[Numpy-discussion] NumPy re-factoring project

Sturla Molden sturla@molden...
Thu Jun 10 17:25:17 CDT 2010

Den 10.06.2010 22:07, skrev Travis Oliphant:
>> 2. The core should be a plain DLL, loadable with ctypes. (I know David Cournapeau and Robert Kern is going to hate this.) But if Python can have a custom loader for .pyd files, so can NumPy for it's core DLL. For ctypes we just need to specify a fully qualified path to the DLL, which can be read from a config file or whatever.
> This approach does not build a new Python type in compiled code.   There are speed disadvantages to this --- especially for the numpy scalars.

There are at least four speed penalties in what I suggested:

- the ctypes overhead is bigger than using Python's C API manually (or 
- there is a speed penalty for scalars and small arrays. (Previously 
seen in numarray vs. numeric.)
- bytearray is a bit slower to create than to malloc a buffer.
- arrays with dtype=object

The advantages are:

- the code is easier to read and maintain (clean separation of Python and C)
- more portable code (no Python C API dependence)
- no obscure memory leaks to track down (bytearray instead of 
malloc/free and no manual ref counting).

By the way: Is Cython mature enough to toss all C out the door? Since 
Cython has syntax for PEP 3118 buffer objects, we should theoretically 
be able to implement NumPy in Cython. Then we don't have the speed 
penalty and no difficult C to maintain. But if the idea is to make a 
Python independent C library from the core, it is probably a bit counter 


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