I'm having trouble with MaskedArray's _sharedmask flag. I would like to create a sub-view of a MaskedArray, fill it, and have the modifications reflected in the original array. This works with regular ndarrays, but only works with MaskedArrays if _sharedmask is set to False. Here's an example:
>>> a = numpy.ma.MaskedArray( ... data=numpy.zeros((4,5), dtype=float), ... mask=numpy.ones((4,5), dtype=numpy.ma.MaskType), ... fill_value=0.0 ... ) >>> sub_a = a[:2,:3] >>> sub_a[0,0] = 1.0 >>> print sub_a [[1.0 -- --] [-- -- --]] >>> print a [[-- -- -- -- --] [-- -- -- -- --] [-- -- -- -- --] [-- -- -- -- --]] >>> print a.data [[ 1. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0.]] The data array receives the new value, but the mask array does not. >>> a._sharedmask = False >>> sub_a = a[:2,:3] >>> sub_a[0,0] = 1.0 >>> print sub_a [[1.0 -- --] [-- -- --]] >>> print a [[1.0 -- -- -- --] [-- -- -- -- --] [-- -- -- -- --] [-- -- -- -- --]] This sort of (for me) unexpected behavior extends to other ways I've been using numpy arrays as well: a[:] = 1.0 (set to constant); a[:] = b (copy into); a[:5] = a[-5:] (rotating copy), etc. I wasn't seeing this behavior before because I was working on an array that had already been sliced and therefore "unshared", which caused a good deal of confusion for me when I started working on an array that wasn't the product of slicing. All of this leads me to some questions. What is the rational for initializing a new MaskedArray with _sharedmask=True when its mask isn't (actively) being shared yet? Is there a better way to say: "a=MaskedArray(...); a._sharedmask=False" that does not require touching a "private" attribute? Or am I going about this all wrong? What's the correct MaskedArray idioms for these actions that doesn't cause a new mask to be created? Thanks! Alex _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
