diref = behavior_performance['DI'][epoch.strip('TAR_').strip('CAT_')]
direfall = behavior_performance_all['DI'][epoch.strip('TAR_').strip('CAT_')]
else:
di = np.inf
diall = np.inf
for tb in time_bins:
sidx = int(tb[0] * options['rasterfs'])
eidx = int(tb[1] * options['rasterfs'])
da = {k: r[:, :, sidx:eidx] for k, r in rec['resp'].extract_epochs([epoch], mask=ra['mask']).items()}
dp = {k: r[:, :, sidx:eidx] for k, r in rec['resp'].extract_epochs([epoch], mask=rp['mask']).items()}
dfa = nc.compute_rsc(da, chans=rec['resp'].chans).rename(columns={'rsc': 'active', 'pval': 'pa'})
dfp = nc.compute_rsc(dp, chans=rec['resp'].chans).rename(columns={'rsc': 'passive', 'pval': 'pp'})
df = pd.concat([dfa, dfp], axis=1)
df['snr'] = thelp.get_snrs([epoch_str])[0]
df['f'] = thelp.get_tar_freqs([epoch])[0]
df['tbin'] = '_'.join([str(t) for t in tb])
df['DI'] = di
df['DIall'] = diall
df['DIref'] = diref
df['DIrefall'] = direfall
df['site'] = site
if batch in [324, 302, 307]: area='A1'
else: area='PEG'
df['area'] = area
df['batch'] = batch
dfs.append(df)
dat = dat.astype({'Spike Count': 'float32'})
g = sns.FacetGrid(dat, col='state', hue='Epoch')
g.map(sns.kdeplot, "Spike Count", fill=True)
g.add_legend(frameon=False)
g.fig.suptitle(cellid)
g.fig.set_size_inches(8, 4)
g.fig.set_tight_layout(True)
g.fig.savefig(fpath + f'{cellid}_tarDist.pdf')
plt.close('all')
_df = pd.DataFrame()
for pair in pairs:
if ('STIM_' in pair[0]) | ('REFERENCE' in pair[0]):
snr1 = np.inf
else:
snr1 = thelp.get_snrs([pair[0]])[0]
if ('STIM_' in pair[1]) | ('REFERENCE' in pair[1]):
snr2 = np.inf
else:
snr2 = thelp.get_snrs([pair[1]])[0]
if ('REFERENCE' in pair[0]): f1 = 0
else: f1 = thelp.get_tar_freqs([pair[0].strip('STIM_')])[0]
if 'REFERENCE' in pair[1]: f2 = 0
else: f2 = thelp.get_tar_freqs([pair[1].strip('STIM_')])[0]
# get behavioral DI
if ('REFERENCE' in pair[0]) & (('TAR_' in pair[1]) |
('CAT_' in pair[1])):
di = behavior_performance['DI'][pair[1].strip(
'TAR_').strip('CAT_')]
if regress_pupil:
f.savefig(fpath + f'{site}{fext}_pr.pdf')
else:
f.savefig(fpath + f'{site}{fext}.pdf')
# get behavior performance for this site
behavior_performance = manager.get_behavior_performance(**options)
# for each pair, project into TDR (overall and pair-specific) and compute dprime
for i, pair in enumerate(pairs):
print(f"pair {i}/{len(pairs)}")
idx = pair[0] + '_' + pair[1]
if ('STIM_' in pair[0]) | ('REFERENCE' in pair[0]):
snr1 = np.inf
else:
snr1 = thelp.get_snrs([pairs_str[i][0]])[0]
if ('STIM_' in pair[1]) | ('REFERENCE' in pair[1]):
snr2 = np.inf
else:
snr2 = thelp.get_snrs([pairs_str[i][1]])[0]
if ('REFERENCE' in pair[0]): f1 = 0
else:
f1 = thelp.get_tar_freqs([pairs_str[i][0].strip('STIM_')
])[0]
if 'REFERENCE' in pair[1]: f2 = 0
else:
f2 = thelp.get_tar_freqs([pairs_str[i][1].strip('STIM_')
])[0]
cat_cat = ('CAT_' in pairs_str[i][0]) & ('CAT_'
in pairs_str[i][1])
b = np.array(projections[k])[np.array(masks[k])==False][np.newaxis, :]
dp, _, _, _, _, _ = compute_dprime(a, b)
chance_dp, pvalue = compute_dprime_noiseFloor(a, b)
chance_dp = np.sqrt(chance_dp)
dp = (dp ** 0.5) - chance_dp.mean()
choice_decoder = choice_decoder.append(pd.DataFrame(index=['sound', 'dprime', 'pvalue', 'nDim', 'axes', 'soundCategory', 'site', 'batch'],
data=['target', dp, pvalue, nAx, k, 'target', site, batch]).T)
# ======================= STIMULUS DECODING ============================
# perform stimulus decoding on each set of axes.
stim_pairs = list(product(catches, targets))
for pair in stim_pairs:
d1 = rcat['resp'].extract_epoch(pair[0], mask=rcat['mask'])[:, :, tstart:tend].mean(axis=-1)
d2 = rtar['resp'].extract_epoch(pair[1], mask=rtar['mask'])[:, :, tstart:tend].mean(axis=-1)
snr = thelp.get_snrs([pair[1]])[0]
d = np.concatenate((d1, d2), axis=0)
mask = np.concatenate((np.zeros(d1.shape[0]), np.ones(d2.shape[0])), axis=0).astype(bool)
for nAx in range(nPCs):
projections = {k: [] for k in axes.keys()}
masks = {k: [] for k in axes.keys()}
for i in range(d.shape[0]):
idx = np.array(list(set(range(d.shape[0])).difference(set([i]))))
for ax_str in axes.keys():
_d = d[idx]
_choice = mask[idx]
# project into space
_d = _d.dot(axes[ax_str][range(0, nAx+1), :].T)
# compute discrimination axis
if nAx > 0:
_, wopt, _, _, _, _= compute_dprime(_d[_choice].T, _d[~_choice].T)
dat = pd.concat([dat, pd.DataFrame(np.stack([np.concatenate([r1, r2]).squeeze(),
r1.shape[0]*['Active'] + r2.shape[0]*['Passive'], [ep]*(r1.shape[0] + r2.shape[0])]).T, columns=['Spike Count', 'state', 'Epoch'])])
dat = dat.astype({'Spike Count': 'float32'})
g = sns.FacetGrid(dat, col='state', hue='Epoch')
g.map(sns.kdeplot, "Spike Count", fill=True)
g.add_legend(frameon=False)
g.fig.suptitle(cellid)
g.fig.set_size_inches(8,4)
g.fig.set_tight_layout(True)
g.fig.savefig(fpath + f'{cellid}_tarDist.pdf')
plt.close('all')
_df = pd.DataFrame()
for pair in pairs:
if ('STIM_' in pair[0]) | ('REFERENCE' in pair[0]): snr1 = np.inf
else: snr1 = thelp.get_snrs([pair[0]])[0]
if ('STIM_' in pair[1]) | ('REFERENCE' in pair[1]): snr2 = np.inf
else: snr2 = thelp.get_snrs([pair[1]])[0]
if ('REFERENCE' in pair[0]): f1 = 0
else: f1 = thelp.get_tar_freqs([pair[0].strip('STIM_')])[0]
if 'REFERENCE' in pair[1]: f2 = 0
else: f2 = thelp.get_tar_freqs([pair[1].strip('STIM_')])[0]
# get behavioral DI
if ('REFERENCE' in pair[0]) & (('TAR_' in pair[1]) | ('CAT_' in pair[1])):
di = behavior_performance['DI'][pair[1].strip('TAR_').strip('CAT_')]
elif ('REFERENCE' in pair[1]) & (('TAR_' in pair[0]) | ('CAT_' in pair[0])):
di = behavior_performance['DI'][pair[0].strip('TAR_').strip('CAT_')]
elif ('STIM_' in pair[0]) | ('STIM_' in pair[1]):
di = np.inf
# need to do some "hacky" stuff for batch 302 / 307 to get names to align with the TBP data
if batch in [324, 325]:
targets = thelp.sort_targets([f for f in _ra['resp'].epochs.name.unique() if 'TAR_' in f])
# only keep target presented at least 5 times
targets = [t for t in targets if (_ra['resp'].epochs.name==t).sum()>=5]
# remove "off-center targets"
on_center = thelp.get_tar_freqs([f.strip('REM_') for f in _ra['resp'].epochs.name.unique() if 'REM_' in f])[0]
targets = [t for t in targets if str(on_center) in t]
catch = [f for f in _ra['resp'].epochs.name.unique() if 'CAT_' in f]
# remove off-center catches
catch = [c for c in catch if str(on_center) in c]
rem = [f for f in rec['resp'].epochs.name.unique() if 'REM_' in f]
#targets += rem
targets_str = targets
catch_str = catch
snrs = thelp.get_snrs(targets)
# compute noise PCs using difference covariance matrix (so they're ordered by change in rsc)
respa = []
respp = []
for t in targets:
_r = rec['resp'].extract_epoch(t, mask=ra['mask'])[:, :, start:end].mean(axis=-1, keepdims=True)
m = _r.mean(axis=0)
sd = _r.std(axis=0)
sd[sd==0] = 1
_r = (_r - m) / sd
respa.append(_r)
_r = rec['resp'].extract_epoch(t, mask=rp['mask'])[:, :, start:end].mean(axis=-1, keepdims=True)
m = _r.mean(axis=0)
sd = _r.std(axis=0)