code: def apply_lcmv_raw(
      raw, filters, start=None, stop=None, *, verbose=None
  ):
      """Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
  
      Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights
      on raw data.
  
      Parameters
      ----------
      raw : mne.io.Raw
          Raw data to invert.
      filters : instance of Beamformer
          LCMV spatial filter (beamformer weights).
          Filter weights returned from :func:`make_lcmv`.
      start : int
          Index of first time sample (index not time is seconds).
      stop : int
          Index of first time sample not to include (index not time is seconds).
      %(verbose)s
  
      Returns
      -------
      stc : SourceEstimate | VolSourceEstimate
          Source time courses.
  
      See Also
      --------
      make_lcmv, apply_lcmv_epochs, apply_lcmv, apply_lcmv_cov
      """
      _check_reference(raw)
  
      info = raw.info
  
      sel = _check_channels_spatial_filter(raw.ch_names, filters)
      data, times = raw[sel, start:stop]
      tmin = times[0]
  
      stc = _apply_lcmv(
          data=data, filters=filters, info=info, tmin=tmin
      )
  
      return next(stc)
  
file_path: mne-python/mne/beamformer/_lcmv.py
imports: import numpy
  from .._fiff.meas_info import _simplify_info
  from .._fiff.pick import pick_channels_cov, pick_info
  from ..forward import _subject_from_forward
  from ..minimum_norm.inverse import _check_depth, _check_reference, combine_xyz
  from ..rank import compute_rank
  from ..source_estimate import _get_src_type, _make_stc
  from ..utils import _check_channels_spatial_filter, _check_info_inv, _check_one_ch_type, logger, verbose
  from ._compute_beamformer import Beamformer, _check_src_type, _compute_beamformer, _compute_power, _prepare_beamformer_input, _proj_whiten_data
label_desc: Use image node_id 5 for calling a global function with example usage: apply_lcmv_raw(raw, filters, start, stop) and returns: next