Fixed missing import.

2015-06-23 21:30:23 -07:00
1 parent 0a33395005
commit 46b3be3139
@@ -389,9 +389,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAD9CAYAAAC1Fh+gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x5b2a4a8>"
+       "<matplotlib.figure.Figure at 0x472f400>"
      ]
     },
     "metadata": {},
@@ -558,9 +558,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x5b2a128>"
+       "<matplotlib.figure.Figure at 0x472f240>"
      ]
     },
     "metadata": {},
@@ -595,9 +595,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x5b2a240>"
+       "<matplotlib.figure.Figure at 0x82c7ba8>"
      ]
     },
     "metadata": {},
@@ -634,7 +634,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x99fdcf8>"
+       "<matplotlib.figure.Figure at 0x82e2390>"
      ]
     },
     "metadata": {},
@@ -672,7 +672,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c1f7b8>"
+       "<matplotlib.figure.Figure at 0x87fc320>"
      ]
     },
     "metadata": {},
@@ -705,7 +705,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a41518>"
+       "<matplotlib.figure.Figure at 0x8155710>"
      ]
     },
     "metadata": {},
@@ -783,7 +783,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b9f048>"
+       "<matplotlib.figure.Figure at 0x7dd5ef0>"
      ]
     },
     "metadata": {},
@@ -819,7 +819,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8baee48>"
+       "<matplotlib.figure.Figure at 0x882f710>"
      ]
     },
     "metadata": {},
@@ -862,7 +862,7 @@
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c11a58>"
+       "<matplotlib.figure.Figure at 0x82f3978>"
      ]
     },
     "metadata": {},
@@ -916,7 +916,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa101ac8>"
+       "<matplotlib.figure.Figure at 0x81fdb38>"
      ]
     },
     "metadata": {},
@@ -947,7 +947,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x99a8b00>"
+       "<matplotlib.figure.Figure at 0x83276a0>"
      ]
     },
     "metadata": {},
@@ -978,7 +978,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb157710>"
+       "<matplotlib.figure.Figure at 0x9133f28>"
      ]
     },
     "metadata": {},
@@ -1016,7 +1016,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb19f0b8>"
+       "<matplotlib.figure.Figure at 0x9137a90>"
      ]
     },
     "metadata": {},
@@ -1047,7 +1047,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb228b00>"
+       "<matplotlib.figure.Figure at 0x91feb70>"
      ]
     },
     "metadata": {},
@@ -1087,7 +1087,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb1ba3c8>"
+       "<matplotlib.figure.Figure at 0x8ccdb38>"
      ]
     },
     "metadata": {},
@@ -1121,7 +1121,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb27bc50>"
+       "<matplotlib.figure.Figure at 0x922d128>"
      ]
     },
     "metadata": {},
@@ -1154,7 +1154,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x83c4da0>"
+       "<matplotlib.figure.Figure at 0x91e92e8>"
      ]
     },
     "metadata": {},
@@ -1203,7 +1203,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb219d30>"
+       "<matplotlib.figure.Figure at 0x9220eb8>"
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     "metadata": {},
@@ -1233,7 +1233,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b29208>"
+       "<matplotlib.figure.Figure at 0x9220668>"
      ]
     },
     "metadata": {},
@@ -1268,7 +1268,7 @@
     "data": {
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      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb27e748>"
+       "<matplotlib.figure.Figure at 0x830aa58>"
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     "metadata": {},
@@ -1297,7 +1297,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b06ef0>"
+       "<matplotlib.figure.Figure at 0x82e2470>"
      ]
     },
     "metadata": {},
@@ -1326,7 +1326,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c15780>"
+       "<matplotlib.figure.Figure at 0x819f080>"
      ]
     },
     "metadata": {},
@@ -1355,7 +1355,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a43da0>"
+       "<matplotlib.figure.Figure at 0x827f240>"
      ]
     },
     "metadata": {},
@@ -1442,7 +1442,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb16ef28>"
+       "<matplotlib.figure.Figure at 0x91f6208>"
      ]
     },
     "metadata": {},
@@ -1920,7 +1920,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a398d0>"
+       "<matplotlib.figure.Figure at 0x880acc0>"
      ]
     },
     "metadata": {},
@@ -2230,9 +2230,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa078b38>"
+       "<matplotlib.figure.Figure at 0x915fa20>"
      ]
     },
     "metadata": {},
@@ -2242,7 +2242,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0986a0>"
+       "<matplotlib.figure.Figure at 0x83442e8>"
      ]
     },
     "metadata": {},
@@ -2312,9 +2312,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEjCAYAAAB+VMTJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa05d9b0>"
+       "<matplotlib.figure.Figure at 0x8cc5748>"
      ]
     },
     "metadata": {},
@@ -2423,7 +2423,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a11080>"
+       "<matplotlib.figure.Figure at 0x7afcb70>"
      ]
     },
     "metadata": {},
@@ -2433,7 +2433,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb11b1d0>"
+       "<matplotlib.figure.Figure at 0x7afcba8>"
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     },
     "metadata": {},
@@ -2491,7 +2491,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa06fcf8>"
+       "<matplotlib.figure.Figure at 0x8221f98>"
      ]
     },
     "metadata": {},
@@ -2521,7 +2521,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa03f978>"
+       "<matplotlib.figure.Figure at 0x8301400>"
      ]
     },
     "metadata": {},
@@ -2552,7 +2552,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x99a9c50>"
+       "<matplotlib.figure.Figure at 0x8834198>"
      ]
     },
     "metadata": {},
@@ -2619,18 +2619,19 @@
   },
   "outputs": [
    {
-     "ename": "NameError",
-     "evalue": "name 'stats' 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-47-99f503c64879>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m P = np.array([[2., 0.], \n\u001b[0;32m      2\u001b[0m               [0., 6.]])\n\u001b[1;32m----> 3\u001b[1;33m stats.plot_covariance_ellipse((0, 0), P, facecolor='g', alpha=0.2,\n\u001b[0m\u001b[0;32m      4\u001b[0m                               title='|2 0|\\n|0 6|')\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'stats' is not defined"
-     ]
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x915f198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
    }
   ],
   "source": [
+    "from filterpy.common import stats\n",
+    "\n",
    "P = np.array([[2., 0.], \n",
    "              [0., 6.]])\n",
    "stats.plot_covariance_ellipse((0, 0), P, facecolor='g', alpha=0.2,\n",
@@ -2646,11 +2647,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAE6CAYAAADZSa6hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x82ba6a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "P = np.array([[2.0, 2.4],\n",
    "              [2.4, 6.0]])\n",
@@ -2698,11 +2710,32 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x91b5080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x87dc898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "# guarantee the noise is the same each time so I can be sure of\n",
    "# what the graphs look like.\n",
@@ -2731,11 +2764,32 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x8151da0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x8821b00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "run_filter(data=zs, R=var, Q=.2, P=1., plot_P=True, \n",
    "           title='$P=1\\, m^2$')"
@@ -2754,11 +2808,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x82c2780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "x = np.array([100., 0.]).T\n",
    "\n",
@@ -2770,18 +2835,29 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "We can see that the initial estimates are terrible and that it takes the filter a long time to start converging onto the signal . This is because we told the Kalman filter that we strongly believe in our initial estimate of 100m and were incorrect in that belief.\n",
+    "We can see that the initial estimates are terrible and that it takes the filter a long time to start converging onto the signal . This is because we told the Kalman filter that we strongly believe in our initial estimate of 100 m and were incorrect in that belief.\n",
    "\n",
    "Now, let's provide a more reasonable value for P and see the difference."
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x83196a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "run_filter(data=zs, R=var, Q=.2, P=500., initial_x=x,\n",
    "           plot_P=False,  title='$P=500\\, m^2$')"
@@ -2805,12 +2881,34 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x815f438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x832e668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
+    "import book_plots as bp\n",
    "def plot_track_ellipses(noise, count, R, Q=0, P=20., plot_P=True, \n",
    "                        title='Kalman Filter'):\n",
    "    dog = DogSimulation(velocity=1, measurement_variance=noise)\n",
@@ -2863,9 +2961,9 @@
    "\n",
    "Think about what this means physically. The x-axis of the ellipse denotes our uncertainty in position, and the y-axis our uncertainty in velocity. So, an ellipse that is taller than it is wide signifies that we are more uncertain about the velocity than the position. Conversely, a wide, narrow ellipse shows high uncertainty in position and low uncertainty in velocity. Finally, the amount of tilt shows the amount of correlation between the two variables. \n",
    "\n",
-    "The first plot, with `R=5 m`, finishes up with an ellipse that is wider than it is tall. If that is not clear I have printed out the variances for the last ellipse in the lower right hand corner. The variance for position is 3.85 m, and the variance for velocity is 3.0 m. \n",
+    "The first plot, with $R=5 m^2$, finishes up with an ellipse that is wider than it is tall. If that is not clear I have printed out the variances for the last ellipse in the lower right hand corner. The variance for position is 3.85 $m^2$, and the variance for velocity is 3.0 $m^2$. \n",
    "\n",
-    "In contrast, the second plot, with `R=0.5 m`, has a final ellipse that is taller than wide. The ellipses in the second plot are all much smaller than the ellipses in the first plot. This stands to reason because a small $\\small\\mathbf{R}$ implies a small amount of noise in our measurements. Small noise means accurate predictions, and thus a strong belief in our position. "
+    "In contrast, the second plot, with `R=0.5` $m^2$, has a final ellipse that is taller than wide. The ellipses in the second plot are all much smaller than the ellipses in the first plot. This stands to reason because a small $\\small\\mathbf{R}$ implies a small amount of noise in our measurements. Small noise means accurate predictions, and thus a strong belief in our position. "
   ]
  },
  {
@@ -2879,7 +2977,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Why are the ellipses for $\\mathbf{R}=5 m$ shorter, and more tilted than the ellipses for $\\mathbf{R}=0.5 m$. Hint: think about this in the context of what these ellipses mean physically, not in terms of the math. If you aren't sure about the answer,change $\\mathbf{R}$ to truly large and small numbers such as 100 m and 0.1 m, observe the changes, and think about what this means. "
+    "Why are the ellipses for $\\mathbf{R}=5 m^2$ shorter, and more tilted than the ellipses for $\\mathbf{R}=0.5 m^2$. Hint: think about this in the context of what these ellipses mean physically, not in terms of the math. If you aren't sure about the answer,change $\\mathbf{R}$ to truly large and small numbers such as 100 $m^2$ and 0.1 $m^2$, observe the changes, and think about what this means. "
   ]
  },
  {
@@ -2897,16 +2995,27 @@
    "\n",
    "**This is a critical point to understand!**. The Kalman filter is just a mathematical model for a real world system. A report of little correlation *does not mean* there is no correlation in the physical system, just that there was no *linear* correlation in the mathematical model. It's just a report of how much measurement vs prediction was incorporated into the model.  \n",
    "\n",
-    "Let's bring that point home with a truly large measurement error. We will set $\\mathbf{R}=500 m$. Think about what the plot will look like before scrolling down. To emphasize the issue, I will set the amount of noise injected into the measurements to 0, so the measurement will exactly equal the actual position. "
+    "Let's bring that point home with a truly large measurement error. We will set $\\mathbf{R}=500 m^2$. Think about what the plot will look like before scrolling down. To emphasize the issue, I will set the amount of noise injected into the measurements to 0, so the measurement will exactly equal the actual position. "
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x91f8588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "plot_track_ellipses(noise=0., R=500., Q=.2, count=7, title='$R = 500\\, m^2$')"
   ]
@@ -3042,11 +3151,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x87796a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "show_residual_chart()"
   ]
@@ -3114,11 +3234,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEPCAYAAACTNwgMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x87fc908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "dog = DogSimulation(velocity=1, measurement_variance=10.)\n",
    "f = pos_vel_filter(x=(0., 0.), R=3., Q=.02, P=P)\n",
@@ -3155,11 +3286,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEPCAYAAACTNwgMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x8283710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "from numpy.random import seed\n",
    "dog = DogSimulation(velocity=1, measurement_variance=40.)\n",
@@ -3186,11 +3328,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEWCAYAAAC68CsYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x915f7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "dog = DogSimulation(velocity=1, measurement_variance=40.)\n",
    "f = pos_vel_filter(x=(0., 0.), R=3., Q=.2, P=P)\n",
@@ -3224,7 +3377,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
@@ -3242,11 +3395,22 @@
  },
  {
   "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 0x81f06d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "dx = np.diff(xs[:, 0], axis=0)\n",
    "plt.plot(range(1,count), dx, c='r',ls='--', lw=2, label='Raw velocity')\n",
@@ -3303,7 +3467,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.4.1"
  }
 },
 "nbformat": 4,