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 psthcorrdiff(rhos, seps, basetitle):
"""Plot difference of a pair of rho matrices (rhos[0] - rhos[1]). seps is the
corresponding distance matrix"""
assert len(rhos) == 2
# calc rho difference matrix:
rhod = rhos[0] - rhos[1]
assert rhod.shape[0] == rhod.shape[1] # square
nn = rhod.shape[0]
# plot rho diff matrix:
figure(figsize=FIGSIZE)
imshow(rhod, vmin=-1, vmax=1, cmap='jet') # cmap='gray' is too bland
ssnidticks = np.arange(0, nn, 10)
xticks(ssnidticks)
yticks(ssnidticks)
if SHOWCOLORBAR:
colorbar()
gcfm().window.setWindowTitle(basetitle + '_rhod_mat')
tight_layout(pad=0.3)
# plot rho difference histogram:
lti = np.tril_indices(nn, -1) # lower triangle (below diagonal) indices
rhol = rhod[lti]
notnanis = np.logical_not(np.isnan(rhol)) # indices of non-nan values
frhol = rhol[notnanis] # rhol filtered out for nans
frholmean = frhol.mean()
t, p = ttest_1samp(frhol, 0) # 2-sided ttest relative to 0
print('mean=%g, t=%g, p=%g' % (frholmean, t, p))
if p < ALPHA0:
pstring = '$p<%g$' % ceilsigfig(p)
else:
pstring = '$p>%g$' % floorsigfig(p)
figure(figsize=FIGSIZE)
rhobins = np.arange(RHODIFFMIN, RHODIFFMAX+0.0333, 0.0333) # left edges + rightmost edge
n = hist(frhol, bins=rhobins, color='k')[0]
axvline(x=frholmean, c='r', ls='--') # draw vertical red line at mean frhol
axvline(x=0, c='e', ls='--') # draw vertical grey line at x=0
xlim(xmin=RHODIFFMIN, xmax=RHODIFFMAX)
ylim(ymax=n.max()) # effectively normalizes the histogram
rhoticks = np.arange(-0.6, 0.6+0.2, 0.2)
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' % (frholmean, pstring), color='k',
transform=gca().transAxes, horizontalalignment='right', verticalalignment='top')
gcfm().window.setWindowTitle(basetitle + '_rhod_hist')
tight_layout(pad=0.3)
# plot rho difference vs separation:
fseps = seps[notnanis] # seps filtered out for nans
figure(figsize=FIGSIZE)
# scatter plot:
pl.plot(fseps, frhol, 'k.')
# bin seps and plot mean rho in each bin:
sortis = np.argsort(fseps)
seps = fseps[sortis]
rhos = frhol[sortis]
sepbins = np.arange(0, seps.max()+SEPBINW, SEPBINW) # left edges
sepis = seps.searchsorted(sepbins)
sepmeans, rhomeans, rhostds = [], [], []
for sepi0, sepi1 in zip(sepis[:-1], sepis[1:]): # iterate over sepbins
sepmeans.append(seps[sepi0:sepi1].mean()) # mean sep of all points in this sepbin
rhoslice = rhos[sepi0:sepi1] # rhos in this sepbin
rhomeans.append(rhoslice.mean()) # mean rho of all points in this sepbin
rhostds.append(rhoslice.std()) # std of rho in this sepbin
#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=RHODIFFMIN, ymax=RHODIFFMAX)
septicks = np.arange(0, seps.max()+100, 500)
xticks(septicks)
yticks(rhoticks)
gcfm().window.setWindowTitle(basetitle + '_rhod_sep')
tight_layout(pad=0.3)
# concatenate rho and sep lists into arrays:
rhos, nrhos, seps = {}, {}, {}
for slabel in ([None]+slabels):
rhos[slabel] = np.hstack(rhoslist[slabel])
nrhos[slabel] = np.hstack(nrhoslist[slabel])
seps[slabel] = np.hstack(sepslist[slabel])
# plot (signal) rho histogram:
dmean = rhos['desynch'].mean()
smean = rhos['synch'].mean()
u, p = mannwhitneyu(rhos['desynch'], rhos['synch']) # 1-sided
if p < ALPHA:
pstring = 'p < %g' % ceilsigfig(p)
else:
pstring = 'p > %g' % floorsigfig(p)
'''
# T-tests of both distribs relative to 0, not so useful:
dt, dp = ttest_1samp(rhos['desynch'], 0) # 2-sided ttest relative to 0
st, sp = ttest_1samp(rhos['synch'], 0) # 2-sided ttest relative to 0
print('dmean=%g, t=%g, p=%g' % (dmean, dt, dp))
print('smean=%g, t=%g, p=%g' % (smean, st, sp))
if dp < ALPHA:
dpstring = 'p < %g' % ceilsigfig(dp)
else:
dpstring = 'p > %g' % floorsigfig(dp)
if sp < ALPHA:
spstring = 'p < %g' % ceilsigfig(sp)
else:
spstring = 'p > %g' % floorsigfig(sp)
'''
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:
pstring = 'p > %g' % floorsigfig(p)
figure(figsize=FIGSIZE)
nd = hist(coups[0], bins=coupbins, histtype='step', color='b')[0]
ns = hist(coups[1], bins=coupbins, histtype='step', color='r')[0]
nmax = max(np.hstack([nd, ns]))
xlim(xmin=COUPMIN, xmax=COUPMAX)
ymin, ymax = ylim(0, 34)
axvline(x=0, c='e', ls='-', alpha=0.5,
zorder=-1) # draw vertical grey line at x=0
# draw arrows at means:
ah = ymax / 8 # arrow height
arrow(dmean,
ymax,
0,
-ah,
head_width=0.05,
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 psthcorrdiff(rhos, seps, basetitle):
"""Plot difference of a pair of rho matrices (rhos[0] - rhos[1]). seps is the
corresponding distance matrix"""
assert len(rhos) == 2
# calc rho difference matrix:
rhod = rhos[0] - rhos[1]
assert rhod.shape[0] == rhod.shape[1] # square
nn = rhod.shape[0]
# plot rho diff matrix:
figure(figsize=FIGSIZE)
imshow(rhod, vmin=-1, vmax=1, cmap='jet') # cmap='gray' is too bland
ssnidticks = np.arange(0, nn, 10)
xticks(ssnidticks)
yticks(ssnidticks)
if SHOWCOLORBAR:
colorbar()
gcfm().window.setWindowTitle(basetitle + '_rhod_mat')
tight_layout(pad=0.3)
# plot rho difference histogram:
lti = np.tril_indices(nn, -1) # lower triangle (below diagonal) indices
rhol = rhod[lti]
notnanis = np.logical_not(np.isnan(rhol)) # indices of non-nan values
frhol = rhol[notnanis] # rhol filtered out for nans
frholmean = frhol.mean()
t, p = ttest_1samp(frhol, 0) # 2-sided ttest relative to 0
print('mean=%g, t=%g, p=%g' % (frholmean, t, p))
if p < ALPHA0:
pstring = '$p<%g$' % ceilsigfig(p)
else:
pstring = '$p>%g$' % floorsigfig(p)
figure(figsize=FIGSIZE)
rhobins = np.arange(RHODIFFMIN, RHODIFFMAX + 0.0333,
0.0333) # left edges + rightmost edge
n = hist(frhol, bins=rhobins, color='k')[0]
axvline(x=frholmean, c='r',
ls='--') # draw vertical red line at mean frhol
axvline(x=0, c='e', ls='--') # draw vertical grey line at x=0
xlim(xmin=RHODIFFMIN, xmax=RHODIFFMAX)
ylim(ymax=n.max()) # effectively normalizes the histogram
rhoticks = np.arange(-0.6, 0.6 + 0.2, 0.2)
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' % (frholmean, pstring),
color='k',
transform=gca().transAxes,
horizontalalignment='right',
verticalalignment='top')
gcfm().window.setWindowTitle(basetitle + '_rhod_hist')
tight_layout(pad=0.3)
# plot rho difference vs separation:
fseps = seps[notnanis] # seps filtered out for nans
figure(figsize=FIGSIZE)
# scatter plot:
pl.plot(fseps, frhol, 'k.')
# bin seps and plot mean rho in each bin:
sortis = np.argsort(fseps)
seps = fseps[sortis]
rhos = frhol[sortis]
sepbins = np.arange(0, seps.max() + SEPBINW, SEPBINW) # left edges
sepis = seps.searchsorted(sepbins)
sepmeans, rhomeans, rhostds = [], [], []
for sepi0, sepi1 in zip(sepis[:-1], sepis[1:]): # iterate over sepbins
sepmeans.append(
seps[sepi0:sepi1].mean()) # mean sep of all points in this sepbin
rhoslice = rhos[sepi0:sepi1] # rhos in this sepbin
rhomeans.append(
rhoslice.mean()) # mean rho of all points in this sepbin
rhostds.append(rhoslice.std()) # std of rho in this sepbin
#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=RHODIFFMIN, ymax=RHODIFFMAX)
septicks = np.arange(0, seps.max() + 100, 500)
xticks(septicks)
yticks(rhoticks)
gcfm().window.setWindowTitle(basetitle + '_rhod_sep')
tight_layout(pad=0.3)
