Updated to Python 3.6

10-Unscented-Kalman-Filter.ipynb

@@ -20,7 +20,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from __future__ import division, print_function\n",
     "%matplotlib inline"
    ]
   },
@@ -36,7 +35,7 @@
        "        <style>\n",
        "        .output_wrapper, .output {\n",
        "            height:auto !important;\n",
-       "            max-height:100000px; \n",
+       "            max-height:100000px;\n",
        "        }\n",
        "        .output_scroll {\n",
        "            box-shadow:none !important;\n",
@@ -80,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -122,7 +121,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -173,7 +172,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -218,12 +217,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in mean x=-0.157, y=43.481\n"
+      "Difference in mean x=0.254, y=42.846\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -293,7 +292,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -350,7 +349,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -414,7 +413,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -462,7 +461,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -508,7 +507,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -563,7 +562,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -752,7 +751,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -869,7 +868,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -931,7 +930,7 @@
     "                  [0,  1, 0,  0],\n",
     "                  [0,  0, 1, dt],\n",
     "                  [0,  0, 0,  1]])\n",
-    "    return np.dot(F, x)\n",
+    "    return F @ x\n",
     "\n",
     "def h_cv(x):\n",
     "    return x[[0, 2]]"
@@ -973,12 +972,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "UKF standard deviation 0.000 meters\n"
+      "UKF standard deviation 0.013 meters\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1008,7 +1007,7 @@
     "uxs = np.array(uxs)\n",
     "\n",
     "plt.plot(uxs[:, 0], uxs[:, 2])\n",
-    "print('UKF standard deviation {:.3f} meters'.format(np.std(uxs - xs)))"
+    "print(f'UKF standard deviation {np.std(uxs - xs):.3f} meters')"
    ]
   },
   {
@@ -1040,7 +1039,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1091,7 +1090,7 @@
     "    F = np.array([[1, dt, 0],\n",
     "                  [0,  1, 0],\n",
     "                  [0,  0, 1]], dtype=float)\n",
-    "    return np.dot(F, x)"
+    "    return F @ x"
    ]
   },
   {
@@ -1208,7 +1207,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1218,7 +1217,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1228,7 +1227,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1306,12 +1305,12 @@
      "output_type": "stream",
      "text": [
       "Actual altitude: 2515.6\n",
-      "UKF altitude   : 1042.0\n"
+      "UKF altitude   : 1042.1\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1342,8 +1341,8 @@
     "    xs.append(kf.x)\n",
     "\n",
     "plot_altitude(xs, time, ys)\n",
-    "print('Actual altitude: {:.1f}'.format(ac.pos[1]))\n",
-    "print('UKF altitude   : {:.1f}'.format(xs[-1][2]))"
+    "print(f'Actual altitude: {ac.pos[1]:.1f}')\n",
+    "print(f'UKF altitude   : {xs[-1][2]:.1f}')"
    ]
   },
   {
@@ -1379,7 +1378,7 @@
     "                  [0,  1, 0, 0],\n",
     "                  [0,  0, 1, dt],\n",
     "                  [0,  0, 0, 1]], dtype=float)\n",
-    "    return np.dot(F, x)\n",
+    "    return F @ x\n",
     "    \n",
     "def cv_UKF(fx, hx, R_std):\n",
     "    points = MerweScaledSigmaPoints(n=4, alpha=.1, beta=2., kappa=-1.)\n",
@@ -1388,7 +1387,7 @@
     "    kf.Q[0:2, 0:2] = Q_discrete_white_noise(2, dt=dt, var=0.1)\n",
     "    kf.Q[2:4, 2:4] = Q_discrete_white_noise(2, dt=dt, var=0.1)\n",
     "    kf.R = np.diag(R_std)\n",
-    "    kf.R = np.dot(kf.R, kf.R)  # square to get variance\n",
+    "    kf.R = kf.R @ kf.R  # square to get variance\n",
     "    kf.x = np.array([0., 90., 1100., 0.])\n",
     "    kf.P = np.diag([300**2, 3**2, 150**2, 3**2])\n",
     "    return kf"
@@ -1404,12 +1403,12 @@
      "output_type": "stream",
      "text": [
       "Actual altitude: 2515.6\n",
-      "UKF altitude   : 2499.7\n"
+      "UKF altitude   : 2500.1\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1436,8 +1435,8 @@
     "    xs.append(kf_cv.x)\n",
     "\n",
     "plot_altitude(xs, time, ys)\n",
-    "print('Actual altitude: {:.1f}'.format(ac.pos[1]))\n",
-    "print('UKF altitude   : {:.1f}'.format(xs[-1][2]))"
+    "print(f'Actual altitude: {ac.pos[1]:.1f}')\n",
+    "print(f'UKF altitude   : {xs[-1][2]:.1f}')"
    ]
   },
   {
@@ -1472,7 +1471,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1508,7 +1507,7 @@
     "\n",
     "xs = np.asarray(xs)\n",
     "plot_radar(xs, time, plot_x=False, plot_vel=True, plot_alt=False)\n",
-    "print('Velocity std {:.1f} m/s'.format(np.std(xs[10:, 1])))"
+    "print(f'Velocity std {np.std(xs[10:, 1]):.1f} m/s')"
    ]
   },
   {
@@ -1550,7 +1549,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1594,7 +1593,7 @@
     "    xs.append(kf_sf2.x)\n",
     "xs = np.asarray(xs)\n",
     "plot_radar(xs, time, plot_x=False, plot_vel=True, plot_alt=False)\n",
-    "print('Velocity std {:.1f} m/s'.format(np.std(xs[10:,1])))"
+    "print(f'Velocity std {np.std(xs[10:,1]):.1f} m/s')"
    ]
   },
   {
@@ -1624,7 +1623,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1731,7 +1730,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1800,7 +1799,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1843,7 +1842,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1893,7 +1892,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1931,7 +1930,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x500 with 2 Axes>"
       ]
@@ -2102,19 +2101,12 @@
      "text": [
       "cholesky:\n",
       "[[1.414 0.071]\n",
-      " [0.    1.731]]\n"
+      " [0.    1.731]]\n",
+      "\n",
+      "square of cholesky:\n",
+      "[[2.005 0.122]\n",
+      " [0.122 2.995]]\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[2.005, 0.122],\n",
-       "       [0.122, 2.995]])"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -2123,7 +2115,8 @@
     "s = scipy.linalg.cholesky(a)\n",
     "print(\"cholesky:\")\n",
     "print(s)\n",
-    "np.dot(s, s.T)"
+    "print(\"\\nsquare of cholesky:\")\n",
+    "print(s @ s.T)"
    ]
   },
   {
@@ -2338,7 +2331,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2348,7 +2341,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2358,7 +2351,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2430,12 +2423,12 @@
      "output_type": "stream",
      "text": [
       "Difference in position in meters:\n",
-      "\t [-1.0928  0.1593  0.3259 -0.2323 -0.1727]\n"
+      "\t [-1.4166 -0.2815  1.2679 -1.2405 -2.1863]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2445,7 +2438,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2455,7 +2448,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2640,7 +2633,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3055,7 +3048,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3067,7 +3060,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [0.0085 0.0177 0.0006]\n"
+      "Final P: [0.0092 0.0187 0.0007]\n"
      ]
     }
    ],
@@ -3140,7 +3133,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3152,7 +3145,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "final covariance [0.0012 0.0042 0.0003]\n"
+      "final covariance [0.0013 0.0043 0.0004]\n"
      ]
     }
    ],
@@ -3178,7 +3171,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3190,7 +3183,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "final covariance [0.0025 0.0654 0.0007]\n"
+      "final covariance [0.0026 0.0657 0.0008]\n"
      ]
     }
    ],