def sample_stats_prior_to_xarray(self):
        """Extract sample_stats from fit."""
        dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64}

        # copy dims and coords
        dims = deepcopy(self.dims) if self.dims is not None else {}
        coords = deepcopy(self.coords) if self.coords is not None else {}

        sampler_params = self.sample_stats_prior
        for j, s_params in enumerate(sampler_params):
            rename_dict = {}
            for key in s_params:
                key_, *end = key.split(".")
                name = re.sub("__$", "", key_)
                name = "diverging" if name == "divergent" else name
                rename_dict[key] = ".".join((name, *end))
                sampler_params[j][key] = s_params[key].astype(dtypes.get(key))
            sampler_params[j] = sampler_params[j].rename(columns=rename_dict)
        data = _unpack_dataframes(sampler_params)
        return dict_to_dataset(data, coords=coords, dims=dims)