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.html

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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