osl_ephys.source_recon.minimum_norm
===================================

.. py:module:: osl_ephys.source_recon.minimum_norm

.. autoapi-nested-parse::

   Minimum norm source localization using MNE-Python.



Attributes
----------

.. autoapisummary::

   osl_ephys.source_recon.minimum_norm.logger


Functions
---------

.. autoapisummary::

   osl_ephys.source_recon.minimum_norm.get_mne_filenames
   osl_ephys.source_recon.minimum_norm.create_inverse_operator
   osl_ephys.source_recon.minimum_norm.apply_inverse_operator_surf
   osl_ephys.source_recon.minimum_norm.apply_inverse_operator_vol
   osl_ephys.source_recon.minimum_norm.calc_noise_cov
   osl_ephys.source_recon.minimum_norm.morph_surface


Module Contents
---------------

.. py:data:: logger
   :value: None


.. py:function:: get_mne_filenames(subjects_dir, subject)

   Get minimum norm (MNE) filenames.

   Files will be in subjects_dir/subject/mne

   :param subjects_dir: Directory containing the subject directories.
   :type subjects_dir: string
   :param subject: Subject name.
   :type subject: string

   :returns: **filenames** -- A dict of files.
   :rtype: dict


.. py:function:: create_inverse_operator(fwd, data, chantypes, rank, depth, loose, filename)

   Creates minimum norm (MNE) inverse operator.

   :param fwd: Forward model.
   :type fwd: mne forward model or str
   :param data: Preprocessed data.
   :type data: mne.io.Raw, mne.Epochs
   :param chantypes: List of channel types to include.
   :type chantypes: list
   :param rank: Rank of the data covariance matrix.
   :type rank: int
   :param depth: Depth weighting.
   :type depth: float
   :param loose: Loose parameter.
   :type loose: float
   :param inv_op_filename: Output filename.
   :type inv_op_filename: str


.. py:function:: apply_inverse_operator_surf(outdir, subject, data, method, lambda2, pick_ori, inverse_operator=None, morph='fsaverage', save=False)

   Apply previously computed minimum norm inverse solution (surface).

   :param outdir: Output directory.
   :type outdir: str
   :param subject: Subject ID.
   :type subject: str
   :param data: Raw or Epochs object.
   :type data: mne.io.Raw, mne.Epochs
   :param inverse_operator: Inverse operator.
   :type inverse_operator: mne.minimum_norm.InverseOperator
   :param method: Inverse method.
                  "MNE" | "dSPM" | "sLORETA" | "eLORETA".
                  (or "mne" | "dspm" | "sloreta" | "eloreta").
   :type method: str
   :param lambda2: Regularization parameter.
   :type lambda2: float
   :param pick_ori: Orientation to pick.
   :type pick_ori: str
   :param morph: Morph method, e.g. fsaverage. Can be False.
   :type morph: bool, str
   :param save: Save source estimate (default: False).
   :type save: bool


.. py:function:: apply_inverse_operator_vol(outdir, subject, data, method, lambda2, pick_ori='pca', inverse_operator=None, transform=None)

   Apply previously computed minimum norm inverse solution (volumetric).

   :param outdir: Output directory.
   :type outdir: str
   :param subject: Subject ID.
   :type subject: str
   :param data: Raw or Epochs object.
   :type data: mne.io.Raw, mne.Epochs
   :param inverse_operator: Inverse operator.
   :type inverse_operator: mne.minimum_norm.InverseOperator
   :param method: Inverse method.
                  "MNE" | "dSPM" | "sLORETA" | "eLORETA".
                  (or "mne" | "dspm" | "sloreta" | "eloreta").
   :type method: str
   :param lambda2: Regularization parameter.
   :type lambda2: float
   :param pick_ori: Orientation to pick.
   :type pick_ori: str
   :param transform: Should we standardise ('ztrans') or de-mean ('demean') the voxel
                     time courses? If None, no transform is applied.
   :type transform: str, optional

   :returns: **ts** -- In native MRI space.
   :rtype: (voxels, time) array


.. py:function:: calc_noise_cov(data, data_cov_rank, chantypes)

   Calculate noise covariance.

   :param raw: Raw object.
   :type raw: mne.io.Raw
   :param data_cov_rank: Rank of the data covariance matrix.
   :type data_cov_rank: int
   :param chantypes: List of channel types to include.
   :type chantypes: list


.. py:function:: morph_surface(subjects_dir, subject, src_from, subject_to='fsaverage', src_to=None, spacing=None)

   Morph source space to another subject's surface.

   :param subject: Subject ID.
   :type subject: str
   :param subjects_dir: Subjects directory.
   :type subjects_dir: str
   :param src_from: Original source space.
   :type src_from: mne.SourceSpaces
   :param src_to: Destination source space. can be 'fsaverage' or a source space.
   :type src_to: str, mne.SourceSpaces