The dummy example does not do much but give you an idea of what can be done. List reduced from 6 to 1 due to restriction In our example checking stats for both functions shows us the following: Welcome to the profile statistics browser. For your particular use case you can just check the stats for your function. This runs the interactive statistics browser which gives you a lot of nice functionality. Once you have the stats file you can run the pstats module as follows: python -m pstats timeStats.profile
Timer function python code#
Note that we did not have to add any code to existing module (timeFunctions.py) and this can be done with any module. What this is doing is using the cProfile module to profile all functions in timeFunctions.py and collecting the stats in the timeStats.profile file. To run the profiler and generate stats for the file you can just run: python -m cProfile -o timeStats.profile timeFunctions.py The python cProfile and pstats modules offer great support for measuring time elapsed in certain functions without having to add any code around the existing functions.įor example if you have a python script timeFunctions.py: import time Ability to keep gc enabled for block timing.Ability to disable printing in block timing (use with timing.MeasureBlockTime() as t and then t.elapsed).Decorator accepts functions with named or unnamed params.You can disable GC during timing if you want.Use timer from timeit instead of time.time for reasons described earlier.There are several half-backed versions floating around so I want to point out few highlights: If you want to time portion of code then just put it inside with block: import timing Now you can time any function just by putting a decorator in front of it: import MyBigFunc(): The only external dependency is runstats which is again small. Just drop this file in your project and start using it. I created below little timing utility module called timing.py. The timebudget is a versatile and very simple library that you can use just in one line of code after pip install. Unfortunately, there is nothing built-in available for this so you have two options: Ideally, you just want a decorator or use with block and measure time. Now the problem is that timeit is not that simple to use because it needs setup and things get ugly when you have a bunch of imports. timeit disables garbage collection, however, this is not something you may or may not want.timeit selects the best timer available on your OS and Python version.
Here are my findings after going through many good answers here as well as a few other articles.įirst, if you are debating between timeit and time.time, the timeit has two advantages: Note that by design of this function, the return value of elapsed() is frozen on block exit, and further calls return the same duration (of about 6 seconds in this toy example). Here's contextmanager code sufficient to do the trick: from contextlib import contextmanagerĮlapser = lambda: default_timer() - start Print( "all done at %.2f seconds" % elapsed() )
Print( "midpoint at %.2f seconds" % elapsed() ) # time so far Once in place, you can do things like: with elapsed_timer() as elapsed: The core library doesn't have this (but probably ought to). With a little trickery, you can even get a running elapsed-time tally inside the block from the same context-manager function. It's fun to do this with a context-manager that automatically remembers the start time upon entry to a with block, then freezes the end time on block exit. On the platform: use perf_counter() or process_time() instead,ĭepending on your requirements, to have a well defined behaviour. The resolution is typicallyĭeprecated since version 3.3: The behaviour of this function depends Win32 function QueryPerformanceCounter(). On Windows, this function returns wall-clock seconds elapsed since theįirst call to this function, as a floating point number, based on the Of the meaning of “processor time”, depends on that of the C function The precision, and in fact the very definition On Unix, return the current processor time as a floating point numberĮxpressed in seconds. Before 3.3 it was recommended to use time.clock (thanks Amber). This gives the execution time in seconds.Īnother option since Python 3.3 might be to use perf_counter or process_time, depending on your requirements. Use time.time() to measure the elapsed wall-clock time between two points: import time
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