Work on animations.

Experimented with adding animations to the book. Also, fixed
the H-Infinity code a bit to more close reflect Simon's code.

Chapter02_Discrete_Bayes/discrete_bayes.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:8c25e49f13c73dcadcb995b7316539e0f420423df6a8583eb5ec8e7a3f0baf6d"
+  "signature": "sha256:3fd1875f54e8ffeb8fd85aa2109497a1ecb4090bbdb017910763f76e12562c32"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f96f4456a20>"
+        "<IPython.core.display.HTML at 0x7fb6bf437cc0>"
        ]
       }
      ],
@@ -356,9 +356,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f96db3def28>"
+        "<matplotlib.figure.Figure at 0x7fb6a2f43ac8>"
        ]
       }
      ],
@@ -433,7 +433,7 @@
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -594,8 +594,12 @@
       "        \n",
       "pos = np.array([.4, .1, .2, .3])\n",
       "print('pos before predict =', pos)\n",
+      "bar_plot.plot (pos, title='Before prediction')\n",
+      "\n",
+      "\n",
       "pos = perfect_predict(pos, 1)\n",
-      "print('pos after predict =', pos)\n"
+      "print('pos after predict =', pos)\n",
+      "bar_plot.plot (pos, title='After prediction')\n"
      ],
      "language": "python",
      "metadata": {},
@@ -605,7 +609,22 @@
        "stream": "stdout",
        "text": [
         "pos before predict = [ 0.4  0.1  0.2  0.3]\n",
-        "pos after predict = [ 0.3  0.4  0.1  0.2]\n"
+        "pos after predict ="
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " [ 0.3  0.4  0.1  0.2]\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb6a2be80f0>"
        ]
       }
      ],
@@ -681,7 +700,8 @@
      "input": [
       "p = np.array([0, 0, .4, .6, 0, 0, 0, 0])\n",
       "res = predict (p, 2, .8, .1, .1)\n",
-      "print(res)"
+      "print(res)\n",
+      "bar_plot.plot(res)"
      ],
      "language": "python",
      "metadata": {},
@@ -692,6 +712,14 @@
        "text": [
         "[ 0.    0.    0.    0.04  0.38  0.52  0.06  0.  ]\n"
        ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb6a2a3c0f0>"
+       ]
       }
      ],
      "prompt_number": 11
@@ -717,9 +745,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f96f4456438>"
+        "<matplotlib.figure.Figure at 0x7fb6a2b93be0>"
        ]
       }
      ],
@@ -755,9 +783,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f96db3844e0>"
+        "<matplotlib.figure.Figure at 0x7fb6a2bd6438>"
        ]
       }
      ],
@@ -794,9 +822,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f96db385b38>"
+        "<matplotlib.figure.Figure at 0x7fb6a2c38ba8>"
        ]
       }
      ],
@@ -850,9 +878,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f96db3a0c50>"
+        "<matplotlib.figure.Figure at 0x7fb6a2aacba8>"
        ]
       }
      ],
@@ -887,9 +915,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f96db35cd68>"
+        "<matplotlib.figure.Figure at 0x7fb6a2b495f8>"
        ]
       }
      ],
@@ -916,9 +944,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f96db33cd68>"
+        "<matplotlib.figure.Figure at 0x7fb6a29365f8>"
        ]
       }
      ],
@@ -945,9 +973,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f96db3c49e8>"
+        "<matplotlib.figure.Figure at 0x7fb6a29e3b38>"
        ]
       }
      ],
@@ -994,14 +1022,6 @@
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f96db36e9e8>"
-       ]
-      },
       {
        "output_type": "stream",
        "stream": "stdout",
@@ -1009,6 +1029,14 @@
         "[ 0.2245871   0.06288015  0.06109133  0.0581008   0.09334062  0.2245871\n",
         "  0.06288015  0.06109133  0.0581008   0.09334062]\n"
        ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb6a28e5a20>"
+       ]
       }
      ],
      "prompt_number": 19
@@ -1034,9 +1062,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f96db385358>"
+        "<matplotlib.figure.Figure at 0x7fb6a2970438>"
        ]
       }
      ],
@@ -1055,10 +1083,11 @@
      "input": [
       "measurements = [0,1,0,1,0,0]\n",
       "\n",
-      "for m in measurements:\n",
+      "for i,m in enumerate(measurements):\n",
       "    pos = update(pos, m, .6, .2)\n",
       "    pos = predict(pos, 1, .8, .1, .1)\n",
-      "bar_plot.plot(pos)"
+      "    plt.subplot(3,2, i+1)\n",
+      "    bar_plot.plot(pos, title='step{}'.format(i+1))"
      ],
      "language": "python",
      "metadata": {},
@@ -1066,13 +1095,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f96db40da58>"
+        "<matplotlib.figure.Figure at 0x7fb6a28b74e0>"
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -1176,15 +1205,6 @@
       "**If you can understand this chapter you will be able to understand and implement Kalman filters** I cannot stress this enough. If anything is murky, go back and reread this chapter and play with the code. The rest of this book will build on the algorithms that we use here. If you don't intuitively understand why this histogram filter works, and can at least work through the math, you will have little success with the rest of the material. However, if you grasp the fundamental insight - multiplying probabilities when we measure, and shifting probabilities when we update leads to a converging solution - then you understand everything important you need to grasp the Kalman filter. "
      ]
     },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "**Author notes:**\n",
-      "    Do I want to go to the multidimensional case? At least describe it, but why not implement it as well"
-     ]
-    },
     {
      "cell_type": "heading",
      "level": 2,

Chapter02_Discrete_Bayes/discrete_bayes_animations.ipynb

@@ -0,0 +1,23 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:fea22f31f443e23e2d336a0742e6bf9a559c588029a2d3c2e8e69066e08c2fe5"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

Chapter12_HInfinity_Filters/HInfinity_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7d487f11e312a7f42c8b0f26c54306db69a8a6b3fcec7863bf7db4070d9fc17e"
+  "signature": "sha256:8c68f422d155e6e256b984c1601ed17d878240844f686e8559510bac3b24b183"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -256,13 +256,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 4,
+       "prompt_number": 2,
        "text": [
-        "<IPython.core.display.HTML at 0x7f16501445f8>"
+        "<IPython.core.display.HTML at 0x7f5f82564358>"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -291,96 +291,54 @@
       "from filterpy.hinfinity import HInfinityFilter\n",
       "\n",
       "dt = 0.1\n",
-      "f = HInfinityFilter(2, 1, 0, gamma=.4)\n",
+      "f = HInfinityFilter(2, 1, dim_u=1, gamma=.01)\n",
       "\n",
       "f.F = array([[1., dt],\n",
       "             [0., 1.]])\n",
       "\n",
       "f.H = array([[0., 1.]])\n",
       "f.x = array([[0., 0.]]).T\n",
-      "#f.G = array([[dt**2 / 2, dt]]).T\n",
+      "f.G = array([[dt**2 / 2, dt]]).T\n",
       "\n",
       "f.P = 0.01\n",
       "f.W = array([[0.0003, 0.005],\n",
-      "             [0.0050, 0.100]])/ 1000\n",
+      "             [0.0050, 0.100]])/ 1000  #process noise\n",
       "\n",
       "f.V = 0.01\n",
       "f.Q = 0.01\n",
+      "u = 1. #acceleration of 1 f/sec**2\n",
       "\n",
       "xs = []\n",
       "vs = []\n",
       "\n",
       "for i in range(1,40):\n",
       "    f.update (5)\n",
-      "    print(f.x.T)\n",
+      "    #print(f.x.T)\n",
       "    xs.append(f.x[0,0])\n",
       "    vs.append(f.x[1,0])\n",
-      "    f.predict()\n",
+      "    f.predict(u=u)\n",
       "\n",
       "plt.subplot(211)\n",
       "plt.plot(xs)\n",
+      "plt.title('position')\n",
       "plt.subplot(212)\n",
-      "plt.plot(vs)\n",
+      "plt.plot(vs) \n",
+      "plt.title('velocity')\n",
       "plt.show()\n"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0.250005  2.50005 ]]\n",
-        "[[ 0.66806083  3.34442646]]\n",
-        "[[ 1.1284151   3.77161316]]\n",
-        "[[ 1.60570517  4.03126553]]\n",
-        "[[ 2.09144161  4.20685183]]\n",
-        "[[ 2.58197401  4.33419113]]\n",
-        "[[ 3.07547127  4.4312003 ]]\n",
-        "[[ 3.57091268  4.50783208]]\n",
-        "[[ 4.06768385  4.57005637]]\n",
-        "[[ 4.56539275  4.62167173]]\n",
-        "[[ 5.06377767  4.66521177]]\n",
-        "[[ 5.56265755  4.7024333 ]]\n",
-        "[[ 6.06190354  4.73459514]]\n",
-        "[[ 6.5614219   4.76262524]]\n",
-        "[[ 7.0611432   4.78722492]]\n",
-        "[[ 7.56101536  4.80893611]]\n",
-        "[[ 8.06099891  4.828186  ]]\n",
-        "[[ 8.56106372  4.8453174 ]]\n",
-        "[[ 9.06118672  4.86060987]]\n",
-        "[[ 9.56135018  4.87429471]]\n",
-        "[[ 10.06154053   4.88656573]]\n",
-        "[[ 10.56174736   4.89758722]]\n",
-        "[[ 11.06196277   4.9074998 ]]\n",
-        "[[ 11.5621808   4.9164249]]\n",
-        "[[ 12.06239701   4.92446815]]\n",
-        "[[ 12.56260818   4.93172204]]\n",
-        "[[ 13.06281197   4.93826801]]\n",
-        "[[ 13.56300681   4.94417805]]\n",
-        "[[ 14.06319168   4.94951612]]\n",
-        "[[ 14.563366     4.95433916]]\n",
-        "[[ 15.0635295    4.95869805]]\n",
-        "[[ 15.56368218   4.96263833]]\n",
-        "[[ 16.06382423   4.96620086]]\n",
-        "[[ 16.56395596   4.96942231]]\n",
-        "[[ 17.06407777   4.97233571]]\n",
-        "[[ 17.56419015   4.97497078]]\n",
-        "[[ 18.0642936    4.97735429]]\n",
-        "[[ 18.56438866   4.97951041]]\n",
-        "[[ 19.06447587   4.98146094]]\n"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f164f16d518>"
+        "<matplotlib.figure.Figure at 0x7f5f616b2ac8>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 14
     }
    ],
    "metadata": {}