Embedding Matplotlib in PyQt4: A Step-by-Step Guide

Integrating an interactive matplotlib graph into a PyQt4 user interface is simpler than it may seem. Here's a step-by-step explanation:

1. Import the Necessary Modules:

   Begin by importing the relevant Qt widgets from matplotlib.backends.backend_qt4agg:

   from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
   from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar

2. Create a Matplotlib Figure:

   Instantiate a Figure object to serve as the canvas for your graph.

   self.figure = Figure()

3. Instantiate a Qt Widget for the Canvas:

   Create an instance of FigureCanvas, which represents the Qt widget that will display the figure.

   self.canvas = FigureCanvas(self.figure)

4. Add a Navigation Toolbar:

   The NavigationToolbar widget provides controls for zooming, panning, and saving the figure.

   self.toolbar = NavigationToolbar(self.canvas, self)

5. Create a Button:

   Create a PyQt button that, when clicked, will trigger a plot function.

   self.button = QtGui.QPushButton('Plot')
   self.button.clicked.connect(self.plot)

6. Design the Layout:

   Arrange the widgets within a Qt layout.

   layout = QtGui.QVBoxLayout()
   layout.addWidget(self.toolbar)
   layout.addWidget(self.canvas)
   layout.addWidget(self.button)
   self.setLayout(layout)

7. Plot Random Data:

   Define a function to generate random data and plot it on the figure.

   def plot(self):
       data = [random.random() for i in range(10)]
       ax = self.figure.add_subplot(111)
       ax.clear()
       ax.plot(data, '*-')
       self.canvas.draw()

By following these steps, you can embed a matplotlib graph within a PyQt4 user interface, allowing you to visualize data and interact with it through Qt widgets.