On 2007-05-09, Robert Kern <[EMAIL PROTECTED]> wrote:
> Grant Edwards wrote:
>> I'm pretty sure the answer is "no", but before I give up on the
>> idea, I thought I'd ask...
>>
>> Is there any way to do single-precision floating point
>> calculations in Python?
>>
>> I know the various array modules generally support arrays of
>> single-precision floats. I suppose I could turn all my
>> variables into single-element arrays, but that would be way
>> ugly...
>
> We also have scalar types of varying precisions in numpy:
>
> In [9]: from numpy import *
>
> In [10]: float32(1.0) + float32(1e-8) == float32(1.0)
> Out[10]: True
Very interesting. Converting a few key variables and
intermediate values to float32 and then back to CPython floats
each time through the loop would probably be more than
sufficient.
So far as I know, I haven't run into any cases where the
differences between 64-bit prototype calculations in Python and
32-bit production calculations in C have been significant. I
certainly try to design the algorithms so that it won't make
any difference, but it's a nagging worry...
> In [11]: 1.0 + 1e-8 == 1.0
> Out[11]: False
>
> If you can afford to be slow,
Yes, I can afford to be slow.
I'm not sure I can afford the decrease in readability.
> I believe there is an ASPN Python Cookbook recipe for
> simulating floating point arithmetic of any precision.
Thanks, I'll go take a look.
--
Grant Edwards grante Yow! It's the RINSE
at CYCLE!! They've ALL IGNORED
visi.com the RINSE CYCLE!!
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