Added section for nonstationary processes

Everyone is asking "can I vary F", or R, or whatever. Yes, you can!

08-Designing-Kalman-Filters.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -234,7 +234,7 @@
        "<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 +255,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -267,6 +267,8 @@
     "%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()"
@@ -310,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -345,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -362,9 +364,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEPCAYAAACA4Ej4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEPCAYAAACA4Ej4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7256b00>"
+       "<matplotlib.figure.Figure at 0x7197748>"
       ]
      },
      "metadata": {},
@@ -453,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -480,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -530,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -594,7 +596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -625,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -634,8 +636,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[5 0]\n",
-      " [0 5]]\n"
+      "[[ 5.  0.]\n",
+      " [ 0.  5.]]\n"
      ]
     }
    ],
@@ -666,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -680,7 +682,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Implement the Filter"
+    "### Implement the Filter"
    ]
   },
   {
@@ -692,7 +694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -708,9 +710,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAEVCAYAAADw59zpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAEPCAYAAAA9LdzIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8504c50>"
+       "<matplotlib.figure.Figure at 0x71d4f60>"
       ]
      },
      "metadata": {},
@@ -767,7 +769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -784,9 +786,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAERCAYAAABW/kAtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAEPCAYAAABvIiPGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8584160>"
+       "<matplotlib.figure.Figure at 0x8039e80>"
       ]
      },
      "metadata": {},
@@ -841,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -867,7 +869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -884,7 +886,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAAEWCAYAAADMypmTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x84e2ac8>"
+       "<matplotlib.figure.Figure at 0x7fba588>"
       ]
      },
      "metadata": {},
@@ -908,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -920,7 +922,7 @@
        "       [ 0.,  0.]])"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -985,7 +987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -994,7 +996,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEQCAYAAAByYPi2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x729bda0>"
+       "<matplotlib.figure.Figure at 0x8095048>"
       ]
      },
      "metadata": {},
@@ -1069,7 +1071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -1133,7 +1135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -1199,7 +1201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -1248,7 +1250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -1274,7 +1276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -1301,7 +1303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
@@ -1310,7 +1312,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAEmCAYAAAANwgSvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x864fba8>"
+       "<matplotlib.figure.Figure at 0x80c3898>"
       ]
      },
      "metadata": {},
@@ -1337,7 +1339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -1353,7 +1355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -1362,7 +1364,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAE8CAYAAACYZL1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8U1XeP/DPTdKk+76lC90opaXsBaHssig6qAPuOiK4\njQsuOPP4MD9FGHnkwUcZFxTHUYFxdERRQUZ22ReBUsrWne5Luu9blnt/f6S5uWnTNknTJmm/79eL\nFznJvTfnLk1y7vme72E4juNACCGEEEIIGTZEtq4AIYQQQgghZHBRI4AQQgghhJBhhhoBhBBCCCGE\nDDPUCCCEEEIIIWSYoUYAIYQQQgghwww1AgghhBBCCBlmqBFACCGEEELIMEONAEIIIYQQQoYZagQQ\nMkwVFBRAJBJhxYoVtq6K3YmMjERUVJStq2EXLLlO6NoyjaXHyRbXp7XP6VNPPQVfX180NTVZZXum\nKCoqgkwmw9q1awftPQmxZ9QIIGSIEYlEvf774IMPDJZnGGZA6nH8+HGr/GhIS0vDk08+idjYWLi7\nu8PDwwNjxozBiy++iJs3b1qptt0N1HExVdfzJhaL4evri1mzZuGzzz4Dy7KDWh/h8TD13Nr6GHZl\n7Jj6+PhgxowZ2LJlC9RqtU3qZclxstWxtcb7Xrt2Ddu2bcOrr74KDw8P/vldu3Zh1apVmDVrFjw9\nPSESifDQQw/1+/10RowYgSeffBKbN29GWVmZ1bZLiKOS2LoChBDrYxgGb775ptHXpk+fDgAICwtD\nZmYmvLy8Brwulnr99dfx9ttvQyKRYP78+Vi6dClYlsWFCxewZcsWfPrpp/jggw/w7LPPWrHG9kN4\nHtVqNXJzc/HTTz/hzJkzOHLkCL777rsBr0Nv10lP53awri1LdD2mBQUF+PHHH3Hu3DkcPnwYe/bs\nGbS62PNxGkhr166Fs7MzXnzxRYPnN2zYgKtXr8LDw4M/NtZu7PzXf/0XPv30U7z99tvYsmWLVbdN\niMPhCCFDCsMwnEgksnU1uGPHjnEMw3CPP/64Retv2LCBYxiGi4iI4K5cuWJ0+76+vhzDMNw333zT\n3+oaiIiI4KKioqy6TXP1dB6vXbvGubq6cgzDcKdOnbJBzfp/bm2lp2OanZ3Nubu7cwzDcMePH7dB\nzcxji+szPz+fYxiGW7FiRb+2U1RUxInFYu6hhx7q9tqxY8e43NxcjuM47vjx4xzDMEaX6685c+Zw\nnp6eXHNzs9W3TYgjoXAgQoapnmJ8dc/PmzcPZWVlWLlyJeRyOSQSCX+XdO/evViwYAFCQkLg7OyM\nkJAQzJw5Exs3bgQArFu3DrfeeisAYMeOHQYhGDt27OizboWFhVi3bh2cnJywd+9ejBs3rtsyc+fO\nxVdffQUAePHFF9HS0mLWPnAchy1btmDMmDFwcXFBWFgYVq1ahYaGhl7rlpqaigcffBAhISGQyWQI\nCQnBY4891i00yZQ6WCIxMRFz5swBAFy4cMHgtR9//BHz5s2Dl5cXXFxckJCQgDfffNPg2Oj0dQ67\n7ofuOjHl3PYVP25qPYXHsKamBk8//TTkcjmcnZ2RmJiI7du3m38AjYiNjcXs2bMBACkpKUaXMfW8\nA5Yf265MvT514Vnr1683up25c+dCJOr+db99+3YsW7YM0dHRcHV1hZeXF2bOnMn/XZnK1P3VvSfL\nsnjwwQeN1jMmJgaA9u9zoDz44INoamrC999/P2DvQYgjoHAgQoa5nrrba2pqkJycDG9vbzzwwANg\nWRa+vr747LPP8Mc//hHBwcH43e9+h8DAQFRXV+PGjRv4+9//jjVr1mDevHkoLCzEjh07MGHCBNxz\nzz38didOnNhnnb788ktoNBosW7YMY8eO7XG5O+64A0lJSUhJScGuXbuwfPnyPvfBz88PAPDyyy/j\no48+glwux9NPPw2pVIo9e/bg/PnzUKlUkMlk3d7v66+/xuOPPw5nZ2fcddddCA8PR05ODv79739j\n7969OH78OMaPH29yHSyl+4EkPHdr167Fhg0b4Ofnh4cffhje3t44dOgQ3nrrLfz88884deoU3N3d\nAcCkc9iV7r3MObfGri1z6qlTX1+PGTNmQCaT4f7770dHRwe+++47rFy5EiKRCI899piFR1JPd0z7\ne977c2yFXnrpJbOvz95CZ4y99txzzyExMRFz586FXC5HdXU19u3bh+XLlyMzMxP/8z//0/MB62Tu\n/h46dAgMw2DGjBl9bnugzJw5k6/L448/brN6EGJztu2IIIRYG8MwHMMw3Lp167g333zT4N+nn37K\nL9dT977ueYZhuOXLl3Majcbg9UmTJnHOzs5cZWVlt/euqanhH+u68y0JH7j11ls5hmG4zz//vM9l\n//KXv3AMw3BPPfWUyftw5swZjmEYLjo62qDOHR0d3MyZMzmGYbqFW+Tk5HAymYwbOXIkV1ZWZvDa\n8ePHOYlEwk2ePNnkOvSlp9CVK1eucM7OzpxIJOLOnDnDcRzHnTt3jmMYhgsPD+fKy8sNll++fDnH\nMAz3wgsv8M+Zeg6F+yE8j32d256uLXPrKTyGTz31FMeyLP9aeno6J5FIuISEBKN1MKanY5qens65\nurpyIpGIu3r1qsFr5p73/h5bjjP/+tSFZ61fv97ofs+ZM8fofufl5XV7TqlUcvPnz+ckEglXUlLS\nZ13N2d/29nZOJpNxMTExRusppNsnU8KBrl69yt11112cp6cnf70I/3333XcGy2s0Gs7NzY0LCwvr\nc9uEDGXUE0DIEGUsNGDChAl45plnTFpfJpPh3XffNRpGIBaLIZF0//jw9fXlH3P96M4vLy8HAISH\nh/e5bFhYGAAYzfbR0z5s27YNAPCXv/zFoM5SqRRvv/02H24jtHXrViiVSvztb3+DXC43eG3OnDlY\nsmQJdu/ejYyMDMTHx/dZB1NwHIf169eD4zio1Wrk5eXhxx9/hFKpxH333Yfk5GQAwBdffMHvT3Bw\nsME23nnnHXz33XfYvn073n//fYjFYgCmncPe6mUJU+q5Y8cO/O1vfzOom5ubGzZv3mxwNzs+Ph7J\nyck4ffo0Wltb4erqalIduh5T3cBgpVKJ9evXd+t5MvW8Z2ZmYvTo0QD6d2wBy65PSxhLM+rk5ITn\nnnsOR48exdGjR/GHP/yhz+2Yur9lZWVQKpX836w17N27Fw8++CCWL1+OHTt2IDs7G2vXrsXKlStx\n7733QiKR8MkQdEQiEeRyOfLy8qBSqeDk5GS1+hDiSKgRQMgQxDAMNBpNv7YRGRkJf3//bs8/+uij\nePXVV5GQkIAHHngAs2bNQnJycrcfSPagp31ITU0FAKM/ppKTk/kfykJnzpwBAJw4ccJo3HhFRQUA\ndGsE9FQHU+kacwzDwMvLC1OnTsWjjz6Kp556qtv+6GL1hQIDAzF27FhcvHgR2dnZiI+Pt9k5NKee\nCQkJ/Gu69LBdhYeHg+M41NXVmdwIAIw3kN99912sXr262/PmnPfRo0db5dhacn1aoqioCJs2bcKv\nv/6K4uJitLW1GbxuShpNc/a3qqoKgOmNob5kZ2fjoYcewvvvv2/w95CRkYGMjAyj15mOn58f8vLy\nUFlZidDQUKvUhxBHQ40AQohRXe/U6rzyyisIDAzE1q1b8fHHH+PDDz8EAEybNg0bN260yl3K4OBg\nZGZmoqioqM9li4uLAQAhISFGt2NMQ0MDGIZBUFBQt9fEYrHRmP2amhoAwHvvvddjXRiG6Ta4tac6\nmMLUxpxuf3p6L90Psvr6egCDcw77U8+ug1+9vb2NLq+7+2xOg1d4TNvb23HhwgU888wz+POf/4yg\noCA88sgjBsubet6bm5sBWOfYWnJ9misvLw9Tp05FfX09Zs+ejdtvvx1eXl4Qi8XIz8/Hjh070NHR\n0ed2zNlfXU9Of3oJhdasWYOxY8caNAAAICAgAL/99luv6+rm2bC3uSwIGUyUHYgQYlRvX46PPPII\nTp8+jbq6Ohw8eBDPP/88UlNTsXjxYuTk5PT7vWfNmgUAOHz4cJ/LHjlyBIB+sJ9QT/ugy8uuUCi6\nvabRaPgffl3XYRgGtbW1YFnW6D+NRtMtfGIwfmTo9kcXRtWV7nlhPvqBPofWqudAcnZ2xuzZs3Hg\nwAG4uLjg2Wef7XZNWHLe+3tszb0+daFmPU12pmv8CW3evBm1tbX48ssvcfToUbz//vtYv3491q5d\ni0WLFvVZRyFT91fXI1ZbW2vW9o2pr6/H3r17uyUDALQ9BBEREb2ur6tDQEBAv+tCiKOiRgAhxGLu\n7u5YuHAhPvroI7z66qtob2/HgQMHAIAPWbAkLGnFihWQSCTYvXs3rl+/3uNy+/fvR0pKCvz9/XHv\nvfeavP3JkyeD4zicOHGi22tnzpwxWufk5GRwHIeTJ0+a/D6DRbc/x44d6/ZaZWUlrl+/Dnd3d8TF\nxXV73dg53L9/f6/vZ+m57U89B1JERARee+01NDc3Y+3atQav9ee8W3JsAfOvTx8fHwAw2nPW0NCA\n7Ozsbs/n5uaCYRgsW7as22vG3tcUfe1vaGgopFIpSkpKLNq+UFZWFtRqNaZOnWrwfEtLC44dO4bf\n//73Pa7LsiwUCgVCQkJoPAAZ1qgRQAgxy9GjR40+r7uLq4vN1oUsFBYWmv0ekZGReP3116FSqbBk\nyRJcu3at2zInTpzAo48+CoZh8MEHH5gVE65LC/j2228b3JXs6OjAX/7yF6PrvPDCC5BKpXj11VeR\nlZXV7XW1Wm30x+1gWLlyJQDt/uhi1AFt2MVrr72GtrY2LF++nP/x3tc5dHNz6/X9LD235tZzML3y\nyivw9/fH9u3bDe5em3ve+3tsAfOvz/j4eHh5eWH37t0Gx1WtVuPll19Ge3t7t3WioLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x864f358>"
+       "<matplotlib.figure.Figure at 0x81609b0>"
       ]
      },
      "metadata": {},
@@ -1386,7 +1388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -1395,7 +1397,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAE8CAYAAACYZL1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x864fe10>"
+       "<matplotlib.figure.Figure at 0x810bb38>"
       ]
      },
      "metadata": {},
@@ -1419,7 +1421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -1428,7 +1430,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAEmCAYAAAANwgSvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9845860>"
+       "<matplotlib.figure.Figure at 0x80c2588>"
       ]
      },
      "metadata": {},
@@ -1456,7 +1458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -1465,7 +1467,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAE8CAYAAACvjyVSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9859400>"
+       "<matplotlib.figure.Figure at 0x81e0470>"
       ]
      },
      "metadata": {},
@@ -1497,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -1506,7 +1508,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAEmCAYAAAANwgSvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9946898>"
+       "<matplotlib.figure.Figure at 0x929c2b0>"
       ]
      },
      "metadata": {},
@@ -1532,7 +1534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -1541,7 +1543,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAE6CAYAAABOPV5SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9908748>"
+       "<matplotlib.figure.Figure at 0x9203d30>"
       ]
      },
      "metadata": {},
@@ -1571,7 +1573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -1580,7 +1582,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAE6CAYAAABOPV5SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8740c88>"
+       "<matplotlib.figure.Figure at 0x9213470>"
       ]
      },
      "metadata": {},
@@ -1614,7 +1616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -1623,7 +1625,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAEmCAYAAAANwgSvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9946978>"
+       "<matplotlib.figure.Figure at 0x92ddc50>"
       ]
      },
      "metadata": {},
@@ -1647,7 +1649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -1656,7 +1658,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAEoCAYAAAC6pSKLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8jef/x/HXfU5ykkgiSwZCYsSmZim1Y6/6okaVqNGf\nGaO0SiXaqtEWRWm1tlq1WqNFKma0UmrvvZqQyCA759y/P04djgQJWeLzfDzykFz3dV/3dd8y3uc6\n133diqqqKkIIIYQQQuRTmtzugBBCCCGEENlJAq8QQgghhMjXJPAKIYQQQoh8TQKvEEIIIYTI1yTw\nCiGEEEKIfE0CrxBCCCGEyNck8AohhBBCiHzNIrcOPHnyZNavX8+5c+ewsrKiTp06TJ48mYoVK5rq\n+Pn5sXTpUrP96tSpQ0hIiOnrpKQkPvjgA1atWkVCQgJNmzZl7ty5FC1a1Gy/mJiY7D0hIYQQIg9y\ncHDI7S4IketybYR39+7dDBkyhAMHDrBz504sLCzw9fUlKirKVEdRFJo1a0ZYWJjpY+vWrWbtDB8+\nnPXr17Nq1Sr27t1LbGwsbdu2xWAw5PQpCSGEEEKIPEjJK09ai4uLw8HBgV9++YU2bdoAxhHeyMhI\nNm3alO4+MTExuLm5sXjxYrp37w7AjRs38PLy4rfffqN58+ZmdYUQQohXjYzwCpGLUxoeFxsbi8Fg\nwMnJyVSmKAr79u3D3d0dR0dHGjZsyKRJk3B1dQXg0KFDpKSkmAVbT09PypcvT0hIiFn5o+SHP3v8\n/fffANSsWTOXe5J/yTXOfnKNs59c4+wngzxCmMszI7xvv/02Fy9e5O+//0ZRFABWr16Nra0tJUqU\n4PLly4wfPx69Xs+hQ4fQ6XSsWLGC3r17k5KSYtZW06ZNKVOmDPPmzTOVPfrDf/78+Zw5KSGEECIX\n+Pj4mD6XQR4h8sgI78iRIwkJCWHfvn2msAvQtWtX0+cVK1akRo0aeHl5sWXLFjp27JgbXRVCCCGE\nEC+ZXA+8I0aMYM2aNQQHB+Pt7f3UuoULF8bT05MLFy4A4OHhgV6vJzIyEhcXF1O9sLAwGjRo8MR2\n5G207CFvU2Y/ucbZT65x9pNrnP1kSoMQ5nJ1HV5/f39Wr17Nzp07KVOmzDPr37lzh5s3b1K4cGEA\natSogaWlJdu3bzfVuXHjBmfOnKFu3brZ1m8hhBBCCPHyyLUR3sGDB7N8+XI2btyIg4MDYWFhANjb\n22Nra0tcXBwBAQF07twZDw8Prly5wtixY3F3dzdNZ3BwcKBv376MGTMGNzc3nJ2dGTlyJK+99hq+\nvr65dWpCCCGEECIPybXAO2/ePBRFoWnTpmblgYGBTJgwAa1Wy4kTJ1i2bBnR0dEULlyYJk2asHbt\nWmxtbU31Z86ciYWFBV27diUhIQFfX1+WL19uNhdYCCGEEEK8unIt8D7rwRDW1tb8/vvvz2xHp9Mx\na9YsZs2alVVdE0IIIV4qqqoyb948Zs+eTVRUFGfPns3tLgmRp+T6TWtCCCGEeH6HDx/Gz8+P48eP\n53ZXhMizJPAKIYQQL4Ho6GgCAwM5dOgQAImJiYSHh3P9+nWzeg9u7BZCPCSBVwghhMjj4uPjadiw\nIceOHUu/gsYanP8H7n359+bXOds5IV4CEniFEEKIPO6nn37i2LFjlChRAkVRuHTpknFDgSrg1hcK\ndULRFQFAjd2Tiz0VIm/K1XV4hRBCCPF0SUlJ7Nu3D4CoqCguXb0D7v2hyl8oVY+Amgw3v3y4g8vb\nudRTIfIuGeEVQgghcpHBYODixYvo9Xqsra05efIklpaWlCxZkkOHDuHv7094eDjYv0G0Sz/wrgMx\nO1HsahkbcOkIZzqiJlyA2wvh9hLgdK6ekxB5jQReIYQQIpdcu3aNjh07cvjw4fQrWLiA67tQpSeK\nXXUA1JS7cO0T1EJvg7Yg3PsTtM7wT1lAzbnOC/ESkcArhBBC5IJr165Ru3ZtwsLC0Gq16PX6/7Yo\n4NAU3PuCc0dQLOGfsqjlt6DY+KBYOqMWmwBXx8PddZB619Smt7c333zzTe6ckBB5mAReIYQQIocd\nP36cunXrcv/+fQBj2NUVBbc+xo/4Y6ArjqLRAaA6d4S7G1EtnCD8B7gfataes7Mz48ePx9/fH41G\nQ0xMTI6fkxB5mQReIYQQIodcuHCB2bNnM3fuXFJTU0GxAKc2xpUWHJqgaAsAoEauh8j14LME9d4B\nSLkDYfPAcD9Nmw0aNGDbtm1YW1vn9OkI8dKQwCuEEEJkg5SUFKKioihUqBB6vZ7//e9/bN682bjR\nujQU6QtuvVF0HqhR2+BsJ6jwm3G7c3uI+wf1SGWIP2nWrk6no127dlSsWBEfHx+6du2KpaVlDp+d\nEC8XCbxCCCFEFjl9+jS//fYb33777cO1ch/QWEOhHsa5uXZ1IHw+WLobtzk0hEsDjSO7ET/D3Y2g\nJqVpf9SoUUyZMgULC/nzLURmyE+MEEIIkUmHDh3i888/5+bNm5QvX54RI0bwzTffsHjx4rSVC1Q2\nrptbsCFYl0TR2qKqBgibA/a1UXXF4PZi4wILZzunezyNRsPXX3/N8OHDs/O0hMi3JPAKIYQQmRAc\nHEzLli1JTk4GIDQ0lKVLl5pXevCoXzc/FEdfANRTbcGtFxR6GzBAoW5wdSzE7gP0Zrs7ODig0+lw\nc3OjZ8+edOvWDW9v7+w/OSHyKQm8QgghxH9Onz7NsGHDOHLkCACenp44OztTrlw5QkJCOH78+CPL\nh6XDqiQUHmoMttcCIO4w/Bd4cekI8adQr35sfDhEyr/pNtGxY0cWLlyIo6NjVp+eEK8sCbxCCCEE\nxmkK9erVIynp4dzZiIgIAHbu3Pn0nQs2hgKVQE1GKeIPgOrYAqK2oBqSIXId3FkBsbt4/OEQjo6O\nzJ8/n1atWqHVarGxscnK0xJCIIFXCCGEYPz48UyaNCmTe1mCpQtU+B3Ftgrqvb/g8oiHm23KQuwe\nOFTMuKzYI/z9/RkyZAiWlpYULlwYnU734ichhHgiCbxCCCFeKWfPnmXcuHGcOXOGpKQkIiMjiYqK\nyngDlu7gMdC4dFjEGhTbKsZyu1pQeT/q3c3w72yICQJUNBoN9g4OFClShJYtWzJq1CiKFi2aLecm\nhEifBF4hhBCvjIMHD9K0aVPTE84yxaYKFOoIRT9C0VgZy2yrAqDq44wrLfw7CxLPm+32ww8/8N57\n771gz4UQL0ICrxBCiHxPVVWGDBnCvHnzUFX12Ts8yqktFBmJ4tAobbtJ1+DfORD+I+ijcXV1JcHC\njvv372Nvb09gYCB9+vTJmpMQQjw3CbxCCCHyrXPnzjFp0iR+/fVXoqOjM7ezUzsoFoBiVz3NJsvE\nv3FMXEp1ryuM/+Yj6tadhqoapy+oqsr9+/extbVFo9Fk0ZkIIV6EBF4hhBD5yv79+9m5cyfHjh1j\n/fr1GAyGzDXg1A6leCDYVjMvV1Nxt9zHokmv0fLNWkAts82Kopj+tbe3f/4TEEJkOQm8QgghXnoh\nISH88ssvbNmyhZMnTz5fI05t/xvRrWFWbGWp573WKYz1s8bTrXEW9FYIkdMk8AohhHipffLJJ3z+\n+efP34BTm/+Cbk2zYhsr+L+OMLqHFg8X+XMpxMtMfoKFEEK8lO7evUu9evU4c+bM8zXg2MoYdO1f\nNyt+GHTBw0XJgp4KIXKbBF4hhBAvndu3b1OqVKnnW17MscV/QbeOWbG1zhh0x7wjQVeI/EYCrxBC\niDwtNjaWXbt2ER8fz6lTp1iyZAnXrl3LfEMF60PxSSgF3zQrttLB+x3gw55QuJAEXSHyIwm8Qggh\n8qxTp07RsmVLrl+//txtaAq+jqHoRBSnFmblOksY0AE+6glFXCXoCpGfSeAVQgiRJ925c4e33nqL\n69evY2FhQWpqauYasCkPxT9FdenEo3HW0gL6toOPe4GnmwRdIV4FEniFEELkKaGhoXTr1o1Lly6Z\nyjIVdq28jXN0XXuCojUVazTQqyVMeA+8C0vQFeJVIoFXCCFEnjFlyhTGjh37XPs2bNadM/c7cUdp\nh4ql2bbOjWFiPyjvLUFXiFeRBF4hhBB5QnBw8HOFXUVXiHbv72HH8XIkPpZnW9WBzwZA9bISdIV4\nlUngFUIIkStiY2NZuXIlp0+fZseOHZw+fTpT+9s5FKZSsx85GdWCTYc0ZtverAKT3of6VSXoCiEk\n8AohhMgFYWFhdOnShStXrmR6X0Vrw+vtF3MpsTN/3TIPtNXLwucDoEVtUBQJu0III82zq2SPyZMn\nU6tWLRwcHHBzc6N9+/bpPv88MDCQokWLUqBAARo3bsypU6fMticlJTF06FBcXV2xs7OjQ4cO3Lx5\nM6dOQwghRAbFxMQQHBzMDz/8wFtvvZXpsKsoFrzZaTEeLWM4GN6FiJiHgbacF/z8OYQugJZ1FAm7\nQggzuRZ4d+/ezZAhQzhw4AA7d+7EwsICX19foqKiTHWmTp3K9OnTmTNnDqGhobi5udGsWTOzJ+sM\nHz6c9evXs2rVKvbu3UtsbCxt27bFYDDkxmkJIYRIx549eyhVqhRNmjRh/vz56PX6DO/7zjs98Ru1\niVKd4tn/by/Coh6+OenlAYvGwfFl0KmxBF0hRPpybUrD77//bvb1smXLcHBwICQkhDZt2qCqKjNn\nzmTs2LF07NgRgCVLluDm5saKFSsYMGAAMTExLFy4kMWLF9O0aVNTO15eXgQFBdG8efMcPy8hhBBG\nqqpy8eJFNmzYwNixYzMVch/QOrfknPUS/j5gHmQ9XGBcb+jXDqx0EnKFEE+XayO8j4uNjcVgMODk\n5ATA5cuXCQ8PNwut1tbWNGjQgJCQEAAOHTpESkqKWR1PT0/Kly9vqiOEECLnLVu2DLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x993c240>"
+       "<matplotlib.figure.Figure at 0x92ddb00>"
       ]
      },
      "metadata": {},
@@ -1666,7 +1668,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAE6CAYAAABOPV5SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x853c7f0>"
+       "<matplotlib.figure.Figure at 0x80e0240>"
       ]
      },
      "metadata": {},
@@ -1699,7 +1701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
@@ -1708,7 +1710,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAArUAAAEmCAYAAAB8umgxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x853ce48>"
+       "<matplotlib.figure.Figure at 0x80e00f0>"
       ]
      },
      "metadata": {},
@@ -1718,7 +1720,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwoAAAE6CAYAAAC23aWlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x988e240>"
+       "<matplotlib.figure.Figure at 0x81b8a58>"
       ]
      },
      "metadata": {},
@@ -1751,7 +1753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
@@ -1760,7 +1762,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x989be48>"
+       "<matplotlib.figure.Figure at 0x808beb8>"
       ]
      },
      "metadata": {},
@@ -1792,7 +1794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
@@ -1801,7 +1803,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAAEWCAYAAAAuFoLqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86ec0b8>"
+       "<matplotlib.figure.Figure at 0x932d978>"
       ]
      },
      "metadata": {},
@@ -1848,7 +1850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
@@ -1857,7 +1859,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAAEmCAYAAAC03xeLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8756198>"
+       "<matplotlib.figure.Figure at 0x92d3e48>"
       ]
      },
      "metadata": {},
@@ -1867,7 +1869,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAE6CAYAAAB51sZPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86846a0>"
+       "<matplotlib.figure.Figure at 0x8049978>"
       ]
      },
      "metadata": {},
@@ -1898,7 +1900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
@@ -1907,7 +1909,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAAEmCAYAAAC03xeLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x979b358>"
+       "<matplotlib.figure.Figure at 0x95bc278>"
       ]
      },
      "metadata": {},
@@ -1917,7 +1919,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAE6CAYAAAB51sZPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x727ec18>"
+       "<matplotlib.figure.Figure at 0x81ef0f0>"
       ]
      },
      "metadata": {},
@@ -1943,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
@@ -1952,7 +1954,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAAEmCAYAAAC03xeLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x871d160>"
+       "<matplotlib.figure.Figure at 0x81b3080>"
       ]
      },
      "metadata": {},
@@ -1962,7 +1964,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwYAAAE6CAYAAACs4UUrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x871d518>"
+       "<matplotlib.figure.Figure at 0x810b5f8>"
       ]
      },
      "metadata": {},
@@ -2023,7 +2025,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "collapsed": true
    },
@@ -2041,7 +2043,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
@@ -2115,7 +2117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -2124,7 +2126,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9830be0>"
+       "<matplotlib.figure.Figure at 0x81c0780>"
       ]
      },
      "metadata": {},
@@ -2156,7 +2158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
@@ -2165,7 +2167,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwkAAAEWCAYAAAAthNvrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86fc5c0>"
+       "<matplotlib.figure.Figure at 0x92ae9e8>"
       ]
      },
      "metadata": {},
@@ -2249,16 +2251,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv4AAAEtCAYAAABwJ2SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAEtCAYAAABQ7mxxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99c1320>"
+       "<matplotlib.figure.Figure at 0x9335898>"
       ]
      },
      "metadata": {},
@@ -2266,6 +2268,7 @@
     }
    ],
    "source": [
+    "dt = 0.1\n",
     "R = 3.\n",
     "kf = KalmanFilter(dim_x=2, dim_z=1, dim_u = 1)\n",
     "kf.P *= 10\n",
@@ -2359,7 +2362,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9abc588>"
+       "<matplotlib.figure.Figure at 0x92f93c8>"
       ]
      },
      "metadata": {},
@@ -2442,7 +2445,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71db208>"
+       "<matplotlib.figure.Figure at 0x933d4e0>"
       ]
      },
      "metadata": {},
@@ -2476,9 +2479,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b3ec88>"
+       "<matplotlib.figure.Figure at 0x937f940>"
       ]
      },
      "metadata": {},
@@ -2528,9 +2531,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x972d1d0>"
+       "<matplotlib.figure.Figure at 0x922f320>"
       ]
      },
      "metadata": {},
@@ -2616,7 +2619,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x861b2e8>"
+       "<matplotlib.figure.Figure at 0x967bc50>"
       ]
      },
      "metadata": {},
@@ -2624,9 +2627,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x979d320>"
+       "<matplotlib.figure.Figure at 0x967bd30>"
       ]
      },
      "metadata": {},
@@ -2634,9 +2637,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9657780>"
+       "<matplotlib.figure.Figure at 0x92ecfd0>"
       ]
      },
      "metadata": {},
@@ -2695,7 +2698,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x964cb00>"
+       "<matplotlib.figure.Figure at 0x80d6668>"
       ]
      },
      "metadata": {},
@@ -2703,9 +2706,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x964ce48>"
+       "<matplotlib.figure.Figure at 0x81b8400>"
       ]
      },
      "metadata": {},
@@ -2713,9 +2716,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdcVuX/x/HXudkggqEMEXPkNk3LNM1BjjLN9OvWcmSS\nua20XDkqTaWcmdsw09yjMnNrjnJlWpZ7RlI4CJQl9/n9wc9b71BDBW7Q9/Px4JHXOOd8Tkfh/nBd\n57oM0zRNRERERERERBzM4ugAREREREREREAJqoiIiIiIiGQTSlBFREREREQkW1CCKiIiIiIiItmC\nElQRERERERHJFpSgioiIiIiISLagBFVEJBuyWCyEhoY6OowMN3ToUCwWC5s3b3Z0KCIiIpINKUEV\nEckAFy5cYObMmTRt2pTixYvj5eWFj48PVapUYcKECVy9evWOz2kYhl25Q4cO2T65u5aARkRE3LTd\nMAzbl4iIiMi/OTs6ABGR+8HChQvp2rUrAQEBhIaG0rx5c/7++29WrlxJ7969Wbx4MevWrcPV1TVd\n5/v999/x9PS8aVtOSO5uFWP37t1p3bo1ISEhWRyRiIiI5ARKUEVEMkCJEiVYsWIFDRs2tEvOPvro\nI2rWrMnWrVv55JNP6NOnT7rOV7x48Vu2maZ5z/FmtlvF6Ofnh5+fXxZHIyIiIjmFpviKiGSA0NBQ\nXnjhhTQjh97e3vTt2xeATZs2pft8/34HtVChQsyZM8d2LYvFYvu6UUJCAuHh4Tz++OPkypWLXLly\nUalSJaZOnZrmGidPnrRdJzIykldeeYWgoCCcnZ1ZsWIFAHv27KFXr16UL18ePz8/PDw8KF68OG+8\n8QYXL160O1+tWrUYPnw4AB07drSL8fTp08D1KcBbtmxJE8+mTZto0KABfn5+uLu7U7RoUfr06UN0\ndHSavjdOd168eDFPPvkkXl5e+Pn50bp1ayIjI9Mcc/z4cV577TWKFSuGp6cnDz30EKVLl6ZTp06c\nOXPmts9DREREsoZGUEVEMpmzs7Pdf9PrxmS3T58+fPbZZ/z888906NCBQoUKpekfGxtLnTp12LVr\nFxUrVuSVV14BYPXq1bz++uv88MMPzJ49O81x58+fp2rVqvj6+tKyZUusVqttlHP69OksX76cWrVq\nUa9ePaxWK7t372bcuHF8++237Nq1i1y5cgGpSalhGGzevJnGjRvz2GOP2a7h4+Nz23udMWMGYWFh\neHl50bx5c4KCgti2bRvjx49n2bJlbNu2jeDg4DTHTZ48mZUrV/Liiy8SGhrKDz/8wIIFC/j555/Z\nt2+fbUr1n3/+SaVKlYiNjaV+/fo0bdqUpKQkTp06xdKlS2nbtq2mHYuIiGQHpoiIZKpnn33WNAzD\nnDZtWrqPMQzDDA0Ntatr3769aRiGuXnz5pse06lTJ9MwDHP06NF29YmJiWaDBg1MwzDMr776ylZ/\n4sQJ0zAM0zAMs3379mZKSkqac546dcq0Wq1p6mfOnGkahmF++OGHdvVDhgwxDcMwIyIibhrjtfYb\n7+H06dOmq6ur6e3tbR48eNCu/+DBg03DMMyGDRve9P+Fj4+P+csvv9i1tWnTxjQMw1y4cKGtbsKE\nCaZhGOb48ePTxJSUlGTGxcXdNF4RERHJWpriKyKSicaNG8eaNWvsRjQzw4ULF4iIiKBixYq2KcXX\nuLq6MnLkSAA+//zzNMe6ubkRHh6eZrowQMGCBW+64FHHjh3x9vZm7dq19xz7nDlzSE5OpmvXrpQq\nVcqubeDAgQQFBfHNN9/w559/pjm2Z8+elClTxq6uc+fOAOzatctWd+0e3N3d05zDxcUFLy+ve74P\nERERuXea4isikkm++OIL3nzzTYKDg1m6dClOTk6Zdq2dO3eSkpKCYRgMHTo0TXtycjIAv/32W5q2\nQoUKkTdv3pueNzk5malTp/Lll19y8OBB/vnnH6xWq639jz/+uOfY9+7dC8AzzzyTps3NzY2nn36a\nRYsW8dNPPxEUFGTX/sQTT6Q5pkCBAgB278g2atSIAQMG0L17d1avXk29evV46qmnKFeuXI5YFVlE\nRORBoQRVRCQTzJkzh44dOxISEsKGDRsoWLBgpl7v/PnzQOqiRnv27LlpH8MwuHz5cpr6wMDAW563\nZcuWLF++nKJFi9KkSRMCAwNxc3PDNE3GjRtHYmLiPcceExNz2ziuJaXX+t3I19c3Td21d31TUlJs\ndQULFmTXrl0MGzaM1atXs3z5cgACAgLo0aMH77zzzk1HkEVERCRrKUEVEclg06ZNo0uXLhQpUiRL\nklO4vghRz549GTdu3B0de6sRxN27d7N8+XLq1q3Lt99+a5fAmabJqFGj7j7gG1yL/c8//6RcuXJp\n2q9N7f2vhZb+S4kSJZg3bx5Wq5UDBw6wYcMGPvnkEwYNGkRKSgqDBw++p/OLiIjIvdOvi0VEMtD4\n8ePp0qULxYsXZ8uWLRmanF6bInzjyOA1VapUueX2LXfr6NGjQOr02H+PLv74448kJCTcUYy38vjj\njwOwcePGNG2JiYls27YNwzCoWLFius95OxaLhfLly9OnTx++/fZbANuIqoiIiDiWElQRkQwycuRI\n+vTpQ5kyZdiyZQv58+fP0PNf2/rl1KlTadry5s3Lyy+/zL59+xg2bNhNE8SzZ89y6NChdF+vcOHC\nQNrE8a+//qJbt253HOOtvPTSS7i6ujJ58uQ08Y0cOZLIyEief/75205F/i979+7l0qVLaeqvjc56\nenre9blFREQk42iKr4hIBoiIiGDgwIEYhkGtWrWYPHlymj558uShV69ed32NevXqER4eTv/+/Tlw\n4AB58uTBMAwGDhwIwMSJEzly5AjDhg3j888/p3r16gQGBnLu3DkOHTrEjz/+yNixYylRokS6rlep\nUiWqVavG0qVLqVatGtWqVSMqKorVq1dTsmRJ8ufPj2madsfUrl0bi8XCuHHjOH/+PAEBAUDq1OPc\nuXPf9DoFCxZkwoQJvP766zzxxBO0aNGCgIAAtm/fzpYtWwgJCeHTTz+96/9vkPpO8NSpU6lWrRpF\nixbFz8+PU6dOsWLFCpydnenXr989nV9EREQyhhJUEZEMcPLkSdufP/nkk5v2KVSo0D0lqHXq1GH8\n+PFMnTqVyZMnk5iYaJeg5sqVi02bNjFz5ky++OILli9fTnx8PAEBARQpUoQPP/yQFi1apPt6FouF\nlStXMmjQIFatWsXEiRMpUKAAnTt3ZuDAgZQuXTrN+6slSpRg7ty5hIeHM2vWLOLj4zEMg3bt2pE7\nd24Mw7jpO69hYWEUK1aMMWPGsHz5cuLi4ihQoAA9e/Zk4MCB5MuXz67/rc5zK23atCE5OZnt27ez\nb98+Ll++THBwMC+++CJ9+vS56WrAIiIikvUM89+//hYRERERERFxAL2DKiIiIiIiItmCElQRERER\nERHJFpSgioiIiIiISLZwXyySFBMT4+gQRERERETumo+Pj6NDEMkWNIIqIiIiIiIi2YISVBERERER\nEckW7ospvjfK6OkRu3fvBtAeeTmQnl3OpWeXc+nZ5Vx6djmXnl3OpdfURNLSCKqIiIiIiIhkC0pQ\nRUREREREJFtQgioiIiIiIiLZghJUERERERERyRaUoIqIiIiIiEi2oARVREREREREsgUlqCIiIiIi\nN2G1mnyz3WT0F6ajQxF5YNx3+6CKiIiIiNyLy/EmEd/ChIVw+Aw4Waw87LmderXKkidPHkeHJ3Jf\n0wiqiIiIiAhwJsrk7ckmIU2g+0epySlAitVCq97fU7hwYb777jvHBilyn9MIqoiIiIg80HYeNBm3\nABZthJQU+zbzagxel+dTwHczh07H0Lx5cw4fPkxgYKBjghW5z2kEVUREREQeOFevmizaYFLtNZMq\nneHLdfbJaeEgK+bxnlh+KsTpLS35bd9qqlSpQmxsLNu3b3dc4CL3OY2gioiIiMgD41KsycyvYdJi\nOHUubXvNCtC7JTzzWCK5c0/CCsTExODl5UVsbCwATk5OWRu0yANECaqIiIiI3PeOnjWZsAg++wbi\n4u3bXJyhdR3o1RIqFDf+v9aTxo0bs3z5ckqWLImXlxcXL14kKCiImjVrZnn8Ig8KJagiIiIicl8y\nTZPNP8G4BfDVNjD/tVtMXl/o0hhebwJBeY00x8+aNYvExES+/fZbkpKSKFq0KIsXL8bX1zeL7kDk\nwaMEVURERETuK4lJJgvWpyam+46kbS9TGHq1gLbPgodb2sT0mjx58rBq1SqioqKIj48nJCRE03tF\nMpkSVBERERG5L/x90WTKcvh0GZw7n7a9fpXU90vrVALDuHVi+m8BAQEZGKWI3I4SVBERERHJ0c5F\nJ9G0505+OPkEpuFm1+bhBu3qQ8/mUKpQ+pNSEXEMJagiIiIikiNdjk/dv3T4jCSSzWpwQ/7p7fYP\n73TITdiL4OejxFQkp1CCKiIiIiI5SlKyybQV8EEERF0A8LK1FcjzN2d+7MOVf5bT6+u/8fT0dFic\nInLnLI4OQEREREQkPVJSTOZ+Z1KqDfQcey05TeVhnGHh+3ByZT78zO+4mnSF6Ojoe7rezz//zPDh\nw+8xahG5E0pQRURERCRbM02Tr7aaVOgA7YbDicjrbYF5kjGPvkrCD8U4+P17dOvWlfPnz5M7d24C\nAwPv+FrJycm2P4eEhDBu3DjOnj2bAXchIumhBFVEREREsq0t+0yqvw4vvg2/HL9e7+cDY7rDsSUu\nDHg1EGtKEkOGDGHKlClYLBY+/fRTXF1d7+haVquV8uXLc/jwYQAeeughvvvuO/z9/TPylkTkNvQO\nqoiIiIhkO/sOmwycCt/+YF/v5QF9WsKbrcEnV+riRx988AEVKlRgzZo1uLi40Lp1aLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71f5898>"
+       "<matplotlib.figure.Figure at 0x92f2c88>"
       ]
      },
      "metadata": {},
@@ -2824,7 +2827,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEmCAYAAAAA3HToAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x970bfd0>"
+       "<matplotlib.figure.Figure at 0x933dcc0>"
       ]
      },
      "metadata": {},
@@ -2868,16 +2871,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Exercise: Different Data Rates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is rare that two different sensor classes output data at the same rate. Assume that the position sensor produces an update at 1 Hz, and the wheel updates at 4 Hz. Design a filter that incorporates all of these measurements.\n",
+    "## Nonstationary Processes\n",
     "\n",
-    "**hint**: This is a difficult problem in that I have not explained how to do this. Think about which matrices incorporate time, and which incorporate knowledge about the number and kind of measurements. All of these will have to be designed to work with this problem. If you can correctly enumerate those matrices you are most of the way to a solution."
+    "So far we have assumed that the various matrices in the Kalman filter are *stationary* - nonchanging over time. For example, in the robot tracking section we assumed that $\\Delta t = 1.0$ seconds, and designed the state transition matrix to be\n",
+    "\n",
+    "$$\n",
+    "\\mathbf{F} = \\begin{bmatrix}1& \\Delta t& 0& 0\\\\0& 1& 0& 0\\\\0& 0& 1& \\Delta t\\\\ 0& 0& 0& 1\\end{bmatrix} = \\begin{bmatrix}1& 1& 0& 0\\\\0& 1& 0& 0\\\\0& 0& 1& 1\\\\ 0& 0& 0& 1\\end{bmatrix}$$\n",
+    "\n",
+    "But what if our data rate changes in some unpredictable manner? Or what if we have two sensors, each running at a different rate? What if the error of the measurement changes? \n",
+    "\n",
+    "Handling this is easy; you just alter the Kalman filter matrices to reflect the current situation. Let's go back to our dog tracking problem and assume that the data input is somewhat sporadic. For this problem we designed\n",
+    "\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\mathbf{\\bar{x}} &= {\\begin{bmatrix}x\\\\\\dot{x}\\end{bmatrix}}^- \\\\\n",
+    "\\mathbf{F} &= \\begin{bmatrix}1&\\Delta t  \\\\ 0&1\\end{bmatrix} \n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "and set the Kalman filter variable `F` during initialization like so:\n",
+    "\n",
+    "```python\n",
+    "dt = 0.1\n",
+    "kf.F = np.array([[1, dt],\n",
+    "                 [0, 1]])\n",
+    "```\n",
+    "\n",
+    "How would we handle $\\Delta t$ changing for each measurement? It's easy - just modify the relevant matrices. In this case `F` is variant, so we will need to update this inside the update/predict loop.  `Q` is also dependent on time, so it must be assigned during each loop as well. Here is an example of how we might code this:"
    ]
   },
   {
@@ -2886,16 +2905,50 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "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"
+     ]
+    }
+   ],
    "source": [
-    "#your solution here!"
+    "kf = KalmanFilter(dim_x=2, dim_z=1)\n",
+    "kf.x = array([0., 1.])\n",
+    "kf.H = array([[1, 0]])\n",
+    "kf.P = np.eye(2) * 50\n",
+    "kf.R = 1.\n",
+    "Q = 0.02\n",
+    "\n",
+    "# measurement tuple: (value, time)\n",
+    "zs = [(1., 1.),  (2., 1.1), (3., 0.9), (4.1, 1.23), (5.01, 0.97)]\n",
+    "for z, dt in zs:\n",
+    "    kf.F = array([[1, dt],\n",
+    "                  [0, 1]])\n",
+    "    kf.Q = Q_discrete_white_noise(dim=2, dt=dt, var=Q)\n",
+    "    kf.predict()\n",
+    "    kf.update(z)\n",
+    "    print(kf.x)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Solution"
+    "### Sensor fusion: Different Data Rates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is rare that two different sensor classes output data at the same rate. Assume that the position sensor produces an update at 1 Hz, and the wheel updates at 4 Hz. "
    ]
   },
   {
@@ -2924,7 +2977,7 @@
     "\n",
     "$$\\mathbf{F} = \\begin{bmatrix}1 & \\Delta t \\\\ 0& 1\\end{bmatrix}.$$\n",
     "\n",
-    "Since the wheel and position sensor reading coincide once every 4 readings we can just set $\\delta t =0.25$ and not modify it while filtering. If the readings did not coincide in each iteration you would have to calculate how much time has passed since the last predict, compute a new $\\mathbf{F}$ and $\\mathbf{Q}$, and then call `predict()` so the filter could make a correct prediction based on the time step required."
+    "Since the wheel and position sensor reading coincide once every 4 readings we can just set $\\Delta t =0.25$ and not modify it while filtering. If the readings did not coincide in each iteration you would have to calculate how much time has passed since the last predict, compute a new $\\mathbf{F}$ and $\\mathbf{Q}$, and then call `predict()` so the filter could make a correct prediction based on the time step required."
    ]
   },
   {
@@ -2945,7 +2998,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x85ccf98>"
+       "<matplotlib.figure.Figure at 0x968c5c0>"
       ]
      },
      "metadata": {},
@@ -3135,7 +3188,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAAERCAYAAAAgxPKmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7202b00>"
+       "<matplotlib.figure.Figure at 0x969e908>"
       ]
      },
      "metadata": {},
@@ -3392,7 +3445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 55,
    "metadata": {
     "collapsed": false
    },
@@ -3433,16 +3486,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b83198>"
+       "<matplotlib.figure.Figure at 0x9934908>"
       ]
      },
      "metadata": {},
@@ -3563,7 +3616,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9692b00>"
+       "<matplotlib.figure.Figure at 0x81ab9e8>"
       ]
      },
      "metadata": {},
@@ -3709,14 +3762,15 @@
    },
    "outputs": [
     {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8504a58>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "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"
+     ]
     }
    ],
    "source": [
@@ -3772,32 +3826,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9848eb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x96ce668>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def plot_ball_with_q(q, r=1., noise=0.3):\n",
     "    x, y = 0., 1.\n",
@@ -3845,32 +3878,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x97c8128>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x97499e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_ball_with_q(0.01, r=3, noise=3.)\n",
     "plot_ball_with_q(0.1, r=3, noise=3.)"