On Thu, Mar 15, 2012 at 1:12 PM, Pierre GM <[email protected]> wrote:
> Ciao Gökhan,
> AFAIR, shrink is used only to force a collapse of a mask full of False,
> not to force the creation of such a mask.
> Now, it should work as you expected, meaning that it needs to be fixed.
> Could you open a ticket? And put me in copy, just in case.
> Anyhow:
> Your trick is a tad dangerous, as it erases the previous mask. I'd prefer
> to create x w/ a full mask, then use masked_values w/ shrink=False... Now,
> if you're sure there's x= no masked values, go for it.
> Cheers
>
This condition checking should make it stronger:
I7 x = np.array([1, 1.1, 2, 1.1, 3])
I8 y = np.ma.masked_values(x, 1.5)
I9 if y.mask == False:
y.mask = np.zeros(len(x), dtype=np.bool)*True
...:
I10 y.mask
O10 array([False, False, False, False, False], dtype=bool)
I11 y
O11
masked_array(data = [1.0 1.1 2.0 1.1 3.0],
mask = [False False False False False],
fill_value = 1.5)
How do you create "x w/ a full mask"?
--
Gökhan
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