epdat_3 = epdat_3[:,mask_trials_3,:]
print('Total Trials Left: ' + str(epdat.shape[1]))
print('Total Trials Left_3: ' + str(epdat_3.shape[1]))
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]
ch_maxAmp = Peak2Peak.mean(axis=1).argmax()
# plt.figure()
# plt.plot(Peak2Peak.T)
# 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, :])
for nf in range(num_nfs):
print('On nf:' + str(nf))
resp = epdat
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]
with open(os.path.join(data_loc, Subject + '_DynBin.pickle'), 'rb') as f:
IAC_epochs, ITD_epochs = pickle.load(f)
#%% Extract epochs when stim is on
t = IAC_epochs.times
fs = IAC_epochs.info['sfreq']
t1 = np.where(t >= 0)[0][0]
t2 = t1 + Mseq.size + int(np.round(0.4 * fs))
t = t[t1:t2]
t = np.concatenate((-t[-int(np.round(0.4 * fs)):0:-1], t[:-1]))
ch_picks = np.arange(32)
IAC_ep = IAC_epochs.get_data()[:, ch_picks, t1:t2].transpose(1, 0, 2)
#ITD_ep = ITD_epochs.get_data()[:,ch_picks,t1:t2].transpose(1,0,2)
IAC_Ht = mseqXcorr(IAC_ep, Mseq[:, 0])
#IAC_Ht = IAC_Ht[:,np.where(t>=0)[0][0]:]
#%% Plot stuff
IAC_evoked = IAC_epochs.average()
fig = mne.viz.plot_evoked_topo(IAC_evoked, legend=False)
plt.savefig(os.path.join(fig_path, 'IAC_evkd_topo.svg'), format='svg')
times_plot = (t > 0) & (t < 0.5)
IAC_evoked.data = IAC_Ht[:, times_plot]
IAC_evoked.times = t[times_plot]
mne.viz.plot_evoked_topo(IAC_evoked, legend=False)
plt.savefig(os.path.join(fig_path, 'IAC_Ht_topo.svg'), format='svg')
#%% Remove epochs with large deflections
Reject_Thresh=150e-6
Peak2Peak = epdat.max(axis=2) - epdat.min(axis=2)
mask_trials = np.all(Peak2Peak < Reject_Thresh,axis=0)
print('rejected ' + str(epdat.shape[1] - sum(mask_trials)) + ' trials due to P2P')
epdat = epdat[:,mask_trials,:]
print('Total Trials Left: ' + str(epdat.shape[1]))
Tot_trials = epdat.shape[1]
plt.figure()
plt.plot(Peak2Peak.T)
#%% Correlation Analysis
Ht = mseqXcorr(epdat,mseq[0,:])
#%% Plot Ht
if ch_picks.size == 31:
sbp = [5,3]
sbp2 = [4,4]
elif ch_picks.size == 32:
sbp = [4,4]
sbp2 = [4,4]
elif ch_picks.size == 30:
sbp = [5,3]
sbp2 = [5,3]
Tot_trials[m] = epdat[m].shape[1]
plt.figure()
plt.plot(Peak2Peak.T)
#%% Correlation Analysis
Ht = []
Htnf = []
Tot_trials = np.zeros([len(mseq)])
# do cross corr
for m in range(len(epdat)):
print('On mseq # ' + str(m + 1))
Tot_trials[m] = epdat[m].shape[1]
Ht_m = mseqXcorr(epdat[m], mseq[m][0, :])
Ht.append(Ht_m)
for nf in range(num_nfs):
resp = epdat[m]
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, :])
#%% Plot Ht
if ch_picks.size == 31:
sbp = [5, 3]
sbp2 = [4, 4]
elif ch_picks.size == 32:
sbp = [4, 4]
print('Total IAC trials: ' + str(IAC_ep.shape[1]))
Tot_trials_IAC = IAC_ep.shape[1]
Peak2Peak = ITD_ep.max(axis=2) - ITD_ep.min(axis=2)
mask_trials = np.all(Peak2Peak < reject_thresh, axis=0)
print('rejected ' + str(ITD_ep.shape[1] - sum(mask_trials)) +
' ITD trials due to P2P')
ITD_ep = ITD_ep[:, mask_trials, :]
print('Total ITD trials: ' + str(ITD_ep.shape[1]))
Tot_trials_ITD = ITD_ep.shape[1]
#%% Calculate Ht
IAC_Ht = mseqXcorr(IAC_ep, Mseq[:, 0])
ITD_Ht = mseqXcorr(ITD_ep, Mseq[:, 0])
#%% Calculate Noise Floors
IAC_Htnf = []
ITD_Htnf = []
for nf in range(Num_noiseFloors):
IAC_nf = IAC_ep.copy()
ITD_nf = ITD_ep.copy()
IACrnd_inds = np.random.permutation(
IAC_ep.shape[1])[:round(IAC_ep.shape[1] / 2)]
ITDrnd_inds = np.random.permutation(
ITD_ep.shape[1])[:round(ITD_ep.shape[1] / 2)]
IAC_nf[:, IACrnd_inds, :] = -IAC_nf[:, IACrnd_inds, :]
ITD_nf[:, ITDrnd_inds, :] = -ITD_nf[:, ITDrnd_inds, :]
# 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))
# resp_m = epdat[m].mean(axis=1)
Ht_m = mseqXcorr(epdat[m],mseq[0,:])
Ht.append(Ht_m)
#%% Plot Ht
if ch_picks.size == 31:
sbp = [5,3]
sbp2 = [4,4]
elif ch_picks.size == 32:
sbp = [4,4]
sbp2 = [4,4]
elif ch_picks.size == 30:
sbp = [5,3]
sbp2 = [5,3]
mask_trials = np.all(Peak2Peak < Reject_Thresh, axis=0)
print('rejected ' + str(epdat[cond].shape[1] - sum(mask_trials)) +
' trials due to P2P')
epdat[cond] = epdat[cond][:, mask_trials, :]
print('Total Trials Left: ' + str(epdat[cond].shape[1]))
Tot_trials.append(epdat[cond].shape[1])
plt.figure()
plt.plot(Peak2Peak.T)
plt.title(cond)
#%% Correlation analysis
Htnf = []
Ht = []
for cond in range(2):
Ht.append(mseqXcorr(epdat[cond], mseq[0, :]))
Htnf_nf = []
for nf in range(num_nfs):
print('On nf:' + str(nf))
resp = epdat[cond]
inv_inds = np.random.permutation(
epdat[cond].shape[1])[:round(epdat[cond].shape[1] / 2)]
resp[:, inv_inds, :] = -resp[:, inv_inds, :]
Ht_nf = mseqXcorr(resp, mseq[0, :])
Htnf_nf.append(Ht_nf)
Htnf.append(Htnf_nf)
#%% Plot Ht
if ch_picks.size == 31:
Peak2Peak = epdat[ep].max(axis=2) - epdat[ep].min(axis=2)
mask_trials = np.all(Peak2Peak < Reject_Thresh, axis=0)
print('rejected ' + str(epdat[ep].shape[1] - sum(mask_trials)) +
' trials due to P2P')
epdat[ep] = epdat[ep][:, mask_trials, :]
print('Total Trials Left: ' + str(epdat[ep].shape[1]))
Tot_trials[ep] = epdat[ep].shape[1]
plt.figure()
plt.plot(Peak2Peak.T)
#%% Correlation Analysis
Ht = []
for ep in range(3):
Ht.append(mseqXcorr(epdat[ep], mseq[0, :]))
#%% Plot Ht
if ch_picks.size == 31:
sbp = [5, 3]
sbp2 = [4, 4]
elif ch_picks.size == 32:
sbp = [4, 4]
sbp2 = [4, 4]
elif ch_picks.size == 30:
sbp = [5, 3]
sbp2 = [5, 3]
fig, axs = plt.subplots(sbp[0], sbp[1], sharex=True, gridspec_kw=None)
for ep in range(3):