Fixed merge problem.

Somehow there was a missing comma in the JSON, making it unloadable.
2015-05-01 06:58:02 -07:00
1 parent cd8c170b7c
commit 4fef1138f7
@@ -324,7 +324,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7037470>"
+       "<matplotlib.figure.Figure at 0x80851d0>"
      ]
     },
     "metadata": {},
@@ -359,7 +359,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7037390>"
+       "<matplotlib.figure.Figure at 0x811ce10>"
      ]
     },
     "metadata": {},
@@ -391,7 +391,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x783fef0>"
+       "<matplotlib.figure.Figure at 0x8866d30>"
      ]
     },
     "metadata": {},
@@ -427,7 +427,7 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Average of measurements is 165.0096\n"
+      "Average of measurements is 165.0162\n"
     ]
    }
   ],
@@ -458,7 +458,7 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Average of measurements is 165.0597\n"
+      "Average of measurements is 165.0291\n"
     ]
    }
   ],
@@ -491,7 +491,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x51ab4a8>"
+       "<matplotlib.figure.Figure at 0x5e0b048>"
      ]
     },
     "metadata": {},
@@ -529,7 +529,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x78fcf98>"
+       "<matplotlib.figure.Figure at 0x5e0b470>"
      ]
     },
     "metadata": {},
@@ -558,7 +558,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x79e57f0>"
+       "<matplotlib.figure.Figure at 0x8a361d0>"
      ]
     },
     "metadata": {},
@@ -587,9 +587,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a63358>"
+       "<matplotlib.figure.Figure at 0x8ab8f98>"
      ]
     },
     "metadata": {},
@@ -619,9 +619,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x79e54a8>"
+       "<matplotlib.figure.Figure at 0x89edcf8>"
      ]
     },
     "metadata": {},
@@ -656,7 +656,7 @@
     "data": {
      "image/png": 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      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7835710>"
+       "<matplotlib.figure.Figure at 0x80d05f8>"
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     },
     "metadata": {},
@@ -685,7 +685,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x784a438>"
+       "<matplotlib.figure.Figure at 0x8895b00>"
      ]
     },
     "metadata": {},
@@ -727,7 +727,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x78a66d8>"
+       "<matplotlib.figure.Figure at 0x88f92e8>"
      ]
     },
     "metadata": {},
@@ -758,7 +758,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
@@ -783,9 +783,9 @@
    },
    {
     "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7cc3828>"
+       "<matplotlib.figure.Figure at 0x898ecf8>"
      ]
     },
     "metadata": {},
@@ -853,16 +853,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d27550>"
+       "<matplotlib.figure.Figure at 0x8d66160>"
      ]
     },
     "metadata": {},
@@ -895,16 +895,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d765f8>"
+       "<matplotlib.figure.Figure at 0x89c5b00>"
      ]
     },
     "metadata": {},
@@ -995,7 +995,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
@@ -1005,7 +1005,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAFoCAYAAAAhN0NQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8e95780>"
+       "<matplotlib.figure.Figure at 0x89724a8>"
      ]
     },
     "metadata": {},
@@ -1097,9 +1097,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x77bd2e8>"
+       "<matplotlib.figure.Figure at 0x89f2ac8>"
      ]
     },
     "metadata": {},
@@ -1184,16 +1184,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9554748>"
+       "<matplotlib.figure.Figure at 0x89a5400>"
      ]
     },
     "metadata": {},
@@ -1251,9 +1251,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x78580b8>"
+       "<matplotlib.figure.Figure at 0x8943a20>"
      ]
     },
     "metadata": {},
@@ -1314,9 +1314,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a26d68>"
+       "<matplotlib.figure.Figure at 0x9dbd2e8>"
      ]
     },
     "metadata": {},
@@ -1379,9 +1379,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a4b080>"
+       "<matplotlib.figure.Figure at 0xa4ed6d8>"
      ]
     },
     "metadata": {},
@@ -1457,9 +1457,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ab9748>"
+       "<matplotlib.figure.Figure at 0xa57d5c0>"
      ]
     },
     "metadata": {},
@@ -1499,9 +1499,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ad7b38>"
+       "<matplotlib.figure.Figure at 0xa59e828>"
      ]
     },
     "metadata": {},
@@ -1544,9 +1544,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a2b2e8>"
+       "<matplotlib.figure.Figure at 0xa5c4828>"
      ]
     },
     "metadata": {},
@@ -1591,9 +1591,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7772c88>"
+       "<matplotlib.figure.Figure at 0x89d9438>"
      ]
     },
     "metadata": {},
@@ -1650,9 +1650,9 @@
    },
    {
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x77bb4a8>"
+       "<matplotlib.figure.Figure at 0x8a1f668>"
      ]
     },
     "metadata": {},
@@ -1719,7 +1719,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b28438>"
+       "<matplotlib.figure.Figure at 0x88e9be0>"
      ]
     },
     "metadata": {},
@@ -1770,9 +1770,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ac3eb8>"
+       "<matplotlib.figure.Figure at 0xa5189b0>"
      ]
     },
     "metadata": {},
@@ -1801,9 +1801,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7693550>"
+       "<matplotlib.figure.Figure at 0x8a62f98>"
      ]
     },
     "metadata": {},
@@ -1834,9 +1834,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a705c0>"
+       "<matplotlib.figure.Figure at 0x8aa0a58>"
      ]
     },
     "metadata": {},
@@ -1884,9 +1884,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAEjCAYAAACrT4XoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAEjCAYAAACrT4XoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8ba99e8>"
+       "<matplotlib.figure.Figure at 0x8da9d30>"
      ]
     },
     "metadata": {},
@@ -1915,9 +1915,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAEjCAYAAACrT4XoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x787ce48>"
+       "<matplotlib.figure.Figure at 0x9dc8400>"
      ]
     },
     "metadata": {},
@@ -1989,7 +1989,7 @@
   "source": [
    "I encourage you to experiment with this filter to develop your understanding of how it reacts. It shouldn't take too many attempts to come to the realization that ad-hoc choices for $g$ and $h$ do not perform very well. A particular choice might perform well in one situation, but very poorly in another. Even when you understand the effect of $g$ and $h$ it can be difficult to choose proper values. In fact, it is extremely unlikely that you will choose values for $g$ and $h$ that is optimal for any given problem. Filters are *designed*, not selected *ad hoc*. \n",
    "\n",
-    "In some ways I do not want to end the chapter here, as there is a significant amount that we can say about selecting $g$ and $h$. But the g-h filter in this form is not the purpose of this book. Designing the Kalman filter requires you to specify a number of parameters - indirectly they do relate to choosing $g$ and $h$, but you will never refer to them directly when designing Kalman filters. Furthermore, $g$ and $h$ will vary at every time step in a very non-obvious manner. "
+    "In some ways I do not want to end the chapter here, as there is a significant amount that we can say about selecting $g$ and $h$. But the g-h filter in this form is not the purpose of this book. Designing the Kalman filter requires you to specify a number of parameters - indirectly they do relate to choosing $g$ and $h$, but you will never refer to them directly when designing Kalman filters. Furthermore, $g$ and $h$ will vary at every time step in a very non-obvious manner. \n",
    "\n",
    "There is another feature of these filters we have barely touched upon - Bayesian statistics. You will note that the term 'Bayesian' is in the title of this book; this is not a coincidence! For the time being we will leave $g$ and $h$ behind, largely unexplored, and develop a very powerful form of probabilistic reasoning about filtering. Yet suddenly this same g-h filter algorithm will appear, this time with a formal mathematical edifice that allows us to create filters from multiple sensors, to accurately estimate the amount of error in our solution, and to control robots."
   ]