<s> """<STR_LIT>""" <EOL> import mne <EOL> from mne . datasets import sample <EOL> from mne . time_frequency import compute_epochs_csd <EOL> from mne . beamformer import dics_source_power <EOL> print ( __doc__ ) <EOL> data_path = sample . data_path ( ) <EOL> raw_fname = data_path + '<STR_LIT>' <EOL> event_fname = data_path + '<STR_LIT>' <EOL> fname_fwd = data_path + '<STR_LIT>' <EOL> subjects_dir = data_path + '<STR_LIT>' <EOL> raw = mne . io . read_raw_fif ( raw_fname ) <EOL> raw . info [ '<STR_LIT>' ] = [ '<STR_LIT>' ] <EOL> picks = mne . pick_types ( raw . info , meg = True , eeg = False , eog = False , <EOL> stim = False , exclude = '<STR_LIT>' ) <EOL> event_id , tmin , tmax = <NUM_LIT:1> , - <NUM_LIT> , <NUM_LIT:0.5> <EOL> events = mne . read_events ( event_fname ) <EOL> epochs = mne . Epochs ( raw , events , event_id , tmin , tmax , proj = True , <EOL> picks = picks , baseline = ( None , <NUM_LIT:0> ) , preload = True , <EOL> reject = dict ( grad = <NUM_LIT> , mag = <NUM_LIT> ) ) <EOL> evoked = epochs . average ( ) <EOL> forward = mne . read_forward_solution ( fname_fwd , surf_ori = True ) <EOL> data_csds = compute_epochs_csd ( epochs , mode = '<STR_LIT>' , tmin = <NUM_LIT> , tmax = <NUM_LIT> , <EOL> fmin = <NUM_LIT:15> , fmax = <NUM_LIT:30> , fsum = False ) <EOL> noise_csds = compute_epochs_csd ( epochs , mode = '<STR_LIT>' , tmin = - <NUM_LIT> , <EOL> tmax = - <NUM_LIT> , fmin = <NUM_LIT:15> , fmax = <NUM_LIT:30> , fsum = False ) <EOL> stc = dics_source_power ( epochs . info , forward , noise_csds , data_csds ) <EOL> clim = dict ( kind = '<STR_LIT:value>' , lims = [ <NUM_LIT> , <NUM_LIT> , <NUM_LIT> ] ) <EOL> for i , csd in enumerate ( data_csds ) : <EOL> message = '<STR_LIT>' % csd . frequencies [ <NUM_LIT:0> ] <EOL> brain = stc . plot ( surface = '<STR_LIT>' , hemi = '<STR_LIT>' , subjects_dir = subjects_dir , <EOL> time_label = message , figure = i , clim = clim ) <EOL> brain . set_data_time_index ( i ) <EOL> brain . show_view ( '<STR_LIT>' ) </s>
