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Data Plotting in wxPython

wx.lib.plot

wxPython has its own plotting library, which provides simple way of drawing large number of data on a canvas. It is convenient to use and it is fast. However you have only one axis per canvas and you can plot 2D graphs only.


To plot a line graph like above, you create line objects using numpy
Source file 
  214                 x = np.linspace(0,10,500)
  215                 y = np.sin(x)
  216 
  217                 # create lines
  218                 line1 = wxplot.PolyLine(list(zip(x, np.sin(x))),
  219                         colour='red', width=3, style=wx.PENSTYLE_DOT_DASH)
  220                 line2 = wxplot.PolyLine(list(zip(x, -np.sin(x))),
  221                         colour='blue', width=3, style=wx.PENSTYLE_LONG_DASH)
Then generate a graphics object and render it on the canvas Source file 
  223                 # create a graphics
  224                 graphics = wxplot.PlotGraphics([line1, line2])
  225                 self.pnlPlot.Draw(graphics)
Here the canvas is implemented on the panel, self.pnlPlot. So you can embed the panel into any wx.Window object.

Matplotlib WXAgg

For more professional plot, you can use matplotlib more specifically matplotlib WXAgg backend, where almost all the matplotlib features are available to wx.Python.  Thus you can plot contour


or more sophisticated plots as shown below very easily.


In this case, the WXAgg figure object and the canvas object are implemented on a wx.Panel: Source file 
   34 class MplGraph(wx.Panel):
   35 
   36     def __init__(self, parent, hideToolbar=False):
   37 
   38         wx.Panel.__init__(self, parent)
   39 
   40         # mpl figure object
   41         self.figure = Figure()
   42         # mpl canvas object
   43         self.canvas = FigureCanvas(self, -1, self.figure)
and exposed: Source file 
   65     ## return canvas object
   66     def GetCanvas(self):
   67         return self.canvas
   68 
   69     ## return figure object
   70     def GetFigure(self):
   71         return self.figure
Thus, you can use them just as you use matplotlib. For the above example, the shade plot on the left was generated by: Source file 
  191             elif evt.GetId() == self.idShade:
  192                 # clear previous plot
  193                 self.pnlPlot.Clear()
  194                 # acquire new axes
  195                 ax1 = self.pnlPlot.AddSubPlot(121)
  196                 # we need figure object too
  197                 fig = self.pnlPlot.GetFigure()
  198 
  199                 # colormap
  200                 cmap = matplotlib.cm.copper
  201 
  202                 # import LightSource
  203                 from matplotlib.colors import LightSource
  204 
  205                 y,x = np.mgrid[-4:2:200j, -4:2:200j]
  206                 z = 10 * np.cos(x**2 + y**2)
  207                 ls = LightSource(315, 45)
  208 
  209                 rgb = ls.shade(z, cmap)
  210 
  211                 ax1.imshow(rgb, interpolation='bilinear')
  212                 im = ax1.imshow(z, cmap=cmap)
  213                 #im.remove()
  214                 #fig.colorbar(im)
  215                 ax1.set_title('shaded plot')
and the right 3D surface plot is generated by: Source file 
  217                 # import Axes3D
  218                 from mpl_toolkits.mplot3d import Axes3D
  219                 ax2 = self.pnlPlot.AddSubPlot(122, projection='3d')
  220 
  221                 X = np.arange(-5,5,0.25)
  222                 Y = np.arange(-5,5,0.25)
  223                 X,Y = np.meshgrid(X,Y)
  224                 R = np.sqrt(X**2 + Y**2)
  225                 Z = np.sin(R)
  226                 
  227                 #surface plot
  228                 surf = ax2.plot_surface(X,Y,Z, cmap = matplotlib.cm.coolwarm,
  229                         linewidth=0, antialiased=False)
  230                 
  231                 ax2.set_title('3d surface plot')
source code
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