Extensive copy editing.

code/ekf_internal.py

@@ -52,7 +52,6 @@ def ball_kf(x, y, omega, v0, dt, r=0.5, q=0.02):
 
 
 def plot_radar(xs, track, time):
-
     plt.figure()
     bp.plot_track(time, track[:, 0])
     bp.plot_filter(time, xs[:, 0])
@@ -76,6 +75,7 @@ def plot_radar(xs, track, time):
     plt.ylim((900, 1600))
     plt.show()
 
+    
 def plot_bicycle():
     plt.clf()
     plt.axes()
@@ -215,11 +215,11 @@ def show_radar_chart():
 
 
 
-    ax.annotate('$\Theta$', xy=(1.2, 1.1), color='b')
+    ax.annotate('$\Theta$', xy=(1.2, 1.05), color='b')
     ax.annotate('Aircraft', xy=(2.04,2.), color='b')
-    ax.annotate('altitude', xy=(2.04,1.5), color='k')
-    ax.annotate('X', xy=(1.5, .9))
-    ax.annotate('Radar', xy=(.95, 0.9))
+    ax.annotate('altitude (y)', xy=(2.04,1.5), color='k')
+    ax.annotate('x', xy=(1.5, .9))
+    ax.annotate('Radar', xy=(.95, 0.8))
     ax.annotate('Slant\n  (r)', xy=(1.5,1.62), color='r')
 
     plt.title("Radar Tracking")
@@ -229,4 +229,4 @@ def show_radar_chart():
     ax.yaxis.set_ticks([])
 
 
-    plt.show()
+    plt.show()

code/nonlinear_plots.py

@@ -16,6 +16,7 @@ for more information.
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
+from filterpy.kalman import MerweScaledSigmaPoints, unscented_transform
 from filterpy.stats import multivariate_gaussian
 from matplotlib import cm
 import matplotlib.pyplot as plt
@@ -47,10 +48,10 @@ def plot_nonlinear_func(data, f, gaussian, num_bins=300):
     #plot output
     h = np.histogram(ys, num_bins, density=False)
     plt.subplot(2,2,4)
-    plt.plot(h[0], h[1][1:], lw=4, alpha=0.5)
+    plt.plot(h[0], h[1][1:], lw=4, alpha=0.8)
     plt.ylim(out_lims[1], out_lims[0])
     plt.gca().xaxis.set_ticklabels([])
-    plt.title('output')
+    plt.title('Output')
 
     plt.axhline(np.mean(ys), ls='--', lw=2)
     plt.axhline(f(x0), lw=1)
@@ -66,16 +67,16 @@ def plot_nonlinear_func(data, f, gaussian, num_bins=300):
     print(max(norm.pdf(xs)))'''
 
     # plot transfer function
-    plt.subplot(2,2,3)
+    plt.subplot(2, 2, 3)
     x = np.arange(in_lims[0], in_lims[1], 0.1)
     y = f(x)
-    plt.plot (x,y, 'k')
+    plt.plot (x, y, 'k')
     isct = f(x0)
     plt.plot([x0, x0, in_lims[1]], [out_lims[1], isct, isct], color='r', lw=1)
     plt.xlim(in_lims)
     plt.ylim(out_lims)
     #plt.axis('equal')
-    plt.title('function')
+    plt.title('f(x)')
 
     # plot input
     h = np.histogram(data, num_bins, density=True)
@@ -84,7 +85,7 @@ def plot_nonlinear_func(data, f, gaussian, num_bins=300):
     plt.plot(h[1][1:], h[0], lw=4)
     plt.xlim(in_lims)
     plt.gca().yaxis.set_ticklabels([])
-    plt.title('input')
+    plt.title('Input')
 
     plt.show()
 
@@ -106,11 +107,9 @@ def plot_ekf_vs_mc():
     d_t = fx(data)
 
     mean_ekf = fx(mean)
-
     slope = dfx(mean)
     std_ekf = abs(slope*std)
 
-
     norm = scipy.stats.norm(mean_ekf, std_ekf)
     xs = np.linspace(-3, 5, 200)
     plt.plot(xs, norm.pdf(xs), lw=2, ls='--', color='b')
@@ -126,8 +125,6 @@ def plot_ekf_vs_mc():
     print('EKF    mean={:.2f}, std={:.2f}'.format(mean_ekf, std_ekf))
 
 
-from filterpy.kalman import MerweScaledSigmaPoints, unscented_transform
-
 def plot_ukf_vs_mc(alpha=0.001, beta=3., kappa=1.):
 
     def fx(x):
@@ -159,12 +156,12 @@ def plot_ukf_vs_mc(alpha=0.001, beta=3., kappa=1.):
 
     norm = scipy.stats.norm(ukf_mean, ukf_std)
     xs = np.linspace(-3, 5, 200)
-    plt.plot(xs, norm.pdf(xs), ls='--', lw=1, color='b')
-    plt.hist(d_t, bins=200, normed=True, histtype='step', lw=1, color='g')
+    plt.plot(xs, norm.pdf(xs), ls='--', lw=2, color='b')
+    plt.hist(d_t, bins=200, normed=True, histtype='step', lw=2, color='g')
 
     actual_mean = d_t.mean()
-    plt.axvline(actual_mean, lw=1, color='g', label='Monte Carlo')
-    plt.axvline(ukf_mean, lw=1, ls='--', color='b', label='UKF')
+    plt.axvline(actual_mean, lw=2, color='g', label='Monte Carlo')
+    plt.axvline(ukf_mean, lw=2, ls='--', color='b', label='UKF')
     plt.legend()
     plt.show()
 

code/pf_internal.py

@@ -118,6 +118,7 @@ def plot_random_pd():
         #plt.setp(plt.gca().get_yticklabels(), visible=False)
         plt.axes(xticks=[], yticks=[], frameon=False)
         plt.plot(x, y2)
+        plt.ylim([0, max(y2)+.1])
 
 
 def plot_monte_carlo_ukf():

code/ukf_internal.py

@@ -279,6 +279,17 @@ def plot_radar(xs, t, plot_x=True, plot_vel=True, plot_alt=True):
         plt.ylabel('altitude')
     plt.show()
 
+    
+def plot_altitude(xs, t, track):
+    xs = np.asarray(xs)
+
+    plt.plot(t, xs[:,2], label='filter', )
+    plt.plot(t, track, label='Aircraft', lw=2, ls='--', c='k')
+    plt.xlabel('time(sec)')
+    plt.ylabel('altitude')
+    plt.legend(loc=4)
+
+    
 def print_sigmas(n=1, mean=5, cov=3, alpha=.1, beta=2., kappa=2):
     points = MerweScaledSigmaPoints(n, alpha, beta, kappa)
     print('sigmas: ', points.sigma_points(mean,  cov).T[0])