def uniform_discr(min_pt, max_pt, shape, dtype=None, impl='numpy', **kwargs):
    """Return a uniformly discretized L^p function space.

    Parameters
    ----------
    min_pt, max_pt : float or sequence of floats
        Minimum/maximum corners of the desired function domain.
    shape : int or sequence of ints
        Number of samples per axis.
    dtype : optional
        Data type for the discretized space, must be understood by the
        `numpy.dtype` constructor. The default for ``None`` depends on the
        ``impl`` backend, usually it is ``'float64'`` or ``'float32'``.
    impl : string, optional
        Implementation of the data storage arrays.

    Other Parameters
    ----------------
    exponent : positive float, optional
        The parameter :math:`p` in :math:`L^p`. If the exponent is not
        equal to the default 2.0, the space has no inner product.
    interp : string or sequence of strings, optional
        Interpolation type to be used for discretization.
        A sequence is interpreted as interpolation scheme per axis.
        Possible values:

        - ``'nearest'`` : use nearest-neighbor interpolation.
        - ``'linear'`` : use linear interpolation.

    nodes_on_bdry : bool or sequence, optional
        If a sequence is provided, it determines per axis whether to
        place the last grid point on the boundary (``True``) or shift it
        by half a cell size into the interior (``False``). In each axis,
        an entry may consist in a single bool or a 2-tuple of
        bool. In the latter case, the first tuple entry decides for
        the left, the second for the right boundary. The length of the
        sequence must be ``len(shape)``.
        A single boolean is interpreted as a global choice for all
        boundaries.
        Default: ``False``.
    weighting : optional
        Use weighted inner product, norm, and dist. The following
        types are supported as ``weighting``:

        - ``None``: Use the cell volume as weighting constant (default).
        - ``float``: Weighting by a constant.
        - array-like: Point-wise weighting by an array.
        - `Weighting`: Use weighting class as-is. Compatibility
          with this space's elements is not checked during init.

    Returns
    -------
    discr : `DiscreteLp`
        The uniformly discretized function space

    Examples
    --------
    Create real space:

    >>> space = uniform_discr([0, 0], [1, 1], (10, 10))
    >>> space
    uniform_discr([ 0.,  0.], [ 1.,  1.], (10, 10))
    >>> space.cell_sides
    array([ 0.1,  0.1])
    >>> space.dtype
    dtype('float64')
    >>> space.is_real
    True

    Create complex space by giving a dtype:

    >>> space = uniform_discr([0, 0], [1, 1], (10, 10), dtype=complex)
    >>> space
    uniform_discr([ 0.,  0.], [ 1.,  1.], (10, 10), dtype=complex)
    >>> space.is_complex
    True
    >>> space.real_space  # Get real counterpart
    uniform_discr([ 0.,  0.], [ 1.,  1.], (10, 10))

    See Also
    --------
    uniform_discr_frompartition : uniform Lp discretization using a given
        uniform partition of a function domain
    uniform_discr_fromspace : uniform discretization from an existing
        function space
    uniform_discr_fromintv : uniform discretization from an existing
        interval product
    """
    intv_prod = IntervalProd(min_pt, max_pt)
    return uniform_discr_fromintv(intv_prod, shape, dtype, impl, **kwargs)
Return a uniformly discretized L^p function space.

    Parameters
    ----------
    min_pt, max_pt : float or sequence of floats
        Minimum/maximum corners of the desired function domain.
    shape : int or sequence of ints
        Number of samples per axis.
    dtype : optional
        Data type for the discretized space, must be understood by the
        `numpy.dtype` constructor. The default for ``None`` depends on the
        ``impl`` backend, usually it is ``'float64'`` or ``'float32'``.
    impl : string, optional
        Implementation of the data storage arrays.

    Other Parameters
    ----------------
    exponent : positive float, optional
        The parameter :math:`p` in :math:`L^p`. If the exponent is not
        equal to the default 2.0, the space has no inner product.
    interp : string or sequence of strings, optional
        Interpolation type to be used for discretization.
        A sequence is interpreted as interpolation scheme per axis.
        Possible values:

        - ``'nearest'`` : use nearest-neighbor interpolation.
        - ``'linear'`` : use linear interpolation.

    nodes_on_bdry : bool or sequence, optional
        If a sequence is provided, it determines per axis whether to
        place the last grid point on the boundary (``True``) or shift it
        by half a cell size into the interior (``False``). In each axis,
        an entry may consist in a single bool or a 2-tuple of
        bool. In the latter case, the first tuple entry decides for
        the left, the second for the right boundary. The length of the
        sequence must be ``len(shape)``.
        A single boolean is interpreted as a global choice for all
        boundaries.
        Default: ``False``.
    weighting : optional
        Use weighted inner product, norm, and dist. The following
        types are supported as ``weighting``:

        - ``None``: Use the cell volume as weighting constant (default).
        - ``float``: Weighting by a constant.
        - array-like: Point-wise weighting by an array.
        - `Weighting`: Use weighting class as-is. Compatibility
          with this space's elements is not checked during init.

    Returns
    -------
    discr : `DiscreteLp`
        The uniformly discretized function space

    Examples
    --------
    Create real space:

    >>> space = uniform_discr([0, 0], [1, 1], (10, 10))
    >>> space
    uniform_discr([ 0.,  0.], [ 1.,  1.], (10, 10))
    >>> space.cell_sides
    array([ 0.1,  0.1])
    >>> space.dtype
    dtype('float64')
    >>> space.is_real
    True

    Create complex space by giving a dtype:

    >>> space = uniform_discr([0, 0], [1, 1], (10, 10), dtype=complex)
    >>> space
    uniform_discr([ 0.,  0.], [ 1.,  1.], (10, 10), dtype=complex)
    >>> space.is_complex
    True
    >>> space.real_space  # Get real counterpart
    uniform_discr([ 0.,  0.], [ 1.,  1.], (10, 10))

    See Also
    --------
    uniform_discr_frompartition : uniform Lp discretization using a given
        uniform partition of a function domain
    uniform_discr_fromspace : uniform discretization from an existing
        function space
    uniform_discr_fromintv : uniform discretization from an existing
        interval product