strangesr08s = [(0, 1500e6), # r08 desynched, us
(1550e6, np.inf)] # r08 synched, us, end is ~ 2300s
ptc22tr1r10s = [ptc22.tr1.r10, ptc22.tr1.r10]
strangesr10s = [(0, 1400e6), # r10 synched, us
(1480e6, np.inf)] # r10 desynched, us, end is ~ 2300s
ptc22tr1recs = [ptc22.tr1.r05, ptc22.tr1.r08, ptc22.tr1.r10, ptc22.tr1.r19]
ptc22tr2recs = [ptc22.tr2.r28, ptc22.tr2.r33]
# ptc15.tr7c:
SEPMAX = 1675
# get superset of active nids for all natexps of both recs in ptc15tr7crecs:
stranges = etrangesr74 + etrangesr95b # 8 stranges in total
recs = [ptc15.tr7c.r74]*4 + [ptc15.tr7c.r95b]*4 # 8 recs corresponding to 8 stranges
ssnids, recsecnids = get_ssnids(recs, stranges)
ssseps = get_seps(ssnids, ptc15.tr7c.alln)
# get separate supersets of active nids for all 4 natexps in each recording:
ptc15tr7crecsecnids = [np.unique(np.hstack(recsecnids[:4])),
np.unique(np.hstack(recsecnids[4:]))]
# do psthcorr plots and collect ssrho matrices:
ssrhos = []
for rec, nids in zip(ptc15tr7crecs, ptc15tr7crecsecnids):
ssrho = psthcorr(rec, nids=nids, ssnids=ssnids, ssseps=ssseps, natexps=True) # in sec
ssrhos.append(ssrho)
# plot differences in superset rho matrices for the two recordings:
psthcorrdiff(ssrhos, ssseps, 'r74-r95b')
## rho for ns1 figure:
#In [124]: np.where(ssnids == 328)
#Out[124]: (array([31]),)
#In [125]: np.where(ssnids == 345)
rates = cs / BINW # normalize counts by binw to get rates
err = rates - psth # 2D error array between trial responses and PSTH
errs.append(
err.ravel()) # flatten across trials for use in corrcoef()
errs = np.asarray(errs)
# calculate corrs between flattened 2D err arrays for all cell pairs:
nrho = np.corrcoef(errs)
nrho[diagis] = np.nan # nan the diagonal, which imshow plots as white
# collect rho and nrho values:
lti = np.tril_indices(nn, -1) # lower triangle indices of rho matrix
rhoslist[slabel].append(rho[lti])
nrhoslist[slabel].append(nrho[lti])
# collect corresponding pairwise neuron separation distances:
sepslist[slabel].append(get_seps(nids, rec.alln))
if PLOTRHOMATRICES:
# plot rho matrix:
figure(figsize=FIGSIZE)
imshow(rho, vmin=VMIN, vmax=VMAX,
cmap='jet') # cmap='gray' is too bland
nidticks = np.arange(0, nn, 10)
xticks(nidticks)
yticks(nidticks)
if SHOWCOLORBAR:
colorbar(ticks=[-1, 0, 1])
titlestr = '_'.join(
[rec.absname, 'rho_mat',
str(slabel), KIND, KERNEL, BINWMS])
gcfm().window.setWindowTitle(titlestr)
cs = n2count[nid] # 2D array of trial spike counts, one row per trial
rates = cs / BINW # normalize counts by binw to get rates
err = rates - psth # 2D error array between trial responses and PSTH
errs.append(err.ravel()) # flatten across trials for use in corrcoef()
errs = np.asarray(errs)
# calculate corrs between flattened 2D err arrays for all cell pairs:
nrho = np.corrcoef(errs)
nrho[diagis] = np.nan # nan the diagonal, which imshow plots as white
# collect rho and nrho values:
lti = np.tril_indices(nn, -1) # lower triangle indices of rho matrix
rhoslist[slabel].append(rho[lti])
nrhoslist[slabel].append(nrho[lti])
# collect corresponding pairwise neuron separation distances:
sepslist[slabel].append(get_seps(nids, rec.alln))
if PLOTRHOMATRICES:
# plot rho matrix:
figure(figsize=FIGSIZE)
imshow(rho, vmin=VMIN, vmax=VMAX, cmap='jet') # cmap='gray' is too bland
nidticks = np.arange(0, nn, 10)
xticks(nidticks)
yticks(nidticks)
if SHOWCOLORBAR:
colorbar(ticks=[-1, 0, 1])
titlestr = '_'.join([rec.absname, 'rho_mat', str(slabel),
KIND, KERNEL, BINWMS])
gcfm().window.setWindowTitle(titlestr)
tight_layout(pad=0.3)
strangesr10s = [
(0, 1400e6), # r10 synched, us
(1480e6, np.inf)
] # r10 desynched, us, end is ~ 2300s
ptc22tr1recs = [ptc22.tr1.r05, ptc22.tr1.r08, ptc22.tr1.r10, ptc22.tr1.r19]
ptc22tr2recs = [ptc22.tr2.r28, ptc22.tr2.r33]
# ptc15.tr7c:
SEPMAX = 1675
# get superset of active nids for all natexps of both recs in ptc15tr7crecs:
stranges = etrangesr74 + etrangesr95b # 8 stranges in total
recs = [ptc15.tr7c.r74] * 4 + [ptc15.tr7c.r95b
] * 4 # 8 recs corresponding to 8 stranges
ssnids, recsecnids = get_ssnids(recs, stranges)
ssseps = get_seps(ssnids, ptc15.tr7c.alln)
# get separate supersets of active nids for all 4 natexps in each recording:
ptc15tr7crecsecnids = [
np.unique(np.hstack(recsecnids[:4])),
np.unique(np.hstack(recsecnids[4:]))
]
# do psthcorr plots and collect ssrho matrices:
ssrhos = []
for rec, nids in zip(ptc15tr7crecs, ptc15tr7crecsecnids):
ssrho = psthcorr(rec,
nids=nids,
ssnids=ssnids,
ssseps=ssseps,
natexps=True) # in sec
ssrhos.append(ssrho)
# plot differences in superset rho matrices for the two recordings:
