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random_points_on_polyline

random_points_on_polyline(V, n, EC=None)

Generate points on the edges of a polyline.

Use a uniform distribution to randomly distribute random points in a given polyline.

Parameters:

Name Type Description Default
V numpy double array

Matrix of polyline vertices

 required
n int

Number of desired points

 required
EC numpy int array, optional (default None)

Matrix of polyline indices into V. If None, the polyline is assumed to be open and ordered.

 None

Returns:

Name Type Description
P numpy double array

Matrix of randomly sampled points that lay on the polyline
N numpy double array

Matrix of outward facing polyline normals at P

See Also

sample_mesh edge_indices

Examples:

TODO

Source code in src/gpytoolbox/random_points_on_polyline.py 
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def random_points_on_polyline(V, n, EC=None):
    """Generate points on the edges of a polyline.

    Use a uniform distribution to randomly distribute random points in a given polyline.

    Parameters
    ----------
    V : numpy double array
        Matrix of polyline vertices 
    n : int
        Number of desired points
    EC : numpy int array, optional (default None)
        Matrix of polyline indices into V. If None, the polyline is assumed to be open and ordered.

    Returns
    -------
    P : numpy double array
        Matrix of randomly sampled points that lay on the polyline
    N : numpy double array
        Matrix of outward facing polyline normals at P

    See Also
    --------
    sample_mesh
    edge_indices

    Examples
    --------
    TODO
    """

    raise Exception("random_points_on_polyline was deprecated in gpytoolbox-0.0.3 in favour of the more general random_points_on_mesh. This error message will disappear in gpytoolbox-0.0.4")

    # warnings.warn("random_points_on_polyline will be deprecated in gpytoolbox-0.0.3 in favour of the more general random_points_on_mesh",DeprecationWarning)

    # if (EC is None):
    #     EC = edge_indices(V.shape[0],closed=False)

    # x,I,_ = random_points_on_mesh(V, EC, n, return_indices=True)

    # vecs = V[EC[:,1],:] - V[EC[:,0],:]
    # vecs /= np.linalg.norm(vecs, axis=1)[:,None]
    # J = np.array([[0., -1.], [1., 0.]])
    # N = vecs @ J.T

    # sampled_N = N[I,:]

    return x, sampled_N