4 ms·

Actually, a lot of people do write them this way. When you take something you can write quickly and with no dependencies other than python, and instead pull in

by jnoller 16y ago

Actually, a lot of people do write them this way. When you take something you can write quickly and with no dependencies other than python, and instead pull in swig or cython - you now have two problems instead of one. As the author shows, writing a python c extension is fundamentally easy which is why Python has such a huge ecosystem of them to begin with.

cdavid 16y ago
Writing C extensions is only easy if you do trivial things. As soon as you do something significant, writing all this C code by hand becomes increasingly complex, especially because of reference counting and error management (http://docs.python.org/release/2.5.2/ext/thinIce.html http://docs.python.org/release/2.5.2/ext/thinIce.html). If dependency for distribution is an issue, you can just include the generated C code (as we do in numpy and scipy, as a matter of fact). Also, cython can generate both python 2 and 3 compatible code.
- mturmon 16y ago
  It depends on what kind of "trivial". I have a lot (> 30000 SLOC; hundreds of functions) of C code that does intricate math/stats/optimization, but from a control flow and memory allocation point of view is simple. This kind of code is perfect for the simple c-extension API of Python as described in the OP. In fact, if you set up the gateway interface to your C code cleanly, you can have the same C code callable directly from (e.g.) Matlab via the "mex" mechanism.
  - cdavid 16y ago
    Yep, in that case, that's relatively easy - but cython is even better. Cython is used a lot in the scipy community - if you don't know it, you may want to look at it. Since I am using cython, I am trying to avoid writing raw C API as much as possible, there is just no point.
    - slug 16y ago
      I wrote a few things in cython to give it a try and it's amazing what static typing can do to improve the efficiency of the code. Just don't look at the auto-generated .c file though, it looks scary, but this seems to be a common occurrence (swig comes to mind).

by jnoller 16y ago

cdavid 16y ago

Writing C extensions is only easy if you do trivial things. As soon as you do something significant, writing all this C code by hand becomes increasingly complex, especially because of reference counting and error management (http://docs.python.org/release/2.5.2/ext/thinIce.html http://docs.python.org/release/2.5.2/ext/thinIce.html). If dependency for distribution is an issue, you can just include the generated C code (as we do in numpy and scipy, as a matter of fact). Also, cython can generate both python 2 and 3 compatible code.

mturmon 16y ago
It depends on what kind of "trivial". I have a lot (> 30000 SLOC; hundreds of functions) of C code that does intricate math/stats/optimization, but from a control flow and memory allocation point of view is simple. This kind of code is perfect for the simple c-extension API of Python as described in the OP. In fact, if you set up the gateway interface to your C code cleanly, you can have the same C code callable directly from (e.g.) Matlab via the "mex" mechanism.
- cdavid 16y ago
  Yep, in that case, that's relatively easy - but cython is even better. Cython is used a lot in the scipy community - if you don't know it, you may want to look at it. Since I am using cython, I am trying to avoid writing raw C API as much as possible, there is just no point.
  - slug 16y ago
    I wrote a few things in cython to give it a try and it's amazing what static typing can do to improve the efficiency of the code. Just don't look at the auto-generated .c file though, it looks scary, but this seems to be a common occurrence (swig comes to mind).