numpy-discussion

[Numpy-discussion] Could numpy.matlib.nanmax return a matrix?

[Keith G. <kwg...@gm...>]

Is anybody interested in making x.max() and nanmax() behave the same
for matrices, except for the NaN part? That is, make
numpy.matlib.nanmax return a matrix instead of an array.

Example
=====

>> import numpy.matlib as M
>> x = M.rand(3,2)

>> x.max(0)
matrix([[ 0.91749823,  0.94942954]])

>> M.nanmax(x, 0)
array([ 0.91749823,  0.94942954])

Re: [Numpy-discussion] Could numpy.matlib.nanmax return a matrix?

[A. M. A. <per...@gm...>]

On 12/11/06, Keith Goodman <kwg...@gm...> wrote:
> Is anybody interested in making x.max() and nanmax() behave the same
> for matrices, except for the NaN part? That is, make
> numpy.matlib.nanmax return a matrix instead of an array.

Sounds good to me; maybe put a patch in the tracker?

A. M. Archibald

Re: [Numpy-discussion] Could numpy.matlib.nanmax return a matrix?

[Pierre GM <pgm...@gm...>]

On Sunday 12 November 2006 17:08, A. M. Archibald wrote:
> On 12/11/06, Keith Goodman <kwg...@gm...> wrote:
> > Is anybody interested in making x.max() and nanmax() behave the same
> > for matrices, except for the NaN part? That is, make
> > numpy.matlib.nanmax return a matrix instead of an array.

Or, you could use masked arrays... In the new implementation, you can add a 
mask to a subclassed array (such as matrix) to get a regular masked array. If 
you fill this masked array, you get an array of the same subclass.

>>> import numpy as N
>>> import numpy.matlib as M
>>> import maskedarray as MA
>>> x=M.rand(3,3)
>>> assert isinstance(x.max(0), M.matrix)
>>> assert isinstance(N.max(x,0), M.matrix)
>>> assert  isinstance(MA.max(x,0).filled(0), M.matrix)
>>> assert isinstance(MA.max(x,0)._data, M.matrix)

>>> x[-1,-1] = N.nan
>>> tmp = MA.max(MA.array(x,mask=N.isnan(x)), 0)
>>> assert (tmp == N.nanmax(x,0)).all()
>>> assert isinstance(tmp.filled(0), M.matrix)

Re: [Numpy-discussion] Could numpy.matlib.nanmax return a matrix?

[Sven S. <sve...@gm...>]

Pierre GM schrieb:
> On Sunday 12 November 2006 17:08, A. M. Archibald wrote:
>> On 12/11/06, Keith Goodman <kwg...@gm...> wrote:
>>> Is anybody interested in making x.max() and nanmax() behave the same
>>> for matrices, except for the NaN part? That is, make
>>> numpy.matlib.nanmax return a matrix instead of an array.
> 
> Or, you could use masked arrays... In the new implementation, you can add a 
> mask to a subclassed array (such as matrix) to get a regular masked array. If 
> you fill this masked array, you get an array of the same subclass.
> 

That is very interesting, but I agree with Keith and would actually call
this a bug. (If still present in 1.0, that is, haven't checked, I think
Keith used some rc?.) One proclaimed goal of numpy for the 1.0 release
has been to be as matrix-friendly as possible, for which I am very
grateful.

Still, the use of masked arrays looks more attractive every time they're
mentioned...

-sven

Re: [Numpy-discussion] Could numpy.matlib.nanmax return a matrix?

[Keith G. <kwg...@gm...>]

On 11/12/06, Pierre GM <pgm...@gm...> wrote:
> On Sunday 12 November 2006 17:08, A. M. Archibald wrote:
> > On 12/11/06, Keith Goodman <kwg...@gm...> wrote:
> > > Is anybody interested in making x.max() and nanmax() behave the same
> > > for matrices, except for the NaN part? That is, make
> > > numpy.matlib.nanmax return a matrix instead of an array.
>
> Or, you could use masked arrays... In the new implementation, you can add a
> mask to a subclassed array (such as matrix) to get a regular masked array. If
> you fill this masked array, you get an array of the same subclass.
>
> >>> import numpy as N
> >>> import numpy.matlib as M
> >>> import maskedarray as MA
> >>> x=M.rand(3,3)
> >>> assert isinstance(x.max(0), M.matrix)
> >>> assert isinstance(N.max(x,0), M.matrix)
> >>> assert  isinstance(MA.max(x,0).filled(0), M.matrix)
> >>> assert isinstance(MA.max(x,0)._data, M.matrix)
>
> >>> x[-1,-1] = N.nan
> >>> tmp = MA.max(MA.array(x,mask=N.isnan(x)), 0)
> >>> assert (tmp == N.nanmax(x,0)).all()
> >>> assert isinstance(tmp.filled(0), M.matrix)

I didn't know you could use masked arrays with matrices. I guess I
took the name literally.

I think an easier way to use masked arrays would be to introduce a new
thing called mis.

I could make a regular matrix

x  = M.rand(3,3)

and assign a missing value

x[0,0] = M.mis

x would then behave as a missing array matrix.

I could also do

x[M.isnan(x)] = M.mis

or

x[mask] = M.mis

To get the mask from x:

x.mask

or

M.ismis(x)

I think that would make missing arrays accessible to everyone.

Re: [Numpy-discussion] Could numpy.matlib.nanmax return a matrix?

[Pierre GM <pgm...@gm...>]

On Wednesday 15 November 2006 12:55, Keith Goodman wrote:
> I didn't know you could use masked arrays with matrices. I guess I
> took the name literally.

:) 
Please check the developer zone:
http://projects.scipy.org/scipy/numpy/wiki/MaskedArray
for an alternative implementation of masked arrays that support subclasses of 
ndarray.

> I think an easier way to use masked arrays would be to introduce a new
> thing called mis.
>
> I could make a regular matrix
>
> x  = M.rand(3,3)
>
> and assign a missing value
>
> x[0,0] = M.mis
>
> x would then behave as a missing array matrix.
....
> I think that would make missing arrays accessible to everyone.

Well, there's already something like that, sort of: MA.masked, or 
MA.masked_singleton. The emphasis here is on "sort of".

That works well if x is already a masked array. Else, a "ValueError: setting 
an array element with a sequence" is raised. I haven't tried to find where 
the problem comes from (ndarray.__setitem__ ? The masked_singleton larger 
than it seems ?), but I wonder whether it's an issue worth solving.

If you want to get a masked_matrix from x, just type x=masked_array(x). You 
won't be able to access some specific matrix attributes (A, T), at least 
directly, but you can fill your masked_matrix and get a matrix back. And 
multiplication of two masked_matrices work as expected ! 

The main advantage of this approach is that we don't overload ndarray or 
matrices, the work is solely on the masked_array side.