This is a Python library for easily comparing function runtimes. It's well-tested on 2.6, 2.7, 3.3, 3.4, PyPy's Python 2 and 3 implementations.
Install with
pip install rumbleand see the examples directory here on GitHub for basic use.
You want to compare runtimes for different implementations of a function -- let's call it primes. It outputs a list of all primes up to and including its argument. The way you used to do this:
- Create a file called
functime.py. Paste in the different implementations of the function into that file -- let's say one uses a naive implementation that checks all numbers below its argument, and the other uses the Sieve of Eratosthenes. - Run a bunch of
timeitcommands:python -m timeit -s 'import functime' 'check_all(50)', and thenpython -m timeit -s 'import functime' 'sieve(50)', and thenpython -m timeit -s 'import functime' 'check_all(100)', and thenpython -m timeit -s 'import functime' 'sieve(100)'
- Look back through your shell history to see what happened.
Isn't that hard? There's a lot of repetition, a lot of unnecessary name duplication, and some reasoning about local imports.
Now, you can do this:
from rumble import rumble
rumble.arguments(50)
rumble.arguments(100)
@rumble.contender
def check_all(n):
...
@rumble.contender
def sieve(n):
...
rumble.run()and it prints out a table showing the runtimes of each of the contenders. I think it's much more readable than your shell history:
args: 100 usec loops best of
----------- ------ ------- ---------
check_all 242.35 1000 3
sieve 138.30 10000 3
args: 500 usec loops best of
----------- ------- ------- ---------
check_all 3539.53 100 3
sieve 2019.23 100 3
See the examples directory for examples of use. At a high level:
- Optionally, create a new
Rumbleobject. If you'd rather, you can simply call methods from the module itself as well. - Decorate some functions as
contendersin theRumble. - Add some arguments with the
argumentsmethod. This method accepts arbitrary numbers of arguments, including keyword arguments.- You can also specify code to be executed before the timing runs with the
_setupkeyword argument. - (For now, each argument
amust conform to the conditiona == exec(repr(a)). So, for instance,[1, None]works, butlambda: Nonedoes not.)
- You can also specify code to be executed before the timing runs with the
- Call the
runmethod to time the functions and print your tables!- You can also call
run(as_string=True)to return the output as a string
- You can also call
For now, the documentation consists of the examples, tests, method definitions, precious few docstrings, and this file. This will improve as part of getting this out of pre-alpha stage.
This is something I made for my personal use because I got annoyed with dealing with interactions between the shell and Python for answering questions like this. I'm also using it as a place to practice versioning, package maintenance, design, and testing.
If you use it, I want your feedback. I want to know problems you have that you think this could solve but doesn't yet, I want proposed solutions, I want pull requests, I want issues. Even the name is changeable at this point! And of course, you are welcome to fork and alter for your own purposes as allowed by the license. I'd love to see what you do with it if you want to share!
If you want to contribute, please consider contributing some tests alongside your changes. You'll notice in tests that each module has its own suite of unit tests. You'll notice in .travis.yml that each of those suites is run independently, and if its respective module does not have 100% coverage, the suite fails. Obviously, you don't have to make that happen for a casual contribution, but recognize that that's my endgame -- I'll write the tests myself if they aren't there. If you can make that easy, that'd be much appreciated! But, if you contribute at all: also much appreciated!
This code is licensed under the MIT License. Code in adaptiverun.py is derived from the Python 3.4 standard library.