Re: [Matplotlib-users] Dark or inverted color scheme
I got pretty good results with the code below. Note that I am reading the
FLIP_COLORS from a gui checkbox.
FLIP_COLORS = self.dark_background_flag.get()
if FLIP_COLORS:
matplotlib.rcParams['figure.facecolor'] = '0.0'
matplotlib.rcParams['axes.edgecolor'] = 'grey'
matplotlib.rcParams['text.color'] = 'white'
matplotlib.rcParams['ytick.color'] = '#00ff00'
matplotlib.rcParams['xtick.color'] = '#0ED5D5'
matplotlib.rcParams['axes.labelcolor'] = '#0ED5D5'
matplotlib.rcParams['axes.facecolor'] = 'black'
matplotlib.rcParams['grid.color'] = '0.3'
matplotlib.rcParams['grid.linestyle'] = '-'
matplotlib.rcParams['lines.markeredgewidth'] = 0.0
else:
matplotlib.rcdefaults()
## I seems like setting matplotlib.rcParams['figure.facecolor']
isn't
## enough. I think this is a bug and set_facecolor() is a
work-around.
fig.set_facecolor(matplotlib.rcParams['figure.facecolor'])
This will flip and also flip back. I found a few colors didn't follow
the crowd. For example, axes.ylabel.color doesn't seem to have
an entry in rcParams. For this, I have to modify the plot generation
statments something like:
ax.set_ylabel('Voltage (volts)',
color=matplotlib.rcParams['ytick.color'])
That sets the ylabel text to be the same as the tick marks. I also have to
do things like that to change line colors and such when
going flipping colors.
DavidS
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Re: [Matplotlib-users] How to do million data-point plots with Matplotlib?
I have experimented with path.simplify and can't see any appreciable improvements. Incidently, I also experimented with other back ends. I found that all the back ends involving Agg behave similarly. However, using the 'GTK' backend it renders the whole 1 million points and does it very fast (about 5x faster than Agg backends). I also found that gnuplot can render the whole million points very fast using the 'x11' terminal. I am guessing that both matplotlib's GTK backend and gnuplot's 'x11' terminal use the hardware accelerated display driver. David -- Learn Windows Azure Live! Tuesday, Dec 13, 2011 Microsoft is holding a special Learn Windows Azure training event for developers. It will provide a great way to learn Windows Azure and what it provides. You can attend the event by watching it streamed LIVE online. Learn more at http://p.sf.net/sfu/ms-windowsazure___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
[Matplotlib-users] How to do million data-point plots with Matplotlib?
I have been working on a program that uses Matplotlib to plot data consisting of around one million points. Sometimes the plots succeed but often I get an exception: OverFlowError: Agg rendering complexity exceeded. I can make this message go away by plotting the data in chunks as illustrated in the demo code below. However, the extra code is a chore which I don't think should be necessary - I hope the developers will be able to fix this issue sometime soon. I know that the development version has some modifications to addressing this issue. I wonder if it is expected to make the problem go away? By the way, this plot takes about 30 seconds to render on my I7 2600k. The main program reaches the show() statement quickly and prints Done plotting?. Then I see that the program reaches 100% usage on one CPU core (4 real, 8 virtual on the 2600k) until the plot is displayed. I wonder if there is any way to persuade Matplotlib to run some of the chunks in parallel so as to use more CPU cores? Plotting something other than random data, the plots run faster and the maximum chunk size is smaller. The maximum chunk size also depends on the plot size - it is smaller for larger plots. I am wondering if I could use this to plot course and fine versions of the plots. The course plot is zoomed in version of the small-sized raster. That would be better than decimation as all the points would at least be there. Thanks in advance, David --- start code - ## Demo program shows how to chunk plots to avoid the exception: ## ## OverflowError: Agg rendering complexity exceeded. ## Consider downsampling or decimating your data. ## ## David Smith December 2011. from pylab import * import numpy as np nPts=600100 x = np.random.rand(nPts) y = np.random.rand(nPts) ## This seems to always succeed if Npts = 2, but fails ## for Npts 3. For points between, it sometimes succeeds ## and sometimes fails. figure(1) plot (x, y) ## Chunking the plot alway succeeds. figure(2) chunk_size=2 iStarts=range(x.size/chunk_size) for iStart in iStarts: print Plotting chunk starting at %d\n % iStart plot(x[iStart:iStart+chunk_size], y[iStart:iStart+chunk_size], '-b') left_overs = nPts % chunk_size if left_overs 0: print Leftovers %d points\n % left_overs plot(x[-left_overs-1:], y[-left_overs-1:], '-r') print done plotting? show() -- end code Please don't reply to this post It is rediculous to plot 1 million points on screen. I am routinely capturing million-point traces from oscilloscopes and other test equipment and to I need to be able to spot features in the data (glitches if you will) that may not show up plotting decimated data. I can then zoom the plot to inspect these features. -- Learn Windows Azure Live! Tuesday, Dec 13, 2011 Microsoft is holding a special Learn Windows Azure training event for developers. It will provide a great way to learn Windows Azure and what it provides. You can attend the event by watching it streamed LIVE online. Learn more at http://p.sf.net/sfu/ms-windowsazure ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
[Matplotlib-users] Cursor corruption with pyqt4 and a work-around.
I have been developing an application using PyQt ant Matplotlib and encountered
a problem with the mouse cursor shape being incorrect. I found a work-around
that seems to work, but I think this is a bug and the fix needs to go into the
Matplotlib code base. Here are details.
My application has a central widget used fo r a Matplotlib Figure.
Additionally there is a menu bar, button bar and two dock panels with
controls for the plot. I see the following behavior for the cursor:
* On start up, the cursor acts normally on startup provided
the mouse cursor is not inside the figure widget on start-up.
* Mouse pointer shapes are set by Windows (in this case VISTA)
and change shape according to location. For example, touching
the application window's border results in the arrow pointer
changing shape to a double-headed arrow indicating the border
can be dragged to adjust the size of the window.
* Once the mouse pointer touches the Matplotlib figure widget
in any way, the mouse pointer will cease to show the double-arrow
shape on the window borders - the pointer remains an arrowhead.
You can still resize the window and the cursor does change to
a double-arrow when you press the left button.
My workaround for this was to add the following lines of code in my
application:
def onleave(self):
QtGui.QApplication.restoreOverrideCursor()
self.fig.canvas.mpl_connect('figure_leave_event', onleave)
I guessed these lines by studying the matplotlib code. Probably they
need to go somewhere inside the Matplotlib class definitions.
I hope this helps developers to correct this problem. My application
code is medium-large and I didn't try to build a smaller example. The
mysterious 3-line workaround solves my problem for the moment.
Perhaps it will help another PyQt and Matplotlib user and perhaps
urge developers to fix the problem in the Matplotlib core.
David Smith
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Re: [Matplotlib-users] Contour with locator=FixedLocator(...) drops first and last contours from the list.
Eric, Thank you, thank you, thank you. This not only fixes the problem I reported with with FixedLocator(..) but also another one where I was using MultipleLocator(..) and getting the same problem issue with dropping first and last contours. The later isn't as easy to work-around but your change fixed it. David Smith On Sat, Mar 13, 2010 at 12:30 PM, Eric Firing efir...@hawaii.edu wrote: David Smith wrote: This is a bug report. I am using matplotlib 0.99.1 on Windows. When using contour with the keyword argument locator=ticker.FixedLocator(levels), the plot is always dropping the first and last contour level. If there are less than 3 levels, contour.py throws an exception. My workaround is to duplicate the first and last levels when using the fixed locator: e.g. my argument becomes locator=FixedLocator( [levels[0]] + levels + [levels[-1]] ) I have traced the problem to the last line in contour.py, method _autolev() which strips the first and last levels if the contours are not filled: return lev[1:-1] This line occurs at line 682 in my version of contour.py which came with the 0.991 installation. I realize that I could specify the levels in the argument V and this does work. However this code is embedded in GUI-ness which allows the user to choose how the contours are selected. Passing the locator seems to be the best option code-wise. I committed a small change to svn trunk (r8190) that I think will handle your use case without fouling anything else up. Eric Thank you, Dave Smith -- Download Intel#174; Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
[Matplotlib-users] Contour with locator=FixedLocator(...) drops first and last contours from the list.
This is a bug report. I am using matplotlib 0.99.1 on Windows. When using contour with the keyword argument locator=ticker.FixedLocator(levels), the plot is always dropping the first and last contour level. If there are less than 3 levels, contour.py throws an exception. My workaround is to duplicate the first and last levels when using the fixed locator: e.g. my argument becomes locator=FixedLocator( [levels[0]] + levels + [levels[-1]] ) I have traced the problem to the last line in contour.py, method _autolev() which strips the first and last levels if the contours are not filled: return lev[1:-1] This line occurs at line 682 in my version of contour.py which came with the 0.991 installation. I realize that I could specify the levels in the argument V and this does work. However this code is embedded in GUI-ness which allows the user to choose how the contours are selected. Passing the locator seems to be the best option code-wise. Thank you, Dave Smith -- Download Intel#174; Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
