Issue #122.

Description of ellipse shape was incorrect.

While I was changing this, I also alterted the plots to make them
more readalbe.

06-Multivariate-Kalman-Filters.ipynb

@@ -396,7 +396,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvAAAADlCAYAAAA1IsN7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e59c1fd0>"
+       "<matplotlib.figure.Figure at 0x178f1631b38>"
       ]
      },
      "metadata": {},
@@ -1004,7 +1004,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAEWCAYAAAA97QBbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e89e87b8>"
+       "<matplotlib.figure.Figure at 0x178f4665828>"
       ]
      },
      "metadata": {},
@@ -1486,7 +1486,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e89e8550>"
+       "<matplotlib.figure.Figure at 0x178f4665358>"
       ]
      },
      "metadata": {},
@@ -1527,7 +1527,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e89e8320>"
+       "<matplotlib.figure.Figure at 0x178f5825908>"
       ]
      },
      "metadata": {},
@@ -1663,7 +1663,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e787f470>"
+       "<matplotlib.figure.Figure at 0x178f46eab00>"
       ]
      },
      "metadata": {},
@@ -1713,7 +1713,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e8a61c50>"
+       "<matplotlib.figure.Figure at 0x178f46ea0b8>"
       ]
      },
      "metadata": {},
@@ -1904,7 +1904,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e81d9400>"
+       "<matplotlib.figure.Figure at 0x178f58f0320>"
       ]
      },
      "metadata": {},
@@ -2037,7 +2037,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e8a21d30>"
+       "<matplotlib.figure.Figure at 0x178f5898d68>"
       ]
      },
      "metadata": {},
@@ -2128,7 +2128,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e81c6198>"
+       "<matplotlib.figure.Figure at 0x178f58e2390>"
       ]
      },
      "metadata": {},
@@ -2157,7 +2157,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e81c6128>"
+       "<matplotlib.figure.Figure at 0x178f56c7d68>"
       ]
      },
      "metadata": {},
@@ -2230,7 +2230,7 @@
     "$$\\begin{aligned}x& = \\bar x + yK_x\\\\\n",
     "\\dot x &= \\bar{\\dot x} + yK_{\\dot x}\\end{aligned}$$\n",
     "\n",
-    "The prediction $\\bar x$ was computed as $x + \\bar x \\Delta t$. If the prediction was perfect then the residual will be $y=0$ (ignoring noise in the measurement) and the velocity estimate will be unchanged. On the other hand, if the velocity estimate was very bad then the prediction will be very bad, and the residual will be large: $y >> 0$. In this case we update the velocity estimate with $yK_{\\dot x}$. $K_{\\dot x}$ is proportional to $COV(x,\\dot x)$. Therefore the velocity is updated by the error in the position times the a value proportional to the covariance between the position and velocity. The higher the correlation the larger the correction. \n",
+    "The prediction $\\bar x$ was computed as $x + \\bar x \\Delta t$. If the prediction was perfect then the residual will be $y=0$ (ignoring noise in the measurement) and the velocity estimate will be unchanged. On the other hand, if the velocity estimate was very bad then the prediction will be very bad, and the residual will be large: $y >> 0$. In this case we update the velocity estimate with $yK_{\\dot x}$. $K_{\\dot x}$ is proportional to $COV(x,\\dot x)$. Therefore the velocity is updated by the error in the position times the value proportional to the covariance between the position and velocity. The higher the correlation the larger the correction. \n",
     "\n",
     "To bring this full circle, $COV(x,\\dot x)$ is the off-diagonal elements of $\\mathbf P$. Recall that those values were computed with $\\mathbf{FPF}^\\mathsf T$. So the covariance of position and velocity is computed during the predict step. The Kalman gain for the velocity is proportional to this covariance, and we adjust the velocity estimate based on how inaccurate it was during the last epoch times a value proportional to this covariance. \n",
     "\n",
@@ -2260,7 +2260,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9ed692ef0>"
+       "<matplotlib.figure.Figure at 0x178f5a77748>"
       ]
      },
      "metadata": {},
@@ -2305,7 +2305,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e9e785f8>"
+       "<matplotlib.figure.Figure at 0x178f93e86a0>"
       ]
      },
      "metadata": {},
@@ -2335,7 +2335,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9ed761c18>"
+       "<matplotlib.figure.Figure at 0x178f93e4f98>"
       ]
      },
      "metadata": {},
@@ -2366,7 +2366,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e8276518>"
+       "<matplotlib.figure.Figure at 0x178f5808320>"
       ]
      },
      "metadata": {},
@@ -2408,7 +2408,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e8a074a8>"
+       "<matplotlib.figure.Figure at 0x178f36a4b00>"
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      "metadata": {},
@@ -2453,7 +2453,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e7913780>"
+       "<matplotlib.figure.Figure at 0x178f392c860>"
       ]
      },
      "metadata": {},
@@ -2487,7 +2487,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e8270cf8>"
+       "<matplotlib.figure.Figure at 0x178f5accef0>"
       ]
      },
      "metadata": {},
@@ -2520,7 +2520,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9ed64b668>"
+       "<matplotlib.figure.Figure at 0x178f5892358>"
       ]
      },
      "metadata": {},
@@ -2552,19 +2552,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9ed64b630>"
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-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e9b24b38>"
+       "<matplotlib.figure.Figure at 0x178f57ddf98>"
       ]
      },
      "metadata": {},
@@ -2587,8 +2577,10 @@
     "        cov.append(f.P)\n",
     "    plot_track_ellipses(count, data[:, 1], xs, cov, title)\n",
     "\n",
+    "plt.subplot(121)\n",
     "plot_covariances(R=5, Q=.02, count=20, title='$R = 5\\, m^2$')\n",
-    "plot_covariances(R=.5, Q=.02, count=20, title='$R = 0.5\\, m^2$')"
+    "plt.subplot(122)\n",
+    "plot_covariances(R=.1, Q=.02, count=20, title='$R = 0.5\\, m^2$')"
    ]
   },
   {
@@ -2618,7 +2610,7 @@
    "source": [
     "## Question: Explain Ellipse Differences\n",
     "\n",
-    "Why are the ellipses for $\\mathbf R=5 m^2$ shorter, and more tilted than the ellipses for $\\mathbf R=0.5 m^2$. Hint: think about this in the context of what these ellipses mean physically, not in terms of the math. If you aren't sure about the answer,change $\\mathbf R$ to truly large and small numbers such as 100 $m^2$ and 0.1 $m^2$, observe the changes, and think about what this means. "
+    "Why are the ellipses for $\\mathbf R=5 m^2$ more tilted towards the horizontal than the ellipses for $\\mathbf R=0.5 m^2$. Hint: think about this in the context of what these ellipses mean physically, not in terms of the math. If you aren't sure about the answer,change $\\mathbf R$ to truly large and small numbers such as 100 $m^2$ and 0.1 $m^2$, observe the changes, and think about what this means. "
    ]
   },
   {
@@ -2631,7 +2623,7 @@
     "\n",
     "**This is a critical point to understand!**. The Kalman filter is a mathematical model for a real world system. A report of little correlation *does not mean* there is no correlation in the physical system, just that there was no *linear* correlation in the mathematical model. It's a report of how much measurement vs prediction was incorporated into the model.  \n",
     "\n",
-    "Let's bring that point home with a truly large measurement error. We will set $\\mathbf R=200\\, m^2$. Think about what the plot will look like before scrolling down."
+    "Let's bring that point home with a truly large measurement error. We will set $\\mathbf R=200\\, m^2$. Think about what the plot will look like before looking at it."
    ]
   },
   {
@@ -2643,9 +2635,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAElCAYAAACbGvqYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9ed6c0c18>"
+       "<matplotlib.figure.Figure at 0x178f36b3b38>"
       ]
      },
      "metadata": {},
@@ -2653,7 +2645,7 @@
     }
    ],
    "source": [
-    "plot_covariances(R=200., Q=.2, count=10, title='$R = 200\\, m^2$')"
+    "plot_covariances(R=200., Q=.2, count=5, title='$R = 200\\, m^2$')"
    ]
   },
   {
@@ -2770,9 +2762,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEPCAYAAABvDuGDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEPCAYAAABvDuGDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e8281438>"
+       "<matplotlib.figure.Figure at 0x178f5767f28>"
       ]
      },
      "metadata": {},
@@ -2816,9 +2808,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEPCAYAAABvDuGDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl4Tdf+x/H3OZlPZpFJgiSEmGdCiJkai6KlSlvV6q2r\nwy29aFVvtUqrqreTak23ooYaKobSIkESxDzWkEgkkUFmkfns3x/n57THCSESieT7eh7PZa119v4e\nu9yPvddeS6UoioIQQgghhCgX6souQAghhBCiOpFwJYQQQghRjiRcCSGEEEKUIwlXQgghhBDlSMKV\nEEIIIUQ5Mn3UJ8zMzHzUpxRCCCGEqDD29vYGv5Y7V0IIIYQQ5UjClRBCCCFEOXrkjwX/7s7baOUp\nMjISgPbt21fYOUTZyfWp+uQaVW1yfao+uUZV38Nco3tNc5I7V0IIIYQQ5UjClRBCCCFEOZJwJYQQ\nQghRjiRcCSGEEEKUo1LD1QcffIBarTb4UadOHYMxc+bMwcPDA41GQ8+ePTl37lyFFSyEEEIIUZLI\nyEimT5/Om2++yc6dOyutjvu6c+Xn50dSUhKJiYkkJiZy+vRpfd/8+fNZtGgRX3/9NZGRkbi4uNC3\nb19ycnIqrGghhBBCiL/bsGED/v7+fPrpp3zxxRcMGDCAefPmVUot9xWuTE1NcXZ2xsXFBRcXF5yc\nnPR9ixcvZsaMGQwbNoymTZuycuVKsrOzCQoKqrCihRBCCCFuKygo4KWXXqK4uJgJEyYwbdo0VCoV\ns2bNIjo6+pHXc1/hKioqCg8PD3x8fBgzZoy+0OjoaBITE+nbt69+rKWlJYGBgYSFhVVMxUIIIYQQ\nfxMfH09mZibOzs4sX76cBQsW0K1bNxRF4fz584+8nlIXEfX392fFihX4+fmRnJzMhx9+SEBAAGfP\nniUxMRGVSoWrq6vBZ1xdXUlISCj15LcX76pIj+Icouzk+lR9co2qNrk+VZ9co4p369YtTExMSElJ\n4T//+Q+Ojo4cOXIEgPT09FKvQVmuka+v7137Sg1X/fv3N/i1v78/3t7erFy5kk6dOj1wMUIIIYQQ\n5Umj0fDss8+yatUq5syZo28PDAykUaNGj7yeB97+RqPR0KxZMy5dusSTTz6JoigkJSXh6empH5OU\nlISbm1upx6rILQFk24GqTa5P1SfXqGqT61P1yTV6tJYvX07r1q1ZvXo1hYWFPPHEE3zwwQdYWlre\n9TNVZvubvLw8Lly4QJ06dfD29sbNzY3du3cb9O/fv5+AgIAHLlQIIYQQoizUajVvvvkmkZGRnDx5\nkvnz598zWFVoLaUNmDZtGqGhoVy9epVDhw4xcuRIbt26xfjx4wF44403mD9/Pps2beLMmTM8//zz\n2NraMmbMmAovXgghhBCPhqIoaLXayi7jrhRFYf8JhVU7lMoupfTHgnFxcYwdO5YbN27g7OyMv78/\nERER1K1bF4Dp06eTl5fHlClTSE9Pp1OnTuzatQtra+sKL14IIYQQFUur1fLhhx+yePFisrKy6NCh\nA8uWLaNJkyaVXRoASWkKq3bAj1vh4jWws4aRPRU0lqpKq6nUcLVmzZpSDzJ79mxmz55dLgUJIYQQ\nour4+OOPDSaJR0RE0Lt3b06fPm2w7mVFyszMJCoqCnd3d9zc3CguVth1WBeofj0ARcV/jc3KgQ17\nYfyAR1JaiWRvQSGEEELc1VdffQXobrakpaXRtm1brl+/zubNmx/J+X/88UdcXV1p27Yt7l7+tB8e\njPdTCoPeho0hhsHKVgMvPwltGz+S0u7qgd8WFEIIIUTNkZ2dDUC3bt1wdHSkZcuWHDt27J5vy5WX\n/fv3M+nl11Ach6JpMYVcswCOJRvfF+raEl4cDKN6gbVV5T0OvE3ClRBCCCHuqkuXLvz+++/07duX\n1q1bs27dOn17RTp9ReH1L7Qo7a6hMqtN7h39zg66R38Th4Bf/coPVH8n4UoIIYQQd7V06VJ69erF\n+fPn9VvJzJ07F39//3I/V+ZNhZ9/h2XBcOQ8QCAqs7/6VSho03fiY72X85sXYG5WtULVbRKuhBBC\nCHFXXl5enDhxguDgYDIzM+nQoUO5LoyqKAqhJ3SBasNeyM0vYUzeVVxVO0i9sBBtThQTPvigygYr\nkHAlhBBCiFLY2dkxduzYcj1mfIpuCYXl2+BynHG/uRmM6A5OxVv4dv5okooKAZgwYQIzZ84s11rK\nm4QrIYQQQjwS+QUKWw/C8mD47TCUtCZpq4a6yenP9odadipgGLNfj+fSpUu4u7vj7e39yOt+UBKu\nhBBCCFGhTlxUWL4dVv8GaVnG/fY2MKYvTBysW0ZBpTJ85Ofs7Iyzs/MjqvbhSbgSQgghRLlLzVQI\n2gUrtsPxiyWP6dkWnh8ET/WgUldUL28SroQQQghRLoqLFXYfgRXbYPN+KCg0HlPPFSYMhOcHgned\n6hOo/k7ClRBCCFEDRUdH89JLLxEeHo6trS1Tpkxh1qxZqNUPvnnLpWsKK7bDqh0Qn2Lcb2Gum5z+\nwiDo1Q7U6uoZqm6TcCWEEELUMJmZmfTq1YurV68CkJuby+zZs9Fqtbz//vv3dYybtxTW79VNTj9w\nquQxHZroHvs90xsc7ap3oPo72VtQCCGEqGGCg4O5evUqTZs2JSEhgTVr1gDw5Zdf3vNzujWpFF78\nSMF9KEz82DhYOTvAG0/DyVVw6AcVrw5X1ahgBXLnSgghhKhxMjIyAGjdujXu7u7069cPgKysLLRa\nrdGjwfgUhZXbdZPTS1qTysQEBnXW3aUa2JkqvcDnoyDhSgghhKhhOnbsCMDPP/+Mqakpp06d0rff\nDlYFhQpbD+gW+dx5qOQ1qZp66QLVuP7g5lSzA9XfyWNBIYQQoobp0KEDH330EVqtllWrVnHixAnq\n1KnDsmXLOBul8NaXCp7DYNS7sD3cMFjZ28ArwyBiKZz+Cd4eq9IHq7i4OEaNGoWPjw8dO3Zkw4YN\nlfQNK5fcuRJCCCFqoJkzZ9KrVy8iIiIws3SiwHY4z39mzaFzJY/v1U73tt+IHmBlYXyXKj09nW7d\nuuknyUdHRzNq1CjWrFnDM888U3FfpAqScCWEEELUQIqiUGTViZM3O7F+K9zKMx7j6aJbj+r5geDj\nce/HfkFBQVy9epUWLVqwatUq1q1bx7x58/j444/vGq5SU1O5desWHh4eZVoCoqqScCWEEELUICnp\nCqt2wo9b4UKMcb+ZKQwL1N2l6tsBTEzuby5VcnIyAP3796d169bY2toyb948kpKSjMZmZ2czYcIE\nNm3aBECDBg1Yt24dbdu2LfsXq0IkXAkhhBDVnFar8Eck/LAVNodCYZHxmBYN/n/D5H5Q2+HBJ6e3\nbNkSgO+//x6NRsPvv/9u0P53r7zyCps2bcLU1BSNRsOVK1cYOHAg586do1atWg987qpGwpUQQghR\nTcWnKCzfBsuC4ep1434bKxjTD14aAu39jDdMfhAjRozgmWee4eeff+Y///kPoNtw+auvvjIYV1hY\nyLp16wA4c+YMXl5e+Pv7c+LECf744w9GjRpV5hqqCglXQgghRDVSVKTwy55b/LAV9p6wQqs1Dkz+\nzWDiEHi6N9hoymcJBZVKxerVqxkyZAjHjh2jdu3aTJgwAXd3d4NxxcXFFBcXA+Dk5IS5uTkODg6A\nLnhVBxKuhBBCiEqUmprK9evX8fLywsbGpsQxSUlJvPLKK+zZswdLS0tefPFFPvzwQ8zMzPRjriUp\n/PCrwqLVGdwsdDQ6hqMtjHtCd5eqRYOKWZNKrVYzduxYxo4de9cxlpaW9OjRg3379tGqVSvc3d05\nevQoGo2Gbt26lfnchUWFnLt6lIybN+jeenCZj1MeJFwJIYQQlUCr1fLOO++wcOFCFEXB0tKS//73\nv7z00ksG4/Lz8+nfvz8nT54EdJPB58+fz61bt1i0aDE7IuD7LbfXo1IBhsFKlRXCvDcbMPVZTyxL\nWEKhMvzvf/9jwIABnDlzhoSEBKytrQkKCqJu3boPfKz4lGhCT27nxOUwcvNzMDe1wL9pbyzMrSqg\n8vtTfd57FEIIIR4jX331FZ999hmKouDl5UVeXh6TJk1i3759BuNCQkI4efIkHh4eXLlyhd9++w3M\nPfh6Sy28nlIYOh2CDxou9Glnlc+0sdDPZSraMz3Ji1teZYIVgKenJ8eOHWP//v3s2LGD6Ohohg4d\nWqZjJaXHE352N7n5OQAUFOVzKupweZb7wCRcCSGEEJXg9qTuZcuWER0dzdSpUwGMVjW/vQ9g06Yt\nOHfdm//u7o2q3VUUz/eJTzEMTJb5B1H+HM2md48y/zUVPu66uU1VcS6TmZkZXbt25YknnsDZ2bnU\n8Tl52SW2N/fpoL9L5WTnSr8Oo/Bya1SutT4oeSwohBBCVILbk7qtra0B9POtbrff5unTEXXd99id\nNpHfpwOo4W+ZytlBt7/fpKGwaN4avjm6gWFP7qJly5YcOHAAQL8x8+MmMe0ap64c4tSVQySmxjJ3\n0gos73jcZ25qwageL1Pb3h1v98YP9cZjeZFwJYQQQlSCwYMHExERwXPPPcdnn33GkSNH9O2316Va\nshm2HKiPUvcD7owM7X0zefs5e4YFgrmZrnfBggVcunSJ3bt3c+DAAdRqNZ9//vlDTRSvDLsjN3Lo\n3B8kp8cbtJ+6EkHHJj2NxpfUVpkkXAkhhBCV4J133uHixYusWrWKI0eOoFarmfn+Is5mDOSNZ+BK\nvPFnrM1v0bnhRd5/1YOAti7G/dbW7Ny5k2PHjpGcnEzz5s2pV6/eI/g25Ss5Lc4oWKnVJqRkJFRS\nRQ9GwpUQQghRCUxNTVm5ciWzZr3LjoM32Xe+EZ/u0VDwm/HY7m3glWEwPFCDhXmbeLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEPCAYAAABvDuGDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e81ed748>"
+       "<matplotlib.figure.Figure at 0x178f92e3828>"
       ]
      },
      "metadata": {},
@@ -2838,7 +2830,7 @@
     "Ms, Ps, _ = f.rts_smoother(Xs, Covs)\n",
     "\n",
     "book_plots.plot_measurements(zs)\n",
-    "plt.plot(xs[:, 0], ls='--', label='Kalman Position')\n",
+    "plt.plot(Xs[:, 0], ls='--', label='Kalman Position')\n",
     "plt.plot(Ms[:, 0], label='RTS Position')\n",
     "plt.legend(loc=4);"
    ]
@@ -2859,9 +2851,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAEWCAYAAAAqz5CSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAEWCAYAAAAqz5CSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9e8a21d68>"
+       "<matplotlib.figure.Figure at 0x178f58331d0>"
       ]
      },
      "metadata": {},
@@ -2918,9 +2910,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAEWCAYAAAAqz5CSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d9ed6de160>"
+       "<matplotlib.figure.Figure at 0x178f57c98d0>"
       ]
      },
      "metadata": {},

code/mkf_internal.py

@@ -60,9 +60,8 @@ def zs_var_275():
 
 
 def plot_track_ellipses(N, zs, ps, cov, title):
-    bp.plot_measurements(range(1,N + 1), zs)
-    plt.plot(range(1, N + 1), ps, c='b', lw=2, label='filter')
-    plt.legend(loc='best')
+    #bp.plot_measurements(range(1,N + 1), zs)
+    #plt.plot(range(1, N + 1), ps, c='b', lw=2, label='filter')
     plt.title(title)
 
     for i,p in enumerate(cov):
@@ -75,11 +74,8 @@ def plot_track_ellipses(N, zs, ps, cov, title):
             plt.text (20, 5, s, fontsize=18)
             s = ('$\sigma^2_{vel} = %.2f$' % p[1, 1])
             plt.text (20, 0, s, fontsize=18)
-    plt.xlim(-10, 80)
+    plt.ylim(-5, 20)
     plt.gca().set_aspect('equal')
-    plt.show()
-
-
 
 
 def plot_gaussian_multiply():