On Jun 30, 2011, at 5:38 PM, Matthew Brett wrote:
Hi,
On Thu, Jun 30, 2011 at 2:58 PM, Pierre GM <pgmdevlist@gmail.com> wrote:
On Jun 30, 2011, at 3:31 PM, Matthew Brett wrote:
############################################### A alternative-NEP on masking and missing values ###############################################
I like the idea of two different special values, np.NA for missing values, np.IGNORE for masked values. np.NA values in an array define what was implemented in numpy.ma as a 'hard mask' (where you can't unmask data), while np.IGNOREs correspond to the .mask in numpy.ma. Looks fairly non ambiguous that way.
************** Initialization **************
First, missing values can be set and be displayed as ``np.NA, NA``::
np.array([1.0, 2.0, np.NA, 7.0], dtype='NA[f8]') array([1., 2., NA, 7.], dtype='NA[<f8]')
As the initialization is not ambiguous, this can be written without the NA dtype::
np.array([1.0, 2.0, np.NA, 7.0]) array([1., 2., NA, 7.], dtype='NA[<f8]')
Masked values can be set and be displayed as ``np.MASKED, MASKED``::
np.array([1.0, 2.0, np.MASKED, 7.0], masked=True) array([1., 2., MASKED, 7.], masked=True)
As the initialization is not ambiguous, this can be written without ``masked=True``::
np.array([1.0, 2.0, np.MASKED, 7.0]) array([1., 2., MASKED, 7.], masked=True)
I'm not happy with this 'masked' parameter, at all. What's the point? Either you have np.NAs and/or np.IGNOREs or you don't. I'm probably missing something here.
If I put np.MASKED (I agree I prefer np.IGNORE) in the init, then obviously I mean it should be masked, so the 'masked=True' here is completely redundant, yes, I agree. And in fact:
np.array([1.0, 2.0, np.MASKED, 7.0], masked=False)
should raise an error. On the other hand, if I make a normal array:
arr = np.array([1.0, 2.0, 7.0])
and then do this:
arr.visible[2] = False
then either I should raise an error (it's not a masked array), or, more magically, construct a mask on the fly. This somewhat breaks expectations though, because you might just have made a largish mask array without having any clue that that had happened.
Well, I'd expect an error to be raised when assigning a NA if the initial array is not NA friendly. The 'magical' creation of a mask would be nice, but is probably too magic and best left alone.
Direct assignnent in the masked case is magic and confusing, and so happens only via the mask::
masked_array = np.array([1.0, 2.0, 7.0], masked=True) masked_arr[2] = np.NA TypeError('dtype does not support NA') masked_arr[2] = np.MASKED TypeError('float() argument must be a string or a number') masked_arr.visible[2] = False masked_arr array([1., 2., MASKED], masked=True)
What about the reverse case ? When you assign a regular value to a np.NA/np.IGNORE item ?
Well, for the np.NA case, this is straightforward:
na_arr[2] = 3
It's just assignment. For ``masked_array[2] = 3`` - I don't know, I guess whatever we are used to. What do you think?
Ahah, that depends. With a = np.array([1., np.NA, 3.]), then a[1]=2. should raise an error, as Mark suggests: you can't "unmask" a missing value, you need to create a view of the initial array then "unmask". It's the equivalent of a hard mask. With a = np.array([1., np.IGNORE, 3.]), then a[1]=2. should give np.array([1.,2.,3.]) and a.mask=[False,False,False]. That's a soft mask. At least, that's how I see it... P.