Updated for Python 3.6

11-Extended-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",
@@ -111,7 +110,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -195,7 +194,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -497,7 +496,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -507,7 +506,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -517,7 +516,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -673,7 +672,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1159,7 +1158,7 @@
     "        M = array([[self.std_vel*u[0]**2, 0], \n",
     "                   [0, self.std_steer**2]])\n",
     "\n",
-    "        self.P = np.dot(F, self.P).dot(F.T) + np.dot(V, M).dot(V.T)\n",
+    "        self.P = F @ self.P @ F.T + V @ M @ V.T\n",
     "\n",
     "    def move(self, x, u, dt):\n",
     "        hdg = x[2, 0]\n",
@@ -1295,7 +1294,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1307,7 +1306,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [0.026 0.041 0.002]\n"
+      "Final P: [0.025 0.042 0.002]\n"
      ]
     }
    ],
@@ -1340,7 +1339,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1352,7 +1351,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [0.021 0.02  0.002]\n"
+      "Final P: [0.02  0.02  0.002]\n"
      ]
     }
    ],
@@ -1380,7 +1379,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1392,7 +1391,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [0.021 0.045 0.   ]\n"
+      "Final P: [0.022 0.045 0.   ]\n"
      ]
     }
    ],
@@ -1417,7 +1416,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1429,7 +1428,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [0.285 0.778 0.004]\n"
+      "Final P: [0.263 0.798 0.004]\n"
      ]
     }
    ],
@@ -1454,7 +1453,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1466,7 +1465,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [0.009 0.008 0.001]\n"
+      "Final P: [0.008 0.009 0.001]\n"
      ]
     }
    ],
@@ -1516,7 +1515,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1552,7 +1551,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1564,7 +1563,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "actual mean=1.30, std=1.13\n",
+      "actual mean=1.30, std=1.12\n",
       "UKF    mean=1.30, std=1.08\n"
      ]
     }
@@ -1598,7 +1597,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

12-Particle-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",
@@ -111,7 +110,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -181,7 +180,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -272,7 +271,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -306,8 +305,8 @@
     "            marker=',', edgecolor='r', s=1)\n",
     "plt.axis('equal')\n",
     "\n",
-    "print('mean pi(N={})= {:.4f}'.format(N, pi))\n",
-    "print('err  pi(N={})= {:.4f}'.format(N, np.pi-pi))"
+    "print(f'mean pi(N={N})= {pi:.4f}')\n",
+    "print(f'err  pi(N={N})= {np.pi-pi:.4f}')"
    ]
   },
   {
@@ -324,7 +323,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -356,7 +355,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -703,7 +702,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -832,7 +831,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -887,7 +886,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -937,7 +936,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -975,7 +974,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1105,7 +1104,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1157,7 +1156,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1239,7 +1238,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1282,7 +1281,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1353,7 +1352,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1427,7 +1426,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1437,7 +1436,7 @@
     },
     {
      "data": {
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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1447,7 +1446,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1457,7 +1456,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1538,7 +1537,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,