viscid.calculator.cluster module

Functions for clustering clouds of neighboring points

viscid.calculator.cluster.distance_to_clusters(point, clusters, alt=())

L2 distance between point and clusters

viscid.calculator.cluster.find_clusters(indx, indy, x, y, multiple=True, periodic=(False, False), diagonals=True)

Cluster and average groups of neighboring points

TODO: If absolutely necessary, could do some K-means clustering

here by calling into scikit-learn.

Parameters:

• indx (sequence) – list of x indices
• indy (sequence) – list of y indices
• x (sequence) – x coordinate array
• y (sequence) – y coordinate array
• multiple (bool) – If False, average all points as a single cluster
• periodic (sequence) – indicate whether that direction is periodic, and if so, whether the coordinate arrays are overlapped or not. Values can be True, False, or ‘+’. ‘+’ indicates that x[0] and x[-1] are not colocated, so assume they’re dx apart where dx = x[-1] - x[-2].
• diagonals (bool) – if true, then diagonal points are considered neighbors

Returns:

2xN for N clusters

Return type:

ndarray