Copy edits.

04-One-Dimensional-Kalman-Filters.ipynb

@@ -348,7 +348,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x73a8470>"
+       "<matplotlib.figure.Figure at 0x72e8cf8>"
       ]
      },
      "metadata": {},
@@ -383,14 +383,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean of readings is 9.972\n"
+      "Mean of readings is 9.996\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuIAAADaCAYAAADjVSfWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFFf7N/AvCBYQwQJKUQxWFBWxJprYFUjsYsXeY8Ea\nfVTEXjCx91hRY1QC9oIaUFQUUAGlCQLSe9tdWHDZ+/eHL/tKWJZdWBiQ87muXNfz7MycuYVh9syZ\n+9xHhYgIDMMwDMMwDMNUKlWuA2AYhmEYhmGYmoh1xBmGYRiGYRiGA6wjzjAMwzAMwzAcYB1xhmEY\nhmEYhuEA64gzDMMwDMMwDAdYR5xhGIZhGIZhOMA64gzDMAzDMAzDAbk64p6enhg5ciSMjIygqqoK\nJyenEvedP38+VFVVsXfvXqUFyTAMwzAMwzDfGrk64nw+H506dcLBgwehoaFR4n7Ozs7w8fGBoaGh\n0gJkGIZhGIZhmG+RXB1xKysrbNu2DWPGjIGKiorUfT59+oTly5fj8uXLUFNTU2qQDMMwDMMwDPOt\nUUqOeEFBASZPngx7e3u0a9dOGU0yDMMwDMMwzDdNKR3xjRs3Qk9PD/PmzVNGcwzDMAzDMAzzzSt3\nDomHhwfOnz8Pf39/ufbPysoq7ykZhmEYhmEYhjPa2tpKaafcI+JPnjxBYmIimjVrBnV1dairq+PT\np0/47bff0KJFC2XEyDAMwzAMwzDfnHKPiC9atAg2NjZFPhs6dCgmT56MuXPnlrd5hmEYhmEYhvkm\nydURFwgECA8PBxFBLBYjOjoa/v7+aNSoEZo3b44mTZoU2V9dXR3NmjVDmzZtZLarrGF95tvj6+sL\nAOjevTvHkTBVGbtOmNKwa4SRB7tOGHlURHq1XKkpvr6+6Nq1K7p16wahUAgHBwdYWFjAwcFB6v4l\nlThkGIZhGIZhGOYLuUbE+/XrB7FYLHejERERZQ6IYRiGYRiGYWoCpZQvZBiGYRiGYRhGMawjzjAM\nwzAMwzAcYB1xhmEYhmEYhuEA64gzDMMwDMMwlSYxMRHv379Hamoq16FwjnXEGYZhGIZhmErzzz//\nYMKECbhy5QrXoXCu3Av6MAzDMAzDMIy8JkyYgB9++KHYOjQ1ERsRZxiGYRiGYSrN/fv3MXPmTDYi\nDjYizjAMwzAMw1SiAQMGwNzcHAYGBlyHwjk2Is4wDMMwDMNUmvPnz2PChAm4d+8e16Fwjo2IMwzD\nMAzDMJVm4cKFsLKygo6ODtehcI6NiDMMwzAMwzCVxtPTEzNnzsTZs2e5DoVzbEScYRiGYRiGqTRd\nunTBxYsX0bx5c65D4RwbEWcYhmEYhmEqzdGjRzFhwgQ8e/aM61A4x0bEGYZhGIZhmEqzbt06TJw4\nEfXr1+c6FM6xEXGGYRiGqSSOjo5YsWIF4uLiuA6FYTjj5+eHWbNm4ciRI1yHwjm5OuKenp4YOXIk\njIyMoKqqCicnpyLbN27cCFNTU9SvXx+NGjXC4MGD4eXlVSEBMwzDMEx1ZWhoCENDQ6irq3MdCsNw\npnnz5rh48SK2bdvGdSick6sjzufz0alTJxw8eBAaGhrFtrdv3x5Hjx7F+/fv8fz5c3z33XewtLRE\nSkqK0gNmGIZhmOoqPj4ez549Q2RkJNehMAxndu/ejfHjx8Pf35/rUDgnV464lZUVrKysAADTp08v\ntn3y5MlF/v/evXtx+vRp+Pn5YciQIUoIk2EYhmGqv969e6NVq1Zo0aIF16EwDGf27t2LDx8+oG7d\nulyHwjml54h//vwZJ06cgLa2NszNzZXdPMMwDMNUW48ePcKzZ89YB4Sp0cLCwjBz5kzs2bOH61A4\np7SqKXfu3MHEiRORk5MDAwMDPHz4ELq6ujKP8fX1VdbpmW8Uu0YYebDrhClNVblGxGIxxGIx/P39\nWcWIKqiqXCffuk+fPmHt2rUwMjKqVj/zNm3aKL1NpY2IDxw4EP7+/vDy8oKlpSVsbGyQlJSkrOYZ\nhmEYptpTV1eHv78/fHx8uA6FYThz4sQJrFu3jlUPghJHxOvVqwcTExOYmJigZ8+eaNu2LU6dOoX1\n69eXeEz37t2VdXrmG1P4hMyuEUYWdp0wpalq14iamho6duyILl26oHXr1lyHw/w/Ve06+dbdvHkT\nwcHBUFdXh5mZGdfhyC0rK0vpbVbYgj5isRh5eXkV1TzDMAzDVDt//fUXCgoK8PPPP3MdCsNwJiEh\nATNnzkTr1q3h7OzMdTickqsjLhAIEB4eDiKCWCxGdHQ0/P390ahRI+jo6MDR0RHDhw+Hvr4+UlJS\ncPjwYcTFxWH8+PEVHT/DMAzDVBvGxsYQCoVQVWXr6TE1l1AoxKVLlyok57q6ketO4Ovri65du6Jb\nt24QCoVwcHCAhYUFHBwcoKamhsDAQIwZMwZt27bFiBEjkJGRAU9Pz2r1uoGpXGz+AMMwNVG7du3w\n4sULnDx5kutQGIYzK1asgI2NDZKTk7kOhXNyjYj369cPYrG4xO0uLi5KC4j59p08eRLz58/HyZMn\nMXfuXK7DYRiGqTQtW7bE5MmTWX44U6PdvHkTgYGBSE1NhZGREdfhcKrCcsQZpiT16tWDlZUVK93F\nMEyNUlBQgEOHDqF27doYO3Ys1+EwDGeys7MxY8YM6Orq4uHDh1yHwynWEWcq3dSpUzF16lSuw2AY\nhql0JiYmMt8wM0xNEB8fj0uXLqF9+/Zch8I5NluEqXRZWVlITExETk4O16EwDMNUmlq1amHQoEF4\n9uyZzNK+DPOtmzdvHmxsbCAUCrkOhXOsI85UupUrV0JfXx8rV67kOhSGYZhKpauri0mTJrHyhUyN\n9vz5c/z1118ICQnhOhTOsdQUjsTExCAjIwOdO3fmOpRKN27cOMTHx6NDhw5ch8IwNVZBQQECAgLQ\nuXNn1KpVi+twagQej4cdO3ZAR0cH48aN4zochuFMQUEBZsyYgTp16uDVq1dch8MpNiLOkRYtWqBL\nly5ISUnhOpRKZ2lpibt372LJkiVch8IwNdb+/fthYWGB33//netQagxVVVWYmJigWbNmXIfCVHPR\n0dG4cuVKtZxvQEQICgrCX3/9VeM74QAbEeeMm5sb8vLyoKWlxXUolS4zMxNCoRDa2tqoV68e1+Ew\nTI1kamoKOzs7mJubcx1KjaGpqYkpU6ZgwYIF8Pb2xtmzZ7kOiammunTpgszMTNStWxcjR47kOhyF\niEQiTJs2DQDg7+/PcTTcq7IdcbFYjN27d6OgoAAbNmzgOhylGzJkCNchcGbp0qW4cOECfv31Vxw5\ncoTrcBimRrK2toa1tTXXYdQ4GhoamDBhAnR0dLgOhanG3r9/j/T0dBgbG3MdisLU1dXh7++PgIAA\nyYKRNTk9rsp2xFVUVJCVlQV1dXWuQ2GUbOnSpUhJSUGTJk24DoVhaqy4uDjk5ORAX1+f1fSvJElJ\nSdixYwf09fWxdu1arsNhqjFDQ0MYGhpyHUa5LFiwAPn5+Xjy5Ak0NTW5DoczVbYjXlBQgHHjxqFO\nnTpch1IhVFRUAABBQUEwNTXlOJrK1b17d9y7d4/rMBimRlu2bBmcnZ0xbdo0nD9/nutwagR1dXWY\nmJigQYMGXIfCVHPZ2dnIzc1FgwYNql2K5+fPnxEUFISzZ8+iXbt2XIfDuSrbEefxeFiwYAHq168P\nDw8PrsNRukePHiEnJ6davlYqr/T0dOTn50NHRwd169blOhyGqZHWrl0LQ0ND9OjRg+tQaoxGjRrB\nzs4OkyZNgrOzM1xdXVG7dm2uw2KqIW1tbQDA6dOnMWvWLI6jUUxmZiamTZsGHR0dPHnyhOtwOFdl\nO+INGzbE5MmTIRAIIBQKv7kO26BBg7gOgTOLFy/G5cuXYWdnh/3793MdDsPUSN26dUO3bt24DqNG\nsrGxQe3ataGqygqXMWUTEREBHo9XLcsA6+rqwt/fH4GBgfD19UWnTp2+2ewHeVTZjjjwZVRcJBKB\niLgOhVGiXbt2ISMjA58/f+Y6FIapsSIjI1FQUAAjI6NvbqCjqoqIiMD+/fvRunVrLF26lOtwmGrs\nu+++4zqEclu1ahVSUlJw69Yt6Ovrcx0OZ6psR1woFOLnn3+GhoZGtct/Kg2Px5PkCL5+/RoWFhYc\nR1S5WrRowXLEGYZjCxcuxIMHD7Bw4UIcPXqU63BqhLp166JVq1bQ1dXlOhSmmuPz+RAIBNDS0oKG\nhgbX4ShEKBQiNDQUhw4dQuvWrbkOh3NyvRfz9PTEyJEjYWRkBFVVVTg5OUm2iUQirFmzBl26dEH9\n+vVhYGCAKVOmICYmplyBJScnY8GCBd/kzHJNTU24u7vj5s2baNOmDdfhVLq0tDQkJiYiLy+P61AY\npsY6ffo0li5dWiPvQVwxMDCAnZ0dfHx88MsvvyAqKorrkBQmEAjA5/NRUFDAdSg1VmpqKrS0tNCs\nWTO4uLhwHY7CYmNjMXXqVKxYsYLrUKoEuUbE+Xw+OnXqhOnTp0uKsBfKycmBn58f7O3t0aVLF2Rl\nZWHFihWwsrJCQEBAmXPgWrRogdmzZyM1NRUZGRlo2LBhmdqpilRVVdG/f3+uw+DMggUL4OzsjBUr\nVuCPP/7gOhyGqZEMDQ1x4MABrsOokSwtLTFgwAA0atSI61AUVljqslu3bvD19eU4mppJR0cHYWFh\nyM/Pr5Y54q1bt0ZAQABCQkLg4+ODjh07VrtRfWWSqyNuZWUFKysrAMD06dOLbGvQoAEePHhQ5LMT\nJ06gY8eOCA4ORseOHcscHJ/PR15eXrVcwpUp2fnz58Hj8RAXF1dsW0BAAOLi4iAWi9G0aVMOomOY\nmiE0NBS1atVCy5YtoaZWZbMUvymBgYE4efIkOnXqhDlz5nAdTpmkp6dDIBBIqnYwlLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b261d0>"
+       "<matplotlib.figure.Figure at 0x7da0e80>"
       ]
      },
      "metadata": {},
@@ -453,7 +453,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuIAAADaCAYAAADjVSfWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x48f52e8>"
+       "<matplotlib.figure.Figure at 0x4650128>"
       ]
      },
      "metadata": {},
@@ -551,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true
    },
@@ -570,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -578,19 +578,17 @@
     {
      "data": {
       "text/plain": [
-       "(25, 0.530)"
+       "(25, 49.040)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from filterpy.kalman import predict\n",
-    "\n",
     "%precision 3\n",
-    "predict(x=10, P=.2**2, u=15, Q=.7**2)"
+    "predict((10, .2**2), (15, 7**2))"
    ]
   },
   {
@@ -713,7 +711,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81482e8>"
+       "<matplotlib.figure.Figure at 0x7de1080>"
       ]
      },
      "metadata": {},
@@ -762,7 +760,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8cadf98>"
+       "<matplotlib.figure.Figure at 0x8bcc7b8>"
       ]
      },
      "metadata": {},
@@ -817,7 +815,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8cada58>"
+       "<matplotlib.figure.Figure at 0x8bb8128>"
       ]
      },
      "metadata": {},
@@ -863,7 +861,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b522b0>"
+       "<matplotlib.figure.Figure at 0x7dd5208>"
       ]
      },
      "metadata": {},
@@ -892,7 +890,7 @@
    "execution_count": 12,
    "metadata": {
     "collapsed": false,
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
@@ -901,15 +899,25 @@
      "text": [
       "PREDICT\t\t\tUPDATE\n",
       "     x      var\t\t  z\t    x      var\n",
+      "(1.0, 401.0) (1.351841154783717, 1.9900744416873448) 1.3535959735108178\n",
       "  1.000  401.000\t1.354\t  1.352   1.990\n",
+      "(2.351841154783717, 2.990074441687345) (2.070349464840893, 1.198408751864744) 1.8820653967131618\n",
       "  2.352    2.990\t1.882\t  2.070   1.198\n",
+      "(3.070349464840893, 2.198408751864744) (3.7357238224806713, 1.0472580836195666) 4.341047429453569\n",
       "  3.070    2.198\t4.341\t  3.736   1.047\n",
+      "(4.735723822480671, 2.0472580836195666) (5.960160449584709, 1.0116765678499313) 7.156332673205118\n",
       "  4.736    2.047\t7.156\t  5.960   1.012\n",
+      "(6.960160449584709, 2.011676567849931) (6.949396530475968, 1.0029106453754295) 6.938695089418526\n",
       "  6.960    2.012\t6.939\t  6.949   1.003\n",
+      "(7.949396530475968, 2.0029106453754295) (7.396252519656308, 1.000727132237836) 6.843912342028484\n",
       "  7.949    2.003\t6.844\t  7.396   1.001\n",
+      "(8.396252519656308, 2.000727132237836) (9.121670120559637, 1.0001817500203842) 9.846824080052299\n",
       "  8.396    2.001\t9.847\t  9.122   1.000\n",
+      "(10.121670120559637, 2.000181750020384) (11.337631330190714, 1.000045435440623) 12.553482049375292\n",
       " 10.122    2.000\t12.553\t 11.338   1.000\n",
+      "(12.337631330190714, 2.000045435440623) (14.30538006966205, 1.0000113587311334) 16.2730841073834\n",
       " 12.338    2.000\t16.273\t 14.305   1.000\n",
+      "(15.30538006966205, 2.0000113587311334) (15.052894906364974, 1.0000028396747196) 14.800411177015299\n",
       " 15.305    2.000\t14.800\t 15.053   1.000\n",
       "\n",
       "final estimate:            15.053\n",
@@ -965,7 +973,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5UAAADTCAYAAAARQTc/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8cd1860>"
+       "<matplotlib.figure.Figure at 0x895a8d0>"
       ]
      },
      "metadata": {},
@@ -1070,7 +1078,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAADlCAYAAAB3YbDsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa2999e8>"
+       "<matplotlib.figure.Figure at 0x8197128>"
       ]
      },
      "metadata": {},
@@ -1230,7 +1238,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8bcd6a0>"
+       "<matplotlib.figure.Figure at 0xa1857f0>"
       ]
      },
      "metadata": {},
@@ -1326,7 +1334,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa299550>"
+       "<matplotlib.figure.Figure at 0xa181128>"
       ]
      },
      "metadata": {},
@@ -1424,7 +1432,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8071908>"
+       "<matplotlib.figure.Figure at 0x81b17f0>"
       ]
      },
      "metadata": {},
@@ -1459,7 +1467,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8ba46a0>"
+       "<matplotlib.figure.Figure at 0xa0ecac8>"
       ]
      },
      "metadata": {},
@@ -1500,9 +1508,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1c19e8>"
+       "<matplotlib.figure.Figure at 0xa0ec128>"
       ]
      },
      "metadata": {},
@@ -1579,7 +1587,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8bdab38>"
+       "<matplotlib.figure.Figure at 0x7e1e390>"
       ]
      },
      "metadata": {},
@@ -1589,7 +1597,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa128da0>"
+       "<matplotlib.figure.Figure at 0x8bc2b00>"
       ]
      },
      "metadata": {},
@@ -1697,7 +1705,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80cc160>"
+       "<matplotlib.figure.Figure at 0x8dec908>"
       ]
      },
      "metadata": {},
@@ -1755,7 +1763,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa277ef0>"
+       "<matplotlib.figure.Figure at 0xa16b128>"
       ]
      },
      "metadata": {},
@@ -1812,7 +1820,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c13c88>"
+       "<matplotlib.figure.Figure at 0x8e31c50>"
       ]
      },
      "metadata": {},
@@ -1866,7 +1874,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1d2ef0>"
+       "<matplotlib.figure.Figure at 0xa20b470>"
       ]
      },
      "metadata": {},
@@ -1914,7 +1922,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa2a4438>"
+       "<matplotlib.figure.Figure at 0xa056518>"
       ]
      },
      "metadata": {},
@@ -2004,7 +2012,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa184c18>"
+       "<matplotlib.figure.Figure at 0x8923f60>"
       ]
      },
      "metadata": {},
@@ -2097,7 +2105,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa394a20>"
+       "<matplotlib.figure.Figure at 0xa27a710>"
       ]
      },
      "metadata": {},
@@ -2180,7 +2188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
@@ -2191,7 +2199,7 @@
        "(11, 7.000)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2216,7 +2224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
@@ -2227,7 +2235,7 @@
        "(11.350, 4.550)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }

code/kf_internal.py

@@ -36,6 +36,7 @@ def plot_dog_track(xs, dog, measurement_var, process_var):
 
 
 def print_gh(predict, update, z):
+    print(predict, update, z)
     predict_template = '{: 7.3f} {: 8.3f}'
     update_template = '{:.3f}\t{: 7.3f} {: 7.3f}'