It seems that object arrays will not pickle if they have a nan when the pickle protocol is set to binary. From what I can tell the object array simply delegates to python (which makes sense) which in turn cannot pickle nans in binary format. It is unfortunate because it is very useful to have heterogenous arrays that include nans. What do other people do in this situation? Does anyone know why python has this limitation? Is there an intelligent workaround other than search and replace? Would it be worth it to put an intelligent workaround into numpy so it is transparent to the user? I was wondering what people thought.
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 --Tom ------------------------------------------------------------------------- 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
