more fixes for code directory

Chapter09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3ee53e118c8313c872ece50f585d138691e58bd895a187ec935ce1a99a6fb7dd"
+  "signature": "sha256:c8a99ddb4d452ae2c13c166e9d8f4b0721d0486697b5aa9a2e8dcd38f1227357"
  },
  "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 0x7fc4d817b9e8>"
+        "<IPython.core.display.HTML at 0x7fb1b0f91a20>"
        ]
       }
      ],
@@ -343,9 +343,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c24a4ef0>"
+        "<matplotlib.figure.Figure at 0x7fb197f4ae10>"
        ]
       }
      ],
@@ -375,9 +375,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c20c2208>"
+        "<matplotlib.figure.Figure at 0x7fb197b4d860>"
        ]
       }
      ],
@@ -409,9 +409,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c1082c50>"
+        "<matplotlib.figure.Figure at 0x7fb197af0ba8>"
        ]
       }
      ],
@@ -445,9 +445,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c10d0518>"
+        "<matplotlib.figure.Figure at 0x7fb197b4aac8>"
        ]
       }
      ],
@@ -474,8 +474,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "input  mean, variance: -0.0002, 1.0019\n",
-        "output mean, variance: -0.0272, 2.2486\n"
+        "input  mean, variance: 0.0006, 1.0019\n",
+        "output mean, variance: -0.0286, 2.2455\n"
        ]
       }
      ],
@@ -504,16 +504,16 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c20b7208>"
+        "<matplotlib.figure.Figure at 0x7fb196a26710>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "output mean, variance: 0.0004, 2.2543\n"
+        "output mean, variance: -0.0009, 2.2544\n"
        ]
       }
      ],
@@ -546,8 +546,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "linear    output mean, variance: 0.0214, 7495.9793\n",
-        "nonlinear output mean, variance: -1.7468, 26292.6295\n"
+        "linear    output mean, variance: -0.0532, 7496.3696\n",
+        "nonlinear output mean, variance: -1.9719, 26248.5350\n"
        ]
       }
      ],
@@ -596,7 +596,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c20933c8>"
+        "<matplotlib.figure.Figure at 0x7fb196ba5128>"
        ]
       }
      ],
@@ -631,7 +631,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c24d1908>"
+        "<matplotlib.figure.Figure at 0x7fb196bb44a8>"
        ]
       }
      ],
@@ -672,7 +672,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c20a49e8>"
+        "<matplotlib.figure.Figure at 0x7fb196b77f98>"
        ]
       }
      ],
@@ -743,7 +743,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c10a5c50>"
+        "<matplotlib.figure.Figure at 0x7fb196b48048>"
        ]
       },
       {
@@ -751,7 +751,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c10a5dd8>"
+        "<matplotlib.figure.Figure at 0x7fb196b89ef0>"
        ]
       }
      ],
@@ -821,7 +821,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c2097be0>"
+        "<matplotlib.figure.Figure at 0x7fb196b14a20>"
        ]
       }
      ],
@@ -859,7 +859,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c1064eb8>"
+        "<matplotlib.figure.Figure at 0x7fb197f6b0b8>"
        ]
       }
      ],
@@ -891,7 +891,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c20c51d0>"
+        "<matplotlib.figure.Figure at 0x7fb196bb4ef0>"
        ]
       }
      ],
@@ -997,9 +997,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc4c2083198>"
+        "<matplotlib.figure.Figure at 0x7fb1b0f91668>"
        ]
       }
      ],

Chapter10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:1cb97d22a87ae9949a15389a2eddc37a6f1666ae331318911586e0cec1af5e32"
+  "signature": "sha256:5b8c10926d18dc83f6553700e669eb52f92d2566a745c9a47d73ec59589e45bb"
  },
  "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 0x7f4586053a20>"
+        "<IPython.core.display.HTML at 0x7fb4016249e8>"
        ]
       }
      ],
@@ -291,9 +291,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f456d00d6a0>"
+        "<matplotlib.figure.Figure at 0x7fb3e85be550>"
        ]
       }
      ],
@@ -327,7 +327,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4586053748>"
+        "<matplotlib.figure.Figure at 0x7fb401624550>"
        ]
       }
      ],
@@ -379,7 +379,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4586053470>"
+        "<matplotlib.figure.Figure at 0x7fb401624470>"
        ]
       }
      ],
@@ -419,7 +419,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f45654fceb8>"
+        "<matplotlib.figure.Figure at 0x7fb3e0aa8c50>"
        ]
       }
      ],
@@ -489,7 +489,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAF2CAYAAABDD3/PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f456549d358>"
+        "<matplotlib.figure.Figure at 0x7fb3e818e048>"
        ]
       }
      ],

Chapter11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:72e5532f67eae7c60cd3bff850e8c1c6a1137cda09c8f937cf7a334bf6d49e3f"
+  "signature": "sha256:32e7c8eabef27ef8d2921c97d1517b8e61b8095a0469741977c8a4099d9b129c"
  },
  "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 0x7fb2c00e89e8>"
+        "<IPython.core.display.HTML at 0x7f227ab379e8>"
        ]
       }
      ],
@@ -397,7 +397,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb2a9647a90>"
+        "<matplotlib.figure.Figure at 0x7f2265b10908>"
        ]
       }
      ],
@@ -815,9 +815,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb2a85fdc88>"
+        "<matplotlib.figure.Figure at 0x7f2264ac0b00>"
        ]
       }
      ],