label min max mean total run count
------- ----------- ----------- ----------- ----------- -----------
imports 0.00283813 0.00283813 0.00283813 0.00283813 1
loop 5.96046e-06 1.50204e-05 6.71864e-06 0.000335932 50
我喜欢它如何给你关于它的统计数字和计时器运行的次数。
使用起来很简单。如果要测量代码进入for循环的时间,只需执行以下操作:
from jackedCodeTimerPY import JackedTiming
JTimer = JackedTiming()
for i in range(50):
JTimer.start('loop') # 'loop' is the name of the timer
doSomethingHere = 'This is really useful!'
JTimer.stop('loop')
print(JTimer.report()) # prints the timing report
您也可以同时运行多个计时器。
JTimer.start('first timer')
JTimer.start('second timer')
do_something = 'amazing'
JTimer.stop('first timer')
do_something = 'else'
JTimer.stop('second timer')
print(JTimer.report()) # prints the timing report
import time
time_start = time.clock()
#run your code
time_elapsed = (time.clock() - time_start)
如Python文档所引用:
time.clock()
On Unix, return the current processor time as a floating
point number expressed in seconds. The precision, and in fact the very
definition of the meaning of “processor time”, depends on that of the
C function of the same name, but in any case, this is the function to
use for benchmarking Python or timing algorithms.
On Windows, this function returns wall-clock seconds elapsed since the
first call to this function, as a floating point number, based on the
Win32 function QueryPerformanceCounter(). The resolution is typically
better than one microsecond.
使用像guppy这样的内存分析器
有一个名为jackedCodeTimerPy的非常好的库来为代码计时。然后应该使用Daniel Li建议的资源包。
jackedCodeTimerPy给出了非常好的报告,比如
我喜欢它如何给你关于它的统计数字和计时器运行的次数。
使用起来很简单。如果要测量代码进入for循环的时间,只需执行以下操作:
您也可以同时运行多个计时器。
回购协议中有更多的使用示例。希望这有帮助。
https://github.com/BebeSparkelSparkel/jackedCodeTimerPY
使用此项计算时间:
如Python文档所引用:
引用:http://docs.python.org/library/time.html
使用此项计算内存:
引用:http://docs.python.org/library/resource.html
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