@verbose
def read_ica(fname):
    """Restore ICA solution from fif file.

    Parameters
    ----------
    fname : str
        Absolute path to fif file containing ICA matrices.
        The file name should end with -ica.fif or -ica.fif.gz.

    Returns
    -------
    ica : instance of ICA
        The ICA estimator.
    """
    check_fname(fname, 'ICA', ('-ica.fif', '-ica.fif.gz'))

    logger.info('Reading %s ...' % fname)
    fid, tree, _ = fiff_open(fname)

    try:
        # we used to store bads that weren't part of the info...
        info, meas = read_meas_info(fid, tree, clean_bads=True)
    except __HOLE__:
        logger.info('Could not find the measurement info. \n'
                    'Functionality requiring the info won\'t be'
                    ' available.')
        info = None
    else:
        info['filename'] = fname

    ica_data = dir_tree_find(tree, FIFF.FIFFB_MNE_ICA)
    if len(ica_data) == 0:
        ica_data = dir_tree_find(tree, 123)  # Constant 123 Used before v 0.11
        if len(ica_data) == 0:
            fid.close()
            raise ValueError('Could not find ICA data')

    my_ica_data = ica_data[0]
    for d in my_ica_data['directory']:
        kind = d.kind
        pos = d.pos
        if kind == FIFF.FIFF_MNE_ICA_INTERFACE_PARAMS:
            tag = read_tag(fid, pos)
            ica_init = tag.data
        elif kind == FIFF.FIFF_MNE_ROW_NAMES:
            tag = read_tag(fid, pos)
            ch_names = tag.data
        elif kind == FIFF.FIFF_MNE_ICA_WHITENER:
            tag = read_tag(fid, pos)
            pre_whitener = tag.data
        elif kind == FIFF.FIFF_MNE_ICA_PCA_COMPONENTS:
            tag = read_tag(fid, pos)
            pca_components = tag.data
        elif kind == FIFF.FIFF_MNE_ICA_PCA_EXPLAINED_VAR:
            tag = read_tag(fid, pos)
            pca_explained_variance = tag.data
        elif kind == FIFF.FIFF_MNE_ICA_PCA_MEAN:
            tag = read_tag(fid, pos)
            pca_mean = tag.data
        elif kind == FIFF.FIFF_MNE_ICA_MATRIX:
            tag = read_tag(fid, pos)
            unmixing_matrix = tag.data
        elif kind == FIFF.FIFF_MNE_ICA_BADS:
            tag = read_tag(fid, pos)
            exclude = tag.data
        elif kind == FIFF.FIFF_MNE_ICA_MISC_PARAMS:
            tag = read_tag(fid, pos)
            ica_misc = tag.data

    fid.close()

    ica_init, ica_misc = [_deserialize(k) for k in (ica_init, ica_misc)]
    current_fit = ica_init.pop('current_fit')
    if ica_init['noise_cov'] == Covariance.__name__:
        logger.info('Reading whitener drawn from noise covariance ...')

    logger.info('Now restoring ICA solution ...')

    # make sure dtypes are np.float64 to satisfy fast_dot
    def f(x):
        return x.astype(np.float64)

    ica_init = dict((k, v) for k, v in ica_init.items()
                    if k in _get_args(ICA.__init__))
    ica = ICA(**ica_init)
    ica.current_fit = current_fit
    ica.ch_names = ch_names.split(':')
    ica._pre_whitener = f(pre_whitener)
    ica.pca_mean_ = f(pca_mean)
    ica.pca_components_ = f(pca_components)
    ica.n_components_ = unmixing_matrix.shape[0]
    ica.pca_explained_variance_ = f(pca_explained_variance)
    ica.unmixing_matrix_ = f(unmixing_matrix)
    ica.mixing_matrix_ = linalg.pinv(ica.unmixing_matrix_)
    ica.exclude = [] if exclude is None else list(exclude)
    ica.info = info
    if 'n_samples_' in ica_misc:
        ica.n_samples_ = ica_misc['n_samples_']
    if 'labels_' in ica_misc:
        ica.labels_ = ica_misc['labels_']

    logger.info('Ready.')

    return ica
dataset/ETHPy150Open mne-tools/mne-python/mne/preprocessing/ica.py/read_ica