def load_features(self, features, image_type=None, from_array=False,<EOL>threshold=<NUM_LIT>):
if from_array:<EOL><INDENT>if isinstance(features, list):<EOL><INDENT>features = features[<NUM_LIT:0>]<EOL><DEDENT>self._load_features_from_array(features)<EOL><DEDENT>elif path.exists(features[<NUM_LIT:0>]):<EOL><INDENT>self._load_features_from_images(features)<EOL><DEDENT>else:<EOL><INDENT>self._load_features_from_dataset(<EOL>features, image_type=image_type, threshold=threshold)<EOL><DEDENT>
Load features from current Dataset instance or a list of files.
        Args:
            features: List containing paths to, or names of, features to
                extract. Each element in the list must be a string containing
                either a path to an image, or the name of a feature (as named
                in the current Dataset). Mixing of paths and feature names
                within the list is not allowed.
            image_type: Optional suffix indicating which kind of image to use
                for analysis. Only used if features are taken from the Dataset;
                if features is a list of filenames, image_type is ignored.
            from_array: If True, the features argument is interpreted as a
                string pointing to the location of a 2D ndarray on disk
                containing feature data, where rows are voxels and columns are
                individual features.
            threshold: If features are taken from the dataset, this is the
                threshold passed to the meta-analysis module to generate fresh
                images.