matplotlib-users Mailing List for matplotlib

Hello,
I can't understand how to refresh FigureCanvasWxAgg instance. Here is
the example:
import wx
import matplotlib
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
from matplotlib.figure import Figure
class MainFrame(wx.Frame):
 def __init__(self):
 wx.Frame.__init__(self, None, wx.NewId(), "Main")
 self.sizer = wx.BoxSizer(wx.VERTICAL)
 self.figure = Figure(figsize=(1,2))
 self.axe = self.figure.add_subplot(111)
 self.figurecanvas = FigureCanvas(self, -1, self.figure)
 self.buttonPlot = wx.Button(self, wx.NewId(), "Plot")
 self.buttonClear = wx.Button(self, wx.NewId(), "Clear")
 self.sizer.Add(self.figurecanvas, proportion=1, border=5,
flag=wx.ALL | wx.EXPAND)
 self.sizer.Add(self.buttonPlot, proportion=0, border=2, flag=wx.ALL)
 self.sizer.Add(self.buttonClear, proportion=0, border=2, flag=wx.ALL)
 self.SetSizer(self.sizer)
 self.figurecanvas.Bind(wx.EVT_LEFT_DCLICK, self.on_dclick)
 self.buttonPlot.Bind(wx.EVT_BUTTON, self.on_button_plot)
 self.buttonClear.Bind(wx.EVT_BUTTON, self.on_button_clear)
 self.subframe_opened = False
 def on_dclick(self, evt):
 self.subframe = SubFrame(self, self.figure)
 self.subframe.Show(True)
 self.subframe_opened = True
 def on_button_plot(self, evt):
 self.axe.plot(range(10), color='green')
 self.figurecanvas.draw()
 def on_button_clear(self, evt):
 if self.subframe_opened:
 self.subframe.Close()
 self.figure.set_canvas(self.figurecanvas)
 self.axe.clear()
 self.figurecanvas.draw()
class SubFrame(wx.Frame):
 def __init__(self, parent, figure):
 wx.Frame.__init__(self, parent, wx.NewId(), "Sub")
 self.sizer = wx.BoxSizer(wx.VERTICAL)
 self.figurecanvas = FigureCanvas(self, -1, figure)
 self.sizer.Add(self.figurecanvas, proportion=1, border=5,
flag=wx.ALL | wx.EXPAND)
 self.SetSizer(self.sizer)
 self.Bind(wx.EVT_CLOSE, self.on_close)
 def on_close(self, evt):
 self.GetParent().subframe_opened = False
 evt.Skip()
class MyApp(wx.App):
 def OnInit(self):
 frame = MainFrame()
 frame.Show(True)
 self.SetTopWindow(frame)
 return True
app = MyApp(0)
app.MainLoop()
I'm interested in the following sequence of operations:
- run a script
- resize the main frame
- press Plot button
- double click on plot
- press Clear button
Now I get a mess on main frame plot. If I resize the frame it redraws
properly. My question is what should I add to my code to do that
without resizing?
Thanks in advance,
Alex

2010年3月14日 David Arnold <dwa...@su...>:
> All,
>
> I am having difficulty with a line on: http://scipy.org/LoktaVolterraTutorial
>
> Here are the lines:
>
> values = linspace(0.3, 0.9, 5)
>
> vcolors = p.cm.autumn_r(linspace(0.3, 1., len(values)))
>
> First of all, I can find no reference to autumn_r in the Matplotlib documentation. Also, using Aptana (eclipse), PyDev complains about the vcolors line with:
The colormap's data is defined in matplotlib._cm.py, there a
dictionary defining the names of the colormaps is defined from line
no. 5814 on. The colormaps are "imported", i.e. added to the
module-local namespace by "patching" locals() in matplolib.cm on line
43, where cmap_d also containes reversed versions of all the data
defined by matplotlib._cm, indicated by an trailing "_r" in the name.
Because the data isn't imported the usual way, your program (PyDev?)
will not find it.
> Undefined variable from import: autumn_r    Lotka.py    /scipy/src/pkg line 44 PyDev Problem
>
> Secondly, I am used to using colormaps in Matlab, but not so much in Matplotlib. In Matlab, g=grey(256) produces an 256x3 matrix and each row is indexed by the numbers 1 through 256. Thus, if you have an image and pixel has a value 124, then row 124 gives an rgb triple that is used to color the pixel.
I think you can reproduce the matlab behaviour by using:
>>> result = some_colormap(numpy.linspace(0.0, 1.0, 256), [bytes = True]).
The optional bytes = True argument specifies to return integer \in [0,
255] colors. The return ndarray will have shape (256, 4), and an
indicing result[i] returns the ndarray array([r, g, b, a]). Note than
numpy and Python use zero-based indices, opposed to matlab's one-based
indices.
> However, in the case of vcolors = p.cm.autumn_r(linspace(0.3, 1., len(values))), I'm really not sure what is going on. The linspace(0.3,1,len(values)) produces len(values) equally spaced numbers from 0.3 to 1. Now, how is autumn_r(array([ 0.3 , 0.475, 0.65 , 0.825, 1.  ])) make any sense?
The first argument to some_colormap.__call__(X, alpha [= 1.0], bytes
[= False]) is the interpolation axis. I embed __call__()'s __doc__
string:
 """
 *X* is either a scalar or an array (of any dimension).
 If scalar, a tuple of rgba values is returned, otherwise
 an array with the new shape = oldshape+(4,). If the X-values
 are integers, then they are used as indices into the array.
 If they are floating point, then they must be in the
 interval (0.0, 1.0).
 Alpha must be a scalar.
 If bytes is False, the rgba values will be floats on a
 0-1 scale; if True, they will be uint8, 0-255.
 """
I myself just did a short dive into the matplotlib code of cm.py,
_cm.py, and colors.py, so this are just my conclusions.
Friedrich

All,
I am having difficulty with a line on: http://scipy.org/LoktaVolterraTutorial
Here are the lines:
values = linspace(0.3, 0.9, 5)
vcolors = p.cm.autumn_r(linspace(0.3, 1., len(values)))
First of all, I can find no reference to autumn_r in the Matplotlib documentation. Also, using Aptana (eclipse), PyDev complains about the vcolors line with: 
Undefined variable from import: autumn_r	Lotka.py	/scipy/src/pkg	line 44	PyDev Problem
Secondly, I am used to using colormaps in Matlab, but not so much in Matplotlib. In Matlab, g=grey(256) produces an 256x3 matrix and each row is indexed by the numbers 1 through 256. Thus, if you have an image and pixel has a value 124, then row 124 gives an rgb triple that is used to color the pixel. 
However, in the case of vcolors = p.cm.autumn_r(linspace(0.3, 1., len(values))), I'm really not sure what is going on. The linspace(0.3,1,len(values)) produces len(values) equally spaced numbers from 0.3 to 1. Now, how is autumn_r(array([ 0.3 , 0.475, 0.65 , 0.825, 1. ])) make any sense?
Thanks.
David.