Copy edit of chapter 8.

Note fully done, still have to figure out what to do with the ball
tracking code, and add a control example to the chapter.

code/book_plots.py

@@ -83,8 +83,13 @@ def plot_measurements(xs, ys=None, color='r', lw=2, label='Measurements', **kwar
 
 
 
-def plot_residual_limits(Ps):
-    std = np.sqrt(Ps)
+def plot_residual_limits(Ps, stds=1.):
+    """ plots standand deviation given in Ps as a yellow shaded region. One std
+    by default, use stds for a different choice (e.g. stds=3 for 3 standard
+    deviations.
+    """
+
+    std = np.sqrt(Ps) * stds
 
     plt.plot(-std, color='k', ls=':', lw=2)
     plt.plot(std, color='k', ls=':', lw=2)

code/kf_design_internal.py

@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jul 13 13:20:27 2015
+
+@author: Roger
+"""
+
+from filterpy.kalman import UnscentedKalmanFilter as UKF
+from filterpy.kalman import JulierSigmaPoints
+from math import atan2
+import numpy as np
+
+def hx(x):
+    """ compute measurements corresponding to state x"""
+    dx = x[0] - hx.radar_pos[0]
+    dy = x[1] - hx.radar_pos[1]
+    return np.array([atan2(dy,dx), (dx**2 + dy**2)**.5])
+
+def fx(x,dt):
+    """ predict state x at 'dt' time in the future"""
+    return x
+
+def set_radar_pos(pos):
+    global hx
+    hx.radar_pos = pos
+
+def sensor_fusion_kf():
+    global hx, fx
+    # create unscented Kalman filter with large initial uncertainty
+    points = JulierSigmaPoints(2, kappa=2)
+    kf = UKF(2, 2, dt=0.1, hx=hx, fx=fx, points=points)
+    kf.x = np.array([100, 100.])
+    kf.P *= 40
+    return kf
+

code/mkf_internal.py

@@ -359,8 +359,6 @@ def plot_track(ps, zs, cov, std_scale=1,
                plot_P=True, y_lim=None,
                title='Kalman Filter'):
 
-    print(std_scale)
-
     count = len(zs)
     actual = np.linspace(0, count - 1, count)
     cov = np.asarray(cov)