Window moving average implementation using numba with speed tests.

gistfile1.py

import time

import numpy as np
from numba import jit, float64, int64


def mean_filter(X, window_size):
    filtered = np.empty(X.shape)
    starts = window_size * [0] + range(1, X.shape[0] - window_size + 1)
    for i in range(X.shape[0]):
        start = starts[i]
        filtered[i] = (X[start:i + 1]).mean(axis=0)
    return filtered

jit_mean_filter = jit(float64[:, :](float64[:, :], int64))(mean_filter)

# Run once to make it compile.
jit_mean_filter(np.empty((3, 1)), 2)


if __name__ == '__main__':
    X = np.random.normal(0, 1, (10000, 5))

    start = time.time()
    for i in range(10):
        mean_filter(X, 10)
    print 'no jit', time.time() - start

    start = time.time()
    for i in range(10):
        jit_mean_filter(X, 10)
    print 'jit', time.time() - start