[Numpy-discussion] Questions about converting to numpy

Robert Kern robert.kern@gmail....
Wed Apr 25 15:11:29 CDT 2007

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

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