#NEWLINE# @ verbose #NEWLINE# 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 ) : #NEWLINE# #INDENT# "Linearly Constrained Minimum Variance (LCMV) beamformer.\n\n    Compute Linearly Constrained Minimum Variance (LCMV) beamformer\n    on raw data.\n\n    NOTE : This implementation has not been heavily tested so please\n    report any issue or suggestions.\n\n    Parameters\n    ----------\n    raw : mne.io.Raw\n        Raw data to invert.\n    forward : dict\n        Forward operator.\n    noise_cov : Covariance\n        The noise covariance.\n    data_cov : Covariance\n        The data covariance.\n    reg : float\n        The regularization for the whitened data covariance.\n    label : Label\n        Restricts the LCMV solution to a given label.\n    start : int\n        Index of first time sample (index not time is seconds).\n    stop : int\n        Index of first time sample not to include (index not time is seconds).\n    picks : array-like of int\n        Channel indices to use for beamforming (if None all channels\n        are used except bad channels).\n    pick_ori : None | 'normal' | 'max-power'\n        If 'normal', rather than pooling the orientations by taking the norm,\n        only the radial component is kept. If 'max-power', the source\n        orientation that maximizes output source power is chosen.\n    rank : None | int | dict\n        Specified rank of the noise covariance matrix. If None, the rank is\n        detected automatically. If int, the rank is specified for the MEG\n        channels. A dictionary with entries 'eeg' and/or 'meg' can be used\n        to specify the rank for each modality.\n    verbose : bool, str, int, or None\n        If not None, override default verbose level (see mne.verbose).\n\n    Returns\n    -------\n    stc : SourceEstimate | VolSourceEstimate\n        Source time courses\n\n    See Also\n    --------\n    lcmv, lcmv_epochs\n\n    Notes\n    -----\n    The original reference is:\n    Van Veen et al. Localization of brain electrical activity via linearly\n    constrained minimum variance spatial filtering.\n    Biomedical Engineering (1997) vol. 44 (9) pp. 867--880\n\n    The reference for finding the max-power orientation is:\n    Sekihara et al. Asymptotic SNR of scalar and vector minimum-variance\n    beamformers for neuromagnetic source reconstruction.\n    Biomedical Engineering (2004) vol. 51 (10) pp. 1726--34\n    " #NEWLINE# _check_reference ( raw ) #NEWLINE# info = raw . info #NEWLINE# picks = _setup_picks ( data_cov , info , forward , noise_cov ) #NEWLINE# ( data , times ) = raw [ picks , start : stop ] #NEWLINE# tmin = times [ 0 ] #NEWLINE# 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 ) #NEWLINE# return six . advance_iterator ( stc )