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 no masked values, go for it. Cheers On Mar 15, 2012 7:56 PM, "Gökhan Sever" <gokhansever@gmail.com> wrote:
Hello,
From the masked_values() documentation -> http://docs.scipy.org/doc/numpy/reference/generated/numpy.ma.masked_values.h...
I10 np.ma.masked_values(x, 1.5) O10 masked_array(data = [ 1. 1.1 2. 1.1 3. ], mask = False, fill_value = 1.5)
I12 np.ma.masked_values(x, 1.5, shrink=False) O12 masked_array(data = [ 1. 1.1 2. 1.1 3. ], mask = False, fill_value = 1.5)
Shouldn't setting the 'shrink' to False return an array of False values for the mask field? If not so, how can I return a set of False values if my masking condition is not met?
Using: I16 np.__version__ O16 '2.0.0.dev-7e202a2'
Thanks.
-- Gökhan
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