Python How to improve numpy array performance?

Question

I have a global numpy.array data which is a 200*200*3 3d-array containing 40000 points in the 3d-space.

My goal is to calculate the distance from each point to the four corners of the unit cube ((0, 0, 0),(1, 0, 0),(0, 1, 0),(0, 0, 1)),so I can determine which corner is the nearest from the it .

def dist(*point):
    return np.linalg.norm(data - np.array(rgb), axis=2)

buffer = np.stack([dist(0, 0, 0), dist(1, 0, 0), dist(0, 1, 0), dist(0, 0, 1)]).argmin(axis=0)

I wrote this piece of code and tested it, it costs me about 10ms each run . My problem is how can I improve the performance of this piece of code ,better ran in less than 1ms .

Answer 1

You could use Scipy cdist -

# unit cube coordinates as array
uc = np.array([[0, 0, 0],[1, 0, 0], [0, 1, 0], [0, 0, 1]])

# buffer output
buf = cdist(data.reshape(-1,3), uc).argmin(1).reshape(data.shape[0],-1)

Runtime test

# Original approach
def org_app():
    return np.stack([dist(0, 0, 0), dist(1, 0, 0), \
       dist(0, 1, 0), dist(0, 0, 1)]).argmin(axis=0)

Timings -

In [170]: data = np.random.rand(200,200,3)

In [171]: %timeit org_app()
100 loops, best of 3: 4.24 ms per loop

In [172]: %timeit cdist(data.reshape(-1,3), uc).argmin(1).reshape(data.shape[0],-1)
1000 loops, best of 3: 1.25 ms per loop