Alexander, The rationale behind the current behavior is to avoid an accidental propagation of the mask. Consider the following example:

m = numpy.array([1,0,0,1,0], dtype=bool_) x = numpy.array([1,2,3,4,5]) y = numpy.sqrt([5,4,3,2,1]) mx = masked_array(x,mask=m) my = masked_array(y,mask=m) mx[0] = 0 print mx,my, m

[0 2 3 -- 5] [-- 4 3 -- 1] [ True False False True False]

At the creation, mx._sharedmask and my._sharedmask are both True. Setting mx[0]=0 forces mx._mask to be copied, so that we don't affect the mask of my.

Now,

m = numpy.array([1,0,0,1,0], dtype=bool_) x = numpy.array([1,2,3,4,5]) y = numpy.sqrt([5,4,3,2,1]) mx = masked_array(x,mask=m) my = masked_array(y,mask=m) mx._sharedmask = False mx[0] = 0 print mx,my, m

[0 2 3 -- 5] [5 4 3 -- 1] [False False False True False]

By mx._sharedmask=False, we deceived numpy.ma into thinking that it's OK to update the mask of mx (that is, m), and my gets updated. Sometimes it's what you want (your case for example), often it is not: I've been bitten more than once before reintroducing the _sharedmask flag.

As you've observed, setting a private flag isn't a very good idea: you should use the .unshare_mask() function instead, that copies the mask and set the _sharedmask to False. OK, in your example, copying the mask is not needed, but in more general cases, it is.

At the initialization, self._sharedmask is set to (not copy). That is, if you didn't specify copy=True at the creation (the default being copy=False), self._sharedmask is True. Now, I recognize it's not obvious, and perhaps we could introduce yet another parameter to masked_array/array/MaskedArray, share_mask, that would take a default value of True and set self._sharedmask=(not copy)&share_mask

So: should we introduce this extra parameter ?

In any case, I hope it helps. P.