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

Chris Withers chris@simplistix.co...
Tue Mar 25 09:33:58 CDT 2008

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.



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