How to use the nimare.utils.listify function in NiMARE

ids : list, optional
            A list of IDs in the Dataset for which to find texts. Default is
            None, in which case all texts of requested type are returned.
        field : str, optional
            Metadata field to extract. Corresponds to column name in
            Dataset.metadata DataFrame. Default is 'sample_sizes'.

        Returns
        -------
        metadata : list
            List of values of requested type for selected IDs.
        """
        return_first = False
        if not isinstance(ids, list) and ids is not None:
            return_first = True
            ids = listify(ids)

        md_fields = [c for c in self.metadata.columns if c not in self._id_cols]
        if field not in md_fields:
            raise ValueError('Metadata field "{0}" not found.\nAvailable fields: '
                             '{1}'.format(field, ', '.join(md_fields)))

        if ids is not None:
            result = self.metadata[field].loc[self.metadata['id'].isin(ids)].tolist()
        else:
            result = self.metadata[field].tolist()

        if return_first:
            return result[0]
        else:
            return result

ids : list, optional
            A list of IDs in the Dataset for which to find texts. Default is
            None, in which case all texts of requested type are returned.
        imtype : str, optional
            Type of image to extract. Corresponds to column name in
            Dataset.images DataFrame. Default is 'z'.

        Returns
        -------
        images : list
            List of images of requested type for selected IDs.
        """
        return_first = False
        if not isinstance(ids, list) and ids is not None:
            return_first = True
            ids = listify(ids)

        imtypes = [c for c in self.images.columns if c not in self._id_cols]
        if imtype not in imtypes:
            raise ValueError('Image type "{0}" not found.\nAvailable types: '
                             '{1}'.format(imtype, ', '.join(imtypes)))

        if ids is not None:
            result = self.images[imtype].loc[self.images['id'].isin(ids)].tolist()
        else:
            result = self.images[imtype].tolist()

        if return_first:
            return result[0]
        else:
            return result

def add_images(self, images):
        ''' Add one or more images to the current list. '''
        for image in listify(images):
            if not isinstance(image, Image) and image is not None:
                raise ValueError('All images inputs must be nimare Images.')
            elif image.type in self.images.keys():
                self.images[image.type] = image

def add_contrasts(self, contrasts):
        self.contrasts.extend(listify(contrasts))

"""
        Extract list of labels for which studies in Dataset have annotations.

        Parameters
        ----------
        ids : list, optional
            A list of IDs in the Dataset for which to find labels. Default is
            None, in which case all labels are returned.

        Returns
        -------
        labels : list
            List of labels for which there are annotations in the Dataset.
        """
        if not isinstance(ids, list) and ids is not None:
            ids = listify(ids)

        result = [c for c in self.annotations.columns if c not in self._id_cols]
        if ids is not None:
            temp_annotations = self.annotations.loc[self.annotations['id'].isin(ids)]
            res = temp_annotations[result].any(axis=0)
            result = res.loc[res].index.tolist()

        return result

ids : list, optional
            A list of IDs in the Dataset for which to find texts. Default is
            None, in which case all texts of requested type are returned.
        text_type : str, optional
            Type of text to extract. Corresponds to column name in
            Dataset.texts DataFrame. Default is 'abstract'.

        Returns
        -------
        texts : list
            List of texts of requested type for selected IDs.
        """
        return_first = False
        if not isinstance(ids, list) and ids is not None:
            return_first = True
            ids = listify(ids)

        text_types = [c for c in self.texts.columns if c not in self._id_cols]
        if text_type not in text_types:
            raise ValueError('Text type "{0}" not found.\nAvailable types: '
                             '{1}'.format(text_type, ', '.join(text_types)))

        if ids is not None:
            result = self.texts[text_type].loc[self.texts['id'].isin(ids)]
        else:
            result = self.texts[text_type]

        if return_first:
            return result[0]
        else:
            return result