Russell E. Owen wrote: > 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).
The standard MemoryError exception. > - 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 > emulation)? Not anywhere particular, but these might not be available/useful on all platforms: float96, float128, float256, complex182, complex256, complex512, int64, uint64, int128, uint128, longlong, ulonglong, longfloat, clongfloat. > - 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 > hints? If you wanted an array scalar of the "default" float dtype (whatever that happened to be), you would have to use float_. Of course the "default" float dtype is always (and will always be, AFAICT) float64, so really, you might as well use that. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
