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" <[email protected]> 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 > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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