Added animation of multivariate tracking.

README.md

@@ -1,6 +1,6 @@
 Introductory textbook for Kalman filters and Bayesian filters. All code is written in Python, and the book itself is written in Ipython Notebook so that you can run and modify the code in the book in place, seeing the results inside the book. What better way to learn?
 
-![alt tag](https://raw.githubusercontent.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter05_Kalman_Filters/dog_track.gif)
+![alt tag](https://raw.githubusercontent.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter06_Multivariate_Kalman_Filter/track1.gif)
 
 
 Reading Online

code/stats.py

@@ -226,7 +226,7 @@ def is_inside_ellipse(x,y, ex, ey, orientation, width, height):
 def plot_covariance_ellipse(mean, cov=None, variance = 1.0,
              ellipse=None, title=None, axis_equal=True,
              facecolor='none', edgecolor='#004080',
-             alpha=1.0):
+             alpha=1.0, xlim=None, ylim=None):
     """ plots the covariance ellipse where
 
     mean is a (x,y) tuple for the mean of the covariance (center of ellipse)
@@ -277,6 +277,11 @@ def plot_covariance_ellipse(mean, cov=None, variance = 1.0,
                     lw=2)
         ax.add_patch(e)
     plt.scatter(mean[0], mean[1], marker='+') # mark the center
+    if xlim is not None:
+        ax.set_xlim(xlim)
+
+    if ylim is not None:
+        ax.set_ylim(ylim)
 
 
 def _to_cov(x,n):