Updated for FilterPy 1.2.4, moved code around

Moved code specific to one notebook to the *_internal.py file
for that book.

Updated to require FilterPy 1.2.4 due to a fix of a bad bug in the
UKF RTS Smoother, and the change of likelihood from a function back
to a property.

01-g-h-filter.ipynb

@@ -333,7 +333,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8921502e8>"
+       "<matplotlib.figure.Figure at 0x16e96d36eb8>"
       ]
      },
      "metadata": {},
@@ -381,7 +381,7 @@
      "data": {
       "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c89218c550>"
+       "<matplotlib.figure.Figure at 0x16e96d36b38>"
       ]
      },
      "metadata": {},
@@ -410,7 +410,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAACICAYAAADjyzHQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADDVJREFUeJzt3X9s1PUdx/HXuy1XwUKxU8gGjKJDhCixFA3L5sABIfNH6jBhsrgRYqYzY2SaIWLU4XQJY5sGsoT9CCyj/kCYREUwkurmQjLEIg7kp24rlgIighdq6x30Pvvjvo1H6d31ftDrfXg+kovffr7f7+c+eefT8vLz/X7vzDknAAAAn5UUegAAAADnG4EHAAB4j8ADAAC8R+ABAADeI/AAAADvleXaQTgc5jEvAAA8U1lZaYUeQz6xwgMAALxH4AEAAN7L+ZJWosrKynx2d8FpbGyUJE2cOLHAI/ED9cwfaplf1DN/qGV+hcPhQg/hvGGFBwAAeI/AAwAAvEfgAQAA3iPwAAAA7xF4AACA9wg8AADAewQeAADgPQIPAADwHoEHAAB4j8ADAAC8R+ABAADeI/AAAADvEXgAAID3CDwAAMB7BB4AAOA9Ag8AAPAegQcAAHiPwAMAALxH4AEAAN4j8AAAAO8ReAAAgPcIPAAAwHsEHgAA4D0CDwAA8B6BBwAAeI/AAwAAvEfgAQAA3iPwAAAA7xF4AACA9wg8AADAewQeIJnFlfEXABQD/malROABAADeI/AAAADvEXgAAEBOqh/c6Kof3Ojyddz5UFaINwUAAMVj+/btJRUVFQ+EQqGxZpZ0saSpqam+J/319LhMOOdi0Wh0b2tr69La2tpY1/0EHgAAkFJFRcUDI0aMmDVgwIBzgkTcbklSdXX12NQ99fS47LS1tV3T3NwsSUu67uOSFgAASCkUCo1NHnb6jgEDBsRCoVC3YYrAAwC9aedaXfP6bNW+Mk166mpp59pCjwhIK9VlrL4m2Vi5pAUAvWXnWmnDfJWfbo//HG6WNsyPb4+fVbhxARcAAg+Qzl9uLvQICm7MqVPxjV0DCzuQYnfobakjcnbb6XbppXnS9r8WZkxFjrl5YVm9evXgOXPmXPHOO+/srqmp+TyTcwk8ANBbuoaddO1AkXp668Gq5a+/P+zjU5HQZQPLo/Onjm65c9LIE7n2u2bNmqoJEya01tfXV9XU1BzO5FwCD/qk6gc3SpKalvSB1ZW5Gws9goLb39goSZo4cWKBR1Lknro6fhmrq8oRzLMsMTcT9JGvlXh668Gqx1/ZMzJyJlYiScdORUKPv7JnZK79hsPhksbGxoqGhob9dXV1X3vyySczCjxFcxMSABS9qY9K/fqf3davf7wd8EDd77eMeWzD7urOsNMpciZW8tiG3dW59P3MM88MnjJlSnj8+PGRwYMHd2zZsmVAJucTeACgt4yfJd26XJH+Q+Rk8ZWdW5dzwzK8crrDWSbtPbV27dqq2bNnn5Sk22+//UR9fX1VJudzSQsAetP4WdoVvVwSl2Hgn5fmfXP/9b9quObYqUio674hA8uj3bX3xNGjR0u3bt066MCBA/3nzZunjo4OMzO3YsWKQyUlPVu7YYUHAADkzfypo1vKy0rO+pDC8rKS2Pypo1uy7bO+vv6SmTNnfnL48OFdLS0tu44ePbpz+PDh0c2bN1f0tA8CDwAAyJs7J4088cgt4w4OGVgeNcVXdh65ZdzBXJ7SWrdu3Zdmzpx5MrGtrq7uZCaXtbikBQAA8urOSSNP5OMx9E7btm3b37Xt4YcfPpZJHwQeeOnFHS16YtPH+qQtpq80vKEFM8botpphhR4WAHip6aLvB1vhvBx3PhB44J0Xd7Ro0fpdaj8dv4Tc8mm7Fq3fJUmEHgC4QBF40Kd974//yvicHR9+qmjH2V/q2366Qw/8baee2/Zhj/t56/NnJUlNGY8AAApgce+vmhQTblqGd7qGnXTtAAD/scKDPu35e76e8TnfWPKGWj5tP6d92OD+GfXX+fUWAIDixwoPvLNgxhj171d6Vlv/fqVaMGNMgUYEACg0Vnjgnc4bk5/YsDP+lNbg/jylBQBFrrS0tHb06NHtzjmVlpa6ZcuWfTh9+vTPeno+gQdeuq1mmIZ3HJHEx/cDQK97e2WV3vz1MLUeC6liSFSTF7bourty+lye8vLy2L59+/ZI0gsvvDDooYceGj59+vRzPp8nGQIPAADIn7dXVum1RSN1JhK/bab1o5BeWzQyn28RDodLKysrz2RyDoEHAADkx59uHKOjuy5W7PTZ34x+JlKiVxdW59J1JBIpueqqq8ZFIhE7fvx4v02bNh3I5HwCDwAAyJ+uYSddew8lXtJqaGi4eO7cuaMOHDiwm29LBwAAvevuv+9XxdBot/uStWdh2rRpn508ebLsyJEjPV64IfAAAID8mbywRWXlZ3/Sa1l5TJMXtuTrLXbs2HFRLBbT0KFDe3wfD5e00Cc1Lbm50EPoE2MAgKLT+TRWd09pbbx/VLbddt7DI0nOOa1YsaKprKznMYbAAwAA8uu6u07k+hh6Vx0dHdtzOZ9LWgAAwHsEHgAA4D0CDwAASMk5F0t/VN+QbKwEHgAAkFI0Gt3b1tbW5zNDW1tbSTQa3dvdPm5aBgAAKbW2ti5tbm5WKBQaa2bnBJ9qqVaSmpqaug0bmR6XDedcLBqN7m1tbV3a3X4CDwAASKm2tjYmaUnSAxaHfyBJ1ek66ulx50GfX54CAADIFYEHAAB4j8ADAAC8R+ABAADeI/AAAADvEXgAAID3CDwAAMB7BB4AAOA9Ag8AAPAegQcAAHiPwAMAALxH4AEAAN4j8AAAAO8ReAAAgPcIPAAAwHsEHgAA4D0CDwAA8B6BBwAAeI/AAwAAvEfgAQAA3iPwAAAA7xF4AACA9wg8AADAewQeAADgPQIPAADwHoEHAAB4j8ADAAC8R+ABAADeI/AAAADvEXgAAID3CDwAAMB7BB4AAOA9Ag8AAPAegQcAAHiPwAMAALxnzrmcOgiHw7l1AAAA+pzKykor9BjyiRUeAADgPQIPAADwXs6XtAAAAPo6VngAAID30gYeM1tlZsfM7L2EtsVm1mJm7wavmxL2LTKzD8xsv5nNOF8DL1aZ1NPMqs2sPaH9D4Ubed/TXS2D9p8G82+3mS1NaGduppBJPZmbqSX5PX8+oV5NZvZuwj7mZgqZ1JO5mV6Sel5rZluDmjWa2fVBu5nZ8mB+7jSzCYUbeY6ccylfkr4laYKk9xLaFkv6eTfHjpP0b0nlkkZJ+o+k0nTvcSG9MqxndeJxvHpUyxslNUgqD34eEvyXuZnfejI3M6xll/2/k/RosM3czG89mZtZ1FPSZknfCbZvkvSPhO1XJZmkSZLeKvT4s32lXeFxzv1T0ol0xwXqJK1xzkWcc/+T9IGk63t47gUhw3oihSS1vFfSEudcJDjmWNDO3Ewjw3oihVS/52ZmkmZJei5oYm6mkWE9kUaSejpJg4LtSkmHg+06Satd3FZJg83sy70z0vzK5R6eecHy1iozuyRoGyapOeGYQ0Eb0uuunpI0ysx2mNmbZnZDwUZXPK6UdIOZvRXU7LqgnbmZnWT1lJib2bpB0kfOufeDn5mbuelaT4m5mY2fSfqNmTVL+q2kRUG7N/Mz28CzQtIVkq6VdETx5UQpvuTVFY+BpZesnkckfdU5VyPpfknPmtmg7rtAoEzSJYovvS6QtDb4P0DmZnaS1ZO5mb3ZOns1grmZm671ZG5m515J9znnRki6T9LKoN2b+ZlV4HHOfeSc63DOxST9WV8svx6SNCLh0OH6YlkMSSSrZ7DE/UmwvV3xa/tXFm6kReGQpPXB8us2STFJl4q5ma1u68nczI6ZlUmaKen5hGbmZpa6qydzM2tzJK0PttfJw3/Xswo8Xa7ffVdS553eL0u6w8zKzWyUpNGStuU2RP8lq6eZXWZmpcH25YrX87+9P8Ki8qKkb0uSmV0pKSTpuJib2eq2nszNrE2TtM85dyihjbmZvXPqydzM2mFJk4Ptb0vqvET4sqQfBk9rTZIUds4dKcQAc1WW7gAze07SFEmXmtkhSb+QNMXMrlV8WatJ0j2S5JzbbWZrJe2RdEbST5xzHedn6MUpk3oqfif9L83sjKQOST92znHDcyBJLVdJWhU8bhmVNMfFHzVgbqaRST3NjLmZQne1dM6tlHSHutxcy9/N9DKpp/i7mVaS3/UfSVoWrJp9Lunu4PBNij+p9YGkNklze33AecInLQMAAO/xScsAAMB7BB4AAOA9Ag8AAPAegQcAAHiPwAMAALxH4AEAAN4j8AAAAO8ReAAAgPf+D+7Y8iFWgF2WAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8925256a0>"
+       "<matplotlib.figure.Figure at 0x16e9713beb8>"
       ]
      },
      "metadata": {},
@@ -443,7 +443,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9961\n"
+      "Average of measurements is 164.9766\n"
      ]
     }
    ],
@@ -472,7 +472,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0258\n"
+      "Average of measurements is 165.0491\n"
      ]
     }
    ],
@@ -503,7 +503,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c890219668>"
+       "<matplotlib.figure.Figure at 0x16e94df3eb8>"
       ]
      },
      "metadata": {},
@@ -511,7 +511,8 @@
     }
    ],
    "source": [
-    "book_plots.plot_hypothesis1()"
+    "import kf_book.gh_internal as gh\n",
+    "gh.plot_hypothesis1()"
    ]
   },
   {
@@ -534,7 +535,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c892605860>"
+       "<matplotlib.figure.Figure at 0x16e9728a630>"
       ]
      },
      "metadata": {},
@@ -542,7 +543,7 @@
     }
    ],
    "source": [
-    "book_plots.plot_hypothesis2()"
+    "gh.plot_hypothesis2()"
    ]
   },
   {
@@ -561,7 +562,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8926ce630>"
+       "<matplotlib.figure.Figure at 0x16e972b8be0>"
       ]
      },
      "metadata": {},
@@ -569,7 +570,7 @@
     }
    ],
    "source": [
-    "book_plots.plot_hypothesis3()"
+    "gh.plot_hypothesis3()"
    ]
   },
   {
@@ -590,7 +591,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8927332b0>"
+       "<matplotlib.figure.Figure at 0x16e9731c588>"
       ]
      },
      "metadata": {},
@@ -598,7 +599,7 @@
     }
    ],
    "source": [
-    "book_plots.plot_hypothesis4()"
+    "gh.plot_hypothesis4()"
    ]
   },
   {
@@ -619,7 +620,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c89275a518>"
+       "<matplotlib.figure.Figure at 0x16e97337f28>"
       ]
      },
      "metadata": {},
@@ -627,7 +628,7 @@
     }
    ],
    "source": [
-    "book_plots.plot_hypothesis5()"
+    "gh.plot_hypothesis5()"
    ]
   },
   {
@@ -652,7 +653,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c89279b940>"
+       "<matplotlib.figure.Figure at 0x16e973641d0>"
       ]
      },
      "metadata": {},
@@ -660,7 +661,7 @@
     }
    ],
    "source": [
-    "book_plots.plot_estimate_chart_1()"
+    "gh.plot_estimate_chart_1()"
    ]
   },
   {
@@ -679,7 +680,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8926d8fd0>"
+       "<matplotlib.figure.Figure at 0x16e970e1e48>"
       ]
      },
      "metadata": {},
@@ -687,7 +688,7 @@
     }
    ],
    "source": [
-    "book_plots.plot_estimate_chart_2()"
+    "gh.plot_estimate_chart_2()"
    ]
   },
   {
@@ -725,7 +726,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c89273a358>"
+       "<matplotlib.figure.Figure at 0x16e972ef208>"
       ]
      },
      "metadata": {},
@@ -733,7 +734,7 @@
     }
    ],
    "source": [
-    "book_plots.plot_estimate_chart_3()"
+    "gh.plot_estimate_chart_3()"
    ]
   },
   {
@@ -779,7 +780,6 @@
    ],
    "source": [
     "from kf_book.book_plots import figsize\n",
-    "import kf_book.gh_internal as gh\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6, \n",
@@ -825,7 +825,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c892693518>"
+       "<matplotlib.figure.Figure at 0x16e971e5438>"
       ]
      },
      "metadata": {},
@@ -859,7 +859,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c892791a20>"
+       "<matplotlib.figure.Figure at 0x16e973c8c18>"
       ]
      },
      "metadata": {},
@@ -904,7 +904,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c89258bc50>"
+       "<matplotlib.figure.Figure at 0x16e97411e10>"
       ]
      },
      "metadata": {},
@@ -990,7 +990,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c89254a5f8>"
+       "<matplotlib.figure.Figure at 0x16e98447e48>"
       ]
      },
      "metadata": {},
@@ -1130,7 +1130,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c893821390>"
+       "<matplotlib.figure.Figure at 0x16e9843da58>"
       ]
      },
      "metadata": {},
@@ -1227,9 +1227,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c89255be80>"
+       "<matplotlib.figure.Figure at 0x16e973f0898>"
       ]
      },
      "metadata": {},
@@ -1262,9 +1262,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8938d2a90>"
+       "<matplotlib.figure.Figure at 0x16e98453550>"
       ]
      },
      "metadata": {},
@@ -1312,9 +1312,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8938d26d8>"
+       "<matplotlib.figure.Figure at 0x16e971f3a90>"
       ]
      },
      "metadata": {},
@@ -1371,9 +1371,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8937e2c88>"
+       "<matplotlib.figure.Figure at 0x16e971e62e8>"
       ]
      },
      "metadata": {},
@@ -1434,7 +1434,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c89264f8d0>"
+       "<matplotlib.figure.Figure at 0x16e9721fa20>"
       ]
      },
      "metadata": {},
@@ -1509,7 +1509,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c89258fa20>"
+       "<matplotlib.figure.Figure at 0x16e97427c50>"
       ]
      },
      "metadata": {},
@@ -1548,7 +1548,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8926d7b70>"
+       "<matplotlib.figure.Figure at 0x16e9716f048>"
       ]
      },
      "metadata": {},
@@ -1590,7 +1590,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c892776f60>"
+       "<matplotlib.figure.Figure at 0x16e9737d198>"
       ]
      },
      "metadata": {},
@@ -1635,7 +1635,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c893944898>"
+       "<matplotlib.figure.Figure at 0x16e9852a128>"
       ]
      },
      "metadata": {},
@@ -1679,7 +1679,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7a6ad8c9fbcd4c4a80c0336aba3b7bfc",
+       "model_id": "28065a8f31a4441aba673aa840e2d970",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1746,7 +1746,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c892761940>"
+       "<matplotlib.figure.Figure at 0x16e98535e10>"
       ]
      },
      "metadata": {},
@@ -1780,7 +1780,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8939a50f0>"
+       "<matplotlib.figure.Figure at 0x16e9714f518>"
       ]
      },
      "metadata": {},
@@ -1843,7 +1843,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8938a15f8>"
+       "<matplotlib.figure.Figure at 0x16e973a1ba8>"
       ]
      },
      "metadata": {},
@@ -1894,7 +1894,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c892504080>"
+       "<matplotlib.figure.Figure at 0x16e972f9da0>"
       ]
      },
      "metadata": {},
@@ -1925,7 +1925,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8938a13c8>"
+       "<matplotlib.figure.Figure at 0x16e98447668>"
       ]
      },
      "metadata": {},
@@ -1956,7 +1956,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8939a3e80>"
+       "<matplotlib.figure.Figure at 0x16e970f8160>"
       ]
      },
      "metadata": {},
@@ -2002,7 +2002,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8938f11d0>"
+       "<matplotlib.figure.Figure at 0x16e97257f28>"
       ]
      },
      "metadata": {},
@@ -2031,7 +2031,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c8927a1390>"
+       "<matplotlib.figure.Figure at 0x16e9846f128>"
       ]
      },
      "metadata": {},

04-One-Dimensional-Kalman-Filters.ipynb

@@ -312,9 +312,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAEgCAYAAAA0WYyMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3XlcVPX+BvBnNvZ9U8cFRESQ1Vwz\nU1Ex15tahktdM9tsudktzbKstH5pt43Kui6pFIF5FSsVKS0zV9Q0BFxZVVxYhm3YZ+b8/kBJEpVh\nO3OG5/16+bLOzJl58Cvj48ezyARBEEBERERERJIlFzsAERERERE1D0s9EREREZHEsdQTEREREUkc\nSz0RERERkcSx1BMRERERSRxLPRERERGRxLHUExERERFJHEs9EREREZHEsdQTEREREUmc0aVeq9Vi\n3rx5UKvVsLKyQmhoKDZs2HDH/davXw+ZTNbgjytXrjQpPBERERERAUpjd5gyZQqOHDmCZcuWwdfX\nFzExMZg+fToMBgNmzJhxx/3XrVsHPz+/ettcXV2NjUFERERERNcYVerj4+Oxc+fOuiIPAGFhYcjO\nzsb8+fMREREBhUJx29cIDAxEv3797vheBoMBBoOh3rbrk30iIiIiIqkRBAGCINTbJpfLIZc3/4h4\no0r9li1bYGdnh6lTp9bbPnv2bMyYMQOJiYkYPHhws0MBtaW+rKysRV6LiIiIiMgU2dratkipN+oV\nUlJS4O/vD6Wy/t8FgoOD6x6/kwkTJkChUMDFxQVTpkxp1D5ERERERHRrRk3qCwoK4O3tfdN2FxeX\nusdvpWPHjli0aBEGDRoEBwcHJCcnY9myZRg0aBD279+PkJAQI6MTERERERHQhBNlb3dM++0eGzNm\nDMaMGVP3/0OHDsX48eMRFBSExYsX44cffjA2ChERERERwchS7+rq2uA0XqPRAPhrYt9YXl5eGDJk\nCA4dOnTTYw39BaGljjki8aSmpkKn00GpVCIgIEDsONRMXE/zwvU0L1xP88L1NA8NnTPaUheBMarU\nBwUFITY2tu431XXJyckAaq9sYyxBEBos6g19gS11djCJx2AwQK/Xcy3NBNfTvHA9zQvX07xwPc1X\nS5V6o35XTJ48GVqtFps3b663PSoqCmq1GgMHDjTqzTMzM7F//34MGjTIqP2IiIiIiOgvRk3qx44d\ni/DwcMydOxclJSXw8fFBbGwsEhISEB0dXXeN+jlz5iAqKgrp6enw9PQEAIwaNQpDhw5FcHBw3Ymy\n77//PmQyGZYuXdryXxkRERERUTth9ImycXFxWLRoERYvXgyNRgM/Pz/ExsZi2rRpdc/R6/XQ6/X1\nLq4fFBSE7777Dh988AEqKirg4eGBESNG4I033oCvr2/LfDVERERERO2Q0aXezs4OkZGRiIyMvOVz\n1q9fj/Xr19fb9vHHHxsdjoiIiIiI7oxnWhARERERSRxLPRERERGRxLHUExERERFJHEs9EREREZHE\nsdQTEREREUkcSz0RERERkcSx1BMRERERSRxLPRERERGRxLHUExERERFJHEs9EREREZHEsdQTERER\nEUkcSz0RERERkcSx1BMRERERSRxLPRERERGRxLHUExERERFJHEs9EREREZHEsdQTEREREUkcSz0R\nERERkcSx1BMRERERSRxLPRERERGRxLHUExERERFJHEs9EREREZHEsdQTEREREUkcSz0RERERkcSx\n1BMRERERSRxLPRERERGRxLHUExERERFJHEs9EREREZHEsdQTEREREUkcSz0RERERkcSx1BMRERER\nSRxLPRERERGRxLHUExERERFJHEs9EREREZHEsdQTEREREUkcSz0RERERkcQZXeq1Wi3mzZsHtVoN\nKysrhIaGYsOGDUa/8euvvw6ZTIbAwECj9yUiIiIior8ojd1hypQpOHLkCJYtWwZfX1/ExMRg+vTp\nMBgMmDFjRqNe488//8QHH3yADh06GB2YiIiIiIjqM6rUx8fHY+fOnXVFHgDCwsKQnZ2N+fPnIyIi\nAgqF4ravodPpMHv2bDz11FNISkpCfn5+09MTEREREZFxh99s2bIFdnZ2mDp1ar3ts2fPxqVLl5CY\nmHjH11i2bBk0Gg3effdd45ISEREREVGDjJrUp6SkwN/fH0pl/d2Cg4PrHh88ePAt9z958iTeeecd\nxMXFwc7OzuiwqampMBgMRu9HpqOmpqbu56SkJJHTUHNxPaXtqlaHk3lVyCqqwRWtDtoqPar1gLVK\nBof9v6KLowrdnVQI8LCEnQWvqyA1/P40L1xP8yCXy9GtW7dWeW2jSn1BQQG8vb1v2u7i4lL3+K0Y\nDAY89thjmDJlCsaNG2dkzFo6nQ56vb5J+5Lpuf4BReaB6ykNBRV67MqowMGcSlwsudPnaQUAQC4D\n/N1UGNrNGkO6WsFSKWv9oNSi+P1pXrie0nWnw9Sbw+gTZWWyW3+Y3+6xjz76COfOncOPP/5o7FvW\nUSqVkMs5LZKyGz+IVCqViEmoJXA9peNSaQ1ik0uw73wFDELtNrkM8HW1QA9nFTo7qGCjMMBCIUOF\nTkBpjQzni2twtqAaF0t0SM2rQWpeDb5JLsW4nnaY7G8PGxU/j00Zvz/NC9fTPLRmjzWq1Lu6ujY4\njddoNAD+mtj/3fnz57F48WIsW7YMFhYWKCoqAlA7eTcYDCgqKoKlpSWsra1v+/4BAQEs9RKXlJSE\nmpoaqFQqhISEiB2HmonrafqKK2rwwU9nEHs4B7prbX6AlwumD+yKEb06wNHmr3Jwq/U8X1CO7cmX\nEXM4Gxc0FdiYWoqdmZX4d7gvZg70hFzOyb0p4veneeF6mgeDwYDS0tJWeW2jGnJQUBBOnToFnU5X\nb3tycjIA3PKa8xkZGaioqMALL7wAZ2fnuh/79+/HqVOn4OzsjFdffbWJXwIRETVk58mrGP3xHnxz\nKBs6g4ARfh7Y9vwQbHz6bkzu06Veob+dbq42mDu8B357OQxfzrwL3u62KCyvwRs/pOKhlQeRnqdt\n5a+EiIjuxKhJ/eTJk7F69Wps3rwZERERddujoqKgVqsxcODABvcLDQ3F7t27b9o+b948FBcXY926\ndejSpYuR0YmIqCFVOj3+b/spRB3MBgB0d7PFu5MDMbiHW7NeVyGXYWxQJ4T37oBvE8/j/YTTOJpd\niAmf7sO7kwMx5S5+jhMRicWoUj927FiEh4dj7ty5KCkpgY+PD2JjY5GQkIDo6Oi6g//nzJmDqKgo\npKenw9PTE05OThg+fPhNr+fk5ASdTtfgY0REZLyrJZV48uujSLpYDAB4cqg3/h3uCytVy52cpVTI\nMWuwF0b17oAFm5KwP60A/96YhKPZhXj7HwFQKXiYJBFRWzP6RNm4uDgsWrQIixcvhkajgZ+fH2Jj\nYzFt2rS65+j1euj1egiC0KJhiYjo1tJySzFr7RHkFFXAyUaFjx8KRZifR6u9X2cna3z92EB89us5\nRP5yDjGJ53FBU44vZt4FeyueyEdE1JZkgok274ZOJLC3t+eJshLHE33MC9fTdBzJ0uDxqKMorqiB\nt5st1s8egG6uNka9RnPW89fTV/FczHGUV+vh19Ee0Y8PhJudpVGvQS2L35/mhetpHlqz37IhExFJ\n3IG0fDy8JhHFFTW4q5sTNs0dbHShb64Rfh3w3ZN3w93eEqevlGLG6kPI11a1aQYiovaMpZ6ISMIO\nZ2owJ+ooqnQGjPDzwLePD4KLrYUoWYK6OGLjU3ejo4MVzl7VYvqqQ8grZbEnImoLLPVERBJ17Hwh\nZq87jIoaPYb5uuPLh++CtUXr3a2wMbq72WLDk4PQ0cEK53K1mLnmEIorePdLIqLWxlJPRCRBGXla\nPLb+CMqq9bjHxxUrH+kLS6W4hf46r2vFvoODJc5e1eLJr4+iSqcXOxYRkVljqScikpgCbRVmrz+C\novIahHZ1wup/9mvRS1a2BC83W6x7dADsLJVIzNTg3xuTYDCY5HUZiIjMAks9EZGEVNbo8eQ3fyC7\noBxdXayxZlY/2FgYfXXiNtFb7YCVj/SFSiHD9hOXsfyn02JHIiIyWyz1REQSIQgCXo1Lxh/ZhXCw\nUmLdo/1N/rKR9/i44T8P1l5+b+WeDGxNuiRyIiIi88RST0QkEd8cysaW4zlQyGX478N94eNhL3ak\nRpnUpzOeHtYDALBg0wmculwiciIiIvPDUk9EJAHHzhdi6baTAICFY/ww2MdN5ETGmX9fL9zb0w0V\nNXo89c0fKCqvFjsSEZFZYaknIjJxBdoqPBN9DDV6AeOCOuLxe7uLHcloCrkMn03vg64u1jivKceC\nTSdgojc0JyKSJJZ6IiITJggC5m86gSsllejhbov3HwyBTCYTO1aTONlY4MuZfWGhkOPnk1cRfShb\n7EhERGaDpZ6IyIRFJ57Hr6dzYaGUY8XMu2BnaZpXummswM6OWDjWDwCwdPspnL7C4+uJiFoCSz0R\nkYlKy9Xi3e21x9G/MsYPfh0dRE7UMmbf44URfh6o1hnwXMxxVFTzxlRERM3FUk9EZIKqdQbM++44\nKmsMuLenG2YP9hI7UouRyWT4z4PB8LC3RFquFh/8fEbsSEREksdST0Rkgj7edRYpOSVwtlHhg6kh\nkMuleRz9rbjaWWL5g8EAgLX7M3EkSyNyIiIiaWOpJyIyMUkXirByTzoA4L0pQejgYCVyotYR1ssD\nU/t2gSAA8/+XxMNwiIiagaWeiMiEVOsMeGXzCRgEYFKoGmMCO4kdqVW9PqE3OjpYIaugHP/5iYfh\nEBE1FUs9EZEJWbknHaevlMLF1gKLJwaIHafVOVqr8N4DQQCAdQcycTiTh+EQETUFSLine truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bb7bafd0>"
+       "<matplotlib.figure.Figure at 0x1c462568e80>"
       ]
      },
      "metadata": {},
@@ -345,14 +345,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean of readings is 9.989\n"
+      "Mean of readings is 10.023\n"
      ]
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEdCAYAAABAJKIUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bc776470>"
+       "<matplotlib.figure.Figure at 0x1c4630277b8>"
       ]
      },
      "metadata": {},
@@ -408,9 +408,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEaCAYAAACo3xkcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bc2ec278>"
+       "<matplotlib.figure.Figure at 0x1c461715358>"
       ]
      },
      "metadata": {},
@@ -512,9 +512,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from collections import namedtuple\n",
@@ -562,9 +560,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from collections import namedtuple\n",
@@ -729,9 +725,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bb5d3f60>"
+       "<matplotlib.figure.Figure at 0x1c4642e27f0>"
       ]
      },
      "metadata": {},
@@ -773,9 +769,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEaCAYAAACo3xkcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bda981d0>"
+       "<matplotlib.figure.Figure at 0x1c46300a0b8>"
       ]
      },
      "metadata": {},
@@ -826,9 +822,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEaCAYAAACo3xkcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdaaa5f8>"
+       "<matplotlib.figure.Figure at 0x1c46301bf98>"
       ]
      },
      "metadata": {},
@@ -836,9 +832,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdb60ef0>"
+       "<matplotlib.figure.Figure at 0x1c464404908>"
       ]
      },
      "metadata": {},
@@ -881,12 +877,27 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c173f87d1d09485781c7f3f43cd920c9",
+       "model_id": "830d919b878a4afbaf89e1676e2ccf3e",
        "version_major": 2,
        "version_minor": 0
       },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
       "text/plain": [
-       "A Jupyter Widget"
+       "interactive(children=(FloatSlider(value=7.5, description='m1', max=10.0, min=5.0, step=0.5), FloatSlider(value=12.0, description='m2', max=15.0, min=10.0, step=0.5), FloatSlider(value=1.0, description='v1', max=2.0, min=0.1), FloatSlider(value=1.0, description='v2', max=2.0, min=0.1), Output()), _dom_classes=('widget-interact',))"
       ]
      },
      "metadata": {},
@@ -920,9 +931,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import kf_book.kf_internal as kf_internal\n",
@@ -1813,7 +1822,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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truncated
+       "<img 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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1991,9 +2000,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdaeb6d8>"
+       "<matplotlib.figure.Figure at 0x1c46459f160>"
       ]
      },
      "metadata": {},
@@ -2142,9 +2151,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAADQCAYAAABV7JR5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdaeb6a0>"
+       "<matplotlib.figure.Figure at 0x1c4644651d0>"
       ]
      },
      "metadata": {},
@@ -2166,9 +2175,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def update(prior, measurement):\n",
@@ -2288,13 +2295,15 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAACICAYAAADjyzHQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwAMAALxH4AEAAN77P+xAYSvHUP+3AAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdb56208>"
+       "<matplotlib.figure.Figure at 0x1c4644fdf98>"
       ]
      },
      "metadata": {},
@@ -2302,7 +2311,7 @@
     }
    ],
    "source": [
-    "book_plots.plot_errorbar2()"
+    "book_plots.plot_errorbars([(160, 8, 'A'), (170, 8, 'B')], xlims=(150, 180))"
    ]
   },
   {
@@ -2325,9 +2334,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdaba128>"
+       "<matplotlib.figure.Figure at 0x1c46454c1d0>"
       ]
      },
      "metadata": {},
@@ -2364,9 +2373,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdaba6d8>"
+       "<matplotlib.figure.Figure at 0x1c46431e6a0>"
       ]
      },
      "metadata": {},
@@ -2412,9 +2421,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def volt(voltage, std):\n",
@@ -2441,9 +2448,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdb5c048>"
+       "<matplotlib.figure.Figure at 0x1c4645e4a90>"
       ]
      },
      "metadata": {},
@@ -2458,9 +2465,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bd9ca780>"
+       "<matplotlib.figure.Figure at 0x1c4645edbe0>"
       ]
      },
      "metadata": {},
@@ -2558,9 +2565,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdb445c0>"
+       "<matplotlib.figure.Figure at 0x1c4648ff898>"
       ]
      },
      "metadata": {},
@@ -2614,9 +2621,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdc0c470>"
+       "<matplotlib.figure.Figure at 0x1c46493ff60>"
       ]
      },
      "metadata": {},
@@ -2669,9 +2676,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bd9020b8>"
+       "<matplotlib.figure.Figure at 0x1c4605a7e48>"
       ]
      },
      "metadata": {},
@@ -2721,9 +2728,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdb44668>"
+       "<matplotlib.figure.Figure at 0x1c46493f8d0>"
       ]
      },
      "metadata": {},
@@ -2767,9 +2774,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdcf7da0>"
+       "<matplotlib.figure.Figure at 0x1c46438e198>"
       ]
      },
      "metadata": {},
@@ -2822,12 +2829,27 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "29c76484c6954891be128263032d967e",
+       "model_id": "5f75552ae3af486fbc9edffaa63f75e8",
        "version_major": 2,
        "version_minor": 0
       },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
       "text/plain": [
-       "A Jupyter Widget"
+       "interactive(children=(IntSlider(value=0, description='start_pos', max=10, min=-10), FloatSlider(value=5.0, description='sensor_noise'), FloatSlider(value=1.0, description='velocity', max=2.0, min=-2.0), FloatSlider(value=5.0, description='process_noise'), Output()), _dom_classes=('widget-interact',))"
       ]
      },
      "metadata": {},
@@ -2869,12 +2891,27 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb4824affda74bca8e684efb12bd6063",
+       "model_id": "77d07d28875145b4a2ea4dfb063e83ba",
        "version_major": 2,
        "version_minor": 0
       },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
       "text/plain": [
-       "A Jupyter Widget"
+       "interactive(children=(IntSlider(value=0, description='start_pos', max=10, min=-10), FloatSlider(value=5.0, continuous_update=False, description='sensor_noise'), FloatSlider(value=1.0, continuous_update=False, description='velocity', max=2.0, min=-2.0), FloatSlider(value=0.1, continuous_update=False, description='process_noise', max=40.0), Output()), _dom_classes=('widget-interact',))"
       ]
      },
      "metadata": {},
@@ -2942,9 +2979,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "#enter your code here."
@@ -2966,9 +3001,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhUAAAC+CAYAAACCoP16AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a4bdd27be0>"
+       "<matplotlib.figure.Figure at 0x1c46434eeb8>"
       ]
      },
      "metadata": {},
@@ -3166,7 +3201,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

05-Multivariate-Gaussians.ipynb

@@ -335,9 +335,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x216687b4438>"
+       "<matplotlib.figure.Figure at 0x2138c3414a8>"
       ]
      },
      "metadata": {},
@@ -464,8 +464,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 0.025,  2.73],\n",
-       "       [ 2.73,  3.27e+02]])"
+       "array([[  0.025,   2.727],\n",
+       "       [  2.727, 327.235]])"
       ]
      },
      "execution_count": 3,
@@ -500,8 +500,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 0.02,  2.18],\n",
-       "       [ 2.18,  2.62e+02]])"
+       "array([[  0.02 ,   2.182],\n",
+       "       [  2.182, 261.788]])"
       ]
      },
      "execution_count": 4,
@@ -532,8 +532,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 6.96,  6.96],\n",
-       "       [ 6.96,  6.96]])"
+       "array([[6.956, 6.956],\n",
+       "       [6.956, 6.956]])"
       ]
      },
      "execution_count": 5,
@@ -563,9 +563,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21669778358>"
+       "<matplotlib.figure.Figure at 0x2138ce062e8>"
       ]
      },
      "metadata": {},
@@ -575,8 +575,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 6.96  -3.08]\n",
-      " [ -3.08  1.39]]\n"
+      "[[ 6.956 -3.084]\n",
+      " [-3.084  1.387]]\n"
      ]
     }
    ],
@@ -603,9 +603,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x216697fafd0>"
+       "<matplotlib.figure.Figure at 0x2138b3a6e80>"
       ]
      },
      "metadata": {},
@@ -615,8 +615,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 10.7  3.15]\n",
-      " [ 3.15  2.38]]\n"
+      "[[11.007  3.032]\n",
+      " [ 3.032  2.311]]\n"
      ]
     }
    ],
@@ -644,9 +644,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x216687b40b8>"
+       "<matplotlib.figure.Figure at 0x2138b3d85f8>"
       ]
      },
      "metadata": {},
@@ -656,8 +656,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.999  0.00277]\n",
-      " [ 0.00277  1.0]]\n"
+      "[[ 0.996 -0.004]\n",
+      " [-0.004  1.004]]\n"
      ]
     }
    ],
@@ -705,9 +705,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAADnCAYAAAAaX/GuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsvXmYJGd95/l5I/KurDOrqm9d3SB0gEQjgTDGY888Xu/w2AKDnwXW+2CPrwWssZ71s17s2V089tiM7QcPPEYMfh7jQc+yI7ANsxbIAl8gGwNueQzCAiyp0dF0V9eRmXVk5R3Hu39EvpERkZFZWVmZVdmp+D5PS92RmRFvXO/3/V3fn5BSEiFChAgRIihoRz2ACBEiRIgwXoiIIUKECBEi+BARQ4QIESJE8CEihggRIkSI4ENEDBEiRIgQwYfYHp9HKUsRIkSIMJkQ3T6ILIYIESJEiOBDRAwRIkSIEMGHiBgiRIgQIYIPETFEiBAhQgQfImKIECFChAg+RMQQIUKECBF8iIghQoQIESL4EBFDhAgRIkTwISKGCBEiRIjgQ0QMESJEiBDBh4gYIkSIECGCDxExRIgQIUIEHyJiiBAhQoQIPuylrhohwshh2zaGYWCaJrquu3+EEAjRVQAyQoQII4KQsqeydiS7HWEkkFJi2zamafr+rj4DEEJgmiaVSoV4PM78/DyapkWkESHCcND1BYoshgiHCiklpmliWZa7rdlssrm5iWVZJBIJ94+u6zSbTdbX10mn02QyGaSULiEIIdA0DSEEuq5HpBEhwpAQEUOEQ4FlWViWhW3b7rbd3V2KxSLlcjn0N0IIYjHnETUMg1Kp5CMNcNxQimy8pKEIQ5GGIo6INCJE2BuRKynCyCCldAlBPWe2bbO1tUWxWKTZbAIOAczOzqLrOoZh0Gw2aTabPhIJQtM0lyTi8bjP0tC0dk6FOq7XPRUkjVgs5m6LEOFFhK4PfEQMEYaOoLtICEG9XqdYLLK1teVO0vF4nFwu58YODMPwTc6WZbG7u8v6+jq6rpPJZFzS6PXc6roeShrxeDyUNNTfvaSh3FJeSyNChAlDFGOIMFqoAHLQXVQqlSgWi1QqFXdbNpsll8sxPT3tTrhh1oGu6ySTSQBisRgnT550j2VZlksSXivDMAwsy6JWq1Gr1Tr2GYvFupKGEAIppW//gDtG0zRpNBokk0mmp6fRNC0ijQgTiYgYIhwIYe4iy7LY3Nxkc3MTwzAAx/UzNzdHLpcjlUqF7icMYZOuij3EYjEymUzHfkzT7EoapmlimibVarVjv0GiUH+PxWIIIahUKqyvrzMzM+PGOJRLSlkiUbpthElARAwRBoJt26ytrVGtVllcXCSdTlOr1SgWi+zs7LgTfSKRcN1FajINQ7cJVG3fw+Xp+348HicejzM1NeX7TErpIwovcZimiWEYGIbhs268+1RjMQyDer3eEQQHfATpJY2geyoijQjjjIgYIvSNoLuoVCpRLpeJxWKsrKz4XDfT09Pkcjmy2ezYTIBCCNcKCEIV2YVZGl63EkCtVuPy5cuAYwl1szSUFaEyp4Ao3TbCNYGIGCLsCeWe8U5wyl0DUCgUAGeSXFhYIJfLhU6+g2C/FsOg0DSNZDLpxjS8sCwLwzDY2tqiVCoRj8fdGgvbtmk0GjQajY7f6brekTGl/h0kjSjdNsI4ISKGCF3hjR2oiblarbruIoV4PM7y8jJzc3O+rJ9JgZqY0+k0pVKJTCbD8ePH3fhKL0vDsizq9XrHPmOxWKiloTKn1DX3kgb4rYxms+lmaylLI0KEYSAihgg+hAWTpZRsb29TLBZ9k1w8HscwDJaXl1lYWDjQcYcVYzhMeIPg6XTa95ma0LuRhgqCh2VOqRhJ0NIIZk698MILWJbFTTfd5BJDlG4bYRiIiCECEF57YBiGW3ugtsdiMebn58nlcqyvr7O1tXWUwx5beIPgYZlTKtAdJA213TCM0Mwpr3WhAt62bbuZU73Sbb2koYr6ItKIEIaIGF7E6FZ7UC6XKRaL7O7uutvS6TS5XI7Z2VnXXXQYq/lxshiGNQZvEDwscyoszVZZGWqbN3PqhRdecPcZ5p5S1kQ30ojSbSMEERHDixC9pCo2NzfdQKqSqsjlch2rXvW52t9hjPnFACFE1yC4bds+olBBf13XsSyraxBcyYeEuae8QXCI0m0jOIiI4UUEZR143UXNZtN1F6nJIRaLkcvlWFhYcEXswtCranlYiCafNjRNI5VKuQWCihjOnj3bQRrev9u2Tb1eDw2CK/mQMEvDe3+jdNsXFyJimHBIKanVauTzeXfCh3Bl06mpKXK5HDMzM3292Gq1+WJxJSmMw6QXvB4qayosCK5cR2HFff3Ih4RZGkHSME2TK1eu0Gw2OXXqFOl0Okq3vYYREcOEwlt7UK/XyefzpFIppJQUi0VXqkII4QaTw6QqemEcJ+0XI3pNtN7MqSC88iHdYhqqeVIQ3vRaRRhKcsQbz+iVbhup244vImKYMARrD1R2EUC9XmdtbQ1wslsWFhZYWFjoKVXRCy+24PM4wTvRDgpv5lTY/r2ZUl7S8P4Jw8rKCslkssPSCAbBFWk0Go1I3XbMEBHDBCDYJlNhZ2eHYrHoS3sMUzYdFMOetPcaj9e//WLHMIihF/qVD/GShnJHdSMN7z6DMY29MqcgSrc9TETEcA0jrPbANE1X2VS5ATRNc3Pdb7zxxqEdf9jB57CJP3rpwzFqYuiFbvIhFy9exLZtzpw5E1rc10/mVK8eGlG67eEhIoZrDN1qD8KUTZPJpCtk98wzzwzdHTMsi0FKSaVSoVKpdEhdR7h2oJ7HVCoVKo3SrYdGv5lT3XpoeI/tTbcFJ3MrFouxtLQUpdvuAxExXCPoJlWh3EXerJKZmRlyuRxTU1MIIXwVssPEQbOSbNtmZ2eHQqHQMSEo/7fXlVGr1UgmkwPHRCYJR2kxhMH7DHQbUz+ZU8NsvKRqczRNY35+3j1WlG67NyJiGHN0k6pQ7iK1Xdd1N5gc9At7V/bD9NMPajGYpkmxWPS5u3RdZ2pqyp0UvBOFgpK69q4gu6VRTjrGlRgGmVS9mVPDbLykMrGU1leYNdpN3TZIGi+2dNuIGMYQ3dxFlUqFYrFIqVRyt6VSKXK5XE9lU/UwHzUxKHfX9va2+xs1/tnZWZfkoC113Ww2WV1dBXBTInutIIOZMMGMmEnBuGVpqed02Oq63sypvRovhYkUqu9tbGx07DPM0vA+J0HSUL99MaTbRsQwRjAMg1qt5j644DzUW1tbFItFX9DOK1XRzwOpiMG27aG9vP0Qg5SS3d1dCoWCT99nenqaxcVF192l3AkKapWWSqXY2NjAsizOnDmDruu+FWS3NMpgF7ZuwU1vb4RrEeMyGR2FBbNX5lSpVGJ9fZ1YLMbU1FRH4yWvNRq2zzDSgLblPcnpthExjAFUqmmhUODq1assLCywuLgYKlWh3EVhuee9oDKThrnS7JWVZFmWS2jqBVS+3lwu15HR0u+L02sFGezC1m9w0+un9v4JBsDHaZU+bq4k9QyMy3hUSis4SRjHjx93P+vVQ2M/jZfCuvWFZU6p33YjsXFERAxHhLDaA/VSlUolNjc33e9mMhlXqmLQ1W1QLG0YCAs+K+2lzc1N91jxeNzVXhokcNyvy6pbGmUwuNnN7RD0UwddDio3fxwIYtyIQY1nnKyvbu4trzUaRK/npFfjpWDcSz03anHh1Si7FhARwyEjGEyGtrJpPp8HcGUF5ubmyOVyHVkcg2AUgnfefVYqFQqFgi/+kclkWFxc7Ft7SbmUho29gpvdrIxuLoednR3K5fKRBsDHgZy8GDeigsHiHv1mTgV7aHSLez355JM0Gg1e97rXcfbs2YOf1CEhIoZDgloxeE3uer3uBmO9E3YikeDs2bM9lU33i1EK3jWbTZ577jn334rQwqS6e6Hb2EYpi9HLTx10OZTLZdfFMC4B8HGZiEcVfD4IhjmmfhYXYe6parXK448/zmc+8xlWV1dJJBJ85Stf6dtyvnz5Mu94xztYW1tD0zR+7ud+jvvvv9/3nccee4w3vvGNbvHqm9/8Zt773vce6HwjYhghwmoPwHEVFYtFX4B0amqKbDbL+vq6m+kwTAzTlaSqq5Xss5TSTZfN5XL7jn+MK4IuB03TyOfzzM3NsbCwMHAAPIw49jt5jdsKfdzGA4dHVt7FRTDudf311/PWt76VeDxOOp12n6l+EYvF+N3f/V3Onz/P7u4ur3rVq/jBH/xBbr31Vt/3Xv/61/PII48M5XwgIoaRIKz2wLIst/ZA+ao1TXNX16lUinq9zvr6+kj6GwzDlVSv1ykUCr50U3DO42Uve9nIXsBxE9I7ygC4wrhNxOMWfIbxsGK8GYYqvXU/OHHiBCdOnACcTL5bbrmFlZWVDmIYNiJiGBLCag+EEKG5+4lEglwux/z8vO9BGUWAOLjv/U6uUkrK5TKFQsHXuyGbzTI/P8/ly5d9WjUHwV4xhnEhhl4YdQBc/Rm3QOa1FHw+Khx0HC+88AJf//rXec1rXtPx2Ve/+lXuuOMOTp48yfvf/35uu+22Ax0rIoYDoptUhXIXeV/06elpV7sobGV1GMTQ775VQNxbPyGEv3eDmpxGPWGP0yp0UAw7AK5Qq9VYW1vr2n3tsBBZDHvjINemXC7zlre8hQ9+8IPMzMz4Pjt//jyXLl0im83y6KOP8qY3vYmLFy8eaKwRMQyIsDaZ3ZRNVe1BWB9fL0ZJDP26kprNZofchko3nZ+f98U+DsvFM26upGFjPwFw7x9lpe7s7HT87rArwMfRYlDP77iMadDrbhgGb3nLW/jxH/9x3vzmN3d87iWKN7zhDbz73e+mUCiwuLg48FgjYtgHpJRUq1WKxaJbbAa427wvaD9SFUGMStMI9nYlVatVCoWC7xzS6TSLi4vMzs6GjmWU443goFvLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x216687b4470>"
+       "<matplotlib.figure.Figure at 0x2138cedc6d8>"
       ]
      },
      "metadata": {},
@@ -745,9 +745,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from filterpy.stats import gaussian, multivariate_gaussian"
@@ -767,9 +765,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = [2.5, 7.3]"
@@ -785,9 +781,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mu = [2.0, 7.0]"
@@ -803,9 +797,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "P = [[8., 0.], \n",
@@ -865,9 +857,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x216687b4160>"
+       "<matplotlib.figure.Figure at 0x2138a368518>"
       ]
      },
      "metadata": {},
@@ -903,9 +895,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166ba2e7b8>"
+       "<matplotlib.figure.Figure at 0x2138b3d8208>"
       ]
      },
      "metadata": {},
@@ -941,9 +933,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166ba27e48>"
+       "<matplotlib.figure.Figure at 0x2138f6a7048>"
       ]
      },
      "metadata": {},
@@ -975,9 +967,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b420eb8>"
+       "<matplotlib.figure.Figure at 0x2138f6d5940>"
       ]
      },
      "metadata": {},
@@ -1008,9 +1000,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x216697ada90>"
+       "<matplotlib.figure.Figure at 0x2138cdc66a0>"
       ]
      },
      "metadata": {},
@@ -1051,9 +1043,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b9af0b8>"
+       "<matplotlib.figure.Figure at 0x2138d53fb38>"
       ]
      },
      "metadata": {},
@@ -1094,9 +1086,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b47f6d8>"
+       "<matplotlib.figure.Figure at 0x2138d531668>"
       ]
      },
      "metadata": {},
@@ -1127,8 +1119,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[   18.5249,   135.701 ],\n",
-       "       [  135.701 ,  1092.29  ]])"
+       "array([[  18.5249,  135.701 ],\n",
+       "       [ 135.701 , 1092.29  ]])"
       ]
      },
      "execution_count": 22,
@@ -1150,9 +1142,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x216691a5dd8>"
+       "<matplotlib.figure.Figure at 0x2138f12eb00>"
       ]
      },
      "metadata": {},
@@ -1181,9 +1173,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166ba000f0>"
+       "<matplotlib.figure.Figure at 0x2138f011390>"
       ]
      },
      "metadata": {},
@@ -1217,12 +1209,27 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ba258f1263764dd7b04d9a305de3fffe",
+       "model_id": "72e55a1b5ba944dc89bacb0771e873e6",
        "version_major": 2,
        "version_minor": 0
       },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
       "text/plain": [
-       "A Jupyter Widget"
+       "interactive(children=(FloatSlider(value=5.0, continuous_update=False, description='var_x', max=20.0), FloatSlider(value=5.0, continuous_update=False, description='var_y', max=20.0), FloatSlider(value=1.5, continuous_update=False, description='cov_xy', max=50.0, step=0.2), Output()), _dom_classes=('widget-interact',))"
       ]
      },
      "metadata": {},
@@ -1337,9 +1344,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b836b70>"
+       "<matplotlib.figure.Figure at 0x2138f048b38>"
       ]
      },
      "metadata": {},
@@ -1368,9 +1375,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x216677395f8>"
+       "<matplotlib.figure.Figure at 0x2138ebae438>"
       ]
      },
      "metadata": {},
@@ -1411,9 +1418,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b267fd0>"
+       "<matplotlib.figure.Figure at 0x2138ea2ecf8>"
       ]
      },
      "metadata": {},
@@ -1470,9 +1477,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21667739438>"
+       "<matplotlib.figure.Figure at 0x2138ea15240>"
       ]
      },
      "metadata": {},
@@ -1500,9 +1507,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b262dd8>"
+       "<matplotlib.figure.Figure at 0x2138ea1eeb8>"
       ]
      },
      "metadata": {},
@@ -1533,9 +1540,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b861978>"
+       "<matplotlib.figure.Figure at 0x2138ebff9b0>"
       ]
      },
      "metadata": {},
@@ -1569,9 +1576,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x216687c7908>"
+       "<matplotlib.figure.Figure at 0x2138ebaecc0>"
       ]
      },
      "metadata": {},
@@ -1598,9 +1605,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21669d69438>"
+       "<matplotlib.figure.Figure at 0x2138ea15a90>"
       ]
      },
      "metadata": {},
@@ -1630,9 +1637,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b20ac50>"
+       "<matplotlib.figure.Figure at 0x2138ce8f7f0>"
       ]
      },
      "metadata": {},
@@ -1641,7 +1648,7 @@
    ],
    "source": [
     "import kf_book.book_plots as book_plots\n",
-    "book_plots.plot_errorbar2()"
+    "book_plots.plot_errorbars([(160, 8, 'A'), (170, 8, 'B')], xlims=(150, 180))"
    ]
   },
   {
@@ -1661,9 +1668,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b94f7f0>"
+       "<matplotlib.figure.Figure at 0x2138f6c49b0>"
       ]
      },
      "metadata": {},
@@ -1692,9 +1699,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b89ed68>"
+       "<matplotlib.figure.Figure at 0x2138ec27828>"
       ]
      },
      "metadata": {},
@@ -1725,9 +1732,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b94cef0>"
+       "<matplotlib.figure.Figure at 0x2138f019c50>"
       ]
      },
      "metadata": {},
@@ -1764,9 +1771,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b221f98>"
+       "<matplotlib.figure.Figure at 0x2138f05f278>"
       ]
      },
      "metadata": {},
@@ -1791,9 +1798,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166ba4a438>"
+       "<matplotlib.figure.Figure at 0x2138f05f3c8>"
       ]
      },
      "metadata": {},
@@ -1825,9 +1832,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b8cf240>"
+       "<matplotlib.figure.Figure at 0x2138f1240f0>"
       ]
      },
      "metadata": {},
@@ -1852,9 +1859,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b4027f0>"
+       "<matplotlib.figure.Figure at 0x2138d53f198>"
       ]
      },
      "metadata": {},
@@ -1875,13 +1882,15 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166ba40a58>"
+       "<matplotlib.figure.Figure at 0x2138ebc7668>"
       ]
      },
      "metadata": {},
@@ -1908,9 +1917,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2166b894da0>"
+       "<matplotlib.figure.Figure at 0x2138c341b70>"
       ]
      },
      "metadata": {},
@@ -1990,7 +1999,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

08-Designing-Kalman-Filters.ipynb

@@ -307,9 +307,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from numpy.random import randn\n",
@@ -344,9 +342,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a37901d0>"
+       "<matplotlib.figure.Figure at 0x214c3eb3eb8>"
       ]
      },
      "metadata": {},
@@ -426,9 +424,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from filterpy.kalman import KalmanFilter\n",
@@ -462,10 +458,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.00025  0.0005  0.0  0.0]\n",
-      " [ 0.0005  0.001  0.0  0.0]\n",
-      " [ 0.0  0.0  0.00025  0.0005]\n",
-      " [ 0.0  0.0  0.0005  0.001]]\n"
+      "[[0.    0.001 0.    0.   ]\n",
+      " [0.001 0.001 0.    0.   ]\n",
+      " [0.    0.    0.    0.001]\n",
+      " [0.    0.    0.001 0.001]]\n"
      ]
     }
    ],
@@ -499,18 +495,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tracker.B"
    ]
@@ -553,9 +538,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "tracker.H = np.array([[1/0.3048, 0, 0,        0],\n",
@@ -584,8 +567,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 5.0,  0.0],\n",
-       "       [ 0.0,  5.0]])"
+       "array([[5., 0.],\n",
+       "       [0., 5.]])"
       ]
      },
      "execution_count": 8,
@@ -621,9 +604,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "tracker.x = np.array([[0, 0, 0, 0]]).T\n",
@@ -653,9 +634,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a48c8080>"
+       "<matplotlib.figure.Figure at 0x214c4c73588>"
       ]
      },
      "metadata": {},
@@ -737,7 +718,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.00656  0.00298  0.00656  0.00298]\n"
+      "[0.007 0.003 0.007 0.003]\n"
      ]
     }
    ],
@@ -761,15 +742,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.00656  0.00278]\n",
-      " [ 0.00278  0.00298]]\n"
+      "[[0.007 0.003]\n",
+      " [0.003 0.003]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a3787780>"
+       "<matplotlib.figure.Figure at 0x214c3eb3080>"
       ]
      },
      "metadata": {},
@@ -799,8 +780,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 0.0,  0.0],\n",
-       "       [ 0.0,  0.0]])"
+       "array([[0., 0.],\n",
+       "       [0., 0.]])"
       ]
      },
      "execution_count": 13,
@@ -875,9 +856,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a49d02e8>"
+       "<matplotlib.figure.Figure at 0x214c4d402e8>"
       ]
      },
      "metadata": {},
@@ -956,9 +937,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def ZeroOrderKF(R, Q, P=20):\n",
@@ -1021,9 +1000,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def FirstOrderKF(R, Q, dt):\n",
@@ -1088,9 +1065,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def SecondOrderKF(R_std, Q, dt, P=100):\n",
@@ -1133,9 +1108,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def simulate_system(Q, count):\n",
@@ -1159,9 +1132,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def filter_data(kf, zs):\n",
@@ -1190,9 +1161,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a49cb390>"
+       "<matplotlib.figure.Figure at 0x214c4cabf98>"
       ]
      },
      "metadata": {},
@@ -1222,9 +1193,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from kf_book.book_plots import plot_residual_limits, set_labels\n",
@@ -1243,9 +1212,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a48a8cf8>"
+       "<matplotlib.figure.Figure at 0x214c4d006d8>"
       ]
      },
      "metadata": {},
@@ -1274,9 +1243,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5af4cf8>"
+       "<matplotlib.figure.Figure at 0x214c4dde908>"
       ]
      },
      "metadata": {},
@@ -1306,9 +1275,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5ac7fd0>"
+       "<matplotlib.figure.Figure at 0x214c4dd80f0>"
       ]
      },
      "metadata": {},
@@ -1339,9 +1308,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5abec50>"
+       "<matplotlib.figure.Figure at 0x214c5e667b8>"
       ]
      },
      "metadata": {},
@@ -1373,9 +1342,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5be16d8>"
+       "<matplotlib.figure.Figure at 0x214c5f44ac8>"
       ]
      },
      "metadata": {},
@@ -1408,9 +1377,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5cc7668>"
+       "<matplotlib.figure.Figure at 0x214c5fc3550>"
       ]
      },
      "metadata": {},
@@ -1445,9 +1414,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5cd3f28>"
+       "<matplotlib.figure.Figure at 0x214c5fe4748>"
       ]
      },
      "metadata": {},
@@ -1487,9 +1456,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5c48a58>"
+       "<matplotlib.figure.Figure at 0x214c5f76438>"
       ]
      },
      "metadata": {},
@@ -1518,9 +1487,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a49d0208>"
+       "<matplotlib.figure.Figure at 0x214c5fdad68>"
       ]
      },
      "metadata": {},
@@ -1528,9 +1497,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a4995470>"
+       "<matplotlib.figure.Figure at 0x214c5fdaba8>"
       ]
      },
      "metadata": {},
@@ -1568,9 +1537,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a3eef0f0>"
+       "<matplotlib.figure.Figure at 0x214c5fda7b8>"
       ]
      },
      "metadata": {},
@@ -1578,9 +1547,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a4995e80>"
+       "<matplotlib.figure.Figure at 0x214c4c61828>"
       ]
      },
      "metadata": {},
@@ -1618,9 +1587,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5c61358>"
+       "<matplotlib.figure.Figure at 0x214c5ebe978>"
       ]
      },
      "metadata": {},
@@ -1652,9 +1621,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5d1a6d8>"
+       "<matplotlib.figure.Figure at 0x214c6004160>"
       ]
      },
      "metadata": {},
@@ -1706,9 +1675,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a49482b0>"
+       "<matplotlib.figure.Figure at 0x214c5fdab70>"
       ]
      },
      "metadata": {},
@@ -1716,9 +1685,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a4948ba8>"
+       "<matplotlib.figure.Figure at 0x214c4cce160>"
       ]
      },
      "metadata": {},
@@ -1754,9 +1723,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5e9b4a8>"
+       "<matplotlib.figure.Figure at 0x214c3ff5e80>"
       ]
      },
      "metadata": {},
@@ -1764,9 +1733,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5e95278>"
+       "<matplotlib.figure.Figure at 0x214c3ff5ba8>"
       ]
      },
      "metadata": {},
@@ -1797,9 +1766,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5a21cf8>"
+       "<matplotlib.figure.Figure at 0x214c4cce0f0>"
       ]
      },
      "metadata": {},
@@ -1807,9 +1776,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5a21208>"
+       "<matplotlib.figure.Figure at 0x214c5e73e10>"
       ]
      },
      "metadata": {},
@@ -1941,9 +1910,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a3787e80>"
+       "<matplotlib.figure.Figure at 0x214c3ff5f98>"
       ]
      },
      "metadata": {},
@@ -2014,9 +1983,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "bad measurement       : [[ 10.1  10.1]] km\n",
-      "before bad measurement: [[ 5.  5.]] km\n",
-      "after bad measurement : [[ 7.84  7.01]] km\n",
+      "bad measurement       : [[10.1 10.1]] km\n",
+      "before bad measurement: [[5. 5.]] km\n",
+      "after bad measurement : [[7.84 7.01]] km\n",
       "estimate shift        : 3.4 km\n",
       "distance from prior   : 7.1 km\n"
      ]
@@ -2114,9 +2083,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a3dc89b0>"
+       "<matplotlib.figure.Figure at 0x214c4d4b7b8>"
       ]
      },
      "metadata": {},
@@ -2144,9 +2113,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a3dc8630>"
+       "<matplotlib.figure.Figure at 0x214c5fe3390>"
       ]
      },
      "metadata": {},
@@ -2181,7 +2150,7 @@
      "output_type": "stream",
      "text": [
       "discarding measurement, error is 39 std, 18 std\n",
-      "y   is [ 5.05  5.05]\n",
+      "y   is [5.05 5.05]\n",
       "std is 0.13 0.29\n"
      ]
     }
@@ -2215,9 +2184,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5cc74e0>"
+       "<matplotlib.figure.Figure at 0x214c60ac860>"
       ]
      },
      "metadata": {},
@@ -2389,9 +2358,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from scipy.linalg import inv\n",
@@ -2413,7 +2380,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean NEES is:  0.889331938901\n",
+      "mean NEES is:  0.8893319389009477\n",
       "passed\n"
      ]
     }
@@ -2490,9 +2457,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAD1CAYAAAAWAiGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsvXl4W9d17v0uzBMJzhQnkZREax5sQZKneEhiWxndNmli3zR1ps9JGufrbXvbOm2TtEl6myZt0iZNGrep43uTJs7kxI7tWB7k2ZYlSNY8SxQHkRRHgAAxA/v+cXBAkMRwABwSILB+z8NHxBlwNreAs85a+11rkRACDMMwDLPUaIo9AIZhGKYyYQPEMAzDFAU2QAzDMExRYAPEMAzDFAU2QAzDMExR0BV7AG63m2V4DMMwZY7dbqf529gDYhiGYYpCVgNERB1E9DwRnSKiE0T0xymOISL6FhGdJ6KjRHRN0r57iOhc/Ocetf8AhmEYZnmiJAQXAfBnQohDRFQF4CARPSOEOJl0zDsA9MR/dgH4dwC7iKgOwBcBOACI+LmPCSGmUl3Ibrfn9Uc4nU4AgMPhyOv8coXnJTU8LwvhOUkNz0tqlM6L2+3OuD+rBySEGBZCHIr/7gFwCkDbvMPuBPB/hcQ+ADVE1ALgDgDPCCEm40bnGQC7s12TYRiGKX8ol1I8RNQF4CUAm4QQ00nbHwfwVSHEK/HXzwH4SwC3ADAJIb4S3/55AH4hxD/J5yaLEM6dO1fAn8IwDMOUEj09PYnfCxIhEJENwC8B/M9k4yPvTnGKyLCdYRiGqXAUybCJSA/J+Py3EOKRFIcMAuhIet0OYCi+/ZZ5219Id51846wcp00Nz0tqeF4WwnOSGp6X1CzZGhAREYD/AnBKCPGNNIc9BuAP42q4awG4hRDDAPYAuJ2IaomoFsDt8W0MwzBMhaPEA7oBwIcBHCOiw/FtfwVgJQAIIb4H4EkA7wRwHoAPwEfj+yaJ6MsADsTP+5IQYlK94TMMw5QeQghIz+5MJrIaoLiwIONMCknJ8Jk0+x4E8GBeo2Oysr9Xsuc7u+uKPJL8CISj+PB/vYHbNjTj3ptWF3s4DKMKf/nLo5icCeF7f7AdOi3n+6ej6KV4mML4058dxuCUHx90dOCv370e1Sb9ol4vFIlBpyFoNOo83f3qzcs4cGkKBy5NwR+K4Y/f3pP9JIYpYY5fduNnzkEAwLf3nsef3HZVkUdUurBpXsaEozEMufxY02TDzw8O4PZvvITnT48u2vUi0Rhu/acX8M1nz6ryfkII/ODVXmxoqcb7t7fjm8+exT8/fQbcpbey+dmBAfzed19dtp+Df9t7HlUmHd65eQW+vfcc3rg4UewhlSxsgJYxQy4/YgL45E2r8Ks/ugHVZh0++tAB/OnPDsPlC6l+vdcuTOCyy48f7etDMBIt+P1ePT+Bs1e8+NiN3fja+7bgrh0d+Pbe8/jaHjZClYyzbxKH+l1w+cLFHkrOnB6ZxlMnRvDR67vwtfdvxco6C/7nTxfn+1gOsAFaxvRP+gAAHXUWbO2owW8+eyM++9Y1ePTwEG775kvYfzmg6vWeODoMImDKF8aeE1cKfr8fvNqLBpsB79naAo2G8L9/dzP+4NqV+PcXLuB/P3mKjVCFMuYJAgAuTcws+rWEEHh4fz9+sr9flff7t73nYTVo8bEbu2Ez6vDtu6/BuDeIv/zlUf48p4AN0DJGNkAr6ywAAKNOiz+7fS0e/cwNaLAZ8bXX3Xj0jDpf4lAkhqdOjOC9W1vRXmvGwwV+YXvHZ/Dc6VF8aFcnjDotAECjIXz5zk34yPVd+M+Xe/Glx0/yl7YCGfMujQEanQ7gIz84gPsfOYa/+80JhCKxgt7v/KgXTxwbxh9e34UaiwEAsLndjr+4Yx32nLiC/35DHSNXTrABWsYMTPqh1xKaq01ztm9qs+Ox+27A2no9XupXxwt69cI43P4w3rOlFXft6MBrFybQV8AN4qFXe2HQavCha1fO2U5E+OJ7NuDjN3bjB69ewhcePYFYjI2QGhzsm8Tbv/EiZoKRYg8lIwkPaNy3aNd46vgI7viXl/BG7wTevaUFgXAMxy5nTprMxnefPw+TTotP3Ng9Z/vHb+zGTVc14suPn8SZEU9B1yg32AAtYwYmfWivtUCbQpGm12qwrdmAfncEbhVi6Y8fGUaVSYe3XNWA33d0QKshPHxgIK/3cvvD+PnBQbx7awuaqkwL9hMR/uZd6/HJm1fhh/v68FNnftdh5vLq+QmcH/Vi2O0v9lDSEosJjHul9ZLF8IC8wQj+4hdH8KkfHUR7rQWPf/Yt+Nv3bgQwm9KQD5fGZ/Drw5fxoV0rUW8zztmn0RD++fe3osqkw2d/cgiBcOHrp+UCG6BlzMCUD+215rT7NzQaICAt6hZCMBLF0ydHcPuGFTDqtGiuNuHWtU34uXMQ4WjuYYufOwfgC0XxsRu60x5DRLh/9zp01Vvw7MnC15uY2ZCtN1i6N8ApXwjRuMd7aUJdD+hg3xTe+a8v4xcHB3HfrWvwy09fjzVNNjTYjFjdaMX+3vzVat994Tz0Wg3uvWlVyv2NVUb88we24ewVL77yxMmUx1QibICWMf2TvsT6TyrW1Omho8Ke7ADglXPj8AQiePfWlsS2u3d2YNwbxHOncpN9R2MCD712CTu76rCpLXP/JyLCjT0N2HdxIi9Dx8ylP35D9wZKNwQnr//UWQ24NK6OBxSNCfzrs+fw+997DTEh8NNPXof/dcdaGHSzt7+d3fVwXppKGL9cGJj04ZFDl3H3zpVoql7o0cvcfFUj7r1pFX60rx9PHR/J628pN9gALVOmA2G4fOGMBsioJayp0+ONAg3Q40eHYTfrccPqhsS2m69qxIpqEx4+kNvC6jMnr2Bwyo+P3dil6Pgb1zRiJhTFm/2unK6zVERjAi+eHcM//PYUPIHSlg33TUo3dG8JrwHJ6z+Ozlq4/eGC5csT3iA+8oP9+OazZ3Hntjb89o/fgh1dC6uGXLuqDp5gBKeG5xf6z86/v3gBGiJ88ubU3k8y/+v2tVjfUo2v7znNAhuwAVq2DCRJsDOxvkGP45fd8IXyu+kEwlE8c/IKdm9cMeeJUafV4AOOdrx4dgyXXcrXFB58tRdtNWbctmGFouOvW10PDQGvnB/PeeyLybDbj3999hxu+trzuOfB/XjgxYvYu4hJwIUSCEdxZVq6uS8HAySXliokDHewbxLv+tYreKN3Ev/we5vxjQ9sRVWaSiGyUcr1YW3Y7ccvnIP4fUc7Wuzpw+EyBp0G/2NnBy6MzeDsFW9O1ypH2AAtUwYmpZt+Jg8IANY3GBCJibw9iBfPjsEbjOBdW1oW7PvADqkDx88UihGOX3Zjf+8kPnJ9V0rhRCrsZj22tNfglXNjyge9SESiMTx9YgQfe+gAbvjqXnzz2bPobrDiGx/YCmA2xFWKyA8sAEpaBZfwgOIGIZ8wnBAC33/5Ij74wD4YdBo88unrcffOlRmLg7bWmNFRZ855HeiBFy8iJgQ+fYvyOoZ3bFoBIuCJY8M5XascYQO0TEl4QLWZDdDaBj00lPuTncwTR4dRa9HjutX1C/a111rwlp5G/Nw5oCh2/oNXL8Fi0CYMl1Le0tOAI4NuTBcxxDXuDeKmrz2Pe394EMcvu/HpW1bjpT+/FT/6xC783jXtaLGb0LsEiZP50pdkHEvdA7IYtFi3ogpEuSvhpgNhfPpHh/CVJ07hreua8JvP3ph1rVFmZ1c99vdOKg6NjU4H8JP9/fi9a9rQnuV7mExTlQk7u+rwJBsgNkDLlf5JH6pNOtgtmYuPWvUabGitxoE8DFAgHMWzp65g96YW6NNU9L17RweG3AG8dDazhzLmCeI3R4bw/u3tsJtzK5h645oGRGMCr18oXk2t08MeDLkD+OJ7NuC1+9+KP79jHVbWz950Oustc27ypUbf5NIYoMmZEJ47dQVf33Mad//HPnz4v97IKY9rzBtEY5URJr0WrXZzTh7QhTEv3vvtV/DMqSv4m3etxwMf3p7TZ23XqjpM+cI4P6osNPb9V3oRiQl85tY1iq8h864tLTg/6sXZK5WdF8TVsJcpA1O+rOs/Mju66vDjN/oRisTmrONk4/nTo/CFonh3ivCbzNvWN6PBZsBP9vfj1nVNaY/77zf6EIrGcM/1XYqvL3P1ylpYDFq8en4cd2xUtnakNkPx3Jm3rWtOWV6/q96KZ0+Vrlx8YNIHq0ELvU6jaghuJhjBo4eHcLBvCm/2T+Fi3GBoNYQ6qwFjniAmZkJorDJmeSeJMU8QjfE8mq4GS05rQN967hwmZkJ4+N5rUwoNsrErvu60r3cSPc1VGY8NRWL4uXMAuzeuQGe9Nedr7d60Al987ASeODqMq27LfK1yhj2gZUo2CXYyu7rrEIzEcOxybutAjx8bRoPNkPhipsKg0+B929vx3OlRjE4vrLoQisTw4zf68eArvbh1bSNWN9pyGoN8jV3ddXjlXPGECMMu6W9rtqe+kXY1WDHuDZWsEq5vYgYr662wGXWqyrD/+40+/NWvjuGFM6NY1WjDX+xei4fvvRbH/vZ2/P3vbAKAnBJfxzzBhLHqrLfmFII7dtmNXd31eRkfQFpPba42Kkpb2Ht6FFO+MN7vaM/rWk1VJuzgMJyiltwPEtEoER1Ps//Piehw/Oc4EUWJqC6+7xIRHYvvc6o9+EolFhMYnPIrNkDyF3J/75Tia/hCEew9NYrdm1Zkbah1146ViMYEfn5wMLEtFIlJXtE/vYC/+tUxrGq04a/ftUHx9edzY08jLo7P5KS4U5Nhtx8NNkOibt18uuLhuFINw/VN+tBZZ5EMkIoe0JArgCqTDs6/eTu+f48Df3TLGly7qh4Wgy6hChtyKS8HNeYNokH2gOotcPmUSbFnghH0js9gY2t1fn8IpLyznd312N87kXUd6JeHBtFLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a49eabe0>"
+       "<matplotlib.figure.Figure at 0x214c4d3c240>"
       ]
      },
      "metadata": {},
@@ -2509,7 +2476,7 @@
     "for z in zs:\n",
     "    kf.predict() \n",
     "    kf.update(z)\n",
-    "    likelihoods.append(kf.likelihood(z))\n",
+    "    likelihoods.append(kf.likelihood)\n",
     "plt.plot(likelihoods);"
    ]
   },
@@ -2529,9 +2496,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a49eab70>"
+       "<matplotlib.figure.Figure at 0x214c5df72b0>"
       ]
      },
      "metadata": {},
@@ -2544,7 +2511,7 @@
     "for z in zs:\n",
     "    kf.predict() \n",
     "    kf.update(z)\n",
-    "    log_likelihoods.append(kf.log_likelihood(z))\n",
+    "    log_likelihoods.append(kf.log_likelihood)\n",
     "plt.plot(log_likelihoods);"
    ]
   },
@@ -2606,15 +2573,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 10.   0.]\n",
-      " [  0.  10.]]\n"
+      "[[10.  0.]\n",
+      " [ 0. 10.]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5c6a518>"
+       "<matplotlib.figure.Figure at 0x214c5df71d0>"
       ]
      },
      "metadata": {},
@@ -2709,9 +2676,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5cf56d8>"
+       "<matplotlib.figure.Figure at 0x214c5ebd358>"
       ]
      },
      "metadata": {},
@@ -2791,9 +2758,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5c64da0>"
+       "<matplotlib.figure.Figure at 0x214c4cc0588>"
       ]
      },
      "metadata": {},
@@ -2826,9 +2793,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a49d0a58>"
+       "<matplotlib.figure.Figure at 0x214c5f419b0>"
       ]
      },
      "metadata": {},
@@ -2877,9 +2844,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5cf5208>"
+       "<matplotlib.figure.Figure at 0x214c5fa9208>"
       ]
      },
      "metadata": {},
@@ -2964,9 +2931,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5a6e518>"
+       "<matplotlib.figure.Figure at 0x214c5eaad30>"
       ]
      },
      "metadata": {},
@@ -2974,9 +2941,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a2709588>"
+       "<matplotlib.figure.Figure at 0x214c601b5c0>"
       ]
      },
      "metadata": {},
@@ -2984,9 +2951,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a49582e8>"
+       "<matplotlib.figure.Figure at 0x214c3ffd4a8>"
       ]
      },
      "metadata": {},
@@ -3041,9 +3008,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5c64550>"
+       "<matplotlib.figure.Figure at 0x214c5f4fc18>"
       ]
      },
      "metadata": {},
@@ -3051,9 +3018,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5a40f98>"
+       "<matplotlib.figure.Figure at 0x214c5f4f9b0>"
       ]
      },
      "metadata": {},
@@ -3061,9 +3028,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a48f2c50>"
+       "<matplotlib.figure.Figure at 0x214c4d00c50>"
       ]
      },
      "metadata": {},
@@ -3167,9 +3134,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5c64780>"
+       "<matplotlib.figure.Figure at 0x214c4cc0eb8>"
       ]
      },
      "metadata": {},
@@ -3250,11 +3217,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.  1.]\n",
-      "[ 2.    0.92]\n",
-      "[ 2.96  1.  ]\n",
-      "[ 4.12  0.97]\n",
-      "[ 5.03  0.96]\n"
+      "[1. 1.]\n",
+      "[2.   0.92]\n",
+      "[2.96 1.  ]\n",
+      "[4.12 0.97]\n",
+      "[5.03 0.96]\n"
      ]
     }
    ],
@@ -3314,9 +3281,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5e56c88>"
+       "<matplotlib.figure.Figure at 0x214c5ed8470>"
       ]
      },
      "metadata": {},
@@ -3438,9 +3405,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from math import radians, sin, cos\n",
@@ -3512,9 +3477,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5c01128>"
+       "<matplotlib.figure.Figure at 0x214c61670f0>"
       ]
      },
      "metadata": {},
@@ -3733,9 +3698,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from math import sin, cos, radians\n",
@@ -3776,9 +3739,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a3dd1128>"
+       "<matplotlib.figure.Figure at 0x214c4cb1518>"
       ]
      },
      "metadata": {},
@@ -3898,9 +3861,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a494ad68>"
+       "<matplotlib.figure.Figure at 0x214c4dd22b0>"
       ]
      },
      "metadata": {},
@@ -3970,9 +3933,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class BaseballPath(object):\n",
@@ -4043,9 +4004,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5c724e0>"
+       "<matplotlib.figure.Figure at 0x214c4cbd9e8>"
       ]
      },
      "metadata": {},
@@ -4112,9 +4073,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a49d4c50>"
+       "<matplotlib.figure.Figure at 0x214c60f9898>"
       ]
      },
      "metadata": {},
@@ -4122,9 +4083,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a495f0f0>"
+       "<matplotlib.figure.Figure at 0x214c5f93c88>"
       ]
      },
      "metadata": {},
@@ -4184,9 +4145,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5df5358>"
+       "<matplotlib.figure.Figure at 0x214c6196630>"
       ]
      },
      "metadata": {},
@@ -4194,9 +4155,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x218a5f7f9e8>"
+       "<matplotlib.figure.Figure at 0x214c625d6a0>"
       ]
      },
      "metadata": {},
@@ -4246,7 +4207,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   },
   "widgets": {
    "state": {},

14-Adaptive-Filtering.ipynb

@@ -303,9 +303,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from math import sin, cos, radians\n",
@@ -373,9 +371,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from numpy.random import randn\n",
@@ -406,9 +402,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2231057eac8>"
+       "<matplotlib.figure.Figure at 0x19f27d3e7b8>"
       ]
      },
      "metadata": {},
@@ -466,9 +462,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from filterpy.kalman import KalmanFilter\n",
@@ -511,9 +505,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2231058e9b0>"
+       "<matplotlib.figure.Figure at 0x19f27d22198>"
       ]
      },
      "metadata": {},
@@ -557,9 +551,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22311681128>"
+       "<matplotlib.figure.Figure at 0x19f27d22e10>"
       ]
      },
      "metadata": {},
@@ -598,9 +592,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x223117447f0>"
+       "<matplotlib.figure.Figure at 0x19f28ba0978>"
       ]
      },
      "metadata": {},
@@ -636,9 +630,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22311778748>"
+       "<matplotlib.figure.Figure at 0x19f28b28908>"
       ]
      },
      "metadata": {},
@@ -672,9 +666,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def make_ca_filter(dt, std):\n",
@@ -701,9 +693,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x223127ce748>"
+       "<matplotlib.figure.Figure at 0x19f28c04ba8>"
       ]
      },
      "metadata": {},
@@ -770,9 +762,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2231280d438>"
+       "<matplotlib.figure.Figure at 0x19f28c1f470>"
       ]
      },
      "metadata": {},
@@ -869,9 +861,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x223128ec860>"
+       "<matplotlib.figure.Figure at 0x19f27d223c8>"
       ]
      },
      "metadata": {},
@@ -919,9 +911,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x223128ec8d0>"
+       "<matplotlib.figure.Figure at 0x19f28018ac8>"
       ]
      },
      "metadata": {},
@@ -1000,9 +992,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x223129c68d0>"
+       "<matplotlib.figure.Figure at 0x19f27d34f60>"
       ]
      },
      "metadata": {},
@@ -1095,9 +1087,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22312b1e7f0>"
+       "<matplotlib.figure.Figure at 0x19f28c2a2b0>"
       ]
      },
      "metadata": {},
@@ -1122,9 +1114,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22312b39208>"
+       "<matplotlib.figure.Figure at 0x19f29e2df60>"
       ]
      },
      "metadata": {},
@@ -1149,9 +1141,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22312a5d160>"
+       "<matplotlib.figure.Figure at 0x19f2a072080>"
       ]
      },
      "metadata": {},
@@ -1159,9 +1151,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22312a66eb8>"
+       "<matplotlib.figure.Figure at 0x19f29fba6d8>"
       ]
      },
      "metadata": {},
@@ -1191,9 +1183,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22312a96198>"
+       "<matplotlib.figure.Figure at 0x19f29d62be0>"
       ]
      },
      "metadata": {},
@@ -1201,9 +1193,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2231177f860>"
+       "<matplotlib.figure.Figure at 0x19f29d0b978>"
       ]
      },
      "metadata": {},
@@ -1211,9 +1203,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22311776470>"
+       "<matplotlib.figure.Figure at 0x19f28082a58>"
       ]
      },
      "metadata": {},
@@ -1221,9 +1213,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x223116f5f28>"
+       "<matplotlib.figure.Figure at 0x19f29d629b0>"
       ]
      },
      "metadata": {},
@@ -1231,9 +1223,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2231177f780>"
+       "<matplotlib.figure.Figure at 0x19f29f30be0>"
       ]
      },
      "metadata": {},
@@ -1302,9 +1294,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x223129e03c8>"
+       "<matplotlib.figure.Figure at 0x19f29f04048>"
       ]
      },
      "metadata": {},
@@ -1430,9 +1422,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22312d139e8>"
+       "<matplotlib.figure.Figure at 0x19f29eaa0f0>"
       ]
      },
      "metadata": {},
@@ -1461,9 +1453,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22312c8d5c0>"
+       "<matplotlib.figure.Figure at 0x19f29f39550>"
       ]
      },
      "metadata": {},
@@ -1516,9 +1508,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2231164f208>"
+       "<matplotlib.figure.Figure at 0x19f2a0a1630>"
       ]
      },
      "metadata": {},
@@ -1566,9 +1558,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22312c8d9b0>"
+       "<matplotlib.figure.Figure at 0x19f28ba3e80>"
       ]
      },
      "metadata": {},
@@ -1594,8 +1586,8 @@
     "        cvfilter.update([z])\n",
     "        cafilter.update([z])\n",
     "        \n",
-    "        cv_likelihood = cvfilter.likelihood([z]) * pv\n",
-    "        ca_likelihood = cafilter.likelihood([z]) * pa\n",
+    "        cv_likelihood = cvfilter.likelihood * pv\n",
+    "        ca_likelihood = cafilter.likelihood * pa\n",
     "        \n",
     "        pv = (cv_likelihood) / (cv_likelihood + ca_likelihood)\n",
     "        pa = (ca_likelihood) / (cv_likelihood + ca_likelihood)\n",
@@ -1642,9 +1634,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2231177e198>"
+       "<matplotlib.figure.Figure at 0x19f29d68588>"
       ]
      },
      "metadata": {},
@@ -1713,7 +1705,7 @@
     {
      "data": {
       "text/plain": [
-       "array([ 0.7,  0.3])"
+       "array([0.7, 0.3])"
       ]
      },
      "execution_count": 26,
@@ -1748,14 +1740,14 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22312d36be0>"
+       "<matplotlib.figure.Figure at 0x19f2a0d4710>"
       ]
      },
      "metadata": {},
@@ -1790,8 +1782,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 0.97,  0.03],\n",
-       "       [ 0.05,  0.95]])"
+       "array([[0.97, 0.03],\n",
+       "       [0.05, 0.95]])"
       ]
      },
      "execution_count": 28,
@@ -1833,7 +1825,7 @@
     {
      "data": {
       "text/plain": [
-       "array([ 0.694,  0.306])"
+       "array([0.694, 0.306])"
       ]
      },
      "execution_count": 29,
@@ -1879,7 +1871,7 @@
     {
      "data": {
       "text/plain": [
-       "array([ 0.802,  0.198])"
+       "array([0.802, 0.198])"
       ]
      },
      "execution_count": 30,
@@ -1956,8 +1948,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 0.987,  0.114],\n",
-       "       [ 0.0125,  0.886]])"
+       "array([[0.987, 0.114],\n",
+       "       [0.013, 0.886]])"
       ]
      },
      "execution_count": 31,
@@ -2018,9 +2010,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2231168bf60>"
+       "<matplotlib.figure.Figure at 0x19f28ba33c8>"
       ]
      },
      "metadata": {},
@@ -2113,9 +2105,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22312bd5908>"
+       "<matplotlib.figure.Figure at 0x19f28b22208>"
       ]
      },
      "metadata": {},
@@ -2189,7 +2181,7 @@
    "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 = "1.2.1"
+    min_version = "1.2.4"
     if LooseVersion(v) < LooseVersion(min_version):
        raise Exception("Minimum FilterPy version supported is {}.\n"
                        "Please install a more recent version.\n"

kf_book/adaptive_internal.py

@@ -23,6 +23,11 @@ import numpy as np
 
 
 def plot_track_and_residuals(dt, xs, z_xs, res):
+    """ plots track and measurement on the left, and the residual
+    of the filter on the right. Helps to visualize the performance of
+    an adaptive filter.
+    """
+    
     assert np.isscalar(dt)
     t = np.arange(0, len(xs)*dt, dt)
     plt.subplot(121)
@@ -44,6 +49,9 @@ def plot_track_and_residuals(dt, xs, z_xs, res):
 
 
 def plot_markov_chain():
+    """ show a markov chain showing relative probability of an object
+    turning"""
+    
     fig = plt.figure(figsize=(4,4), facecolor='w')
     ax = plt.axes((0, 0, 1, 1),
                   xticks=[], yticks=[], frameon=False)
@@ -103,7 +111,7 @@ def plot_markov_chain():
 
 
 def turning_target(N=600, turn_start=400):
-    """ simulate a moving target blah"""
+    """ simulate a moving target"""
 
     #r = 1.
     dt = 1.

kf_book/book_plots.py

@@ -34,19 +34,24 @@ except:
 
 
 def equal_axis(sz=10):
+    """ set size of axis in inches, using the same for each"""
     pylab.rcParams['figure.figsize'] = sz, sz
     plt.axis('equal')
 
 
 def reset_axis():
+    """ reest axis size in inches to the default size for the book"""
     pylab.rcParams['figure.figsize'] = 8, 3
 
     
 def reset_figsize():
+    """ reest axis size in inches to the default size for the book"""
     pylab.rcParams['figure.figsize'] = 8, 3
 
 
 def set_figsize(x=8, y=3):
+    """ set the figure size of the plot to the specified size in inches"""
+    
     pylab.rcParams['figure.figsize'] = x, y
 
 
@@ -139,169 +144,6 @@ def plot_errorbars(bars, xlims, ylims=(-1, 1)):
         plt.show()
 
 
-def plot_errorbar1():
-    with figsize(y=2):
-        plt.figure()
-        plot_errorbars([(160, 8, 'A'), (170, 8, 'B')],
-                       xlims=(145, 185), ylims=(-1, 1))
-        plt.show()
-
-
-def plot_errorbar2():
-    with figsize(y=2):
-        plt.figure()
-        plot_errorbars([(160, 3, 'A'), (170, 9, 'B')],
-                       xlims=(145, 185), ylims=(-1, 1))
-
-def plot_errorbar3():
-    with figsize(y=2):
-        plt.figure()
-        plot_errorbars([(160, 1, 'A'), (170, 9, 'B')],
-                       xlims=(145, 185), ylims=(-1, 1))
-
-
-def plot_hypothesis1():
-    with figsize(y=3.5):
-        plt.figure()
-        plt.errorbar([1, 2, 3], [170, 161, 169],
-                     xerr=0, yerr=10, fmt='bo', capthick=2, capsize=10)
-
-        plt.plot([1, 3], [180, 160], color='g', ls='--')
-        plt.plot([1, 3], [170, 170], color='g', ls='--')
-        plt.plot([1, 3], [160, 175], color='g', ls='--')
-        plt.plot([1, 2, 3], [180, 152, 179], color='g', ls='--')
-        plt.xlim(0,4)
-        plt.ylim(150, 185)
-        plt.xlabel('day')
-        plt.ylabel('lbs')
-        plt.tight_layout()
-
-
-def plot_hypothesis2():
-    with figsize(y=2.5):
-        plt.figure()
-        plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170],
-                     xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
-        plt.plot([1, 10], [169, 170.5], color='g', ls='--')
-        
-        plt.xlim(0, 11)
-        plt.ylim(150, 185)
-        plt.xlabel('day')
-        plt.ylabel('lbs')
-
-
-def plot_hypothesis3():
-    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
-           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
-
-    with figsize(y=2.5):
-        plt.figure()
-
-        plt.errorbar(range(1, 13), weights,
-                     xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)
-
-        plt.xlim(0, 13)
-        plt.ylim(145, 185)
-        plt.xlabel('day')
-        plt.ylabel('weight (lbs)')
-
-
-def plot_hypothesis4():
-    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
-           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
-
-    with figsize(y=2.5):
-        plt.figure()
-        ave = np.sum(weights) / len(weights)
-        plt.errorbar(range(1,13), weights, label='weights',
-                     yerr=6, fmt='o', capthick=2, capsize=10)
-        plt.plot([1, 12], [ave,ave], c='r', label='hypothesis')
-        plt.xlim(0, 13)
-        plt.ylim(145, 185)
-        plt.xlabel('day')
-        plt.ylabel('weight (lbs)')
-        show_legend()
-
-
-def plot_hypothesis5():
-    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
-           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
-
-    xs = range(1, len(weights)+1)
-    line = np.poly1d(np.polyfit(xs, weights, 1))
-
-    with figsize(y=2.5):
-        plt.figure()
-        plt.errorbar(range(1, 13), weights, label='weights',
-                     yerr=5, fmt='o', capthick=2, capsize=10)
-        plt.plot (xs, line(xs), c='r', label='hypothesis')
-        plt.xlim(0, 13)
-        plt.ylim(145, 185)
-        plt.xlabel('day')
-        plt.ylabel('weight (lbs)')
-        show_legend()
-
-
-def plot_estimate_chart_1():
-    with figsize(y=2.5):
-        plt.figure()
-        ax = plt.axes()
-        ax.annotate('', xy=[1,159], xytext=[0,158],
-                    arrowprops=dict(arrowstyle='->', ec='r',shrinkA=6, lw=3,shrinkB=5))
-        plt.scatter ([0], [158], c='b')
-        plt.scatter ([1], [159], c='r')
-        plt.xlabel('day')
-        plt.ylabel('weight (lbs)')
-        ax.xaxis.grid(True, which="major", linestyle='dotted')
-        ax.yaxis.grid(True, which="major", linestyle='dotted')
-        plt.tight_layout()
-
-
-def plot_estimate_chart_2():
-    with figsize(y=2.5):
-        plt.figure()
-        ax = plt.axes()
-        ax.annotate('', xy=[1,159], xytext=[0,158],
-                    arrowprops=dict(arrowstyle='->',
-                                    ec='r', lw=3, shrinkA=6, shrinkB=5))
-        plt.scatter ([0], [158.0], c='k',s=128)
-        plt.scatter ([1], [164.2], c='b',s=128)
-        plt.scatter ([1], [159], c='r', s=128)
-        plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
-        plt.text (1.0, 164.4, "measurement ($z_t$)",ha='center',va='bottom',fontsize=18,color='blue')
-        plt.text (0.0, 159.8, "last estimate ($\hat{x}_{t-1}$)", ha='left', va='top',fontsize=18)
-        plt.xlabel('day')
-        plt.ylabel('weight (lbs)')
-        ax.xaxis.grid(True, which="major", linestyle='dotted')
-        ax.yaxis.grid(True, which="major", linestyle='dotted')
-        plt.ylim(157, 164.5)
-
-
-def plot_estimate_chart_3():
-    with figsize(y=2.5):
-        plt.figure()
-        ax = plt.axes()
-        ax.annotate('', xy=[1,159], xytext=[0,158],
-                    arrowprops=dict(arrowstyle='->',
-                                    ec='r', lw=3, shrinkA=6, shrinkB=5))
-
-        ax.annotate('', xy=[1,159], xytext=[1,164.2],
-                    arrowprops=dict(arrowstyle='-',
-                                    ec='k', lw=3, shrinkA=8, shrinkB=8))
-
-        est_y = (158 + .4*(164.2-158))
-        plt.scatter ([0,1], [158.0,est_y], c='k',s=128)
-        plt.scatter ([1], [164.2], c='b',s=128)
-        plt.scatter ([1], [159], c='r', s=128)
-        plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
-        plt.text (1.0, 164.4, "measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
-        plt.text (0, 159.8, "last estimate ($\hat{x}_{t-1}$)", ha='left', va='top',fontsize=18)
-        plt.text (0.95, est_y, "estimate ($\hat{x}_{t}$)", ha='right', va='center',fontsize=18)
-        plt.xlabel('day')
-        plt.ylabel('weight (lbs)')
-        ax.xaxis.grid(True, which="major", linestyle='dotted')
-        ax.yaxis.grid(True, which="major", linestyle='dotted')
-        plt.ylim(157, 164.5)
 
 
 def predict_update_chart(box_bg = '#CCCCCC',

kf_book/gh_internal.py

@@ -18,10 +18,155 @@ from __future__ import (absolute_import, division, print_function,
 
 
 import kf_book.book_plots as book_plots
+from kf_book.book_plots import figsize
 import numpy as np
 from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow
 import matplotlib.pylab as plt
 
+
+def plot_hypothesis1():
+    with figsize(y=3.5):
+        plt.figure()
+        plt.errorbar([1, 2, 3], [170, 161, 169],
+                     xerr=0, yerr=10, fmt='bo', capthick=2, capsize=10)
+
+        plt.plot([1, 3], [180, 160], color='g', ls='--')
+        plt.plot([1, 3], [170, 170], color='g', ls='--')
+        plt.plot([1, 3], [160, 175], color='g', ls='--')
+        plt.plot([1, 2, 3], [180, 152, 179], color='g', ls='--')
+        plt.xlim(0,4)
+        plt.ylim(150, 185)
+        plt.xlabel('day')
+        plt.ylabel('lbs')
+        plt.tight_layout()
+
+
+def plot_hypothesis2():
+    with book_plots.figsize(y=2.5):
+        plt.figure()
+        plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170],
+                     xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
+        plt.plot([1, 10], [169, 170.5], color='g', ls='--')
+        
+        plt.xlim(0, 11)
+        plt.ylim(150, 185)
+        plt.xlabel('day')
+        plt.ylabel('lbs')
+
+def plot_hypothesis3():
+    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
+           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
+
+    with book_plots.figsize(y=2.5):
+        plt.figure()
+
+        plt.errorbar(range(1, 13), weights,
+                     xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)
+
+        plt.xlim(0, 13)
+        plt.ylim(145, 185)
+        plt.xlabel('day')
+        plt.ylabel('weight (lbs)')
+
+
+def plot_hypothesis4():
+    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
+           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
+
+    with book_plots.figsize(y=2.5):
+        plt.figure()
+        ave = np.sum(weights) / len(weights)
+        plt.errorbar(range(1,13), weights, label='weights',
+                     yerr=6, fmt='o', capthick=2, capsize=10)
+        plt.plot([1, 12], [ave,ave], c='r', label='hypothesis')
+        plt.xlim(0, 13)
+        plt.ylim(145, 185)
+        plt.xlabel('day')
+        plt.ylabel('weight (lbs)')
+        book_plots.show_legend()
+    
+    
+def plot_hypothesis5():
+    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
+           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
+
+    xs = range(1, len(weights)+1)
+    line = np.poly1d(np.polyfit(xs, weights, 1))
+
+    with figsize(y=2.5):
+        plt.figure()
+        plt.errorbar(range(1, 13), weights, label='weights',
+                     yerr=5, fmt='o', capthick=2, capsize=10)
+        plt.plot (xs, line(xs), c='r', label='hypothesis')
+        plt.xlim(0, 13)
+        plt.ylim(145, 185)
+        plt.xlabel('day')
+        plt.ylabel('weight (lbs)')
+        book_plots.show_legend()
+        
+    
+def plot_estimate_chart_1():
+    with figsize(y=2.5):
+        plt.figure()
+        ax = plt.axes()
+        ax.annotate('', xy=[1,159], xytext=[0,158],
+                    arrowprops=dict(arrowstyle='->', ec='r',shrinkA=6, lw=3,shrinkB=5))
+        plt.scatter ([0], [158], c='b')
+        plt.scatter ([1], [159], c='r')
+        plt.xlabel('day')
+        plt.ylabel('weight (lbs)')
+        ax.xaxis.grid(True, which="major", linestyle='dotted')
+        ax.yaxis.grid(True, which="major", linestyle='dotted')
+        plt.tight_layout()
+
+
+def plot_estimate_chart_2():
+    with figsize(y=2.5):
+        plt.figure()
+        ax = plt.axes()
+        ax.annotate('', xy=[1,159], xytext=[0,158],
+                    arrowprops=dict(arrowstyle='->',
+                                    ec='r', lw=3, shrinkA=6, shrinkB=5))
+        plt.scatter ([0], [158.0], c='k',s=128)
+        plt.scatter ([1], [164.2], c='b',s=128)
+        plt.scatter ([1], [159], c='r', s=128)
+        plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
+        plt.text (1.0, 164.4, "measurement ($z_t$)",ha='center',va='bottom',fontsize=18,color='blue')
+        plt.text (0.0, 159.8, "last estimate ($\hat{x}_{t-1}$)", ha='left', va='top',fontsize=18)
+        plt.xlabel('day')
+        plt.ylabel('weight (lbs)')
+        ax.xaxis.grid(True, which="major", linestyle='dotted')
+        ax.yaxis.grid(True, which="major", linestyle='dotted')
+        plt.ylim(157, 164.5)
+
+
+def plot_estimate_chart_3():
+    with figsize(y=2.5):
+        plt.figure()
+        ax = plt.axes()
+        ax.annotate('', xy=[1,159], xytext=[0,158],
+                    arrowprops=dict(arrowstyle='->',
+                                    ec='r', lw=3, shrinkA=6, shrinkB=5))
+
+        ax.annotate('', xy=[1,159], xytext=[1,164.2],
+                    arrowprops=dict(arrowstyle='-',
+                                    ec='k', lw=3, shrinkA=8, shrinkB=8))
+
+        est_y = (158 + .4*(164.2-158))
+        plt.scatter ([0,1], [158.0,est_y], c='k',s=128)
+        plt.scatter ([1], [164.2], c='b',s=128)
+        plt.scatter ([1], [159], c='r', s=128)
+        plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
+        plt.text (1.0, 164.4, "measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
+        plt.text (0, 159.8, "last estimate ($\hat{x}_{t-1}$)", ha='left', va='top',fontsize=18)
+        plt.text (0.95, est_y, "estimate ($\hat{x}_{t}$)", ha='right', va='center',fontsize=18)
+        plt.xlabel('day')
+        plt.ylabel('weight (lbs)')
+        ax.xaxis.grid(True, which="major", linestyle='dotted')
+        ax.yaxis.grid(True, which="major", linestyle='dotted')
+        plt.ylim(157, 164.5)
+
+
 def plot_gh_results(weights, estimates, predictions, time_step=0):
 
     n = len(weights)
@@ -75,4 +220,4 @@ def plot_g_h_results(measurements, filtered_data,
             time.sleep(0.5)
 
 if __name__ == '__main__':
-    plot_errorbar1()
+    pass