python 性能调试工具（line_profiler）使用

python性能调试过程中最突出的问题就是耗时，性能测试工具有很多，像profiler，cprofiler等等，都是只能返回函数整体的耗时，而line_profiler就能够很好解决这个问题（大家可以试试就知道了）。

怎么使用这个工具呢？网上大部分都是说在所需要测的函数前面加一个@profile，如文档所说。但是加了@profile后函数无法直接运行，只能优化的时候加上，调试的时候又得去掉。文章中提到了这个问题的解决办法，个人觉得还是有点麻烦，不太能理解这是为什么。我在stackoverflow上看到了另一种关于line_profile的使用方法，简单而且实用。

这是使用案例：

from line_profiler import LineProfiler
import random

def do_stuff(numbers):
    s = sum(numbers)
    l = [numbers[i]/43 for i in range(len(numbers))]
    m = ['hello'+str(numbers[i]) for i in range(len(numbers))]

numbers = [random.randint(1,100) for i in range(1000)]
lp = LineProfiler()
lp_wrapper = lp(do_stuff)
lp_wrapper(numbers)
lp.print_stats()

输出结果：

Timer unit: 1e-06 s

Total time: 0.000649 s
File: <ipython-input-2-2e060b054fea>
Function: do_stuff at line 4

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
     4                                           def do_stuff(numbers):
     5         1           10     10.0      1.5      s = sum(numbers)
     6         1          186    186.0     28.7      l = [numbers[i]/43 for i in range(len(numbers))]
     7         1          453    453.0     69.8      m = ['hello'+str(numbers[i]) for i in range(len(numbers))]

这种方法就比较好用了，如果你想在其他文件中调用这个包含line_profiler函数测试的文件，又不想把这个信息显示出来，使用

if __name__ == '__main__':

把需要测试的函数放到这个下面，这样就能完美解决上述问题。

对于函数内部调用函数的情况：

from line_profiler import LineProfiler
import random

def do_other_stuff(numbers):
    s = sum(numbers)

def do_stuff(numbers):
    do_other_stuff(numbers)
    l = [numbers[i]/43 for i in range(len(numbers))]
    m = ['hello'+str(numbers[i]) for i in range(len(numbers))]

numbers = [random.randint(1,100) for i in range(1000)]
lp = LineProfiler()
lp_wrapper = lp(do_stuff)
lp_wrapper(numbers)
lp.print_stats()

这样做的话，只能显示子函数的总时间。输出如下：

Timer unit: 1e-06 s

Total time: 0.000773 s
File: <ipython-input-3-ec0394d0a501>
Function: do_stuff at line 7

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
     7                                           def do_stuff(numbers):
     8         1           11     11.0      1.4      do_other_stuff(numbers)
     9         1          236    236.0     30.5      l = [numbers[i]/43 for i in range(len(numbers))]
    10         1          526    526.0     68.0      m = ['hello'+str(numbers[i]) for i in range(len(numbers))]

为了能够同时显示函数每行所用时间和调用函数每行所用时间，加入add_function就能够解决。

from line_profiler import LineProfiler
import random

def do_other_stuff(numbers):
    s = sum(numbers)

def do_stuff(numbers):
    do_other_stuff(numbers)
    l = [numbers[i]/43 for i in range(len(numbers))]
    m = ['hello'+str(numbers[i]) for i in range(len(numbers))]

numbers = [random.randint(1,100) for i in range(1000)]
lp = LineProfiler()
lp.add_function(do_other_stuff)   # add additional function to profile
lp_wrapper = lp(do_stuff)
lp_wrapper(numbers)
lp.print_stats()

Timer unit: 1e-06 s

Total time: 9e-06 s
File: <ipython-input-4-dae73707787c>
Function: do_other_stuff at line 4

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
     4                                           def do_other_stuff(numbers):
     5         1            9      9.0    100.0      s = sum(numbers)

Total time: 0.000694 s
File: <ipython-input-4-dae73707787c>
Function: do_stuff at line 7

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
     7                                           def do_stuff(numbers):
     8         1           12     12.0      1.7      do_other_stuff(numbers)
     9         1          208    208.0     30.0      l = [numbers[i]/43 for i in range(len(numbers))]
    10         1          474    474.0     68.3      m = ['hello'+str(numbers[i]) for i in range(len(numbers))]

结束