@verbose
def lcmv(evoked, forward, noise_cov, data_cov, reg=0.01, label=None, pick_ori=None, picks=None, rank=None, verbose=None):
 _check_reference(evoked)
 info = evoked.info
 data = evoked.data
 tmin = evoked.times[0]
 picks = _setup_picks(picks, info, forward, noise_cov)
 data = data[picks]
 stc = _apply_lcmv(data=data, info=info, tmin=tmin, forward=forward, noise_cov=noise_cov, data_cov=data_cov, reg=reg, label=label, picks=picks, rank=rank, pick_ori=pick_ori)
 return six.advance_iterator(stc)

@ verbose def lcmv ( evoked , forward , noise_cov , data_cov , reg = 0.01 , label = None , pick_ori = None , picks = None , rank = None , verbose = None ) : _check_reference ( evoked ) info = evoked . info data = evoked . data tmin = evoked . times [ 0 ] picks = _setup_picks ( picks , info , forward , noise_cov ) data = data [ picks ] stc = _apply_lcmv ( data = data , info = info , tmin = tmin , forward = forward , noise_cov = noise_cov , data_cov = data_cov , reg = reg , label = label , picks = picks , rank = rank , pick_ori = pick_ori ) return six . advance_iterator ( stc )
linearly constrained minimum variance beamformer .