goog

import numpy as np
from numba import double
from numba.decorators import jit

@jit(argtypes=[double[:,:], double[:,:]])
def pairwise_numba(X, D):
    M = X.shape[0]
    N = X.shape[1]
    for i in range(M):
        for j in range(M):

goog

from numba import autojit
import numpy as np


@autojit
def sum1d(my_double_array):
    sum = 0.0
    for i in range(my_double_array.shape[0]):
        sum += my_double_array[i]
    return sum

goog

from numba import jit
import numpy as np


@jit('f8(f8[:])')
def sum1d(my_double_array):
    sum = 0.0
    for i in range(my_double_array.shape[0]):
        sum += my_double_array[i]
    return sum

goog

A closure occurs when a function has access to a local variable from an enclosing scope that has finished its execution.

def make_printer(msg): def printer(): print msg return printer

printer = make_printer('Foo!') printer()

goog

from numpy import zeros
from scipy import weave
import numpy as np
import time
import timeit
import scipy.weave as weave
import pyximport
pyximport.install(setup_args={'include_dirs':[np.get_include()]})

dx = 0.1

goog

In [328]: timeit.Timer('if done is None:pass','done = 1').timeit()
Out[328]: 0.09523200988769531

In [329]: timeit.Timer('if done!=None:pass','done = 1').timeit()
Out[329]: 0.2190079689025879

goog

my_sum: 0.0396
np.sum: 0.0396
sum: 5.47

goog

# -*- coding: utf-8 -*-
"""
使用weave将C++嵌入到Python程序中，加快程序的运行。
"""
import scipy.weave as weave
import numpy as np
import time

def my_sum(a):
    n=int(len(a))

goog

#!/usr/bin/python
# -*- coding: utf-8 -*-
import time
import math
import numpy as np

x = [i * 0.001 for i in xrange(1000000)]
start = time.clock()
for i,t in enumerate(x):
    x[i] = math.sin(t)

goog

the loop of the math.sin: 0.78
math.sin: 0.56
numpy.sin: 0.08
the for loop of numpy.sin 3.66