On Thu, Dec 15, 2011 at 12:33 PM, Chao YUE chaoyue...@gmail.com wrote:
Dear matplotlib users,
How can I surpress the figure pop out when I make plot within the ipython
interactive shell?
suppose I make a figure first and I want to save it:
fig=plt.figure()
ax=fig.add_subplot(111)
On Wed, Jul 13, 2011 at 6:49 PM, Benjamin Root ben.r...@ou.edu wrote:
On Wednesday, July 13, 2011, Justin McCann jne...@gmail.com wrote:
$ ipython -pylab
#
from matplotlib.collections import LineCollection
f = figure()
plot()
ax = gca()
vec = numpy.random.random((10,3))
segs
On Thu, Jul 14, 2011 at 3:57 PM, T. Tofus von Blisstein
tuffst...@googlemail.com wrote:
Hi,
how can I invert the colors of axes/background from black/white to
white/black?
thanks... I have been googling for a while...
If you want to do it for all your plots, you can mess with all of the
2011/7/13 SULSEUNG-JIN sulsj0...@hotmail.com:
Hi,
I'm plotting thousands of short lines on a plot. Because plot and Line2D
are quite slow for this case, I'm trying to use lineCollection. Here comes
the part of my testing code:
...
segs = []
# Manual set for testing
x2 =
2011/7/13 SULSEUNG-JIN sulsj0...@hotmail.com:
Thanks, Justin
I think I made a confusing example code. Here comes new one:
Maybe you just need to force a call to draw() and set your x/y limits.
This works for me on matplotlib 1.0.1
$ ipython -pylab
#
from matplotlib.collections import
On Tue, Jun 21, 2011 at 1:50 PM, Frank frank.t.win...@googlemail.com wrote:
matplotlib python: How do you change the background color of a line plot
according to a given column? Say I have the following data file
...
2. 1
3. 1
3. 2
The first column represents the y-values, and the 2nd
On Wed, May 18, 2011 at 3:01 PM, Neal Becker ndbeck...@gmail.com wrote:
Using scatter, it seems less probably (numerous) points show just as much
as
more probable points. Can anyone suggest a good way to emphasize the more
probable points?
I was thinking maybe the easy way is just scale
On Wed, May 11, 2011 at 1:59 PM, Benjamin Root ben.r...@ou.edu wrote:
...
Most things, we do know the sizes of. It is my understanding that it is
the text objects that is the unknown. If this could be solved, then a
layout engine would be much more feasible. The problem is that even LaTeX
You'll want to use event handling to figure out where the user clicked, and
then you have a couple of options: Axes.vlines(), or pylab.axvline(). It
seems like pylab.axvline() will always span the entire y-axis by default,
but with Axes.vlines() you need to specify the ymin/ymax. Maybe someone
On Wed, Dec 1, 2010 at 11:58 AM, Justin McCann jne...@gmail.com wrote:
Is there a straightforward way to limit the legend only to lines that
appear within the current display limits? I have a plot that has too
many separate data series to show on the legend at once, but once I
zoom in it would
Is there a straightforward way to limit the legend only to lines that
appear within the current display limits? I have a plot that has too
many separate data series to show on the legend at once, but once I
zoom in it would be good to re-set the legend to show only the visible
data points/lines.
On Wed, Nov 3, 2010 at 1:18 AM, David Frey dpf...@shaw.ca wrote:
...
My data in the y-axis (address space usage) is fairly uniform (0-2000 MB
values), but my data in the x-axis (the time at which the the trace statements
were executed) is highly clustered. For example, I have approximately
On Fri, Oct 8, 2010 at 11:10 PM, Jae-Joon Lee lee.j.j...@gmail.com wrote:
The label_mode need to be capital L, instead of l. I guess this
will fix your first problem.
While we make l same as L, but I think it actually degrade the
readability of the code, and I;m inclined to leave it as is. Let
I just refactored some custom code to make use of
axes_grid1.ImageGrid, and I think I've come across a bug (see below).
It looks like the tick labelsize doesn't get passed properly to the
parasite axes.
I'm using Python2.6, matplotlib-1.0.0 release, and the Qt4Agg backend.
Also, I noticed that
I have several heatmap images, which I place in subplots stacked vertically.
I've been using
ax = figure.add_subplot(nplots, 1, plotnum)
ax.imshow(...)
to add each subsequent heatmap, and then place
--
Beautiful is
Sorry about that; don't know what key combo I pushed. Completed email is
below.
On Thu, Oct 7, 2010 at 3:09 PM, Justin McCann jne...@gmail.com wrote:
I have several heatmap images, which I place in subplots stacked
vertically. I've been using
ax = figure.add_subplot(nplots, 1, plotnum
On Thu, Oct 7, 2010 at 4:08 PM, Benjamin Root ben.r...@ou.edu wrote:
...
On Thu, Oct 7, 2010 at 3:09 PM, Justin McCann jne...@gmail.com wrote:
...
I'd like to annotate across all of the subplots by placing a vertical line
(or vspan) across the entire figure-- to extend from the bottom
On Mon, Oct 4, 2010 at 4:50 PM, Sanjay Kairam sanjay.kai...@gmail.com wrote:
Hi there,
I'm having a problem installing matplotlib, I'm guessing that I am missing
some dependency, but I am having trouble figuring out what the issue is (I
...
REQUIRED DEPENDENCIES
numpy: 1.5.0
On Wed, Sep 29, 2010 at 9:58 PM, Jason Grout
jason-s...@creativetrax.com wrote:
...
I made the FAQ entry code a little more general (and hopefully more
robust) a while ago. I don't know if it takes care of the problem
you're talking about, though.
I posted it to the matplotlib-devel mailing
Not to pile on the auto-adjust to make labels fit bandwagon, but
I've been following the FAQ on adjusting the subplot locations to make
room for too-long tick labels:
http://matplotlib.sourceforge.net/faq/howto_faq.html#automatically-make-room-for-tick-labels
and have found that the FAQ code
Here are a couple of functions you might try, with a few colors and line
styles I use:
import itertools
from pylab import *
COLORS = ['#990033', '#FF', '#00FF00', '#F79E00',
'#ff00ff', '#0080FF', '#FF', '#2D0668',
'#2EB42E', '#ff6633', '#8000ff', '#33',
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