def empirical_SVD(stream_list, linear=True):
"""
Empirical subspace detector generation function.
Takes a list of \
templates and computes the stack as the first order subspace detector, \
and the differential of this as the second order subspace detector \
following the empirical subspace method of
`Barrett & Beroza, 2014 - SRL
<http://srl.geoscienceworld.org/content/85/3/594.extract>`_.
:type stream_list: list
:param stream_list:
list of streams to compute the subspace detectors from, where streams
are :class:`obspy.core.stream.Stream` objects.
:type linear: bool
:param linear: Set to true by default to compute the linear stack as the \
first subspace vector, False will use the phase-weighted stack as the \
first subspace vector.
:returns: list of two :class:`obspy.core.stream.Stream` s
"""
from eqcorrscan.utils import stacking
# Run a check to ensure all traces are the same length
stachans = list(
set([(tr.stats.station, tr.stats.channel) for st in stream_list
for tr in st]))
for stachan in stachans:
lengths = []
for st in stream_list:
lengths.append(
len(st.select(station=stachan[0], channel=stachan[1])[0]))
min_length = min(lengths)
for st in stream_list:
tr = st.select(station=stachan[0], channel=stachan[1])[0]
if len(tr.data) > min_length:
if abs(len(tr.data) - min_length) > (0.1 *
tr.stats.sampling_rate):
raise IndexError('More than 0.1 s length '
'difference, align and fix')
warnings.warn(
str(tr) + ' is not the same length as others, ' +
'trimming the end')
tr.data = tr.data[0:min_length]
if linear:
first_subspace = stacking.linstack(stream_list)
else:
first_subspace = stacking.PWS_stack(streams=stream_list)
second_subspace = first_subspace.copy()
for i in range(len(second_subspace)):
second_subspace[i].data = np.diff(second_subspace[i].data)
second_subspace[i].stats.starttime += 0.5 * \
second_subspace[i].stats.delta
return [first_subspace, second_subspace]
def multi_event_singlechan(streams, picks, clip=10.0, pre_pick=2.0,\
freqmin=False, freqmax=False, realign=False, \
cut=(-3.0,5.0), PWS=False, title=False):
"""
Function to plot data from a single channel at a single station for multiple
events - data will be alligned by their pick-time given in the picks
:type streams: List of :class:obspy.stream
:param streams: List of the streams to use, can contain more traces than\
you plan on plotting
:type picks: List of :class:PICK
:param picks: List of picks, one for each stream
:type clip: float
:param clip: Length in seconds to plot, defaults to 10.0
:type pre_pick: Float
:param pre_pick: Length in seconds to extract and plot before the pick,\
defaults to 2.0
:type freqmin: float
:param freqmin: Low cut for bandpass in Hz
:type freqmax: float
:param freqmax: High cut for bandpass in Hz
:type realign: Bool
:param realign: To compute best alignement based on correlation or not.
:type cut: tuple:
:param cut: tuple of start and end times for cut in seconds from the pick
:type PWS: bool
:param PWS: compute Phase Weighted Stack, if False, will compute linear stack
:type title: str
:param title: Plot title.
:returns: Alligned and cut traces, and new picks
"""
from eqcorrscan.utils import stacking
import copy
from eqcorrscan.core.match_filter import normxcorr2
from obspy import Stream
fig, axes = plt.subplots(len(picks) + 1, 1, sharex=True, figsize=(7, 12))
axes = axes.ravel()
traces = []
al_traces = []
# Keep input safe
plist = copy.deepcopy(picks)
st_list = copy.deepcopy(streams)
for i, pick in enumerate(plist):
if st_list[i].select(station=pick.station, \
channel='*'+pick.channel[-1]):
tr=st_list[i].select(station=pick.station, \
channel='*'+pick.channel[-1])[0]
else:
print 'No data for ' + pick.station + '.' + pick.channel
continue
tr.detrend('linear')
if freqmin:
tr.filter('bandpass', freqmin=freqmin, freqmax=freqmax)
if realign:
tr_cut = tr.copy()
tr_cut.trim(pick.time+cut[0], pick.time+cut[1],\
nearest_sample=False)
if len(tr_cut.data) <= 0.5 * (cut[1] -
cut[0]) * tr_cut.stats.sampling_rate:
print 'Not enough in the trace for ' + pick.station + '.' + pick.channel
print 'Suggest removing pick from sfile at time ' + str(
pick.time)
else:
al_traces.append(tr_cut)
else:
tr.trim(pick.time-pre_pick, pick.time+clip-pre_pick,\
nearest_sample=False)
if len(tr.data) == 0:
print 'No data in the trace for ' + pick.station + '.' + pick.channel
print 'Suggest removing pick from sfile at time ' + str(pick.time)
continue
traces.append(tr)
if realign:
shift_len = int(0.25 * (cut[1] - cut[0]) *
al_traces[0].stats.sampling_rate)
shifts = stacking.align_traces(al_traces, shift_len)
for i in xrange(len(shifts)):
print 'Shifting by ' + str(shifts[i]) + ' seconds'
pick.time -= shifts[i]
traces[i].trim(pick.time - pre_pick, pick.time + clip-pre_pick,\
nearest_sample=False)
# We now have a list of traces
traces = [(trace, trace.stats.starttime.datetime) for trace in traces]
traces.sort(key=lambda tup: tup[1])
traces = [trace[0] for trace in traces]
# Plot the traces
for i, tr in enumerate(traces):
y = tr.data
x = np.arange(len(y))
x = x / tr.stats.sampling_rate # convert to seconds
axes[i + 1].plot(x, y, 'k', linewidth=1.1)
# axes[i+1].set_ylabel(tr.stats.starttime.datetime.strftime('%Y/%m/%d %H:%M'),\
# rotation=0)
axes[i + 1].yaxis.set_ticks([])
traces = [Stream(trace) for trace in traces]
if PWS:
linstack = stacking.PWS_stack(traces)
else:
linstack = stacking.linstack(traces)
tr=linstack.select(station=picks[0].station, \
channel='*'+picks[0].channel[-1])[0]
y = tr.data
x = np.arange(len(y))
x = x / tr.stats.sampling_rate
axes[0].plot(x, y, 'r', linewidth=2.0)
axes[0].set_ylabel('Stack', rotation=0)
axes[0].yaxis.set_ticks([])
for i, slave in enumerate(traces):
cc = normxcorr2(tr.data, slave[0].data)
axes[i + 1].set_ylabel('cc=' + str(round(np.max(cc), 2)), rotation=0)
axes[i+1].text(0.9, 0.15, str(round(np.max(slave[0].data))), \
bbox=dict(facecolor='white', alpha=0.95),\
transform=axes[i+1].transAxes)
axes[i+1].text(0.7, 0.85, slave[0].stats.starttime.datetime.strftime('%Y/%m/%d %H:%M:%S'), \
bbox=dict(facecolor='white', alpha=0.95),\
transform=axes[i+1].transAxes)
axes[-1].set_xlabel('Time (s)')
if title:
axes[0].set_title(title)
plt.subplots_adjust(hspace=0)
plt.show()
return traces, plist
def multi_event_singlechan(streams,
catalog,
clip=10.0,
pre_pick=2.0,
freqmin=False,
freqmax=False,
realign=False,
cut=(-3.0, 5.0),
PWS=False,
title=False,
save=False,
savefile=None):
r"""Function to plot data from a single channel at a single station for \
multiple events - data will be alligned by their pick-time given in the \
picks.
:type streams: list of :class:obspy.stream
:param streams: List of the streams to use, can contain more traces than \
you plan on plotting
:type catalog: obspy.core.event.Catalog
:param catalog: Catalog of events, one for each trace, with a single pick
:type clip: float
:param clip: Length in seconds to plot, defaults to 10.0
:type pre_pick: float
:param pre_pick: Length in seconds to extract and plot before the pick, \
defaults to 2.0
:type freqmin: float
:param freqmin: Low cut for bandpass in Hz
:type freqmax: float
:param freqmax: High cut for bandpass in Hz
:type realign: bool
:param realign: To compute best alignement based on correlation or not.
:type cut: tuple
:param cut: tuple of start and end times for cut in seconds from the pick
:type PWS: bool
:param PWS: compute Phase Weighted Stack, if False, will compute linear \
stack.
:type title: str
:param title: Plot title.
:type save: bool
:param save: False will plot to screen, true will save plot and not show \
to screen.
:type savefile: str
:param savefile: Filename to save to, required for save=True
:returns: Alligned and cut traces, and new picks
"""
_check_save_args(save, savefile)
from eqcorrscan.utils import stacking
import copy
from eqcorrscan.core.match_filter import normxcorr2
from obspy import Stream
import warnings
fig, axes = plt.subplots(len(catalog) + 1, 1, sharex=True, figsize=(7, 12))
axes = axes.ravel()
traces = []
al_traces = []
# Keep input safe
clist = copy.deepcopy(catalog)
st_list = copy.deepcopy(streams)
for i, event in enumerate(clist):
if st_list[i].select(station=event.picks[0].waveform_id.station_code,
channel='*' +
event.picks[0].waveform_id.channel_code[-1]):
tr = st_list[i].select(
station=event.picks[0].waveforms_id.station_code,
channel='*' + event.picks[0].waveform_id.channel_code[-1])[0]
else:
print('No data for ' + event.pick[0].waveform_id)
continue
tr.detrend('linear')
if freqmin:
tr.filter('bandpass', freqmin=freqmin, freqmax=freqmax)
if realign:
tr_cut = tr.copy()
tr_cut.trim(event.picks[0].time + cut[0],
event.picks[0].time + cut[1],
nearest_sample=False)
if len(tr_cut.data) <= (0.5 * (cut[1] - cut[0]) *
tr_cut.stats.sampling_rate):
msg = ''.join([
'Not enough in the trace for ', tr.stats.station, '.',
tr.stats.channel, '\n',
'Suggest removing pick from sfile at time ',
str(event.picks[0].time)
])
warnings.warn(msg)
else:
al_traces.append(tr_cut)
else:
tr.trim(event.picks[0].time - pre_pick,
event.picks[0].time + clip - pre_pick,
nearest_sample=False)
if len(tr.data) == 0:
msg = ''.join([
'No data in the trace for ', tr.stats.station, '.',
tr.stats.channel, '\n',
'Suggest removing pick from sfile at time ',
str(event.picks[0].time)
])
warnings.warn(msg)
continue
traces.append(tr)
if realign:
shift_len = int(0.25 * (cut[1] - cut[0]) *
al_traces[0].stats.sampling_rate)
shifts = stacking.align_traces(al_traces, shift_len)
for i in xrange(len(shifts)):
print('Shifting by ' + str(shifts[i]) + ' seconds')
event.picks[0].time -= shifts[i]
traces[i].trim(event.picks[0].time - pre_pick,
event.picks[0].time + clip - pre_pick,
nearest_sample=False)
# We now have a list of traces
traces = [(trace, trace.stats.starttime.datetime) for trace in traces]
traces.sort(key=lambda tup: tup[1])
traces = [trace[0] for trace in traces]
# Plot the traces
for i, tr in enumerate(traces):
y = tr.data
x = np.arange(len(y))
x = x / tr.stats.sampling_rate # convert to seconds
axes[i + 1].plot(x, y, 'k', linewidth=1.1)
axes[i + 1].yaxis.set_ticks([])
traces = [Stream(trace) for trace in traces]
if PWS:
linstack = stacking.PWS_stack(traces)
else:
linstack = stacking.linstack(traces)
tr = linstack.select(station=event[0].picks[0].waveform_id.station_code,
channel='*' +
event[0].picks[0].waveform_id.channel_code[-1])[0]
y = tr.data
x = np.arange(len(y))
x = x / tr.stats.sampling_rate
axes[0].plot(x, y, 'r', linewidth=2.0)
axes[0].set_ylabel('Stack', rotation=0)
axes[0].yaxis.set_ticks([])
for i, slave in enumerate(traces):
cc = normxcorr2(tr.data, slave[0].data)
axes[i + 1].set_ylabel('cc=' + str(round(np.max(cc), 2)), rotation=0)
axes[i + 1].text(0.9,
0.15,
str(round(np.max(slave[0].data))),
bbox=dict(facecolor='white', alpha=0.95),
transform=axes[i + 1].transAxes)
axes[i + 1].text(
0.7,
0.85,
slave[0].stats.starttime.datetime.strftime('%Y/%m/%d %H:%M:%S'),
bbox=dict(facecolor='white', alpha=0.95),
transform=axes[i + 1].transAxes)
axes[-1].set_xlabel('Time (s)')
if title:
axes[0].set_title(title)
plt.subplots_adjust(hspace=0)
if not save:
plt.show()
else:
plt.savefig(savefile)
return traces, clist
