Edit to fit on 6x9 book.

Somewhat arbitrary, but I want the code to be readable on
narrow paper than 8.5x11.

01-g-h-filter.ipynb

@@ -1011,11 +1011,11 @@
     "\n",
     "    1. Initialize the state of the filter\n",
     "    2. Initialize our belief in the state\n",
-    "    \n",
+    "\n",
     "**Predict**\n",
     "\n",
-    "    1. Based on the system behavior, predict state at the next time step\n",
-    "    2. Adjust beliefto account for the uncertainty in prediction\n",
+    "    1.Use system behavior to predict state at the next time step\n",
+    "    2. Adjust belief to account for the uncertainty in prediction\n",
     "    \n",
     "**Update**\n",
     "\n",

code/DogSimulation.py

@@ -13,7 +13,7 @@ import math
 class DogSimulation(object):
 
     def __init__(self, x0=0, velocity=1,
-                 measurement_variance=0.0, process_variance=0.0):
+                 measurement_var=0.0, process_var=0.0):
         """ x0 - initial position
             velocity - (+=right, -=left)
             measurement_variance - variance in measurement m^2
@@ -21,8 +21,8 @@ class DogSimulation(object):
         """
         self.x = x0
         self.velocity = velocity
-        self.measurement_noise = math.sqrt(measurement_variance)
-        self.process_noise = math.sqrt(process_variance)
+        self.measurement_noise = math.sqrt(measurement_var)
+        self.process_noise = math.sqrt(process_var)
 
 
     def move(self, dt=1.0):

code/book_plots.py

@@ -58,6 +58,27 @@ def set_limits(x, y):
     plt.gca().set_xlim(x)
     plt.gca().set_ylim(y)
 
+def plot_predictions(p, rng=None):
+    if rng is None:
+        rng = range(len(p))
+    plt.scatter(rng, p, marker='v', s=40, edgecolor='r',
+                facecolor='None', lw=2, label='prediction')
+
+
+
+def plot_kf_output(xs, filter_xs, zs, title=None, aspect_equal=True):
+    plot_filter(filter_xs[:, 0])
+    plot_track(xs[:, 0])
+
+    if zs is not None:
+        plot_measurements(zs)
+    show_legend()
+    set_labels(title=title, x='meters', y='time (sec)')
+    if aspect_equal:
+        plt.gca().set_aspect('equal')
+    plt.xlim((-1, len(xs)))
+    plt.show()
+
 
 def plot_measurements(xs, ys=None, color='k', lw=2, label='Measurements',
                       lines=False, **kwargs):
@@ -84,9 +105,11 @@ def plot_measurements(xs, ys=None, color='k', lw=2, label='Measurements',
             plt.plot(xs, color=color, lw=lw, ls='--', label=label, **kwargs)
     else:
         if ys is not None:
-            plt.scatter(xs, ys, edgecolor=color, facecolor='none', lw=2, label=label, **kwargs)
+            plt.scatter(xs, ys, edgecolor=color, facecolor='none',
+                        lw=2, label=label, **kwargs)
         else:
-            plt.scatter(range(len(xs)), xs, edgecolor=color, facecolor='none', lw=2, label=label, **kwargs)
+            plt.scatter(range(len(xs)), xs, edgecolor=color, facecolor='none',
+                        lw=2, label=label, **kwargs)
 
 
 def plot_residual_limits(Ps, stds=1.):

code/kf_internal.py

@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul 25 17:13:23 2015
+
+@author: rlabbe
+"""
+
+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 = 'PREDICT: {: 6.2f} {: 6.2f}'
+    update_template = 'UPDATE: {: 6.2f} {: 6.2f}\tZ: {:.2f}'
+
+    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]]))

code/mkf_internal.py

@@ -13,6 +13,50 @@ from matplotlib import cm
 from mpl_toolkits.mplot3d import Axes3D
 from numpy.random import multivariate_normal
 
+
+
+def zs_var_240():
+    return [3.59, 1.73, -2.575, 4.38, 9.71, 2.88, 10.08,
+      8.97, 3.74, 12.81, 11.15, 9.25, 3.93, 11.11,
+      19.29, 16.20, 19.63, 9.54, 26.27, 23.29, 25.18,
+      26.21, 17.1, 25.27, 26.86,33.70, 25.92, 28.82,
+      32.13, 25.0, 38.56, 26.97, 22.49, 40.77, 32.95,
+      38.20, 40.93, 39.42, 35.49, 36.31, 31.56, 50.29,
+      40.20, 54.49, 50.38, 42.79, 37.89, 56.69, 41.47, 53.66]
+
+def zs_var_375():
+    return [-6.947, 12.467, 6.899, 2.643, 6.980, 5.820, 5.788, 10.614, 5.210,
+      14.338, 11.401, 19.138, 14.169, 19.572, 25.471, 13.099, 27.090,
+      12.209, 14.274, 21.302, 14.678, 28.655, 15.914, 28.506, 23.181,
+      18.981, 28.197, 39.412, 27.640, 31.465, 34.903, 28.420, 33.889,
+      46.123, 31.355, 30.473, 49.861, 41.310, 42.526, 38.183, 41.383,
+      41.919, 52.372, 42.048, 48.522, 44.681, 32.989, 37.288, 49.141,
+      54.235, 62.974, 61.742, 54.863, 52.831, 61.122, 61.187, 58.441,
+      47.769, 56.855, 53.693, 61.534, 70.665, 60.355, 65.095, 63.386]
+
+
+
+def plot_track_ellipses(N, zs, ps, cov, title):
+    bp.plot_measurements(range(1,N + 1), zs)
+    plt.plot(range(1, N + 1), ps, c='b', lw=2, label='filter')
+    plt.legend(loc='best')
+    plt.title(title)
+
+    for i,p in enumerate(cov):
+        plot_covariance_ellipse(
+              (i+1, ps[i]), cov=p, variance=4,
+               axis_equal=False, ec='g', alpha=0.5)
+
+        if i == len(cov)-1:
+            s = ('$\sigma^2_{pos} = %.2f$' % p[0,0])
+            plt.text (20, 5, s, fontsize=18)
+            s = ('$\sigma^2_{vel} = %.2f$' % p[1, 1])
+            plt.text (20, 0, s, fontsize=18)
+    plt.xlim(-10, 80)
+    plt.gca().set_aspect('equal')
+    plt.show()
+
+
 def show_residual_chart():
     est_y = ((164.2-158)*.8 + 158)
 
@@ -23,7 +67,7 @@ def show_residual_chart():
 
     ax.annotate('', xy=[1,159], xytext=[1,164.2],
                 arrowprops=dict(arrowstyle='-',
-                                ec='k', lw=1, shrinkA=8, shrinkB=8))
+                                ec='k', lw=3, shrinkA=8, shrinkB=8))
 
     ax.annotate('', xy=(1., est_y), xytext=(0.9, est_y),
                 arrowprops=dict(arrowstyle='->', ec='#004080',

code/nonlinear_plots.py

@@ -266,7 +266,7 @@ def plot_cov_ellipse_colormap(cov=[[1,1],[1,1]]):
 
 
 
-def plot_multiple_gaussians(xs, ps, x_range, y_range, N):
+def plot_gaussians(xs, ps, x_range, y_range, N):
     """ given a list of 2d states (x,y) and 2x2 covariance matrices, produce
     a surface plot showing all of the gaussians"""
 

code/ukf_internal.py

@@ -301,7 +301,7 @@ def plot_rts_output(xs, Ms, t):
     plt.legend(loc=4)
 
     np.set_printoptions(precision=4)
-    print('Difference in position in meters:', xs[-6:-1, 0] - Ms[-6:-1, 0])
+    print('Difference in position in meters:\n\t', xs[-6:-1, 0] - Ms[-6:-1, 0])
 
 
 def plot_scatter_of_bearing_error():

pdf/6x9build_book.bat

@@ -1,4 +1,5 @@
-copy /Y  ..\01-g-h-filter.ipynb book.ipynb
+copy /Y  ..\14-Adaptive-Filtering.ipynb book.ipynb
+REM python merge_book.py
 
 ipython nbconvert --to latex --template book6x9 book.ipynb
 ipython to_pdf.py