Hi, On Sat, Oct 29, 2011 at 3:55 PM, Matthew Brett <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, Matthew

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

Hi, On Sun, Oct 30, 2011 at 2:38 AM, Berthold Höllmann <berthold@xn--hllmanns-n4a.de> wrote:

Matthew Brett <matthew.brett@gmail.com> writes:

...

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?

Just use float128 all the was through, and avoid casting to float in between:

.>>> "%20.1f"%float(2**64+2) '18446744073709551616.0' .>>> np.float128(np.float128(2)**64+2) 18446744073709551618.0

Ah yes - sorry - that would work in this example where I know the component parts of the number, but I was thinking in the general case where I have been given any int. I think my code works for that, by casting to float64 to break up the number into parts: In [35]: def int_to_float128(val): ....: f64 = np.float64(val) ....: res = val - int(f64) ....: return np.float128(f64) + np.float128(res) ....: In [36]: int_to_float128(2**64) Out[36]: 18446744073709551616.0 In [37]: int_to_float128(2**64+2) Out[37]: 18446744073709551618.0 Thanks, Matthew