Hi Folks, I'm pretty new to Numpy, and I hope someone can help me out with a couple newbie questions. First, I'd like, for diagnostic purposes, to print arrays of floating point numbers in a format of my choosing. Specifically, I have a formatting function that generates strings which represent all the bits of an FP number exactly, but it formats the exponent, exponent sign, and sign separately from the mantissa so I can get a rough idea of the magnitude at a glance. We use this to verify that certain operations are yielding exactly the same FP values either across platforms or across algorithmic changes. I'd like to be able to print numpy arrays in this format. At this point I've thought of three ways to try this. First, there's using set_string_function() and then writing my own version of array2string() that uses my function for FP arrays. OK, but I'd kind of like to avoid the work of duplicating the nice way that array2string() formats arrays of different ranks. Enticingly, I see code in core/arrayprint.py that's looking for an _format attribute and using it if it exists. So the second idea is to hook into this somehow, but I don't see how (indeed, I'm not sure when an array ever has an _format attribute?). Finally, I imagine I could make my own dtype so that array2string will format with %s, then convert FP arrays into arrays of that dtype for printing. Any ideas which of these would be better, or other ideas, would be most appreciated. Second, I wanted to find out how numpy developers and experts feel about cross-platform differences in FP behavior. Is this considered unavoidable and expected, a bug, or somewhere in between? Until recently I was about to get exact matches everywhere, but I now have at least one example of a difference. I should clarify that I'm not talking about processor-family dependency; all my machines are x86 at this point. Indeed, the differences appear across virtual machines running different OSs on the same physical machine. Thanks in advance, Ken Basye kbasye1@jhu.edu