Moved chapter 01 as part of book reorg

01_g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:baea7682f826fcbcca71a2693a7d43c4f2b9b361029d9bab12908795ea315c61"
+  "signature": "sha256:618a6f3ed753b80e339ceabebc203d9c01d69a0a78da364d1584314ce521f8c0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -31,7 +31,7 @@
       "%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",
+      "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 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f12aedfe1d0>"
+        "<IPython.core.display.HTML at 0x7fc8fe8b1400>"
        ]
       }
      ],
@@ -335,7 +335,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295f1b320>"
+        "<matplotlib.figure.Figure at 0x7fc8e5a03518>"
        ]
       }
      ],
@@ -372,7 +372,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295ed3a20>"
+        "<matplotlib.figure.Figure at 0x7fc8e5a03c18>"
        ]
       }
      ],
@@ -406,7 +406,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295f1b1d0>"
+        "<matplotlib.figure.Figure at 0x7fc8e5a03358>"
        ]
       }
      ],
@@ -448,7 +448,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Average of measurements is 164.9904\n"
+        "Average of measurements is 164.9541\n"
        ]
       }
      ],
@@ -484,7 +484,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Average of measurements is 164.9898\n"
+        "Average of measurements is 164.9308\n"
        ]
       }
      ],
@@ -527,7 +527,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAzsAAAGkCAYAAAAWgDuQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295ddda20>"
+        "<matplotlib.figure.Figure at 0x7fc8e58c7dd8>"
        ]
       }
      ],
@@ -563,7 +563,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295da9e10>"
+        "<matplotlib.figure.Figure at 0x7fc8e5916e48>"
        ]
       }
      ],
@@ -598,7 +598,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295eca8d0>"
+        "<matplotlib.figure.Figure at 0x7fc8e58cc160>"
        ]
       }
      ],
@@ -636,7 +636,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295f106d8>"
+        "<matplotlib.figure.Figure at 0x7fc8e59caba8>"
        ]
       }
      ],
@@ -674,7 +674,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295cb6b00>"
+        "<matplotlib.figure.Figure at 0x7fc8e5794ac8>"
        ]
       }
      ],
@@ -713,7 +713,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295de8358>"
+        "<matplotlib.figure.Figure at 0x7fc8e58cb9e8>"
        ]
       }
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@@ -740,7 +740,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295ba4a90>"
+        "<matplotlib.figure.Figure at 0x7fc8e5690cc0>"
        ]
       }
      ],
@@ -780,7 +780,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295ca4048>"
+        "<matplotlib.figure.Figure at 0x7fc8e57a9748>"
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@@ -848,7 +848,7 @@
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295ee3278>"
+        "<matplotlib.figure.Figure at 0x7fc8e58d1438>"
        ]
       }
      ],
@@ -877,7 +877,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295d9e470>"
+        "<matplotlib.figure.Figure at 0x7fc8e59a01d0>"
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295de53c8>"
+        "<matplotlib.figure.Figure at 0x7fc8e5773f60>"
        ]
       }
      ],
@@ -1012,7 +1012,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295c923c8>"
+        "<matplotlib.figure.Figure at 0x7fc8e574d400>"
        ]
       }
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@@ -1151,7 +1151,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295cfd400>"
+        "<matplotlib.figure.Figure at 0x7fc8e5743ef0>"
        ]
       }
      ],
@@ -1228,9 +1228,9 @@
       {
        "metadata": {},
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295f418d0>"
+        "<matplotlib.figure.Figure at 0x7fc8e56afc18>"
        ]
       }
      ],
@@ -1284,9 +1284,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295e3def0>"
+        "<matplotlib.figure.Figure at 0x7fc8e577d908>"
        ]
       }
      ],
@@ -1347,9 +1347,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295ce2470>"
+        "<matplotlib.figure.Figure at 0x7fc8e572bb38>"
        ]
       }
      ],
@@ -1423,7 +1423,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295cc1d68>"
+        "<matplotlib.figure.Figure at 0x7fc8e58a4400>"
        ]
       }
      ],
@@ -1496,25 +1496,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295d21908>"
+        "<matplotlib.figure.Figure at 0x7fc8e5908400>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295c7a710>"
+        "<matplotlib.figure.Figure at 0x7fc8e590b8d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295c804e0>"
+        "<matplotlib.figure.Figure at 0x7fc8e59dc0f0>"
        ]
       }
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@@ -1553,7 +1553,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295bad518>"
+        "<matplotlib.figure.Figure at 0x7fc8e56a9358>"
        ]
       },
       {
@@ -1561,7 +1561,7 @@
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295bad9e8>"
+        "<matplotlib.figure.Figure at 0x7fc8e56a90b8>"
        ]
       },
       {
@@ -1569,7 +1569,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295e7d5c0>"
+        "<matplotlib.figure.Figure at 0x7fc8e58b87f0>"
        ]
       }
      ],
@@ -1604,7 +1604,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295c88630>"
+        "<matplotlib.figure.Figure at 0x7fc8e56a92e8>"
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       },
       {
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295bf7f28>"
+        "<matplotlib.figure.Figure at 0x7fc8e45cdeb8>"
        ]
       }
      ],
@@ -1660,7 +1660,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1294302160>"
+        "<matplotlib.figure.Figure at 0x7fc8e45eaf60>"
        ]
       },
       {
@@ -1668,7 +1668,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1294302860>"
+        "<matplotlib.figure.Figure at 0x7fc8e458b4a8>"
        ]
       },
       {
@@ -1676,7 +1676,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1294382be0>"
+        "<matplotlib.figure.Figure at 0x7fc8e465c7b8>"
        ]
       }
      ],
@@ -1753,9 +1753,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295de43c8>"
+        "<matplotlib.figure.Figure at 0x7fc8e570d7b8>"
        ]
       }
      ],
@@ -1784,9 +1784,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295e8f0f0>"
+        "<matplotlib.figure.Figure at 0x7fc8e58d16d8>"
        ]
       }
      ],
@@ -1813,9 +1813,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295de5278>"
+        "<matplotlib.figure.Figure at 0x7fc8e5892630>"
        ]
       }
      ],
@@ -1844,9 +1844,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295e83400>"
+        "<matplotlib.figure.Figure at 0x7fc8e59597f0>"
        ]
       }
      ],
@@ -1892,9 +1892,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295cccac8>"
+        "<matplotlib.figure.Figure at 0x7fc8e45d6c18>"
        ]
       }
      ],
@@ -1921,9 +1921,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1295ccdc50>"
+        "<matplotlib.figure.Figure at 0x7fc8e578a278>"
        ]
       }
      ],

01_gh_filter/README.md

@@ -1,2 +0,0 @@
-You may read this book online via nbviewer by using this link:
-[*Read Online Now*](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)

code/book_format.py

@@ -64,14 +64,14 @@ def _decode_dict(data):
     return rv
 
 
-def load_style(name='../styles/custom2.css'):
+def load_style(name='./styles/custom2.css'):
     if sys.version_info[0] >= 3:
-        s = json.load( open("../code/538.json"))
+        s = json.load( open("./code/538.json"))
     else:
-        s = json.load( open("../code/538.json"), object_hook=_decode_dict)
+        s = json.load( open("./code/538.json"), object_hook=_decode_dict)
     plt.rcParams.update(s)
     reset_axis ()
     np.set_printoptions(suppress=True)
 
     styles = open(name, 'r').read()
-    return HTML(styles)
+    return HTML(styles)