So I finally bit the bullet and converted most of my code from Numeric
and numarray to numpy. (I haven't yet tried to convert one package that
makes heavy use of nd_image and has C extensions).
But it left me with a few questions:
- What exception does numpy throw if it runs out of memory? (I can try
harder to make it do that, but trying to chew up all memory tends to
slow the machine down and my first tests weren't successful) -- the
equivalent to numarray.memory.error. The numpy book is silent on the
issue of what exceptions numpy can throw (at least the index was).
- Is there a list of the data types that we can expect to be available
on all regular platforms (including 32-bit linux, MacOS X and Windows)
and of usual speed for computations (instead of some kind of slow
- Even after reading the book I'm not really clear on why one would use
numpy.float_ instead of numpy.float or float for day-to-day programming
(where the size doesn't matter enough to use float64 or whatever). Any
Overall it went fine. I'm not entirely sure I caught everything yet, but
the new code seems to be working.