# n2count is needed for calculating reliability:
n2count = rec.bintraster(nids=nids, blank=BLANK, strange=strange,
binw=TRASTERBINW, tres=TRASTERTRES, gauss=GAUSS)[0]
ntrials = n2count[nids[0]].shape[0] # should be the same for all nids
print('ntrials: %d' % ntrials)
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, fmt)
npeaks = len(peakis)
if npeaks == 0:
nrnids[statei].append(nid) # save nonresponsive nids, indexed by state
continue # this PSTH has no peaks, skip all subsequent measures
rnids[statei].append(nid) # save responsive nids, indexed by state
rpsths[statei].append(psth) # save responsive PSTH, indexed by state
# calculate peak precision:
widths = (ris - lis) * TRES * 1000 # ms
psthswidths.append(widths)
psthsts.append(peakis * TRES) # sec
psthsheights.append(psth[peakis] - baseline) # peak height above baseline
#psthsheights.append(psth[peakis]) # peak height above 0
lspikeis, rspikeis = ts.searchsorted(lis*TRES), ts.searchsorted(ris*TRES)
nspikes = rspikeis - lspikeis # nspikes of each detected peak
assert (nspikes > 0).all()
EPS = np.spacing(1) # epsilon, smallest representable non-zero number
BINW, TRES = 0.02, 0.0001 # PSTH time bins, sec
# 2.5 Hz thresh is 1 spike in the same 20 ms wide bin every 20 trials, assuming 0 baseline:
MINTHRESH = 3 # peak detection thresh, Hz
MEDIANX = 2 # PSTH median multiplier, Hz
FWFRACTION = 0.5 # full width fraction of max
WIDTHMAX = 200 # maximum width, ms
WIDTHMAXPOINTS = intround(WIDTHMAX / 1000 / TRES) # maximum width, number of PSTH timepoints
# plotting params:
PLOTPSTH = True
FIGSIZE = 3.14, 2
YMAX = 6 # Hz
YTICKS = 0, 3, 6
MS = 5
t, psths, spikets = rec.psth(nids=nids, natexps=False, strange=strange, plot=False,
binw=BINW, tres=TRES, norm='ntrials')
psthparams = {} # params returned for each PSTH by get_psth_peaks
for nid, psth in zip(nids, psths):
psthparams[nid] = get_psth_peaks_simple(t, psth, nid, WIDTHMAXPOINTS, minthresh=MINTHRESH,
medianx=MEDIANX, fwfraction=FWFRACTION)
if PLOTPSTH:
plot_psth(psthparams, nid, fmt='k-', ms=MS, ymax=YMAX, yticks=YTICKS, figsize=FIGSIZE)
#t, psth, thresh, baseline, peakis, lis, ris = psthparams[nid] # unpack
pl.show()
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
# calculate peak precision:
widths = (ris - lis) * TRES * 1000 # ms
peakwidths[state].append(widths)
peakheights[state].append(
psth[peakis] - baseline) # peak height above baseline
MS = 5
t, psths, spikets = rec.psth(nids=nids,
natexps=False,
strange=strange,
plot=False,
binw=BINW,
tres=TRES,
norm='ntrials')
psthparams = {} # params returned for each PSTH by get_psth_peaks
for nid, psth in zip(nids, psths):
psthparams[nid] = get_psth_peaks_simple(t,
psth,
nid,
WIDTHMAXPOINTS,
minthresh=MINTHRESH,
medianx=MEDIANX,
fwfraction=FWFRACTION)
if PLOTPSTH:
plot_psth(psthparams,
nid,
fmt='k-',
ms=MS,
ymax=YMAX,
yticks=YTICKS,
figsize=FIGSIZE)
#t, psth, thresh, baseline, peakis, lis, ris = psthparams[nid] # unpack
pl.show()
binw=TRASTERBINW,
tres=TRASTERTRES,
gauss=GAUSS)[0]
ntrials = n2count[nids[0]].shape[0] # should be the same for all nids
print('ntrials: %d' % ntrials)
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, fmt)
npeaks = len(peakis)
if npeaks == 0:
nrnids[statei].append(
nid) # save nonresponsive nids, indexed by state
continue # this PSTH has no peaks, skip all subsequent measures
rnids[statei].append(nid) # save responsive nids, indexed by state
rpsths[statei].append(psth) # save responsive PSTH, indexed by state
# calculate peak precision:
widths = (ris - lis) * TRES * 1000 # ms
psthswidths.append(widths)
psthsts.append(peakis * TRES) # sec
psthsheights.append(psth[peakis] -
baseline) # peak height above baseline
#psthsheights.append(psth[peakis]) # peak height above 0
lspikeis, rspikeis = ts.searchsorted(lis * TRES), ts.searchsorted(ris *
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
# calculate peak precision:
widths = (ris - lis) * TRES * 1000 # ms
peakwidths[state].append(widths)
peakheights[state].append(psth[peakis] - baseline) # peak height above baseline
#peakheights[state].append(psth[peakis]) # peak height above 0
lspikeis, rspikeis = ts.searchsorted(lis*TRES), ts.searchsorted(ris*TRES)