more changes for code directory

Chapter04_Gaussians/Gaussians.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:4ced6bfb1e08c63d260e43842dcb48d678646bfb82a467f54245e8e1f7d1dabb"
+  "signature": "sha256:03a16f585bb3f4a954520990ebb17e6cf9ceb7f9e247874263f66bfe4ac0da5a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -24,6 +24,8 @@
       "%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()"
      ],
@@ -248,13 +250,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 12,
        "text": [
-        "<IPython.core.display.HTML at 0x7f4270036748>"
+        "<IPython.core.display.HTML at 0x7f8a50209cf8>"
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 12
     },
     {
      "cell_type": "heading",
@@ -289,13 +291,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f425801cba8>"
+        "<matplotlib.figure.Figure at 0x7f8a502092b0>"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -395,13 +397,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f424d69f978>"
+        "<matplotlib.figure.Figure at 0x7f8a50276630>"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -448,13 +450,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f424d668080>"
+        "<matplotlib.figure.Figure at 0x7f8a501220b8>"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -490,13 +492,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f424d5dcbe0>"
+        "<matplotlib.figure.Figure at 0x7f8a50039be0>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -545,21 +547,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f424d60aeb8>"
+        "<matplotlib.figure.Figure at 0x7f8a50078a90>"
        ]
       },
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 6,
+       "prompt_number": 17,
        "text": [
         "<function __main__.plt_g>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 17
     },
     {
      "cell_type": "heading",
@@ -617,7 +619,7 @@
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -648,7 +650,7 @@
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -672,13 +674,13 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[ 7.34214043  4.01197645  2.72519952  6.59756375  1.23150108  5.35084192\n",
-        "  5.13531457 -1.17205809  3.62692992 -2.15541009  1.50868156  6.60801968\n",
-        "  4.56818095  0.22491041 -2.23888555]\n"
+        "[ 0.38741053 -4.91803911 -2.00879426 -1.15175952  2.63354369 -0.96097707\n",
+        "  2.45698146  4.22587531  3.69924957 -0.27811435 -0.94132557  4.96510082\n",
+        "  3.43324112  4.09462176  0.6446632 ]\n"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -705,7 +707,7 @@
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -735,13 +737,13 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "There are many other functions available, and if you are interested I urge you to peruse the documentation. I find the documentation to be excessively terse, but with a bit of googling you can find out what a function does and some examples of how to use it. Most of this functionality is not of immediate interest to the book, so I will leave the topic in your hands to explore. The tutorial is quite approachable, and I suggest starting there. <a href=\"http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html\">(tutorial)</a>[2]"
+      "There are many other functions available, and if you are interested I urge you to peruse the documentation. I find the documentation to be excessively terse, but with a bit of googling you can find out what a function does and some examples of how to use it. Most of this functionality is not of immediate interest to the book, so I will leave the topic in your hands to explore. The tutorial is quite approachable, and I suggest starting there. <a href=\"http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html\">tutorial</a>[2]"
      ]
     },
     {

Chapter05_Kalman_Filters/Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:293a09cc844ccb8d9ee5a2a79a6375ecb56770e0e5e90dd5d3698900e6006206"
+  "signature": "sha256:dea8b0a50eff26f334abc7863ef4ec47ce0257f7405dc287360ef650b1855279"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -23,7 +23,7 @@
       "#format the book\n",
       "%matplotlib inline\n",
       "import sys\n",
-      "sys.path.insert(0,'../') # allow us to import book_format\n",
+      "sys.path.insert(0,'../code') # allow us to import book_format\n",
       "import book_format\n",
       "book_format.load_style()"
      ],
@@ -250,7 +250,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f4ac50ad828>"
+        "<IPython.core.display.HTML at 0x7f9a41e44828>"
        ]
       }
      ],
@@ -342,10 +342,10 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        " 0.4900\t-1.5190\t 0.0639\t 0.5312\t-0.4402\t\n",
-        "-0.8721\t 0.4444\t 0.0782\t 1.0500\t-1.4336\t\n",
-        " 0.3928\t 1.6109\t 0.3260\t 0.4645\t 0.2985\t\n",
-        " 1.2323\t 0.6590\t 0.9813\t-0.7694\t 0.2915\t\n"
+        "-1.8386\t 0.7546\t-0.5046\t-2.9576\t-0.2675\t\n",
+        " 1.4816\t-0.9532\t-0.8862\t-0.3574\t-0.4382\t\n",
+        " 0.1608\t 1.1827\t 0.4886\t 2.0902\t-0.2451\t\n",
+        "-0.4182\t 0.5224\t 0.1251\t 2.1121\t-0.6231\t\n"
        ]
       }
      ],
@@ -392,7 +392,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4abc066128>"
+        "<matplotlib.figure.Figure at 0x7f9a28e15f98>"
        ]
       }
      ],
@@ -435,9 +435,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aaffc01d0>"
+        "<matplotlib.figure.Figure at 0x7f9a28db9400>"
        ]
       }
      ],
@@ -469,9 +469,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4abc053be0>"
+        "<matplotlib.figure.Figure at 0x7f9a28bfb7f0>"
        ]
       }
      ],
@@ -489,9 +489,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aafdf2eb8>"
+        "<matplotlib.figure.Figure at 0x7f9a28da1e48>"
        ]
       }
      ],
@@ -534,7 +534,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aaffc61d0>"
+        "<matplotlib.figure.Figure at 0x7f9a28d9ee10>"
        ]
       }
      ],
@@ -571,9 +571,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aa0095940>"
+        "<matplotlib.figure.Figure at 0x7f9a21695d30>"
        ]
       }
      ],
@@ -670,7 +670,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4a9afc1e48>"
+        "<matplotlib.figure.Figure at 0x7f9a215e8c50>"
        ]
       }
      ],
@@ -718,7 +718,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aaff81358>"
+        "<matplotlib.figure.Figure at 0x7f9a21634f28>"
        ]
       }
      ],
@@ -768,7 +768,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aa0052630>"
+        "<matplotlib.figure.Figure at 0x7f9a28dd1908>"
        ]
       }
      ],
@@ -891,26 +891,26 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "time: 0 \tposition = 0.655 \tvariance = 2.500\n",
-        "time: 1 \tposition = 0.170 \tvariance = 1.667\n",
-        "time: 2 \tposition = 0.110 \tvariance = 1.250\n",
-        "time: 3 \tposition = 0.047 \tvariance = 1.000\n",
-        "time: 4 \tposition = 0.053 \tvariance = 0.833\n",
-        "time: 5 \tposition = 0.174 \tvariance = 0.714\n",
-        "time: 6 \tposition = 0.159 \tvariance = 0.625\n",
-        "time: 7 \tposition = 0.167 \tvariance = 0.556\n",
-        "time: 8 \tposition = 0.039 \tvariance = 0.500\n",
-        "time: 9 \tposition = 0.070 \tvariance = 0.455\n",
-        "time: 10 \tposition = 0.074 \tvariance = 0.417\n",
-        "time: 11 \tposition = 0.036 \tvariance = 0.385\n",
-        "time: 12 \tposition = 0.133 \tvariance = 0.357\n",
-        "time: 13 \tposition = 0.344 \tvariance = 0.333\n",
-        "time: 14 \tposition = 0.252 \tvariance = 0.312\n",
-        "time: 15 \tposition = 0.273 \tvariance = 0.294\n",
-        "time: 16 \tposition = 0.210 \tvariance = 0.278\n",
-        "time: 17 \tposition = 0.210 \tvariance = 0.263\n",
-        "time: 18 \tposition = 0.168 \tvariance = 0.250\n",
-        "time: 19 \tposition = 0.206 \tvariance = 0.238\n"
+        "time: 0 \tposition = 0.827 \tvariance = 2.500\n",
+        "time: 1 \tposition = 0.427 \tvariance = 1.667\n",
+        "time: 2 \tposition = 0.192 \tvariance = 1.250\n",
+        "time: 3 \tposition = 0.548 \tvariance = 1.000\n",
+        "time: 4 \tposition = 0.416 \tvariance = 0.833\n",
+        "time: 5 \tposition = 0.409 \tvariance = 0.714\n",
+        "time: 6 \tposition = 0.297 \tvariance = 0.625\n",
+        "time: 7 \tposition = 0.490 \tvariance = 0.556\n",
+        "time: 8 \tposition = 0.407 \tvariance = 0.500\n",
+        "time: 9 \tposition = 0.291 \tvariance = 0.455\n",
+        "time: 10 \tposition = 0.120 \tvariance = 0.417\n",
+        "time: 11 \tposition = -0.043 \tvariance = 0.385\n",
+        "time: 12 \tposition = -0.082 \tvariance = 0.357\n",
+        "time: 13 \tposition = -0.130 \tvariance = 0.333\n",
+        "time: 14 \tposition = -0.208 \tvariance = 0.312\n",
+        "time: 15 \tposition = -0.144 \tvariance = 0.294\n",
+        "time: 16 \tposition = -0.075 \tvariance = 0.278\n",
+        "time: 17 \tposition = -0.073 \tvariance = 0.263\n",
+        "time: 18 \tposition = -0.030 \tvariance = 0.250\n",
+        "time: 19 \tposition = -0.045 \tvariance = 0.238\n"
        ]
       }
      ],
@@ -1030,24 +1030,24 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "PREDICT:     1.0000   502.0000\tUPDATE:    -5.0106     9.8047\n",
-        "PREDICT:    -4.0106    11.8047\tUPDATE:    -1.9323     5.4138\n",
-        "PREDICT:    -0.9323     7.4138\tUPDATE:     2.3085     4.2574\n",
-        "PREDICT:     3.3085     6.2574\tUPDATE:     4.5496     3.8490\n",
-        "PREDICT:     5.5496     5.8490\tUPDATE:     4.5375     3.6904\n",
-        "PREDICT:     5.5375     5.6904\tUPDATE:     6.3341     3.6267\n",
-        "PREDICT:     7.3341     5.6267\tUPDATE:     6.4186     3.6007\n",
-        "PREDICT:     7.4186     5.6007\tUPDATE:     8.5696     3.5900\n",
-        "PREDICT:     9.5696     5.5900\tUPDATE:    10.7097     3.5856\n",
-        "PREDICT:    11.7097     5.5856\tUPDATE:     9.8509     3.5838\n"
+        "PREDICT:     1.0000   502.0000\tUPDATE:    -1.9271     9.8047\n",
+        "PREDICT:    -0.9271    11.8047\tUPDATE:     1.4652     5.4138\n",
+        "PREDICT:     2.4652     7.4138\tUPDATE:     0.8840     4.2574\n",
+        "PREDICT:     1.8840     6.2574\tUPDATE:     2.2629     3.8490\n",
+        "PREDICT:     3.2629     5.8490\tUPDATE:     5.3172     3.6904\n",
+        "PREDICT:     6.3172     5.6904\tUPDATE:     4.8154     3.6267\n",
+        "PREDICT:     5.8154     5.6267\tUPDATE:     6.7095     3.6007\n",
+        "PREDICT:     7.7095     5.6007\tUPDATE:     7.0910     3.5900\n",
+        "PREDICT:     8.0910     5.5900\tUPDATE:     9.0888     3.5856\n",
+        "PREDICT:    10.0888     5.5856\tUPDATE:    11.0984     3.5838\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aaffae828>"
+        "<matplotlib.figure.Figure at 0x7f9a2169e208>"
        ]
       }
      ],
@@ -1183,9 +1183,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aaffee278>"
+        "<matplotlib.figure.Figure at 0x7f9a28d8c780>"
        ]
       },
       {
@@ -1193,7 +1193,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aafe0b9e8>"
+        "<matplotlib.figure.Figure at 0x7f9a2161a278>"
        ]
       },
       {
@@ -1405,9 +1405,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4a9afed940>"
+        "<matplotlib.figure.Figure at 0x7f9a28dbdeb8>"
        ]
       },
       {
@@ -1415,7 +1415,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aaffee358>"
+        "<matplotlib.figure.Figure at 0x7f9a2169e048>"
        ]
       },
       {
@@ -1423,7 +1423,7 @@
        "stream": "stdout",
        "text": [
         "Variance converges to 0.09072976736236911\n",
-        "Last voltage is 15.592455113316198\n"
+        "Last voltage is 16.160958715064073\n"
        ]
       }
      ],
@@ -1486,9 +1486,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4a9af3a160>"
+        "<matplotlib.figure.Figure at 0x7f9a2163a518>"
        ]
       }
      ],
@@ -1555,9 +1555,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aaffeeb70>"
+        "<matplotlib.figure.Figure at 0x7f9a28d8c198>"
        ]
       },
       {
@@ -1565,7 +1565,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4a9adb7eb8>"
+        "<matplotlib.figure.Figure at 0x7f9a28bd72e8>"
        ]
       }
      ],
@@ -1641,9 +1641,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4a9add02e8>"
+        "<matplotlib.figure.Figure at 0x7f9a20599a90>"
        ]
       }
      ],
@@ -1705,9 +1705,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4a9af45f28>"
+        "<matplotlib.figure.Figure at 0x7f9a216a1e10>"
        ]
       }
      ],
@@ -1768,9 +1768,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aa0041550>"
+        "<matplotlib.figure.Figure at 0x7f9a216e42e8>"
        ]
       }
      ],
@@ -1820,9 +1820,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4a9af80c88>"
+        "<matplotlib.figure.Figure at 0x7f9a21680588>"
        ]
       }
      ],
@@ -1931,7 +1931,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aaffaef28>"
+        "<matplotlib.figure.Figure at 0x7f9a2169e630>"
        ]
       },
       {
@@ -2022,7 +2022,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aaffd7390>"
+        "<matplotlib.figure.Figure at 0x7f9a41e44780>"
        ]
       }
      ],
@@ -2107,7 +2107,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4aa0032b38>"
+        "<matplotlib.figure.Figure at 0x7f9a205b3a90>"
        ]
       }
      ],

Chapter07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:b48c3519700b0fb4870898b3dc76b4f979d302dcdde562eb556ccc8e77d56013"
+  "signature": "sha256:107480012317adfa7c7e4b7cb599197924e13638e3127ea101fc5684332f25b8"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -25,7 +25,7 @@
       "from __future__ import division, print_function\n",
       "import matplotlib.pyplot as plt\n",
       "import sys\n",
-      "sys.path.insert(0,'../') # allow us to format the book\n",
+      "sys.path.insert(0,'../code') # allow us to format the book\n",
       "import book_format\n",
       "book_format.load_style()"
      ],
@@ -252,7 +252,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f794933e9e8>"
+        "<IPython.core.display.HTML at 0x7efefefbf9e8>"
        ]
       }
      ],
@@ -380,11 +380,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAGDCAYAAAAs4AbKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYU9fjBvD3hi2IQCxUQFG0Cg4UkboQFQWLOFp3VdSq\ndRRH+VZxo3XVrXVVW2nrKE60orW2KIKjDqhaW2XUjeBCUKYykt8f/JIaZtgheT/P4yO59+Scc+/N\nhTcn594IMTExUhARERERaShRdXeAiIiIiKg6MRATERERkUZjICYiIiIijcZATEREREQajYGYiIiI\niDQaAzERERERaTQGYiIluLu7w87OTv4vISGhurtU5Q4fPlzktkdGRirsH29v72rooXorbv8DwOzZ\ns+Hm5lbFvcqzadMm2NnZVUvbqqI69z8RlR8DMakdNzc3DB8+vMDyQ4cOwc7ODvPnzy91nbNnz8bq\n1asxZMiQiuhitZOFq8L+OTg4FPk8QRAKXW5ra4vVq1dj1apVsLW1LbLc206dOoWdO3eWeRsq06NH\njxT2ib29Pdq3b49x48bh0qVL1d29QgmCoNR+V9aPP/6IU6dOVUvbFUX2Or948aLC8gcPHsDOzg6b\nN2+u0PbKuw8uX75c4X1SRnR0NHx8fNC+fXu0bt0aHh4e8PPzw19//VVo+U2bNuHKlStV3EuiyqVd\n3R0gqgz5/zCdOnUK/v7+8PDwwJIlS0pdX48ePQAA2dnZOHDgQIX0URVMnDgRTZo0UVimpaVVaNkP\nP/wQffr0ga6uboF1ZmZm6Nu3LwDg4MGDSrV96tQpXLlyBaNHjy5lr6tOz5490atXL+Tm5uL+/fvY\nu3cvxo0bhx07dqBjx47V3T0FS5cuhUQiqbD6du3ahfbt26Nnz54llp08eTImTJhQYW3XVFJp+b7n\n6sqVK9iyZQumTJlSQT0qWXR0ND7++GOYmppizJgxsLCwQEJCAn777TeEhYWhdevWBZ6zZcsWCIKA\n999/v8r6SVTZGIhJ7V2+fBm+vr54//33sXbtWpUcyaouHTp0UDrYiUSiQsOwOnvvvffkQR/Imzoz\ncOBABAQEqFwg1tLSKvLNjDq3TeWzdetWCIKAAwcOoG7duvLlU6ZMwdOnT6uxZ0RVi1MmSK3dunUL\nn332GZo2bYqtW7dCR0dHYf2RI0cwbtw4dOnSBa1atYKbmxuWLVuGlJSUMrVnZ2eHlStXws3NDZ07\nd8bZs2fh5+cHR0dHTJ06FVlZWfKyoaGh8PHxQffu3dGqVSu4uLhgzpw5Bf4IyeZnPnz4EJ988gkc\nHR3Rq1cv/Pzzz2XqY2n16dOnwuZPvz0V4eeff0ZCQoJC3fk/Ls7KysLGjRvh7u6OVq1aoXv37li/\nfj1ycnIK1C3bT5mZmVi4cCE6duwIR0dHDBw4EE+ePClzn9/WokUL1KlTB48ePVJYLvto/uHDh1i/\nfj1cXV3RunVr9O3bFzdv3pSXS0lJwbJly9C1a1e0atUKvXr1wo8//lignbS0NPj7+6N9+/ZwcnLC\nnDlzkJGRUaBcUlKSwv4raQ7rvXv38MUXX6Bz585wcHCAl5cXtm7ditzcXIXtkB3nI0eOKNQfERGh\nUJ+jo6PC+qJkZGTgq6++gqurK1q2bAl3d3ds27atwIiqt7c3vL29ER4ejn79+qFNmzYYNGgQbt26\nVex2VQTZHODw8HD07t0bDg4O6N+/P8LDwwuUvXfvHsaNG4c2bdrAxcUF3377baF1KnuOy/bfli1b\nFB7b2dlhzpw5CmWlUil27twJLy8vODg4wMXFBYsXL0Z6enqZtjs2NhYNGzZUCMMyFhYW8p9l55fs\nOG/evLnY3wt3797FtGnT0L59ezg4OGDw4ME4d+6cQpnLly/Dzs4OJ06cwGeffQZHR0e4uLhg9erV\nhZ7jRJWJI8Sktu7fv4/x48fD3NwcO3bsQK1atQqU+f7772Fubo5x48bByMgI0dHR2LdvH/7++2/s\n27evTO2GhIRg/Pjx+PHHHzF16lR069YNEyZMwMaNGxESEgIvLy8AwIEDB/Dy5Ut8/PHHqFu3Lu7f\nv4/AwEBcuXIFv/76a4HRWB8fH7i6usLd3R2HDh3CnDlzSgwiJUlLS0NSUpLCMiMjI4W2Z8yYgdTU\nVERERJR7uohYLMbq1asBAPv378edO3cwd+5c+fpmzZrJf5ZIJJg8eTIuX76MIUOGwM7ODtHR0QgI\nCMCTJ0+wcuXKQtuYMWMGkpOTMWHCBIhEIly4cAEvX77Eu+++W66+A3kBNCUlBba2toWuX7lyJR4+\nfIiRI0fC0NAQf/75J549e4YWLVogMzMTI0eORFxcHEaMGIEGDRogMjISK1asQEpKCqZNmyavx9fX\nF+fPn8fw4cNha2uLY8eO4cyZMwU+3TAyMlLYn48fPy6y7zExMRg+fDi0tLQwbNgw2NjY4OHDhwgM\nDMSoUaNgZGQEZ2dnrF69GlKpFF999RWaNGmiMG8+/3YvX74cOTk5+P3334udbzx9+nScO3cOAwcO\nRMuWLfHHH39gw4YNSExMLDCnPz4+HosXL8bHH38MQRCwY8cOTJgwAWfOnCnwhrYiCYKAly9fYsaM\nGfJz8uDBg/Dx8cHu3bvh6OgIIO9NzahRo5CVlYWJEydCR0cHe/bswZs3bwr8jlH2HJcdw99//x0h\nISHyxwDQoEEDhToXLFiAoKAg9OvXD6NHj0ZcXBz27NmDf//9F7t27Sr1J2AWFha4fv06YmNj0bRp\n0yLLeXh4oGHDhpBKpfDz84OHhwfc3d3l601NTeU///vvv/j4449Rp04djB8/HrVq1cKJEycwadIk\nfPfdd+jUqZNC3UuXLoWDgwNmzpyJq1evIiAgABkZGVi4cGGptoWoPBiISS0lJSVh7NixSEpKwowZ\nMxR+Wb8tICAA5ubmCstMTEywefNm3Lhxo9gLzIoyaNAgDB8+HCkpKdiwYQPmzp0LCwsLHD16FLGx\nsfJA/OWXXyqMwABAo0aNMGfOHJw+fRqenp4K64YMGSK/e0O3bt3Qo0cPhIaGlisQT506tcCyuXPn\nYtSoUfLH3bp1A1Ax86cNDAzkUxAuXLiAx48fK0xJeFtwcDAuXLiANWvWoE+fPvLlFhYWWL9+PSZM\nmIDGjRsXeJ5EIsFPP/0kDwajR48u89za169fIykpCRKJBPfv38e6desglUoV+vO2J0+eICgoSB50\nRowYIW87ICAA//77L/bs2QMnJycAecdUX18fAQEBGDNmDIyNjXH16lWcO3cOEyZMwP/+9z8AwODB\ng/HBBx/g1atXCu3p6uoq7M/iRu+XLFmC3NxcHDlyRCFkjR07Fvr6+gCA+vXro379+gCADRs2wNra\nusjjA0D+Gr1//z5CQkIKLRMZGYlz585h9OjR8tHOjz/+GD4+PggMDMSnn36qcB4kJCTg6NGj8jdH\ndevWxaxZs/DXX3+hXbt2RfalvKRSKTIyMrBkyRIMHjwYANC3b1+4ublh8+bNCAgIAAAEBgbi+fPn\n2LFjB1xcXAAA3bt3R58+fQoEYmXPcdk+lu3HovZ5REQEDh06BF9fX0ycOFG+vFmzZpgxYwbOnTsH\nV1fXUm33J598gsmTJ2PAgAHo1KkTOnXqhB49eshfB2+3ITsmfn5+aNq0aZH9XLJkCQwMDBAUFAQT\nExMAwLBhw9CvXz9s2LChQCC2tbXFtm3bAADDhw9Hbm4u9u/fj0mTJhXYf0SVhVMmSC3du3cPWVlZ\nqFevHtavX4/U1NRCy70dhmWjpbJRsIcPH5apbbFYDCDvQjMA8o8iTU1N8fLlS3m5t3/RZ2RkICkp\nCQ0bNiyy7V69esl/trS0hImJSbmnAvj5+eGHH35Q+Ofh4VGuOkujuIuQTp48CSMjI3Tq1AlJSUny\nf61atQKAAh/fy0ycOLHAKJlIVLZfdT/88AM6deoEFxcXjBw5EtHR0Zg8eTJGjBhRaPmxY8cWGNmX\ntf3bb7+hSZMmsLW1Vdie1q1b482bN7hx4wYAyO+I8HbY0NXVxQcffFDmi7aSkpIQGRkJLy+vAiOO\nderUgbZ25Y2NyO7KMWDAAIXlAwYMgEQiweXLlxWW29jYKHxS0Lx5cwCokvmsIpFIYb+bmprCxcUF\nkZGR8jc2Fy9ehJmZmTwMA0Djxo3RokWLAvWV9hwvycmTJyEIAj744AOF15Cs7aLOieJ069YNu3fv\nRseOHXHx4kWsWLEC7u7uGDduHJ49e1bq+pKSknDlyhV0794dEolE3sdXr16hTZs2+Pvvv/HmzRuF\n5+R/g9mvXz9IJBLeyYKqFEeISS0ZGhriu+++w9OnTzFp0iR89dVXWL58eYFyN2/exKZNmxAZGYm0\ntDSFdfl/aStL9rGuLGTILjbS1tZGdna2vNyDBw+wadMmnDt3rsDIX2Ft5x/JNjAwUKivLOzt7VXu\n4jCZBw8eIC0trcBoEpD38XZycnKhz8t/14zy6NevHz766COIRCIYGxujcePGxV5YWNiItcyDBw+Q\nlZVV6P4WBEE+dUUW/OrVq6dQxtLSsiybAACIi4sDULH7RlmyUGVlZaWwXLY9+YNuYa9zAOV+rRcm\n/xsnExMT+Wi5zLvvvos3b94gOTkZYrEYT548KXT6jaWlZYHXZGnP8ZI8ePAAUqlU4c3x29tS1DlR\nknbt2qFdu3bIyMhAREQEgoOD8csvv2D69OnYu3dvqeqSvdYOHDhQ6CdKsqkpb79ZyP9al+3fipr7\nT6QMBmJSS7J7x9rb26NXr144fPgw+vTpoxCu4uPjMWLECFhaWsLX1xf169eHjo4Obt68iTVr1pT7\nFkqFkdWZlpaGESNGQEtLCxMnToStrS309PTw9OlTzJ49u1LarolsbGywaNGiQtdZW1sXutLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f79302f9780>"
+        "<matplotlib.figure.Figure at 0x7efefefbf6d8>"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -465,7 +465,8 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 2
     }
    ],
    "metadata": {}

Chapter08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:4ab6426303b8e42b01e0c28dbe8acb5f6f138ede5afd19f2ba0ca549d9b04260"
+  "signature": "sha256:d37e9748673df0d583528e8e9ad936fea340b855a9618f62dedb4ee0dd131422"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -25,7 +25,7 @@
       "from __future__ import division, print_function\n",
       "import matplotlib.pyplot as plt\n",
       "import sys\n",
-      "sys.path.insert(0,'../') # allow us to format the book\n",
+      "sys.path.insert(0,'../code') # allow us to format the book\n",
       "import book_format\n",
       "book_format.load_style()"
      ],
@@ -252,7 +252,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f5aff338a20>"
+        "<IPython.core.display.HTML at 0x7f413579c9e8>"
        ]
       }
      ],
@@ -345,9 +345,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5aea2db470>"
+        "<matplotlib.figure.Figure at 0x7f411c73f358>"
        ]
       }
      ],
@@ -698,9 +698,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5aea2a5908>"
+        "<matplotlib.figure.Figure at 0x7f411c73f2e8>"
        ]
       }
      ],
@@ -763,7 +763,8 @@
       " \n",
       "    #e = stats.sigma_ellipse (cov=cov, x=f1.x[0,0], y=f1.x[2,0])\n",
       "    #stats.plot_sigma_ellipse(ellipse=e)\n",
-      "    stats.plot_covariance_ellipse((f1.x[0,0], f1.x[2,0]), cov=cov)\n",
+      "    stats.plot_covariance_ellipse((f1.x[0,0], f1.x[2,0]), cov=cov,\n",
+      "                                  facecolor='g', alpha=0.2)\n",
       "\n",
       "        \n",
       "p1, = plt.plot (xs, ys, 'r--')\n",
@@ -779,9 +780,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5aea2bb668>"
+        "<matplotlib.figure.Figure at 0x7f411c73ac18>"
        ]
       },
       {
@@ -796,7 +797,7 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -848,7 +849,7 @@
      "input": [
       "c = f1.P[0:2,0:2]\n",
       "print(c)\n",
-      "stats.plot_covariance_ellipse((0,0), cov=c)"
+      "stats.plot_covariance_ellipse((0,0), cov=c, facecolor='g', alpha=0.2)"
      ],
      "language": "python",
      "metadata": {},
@@ -857,20 +858,20 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[[ 0.30660483  0.12566239]\n",
-        " [ 0.12566239  0.24399092]]\n"
+        "[[ 0.08204134  0.02434904]\n",
+        " [ 0.02434904  0.00955614]]\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5aea1f3ba8>"
+        "<matplotlib.figure.Figure at 0x7f4114e6edd8>"
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -1016,15 +1017,15 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f5aea205a90>"
+        "<matplotlib.figure.Figure at 0x7f411c497dd8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5ae2a78e10>"
+        "<matplotlib.figure.Figure at 0x7f4114e64a90>"
        ]
       }
      ],
@@ -1206,7 +1207,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5ae103d748>"
+        "<matplotlib.figure.Figure at 0x7f411451b358>"
        ]
       }
      ],
@@ -1400,9 +1401,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5ae2ae45f8>"
+        "<matplotlib.figure.Figure at 0x7f4114f44780>"
        ]
       }
      ],
@@ -1543,7 +1544,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5ae111bf98>"
+        "<matplotlib.figure.Figure at 0x7f411460ed68>"
        ]
       }
      ],
@@ -1674,9 +1675,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5ae2af1668>"
+        "<matplotlib.figure.Figure at 0x7f411c6ac898>"
        ]
       }
      ],
@@ -1742,17 +1743,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5aea21ddd8>"
+        "<matplotlib.figure.Figure at 0x7f411456b320>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f5aea1ed630>"
+        "<matplotlib.figure.Figure at 0x7f4114f3dac8>"
        ]
       }
      ],
@@ -1780,17 +1781,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f5ae2a81d30>"
+        "<matplotlib.figure.Figure at 0x7f4115281da0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5ae2a81278>"
+        "<matplotlib.figure.Figure at 0x7f4114eb2208>"
        ]
       }
      ],