def get_responsive_nids(rec):
"""Get responsive nids (those with at least 1 detected response event in their PSTH) by
finding the responsive nids in each state separately, then taking their superset"""
rnids = []
allnids = sorted(rec.alln)
for statei in [0, 1]: # 0 is desynched, 1 is synched
tranges = REC2STATETRANGES[rec.absname]
trange = tranges[statei]
t, psths, spikets = rec.psth(nids=allnids,
natexps=False,
blank=BLANK,
strange=trange,
plot=False,
binw=BINW,
tres=TRES,
gauss=GAUSS,
norm='ntrials')
for nid, psth, ts in zip(allnids, psths, spikets):
# run PSTH peak detection:
baseline = MEDIANX * np.median(psth)
thresh = baseline + MINTHRESH # peak detection threshold
print("n%d" % nid, end='')
peakis, lis, ris = get_psth_peaks_gac(ts, t, psth, thresh)
npeaks = len(peakis)
if npeaks > 0:
rnids.append(nid)
print() # newline
rnids = np.unique(rnids)
return rnids
def get_responsive_nids(rec):
"""Get responsive nids (those with at least 1 detected response event in their PSTH) by
finding the responsive nids in each state separately, then taking their superset"""
rnids = []
allnids = sorted(rec.alln)
for statei in [0, 1]: # 0 is desynched, 1 is synched
tranges = REC2STATETRANGES[rec.absname]
trange = tranges[statei]
t, psths, spikets = rec.psth(nids=allnids, natexps=False, blank=BLANK, strange=trange,
plot=False, binw=BINW, tres=TRES, gauss=GAUSS,
norm='ntrials')
for nid, psth, ts in zip(allnids, psths, spikets):
# run PSTH peak detection:
baseline = MEDIANX * np.median(psth)
thresh = baseline + MINTHRESH # peak detection threshold
print("n%d" % nid, end='')
peakis, lis, ris = get_psth_peaks_gac(ts, t, psth, thresh)
npeaks = len(peakis)
if npeaks > 0:
rnids.append(nid)
print() # newline
rnids = np.unique(rnids)
return rnids
rts = {} # index into by [rec.absname][statei]
rpsths = {} # index into by [rec.absname][statei][nid]
for rec, nids, strange, state in zip(recs, recsecnids, stranges, states):
print(rec.absname, state)
statei = {'desynch': 0, 'synch': 1}[state]
if rec.absname not in rts:
rts[rec.absname] = [None, None] # init, index into using statei
rpsths[rec.absname] = [{}, {}] # init, index into using statei
t, psths, spikets = rec.psth(nids=nids, natexps=False, blank=BLANK, strange=strange,
plot=False, binw=BINW, tres=TRES, gauss=GAUSS, norm='ntrials')
rts[rec.absname][statei] = t
for nid, psth, ts in zip(nids, psths, spikets):
# run PSTH peak detection:
baseline = MEDIANX * np.median(psth)
thresh = baseline + MINTHRESH # peak detection threshold
peakis, lis, ris = get_psth_peaks_gac(ts, t, psth, thresh)
if len(peakis) == 0: # not a responsive PSTH
continue
rpsths[rec.absname][statei][nid] = psth
print('\n') # two newlines
# correlate each PSTH in each recording section with movie signals. Also, collect
# movie motion-PSTH sparseness pairs:
motrhostats, conrhostats, lumrhostats = [], [], []
for corrdelay in CORRDELAYS:
motrhos = {'desynch': [], 'synch': []}
conrhos = {'desynch': [], 'synch': []}
lumrhos = {'desynch': [], 'synch': []}
motscatspars = {'desynch': [], 'synch': []}
NULLRHO = -1
recsecscatmotrhos = {} # index into with [rec.absname][nid][statei], first 2 are dict keys
for n in ns:
nid = n.id
rec = n.sort.r
stranges = REC2STATETRANGES[rec.absname] # [desynched, synched]
figure(figsize=figsize)
for strange, c in zip(stranges[::-1], ['r', 'b']): # [synched, desynched]
# calculate PSTH:
t, psths, spikets = rec.psth(nids=[nid], strange=strange, binw=BINW, tres=TRES,
gauss=True, norm='ntrials', plot=False)
psth, ts = psths[0], spikets[0] # lists of len 1
# run PSTH peak detection:
baseline = MEDIANX * np.median(psth)
thresh = baseline + MINTHRESH # peak detection threshold
print("n%d" % nid, end='')
peakis, lis, ris = get_psth_peaks_gac(ts, t, psth, thresh, sigma=sigma)
plot(t, psth, c=c, marker=None, ls='-')
if len(peakis) > 0:
plot(t[peakis], psth[peakis], c=c, marker='.', linestyle='none', ms=ms, mec='none')
'''
if len(lis) > 0:
plot(t[lis], psth[lis], c='e', marker='.', linestyle='none', ms=ms, mec='none')
if len(ris) > 0:
plot(t[ris], psth[ris], c='k', marker='.', linestyle='none', ms=ms, mec='none')
'''
xlim(xmin=0, xmax=t[-1])
#ylim(0, 1) # arbitrary units
#yticks([0, 1])
yticks([])
gcfm().window.setWindowTitle(rec.absname+'_n'+str(nid)+'_PSTHs')
tight_layout(pad=0.3) # crop figure to contents
show()
# n2count is needed for calculating reliability:
n2count = rec.bintraster(nids=nids,
ttranges=ttranges,
natexps=False,
blank=BLANK,
strange=None,
binw=TRASTERBINW,
tres=TRASTERTRES,
gauss=GAUSS)[0]
for nid, psth, ts in zip(nids, psths, spikets):
# run PSTH peak detection:
baseline = MEDIANX * np.median(psth)
thresh = baseline + MINTHRESH # peak detection threshold
print("n%d" % nid, end='')
peakis, lis, ris = get_psth_peaks_gac(ts, t, psth, thresh)
psthparams[nid] = t, psth, thresh, baseline, peakis, lis, ris
#psthparams[nid] = get_psth_peaks(t, psth, nid)
#t, psth, thresh, baseline, peakis, lis, ris = psthparams[nid] # unpack
if PLOTPSTH:
plot_psth(psthparams, nid, FMTS[state])
show()
npeaks = len(peakis)
if npeaks == 0:
nrnids[state].append(
nid) # save nonresponsive nids by state
continue # this PSTH has no peaks, skip all subsequent measures
ntotpeaks += npeaks
rnids[state].append(nid) # save responsive nids by state
rpsths[state].append(psth) # save responsive PSTH by state
peaktimes[state].append(peakis * TRES) # save peak times, s
