On 2013/09/30 4:05 PM, [email protected] wrote: > On Mon, Sep 30, 2013 at 9:38 PM, Charles R Harris > <[email protected]> wrote: >> >> >> >> On Mon, Sep 30, 2013 at 7:05 PM, Ondřej Čertík <[email protected]> >> wrote: >>> >>> Hi, >>> >>> What is the rationale for using False in 'mask' for elements that >>> should be included? >>> >>> http://docs.scipy.org/doc/numpy/reference/maskedarray.generic.html >>> >>> As opposed to using True for elements that should be included, which >>> is what I was intuitively expecting when I started using the masked >>> arrays. This "True convention" also happens to be the one used in >>> Fortran, see e.g.: >>> >>> http://gcc.gnu.org/onlinedocs/gfortran/SUM.html >>> >>> So it's confusing why NumPy would chose a "False convention". Could it >>> be, that NumPy views 'mask' as opacity? Then it would make sense to >>> use True to make a value 'opaque'. >> >> >> There was a lengthy discussion of this point back when the NA work was done. >> You might be able to find the thread with a search. >> >> As to why it is as it is, I suspect it is historical consistency. Pierre >> wrote the masked array package for numpy, but it may very well go back to >> the masked array package implemented for Numeric. > > I don't know ancient history, but I thought it's "natural". (Actually, > I never thought about it.) > > I always thought `mask` indicates the "masked" (invalid, hidden) > values, and masked arrays mask the missing values.
Exactly. It is also consistent with the C and Unix convention of returning 0 on success and 1, or a non-zero error code on failure. In a similar vein, it works nicely with bit-mapped quality control flags, etc. When nothing is flagged, the value is good, and consequently not masked out. Eric > > http://en.wikipedia.org/wiki/Masking_tape > > Josef > >> >> Chuck >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> [email protected] >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
