def calc(self):
t0 = time.time()
dts = util.xcorr(self.n0.spikes, self.n1.spikes, trange=self.trange) # in us
print('xcorr calc took %.3f sec' % (time.time()-t0))
self.dts = np.array(dts)
if self.autocorr:
self.dts = self.dts[self.dts != 0] # remove 0s for autocorr
return self
the resulting mean CCH is a result of some kind of causal regularity as a function of cell
depth. To do this properly, when shuffling, should really generate lots of mean CCHs with
shuffled nids and average them. That would guarantee no assymmetry in the super averaged CCH:
"""
np.random.shuffle(nids)
nn = len(nids)
binw = 2000 # us
trange = np.array([-100000, 100000]) # us
bins = np.arange(trange[0], trange[1]+binw, binw)
hists = []
for nii0 in range(nn):
for nii1 in range(nii0+1, nn):
spikes0 = rec.n[nids[nii0]].spikes
spikes1 = rec.n[nids[nii1]].spikes
dts = util.xcorr(spikes0, spikes1, trange) # spike time differences in us
hist = np.histogram(dts, bins=bins)[0]
# if we don't normalize, we treat our confidence in the CCHs of cell pairs
# proportional to the firing rates of cell pairs, which may be the optimal thing to do:
#hist = hist / hist.sum() # pmf: normalize so that sum of each hist is 1
hists.append(hist)
#hists = np.vstack(hists).sum(axis=0)
hists = np.vstack(hists).mean(axis=0)
figure()
bar(bins[:-1]/1000, hists, width=binw/1000)
xlim(trange/1000)
xlabel('time (ms)')
ylabel('mean cross-correlation histogram across all pairs')
title(rec.name)
tight_layout(pad=0.3)
farrdtype=[('nid0', np.int64), ('nid1', np.int64),
('f0', np.float64), ('f1', np.float64), ('df', np.float64)]
# convert RP to half trange array, in us
trange = np.array([0, RP]) # only need to work on one half of the autocorrelogram
for tracki, (track, c) in enumerate(zip(tracks, colours)):
print(track.absname)
neurons = track.alln # dict
nids = sorted(neurons) # sorted keys
nn = len(nids)
# precalculate nrpvs and f values for reuse in pair loop:
nrpvs = np.zeros(nn, dtype=np.int64) # number of refractory period violations, per nid
fs = np.zeros(nn, dtype=np.float64) # f values, per nid
for nidi, nid in enumerate(nids):
s = neurons[nid].spikes # should be sorted
dts = util.xcorr(s, s, trange)
dts = dts[dts != 0] # remove 0s for autocorr
nrpvs[nidi] = len(dts)
nISI = len(s) - 1
fs[nidi] = nrpvs[nidi] / nISI
maxnpairs = nCr(nn, 2) # maximum number of pairs, actual will be less given REXCL
farr = np.zeros(maxnpairs, dtype=farrdtype)
pairi = 0
for nidi0 in range(nn):
for nidi1 in range(nidi0+1, nn): # for all neuron pairs
nid0 = nids[nidi0]
nid1 = nids[nidi1]
n0, n1 = neurons[nid0], neurons[nid1]
if core.dist(n0.pos, n1.pos) <= REXCL:
continue # skip this pair, they're too close together
the resulting mean CCH is a result of some kind of causal regularity as a function of cell
depth. To do this properly, when shuffling, should really generate lots of mean CCHs with
shuffled nids and average them. That would guarantee no assymmetry in the super averaged CCH:
"""
np.random.shuffle(nids)
nn = len(nids)
binw = 2000 # us
trange = np.array([-100000, 100000]) # us
bins = np.arange(trange[0], trange[1] + binw, binw)
hists = []
for nii0 in range(nn):
for nii1 in range(nii0 + 1, nn):
spikes0 = rec.n[nids[nii0]].spikes
spikes1 = rec.n[nids[nii1]].spikes
dts = util.xcorr(spikes0, spikes1,
trange) # spike time differences in us
hist = np.histogram(dts, bins=bins)[0]
# if we don't normalize, we treat our confidence in the CCHs of cell pairs
# proportional to the firing rates of cell pairs, which may be the optimal thing to do:
#hist = hist / hist.sum() # pmf: normalize so that sum of each hist is 1
hists.append(hist)
#hists = np.vstack(hists).sum(axis=0)
hists = np.vstack(hists).mean(axis=0)
figure()
bar(bins[:-1] / 1000, hists, width=binw / 1000)
xlim(trange / 1000)
xlabel('time (ms)')
ylabel('mean cross-correlation histogram across all pairs')
title(rec.name)
tight_layout(pad=0.3)
def cch(self,
nid0,
nid1=None,
trange=50,
binw=None,
shift=None,
nshifts=10,
rate=False,
norm=False,
c='k',
title=True,
figsize=(7.5, 6.5)):
"""Copied from Recording.cch(). Plot cross-correlation histogram given nid0 and nid1.
If nid1 is None, calculate autocorrelogram. +/- trange and binw are in ms. If shift
(in ms) is set, calculate the average of +/- nshift CCHs shifted by shift, and then
subtract that from the unshifted CCH to get the shift corrected CCH"""
if nid1 == None:
nid1 = nid0
autocorr = nid0 == nid1
n0 = self.alln[nid0]
n1 = self.alln[nid1]
calctrange = trange * 1000 # calculation trange, in us
if shift:
assert nshifts > 0
shift *= 1000 # convert to us
maxshift = nshifts * shift
calctrange = trange + maxshift # expand calculated trange to encompass shifts
calctrange = np.array([-calctrange,
calctrange]) # convert to a +/- array, in us
dts = util.xcorr(n0.spikes, n1.spikes, calctrange) # in us
if autocorr:
dts = dts[dts != 0] # remove 0s for autocorr
if shift: # calculate dts for shift corrector
shiftis = range(-nshifts, nshifts + 1)
shiftis.remove(
0
) # don't shift by 0, that's the original which we'll subtract from
shifts = np.asarray(shiftis) * shift
shiftdts = np.hstack([dts + s for s in shifts]) # in us
print('shifts =', shifts / 1000)
if not binw:
nbins = intround(np.sqrt(len(dts))) # good heuristic
nbins = max(20, nbins) # enforce min nbins
nbins = min(200, nbins) # enforce max nbins
else:
nbins = intround(2 * trange / binw)
dts = dts / 1000 # in ms, converts to float64 array
t = np.linspace(start=-trange,
stop=trange,
num=nbins + 1,
endpoint=True) # ms
binw = t[1] - t[0] # all should be equal width, ms
n = np.histogram(dts, bins=t, density=False)[0]
if shift: # subtract shift corrector
shiftdts = shiftdts / 1000 # in ms, converts to float64 array
shiftn = np.histogram(shiftdts, bins=t,
density=False)[0] / (nshifts * 2)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.bar(left=t[:-1], height=shiftn,
width=binw) # omit last right edge in t
a.set_xlim(t[0], t[-1])
a.set_xlabel('spike interval (ms)')
n -= shiftn
if norm: # normalize and convert to float:
n = n / n.max()
elif rate: # normalize by binw and convert to float:
n = n / binw
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.bar(left=t[:-1], height=n, width=binw, color=c,
ec=c) # omit last right edge in t
a.set_xlim(t[0], t[-1])
a.set_xlabel('spike interval (ms)')
if norm:
a.set_ylabel('coincidence rate (AU)')
a.set_yticks([0, 1])
elif rate:
a.set_ylabel('coincidence rate (Hz)')
else:
a.set_ylabel('count')
if title:
a.set_title('spike times of n%d wrt n%d' %
(self.n1.id, self.n0.id))
wtitlestr = lastcmd() # + ', binw=%.1f ms' % binw
gcfm().window.setWindowTitle(wtitlestr)
f.tight_layout(pad=0.3) # crop figure to contents
# convert RP to half trange array, in us
trange = np.array([0,
RP]) # only need to work on one half of the autocorrelogram
for track in tracks:
print(track.absname)
neurons = track.alln # dict
nids = sorted(neurons) # sorted keys
nn = len(nids)
# precalculate nrpvs and f values for reuse in pair loop:
nrpvs = np.zeros(
nn, dtype=np.int64) # number of refractory period violations, per nid
fs = np.zeros(nn, dtype=np.float64) # f values, per nid
for nidi, nid in enumerate(nids):
s = neurons[nid].spikes # should be sorted
dts = util.xcorr(s, s, trange)
dts = dts[dts != 0] # remove 0s for autocorr
nrpvs[nidi] = len(dts)
nISI = len(s) - 1
fs[nidi] = nrpvs[nidi] / nISI
fss.append(fs)
maxnpairs = nCr(
nn, 2) # maximum number of pairs, actual will be less given REXCL
farr = np.zeros(maxnpairs, dtype=farrdtype)
pairi = 0
for nidi0 in range(nn):
for nidi1 in range(nidi0 + 1, nn): # for all neuron pairs
nid0 = nids[nidi0]
nid1 = nids[nidi1]
n0, n1 = neurons[nid0], neurons[nid1]
def cch(self, nid0, nid1=None, trange=50, binw=None, shift=None, nshifts=10,
rate=False, norm=False, c='k', title=True, figsize=(7.5, 6.5)):
"""Copied from Recording.cch(). Plot cross-correlation histogram given nid0 and nid1.
If nid1 is None, calculate autocorrelogram. +/- trange and binw are in ms. If shift
(in ms) is set, calculate the average of +/- nshift CCHs shifted by shift, and then
subtract that from the unshifted CCH to get the shift corrected CCH"""
if nid1 == None:
nid1 = nid0
autocorr = nid0 == nid1
n0 = self.alln[nid0]
n1 = self.alln[nid1]
calctrange = trange * 1000 # calculation trange, in us
if shift:
assert nshifts > 0
shift *= 1000 # convert to us
maxshift = nshifts * shift
calctrange = trange + maxshift # expand calculated trange to encompass shifts
calctrange = np.array([-calctrange, calctrange]) # convert to a +/- array, in us
dts = util.xcorr(n0.spikes, n1.spikes, calctrange) # in us
if autocorr:
dts = dts[dts != 0] # remove 0s for autocorr
if shift: # calculate dts for shift corrector
shiftis = range(-nshifts, nshifts+1)
shiftis.remove(0) # don't shift by 0, that's the original which we'll subtract from
shifts = np.asarray(shiftis) * shift
shiftdts = np.hstack([ dts+s for s in shifts ]) # in us
print('shifts =', shifts / 1000)
if not binw:
nbins = intround(np.sqrt(len(dts))) # good heuristic
nbins = max(20, nbins) # enforce min nbins
nbins = min(200, nbins) # enforce max nbins
else:
nbins = intround(2 * trange / binw)
dts = dts / 1000 # in ms, converts to float64 array
t = np.linspace(start=-trange, stop=trange, num=nbins+1, endpoint=True) # ms
binw = t[1] - t[0] # all should be equal width, ms
n = np.histogram(dts, bins=t, density=False)[0]
if shift: # subtract shift corrector
shiftdts = shiftdts / 1000 # in ms, converts to float64 array
shiftn = np.histogram(shiftdts, bins=t, density=False)[0] / (nshifts*2)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.bar(left=t[:-1], height=shiftn, width=binw) # omit last right edge in t
a.set_xlim(t[0], t[-1])
a.set_xlabel('spike interval (ms)')
n -= shiftn
if norm: # normalize and convert to float:
n = n / n.max()
elif rate: # normalize by binw and convert to float:
n = n / binw
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.bar(left=t[:-1], height=n, width=binw, color=c, ec=c) # omit last right edge in t
a.set_xlim(t[0], t[-1])
a.set_xlabel('spike interval (ms)')
if norm:
a.set_ylabel('coincidence rate (AU)')
a.set_yticks([0, 1])
elif rate:
a.set_ylabel('coincidence rate (Hz)')
else:
a.set_ylabel('count')
if title:
a.set_title('spike times of n%d wrt n%d' % (self.n1.id, self.n0.id))
wtitlestr = lastcmd()# + ', binw=%.1f ms' % binw
gcfm().window.setWindowTitle(wtitlestr)
f.tight_layout(pad=0.3) # crop figure to contents