Tom Denniston wrote: >Code that reproduces the problem: > >This is regular python: >pickle.dumps(numpy.nan, 2) >SystemError: frexp() result out of range > >This is fine in numpy: >pickle.dumps(numpy.array([numpy.nan]), 2) > >This breaks: >pickle.dumps(numpy.array([numpy.nan], numpy.PyObject), 2) >SystemError: frexp() result out of range > > It seems to me that the example that shows what you want is the only example that numpy has control over, anyway. In the other two cases, you're really using a Python object.
So it's really a Python question as to why it can't pickle nans. My understanding on this is weak, but here's one summary, albeit dated: http://www.python.org/dev/summary/2000-10-1.html Here's something more recent: http://mail.python.org/pipermail/python-list/2005-July/290272.html NumPy sidesteps these issues by "forcing" IEEE-754 behavior with clever C written AFAIK by the numarray people. These apparently work even if the C compiler doesn't "officially" support IEEE-754, but it's not going to work on every platform that Python targets. You may also want to take a look at http://scipy.org/FAQ#head-fff4d6fce7528974185715153cfbc1a191dcb915 ------------------------------------------------------------------------- Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT & business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
