Fixed wording.

08-Designing-Kalman-Filters.ipynb

@@ -283,8 +283,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Author's note: clean up ball example, add control input example.**\n",
-    "\n",
     "In the last chapter we worked with 'textbook' problems. These are problems that are easy to state, program in a few lines of code, and teach. Real world problems are rarely 'textbook'. In this chapter, and the remainder of the book, we will work with more realistic examples. \n",
     "\n",
     "We will begin by tracking a robot in a 2D space, such as a field or warehouse. We will start with a simple noisy sensor that outputs noisy $(x,y)$ coordinates which we will need to filter to generate a 2D track. Once we have mastered this concept, we will extend the problem significantly with more sensors and then adding control inputs. \n",
@@ -307,7 +305,7 @@
     "\n",
     "Implementation of code to interact with real sensors is beyond the scope of this book, so as before we will program simple simulations of the sensors. We will develop several of these sensors as we go, each with more complications, so as I program them I will just append a number to the function name. \n",
     "\n",
-    "So let's start with a very simple sensor, one that travels in a straight line. It is initialized with the initial position, velocity, and noise standard deviation. Each call to `read()` updates the position by one time step and returns the new measurement."
+    "So let's start with a very simple sensor, one that simulates tracking an object traveling in a straight line. It is initialized with the initial position, velocity, and noise standard deviation. Each call to `read()` updates the position by one time step and returns the new measurement."
    ]
   },
   {
@@ -364,9 +362,9 @@
     },
     {
      "data": {
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+      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6dde4e0>"
+       "<matplotlib.figure.Figure at 0x7106cc0>"
       ]
      },
      "metadata": {},
@@ -710,9 +708,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ec82e8>"
+       "<matplotlib.figure.Figure at 0x83c7978>"
       ]
      },
      "metadata": {},
@@ -786,9 +784,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f397b8>"
+       "<matplotlib.figure.Figure at 0x843bcf8>"
       ]
      },
      "metadata": {},
@@ -852,12 +850,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 500.  500.  500.  500.]\n"
+      "[ 0.25675401  0.10095634  0.25675401  0.10095634]\n"
      ]
     }
    ],
    "source": [
-    "print(np.diag(tracker.P))"
+    "print(np.diag(robot_tracker.P))"
    ]
   },
   {
@@ -878,15 +876,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 500.    0.]\n",
-      " [   0.  500.]]\n"
+      "[[ 0.25675401  0.10192183]\n",
+      " [ 0.10192183  0.10095634]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7eaaf60>"
+       "<matplotlib.figure.Figure at 0x83c7240>"
       ]
      },
      "metadata": {},
@@ -894,7 +892,7 @@
     }
    ],
    "source": [
-    "c = tracker.P[0:2, 0:2]\n",
+    "c = robot_tracker.P[0:2, 0:2]\n",
     "print(c)\n",
     "plot_covariance_ellipse((0, 0), cov=c, fc='g', alpha=0.2)"
    ]
@@ -928,7 +926,7 @@
     }
    ],
    "source": [
-    "tracker.P[2:4, 0:2]"
+    "robot_tracker.P[2:4, 0:2]"
    ]
   },
   {
@@ -996,7 +994,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7eaac88>"
+       "<matplotlib.figure.Figure at 0x84c8b38>"
       ]
      },
      "metadata": {},
@@ -1142,7 +1140,7 @@
    "outputs": [],
    "source": [
     "def FirstOrderKF(R, Q, dt):\n",
-    "    \"\"\" Create zero order Kalman filter. \n",
+    "    \"\"\" Create first order Kalman filter. \n",
     "    Specify R and Q as floats.\"\"\"\n",
     "    kf = KalmanFilter(dim_x=2, dim_z=1)\n",
     "    kf.x = np.zeros(2)\n",
@@ -1208,7 +1206,7 @@
    "outputs": [],
    "source": [
     "def SecondOrderKF(R_std, Q, dt, P=100):\n",
-    "    \"\"\" Create zero order Kalman filter. \n",
+    "    \"\"\" Create second order Kalman filter. \n",
     "    Specify R and Q as floats.\"\"\"\n",
     "    kf = KalmanFilter(dim_x=3, dim_z=1)\n",
     "    kf.x = np.zeros(3)\n",
@@ -1310,9 +1308,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8050dd8>"
+       "<matplotlib.figure.Figure at 0x855d668>"
       ]
      },
      "metadata": {},
@@ -1364,7 +1362,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7facb38>"
+       "<matplotlib.figure.Figure at 0x84c2b38>"
       ]
      },
      "metadata": {},
@@ -1397,7 +1395,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f05dd8>"
+       "<matplotlib.figure.Figure at 0x84246a0>"
       ]
      },
      "metadata": {},
@@ -1428,9 +1426,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAEmCAYAAAANwgSvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAEmCAYAAAANwgSvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f99fd0>"
+       "<matplotlib.figure.Figure at 0x84c3eb8>"
       ]
      },
      "metadata": {},
@@ -1467,7 +1465,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAE8CAYAAACvjyVSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa3358>"
+       "<matplotlib.figure.Figure at 0x95b0828>"
       ]
      },
      "metadata": {},
@@ -1506,9 +1504,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x914c128>"
+       "<matplotlib.figure.Figure at 0x967f748>"
       ]
      },
      "metadata": {},
@@ -1543,7 +1541,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90efd68>"
+       "<matplotlib.figure.Figure at 0x96283c8>"
       ]
      },
      "metadata": {},
@@ -1582,7 +1580,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAE6CAYAAABOPV5SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91c48d0>"
+       "<matplotlib.figure.Figure at 0x965ee10>"
       ]
      },
      "metadata": {},
@@ -1623,9 +1621,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90e4a90>"
+       "<matplotlib.figure.Figure at 0x96b2908>"
       ]
      },
      "metadata": {},
@@ -1656,9 +1654,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x912cf60>"
+       "<matplotlib.figure.Figure at 0x96b2d30>"
       ]
      },
      "metadata": {},
@@ -1666,9 +1664,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f05080>"
+       "<matplotlib.figure.Figure at 0x83acef0>"
       ]
      },
      "metadata": {},
@@ -1684,7 +1682,7 @@
     "\n",
     "plot_kf_output(xs500, filter_xs2, zs500)\n",
     "plot_residuals(xs500[:, 0], filter_xs2[:, 0], ps2[:, 0], \n",
-    "               'Zero Order Position Residuals',\n",
+    "               'Second Order Position Residuals',\n",
     "               'meters') "
    ]
   },
@@ -1708,9 +1706,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f05a58>"
+       "<matplotlib.figure.Figure at 0x84bed30>"
       ]
      },
      "metadata": {},
@@ -1718,9 +1716,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80382e8>"
+       "<matplotlib.figure.Figure at 0x7842cf8>"
       ]
      },
      "metadata": {},
@@ -1736,7 +1734,7 @@
     "\n",
     "plot_kf_output(xs2000, filter_xs2, zs2000)\n",
     "plot_residuals(xs2000[:, 0], filter_xs2[:, 0], ps2[:, 0], \n",
-    "               'Seceond Order Position Residuals',\n",
+    "               'Second Order Position Residuals',\n",
     "               'meters') "
    ]
   },
@@ -1762,7 +1760,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8058358>"
+       "<matplotlib.figure.Figure at 0x85a89e8>"
       ]
      },
      "metadata": {},
@@ -1803,7 +1801,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAAEWCAYAAAAuFoLqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x807f860>"
+       "<matplotlib.figure.Figure at 0x96ad048>"
       ]
      },
      "metadata": {},
@@ -1857,9 +1855,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80a3ba8>"
+       "<matplotlib.figure.Figure at 0x783c6a0>"
       ]
      },
      "metadata": {},
@@ -1869,7 +1867,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e67e48>"
+       "<matplotlib.figure.Figure at 0x84efeb8>"
       ]
      },
      "metadata": {},
@@ -1907,9 +1905,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAAEmCAYAAAC03xeLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAAEmCAYAAAC03xeLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd0b70>"
+       "<matplotlib.figure.Figure at 0x84efef0>"
       ]
      },
      "metadata": {},
@@ -1919,7 +1917,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAE6CAYAAAB51sZPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92939b0>"
+       "<matplotlib.figure.Figure at 0x985c3c8>"
       ]
      },
      "metadata": {},
@@ -1952,9 +1950,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91ffac8>"
+       "<matplotlib.figure.Figure at 0x9737c50>"
       ]
      },
      "metadata": {},
@@ -1962,9 +1960,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91ff518>"
+       "<matplotlib.figure.Figure at 0x849e748>"
       ]
      },
      "metadata": {},
@@ -1977,7 +1975,7 @@
     "\n",
     "plot_kf_output(xs, fxs1, zs, aspect_equal=False)\n",
     "plot_residuals(xs[:, 0], fxs1[:, 0], ps2[:, 0], \n",
-    "               'Second Order Position Residuals',\n",
+    "               'First Order Position Residuals',\n",
     "               'meters') "
    ]
   },
@@ -2126,7 +2124,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7380b70>"
+       "<matplotlib.figure.Figure at 0x8404390>"
       ]
      },
      "metadata": {},
@@ -2167,7 +2165,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9174550>"
+       "<matplotlib.figure.Figure at 0x849e5f8>"
       ]
      },
      "metadata": {},
@@ -2264,9 +2262,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x956a518>"
+       "<matplotlib.figure.Figure at 0x966de48>"
       ]
      },
      "metadata": {},
@@ -2304,18 +2302,20 @@
     "        zs.append(z.T[0])\n",
     "        nom.append(i)\n",
     "\n",
-    "    xs = asarray(xs)\n",
-    "    zs = asarray(zs)\n",
-    "    nom = asarray(nom)\n",
+    "    xs = array(xs)\n",
+    "    zs = array(zs)\n",
+    "    nom = array(nom)\n",
     "    \n",
     "    res = nom - xs[:, 0]\n",
     "    print('fusion std: {:.3f}'.format(np.std(res)))\n",
     "    if do_plot:\n",
-    "        bp.plot_measurements(zs[:, 0], label='Wheel')\n",
-    "        plt.plot(zs[:, 1], ls='--', label='Pos Sensor')\n",
-    "        bp.plot_filter(xs[:, 0], label='Kalman filter')\n",
+    "        ts = np.arange(0.1, 10, .1)\n",
+    "        bp.plot_measurements(ts, zs[:, 0], label='Wheel')\n",
+    "        plt.plot(ts, zs[:, 1], ls='--', label='Pos Sensor')\n",
+    "        bp.plot_filter(ts, xs[:, 0], label='Kalman filter')\n",
     "        plt.legend(loc=4)\n",
     "        plt.ylim(0, 100)\n",
+    "        bp.set_labels(x='time (sec)', y='meters')\n",
     "\n",
     "fusion_test(1.5, 3.0)"
    ]
@@ -2347,7 +2347,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91ba8d0>"
+       "<matplotlib.figure.Figure at 0x84715f8>"
       ]
      },
      "metadata": {},
@@ -2381,9 +2381,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91ba748>"
+       "<matplotlib.figure.Figure at 0x9628198>"
       ]
      },
      "metadata": {},
@@ -2433,9 +2433,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91e0be0>"
+       "<matplotlib.figure.Figure at 0x980b128>"
       ]
      },
      "metadata": {},
@@ -2519,9 +2519,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e15940>"
+       "<matplotlib.figure.Figure at 0x713d550>"
       ]
      },
      "metadata": {},
@@ -2529,9 +2529,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91ffbe0>"
+       "<matplotlib.figure.Figure at 0x9737358>"
       ]
      },
      "metadata": {},
@@ -2539,9 +2539,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x919b710>"
+       "<matplotlib.figure.Figure at 0x96f5e80>"
       ]
      },
      "metadata": {},
@@ -2598,9 +2598,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4FNXbxvHvpIcWamhSpHcRpEkREEKVoiC9iqh0EPhR\nRCJNmqBSBKRKU0AE5AUJSARp0qSEElqC1NBCkJI+7x8rG1aKCSmbkPtzXbmSc2Z25lknhtw5M+cY\npmmaiIiIiIiIiNiZg70LEBEREREREQEFVBEREREREUkmFFBFREREREQkWVBAFRERERERkWRBAVVE\nRERERESSBQVUERERERERSRYUUEVERERERCRZUEAVEUmlHBwccHB48j8DgYGBFC1aFAcHB/r27cvD\nJbNr1qxpfd2TPpo3b56Ub0FEREReME72LkBEROzHMIzH+g4fPkyDBg0ICgri888/53//+99j+3Tu\n3Jn8+fM/1l+sWLHEKFNERERSCQVUERGx8vX1pVmzZoSGhrJw4UI6dOjwxP06d+5MjRo1krg6ERER\nedHpFl8REQFgxYoV1K9fH9M0+fnnn58aTkVEREQSiwKqiIgwbdo02rRpQ6ZMmfD19cXLy+uZ+z98\nJlVEREQkIekWXxGRVG7o0KFMmDCBggULsmnTJgoUKPCfr1m4cCG+vr42fYZhMGTIEFxdXROrVBER\nEXnBGab+DC4ikio9OoOvs7Mzx44do1ChQs98Tc2aNdm+ffsTtxmGQXBwMBkyZEjQOkVERCT10C2+\nIiKpXP369YmIiKBt27YEBwfH6jW//fYb0dHRNh9RUVEKpyIiIhIvCqgiIqmYYRisW7eOZs2asX//\nfmrWrMn169ftXZaIiIikUgqoIiKpnJOTEytXrqRt27YcPXqUGjVqcOnSJXuXJSIiIqmQAqqIiODo\n6MjixYvp1q0b/v7+1KhRg8DAQHuXJSIiIqmMAqqIiACW233nzJlD3759CQgIoHr16pw6dcreZYmI\niEgqooAqIiI2pk6dytChQ7l06RJvvPEGfn5+9i5JREREUgkFVBEReczYsWMZO3YsQUFB1KpVi4MH\nDwKWUVbDMOxcnYiIiLyotA6qiIiIiIiIJAsaQRUREREREZFkQQFVREREREREkgUFVBEREREREUkW\nnOxdQEIICQmxdwkiIiIiIs/Nw8PD3iWIJAsaQRUREREREZFkQQFVREREREREkoUX4hbfRyX07RH7\n9+8H4LXXXkvQ40ri07VLuXTtUi5du5RL1y7l0rVLufSYmsjjNIIqIiIiIiIiyYICqoiIiIiIiCQL\nCqgiIiIiIiKSLCigioiIiIiISLKggCoiIiIiIiLJggKqiIiIiIiIJAsKqCIiIiKSooWFhTF06FDK\nly9P5cqVmT59OqZp2rssEXkOL9w6qCIiIiKSepimSatWrVi7dq21748//iAoKIjRo0fbsTIReR4a\nQRURERGRFOvgwYOsXbuWdOnSsX79eubOnQvA+PHjuX//vp2rE5G4UkAVERERkRTr8uXLAFSoUIFG\njRrRtWtXsmTJQmRkJDdu3LBzdSISV7rFV0RERERSrOLFiwOwbds2hg8fzpUrV7h58yYZM2YkZ86c\ndq5OROJKAVVEREREUqxChQrh7e2Nt7c348aNA8DR0ZE5c+bg7Owcq2NERZkE/w03Q+DmHbhx+/HP\n0weAm6uRmG9FRFBAFREREZEUbuTIkZQrVw4fHx8cHF2p/1Zb8hV4le2HTG6GwI0QrJ9vPfL1w8/B\nf8N/Tfrr/R685Jk070ckNVNAFREREZEUzyFLY7bcaIz/XzBtX8If/2aIAqpIUlBAFREREZEUKzzC\nZOgsmPp9/I7jkQ6yekAWj5jPWR5p58yaMPWKyLMpoIqIiIhIinTukkmbkbDvREyfYUDmDJAlA2TN\naPn877Bp8zkjZE4PTk56vlQkOVBAFREREZEU54ctJh9MhDv3YvoavQ4LhkPWjAkTNidMmECFChWo\nXbt2ghxPRP6b1kEVERERkRTjfqhJ9wmWkdOH4dTZCSb3hnUT4x9OHzx4YP06Z86cjB07Nl7HE5G4\nUUAVERERkRTheIBJpW4wd11MX4FcsGMWDGhtYBjxC6c7d+6kZs2amP9M6dumTRumTZsWr2OKSNwo\noIqIiIhIsmaaJvN+NqnwHhwLiOlv9SYcWAAVij9fMDVNk61btxIdHQ1AlSpViIyM5MyZMwA4OztT\nokSJeNcvIrGngCoiIiIiydadeybtP4P3x8ODMEufmwvM/h8s+ww80sVv1HTQoEH8/PPPADg4OLB3\n714KFy4c37JF5DlpkiQRERERSZYOnLQ8a3rmYkxfifzw/WgoVeD5gumGDRuIiorirbfewjAMRo8e\nTUhIiHW7o6NjPKsWkfhQQBURERGRZMU0Tb5eCYNnQERkTH/XxvB1f0jjFrdwapqm9flUBwcHBg8e\nTKNGjXBwcKBhw4YJWbqIxJMCqoiIiIgkGzdDTN4bB+t2xPSlTwOzBkObunEfNb18+TJNmzZl9+7d\nODk5Ua9ePSIiIhKwYhFJSHoGVURERESSzKFDh1i0aBEbNmwgPDzcZtuOwyavdrYNp+WLWiZCiks4\nvXjxIqGhoQDkypULd3d3Nm/eDIBhGLz11ls4OMTu1+C9e/fSu3dvunXrxrJly6wz/IpI4tAIqoiI\niIgkiZEjRzJq1Chru3z58vj4+ODhkYnxS8B7HkRFxezf910Y/xG4usRt5LRHjx40btyY7t27A7B+\n/XoyZMgQ53rXrFlDixYtiPqnqHnz5vHHH3/w1VdfxflYIhI7GkEVERERkUS3efNmRo0ahaOjI82b\nNydXrlwcOHCA7j0/pf4AGDEnJpxmzgBrxsPUvkaswqmfnx+rV6+2tgcPHmxdKgZ4rnBqmiYffPAB\nUVFRdOnShbFjx+Li4sLXX3/Nn3/+GefjiUjsKKCKiIiISKLbvn07AL169WL16tWWpV0yerEm4FN+\n3R+zX7Uy8OdCaFL92cH00VttIyMj6d27t/WW4WrVqjFx4sR41Xvr1i2uXbuGm5sb8+bNY9iwYdSr\nVw8Af3//eB1bRJ4u2QTUzz//HAcHB3r37m3T7+3tTe7cuUmTJg21atXi+PHjdqpQRERERJ5X2rRp\nATh58iT37oczYq4TRolfiHbMBoBhwPBOsHUa5Mn+7HD64MEDypcvz927dwEoW7YsY8aMSdDJjzJm\nzEjatGkJDQ1lypQprFu3zhqyc+fOnWDnERFbySKg7tmzh2+//ZYyZcpYpwAHmDBhAlOmTGH69Ons\n27cPT09P6tata/1hJCIiIiIpw7vvvkv69OnZ9Ntp0lfez8bDpa3bcmQBny9hdHcDJ6cnh9PAwEBu\n3boFgLu7OwUKFGDFihXW7V26dLGG4ITg6OjI6NGjARg4cCBNmzYlJCSEBg0aULVq1QQ7j4jYsntA\nDQkJoX379ixYsIBMmTJZ+03T5Msvv2To0KE0b96ckiVLsmjRIv7++2+WLVtmx4pFREREJK5y5n6Z\nlgP8cCh3DNJXsfbXrWi5pffN1549ajpu3DimT59ubc+dO5fOnTsnUrUW/fr147vvvqNWrVpUrlyZ\noUOHsnr16ljPACwicWf3/7u6d+9Oy5YteeONN2yeJQgICCAoKAgvLy9rn5ubGzVq1GDXrl32KFVE\nREREnsP/7TIp3R4WbM6DabgC4Oho8vlHsPELyJ758XB65MgRZs6caW3379+f4OBgaztjxoyJHhQN\nw6BDhw5s3bqV3bt3M27cONzc3BL1nCKpnV0D6rfffsu5c+cYM2YMgM3tvVevXgUge/bsNq/x9PS0\nbhMRERFJzS5fvszixYv55ptv2LBhg73LeUzAZZNm/zN5axCcuxzT/1ox2D3b4H/tDRwcYn7/i4yM\ntH6dOXNmPvnkE27fvg1A8eLFmTp1apLVLiL2Ybd1UP39/Rk+fDg7duzA0dERsNzWG5vFjx8Nsv+2\nf//+p26Lj8Q6riQ+XbuUS9cu5dK1S7l07VKO06dP89FHHxESEgLA/PnzadOmDQMGDLBzZRAWYbBk\na3YWbs5JWETMeEiGNJH0aHyJplVuwD149NstMjKSFi1aMHfuXLJmzQpYJsv08/N7YUctCxcubO8S\nRJIdu42g7t69mxs3blCyZEmcnZ1xdnZm+/btzJw5ExcXF+sPpqCgIJvXBQUFkSNHDnuULCIiIpJs\njB07lpCQEMqWLUvbtm1xcXFh+fLlHDhwwK517TqegTbjSzB7Q26bcNq0ynVWDffj7ao3cPyn+8yZ\nM9Y745ycnHj99dfx9fW1vub1119/YcOpiDyZ3UZQmzdvTsWKFa1t0zTp0qULRYoUYdiwYRQuXJgc\nOXLg4+ND+fLlAQgNDWXHjh1Mnjz5qcd97bXXErTOh39JTujjSuLTtUu5dO1SLl27lEvXLuU5deoU\nAJMnT8bDwwNXV1cWLFhAaGioXa5j4BWTAV/Dmu22/eWKwvQBULmUJ+Bps23NmjUEBwczY8YMAJYs\nWYKLi0sSVWx/D0e/RSSG3UZQPTw8KFGihPWjZMmSpEmThkyZMlGiRAkMw6Bfv35MmDCBn376CT8/\nPzp37kz69Olp27atvcoWERERSRayZbOsH7px40bOnDnDjh07AKx3oSWVsHCTsYtMSrazDacZ08OM\ngfDHt1C5lOXxrMOHDzN48GDrPr169cLDw8PaTk3hVESezG4jqE9iGIbN86WDBw/mwYMH9OzZk+Dg\nYCpXroyPj0+CrnElIiIikhINHjyYfv368cUXX1j7ihYtStOmTZOshk1/mPSZCqcv2PZ3aQzjP4Rs\nmQwuXLhAnjx5AHj55ZeZO3cuffr04aWXXiJHjhyMGzcuyeoVkeQvWQXUR585eGjkyLine truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92d9358>"
+       "<matplotlib.figure.Figure at 0x96f51d0>"
       ]
      },
      "metadata": {},
@@ -2608,9 +2608,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90b3e10>"
+       "<matplotlib.figure.Figure at 0x96f5160>"
       ]
      },
      "metadata": {},
@@ -2618,9 +2618,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f53dd8>"
+       "<matplotlib.figure.Figure at 0x84dda20>"
       ]
      },
      "metadata": {},
@@ -2727,9 +2727,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91ba5f8>"
+       "<matplotlib.figure.Figure at 0x8464278>"
       ]
      },
      "metadata": {},
@@ -2751,14 +2751,15 @@
     "nom = range(1, 100)\n",
     "zs = np.array([i + randn()*wheel_sigma for i in nom])\n",
     "xs, _, _, _ = kf.batch_filter(zs)\n",
+    "ts = np.arange(0.1, 10, .1)\n",
     "\n",
     "res = nom - xs[:, 0]\n",
     "print('std: {:.3f}'.format(np.std(res)))\n",
     "\n",
-    "bp.plot_filter(xs[:, 0], label='Kalman filter')\n",
-    "bp.plot_measurements(zs, label='Wheel')\n",
-    "plt.legend(loc=4)\n",
-    "plt.xlim((-1, 100));"
+    "bp.plot_filter(ts, xs[:, 0], label='Kalman filter')\n",
+    "bp.plot_measurements(ts, zs, label='Wheel')\n",
+    "bp.set_labels(x='time (sec)', y='meters')\n",
+    "plt.legend(loc=4);"
    ]
   },
   {
@@ -2849,9 +2850,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91f9470>"
+       "<matplotlib.figure.Figure at 0x78210b8>"
       ]
      },
      "metadata": {},
@@ -2873,10 +2874,7 @@
     "    kf.R[1, 1] = ps_sigma**2 \n",
     "\n",
     "    random.seed(1123)\n",
-    "    xs = []\n",
-    "    zs_wheel = []\n",
-    "    zs_ps = []\n",
-    "    nom = []\n",
+    "    xs, zs_wheel, zs_ps, nom = [], [], [], []\n",
     "    for i in range(1, 101):\n",
     "        if i % 4 == 0:\n",
     "            m0 = i + randn()*wheel_sigma\n",
@@ -2888,7 +2886,7 @@
     "            R[1, 1] = ps_sigma**2      \n",
     "            \n",
     "            zs_wheel.append(m0)\n",
-    "            zs_ps.append((i, m1))\n",
+    "            zs_ps.append((i*dt, m1))\n",
     "        else:  \n",
     "            m0 = i + randn()*wheel_sigma\n",
     "            z = array([m0])\n",
@@ -2906,11 +2904,13 @@
     "    res = nom - xs[:, 0]\n",
     "    print('fusion std: {:.3f}'.format(np.std(res)))\n",
     "    if do_plot:\n",
-    "        bp.plot_measurements(zs_wheel,  label='Wheel')\n",
+    "        ts = np.arange(0, 25, .25)\n",
+    "        bp.plot_measurements(ts, zs_wheel,  label='Wheel')\n",
     "        plt.plot(*zip(*zs_ps), ls='--', label='Pos Sensor')\n",
-    "        bp.plot_filter(xs[:, 0], label='Kalman filter')\n",
+    "        bp.plot_filter(ts,xs[:, 0], label='Kalman filter')\n",
     "        plt.legend(loc=4)\n",
     "        plt.ylim(0, 100)\n",
+    "        bp.set_labels(x='time (sec)', y='meters')\n",
     "\n",
     "fusion_test(1.5, 3.0);"
    ]
@@ -3049,7 +3049,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8058518>"
+       "<matplotlib.figure.Figure at 0x714f160>"
       ]
      },
      "metadata": {},
@@ -3099,7 +3099,7 @@
     "\n",
     "$$\\begin{aligned}\n",
     "x_t &=  x_{t-1} + v_{x(t-1)} {\\Delta t} \\\\\n",
-    "v_{xt} &= vx_{t-1}\n",
+    "v_{xt} &= v_{x(t-1)}\n",
     "\\\\\n",
     "y_t &= y_{t-1} + v_{y(t-1)} {\\Delta t}\\\\\n",
     "v_{yt} &= -g {\\Delta t} + v_{y(t-1)} \\\\\n",
@@ -3360,7 +3360,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x930c780>"
+       "<matplotlib.figure.Figure at 0x9765630>"
       ]
      },
      "metadata": {},
@@ -3483,7 +3483,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92eb6a0>"
+       "<matplotlib.figure.Figure at 0x980d160>"
       ]
      },
      "metadata": {},
@@ -3582,7 +3582,6 @@
     "\n",
     "        self.x = x0\n",
     "        self.y = y0\n",
-    "\n",
     "        self.noise = noise\n",
     "\n",
     "\n",
@@ -3632,9 +3631,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x806ebe0>"
+       "<matplotlib.figure.Figure at 0x9ba6898>"
       ]
      },
      "metadata": {},
@@ -3642,8 +3641,8 @@
     }
    ],
    "source": [
-    "y = 1.\n",
-    "x = 0.\n",
+    "x, y = 0, 1.\n",
+    "\n",
     "theta = 35. # launch angle\n",
     "v0 = 50.\n",
     "dt = 1/10.   # time step\n",
@@ -3653,24 +3652,22 @@
     "f1 = ball_kf(x, y, theta, v0, dt, r=1.)\n",
     "f2 = ball_kf(x, y, theta, v0, dt, r=10.)\n",
     "t = 0\n",
-    "xs = []\n",
-    "ys = []\n",
-    "xs2 = []\n",
-    "ys2 = []\n",
+    "xs, ys = [], []\n",
+    "xs2, ys2 = [], []\n",
     "\n",
-    "while f1.x[2, 0] > 0:\n",
+    "while f1.x[2] > 0:\n",
     "    t += dt\n",
     "    x, y = ball.update(dt)\n",
     "    z = np.array([[x, y]]).T\n",
     "\n",
     "    f1.update(z)\n",
     "    f2.update(z)\n",
-    "    xs.append(f1.x[0, 0])\n",
-    "    ys.append(f1.x[2, 0])\n",
-    "    xs2.append(f2.x[0, 0])\n",
-    "    ys2.append(f2.x[2, 0])    \n",
-    "    f1.predict() \n",
-    "    f2.predict()\n",
+    "    xs.append(f1.x[0])\n",
+    "    ys.append(f1.x[2])\n",
+    "    xs2.append(f2.x[0])\n",
+    "    ys2.append(f2.x[2])    \n",
+    "    f1.predict(u=-g) \n",
+    "    f2.predict(u=-g)\n",
     "    \n",
     "    p1 = plt.scatter(x, y, color='r', marker='o', s=75)\n",
     "\n",
@@ -3703,9 +3700,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4fd68>"
+       "<matplotlib.figure.Figure at 0x9735b00>"
       ]
      },
      "metadata": {},
@@ -3713,9 +3710,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x801c5c0>"
+       "<matplotlib.figure.Figure at 0x95fd358>"
       ]
      },
      "metadata": {},
@@ -3724,8 +3721,7 @@
    ],
    "source": [
     "def plot_ball_with_q(q, r=1., noise=0.3):\n",
-    "    y = 1.\n",
-    "    x = 0.\n",
+    "    x, y = 0., 1.\n",
     "    theta = 35. # launch angle\n",
     "    v0 = 50.\n",
     "    dt = 1/10.   # time step\n",
@@ -3737,18 +3733,17 @@
     "                        noise=[noise,noise])\n",
     "    f1 = ball_kf(x, y, theta, v0, dt, r=r, q=q)\n",
     "    t = 0\n",
-    "    xs = []\n",
-    "    ys = []\n",
+    "    xs, ys = [], []\n",
     "\n",
-    "    while f1.x[2, 0] > 0:\n",
+    "    while f1.x[2] > 0:\n",
     "        t += dt\n",
     "        x, y = ball.update(dt)\n",
     "        z = np.array([[x, y]]).T\n",
     "\n",
     "        f1.update(z)\n",
-    "        xs.append(f1.x[0, 0])\n",
-    "        ys.append(f1.x[2, 0]) \n",
-    "        f1.predict() \n",
+    "        xs.append(f1.x[0])\n",
+    "        ys.append(f1.x[2]) \n",
+    "        f1.predict(u=-g) \n",
     "\n",
     "        p1 = plt.scatter(x, y, color='r', marker='o', s=75)\n",
     "\n",
@@ -3778,9 +3773,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90d1c88>"
+       "<matplotlib.figure.Figure at 0x95b24e0>"
       ]
      },
      "metadata": {},
@@ -3788,9 +3783,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x98040b8>"
+       "<matplotlib.figure.Figure at 0x9ae7978>"
       ]
      },
      "metadata": {},
@@ -3857,7 +3852,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,