Vincent, > I think the > idea that when a.mask returns False, that actually means nomask instead > of the False I'm used to, is what caused a major part of my confusion.
Indeed. > It might actually be nice to give you some context of why I asked this: > during my (satellite image) processing, I use maskedarrays by default > for each step in the processing chain Ah. You'll probably notice it might be faster to process the array and its mask in parallel, and then recreate a masked array at the end. If you need to save a mask that hasn't been set yet, you could use the `ma.getmaskarray` command instead of trying to access the mask as an attribute, that is, use: >>> mask=ma.getmaskarray(a) instead of >>> mask=a.mask or >>> mask=ma.getmask(a) `ma.getmaskarray(a)` always return an array with the same shape as `a`, even if full of False. If you need to create a new mask from the shape and dtype of `a`, you can use `ma.make_mask_none(a)`. Both functions are documented. > force expanding a mask, or e.g. an ma.mask.as_full_shaped_mask() method > that returns either the mask, or (if nomask) a new array of Falses. I > just supposed it existed and I could not find it, but now I understand > it does not exist. Actually, it does, that's the ma.getmaskarray() I was just telling you about. > Just for completeness (in case someone else is reading this and > wondering how to *unmask* values): just setting ma[idx] to some valid > number will unset the mask for that index. No need to do ma[idx] = > ma.unmask or whatever, just ma[idx] = v. Exactly. Except that for the sake of completeness, there's a little known attribute (_hardmask) that prevents you to unmask some data by mistake. By default, `_hardmask` is False, which means that if you set a masked value to a non-masked one, you update the mask as well. If you set a._hardmask to True or use the `harden_mask` method, you won't be able to unmask the data that way, you'll have to modify the mask directly. It's useful when the mask cannot or should not be changed. > Btw, in future versions, would it be an idea to separate 'nomask' and > 'False' a little more? I always assumed (correctly?) that in python, > True and False are singletons (as far as that is possible in python), > just like None. 'False is False' should always compare to True, then. In > this case (a.mask is False) it at least *seems* to break that 'rule'... You mean, returning something that says 'nomask' instead of 'False' ? I'm not sure how I can do that. Sure, it'd be nice. > > No. I'm afraid you're confusing `nomask` and `False`. Once again, nomask > > is NOT the same thing as False. It's the same value, but not the same > > object. > > Exactly. It might not be inconsistent, but imho it does a lot of effort > to /feel/ inconsistent to Joe Average. It's not intentional. I guess most of your problems would never have come if we had a proper documentation. As you're starting to use MaskedArrays, you could take note and that would provide us with a basis we could improve on... _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
