On Tuesday 25 March 2008 10:33:58 Chris Withers wrote: > 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 ;-)
My bad, I neglected an overall doc for the functions and their docstring. But you know what ? As you're now at an intermediary level, you'll be able to help: just write down the problems you encountered, and the solutions you came up with, so that we could use your experience as the backbone for a proper MaskedArray documentation > > Oh, and NaNs will be transformed to 0 if you use ints... > > "use ints" in what context? Try that: >>>x = numpy.ma.array([0,1,2,3,]) >>>x[-1] = numpy.nan >>>print x >>>[0 1 2 0] See? No NaNs with an int array. > > 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... Well, no problem, they should stick around. Note that if a NaN/Inf should normally show up as the result of some operation (divide by zero for example), it'll probably won't: >>>x = numpy.ma.array([0,1,2,numpy.nan],dtype=float) >>>print 1./x >>>[-- 1.0 0.5 nan] >>>print (1./x)._data >>>[ 1. 1. 0.5 NaN] >>>print 1./x._data >>>[ Inf 1. 0.5 NaN] > I'd argue that the masked singleton having a different fill value to the > ma it comes from is a bug. "It's not a bug, it's a feature"TM > > 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. The fill_value for the mask singleton is meaningless, correct. However, having numpy.ma.masked as a constant is really helpful to test whether a particular value is masked, or to mask a particular value: >>>x = numpy.ma.array([0,1,2,3]) >>>x[-1] = masked >>>x[-1] is masked >>>True _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion