python加速器numba使用详解_python

from numba import jit
@jit(nopython=True)
def f(x, y):
    return x+y

if __name__ == '__main__':
    a = 1
    b = 1
    print(f(a,b))

from numba import njit
@njit  # 也就是jit(nopython=True)
def f(x, y):
    return x+y

if __name__ == '__main__':
    a = 1
    b = 1
    print(f(a,b))

from numba import generated_jit, typed

@generated_jit(nopython=True)
def f2(x):
    if x==1:
        return lambda x: x+1
    else:
        return lambda x: [1,2]

if __name__ == '__main__':
    a = 1
    print(f2(a))

from numba import vectorize, float64
import numpy as np

@vectorize([float64(float64, float64)])  # 注意有中括号
def f(x, y):
    return x + y

if __name__ == '__main__':
    a = np.array([1,2])
    b = np.array([2,2])
    print(f(a,b))

import numpy as np
import time
NUM = 160
from numba import jit
from numba.typed import List

a = List()
a.append(1) # 同样需要指定数据类型，塞个1，数据类型就是int

@jit(nopython=True)
def f(a):
    for i in range(NUM):
        a.append(i)

if __name__ == '__main__':
   for i in range(5):
    start = time.time()
    f(a)
    print(time.time()-start)