code stringlengths 3 6.57k |
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range(nshifts) |
range(nshifts) |
zip(sti0s, sti1s) |
print("Padding waveforms with up to +/- %d points of fake data" % maxshift) |
np.zeros((nspikes, nchans, subnt) |
np.zeros((nspikes, nchans) |
time.time() |
enumerate(sids) |
spikechans.searchsorted(chans) |
print('Mean prep loop for best shift took %.3f sec' % (time.time() |
time.time() |
subsd.mean(axis=0) |
print('Mean for best shift took %.3f sec' % (time.time() |
max() |
np.zeros((maxnchans, maxshift+nt+maxshift) |
subsd.copy() |
np.zeros((nshifts, nchans, subnt) |
time.time() |
enumerate(sids) |
enumerate(sti0ssti1s) |
sum(axis=2) |
sum(axis=1) |
sserrors.argmin() |
dirtysids.append(sid) |
print('Shifting loop took %.3f sec' % (time.time() |
AD2uV(subsd.std(axis=0) |
mean() |
AD2uV(shiftedsubsd.std(axis=0) |
mean() |
print('stdev went from %.3f to %.3f uV' % (stdevbefore, stdevafter) |
alignminmax(self, sids, to) |
self.stream.is_open() |
RuntimeError("No open stream to reload spikes from") |
np.column_stack((V0s, V1s) |
np.column_stack((alignis==0, alignis==1) |
ValueError('Unknown to %r' % to) |
len(sids) |
print("Realigning %d spikes" % nspikes) |
np.int32(multichantis[np.arange(nspikes) |
np.zeros(nspikes, dtype=int) |
np.int32(alignis) |
abs(dpeaktis) |
self.reload_spikes(sids) |
choose_new_meanchans(self, sids) |
print('Choosing new channel set for all selected spikes') |
self.get_mean_wave(sids) |
meanwave.data.ptp(axis=1) |
argmax() |
det.chans.searchsorted(maxchan) |
distances.argsort() |
meanchans.sort() |
print('meanchans: %r' % list(meanchans) |
print('furthestchan: %d' % furthestchan) |
meanchans.searchsorted(furthestchan) |
len(meanchans) |
reload_spikes(self, sids, usemeanchans=False) |
len(sids) |
print('(Re) |
stream.is_open() |
RuntimeError("No open stream to reload spikes from") |
float(self.__version__) |
print('Fixing potentially incorrect time values during spike reloading') |
time.time() |
self.choose_new_meanchans(sids) |
len(meanchans) |
not (np.diff(ts) |
all() |
print("Selected sids aren't in temporal order, sorting by time...") |
ts.argsort() |
print("Done sorting sids by time") |
np.where(np.diff(ts) |
np.split(sids, splitis) |
len(groups) |
np.where(np.diff(relts // MAXGROUPDT) |
len(splitis) |
np.split(group, splitis) |
len(subgroups) |
print('ngroups: %d' % len(groups) |
enumerate(groups) |
printflush('<%d>' % groupi, end='') |
len(group) |
np.unique(spikes['chans'][group]) |
np.unique(np.hstack((unionchans, meanchans) |
stream() |
stream(t0, t1, unionchans) |
printflush(sidi, end='') |
printflush('.', end='') |
chans.sort() |
self.reload_spike_ver_lte_03(sid, nchans, tempwave, rd) |
print() |
print('Fixed time values of %d spikes' % nfixed) |
print('(Re) |
len(sids) |
time.time() |
reload_spike_ver_lte_03(self, sid, nchans, tempwave, rd) |
alignbest() |
print('Reloading sid from ver_lte_03: %d' % sid) |
lrrep2Darrstripis(od) |
print('') |
pdb.set_trace() |
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