@verbose
def lcmv_raw(raw, forward, noise_cov, data_cov, reg=0.01, label=None, start=None, stop=None, picks=None, pick_ori=None, rank=None, verbose=None):
 _check_reference(raw)
 info = raw.info
 picks = _setup_picks(picks, info, forward, noise_cov)
 (data, times) = raw[picks, start:stop]
 tmin = times[0]
 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_raw ( raw , forward , noise_cov , data_cov , reg = 0.01 , label = None , start = None , stop = None , picks = None , pick_ori = None , rank = None , verbose = None ) : _check_reference ( raw ) info = raw . info picks = _setup_picks ( picks , info , forward , noise_cov ) ( data , times ) = raw [ picks , start : stop ] tmin = times [ 0 ] 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 .