Updated for FilterPy 1.2.0, added section

Added a section on Mahalanobis distance and on gating.
Updated to require filterpy 1.2.0.

06-Multivariate-Kalman-Filters.ipynb

@@ -364,7 +364,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x229879e96a0>"
+       "<matplotlib.figure.Figure at 0x2b9c7fa8710>"
       ]
      },
      "metadata": {},
@@ -1015,7 +1015,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAEaCAYAAAB+esN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22989b592e8>"
+       "<matplotlib.figure.Figure at 0x2b9ca12b358>"
       ]
      },
      "metadata": {},
@@ -1556,9 +1556,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22989b66080>"
+       "<matplotlib.figure.Figure at 0x2b9ca194b70>"
       ]
      },
      "metadata": {},
@@ -1568,7 +1568,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22989b8ec50>"
+       "<matplotlib.figure.Figure at 0x2b9cb2026d8>"
       ]
      },
      "metadata": {},
@@ -1608,7 +1608,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298ac7ceb8>"
+       "<matplotlib.figure.Figure at 0x2b9cb272ba8>"
       ]
      },
      "metadata": {},
@@ -1760,7 +1760,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298ad85da0>"
+       "<matplotlib.figure.Figure at 0x2b9cbe4f5c0>"
       ]
      },
      "metadata": {},
@@ -1805,7 +1805,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1aa065e3f20d40bf983c511301f39663",
+       "model_id": "e1ad782948d04785983c0c5b1e922df9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1998,7 +1998,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22989bc5d30>"
+       "<matplotlib.figure.Figure at 0x2b9cb2091d0>"
       ]
      },
      "metadata": {},
@@ -2130,7 +2130,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22989b8be80>"
+       "<matplotlib.figure.Figure at 0x2b9cb229f28>"
       ]
      },
      "metadata": {},
@@ -2219,7 +2219,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22989bb3320>"
+       "<matplotlib.figure.Figure at 0x2b9cb3629e8>"
       ]
      },
      "metadata": {},
@@ -2246,7 +2246,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298b38b0f0>"
+       "<matplotlib.figure.Figure at 0x2b9cb637940>"
       ]
      },
      "metadata": {},
@@ -2348,7 +2348,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298b07aba8>"
+       "<matplotlib.figure.Figure at 0x2b9cb600128>"
       ]
      },
      "metadata": {},
@@ -2358,7 +2358,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298b3992b0>"
+       "<matplotlib.figure.Figure at 0x2b9cba3b898>"
       ]
      },
      "metadata": {},
@@ -2402,7 +2402,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298b2ad908>"
+       "<matplotlib.figure.Figure at 0x2b9cb1f9828>"
       ]
      },
      "metadata": {},
@@ -2430,7 +2430,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298ac4ed68>"
+       "<matplotlib.figure.Figure at 0x2b9cb237668>"
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@@ -2459,7 +2459,7 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298b8bfda0>"
+       "<matplotlib.figure.Figure at 0x2b9cb202a20>"
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      },
      "metadata": {},
@@ -2499,7 +2499,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298ad25128>"
+       "<matplotlib.figure.Figure at 0x2b9cb27f748>"
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      "metadata": {},
@@ -2509,7 +2509,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298ad25470>"
+       "<matplotlib.figure.Figure at 0x2b9cb38f080>"
       ]
      },
      "metadata": {},
@@ -2552,7 +2552,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298adbe748>"
+       "<matplotlib.figure.Figure at 0x2b9cbbb7320>"
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      },
      "metadata": {},
@@ -2562,7 +2562,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298b291400>"
+       "<matplotlib.figure.Figure at 0x2b9cb9b5358>"
       ]
      },
      "metadata": {},
@@ -2594,7 +2594,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298ac887b8>"
+       "<matplotlib.figure.Figure at 0x2b9ca138c18>"
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      "metadata": {},
@@ -2625,7 +2625,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298909eb00>"
+       "<matplotlib.figure.Figure at 0x2b9cb1e3ac8>"
       ]
      },
      "metadata": {},
@@ -2658,7 +2658,7 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22989b9b518>"
+       "<matplotlib.figure.Figure at 0x2b9cb30e198>"
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@@ -2739,7 +2739,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298b51e978>"
+       "<matplotlib.figure.Figure at 0x2b9cb63fcf8>"
       ]
      },
      "metadata": {},
@@ -2861,7 +2861,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298ad2a048>"
+       "<matplotlib.figure.Figure at 0x2b9cbbc2ba8>"
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      },
      "metadata": {},
@@ -2904,7 +2904,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22989baff60>"
+       "<matplotlib.figure.Figure at 0x2b9cbbc26a0>"
       ]
      },
      "metadata": {},
@@ -2944,7 +2944,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAukAAAEaCAYAAACsKskhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22989baf128>"
+       "<matplotlib.figure.Figure at 0x2b9ca15edd8>"
       ]
      },
      "metadata": {},
@@ -3001,7 +3001,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAEaCAYAAAB+esN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2298b3b7e80>"
+       "<matplotlib.figure.Figure at 0x2b9cbbc2f28>"
       ]
      },
      "metadata": {},

14-Adaptive-Filtering.ipynb

@@ -239,11 +239,14 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
+       "                    scale: 95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                },\n",
+       "                 \"fast-preview\": {\n",
+       "                    \"disabled\": false\n",
        "                }\n",
        "        });\n",
        "</script>\n"
@@ -403,9 +406,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a469dca908>"
+       "<matplotlib.figure.Figure at 0x2231057eac8>"
       ]
      },
      "metadata": {},
@@ -508,9 +511,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a469dd5438>"
+       "<matplotlib.figure.Figure at 0x2231058e9b0>"
       ]
      },
      "metadata": {},
@@ -554,9 +557,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46aefac88>"
+       "<matplotlib.figure.Figure at 0x22311681128>"
       ]
      },
      "metadata": {},
@@ -595,9 +598,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a469dd5518>"
+       "<matplotlib.figure.Figure at 0x223117447f0>"
       ]
      },
      "metadata": {},
@@ -633,9 +636,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46bfae198>"
+       "<matplotlib.figure.Figure at 0x22311778748>"
       ]
      },
      "metadata": {},
@@ -698,9 +701,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c02d978>"
+       "<matplotlib.figure.Figure at 0x223127ce748>"
       ]
      },
      "metadata": {},
@@ -767,9 +770,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c01c358>"
+       "<matplotlib.figure.Figure at 0x2231280d438>"
       ]
      },
      "metadata": {},
@@ -866,9 +869,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c02d908>"
+       "<matplotlib.figure.Figure at 0x223128ec860>"
       ]
      },
      "metadata": {},
@@ -916,9 +919,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c03f550>"
+       "<matplotlib.figure.Figure at 0x223128ec8d0>"
       ]
      },
      "metadata": {},
@@ -997,9 +1000,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c2a78d0>"
+       "<matplotlib.figure.Figure at 0x223129c68d0>"
       ]
      },
      "metadata": {},
@@ -1092,9 +1095,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c2bfc88>"
+       "<matplotlib.figure.Figure at 0x22312b1e7f0>"
       ]
      },
      "metadata": {},
@@ -1119,9 +1122,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c244c18>"
+       "<matplotlib.figure.Figure at 0x22312b39208>"
       ]
      },
      "metadata": {},
@@ -1146,9 +1149,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c184278>"
+       "<matplotlib.figure.Figure at 0x22312a5d160>"
       ]
      },
      "metadata": {},
@@ -1156,9 +1159,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c387b38>"
+       "<matplotlib.figure.Figure at 0x22312a66eb8>"
       ]
      },
      "metadata": {},
@@ -1188,9 +1191,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c0a4da0>"
+       "<matplotlib.figure.Figure at 0x22312a96198>"
       ]
      },
      "metadata": {},
@@ -1198,9 +1201,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c137240>"
+       "<matplotlib.figure.Figure at 0x2231177f860>"
       ]
      },
      "metadata": {},
@@ -1208,9 +1211,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c079208>"
+       "<matplotlib.figure.Figure at 0x22311776470>"
       ]
      },
      "metadata": {},
@@ -1218,9 +1221,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c081a90>"
+       "<matplotlib.figure.Figure at 0x223116f5f28>"
       ]
      },
      "metadata": {},
@@ -1228,9 +1231,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c0227b8>"
+       "<matplotlib.figure.Figure at 0x2231177f780>"
       ]
      },
      "metadata": {},
@@ -1299,9 +1302,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c2a1198>"
+       "<matplotlib.figure.Figure at 0x223129e03c8>"
       ]
      },
      "metadata": {},
@@ -1427,9 +1430,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c63aac8>"
+       "<matplotlib.figure.Figure at 0x22312d139e8>"
       ]
      },
      "metadata": {},
@@ -1458,9 +1461,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c518c50>"
+       "<matplotlib.figure.Figure at 0x22312c8d5c0>"
       ]
      },
      "metadata": {},
@@ -1513,9 +1516,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c45c8d0>"
+       "<matplotlib.figure.Figure at 0x2231164f208>"
       ]
      },
      "metadata": {},
@@ -1563,9 +1566,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEaCAYAAABdIZKsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c499da0>"
+       "<matplotlib.figure.Figure at 0x22312c8d9b0>"
       ]
      },
      "metadata": {},
@@ -1591,8 +1594,8 @@
     "        cvfilter.update([z])\n",
     "        cafilter.update([z])\n",
     "        \n",
-    "        cv_likelihood = cvfilter.likelihood * pv\n",
-    "        ca_likelihood = cafilter.likelihood * pa\n",
+    "        cv_likelihood = cvfilter.likelihood([z]) * pv\n",
+    "        ca_likelihood = cafilter.likelihood([z]) * pa\n",
     "        \n",
     "        pv = (cv_likelihood) / (cv_likelihood + ca_likelihood)\n",
     "        pa = (ca_likelihood) / (cv_likelihood + ca_likelihood)\n",
@@ -1639,9 +1642,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c396f98>"
+       "<matplotlib.figure.Figure at 0x2231177e198>"
       ]
      },
      "metadata": {},
@@ -1752,7 +1755,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c45cb00>"
+       "<matplotlib.figure.Figure at 0x22312d36be0>"
       ]
      },
      "metadata": {},
@@ -1761,8 +1764,6 @@
    ],
    "source": [
     "import kf_book.adaptive_internal as adaptive_internal\n",
-    "from kf_book.book_plots import interactive_plot\n",
-    "\n",
     "adaptive_internal.plot_markov_chain()"
    ]
   },
@@ -2017,9 +2018,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwcAAAEyCAYAAACvcKx4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwcAAAEyCAYAAACvcKx4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c185dd8>"
+       "<matplotlib.figure.Figure at 0x2231168bf60>"
       ]
      },
      "metadata": {},
@@ -2112,9 +2113,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAx8AAAE0CAYAAABNUI55AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAx8AAAE0CAYAAABNUI55AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a46c162198>"
+       "<matplotlib.figure.Figure at 0x22312bd5908>"
       ]
      },
      "metadata": {},

Appendix-G-Designing-Nonlinear-Kalman-Filters.ipynb

@@ -17,9 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -408,9 +406,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -528,9 +524,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -563,9 +557,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -595,9 +587,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -626,9 +616,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -716,9 +704,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from math import sin, cos, atan2\n",
@@ -746,9 +732,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from numpy.random import randn\n",
@@ -781,9 +765,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -894,9 +876,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1028,7 +1008,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1042,9 +1022,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

book_format.py

@@ -46,7 +46,7 @@ def test_filterpy_version():
     from distutils.version import LooseVersion
 
     v = filterpy.__version__
-    min_version = "1.1.0"
+    min_version = "1.2.0"
     if LooseVersion(v) < LooseVersion(min_version):
        raise Exception("Minimum FilterPy version supported is {}.\n"
                        "Please install a more recent version.\n"