Updated to Python 3.6

07-Kalman-Filter-Math.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",
@@ -1209,7 +1208,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu0AAAFfCAYAAAD6VPKEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAA9hAAAPYQGoP6dpAABgg0lEQVR4nO3dd3iV9f3/8ec5ycneCQmBDLYsRQjIKkNlKysnjjqqrbW1WqtSa6Valdaq/X2rUutAW5Uq0qo57I2DISDI3hsSCAkZkEXWSc79+yNwakwYOQk5Ga/HdXGV8zn3Off7fvfO8ZWbz/ncJsMwDEREREREpNEyu7sAERERERG5NIV2EREREZFGTqFdRERERKSRU2gXEREREWnkFNpFRERERBo5hXYRERERkUZOoV1EREREpJHzdHcBV8LhcHDq1CkCAwMxmUzuLkdEREREpM4Mw6CgoIA2bdpgNl/6WnqTCO2nTp0iNjbW3WWIiIiIiNS7EydOEBMTc8ltmkRoDwwMBCoPKCgoqMH3b7fbWbFiBaNGjcJisTT4/psy9c516p3r1Lu6Uf9cp965Tr1znXpXN+7sX35+PrGxsc6seylNIrRfmBITFBTkttDu5+dHUFCQfhhqSb1znXrnOvWubtQ/16l3rlPvXKfe1U1j6N+VTP/WF1FFRERERBo5hXYRERERkUZOoV1EREREpJFTaBcRERERaeQU2kVEREREGjmFdhERERGRRk6hXURERESkkVNoFxERERFp5BTaRUREREQaOYV2EREREWmxSuwV7i7hiii0i4iIiEiLtOdUHmP/sZ7NWSZ3l3JZCu0iIiIi0uLM25aG9Z31nDxbzBdpZiochrtLuiRPdxcgIiIiItJQ7BUOXlqyjw/XHQdgWOcIxoRk4GFu3FfbdaVdRERERFqErIJS7v7XRmdgf/SmTrx7T2/8msBl7CZQooiIiIhI3WxNPcuvZm3hdH4pAd6evHp7L0b3aI3dbnd3aVdEoV1EREREmrXZG1N5YcEeyiocdIoM4N17E+jYKsDdZdWKQruIiIiINEul5RU8P38P//3uBABjerTmb7f3IsC76UXgplexiIiIiMhlnMot5lefbGXHiVxMJvjd6Gv41bCOmEyN+wunF6PQLiIiIiLNyrdHc3jkk63knCsjxM/CG3f2ZmiXVu4uq04U2kVERESkWTAMgw/WHeelJfuocBh0jw7i3XsTiA3zc3dpdabQLiIiIiJNXnFZBU/P2cn87acAmNy7LS9NvhZfLw83V1Y/FNpFREREpElLzSniFx9vZn9GAR5mE8/e0o37B7VrsvPXa6LQLiIiIiJN1qoDmTz23+3kFduJCPDirbv60L9DuLvLqncK7SIiIiLS5DgcBu+sPsLfVhzAMOD62BBm3JNA62Afd5d2VSi0i4iIiEiTUlBi57ef7WDF3tMA/PiGOF6Y0B1vz+Yxf70mCu0iIiIi0mQczizklx9v5kjWObw8zPxpYg/uvCHO3WVddQrtIiIiItIkLN+TwW8/20FhaTnRwT68c08C18eGuLusBqHQLiIiIiKNWoXD4LWVB3jr6yMA9G8fxlt39yEiwNvNlTUchXYRERERabRyi8r4zX+3s+ZgFgA/G9yeqeO6YvEwu7myhqXQLiIiIiKN0t5T+fxy1mZOnCnGx2Lmr9brmHh9W3eX5Ra1+hXl5Zdfpl+/fgQGBhIZGcmkSZM4cODAJV+zatUqTCZTtT/79++vU+EiIiIi0nzN355G4jvrOHGmmNgwX+b8anCLDexQyyvtq1ev5pFHHqFfv36Ul5fzzDPPMGrUKPbu3Yu/v/8lX3vgwAGCgoKcj1u1auVaxSIiIiLSbNkrHLy8ZD8frDsGwNAurXjjzusJ8fNyc2XuVavQvmzZsiqPP/zwQyIjI9myZQtDhw695GsjIyMJCQmpdYEiIiIi0jJkFZTy69lb2XjsDAC/vrETT4zsgofZ5ObK3K9Oc9rz8vIACAsLu+y2vXv3pqSkhO7du/Pss89y4403XnTb0tJSSktLnY/z8/MBsNvt2O32upTskgv7dMe+mzr1znXqnevUu7pR/1yn3rlOvXNdc+ndjpN5PPKf7ZzOL8Xf24P/l9iTUd2jcFSU46i4evt1Z/9qs0+TYRiGKzsxDIOJEydy9uxZ1q5de9HtDhw4wJo1a0hISKC0tJSPP/6YGTNmsGrVqotenX/hhReYNm1atfHZs2fj5+fnSrkiIiIi0khtOG3i82NmKgwTUb4GD1xTQZSvu6u6+oqKirjrrrvIy8urMo28Ji6H9kceeYTFixfzzTffEBMTU6vXjh8/HpPJxIIFC2p8vqYr7bGxsWRnZ1/2gK4Gu93OypUrGTlyJBaLpcH335Spd65T71yn3tWN+uc69c516p3rmnLvSssd/Hnxfj7dfBKAkd0i+WtiTwJ9Gm6BQ3f2Lz8/n4iIiCsK7S515NFHH2XBggWsWbOm1oEdYMCAAcyaNeuiz3t7e+PtXX2xfIvF4taT0d37b8rUO9epd65T7+pG/XOdeuc69c51Ta136XnF/GrWVrafyMVkgidHXcOvhnXE7Kb56+7oX232V6vQbhgGjz76KHPnzmXVqlW0b9++1sUBbNu2jejoaJdeKyIiIiJN28ajOTwyeyvZhWUE+1p448e9GdZFKwteSq1C+yOPPMLs2bOZP38+gYGBZGRkABAcHIyvb+XEo6lTp5KWlsZHH30EwPTp02nXrh09evSgrKyMWbNmYbPZsNls9XwoIiIiItKYGYbBzPXH+cvifZQ7DLpFB/HuPQnEhes7i5dTq9D+zjvvADB8+PAq4x9++CH3338/AOnp6aSmpjqfKysr48knnyQtLQ1fX1969OjB4sWLGTduXN0qFxEREZEmo7isgj/M3cXcbWkATLy+Da8kXoevl4ebK2saaj095nJmzpxZ5fFTTz3FU089VauiRERERKT5OHGmiF9+vIW96fl4mE08M64bPx3cDpNJ669fqYb7aq6IiIiItDhrDmbx6H+2kVdsJyLAizfv6sOADuHuLqvJUWgXERERkXpnGAZvrzrC31YcwDCgV2wIM+7pQ3RwC1iA/SpQaBcRERGRelVYWs6Tn+1g2Z7KRUvu7BfLtIk98PbU/HVXKbSLiIiISL05klXILz7azJGsc3h5mJk2sQc/viHO3WU1eQrtIiIiIlIvVuzJYMpnOygsLad1kA/v3NOH3nGh7i6rWVBoFxEREZE6qXAYTP/iIP/46jAAN7QP4627+tAqsPod7sU1Cu0iIiIi4rK8Iju/+e82Vh/MAuBng9szdVxXLB5mN1fWvCi0i4iIiIhL9qXn88uPt5B6pggfi5lXEq9jUu+27i6rWVJoFxEREZFaW7DjFL9P3kmxvYLYMF9m3JNAjzbB7i6r2VJoFxEREZErVl7h4JWl+/nXN8cAGNI5gn/8uDchfl5urqx5U2gXERERkSuSXVjKr2dv5dujZwB45MaOTBl5DR5mk5sra/4U2kVERETksnacyOWhWVtIzyvB38uDV2+/njE9W7u7rBZDoV1ERERELumz707w7PzdlJU76NDKn/fuTaBTZKC7y2pRFNpFREREpEal5RVMW7iX2RtTARjZPYrXbu9FoI/FzZW1PArtIiIiIlJNRl4Jv/pkC9tSczGZ4Lcju/Dw8E6YNX/dLRTaRURERKSKTcfO8PAnW8kuLCXIx5O//7g3N14T6e6yWjSFdhEREREBwDAM/r3+OC8u3ke5w6Br60DevTeB+HB/d5fW4im0i4iIiAjFZRU8M3cXc7alATChVxtesV6Ln5fiYmOg/xdEREREWrgTZ4r45cdb2Juej4fZxB/GdeNng9thMmn+emOh0C4iIiLSgq09lMWj/9lGbpGdcH8v3ryrDwM7hru7LPkBhXYRERGRFsgwDGasPsr/Ld+Pw4BeMcG8c08CbUJ83V2a1EChXURERKSFKSwt53ef72Dp7gwA7ugby7SJPfCxeLi5MrkYhXYRERGRFuRoViG//HgLhzILsXiYmDahJ3f1j3N3WXIZCu0iIiIiLcTKvaeZ8ul2CkrLiQry5p17EugTF+rusuQKKLSLiIiINHMOh8H0Lw/xxpeHALihXRhv3t2byEAfN1cmV0qhXURERKQZS80p4g9zd/HN4WwA7h/Ujmdu6YbFw+zmyqQ2FNpFREREmqHyCgfvf3OM1784SIndgbenmVes1zK5d4y7SxMXKLSLiIiINDM7T+bytG0Xe9PzARjcKZy/TLqWdhH+bq5MXKXQLiIiItJMnCst59UVB5m5/hgOA0L8LDx7S3esfdrq7qZNnEK7iIiISDOw6mAWLyzcT1puMQCTrm/Ds7d2JyLA282VSX1QaBcRERFpwrILS/n3QTNbN2wDICbUl79MvpZhXVq5uTKpTwrtIiIiIk2QYRh8tvkEf1m8j/wSM2YT/HxIBx4f0Rk/L0W85kb/j4qIiIg0MUezCpk6Zxcbj50BIMbf4M2fDOT6+HA3VyZXi0K7iIiISBNRVu7g3dVH+MfXhykrd+Br8eDxmzvSKncvPdoEubs8uYoU2kVERESagC0pZ5k6ZycHTxcCMKxLK16c1JPWgRaWLNnr5urkalNoFxEREWnE8kvs/N+yA8zamIJhQLi/F8+N786EXm0wmUzY7XZ3lygNQKFdREREpJFavieD5+bv5nR+KQC3943hD+O6EeLn5ebKpKEptIuIiIg0Mhl5JTy/YDfL95wGoF24Hy8lXsugjhFurkzcRaFdREREpJFwOAw+2ZTK/1u6n4LScjzNJn45rAOP3tQZH4uHu8sTN1JoFxEREWkEDp4uYOqcXWxJOQvA9bEhvGK9lq6ttSqMgLk2G7/88sv069ePwMBAIiMjmTRpEgcOHLjs61avXk1CQgI+Pj506NCBGTNmuFywiIiISHNSYq/g1RUHuOWNtWxJOYu/lwfTJvTA9qtBCuziVKvQvnr1ah555BG+/fZLine truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1242,7 +1241,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1318,7 +1317,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1469,7 +1468,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1501,7 +1500,7 @@
     "plt.plot(ts, exact)\n",
     "\n",
     "error = np.array(exact) - np.array(ys)\n",
-    "print(\"max error {:.5f}\".format(max(error)))"
+    "print(f\"max error {max(error):.5f}\")"
    ]
   },
   {
@@ -1668,7 +1667,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

08-Designing-Kalman-Filters.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",
@@ -134,7 +133,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -426,7 +425,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -540,7 +539,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -648,7 +647,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -950,7 +949,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1000,7 +999,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1031,7 +1030,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1062,7 +1061,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1095,7 +1094,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1129,7 +1128,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1164,7 +1163,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1201,7 +1200,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1243,7 +1242,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1274,7 +1273,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1284,7 +1283,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1324,7 +1323,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1334,7 +1333,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1374,7 +1373,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1408,7 +1407,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1462,7 +1461,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1472,7 +1471,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1510,7 +1509,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1520,7 +1519,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1553,7 +1552,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1563,7 +1562,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1697,7 +1696,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1799,16 +1798,16 @@
     "p1 = kf.x[0:2]\n",
     "\n",
     "# compute error of measurement from prior\n",
-    "y = np.abs(z - np.dot(kf.H, prior))\n",
+    "y = np.abs(z - kf.H @ prior)\n",
     "dist = np.linalg.norm(y)\n",
     "\n",
     "np.set_printoptions(precision=2, suppress=True)\n",
     "\n",
-    "print('bad measurement       : {} km'.format(z.T))\n",
-    "print('before bad measurement: {} km'.format(p0.T))\n",
-    "print('after bad measurement : {} km'.format(p1.T))\n",
-    "print('estimate shift        : {:.1f} km'.format(np.linalg.norm(p1 - prior[:2])))\n",
-    "print('distance from prior   : {:.1f} km'.format(dist))\n"
+    "print(f'bad measurement       : {z.T} km')\n",
+    "print(f'before bad measurement: {p0.T} km')\n",
+    "print(f'after bad measurement : {p1.T} km')\n",
+    "print(f'estimate shift        : {np.linalg.norm(p1 - prior[:2]):.1f} km')\n",
+    "print(f'distance from prior   : {dist:.1f} km')"
    ]
   },
   {
@@ -1838,7 +1837,7 @@
     }
    ],
    "source": [
-    "print('error = {:.1f} km, at a speed of {:.0f} kph'.format(np.linalg.norm(kf.y), dist*3600))"
+    "print(f'error = {np.linalg.norm(kf.y):.1f} km, at a speed of {dist*3600:.0f} kph')"
    ]
   },
   {
@@ -1869,7 +1868,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1899,7 +1898,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1948,10 +1947,10 @@
     "y = kf.residual_of(z)[:,0]\n",
     "\n",
     "if y[0] > GATE_LIMIT * std_x or y[1] > GATE_LIMIT * std_y:\n",
-    "    print('discarding measurement, error is {:.0f} std, {:.0f} std'.format(y[0]/std_x, y[1]/std_y))\n",
+    "    print(f'discarding measurement, error is {y[0]/std_x:.0f} std, {y[1]/std_y:.0f} std')\n",
     "    \n",
     "print('y   is', y)\n",
-    "print('std is {:.2f} {:.2f}'.format(std_x, std_y))"
+    "print(f'std is {std_x:.2f} {std_y:.2f}')"
    ]
   },
   {
@@ -1970,7 +1969,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1981,7 +1980,7 @@
    ],
    "source": [
     "plot_covariance_ellipse(x, P, std=[1,2,3])\n",
-    "plt.scatter(8.08, 7.7, marker='x');\n",
+    "plt.scatter(8.08, 7.7, marker='x')\n",
     "plt.scatter(8.2, 7.65, marker='x');"
    ]
   },
@@ -2007,8 +2006,8 @@
    ],
    "source": [
     "from filterpy.stats import mahalanobis\n",
-    "\n",
-    "print('mahalanobis distance = {:.1f}'.format(mahalanobis(x=z, mean=x, cov=P)))"
+    "m = mahalanobis(x=z, mean=x, cov=P)\n",
+    "print(f'mahalanobis distance = {m:.1f}')"
    ]
   },
   {
@@ -2033,8 +2032,8 @@
     }
    ],
    "source": [
-    "print('mahalanobis distance = {:.1f}'.format(mahalanobis(x=[8.08, 7.7], mean=x, cov=P)))\n",
-    "print('mahalanobis distance = {:.1f}'.format(mahalanobis(x=[8.2, 7.65], mean=x, cov=P)))"
+    "print(f'mahalanobis distance = {mahalanobis(x=[8.08, 7.7], mean=x, cov=P):.1f}')\n",
+    "print(f'mahalanobis distance = {mahalanobis(x=[8.2, 7.65], mean=x, cov=P):.1f}')"
    ]
   },
   {
@@ -2153,7 +2152,7 @@
     "    est_err = xs - est_xs\n",
     "    err = []\n",
     "    for x, p in zip(est_err, Ps):\n",
-    "        err.append(np.dot(x.T, inv(p)).dot(x))\n",
+    "        err.append(x.T @ inv(p) @ x)\n",
     "    return err"
    ]
   },
@@ -2166,7 +2165,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean NEES is:  0.8893319389009477\n",
+      "mean NEES is: 0.8893\n",
       "passed\n"
      ]
     }
@@ -2180,7 +2179,7 @@
     "nees = NEES (xs, est_xs, ps)\n",
     "eps = np.mean(nees)\n",
     "\n",
-    "print('mean NEES is: ', eps)\n",
+    "print(f'mean NEES is: {eps:.4f}')\n",
     "if eps < kf2.dim_x:\n",
     "    print('passed')\n",
     "else:\n",
@@ -2230,8 +2229,8 @@
     "\n",
     "```python\n",
     "from scipy.stats import multivariate_normal\n",
-    "hx = np.dot(H, x).flatten()\n",
-    "S = np.dot(H, P).dot(H.T) + R\n",
+    "hx = (H @ x).flatten()\n",
+    "S = H @ P @ H.T  + R\n",
     "likelihood = multivariate_normal.pdf(z.flatten(), mean=hx, cov=S)\n",
     "```\n",
     "\n",
@@ -2247,7 +2246,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2283,7 +2282,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2362,7 +2361,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2459,7 +2458,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2494,7 +2493,7 @@
     "        kf.update(array([[m0], [m1]]))\n",
     "        s.save()\n",
     "    s.to_array()\n",
-    "    print('fusion std: {:.3f}'.format(np.std(s.y[:, 0])))\n",
+    "    print(f'fusion std: {np.std(s.y[:, 0]):.3f}')\n",
     "    if do_plot:\n",
     "        ts = np.arange(0.1, 10, .1)\n",
     "        plot_measurements(ts, s.z[:, 0], label='Wheel')\n",
@@ -2531,7 +2530,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2566,7 +2565,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2617,7 +2616,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2702,7 +2701,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2712,7 +2711,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2722,7 +2721,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2779,7 +2778,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2789,7 +2788,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2799,7 +2798,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2903,7 +2902,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2930,7 +2929,7 @@
     "ts = np.arange(0.1, 10, .1)\n",
     "\n",
     "res = nom - xs[:, 0, 0]\n",
-    "print('std: {:.3f}'.format(np.std(res)))\n",
+    "print(f'std: {np.std(res):.3f}')\n",
     "\n",
     "plot_filter(ts, xs[:, 0], label='Kalman filter')\n",
     "plot_measurements(ts, zs, label='Wheel')\n",
@@ -3052,7 +3051,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -3248,7 +3247,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3510,7 +3509,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3632,7 +3631,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3775,7 +3774,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3844,7 +3843,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3854,7 +3853,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3916,7 +3915,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3926,7 +3925,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3978,7 +3977,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "widgets": {
    "state": {},

09-Nonlinear-Filtering.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",
@@ -142,7 +141,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -194,7 +193,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -226,7 +225,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -264,7 +263,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -303,7 +302,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -338,7 +337,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -368,8 +367,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "input  mean, variance: -0.0014, 1.0015\n",
-      "output mean, variance: -0.1223, 2.4074\n"
+      "input  mean, variance: -0.0006, 1.0033\n",
+      "output mean, variance: -0.1243, 2.4105\n"
      ]
     }
    ],
@@ -396,7 +395,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -408,7 +407,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output mean, variance: 0.0020, 2.2533\n"
+      "output mean, variance: 0.0009, 2.2575\n"
      ]
     }
    ],
@@ -438,8 +437,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "linear    output mean, variance: 0.1173, 7492.8388\n",
-      "nonlinear output mean, variance: -8.9527, 30531.3374\n"
+      "linear    output mean, variance: 0.0511, 7506.6256\n",
+      "nonlinear output mean, variance: -9.0540, 30529.4381\n"
      ]
     }
    ],
@@ -474,7 +473,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -519,7 +518,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -548,7 +547,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -575,7 +574,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -604,7 +603,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -706,7 +705,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,