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 ?
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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 m
Submitted the ticket at http://projects.scipy.org/numpy/ticket/2082
On Thu, Mar 15, 2012 at 1:24 PM, Gökhan Sever wrote:
>
>
> On Thu, Mar 15, 2012 at 1:12 PM, Pierre GM wrote:
>
>> Ciao Gökhan,
>> AFAIR, shrink is used only to force a collapse of a mask full of False,
>> not to force the cre
On Thu, Mar 15, 2012 at 1:12 PM, Pierre GM 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
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
On Thu, Mar 15, 2012 at 12:56 PM, Gökhan Sever 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 =
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.
