Final updates before merge to trunk.

Updated to work with FilterPy 0.0.19.

.gitignore

@@ -4,4 +4,10 @@ __pycache__
 *.pyc
 short.pdf
 11*
-Kalman_and_Bayesian_Filters_in_Python.pdf
+*.aux
+*.out
+book.ipynb
+*.tex
+*.toc
+Kalman_and_Bayesian_Filters_in_Python.pdf
+book_files

09_Unscented_Kalman_Filter.ipynb

@@ -289,9 +289,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x761bdd8>"
+       "<matplotlib.figure.Figure at 0x74c9e10>"
       ]
      },
      "metadata": {},
@@ -351,7 +351,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x57a72b0>"
+       "<matplotlib.figure.Figure at 0x525a358>"
       ]
      },
      "metadata": {},
@@ -387,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -397,14 +397,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in mean x=0.029, y=43.109\n"
+      "Difference in mean x=0.028, y=43.204\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xab1bac8>"
+       "<matplotlib.figure.Figure at 0x8a7beb8>"
       ]
      },
      "metadata": {},
@@ -489,7 +489,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d69978>"
+       "<matplotlib.figure.Figure at 0x8a590b8>"
       ]
      },
      "metadata": {},
@@ -527,7 +527,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7bfebe0>"
+       "<matplotlib.figure.Figure at 0x790ac18>"
       ]
      },
      "metadata": {},
@@ -572,7 +572,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa983e80>"
+       "<matplotlib.figure.Figure at 0x90abda0>"
       ]
      },
      "metadata": {},
@@ -613,7 +613,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa9a2cf8>"
+       "<matplotlib.figure.Figure at 0x8a73080>"
       ]
      },
      "metadata": {},
@@ -726,7 +726,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x82ca978>"
+       "<matplotlib.figure.Figure at 0xa4f3198>"
       ]
      },
      "metadata": {},
@@ -756,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -765,14 +765,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in mean x=0.054, y=0.408\n"
+      "Difference in mean x=-0.061, y=-0.124\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x763a908>"
+       "<matplotlib.figure.Figure at 0xa673978>"
       ]
      },
      "metadata": {},
@@ -981,7 +981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -991,7 +991,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xab73ba8>"
+       "<matplotlib.figure.Figure at 0xa55be80>"
       ]
      },
      "metadata": {},
@@ -1039,7 +1039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1093,7 +1093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1103,7 +1103,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xabc1550>"
+       "<matplotlib.figure.Figure at 0xa538cf8>"
       ]
      },
      "metadata": {},
@@ -1178,7 +1178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1188,7 +1188,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xabdc1d0>"
+       "<matplotlib.figure.Figure at 0xa5c68d0>"
       ]
      },
      "metadata": {},
@@ -1224,7 +1224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1260,7 +1260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1288,7 +1288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1356,7 +1356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1366,7 +1366,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x82ed4a8>"
+       "<matplotlib.figure.Figure at 0xa584eb8>"
       ]
      },
      "metadata": {},
@@ -1376,7 +1376,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x878d4e0>"
+       "<matplotlib.figure.Figure at 0x8a1b2b0>"
       ]
      },
      "metadata": {},
@@ -1386,7 +1386,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7bf8668>"
+       "<matplotlib.figure.Figure at 0xa678080>"
       ]
      },
      "metadata": {},
@@ -1482,7 +1482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1492,7 +1492,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d4fb00>"
+       "<matplotlib.figure.Figure at 0xa8dddd8>"
       ]
      },
      "metadata": {},
@@ -1555,7 +1555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1565,7 +1565,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xaa4b2b0>"
+       "<matplotlib.figure.Figure at 0x79124a8>"
       ]
      },
      "metadata": {},
@@ -1647,7 +1647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1664,7 +1664,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d4f0f0>"
+       "<matplotlib.figure.Figure at 0xa5842b0>"
       ]
      },
      "metadata": {},
@@ -1707,7 +1707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1724,7 +1724,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv8AAAEtCAYAAACf5Q+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xabdff60>"
+       "<matplotlib.figure.Figure at 0xa966860>"
       ]
      },
      "metadata": {},
@@ -1810,7 +1810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1818,9 +1818,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xabae7f0>"
+       "<matplotlib.figure.Figure at 0x7ff7a58>"
       ]
      },
      "metadata": {},
@@ -1862,7 +1862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1872,7 +1872,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xac85278>"
+       "<matplotlib.figure.Figure at 0x8a123c8>"
       ]
      },
      "metadata": {},
@@ -1964,7 +1964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1974,7 +1974,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xac965f8>"
+       "<matplotlib.figure.Figure at 0xa6ba630>"
       ]
      },
      "metadata": {},
@@ -2012,7 +2012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2038,7 +2038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2076,7 +2076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2086,7 +2086,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xac96668>"
+       "<matplotlib.figure.Figure at 0x85e2cf8>"
       ]
      },
      "metadata": {},
@@ -2117,7 +2117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2127,7 +2127,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d0a710>"
+       "<matplotlib.figure.Figure at 0xa59d940>"
       ]
      },
      "metadata": {},
@@ -2165,7 +2165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2191,7 +2191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2201,7 +2201,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8772a58>"
+       "<matplotlib.figure.Figure at 0xa6b7908>"
       ]
      },
      "metadata": {},
@@ -2242,7 +2242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2268,7 +2268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2310,7 +2310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2320,7 +2320,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xab4a550>"
+       "<matplotlib.figure.Figure at 0xa704f28>"
       ]
      },
      "metadata": {},
@@ -2373,7 +2373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2381,9 +2381,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c0d400>"
+       "<matplotlib.figure.Figure at 0xa6bb048>"
       ]
      },
      "metadata": {},
@@ -2429,7 +2429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2439,7 +2439,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xab66128>"
+       "<matplotlib.figure.Figure at 0xa4adcf8>"
       ]
      },
      "metadata": {},
@@ -2525,7 +2525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2535,7 +2535,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d0af98>"
+       "<matplotlib.figure.Figure at 0xa8c39b0>"
       ]
      },
      "metadata": {},
@@ -2693,7 +2693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2705,7 +2705,7 @@
        "array([410, 520, 630])"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2802,7 +2802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2876,7 +2876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2886,7 +2886,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xac99710>"
+       "<matplotlib.figure.Figure at 0xa66c278>"
       ]
      },
      "metadata": {},
@@ -2896,7 +2896,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xab66518>"
+       "<matplotlib.figure.Figure at 0x80365f8>"
       ]
      },
      "metadata": {},
@@ -2906,7 +2906,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x82c4a20>"
+       "<matplotlib.figure.Figure at 0x8a1bf28>"
       ]
      },
      "metadata": {},
@@ -2954,7 +2954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2968,7 +2968,7 @@
        "       [  -5.26709207,    0.01521311,  195.7945557 ]])"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2976,7 +2976,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xaab2710>"
+       "<matplotlib.figure.Figure at 0xa907828>"
       ]
      },
      "metadata": {},
@@ -3014,7 +3014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -3024,7 +3024,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xabbfc18>"
+       "<matplotlib.figure.Figure at 0x85eddd8>"
       ]
      },
      "metadata": {},
@@ -3034,7 +3034,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8cfcbe0>"
+       "<matplotlib.figure.Figure at 0xa8a8550>"
       ]
      },
      "metadata": {},
@@ -3044,7 +3044,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xaa21e48>"
+       "<matplotlib.figure.Figure at 0x847a780>"
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@@ -3115,7 +3115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -3171,7 +3171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -3215,7 +3215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
@@ -3245,7 +3245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
@@ -3268,7 +3268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
@@ -3291,7 +3291,7 @@
   },
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    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "collapsed": false
    },
@@ -3334,7 +3334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "collapsed": false
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@@ -3343,7 +3343,7 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7bf8978>"
+       "<matplotlib.figure.Figure at 0xa5957f0>"
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@@ -3427,7 +3427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 50,
    "metadata": {
     "collapsed": true
    },
@@ -3463,7 +3463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "collapsed": true
    },
@@ -3504,7 +3504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 52,
    "metadata": {
     "collapsed": true
    },
@@ -3551,7 +3551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "collapsed": true
    },
@@ -3574,7 +3574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "collapsed": true
    },
@@ -3607,7 +3607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 55,
    "metadata": {
     "collapsed": true
    },
@@ -3661,7 +3661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false
    },
@@ -3719,7 +3719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 57,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -3727,9 +3727,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEjCAYAAADwjo21AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEjCAYAAADwjo21AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8XWWd+PHPOXffcnOz3OxrSfeWbqytbBVoWQqOIyOL\npfgTGOWlwJQBHa1BRBgRZmSk4/JCbUFF0RFZBESBppRS20JL9zZt0+z7eu/N3c/5/RETcpu1Sbp/\n369XXk3Pec45z3NzkvvNk+/5Poqu6zpCCCGEEEKIY6ae7A4IIYQQQghxupJgWgghhBBCiDGSYFoI\nIYQQQogxkmBaCCGEEEKIMZJgWgghhBBCiDGSYFoIIYQQQogxkmBaCCGOwYoVK1BVlfXr15/srgxw\n5MgRVFXljjvuONldGVZhYSFFRUUJ29asWYOqqqxdu/Yk9WogVVW5/PLLT3Y3hBCnOAmmhRAJ1q1b\nN6ogQlVVVDXxR8hIx7711lu4XC4sFgsvvPDCgHMN9fH000+Pqu9HH2cwGPB4PCxcuJBnnnmGWCw2\nqvOcbA8//PC4AktFUSa4RxPv6D4qitL3caKoqjogqD/a6fBaCiFOLuPJ7oAQ4tQ0miBiqDaDbf/V\nr37FF7/4Rex2O2+88QZXXHHFgGNKS0sHPd9FF100ih4PPE8sFuPIkSP88Y9/5IMPPuCvf/0rL7/8\n8qjPdbKdTYHcZz7zGS666CIyMzNP6HWHe4337duH3W4/gb0RQpyOJJgWQhx3Tz75JA8++CCZmZm8\n/vrrzJkzZ9B23/72tyfkekefZ9WqVcybN49XX32VsrIyLr300gm5zvF2Ni1Qm5SURFJS0snuRoLJ\nkyef7C4IIU4DkuYhhDhudF3n3/7t33jwwQeZPHkyH3zwwZCB9PFUUlLCJZdcAsDWrVsH7F+3bh3X\nXnstqampWK1WJk2axP33309LS8uQ59R1nV/+8pfMmTMHu91OZmYmd955J01NTYO2P3z4MHfccQe5\nublYLBYyMzP5l3/5F3bu3JnQ7rLLLuORRx4B4I477khIW6mqqhrrS0BjYyNf+9rXKC4uxmq1kpaW\nxvXXX89777035DF//etfWbZsGRkZGVitVvLy8rjuuut47bXX+tpEo1GeeeYZrrnmGgoKCrBaraSk\npPDpT3+aP//5z6Pu32A507356cN9jKUfvelI8Emeee9H/3zzoVKWfD4f3/rWt5g6dSo2mw2Px8Pi\nxYt55ZVXBrTtPf/ll19Oa2srd911F1lZWVitVmbOnMmaNWtG/RoJIU5NMjMthDguYrEYt99+Oy+8\n8ALnn38+f/7zn0lNTT1p/emd5bVYLAnbn332We666y4cDgef+9znyMrK4v333+fpp5/mpZde4v33\n3ycnJ2fA+Z566inefvttPv/5z3PttddSVlbGz3/+c9599102b95MSkpKX9uPPvqIxYsX09XVxXXX\nXcesWbM4ePAgf/zjH3n11Vd5+eWXufLKK4GeAFpRFMrKyrjxxhsTfvlwu91jGntlZSWLFi2itraW\nyy67jJtvvpm6ujpefPFF3njjDX7+859z++23JxxTWlrKd7/7XZxOJzfeeCP5+fnU19ezadMmfvGL\nX3DdddcB0Nrayn333cfChQu5+uqrSU9Pp66ujldffZXrr7+en/zkJ9x1112j7mv/tIvPfOYzFBcX\nD2hz6NAhnn/++YQUjGPpR1FREaWlpXznO9/B7XZz//33953n6F/2jk4D6ezsZNGiRezevZt58+Zx\n33330d7ezu9//3tuvPFGvvOd77Bq1aoBfe7o6GDhwoVYLBZuuukmwuEwL774Il/84hdRVZXly5eP\n+jUSQpxidCGE6Ofdd9/VFUXRL7/88mHbKYqiq6o66LELFizQr7zySl1RFP3aa6/Vu7u7RzyXoij6\nww8/rJeWliZ8/OQnPxl13wfrk67r+p49e3S73a6rqqrv2LGjb3tVVZVuNpt1l8ul79mzJ+GYVatW\n6Yqi6Nddd13C9ttvv11XFEW3WCz69u3bE/Z99atf1RVF0e++++6+bZqm6dOnT9cVRdGfe+65hPZ/\n+9vfdFVVda/Xm/AalZaW6oqi6GvXrh312HVd1ysqKnRFUfQ77rgjYfuSJUt0RVH0Rx55JGH7zp07\ndbvdrlutVr2mpqZv+1/+8hddURS9qKgoYXuv/tvC4bBeW1s7oE1nZ6c+c+ZMPSUlRQ8Ggwn7CgoK\n9KKiooRtv/zlL0c15paWFn3y5Mm60WjUX3nllXH1o3eMQxns++Bf//VfdUVR9P/3//5fwvaamho9\nKytLV1VV37JlS9/23q+Joij6nXfeqWua1rdvz549utFo1KdPnz7smIUQpzYJpoUQCSYimO79mDx5\nsh6LxUa8Zv9jjv6YO3fuqPt+dFD+zW9+U7/11lt1m82mGwwG/bvf/W5C+0cffVRXFEV/6KGHBpwr\nFArp2dnZuqIoel1dXd/23mD6S1/60oBj2tradIfDoTudzr5xb9iwQVcURb/gggsG7fNnP/tZXVEU\n/YUXXujbNpHBdE1Nja4oip6fn69Ho9EBx6xcuVJXFEV//PHH+7Zdd911uqIo+h/+8Idjuv7Rnnrq\nKV1RFH39+vUJ28caTAeDQX3hwoW6oij6M888M+5+HGswHYlEdLvdrjudTr21tXVA+x/96EcDfpnq\n/Zo4nU7d5/MNOOaSSy7RVVXVA4HAqMcjhDi1SM60EGLCTZkyhcLCQsrLy/nyl788qgfpFEVB07QB\nHx999NExX/873/kOjzzyCI899hi/+c1vCIVCPPHEE3zrW99KaNd77qMri0BPOsiiRYsA2LZt24D9\ngz3E6PF4mDVrFoFAgP379494DYBPf/rTQ15jIvRef+HChRiNAzP7Brv+pk2bUBSFpUuXjuoau3fv\nZsWKFRQXF2O32/vyjx944AEA6urqxjsMdF3nC1/4Ahs3buSBBx7gnnvuOeH92LdvH8FgkFmzZiWk\n8fQa7mtZUlKC0+kcsD0vLw9d12lvbx9X34QQJ4/kTAshEvQ+mKVp2pBtevcNVVYsKyuL559/nsWL\nF/Pss8/S3d3N2rVrMRgME9/hoyiKQjweByAUCrF582buvvtu/v3f/52MjAxuvfXWvradnZ0AQ5Zj\ny8rKSmjXX0ZGxqDH9G7vPWaka/Ru7+joGH5gYzSW63d0dJCUlDSqsnCbNm3iiiuuQNM0Fi9ezI03\n3khSUhKqqrJt2zZefvllwuHwuMfxwAMP8H//93/cdNNNPPHEEyelH+P5WiYnJw96TO8vOL33rBDi\n9CPBtBAiQe9Dbq2trUO26a1yMVSAAJCTk8P69eu58sor+c1vfkMwGOS3v/0tJpNpYjs8DKvVyiWX\nXMKbb77JjBkz+PKXv8zixYv7gp7esdbX1zN79uwBx9fX1ye066+xsXHQa/Zu7z2m99+GhoZB2w93\njYkwlusnJyfT1tZGIBDA4XAMe/5HH32UUCjEunXr+iqm9Hr88ccnpK73j370I/77v/+bRYsW8dxz\nz520fpzsr6UQ4tQkaR5CiARTp07FbDZz4MCBIQPqjRs3AnDuuecOey6v18u6detYsGABL730Ejfc\ncAOhUGjC+zySgoICHnroIfx+f0IN6vnz5wPw7rvvDjgmHA7z/vvvoygK8+bNG7B/3bp1A7a1t7ez\nc+dOHA4HU6ZMSbjGO++8M2jf3n777YR2QN8M/kTMVvb2/f333ycajY7q+hdddBG6rvPGG2+MeP6D\nBw+Smpo6IIAFKCsrG2u3+/zpT3/ivvvuY8qUKbz88suYzeYJ60f/v2KMxrRp07DZbOzcuXPQ743B\nXkshxJlPgmkhRAKLxcItt9xCNBpl5cqVA/Kd29vb+wLSL37xiyOez+Px8Pbbb7No0SLefPNNrr32\nWgKBwHHp+3Duv/9+0tLSWLNmDeXl5QDcdtttmM1m/vd//7cvx7nX448/Tl1dHddcc82gf9Z//vnn\n2b59e8K2b3/723R3d3Prrbf2BcQXX3wx06ZNY/Pmzfz6179OaP/OO+/wxz/+kfT0dG644Ya+7b0l\nBCsrK8c97pycHK6++mqqq6sHpEfs3r2bH//4x1itVm677ba+7V/96lcB+Pd//3dqamoGnLO2trbv\n86KiIlpbWwfUy/75z3/OW2+9Na6+//3vf+eWW27B6/Xyxhtv4PF4hmw7ln6kpqbS3Nw86l/wjEYj\ny5cvJxAI8I1vfCNhX11dHY8//jiqqo7q+0IIceaQNA8hxABPPfUUW7du5bnnnuODDz7gqquuwu12\nU1dXxyuvvEJbWxtf+MIXEvKPh+NyufjLX/7CDTfcwN/+9jeuuuoq3njjjRO64p3T6eTrX/86Dzzw\nAKtWreK3v/0t+fn5/M///A9f/vKXWbBgATfddBMZGRls3LiR9evXk5eXx49//ONBz7dkyRIWLlzI\nv/zLv5CRkcH69ev54IMPmDRpEo899lhC27Vr1/LpT3+a5cuX8+KLLzJz5kwOHTrE//3f/2G1Wnnu\nueewWq197RcvXoyqqvzwhz+ktbW1Lw/7a1/72phes5/85CcsXLiQVatW8c4773DBBRdQX1/Piy++\nSCQS4Wc/+1lCLe0rr7ySVatW8d3vfpfp06dzww03kJ+fT1NTE5s2beKcc87hpZdeAuC+++7jL3/5\nC4sWLeKmm24iKSmJrVu38v777/PP//zP/OEPfzjm/va64447CIVCXHnllYMubtJ/6fix9OOqq67i\nN7/5DUuWLOFTn/oUFouFOXPm9NXQHsx//ud/8t577/Hss8+ybds2Fi9eTEdHB7///e/p6Ojg29/+\nNuedd96YxyyEOA0NV+rjscce0xcsWKAnJSXp6enp+vXXX6/v2rVrQLvS0lI9OztbtLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xaeaf048>"
+       "<matplotlib.figure.Figure at 0x8a1ac50>"
       ]
      },
      "metadata": {},
@@ -3739,7 +3739,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.0133  0.0385  0.0013]\n"
+      "[ 0.0135  0.0376  0.0013]\n"
      ]
     }
    ],
@@ -3751,39 +3751,6 @@
     "print(ukf.P.diagonal())"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xaf04630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.0435  0.0128  0.0003]\n"
-     ]
-    }
-   ],
-   "source": [
-    "landmarks = np.array([[5, 10], [10, 5], [15, 15], [20, 5]])\n",
-    "\n",
-    "ukf = run_localization(landmarks, sigma_vel=0.1, sigma_steer=np.radians(1),\n",
-    "                      sigma_range=0.3, sigma_bearing=0.1)\n",
-    "print(ukf.P.diagonal())"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3791,159 +3758,6 @@
     "The rest of the code runs the simulation and plots the results, and shouldn't need too much comment by now. I create a variable `landmarks` that contains the coordinates of the landmarks. I update the simulated robot position 10 times a second, but run the EKF only once. This is for two reasons. First, we are not using Runge Kutta to integrate the differental equations of motion, so a narrow time step allows our simulation to be more accurate. Second, it is fairly normal in embedded systems to have limited processing speed. This forces you to run your Kalman filter only as frequently as absolutely needed."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

book_format.py

@@ -12,7 +12,7 @@ sys.path.insert(0,'./code') # allow us to import book_format
 
 def test_filterpy_version():
     import filterpy
-    min_version = [0,0,18]
+    min_version = [0,0,19]
     v = filterpy.__version__
     tokens = v.split('.')
     for i,v in enumerate(tokens):