Tot_trials = epdat.shape[1] + epdat_3.shape[1]
plt.figure()
plt.plot(Peak2Peak.T)
plt.title(subject + ' Tot trials:' + str(Tot_trials))
# plt.figure()
# plt.plot(Peak2Peak_3.T)
#%% Correlation Analysis
Ht_12 = mseqXcorr(epdat,mseq[0,:])
Ht_3 = mseqXcorr(epdat_3,mseq_shift[0,:])
Ht_epochs_12, t_epochs = mseqXcorrEpochs_fft(epdat,mseq[0,:],fs)
Ht_epochs_3, t_epochs = mseqXcorrEpochs_fft(epdat_3,mseq_shift[0,:],fs)
Ht_epochs = np.concatenate([Ht_epochs_12,Ht_epochs_3],axis=1)
Ht = Ht_12 * epdat.shape[1]/Tot_trials + Ht_3 * epdat_3.shape[1]/Tot_trials
#%% Plot Ht
# if ch_picks.size == 31:
# sbp = [5,3]
# sbp2 = [4,4]
# elif ch_picks.size == 32:
# sbp = [4,4]
# sbp2 = [4,4]
plt.plot(Peak2Peak.T)
plt.title(subject + ' ' + str(m))
#%% Correlation Analysis
Ht = []
Htnf = []
Ht_epochs = []
t_epochs = []
# do cross corr
for m in range(len(epdat)):
print('On mseq # ' + str(m + 1))
Ht_m = mseqXcorr(epdat[m], mseq[m][0, :])
Ht_epochs_m, t_epochs_m = mseqXcorrEpochs_fft(epdat[m], mseq[m][0, :],
fs)
Ht.append(Ht_m)
Ht_epochs.append(Ht_epochs_m)
t_epochs.append(t_epochs_m)
for nf in range(num_nfs):
print('On nf: ' + str(nf))
resp = epdat[m].copy()
inv_inds = np.random.permutation(
epdat[m].shape[1])[:round(epdat[m].shape[1] / 2)]
resp[:, inv_inds, :] = -resp[:, inv_inds, :]
Ht_nf = mseqXcorr(resp, mseq[m][0, :])
Htnf.append(Ht_nf)
#%% Plot Ht
plt.figure()
plt.plot(Peak2Peak.T)
plt.title(subject + ' ' + str(Tot_trials))
# plt.figure()
# plt.plot(Peak2Peak[:,mask_trials].T)
# plt.figure()
# plt.plot(Peak2Peak[np.delete(ch_picks,ch_maxAmp),:].T)
#%% Correlation Analysis
Htnf = []
Ht = mseqXcorr(epdat,mseq[0,:])
Ht_epochs, t_epochs = mseqXcorrEpochs_fft(epdat,mseq[0,:],fs)
for nf in range(num_nfs):
print('On nf:' + str(nf))
resp = epdat.copy()
inv_inds = np.random.permutation(epdat.shape[1])[:round(epdat.shape[1]/2)]
resp[:,inv_inds,:] = -resp[:,inv_inds,:]
Ht_nf = mseqXcorr(resp,mseq[0,:])
Htnf.append(Ht_nf)
#%% Plot Ht
if ch_picks.size == 31:
sbp = [5,3]
sbp2 = [4,4]
elif ch_picks.size == 32:
#%% Remove epochs with large deflections
# Reject_Thresh=150e-6
Tot_trials = np.zeros([len(mseq)])
for m in range(len(epdat)):
Peak2Peak = epdat[m].max(axis=2) - epdat[m].min(axis=2)
mask_trials = np.all(Peak2Peak < Reject_Thresh, axis=0)
print('rejected ' + str(epdat[m].shape[1] - sum(mask_trials)) +
' trials due to P2P')
epdat[m] = epdat[m][:, mask_trials, :]
print('Total Trials Left: ' + str(epdat[m].shape[1]))
Tot_trials[m] = epdat[m].shape[1]
plt.figure()
plt.plot(Peak2Peak.T)
#%% Correlation Analysis
Ht = []
Htnf = []
# do cross corr
for m in range(len(epdat)):
print('On mseq # ' + str(m + 1))
Ht_m, t_keep = mseqXcorrEpochs_fft(epdat[m], mseq[:, 0], fs)
Ht.append(Ht_m[0, :, :])
#%% Save Data
with open(os.path.join(pickle_loc, subject + '_TMTF_cz.pickle'),
'wb') as file:
pickle.dump([t_keep, Ht, info_obj, ch_picks], file)
del data_eeg, data_evnt, epdat, Ht, info_obj, Htnf, Ht_m