matplotlib-users Mailing List for matplotlib

On Wed, Feb 11, 2009 at 7:30 PM, John Hunter <jd...@gm...> wrote:
> On Wed, Feb 11, 2009 at 2:49 PM, Ryan May <rm...@gm...> wrote:
>
> > You're looking for 'xlim_changed' and 'ylim_changed'.
> >
> > I know this can be found here:
> > http://matplotlib.sourceforge.net/api/axes_api.html
> >
> > Should we add this to
> > http://matplotlib.sourceforge.net/users/event_handling.html ?
>
> We should - perhaps you can prepare a small example ( I notice we
> don't have any) illustrating it and add a description with a pointer
> to the example in the event handling tutorial.
>
> JDH
>
Well, I checked in an example that shows the functionality. The problem is
that using these events doesn't follow the standard event API. You don't
connect using figure.canvas.mpl_connect() (it doesn't like the names
'xlim_changed' and 'ylim_changed'), but rather you use
Axes.callbacks.connect(). Also, the an event object is not passed into the
callback, but rather the originating axes instance. Are these events relics
to the older version of event handling that haven't been moved to the
present?
Otherwise, should I add a special section to the event handling docs to
handle these?
Ryan
-- 
Ryan May
Graduate Research Assistant
School of Meteorology
University of Oklahoma
Sent from: Norman Oklahoma United States.

On Wed, Feb 11, 2009 at 8:00 PM, Marjolaine Rouault <mro...@cs...>wrote:
> Hi,
>
> Thanks a lot for your comments. I did try earlier on to remove the bad
> points but came across some problems when re-ordering my array. I will try
> out the method sent to me and check the reference.
Yep, the compacting/reordering method is appropriate for fixed missing
values (typically a grid mask) but not approriate for randomly placed
missing values.
I didn't read these references, but a simple approach you can implement in
python (using for example numpy.linalg.eig applied to you covariance matrix
to compute your eofs, and then your pcs) consist in a interative method to
fill your missing value and let converge your EOF/PC.
0) first save your mask once: mask = data.mask.copy()
1) fill the missing values with the mean data.filled(data.mean())
2) make a reconstruction of your data (EOF[1:10] . PC[1:10]) after your PCA
analysis using a limited number of modes (met's say 10) : datarec
3) replace you original missing data with your reconstructed field: data =
numpy.where(mask, datarec, data)
4) restart from 1) a number of time you can fixe or detect with a criteria
based for example on the eigenvalues
5) the finally, you can use you EOFs et PCs, and has a bonus, you filled
your data!
>
>
> Regards, Marjolaine.
>
>
>
> >>> <kgd...@gm... <kgdunn%2Bn...@gm...>> 02/11/09 4:06 PM
> >>>
> Marjolaine,
>
> I am assuming your masked array entries are missing data. Multivariate
> analysis with missing data can be handled in several standard ways, however
> these methods don't appear in most Python libraries.
>
> Here are some references on the topic that will help you:
>
> [1] P.R.C. Nelson and J.F. MacGregor, 1996, "Missing data methods in PCA
> and PLS: Score calculations with incomplete observations", Chemometrics and
> Intelligent Laboratory Systems, v35, p 45-65.
>
> [2] F. Arteaga and A. Ferrer, 2002, "Dealing with missing data in MSPC:
> several methods, different interpretations, some examples", Journal of
> Chemometrics, v16, p408-418.
>
> Paper [1] deals with building a model with missing data, while paper [2]
> looks at applying an existing PCA model to new data that contains missing
> entries.
>
> Hope these help,
> Kevin
>
> Marjolaine Rouault wrote:
> >
> > Hi,
> >
> > I am struggling to do a PCA analysis on a masked array. Anybody has
> > suggestions on how to deal with masked array when doing PCAs?
> >
> > Best regards, Marjolaine.
> >
>
> Quoted from:
> http://www.nabble.com/matplotlib.mlab-PCA-analysis-tp21932808p21932808.html
>
>
>
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-- 
Stephane Raynaud

On Wed, Feb 11, 2009 at 2:49 PM, Ryan May <rm...@gm...> wrote:
> You're looking for 'xlim_changed' and 'ylim_changed'.
>
> I know this can be found here:
> http://matplotlib.sourceforge.net/api/axes_api.html
>
> Should we add this to
> http://matplotlib.sourceforge.net/users/event_handling.html ?
We should - perhaps you can prepare a small example ( I notice we
don't have any) illustrating it and add a description with a pointer
to the example in the event handling tutorial.
JDH