The base python installation has a file code.py; this causes

problems when trying to import files from the code subdirectory.

I also tried to rearange some code so that book_format does not need
to be imported if you are not running in a Jupyter Notebook.

kf_book/DogSimulation.py

@@ -0,0 +1,73 @@
+# -*- 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 copy
+import math
+import numpy as np
+from numpy.random import randn
+
+class DogSimulation(object):
+
+    def __init__(self, x0=0, velocity=1,
+                 measurement_var=0.0, process_var=0.0):
+        """ x0 - initial position
+            velocity - (+=right, -=left)
+            measurement_variance - variance in measurement m^2
+            process_variance - variance in process (m/s)^2
+        """
+        self.x = x0
+        self.velocity = velocity
+        self.measurement_noise = math.sqrt(measurement_var)
+        self.process_noise = math.sqrt(process_var)
+
+
+    def move(self, dt=1.0):
+        '''Compute new position of the dog assuming `dt` seconds have
+        passed since the last update.'''
+        # compute new position based on velocity. Add in some
+        # process noise
+        velocity = self.velocity + randn() * self.process_noise * dt
+        self.x += velocity * dt
+
+
+    def sense_position(self):
+        # simulate measuring the position with noise
+        return self.x + randn() * self.measurement_noise
+
+
+    def move_and_sense(self, dt=1.0):
+        self.move(dt)
+        x = copy.deepcopy(self.x)
+        return x, self.sense_position()
+
+
+    def run_simulation(self, dt=1, count=1):
+        """ simulate the dog moving over a period of time.
+
+        **Returns**
+        data : np.array[float, float]
+            2D array, first column contains actual position of dog,
+            second column contains the measurement of that position
+        """
+        return np.array([self.move_and_sense(dt) for i in range(count)])
+
+
+
+
+