matplotlib-users Mailing List for matplotlib

Best to hold off for a little while. The patch was committed to CVS, 
but turned out to have a major problem, so John is working on it.
Eric
Fernando Perez wrote:
> Chris Fonnesbeck wrote:
> 
>> I am trying to compile matplotlib with scipy_core (latest release),
>> rather than Numeric or nunmarray. However, I get the following error
>> when trying to build:
>>
>> Traceback (most recent call last):
>> File "setup.py", line 110, in ?
>> raise RuntimeError("You must install Numeric, numarray, or both to
>> build matplotlib")
>> RuntimeError: You must install Numeric, numarray, or both to build 
>> matplotlib
>>
>> Is there no way to base matplotlib on scipy_core?
> 
> 
> Chris, are you using Daishi's patches? I'm not quite sure if they're in 
> CVS yet, but it's certain that you can NOT build mpl 0.85 + scipy_core 
> without them.
> 
> Cheers,
> 
> f
> 
> 
> -------------------------------------------------------
> This SF.net email is sponsored by: Splunk Inc. Do you grep through log 
> files
> for problems? Stop! Download the new AJAX search engine that makes
> searching your log files as easy as surfing the web. DOWNLOAD SPLUNK!
> http://ads.osdn.com/?ad_id=7637&alloc_id=16865&op=click
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

Chris Fonnesbeck wrote:
> I am trying to compile matplotlib with scipy_core (latest release),
> rather than Numeric or nunmarray. However, I get the following error
> when trying to build:
> 
> Traceback (most recent call last):
> File "setup.py", line 110, in ?
> raise RuntimeError("You must install Numeric, numarray, or both to
> build matplotlib")
> RuntimeError: You must install Numeric, numarray, or both to build matplotlib
> 
> Is there no way to base matplotlib on scipy_core?
Chris, are you using Daishi's patches? I'm not quite sure if they're in CVS 
yet, but it's certain that you can NOT build mpl 0.85 + scipy_core without them.
Cheers,
f

I am trying to compile matplotlib with scipy_core (latest release),
rather than Numeric or nunmarray. However, I get the following error
when trying to build:
Traceback (most recent call last):
 File "setup.py", line 110, in ?
 raise RuntimeError("You must install Numeric, numarray, or both to
build matplotlib")
RuntimeError: You must install Numeric, numarray, or both to build matplotl=
ib
Is there no way to base matplotlib on scipy_core?
Thanks,
--
Chris Fonnesbeck
Atlanta, GA

Hi Ken,
That's nice, and coordinated zooming of separate maps is certainly a
good feature,
but that's not what I had in mind. Sorry I was imprecise and probably hurr=
ied.
The case is to help visualize L maps of 'element' values that share
the same pixel grid (NxM). You make a LxL (in your case 3x3) grid and
in each diagonal grid i =3D 1,...L, you draw the map for the ith
element. On the off-diagonal cells you draw a 2d plot of pixel
intensities for a pair of element: value in element j v. value in
element i for all NxM pixels. If elemetns i and j are highly
correlated, this plot is a straight line, if they are uncorrelated
it's a blob. There may be multiple trends that show (several points
with high value for i and lower value for j, say). I think each of
the correlation plots is what mpl calls a scatterplot with parameter
's' a scalar.
The correlation plot shows trends between element at the expense of
spatial information. The maps of individual element intensities shows
spatial information at the expense of showing trends between elements.
 But if you could highlight or mask out selected pixels on a map or
regions on a correlation plot (with a rubberband box) and have those
those pixels light up or change color on other the maps and 2-element
correlation plots, then you can better understand the spatial and
correlation data. Does that make sense?
I say I believe this would be better handled by GUI code than directly
in mpl because someone has to keep track of the pixels for the
correlation plot data, then change some set of plot attribute (color,
symbol, etc) for those pixels on _all_ the correlation plots and
false-color maps. That is, the series of plots has to be managed
and altered as a unit in a way that is depends on the data, not by the
plot characteristics alone. And presumably the GUI would allow you
to change the color used to 'highlight' the selected points. I'm sure
it _could_ be done other ways, and perhaps I'm missing something.=20
Anyway, what's wrong with having multiple wx.Panel each of which has a
Figure.Axes on it?
That's one example.... What about linking together plots in different
frames or having draggable panels so that a user can orgranize them on
the desktop as they choose?
This doesn't have a lot to do with the 'should I use MPlot or wxMPL?'
question, and all I'm saying is that as plots get more complicated and
have more complex user interaction, some interactions will most
naturally be handled at the GUI level.
Sorry to stir up flames and run.....
--Matt
PS: Chris Barker asked:
> Matt Newville wrote:
> > the users of
> > the library should probably not have to know anything about
> > matplotlib, because they're going to be asking "I'm writing this wx
> > Application and want to include a graph -- what should I use?".
>
>Huh? to put a graph in your app, you'd need to know the API to create
>and manipulate that graph, That API should be MPL's API, why write and
>document another one?
Well MPlot and wxMPL both did just that (create other APIs). wxMPL is
very close (derived from) MPL but it's not identical. I do think the
MPL API is a little complicated if all you want to do is make a 2D
line plot. With MPlot, it's:
 plotter =3D MPlot.PlotPanel(...)
 plotter.plot(x2,y2)
 plotter.oplot(x2,y2)
and MPlot somehow neglects to make the user turn on or select how
zooming works, turn on or place the legend (that's a
user-configuration) or reveal that a PlotPanel has a FigureCanvas has
a Figure which has an Axes which plot()s. Yes, it is less general
than MPL, but it's simpler to use and includes functionality that MPL
does not have. It's simply a different thing: it's not MPL, but it
does use MPL to achieve its ends.
So a wxPython plotting widget based on MPL doesn't need to expose the
MPL API. If you want the MPL API, it already exists. I used this
for MPlot (nothing else existed) -- it works fine. One of the
questions here is whether it might be worth it to use the additional
layer of wxMPL in MPlot. I could be convinced, and would not mind if
someone else did this, but I'm not sure how big the gain would be.

>>>>> "Christian" == Christian Kristukat <ck...@ho...> writes:
 Christian> Hi, interpolation seems not to be supported for pcolor
 Christian> plots. Is that true? I want to plot nonaequidistant
 Christian> gridded data, so imshow is not the right choice. Using
 Christian> contourf with a large number of contour levels works
 Christian> fine but the eps output is huge. I'd prefer to have the
 Christian> image embedded as bitmap in an eps, that's why I'd like
 Christian> to use pcolor. Regards, Christian
Nicholas Young contributed a patch which supports a NonUniformImage
Make sure you have the most recent CVS, eg
Checking in lib/matplotlib/image.py;
/cvsroot/matplotlib/matplotlib/lib/matplotlib/image.py,v <--
image.py
new revision: 1.25; previous revision: 1.24
done
or later
Below is an example.
from pylab import figure, show
import matplotlib.numerix as nx
from matplotlib.image import NonUniformImage
x = nx.arange(-4, 4, 0.005)
y = nx.arange(-4, 4, 0.005)
print 'Size %d points' % (len(x) * len(y))
z = nx.sqrt(x[nx.NewAxis,:]**2 + y[:,nx.NewAxis]**2)
fig = figure()
ax = fig.add_subplot(111)
im = NonUniformImage(ax, extent=(-4,4,-4,4))
im.set_data(x, y, z)
ax.images.append(im)
ax.set_xlim(-4,4)
ax.set_ylim(-4,4)
fig2 = figure()
ax = fig2.add_subplot(111)
x2 = x**3
im = NonUniformImage(ax, extent=(-64,64,-4,4))
im.set_data(x2, y, z)
ax.images.append(im)
ax.set_xlim(-64,64)
ax.set_ylim(-4,4)
show()

On 12/3/05, Willi Richert <w.r...@gm...> wrote:
> Hi,
>
> I tried that but got:
> python plot.py
> file h433x560.gz loaded and given shape (433, 560)
> data ok
so you got the data loaded, try commenting out one of the below lines
near the end until you see something, you probably hit a bug in one of
the qt routines
...
hsv()
contourf(x, y, h, levels)
colorbar(clabels=3Dlevels[::8])
axis([0, im, 0, jm] )
axis('scaled')
pldj(seg)
...
I use the gtk backend btw.
Helge

Hi,
I tried that but got:
python plot.py
file h433x560.gz loaded and given shape (433, 560)
data ok
Traceback (most recent call last):
 File=20
"/usr/lib/python2.3/site-packages/matplotlib/backends/backend_qtagg.py", li=
ne=20
75, in paintEvent
 FigureCanvasAgg.draw( self )
 File "/usr/lib/python2.3/site-packages/matplotlib/backends/backend_agg.py=
",=20
line 383, in draw
 self.figure.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/figure.py", line 520, i=
n=20
draw
 for a in self.axes: a.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/axes.py", line 1441, in=
=20
draw
 self.yaxis.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/axis.py", line 562, in=
=20
draw
 tick.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/axis.py", line 162, in=
=20
draw
 if self.label2On: self.label2.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/text.py", line 854, in=
=20
draw
 self._mytext.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/text.py", line 339, in=
=20
draw
 bbox, info =3D self._get_layout(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/text.py", line 264, in=
=20
_get_layout
 xys =3D [self._transform.inverse_xy_tup( xy ) for xy in zip(tx, ty)]
RuntimeError: Transformation is not invertible
Traceback (most recent call last):
 File=20
"/usr/lib/python2.3/site-packages/matplotlib/backends/backend_qtagg.py", li=
ne=20
75, in paintEvent
 FigureCanvasAgg.draw( self )
 File "/usr/lib/python2.3/site-packages/matplotlib/backends/backend_agg.py=
",=20
line 383, in draw
 self.figure.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/figure.py", line 520, i=
n=20
draw
 for a in self.axes: a.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/axes.py", line 1441, in=
=20
draw
 self.yaxis.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/axis.py", line 562, in=
=20
draw
 tick.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/axis.py", line 162, in=
=20
draw
 if self.label2On: self.label2.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/text.py", line 854, in=
=20
draw
 self._mytext.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/text.py", line 339, in=
=20
draw
 bbox, info =3D self._get_layout(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/text.py", line 264, in=
=20
_get_layout
 xys =3D [self._transform.inverse_xy_tup( xy ) for xy in zip(tx, ty)]
RuntimeError: Transformation is not invertible
Traceback (most recent call last):
 File=20
"/usr/lib/python2.3/site-packages/matplotlib/backends/backend_qtagg.py", li=
ne=20
75, in paintEvent
 FigureCanvasAgg.draw( self )
 File "/usr/lib/python2.3/site-packages/matplotlib/backends/backend_agg.py=
",=20
line 383, in draw
 self.figure.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/figure.py", line 520, i=
n=20
draw
 for a in self.axes: a.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/axes.py", line 1441, in=
=20
draw
 self.yaxis.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/axis.py", line 562, in=
=20
draw
 tick.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/axis.py", line 162, in=
=20
draw
 if self.label2On: self.label2.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/text.py", line 854, in=
=20
draw
 self._mytext.draw(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/text.py", line 339, in=
=20
draw
 bbox, info =3D self._get_layout(renderer)
 File "/usr/lib/python2.3/site-packages/matplotlib/text.py", line 264, in=
=20
_get_layout
 xys =3D [self._transform.inverse_xy_tup( xy ) for xy in zip(tx, ty)]
RuntimeError: Transformation is not invertible
QWidget (QWidget figure): deleted while being painted
QPaintDevice: Cannot destroy paint device that is being painted
Any hint how to solve this? My installation:
=46C3
matplotlib-0.85
PyQt-3.15-0.1.fc3.kde
PyQt-qscintilla-3.15-0.1.fc3.kde
python-2.3.4-13.1
scipy_version: '0.3.2'
Thanks,
wr
Am Freitag, 2. Dezember 2005 20:48 schrieb Helge Avlesen:
> On 12/1/05, Charlie Moad <cw...@gm...> wrote:
> > It would help if you were more specific. Are you referring to
> > animation or static images? I can generate a million point scatter
> > plot in under a minute, and I would consider this pretty good for a
> > general purpose plotting package.
>
> Hi matplotlib'ers!
> while the end result in matplotlib is starting to "get there", the speed =
is
> not yet where it has to be to be useful IMO. I hereby post a little
> challenge for the matplotlib developers;
>
> get the actual plotting time (from the first plot command until show is
> done) on the below dataset down to less than a second,(including the quiv=
er
> command in the plot.py file).
>
> download the 3 files here (ca 600kb in total)
>
> http://www.ii.uib.no/~avle/slow1/
>
> execfile('plot.py') to plot the dataset (works for me with cvs as of
> today). the first part of the file is just some convenience funcs for
> loading the data. the plotting happen near the end.
>
> this is a moderately sized grid, 433x560 cells (those of you into
> oceanography may recognize the area). what I observe on my pentium
> 2.54ghz linux box:
>
> -after "data ok" on screen, it takes ca15s to render. pygist uses < 0.5s
> -zoom operations take ca 5s. pygist is "instant".
> -you don't want to wait for an additional quiver layer. it must take
> minutes to finish. pygist is instant. with quiver, also zooming is
> equally slow, the ui freeze for ages.
> -often one wants to add contours from other fields on top of this, in
> pygist this adds no
> visible delay, matplotlib easily doubles the rendering time.
>
> typical usage for me is to load many such datasets, and do a lot of
> zoom in/out/pan to various features, modify colors/levels, add
> velocity vectors etc. currently this is quite
> painful in matplotlib, as one zoom operation on realistic datasets
> easily takes 10 seconds on a multi ghz machine. pygist is very fast,
> even on a 400mhz laptop, memory usage is also a lot lower.
>
> so, I think matplotlib is a great effort, and shows a lot of promise, but
> for realistic use, it is (at least for me) still far too slow!
>
> Helge
>
>
> -------------------------------------------------------
> This SF.net email is sponsored by: Splunk Inc. Do you grep through log
> files for problems? Stop! Download the new AJAX search engine that makes
> searching your log files as easy as surfing the web. DOWNLOAD SPLUNK!
> http://ads.osdn.com/?ad_idv37&alloc_id=16865&op=CCk
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
=2D-=20
Gruss,
wr
=2D-
Dipl.-Inform. Willi Richert
C-LAB - Cooperative Computing & Communication Laboratory
 der Universit=E4t Paderborn und Siemens
=46U.323
=46=FCrstenallee 11
D-33102 Paderborn
Tel: +49 5251 60 6120
=わ46ax: +49 5251 60 6165
http://www.c-lab.de

Chris,
Christopher Barker wrote:
> Ken McIvor wrote:
> 
>> I haven't really given it much thought. Most people presumably use 
>> MPL from IPython or scripts via pylab, so focusing documentation 
>> efforts on it make sense.
> 
> 
> Not really. Most people use pylab, because that's what's well 
> documented, not the other way around. Also, a lot of us have matlab 
> experience, so pylab is easier that way.
Don't understimate the importance of this group of users, present and 
potential. For a lot of people doing a lot of things, the Matlab/pylab 
approach works very well. Sometimes OO programming is a good way to go, 
but it is not the best approach in all cases. One of the strengths of 
python itself is that it works fine with or without OO style; mpl brings 
that strength to 2D graphics.
Eric

On 12/02/05 15:20, Matt Newville wrote:
<snip>
> At each cell along the diagonals of the grid is a NxM false color map for
> an element's intensity, the off-diagonal cells have the correlation plots
> for each i,j pair of elements.
How are you planning on implementing the correlation plots?
<snip>
> I humbly submit that this would be best handled at the GUI level.
I disagree, and am willing to put my money where my mouth is! :-)
Ken
P.S. Watch out: to unzoom, you have to right-click on the same axes you 
zoomed in on. This is a bug.

Matt Newville wrote:
> the users of
> the library should probably not have to know anything about
> matplotlib, because they're going to be asking "I'm writing this wx
> Application and want to include a graph -- what should I use?".
Huh? to put a graph in your app, you'd need to know the API to create 
and manipulate that graph, That API should be MPL's API, why write and 
document another one?
-Chris
-- 
Christopher Barker, Ph.D.
Oceanographer
 		
NOAA/OR&R/HAZMAT (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
Chr...@no...