Function _prepare_forward

mne/minimum_norm/inverse.py:1691–1867

Prepare a gain matrix and noise covariance for localization.

(
    forward,
    info,
    noise_cov,
    fixed,
    loose,
    rank,
    pca,
    use_cps,
    exp,
    limit_depth_chs,
    combine_xyz,
    allow_fixed_depth,
    limit,
)

def _prepare_forward(
    forward,
    info,
    noise_cov,
    fixed,
    loose,
    rank,
    pca,
    use_cps,
    exp,
    limit_depth_chs,
    combine_xyz,
    allow_fixed_depth,
    limit,
):
    """Prepare a gain matrix and noise covariance for localization."""
    # Steps (according to MNE-C, we change the order of various steps
    # because our I/O is already done, and we create the objects
    # on the fly more easily):
    #
    # 1. Read the bad channels
    # 2. Read the necessary data from the forward solution matrix file
    # 3. Load the projection data
    # 4. Load the sensor noise covariance matrix and attach it to the forward
    # 5. Compose the depth-weighting matrix
    # 6. Compose the source covariance matrix
    # 7. Apply fMRI weighting (not done)
    # 8. Apply the linear projection to the forward solution
    # 9. Apply whitening to the forward computation matrix
    # 10. Exclude the source space points within the labels (not done)
    # 11. Do appropriate source weighting to the forward computation matrix
    #

    # make a copy immediately so we do it exactly once
    forward = forward.copy()

    # Deal with "fixed" and "loose"
    loose = _triage_loose(forward["src"], loose, fixed)
    del fixed

    # Figure out what kind of inverse is requested
    fixed_inverse = all(v == 0.0 for v in loose.values())
    constrained_inverse = any(v < 1.0 for v in loose.values())

    # We only support fixed orientations for surface and discrete source
    # spaces, not volume.
    if fixed_inverse and "volume" in loose:
        raise ValueError(
            "Computing inverse solutions for volume source "
            "spaces with fixed orientations is not "
            "supported."
        )
    if loose.get("volume", 1) < 1:
        raise ValueError(
            "Computing inverse solutions with restricted "
            "orientations (loose < 1) is not supported for "
            "volume source spaces."
        )