Updated for FilterPy 1.4.0

09-Nonlinear-Filtering.ipynb

@@ -239,11 +239,14 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
+       "                    scale: 95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                },\n",
+       "                 \"fast-preview\": {\n",
+       "                    \"disabled\": false\n",
        "                }\n",
        "        });\n",
        "</script>\n"
@@ -347,9 +350,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEaCAYAAABdIZKsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bcf040c3c8>"
+       "<matplotlib.figure.Figure at 0x229382d3da0>"
       ]
      },
      "metadata": {},
@@ -361,16 +364,17 @@
     "from numpy.random import randn\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "N = 3000\n",
+    "N = 5000\n",
     "a = np.pi/2. + (randn(N) * 0.35)\n",
     "r = 50.0     + (randn(N) * 0.4)\n",
     "xs = r * np.cos(a)\n",
     "ys = r * np.sin(a)\n",
     "\n",
-    "plt.scatter(xs, ys, label='Sensor', color='k', marker='.', s=2)\n",
-    "xs, ys = sum(xs)/N, sum(ys)/N\n",
-    "plt.scatter(xs, ys, c='r', marker='*', s=200, label='Mean')\n",
-    "plt.scatter(0, 50, c='k', marker='o', s=300, label='Intuition')\n",
+    "plt.scatter(xs, ys, label='Sensor', color='k', \n",
+    "            alpha=0.4, marker='.', s=1)\n",
+    "xmean, ymean = sum(xs) / N, sum(ys) / N\n",
+    "plt.scatter(0, 50, c='k', marker='o', s=200, label='Intuition')\n",
+    "plt.scatter(xmean, ymean, c='r', marker='*', s=200, label='Mean')\n",
     "plt.axis('equal')\n",
     "plt.legend();"
    ]
@@ -398,9 +402,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaMAAAD1CAYAAAD9NZqqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAGJhJREFUeJzt3X+Q3PVdx/Hny1Dq1OIRrMSURA+cLQodpbBDUWwHpYUQOqXtnJr8UWJhhqIwU7TO9LCOMGUYY5V2ZKipUjKAIi09SpMhoTTF1toZQrliCEkB70JjORIvY4NLHRyU+vaP/ez5ZbO7t3f74/vd3ddjZue++/l+9rvv/d5397Xfz35uTxGBmZlZnn4s7wLMzMwcRmZmljuHkZmZ5c5hZGZmuXMYmZlZ7o7Lu4CsSqXiqX1mZkNubGxM9W0+MzIzs9w5jMzMLHeFGqbLGhsb68l2p6enASiXyz3Zfi+45v5wzf3hmvujaDVXKpWW631mZGZmuXMYmZlZ7hYNI0lrJX1d0tOS9kv6SGo/SdIuSTPp58rULkm3SpqVtFfS2ZltbUr9ZyRt6t3DMjOzQdLOmdGrwEcj4heB84BrJJ0BTAKPREQJeCRdB7gEKKXLVcAWqIYXcAPwduBc4IZagJmZ2WjTUr+1W9I24LZ0uSAiDktaDXwjIk6X9Ndp+d7U/1nggtolIj6c2l/TD177d0YzMzOdPC4zMyuQUqm0sNzx3xlJGgfeBjwGrIqIwwDp58mp2ynA85mbzaW2Zu1mZjbi2p7aLemNwP3AdRHxknRMsC10bdAWLdob6tV0xKJNd2yHa+4P19wfrrk/ilZzV6Z2S3od1SC6JyK+lJrn0/Ac6eeR1D4HrM3cfA1wqEW72dAbn9yRdwlmhdbObDoBdwBPR8SnMqu2A7UZcZuAbZn2y9OsuvOAShrGexi4SNLKNHHhotRmZmYjrp1huvOBDwJPSdqT2v4I2AzcJ+lK4PvAb6Z1O4H1wCzwMvAhgIg4Kukm4PHU7xMRcbQrj8LMzAbaomEUEd+i8ec9ABc26B/ANU22tRXYupQCzQbZ+OQODm6+NO8yzArP38BgZma5cxiZ9YknMZg15zAy65J2w8ahZHYsh5HZMrQTKA4ds/Y5jMx6wEFktjQOI7MucxCZLZ3DyKwDDh6z7nAYmZlZ7hxGZl2w1DMkn1GZvZbDyGyZGgXKYiHjEDJrzGFkZma5cxiZ5cxnS2YOIzMzKwCHkZmZ5c5hZNYhT1ow65zDyKxNtVBxuJh1n8PIbAkcRGa9sWgYSdoq6YikfZm2L0jaky4Ha/+OXNK4pP/KrPts5jbnSHpK0qykWyU1+++xZoXSjwByyNmoW/TfjgN3ArcBd9caIuK3a8uSbgEqmf4HIuKsBtvZAlwF7AZ2AuuAh5Zesln/9SosHEJmVYqIxTtJ48CDEfHWunYB3wd+IyJmWvRbDXw9In4hXd8IXBARH872q1QqC8XMzMws4+GYdc/E1DxTE6uYmJrvyfabbXtqYlVP7s8sT6VSaWF5bGzsmJGxTj8zegcwHxHZ5DhV0j9L+kdJ70htpwBzmT5zqc2scHoVPnndj9kgaGeYrpWNwL2Z64eBn42IH0g6B/iypDOBRp8PtTwlK5fLHZbW2PT0dE+33wuuuT9qNUOqe2rHws9+Wso+G+T97Jp7q2g1VyqVluuXHUaSjgM+AJxTa4uIV4BX0vJ3JB0A3kL1TGhN5uZrgEPLvW+zfvFnOmb90ckw3buAZyJiYfhN0k9LWpGWTwNKwHMRcRj4oaTz0udMlwPbOrhvs55yCJn1VztTu+8FHgVOlzQn6cq0agOvHaIDeCewV9KTwBRwdUQcTet+F/gcMAscwDPpzMwsWXSYLiI2Nmn/nQZt9wP3N+k/Dby10TozMxtt/gYGG3lFHJIrYk1mveQwMiuYbBCNT+5wMNlI6HRqt9nQ8N/9mOXHZ0ZmZpY7h5FZQXl4zkaJw8jMzHLnMDIzs9w5jMzMLHcOIzMzy53DyEZabZKAJwuY5cthZDYAHJY27BxGZmaWO4eRmZnlzmFkZma5cxiZmVnuHEZmZpY7h5GNrEGboTZo9ZotRTv/dnyrpCOS9mXabpT0gqQ96bI+s+56SbOSnpV0caZ9XWqblTTZ/YdiZmaDqp0zozuBdQ3aPx0RZ6XLTgBJZwAbgDPTbf5K0gpJK4DPAJcAZwAbU1+zvvLZhVkxLfrP9SLim5LG29zeZcDnI+IV4HuSZoFz07rZiHgOQNLnU9/vLrlisy5wKJkVSyf/6fVaSZcD08BHI+JF4BRgd6bPXGoDeL6u/e2tNj49Pd1BaYvr9fZ7wTV3RxFralez2gfxMbnm/ihKzaVSqeX65U5g2AL8PHAWcBi4JbWrQd9o0W5mSzAxNb9wMRsmyzozioiFZ4Kk24EH09U5YG2m6xrgUFpu1t5QuVxeTmmLqr1L6NX2e8E1d9HUjmpNU4M/TFcul4u7n1twzf1RtJorlUrL9cs6M5K0OnP1/UBtpt12YIOk10s6FSgB3wYeB0qSTpV0PNVJDtuXc99mZjZ8Fj0zknQvcAHwJklzwA3ABZLOojrUdhD4MEBE7Jd0H9WJCa8C10TEj9J2rgUeBlYAWyNif9cfjVkLnrRgVlztzKbb2KD5jhb9bwZubtC+E9i5pOrMemBYQml8cgdTE6vyLsOsK/wNDGYDzBMZbFg4jMzMLHcOIzMzy53DyMzMcucwMjOz3DmMzMwsdw4jMzPLncPIzMxy5zAyM7PcOYzMBpz/8NWGgcPIht6wfP3PYkblcdpwchjZUBuVF+hReZw2vBxGNrRG9QV6VB+3DTaHkZmZ5c5hZCNhVM4WRuVx2vBxGJmZWe4cRmZmlrtFw0jSVklHJO3LtP25pGck7ZX0gKQTU/u4pP+StCddPpu5zTmSnpI0K+lWSerNQzLzcJXZoGnnzOhOYF1d2y7grRHxS8C/ANdn1h2IiLPS5epM+xbgKqCULvXbNLMucRjboFk0jCLim8DRuravRsSr6epuYE2rbUhaDfxkRDwaEQHcDbxveSWbmdmwOa4L27gC+ELm+qmS/hl4CfjjiPgn4BRgLtNnLrU1NT093YXS8tt+L7hmW4qi7/ui19eIa16+UqnUcn1HExgkfRx4FbgnNR0GfjYi3gb8AfD3kn4SaPT5UHRy32b1at/R5u9qMxs8yz4zkrQJeA9wYRp6IyJeAV5Jy9+RdAB4C9UzoexQ3hrgUKvtl8vl5ZbWUu1dQq+23wuuuU1TO6r3N+XPS6C4x4uP5/4oWs2VSqXl+mWdGUlaB3wMeG9EvJxp/2lJK9LyaVQnKjwXEYeBH0o6L82iuxzYtpz7NjOz4dPO1O57gUeB0yXNSboSuA04AdhVN4X7ncBeSU8CU8DVEVGb/PC7wOeAWeAA8FB3H4qZNePZdVZ0iw7TRcTGBs13NOl7P3B/k3XTwFuXVJ3ZEvlF9/+NT+7g4OZLvU9sIPgbGMyGmIPIBoXDyMzMcucwsqHgMwCzweYwMjOz3DmMzMwsdw4jMzPLncPIzMxy5zAyM7PcOYzMzCx3DiMzM8udw8jMzHLnMLKBNj65w3/w2ibvJysyh5GZmeXOYWQDye/yl8/7zorIYWQDyy+qZsPDYWRmZrlzGJmNEJ9NWlG1FUaStko6Imlfpu0kSbskzaSfK1O7JN0qaVbSXklnZ26zKfWfkbSp+w/HzMwGUbtnRncC6+raJoFHIqIEPJKuA1wClNLlKmALVMMLuAF4O3AucEMtwMyWwu/uzYZPW2EUEd8EjtY1XwbclZbvAt6Xab87qnYDJ0paDVwM7IqIoxHxIrCLYwPOzMxG0HEd3HZVRBwGiIjDkk5O7acAz2f6zaW2Zu0NTU9Pd1Da4nq9/V5wzdYtRfi9FKGGpXLNy1cqlVqu78UEBjVoixbtZmY24jo5M5qXtDqdFa0GjqT2OWBtpt8a4FBqv6Cu/RvNNl4ulzsorbnau4Rebb8XXHOdKX9m1KmJqXkObr40l/v28dwfRau5Uqm0XN/JmdF2oDYjbhOwLdN+eZpVdx5QScN5DwMXSVqZJi5clNrM2ubJC2bDqd2p3fcCjwKnS5qTdCWwGXi3pBng3ek6wE7gOWAWuB34PYCIOArcBDyeLp9IbWaWE4e7FUVbw3QRsbHJqgsb9A3gmibb2Qpsbbs6MzMbCf4GBrMR5bMiKxKHkQ0Mv3iaDS+HkQ0EB5HZcHMYmZlZ7hxGZmaWO4eRmXkY1HLnMDIzs9w5jKzw/K7dbPg5jMxGnMPeisBhZGaAQ8ny5TCyQvMLpNlocBhZYTmIzEaHw8jMzHLnMDKzBT4btbw4jMzsGA4l6zeHkZmZ5c5hZGZmuVt2GEk6XdKezOUlSddJulHSC5n29ZnbXC9pVtKzki7uzkMws27yEJ3loa1/O95IRDwLnAUgaQXwAvAA8CHg0xHxF9n+ks4ANgBnAm8GvibpLRHxo+XWYGZmw6Fbw3QXAgci4l9b9LkM+HxEvBIR3wNmgXO7dP9mZjbAFBGdb0TaCjwREbdJuhH4HeAlYBr4aES8KOk2YHdE/F26zR3AQxExVdtOpVJZKGZmZqbjumxwTUzN513CyJuaWJV3CTZESqXSwvLY2Jjq13d8ZiTpeOC9wBdT0xbg56kO4R0Gbql1bXDzzpPQhopDyGw0Lfszo4xLqJ4VzQPUfgJIuh14MF2dA9ZmbrcGONRso+VyuQulHWt6erqn2++Fkap5aocDqSDK5TLjkzs4uPnSrm53pI7nHBWt5kql0nJ9Nz4z2gjcW7siaXVm3fuBfWl5O7BB0uslnQqUgG934f7NrAc8q876qaMzI0lvAN4NfDjT/ElJZ1EdgjtYWxcR+yXdB3wXeBW4xjPpzMwMOgyjiHgZ+Km6tg+26H8zcHMn92lmZsPH38BgheAhIbPR5jCywnAgmY0uh5GZteQ3CdYPDiMzM8udw8jMzHLLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bcf07a27f0>"
+       "<matplotlib.figure.Figure at 0x229382bd7b8>"
       ]
      },
      "metadata": {},
@@ -409,8 +413,8 @@
    ],
    "source": [
     "from numpy.random import normal\n",
-    "gaussian = (0., 1.)\n",
-    "data = normal(loc=gaussian[0], scale=gaussian[1], size=500000)\n",
+    "\n",
+    "data = normal(loc=0., scale=1., size=500000)\n",
     "plt.hist(2*data + 1, 1000);"
    ]
   },
@@ -430,9 +434,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bcef378b00>"
+       "<matplotlib.figure.Figure at 0x229382bd7f0>"
       ]
      },
      "metadata": {},
@@ -446,7 +450,7 @@
     "def g1(x):\n",
     "    return 2*x+1\n",
     "\n",
-    "plot_nonlinear_func(data, g1, gaussian)"
+    "plot_nonlinear_func(data, g1)"
    ]
   },
   {
@@ -468,9 +472,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bcf3316a90>"
+       "<matplotlib.figure.Figure at 0x2293aeba080>"
       ]
      },
      "metadata": {},
@@ -481,7 +485,7 @@
     "def g2(x):\n",
     "    return (np.cos(3*(x/2 + 0.7))) * np.sin(0.3*x) - 1.6*x\n",
     "\n",
-    "plot_nonlinear_func(data, g2, gaussian)"
+    "plot_nonlinear_func(data, g2)"
    ]
   },
   {
@@ -497,19 +501,54 @@
     "\n",
     "These equations do not hold for non-Gaussians, and certainly do not hold for the probability distribution shown in the 'Output' chart above. \n",
     "\n",
+    "Here's another way to look at the same data as scatter plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2293974ea20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N = 30000\n",
+    "plt.subplot(121)\n",
+    "plt.scatter(data[:N], range(N), alpha=.1, s=1.5)\n",
+    "plt.title('Input')\n",
+    "plt.subplot(122)\n",
+    "plt.title('Output')\n",
+    "plt.scatter(g2(data[:N]), range(N), alpha=.1, s=1.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The original data is clearly Gaussian, but the data passed through `g2(x)` is no longer normally distributed. There is a thick band near -3, and the points are unequally distributed on either side of the band. If you compare this to the pdf labelled 'output' in the previous chart you should be able to see how the pdf shape matches the distribution of `g(data)`.\n",
+    "\n",
     "Think of what this implies for the Kalman filter algorithm of the previous chapter. All of the equations assume that a Gaussian passed through the process function results in another Gaussian. If this is not true then all of the assumptions and guarantees of the Kalman filter do not hold. Let's look at what happens when we pass the output back through the function again, simulating the next step time step of the Kalman filter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bcf331c6d8>"
+       "<matplotlib.figure.Figure at 0x2293afae278>"
       ]
      },
      "metadata": {},
@@ -518,9 +557,7 @@
    ],
    "source": [
     "y = g2(data)\n",
-    "gaussian2 = (np.mean(y), np.var(y))\n",
-    "\n",
-    "plot_nonlinear_func(y, g2, gaussian2)"
+    "plot_nonlinear_func(y, g2)"
    ]
   },
   {
@@ -532,15 +569,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "input  mean, variance: -0.0018, 1.0010\n",
-      "output mean, variance: -0.1223, 2.4067\n"
+      "input  mean, variance: -0.0029, 1.0011\n",
+      "output mean, variance: -0.1203, 2.4076\n"
      ]
     }
    ],
@@ -562,14 +599,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvkAAAEyCAYAAABge8+SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bcf3279be0>"
+       "<matplotlib.figure.Figure at 0x22938b38860>"
       ]
      },
      "metadata": {},
@@ -579,7 +616,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output mean, variance: 0.0026, 2.2522\n"
+      "output mean, variance: 0.0044, 2.2524\n"
      ]
     }
    ],
@@ -587,7 +624,7 @@
     "def g3(x): \n",
     "    return -1.5 * x\n",
     "\n",
-    "plot_nonlinear_func(data, g3, gaussian)\n",
+    "plot_nonlinear_func(data, g3)\n",
     "out = g3(data)\n",
     "print('output mean, variance: %.4f, %.4f' % \n",
     "      (np.mean(out), np.var(out)))"
@@ -602,15 +639,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "linear    output mean, variance: 0.1520, 7489.2679\n",
-      "nonlinear output mean, variance: -8.8097, 30501.0215\n"
+      "linear    output mean, variance: 0.2528, 7489.7719\n",
+      "nonlinear output mean, variance: -8.6036, 30495.7586\n"
      ]
     }
    ],
@@ -638,16 +675,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAukAAAEyCAYAAABH1U56AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bcf0fee780>"
+       "<matplotlib.figure.Figure at 0x22938cae828>"
       ]
      },
      "metadata": {},
@@ -657,9 +694,9 @@
    "source": [
     "def g3(x): \n",
     "    return -x*x\n",
-    "x0 = (1, 1)\n",
-    "data = normal(loc=x0[0], scale=x0[1], size=500000)\n",
-    "plot_nonlinear_func(data, g3, gaussian=x0)"
+    "\n",
+    "data = normal(loc=1, scale=1, size=500000)\n",
+    "plot_nonlinear_func(data, g3)"
    ]
   },
   {
@@ -685,14 +722,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEaCAYAAACo3xkcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bcf103b7f0>"
+       "<matplotlib.figure.Figure at 0x22938ddb4e0>"
       ]
      },
      "metadata": {},
@@ -714,14 +751,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEaCAYAAACo3xkcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAD1CAYAAACC5IhbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xd4XOdh5/vvmd4bMOggQBR2UpREUbK6Jctqjh05cmzFcqRsvHeTdXJ3924S33U2tuPd5Kbsda4T58b2rteSbK9c5SK5SLItSqKqSYmUKDZQBIhepvc5c855948BIQwJkiBRZgC8n+fBM8CZMzMvBpj5zdsVIQSSJEmStBRM1S6AJEmStHrJkJEkSZKWjAwZSZIkacnIkJEkSZKWjAwZSZIkaclYluuBksmkHMYmSZK0yvn9fmX2z7ImI0mSJC0ZGTKSJEnSklm25rLZ/H5/NR52Ue3btw+AXbt2VbkktUE+H5Xk8/EO+VxUWo3PRzKZPOd1F6zJKIriUBTlVUVRDiqK8paiKH85xzkPKooypSjKgemvjy+wzJIkSdIqMJ+aTBG4RQiRURTFCuxVFOVnQoiXzzjv20KIP1r8IkqSJEkr1QVDRpQXN8tM/2id/pIjxSRJkqQLUuazQKaiKGZgP9AD/LMQ4pNnXP8g8P8AU8Bx4D8IIYZmnzN7CHNfX9+CCy5JkiTVht7e3pnvL2kIsxBCF0LsBNqA3YqibDvjlMeBTiHEDuAXwMMLKrEkSZK0KsyrJlNxA0X5DJAVQvy3c1xvBmJCiIohZLNrMnJ02eojn49K8vl4h3wuKq3G52P26LKLrskoihJWFCUw/b0TeA9w9Ixzmmf9+H7gyALKK0mSJK0S8xld1gw8PF1DMQHfEUI8oSjK54B9QogfA/+noijvBzQgBjy4VAWWJEmSVo75jC57A7h8juOfnvX9fwL+0+IWTZIkSVrp5LIykiRJ0pKRISNJkiQtGRkykiRJ0pKRISNJkiQtGRkykiRJ0pKRISNJkiQtGRkykiRJ0pKRISNJkiQtGRkykiRJ0pKRISNJkiQtGRkykiRJ0pKRISNJkiQtGRkykiRJ0pKRISNJkiQtGRkykiRJ0pKRISNJkiQtGRkykiRJ0pKRISNJkiQtGRkykiRJ0pKxVLsAkiRJK51hCDTdwBACwxDnvYykCgBMxDKYTAomRZm5VGZ9b7WYsFrMVf7NFk6GjCRJ0jnoukFJNyhpOiVt+nKOnzVdRzM0DCEQwsBAYAij/P2sY0II+qYmADANDpSDBRMmxYRJUUBRyt9jwmIyY7NYsVst2K1m7LbypcNmwW61YLOujACSISNJ0pqmlnSKJY1iSaeoTl9O/6xqGprxzpcudDRdQxNaxaUhdMzm07UQpmskzNROFGX6exMIewoAzZxACGZqOcJgOpBO14wEhgE2iw2b2YbNZMNmtmI127CbbbjsdvxuB363Ha/LjsmkVPmZnJsMGUmSVj3DEBRUreKrWCpfqloJ1SihakVUvYRqqJR0FVUvITCwmBUsZhMWy/SlVcFlNpWPW6xYzDYs5vl3b0cny2+7jXXOC56rG4JSyUDVNNSSSq5koBYM1JKBWbHisXnw2Dy4rS587nLoBL2OmmpmkyEjSdKqUtJ08kWNXLFEvlgiVyiRV0sUNRVVV1H1IsXpS1UvoSgCm9VU/rKb8FpN2CwWrNaLC4+lYDYpmO1mHPazQyNf0Mjms0zlkwynBO6UC4/NS9DppynkpTHoxlzl8oMMGUmSVighBMWSXg6RYmk6VDTyJZWiVqSgFSjq5UtVL2K1mLBPh4nbZSZktWCz2THXaDPThTgdFpwOC/VB0HSDTE4jnZ0iEp0inq9nMhGkOeQlHHBXtSntgiGjKIoDeA6wT5//PSHEZ844xw48AlwJRIEPCyEGFr20kiStWQVVI5tXyRZKM7WUQkmdDpNC+VIrogsNu6386d/lNBOyWbFZa7fPYjFYzCYCXhsBr418QWMiNkU0EiORDzOZCNDVHMTttFWnbPM4pwjcIoTIKIpiBfYqivIzIcTLs875fSAuhOhRFOUjwN8CH16C8kpS1Z3+BF1QtfIbnarNDE8Vonx9/0QaRVHwDkYwmaaHpioKdqsZp92Kw2bBabegKKv3jW8hdN0gWyiRLZRDJZNXyatF8lqefCk/U0sxmwQOuxm7zUzAY8Zuc6yYUVdLxemw0NniIZMrMRkbI1FIoGoaG9rq8brsy16eC4aMEEIAmekfrdNf4ozTPgB8dvr77wFfVBRFmb6tJNUsXTdIZArE0nliqTzxTIFYKk8snSeRKZCfboLJTYdJvqiRV0tc6D87kUgAENg7dt7zHDYLTptlJnRcjnIA+d0Ogh4HIZ+TkNc5cxn0Olflm+jpWkpmuqaSLZZrKPlSnnwpR17Lg2LgtJtxuix4bWbsNnfV+0xqmcdlxe20MDqV51RiCIDe1np87uUNGmU+OaAoihnYD/QA/yyE+OQZ1x8C7hBCDE///DZwtRAicvqcZDI580B9fX2LU3pJugAhBPGsylSywFSqwGSySDRVIJkvkcqVyBQuHBi1xmW34HVa8LtshDw2GvxOwn47YZ+DOq+95t94hRDkVZ1cUSOv6uRVnaJWoqAXKBoFirpKyVCxWsFuNWG3KThs5dFc0sUTQhBNaqhFK23uFrqbvIv+P9Lb2zvzvd/vr/hDzavjXwihAzsVRQkAP1AUZZsQ4tCsU+b666+wl660khVLOmPxPJPJAlPJApOpApFUgalkkZJuVLt4iypX1MgVNSYShbOuMykKIa+NBp+DsN9B2Oegwe+gOejE47RWobTlN7lCSSdb0MgWy7XBgl4sh4pepGAUQdGxW004HApumwmb1VqenCgtmKIo1AesTMRKxAoJ3HELbfXuZXv8ixpdJoRIKIqyB7gDmB0yw0A7MKwoigXwA7Fz3c+uXbsuvqQ1Zt++fcDq+F0Ww3I+HwVV4+RonBMjsZmv4UhqjhqJHbd3aZoGrBYTTpsVp73czHV6aRCF8uX4+DgAjY2NMxPudMOgqOrkp/tyiiV9ScqmAaMZGM0UYaQIJAGo97voaQ3S0xKip7X85fc4Fv3xhRDkixrpXJFUrsir+w9Q0Iq0d6/DXMpiKuXxWyw0O/047WZcDgtWS23XvhbT4SOHAdiyecuyPu4GzeDkSIamQCdb1rfgcizeh45kMnnO6+YzuiwMlKYDxgm8h3LH/mw/Bh4AXgLuBX4l+2OkxVDSdE6MxDg+FOXt6WCZO1Aunc9lJ+RzzPR5nO4DCXocuJ02nDYLTvvpQCn3mVyouWE+oXt6gmC+WJoJnnyxfHm6byieLvcPlfuMCsQz+Uv+3SPJHJFkjpcPj8wcOzN4Nq2rv6RRSPliiXROJZ0rks6rZIt5clqOnJplIHMKk1nQZPLhD5hpdsi+lGqwWEz4PFYShQTRVACXw788jzuPc5qBh6f7ZUzAd4QQTyiK8jlgnxDix8BXga8rinKCcg3mI0tWYmlV03SD40NR3jw5wRsnJzg6GEXVFvaJ3+O00Vrvnf7y0VLvJex3lYPE66zaG57JpOByWC/qE6VhCJLZAvF0gUgyx1g0zfBUitFompFImmgqf1FlODN4FAW6W4JsX9/I9q4GtnSE5wwdtaSTzBZmgiWrFsiVsmRLOXJqDpPZwO2w4PVbaAlbsJgVmuovPMNdWlo+t5WJqQzpnLpsjzmf0WVvAJfPcfzTs74vAB9a3KJJa4GmG/QNR3nz5CRv9k9weCByyaHSWu+lo9FPa72vHCjh8mU1hm0uFZNJIThd4+pqCZ51fUHVGI2kGYmkGImkGY2kGZxMcmoiiTaPvikh4MRInBMjcX6w9yiKAj0tIbatb6CrJUhr2IeqamSKRTLFDNlSllwpi6IYuJwWPF4LjU5XRfOX7LCvHXabmaKWo6BqCCGWZQi9nPEvLbtIMserR0Z49egIh/onL6lvorXeS3dLkN62OnpaQ3Q1Bxe1jXmlctgsdLUEzwqgkqZzaiLJiZEYb4/E6BuJXTB4DMNA0w0OvD3OvmOj6ELHEDrhkI2OFhfbNwToavESdq7OYdWrkdlUXrizZGjohliWDwAyZKQlJ4Tg5GicV46M8MqRYU6OJS7q9nU+J1s7wzJQFsBqMc909p92ZvAcPhXh5Ghseun68pcuNDRDRzc0FFN5Znk0pRPPFDh4PEbQZ2dbT4CtXQG62xd/aKy0+AxDYD69tcAykCEjLQm1pPPmyQlePTrCq0dHiSRz875tyOtkR3fDTL9AU8gjZ8YvAavFTHvYh89lp6c1xLu2tjGRSvLWqTGODU8xMJokkVGxWBTsZgtzrcoSTxV5/rUJnn9tAofNzMb1frZ1B9i8PoDbKd9eao1hlEeNmBTTsi2zI/8LpEWjG4L9x0Z55sAArxwZoaBq87pdyOtke1cD29c3sL2rkeY6GSpLKZtXSWQKJDIFMoUCGTVDppQlq2axWWHbJjtXX96J024mk9N4ezjNiaEUfYNpJmPnHlhQUHUOHotx8FgMRVHoXefjys112AwDm1XWcGqBphuYTeZlXRRUhoy0IEII+oZjPPbyIK+fjGJxnLjgbRQFNrXXs3tzK7s3tdLe4JOhsoSEEKRzs4KlmCetpskU0xT1Am6XBY/XSpPTjeWM+Spet5WdG0Ps3FhuZktmVA6fTHDoRILjgyk0be4+HSEEx08lOX4qST6fobvVgbAl2Njpk01qVZTOaXhsnmUdDCNDRrokY9E0zx48xTOv9zMazbyzVtc55vbZrWYu723i6s1tXLWxZUkmAUrv0HWDVK5IIlMgmS2SLmZJF9Nk1AyCEh63lXC9FZfj4gLe77Hxrh0NvGtHA0VVp28wxaETCd46mSCTK815G02HY4MFRn9wHLfTws6NdezaUkdHs1t+uFhmqYxKg7OBkG/5hpPLkJHmLV8ssefAAL96vZ+jg9ELnh/yOtmLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bcf11a8400>"
+       "<matplotlib.figure.Figure at 0x22938e19be0>"
       ]
      },
      "metadata": {},
@@ -741,14 +778,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEaCAYAAACo3xkcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bcf1219470>"
+       "<matplotlib.figure.Figure at 0x22938e7fe80>"
       ]
      },
      "metadata": {},
@@ -768,14 +805,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEaCAYAAACo3xkcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bcf12e4320>"
+       "<matplotlib.figure.Figure at 0x22939316630>"
       ]
      },
      "metadata": {},
@@ -875,7 +912,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,

11-Extended-Kalman-Filters.ipynb

@@ -239,11 +239,14 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
+       "                    scale: 95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                },\n",
+       "                 \"fast-preview\": {\n",
+       "                    \"disabled\": false\n",
        "                }\n",
        "        });\n",
        "</script>\n"
@@ -316,9 +319,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAFJCAYAAAAG+busAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0a358fd0>"
+       "<matplotlib.figure.Figure at 0x24f12ef42e8>"
       ]
      },
      "metadata": {},
@@ -400,9 +403,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAEeCAYAAACez0OjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0c5ef390>"
+       "<matplotlib.figure.Figure at 0x24f15cd6978>"
       ]
      },
      "metadata": {},
@@ -555,9 +558,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from math import sqrt\n",
@@ -580,9 +581,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def hx(x):\n",
@@ -603,9 +602,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from numpy.random import randn\n",
@@ -708,9 +705,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0d64d048>"
+       "<matplotlib.figure.Figure at 0x24f154194a8>"
       ]
      },
      "metadata": {},
@@ -718,9 +715,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0d68f5c0>"
+       "<matplotlib.figure.Figure at 0x24f16d4a588>"
       ]
      },
      "metadata": {},
@@ -728,9 +725,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0d6d0b00>"
+       "<matplotlib.figure.Figure at 0x24f16d8bba8>"
       ]
      },
      "metadata": {},
@@ -795,7 +792,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMsAAAAmBAMAAACYD3JcAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nbvXGjjdK38CrVvEPoeYc6sIOZHkAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/latex": [
        "$$\\left[\\begin{matrix}\\frac{x}{\\sqrt{x^{2} + y^{2}}} & 0 & \\frac{y}{\\sqrt{x^{2} + y^{2}}}\\end{matrix}\\right]$$"
       ],
@@ -866,9 +863,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0d65fc88>"
+       "<matplotlib.figure.Figure at 0x24f16d15198>"
       ]
      },
      "metadata": {},
@@ -1222,9 +1219,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from math import sqrt\n",
@@ -1256,9 +1251,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from math import atan2\n",
@@ -1315,9 +1308,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from filterpy.kalman import ExtendedKalmanFilter as EKF\n",
@@ -1396,9 +1387,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def residual(a, b):\n",
@@ -1422,7 +1411,6 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -1505,9 +1493,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0e63eb00>"
+       "<matplotlib.figure.Figure at 0x24f17cf84e0>"
       ]
      },
      "metadata": {},
@@ -1517,7 +1505,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.0242  0.0419  0.00219]\n"
+      "Final P: [0.023 0.043 0.002]\n"
      ]
     }
    ],
@@ -1550,9 +1538,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0d747748>"
+       "<matplotlib.figure.Figure at 0x24f17e4d630>"
       ]
      },
      "metadata": {},
@@ -1562,7 +1550,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.0206  0.0201  0.00154]\n"
+      "Final P: [0.02  0.02  0.002]\n"
      ]
     }
    ],
@@ -1592,9 +1580,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0e63e630>"
+       "<matplotlib.figure.Figure at 0x24f17cf86a0>"
       ]
      },
      "metadata": {},
@@ -1604,7 +1592,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.0204  0.0451  0.000224]\n"
+      "Final P: [0.019 0.047 0.   ]\n"
      ]
     }
    ],
@@ -1631,9 +1619,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0e61ecf8>"
+       "<matplotlib.figure.Figure at 0x24f17f691d0>"
       ]
      },
      "metadata": {},
@@ -1643,7 +1631,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.259  0.848  0.00365]\n"
+      "Final P: [0.262 0.805 0.004]\n"
      ]
     }
    ],
@@ -1670,9 +1658,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0e96ae48>"
+       "<matplotlib.figure.Figure at 0x24f17fd27f0>"
       ]
      },
      "metadata": {},
@@ -1682,7 +1670,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.00826  0.00915  0.000762]\n"
+      "Final P: [0.008 0.009 0.001]\n"
      ]
     }
    ],
@@ -1732,9 +1720,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0e962c18>"
+       "<matplotlib.figure.Figure at 0x24f17cf8da0>"
       ]
      },
      "metadata": {},
@@ -1744,7 +1732,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "actual mean=1.30, std=1.14\n",
+      "actual mean=1.30, std=1.12\n",
       "EKF    mean=1.00, std=0.95\n"
      ]
     }
@@ -1768,9 +1756,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19d0ea88358>"
+       "<matplotlib.figure.Figure at 0x24f18194f28>"
       ]
      },
      "metadata": {},
@@ -1814,7 +1802,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,

12-Particle-Filters.ipynb

@@ -239,11 +239,14 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
+       "                    scale: 95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                },\n",
+       "                 \"fast-preview\": {\n",
+       "                    \"disabled\": false\n",
        "                }\n",
        "        });\n",
        "</script>\n"
@@ -316,9 +319,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fad468f60>"
+       "<matplotlib.figure.Figure at 0x20494475208>"
       ]
      },
      "metadata": {},
@@ -386,9 +389,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20faf4d5e48>"
+       "<matplotlib.figure.Figure at 0x2049614f4e0>"
       ]
      },
      "metadata": {},
@@ -477,9 +480,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20faf4fb438>"
+       "<matplotlib.figure.Figure at 0x204944750f0>"
       ]
      },
      "metadata": {},
@@ -529,9 +532,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20faf4d5588>"
+       "<matplotlib.figure.Figure at 0x204961ee6d8>"
       ]
      },
      "metadata": {},
@@ -561,9 +564,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAD3CAYAAAA+J3LdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3Wd0VPeBBfD7pmjURl1CqBeEhCgCIUyvxoALuOAWJ66xHZf4pNnr2JvNJrtZO16nucV24hI34gaOjW1swBSD6QKEQAUkIaE+6jMq099+mJEWY0AgvZk3b+b+viQnR+hdBaGr96+CKIoiiIiIJKKSOwAREfkXFgsREUmKxUJERJJisRARkaRYLEREJCkWCxERSYrFQkREkmKxEBGRpFgsREQkKRYLERFJisVCRESSYrEQEZGkWCxERCQpFgsREUmKxUJERJJisRARkaRYLEREJCkWCxERSYrFQkREkmKxEBGRpFgsREQkKRYLERFJisVCRESSYrEQEZGkWCxERCQpFgsREUmKxUJERJJisRARkaRYLEREJCkWCxERSYrFQkREkmKxEBGRpDRyByAiGgmL3YH1Jc1oM1lwy8w0RIZo5Y5EboIoiqLcIYiILlRHrwXv7q/H69/Uor3XAgAI0qgwf1wcritMwZVTxsqckFgsRKQYe2s6cOc/9qPf6gAAZMWFIUijQmWrCYM/yZ793jSsKkiSMSWxWIhIEY63mrD6xV0wme2YmRmDh5bkYO64WAiCAIPRjOe2VOGtPXUI0arx4f2zMTEpUu7IAYvFQkQ+r6XHjOv++g2aesxYMTERL3y/EGqV8K2PEUURv3i/BOsONSIxIhjrHpiDpKgQmRIHNq4KIyKf1t5rwS2v7EFTjxnT06Pxl5unfqdUAEAQBDxx3WTMyIhGi9GMu/6xH2abQ4bExGIhIp/ldIq4/+1i1LT1IS9Rj1duK0KwVn3Ojw/WqvHKbTOQERuKihYT1h5s8GJaGsRiISKf9a/Djdhf24UEvQ5v/vASRIcFDftnIkO1+MWyXADA81uq+NYiAxYLEfmkXosdT31RAQB4ZHkuEvTBF/xnr5w8FnmJejT3mPH2njpPRaRzYLEQkU96fksVWo0WFKRGYXVhykX9WZVKwCPLXW8tL2ytgtFs80REOgcWCxH5nFMd/Xht50kAwH+tmgjVWSbrh7MkLwGXZMagq9+Gl7ZVSx2RzoPFQkQ+RRRF/PqTo7A6nLhuWjIKUqNG9HkEQcBjl+cBAF7deRLNPQNSxqTzYLEQkU/5rLQZ2yrbEBGswS+vyBvV55qWFo0rJ4+Fxe7E69/UShOQhsViISKf4XCK+MOXlQCARy/Pu6gJ+3P54fxMAMCHxQ2w2LlCzBtYLETkMzaXt6K2ox+pMSG4qShVks85LTUKE8ZGoLPPii+OtkjyOen8WCxE5DNe3eGasL9jTiY0aml+PAmCgB/MSgMAvLmbS4+9gcVCRD5hf20n9tV2IiJYgxuLLm558XCumZoMfbAGxXVdONrYI+nnpu9isRCRTxhcXnzb7Azog6W9tCtMp8GN7qG1N3bVSvq56btYLEQku6buAWwsa4VGJeDW2ekeecats9IhCMDHJU1DF4SRZ7BYiEh2b+yuhcMp4vLJYzEmYvQrwc4mIy4Ml+YlwGp34p09pzzyDHJhsRCRrHotdqzZ6/pBf9fcDI8+6665rqXHb++tg93h9OizAhmLhYhk9dHBBpjMdhSlR2NaWrRHnzU7OxaZcWFoM1nw9Yk2jz4rkLFYiEhW7x9w3Zly25wMjz9LEIShSfw1e+s9/rxAxWIhItlUt/WitLEH4ToNluWP8cozr5+eArVKwLZKAzr7rF55ZqBhsRCRbD4+1AgAuHxS4nlvhpRSvF6H+TlxsDtF/Mv9fJIWi4WIZCGKIj4paQIAXD012avPvnmGazjs7T11cDpFrz47ELBYiEgWh+u7UdvRjwS9DrOzY7367KUTxmBsZDBq2vuwq7rDq88OBCwWIpLF4NvKVVOSoB7BRV6joVGrcJP7reWDYk7iS43FQkRe53SK+Ly0GQCwsmCsLBkGrzv+8lgLTLy6WFIsFiLyuoOnutBqtCA5KgRTR3hD5GilxoRiZmYMzDYn1hY3yJLBX7FYiMjrPi913YtyxeRECIJ3h8FOd9vsDADAewdYLFJisRCRV4miiE3lrmJZMSlR1ixL8xOgD9agvNmImrZeWbP4ExYLEXlVRYsJ9Z0DiAsPwtRUzx7hMhydRo2lE1wbMzfwdknJsFiIyKu2VhoAAItzE7y+GuxsrpriWjzw0aFGiCL3tEiBxUJEXvX1cdfhjwtz42VO4rJgfDyiQ7WoMvSistUkdxy/wGIhIq/pt9pRXNcFlQDMGxcndxwAgFatwvKJrrmez440y5zGP7BYiMhriuu6YHOImJQciajQILnjDFk1NQkA8MGBBh7xIgEWCxF5zc6qdgDA7CzvHuEynNlZsUiOCkGL0YxD9d1yx1E8FgsRec037mKZl+Mbw2CDBEHgcJiEWCxE5BU9AzaUNRmhUQkoSo+RO853XO0eDvukpAkODoeNCouFiLxid3UHnCJQmBaNkCDv3L1yMaakRCI9NhTtvRbsr+2UO46isViIyCt2V7uGweb6yGqwMwmCMHQSwBfcLDkqLBYi8oo9Na63AG/fvXIxBudZNpW1cnXYKLBYiMjj+ix2HDeYoFEJmJISKXecc5qaEoWkyGA0dg9gH4fDRozFQkQeV9rYA1EExo/Re+1u+5FQqQRcW+i6Jvmjg40yp1EuFgsRedxu9/W/M7N8bzXYma6e6iqWDUebYbE7ZE6jTCwWIvK4Iw2uTYczMny/WMaP0SMvUQ+j2Y7tlW1yx1EkFgsReVxli+twx9xEvcxJLszgES+flXKz5EiwWIjIo4xmG5p6zAjSqJAeEyp3nAty5WTXUfpbyg2w2p0yp1EeFgsRedSR+h4AwIREPTRqZfzISY8NQ16iHiaLHbtrOuSOozjK+FsmIsUqbzYCACb78DLjsxm8WXJrhUHmJMrDYiEijzra5H5jGRshc5KLszjPdRHZ4MVkdOFYLETkUYdOuVaEFaREyZzk4hSkRCEiWIOa9j7Ud/bLHUdRWCxE5DFtJgtOdfYjNEiNPIWsCBukUaswJ9t1rtmOE+0yp1EWFgsReUxxnetYlKmpUYqZuD/dXPe9Md9Us1guhvL+polIMYrrugAARQrYGHk2c90HZu6p7oAo8lDKC8ViISKP2V/rKpbp6dEyJxmZzLgwJOh16OizorqtV+44isFiISKP6LXYUdrYA7VKQGGasibuBwmCgKIMVykecJckDY/FQkQecaShGw6niIlJEdAHa+WOM2KFaa5iGVzdRsNjsRCRR5S4d9xPTlbWxsgzTXMXS/EpvrFcKBYLEXnE4Iowpc6vDJqcHIkgjQpVhl709NvkjqMILBYikpzTKQ5N3CvhqPzzCdKohk4NONbcI3MaZWCxEJHkKltN6BmwISkyGCnRIXLHGbV8d7GUNRllTqIMLBYiktwu942Rs7JiIQiCzGlGb1Kyq1iONvKN5UKwWIhIcgdqXfMrs7JiZU4ijcE3lgr3hWV0fiwWIpKUKIpDO+4L05W5f+VMOWP0EASguq0XFrtD7jg+j8VCRJKqae+DwWRBTFgQsuLC5Y4jiXCdBllxYbA5RJQ3861lOCwWIpLU4PzKnOxYqFTKn18ZNHjsP+dZhsdiISJJ7Tzhuhhr8Mh5f5HrPva/ysAzw4bDYiEiyVjsDux0312yMDde5jTSynNP4Jc08GiX4bBYiEgy+052os/qQF6iHslRyt+/crqp7qGw8mYjj9AfBouFiCTzVbkBALAkL0HmJNKLDNUiKlQLs82Jtl6L3HF8GouFiCQhiiI2lbUCAC6dMEbmNJ6RHhsGgPMsw2GxEJEkjjUZ0dg9gHi9DtNS/WP/ypkmJrnmWbjk+PxYLEQkiQ1HmwEAy/LH+NUy49PlJLj25VQZWCznw2IholETRRGfl7YAAK6cPFbmNJ6Tk+Baclxt6JM5iW9jsRDRqJU3m3CyvQ8xYUG4JFPZx+SfT3psKADgVGe/zEl8G4uFiEZt/ZEmAMCKSYnQqP33x0piZDCC1Cq0GM0wmXnp17n473cAEXmFaxjMNb9y1RT/HQYDAK1ahbyxruGwo428m+VcNHIHIPJXFrsDVYZeHGs0oqzZiJYeMzr6LGjvtcJqdyIyxLUvIjEyGLOyYnFpXgJiw3Vyx75ox5qMqOvoR1x4EGZm+scx+eczKTkSRxp6UNrYjdnZ/v/1jgSLhUgioiiiytCL7cfb8FW5AftrO2F3nnuHdmP3wNB/X3ewEYIAFKZF4+YZqVhdmKKYlVXvH6gHAFwxeSzUCsk8GlOSI7EGQCnfWM6JxUI0SgajGR8ebMAHBxpwsv3/VwsJApAVF4b8pAhMTIpEakwIYsN0iNcHIUithtFsQ2efFSfb+/BVhQG7q9tRXNeF4rou/GNXLf79igmYM863D3Lss9jx0cFGAMDNM9JkTuMdE5MiAQAVzSyWc2GxEI1QaUMPnvnqBLZWGuBwv5nEhgVhwfh4LM5LwMKceESGaof9PAvGx+P2ORnotdjxeWkz/rTxOI41GXHLK3sxPycOT19fgMTIYE9/OSOy9mADTBY7itKjke/ePOjvshNcu+9PtvfB5nBC68eLFUaKxUJ0karbevGnTcfx2RHXhLVGJWD5xDG4aUYqFuTEj3hVVLhOgxuLUrGqIAmv7jyJl7ZVY8eJdlzx7A48ff0UnzsmRRRFvLW7DgBw59xMmdN4T2iQBhmxoajt6EdliwmTkiPljuRzWCxEF6hnwIYnPy/HewfqIYpAkEaFO+dk4J4FWYiTcNI9WKvGg4vH4eYZqfjpe4ex40Q7fvjGATy4OBs/vyzXZ+Yxdla144ShF2MidFg20bdKz9OmpUWjtqMfh051sVjOgu9Line truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb07379e8>"
+       "<matplotlib.figure.Figure at 0x20496127208>"
       ]
      },
      "metadata": {},
@@ -607,9 +610,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from numpy.random import uniform\n",
@@ -646,10 +647,10 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 0.772,  0.336,  4.17],\n",
-       "       [ 0.333,  0.34,  4.32],\n",
-       "       [ 0.6,  0.274,  5.02],\n",
-       "       [ 0.0539,  0.0218,  5.03]])"
+       "array([[0.772, 0.336, 4.171],\n",
+       "       [0.333, 0.34 , 4.319],\n",
+       "       [0.6  , 0.274, 5.02 ],\n",
+       "       [0.054, 0.022, 5.034]])"
       ]
      },
      "execution_count": 8,
@@ -673,9 +674,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def predict(particles, u, std, dt=1.):\n",
@@ -729,9 +728,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def update(particles, weights, z, R, landmarks):\n",
@@ -769,9 +766,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def estimate(particles, weights):\n",
@@ -798,7 +793,7 @@
     {
      "data": {
       "text/plain": [
-       "(array([ 0.494,  0.514]), array([ 0.0833,  0.0846]))"
+       "(array([0.494, 0.514]), array([0.083, 0.085]))"
       ]
      },
      "execution_count": 12,
@@ -830,9 +825,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def simple_resample(particles, weights):\n",
@@ -860,9 +853,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def neff(weights):\n",
@@ -890,9 +881,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def resample_from_index(particles, weights, indexes):\n",
@@ -920,14 +909,14 @@
      "output_type": "stream",
      "text": [
       "final position error, variance:\n",
-      "\t [ -0.106  0.106] [ 0.0086  0.00757]\n"
+      "\t [-0.106  0.106] [0.009 0.008]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb07c6198>"
+       "<matplotlib.figure.Figure at 0x20496294160>"
       ]
      },
      "metadata": {},
@@ -1049,14 +1038,14 @@
      "output_type": "stream",
      "text": [
       "final position error, variance:\n",
-      "\t [ -0.0187  -0.00526] [ 0.00515  0.0056]\n"
+      "\t [-0.019 -0.005] [0.005 0.006]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb07b4240>"
+       "<matplotlib.figure.Figure at 0x204962678d0>"
       ]
      },
      "metadata": {},
@@ -1106,14 +1095,14 @@
      "output_type": "stream",
      "text": [
       "final position error, variance:\n",
-      "\t [ -0.17  0.0845] [ 0.00548  0.00501]\n"
+      "\t [-0.17   0.084] [0.005 0.005]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb0864e80>"
+       "<matplotlib.figure.Figure at 0x2049737eb70>"
       ]
      },
      "metadata": {},
@@ -1156,14 +1145,14 @@
      "output_type": "stream",
      "text": [
       "final position error, variance:\n",
-      "\t [ -2.69  -31.5] [ 47.1  47.0]\n"
+      "\t [ -2.688 -31.47 ] [47.065 47.03 ]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb0895518>"
+       "<matplotlib.figure.Figure at 0x20496141f98>"
       ]
      },
      "metadata": {},
@@ -1194,14 +1183,14 @@
      "output_type": "stream",
      "text": [
       "final position error, variance:\n",
-      "\t [ 0.0353  -0.0766] [ 0.00741  0.00933]\n"
+      "\t [ 0.035 -0.077] [0.007 0.009]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb09a0860>"
+       "<matplotlib.figure.Figure at 0x2049743d240>"
       ]
      },
      "metadata": {},
@@ -1298,9 +1287,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def resample_from_index(particles, weights, indexes):\n",
@@ -1329,14 +1316,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cumulative sume is [ 0.1  0.3  0.4  1.0]\n"
+      "cumulative sume is [0.1 0.3 0.4 1. ]\n"
      ]
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv4AAABUCAYAAAD6fTMYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAI/5vdhshOR1AAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb09e6c88>"
+       "<matplotlib.figure.Figure at 0x2049746d3c8>"
       ]
      },
      "metadata": {},
@@ -1361,9 +1348,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def multinomal_resample(weights):\n",
@@ -1388,9 +1373,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAABYCAYAAAC9M+JCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb0b05710>"
+       "<matplotlib.figure.Figure at 0x204975a69e8>"
       ]
      },
      "metadata": {},
@@ -1429,9 +1414,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def residual_resample(weights):\n",
@@ -1472,9 +1455,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb0a53080>"
+       "<matplotlib.figure.Figure at 0x20497603710>"
       ]
      },
      "metadata": {},
@@ -1515,9 +1498,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb09ba4e0>"
+       "<matplotlib.figure.Figure at 0x204975fd240>"
       ]
      },
      "metadata": {},
@@ -1539,9 +1522,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def stratified_resample(weights):\n",
@@ -1588,9 +1569,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb08c8cc0>"
+       "<matplotlib.figure.Figure at 0x204960ff2e8>"
       ]
      },
      "metadata": {},
@@ -1612,9 +1593,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def systematic_resample(weights):\n",
@@ -1666,9 +1645,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20fb070ec50>"
+       "<matplotlib.figure.Figure at 0x204961845c0>"
       ]
      },
      "metadata": {},
@@ -1676,9 +1655,9 @@
     },
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20faeec4fd0>"
+       "<matplotlib.figure.Figure at 0x2049624b7b8>"
       ]
      },
      "metadata": {},
@@ -1686,9 +1665,9 @@
     },
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20faf553b00>"
+       "<matplotlib.figure.Figure at 0x204962125f8>"
       ]
      },
      "metadata": {},
@@ -1696,9 +1675,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAABYCAYAAAC9M+JCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20faf536160>"
+       "<matplotlib.figure.Figure at 0x2049627d438>"
       ]
      },
      "metadata": {},
@@ -1777,7 +1756,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,

book_format.py

@@ -45,7 +45,7 @@ def test_filterpy_version():
     from distutils.version import LooseVersion
 
     v = filterpy.__version__
-    min_version = "1.3.2"
+    min_version = "1.4.0"
     if LooseVersion(v) < LooseVersion(min_version):
        raise Exception("Minimum FilterPy version supported is {}.\n"
                        "Please install a more recent version.\n"

kf_book/nonlinear_plots.py

@@ -28,34 +28,31 @@ from filterpy.kalman import MerweScaledSigmaPoints, unscented_transform
 from filterpy.stats import multivariate_gaussian
 
 
-def plot_nonlinear_func(data, f, gaussian, num_bins=300):
-    # linearize at mean to simulate EKF
-    #x = gaussian[0]
+def plot_nonlinear_func(data, f, out_lim=None, num_bins=300):
 
-    # equation of linearization
-    #m = df(x)
-    #b = f(x) - x*m
-
-    # compute new mean and variance based on EKF equations
     ys = f(data)
-    x0 = gaussian[0]
-    in_std = np.sqrt(gaussian[1])
+    x0 = np.mean(data)
+    in_std = np.std(data)
+    
     y = f(x0)
     std = np.std(ys)
 
-    in_lims = [x0-in_std*3, x0+in_std*3]
-    out_lims = [y-std*3, y+std*3]
+    in_lims = [x0 - in_std*3, x0 + in_std*3]
+    if out_lim is None:
+        out_lim = [y - std*3, y + std*3]
 
-    #plot output
+    # plot output
     h = np.histogram(ys, num_bins, density=False)
-    plt.subplot(2,2,4)
-    plt.plot(h[0], h[1][1:], lw=2, alpha=0.8)
-    plt.ylim(out_lims[1], out_lims[0])
-    plt.gca().xaxis.set_ticklabels([])
+    plt.subplot(221)
+    plt.plot(h[1][1:], h[0], lw=2, alpha=0.8)
+    if out_lim is not None:
+        plt.xlim(out_lim[0], out_lim[1])
+
+    plt.gca().yaxis.set_ticklabels([])
     plt.title('Output')
 
-    plt.axhline(np.mean(ys), ls='--', lw=2)
-    plt.axhline(f(x0), lw=1)
+    plt.axvline(np.mean(ys), ls='--', lw=2)
+    plt.axvline(f(x0), lw=1)
 
 
     norm = scipy.stats.norm(y, in_std)
@@ -73,20 +70,21 @@ def plot_nonlinear_func(data, f, gaussian, num_bins=300):
     y = f(x)
     plt.plot (x, y, 'k')
     isct = f(x0)
-    plt.plot([x0, x0, in_lims[1]], [out_lims[1], isct, isct], color='r', lw=1)
+    plt.plot([x0, x0, in_lims[1]], [out_lim[1], isct, isct], color='r', lw=1)
     plt.xlim(in_lims)
-    plt.ylim(out_lims)
+    plt.ylim(out_lim)
     #plt.axis('equal')
     plt.title('f(x)')
 
     # plot input
     h = np.histogram(data, num_bins, density=True)
 
-    plt.subplot(2,2,1)
-    plt.plot(h[1][1:], h[0], lw=2)
-    plt.xlim(in_lims)
-    plt.gca().yaxis.set_ticklabels([])
+    plt.subplot(2,2,4)
+    plt.plot(h[0], h[1][1:], lw=2)
+    #plt.ylim(in_lims)
+    plt.gca().xaxis.set_ticklabels([])
     plt.title('Input')
+    plt.tight_layout()
     plt.show()
 
     
@@ -141,7 +139,7 @@ def plot_ukf_vs_mc(alpha=0.001, beta=3., kappa=1.):
 
 
     points = MerweScaledSigmaPoints(1, alpha, beta, kappa)
-    Wm, Wc = points.weights()
+    Wm, Wc = points.Wm, points.Wc
     sigmas = points.sigma_points(mean, var)
 
     sigmas_f = np.zeros((3, 1))

kf_book/ukf_internal.py

@@ -179,21 +179,21 @@ def show_sigma_selections():
 
     points = MerweScaledSigmaPoints(2, .09, 2., 1.)
     sigmas = points.sigma_points(x, P)
-    Wm, Wc = points.weights()
+    Wm, Wc = points.Wm, points.Wc
     plot_covariance_ellipse(x, P, facecolor='b', alpha=.3, variance=[.5])
     _plot_sigmas(sigmas, Wc, alpha=1.0, facecolor='k')
 
     x = np.array([5, 5])
     points = MerweScaledSigmaPoints(2, .15, 1., .15)
     sigmas = points.sigma_points(x, P)
-    Wm, Wc = points.weights()
+    Wm, Wc = points.Wm, points.Wc
     plot_covariance_ellipse(x, P, facecolor='b', alpha=0.3, variance=[.5])
     _plot_sigmas(sigmas, Wc, alpha=1.0, facecolor='k')
 
     x = np.array([8, 5])
     points = MerweScaledSigmaPoints(2, .2, 3., 10)
     sigmas = points.sigma_points(x, P)
-    Wm, Wc = points.weights()
+    Wm, Wc = points.Wm, points.Wc
     plot_covariance_ellipse(x, P, facecolor='b', alpha=0.3, variance=[.5])
     _plot_sigmas(sigmas, Wc, alpha=1.0, facecolor='k')
 
@@ -232,17 +232,25 @@ def show_sigmas_for_2_kappas():
     plt.show()
 
 
+def plot_sigmas(sigmas, x, cov):
+    if not np.isscalar(cov):
+        cov = np.atleast_2d(cov)
+    pts = sigmas.sigma_points(x=x, P=cov)
+    plt.scatter(pts[:, 0], pts[:, 1], s=sigmas.Wm*1000)
+    plt.axis('equal')
+    
+
 def plot_sigma_points():
     x = np.array([0, 0])
     P = np.array([[4, 2], [2, 4]])
 
     sigmas = MerweScaledSigmaPoints(n=2, alpha=.3, beta=2., kappa=1.)
     S0 = sigmas.sigma_points(x, P)
-    Wm0, Wc0 = sigmas.weights()
+    Wm0, Wc0 = sigmas.Wm, sigmas.Wc
 
     sigmas = MerweScaledSigmaPoints(n=2, alpha=1., beta=2., kappa=1.)
     S1 = sigmas.sigma_points(x, P)
-    Wm1, Wc1 = sigmas.weights()
+    Wm1, Wc1 = sigmas.Wm, sigmas.Wc
 
     def plot_sigmas(s, w, **kwargs):
         min_w = min(abs(w))
@@ -296,7 +304,7 @@ def plot_altitude(xs, t, track):
 def print_sigmas(n=1, mean=5, cov=3, alpha=.1, beta=2., kappa=2):
     points = MerweScaledSigmaPoints(n, alpha, beta, kappa)
     print('sigmas: ', points.sigma_points(mean,  cov).T[0])
-    Wm, Wc = points.weights()
+    Wm, Wc = points.Wm, points.Wc
     print('mean weights:', Wm)
     print('cov weights:', Wc)
     print('lambda:', alpha**2 *(n+kappa) - n)