matplotlib-users Mailing List for matplotlib

On 1/8/2010 3:08 PM, David Arnold wrote:
> Does anyone have some Tkinter scripts using matplotlib they can share?
You may find the TSPlot and Histogram classes here
http://code.google.com/p/econpy/source/browse/trunk/abm/gridworld/gridworld.py
to be helpful.
fwiw,
Alan Isaac

All,
Does anyone have some Tkinter scripts using matplotlib they can share? I am just starting to learn Tkinter and I could use some examples.
David Arnold
College of the Redwoods
Department of Mathematics

Simply beautiful !
Thank's a lot, it's exactly what I wanted.
Le vendredi 08 janvier 2010 à 13:08 -0500, Jae-Joon Lee a écrit :
> this_annotation.xytext = (event.x, event.y)

This is because "legend" creates a legend only in the current axes.
Note that "twinx" creates a separate axes.
You need to manually specify which plot items to show in the legend.
http://matplotlib.sourceforge.net/users/legend_guide.html
Or, you may use axes_grid.parasite_axes which does this job for you
(but do not use this toolkit blindly but read the documentation)
http://matplotlib.sourceforge.net/examples/axes_grid/demo_parasite_axes2.html
-JJ
On Thu, Jan 7, 2010 at 9:19 AM, Manuel Wittchen
<man...@gm...> wrote:
> Hi,
>
> I'm plotting two y-axis with the twinx() command. But when I'm
> creating my legend there is only one entry in the legend-box instead
> of two.
> Here is my code:
>
> grafik1 = plt.figure()
>
> ax1 = grafik1.add_subplot(111)
> ax1.plot(TIME, BTM, color='red', label='Biomass')
> ax1.set_ylabel('Biomass')
>
> ax2 = ax1.twinx()
> ax2.plot(TIME, FLUOR, label='Productivity')
> ax2.set_ylabel('Produktivity')
> ax2.set_xlabel('Time')
>
> grafik1_legend = legend(loc=10)
>
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What version of matplotlib are you using?
This may be the bug that already have been fixed.
Neither with the maintenance branch nor the current svn reproduce this problem,
as can be easily seen from the gallery
http://matplotlib.sourceforge.net/examples/axes_grid/demo_parasite_axes2.html
-JJ
On Thu, Jan 7, 2010 at 5:16 AM, Burly Cumberland <bu...@mt...> wrote:
> Hi,
>
> I've been playing around with the parasite_axes toolkit. It's very nice but
> I discovered from the example that I always get an extra set of x and y axis
> labels. See attached png. I've got around this by simply turning the first
> axis off. Is this a bug in the code or the example? Also there appears to be
> an additional set of axis label commands in the example.
>
> From demo_parasite_axes2.py
> ----------------------------------------------------------------------
>
> from mpl_toolkits.axes_grid.parasite_axes import SubplotHost
> import matplotlib.pyplot as plt
>
> if 1:
>  fig = plt.figure(1)
>  plt.axis('off') # Extra line to remove unwanted axis labels
>  host = SubplotHost(fig, 111)
>
>  host.set_ylabel("Density")
>  host.set_xlabel("Distance")
>
>  par1 = host.twinx()
>  par2 = host.twinx()
>
>  par1.set_ylabel("Temperature")
>
>  par2.axis["right"].set_visible(False)
>
>  offset = 60, 0
>  new_axisline = par2.get_grid_helper().new_fixed_axis
>  par2.axis["right2"] = new_axisline(loc="right",
>                    axes=par2,
>                    offset=offset)
>
>  par2.axis["right2"].label.set_visible(True)
>  par2.axis["right2"].set_label("Velocity")
>
>  fig.add_axes(host)
>  plt.subplots_adjust(right=0.75)
>
>  host.set_xlim(0, 2)
>  host.set_ylim(0, 2)
>
>  host.set_xlabel("Distance") # Why reset these three labels..
>  host.set_ylabel("Density")
>  par1.set_ylabel("Temperature")
>
>  p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density")
>  p2, = par1.plot([0, 1, 2], [0, 3, 2], label="Temperature")
>  p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity")
>
>  par1.set_ylim(0, 4)
>  par2.set_ylim(1, 65)
>
>  host.legend()
>
>  host.axis["left"].label.set_color(p1.get_color())
>  par1.axis["right"].label.set_color(p2.get_color())
>  par2.axis["right2"].label.set_color(p3.get_color())
>
>  plt.draw()
>  plt.show()
>
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On Wed, Jan 6, 2010 at 5:50 AM, Gergely Ungvary
<ger...@co...> wrote:
> I guess I'm not
> the only one plotting statistical data with manually specified bins.
Yes, and mpl works fine with manually specified bins.
On the other hand, I don't see why you want to change the widths of
the bar. As far as I can see, that is not the correct representation
of the data.
Anyhow, here is a little example that makes your bars in equal width.
I never used "fixed width" bar so I'm not sure if this is what you
want.
-JJ
aa = np.random.rand(100)
bins = np.array([0., 0.2, 0.3, 0.8, 1.])
p = hist(aa, bins)
rects = p[2]
for r in rects:
 r.set_width(0.1)

I think the current method names of Annotation class (originally from
the Text class) is a bit confusing.
And this needs to be fixed.
Anyhow, instead of calling set_position method, you need to set the
"xytext" attribute directly.
 this_annotation.xytext = (event.x, event.y)
Regards,
-JJ
On Wed, Jan 6, 2010 at 9:49 AM, <cha...@gm...> wrote:
> Dear all,
>
> I have a little problem on understanding how to move an annotation on a
> plot.
>
> My idea is to make a plot where some informations follow the cursor when
> moving on the data, like you can see in plots from piwik (
> http://piwik.org/demo )
>
> In the script in attachment, I can follow the cursor coordinate, change the
> text of the annotation, but I cannot move it.
>
> I tried several things, but none worked.
>
> If you have any advices, I would be very happy.
>
> =============================
> Script : try_move_annotate.py
> =============================
> import numpy
> import matplotlib
> import matplotlib.pyplot as plt
>
> def on_motion(event):
> if event.inaxes:
> print "before setting position : "
> print this_annotation.get_position()
> this_annotation.set_text('coordinate : ' + str(event.x) + ' ' +
> str(event.y))
> this_annotation.set_position((event.x, event.y))
> print "after setting position : "
> print this_annotation.get_position()
> ax.draw_artist(this_annotation)
> plt.draw()
>
> fig = plt.figure()
> ax = fig.add_subplot(111)
>
> ax.plot(range(10), range(10))
> this_annotation = ax.annotate("coordinate : ",
> xy = (100,100),
> xycoords = 'figure pixels',
> horizontalalignment = 'left',
> verticalalignment = 'top',
> fontsize = 20,
> fontweight = 'bold',# animated=True,
> bbox = dict(boxstyle="round", fc='black', ec="0.5", alpha=0.5)
> )
>
> fig.canvas.mpl_connect('motion_notify_event', on_motion)
> plt.show()
> ==========
> END Script
> ==========
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>

Mag,
Are you looking at time series or just a single value per user? 
Time series lends itself to a line plot. There are plenty of style options,
etc. to highlight each user and the average. 
If you're looking at a single value per user, a bar chart may be a good
choice:
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.bar
You could then show the average using a horizontal line? 
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.axhline
Mag Gam wrote:
> 
> I am new to matplot but I am liking it a lot. I am creating a webpage
> with Django and I would like to plot a user's usage and average usage.
> Therefore I am not sure what type of plot is the best type.
> 
> 
It sounds like you've already worked out using MPL in Django. I found the
following links useful if not:
http://bitsofpy.blogspot.com/2009/07/matplotlib-in-django.html
http://www.scipy.org/Cookbook/Matplotlib/Django
http://matplotlib.sourceforge.net/faq/howto_faq.html#matplotlib-with-django
I'm sure others have better ideas, just my 2c. 
-Erik Schweller
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Hi,
I have problems using mpl_toolkits.mplot3d.Axes3D. I plotted two surfaces, one using plot_surface, the other using plot_wireframe. However, no occlusions are handled. One surface is always in front of the other. Sometimes, the surface hides the wireframe, sometimes the other way (see attachment). How can I achieve a real 3D plot of two surfaces?
Thanks for your help
Sven

I am new to matplot but I am liking it a lot. I am creating a webpage
with Django and I would like to plot a user's usage and average usage.
Therefore I am not sure what type of plot is the best type.
The data looks like this:
user: 13.4
average: 17.5
It would also be nice if I can create a bell curve (normal
distribution) and show where the user fits into the curve.
TIA