On Tue, May 10, 2022 at 3:15 AM Chris Angelico <ros...@gmail.com> wrote:
> > It is often the case that developer write Code in Python and then > convert to a C extension module for performance regions. > > > > A C extension module has a lot of boiler plate code - for instance the > Structures required for each class, the functions for Module initialization > etc. > > > > My Idea is a simple tool that uses introspection tools to take a Python > module and to generate the relevant boiler plate for the module - including > blank functions for the module classes and for methods. This tool would use > type annotations (if given) to make sensible choices for parameter and > attribute types, including using int and float directly rather than > Internal objects (depending on tool options). > Two things to say about this: 1) Sometimes abandoning a pure python module for a C extension for performance is a mistake - because Pypy is probably going to be much faster with the pure python module 2) I've had some luck using m4 to maintain a single source file that is used to automatically generate both pure python and cython. This is a little like using cpp in a C project. For examples of #2, perhaps see: https://stromberg.dnsalias.org/~strombrg/treap/ https://stromberg.dnsalias.org/svn/rolling_checksum_mod/trunk/ https://stromberg.dnsalias.org/~strombrg/sort-comparison/ It's often nice to keep the lines of the pure-python and cython having a 1-1 relationship, so that tracebacks report useful line numbers either way. However, in the treap example I've dispensed with that because some methods were almost identical but had some boilerplate - and m4 was able to handle that nicely at the cost of lines being 1-1. HTH -- https://mail.python.org/mailman/listinfo/python-list
