01d18e835815feedeac2fbeff8025cdd94128eaa,pynets/fmri/clustools.py,NilParcellate,create_clean_mask,#NilParcellate#,411

Before Change

        self._mask_data = np.asarray(self._clust_mask_img.dataobj).astype("bool").astype("uint8")

        // Ensure mask does not inclue voxels outside of the brain
        self._masked_fmri_vol = np.asarray(self._func_img.dataobj)[:, :, :, 0]
        self._masked_fmri_vol = self._masked_fmri_vol.astype("bool")
        self._mask_data[~self._masked_fmri_vol] = 0
        del self._masked_fmri_vol

After Change

        clust_mask_res_img = resample_img(nib.load(self.clust_mask), target_affine=func_vol_img.affine,
                                          target_shape=func_vol_img.shape, interpolation="nearest")
        if self._mask_img is not None:
            mask_res_img = compute_gray_matter_mask(resample_img(self._mask_img, target_affine=func_vol_img.affine,
                                                                 target_shape=func_vol_img.shape,
                                                                 interpolation="nearest"))
            self._clust_mask_corr_img = intersect_masks([math_img("img > 0", img=func_vol_img),
                                                         math_img("img > 0", img=clust_mask_res_img),
                                                         math_img("img > 0", img=mask_res_img)],
                                                        threshold=1, connected=False)
            self._mask_img.uncache()
            mask_res_img.uncache()
        else:
            self._clust_mask_corr_img = intersect_masks([math_img("img > 0", img=func_vol_img),

Instances