The parametric space is called the isoparametric space.

In docstrings :
- the types are the same as in the definition of the function (type hints).
- except for the types of the parameters in their "introductory" line, the python objects, types, variables and python code must be written between backtick (e.g. `int`).
- make a clear difference between tuples, displayed with parentheses, and lists, displayed with square brackets.
- don't forget to talk abaout the optional parameters values (e.g. "n_eval_per_elem : Union[int, Iterable[int]], optional [...] By default, 10.").

I prefer the docstrings to be displayed in the chat, not in a separate file.
Be careful with the indentation of the docstring : it should match the indentation of the function/method/class.

If a docstring already exists and seems lacking in terms of content compared to the one that follows, enhance it.
If it is already good enough in your opinion, tell me in plain text your opinion afterwards.


Exemple of docstring :

    def gauss_legendre_for_integration(
        self, 
        n_eval_per_elem: Union[int, Iterable[int], None]=None, 
        bounding_box: Union[Iterable, None]=None
        ) -> tuple[tuple[npt.NDArray[np.floating], ...], tuple[npt.NDArray[np.floating], ...]]:
        """
        Generate sets of evaluation points and their Gauss-Legendre integration weights over each basis span.

        This method creates Gauss-Legendre quadrature points and their corresponding integration weights
        for each isoparametric dimension by calling `gauss_legendre_for_integration` on each
        `BSplineBasis` instance stored in the `bases` array.

        Parameters
        ----------
        n_eval_per_elem : Union[int, Iterable[int], None], optional
            Number of evaluation points per element for each isoparametric dimension.
            If an `int` is provided, the same number is used for all dimensions.
            If an `Iterable` is provided, each value corresponds to a different dimension.
            If `None`, uses `(p + 2)//2` points per element where `p` is the degree of each basis.
            This number of points ensures an exact integration of a `p`-th degree polynomial.
            By default, None.

        bounding_box : Union[Iterable[tuple[float, float]], None], optional
            Lower and upper bounds for each isoparametric dimension.
            If `None`, uses the span of each basis.
            Format: [(`xi_min`, `xi_max`), (`eta_min`, `eta_max`), ...].
            By default, None.

        Returns
        -------
        XI : tuple[npt.NDArray[np.floating], ...]
            Tuple containing arrays of Gauss-Legendre points for each isoparametric dimension.
            The tuple has length `NPa` (dimension of isoparametric space).

        dXI : tuple[npt.NDArray[np.floating], ...]
            Tuple containing arrays of Gauss-Legendre weights for each isoparametric dimension.
            The tuple has length `NPa` (dimension of isoparametric space).

        Notes
        -----
        - For a curve (1D), returns ((`xi` points), (`xi` weights))
        - For a surface (2D), returns ((`xi` points, `eta` points), (`xi` weights, `eta` weights))
        - For a volume (3D), returns ((`xi` points, `eta` points, `zeta` points), 
                                    (`xi` weights, `eta` weights, `zeta` weights))
        - The points and weights follow the Gauss-Legendre quadrature rule
        - When `n_eval_per_elem` is `None`, uses `(p + 2)//2` points per element for exact
        integration of polynomials up to degree `p`

        Examples
        --------
        >>> degrees = [2, 2]
        >>> knots = [np.array([0, 0, 0, 0.5, 1, 1, 1], dtype='float'),
        ...          np.array([0, 0, 0, 0.5, 1, 1, 1], dtype='float')]
        >>> spline = BSpline(degrees, knots)
        >>> (xi, eta), (dxi, deta) = spline.gauss_legendre_for_integration()
        >>> xi  # xi points
        array([0.10566243, 0.39433757, 0.60566243, 0.89433757])
        >>> dxi  # xi weights
        array([0.25, 0.25, 0.25, 0.25])
        """