stds.append(std)
# keeping all 10 chans for each LFP for all tracks gives MemoryError:
del data
del rec.lfp.data
#print('.', end='')
#print()
sis = np.hstack(sis)
Vpps = np.hstack(Vpps) / 1e3 # convert from uV to mV
stds = np.hstack(stds)
figure(figsize=figsize)
#plot(Vpps, sis, 'k.', ms=4, alpha=ALPHA)
Vppedges = np.arange(0.25, VPPMAX + 0.25, 0.25) # mV
Vppmeans, sismeans, sisstds = scatterbin(Vpps,
sis,
Vppedges,
xaverage=None,
yaverage=np.mean)
errorbar(Vppmeans, sismeans, yerr=sisstds, fmt='k.-', ms=6, lw=1, zorder=9999)
xlim(xmin=0, xmax=VPPMAX)
ylim(0.2, 1)
yticks([0.2, 0.4, 0.6, 0.8, 1])
xlabel('LFP $V_{pp}$ (mV)')
ylabel('SI (L/(L+H))')
gcfm().window.setWindowTitle('SI vs Vpp lfpwidth=%g lfptres=%g' %
(lfpwidth, lfptres))
tight_layout(pad=0.3)
figure(figsize=figsize)
#plot(stds, sis, 'k.', ms=4, alpha=ALPHA)
stdedges = np.arange(25, STDMAX + 25, 25) # uV
std = [ data[tirange[0]:tirange[1]].std() for tirange in tiranges ]
Vpps.append(Vpp)
stds.append(std)
# keeping all 10 chans for each LFP for all tracks gives MemoryError:
del data
del rec.lfp.data
#print('.', end='')
#print()
sis = np.hstack(sis)
Vpps = np.hstack(Vpps) / 1e3 # convert from uV to mV
stds = np.hstack(stds)
figure(figsize=figsize)
#plot(Vpps, sis, 'k.', ms=4, alpha=ALPHA)
Vppedges = np.arange(0.25, VPPMAX+0.25, 0.25) # mV
Vppmeans, sismeans, sisstds = scatterbin(Vpps, sis, Vppedges,
xaverage=None, yaverage=np.mean)
errorbar(Vppmeans, sismeans, yerr=sisstds, fmt='k.-', ms=6, lw=1, zorder=9999)
xlim(xmin=0, xmax=VPPMAX)
ylim(0.2, 1)
yticks([0.2, 0.4, 0.6, 0.8, 1])
xlabel('LFP $V_{pp}$ (mV)')
ylabel('SI (L/(L+H))')
gcfm().window.setWindowTitle('SI vs Vpp lfpwidth=%g lfptres=%g' % (lfpwidth, lfptres))
tight_layout(pad=0.3)
figure(figsize=figsize)
#plot(stds, sis, 'k.', ms=4, alpha=ALPHA)
stdedges = np.arange(25, STDMAX+25, 25) # uV
stdmeans, sismeans, sisstds = scatterbin(stds, sis, stdedges,
xaverage=None, yaverage=np.mean)
errorbar(stdmeans, sismeans, yerr=sisstds, fmt='k.-', ms=6, lw=1, zorder=9999)
def psthcorr(rec, nids=None, ssnids=None, ssseps=None, natexps=False, strange=None, plot=True):
if nids == None:
nids = sorted(rec.n) # use active neurons
if ssnids == None:
ssnids = nids # use nids as the superset
nn = len(nids)
nnss = len(ssnids)
# note that using norm=True or norm='ntrials' doesn't seem to make a difference to the
# results, probably doesn't matter for calculating corrs:
midbins, psths, spikets = rec.psth(nids=nids, natexps=natexps, strange=strange, plot=False,
binw=0.02, tres=0.005, norm=True)
rho = np.corrcoef(psths) # defaults to bias=1
rho[np.diag_indices(nn)] = np.nan # nan the diagonal, which imshow plots as white
ssrho = np.zeros((nnss, nnss)) # superset rho matrix
ssrho.fill(np.nan) # init with nans
# load up values into appropriate spots in superset rho matrix:
for i in range(nn):
for j in range(nn):
ssi, ssj = ssnids.searchsorted([nids[i], nids[j]])
ssrho[ssi, ssj] = rho[i, j]
if plot == False:
return ssrho
# plot superset rho matrix:
figure(figsize=FIGSIZE)
imshow(ssrho, vmin=-1, vmax=1, cmap='jet') # cmap='gray' is too bland
ssnidticks = np.arange(0, nnss, 10)
xticks(ssnidticks)
yticks(ssnidticks)
if SHOWCOLORBAR:
colorbar()
basetitle = rec.absname
if strange != None:
strange_sec = tuple(np.array(strange)/1e6) # convert to sec for display
basetitle += '_strange=(%.f, %.f)' % strange_sec
gcfm().window.setWindowTitle(basetitle + '_rho_mat')
tight_layout(pad=0.3)
# plot rho histogram:
lti = np.tril_indices(nnss, -1) # lower triangle (below diagonal) indices of ssrho
ssrhol = ssrho[lti]
notnanis = np.logical_not(np.isnan(ssrhol)) # indices of non-nan values
fssrhol = ssrhol[notnanis] # ssrhol filtered out for nans
fssrholmean = fssrhol.mean()
t, p = ttest_1samp(fssrhol, 0) # 2-sided ttest relative to 0
print('mean=%g, t=%g, p=%g' % (fssrholmean, t, p))
if p < ALPHA0:
pstring = '$p<%g$' % ceilsigfig(p)
else:
pstring = '$p>%g$' % floorsigfig(p)
figure(figsize=FIGSIZE)
rhobins = np.arange(RHOMIN, RHOMAX+0.0333, 0.0333) # left edges + rightmost edge
n = hist(fssrhol, bins=rhobins, color='k')[0]
axvline(x=fssrholmean, c='r', ls='--') # draw vertical red line at mean fssrhol
axvline(x=0, c='e', ls='--') # draw vertical grey line at x=0
xlim(xmin=RHOMIN, xmax=RHOMAX)
ylim(ymax=n.max()) # effectively normalizes the histogram
rhoticks = np.arange(-0.2, 1+0.2, 0.2) # excluding the final 1
xticks(rhoticks)
yticks([n.max()]) # turn off y ticks to save space
#yticks([0, n.max()])
text(0.98, 0.98, '$\mu$=%.2g\n%s' % (fssrholmean, pstring), color='k',
transform=gca().transAxes, horizontalalignment='right', verticalalignment='top')
gcfm().window.setWindowTitle(basetitle + '_rho_hist')
tight_layout(pad=0.3)
# plot rho vs separation:
fssseps = ssseps[notnanis] # ssseps filtered out for nans
figure(figsize=FIGSIZE)
# scatter plot:
pl.plot(fssseps, fssrhol, 'k.')
# bin seps and plot mean rho in each bin:
sepbins = np.arange(0, fssseps.max()+SEPBINW, SEPBINW) # left edges
sepmeans, rhomeans, rhostds = scatterbin(fssseps, fssrhol, sepbins)
#pl.plot(sepmeans, rhomeans, 'r.-', ms=10, lw=2)
errorbar(sepmeans, rhomeans, yerr=rhostds, fmt='r.-', ms=10, lw=2, zorder=9999)
xlim(xmin=0, xmax=SEPMAX)
ylim(ymin=RHOMIN, ymax=RHOMAX)
septicks = np.arange(0, fssseps.max()+100, 500)
xticks(septicks)
yticks(rhoticks)
gcfm().window.setWindowTitle(basetitle + '_rho_sep')
tight_layout(pad=0.3)
return ssrho
def psthcorr(rec,
nids=None,
ssnids=None,
ssseps=None,
natexps=False,
strange=None,
plot=True):
if nids == None:
nids = sorted(rec.n) # use active neurons
if ssnids == None:
ssnids = nids # use nids as the superset
nn = len(nids)
nnss = len(ssnids)
# note that using norm=True or norm='ntrials' doesn't seem to make a difference to the
# results, probably doesn't matter for calculating corrs:
midbins, psths, spikets = rec.psth(nids=nids,
natexps=natexps,
strange=strange,
plot=False,
binw=0.02,
tres=0.005,
norm=True)
rho = np.corrcoef(psths) # defaults to bias=1
rho[np.diag_indices(
nn)] = np.nan # nan the diagonal, which imshow plots as white
ssrho = np.zeros((nnss, nnss)) # superset rho matrix
ssrho.fill(np.nan) # init with nans
# load up values into appropriate spots in superset rho matrix:
for i in range(nn):
for j in range(nn):
ssi, ssj = ssnids.searchsorted([nids[i], nids[j]])
ssrho[ssi, ssj] = rho[i, j]
if plot == False:
return ssrho
# plot superset rho matrix:
figure(figsize=FIGSIZE)
imshow(ssrho, vmin=-1, vmax=1, cmap='jet') # cmap='gray' is too bland
ssnidticks = np.arange(0, nnss, 10)
xticks(ssnidticks)
yticks(ssnidticks)
if SHOWCOLORBAR:
colorbar()
basetitle = rec.absname
if strange != None:
strange_sec = tuple(np.array(strange) /
1e6) # convert to sec for display
basetitle += '_strange=(%.f, %.f)' % strange_sec
gcfm().window.setWindowTitle(basetitle + '_rho_mat')
tight_layout(pad=0.3)
# plot rho histogram:
lti = np.tril_indices(
nnss, -1) # lower triangle (below diagonal) indices of ssrho
ssrhol = ssrho[lti]
notnanis = np.logical_not(np.isnan(ssrhol)) # indices of non-nan values
fssrhol = ssrhol[notnanis] # ssrhol filtered out for nans
fssrholmean = fssrhol.mean()
t, p = ttest_1samp(fssrhol, 0) # 2-sided ttest relative to 0
print('mean=%g, t=%g, p=%g' % (fssrholmean, t, p))
if p < ALPHA0:
pstring = '$p<%g$' % ceilsigfig(p)
else:
pstring = '$p>%g$' % floorsigfig(p)
figure(figsize=FIGSIZE)
rhobins = np.arange(RHOMIN, RHOMAX + 0.0333,
0.0333) # left edges + rightmost edge
n = hist(fssrhol, bins=rhobins, color='k')[0]
axvline(x=fssrholmean, c='r',
ls='--') # draw vertical red line at mean fssrhol
axvline(x=0, c='e', ls='--') # draw vertical grey line at x=0
xlim(xmin=RHOMIN, xmax=RHOMAX)
ylim(ymax=n.max()) # effectively normalizes the histogram
rhoticks = np.arange(-0.2, 1 + 0.2, 0.2) # excluding the final 1
xticks(rhoticks)
yticks([n.max()]) # turn off y ticks to save space
#yticks([0, n.max()])
text(0.98,
0.98,
'$\mu$=%.2g\n%s' % (fssrholmean, pstring),
color='k',
transform=gca().transAxes,
horizontalalignment='right',
verticalalignment='top')
gcfm().window.setWindowTitle(basetitle + '_rho_hist')
tight_layout(pad=0.3)
# plot rho vs separation:
fssseps = ssseps[notnanis] # ssseps filtered out for nans
figure(figsize=FIGSIZE)
# scatter plot:
pl.plot(fssseps, fssrhol, 'k.')
# bin seps and plot mean rho in each bin:
sepbins = np.arange(0, fssseps.max() + SEPBINW, SEPBINW) # left edges
sepmeans, rhomeans, rhostds = scatterbin(fssseps, fssrhol, sepbins)
#pl.plot(sepmeans, rhomeans, 'r.-', ms=10, lw=2)
errorbar(sepmeans,
rhomeans,
yerr=rhostds,
fmt='r.-',
ms=10,
lw=2,
zorder=9999)
xlim(xmin=0, xmax=SEPMAX)
ylim(ymin=RHOMIN, ymax=RHOMAX)
septicks = np.arange(0, fssseps.max() + 100, 500)
xticks(septicks)
yticks(rhoticks)
gcfm().window.setWindowTitle(basetitle + '_rho_sep')
tight_layout(pad=0.3)
return ssrho
