def test_xcorr_plot(self):
st = self.st.copy().detrend('simple').filter(
'bandpass', freqmin=2, freqmax=15)
shifts, ccs = align_traces([st[0], st[1]], 40)
shift = shifts[1] * st[1].stats.sampling_rate
cc = ccs[1]
fig = xcorr_plot(template=st[1].data, image=st[0].data, shift=shift,
cc=cc, show=False, return_figure=True)
return fig
def test_xcorr_plot_cc_vec(self):
st = self.st.copy().detrend('simple').filter(
'bandpass', freqmin=2, freqmax=15)
cc_vec = normxcorr2(
template=st[0].data.astype(np.float32)[40:-40],
image=st[1].data.astype(np.float32))
cc_vec = cc_vec[0]
fig = xcorr_plot(
template=st[1].data[40:-40], image=st[0].data, cc_vec=cc_vec,
show=False, return_figure=True)
return fig
def align_traces(trace_list,
shift_len,
master=False,
positive=False,
plot=False):
"""
Align traces relative to each other based on their cross-correlation value.
Uses the :func:`eqcorrscan.core.match_filter.normxcorr2` function to find
the optimum shift to align traces relative to a master event. Either uses
a given master to align traces, or uses the trace with the highest MAD
amplitude.
:type trace_list: list
:param trace_list: List of traces to align
:type shift_len: int
:param shift_len: Length to allow shifting within in samples
:type master: obspy.core.trace.Trace
:param master: Master trace to align to, if set to False will align to \
the largest amplitude trace (default)
:type positive: bool
:param positive: Return the maximum positive cross-correlation, or the \
absolute maximum, defaults to False (absolute maximum).
:type plot: bool
:param plot: If true, will plot each trace aligned with the master.
:returns: list of shifts and correlations for best alignment in seconds.
:rtype: list
"""
from eqcorrscan.core.match_filter import normxcorr2
from eqcorrscan.utils.plotting import xcorr_plot
traces = deepcopy(trace_list)
if not master:
# Use trace with largest MAD amplitude as master
master = traces[0]
MAD_master = np.median(np.abs(master.data))
for i in range(1, len(traces)):
if np.median(np.abs(traces[i].data)) > MAD_master:
master = traces[i]
MAD_master = np.median(np.abs(master.data))
else:
print('Using master given by user')
shifts = []
ccs = []
for i in range(len(traces)):
if not master.stats.sampling_rate == traces[i].stats.sampling_rate:
raise ValueError('Sampling rates not the same')
cc_vec = normxcorr2(template=traces[i].data.astype(
np.float32)[shift_len:-shift_len],
image=master.data.astype(np.float32))
cc_vec = cc_vec[0]
shift = np.abs(cc_vec).argmax()
cc = cc_vec[shift]
if plot:
xcorr_plot(template=traces[i].data.astype(
np.float32)[shift_len:-shift_len],
image=master.data.astype(np.float32),
shift=shift,
cc=cc)
shift -= shift_len
if cc < 0 and positive:
cc = cc_vec.max()
shift = cc_vec.argmax() - shift_len
shifts.append(shift / master.stats.sampling_rate)
ccs.append(cc)
return shifts, ccs
