Prototyping with Python code before compiling
<p>I have been mulling over writing a peak fitting library for a while. I know Python fairly well and plan on implementing everything in Python to begin with but envisage that I may have to re-implement some core routines in a compiled language eventually.</p>

<p>IIRC, one of Python's original remits was as a prototyping language, however Python is pretty liberal in allowing functions, functors, objects to be passed to functions and methods, whereas I suspect the same is not true of say C or Fortran.</p>

<p>What should I know about designing functions/classes which I envisage will have to interface into the compiled language? And how much of these potential problems are dealt with by libraries such as cTypes, bgen, <a href="http://www.swig.org/">SWIG</a>, <a href="http://www.boost.org/doc/libs/1_35_0/libs/python/doc/index.html">Boost.Python</a>, <a href="http://cython.org/">Cython</a> or <a href="http://www.riverbankcomputing.co.uk/software/sip/intro">Python SIP</a>?</p>

<p>For this particular use case, (a fitting library) I imagine allowing users to define mathematical functions (Guassian, Lorentzian etc.) as Python functions which can then to be passed an interpreted by the compiled code fitting library. Passing and returning arrays is also essential.</p>

<p>Finally a question that I can really put a value answer to :). </p>

<p>I have investigated f2py, boost.python, swig, cython and pyrex for my work (PhD in optical measurement techniques). I used swig extensively, boost.python some and pyrex and cython a lot. I also used ctypes. This is my breakdown:</p>

<p><strong>Disclaimer</strong>: This is my personal experience. I am not involved with any of these projects. </p>

<p><strong>swig:</strong>
does not play well with c++. It should, but name mangling problems in the linking step was a major headache for me on linux &amp; Mac OS X. If you have C code and want it interfaced to python, it is a good solution. I wrapped the GTS for my needs and needed to write basically a C shared library which I could connect to. I would not recommend it.</p>

<p><strong>Ctypes:</strong>
I wrote a libdc1394 (IEEE Camera library) wrapper using ctypes and it was a very straigtforward experience. You can find the code on <a href="https://launchpad.net/pydc1394">https://launchpad.net/pydc1394</a>. It is a lot of work to convert headers to python code, but then everything works reliably. This is a good way if you want to interface an external library. Ctypes is also in the stdlib of python, so everyone can use your code right away. This is also a good way to play around with a new lib in python quickly. I can recommend it to interface to external libs. </p>

<p><strong>Boost.Python</strong>: Very enjoyable. If you already have C++ code of your own that you want to use in python, go for this. It is very easy to translate c++ class structures into python class structures this way. I recommend it if you have c++ code that you need in python. </p>

<p><strong>Pyrex/Cython:</strong> Use Cython, not Pyrex. Period. Cython is more advanced and more enjoyable to use. Nowadays, I do everything with cython that i used to do with SWIG or Ctypes. It is also the best way if you have python code that runs too slow. The process is absolutely fantastic: you convert your python modules into cython modules, build them and keep profiling and optimizing like it still was python (no change of tools needed). You can then apply as much (or as little) C code mixed with your python code. This is by far faster then having to rewrite whole parts of your application in C; you only rewrite the inner loop. </p>

<p><strong>Timings</strong>: ctypes has the highest call overhead (~700ns), followed by boost.python (322ns), then directly by swig (290ns). Cython has the lowest call overhead (124ns) and the best feedback where it spends time on (cProfile support!). The numbers are from my box calling a trivial function that returns an integer from an interactive shell; module import overhead is therefore not timed, only function call overhead is. It is therefore easiest and most productive to get python code fast by profiling and using cython.</p>

<p><strong>Summary</strong>: For your problem, use Cython ;). I hope this rundown will be useful for some people. I'll gladly answer any remaining question.</p>

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<p><strong>Edit</strong>: I forget to mention: for numerical purposes (that is, connection to NumPy) use Cython; they have support for it (because they basically develop cython for this purpose). So this should be another +1 for your decision. </p>
