Edits for reorg

I moved book_format.py to the root directory so that all of the notebooks do not need to modify the system path to import it. It modifies the path on import so that all of the code in ./code can then be accessed. Altered links to nbviewer to account for no longer using subdirectories.
2015-01-27 15:14:06 -08:00
1 parent 892d1c04ac
commit 33c745d997
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:618a6f3ed753b80e339ceabebc203d9c01d69a0a78da364d1584314ce521f8c0"
+  "signature": "sha256:c072eb076dee3ffbdd6cfe02a570292c00848b0aa38c419ab3f0f7c08f2bf392"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -30,8 +30,6 @@
      "#format the book\n",
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
-      "import sys\n",
-      "sys.path.insert(0,'./code') # allow us to import book_format\n",
      "import matplotlib.pyplot as plt\n",
      "import book_format\n",
      "book_format.load_style()"
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7fc8fe8b1400>"
+        "<IPython.core.display.HTML at 0x7fc14f36d6a0>"
       ]
      }
     ],
@@ -335,7 +333,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e5a03518>"
+        "<matplotlib.figure.Figure at 0x7fc1324ae518>"
       ]
      }
     ],
@@ -372,7 +370,7 @@
       "output_type": "display_data",
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e5a03c18>"
+        "<matplotlib.figure.Figure at 0x7fc1324aeba8>"
       ]
      }
     ],
@@ -406,7 +404,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e5a03358>"
+        "<matplotlib.figure.Figure at 0x7fc1324ae358>"
       ]
      }
     ],
@@ -448,7 +446,7 @@
       "output_type": "stream",
       "stream": "stdout",
       "text": [
-        "Average of measurements is 164.9541\n"
+        "Average of measurements is 165.0624\n"
       ]
      }
     ],
@@ -484,7 +482,7 @@
       "output_type": "stream",
       "stream": "stdout",
       "text": [
-        "Average of measurements is 164.9308\n"
+        "Average of measurements is 164.9074\n"
       ]
      }
     ],
@@ -527,7 +525,7 @@
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAzsAAAGkCAYAAAAWgDuQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e58c7dd8>"
+        "<matplotlib.figure.Figure at 0x7fc132372f98>"
       ]
      }
     ],
@@ -563,7 +561,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e5916e48>"
+        "<matplotlib.figure.Figure at 0x7fc1323b3f98>"
       ]
      }
     ],
@@ -598,7 +596,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e58cc160>"
+        "<matplotlib.figure.Figure at 0x7fc1322a1828>"
       ]
      }
     ],
@@ -636,7 +634,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e59caba8>"
+        "<matplotlib.figure.Figure at 0x7fc13247a710>"
       ]
      }
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@@ -674,7 +672,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e5794ac8>"
+        "<matplotlib.figure.Figure at 0x7fc13223cac8>"
       ]
      }
     ],
@@ -713,7 +711,7 @@
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       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e58cb9e8>"
+        "<matplotlib.figure.Figure at 0x7fc1323775f8>"
       ]
      }
     ],
@@ -740,7 +738,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e5690cc0>"
+        "<matplotlib.figure.Figure at 0x7fc132150080>"
       ]
      }
     ],
@@ -780,7 +778,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e57a9748>"
+        "<matplotlib.figure.Figure at 0x7fc1321c2e10>"
       ]
      }
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@@ -848,7 +846,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e58d1438>"
+        "<matplotlib.figure.Figure at 0x7fc13237a588>"
       ]
      }
     ],
@@ -877,7 +875,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e59a01d0>"
+        "<matplotlib.figure.Figure at 0x7fc132166908>"
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@@ -948,7 +946,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e5773f60>"
+        "<matplotlib.figure.Figure at 0x7fc132213320>"
       ]
      }
     ],
@@ -1012,7 +1010,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e574d400>"
+        "<matplotlib.figure.Figure at 0x7fc1324084a8>"
       ]
      }
     ],
@@ -1151,7 +1149,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e5743ef0>"
+        "<matplotlib.figure.Figure at 0x7fc13214eda0>"
       ]
      }
     ],
@@ -1228,9 +1226,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e56afc18>"
+        "<matplotlib.figure.Figure at 0x7fc132157128>"
       ]
      }
     ],
@@ -1284,9 +1282,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e577d908>"
+        "<matplotlib.figure.Figure at 0x7fc132141d68>"
       ]
      }
     ],
@@ -1347,9 +1345,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e572bb38>"
+        "<matplotlib.figure.Figure at 0x7fc1321ce358>"
       ]
      }
     ],
@@ -1423,7 +1421,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e58a4400>"
+        "<matplotlib.figure.Figure at 0x7fc132235160>"
       ]
      }
     ],
@@ -1496,25 +1494,25 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e5908400>"
+        "<matplotlib.figure.Figure at 0x7fc132378128>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e590b8d0>"
+        "<matplotlib.figure.Figure at 0x7fc1323782e8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e59dc0f0>"
+        "<matplotlib.figure.Figure at 0x7fc13225d4a8>"
       ]
      }
     ],
@@ -1553,7 +1551,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e56a9358>"
+        "<matplotlib.figure.Figure at 0x7fc132277550>"
       ]
      },
      {
@@ -1561,7 +1559,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e56a90b8>"
+        "<matplotlib.figure.Figure at 0x7fc132145da0>"
       ]
      },
      {
@@ -1569,7 +1567,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e58b87f0>"
+        "<matplotlib.figure.Figure at 0x7fc13220eef0>"
       ]
      }
     ],
@@ -1604,7 +1602,7 @@
       "output_type": "display_data",
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e56a92e8>"
+        "<matplotlib.figure.Figure at 0x7fc1322775f8>"
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      },
      {
@@ -1612,7 +1610,7 @@
       "output_type": "display_data",
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e45cdeb8>"
+        "<matplotlib.figure.Figure at 0x7fc1321b7630>"
       ]
      }
     ],
@@ -1660,7 +1658,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e45eaf60>"
+        "<matplotlib.figure.Figure at 0x7fc130895b00>"
       ]
      },
      {
@@ -1668,7 +1666,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e458b4a8>"
+        "<matplotlib.figure.Figure at 0x7fc130895ac8>"
       ]
      },
      {
@@ -1676,7 +1674,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e465c7b8>"
+        "<matplotlib.figure.Figure at 0x7fc132363080>"
       ]
      }
     ],
@@ -1753,9 +1751,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e570d7b8>"
+        "<matplotlib.figure.Figure at 0x7fc132335518>"
       ]
      }
     ],
@@ -1784,9 +1782,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e58d16d8>"
+        "<matplotlib.figure.Figure at 0x7fc13222d5f8>"
       ]
      }
     ],
@@ -1813,9 +1811,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e5892630>"
+        "<matplotlib.figure.Figure at 0x7fc132403ef0>"
       ]
      }
     ],
@@ -1844,9 +1842,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e59597f0>"
+        "<matplotlib.figure.Figure at 0x7fc1323f5518>"
       ]
      }
     ],
@@ -1892,9 +1890,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e45d6c18>"
+        "<matplotlib.figure.Figure at 0x7fc13219b7f0>"
       ]
      }
     ],
@@ -1921,9 +1919,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fc8e578a278>"
+        "<matplotlib.figure.Figure at 0x7fc13218abe0>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:91108c3755b10babb902a5c6bcf9bba324a0fda3d1f3e5fb51cf65f3665c51e1"
+  "signature": "sha256:161cde2172ad51a96760453968bfc1209c772d9e287deb485626173c4e558e4f"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0, './code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -259,13 +257,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 2,
+       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7f1c609379e8>"
+        "<IPython.core.display.HTML at 0x7ff9e3a526a0>"
       ]
      }
     ],
-     "prompt_number": 2
+     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
@@ -361,7 +359,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200c318f28>"
+        "<matplotlib.figure.Figure at 0x7ff9c6e89eb8>"
       ]
      }
     ],
@@ -504,7 +502,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200bfd3128>"
+        "<matplotlib.figure.Figure at 0x7ff9e3a570b8>"
       ]
      }
     ],
@@ -556,7 +554,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200bf8d080>"
+        "<matplotlib.figure.Figure at 0x7ff9c6aea748>"
       ]
      }
     ],
@@ -636,7 +634,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7f200be5ac50>"
+        "<matplotlib.figure.Figure at 0x7ff9c6a27ac8>"
       ]
      }
     ],
@@ -702,7 +700,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200bf89550>"
+        "<matplotlib.figure.Figure at 0x7ff9c6a38668>"
       ]
      }
     ],
@@ -739,7 +737,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200befbba8>"
+        "<matplotlib.figure.Figure at 0x7ff9c6af2c50>"
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      }
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@@ -768,7 +766,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200bf9b390>"
+        "<matplotlib.figure.Figure at 0x7ff9d1447fd0>"
       ]
      }
     ],
@@ -806,7 +804,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200bfbae80>"
+        "<matplotlib.figure.Figure at 0x7ff9c6aa3320>"
       ]
      }
     ],
@@ -893,7 +891,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200bf1bb70>"
+        "<matplotlib.figure.Figure at 0x7ff9c6aa39e8>"
       ]
      }
     ],
@@ -929,7 +927,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200bd6ae10>"
+        "<matplotlib.figure.Figure at 0x7ff9c687e978>"
       ]
      }
     ],
@@ -958,7 +956,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200bfa16a0>"
+        "<matplotlib.figure.Figure at 0x7ff9c6a05400>"
       ]
      }
     ],
@@ -987,7 +985,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200c321dd8>"
+        "<matplotlib.figure.Figure at 0x7ff9c6a5eb38>"
       ]
      }
     ],
@@ -1054,7 +1052,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200be067f0>"
+        "<matplotlib.figure.Figure at 0x7ff9c69e43c8>"
       ]
      }
     ],
@@ -1083,7 +1081,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200bef7fd0>"
+        "<matplotlib.figure.Figure at 0x7ff9c6b70cc0>"
       ]
      }
     ],
@@ -1116,7 +1114,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200bd075f8>"
+        "<matplotlib.figure.Figure at 0x7ff9c683c358>"
       ]
      }
     ],
@@ -1170,7 +1168,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f200c30dfd0>"
+        "<matplotlib.figure.Figure at 0x7ff9c6827208>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:982ce55fb5f6907f7fa52cec3da6cfc83f03f40888d7527a039bcd600fc9e95e"
+  "signature": "sha256:0daafb03588df165f6ab3a24032dff6c9dd058dadf3e9c412f54c22856ae3d6c"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0,'./code') # allow us to import book_format\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7fecdc400400>"
+        "<IPython.core.display.HTML at 0x7f5ffa3589e8>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:66e678fa200bb5f5cf2b969a860179e3c9d92699768f7e8146e5160a54619519"
+  "signature": "sha256:2130dc4c97d74b7134afa090e42b6b505dc965f30bb05abe569a3168f652856d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0, './code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7fa00b478400>"
+        "<IPython.core.display.HTML at 0x7f194f549b38>"
       ]
      }
     ],
@@ -302,7 +300,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f9fe97132e8>"
+        "<matplotlib.figure.Figure at 0x7f1948330128>"
       ]
      }
     ],
@@ -348,7 +346,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7f9fe6e770b8>"
+        "<matplotlib.figure.Figure at 0x7f1947b15b70>"
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      }
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@@ -425,7 +423,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f9fe6c91e48>"
+        "<matplotlib.figure.Figure at 0x7f1947ab20b8>"
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      }
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@@ -556,7 +554,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f9fe6add940>"
+        "<matplotlib.figure.Figure at 0x7f1947a215c0>"
       ]
      }
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@@ -598,7 +596,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f9fe6e08c50>"
+        "<matplotlib.figure.Figure at 0x7f196c12ee80>"
       ]
      }
     ],
@@ -654,7 +652,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f9fe6d01f60>"
+        "<matplotlib.figure.Figure at 0x7f19477dff28>"
       ]
      },
      {
@@ -788,9 +786,9 @@
       "output_type": "stream",
       "stream": "stdout",
       "text": [
-        "[-0.56643549  5.55377853  3.20392736 -2.46707904  5.05869208  6.0326174\n",
-        "  1.80130981  1.41284223  3.22560866  5.97999268  4.22365687  5.92671618\n",
-        " -6.25729187  6.92417721 -2.47483372]\n"
+        "[ -2.88454926   1.33359582   1.97523353   5.02976188  -0.92369843\n",
+        "  -1.2615812    0.13582678   3.43281277   5.15944677   0.197765\n",
+        "   2.84795422   1.66185525  -0.67031453   1.95990484  10.08883809]\n"
       ]
      }
     ],
@@ -896,7 +894,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f9ff81a0320>"
+        "<matplotlib.figure.Figure at 0x7f1947b004e0>"
       ]
      }
     ],
@@ -950,9 +948,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f9fe6e30cc0>"
+        "<matplotlib.figure.Figure at 0x7f19479bbf60>"
       ]
      }
     ],
@@ -1004,9 +1002,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f9fe6e400f0>"
+        "<matplotlib.figure.Figure at 0x7f1950105828>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:fd5c9b799c18c3ea9b9d184177fe5d044cef5d0effd57570a21d613ebd67f799"
+  "signature": "sha256:45cdaf9dfe065a427fbf85952655b40f5792aae65f05bc5579790995cb4314c0"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -29,8 +29,6 @@
     "input": [
      "#format the book\n",
      "%matplotlib inline\n",
-      "import sys\n",
-      "sys.path.insert(0,'./code') # allow us to import book_format\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -259,7 +257,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7fcdc33e2c18>"
+        "<IPython.core.display.HTML at 0x7f4e011cc470>"
       ]
      }
     ],
@@ -361,10 +359,10 @@
       "output_type": "stream",
       "stream": "stdout",
       "text": [
-        "-1.0017\t-1.5845\t-0.9908\t 0.9088\t-0.7612\t\n",
-        " 0.2126\t-0.3131\t 1.4439\t-0.5407\t 0.2265\t\n",
-        "-0.9048\t-0.5614\t 0.4823\t-0.6600\t 0.1101\t\n",
-        " 0.0350\t 1.2220\t 1.9535\t 0.3303\t-0.1984\t\n"
+        " 0.3847\t 2.4259\t-2.2281\t 0.6590\t 0.1281\t\n",
+        " 0.0537\t-0.5158\t-0.6354\t 1.1974\t-0.1386\t\n",
+        "-0.8091\t 0.2634\t 0.0860\t-1.8625\t-0.2827\t\n",
+        "-0.0008\t-0.6972\t-2.3696\t-1.0556\t 0.8438\t\n"
       ]
      }
     ],
@@ -411,7 +409,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda652dc50>"
+        "<matplotlib.figure.Figure at 0x7f4de831ec88>"
       ]
      }
     ],
@@ -457,9 +455,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda64e83c8>"
+        "<matplotlib.figure.Figure at 0x7f4de829eb00>"
       ]
      }
     ],
@@ -491,9 +489,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda652d470>"
+        "<matplotlib.figure.Figure at 0x7f4de80f5470>"
       ]
      }
     ],
@@ -511,9 +509,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda64aafd0>"
+        "<matplotlib.figure.Figure at 0x7f4de823cf28>"
       ]
      }
     ],
@@ -544,7 +542,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda63d3b38>"
+        "<matplotlib.figure.Figure at 0x7f4de81c8c88>"
       ]
      }
     ],
@@ -573,7 +571,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda6399e10>"
+        "<matplotlib.figure.Figure at 0x7f4de817f780>"
       ]
      }
     ],
@@ -609,7 +607,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcdb00ba3c8>"
+        "<matplotlib.figure.Figure at 0x7f4de80c7e10>"
       ]
      }
     ],
@@ -649,7 +647,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda406fd30>"
+        "<matplotlib.figure.Figure at 0x7f4de831edd8>"
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@@ -752,7 +750,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd9c0b10b8>"
+        "<matplotlib.figure.Figure at 0x7f4de00c7a20>"
       ]
      }
     ],
@@ -800,7 +798,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda40381d0>"
+        "<matplotlib.figure.Figure at 0x7f4de00c3438>"
       ]
      }
     ],
@@ -850,7 +848,7 @@
       "output_type": "display_data",
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd9c034f98>"
+        "<matplotlib.figure.Figure at 0x7f4de0085080>"
       ]
      }
     ],
@@ -1124,7 +1122,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd9c02e780>"
+        "<matplotlib.figure.Figure at 0x7f4de00c8240>"
       ]
      }
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@@ -1265,7 +1263,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda4046e10>"
+        "<matplotlib.figure.Figure at 0x7f4de0103ac8>"
       ]
      },
      {
@@ -1273,7 +1271,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd95327d30>"
+        "<matplotlib.figure.Figure at 0x7f4de0041cf8>"
       ]
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd9c0d5e80>"
+        "<matplotlib.figure.Figure at 0x7f4de81c83c8>"
       ]
      },
      {
@@ -1609,7 +1607,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd9c0384a8>"
+        "<matplotlib.figure.Figure at 0x7f4dd80c54e0>"
       ]
      },
      {
@@ -1729,7 +1727,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda63caac8>"
+        "<matplotlib.figure.Figure at 0x7f4de817f550>"
       ]
      }
     ],
@@ -1809,7 +1807,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda4984f28>"
+        "<matplotlib.figure.Figure at 0x7f4de815a588>"
       ]
      },
      {
@@ -1817,7 +1815,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd9c0e97f0>"
+        "<matplotlib.figure.Figure at 0x7f4de8229b70>"
       ]
      }
     ],
@@ -1893,13 +1891,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd9521c978>"
+        "<matplotlib.figure.Figure at 0x7f4de821ce80>"
       ]
      }
     ],
-     "prompt_number": 28
+     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
@@ -1957,13 +1955,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd9521c198>"
+        "<matplotlib.figure.Figure at 0x7f4dd2faa358>"
       ]
      }
     ],
-     "prompt_number": 29
+     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
@@ -2020,13 +2018,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda638b630>"
+        "<matplotlib.figure.Figure at 0x7f4de0140710>"
       ]
      }
     ],
-     "prompt_number": 30
+     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
@@ -2072,13 +2070,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcda638b2e8>"
+        "<matplotlib.figure.Figure at 0x7f4dd2ff5cc0>"
       ]
      }
     ],
-     "prompt_number": 31
+     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
@@ -2133,13 +2131,13 @@
      {
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 32,
+       "prompt_number": 30,
       "text": [
        "<function __main__.plot_kalman_filter>"
       ]
      }
     ],
-     "prompt_number": 32
+     "prompt_number": 30
    },
    {
     "cell_type": "heading",
@@ -2201,13 +2199,21 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd9c0d5160>"
+        "<matplotlib.figure.Figure at 0x7f4de80f5e10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 31,
+       "text": [
+        "<function __main__.plot_kalman_filter>"
       ]
      }
     ],
-     "prompt_number": 33
+     "prompt_number": 31
    },
    {
     "cell_type": "heading",
@@ -2242,7 +2248,8 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 32
    },
    {
     "cell_type": "heading",
@@ -2284,11 +2291,11 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd9529fbe0>"
+        "<matplotlib.figure.Figure at 0x7f4de82275c0>"
       ]
      }
     ],
-     "prompt_number": 34
+     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
@@ -2326,7 +2333,8 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 34
    },
    {
     "cell_type": "heading",
@@ -2368,7 +2376,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fcd951fa0b8>"
+        "<matplotlib.figure.Figure at 0x7f4de003d400>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:cc93f0602c44f29149840aa5f8363ab5a6b42db6007e6e1c3826a7a4e7c742cd"
+  "signature": "sha256:e6b03302db96abb18b25b58b48ed64177e88c5da12333f9bf9dadc3fd9843de7"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0, './code') # allow us to import book_format\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7f579724c438>"
+        "<IPython.core.display.HTML at 0x7fdf3a08e9e8>"
       ]
      }
     ],
@@ -395,7 +393,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7f579724c4a8>"
+        "<matplotlib.figure.Figure at 0x7fdf214d0b00>"
       ]
      }
     ],
@@ -563,7 +561,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f577276bf28>"
+        "<matplotlib.figure.Figure at 0x7fdf3a0939b0>"
       ]
      }
     ],
@@ -595,9 +593,9 @@
      {
       "metadata": {},
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f579723dc18>"
+        "<matplotlib.figure.Figure at 0x7fdf3a0937b8>"
       ]
      }
     ],
@@ -655,7 +653,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f577275d438>"
+        "<matplotlib.figure.Figure at 0x7fdf200e9c88>"
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@@ -758,7 +756,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f5772358198>"
+        "<matplotlib.figure.Figure at 0x7fdf1533e400>"
       ]
      }
     ],
@@ -785,7 +783,7 @@
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       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f5772413780>"
+        "<matplotlib.figure.Figure at 0x7fdf153ea198>"
       ]
      }
     ],
@@ -812,7 +810,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f57726016a0>"
+        "<matplotlib.figure.Figure at 0x7fdf151469e8>"
       ]
      }
     ],
@@ -843,7 +841,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f57721fd4e0>"
+        "<matplotlib.figure.Figure at 0x7fdf152479b0>"
       ]
      }
     ],
@@ -908,7 +906,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f57724a8400>"
+        "<matplotlib.figure.Figure at 0x7fdf14fbb240>"
       ]
      }
     ],
@@ -2058,9 +2056,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f577239c470>"
+        "<matplotlib.figure.Figure at 0x7fdf1515ef98>"
       ]
      },
      {
@@ -2068,7 +2066,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f577b208400>"
+        "<matplotlib.figure.Figure at 0x7fdf15131a58>"
       ]
      }
     ],
@@ -2235,9 +2233,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f57725f0198>"
+        "<matplotlib.figure.Figure at 0x7fdf152d57b8>"
       ]
      },
      {
@@ -2245,7 +2243,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f5772446470>"
+        "<matplotlib.figure.Figure at 0x7fdf14fb87f0>"
       ]
      }
     ],
@@ -2479,9 +2477,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f57724565c0>"
+        "<matplotlib.figure.Figure at 0x7fdf2011ccc0>"
       ]
      }
     ],
@@ -2551,7 +2549,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7f577261e128>"
+        "<matplotlib.figure.Figure at 0x7fdf14fd1e80>"
       ]
      },
      {
@@ -2559,7 +2557,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f57721f5438>"
+        "<matplotlib.figure.Figure at 0x7fdf14fcbf28>"
       ]
      }
     ],
@@ -2602,7 +2600,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f57726a82b0>"
+        "<matplotlib.figure.Figure at 0x7fdf152ebc88>"
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@@ -2631,7 +2629,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f577239d358>"
+        "<matplotlib.figure.Figure at 0x7fdf1520e710>"
       ]
      }
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@@ -2660,7 +2658,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f5772740fd0>"
+        "<matplotlib.figure.Figure at 0x7fdf2009efd0>"
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@@ -2729,7 +2727,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f57724c1ba8>"
+        "<matplotlib.figure.Figure at 0x7fdf151c4518>"
       ]
      }
     ],
@@ -2759,7 +2757,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f577260fc50>"
+        "<matplotlib.figure.Figure at 0x7fdf2012ff28>"
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f57725728d0>"
+        "<matplotlib.figure.Figure at 0x7fdf1536d630>"
       ]
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@@ -2861,7 +2859,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f5772572940>"
+        "<matplotlib.figure.Figure at 0x7fdf15250080>"
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@@ -2894,7 +2892,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f57723509e8>"
+        "<matplotlib.figure.Figure at 0x7fdf20077860>"
       ]
      }
     ],
@@ -2925,7 +2923,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f57725cda58>"
+        "<matplotlib.figure.Figure at 0x7fdf14fe3a90>"
       ]
      }
     ],
@@ -2996,17 +2994,17 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f577260ff28>"
+        "<matplotlib.figure.Figure at 0x7fdf2012fd68>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f5772520470>"
+        "<matplotlib.figure.Figure at 0x7fdf1526bd68>"
       ]
      }
     ],
@@ -3081,7 +3079,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f577b1bdbe0>"
+        "<matplotlib.figure.Figure at 0x7fdf152eb160>"
       ]
      }
     ],
@@ -3159,9 +3157,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f57722e9240>"
+        "<matplotlib.figure.Figure at 0x7fdf151116a0>"
       ]
      }
     ],
@@ -3219,7 +3217,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7f57722e94e0>"
+        "<matplotlib.figure.Figure at 0x7fdf151115c0>"
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      }
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@@ -3263,7 +3261,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f57722e9e48>"
+        "<matplotlib.figure.Figure at 0x7fdf152e6f98>"
       ]
      }
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@@ -3297,7 +3295,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f577262b668>"
+        "<matplotlib.figure.Figure at 0x7fdf15111630>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:f8dcc12ca7ce84bf0c76ff9c37aab41847d1661cdde92d0fcb8b409ae6520cc6"
+  "signature": "sha256:27a5ed2ddd67c228610a6ba471f1ec1e67b9a62114cc2be9574fab84b11cb41b"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0, './code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7f84da330438>"
+        "<IPython.core.display.HTML at 0x7f620295c9e8>"
       ]
      }
     ],
@@ -739,7 +737,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f84b64b7c50>"
+        "<matplotlib.figure.Figure at 0x7f61e81c98d0>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:1e1380354f6dfc1cf9470d633eebdacba2529a712807045e1d9435b76c925336"
+  "signature": "sha256:186b373fb97e5bd784cf9974b5baccbdf054953ba932072b306358451ef8169e"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0,'./code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7fbb50f2e400>"
+        "<IPython.core.display.HTML at 0x7f923518b9e8>"
       ]
      }
     ],
@@ -354,9 +352,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb50f29ac8>"
+        "<matplotlib.figure.Figure at 0x7f923518bf28>"
       ]
      }
     ],
@@ -707,9 +705,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb3409b080>"
+        "<matplotlib.figure.Figure at 0x7f921c2e3048>"
       ]
      }
     ],
@@ -788,9 +786,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb33fd9fd0>"
+        "<matplotlib.figure.Figure at 0x7f9235190400>"
       ]
      },
      {
@@ -875,7 +873,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb34058128>"
+        "<matplotlib.figure.Figure at 0x7f92108c98d0>"
       ]
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@@ -997,7 +995,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb34070b38>"
+        "<matplotlib.figure.Figure at 0x7f9217f72588>"
       ]
      }
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@@ -1336,7 +1334,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb33d7ea90>"
+        "<matplotlib.figure.Figure at 0x7f921c0a4908>"
       ]
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@@ -1386,7 +1384,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2c687860>"
+        "<matplotlib.figure.Figure at 0x7f92108bfdd8>"
       ]
      }
     ],
@@ -1417,7 +1415,7 @@
       "output_type": "display_data",
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb33e4a2e8>"
+        "<matplotlib.figure.Figure at 0x7f921c2471d0>"
       ]
      }
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@@ -1450,7 +1448,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb33d3d470>"
+        "<matplotlib.figure.Figure at 0x7f9217f518d0>"
       ]
      }
     ],
@@ -1481,7 +1479,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2c5f32b0>"
+        "<matplotlib.figure.Figure at 0x7f9210810e48>"
       ]
      }
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@@ -1520,7 +1518,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2c571da0>"
+        "<matplotlib.figure.Figure at 0x7f9210787ac8>"
       ]
      }
     ],
@@ -1561,7 +1559,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2c5a9550>"
+        "<matplotlib.figure.Figure at 0x7f9210804780>"
       ]
      }
     ],
@@ -1601,7 +1599,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb33d3d4a8>"
+        "<matplotlib.figure.Figure at 0x7f9217f5ac50>"
       ]
      }
     ],
@@ -1638,7 +1636,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2c5d5208>"
+        "<matplotlib.figure.Figure at 0x7f921078bf28>"
       ]
      }
     ],
@@ -1676,7 +1674,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2c5d5b00>"
+        "<matplotlib.figure.Figure at 0x7f92107e0e10>"
       ]
      },
      {
@@ -1684,7 +1682,7 @@
       "output_type": "display_data",
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb33d5fd68>"
+        "<matplotlib.figure.Figure at 0x7f92107e0240>"
       ]
      }
     ],
@@ -1724,7 +1722,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2ace9668>"
+        "<matplotlib.figure.Figure at 0x7f920ef08908>"
       ]
      },
      {
@@ -1732,7 +1730,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2c5c0d30>"
+        "<matplotlib.figure.Figure at 0x7f9217f0ea58>"
       ]
      }
     ],
@@ -1765,7 +1763,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb34387da0>"
+        "<matplotlib.figure.Figure at 0x7f9217fdbd30>"
       ]
      }
     ],
@@ -1833,7 +1831,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2c5d09b0>"
+        "<matplotlib.figure.Figure at 0x7f921c09ba20>"
       ]
      }
     ],
@@ -1866,7 +1864,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb3408ccf8>"
+        "<matplotlib.figure.Figure at 0x7f923518bd30>"
       ]
      },
      {
@@ -1874,7 +1872,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb33ffe908>"
+        "<matplotlib.figure.Figure at 0x7f920eeb2828>"
       ]
      }
     ],
@@ -1909,7 +1907,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb33ffef28>"
+        "<matplotlib.figure.Figure at 0x7f920ef6b470>"
       ]
      },
      {
@@ -1917,7 +1915,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2c5f5b70>"
+        "<matplotlib.figure.Figure at 0x7f920ee65e80>"
       ]
      }
     ],
@@ -1950,7 +1948,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2c58b9e8>"
+        "<matplotlib.figure.Figure at 0x7f920ee7a898>"
       ]
      },
      {
@@ -1958,7 +1956,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb2c58bc50>"
+        "<matplotlib.figure.Figure at 0x7f920eea3048>"
       ]
      }
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@@ -2083,7 +2081,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb33e35978>"
+        "<matplotlib.figure.Figure at 0x7f9217f057f0>"
       ]
      }
     ],
@@ -2262,7 +2260,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fbb33e35470>"
+        "<matplotlib.figure.Figure at 0x7f9217f056d8>"
       ]
      }
     ],
@@ -2386,7 +2384,7 @@
      "        else:  \n",
      "            m0 = i + randn()*wheel_sigma\n",
      "            z = array([m0])\n",
-      "            kf._H = array([[1., 0.]])\n",
+      "            kf.H = array([[1., 0.]])\n",
      "            R = np.eye(1) * wheel_sigma**2\n",
      "            zs_wheel.append(m0)\n",
      "\n",
@@ -2415,16 +2413,18 @@
     "metadata": {},
     "outputs": [
      {
-       "ename": "LinAlgError",
-       "evalue": "singular matrix",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-43-bda69a291b49>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     63\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m \u001b[0mfusion_test\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;32m<ipython-input-43-bda69a291b49>\u001b[0m in \u001b[0;36mfusion_test\u001b[1;34m(wheel_sigma, ps_sigma, do_plot)\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m         \u001b[0mkf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m         \u001b[0mkf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mR\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m         \u001b[0mxs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/home/rlabbe/Dropbox/filterpy/filterpy/kalman/kalman_filter.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, z, R, H)\u001b[0m\n\u001b[0;32m    166\u001b[0m         \u001b[1;31m# K = PH'inv(S)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m         \u001b[1;31m# map system uncertainty into kalman gain\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 168\u001b[1;33m         \u001b[0mK\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdot3\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mH\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    170\u001b[0m         \u001b[1;31m# x = x + Ky\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/home/rlabbe/anaconda3/lib/python3.4/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36minv\u001b[1;34m(a, overwrite_a, check_finite)\u001b[0m\n\u001b[0;32m    381\u001b[0m         \u001b[0minv_a\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetri\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpiv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlwork\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlwork\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moverwrite_lu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    382\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 383\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"singular matrix\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    384\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    385\u001b[0m         raise ValueError('illegal value in %d-th argument of internal '\n",
-        "\u001b[1;31mLinAlgError\u001b[0m: singular matrix"
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "fusion std: 0.452\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f921082d860>"
       ]
      }
     ],
@@ -2534,7 +2534,8 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
@@ -2573,7 +2574,25 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f921c03bb38>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f9217fe02b0>"
+       ]
+      }
+     ],
+     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
@@ -2709,7 +2728,8 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
@@ -2747,7 +2767,17 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f92107e9ef0>"
+       ]
+      }
+     ],
+     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
@@ -2887,7 +2917,8 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
@@ -2932,7 +2963,17 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f921c308cc0>"
+       ]
+      }
+     ],
+     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
@@ -3065,7 +3106,17 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f921c29eef0>"
+       ]
+      }
+     ],
+     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
@@ -3138,7 +3189,8 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
@@ -3192,7 +3244,17 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f921c2c12e8>"
+       ]
+      }
+     ],
+     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
@@ -3250,7 +3312,25 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f9217f9a278>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f9217f2ec50>"
+       ]
+      }
+     ],
+     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
@@ -3270,7 +3350,25 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f921c235860>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f9217f1f320>"
+       ]
+      }
+     ],
+     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:5995720ed3ceb08c4be39c6feddca4bc39bad7116b8f07678560ea256675af70"
+  "signature": "sha256:0d44e081a2149e5b4781c1b684f7e6682b1af05cf278c8fc3e56745828aa6b87"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0,'./code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7ffc0bc93400>"
+        "<IPython.core.display.HTML at 0x7f80f279d6a0>"
       ]
      }
     ],
@@ -358,9 +356,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffbef0bfcc0>"
+        "<matplotlib.figure.Figure at 0x7f80e1bf0cf8>"
       ]
      }
     ],
@@ -390,9 +388,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffbeedd1400>"
+        "<matplotlib.figure.Figure at 0x7f80e19361d0>"
       ]
      }
     ],
@@ -424,9 +422,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffbed899550>"
+        "<matplotlib.figure.Figure at 0x7f80e1bf0b00>"
       ]
      }
     ],
@@ -460,9 +458,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffc0bc84978>"
+        "<matplotlib.figure.Figure at 0x7f80e151e240>"
       ]
      }
     ],
@@ -489,8 +487,8 @@
       "output_type": "stream",
       "stream": "stdout",
       "text": [
-        "input  mean, variance: 0.0009, 0.9984\n",
-        "output mean, variance: -0.0290, 2.2380\n"
+        "input  mean, variance: -0.0003, 0.9968\n",
+        "output mean, variance: -0.0272, 2.2340\n"
       ]
      }
     ],
@@ -519,16 +517,16 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxgAAAGaCAYAAACMmuWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffbee781c18>"
+        "<matplotlib.figure.Figure at 0x7f80e12d96d8>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
-        "output mean, variance: -0.0013, 2.2464\n"
+        "output mean, variance: 0.0005, 2.2428\n"
       ]
      }
     ],
@@ -563,8 +561,8 @@
       "output_type": "stream",
       "stream": "stdout",
       "text": [
-        "linear    output mean, variance: -0.0748, 7469.8042\n",
-        "nonlinear output mean, variance: -1.9234, 26155.6443\n"
+        "linear    output mean, variance: 0.0267, 7457.7205\n",
+        "nonlinear output mean, variance: -1.8258, 26091.4544\n"
       ]
      }
     ],
@@ -613,7 +611,7 @@
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAysAAAGNCAYAAAARje6GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffbee7b25f8>"
+        "<matplotlib.figure.Figure at 0x7f80e142f0f0>"
       ]
      }
     ],
@@ -648,7 +646,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffbee9f69e8>"
+        "<matplotlib.figure.Figure at 0x7f80e14b4ef0>"
       ]
      }
     ],
@@ -689,7 +687,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffbee8e8d30>"
+        "<matplotlib.figure.Figure at 0x7f80e13f2cc0>"
       ]
      }
     ],
@@ -760,7 +758,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffc0bc84470>"
+        "<matplotlib.figure.Figure at 0x7f80e276b2e8>"
       ]
      }
     ],
@@ -1141,25 +1139,25 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffbee7c0cc0>"
+        "<matplotlib.figure.Figure at 0x7f80f27a27b8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffbed82fb38>"
+        "<matplotlib.figure.Figure at 0x7f80e035bf60>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffbed7ffbe0>"
+        "<matplotlib.figure.Figure at 0x7f80e032cba8>"
       ]
      }
     ],
@@ -1336,9 +1334,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7ffbee7f50b8>"
+        "<matplotlib.figure.Figure at 0x7f80e14c76d8>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:66e7c1ac6733ab9cc3c8a71c3a2a0dc29d1b3123baa0531c902b83527076bbf3"
+  "signature": "sha256:25a07fbbd0e9ae161c0147f461a1bdd2aeaf6a8adb9675a094737edd26ff3aff"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0,'./code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7f3a3b77a400>"
+        "<IPython.core.display.HTML at 0x7fcf977a16a0>"
       ]
      }
     ],
@@ -309,9 +307,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1e8dd0f0>"
+        "<matplotlib.figure.Figure at 0x7fcf7a917128>"
       ]
      }
     ],
@@ -347,9 +345,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1e8d94a8>"
+        "<matplotlib.figure.Figure at 0x7fcf7a90e518>"
       ]
      }
     ],
@@ -396,7 +394,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1ebbdf28>"
+        "<matplotlib.figure.Figure at 0x7fcf7abfea90>"
       ]
      }
     ],
@@ -514,16 +512,16 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1e8ff5c0>"
+        "<matplotlib.figure.Figure at 0x7fcf7a8b9f98>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
-        "Difference in mean x=0.074, y=43.423\n"
+        "Difference in mean x=-0.037, y=43.337\n"
       ]
      }
     ],
@@ -575,7 +573,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1e778588>"
+        "<matplotlib.figure.Figure at 0x7fcf7a80f240>"
       ]
      }
     ],
@@ -615,7 +613,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a15160470>"
+        "<matplotlib.figure.Figure at 0x7fcf711a0d68>"
       ]
      }
     ],
@@ -670,7 +668,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a19b63630>"
+        "<matplotlib.figure.Figure at 0x7fcf7112a080>"
       ]
      }
     ],
@@ -1081,9 +1079,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a151354e0>"
+        "<matplotlib.figure.Figure at 0x7fcf729e1cc0>"
       ]
      }
     ],
@@ -1113,9 +1111,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1e756da0>"
+        "<matplotlib.figure.Figure at 0x7fcf7110dfd0>"
       ]
      }
     ],
@@ -1161,7 +1159,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a169d4cc0>"
+        "<matplotlib.figure.Figure at 0x7fcf7118d630>"
       ]
      }
     ],
@@ -1284,7 +1282,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a151131d0>"
+        "<matplotlib.figure.Figure at 0x7fcf7117e160>"
       ]
      }
     ],
@@ -1316,7 +1314,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1f6ec0f0>"
+        "<matplotlib.figure.Figure at 0x7fcf72a3c7f0>"
       ]
      }
     ],
@@ -1416,7 +1414,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1698b8d0>"
+        "<matplotlib.figure.Figure at 0x7fcf72a369b0>"
       ]
      }
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@@ -1467,7 +1465,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1e863630>"
+        "<matplotlib.figure.Figure at 0x7fcf710e8320>"
       ]
      }
     ],
@@ -1542,7 +1540,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1e7c8a90>"
+        "<matplotlib.figure.Figure at 0x7fcf7116b2b0>"
       ]
      }
     ],
@@ -1663,7 +1661,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1509e8d0>"
+        "<matplotlib.figure.Figure at 0x7fcf72a46780>"
       ]
      }
     ],
@@ -1734,7 +1732,7 @@
       "output_type": "display_data",
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a16a3ff98>"
+        "<matplotlib.figure.Figure at 0x7fcf71143630>"
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      }
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@@ -1834,7 +1832,7 @@
       "output_type": "display_data",
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a169ba940>"
+        "<matplotlib.figure.Figure at 0x7fcf7a782080>"
       ]
      }
     ],
@@ -1869,7 +1867,7 @@
       "output_type": "display_data",
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a150afeb8>"
+        "<matplotlib.figure.Figure at 0x7fcf72a7a908>"
       ]
      }
     ],
@@ -1981,7 +1979,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1e8b31d0>"
+        "<matplotlib.figure.Figure at 0x7fcf72a36ac8>"
       ]
      },
      {
@@ -2753,7 +2751,7 @@
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1e621390>"
+        "<matplotlib.figure.Figure at 0x7fcf7a6a18d0>"
       ]
      }
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@@ -2871,7 +2869,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1e80a3c8>"
+        "<matplotlib.figure.Figure at 0x7fcf729f1780>"
       ]
      }
     ],
@@ -2981,7 +2979,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1514a2b0>"
+        "<matplotlib.figure.Figure at 0x7fcf72a36358>"
       ]
      }
     ],
@@ -3045,7 +3043,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f3a1e60c6a0>"
+        "<matplotlib.figure.Figure at 0x7fcf7a6a2320>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:5346f4c3acd322f6b0d64eb36febeafe9c3d7e392f241c7bdd3b7950924dbe44"
+  "signature": "sha256:a404121cbe8fa33c5a1840055688ac0af387004638bb55fdf856d15a24a90c2d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0,'./code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7f970a241400>"
+        "<IPython.core.display.HTML at 0x7f0170b389e8>"
       ]
      }
     ],
@@ -312,9 +310,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7f96f0d98940>"
+        "<matplotlib.figure.Figure at 0x7f01536eb4a8>"
       ]
      }
     ],
@@ -634,7 +632,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f96e5a34a58>"
+        "<matplotlib.figure.Figure at 0x7f014c4f1cf8>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:b5fb5804ede73bfa10fc7023f5c5db78201332729c4ab675b5f971ed71273b41"
+  "signature": "sha256:c881a294028594ad9b0c22639019307db6edb57f4ccf0d8621859f194972fd02"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0, './code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7f1ee7140438>"
+        "<IPython.core.display.HTML at 0x7f470e8c4630>"
       ]
      }
     ],
@@ -406,7 +404,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f1eca2736a0>"
+        "<matplotlib.figure.Figure at 0x7f470e8c9668>"
       ]
      }
     ],
@@ -825,9 +823,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f1ec92b03c8>"
+        "<matplotlib.figure.Figure at 0x7f46f4a8a438>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:57f2862cd8540acc35671d93baee8bd8128cca71a0fb8b85fc734bca546eecac"
+  "signature": "sha256:78c13872e6c2cb52bc6db77a5e5449ba5b096adea22234e39ae1fdcb9adbea78"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -29,8 +29,6 @@
      "#format the book\n",
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
-      "import sys\n",
-      "sys.path.insert(0,'./code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -259,7 +257,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7f5c461e4978>"
+        "<IPython.core.display.HTML at 0x7f745f93eb00>"
       ]
      }
     ],
@@ -334,7 +332,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f5c29325c18>"
+        "<matplotlib.figure.Figure at 0x7f7442adbc18>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:5a4ad28382e572d04a1a7439f49a860005d391b9ebc799a3c5099f681dea1ac9"
+  "signature": "sha256:c0caab3016d3aa22ec9277ab90ab046e1fe26bbbe4c1423ed8ba2c60cb49bd57"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0, './code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7f58495a0400>"
+        "<IPython.core.display.HTML at 0x7f36d98c06a0>"
       ]
      }
     ],
@@ -300,7 +298,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f5849591ba8>"
+        "<matplotlib.figure.Figure at 0x7f36bcd34b38>"
       ]
      }
     ],
@@ -341,7 +339,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f5849591c50>"
+        "<matplotlib.figure.Figure at 0x7f36bca77160>"
       ]
      }
     ],
@@ -377,7 +375,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f582c67eb38>"
+        "<matplotlib.figure.Figure at 0x7f36bc9daf98>"
       ]
      }
     ],
@@ -558,7 +556,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f582c5f7b38>"
+        "<matplotlib.figure.Figure at 0x7f36bc94fc18>"
       ]
      }
     ],
@@ -587,7 +585,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f582c641240>"
+        "<matplotlib.figure.Figure at 0x7f36bc996eb8>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:0090b1f43955c2b9c9ef7085e20cf6a3351f6af5daba427429e49d1d5f2f81ca"
+  "signature": "sha256:96201d2c9d8ab085f9c9aa24bd26c0287ee00d2e33a90966c38cda8a4c88c8e3"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -30,9 +30,6 @@
      "#format the book\n",
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
-      "import sys\n",
-      "\n",
-      "sys.path.insert(0,'./code') # allow us to import book_format\n",
      "import matplotlib.pyplot as plt\n",
      "import book_format\n",
      "book_format.load_style()"
@@ -262,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7fbaa956d470>"
+        "<IPython.core.display.HTML at 0x7f218207a9e8>"
       ]
      }
     ],
@@ -447,7 +444,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c9dee80>"
+        "<matplotlib.figure.Figure at 0x7f21654d0e48>"
       ]
      }
     ],
@@ -479,9 +476,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c6edf28>"
+        "<matplotlib.figure.Figure at 0x7f21651eb940>"
       ]
      }
     ],
@@ -579,9 +576,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c4e62b0>"
+        "<matplotlib.figure.Figure at 0x7f2164fc9f98>"
       ]
      }
     ],
@@ -622,9 +619,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c6edf98>"
+        "<matplotlib.figure.Figure at 0x7f21650e2b00>"
       ]
      }
     ],
@@ -662,9 +659,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c6c92b0>"
+        "<matplotlib.figure.Figure at 0x7f21651ac5f8>"
       ]
      }
     ],
@@ -703,9 +700,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c450978>"
+        "<matplotlib.figure.Figure at 0x7f2164f38128>"
       ]
      }
     ],
@@ -763,9 +760,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c3c52e8>"
+        "<matplotlib.figure.Figure at 0x7f2164f146a0>"
       ]
      }
     ],
@@ -816,7 +813,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c445cf8>"
+        "<matplotlib.figure.Figure at 0x7f2164e79668>"
       ]
      }
     ],
@@ -870,9 +867,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c67c780>"
+        "<matplotlib.figure.Figure at 0x7f21651ac6d8>"
       ]
      }
     ],
@@ -976,9 +973,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c67c898>"
+        "<matplotlib.figure.Figure at 0x7f2164f69be0>"
       ]
      }
     ],
@@ -1044,9 +1041,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba84a31e80>"
+        "<matplotlib.figure.Figure at 0x7f215d51b860>"
       ]
      }
     ],
@@ -1162,9 +1159,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c37f2e8>"
+        "<matplotlib.figure.Figure at 0x7f2164f94fd0>"
       ]
      }
     ],
@@ -1196,9 +1193,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fba84a37940>"
+        "<matplotlib.figure.Figure at 0x7f2164fa8198>"
       ]
      }
     ],
@@ -1223,9 +1220,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c71ef60>"
+        "<matplotlib.figure.Figure at 0x7f21651ac710>"
       ]
      }
     ],
@@ -1251,17 +1248,17 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fba84cffa90>"
+        "<matplotlib.figure.Figure at 0x7f2164ecfd68>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba84cb7ef0>"
+        "<matplotlib.figure.Figure at 0x7f215d22c860>"
       ]
      }
     ],
@@ -1292,41 +1289,41 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fba847812e8>"
+        "<matplotlib.figure.Figure at 0x7f215d7a7710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba84781d30>"
+        "<matplotlib.figure.Figure at 0x7f215d7a7b70>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c6e5cf8>"
+        "<matplotlib.figure.Figure at 0x7f2166060cf8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c497f60>"
+        "<matplotlib.figure.Figure at 0x7f215d52b400>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7fba8c474780>"
+        "<matplotlib.figure.Figure at 0x7f215d270ef0>"
       ]
      }
     ],
@@ -1474,9 +1471,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7fba84a389e8>"
+        "<matplotlib.figure.Figure at 0x7f216518ceb8>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:57dbf656364273c92d44507fa244b442d18883c499a9648624b3a44b5047cdd4"
+  "signature": "sha256:4bdc568be195aa4a12705bb1ecde8c5a68b6eab796138620bf9ad7e459a003d8"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -31,8 +31,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0,'./code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -261,7 +259,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7f997b223438>"
+        "<IPython.core.display.HTML at 0x7f098c8276a0>"
       ]
      }
     ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:88870ba7ba4c3aad2c18e5701a8625b32b4d4f124879f4f9d66c4474bc2f93be"
+  "signature": "sha256:50f42ee2e006c1e6799c0cb38dfead8dba5bfdb7035dedbdd6496865de36531e"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -23,8 +23,6 @@
      "%matplotlib inline\n",
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
-      "import sys\n",
-      "sys.path.insert(0,'./code') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -251,13 +249,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 2,
+       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7f2372fae7f0>"
+        "<IPython.core.display.HTML at 0x7f1f6b0279e8>"
       ]
      }
     ],
-     "prompt_number": 2
+     "prompt_number": 1
    },
    {
     "cell_type": "heading",
@@ -30,6 +30,9 @@
      "#Introduction\n",
      "##### Version 0.0\n",
      "\n",
+      "**Author's note - this is obsolete, read the preface instead.**\n",
+      "\n",
+      "\n",
      "The Kalman filter was introduced to the world via papers published in 1958 and 1960 by Rudolph E Kalman. This work built on work by Nobert Wiener. Kalman's early papers were extremely abstract, but researchers quickly realized that the papers described a very practical technique to  filter noisy data. From then until now it has been an ongoing topic of research, and there are many books and papers devoted not only to the basics, but many specializations and extensions to the technique. If you are reading this, you have likely come across some of them.\n",
      "\n",
      "If you are like me, you probably find them nearly impenetrable. I find that almost all start with very abstract math, assume familiarity with notation and naming conventions that I haven't seen before, and focus heavily on proof rather than exposition and teaching. This is perhaps understandable, but it is a regrettable situation and not necessary. \n",
@@ -1,7 +1,7 @@
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  "name": "",
-  "signature": "sha256:3fdf88b217a0cfa4dc5fc3503e5a7ae1d67eecb3dd2463a7004fb5b9bebbadd7"
+  "signature": "sha256:7715a628d7d3a81a6ebba0dab92cbcdf90be862af19aeacf9530984b10c651ab"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -28,13 +28,8 @@
     "collapsed": false,
     "input": [
      "#format the book\n",
-      "%matplotlib inline\n",
-      "from __future__ import division, print_function\n",
-      "import sys\n",
-      "sys.path.insert(0,'./code') # allow us to import book_format\n",
-      "import matplotlib.pyplot as plt\n",
      "import book_format\n",
-      "book_format.load_style('./styles/custom2.css')"
+      "book_format.load_style()"
     ],
     "language": "python",
     "metadata": {},
@@ -43,6 +38,8 @@
       "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",
@@ -129,9 +126,9 @@
        "        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",
@@ -141,12 +138,12 @@
        "    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",
@@ -257,13 +254,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 3,
       "text": [
-        "<IPython.core.display.HTML at 0x7f9ee410c2e8>"
+        "<IPython.core.display.HTML at 0x7f7e2e35f588>"
       ]
      }
     ],
-     "prompt_number": 1
+     "prompt_number": 3
    },
    {
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@@ -89,78 +89,78 @@ Contents
 Motivation for the book. Where to download, how to use.


-* [**Chapter 1: The g-h Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/01_gh_filter/g-h_filter.ipynb)
+* [**Chapter 1: The g-h Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/01_g-h_filter.ipynb)

 Intuitive introduction to the g-h filter, which is a family of filters that includes the Kalman filter. Not filler - once you understand this chapter you will understand the concepts behind the Kalman filter. 


-* [**Chapter 2: The Discrete Bayes Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/02_Discrete_Bayes/discrete_bayes.ipynb)
+* [**Chapter 2: The Discrete Bayes Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/02_Discrete_Bayes.ipynb)

 Introduces the Discrete Bayes Filter. From this you will learn the probabilistic reasoning that underpins the Kalman filter in an easy to digest form.


-* [**Chapter 3: Least Squares Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/03_Least_Squares/Least_Squares_Filters.ipynb)
+* [**Chapter 3: Least Squares Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/03_Least_Squares_Filters.ipynb)

 Introduces the least squares filter in batch and recursive forms.


-* [**Chapter 4: Gaussian Probabilities**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/04_Gaussians/Gaussians.ipynb)
+* [**Chapter 4: Gaussian Probabilities**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/04_Gaussians.ipynb)

 Introduces using Gaussians to represent beliefs. Gaussians allow us to implement the algorithms used in the Discrete Bayes Filter to work in continuous domains.


-* [**Chapter 5: One Dimensional Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/05_Kalman_Filters/Kalman_Filters.ipynb)
+* [**Chapter 5: One Dimensional Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/05_Kalman_Filters.ipynb)

 Implements a Kalman filter by modifying the Discrete Bayesian Filter to use Gaussians. This is a full featured Kalman filter, albeit only useful for 1D problems. 


-* [**Chapter 6: Multivariate Kalman Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/06_Multivariate_Kalman_filter/Multivariate_Kalman_Filters.ipynb)
+* [**Chapter 6: Multivariate Kalman Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/06_Multivariate_Kalman_Filters.ipynb)

 We extend the Kalman filter developed in the previous chapter to the full, generalized filter. 


-* [**Chapter 7: Kalman Filter Math**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb)
+* [**Chapter 7: Kalman Filter Math**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/07_Kalman_Filter_Math.ipynb)

 We gotten about as far as we can without forming a strong mathematical foundation. This chapter is optional, especially the first time, but if you intend to write robust, numerically stable filters, or to read the literature, you will need to know this.


-* [**Chapter 8: Designing Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb)
+* [**Chapter 8: Designing Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/08_Designing_Kalman_Filters.ipynb)

 Building on material in Chapter 6, walks you through the design of several Kalman filters. Discusses, but does not solve issues like numerical stability.


-* [**Chapter 9: Extended Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb)
+* [**Chapter 9: Extended Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/09_Extended_Kalman_Filters.ipynb)

 Kalman filter as covered only work for linear problems. Extended Kalman filters (EKF) are the most common approach to linearizing non-linear problems.


-* [**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb)
+* [**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/10_Unscented_Kalman_Filter.ipynb)

 Unscented Kalman filters (UKF) are a recent development in Kalman filter theory. They allow you to filter nonlinear problems without requiring a closed form solution like the Extended Kalman filter requires.


-[**Chapter 11: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filters.ipynb)
+[**Chapter 11: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11_Ensemble_Kalman_Filters.ipynb)

 Discusses the ensemble Kalman Filter, which uses a Monte Carlo approach to deal with very large Kalman filter states in nonlinear systems.


-* [**Chapter 12: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb)
+* [**Chapter 12: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12_Designing_Nonlinear_Kalman_Filters.ipynb)

 EKF and UKF are linear approximations of nonlinear problems. Unless programmed carefully, they are not numerically stable. We discuss some common approaches to this problem.


-* [**Chapter 13: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13_HInfinity_Filters/HInfinity_Filters.ipynb)
+* [**Chapter 13: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13_HInfinity_Filters.ipynb)

 H-inifinity filters are a form of filter that is very robust in the presence of non-Gaussian noise. They do not perform as well as Kalman filters, but are less likely to diverge.


-* [**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/Smoothing.ipynb)
+* [**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing.ipynb)
    
 Kalman filters are recursive, and thus very suitable for real time filtering. However, they work well for post-processing data. We discuss some common approaches.
    


-* [**Chapter 15: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/15_Adaptive_Filtering/Adaptive_Filtering.ipynb)
+* [**Chapter 15: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/15_Adaptive_Filtering.ipynb)
    
 Kalman filters assume a single process model, but manuevering targets typically need to be described by several different process models. Adaptive filtering uses several techniques to allow the Kalman filter to adapt to the changing behavior of the target.

@@ -179,14 +179,14 @@ Not written yet
 Not written yet.


-* [**Appendix: Installation, Python, NumPy, and FilterPy**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_A_Installation/Appendix_Installation.ipynb)
+* [**Appendix: Installation, Python, NumPy, and FilterPy**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_A_Installation.ipynb)


 Brief introduction of Python and how it is used in this book. Description of the companion
 library FilterPy. 
    

-* [**Appendix: Symbols and Notations**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_B_Symbols_and_Notations/Appendix_Symbols_and_Notations.ipynb)
+* [**Appendix: Symbols and Notations**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_B_Symbols_and_Notations.ipynb)

 Symbols and notations used in this book. Comparison with notations used in the literature.

@@ -1,7 +1,7 @@
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 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -15,7 +15,7 @@
      "from __future__ import division, print_function\n",
      "%matplotlib inline\n",
      "import sys\n",
-      "sys.path.insert(0,'../code') # allow us to format the book\n",
+      "sys.path.insert(0,'..') # allow us to format the book\n",
      "\n",
      "# use same formatting as rest of book so that the plots are\n",
      "# consistant with that look and feel.\n",
@@ -1,7 +1,7 @@
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 "metadata": {
  "name": "",
-  "signature": "sha256:42fcc233db58d607109d988acbd2c05f37460792df171ef7155393554328c969"
+  "signature": "sha256:8dfeca3728647e0419c6f2c7f141dc89f294f6d749e8e29d6baf3d93643fb2a2"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -12,11 +12,10 @@
     "cell_type": "code",
     "collapsed": false,
     "input": [
-      "\n",
      "from __future__ import division, print_function\n",
      "%matplotlib inline\n",
      "import sys\n",
-      "sys.path.insert(0,'../code') # allow us to format the book\n",
+      "sys.path.insert(0,'..') # allow us to format the book\n",
      "\n",
      "# use same formatting as rest of book so that the plots are\n",
      "# consistant with that look and feel.\n",
@@ -1,7 +1,7 @@
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-  "signature": "sha256:8ca39e02795ea489851809c56e3e92eaf9d7fe6e5fb1b83cde4ac7605fcdccb0"
+  "signature": "sha256:433f3b1a6dada8b107fd2ee6433183f04ca080c6c5ef055d0f83e14f9c714fb3"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -23,7 +23,7 @@
      "from __future__ import division, print_function\n",
      "import matplotlib.pyplot as plt\n",
      "import sys\n",
-      "sys.path.insert(0,'../code') # allow us to format the book\n",
+      "sys.path.insert(0,'..') # allow us to format the book\n",
      "import book_format\n",
      "book_format.load_style(directory='..')"
     ],
@@ -250,13 +250,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 2,
       "text": [
-        "<IPython.core.display.HTML at 0x7eff87e216d8>"
+        "<IPython.core.display.HTML at 0x7ff8d0752630>"
       ]
      }
     ],
-     "prompt_number": 1
+     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
@@ -290,7 +290,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 3
    },
    {
     "cell_type": "code",
@@ -327,7 +327,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 4
    },
    {
     "cell_type": "code",
@@ -353,17 +353,17 @@
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOElSkyw4SVKTLDhJUpMsOElSk/4Po5u82b9IDysAAAAASUVORK5CYII=\n",
       "text": [
-        "<matplotlib.figure.Figure at 0x7eff87e21780>"
+        "<matplotlib.figure.Figure at 0x7ff8d0768198>"
       ]
      }
     ],
-     "prompt_number": 4
+     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
-      "<img src=\"./animations/no_info.gif\">"
+      "<img src=\"02_no_info.gif\">"
     ]
    },
    {
@@ -400,17 +400,17 @@
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text": [
-        "<matplotlib.figure.Figure at 0x7eff66cffac8>"
+        "<matplotlib.figure.Figure at 0x7ff8d065cfd0>"
       ]
      }
     ],
-     "prompt_number": 5
+     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
-      " <img src=\"simulate.gif\">"
+      " <img src=\"02_simulate.gif\">"
     ]
    }
   ],
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:c82e943808236de492b791895b0610ea0864057ea274158ee910a721ed9ce67c"
+  "signature": "sha256:305dfb2c678185fb26ee4d55068703451293f5ef20a3de5c28eccffec4e02261"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -15,7 +15,7 @@
      "from __future__ import division, print_function\n",
      "%matplotlib inline\n",
      "import sys\n",
-      "sys.path.insert(0,'../code') # allow us to format the book\n",
+      "sys.path.insert(0,'..') # allow us to format the book\n",
      "\n",
      "# use same formatting as rest of book so that the plots are\n",
      "# consistant with that look and feel.\n",
@@ -6,6 +6,8 @@ import json
 import numpy as np
 import sys

+sys.path.insert(0,'./code') # allow us to import book_format
+
 def test_filterpy_version():
    import filterpy
    min_version = [0,0,10]
@@ -7,6 +7,6 @@ python merge_book.py > Kalman_and_Bayesian_Filters_in_Python.ipynb
 echo "creating pdf..."
 ipython nbconvert --to latex --template book --post PDF Kalman_and_Bayesian_Filters_in_Python.ipynb

-mv Kalman_and_Bayesian_Filters_in_Python ..
+mv Kalman_and_Bayesian_Filters_in_Python.pdf ..
 echo "done."

@@ -62,20 +62,20 @@ if __name__ == '__main__':
    #merge_notebooks(sys.argv[1:])
    merge_notebooks(
        ['../Preface.ipynb',
-         '../01_gh_filter/g-h_filter.ipynb',
-         '../02_Discrete_Bayes/discrete_bayes.ipynb',
-         '../03_Least_Squares/Least_Squares_Filters.ipynb',
-         '../04_Gaussians/Gaussians.ipynb',
-         '../05_Kalman_Filters/Kalman_Filters.ipynb',
-         '../06_Multivariate_Kalman_filter/Multivariate_Kalman_Filters.ipynb',
-         '../07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb',
-         '../08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb',
-         '../09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb',
-         '../10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb',
-         '../11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filters.ipynb',
-         '../12_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb',
-         '../13_HInfinity_Filters/HInfinity_Filters.ipynb',
-         '../14_Smoothing/Smoothing.ipynb',
-         '../15_Adaptive_Filtering/Adaptive_Filtering.ipynb',
-         '../Appendix_A_Installation/Appendix_Installation.ipynb',
-         '../Appendix_B_Symbols_and_Notations/Appendix_Symbols_and_Notations.ipynb'])
+         '../01_g-h_filter.ipynb',
+         '../02_Discrete_Bayes.ipynb',
+         '../03_Least_Squares_Filters.ipynb',
+         '../04_Gaussians.ipynb',
+         '../05_Kalman_Filters.ipynb',
+         '../06_Multivariate_Kalman_Filters.ipynb',
+         '../07_Kalman_Filter_Math.ipynb',
+         '../08_Designing_Kalman_Filters.ipynb',
+         '../09_Extended_Kalman_Filters.ipynb',
+         '../10_Unscented_Kalman_Filter.ipynb',
+         '../11_Ensemble_Kalman_Filters.ipynb',
+         '../12_Designing_Nonlinear_Kalman_Filters.ipynb',
+         '../13_HInfinity_Filters.ipynb',
+         '../14_Smoothing.ipynb',
+         '../15_Adaptive_Filtering.ipynb',
+         '../Appendix_A_Installation.ipynb',
+         '../Appendix_B_Symbols_and_Notations.ipynb'])
@@ -1,7 +1,7 @@
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@@ -23,80 +23,80 @@
      "Motivation behind writing the book. How to download and read the book. Requirements for IPython Notebook and Python. github links.\n",
      "\n",
      "\n",
-      "[**Chapter 1: The g-h Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/01_gh_filter/g-h_filter.ipynb)\n",
+      "[**Chapter 1: The g-h Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/01_g-h_filter.ipynb)\n",
      "\n",
      "Intuitive introduction to the g-h filter, which is a family of filters that includes the Kalman filter. Not filler - once you understand this chapter you will understand the concepts behind the Kalman filter. \n",
      "\n",
      "\n",
-      "[**Chapter 2: The Discrete Bayes Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/02_Discrete_Bayes/discrete_bayes.ipynb)\n",
+      "[**Chapter 2: The Discrete Bayes Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/02_Discrete_Bayes.ipynb)\n",
      "\n",
      "Introduces the Discrete Bayes Filter. From this you will learn the probabilistic reasoning that underpins the Kalman filter in an easy to digest form.\n",
      "\n",
      "\n",
-      "[**Chapter 3: Least Squares Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/03_Least_Squares/Least_Squares_Filters.ipynb)\n",
+      "[**Chapter 3: Least Squares Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/03_Least_Squares_Filters.ipynb)\n",
      "\n",
      "Introduces the least squares filter in batch and recursive forms. I've not made a start on authoring this yet.\n",
      "\n",
      "\n",
-      "[**Chapter 4: Gaussian Probabilities**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/04_Gaussians/Gaussians.ipynb)\n",
+      "[**Chapter 4: Gaussian Probabilities**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/04_Gaussians.ipynb)\n",
      "\n",
      "Introduces using Gaussians to represent beliefs in the Bayesian sense. Gaussians allow us to implement the algorithms used in the Discrete Bayes Filter to work in continuous domains.\n",
      "\n",
      "\n",
-      "[**Chapter 5: One Dimensional Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/05_Kalman_Filters/Kalman_Filters.ipynb)\n",
+      "[**Chapter 5: One Dimensional Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/05_Kalman_Filters.ipynb)\n",
      "\n",
      "Implements a Kalman filter by modifying the Discrete Bayesian Filter to use Gaussians. This is a full featured Kalman filter, albeit only useful for 1D problems. \n",
      "\n",
      "\n",
-      "[**Chapter 6: Multivariate Kalman Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/06_Multivariate_Kalman_filter/Multivariate_Kalman_Filters.ipynb)\n",
+      "[**Chapter 6: Multivariate Kalman Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/06_Multivariate_Kalman_Filters.ipynb)\n",
      "\n",
      "We extend the Kalman filter developed in the previous chapter to the full, generalized filter. \n",
      "\n",
      "\n",
-      "[**Chapter 7: Kalman Filter Math**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb)\n",
+      "[**Chapter 7: Kalman Filter Math**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/07_Kalman_Filter_Math.ipynb)\n",
      "\n",
      "We gotten about as far as we can without forming a strong mathematical foundation. This chapter is optional, especially the first time, but if you intend to write robust, numerically stable filters, or to read the literature, you will need to know this. \n",
      "\n",
      "*This still needs a lot of work. *\n",
      "\n",
      "\n",
-      "[**Chapter 8: Designing Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb)\n",
+      "[**Chapter 8: Designing Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/08_Designing_Kalman_Filters.ipynb)\n",
      "\n",
      "Building on material in Chapter 6, walks you through the design of several Kalman filters. Discusses, but does not solve issues like numerical stability.\n",
      "\n",
      "\n",
-      "[**Chapter 9: Extended Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb)\n",
+      "[**Chapter 9: Extended Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/09_Extended_Kalman_Filters.ipynb)\n",
      "\n",
      "Kalman filter as covered only work for linear problems. Extended Kalman filters (EKF) are the most common approach to linearizing non-linear problems.\n",
      "\n",
      "*Still very early going on this chapter.*\n",
      "\n",
-      "[**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb)\n",
+      "[**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/10_Unscented_Kalman_Filter.ipynb)\n",
      "\n",
      "Unscented Kalman filters (UKF) are a recent development in Kalman filter theory. They allow you to filter nonlinear problems without requiring a closed form solution like the Extended Kalman filter requires.\n",
      "\n",
-      "[**Chapter 11: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filters.ipynb)\n",
+      "[**Chapter 11: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11_Ensemble_Kalman_Filters.ipynb)\n",
      "\n",
      "Discusses the ensemble Kalman Filter, which uses a Monte Carlo approach to deal with very large Kalman filter states in nonlinear systems.\n",
      "\n",
-      "[**Chapter 12: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb)\n",
+      "[**Chapter 12: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12_Designing_Nonlinear_Kalman_Filters.ipynb)\n",
      "\n",
      "Works through some examples of the design of Kalman filters for nonlinear problems. *This is still very much a work in progress.*\n",
      "\n",
      "\n",
-      "[**Chapter 13: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13_HInfinity_Filters/HInfinity_Filters.ipynb)\n",
+      "[**Chapter 13: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13_HInfinity_Filters.ipynb)\n",
      "    \n",
      "Describes the $H_\\infty$ filter. \n",
      "\n",
      "*I have code that implements the filter, but no supporting text yet.*\n",
      "\n",
      "\n",
-      "[**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/Smoothing.ipynb)\n",
+      "[**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing.ipynb)\n",
      "\n",
      "Kalman filters are recursive, and thus very suitable for real time filtering. However, they work well for post-processing data. We discuss some common approaches.\n",
      "\n",
      "\n",
-      "[**Chapter 15: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/15_Adaptive_Filtering/Adaptive_Filtering.ipynb)\n",
+      "[**Chapter 15: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/15_Adaptive_Filtering.ipynb)\n",
      "    \n",
      "Kalman filters assume a single process model, but manuevering targets typically need to be described by several different process models. Adaptive filtering uses several techniques to allow the Kalman filter to adapt to the changing behavior of the target.\n",
      "\n",
@@ -116,13 +116,13 @@
      "\n",
      "\n",
      "\n",
-      "[**Appendix: Installation, Python, NumPy, and filterpy**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_A_Installation/Appendix_Installation.ipynb)\n",
+      "[**Appendix: Installation, Python, NumPy, and filterpy**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_A_Installation.ipynb)\n",
      "\n",
      "Brief introduction of Python and how it is used in this book. Description of the companion\n",
      "library filterpy. \n",
      "    \n",
      "\n",
-      "[**Appendix: Symbols and Notations**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_B_Symbols_and_Notations/Appendix_Symbols_and_Notations.ipynb)\n",
+      "[**Appendix: Symbols and Notations**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_B_Symbols_and_Notations.ipynb)\n",
      "\n",
      "Symbols and notations used in this book. Comparison with notations used in the literature.\n",
      "\n",
@@ -132,234 +132,6 @@
      "### Github repository\n",
      "http://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n"
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