python_embed/Lib/site-packages/mne/preprocessing/_regress.py

# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import numpy as np

from .._fiff.pick import _picks_to_idx, pick_info
from .._fiff.proj import _needs_eeg_average_ref_proj
from ..defaults import _BORDER_DEFAULT, _EXTRAPOLATE_DEFAULT, _INTERPOLATION_DEFAULT
from ..epochs import BaseEpochs
from ..evoked import Evoked
from ..io import BaseRaw
from ..utils import (
    _check_fname,
    _check_option,
    _check_preload,
    _import_h5io_funcs,
    _validate_type,
    copy_function_doc_to_method_doc,
    fill_doc,
    verbose,
)
from ..viz import plot_regression_weights


@verbose
def regress_artifact(
    inst,
    picks=None,
    *,
    exclude="bads",
    picks_artifact="eog",
    betas=None,
    proj=True,
    copy=True,
    verbose=None,
):
    """Remove artifacts using regression based on reference channels.

    Parameters
    ----------
    inst : instance of Epochs | Raw
        The instance to process.
    %(picks_good_data)s
    exclude : list | 'bads'
        List of channels to exclude from the regression, only used when picking
        based on types (e.g., exclude="bads" when picks="meg").
        Specify ``'bads'`` (the default) to exclude all channels marked as bad.

        .. versionadded:: 1.2
    picks_artifact : array-like | str
        Channel picks to use as predictor/explanatory variables capturing
        the artifact of interest (default is "eog").
    betas : ndarray, shape (n_picks, n_picks_ref) | None
        The regression coefficients to use. If None (default), they will be
        estimated from the data.
    proj : bool
        Whether to automatically apply SSP projection vectors before performing
        the regression. Default is ``True``.
    copy : bool
        If True (default), copy the instance before modifying it.
    %(verbose)s

    Returns
    -------
    inst : instance of Epochs | Raw
        The processed data.
    betas : ndarray, shape (n_picks, n_picks_ref)
        The betas used during regression.

    Notes
    -----
    To implement the method outlined in :footcite:`GrattonEtAl1983`,
    remove the evoked response from epochs before estimating the
    regression coefficients, then apply those regression coefficients to the
    original data in two calls like (here for a single-condition ``epochs``
    only):

        >>> epochs_no_ave = epochs.copy().subtract_evoked()  # doctest:+SKIP
        >>> _, betas = mne.preprocessing.regress(epochs_no_ave)  # doctest:+SKIP
        >>> epochs_clean, _ = mne.preprocessing.regress(epochs, betas=betas)  # doctest:+SKIP

    References
    ----------
    .. footbibliography::
    """  # noqa: E501
    if betas is None:
        model = EOGRegression(
            picks=picks, exclude=exclude, picks_artifact=picks_artifact, proj=proj
        )
        model.fit(inst)
    else:
        # Create an EOGRegression object and load the given betas into it.
        picks = _picks_to_idx(inst.info, picks, exclude=exclude, none="data")
        picks_artifact = _picks_to_idx(inst.info, picks_artifact)
        want_betas_shape = (len(picks), len(picks_artifact))
        _check_option("betas.shape", betas.shape, (want_betas_shape,))
        model = EOGRegression(picks, picks_artifact, proj=proj)
        model.info_ = inst.info.copy()
        model.coef_ = betas
    return model.apply(inst, copy=copy), model.coef_


@fill_doc
class EOGRegression:
    """Remove EOG artifact signals from other channels by regression.

    Employs linear regression to remove signals captured by some channels,
    typically EOG, as described in :footcite:`GrattonEtAl1983`. You can also
    choose to fit the regression coefficients on evoked blink/saccade data and
    then apply them to continuous data, as described in
    :footcite:`CroftBarry2000`.

    Parameters
    ----------
    %(picks_good_data)s
    exclude : list | 'bads'
        List of channels to exclude from the regression, only used when picking
        based on types (e.g., exclude="bads" when picks="meg").
        Specify ``'bads'`` (the default) to exclude all channels marked as bad.
    picks_artifact : array-like | str
        Channel picks to use as predictor/explanatory variables capturing
        the artifact of interest (default is "eog").
    proj : bool
        Whether to automatically apply SSP projection vectors before fitting
        and applying the regression. Default is ``True``.

    Attributes
    ----------
    coef_ : ndarray, shape (n, n)
        The regression coefficients. Only available after fitting.
    info_ : Info
        Channel information corresponding to the regression weights.
        Only available after fitting.
    picks : array-like | str
        Channels to perform the regression on.
    exclude : list | 'bads'
        Channels to exclude from the regression.
    picks_artifact : array-like | str
        The channels designated as containing the artifacts of interest.
    proj : bool
        Whether projections will be applied before performing the regression.

    Notes
    -----
    .. versionadded:: 1.2

    References
    ----------
    .. footbibliography::
    """

    def __init__(self, picks=None, exclude="bads", picks_artifact="eog", proj=True):
        self.picks = picks
        self.exclude = exclude
        self.picks_artifact = picks_artifact
        self.proj = proj

    def fit(self, inst):
        """Fit EOG regression coefficients.

        Parameters
        ----------
        inst : Raw | Epochs | Evoked
            The data on which the EOG regression weights should be fitted.

        Returns
        -------
        self : EOGRegression
            The fitted ``EOGRegression`` object. The regression coefficients
            are available as the ``.coef_`` and ``.intercept_`` attributes.

        Notes
        -----
        If your data contains EEG channels, make sure to apply the desired
        reference (see :func:`mne.set_eeg_reference`) before performing EOG
        regression.
        """
        picks, picks_artifact = self._check_inst(inst)

        # Calculate regression coefficients. Add a row of ones to also fit the
        # intercept.
        _check_preload(inst, "artifact regression")
        artifact_data = inst._data[..., picks_artifact, :]
        ref_data = artifact_data - np.mean(artifact_data, axis=-1, keepdims=True)
        if ref_data.ndim == 3:
            ref_data = ref_data.transpose(1, 0, 2)
            ref_data = ref_data.reshape(len(picks_artifact), -1)
        cov_ref = ref_data @ ref_data.T

        # Process each channel separately to reduce memory load
        coef = np.zeros((len(picks), len(picks_artifact)))
        for pi, pick in enumerate(picks):
            this_data = inst._data[..., pick, :]  # view
            # Subtract mean over time from every trial/channel
            cov_data = this_data - np.mean(this_data, -1, keepdims=True)
            cov_data = cov_data.reshape(1, -1)
            # Perform the linear regression
            coef[pi] = np.linalg.solve(cov_ref, ref_data @ cov_data.T).T[0]

        # Store relevant parameters in the object.
        self.coef_ = coef
        self.info_ = inst.info.copy()
        return self

    @fill_doc
    def apply(self, inst, copy=True):
        """Apply the regression coefficients to data.

        Parameters
        ----------
        inst : Raw | Epochs | Evoked
            The data on which to apply the regression.
        %(copy_df)s

        Returns
        -------
        inst : Raw | Epochs | Evoked
            A version of the data with the artifact channels regressed out.

        Notes
        -----
        Only works after ``.fit()`` has been used.

        References
        ----------
        .. footbibliography::
        """
        if copy:
            inst = inst.copy()
        picks, picks_artifact = self._check_inst(inst)

        # Check that the channels are compatible with the regression weights.
        ref_picks = _picks_to_idx(
            self.info_, self.picks, none="data", exclude=self.exclude
        )
        ref_picks_artifact = _picks_to_idx(self.info_, self.picks_artifact)
        if any(
            inst.ch_names[ch1] != self.info_["chs"][ch2]["ch_name"]
            for ch1, ch2 in zip(picks, ref_picks)
        ):
            raise ValueError(
                "Selected data channels are not compatible with "
                "the regression weights. Make sure that all data "
                "channels are present and in the correct order."
            )
        if any(
            inst.ch_names[ch1] != self.info_["chs"][ch2]["ch_name"]
            for ch1, ch2 in zip(picks_artifact, ref_picks_artifact)
        ):
            raise ValueError(
                "Selected artifact channels are not compatible "
                "with the regression weights. Make sure that all "
                "artifact channels are present and in the "
                "correct order."
            )

        _check_preload(inst, "artifact regression")
        artifact_data = inst._data[..., picks_artifact, :]
        ref_data = artifact_data - np.mean(artifact_data, -1, keepdims=True)
        for pi, pick in enumerate(picks):
            this_data = inst._data[..., pick, :]  # view
            this_data -= (self.coef_[pi] @ ref_data).reshape(this_data.shape)
        return inst

    @copy_function_doc_to_method_doc(plot_regression_weights)
    def plot(
        self,
        ch_type=None,
        sensors=True,
        show_names=False,
        mask=None,
        mask_params=None,
        contours=6,
        outlines="head",
        sphere=None,
        image_interp=_INTERPOLATION_DEFAULT,
        extrapolate=_EXTRAPOLATE_DEFAULT,
        border=_BORDER_DEFAULT,
        res=64,
        size=1,
        cmap=None,
        vlim=(None, None),
        cnorm=None,
        axes=None,
        colorbar=True,
        cbar_fmt="%1.1e",
        title=None,
        show=True,
    ):
        return plot_regression_weights(
            self,
            ch_type=ch_type,
            sensors=sensors,
            show_names=show_names,
            mask=mask,
            mask_params=mask_params,
            contours=contours,
            outlines=outlines,
            sphere=sphere,
            image_interp=image_interp,
            extrapolate=extrapolate,
            border=border,
            res=res,
            size=size,
            cmap=cmap,
            vlim=vlim,
            cnorm=cnorm,
            axes=axes,
            colorbar=colorbar,
            cbar_fmt=cbar_fmt,
            title=title,
            show=show,
        )

    def _check_inst(self, inst):
        """Perform some sanity checks on the input."""
        _validate_type(
            inst, (BaseRaw, BaseEpochs, Evoked), "inst", "Raw, Epochs, Evoked"
        )
        picks = _picks_to_idx(inst.info, self.picks, none="data", exclude=self.exclude)
        picks_artifact = _picks_to_idx(inst.info, self.picks_artifact)
        all_picks = np.unique(np.concatenate([picks, picks_artifact]))
        use_info = pick_info(inst.info, all_picks)
        del all_picks
        if _needs_eeg_average_ref_proj(use_info):
            raise RuntimeError(
                "No average reference for the EEG channels has been "
                "set. Use inst.set_eeg_reference(projection=True) to do so."
            )
        if self.proj and not inst.proj:
            inst.apply_proj()
        if not inst.proj and len(use_info.get("projs", [])) > 0:
            raise RuntimeError(
                "Projections need to be applied before "
                "regression can be performed. Use the "
                ".apply_proj() method to do so."
            )
        return picks, picks_artifact

    def __repr__(self):
        """Produce a string representation of this object."""
        s = "<EOGRegression | "
        if hasattr(self, "coef_"):
            n_art = self.coef_.shape[1]
            plural = "s" if n_art > 1 else ""
            s += f"fitted to {n_art} artifact channel{plural}>"
        else:
            s += "not fitted>"
        return s

    @fill_doc
    def save(self, fname, overwrite=False):
        """Save the regression model to an HDF5 file.

        Parameters
        ----------
        fname : path-like
            The file to write the regression weights to. Should end in ``.h5``.
        %(overwrite)s
        """
        _, write_hdf5 = _import_h5io_funcs()
        _validate_type(fname, "path-like", "fname")
        fname = _check_fname(fname, overwrite=overwrite, name="fname")
        write_hdf5(fname, self.__dict__, overwrite=overwrite)


def read_eog_regression(fname):
    """Read an EOG regression model from an HDF5 file.

    Parameters
    ----------
    fname : path-like
        The file to read the regression model from. Should end in ``.h5``.

    Returns
    -------
    model : EOGRegression
        The regression model read from the file.

    Notes
    -----
    .. versionadded:: 1.2
    """
    read_hdf5, _ = _import_h5io_funcs()
    _validate_type(fname, "path-like", "fname")
    fname = _check_fname(fname, overwrite="read", must_exist=True, name="fname")
    model = EOGRegression()
    model.__dict__.update(read_hdf5(fname))
    return model