Python并行运行函数

是的,你可以。只需按照说明书中的说明操作即可 multiprocessing documentation 并衡量使用多个工人是否会更快。

以下是我测试的代码:

from multiprocessing import Pool
import numpy as np
from scipy import ndimage
from time import time

n=6
x0=350
y0=350
r=150
num=10000
#z = np.gradient(sensor_dat, axis=1)
z = np.random.randn(700,700)

def f(i):
    x1, y1 = x0 + r * np.cos(2 * np.pi * i / n), y0 + r * np.sin(2 * np.pi * i / n)
    x, y = np.linspace(x0, x1, num), np.linspace(y0, y1, num)
    zi = ndimage.map_coordinates(z, np.vstack((y, x)))
    return zi

if __name__ == '__main__':
    begin = time()
    [f(i) for i in range(36)]
    end = time()
    print('Single worker took {:.3f} secs'.format(end - begin))

    begin = time()
    with Pool() as p:
        p.map(f, list(range(36)))
    end = time()
    print('Parallel workers took {:.3f} secs'.format(end - begin))

这将在我的机器上产生以下输出:

Single worker took 0.793 secs
Parallel workers took 0.217 secs