libpysal.cg.
distance_matrix
(X, p=2.0, threshold=50000000.0)[source]¶Calculate a distance matrix
XXX Needs optimization/integration with other weights in pysal
An n by k array where n is the number of observations and k is the number of dimensions (2 for x,y).
Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance
If (n**2)*32 > threshold use scipy.spatial.distance_matrix instead of working in RAM, this is roughly the amount of RAM (in bytes) that will be used.
An n by m p-norm distance matrix.
Examples
>>> x, y = [r.flatten() for r in np.indices((3, 3))]
>>> data = np.array([x, y]).T
>>> d = distance_matrix(data)
>>> np.array(d)
array([[0. , 1. , 2. , 1. , 1.41421356,
2.23606798, 2. , 2.23606798, 2.82842712],
[1. , 0. , 1. , 1.41421356, 1. ,
1.41421356, 2.23606798, 2. , 2.23606798],
[2. , 1. , 0. , 2.23606798, 1.41421356,
1. , 2.82842712, 2.23606798, 2. ],
[1. , 1.41421356, 2.23606798, 0. , 1. ,
2. , 1. , 1.41421356, 2.23606798],
[1.41421356, 1. , 1.41421356, 1. , 0. ,
1. , 1.41421356, 1. , 1.41421356],
[2.23606798, 1.41421356, 1. , 2. , 1. ,
0. , 2.23606798, 1.41421356, 1. ],
[2. , 2.23606798, 2.82842712, 1. , 1.41421356,
2.23606798, 0. , 1. , 2. ],
[2.23606798, 2. , 2.23606798, 1.41421356, 1. ,
1.41421356, 1. , 0. , 1. ],
[2.82842712, 2.23606798, 2. , 2.23606798, 1.41421356,
1. , 2. , 1. , 0. ]])