On June 11, 2013 at 00:40:31, Pierre GM ([email protected]) wrote: On June 10, 2013 at 23:07:24 , Eric Firing ([email protected]) wrote:
On 2013/06/10 10:17 AM, Aldcroft, Thomas wrote:
> I use np.ma <http://np.ma>, and for me the most intuitive would be the
> second option where the new array matches the original array in shape
> and dtype, but always has an empty mask. I always think of the *_like()
> functions as just copying shape and dtype, so it would be a bit
> surprising to get part of the data (the mask) from the original. If you
> do need the mask then on the next line you have an explicit statement to
> copy the mask and the code and intent will be clear. Also, most of the
> time the mask is set because that particular data value was bad or
> missing, so it seems like it would be a less-common use case to want a
> new empty array with the same mask.
>
I also use np.ma (and it is used internally in matplotlib). I agree
with Tom. I think all of the *_like() functions should start with
mask=False, meaning nothing is masked by default. I don't see what the
reasonable use cases would be for any alternative.
Thinking about it, a replacement to `_convert2ma` should work something like `func(input.view(np.ndarray)).view(np.ma.MaskedArray)`
The `input.view(np.ndarray)` will get the `.data` part of a MaskedArray and work seamlessly with a regular ndarray, the `.view(MaskedArray)` will attach a `np.ma.nomask` to the result of the previous view (therefore making the output a MaskedArray). Depending on your decision, we could add a flag to copy the initial mask (if any) |
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