Disable interactive plots for PDF.

nbconvert cannot output interactive plots to PDF. I wrote code
to remove all of the %matplotlib notebook magic, rerun the notebooks,
and then create the PDF.

.gitignore

@@ -12,4 +12,5 @@ book.pdf
 book6x9.pdf
 Kalman_and_Bayesian_Filters_in_Python6x9.pdf
 Kalman_and_Bayesian_Filters_in_Python.pdf
-book_files
+book_files
+tmp

01-g-h-filter.ipynb

@@ -375,9 +375,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp8AAACXCAYAAABTJDBMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAADSCAYAAACy7maiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6d2e48d0>"
+       "<matplotlib.figure.Figure at 0x1cc09cd2400>"
       ]
      },
      "metadata": {},
@@ -412,9 +412,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6d2e4898>"
+       "<matplotlib.figure.Figure at 0x1cc09cd2470>"
       ]
      },
      "metadata": {},
@@ -445,9 +445,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dca0f98>"
+       "<matplotlib.figure.Figure at 0x1cc0a141b00>"
       ]
      },
      "metadata": {},
@@ -484,7 +484,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9946\n"
+      "Average of measurements is 164.9692\n"
      ]
     }
    ],
@@ -517,7 +517,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9994\n"
+      "Average of measurements is 165.0343\n"
      ]
     }
    ],
@@ -550,9 +550,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6d2e4518>"
+       "<matplotlib.figure.Figure at 0x1cc07e9f860>"
       ]
      },
      "metadata": {},
@@ -582,9 +582,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dd5d668>"
+       "<matplotlib.figure.Figure at 0x1cc0a6912b0>"
       ]
      },
      "metadata": {},
@@ -611,9 +611,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dde9a20>"
+       "<matplotlib.figure.Figure at 0x1cc0a7a0c88>"
       ]
      },
      "metadata": {},
@@ -642,9 +642,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6de33470>"
+       "<matplotlib.figure.Figure at 0x1cc0a80db70>"
       ]
      },
      "metadata": {},
@@ -673,9 +673,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dd1ec50>"
+       "<matplotlib.figure.Figure at 0x1cc0b86bb00>"
       ]
      },
      "metadata": {},
@@ -708,9 +708,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6d74d8d0>"
+       "<matplotlib.figure.Figure at 0x1cc0ba2b860>"
       ]
      },
      "metadata": {},
@@ -737,9 +737,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dc59940>"
+       "<matplotlib.figure.Figure at 0x1cc0ba660f0>"
       ]
      },
      "metadata": {},
@@ -779,9 +779,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dd95828>"
+       "<matplotlib.figure.Figure at 0x1cc0bc56908>"
       ]
      },
      "metadata": {},
@@ -837,9 +837,9 @@
     },
     {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dc7c6d8>"
+       "<matplotlib.figure.Figure at 0x1cc0b87b3c8>"
       ]
      },
      "metadata": {},
@@ -1617,12 +1617,9 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\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",
+       "        // 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",
        "    }\n",
        "}\n",
        "\n",
@@ -1671,7 +1668,7 @@
     {
      "data": {
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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1684,7 +1681,7 @@
    "source": [
     "%matplotlib notebook\n",
     "predict_using_gain_guess(weight=initial_guess, gain_rate=1, do_print=False, sim_rate=.4)\n",
-    "%matplotlib inline"
+    "%matplotlib inline #reset"
    ]
   },
   {
@@ -1705,6 +1702,8 @@
     "\n",
     "to return to the static form of plotting. You can remove this line if you want. I always do it because the plot must be in static form to be converted to PDF format, which I do for the printed version of this book.\n",
     "\n",
+    "If you are reading this in a PDF than you will see the interactive code commented out. I wrote a batch file that does this automatically. You can see that I added the comment `#reset` to the end of the `%matplotlib inline #reset`. That is used by the batch file so it knows to comment out this line; it is not a normal part of the `%matplotlib` magic.\n",
+    "\n",
     "To get the animation to work you have to call `plt.gcf().canvas.draw()` each time you want an update, and call `time.sleep()` to slow down the animation. If you look at the code in `gh.plot_gh_results()` you'll see it using this to create the animation. For example, the following code draws a line once a second:"
    ]
   },
@@ -2428,12 +2427,9 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\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",
+       "        // 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",
        "    }\n",
        "}\n",
        "\n",
@@ -2482,7 +2478,7 @@
     {
      "data": {
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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2501,7 +2497,7 @@
     "    plt.gcf().canvas.draw()\n",
     "    time.sleep(1)\n",
     "\n",
-    "%matplotlib inline"
+    "%matplotlib inline #reset"
    ]
   },
   {
@@ -2520,9 +2516,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dd9d4a8>"
+       "<matplotlib.figure.Figure at 0x1cc0bbf84e0>"
       ]
      },
      "metadata": {},
@@ -3273,12 +3269,9 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\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",
+       "        // 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",
        "    }\n",
        "}\n",
        "\n",
@@ -3327,7 +3320,7 @@
     {
      "data": {
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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3363,7 +3356,7 @@
     "\n",
     "%matplotlib notebook\n",
     "gh.plot_gh_results(weights, estimates, predictions, time_step=0.1)\n",
-    "%matplotlib inline"
+    "%matplotlib inline #reset"
    ]
   },
   {
@@ -3418,9 +3411,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dcb8400>"
+       "<matplotlib.figure.Figure at 0x1cc0bc56a58>"
       ]
      },
      "metadata": {},
@@ -3971,9 +3964,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6de7f828>"
+       "<matplotlib.figure.Figure at 0x1cc0a632da0>"
       ]
      },
      "metadata": {},
@@ -4065,9 +4058,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6deadcc0>"
+       "<matplotlib.figure.Figure at 0x1cc0bc519b0>"
       ]
      },
      "metadata": {},
@@ -4102,19 +4095,9 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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truncated
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15a6dde0550>]"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6df00a90>"
+       "<matplotlib.figure.Figure at 0x1cc0b868c88>"
       ]
      },
      "metadata": {},
@@ -4166,9 +4149,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6d2f6ef0>"
+       "<matplotlib.figure.Figure at 0x1cc0bbdf0f0>"
       ]
      },
      "metadata": {},
@@ -4229,9 +4212,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6d700c18>"
+       "<matplotlib.figure.Figure at 0x1cc0a1202b0>"
       ]
      },
      "metadata": {},
@@ -4294,9 +4277,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dc60a90>"
+       "<matplotlib.figure.Figure at 0x1cc0bbeb5f8>"
       ]
      },
      "metadata": {},
@@ -4372,9 +4355,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dd49518>"
+       "<matplotlib.figure.Figure at 0x1cc0bc4b710>"
       ]
      },
      "metadata": {},
@@ -4424,9 +4407,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6df05940>"
+       "<matplotlib.figure.Figure at 0x1cc0b9dbd68>"
       ]
      },
      "metadata": {},
@@ -4468,9 +4451,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dc892b0>"
+       "<matplotlib.figure.Figure at 0x1cc0a833e10>"
       ]
      },
      "metadata": {},
@@ -4514,9 +4497,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6df34be0>"
+       "<matplotlib.figure.Figure at 0x1cc0a754668>"
       ]
      },
      "metadata": {},
@@ -4561,9 +4544,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6df34c50>"
+       "<matplotlib.figure.Figure at 0x1cc0a6f7390>"
       ]
      },
      "metadata": {},
@@ -4610,9 +4593,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6efb0eb8>"
+       "<matplotlib.figure.Figure at 0x1cc0a6ec5f8>"
       ]
      },
      "metadata": {},
@@ -4646,9 +4629,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6f28c4a8>"
+       "<matplotlib.figure.Figure at 0x1cc0a6ba9e8>"
       ]
      },
      "metadata": {},
@@ -4711,9 +4694,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6f28c208>"
+       "<matplotlib.figure.Figure at 0x1cc09ebf3c8>"
       ]
      },
      "metadata": {},
@@ -4763,9 +4746,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6f18d668>"
+       "<matplotlib.figure.Figure at 0x1cc0bbebe10>"
       ]
      },
      "metadata": {},
@@ -4795,9 +4778,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dcbbd68>"
+       "<matplotlib.figure.Figure at 0x1cc0b876780>"
       ]
      },
      "metadata": {},
@@ -4828,9 +4811,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dd09f28>"
+       "<matplotlib.figure.Figure at 0x1cc0bedf8d0>"
       ]
      },
      "metadata": {},
@@ -4878,9 +4861,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6dd80e80>"
+       "<matplotlib.figure.Figure at 0x1cc0a7c6f60>"
       ]
      },
      "metadata": {},
@@ -4909,9 +4892,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15a6deaf4a8>"
+       "<matplotlib.figure.Figure at 0x1cc0a60c668>"
       ]
      },
      "metadata": {},
@@ -5141,6 +5124,7 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
    "display_name": "Python [default]",
    "language": "python",
@@ -5156,7 +5140,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,

04-One-Dimensional-Kalman-Filters.ipynb

@@ -344,7 +344,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2151389ca90>"
+       "<matplotlib.figure.Figure at 0x1b25d282c18>"
       ]
      },
      "metadata": {},
@@ -380,14 +380,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean of readings is 10.043\n"
+      "Mean of readings is 10.024\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x215143909b0>"
+       "<matplotlib.figure.Figure at 0x1b25de49dd8>"
       ]
      },
      "metadata": {},
@@ -446,7 +446,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21511977ac8>"
+       "<matplotlib.figure.Figure at 0x1b25b2459b0>"
       ]
      },
      "metadata": {},
@@ -705,7 +705,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21514a2d668>"
+       "<matplotlib.figure.Figure at 0x1b25e37b128>"
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      },
      "metadata": {},
@@ -754,7 +754,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x215149340b8>"
+       "<matplotlib.figure.Figure at 0x1b25e3e5898>"
       ]
      },
      "metadata": {},
@@ -810,7 +810,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21515cfa2e8>"
+       "<matplotlib.figure.Figure at 0x1b25f78b668>"
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      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21515cfafd0>"
+       "<matplotlib.figure.Figure at 0x1b25f78b6d8>"
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      },
      "metadata": {},
@@ -864,7 +864,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21514b8a898>"
+       "<matplotlib.figure.Figure at 0x1b25e638208>"
       ]
      },
      "metadata": {},
@@ -981,7 +981,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x215149c1e80>"
+       "<matplotlib.figure.Figure at 0x1b25e6fcd30>"
       ]
      },
      "metadata": {},
@@ -1112,7 +1112,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x215148ab7f0>"
+       "<matplotlib.figure.Figure at 0x1b25de521d0>"
       ]
      },
      "metadata": {},
@@ -1278,7 +1278,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x215149b23c8>"
+       "<matplotlib.figure.Figure at 0x1b25de99978>"
       ]
      },
      "metadata": {},
@@ -1447,7 +1447,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21514a046a0>"
+       "<matplotlib.figure.Figure at 0x1b25e620ef0>"
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21515ce4e48>"
+       "<matplotlib.figure.Figure at 0x1b25e5fdc50>"
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      },
      "metadata": {},
@@ -1530,9 +1530,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21515c3ccf8>"
+       "<matplotlib.figure.Figure at 0x1b25e4b3ba8>"
       ]
      },
      "metadata": {},
@@ -1610,7 +1610,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21515e044a8>"
+       "<matplotlib.figure.Figure at 0x1b25f8d4908>"
       ]
      },
      "metadata": {},
@@ -1627,7 +1627,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21515ee7e10>"
+       "<matplotlib.figure.Figure at 0x1b25f98ec50>"
       ]
      },
      "metadata": {},
@@ -1729,7 +1729,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x215149b2a58>"
+       "<matplotlib.figure.Figure at 0x1b25e481c18>"
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      },
      "metadata": {},
@@ -1787,7 +1787,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21515fcbbe0>"
+       "<matplotlib.figure.Figure at 0x1b25fa64198>"
       ]
      },
      "metadata": {},
@@ -1844,7 +1844,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21515fda4e0>"
+       "<matplotlib.figure.Figure at 0x1b25fa6e710>"
       ]
      },
      "metadata": {},
@@ -1898,7 +1898,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21514b7ca58>"
+       "<matplotlib.figure.Figure at 0x1b25f9e1048>"
       ]
      },
      "metadata": {},
@@ -1946,7 +1946,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21514c49748>"
+       "<matplotlib.figure.Figure at 0x1b25f7c31d0>"
       ]
      },
      "metadata": {},
@@ -2001,7 +2001,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21515f59898>"
+       "<matplotlib.figure.Figure at 0x1b25e613d68>"
       ]
      },
      "metadata": {},
@@ -2047,7 +2047,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x215149eaa20>"
+       "<matplotlib.figure.Figure at 0x1b25de7cfd0>"
       ]
      },
      "metadata": {},
@@ -2142,7 +2142,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAEWCAYAAADSL2tlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21515cb03c8>"
+       "<matplotlib.figure.Figure at 0x1b25fa89588>"
       ]
      },
      "metadata": {},

06-Multivariate-Kalman-Filters.ipynb

@@ -394,9 +394,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvAAAADlCAYAAAA1IsN7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABBIAAAE6CAYAAACrn3E6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d20e2879e8>"
+       "<matplotlib.figure.Figure at 0x1fc154cf7b8>"
       ]
      },
      "metadata": {},
@@ -1069,9 +1069,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAEWCAYAAAA97QBbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAGCCAYAAAASZwn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d21132dac8>"
+       "<matplotlib.figure.Figure at 0x1fc19a4ba20>"
       ]
      },
      "metadata": {},
@@ -1579,9 +1579,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAEnCAYAAAAO3v+ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d211359518>"
+       "<matplotlib.figure.Figure at 0x1fc19aad3c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA00AAAGaCAYAAADTp6iSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fc19b734a8>"
       ]
      },
      "metadata": {},
@@ -1620,9 +1630,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d2118d82e8>"
+       "<matplotlib.figure.Figure at 0x1fc19ab7908>"
       ]
      },
      "metadata": {},
@@ -1756,9 +1766,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d211b13ac8>"
+       "<matplotlib.figure.Figure at 0x1fc1772a4a8>"
       ]
      },
      "metadata": {},
@@ -1806,9 +1816,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d211905550>"
+       "<matplotlib.figure.Figure at 0x1fc181432b0>"
       ]
      },
      "metadata": {},
@@ -1997,9 +2007,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d2130c2d30>"
+       "<matplotlib.figure.Figure at 0x1fc19af8ba8>"
       ]
      },
      "metadata": {},
@@ -2130,9 +2140,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d2118f52e8>"
+       "<matplotlib.figure.Figure at 0x1fc19b21748>"
       ]
      },
      "metadata": {},
@@ -2221,9 +2231,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d2116c6630>"
+       "<matplotlib.figure.Figure at 0x1fc176d5390>"
       ]
      },
      "metadata": {},
@@ -2250,9 +2260,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d2115f0358>"
+       "<matplotlib.figure.Figure at 0x1fc19b1ae48>"
       ]
      },
      "metadata": {},
@@ -2353,9 +2363,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d211c08d30>"
+       "<matplotlib.figure.Figure at 0x1fc1a297a58>"
       ]
      },
      "metadata": {},
@@ -2363,9 +2373,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d211ce6390>"
+       "<matplotlib.figure.Figure at 0x1fc19b20e48>"
       ]
      },
      "metadata": {},
@@ -2408,9 +2418,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d212fbdc88>"
+       "<matplotlib.figure.Figure at 0x1fc1a2874a8>"
       ]
      },
      "metadata": {},
@@ -2438,9 +2448,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d2119471d0>"
+       "<matplotlib.figure.Figure at 0x1fc180043c8>"
       ]
      },
      "metadata": {},
@@ -2469,9 +2479,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d2118dd208>"
+       "<matplotlib.figure.Figure at 0x1fc177206d8>"
       ]
      },
      "metadata": {},
@@ -2511,9 +2521,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d21161a4e0>"
+       "<matplotlib.figure.Figure at 0x1fc19a6ac50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fc18149d30>"
       ]
      },
      "metadata": {},
@@ -2556,9 +2576,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3wAAAG0CAYAAABpMCXSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d2113e2128>"
+       "<matplotlib.figure.Figure at 0x1fc17ef00b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0EAAAGaCAYAAADJm0gcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fc19af84a8>"
       ]
      },
      "metadata": {},
@@ -2590,9 +2620,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d2113da6d8>"
+       "<matplotlib.figure.Figure at 0x1fc17323390>"
       ]
      },
      "metadata": {},
@@ -2623,9 +2653,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d211369e10>"
+       "<matplotlib.figure.Figure at 0x1fc17312780>"
       ]
      },
      "metadata": {},
@@ -2657,9 +2687,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d211be8c18>"
+       "<matplotlib.figure.Figure at 0x1fc19a956d8>"
       ]
      },
      "metadata": {},
@@ -2740,9 +2770,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d2119b0f98>"
+       "<matplotlib.figure.Figure at 0x1fc19eac0f0>"
       ]
      },
      "metadata": {},
@@ -2866,9 +2896,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d2119ab5c0>"
+       "<matplotlib.figure.Figure at 0x1fc18143860>"
       ]
      },
      "metadata": {},
@@ -2911,9 +2941,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d212fa6dd8>"
+       "<matplotlib.figure.Figure at 0x1fc19b05a58>"
       ]
      },
      "metadata": {},
@@ -2953,9 +2983,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d211963a20>"
+       "<matplotlib.figure.Figure at 0x1fc19c564e0>"
       ]
      },
      "metadata": {},
@@ -3012,9 +3042,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d211374fd0>"
+       "<matplotlib.figure.Figure at 0x1fc19c509e8>"
       ]
      },
      "metadata": {},
@@ -3088,7 +3118,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
@@ -3103,6 +3133,18 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.5.2"
+  },
+  "widgets": {
+   "state": {
+    "b908e63da2d8435487b14f9e37dd89de": {
+     "views": [
+      {
+       "cell_index": 73
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
   }
  },
  "nbformat": 4,

07-Kalman-Filter-Math.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -289,7 +289,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -592,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -604,7 +604,7 @@
        "       [ 0.0,  1.0]])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -805,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -832,7 +832,7 @@
        "⎣    6           2                 ⎦      "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -867,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -888,7 +888,7 @@
        "[\\Delta{t}⋅\\Phiₛ]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -903,7 +903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -932,7 +932,7 @@
        "⎣    2                 ⎦      "
       ]
      },
-     "execution_count": 25,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -982,7 +982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -1004,7 +1004,7 @@
        "⎣    2                 ⎦    "
       ]
      },
-     "execution_count": 26,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1044,7 +1044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -1071,7 +1071,7 @@
        "⎣    2                             ⎦    "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1109,7 +1109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -1133,7 +1133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -1162,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -1183,7 +1183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -1214,7 +1214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -1296,7 +1296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1304,9 +1304,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAEWCAYAAAAjJDDoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c3e2f42f60>"
+       "<matplotlib.figure.Figure at 0x1ee2403f0b8>"
       ]
      },
      "metadata": {},
@@ -1331,16 +1331,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAEtCAYAAABnKTYpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c3e2678e10>"
+       "<matplotlib.figure.Figure at 0x1ee2415ac18>"
       ]
      },
      "metadata": {},
@@ -1366,7 +1366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 15,
    "metadata": {
     "collapsed": true
    },
@@ -1383,7 +1383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -1413,16 +1413,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 17,
    "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 0x1c3e26a99e8>"
+       "<matplotlib.figure.Figure at 0x1ee24196518>"
       ]
      },
      "metadata": {},
@@ -1442,7 +1442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -1525,7 +1525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -1563,7 +1563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -1577,9 +1577,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAEWCAYAAAA97QBbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c3e2602e48>"
+       "<matplotlib.figure.Figure at 0x1ee2604ee80>"
       ]
      },
      "metadata": {},
@@ -1758,7 +1758,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

code/mkf_internal.py

@@ -17,7 +17,6 @@ from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
 import code.book_plots as bp
-from code.book_plots import interactive_plot
 import filterpy.stats as stats
 from filterpy.stats import plot_covariance_ellipse
 from matplotlib.patches import Ellipse
@@ -420,37 +419,36 @@ def plot_track(ps, actual, zs, cov, std_scale=1,
                xlabel='time', ylabel='position',
                title='Kalman Filter'):
 
-    with interactive_plot():
-        count = len(zs)
-        zs = np.asarray(zs)
+    count = len(zs)
+    zs = np.asarray(zs)
 
-        cov = np.asarray(cov)
-        std = std_scale*np.sqrt(cov[:,0,0])
-        std_top = np.minimum(actual+std, [count + 10])
-        std_btm = np.maximum(actual-std, [-50])
+    cov = np.asarray(cov)
+    std = std_scale*np.sqrt(cov[:,0,0])
+    std_top = np.minimum(actual+std, [count + 10])
+    std_btm = np.maximum(actual-std, [-50])
 
-        std_top = actual + std
-        std_btm = actual - std
+    std_top = actual + std
+    std_btm = actual - std
 
-        bp.plot_track(actual,c='k')
-        bp.plot_measurements(range(1, count + 1), zs)
-        bp.plot_filter(range(1, count + 1), ps)
+    bp.plot_track(actual,c='k')
+    bp.plot_measurements(range(1, count + 1), zs)
+    bp.plot_filter(range(1, count + 1), ps)
 
-        plt.plot(std_top, linestyle=':', color='k', lw=1, alpha=0.4)
-        plt.plot(std_btm, linestyle=':', color='k', lw=1, alpha=0.4)
-        plt.fill_between(range(len(std_top)), std_top, std_btm,
-                         facecolor='yellow', alpha=0.2, interpolate=True)
-        plt.legend(loc=4)
-        plt.xlabel(xlabel)
-        plt.ylabel(ylabel)
-        if y_lim is not None:
-            plt.ylim(y_lim)
-        else:
-            plt.ylim((-50, count + 10))
+    plt.plot(std_top, linestyle=':', color='k', lw=1, alpha=0.4)
+    plt.plot(std_btm, linestyle=':', color='k', lw=1, alpha=0.4)
+    plt.fill_between(range(len(std_top)), std_top, std_btm,
+                     facecolor='yellow', alpha=0.2, interpolate=True)
+    plt.legend(loc=4)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    if y_lim is not None:
+        plt.ylim(y_lim)
+    else:
+        plt.ylim((-50, count + 10))
 
-        plt.xlim((0,count))
-        plt.title(title)
-        plt.show()
+    plt.xlim((0,count))
+    plt.title(title)
+    plt.show()
 
     if plot_P:
         ax = plt.subplot(121)

experiments/Untitled2.ipynb

@@ -0,0 +1,839 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.plot([1,3,6, 2]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // 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",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"data:image/png;base64,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 truncated
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import time\n",
+    "%matplotlib notebook\n",
+    "plt.plot([1,3,6,1]);\n",
+    "time.sleep(0.05)\n",
+    "plt.gcf().canvas.draw()\n",
+    "%matplotlib inline\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

pdf/build_book.bat

@@ -1,3 +1,5 @@
+rem call run_notebooks
+rem cd ..
 ipython merge_book.py
 
 jupyter nbconvert --to latex --template book book.ipynb

pdf/merge_book.py

@@ -45,25 +45,25 @@ if __name__ == '__main__':
             ['../04-One-Dimensional-Kalman-Filters.ipynb'])'''
 
         merge_notebooks(f,
-            ['../00-Preface.ipynb',
-             '../01-g-h-filter.ipynb',
-             '../02-Discrete-Bayes.ipynb',
-             '../03-Gaussians.ipynb',
-             '../04-One-Dimensional-Kalman-Filters.ipynb',
-             '../05-Multivariate-Gaussians.ipynb',
-             '../06-Multivariate-Kalman-Filters.ipynb',
-             '../07-Kalman-Filter-Math.ipynb',
-             '../08-Designing-Kalman-Filters.ipynb',
-             '../09-Nonlinear-Filtering.ipynb',
-             '../10-Unscented-Kalman-Filter.ipynb',
-             '../11-Extended-Kalman-Filters.ipynb',
-             '../12-Particle-Filters.ipynb',
-             '../13-Smoothing.ipynb',
-             '../14-Adaptive-Filtering.ipynb',
-             '../Appendix-A-Installation.ipynb',
-             '../Appendix-B-Symbols-and-Notations.ipynb',
-             '../Appendix-D-HInfinity-Filters.ipynb',
-             '../Appendix-E-Ensemble-Kalman-Filters.ipynb'])
+            ['./tmp/00-Preface.ipynb',
+             './tmp/01-g-h-filter.ipynb',
+             './tmp/02-Discrete-Bayes.ipynb',
+             './tmp/03-Gaussians.ipynb',
+             './tmp/04-One-Dimensional-Kalman-Filters.ipynb',
+             './tmp/05-Multivariate-Gaussians.ipynb',
+             './tmp/06-Multivariate-Kalman-Filters.ipynb',
+             './tmp/07-Kalman-Filter-Math.ipynb',
+             './tmp/08-Designing-Kalman-Filters.ipynb',
+             './tmp/09-Nonlinear-Filtering.ipynb',
+             './tmp/10-Unscented-Kalman-Filter.ipynb',
+             './tmp/11-Extended-Kalman-Filters.ipynb',
+             './tmp/12-Particle-Filters.ipynb',
+             './tmp/13-Smoothing.ipynb',
+             './tmp/14-Adaptive-Filtering.ipynb',
+             './tmp/Appendix-A-Installation.ipynb',
+             './tmp/Appendix-B-Symbols-and-Notations.ipynb',
+             './tmp/Appendix-D-HInfinity-Filters.ipynb',
+             './tmp/Appendix-E-Ensemble-Kalman-Filters.ipynb'])
 
 
     #remove text printed for matplotlib charts

pdf/rm_notebook.py

@@ -0,0 +1,15 @@
+import os, sys
+print(sys.argv[1])
+infile = open(sys.argv[1], encoding="utf8")
+data = infile.read()
+infile.close()
+outfile = open(sys.argv[1], "w", encoding="utf8")
+
+data = data.replace("%matplotlib notebook", "#%matplotlib notebook")
+data = data.replace("%matplotlib inline #reset", "#%matplotlib inline #reset")
+data = data.replace("time.sleep", "#time.sleep")
+data = data.replace("fig.canvas.draw", "#fig.canvas.draw")
+data = data.replace("plt.gcf().canvas.draw", "#plt.gcf().canvas.draw")
+
+outfile.write(data)
+outfile.close()

pdf/run_notebooks.bat

@@ -0,0 +1,10 @@
+mkdir tmp
+REM copy ..\*.ipynb .\tmp
+REM copy ..\*.py .\tmp
+REM cp -r ..\code\ .\tmp\code\
+
+cd tmp
+
+REM forfiles /m *.ipynb /c "cmd /c ipython ..\rm_notebook.py @file"
+REM forfiles /m *.ipynb /c "cmd /c jupyter nbconvert --to notebook --execute @file --output @file"
+