[Numpy-discussion] Question on numpy.ma.masked_values

[Gökhan Sever]

Hello,

From the masked_values() documentation -> http://docs.scipy.org/doc/numpy/reference/generated/numpy.ma.masked_values.html

I10 np.ma.masked_values(x, 1.5)

O10 

masked_array(data = [ 1.   1.1  2.   1.1  3. ],

             mask = False,

       fill_value = 1.5)

I12 np.ma.masked_values(x, 1.5, shrink=False)

O12 

masked_array(data = [ 1.   1.1  2.   1.1  3. ],

             mask = False,

       fill_value = 1.5)

Shouldn't setting the 'shrink' to False return an array of False values for the mask field? 

If not so, how can I return a set of False values if my masking condition is not met?

Using:

I16 np.__version__

O16 '2.0.0.dev-7e202a2'

Thanks.

--
Gökhan

[Gökhan Sever]

On Thu, Mar 15, 2012 at 12:56 PM, Gökhan Sever <gokha...@gmail.com> wrote:

If not so, how can I return a set of False values if my masking condition is not met?

Self-answer: I can force the mask to be filled with False's, however unsure if this is a safe operation.

I50 x = np.array([1, 1.1, 2, 1.1, 3])

I51 y = np.ma.masked_values(x, 1.5, shrink=0)

I52 y

O52 

masked_array(data = [1.0 1.1 2.0 1.1 3.0],

             mask = False,

       fill_value = 1.5)

I53 y.mask = np.zeros(len(x), dtype=np.bool)*True

I54 y

O54 

masked_array(data = [1.0 1.1 2.0 1.1 3.0],

             mask = [False False False False False],

       fill_value = 1.5)

[Pierre GM]

Ciao Gökhan,
AFAIR, shrink is used only to force a collapse of a mask full of False, not to force the creation of such a mask.
Now, it should work as you expected, meaning that it needs to be fixed. Could you open a ticket? And put me in copy, just in case.
Anyhow:
Your trick is a tad dangerous, as it erases the previous mask. I'd prefer to create x w/ a full mask, then use masked_values w/ shrink=False... Now, if you're sure there's no masked values, go for it.
Cheers

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[Gökhan Sever]

On Thu, Mar 15, 2012 at 1:12 PM, Pierre GM <pgmde...@gmail.com> wrote:

Ciao Gökhan,
AFAIR, shrink is used only to force a collapse of a mask full of False, not to force the creation of such a mask.
Now, it should work as you expected, meaning that it needs to be fixed. Could you open a ticket? And put me in copy, just in case.
Anyhow:

Your trick is a tad dangerous, as it erases the previous mask. I'd prefer to create x w/ a full mask, then use masked_values w/ shrink=False... Now, if you're sure there's x= no masked values, go for it.
Cheers

This condition checking should make it stronger:

I7 x = np.array([1, 1.1, 2, 1.1, 3])

I8 y = np.ma.masked_values(x, 1.5)

I9 if y.mask == False:

    y.mask = np.zeros(len(x), dtype=np.bool)*True

   ...:     

I10 y.mask

O10 array([False, False, False, False, False], dtype=bool)

I11 y

O11 

masked_array(data = [1.0 1.1 2.0 1.1 3.0],

             mask = [False False False False False],

       fill_value = 1.5)

How do you create "x w/ a full mask"?

--
Gökhan

[Gökhan Sever]

Submitted the ticket at http://projects.scipy.org/numpy/ticket/2082

--
Gökhan

[Pierre GM]

Gökhan,
By default, the mask of a MaskedArray is set to the special value
`np.ma.nomask`. In other terms::
np.ma.array(...) <=> np.ma.array(..., mask=np.ma.nomask)

In practice, np.ma.nomask lets us quickly check whether a MaskedArray
has a masked value : if its .mask is np.ma.nomask, then no masked
value, otherwise it's a full boolean array and we can use any.

If you want to create a MaskedArray w/ a full boolean mask, just use::
np.ma.array(..., mask=False)
In that case, the mask is automatically created as a boolean array
with the same shape as the data, with False everywhere. If you used
True, the mask would be full of True...

Now, just to be clear, you'd want
'np.ma.masked_values(...,shrink=False) to create a maked array w/ a
full boolean mask by default, right ?

[Gökhan Sever]

Yes, that's the behaviour that I expect setting the 'shrink' keyword to 'False'

> Now, just to be clear, you'd want
> 'np.ma.masked_values(...,shrink=False) to create a maked array w/ a
> full boolean mask by default, right ?