d55bad55b6a9e97f800c97c73038bc5ed8d4b31f,arviz/data/io_pystan.py,PyStanConverter,prior_to_xarray,#PyStanConverter#,201

Before Change

        nchain = self.fit.sim["chains"]
        data = {}
        for key, values in self.prior.items():
            if len(values.shape) == 0:
                values = np.atleast_2d(values)
            elif len(values.shape) == 1:
                if nchain == 1:
                    values = np.expand_dims(values, -1)
                else:
                    values = np.expand_dims(values, 0)
            values = np.swapaxes(values, 0, 1)
            data[key] = values
        return dict_to_dataset(data, library=self.pystan, coords=self.coords, dims=self.dims)

After Change

        prior = self.prior
        // filter posterior_predictive and log_likelihood
        prior_predictive = self.prior_predictive
        if prior_predictive is None:
            prior_predictive = []
        elif isinstance(prior_predictive, str):
            prior_predictive = [prior_predictive]

        ignore = prior_predictive + ["lp__"]

        data = get_draws(prior, ignore=ignore)
        return dict_to_dataset(data, library=self.pystan, coords=self.coords, dims=self.dims)

Instances