Book format changes.

Moved book_format out of code to simplify importing it into the
notebooks. Added code to it to suppress future warnings from
matplotlib 1.4.3, and to set the numpy print precision to 3
decimal places.

00-Preface.ipynb

@@ -270,8 +270,6 @@
    ],
    "source": [
     "#format the book\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style() "
    ]

01-g-h-filter.ipynb

@@ -264,13 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -333,7 +327,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x490e240>"
+       "<matplotlib.figure.Figure at 0x491e048>"
       ]
      },
      "metadata": {},
@@ -367,7 +361,7 @@
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7245c18>"
+       "<matplotlib.figure.Figure at 0x73a7b38>"
       ]
      },
      "metadata": {},
@@ -398,7 +392,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74690b8>"
+       "<matplotlib.figure.Figure at 0x76ed198>"
       ]
      },
      "metadata": {},
@@ -433,7 +427,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0467\n"
+      "Average of measurements is 165.0058\n"
      ]
     }
    ],
@@ -464,7 +458,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9520\n"
+      "Average of measurements is 164.9607\n"
      ]
     }
    ],
@@ -498,7 +492,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x779eb38>"
+       "<matplotlib.figure.Figure at 0x7f23e48>"
       ]
      },
      "metadata": {},
@@ -529,7 +523,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb400>"
+       "<matplotlib.figure.Figure at 0x7fbf5f8>"
       ]
      },
      "metadata": {},
@@ -558,7 +552,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8035ac8>"
+       "<matplotlib.figure.Figure at 0x7ffde48>"
       ]
      },
      "metadata": {},
@@ -587,9 +581,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb860>"
+       "<matplotlib.figure.Figure at 0x7f72a90>"
       ]
      },
      "metadata": {},
@@ -619,9 +613,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7445ef0>"
+       "<matplotlib.figure.Figure at 0x48ce240>"
       ]
      },
      "metadata": {},
@@ -656,7 +650,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81565c0>"
+       "<matplotlib.figure.Figure at 0x739f518>"
       ]
      },
      "metadata": {},
@@ -685,7 +679,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76fbda0>"
+       "<matplotlib.figure.Figure at 0x73d50b8>"
       ]
      },
      "metadata": {},
@@ -728,7 +722,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8098d68>"
+       "<matplotlib.figure.Figure at 0x803d908>"
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      },
      "metadata": {},
@@ -785,7 +779,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9427ef0>"
+       "<matplotlib.figure.Figure at 0x83d1f98>"
       ]
      },
      "metadata": {},
@@ -849,7 +843,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x946f160>"
+       "<matplotlib.figure.Figure at 0x8363358>"
       ]
      },
      "metadata": {},
@@ -891,7 +885,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8157438>"
+       "<matplotlib.figure.Figure at 0x8397978>"
       ]
      },
      "metadata": {},
@@ -984,7 +978,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff75f8>"
+       "<matplotlib.figure.Figure at 0x8046a20>"
       ]
      },
      "metadata": {},
@@ -1393,7 +1387,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x72f2b38>"
+       "<matplotlib.figure.Figure at 0x80120f0>"
       ]
      },
      "metadata": {},
@@ -1485,9 +1479,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7456748>"
+       "<matplotlib.figure.Figure at 0x809b5c0>"
       ]
      },
      "metadata": {},
@@ -1522,9 +1516,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76f0240>"
+       "<matplotlib.figure.Figure at 0x7714278>"
       ]
      },
      "metadata": {},
@@ -1576,9 +1570,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x949d0f0>"
+       "<matplotlib.figure.Figure at 0x772f908>"
       ]
      },
      "metadata": {},
@@ -1639,9 +1633,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x96231d0>"
+       "<matplotlib.figure.Figure at 0x95780f0>"
       ]
      },
      "metadata": {},
@@ -1706,7 +1700,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x96768d0>"
+       "<matplotlib.figure.Figure at 0x9585080>"
       ]
      },
      "metadata": {},
@@ -1784,7 +1778,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7797550>"
+       "<matplotlib.figure.Figure at 0x95d3cf8>"
       ]
      },
      "metadata": {},
@@ -1826,7 +1820,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9455fd0>"
+       "<matplotlib.figure.Figure at 0x957a828>"
       ]
      },
      "metadata": {},
@@ -1871,7 +1865,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9439048>"
+       "<matplotlib.figure.Figure at 0x7f25278>"
       ]
      },
      "metadata": {},
@@ -1918,7 +1912,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x83e2f28>"
+       "<matplotlib.figure.Figure at 0x7f243c8>"
       ]
      },
      "metadata": {},
@@ -1966,7 +1960,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8384ac8>"
+       "<matplotlib.figure.Figure at 0x73db630>"
       ]
      },
      "metadata": {},
@@ -2012,7 +2006,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8146f60>"
+       "<matplotlib.figure.Figure at 0x9591278>"
       ]
      },
      "metadata": {},
@@ -2048,7 +2042,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x77cdc88>"
+       "<matplotlib.figure.Figure at 0x80bc160>"
       ]
      },
      "metadata": {},
@@ -2114,7 +2108,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9693cf8>"
+       "<matplotlib.figure.Figure at 0x802aac8>"
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      "metadata": {},
@@ -2165,7 +2159,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x77c3320>"
+       "<matplotlib.figure.Figure at 0x83f6f60>"
       ]
      },
      "metadata": {},
@@ -2196,7 +2190,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8180470>"
+       "<matplotlib.figure.Figure at 0x96009b0>"
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      },
      "metadata": {},
@@ -2229,7 +2223,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8174048>"
+       "<matplotlib.figure.Figure at 0x9418828>"
       ]
      },
      "metadata": {},
@@ -2279,7 +2273,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x963a2e8>"
+       "<matplotlib.figure.Figure at 0x96033c8>"
       ]
      },
      "metadata": {},
@@ -2310,7 +2304,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9462908>"
+       "<matplotlib.figure.Figure at 0x93e8940>"
       ]
      },
      "metadata": {},
@@ -2467,10 +2461,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 1.965   0.375 ]\n",
-      " [ 2.868   0.507 ]\n",
-      " [ 3.875   0.632 ]\n",
-      " [ 4.9014  0.7306]]\n"
+      "[[ 1.965  0.375]\n",
+      " [ 2.868  0.507]\n",
+      " [ 3.875  0.632]\n",
+      " [ 4.901  0.731]]\n"
      ]
     }
    ],

02-Discrete-Bayes.ipynb

@@ -120,8 +120,9 @@
        "    }\n",
        "    div.text_cell_render{\n",
        "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 115%;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -234,7 +235,7 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -263,11 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -369,9 +366,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e52080>"
+       "<matplotlib.figure.Figure at 0x71f4a20>"
       ]
      },
      "metadata": {},
@@ -500,9 +497,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e8ee80>"
+       "<matplotlib.figure.Figure at 0x7949908>"
       ]
      },
      "metadata": {},
@@ -550,9 +547,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7446dd8>"
+       "<matplotlib.figure.Figure at 0x7e3b4e0>"
       ]
      },
      "metadata": {},
@@ -624,9 +621,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74b3cc0>"
+       "<matplotlib.figure.Figure at 0x7e977b8>"
       ]
      },
      "metadata": {},
@@ -634,9 +631,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7525588>"
+       "<matplotlib.figure.Figure at 0x7e7f6a0>"
       ]
      },
      "metadata": {},
@@ -700,9 +697,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7570208>"
+       "<matplotlib.figure.Figure at 0x797d128>"
       ]
      },
      "metadata": {},
@@ -742,9 +739,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x436c6a0>"
+       "<matplotlib.figure.Figure at 0x797ab38>"
       ]
      },
      "metadata": {},
@@ -775,9 +772,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75ba128>"
+       "<matplotlib.figure.Figure at 0x491b748>"
       ]
      },
      "metadata": {},
@@ -804,9 +801,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x748de80>"
+       "<matplotlib.figure.Figure at 0x803b8d0>"
       ]
      },
      "metadata": {},
@@ -896,9 +893,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x749cb38>"
+       "<matplotlib.figure.Figure at 0x80286d8>"
       ]
      },
      "metadata": {},
@@ -930,9 +927,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x765aeb8>"
+       "<matplotlib.figure.Figure at 0x80286a0>"
       ]
      },
      "metadata": {},
@@ -1016,9 +1013,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ec5cc0>"
+       "<matplotlib.figure.Figure at 0x7e9e4a8>"
       ]
      },
      "metadata": {},
@@ -1041,9 +1038,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74663c8>"
+       "<matplotlib.figure.Figure at 0x8050278>"
       ]
      },
      "metadata": {},
@@ -1072,9 +1069,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76b7c50>"
+       "<matplotlib.figure.Figure at 0x8050438>"
       ]
      },
      "metadata": {},
@@ -1102,9 +1099,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75c8898>"
+       "<matplotlib.figure.Figure at 0x8105668>"
       ]
      },
      "metadata": {},
@@ -1133,9 +1130,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75df4e0>"
+       "<matplotlib.figure.Figure at 0x81007f0>"
       ]
      },
      "metadata": {},
@@ -1166,9 +1163,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75c8c18>"
+       "<matplotlib.figure.Figure at 0x7e85dd8>"
       ]
      },
      "metadata": {},
@@ -1240,9 +1237,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74e07f0>"
+       "<matplotlib.figure.Figure at 0x80d4080>"
       ]
      },
      "metadata": {},
@@ -1277,9 +1274,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ecd080>"
+       "<matplotlib.figure.Figure at 0x7faa7b8>"
       ]
      },
      "metadata": {},
@@ -1308,9 +1305,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x763f710>"
+       "<matplotlib.figure.Figure at 0x8071320>"
       ]
      },
      "metadata": {},
@@ -1368,9 +1365,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74b73c8>"
+       "<matplotlib.figure.Figure at 0x80c4e10>"
       ]
      },
      "metadata": {},
@@ -1545,11 +1542,18 @@
       "predicted position is 6 withconfidence 100.0%:\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:16: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7844cf8>"
+       "<matplotlib.figure.Figure at 0x7e5a710>"
       ]
      },
      "metadata": {},
@@ -1596,11 +1600,18 @@
       "predicted position is 5 withconfidence 47.83%:\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:16: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76efe48>"
+       "<matplotlib.figure.Figure at 0x81f7780>"
       ]
      },
      "metadata": {},
@@ -1632,9 +1643,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7997198>"
+       "<matplotlib.figure.Figure at 0x7f2ce48>"
       ]
      },
      "metadata": {},

03-Gaussians.ipynb

@@ -264,13 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -636,7 +630,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4940160>"
+       "<matplotlib.figure.Figure at 0x71f4668>"
       ]
      },
      "metadata": {},
@@ -670,9 +664,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7cc9828>"
+       "<matplotlib.figure.Figure at 0x802a0b8>"
       ]
      },
      "metadata": {},
@@ -682,8 +676,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean = 1.788\n",
-      "std  = 0.139\n"
+      "mean = 1.818\n",
+      "std  = 0.131\n"
      ]
     }
    ],
@@ -798,7 +792,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAACxCAYAAAALSUEOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npysLuOkAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7afb710>"
+       "<matplotlib.figure.Figure at 0x8005ac8>"
       ]
      },
      "metadata": {},
@@ -892,7 +886,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b18da0>"
+       "<matplotlib.figure.Figure at 0x7d24a20>"
       ]
      },
      "metadata": {},
@@ -930,7 +924,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x887c940>"
+       "<matplotlib.figure.Figure at 0x88d3978>"
       ]
      },
      "metadata": {},
@@ -969,7 +963,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x88a95f8>"
+       "<matplotlib.figure.Figure at 0x88d3f28>"
       ]
      },
      "metadata": {},
@@ -1052,7 +1046,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89a5828>"
+       "<matplotlib.figure.Figure at 0x88d8860>"
       ]
      },
      "metadata": {},
@@ -1176,9 +1170,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9ab1b70>"
+       "<matplotlib.figure.Figure at 0x9a98da0>"
       ]
      },
      "metadata": {},
@@ -1233,7 +1227,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b77be0>"
+       "<matplotlib.figure.Figure at 0x9b73630>"
       ]
      },
      "metadata": {},
@@ -1271,7 +1265,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9ac8710>"
+       "<matplotlib.figure.Figure at 0x9ad9780>"
       ]
      },
      "metadata": {},
@@ -1410,9 +1404,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 2.447  4.083 -0.846  0.879  3.904  0.432  5.224\n",
-      "  2.652  2.469  5.71  -1.672 -2.814 -3.919 -2.951\n",
-      " -1.152]\n"
+      "[-2.166 -0.353  6.399  5.244  5.104  4.299 -0.337\n",
+      "  4.528  2.062 -1.354 -3.177  6.797  2.646  5.988\n",
+      "  1.212]\n"
      ]
     }
    ],
@@ -1516,7 +1510,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89fc828>"
+       "<matplotlib.figure.Figure at 0x89fa588>"
       ]
      },
      "metadata": {},
@@ -1571,9 +1565,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAADaCAYAAAAMl0zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAADaCAYAAAAMl0zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a17860>"
+       "<matplotlib.figure.Figure at 0x88fc080>"
       ]
      },
      "metadata": {},
@@ -1624,9 +1618,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAADaCAYAAAAMl0zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAADaCAYAAAAMl0zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x88432b0>"
+       "<matplotlib.figure.Figure at 0x7d1aa20>"
       ]
      },
      "metadata": {},

04-One-Dimensional-Kalman-Filters.ipynb

@@ -264,13 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -450,7 +444,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1YAAAEWCAYAAABlmwYuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71faf28>"
+       "<matplotlib.figure.Figure at 0x489e198>"
       ]
      },
      "metadata": {},
@@ -491,9 +485,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71fa7f0>"
+       "<matplotlib.figure.Figure at 0x72039e8>"
       ]
      },
      "metadata": {},
@@ -536,9 +530,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4cde9e8>"
+       "<matplotlib.figure.Figure at 0x7206128>"
       ]
      },
      "metadata": {},
@@ -559,9 +553,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8056ef0>"
+       "<matplotlib.figure.Figure at 0x7fe3470>"
       ]
      },
      "metadata": {},
@@ -592,7 +586,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81d90b8>"
+       "<matplotlib.figure.Figure at 0x81606d8>"
       ]
      },
      "metadata": {},
@@ -624,7 +618,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81d9438>"
+       "<matplotlib.figure.Figure at 0x81609b0>"
       ]
      },
      "metadata": {},
@@ -660,7 +654,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81c3dd8>"
+       "<matplotlib.figure.Figure at 0x814ca20>"
       ]
      },
      "metadata": {},
@@ -698,7 +692,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8824a58>"
+       "<matplotlib.figure.Figure at 0x4cfcda0>"
       ]
      },
      "metadata": {},
@@ -793,7 +787,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x88462e8>"
+       "<matplotlib.figure.Figure at 0x8841a58>"
       ]
      },
      "metadata": {},
@@ -849,7 +843,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x884df98>"
+       "<matplotlib.figure.Figure at 0x8916d68>"
       ]
      },
      "metadata": {},
@@ -900,7 +894,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89430b8>"
+       "<matplotlib.figure.Figure at 0x89346a0>"
       ]
      },
      "metadata": {},
@@ -991,7 +985,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89bf8d0>"
+       "<matplotlib.figure.Figure at 0x89b60b8>"
       ]
      },
      "metadata": {},
@@ -1249,7 +1243,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a799e8>"
+       "<matplotlib.figure.Figure at 0x9a74cf8>"
       ]
      },
      "metadata": {},
@@ -1310,7 +1304,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9afe5c0>"
+       "<matplotlib.figure.Figure at 0x9ae6588>"
       ]
      },
      "metadata": {},
@@ -1461,7 +1455,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a4ec88>"
+       "<matplotlib.figure.Figure at 0x9a66f98>"
       ]
      },
      "metadata": {},
@@ -1576,7 +1570,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9ab9b70>"
+       "<matplotlib.figure.Figure at 0x9a4bac8>"
       ]
      },
      "metadata": {},
@@ -1643,7 +1637,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x83559e8>"
+       "<matplotlib.figure.Figure at 0x82dd0f0>"
       ]
      },
      "metadata": {},
@@ -1673,7 +1667,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvwAAAEtCAYAAAB00rSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcVOX+B/DPGXbZRdwCxQ13NMQlFQU1ASs1r2luQN5r\nZtmVNC20giy5peV2XUpzy7o/zTJNr6WpuOSORuVCaplLaa6AkIDA8/vjuTMyrAMMzAN83q/XvOCc\n88w5z5mvjN955nueowkhBIiIiIiIqFrSWboDRERERERUcZjwExERERFVY0z4iYiIiIiqMSb8RERE\nRETVGBN+IiIiIqJqjAk/EREREVE1xoSfiIiIiKgas2jCv2/fPgwcOBBeXl7Q6XRYs2ZNgTZnz57F\nkCFD4O7uDkdHR3Tq1AlJSUmG7UFBQdDpdEaPkSNHVuZpEBEREREpy6IJf3p6Ovz8/LBgwQI4ODhA\n0zSj7RcuXECPHj3QrFkzxMfH49SpU5g1axacnJwMbTRNw9ixY3Ht2jXD48MPP6zsUyEiIiIiUpK1\nJQ8eFhaGsLAwAEBkZGSB7TNmzEBoaCjmzJljWOfj41OgnYODA+rWrVtR3SQiIiIiqrKUreHPzc3F\n1q1b0bp1a4SGhqJu3bro0qULPvvsswJt161bB09PT7Rr1w5Tp05FWlqaBXpMRERERKQei47wF+f6\n9etIS0tDXFwc3n77bcyePRu7du3CqFGj4OTkhAEDBgAARo4cCR8fHzRs2BAnT55EdHQ0fvzxR2zf\nvt3CZ0BEREREZHnKJvy5ubkAgMGDByMqKgoA4Ofnh4SEBCxatMiQ8I8bN87wnLZt26JZs2bo0qUL\nvv/+ezz88MMAgJSUlEruPRERERGR+bi6upb5ucqW9NSpUwfW1tZo06aN0fpWrVrh0qVLRT7P398f\nVlZWOH/+fEV3kYiIiIiquNOndfDwcMHevValet5//2uNunVd8OuvyqbTBsr20NbWFp07dzaaghOQ\n03QWduGu3k8//YScnBw0aNCggntIRERERCr48Ucd/vUvO1y6pJXcOJ8ZM+zxyCM56N07p1TPe+yx\nbLRpk4OYGPtSH7OyWbSkJz09HefOnQMgS3guXryIxMREeHh4wNvbG9OmTcOwYcMQGBiI4OBgxMfH\nY/369di8eTMA4Ndff8Unn3yCxx57DB4eHjh9+jSmTJkCf39/9OjRo9BjlufrECq/hIQEAEBAQICF\ne1KzMQ7qYCzUwDiog7FQQ1WLwy+/ALNnAwMG2KN9e9Ofd+gQsGcPsHlz2XLEyZOBiAjg999dka8o\nxSzMVZauCSGEWfZUBnv27EGfPn1kRzQN+q5ERkZi5cqVAIA1a9YgLi4Oly9fhq+vL6KjozF8+HAA\nwJUrVzB69GicPHkSaWlp8Pb2xuOPP46YmBi4ubkZjpP3xWLCT0RERDVZTg6QlQU4OFi6J+azejUw\ndiwQHw/07m3688aMAXbsAP74A7AqXUUPACA9HahXTx574cLSP78k5sphLVrSExQUhNzcXOTm5iIn\nJ8fwuz7ZB4CIiAj8/PPP+Ouvv5CYmGhI9gHAy8sLe/bswc2bN5GRkYFz585h3rx5Rsk+ERERkd7q\n1YBOB+zeDcycCTRuDNSqBXTrBhw5Itvs3Qv07Ak4OQENGwJvv11wP5mZQFwc0LatTJzd3YGBA4HE\nRON2aWnAa68BXbsCnp6AvT3QogUQHQ3cu2fcNiMDiI0FWrYEHB3lPv38gGnTjNvFxspzKOySRh8f\nIDi44Pnu2gW89RbQrJnsr36Wc1PPw1yvW1mOGR8PvPee7Lu9vXx9Pv7Y+PUYO1b+Hhwsn6PTAc88\nU/jx9bKzgU2bgH79ypbsAzJOgYHA55+X7fmVRdlZeoiIiIgqyquvArm5wEsvyQT0/feB/v2BNWuA\nf/wDGD9ejv6uXw+88QbQpAkwapR87v37QGioLAcJDwf++U8gORlYvhzo0QPYtw/o1Em2vXIFWLEC\nGDoUGD0asLaWJSSzZwPffw98882DPr3wArBqlSwR6d5dJqRnz8qE11SaJh/5vfyy3N/48YCLC9Cq\nVenOwxyvW2lfO73p0+WHoQkTAFtbYOlSIDISaN5cvk5/+xtw7RqwbBkwYwbQurV8XrNmxb9Wx4/L\nEfouXUx/fQvTrRuwfTvw88/yw4iSRA2QnJxseJBlHTt2TBw7dszS3ajxGAd1MBZqYBzUUdGxWLVK\nCE0TolMnIe7ff7D+q6/kehsbIY4ff7A+K0uIBg2EeOSRB+vmzpVtd+ww3ndqqhCNGgkRFGT8/Ozs\ngv14/XW5j6NHH6xzdxfiscdKPoeYGPncixcLbmvcWIjg4ILn26qVEPfuGbct7jzq188QnTqlFNhP\neV63ko6Z/7XTH9Pf3/iYv/8uhJ2dECNGFGy7d2/B16QoK1fK52zZUvj2tDQhhgwRwsVFiH//W65b\ns0aIhATjdmvXyv1s3Gj6sU1lrhyWI/xUqarKxT/VHeOgDsZCDYyDOiorFhMmyNF2vZ495c9u3QB/\n/wfrbWyAzp2BgwcfrPvkEzmK7O8P3LxpvN9+/WS5SWYmYGcnn6+XnQ3cvStr6Pv2lSUvR4/K/QOA\nmxtw8iRw6pQsdzH3+drnm0ymuPMYMMAOH39sZziPvPsp6+tW0jHzv3Z6zz9vfMyGDQFfX6C8M7Df\nuCF/1q5d+PbZs+W3DqNHyziNHCm/Dcj/DYSHh/x5/Xr5+lORmPATERFRjdO0qfGyu7v82aRJwbbu\n7sCtWw+Wz5yRJSaenoXvW9NkMvvQQ3J5yRLggw+A06dlOUxed+48+H3+fFkO07697F9wMPDEE/JR\nWJlOafj6FlxX2vMAyve6meuYgEzSL18ufB+m0r+mRU1fExX14PwaNZJlO/+7F6wR/fPLG6OKxISf\niIiIapyiLtI05eJNIeTFtHPnFt2mTh35c+5cWT8fEiKTxYYNZR36lSuyDj3vB4CBA4HffgO2bZMX\nwO7cKev/AwPl7/pvC4pLLLOzC19fq1b5zkOvPK+buY9Z3nkm9R86bt8ufLs+2d+xQ35QmT698Hb6\n5xf1IUYFTPipUlW1eX2rK8ZBHYyFGhgHdVSFWPj6yvKN4OCSR3XXrpWj319/bbw+78W6ebm7y4tc\n9Re6vvqqLC3ZvFle+As8KEG5fVuOPOtlZABXrxY+ml/a89DHwc7OvHEozWtXGmXZl36+/v/dEqpQ\n//d/sgRr0qSi2+hLi9q1K30fKouyd9ql6ikgIEDpN/GagnFQB2OhBsZBHarGIm9CGR4uZ4UpapT6\nzz8f/K6vPc87kp+dDbzzjvFzcnPlbDX5dewof+Yt/dHPBPPtt8Zt580r3ah3cecREBAAb+/yxyF/\nIl6a1640nJzkz/wlRMXp2FHOWHToUOHbV6+W026OHi2XhZDTqeZ3+DBQv76cblVVHOEnIiIiKkHe\nRHrSJJlsT50q56UPDpaJ46VLcr57Bwe5HpCj8tHRQFgY8OSTQGoq8J//yLKevFJTgQYNgEGDZCJa\nty5w4YKcgrJ2bVnHr9evn0z633hDJrg+PsB338n58OvUMT3pL815lFX+vpjzmHn33aWLnHt/1iz5\nzYejo6z9L27KTSsrYMgQORd/VpZxTDZulN+q+PgAv/8u+/jpp3La0bzS0oD9++WUpCpjwk9EREQ1\nSmnLP/LPbW9tDfz3v/Ji3LVr5Y2fAHmhaZcuch59valTZWK6YoWs4W/QABg+XNbvt2nzoJ2jo5zb\nftcuWa+flibr/QcPlh8Y6td/0FanA776Ss5h/+9/y0Q1JETW/ffoUfD8ijrf0pxHcfspSmH3BDDX\nMfPv29sbWLkSePddOavP/fvyNS5pjv0JE+RI/tatMvkH5LcMP/8MfPml/NA1eLC8kHj2bPmBLK8v\nvpA3UMv/QUA1mhDlveRBfea6LTGVX1WozawJGAd1MBZqYBzUwViooSbFISxM3oBr377SP9ffX36T\nUFF32jVXDsuEn4iIiIhqrNOngQ4d5IXV/fqZ/rxNm4Cnn5b3TSjprr5lxYS/FJjwExEREVFVY64c\nlrP0EBERERFVY7xolypVTaoJVBnjoA7GQg2MgzoYC8sRQiA1NRsnT2bj7Nl7AABfXwe0a2cNFxdr\naCrfSpaKxZIeIiIiohpOCIEjRzIQE2ODHTusAOiTe4GQkBy8+eZ9dOliz6S/krGGvxSY8BMREREV\nTp/sh4baIyWl8ITe1VXgm28y0LUrk/7KxBp+IiIiIiq31NRsxMTYFJnsA0BKiobYWBukpmZXYs/I\nXJjwU6VKSEgw1GeS5TAO6mAs1MA4qIOxqHwnT2b/r4yneNu3W+HUKSb8VREv2qVKxYuw1MA4qIOx\nUAPjoA7GovJduaLhQc1+cTRcvsxynqrIYiP8+/btw8CBA+Hl5QWdToc1a9YUaHP27FkMGTIE7u7u\ncHR0RKdOnZCUlFSgnRACYWFh0Ol0+OKLLyqj+0REREREVYLFEv709HT4+flhwYIFcHBwKHAByIUL\nF9CjRw80a9YM8fHxOHXqFGbNmgUnJ6cC+3r//fdhZSW/iuKFJERERESm8/ISAEyZw0XA27vaz/VS\nLVmspCcsLAxhYWEAgMjIyALbZ8yYgdDQUMyZM8ewzsfHp0C7Y8eOYeHChTh+/Djq1atXUd0lM+H8\nympgHNTBWKiBcVAHY1H52rWzRv/+Odixo/i0MCQkB23bshq8KlIyarm5udi6dSteffVVhIaG4sSJ\nE/Dx8cHLL7+MYcOGGdrdvXsXI0eOxPLly+Hp6WnBHpOp+AauBsZBHYyFGhgHdTAWlc/FxRozZ2bg\nyBGrImfqcXPLRWzsfbi42Fdy78gclJyl5/r160hLS0NcXBxCQ0Oxc+dOjBgxAqNGjLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x887c978>"
+       "<matplotlib.figure.Figure at 0x880b438>"
       ]
      },
      "metadata": {},
@@ -1736,9 +1730,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8846240>"
+       "<matplotlib.figure.Figure at 0x8974588>"
       ]
      },
      "metadata": {},
@@ -1772,7 +1766,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9af99b0>"
+       "<matplotlib.figure.Figure at 0x9ae22e8>"
       ]
      },
      "metadata": {},
@@ -1813,9 +1807,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAAEWCAYAAAA9wcIeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlYVdX+x/H3YRScynDACc0cMk1NTCVNLaQULQXBKaf0\nOvzKi3otrzfLBrNBr9dMMUtzHlCcpxRzKIdKMgwVK0NNg4PiPILA+f3hdRtXBFRwnwOf1/P4PK3F\n2vt8Nm0437NZe22LzWazISIiIiIidsnJ7AAiIiIiInJ7KthFREREROyYCnYRERERETumgl1ERERE\nxI6pYBcRERERsWMq2EVERERE7JgKdhERERERO5argj08PJyqVavi4eGBr68v27dvv+3YlJQUevfu\nTb169XBzc6NVq1bZ7nv79u24uLhQt27dO0suIiIiIlII5FiwR0REMGTIEEaNGkVMTAx+fn60adOG\nY8eOZTk+PT0dDw8PBg8eTGBgIBaL5bb7PnPmDD179sTf3z/bcSIiIiIihZUlpyedNm7cmPr16zNt\n2jSjr0aNGnTq1ImxY8dmu/NXX32V/fv3s2XLliy/HhQURIMGDcjIyCAyMpLY2Ni7OAQRERERkYIr\n2yvsqamp7Nmzh4CAgEz9AQEB7Ny5855eODw8nJMnTzJq1Chy+MwgIiIiIlJouWT3xeTkZNLT0ylb\ntmym/jJlymC1Wu/6RWNjY3n33Xf5/vvvNRVGRERERCQb2Rbs+SElJYXOnTszfvx4fHx8chx/7ty5\n+5BKRERERCR/lCxZ8p62z7Zg9/LywtnZmaSkpEz9SUlJeHt739ULJiYmcvDgQfr06UOfPn0AyMjI\nwGaz4erqyvr16/H397+rfYuIiIiIFDTZzmF3c3OjYcOGbNy4MVN/VFQUfn5+d/WCFStWZN++fezd\nu9f4N3DgQB555BH27t1L06ZN72q/IiIiIiIFUY5TYoYNG0aPHj148skn8fPz47PPPsNqtTJw4EAA\nRo4cye7du9m0aZOxzYEDB0hNTSU5OZmLFy+yd+9ebDYb9evXx8XFhdq1a2d6jdKlS+Pu7n5L//+6\n1z8niOS36OhoAHx9fU1OIpIzna/iSHS+iqPJy2ndORbsoaGhnDp1ijFjxpCYmEjdunVZt24dlSpV\nAsBqtRIfH59pm8DAQI4ePQqAxWKhQYMGWCwW0tPTs3wNi8Wim09FRERERLKQ4zrsZvvrpxNdYRd7\npytA4kh0vooj0fkqjiYva9gcn3QqIiIiIiLmUcEuIiIiImLHVLCLiIiIiNgxFewiIiIiInZMBbuI\niIiIiB3LcVlHERFxTOkZ6fx+5nd+O/Ubx88f5/j54yRcSOBC6gWupF3BesqKzWajzC9l8HT1xNPV\nk3LFylGxREUqlqhI1QeqUrt0bdxd3M0+FBGRQk0Fu4hIAWG9aGXL4S18c/QbfrL+ROyJWC5fu5zz\nhqdv/yVnizO1vGpRv1x9nqr0FK2qtqLmQzX17AwRkftIBbuIiIPKsGXw3fHvWHpgKesPrScuOS7P\nXyPdls7+k/vZf3I/82PnA+BdzJvW1VoT/GgwAdUCKOJSJM9fV0REblLBLiLiYGKsMcz8aSaRcZEk\nXEjIdmzZomV5rMxj+JT0oWKJilQoXoEHijyAh6sHx+KPYbFYqPxwZa5cu8LF1IskXEjg+PnjHDt/\njLjkOOLPxN+yz8SLiczZO4c5e+dQzK0Y7Wq0o+fjPQmoFoCzk3N+HbaISKGlgl1ExAFcSr3Ewn0L\n+fzHz9mdsDvLMW7ObjSp2IRWVVrhV8mPemXrUbZY2dvuM/rCf58cWeP2T468kHKB2BOx/PDnD2w5\nsoVtR7ZxLuXm0/supl5k0b5FLNq3iMolK9O3QV/6NuhLhRIV7vJIRUTkf1lsNpvN7BDZycvHuork\nNz06W/LayUsn+fSHT5n8w2TOXD1zy9cf8niIjrU6EvRoEC2rtMTD1SPX+76b8zU9I509iXtYfnA5\nkQci+e30b7eMcXFyoXvd7rz+1OvULl071/sWyY5+v4qjycsaVlfYRUTsUOKFRMZ+O5YZP83gStqV\nTF9zc3Yj+NFgXm7wMi2rtMTF6f79Knd2cqZRhUY0qtCI9595n9gTsczZO4dZMbM4deUUAGkZacze\nO5vZe2fTvkZ73m75Nk94P3HfMoqIFDRah11ExI6cvXqWf339L6pNqsbk3ZMzFesPP/gwEwIm8Oew\nP1kQvAD/h/3va7H+vywWC4+XfZzxAeP5c9ifLApeRLPKzTKNWf3rahp+3pAukV04dPqQSUlFRByb\nCnYRETuQlpHGJ999wsOfPMwH2z/IVKg3KNeARcGL+OXVXxjadChenl4mJs2au4s7net05ts+37Lj\n5R28UPOFTF+P2B/Bo1Me5ZW1r3D6SjbrSIqIyC1UsIuImGzbkW00mNaAIRuGZJqnXr9cfdZ2W8uP\n/X+kc53Opl5NvxN+lfxY2WUl+wbto2OtjkZ/WkYa4dHh1Pi0Bl/8+AUZtgwTU4qIOA4V7CIiJjl5\n6STdl3Wn5eyW7Duxz+iv9mA1FgYv5Mf+P9K2eluHfUjRY2UeY1nnZezqu4uWVVoa/aeunKL/mv40\nmd6Evda95gUUEXEQKthFREywZP8SaofXZkHsAqOvqGtRPvL/iAOvHKBLnS44WQrGr+gmFZuwuedm\nlndejk9JH6N/d8JufL/w5e2tb5OanmpiQhER+1Yw3g1ERBzEiUsn6LS4E6GRoSRfTjb6Oz/WmYOv\nHuT1p17HzdnNxIT5w2Kx0KFWBw68coA3n34Td2d34Po0mXe2vUOjLxrxU+JPJqcUEbFPKthFRO6T\njb9v5PGpj7M0bqnRV7FERdZ1W8eiTouoWKKiienuD09XT95t9S57B+7Fr5Kf0f9z0s80nt6Yf+/8\nt+a2i4j8DxXsIiL5LDU9ldc2vsZz854j6VKS0d+vQT/2DdpHm+ptTExnjppeNfmm9zdMCJiAh8v1\nhz1dy7jG8KjhtJnfButFq8kJRUTshwp2EZF8dOTsEZ768inG7xpv9JUtWpb13dfzxQtfULJI4X2C\ns7OTM0ObDmXvwL34lr/59MqNv2+k3mf12Hx4s4npRETshwp2EZF8suHQBhp+3pDohGij7/lHnufn\nQT/z/CPPm5jMvlR/qDo7Xt7B636vG30nLp2g9dzWfLzjY2w2m4npRETMp4JdRCSPZdgyGPPNGNrM\nb2M8JMjVyZUJARNY220tZYqWMTmh/XFzduOj1h8R1SOKskXLAte/jyM2jaDTkk6cTzlvckIREfPk\numAPDw+natWqeHh44Ovry/bt2287NiUlhd69e1OvXj3c3Nxo1arVLWOWLVtGQEAAZcqUoUSJEjRp\n0oTVq1ff3VGIiNiJS6mXCFkSwptb3sTG9SvD5YuXZ1vvbQxtOrTALNWYX/wf9mfPgD2ZbkhdFreM\nJtOb8Pvp301MJiJinly9c0RERDBkyBBGjRpFTEwMfn5+tGnThmPHjmU5Pj09HQ8PDwYPHkxgYGCW\nD/345ptv8Pf3Z926dcTExNC2bVs6duyY7QcBERF7dvz8cZrPbM6yuGVGXwufFuzpv4emlZqamMyx\nlC9eni29tvD3J/9u9MUlx9F4emO+OfqNiclERMxhseVicmDjxo2pX78+06ZNM/pq1KhBp06dGDt2\nbLbbvvrqq+zfv58tW7bkGKZx48Y0b96c8eNv3px17tw5479Lliy8N2eJY4iOvj5X2dfXN4eRUtDs\n/nM3Ly56kcSLiUZfWOMwxgeMx8XJxcRkt+cI5+u8n+fRb1U/UtJTgOtTi6a1m0afBn1MTib3myOc\nryJ/lZc1bI5X2FNTU9mzZw8BAQGZ+gMCAti5c+c9vfj/On/+PKVKlcrTfYqI5Lc1v66hxawWRrHu\n4uTCtHbTmPj8RLst1h3FS4+/xJZeW4x5/9cyrvHyqpcZvWW0bkYVkUIjx4I9OTmZ9PR0ypYtm6m/\nTJkyWK15t07ulClTSEhIoEePHnm2TxGR/DZ9z3ReXPQiV9KuAPBgkQfZ8NIG+jfsb3KygqNppab8\n0O8H6papa/S9+827/G3130jLSDMxmYjI/WEXl36WLl3K66+/zuLFi6lUqdJtx934c5iIvdO5WvDZ\nbDamH5rO579+bvSV9yjPJ09+QolTJYg+5TjngKOcr582+JR/7vkn3538DoAZP83glz9/YWyDscbD\nl6Tgc5TzVaR69ep5tq8cr7B7eXnh7OxMUlJSpv6kpCS8vb3vOUBkZCQ9e/Zk7ty5BAYG3vP+RETy\nW4Ytg3H7x2Uq1muVqMUMvxlUKVbFvGAFXFGXovzH9z8EVrz5XrH9xHZe+f4VLly7YGIyEZH8leMV\ndjc3Nxo2bMjGjRsJDg42+qOioggJCbmnF1+8eDG9e/dmzpw5BAUF5TheN5qIvdNNUQVfWkYaL698\nmSVHlxh9rR9uzdLQpRR3L25isjvnqOfr6karGbV5FGO3X1/0IPZsLEP3DmVjj41a474Ac9TzVQqv\nv950eq9ytazjsGHDmDVrFjNmzCAuLo6wsDCsVisDBw4EYOTIkfj7+2fa5sCBA8TExJCcnMzFixfZ\nu3cvMTExxtcXLVpE9+7d+eijj2jWrBlWqxWr1crp06fz7OBERPJSSloKoUtCmfvzXKOvS50urOm2\nxuGKdUdmsVh4/9n3mfT8JKNvb9Jenp75NMfOZb3csIiII8vVHPbQ0FBOnTrFmDFjSExMpG7duqxb\nt86Yb261WomPj8+0TWBgIEePHgWu/3Jt0KABFouF9PR0AKZNm0ZGRgZhYWGEhYUZ27Vs2ZLNmzfn\nycGJiOSVq2lX6RjRka8OfWX09WvQj8/afYazk7OJyQqvwY0HU9y9OH1X9SXDlsEvpLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b0b390>"
+       "<matplotlib.figure.Figure at 0x87f6080>"
       ]
      },
      "metadata": {},
@@ -1980,14 +1974,30 @@
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'variance' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-29-6516ba48a651>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     12\u001b[0m                     \u001b[0mP\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m        \u001b[1;31m# initial variance\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m                                    \u001b[1;31m# large # says 'who knows?'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m                     \u001b[0mR\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvariance\u001b[0m\u001b[1;33m,\u001b[0m    \u001b[1;31m# sensor noise\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m                     Q=process_var) # error in prediction\n\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'variance' is not defined"
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEPCAYAAAAqKhsoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2a7b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9b34e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variance converges to 0.005\n"
      ]
     }
    ],
@@ -2002,11 +2012,11 @@
     "ps = []\n",
     "estimates = []\n",
     "\n",
-    "kf = KalmanFilter1D(x0=25,         # initial state\n",
-    "                    P=1000,        # initial variance \n",
-    "                                   # large # says 'who knows?'\n",
-    "                    R=variance,    # sensor noise\n",
-    "                    Q=process_var) # error in prediction\n",
+    "kf = KalmanFilter1D(x0=25,            # initial state\n",
+    "                    P=1000,           # initial variance \n",
+    "                                      # large # says 'who knows?'\n",
+    "                    R=voltage_std**2, # sensor noise\n",
+    "                    Q=process_var)    # error in prediction\n",
     "\n",
     "for i in range(N):\n",
     "    kf.predict(temp_change)\n",
@@ -2088,7 +2098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -2106,11 +2116,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAAEWCAYAAAAuFoLqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9b34f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "variance = 2.13**2\n",
     "process_var = 0.1**2\n",
@@ -2161,11 +2182,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x8919ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "velocity = 1\n",
     "sensor_var = 300**2\n",
@@ -2208,11 +2240,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAEPCAYAAABx6xN1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x89931d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "velocity = 1\n",
     "sensor_var = 20\n",
@@ -2260,11 +2303,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9a2f9e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "velocity = 1\n",
     "sensor_var = 30\n",
@@ -2308,11 +2362,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAEPCAYAAADh+Gq6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x8166940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sensor_var = 30000\n",
     "process_var = 2\n",
@@ -2343,11 +2408,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x885aa20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sensor_var = 30000\n",
     "process_var = 2\n",
@@ -2392,7 +2468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
@@ -2431,11 +2507,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x82c7ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from numpy.random import seed \n",
     "def plot_kalman_filter(start_pos, \n",
@@ -2497,7 +2584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
@@ -2515,11 +2602,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ea97b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sensor_var = 30\n",
     "process_var = 2\n",
@@ -2575,7 +2673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
@@ -2593,11 +2691,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9e642b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sensor_var = 30\n",
     "process_var = 2\n",

06-Multivariate-Kalman-Filters.ipynb

@@ -16,19 +16,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -264,7 +256,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -272,13 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -369,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -379,7 +365,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x806e5c0>"
+       "<matplotlib.figure.Figure at 0x47da780>"
       ]
      },
      "metadata": {},
@@ -409,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 3,
    "metadata": {
     "collapsed": true
    },
@@ -481,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -493,7 +479,7 @@
        "       [  4.5]])"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -516,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -528,7 +514,7 @@
        "       [  4.5]])"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -547,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 6,
    "metadata": {
     "collapsed": true
    },
@@ -586,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -598,7 +584,7 @@
        "       [   0.,  400.]])"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -617,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -629,7 +615,7 @@
        "       [   0.,  400.]])"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -734,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -764,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -796,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -833,7 +819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -845,7 +831,7 @@
        "       [ 120.,  400.]])"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -865,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -875,7 +861,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x896ef28>"
+       "<matplotlib.figure.Figure at 0x800dba8>"
       ]
      },
      "metadata": {},
@@ -937,7 +923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -946,11 +932,11 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 0.00005875,  0.001175  ],\n",
-       "       [ 0.001175  ,  0.0235    ]])"
+       "array([[ 0.   ,  0.001],\n",
+       "       [ 0.001,  0.024]])"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -987,7 +973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -1031,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -1040,7 +1026,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e7b518>"
+       "<matplotlib.figure.Figure at 0x802df98>"
       ]
      },
      "metadata": {},
@@ -1106,7 +1092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 17,
    "metadata": {
     "collapsed": true
    },
@@ -1145,7 +1131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 18,
    "metadata": {
     "collapsed": true
    },
@@ -1185,7 +1171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -1204,7 +1190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -1245,7 +1231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 21,
    "metadata": {
     "collapsed": true
    },
@@ -1282,7 +1268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -1366,7 +1352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1374,9 +1360,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv0AAAE6CAYAAACFw+2FAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xc73c908>"
+       "<matplotlib.figure.Figure at 0x98e3400>"
       ]
      },
      "metadata": {},
@@ -1386,7 +1372,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c99470>"
+       "<matplotlib.figure.Figure at 0x99496a0>"
       ]
      },
      "metadata": {},
@@ -1425,7 +1411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1435,7 +1421,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xbc629e8>"
+       "<matplotlib.figure.Figure at 0x9a8fa90>"
       ]
      },
      "metadata": {},
@@ -1512,7 +1498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -1521,7 +1507,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b23d68>"
+       "<matplotlib.figure.Figure at 0x7eadc18>"
       ]
      },
      "metadata": {},
@@ -1560,7 +1546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1570,7 +1556,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xb567908>"
+       "<matplotlib.figure.Figure at 0x9888f60>"
       ]
      },
      "metadata": {},
@@ -1624,7 +1610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1634,7 +1620,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9992ba8>"
+       "<matplotlib.figure.Figure at 0x9a9d7f0>"
       ]
      },
      "metadata": {},
@@ -1794,7 +1780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -1814,7 +1800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1824,7 +1810,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9c7c240>"
+       "<matplotlib.figure.Figure at 0x9aa0780>"
       ]
      },
      "metadata": {},
@@ -1905,7 +1891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -1914,7 +1900,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xbc0de80>"
+       "<matplotlib.figure.Figure at 0x9dbb9e8>"
       ]
      },
      "metadata": {},
@@ -1934,7 +1920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -1943,7 +1929,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b7a0f0>"
+       "<matplotlib.figure.Figure at 0x98f2be0>"
       ]
      },
      "metadata": {},
@@ -1986,7 +1972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1996,7 +1982,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xc768be0>"
+       "<matplotlib.figure.Figure at 0x999f390>"
       ]
      },
      "metadata": {},
@@ -2006,7 +1992,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c869e8>"
+       "<matplotlib.figure.Figure at 0x8826128>"
       ]
      },
      "metadata": {},
@@ -2046,7 +2032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2056,7 +2042,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9c4eef0>"
+       "<matplotlib.figure.Figure at 0x882c710>"
       ]
      },
      "metadata": {},
@@ -2077,7 +2063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
@@ -2086,7 +2072,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xbc41e48>"
+       "<matplotlib.figure.Figure at 0x9b74438>"
       ]
      },
      "metadata": {},
@@ -2108,7 +2094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
@@ -2117,7 +2103,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xb6465f8>"
+       "<matplotlib.figure.Figure at 0x9d8d160>"
       ]
      },
      "metadata": {},
@@ -2155,7 +2141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
@@ -2164,7 +2150,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xbbcf9b0>"
+       "<matplotlib.figure.Figure at 0x7b1a438>"
       ]
      },
      "metadata": {},
@@ -2174,7 +2160,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a10c88>"
+       "<matplotlib.figure.Figure at 0x478e160>"
       ]
      },
      "metadata": {},
@@ -2209,7 +2195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
@@ -2218,7 +2204,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9bf6e10>"
+       "<matplotlib.figure.Figure at 0x8888cf8>"
       ]
      },
      "metadata": {},
@@ -2228,7 +2214,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9c3a9e8>"
+       "<matplotlib.figure.Figure at 0x9a66320>"
       ]
      },
      "metadata": {},
@@ -2253,7 +2239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
@@ -2262,7 +2248,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9cdc940>"
+       "<matplotlib.figure.Figure at 0x9ab5710>"
       ]
      },
      "metadata": {},
@@ -2287,7 +2273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
@@ -2296,7 +2282,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9c28828>"
+       "<matplotlib.figure.Figure at 0x80291d0>"
       ]
      },
      "metadata": {},
@@ -2327,7 +2313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -2336,7 +2322,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xbc16a20>"
+       "<matplotlib.figure.Figure at 0x99a6710>"
       ]
      },
      "metadata": {},
@@ -2346,7 +2332,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xb56ef28>"
+       "<matplotlib.figure.Figure at 0x9db4b70>"
       ]
      },
      "metadata": {},
@@ -2429,7 +2415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
@@ -2438,7 +2424,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a290b8>"
+       "<matplotlib.figure.Figure at 0x9a4c940>"
       ]
      },
      "metadata": {},
@@ -2489,7 +2475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
@@ -2564,7 +2550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false
    },
@@ -2573,7 +2559,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1de278>"
+       "<matplotlib.figure.Figure at 0x992ff98>"
       ]
      },
      "metadata": {},
@@ -2615,7 +2601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 44,
    "metadata": {
     "collapsed": false
    },
@@ -2624,7 +2610,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b35cf8>"
+       "<matplotlib.figure.Figure at 0x9ac9320>"
       ]
      },
      "metadata": {},
@@ -2656,7 +2642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
@@ -2665,7 +2651,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b0e2e8>"
+       "<matplotlib.figure.Figure at 0x88603c8>"
       ]
      },
      "metadata": {},
@@ -2697,7 +2683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 46,
    "metadata": {
     "collapsed": true
    },
@@ -2715,7 +2701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
@@ -2724,7 +2710,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9c50828>"
+       "<matplotlib.figure.Figure at 0xa706390>"
       ]
      },
      "metadata": {},

07-Kalman-Filter-Math.ipynb

@@ -264,11 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -417,9 +413,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7941240>"
+       "<matplotlib.figure.Figure at 0x7fcd978>"
       ]
      },
      "metadata": {},
@@ -451,9 +447,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x43bc518>"
+       "<matplotlib.figure.Figure at 0x71ef6d8>"
       ]
      },
      "metadata": {},
@@ -481,9 +477,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f5c0>"
+       "<matplotlib.figure.Figure at 0x869a470>"
       ]
      },
      "metadata": {},
@@ -915,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -1126,7 +1122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -1153,7 +1149,7 @@
        "⎣    6           2                 ⎦       "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1188,7 +1184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -1209,7 +1205,7 @@
        "[\\Delta{t}⋅\\Phi_s]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1224,7 +1220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -1253,7 +1249,7 @@
        "⎣    2                 ⎦       "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1303,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -1325,7 +1321,7 @@
        "⎣    2                 ⎦    "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1365,7 +1361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -1392,7 +1388,7 @@
        "⎣    2                             ⎦    "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1430,7 +1426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -1439,8 +1435,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.33333333  0.5       ]\n",
-      " [ 0.5         1.        ]]\n"
+      "[[ 0.333  0.5  ]\n",
+      " [ 0.5    1.   ]]\n"
      ]
     }
    ],
@@ -1454,7 +1450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -1463,9 +1459,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.05        0.125       0.16666667]\n",
-      " [ 0.125       0.33333333  0.5       ]\n",
-      " [ 0.16666667  0.5         1.        ]]\n"
+      "[[ 0.05   0.125  0.167]\n",
+      " [ 0.125  0.333  0.5  ]\n",
+      " [ 0.167  0.5    1.   ]]\n"
      ]
     }
    ],
@@ -1483,7 +1479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -1504,7 +1500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -1535,7 +1531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -1544,9 +1540,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.00000002  0.00000078  0.00002083]\n",
-      " [ 0.00000078  0.00004167  0.00125   ]\n",
-      " [ 0.00002083  0.00125     0.05      ]]\n"
+      "[[ 0.     0.     0.   ]\n",
+      " [ 0.     0.     0.001]\n",
+      " [ 0.     0.001  0.05 ]]\n"
      ]
     }
    ],
@@ -1634,7 +1630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1642,9 +1638,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8042400>"
+       "<matplotlib.figure.Figure at 0xa4aac18>"
       ]
      },
      "metadata": {},
@@ -1653,6 +1649,7 @@
    ],
    "source": [
     "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
     "t = np.linspace(-1, 1, 10)\n",
     "plt.plot(t, np.exp(t))\n",
     "t = np.linspace(0, 1, 2)\n",
@@ -1668,16 +1665,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEtCAYAAACxoC4JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcXOW9P/DPzMAMMywT9j1AEghCViALxGh2TaLRal1S\nl0T7qre2ejX+Wm/rtWprql7bq1WvsVWrscaoca3WNJqYfTMhkAWyQQIk7PsAwyww8/z+GJhhwhLC\ndmaGz/v14sXMc55z5kuOx3x48pznyIQQAkRERERE5HLkUhdAREREREQ9Y1gnIiIiInJRDOtERERE\nRC6KYZ2IiIiIyEUxrBMRERERuSiGdSIiIiIiF8WwTkRERETkoiQN6xaLBb/73e8wbtw4qNVqjBs3\nDr/73e9gsVikLIuIiIiIyCV4Sfnh//M//4N169bhH//4ByZPnoxjx45h9erVUKlUePLJJ6UsjYiI\niIhIcpKG9f3792PFihVYvnw5AGDs2LG44YYbcOjQISnLIiIiIiJyCZJOg5k7dy62b9+OM2fOAABO\nnjyJHTt2YNmyZVKWRURERETkEiQdWf+v//ovNDU1ISUlBQqFAu3t7XjyySfx85//XMqyiIiIiIhc\ngqRh/aOPPsL777+PDz/8EKmpqcjNzcUjjzyC+Ph43H///fZ+Op1OwiqJiIiIiAZHq9UOaD+ZEEIM\ncS39Fhsbi8cffxwPP/ywve2Pf/wj1q9fj4KCAnsbwzoRERERubOBhnVJ56wbDAbI5c4lyOVySPj7\nAxERERGRy5B0GsyNN96IF154AQkJCUhJSUFubi5efvllrFq1qtd9BvpbCUkvOzsbAJCRkSFxJTRY\nPJeeg+fSc/Bceg6eS88xFLNDJA3rr732Gn73u9/hF7/4BaqrqxEZGYkHHngATz31lJRlERERERG5\nBEnDup+fH15++WW8/PLLUpZBREREROSSJJ2zTkREREREvWNYJyIiIiJyUQzrREREREQuimGdiIiI\niMhFMawTEREREbkohnUiIiIiIhfFsE5ERERE5KIY1omIiIiIXBTDOhERERGRi2JYJyIiIiJyUQzr\nREREREQuimGdiIiIiGiIWa2WITkOwzoRERER0RCqrL+I5zc8MiTH8hqSoxAREREREc6VncRbXz+H\nVlPLkByPYZ2IiIiIaAgcKzyA97a8hHZL25Adk2GdiIiIiGiQdh/7Bp/tfBsCAgDgr9YOyXEZ1omI\niIiIBsgqrPjXvg3YduRze1vomCg8ePNTQ3J8hnUiIiIiogFot7Rh47b/Q/bpXfa2uIgk/MeKJ+Gn\nDoBOpxv0ZzCsExERERFdIYOpFe988z84c/GYvW1SwgysXvorKL1VQ/Y5ki7dGB8fD7lc3u3rhhtu\nkLIsIiIiIqJe6Vrq8eqnTzgF9axJS/DTG34zpEEdkHhk/ciRI7BYHAvGl5eXIz09HXfccYeEVRER\nERER9ayy/iLe+PIPaGiusbctz/wJlsy4DTKZbMg/T9KwHhwc7PT+rbfeglarxe233y5RRURERERE\nPTtffgpvfvVH+xrqcpkcdy78JWanLhy2z3SZOetCCPz973/H3XffDZVqaP/5gIiIiIhoMI4VHsQ/\ntryENosZAKD09sH9y36NlPj0Yf1clwnrW7duRXFxMX72s59JXQoRERERkV1Pa6j/x02/w9jwCcP+\n2TIhhBj2T+mH2267DRcvXsTBgwe7beu67E1BQcFIlkVEREREo5QQAjklO5Bftt/e5u8ThEWpK+Hv\nE3jZ/RMTE+2vtdqBPSTJJUbWq6ur8dVXX2HdunVSl0JEREREBIvVggOFX+N8TZ69LcQvCgtS7oCP\nt++I1eESYX39+vXw8fHBypUrL9s3IyNjBCqi4ZCdnQ2A59AT8Fx6Dp5Lz8Fz6Tl4LqXXuYZ616Ce\nmpCB1Ut/BZW3T7+P4xEPRRJC4O2338add94JjUYjdTlERERENIrpWurx13/+AWW1xfa2rEmLcdv8\nn0MhV4x4PZKH9Z07d+LcuXPYuHGj1KUQERER0ShWVV+KN778Peq7rKG+bPZKXDfz9mFZQ70/JA/r\n8+fPd3owEhERERHRSDtffgpvfv0cWo3NAGxrqN+x8BfITF0kaV2Sh3UiIiIiIil1W0PdS4X7lz8+\n7Guo9wfDOhERERGNWruPbcZnO9+SZA31/mBYJyIiIqJRRwiBr/dvwLbsz+xtodpI/PzmpxA6JlLC\nypwxrBMRERHRqNJuacOH217H4dM77W1x4Yl4YMWT8NcM7OFFw4VhnYiIiIhGjc411M9cPGZvG8ga\n6iOFYZ2IiIiIRgWdvh5//eezKKspsrdJuYZ6fzCsExEREZHH62kN9aWzV+J6CddQ7w+GdSIiIiLy\naD2uob7gQWROWixxZZfHsE5EREREHqunNdTvW/ZrpCZkSFxZ/zCsExEREZFHunQNdT+1Fv+x4knE\nRSRKXFn/MawTERERkUdxlzXU+4NhnYiIiIg8hjutod4fDOtERERE5BGMZgP+/s0LOHOhyxrq8RlY\nvcw111DvD4Z1IiIiInJ7On09/vbPtSitOW9vy0xdjNsXuO4a6v3BsE5EREREbq2o4jTWb/4zGlpq\n7W1LZ92J62fd4dJrqPcHwzoRERERuSUhBHYe/Rr/3PserFYLAPdaQ70/GNaJiIiIyO0YTHps3Poa\njp07aG9Tq3yx6vrHkBKfLmFlQ4thnYiIiIjcysXqc3hn84uo01XZ28aGJ+K+Zb9CcEC4hJUNPYZ1\nIiIiInILQgjsz/sOn+16G+2WNnv7NVOX46arV8Pby1vC6oaHXOoCKioqsGrVKoSFhUGtViM1NRW7\nd++WuiwiIiIiciEmswH/+PZlfLz9DXtQVynVWL30V/jxvJ95ZFAHJB5Zb2xsxJw5c3DNNddg8+bN\nCA0Nxfnz5xEWFiZlWURERETkQirqLuCdb15EVUOpvS0qJB73L/s1wgKjJaxs+Eka1l988UVER0dj\n/fr19ra4uDjpCiIiIiIil3Lo1A5s2v5XmNtN9rbZqYvw43k/g9JLJWFlI0PSaTBffvklZs6ciTvu\nuAPh4eGYPn06Xn/9dSlLIiIiIiIXYG434cNtr2PDd6/Yg7q3lxJ3Lf5P/GTRQ6MiqAMSj6yfP38e\n69atw2OPPYYnnngCubm5ePjhhwEAv/zlL6UsjYiIiIgkUt1Qjnc3v4iy2mJ7W1hgNO5f9jiiQkbX\nLAyZEEJI9eFKpRIzZ87E3r177W3//d//jS+++AInT560t+l0OvvrgoKCEa2RiIiIiEZOSe0p7C/8\nGm0Ws70tPiQVmeOXwdvNRtMTExPtr7Va7YCOIenIelRUFFJSUpzakpOTceHCBYkqIiIiIiIpWKwW\nHCnehtMVh+1tcpkCMxKWICkiDTKZTMLqpCNpWJ8zZw5Onz7t1Hb27FnEx8f3uk9GRsYwV0XDJTs7\nGwDPoSfgufQcPJeeg+fSc4zGc1nfVI13N/8JJVWOGRTBAeG4f/njiA0bL2Flg9N1dshASRrW16xZ\ng6ysLDz33HO4/fbbkZubi9deew3PP/+8lGURERER0QjJL8rG+9/+Ba2mFnvblPGz8JPFD0Oj8pOw\nMtcgaVjPyMjAl19+iSeeeALPPvss4uLisHbtWjz44INSlkVEREREw8xiteCbAxuxLfsze5tcrsCK\nOfdi/vQVo3bay6UkDesAsGzZMixbtkzqMoiIiIhohOha6rF+y//iXFm+vW2MXzBWL/01xkUlS1iZ\n65E8rBMRERHR6HHmwjH8Y8tLaDY45nMnx03HvdetgZ86QMLKXBPDOhERERENO6uw4rtDn+DfBz+C\ngG3lcJlMjmWzV2LxjFshl0n6rE6XxbBORERERMOquVWH9799GacvHLW3+WvGYNX1/w9JsZMlrMz1\nMawTERER0bA5X34K7/77z9C11NnbJkSnYtXS/wetb5CElbkHhnUiIiIiGnJCCGzP+Se+3vcPWIXV\n3r5kxo+xdPZKKOQKCatzHwzrRERERDSkWo0t2LD1VeSdP2Rv0/j4497rHkVKfLqElbkfhnUiIiIi\nGjIXqgrxzuYXUd9UbW+Lj5iI1Ut/haCAUAkrc08M60REREQ0aEII7D3+b3y+5x1YLO329nnTbsSK\nq++Fl8JbwurcF8M6EREREQ2K0WzAR9+vQ87ZPfY2H6UGdy1+GFMnZEpYmftjWCciIiKiASuvLcY7\n37yI6sZye1tM6Djct+zXCB0TKWFlnoFhnYiIiIgG5IeT32PTjr+hrd1sb5sz6Trccu1P4e2llLAy\nz8GwTkRERERXxGBqxee73sYPp7bb25TePrhjwYOYkXythJV5HoZ1IiIiIuq3/KJsfLz9DTR2echR\nRFAs7l/+OCKCYiWszDMxrBMRERHRZTW36vD57r/jyJndTu0zkufh9gU/h8rbR6LKPBvDOhERERH1\nSgiBnLN78Omut6E3NNnb/dRa/HjezzA9cQ5kMpmEFXo2hnUiIiIi6lFDcy027fgr8ouyndpnJM/D\nLdfcD191gESVjR4M60RERETkxCqsOJC3FV/uXQ+T2WBvD/QLwR0LH0RKfLqE1Y0uDOtEREREZFfd\nUI6Pvn8dhWX5Tu1zpyzDjXPugY9SLVFloxPDOhERERHBYrVgZ+5X2HzgQ7RZHOumh42JwspFv8T4\n6FQJqxu95FJ++DPPPAO5XO70FRUVJWVJRERERKNOWU0RXvr4cfxz73v2oC6XybE441b8111/YVCX\nkOQj68nJydi5c6f9vUKhkK4YIiIiolGkrb0N3x3ehK3Zn8NqtdjbY0LHYeWihxAbNk7C6ghwgbCu\nUCgQFhYmdRlEREREo8r58tP48Pv/Q1V9qb3NS+GNpbPuxIK0m6BQSB4TCS4Q1s+fPLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8df28d0>"
+       "<matplotlib.figure.Figure at 0xa4c9908>"
       ]
      },
      "metadata": {},
@@ -1702,7 +1699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 19,
    "metadata": {
     "collapsed": true
    },
@@ -1719,7 +1716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -1749,16 +1746,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEjCAYAAADuR70GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8e504a8>"
+       "<matplotlib.figure.Figure at 0xa540a90>"
       ]
      },
      "metadata": {},
@@ -1778,7 +1775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -1812,7 +1809,7 @@
     "\n",
     "Here are the first 3 terms of the Euler expansion of $y$. An infinite expansion would give an exact answer, so $O(h^4)$ denotes the error due to the finite expansion.\n",
     "\n",
-    "$$y(t_0 + h) = y(t_0) + h y'(t_0) + \\frac{1}{2!}h^2 y''(t_0) + \\frac{1}{3!}h^3 y'''(t_0) +  O(h^4).$$\n",
+    "$$y(t_0 + h) = y(t_0) + h y'(t_0) + \\frac{1}{2!}h^2 y''(t_0) + \\frac{1}{3!}h^3 y'''(t_0) +  O(h^4)$$\n",
     "\n",
     "Here we can see that Euler's method is using the first two terms of the Taylor expansion. Each subsequent term is smaller than the previous terms, so we are assured that the estimate will not be too far off from the correct value. "
    ]
@@ -1859,7 +1856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -1897,7 +1894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -1911,9 +1908,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAAEWCAYAAADMypmTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9e795f8>"
+       "<matplotlib.figure.Figure at 0xa5740f0>"
       ]
      },
      "metadata": {},
@@ -1967,7 +1964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -1976,7 +1973,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp8AAAGNCAYAAABXHhoFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9eb0da0>"
+       "<matplotlib.figure.Figure at 0xa5e8908>"
       ]
      },
      "metadata": {},
@@ -2192,7 +2189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -2325,9 +2322,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [
     {
@@ -2369,7 +2367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -2403,7 +2401,7 @@
       "pos       [ 799.9476  198.6025]\n",
       "\n",
       "\n",
-      "Iterated solution:  [ 799.9475854   198.60245871]\n"
+      "Iterated solution:  [ 799.948  198.602]\n"
      ]
     }
    ],

08-Designing-Kalman-Filters.ipynb

@@ -120,8 +120,9 @@
        "    }\n",
        "    div.text_cell_render{\n",
        "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 115%;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -263,13 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "import warnings\n",
-    "warnings.simplefilter(action = \"ignore\", category = FutureWarning)\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -326,10 +321,6 @@
     "        self.noise_std = noise_std\n",
     "        self.pos = [pos[0], pos[1]]\n",
     "        \n",
-    "        print(self.vel)\n",
-    "        print(self.noise_std)\n",
-    "        print(self.pos)\n",
-    "        \n",
     "    def read(self):\n",
     "        self.pos[0] += self.vel[0]\n",
     "        self.pos[1] += self.vel[1]\n",
@@ -353,20 +344,11 @@
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 1)\n",
-      "1\n",
-      "[4, 3]\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7197748>"
+       "<matplotlib.figure.Figure at 0x7231518>"
       ]
      },
      "metadata": {},
@@ -492,10 +474,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.0125  0.025   0.      0.    ]\n",
-      " [ 0.025   0.05    0.      0.    ]\n",
-      " [ 0.      0.      0.0125  0.025 ]\n",
-      " [ 0.      0.      0.025   0.05  ]]\n"
+      "[[ 0.013  0.025  0.     0.   ]\n",
+      " [ 0.025  0.05   0.     0.   ]\n",
+      " [ 0.     0.     0.013  0.025]\n",
+      " [ 0.     0.     0.025  0.05 ]]\n"
      ]
     }
    ],
@@ -636,8 +618,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 5.  0.]\n",
-      " [ 0.  5.]]\n"
+      "[[5 0]\n",
+      " [0 5]]\n"
      ]
     }
    ],
@@ -699,20 +681,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 1)\n",
-      "1.0\n",
-      "[0, 0]\n"
-     ]
-    },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71d4f60>"
+       "<matplotlib.figure.Figure at 0x852c8d0>"
       ]
      },
      "metadata": {},
@@ -775,20 +748,11 @@
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 1)\n",
-      "1.0\n",
-      "[0, 0]\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8039e80>"
+       "<matplotlib.figure.Figure at 0x8727d68>"
       ]
      },
      "metadata": {},
@@ -852,7 +816,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.25675401  0.10095634  0.25675401  0.10095634]\n"
+      "[ 0.257  0.101  0.257  0.101]\n"
      ]
     }
    ],
@@ -878,15 +842,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.25675401  0.10192183]\n",
-      " [ 0.10192183  0.10095634]]\n"
+      "[[ 0.257  0.102]\n",
+      " [ 0.102  0.101]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fba588>"
+       "<matplotlib.figure.Figure at 0x86c2898>"
       ]
      },
      "metadata": {},
@@ -994,9 +958,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8095048>"
+       "<matplotlib.figure.Figure at 0x86c2518>"
       ]
      },
      "metadata": {},
@@ -1310,9 +1274,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80c3898>"
+       "<matplotlib.figure.Figure at 0x885a390>"
       ]
      },
      "metadata": {},
@@ -1362,9 +1326,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81609b0>"
+       "<matplotlib.figure.Figure at 0x86e3828>"
       ]
      },
      "metadata": {},
@@ -1395,9 +1359,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x810bb38>"
+       "<matplotlib.figure.Figure at 0x887c160>"
       ]
      },
      "metadata": {},
@@ -1428,9 +1392,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80c2588>"
+       "<matplotlib.figure.Figure at 0x99ffef0>"
       ]
      },
      "metadata": {},
@@ -1465,9 +1429,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81e0470>"
+       "<matplotlib.figure.Figure at 0x9a13940>"
       ]
      },
      "metadata": {},
@@ -1506,9 +1470,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x929c2b0>"
+       "<matplotlib.figure.Figure at 0x99e7668>"
       ]
      },
      "metadata": {},
@@ -1541,9 +1505,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9203d30>"
+       "<matplotlib.figure.Figure at 0x9a802e8>"
       ]
      },
      "metadata": {},
@@ -1580,9 +1544,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9213470>"
+       "<matplotlib.figure.Figure at 0x991aeb8>"
       ]
      },
      "metadata": {},
@@ -1623,9 +1587,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92ddc50>"
+       "<matplotlib.figure.Figure at 0x9b01f28>"
       ]
      },
      "metadata": {},
@@ -1656,9 +1620,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92ddb00>"
+       "<matplotlib.figure.Figure at 0x9a2cb38>"
       ]
      },
      "metadata": {},
@@ -1666,9 +1630,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80e0240>"
+       "<matplotlib.figure.Figure at 0x9a2c240>"
       ]
      },
      "metadata": {},
@@ -1708,9 +1672,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80e00f0>"
+       "<matplotlib.figure.Figure at 0x884c438>"
       ]
      },
      "metadata": {},
@@ -1718,9 +1682,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81b8a58>"
+       "<matplotlib.figure.Figure at 0x87a35c0>"
       ]
      },
      "metadata": {},
@@ -1760,9 +1724,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x808beb8>"
+       "<matplotlib.figure.Figure at 0x8717ef0>"
       ]
      },
      "metadata": {},
@@ -1801,9 +1765,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x932d978>"
+       "<matplotlib.figure.Figure at 0x87b8d30>"
       ]
      },
      "metadata": {},
@@ -1857,9 +1821,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92d3e48>"
+       "<matplotlib.figure.Figure at 0x8717a20>"
       ]
      },
      "metadata": {},
@@ -1867,9 +1831,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8049978>"
+       "<matplotlib.figure.Figure at 0x883bef0>"
       ]
      },
      "metadata": {},
@@ -1907,9 +1871,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x95bc278>"
+       "<matplotlib.figure.Figure at 0x87c9630>"
       ]
      },
      "metadata": {},
@@ -1917,9 +1881,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81ef0f0>"
+       "<matplotlib.figure.Figure at 0x87c9400>"
       ]
      },
      "metadata": {},
@@ -1952,9 +1916,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81b3080>"
+       "<matplotlib.figure.Figure at 0x88db550>"
       ]
      },
      "metadata": {},
@@ -1962,9 +1926,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x810b5f8>"
+       "<matplotlib.figure.Figure at 0x9925588>"
       ]
      },
      "metadata": {},
@@ -2124,9 +2088,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81c0780>"
+       "<matplotlib.figure.Figure at 0x99ff780>"
       ]
      },
      "metadata": {},
@@ -2165,9 +2129,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92ae9e8>"
+       "<matplotlib.figure.Figure at 0x9ac3208>"
       ]
      },
      "metadata": {},
@@ -2258,9 +2222,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9335898>"
+       "<matplotlib.figure.Figure at 0x88d23c8>"
       ]
      },
      "metadata": {},
@@ -2360,9 +2324,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92f93c8>"
+       "<matplotlib.figure.Figure at 0x886ada0>"
       ]
      },
      "metadata": {},
@@ -2443,9 +2407,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x933d4e0>"
+       "<matplotlib.figure.Figure at 0x86c29e8>"
       ]
      },
      "metadata": {},
@@ -2479,9 +2443,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x937f940>"
+       "<matplotlib.figure.Figure at 0x9a46390>"
       ]
      },
      "metadata": {},
@@ -2531,9 +2495,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x922f320>"
+       "<matplotlib.figure.Figure at 0x9a98a20>"
       ]
      },
      "metadata": {},
@@ -2617,9 +2581,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x967bc50>"
+       "<matplotlib.figure.Figure at 0x9b4f940>"
       ]
      },
      "metadata": {},
@@ -2627,9 +2591,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x967bd30>"
+       "<matplotlib.figure.Figure at 0x9b4f048>"
       ]
      },
      "metadata": {},
@@ -2637,9 +2601,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92ecfd0>"
+       "<matplotlib.figure.Figure at 0x9b165f8>"
       ]
      },
      "metadata": {},
@@ -2696,9 +2660,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80d6668>"
+       "<matplotlib.figure.Figure at 0x9c7a7b8>"
       ]
      },
      "metadata": {},
@@ -2706,9 +2670,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81b8400>"
+       "<matplotlib.figure.Figure at 0x879fc18>"
       ]
      },
      "metadata": {},
@@ -2716,9 +2680,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92f2c88>"
+       "<matplotlib.figure.Figure at 0x9b01470>"
       ]
      },
      "metadata": {},
@@ -2825,9 +2789,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x933dcc0>"
+       "<matplotlib.figure.Figure at 0x9a886a0>"
       ]
      },
      "metadata": {},
@@ -2911,10 +2875,10 @@
      "output_type": "stream",
      "text": [
       "[ 1.  1.]\n",
-      "[ 2.00297178  0.91591901]\n",
-      "[ 2.96323207  0.99653588]\n",
-      "[ 4.12415719  0.97035201]\n",
-      "[ 5.03224847  0.95933159]\n"
+      "[ 2.003  0.916]\n",
+      "[ 2.963  0.997]\n",
+      "[ 4.124  0.97 ]\n",
+      "[ 5.032  0.959]\n"
      ]
     }
    ],
@@ -2996,9 +2960,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x968c5c0>"
+       "<matplotlib.figure.Figure at 0x8705b70>"
       ]
      },
      "metadata": {},
@@ -3186,9 +3150,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x969e908>"
+       "<matplotlib.figure.Figure at 0x9af6550>"
       ]
      },
      "metadata": {},
@@ -3493,9 +3457,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9934908>"
+       "<matplotlib.figure.Figure at 0x87ae320>"
       ]
      },
      "metadata": {},
@@ -3614,9 +3578,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81ab9e8>"
+       "<matplotlib.figure.Figure at 0x8718128>"
       ]
      },
      "metadata": {},
@@ -3762,15 +3726,14 @@
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'g' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-59-bd0b26dbf187>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[0mxs2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\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[0;32m     25\u001b[0m     \u001b[0mys2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m     \u001b[0mf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m     \u001b[0mf2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'g' is not defined"
-     ]
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x8723eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -3779,6 +3742,7 @@
     "theta = 35. # launch angle\n",
     "v0 = 50.\n",
     "dt = 1/10.   # time step\n",
+    "g = 9.8\n",
     "\n",
     "ball = BaseballPath(x0=x, y0=y, launch_angle_deg=theta,\n",
     "                    velocity_ms=v0, noise=[.3,.3])\n",
@@ -3826,17 +3790,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9918908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9a10b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def plot_ball_with_q(q, r=1., noise=0.3):\n",
     "    x, y = 0., 1.\n",
     "    theta = 35. # launch angle\n",
     "    v0 = 50.\n",
     "    dt = 1/10.   # time step\n",
+    "    g = 9.8\n",
     "\n",
     "    ball = BaseballPath(x0=x, \n",
     "                        y0=y, \n",
@@ -3878,11 +3864,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9be49e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9b014a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot_ball_with_q(0.01, r=3, noise=3.)\n",
     "plot_ball_with_q(0.1, r=3, noise=3.)"
@@ -3899,24 +3906,6 @@
     "For now we will set this problem aside, as we are clearly misapplying the Kalman filter in this example. We will revisit this problem in subsequent chapters to see the effect of using various nonlinear techniques. In some domains you will be able to get away with using a linear Kalman filter for a nonlinear problem, but usually you will have to use one or more of the techniques you will learn in the rest of this book."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tracking Noisy Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we are applying a Kalman filter to a thermometer in an oven in a factory then our task is done once the Kalman filter is designed. The data from the thermometer may be noisy, but there is never doubt that the thermometer is reading the temperature of *some other* oven. Contrast this to our current situation, where we are using computer vision to detect ball blobs from a video camera. For any frame we may detect or may not detect the ball, and we may have one or more spurious blobs - blobs not associated with the ball at all. This can occur because of limitations of the computer vision code, or due to foreign objects in the scene, such as a bird flying through the frame. Also, in the general case we may have no idea where the ball starts from. A ball may be picked up, carried, and thrown from any position, for example. A ball may be launched within view of the camera, or the initial launch might be off screen and the ball merely travels through the scene. There is the possibility of bounces and deflections - the ball can hit the ground and bounce, it can bounce off a wall, a person, or any other object in the scene.\n",
-    "\n",
-    "Consider some of the problems that can occur. We could be waiting for a ball to appear, and a blob is detected. We initialize our Kalman filter with that blob, and look at the next frame to detect where the ball is going. Maybe there is no blob in the next frame. Can we conclude that the blob in the previous frame was noise? Or perhaps the blob was valid, but we did not detect the blob in this frame.\n",
-    "\n",
-    "**author's note: not sure if I want to cover this. If I do, not sure I want to cover this here.**"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

09-Nonlinear-Filtering.ipynb

@@ -28,6 +28,7 @@
        "@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",
+       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
        "\n",
        "    div.cell{\n",
        "        width: 900px;\n",
@@ -118,9 +119,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 120%;\n",
+       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -233,7 +235,7 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -262,11 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -374,9 +372,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEPCAYAAACTNwgMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEPCAYAAACTNwgMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e01f28>"
+       "<matplotlib.figure.Figure at 0x7634c18>"
       ]
      },
      "metadata": {},
@@ -432,9 +430,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x435cac8>"
+       "<matplotlib.figure.Figure at 0x76219e8>"
       ]
      },
      "metadata": {},
@@ -466,9 +464,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x435c8d0>"
+       "<matplotlib.figure.Figure at 0x86b7dd8>"
       ]
      },
      "metadata": {},
@@ -508,9 +506,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa248cf8>"
+       "<matplotlib.figure.Figure at 0xad12978>"
       ]
      },
      "metadata": {},
@@ -550,9 +548,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa243438>"
+       "<matplotlib.figure.Figure at 0xade7ac8>"
       ]
      },
      "metadata": {},
@@ -584,8 +582,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "input  mean, variance: -0.0008, 1.0010\n",
-      "output mean, variance: -0.1237, 2.4052\n"
+      "input  mean, variance: -0.0007, 1.0005\n",
+      "output mean, variance: -0.1234, 2.4021\n"
      ]
     }
    ],
@@ -614,9 +612,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f25ef0>"
+       "<matplotlib.figure.Figure at 0xae6d828>"
       ]
      },
      "metadata": {},
@@ -626,7 +624,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output mean, variance: 0.0012, 2.2522\n"
+      "output mean, variance: 0.0011, 2.2510\n"
      ]
     }
    ],
@@ -659,8 +657,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "linear    output mean, variance: 0.0714, 7489.0102\n",
-      "nonlinear output mean, variance: -9.1074, 30507.3721\n"
+      "linear    output mean, variance: 0.0639, 7485.2965\n",
+      "nonlinear output mean, variance: -8.9982, 30499.3277\n"
      ]
     }
    ],
@@ -695,9 +693,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7931f60>"
+       "<matplotlib.figure.Figure at 0x8763048>"
       ]
      },
      "metadata": {},
@@ -744,9 +742,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7947c18>"
+       "<matplotlib.figure.Figure at 0x8775ba8>"
       ]
      },
      "metadata": {},
@@ -774,9 +772,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b4bbe0>"
+       "<matplotlib.figure.Figure at 0x8a190b8>"
       ]
      },
      "metadata": {},
@@ -803,9 +801,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc6908>"
+       "<matplotlib.figure.Figure at 0x7b97d30>"
       ]
      },
      "metadata": {},
@@ -832,9 +830,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe6278>"
+       "<matplotlib.figure.Figure at 0x897f438>"
       ]
      },
      "metadata": {},
@@ -933,7 +931,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,

10-Unscented-Kalman-Filter.ipynb

@@ -120,8 +120,9 @@
        "    }\n",
        "    div.text_cell_render{\n",
        "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 115%;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -234,7 +235,7 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -263,11 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -295,9 +292,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x713be48>"
+       "<matplotlib.figure.Figure at 0x7990320>"
       ]
      },
      "metadata": {},
@@ -358,7 +355,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4f43ef0>"
+       "<matplotlib.figure.Figure at 0x71f3518>"
       ]
      },
      "metadata": {},
@@ -404,14 +401,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in mean x=-0.084, y=43.447\n"
+      "Difference in mean x=0.062, y=45.093\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApoAAAEWCAYAAADGljfBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7db60b8>"
+       "<matplotlib.figure.Figure at 0x90fd7b8>"
       ]
      },
      "metadata": {},
@@ -466,7 +463,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAD9CAYAAAC1Fh+gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7dc0898>"
+       "<matplotlib.figure.Figure at 0x83b9908>"
       ]
      },
      "metadata": {},
@@ -502,9 +499,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d2c2e8>"
+       "<matplotlib.figure.Figure at 0x8742a90>"
       ]
      },
      "metadata": {},
@@ -549,7 +546,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f405c0>"
+       "<matplotlib.figure.Figure at 0x87e8f28>"
       ]
      },
      "metadata": {},
@@ -598,9 +595,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAD9CAYAAAC1Fh+gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc2588>"
+       "<matplotlib.figure.Figure at 0x8716dd8>"
       ]
      },
      "metadata": {},
@@ -647,9 +644,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApoAAAEWCAYAAADGljfBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8fa8eb8>"
+       "<matplotlib.figure.Figure at 0x897d748>"
       ]
      },
      "metadata": {},
@@ -863,9 +860,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtkAAAEjCAYAAADnrB18AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtkAAAEjCAYAAADnrB18AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x93ed3c8>"
+       "<matplotlib.figure.Figure at 0x890b6a0>"
       ]
      },
      "metadata": {},
@@ -986,9 +983,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99c9a20>"
+       "<matplotlib.figure.Figure at 0x838bb38>"
       ]
      },
      "metadata": {},
@@ -1111,9 +1108,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl0VNed7v2nNM8DkkqleUJCA4hBYjDYeIih4yH29dtv\n7I47caa+vE4IbcxaIRfbCcRNICRebqdtcCes7oQMxElu5zrd6cTGsXFsWYAZJCbNaEaq0jwiNFSd\n9w9sXStIQsGlUkn6ftbSkjnnt49+8toUj4527WMyDMMQAAAAALfiMdMNAAAAALgeQR0AAABwQwR1\nAAAAwA0R1AEAAAA3RFAHAAAA3BBBHQAAAHBDBHUAAADADU05qL/zzjt64IEHFB8fLw8PDx06dOi6\nml27dikuLk4BAQG68847VVJSMub84OCgtmzZoqioKAUFBenBBx/U5cuXP/53AQAAAMwxUw7q/f39\nys3N1Q9+8AP5+/vLZDKNOb9v3z49//zzeumll3Ty5EmZzWZt2LBBfX19ozVbt27Vb3/7W73yyit6\n99131dPTo/vvv18Oh8N53xEAAAAwB5hu5smkwcHB2r9/vx577DFJkmEYio2N1T/+4z9qx44dkqSr\nV6/KbDbrueee06ZNm9Td3S2z2ayf/OQn+sxnPiNJamxsVFJSkv74xz9q48aNTvy2AAAAgNnNKWvU\na2pqZLPZxoRtPz8/rV+/XoWFhZKk06dPa3h4eExNfHy8srKyRmsAAAAAXOOUoG61WiVJ0dHRY46b\nzebRc1arVZ6enoqIiBhTEx0dLZvN5ow2AAAAgDnDa7q/wF+uZZ+K7u7uaegEAAAAcI3Q0NCPfQ2n\n3FG3WCySdN2dcZvNNnrOYrHIbrervb19TI3Vah2tAQAAAHCNU4J6SkqKLBaLjhw5Mnrs6tWrKigo\n0Nq1ayVJeXl58vb2HlPT2NiosrKy0RoAAAAA10x56Ut/f78qKyslSQ6HQ3V1dSouLlZERIQSEhK0\ndetW7dmzR5mZmUpPT9fu3bsVHBysRx99VNK12/9f/vKXtX37dpnNZi1YsEDbtm3T0qVLdffdd0/4\ndZ3xawPMTadOnZIk5efnz3AncGfME0wF8wQ3whyZHwzDUNXlCyo8f0RnLx3XiH143DqTyUPfe/wX\n8vXxH3Pc2cu3pxzUT548qbvuuuuD5kzauXOndu7cqS984Qv693//d23fvl0DAwPavHmzOjs7tWbN\nGh05ckSBgYGj13jhhRfk5eWlRx55RAMDA7r77rv185///KbWsQMAAADO0HulW++XHlXhhSNq7Wqa\nsM7Dw1OxkUlKis7Q0MjgdUHd2W5qH/Xp9tGfRrijjolwdwNTwTzBVDBPcCPMkbmn/2qvqptKdbr8\nHZ2tOi67Y+S6mqjQGCVZMpRkSVdidLriopLl4+U74TWdnWGnfdcXAAAAYCYZhqHWrmbVNJequqlM\n1c2lsnU0jlvr5xOglZl3aO3iDYqLSnFxp2MR1AEAADCnGIahelulLjWVqLqpTDVNpeodmHz9eHLM\nIq1bvFHL0tfJ19vPRZ1OjqAOAACAOaH3SpdOlLylYxfeUGt386S1Hh6eSohKVVpcjlZl3aHYyGTX\nNPlXIKgDAABg1rI77KpoOKdjF97Q+er3x11rLkn+voFKiclUakymUmKzlBSdLh/videbuwOCOgAA\nAGaVnv5Oldad0cXa0yqvP6uBwf7ravx9ArQ4dZVSY7OUGpul6AXx8jA55RFCLkNQBwAAgFtzOOyq\ntVaqpPa0SupOq7GlesLalJhMrV28UcvT17n9HfMbIagDAADArRiGIWtHgyoazqmy8bwqGy+Me9f8\nQ2FBEVq68BatXbxRMRGJLux0ehHUAQAAMOO6+zp0sfa0KhvOqaLxvHqvdE1Y6+HhqdTYLGUnrVB2\n8grFRCTNyQdoEtQBAAAwY2qtFXq76L9UXPmeHIZjwrrQwAXKSl6hnOQ8ZSTkyt830IVdzgyCOgAA\nAFxqcGhAF2tP6+3i/1Jtc/m4NQG+QUqPX6z0hFwtSsiVOTxuTt41nwxBHQAAAE5nGIb6Brpl7WhU\nS+dlWTsaZOtolK2jUZ19beOOSYvN1uLUVcpIyFVcVPKs26XF2QjqAAAA+FgMw1BHb8voU0Cb2upk\n7WzUlau9Nxzr6emlvIzbdPuyTynBnOqCbmcPgjoAAAD+KnaHXZdba1TdVKrq5lLVNJWpu79jyuM9\nPDxlDovVsoVrdWvuJxUSGD6N3c5eBHUAAABMymE41NhSrYu1p3Wp8YJqbZUaGr56w3E+3n6KDo9T\n9IJ4WcLjFb0gQZYF8YoMtcjTkxh6I/wfAgAAwHUMw9DFmlM6d+m4SmrPqOdK56T1vj7+SrZkKDU2\nW8mWDFkWxCssKHLevQHUmQjqAAAAGKP3SrcOv/GiLtaemrAmPChSqbFZSonNUmpspmIjkuTh4enC\nLuc+gjoAAABGldef1c9ef+G6O+iB/iHKTlqhrKTlSovLVnhw1Ax1OH8Q1AEAAOYxh+FQa2eTqpvL\nVNl4XqfL3pEhY/T8rUs+qZVZdyopeiF3zF2MoA4AADCPXB0aUJ21QrXWctU0lanWWqErg33X1QX7\nh+qzf7NVWUnLZ6BLSAR1AACAOe3K1T6V1RerqvGCaprL1NReL8NwTDomM2m5PrvhCYUEhrmoS4yH\noA4AADCHGIahxtYaldaeVkndGdU2l8txg2Ae6B+iFMsipcRkKi0uWykxmezW4gYI6gAAALPclcE+\nldefVUntGZXWnVFP/8RbKZpMHoqJSFRKTKZSYq6F88hQC8HcDRHUAQAAZpm/5q65SSYlRi9UVtIK\npcVlKzE6Xf6+AS7uGDeDoA4AADALdPS0qqLhnCoaz6mi4dykd80D/YKVmbRc2ckrlJm4XMEBoS7s\nFM5CUAcAAHBD/Vd7VV5/9lo4bzintm7rhLUmmZQQvfDaPufJK9hKcY4gqAMAALgBwzDU3F6nizWn\ndbH2lGqayyfdnSXAL1hZicuUlXztIUTBAezQMtcQ1AEAAGbI0PCgKhrO6WLtaZXUnFJnX9uEtd5e\nPkqLzVZ6Qq4WJeQqPiqFu+ZzHEEdAADARewOuxpaLqmi4ZwqG86ruqlUw/ahcWtNMinJkqHMxGXK\nSMxVUnSGvL28XdwxZhJBHQAAYJo4DIea2mpV0XBelQ3nVdV0UYNDAxPW+/sGKitpubKT85SVtII3\ngc5zTgvqdrtdu3bt0i9+8Qs1NzcrJiZGf//3f69du3bJ0/P//lpm165dOnjwoDo7O7V69Wrt379f\n2dnZzmoDAABgRjkcdpXVF+v90qMqqz+rK1d7J623LEhQTkq+clLylRKTKU+Ws+ADTgvq+/bt04ED\nB/TTn/5US5Ys0dmzZ/WFL3xBvr6+euaZZ0Zrnn/+eR06dEgZGRl69tlntWHDBpWXlysoKMhZrQAA\nALhce7dNx0v+pBMlb6mrr33CuvCgSKUnLFFGQq7S4xcrPDjKhV1iNnFaUC8sLNQDDzyg++67T5KU\nmJio+++/XydOnJB07Z3ML7zwgnbs2KGHHnpIknTo0CGZzWYdPnxYmzZtclYrAAAALtF7pVtl9cU6\nUfKmKhrOjVsTHBCm9PglykhYovT4JTwFFFPmtKB+22236cCBAyovL9eiRYtUUlKio0eP6qmnnpIk\n1dTUyGazaePGjaNj/Pz8tH79ehUWFhLUAQCA2xscGtClphJVNJxTef1ZXW6rHbcuyD9Uq7Lu0MrM\nOxUbmUQwx01xWlD/xje+oZ6eHmVnZ8vT01MjIyN65pln9Pjjj0uSrNZrm/RHR0ePGWc2m9XU1OSs\nNgAAAJzCMAx19raqoaVaRXUFsvXU6RfHmmV3jIxbbzJ5KCtxmdbk3K3FqSvl5ckOLfh4nBbUX3nl\nFf3sZz/TL3/5S+Xk5KioqEhPPPGEkpOT9aUvfWnSsZP9lHnq1ClntYg5ijmCqWCeYCqYJ/OXYRjq\nvdqh9j6rOvqt6uizqr3fqqGRiXdoka6F88igWMWFL1SaeYkCfUM10i0VF511UedwJ+np6U69ntOC\n+te//nVt375dDz/8sCQpJydHdXV12rt3r770pS/JYrFIkmw2m+Lj40fH2Wy20XMAAACuMjg8oPqO\nctW1laqlp0EjjvH3M/9LYQFmxYSlKCY0WdEhifL28p3mTjFfOS2oDwwMyMPDY8wxDw8PGYYhSUpJ\nSZHFYtGRI0eUl5cnSbp69aoKCgr03HPPTXjd/Px8Z7WIOebDO1/MEUyGeYKpYJ7MHwOD/Tp36YSK\nKt9TWX2xHA77pPX+voGKj0qVtyNQEUEx+uTtDyo4IMxF3WK26e7udur1nBbUP/WpT+m73/2uUlJS\nlJ2draKiIv3zP/+zPv/5z0u6trxl69at2rNnjzIzM5Wenq7du3crODhYjz76qLPaAAAAuE5Fwzm9\nXfx7ldadkd0+/hrzYP9QxZvTlGBOVXxUqhLMaVoQYpbJZBr9YY6QDldyWlB/8cUX9c1vflNf/epX\n1dLSopiYGG3atEnf+ta3Rmu2b9+ugYEBbd68WZ2dnVqzZo2OHDmiwMBAZ7UBAAAwqrO3Tf/n3X9X\ncWXhuOeTotO1PGOdli68RQuCzezOArdiMj5cm+JGPvprg9BQHp2L8fGrakwF8wRTwTyZO3qvdMva\n0SBbR6Oa2uv0fulRDQ1fHVMTb07VivRbtTx9nSJCoye40ljMEUyFszOs0+6oAwAAuMrA4BXV2ypl\n7WiQtb3h2ufORvUP9Ew4Jj/zdn1y1SMyh8e6sFPg5hHUAQCA2xseGVattfzag4YazqreWimH4ZjS\n2JiIRH36zv9PC+NyprlLwLkI6gAAwO3YHXY1tlxSReMFVTae16XLFzU8cuPtE328fLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99c9748>"
+       "<matplotlib.figure.Figure at 0x83309b0>"
       ]
      },
      "metadata": {},
@@ -1181,9 +1178,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAERCAYAAABy58/tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAERCAYAAABy58/tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9504860>"
+       "<matplotlib.figure.Figure at 0x8728320>"
       ]
      },
      "metadata": {},
@@ -1359,9 +1356,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvcAAAEtCAYAAACMMk9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8fa8ef0>"
+       "<matplotlib.figure.Figure at 0x897d6a0>"
       ]
      },
      "metadata": {},
@@ -1369,9 +1366,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff8908>"
+       "<matplotlib.figure.Figure at 0x8a11588>"
       ]
      },
      "metadata": {},
@@ -1379,9 +1376,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwcAAAEtCAYAAABd8Bw2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7dd3240>"
+       "<matplotlib.figure.Figure at 0x8a07a58>"
       ]
      },
      "metadata": {},
@@ -1458,9 +1455,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwcAAAEtCAYAAABd8Bw2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwcAAAEtCAYAAABd8Bw2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7df6ef0>"
+       "<matplotlib.figure.Figure at 0x89880b8>"
       ]
      },
      "metadata": {},
@@ -1558,9 +1555,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAEtCAYAAACs+0e7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAEtCAYAAACs+0e7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7df6828>"
+       "<matplotlib.figure.Figure at 0x89881d0>"
       ]
      },
      "metadata": {},
@@ -1629,9 +1626,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv8AAAEtCAYAAACf5Q+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv8AAAEtCAYAAACf5Q+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8fad780>"
+       "<matplotlib.figure.Figure at 0x8aa5710>"
       ]
      },
      "metadata": {},
@@ -1678,9 +1675,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv8AAAEtCAYAAACf5Q+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv8AAAEtCAYAAACf5Q+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7df3cc0>"
+       "<matplotlib.figure.Figure at 0x8aa5240>"
       ]
      },
      "metadata": {},
@@ -1772,9 +1769,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAEWCAYAAACpJ2vsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAEWCAYAAACpJ2vsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x94a4240>"
+       "<matplotlib.figure.Figure at 0x87974a8>"
       ]
      },
      "metadata": {},
@@ -1816,9 +1813,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAAEWCAYAAAA0KmJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAAEWCAYAAAA0KmJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d8ef28>"
+       "<matplotlib.figure.Figure at 0x87961d0>"
       ]
      },
      "metadata": {},
@@ -1915,9 +1912,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAAEWCAYAAAA0KmJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAAEWCAYAAAA0KmJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ca8e48>"
+       "<matplotlib.figure.Figure at 0x8378d30>"
       ]
      },
      "metadata": {},
@@ -1963,9 +1960,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xt4VdW97vHvnOu+cgUhhtwgIBADBBCQm9bqqQp7H6xK\ndTfeUKpoi1xElFAOlwJKqFQBpUWtt2qlemCrtVWx9dRqK6lF6wYVRAW5B4tCwsq6rznPH1MWRNBK\nRVYC7+d5eJ6VlTWTMZ4Zkjcjv/Ebhm3bNiIiIiIi0qqYmR6AiIiIiIgcOQV5EREREZFWSEFeRERE\nRKQVUpAXEREREWmFFORFRERERFohBXkRERERkVZIQV5EREREpBXKaJCfNWsWpmk2+1dUVNTsNRs2\nbODiiy+mTZs2ZGVl0a9fP9avX5+hEYuIiIiItAzuTA+goqKCl19+Of22y+VKP960aRNDhw7l6quv\nZsaMGeTn57N+/Xqys7MzMFIRERERkZYj40He5XJRUFBw2PdNmzaNYcOGcccdd6Sf69Sp0zEamYiI\niIhIy5XxGvmNGzdSXFxM586dqa6uZtOmTQBYlsXvfvc7Tj31VIYNG0ZBQQGnn346Tz75ZIZHLCIi\nIiKSeYZt23amPvkLL7xAKBSioqKCXbt2MXfuXNavX88777xDPB6nqKiIYDDI3LlzOeecc3jppZe4\n9dZbeeaZZ/iP//iPTA1bRERERCTjMhrkPy8cDlNeXk5NTQ3f//73KS4u5rLLLuOxxx5Lv+byyy9n\nz549PPfcc+nnGhoaMjFcEREREZGjJi8v74hen/HSmoMFg0F69OjBBx98QLt27XC73VRWVjZ7TUVF\nBVu2bMnQCEVEREREWoYWFeSj0Sjr1q2jQ4cOeDweBgwYcEiryQ0bNmjDq4iIiIic8DLatWby5Mlc\ncMEFlJaW8vHHHzNnzhwikQijRo0C4NZbb+XSSy/lzDPP5Oyzz+ZPf/oTTzzxBM8888wXfswj/ZOE\nHF2rV68GoH///hkeyYlN96Hl0L1oGXQfWg7di5ZB96Hl+Dol4hkN8tu3b6e6uprdu3fTvn17Bg8e\nTF1dHaWlpQB897vf5b777uP2229nwoQJdOvWjUcffZThw4dnctgiIiIiIhmX0SC/bNmyf/maUaNG\npVfoRURERETE0aJq5EVERERE5KtRkBcRERERaYUU5EVEREREWiEFeRERERGRVkhBXkRERESkFVKQ\nFxERERFphRTkRURERERaIQV5EREREZFWSEFeRERERKQVUpAXEREREWmFFORFRERERFohBXkRERER\nkVZIQV5EREREpBVSkBcRERERaYUU5EVEREREWiEFeRERERGRVkhBXkRERESkFVKQFxERERFphRTk\nRURERERaIQV5EREREZFWSEFeRERERKQVcmd6AJJ5lmWxbVucvXstcnNNysq8mKZ+xxMRERFpyZTW\nTnCWZfH//l+Up56yOP98P4MHe1m5MoplWZkemoiIiIh8CQX5E9zWrQmee86kttZPfb1Jfb3J6NF+\ntm5NZHpoIiIiIvIlFORFRERERFoh1cif4EpLPfzHf0Tp2DFKba0fgAcfjFJa6s/wyERERETkyyjI\nn+BM0+Scc/x06xbn7LOjn2129Wuzq4iIiEgLpyAvmKYT3svKMj0SEREREfmqFOTlG5dMJnn33TgA\nlZVe3G592YmIiIh8XRmtn5g1axamaTb7V1RUdNjXXn/99Zimyc9+9rNjPEr5OpLJJL//fZSHHzZ5\n4gmDP/0pwqZNEbW3FBEREfmaMl4IXVFRQX19ffrf2rVrD3nN8uXL+fvf/05RURGGYWRglPLvWr8+\nxkcfmTz/vJuCApurrspiyBAfv/99hC1b1K9eRERE5N+V8RoHl8tFQUHBF75/8+bNTJw4kZdeeolh\nw4Ydw5HJ0RCJ2NTWBhgxIpHuVQ8wZkyA6uo4554bpWdPgz17bJ0qKyIiInIEMp6YNm7cSHFxMZ07\nd6a6uppNmzal35dMJqmurmb69Ol07949g6OUf1e7dq4vfF8oZDBpkpeXXnJOlR0+3M3LL4f56KMo\nyWSSzZtjbN4c06q9iIiIyGFkNMgPGjSIRx55hJUrV3L//fdTX1/PkCFD+PTTTwGYOXMmBQUFXH/9\n9ZkcpnwNHTv6ePDBKK++6qKmJkphoUVhocXkyTFWrPAwfHiSqVMDxONw7bUJLr88yODBXp5+Osaw\nYS4GDfLw4osK8yIiIiKfZ9i2bWd6EPuFw2HKy8upqamhb9++XHHFFbz11lu0a9cOgPLycm688UZu\nvvnmZtc1NDSkH7///vvHdMzyr5mmSTzeFpfLwLZh7948JkzIZu9ek8WLw4wfH2TEiATPPutJl94U\nFlqMGJHg/vt9FBZaPPXUFrzeT4nH2wLg9X6qcC8iIiKtXteuXdOP8/LyjujajNfIHywYDNKjRw/e\nf/99Ghsb2blzJx06dEi/P5VKMWXKFBYtWsSWLVsyOFI5EpZl4XbvBsAwoF27T3joobYYhsGePTnU\n1Jhs3vzlfxwyDIPNmzvxox/lA3D//bmcfHIT8XgWbncSn28HyWTym56KiIiISIvRooJ8NBpl3bp1\nnHPOOVx//fVccskl6ffZts3555/PZZddxnXXXfeFH6N///7HYqjyBVavXg189ftgWRaVlXEaG1Oc\nfXaEMWMCANx9d4Tp053V+IceitGhw8lceKGX+nqTtm0ttmzxcd11TqifNi3Kqad2pqDARV6ei5IS\nbZg90vsg3xzdi5ZB96Hl0L1oGXQfWo6DK0uOVEaD/OTJk7ngggsoLS3l448/Zs6cOUQiEUaNGkX7\n9u1p3759s9d7PB4KCwub/QlCWrf9p8oCVFZa1NUlACgu9jFgQApIUVrqY+vWRPqakSMTzJkTSJfh\n3Habn+rqOMuWeampidKjR5Rvf9vL9u0pAEpLPSd8sBcREZHjT0aD/Pbt26murmb37t20b9+ewYMH\nU1dXR2lpaSaHJRlimiYdO/rSb3fseODLs7TUw0MPxbjmGh/Z2Ydu6wiFDOrrTWpr/YweHSMSiaVX\n9++7L0Lv3lqpFxERkeNLRoP8smXLjuj1B7emlBOLaZqcd56PuroEhgHnnhth9GgnqNfURJk9259+\nbXm5xZgxgUN61p99doTzz/ewfr1TS19Z6cXtblHVZSIiIiJfmVKMtBoHr9iXlFi89lqMTz5JsmuX\ngdfrdLqpqYlyyimHX7EfMybAgw82MXp0Fh07ppg3L4LPZ9O2LZxySlChXkRERFoVJRdplUzTpLw8\nQHm5s2F21ao4jY0W+fkmRUXedBkOwOTJMW6/3YfXC3/4g4esLIsbb4xz2WVZ5OdbLFkS5p//DOPx\ngM8HPXsG8Hg8GZ6hiIiIyJdTkJdWzzRNOnXyN3vuvPNMVq2K88EHCcaNC+D1wm23RbjjDh+TJsW5\n5RbnEKrx42OsXeumtta5/uc/DxOJRPB6o7Rv76a01Ke6ehEREWmRFOTluLQ/3JeUuFmyJMbvfufi\nvvu8/OQnMRobndeMHJng/fddLFt2cFtLkx/9KAjAT38aoXPnEDk5JqZpkJurDbMiIiLScijIy3HN\n7Xbz7W+bdOmSYMIEi6IiLx98EGXRogh1da5mrx05MkFtrT+9SfbWWwPptpbTp0coKkqwcWOCM84I\nqJ5eREREMk5pRI57n29reeqpHrp0iVNeHqW+3qBjR4vaWv+XtrWcM+dAqL/vvgj/+Z8mlmXx7rtx\nQB1wRERE5NhT8pATktfrZcAAL5ZlsXVrjMGDm4jFLE45JcWcOU5by2nTosyceaD2fn+oHzMmwGuv\nxXjjDYvp031ceGGC4cPD5Oeb5Oa6KStT+Y2IiIh88xTk5YTmrNYH6NgRkskkbdpEefrpJsJheO89\ns1lby4N71e/enWL6dD/XXx/n3nu9FBba1Nb6yc+3WLSoibZt4eST3RQXa7OsiIiIfDMU5EU+43a7\n6dkzG3BaWnbpEmfw4AihUIqdOw+E+gcfjBIIGAwfnmT+fD8jRji19fE4XHttglGjsgBYuDBCcfE+\nfD6TggJ1wBEREZGjS0Fe5DBM06SszE9ZmRPqt22Ls3JllNxc53nLsjj//AjLlnnT14wcmWDBAl96\ns+zEiQfq6ufPj9Cjxz7icRO3G/r08atXvYiIiHwtWh4U+Rf2h/qqqiCdOvkxTRO328055/j5xS8i\nvPqqi3nzIl+6WXbePB8bN7q4+OIsLrggi6eeirNuXQN//3uIRCKRgVmJiIhIa6cVeZF/k8fj4YIL\nXPTuHaO+PklxcYqBA1NMnOhslj24rn748CTjxwfTq/UTJgT4zW9CNDSkiEaj+P1RrdKLiIjIEVGQ\nF/kaTNOkvDxAx44+tm2LEwolePLJJjZvNojFSNfVf+c7iWZlOPn5Fnv3GoRCLq6/3gn+P/95mC5d\nooBJfr4OnxIREZEvpyAvchTsL78Bp36+tDROKJTk979vYs8e+OQTm0WLIkyY4IT2O+6IUF9vMn16\n4HOnyjor+AsXhunUKUYyaeDzgd9vUlHhV696ERERSVMqEDnKTNOkU6cDrSr3b5aNxWL8+tdNbNxo\n8sYbLoqLD9TUf/5U2YkTgyxeHGb8+CAAs2dH+PjjMHl5Bj6fSUWFT6FeRETkBKckIPINO3i1vlOn\nBDk5Mfr2TbFrFyxYEGHy5MBhN8quWuVOB/sZM5wOOK+95mLevCgNDWGysmzy8z2UlflVgiMiInIC\nUpAXOYY8Hg8DBjgbWhOJBG+/HWb58iZs2252quy8eRHmz/d97lqbG2+Mc9llB/rUt2uXYPPmFGVl\nLjp2VKAXERE5kSjIi2SIx+Ohb988wCm/KSuLM3RohKamFJ98YnDDDXFqa51gPm1alLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ca81d0>"
+       "<matplotlib.figure.Figure at 0x881eb70>"
       ]
      },
      "metadata": {},
@@ -2016,9 +2013,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAEWCAYAAABL+3CVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAEWCAYAAABL+3CVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7cc2c18>"
+       "<matplotlib.figure.Figure at 0x8868518>"
       ]
      },
      "metadata": {},
@@ -2055,9 +2052,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAEWCAYAAAAerO46AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99b4898>"
+       "<matplotlib.figure.Figure at 0x87a1780>"
       ]
      },
      "metadata": {},
@@ -2087,9 +2084,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAO8AAAEPCAYAAACqQ5OPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl0FGW6x/FvVa9ZoBFJyEJIiAQxiSAaEMQFVFTA6yC4\nkDkqOi5XBUV0lgAuDIowjoO4wDDCjOiooAJyHYWjjLKpBAkSAQMCJiwBgiAm0Em609313j8iLZGs\nTRYqPJ9zck53VXW975POr99auiqaUkohhDAdvaU7IIQIjYRXCJOS8AphUhJeIUxKwiuESVkba0Ul\nJSWNtSohzhgulyvk18rIK4RJSXiFMKlG22w+0alsCtQmJycHgIyMjCZZf3O10VztSC2nZzuNtYsp\nI68QJtUkI68QZhWum2c8k/AK8TO/203SG29UPu7WDWtkZAv3qHbm+ZgRQlQhI68QP7NGRrL9jjsA\nSD3NR12Q8ApRRZlhtHQX6k02m4UwKQmvECYl4RXCpCS84ozhd7vxu90t3Y1GIwesRKt1PKjWyEj8\nbjeBJ5+snDF5cnCZ0/1cbm0kvKJVOCmo5eUwdWrlzBPCCpw0z6wBlvAK0wqGFCrDqBTesWOxvvQS\nRlwcxMUBYOHnEfaEEAdaoL+NTcIrTlt+v5+8vAoArFYrfr+/yj5r4MknMTp0IHDuuViVQquoQFuw\ngIpBg9DKyrB9/jkK8BUXY42MrDrC/hxks466IAesRAvz+Xzk5pbwxRfH+PLLo5T/PJL6/X7+8x8P\n117r5NprneTnn0Mbux3f7NmVP8XFGLGx6IWFWFetwte/P0Z8PEZyMralS9F37ED5/WgVFfDvf+M9\ndKhKuyeF2YRk5BUtxufzsWyZh4ICG9OmOWnXzmDGjHJcrqOEh8ODD0ZSVFQ5voweHcGyj+LQ8/Mr\nX7tuHVqHDmj796OiotB/+olAdDR6QQEqNhYjKgoAzUTfmGooGXlFi8nN9bJyZWVwKypg9OgKPv7Y\nxv/9n42yspOXLz5qJ5CcDJqGvmMH/pQUvCNGoO/bhyU3l0BaGloggNJ19CNH0KxW9EOH4Pbbcfwc\n5takzvD6/X4mTJhAcnIyYWFhJCcn88QTTxAItIZdfnG6uP32CgIBmD/fzrx5DgwDsrI8dO/uZ9w4\nD6++WorDodhx1d1UDBmC0aEDts8/x7J1K8pauQFpX7GCiquvxrJnD5aCAoybbsLy9NOEderUwtU1\njTo3m5999ln+8Y9/8MYbb3D++efzzTffcOedd+JwOHj88cebo4+ilbrgAgcHDnhITDSIiFA88URY\ncDM5EICPPrIyaZKXRx4JY/58O6+8UkZaGtiXLsV7/fXohw+j//gjFdddh6WwEADL5s0YCQlo+/cD\n5j4gVZc6w7t+/XpuuOEGhg4dCkDnzp25/vrr+eqrr5q8c6J1s9lsDB4MW7eWceyYVmXe3Ll2xo/3\n8tvfRgQDPWZMOP/6VymxEyZg+eILLHv2oDQN28//K6/iqqtwzpuH0jR8Q4ZgXbgQ//33t9oA17nZ\nPHjwYD777DO+++47APLy8lixYgVDhgxp8s6J1s9ms9Gjh4v0bl7+/vcyYmIMYmIMBg4MYLef/A8s\nly+3seNAGwL9+6MsFtB1Ah06oO/di23VquA0rbQU/efRt7XS6vMvPidMmMC0adOC59oef/xxJv/q\nWysn3hFvx44djd9T0WqF6zqdP/2UwKWXsrfNuRw6ZMUwoGtXxYYNFkaPDgfg97/3MneujRdfLCct\nzeDIEeh8YB107Ihj7lwCnTsT6NIFvaCAI4MG4TaM0/L63JSUlODjU7nTap2bzS+99BKvvfYaCxYs\nIC0tjY0bNzJ27FiSkpL43e9+F3LDQhwXqetw1lnYV6wgKWoLCV26sKdjb3JzdRITA8ybV8rHH9uY\nO9fG/fdXkJ+v43IZFBXpHG7bjwt/yKZi2DDs//kP2r59HBg1ip/8/pYuq8nVOfJ27NiRxx9/nIce\neig4bcqUKcybN6/KCHviyCv3bT492jnda/EeOoTh9aL/7W9QUYHviivQSkow4uIwnE7o3p3t+yPx\n+WDhQgdut8aiRTbsdli40M0994QzeLCfa6/10THaoOvat7Dt3InlFL6v3Nz3bW7SkVcphf6r22Hq\nuk49traFqFZ5YSEBrxfLO+9gREWhKQUWC4GkJBz//jdGXBzORYtQFgvdsrLYsqsN8+fbgweuYmIM\nNA3mzCnl5psr502dWs6uuNu56mYPbVrpAapfq/OA1bBhw5g2bRpLly5l165dvP/++7zwwgvceOON\nzdE/0Yr43W7c338Pb7yB5d130fbtQzt8GH9aGiomBsebb+Lv0QOttBSo/HaU7c03cTgUEyd6ggez\nJk70UFioYbVqFBXpFBXpjB8fxuHDOrnbnPh8vhautHnUOfK+8MILtG3bltGjR3Pw4EFiY2O57777\nePL4tZFC1IPf7cb73ntYN25E8/vx9+qF5eBB0DQ0QN+3j0BCAoH0dBxvv41vwAACHTpgW7eOlKhi\nCju1JzOz8iKFjh0N/vY3J3/9q4f27Q2OHKkcg/LyLDzxRBivvFLOb36jYbW27m//1lldREQEzz//\nPM8//3xz9Ee0Ur7iYvQDB9D8fpTdjr9nz+A8y6ZN+Hr1wvrNNzj//W+8I0Zg++orbCtXUnHllehe\nL926GbRp46OgQOdvf3OQmemjtFTx8MMeZs92kpXlYfJkJ0eO6IwZE0ZKiocePVp3eOW7zaLZ6Pv2\n4bvwQoyOHXHOmweAgsrvKh8+DD+f1rHk5+Pr2ZNAUhL2L77AGh5O/JoFHD2qkZdn4ZJLAiQkGEyf\n7uTqq/0sXOhm9mx7cAQ+U7TujyZx2rC1a0f59dejDAPb118DEDjnHFRsLHpxMZrfj2/IELSDB7Fu\n3Fg5//zz0XQdffp0LLGxpKYV4bq+I8eOwcyZDoYO9eNyWTj3XDvPPONlzJjK8L78cjmpqY4Wq7W5\nSHhFswiUl2PdsgW9oADfgAGoiAgcCxdiJCSg//ADeL1YbTb0UaMwrrkGHbA6HLB8OQBHBg0idt2n\nbOswklVfOEhPN0hKMuje3YnVauU3v9FISfEAkJrqaPX7uyDhFc3A73bD1KnoHg9GcjKWSy7B8Pth\n6VL0vXsxJkxAdziwhYWddH7W//M3+X7Ytg13794M7qrRJaVy8zo11RkMqdVqbfX7uL92ZlUrWpbT\niT5qVPDaWu+kSQCE1XKt7YlhLjMM7G3b0qNHk/bSNCS8osmdePO3E8PYGi+Qb04SXtEsWutleS3p\nzDq2LkQrIuEVwqQkvEKYlIRXCJOS8AphUhJeIUxKwiuESUl4hTApCa8QJiXhFcKkJLxCmJSEVwiT\nkvAKYVISXiFMSsIrhElJeIUwKQmvECYl4RXCpCS8QpiUhFcIk5LwCmFSEl4hTErCK4RJSXiFMCkJ\nrxAmJeEVwqQkvEKYlIRXCJOS8AphUhJeIUxK/sWnqJXf7ycvrwKA1FR78D/RH2cYBn5/h+BjXZfx\noLnIb1rUyO/3s2SJl/vus5Gba/DFF+Vs2XIMv98PVIZ17Vo3x46dzbFjZ7N2rRvfsWP43e4W7vmZ\noc7wJiUloev6ST/XX399c/QvSCmF0+nE6XSilGrWts9UeXkVPP+8nUcf9XL0qM7IkREMGhTBBx94\n2JBTwu78EtxuRVkZlJWB263Yt34nvtmzKS8sxO92V/kRjavOzeYNGzYQCASCz/fv389FF13Erbfe\n2qQdO04pxebNXrKzYcmSbgAMG+alXz9IT3egaVqz9ONMdc89FWzcaGHePAdFRTrt2xusX2/BYlFY\nrOD16qxaZQNgwAAfxR2Tidn9L4y33sIbFYX+44/o+/eDpsHkycH1WiMjW6qkVqPO8J599tlVns+Z\nMweXy8Utt9zSZJ06TinFihUehg93UlLyS0iXLQOXS7F4sYeBA50S4CaSmmrn8OHy4PP27Q2eeMLD\nX/7iZN48Bx99dIzDhzVSUys/3A8f1ujUCfw9e2L7+mtUYSG+667DvmsX2O0Eysth6lQAAuPH44iK\naomyWo0G7fMqpfjnP//JbbfdhsPhaKo+BW3e7D0puMeVlGgMH+5kyxZvk/fjTGW1Wul7kUFiYoCp\nU8sZNaqCv/zFSVGRTkUFGAZERkJenoW8PAuRkZXTjNhYfBkZaIEA+v79VAwZQsUDD/yyYo8H/4cf\n4j10qOWKawUaFN7ly5eza9cu7r333qbqT5BSiuxsqg3ucSUlGuvWIfvATcR76BDGf/9L351vEdPR\nz3XX+YLzRozw4ffDgQM68+fbmT/fzoEDOn4/qKgobDk5eDIzsebmYtmyBdsrr8Czz+J/+GH8vXtj\nXb8epk6VfeFToKkG/OXffPPN7N27l+zs7JPmlZSUBB/v2LHjlDvmdDr54x+7sWyZvdblBg+u4Lnn\ntuPxeE65TVEpXNeJ1HXOXr4c3edD27cPwsIovPtPbN4WxoMPhpOZWcGIERXcdFMkRUWVY0BMjMHC\nhW5iYxVnW4ph506cixYR6NwZfd8+tEAAX5cuHBk0iA7z5gGw6447KDOMFqy2+aWkpAQfu1yukNdT\n7/O8P/zwAx988AGzZs0KuTFx+gvXdTp/+mllaAG9sJBA5874rrySTrMmE/XQQyxYEIPNBlYrtGtn\n8D//Uzkir1ljwWaDnBwrqantOOuc7miXXYbF7cbXtSt6QQH6vn24DQP3HXcAnHHBbUz1Hnmfe+45\npkyZwoEDBwgPDz9p/okj76l8mhynlGLOHC//+7/OWpebM8fD3Xc33lHnnJwcADIyMhplfS3ZTiht\n+N1ufLNno+fnY3Tpgu+ii9C8XiybNqHv2weahnfwYIq69KO8HLZvtzJ2bBgAL75YTLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAO8AAAEPCAYAAACqQ5OPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f98080>"
+       "<matplotlib.figure.Figure at 0x8a3d710>"
       ]
      },
      "metadata": {},
@@ -2454,9 +2451,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvcAAAEtCAYAAACMMk9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff7710>"
+       "<matplotlib.figure.Figure at 0x8aa5d30>"
       ]
      },
      "metadata": {},
@@ -2464,9 +2461,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x952df28>"
+       "<matplotlib.figure.Figure at 0x9001c50>"
       ]
      },
      "metadata": {},
@@ -2474,9 +2471,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x952ec88>"
+       "<matplotlib.figure.Figure at 0x9012e80>"
       ]
      },
      "metadata": {},
@@ -2561,9 +2558,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9476e80>"
+       "<matplotlib.figure.Figure at 0x8fc5198>"
       ]
      },
      "metadata": {},
@@ -2571,9 +2568,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa27ca90>"
+       "<matplotlib.figure.Figure at 0x89cccc0>"
       ]
      },
      "metadata": {},
@@ -2581,9 +2578,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwcAAAEtCAYAAABd8Bw2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa23a4e0>"
+       "<matplotlib.figure.Figure at 0x89a8a58>"
       ]
      },
      "metadata": {},
@@ -2841,9 +2838,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAEWCAYAAAA+Wg5zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAEWCAYAAAA+Wg5zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x948ffd0>"
+       "<matplotlib.figure.Figure at 0x89b7b70>"
       ]
      },
      "metadata": {},
@@ -3271,9 +3268,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAEjCAYAAAAfTOaLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAEjCAYAAAAfTOaLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9566a58>"
+       "<matplotlib.figure.Figure at 0x8f9c0b8>"
       ]
      },
      "metadata": {},
@@ -3361,9 +3358,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEjCAYAAADjWc1BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEjCAYAAADjWc1BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2d240>"
+       "<matplotlib.figure.Figure at 0x87aff98>"
       ]
      },
      "metadata": {},
@@ -3401,9 +3398,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEjCAYAAADwjo21AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEjCAYAAADwjo21AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x800e9b0>"
+       "<matplotlib.figure.Figure at 0x87de080>"
       ]
      },
      "metadata": {},
@@ -3479,7 +3476,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,

11-Extended-Kalman-Filters.ipynb

@@ -28,6 +28,7 @@
        "@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",
+       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
        "\n",
        "    div.cell{\n",
        "        width: 900px;\n",
@@ -118,9 +119,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 120%;\n",
+       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -233,7 +235,7 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -262,11 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -305,9 +303,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu0AAAEWCAYAAADSA6kgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4VOX5xvF7ZrLv+x7CDiKKAi6Aa1UKuFGpu1LQ1kpt\nRRC1WFwQN6RS0YrULkDVulVbN2zFn+JKW6OCirIZCNkm+75n5vz+OJMZohASmGQmyfdzXXOded45\nZ+ZNr1Nye/LOeSyGYRgCAAAA4Lesvp4AAAAAgM4R2gEAAAA/R2gHAAAA/ByhHQAAAPBzhHYAAADA\nzxHaAQAAAD9HaAcAAAD8nFdD+/vvv68LLrhAGRkZslqtWr9+/SGP+fLLL3X66acrLCxMGRkZWrZs\nmTenBAAAAPR5Xg3t9fX1OvbYY7Vq1SqFhobKYrF0un9NTY3OOeccpaamKjs7W6tWrdKKFSu0cuVK\nb04LAAAA6NMsPdURNTIyUo8//rhmz5590H2eeOIJLV68WMXFxQoODpYk3XfffXriiSeUn5/fE9MC\nAAAA+hyfrmnfvHmzTj31VHdgl6SpU6eqsLBQubm5PpwZAAAA4D98GtrtdruSk5M7jLXXdrvdF1MC\nAAAA/E6ALz/8UGve91ddXd2DMwEAAAB6VnR09GEf69Mr7SkpKd+7ol5cXOx+DQAAAICPQ/ukSZP0\nwQcfqLm52T22ceNGpaenKysry4czAwAAAPyHV5fH1NfXa9euXZIkp9Op3NxcbdmyRfHx8crMzNTi\nxYv1ySef6O2335YkXXHFFVq6dKnmzJmjJUuWaMeOHVq+fLnuvvvuTj/nSP60gP4tOztbkjRx4kQf\nzwT+inMEXcF5cngWP2Fo+dOe+jc/kZZd1/WlsH0N5wm6wltLvL16pf2TTz7R+PHjNX78eDU1Nemu\nu+7S+PHjddddd0kyv1yak5Pj3j8qKkobN25UYWGhJk6cqF/96ldatGiRFixY4M1pAQCAHvboix0D\n+zXnSff8zHfzAfobr15pP+OMM+R0Og/6+tq1a783NnbsWL333nvenAYAAOhFz79taMEqT33+FGnN\nLd274QSAzvl0TTsAAOjb3vnU0OxlUnurxkljpWfvkQICCOyANxHaAQDAYfl8p6Ef/VpqbTProwZL\nrz4khYUQ2AFvI7QDAIBu21NoaMbNUm2DWaclSG8+LMVHE9iBnkBoBwAA3VJaaWjaQqm4wqyjI6Q3\nV0qDUgjsQE8htAMAgC6razB03i3SrjyzDg6SXlkuHTOMwA70JEI7AADoktY2QxcvkT75xqytVulv\nd0unHUdgB3oaoR0AAByS02nopw9I//6vZ+z3C6UfnU5gB3oDoR0AABzS4jXSU//y1HfMla7/EYEd\n6C2EdgAA0KlHnje04hlP/dMLpLuv9d18gIGI0A4AAA7q2Y2GFj7qqS84RVp9M91Ogd5GaAcAAAf0\n9ieG5tzrqScfQ7dTwFcI7QAA4Hs+22HoosWebqdjBpvdTkODCeyALxDaAQBAB9/mm91O6xrNOj3R\nbJ4UF0VgB3yF0A4AANxKKg1Nv1kqqTTrmEjpXyulzGQCO+BLhHYAACBJqq03dO7N0u58sw4Jkl5d\nLh09lMAO+BqhHQAAqKXV7Hb66Q6ztlqlvy2VThlHYAf8AaEdAIABzuk0dO390lv/84ytXiTNPI3A\nDvgLQjsAAAPcbaulZ97y1HddK113IYEd8CeEdgAABrCVzxl6+FlP/bMLpTvn+m4+AA6M0A4AwAD1\nzL8NLXrMU888jW6ngL8itAMAMAC99V9Dc+/z1KccKz1zt2SzEdgBf0RoBwBggPl0u6Ef/0Zqc5j1\n0UOkV5bT7RTwZ4R2AAAGkN3f6XaakWR2O42l2yng1wjtAAAMEMUVhqYtkEqrzDrW1e00I4nADvg7\nQjsAAANAbb2hcxdJOYVmHRIkvbZCGjOEwA70BYR2AAD6uZZWQ7Nulz7br9vpc/dIk48hsAN9BaEd\nAIB+zOk07xLzdrZnbM0t0gWnEtiBvoTQDgBAP3bL49KzGz310p9KP72AwA70NYR2AAD6qd/+zdDv\nnvPUP58pLZnjs+kAOAKEdgAA+qGn/mXo1sc99UWnS79fSLdToK8itAMA0M/8+7+Grr3fU592nPT0\nXXQ7BfoyQjsAAP3IJ9907HY6dqj0zwelELqdAn0aoR0AgH5iV56h8xZJ9a5up5nJZrfTmEgCO9DX\nEdoBAOgH7OWGpi30dDuNizK7naYnEtiB/oDQDgBAH1dTb2jGzdIeV7fT0GCz2+lRgwnsQH9BaAcA\noA9rbjG7nW7ZZdY2m/T8MmnSWAI70J8Q2gEA6KOcTkNz7pX+7zvdTs+bQmAH+pseCe2rV6/WkCFD\nFBoaqokTJ+rDDz886L579+6V1Wr93uOtt97qiakBANAvGIahhY9Kz/+fZ2zZddK15xPYgf7I66H9\n+eef10033aQlS5Zoy5Ytmjx5sqZPn668vLxOj/v3v/8tu93ufpx55pnenhoAAP3Gir9Jj77oqX9x\nkXT7bN/NB0DP8npoX7lypebOnatrr71Wo0aN0qOPPqrU1FQ98cQTnR4XFxenpKQk9yMwMNDbUwMA\noF/465uGfr3aU//4TGnVTXQ7Bfozr4b2lpYWffbZZ5o6dWqH8alTp+rjjz/u9NiLLrpIycnJOuWU\nU/TSSy95c1oAAPQbb242dO0Dnvr046W/3kG3U6C/82poLysrk8PhUHJycofxpKQk2e32Ax4TGRmp\nhx9+WC+++KLefPNNnXXWWbr00kv1zDPPeHNqAAD0ef/72tDFSySHq9vpMcOkfzxAt1NgIAjw9QTi\n4+O1YMECdz1+/HiVl5froYce0pVXXnnAY7Kzsw84DrTjHMGhcI6gK/zpPMktCdbPHhmlhiZz+WhK\nbLMe/MkO7d7R6uOZwZ/OE/ifESNGeOV9vHqlPSEhQTabTcXFxR3Gi4uLlZqa2uX3OeGEE7Rr1y5v\nTg0AgD6rrDpANz4xQlX1ZmCPDm/To/N2KTGawA4MFF690h4UFKQJEyborbfe0qxZs9zjGzdu1MUX\nX9zl99myZYvS0tIO+vrEiROPaJ7ov9qvdnCO4GA4R9AV/nSeVNcZ+tkNUlGFWYcGS2+uDNDJY4/x\n7cTgV+cJ/Fd1dbVX3sfry2MWLlyoq6++WieeeKImT56sNWvWyG636/rrr5ckLV68WJ988onefvtt\nSdL69esVFBSk4447TlarVa+99ppWr16thx56yNtTAwCgT2luMXTRYmnrbrO22aQX7pVOptspMOB4\nPbRfcsklKi8v17333quioiIdc8wx2rBhgzIzMyVJdrtdOTk57v0tFovuvfde5ebmymazadSoUVq7\ndq2uuOIKb08NAIA+w+k09JNl0rufecaevE06dzKBHRiIeuSLqPPmzdO8efMO+NratWs71LNnz9bs\n2XSDAACgnWEYummV9MI7nrH7fi7NPZfADgxUXm+uBAAAjszyp6Xf/91T3zBL+vXVvpsPAN8jtAMA\n4EfWvWHo9jWe+uIfSI/Mp9spMNAR2gEA8BMbPjb0s+We+gy6nQJwIbQDAOAH/rvN0CV3eLqdjhsu\n/eNBKTiIwA6A0A4AgM/tyDV03i1SQ5NZZ6VIGx6WoiMI7ABMhHYAAHyosNTQtIVSuav/Sny09K+V\nUmoCgR2AB6EdAAAfqa4zNGORlGs367AQ6fUV0qgsAjuAjgjtAAD4QFOzoZm/lr7Yr9vpi/dKJx1N\nYAfwfYR2AAB6mcNhaPYy6b3PPWN/+rU0fRKBHcCBEdoBAOhFhmFo/iPS39/1jD0wT/rJDAI7gIMj\ntAMA0Ise+Ku0+mVP/auLpVuv9N18APQNhHYAAHrJX143tORJT33pWdLvbqTbKYBDI7QDANALXv/I\n0M8f8tQ/mCCtWyJZrQR2AIdGaAcAoIdt/srQpft1Oz1uhPTyA3Q7BdB1hHYAAHrQ9lxD598iNTab\n9ZA0s9tpVDiBHUDXEdoBAOghBaWGpi2QKmrMOiFGevNhKSWewA6gewjtAAD0gKpaQ9MXSvuKzTo8\nVHrjt9LIQQR2AN1HaAcAwMvau51+lWPWAa5upyccRWAHcHgI7QAAeJHDYeiqpdL7Wzxjf75dmnYy\ngR3A4SO0AwDgJYZh6MZHpJff84wt/4V09TQCO4AjQ2gHAMBL7lsvPbFft9P5l0iLrvDdfAD0H4R2\nAAC84E+vGrrzj5768nOkh39Ft1MA3kFoBwDgCL36gaHrV3jqsydKa39Dt1MA3kNoBwDgCHz8paHL\n7pScTrM+fqT09/uloEACOwDvIbQDAHCYvtlrdjttajHroXQ7BdBDCO0AAByG/BJD0xZKlbVmnRgj\n/et3UnIcgR2A9xHaAQDopsoas9tp3ne6nQ7PILAD6BmEdgAAuqHR1e102x6zDrBJL90nTaTbKYAe\nRGgHAKCLHA5DV90tfbDVM7b2N9LUkwjsAHoWoR0AgC4wDEM3PCz9433P2IpfSlf+kMAOoOcR2gEA\n6IJla6UnX/HUCy+Xbr6cwA6gdxDaAQA4hCdfMXT3nz31lVOlh37hu/kAGHgI7QAAdOKVDwz94ree\n+pwTpD/fTrdTAL2L0A4AwEF89IWhy/frdjphlPT3++h2CqD3EdoBADiAb4tCdP6tnm6nw9KlNx6W\nIul2CsAHCO0AAHxHcWWg5q8ZoSpXt9OkWOlfK6WkWAI7AN8gtAMAsJ+KGkM3rhmhkqogSVJEqLTh\nYWkY3U4B+JDXQ/vq1as1ZMgQhYaGauLEifrwww873f/LL7/U6aefrrCwMGVkZGjZsmXenhIAAF3S\n2GzowtukPfZQSVJggPTS/dL4UQR2AL7l1dD+/PPP66abbtKSJUu0ZcsWTZ48WdOnTLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu0AAAEWCAYAAADSA6kgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6dda518>"
+       "<matplotlib.figure.Figure at 0x7200160>"
       ]
      },
      "metadata": {},
@@ -353,9 +351,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu0AAAEWCAYAAADSA6kgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtwW/Wd//+XJNvy3Y4d25Kv8S0JKRAI4RLuXSDb0i1l\nykJb2vIDusOW6W4hfJntZCe7bYB2hzLLEDoFlt3ZwFK2t2F3thf6XcLQQFuyv8WlSUMgiR3fL5Lv\nkiXLkiWd7x+SZTuxHUdR7GPn+Zg5c6Sj8zn6qHMKLz7+vD/HYhiGIQAAAACmZV3uDgAAAABYGKEd\nAAAAMDlCOwAAAGByhHYAAADA5AjtAAAAgMkR2gEAAACTI7QDAAAAJpfS0P7OO+/otttuU2VlpaxW\nq15++eXTtjl8+LBuuOEGZWdnq7KyUo8//ngquwQAAACseCkN7X6/XxdffLH27NmjrKwsWSyWBc/3\ner265ZZb5HQ61dTUpD179uipp57S008/ncpuAQAAACua5Vw9ETUvL0/f//73dc8998x7zvPPP6+d\nO3fK7XbLbrdLkr797W/r+eefV3d397noFgAAALDiLOuc9gMHDui6665LBHZJ2r59u3p7e9XR0bGM\nPQMAAADMY1lDu8vlUllZ2axjU+9dLtdydAkAAAAwnbTl/PLTzXmfyePxnMOeAAAAAOdWQUFB0m2X\ndaTd4XCcMqLudrsTnwEAAABY5tC+bds2/eY3v1EwGEwc27dvnyoqKlRTU7OMPQMAAADMI6XTY/x+\nv5qbmyVJ0WhUHR0dOnjwoIqLi1VVVaWdO3fqvffe05tvvilJuvvuu7V7927de++92rVrl44dO6Yn\nn3xS3/rWtxb8nrP50wJWt6amJknS1q1bl7knMCvuESwG9wkWg/sEi5GqKd4pHWl/7733tGXLFm3Z\nskUTExP65je/qS1btuib3/ympFhxaWtra+L8/Px87du3T729vdq6dav++q//Wo8++qh27NiRym4B\nAAAAK1pKR9pvvPFGRaPReT/fu3fvKccuvPBCvf3226nsBgAAALCqLOucdgAAAACnR2gHAAAATI7Q\nDgAAAJgcoR0AAAAwOUI7AAAAYHKEdgAAAMDkCO0AAACAyRHaAQAAAJMjtAMAAAAmR2gHAAAATI7Q\nDgAAAJgcoR0AAAAwOUI7AAAAYHKEdgAAAMDkCO0AAACAyRHaAQAAAJMjtAMAAAAmR2gHAAAATI7Q\nDgAAAJgcoR0AAAAwOUI7AAAAYHKEdgAAAMDkCO0AAACAyRHaAQAAAJMjtAMAAAAmR2gHAAAATI7Q\nDgAAAJgcoR0AAAAwOUI7AAAAYHKEdgAAAMDkCO0AAACAyRHaAQAAAJMjtAMAAAAmR2gHAAAATI7Q\nDgAAAJgcoR0AAAAwOUI7AAAAYHLnJLQ/99xzqq2tVVZWlrZu3arf/va3857b3t4uq9V6yvbGG2+c\ni64BAAAAK07KQ/uPf/xjPfzww9q1a5cOHjyoq6++Wp/85CfV1dW1YLv//u//lsvlSmwf//jHU901\nAAAAYEVKeWh/+umndd999+krX/mKNmzYoGeffVZOp1PPP//8gu2KiopUWlqa2NLT01PdNQAAAGBF\nSmloD4VCev/997V9+/ZZx7dv36533313wbaf/exnVVZWpmuvvVavvfZaKrsFAAAArGgpDe2Dg4OK\nRCIqKyubdby0tFQul2vONnl5efrHf/xH/fSnP9WvfvUr3XTTTfrc5z6nV199NZVdAwAAAFastOXu\nQHFxsXbs2JF4v2XLFg0NDem73/2uvvjFL87Zpqmpaam6hxWKewSnwz2CxeA+wWJwn2AhjY2NKblO\nSkfa165dK5vNJrfbPeu42+2W0+lc9HUuv/xyNTc3p7JrAAAAwIqV0pH2jIwMXXbZZXrjjTd0xx13\nJI7v27dPd95556Kvc/DgQZWXl8/7+datW8+qn1i9pkY7uEcwH+4RLAb3CRaD+wSL4fF4UnKdlE+P\neeSRR/TlL39ZV1xxha6++mq98MILcrlc+upXvypJ2rlzp9577z29+eabkqSXX35ZGRkZuuSSS2S1\nWvXzn/9czz33nL773e+mumsAAADAipTy0H7XXXdpaGhITzzxhPr6+nTRRRfp9ddfV1VVlSTJ5XKp\ntbU1cb7FYtETTzyhjo4O2Ww2bdiwQXv37tXdd9+d6q4BAAAAK5LFMAxjuTuxGDP/tFBQULCMPYGZ\n8adKnA73CBaD+wSLwX2CxUhVhk35w5UAAAAApBahHQAAADA5QjsAAABgcoR2AAAAwOQI7QAAAIDJ\nEdoBAAAAkyO0AwAAACZHaAcAAABMjtAOAAAAmByhHQAAADA5QjsAAABgcoR2AAAAwOQI7QAAAIDJ\nEdoBAAAAkyO0AwAAACZHaAcAAABMjtAOAAAAmByhHQAAADA5QjsAAABgcoR2AAAAwOQI7QAAAIDJ\nEdoBAAAAkyO0AwAAACZHaAcAAABMjtAOAAAAmByhHQAAADA5QjsAAABgcoR2AAAAwOQI7QAAAIDJ\nEdoBAAAAkyO0AwAAACZHaAcAAABMjtAOAAAAmByhHQAAADA5QjsAAABgcoR2AAAAwOQI7QAAAIDJ\npTy0P/fcc6qtrVVWVpa2bt2q3/72twuef/jwYd1www3Kzs5WZWWlHn/88VR3CQAAAFjRUhraf/zj\nH+vhhx/Wrl27dPDgQV199dX65Cc/qa6urjnP93q9uuWWW+R0OtXU1KQ9e/boqaee0tNPP53KbgEA\nAAArWkpD+9NPP6377rtPX/nKV7RhwwY9++yzcjqdev755+c8/9VXX9XExIRefvllbdq0SXfccYe+\n8Y1vENoBAACAGVIW2kOhkN5//31t37591vHt27fr3XffnbPNgQMHdN1118lut886v7e3Vx0dHanq\nGgAAALCipaXqQoODg4pEIiorK5t1vLS0VC6Xa842LpdL1dXVs45NtXe5XKqpqZmzXVNTUwp6jNWM\newSnwz2CxeA+wWJwn2AhjY2NKbnOsq4eY7FYlvPrAQAAgLNiGIZC4eA5/56UjbSvXbtWNptNbrd7\n1nG32y2n0zlnG4fDccoo/FR7h8Mx73dt3br1LHuL1WpqtIN7BPPhHsFicJ9gMbhPzl9e/6iOdR3U\n0Y6DOtZ5SI2VF+r/++T/mfNcj8eTku9MWWjPyMjQZZddpjfeeEN33HFH4vi+fft05513ztlm27Zt\n+sY3vqFgMJiY175v3z5VVFTMOzUGAAAAWEqT4ZBaez/S0c4/6GjHQfUMts/6/GjXIUWNqKyWczeJ\nJWWhXZIeeeQRffnLX9YVV1yhq6++Wi+88IJcLpe++tWvSpJ27typ9957T2+++aYk6e6779bu3bt1\n7733ateuXTp27JiefPJJfetb30pltwAAAIBFixpR9Q126GjnIR3t/INaez7SZCQ07/mGYWjEO6Di\ngrJ5zzlbKQ3td911l4aGhvTEE0+or69PF110kV5//XVVVVVJihWXtra2Js7Pz8/Xvn379LWvfU1b\nt25VUVGRHn30Ue3YsSOV3QIAAAAWNOob0rHOgzraeUjHOw9pLDD/tBar1aY650ZtrL5EG6ovUVVp\nnaxW2zntX0pDuyQ9+OCDevDBB+f8bO/evaccu/DCC/X222+nuhsAAADAvALBcbX0fKBjnYd0rOuQ\n3MPdC55fuqZCG6s3a2P1pWqovFCZGVlL1NOYlId2AAAAwGwikbDaXcd1rOuQjnf+Ue2uY4oa0XnP\nz8nM04bqzdpQfYk2VG1WUX7JEvb2VIR2AAAArDqGYahvqDMR0lt6PlBwcmLe89Ns6aorv0AbqjZr\nY80lqiipPaeFpWeK0A4AAIBVYdjbr2Ndf9Txrj+queuwvOMjC55fWVKnDdUXa0PVJaorv0AZ6fYl\n6umZI7QDAABgRfIFvGruPqzjnbGgPuDpW/D8orwSbai+ROurLtb6qouVl12wRD09e4R2AAAArAgT\noYBO9BzR8a4/6nj3YfUMtC14fnZmntZXXaQNVZu1vupirS1wyGKxLFFvU4vQDgAAAFOaDIfU1ndM\nzd1/1LGuP6rT1bxg8Wh6WobqyzclRtIrS+tMNS/9bBDaAQAAYAqRSFgd7hY1dx9Wc9cf1dp3VOHI\n5LznWy1W1TjWJ0L6OscGpaelL2GPlw6hHQAAAMsiGo2oe6BNzd0fqLnrj2rp/VChBVZ4kaSKklqt\nr7xI66suVn3Fx5Z8vfTlQmgHAADAkogaUfUNduh492E1d3+gE90fKBAaX7BNaWG5GuMj6Y2VFyo3\nK3+JemsuhHYAAACcE1EjKtdQl1p6PlBz12G19ByRf2JswTZFeSXxkH6RGisvUmFu8RL11twI7QAA\nAEiJWSG9+4NYSA94F2yTn7NGjZWxgL6h6mIVF5QtUW9XFkI7AAAAkhIL6Z1q6TkSm+7S86F8Ac+C\nbfKyCtQYH0VvqLxQpYXlK3YZxqVEaAcAAMCiTM1Jb+k5opapkfTTTHfJycpXY8WFaqi8UI2VF8pR\nVEVITwKhHQAAAHOaWt2lpeeIWnqOqLXnQ40HfQu2ycnMU0PFx9RYdZEaKi6Uo7hq1ayVvpwI7QAA\nAJAUWye9s/+ETkyF9N6PNHGa1V1ysvLVUPGxWFCvvFCO4mpC+jlAaAcAADhPTYZDancdT4T09r5j\nCoWDC7bJyypQfcXHEtNdyooqCelLgNAOAABwnggEx9XWd1StvR+qpeeIOtzNikTCC7YpyCmKjaRX\nXqiGio+pdE0Fc9IXacRrKFX/OUNoBwAAWKXGxkfV2vuRWnqO6ETvh+oZaJdhRBdsU5xfpvqKTbHR\n9IqPaW2Bg5A+D8Mw5B6WTvRILd2x/dTW0i0Ne6WR11PzXYR2AACAVcAwDA153TrR8Line truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu0AAAEWCAYAAADSA6kgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6dbefd0>"
+       "<matplotlib.figure.Figure at 0x74fc978>"
       ]
      },
      "metadata": {},
@@ -431,9 +429,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAERCAYAAABy58/tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAERCAYAAABy58/tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x434c6d8>"
+       "<matplotlib.figure.Figure at 0x4b0a6d8>"
       ]
      },
      "metadata": {},
@@ -777,9 +775,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwYAAAEtCAYAAACyMncIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7539b70>"
+       "<matplotlib.figure.Figure at 0x873a1d0>"
       ]
      },
      "metadata": {},
@@ -787,9 +785,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x758e898>"
+       "<matplotlib.figure.Figure at 0x7e7cd68>"
       ]
      },
      "metadata": {},
@@ -797,9 +795,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwMAAAEtCAYAAABUG7xMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75d6748>"
+       "<matplotlib.figure.Figure at 0x87c3588>"
       ]
      },
      "metadata": {},
@@ -1017,9 +1015,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAEWCAYAAAA+Wg5zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAEWCAYAAAA+Wg5zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl0VGWexvHnVvYggUACIpBEEGSNiNhRNiERggnQbK3A\nNCiKAjNz2gWDDLaCnKMtHkGmZ0ZxQFnalqZBaBZB2cISBQQGRCWCYEASgQBNEgghqSR3/qBTTVnZ\nK6miqr6fc3K68963bv0iSz2893ffa5imaQoAAMAJFncXAAAAPB+BAgAAOI1AAQAAnEagAAAATiNQ\nAAAApxEoAACA0wgUAADAaQQKAADgNAIFAABwGoECAAA4jUABAACcRqAAAABOI1AAAACnESgAwAt0\n795dW7ZsceocZ8+eVYsWLeqoIvgaf3cXAABw3sSJE7Vo0SINGDCg3OOLFy/Wnj17FBUVpZMnT2rc\nuHGKj4+XJF27dk2hoaEKDQ1Vfn6+K8uGFzFM0zTdXQQAwDk5OTmKiYnRiRMnFBERYRs3TVPjx4+X\n1WrVxx9/LIvFoitXrqhNmzbat2+f2rRpo2nTpumtt96S1WpVcHCwiouLZRiGG38aeCIueQCAF2jc\nuLGGDh2qZcuW2Y3PmzdPmzZt0qJFi2Sx3Pgrv2HDhurevbs++ugj7d+/X3FxcZKkgIAA+fn5qaio\nyOX1w/MRKADASzz99NNatGiRyhaei4qKNGfOHE2YMEG33Xab3dxmzZrp9OnTWr58uUaOHGkbb9Cg\nAZc9UCsECgDwEr1791ZJSYn27NkjSfr+++918eLFcvsq/Pz8tGXLFg0bNsxunECB2iJQAICXMAzD\n1pwpSSUlJZKk1q1bO8z18/PTAw88oL59+9qNh4aG6tq1a/VfLLwOgQIAvMj48eO1Zs0a5eXl6Z57\n7lG7du2Unp5uO26appYvX66MjAxZrVZJ0qFDh2zHWaFAbXHbKAB4kebNmyshIUHLly/XpEmTtGHD\nBs2YMUNHjhxRQECArFarhg4dqhUrVujRRx9VSkqK3WUPAgVqi9tGAcDLfPbZZ3rllVe0f//+Gr92\n4MCBmjp1qhITE+uhMngzLnkAgJcZMGCAsrOzdfjw4Rq/lhUK1BaBAgC8jJ+fn5588klbc2ZNEChQ\nWwQKAPBCEyZM0PLly1VQUFCj13GXB2qLQAEAXigqKkq/+tWv9Mknn9TodaxQoLYIFADgpSZOnKiF\nCxfW6DUECtQWgQIAvNSQIUN07NgxHT9+vNqv4ZIHaotAAQBeKjAwUOPHj9cHH3xQ7dewQoHaIlAA\ngBd76qmntHTpUtuumFUhUKC2CBQA4MXuvvtu3X333Vq/fn215nPJA7VFoAAAL3fzA8OqwgoFaotA\nAQBebuTIkdq3b5/OnDlT5VwCBWqLQAEAXi40NFSjR4/Whx9+WK25XPJAbRAoAMAHPP300/rwww9V\nUlJS6TxWKFBbBAoA8AHdunVTZGSktm7dWuk8AgVqi0ABAD6iOs2ZXPJAbRmmaZruLgIAUP/y8vIU\nHR2tY8eOqVmzZuXOyc3NVevWrZWXl+fi6uDpWKEAAB8RFhamYcOGadmyZRXOCQ0NVX5+vvi3JmqK\nQAEAPqTsskdFgSEgIEB+fn4qKipycWXwdAQKAPAhPXv2lMVi0RdffFHhHBozURsECgDwIYZhVPlY\ncwIFaoNAAQA+Zty4cVq7dq1ycnLKPc6dHqgNAgUA+JjIyEgNHDhQy5cvdziWmZmp/Px8bd++3Q2V\nwZMRKADABz399NO2yx5XrlzR0qVLNX36dK1Zs0atWrXS7t273VwhPA37UACADyoqKlLr1q2VlJSk\ntm3basSIEerUqZMkaeDAgWrdurXmzJmjiIgIN1cKT0GgAAAfYZqmvv76a61du1ZFRUU6e/asgoKC\n9N5779nNGz58uBITE2WapqZMmeKmauFp/N1dAACgfuXl5WnRokXKzs5Wt27dlJKSotDQUGVmZio2\nNlZz585VaGiobX6DBg3UoEEDfffdd26sGp6GHgoA8AG5ubl68803NXr0aFt4aNWqlXr27KmVK1fa\nzS27y6Ndu3Y6fvy4O8qFByJQAICXCwsLU2FhoUpLSx2OlffAsLJ9KEaOHKlVq1a5qkx4OAIFAPiA\nvn37lnvnRnJysk6cOKHvv//eNlYWKBo3bqzc3NxygwjwSwQKAPABAwcO1ObNmx3GAwIC9MQTT+iD\nDz6wjd28sVWfPn0q3aYbKEOgAAAf4O/vr4CAABUUFDgce/LJJ7Vs2TLbA8Fu3no7MTFRn3/+uUtr\nhWciUACAjxgyZIjWr1/vMN6uXTt16tRJ69atk2QfKMqePnr9+nWX1grPQ6AAAB/RvXt3HTx4sNxj\nNzdnhoaG2j0cbPDgwdqwYYNLaoTnIlAAgI8wDEPNmjVTdna2w7GRI0fqwIEDOnXqlBo0aGD3cLAe\nPXpo//79riwVHohAAQA+5NFHH9Vf//pXh/Hg4GCNHTtWixcvdnh8uWEYioiI0IULF1xZKjwMgQIA\nfEjr1q2VmZlZ7rGJEyfqww8/VFBQkF2gkG4EkV9ugAXcjEABAD7m7rvvttt3okxsbKzuuOMOHTly\nxO6ShyRFR0frp59+clWJ8EAECgDwMZXtgDlx4kStXbvWYYVCunE3yLFjx+q7PHgoAgUA+JiwsDDl\n5+eXuwPm6NGj9dVXXykvL8/h2KhRo9iKGxUiUACAD+rbt6927drlMN6wYUMNGTKk3EDRqFEjXbly\nha24US4CBQD4oAEDBmjLli3lHps0aZKsVqtM03Q41qdPH6WlpdV3efBABAoA8EGVbcXdrl07RURE\nlLtfRUXPBAEIFADgo4YOHWrbbvtm//Ef/6ELFy5o+vTpDsfYihsVIVAAgI+69957dejQIbux7Oxs\npaamSpJSU1N1/vx5h9dV9EwQ+DYCBQD4KMMwFBkZaRcapk+frlOnTkmSTp8+Xe4qxX333acDBw64\nqkx4CAIFAPiwxx57zLYV982rE2XKW6UoCyJsxY2bESgAwIe1atVKWVlZkuxXJ8pUtEpR0TNB4LsI\nFADg4zp06KC0tDSH1Yky5a1SREVF6cyZM64oDx6CQAEAPm7kyJF69tlnHVYnylS0SlHRM0Hgmwyz\nvJ1LAAA+4/r162rXrp1iYmJsY3v27NGDDz5o+940TW3ZskUhISG2sby8PP3Xf/2XXn75ZVeWi1uU\nv7sLAAC4V3BwsBYuXKigoCD1799f0o3Gy927d1f6urCwMF29elWlpaWyWFjw9nX8DgAA6OGHH9bW\nrVtr/Lo+ffpUGTzgGwgUAAD5+/srMDBQ165dq9Hr2IobZQgUAABJFW/FXZmffvpJ3377rYqLi+up\nKngKeigAAJKkbt26acWKFRo9enSl8y5fvqyVK1fq5MmTatOmjRYvXix/fz5OfB2/AwAAkm40YjZr\n1qzc53cUFRVp06ZN+vLLLxUeHq5Ro0bpmWeecUOVuFVx2ygAwCYzM1Nr1qzR7373O5WWlmrfvn36\n9NNPJUmPPPKIHnzwQRmG4eYqcStihQIAYNOqVStlZGRIuvEY87i4OP3+979XUFCQmyvDrY4VCgCA\nnWPHjqlDhw7i4wE1QaAAADgwDINAgRrhtlEAAOA0AgUAAHAagQIAADiNQAEAAJxGoAAAAE4jUAAA\nAKcRKAAAgNMIFAAAwGkECgAA4DQCBQAAcBqBAgAAOI1AAQAAnEagAAAATiNQAAAApxEoAACA0wgU\nAADAaQQKAADgNAIFAABwGoECAAA4jUABAACcRqAAAABOI1AAAACnESgAAIDTCBQAAMBpBAoAAOA0\nAgUAAHAagQIAADiNQAEAAJxGoAAAAE4jUAAAAKcRKAAAgNMIFAAAwGkECgAA4DQCBQAAcBqBAgAA\nOI1AAQAAnEagAAC43WOPPabevXvXyblOnToli8Wi1157rU7OV1tLliyRxWLRrl27Kh27VR0+fLhG\ntfrXcz0AAFTqiy++0MqVK5Wamlqn5zUMo07P52u6deum4cOHa+rUqdq/f3+V81mhAAC41ezZs3Xv\nvffqoYcecncp+IXnnntOBw8e1MaNG6ucS6AAALjNiRMntHXrVo0fP97dpaAcffr0UUxMjBYsWFDl\nXAIFAKBKp0+flsVi0axZs+zGExMTZbFYNH/+fLvxuLg4derUqcrzrlq1SqZpKikpyW58woQJCgkJ\nUWFhoW1sz549slgsatq0qUzTtI1v2rRJFotFK1eutDuHaZrasGGD7r//foWEhOiOO+7QtGnTVFJS\n4lDHDz/8oHHjxqlFixYKCgrSnXfeqWnTpunatWsOc8+ePaspU6YoKipKQUFBatmypSZNmqQLFy5U\n+fOWsVqtmjVrlqKjoxUcHKx77rlHK1ascJi3efNmPfbYY2rTpo1CQ0MVHh6uxMTEcvsavvvuO/3m\nN79Ry5YtFRwcrBYtWig+Pt5hdaGwsFBvvPGGOnfurJCQEIWHh2vo0KE6fPhwubUmJibqs88+U35+\nfqU/E4ECAFCl6OhotWnTRtu3b7eNFRUVKS0tTRaLxW48Ly9P//d//6eEhIQqz7tz506Fh4erXbt2\nduMJCQkqLCzUF198YRvbtm2bLBaLcnJydOjQIdv49u3bZbFY1L9/f7tzbNy4UU899ZSSk5M1f/58\n3XPPPXr77bf11ltv2c07ePCgevToobS0NE2ZMkXvvvuuBg8erD/+8Y8aMGCAiouLbXN/+ukn9ejR\nQ6tXr9Zvf/tbvfvuuxo3bpz+8pe/qFevXsrLy6vyZ5akl156SX/961/17//+75o9e7aKioo0ZswY\nLV261G7e0qVLlZOToyeeeEL//d//reeff17p6elKSEhQWlqabd6lS5cUHx+vtLQ0PLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75015f8>"
+       "<matplotlib.figure.Figure at 0x7e69710>"
       ]
      },
      "metadata": {},
@@ -1636,9 +1634,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4ffecc0>"
+       "<matplotlib.figure.Figure at 0xa5e51d0>"
       ]
      },
      "metadata": {},
@@ -1648,7 +1646,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.02356385  0.04184256  0.00219828]\n"
+      "[ 0.024  0.043  0.002]\n"
      ]
     }
    ],
@@ -1671,9 +1669,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmQnHd97/v3s/bT2/T0TE/PohnNSJYsycYyCEEcVhsC\nWHGVcpNcUsEGWXXuxZzEJhCoCyEkiOT6kCIQLiT24SR1LgcFJzekqDphOSHxCcRyjJ2DhS1sS9Yu\nzT7TM9M9vT/r77l/jCVL1mZrsbV8X1Uqj/t55unfPPVM92d+/X2+Py2O4xghhBBCCCHEK6a/1gMQ\nQgghhBDiSiVhWgghhBBCiPMkYVoIIYQQQojzJGFaCCGEEEKI8yRhWgghhBBCiPMkYVoIIYQQQojz\nJGFaCCGEEEKI83TWMP0nf/InvOlNbyKXy1EsFtm8eTO7d+8+Zb/Pf/7zLFu2jFQqxW233caePXsu\n2YCFEEIIIYS4XJw1TO/YsYP77ruPJ554gh//+MeYpskv/dIvUalUju/zxS9+ka985Ss88MADPPnk\nkxSLRd7znvfQaDQu+eCFEEIIIYR4LWmvZAXEZrNJLpfju9/9LnfccQdxHDMwMMDv/M7v8JnPfAYA\n13UpFot8+ctf5p577rlkAxdCCCGEEOK19opqpmu1Gkop8vk8AEeOHGF2dpb3vve9x/dxHId3vOMd\nPP744xd3pEIIIYQQQlxmXlGY/tjHPsYb3vAGfvEXfxGAmZkZAHp7e0/ar1gsHt8mhBBCCCHE1cp8\nuTt+4hOf4PHHH+exxx5D07Rz7v/SfarV6isfnRBCCCGEEJeJXC53ymMva2b6d3/3d/n2t7/Nj3/8\nY0ZGRo4/3tfXB8Ds7OxJ+8/Ozh7fJoQQQgghxNXqnGH6Yx/72PEgff3115+0bcWKFfT19fHwww8f\nf8x1XR577DHe8pa3XPzRCiGEEEIIcRk5a5nHvffey0MPPcQ//MM/kMvljtdBZ7NZ0uk0mqbx8Y9/\nnC984QusXbuW1atXc//995PNZrnzzjvPeNzTTZGLM9u5cycAGzdufI1HIi4Xck2IE8n1IF5Krgnx\nUnJNnL9zlSqfNUx//etfR9M03v3ud5/0+Oc//3k+97nPAfCpT32KdrvNvffeS6VS4ZZbbuHhhx8m\nnU5f4NCFEEIIIYS4vJ01TCulXtZBtm3bxrZt2y7KgIQQQgghhLhSvKLWeEIIIYQQQogXSZgWQggh\nhBDiPEmYFkIIIYQQ4jxJmBZCCCGEEOI8SZgWQgghhBDiPEmYFkIIIYQQ4jxJmBZCCCGEEOI8SZgW\nQgghhBDiPEmYFkIIIYQQ4jxJmBZCCCGEEOI8SZgWQgghhBDiPEmYFkIIIYQQ4jxJmBZCCCGEEOI8\nSZgWQgghhBDiPEmYFkIIIYQQ4jxJmBZCCCGEEOI8nTNMP/roo2zevJnBwUF0XWf79u0nbW80Gnz0\nox9laGiIVCrF2rVr+epXv3rJBiyEEEIIIcTlwjzXDs1mk/Xr13P33XezZcsWNE07afsnPvEJfvSj\nH/HQQw+xYsUKduzYwYc//GEKhQIf/OAHL9nAhRBCCCGEeK2dc2Z606ZN3H///fz6r/86un7q7k88\n8QRbtmzhne98J8uXL+dDH/oQt9xyCz/96U8vyYCFEEIIIYS4XFxwzfTb3vY2vve97zExMQHA448/\nzq5du7j99tsveHBCCCGEEEJczrQ4juOXu3M2m+XBBx9ky5Ytxx8LgoB77rmH7du3Y5pLVSMPPPAA\n99xzz0nfW61Wj3994MCBCx23EEIIIYQQl9zq1auPf53L5U7Zfs6a6XP58z//c5544gm+//3vMzw8\nzI4dO/jkJz/J8PAw73vf+y708EIIIYQQQly2LihMt9ttfv/3f5/vfOc73HHHHQC87nWvY9euXXz5\ny18+Y5jeuHHjhTztNWfnzp2AnDfxIrkmxInkehAvdSHXhFIxYaRQL3xwrQGapmFbxsUconiVyevE\n+TuxuuJ0LihMB0FAEASn3Jio6zqvoHpECCGEEK+ROI7xggg/iAgjRRjFhCEotbRd00DXIWFrJCyD\nZMJC17WzH1SIa8jLao13rMZZKcXo6Ci7du2iu7uboaEh3vnOd/J7v/d7ZDIZli9fzo4dO/jWt77F\nl770pUs+eCGEEEKcHz+IcP0QP4jwfAiCpX/EGqaho+sax+bFlFI0m4pEIiSdiuhIJzANWfdNCHgZ\nYfrJJ5/kXe96F7D0Mc+2bdvYtm0bW7du5Rvf+AZ/93d/x2c+8xnuuusuyuUyIyMj3H///dx7772X\nfPBCCCGEeGU8P6TlBXh+jOsuBWjLMLBMg1TKOOOscxQpWl5ALYrQNI/OjHPK2hNCXIvOGaZvvfVW\n1LHPek6jt7eXb3zjGxd1UEIIIYS4uPwgouUFuJ6i1QIVaTi2RTptvKxQbBg62VSCWtOl1VY4dkgy\nYb0KIxfi8nbB3TyEEEIIcfkKI0Wj7eN6inYbolAjmbBIJM8vAji2hR94BKEimbjIgxXiCiRhWggh\nhLgKxXFMs+3TdENaLQiDF0K0c2Fv/Zap02xCEEYXaaRCXNkkTAshhBBXmSBUtP2ISi3EdcE2LToz\nF6ckQ9OWbkyMWQrsUjctrnUSpoUQQoirRBzHNNo+9XZIu63juzpZx8a4BJ03jvWfFuJaJ2FaCCGE\nuApEkaLW8mi2Yhp1A8c26Ug7AC/0j1aEYcSJy0Bo+lLvaMt8+QuyKBWjaRKkhThGwrQQQghxhfP8\ncGlGugFRqJNJ2oSRot70CKKIIIQohDDipDCt65BIhNiWRib58npHh5HCNMGQhVuEACRMCyGEEFe0\nRtun2Q5pNEDHwNQ1Gu2AINSo1CKCAHRNx9B1LENHOyEEh0rRqIcYZoxSLrm0c86SED+MsGxkeXEh\nXiBhWgghhLgCxXFMrenRbCvq9RjQII5wXajX9aVgrdmkUmfuI21hkLQtmq5Poxli6D65jHPW5w3C\niFQG7FdQGiLE1UzCtBBCCHGFUSqm2nSpNxSL1RDLNPA9Dc8HUzdJ2jaGrpGwXt7bfNqxWWxEBJFC\nqfiMqyAGYYRuxFimdkluahTiSiRhWgghhLiCRJGi2vSYK/vUGxEmCeptDVM30YmZKi+ye3KOWttj\nXy0kjmOStk02maDYmWW42E3SPrVNnmnorHvIYeLDAQn79PHA9UMcB5wzbBfiWiS/DUIIIcQVIowU\nlVqb6ZJHq62hqQSabhIGAXumJzhamqfsznGkdIi2apLlEDExtpHAMZLk7C66DxdZ2VvkDSsHsU+Y\nud74XYdd/3sTdeIdiieIIkUURzgJCdNCnEh+G4QQQogrQBgpphfqlOYCWi2ThJEgYZnsnyzx3PgE\nU80xFtwZsh2Q6lqgkITlQ12gge/HtN0GlWqViYUjzLWHmV2s8ZZ1Kyl0ZFj7dxo/+988dD1EP0N9\ndcsLjs9KS1s8IV4kYVoIIYS4zHl+yNRCndlSRLtl0plOAfDo7oMcXhhnvHGQzrzihmEL29aZmlr6\nPsNYCr1JRyPp6HR1mrie4uj4UWqlCp86cjOP39Fg72/GNNoems5p66WDMCKKIzocSDkXZyVFIa4W\ncveAEEIIcRlrtX3GS1UmpiLclkUx1wHAvzy9l12Tz3Gk8RzLBiN6eiAixA8CQqWIz1Cu4SR01lyX\n4KHmVj6U+Wt+8vxhwkgRKYVhgKGfGg2ark8qtRSkZVZaiJNJmBZCCCEuU/Wmx1ipzuR0TBwmKGQz\nNNse//yzPfx8eg9z3hhDA0sBt92Cdguaboznx/jhmQP153/2H9n2xv+Cr5eZqE7y3NFJdCPGNvVT\nZqZbXoBpxaSSOsmEzEoL8VLnDNOPPvoomzdvZnBwEF3X2b59+yn77N+/n1/7tV8jn8+TTqd54xvf\nyN69ey/JgIUQQoirnVIxlXqb8VKTmVlF5JskTJNKw+OJ58fZOz1O2Z9ixXASx7awDZOEZWGbJgYG\nYaDj++AGAXByoN628yP80ca/RNc0li+zmWgeYc/4DIahTlmIJYwUfhiQSUMmab+KZ0CIK8c5a6ab\nzSbr16/n7rvvZsuWLad8vHPkyBHe+ta3snXrVj73uc/R2dnJ3r17yWQyl2zQQgghxNUqjBS1psdk\nyWVmNiAOTfxAcXB8nrFShafGDjId7magP2am4pJ2bHKpFAlr6cZAHbBNHT+EMITIUBiGwbadHwHg\njzb+5fHnyqQNTLtNub1AudlgoCd10liark8yCcmE+bKWGhfiWnTOML1p0yY2bdoEwNatW0/Z/tnP\nfpbbb7+dL33pS8cfGxkZuWgDFEIIIa4VfhBRa3qMTbeZW/AYn/IYLzWYb9RoRw0OlaaYah+GTImp\ndoDhWjiGQ8rM0JVNs6w7j2UYoMF8zaU/giiO+eMXZqNPJ5PRaNQWWWw0T1qIpdH2MUxFKqmRlllp\nIc7ogv7MVErxgx/8gHXr1nH77bdTLBZ585vfzN///d9frPEJIYQQ1wTXD1motTk01uTQeI0f75zi\nf+0/yu6FPYy2n2ORowRmGTOzyOByj858RDLr4ullptujjFWm2Ts+Q8N1AZitusQK/tOue88YpCHG\nMGOU7p7UX9r1Q6I4JJuBjlTiVfjphbhyXVBrvFKpRKPR4Atf+AL3338/f/qnf8qPfvQj7rrrLjKZ\nDL/8y798scYphBBCXLVabkCp3GR6zmfXgQV+vn+esjdDWy9T7DZZkXcoLXjMNppkkgF2QgM0bCCV\n1ImimPJihelWm/l9TRp1jz2jFZ7t/Ch39t5/xucNIkXChjZLS4nDUpmJG/h0dEA2lZBlw4U4By0+\n062+p5HNZnnwwQfZsmULAFNTUwwODnLnnXfy0EMPHd/vrrvuolKp8I//+I/HH6tWqLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4ffe780>"
+       "<matplotlib.figure.Figure at 0xa5e5a58>"
       ]
      },
      "metadata": {},
@@ -1683,7 +1681,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.02010224  0.02051038  0.00154269]\n"
+      "[ 0.02   0.02   0.002]\n"
      ]
     }
    ],
@@ -1717,9 +1715,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4ffe080>"
+       "<matplotlib.figure.Figure at 0xa5e5f28>"
       ]
      },
      "metadata": {},
@@ -1729,7 +1727,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.01975584  0.04575349  0.00022389]\n"
+      "[ 0.02   0.045  0.   ]\n"
      ]
     }
    ],
@@ -1757,9 +1755,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmM3Od54Pnvr+7q6q7quu+zL94iJZG25EPyoTiZeGR7\nHGSAWWCj+ceZhbLr2AkCBDCizCKDTBbjrDObwMgfa8RwkoXtDeJZwLYkWyIlUSTF+2qyj7rv6uqq\nvs/qqto/iuxmsWjZ1GFezwdoQOrf29VvvQ2QTz983udR2u12GyGEEEIIIcRdU93rDQghhBBCCPGg\nkmBaCCGEEEKI90iCaSGEEEIIId4jCaaFEEIIIYR4jySYFkIIIYQQ4j2SYFoIIYQQQoj3SIJpIYQQ\nQggh3qN3Dab/8i//ksOHD2OxWHC5XDz//POMj493rXnhhRdQqVRdH08//fSHumkhhBBCCCHuB+8a\nTL/xxhv8wR/8ASdPnuT1119Ho9Hw2c9+lrm5ue01iqLw3HPPUS6Xtz9+8pOffOgbF0IIIYQQ4l7T\nvNvDl19+uev/v/e972GxWDhx4gS//du/DUC73Uan0+FyuT68XQohhBBCCHEfuqua6cXFRVqtFlar\ndftziqJw/Phx3G43Y2NjfOUrX6FarX7gGxVCCCGEEOJ+o7Tb7favuvh3f/d3SSQSnD17FkVRAPj+\n97+PyWQiGo2SSqX4xje+QbPZ5Ny5c+h0ug9t40IIIYQQQtxrv3Iw/fWvf50f/OAHHD9+nEgk8gvX\nlUolwuEw3//+9/nSl760/fmFhYX3vVkhhBBCCCHuFYvF0vO5d62ZvulrX/saP/jBDzh69Oi7BtIA\nXq+XQCBAPB5/T5sUQgghhBDiQfFLg+mvfvWr/PCHP+To0aOMjo7+0hesVqsUCgW8Xu8HskEhhBBC\nCCHuV+8aTL/44ov84z/+Iz/60Y+wWCyUy2UABgYGMJlMrKys8NJLL/E7v/M7eDwe0uk0f/qnf4rb\n7e4q8bjdnVLkD4uzZ88C8OSTT97jnTx65OzvLTn/e0fO/t6S87+35PzvnUfl7H9ZqfK7dvP49re/\nzfLyMp/5zGfw+XzbH9/85jcBUKvVXL16lS984QuMjY3xwgsvsHv3bk6ePInJZPrg3oUQQgghhBD3\noXfNTLdarXf9YoPB0NOLWgghhBBCiEfFXfWZFkIIIYQQQuyQYFoIIYQQQoj3SIJpIYQQQggh3iMJ\npoUQQgghhHiPJJgWQgghhBDiPZJgWgghhBBCiPdIgmkhhBBCCCHeIwmmhRBCCCGEeI8kmBZCCCGE\nEOI9etcJiEIIIcT9oNVqs7CyjqIomPv0qFTKvd6SEEIAEkwLIYR4AGw0triSqHF+qsRw0MLnnhxC\nq1Xf620JIYQE00IIIe5/Wo2adKXO5dIExQUbK2ubfPHju9Dr5K8xIcS9JX8KCSGEuO9p1Cp8jn4M\nGj1T9Qka7U0UlcKXP7EbtVqu/wgh7h35E0gIIcQDIea14jTZQIH8cooLyQyvnU/d620JIR5xEkwL\nIYR4IITdFgIOG/3aAdwODdP1KU5eT3N2snivtyaEeIRJMC2EEOKBoFarODTiwW/2M7fYJBbWMD5z\nndcvJEgW6/d6e0KIR5QE00IIIR4YB6IuwnYXzQ09er0Kl7vFlfIEPz41TW1h9V5vTwjxCJJgWggh\nxANDp9Pw2JAbv9lPrrhJNKhH27fC5cIU/+PEJJuNrXu9RSHEI0aCaSGEEA+UQyNeQlYPq8sallda\n7B42styuMp5P8+rZxL3enhDiESPBtBBCiPtOfXGVH5+c4p1r+Z5nfQYt+2NOfAM+ssVN1GqFPaNG\nMgtpzifyXElW7sGOhRCPKgmmhRBC3HfeuV7g6NXr/PTsdV49E6fdbnc9f3LMT9DqYXFBYXG5icmo\nJhrWcq0yyc/PJaV+WgjxayPBtBBCiPuO2WSg0drkcvkqb1yd5pUz8due63l8xEvAHCSVXQfA69LR\nb97k+kycH5+aptls3YutCyEeMRJMCyGEuO+EXGYGjRa2Wk0maxO8M5nh3G39pD+yy0/M4WNjVU99\nrgHA2JCRxUaV8UKWoxfT92DnQohHjQTTQggh7jt+hxmPxUaf1ojDrmK8cp3XLiRIl+e31/QZdRzZ\n5Sc8GCaZ3aDdbqNWK+weMRCvxXlnIsNUrnYP34UQ4lEgwbQQQoj7jkqlcCDmwmf2srHZwulqc7U8\nwY9PTjG/tLa97vERL0MuD6pmP5XZTnZ6oF9DKKDh+swUPz+XYHl18169DSHEI0CCaSGEEPelQyMe\nAhY3i4sKXrcWlWGZK8Xprn7SWq2ap/YGiFojpHMbtFqdi4pBnx61cZXr5RQvn+69wCiEEB8UCaaF\nEELcl/oMOnaFXLj73RTKm+wZ6WN+q8KVXJqXT+/0k94bcTLsdWNS28iXdrLQu4aMlFcKXMnmOHtb\nvbUQQnxQJJgWQghx3zo04iFg9lKtNoE2e8eMpBeSnE9kuZzo9JNWqVR8fH+QmC1MvtSg0ehkofU6\nFSNRPddnpnnzcprq3Mo9fCdCiIeVBNNCCCHuW177AFGPHbPeSrnawGRUEwvpmKhO8/qFJHOLnfrp\nIZ+NPSEPTqOHZG59++sddi1We5OJSoKfnJZ2eUKID54E00IIIe65q6kZXjkdZ2F5vefZ4yNeQoN+\nssVNWq02HpcO08AmE5UkP3lnertO+pMHwsTsIWqzsLLa3P764bCBpdYs4/ksx6RdnhDiAybBtBBC\niHtqdX2TV8/E+fGF8/zzz69QqS93PR8N2om53AxorBTKnZro0ZiB2maZ8Xyet69kAXAOmjg41Bnk\nEk/vBOVqtcLuYQPJuSTvTGZJFuu/vjcnhHjoSTAthBDinjLotKhUKvILeU5nr/CDY1e7xoErisLT\n+wJErEHypU22mi20WhW7hgxM16Y5cS1DbmYBgI/tDTLk9LOxqqd2Y5ALdNrl+bwqJmam+dnZJOsb\njZ59CCHEeyHBtBBCiHtKpVJwDZro1w1QX6tzuTjN/3dL+zuAYb+dYY+bAa1tOztttWhxuRQmqtO8\nfDrOZmNre5BL1BohmdlplQcQ9utpaZeYqGRkOqIQ4gMjwbQQQoh7LuodxNnvYNCiZrlV5Wo+zU9P\nx7vWPL0vSNQaolBqsLXVuUgYDerZUBaYKGV5/UIKgCfHfAy7PWjbZoqVnQy0oiiMxQxkFzKcj+eI\nF2Q6ohDi/ZNgWgghxD13IObGO+BieanNSERPZjHF+USWU9fy22uiXivDXheDOge5G/2kVSqFXcNG\n0vNpzk3niRfqqNUqPn4gzJAtSja/ud0qD8DUpyHo1zJZjfPauZSUewgh3jcJpoUQQtxzJqOOYb8d\nm9HJ/FKTkZieazMTvHE5Sao0t73u4/tDRG1BiuUtGo1Odrq/T03Qr2GiOs3PziZY32gwFrSzO+TB\ncVurPICgV0dbu8z1cprXL6R/nW9TCPEQkmBaCCHEfeFAzIXf4qZcbeCwavB44Gp5gp++M83yaicT\nHXRZGPN7cBhdpAsb218b9OlAt8xkOcMrZ5MAfPpQlCFHmPosLC7v1F8risLYkIHcYpbziRzTeSn3\nEEK8d+8aTP/lX/4lhw8fxmKx4HK5eP755xkfH+9Z9+d//uf4/X76+vr41Kc+xbVr1z60DQshhHg4\nRb1W/FY7mlYfc/NbRAJ6VIYVrpdTvHp2Z3z4Jw90stMzMy3WNjr9pDsBspH8Uo5LqTzX0lUclj4O\nj/kJWyNMp9Zpt3fKPfqMt5R7nJfuHkKI9+5dg+k33niDP/iDP+DkyZO8/vrraDQaPvvZzzI3t/NP\nbn/1V3/FX//1X/O3f/u3nDlzBpfLxXPPPcfy8vK7vLIQQgjRTVEU9kVdeM0eCpXN7QuDpeUClzN5\nLsfLALhs/eyPePEO+Ehnd7LTfQY14aCGyeo0r59Psry6ycf2BRl1B1Aa/V2XEaFT7oF2mYlyhtfO\np36t71UI8fB412D65Zdf5vd+7/fYs2cP+/bt43vf+x7VapUTJ04A0G63+da3vsWf/umf8qUvfYm9\ne/fy3e9+l6WlJf75n//51/IGhBBCPFhm51c4eiHFVG6259ljQ26Cgx6WFlUsr2xh0KuJRrRMVqc5\nejG1PSHxY/tCRKwB5uZgeWWnhMPv0aM1rjE5k+bVswm0GjWffCzMsD1GJtd9GfHWco8LyTxTOSn3\nEELcvbuqmV5cXKTVamG1WgFIpVJUKhV+4zd+Y3uNwWDgk5/85HbALYQQQtzq7as5fnzmGv/vm+Oc\nmyx2Pesz6NgfdeM3+8jcqIn2OvUY+zeYrKZ5+XScdrvN4IChM+3QEiCV2+h6jbEhI6XlAlcyBa6l\nq+wKOdgT8nYuI2a7LyP2GTWEA1omqnFeO5dkTco9hBB36a6C6a9+9ascOnSIp556CoByufNPbm63\nu2udy+XafiaEEELcSq1WsbBR52LpCj85M8mlePffF0d2+QkO+liYV1hZ7WSdR2NGqmtFxnN5zk2V\nADpTEW1+lpc0zC/uBMF6nYpwSMvUbIJjF1OsbTT4zI3LiLVa92VEAL9Hh6JdYXJGyj2EEHdP86su\n/PrXv86JEyc4fvw4iqL80vXvtubs2bO/6rd9YD0K7/F+JWd/b8n53zsPytnPFOoszS0xs1bmrbl5\n6jMz/OZBP3azYXuNvrGAalXNuUsFooFO3qff0Obk1GmWawssloOYTTr6W0sY1nWcuZBjV6z77525\n5TZvXWmyuTTLx3e7MLOMYcPIqbNZdsUUbv1rqt/Y5nKizsb8GhtzRcKu/rt+Xw/K+T+s5PzvnYf9\n7EdGRt71+a+Umf7a177G97//fV5//XUikcj25z0eDwCVSqVrfaVS2X4mhBBC3MprNLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f749e8>"
+       "<matplotlib.figure.Figure at 0xa5e5860>"
       ]
      },
      "metadata": {},
@@ -1769,7 +1767,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.26397302  0.7937892   0.00351629]\n"
+      "[ 0.305  0.753  0.004]\n"
      ]
     }
    ],
@@ -1797,9 +1795,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9ea99e8>"
+       "<matplotlib.figure.Figure at 0xa9adb70>"
       ]
      },
      "metadata": {},
@@ -1809,7 +1807,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.00871873  0.00883399  0.00077378]\n"
+      "[ 0.009  0.008  0.001]\n"
      ]
     }
    ],
@@ -1860,9 +1858,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x73db128>"
+       "<matplotlib.figure.Figure at 0xa9ad320>"
       ]
      },
      "metadata": {},
@@ -1900,9 +1898,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0ce160>"
+       "<matplotlib.figure.Figure at 0xa869c50>"
       ]
      },
      "metadata": {},
@@ -1912,7 +1910,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "actual mean=1.30, std=1.14\n",
+      "actual mean=1.31, std=1.14\n",
       "UKF    mean=1.30, std=1.08\n"
      ]
     }
@@ -1947,7 +1945,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,

12-Particle-Filters.ipynb

@@ -120,8 +120,9 @@
        "    }\n",
        "    div.text_cell_render{\n",
        "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 115%;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -234,7 +235,7 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -263,11 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -323,9 +320,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x49a6e10>"
+       "<matplotlib.figure.Figure at 0x79722b0>"
       ]
      },
      "metadata": {},
@@ -399,7 +396,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x84e1ba8>"
+       "<matplotlib.figure.Figure at 0x7d6cf98>"
       ]
      },
      "metadata": {},
@@ -475,9 +472,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x896d550>"
+       "<matplotlib.figure.Figure at 0x82e1a20>"
       ]
      },
      "metadata": {},
@@ -531,7 +528,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x894be10>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x8395908>"
       ]
      },
      "execution_count": 5,
@@ -540,9 +537,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x84e4f60>"
+       "<matplotlib.figure.Figure at 0x7db4518>"
       ]
      },
      "metadata": {},
@@ -576,7 +573,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAD9CAYAAAC1Fh+gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8905080>"
+       "<matplotlib.figure.Figure at 0x8331908>"
       ]
      },
      "metadata": {},
@@ -812,14 +809,14 @@
      "output_type": "stream",
      "text": [
       "final position error, variance:\n",
-      "\t [ 18.00486271  18.00176074] [ 0.00458291  0.00448719]\n"
+      "\t [ 18.005  18.002] [ 0.005  0.004]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x84a4668>"
+       "<matplotlib.figure.Figure at 0x7b69630>"
       ]
      },
      "metadata": {},
@@ -924,14 +921,14 @@
      "output_type": "stream",
      "text": [
       "final position error, variance:\n",
-      "\t [ 8.06115913  7.89412269] [ 0.0029203   0.00299334]\n"
+      "\t [ 8.061  7.894] [ 0.003  0.003]\n"
      ]
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAEWCAYAAAB2aRHzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvX9wG9d57/3d5ZLib1AmQIKk6FjqiKLIa8uK5NROWubH\nNDOKo3GTmURpMpnaTW02TSa3k077OmPZTW5jOT9nMjeeuDeUMuab6Ti9Sto4fT21ZprWLps7diMx\ntJKaEqlbRjZEEARAmiAIkBQXu+8fyK4W4ALYX2f37OJ8/pEALnbPnvOc53nOc55zDifLsgwGg8Fg\nMBgMBoNRFd7rAjAYDAaDwWAwGH6AOc4MBoPBYDAYDIYBmOPMYDAYDAaDwWAYgDnODAaDwWAwGAyG\nAZjjzGAwGAwGg8FgGIA5zgwGg8FgMBgMhgGY48xgMBgMBoPBYBigpuMsiiIee+wxHDhwAC0tLThw\n4ACeeOIJFAoFN8rHYDAYDAaDwWBQgVDrgqeeegrf/e538f3vfx933nknLl++jIceegh79uzB448/\n7kYZGQwGg8FgMBgMz6npOF+8eBEPPPAAPvjBDwIAbr/9dpw8eRI///nPiReOwWAwGAwGg8GghZqp\nGh/4wAfwr//6r5ibmwMAzM7O4qWXXsL9999PvHAMBoPBYDAYDAYt1Iw4f+Yzn8GNGzdw+PBhCIIA\nURTx+OOP49Of/rQb5WMwGAwGg8FgMKigpuP87W9/G88++yz+7u/+DqOjo5iZmcGf/dmf4Y477sCn\nPvUp9bpMJkO0oAwGg8FgMBgMBklCoVDVv3OyLMvVLujt7cXjjz+Oz33uc+p3Z86cweTkJK5du6Z+\nxxxnBoPBYDAYDIafqeU418xxlmUZPF96Gc/zqOFvMxgMBoPBYDAYgaJmqsaHPvQhfPWrX8X+/fsx\nMjKCmZkZfOtb38KDDz5Y8Te1vHUriJKIi4sXwXNFJ16SJdwzcA8EvuYr+AZRErGUXQIA9HX0QeAF\nXLp0CQBw/PhxL4sWOF79+auYzcxi9PAogMryVEnulrJLiGfj6vWiJKK/ox+DocGKz3RShmOZWMnz\nt8QtpHIp9HX02b63meeWv7dWXmtdyzCOX/SAkzKupw+NYlT2qtWrneebLVdfR1/FZ4mSiOn4NBIb\nCUQ7ohA4gfq+5Ia81qt+MWq79LBTZ1b6dqU+5JY/Nz4+jqmpKfWzssmFHmtra4bvW7OU3/rWt9DZ\n2YnPfvazWF5eRl9fH8bHx/FXf/VXhh/iBAIvqA4L4Jwio4VyQbqxfgP3DNzjcamCi8ALGAmNoL+j\nH0Bleaokd30dfbixfgOiJAIodnzFaa3EUnYJPMeXKI+l7JIjil7gBRyJHlHvHbT+wfAHTunpSvrQ\nLZkm9Xw9vRFpi1R8llIOSZaQyqWQzCUx0jMCHnxNfRN0rOjgIJDeToMDZ8mO2KkzK/ZL4AXdv7vl\nz01MTJR87urqciStuGZJ29ra8M1vfhPf/OY3bT/MLpUaIQhUEkoGOYzKk951Xg/k9BTgYGiQeBnM\nKN56NWz1jhN6ulwfbolbmI5Pq9HZWnJuV/ZIDXL19Ea1Zyl/a2powp29d2J5YxkCJ+BY/7G6Hxh7\nrYP9CE115rY/Nz4+jmg0inw+j52dHVv3YlLGYFjEbMd30pHUU4BAcSpOlESAAwROcFwxmlG8NClp\nhj1IpC2YefaV1BVE2iIAjEV/aZY9qw6DwAvobe9Ff0c/Ne/iNZXq0kt5JU14TxjJraRlO2JV/oIQ\nCBkbG8PVq1cxPDyMRCIBwNrGFr6VpqB1jEpCuYxlj0vGcAqnjblWAWqndK+krkCGjJGeESJT3GYU\nb5BnieqFamkLpPSwVh8ubyxDhlyS32sk+mtH9sw6CXbqodqzguCsuI3XaT6kMZpmSOK5JAajbvly\n2rSNq1evlvxtfHzc1L18KUlB7Bg0R0gYzkHKkVSmdFc3VyHwAjiOw9rmGsKtYcfyqJ0gaAPeIFLe\nRpVSCfo6+ojp4XJ92N3WDYHTvy8JmVKeH8vEkMwlEe2IVrzWrj2qpvuZXTAPybUktOBVQMLp59Li\ny01MTJiKPLtaOqcUXFA7BovOBQ/mKN6CFiXJqIxeG0Xb9Z1G0npY0YeKg64XdSUtU4mNBHiOR3Ij\niUQ2oXtvJ+qhmu5ndoERVPzqy7lmsfxqNJWGJJk3arYsAJ1OGO3lcxs3ZV6Z0u1q7sJSdgkyZHS1\ndFE1tetXJVlP6LURuKKzWu64Or14uZL+KI+6Rtoi6v9FWSQmU0xe/Qnp9JZyOQVg2e55vXaA2Wtr\nuFZTTioht/K+3MwbNVoW0k6Y1c7k14GRUazUi1uGV7lvtCMKyCjmvnk8yGPQi1lZFjjntmSsVqZq\n+kOJupZft5RdQqQ9AsHDrEPa85DrzUEimd5SLn/X166D4zg08o0AzNk9uzazWrvWanNa7DXtfacS\nvuxB1SIQTnYSmvJGqzlhTilGO50pyNEZWpSMHn46GMgNJVlvToJZaslypTYivSWjUf1Rfl2kLYLU\nRunBP07JlFF5pTkPmWbdRRISubjKoVeSLKGpoQkA1IWr+zr3lVxn5Nl2bGatBbu12pwWe01z36mG\nayV02mhWikDUi2JQcPL9tZ1JlESkcilMx6frfs9Qq0rGDUeRFgVoBNJKkrQuCIJTXktezLYRqfxb\nURIRz8ZrlkHgyR38Y6YuaM1DtqofgiDrTqHVK4mNBFK5FO7svZNYnRip+/R2GhEuotuufrIJAL19\npxq8Ww9SlFB/Rz/6O/odM2haIRF4ATzHO5Z719fRB0mW0NXcBVEScbNw07O8UaUsoiRClMSSPEOn\n31+URMymZpHKp5DYSJQszDFbvnqGlMz7GUVJkjishaQuUIxnPBtHPBs31Cf8Csk2qoRWf2yJW3g9\n+TpEWdxV13p6RikrifKSqAtREhHLxG7tuU4Z9STrRtDqlWhHFDJkLG8sQ5RE7G3Zi+7Wbkt2T0+W\nlVMkSdc9s9f2cNWK+21koY04eJ036saUhhIhTeVSkGQJPMerebO1Rqx+nXIxMrq3EzkmLfN+zRHz\nG6SiOG5H9miVF63+iGfjGO0dRXNDM4DSuvarnlFwe4bUSnv7LWLpJgInYKRnBAInqKdYAtYWB+rJ\nstG6D+8J6y7YVe5Tq8393o+8xvc1RdoQ0OTs65XFyfdXOtN0fLr0wAHZ2IiXproyglEjRrOSobls\nbkOrU1gJL9LMaJYXrf5Q0jRqXUcj1QZDbjulNLe3XyjXKzz4XemLVtvPqiwLvIC7B+62tfc37f2I\nZqjvQbUiMvWuGJx+f4EXcKz/GC4uXgTk4nZPNws3IcrF6cUg1W+1PLFyaFYyNJfNTUjqAjNOudEo\nsleRPdrlxW8DIC00rrkx295+rn8SuOljmKn7au1Kex/3O1R7QGYigvUsJE6/v1ZRiJKIxewikhtJ\nAHQYAgajEqR0gVHjSaPj5Df8HAypNRjyg1Pq5/onhVs+hlL3Rk6sDCp+WJhKX4k00JBr5YdGJIGi\nKGKZGBr5xkDmu1XLE2MwyjFiPM3oLKtOVD3oJD8HQ0RJRDqfBgB0NXft+nu0PYpkLome9h4Mduov\nOvS6jf1c/0HAyImVQcQvgQe6SkMZfmlEhjWq5YkxGKSxEtljOoluIm0RvHjtRTTwDQCAxfVFHOs/\nBmB32yWyCQx27nZOWRvXNzQEDL3CL+/u2nZ0VvB6yxSS21v5Ba/bgDRebL0VJGjfWsttzPYXs/Jn\nVic51T6snY2RyqUw2juKaHsU0fYoRntHkcqlABhvO2Z3GOUoa4z0+h/rm+5DtacQpFwrq1NvoiQi\nvZ32bGFekNqgHnBzilcvMsZJXF3LB039xanIJYuAmkPgBETbi7mpzJFhmKU8hWtH2sGNzA31tMLZ\nzCxGQiMAgtc3/bAGAKDccQbIHZ0J1DZqTjWiVeEWJRGzmVlw4BDPxj3rFCzfzR+4rUT1ptVS2ylE\nW+pvQYsWkv3FjE5yatrTL9OnNFCtfZS/bYlbWMmvQJIlHIkeMXUPhveQDk6UD75FSUQyl1Sfw4FD\neruYQ+9233T73WkN1LlWIq8XOyhlMONYONWIVoV7KbsEDpw6ZccMFhmckE0797AzG2Fl83wS78Bw\nB78YlnpFr30AIJaJAQDu7L0TP/2vn4LjOETaI5hZmtllg2hv43rWE24FJ7SDb0V2FJ7//vNoE9pw\n6V8u4Y8//8eOPrcaXrw7abSy3IlOw7+r+cZ33HEH3nzzzV3f33///XjhhRcMF46G6QQrjgWLtgYb\nJ2TTzj3Kf/tG5g0MdAxA4KufUKn3TGV62ApW3kEvMhbeE7ZcBoYxjOokpyKXLAJqDm37lPer6fg0\nIm0RNAu7T0Wsdg/FefLaUaXFlnuFmxHe8fFxAMAz/+uZkv73j3/7j+o1j//V4671zaDNPJXLcmeH\ng47z9PQ0CoWC+jkej+PYsWP42Mc+ZvghQatws1g1PH0dfZAhB3ZhHg04IZt27qH9rSiLuJK6gtX8\nKnrbe6saJb1ngoPl7fWsDirLI2OvLb9m6HkM8pjZe5odMkWG8n7FcRxW8isY6Bww9HvaHNV6t+V2\nUBzhiYmJXZ/1/q+g9L8//KM/3HVP1jetUy7LZqj5i+7u7pLPZ8+eRSgUwqlTp0w/zGu8ipxYFW6B\nFzASGkF6O43+jn7WKSoQlKnDdC4NnuPRwDeYTs0RZRHJjd9smC+jZsS64n1+swdtQSqgp62n5vVs\nRoZuarUPO2TKXcJtYaQ2UoZtUFAc1aDoaKd9iPPnz2NqagqJREL9nMlk0NjYiNbWVkSjUQwPD2Nh\nYQEAcODAgZLfj4+PY2JiwhV5YDNPtzAlvbIs43vf+x4++clPYs+ePYZ/R0uFezk6sLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAEWCAYAAAB2aRHzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x84cb630>"
+       "<matplotlib.figure.Figure at 0x7ad2978>"
       ]
      },
      "metadata": {},
@@ -979,14 +976,14 @@
      "output_type": "stream",
      "text": [
       "final position error, variance:\n",
-      "\t [ 8.03991809  7.93388899] [ 0.00275743  0.00305754]\n"
+      "\t [ 8.04   7.934] [ 0.003  0.003]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAEWCAYAAAB2aRHzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8ea6b70>"
+       "<matplotlib.figure.Figure at 0x8918320>"
       ]
      },
      "metadata": {},
@@ -1035,14 +1032,14 @@
      "output_type": "stream",
      "text": [
       "final position error, variance:\n",
-      "\t [ 13.65600711 -15.87383315] [ 51.52147194  34.40736373]\n"
+      "\t [ 13.656 -15.874] [ 51.521  34.407]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEWCAYAAAAq41LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9510940>"
+       "<matplotlib.figure.Figure at 0x9995470>"
       ]
      },
      "metadata": {},
@@ -1075,14 +1072,14 @@
      "output_type": "stream",
      "text": [
       "final position error, variance:\n",
-      "\t [ 17.92304664  18.07173658] [ 0.00451171  0.00429415]\n"
+      "\t [ 17.923  18.072] [ 0.005  0.004]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXtwW+d55/99Dw6vAAiSuBCgRFuyLYmSLcmSrVjOZFU7\nTTLy1Ekn2Y6b9XQc/xHRv9lkmrTTTZtpG1LZNG1+vzTT7iTtmpxtlct6G01nmsx6Jxo3TVyOx/Ev\nEnWxZEmkZFsyRNxBEgTA68F594+jc3QA4o4D4IB8PjMYUcC5POf6ft/nfd7nYZxzDoIgCIIgCIIg\nDEdotgEEQRAEQRAEsVkhsU0QBEEQBEEQdYLENkEQBEEQBEHUCRLbBEEQBEEQBFEnSGwTBEEQBEEQ\nRJ0gsU0QBEEQBEEQdYLENkEQBEEQBEHUiaJi+y//8i9x5MgROBwOeDwefOpTn8I777yzYbmxsTFs\n27YN3d3dePrpp3H16tW6GUwQBEEQBEEQrUJRsf3v//7v+OIXv4hf/epX+MUvfgFRFPGxj30M8/Pz\n2jLf+ta38J3vfAff/e53cfbsWXg8Hnz84x9HKpWqu/EEQRAEQRAEYWZYJRUk0+k0HA4HfvrTn+K3\nfuu3wDnH4OAgfv/3fx9f/epXAQArKyvweDz49re/jZGRkboZThAEQRAEQRBmp6KY7cXFRciyjL6+\nPgDA+++/j3A4jE984hPaMp2dnTh27BjefPNNYy0lCIIgCIIgiBajIrH9pS99CYcOHcKTTz4JAAiF\nQgCAgYGBrOU8Ho/2G0EQBEEQBEFsVcRyF/zDP/xDvPnmm3jjjTfAGCu5fO4yiUSicusIgiAIgiAI\nwiQ4HI6K1ynLs/0Hf/AH+PGPf4xf/OIX2LFjh/a91+sFAITD4azlw+Gw9htBEARBEARBbFVKiu0v\nfelLmtDevXt31m87d+6E1+vFa6+9pn23srKCN954Ax/+8IeNt5YgCIIgCIIgWoiiYSRf+MIX8KMf\n/Qg/+clP4HA4tDhsu90Oq9UKxhi+/OUv45vf/CaGh4exa9cufOMb34Ddbsfzzz9fcLvVuOAJopGc\nO3cOAPD444832RKCKA3dr0SrQfcs0UrUGgpdVGz//d//PRhj+M3f/M2s78fGxvC1r30NAPCVr3wF\ny8vL+MIXvoD5+XkcPXoUr732GqxWa02GEQRBEARBEESrU1Rsy7Jc1kZGR0cxOjpqiEEEQRAEQRAE\nsVmoKPUfQRAEQRAEQRDlQ2KbIAiCIAiCIOoEiW2CIAiCIAiCqBMktgmCIAiCIAiiTpDYJgiCIAiC\nIIg6QWKbIAiCIAiCIOoEiW2CIAiCIAiCqBMktgmCIAiCIAiiTpDYJgiCIAiCIIg6QWKbIAiCIAiC\nIOoEiW2CIAiCIAiCqBMktgmCIAiCIAiiTojNNoAgCIIgCIIwB2NjY3n/JqqHxDZBEARBEMQmY2Rk\nBKdOnUJ3dzcWFhby/g4A4+PjWd+fPHlS+5vEtjGQ2CYIgiAIgthkTE5OYn19HYlEAoKgRA1zzjcs\nNzExAQA4ceLEBuFNGAPFbBMEQRAEQWwyjh07hra2NgCKyM4ntPVMTEyAMdYI07Yc5NkmCIIgCILY\nZKhe6tOnT2NpaQnr6+t5l3sZwG7d/2cAvHT370KhJkRlkGebIAiCIAhiEzI+Po6FhQW8+OKLmpc7\nl90AntJ99MJ7YmJC83gPDw/X2drNC4ltgiAIgiCITcz4+DjW1tbAOceJEycKCu9iTE9PgzGGzs7O\nOli4uSGxTRAEQRAEsUVQhbfT6axq/dXVVQiCoIWYEKUhsU0QBEEQBLHF+OIXvwhAidF+XfeZKWNd\nzjlOnz5NgrtMTCm2JVmCP+GHP+GHJEvNNocgCIIgCGJTMTY2Bs45RjjHU7rPS7plHA5HwfW9Xm/9\njdwkmC4biSRLODt7FgJT+gF3Fu/gyLYjEAXTmUoQBEEQBLFpUYvh6D3Yk5OTOHbsGGUoqQDTKdhg\nMgiBCZq4lmQJwWQQQ46hJltGEARBEASx9SBhXRumE9sEQRAEQRCtTDX5qfWl0alM+ubCdGLbZ/fh\nzuIdLVZb5jJ8dl+TrSIIgiAIgihO7oTBkZERnD59Gl6vF9evXy+67smTJ7W/SWxvLkwntkVBxJFt\nRxBMBgEo4pvitQmCIAiCaBXGx8cxMjKCU6dOYX19HYlEIqsUeltbG9bW1kyZzWN0dLTZJmw6TKli\nRUGkGG2CIAiCIFoKfdjI5ORkwRLp6+vrWeLbTJBX3XhMmfqPIAiCIAiilTl27FjR1HnFoKIxmwsS\n2wRBEARBEAYzPj6OhYUF7Nmzp6zlXwbwy7ufX3COxycmTOv9JiqDxDZBEARBEESduH79Ojjn4Jzj\nxIkTBZfbDeAp3Wf33e8ZY9qnvb0d7e3tdbaYMBoS2wRBEARBEA1gfHwchw8fbrYZRIMhsU0QBEEQ\nBNEgPvnJT1a97vr6uja5cnh4mOK6WwQS2yZAkiX4E374E34tvzhBEARBEJuPsbExLaxE/5kB8Lru\nM1NiOzMzM5icnKyvsYQhmDL131ZCkiWcnT0LgSn9njuLd3Bk2xHKLU4QBEEQOnp7ewEACwsLTbak\nPryk+5tzjqcAvFRkgiTnvN4mEQZBnu0mE0wGITABoiBCFEQITNAK+hAEQRAEcY/FxUUMDw9rn82O\n6vV2OBxwOBzaJEv1/8eOHWu2iUQZkPuUIAiCIAjTs7CwgOHhYYRCISQSCQDYkBpPTbNXqjS6ir6A\ni5mLueR685977rmsAjqEuWG8QeMQ6oMBoGSSd0mWtky59twwkrXMGrY7tkNk4qY/djNz7tw5AMDj\njz/eZEsIojR0vxKtRi337MjICCYmJipax+Fw5A0/0Yv1ZodlmMkWIptKNGw+TKfktloMsyiIOLLt\nCILJICRZwmxyFpFUBMDmP3aCIAiCqBTVo3v69OksEVSMRCIBxpipRezo6GizTSDqhOlUnD6GGbjn\n5R5yDDXZsvohCiKGHEPwJ/xoE9q21LETBEEQRKWMj49jfHy8ZIXFl3GvOAwAjDOmTURsa2url3lV\nYeYwFqI2aIIkQRAEQRAtiTphsJDoLlSVEVByVhNEIzCd2PbZfZC5DEmWIMkSZC7DZ/c126yGsJWP\nnSAIgiCqYXx8HLIsa5k7qkUtEkOFYgijMV0YiT6GGdj8EyT1tPqxb6WJrQRBEIQ5UQV3qRCTXKan\npzE9PQ0A2gTM0dFRCu8gasaUakiNYd6KtOqxb7WJrQRBEIS54ZwDIyN44x/+AVImA6B0VUaCqAem\nCyMhWhMqzkMQBEGYjvFxfESS8ProKJ5GdpVGgmgUJLYJIg+SLCG0HII/4YckS802hyAIgqiBsbEx\nLaZbnVSpFsApRFtbG8bGxiiOm6gZGuMnDMFn9+HO4h1NmLby5E5JlnA1cRUMDIFkgEJiCIIgNhlq\nru58hWRUYU0VGgmjIPVAGEKrT+7UE0wGwcC0kBjKd04QBLF1yBXZJLqJWmlNNUSYklad3EkQBEE0\nFlXAvvrqq5Ttg9j0lIzZnpycxKc+9Sls374dgiDg+9//ftbvL774IgRByPp8+MMfrpvBBFFvfHYf\nODjlOycIgigDfUxzob9zmZiYwMTEBE6ePNkwOwmiWZT0bKfTaRw4cACf+9zn8MILL2zIW8kYw8c/\n/nH88Ic/1L5rb2833lKCaBCiIGKfYx9iqzEM2gdbOiSGIAii3kxOTiIUCmn/P3XqFCYmJsAYw+7d\nuzEyMmLaUIzR0dFmm0BsAUoqiGeeeQbPPPMMAMWLnQvnHO3t7fB4PIYbRxDNQhREeLu8FBZDEARR\ngmPHjmFychKTk5MA7pVB55xrhWLUIjGMMXz+859vmq25UAgL0QhqTv3HGMMbb7yBgYEB7NmzByMj\nI4hGo0bYRhAEQRCEyRkfH8f169cBIMvDnQ/OuSa8VSitHrHZqVlsHz9+HD/84Q/xi1/8An/913+N\nX//61/joRz+KtbU1I+wjCCIPkizBn/BTHnCCIEzDsWPH8Nxzz+HEiRMVraeGnJDoJjYrjKuJJcvA\nbrfje9/7Hl544YWCywSDQdx///348Y9/jE9/+tPa94lEQvv7xo0bVZpLEIQ+DzgAcHDsc+yjuHKC\nIEzD7/zO7+D27dsFf38ZwG7d/2eQXd1xz549+NGPflQn6wiiMnbt2qX97XA4Kl7f8NbZ5/Nh+/bt\nuHnzptGbJghTIMkSYqsxAICrw9VwkRtbjWl5wPX2eLu8DbWDIOpNs581onr++Z//GQDwoQ99CPl8\nersBPFVk/enp6brYRRDNwPA3VzQaxezsLHy+wqnSHn/8caN3SxCGcu7cOQAb71VJlnB29izczA1A\nqZT56LZHGyoC/Ak/AslAltgetA/SZM4tTKH7tZUxw7NG1M7Xvva1qtP7HTlyBABw4sQJ02YzIbYG\n+uiMaigZs51Op3Hx4kVcvHgRsizj9u3buHjxIvx+P9LpNP7oj/4Ib731Fm7duoXXX38dn/rUpzAw\nMJAVQkIQm4VgMgiBCVp1SYEJWtXMRuGz+yBzmfKAb2IoJt8czxpRO2NjY+Ccax+Hw4G2traKtjEx\nMUEphYmWpqTYPnv2LA4fPozDhw9jZWUFo6OjOHz4MEZHR2GxWHDlyhX89m//Nvbs2YMXX3wRe/fu\nxa9+9StYrdZG2E8QWw5REHFk2xEM2gcxaB/EkW1HyNtXRxotfFWPbiAZQCAZwNnZs1tWcBObj4WF\nBaytrWEGwOu6z0yJ9dbX12kCJdGylGyhn3rqKciyXPD3M2fOGGoQQZgZn92HO4t3NPHTLK+yKIgb\nwkYkWdI8f1SIxxhU4SswxS9xZ/FO3Ts3eo+uakMwGdxyYUJmedY2E/qc0s3OL62fDLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xbf706a0>"
+       "<matplotlib.figure.Figure at 0xbbba0b8>"
       ]
      },
      "metadata": {},
@@ -1208,9 +1205,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAABACAYAAAC+2bgWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOP8CSdsd9j7kQ7EAAAAASUVORK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAABACAYAAAC+2bgWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAABfdJREFUeJzt3E1oE1sYxvF34m1qQsbchZhoAraIIrgQahRaqDYI3Qji\nQqguhCqlKKLopihdGRXxgm6ECtdCjeBHtW6ECOqi0IruHGs/FKwLuygtuLgBBaPG9y4uLab2NiYz\nJUf9/6CUTs6ZPj2FPJnhJJaqqgAAYBBfpQMAADAX5QQAMA7lBAAwDuUEADDOH25PkM1mvcgBAPjF\nhMPhsudy5QQAMA7lBAAwjuvbet/6s6P8S7jFou1WpSP8lKy/zXz7mwr/T8Bk2b/+8eQ8XDkBAIxD\nOQEAjEM5AQCMQzkBAIxDOQEAjEM5AQCMQzkBAIxDOQEAjEM5AQCMQzkBAIxDOQEAjEM5AQCMQzkB\nAIxDOQEAjEM5AQCMQzkBAIxDOQEAjEM5AQCMQzkBAIxDOQEAjEM5AQCMQzkBAIxDOQEAjEM5AQCM\nQzkBAIxDOQEAjEM5AQCMQzkBAIxDOQEAjGOpqro5QTab9SoLAOAXEg6Hy57LlRMAwDiUEwDAOK5v\n6wEA4DWunAAAxqGcAADGoZwAAMb5oXLq6uqS2tpaCQQCkkgk5PHjxwuOHx4elm3btkkwGJR4PC6n\nT5/2JKzbbLlcTlpbW2Xjxo3i9/slmUwuWi7TlbJuY2NjkkwmJRqNSiAQkDVr1khnZ6d8/vy54tm+\n9fr1a7FtW2zbXpRcwO9uYGBAdu7cKfF4XHw+n6TT6aJzyu4DLeLWrVtaVVWl3d3d+urVKz1y5IiG\nQiGdmJiYd3w2m9VIJKItLS06OjqqfX19atu2XrhwodivKlmp2T58+KAHDx7UK1eu6K5duzSZTHqe\n6WdQ6rqNj49rOp3WFy9e6MTEhN67d08jkYh2dHRUPNuMXC6ndXV1umPHDrVt2/NcAFTv37+vnZ2d\n2tfXp8FgUNPp9ILj3fRB0XLasmWLtre3Fxxbu3atnjx5ct7xXV1dGg6H9ePHj7PHzpw5o7FYrGiY\nUpWa7VuHDx/WpqYmzzP9DNys24zjx49rfX2919HKznbs2DE9cOCAXr16VUOhkOe5ABQKhUJFy8lN\nHyx4W+/Tp0/y7NkzaW5uLjje3NwsT548mXfO06dPpbGxUaqrqwvGT05Oytu3b3/scu4HlJMN3qzb\n+Pi4PHjwQJqamozIlslkJJPJyKVLl0R5ZwRgDDd9sGA5vXv3TvL5vEQikYLjK1askKmpqXnnTE1N\nfTd+5uf/m1OOcrLB3bo1NDRIIBCQdevWSWNjo5w9e7bi2SYnJ6W9vV2uX78uwWDQ0zwA3HHTB57v\n1rMsy+tTwhC3b98Wx3Hkxo0bkslk5Pz585WOJPv27ZNDhw7J5s2bKx0FwBxu+uCPhR5cvny5LFmy\nRKanpwuOT09Py8qVK+edE41Gv2vEmfnRaLTsoF5kg7t1i8fjIiKyfv16yefz0tbWJh0dHeLzefMa\np5xs/f39MjAwIKdOnRIREVWVr1+/SlVVlVy+fFna2to8yQagdG76YMFnFb/fL5s2bZKHDx8WHH/0\n6JE0NDTMO6e+vl4GBwcll8sVjI/FYrJ69eoFw5SinGzwbt3y+bx8+fJF8vl8RbONjIzI0NDQ7Fcq\nlZJAICBDQ0Oye/duz7IBKJ2rPii2Y6K3t1f9fr92d3fr2NiYHj16VG3bnt3ae+LECd2+ffvs+Gw2\nq9FoVPfs2aMjIyN69+5dXbZsmV68eLHo7oxSlZpNVXV0dFQdx9GWlhZNJBL6/PlzdRzH82wmK3Xd\nrl27pnfu3NGXL1/qmzdvtLe3V2OxmO7du7fi2ebq6elhtx6wSN6/f6+O46jjOBoMBjWVSqnjOIvS\nB0XLSfW/7YA1NTVaXV2tiURCBwcHZx9rbW3V2tragvHDw8O6detWXbp0qa5atUpTqdQP/eHlKDVb\nTU2NWpallmWpz+eb/f67KWXdbt68qXV1dWrbtoZCId2wYYOeO3euYHtopbLN1dPTw/ucgEXS39//\n3fOnZVm6f/9+VfW2D/hUcgCAcfhsPQCAcSgnAIBxKCcAgHEoJwCAcSgnAIBxKCcAgHEoJwCAcSgn\nAIBx/gXyNx3ze6skVAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xbf52518>"
+       "<matplotlib.figure.Figure at 0x8871240>"
       ]
      },
      "metadata": {},
@@ -1251,9 +1248,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f7dac8>"
+       "<matplotlib.figure.Figure at 0x7d2b400>"
       ]
      },
      "metadata": {},
@@ -1325,9 +1322,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89176a0>"
+       "<matplotlib.figure.Figure at 0x7d84da0>"
       ]
      },
      "metadata": {},
@@ -1370,9 +1367,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7deab70>"
+       "<matplotlib.figure.Figure at 0x7d339b0>"
       ]
      },
      "metadata": {},
@@ -1433,9 +1430,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x84e60f0>"
+       "<matplotlib.figure.Figure at 0x890bf28>"
       ]
      },
      "metadata": {},
@@ -1499,9 +1496,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8e2cd30>"
+       "<matplotlib.figure.Figure at 0xbb13cf8>"
       ]
      },
      "metadata": {},
@@ -1509,9 +1506,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8e812b0>"
+       "<matplotlib.figure.Figure at 0x938a710>"
       ]
      },
      "metadata": {},
@@ -1519,9 +1516,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8ec03c8>"
+       "<matplotlib.figure.Figure at 0x8848860>"
       ]
      },
      "metadata": {},
@@ -1529,9 +1526,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f725f8>"
+       "<matplotlib.figure.Figure at 0x88c0a90>"
       ]
      },
      "metadata": {},
@@ -1604,7 +1601,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,

13-Smoothing.ipynb

@@ -28,6 +28,7 @@
        "@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",
+       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
        "\n",
        "    div.cell{\n",
        "        width: 900px;\n",
@@ -118,9 +119,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 120%;\n",
+       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -233,7 +235,7 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -262,11 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -294,9 +292,9 @@
    "outputs": [
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x436c978>"
+       "<matplotlib.figure.Figure at 0x7211630>"
       ]
      },
      "metadata": {},
@@ -339,9 +337,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e6ccc0>"
+       "<matplotlib.figure.Figure at 0x725cc88>"
       ]
      },
      "metadata": {},
@@ -371,9 +369,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7457a20>"
+       "<matplotlib.figure.Figure at 0x74718d0>"
       ]
      },
      "metadata": {},
@@ -515,9 +513,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74bcfd0>"
+       "<matplotlib.figure.Figure at 0x7ec72e8>"
       ]
      },
      "metadata": {},
@@ -590,9 +588,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x746f080>"
+       "<matplotlib.figure.Figure at 0x7eab198>"
       ]
      },
      "metadata": {},
@@ -619,9 +617,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76109e8>"
+       "<matplotlib.figure.Figure at 0x8732320>"
       ]
      },
      "metadata": {},
@@ -658,9 +656,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7664d68>"
+       "<matplotlib.figure.Figure at 0x74ad588>"
       ]
      },
      "metadata": {},
@@ -698,9 +696,9 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76f1e10>"
+       "<matplotlib.figure.Figure at 0x882fc18>"
       ]
      },
      "metadata": {},
@@ -747,9 +745,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7616ef0>"
+       "<matplotlib.figure.Figure at 0x99b3f60>"
       ]
      },
      "metadata": {},
@@ -846,7 +844,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,

14-Adaptive-Filtering.ipynb

@@ -120,8 +120,9 @@
        "    }\n",
        "    div.text_cell_render{\n",
        "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 115%;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -234,7 +235,7 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -263,11 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -409,9 +406,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71523c8>"
+       "<matplotlib.figure.Figure at 0x72136a0>"
       ]
      },
      "metadata": {},
@@ -471,9 +468,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7138160>"
+       "<matplotlib.figure.Figure at 0x7206c18>"
       ]
      },
      "metadata": {},
@@ -564,9 +561,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78aa2e8>"
+       "<matplotlib.figure.Figure at 0x74281d0>"
       ]
      },
      "metadata": {},
@@ -610,9 +607,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x84fd128>"
+       "<matplotlib.figure.Figure at 0x747a358>"
       ]
      },
      "metadata": {},
@@ -653,9 +650,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x711dfd0>"
+       "<matplotlib.figure.Figure at 0x724d4e0>"
       ]
      },
      "metadata": {},
@@ -692,9 +689,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x710eb70>"
+       "<matplotlib.figure.Figure at 0x87d34e0>"
       ]
      },
      "metadata": {},
@@ -757,9 +754,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x84e8160>"
+       "<matplotlib.figure.Figure at 0x87ef588>"
       ]
      },
      "metadata": {},
@@ -816,9 +813,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79026d8>"
+       "<matplotlib.figure.Figure at 0x87ec0f0>"
       ]
      },
      "metadata": {},
@@ -846,9 +843,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x865cd68>"
+       "<matplotlib.figure.Figure at 0x742b278>"
       ]
      },
      "metadata": {},
@@ -956,9 +953,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78aaa90>"
+       "<matplotlib.figure.Figure at 0x9a23160>"
       ]
      },
      "metadata": {},
@@ -1007,9 +1004,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86d96d8>"
+       "<matplotlib.figure.Figure at 0x9930c50>"
       ]
      },
      "metadata": {},
@@ -1092,9 +1089,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8802860>"
+       "<matplotlib.figure.Figure at 0x99bc278>"
       ]
      },
      "metadata": {},
@@ -1192,9 +1189,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87e3898>"
+       "<matplotlib.figure.Figure at 0x72bc6a0>"
       ]
      },
      "metadata": {},
@@ -1221,9 +1218,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8514e80>"
+       "<matplotlib.figure.Figure at 0x99b46a0>"
       ]
      },
      "metadata": {},
@@ -1250,9 +1247,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8638ef0>"
+       "<matplotlib.figure.Figure at 0x99e5048>"
       ]
      },
      "metadata": {},
@@ -1260,9 +1257,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87372e8>"
+       "<matplotlib.figure.Figure at 0x729e198>"
       ]
      },
      "metadata": {},
@@ -1292,9 +1289,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9982860>"
+       "<matplotlib.figure.Figure at 0x9b73cf8>"
       ]
      },
      "metadata": {},
@@ -1302,9 +1299,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9921278>"
+       "<matplotlib.figure.Figure at 0x882bef0>"
       ]
      },
      "metadata": {},
@@ -1312,9 +1309,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8621da0>"
+       "<matplotlib.figure.Figure at 0x72e0e80>"
       ]
      },
      "metadata": {},
@@ -1322,9 +1319,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78cf470>"
+       "<matplotlib.figure.Figure at 0x99bcda0>"
       ]
      },
      "metadata": {},
@@ -1332,9 +1329,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x882d978>"
+       "<matplotlib.figure.Figure at 0x9baea58>"
       ]
      },
      "metadata": {},
@@ -1405,9 +1402,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a718d0>"
+       "<matplotlib.figure.Figure at 0x99fd940>"
       ]
      },
      "metadata": {},
@@ -1566,9 +1563,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86709b0>"
+       "<matplotlib.figure.Figure at 0x72c15f8>"
       ]
      },
      "metadata": {},
@@ -1597,9 +1594,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99f87f0>"
+       "<matplotlib.figure.Figure at 0x9b781d0>"
       ]
      },
      "metadata": {},
@@ -1654,9 +1651,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8798438>"
+       "<matplotlib.figure.Figure at 0x9b44c88>"
       ]
      },
      "metadata": {},
@@ -1702,9 +1699,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x885deb8>"
+       "<matplotlib.figure.Figure at 0x9b46f98>"
       ]
      },
      "metadata": {},
@@ -1777,9 +1774,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9935470>"
+       "<matplotlib.figure.Figure at 0x72e04a8>"
       ]
      },
      "metadata": {},
@@ -1827,9 +1824,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9920ac8>"
+       "<matplotlib.figure.Figure at 0x9871f98>"
       ]
      },
      "metadata": {},
@@ -1904,7 +1901,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,

Appendix-A-Installation.ipynb

@@ -37,6 +37,7 @@
        "@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",
+       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
        "\n",
        "    div.cell{\n",
        "        width: 900px;\n",
@@ -127,9 +128,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 120%;\n",
+       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -242,7 +244,7 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -271,12 +273,8 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
-    "from book_format import load_style, set_figsize, figsize\n",
+    "from book_format import load_style\n",
     "load_style()"
    ]
   },

Appendix-B-Symbols-and-Notations.ipynb

@@ -1,405 +1,405 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
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-   ],
-   "source": [
-    "#format the book\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
-    "from book_format import load_style\n",
-    "load_style()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Symbols and Notations\n",
-    "\n",
-    "Here is a collection of the notation used by various authors for the linear Kalman filter. I have ordered them in the same order so that you can compare and contrast the choices. I use the original variable names; I did not try to normalize them."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Labbe\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\overline{\\mathbf{x}} &= \\mathbf{F}\\hat{\\mathbf{x}} + \\mathbf{B}\\mathbf{u} \\\\\n",
-    "\\overline{\\mathbf{P}} &=  \\mathbf{F} \\mathbf{P}\\mathbf{F}^\\mathsf{T} + \\textbf{Q} \\\\ \\\\\n",
-    "\\mathbf{y} &= \\mathbf{z} - \\mathbf{H}\\overline{\\textbf{x}} \\\\\n",
-    "\\mathbf{S} &= \\mathbf{H}\\overline{\\mathbf{P}}\\mathbf{H}^\\mathsf{T} + \\mathbf{R} \\\\\n",
-    "\\mathbf{K} &= \\overline{\\mathbf{P}}\\mathbf{H}^\\mathsf{T}\\mathbf{S}^{-1} \\\\\n",
-    "\\hat{\\textbf{x}}  &= \\overline{\\mathbf{x}} +\\mathbf{K}\\mathbf{y} \\\\\n",
-    "\\mathbf{P} &= (\\mathbf{I}-\\mathbf{K}\\mathbf{H})\\overline{\\mathbf{P}}\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "\n",
-    "## Wikipedia\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\hat{\\textbf{x}}_{k\\mid k-1} &= \\textbf{F}_{k}\\hat{\\textbf{x}}_{k-1\\mid k-1} + \\textbf{B}_{k} \\textbf{u}_{k} \\\\\n",
-    "\\textbf{P}_{k\\mid k-1} &=  \\textbf{F}_{k} \\textbf{P}_{k-1\\mid k-1} \\textbf{F}_{k}^{\\textsf{T}} + \\textbf{Q}_{k}\\\\\n",
-    "\\tilde{\\textbf{y}}_k &= \\textbf{z}_k - \\textbf{H}_k\\hat{\\textbf{x}}_{k\\mid k-1} \\\\\n",
-    "\\textbf{S}_k &= \\textbf{H}_k \\textbf{P}_{k\\mid k-1} \\textbf{H}_k^\\textsf{T} + \\textbf{R}_k \\\\\n",
-    "\\textbf{K}_k &= \\textbf{P}_{k\\mid k-1}\\textbf{H}_k^\\textsf{T}\\textbf{S}_k^{-1} \\\\\n",
-    "\\hat{\\textbf{x}}_{k\\mid k} &= \\hat{\\textbf{x}}_{k\\mid k-1} + \\textbf{K}_k\\tilde{\\textbf{y}}_k \\\\\n",
-    "\\textbf{P}_{k|k} &= (I - \\textbf{K}_k \\textbf{H}_k) \\textbf{P}_{k|k-1}\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "## Brookner\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "X^*_{n+1,n} &= \\Phi X^*_{n,n} \\\\\n",
-    "X^*_{n,n}  &= X^*_{n,n-1} +H_n(Y_n - MX^*_{n,n-1}) \\\\\n",
-    "H_n &= S^*_{n,n-1}M^\\mathsf{T}[R_n + MS^*_{n,n-1}M^\\mathsf{T}]^{-1} \\\\\n",
-    "S^*_{n,n-1} &= \\Phi S^*_{n-1,n-1}\\Phi^\\mathsf{T} + Q_n \\\\\n",
-    "S^*_{n-1,n-1} &= (I-H_{n-1}M)S^*_{n-1,n-2}\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "## Gelb\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\underline{\\hat{x}}_k(-) &= \\Phi_{k-1} \\underline{\\hat{x}}_{k-1}(+) \\\\\n",
-    "\\underline{\\hat{x}}_k(+)  &= \\underline{\\hat{x}}_k(-) +K_k[Z_k - H_k\\underline{\\hat{x}}_k(-)] \\\\\n",
-    "K_k &= P_k(-)H_k^\\mathsf{T}[H_kP_k(-)H_k^\\mathsf{T} + R_k]^{-1}\\\\\n",
-    "P_k(+) &=  \\Phi_{k-1} P_{k-1}(+)\\Phi_{k-1}^\\mathsf{T} + Q_{k-1} \\\\\n",
-    "P_k(-) &= (I-K_kH_k)P_k(-)\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "\n",
-    "## Brown\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\hat{\\textbf{x}}^-_{k+1} &= \\mathbf{\\phi}_{k}\\hat{\\textbf{x}}_{k} \\\\\n",
-    "\\hat{\\textbf{x}}_k  &= \\hat{\\textbf{x}}^-_k +\\textbf{K}_k[\\textbf{z}_k - \\textbf{H}_k\\hat{\\textbf{}x}^-_k] \\\\\n",
-    "\\textbf{K}_k &= \\textbf{P}^-_k\\textbf{H}_k^\\mathsf{T}[\\textbf{H}_k\\textbf{P}^-_k\\textbf{H}_k^T + \\textbf{R}_k]^{-1}\\\\\n",
-    "\\textbf{P}^-_{k+1} &=  \\mathbf{\\phi}_k \\textbf{P}_k\\mathbf{\\phi}_k^\\mathsf{T} + \\textbf{Q}_{k} \\\\\n",
-    "\\mathbf{P}_k &= (\\mathbf{I}-\\mathbf{K}_k\\mathbf{H}_k)\\mathbf{P}^-_k\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "\n",
-    "## Zarchan\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\hat{x}_{k} &= \\Phi_{k}\\hat{x}_{k-1} + G_ku_{k-1} + K_k[z_k - H\\Phi_{k}\\hat{x}_{k-1} - HG_ku_{k-1} ] \\\\\n",
-    "M_{k} &=  \\Phi_k P_{k-1}\\phi_k^\\mathsf{T} + Q_{k} \\\\\n",
-    "K_k &= M_kH^\\mathsf{T}[HM_kH^\\mathsf{T} + R_k]^{-1}\\\\\n",
-    "P_k &= (I-K_kH)M_k\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "\n",
-    "## Bar-Shalom\n",
-    "\n",
-    "## Thrun"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Terminology\n",
-    "\n",
-    "### Bar-Shalom\n",
-    "\n",
-    "* State-space\n",
-    "* x: state vector\n",
-    "* u: input vector\n",
-    "* Process noise or plant noise\n",
-    "* system matrix: A  (for $\\dot(x) = Ax + Bu + Dv$)\n",
-    "* F: state transition matrix\n",
-    "* H : Measurement matrix\n",
-    "* y : measurement residual\n",
-    "* $\\overline{P}$ : state prediction covariance\n",
-    "* P: updated state covariance\n",
-    "* S: innovatin covariance\n",
-    "* K(W): filter gain\n",
-    "* $\\hat{x}$ updated state estimate\n",
-    "\n",
-    "\n",
-    "smoothed state = retrodicted state\n",
-    "\n",
-    "x0 = initial estimate\n",
-    "\n",
-    "P0 : initial covariance\n"
-   ]
-  }
- ],
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-   "display_name": "Python 3",
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+    "[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)"
+   ]
+  },
+  {
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+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li li{\n",
+       "        padding-left: 0.2em; \n",
+       "        margin-bottom: 0.2em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    ol{\n",
+       "        margin: 2em;\n",
+       "    }\n",
+       "    ol li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    a:link{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:visited{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:hover{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:focus{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:active{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    .rendered_html :link {\n",
+       "       text-decoration: underline; \n",
+       "    }\n",
+       "    .rendered_html :hover {\n",
+       "       text-decoration: none; \n",
+       "    }\n",
+       "    .rendered_html :visited {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :focus {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :active {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .warning{\n",
+       "        color: rgb( 240, 20, 20 )\n",
+       "    } \n",
+       "    hr {\n",
+       "      color: #f3f3f3;\n",
+       "      background-color: #f3f3f3;\n",
+       "      height: 1px;\n",
+       "    }\n",
+       "    blockquote{\n",
+       "      display:block;\n",
+       "      background: #fcfcfc;\n",
+       "      border-left: 5px solid #c76c0c;\n",
+       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "      width:680px;\n",
+       "      padding: 10px 10px 10px 10px;\n",
+       "      text-align:justify;\n",
+       "      text-justify:inter-word;\n",
+       "      }\n",
+       "      blockquote p {\n",
+       "        margin-bottom: 0;\n",
+       "        line-height: 125%;\n",
+       "        font-size: 100%;\n",
+       "      }\n",
+       "</style>\n",
+       "<script>\n",
+       "    MathJax.Hub.Config({\n",
+       "                        TeX: {\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
+       "                           },\n",
+       "                tex2jax: {\n",
+       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
+       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
+       "                },\n",
+       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
+       "                \"HTML-CSS\": {\n",
+       "                    scale:100,\n",
+       "                        availableFonts: [],\n",
+       "                        preferredFont:null,\n",
+       "                        webFont: \"TeX\",\n",
+       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                }\n",
+       "        });\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#format the book\n",
+    "from book_format import load_style\n",
+    "load_style()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Symbols and Notations\n",
+    "\n",
+    "Here is a collection of the notation used by various authors for the linear Kalman filter. I have ordered them in the same order so that you can compare and contrast the choices. I use the original variable names; I did not try to normalize them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Labbe\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\overline{\\mathbf{x}} &= \\mathbf{F}\\hat{\\mathbf{x}} + \\mathbf{B}\\mathbf{u} \\\\\n",
+    "\\overline{\\mathbf{P}} &=  \\mathbf{F} \\mathbf{P}\\mathbf{F}^\\mathsf{T} + \\textbf{Q} \\\\ \\\\\n",
+    "\\mathbf{y} &= \\mathbf{z} - \\mathbf{H}\\overline{\\textbf{x}} \\\\\n",
+    "\\mathbf{S} &= \\mathbf{H}\\overline{\\mathbf{P}}\\mathbf{H}^\\mathsf{T} + \\mathbf{R} \\\\\n",
+    "\\mathbf{K} &= \\overline{\\mathbf{P}}\\mathbf{H}^\\mathsf{T}\\mathbf{S}^{-1} \\\\\n",
+    "\\hat{\\textbf{x}}  &= \\overline{\\mathbf{x}} +\\mathbf{K}\\mathbf{y} \\\\\n",
+    "\\mathbf{P} &= (\\mathbf{I}-\\mathbf{K}\\mathbf{H})\\overline{\\mathbf{P}}\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "\n",
+    "## Wikipedia\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\hat{\\textbf{x}}_{k\\mid k-1} &= \\textbf{F}_{k}\\hat{\\textbf{x}}_{k-1\\mid k-1} + \\textbf{B}_{k} \\textbf{u}_{k} \\\\\n",
+    "\\textbf{P}_{k\\mid k-1} &=  \\textbf{F}_{k} \\textbf{P}_{k-1\\mid k-1} \\textbf{F}_{k}^{\\textsf{T}} + \\textbf{Q}_{k}\\\\\n",
+    "\\tilde{\\textbf{y}}_k &= \\textbf{z}_k - \\textbf{H}_k\\hat{\\textbf{x}}_{k\\mid k-1} \\\\\n",
+    "\\textbf{S}_k &= \\textbf{H}_k \\textbf{P}_{k\\mid k-1} \\textbf{H}_k^\\textsf{T} + \\textbf{R}_k \\\\\n",
+    "\\textbf{K}_k &= \\textbf{P}_{k\\mid k-1}\\textbf{H}_k^\\textsf{T}\\textbf{S}_k^{-1} \\\\\n",
+    "\\hat{\\textbf{x}}_{k\\mid k} &= \\hat{\\textbf{x}}_{k\\mid k-1} + \\textbf{K}_k\\tilde{\\textbf{y}}_k \\\\\n",
+    "\\textbf{P}_{k|k} &= (I - \\textbf{K}_k \\textbf{H}_k) \\textbf{P}_{k|k-1}\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "## Brookner\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "X^*_{n+1,n} &= \\Phi X^*_{n,n} \\\\\n",
+    "X^*_{n,n}  &= X^*_{n,n-1} +H_n(Y_n - MX^*_{n,n-1}) \\\\\n",
+    "H_n &= S^*_{n,n-1}M^\\mathsf{T}[R_n + MS^*_{n,n-1}M^\\mathsf{T}]^{-1} \\\\\n",
+    "S^*_{n,n-1} &= \\Phi S^*_{n-1,n-1}\\Phi^\\mathsf{T} + Q_n \\\\\n",
+    "S^*_{n-1,n-1} &= (I-H_{n-1}M)S^*_{n-1,n-2}\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "## Gelb\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\underline{\\hat{x}}_k(-) &= \\Phi_{k-1} \\underline{\\hat{x}}_{k-1}(+) \\\\\n",
+    "\\underline{\\hat{x}}_k(+)  &= \\underline{\\hat{x}}_k(-) +K_k[Z_k - H_k\\underline{\\hat{x}}_k(-)] \\\\\n",
+    "K_k &= P_k(-)H_k^\\mathsf{T}[H_kP_k(-)H_k^\\mathsf{T} + R_k]^{-1}\\\\\n",
+    "P_k(+) &=  \\Phi_{k-1} P_{k-1}(+)\\Phi_{k-1}^\\mathsf{T} + Q_{k-1} \\\\\n",
+    "P_k(-) &= (I-K_kH_k)P_k(-)\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "\n",
+    "## Brown\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\hat{\\textbf{x}}^-_{k+1} &= \\mathbf{\\phi}_{k}\\hat{\\textbf{x}}_{k} \\\\\n",
+    "\\hat{\\textbf{x}}_k  &= \\hat{\\textbf{x}}^-_k +\\textbf{K}_k[\\textbf{z}_k - \\textbf{H}_k\\hat{\\textbf{}x}^-_k] \\\\\n",
+    "\\textbf{K}_k &= \\textbf{P}^-_k\\textbf{H}_k^\\mathsf{T}[\\textbf{H}_k\\textbf{P}^-_k\\textbf{H}_k^T + \\textbf{R}_k]^{-1}\\\\\n",
+    "\\textbf{P}^-_{k+1} &=  \\mathbf{\\phi}_k \\textbf{P}_k\\mathbf{\\phi}_k^\\mathsf{T} + \\textbf{Q}_{k} \\\\\n",
+    "\\mathbf{P}_k &= (\\mathbf{I}-\\mathbf{K}_k\\mathbf{H}_k)\\mathbf{P}^-_k\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "\n",
+    "## Zarchan\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\hat{x}_{k} &= \\Phi_{k}\\hat{x}_{k-1} + G_ku_{k-1} + K_k[z_k - H\\Phi_{k}\\hat{x}_{k-1} - HG_ku_{k-1} ] \\\\\n",
+    "M_{k} &=  \\Phi_k P_{k-1}\\phi_k^\\mathsf{T} + Q_{k} \\\\\n",
+    "K_k &= M_kH^\\mathsf{T}[HM_kH^\\mathsf{T} + R_k]^{-1}\\\\\n",
+    "P_k &= (I-K_kH)M_k\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "\n",
+    "## Bar-Shalom\n",
+    "\n",
+    "## Thrun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Terminology\n",
+    "\n",
+    "### Bar-Shalom\n",
+    "\n",
+    "* State-space\n",
+    "* x: state vector\n",
+    "* u: input vector\n",
+    "* Process noise or plant noise\n",
+    "* system matrix: A  (for $\\dot(x) = Ax + Bu + Dv$)\n",
+    "* F: state transition matrix\n",
+    "* H : Measurement matrix\n",
+    "* y : measurement residual\n",
+    "* $\\overline{P}$ : state prediction covariance\n",
+    "* P: updated state covariance\n",
+    "* S: innovatin covariance\n",
+    "* K(W): filter gain\n",
+    "* $\\hat{x}$ updated state estimate\n",
+    "\n",
+    "\n",
+    "smoothed state = retrodicted state\n",
+    "\n",
+    "x0 = initial estimate\n",
+    "\n",
+    "P0 : initial covariance\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

Appendix-C-Walking-Through-KF-Code.ipynb

@@ -1,483 +1,481 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Walking through the Kalman Filter code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
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-       "      blockquote p {\n",
-       "        margin-bottom: 0;\n",
-       "        line-height: 125%;\n",
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-       "      }\n",
-       "</style>\n",
-       "<script>\n",
-       "    MathJax.Hub.Config({\n",
-       "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
-       "                           },\n",
-       "                tex2jax: {\n",
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-       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
-       "                },\n",
-       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
-       "                \"HTML-CSS\": {\n",
-       "                    scale:100,\n",
-       "                        availableFonts: [],\n",
-       "                        preferredFont:null,\n",
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-       "                }\n",
-       "        });\n",
-       "</script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#format the book\n",
-    "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
-    "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
-    "from book_format import load_style, set_figsize, figsize\n",
-    "load_style()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "** author's note: this code is somewhat old. This section needs to be edited; I would not pay a lot of attention to it right now. **\n",
-    "\n",
-    "The kalman filter code that we are using is implemented in my Python library `FilterPy`. If you are interested in the full implementation of the filter you should look in `filterpy\\kalman\\kalman_filter.py`. In the following I will present a simplified implementation of the same code. The code in the library handles issues that are beyond the scope of this chapter, such as numerical stability and support for the extended Kalman filter, subject of a later chapter. \n",
-    "\n",
-    "The code is implemented as the class `KalmanFilter`. Some Python programmers are not a fan of object oriented (OO) Python, and eschew classes. I do not intend to enter into that battle other than to say that I have often seen OO abused. Here I use the class to encapsulate the data that is pertinent to the filter so that you do not have to store and pass around a half dozen variables everywhere.\n",
-    "\n",
-    "The method `__init__()` is used by Python to create the object. Here is the method \n",
-    "\n",
-    "    def __init__(self, dim_x, dim_z):\n",
-    "        \"\"\" Create a Kalman filter. You are responsible for setting the \n",
-    "        various state variables to reasonable values; the defaults below will\n",
-    "        not give you a functional filter.\n",
-    "        \n",
-    "        Parameters\n",
-    "        ----------\n",
-    "        dim_x : int\n",
-    "            Number of state variables for the Kalman filter. For example, if\n",
-    "            you are tracking the position and velocity of an object in two\n",
-    "            dimensions, dim_x would be 4.\n",
-    "            \n",
-    "            This is used to set the default size of P, Q, and u\n",
-    "           \n",
-    "        dim_z : int\n",
-    "            Number of of measurement inputs. For example, if the sensor\n",
-    "            provides you with position in (x,y), dim_z would be 2.            \n",
-    "        \"\"\"\n",
-    "        \n",
-    "        self.dim_x = dim_x\n",
-    "        self.dim_z = dim_z\n",
-    "\n",
-    "        self.x = np.zeros((dim_x, 1)) # state\n",
-    "        self.P = np.eye(dim_x)        # uncertainty covariance\n",
-    "        self.Q = np.eye(dim_x)        # process uncertainty\n",
-    "        self.u = 0                    # control input vector\n",
-    "        self.B = np.zeros((dim_x, 1))\n",
-    "        self.F = 0                    # state transition matrix\n",
-    "        self.H = 0                    # Measurement function\n",
-    "        self.R = np.eye(dim_z)        # state uncertainty\n",
-    "\n",
-    "        # identity matrix. Do not alter this.\n",
-    "        self._I = np.eye(dim_x)\n",
-    "\n",
-    "More than anything this method exists to document for you what the variable names are in the filter. To do anything useful with this filter you will have to modify most of these values. Some are set to useful values. For example, `R` is set to an identity matrix; if you want the diagonals of `R` to be 10. you may write (as we did earlier in this chapter) `my_filter.R += 10.`.\n",
-    "\n",
-    "The names used for each variable matches the math symbology used in this chapter. Thus, `self.P` is the covariance matrix, `self.x` is the state, and so on.\n",
-    "\n",
-    "The predict function implements the predict step of the Kalman equations, which are \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mathbf{x}^- &= \\mathbf{F x} + \\mathbf{B u} \\\\\n",
-    "\\mathbf{P}^- &= \\mathbf{FP{F}}^\\mathsf{T} + \\mathbf{Q} \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "The corresponding code is\n",
-    "\n",
-    "    def predict(self):        \n",
-    "        self.x = self.F.dot(self.x) + self.B.dot(self.u)\n",
-    "        self.P = self.F.dot(self.P).dot(self.F.T) + self.Q\n",
-    "\n",
-    "I haven't discussed the use of NumPy much until now, but this method illustrates the power of that package. We use NumPy's `array` class to store our data and perform the linear algebra for our filters. `array` implements matrix multiplication using the `.dot()` method; if you use `*` you will get element-wise multiplication. As a heavy user of linear algebra this design is somewhat distressing as I use matrix multiplication far more often than element-wise multiplication. However, this design is due to historical developments in the library and we must live with it. The Python community has recognized this problem, and in Python 3.5 we will have the `@` operator to implement matrix multiplication. \n",
-    "\n",
-    "With that in mind, the Python code `self.F.dot(self.x)` implements the math expression $\\mathbf{F x}$.\n",
-    "\n",
-    "NumPy's `array` implements matrix transposition by using the `.T` property. Therefore, `F.T` is the python implementation of $\\mathbf{F}^\\mathsf{T}$.\n",
-    "\n",
-    "The `update()` method implements the update equations of the Kalman filter, which are\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mathbf{y} &= \\mathbf{z} - \\mathbf{H}\\mathbf{x^-} \\\\\n",
-    "\\mathbf{K} &= \\mathbf{P} \\mathbf{H}^\\mathsf{T} (\\mathbf{H} \\mathbf{P^-} \\mathbf{H}^\\mathsf{T} +\\mathbf{R})^{-1} \\\\\n",
-    "\\mathbf{x} &= \\mathbf{x}^- + \\mathbf{K} \\mathbf{y} \\\\\n",
-    "\\mathbf{P} &= (\\mathbf{I} - \\mathbf{K} \\mathbf{H})\\mathbf{P^-}\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "The corresponding code is:\n",
-    "\n",
-    "    def update(self, Z, R=None):\n",
-    "        \"\"\"\n",
-    "        Add a new measurement (Z) to the kalman filter. If Z is None, nothing\n",
-    "        is changed.\n",
-    "\n",
-    "        Optionally provide R to override the measurement noise for this\n",
-    "        one call, otherwise  self.R will be used.\n",
-    "\n",
-    "        self.residual, self.S, and self.K are stored in case you want to\n",
-    "        inspect these variables. Strictly speaking they are not part of the\n",
-    "        output of the Kalman filter, however, it is often useful to know\n",
-    "        what these values are in various scenarios.\n",
-    "        \"\"\"\n",
-    "\n",
-    "        if Z is None:\n",
-    "            return\n",
-    "\n",
-    "        if R is None:\n",
-    "            R = self.R\n",
-    "        elif np.isscalar(R):\n",
-    "            R = np.eye(self.dim_z) * R\n",
-    "\n",
-    "        # error (residual) between measurement and prediction\n",
-    "        self.residual = Z - self.H.dot(self.x)\n",
-    "\n",
-    "        # project system uncertainty into measurement space\n",
-    "        self.S = self.H.dot(self.P).dot(self.H.T) + R\n",
-    "\n",
-    "        # map system uncertainty into kalman gain\n",
-    "        self.K = self.P.dot(self.H.T).dot(linalg.inv(self.S))\n",
-    "\n",
-    "        # predict new x with residual scaled by the kalman gain\n",
-    "        self.x += self.K.dot(self.residual)\n",
-    "\n",
-    "        KH = self.K.dot(self.H)\n",
-    "        I_KH = self._I - KH\n",
-    "        self.P = (I_KH.dot(self.P.dot(I_KH.T)) +\n",
-    "                 self.K.dot(self.R.dot(self.K.T)))\n",
-    "\n",
-    "There are a few more complications in this piece of code compared to `predict()` but it should still be quite clear. \n",
-    "\n",
-    "The first complication are the lines:\n",
-    "\n",
-    "        if Z is None:\n",
-    "            return\n",
-    "            \n",
-    "This just lets you deal with missing data in a natural way. It is typical to use `None` to indicate the absence of data. If there is no data for an update we skip the update equations. This bit of code means you can write something like:\n",
-    "\n",
-    "    z = read_sensor()    # may return None if no data\n",
-    "    my_kf.update(z)\n",
-    "    \n",
-    "instead of:\n",
-    "    z = read_sensor()\n",
-    "    if z is not None:\n",
-    "        my_kf.update(z)\n",
-    "        \n",
-    "Reasonable people will argue whether my choice is cleaner, or obscures the fact that we do not update if the measurement is `None`. Having written a lot of avionics code my proclivity is always to do the safe thing. If we pass 'None' into the function I do not want an exception to occur; instead, I want the reasonable thing to happen, which is to just return without doing anything. If you feel that my choice obscures that fact, go ahead and write the explicit `if` statement prior to calling `update()` and get the best of both worlds.\n",
-    "\n",
-    "The next bit of code lets you optionally pass in a value to override `R`. It is common for the sensor noise to vary over time; if it does you can pass in the value as the optional parameter `R`.\n",
-    "\n",
-    "        if R is None:\n",
-    "            R = self.R\n",
-    "        elif np.isscalar(R):\n",
-    "            R = np.eye(self.dim_z) * R\n",
-    "            \n",
-    "This code will use self.R if you do not provide a value for `R`. If you did provide a value, it will check if you provided a scalar (number); if so it constructs a matrix of the correct dimension for you. Otherwise it assumes that you passed in a matrix of the correct dimension.\n",
-    "\n",
-    "The rest of the code implements the Kalman filter equations, with one exception. Instead of implementing \n",
-    "\n",
-    "$$\\mathbf{P} = (\\mathbf{I} - \\mathbf{KH})\\mathbf{P}^-$$\n",
-    "\n",
-    "it implements the somewhat more complicated form \n",
-    "\n",
-    "$$\\mathbf{P} = (\\mathbf{I} - \\mathbf{KH})\\mathbf{P}^-(\\mathbf{I} - \\mathbf{KH})^\\mathsf{T} + \\mathbf{KRK}^\\mathsf{T}$$.\n",
-    "\n",
-    "The reason for this altered equation is that it is more numerically stable than the former equation, at the cost of being a bit more expensive to compute. It is not always possible to find the optimal value for $\\text{K}$, in which case the former equation will not produce good results because it assumes optimality. The longer reformulation used in the code is derived from more general math that does not assume optimality, and hence provides good results for non-optimal filters (such as when we can not correctly model our measurement error).\n",
-    "\n",
-    "Various texts differ as to whether this form of the equation should always be used, or only used when you know you need it. I choose to expend a bit more processing power to ensure stability; if your hardware is very constrained and you are able to prove that the simpler equation is correct for your problem then you might choose to use it instead. Personally, I find that a risky approach and do not recommend it to non-experts. Brown's *Introduction to Random Signals and Applied Kalman Filtering* [3] discusses this issue in some detail, if you are interested."
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+{
+ "cells": [
+  {
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+   "metadata": {},
+   "source": [
+    "[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Walking through the Kalman Filter code"
+   ]
+  },
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+   ],
+   "source": [
+    "#format the book\n",
+    "%matplotlib inline\n",
+    "from __future__ import division, print_function\n",
+    "from book_format import load_style\n",
+    "load_style()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "** author's note: this code is somewhat old. This section needs to be edited; I would not pay a lot of attention to it right now. **\n",
+    "\n",
+    "The kalman filter code that we are using is implemented in my Python library `FilterPy`. If you are interested in the full implementation of the filter you should look in `filterpy\\kalman\\kalman_filter.py`. In the following I will present a simplified implementation of the same code. The code in the library handles issues that are beyond the scope of this chapter, such as numerical stability and support for the extended Kalman filter, subject of a later chapter. \n",
+    "\n",
+    "The code is implemented as the class `KalmanFilter`. Some Python programmers are not a fan of object oriented (OO) Python, and eschew classes. I do not intend to enter into that battle other than to say that I have often seen OO abused. Here I use the class to encapsulate the data that is pertinent to the filter so that you do not have to store and pass around a half dozen variables everywhere.\n",
+    "\n",
+    "The method `__init__()` is used by Python to create the object. Here is the method \n",
+    "\n",
+    "    def __init__(self, dim_x, dim_z):\n",
+    "        \"\"\" Create a Kalman filter. You are responsible for setting the \n",
+    "        various state variables to reasonable values; the defaults below will\n",
+    "        not give you a functional filter.\n",
+    "        \n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        dim_x : int\n",
+    "            Number of state variables for the Kalman filter. For example, if\n",
+    "            you are tracking the position and velocity of an object in two\n",
+    "            dimensions, dim_x would be 4.\n",
+    "            \n",
+    "            This is used to set the default size of P, Q, and u\n",
+    "           \n",
+    "        dim_z : int\n",
+    "            Number of of measurement inputs. For example, if the sensor\n",
+    "            provides you with position in (x,y), dim_z would be 2.            \n",
+    "        \"\"\"\n",
+    "        \n",
+    "        self.dim_x = dim_x\n",
+    "        self.dim_z = dim_z\n",
+    "\n",
+    "        self.x = np.zeros((dim_x, 1)) # state\n",
+    "        self.P = np.eye(dim_x)        # uncertainty covariance\n",
+    "        self.Q = np.eye(dim_x)        # process uncertainty\n",
+    "        self.u = 0                    # control input vector\n",
+    "        self.B = np.zeros((dim_x, 1))\n",
+    "        self.F = 0                    # state transition matrix\n",
+    "        self.H = 0                    # Measurement function\n",
+    "        self.R = np.eye(dim_z)        # state uncertainty\n",
+    "\n",
+    "        # identity matrix. Do not alter this.\n",
+    "        self._I = np.eye(dim_x)\n",
+    "\n",
+    "More than anything this method exists to document for you what the variable names are in the filter. To do anything useful with this filter you will have to modify most of these values. Some are set to useful values. For example, `R` is set to an identity matrix; if you want the diagonals of `R` to be 10. you may write (as we did earlier in this chapter) `my_filter.R += 10.`.\n",
+    "\n",
+    "The names used for each variable matches the math symbology used in this chapter. Thus, `self.P` is the covariance matrix, `self.x` is the state, and so on.\n",
+    "\n",
+    "The predict function implements the predict step of the Kalman equations, which are \n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\mathbf{x}^- &= \\mathbf{F x} + \\mathbf{B u} \\\\\n",
+    "\\mathbf{P}^- &= \\mathbf{FP{F}}^\\mathsf{T} + \\mathbf{Q} \n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "The corresponding code is\n",
+    "\n",
+    "    def predict(self):        \n",
+    "        self.x = self.F.dot(self.x) + self.B.dot(self.u)\n",
+    "        self.P = self.F.dot(self.P).dot(self.F.T) + self.Q\n",
+    "\n",
+    "I haven't discussed the use of NumPy much until now, but this method illustrates the power of that package. We use NumPy's `array` class to store our data and perform the linear algebra for our filters. `array` implements matrix multiplication using the `.dot()` method; if you use `*` you will get element-wise multiplication. As a heavy user of linear algebra this design is somewhat distressing as I use matrix multiplication far more often than element-wise multiplication. However, this design is due to historical developments in the library and we must live with it. The Python community has recognized this problem, and in Python 3.5 we will have the `@` operator to implement matrix multiplication. \n",
+    "\n",
+    "With that in mind, the Python code `self.F.dot(self.x)` implements the math expression $\\mathbf{F x}$.\n",
+    "\n",
+    "NumPy's `array` implements matrix transposition by using the `.T` property. Therefore, `F.T` is the python implementation of $\\mathbf{F}^\\mathsf{T}$.\n",
+    "\n",
+    "The `update()` method implements the update equations of the Kalman filter, which are\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\mathbf{y} &= \\mathbf{z} - \\mathbf{H}\\mathbf{x^-} \\\\\n",
+    "\\mathbf{K} &= \\mathbf{P} \\mathbf{H}^\\mathsf{T} (\\mathbf{H} \\mathbf{P^-} \\mathbf{H}^\\mathsf{T} +\\mathbf{R})^{-1} \\\\\n",
+    "\\mathbf{x} &= \\mathbf{x}^- + \\mathbf{K} \\mathbf{y} \\\\\n",
+    "\\mathbf{P} &= (\\mathbf{I} - \\mathbf{K} \\mathbf{H})\\mathbf{P^-}\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "The corresponding code is:\n",
+    "\n",
+    "    def update(self, Z, R=None):\n",
+    "        \"\"\"\n",
+    "        Add a new measurement (Z) to the kalman filter. If Z is None, nothing\n",
+    "        is changed.\n",
+    "\n",
+    "        Optionally provide R to override the measurement noise for this\n",
+    "        one call, otherwise  self.R will be used.\n",
+    "\n",
+    "        self.residual, self.S, and self.K are stored in case you want to\n",
+    "        inspect these variables. Strictly speaking they are not part of the\n",
+    "        output of the Kalman filter, however, it is often useful to know\n",
+    "        what these values are in various scenarios.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        if Z is None:\n",
+    "            return\n",
+    "\n",
+    "        if R is None:\n",
+    "            R = self.R\n",
+    "        elif np.isscalar(R):\n",
+    "            R = np.eye(self.dim_z) * R\n",
+    "\n",
+    "        # error (residual) between measurement and prediction\n",
+    "        self.residual = Z - self.H.dot(self.x)\n",
+    "\n",
+    "        # project system uncertainty into measurement space\n",
+    "        self.S = self.H.dot(self.P).dot(self.H.T) + R\n",
+    "\n",
+    "        # map system uncertainty into kalman gain\n",
+    "        self.K = self.P.dot(self.H.T).dot(linalg.inv(self.S))\n",
+    "\n",
+    "        # predict new x with residual scaled by the kalman gain\n",
+    "        self.x += self.K.dot(self.residual)\n",
+    "\n",
+    "        KH = self.K.dot(self.H)\n",
+    "        I_KH = self._I - KH\n",
+    "        self.P = (I_KH.dot(self.P.dot(I_KH.T)) +\n",
+    "                 self.K.dot(self.R.dot(self.K.T)))\n",
+    "\n",
+    "There are a few more complications in this piece of code compared to `predict()` but it should still be quite clear. \n",
+    "\n",
+    "The first complication are the lines:\n",
+    "\n",
+    "        if Z is None:\n",
+    "            return\n",
+    "            \n",
+    "This just lets you deal with missing data in a natural way. It is typical to use `None` to indicate the absence of data. If there is no data for an update we skip the update equations. This bit of code means you can write something like:\n",
+    "\n",
+    "    z = read_sensor()    # may return None if no data\n",
+    "    my_kf.update(z)\n",
+    "    \n",
+    "instead of:\n",
+    "    z = read_sensor()\n",
+    "    if z is not None:\n",
+    "        my_kf.update(z)\n",
+    "        \n",
+    "Reasonable people will argue whether my choice is cleaner, or obscures the fact that we do not update if the measurement is `None`. Having written a lot of avionics code my proclivity is always to do the safe thing. If we pass 'None' into the function I do not want an exception to occur; instead, I want the reasonable thing to happen, which is to just return without doing anything. If you feel that my choice obscures that fact, go ahead and write the explicit `if` statement prior to calling `update()` and get the best of both worlds.\n",
+    "\n",
+    "The next bit of code lets you optionally pass in a value to override `R`. It is common for the sensor noise to vary over time; if it does you can pass in the value as the optional parameter `R`.\n",
+    "\n",
+    "        if R is None:\n",
+    "            R = self.R\n",
+    "        elif np.isscalar(R):\n",
+    "            R = np.eye(self.dim_z) * R\n",
+    "            \n",
+    "This code will use self.R if you do not provide a value for `R`. If you did provide a value, it will check if you provided a scalar (number); if so it constructs a matrix of the correct dimension for you. Otherwise it assumes that you passed in a matrix of the correct dimension.\n",
+    "\n",
+    "The rest of the code implements the Kalman filter equations, with one exception. Instead of implementing \n",
+    "\n",
+    "$$\\mathbf{P} = (\\mathbf{I} - \\mathbf{KH})\\mathbf{P}^-$$\n",
+    "\n",
+    "it implements the somewhat more complicated form \n",
+    "\n",
+    "$$\\mathbf{P} = (\\mathbf{I} - \\mathbf{KH})\\mathbf{P}^-(\\mathbf{I} - \\mathbf{KH})^\\mathsf{T} + \\mathbf{KRK}^\\mathsf{T}$$.\n",
+    "\n",
+    "The reason for this altered equation is that it is more numerically stable than the former equation, at the cost of being a bit more expensive to compute. It is not always possible to find the optimal value for $\\text{K}$, in which case the former equation will not produce good results because it assumes optimality. The longer reformulation used in the code is derived from more general math that does not assume optimality, and hence provides good results for non-optimal filters (such as when we can not correctly model our measurement error).\n",
+    "\n",
+    "Various texts differ as to whether this form of the equation should always be used, or only used when you know you need it. I choose to expend a bit more processing power to ensure stability; if your hardware is very constrained and you are able to prove that the simpler equation is correct for your problem then you might choose to use it instead. Personally, I find that a risky approach and do not recommend it to non-experts. Brown's *Introduction to Random Signals and Applied Kalman Filtering* [3] discusses this issue in some detail, if you are interested."
+   ]
+  },
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Appendix-D-HInfinity-Filters.ipynb

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-   "source": [
-    "#format the book\n",
-    "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
-    "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
-    "from book_format import load_style\n",
-    "load_style()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I am still mulling over how to write this chapter. In the meantime, Professor Dan Simon at Clevant State University has an accessible introduction here:\n",
-    "\n",
-    "http://academic.csuohio.edu/simond/courses/eec641/hinfinity.pdf\n",
-    "\n",
-    "In one sentence the $H_\\infty$ (H infinity) filter is like a Kalman filter, but it is robust in the face of non-Gaussian, non-predictable inputs.\n",
-    "\n",
-    "\n",
-    "My FilterPy library contains an H-Infinity filter. I've pasted some test code below which implements the filter designed by Simon in the article above. Hope it helps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x6a96400>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from __future__ import (absolute_import, division, print_function,\n",
-    "                        unicode_literals)\n",
-    "\n",
-    "from numpy import array\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from filterpy.hinfinity import HInfinityFilter\n",
-    "\n",
-    "dt = 0.1\n",
-    "f = HInfinityFilter(2, 1, dim_u=1, gamma=.01)\n",
-    "\n",
-    "f.F = array([[1., dt],\n",
-    "             [0., 1.]])\n",
-    "\n",
-    "f.H = array([[0., 1.]])\n",
-    "f.G = array([[dt**2 / 2, dt]]).T\n",
-    "\n",
-    "f.P = 0.01\n",
-    "f.W = array([[0.0003, 0.005],\n",
-    "             [0.0050, 0.100]])/ 1000  #process noise\n",
-    "\n",
-    "f.V = 0.01\n",
-    "f.Q = 0.01\n",
-    "u = 1. #acceleration of 1 f/sec**2\n",
-    "\n",
-    "xs = []\n",
-    "vs = []\n",
-    "\n",
-    "for i in range(1,40):\n",
-    "    f.update (5)\n",
-    "    #print(f.x.T)\n",
-    "    xs.append(f.x[0,0])\n",
-    "    vs.append(f.x[1,0])\n",
-    "    f.predict(u=u)\n",
-    "\n",
-    "plt.subplot(211)\n",
-    "plt.plot(xs)\n",
-    "plt.title('position')\n",
-    "plt.subplot(212)\n",
-    "plt.plot(vs) \n",
-    "plt.title('velocity');"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# H Infinity filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/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",
+       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "\n",
+       "    div.cell{\n",
+       "        width: 900px;\n",
+       "        margin-left: 0% !important;\n",
+       "        margin-right: auto;\n",
+       "    }\n",
+       "    div.text_cell code {\n",
+       "        background: transparent;\n",
+       "        color: #000000;\n",
+       "        font-weight: 600;\n",
+       "        font-size: 11pt;\n",
+       "        font-style: bold;\n",
+       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
+       "   }\n",
+       "    h1 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "\t}\n",
+       "\t\n",
+       "    div.input_area {\n",
+       "        background: #F6F6F9;\n",
+       "        border: 1px solid #586e75;\n",
+       "    }\n",
+       "\n",
+       "    .text_cell_render h1 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 30pt;\n",
+       "        line-height: 100%;\n",
+       "        color:#c76c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 1em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    } \n",
+       "    h2 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "    .text_cell_render h2 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 16pt;\n",
+       "        font-style: italic;\n",
+       "        line-height: 100%;\n",
+       "        color:#c76c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 1.5em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    } \n",
+       "    h3 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "    .text_cell_render h3 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 14pt;\n",
+       "        line-height: 100%;\n",
+       "        color:#d77c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 2em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "    h4 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "    .text_cell_render h4 {\n",
+       "        font-weight: 100;\n",
+       "        font-size: 14pt;\n",
+       "        color:#d77c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 0.5em;\n",
+       "        display: block;\n",
+       "        white-space: nowrap;\n",
+       "    }\n",
+       "    h5 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "    .text_cell_render h5 {\n",
+       "        font-weight: 200;\n",
+       "        font-style: normal;\n",
+       "        color: #1d3b84;\n",
+       "        font-size: 16pt;\n",
+       "        margin-bottom: 0em;\n",
+       "        margin-top: 0.5em;\n",
+       "        display: block;\n",
+       "        white-space: nowrap;\n",
+       "    }\n",
+       "    div.text_cell_render{\n",
+       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
+       "        text-align:justify;\n",
+       "        text-justify:inter-word;\n",
+       "    }\n",
+       "    div.output_subarea.output_text.output_pyout {\n",
+       "        overflow-x: auto;\n",
+       "        overflow-y: scroll;\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: 50000px;\n",
+       "    }\n",
+       "    div.output_wrapper{\n",
+       "        margin-top:0.2em;\n",
+       "        margin-bottom:0.2em;\n",
+       "}\n",
+       "\n",
+       "    code{\n",
+       "      font-size: 70%;\n",
+       "    }\n",
+       "    .rendered_html code{\n",
+       "    background-color: transparent;\n",
+       "    }\n",
+       "    ul{\n",
+       "        margin: 2em;\n",
+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li li{\n",
+       "        padding-left: 0.2em; \n",
+       "        margin-bottom: 0.2em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    ol{\n",
+       "        margin: 2em;\n",
+       "    }\n",
+       "    ol li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    a:link{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:visited{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:hover{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:focus{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:active{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    .rendered_html :link {\n",
+       "       text-decoration: underline; \n",
+       "    }\n",
+       "    .rendered_html :hover {\n",
+       "       text-decoration: none; \n",
+       "    }\n",
+       "    .rendered_html :visited {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :focus {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :active {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .warning{\n",
+       "        color: rgb( 240, 20, 20 )\n",
+       "    } \n",
+       "    hr {\n",
+       "      color: #f3f3f3;\n",
+       "      background-color: #f3f3f3;\n",
+       "      height: 1px;\n",
+       "    }\n",
+       "    blockquote{\n",
+       "      display:block;\n",
+       "      background: #fcfcfc;\n",
+       "      border-left: 5px solid #c76c0c;\n",
+       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "      width:680px;\n",
+       "      padding: 10px 10px 10px 10px;\n",
+       "      text-align:justify;\n",
+       "      text-justify:inter-word;\n",
+       "      }\n",
+       "      blockquote p {\n",
+       "        margin-bottom: 0;\n",
+       "        line-height: 125%;\n",
+       "        font-size: 100%;\n",
+       "      }\n",
+       "</style>\n",
+       "<script>\n",
+       "    MathJax.Hub.Config({\n",
+       "                        TeX: {\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
+       "                           },\n",
+       "                tex2jax: {\n",
+       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
+       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
+       "                },\n",
+       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
+       "                \"HTML-CSS\": {\n",
+       "                    scale:100,\n",
+       "                        availableFonts: [],\n",
+       "                        preferredFont:null,\n",
+       "                        webFont: \"TeX\",\n",
+       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                }\n",
+       "        });\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#format the book\n",
+    "%matplotlib inline\n",
+    "from __future__ import division, print_function\n",
+    "from book_format import load_style\n",
+    "load_style()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I am still mulling over how to write this chapter. In the meantime, Professor Dan Simon at Clevant State University has an accessible introduction here:\n",
+    "\n",
+    "http://academic.csuohio.edu/simond/courses/eec641/hinfinity.pdf\n",
+    "\n",
+    "In one sentence the $H_\\infty$ (H infinity) filter is like a Kalman filter, but it is robust in the face of non-Gaussian, non-predictable inputs.\n",
+    "\n",
+    "\n",
+    "My FilterPy library contains an H-Infinity filter. I've pasted some test code below which implements the filter designed by Simon in the article above. Hope it helps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAEjCAYAAABzSrSOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7378f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from __future__ import (absolute_import, division, print_function,\n",
+    "                        unicode_literals)\n",
+    "\n",
+    "from numpy import array\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from filterpy.hinfinity import HInfinityFilter\n",
+    "\n",
+    "dt = 0.1\n",
+    "f = HInfinityFilter(2, 1, dim_u=1, gamma=.01)\n",
+    "\n",
+    "f.F = array([[1., dt],\n",
+    "             [0., 1.]])\n",
+    "\n",
+    "f.H = array([[0., 1.]])\n",
+    "f.G = array([[dt**2 / 2, dt]]).T\n",
+    "\n",
+    "f.P = 0.01\n",
+    "f.W = array([[0.0003, 0.005],\n",
+    "             [0.0050, 0.100]])/ 1000  #process noise\n",
+    "\n",
+    "f.V = 0.01\n",
+    "f.Q = 0.01\n",
+    "u = 1. #acceleration of 1 f/sec**2\n",
+    "\n",
+    "xs = []\n",
+    "vs = []\n",
+    "\n",
+    "for i in range(1,40):\n",
+    "    f.update (5)\n",
+    "    #print(f.x.T)\n",
+    "    xs.append(f.x[0,0])\n",
+    "    vs.append(f.x[1,0])\n",
+    "    f.predict(u=u)\n",
+    "\n",
+    "plt.subplot(211)\n",
+    "plt.plot(xs)\n",
+    "plt.title('position')\n",
+    "plt.subplot(212)\n",
+    "plt.plot(vs) \n",
+    "plt.title('velocity');"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

Appendix-E-Ensemble-Kalman-Filters.ipynb

@@ -28,6 +28,7 @@
        "@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",
+       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
        "\n",
        "    div.cell{\n",
        "        width: 900px;\n",
@@ -118,9 +119,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 120%;\n",
+       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -233,7 +235,7 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -262,11 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -298,9 +296,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6adad68>"
+       "<matplotlib.figure.Figure at 0x7248f98>"
       ]
      },
      "metadata": {},
@@ -581,9 +579,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78dcc18>"
+       "<matplotlib.figure.Figure at 0x7efa470>"
       ]
      },
      "metadata": {},

Appendix-G-Designing-Nonlinear-Kalman-Filters.ipynb

@@ -262,12 +262,8 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
-    "from book_format import load_style, set_figsize, figsize\n",
+    "from book_format import load_style\n",
     "load_style()"
    ]
   },
@@ -1012,7 +1008,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,

Appendix-H-Least-Squares-Filters.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -120,8 +120,9 @@
        "    }\n",
        "    div.text_cell_render{\n",
        "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 115%;\n",
+       "        line-height: 150%;\n",
+       "        font-size: 110%;\n",
+       "        font-weight: 400;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -234,7 +235,7 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
+       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -255,7 +256,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -263,11 +264,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -320,7 +317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,

Appendix-I-Analytic-Evaluation-of-Performance.ipynb

@@ -263,11 +263,7 @@
    "source": [
     "#format the book\n",
     "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
-    "import sys\n",
-    "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
    ]
@@ -316,7 +312,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,

book_format.py

@@ -1,138 +1,145 @@
-# -*- coding: utf-8 -*-
-
-"""Copyright 2015 Roger R Labbe Jr.
-
-
-Code supporting the book
-
-Kalman and Bayesian Filters in Python
-https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
-
-
-This is licensed under an MIT license. See the LICENSE.txt file
-for more information.
-"""
-
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-from contextlib import contextmanager
-from distutils.version import LooseVersion
-from IPython.core.display import HTML
-import json
-import matplotlib
-import matplotlib.pylab as pylab
-import matplotlib.pyplot as plt
-import numpy as np
-import os.path
-import sys
-
-
-sys.path.insert(0, './code') # allow us to import book_format
-
-def test_filterpy_version():
-    import filterpy
-    min_version = [0,0,28]
-    v = filterpy.__version__
-    tokens = v.split('.')
-    for i,v in enumerate(tokens):
-        if int(v) > min_version[i]:
-            return
-
-    i = len(tokens) - 1
-    if min_version[i] > int(tokens[i]):
-       raise Exception("Minimum FilterPy version supported is {}.{}.{}.\n"
-                       "Please install a more recent version.\n"
-                       "   ex: pip install filterpy --upgrade".format(
-             *min_version))
-    v = int(tokens[0]*1000)
-
-
-# ensure that we have the correct filterpy loaded. This is
-# called when this module is imported at the top of each book
-# chapter so the reader can see that they need to update FilterPy.
-test_filterpy_version()
-
-
-def equal_axis():
-    pylab.rcParams['figure.figsize'] = 10,10
-    plt.axis('equal')
-
-
-def reset_axis():
-    pylab.rcParams['figure.figsize'] = 11, 4
-
-def set_figsize(x=11, y=4):
-    pylab.rcParams['figure.figsize'] = x, y
-
-
-@contextmanager
-def figsize(x=11, y=4):
-    """Temporarily set the figure size using 'with figsize(a,b):'"""
-
-    size = pylab.rcParams['figure.figsize']
-    set_figsize(x, y)
-    yield
-    pylab.rcParams['figure.figsize'] = size
-
-@contextmanager
-def numpy_precision(precision):
-	old = np.get_printoptions()['precision']
-	np.set_printoptions(precision=precision)
-	yield
-	np.set_printoptions(old)
-
-@contextmanager
-def printoptions(*args, **kwargs):
-    original = np.get_printoptions()
-    np.set_printoptions(*args, **kwargs)
-    yield
-    np.set_printoptions(**original)
-
-def _decode_list(data):
-    rv = []
-    for item in data:
-        if isinstance(item, unicode):
-            item = item.encode('utf-8')
-        elif isinstance(item, list):
-            item = _decode_list(item)
-        elif isinstance(item, dict):
-            item = _decode_dict(item)
-        rv.append(item)
-    return rv
-
-def _decode_dict(data):
-    rv = {}
-    for key, value in data.iteritems():
-        if isinstance(key, unicode):
-            key = key.encode('utf-8')
-        if isinstance(value, unicode):
-            value = value.encode('utf-8')
-        elif isinstance(value, list):
-            value = _decode_list(value)
-        elif isinstance(value, dict):
-            value = _decode_dict(value)
-        rv[key] = value
-    return rv
-
-
-def load_style(directory = '.', name='code/custom.css'):
-    if sys.version_info[0] >= 3:
-        style = json.load(open(os.path.join(directory, "code/538.json")))
-    else:
-        style = json.load(open(directory + "/code/538.json"), object_hook=_decode_dict)
-
-    # matplotlib has deprecated the use of axes.color_cycle as of version
-
-    version = [int(version_no) for version_no in matplotlib.__version__.split('.')]
-    if version[0] > 1 or (version[0] == 1 and version[1] >= 5):
-        style["axes.prop_cycle"] = "cycler('color', ['#6d904f','#013afe', '#202020','#fc4f30','#e5ae38','#A60628','#30a2da','#008080','#7A68A6','#CF4457','#188487','#E24A33'])"
-        style.pop("axes.color_cycle", None)
-
-    plt.rcParams.update(style)
-    reset_axis ()
-    np.set_printoptions(suppress=True)
-
-    styles = open(os.path.join(directory, name), 'r').read()
-    return HTML(styles)
-
+# -*- coding: utf-8 -*-
+
+"""Copyright 2015 Roger R Labbe Jr.
+
+
+Code supporting the book
+
+Kalman and Bayesian Filters in Python
+https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
+
+
+This is licensed under an MIT license. See the LICENSE.txt file
+for more information.
+"""
+
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+from contextlib import contextmanager
+from distutils.version import LooseVersion
+from IPython.core.display import HTML
+import json
+import matplotlib
+import matplotlib.pylab as pylab
+import matplotlib.pyplot as plt
+import numpy as np
+import os.path
+import sys
+import warnings
+
+# version 1.4.3 of matplotlib has a bug that makes
+# it issue a spurious warning on every plot that
+# clutters the notebook output
+if matplotlib.__version__ == '1.4.3':
+    warnings.simplefilter(action="ignore", category=FutureWarning)
+
+np.set_printoptions(precision=3)
+sys.path.insert(0, './code') # allow us to import book_format
+
+def test_filterpy_version():
+    import filterpy
+    min_version = [0,0,28]
+    v = filterpy.__version__
+    tokens = v.split('.')
+    for i,v in enumerate(tokens):
+        if int(v) > min_version[i]:
+            return
+
+    i = len(tokens) - 1
+    if min_version[i] > int(tokens[i]):
+       raise Exception("Minimum FilterPy version supported is {}.{}.{}.\n"
+                       "Please install a more recent version.\n"
+                       "   ex: pip install filterpy --upgrade".format(
+             *min_version))
+    v = int(tokens[0]*1000)
+
+
+# ensure that we have the correct filterpy loaded. This is
+# called when this module is imported at the top of each book
+# chapter so the reader can see that they need to update FilterPy.
+test_filterpy_version()
+
+
+def equal_axis():
+    pylab.rcParams['figure.figsize'] = 10,10
+    plt.axis('equal')
+
+
+def reset_axis():
+    pylab.rcParams['figure.figsize'] = 11, 4
+
+def set_figsize(x=11, y=4):
+    pylab.rcParams['figure.figsize'] = x, y
+
+
+@contextmanager
+def figsize(x=11, y=4):
+    """Temporarily set the figure size using 'with figsize(a,b):'"""
+
+    size = pylab.rcParams['figure.figsize']
+    set_figsize(x, y)
+    yield
+    pylab.rcParams['figure.figsize'] = size
+
+@contextmanager
+def numpy_precision(precision):
+	old = np.get_printoptions()['precision']
+	np.set_printoptions(precision=precision)
+	yield
+	np.set_printoptions(old)
+
+@contextmanager
+def printoptions(*args, **kwargs):
+    original = np.get_printoptions()
+    np.set_printoptions(*args, **kwargs)
+    yield
+    np.set_printoptions(**original)
+
+def _decode_list(data):
+    rv = []
+    for item in data:
+        if isinstance(item, unicode):
+            item = item.encode('utf-8')
+        elif isinstance(item, list):
+            item = _decode_list(item)
+        elif isinstance(item, dict):
+            item = _decode_dict(item)
+        rv.append(item)
+    return rv
+
+def _decode_dict(data):
+    rv = {}
+    for key, value in data.iteritems():
+        if isinstance(key, unicode):
+            key = key.encode('utf-8')
+        if isinstance(value, unicode):
+            value = value.encode('utf-8')
+        elif isinstance(value, list):
+            value = _decode_list(value)
+        elif isinstance(value, dict):
+            value = _decode_dict(value)
+        rv[key] = value
+    return rv
+
+
+def load_style(directory = '.', name='code/custom.css'):
+    if sys.version_info[0] >= 3:
+        style = json.load(open(os.path.join(directory, "code/538.json")))
+    else:
+        style = json.load(open(directory + "/code/538.json"), object_hook=_decode_dict)
+
+    # matplotlib has deprecated the use of axes.color_cycle as of version
+
+    version = [int(version_no) for version_no in matplotlib.__version__.split('.')]
+    if version[0] > 1 or (version[0] == 1 and version[1] >= 5):
+        style["axes.prop_cycle"] = "cycler('color', ['#6d904f','#013afe', '#202020','#fc4f30','#e5ae38','#A60628','#30a2da','#008080','#7A68A6','#CF4457','#188487','#E24A33'])"
+        style.pop("axes.color_cycle", None)
+
+    plt.rcParams.update(style)
+    reset_axis ()
+    np.set_printoptions(suppress=True)
+
+    styles = open(os.path.join(directory, name), 'r').read()
+    return HTML(styles)
+