Added base implementation of data visualization framework for use with GP-LVM.

GPy/util/visualize.py

@@ -0,0 +1,159 @@
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import GPy
+import numpy as np
+
+class lvm:
+    def __init__(self, model, data_visualize, ax):
+        self.cid = ax.figure.canvas.mpl_connect('button_press_event', self.on_click)
+        self.cid = ax.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
+        self.data_visualize = data_visualize
+        self.model = model
+        self.ax = ax
+        self.called = False
+        self.move_on = False
+
+    def on_click(self, event):
+        print 'click', event.xdata, event.ydata
+        if event.inaxes!=self.ax: return
+        self.move_on = not self.move_on
+        print
+        if self.called:
+            self.xs.append(event.xdata)
+            self.ys.append(event.ydata)
+            self.line.set_data(self.xs, self.ys)
+            self.line.figure.canvas.draw()
+        else:
+            self.xs = [event.xdata]
+            self.ys = [event.ydata]
+            self.line, = ax.plot(event.xdata, event.ydata)
+        self.called = True
+    def on_move(self, event):
+        if event.inaxes!=self.ax: return
+        if self.called and self.move_on:
+            # Call modify code on move
+            #print 'move', event.xdata, event.ydata
+            latent_values = np.array((event.xdata, event.ydata))
+            y = self.model.predict(latent_values)[0]
+            self.data_visualize.modify(y)
+            #print 'y', y
+
+class data_show:
+    """The data show class is a base class which describes how to visualize a particular data set. For example, motion capture data can be plotted as a stick figure, or images are shown using imshow. This class enables latent to data visualizations for the GP-LVM."""
+
+    def __init__(self, vals, axis=None):
+        self.vals = vals
+        # If no axes are defined, create some.
+        if axis==None:
+            fig = plt.figure()
+            self.axis = fig.add_subplot(111)            
+        else:
+            self.axis = axis
+
+    def modify(self, vals):
+        raise NotImplementedError, "this needs to be implemented to use the data_show class"
+
+class vector_show(data_show):
+    """A base visualization class that just shows a data vector as a plot of vector elements alongside their indices."""
+    def __init__(self, vals, axis=None):
+        data_show.__init__(self, vals, axis)
+        self.vals = vals.T
+        self.handle = plt.plot(np.arange(0, len(vals))[:, None], self.vals)[0]
+
+    def modify(self, vals):
+        xdata, ydata = self.handle.get_data()
+        self.vals = vals.T
+        self.handle.set_data(xdata, self.vals)
+        self.axis.figure.canvas.draw()
+
+class image_show(data_show):
+    """Show a data vector as an image."""
+    def __init__(self, vals, axis=None, dimensions=(16,16), transpose=False, invert=False):
+        data_show.__init__(self, vals, axis)
+        self.dimensions = dimensions
+        self.fig_display = plt.figure()
+        self.set_image(vals)
+        self.handle = plt.imshow(self.vals)
+        self.transpose = transpose
+        self.invert = invert
+
+    def modify(self, vals):
+        self.set_image(vals)
+        self.handle.set_array(self.vals)
+        self.axis.figure.canvas.draw()
+
+    def set_image(self, vals):
+        self.vals = np.reshape(vals, self.dimensions)
+        if self.transpose:
+            self.vals = self.vals.T
+        if self.invert:
+            self.vals = -self.vals
+
+class stick_show(data_show):
+    """Show a three dimensional point cloud as a figure. Connect elements of the figure together using the matrix connect."""
+
+    def __init__(self, vals, axis=None, connect=None):
+        if axis==None:
+            fig = plt.figure()
+            axis = fig.add_subplot(111, projection='3d')
+        data_show.__init__(self, vals, axis)
+        self.vals = vals.reshape((3, vals.shape[1]/3)).T
+        self.x_lim = np.array([self.vals[:, 0].min(), self.vals[:, 0].max()])
+        self.y_lim = np.array([self.vals[:, 1].min(), self.vals[:, 1].max()])
+        self.z_lim = np.array([self.vals[:, 2].min(), self.vals[:, 2].max()])
+        self.points_handle = self.axis.scatter(self.vals[:, 0], self.vals[:, 1], self.vals[:, 2])
+        self.axis.set_xlim(self.x_lim)
+        self.axis.set_ylim(self.y_lim)
+        self.axis.set_zlim(self.z_lim)
+        self.axis.set_aspect(1)
+        self.axis.autoscale(enable=False)
+
+        self.connect = connect
+        if not self.connect==None:
+            x = []
+            y = []
+            z = []
+            self.I, self.J = np.nonzero(self.connect)
+            for i in range(len(self.I)):
+                x.append(self.vals[self.I[i], 0])
+                x.append(self.vals[self.J[i], 0])
+                x.append(np.NaN)
+                y.append(self.vals[self.I[i], 1])
+                y.append(self.vals[self.J[i], 1])
+                y.append(np.NaN)
+                z.append(self.vals[self.I[i], 2])
+                z.append(self.vals[self.J[i], 2])
+                z.append(np.NaN)
+            self.line_handle = self.axis.plot(np.array(x), np.array(y), np.array(z), 'b-')
+        self.axis.figure.canvas.draw()
+
+    def modify(self, vals):
+        self.points_handle.remove()
+        self.line_handle[0].remove()
+        self.vals = vals.reshape((3, vals.shape[1]/3)).T
+        self.points_handle = self.axis.scatter(self.vals[:, 0], self.vals[:, 1], self.vals[:, 2])
+        self.axis.set_xlim(self.x_lim)
+        self.axis.set_ylim(self.y_lim)
+        self.axis.set_zlim(self.z_lim)
+        self.line_handle = []
+        if not self.connect==None:
+            x = []
+            y = []
+            z = []
+            self.I, self.J = np.nonzero(self.connect)
+            for i in range(len(self.I)):
+                x.append(self.vals[self.I[i], 0])
+                x.append(self.vals[self.J[i], 0])
+                x.append(np.NaN)
+                y.append(self.vals[self.I[i], 1])
+                y.append(self.vals[self.J[i], 1])
+                y.append(np.NaN)
+                z.append(self.vals[self.I[i], 2])
+                z.append(self.vals[self.J[i], 2])
+                z.append(np.NaN)
+            self.line_handle = self.axis.plot(np.array(x), np.array(y), np.array(z), 'b-')
+
+        self.axis.figure.canvas.draw()
+        
+
+