Alexander,
> create the MaskedArray to:
> >>> a = numpy.ma.MaskedArray(
>
> ... data=numpy.zeros((4,5), dtype=float),
> ... mask=True,
> ... fill_value=0.0
> ... )
By far the easiest indeed.
> > So: should we introduce this extra parameter ?
>
> The propagation semantics and mechan
On Tue, Feb 26, 2008 at 2:32 PM, Pierre GM <[EMAIL PROTECTED]> wrote:
> Alexander,
> The rationale behind the current behavior is to avoid an accidental
> propagation of the mask. Consider the following example:
>
> >>>m = numpy.array([1,0,0,1,0], dtype=bool_)
> >>>x = numpy.array([1,2,3,4,5])
Alexander,
The rationale behind the current behavior is to avoid an accidental
propagation of the mask. Consider the following example:
>>>m = numpy.array([1,0,0,1,0], dtype=bool_)
>>>x = numpy.array([1,2,3,4,5])
>>>y = numpy.sqrt([5,4,3,2,1])
>>>mx = masked_array(x,mask=m)
>>>my = masked_array(y
I'm having trouble with MaskedArray's _sharedmask flag. I would like to
create a sub-view of a MaskedArray, fill it, and have the modifications
reflected in the original array. This works with regular ndarrays, but
only works with MaskedArrays if _sharedmask is set to False. Here's an example:
>>>
