原文： http://numba.pydata.org/numba-doc/latest/user/examples.html
#! /usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function, division, absolute_import
from timeit import default_timer as timer
from matplotlib.pylab import imshow, jet, show, ion
import numpy as np
from numba import jit
@jit
def mandel(x, y, max_iters):
"""
Given the real and imaginary parts of a complex number,
determine if it is a candidate for membership in the Mandelbrot
set given a fixed number of iterations.
"""
i = 0
c = complex(x,y)
z = 0.0j
for i in range(max_iters):
z = z*z + c
if (z.real*z.real + z.imag*z.imag) >= 4:
return i
return 255
@jit
def create_fractal(min_x, max_x, min_y, max_y, image, iters):
height = image.shape[0]
width = image.shape[1]
pixel_size_x = (max_x - min_x) / width
pixel_size_y = (max_y - min_y) / height
for x in range(width):
real = min_x + x * pixel_size_x
for y in range(height):
imag = min_y + y * pixel_size_y
color = mandel(real, imag, iters)
image[y, x] = color
return image
image = np.zeros((500 * 2, 750 * 2), dtype=np.uint8)
s = timer()
create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20)
e = timer()
print(e - s)
imshow(image)
#jet()
#ion()
show()#!/usr/bin/env python
"""
A moving average function using @guvectorize.
"""
import numpy as np
from numba import guvectorize
@guvectorize(['void(float64[:], intp[:], float64[:])'], '(n),()->(n)')
def move_mean(a, window_arr, out):
window_width = window_arr[0]
asum = 0.0
count = 0
for i in range(window_width):
asum += a[i]
count += 1
out[i] = asum / count
for i in range(window_width, len(a)):
asum += a[i] - a[i - window_width]
out[i] = asum / count
arr = np.arange(20, dtype=np.float64).reshape(2, 10)
print(arr)
print(move_mean(arr, 3))下面的代码展示了使用 nogil 功能时潜在的性能提升。例如，在 4 核机器上，我打印出以下结果：
numpy (1 thread) 145 ms
numba (1 thread) 128 ms
numba (4 threads) 35 ms注意
在 Python 3 下，您可以使用标准的 concurrent.futures 模块，而不是手工生成线程和调度任务。
#!/usr/bin/env python
from __future__ import print_function, division, absolute_import
import math
import threading
from timeit import repeat
import numpy as np
from numba import jit
nthreads = 4
size = 10**6
def func_np(a, b):
"""
Control function using Numpy.
"""
return np.exp(2.1 * a + 3.2 * b)
@jit('void(double[:], double[:], double[:])', nopython=True, nogil=True)
def inner_func_nb(result, a, b):
"""
Function under test.
"""
for i in range(len(result)):
result[i] = math.exp(2.1 * a[i] + 3.2 * b[i])
def timefunc(correct, s, func, *args, **kwargs):
"""
Benchmark *func* and print out its runtime.
"""
print(s.ljust(20), end=" ")
# Make sure the function is compiled before we start the benchmark
res = func(*args, **kwargs)
if correct is not None:
assert np.allclose(res, correct), (res, correct)
# time it
print('{:>5.0f} ms'.format(min(repeat(lambda: func(*args, **kwargs),
number=5, repeat=2)) * 1000))
return res
def make_singlethread(inner_func):
"""
Run the given function inside a single thread.
"""
def func(*args):
length = len(args[0])
result = np.empty(length, dtype=np.float64)
inner_func(result, *args)
return result
return func
def make_multithread(inner_func, numthreads):
"""
Run the given function inside *numthreads* threads, splitting its
arguments into equal-sized chunks.
"""
def func_mt(*args):
length = len(args[0])
result = np.empty(length, dtype=np.float64)
args = (result,) + args
chunklen = (length + numthreads - 1) // numthreads
# Create argument tuples for each input chunk
chunks = [[arg[i * chunklen:(i + 1) * chunklen] for arg in args]
for i in range(numthreads)]
# Spawn one thread per chunk
threads = [threading.Thread(target=inner_func, args=chunk)
for chunk in chunks]
for thread in threads:
thread.start()
for thread in threads:
thread.join()
return result
return func_mt
func_nb = make_singlethread(inner_func_nb)
func_nb_mt = make_multithread(inner_func_nb, nthreads)
a = np.random.rand(size)
b = np.random.rand(size)
correct = timefunc(None, "numpy (1 thread)", func_np, a, b)
timefunc(correct, "numba (1 thread)", func_nb, a, b)
timefunc(correct, "numba (%d threads)" % nthreads, func_nb_mt, a, b)