Pierre GM wrote: > > Well, yeah, my bad, that depends on whether you use masked_invalid or > fix_invalid or just build a basic masked array.
Yeah, well, if there were any docs I'd have a *clue* what you were talking about ;-) >>>> y=ma.fix_invalid(x) I've never done this ;-) > Having NaNs in an array usually reduces performance: the option we follow w/ > fix_invalid is to clear the masked array of the NaNs, and keeping track of > where they were by setting the mask to True at the appropriate location. That's good to know.... > That > way, you don't have the drop of performance of having NaNs in your underlying > array. > Oh, and NaNs will be transformed to 0 if you use ints... "use ints" in what context? > Nope, the idea is really is to make things as efficient as possible. For you, maybe. And for me, yes, except I wanted the NaNs to stick around... > y=ma.masked_invalid(x) I'm not using masked_invalid. I didn't even know it existed. > Because in your particular case, you're inspecting elements one by one, and > then, your masked data becomes the masked singleton which is a special value. I'd argue that the masked singleton having a different fill value to the ma it comes from is a bug. > And once again, it's not. numpy.ma.masked is a special value, like numpy.nan > or numpy.inf ...which is silly, since that forces it to have a fixed fill value, which it should not. cheers, Chris -- Simplistix - Content Management, Zope & Python Consulting - http://www.simplistix.co.uk _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion