Updated Table of Contents

Removed the Appendixes that I deleted, and added all of the supporting
notebook.

Supporting_Notebooks/Computing_and_plotting_PDFs.ipynb

@@ -1,5 +1,266 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
+       "\n",
+       ".CodeMirror pre {\n",
+       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
+       "}\n",
+       "    div.cell{\n",
+       "        width: 900px;\n",
+       "        margin-left: 0% !important;\n",
+       "        margin-right: auto;\n",
+       "    }\n",
+       "    div.text_cell code {\n",
+       "        background: transparent;\n",
+       "        color: #000000;\n",
+       "        font-weight: 600;\n",
+       "        font-size: 11pt;\n",
+       "        font-style: bold;\n",
+       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
+       "   }\n",
+       "    h1 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "\t}\n",
+       "\t\n",
+       "    div.input_area {\n",
+       "        background: #F6F6F9;\n",
+       "        border: 1px solid #586e75;      \n",
+       "    }\n",
+       "\n",
+       "    .text_cell_render h1 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 30pt;\n",
+       "        line-height: 100%;\n",
+       "        color:#c76c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 1em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    } \n",
+       "    h2 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "    .text_cell_render h2 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 16pt;\n",
+       "        font-style: italic;\n",
+       "        line-height: 100%;\n",
+       "        color:#c76c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 1.5em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    } \n",
+       "    h3 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "    .text_cell_render h3 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 14pt;\n",
+       "        line-height: 100%;\n",
+       "        color:#d77c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 2em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "    h4 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "    .text_cell_render h4 {\n",
+       "        font-weight: 100;\n",
+       "        font-size: 14pt;\n",
+       "        color:#d77c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 0.5em;\n",
+       "        display: block;\n",
+       "        white-space: nowrap;\n",
+       "    }\n",
+       "    h5 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "\n",
+       "    .text_cell_render h5 {\n",
+       "        font-weight: 200;\n",
+       "        font-style: normal;\n",
+       "        color: #1d3b84;\n",
+       "        font-size: 16pt;\n",
+       "        margin-bottom: 0em;\n",
+       "        margin-top: 0.5em;\n",
+       "        display: block;\n",
+       "        white-space: nowrap;\n",
+       "    }\n",
+       "    div.text_cell_render{\n",
+       "        font-family: 'Vollkorn', verdana,arial,sans-serif;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 130%;\n",
+       "        font-weight: 400;\n",
+       "        text-align:justify;\n",
+       "        text-justify:inter-word;\n",
+       "    }\n",
+       "    div.output_subarea.output_text.output_pyout {\n",
+       "        overflow-x: auto;\n",
+       "        overflow-y: scroll;\n",
+       "        max-height: 50000px;\n",
+       "    }\n",
+       "    div.output_subarea.output_stream.output_stdout.output_text {\n",
+       "        overflow-x: auto;\n",
+       "        overflow-y: scroll;\n",
+       "        max-height: 50000px;\n",
+       "    }\n",
+       "    div.output_wrapper{\n",
+       "        margin-top:0.2em;\n",
+       "        margin-bottom:0.2em;\n",
+       "}\n",
+       "\n",
+       "    code{\n",
+       "        font-size: 6pt;\n",
+       "\n",
+       "    }\n",
+       "    .rendered_html code{\n",
+       "    background-color: transparent;\n",
+       "    }\n",
+       "    ul{\n",
+       "        margin: 2em;\n",
+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li li{\n",
+       "        padding-left: 0.2em; \n",
+       "        margin-bottom: 0.2em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    ol{\n",
+       "        margin: 2em;\n",
+       "    }\n",
+       "    ol li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    a:link{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:visited{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:hover{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:focus{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:active{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    .rendered_html :link {\n",
+       "       text-decoration: underline; \n",
+       "    }\n",
+       "    .rendered_html :hover {\n",
+       "       text-decoration: none; \n",
+       "    }\n",
+       "    .rendered_html :visited {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :focus {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :active {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .warning{\n",
+       "        color: rgb( 240, 20, 20 )\n",
+       "    } \n",
+       "    hr {\n",
+       "      color: #f3f3f3;\n",
+       "      background-color: #f3f3f3;\n",
+       "      height: 1px;\n",
+       "    }\n",
+       "    blockquote{\n",
+       "      display:block;\n",
+       "      background: #fcfcfc;\n",
+       "      border-left: 5px solid #c76c0c;\n",
+       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "      width:680px;\n",
+       "      padding: 10px 10px 10px 10px;\n",
+       "      text-align:justify;\n",
+       "      text-justify:inter-word;\n",
+       "      }\n",
+       "      blockquote p {\n",
+       "        margin-bottom: 0;\n",
+       "        line-height: 125%;\n",
+       "        font-size: 100%;\n",
+       "      }\n",
+       "</style>\n",
+       "<script>\n",
+       "    MathJax.Hub.Config({\n",
+       "                        TeX: {\n",
+       "                           extensions: [\"AMSmath.js\"],\n",
+       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
+       "                           },\n",
+       "                tex2jax: {\n",
+       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
+       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
+       "                },\n",
+       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
+       "                \"HTML-CSS\": {\n",
+       "                    scale:95,\n",
+       "                        availableFonts: [],\n",
+       "                        preferredFont:null,\n",
+       "                        webFont: \"TeX\",\n",
+       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                }\n",
+       "        });\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#format the book\n",
+    "%matplotlib inline\n",
+    "from __future__ import division, print_function\n",
+    "import sys;sys.path.insert(0,'..')\n",
+    "from  book_format import load_style;load_style('..')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -19,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -29,8 +290,8 @@
      "output_type": "stream",
      "text": [
       "50000\n",
-      "3.00043200502\n",
-      "2.01072423406\n"
+      "2.99685400941\n",
+      "1.99912142836\n"
      ]
     }
    ],
@@ -38,7 +299,6 @@
     "import numpy as np\n",
     "import numpy.random as random\n",
     "\n",
-    "\n",
     "mean = 3\n",
     "std = 2\n",
     "\n",
@@ -59,16 +319,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEACAYAAAD4NNLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79d6550>"
+       "<matplotlib.figure.Figure at 0x7b851d0>"
       ]
      },
      "metadata": {},
@@ -99,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -107,9 +367,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaMAAAEACAYAAAAeHRm0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x46db518>"
+       "<matplotlib.figure.Figure at 0x4b35ba8>"
       ]
      },
      "metadata": {},
@@ -130,16 +390,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEACAYAAAD4NNLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd8b00>"
+       "<matplotlib.figure.Figure at 0x8a54e48>"
       ]
      },
      "metadata": {},
@@ -160,16 +420,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEACAYAAAD4NNLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x82595f8>"
+       "<matplotlib.figure.Figure at 0x8acabe0>"
       ]
      },
      "metadata": {},
@@ -177,7 +437,7 @@
     }
    ],
    "source": [
-    "plt.hist(data, bins=200, normed=True, histtype='step')\n",
+    "plt.hist(data, bins=200, normed=True, histtype='step', lw=2)\n",
     "plt.show()"
    ]
   },
@@ -190,16 +450,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEACAYAAAD4NNLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x851f860>"
+       "<matplotlib.figure.Figure at 0x9dbac88>"
       ]
      },
      "metadata": {},
@@ -207,7 +467,7 @@
     }
    ],
    "source": [
-    "plt.hist(data, bins=200, normed=True, histtype='step')\n",
+    "plt.hist(data, bins=200, normed=True, histtype='step', lw=2)\n",
     "norm = stats.norm(mean, std)\n",
     "plt.plot(xs, norm.pdf(xs), color='k', lw=2)\n",
     "plt.show()"
@@ -222,16 +482,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEACAYAAAD4NNLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a13710>"
+       "<matplotlib.figure.Figure at 0x9de1780>"
       ]
      },
      "metadata": {},
@@ -260,16 +520,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEACAYAAAD4NNLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x859ba90>"
+       "<matplotlib.figure.Figure at 0x9e29ac8>"
       ]
      },
      "metadata": {},
@@ -279,8 +539,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean = 13.96\n",
-      "std  = 2.81\n"
+      "mean = 13.99\n",
+      "std  = 2.80\n"
      ]
     }
    ],
@@ -294,8 +554,8 @@
     "\n",
     "d_t = f(data) # transform data through f(x)\n",
     "\n",
-    "plt.hist(data, bins=200, normed=True, histtype='step')\n",
-    "plt.hist(d_t, bins=200, normed=True, histtype='step')\n",
+    "plt.hist(data, bins=200, normed=True, histtype='step', lw=2)\n",
+    "plt.hist(d_t, bins=200, normed=True, histtype='step', lw=2)\n",
     "\n",
     "plt.ylim(0, .35)\n",
     "plt.show()\n",
@@ -324,16 +584,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEACAYAAAD4NNLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x95bb7f0>"
+       "<matplotlib.figure.Figure at 0x8dc9eb8>"
       ]
      },
      "metadata": {},
@@ -343,8 +603,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean = -1.57\n",
-      "std  = 2.10\n"
+      "mean = -1.59\n",
+      "std  = 2.09\n"
      ]
     }
    ],
@@ -354,7 +614,7 @@
     "\n",
     "d_t = f2(data)\n",
     "plt.subplot(121)\n",
-    "plt.hist(d_t, bins=200, normed=True, histtype='step')\n",
+    "plt.hist(d_t, bins=200, normed=True, histtype='step', lw=2)\n",
     "\n",
     "plt.subplot(122)\n",
     "kde = stats.gaussian_kde(d_t)\n",
@@ -379,7 +639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -390,7 +650,7 @@
        "-1.5*sin(x/3)*sin(1.5*x + 2.1) + cos(x/3)*cos(1.5*x + 2.1)/3 - 1.6"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -423,7 +683,7 @@
        "-1.66528051815545"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -442,16 +702,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEACAYAAAD4NNLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xaa5bac8>"
+       "<matplotlib.figure.Figure at 0x9e02b00>"
       ]
      },
      "metadata": {},
@@ -459,7 +719,7 @@
     }
    ],
    "source": [
-    "plt.hist(d_t, bins=200, normed=True, histtype='step')\n",
+    "plt.hist(d_t, bins=200, normed=True, histtype='step', lw=2)\n",
     "plot_normal(xs, f2(mean), abs(float(m)*std), color='k', lw=3, label='EKF')\n",
     "plot_normal(xs, d_t.mean(), d_t.std(), color='r', lw=3, label='MC')\n",
     "plt.legend()\n",

Supporting_Notebooks/Iterative-Least-Squares-for-Sensor-Fusion.ipynb

@@ -21,7 +21,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -116,9 +116,9 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        font-family: 'Vollkorn', verdana,arial,sans-serif;\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
+       "        font-size: 130%;\n",
        "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
@@ -264,11 +264,8 @@
     "#format the book\n",
     "%matplotlib inline\n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'..')\n",
-    "sys.path.insert(0,'../code') \n",
-    "from book_format import load_style\n",
-    "load_style('..')"
+    "import sys;sys.path.insert(0,'..');sys.path.insert(0, '../code')\n",
+    "import book_format; book_format.load_style('..')"
    ]
   },
   {
@@ -293,7 +290,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqIAAAGNCAYAAADKExONAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22cc3fe0b00>"
+       "<matplotlib.figure.Figure at 0x7bec9e8>"
       ]
      },
      "metadata": {},
@@ -374,7 +371,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Derivation of ILS Equations (Optional)"
+    "### Derivation of ILS Equations"
    ]
   },
   {

Supporting_Notebooks/Linearizing-with-Taylor-Series.ipynb

@@ -1,92 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Linearizing with Taylor Series\n",
-    "\n",
-    "Taylor series represents a function as an infinite sum of terms. The terms are linear, even for a nonlinear function, so we can express any arbitrary nonlinear function using linear algebra. The cost of this choice is that unless we use an infinite number of terms the value we compute will be approximate rather than exact.\n",
-    "\n",
-    "Before applying it to a matrix lets do the Taylor expansion of a real function since this is much easier to visualize. I choose sin(x). The Taylor series for a real or complex function f(x) at x=a is the infinite series\n",
-    "\n",
-    "$$f(x) = f(a) + f'(a)(x-a) + \\frac{f''(a)}{2!}(x-a)^2 + \\, ...\\,  + \\frac{f^{(n)}(a)}{n!}(x-a)^n + \\, ...$$\n",
-    "\n",
-    "where $f^{n}$ is the nth derivative of f. To compute the Taylor series for $f(x)=sin(x)$ at $x=0$ Let's first work out the terms for f.\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "f^{0}(x) &= sin(x) ,\\ \\  &f^{0}(0) &= 0 \\\\\n",
-    "f^{1}(x) &= cos(x),\\ \\  &f^{1}(0) &= 1 \\\\\n",
-    "f^{2}(x) &= -sin(x),\\ \\  &f^{2}(0) &= 0 \\\\\n",
-    "f^{3}(x) &= -cos(x),\\ \\  &f^{3}(0) &= -1 \\\\\n",
-    "f^{4}(x) &= sin(x),\\ \\  &f^{4}(0) &= 0 \\\\\n",
-    "f^{5}(x) &= cos(x),\\ \\  &f^{5}(0) &= 1\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "Now we can substitute these values into the equation.\n",
-    "\n",
-    "$$\\sin(x) = \\frac{0}{0!}(x)^0 + \\frac{1}{1!}(x)^1 + \\frac{0}{2!}(x)^2 + \\frac{-1}{3!}(x)^3 + \\frac{0}{4!}(x)^4 + \\frac{-1}{5!}(x)^5 + ... $$\n",
-    "\n",
-    "And let's test this with some code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "estimate of sin(.3) is 0.30452025\n",
-      "exact value of sin(.3) is 0.295520206661\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "x = .3\n",
-    "estimate = x + x**3/6 + x**5/120\n",
-    "exact = np.sin(.3)\n",
-    "\n",
-    "print('estimate of sin(.3) is', estimate)\n",
-    "print('exact value of sin(.3) is', exact)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is not bad for only three terms. If you are curious, go ahead and implement this as a Python function to compute the series for an arbitrary number of terms.\n",
-    "\n",
-    "Now we can consider how to linearize a nonlinear "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

Supporting_Notebooks/Taylor-Series.ipynb

@@ -0,0 +1,351 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
+       "\n",
+       ".CodeMirror pre {\n",
+       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
+       "}\n",
+       "    div.cell{\n",
+       "        width: 900px;\n",
+       "        margin-left: 0% !important;\n",
+       "        margin-right: auto;\n",
+       "    }\n",
+       "    div.text_cell code {\n",
+       "        background: transparent;\n",
+       "        color: #000000;\n",
+       "        font-weight: 600;\n",
+       "        font-size: 11pt;\n",
+       "        font-style: bold;\n",
+       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
+       "   }\n",
+       "    h1 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "\t}\n",
+       "\t\n",
+       "    div.input_area {\n",
+       "        background: #F6F6F9;\n",
+       "        border: 1px solid #586e75;      \n",
+       "    }\n",
+       "\n",
+       "    .text_cell_render h1 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 30pt;\n",
+       "        line-height: 100%;\n",
+       "        color:#c76c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 1em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    } \n",
+       "    h2 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "    .text_cell_render h2 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 16pt;\n",
+       "        font-style: italic;\n",
+       "        line-height: 100%;\n",
+       "        color:#c76c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 1.5em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    } \n",
+       "    h3 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "    .text_cell_render h3 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 14pt;\n",
+       "        line-height: 100%;\n",
+       "        color:#d77c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 2em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "    h4 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "    .text_cell_render h4 {\n",
+       "        font-weight: 100;\n",
+       "        font-size: 14pt;\n",
+       "        color:#d77c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 0.5em;\n",
+       "        display: block;\n",
+       "        white-space: nowrap;\n",
+       "    }\n",
+       "    h5 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "\n",
+       "    .text_cell_render h5 {\n",
+       "        font-weight: 200;\n",
+       "        font-style: normal;\n",
+       "        color: #1d3b84;\n",
+       "        font-size: 16pt;\n",
+       "        margin-bottom: 0em;\n",
+       "        margin-top: 0.5em;\n",
+       "        display: block;\n",
+       "        white-space: nowrap;\n",
+       "    }\n",
+       "    div.text_cell_render{\n",
+       "        font-family: 'Vollkorn', verdana,arial,sans-serif;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 130%;\n",
+       "        font-weight: 400;\n",
+       "        text-align:justify;\n",
+       "        text-justify:inter-word;\n",
+       "    }\n",
+       "    div.output_subarea.output_text.output_pyout {\n",
+       "        overflow-x: auto;\n",
+       "        overflow-y: scroll;\n",
+       "        max-height: 50000px;\n",
+       "    }\n",
+       "    div.output_subarea.output_stream.output_stdout.output_text {\n",
+       "        overflow-x: auto;\n",
+       "        overflow-y: scroll;\n",
+       "        max-height: 50000px;\n",
+       "    }\n",
+       "    div.output_wrapper{\n",
+       "        margin-top:0.2em;\n",
+       "        margin-bottom:0.2em;\n",
+       "}\n",
+       "\n",
+       "    code{\n",
+       "        font-size: 6pt;\n",
+       "\n",
+       "    }\n",
+       "    .rendered_html code{\n",
+       "    background-color: transparent;\n",
+       "    }\n",
+       "    ul{\n",
+       "        margin: 2em;\n",
+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li li{\n",
+       "        padding-left: 0.2em; \n",
+       "        margin-bottom: 0.2em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    ol{\n",
+       "        margin: 2em;\n",
+       "    }\n",
+       "    ol li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    a:link{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:visited{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:hover{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:focus{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:active{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    .rendered_html :link {\n",
+       "       text-decoration: underline; \n",
+       "    }\n",
+       "    .rendered_html :hover {\n",
+       "       text-decoration: none; \n",
+       "    }\n",
+       "    .rendered_html :visited {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :focus {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :active {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .warning{\n",
+       "        color: rgb( 240, 20, 20 )\n",
+       "    } \n",
+       "    hr {\n",
+       "      color: #f3f3f3;\n",
+       "      background-color: #f3f3f3;\n",
+       "      height: 1px;\n",
+       "    }\n",
+       "    blockquote{\n",
+       "      display:block;\n",
+       "      background: #fcfcfc;\n",
+       "      border-left: 5px solid #c76c0c;\n",
+       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "      width:680px;\n",
+       "      padding: 10px 10px 10px 10px;\n",
+       "      text-align:justify;\n",
+       "      text-justify:inter-word;\n",
+       "      }\n",
+       "      blockquote p {\n",
+       "        margin-bottom: 0;\n",
+       "        line-height: 125%;\n",
+       "        font-size: 100%;\n",
+       "      }\n",
+       "</style>\n",
+       "<script>\n",
+       "    MathJax.Hub.Config({\n",
+       "                        TeX: {\n",
+       "                           extensions: [\"AMSmath.js\"],\n",
+       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
+       "                           },\n",
+       "                tex2jax: {\n",
+       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
+       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
+       "                },\n",
+       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
+       "                \"HTML-CSS\": {\n",
+       "                    scale:95,\n",
+       "                        availableFonts: [],\n",
+       "                        preferredFont:null,\n",
+       "                        webFont: \"TeX\",\n",
+       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                }\n",
+       "        });\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#format the book\n",
+    "%matplotlib inline\n",
+    "from __future__ import division, print_function\n",
+    "import sys;sys.path.insert(0,'..')\n",
+    "from  book_format import load_style;load_style('..')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linearizing with Taylor Series\n",
+    "\n",
+    "Taylor series represents a function as an infinite sum of terms. The terms are linear, even for a nonlinear function, so we can express any arbitrary nonlinear function using linear algebra. The cost of this choice is that unless we use an infinite number of terms the value we compute will be approximate rather than exact.\n",
+    "\n",
+    "The Taylor series for a real or complex function f(x) at x=a is the infinite series\n",
+    "\n",
+    "$$f(x) = f(a) + f'(a)(x-a) + \\frac{f''(a)}{2!}(x-a)^2 + \\, ...\\,  + \\frac{f^{(n)}(a)}{n!}(x-a)^n + \\, ...$$\n",
+    "\n",
+    "where $f^{n}$ is the nth derivative of f. To compute the Taylor series for $f(x)=sin(x)$ at $x=0$ let's first work out the terms for f.\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "f^{0}(x) &= sin(x) ,\\ \\  &f^{0}(0) &= 0 \\\\\n",
+    "f^{1}(x) &= cos(x),\\ \\  &f^{1}(0) &= 1 \\\\\n",
+    "f^{2}(x) &= -sin(x),\\ \\  &f^{2}(0) &= 0 \\\\\n",
+    "f^{3}(x) &= -cos(x),\\ \\  &f^{3}(0) &= -1 \\\\\n",
+    "f^{4}(x) &= sin(x),\\ \\  &f^{4}(0) &= 0 \\\\\n",
+    "f^{5}(x) &= cos(x),\\ \\  &f^{5}(0) &= 1\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "Now we can substitute these values into the equation.\n",
+    "\n",
+    "$$\\sin(x) = \\frac{0}{0!}(x)^0 + \\frac{1}{1!}(x)^1 + \\frac{0}{2!}(x)^2 + \\frac{-1}{3!}(x)^3 + \\frac{0}{4!}(x)^4 + \\frac{-1}{5!}(x)^5 + ... $$\n",
+    "\n",
+    "And let's test this with some code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "estimate of sin(.3) is 0.30452025\n",
+      "exact value of sin(.3) is 0.295520206661\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "x = .3\n",
+    "estimate = x + x**3/6 + x**5/120\n",
+    "exact = np.sin(.3)\n",
+    "\n",
+    "print('estimate of sin(.3) is', estimate)\n",
+    "print('exact value of sin(.3) is', exact)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is not bad for only three terms. If you are curious, go ahead and implement this as a Python function to compute the series for an arbitrary number of terms."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

table_of_contents.ipynb

@@ -1,161 +1,432 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<center><h1>Kalman and Bayesian Filters in Python</h1></center>\n",
-    "<p>\n",
-    " <p>\n",
-    "Table of Contents\n",
-    "-----\n",
-    "\n",
-    "[**Preface**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/00-Preface.ipynb)\n",
-    " \n",
-    "Motivation behind writing the book. How to download and read the book. Requirements for IPython Notebook and Python. github links.\n",
-    "\n",
-    "\n",
-    "[**Chapter 1: The g-h Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/01-g-h-filter.ipynb)\n",
-    "\n",
-    "Intuitive introduction to the g-h filter, also known as the $\\alpha$-$\\beta$ Filter, which is a family of filters that includes the Kalman filter. Once you understand this chapter you will understand the concepts behind the Kalman filter. \n",
-    "\n",
-    "\n",
-    "[**Chapter 2: The Discrete Bayes Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/02-Discrete-Bayes.ipynb)\n",
-    "\n",
-    "Introduces the discrete Bayes filter. From this you will learn the probabilistic (Bayesian) reasoning that underpins the Kalman filter in an easy to digest form.\n",
-    "\n",
-    "[**Chapter 3: Gaussian Probabilities**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/03-Gaussians.ipynb)\n",
-    "\n",
-    "Introduces using Gaussians to represent beliefs in the Bayesian sense. Gaussians allow us to implement the algorithms used in the discrete Bayes filter to work in continuous domains.\n",
-    "\n",
-    "\n",
-    "[**Chapter 4: One Dimensional Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/04-One-Dimensional-Kalman-Filters.ipynb)\n",
-    "\n",
-    "Implements a Kalman filter by modifying the discrete Bayes filter to use Gaussians. This is a full featured Kalman filter, albeit only useful for 1D problems. \n",
-    "\n",
-    "\n",
-    "[**Chapter 5: Multivariate Gaussians**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/05-Multivariate-Gaussians.ipynb)\n",
-    "\n",
-    "Extends Gaussians to multiple dimensions, and demonstrates how 'triangulation' and hidden variables can vastly improve estimates.\n",
-    "\n",
-    "[**Chapter 6: Multivariate Kalman Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/06-Multivariate-Kalman-Filters.ipynb)\n",
-    "\n",
-    "We extend the Kalman filter developed in the univariate chapter to the full, generalized filter for linear problems. After reading this you will understand how a Kalman filter works and how to design and implement one for a (linear) problem of your choice.\n",
-    "\n",
-    "[**Chapter 7: Kalman Filter Math**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/07-Kalman-Filter-Math.ipynb)\n",
-    "\n",
-    "We gotten about as far as we can without forming a strong mathematical foundation. This chapter is optional, especially the first time, but if you intend to write robust, numerically stable filters, or to read the literature, you will need to know the material in this chapter. Some sections will be required to understand the later chapters on nonlinear filtering. \n",
-    "\n",
-    "\n",
-    "[**Chapter 8: Designing Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/08-Designing-Kalman-Filters.ipynb)\n",
-    "\n",
-    "Building on material in Chapters 5 and 6, walks you through the design of several Kalman filters. Only by seeing several different examples can you really grasp all of the theory. Examples are chosen to be realistic, not 'toy' problems to give you a start towards implementing your own filters. Discusses, but does not solve issues like numerical stability.\n",
-    "\n",
-    "\n",
-    "[**Chapter 9: Nonlinear Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/09-Nonlinear-Filtering.ipynb)\n",
-    "\n",
-    "Kalman filters as covered only work for linear problems. Yet the world is nonlinear. Here I introduce the problems that nonlinear systems pose to the filter, and briefly discuss the various algorithms that we will be learning in subsequent chapters.\n",
-    "\n",
-    "\n",
-    "[**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/10-Unscented-Kalman-Filter.ipynb)\n",
-    "\n",
-    "Unscented Kalman filters (UKF) are a recent development in Kalman filter theory. They allow you to filter nonlinear problems without requiring a closed form solution like the Extended Kalman filter requires.\n",
-    "\n",
-    "This topic is typically either not mentioned, or glossed over in existing texts, with Extended Kalman filters receiving the bulk of discussion. I put it first because the UKF is much simpler to understand, implement, and the filtering performance is usually as good as or better then the Extended Kalman filter. I always try to implement the UKF first for real world problems, and you should also.\n",
-    "\n",
-    "\n",
-    "[**Chapter 11: Extended Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11-Extended-Kalman-Filters.ipynb)\n",
-    "\n",
-    "Extended Kalman filters (EKF) are the most common approach to linearizing non-linear problems. A majority of real world Kalman filters are EKFs, so will need to understand this material to understand existing code, papers, talks, etc. \n",
-    "\n",
-    "\n",
-    "[**Chapter 12: Particle Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12-Particle-Filters.ipynb)\n",
-    "\n",
-    "Particle filters uses Monte Carlo techniques to filter data. They easily handle highly nonlinear and non-Gaussian systems, as well as multimodal distributions (tracking multiple objects simultaneously) at the cost of high computational requirements.\n",
-    "\n",
-    "\n",
-    "[**Chapter 13: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13-Smoothing.ipynb)\n",
-    "\n",
-    "Kalman filters are recursive, and thus very suitable for real time filtering. However, they work extremely well for post-processing data. After all, Kalman filters are predictor-correctors, and it is easier to predict the past than the future! We discuss some common approaches.\n",
-    "\n",
-    "\n",
-    "[**Chapter 14: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14-Adaptive-Filtering.ipynb)\n",
-    "    \n",
-    "Kalman filters assume a single process model, but manuevering targets typically need to be described by several different process models. Adaptive filtering uses several techniques to allow the Kalman filter to adapt to the changing behavior of the target.\n",
-    "\n",
-    "\n",
-    "[**Appendix A: Installation, Python, NumPy, and FilterPy**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-A-Installation.ipynb)\n",
-    "\n",
-    "Brief introduction of Python and how it is used in this book. Description of the companion\n",
-    "library FilterPy. \n",
-    "    \n",
-    "\n",
-    "[**Appendix B: Symbols and Notations**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-B-Symbols-and-Notations.ipynb)\n",
-    "\n",
-    "Most books opt to use different notations and variable names for identical concepts. This is a large barrier to understanding when you are starting out. I have collected the symbols and notations used in this book, and built tables showing what notation and names are used by the major books in the field.\n",
-    "\n",
-    "*Still just a collection of notes at this point.*\n",
-    "\n",
-    "\n",
-    "[**Appendix C: Walking through the Kalman Filter code**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-C-Walking-Through-KF-Code.ipynb)\n",
-    "\n",
-    "A brief walkthrough of the KalmanFilter class from FilterPy.\n",
-    "\n",
-    "\n",
-    "[**Appendix D: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-D-HInfinity-Filters.ipynb)\n",
-    "    \n",
-    "Describes the $H_\\infty$ filter. \n",
-    "\n",
-    "*I have code that implements the filter, but no supporting text yet.*\n",
-    "\n",
-    "\n",
-    "[**Appendix E: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-E-Ensemble-Kalman-Filters.ipynb)\n",
-    "\n",
-    "Discusses the ensemble Kalman Filter, which uses a Monte Carlo approach to deal with very large Kalman filter states in nonlinear systems.\n",
-    "\n",
-    "\n",
-    "[**Appendix F: FilterPy Source Code**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-F-Filterpy-Code.ipynb)\n",
-    "\n",
-    "Listings of important classes from FilterPy that are used in this book.\n",
-    "\n",
-    "\n",
-    "[*Appendix G: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-G-Designing-Nonlinear-Kalman-Filters.ipynb)\n",
-    "\n",
-    "Works through some examples of the design of Kalman filters for nonlinear problems. *This is still very much a work in progress.*\n",
-    "\n",
-    "\n",
-    "[**Appendix H: Least Squares Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-H-Least-Squares-Filters.ipynb)\n",
-    "\n",
-    "**not written yet**\n",
-    "\n",
-    "Introduces the least squares filter in batch and recursive forms. I've not made a start on authoring this yet. Many authors develop KF explanations by covering least squares first. I am not, so I may move this chapter deeper in the book, or remove it.\n",
-    "\n",
-    "\n",
-    "\n",
-    "### Github repository\n",
-    "http://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<center><h1>Kalman and Bayesian Filters in Python</h1></center>\n",
+    "<p>\n",
+    " <p>\n",
+    "\n",
+    "## Table of Contents\n",
+    "\n",
+    "[**Preface**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/00-Preface.ipynb)\n",
+    " \n",
+    "Motivation behind writing the book. How to download and read the book. Requirements for IPython Notebook and Python. github links.\n",
+    "\n",
+    "\n",
+    "[**Chapter 1: The g-h Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/01-g-h-filter.ipynb)\n",
+    "\n",
+    "Intuitive introduction to the g-h filter, also known as the $\\alpha$-$\\beta$ Filter, which is a family of filters that includes the Kalman filter. Once you understand this chapter you will understand the concepts behind the Kalman filter. \n",
+    "\n",
+    "\n",
+    "[**Chapter 2: The Discrete Bayes Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/02-Discrete-Bayes.ipynb)\n",
+    "\n",
+    "Introduces the discrete Bayes filter. From this you will learn the probabilistic (Bayesian) reasoning that underpins the Kalman filter in an easy to digest form.\n",
+    "\n",
+    "[**Chapter 3: Gaussian Probabilities**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/03-Gaussians.ipynb)\n",
+    "\n",
+    "Introduces using Gaussians to represent beliefs in the Bayesian sense. Gaussians allow us to implement the algorithms used in the discrete Bayes filter to work in continuous domains.\n",
+    "\n",
+    "\n",
+    "[**Chapter 4: One Dimensional Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/04-One-Dimensional-Kalman-Filters.ipynb)\n",
+    "\n",
+    "Implements a Kalman filter by modifying the discrete Bayes filter to use Gaussians. This is a full featured Kalman filter, albeit only useful for 1D problems. \n",
+    "\n",
+    "\n",
+    "[**Chapter 5: Multivariate Gaussians**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/05-Multivariate-Gaussians.ipynb)\n",
+    "\n",
+    "Extends Gaussians to multiple dimensions, and demonstrates how 'triangulation' and hidden variables can vastly improve estimates.\n",
+    "\n",
+    "[**Chapter 6: Multivariate Kalman Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/06-Multivariate-Kalman-Filters.ipynb)\n",
+    "\n",
+    "We extend the Kalman filter developed in the univariate chapter to the full, generalized filter for linear problems. After reading this you will understand how a Kalman filter works and how to design and implement one for a (linear) problem of your choice.\n",
+    "\n",
+    "[**Chapter 7: Kalman Filter Math**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/07-Kalman-Filter-Math.ipynb)\n",
+    "\n",
+    "We gotten about as far as we can without forming a strong mathematical foundation. This chapter is optional, especially the first time, but if you intend to write robust, numerically stable filters, or to read the literature, you will need to know the material in this chapter. Some sections will be required to understand the later chapters on nonlinear filtering. \n",
+    "\n",
+    "\n",
+    "[**Chapter 8: Designing Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/08-Designing-Kalman-Filters.ipynb)\n",
+    "\n",
+    "Building on material in Chapters 5 and 6, walks you through the design of several Kalman filters. Only by seeing several different examples can you really grasp all of the theory. Examples are chosen to be realistic, not 'toy' problems to give you a start towards implementing your own filters. Discusses, but does not solve issues like numerical stability.\n",
+    "\n",
+    "\n",
+    "[**Chapter 9: Nonlinear Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/09-Nonlinear-Filtering.ipynb)\n",
+    "\n",
+    "Kalman filters as covered only work for linear problems. Yet the world is nonlinear. Here I introduce the problems that nonlinear systems pose to the filter, and briefly discuss the various algorithms that we will be learning in subsequent chapters.\n",
+    "\n",
+    "\n",
+    "[**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/10-Unscented-Kalman-Filter.ipynb)\n",
+    "\n",
+    "Unscented Kalman filters (UKF) are a recent development in Kalman filter theory. They allow you to filter nonlinear problems without requiring a closed form solution like the Extended Kalman filter requires.\n",
+    "\n",
+    "This topic is typically either not mentioned, or glossed over in existing texts, with Extended Kalman filters receiving the bulk of discussion. I put it first because the UKF is much simpler to understand, implement, and the filtering performance is usually as good as or better then the Extended Kalman filter. I always try to implement the UKF first for real world problems, and you should also.\n",
+    "\n",
+    "\n",
+    "[**Chapter 11: Extended Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11-Extended-Kalman-Filters.ipynb)\n",
+    "\n",
+    "Extended Kalman filters (EKF) are the most common approach to linearizing non-linear problems. A majority of real world Kalman filters are EKFs, so will need to understand this material to understand existing code, papers, talks, etc. \n",
+    "\n",
+    "\n",
+    "[**Chapter 12: Particle Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12-Particle-Filters.ipynb)\n",
+    "\n",
+    "Particle filters uses Monte Carlo techniques to filter data. They easily handle highly nonlinear and non-Gaussian systems, as well as multimodal distributions (tracking multiple objects simultaneously) at the cost of high computational requirements.\n",
+    "\n",
+    "\n",
+    "[**Chapter 13: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13-Smoothing.ipynb)\n",
+    "\n",
+    "Kalman filters are recursive, and thus very suitable for real time filtering. However, they work extremely well for post-processing data. After all, Kalman filters are predictor-correctors, and it is easier to predict the past than the future! We discuss some common approaches.\n",
+    "\n",
+    "\n",
+    "[**Chapter 14: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14-Adaptive-Filtering.ipynb)\n",
+    "    \n",
+    "Kalman filters assume a single process model, but manuevering targets typically need to be described by several different process models. Adaptive filtering uses several techniques to allow the Kalman filter to adapt to the changing behavior of the target.\n",
+    "\n",
+    "\n",
+    "[**Appendix A: Installation, Python, NumPy, and FilterPy**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-A-Installation.ipynb)\n",
+    "\n",
+    "Brief introduction of Python and how it is used in this book. Description of the companion\n",
+    "library FilterPy. \n",
+    "    \n",
+    "\n",
+    "[**Appendix B: Symbols and Notations**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-B-Symbols-and-Notations.ipynb)\n",
+    "\n",
+    "Most books opt to use different notations and variable names for identical concepts. This is a large barrier to understanding when you are starting out. I have collected the symbols and notations used in this book, and built tables showing what notation and names are used by the major books in the field.\n",
+    "\n",
+    "*Still just a collection of notes at this point.*\n",
+    "\n",
+    "\n",
+    "[**Appendix D: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-D-HInfinity-Filters.ipynb)\n",
+    "    \n",
+    "Describes the $H_\\infty$ filter. \n",
+    "\n",
+    "*I have code that implements the filter, but no supporting text yet.*\n",
+    "\n",
+    "\n",
+    "[**Appendix E: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-E-Ensemble-Kalman-Filters.ipynb)\n",
+    "\n",
+    "Discusses the ensemble Kalman Filter, which uses a Monte Carlo approach to deal with very large Kalman filter states in nonlinear systems.\n",
+    "\n",
+    "\n",
+    "[**Appendix F: FilterPy Source Code**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-F-Filterpy-Code.ipynb)\n",
+    "\n",
+    "Listings of important classes from FilterPy that are used in this book.\n",
+    "\n",
+    "\n",
+    "## Supporting Notebooks\n",
+    "\n",
+    "These notebooks are not a primary part of the book, but contain information that might be interested to a subest of readers.\n",
+    "\n",
+    "\n",
+    "[**Computing and plotting PDFs**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Supporting_Notebooks/Computing_and_plotting_PDFs.ipynb)\n",
+    "\n",
+    "Describes how I implemented the plotting of various pdfs in the book.\n",
+    "\n",
+    "\n",
+    "\n",
+    "[**Iterative Least Squares for Sensor Fusion**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Supporting_Notebooks/Iterative-Least-Squares-for-Sensor-Fusion.ipynb)\n",
+    "\n",
+    "Deep dive into using an iterative least squares technique to solve the nonlinear problem of finding position from multiple GPS pseudorange measurements.\n",
+    "\n",
+    "\n",
+    "[**Taylor Series**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Supporting_Notebooks/Taylor-Series.ipynb)\n",
+    "\n",
+    "A very brief introduction to Taylor series.\n",
+    "\n",
+    "\n",
+    "### Github repository\n",
+    "http://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
+       "\n",
+       ".CodeMirror pre {\n",
+       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
+       "}\n",
+       "    div.cell{\n",
+       "        width: 900px;\n",
+       "        margin-left: 0% !important;\n",
+       "        margin-right: auto;\n",
+       "    }\n",
+       "    div.text_cell code {\n",
+       "        background: transparent;\n",
+       "        color: #000000;\n",
+       "        font-weight: 600;\n",
+       "        font-size: 11pt;\n",
+       "        font-style: bold;\n",
+       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
+       "   }\n",
+       "    h1 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "\t}\n",
+       "\t\n",
+       "    div.input_area {\n",
+       "        background: #F6F6F9;\n",
+       "        border: 1px solid #586e75;      \n",
+       "    }\n",
+       "\n",
+       "    .text_cell_render h1 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 30pt;\n",
+       "        line-height: 100%;\n",
+       "        color:#c76c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 1em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    } \n",
+       "    h2 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "    .text_cell_render h2 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 16pt;\n",
+       "        font-style: italic;\n",
+       "        line-height: 100%;\n",
+       "        color:#c76c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 1.5em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    } \n",
+       "    h3 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "    .text_cell_render h3 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 14pt;\n",
+       "        line-height: 100%;\n",
+       "        color:#d77c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 2em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "    h4 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "    .text_cell_render h4 {\n",
+       "        font-weight: 100;\n",
+       "        font-size: 14pt;\n",
+       "        color:#d77c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 0.5em;\n",
+       "        display: block;\n",
+       "        white-space: nowrap;\n",
+       "    }\n",
+       "    h5 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "\n",
+       "    .text_cell_render h5 {\n",
+       "        font-weight: 200;\n",
+       "        font-style: normal;\n",
+       "        color: #1d3b84;\n",
+       "        font-size: 16pt;\n",
+       "        margin-bottom: 0em;\n",
+       "        margin-top: 0.5em;\n",
+       "        display: block;\n",
+       "        white-space: nowrap;\n",
+       "    }\n",
+       "    div.text_cell_render{\n",
+       "        font-family: 'Vollkorn', verdana,arial,sans-serif;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 130%;\n",
+       "        font-weight: 400;\n",
+       "        text-align:justify;\n",
+       "        text-justify:inter-word;\n",
+       "    }\n",
+       "    div.output_subarea.output_text.output_pyout {\n",
+       "        overflow-x: auto;\n",
+       "        overflow-y: scroll;\n",
+       "        max-height: 50000px;\n",
+       "    }\n",
+       "    div.output_subarea.output_stream.output_stdout.output_text {\n",
+       "        overflow-x: auto;\n",
+       "        overflow-y: scroll;\n",
+       "        max-height: 50000px;\n",
+       "    }\n",
+       "    div.output_wrapper{\n",
+       "        margin-top:0.2em;\n",
+       "        margin-bottom:0.2em;\n",
+       "}\n",
+       "\n",
+       "    code{\n",
+       "        font-size: 6pt;\n",
+       "\n",
+       "    }\n",
+       "    .rendered_html code{\n",
+       "    background-color: transparent;\n",
+       "    }\n",
+       "    ul{\n",
+       "        margin: 2em;\n",
+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li li{\n",
+       "        padding-left: 0.2em; \n",
+       "        margin-bottom: 0.2em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    ol{\n",
+       "        margin: 2em;\n",
+       "    }\n",
+       "    ol li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    a:link{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:visited{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:hover{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:focus{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:active{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    .rendered_html :link {\n",
+       "       text-decoration: underline; \n",
+       "    }\n",
+       "    .rendered_html :hover {\n",
+       "       text-decoration: none; \n",
+       "    }\n",
+       "    .rendered_html :visited {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :focus {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :active {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .warning{\n",
+       "        color: rgb( 240, 20, 20 )\n",
+       "    } \n",
+       "    hr {\n",
+       "      color: #f3f3f3;\n",
+       "      background-color: #f3f3f3;\n",
+       "      height: 1px;\n",
+       "    }\n",
+       "    blockquote{\n",
+       "      display:block;\n",
+       "      background: #fcfcfc;\n",
+       "      border-left: 5px solid #c76c0c;\n",
+       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "      width:680px;\n",
+       "      padding: 10px 10px 10px 10px;\n",
+       "      text-align:justify;\n",
+       "      text-justify:inter-word;\n",
+       "      }\n",
+       "      blockquote p {\n",
+       "        margin-bottom: 0;\n",
+       "        line-height: 125%;\n",
+       "        font-size: 100%;\n",
+       "      }\n",
+       "</style>\n",
+       "<script>\n",
+       "    MathJax.Hub.Config({\n",
+       "                        TeX: {\n",
+       "                           extensions: [\"AMSmath.js\"],\n",
+       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
+       "                           },\n",
+       "                tex2jax: {\n",
+       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
+       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
+       "                },\n",
+       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
+       "                \"HTML-CSS\": {\n",
+       "                    scale:95,\n",
+       "                        availableFonts: [],\n",
+       "                        preferredFont:null,\n",
+       "                        webFont: \"TeX\",\n",
+       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                }\n",
+       "        });\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#format the book\n",
+    "from book_format import load_style\n",
+    "load_style()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}