fmri.py 文件源码

def transform(self, imgs, confounds=None):
        """Compute the mask and the ICA maps across subjects

        Parameters
        ----------
        batch_size
        imgs: list of Niimg-like objects
            See http://nilearn.github.io/building_blocks/manipulating_mr_images.html#niimg.
            Data on which PCA must be calculated. If this is a list,
            the affine is considered the same for all.

        confounds: CSV file path or 2D matrix
            This parameter is passed to nilearn.signal.clean. Please see the
            related documentation for details

        Returns
        -------
        codes, list of ndarray, shape = n_images * (n_samples, n_components)
            Loadings for each of the images, and each of the time steps
        """
        if (isinstance(imgs, str) or not hasattr(imgs, '__iter__')):
            imgs = [imgs]
        if confounds is None:
            confounds = itertools.repeat(None)
        codes = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
            delayed(self._cache(_transform_img, func_memory_level=1))(
                self.coder_, self.masker_, img, these_confounds)
            for img, these_confounds in zip(imgs, confounds))
        return codes