def generate_noise(shape: List[int], porosity=None, octaves: int = 3,
                   frequency: int = 32, mode: str = 'simplex'):
    r"""
    Generate a field of spatially correlated random noise using the Perlin
    noise algorithm, or the updated Simplex noise algorithm.

    Parameters
    ----------
    shape : array_like
        The size of the image to generate in [Nx, Ny, Nz] where N is the
        number of voxels.

    porosity : float
        If specified, this will threshold the image to the specified value
        prior to returning.  If no value is given (the default), then the
        scalar noise field is returned.

    octaves : int
        Controls the *texture* of the noise, with higher octaves giving more
        complex features over larger length scales.

    frequency : array_like
        Controls the relative sizes of the features, with higher frequencies
        giving larger features.  A scalar value will apply the same frequency
        in all directions, given an isotropic field; a vector value will
        apply the specified values along each axis to create anisotropy.

    mode : string
        Which noise algorithm to use, either ``'simplex'`` (default) or
        ``'perlin'``.

    Returns
    -------
    image : ND-array
        If porosity is given, then a boolean array with ``True`` values
        denoting the pore space is returned.  If not, then normally
        distributed and spatially correlated randomly noise is returned.

    Notes
    -----
    This method depends the a package called 'noise' which must be
    compiled. It is included in the Anaconda distribution, or a platform
    specific binary can be downloaded.

    See Also
    --------
    porespy.tools.norm_to_uniform

    """
    try:
        import noise
    except ModuleNotFoundError:
        raise Exception("The noise package must be installed")
    shape = sp.array(shape)
    if sp.size(shape) == 1:
        Lx, Ly, Lz = sp.full((3, ), int(shape))
    elif len(shape) == 2:
        Lx, Ly = shape
        Lz = 1
    elif len(shape) == 3:
        Lx, Ly, Lz = shape
    if mode == 'simplex':
        f = noise.snoise3
    else:
        f = noise.pnoise3
    frequency = sp.atleast_1d(frequency)
    if frequency.size == 1:
        freq = sp.full(shape=[3, ], fill_value=frequency[0])
    elif frequency.size == 2:
        freq = sp.concatenate((frequency, [1]))
    else:
        freq = sp.array(frequency)
    im = sp.zeros(shape=[Lx, Ly, Lz], dtype=float)
    for x in range(Lx):
        for y in range(Ly):
            for z in range(Lz):
                im[x, y, z] = f(x=x/freq[0], y=y/freq[1], z=z/freq[2],
                                octaves=octaves)
    im = im.squeeze()
    if porosity:
        im = norm_to_uniform(im, scale=[0, 1])
        im = im < porosity
    return im
r"""
    Generate a field of spatially correlated random noise using the Perlin
    noise algorithm, or the updated Simplex noise algorithm.

    Parameters
    ----------
    shape : array_like
        The size of the image to generate in [Nx, Ny, Nz] where N is the
        number of voxels.

    porosity : float
        If specified, this will threshold the image to the specified value
        prior to returning.  If no value is given (the default), then the
        scalar noise field is returned.

    octaves : int
        Controls the *texture* of the noise, with higher octaves giving more
        complex features over larger length scales.

    frequency : array_like
        Controls the relative sizes of the features, with higher frequencies
        giving larger features.  A scalar value will apply the same frequency
        in all directions, given an isotropic field; a vector value will
        apply the specified values along each axis to create anisotropy.

    mode : string
        Which noise algorithm to use, either ``'simplex'`` (default) or
        ``'perlin'``.

    Returns
    -------
    image : ND-array
        If porosity is given, then a boolean array with ``True`` values
        denoting the pore space is returned.  If not, then normally
        distributed and spatially correlated randomly noise is returned.

    Notes
    -----
    This method depends the a package called 'noise' which must be
    compiled. It is included in the Anaconda distribution, or a platform
    specific binary can be downloaded.

    See Also
    --------
    porespy.tools.norm_to_uniform