Python Implementation of OPTICS (Clustering) Algorithm

I'm looking for a decent implementation of the OPTICS algorithm in Python. I will use it to form density-based clusters of points ((x,y) pairs).

I'm looking for something that takes in (x,y) pairs and outputs a list of clusters, where each cluster in the list contains a list of (x, y) pairs belonging to that cluster.

EDIT: the following is known to not be a complete implementation of OPTICS.

I did a quick search and found the following (Optics). I can't vouch for its quality, however the algorithm seems pretty simple, so you should be able to validate/adapt it quickly.

Here is a quick example of how to build clusters on the output of the optics algorithm:

def cluster(order, distance, points, threshold):
    ''' Given the output of the options algorithm,
    compute the clusters:

    @param order The order of the points
    @param distance The relative distances of the points
    @param points The actual points
    @param threshold The threshold value to cluster on
    @returns A list of cluster groups
    '''
    clusters = [[]]
    points   = sorted(zip(order, distance, points))
    splits   = ((v > threshold, p) for i,v,p in points)
    for iscluster, point in splits: 
        if iscluster: clusters[-1].append(point)
        elif len(clusters[-1]) > 0: clusters.append([])
    return clusters

    rd, cd, order = optics(points, 4)
    print cluster(order, rd, points, 38.0)