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@scipy.org [numpy-discussion-bounces@scipy.org] On Behalf Of Charles R Harris [charlesr.harris@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@gmail.com<mailto:matthew.brett@gmail.com>> wrote: Hi, On Sat, Oct 29, 2011 at 3:55 PM, Matthew Brett <matthew.brett@gmail.com<mailto:matthew.brett@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/e18e94c5b0f54775c46b1c690491b8... 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