matplotlib-users Mailing List for matplotlib

In article <4CF...@hm...>,
 Kaushik Ghose <Kau...@hm...> 
 wrote:
> Hi Guys,
> 
> I did a search on the mailing list but could not come up with a solution, so 
> I 
> am crying "Uncle" and writing.
> 
> I installed matplotlib 1.0.0 from the package on SF 
> (http://sourceforge.net/projects/matplotlib/files/matplotlib/matplotlib-1.0/ma
> tplotlib-1.0.0-python.org-py2.6-macosx10.4.dmg/download)
> 
> I assumed that 10.4 would work on my 10.5 - perhaps that is where I went 
> wrong?
> 
> I installed it and got, satisfactorily:
> 
> import matplotlib
> matplotlib.__version__
> ---> '1.0.0'
> 
> But when I do
> 
> import pylab
> 
> I get a bus error that throws me out of ipython.
It should work on Mac OS X 10.4.
A newer version of this binary is available here, which I recommend 
instead:
<http://www.astro.washington.edu/users/rowen/python/>
A few things to try:
- Make sure you have Python 2.6 from python.org
- Make sure you have a good numpy build, preferably of 1.5 or 1.5.1
- matplotlib 1.0.0 will crash if it has a problem with the font cache 
(though I don't think that is what caused your crash); to fix that 
delete the font cache in the ~/.matplotlib directory. That problem 
should be fixed in 1.0.1.
-- Russell

I explored the memory leak in my strip chart widget some more and found 
that it is caused by calling canvas.draw(), where canvas is:
figure = matplotlib.figure.Figure(figsize=(8, 2), frameon=True)
canvas = FigureCanvasTkAgg(figure, self)
canvas.show() exhibits exactly the same problem.
So...what is the right way to redraw a plot after its X axes have been 
changed?
-- Russell

Hi,
Has anyone ever managed to draw a taylor diagram in Matplotlib? For example
like this
http://www.mathworks.com/matlabcentral/fx_files/20559/2/taylordiag_fig.jpg
Cheers,
Martin
-- 
View this message in context: http://old.nabble.com/Taylor-diagram-tp30421393p30421393.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

2010年12月9日 C M <cmp...@gm...>:
>
>> I have created a runnable sample app that demonstrates the problem
>
> Here is a much simpler 10 line sample that doesn't require wxPython and
> demonstrates the problem: you can't pick the red line. This seems like a
> bug in mpl 1.0.
Confirmed using tkagg, mpl 1.0.0 and Ubuntu 10.10. I do not know much
about the semantics of the pick event but it I don't think it should
discriminate between lines in the same canvas si I guess it's a bug.
Goyo

> I have created a runnable sample app that demonstrates the problem
Here is a much simpler 10 line sample that doesn't require wxPython and
demonstrates the problem: you can't pick the red line. This seems like a
bug in mpl 1.0.
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twinx()
line, = ax.plot([1,2,3], 'r-o', markersize=15, picker=5)
line2, = ax2.plot([4,2,12], 'b-o', markersize=15, picker=5)
def onpick(event):
 print 'picked line: ', event.artist
fig.canvas.mpl_connect('pick_event', onpick)
plt.show()

On Sun, Dec 5, 2010 at 6:15 PM, Daniel Hyams <dh...@gm...> wrote:
> Hi all, ran into an issue using matplotib on OSX, Cocoa toolkit, that I
> haven't run into anywhere else. Basically, if a dialog is started from an
> event generated from the canvas, the application freezes.
>
> I went through the code in backend_wx.py, but didn't see anything there
> that could cause the problem. The small code that reproduces the problem is
> attached.
>
> Not sure if this is a wxPython problem or matplotlib problem, but figured
> I'd throw it out there and see if it also freezes on other people's
> platforms.
>
Well ... here's what I see:
A figure window opens.
If I right-click on the figure, a dialog appears with a default title in it.
I change the text, click OK,
The dialog closes, but nothing else happens.
But I can move the figure window around, and I can right-click again and
repeat the process indefinitely.
Some vital statitstics:
OS X 10.6.5
Python 2.6.6 |EPD 6.3-1 (32-bit)| (r266:84292, Sep 23 2010, 11:52:53)
[GCC 4.0.1 (Apple Inc. build 5488)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import matplotlib
m>>> matplotlib.__version__
'1.0.0'
>>> import wx
>>> wx.__version__
'2.8.10.1'
>>>
hth a little,
gary
>
> --
> Daniel Hyams
> dh...@gm...
>
>
> ------------------------------------------------------------------------------
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>
>

Hi all,
I am having a problem with manipulating colorbars. I want to take the
cm.Blues colorbar and edit it so that the lowest end of the colorbar is
light blue instead of white, or in other words I want to remove the lightest
1/4 of the colorbar and just keep the darker end.
Is there any easy way to do this?
Any help would be greatly appreciated!
Thanks
Ian Harry
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Hi
I have a text file containing experimental data obtained over the course of two years. The data were obtained on a daily basis. Having inspected some of the data in Excel (there are over 15 000 points), I noticed that there are a couple of points that should not be included in the final data analysis. I want to write a python programme that does the following:
1. Creates a plot of the data taken on day 1.
2. Allows the user to click on the "bad data" and then writes these points to either a text file or a list(which can then be written to a text file at a later stage).
3. If all the bad data points have been selected ( or if there are no bad points) then, following an indication by the user (i.e. either via a key press event or the user clicking outside the plot area), plot the data of the next day.
What I want the programme to do is repeat the above steps until it reaches the end of the data file and then at the end, I want a text file containing the information of the bad points which I can then remove from the main data file at a later stage. I know that one way of obtaining the information of the bad points is to use event handling, especially the 'button_press_event' and creating a MouseMonitor class. What I'm specifically having trouble with is figuring out how I can get the programme to run through all the data, all the while keeping the 'button_press_event' connected/active and writing all the bad data to a text file. Only at the end of all the data, do I want to disconnect the 'button_press_event' (unless of course there is a better way of doing this).
Any help will be greatly appreciated.
Thanks
Pat

Hello,
I'm trying to teach myself to create custom colormaps to highlight
certain aspects of a dataset I am working with. The script below
produces two plots -- the first shows a 4x4 array foo of random floats
between 0.0 and 1.0, and the second shows the same array, but normalized
such that [foo.min(), foo.max()] is mapped to [0.0, 1.0].
As I understand it, I am plotting two slightly different datasets using
the same colormap, yet the two colorbars are different -- note the
value at the transition from grayscale to red.
I am not sure whether the colors are being assigned to slightly
different data values in the two plots, or if the problem is in plotting
the colorbar. I'd appreciate any help!
Thanks,
Tim
--
Timothy W. Hilton
PhD Candidate, Department of Meteorology
The Pennsylvania State University
503 Walker Building, University Park, PA 16802
hi...@me...
=========
import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
import matplotlib as mpl
mycmdata1 = {'red': ((0.0, 0.0, 0.0),
 (0.5, 1.0, 0.7),
 (1.0, 1.0, 1.0)),
 'green': ((0.0, 0.0, 0.0),
 (0.5, 1.0, 0.0),
 (1.0, 1.0, 1.0)),
 'blue': ((0.0, 0.0, 0.0),
 (0.5, 1.0, 0.0),
 (1.0, 0.5, 1.0))}
mycm1 = mpl.colors.LinearSegmentedColormap('mycm1', mycmdata1)
N = 4
np.random.seed(0)
foo = np.random.rand(N, N)
plt.figure()
plt.pcolor(foo, cmap=mycm1)
plt.colorbar()
plt.figure()
norm = mpl.colors.Normalize()
plt.pcolor(norm(foo), cmap=mycm1)
plt.colorbar()

Hi Friedrich,
Many thanks for your detailed response. I've had to turn my attention
to other things in the past few weeks, but I am back to this task now.
I've implemented the Norm that you suggested by subclassing Normalize;
that was a great suggestion. Now I have a two dimensional array where
water has value -1.5, missing data have -0.5, and valid data over land
have values in [0, 1].
After poking around in cm.py, _cm.py, and colors.py, I understand better
how to define a colormap, but I am not sure how to map negative values
like -0.5 or -1.5 to a color -- I believe the x values in the color
dictionary must be positive?
I can imagine various schemes to sidestep this by assigning water and
missing values positive discrete values (say, 0.1 and 0.2 or something)
and then putting valid data in the remaining portion of [0,1] -- maybe
[0.3, 1] or something. But there's an elegance to valid data going into
[0, 1] and other invalid data getting values outside of that range... I
think I'd lose a little readability in giving that up.
Anyway, thanks a whole lot for your helpful suggestions.
Best,
Tim
On Sat, Nov 2010, 06 at 10:52:48PM +0100, Friedrich Romstedt wrote:
> 2010年11月1日 Timothy W. Hilton <hi...@me...>:
> > [...]
> >
> > I want to have blue water, some other (bright) color for missing data,
> > and a nice-looking color transition (matplotlib.cm.Blues or something
> > similar) for the valid data over land (values from 0 to 50). The
> > Cookbook example at
> > <http://www.scipy.org/Cookbook/Matplotlib/Plotting_Images_with_Special_Values>
> > addresses my problem, but I cannot get it to work. After changing
> > instances of matplotlib.numerix to numpy, I get a long list of
> > exceptions, the last of which is
> > TypeError: __call__() got an unexpected keyword argument 'bytes'.
> > This has to do with sentinelNorm, I think, but I'm not sure how to fix it.
> 
> I think I would tackle this by writing a Norm that doesn't change
> negative values, and you might mask then the oceans by -0.5, and the
> nans by -1.5. Then, you might create a colormap comprised of the
> ocean color for [-1, 0] and the nan color for [-2, -1], and for the
> normal normed range [0, 1] the normal Blues cm. Have a look at cm.py
> and _cm.py how it works. Basically, you can specify for all sections
> of the colormap the left and right color. So you can mix discrete
> maps with continuous ones, because the continuous ones are just
> linearly interpolated with matching colors for left/right at each
> boundary position. Looking at the code will clarify things a lot I
> believe.
> 
> I don't know what went wrong with the cb example you said. From a
> quick look, it seems to have "sentinel rgb values", but this is not
> what we want, right?
> 
> > Eventually I would like to sub-classify missing data by the type of
> > missing input that caused a missing value, but for now a single
> > missing data color is enough.
> 
> That would be possible with the approach above, by just adding
> sections below zero.
> 
> _cm.py: Definitions of colormaps, like Blues.
> cm.py: among other things, how to load such specifications.
> colors.py: Defines Colormaps, and Norms. Have a look at both of them,
> esp. at :class:`Normalize`. I would subclass the Norm mentioned above
> from Normalize.
> 
> I hope this helps you,
> Friedrich
>