## Differing behaviour of numpy’s log1p function across platforms

September 5th, 2015

The test suite of a project I’m working on is poking around at the extreme edges of the range of double precision numbers. I noticed a difference between Windows and other platforms that I can’t yet fully explain. On Windows, the test suite was pumping out RuntimeWarnings that we don’t see in Linux or Mac. I’ve distilled the issue down to a single numpy command:

np.log1p(1.7976931348622732e+308)

On Windows 7 Anaconda Python 2.3, this gives a RuntimeWarning and returns inf whereas on Linux and Mac OS X it evaluates to 709.78-ish

Numpy version is 1.9.2 in all cases.

64 bit Windows 7

Python 2.7.10 |Continuum Analytics, Inc.| (default, May 28 2015, 16:44:52) [MSC
v.1500 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://binstar.org
>>> import numpy as np
>>> np.log1p(1.7976931348622732e+308)
__main__:1: RuntimeWarning: overflow encountered in log1p
inf


64 bit Linux

Python 2.7.9 (default, Apr  2 2015, 15:33:21)
[GCC 4.9.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.log1p(1.7976931348622732e+308)
709.78271289338397


Mac OS X

Python 2.7.10 |Anaconda 2.3.0 (x86_64)| (default, May 28 2015, 17:04:42)
[GCC 4.2.1 (Apple Inc. build 5577)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://binstar.org
>>> import numpy as np
>>> np.log1p(1.7976931348622732e+308)
709.78271289338397


The argument to log1p is getting close to the largest double precision number:

>>> sys.float_info.max
1.7976931348623157e+308