else:
continue
params = dict(Fs=4096.0, fpass=[5, 600], tapers=[1, 1], pad=1,
Npairs=2000, itc=1)
nPerDraw = 400
nDraws = 100
print 'Running Pairwise Spectrum Estimation'
(pS, f) = spectral.mtpspec(x, params, verbose='DEBUG')
print 'Running Raw Spectrum Estimation'
(Sraw, f) = spectral.mtspecraw(x, params, verbose=True)
print 'Running Mean Spectrum Estimation'
(S, N, f) = spectral.mtspec(x, params, verbose=True)
print 'Running CPCA PLV Estimation'
(cplv, f) = spectral.mtcpca(x, params, verbose=True)
# print 'Running CPCA Power Estimation'
# (cpow, f) = spectral.mtcspec(x, params, verbose=True)
# Saving Results
res = dict(pS=pS, cplv=cplv, Sraw=Sraw, f=f, S=S, N=N)
# res = dict(cpow = cpow, f = f)
save_name = subj + condstem + '.mat'
if (not os.path.isdir(respath)):
os.mkdir(respath)
if (not os.path.isdir(resonly_backup)):
def bdf2mat(froot, fs, Fs_new, hpf, t_stim, topchans, trial_name):
full_raw, full_eves = EEGconcatenateFolder(froot,
nchans=34,
refchans=['EXG1', 'EXG2'],
exclude=[],
fs_new=Fs_new)
event_id = {'Positive': 1}
epochs = mne.Epochs(full_raw,
full_eves,
event_id,
tmin=t_stim[0],
tmax=t_stim[1],
reject_tmax=1.3,
picks=topchans,
reject=dict(eeg=100e-6))
epochs.load_data()
epochs_filtered = epochs.filter(hpf, None)
pos_data = epochs_filtered.get_data()
event_id = {'Negative': 2}
epochs = mne.Epochs(full_raw,
full_eves,
event_id,
tmin=t_stim[0],
tmax=t_stim[1],
reject_tmax=1.3,
picks=topchans,
reject=dict(eeg=100e-6))
epochs.load_data()
epochs_filtered = epochs.filter(hpf, None)
neg_data = epochs_filtered.get_data()
neg_l = len(neg_data)
pos_l = len(pos_data)
length = (neg_l >= pos_l) * pos_l + (neg_l < pos_l) * neg_l
tot_arr = np.zeros(2 * length, dtype=np.object)
ind = 0
for i in range(0, length):
tot_arr[ind] = pos_data[i]
tot_arr[ind + 1] = neg_data[i]
ind = ind + 2
os.chdir(froot)
savemat(trial_name + '_Data_full.mat',
{(trial_name + '_tot_arr'): tot_arr})
###########################PLOT SPECTROGRAM/PLV####################################
x = np.add(neg_data[:length, :, :], pos_data[:length, :, :])
params = dict(Fs=4e3, tapers=[1, 1], fpass=[0, 1000], itc=0)
S_psd, N_psd, f_psd = mtspec(x, params)
mt_plv, f_plv = mtplv(x, params)
fig, ax = plt.subplots(num=2, figsize=(12, 8))
ax.plot(f_psd, np.subtract(S_psd, N_psd))
#ax.plot(f_psd, N_psd)
ax.grid(color='k', linestyle='-', linewidth=1)
ax.set_xlabel('Freq (Hz)', fontsize=18)
ax.set_ylabel('PSD', fontsize=18)
ax.set_xlim([70., 500.])
ax.set_title('psd all')
fig, ax = plt.subplots(num=1, figsize=(12, 8))
ax.plot(f_plv, mt_plv)
#ax.plot(f_psd, N_psd)
ax.grid(color='k', linestyle='-', linewidth=1)
ax.set_xlabel('Freq (Hz)', fontsize=18)
ax.set_ylabel('PLV', fontsize=18)
ax.set_xlim([70., 500.])
ax.set_title('PLV all')
epochs = mne.Epochs(raw,
eves,
1,
tmin=-0.1,
proj=False,
tmax=1.2,
baseline=(-0.1, 0.0),
picks=31,
reject=dict(eeg=200e-6))
evoked = epochs.average()
params = dict(Fs=raw.info['sfreq'], tapers=[1, 1], fpass=[70, 1000], itc=0)
x = epochs.get_data()
x = x[:, :32, :]
x = x.transpose((1, 0, 2))
plv, f_plv = mtplv(x[30, :, :], params)
S_psd, N_psd, f_psd = mtspec(x[30, :, :], params)
if plotYes:
fig, ax = plt.subplots(num=1, figsize=(12, 8))
ax.plot(f_plv, plv)
ax.grid(color='k', linestyle='-', linewidth=1)
ax.set_xlabel('Freq (Hz)', fontsize=18)
ax.set_ylabel('PLV', fontsize=18)
ax.set_xlim([70., 500.])
ax.set_title('plv all')
if saveYes:
plt.savefig(dir_dict['figOut'] + 'plv_all.png')
if plotYes:
fig, ax = plt.subplots(num=2, figsize=(12, 8))
ax.plot(f_psd, S_psd)
