def disparity_tuning(Einfo,
r,
used_inds=None,
num_dlags=8,
fr1or3=3,
to_plot=False):
if used_inds is None:
used_inds = range(len(r))
dmat = disparity_matrix(Einfo['dispt'], Einfo['corrt'])
ND = (dmat.shape[1] - 2) // 2
# Weight all by their frequency of occurance
if (fr1or3 == 3) or (fr1or3 == 1):
frs_valid = Einfo['frs'] == fr1or3
else:
frs_valid = Einfo['frs'] > 0
to_use = frs_valid[used_inds]
#dmatN = dmat / np.mean(dmat[used_inds[to_use],:], axis=0) * np.mean(dmat[used_inds[to_use],:])
dmatN = dmat / np.mean(dmat[used_inds[to_use], :],
axis=0) # will be stim rate
# if every stim resulted in 1 spk, the would be 1 as is
#nrms = np.mean(dmat[used_inds[to_use],:], axis=0) # number of stimuli of each type
Xmat = NDNutils.create_time_embedding(dmatN[:, range(ND * 2)],
[num_dlags, 2 * ND, 1])[used_inds, :]
# uncorrelated response
Umat = NDNutils.create_time_embedding(dmatN[:, [-2]],
[num_dlags, 1, 1])[used_inds, :]
#if len(r) > len(used_inds):
resp = deepcopy(r[used_inds])
#else:
# resp = r
#Nspks = np.sum(resp[to_use, :], axis=0)
Nspks = len(
to_use
) # this will end up being number of spikes associated with each stim
# at different lags, divided by number of time points used. (i.e. prob of spike per bin)
Dsta = np.reshape(Xmat[to_use, :].T @ resp[to_use],
[2 * ND, num_dlags]) / Nspks
Usta = (Umat[to_use, :].T @ resp[to_use])[:, 0] / Nspks
# Rudimentary analysis
best_lag = np.argmax(np.max(Dsta[range(ND), :], axis=0))
Dtun = np.reshape(Dsta[:, best_lag], [2, ND]).T
uncor_resp = Usta[best_lag]
Dinfo = {
'Dsta': Dsta,
'Dtun': Dtun,
'uncor_resp': uncor_resp,
'best_lag': best_lag,
'uncor_sta': Usta,
'disp_list': Einfo['disp_list'][2:]
}
if to_plot:
DU.subplot_setup(1, 2)
plt.subplot(1, 2, 1)
DU.plot_norm(Dsta.T - uncor_resp, cmap='bwr')
plt.plot([ND - 0.5, ND - 0.5], [-0.5, num_dlags - 0.5], 'k')
plt.plot([-0.5, 2 * ND - 0.5], [best_lag, best_lag], 'k--')
plt.subplot(1, 2, 2)
plt.plot(Dtun)
plt.plot(-Dtun[:, 1] + 2 * uncor_resp, 'm--')
plt.plot([0, ND - 1], [uncor_resp, uncor_resp], 'k')
plt.xlim([0, ND - 1])
plt.show()
return Dinfo
def disparity_tuning(Einfo,
r,
used_inds=None,
num_dlags=8,
fr1or3=3,
to_plot=False):
if used_inds is None:
used_inds = range(len(r))
dmat = disparity_matrix(Einfo['dispt'], Einfo['corrt'])
ND = (dmat.shape[1] - 2) // 2
# Weight all by their frequency of occurance
if (fr1or3 == 3) or (fr1or3 == 1):
frs_valid = Einfo['frs'] == fr1or3
else:
frs_valid = Einfo['frs'] > 0
to_use = frs_valid[used_inds]
dmatN = dmat / np.mean(dmat[used_inds[to_use], :], axis=0) * np.mean(
dmat[used_inds[to_use], :])
Xmat = NDNutils.create_time_embedding(dmatN[:, range(ND * 2)],
[num_dlags, 2 * ND, 1])[used_inds, :]
# uncorrelated response
Umat = NDNutils.create_time_embedding(dmatN[:, [-2]],
[num_dlags, 1, 1])[used_inds, :]
#if len(r) > len(used_inds):
resp = deepcopy(r[used_inds])
#else:
# resp = r
Nspks = np.sum(resp[to_use, :], axis=0)
Dsta = np.reshape(Xmat[to_use, :].T @ resp[to_use],
[2 * ND, num_dlags]) / Nspks
Usta = (Umat[to_use, :].T @ resp[to_use])[:, 0] / Nspks
# Rudimentary analysis
best_lag = np.argmax(np.max(Dsta[range(ND), :], axis=0))
Dtun = np.reshape(Dsta[:, best_lag], [2, ND]).T
uncor_resp = Usta[best_lag]
Dinfo = {
'Dsta': Dsta,
'Dtun': Dtun,
'uncor_resp': uncor_resp,
'best_lag': best_lag,
'uncor_sta': Usta
}
if to_plot:
DU.subplot_setup(1, 2)
plt.subplot(1, 2, 1)
DU.plot_norm(Dsta.T - uncor_resp, cmap='bwr')
plt.plot([ND - 0.5, ND - 0.5], [-0.5, num_dlags - 0.5], 'k')
plt.plot([-0.5, 2 * ND - 0.5], [best_lag, best_lag], 'k--')
plt.subplot(1, 2, 2)
plt.plot(Dtun)
plt.plot(-Dtun[:, 1] + 2 * uncor_resp, 'm--')
plt.plot([0, ND - 1], [uncor_resp, uncor_resp], 'k')
plt.xlim([0, ND - 1])
plt.show()
return Dinfo