2009/12/21 David Goldsmith <d.l.goldsmith@gmail.com>:
On Mon, Dec 21, 2009 at 9:57 AM, Christopher Barker <Chris.Barker@noaa.gov> wrote:
Dag Sverre Seljebotn wrote:
I recently got motivated to get better linear algebra for Python;
wonderful!
To me that seems like the ideal way to split up code -- let NumPy/SciPy deal with the array-oriented world and Sage the closer-to-mathematics notation.
well, maybe -- but there is a lot of call for pure-computational linear algebra. I do hope you'll consider building the computational portion of it in a way that might be included in numpy or scipy by itself in the future.
My personal opinion is that the LA status quo is acceptably good: there's maybe a bit of an adjustment to make for newbies, but I don't see it as a very big one, and this list strikes me as very efficient at getting people over little bumps (e.g., someone emails in: "how do you matrix-multiply two arrays?" within minutes (:-)) Robert or Charles replies with "np.dot: np.dot([[1,2],[3,4]],[[1,2],[3,4]]) = array([[7,10],[15,22]])"). Certainly any significant changes to the base should need to run the gauntlet of an NEP process.
I think we have one major lacuna: vectorized linear algebra. If I have to solve a whole whack of four-dimensional linear systems, right now I need to either write a python loop and use linear algebra on them one by one, or implement my own linear algebra. It's a frustrating lacuna, because all the machinery is there: generalized ufuncs and LAPACK wrappers. Somebody just needs to glue them together. I've even tried making a start on it, but numpy's ufunc machinery and generic type system is just too much of a pain for me to make any progress as is. I think if someone wanted to start building a low-level generalized ufunc library interface to LAPACK, that would be a big improvement in numpy/scipy's linear algebra. Pretty much everything else strikes me as a question of notation. (Not to trivialize it: good notation makes a tremendous difference.) Anne