CorrsearchSnuffling()]
if __name__ == '__main__':
snuf = CorrsearchSnuffling()
snuf.setup()
snuf.apply_to_all = True
snuf.corner_highpass = 0.1
markers = Marker.load_markers('event.picks')
m = markers[0]
snuf.master_tmin, snuf.master_tmax = m.tmin, m.tmax
snuf.out_path = 'corr/%(tmin)s.yaff'
snuf.call()