def get_seps(nids, nd):
"""Build flattened array of distances between all unique pairs in nids, given neuron
dict nd"""
nn = len(nids)
lti = np.tril_indices(nn, -1) # lower triangle (below diagonal) indices, ie unique pairs
seps = []
for nidi0, nidi1 in np.asarray(lti).T:
sep = dist(nd[nids[nidi0]].pos, nd[nids[nidi1]].pos)
seps.append(sep)
seps = np.hstack(seps)
return seps
def get_seps(nids, nd):
"""Build flattened array of distances between all unique pairs in nids, given neuron
dict nd"""
nn = len(nids)
lti = np.tril_indices(
nn, -1) # lower triangle (below diagonal) indices, ie unique pairs
seps = []
for nidi0, nidi1 in np.asarray(lti).T:
sep = dist(nd[nids[nidi0]].pos, nd[nids[nidi1]].pos)
seps.append(sep)
seps = np.hstack(seps)
return seps
xs = np.unique(track.chanpos[:, 0])
ys = np.unique(track.chanpos[:, 1])
dx = np.diff(xs)[0]
dy = np.diff(ys)[0]
w = max(xs) - min(xs) + dx
h = max(ys) - min(ys) + dy
xlims.append([min(xs)-dx/2, max(xs)+dx/2])
ylims.append([min(ys)-dy/2, max(ys)+dy/2])
areas.append(w*h) # in square um
N = 0
ds = []
for track in tracks:
neurons = track.alln.values()
for neuron in neurons:
d = [ dist(neuron.pos, cp) for cp in neuron.sort.chanpos ]
ds.append(min(d))
N += len(neurons)
ds = np.hstack(ds)
# calculate theoretical distribution, derived from concentric annuli:
rho = N / sum(areas) # average cell density per unit polytrode area (1/um^2)
midbins = edges[:-1] + intround(binw/2) # middle of each bin
theory = rho*2*pi*midbins/binw # normed distrib expected for randomly distributed units
# simulate numerically by randomly distributing points aroound a polytrode and measuring
# the distribution of distances between each point and its nearest electrode site:
umap1a = ptc22.tr1.sort.chanpos
area1a, xlims1a, ylims1a = areas[1], xlims[1], ylims[1]
nseed = intround(rho * area)
nrandom = 100000
def check_wave(self, wave, cutrange):
"""Check which threshold-exceeding peaks in wave data look like spikes
and return only events that fall within cutrange. Search local spatiotemporal
window around threshold-exceeding peak for biggest peak-to-peak sharpness.
Finally, test that the sharpest peak and its neighbour exceed Vp and Vpp thresholds"""
sort = self.sort
AD2uV = sort.converter.AD2uV
if self.extractparamsondetect:
weights2f = sort.extractor.weights2spatial
f = sort.extractor.f
# holds time indices for each enabled chan until which each enabled chani is
# locked out, updated on every found spike
lockouts = np.zeros(self.nchans, dtype=np.int64)
tsharp = time.time()
sharp = util.sharpness2D(
wave.data) # sharpness of all zero-crossing separated peaks
info('%s: sharpness2D() took %.3f sec' %
(ps().name, time.time() - tsharp))
targthreshsharp = time.time()
# threshold-exceeding peak indices (2D, columns are [tis, cis])
peakis = util.argthreshsharp(wave.data, self.thresh, sharp)
info('%s: argthreshsharp() took %.3f sec' %
(ps().name, time.time() - targthreshsharp))
maxti = len(wave.ts) - 1
dti = self.dti
twi = sort.twi
sdti = dti // 2 # spatial dti: max dti allowed between maxchan and all other chans
nspikes = 0
npeaks = len(peakis)
spikes = np.zeros(npeaks,
self.SPIKEDTYPE) # nspikes will always be <= npeaks
## TODO: test whether np.empty or np.zeros is faster overall in this case
wavedata = np.empty((npeaks, self.maxnchansperspike, self.maxnt),
dtype=np.int16)
# check each threshold-exceeding peak for validity:
for peaki, (ti, chani) in enumerate(peakis):
if DEBUG:
self.log('*** trying thresh peak at t=%r chan=%d' %
(wave.ts[ti], self.chans[chani]))
# is this threshold-exceeding peak locked out?
tlockoutchani = lockouts[chani]
if ti <= tlockoutchani:
if DEBUG: self.log('peak is locked out')
continue # skip to next peak
# find all enabled chanis within inclnbh of chani, lockouts are checked later:
chanis = self.inclnbhdi[chani]
nchans = len(chanis)
# get search window DT on either side of this peak, for checking sharpness
t0i = max(ti - dti, 0) # check for lockouts a bit later
t1i = ti + dti + 1 # +1 makes it end inclusive, don't worry about slicing past end
window = wave.data[chanis,
t0i:t1i] # search window, might not be contig
if DEBUG:
self.log(
'searching window (%d, %d) on chans=%r' %
(wave.ts[t0i], wave.ts[t1i], list(self.chans[chanis])))
# Collect peak-to-peak sharpness for all chans. Save max and adjacent sharpness
# timepoints for each chan, and keep track of which of the two adjacent non locked
# out peaks is the sharpest. Note that the localsharp array contain sharpness of
# all local peaks, not just those that exceed threshold, as in peakis array.
localsharp = sharp[chanis,
t0i:t1i] # sliced the same way as window
ppsharp = np.zeros(nchans, dtype=np.float32)
maxsharpis = np.zeros(nchans, dtype=int)
adjpeakis = np.zeros((nchans, 2), dtype=int)
maxadjiis = np.zeros(nchans, dtype=int)
continuepeaki = False # signal to skip to next peaki
for cii in range(nchans):
localpeakis, = np.where(localsharp[cii] != 0.0)
# keep only non-locked out localpeakis on this channel:
localpeakis = localpeakis[(
t0i + localpeakis) > lockouts[chanis[cii]]]
if len(localpeakis) == 0:
continue # localpeakis is empty
lastpeakii = len(localpeakis) - 1
maxsharpii = abs(localsharp[cii, localpeakis]).argmax()
maxsharpi = localpeakis[maxsharpii]
maxsharpis[cii] = maxsharpi
# Get one adjacent peak to left and right each. Due to limits, either or
# both may be identical to the max sharpness peak
adjpeakis[cii] = localpeakis[[
max(maxsharpii - 1, 0),
min(maxsharpii + 1, lastpeakii)
]]
if localsharp[cii, maxsharpi] < 0:
maxadjii = localsharp[
cii, adjpeakis[cii]].argmax() # look for +ve adj peak
else:
maxadjii = localsharp[
cii, adjpeakis[cii]].argmin() # look for -ve adj peak
maxadjiis[cii] = maxadjii # save
adjpi = adjpeakis[cii, maxadjii]
if maxsharpi != adjpi:
ppsharp[cii] = localsharp[cii,
maxsharpi] - localsharp[cii,
adjpi]
else: # monophasic spike, set ppsharp == sharpness of single peak:
ppsharp[cii] = localsharp[cii, maxsharpi]
if chanis[cii] == chani: # trigger chan is monophasic
# ensure ppsharp of monophasic spike >= Vppthresh**2/dt, ie ensure that
# its Vpp exceeds Vppthresh and has zero crossings on either side,
# with no more than dt between. Avoids excessively wide
# monophasic peaks from being considered as spikes:
if DEBUG: self.log("found monophasic spike")
if abs(ppsharp[cii]) < self.ppthresh[chani]**2 / dti:
continuepeaki = True
if DEBUG:
self.log(
"peak wasn't sharp enough for a monophasic "
"spike")
break # out of cii loop
if continuepeaki:
continue # skip to next peak
# Choose chan with biggest ppsharp as maxchan and its sharpest peak as the primary
# peak, check that these new chani and ti values are identical to the trigger
# values in peakis, that the peak at [chani, ti] isn't locked out, that it falls
# within cutrange, and that it meets both Vp and Vpp threshold criteria.
oldchani, oldti = chani, ti # save
maxcii = abs(ppsharp).argmax(
) # choose chan with sharpest peak as new maxchan
chani = chanis[maxcii] # update maxchan
maxsharpi = maxsharpis[
maxcii] # choose sharpest peak of maxchan, absolute
ti = t0i + maxsharpi # update ti
# Search forward through peakis for a future (later) row that matches the
# (potentially new) [chani, ti] calculated above based on sharpness of local
# peaks. If that particular tuple is indeed coming up, it is therefore
# thresh exceeding, and should be waited for. If not, don't wait for it. Something
# that was thresh exceeding caused the trigger, but this nearby [chani, ti] tuple
# is according to the sharpness measure the best estimate of the spatiotemporal
# origin of the trigger-causing event.
newpeak_coming_up = (peakis[peaki + 1:] == [ti, chani
]).prod(axis=1).any()
if chani != oldchani:
if newpeak_coming_up:
if DEBUG:
self.log(
"triggered off peak on chan that isn't max ppsharpness for "
"this event, pass on this peak and wait for the true "
"sharpest peak to come later")
continue # skip to next peak
else:
# update all variables that depend on chani that wouldn't otherwise be
# updated:
tlockoutchani = lockouts[chani]
chanis = self.inclnbhdi[chani]
nchans = len(chanis)
if ti > oldti:
if newpeak_coming_up:
if DEBUG:
self.log(
"triggered off early adjacent peak for this event, pass on "
"this peak and wait for the true sharpest peak to come later"
)
continue # skip to next peak
else:
# unlike chani, it seems that are no variables that depend on ti that
# wouldn't otherwise be updated:
pass
if ti <= tlockoutchani: # sharpest peak is locked out
if DEBUG:
self.log('sharpest peak at t=%d chan=%d is locked out' %
(wave.ts[ti], self.chans[chani]))
continue # skip to next peak
if not (cutrange[0] <= wave.ts[ti] <= cutrange[1]):
# use %r since wave.ts[ti] is np.int64 and %d gives TypeError if > 2**31:
if DEBUG:
self.log(
"spike time %r falls outside cutrange for this searchblock "
"call, discarding" % wave.ts[ti])
continue # skip to next peak
# check that Vp threshold is exceeded by at least one of the two sharpest peaks
adjpi = adjpeakis[maxcii, maxadjiis[maxcii]]
# relative to t0i, not necessarily in temporal order:
maxchantis = np.array([maxsharpi, adjpi])
# voltages of the two sharpest peaks, convert int16 to int64 to prevent overflow
Vs = np.int64(window[maxcii, maxchantis])
Vp = abs(Vs).max() # grab biggest peak
if Vp < self.thresh[chani]:
if DEBUG:
self.log(
'peak at t=%d chan=%d and its adjacent peak are both '
'< Vp=%f uV' %
(wave.ts[ti], self.chans[chani], AD2uV(Vp)))
continue # skip to next peak
# check that the two sharpest peaks together exceed Vpp threshold:
Vpp = abs(Vs[0] -
Vs[1]) # Vs are of opposite sign, unless monophasic
if Vpp == 0: # monophasic spike
Vpp = Vp # use Vp as Vpp
if Vpp < self.ppthresh[chani]:
if DEBUG:
self.log('peaks at t=%r chan=%d are < Vpp = %f' %
(wave.ts[[ti, t0i + adjpi
]], self.chans[chani], AD2uV(Vpp)))
continue # skip to next peak
if DEBUG:
self.log(
'found biggest thresh exceeding ppsharp at t=%d chan=%d' %
(wave.ts[ti], self.chans[chani]))
# get new spatiotemporal neighbourhood, with full window,
# align to -ve of the two sharpest peaks
aligni = localsharp[maxcii, maxchantis].argmin()
#oldti = ti # save
ti = t0i + maxchantis[
aligni] # new absolute time index to align to
# cut new window
oldt0i = t0i
t0i = max(ti + twi[0], 0)
t1i = min(ti + twi[1] + 1, maxti) # end inclusive
window = wave.data[
chanis, t0i:t1i] # multichan data window, might not be contig
maxcii, = np.where(chanis == chani)
maxchantis += oldt0i - t0i # relative to new t0i
tis = np.zeros((nchans, 2),
dtype=int) # holds time indices for each lockchani
tis[maxcii] = maxchantis
# pick corresponding peaks on other chans according to how close they are
# to those on maxchan, Don't consider the sign of the peaks on each
# chan, just their proximity in time. In other words, allow for spike
# inversion across space
localsharp = sharp[chanis, t0i:t1i]
peak0ti, peak1ti = maxchantis # primary and 2ndary peak tis of maxchan
for cii in range(nchans):
if cii == maxcii: # already set
continue
localpeakis, = np.where(localsharp[cii] != 0.0)
# keep only non-locked out localpeakis on this channel:
localpeakis = localpeakis[(
t0i + localpeakis) > lockouts[chanis[cii]]]
if len(localpeakis) == 0: # localpeakis is empty
tis[cii] = maxchantis # use same tis as maxchan
continue
lastpeakii = len(localpeakis) - 1
# find peak on this chan that's temporally closest to primary peak on maxchan.
# If two peaks are equally close, pick the sharpest one
dt0is = abs(localpeakis - peak0ti)
if (np.diff(dt0is) == 0
).any(): # two peaks equally close, pick sharpest one
peak0ii = abs(localsharp[cii, localpeakis]).argmax()
else:
peak0ii = dt0is.argmin()
# save primary peak for this cii
dt0i = dt0is[peak0ii]
if dt0i > sdti: # too distant in time
tis[cii, 0] = peak0ti # use same t0i as maxchan
else: # give it its own t0i
tis[cii, 0] = localpeakis[peak0ii]
# save 2ndary peak for this cii
if len(localpeakis
) == 1: # monophasic, set 2ndary peak same as primary
tis[cii, 1] = tis[cii, 0]
continue
if peak0ti <= peak1ti: # primary peak comes first (more common case)
peak1ii = min(peak0ii + 1,
lastpeakii) # 2ndary peak is 1 to the right
else: # peak1ti < peak0ti, ie 2ndary peak comes first
peak1ii = max(peak0ii - 1,
0) # 2ndary peak is 1 to the left
dt1is = abs(localpeakis - peak1ti)
dt1i = dt1is[peak1ii]
if dt1i > sdti: # too distant in time
tis[cii, 1] = peak1ti # use same t1i as maxchan
else:
tis[cii, 1] = localpeakis[peak1ii]
# based on maxchan (chani), find inclchanis, incltis, and inclwindow:
inclchanis = self.inclnbhdi[chani]
ninclchans = len(inclchanis)
inclchans = self.chans[inclchanis]
chan = self.chans[chani]
inclchani = int(np.where(inclchans == chan)[0]) # != chani!
inclciis = chanis.searchsorted(inclchanis)
incltis = tis[inclciis]
inclwindow = window[inclciis]
if DEBUG:
self.log(
"final window params: t0=%r, t1=%r, Vs=%r, peakts=\n%r" %
(wave.ts[t0i], wave.ts[t1i], list(
AD2uV(Vs)), wave.ts[t0i + tis]))
if self.extractparamsondetect:
# Get Vpp at each inclchan's tis, use as spatial weights:
# see core.rowtake() or util.rowtake_cy() for indexing explanation:
w = np.float32(inclwindow[np.arange(ninclchans)[:, None],
incltis])
w = abs(w).sum(axis=1)
x = self.siteloc[inclchanis, 0] # 1D array (row)
y = self.siteloc[inclchanis, 1]
params = weights2f(f, w, x, y, inclchani)
if params == None: # presumably a non-localizable many-channel noise event
if DEBUG:
treject = intround(wave.ts[ti]) # nearest us
self.log("reject spike at t=%d based on fit params" %
treject)
# no real need to lockout chans for a params-rejected spike
continue # skip to next peak
# build up spike record:
s = spikes[nspikes]
# wave.ts might be floats, depending on sampfreq
s['t'] = intround(wave.ts[ti]) # nearest us
# leave each spike's chanis in sorted order, as they are in self.inclnbhdi,
# important assumption used later on, like in sort.get_wave() and
# Neuron.update_wave()
ts = wave.ts[t0i:t1i] # potentially floats
# use ts = np.arange(s['t0'], s['t1'], stream.tres) to reconstruct
s['t0'], s['t1'] = intround(wave.ts[t0i]), intround(
wave.ts[t1i]) # nearest us
s['tis'][:ninclchans] = incltis # wrt t0i=0
s['aligni'] = aligni # 0 or 1
s['dt'] = intround(abs(ts[tis[maxcii, 0]] -
ts[tis[maxcii, 1]])) # nearest us
s['V0'], s['V1'] = AD2uV(Vs) # in uV
s['Vpp'] = AD2uV(Vpp) # in uV
s['chan'], s['chans'][:ninclchans], s[
'nchans'] = chan, inclchans, ninclchans
s['chani'] = inclchani
nt = inclwindow.shape[
1] # isn't always full width if recording has gaps
wavedata[nspikes, :ninclchans, :nt] = inclwindow
if self.extractparamsondetect:
# Save spatial fit params, and lockout only the channels within lockrx*sx
# of the fit spatial location of the spike, up to a max of self.inclr.
s['x0'], s['y0'], s['sx'], s['sy'] = params
x0, y0 = s['x0'], s['y0']
# lockout radius for this spike:
lockr = min(self.lockrx * s['sx'], self.inclr) # in um
# test y coords of inclchans in y array, ylockchaniis can be used to index
# into x, y and inclchans:
ylockchaniis, = np.where(
np.abs(y - y0) <= lockr) # convert bool arr to int
# test Euclid distance from x0, y0 for each ylockchani:
lockchaniis = ylockchaniis.copy()
for ylockchanii in ylockchaniis:
if dist((x[ylockchanii], y[ylockchanii]),
(x0, y0)) > lockr:
lockchaniis = np.delete(
lockchaniis, ylockchanii) # dist is too great
lockchans = inclchans[lockchaniis]
lockchanis = inclchanis[lockchaniis]
nlockchans = len(lockchans)
s['lockchans'][:nlockchans], s[
'nlockchans'] = lockchans, nlockchans
# just for testing:
#assert (lockchanis == self.chans.searchsorted(lockchans)).all()
#assert (lockchaniis == chanis.searchsorted(lockchanis)).all()
else: # in this case, the inclchans and lockchans fields are redundant
s['lockchans'][:ninclchans], s[
'nlockchans'] = inclchans, ninclchans
lockchanis = chanis
lockchaniis = np.arange(ninclchans)
# give each chan a distinct lockout, based on how each chan's
# sharpest peaks line up with those of the maxchan. Respect existing lockouts:
# on each of the relevant chans, keep whichever lockout ends last
thislockout = t0i + tis.max(axis=1)[lockchaniis]
lockouts[lockchanis] = np.max([lockouts[lockchanis], thislockout],
axis=0)
if DEBUG:
self.log('lockouts=%r\nfor chans=%r' %
(list(wave.ts[lockouts[lockchanis]]),
list(self.chans[lockchanis])))
self.log('*** found new spike %d: t=%d chan=%d (%d, %d)' %
(nspikes + self.nspikes, s['t'], chan,
self.siteloc[chani, 0], self.siteloc[chani, 1]))
nspikes += 1
# trim spikes and wavedata arrays down to size
spikes.resize(nspikes, refcheck=False)
wds = wavedata.shape
wavedata.resize((nspikes, wds[1], wds[2]), refcheck=False)
return spikes, wavedata
xs = np.unique(track.chanpos[:, 0])
ys = np.unique(track.chanpos[:, 1])
dx = np.diff(xs)[0]
dy = np.diff(ys)[0]
w = max(xs) - min(xs) + dx
h = max(ys) - min(ys) + dy
xlims.append([min(xs) - dx / 2, max(xs) + dx / 2])
ylims.append([min(ys) - dy / 2, max(ys) + dy / 2])
areas.append(w * h) # in square um
N = 0
ds, sigmas = [], []
for track in tracks:
neurons = list(track.alln.values())
for neuron in neurons:
d = [dist(neuron.pos, cp) for cp in neuron.sort.chanpos]
ds.append(min(d))
sigmas.append(neuron.sigma)
N += len(neurons)
ds = np.hstack(ds)
sigmas = np.hstack(sigmas)
# calculate theoretical distribution, derived from concentric annuli:
rho = N / sum(areas) # average cell density per unit polytrode area (1/um^2)
midbins = edges[:-1] + intround(binw / 2) # middle of each bin
theory = rho * 2 * pi * midbins / binw # normed distrib expected for randomly distributed units
# simulate numerically by randomly and uniformly distributing points around a polytrode and
# measuring the distribution of distances between each point and its nearest electrode site:
if MODEL: # can take a long time to run
umap1a = ptc22.tr1.sort.chanpos
def check_wave(self, wave, cutrange):
"""Check which threshold-exceeding peaks in wave data look like spikes
and return only events that fall within cutrange. Search local spatiotemporal
window around threshold-exceeding peak for biggest peak-to-peak sharpness.
Finally, test that the sharpest peak and its neighbour exceed Vp and Vpp thresholds"""
sort = self.sort
AD2uV = sort.converter.AD2uV
if self.extractparamsondetect:
weights2f = sort.extractor.weights2spatial
f = sort.extractor.f
# holds time indices for each enabled chan until which each enabled chani is
# locked out, updated on every found spike
lockouts = np.zeros(self.nchans, dtype=np.int64)
tsharp = time.time()
sharp = util.sharpness2D(wave.data) # sharpness of all zero-crossing separated peaks
info('%s: sharpness2D() took %.3f sec' % (ps().name, time.time()-tsharp))
targthreshsharp = time.time()
# threshold-exceeding peak indices (2D, columns are [tis, cis])
peakis = util.argthreshsharp(wave.data, self.thresh, sharp)
info('%s: argthreshsharp() took %.3f sec' % (ps().name, time.time()-targthreshsharp))
maxti = len(wave.ts) - 1
dti = self.dti
twi = sort.twi
sdti = dti // 2 # spatial dti: max dti allowed between maxchan and all other chans
nspikes = 0
npeaks = len(peakis)
spikes = np.zeros(npeaks, self.SPIKEDTYPE) # nspikes will always be <= npeaks
## TODO: test whether np.empty or np.zeros is faster overall in this case
wavedata = np.empty((npeaks, self.maxnchansperspike, self.maxnt), dtype=np.int16)
# check each threshold-exceeding peak for validity:
for peaki, (ti, chani) in enumerate(peakis):
if DEBUG: self.log('*** trying thresh peak at t=%r chan=%d'
% (wave.ts[ti], self.chans[chani]))
# is this threshold-exceeding peak locked out?
tlockoutchani = lockouts[chani]
if ti <= tlockoutchani:
if DEBUG: self.log('peak is locked out')
continue # skip to next peak
# find all enabled chanis within inclnbh of chani, lockouts are checked later:
chanis = self.inclnbhdi[chani]
nchans = len(chanis)
# get search window DT on either side of this peak, for checking sharpness
t0i = max(ti-dti, 0) # check for lockouts a bit later
t1i = ti+dti+1 # +1 makes it end inclusive, don't worry about slicing past end
window = wave.data[chanis, t0i:t1i] # search window, might not be contig
if DEBUG: self.log('searching window (%d, %d) on chans=%r'
% (wave.ts[t0i], wave.ts[t1i], list(self.chans[chanis])))
# Collect peak-to-peak sharpness for all chans. Save max and adjacent sharpness
# timepoints for each chan, and keep track of which of the two adjacent non locked
# out peaks is the sharpest. Note that the localsharp array contain sharpness of
# all local peaks, not just those that exceed threshold, as in peakis array.
localsharp = sharp[chanis, t0i:t1i] # sliced the same way as window
ppsharp = np.zeros(nchans, dtype=np.float32)
maxsharpis = np.zeros(nchans, dtype=int)
adjpeakis = np.zeros((nchans, 2), dtype=int)
maxadjiis = np.zeros(nchans, dtype=int)
continuepeaki = False # signal to skip to next peaki
for cii in range(nchans):
localpeakis, = np.where(localsharp[cii] != 0.0)
# keep only non-locked out localpeakis on this channel:
localpeakis = localpeakis[(t0i+localpeakis) > lockouts[chanis[cii]]]
if len(localpeakis) == 0:
continue # localpeakis is empty
lastpeakii = len(localpeakis) - 1
maxsharpii = abs(localsharp[cii, localpeakis]).argmax()
maxsharpi = localpeakis[maxsharpii]
maxsharpis[cii] = maxsharpi
# Get one adjacent peak to left and right each. Due to limits, either or
# both may be identical to the max sharpness peak
adjpeakis[cii] = localpeakis[[max(maxsharpii-1, 0), min(maxsharpii+1,
lastpeakii)]]
if localsharp[cii, maxsharpi] < 0:
maxadjii = localsharp[cii, adjpeakis[cii]].argmax() # look for +ve adj peak
else:
maxadjii = localsharp[cii, adjpeakis[cii]].argmin() # look for -ve adj peak
maxadjiis[cii] = maxadjii # save
adjpi = adjpeakis[cii, maxadjii]
if maxsharpi != adjpi:
ppsharp[cii] = localsharp[cii, maxsharpi] - localsharp[cii, adjpi]
else: # monophasic spike, set ppsharp == sharpness of single peak:
ppsharp[cii] = localsharp[cii, maxsharpi]
if chanis[cii] == chani: # trigger chan is monophasic
# ensure ppsharp of monophasic spike >= Vppthresh**2/dt, ie ensure that
# its Vpp exceeds Vppthresh and has zero crossings on either side,
# with no more than dt between. Avoids excessively wide
# monophasic peaks from being considered as spikes:
if DEBUG: self.log("found monophasic spike")
if abs(ppsharp[cii]) < self.ppthresh[chani]**2 / dti:
continuepeaki = True
if DEBUG: self.log("peak wasn't sharp enough for a monophasic "
"spike")
break # out of cii loop
if continuepeaki:
continue # skip to next peak
# Choose chan with biggest ppsharp as maxchan and its sharpest peak as the primary
# peak, check that these new chani and ti values are identical to the trigger
# values in peakis, that the peak at [chani, ti] isn't locked out, that it falls
# within cutrange, and that it meets both Vp and Vpp threshold criteria.
oldchani, oldti = chani, ti # save
maxcii = abs(ppsharp).argmax() # choose chan with sharpest peak as new maxchan
chani = chanis[maxcii] # update maxchan
maxsharpi = maxsharpis[maxcii] # choose sharpest peak of maxchan, absolute
ti = t0i + maxsharpi # update ti
# Search forward through peakis for a future (later) row that matches the
# (potentially new) [chani, ti] calculated above based on sharpness of local
# peaks. If that particular tuple is indeed coming up, it is therefore
# thresh exceeding, and should be waited for. If not, don't wait for it. Something
# that was thresh exceeding caused the trigger, but this nearby [chani, ti] tuple
# is according to the sharpness measure the best estimate of the spatiotemporal
# origin of the trigger-causing event.
newpeak_coming_up = (peakis[peaki+1:] == [ti, chani]).prod(axis=1).any()
if chani != oldchani:
if newpeak_coming_up:
if DEBUG:
self.log("triggered off peak on chan that isn't max ppsharpness for "
"this event, pass on this peak and wait for the true "
"sharpest peak to come later")
continue # skip to next peak
else:
# update all variables that depend on chani that wouldn't otherwise be
# updated:
tlockoutchani = lockouts[chani]
chanis = self.inclnbhdi[chani]
nchans = len(chanis)
if ti > oldti:
if newpeak_coming_up:
if DEBUG:
self.log("triggered off early adjacent peak for this event, pass on "
"this peak and wait for the true sharpest peak to come later")
continue # skip to next peak
else:
# unlike chani, it seems that are no variables that depend on ti that
# wouldn't otherwise be updated:
pass
if ti <= tlockoutchani: # sharpest peak is locked out
if DEBUG: self.log('sharpest peak at t=%d chan=%d is locked out'
% (wave.ts[ti], self.chans[chani]))
continue # skip to next peak
if not (cutrange[0] <= wave.ts[ti] <= cutrange[1]):
# use %r since wave.ts[ti] is np.int64 and %d gives TypeError if > 2**31:
if DEBUG: self.log("spike time %r falls outside cutrange for this searchblock "
"call, discarding" % wave.ts[ti])
continue # skip to next peak
# check that Vp threshold is exceeded by at least one of the two sharpest peaks
adjpi = adjpeakis[maxcii, maxadjiis[maxcii]]
# relative to t0i, not necessarily in temporal order:
maxchantis = np.array([maxsharpi, adjpi])
# voltages of the two sharpest peaks, convert int16 to int64 to prevent overflow
Vs = np.int64(window[maxcii, maxchantis])
Vp = abs(Vs).max() # grab biggest peak
if Vp < self.thresh[chani]:
if DEBUG: self.log('peak at t=%d chan=%d and its adjacent peak are both '
'< Vp=%f uV' % (wave.ts[ti], self.chans[chani], AD2uV(Vp)))
continue # skip to next peak
# check that the two sharpest peaks together exceed Vpp threshold:
Vpp = abs(Vs[0] - Vs[1]) # Vs are of opposite sign, unless monophasic
if Vpp == 0: # monophasic spike
Vpp = Vp # use Vp as Vpp
if Vpp < self.ppthresh[chani]:
if DEBUG: self.log('peaks at t=%r chan=%d are < Vpp = %f'
% (wave.ts[[ti, t0i+adjpi]], self.chans[chani], AD2uV(Vpp)))
continue # skip to next peak
if DEBUG: self.log('found biggest thresh exceeding ppsharp at t=%d chan=%d'
% (wave.ts[ti], self.chans[chani]))
# get new spatiotemporal neighbourhood, with full window,
# align to -ve of the two sharpest peaks
aligni = localsharp[maxcii, maxchantis].argmin()
#oldti = ti # save
ti = t0i + maxchantis[aligni] # new absolute time index to align to
# cut new window
oldt0i = t0i
t0i = max(ti+twi[0], 0)
t1i = min(ti+twi[1]+1, maxti) # end inclusive
window = wave.data[chanis, t0i:t1i] # multichan data window, might not be contig
maxcii, = np.where(chanis == chani)
maxchantis += oldt0i - t0i # relative to new t0i
tis = np.zeros((nchans, 2), dtype=int) # holds time indices for each lockchani
tis[maxcii] = maxchantis
# pick corresponding peaks on other chans according to how close they are
# to those on maxchan, Don't consider the sign of the peaks on each
# chan, just their proximity in time. In other words, allow for spike
# inversion across space
localsharp = sharp[chanis, t0i:t1i]
peak0ti, peak1ti = maxchantis # primary and 2ndary peak tis of maxchan
for cii in range(nchans):
if cii == maxcii: # already set
continue
localpeakis, = np.where(localsharp[cii] != 0.0)
# keep only non-locked out localpeakis on this channel:
localpeakis = localpeakis[(t0i+localpeakis) > lockouts[chanis[cii]]]
if len(localpeakis) == 0: # localpeakis is empty
tis[cii] = maxchantis # use same tis as maxchan
continue
lastpeakii = len(localpeakis) - 1
# find peak on this chan that's temporally closest to primary peak on maxchan.
# If two peaks are equally close, pick the sharpest one
dt0is = abs(localpeakis-peak0ti)
if (np.diff(dt0is) == 0).any(): # two peaks equally close, pick sharpest one
peak0ii = abs(localsharp[cii, localpeakis]).argmax()
else:
peak0ii = dt0is.argmin()
# save primary peak for this cii
dt0i = dt0is[peak0ii]
if dt0i > sdti: # too distant in time
tis[cii, 0] = peak0ti # use same t0i as maxchan
else: # give it its own t0i
tis[cii, 0] = localpeakis[peak0ii]
# save 2ndary peak for this cii
if len(localpeakis) == 1: # monophasic, set 2ndary peak same as primary
tis[cii, 1] = tis[cii, 0]
continue
if peak0ti <= peak1ti: # primary peak comes first (more common case)
peak1ii = min(peak0ii+1, lastpeakii) # 2ndary peak is 1 to the right
else: # peak1ti < peak0ti, ie 2ndary peak comes first
peak1ii = max(peak0ii-1, 0) # 2ndary peak is 1 to the left
dt1is = abs(localpeakis-peak1ti)
dt1i = dt1is[peak1ii]
if dt1i > sdti: # too distant in time
tis[cii, 1] = peak1ti # use same t1i as maxchan
else:
tis[cii, 1] = localpeakis[peak1ii]
# based on maxchan (chani), find inclchanis, incltis, and inclwindow:
inclchanis = self.inclnbhdi[chani]
ninclchans = len(inclchanis)
inclchans = self.chans[inclchanis]
chan = self.chans[chani]
inclchani = int(np.where(inclchans == chan)[0]) # != chani!
inclciis = chanis.searchsorted(inclchanis)
incltis = tis[inclciis]
inclwindow = window[inclciis]
if DEBUG: self.log("final window params: t0=%r, t1=%r, Vs=%r, peakts=\n%r"
% (wave.ts[t0i], wave.ts[t1i], list(AD2uV(Vs)),
wave.ts[t0i+tis]))
if self.extractparamsondetect:
# Get Vpp at each inclchan's tis, use as spatial weights:
# see core.rowtake() or util.rowtake_cy() for indexing explanation:
w = np.float32(inclwindow[np.arange(ninclchans)[:, None], incltis])
w = abs(w).sum(axis=1)
x = self.siteloc[inclchanis, 0] # 1D array (row)
y = self.siteloc[inclchanis, 1]
params = weights2f(f, w, x, y, inclchani)
if params == None: # presumably a non-localizable many-channel noise event
if DEBUG:
treject = intround(wave.ts[ti]) # nearest us
self.log("reject spike at t=%d based on fit params" % treject)
# no real need to lockout chans for a params-rejected spike
continue # skip to next peak
# build up spike record:
s = spikes[nspikes]
# wave.ts might be floats, depending on sampfreq
s['t'] = intround(wave.ts[ti]) # nearest us
# leave each spike's chanis in sorted order, as they are in self.inclnbhdi,
# important assumption used later on, like in sort.get_wave() and
# Neuron.update_wave()
ts = wave.ts[t0i:t1i] # potentially floats
# use ts = np.arange(s['t0'], s['t1'], stream.tres) to reconstruct
s['t0'], s['t1'] = intround(wave.ts[t0i]), intround(wave.ts[t1i]) # nearest us
s['tis'][:ninclchans] = incltis # wrt t0i=0
s['aligni'] = aligni # 0 or 1
s['dt'] = intround(abs(ts[tis[maxcii, 0]] - ts[tis[maxcii, 1]])) # nearest us
s['V0'], s['V1'] = AD2uV(Vs) # in uV
s['Vpp'] = AD2uV(Vpp) # in uV
s['chan'], s['chans'][:ninclchans], s['nchans'] = chan, inclchans, ninclchans
s['chani'] = inclchani
nt = inclwindow.shape[1] # isn't always full width if recording has gaps
wavedata[nspikes, :ninclchans, :nt] = inclwindow
if self.extractparamsondetect:
# Save spatial fit params, and lockout only the channels within lockrx*sx
# of the fit spatial location of the spike, up to a max of self.inclr.
s['x0'], s['y0'], s['sx'], s['sy'] = params
x0, y0 = s['x0'], s['y0']
# lockout radius for this spike:
lockr = min(self.lockrx*s['sx'], self.inclr) # in um
# test y coords of inclchans in y array, ylockchaniis can be used to index
# into x, y and inclchans:
ylockchaniis, = np.where(np.abs(y - y0) <= lockr) # convert bool arr to int
# test Euclid distance from x0, y0 for each ylockchani:
lockchaniis = ylockchaniis.copy()
for ylockchanii in ylockchaniis:
if dist((x[ylockchanii], y[ylockchanii]), (x0, y0)) > lockr:
lockchaniis = np.delete(lockchaniis, ylockchanii) # dist is too great
lockchans = inclchans[lockchaniis]
lockchanis = inclchanis[lockchaniis]
nlockchans = len(lockchans)
s['lockchans'][:nlockchans], s['nlockchans'] = lockchans, nlockchans
# just for testing:
#assert (lockchanis == self.chans.searchsorted(lockchans)).all()
#assert (lockchaniis == chanis.searchsorted(lockchanis)).all()
else: # in this case, the inclchans and lockchans fields are redundant
s['lockchans'][:ninclchans], s['nlockchans'] = inclchans, ninclchans
lockchanis = chanis
lockchaniis = np.arange(ninclchans)
# give each chan a distinct lockout, based on how each chan's
# sharpest peaks line up with those of the maxchan. Respect existing lockouts:
# on each of the relevant chans, keep whichever lockout ends last
thislockout = t0i+tis.max(axis=1)[lockchaniis]
lockouts[lockchanis] = np.max([lockouts[lockchanis], thislockout], axis=0)
if DEBUG:
self.log('lockouts=%r\nfor chans=%r' %
(list(wave.ts[lockouts[lockchanis]]),
list(self.chans[lockchanis])))
self.log('*** found new spike %d: t=%d chan=%d (%d, %d)' %
(nspikes+self.nspikes, s['t'], chan, self.siteloc[chani, 0],
self.siteloc[chani, 1]))
nspikes += 1
# trim spikes and wavedata arrays down to size
spikes.resize(nspikes, refcheck=False)
wds = wavedata.shape
wavedata.resize((nspikes, wds[1], wds[2]), refcheck=False)
return spikes, wavedata
def Hom_loss(H, p, p_):
Hp_ = H @ p
Hp = inv(H) @ p_
return core.dist(Hp_,p_) + core.dist(Hp, p)