Pierre GM wrote: > 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,
That's pretty unkind to your userbase. I know a lot about python, but I'm a total novice with numpy and even the maths it's based on. > 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 Blind leading the blind seems like a terrible idea to me... > 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. Right. "Array types" and whatever a dtype is are things that could be much better documented too :-( > 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] NaN/inf is still NaN in my books, so why would I be surprised by this? >> 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 One which sucks and is unintuitive. > 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 I may not know much about maths, but I know about these funny things in python we have called "classes" to solve exactly this problem ;-) >>> x[-1] = Masked(fill_value=50) >>> isinstance(x[-1],Masked) True ...which gives you what you want without forcing me to experience the resultant suck. 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