[Numpy-discussion] Large numbers into float128

[Nadav Horesh]

A quick and dirty cython code is attached

Use:

>> import Float128
>> a = Float128.Float128('1E500')

array([ 1e+500], dtype=float128)

or

>> b = np.float128(1.34) * np.float128(10)**2500
>> b
1.3400000000000000779e+2500


Maybe there is also a way to do it in a pure python code via ctypes?

   Nadav
________________________________
From: numpy-discussion-bounces at scipy.org [numpy-discussion-bounces at scipy.org] On Behalf Of Charles R Harris [charlesr.harris at gmail.com]
Sent: 30 October 2011 05:02
To: Discussion of Numerical Python
Subject: Re: [Numpy-discussion] Large numbers into float128



On Sat, Oct 29, 2011 at 8:49 PM, Matthew Brett <matthew.brett at gmail.com<mailto:matthew.brett at gmail.com>> wrote:
Hi,

On Sat, Oct 29, 2011 at 3:55 PM, Matthew Brett <matthew.brett at gmail.com<mailto:matthew.brett at gmail.com>> wrote:
> Hi,
>
> Can anyone think of a good way to set a float128 value to an
> arbitrarily large number?
>
> As in
>
> v = int_to_float128(some_value)
>
> ?
>
> I'm trying things like
>
> v = np.float128(2**64+2)
>
> but, because (in other threads) the float128 seems to be going through
> float64 on assignment, this loses precision, so although 2**64+2 is
> representable in float128, in fact I get:
>
> In [35]: np.float128(2**64+2)
> Out[35]: 18446744073709551616.0
>
> In [36]: 2**64+2
> Out[36]: 18446744073709551618L
>
> So - can anyone think of another way to assign values to float128 that
> will keep the precision?

To answer my own question - I found an unpleasant way of doing this.

Basically it is this:

def int_to_float128(val):
   f64 = np.float64(val)
   res = val - int(f64)
   return np.float128(f64) + np.float128(res)

Used in various places here:

https://github.com/matthew-brett/nibabel/blob/e18e94c5b0f54775c46b1c690491b8bd6f07eb49/nibabel/floating.py

Best,


It might be useful to look into mpmath. I didn't see any way to export mp values into long double, but they do offer a number of resources for working with arbitrary precision. We could maybe even borrow some of their stuff for parsing values from strings

Chuck
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