[Numpy-discussion] bug with with fill_values in masked arrays?

Chris Withers chris@simplistix.co...
Wed Mar 26 14:42:41 CDT 2008

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)

...which gives you what you want without forcing me to experience the 
resultant suck.



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