Embedding in Traits GUI

Traits, part of the Enthought Tools Suit, provides a great framework for creating GUI Apps without a lot of the normal boilerplate required to connect the UI the rest of the application logic. A brief introduction to Traits can be found here. Although ETS comes with it’s own traits-aware plotting framework (Chaco), if you already know matplotlib it is just as easy to embed this instead. The advantages of Chaco (IMHO) are its interactive “tools”, an (in development) OpenGL rendering backend and an easy-to-understand codebase. However, matplotlib has more and better documentation and better defaults; it just works. The key to getting TraitsUI and matplotlib to play nice is to use the mpl object-oriented API, rather than pylab / pyplot. This recipe requires the following packages:

  • numpy
  • wxPython
  • matplotlib
  • Traits > 3.0
  • TraitsGUI > 3.0
  • TraitsBackendWX > 3.0

For this example, we will display a function (y, a sine wave) of one variable (x, a numpy ndarray) and one parameter (scale, a float value with bounds). We want to be able to vary the parameter from the UI and see the resulting changes to y in a plot window. Here’s what the final result looks like: image0 The TraitsUI ”!CustomEditor” can be used to display any wxPython window as the editor for the object. You simply pass the !CustomEditor a callable which, when called, returns the wxPython window you want to display. In this case, our !MakePlot() function returns a wxPanel containing the mpl !FigureCanvas and Navigation toolbar. This example exploits a few of Traits’ features. We use “dynamic initialisation” to create the Axes and Line2D objects on demand (using the xxxdefault methods). We use Traits “notification”, to call update_line(...) whenever the x- or y-data is changed. Further, the y-data is declared as a Property trait which depends on both the ‘scale’ parameter and the x-data. ‘y’ is then recalculated on demand, whenever either ‘scale’ or ‘x’ change. The ‘cached_property’ decorator prevents recalculation of y if it’s dependancies *are)#not* modified.`

Finally, there’s a bit of wx-magic in the redraw() method to limit the redraw rate by delaying the actual drawing by 50ms. This uses the wx.!CallLater class. This prevents excessive redrawing as the slider is dragged, keeping the UI from lagging. Here's the full listing:

A simple demonstration of embedding a matplotlib plot window in
a traits-application. The CustomEditor allow any wxPython window
to be used as an editor. The demo also illustrates Property traits,
which provide nice dependency-handling and dynamic initialisation, using
the _xxx_default(...) method.
from enthought.traits.api import HasTraits, Instance, Range,\
                                Array, on_trait_change, Property,\
                                cached_property, Bool
from enthought.traits.ui.api import View, Item
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg
from matplotlib.backends.backend_wx import NavigationToolbar2Wx
from matplotlib.figure import Figure
from matplotlib.axes import Axes
from matplotlib.lines import Line2D
from enthought.traits.ui.api import CustomEditor
import wx
import numpy
def MakePlot(parent, editor):
    Builds the Canvas window for displaying the mpl-figure
    fig = editor.object.figure
    panel = wx.Panel(parent, -1)
    canvas = FigureCanvasWxAgg(panel, -1, fig)
    toolbar = NavigationToolbar2Wx(canvas)
    sizer = wx.BoxSizer(wx.VERTICAL)
    return panel
class PlotModel(HasTraits):
    """A Model for displaying a matplotlib figure"""
    #we need instances of a Figure, a Axes and a Line2D
    figure = Instance(Figure, ())
    axes = Instance(Axes)
    line = Instance(Line2D)
    _draw_pending = Bool(False) #a flag to throttle the redraw rate
    #a variable paremeter
    scale = Range(0.1,10.0)
    #an independent variable
    x = Array(value=numpy.linspace(-5,5,512))
    #a dependent variable
    y = Property(Array, depends_on=['scale','x'])
    traits_view = View(
    def _axes_default(self):
        return self.figure.add_subplot(111)
    def _line_default(self):
        return self.axes.plot(self.x, self.y)[0]
    def _get_y(self):
        return numpy.sin(self.scale * self.x)
    @on_trait_change("x, y")
    def update_line(self, obj, name, val):
        attr = {'x': "set_xdata", 'y': "set_ydata"}[name]
        getattr(self.line, attr)(val)
    def redraw(self):
        if self._draw_pending:
        canvas = self.figure.canvas
        if canvas is None:
        def _draw():
            self._draw_pending = False
        wx.CallLater(50, _draw).Start()
        self._draw_pending = True
if __name__=="__main__":
    model = PlotModel(scale=2.0)

Section author: Unknown[47], GaelVaroquaux