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

problems when trying to import files from the code subdirectory.

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

00-Preface.ipynb

@@ -57,7 +57,7 @@
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
        "}\n",
        "    div.cell{\n",
-       "        width: 800px;\n",
+       "        //width: 950px;\n",
        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
@@ -71,7 +71,7 @@
        "        //font-family: 'Chivo',verdana,arial,sans-serif;\n",
        "        //font-family: 'Cardo',verdana,arial,sans-serif;\n",
        "        //font-family: 'Arvo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Poppins',verdana,arial,sans-serif;   \n",
+       "        //font-family: 'Poppins',verdana,arial,sans-serif;\n",
        "        //font-family: 'Ubuntu',verdana,arial,sans-serif;\n",
        "        //font-family: 'Fontin',verdana,arial,sans-serif;\n",
        "        //font-family: 'Raleway',verdana,arial,sans-serif;\n",
@@ -95,10 +95,10 @@
        "    h1 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "\t}\n",
-       "\t\n",
+       "\n",
        "    div.input_area {\n",
        "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;      \n",
+       "        border: 1px solid #586e75;\n",
        "    }\n",
        "\n",
        "    .text_cell_render h1 {\n",
@@ -111,7 +111,7 @@
        "        display: block;\n",
        "        white-space: wrap;\n",
        "        text-align: left;\n",
-       "    } \n",
+       "    }\n",
        "    h2 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "        text-align: left;\n",
@@ -127,7 +127,7 @@
        "        display: block;\n",
        "        white-space: wrap;\n",
        "        text-align: left;\n",
-       "    } \n",
+       "    }\n",
        "    h3 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "    }\n",
@@ -194,42 +194,38 @@
        "        margin: 2em;\n",
        "    }\n",
        "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \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",
+       "        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",
+       "        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",
+       "        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",
@@ -237,10 +233,10 @@
        "       color:#447adb;\n",
        "    }\n",
        "    .rendered_html :link {\n",
-       "       text-decoration: underline; \n",
+       "       text-decoration: underline;\n",
        "    }\n",
        "    .rendered_html :hover {\n",
-       "       text-decoration: none; \n",
+       "       text-decoration: none;\n",
        "    }\n",
        "    .rendered_html :visited {\n",
        "      text-decoration: none;\n",
@@ -253,7 +249,7 @@
        "    }\n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
-       "    } \n",
+       "    }\n",
        "    hr {\n",
        "      color: #f3f3f3;\n",
        "      background-color: #f3f3f3;\n",
@@ -581,7 +577,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -595,7 +591,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

01-g-h-filter.ipynb

@@ -306,15 +306,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Before we continue, some words about using Jupyter notebooks with this book. This book is interactive. If you want to run code examples, and especially if you want to see animated plots, you will need to run the code cells. A few things trip readers up. \n",
+    "Before we continue, some words about using Jupyter Notebooks with this book. This book is interactive. If you want to run code examples, and especially if you want to see animated plots, you will need to run the code cells. I cannot teach you everything about Jupyter Notebooks. However, a few things trip readers up. You can go to http://jupyter.org/ for detailed documentation.\n",
     "\n",
-    "First, you must always run the topmost code cell, the one with the comment `#format the book`. It is directly above. This does not just set up formatting, which you might not care about, but it also loads some necessary modules and makes some global settings regarding plotting and printing. So, always run this cell unless you are just passively reading.\n",
+    "First, you must always run the topmost code cell, the one with the comment `#format the book`. It is directly above. This does not just set up formatting, which you might not care about, but it also loads some necessary modules and makes some global settings regarding plotting and printing. So, always run this cell unless you are just passively reading. The import from `__future__` helps Python 2.7 work like Python 3.X. Division of integers will return a `float` (`3/10 == 0.3`) instead of an `int` (`3/10 == 0`), and printing requires parens: `print(3)`, not `print 3`. The line\n",
+    "\n",
+    "```python\n",
+    "%matplotlib inline\n",
+    "```\n",
+    "causes plots to be displayed inside the noteboook. For reasons I don't understand the default behavior of Jupyter Notebooks is to generate plots in an external window. \n",
     "\n",
     "Running a cell is easy. Click on it so that it has focus (a box will be drawn around it), and then press CTRL-Enter. \n",
     "\n",
     "Second, cells must be run in order. I break problems up over several cells; if you try to just skip down and run the tenth code cell it almost certainly won't work. If you haven't run anything yet just choose *Run All Above* from the **Cell** menu item. That's the easiest way to ensure everything has been run.\n",
     "\n",
-    "Once cells are run you can often jump around and rerun cells in different orders, but not always. I'm trying to fix this, but there is a tradeoff. I'll define a variable in cell 10 (say), and then run code that modifies that variable in cells 11 and 12. If you go back and run cell 11 again the variable will have the value that was set in cell 12, and the code expects the value that was set in cell 10. So, occasionally you'll get *weird* results if you run cells out of order. My advise is to backtrack a bit, and run cells in order again to get back to a proper state. It's annoying, but the interactive aspect of Jupyter notebooks more than makes up for it. \n",
+    "Once cells are run you can often jump around and rerun cells in different orders, but not always. I'm trying to fix this, but there is a tradeoff. I'll define a variable in cell 10 (say), and then run code that modifies that variable in cells 11 and 12. If you go back and run cell 11 again the variable will have the value that was set in cell 12, and the code expects the value that was set in cell 10. So, occasionally you'll get weird results if you run cells out of order. My advise is to backtrack a bit, and run cells in order again to get back to a proper state. It's annoying, but the interactive aspect of Jupyter notebooks more than makes up for it. Better yet, submit an issue on GitHub so I know about the problem and fix it!\n",
     "\n",
     "Finally, some readers have reported problems with the animated plotting features in some browsers. I have not been able to duplicate this. In the code cell above `%matplot notebook` enables the interactive plots. If plots are not working for you, try changing this to read `%matplotlib inline`. You will lose the animated plotting, but it seems to work on all platforms and browsers."
    ]
@@ -375,9 +380,9 @@
    "outputs": [
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp8AAACXCAYAAABTJDBMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21541ec1390>"
+       "<matplotlib.figure.Figure at 0x1fc4fabb320>"
       ]
      },
      "metadata": {},
@@ -385,11 +390,10 @@
     }
    ],
    "source": [
-    "from book_format import figsize\n",
-    "from code.book_plots import plot_errorbars\n",
+    "from kf_book.book_plots import figsize, plot_errorbars\n",
     "with figsize(y=2):\n",
     "    plot_errorbars([(160, 8, 'A'), (170, 8, 'B')],\n",
-    "                   xlims=(145, 185), ylims=(-1, 1));\n"
+    "                   xlims=(145, 185), ylims=(-1, 1));"
    ]
   },
   {
@@ -412,9 +416,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAADSCAYAAACy7maiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp8AAACXCAYAAABTJDBMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b57b83400>"
+       "<matplotlib.figure.Figure at 0x1fc4fabb2b0>"
       ]
      },
      "metadata": {},
@@ -445,9 +449,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAADSCAYAAACy7maiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp8AAACXCAYAAABTJDBMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b57f804e0>"
+       "<matplotlib.figure.Figure at 0x1fc5045eeb8>"
       ]
      },
      "metadata": {},
@@ -484,7 +488,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0241\n"
+      "Average of measurements is 164.9963\n"
      ]
     }
    ],
@@ -517,7 +521,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9138\n"
+      "Average of measurements is 165.0785\n"
      ]
     }
    ],
@@ -550,9 +554,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2wAAADhCAYAAAC0qjbKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnkAAAClCAYAAAAgauvcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b55d50208>"
+       "<matplotlib.figure.Figure at 0x1fc4dbfbbe0>"
       ]
      },
      "metadata": {},
@@ -560,7 +564,7 @@
     }
    ],
    "source": [
-    "import code.book_plots as book_plots\n",
+    "import kf_book.book_plots as book_plots\n",
     "book_plots.plot_hypothesis1()"
    ]
   },
@@ -582,9 +586,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2sAAAEjCAYAAACsIZm4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAADTCAYAAADj2DWzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b585abbe0>"
+       "<matplotlib.figure.Figure at 0x1fc5056c3c8>"
       ]
      },
      "metadata": {},
@@ -611,9 +615,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2sAAAEjCAYAAACsIZm4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAADTCAYAAADj2DWzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b585f2080>"
+       "<matplotlib.figure.Figure at 0x1fc505f60b8>"
       ]
      },
      "metadata": {},
@@ -642,9 +646,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABEoAAAEjCAYAAAAlheTVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxcAAADTCAYAAADzse4DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b586af240>"
+       "<matplotlib.figure.Figure at 0x1fc50581b70>"
       ]
      },
      "metadata": {},
@@ -673,9 +677,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABEoAAAEjCAYAAAAlheTVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxcAAADTCAYAAADzse4DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b5970fc18>"
+       "<matplotlib.figure.Figure at 0x1fc504dc6d8>"
       ]
      },
      "metadata": {},
@@ -708,9 +712,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2wAAADhCAYAAAC0qjbKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnkAAAClCAYAAAAgauvcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b598ddb00>"
+       "<matplotlib.figure.Figure at 0x1fc5048d198>"
       ]
      },
      "metadata": {},
@@ -737,9 +741,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAEkCAYAAACWiijoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoIAAADVCAYAAADD5JFzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59918240>"
+       "<matplotlib.figure.Figure at 0x1fc4fedc3c8>"
       ]
      },
      "metadata": {},
@@ -779,9 +783,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAEkCAYAAACWiijoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59936dd8>"
+       "<matplotlib.figure.Figure at 0x1fc506c1978>"
       ]
      },
      "metadata": {},
@@ -837,9 +841,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59723b00>"
+       "<matplotlib.figure.Figure at 0x1fc5044e4e0>"
       ]
      },
      "metadata": {},
@@ -848,16 +852,16 @@
    ],
    "source": [
     "from book_format import set_figsize\n",
-    "from code import book_plots\n",
-    "import code.gh_internal as gh\n",
+    "from kf_book import book_plots\n",
+    "import kf_book.gh_internal as gh\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "\n",
     "weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6, \n",
     "           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]\n",
     "\n",
-    "time_step = 1 # day\n",
-    "scale_factor = 4/10\n",
+    "time_step = 1.0 # day\n",
+    "scale_factor = 4.0/10\n",
     "\n",
     "def predict_using_gain_guess(weight, gain_rate, do_print=True, sim_rate=0): \n",
     "    set_figsize(y=4)\n",
@@ -1617,9 +1621,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -1668,7 +1675,7 @@
     {
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+       "<img 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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1680,7 +1687,7 @@
    ],
    "source": [
     "%matplotlib notebook\n",
-    "predict_using_gain_guess(weight=initial_guess, gain_rate=1, do_print=False, sim_rate=.4)\n",
+    "predict_using_gain_guess(weight=initial_guess, gain_rate=1., do_print=False, sim_rate=.4)\n",
     "%matplotlib inline"
    ]
   },
@@ -2427,9 +2434,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -2478,7 +2488,7 @@
     {
      "data": {
       "text/html": [
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truncated
+       "<img src=\"data:image/png;base64,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 truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2516,9 +2526,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b585eec50>"
+       "<matplotlib.figure.Figure at 0x1fc5062eba8>"
       ]
      },
      "metadata": {},
@@ -2526,7 +2536,7 @@
     }
    ],
    "source": [
-    "predict_using_gain_guess(initial_guess, -1, do_print=False)"
+    "predict_using_gain_guess(initial_guess, -1., do_print=False)"
    ]
   },
   {
@@ -2553,7 +2563,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": false
    },
    "outputs": [
     {
@@ -3269,9 +3280,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -3320,7 +3334,7 @@
     {
      "data": {
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truncated
+       "<img src=\"data:image/png;base64,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 truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3331,12 +3345,12 @@
     }
    ],
    "source": [
-    "weight = 160  # initial guess\n",
+    "weight = 160.  # initial guess\n",
     "gain_rate = -1.0 # initial guess\n",
     "\n",
-    "time_step = 1\n",
-    "weight_scale = 4/10\n",
-    "gain_scale = 1/3\n",
+    "time_step = 1.\n",
+    "weight_scale = 4./10\n",
+    "gain_scale = 1./3\n",
     "estimates = [weight]\n",
     "predictions = []\n",
     "\n",
@@ -3411,9 +3425,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAS4AAADmCAYAAABrjGguAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b5865fe80>"
+       "<matplotlib.figure.Figure at 0x1fc4fef8f98>"
       ]
      },
      "metadata": {},
@@ -3899,7 +3913,7 @@
    "source": [
     "def one_tenth(x):\n",
     "    x = np.asarray(x)\n",
-    "    return x / 10\n",
+    "    return x / 10.\n",
     "\n",
     "print(one_tenth([1, 2, 3]))            # I work!\n",
     "print(one_tenth(np.array([4, 5, 6])))  # so do I!"
@@ -3964,9 +3978,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAFdCAYAAAAJ5smXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaMAAAD7CAYAAADHP/vaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59ad0e80>"
+       "<matplotlib.figure.Figure at 0x1fc50622c88>"
       ]
      },
      "metadata": {},
@@ -3974,7 +3988,7 @@
     }
    ],
    "source": [
-    "from code.gh_internal import plot_g_h_results\n",
+    "from kf_book.gh_internal import plot_g_h_results\n",
     "\n",
     "def g_h_filter(data, x0, dx, g, h, dt=1.):\n",
     "    x = x0\n",
@@ -3992,7 +4006,7 @@
     "    return np.array(results)\n",
     "\n",
     "book_plots.plot_track([0, 11], [160, 172], label='Actual weight')\n",
-    "data = g_h_filter(data=weights, x0=160, dx=1, g=6./10, h=2./3, dt=1.)\n",
+    "data = g_h_filter(data=weights, x0=160., dx=1., g=6./10, h=2./3, dt=1.)\n",
     "plot_g_h_results(weights, data)"
    ]
   },
@@ -4058,9 +4072,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAFdCAYAAAADumfFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAD7CAYAAACxBUlRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59d055c0>"
+       "<matplotlib.figure.Figure at 0x1fc50408400>"
       ]
      },
      "metadata": {},
@@ -4073,7 +4087,7 @@
     "    return [x0 + dx*i + randn()*noise_factor for i in range(count)]\n",
     "\n",
     "measurements = gen_data(0, 1, 30, 1)\n",
-    "data = g_h_filter(data=measurements, x0=0, dx=1, dt=1, g=.2, h=0.02)\n",
+    "data = g_h_filter(data=measurements, x0=0., dx=1., dt=1., g=.2, h=0.02)\n",
     "plot_g_h_results(measurements, data)"
    ]
   },
@@ -4095,9 +4109,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkkAAAFkCAYAAAA0Wq9BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAEACAYAAAATA2nzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59abf400>"
+       "<matplotlib.figure.Figure at 0x1fc5066cf98>"
       ]
      },
      "metadata": {},
@@ -4149,9 +4163,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAFdCAYAAAAT2ikZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAD7CAYAAAAhFjCeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59d05b38>"
+       "<matplotlib.figure.Figure at 0x1fc4fe9d780>"
       ]
      },
      "metadata": {},
@@ -4159,7 +4173,7 @@
     }
    ],
    "source": [
-    "zs = gen_data(x0=5, dx=2, count=100, noise_factor=10)\n",
+    "zs = gen_data(x0=5., dx=2., count=100, noise_factor=10)\n",
     "data = g_h_filter(data=zs, x0=100., dx=2., dt=1., g=0.2, h=0.01)\n",
     "plot_g_h_results(measurements=zs, filtered_data=data)"
    ]
@@ -4212,9 +4226,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAFdCAYAAAAuSEh/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa8AAAD7CAYAAADdAxtUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59d60c50>"
+       "<matplotlib.figure.Figure at 0x1fc4fef4668>"
       ]
      },
      "metadata": {},
@@ -4222,7 +4236,7 @@
     }
    ],
    "source": [
-    "zs = gen_data(x0=5, dx=2, count=100, noise_factor=100)\n",
+    "zs = gen_data(x0=5., dx=2., count=100, noise_factor=100)\n",
     "data = g_h_filter(data=zs, x0=5., dx=2., g=0.2, h=0.02)\n",
     "plot_g_h_results(measurements=zs, filtered_data=data)"
    ]
@@ -4277,9 +4291,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAFdCAYAAAAJ5smXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaMAAAD7CAYAAADHP/vaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b598defd0>"
+       "<matplotlib.figure.Figure at 0x1fc4feed780>"
       ]
      },
      "metadata": {},
@@ -4287,7 +4301,7 @@
     }
    ],
    "source": [
-    "def gen_data(x0, dx, count, noise_factor, accel=0):\n",
+    "def gen_data(x0, dx, count, noise_factor, accel=0.):\n",
     "    zs = []\n",
     "    for i in range(count):\n",
     "        zs.append(x0 + dx*i + randn()*noise_factor)\n",
@@ -4295,8 +4309,8 @@
     "    return zs\n",
     "   \n",
     "predictions = []\n",
-    "zs = gen_data(x0=10, dx=0, count=20, noise_factor=0, accel=2)\n",
-    "data = g_h_filter(data=zs, x0=10, dx=0, g=0.2, h=0.02)\n",
+    "zs = gen_data(x0=10., dx=0., count=20, noise_factor=0, accel=2.)\n",
+    "data = g_h_filter(data=zs, x0=10., dx=0., g=0.2, h=0.02)\n",
     "plt.xlim([0, 20])\n",
     "plot_g_h_results(measurements=zs, filtered_data=data)"
    ]
@@ -4355,9 +4369,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0wAAAFkCAYAAAAT/bvFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl8AAAEACAYAAACNjxOiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59d2d710>"
+       "<matplotlib.figure.Figure at 0x1fc506c6358>"
       ]
      },
      "metadata": {},
@@ -4365,14 +4379,13 @@
     }
    ],
    "source": [
-    "import book_format\n",
     "np.random.seed(100)\n",
-    "zs = gen_data(x0=5, dx=5, count=50, noise_factor=50)\n",
+    "zs = gen_data(x0=5., dx=5., count=50, noise_factor=50)\n",
     "data1 = g_h_filter(data=zs, x0=0., dx=5., dt=1., g=0.1, h=0.01)\n",
     "data2 = g_h_filter(data=zs, x0=0., dx=5., dt=1., g=0.4, h=0.01)\n",
     "data3 = g_h_filter(data=zs, x0=0., dx=5., dt=1., g=0.8, h=0.01)\n",
     "\n",
-    "with book_format.figsize(y=4):\n",
+    "with book_plots.figsize(y=4):\n",
     "    book_plots.plot_measurements(zs, lw=1, color='k')\n",
     "    book_plots.plot_filter(data1, label='g=0.1', marker='s')\n",
     "    book_plots.plot_filter(data2, label='g=0.4', marker='v')\n",
@@ -4407,9 +4420,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEACAYAAACQx1DIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b597501d0>"
+       "<matplotlib.figure.Figure at 0x1fc506df2b0>"
       ]
      },
      "metadata": {},
@@ -4451,9 +4464,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjUAAAFdCAYAAAD7WpwyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAD7CAYAAABJ5bKmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59d031d0>"
+       "<matplotlib.figure.Figure at 0x1fc50665a90>"
       ]
      },
      "metadata": {},
@@ -4497,9 +4510,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAFdCAYAAAAOpBeCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAD7CAYAAABXLIIVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b585195c0>"
+       "<matplotlib.figure.Figure at 0x1fc505c0518>"
       ]
      },
      "metadata": {},
@@ -4544,9 +4557,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAFkCAYAAAANPR4aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa8AAAEACAYAAAAEIfk6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59da65f8>"
+       "<matplotlib.figure.Figure at 0x1fc50578cf8>"
       ]
      },
      "metadata": {},
@@ -4556,7 +4569,7 @@
    "source": [
     "from ipywidgets import interact, FloatSlider\n",
     "\n",
-    "zs1 = gen_data(x0=5, dx=5, count=100, noise_factor=50)\n",
+    "zs1 = gen_data(x0=5, dx=5., count=100, noise_factor=50)\n",
     "\n",
     "fig = None\n",
     "def interactive_gh(x, dx, g, h):\n",
@@ -4593,9 +4606,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkkAAAFdCAYAAAAXL/kkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAD7CAYAAADKIYudAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59d82ef0>"
+       "<matplotlib.figure.Figure at 0x1fc50686cc0>"
       ]
      },
      "metadata": {},
@@ -4603,8 +4616,8 @@
     }
    ],
    "source": [
-    "zs = gen_data(x0=5, dx=.2, count=100, noise_factor=100)\n",
-    "data = g_h_filter(data=zs, x0=5, dx=.2, dt=1., g=0, h=0)\n",
+    "zs = gen_data(x0=5., dx=.2, count=100, noise_factor=100)\n",
+    "data = g_h_filter(data=zs, x0=5., dx=.2, dt=1., g=0., h=0.)\n",
     "\n",
     "book_plots.plot_measurements(zs)\n",
     "book_plots.plot_filter(data, label='filter', lw=2)\n",
@@ -4629,9 +4642,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkkAAAFfCAYAAABa51gvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAD9CAYAAAAceGiAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59d03dd8>"
+       "<matplotlib.figure.Figure at 0x1fc51a28da0>"
       ]
      },
      "metadata": {},
@@ -4640,7 +4653,7 @@
    ],
    "source": [
     "zs = gen_data(x0=5, dx=-2, count=100, noise_factor=5)\n",
-    "data = g_h_filter(data=zs, x0=5, dx=2., dt=1., g=.005, h=0.001)\n",
+    "data = g_h_filter(data=zs, x0=5., dx=2., dt=1., g=.005, h=0.001)\n",
     "book_plots.plot_measurements(zs)\n",
     "book_plots.plot_filter(data, label='filter', lw=2)\n",
     "plt.legend(loc=1);"
@@ -4694,9 +4707,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAGMCAYAAABu0l5AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAEdCAYAAAB38NKUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b5992e6d8>"
+       "<matplotlib.figure.Figure at 0x1fc5196bc18>"
       ]
      },
      "metadata": {},
@@ -4706,7 +4719,7 @@
    "source": [
     "from numpy.random import randn\n",
     "\n",
-    "def compute_new_position(pos, vel, dt=1):\n",
+    "def compute_new_position(pos, vel, dt=1.):\n",
     "    \"\"\" dt is the time delta in seconds.\"\"\"\n",
     "    return pos + (vel * dt)\n",
     "\n",
@@ -4720,7 +4733,7 @@
     "        zs.append(measure_position(pos))\n",
     "    return np.asarray(zs)\n",
     "  \n",
-    "pos, vel = 23*1000, 15\n",
+    "pos, vel = 23.*1000, 15.\n",
     "zs = gen_train_data(pos, vel, 100)\n",
     "\n",
     "plt.plot(zs / 1000.)  # convert to km\n",
@@ -4746,9 +4759,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk8AAAFzCAYAAAAnl+0lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAELCAYAAACIz8K9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59905470>"
+       "<matplotlib.figure.Figure at 0x1fc51a28e10>"
       ]
      },
      "metadata": {},
@@ -4756,9 +4769,9 @@
     }
    ],
    "source": [
-    "zs = gen_train_data(pos, 15, 100)\n",
+    "zs = gen_train_data(pos=pos, vel=15., count=100)\n",
     "data = g_h_filter(data=zs, x0=pos, dx=15., dt=1., g=.01, h=0.0001)\n",
-    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.0001')"
+    "plot_g_h_results(zs/1000., data/1000., 'g=0.01, h=0.0001')"
    ]
   },
   {
@@ -4778,9 +4791,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAFzCAYAAADSaWaVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3XdYleUbwPHvg0xRIXduLUc5MveWcqXmzL0QNfWXpZWp\naW7NNHOUq7IUzSR3aU5caY4cuXOUe28cgKzz/P544YUDqIDAOcj9ua5zec7zrpvzIuc+z1Raa4QQ\nQggh0iMHWwcghBBCCGErkggJIYQQIt2SREgIIYQQ6ZYkQkIIIYRItyQREkIIIUS6JYmQEEIIIdIt\nSYSEEEIIkW5JIiSEEEKIdEsSISGEEEKkW5IICSFShVKqoFLKopSaY+tYhBAiiiRCQqRxSilvpdRf\nSqkHSqkApdQWpVTjRJ6jmlLqS6XUHqXUDaXUI6XUGaXUbKXUSykVe2pKjvcpqedSSrkqpUYppU4o\npYKVUteVUouUUiWecExepdQcpdTlyPtxVik1RSnlGc++jkqpfpH7H1BKhUQmnd2S8vMJkZ4oWWtM\niLRLKfUV8DFwEVgKOAPtgGzA+1rrmQk8z1UgO7AT2A+EA1WB6kAgUFdr/dczxloQOAv4aq1T9QM6\nud6npJxLKeUMbAaqAXsjn+cH2gChwBta672xjikC7MK4J78CJ4FKwJvACaC61vpujP09gLuABq5H\nnjc/8K7WWmrghHgSrbU85CGPNPjASFQsGB+SWWKUFwBuAUFAgQSeawCQK57yTyOvcSgZ4i0Yea45\nafh9SvS5gMGRx/wSq7xJZPmReK6zHogA3otVPinymJmxyp2ABlH3EBgReXw3W/+eykMe9v6QpjEh\nkklk08SxyKaPS0qpaUqpLEqpc0qpMylwyf9h1AB8rrW+H1Wotb4AzABcAJ+EnEhrPVFrfT2eTV8C\nwUAppdQLzx6yIbK/0C9KqZuR79fepDZTJUCyvU9JPFfvyGMGxSzUWq8CtgOvKqVqR5VH1gbVA87p\nuDVVIzBq6DorpdxinCtMa73+MfdQCPEEkggJkQyUUjOBKUAW4DtgIcaHmT/gmEKXfSPy3/XxbFsL\nKIymlGehMZrJwKhhSA6FgD0YtSjzgV+AksCvMROCZJSc71OizhXZvyo/cEprfT6B14+6xobYO2ut\nHwI7gIxAlQTGLIR4gpT6Ay1EuqGUqoHxrf8EUFlr/SCyfAiwCcgDnIt1jAfwEUaikVArtNaHI4/P\nCOQFHjymFuDfyH+LJeL88WkDZAZ2xqwBeUa1gRFa67FRBUopP2AdRhPdHzHK7eZ9SuK5ikf+e+ox\np33cMfopx9SLPGbLU8IWQjyFJEJCPLuuRDeXPIgq1FqHK6UGA3/Gc4wnMJzEfcCfBQ5HPveI/Pfe\nY/aNKo8zwiihlFKFgWlAGEbn4ORyHvg8ZoHWeoNS6gJGh+CY7Ol9Ssq5UusYIUQSSSIkxLMrG/nv\njni27Sa6ackU2Uxit03TSqkcGM022TA67O5JxtMf1FrHl9hcJFZzj72/T0KItE/+wAjx7KK+wcdp\nLtFaW4DbKXDNqFoBj8dsjyoPSOyJI5OgLUBRoK/W+rvEh/dEj4spnOT/m5Sc71NSzpVaxwghkkhq\nhIR4dlF9Z3IRty+QA0atyqVY5c/U90VrHaSUugzkUUrliqfPStHIfx/XzyReSqkXMfo1FcOoCUru\nJChR7Ol9SuK5Tkb++7g+SI87RiXyGCFEEkkiJMSzO4DRPFaDWIkQxrwz8f0/e9a+L2BMzNcJeAuY\nF2vfRpH/bkroyZVS+SLPWQTopbX+MRGxpRR7e58SdS6t9enIvk/FlFIF4xk51gjjZ9scoyyqA3T9\n2BdXSmXCmOQyCKPZVQjxjKRpTIhnNx/jG/xnSqksUYWRMwqPi+8ArfV5rbWD1jpDIh7zY53m2xjX\nNTvOKqUKAX2AR4BvzAOUUrmVUsVjxhlZXhDYBhQGfBKSBEUuM5Gia4fZ2/uUlHPFOOZLpZSKcUwz\njOT5mNbaHCmntT6DMXS+kFLq/VjnGg24A/O11sHxvWdCiMSRGiEhnpHWeptS6nvgXeCYUmoZxkir\nJhj9OK5gzAac3NfdpZSajNF0dFgpFbXcQ1uMmpT3Iyf6i2k80AVjpFvMhGErxszP+4AiSqkR8Vxy\nbqzzRX2RitMZ3J4k5/uUxHNNBt4GWgF/KaU2YbzXrYCHQHzLjbyH0fn+a6VUHeA4RkdyL4xpGobG\nPkApNQiIWrusLEby1U0pVTOy7E87qeUTwq5IIiREMtBa91ZKHQd6RT5uA8uBz4DLwI0Uuu4nSqnD\nGLUR72IkXPuBiVrrtfEdQvzNTAUiy8tHPuKzBYj5IV868hi/xIT8mOvH3J7skvF9SvS5tNahSqm6\nGMuVtAc+xOhXthwYqbU+Ec8xZ5RSFTBqgN4CGgJXMSbtHK21jm9o/VtArVg/Q9XIR9RrSYSEiEUW\nXRUiBSmlimJ0fvXTWne0dTzJSSn1NxCita761J2FEMJOSY2QEMlAKZULuBFzfpzImYinYnwTX26r\n2FJCZN+Z0kBLW8cihBDPQmqEhEgGSqkvMJo9tmI0YeQG6mAsybBGa93EdtEJIYR4HJvVCN27dy8D\n0fNhRLlDCvURECIllS5des+pU6cqhIWFNbBYLJ5KqYgMGTKczpo162x/f//v7927l8PWMQohRBqj\ngKyxyv718PBIrgWgjYvYqkbo3r17JTBGQgghhBBCJMQrHh4ecQYYPAuZR0gIIYQQ6ZYkQkIIIYRI\ntyQREkIIIUS6Zcvh83diF7i7u+PgILmZvTl27Bjh4eE4OjpSsmRJW4cjYpH7Y//kHtk3uT/2yWKx\nEBgYGLs4Tu7wrGyZCMXppe3g4CCJkB2yWCxERETI/bFTcn/sn9wj+yb3J01J9hFecseFEEIIkW5J\nIiSEEEKIdEsSISGEEEKkW5IICSGEECLdkkRICCGEEOmWJEJCCCGESLckERJCCCFEumXLeYSEEEKk\nI0eOHMHPz4+bN29SvHhxOnfuTK5cuWwdlkjnJBESQgiRorTWfPjhh3zzzTdW5cOHD8fX15c2bdrY\nKDIhpGlMCCFECps2bVqcJAggODiYjh07cvjwYRtEJYRBEiEhhBApxmKxMGnSJPP1F198wa5du8xa\noPDwcL7++mtbhSeEJEJCCCFSztWrV7lw4QIAXl5efPrpp1SpUoU5c+bg5OQEwK5du2wZokjnJBES\nQgiRYlxcXMznN2/eRGtjzcw7d+4QFhYWZx8hUpskQkIIIVJM9uzZqVSpEgDHjh2jcePGTJgwgfr1\n65v7NG7c2FbhCSGjxoQQQqSs0aNH07BhQ7TWrF27lrVr15rbcuXKxQcffGDD6ER6JzVCQgghUlSD\nBg1YvHhxnDmDypUrx+bNm2UuIWFTUiMkhBAixbVq1YqmTZuyZcsWbt26RbFixahQoQJKKVuHJtI5\nSYSEEEKkCmdnZxo0aGDrMISwIk1jQgghhEi3JBESQgghRLoliZAQQggh0i1JhIQQQgiRbkkiJIQQ\nQoh0S0aNCSGEEMLmDh48yPr16wkLC6N69erUqlUrVa4riZAQQgghbCYwMJAuXbqwfPlyq/K6deuy\ndOnSFL++NI0JIYQQwmZ69eoVJwkCOHDgQKpcX2qEhBBCiKcICgpi3bp13L59mxIlSlCjRg2ZFTsZ\nnDlzhoULFwKQKVMmxo4di4eHB8OGDSM4ODhVYpBESAghhHgCX19fPvroIwICAsyyUqVKsXDhQkqX\nLm3DyNK+bdu2obUGoH///vTr1w8wkqLevXunSgzSNCaEEEI8xooVK/Dx8bFKggCOHj1KnTp1uHbt\nmo0iez7ErFWLiIgwn4eHh6daDFIjJIQQQsRDa83w4cPN161bt6ZGjRrMnj2bo0ePcvPmTaZPn87Y\nsWNtGGXa5uXlhVIKrTWTJ08mY8aMeHp6Mnr06FSLIVE1QkqpwUqpPUqp+0qp60qpFUqpYrH2mauU\nssR6rEnesIUQQoiUdfXqVY4ePQpA+fLlWbRoEX379mXNmuiPtPXr19sqvOdCwYIF8fb2Box+WEOG\nDOG9995L1Zq2xDaN1QSmAZWBuoATsEEp5RZrv7VALiB35KP9M8YphBBCpKqwsDDzuYeHh9mMkzlz\nZhwcHOLsI5Jm1qxZdOzYMU551apVU+X6iWoa01o3ivlaKdUVuAGUB/6MsSlEa33zmaMTQgghbCR/\n/vzkz5+fixcvsnnzZkaPHk2NGjWYNGkSFosFgBo1atg4yrTP1dWVBQsWMHToUDZs2EBYWBg1atSg\nQoUKPHz4MMWv/6x9hDwBDdyJVe6llLoO3AU2A0O11rH3EUIIIeyWg4MDAwYMoG/fvgCMGDHCaruL\niwsffPCBLUJ7LpUoUYISJUqYr6OSzZSW5FFjyqgjnAr8qbX+J8amtUAX4E1gIFAbWKNkwgUhhBBp\nzPvvv0///v3jlGfJkoVly5ZRvHhxG0QlkpOKGr+f6AOVmgU0AKprra8+Yb/CwGmgjtZ6S1T5vXv3\ncmA0q5kuXLiQahmgSLiYbeBOTk42jETER+6P/ZN7ZN8Scn/OnTvH+vXrCQgIoHDhwrz11ltkyZIl\ntUJMlxwcHChQoEDs4pweHh7J2vUmSU1jSqnpQCOg5pOSIACt9Vml1C3gZWDLk/YNDw+3mkdA2B/p\nGGjf5P7YP7lH9u1x9ydv3rx069YtQfuK5JEhQ4ZUuU6iE6HIJKgZUFtrfSEB++cDsgFPTJgAHB0d\nzZ74wn7It1n7JvfH/sk9sm9yf+xTauUDiUqElFIzMYbCNwUClVK5Ijfd01o/Ukq5AyOAZcA1jFqg\nCcAp4KmTLZQsWVISITt06NAhwsLCcHJy4rXXXrN1OCIWuT/2T+7R41ksFrZv387p0Line truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAELCAYAAACf7VJ0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b584a6b38>"
+       "<matplotlib.figure.Figure at 0x1fc506d41d0>"
       ]
      },
      "metadata": {},
@@ -4788,9 +4801,9 @@
     }
    ],
    "source": [
-    "zs = gen_train_data(pos, 15, 100)\n",
+    "zs = gen_train_data(pos=pos, vel=15., count=100)\n",
     "data = g_h_filter(data=zs, x0=pos, dx=15., dt=1., g=.2, h=0.0001)\n",
-    "plot_g_h_results(zs/1000, data/1000, 'g=0.2, h=0.0001')"
+    "plot_g_h_results(zs/1000., data/1000., 'g=0.2, h=0.0001')"
    ]
   },
   {
@@ -4811,9 +4824,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk8AAAFzCAYAAAAnl+0lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3Xd4VEXbwOHfpEAKIaE3Cb0IUkREeq8CEVARVEBBBcEG\nIljx9XtFfEEFFUGKgICIUgSkht6bgPTeeyehhLSd749NTvZsEsgmWxJ47uvKlT2zc86Z3QO7T2bm\nPKO01gghhBBCiLTx8nQDhBBCCCGyEgmehBBCCCEcIMGTEEIIIYQDJHgSQgghhHCABE9CCCGEEA6Q\n4EkIIYQQwgESPAkhhBBCOECCJyGEEEIIB0jwJIQQQgjhAAmehBAPPaVUN6WURSnV1dNtEUJkfhI8\nCSHSJCHA2KyUuqmUuqGUWqmUau3qYymlnlRKDVFKLVRKnU8Ick5l7NWkyCNrVSml/JRSXyilDiil\nopRSF5VSfyilyjt4HHe9T0I89CR4EkLcl1LqG2AiUBAYC0wBHgP+Vkr1dvGxXgQGAI2B8+l9DZmR\nUiobsAz4DIgARgBLgfbAP0qpJx043AP7PgmR2ShZGFgIcS9KqVrAeuAw8KTWOjKhPBTYDgQA5bXW\n9+3lSM+xlFKVAQXs1VrHKaUswBmtdagTX2M3YALwqtZ6srOOm4bzfgQMBv7UWneyKW8LzMX6miul\n8Vguf5+EEFbS8yREFqOUelcptTdhiOeMUupHpVROpdQJpdQxF5zyTaxDWoMTgx2AhADnJyA78Kqr\njqW13qW13qm1jsvQq0gjpVSjhGHESKVUhFJqvqNDaA7ohfX9GGhbqLX+G1gLVFBKNUjLgdz9Pgnx\nMJPgSYgsRCk1ChgO5ATGANOAZliHenxcdNpGCb+XpPDcIqy9HY09cCxnU0BbrG2LAEYDa4CngVVK\nqdxOPZlSpYCiwCGt9ckUqnj6/RBCpMJVH7ZCCCdTStXF2lNxAHhKa30zofxjYDlQGDhht08w0BfH\nJkP/pbXelbB/AFAEuKm1vphC3cMJv8umof1OO5YLPQM011qvSixQSn2FtWeoO/CNTXkVoJ2Dxx9u\n0+NWLuH3oVTqZob3QwiRAgmehMg6XiFpyOtmYmHC/JaPgHUp7BMCDMKx4Ok4sCvhcXDC74hU6iaW\nh6ThuM48lqv8bhs4JRgLfAjUsCuvivW9dcREIDF4ygrvhxAiBRI8CZF1VE34vT6F5zYByea6JAwH\nyfB82m1Loex0wu9ctoVa61+BX13eIiFEpiMfqkJkHYk9FcmGvLTWFuCqC86Z2PsRnMrzieU33Hws\nV9ApnVtrHZ/w0NvJ58vs74cQIhXS8yRE1pE43FOA5HObvIA8wBm78gzNedJa31FKnQUKK6UKpDBX\nqUzC79Tm7RiceazMwAlzng4m/E5tTlOWej+EeJhI8CRE1rED69BdXeyCJ6AWKf9/zuicJ4AVwMtA\nS5IPUz2d8Ht5Go/tzGN5WobmPGmtjyZkAC+rlCqWwh13T2O9bisy3FIhhFPJsJ0QWcdkrLeuf6KU\nyplYmJCl+quUdtBan9Rae2mtvR34sU8S+bPNeY3Jy0qp4kAf4C4wyXYHpVRBpVQ523am91jpoZSa\n5Oq16rTWvzr4vnqnkEg08f0YqpRSNu1/BmuQvFdrvdrutZVMeG+dPYwohEgj6XkSIovQWq9RSo0F\nXgf2KqVmAbFYcxPdAM4BFhecd6NS6jusw3+7lFIzgWzAC1h7tt5KISj4GuiK9Q7ByRk5llKqHPAR\n1l6YxAAjl1Jqok2197XW12x3S6jvSMJIdf8qTvcd0AZ4DtislFoOFEvYvoU1PYK9FUAoUBywzcSe\nnvdJCJEOEjwJkYVorXsppfYDPRN+rgKzgU+As8AlF523v1JqF9beodexBmnbgGFa60Up7UIqQ4Xp\nOFZBoIvdsQOwBmeJ258DtkFBJeAmsCBNLzDpOPd6zulrWWmtY5RSTbGmQugMvId1WG828B+t9YFU\n2pJSkJye90kIkQ6ytp0QDwClVBmsE5B/11q/5On2eFLCJPmrWIOxjzzdHiHEg0fmPAmRhSilCtjO\njUkoCwBGYO1ZmO2RhmUu9YAYrMvYCCGE00nPkxBZiFJqCNbhnVXAeaxDNU2wLnuyUGvd1nOtE0KI\nh0OWm/MUERHhTVL+k0TXcMF8BCEym0qVKm05dOhQ9djY2BYWiyVEKRXv7e19NHfu3OOWLl06NiIi\nIp+n2yiEEB6mAPuFvA8HBwfHp1Q5XSfIaj1PERER5YH9nm6HEEIIIbKMR4ODg1O6ASNdZM6TEEII\nIYQDJHgSQgghhHCABE9CCCGEEA7IchPGSSHBW2BgIF5eEgdmJnv37iUuLg4fHx8qVqzo6eYIO3J9\nMj+5RpmbXJ/My2KxcPv2bftipyaHzYrBU7IZ7l5eXhI8ZTIWi4X4+Hi5NpmUXJ/MT65R5ibXJ8tx\n6t1xcsWFEEIIIRwgwZMQQgghhAMkeBJCCCGEcIAET0IIIYQQDpDgSQghhBDCARI8CSGEEEI4QIIn\nIYQQQggHSPAkhBBCCOGArJgkUwghhHig3bip+fcw7DgEvj7Qti4UK6g83SyRQIInIYQQwsNuR2km\nLoA1/8L2g3DsnPn5b6bB5vGaArklgMoMJHgSQgghPEhrTadBsGBD6nVOXYQRf8CQN93XLpE6mfMk\nhBBCeNCG3fcOnBL9PAdu3nbqEm0inSR4EkIIITzou99TLs/ma96OuAW/zHd9e8T9SfAkhBBCeMiR\nM5o5a81lvTvA9okQuRSeqWd+bsQfEBcnvU+eJsGTEEII4SEj/gBtEwvlDISvekHVsopsvor+L5rr\nn7oIM1a6t40iOQmehBBCCA+4FqmZtNBc9noY5AxMuqOudiWoWdFc59vfrZPMhedI8CSEEEJkQGxs\nLBMnTqRZs2ZUrVqVjh07snz58vvu9/NfcOdu0raPN7zzvLmOUsl7n7YfhJXbndBwkW4OBU9KqY+U\nUluUUpFKqYtKqb+UUmXt6kxUSlnsfhamdsyEfbol1Iu32edOel6QEEII4S5RUVE0a9aM7t27s2zZ\nMnbu3MmMGTNo2rQpAwcOTLWHKDpGM3KWuaxjYyhaIHkep2fqQelHzGXfTnPWKxDp4WjPUz3gR+Ap\noCngC4Qrpfzt6i0CCgAFE346p+HYETb1CwLFHGybEEII4VZjxoxh9erVxrZSScHP0KFDWbx4cYr7\n/b4ULlw1l/VL5ZvS21vRt5O5bNEm2HNMhu48xaHgSWv9tNZ6itZ6v9Z6N/AKEAo8YVc1Wmt9WWt9\nKeEnIm2HN+1z2ZG2CSGEEO4UFxfH7NmzAfD19WXp0qXExMQwePBgo87o0aOT7ae1Zvgf5rKGj0O1\ncqlnD+/WCvIEm8tSS3EgXC+jc55CAA1csytvmDCsd0ApNUoplTsNx8qhlDqhlDqllJqjlKqQwbYJ\nIYQQLnPt2jUiIyMBaNy4MU2bNsXHx4eBAwfi728dkNm/f3+y/ZZugd1HzWWp9TolCvBT9O5gLvst\nHM5dlt4nT0j38izK2jc5Alintd5n89QiYBZwHCgFDAEWKqVq6dRvDzgIdAd2AcHAB8AGpVQFrfW5\nVPYx7N27F4vFkt6XIlwgNjbW+L1z504Pt0bYk+uT+ck1ytxiY2PJkSMHXl5eWCwWtm3bxsaNGwkI\nCGDfvn1ERUUB4O/vn+z6fTGuBBBkbBcvcJciAYe432VuWN6bob6PEh1r7feIjYMhv1zgtVaXnPra\nsjovLy9CQ0Ndeo6MrG03CqgA1LEt1Fr/abO5Vym1GzgKNARSzE6htd4EbErcVkptBPYDPYHP79eQ\nuLg44uPjHWy+cJfELwGROcn1yfzkGmVOAQEB1K1blzVr1nDlyhXatWvHY489xsaNG406TZs2NV2/\nU5eys3F/kOk4nRpcJD4+lvt9jQX5xdKy+lXmbsxnlP1zKIBuTeXfhy1vb2+XnyNdwZNSaiTwNFBP\na33+XnW11seVUleA0qQSPKWwT5xSakfCPvfl4+ODl5dkXchMbD8sfH1971FTeIJcn8xPrlHmlnh9\n3nrrLbZv386tW7e4dOkSK1asMOqUK1eOjh07mq7fxgPmWSwhgXE8Uzsyzde47mO3TcHTnpOBeHn7\n4i1fgQZ3xAMOB08JgdMzQAOt9ak01H8EyAPcM8iy28cLqAQsSEv9ihUrSvCUyezcuZPY2Fh8fX2p\nUqWKp5sj7Mj1yfzkGmVuidenbNmybNq0iQ8++IDFixejtcbf358uXbrw9ddfkytXLtN+AyeZZ690\naOTDU09WTvN5Czyi+WBc0vbtu954BVamStnUJ5s/bCwWCzdv3nTpORwKnpRSo7CmHQgDbiulCiQ8\nFaG1vquUCsQ6zDYLuIC15+h/wCFgic1xfgXOaq0/Ttj+DOuw3RGsk9AHYL2Lb3z6X5oQQgjhehUr\nVmThwoVcvXqVq1evUqRIEQIDA5PVi4rWrPnXXNaypmPnKphHUbKw5pjNbOANe6Bq2dT3Ec7naHdN\nLyAnsAo4Z/PTMeH5eKAyMBfrJPBxwFagvtbadlC2KNZcTolyAWOBfVh7m3IAtbTWBxxsnxBCCOER\nefLkoWzZsikGTgCrd8DdmKRtb29oWt3x89Sx66jasMvxY4iMcajnSWt9z2BLa30XaJmG4zS22+4H\n9HOkLUIIIURWsmSzefupChAS5PhwW+1KMMUm9+b63RlsmHCYTBQSQggh3MA+eGrxVPqOU7uSefvk\nBTgr+Z7cSoInIYQQwsVOXtAcOGkuS2/wVLEEBOcwl62XoTu3kuBJCCGEcDH7Xqc8wfBEufQdy8tL\nUesxc9kGGbpzKwmehBBCCBdbssm83exJ64K/6WU/dCfBk3tJ8CSEEEK4UGycZvk2cLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAELCAYAAACIz8K9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59da0eb8>"
+       "<matplotlib.figure.Figure at 0x1fc506b7978>"
       ]
      },
      "metadata": {},
@@ -4821,9 +4834,9 @@
     }
    ],
    "source": [
-    "zs = gen_train_data(pos, 15, 100)\n",
+    "zs = gen_train_data(pos=pos, vel=15., count=100)\n",
     "data = g_h_filter(data=zs, x0=pos, dx=15., dt=1., g=0.01, h=0.1)\n",
-    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.1')"
+    "plot_g_h_results(zs/1000., data/1000., 'g=0.01, h=0.1')"
    ]
   },
   {
@@ -4861,9 +4874,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk8AAAFzCAYAAAAnl+0lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAELCAYAAACIz8K9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b59b296a0>"
+       "<matplotlib.figure.Figure at 0x1fc505c4e10>"
       ]
      },
      "metadata": {},
@@ -4871,9 +4884,9 @@
     }
    ],
    "source": [
-    "zs = gen_train_data_with_acc(pos, 15, 100)\n",
+    "zs = gen_train_data_with_acc(pos=pos, vel=15., count=100)\n",
     "data = g_h_filter(data=zs, x0=pos, dx=15., dt=1., g=.01, h=0.0001)\n",
-    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.0001')"
+    "plot_g_h_results(zs/1000., data/1000., 'g=0.01, h=0.0001')"
    ]
   },
   {
@@ -4892,9 +4905,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk8AAAFzCAYAAAAnl+0lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAELCAYAAACIz8K9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b586daa20>"
+       "<matplotlib.figure.Figure at 0x1fc504f7f28>"
       ]
      },
      "metadata": {},
@@ -4902,9 +4915,9 @@
     }
    ],
    "source": [
-    "zs = gen_train_data_with_acc(pos, 15, 100)\n",
+    "zs = gen_train_data_with_acc(pos=pos, vel=15., count=100)\n",
     "data = g_h_filter(data=zs, x0=pos, dx=15., dt=1., g=.01, h=0.001)\n",
-    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.001')"
+    "plot_g_h_results(zs/1000., data/1000., 'g=0.01, h=0.001')"
    ]
   },
   {
@@ -5126,7 +5139,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -5140,7 +5153,19 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
+  },
+  "widgets": {
+   "state": {
+    "f19855eaf6b74009abbcaa78076a1c84": {
+     "views": [
+      {
+       "cell_index": 121
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
   }
  },
  "nbformat": 4,

02-Discrete-Bayes.ipynb

@@ -397,9 +397,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0cAAAGCCAYAAAArALitAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAEWCAYAAACzN0knAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6c4c86d8>"
+       "<matplotlib.figure.Figure at 0x246f5d3d6a0>"
       ]
      },
      "metadata": {},
@@ -407,8 +407,8 @@
     }
    ],
    "source": [
-    "from book_format import figsize, set_figsize\n",
-    "import code.book_plots as book_plots\n",
+    "from kf_book.book_plots import figsize, set_figsize\n",
+    "import kf_book.book_plots as book_plots\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "belief = np.array([1./3, 1./3, 0, 0, 0, 0, 0, 0, 1/3, 0])\n",
@@ -528,9 +528,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6c95ae10>"
+       "<matplotlib.figure.Figure at 0x246f617b940>"
       ]
      },
      "metadata": {},
@@ -662,9 +662,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAACeCAYAAABzRX3UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6cf85748>"
+       "<matplotlib.figure.Figure at 0x246f6690be0>"
       ]
      },
      "metadata": {},
@@ -804,9 +804,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAADvCAYAAAAXWI4PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6c99f2b0>"
+       "<matplotlib.figure.Figure at 0x246f6a97860>"
       ]
      },
      "metadata": {},
@@ -1562,9 +1562,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -1613,7 +1616,7 @@
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truncated
+       "<img 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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1700,9 +1703,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6cf737f0>"
+       "<matplotlib.figure.Figure at 0x246f6a74b00>"
       ]
      },
      "metadata": {},
@@ -1752,9 +1755,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6d6393c8>"
+       "<matplotlib.figure.Figure at 0x246f6aac160>"
       ]
      },
      "metadata": {},
@@ -2499,9 +2502,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -2550,7 +2556,7 @@
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truncated
+       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAtAAAACgCAYAAAAl6lt0AAAgAElEQVR4Xu2dD7ylZVXvv+eMzAQMMHNE760LzEevDoMR5b9L2E36o1eIxPJaViJ6bwTe4OoFQbOUP1nXyiI/XSz7mHmvhoFlZqlF3jRKKhKUhsDBBBnwginMjICDA7LP/azh2TOb3Tln733Oft/9nL2+7+fjx+Hs533ed/3Wep71e9a7nvXM4CUCIiACIrAUAu8FfgJ4KfCHA6DaBLwCuBi4HfjfPe3j33cA64CPAd8F/APwd8DBwIuAxwM/Avxxz32fAJ4LfBQ4BvggcCDwY8Ac8F/7njOsNrv9bgTuW+Cm9xWZjweu6/v9BcCfAf8TeGP5Lf79+kVwOhr4LBB9njbsC9pOBERABGpFYKbWF/O9REAERKASBE4thPZrwDuBPy/Ed9cS79cB/gr4vgXavAV4HXABcGnP70Geg6geBBwF7Cm/BdE9EdgKfCfw9fL3IOv/WP59JHD/iHgNItCfAp5RSH2/rE8GPg+8vxD5ePQfAC8Gngnc0PcujyvyRJ8hg5cIiIAIrGoEJNCrWn2+vAiIQEsInA9cVMhtd968Bfgw8Dbg//W9x2IEOu69F7gT+PYF3v1s4DeAF5aIczTpEt2IOAdJ7b1+GYh3i6j3742IxSACHfI9BTgACHl6r8OBLwNXASeXH+LfzwOeCty2wLs8UOSOKLqXCIiACKxqBCTQq1p9vrwIiECLCBwC/ADwHODZwLOAiKx+FTgJuLbnXRYj0FuAm0uE9kMLvHuQz0gXCVLcjU53iW5Eme/quyeIdvTzayWiPQocEuhR0LKtCIiACPQgIIHWHERABERgeQhEFPY3gZeU9IrvGIJAB/n+JDA/4JGRQ/3m0qZLdL8JeLjvvv8A/H1JLTlrRDEGEWhTOEYE1OYiIAJ5EJBA59G1koqACIwfgSC1O4G1QBDq+Hdci0Wgjy1kO9ItTh/ydSYVgXYT4ZAKspkIiEA+BCTQ+XSuxCIgAuNDIObQSOGIKhpPAHaUrr8BXFM2//U+bU1pE6kYTxsiEh33dgn0j5dNe739/Qrw2oZyoCOi/VvAzwGx8bH3+nXg1SX/+S/KD93KHLHRsj8a/hog7om/x+9eIiACIrCqEZBAr2r1+fIiIAItIPBTpTrGZxZ4VpDI2EQYJdq+tef32GAXpeFiE17/FRv/ogLHL5UScP0b9CItIypudKtt9FbhiBSQ3aXDJwHdd2qiCkeUyIvNgA+Wahx3l+eGTNeXTYSR0/1I+Xvkg38OeGKptPFP5e8Rmf80EDnk8c5LVS9pQZ0+QgREQARWjoAEeuUY2oMIiMB0IxB1l6NGc1Sl+FsgiORhhSRGybYgmLG58OoeGK4sudFxb5DhIJnvKVUoog70R4DvLX1GTnREroMER3+xkfCbC0GNLrsR6LgnSHpvHegNpQ70/xlSBb9aytJF89j4GGT3CuChcn9Es7tR9PjTK4F3lXcJmSKCHpHwIMNxf7xb7xVVOOI9A5PfL2T/R4FvKX1FTW0vERABEVj1CEigV70KFUAERKBhBILQBoF+fokoB7mNqHGUogsCGakJ/9z3Dv+2lKMLkhwHlcRcG//+69JutqQzvLykckSpuCDmQbY/UMhnNzLdJdCRJvKL5aCSiA5H1Dv++49GkP8Lpcb0QrfExsao7xyHvfResTh4AxCbJGMhEAe/REm/OARmoSvqPMcmyPj/INwRJY8UkDh4xUsEREAEpgIBCfRUqFEhREAEphiBLoEOMuolAiIgAiJQAQK1EOg4IjcOEIiDBdYvUri/F674bPl24JQSCYpPhueUzTwVwOoriIAIiMDYEJBAjw1KOxIBERCB8SBQC4GOT6PxSTKOsP2dIQh0EOb45BnEO2SI3Lw4ZveHxgOLvYiACIhANQhIoKtRhS8iAiIgAo8iUAuB7urjRODjAwj0UcDtwHFAd5d3/PuGktv3RZUrAiIgAlOEQBDo7y6nHk6RWIoiAiIgAqsXgdVIoE8tu8YjWt17RcmnOBHsw71/3LFjx2EzMzNBzB9dMczM3NnpdLo7zlev5nxzERABERABERABEZhyBGZnZ9fOz89HlaK91/z8/NVzc3NRf3+i12ok0KcBby1lnnrB+xJwHhCnZ+27du7ceerMzMyHJoqyDxcBERABERABERABEVgxAvPz8y/auHHjn6y4oxV2sBoJ9EgRaAn0Ci3E20VABERABERABESgEgQk0AsrYtgc6KhlGhU7ujnQ8e846WoT8JgcaAl0JRbva4iACIiACIiACIjAChGQQD8WwDhUIKpqBIGOYvtxylUU7I9c5Sju33/9aWn/srIRMk68egD44f6Gu3btenoh13t/OvDAA1mzZrzlVB9++GE+97k4wRY2b97MAQeEKNN/ZZU7NKvs2nuWcZ7Z3h3n+ca59l6fzh955BEefDAON913PWPDhg1xQNNEr1pSOF4BvLuHLMd7BXGOk7si2nxzOTb2moJW1IG+DPjB0i4IddSBvq8fzR07dnzr7OxsN1LN+vXrx06gH3roIbZujQPE4LjjjmPt2rUTVWpbD88qd+Cr7Np7lnGe2d4d5/nGufZen86DQD/wQMRIH706nc6xc3NzN7XFdRZ7Ti0EujEcJNCNQSuJTLho0rnU51yaG+H7e85KJLPKnXmcZ5a9VnuXQLcxyy/wDAl0c8DXOtiak1hCoXORQPuVrY0ZZvLPcH7PN9Zr1bkEekLzgQS6OeBrHWzNSSyBlkDnc6rqXJ1nWjRp7/XZuwS6DVZjBLpVlCXQ9U00bRhAVr1nlVtC4TiXQLuvqQ3fstgzJNATQt8IdHPASyh0rJkcq/aez97VeT6du2CsT+cS6OZ43JI9S6CbA17nUt9E05y2TV/R3vPZuzrPp3MJdH06l0C34dlN4WgVZZ1LfRNNGwaQVe9Z5ZZQOM4zfWnS3uuzdwl0G55dAt0qyhKK+iaaNgwgq96zyi2hcJxLoM2BbsO3mAM9SZQl0K2iL6HQsWZyrNp7PntX5/l07oKxPp0bgW6V2u1/mDnQzQGvc6lvomlO2+ZAa+/57F2d59O5BLo+nUug2/DsRqBbRVnnUt9E04YBZNV7VrklFI7zTF+atPf67F0C3YZnl0C3irKEor6Jpg0DyKr3rHJLKBznEmhzoNvwLeZATxJlCXSr6EsodKyZHKv2ns/e1Xk+nbtgrE/nRqBbpXbmQLcBt86lvolGvTeHgPaez97VeT6dS6Dr07kEujm/tmTPbiJsDnidS30TTXPadhOh9p7P3tV5Pp1LoOvTuQS6Dc9uCkerKOtc6pto2jCArHrPKreEwnGeKVVLe6/P3iXQw3n2S4AzgEOB64GzgZsWufVZwK8A3wF8A/gb4Fzgjt72RqCHA345rSQU9U00y9HjqPdk1XtWuSUUjnMJtJsIR/UT42wvgR6M5gXAOcDJwK3ARcDpwGZgd9/tM8CXgMuB1wHrgN8FjgC+SwI9GOxxtJBQ6FgzOVbtPZ+9q/N8OnfBWJ/OJdCDGdttwKXAZaXpGuAu4LxClHt72ADcW6LPN5YfTgHeDxwsgR4M9jha6Fzqm2jGoddBfWTVe1a5JRSO80wLZe29PnuXQC/tlSNlYxdwAnBtT9OrgCDI5y9w+2+Uv72+RKDfCTxYotb7mpvCMYgOLf93CUV9E83ytTn8nVn1nlVuCYXjXAJtCsfwHmL8LSXQS2MaqReRu3wMcEtP0yuA+4AzF7j9ucA7gKcCkdJxQ0n/+MpSEeh169YxOzs7Vg2HY922bdvePrds2cLatXkGW0a5u4RC2bX3sU4kFXfmHJfL1p3j9Ok1cZlOp8OePXv2zZCdTufYubm5xfbHtTaTBvGs4Ro1Av2Usrnw1SX3+XHAzwAvA76tRKL3ytUfgd6+fTvhDLxEQAREQAREQAREQATqRiCCkps2bZJAL6GmhXKg7y6VNWKzYO/1YuBdwMaePx4CfBU4HvhU9+8S6LoHhm8nAiIgAiIgAiIgAoshIIEebBuR5xxVOGIzYJDpC4HTgKMXqMJxFPBZ4DXAu4EDgMiFjg2H8VsQ6b1XP4E2hWOwIoZtkfWzbuCj7KYsZUnVymzvjvN841x7r0/npnAMx8ouBs4CIpp8XU8d6COBm4GTgGtKV88Dfj7SjoH5stnwjcAnex/lJsLhgF9OKzdVubko0+Yi7T2fvavzfDrvEuitW/PJXqu9u4lwOQxtDPdIoMcA4iJd1DrYmpN4f8/KrnPJFoGWUByXZoN4ZhKZWfZa/ZoEug1Ws8AzJNDNAV/rYGtOYgm0ziXfwkGdq/NMX5q09/rsXQLdBquRQLeKsgS6vommDQPIqvescksoHOcS6DylaWv80iSBbsOzS6BbRVlCoWPN5Fi193z2rs7z6dwFY306l0C3Su32P8wUjuaA17nUN9E0p23TV7T3fPauzvPpXAJdn84l0G14diPQraKsc6lvomnDALLqPavcEgrHeaYvTdp7ffYugW7Ds0ugW0VZQlHfRNOGAWTVe1a5JRSOcwm0OdBt+JbFniGBnhD6pnA0B7yEQseaybFq7/nsXZ3n07kLxvp0LoFujsct2bMEujngdS71TTTNadscaO09n72r83w6l0DXp3MJdBue3RSOVlHWudQ30bRhAFn1nlVuCYXjPNOXJu29PnuXQLfh2SXQraIsoahvomnDALLqPavcEgrHuQTaHOg2fIs50JNEWQLdKvoSCh1rJseqveezd3WeT+cuGOvTuRHoVqnd/oeZA90c8DqX+iaa5rRtDrT2ns/e1Xk+nUug69O5BLoNz24EulWUdS71TTRtGEBWvWeVW0LhOM/0pUl7r8/eJdBteHYJdKsoSyjqm2jaMICses8qt4TCcS6BNge6Dd9iDvQkUZZAt4q+hELHmsmxau/57F2d59O5C8b6dG4EulVqZw50G3DrXOqbaNR7cwho7/nsXZLine truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2672,9 +2678,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAACcCAYAAAC9SeXkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6c8a8e10>"
+       "<matplotlib.figure.Figure at 0x246f66e4198>"
       ]
      },
      "metadata": {},
@@ -2710,9 +2716,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAADYCAYAAAAqPjbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3XucZHV55/HP0z3NEJBpEsEZyDKK8ZpxM4JiQkRYg0pM\nXiZxo0HUFYUYUbJxSTau2VxAsrsEXdCgspIFHRFjwrobg7sxGFDXoAIRZLIZAQ2iowSGa3pGQOnp\nfvaPU41NTXVPneqqPr8+/Xm/Xv2aqVPn8q063fWc59wqMhNJkiRJarOxpgNIkiRJ0qjZ+EiSJElq\nPRsfSZIkSa1n4yNJkiSp9Wx8JEmSJLWejY8kSZKk1rPxkSRJktR6Nj6SJEmSWs/GR5IkSVLr2fho\nVYqIJ0bEbER8cITLmI2Iz3QNO6sz/NghzP8pEfEXEXFnZ573L3WekqTVpVetktpqTdMBpFUmOz9L\nEhFjwF8CTwY+AnwH+N5S5ytJWnWGUpeklcDGR1pe7wU+Bmxf4nwOB54JXJSZb15yKknSavVM4KGm\nQ0jLwcZHWkaZeT8wjFPSfrTz751DmJckaZXKzK81nUFaLl7jo1UvIp4eEZ+IiPsi4rsR8bcR8eIF\nxj0pIj4bEQ9ExMMR8dWI+N2I2KfPZS14jU8nx5aI2B4R34+IuyLioxHxtK7xZoHPdR7OzW82Iv6g\n5kuXJK0g869P7ad2RcTJnfFfFxE/26lf/xwRM/PG6XmNT0Ssi4hzIuKWTr27PyL+OiKO7zHucXN1\nKCKOioj/08k1ExEbR/NuSPXZ+Gi1ezLwJeBA4APA5cCRwKci4pXzR+zcCOGjnWk+DrwPuA/4w874\n/fw99TyXOiJ+FrgROAm4Hng3cBXwcuD6iHj2vNHPArZ0/v+5zuOz+EEzJElqt75rF1XNeSXwSWAn\n8N+AP1ts5hEx2Zn/24B/pqpJHwd+Cvh0RLxxgUl/GvhbYB/gEuDDwCM1X5s0Mp7qptXuBcC7MvPt\ncwMi4n3AtcAHIuJTmfndiHg98HrgfwKvycxH5o3/B8CZwOlU1/DUEhEHUl33813g2My8dd5zPw5c\nB1wMPBcgM8+OiOM6eT6XmWfXXaYkaUXrq3bNPQW8FHhpZv5Nn/N/J9W1Px/IzLfMW8a5wA3ABRFx\nZWZ2X6/6YuBNmXnxQK9KGjGP+Gi1m6I6YvOozLyR6sjOgVRHXADeCkwDp85vejr+E9V1O68ZMMPJ\nwDrgrPlNTyfLV4H/DhwREc8YcP6SpHbpt3bN+US/TU9ETFDVs13Af+xaxm3ABVRHdF7XY/KbbHpU\nMo/4aLW7MTMf7DH8c1QNyRER8XHgJ4B7gDMionvcAL5PtXdsED/V+ffZEXFmj+fnrvF5JnDLgMuQ\nJLXHXmsX1VcdzPm7GvN+OrAfcE1m/nOP5z8D/F5nGd2ur7EcadnZ+Gi127HA8Ls6/04CP0zV3BwM\nLHYDgUG/B+Hxnfn/6l7Ge9yA85cktUs/tavX8H7MTbvQXUPnhh+4yPKlItn4aLVbv8DwDZ1/pzo/\nAF/JzOeOIMMUVdP0E5m5bQTzlyS1Sz+1a07dLyidm3bDAs8f0mMZ85clFctrfLTaHRkR+/cY/kKq\nD/C50wm2AZs6NyIYtmupjvjscYtrSZJ62GvtWsK8b6X6QtPNEbGux/M/0/l3KcuQGmHjo9VukuqO\nbI+KiOcCr6a6hecnOoPPB9YCH+rc5pOuaQ6MiF7nO/fjQ51lnRkRR/WYd3Tu4iZJEvRfu2rLzGmq\nmySso+sGChHxY8BvUN2i+iN7Ti2VzVPdtNp9Hjg1In4S+AJwKPArVEdg3jR3O9DM/FBEHAm8Bbgt\nIq4EtgM/AhxOdbTmg53na8nM+yPiFcD/Aq6NiKupjjAlcBhwdGc5+y3lhUqSWqOv2tWxxx15+vB2\nqltm/3pEPA/4LNV1rq+kut709Mz81hLyS43wiI9WswS+QfWFa/cDbwJeAXyZ6vsOPv6YkTP/LfAy\n4IvA8cAZncfrgHOB9/SYf1/nO2fmZ6juHPd+4ImdLKcAm4CrgRMXyO/51JK0+txOn7WLvdeJPWpJ\nZj5AdcfRd1LteDsD+GWqU7NPyMyL+pmPVJrI9HdUkiSpdBHxRKqmZ0tmntJ0Hmml8YiPJEmSpNaz\n8ZEkSZLUekXd3GBqamoceGrX4PvxnFFJWm5BdW7/fF+fnJycaSJMSaxVaspJJ530Ix/72Mdy//33\nXzs1NXVw03mkAtSqVUVd4zM1NfUM4Oamc0iSenrm5OTkLU2HaJq1SpKKtmCt8lQ3SZIkSa1n4yNJ\nkiSp9Wx8JEmSJLVeUTc3oLo49DH2339/xsb668+2bdvG7t27WbNmDZs2bRp6uLpKylNSltLylJSl\ntDwlZTHP8maZnZ3lwQcf7B68x2f0KmWtWgVZSstTUhbzrJwspeVpulaV1vjscaeFsbGxvovJ7Ows\nMzMztaYZpZLylJSltDwlZSktT0lZzFNElnLuhtMsa9UqyFJanpKymGflZCktT9O1qvm1IUmSJEkj\nZuMjSZIkqfVsfCRJkiS1no2PJEmSpNaz8ZEkSZLUejY+kiRJklrPxkeSJElS69n4SJIkSWo9Gx9J\nkiRJrbem6QDSMH37vp3sfGSm1jQz69YzRjJDsO3OB2ovc90+4xz2+HW1p5MkSdLysfFRq+x8ZIbX\nXnXvEuawq/YUl73ooCUsT5IkScvBU90kSZIktZ6NjyRJkqTWs/GRJEmS1Ho2PpIkSZJaz8ZHkiRJ\nUuvZ+EiSJElqvYEan4g4PSJuj4iHI+LaiDhqkXGPi4jZrp+ZiHjC4LElSVqctUqSNF/txiciTgTO\nA84EjgC2AldGxGJfZpLAU4ENnZ9DMvPu+nElSdo7a5UkqdsgR3zOAC7KzEsz8xbgNOAh4JS9THdP\nZt499zPAciVJ6pe1SpL0GLUan4iYAJ4DXD03LDMTuAo4erFJgZsi4p8i4tMR8dODhJUkaW+sVZKk\nXqKqBX2OHHEIcAdwdGZeN2/4ucCxmblHQYmIpwHHAV8G1gJvBP4N8LzMvGn+uFNTUwcDj9nDtn37\ndmZnZ/vKNz09/ej/JyYm+ntRI1RSnpKywOjyzKxbz6nX7Bra/PpxyTEHML5zx9DmV9K6KikLmGcx\nw84yNjbGxo0buwc/YXJy8p4lz3zErFX1lJSnpCxQVp6SsoB5FlNSFigrT9O1as2Sl7gXmfk14Gvz\nBl0bET9GdRrCyXubfvfu3czMzNRe7vw3tgQl5SkpCww3zxj9N/LDkyN7T0taVyVlAfMsZhhZxsfH\nh5Bk5bBWVUrKU1IWKCtPSVnAPIspKQuUlaeJWlW38bkXmAHWdw1fD9xVYz7XA8/vZ8Q1a9YwNtbf\nGXkldbRQVp6SssAIj/gQQ5tX/2Kor6GkdVVSFjDPYkaxF20Fs1bVUFKekrJAWXlKygLmWUxJWaCs\nPE3XqlqNT2ZOR8QNwPHAFQAREZ3HF9SY1bOBO/sZcdOmTX2/qK1btzI9Pc3ExASbN2+uEWc0SspT\nUpZR5tl25wPA8p7qtnbftWw6fHivoaR1VVIW8yxvltnZWXbtWt6/pWGxVtVTUp6SspSWp6Qs5lk5\nWUrL03StGuRUt/OBLZ2icj3VaQD7AVsAIuIc4NDMPLnz+K3A7cA2YF+q86ZfCLx4gGVLktQPa5Uk\n6TFqNz6ZeXnnexDOpjpt4CbghMycu4hoA3DYvEn2ofouhUOpbiX698Dxmfn5pQSXJGkh1ipJUreB\nbm6QmRcCFy7w3Bu6Hr8LeNcgy5EkaVDWKknSfCv66lVJkiRJ6oeNjyRJkqTWs/GRJEmS1Ho2PpIk\nSZJaz8ZHkiRJUuvZ+EiSJElqPRsfSZIkSa1n4yNJkiSp9Wx8JEmSJLWejY8kSZKk1rPxkSRJktR6\nNj6SJEmSWs/GR5IkSVLr2fhIkiRJaj0bH0mSJEmtN1DjExGnR8TtEfFwRFwbEUf1Od3zI2I6Im4c\nZLmSJPXLWiVJmq924xMRJwLnAWcCRwBbgSsj4qC9TDcJfBi4aoCckiT1zVolSeo2yBGfM4CLMvPS\nzLwFOA14CDhlL9N9APgocO0Ay5QkqQ5rlSTpMWo1PhExATwHuHpuWGYm1Z6xoxeZ7g3A4cA7Bosp\nSVJ/rFWSpF6iqgV9jhxxCHAHcHRmXjdv+LnAsZm5R0GJiKcCnweOyczbIuJM4Bcz88jucaempg4G\n7p4/bPv27czOzvaVb3p6+tH/T0xM9DXNKJWUp6QsMLo8M+vWc+o1u4Y2v35ccswBjO/cMbT5lbSu\nSsoC5lnMsLOMjY2xcePG7sFPmJycvGfJMx8xa1U9JeUpKQuUlaekLGCexZSUBcrK03StWrPkJS4i\nIsaoThk4MzNvmxtcZx67d+9mZmam9rLnv7ElKClPSVlguHnG6L+RH54c2Xta0roqKQuYZzHDyDI+\nPj6EJCuDteoHSspTUhYoK09JWcA8iykpC5SVp4laVbfxuReYAdZ3DV8P3NVj/AOA5wLPjoj3d4aN\nARERjwAvyczPLRpwzRrGxvo7I6+kjhbKylNSFhjhEZ962ypDEkN9DSWtq5KygHkWM4q9aCuYtaqG\nkvKUlAXKylNSFjDPYkrKAmXlabpW1Wp8MnM6Im4AjgeugKoqdB5f0GOSncCzuoadDrwQ+GXgm3tb\n5qZNm/p+UVu3bmV6epqJiQk2b97c1zSjVFKekrKMMs+2Ox8AlvdUt7X7rmXT4cN7DSWtq5KymGd5\ns8zOzrJr1/L+LQ2LtaqekvKUlKW0PCVlMc/KyVJanqZr1SCnup0PbOkUleup7pyzH7AFICLOAQ7N\nzJM7F5N+df7EEXE38L3MvHmAZUuS1A9rlSTpMWo3Ppl5eed7EM6mOm3gJuCEzJy7iLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAACcCAYAAAC9SeXkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6e7f0278>"
+       "<matplotlib.figure.Figure at 0x246f6a7b748>"
       ]
      },
      "metadata": {},
@@ -2768,9 +2774,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAACcCAYAAAC9SeXkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6d6fce48>"
+       "<matplotlib.figure.Figure at 0x246f6bf5320>"
       ]
      },
      "metadata": {},
@@ -2803,9 +2809,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAADYCAYAAAAqPjbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAACcCAYAAAC9SeXkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6d6daa58>"
+       "<matplotlib.figure.Figure at 0x246f6c18780>"
       ]
      },
      "metadata": {},
@@ -2834,9 +2840,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAADYCAYAAAAqPjbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAACcCAYAAAC9SeXkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6e859400>"
+       "<matplotlib.figure.Figure at 0x246f6e8d278>"
       ]
      },
      "metadata": {},
@@ -2865,9 +2871,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAADYCAYAAAAqPjbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAACcCAYAAAC9SeXkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6d09cac8>"
+       "<matplotlib.figure.Figure at 0x246f6d797b8>"
       ]
      },
      "metadata": {},
@@ -2898,9 +2904,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAADYCAYAAAAqPjbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAACcCAYAAAC9SeXkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6e942dd8>"
+       "<matplotlib.figure.Figure at 0x246f6b07438>"
       ]
      },
      "metadata": {},
@@ -2941,9 +2947,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaMAAAFACAYAAAARRRPYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6c8d3da0>"
+       "<matplotlib.figure.Figure at 0x246f6d02d30>"
       ]
      },
      "metadata": {},
@@ -3713,9 +3719,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -3764,7 +3773,7 @@
     {
      "data": {
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+       "<img 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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -4552,9 +4561,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -4603,7 +4615,7 @@
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+       "<img 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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -5357,9 +5369,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -5408,7 +5423,7 @@
     {
      "data": {
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truncated
+       "<img 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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -5447,9 +5462,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6d72fcc0>"
+       "<matplotlib.figure.Figure at 0x246f667e6a0>"
       ]
      },
      "metadata": {},
@@ -5650,9 +5665,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6c8e1978>"
+       "<matplotlib.figure.Figure at 0x246f610e390>"
       ]
      },
      "metadata": {},
@@ -5701,9 +5716,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAACRCAYAAAAB1/Y6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6e8619b0>"
+       "<matplotlib.figure.Figure at 0x246f6d07c88>"
       ]
      },
      "metadata": {},
@@ -5735,9 +5750,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAHnCAYAAAB5ZYgWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAFfCAYAAACMQoJKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20e6e894ac8>"
+       "<matplotlib.figure.Figure at 0x246f6e45a20>"
       ]
      },
      "metadata": {},
@@ -5894,7 +5909,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -5908,7 +5923,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

03-Gaussians.ipynb

@@ -653,7 +653,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAAEtCAYAAABqN0ZuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2100f416390>"
+       "<matplotlib.figure.Figure at 0x1ef75173f60>"
       ]
      },
      "metadata": {},
@@ -661,8 +661,8 @@
     }
    ],
    "source": [
-    "from book_format import set_figsize, figsize\n",
-    "from code.gaussian_internal import plot_height_std\n",
+    "from kf_book.book_plots import set_figsize, figsize\n",
+    "from kf_book.gaussian_internal import plot_height_std\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "plot_height_std(X)"
@@ -688,15 +688,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean = 1.782\n",
-      "std  = 0.140\n"
+      "mean = 1.809\n",
+      "std  = 0.139\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2100fb2c1d0>"
+       "<matplotlib.figure.Figure at 0x1ef75d584a8>"
       ]
      },
      "metadata": {},
@@ -818,7 +818,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2100fe4ef28>"
+       "<matplotlib.figure.Figure at 0x1ef75dc7748>"
       ]
      },
      "metadata": {},
@@ -901,7 +901,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2100ff2e978>"
+       "<matplotlib.figure.Figure at 0x1ef732cb630>"
       ]
      },
      "metadata": {},
@@ -942,7 +942,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2100ff885f8>"
+       "<matplotlib.figure.Figure at 0x1ef76496438>"
       ]
      },
      "metadata": {},
@@ -950,7 +950,7 @@
     }
    ],
    "source": [
-    "import code.book_plots as book_plots\n",
+    "import kf_book.book_plots as book_plots\n",
     "belief = [ 0.,0., 0., 0.1, 0.15, 0.5, 0.2, .15, 0, 0]\n",
     "book_plots.bar_plot(belief)"
    ]
@@ -982,7 +982,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21010185240>"
+       "<matplotlib.figure.Figure at 0x1ef7653c630>"
       ]
      },
      "metadata": {},
@@ -1049,7 +1049,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21011868a20>"
+       "<matplotlib.figure.Figure at 0x1ef7655ce80>"
       ]
      },
      "metadata": {},
@@ -1168,7 +1168,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21011ae16d8>"
+       "<matplotlib.figure.Figure at 0x1ef7756b748>"
       ]
      },
      "metadata": {},
@@ -1223,7 +1223,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21011c181d0>"
+       "<matplotlib.figure.Figure at 0x1ef777576a0>"
       ]
      },
      "metadata": {},
@@ -1231,7 +1231,7 @@
     }
    ],
    "source": [
-    "from code.gaussian_internal import display_stddev_plot\n",
+    "from kf_book.gaussian_internal import display_stddev_plot\n",
     "display_stddev_plot()"
    ]
   },
@@ -1246,7 +1246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -1255,7 +1255,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2100fb7b630>"
+       "<matplotlib.figure.Figure at 0x1ef7655c860>"
       ]
      },
      "metadata": {},
@@ -1512,9 +1512,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.441 -1.375  1.349  0.612  3.477  4.749  1.203\n",
-      " -2.042  7.189 -0.289  0.525  4.164 -0.256 -0.485\n",
-      " -0.805]\n"
+      "[ 1.313  2.32   7.222  1.482 -2.586  6.08  -0.536\n",
+      "  1.988  1.712  1.512  2.502  1.878  0.834  4.719\n",
+      "  0.326]\n"
      ]
     }
    ],
@@ -1606,7 +1606,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2100ff4eef0>"
+       "<matplotlib.figure.Figure at 0x1ef7651b4e0>"
       ]
      },
      "metadata": {},
@@ -1661,19 +1661,9 @@
    "outputs": [
     {
      "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAADaCAYAAAD9sNUgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x21011bbddd8>]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAADaCAYAAAD9sNUgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2100fd8fbe0>"
+       "<matplotlib.figure.Figure at 0x1ef75db39e8>"
       ]
      },
      "metadata": {},
@@ -1682,7 +1672,7 @@
    ],
    "source": [
     "zs = [sense() for i in range(5000)]\n",
-    "plt.plot(zs, lw=1)"
+    "plt.plot(zs, lw=1);"
    ]
   },
   {
@@ -1724,19 +1714,9 @@
    "outputs": [
     {
      "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmwAAADaCAYAAAAbmR5kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8FMX7xz+XXgghQAoJLSC9iIDSFLCgYANRUSyoKIqo\nNIWvigoKiNgQEPVnBUQRRcGGAioqhGYQAoQqCSVAQiAhlYQkt78/7nZvd2+23e215Hl/X37JbZmZ\n3ZmdeeaZ53nGwnEcB4IgCIIgCMJvCfJ1AQiCIAiCIAh1SGAjCIIgCILwc0hgIwiCIAiC8HNIYCMI\ngiAIgvBzSGAjCIIgCILwc0hgIwiCIAiC8HNIYCMIgiAIgvBzDAlsc+bMwRVXXIHY2FgkJCTg1ltv\nRWZmptN1M2bMQEpKCqKionD11Vdj3759phWYIAiCIAiirmFIYPv777/x5JNPYsuWLdiwYQNCQkJw\n3XXX4fz588I1c+fOxbx587Bo0SKkp6cjISEBgwYNQllZmemFJwiCIAiCqAtY3NnpoKysDLGxsfj+\n++9x0003AQCSk5Mxfvx4PPvsswCAiooKJCQk4K233sKYMWPMKTVBEARBEEQdwi0btuLiYlitVsTF\nxQEAsrOzkZubi0GDBgnXREREoH///ti8ebN7JSUIgiAIgqijuCWwTZgwAd27d0efPn0AALm5ubBY\nLEhMTJRcl5iYiNzcXHeyIgiCIAiCqLOEuHrj5MmTsXnzZqSlpcFisbiURlFRkavZEwRBEARB+B2x\nsbEeSdclDdukSZOwYsUKbNiwAS1atBCOJyUlgeM45OXlSa7Py8tDUlKSeyUlCIIgCIKooxgW2CZM\nmCAIa23atJGcS01NRVJSEtavXy8cq6iowMaNG9GvXz/3S0sQBEEQBFEHMbQk+sQTT2DZsmX4/vvv\nERsbK2jS6tWrh+joaADAxIkTMWfOHLRr1w5t2rTBrFmzEBMTg5EjR6qm7SkVIuFZ0tPTAQA9e/b0\ncUkIV6D6C3yoDgMfqsPAxxsmXoYEtvfffx8WiwXXXnut5Pj06dPx0ksvAQCmTp2KiooKPPnkkygs\nLESvXr2wbt06QaAjCIIgCIIgjGFIYLNarbque+mllwQBjiAIgiAIgnAP2kuUIAiCIAjCzyGBjSAI\ngiAIws8hgY0gCIIgCMLPIYGNIAiCIAjCzyGBjSAIgiAIws8hgY0gCIIgCMLPIYGNIAiCqLV8+MNs\nVFVf9HUxCMJtSGAjCIIgai17s/9BcVmhr4tBEG5DAhtBEARBEISfQwIbQRAEUbux+LoABOE+JLAR\nBEEQBEH4OSSwEQRBELUaC6nYiFoACWwEQRAEQRB+DglsBEEQhMexclZwHOfrYhBEwEICG0EQBOFx\nJi4Yjr8zfvZR7rQkSgQ+JLARBEG4wbJ18/H68sm+LobbjJ8/DBUXL3g0j9Pnjgt/11hrUF5Z6tH8\nCKI2QQKbm+w4uBHP/d/9vi4GQRA+4tCJ3cg5k+XrYphCeYWnBSjHkui6f1bi2Q/u83B+BFF7IIHN\nTY6c2oeyihJfF4MgCCKgKCw+47W8LLQiWud5fflk/LBpqa+L4RYksLkJuYsTBEG4AElRhIlkZqdj\n2kcPKp7POZOF/cd3eq9AHoAENoKoQ1g5K6prqnxdjFpFbZq0eVqG8p2TaO2pI4LNkZP7UFJ+XvWa\nQP9WSWDzIFZrjRdsQghCP2u2fInJ797p62IQfgpF3SACFQ61v/GSwOYmajPSdf+sxLP/R0a1hP9w\nSuSlR5hDXRgoPEGgazsIf0PHdxjgTY4ENjdRm5EWlpz1XkEIgiAIJhayl6v16NEOB/okgQS2Osb5\n0nMoKS/ydTEIotYQ6IOAq/yd8TOKywoN3SPWRpIMRRDGIIHNTQKt03npk4ex4Ntpvi4G4SMCrLkS\nfszKPz/Ctn1/GLuJjOQIj6HdtgJ9cmVYYNu4cSOGDh2Kpk2bIigoCEuXSuOaPPTQQwgKCpL817dv\nX9MKTLhP+QXvx42zclZkZqd7PV+CIPwV7w2egT5QE9rUhX1qDQtspaWl6NKlCxYsWICoqCjmNYMG\nDUJeXh5yc3ORm5uLNWvWuF1QIrA5mZ+N//thlq+LQRCEiRh1uKCoHoSn0NW2dCyJHc7Zg6rqi26X\nxxMYFtiGDBmCWbNmYfjw4YqGnOHh4YiPj0dCQgISEhLQoEEDtwvqvwRgT+CDdVxvzn4qLl5A1qn9\nXsuPcJ2q6osYP3+YT/J+f/UrWPfPSp/k7b9Iv9ODxzM8tr8oab28R3HZeVg5q6+L4Vl0jDF6WtzC\nb1/ElszfcL70HFb8/r775TIRj9iwbdq0CYmJiWjXrh0effRR5OfneyIbgmDyW/q3eOeb53xdDJf4\nZsOHKCot8Fj6nvaW4zgOJ/OzdV9/sarCg6VRZ/+xf7Hz0Cb3Ewo0Q1YDLFo1HRt2/qB8gRsTsdoY\nDsXKWXGxutKleyurPCMYA8ALHz+IrZm/eyx9f8DM9sRxVuw/+i/S9q41LU0zCDE7wSFDhuD2229H\namoqjh49imnTpuHaa6/Fjh07EBoaqnhfenpg2jedOWPbD49V/vyz+YrnfEl1VbXpZdJK71zpaV3X\naWHlrDh4egc6JF+ueM3JUydNycsXbNy9BtXlQWiT2M0j6RcW2iKBy9+NWe+qoCwPP+36CKP6vaDr\nen6Q8lVdlZeXu533xYu2AdrX7c2M/Hfv3o16EdIVkVMnTyqmfVLlHIuzZ/OF6/mJvCffG6/Zz8jI\nQFRYjMfy4dmTk4adxzbobv9iVmx/C+UXixEXneiBkgEHD2cirCLOI2n7A3l5eQDU21NZmb7v/fjx\n4wgJDtNMT0ybNm10XecOpgtsI0aMEP7u1KkTunfvjhYtWuDnn3/GsGG+Wfogag/llcX4J3utRGCz\nWmtwsaYSEaFsm0rCe1it1b4ugiFqn47HfNQ0F+68v9q4JFp84Zxb91+s9pzGWV5X58vzUVCai1YJ\nXTyWpxY11mpYYEFQULDPyqCEP7ZO0wU2OU2aNEHTpk1x+PBh1et69uzp6aJ4hGNlu3DgNLv8h89v\nw395/vVsS9OA0NBQ08rEzz600jue9x9+znD/XRQUn8F3O6TpfL9pMX7fsRoLJqwGAJyq2IfMk/71\n3vWyNA1o2bIlenbyTNl35a7HiQLHu9Fbf3o5mlsfa3brT6+8ohQrthvLv8ZagxNnjqBlUltXiwnA\n9q6jIiPdfvYfd4ejrNJ37c2sOlyaBnTt2hUN6ydIjiUnJzPTXpoGpKSk6M53aRrQuFFj4fojRf/g\nkIf7RytnxeebgUu7XorYeg09lg/PgYI0HDlj/Jn4Omzbrh3aNO1serlsdSWtxw9Wv4J9x/7FiBsf\nMj0/vcxc/DgaxSZi3G0z3EqnsqoCR4rTAYWxGLC9g3r16mnWzdI0oHnz5ogIi8Tm//TXZVGR5+Ob\nejwOW35+Pk6ePIkmTZp4OivCTzHTJos12y8scW9WW7fwzbyR4ziUVzL21XWhOLsOp+HtFVPdLxRc\nt3vJ+G+Lz5wlvI6n1JAm2P5VVVch//xp5QsCLtSD58rrj2Ev8otO40R+ltvpTHnvbqTt+VX7Qp1N\nzh/fFeCCwFZWVoaMjAzs2rULVqsVx48fR0ZGBk6cOIGysjJMmTIFW7duxbFjx/Dnn39i6NChSEpK\nwm233WZ64cfPH4aDxzNMT5cwj2O5h/HiJ6M9moc/qK7/+Hc1DufscSsN1YFHhbILxbBaa9zKWw9F\nZQU4V5yneo1SR7d9/x949gNz9tWtrvH9suvxvP98XQSvwcE870KzHQ3Wp6/EzCWPq+TnzJ6s7Xj3\nu5dMLYdZsD6fGpO+bf8UQfwZfxhZpBgW2NLT03HZZZehR48eqKiowPTp09G9e3dMnz4dwcHB2LNn\nD4YNG4Z27drhoYceQocOHbB582ZER0d7ovw4dfaYR9L1N/ZkbTdP6vdiO6y4WG7L0oOedE5vxQde\ne6s3LsYvW79yK425X0y0/WGwnp/7cBT+zvB8rMN5Xz+Llz97zKV7zytoWX1tx+TqN1VbBz9veW5O\nff8eiUak4uIF/LptheF0KirL1S+w16/4uTL+24JDJ3YbzksPnmjPkxbeXiuCjv97aBP+NcMr2w30\n1o/FYvHL/WcN27ANGDAAVqvyjOvXX3WoJc1E5Z3O/+Z53DFwDFLiU53OcRznlxWixEc/voppoxYh\nMS7F10VxCX9VMZuJu09Y7YbBflGZ50KB8GgOjgDMEmWKy86jfrTUW7H0QjEiwiJNSZ/HFQGluqYK\n+47uMLUcatRYa2C1WhEaouxl70lYn+4H38/kzyred6bwFEKCQ9GwfrzTOX4ix3PkZCbWbF2Owb3u\nMlY2PxOd3S8P+34tzba+pH37rhb/8iYAoHvbK72et9F4i/46XgX8XqJqEvORU/tw8AR7yXTCgttw\ntijXU8XyCGaJl77WargDS8PjL0/D+TAwpTfqlB+Msk8fNN1+S95BvvDxgzhycp/k2PMfjsIPm6Rb\n4bnCnzt/dCuIaGZ2Ok6dPep2OeSs+OMDZrpLf30bMz4bo3jf0rRZbnsnCu+fOU45H9QjsM5aOg7z\nvnnWrXK5i9kC3YXKclysci3Omh48KSjI34X4V3VNFYrKCvDrthXYd/Rfj5XBYtEncmza/StOnztu\nWr4/bV5my99vRgvX8HuBbeaScfg742fF89paMuXzpReKXSwVYRRWPe0/thO7j2wzPy/TU9SJDydl\nXtE02AeTHLuR8Fe/L0KZ7BsyOt7w5RaXv6zCttetXAsDAOfL3Hcw+e7vT1BeUcoXwDB6hb3x84cp\naj73ZG13EnrT9vyKHQc3Ol2bk5+NkvLzqnmVV7q3P7Bq6A6VStUSMKp9vMWPIIeKyLine truncated
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x21011b11eb8>]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x210118d1f98>"
+       "<matplotlib.figure.Figure at 0x1ef7654ca20>"
       ]
      },
      "metadata": {},
@@ -1748,7 +1728,7 @@
     "    return 10 + rand_student_t(7)*2\n",
     "\n",
     "zs = [sense_t() for i in range(5000)]\n",
-    "plt.plot(zs, lw=1)"
+    "plt.plot(zs, lw=1);"
    ]
   },
   {
@@ -1830,7 +1810,19 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
+  },
+  "widgets": {
+   "state": {
+    "403fcb72354342e7bc987bddee6b66e4": {
+     "views": [
+      {
+       "cell_index": 54
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
   }
  },
  "nbformat": 4,

04-One-Dimensional-Kalman-Filters.ipynb

@@ -344,7 +344,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAEWCAYAAAAn0eDVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25d282c18>"
+       "<matplotlib.figure.Figure at 0x23be8649208>"
       ]
      },
      "metadata": {},
@@ -352,7 +352,7 @@
     }
    ],
    "source": [
-    "from book_format import set_figsize, figsize\n",
+    "from kf_book.book_plots import set_figsize, figsize\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "import filterpy.stats as stats\n",
@@ -380,14 +380,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean of readings is 10.024\n"
+      "Mean of readings is 10.037\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25de49dd8>"
+       "<matplotlib.figure.Figure at 0x23be92543c8>"
       ]
      },
      "metadata": {},
@@ -395,7 +395,7 @@
     }
    ],
    "source": [
-    "import code.book_plots as bp\n",
+    "import kf_book.book_plots as bp\n",
     "import numpy as np\n",
     "from numpy.random import randn\n",
     "\n",
@@ -446,7 +446,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25b2459b0>"
+       "<matplotlib.figure.Figure at 0x23be678b898>"
       ]
      },
      "metadata": {},
@@ -454,7 +454,7 @@
     }
    ],
    "source": [
-    "import code.kf_internal as kf_internal\n",
+    "import kf_book.kf_internal as kf_internal\n",
     "kf_internal.gaussian_vs_histogram()"
    ]
   },
@@ -705,7 +705,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25e37b128>"
+       "<matplotlib.figure.Figure at 0x23be8f22b00>"
       ]
      },
      "metadata": {},
@@ -754,7 +754,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25e3e5898>"
+       "<matplotlib.figure.Figure at 0x23bea953160>"
       ]
      },
      "metadata": {},
@@ -810,7 +810,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25f78b668>"
+       "<matplotlib.figure.Figure at 0x23be9922710>"
       ]
      },
      "metadata": {},
@@ -820,7 +820,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25f78b6d8>"
+       "<matplotlib.figure.Figure at 0x23be9922390>"
       ]
      },
      "metadata": {},
@@ -864,7 +864,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25e638208>"
+       "<matplotlib.figure.Figure at 0x23be99502e8>"
       ]
      },
      "metadata": {},
@@ -919,8 +919,8 @@
     }
    ],
    "source": [
-    "import code.kf_internal as kf_internal\n",
-    "from code.kf_internal import DogSimulation\n",
+    "import kf_book.kf_internal as kf_internal\n",
+    "from kf_book.kf_internal import DogSimulation\n",
     "\n",
     "np.random.seed(13)\n",
     "\n",
@@ -981,7 +981,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25e6fcd30>"
+       "<matplotlib.figure.Figure at 0x23be99369e8>"
       ]
      },
      "metadata": {},
@@ -1112,7 +1112,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25de521d0>"
+       "<matplotlib.figure.Figure at 0x23beaaa8908>"
       ]
      },
      "metadata": {},
@@ -1120,7 +1120,7 @@
     }
    ],
    "source": [
-    "import code.book_plots as book_plots\n",
+    "import kf_book.book_plots as book_plots\n",
     "book_plots.show_residual_chart()"
    ]
   },
@@ -1278,7 +1278,7 @@
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25de99978>"
+       "<matplotlib.figure.Figure at 0x23bea9e6860>"
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@@ -1447,7 +1447,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25e620ef0>"
+       "<matplotlib.figure.Figure at 0x23beaa4ea58>"
       ]
      },
      "metadata": {},
@@ -1481,7 +1481,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25e5fdc50>"
+       "<matplotlib.figure.Figure at 0x23be8e3c240>"
       ]
      },
      "metadata": {},
@@ -1530,9 +1530,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25e4b3ba8>"
+       "<matplotlib.figure.Figure at 0x23beab1fcf8>"
       ]
      },
      "metadata": {},
@@ -1610,7 +1610,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzwAAAEtCAYAAADN42X5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25f8d4908>"
+       "<matplotlib.figure.Figure at 0x23be9778e10>"
       ]
      },
      "metadata": {},
@@ -1627,7 +1627,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25f98ec50>"
+       "<matplotlib.figure.Figure at 0x23beabd5780>"
       ]
      },
      "metadata": {},
@@ -1729,7 +1729,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25e481c18>"
+       "<matplotlib.figure.Figure at 0x23bea9e6c88>"
       ]
      },
      "metadata": {},
@@ -1787,7 +1787,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25fa64198>"
+       "<matplotlib.figure.Figure at 0x23beac357b8>"
       ]
      },
      "metadata": {},
@@ -1844,7 +1844,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25fa6e710>"
+       "<matplotlib.figure.Figure at 0x23beaa099e8>"
       ]
      },
      "metadata": {},
@@ -1898,7 +1898,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25f9e1048>"
+       "<matplotlib.figure.Figure at 0x23be8e3c7b8>"
       ]
      },
      "metadata": {},
@@ -1946,7 +1946,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmwAAAEPCAYAAAD/HZhMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25f7c31d0>"
+       "<matplotlib.figure.Figure at 0x23be978a940>"
       ]
      },
      "metadata": {},
@@ -2001,7 +2001,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25e613d68>"
+       "<matplotlib.figure.Figure at 0x23be98527f0>"
       ]
      },
      "metadata": {},
@@ -2018,7 +2018,7 @@
     "                       process_noise):\n",
     "    plt.figure()\n",
     "    # your code goes here\n",
-    "    pass\n",
+    "    pass;\n",
     "\n",
     "interact(plot_kalman_filter,\n",
     "         start_pos=(-10, 10), \n",
@@ -2047,7 +2047,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25de7cfd0>"
+       "<matplotlib.figure.Figure at 0x23beab63f98>"
       ]
      },
      "metadata": {},
@@ -2142,7 +2142,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAEWCAYAAADSL2tlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b25fa89588>"
+       "<matplotlib.figure.Figure at 0x23be8e4e5f8>"
       ]
      },
      "metadata": {},
@@ -2317,7 +2317,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -2331,7 +2331,33 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
+  },
+  "widgets": {
+   "state": {
+    "58ed37dc23fe44cc9a0e20ffc9c47414": {
+     "views": [
+      {
+       "cell_index": 79
+      }
+     ]
+    },
+    "8b8fb12fbc4048a0af7d29dfc57d5fb9": {
+     "views": [
+      {
+       "cell_index": 29
+      }
+     ]
+    },
+    "ec6802d3ecf14e67bbcf34b40aa0e3c4": {
+     "views": [
+      {
+       "cell_index": 77
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
   }
  },
  "nbformat": 4,

05-Multivariate-Gaussians.ipynb

@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
@@ -291,13 +291,14 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "#format the book\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
     "%matplotlib inline\n",
     "from __future__ import division, print_function\n",
     "from book_format import load_style\n",
@@ -364,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
@@ -373,7 +374,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df2ea2eeb8>"
+       "<matplotlib.figure.Figure at 0x1a7ad8bf860>"
       ]
      },
      "metadata": {},
@@ -381,7 +382,7 @@
     }
    ],
    "source": [
-    "from code.gaussian_internal import plot_correlated_data\n",
+    "from kf_book.gaussian_internal import plot_correlated_data\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "height = [60, 62, 63, 65, 65.1, 68, 69, 70, 72, 74]\n",
@@ -495,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 48,
    "metadata": {
     "collapsed": false
    },
@@ -503,11 +504,11 @@
     {
      "data": {
       "text/plain": [
-       "array([[   0.025 ,    2.7275],\n",
-       "       [   2.7275,  327.235 ]])"
+       "array([[ 0.025,  2.73],\n",
+       "       [ 2.73,  3.27e+02]])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -533,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 49,
    "metadata": {
     "collapsed": false
    },
@@ -541,11 +542,11 @@
     {
      "data": {
       "text/plain": [
-       "array([[   0.02 ,    2.182],\n",
-       "       [   2.182,  261.788]])"
+       "array([[ 0.02,  2.18],\n",
+       "       [ 2.18,  2.62e+02]])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -567,7 +568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 50,
    "metadata": {
     "collapsed": false
    },
@@ -576,8 +577,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 6.9559  6.9559]\n",
-      " [ 6.9559  6.9559]]\n"
+      "[[ 6.96  6.96]\n",
+      " [ 6.96  6.96]]\n"
      ]
     }
    ],
@@ -598,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 51,
    "metadata": {
     "collapsed": false
    },
@@ -607,7 +608,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df2ea2ec50>"
+       "<matplotlib.figure.Figure at 0x1a7a91a4780>"
       ]
      },
      "metadata": {},
@@ -617,8 +618,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 6.9559 -3.0843]\n",
-      " [-3.0843  1.3868]]\n"
+      "[[ 6.96  -3.08]\n",
+      " [ -3.08  1.39]]\n"
      ]
     }
    ],
@@ -640,16 +641,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 52,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAEICAYAAAATE/N5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df2faa4dd8>"
+       "<matplotlib.figure.Figure at 0x1a7aa4ae0f0>"
       ]
      },
      "metadata": {},
@@ -659,8 +660,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 10.9154   2.9862]\n",
-      " [  2.9862   2.3052]]\n"
+      "[[ 10.4  2.99]\n",
+      " [ 2.99  2.31]]\n"
      ]
     }
    ],
@@ -683,16 +684,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 53,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARoAAAEWCAYAAAC5cVjBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df2ea2eac8>"
+       "<matplotlib.figure.Figure at 0x1a7ad88acc0>"
       ]
      },
      "metadata": {},
@@ -702,8 +703,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 1.003  -0.0008]\n",
-      " [-0.0008  1.0014]]\n"
+      "[[ 0.997  -0.00287]\n",
+      " [ -0.00287  1.01]]\n"
      ]
     }
    ],
@@ -744,16 +745,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 54,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30e7f908>"
+       "<matplotlib.figure.Figure at 0x1a7ad9708d0>"
       ]
      },
      "metadata": {},
@@ -761,14 +763,16 @@
     }
    ],
    "source": [
-    "import code.mkf_internal as mkf_internal\n",
+    "import kf_book.mkf_internal as mkf_internal\n",
     "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "%matplotlib inline\n",
     "\n",
-    "mean = (2, 17)\n",
-    "cov = [[10., 0], \n",
-    "       [0, 4.]]\n",
+    "mean = [2., 17.]\n",
+    "cov = [[10., 0.], \n",
+    "       [0., 4.]]\n",
     "\n",
-    "plt.figure(figsize=(10,5))\n",
+    "#plt.figure(figsize=(10,5))\n",
     "mkf_internal.plot_3d_covariance(mean, cov)"
    ]
   },
@@ -792,7 +796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 55,
    "metadata": {
     "collapsed": false
    },
@@ -814,7 +818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false
    },
@@ -832,7 +836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 57,
    "metadata": {
     "collapsed": false
    },
@@ -850,7 +854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 58,
    "metadata": {
     "collapsed": false
    },
@@ -869,7 +873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 59,
    "metadata": {
     "collapsed": false
    },
@@ -880,7 +884,7 @@
        "0.0315"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -907,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 60,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -915,9 +919,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df31226f60>"
+       "<matplotlib.figure.Figure at 0x1a7ad970d30>"
       ]
      },
      "metadata": {},
@@ -926,7 +930,10 @@
    ],
    "source": [
     "plt.figure(figsize=(10,4))\n",
-    "mkf_internal.plot_3d_sampled_covariance(mu, P)"
+    "try:\n",
+    "   mkf_internal.plot_3d_sampled_covariance(mu, P)\n",
+    "except:\n",
+    "    pass"
    ]
   },
   {
@@ -947,7 +954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 61,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -955,9 +962,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30dc9f98>"
+       "<matplotlib.figure.Figure at 0x1a7ad8da518>"
       ]
      },
      "metadata": {},
@@ -965,7 +972,7 @@
     }
    ],
    "source": [
-    "from book_format import set_figsize, figsize\n",
+    "from kf_book.book_plots import set_figsize, figsize\n",
     "mkf_internal.plot_3_covariances()"
    ]
   },
@@ -989,16 +996,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 62,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30dfccc0>"
+       "<matplotlib.figure.Figure at 0x1a7aa33b518>"
       ]
      },
      "metadata": {},
@@ -1025,16 +1032,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 63,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30dfc128>"
+       "<matplotlib.figure.Figure at 0x1a7a90b0748>"
       ]
      },
      "metadata": {},
@@ -1042,7 +1049,7 @@
     }
    ],
    "source": [
-    "from code.nonlinear_plots import plot_cov_ellipse_colormap\n",
+    "from kf_book.nonlinear_plots import plot_cov_ellipse_colormap\n",
     "plot_cov_ellipse_colormap(cov=[[2, 1.2], [1.2, 1.3]]);"
    ]
   },
@@ -1060,16 +1067,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 64,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30d92f28>"
+       "<matplotlib.figure.Figure at 0x1a7aa427748>"
       ]
      },
      "metadata": {},
@@ -1105,16 +1112,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 65,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30ecf630>"
+       "<matplotlib.figure.Figure at 0x1a7ad979780>"
       ]
      },
      "metadata": {},
@@ -1150,16 +1157,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 66,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30f09c88>"
+       "<matplotlib.figure.Figure at 0x1a7aa423da0>"
       ]
      },
      "metadata": {},
@@ -1184,7 +1191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 67,
    "metadata": {
     "collapsed": false
    },
@@ -1192,11 +1199,11 @@
     {
      "data": {
       "text/plain": [
-       "array([[   18.5249,   135.701 ],\n",
-       "       [  135.701 ,  1092.29  ]])"
+       "array([[ 18.5,  1.36e+02],\n",
+       "       [ 1.36e+02,  1.09e+03]])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1209,7 +1216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 68,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1217,9 +1224,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30fff080>"
+       "<matplotlib.figure.Figure at 0x1a7aa416588>"
       ]
      },
      "metadata": {},
@@ -1243,16 +1250,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 69,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df31127940>"
+       "<matplotlib.figure.Figure at 0x1a7aa50a390>"
       ]
      },
      "metadata": {},
@@ -1280,16 +1287,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 70,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df311536d8>"
+       "<matplotlib.figure.Figure at 0x1a7aa42add8>"
       ]
      },
      "metadata": {},
@@ -1299,7 +1306,7 @@
    "source": [
     "from IPython.html.widgets import interact, interactive, fixed\n",
     "from IPython.html.widgets import FloatSlider\n",
-    "from code.book_plots import end_interactive\n",
+    "from kf_book.book_plots import end_interactive\n",
     "\n",
     "fig = None\n",
     "def plot_covariance(var_x, var_y, cov_xy):\n",
@@ -1326,7 +1333,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Pearson's Correlation Coefficient\n",
+    "kf_book### Pearson's Correlation Coefficient\n",
     "\n",
     "We will not be using this coefficient in this book, but you may see it elsewhere. You can safely skip this section if uninterested.\n",
     "\n",
@@ -1343,7 +1350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 71,
    "metadata": {
     "collapsed": false
    },
@@ -1354,7 +1361,7 @@
        "0.9540"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1373,7 +1380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 72,
    "metadata": {
     "collapsed": false
    },
@@ -1384,7 +1391,7 @@
        "-0.9178"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1404,16 +1411,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 73,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df3107add8>"
+       "<matplotlib.figure.Figure at 0x1a7aa50a940>"
       ]
      },
      "metadata": {},
@@ -1437,16 +1444,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 74,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30e1feb8>"
+       "<matplotlib.figure.Figure at 0x1a7aa410320>"
       ]
      },
      "metadata": {},
@@ -1482,16 +1489,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 75,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df31150d68>"
+       "<matplotlib.figure.Figure at 0x1a7aa585048>"
       ]
      },
      "metadata": {},
@@ -1541,7 +1548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 76,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1549,9 +1556,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df3108aac8>"
+       "<matplotlib.figure.Figure at 0x1a7aa545e80>"
       ]
      },
      "metadata": {},
@@ -1572,7 +1579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 77,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1580,9 +1587,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df3104d390>"
+       "<matplotlib.figure.Figure at 0x1a7aa30b550>"
       ]
      },
      "metadata": {},
@@ -1606,7 +1613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 78,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1614,9 +1621,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df312e9b00>"
+       "<matplotlib.figure.Figure at 0x1a7aa524d68>"
       ]
      },
      "metadata": {},
@@ -1646,16 +1653,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 79,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30de36a0>"
+       "<matplotlib.figure.Figure at 0x1a7a912a080>"
       ]
      },
      "metadata": {},
@@ -1677,16 +1684,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 80,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30f096a0>"
+       "<matplotlib.figure.Figure at 0x1a7aa53e9e8>"
       ]
      },
      "metadata": {},
@@ -1711,16 +1718,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 81,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df3102d2e8>"
+       "<matplotlib.figure.Figure at 0x1a7a9061e10>"
       ]
      },
      "metadata": {},
@@ -1728,7 +1735,7 @@
     }
    ],
    "source": [
-    "import code.book_plots as book_plots\n",
+    "import kf_book.book_plots as book_plots\n",
     "book_plots.plot_errorbar2()"
    ]
   },
@@ -1744,16 +1751,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 82,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30da9860>"
+       "<matplotlib.figure.Figure at 0x1a7ad90ad68>"
       ]
      },
      "metadata": {},
@@ -1777,16 +1784,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 83,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30fe3978>"
+       "<matplotlib.figure.Figure at 0x1a7ad9b9fd0>"
       ]
      },
      "metadata": {},
@@ -1812,16 +1819,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 84,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df31902f98>"
+       "<matplotlib.figure.Figure at 0x1a7a9168518>"
       ]
      },
      "metadata": {},
@@ -1853,16 +1860,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 85,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30d7b240>"
+       "<matplotlib.figure.Figure at 0x1a7a9061240>"
       ]
      },
      "metadata": {},
@@ -1882,16 +1889,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 86,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df31921128>"
+       "<matplotlib.figure.Figure at 0x1a7aa34e2b0>"
       ]
      },
      "metadata": {},
@@ -1918,16 +1925,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 87,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df313bfda0>"
+       "<matplotlib.figure.Figure at 0x1a7aa36c940>"
       ]
      },
      "metadata": {},
@@ -1947,16 +1954,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 88,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30ed4dd8>"
+       "<matplotlib.figure.Figure at 0x1a7aa489b70>"
       ]
      },
      "metadata": {},
@@ -1976,16 +1983,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 89,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df2fb57160>"
+       "<matplotlib.figure.Figure at 0x1a7a91a4a90>"
       ]
      },
      "metadata": {},
@@ -2007,16 +2014,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 90,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1df30e7f668>"
+       "<matplotlib.figure.Figure at 0x1a7a9168898>"
       ]
      },
      "metadata": {},
@@ -2080,6 +2087,7 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -2095,7 +2103,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

06-Multivariate-Kalman-Filters.ipynb

@@ -394,9 +394,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc154cf7b8>"
+       "<matplotlib.figure.Figure at 0x18dc3a7bc50>"
       ]
      },
      "metadata": {},
@@ -404,7 +404,7 @@
     }
    ],
    "source": [
-    "import code.book_plots as book_plots\n",
+    "import kf_book.book_plots as book_plots\n",
     "book_plots.show_residual_chart()"
    ]
   },
@@ -1069,9 +1069,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19a4ba20>"
+       "<matplotlib.figure.Figure at 0x18dc6abeeb8>"
       ]
      },
      "metadata": {},
@@ -1503,7 +1503,7 @@
    },
    "outputs": [],
    "source": [
-    "from code.mkf_internal import plot_track\n",
+    "from kf_book.mkf_internal import plot_track\n",
     "\n",
     "def run(x0=(0.,0.), P=500, R=0, Q=0, dt=1.0, \n",
     "        track=None, zs=None,\n",
@@ -1579,9 +1579,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19aad3c8>"
+       "<matplotlib.figure.Figure at 0x18dc6b3ecc0>"
       ]
      },
      "metadata": {},
@@ -1589,9 +1589,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19b734a8>"
+       "<matplotlib.figure.Figure at 0x18dc7bd0828>"
       ]
      },
      "metadata": {},
@@ -1630,9 +1630,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19ab7908>"
+       "<matplotlib.figure.Figure at 0x18dc7ce77b8>"
       ]
      },
      "metadata": {},
@@ -1640,8 +1640,8 @@
     }
    ],
    "source": [
-    "from book_format import set_figsize, figsize\n",
-    "from code.nonlinear_plots import plot_gaussians\n",
+    "from kf_book.book_plots import set_figsize, figsize\n",
+    "from kf_book.nonlinear_plots import plot_gaussians\n",
     "\n",
     "P = np.diag([3., 1.])\n",
     "np.random.seed(3)\n",
@@ -1766,9 +1766,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc1772a4a8>"
+       "<matplotlib.figure.Figure at 0x18dc6b28cf8>"
       ]
      },
      "metadata": {},
@@ -1816,9 +1816,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc181432b0>"
+       "<matplotlib.figure.Figure at 0x18dc7b9e6d8>"
       ]
      },
      "metadata": {},
@@ -2007,9 +2007,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19af8ba8>"
+       "<matplotlib.figure.Figure at 0x18dc637a710>"
       ]
      },
      "metadata": {},
@@ -2140,9 +2140,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19b21748>"
+       "<matplotlib.figure.Figure at 0x18dc7b3b048>"
       ]
      },
      "metadata": {},
@@ -2231,9 +2231,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc176d5390>"
+       "<matplotlib.figure.Figure at 0x18dc633ed30>"
       ]
      },
      "metadata": {},
@@ -2260,9 +2260,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19b1ae48>"
+       "<matplotlib.figure.Figure at 0x18dc7e002b0>"
       ]
      },
      "metadata": {},
@@ -2363,9 +2363,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc1a297a58>"
+       "<matplotlib.figure.Figure at 0x18dc7e000b8>"
       ]
      },
      "metadata": {},
@@ -2373,9 +2373,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19b20e48>"
+       "<matplotlib.figure.Figure at 0x18dc7e152e8>"
       ]
      },
      "metadata": {},
@@ -2418,9 +2418,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc1a2874a8>"
+       "<matplotlib.figure.Figure at 0x18dc7ff15c0>"
       ]
      },
      "metadata": {},
@@ -2448,9 +2448,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc180043c8>"
+       "<matplotlib.figure.Figure at 0x18dcbaced68>"
       ]
      },
      "metadata": {},
@@ -2479,9 +2479,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc177206d8>"
+       "<matplotlib.figure.Figure at 0x18dc5d6ef28>"
       ]
      },
      "metadata": {},
@@ -2521,9 +2521,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19a6ac50>"
+       "<matplotlib.figure.Figure at 0x18dc7cfb668>"
       ]
      },
      "metadata": {},
@@ -2531,9 +2531,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc18149d30>"
+       "<matplotlib.figure.Figure at 0x18dc7dfcc18>"
       ]
      },
      "metadata": {},
@@ -2541,7 +2541,7 @@
     }
    ],
    "source": [
-    "import code.mkf_internal as mkf_internal\n",
+    "import kf_book.mkf_internal as mkf_internal\n",
     "\n",
     "var = 27.5\n",
     "data = mkf_internal.zs_var_275()\n",
@@ -2576,9 +2576,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc17ef00b8>"
+       "<matplotlib.figure.Figure at 0x18dc625ea20>"
       ]
      },
      "metadata": {},
@@ -2586,9 +2586,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19af84a8>"
+       "<matplotlib.figure.Figure at 0x18dc6ae6908>"
       ]
      },
      "metadata": {},
@@ -2620,9 +2620,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc17323390>"
+       "<matplotlib.figure.Figure at 0x18dc5d4f2e8>"
       ]
      },
      "metadata": {},
@@ -2653,9 +2653,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc17312780>"
+       "<matplotlib.figure.Figure at 0x18dc637a198>"
       ]
      },
      "metadata": {},
@@ -2687,9 +2687,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19a956d8>"
+       "<matplotlib.figure.Figure at 0x18dc7edb4e0>"
       ]
      },
      "metadata": {},
@@ -2697,7 +2697,7 @@
     }
    ],
    "source": [
-    "from code.mkf_internal import plot_track_ellipses\n",
+    "from kf_book.mkf_internal import plot_track_ellipses\n",
     "\n",
     "def plot_covariances(count, R, Q=0, P=20., title=''):    \n",
     "    track, zs = compute_dog_data(R, Q, count)\n",
@@ -2770,9 +2770,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19eac0f0>"
+       "<matplotlib.figure.Figure at 0x18dc6af19e8>"
       ]
      },
      "metadata": {},
@@ -2896,9 +2896,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc18143860>"
+       "<matplotlib.figure.Figure at 0x18dc7ce0ac8>"
       ]
      },
      "metadata": {},
@@ -2941,9 +2941,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19b05a58>"
+       "<matplotlib.figure.Figure at 0x18dc7bcbe48>"
       ]
      },
      "metadata": {},
@@ -2983,9 +2983,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19c564e0>"
+       "<matplotlib.figure.Figure at 0x18dc6b15f60>"
       ]
      },
      "metadata": {},
@@ -3042,9 +3042,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc19c509e8>"
+       "<matplotlib.figure.Figure at 0x18dcb6e0470>"
       ]
      },
      "metadata": {},
@@ -3118,7 +3118,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -3132,11 +3132,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
   },
   "widgets": {
    "state": {
-    "b908e63da2d8435487b14f9e37dd89de": {
+    "ecc568901d6747c293ad68dc86ceb7f5": {
      "views": [
       {
        "cell_index": 73

07-Kalman-Filter-Math.ipynb

@@ -1306,7 +1306,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2403f0b8>"
+       "<matplotlib.figure.Figure at 0x20203c7f940>"
       ]
      },
      "metadata": {},
@@ -1340,7 +1340,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAEtCAYAAABnKTYpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2415ac18>"
+       "<matplotlib.figure.Figure at 0x20203d20a90>"
       ]
      },
      "metadata": {},
@@ -1348,7 +1348,7 @@
     }
    ],
    "source": [
-    "import code.book_plots as book_plots\n",
+    "import kf_book.book_plots as book_plots\n",
     "\n",
     "t = np.linspace(-1, 2, 20)\n",
     "plt.plot(t, np.exp(t))\n",
@@ -1422,7 +1422,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee24196518>"
+       "<matplotlib.figure.Figure at 0x202051f3320>"
       ]
      },
      "metadata": {},
@@ -1579,7 +1579,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2604ee80>"
+       "<matplotlib.figure.Figure at 0x20205bbc390>"
       ]
      },
      "metadata": {},
@@ -1772,7 +1772,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

08-Designing-Kalman-Filters.ipynb

@@ -379,9 +379,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108bb29390>"
+       "<matplotlib.figure.Figure at 0x16a15dc5f28>"
       ]
      },
      "metadata": {},
@@ -391,7 +391,7 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import code.book_plots as bp\n",
+    "import kf_book.book_plots as bp\n",
     "\n",
     "pos, vel = (4, 3), (2, 1)\n",
     "sensor = PosSensor1(pos, vel, noise_std=1)\n",
@@ -696,9 +696,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cc56a90>"
+       "<matplotlib.figure.Figure at 0x16a16ec7b00>"
       ]
      },
      "metadata": {},
@@ -816,7 +816,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cc3f048>"
+       "<matplotlib.figure.Figure at 0x16a16f8b128>"
       ]
      },
      "metadata": {},
@@ -927,7 +927,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108bb09eb8>"
+       "<matplotlib.figure.Figure at 0x16a16fbfa20>"
       ]
      },
      "metadata": {},
@@ -1239,7 +1239,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cdaea20>"
+       "<matplotlib.figure.Figure at 0x16a17031908>"
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      },
      "metadata": {},
@@ -1291,7 +1291,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108ccb2828>"
+       "<matplotlib.figure.Figure at 0x16a170a8128>"
       ]
      },
      "metadata": {},
@@ -1324,7 +1324,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cefaac8>"
+       "<matplotlib.figure.Figure at 0x16a181cf358>"
       ]
      },
      "metadata": {},
@@ -1357,7 +1357,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cfa1320>"
+       "<matplotlib.figure.Figure at 0x16a1815c208>"
       ]
      },
      "metadata": {},
@@ -1365,7 +1365,7 @@
     }
    ],
    "source": [
-    "from code.book_plots import plot_kf_output\n",
+    "from kf_book.book_plots import plot_kf_output\n",
     "\n",
     "kf0 = ZeroOrderKF(R, Q)\n",
     "filter_xs0, ps0 = filter_data(kf0, zs)\n",
@@ -1395,7 +1395,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cf9fef0>"
+       "<matplotlib.figure.Figure at 0x16a1827fb70>"
       ]
      },
      "metadata": {},
@@ -1431,7 +1431,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e068f28>"
+       "<matplotlib.figure.Figure at 0x16a182de9e8>"
       ]
      },
      "metadata": {},
@@ -1468,7 +1468,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e0f7048>"
+       "<matplotlib.figure.Figure at 0x16a1826c470>"
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@@ -1508,7 +1508,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e0f7cf8>"
+       "<matplotlib.figure.Figure at 0x16a1711fe48>"
       ]
      },
      "metadata": {},
@@ -1552,7 +1552,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e107160>"
+       "<matplotlib.figure.Figure at 0x16a182de630>"
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      },
      "metadata": {},
@@ -1586,7 +1586,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e107518>"
+       "<matplotlib.figure.Figure at 0x16a170f82b0>"
       ]
      },
      "metadata": {},
@@ -1596,7 +1596,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108dfd22b0>"
+       "<matplotlib.figure.Figure at 0x16a18222048>"
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@@ -1639,7 +1639,7 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e11afd0>"
+       "<matplotlib.figure.Figure at 0x16a16ed1ac8>"
       ]
      },
      "metadata": {},
@@ -1649,7 +1649,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e10f0f0>"
+       "<matplotlib.figure.Figure at 0x16a1712fc50>"
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      "metadata": {},
@@ -1692,7 +1692,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cd85128>"
+       "<matplotlib.figure.Figure at 0x16a182942e8>"
       ]
      },
      "metadata": {},
@@ -1728,7 +1728,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108dfd1b70>"
+       "<matplotlib.figure.Figure at 0x16a16f359b0>"
       ]
      },
      "metadata": {},
@@ -1784,7 +1784,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e11a2e8>"
+       "<matplotlib.figure.Figure at 0x16a18350390>"
       ]
      },
      "metadata": {},
@@ -1794,7 +1794,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e00a630>"
+       "<matplotlib.figure.Figure at 0x16a16fbf048>"
       ]
      },
      "metadata": {},
@@ -1835,7 +1835,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e2249b0>"
+       "<matplotlib.figure.Figure at 0x16a17102518>"
       ]
      },
      "metadata": {},
@@ -1845,7 +1845,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cef7898>"
+       "<matplotlib.figure.Figure at 0x16a182f7c50>"
       ]
      },
      "metadata": {},
@@ -1881,7 +1881,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cdcc358>"
+       "<matplotlib.figure.Figure at 0x16a16f4c518>"
       ]
      },
      "metadata": {},
@@ -1891,7 +1891,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e1b6080>"
+       "<matplotlib.figure.Figure at 0x16a17126ac8>"
       ]
      },
      "metadata": {},
@@ -2061,7 +2061,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108dfd1be0>"
+       "<matplotlib.figure.Figure at 0x16a170a2b38>"
       ]
      },
      "metadata": {},
@@ -2102,7 +2102,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cd49860>"
+       "<matplotlib.figure.Figure at 0x16a16ee2e80>"
       ]
      },
      "metadata": {},
@@ -2179,7 +2179,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108dfee048>"
+       "<matplotlib.figure.Figure at 0x16a1709c358>"
       ]
      },
      "metadata": {},
@@ -2277,7 +2277,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cc43c88>"
+       "<matplotlib.figure.Figure at 0x16a17078550>"
       ]
      },
      "metadata": {},
@@ -2360,7 +2360,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAEWCAYAAADB+CuRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108dff1a90>"
+       "<matplotlib.figure.Figure at 0x16a1709c470>"
       ]
      },
      "metadata": {},
@@ -2368,7 +2368,7 @@
     }
    ],
    "source": [
-    "from code.kf_design_internal import sensor_fusion_kf, set_radar_pos\n",
+    "from kf_book.kf_design_internal import sensor_fusion_kf, set_radar_pos\n",
     "\n",
     "kf = sensor_fusion_kf()\n",
     "x0, p0 = kf.x.copy(), kf.P.copy()\n",
@@ -2392,21 +2392,11 @@
     "scrolled": true
    },
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2108e280438>]"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e19f908>"
+       "<matplotlib.figure.Figure at 0x16a182a1630>"
       ]
      },
      "metadata": {},
@@ -2435,7 +2425,7 @@
     "plt.scatter([100], [100], c='y', label='Initial')\n",
     "plt.scatter([100], [100], c='g', label='1st station')\n",
     "plt.legend(scatterpoints=1, markerscale=3)\n",
-    "plt.plot([92, 100], [92, 100], c='g', lw=2, ls='--')"
+    "plt.plot([92, 100], [92, 100], c='g', lw=2, ls='--');"
    ]
   },
   {
@@ -2458,7 +2448,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108bb3f978>"
+       "<matplotlib.figure.Figure at 0x16a182010b8>"
       ]
      },
      "metadata": {},
@@ -2546,7 +2536,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cfcf748>"
+       "<matplotlib.figure.Figure at 0x16a170c3208>"
       ]
      },
      "metadata": {},
@@ -2556,7 +2546,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e0fefd0>"
+       "<matplotlib.figure.Figure at 0x16a18135748>"
       ]
      },
      "metadata": {},
@@ -2566,7 +2556,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e0feba8>"
+       "<matplotlib.figure.Figure at 0x16a16f54da0>"
       ]
      },
      "metadata": {},
@@ -2625,7 +2615,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e27c7b8>"
+       "<matplotlib.figure.Figure at 0x16a182afa20>"
       ]
      },
      "metadata": {},
@@ -2635,7 +2625,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e27cf28>"
+       "<matplotlib.figure.Figure at 0x16a182b4dd8>"
       ]
      },
      "metadata": {},
@@ -2645,7 +2635,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e26f828>"
+       "<matplotlib.figure.Figure at 0x16a182d7e48>"
       ]
      },
      "metadata": {},
@@ -2754,7 +2744,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e0fb940>"
+       "<matplotlib.figure.Figure at 0x16a18153630>"
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108cd2bd30>"
+       "<matplotlib.figure.Figure at 0x16a16eeac88>"
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      "metadata": {},
@@ -3105,7 +3095,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e18cb00>"
+       "<matplotlib.figure.Figure at 0x16a1815ecc0>"
       ]
      },
      "metadata": {},
@@ -3371,7 +3361,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e2382b0>"
+       "<matplotlib.figure.Figure at 0x16a1707ea90>"
       ]
      },
      "metadata": {},
@@ -3495,7 +3485,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e2d7da0>"
+       "<matplotlib.figure.Figure at 0x16a18281d30>"
       ]
      },
      "metadata": {},
@@ -3644,7 +3634,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108dfd1780>"
+       "<matplotlib.figure.Figure at 0x16a13efb6a0>"
       ]
      },
      "metadata": {},
@@ -3715,7 +3705,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108dffb7b8>"
+       "<matplotlib.figure.Figure at 0x16a17040f98>"
       ]
      },
      "metadata": {},
@@ -3725,7 +3715,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e040be0>"
+       "<matplotlib.figure.Figure at 0x16a17139588>"
       ]
      },
      "metadata": {},
@@ -3789,7 +3779,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108f376b00>"
+       "<matplotlib.figure.Figure at 0x16a17089208>"
       ]
      },
      "metadata": {},
@@ -3799,7 +3789,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2108e24eda0>"
+       "<matplotlib.figure.Figure at 0x16a17108f98>"
       ]
      },
      "metadata": {},
@@ -3849,7 +3839,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

09-Nonlinear-Filtering.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -289,7 +289,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -379,16 +379,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1923835f978>"
+       "<matplotlib.figure.Figure at 0x1c252045780>"
       ]
      },
      "metadata": {},
@@ -433,16 +433,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1923830f940>"
+       "<matplotlib.figure.Figure at 0x1c2520455c0>"
       ]
      },
      "metadata": {},
@@ -467,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -475,9 +475,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x192376c9668>"
+       "<matplotlib.figure.Figure at 0x1c252dbfeb8>"
       ]
      },
      "metadata": {},
@@ -485,8 +485,8 @@
     }
    ],
    "source": [
-    "from book_format import set_figsize, figsize\n",
-    "from code.nonlinear_plots import plot_nonlinear_func\n",
+    "from kf_book.book_plots import set_figsize, figsize\n",
+    "from kf_book.nonlinear_plots import plot_nonlinear_func\n",
     "\n",
     "def g1(x):\n",
     "    return 2*x+1\n",
@@ -508,16 +508,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19237dabba8>"
+       "<matplotlib.figure.Figure at 0x1c2553aadd8>"
       ]
      },
      "metadata": {},
@@ -550,16 +550,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19237d53c50>"
+       "<matplotlib.figure.Figure at 0x1c2552f5f28>"
       ]
      },
      "metadata": {},
@@ -582,7 +582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -591,8 +591,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "input  mean, variance: 0.0010, 1.0009\n",
-      "output mean, variance: -0.1263, 2.4044\n"
+      "input  mean, variance: 0.0008, 0.9971\n",
+      "output mean, variance: -0.1258, 2.3978\n"
      ]
     }
    ],
@@ -614,16 +614,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19237d89ac8>"
+       "<matplotlib.figure.Figure at 0x1c252e7dac8>"
       ]
      },
      "metadata": {},
@@ -633,7 +633,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output mean, variance: -0.0014, 2.2519\n"
+      "output mean, variance: -0.0011, 2.2434\n"
      ]
     }
    ],
@@ -657,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -666,8 +666,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "linear    output mean, variance: -0.0822, 7488.2247\n",
-      "nonlinear output mean, variance: -9.3045, 30515.9658\n"
+      "linear    output mean, variance: -0.0657, 7459.8773\n",
+      "nonlinear output mean, variance: -9.3367, 30398.0469\n"
      ]
     }
    ],
@@ -695,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -703,9 +703,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x192386dc0f0>"
+       "<matplotlib.figure.Figure at 0x1c25294c7f0>"
       ]
      },
      "metadata": {},
@@ -745,16 +745,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x192383d76d8>"
+       "<matplotlib.figure.Figure at 0x1c252e75a90>"
       ]
      },
      "metadata": {},
@@ -762,7 +762,7 @@
     }
    ],
    "source": [
-    "import code.nonlinear_internal as nonlinear_internal\n",
+    "import kf_book.nonlinear_internal as nonlinear_internal\n",
     "\n",
     "nonlinear_internal.plot1()"
    ]
@@ -776,16 +776,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x192371ef7f0>"
+       "<matplotlib.figure.Figure at 0x1c253548e80>"
       ]
      },
      "metadata": {},
@@ -805,16 +805,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x192377333c8>"
+       "<matplotlib.figure.Figure at 0x1c2535a9588>"
       ]
      },
      "metadata": {},
@@ -834,16 +834,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19237d4bd30>"
+       "<matplotlib.figure.Figure at 0x1c2530e0eb8>"
       ]
      },
      "metadata": {},
@@ -929,7 +929,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

10-Unscented-Kalman-Filter.ipynb

@@ -325,9 +325,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3cb83f60>"
+       "<matplotlib.figure.Figure at 0x1d3249c1ac8>"
       ]
      },
      "metadata": {},
@@ -335,9 +335,9 @@
     }
    ],
    "source": [
-    "from book_format import set_figsize, figsize\n",
+    "from kf_book.book_plots import set_figsize, figsize\n",
     "import matplotlib.pyplot as plt\n",
-    "from code.nonlinear_plots import plot_nonlinear_func\n",
+    "from kf_book.nonlinear_plots import plot_nonlinear_func\n",
     "from numpy.random import normal\n",
     "import numpy as np\n",
     "\n",
@@ -388,7 +388,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3ad02cf8>"
+       "<matplotlib.figure.Figure at 0x1d322afbdd8>"
       ]
      },
      "metadata": {},
@@ -396,7 +396,7 @@
     }
    ],
    "source": [
-    "import code.ukf_internal as ukf_internal\n",
+    "import kf_book.ukf_internal as ukf_internal\n",
     "\n",
     "ukf_internal.show_2d_transform()"
    ]
@@ -435,14 +435,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in mean x=0.121, y=43.207\n"
+      "Difference in mean x=0.090, y=43.831\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAEWCAYAAAC+KTlgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e138048>"
+       "<matplotlib.figure.Figure at 0x1d325f24390>"
       ]
      },
      "metadata": {},
@@ -452,7 +452,7 @@
    "source": [
     "import numpy as np\n",
     "from numpy.random import multivariate_normal\n",
-    "from code.nonlinear_plots import plot_monte_carlo_mean\n",
+    "from kf_book.nonlinear_plots import plot_monte_carlo_mean\n",
     "\n",
     "def f_nonlinear_xy(x, y):\n",
     "    return np.array([x + y, .1*x**2 + y*y])\n",
@@ -505,7 +505,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e1abda0>"
+       "<matplotlib.figure.Figure at 0x1d325f12fd0>"
       ]
      },
      "metadata": {},
@@ -556,7 +556,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e263d68>"
+       "<matplotlib.figure.Figure at 0x1d327033be0>"
       ]
      },
      "metadata": {},
@@ -605,7 +605,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3dc36f28>"
+       "<matplotlib.figure.Figure at 0x1d3270597b8>"
       ]
      },
      "metadata": {},
@@ -661,7 +661,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f2e0ef0>"
+       "<matplotlib.figure.Figure at 0x1d3270fddd8>"
       ]
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      "metadata": {},
@@ -853,7 +853,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f37f1d0>"
+       "<matplotlib.figure.Figure at 0x1d327145e80>"
       ]
      },
      "metadata": {},
@@ -975,7 +975,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f4336a0>"
+       "<matplotlib.figure.Figure at 0x1d327250c18>"
       ]
      },
      "metadata": {},
@@ -1090,7 +1090,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3d2b4400>"
+       "<matplotlib.figure.Figure at 0x1d32711c358>"
       ]
      },
      "metadata": {},
@@ -1155,7 +1155,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f445ba8>"
+       "<matplotlib.figure.Figure at 0x1d3271ac080>"
       ]
      },
      "metadata": {},
@@ -1163,7 +1163,7 @@
     }
    ],
    "source": [
-    "import code.ekf_internal as ekf_internal\n",
+    "import kf_book.ekf_internal as ekf_internal\n",
     "ekf_internal.show_radar_chart();"
    ]
   },
@@ -1334,7 +1334,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f343358>"
+       "<matplotlib.figure.Figure at 0x1d3271a60b8>"
       ]
      },
      "metadata": {},
@@ -1344,7 +1344,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e169048>"
+       "<matplotlib.figure.Figure at 0x1d327282630>"
       ]
      },
      "metadata": {},
@@ -1354,7 +1354,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e1db7b8>"
+       "<matplotlib.figure.Figure at 0x1d3270ef908>"
       ]
      },
      "metadata": {},
@@ -1364,7 +1364,7 @@
    "source": [
     "from filterpy.common import Q_discrete_white_noise\n",
     "import math\n",
-    "from code.ukf_internal import plot_radar\n",
+    "from kf_book.ukf_internal import plot_radar\n",
     "\n",
     "dt = 3. # 12 seconds between readings\n",
     "range_std = 5 # meters\n",
@@ -1441,7 +1441,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3dc4c2b0>"
+       "<matplotlib.figure.Figure at 0x1d32725a320>"
       ]
      },
      "metadata": {},
@@ -1449,7 +1449,7 @@
     }
    ],
    "source": [
-    "from code.ukf_internal import plot_altitude\n",
+    "from kf_book.ukf_internal import plot_altitude\n",
     "\n",
     "# reset aircraft position\n",
     "kf.x = np.array([0., 90., 1100.])\n",
@@ -1544,7 +1544,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo0AAAEtCAYAAABtWVo+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e187b38>"
+       "<matplotlib.figure.Figure at 0x1d325a15780>"
       ]
      },
      "metadata": {},
@@ -1610,7 +1610,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e187da0>"
+       "<matplotlib.figure.Figure at 0x1d325a15748>"
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      },
      "metadata": {},
@@ -1693,7 +1693,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e234d30>"
+       "<matplotlib.figure.Figure at 0x1d3270dcba8>"
       ]
      },
      "metadata": {},
@@ -1770,7 +1770,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f276278>"
+       "<matplotlib.figure.Figure at 0x1d32736ad30>"
       ]
      },
      "metadata": {},
@@ -1886,7 +1886,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f52a940>"
+       "<matplotlib.figure.Figure at 0x1d3249a6f98>"
       ]
      },
      "metadata": {},
@@ -1958,7 +1958,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f285208>"
+       "<matplotlib.figure.Figure at 0x1d3270e7240>"
       ]
      },
      "metadata": {},
@@ -2004,7 +2004,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e126160>"
+       "<matplotlib.figure.Figure at 0x1d325f1c9b0>"
       ]
      },
      "metadata": {},
@@ -2057,7 +2057,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f541320>"
+       "<matplotlib.figure.Figure at 0x1d32713f198>"
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      "metadata": {},
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      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e13b470>"
+       "<matplotlib.figure.Figure at 0x1d325fdc908>"
       ]
      },
      "metadata": {},
@@ -2507,7 +2507,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f5119b0>"
+       "<matplotlib.figure.Figure at 0x1d3270dc2b0>"
       ]
      },
      "metadata": {},
@@ -2517,7 +2517,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e25fda0>"
+       "<matplotlib.figure.Figure at 0x1d325fca128>"
       ]
      },
      "metadata": {},
@@ -2527,7 +2527,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f4a72e8>"
+       "<matplotlib.figure.Figure at 0x1d3273092b0>"
       ]
      },
      "metadata": {},
@@ -2607,7 +2607,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f3ef9e8>"
+       "<matplotlib.figure.Figure at 0x1d3272067b8>"
       ]
      },
      "metadata": {},
@@ -2617,7 +2617,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f3a3c18>"
+       "<matplotlib.figure.Figure at 0x1d327012e10>"
       ]
      },
      "metadata": {},
@@ -2627,7 +2627,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo0AAAEtCAYAAABtWVo+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e237cc0>"
+       "<matplotlib.figure.Figure at 0x1d325fca898>"
       ]
      },
      "metadata": {},
@@ -2678,7 +2678,7 @@
     }
    ],
    "source": [
-    "from code.ukf_internal import print_sigmas\n",
+    "from kf_book.ukf_internal import print_sigmas\n",
     "print_sigmas(mean=0, cov=3, alpha=1)"
    ]
   },
@@ -2820,7 +2820,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3dbf44e0>"
+       "<matplotlib.figure.Figure at 0x1d3259f9cc0>"
       ]
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      "metadata": {},
@@ -3257,7 +3257,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3e263e80>"
+       "<matplotlib.figure.Figure at 0x1d32601cba8>"
       ]
      },
      "metadata": {},
@@ -3347,7 +3347,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f3b8e80>"
+       "<matplotlib.figure.Figure at 0x1d327001eb8>"
       ]
      },
      "metadata": {},
@@ -3388,7 +3388,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b3f2b93c8>"
+       "<matplotlib.figure.Figure at 0x1d327022f28>"
       ]
      },
      "metadata": {},
@@ -3465,7 +3465,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

11-Extended-Kalman-Filters.ipynb

@@ -357,7 +357,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAAE8CAYAAACrVjcqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e3791908>"
+       "<matplotlib.figure.Figure at 0x1c0ee9fbc88>"
       ]
      },
      "metadata": {},
@@ -365,7 +365,7 @@
     }
    ],
    "source": [
-    "import code.ekf_internal as ekf_internal\n",
+    "import kf_book.ekf_internal as ekf_internal\n",
     "ekf_internal.show_linearization()"
    ]
   },
@@ -449,7 +449,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAERCAYAAACXVcjEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e679ac88>"
+       "<matplotlib.figure.Figure at 0x1c0f1a2c588>"
       ]
      },
      "metadata": {},
@@ -757,9 +757,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e688bdd8>"
+       "<matplotlib.figure.Figure at 0x1c0f2b2b4a8>"
       ]
      },
      "metadata": {},
@@ -767,9 +767,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e78d1748>"
+       "<matplotlib.figure.Figure at 0x1c0f2b8b518>"
       ]
      },
      "metadata": {},
@@ -777,9 +777,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e7920ba8>"
+       "<matplotlib.figure.Figure at 0x1c0f2bdab00>"
       ]
      },
      "metadata": {},
@@ -920,7 +920,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e674a198>"
+       "<matplotlib.figure.Figure at 0x1c0f2b26470>"
       ]
      },
      "metadata": {},
@@ -1565,9 +1565,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEjCAYAAAAfYCTOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e86c16a0>"
+       "<matplotlib.figure.Figure at 0x1c0f39d9da0>"
       ]
      },
      "metadata": {},
@@ -1577,7 +1577,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.0244  0.042  0.00223]\n"
+      "Final P: [ 0.0244  0.0406  0.0022]\n"
      ]
     }
    ],
@@ -1612,9 +1612,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEjCAYAAAAfYCTOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEjCAYAAAAfYCTOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e679a748>"
+       "<matplotlib.figure.Figure at 0x1c0f397ec18>"
       ]
      },
      "metadata": {},
@@ -1624,7 +1624,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.0201  0.0204  0.00153]\n"
+      "Final P: [ 0.0198  0.0211  0.00154]\n"
      ]
     }
    ],
@@ -1655,9 +1655,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEjCAYAAAAfYCTOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEjCAYAAAAfYCTOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e87445c0>"
+       "<matplotlib.figure.Figure at 0x1c0f39d9e10>"
       ]
      },
      "metadata": {},
@@ -1667,7 +1667,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.0205  0.0457  0.000224]\n"
+      "Final P: [ 0.0214  0.0457  0.000228]\n"
      ]
     }
    ],
@@ -1695,9 +1695,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEjCAYAAAAfYCTOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEjCAYAAAAfYCTOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e88c3470>"
+       "<matplotlib.figure.Figure at 0x1c0f3a26d30>"
       ]
      },
      "metadata": {},
@@ -1707,7 +1707,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.285  0.759  0.00353]\n"
+      "Final P: [ 0.269  0.829  0.00361]\n"
      ]
     }
    ],
@@ -1735,9 +1735,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEjCAYAAAAfYCTOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl0ldW9x//385x5ypyczHMCYQYBURRExZZqhVtaq3f1\nV9S2667+bm+rbZcdbgU7t6u97bW9g133/qzYwVvbqldvpTgiiKhMMgYyzzkn00ly5nH//qCkpgFE\nCATC97VWlvEZ9rN3npOcD8/eZ29NKaUQQgghhBCTQp/qCgghhBBCTCcSroQQQgghJpGEKyGEEEKI\nSSThSgghhBBiEkm4EkIIIYSYRBKuhBBCCCEmkYQrIcQ5ueGGG9B1nY6OjqmuygSvvfYauq7zrW99\na6qrcka6rnPjjTeO2/bQQw+h6zrbt2+folqN197ejq7r3HvvvVNdFSEuGxKuhHgPuq6f8ctgMIx7\nIzz5xv63b5onvfDCC7hcLqxWK0888cRZX+dnP/vZOdXXYDCQmZnJ8uXL+bd/+zcSicT5/UD+QtM0\nNE2blLJO5+67776kgsbFcDF+ru92Mjyd7vU6FXUS4nJnnOoKCHE50DSNhx56iNPNuVteXn5W5fz6\n17/mU5/6FHa7nWeffZZVq1ad9XWWLVt2TvVNJBK0t7fz1FNPsWvXLl5++WWefvrpsy5rKl2Jb+r/\n9E//xF133UVpaelUVwWAoqIi6uvrSU9Pn+qqCHHZkHAlxFl68MEHz+v8H//4x3zlK18hPz+f559/\nnvnz51+Q65yunAcffJCFCxfy7LPPsmPHDq6//vpJuc6FdCUuIJGVlUVWVtZFu957/YyNRiO1tbUX\nqTZCTA/SLSjERfDFL36RBx54gNraWnbt2nXaYHUhVVdXs3LlSgB27959ymOeeuopVq1aRUZGBjab\njVmzZrFp0yaCweBpy02lUvz4xz9m5syZ2Gw2SktL+fKXv0wgEDjl8QcOHOCOO+4gPz8fi8VCaWkp\nn/70p2lraxt3XEVFBY8//jjw1/FdJ7s5z0drayv33HMPJSUlWCwW8vPz+fjHP86hQ4dOe86TTz7J\n6tWrycnJwWq1Ul5ezh133MHOnTvHjhkdHeVHP/oRN91001jZeXl53H777ezateus63eqMVerVq06\nY5dxZWXlOdVj8+bNVFZWomka27ZtG1fmyfFqZxpz1dfXx+c//3mqqqqwWq3k5OTw4Q9/mB07dkw4\n9mR3+b333kt7ezt33nknubm52Gw2lixZwp/+9Kez/hkJcamTJ1dCXECJRIINGzbwxBNPcPXVV/N/\n//d/ZGdnT1l9Tj6lMJlME/Zt3LiR73znO2RnZ3PXXXeRkZHBiy++yLe//W2ee+45duzYgcPhmHDe\nF77wBXbu3Mkdd9xBeno6W7Zs4Sc/+Qk7d+5k+/bt4661ZcsWPvKRj5BKpfjIRz5CVVUVBw8e5NFH\nH+Xpp5/m1VdfZd68eQDcf//9/PKXv+TgwYPcfffdY12v59NNuG/fPm666Sb8fj+33norc+fOpbm5\nmaeeeornnnuOZ599lptvvnncOXfffTePP/44OTk5rFu3DrfbTXd3N9u3b+ePf/wjy5cvB6C+vp5v\nfOMbrFy5kttuu43MzEw6Ojp49tln2bJlC8899xwf/OAH37OOp+oKveeeeyZ0IQPs3buX5557btx9\neT/1WLhwIffddx//+q//Snl5OXffffdYOTfccMMZ69nR0cHy5cvp6elh5cqV3HnnnfT29vLkk0+y\nZcsWHn30UT75yU9OOK+trY2lS5dSVVXFJz/5SYaGhvjd737HunXreOmll8b+ASDEZU0JIc5I0zSl\n67p66KGHTvv1btu2bVOapqklS5aoW265RWmapm677TYVCoXO+TqPPPLI+67v3zp69KhyOBxK13W1\nb9++cfvefPNNpWmaKi0tVR6PZ9y+DRs2KF3X1T/90z+N237DDTcoTdNUbm6u6urqGtueSqXUunXr\nlK7r6gc/+MHY9mAwqHJycpTRaFSvvfbauLIeffRRpWmamjdv3rjtd999t9J1fcLx7+XkPfjmN785\nbvusWbOUruvqV7/61bjtL7/8stJ1XbndbhUOh8e2/+IXv1CapqmlS5eqkZGRCdfp6ekZ+350dFQN\nDg5OOKa7u1sVFhaqurq6Cfs0TVOrVq0at+2hhx46qza3tLQot9utHA6H2r179znXo62t7ZT1+Nv9\n99xzz7jtH/zgB5Wu6+rb3/72uO2HDx9Wdrtd2Ww21d3dPbb95D051Tlbt25VmqapW2+99YxtFuJy\nIeFKiPdw8g3hdF8Gg2Hc8e9+E9E0TdXW1qpEInFe11m4cOH7ru/JYPaNb3xDfeITn1B2u13puq6+\n8pWvTDjn05/+tNJ1/ZQhzuv1Krvdrlwu17h23HDDDUrXdfXd7353wjnHjx9Xuq6r2trasW2/+c1v\nlKZp6s477zxlva+66iql67p68803x7ZNZrjauXOn0jRNLVu27JTnrF+/Xum6rv7nf/5nbNucOXOU\nrutq79697+v6f+vzn/+80nVddXZ2jtt+ruFqcHBQ1dbWKqPRqJ577rnzqse5hKvu7u6xMB6Pxyec\n86UvfWlCuD55TyoqKlQqlZpwTllZmcrNzT3rtghxKZMxV0KcpWQyecqv001tMGPGDCoqKmhsbOSz\nn/3sWQ/OPtU19u3b977r+61vfYtvfetbfO973+O3v/0tkUiE73znO/zgBz+YcOz+/fsBTtn1lJeX\nx9y5cwkGgzQ0NEzYv2LFignbamtrcbvdNDU1jY3X2rdvH5qmnfIawFh33Lm09WycLPdM11dKjR0X\nCoU4cuQI2dnZLFq06KyucbJ7tLS0FKvVOjZ+6ec//zkA3d3d592OaDTK7bffTlNTEz/72c+47bbb\nLno9Tv6Mli9fjtE4cXTJ3/4s323BggWn7NotKSnB5/OdV72EuFTImCshLpD8/Hx+/etfc+ONN/Lf\n//3fhMNhHnvssfMekH22kskkAJFIhN27d/MP//APfOMb36CiooI777xz3LEjIyNjdT6VgoICAIaH\nhyfsc7vdpzzH7Xbj9XoZHR3F4XCc1TWUUqe8xmQYGRlB07SzbuPJ/xYVFZ1V+U8//TQf+9jHsNls\nrF69mqqqKhwOB7qu8+qrr7J9+3ai0eh5t+MTn/gEu3bt4stf/jKf/exnp6Qe5/N6ycjIOOU5RqOR\nVCp1XvUS4lIh4UqIC6iwsJDt27dzyy238Jvf/IZwOMwTTzxxygHlF4rVauX6669ny5YtzJ49m3/4\nh39g1apV40LRyTmMPB4PaWlpE8ro7e0dd9y7eb1eampqTrkdGCvv3dc4ld7eXjRNu2DzKaWnp6OU\nOuP1313PkyHgbJ/yPPjgg1gsFvbu3Tth6oKenp5JmQj1S1/6En/84x/5+Mc/zg9/+MMpq8fZ3Mt3\nHyfElUa6BYW4wPLy8ti2bRtLlizhqaeeYt26dUQikYtej7KyMr7yla/g9/vZuHHjuH0nu71effXV\nCef19/dz+PBhHA4HM2bMmLD/tddem7CtoaEBr9dLdXX12CfZFi1ahFLqlNcAePnllwG46qqrxrad\nfMp38inc+ThTGwFeeuklNE0bu77dbmfOnDkMDg6yd+/e9yy/ubmZWbNmTQg0SqlTTk3wfv385z/n\npz/9Kddffz2bN2+etHqcy8944cKFwInux1N1i7/00kvA+HspxJVEwpUQF0FGRgYvv/zy2BOk2267\n7YxzR10o999/Pzk5OTz22GM0NTWNbb/33ntRSvG9731v7InTSQ888AChUIi77757QpemUoqHH36Y\nzs7OsW2pVIoHHnhgrNyT1q1bR3Z2Nn/4wx8mvMk/9thj7N27lzlz5nD11VePbT85bcVkrF947bXX\nUldXx9tvv81vfvObcfteeeUVnn76aXJzc1m7du3Y9s9//vMopfjsZz871hX2bief0MCJWfobGxsn\nPM3ZtGkT9fX151X3Z555hvvvv5+ZM2fyzDPPYDabT3vs+61HZmYmmqaNu4fvpaioiA984AN0dXVN\neIJ25MgRHnnkEaxWK5/4xCfOukwhphPpFhTiLH3zm9887b4PfvCD40LBqTidTrZu3cratWt58cUX\nueWWW9iyZcspu+EuFKfTyVe/+lW+/OUv8+CDD46tbbhs2TK+9rWv8YMf/IA5c+bwsY99jPT0dF58\n8UX27dvH/Pnz+d73vjehPE3TuPbaa1mwYMG4ea4OHTrE1VdfzRe/+MWxY+12O4899hgf/ehHufnm\nm1m/fj2VlZUcOHCA559/nqysrLFJQ09avXo1P/rRj/jqV7/KoUOHyMzMBOCf//mfz6n9mzdvZvXq\n1WzYsIEnn3ySuXPn0tTUxFNPPYXFYuHxxx/HarWOHf/pT3+anTt38vjjj1NdXc26devIy8ujt7eX\n1157jbVr1/KTn/wEOBFcP/vZz7JgwQLWr1+PyWRi586d1NfXc/vtt/Pcc8+dU50B/v7v/x6lFEuX\nLj3lGpMZGRl84QtfOKd6OBwOrrnmGnbt2sXtt9/OokWLMJlMrFix4oyz+D/yyCNcd911bNy4kVde\neYVly5bR09PD73//e6LRKP/1X/911uPVhJh2puZDikJcPs40DcPJr4cffnjs+G3btild19WNN954\nyvJisZhau3at0nVdLV68eGxOIk3TJkzrcC7eq5xwOKyKioqU0WhU77zzzrh9f/jDH9QNN9yg0tPT\nldVqVXV1dWrjxo0qEAhMKOeGG25QBoNBtba2qh//+Mdq5syZymq1qpKSEvXlL3/5lOcopdT+/fvV\nxz72MeV2u5XZbFbFxcXqU5/6lGptbT3l8T//+c/VnDlzlM1mO+XUF6dy8h5861vfmLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEjCAYAAAAfYCTOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XeUHNWd//13Vec0PbknR81Io5wlECAJEEaGBa1ZY9jH\nPwtY7/Hxs87rgyPBrONZr73Ye2zvs/tjEQ6ssY1sWKMlGSEhECihrAmaHLon9UznfJ8/xhqQFRDS\nSCPNfF/nzGGoulV1b1f39EdVt+7VlFIKIYQQQggxIfTJroAQQgghxFQi4UoIIYQQYgJJuBJCCCGE\nmEASroQQQgghJpCEKyGEEEKICSThSgghhBBiAkm4EkKclzVr1qDrOp2dnZNdlVO8+uqr6LrOI488\nMtlVOStd17n++utPWvbwww+j6zrbtm2bpFqdrKOjA13Xue+++ya7KkJcMSRcCfEedF0/64/BYDjp\ni/DEF/tffmme8MILL+ByubBarTz55JPnfJwf/ehH51Vfg8FATk4Oq1at4t/+7d9IpVIX9oL8maZp\naJo2Ifs6k3vuueeyChqXwqV4Xd/tRHg60/t1MuokxJXOONkVEOJKoGkaDz/8MGcac7eqquqc9vOL\nX/yCv/u7v8Nut/PMM8+wdu3acz7OypUrz6u+qVSKjo4Onn76ad544w1efvllNm/efM77mkzT8Uv9\n05/+NHfffTcVFRWTXRUASktLOXr0KG63e7KrIsQVQ8KVEOfogQceuKDtv//97/OlL32JoqIinnvu\nORYsWHBRjnOm/TzwwAMsWrSIZ555hu3bt3PttddOyHEupuk4gURubi65ubmX7Hjv9RobjUbq6+sv\nUW2EmBrktqAQl8AXvvAF7r//furr63njjTfOGKwuphkzZrB69WoAdu3addoyTz/9NGvXriU7Oxub\nzcbs2bN56KGHCIfDZ9xvJpPh+9//PrNmzcJms1FRUcEXv/hFQqHQacvv37+fO++8k6KiIiwWCxUV\nFXz84x+nvb39pHLV1dU88cQTwDv9u07c5rwQbW1t3HvvvZSXl2OxWCgqKuIjH/kIBw8ePOM2Tz31\nFOvWrSM/Px+r1UpVVRV33nknO3bsGC8TCAT453/+Z2644YbxfRcWFnLbbbfxxhtvnHP9Ttfnau3a\ntWe9ZVxTU3Ne9di0aRM1NTVomsbWrVtP2ueJ/mpn63PV39/PZz7zGWpra7FareTn5/NXf/VXbN++\n/ZSyJ26X33fffXR0dHDXXXdRUFCAzWZj2bJl/PGPfzzn10iIy51cuRLiIkqlUmzcuJEnn3ySFStW\n8D//8z/k5eVNWn1OXKUwmUynrHvwwQf55je/SV5eHnfffTfZ2dm8+OKL/NM//RPPPvss27dvx+Fw\nnLLdZz/7WXbs2MGdd96J2+1my5Yt/OAHP2DHjh1s27btpGNt2bKFD33oQ2QyGT70oQ9RW1vLgQMH\neOyxx9i8eTOvvPIK8+fPB+Dzn/88//Vf/8WBAwe45557xm+9Xshtwr1793LDDTcQDAa55ZZbmDdv\nHsePH+fpp5/m2Wef5ZlnnuHGG288aZt77rmHJ554gvz8fDZs2IDH46Gnp4dt27bxu9/9jlWrVgFw\n9OhRvv71r7N69WpuvfVWcnJy6Ozs5JlnnmHLli08++yz3Hzzze9Zx9PdCr333ntPuYUMsGfPHp59\n9tmTzsv7qceiRYv43Oc+x7/+679SVVXFPffcM76fNWvWnLWenZ2drFq1it7eXlavXs1dd91FX18f\nTz31FFu2bOGxxx7jYx/72Cnbtbe3s3z5cmpra/nYxz7G8PAwv/71r9mwYQMvvfTS+D8AhLiiKSHE\nWWmapnRdVw8//PAZf95t69atStM0tWzZMnXTTTcpTdPUrbfeqiKRyHkf52c/+9n7ru9fOnLkiHI4\nHErXdbV3796T1u3cuVNpmqYqKiqU1+s9ad3GjRuVruvq05/+9EnL16xZozRNUwUFBaq7u3t8eSaT\nURs2bFC6rqvvfve748vD4bDKz89XRqNRvfrqqyft67HHHlOapqn58+eftPyee+5Ruq6fUv69nDgH\n3/jGN05aPnv2bKXruvr5z39+0vKXX35Z6bquPB6Pikaj48v//d//XWmappYvX65GR0dPOU5vb+/4\n74FAQA0NDZ1SpqenR5WUlKiGhoZT1mmaptauXXvSsocffvic2tza2qo8Ho9yOBxq165d512P9vb2\n09bjL9ffe++9Jy2/+eabla7r6p/+6Z9OWn7o0CFlt9uVzWZTPT0948tPnJPTbfP8888rTdPULbfc\nctY2C3GlkHAlxHs48YVwph+DwXBS+Xd/iWiapurr61Uqlbqg4yxatOh91/dEMPv617+uPvrRjyq7\n3a50XVdf+tKXTtnm4x//uNJ1/bQhzufzKbvdrlwu10ntWLNmjdJ1XX3rW986ZZvGxkal67qqr68f\nX/bLX/5SaZqm7rrrrtPWe8mSJUrXdbVz587xZRMZrnbs2KE0TVMrV6487TZ33HGH0nVd/fd///f4\nsrlz5ypd19WePXve1/H/0mc+8xml67rq6uo6afn5hquhoSFVX1+vjEajevbZZy+oHucTrnp6esbD\neDKZPGWbf/zHfzwlXJ84J9XV1SqTyZyyTWVlpSooKDjntghxOZM+V0Kco3Q6fdqfMw1tMHPmTKqr\nq2lubuaTn/zkOXfOPt0x9u7d+77r+8gjj/DII4/w7W9/m1/96lfEYjG++c1v8t3vfveUsvv27QM4\n7a2nwsJC5s2bRzgcpqmp6ZT111133SnL6uvr8Xg8tLS0jPfX2rt3L5qmnfYYwPjtuPNp67k4sd+z\nHV8pNV4uEolw+PBh8vLyWLx48Tkd48Tt0YqKCqxW63j/pR//+McA9PT0XHA74vE4t912Gy0tLfzo\nRz/i1ltvveT1OPEarVq1CqPx1N4lf/lavtvChQtPe2u3vLwcv99/QfUS4nIhfa6EuEiKior4xS9+\nwfXXX89//ud/Eo1Gefzxxy+4Q/a5SqfTAMRiMXbt2sUnPvEJvv71r1NdXc1dd911UtnR0dHxOp9O\ncXExACMjI6es83g8p93G4/Hg8/kIBAI4HI5zOoZS6rTHmAijo6NomnbObTzx39LS0nPa/+bNm/nw\nhz+MzWZj3bp11NbW4nA40HWdV155hW3bthGPxy+4HR/96Ed54403+OIXv8gnP/nJSanHhbxfsrOz\nT7uN0Wgkk8lcUL2EuFxIuBLiIiopKWHbtm3cdNNN/PKXvyQajfLkk0+etkP5xWK1Wrn22mvZsmUL\nc+bM4ROf+ARr1649KRSdGMPI6/WSlZV1yj76+vpOKvduPp+Purq60y4Hxvf37mOcTl9fH5qmXbTx\nlNxuN0qpsx7/3fU8EQLO9SrPAw88gMViYc+ePacMXdDb2zshA6H+4z/+I7/73e/4yEc+wve+971J\nq8e5nMt3lxNiupHbgkJcZIWFhWzdupVly5bx9NNPs2HDBmKx2CWvR2VlJV/60pcIBoM8+OCDJ607\ncdvrlVdeOWW7gYEBDh06hMPhYObMmaesf/XVV09Z1tTUhM/nY8aMGeNPsi1evBil1GmPAfDyyy8D\nsGTJkvFlJ67ynbgKdyHO1kaAl156CU3Txo9vt9uZO3cuQ0ND7Nmz5z33f/z4cWbPnn1KoFFKnXZo\ngvfrxz/+MT/84Q+59tpr2bRp04TV43xe40WLFgFjtx9Pd1v8pZdeAk4+l0JMJxKuhLgEsrOzefnl\nl8evIN16661nHTvqYvn85z9Pfn4+jz/+OC0tLePL77vvPpRSfPvb3x6/4nTC/fffTyQS4Z577jnl\nlqZSikcffZSurq7xZZlMhvvvv398vyds2LCBvLw8fvvb357yJf/444+zZ88e5s6dy4oVK8aXnxi2\nYiLmL7z66qtpaGjgrbfe4pe//OVJ6/70pz+xefNmCgoKuP3228eXf+Yzn0EpxSc/+cnxW2HvduIK\nDYyN0t/c3HzK1ZyHHnqIo0ePXlDdf//73/P5z3+eWbNm8fvf/x6z2XzGsu+3Hjk5OWiadtI5fC+l\npaV84AMfoLu7+5QraIcPH+ZnP/sZVquVj370o+e8TyGmErktKMQ5+sY3vnHGdTfffPNJoeB0nE4n\nzz//PLfffjsvvvgiN910E1u2bDntbbiLxel08uUvf5kvfvGLPPDAA+NzG65cuZKvfOUrfPe732Xu\n3Ll8+MMfxu128+KLL7J3714WLFjAt7/97VP2p2kaV199NQsXLjxpnKuDBw+yYsUKvvCFL4yXtdvt\nPP744/zN3/wNN954I3fccQc1NTXs37+f5557jtzc3PFBQ09Yt24d//zP/8yXv/xlDh48SE5ODgBf\n+9rXzqv9mzZtYt26dWzcuJGnnnqKefPm0dLSwtNPP43FYuGJJ57AarWOl//4xz/Ojh07eOKJJ5gx\nYwYbNmygsLCQvr4+Xn31VW6//XZ+8IMfAGPB9ZOf/CQLFy7kjjvuwGQysWPHDo4ePcptt93Gs88+\ne151Bvjbv/1blFIsX778tHNMZmdn89nPfva86uFwOLjqqqt44403uO2221i8eDEmk4nrrrvurKP4\n/+xnP+Oaa67hwQcf5E9/+hMrV66kt7eX3/zmN8Tjcf7jP/7jnPurCTHlTM5DikJcOc42DMOJn0cf\nfXS8/NatW5Wu6+r6668/7f4SiYS6/fbbla7raunSpeNjEmmadsqwDufjvfYTjUZVaWmpMhqN6u23\n3z5p3W9/+1u1Zs0a5Xa7ldVqVQ0NDerBBx9UoVDolP2sWbNGGQwG1dbWpr7//e+rWbNmKavVqsrL\ny9UXv/jF026jlFL79u1TH/7wh5XH41Fms1mVlZWpv/u7v1NtbW2nLf/jH/9YzZ07VLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e862d668>"
+       "<matplotlib.figure.Figure at 0x1c0f393e390>"
       ]
      },
      "metadata": {},
@@ -1747,7 +1747,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.00842  0.00879  0.000761]\n"
+      "Final P: [ 0.00856  0.00863  0.000762]\n"
      ]
     }
    ],
@@ -1799,9 +1799,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAEWCAYAAAAn0eDVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlclFX///HXzLALLomCikuaa5ipqCVqZi5pZqvkkktU\nRlpqdltZ2fbNrG7TslzKzDRzS0vLLdG0XMrQ1FJxKfdbIFdwAYVhfn/Mz6mRVWC4Bub9fDzmMTPn\nOtd1fc6A+Jlznesck81msyEiIiIihjIbHYCIiIiIKCkTERERcQtKykRERETcgJIyERERETegpExE\nRETEDSgpExEREXEDSspERERE3EC+k7LJkydTu3Zt/P39iYiIYMOGDXnu8/7779OwYUP8/PyoVq0a\nL774YqGCFRERESmtvPJTaf78+QwfPpypU6cSGRnJpEmT6Nq1K/Hx8YSFhWW7z4gRI1i+fDnjxo0j\nPDyc5ORkEhISijR4ERERkdLClJ8Z/W+55RZuvvlmpk6d6iirV68ePXv2ZMyYMVnq7927l8aNG7Nz\n507q1atXtBGLiIiIlEJ5Xr5MT09n69atdOrUyam8c+fObNq0Kdt9vv32W+rUqcPy5cupU6cO119/\nPQMHDuTEiRNFE7WIiIhIKZNnUnby5EmsVishISFO5SEhISQmJma7z4EDBzh06BDz589n1qxZzJ49\nmz179tCjR4+iiVpERESklMnXmLJrlZmZyeXLl5k9ezZ16tQB4IsvvqB+/frExcXRokULR93k5GRX\nhCAiIiJiiHLlyhVovzx7yoKDg7FYLCQlJTmVJyUlERoamu0+VapUwcvLy5GQAdStWxeLxcKRI0cK\nFKiIiIhIaZZnUubt7U3z5s2JjY11Ko+NjSUyMjLbfSIjI8nIyODgwYOOsr/++gur1UrNmjULGbKI\niIhI6ZOvuy8XLFhA//79mTRpEpGRkUyZMoUZM2awe/duwsLCGDVqFHFxcaxevRoAm81Gy5YtCQwM\nZMKECdhsNp555hnS09PZuHGj07H/ffmyoN19JdWWLVsAiIiIMDiS4ufubTe9bgLA9mqe/zyuibu3\n25XUds9ru6e2G9R28Ly2F0U+k68xZVFRUZw+fZoxY8aQkJBAeHg4K1ascMxRlpiY6NQrZjKZWLp0\nKUOHDuW2227D39+fzp0789577xUoSBEREZHSLt8D/WNiYoiJicl224wZM7KUhYSEMH/+/IJHJiIi\nIuJBtPaleByTyf4QERFxJ0rKRERERNyAkjIRERERN+CSyWNFRESKms1mIz09nczMTKNDydOV6Z/S\n0tIMjqT4lca2m81mvL29Mbl47IuSMhERcXs2m420tDR8fHyK5T/HwvLz8zM6BMOUtrbbbDYyMzNJ\nS0vDz8/Ppb97unwpIiJuLz09HR8fHywWi9snZFK6mEwmLBYLPj4+pKenu/RcSsrE49hs9oeIlByZ\nmZmYzfovS4xjNptdfulcv+EiIlIiqIdMjFQcv39KykRERETcgJIyERERETegpExERERKncOHD2M2\nm5k1a5bRoeSbkjIREREDzZw5E7PZjNlsZuPGjdnWueGGGzCbzXTo0MGlsfz888+8/vrrpKSkuOT4\n6enpfPTRR7Rp04YKFSrg6+tL7dq1efTRR/ntt99ccs6SREmZeBytfSki7sjf3585c+ZkKd+8eTMH\nDhzA39/f5TFs2rSJN954g7Nnzxb5sc+cOUObNm0YNmwY5cqV47XXXmPq1Kk8/PDDbNq0iZYtW3L8\n+PEiP29Josljxe3ZbDbe+PENp7KQwBCSzic53t8SdgtdbuhS3KGJiBSZbt268dVXXzFx4kQsFouj\nfM6cOTRs2NCpzFVsLpwvaMCAAWzdupX58+fz4IMPOm17/fXXGT9+fJGcJz09vVg+K1dQT5m4PRs2\nXvvxNafH1C1Tnd6v/HOl0WGKiBSYyWSid+/enD59mu+//95RnpmZyfz58+nTp0+2CVNqaiojR46k\nZs2a+Pn5Ua9ePd55550sdc1mM4MHD2bJkiU0btwYPz8/wsPDnc71+uuv89xzzwFQq1YtzGYzFouF\nn376yVFn1apVtG/fnqCgIIKCgujatSs7duzIs31xcXEsXbqURx99NEtCdqX9zz77LFWrVgXgyJEj\nDBkyhEaNGlGmTBkqVKjA3Xffzc6dO532+/HHHzGbzcyZM4fXXnuNmjVrEhAQwLFjx3KM5ffff6db\nt26UK1eOwMBAbr/9djZs2JBnG4qDesqkRKlcpjJ/X/g7x/ciIiVVWFgYbdq0Yc6cOXTr1g2A2NhY\nTpw4QZ8+fZg3b16Wfe655x7WrFnDo48+SrNmzVizZg2jRo3i8OHDTJ482anupk2b+O6773jyyScJ\nCgpi4sSJPPjggxw5coQKFSrwwAMPsG/fPubNm8cHH3xAxYoVAWjYsCFg77Hr168fnTt35u233+bS\npUt88skntGvXjri4OOrVq5dj27799ltMJhP9+vXL12cRFxfHhg0b6NmzJzVq1OD48eN8/PHHtG/f\nnl27dhESEuJU/6233sJisTB8+HBsNhuBgYGcO3cuy3H37NlDmzZtCAoK4vnnn8fX15dp06bRsWNH\nVq9eTZs2bfIVn6soKZMSw4SJ0MBQpySsSmAVJWUiUmr06dOHZ599ltTUVPz9/fnyyy9p1aoV119/\nfZa63377LatXr+aNN97g5ZdfBiAmJobo6Gg+/vhjnnrqKRo1auSov2fPHuLj4x3Hat++PU2aNGHu\n3LkMHjyY8PBwmjVrxrx587jnnnuoUaOGY9+LFy/y9NNPEx0dzbRp0xzljz76KPXq1eONN95g9uzZ\nObZr9+7dANx00035+hy6d+/OAw884FTWr18/GjZsyPTp03nxxRedtp0/f549e/Y4rbuZXVL24osv\ncvnyZdavX0/t2rUBGDhwIA0aNGDEiBH8+uuv+YrPVZSUiYhIqWN63XV389hedd24q549e/L000+z\nePFi7rvvPpYsWcLYsWOzrbt8+XIsFgtDhw51Kn/22Wf5/PPPWbZsmVNS1qFDB6fkrnHjxpQtW5YD\nBw7kGVdsbCxnz56ld+/enDp1ylFus9lo27Yta9euzXX/K3dzBgUF5XkuAF9fX8fr1NRUUlNTCQwM\npH79+mzdujVL/QEDBuS5EHpmZiarVq2iR48ejoQMoGLFigwcOJDx48dz4sQJKlWqlK8YXUFJmZRI\n5y+fL/C+WvdSRNxVhQoV6NKlC19++SUWi4XU1FQeeuihbOsePnyYkJAQypYt61Rev359zGYzhw4d\nciqvXr16tuc7c+ZMnnHt27cPm81Gx44ds2y7smB3bq7EeO7cuSzxZufSpUuMHj2aL7/8koSEBKdz\nBQcHZ6n/7yQrJydOnODixYvZXma9con20KFDSspE/m3uH3N55vtnHO9vrHxjljp/nfmrOEMSkRLG\nlb1ZrtanTx/69+9PcnIyHTt2dIztKqycEqf83HGZmZmJyWRi5syZjsH416Jhw4YsXryYP/74g8jI\nyDzrP/XUU3z++ecMHTqUW2+9lfLly2M2mxk2bFi2i4IXx3QhxUFJmbid1IxUki78M91F6MVQx+uI\nKhFU8KvgeO/r5QtJiIiUGvfccw++vr5s2rSJmTNn5livZs2arF69mnPnzjldFty7dy+ZmZnUqlXr\nms+d06LbderUwWazERwcXKAJbHv06MFbb73FrFmz8pWULVy4kAEDBvDee+85lZ85c6bAPVmVKlUi\nICCAvXv3ZtkWHx8PUKDPrChpSgxxW40qNcpSNv2e6awbuM7xuLPOnQZEJiLiOv7+/kydOpVXX32V\n++67L8d63bt3x2q1MnHiRKfy8ePHYzKZuOuuu6753GXKlAHIckmzS5culC9fnrfeeov09PQs+508\neTLX47Zs2ZJu3brx2WefsWjRoizbbTYb48ePd0wea7FYsvSIzZ07t1CTy5rNZu68806+++47Dh48\n6Cg/ffo0s2bNokWLFoZeugT1lIkb8zZ7Gx2CiEixuPoSYt++ffPcp3v37nTq1IlXX32VQ4cOOabE\n+Oabb4iJiXEa5J9fERER2Gw2XnjhBfr06YOPjw933HEHwcHBjtn3mzZtSu/evQkJCeHIkSOsXLmS\n8PBwPvvss1yPPXPmTLp160ZUVBRdu3alU6dOlC1blkOHDrFw4UL27dtH7969AXvP2hdffEFQUBDh\n4eFs376d+fPnU6dOnWtu07+9+eabxMbGEhkZyZAhQ/D19eXTTz8lOTk5S6+cEZSUieGSzidxyXqJ\n1QdWc+jsIeJPxjttP5OW9yBUEZGSLKfLhnnVW7x4Ma+++irz5s3jiy++oEaNGowdO5aRI0dm2S+7\nc1xd3rx5c95++20mT55MdHQ0mZmZrF27lnbt2hEVFUW1atV46623GD9+PGlpaVStWpXIyEieeOKJ\nPGOvWLEiGzZs4OOPP2bu3Lm89tprpKamUqVKFTp06MDcuXOpUqUKABMnTsTHx4cFCxbw2Wef0aJF\nC77//ntGjhyZpR25fXZXb2vQoAEbNmxg1KhRvPvuu2RmZtKiRQumT5+er8uqrmayuXJNhXxITk52\nvC5XrpyBkRS/LVu2APZvJp7m321vN6Md64+sz1KnSUgTdiT9M1O0CROZrzp3Z0/4eQIjVo1geKvh\nTLhzQr7OfeXfaG6/+Vdupy/qwcL6mavtnqQo252WlpbnlAcirpbb72FR5DPqKRO3USmgEicunqCi\nf0Wim0YTHBBMw0oNHdtNaBVxEREpvZSUidtYGLWQdjXbFWjfj+I+Ytpv02hbsy0r+q4o4shERERc\nT0mZlAoZmRlkZGaQlpFmdCgiIiIFoikxpEQb0nII50adY1mfZUaHIiIiUij5TsomTLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAEWCAYAAAAn0eDVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcVFX/B/DPzLDLIgoCiqK4IIahAppiaebyA00fM8nl\ncaNS3Le0qCz1ce1xKUowrVBzwzT3LTQ3xBJULBXNErcEFJFF2YaZ+f0xD6MT2yAzc2f5vF+vec3c\nc8+993sA8cu5554jUigUChARERGRoMRCB0BERERETMqIiIiIDAKTMiIiIiIDwKSMiIiIyAAwKSMi\nIiIyAEzKiIiIiAwAkzIiIiIiA6BxUhYdHQ1vb2/Y2toiMDAQCQkJ1R7z+eefw9fXFzY2NmjUqBE+\n/PDDWgVLREREZKosNKkUFxeHadOmYfXq1QgODsaqVasQEhKC1NRUeHp6VnjMjBkzcODAASxbtgx+\nfn7Izc1Fenq6VoMnIiIiMhUiTWb0f+mll9CuXTusXr1aVdaqVSsMHjwYCxcuLFf/2rVraNu2LS5d\nuoRWrVppN2IiIiIiE1Tt7UupVIpz586hV69eauW9e/dGYmJihcfs2bMHzZs3x4EDB9C8eXM0a9YM\no0ePxoMHD7QTNREREZGJqTYpy8rKgkwmg5ubm1q5m5sbMjIyKjzmxo0buHnzJuLi4rBhwwZs3LgR\nV69eRf/+/bUTNREREZGJ0WhMWU3J5XKUlJRg48aNaN68OQDg+++/h4+PD5KSkhAUFKSqm5ubq4sQ\niIiIiATh5OT0XMdV21Pm4uICiUSCzMxMtfLMzEy4u7tXeIyHhwcsLCxUCRkAtGzZEhKJBLdv336u\nQImIiIhMWbVJmaWlJQICAhAfH69WHh8fj+Dg4AqPCQ4ORmlpKdLS0lRlf/31F2QyGby8vGoZMhER\nEZHp0ejpy23btmHkyJFYtWoVgoODERMTg9jYWFy5cgWenp6IjIxEUlISjhw5AgBQKBTo2LEj7O3t\nsXLlSigUCkyfPh1SqRSnT59WO/ezty+ft7vPWCUnJwMAAgMDBY5E/4yh7aJ5IgCA4tNq/4lozBja\nrStsu/m13VzbDbDtgPm1XRv5jEZjysLCwpCdnY2FCxciPT0dfn5+OHjwoGqOsoyMDLVeMZFIhH37\n9mHKlCno1q0bbG1t0bt3byxfvvy5giQiIiIydRoP9I+IiEBERESF+2JjY8uVubm5IS4u7vkjIyIi\nIjIjXPuSzI5IpHwREREZEiZlRERERAaASRkRERGRAdDJ5LFERETaplAoIJVKIZfLhQ6lWmXTPxUV\nFQkcif6ZYtvFYjEsLS0h0vHYFyZlRERk8BQKBYqKimBlZaWX/xxry8bGRugQBGNqbVcoFJDL5Sgq\nKoKNjY1Of/Z4+5KIiAyeVCqFlZUVJBKJwSdkZFpEIhEkEgmsrKwglUp1ei0mZWR2FArli4iMh1wu\nh1jM/7JIOGKxWOe3zvkTTkRERoE9ZCQkffz8MSkjIiIiMgBMyoiIiIgMAJMyIiIiMjm3bt2CWCzG\nhg0bhA5FY0zKiIiIBLR+/XqIxWKIxWKcPn26wjotWrSAWCxGjx49dBrLmTNnMG/ePOTl5enk/FKp\nFF999RW6du0KZ2dnWFtbw9vbG2+//TbOnz+vk2saEyZlZHa49iURGSJbW1ts3ry5XPmvv/6KGzdu\nwNbWVucxJCYmYv78+cjJydH6uR89eoSuXbti6tSpcHJywty5c7F69Wr8+9//RmJiIjp27Ih79+5p\n/brGhJPHEhERGYDQ0FD88MMPiIqKgkQiUZVv3rwZvr6+amW6otDhfEGjRo3CuXPnEBcXhzfffFNt\n37x587BixQqtXEcqlerla6UL7Ckjg5VblItf7v6iel3LuiZ0SEREOiESiTB06FBkZ2fj8OHDqnK5\nXI64uDgMGzaswoSpsLAQs2bNgpeXF2xsbNCqVSssXbq0XF2xWIwJEyZg9+7daNu2LWxsbODn56d2\nrXnz5mH27NkAgKZNm0IsFkMikeDkyZOqOj/99BO6d+8OBwcHODg4ICQkBBcvXqy2fUlJSdi3bx/e\nfvvtcglZWftnzpyJhg0bAgBu376NiRMnok2bNqhTpw6cnZ3x+uuv49KlS2rHnThxAmKxGJs3b8bc\nuXPh5eUFOzs73L17t9JYfvvtN4SGhsLJyQn29vZ49dVXkZCQUG0b9IE9ZWSwzqefR48NT8dP/Kv1\nv7DzrZ0CRkREpDuenp7o2rUrNm/ejNDQUABAfHw8Hjx4gGHDhmHr1q3ljhkwYACOHj2Kt99+Gx06\ndMDRo0cRGRmJW7duITo6Wq1uYmIi9u7di/Hjx8PBwQFRUVF48803cfv2bTg7O2PQoEH4448/sHXr\nVnzxxReoX78+AMDX1xeAssduxIgR6N27N5YsWYLi4mKsWbMGr7zyCpKSktCqVatK27Znzx6IRCKM\nGDFCo69FUlISEhISMHjwYDRp0gT37t3D119/je7du+Py5ctwc3NTq79o0SJIJBJMmzYNCoUC9vb2\nyM/PL3feq1evomvXrnBwcMD7778Pa2trrF27Fj179sSRI0fQtWtXjeLTFSZlZHDkCjlKZCUokZUI\nHQoRkV4NGzYMM2fORGFhIWxtbbFp0yZ06tQJzZo1K1d3z549OHLkCObPn4+PP/4YABAREYHw8HB8\n/fXXmDRpEtq0aaOqf/XqVaSmpqrO1b17d/j7+2PLli2YMGEC/Pz80KFDB2zduhUDBgxAkyZNVMcW\nFBRg8uTJCA8Px9q1a1Xlb7/9Nlq1aoX58+dj48aNlbbrypUrAIAXX3xRo69Dv379MGjQILWyESNG\nwNfXF99++y0+/PBDtX2PHz/G1atX1dbdrCgp+/DDD1FSUoJTp07B29sbADB69Gi0bt0aM2bMwNmz\nZzWKT1eYlJHBuZhxER3WdBA6DCIyYqJ5unuaR/Gp7sZdDR48GJMnT8auXbswcOBA7N69G4sXL66w\n7oEDByCRSDBlyhS18pkzZ2LdunXYv3+/WlLWo0cPteSubdu2cHR0xI0bN6qNKz4+Hjk5ORg6dCge\nPnyoKlcoFHj55Zdx7NixKo8ve5rTwcGh2msBgLW1tepzYWEhCgsLYW9vDx8fH5w7d65c/VGjRlW7\nELpcLsdPP/2E/v37qxIyAKhfvz5Gjx6NFStW4MGDB3B1ddUoRl1gUkYGzVpijWJZsVbPyXUvichQ\nOTs7o0+fPti0aRMkEgkKCwvx1ltvVVj31q1bcHNzg6Ojo1q5j48PxGIxbt68qVbeuHHjCq/36NGj\nauP6448/oFAo0LNnz3L7yhbsrkpZjPn5+eXirUhxcTHmzJmDTZs2IT09Xe1aLi4u5eo/m2RV5sGD\nBygoKKjwNmvZLdqbN28yKSOqSDv3drgw7gJ2pu7EG9veEDocIjIiuuzN0rVhw4Zh5MiRyM3NRc+e\nPVVju2qrssRJkycu5XI5RCIR1q9frxqMXxO+vr7YtWsXfv/9dwQHB1dbf9KkSVi3bh2mTJmCzp07\no27duhCLxZg6dWqFi4LrY7oQfWBSRkbnYcFDZDzOUG271nFFgzoNBIyIiEh7BgwYAGtrayQmJmL9\n+vWV1vPy8sKRI0eQn5+vdlvw2rVrkMvlaNq0aY2vXdmi282bN4dCoYCLi8tzTWDbv39/LFq0CBs2\nbNAoKdu+fTtGjRqF5cuXq5U/evTouXuyXF1dYWdnh2vXyj/Jn5qaCgDP9TXTJk6JQUbjwPUDcFvm\nBpf/usAvxk/1ikmKETo0IiKtsbW1xerVq/Hpp59i4MCBldbr168fZDIZoqKi1MpXrFgBkUiEvn37\n1vjaderUAYBytzT79OmDunXrYtGiRZBKpeWOy8rKqvK8HTt2RGhoKL777jvs2LGj3H6FQoEVK1ao\nJo+VSCTlesS2bNlSq8llxWIx/u///g979+5FWlqaqjw7OxsbNmxAUFCQoLcuAfaUkREpkZXg/pP7\nQodBRKR1/7yFOHz48GqP6devH3r16oVPP/0UN2/eVE2JsXPnTkRERKgN8tdUYGAgFAoFPvjgAwwb\nNgxWVlZ47bXX4OLiopp9v3379hg6dCjc3Nxw+/ZtHDp0CH5+fvjuu++qPPf69esRGhqKsLAwhISE\noFevXnB0dMTNmzexfft2/PHHHxg6dCgAZc/a999/DwcHB/j5+SElJQVxcXFo3rx5jdv0rAULFiA+\nPh7BwcGYOHEirK2t8c033yA3N7dcr5wQmJSRwQtpGYKMmRmITYlF5NFIocMhItK6ym4bVldv165d\n+PTTT7F161Z8//33aNKkCRYvXoxZs2aVO66ia/yzPCAgAEuWLEF0dDTCw8Mhl8tx7NgxvPLKKwgL\nC0OjRo2waNEirFixAkVFRWjYsCGCg4Mxbty4amOvX78+EhIS8PXXX2PLli2YO3cuCgsL4eHhgR49\nemDLli3w8PAAAERFRcHKygrbtm3Dd999h6CgIBw+fBizZs0q146qvnb/3Ne6dWskJCQgMjISn332\nGeRyOYKCgvDtt99qdFtV10QKXa6poIHc3FzVZycnJwEj0b/k5GQAyr9MzE1Vbb+QfgEd1nRQDfQv\nc/3hdSTcfjrrctzlOBz+6zDmdpuLT7t/qvG1y/6NVveTX/ZIvTYHDPN7zrabE222u6ioqNopD4h0\nraqfQ23kM+wpI6PRsn5LtKzfUrV9M+cmDv91uIojiIiIjAcH+hMREREZACZlRERERAaAty/J6J25\newYfHPkArV1ao717ezR2aox6tvWEDouIiKhGNO4pi46Ohre3N2xtbREYGIiEhIRK6966dQtisVjt\nJZFI8NNPP2klaDItCoVCtQD58yxEfvivw1h6einG7B6Ddl+3w95re3UUKRERke5o1Line truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e8685a20>"
+       "<matplotlib.figure.Figure at 0x1c0f3a2de48>"
       ]
      },
      "metadata": {},
@@ -1811,13 +1811,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "actual mean=1.29, std=1.12\n",
+      "actual mean=1.31, std=1.13\n",
       "EKF    mean=1.00, std=0.95\n"
      ]
     }
    ],
    "source": [
-    "import code.nonlinear_plots as nonlinear_plots\n",
+    "import kf_book.nonlinear_plots as nonlinear_plots\n",
     "nonlinear_plots.plot_ekf_vs_mc()"
    ]
   },
@@ -1837,9 +1837,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAEWCAYAAAAn0eDVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1e8724940>"
+       "<matplotlib.figure.Figure at 0x1c0f3afca20>"
       ]
      },
      "metadata": {},
@@ -1883,7 +1883,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

12-Particle-Filters.ipynb

@@ -355,9 +355,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnAAAAEWCAYAAADmVqp5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x230056e1ba8>"
+       "<matplotlib.figure.Figure at 0x21c6985f978>"
       ]
      },
      "metadata": {},
@@ -365,7 +365,7 @@
     }
    ],
    "source": [
-    "import code.pf_internal as pf_internal\n",
+    "import kf_book.pf_internal as pf_internal\n",
     "pf_internal.plot_monte_carlo_ukf()"
    ]
   },
@@ -429,7 +429,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAngAAAEQCAYAAAAj2AmQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x230086a9fd0>"
+       "<matplotlib.figure.Figure at 0x21c6994ff60>"
       ]
      },
      "metadata": {},
@@ -522,7 +522,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2300873db00>"
+       "<matplotlib.figure.Figure at 0x21c69a8c470>"
       ]
      },
      "metadata": {},
@@ -576,7 +576,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2300875a978>"
+       "<matplotlib.figure.Figure at 0x21c6a32bba8>"
       ]
      },
      "metadata": {},
@@ -610,7 +610,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2300879a390>"
+       "<matplotlib.figure.Figure at 0x21c6a3acda0>"
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      },
      "metadata": {},
@@ -982,7 +982,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23009836f98>"
+       "<matplotlib.figure.Figure at 0x21c6a425390>"
       ]
      },
      "metadata": {},
@@ -1113,7 +1113,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x230087e9eb8>"
+       "<matplotlib.figure.Figure at 0x21c6a3cd9b0>"
       ]
      },
      "metadata": {},
@@ -1171,7 +1171,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2300877a4a8>"
+       "<matplotlib.figure.Figure at 0x21c6b4f6898>"
       ]
      },
      "metadata": {},
@@ -1223,7 +1223,7 @@
      "data": {
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-       "<matplotlib.figure.Figure at 0x2300996cc18>"
+       "<matplotlib.figure.Figure at 0x21c6a3cd4a8>"
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@@ -1263,7 +1263,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x230099bd1d0>"
+       "<matplotlib.figure.Figure at 0x21c6c701080>"
       ]
      },
      "metadata": {},
@@ -1399,7 +1399,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAAA2CAYAAACYw97kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncbr/CePRAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23008773358>"
+       "<matplotlib.figure.Figure at 0x21c69a35320>"
       ]
      },
      "metadata": {},
@@ -1407,7 +1407,7 @@
     }
    ],
    "source": [
-    "from code.pf_internal import plot_cumsum\n",
+    "from kf_book.pf_internal import plot_cumsum\n",
     "print('cumulative sume is', np.cumsum([.1, .2, .1, .6]))\n",
     "plot_cumsum([.1, .2, .1, .6])"
    ]
@@ -1454,7 +1454,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAA4CAYAAADU8w0fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23008173e48>"
+       "<matplotlib.figure.Figure at 0x21c69a89668>"
       ]
      },
      "metadata": {},
@@ -1462,7 +1462,7 @@
     }
    ],
    "source": [
-    "from code.pf_internal import plot_multinomial_resample\n",
+    "from kf_book.pf_internal import plot_multinomial_resample\n",
     "plot_multinomial_resample([.1, .2, .3, .4, .2, .3, .1])"
    ]
   },
@@ -1540,7 +1540,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAA4CAYAAADU8w0fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23008710470>"
+       "<matplotlib.figure.Figure at 0x21c6a32e780>"
       ]
      },
      "metadata": {},
@@ -1548,7 +1548,7 @@
     }
    ],
    "source": [
-    "from code.pf_internal import plot_residual_resample\n",
+    "from kf_book.pf_internal import plot_residual_resample\n",
     "plot_residual_resample([.1, .2, .3, .4, .2, .3, .1])"
    ]
   },
@@ -1585,7 +1585,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAA4CAYAAADU8w0fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x230086dc588>"
+       "<matplotlib.figure.Figure at 0x21c6a2857f0>"
       ]
      },
      "metadata": {},
@@ -1593,7 +1593,7 @@
     }
    ],
    "source": [
-    "from code.pf_internal import plot_stratified_resample\n",
+    "from kf_book.pf_internal import plot_stratified_resample\n",
     "plot_stratified_resample([.1, .2, .3, .4, .2, .3, .1])"
    ]
   },
@@ -1660,7 +1660,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x230086ef128>"
+       "<matplotlib.figure.Figure at 0x21c6b4ba128>"
       ]
      },
      "metadata": {},
@@ -1668,7 +1668,7 @@
     }
    ],
    "source": [
-    "from code.pf_internal import plot_systematic_resample\n",
+    "from kf_book.pf_internal import plot_systematic_resample\n",
     "plot_systematic_resample([.1, .2, .3, .4, .2, .3, .1])"
    ]
   },
@@ -1739,7 +1739,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x230098f2940>"
+       "<matplotlib.figure.Figure at 0x21c6a421438>"
       ]
      },
      "metadata": {},
@@ -1749,7 +1749,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x230087a4550>"
+       "<matplotlib.figure.Figure at 0x21c6a2c5c50>"
       ]
      },
      "metadata": {},
@@ -1759,7 +1759,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x230087a4630>"
+       "<matplotlib.figure.Figure at 0x21c6a3d4080>"
       ]
      },
      "metadata": {},
@@ -1769,7 +1769,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x230098702b0>"
+       "<matplotlib.figure.Figure at 0x21c6b46bfd0>"
       ]
      },
      "metadata": {},
@@ -1834,7 +1834,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

13-Smoothing.ipynb

@@ -327,7 +327,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoUAAAEtCAYAAAB+jhrKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x294001c54a8>"
+       "<matplotlib.figure.Figure at 0x19fffa9a400>"
       ]
      },
      "metadata": {},
@@ -376,7 +376,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x294001c50f0>"
+       "<matplotlib.figure.Figure at 0x19fffa9a080>"
       ]
      },
      "metadata": {},
@@ -408,7 +408,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x29400b2fcc0>"
+       "<matplotlib.figure.Figure at 0x19ffff79d30>"
       ]
      },
      "metadata": {},
@@ -539,7 +539,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x29400c36320>"
+       "<matplotlib.figure.Figure at 0x19f804a9e80>"
       ]
      },
      "metadata": {},
@@ -552,7 +552,7 @@
     "from numpy.random import randn\n",
     "import matplotlib.pyplot as plt\n",
     "from filterpy.kalman import KalmanFilter\n",
-    "import code.book_plots as bp\n",
+    "import kf_book.book_plots as bp\n",
     "\n",
     "def plot_rts(noise, Q=0.001, show_velocity=False):\n",
     "    random.seed(123)\n",
@@ -614,7 +614,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x29400b6f978>"
+       "<matplotlib.figure.Figure at 0x19ffffacc18>"
       ]
      },
      "metadata": {},
@@ -643,7 +643,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x29400bb7ac8>"
+       "<matplotlib.figure.Figure at 0x19f80c23668>"
       ]
      },
      "metadata": {},
@@ -682,7 +682,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x29400b26f98>"
+       "<matplotlib.figure.Figure at 0x19ffffacb70>"
       ]
      },
      "metadata": {},
@@ -721,7 +721,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAACeCAYAAACVbLaQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAADJBBgAQLL/AKEXD+CesnhpAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x29401464588>"
+       "<matplotlib.figure.Figure at 0x19f80d5acc0>"
       ]
      },
      "metadata": {},
@@ -729,8 +729,8 @@
     }
    ],
    "source": [
-    "from book_format import figsize\n",
-    "from code.smoothing_internal import *\n",
+    "from kf_book.book_plots import figsize\n",
+    "from kf_book.smoothing_internal import *\n",
     "\n",
     "with figsize(y=2):\n",
     "    show_fixed_lag_numberline()"
@@ -770,7 +770,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmwAAAEWCAYAAADbxMsfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x29401464f60>"
+       "<matplotlib.figure.Figure at 0x19f81e76438>"
       ]
      },
      "metadata": {},
@@ -852,8 +852,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

14-Adaptive-Filtering.ipynb

@@ -442,9 +442,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef3054898>"
+       "<matplotlib.figure.Figure at 0x2037529d668>"
       ]
      },
      "metadata": {},
@@ -452,7 +452,7 @@
     }
    ],
    "source": [
-    "import code.book_plots as bp\n",
+    "import kf_book.book_plots as bp\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -541,16 +541,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef3076978>"
+       "<matplotlib.figure.Figure at 0x2037529c9e8>"
       ]
      },
      "metadata": {},
@@ -586,16 +586,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef41e4978>"
+       "<matplotlib.figure.Figure at 0x203763f9dd8>"
       ]
      },
      "metadata": {},
@@ -629,16 +629,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef30762b0>"
+       "<matplotlib.figure.Figure at 0x20375bbb048>"
       ]
      },
      "metadata": {},
@@ -668,16 +668,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef42799e8>"
+       "<matplotlib.figure.Figure at 0x203756ccbe0>"
       ]
      },
      "metadata": {},
@@ -708,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -733,16 +733,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef313f908>"
+       "<matplotlib.figure.Figure at 0x2037640dba8>"
       ]
      },
      "metadata": {},
@@ -801,16 +801,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef530b2e8>"
+       "<matplotlib.figure.Figure at 0x203764d66a0>"
       ]
      },
      "metadata": {},
@@ -818,7 +818,7 @@
     }
    ],
    "source": [
-    "from code.adaptive_internal import plot_track_and_residuals\n",
+    "from kf_book.adaptive_internal import plot_track_and_residuals\n",
     "\n",
     "def show_residual_chart():\n",
     "    dt = 0.1\n",
@@ -900,7 +900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -908,9 +908,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef5352be0>"
+       "<matplotlib.figure.Figure at 0x2037529c898>"
       ]
      },
      "metadata": {},
@@ -951,16 +951,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef53113c8>"
+       "<matplotlib.figure.Figure at 0x2037648d518>"
       ]
      },
      "metadata": {},
@@ -1035,7 +1035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1043,9 +1043,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef54da4a8>"
+       "<matplotlib.figure.Figure at 0x20375fedb38>"
       ]
      },
      "metadata": {},
@@ -1134,16 +1134,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef5510780>"
+       "<matplotlib.figure.Figure at 0x203776c27f0>"
       ]
      },
      "metadata": {},
@@ -1163,16 +1163,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef53820f0>"
+       "<matplotlib.figure.Figure at 0x203775537b8>"
       ]
      },
      "metadata": {},
@@ -1192,16 +1192,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef53064e0>"
+       "<matplotlib.figure.Figure at 0x203776c22b0>"
       ]
      },
      "metadata": {},
@@ -1209,9 +1209,9 @@
     },
     {
      "data": {
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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef53320b8>"
+       "<matplotlib.figure.Figure at 0x2037756fb00>"
       ]
      },
      "metadata": {},
@@ -1234,16 +1234,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef42a6128>"
+       "<matplotlib.figure.Figure at 0x2037756f390>"
       ]
      },
      "metadata": {},
@@ -1251,9 +1251,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef5619e10>"
+       "<matplotlib.figure.Figure at 0x203778399b0>"
       ]
      },
      "metadata": {},
@@ -1261,9 +1261,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef397dcf8>"
+       "<matplotlib.figure.Figure at 0x203778396d8>"
       ]
      },
      "metadata": {},
@@ -1271,9 +1271,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef5619f60>"
+       "<matplotlib.figure.Figure at 0x203775726d8>"
       ]
      },
      "metadata": {},
@@ -1281,9 +1281,9 @@
     },
     {
      "data": {
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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef5619908>"
+       "<matplotlib.figure.Figure at 0x203776e0b38>"
       ]
      },
      "metadata": {},
@@ -1347,16 +1347,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef543bcf8>"
+       "<matplotlib.figure.Figure at 0x2037763b9e8>"
       ]
      },
      "metadata": {},
@@ -1364,9 +1364,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef558ff60>"
+       "<matplotlib.figure.Figure at 0x2037771fcf8>"
       ]
      },
      "metadata": {},
@@ -1485,7 +1485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1493,9 +1493,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef41ae4a8>"
+       "<matplotlib.figure.Figure at 0x203778e1be0>"
       ]
      },
      "metadata": {},
@@ -1517,16 +1517,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef5ba9b38>"
+       "<matplotlib.figure.Figure at 0x203778e1c88>"
       ]
      },
      "metadata": {},
@@ -1573,7 +1573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1581,9 +1581,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef55dd748>"
+       "<matplotlib.figure.Figure at 0x20375bc7748>"
       ]
      },
      "metadata": {},
@@ -1626,16 +1626,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef54c7320>"
+       "<matplotlib.figure.Figure at 0x20375bc7b00>"
       ]
      },
      "metadata": {},
@@ -1705,16 +1705,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef5509908>"
+       "<matplotlib.figure.Figure at 0x20376496748>"
       ]
      },
      "metadata": {},
@@ -1777,7 +1777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -1788,7 +1788,7 @@
        "array([ 0.7,  0.3])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1818,7 +1818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1828,7 +1828,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef42becc0>"
+       "<matplotlib.figure.Figure at 0x2037782a588>"
       ]
      },
      "metadata": {},
@@ -1836,8 +1836,8 @@
     }
    ],
    "source": [
-    "import code.adaptive_internal as adaptive_internal\n",
-    "from code.book_plots import interactive_plot\n",
+    "import kf_book.adaptive_internal as adaptive_internal\n",
+    "from kf_book.book_plots import interactive_plot\n",
     "\n",
     "adaptive_internal.plot_markov_chain()"
    ]
@@ -1859,7 +1859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -1871,7 +1871,7 @@
        "       [ 0.05,  0.95]])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1904,7 +1904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -1915,7 +1915,7 @@
        "array([ 0.694,  0.306])"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1952,7 +1952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -1963,7 +1963,7 @@
        "array([ 0.802,  0.198])"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2031,7 +2031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -2043,7 +2043,7 @@
        "       [ 0.0125,  0.886]])"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2096,16 +2096,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef53470f0>"
+       "<matplotlib.figure.Figure at 0x203752e6c50>"
       ]
      },
      "metadata": {},
@@ -2191,7 +2191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2203,15 +2203,15 @@
        "(4200.0, 3200.0, 1980.0, 2120.0)"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21ef55c9518>"
+       "<matplotlib.figure.Figure at 0x203763d3c50>"
       ]
      },
      "metadata": {},
@@ -2285,7 +2285,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

Appendix-A-Installation.ipynb

@@ -59,7 +59,7 @@
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
        "}\n",
        "    div.cell{\n",
-       "        width: 850px;\n",
+       "        //width: 950px;\n",
        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
@@ -73,7 +73,7 @@
        "        //font-family: 'Chivo',verdana,arial,sans-serif;\n",
        "        //font-family: 'Cardo',verdana,arial,sans-serif;\n",
        "        //font-family: 'Arvo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Poppins',verdana,arial,sans-serif;   \n",
+       "        //font-family: 'Poppins',verdana,arial,sans-serif;\n",
        "        //font-family: 'Ubuntu',verdana,arial,sans-serif;\n",
        "        //font-family: 'Fontin',verdana,arial,sans-serif;\n",
        "        //font-family: 'Raleway',verdana,arial,sans-serif;\n",
@@ -97,10 +97,10 @@
        "    h1 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "\t}\n",
-       "\t\n",
+       "\n",
        "    div.input_area {\n",
        "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;      \n",
+       "        border: 1px solid #586e75;\n",
        "    }\n",
        "\n",
        "    .text_cell_render h1 {\n",
@@ -113,7 +113,7 @@
        "        display: block;\n",
        "        white-space: wrap;\n",
        "        text-align: left;\n",
-       "    } \n",
+       "    }\n",
        "    h2 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "        text-align: left;\n",
@@ -129,7 +129,7 @@
        "        display: block;\n",
        "        white-space: wrap;\n",
        "        text-align: left;\n",
-       "    } \n",
+       "    }\n",
        "    h3 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "    }\n",
@@ -196,42 +196,38 @@
        "        margin: 2em;\n",
        "    }\n",
        "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \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",
+       "        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",
+       "        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",
+       "        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",
@@ -239,10 +235,10 @@
        "       color:#447adb;\n",
        "    }\n",
        "    .rendered_html :link {\n",
-       "       text-decoration: underline; \n",
+       "       text-decoration: underline;\n",
        "    }\n",
        "    .rendered_html :hover {\n",
-       "       text-decoration: none; \n",
+       "       text-decoration: none;\n",
        "    }\n",
        "    .rendered_html :visited {\n",
        "      text-decoration: none;\n",
@@ -255,7 +251,7 @@
        "    }\n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
-       "    } \n",
+       "    }\n",
        "    hr {\n",
        "      color: #f3f3f3;\n",
        "      background-color: #f3f3f3;\n",
@@ -496,10 +492,9 @@
        "$$\\frac{1}{2 \\sqrt{\\phi}}$$"
       ],
       "text/plain": [
-       "   1   \n",
-       "───────\n",
-       "    ___\n",
-       "2⋅╲╱ φ "
+       " 1  \n",
+       "────\n",
+       "2⋅√φ"
       ]
      },
      "execution_count": 4,
@@ -749,7 +744,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

Appendix-D-HInfinity-Filters.ipynb

@@ -327,7 +327,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b4f7a8e8d0>"
+       "<matplotlib.figure.Figure at 0x223bfe03c18>"
       ]
      },
      "metadata": {},
@@ -382,7 +382,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

Appendix-E-Ensemble-Kalman-Filters.ipynb

@@ -329,9 +329,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2a0d88c4cf8>"
+       "<matplotlib.figure.Figure at 0x1757b8daf60>"
       ]
      },
      "metadata": {},
@@ -612,9 +612,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2a0d99f26d8>"
+       "<matplotlib.figure.Figure at 0x1757c9d31d0>"
       ]
      },
      "metadata": {},
@@ -622,7 +622,7 @@
     }
    ],
    "source": [
-    "import code.book_plots as bp\n",
+    "import kf_book.book_plots as bp\n",
     "from numpy.random import randn\n",
     "from filterpy.kalman import EnsembleKalmanFilter as EnKF\n",
     "from filterpy.kalman import KalmanFilter\n",
@@ -781,7 +781,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

Appendix-G-Designing-Nonlinear-Kalman-Filters.ipynb

@@ -797,7 +797,7 @@
     }
    ],
    "source": [
-    "import code.book_plots as bp\n",
+    "import kf_book.book_plots as bp\n",
     "from filterpy.kalman import KalmanFilter\n",
     "import math\n",
     "import numpy as np\n",
@@ -910,7 +910,7 @@
     }
    ],
    "source": [
-    "import code.ekf_internal as ekf\n",
+    "import kf_book.ekf_internal as ekf\n",
     "ekf.plot_ball()"
    ]
   },
@@ -1042,7 +1042,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,

Appendix-H-Least-Squares-Filters.ipynb

@@ -337,7 +337,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

book_format.py

@@ -17,7 +17,6 @@ from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
 from contextlib import contextmanager
-from distutils.version import LooseVersion
 from IPython.core.display import HTML
 import json
 import matplotlib
@@ -27,7 +26,7 @@ import numpy as np
 import os.path
 import sys
 import warnings
-
+from kf_book.book_plots import set_figsize, reset_axis
 
 # version 1.4.3 of matplotlib has a bug that makes
 # it issue a spurious warning on every plot that
@@ -60,27 +59,6 @@ pylab.rcParams['figure.max_open_warning'] = 50
 
 
 
-def equal_axis():
-    pylab.rcParams['figure.figsize'] = 10,10
-    plt.axis('equal')
-
-
-def reset_axis():
-    pylab.rcParams['figure.figsize'] = 9, 3
-
-def set_figsize(x=9, y=4):
-    pylab.rcParams['figure.figsize'] = x, y
-
-
-@contextmanager
-def figsize(x=9, y=4):
-    """Temporarily set the figure size using 'with figsize(a,b):'"""
-
-    size = pylab.rcParams['figure.figsize']
-    set_figsize(x, y)
-    yield
-    pylab.rcParams['figure.figsize'] = size
-
 
 @contextmanager
 def numpy_precision(precision):
@@ -123,27 +101,27 @@ def _decode_dict(data):
     return rv
 
 
-def load_style(directory = '.', name='code/custom.css'):
-    if sys.version_info[0] >= 3:
-        style = json.load(open(os.path.join(directory, "code/538.json")))
-    else:
-        style = json.load(open(directory + "/code/538.json"), object_hook=_decode_dict)
-
-    # matplotlib has deprecated the use of axes.color_cycle as of version
-
-    try:
-        import seaborneee
-    except:
+def load_style(directory = '.', name='kf_book/custom.css'):
         version = [int(version_no) for version_no in matplotlib.__version__.split('.')]
-        if version[0] > 1 or (version[0] == 1 and version[1] >= 5):
-            style["axes.prop_cycle"] = "cycler('color', ['#6d904f','#013afe', '#202020','#fc4f30','#e5ae38','#A60628','#30a2da','#008080','#7A68A6','#CF4457','#188487','#E24A33'])"
-            style.pop("axes.color_cycle", None)
-        plt.rcParams.update(style)
-    set_figsize()
-    reset_axis ()
-    np.set_printoptions(suppress=True,precision=3, linewidth=70,
-                        formatter={'float':lambda x:' {:.3}'.format(x)})
 
-    styles = open(os.path.join(directory, name), 'r').read()
-    set_figsize()
-    return HTML(styles)
+        try:
+            if sys.version_info[0] >= 3:
+                style = json.load(open(os.path.join(directory, "kf_book/538.json")))
+            else:
+                style = json.load(open(directory + "/kf_book/538.json"), object_hook=_decode_dict)
+
+            # matplotlib has deprecated the use of axes.color_cycle as of version
+            if version[0] > 1 or (version[0] == 1 and version[1] >= 5):
+                style["axes.prop_cycle"] = "cycler('color', ['#6d904f','#013afe', '#202020','#fc4f30','#e5ae38','#A60628','#30a2da','#008080','#7A68A6','#CF4457','#188487','#E24A33'])"
+                style.pop("axes.color_cycle", None)
+            plt.rcParams.update(style)
+        except:
+            pass
+        set_figsize()
+        reset_axis ()
+        np.set_printoptions(suppress=True,precision=3, linewidth=70,
+                            formatter={'float':lambda x:' {:.3}'.format(x)})
+
+        styles = open(os.path.join(directory, name), 'r').read()
+        set_figsize()
+        return HTML(styles)

kf_book/adaptive_internal.py

@@ -16,7 +16,7 @@ for more information.
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
-import code.book_plots as bp
+import kf_book.book_plots as bp
 from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow
 import matplotlib.pyplot as plt
 import numpy as np

kf_book/book_plots.py

@@ -18,9 +18,9 @@ from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
 
-from book_format import figsize
 from contextlib import contextmanager
 import matplotlib as mpl
+import matplotlib.pylab as pylab
 import matplotlib.pyplot as plt
 from matplotlib.patches import Circle
 import numpy as np
@@ -32,10 +32,34 @@ try:
 except:
     pass
 
+
+def equal_axis():
+    pylab.rcParams['figure.figsize'] = 10,10
+    plt.axis('equal')
+
+
+def reset_axis():
+    pylab.rcParams['figure.figsize'] = 9, 3
+
+def set_figsize(x=9, y=4):
+    pylab.rcParams['figure.figsize'] = x, y
+
+
+@contextmanager
+def figsize(x=9, y=4):
+    """Temporarily set the figure size using 'with figsize(a,b):'"""
+
+    size = pylab.rcParams['figure.figsize']
+    set_figsize(x, y)
+    yield
+    pylab.rcParams['figure.figsize'] = size
+
+
+
 """ If the plot is inline (%matplotlib inline) we need to
 do special processing for the interactive_plot context manager,
 otherwise it outputs a lot of extra <matplotlib.figure.figure
-type output into the notebook.""" 
+type output into the notebook."""
 
 IS_INLINE = mpl.get_backend().find('backend_inline') != -1
 
@@ -50,12 +74,12 @@ def end_interactive(fig):
     time.sleep(1.)
     plt.close(fig)
 
-    
+
 @contextmanager
 def interactive_plot(close=True, fig=None):
     if fig is None and not IS_INLINE:
         fig = plt.figure()
-    
+
     yield
     try:
         # if the figure only uses annotations tight_output
@@ -64,7 +88,7 @@ def interactive_plot(close=True, fig=None):
     except:
         pass
 
-    if not IS_INLINE: 
+    if not IS_INLINE:
         plt.show()
 
     if close and not IS_INLINE:

kf_book/ekf_internal.py

@@ -16,7 +16,7 @@ for more information.
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
-import code.book_plots as bp
+import kf_book.book_plots as bp
 import filterpy.kalman as kf
 from math import radians, sin, cos, sqrt, exp
 import matplotlib.pyplot as plt

kf_book/gh_internal.py

@@ -17,7 +17,7 @@ from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
 
-import code.book_plots as book_plots
+import kf_book.book_plots as book_plots
 import numpy as np
 from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow
 import pylab as plt

kf_book/kf_internal.py

@@ -16,7 +16,7 @@ for more information.
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
-import code.book_plots as bp
+import kf_book.book_plots as bp
 import filterpy.stats as stats
 from math import sqrt
 import matplotlib.pyplot as plt

kf_book/mkf_internal.py

@@ -16,13 +16,18 @@ for more information.
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
-import code.book_plots as bp
+from mpl_toolkits.mplot3d import Axes3D
+
+try:
+    import kf_book.book_plots as bp
+except:
+    import book_plots as bp
+
 import filterpy.stats as stats
 from filterpy.stats import plot_covariance_ellipse
 from matplotlib.patches import Ellipse
 import matplotlib.pyplot as plt
 from matplotlib import cm
-from mpl_toolkits.mplot3d import Axes3D
 import numpy as np
 from numpy.random import multivariate_normal
 
@@ -273,33 +278,44 @@ def plot_3d_covariance(mean, cov):
 
     xs = np.arange(minx, maxx, (maxx-minx)/40.)
     ys = np.arange(miny, maxy, (maxy-miny)/40.)
-    xv, yv = np.meshgrid (xs, ys)
+    xv, yv = np.meshgrid(xs, ys)
 
     zs = np.array([100.* stats.multivariate_gaussian(np.array([x,y]),mean,cov) \
                    for x, y in zip(np.ravel(xv), np.ravel(yv))])
     zv = zs.reshape(xv.shape)
 
     maxz = np.max(zs)
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection='3d')
 
-    #ax = plt.figure().add_subplot(111, projection='3d')
-    ax = plt.gca(projection='3d')
+    #ax = plt.gca(projection='3d')
     ax.plot_surface(xv, yv, zv, rstride=1, cstride=1, cmap=cm.autumn)
 
     ax.set_xlabel('X')
     ax.set_ylabel('Y')
 
-
+    # For unknown reasons this started failing in Jupyter notebook when
+    # using `%matplotlib inline` magic. Still works fine in IPython or when
+    # `%matplotlib notebook` magic is used.
     x = mean[0]
     zs = np.array([100.* stats.multivariate_gaussian(np.array([x, y]),mean,cov)
                    for _, y in zip(np.ravel(xv), np.ravel(yv))])
     zv = zs.reshape(xv.shape)
-    ax.contour(xv, yv, zv, zdir='x', offset=minx-1, cmap=cm.binary)
+    try:
+        pass
+        #ax.contour(xv, yv, zv, zdir='x', offset=minx-1, cmap=cm.binary)
+    except:
+        pass
 
     y = mean[1]
     zs = np.array([100.* stats.multivariate_gaussian(np.array([x, y]),mean,cov)
                    for x, _ in zip(np.ravel(xv), np.ravel(yv))])
     zv = zs.reshape(xv.shape)
-    ax.contour(xv, yv, zv, zdir='y', offset=maxy, cmap=cm.binary)
+    try:
+        pass
+        #ax.contour(xv, yv, zv, zdir='y', offset=maxy, cmap=cm.binary)
+    except:
+        pass
 
 
 def plot_3d_sampled_covariance(mean, cov):

kf_book/pf_internal.py

@@ -17,16 +17,14 @@ from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
 
-from code.book_plots import figsize, end_interactive
+from kf_book.book_plots import figsize, end_interactive
 from filterpy.monte_carlo import stratified_resample, residual_resample
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 
 import numpy as np
 from numpy.random import randn, random, uniform, multivariate_normal, seed
-#from nonlinear_plots import plot_monte_carlo_mean
 import scipy.stats
-#from RobotLocalizationParticleFilter import *