m, c, l = mot[rec.absname], con[rec.absname], lum[rec.absname]
tm = tmovie[rec.absname]
ms = motspars[rec.absname]
psthis0d = t.searchsorted(tm) # PSTH indices closest to movie frame times, no delay
dt = rec.e0.d.sweepSec # frame duration in seconds
corrdelaysec = corrdelay / 1000 # convert from ms to s
ntdelay = intround(corrdelaysec / dt) # ntimepoints to delay movie-PSTH correlation by
if ntdelay >= 0:
psthis = psthis0d[ntdelay:]
movieis = np.arange(0, len(tm)-ntdelay)
else: # ntdelay is -ve
psthis = psthis0d[:ntdelay]
movieis = np.arange(abs(ntdelay), len(tm))
for nid in sorted(rpsths[rec.absname][statei]):
psth = rpsths[rec.absname][statei][nid]
motrho = core.corrcoef(m[movieis], psth[psthis])
conrho = core.corrcoef(c[movieis], psth[psthis])
lumrho = core.corrcoef(l[movieis], psth[psthis])
motrhos[state].append(motrho)
conrhos[state].append(conrho)
lumrhos[state].append(lumrho)
motscatspars[state].append([ms, core.sparseness(psth)])
if nid not in recsecscatmotrhos[rec.absname]:
recsecscatmotrhos[rec.absname][nid] = [NULLRHO, NULLRHO] # init list for this nid
recsecscatconrhos[rec.absname][nid] = [NULLRHO, NULLRHO]
recsecscatlumrhos[rec.absname][nid] = [NULLRHO, NULLRHO]
recsecscatmotrhos[rec.absname][nid][statei] = motrho
recsecscatconrhos[rec.absname][nid][statei] = conrho
recsecscatlumrhos[rec.absname][nid][statei] = lumrho
if rec.absname == EXAMPLERECNAME and nid == EXAMPLENID:
stranges = REC2STATETRANGES[rec.absname]
for statei, strange in enumerate(stranges): # desynched, then synched
lfpt, lfps = rec.tlfps(trange=strange, blank=BLANK, plot=False)
muat, muas = rec.tmuas(trange=strange, blank=BLANK, plot=False) # Hz/unit
TLFPs[rec.absname].append((lfpt, lfps))
TMUAs[rec.absname].append((muat, muas))
MAXMUA[rec.absname] = max(MAXMUA[rec.absname], muas.max())
ntrials = len(lfps)
assert ntrials == len(muas)
for triali in range(ntrials):
# measure reliability as correlation of each trial with mean of all others.
# To exclude last sec of blankscreen in each trial, set BLANK=False:
lfptrial, muatrial = lfps[triali], muas[triali]
otheris = np.ones(ntrials, dtype=bool)
otheris[triali] = False # exclude current trial
LFPCORRS[statei].append(corrcoef(lfptrial, lfps[otheris].mean(axis=0)))
MUACORRS[statei].append(corrcoef(muatrial, muas[otheris].mean(axis=0)))
LFPSPARS[statei].append(sparseness(abs(lfptrial)))
MUASPARS[statei].append(sparseness(muatrial))
for statei in range(2): # desynched, then synched
LFPCORRS[statei] = np.asarray(LFPCORRS[statei]) # convert from list to array
MUACORRS[statei] = np.asarray(MUACORRS[statei])
LFPSPARS[statei] = np.asarray(LFPSPARS[statei])
MUASPARS[statei] = np.asarray(MUASPARS[statei])
# plot LFP and MUA time series, plus mean and stdevs, and SNR time series:
for rec in urecs:
print(rec.absname)
# subplotting trickery from
# http://stackoverflow.com/questions/22511550/gridspec-with-shared-axes-in-python
cs = n2count[nid] # 2D array of spike counts over trial time, one row per trial
rhos, weights = core.pairwisecorr(cs, weight=WEIGHT, invalid='ignore')
# set rho to 0 for trial pairs with undefined rho (one or both trials
# with 0 spikes):
nanis = np.isnan(rhos)
rhos[nanis] = 0.0
# for log plotting convenience, replace any mean rhos < NULLREL with NULLREL
rel = np.mean(rhos)
if rel < NULLREL:
rel = NULLREL
nreplacedbynullrel += 1
rels[statei].append(rel)
# calculate sparseness of this PSTH:
spars[statei].append(sparseness(psth))
# calculate coupling of this PSTH with tMUA:
coup = core.corrcoef(psth, tmua)
coups[statei].append(coup)
print()
for statei in stateis:
rels[statei] = np.asarray(rels[statei])
spars[statei] = np.asarray(spars[statei])
coups[statei] = np.asarray(coups[statei])
# plot MUA coupling histogram:
dmean = coups[0].mean()
smean = coups[1].mean()
u, p = mannwhitneyu(coups[0], coups[1]) # 1-sided
if p < ALPHA:
pstring = 'p < %g' % ceilsigfig(p)
else:
weight=WEIGHT,
invalid='ignore')
# set rho to 0 for trial pairs with undefined rho (one or both trials
# with 0 spikes):
nanis = np.isnan(rhos)
rhos[nanis] = 0.0
# for log plotting convenience, replace any mean rhos < NULLREL with NULLREL
rel = np.mean(rhos)
if rel < NULLREL:
rel = NULLREL
nreplacedbynullrel += 1
rels[statei].append(rel)
# calculate sparseness of this PSTH:
spars[statei].append(sparseness(psth))
# calculate coupling of this PSTH with tMUA:
coup = core.corrcoef(psth, tmua)
coups[statei].append(coup)
print()
for statei in stateis:
rels[statei] = np.asarray(rels[statei])
spars[statei] = np.asarray(spars[statei])
coups[statei] = np.asarray(coups[statei])
# plot MUA coupling histogram:
dmean = coups[0].mean()
smean = coups[1].mean()
u, p = mannwhitneyu(coups[0], coups[1]) # 1-sided
if p < ALPHA:
pstring = 'p < %g' % ceilsigfig(p)
else:
psthis0d = t.searchsorted(
tm) # PSTH indices closest to movie frame times, no delay
dt = rec.e0.d.sweepSec # frame duration in seconds
corrdelaysec = corrdelay / 1000 # convert from ms to s
ntdelay = intround(
corrdelaysec /
dt) # ntimepoints to delay movie-PSTH correlation by
if ntdelay >= 0:
psthis = psthis0d[ntdelay:]
movieis = np.arange(0, len(tm) - ntdelay)
else: # ntdelay is -ve
psthis = psthis0d[:ntdelay]
movieis = np.arange(abs(ntdelay), len(tm))
for nid in sorted(rpsths[rec.absname][statei]):
psth = rpsths[rec.absname][statei][nid]
motrho = core.corrcoef(m[movieis], psth[psthis])
conrho = core.corrcoef(c[movieis], psth[psthis])
lumrho = core.corrcoef(l[movieis], psth[psthis])
motrhos[state].append(motrho)
conrhos[state].append(conrho)
lumrhos[state].append(lumrho)
motscatspars[state].append([ms, core.sparseness(psth)])
if nid not in recsecscatmotrhos[rec.absname]:
recsecscatmotrhos[rec.absname][nid] = [
NULLRHO, NULLRHO
] # init list for this nid
recsecscatconrhos[rec.absname][nid] = [NULLRHO, NULLRHO]
recsecscatlumrhos[rec.absname][nid] = [NULLRHO, NULLRHO]
recsecscatmotrhos[rec.absname][nid][statei] = motrho
recsecscatconrhos[rec.absname][nid][statei] = conrho
recsecscatlumrhos[rec.absname][nid][statei] = lumrho
lfpt, lfps = rec.tlfps(trange=strange, blank=BLANK, plot=False)
muat, muas = rec.tmuas(trange=strange, blank=BLANK,
plot=False) # Hz/unit
TLFPs[rec.absname].append((lfpt, lfps))
TMUAs[rec.absname].append((muat, muas))
MAXMUA[rec.absname] = max(MAXMUA[rec.absname], muas.max())
ntrials = len(lfps)
assert ntrials == len(muas)
for triali in range(ntrials):
# measure reliability as correlation of each trial with mean of all others.
# To exclude last sec of blankscreen in each trial, set BLANK=False:
lfptrial, muatrial = lfps[triali], muas[triali]
otheris = np.ones(ntrials, dtype=bool)
otheris[triali] = False # exclude current trial
LFPCORRS[statei].append(
corrcoef(lfptrial, lfps[otheris].mean(axis=0)))
MUACORRS[statei].append(
corrcoef(muatrial, muas[otheris].mean(axis=0)))
LFPSPARS[statei].append(sparseness(abs(lfptrial)))
MUASPARS[statei].append(sparseness(muatrial))
for statei in range(2): # desynched, then synched
LFPCORRS[statei] = np.asarray(
LFPCORRS[statei]) # convert from list to array
MUACORRS[statei] = np.asarray(MUACORRS[statei])
LFPSPARS[statei] = np.asarray(LFPSPARS[statei])
MUASPARS[statei] = np.asarray(MUASPARS[statei])
# plot LFP and MUA time series, plus mean and stdevs, and SNR time series:
for rec in urecs:
print(rec.absname)