Compute the raw row indices to load for each asset on a query for the
        given dates after applying a shift.

        Parameters
        ----------
        start_idx : int
            Index of first date for which we want data.
        end_idx : int
            Index of last date for which we want data.
        assets : pandas.Int64Index
            Assets for which we want to compute row indices

        Returns
        -------
        A 3-tuple of (first_rows, last_rows, offsets):
        first_rows : np.array[intp]
            Array with length == len(assets) containing the index of the first
            row to load for each asset in `assets`.
        last_rows : np.array[intp]
            Array with length == len(assets) containing the index of the last
            row to load for each asset in `assets`.
        offset : np.array[intp]
            Array with length == (len(asset) containing the index in a buffer
            of length `dates` corresponding to the first row of each asset.

            The value of offset[i] will be 0 if asset[i] existed at the start
            of a query.  Otherwise, offset[i] will be equal to the number of
            entries in `dates` for which the asset did not yet exist.

def _compute_slices(self, start_idx, end_idx, assets):
        """
        Compute the raw row indices to load for each asset on a query for the
        given dates after applying a shift.

        Parameters
        ----------
        start_idx : int
            Index of first date for which we want data.
        end_idx : int
            Index of last date for which we want data.
        assets : pandas.Int64Index
            Assets for which we want to compute row indices

        Returns
        -------
        A 3-tuple of (first_rows, last_rows, offsets):
        first_rows : np.array[intp]
            Array with length == len(assets) containing the index of the first
            row to load for each asset in `assets`.
        last_rows : np.array[intp]
            Array with length == len(assets) containing the index of the last
            row to load for each asset in `assets`.
        offset : np.array[intp]
            Array with length == (len(asset) containing the index in a buffer
            of length `dates` corresponding to the first row of each asset.

            The value of offset[i] will be 0 if asset[i] existed at the start
            of a query.  Otherwise, offset[i] will be equal to the number of
            entries in `dates` for which the asset did not yet exist.
        """
        # The core implementation of the logic here is implemented in Cython
        # for efficiency.
        return _compute_row_slices(
            self._first_rows,
            self._last_rows,
            self._calendar_offsets,
            start_idx,
            end_idx,
            assets,
        )