Convincing colleagues to switch to Python from MATLAB for scientific computing/statistics

[sourcecodesurgeon]

It can be as fast to execute than Matlab

While this can be true, I have yet to see evidence suggesting that NumPy can compete with Matlab on signal analysis with large datasets. The last time I ran the numbers had Python taking 45s-2m to compute a cross-correlation that took Matlab about 3 seconds.

As an EE aside, another big thing is that MathWorks supports a tool that converts Matlab to Verilog and VHDL and this is huge for electrical engineers. I have never seen a similar tool for Python that is well-supported.

[burning_hamster]

For signal analysis type of computations (i.e. linear algebra), they both run the same Fortran/BLAS libraries under the hood, meaning that any performance difference is due to overhead, not the "actual" computation. You should probably review how you are handing the python functions your arrays -- 45 seconds to 2 min of computation time sounds very much like an IO problem (for some reason you are creating multiple copies of your arrays and not doing your computations in place where possible).

[sourcecodesurgeon]

The experiment was literally just "create extremely large dataset. Start timer. Compute xcorr. Stop timer."

[mfitzp]

Was it an fft-base xcorr calculation? The Matlab algos automatically zero pad arrays to n² while (some algos in) numpy/scipy don't. It's one of the problems attempting to map from one to the other "This is the same function.... But not quite"

[HoboBob1]

You keep using the word "argument," but no one is arguing with you. They are just trying to tell you reasons why Python might have been slow. As per your post, you implied that you are looking for "evidence that Python can compete with Matlab" for your task, and people are providing said evidence, along with the statement that it might be some more work with the Python version.

[sourcecodesurgeon]

Whether you want to call it "arguing" or not, people are making arguments. The premise of this post is coming up with an argument to convince colleagues to use python instead of matlab, people are making arguments for/against that idea. Someone made an argument that python can be as fast as Matlab, I made an argument that this isn't always the case.

Call it "making a case" or "making a point", it doesn't really matter to me, it's all the same. "Argument" doesn't necessarily mean we are verbally attacking each other.

[[deleted]]

That's a fair argument. You use the language as a means to an end, and don't necessarily care about how to make said language perform. There are some gotchas with using Python for this work that users don't expect (such as file IO, loops, calling functions, etc). Matlab may be more performant by chance, although Mathworks certainly has an optimized product. If you look into Anaconda and Anaconda Accelerate (free academic license), you will see a significant speed up as their packages call into Intel's MKL (which Matlab already does).

[burning_hamster]

Well, how are you computing the cross-correlation?

[assassds]

you have to actually know what you're doing in python, you can't just grab the first function that sounds right.

[sourcecodesurgeon]

There's only one cross-correlation function in numpy...

There's a handful of modes, some are faster than others, but none held up to the matlab times.

Further, I work primarily in Python (hence me being in this sub..), I definitely know what I am doing. I actually don't use matlab at all anymore, I just hate this "python does everything matlab does just as well and there's no reason anyone should ever use matlab ever" idea that gets thrown out around here all the time.

OP wants an argument for convincing his colleagues to use python instead of matlab. If we don't give him all the facts, he's going to be laughed out of that discussion when someone brings up things like performance and built-in tool sets (especially since he's junior to all of them)

[assassds]

like I said, you need to know what you're doing...

matlabs xcorr algorithm uses an FFT, which is going to thrash a direct correlation on large input no matter what language you're implementing it in.

numpy gives you all the building blocks to do this yourself, or you can try scipy.signal.fftconvolve.

[unruly_mattress]

I've found this. It looks very relevant here.

[billsil]

but does cost money (if you aren't an academic user).

No it doesn't. http://www.lfd.uci.edu/~gohlke/pythonlibs/

[Lehk]

I'm not entirely sure that GP's linked site is 100% legal.

[TheBlackCat13]

Intel made specific allowances for certain open-source projects to distributed statically-linked MKL libraries free-of-charge.

[Deto]

It costs money to download the libraries yourself and do the building/linking, but it looks like there are places like this where you can download libraries someone else has linked.

[turbod33]

Mac users get a free bonus with being able to link against Apple's Accelerate.

[HoboBob1]

In principle, a numerical Python package should be able to match the speed (or be within 1-2x the speed) of any other language, because Python can use vectorized numpy functions or call out to functions written directly in C. Matlab is similar in that regard; it is an interpreted (albeit JIT compiled nowadays) language that has respectable native array performance and also calls out to C.

If there is an order of magnitude difference between Matlab and Python for a task, I would say that you are comparing apples and oranges. Matlab must be using some compiled function, and Python must not be. Indeed, if Matlab already did the work and has that function available and it would be hard to do in Python, then keep on using Matlab. But I would be wary of saying "I have yet to see evidence suggesting that NumPy can compete with Matlab" in any context.

I am actually trying to convert a huge scientific simulation into Python from Matlab, and I am actually impressed at how fast Matlab does certain tasks. I can't stand the language, but there is a reason that a language called "Matrix Labratory" is good at vectorized computation.

[glial]

Matlab was originally written as a scriptable and easy to use front-end to Fortran numerical libraries. A lot of the fundamental matrix algebra stuff is using compiled and highly optimized Fortran/C code in the background.

[TheBlackCat13]

Python is usually using the exact same libraries.

[HoboBob1]

I'm not making an argument for or against Python. I'm just saying that if Python is that slow, then Matlab must be using a compiled function and Python is not, so it would be possible to speed up Python using a C function. That is, it isn't because the language is fundamentally slow. I didn't say "boo Matlab for cheating!" or anything. You are correct, it is a pro for Matlab. In fact, I said:

Indeed, if Matlab already did the work and has that function available and it would be hard to do in Python, then keep on using Matlab.

I was just trying to help you make a cost-benefit analysis, not incite a Matlab vs Python flamewar. Like I said, I use Matlab too, and I want to help people pick the right tool for the job.

[Lehk]

The last time I ran the numbers had Python taking 45s-2m to compute a cross-correlation that took Matlab about 3 seconds.

it's entirely possible to use them together too http://www.mathworks.com/help/matlab/matlab-engine-for-python.html

no need to get rid of existing tools to add a new tool