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
for recname in sorted(mot):
rec = eval(recname)
mviname = os.path.basename(rec.e0.s.fname)
framei0, framei1 = rec.e0.d.framei[0], rec.e0.d.framei[-1]
print('%s: %s, frameis %d:%d' % (recname, mviname, framei0, framei1))
mvi2mot[(mviname, framei0)] = mot[recname]
allmotion = np.hstack(list(mvi2mot.values()))
allmotion = np.hstack([allmotion, -allmotion]) # make it symmetric around 0
motionbins = np.arange(-300, 300+MOTIONBINW, MOTIONBINW) # deg/s, symmetric around 0
midbins = motionbins[:-1] + MOTIONBINW / 2
motioncount = np.histogram(allmotion, bins=motionbins)[0]
k = kurtosis(allmotion)
# kurtosistest() seems to use the method of Anscombe & Glynn (1983),
# http://biomet.oxfordjournals.org/content/70/1/227
z, p = kurtosistest(allmotion)
pstring = 'p < %g' % ceilsigfig(p)
# normally distributed signal with same std as data, to check that its kurtosis is 0:
#nsamples = 10000000
#normal = scipy.random.normal(0, allmotion.std(), nsamples)
#normalcount = np.histogram(normal, bins=motionbins)[0]
normalcount = core.g(0, allmotion.std(), midbins) # generate normal distrib directly
# normalize to get same probability mass:
normalcount = normalcount / normalcount.sum() * motioncount.sum()
plot(midbins, normalcount, marker=None, ls='-', c='0.7', lw=2)
plot(midbins, motioncount, marker=None, ls='-', c='k', lw=2)
text(0.98, 0.98, 'k = %.1f' % k, # kurtosis
horizontalalignment='right', verticalalignment='top',
transform=gca().transAxes, color='k')
text(0.98, 0.90, '%s' % pstring, # p-value of null (normal) hypothesis of kurtosis test
horizontalalignment='right', verticalalignment='top',
transform=gca().transAxes, color='k')
u, p = mannwhitneyu(rpsths[0], rpsths[1]) # 1-sided
dfiltrpsths = rpsths[1][rpsths[1] != 0] # filter out 0 values
sfiltrpsths = rpsths[0][rpsths[0] != 0]
smean = 10**(log10(dfiltrpsths).mean()) # geometric mean, excluding 0 values
dmean = 10**(log10(sfiltrpsths).mean())
logu, logp = mannwhitneyu(log10(dfiltrpsths), log10(sfiltrpsths)) # 1-sided, filtered log values
print('u, p:', u, p)
print('logu, logp:', logu, logp)
# display means and p value:
text(0.02, 0.18, '$\mu$ = %.1f Hz' % smean, # synched
horizontalalignment='left', verticalalignment='bottom',
transform=gca().transAxes, color='r')
text(0.02, 0.10, '$\mu$ = %.1f Hz' % dmean, # desynched
horizontalalignment='left', verticalalignment='bottom',
transform=gca().transAxes, color='b')
text(0.02, 0.02, 'p < %.1g' % ceilsigfig(p, 1),
horizontalalignment='left', verticalalignment='bottom',
transform=gca().transAxes, color='k')
# arrow doesn't display correctly on log axis, use annotate instead:
ymin, ymax = ylim()
logymin, logymax = log10(ymin), log10(ymax)
logyrange = logymax - logymin
annotate('', xy=(smean, 10**(logymin+(6/7)*logyrange)), xycoords='data', # synched
xytext=(smean, ymax), textcoords='data',
arrowprops=dict(fc='r', ec='none', width=1.3, headwidth=7, frac=0.5))
annotate('', xy=(dmean, 10**(logymin+(6/7)*logyrange)), xycoords='data', # desynched
xytext=(dmean, ymax), textcoords='data',
arrowprops=dict(fc='b', ec='none', width=1.3, headwidth=7, frac=0.5))
titlestr = 'PSTH distribution %s' % urecnames
gcfm().window.setWindowTitle(titlestr)
tight_layout(pad=0.3)
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)
plot(modelx, dmodel, 'b--', lw=1, alpha=0.5)
xscale('log')
xlim(xmin=10**logmin, xmax=10**logmax)
ylim(ymax=ymax)
xticks(ticks)
yticks(range(0, 40+10, 10))
xlabel('mean firing rate (Hz)')
ylabel('unit count')
# display geometric means and p value:
text(0.02, 0.98, '$\mu$ = %.2f Hz' % smean, # synched
horizontalalignment='left', verticalalignment='top',
transform=gca().transAxes, color='r')
text(0.02, 0.90, '$\mu$ = %.2f Hz' % dmean, # desynched
horizontalalignment='left', verticalalignment='top',
transform=gca().transAxes, color='b')
text(0.02, 0.82, 'p < %.1g' % ceilsigfig(p, 1),
horizontalalignment='left', verticalalignment='top',
transform=gca().transAxes, color='k')
text(0.98, 0.98, '$\sigma$ = %.2f' % slogstd, # synched
horizontalalignment='right', verticalalignment='top',
transform=gca().transAxes, color='r')
text(0.98, 0.90, '$\sigma$ = %.2f' % dlogstd, # desynched
horizontalalignment='right', verticalalignment='top',
transform=gca().transAxes, color='b')
# arrow doesn't display correctly on log axis, use annotate instead:
annotate('', xy=(smean, (6/7)*40), xycoords='data', # synched
xytext=(smean, 40), textcoords='data',
arrowprops=dict(fc='r', ec='none', width=1.3, headwidth=7, frac=0.5))
annotate('', xy=(dmean, (6/7)*40), xycoords='data', # desynched
xytext=(dmean, 40), textcoords='data',
arrowprops=dict(fc='b', ec='none', width=1.3, headwidth=7, frac=0.5))
gcfm().window.setWindowTitle(titlestr)
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:
'$\mu$ = %.1f ms' % smean, # synched
horizontalalignment='left',
verticalalignment='top',
transform=gca().transAxes,
color='r')
text(
0.03,
0.90,
'$\mu$ = %.1f ms' % dmean, # desynched
horizontalalignment='left',
verticalalignment='top',
transform=gca().transAxes,
color='b')
text(0.03,
0.82,
'p < %.1g' % ceilsigfig(p, 1),
horizontalalignment='left',
verticalalignment='top',
transform=gca().transAxes,
color='k')
statesstr = 'manualstates'
else:
statesstr = 'autostates'
# now that ymax is calculated, plot arrows:
for state in states:
meanpeakwidth = meanpeakwidths[state]
clr = STATECOLOURS[state]
annotate(
'',
xy=(meanpeakwidth, (6 / 7) * ymax),
xycoords='data', # desynched
arrow(dmean, dmax+ah*1.1, 0, -ah, head_width=aw, head_length=ah/2,
length_includes_head=True, color='b')
arrow(smean, smax+ah*1.1, 0, -ah, head_width=aw, head_length=ah/2,
length_includes_head=True, color='r')
xlabel('LFP reliability')
ylabel('trial count')
xticks([0, 0.2, 0.4, 0.6, 0.8, 1], ['0', '0.2', '0.4', '0.6', '0.8', '1'])
ylim(0, nmax+ah*1.1)
# display means and p value:
text(0.98, 0.98, '$\mu$ = %.2f' % smean, # synched
horizontalalignment='right', verticalalignment='top',
transform=gca().transAxes, color='r')
text(0.98, 0.90, '$\mu$ = %.2f' % dmean, # desynched
horizontalalignment='right', verticalalignment='top',
transform=gca().transAxes, color='b')
text(0.98, 0.82, 'p < %.1g' % ceilsigfig(lfpcorrsp, 1),
horizontalalignment='right', verticalalignment='top',
transform=gca().transAxes, color='k')
gcfm().window.setWindowTitle('LFP_reliability_hist')
tight_layout(pad=0.3)
pl.show() # ensure figures pop up in order
# plot MUACORRS PDFs:
nd = np.histogram(MUACORRS[0], bins=corrbins, density=False)[0]
ns = np.histogram(MUACORRS[1], bins=corrbins, density=False)[0]
dmax, smax = nd.max(), ns.max()
nmax = max(np.hstack([nd, ns]))
ah = nmax / 7 # arrow height
nd = np.hstack([nd, nd[-1]]) # repeat last point for right edge
ns = np.hstack([ns, ns[-1]])
figure(figsize=(3, 3))
# 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:
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,
rec = eval(recname)
mviname = os.path.basename(rec.e0.s.fname)
framei0, framei1 = rec.e0.d.framei[0], rec.e0.d.framei[-1]
print('%s: %s, frameis %d:%d' % (recname, mviname, framei0, framei1))
mvi2mot[(mviname, framei0)] = mot[recname]
allmotion = np.hstack(list(mvi2mot.values()))
allmotion = np.hstack([allmotion, -allmotion]) # make it symmetric around 0
motionbins = np.arange(-300, 300 + MOTIONBINW,
MOTIONBINW) # deg/s, symmetric around 0
midbins = motionbins[:-1] + MOTIONBINW / 2
motioncount = np.histogram(allmotion, bins=motionbins)[0]
k = kurtosis(allmotion)
# kurtosistest() seems to use the method of Anscombe & Glynn (1983),
# http://biomet.oxfordjournals.org/content/70/1/227
z, p = kurtosistest(allmotion)
pstring = 'p < %g' % ceilsigfig(p)
# normally distributed signal with same std as data, to check that its kurtosis is 0:
#nsamples = 10000000
#normal = scipy.random.normal(0, allmotion.std(), nsamples)
#normalcount = np.histogram(normal, bins=motionbins)[0]
normalcount = core.g(0, allmotion.std(),
midbins) # generate normal distrib directly
# normalize to get same probability mass:
normalcount = normalcount / normalcount.sum() * motioncount.sum()
plot(midbins, normalcount, marker=None, ls='-', c='0.7', lw=2)
plot(midbins, motioncount, marker=None, ls='-', c='k', lw=2)
text(
0.98,
0.98,
'k = %.1f' % k, # kurtosis
horizontalalignment='right',
'$\mu$ = %.2f' % smean, # synched
horizontalalignment='right',
verticalalignment='top',
transform=gca().transAxes,
color='r')
text(
0.98,
0.90,
'$\mu$ = %.2f' % dmean, # desynched
horizontalalignment='right',
verticalalignment='top',
transform=gca().transAxes,
color='b')
text(0.98,
0.82,
'p < %.1g' % ceilsigfig(lfpcorrsp, 1),
horizontalalignment='right',
verticalalignment='top',
transform=gca().transAxes,
color='k')
gcfm().window.setWindowTitle('LFP_reliability_hist')
tight_layout(pad=0.3)
pl.show() # ensure figures pop up in order
# plot MUACORRS PDFs:
nd = np.histogram(MUACORRS[0], bins=corrbins, density=False)[0]
ns = np.histogram(MUACORRS[1], bins=corrbins, density=False)[0]
dmax, smax = nd.max(), ns.max()
nmax = max(np.hstack([nd, ns]))
ah = nmax / 7 # arrow height
nd = np.hstack([nd, nd[-1]]) # repeat last point for right edge
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)
