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Hello, I have just chaged over to the Qt4Agg backend so I can use Pierre Raybaut's addition of editing cuves and parameters (thanks for this functionality). I have found a small issue. Whenever I apply changes with more than one trace/curve, the colour of the second curve changes. The example below is enough to show the issue: from matplotlib import pyplot import numpy as np a = np.random.rand(100) b =a*1.5 fig = pyplot.figure() ax = fig.add_subplot(111) ax.plot(a) ax.plot(b) pyplot.show() The example above changes second trace from a default of green to a light green when I use the edit button. Just pressing 'apply' without any changes should be enough to trigger the change. It appears that the addin is not getting the correct color from the plot and so apply is appying the incorrect color back into the plot. Also if I try to set color for a trace like this: fig = pyplot.figure() ax = fig.add_subplot(111) ax.plot(a,'r') pyplot.show() It works but then when I press the edit curves button, I get: Traceback (most recent call last): File "C:\Python26\lib\site-packages\matplotlib\backends\backend_qt4.py", line 469, in edit_parameters figureoptions.figure_edit(axes, self) File "C:\Python26\lib\site-packages\matplotlib\backends\qt4_editor\figureoptions.py", line 154, in figure_edit icon=get_icon('qt4_editor_options.svg'), apply=apply_callback) File "C:\Python26\lib\site-packages\matplotlib\backends\qt4_editor\formlayout.py", line 460, in fedit dialog = FormDialog(data, title, comment, icon, parent, apply) File "C:\Python26\lib\site-packages\matplotlib\backends\qt4_editor\formlayout.py", line 384, in __init__ parent=self) File "C:\Python26\lib\site-packages\matplotlib\backends\qt4_editor\formlayout.py", line 362, in __init__ widget = FormComboWidget(data, comment=comment, parent=self) File "C:\Python26\lib\site-packages\matplotlib\backends\qt4_editor\formlayout.py", line 344, in __init__ widget = FormWidget(data, comment=comment, parent=self) File "C:\Python26\lib\site-packages\matplotlib\backends\qt4_editor\formlayout.py", line 233, in __init__ self.setup() File "C:\Python26\lib\site-packages\matplotlib\backends\qt4_editor\formlayout.py", line 256, in setup selindex = value.pop(0) AttributeError: 'tuple' object has no attribute 'pop' I am on version 1.0.0 and XP Thanks, Bevan -- View this message in context: http://old.nabble.com/Qt4Agg-backend---edit-curves-and-axis-parameters-tp29754925p29754925.html Sent from the matplotlib - users mailing list archive at Nabble.com.
On Sat, Sep 18, 2010 at 1:03 PM, Benjamin Root <ben...@ou...> wrote: > > I'll admit that I am not very familiar with how these step plots are done. > Maybe it should be the 'drawstyle' kwarg that should be over-riden, not > 'linestyle'? > > Ben Root > This seems like more reasonable to me. When I update /matplotlib/lib/axes.py: kwargs['linestyle'] = 'steps-' + where -> kwargs['drawstyle'] = 'steps-' + where then I get the expected behavior. -- Gökhan
Thanks, that works fantastically ! -- Forest. On Fri, Sep 17, 2010 at 9:23 PM, Benjamin Root <ben...@ou...> wrote: > On Fri, Sep 17, 2010 at 8:05 PM, Forest Yang <yzi...@gm...> wrote: >> >> Hi >> >> I have a function z(x, y) on a regular grid. But some of the value >> z are not defined on (x,y). I want to plot the contour or contourf of >> z on (x,y) but exclude specific (x,y) points. >> How can I do it ? Right now I just draw small colored square >> (rectangular) around defined (x,y) the color is not smooth since no >> interpolation like contour or contourf. >> >> Thanks. >> >> Forest. >> > > Forest, > > There are a few ways to do this. If you have a recent enough version of > matplotlib, you can use masked arrays, and the contourf will just ignore > those data points. One could also use NaNs and make sure that the clim (the > limits on z that you wish to display a color for) is defined. > > To make a masked array is easy. Imagine you wish to exclude any value less > than zero (assume z is defined): > > import numpy.ma as ma > z_masked = ma.masked_array(z, mask=(z < 0.)) > > And then just use the masked array in your contourf as you would the > regular numpy array. > > I hope that helps! > Ben Root > >