Reran for 538 plot formatting.

Ran all notebooks so that all images are properly plotted using
the 538 matplotlib style.

01_gh_filter/g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9b4d23397adb86da71441f61890f0e812c9ed1da66095c62d80570114907ac90"
+  "signature": "sha256:eb9d7a1739e4d5ad6d5727662f733d2138a26ece0b70575d410a7013f6d76aa3"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fbe7f3b06d8>"
+        "<IPython.core.display.HTML at 0x7fa5dc7fa6d8>"
        ]
       }
      ],
@@ -356,7 +356,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAGkCAYAAAD9t4CTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62ed9390>"
+        "<matplotlib.figure.Figure at 0x7fa5c0358390>"
        ]
       }
      ],
@@ -392,7 +392,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAGkCAYAAAD9t4CTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62c2b240>"
+        "<matplotlib.figure.Figure at 0x7fa5c00b4518>"
        ]
       }
      ],
@@ -428,7 +428,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62bc2c88>"
+        "<matplotlib.figure.Figure at 0x7fa5bfd23d68>"
        ]
       }
      ],
@@ -467,7 +467,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe7f3b5d68>"
+        "<matplotlib.figure.Figure at 0x7fa5dc7ffcf8>"
        ]
       }
      ],
@@ -494,7 +494,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62abc7f0>"
+        "<matplotlib.figure.Figure at 0x7fa5bff35dd8>"
        ]
       }
      ],
@@ -529,7 +529,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62bc2cc0>"
+        "<matplotlib.figure.Figure at 0x7fa5bff661d0>"
        ]
       }
      ],
@@ -597,7 +597,7 @@
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        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe629ed400>"
+        "<matplotlib.figure.Figure at 0x7fa5bffb0b00>"
        ]
       }
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@@ -626,7 +626,7 @@
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe629c31d0>"
+        "<matplotlib.figure.Figure at 0x7fa5bfcee5f8>"
        ]
       }
      ],
@@ -698,7 +698,7 @@
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62927f60>"
+        "<matplotlib.figure.Figure at 0x7fa5bfe330b8>"
        ]
       }
      ],
@@ -763,7 +763,7 @@
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        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62920f98>"
+        "<matplotlib.figure.Figure at 0x7fa5c003bb70>"
        ]
       }
      ],
@@ -876,7 +876,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe6288afd0>"
+        "<matplotlib.figure.Figure at 0x7fa5bfdfd4a8>"
        ]
       }
      ],
@@ -953,9 +953,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62b87080>"
+        "<matplotlib.figure.Figure at 0x7fa5c0011ef0>"
        ]
       }
      ],
@@ -1009,9 +1009,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62aba438>"
+        "<matplotlib.figure.Figure at 0x7fa5bfe3fe48>"
        ]
       }
      ],
@@ -1072,9 +1072,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62b03320>"
+        "<matplotlib.figure.Figure at 0x7fa5bff70b70>"
        ]
       }
      ],
@@ -1148,7 +1148,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62b62400>"
+        "<matplotlib.figure.Figure at 0x7fa5bffd1dd8>"
        ]
       }
      ],
@@ -1221,25 +1221,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62858d68>"
+        "<matplotlib.figure.Figure at 0x7fa5bfcd6780>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe629979b0>"
+        "<matplotlib.figure.Figure at 0x7fa5bfd9ef60>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62c04da0>"
+        "<matplotlib.figure.Figure at 0x7fa5c0081550>"
        ]
       }
      ],
@@ -1278,7 +1278,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62b2b7f0>"
+        "<matplotlib.figure.Figure at 0x7fa5bff3cef0>"
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe6297de48>"
+        "<matplotlib.figure.Figure at 0x7fa5bff3ceb8>"
        ]
       },
       {
@@ -1294,7 +1294,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe62b2be80>"
+        "<matplotlib.figure.Figure at 0x7fa5bfe45e10>"
        ]
       }
      ],
@@ -1329,7 +1329,7 @@
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe6296aa90>"
+        "<matplotlib.figure.Figure at 0x7fa5c006aef0>"
        ]
       },
       {
@@ -1337,7 +1337,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe6296a9b0>"
+        "<matplotlib.figure.Figure at 0x7fa5bfe90198>"
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe60f5a0b8>"
+        "<matplotlib.figure.Figure at 0x7fa5be485978>"
        ]
       },
       {
@@ -1393,7 +1393,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe60ff2c18>"
+        "<matplotlib.figure.Figure at 0x7fa5be46c048>"
        ]
       },
       {
@@ -1401,7 +1401,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbe628f66d8>"
+        "<matplotlib.figure.Figure at 0x7fa5bfd6fa20>"
        ]
       }
      ],

02_Discrete_Bayes/discrete_bayes.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:e81fff935ccbcaa4a91a285963630db8aa78e1a28f862ed30e1fd33af76d4504"
+  "signature": "sha256:6a36862b345fff6ec9ecb2e3c5f3a2b4c5027e92f54547c6785bf7854d842d2d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fd93d8320f0>"
+        "<IPython.core.display.HTML at 0x7f8174d31128>"
        ]
       }
      ],
@@ -359,9 +359,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d8601d0>"
+        "<matplotlib.figure.Figure at 0x7f81588865c0>"
        ]
       }
      ],
@@ -502,9 +502,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d4fdba8>"
+        "<matplotlib.figure.Figure at 0x7f81585b7a90>"
        ]
       }
      ],
@@ -554,9 +554,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d4b70f0>"
+        "<matplotlib.figure.Figure at 0x7f81583af320>"
        ]
       }
      ],
@@ -634,9 +634,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d47ad30>"
+        "<matplotlib.figure.Figure at 0x7f81584fc048>"
        ]
       }
      ],
@@ -700,9 +700,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d405160>"
+        "<matplotlib.figure.Figure at 0x7f8158455588>"
        ]
       }
      ],
@@ -737,9 +737,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d431d68>"
+        "<matplotlib.figure.Figure at 0x7f8158483780>"
        ]
       }
      ],
@@ -766,9 +766,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d419cc0>"
+        "<matplotlib.figure.Figure at 0x7f815847dc88>"
        ]
       }
      ],
@@ -804,9 +804,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d4be198>"
+        "<matplotlib.figure.Figure at 0x7f81585013c8>"
        ]
       }
      ],
@@ -891,9 +891,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d4be898>"
+        "<matplotlib.figure.Figure at 0x7f8158501898>"
        ]
       }
      ],
@@ -927,9 +927,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d254048>"
+        "<matplotlib.figure.Figure at 0x7f81582a7390>"
        ]
       }
      ],
@@ -956,9 +956,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d2c1240>"
+        "<matplotlib.figure.Figure at 0x7f815841f390>"
        ]
       }
      ],
@@ -985,9 +985,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d30e160>"
+        "<matplotlib.figure.Figure at 0x7f81582cdb38>"
        ]
       }
      ],
@@ -1052,9 +1052,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d5a0710>"
+        "<matplotlib.figure.Figure at 0x7f8158373fd0>"
        ]
       }
      ],
@@ -1081,9 +1081,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d405cf8>"
+        "<matplotlib.figure.Figure at 0x7f81584555c0>"
        ]
       }
      ],
@@ -1114,9 +1114,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d4cbb70>"
+        "<matplotlib.figure.Figure at 0x7f8158472ef0>"
        ]
       }
      ],
@@ -1168,9 +1168,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d3f3eb8>"
+        "<matplotlib.figure.Figure at 0x7f8158254ef0>"
        ]
       }
      ],

02_Discrete_Bayes/discrete_bayes_animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:47d64bacc2f597ef2e39688a14b58dc93224617c01ee343a7bb71c0cc97e6efa"
+  "signature": "sha256:11f30a7baee4cbae7a0eecac272e77e8b4d31b1f2536db69aebf87e33b7a7458"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -34,6 +34,8 @@
        "html": [
         "<style>\n",
         "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
+        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
+        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
         "\n",
         "    div.cell{\n",
         "        width: 850px;\n",
@@ -120,9 +122,9 @@
         "        white-space: nowrap;\n",
         "    }\n",
         "    div.text_cell_render{\n",
-        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+        "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
         "        line-height: 135%;\n",
-        "        font-size: 110%;\n",
+        "        font-size: 125%;\n",
         "        width:750px;\n",
         "        margin-left:auto;\n",
         "        margin-right:auto;\n",
@@ -132,12 +134,12 @@
         "    div.output_subarea.output_text.output_pyout {\n",
         "        overflow-x: auto;\n",
         "        overflow-y: scroll;\n",
-        "        max-height: 300px;\n",
+        "        max-height: 50000px;\n",
         "    }\n",
         "    div.output_subarea.output_stream.output_stdout.output_text {\n",
         "        overflow-x: auto;\n",
         "        overflow-y: scroll;\n",
-        "        max-height: 300px;\n",
+        "        max-height: 50000px;\n",
         "    }\n",
         "    code{\n",
         "      font-size: 70%;\n",
@@ -250,7 +252,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f77b8e02fd0>"
+        "<IPython.core.display.HTML at 0x7f7d7f3b0f60>"
        ]
       }
      ],
@@ -349,9 +351,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f77b8e02b38>"
+        "<matplotlib.figure.Figure at 0x7f7d5eabb898>"
        ]
       }
      ],
@@ -387,22 +389,12 @@
       "\n",
       "fig = plt.figure(figsize=(6.5, 2.5))\n",
       "anim = animation.FuncAnimation(fig, bar_animate,\n",
-      "                               frames=60, interval=200)\n",
+      "                               frames=60, interval=75)\n",
       "anim.save('simulate.gif', writer='imagemagick')"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f779abe8c18>"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "outputs": []
     },
     {
      "cell_type": "markdown",

04_Gaussians/Gaussians.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:e15041853bd034fbae3600dbd7c0acc9dff195a71aa98cf1ec267baca0b5dcb0"
+  "signature": "sha256:f315c4edb8acd4ca7bed4bf2ad1a08de3002a20feab253ac00f882073b882911"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fd679b4a4e0>"
+        "<IPython.core.display.HTML at 0x7f7f2a69e128>"
        ]
       }
      ],
@@ -300,9 +300,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd656243668>"
+        "<matplotlib.figure.Figure at 0x7f7f117465f8>"
        ]
       }
      ],
@@ -346,9 +346,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd655a4cb70>"
+        "<matplotlib.figure.Figure at 0x7f7f09ead5c0>"
        ]
       }
      ],
@@ -423,9 +423,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd65572d630>"
+        "<matplotlib.figure.Figure at 0x7f7f06501748>"
        ]
       }
      ],
@@ -548,9 +548,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd655902860>"
+        "<matplotlib.figure.Figure at 0x7f7f06403668>"
        ]
       }
      ],
@@ -590,9 +590,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd65572d940>"
+        "<matplotlib.figure.Figure at 0x7f7f09e03e80>"
        ]
       }
      ],
@@ -646,9 +646,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd65574eb38>"
+        "<matplotlib.figure.Figure at 0x7f7f09e32f98>"
        ]
       },
       {
@@ -782,9 +782,10 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[ 0.8890914  -1.21963858 -0.36342745 -1.69175021  1.04580929 -4.03699521\n",
-        "  0.62282703  2.86612581  3.95733631  0.29548548  4.90591126  1.03687335\n",
-        " -0.75739343 -3.71850042  1.8192876 ]\n"
+        "[  2.07782254e+00   2.92284291e+00   3.41939607e+00  -2.74017280e+00\n",
+        "  -9.35326550e-01   2.12233068e+00  -2.40918322e-01  -1.43619992e+00\n",
+        "   3.98720795e+00   6.65972692e-02   4.31909653e+00  -3.55998844e+00\n",
+        "   1.53055228e+00   2.65472646e+00   1.13045111e-03]\n"
        ]
       }
      ],

04_Gaussians/Gaussians_Animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:c37d0ec0133c79aa5807b6f8cb1fc339f0365aa42cb11bfe3a91cba980759b23"
+  "signature": "sha256:a64f35e7df2ba17ebd70c1debdf2b30922efaea2cea89784b53ec3b424b2eadc"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -29,6 +29,8 @@
        "html": [
         "<style>\n",
         "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
+        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
+        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
         "\n",
         "    div.cell{\n",
         "        width: 850px;\n",
@@ -115,9 +117,9 @@
         "        white-space: nowrap;\n",
         "    }\n",
         "    div.text_cell_render{\n",
-        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+        "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
         "        line-height: 135%;\n",
-        "        font-size: 110%;\n",
+        "        font-size: 125%;\n",
         "        width:750px;\n",
         "        margin-left:auto;\n",
         "        margin-right:auto;\n",
@@ -127,12 +129,12 @@
         "    div.output_subarea.output_text.output_pyout {\n",
         "        overflow-x: auto;\n",
         "        overflow-y: scroll;\n",
-        "        max-height: 300px;\n",
+        "        max-height: 50000px;\n",
         "    }\n",
         "    div.output_subarea.output_stream.output_stdout.output_text {\n",
         "        overflow-x: auto;\n",
         "        overflow-y: scroll;\n",
-        "        max-height: 300px;\n",
+        "        max-height: 50000px;\n",
         "    }\n",
         "    code{\n",
         "      font-size: 70%;\n",
@@ -245,7 +247,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f9c484ada20>"
+        "<IPython.core.display.HTML at 0x7fba31a08f98>"
        ]
       }
      ],
@@ -292,13 +294,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAGrCAYAAAC41CzUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8VPW9//H3zCSzJkBMSEIIYQ2ECGErIK4EFa7aFrHg\nUpSKe4n9ab30ov1568X2p7co7ooKolClLSjuG1VTRWRRscGFJSyBACEJgUCSycxklt8fQ2NTkSAE\nJsn39Xw8+CMn5ySf+TI57znf8/1+j6W6ujoiAAAMZI11AQAAxAohCAAwFiEIADAWIQgAMBYhCAAw\nFiEIADAWIQgAMFazIbhixQpdfvnlys3NVVJSkhYtWtTsD/3666914YUXqkuXLsrNzdWsWbNapFgA\nAFpSsyHo9Xo1YMAA3XvvvXK5XLJYLEfc/+DBg5owYYLS09NVWFioe++9V48++qgee+yxFisaAICW\nENfcDueff77OP/98SVJBQUGzP3DJkiXy+XyaM2eOHA6HcnJyVFxcrCeeeEI333zz8VcMAEALafF7\ngmvWrNGoUaPkcDgat40ZM0ZlZWXasWNHS/86AACOWYuHYEVFhVJTU5ts69y5c+P3AABoLVo8BJu7\nZwgAQGvR4iGYmpr6nSu+ysrKxu8BANBatHgIjhgxQitXrpTf72/cVlhYqIyMDGVlZbX0r2t3iouL\nY11Cq0FbNEV7NEV7NEV7HJtmQ7Curk7r1q3TunXrFA6HVVpaqnXr1mnnzp2SpJkzZ2r8+PGN+0+c\nOFEul0vTpk3T+vXr9dprr+nhhx/WtGnTTtyrAADgGDQbgmvXrtU555yjc845Rz6fT/fee6/OOecc\n3XvvvZKk8vJylZSUNO7foUMHvfzyyyorK1N+fr5mzJihm2+++aimVwAAcDI1O0/wrLPO0v79+7/3\n+0888cR3tuXm5uqtt946vsoAADjBWDsUAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAs\nQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQ\nAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABg\nLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxC\nEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAA\nYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAs\nQhAAYCxCEABgLEIQAGCsowrBefPmKS8vT+np6Ro9erRWrlx5xP2XLVum8847T926dVPv3r3185//\nXFu2bGmRggEAaCnNhuDSpUt1xx13aPr06Vq+fLlGjBihSZMmaefOnYfdf+vWrZo8ebLOPPNMLV++\nXK+88or8fr8mTZrU4sUDAHA8mg3Bxx9/XJMnT9aUKVOUnZ2tWbNmKS0tTfPnzz/s/l9++aXC4bDu\nuusu9ejRQwMHDtQtt9yibdu2af/+/S3+AgAAOFZHDMFAIKCioiLl5+c32T5mzBitXr36sMeMHDlS\nHo9HCxYsUCgUUk1NjRYtWqRhw4YpKSmp5SoHAOA4HTEEq6qqFAqFlJqa2mR7SkqKKioqDntMenq6\nlixZoj/84Q9KS0tT9+7dtWHDBv3lL39puaoBAGgBcS39A0tKSjR58mRNnjxZEydOVE1Nje655x5d\nffXVev3112WxWA57XHFxcUuX0mbRFt+iLZqiPZqiPZqiPaKys7OPet8jhmBycrJsNtt3rvoqKyuV\nlpZ22GOeffZZde3aVTNnzmzc9vTTT+vUU0/VmjVrNHLkyOMuuj0rLi6mLQ6hLZqiPZqiPZqiPY7N\nEbtD7Xa7Bg8erMLCwibbCwsLvzfMIpGIrNamP/afX4fD4eOpFQCAFtXs6NCCggItWrRICxcu1MaN\nGzVjxgxVVFRo6tSpkqSZM2dq/PjxjftfcMEFKioq0qxZs7Rlyxb94x//UEFBgTIzMzV48OAT90oA\nAPiBmr0nOGHCBO3bt0/333+/ysvLlZubq8WLFyszM1OSVF5erpKSksb9R40apWeffVYPPvigHnnk\nEblcLg0fPlwvvfSSXC7XCXshAAD8UJbq6upIrIvAt+jX/xZt0RTt0RTt0RTtcWxYOxQAYCxCEABg\nLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxC\nEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAA\nYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAs\nQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQ\nAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABg\nLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYCxCEABgLEIQAGAsQhAAYKyjCsF58+YpLy9P6enpGj16\ntFauXNnsMU888YSGDx+utLQ05eTkaObMmcddLAAALSmuuR2WLl2qO+64Q7Nnz9aoUaM0d+5cTZo0\nSatWrVJmZuZhj/ntb3+rZcuW6fe//71yc3N18OBBlZeXt3jxAAAcj2ZD8PHHH9fkyZM1ZcoUSdKs\nWbP0/vvva/78+frd7373nf2Li4s1d+5cffLJJ8rOzm7cPnDgwBYsGwCA43fE7tBAIKCioiLl5+c3\n2T5mzBitXr36sMe89dZb6tGjh5YtW6ZBgwYpLy9Pv/zlL7V3796WqxoAgBZwxBCsqqpSKBRSampq\nk+0pKSmqqKg47DElJSUqLS3VK6+8oieffFJPPfWUiouLdfnllysSibRc5QAAHKdmu0N/qHA4LL/f\nr6eeekq9evWSJD311FP60Y9+pC+++EJDhw497HHFxcUtXUqbRVt8i7ZoivZoivZoivaI+tdbcc05\nYggmJyfLZrN956qvsrJSaWlphz0mLS1NcXFxjQEoSb169ZLNZlNpaen3huAPKbo9Ky4upi0OoS2a\noj2aoj2aoj2OzRG7Q+12uwYPHqzCwsIm2wsLCzVy5MjDHjNq1CgFg0GVlJQ0bispKVEoFFJWVtbx\nVwwAQAtpdp5gQUGBFi1apIULF2rjxo2aMWOGKioqNHXqVEnSzJkzNX78+Mb9R48erUGDBqmgoEDr\n1q1TUVGRCgoKNHz4cA0ZMuTEvRIAAH6gZu8JTpgwQfv27dP999+v8vJy5ebmavHixY1zBMvLy5tc\n9VksFv31r3/VjBkzdNFFF8npdCo/P1/33HPPCXsRAAAcC0t1dTVDNlsR+vW/RVs0RXs0RXs0RXsc\nG9YOBQAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiL\nEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAE\nABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAY\nixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQ\nBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQA\nGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGIsQBAAYixAEABiLEAQAGOuoQnDevHnKy8tT\nenq6Ro8erZUrVx7VD9+yZYsyMzOVmZl5XEUCAHAiNBuCS5cu1R133KHp06dr+fLlGjFihCZNmqSd\nO3ce8bhAIKBrrrlGZ5xxhiwWS4sVDABAS2k2BB9//HFNnjxZU6ZMUXZ2tmbNmqW0tDTNnz//iMfd\nddddGjhwoMaPH69IJNJiBQMA0FKOGIKBQEBFRUXKz89vsn3MmDFavXr19x737rvvatmyZZo1axYB\nCABoteKO9M2qqiqFQiGlpqY22Z6SkqKKiorDHlNWVqZbb71VL7zwgtxud8tVCgBACztiCB6LG2+8\nUddcc42GDh36g44rLi5u6VLaLNriW7RFU7RHU7RHU7RHVHZ29lHve8QQTE5Ols1m+85VX2VlpdLS\n0g57zPLly/XJJ5/oj3/8oyQpEokoHA4rJSVFDzzwgKZMmXLcRbdnxcXFtMUhtEVTtEdTtEdTtMex\nOWII2u12DR48WIWFhRo/fnzj9sLCQl188cWHPebfp0+8+eabmj17tj744AOlp6e3QMkAALSMZrtD\nCwoKdOONN2ro0KEaOXKk5s+fr4qKCk2dOlWSNHPmTK1du1avvvqqJCknJ6fJ8Z9//rmsVut3tgMA\nEGvNhuCECRO0b98+3X///SovL1dubq4WL17cOAG+vLxcJSUlR/wZzBMEALRGlurqauYwtCL063+L\ntmiK9miK9miK9jg2rB0KADAWIQgAMBYhCAAwFiEIADAWIQgAMBYhCAAwFiEIADAWIQgAMBYhCAAw\nFiEIADAWIQgAMBYhCAAwFiEIADAWIQgAMBYhCAAwFiEIADAWIQgAMBYhCAAwFiEIADAWIQLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f9c26dfab38>"
+        "<matplotlib.figure.Figure at 0x7fba1557def0>"
        ]
       }
      ],
-     "prompt_number": 46
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -306,7 +308,8 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",

05_Kalman_Filters/Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:25e5773098f32686e5a284ee397bd2ea9060a96f46a38df75833442ebd3db7d6"
+  "signature": "sha256:156f79b0088cce4bbd51d29b0e44f749e54646e15cb6b070627a4348cde0149f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fce9a598978>"
+        "<IPython.core.display.HTML at 0x7fc586ba4b38>"
        ]
       }
      ],
@@ -353,10 +353,10 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        " 1.2921\t 0.1444\t 0.7473\t 0.3516\t 0.4305\t\n",
-        " 1.8350\t-0.1108\t-0.1800\t 0.3818\t-0.2313\t\n",
-        "-0.2557\t 1.3211\t 0.1475\t 0.9038\t-0.1875\t\n",
-        "-0.3380\t-1.1683\t 0.6501\t-1.3499\t-0.9251\t\n"
+        "-0.6848\t-0.8225\t 0.5694\t-0.6209\t-0.4933\t\n",
+        "-0.1442\t 0.5606\t 0.8722\t 0.5783\t-1.0106\t\n",
+        " 0.4380\t 0.6382\t-0.0738\t 0.4447\t 1.1688\t\n",
+        " 0.2339\t-0.7171\t-1.2632\t-2.2643\t 0.5534\t\n"
        ]
       }
      ],
@@ -401,9 +401,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7e0e9978>"
+        "<matplotlib.figure.Figure at 0x7fc56a6f4f98>"
        ]
       }
      ],
@@ -447,9 +447,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7dc2d3c8>"
+        "<matplotlib.figure.Figure at 0x7fc56a24ba90>"
        ]
       }
      ],
@@ -481,9 +481,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7e0e9278>"
+        "<matplotlib.figure.Figure at 0x7fc56a6f4fd0>"
        ]
       }
      ],
@@ -501,9 +501,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7dd79470>"
+        "<matplotlib.figure.Figure at 0x7fc56a3921d0>"
        ]
       }
      ],
@@ -537,9 +537,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7dae7518>"
+        "<matplotlib.figure.Figure at 0x7fc56a3e7390>"
        ]
       }
      ],
@@ -576,9 +576,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79cf12b0>"
+        "<matplotlib.figure.Figure at 0x7fc56a2cbc88>"
        ]
       }
      ],
@@ -679,9 +679,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79d0dc18>"
+        "<matplotlib.figure.Figure at 0x7fc568016080>"
        ]
       }
      ],
@@ -727,9 +727,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79c932e8>"
+        "<matplotlib.figure.Figure at 0x7fc568016b70>"
        ]
       }
      ],
@@ -777,9 +777,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74a743c8>"
+        "<matplotlib.figure.Figure at 0x7fc56013b7f0>"
        ]
       }
      ],
@@ -895,26 +895,26 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "time: 0 \tposition = 1.848 \tvariance = 2.500\n",
-        "time: 1 \tposition = 1.440 \tvariance = 1.667\n",
-        "time: 2 \tposition = 1.032 \tvariance = 1.250\n",
-        "time: 3 \tposition = 0.551 \tvariance = 1.000\n",
-        "time: 4 \tposition = 0.504 \tvariance = 0.833\n",
-        "time: 5 \tposition = 0.379 \tvariance = 0.714\n",
-        "time: 6 \tposition = 0.244 \tvariance = 0.625\n",
-        "time: 7 \tposition = 0.321 \tvariance = 0.556\n",
-        "time: 8 \tposition = 0.381 \tvariance = 0.500\n",
-        "time: 9 \tposition = 0.305 \tvariance = 0.455\n",
-        "time: 10 \tposition = 0.435 \tvariance = 0.417\n",
-        "time: 11 \tposition = 0.423 \tvariance = 0.385\n",
-        "time: 12 \tposition = 0.445 \tvariance = 0.357\n",
-        "time: 13 \tposition = 0.404 \tvariance = 0.333\n",
-        "time: 14 \tposition = 0.397 \tvariance = 0.312\n",
-        "time: 15 \tposition = 0.282 \tvariance = 0.294\n",
-        "time: 16 \tposition = 0.239 \tvariance = 0.278\n",
-        "time: 17 \tposition = 0.161 \tvariance = 0.263\n",
-        "time: 18 \tposition = 0.204 \tvariance = 0.250\n",
-        "time: 19 \tposition = 0.160 \tvariance = 0.238\n"
+        "time: 0 \tposition = 0.257 \tvariance = 2.500\n",
+        "time: 1 \tposition = 0.290 \tvariance = 1.667\n",
+        "time: 2 \tposition = 0.092 \tvariance = 1.250\n",
+        "time: 3 \tposition = 0.276 \tvariance = 1.000\n",
+        "time: 4 \tposition = 0.247 \tvariance = 0.833\n",
+        "time: 5 \tposition = 0.510 \tvariance = 0.714\n",
+        "time: 6 \tposition = 0.516 \tvariance = 0.625\n",
+        "time: 7 \tposition = 0.620 \tvariance = 0.556\n",
+        "time: 8 \tposition = 0.454 \tvariance = 0.500\n",
+        "time: 9 \tposition = 0.478 \tvariance = 0.455\n",
+        "time: 10 \tposition = 0.478 \tvariance = 0.417\n",
+        "time: 11 \tposition = 0.374 \tvariance = 0.385\n",
+        "time: 12 \tposition = 0.394 \tvariance = 0.357\n",
+        "time: 13 \tposition = 0.225 \tvariance = 0.333\n",
+        "time: 14 \tposition = 0.296 \tvariance = 0.312\n",
+        "time: 15 \tposition = 0.302 \tvariance = 0.294\n",
+        "time: 16 \tposition = 0.186 \tvariance = 0.278\n",
+        "time: 17 \tposition = 0.156 \tvariance = 0.263\n",
+        "time: 18 \tposition = 0.114 \tvariance = 0.250\n",
+        "time: 19 \tposition = 0.030 \tvariance = 0.238\n"
        ]
       }
      ],
@@ -1034,24 +1034,24 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "PREDICT:     1.0000   502.0000\tUPDATE:    -2.4457     9.8047\n",
-        "PREDICT:    -1.4457    11.8047\tUPDATE:     0.7765     5.4138\n",
-        "PREDICT:     1.7765     7.4138\tUPDATE:     1.4225     4.2574\n",
-        "PREDICT:     2.4225     6.2574\tUPDATE:     2.3347     3.8490\n",
-        "PREDICT:     3.3347     5.8490\tUPDATE:     4.8032     3.6904\n",
-        "PREDICT:     5.8032     5.6904\tUPDATE:     5.4486     3.6267\n",
-        "PREDICT:     6.4486     5.6267\tUPDATE:     6.5119     3.6007\n",
-        "PREDICT:     7.5119     5.6007\tUPDATE:     8.9309     3.5900\n",
-        "PREDICT:     9.9309     5.5900\tUPDATE:     9.5451     3.5856\n",
-        "PREDICT:    10.5451     5.5856\tUPDATE:     9.8194     3.5838\n"
+        "PREDICT:     1.0000   502.0000\tUPDATE:     6.3512     9.8047\n",
+        "PREDICT:     7.3512    11.8047\tUPDATE:     3.3566     5.4138\n",
+        "PREDICT:     4.3566     7.4138\tUPDATE:     5.1701     4.2574\n",
+        "PREDICT:     6.1701     6.2574\tUPDATE:     5.6607     3.8490\n",
+        "PREDICT:     6.6607     5.8490\tUPDATE:     6.0900     3.6904\n",
+        "PREDICT:     7.0900     5.6904\tUPDATE:     3.6639     3.6267\n",
+        "PREDICT:     4.6639     5.6267\tUPDATE:     3.5041     3.6007\n",
+        "PREDICT:     4.5041     5.6007\tUPDATE:     4.4744     3.5900\n",
+        "PREDICT:     5.4744     5.5900\tUPDATE:     7.0620     3.5856\n",
+        "PREDICT:     8.0620     5.5856\tUPDATE:     9.5519     3.5838\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79c8bfd0>"
+        "<matplotlib.figure.Figure at 0x7fc586ba7400>"
        ]
       }
      ],
@@ -1186,17 +1186,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74a4a710>"
+        "<matplotlib.figure.Figure at 0x7fc5601688d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79d0d320>"
+        "<matplotlib.figure.Figure at 0x7fc560133588>"
        ]
       },
       {
@@ -1522,17 +1522,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7dd40780>"
+        "<matplotlib.figure.Figure at 0x7fc560a51ac8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74adaa90>"
+        "<matplotlib.figure.Figure at 0x7fc56010e5f8>"
        ]
       },
       {
@@ -1648,9 +1648,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74aac3c8>"
+        "<matplotlib.figure.Figure at 0x7fc5601a7780>"
        ]
       }
      ],
@@ -1719,17 +1719,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74aa2358>"
+        "<matplotlib.figure.Figure at 0x7fc560168358>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79d25080>"
+        "<matplotlib.figure.Figure at 0x7fc5680c42e8>"
        ]
       }
      ],
@@ -1805,9 +1805,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74a748d0>"
+        "<matplotlib.figure.Figure at 0x7fc5600bc940>"
        ]
       }
      ],
@@ -1869,9 +1869,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce749750b8>"
+        "<matplotlib.figure.Figure at 0x7fc558ee9d30>"
        ]
       }
      ],
@@ -1932,9 +1932,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74943a90>"
+        "<matplotlib.figure.Figure at 0x7fc558f14358>"
        ]
       }
      ],
@@ -1984,9 +1984,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fce748d07b8>"
+        "<matplotlib.figure.Figure at 0x7fc558e757f0>"
        ]
       }
      ],
@@ -2106,9 +2106,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79c68c88>"
+        "<matplotlib.figure.Figure at 0x7fc558e32400>"
        ]
       },
       {
@@ -2195,9 +2195,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79c28ba8>"
+        "<matplotlib.figure.Figure at 0x7fc56003a3c8>"
        ]
       }
      ],
@@ -2279,9 +2279,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce749f6128>"
+        "<matplotlib.figure.Figure at 0x7fc568032470>"
        ]
       }
      ],

05_Kalman_Filters/Kalman_Filters_Animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:08cdb1e902d069b45293bbf627273884dedc0356c9048d8247276cd852dee4c4"
+  "signature": "sha256:be92c78bd7810e38db1917d19689572306a78961e048308a9322a3f068c68208"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -36,7 +36,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -102,7 +102,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -167,7 +168,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7ffa64af50b8>"
+       ]
+      }
+     ],
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -237,13 +248,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f3d805b4d68>"
+        "<matplotlib.figure.Figure at 0x7ffa60799358>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",

06_Multivariate_Kalman_filter/animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ccfeea4057cfb93e8b563fb0ec892cef60906a7bd909fb3ea390ab1cc98ff004"
+  "signature": "sha256:8eb49d934e443793dcd73543ad252888cd7577420d8e703ec46142d3362c7099"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -245,13 +245,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 2,
+       "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f3431146c50>"
+        "<IPython.core.display.HTML at 0x7ff1356b1f60>"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -335,7 +335,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -372,19 +372,19 @@
        "metadata": {},
        "output_type": "display_data",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f34310bbd68>"
+        "<matplotlib.figure.Figure at 0x7ff11d2473c8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f34310bbe48>"
+        "<matplotlib.figure.Figure at 0x7ff11d2474e0>"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -406,20 +406,19 @@
       "import numpy.random as random\n",
       "from DogSensor import DogSensor\n",
       "import stats\n",
-      "from filterpy.common import Q_DWPA\n",
+      "from filterpy.common import Q_discrete_white_noise\n",
       "from filterpy.kalman import KalmanFilter\n",
       "\n",
       "def dog_tracking_filter(R,Q=0,cov=1.):\n",
       "    dog_filter = KalmanFilter (dim_x=2, dim_z=1)\n",
-      "    dog_filter.X = np.array([[0], \n",
-      "                             [0]])   # initial state (location and velocity)\n",
+      "    dog_filter.x = np.array([0, 0])   # initial state (location and velocity)\n",
       "    dog_filter.F = np.array([[1,1],\n",
       "                             [0,1]])  # state transition matrix\n",
       "    dog_filter.H = np.array([[1,0]])  # Measurement function\n",
       "    dog_filter.R *= R                 # measurement uncertainty\n",
       "    dog_filter.P *= cov               # covariance matrix \n",
       "    if np.isscalar(Q):\n",
-      "        dog_filter.Q = Q_DWPA(2, var=Q)\n",
+      "        dog_filter.Q = Q_discrete_white_noise(2, var=Q)\n",
       "    else:\n",
       "        dog_filter.Q = Q\n",
       "    return dog_filter\n",
@@ -429,7 +428,7 @@
       "Q = .01\n",
       "noise = 2.\n",
       "P=20.\n",
-      "dog = DogSensor(velocity=1, noise=noise)\n",
+      "dog = DogSensor(velocity=1,noise_var=noise)\n",
       "f = dog_tracking_filter(R=R, Q=Q, cov=P)\n",
       "random.seed(200)\n",
       "zs = []\n",
@@ -439,16 +438,16 @@
       "    \n",
       "    plt.gca().set_xlim(-100,100)\n",
       "    if frame == 0:\n",
-      "        stats.plot_covariance_ellipse ((0, f.X[0,0]), cov=f.P, axis_equal=True, \n",
+      "        stats.plot_covariance_ellipse ((0, f.x[0]), cov=f.P, axis_equal=True, \n",
       "                                       facecolor='g', edgecolor=None, alpha=0.2)\n",
-      "        xs.append (f.X[0,0])\n",
+      "        xs.append (f.x[0])\n",
       "        \n",
       "    z = dog.sense()\n",
       "    zs.append(z)\n",
       "    f.update(z)\n",
-      "    xs.append (f.X[0,0])\n",
+      "    xs.append (f.x[0])\n",
       "    \n",
-      "    stats.plot_covariance_ellipse ((frame+1, f.X[0,0]), cov=f.P, axis_equal=True, \n",
+      "    stats.plot_covariance_ellipse ((frame+1, f.x[0]), cov=f.P, axis_equal=True, \n",
       "                                       facecolor='g', edgecolor=None, alpha=0.5,\n",
       "                                       xlim=(-5,30), ylim=(-5,30))\n",
       "    \n",
@@ -464,13 +463,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f342966a048>"
+        "<matplotlib.figure.Figure at 0x7ff1113c7080>"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",

07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:1049b9f0fbcda4862cb5dc5365b05cd5652e351863df7664b681da3fc3591c59"
+  "signature": "sha256:134ea54474b96f29362399ed17bc3f4d1772dfef4d9762d9ebebd642331d972d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f9d3d7a04e0>"
+        "<IPython.core.display.HTML at 0x7f98fbb38128>"
        ]
       }
      ],
@@ -737,9 +737,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f9d2131da90>"
+        "<matplotlib.figure.Figure at 0x7f98dc7bdb00>"
        ]
       }
      ],

08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:75c473df8ee167446e82e1b07397a1f60535a0ab7d37591e1626e3798a42d126"
+  "signature": "sha256:402b3ada55be4953c3dcb978c1f15af1b8d8cb4b33be31ce0955749f1bdff3ce"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fa058b4a4e0>"
+        "<IPython.core.display.HTML at 0x7f01f6738128>"
        ]
       }
      ],
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa03c6c5550>"
+        "<matplotlib.figure.Figure at 0x7f01de29a278>"
        ]
       }
      ],
@@ -707,9 +707,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa03c6c55c0>"
+        "<matplotlib.figure.Figure at 0x7f01de29a2e8>"
        ]
       }
      ],
@@ -788,9 +788,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa03c3f2048>"
+        "<matplotlib.figure.Figure at 0x7f01d5d19438>"
        ]
       },
       {
@@ -873,9 +873,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa034824c18>"
+        "<matplotlib.figure.Figure at 0x7f01ddd604a8>"
        ]
       }
      ],
@@ -1006,9 +1006,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa0347fd0b8>"
+        "<matplotlib.figure.Figure at 0x7f01d5ccada0>"
        ]
       }
      ],
@@ -1125,9 +1125,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa0347d9cc0>"
+        "<matplotlib.figure.Figure at 0x7f01d5cc3b38>"
        ]
       }
      ],
@@ -1279,17 +1279,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa03c050668>"
+        "<matplotlib.figure.Figure at 0x7f01ddfbf780>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa03c050748>"
+        "<matplotlib.figure.Figure at 0x7f01ddd60240>"
        ]
       }
      ],
@@ -1472,9 +1472,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa0346450f0>"
+        "<matplotlib.figure.Figure at 0x7f01d2254e10>"
        ]
       }
      ],
@@ -1668,13 +1668,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa0346ad2e8>"
+        "<matplotlib.figure.Figure at 0x7f01d1fe3e80>"
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -1809,13 +1809,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa03c32db38>"
+        "<matplotlib.figure.Figure at 0x7f01de262f28>"
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -1889,7 +1889,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 25
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -1947,13 +1947,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa03c322fd0>"
+        "<matplotlib.figure.Figure at 0x7f01d2245f28>"
        ]
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -2015,21 +2015,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa034705b38>"
+        "<matplotlib.figure.Figure at 0x7f01d2254cc0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa03c0a0860>"
+        "<matplotlib.figure.Figure at 0x7f01d5ca0ef0>"
        ]
       }
      ],
-     "prompt_number": 27
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -2053,21 +2053,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa03c3a9cc0>"
+        "<matplotlib.figure.Figure at 0x7f01d5c64278>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa034680ac8>"
+        "<matplotlib.figure.Figure at 0x7f01ddcb52e8>"
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",

09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:4db242846680cf526af37ab95e44669118d013f7246b8266ac5f982419b68658"
+  "signature": "sha256:6abee3512debdaedc327c17465301250dd3151d001f8cfc9c2668cfa689b79ef"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fb60cedb5c0>"
+        "<IPython.core.display.HTML at 0x7f6156366128>"
        ]
       }
      ],
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5f0343e48>"
+        "<matplotlib.figure.Figure at 0x7f6139e9d240>"
        ]
       }
      ],
@@ -386,9 +386,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5efc73978>"
+        "<matplotlib.figure.Figure at 0x7f6139e90f28>"
        ]
       }
      ],
@@ -420,9 +420,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5eeb7e0b8>"
+        "<matplotlib.figure.Figure at 0x7f61396da358>"
        ]
       }
      ],
@@ -456,9 +456,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb60cecea58>"
+        "<matplotlib.figure.Figure at 0x7f61563666d8>"
        ]
       }
      ],
@@ -485,8 +485,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "input  mean, variance: -0.0021, 1.0008\n",
-        "output mean, variance: -0.0242, 2.2456\n"
+        "input  mean, variance: -0.0016, 0.9987\n",
+        "output mean, variance: -0.0250, 2.2411\n"
        ]
       }
      ],
@@ -515,16 +515,16 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5efa83f98>"
+        "<matplotlib.figure.Figure at 0x7f615636af28>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "output mean, variance: 0.0032, 2.2518\n"
+        "output mean, variance: 0.0024, 2.2470\n"
        ]
       }
      ],
@@ -559,8 +559,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "linear    output mean, variance: 0.1856, 7487.7480\n",
-        "nonlinear output mean, variance: -1.4677, 26256.5001\n"
+        "linear    output mean, variance: 0.1369, 7471.9216\n",
+        "nonlinear output mean, variance: -1.4870, 26209.4562\n"
        ]
       }
      ],
@@ -607,9 +607,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5efbd2c18>"
+        "<matplotlib.figure.Figure at 0x7f61395a9f60>"
        ]
       }
      ],
@@ -642,9 +642,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5eeb53710>"
+        "<matplotlib.figure.Figure at 0x7f61395c8dd8>"
        ]
       }
      ],
@@ -683,9 +683,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5eeb21160>"
+        "<matplotlib.figure.Figure at 0x7f61396ed9b0>"
        ]
       }
      ],
@@ -754,17 +754,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5eeb20860>"
-       ]
-      },
-      {
-       "metadata": {},
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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fb5efbe2128>"
+        "<matplotlib.figure.Figure at 0x7f6139592a20>"
        ]
       }
      ],
@@ -1124,25 +1116,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5eebfcf28>"
+        "<matplotlib.figure.Figure at 0x7f61354f0128>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5eea7c898>"
+        "<matplotlib.figure.Figure at 0x7f613873c668>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5eea48b38>"
+        "<matplotlib.figure.Figure at 0x7f6138607b00>"
        ]
       }
      ],
@@ -1305,9 +1297,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb60cecea20>"
+        "<matplotlib.figure.Figure at 0x7f6139572828>"
        ]
       }
      ],

10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:60173df6b743b480365441ded715da063d4cfe73678f31166e4b6902098929a7"
+  "signature": "sha256:b10e3461aef2e9945cab08b3c6d4ed07cf3bf5cd256f4859d14ae941048e9e0d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fadfd2b25c0>"
+        "<IPython.core.display.HTML at 0x7fa649670128>"
        ]
       }
      ],
@@ -309,9 +309,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fadfd2a7908>"
+        "<matplotlib.figure.Figure at 0x7fa62d1e2e48>"
        ]
       }
      ],
@@ -347,9 +347,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade46d2d68>"
+        "<matplotlib.figure.Figure at 0x7fa62d1d0da0>"
        ]
       }
      ],
@@ -394,9 +394,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fadfd2a49e8>"
+        "<matplotlib.figure.Figure at 0x7fa6496756a0>"
        ]
       }
      ],
@@ -494,16 +494,16 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade43109b0>"
+        "<matplotlib.figure.Figure at 0x7fa62cd9d4e0>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Difference in mean x=0.070, y=43.905\n"
+        "Difference in mean x=0.001, y=43.287\n"
        ]
       }
      ],
@@ -540,9 +540,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade42d74e0>"
+        "<matplotlib.figure.Figure at 0x7fa62cd08358>"
        ]
       }
      ],
@@ -580,9 +580,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade432add8>"
+        "<matplotlib.figure.Figure at 0x7fa62cc6cda0>"
        ]
       }
      ],
@@ -635,9 +635,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade41ab4a8>"
+        "<matplotlib.figure.Figure at 0x7fa62cc61860>"
        ]
       }
      ],
@@ -978,9 +978,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fadd8cd6d30>"
+        "<matplotlib.figure.Figure at 0x7fa62cdcf908>"
        ]
       }
      ],
@@ -1010,9 +1010,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade415e978>"
+        "<matplotlib.figure.Figure at 0x7fa62cc610f0>"
        ]
       }
      ],
@@ -1057,9 +1057,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fadd6d38940>"
+        "<matplotlib.figure.Figure at 0x7fa62ce960f0>"
        ]
       }
      ],
@@ -1180,9 +1180,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade425b5f8>"
+        "<matplotlib.figure.Figure at 0x7fa62887edd8>"
        ]
       }
      ],
@@ -1212,9 +1212,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade432a2b0>"
+        "<matplotlib.figure.Figure at 0x7fa62ccaecc0>"
        ]
       }
      ],
@@ -1300,9 +1300,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade4135eb8>"
+        "<matplotlib.figure.Figure at 0x7fa62cbf9e48>"
        ]
       }
      ],
@@ -1345,9 +1345,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade42a7320>"
+        "<matplotlib.figure.Figure at 0x7fa62ce810b8>"
        ]
       }
      ],
@@ -1403,9 +1403,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade41e8da0>"
+        "<matplotlib.figure.Figure at 0x7fa62ce81f98>"
        ]
       }
      ],
@@ -1502,9 +1502,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade4326470>"
+        "<matplotlib.figure.Figure at 0x7fa62cc61860>"
        ]
       }
      ],
@@ -1561,9 +1561,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade41245f8>"
+        "<matplotlib.figure.Figure at 0x7fa62cc6c6a0>"
        ]
       }
      ],
@@ -1661,9 +1661,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade433d8d0>"
+        "<matplotlib.figure.Figure at 0x7fa6287c5be0>"
        ]
       }
      ],
@@ -1696,9 +1696,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fade41587f0>"
+        "<matplotlib.figure.Figure at 0x7fa6287d1908>"
        ]
       }
      ],
@@ -2403,13 +2403,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fadd85686d8>"
+        "<matplotlib.figure.Figure at 0x7fa62ce78b70>"
        ]
       }
      ],
-     "prompt_number": 37
+     "prompt_number": 34
     },
     {
      "cell_type": "markdown",
@@ -2521,13 +2521,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fade42e3278>"
+        "<matplotlib.figure.Figure at 0x7fa62ccd37b8>"
        ]
       }
      ],
-     "prompt_number": 39
+     "prompt_number": 35
     },
     {
      "cell_type": "markdown",
@@ -2619,13 +2619,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fadd857e3c8>"
+        "<matplotlib.figure.Figure at 0x7fa62cdbda90>"
        ]
       }
      ],
-     "prompt_number": 40
+     "prompt_number": 36
     },
     {
      "cell_type": "markdown",
@@ -2677,13 +2677,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fadd6ac9710>"
+        "<matplotlib.figure.Figure at 0x7fa6286e63c8>"
        ]
       }
      ],
-     "prompt_number": 42
+     "prompt_number": 37
     },
     {
      "cell_type": "markdown",

11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:27673fb70676d96004e87e31970b90211c1085f207b54be7493d754343d3e67f"
+  "signature": "sha256:0d7649d8c899af47bfd8eb588286dcfbc6c6ecdfe43eb8952badfd052abd4b2c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fd6b1d87588>"
+        "<IPython.core.display.HTML at 0x7fabe005f128>"
        ]
       }
      ],
@@ -312,9 +312,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd6b1d7c240>"
+        "<matplotlib.figure.Figure at 0x7fabc3ba8e48>"
        ]
       }
      ],
@@ -610,13 +610,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd68d6a6cf8>"
+        "<matplotlib.figure.Figure at 0x7fabbf800400>"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",

12_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:6d9143dda084a9e33666307fde3fad048b8c7b69e25586be9225356cbb448ef4"
+  "signature": "sha256:2a0bbfa4cad443230799503aa8753ecf0321463983d5923db288ae86321f739e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f30e36cb4e0>"
+        "<IPython.core.display.HTML at 0x7f190149d160>"
        ]
       }
      ],
@@ -404,9 +404,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f30c7268748>"
+        "<matplotlib.figure.Figure at 0x7f18e5012438>"
        ]
       }
      ],
@@ -825,9 +825,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f30c5fa8e10>"
+        "<matplotlib.figure.Figure at 0x7f18e3d18080>"
        ]
       }
      ],

13_HInfinity_Filters/HInfinity_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:5c842924509a6cfd6bb712a9a7e984f9b068e34de67e1bb6c963cb48309b6e72"
+  "signature": "sha256:cf83c4f12a24f5198d1dfcb28bc9d51c92ce9cb54569170bbd9bb0747a26493e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -260,7 +260,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fbdc658afd0>"
+        "<IPython.core.display.HTML at 0x7f8ab7baaf60>"
        ]
       }
      ],
@@ -334,9 +334,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fbdaa100c50>"
+        "<matplotlib.figure.Figure at 0x7f8aa4074a58>"
        ]
       }
      ],

14_Smoothing/Smoothing.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:492530ff0ddd97db5e24c23a8bdc358b1994c47f912d2c7ad0fe4b85f11ec641"
+  "signature": "sha256:d490a5cbf987e71d27897004107aa17320363f945ffb43cf718c4bc0c015216c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f37de3cbf98>"
+        "<IPython.core.display.HTML at 0x7f4973273128>"
        ]
       }
      ],
@@ -298,9 +298,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f37de3d54e0>"
+        "<matplotlib.figure.Figure at 0x7f49732732b0>"
        ]
       }
      ],
@@ -339,9 +339,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f37c155d208>"
+        "<matplotlib.figure.Figure at 0x7f4956aa4ba8>"
        ]
       }
      ],
@@ -375,9 +375,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f37c14ba5f8>"
+        "<matplotlib.figure.Figure at 0x7f4956a35d68>"
        ]
       }
      ],
@@ -556,13 +556,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f37c1286320>"
+        "<matplotlib.figure.Figure at 0x7f495699bf98>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -585,13 +585,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f37ba2b7080>"
+        "<matplotlib.figure.Figure at 0x7f49569cee10>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 6
     },
     {
      "cell_type": "heading",