Revised particle filter chapter.

Pretty happy with it now. Needs copy editing, and probably an
easier introduction to convey the basic idea. Moved from a class
based approach to a procedural approach, and I like that very much.

code/kf_internal.py

@@ -1,45 +1,45 @@
-# -*- coding: utf-8 -*-
-
-"""Copyright 2015 Roger R Labbe Jr.
-
-
-Code supporting the book
-
-Kalman and Bayesian Filters in Python
-https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
-
-
-This is licensed under an MIT license. See the LICENSE.txt file
-for more information.
-"""
-
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-import book_plots as bp
-import matplotlib.pyplot as plt
-
-def plot_dog_track(xs, measurement_var, process_var):
-    N = len(xs)
-    bp.plot_track([0, N-1], [1, N])
-    bp.plot_measurements(xs, label='Sensor')
-    bp.set_labels('variance = {}, process variance = {}'.format(
-              measurement_var, process_var), 'time', 'pos')
-    plt.ylim([0, N])
-    bp.show_legend()
-    plt.show()
-
-
-def print_gh(predict, update, z):
-    predict_template = '         {: 7.3f} {: 8.3f}'
-    update_template = '{: 7.3f} {: 7.3f}\t   {:.3f}'
-
-    print(predict_template.format(predict[0], predict[1]),end='\t')
-    print(update_template.format(update[0], update[1], z))
-
-
-def print_variance(positions):
-    print('Variance:')
-    for i in range(0, len(positions), 5):
-        print('\t{:.4f} {:.4f} {:.4f} {:.4f} {:.4f}'.format(
+# -*- coding: utf-8 -*-
+
+"""Copyright 2015 Roger R Labbe Jr.
+
+
+Code supporting the book
+
+Kalman and Bayesian Filters in Python
+https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
+
+
+This is licensed under an MIT license. See the LICENSE.txt file
+for more information.
+"""
+
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import book_plots as bp
+import matplotlib.pyplot as plt
+
+def plot_dog_track(xs, measurement_var, process_var):
+    N = len(xs)
+    bp.plot_track([0, N-1], [1, N])
+    bp.plot_measurements(xs, label='Sensor')
+    bp.set_labels('variance = {}, process variance = {}'.format(
+              measurement_var, process_var), 'time', 'pos')
+    plt.ylim([0, N])
+    bp.show_legend()
+    plt.show()
+
+
+def print_gh(predict, update, z):
+    predict_template = '         {: 7.3f} {: 8.3f}'
+    update_template = '{: 7.3f} {: 7.3f}\t   {:.3f}'
+
+    print(predict_template.format(predict[0], predict[1]),end='\t')
+    print(update_template.format(update[0], update[1], z))
+
+
+def print_variance(positions):
+    print('Variance:')
+    for i in range(0, len(positions), 5):
+        print('\t{:.4f} {:.4f} {:.4f} {:.4f} {:.4f}'.format(
                 *[v[1] for v in positions[i:i+5]]))