[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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