Added my own FloatSlider, IntSlider

They create a slider with continuous_update=False. Just keeping
code more readable by avoiding long lines.

01-g-h-filter.ipynb

@@ -333,7 +333,7 @@
      "data": {
       "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e96d36eb8>"
+       "<matplotlib.figure.Figure at 0x1a70b76d7f0>"
       ]
      },
      "metadata": {},
@@ -381,7 +381,7 @@
      "data": {
       "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e96d36b38>"
+       "<matplotlib.figure.Figure at 0x1a70baa1630>"
       ]
      },
      "metadata": {},
@@ -410,7 +410,7 @@
      "data": {
       "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e9713beb8>"
+       "<matplotlib.figure.Figure at 0x1a70bb49fd0>"
       ]
      },
      "metadata": {},
@@ -443,7 +443,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9766\n"
+      "Average of measurements is 164.9808\n"
      ]
     }
    ],
@@ -472,7 +472,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0491\n"
+      "Average of measurements is 164.9809\n"
      ]
     }
    ],
@@ -503,7 +503,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e94df3eb8>"
+       "<matplotlib.figure.Figure at 0x1a70a8144a8>"
       ]
      },
      "metadata": {},
@@ -535,7 +535,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e9728a630>"
+       "<matplotlib.figure.Figure at 0x1a70bca20b8>"
       ]
      },
      "metadata": {},
@@ -562,7 +562,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e972b8be0>"
+       "<matplotlib.figure.Figure at 0x1a70bba2d30>"
       ]
      },
      "metadata": {},
@@ -591,7 +591,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e9731c588>"
+       "<matplotlib.figure.Figure at 0x1a70bd78ac8>"
       ]
      },
      "metadata": {},
@@ -620,7 +620,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e97337f28>"
+       "<matplotlib.figure.Figure at 0x1a70bd9f240>"
       ]
      },
      "metadata": {},
@@ -653,7 +653,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e973641d0>"
+       "<matplotlib.figure.Figure at 0x1a70bccf5f8>"
       ]
      },
      "metadata": {},
@@ -680,7 +680,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e970e1e48>"
+       "<matplotlib.figure.Figure at 0x1a70bbc3b38>"
       ]
      },
      "metadata": {},
@@ -726,7 +726,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e972ef208>"
+       "<matplotlib.figure.Figure at 0x1a70bbfcb38>"
       ]
      },
      "metadata": {},
@@ -825,7 +825,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e971e5438>"
+       "<matplotlib.figure.Figure at 0x1a70bae1630>"
       ]
      },
      "metadata": {},
@@ -859,7 +859,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e973c8c18>"
+       "<matplotlib.figure.Figure at 0x1a70bb673c8>"
       ]
      },
      "metadata": {},
@@ -904,7 +904,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e97411e10>"
+       "<matplotlib.figure.Figure at 0x1a70ce494e0>"
       ]
      },
      "metadata": {},
@@ -990,7 +990,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e98447e48>"
+       "<matplotlib.figure.Figure at 0x1a70ce528d0>"
       ]
      },
      "metadata": {},
@@ -1128,9 +1128,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e9843da58>"
+       "<matplotlib.figure.Figure at 0x1a70ce6ac50>"
       ]
      },
      "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 0x16e973f0898>"
+       "<matplotlib.figure.Figure at 0x1a70ce12cf8>"
       ]
      },
      "metadata": {},
@@ -1262,9 +1262,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAD1CAYAAABtJuNlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJztnXd8HMX5/z+jbsuS3LuNC3JvmAMDBkwxxsaUUJJASEISSCCQhBR+CQQI8CUEUmhJCCQkEAKEEtERYLDBBXA79y65yLbkbstqVtf8/rjbu7292d3Zdnt3et6vl166u92dtjPzzDzzzDOMcw6CIAiCcJsMvxNAEARBpCckYAiCIAhPIAFDEARBeAIJGIIgCMITSMAQBEEQnpDldQQ1NTVkpkYQBJHmFBUVMe1vNIMhCIIgPME1AcMYy2SMrWGMve9WmARBEETq4qaK7HYAWwAU6t1QVFRkO/BgMAgACAQCtsNIZtI9fwDlMV2gPKYHbuSxpqbG8DpzYyc/Y2wwgBcAPATg55zzS1UJiERQXl7uOC6CIAgiOSguLo589nIN5gkAvwTQ4VJ4BEEQRIrjWEXGGLsUwCHO+SrG2HlG9zqZiqX7lDXd8wdQHtMFymN6kAgVmRszmOkALmeMVQB4FcAFjLGXXAiXIAiCSGEcCxjO+V2c88Gc82EArgXwKef8m45TRhAEQaQ0tA+GIAiC8ARXBQznfKHagowgiOTntv+uxpb9tX4ng0hDaAZDEJ2c0vX78cnmg34ng0hDSMAQBAE62JbwAhIwBEGAgyQM4T4kYAiCIAhPIAFDEARhkemPfIrHPinzOxlJDwkYgiBoDcYiVccbsWLXUb+TkfSQgCEIgiA8gQQMQRCEDWjWZw4JGIIgyIaM8AQSMCnKzsP1GHZnqd/JIAiC0IUETIqy/VC930kgiE4NzfrMIQFDEAQtKBCeQAKGIAiC8AQSMASRpJSsqsTavcf9TgZB2IYEDEEkKXf8bx0eKt2ckLhIQUZ4AQkYgiAIO5BUNoUEDEEQBOEJjgUMYyyPMbaCMbaOMbaJMfaAGwkjCCJxkBEZ4QVZLoTRDOACznk9YywbwOeMsQ8558tcCJsgCIJIURwLGM45B6Ds+ssO/9F4iCBcgIElJB46cIzwAjdmMGCMZQJYBeBkAE9xzpeL7gsGg47jciOMZEY2f9v3NVm6P5lIxTRbxa081tXXJaS89u8/gGDQmneIzv4ea+sS8268xkkeiouLDa+7ssjPOW/nnE8BMBjA6YyxCW6ES+hjZ1zbzjneL29wPS0EQRAiXJnBKHDOjzPGFgKYDWCj9nogELAdtiJlnYSRzFjN37FNB4AvV1kqjwM1Tfj3Gwtw/3UzDO/jnKOyuhFDenaVDluGdH+HgMt5LClFQbcC78urpBT9+/dHIDBG6na/3uPWA7Xo0TUH/QrzPI/LNI8lpSgo6JbSddmN91hTU2N43Q0rsj6Mse7hz10AzASw1Wm4hH+srKjGOX/4zO9kEEQMs59Ygh++tMrvZBAWcGMGMwDAC+F1mAwAr3PO33chXMIAxrxb/G1oafMsbIJwQlsHGSOkEm5Yka0HcIoLaSEswD3cuJAYuyVjqo43YlD3Ln4nw38S9DLs1Ka/flqOxeVH8PrNZ7qenlSA9g6ZQzv5OxGykx4vZ0cytLZ3YPojn/qahs6Gnc7yo00HsGLXMfcTQ6QNJGBSFL+FgB3+vroWC3c3mt7XQUPDlCBRe3SI1IUEjARznlyCQ7VNficjYXjVbXyysxEfbT/hUegEIeZYQ4vfSei0kICRYMv+Wmw9UOd3MhKGl5MjZW6yane1Z0K7qbXdk3DTmXTeyT/1wU9wsBMNEJMJEjCSpG/zSyxKOV799Jf41RvrXQ//s62HMObej1wPl4gnlbS0jS006PADEjBEHInzf2WdyQ98jD/N26Z7fV+N+RoP0fnwYoBIg05zSMBI4qVZcLLh98jUqKhrGluxZm+17vV0XXh+cdlubKwy3jXtCBvVO5VKujO132SCBIwkVD1dQlWQ1OblufftjXhyQTk2VtWgoZk2wlqFqpo/kIDxgJ++ugZ7jiaftVQyjDhlGrrfM6hkQl0UnAOX/uVz/PnTct/Sk6qk2mDmWEMLDtWlvmECCRhZLFTQt9fuw8KyQ96lxWO87N+5zueYe0zKOtU6C7dpaetwPUxbRaoaCQy7sxR7jyXfoCqKfA7fWVuFF9b5azV61d++wIw/LPQ1DW7Q6QXMwdomlB00r0zpbMYZR4rOIO58Yz2W7TzqdzIco99Rh+pgsgrYownYbyKqmofrmk2fs1Jmzy7ZiffKzYWlel1n64FaNLdFLdWc7r05Ut+CRgfm9m+tqcRDpZsdpcENOr2AuemFIGY9vtj0vj/NK0tAajoBMWsw4lZvpiLT6yxeXbkX767bJ5WM5rZ21CfhWkZbe0faebJuam331EDhtIfmY9cR43OOrMhkOwJ89hNL8PwXFZHvUx/8BJv31VoPKIzTMd7TC3fg2SW7HIbinLQRMBurajDWxv4HWXXD5v32K4uIplb5Dq7mRCuW7kjcyNxLSyyZtpuIEfovXl+HyQ987H1EFjFyFqyUixcWUXbClK0lzyzagUv/8rnl8K1gts9FL3szH1vkmj817QZfRwMYh00wWbxOp5WAaWxtx7A7S7F273G/k2PK9/69Euf8Xs6h4xMLynDds8tsxcM5R1u7uRCta2qNfE7ETn5nYTgPZdeRBrQnSSO0SrKmWk9INbW6t2ZkN+96dWb7oXrbalUv34PTJpgsdTttBIy6U7Sy2OiXxdKOw/WoPtFqfqND3l+/Hyff/aHhPZv21WDi/QkazVuo954eSZCk60zajlCdzuToMqLIlmGyr18mY1UQObNt7+DSfVtbe2yZv7i0AsPuLHUjaZZIHwGTlNVEn0Qt1MbopsNF1NregS0qld9xjaBLVEkm62K1nxiViSJwnZTb6Q/Nj6iEOjo4Lg6vP3r6LlwM227dTERdczMOkfB+deUe6fU57Qxmg5ebdA1IGwGDJB7p+YmoQb68bDfmPLlE9x43jgJYXHYYNY3xMzQ3RrNuNGQ/BiRH65sd6eV55L/9AjhU14xVu0OeENo5xzYJC0pZ/DSasGsYkqyIslPbKF++2jUYv/LvWMAwxoYwxj5jjG1hjG1ijN3uRsIsp8OPSBOEyBBBNr+ietWo1Yl7UHjffm4Fnlm0w/Aes45SplE8u3gn9tvwP+aHiuzU387HzS8GbT/vVichKnc7QWuL8IH3xGaxydC3G50xpK4Lh+uascmB9ZdbiAZ5sgOL7YfqkuZMJTdmMG0AfsE5HwvgDAC3McbGuRCuJVLtAC4rr//l5Xt0n9933HrnGqfn13QVbhXl0wt34ERL7KjLDSsy9eWHPtiCN1ZVmob54Yb9MWs6XteWptZ2oZnqgRrj3dlKElsNDDPc6jucloG2zZ3QmcEkyg/Y3W9twNYD1oWDOh+//2ir9HNJ0ofH0N7BMfMx820XicKxgOGc7+ecrw5/rgOwBcAgp+FaxU5jaWyJNxWua2rFGb9b4Dg91Q3eL+ADQLOJmbWdcrH6jNH5K0frvd98J9PQf/jy6lijCo8HJM99sQuX/HlJ3O9mAyFF+N/84irBtdj/yYZZug7VNnm60Pzy8j14T3IflB5OakUH5zFrH3skFuQP1zXjSH38RtEMmwmJrtOJ34adAakTstwMjDE2DMApAJaLrgeD9tUDAFDT3KEbRoXqKN6dO3ci2Gpe0f5vcTUqq1ti0lZZ24YDtU3CeKyk//H5ZZjeXX9hrbW1VRimURzqazv2hSrlxg0bcLRA/zVW7auPPFvdFBIEVZVVMeGVHY4tg61HYr8bsWp/Mx7+4jhKruknvL5+wwYczM+MfOc8Gm5tbZ0wjua2UONYuSqITEGHXFcX+1xlVRWCwdiyFoW7du1aFOaGxlQnGhp073ODXbsrheE3NTYaxtkYHjCsrTgCIDavtTWhPB4+fDjy26bDLRjWPQv52fJjxR0VlSiZfxiDC6P15sCBgwgGrbl6qa+P1i0AaG1pEebtQPiwvkUr1sTcb4eGhgbh85s2bQIA7N9/AMFgffz1zZvRtD9bGGZVVRWCwdDWhqNHzeuRXlr+8ul2fLJuN357fk8AwL1vrccwfkD47HtlDXhlUz2yMxjyshj+PrdPzPW21ra4+KsqzeusIuBa22KfP3IklK/3lqzGqQNypfNoRnFxseF11xb5GWPdALwB4Kecc0+UmDe+dxjrDordQqi7Ia2JnsI33zqEY43R0faB+uTbyS2Lk4VeN0fAR05Yc2dRVRe9X2/mkYgRulJf5u3w3n/W/zbXR3XiZiNTHvMv1tll3AfgvkXVeGur8S52LW9sbcAd8+1vLqyqbcOflgr2mpnkLRFKbCN1XF2L+X4cp2ncejQ6Szbai7Kjug0t7UBDK0dNs366mtrcbQ2JVuu5MoNhjGUjJFxe5py/qXdfIBCwHYciZRcdzMbHla147eYzY65XZlYBK9cCAP4arMUd15wTF0ZTSSm6Dy5GYHhohJH76afAicaYtBUdrAM+Xozr3z6Mbb+dE3qwpNRa+iXuz/poPoDmyD1K/pTvsx5fhLNG9sb9l48Xhle9+SDwZRATJk7E8N75MWHXN7chPycTjDEsrSkHNpUhEAiEvLO+vwCDBg0CNm6LhNe+8yiwaFnkO9t9DFi4VCq/W9t2A2s2xt8bTvPECRMxtFfX+HIpKUVhYYEwjhMtbcDb8xA4NYBMra6gpBQF3VTPlZRi4MBBCASKI+Fry0q5b/LkyejVLTR667biC+DYcTy7pg53ffUcZNjVSaiY8cfPcP9Z+SjIzcCgQQOBLeWoyR+C1zYHce/XzwbemIcueV0My7WuqRV452NkZWcDTc0oKChUlVchcPAIevfpjUBgUiRf/fr3RyAwVi6RqjKaOvVU4M3QHql+/fohEBALine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e98453550>"
+       "<matplotlib.figure.Figure at 0x1a70baa7e48>"
       ]
      },
      "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 0x16e971f3a90>"
+       "<matplotlib.figure.Figure at 0x1a70be2eda0>"
       ]
      },
      "metadata": {},
@@ -1371,9 +1371,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e971e62e8>"
+       "<matplotlib.figure.Figure at 0x1a70bad6b38>"
       ]
      },
      "metadata": {},
@@ -1432,9 +1432,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e9721fa20>"
+       "<matplotlib.figure.Figure at 0x1a70bbc3c18>"
       ]
      },
      "metadata": {},
@@ -1509,7 +1509,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e97427c50>"
+       "<matplotlib.figure.Figure at 0x1a70be12908>"
       ]
      },
      "metadata": {},
@@ -1546,9 +1546,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e9716f048>"
+       "<matplotlib.figure.Figure at 0x1a70bd08710>"
       ]
      },
      "metadata": {},
@@ -1588,9 +1588,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e9737d198>"
+       "<matplotlib.figure.Figure at 0x1a70bd39358>"
       ]
      },
      "metadata": {},
@@ -1633,9 +1633,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e9852a128>"
+       "<matplotlib.figure.Figure at 0x1a70cf48320>"
       ]
      },
      "metadata": {},
@@ -1679,7 +1679,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "28065a8f31a4441aba673aa840e2d970",
+       "model_id": "3c6019dc7f974a9a8191111895c6872d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1707,7 +1707,12 @@
     }
    ],
    "source": [
-    "from ipywidgets import interact, FloatSlider\n",
+    "from ipywidgets import interact\n",
+    "\n",
+    "# my FloatSlider returns an ipywidgets.FloatSlider with\n",
+    "# continuous_update=False. Filtering code runs too slowly\n",
+    "# to instantly react to slider changes.\n",
+    "from kf_book.book_plots import FloatSlider\n",
     "\n",
     "zs1 = gen_data(x0=5, dx=5., count=100, noise_factor=50)\n",
     "\n",
@@ -1722,10 +1727,10 @@
     "    plt.plot(data, color='b')\n",
     "\n",
     "interact(interactive_gh,           \n",
-    "         x=FloatSlider(value=0., min=-200, max=200., continuous_update=False), \n",
-    "         dx=FloatSlider(value=5., min=-50., max=50., continuous_update=False), \n",
-    "         g=FloatSlider(value=0.1, min=0.01, max=2, step=.02, continuous_update=False), \n",
-    "         h=FloatSlider(value=0.02, min=0.0, max=0.5, step=0.01, continuous_update=False));"
+    "         x=FloatSlider(value=0, min=-200, max=200), \n",
+    "         dx=FloatSlider(value=5, min=-50, max=50), \n",
+    "         g=FloatSlider(value=.1, min=.01, max=2, step=.02), \n",
+    "         h=FloatSlider(value=.02, min=.0, max=.5, step=.01));"
    ]
   },
   {
@@ -1744,9 +1749,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e98535e10>"
+       "<matplotlib.figure.Figure at 0x1a70bce0208>"
       ]
      },
      "metadata": {},
@@ -1778,9 +1783,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e9714f518>"
+       "<matplotlib.figure.Figure at 0x1a70bc0dc50>"
       ]
      },
      "metadata": {},
@@ -1843,7 +1848,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e973a1ba8>"
+       "<matplotlib.figure.Figure at 0x1a70bb94128>"
       ]
      },
      "metadata": {},
@@ -1892,9 +1897,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e972f9da0>"
+       "<matplotlib.figure.Figure at 0x1a70bc067f0>"
       ]
      },
      "metadata": {},
@@ -1923,9 +1928,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e98447668>"
+       "<matplotlib.figure.Figure at 0x1a70bdea908>"
       ]
      },
      "metadata": {},
@@ -1954,9 +1959,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e970f8160>"
+       "<matplotlib.figure.Figure at 0x1a70ce52a90>"
       ]
      },
      "metadata": {},
@@ -2000,9 +2005,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e97257f28>"
+       "<matplotlib.figure.Figure at 0x1a70cf94898>"
       ]
      },
      "metadata": {},
@@ -2029,9 +2034,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16e9846f128>"
+       "<matplotlib.figure.Figure at 0x1a70ce80128>"
       ]
      },
      "metadata": {},

05-Multivariate-Gaussians.ipynb

@@ -337,7 +337,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dae4b828>"
+       "<matplotlib.figure.Figure at 0x17681eabb70>"
       ]
      },
      "metadata": {},
@@ -565,7 +565,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dbbb94a8>"
+       "<matplotlib.figure.Figure at 0x17682c42d30>"
       ]
      },
      "metadata": {},
@@ -603,9 +603,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAADNCAYAAACFD2alAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsnXl8lNW9/z9n1sySdUIGDIkiIAqIoCK4YLUV2yq97a32NmJbrre2vbe/tqmC1drWratlaem1t/eq1O2K6a22tcW2GrVVEUGWgAVCQEASlkySmWwzk9nP74/JMzwzzznPPLNmEs779fJlmOeZZ86ceZ7zOed7vguhlEIgEAgEgnyjG+sGCAQCgWBiIgRGIBAIBAVBCIxAIBAICoIQGIFAIBAUBCEwAoFAICgIQmAEAoFAUBDyIjCEkDsIIfsIIXsJIc8RQsrycV2BQCAQjF9IrnEwhJB6AJsBzKaUjhBC/g/AnymlTwLA4OCgCLQRCASCCU5lZSVJfS1fJjIDAAshxADACuBknq4rEAgEgnFKzgJDKT0BYA2ATgCnAAxSSl/J9boCgUAgGN8Ycr0AIaQawCcBTAMwAOC3hJDPUUr/N/XcysrKXD8u7+zYsQMAcOmll45xS9IzntoKjK/2jqe2AuOrveOprcD4au9Yt3VwcFD1eD5MZNcBOEop7aWUhgH8DsAVebiuQCAQCMYx+RCYTgCLCSFWQggB8BEA7Xm4rkAgEAjGMTmbyCil2wghzwPYBSACoA3Ao7leVyAQFIcutx9rWjvgGgrAWVGGVUtnocFhHetmCSYAOQsMAFBK7wdwfz6uJRAIikeX249bN2xDp8efeK2tcwDPfnGREBlBzohIfoHgDGZNa0eSuABApye+ohEIckUIjEBwBuMaCjBf7+G8LhBkghAYgeAMxlnBzupUx3ldIMgEITACwRnMqqWz0FiTvNfSWGPFqqWzxqhFgolEXjb5BQLB+KTBYcWzX1yENa0d6BkKoE54kQnyiBAYgeAMp8FhxfqmBWPdDMEERJjIBAKBQFAQxApGICgyIrBRcKYgBEYgKCKlENgoF7hysxEUMXiDUSF2grwjBEYgKCJqgY3F2AdhCZwcEcUvyCdiD0YgKCJjHdjIEjg5IopfkE+EwAgERWSsAxt5AidHRPEL8oUwkQkERWTV0llo6xxIWkUUM7CRJ3BytIqdcFYQpEMIjEBQRMY6sJElcHK0il0pOCsISh8hMAJBkclnYGOmq4hUgbOPepH5gtGMxG6snRVyRay+ioMQGIFgnOLyRnBnFquIfAjcWDsr5IJYfRUPsckvEIxTWvb5xqyWy1g7K+SCqIFTPITACATjEJc3gvd6gsxjxVhFjOcszON59TXeECYygSADCmG7z/SaXW4/HnprAINByjxejFXEWDsrZEJq/9rNeuZ542H1Nd4QAiMQaCQb23068cjmmmtaO+DyRZnHrCY9Ot1+NLe0FXzAHw9ZmFn9W19lwZQKM04NnV4BjpfV13hDmMgEAo1karuXBrcXd5/E1iMevLj7JG7dsA1d7tPXyGY/gGfi0RHAH4qirWuA+VlnIqz+PTEwgrn1Vfjk/LNw+bk1+OT8s8QGf4EQKxiBQCOZ2u61uPJmsx/A22CPpVjMxpPbcKHg9a83GMZjKy4tcmvOPMQKRiDQSKaeU1rEIxtvrFVLZ8FpS95HMBvYj/KZvnE9nr3dJgJiBSM4o5H2SN4/4UFNmR4/mubnmkoyTfOiZXDLJnVMg8OK+5ZUoWWfDxGjFXUVZfAFw3i1vVf1s3hM5KBDVv+aDTr4gmF0ufm/tSA/CIERnLEoN4DDuHXDNq49PlPPKS3ika03ltNuQPOiSlx66aWJ73LQtS3jHGfFDDocCyGT+vehTfvx5qFeBCMxBCMxvNrei4Mu/m8tyA9CYARnLJmmO8k1LQtPPPLhjZWtUGWb8iXTvsg260A+aHBYYTXrEYzEkl4Xe1SFJy8CQwipAvA4gLkAKIB/o5S+k49rCwSFoMvtx+ZDfcxjrH2LbGf6uYhHNoKm9bOka79+oId5XN4Hqe1YvrARd73wXkZ9Ec86kNyvxRzgRXDl2JCvFcx6AH+llN5MCDEBEGtOQckiiYXbF2IeZ+1bFDu5YyFNV11uP/7lf7YkxYGkIvUBqx2t+13wh5LjcNL1hSfAjtsp1gAvNvvHBkIpOxpY8wUIqQCwB8C5lHGxwcHBxGuHDh3K6bMEgnywftsg3upiD2xOmx73LamC054897r/DQ/29YYV58+dZMQDH6opWhuXNJSheVFlTtf+yeZ+7OhmiyuQ3AdqfZWKWl/k+/u4vBG07PPBE4iipkyPpjk2xW+Wev5Dbw0kBajyfmuBdmbOnJn4u7KykqQez0fPngugF8AThJCLAOwE0Ewp9eXh2gJB3uHNpivNhDvg1JTpASgFprqMnXYkFZc3gif2DOOQOwwQYGa1EbfNL+cObrw29nNez4RD/crvAQB6AlwxtSxpsOa1g4VaXzTNseGgJ6wY4Jvm2JLO0yIcSrEI46AnrCoWTrsh4XnXH4iiWoMoCXInH71rAHAxgK9TSrcRQtYDuAfA91JPlDxeSokdO3YAKM22pTKe2gqUbntnvN+Gfb0nFa/PqzPjxmsWM9/zo2lKU1FjjRU/akpvsupy+9H82FacGDi9atjRHcKJLV7831euYL6f18bp9ZNw6aULcupbw19agaByBVNlNeGZ//cRTe1IxWrS40dNlzO/y44dO+C0G/D8V69WdULocvtHHQGklU4Yx3w6hVmwuaVNkSrH5YvilW5zWnPljdek/Sole9+yGOu2Dg4Oqh7PR6DlcQDHKaXbRv/9POKCIxCUJKxMwKzZtBzJSyub9CJrWjtwYmBE8fqpoSA3JUyhshV3uf0w6tmP/cWN1ZraweJ8Z7miL7pGc6Ld/4YH67fFB6L1TQvw3Jcvx/qmBYrzeftcn/rV22huaUukvSn0hr3LG8H6bYNoevSdpM+dCITDYfzqV7+C1WoFIQT33ntvQT8v5xUMpbSbENJFCJlFKe0A8BEA+3NvmkBQGFguvddPDqY1l2TrEcYbEAH+oFiIbMXShn034zOdFWbct2x22nZ09Y/geL9SLB12c3xlwfU0i8cYrb5pHjZu72R6xvH6ye0N4cXdJ/HXvd1YMtOBcrOReV4+NuylTNXxFVK8PeO9GNn27duxatUqvPnmm4pj1dXKSUU+yZcB8usAnh31IDsC4LY8XVcgKAipYiGZGgoBz4MJUB8U852tmLVCkBj0h/GNljY0OqwKIWsYfW1NawdGQhF4fKEkL7L6Kgv2nhhI8kp7eW83Aoy4k9ue2p70XvngrdZPABIBklMqzKivsiStCvOVDZmVqXq8xct4PB788Ic/xLp165jHly1bhocffhizZysnFPkmLwJDKd0NoPQNloIzDq2xJNLmcnjXO9zzso1EX7V0FnZ80K8wk02pMGc1KHa5/Vi/bRCeQBQz3teell9tJRWIxNDWNRD/L2XGznJVtpr0CbMYK01NqrhIqLk3szIfsDg1FMTFDRXw+EMIhqMwG/VYtfS8vKwwxmO8TCwWQ0tLC1auXImTJ5X7ZU6nE2vXrsUtt9wCna646SeFC4VgwqI1lkSLWUTLtXgC1OCwouVLi/HQpv3Y1dkPQoD5DZW4f9ncjAfF0+2It3Nf70m07nfhiRULsWi6Q/W96VYIEp0ePx7atD+RbZi18vGHookV1qceeSuj75CKNHjL07q8cbAHoSg/hGJX11Di75FQFHf+dg+cFWVp+yAd2cTLjEUKnAMHDuDuu+/GH//4R+bxb37zm/jud78LhyO3/sgVITCCCYvW4EieWeSWx7diarUFzooy+INR1WttO+xWNf80OKx5SQ/PG+xve2o7Xm6+WnVgW7V0FjNIksUbB3sSySDVZvVdbj8O9miLSNARZUkBQDl4d7iGVcWFRSRGsfL5Pdh894czel8qq5bOwtZDrqT7Qc38Vqxcbn6/H+vWrcP3vqdwzgUAXHnllVi9ejUuv/zyvH1mPhDp+gUTFq3mDt55x/tHEoXC3jykzFQMAJ19XnS5lXsLQPrCYdnAa6s/FFV4W6XS4LBilrNc0+eEohQPbdqPLrefuakPxIVhTWuHJsECgMXnVqf1jFPbJ0rH0Ag7vicTpEzVSxrKNHkLZlMwTisvv/wy5s6dC0IIbDZbkriYTCb853/+J7Zs2YLt27dj8+bNJScugFjBCCYwWs0dWkxHqYkSJTp6fHho037uIJtv271aWyVvK7UZdK3drPmz3j3qxq0btjEFRhKGu17Yo+laRh3w8KfnA0CSZ9zyhY3xQdrtR683CI+Pn75GosygY+7xVFjY3mXpSDVxXT8ZaF5UCee02VjT2oG7XtjDNX3lc8/m+PHj+N73vocnn3ySefwLX/gCvv/976OxsTHxWiGdU/KBEBjBuCAbO7fWWissswgLlonHH4piV2c/9z35znWlxczF83rqcvux98SA5s8KRGIYZKwmplZbNHt+SZSb4saSkwMj2HmsH4MjYVj7fNh6xA2XSk60VOK/33m487d7EJH9GAYdwdqbL9J8HQmWiWurTY+vXmLHna+nN33lkuMsHA7j0UcfxcqVKxEMKvtg9uzZWLt2LT760Y+CEEUWlpwo1r6REBhByZNLJmOt6fLlBbx4sR4VFgMG/BHF67xn32rSa/ISy+Rhb3BY8cSKhVjx661Qy+LCmkE/uGmvaoLLVCwmHXPlNhKKJmb1yxc2atrX8QRi+PR/bYbHH4a0vTIcUPYlC6tJj1l1NjTW2hN946wow8rn92BoJIwKixFrb74oqw1+lonL5YvikR3D6PWnT++facG4bdu2YdWqVdi8eTPz+EMPPYQ777wTNhs/6DdXilkDSAjMOMPljSQFtE2k6oM8MslkzBqstcQvyAt4sR7Axpr4/kVru0vx3vkNlTjo8inLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dae4b208>"
+       "<matplotlib.figure.Figure at 0x17682c89320>"
       ]
      },
      "metadata": {},
@@ -615,8 +615,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[11.54   3.224]\n",
-      " [ 3.224  2.378]]\n"
+      "[[10.6    2.923]\n",
+      " [ 2.923  2.365]]\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 0x1c9da2f95f8>"
+       "<matplotlib.figure.Figure at 0x17682caf208>"
       ]
      },
      "metadata": {},
@@ -656,8 +656,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 1.002 -0.003]\n",
-      " [-0.003  1.   ]]\n"
+      "[[0.994 0.006]\n",
+      " [0.006 1.   ]]\n"
      ]
     }
    ],
@@ -708,7 +708,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dbf425f8>"
+       "<matplotlib.figure.Figure at 0x17682caf470>"
       ]
      },
      "metadata": {},
@@ -886,9 +886,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dbce6b70>"
+       "<matplotlib.figure.Figure at 0x17684c90f28>"
       ]
      },
      "metadata": {},
@@ -912,7 +912,9 @@
    "source": [
     "As beautiful as these plots are, it is hard to get useful information from them. For example, it is not easy to tell if $X$ and $Y$ both have the same variance, and how much they are correlated. In most of the book I'll display Gaussians as contour plots.\n",
     "\n",
-    "The contour plots display the range of values that the multivariate Gaussian takes for a specific standard deviation. This is like taking a horizontal slice out of the 3D plot. These plots show the shape of the slice for 3 standard deviations."
+    "The contour plots display the range of values that the multivariate Gaussian takes for a specific standard deviation. This is like taking a horizontal slice out of the 3D plot.\n",
+    "\n",
+    "These plots show the shape of the slice for 3 standard deviations."
    ]
   },
   {
@@ -926,7 +928,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dc01d518>"
+       "<matplotlib.figure.Figure at 0x17684d48860>"
       ]
      },
      "metadata": {},
@@ -952,19 +954,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "These plots look like circles and ellipses. Indeed, it turns out that any slice through the multivariate Gaussian is an ellipse. Hence, in statistics we do not call these 'contour plots', but either *error ellipses* or *confidence ellipses*; the terms are interchangable.\n",
+    "\n",
     "This code uses the function `plot_covariance_ellipse()` from `filterpy.stats`. By default the function displays one standard deviation, but you can use either the `variance` or `std` parameter to control what is displayed. For example, `variance=3**2` or `std=3` would display the 3rd standard deviation, and `variance=[1,4,9]` or `std=[1,2,3]` would display the 1st, 2nd, and 3rd standard deviations. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dbe87080>"
+       "<matplotlib.figure.Figure at 0x176846cc278>"
       ]
      },
      "metadata": {},
@@ -992,13 +998,15 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dc0b55f8>"
+       "<matplotlib.figure.Figure at 0x17684bedb70>"
       ]
      },
      "metadata": {},
@@ -1031,7 +1039,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dc20cd30>"
+       "<matplotlib.figure.Figure at 0x17682c28d68>"
       ]
      },
      "metadata": {},
@@ -1074,7 +1082,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dc010b38>"
+       "<matplotlib.figure.Figure at 0x17682c84278>"
       ]
      },
      "metadata": {},
@@ -1117,7 +1125,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dc0ce278>"
+       "<matplotlib.figure.Figure at 0x176846da550>"
       ]
      },
      "metadata": {},
@@ -1173,7 +1181,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dc160470>"
+       "<matplotlib.figure.Figure at 0x17684b9b710>"
       ]
      },
      "metadata": {},
@@ -1204,7 +1212,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9de1eb320>"
+       "<matplotlib.figure.Figure at 0x17684c66be0>"
       ]
      },
      "metadata": {},
@@ -1238,7 +1246,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c5f05c3a9276482983846d668e17c601",
+       "model_id": "572eb91274114c8b956ddfa873a0b2f7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1267,8 +1275,8 @@
    ],
    "source": [
     "from ipywidgets import interact\n",
-    "from ipywidgets import FloatSlider\n",
-    "from kf_book.book_plots import figsize\n",
+    "from kf_book.book_plots import figsize, FloatSlider\n",
+    "\n",
     "fig = None\n",
     "def plot_covariance(var_x, var_y, cov_xy):\n",
     "    global fig\n",
@@ -1283,11 +1291,12 @@
     "    plt.gca().set_aspect('equal')\n",
     "    plt.ylim(4, 16)\n",
     "\n",
+    "    \n",
     "with figsize(y=6):\n",
     "    interact (plot_covariance,           \n",
-    "          var_x=FloatSlider(value=5., min=0, max=20., continuous_update=False), \n",
-    "          var_y=FloatSlider(value=5., min=0., max=20., continuous_update=False), \n",
-    "          cov_xy=FloatSlider(value=1.5, min=0.0, max=50, step=.2, continuous_update=False));"
+    "          var_x=FloatSlider(5, min=0, max=20), \n",
+    "          var_y=FloatSlider(5, min=0, max=20), \n",
+    "          cov_xy=FloatSlider(1.5, min=0, max=50, step=.2));"
    ]
   },
   {
@@ -1375,7 +1384,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dc21fd68>"
+       "<matplotlib.figure.Figure at 0x17684d07588>"
       ]
      },
      "metadata": {},
@@ -1406,7 +1415,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAEiCAYAAAC2pabwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xd8zdcbwPHPSWJFiFVqFq2qoqW0/bVVbQWJvWLvmdp7l1otjdoEbRFbEDNKBlFSKyg122hr1ZZB9rjn90fk1nUzycLzfr3yanLPud9z7pXeJ+d8z3mO0lojhBBCZDWLrO6AEEIIARKQhBBCZBMSkIQQQmQLEpCEEEJkCxKQhBBCZAsSkIQQQmQLEpCEEEJkCxKQhEgFpdQkpdSkrO5HWimlLiulymZ1P4RIDQlIQjwDpVQFpVSkUmpNMnWUUuo7pdT9R1/OSimVRN23lVLHlVJBj758lFJvJ3PtSkqpfUqpEKXUJaVUi/R4XUJkBQlIQjybRYB/CnX6AM2Bd4F3gMaAUxJ1bwCOQCGgCLAD2JBYRaWUFbAd8HhUvw+wRin1ZtpeghDZgwQkIZ6SUqodEAzsTaFqV2CW1vq61vpfYBbQLbGKWutgrfVlHZ/TSwFxwBtJXPctoAQwR2sdp7XeB/wKdE7zixEiG7DK6g4I8TxSSuUHpgB2QM8UqlcGTj/28+lHjyV3/WDAhvg/GicmVS2Jx6qk0B8hsiUZIQnxdKYCy7TW11JR1wYIeeznEMAmqftIAFrrAoAtMAD4LYlqF4E7wEilVA6lVH3gM8A6FX0SItuREZIQaaSUqgbUBaqn8imhQP7Hfs4PhOoUUu1rrcOUUkuAu0qpSlrrO0+UxyilmgMLgNHAcWAjEJXKfgmRrUhAEiLtPgfKAlcfDXJsAEul1Nta6/cSqX+O+AUNxx79/O6jx1LDgvgRT0niR0MmtNa/Ez8qAkApdQhYmcprC5GtyJSdEGn3A/A6UO3R1xJgF2CfRP1VwDClVEmlVAlgOOCaWEWlVD2lVHWllOWj+1SzgSDgQhL131FK5VZKWSulRgDFk7q2ENmdBCQh0khrHa61vpXwRfyUXKTW+i6AUupTpVToY09ZCuwEzgBniQ9eS5O4fAFgPfH3mf4ifoWdg9Y68tG1xymldj9WvzNwk/jRkx1QT2stU3biuaTkxFghUpaQpUFrPSlre5I2SqnLwOda68tZ3BUhUiQjJCGEENmCLGoQInX2Z3UHntJc4jfvCpHtyZSdEEKIbEGm7IQQQmQLEpCESAU5fkKIjCcBSYinoJRao5S6qZR6oJT6UynVK5m6uZRSc5RSNx4dKeGilMqRijY+U0pppdS0FK69/FE/bimlhj3taxIiq0lAEuLpTAfKaq3zA02BaUqpGknUHQPUJD7p6ZvAe8BXyV38UcCaBxxNoR+TgArAa8AXwCillEMqX4MQ2YoEJCGegtb63GMbUPWjr9eTqN4EmK+1Dny0eXY+0COFJoYDXsQnUE1OF2Cq1jpIa30B+JEkjrYQIruTgCTEU3o09RZOfNC4CfycVFVMj4pQQCmllG0S132N+IA1JYX2CxJ/HlKajrYQIruSgCTEU9Ja9wPyAZ8CW0g6y/ZuYLBS6hWl1KvAoEePJ3VMxHxggtY6NInyBDaP/vvk0Rb5Uuq7ENmRBCQhnsGjk1r9gFJA3ySqfUP8mUangEPANiCGRLJ3K6WaAPm01m6paD4hYD15tMXD1PVeiOxFApIQ6cOKJO4haa0jtNYDtNYltdblgfvACa11XCLV7YCaj1bM3QLaAkOUUtsTuW4Q8VOF7z72cFqOthAiW5GAJEQaKaWKKqXaKaVsHh0TYQ+0B/YlUb+kUqqEivc/YALwdRKXn0D8SryEoy12EL9QoXsS9VcBXymlCiql3gJ6I8dPiOeUBCQh0k4TPz13nfizir4HhmittwMopcoopUKVUmUe1X+d+Km6MOIPzxujtfZK9MJaP3ziaIsIIExrHfjo2h2VUo+PgL4m/piKK8AvwEyt9Z50fr1CZArJZSdEKsjxE0JkPBkhCSGEyBbk+AkhUmd/VnfgKcnxE+K5IVN2QgghsoUMGyEppZYDjYE7Wusqjz0+EBgAxAK7tNajHn9eSEiIREghhHjB2draqicfy8h7SK6ASZJHpdQXQDPgHa11ZeJXJwkhhBAZF5C01geAwCce7gvMSEhKqbU226kuhBDi5ZTZixreBD5VSn0DRAIjtNb+SVUOCAjItI4JIVLPcfNtNjsWy+pupKvz58+zbt06fv31V0JDzdMIVqtWDRcXF3LkSPEoK5GEChUqJFue2QHJCigI/A94H9iolCqvk1hZUbNmzWQvdvz48VTVy06etz5LfzPW89ZfeP76nFx/tdbs2bOHmTNn4uvra3z8zTffxMHBgUqVKnH//n3mz5/PqVOnOHPmDH369Mmy/mZHaelvSEhIsuWZHZCuA1seBaBjSikDUAS4m8n9EEK85Pbu3cvw4cM5fTr+9I58+fLx5Zdf4uTkxOuvm6YlDA0NZcaMGdy8eTMruvrSyOyAtA2oA+xXSr0J5ATuZXIfhBDPqHWlvFndhad25coVhg8fjru7OwDFixdnyJAhODk5YWub6BFVFCpUCICgoKBM6+fLKCOXfa8HPgeKKKWuE59zazmwXCl1FogGuiY1XSeEyL7aVrZJuVI2ExERgbOzMzNmzCAyMhJra2vGjx/PsGHDyJ07d7LP/f333wEoU6ZMsvXEs8mwgKS1bp9EUaeMalMIkTl6e9zlt+fjFgdaa3x9fXF0dOTKlSsAtG/fHmdnZ0qVKpXi8w0GA15e8blwHRwcUqgtnoWkDhJCpFlQpCGru5AqFy5cYMCAARw7dgyAd955hwULFlC7du1UX+PkyZPcuXOH0qVLU6lSpYzqqkCSqwohXkBaa+bNm0f16tU5duwY+fPnZ9GiRZw4cSJNwQjAxcUFgObNm6OUWXIBkY5khCSESLPyBbLvR8etW7fo3r07e/bEHwvVtGlTBg4cSN26ddN8rX///Zc1a9ZgYWHB4MGD07ur4gnZ97dKCJFtOdctnNVdSNTOnTvp0aMH9+7do3Dhwvz000+puk+UlHnz5hETE0Pr1q3NloKL9CdTdkKINFty4kFWd8FEeHg4/fr1o2nTpty7d4+6devy+++/07x586e+ZnBwMEuWLAFg5MiR6dVVkQwZIQkh0sznn4is7oLRb7/9RocOHbh48SI5c+Zk+vTpDBkyBAuLZ/t7e9KkSTx8+JA6derw/vvvp1NvRXIkIAkhnksGg4HZs2czbtw4YmJiqFSpEuvWraNatWrPfO1z586xcOFCLCwsmDVrVjr0VqSGTNkJIZ47//77L/Xr12fkyJHExMTQv39/jh8/ni7BSGvN4MGDiYuLw8nJKV2uKVJHRkhCiDT7oVGRLGt7y5Yt9O7dm8DAQF555RWWL19O48aN0/X6e/fupVChQkydOjXdritSJiMkIUSa/R0Um+lthoaG0qtXL1q1akVgYCANGjTgzJkz6RqMwsPDGTZsGADTpk2jcOHsuZrwRSUBSQiRZjMOBWdqe6dPn+a9995j2bJl5MqViwULFrBr1y6KFUvfM5mcnZ25evUq7777boYfMyHMyZSdECJbW716NX369CEyMpKqVauybt06qlSpku7tXL58me+++w6ABQsWYGlpme5tiOTJCEkIkS1FR0czYMAAunTpQmRkJN27d+fo0aMZEoy01gwcOJDIyEg6dOjAp59+mu5tiJTJCEkIkWZO7+XL0OvfuHGD1q1bc+jQIXLmzMmCBQvo3bt3huWS++GHH/Dw8MDW1hZnZ+cMaUOkTAKSECLN6pW3zrBrHzhwgDZt2nD79m1KlSqFu7s7H3zwQYa1d/HiRYYOHQrA0qVLKVmyZIa1JZInU3ZCiDRz3Hw73a+ptWbu3LnUqVOH27dv88UXX3DixIkMDUbR0dF06NCBiIgIunTpQtu2bTOsLZEyCUhCiCwXFhZGx44dGTp0KHFxcYwcORIvLy+KFi2aoe1OmDCB3377jXLlyrFgwYIMbUukTKbshBBZ6vLlyzRv3pzTp09jY2PDihUrcHR0zPB29+3bx8yZM7G0tGTt2rXkz58/w9sUyZOAJIRIsxrFc6bLdfbv34+joyP379+nQoUKbNu2jbfffjtdrp2cwMBAunTpgtaaCRMm8NFHH2V4myJlMmUnhEizsZ8UfKbna61ZsGABdevW5f79+zg4OHDs2LFMCUZaa/r06cO///7Lxx9/zPjx4zO8TZE6EpCEEGk2/degp35uVFQUvXr1YtCgQcTFxTF69Gg8PDwoUKBAOvYwaa6urri7u5MvXz7WrFmDlZVMFGUX8i8hhEizEzejn+p5N2/epGXLlhw5coQ8efKwbNky2rdvn869S9qlS5cYOHAgAC4uLpQrVy7T2hYpk4AkhMgUR48epWXLlty4cYMyZcqwbds2qlevnmntx8TE0KFDB8LCwmjfvj0dO3bMtLZF6siUnRAiw61atYratWtz48YNateujb+/f6YGI4DJkyfj7+9PmTJlcHFxybCsD+LpSUASQqTZZsfUZdlOWMXWtWtXoqOj6devHz4+Phm+v+hJJ0+e5Ntvv8XCwoI1a9Zk2v0qkTYSkIQQaeb9d3iKdSIjI+nUqRPTpk3DwsICFxcXFi1aRI4cOTKhh/95+PAhX3/9NVprxo4dK4lTs7EMC0hKqeVKqTtKqbOJlI1QSmmlVNYdOymEeGpLTz5MtvzevXvUq1ePdevWYWNjg4eHB3379s2k3v3HYDAwceJEbt26xfvvv8/XX3+d6X0QqZeRIyRXwOHJB5VSpYF6wNUMbFsIkUUCAgL46KOP8PPzo2TJkvj5+dGgQYMs6cu0adPw8/PD1taWjRs3ZvroTKRNhgUkrfUBIDCRojnAKEBnVNtCiKxx8OBB/ve//3Hp0iWqV6/O0aNLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dc1d34e0>"
+       "<matplotlib.figure.Figure at 0x17684da3dd8>"
       ]
      },
      "metadata": {},
@@ -1449,7 +1458,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9ddcd4780>"
+       "<matplotlib.figure.Figure at 0x1768456db00>"
       ]
      },
      "metadata": {},
@@ -1508,7 +1517,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9de1e1978>"
+       "<matplotlib.figure.Figure at 0x17684d0d400>"
       ]
      },
      "metadata": {},
@@ -1538,7 +1547,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9de1e1c88>"
+       "<matplotlib.figure.Figure at 0x17684d0d390>"
       ]
      },
      "metadata": {},
@@ -1571,7 +1580,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dc117978>"
+       "<matplotlib.figure.Figure at 0x17682c3ef28>"
       ]
      },
      "metadata": {},
@@ -1607,7 +1616,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9ddca8390>"
+       "<matplotlib.figure.Figure at 0x17682ca7da0>"
       ]
      },
      "metadata": {},
@@ -1636,7 +1645,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dbce6b38>"
+       "<matplotlib.figure.Figure at 0x17684bea940>"
       ]
      },
      "metadata": {},
@@ -1668,7 +1677,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9ddc9eb70>"
+       "<matplotlib.figure.Figure at 0x176845a2ef0>"
       ]
      },
      "metadata": {},
@@ -1699,7 +1708,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dbe58518>"
+       "<matplotlib.figure.Figure at 0x17684781b00>"
       ]
      },
      "metadata": {},
@@ -1730,7 +1739,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9dbe6dcc0>"
+       "<matplotlib.figure.Figure at 0x17684bc3048>"
       ]
      },
      "metadata": {},
@@ -1763,7 +1772,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9ddcf9898>"
+       "<matplotlib.figure.Figure at 0x176845d2b38>"
       ]
      },
      "metadata": {},
@@ -1802,7 +1811,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9ddc9e4a8>"
+       "<matplotlib.figure.Figure at 0x17681eab828>"
       ]
      },
      "metadata": {},
@@ -1829,7 +1838,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9ddd3e0b8>"
+       "<matplotlib.figure.Figure at 0x176fd038c18>"
       ]
      },
      "metadata": {},
@@ -1863,7 +1872,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9ddcf9da0>"
+       "<matplotlib.figure.Figure at 0x176845c4860>"
       ]
      },
      "metadata": {},
@@ -1890,7 +1899,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9ddcf9278>"
+       "<matplotlib.figure.Figure at 0x1768286c358>"
       ]
      },
      "metadata": {},
@@ -1919,7 +1928,7 @@
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9ddcf49e8>"
+       "<matplotlib.figure.Figure at 0x17684d905c0>"
       ]
      },
      "metadata": {},
@@ -1948,7 +1957,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c9ddcf4a20>"
+       "<matplotlib.figure.Figure at 0x176ffe221d0>"
       ]
      },
      "metadata": {},
@@ -1984,6 +1993,72 @@
     "It is important to understand that we are taking advantage of the fact that velocity and position are correlated. We get a rough estimate of velocity from the distance and time between two measurements, and use Bayes theorem to produce very accurate estimates after only a few observations. Please reread this section if you have any doubts. If you do not understand this you will quickly find it impossible to reason about what you will learn in the following chapters."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Higher Dimensions\n",
+    "\n",
+    "So far I have shown you two dimensional Gaussians, but the math does not limit you to two dimensions. In later chapters we will be working in 9, or even 12 dimensions. If you work in areas such as weather prediction, you can end up with thousands of dimensions.\n",
+    "\n",
+    "What do these higher dimensions 'look like? Well, a two dimensional Gaussian can be represented by an error ellipse, so it stands to reason a three dimensional Gaussian could be represented by a 3D error ellipsoid. We won't delve into the math here, but this turns out to be true. `FilterPy` provides a function to plot this ellipse.\n",
+    "\n",
+    "First, let's make some noisy data with a given covariance, just so we can plot it inside the ellipsoid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from filterpy.stats import plot_3d_covariance\n",
+    "\n",
+    "mu = [0.3, 5., 10.]\n",
+    "C = np.array([[1.0, .03, .2],\n",
+    "              [.03,  4.0, .0],\n",
+    "              [.2,  .0, 16.1]])\n",
+    "\n",
+    "sample = np.random.multivariate_normal(mu, C, size=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we plot the ellipsoid with the `FilterPy` function `plot_3d_covariance`, and then scatter plot the samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x17684804940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plot_3d_covariance(mu, C, alpha=.4, std=3, limit_xyz=True)\n",
+    "ax.scatter(sample[:, 0], sample[:, 1], zs=sample[:, 2],);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Theory states that roughly 99% of a distribution will fall withing 3 standard deviations, and this appears to be the case.\n",
+    "\n",
+    "Nine dimensions? I haven't quite figured out how to plot a 9D ellipsoid on a 2D screen, so there will be no graphs. The concept is the same; the standard deviation error of the distribution can be described by a 9D ellipsoid."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2039,6 +2114,12 @@
       "model_name": "LayoutModel",
       "state": {}
      },
+     "05f17d48639f484d9e6af045216337c3": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
      "19b08fefd01940b6b840f82802387cee": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.0.0",
@@ -2063,29 +2144,132 @@
        ]
       }
      },
+     "44fe3d92631c4944b9e1653635906b1c": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
      "5627b97ec1154b188f6bb1349bcfc816": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.0.0",
       "model_name": "LayoutModel",
       "state": {}
      },
+     "572eb91274114c8b956ddfa873a0b2f7": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.1.0",
+      "model_name": "VBoxModel",
+      "state": {
+       "_dom_classes": [
+        "widget-interact"
+       ],
+       "children": [
+        "IPY_MODEL_b939ec299c974353b57b8ed295bd939d",
+        "IPY_MODEL_79f3aca46eab4c839194a41a94cb268f",
+        "IPY_MODEL_778a1bfb3fdb4139a25556f3810ec708",
+        "IPY_MODEL_a4be955df7354a558734ff34c97418d0"
+       ],
+       "layout": "IPY_MODEL_05f17d48639f484d9e6af045216337c3"
+      }
+     },
      "7120efb9d05e43e8ab7febb0367dded5": {
       "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.0.0",
+      "model_module_version": "1.1.0",
       "model_name": "SliderStyleModel",
       "state": {
        "description_width": ""
       }
      },
+     "778a1bfb3fdb4139a25556f3810ec708": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.1.0",
+      "model_name": "FloatSliderModel",
+      "state": {
+       "continuous_update": false,
+       "description": "cov_xy",
+       "layout": "IPY_MODEL_44fe3d92631c4944b9e1653635906b1c",
+       "max": 50,
+       "step": 0.2,
+       "style": "IPY_MODEL_bd2dcab04bb4412288a3ea17f36b247d",
+       "value": 1.5
+      }
+     },
+     "79f3aca46eab4c839194a41a94cb268f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.1.0",
+      "model_name": "FloatSliderModel",
+      "state": {
+       "continuous_update": false,
+       "description": "var_y",
+       "layout": "IPY_MODEL_9373888e43644de5811dd49145d4ee18",
+       "max": 20,
+       "step": 0.1,
+       "style": "IPY_MODEL_e70d92ee5d894cb5b761e1e7df34ba6f",
+       "value": 5
+      }
+     },
      "8de94174042a4d7c96267e95167f564d": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.0.0",
       "model_name": "LayoutModel",
       "state": {}
      },
+     "9373888e43644de5811dd49145d4ee18": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "9f73e1352c834bfba0b6e9739a34e5d3": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "a4be955df7354a558734ff34c97418d0": {
+      "model_module": "@jupyter-widgets/output",
+      "model_module_version": "1.0.0",
+      "model_name": "OutputModel",
+      "state": {
+       "layout": "IPY_MODEL_9f73e1352c834bfba0b6e9739a34e5d3",
+       "outputs": [
+        {
+         "data": {
+          "image/png": 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truncated
+          "text/plain": "<matplotlib.figure.Figure at 0x17684ca49b0>"
+         },
+         "metadata": {},
+         "output_type": "display_data"
+        }
+       ]
+      }
+     },
+     "b1673be7013d43e8900fd9982384e4f9": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.1.0",
+      "model_name": "SliderStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "b939ec299c974353b57b8ed295bd939d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.1.0",
+      "model_name": "FloatSliderModel",
+      "state": {
+       "continuous_update": false,
+       "description": "var_x",
+       "layout": "IPY_MODEL_de070b4ffec5427cb4b8a01304ac8a42",
+       "max": 20,
+       "step": 0.1,
+       "style": "IPY_MODEL_b1673be7013d43e8900fd9982384e4f9",
+       "value": 5
+      }
+     },
      "ba258f1263764dd7b04d9a305de3fffe": {
       "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.0.0",
+      "model_module_version": "1.1.0",
       "model_name": "VBoxModel",
       "state": {
        "_dom_classes": [
@@ -2102,7 +2286,15 @@
      },
      "bbcbcccb6d424fc1a101689156efe57b": {
       "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.0.0",
+      "model_module_version": "1.1.0",
+      "model_name": "SliderStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "bd2dcab04bb4412288a3ea17f36b247d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.1.0",
       "model_name": "SliderStyleModel",
       "state": {
        "description_width": ""
@@ -2110,7 +2302,7 @@
      },
      "cb02d85576534087867908af668a32f3": {
       "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.0.0",
+      "model_module_version": "1.1.0",
       "model_name": "FloatSliderModel",
       "state": {
        "continuous_update": false,
@@ -2130,7 +2322,7 @@
      },
      "d2001cdea13145efa7d9e454100c1bc8": {
       "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.0.0",
+      "model_module_version": "1.1.0",
       "model_name": "FloatSliderModel",
       "state": {
        "continuous_update": false,
@@ -2144,15 +2336,21 @@
      },
      "dda53a86c0ad4ec3a3d7c5d57c47057c": {
       "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.0.0",
+      "model_module_version": "1.1.0",
       "model_name": "SliderStyleModel",
       "state": {
        "description_width": ""
       }
      },
+     "de070b4ffec5427cb4b8a01304ac8a42": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
      "e6bf57c2b08e45ce8b94f6286cb77b7c": {
       "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.0.0",
+      "model_module_version": "1.1.0",
       "model_name": "FloatSliderModel",
       "state": {
        "continuous_update": false,
@@ -2163,6 +2361,14 @@
        "style": "IPY_MODEL_7120efb9d05e43e8ab7febb0367dded5",
        "value": 1.5
       }
+     },
+     "e70d92ee5d894cb5b761e1e7df34ba6f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.1.0",
+      "model_name": "SliderStyleModel",
+      "state": {
+       "description_width": ""
+      }
      }
     },
     "version_major": 2,

08-Designing-Kalman-Filters.ipynb

@@ -342,9 +342,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c3eb3eb8>"
+       "<matplotlib.figure.Figure at 0x1fed30b2780>"
       ]
      },
      "metadata": {},
@@ -634,9 +634,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4c73588>"
+       "<matplotlib.figure.Figure at 0x1fed3e7cdd8>"
       ]
      },
      "metadata": {},
@@ -750,7 +750,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c3eb3080>"
+       "<matplotlib.figure.Figure at 0x1fed3e65470>"
       ]
      },
      "metadata": {},
@@ -856,9 +856,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4d402e8>"
+       "<matplotlib.figure.Figure at 0x1fed3f78438>"
       ]
      },
      "metadata": {},
@@ -1126,7 +1126,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And now a routine to perform the filtering."
+    "And now a routine to perform the filtering and save the output in a `Saver` object."
    ]
   },
   {
@@ -1135,16 +1135,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def filter_data(kf, zs):\n",
-    "    xs, ps = [], []\n",
-    "    for z in zs:\n",
-    "        kf.predict()\n",
-    "        kf.update(z)\n",
+    "from filterpy.common import Saver\n",
     "\n",
-    "        xs.append(kf.x)\n",
-    "        ps.append(kf.P.diagonal()) # just save variances\n",
-    "    \n",
-    "    return np.array(xs), np.array(ps)"
+    "def filter_data(kf, zs):\n",
+    "    s = Saver(kf)\n",
+    "    kf.batch_filter(zs, saver=s)\n",
+    "    s.to_array()\n",
+    "    return s"
    ]
   },
   {
@@ -1161,9 +1158,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhcAAADLCAYAAADdurE1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3XdYFMf/wPH3cnB0EBBBFASRoCA27Bo7qLEX7CXYNWqa5asxlkSj5meJGmuMvWHvGrE3RAF7AUWqCIh06Xf7+wO9aGJBA9Z5Pc89HrO7M7MjcB9mp0iyLCMIgiAIglBYtN51BQRBEARB+LiI4EIQBEEQhEIlggtBEARBEAqVCC4EQRAEQShU2u+6Av+UkpIiRpgKgiB85ExNTaV3XQeh6IieC0EQBEEQCpUILgRBEARBKFTv3WORp5mamv4rLSAgAIDq1au/7ep8UEQ7vZpoo4IR7VQw70M7jRo1itmzZzNt2jTGjx+vSR8yZAhLly5l3rx5jBw58p3VLyUl5Z2VLbxdoudCEAThI+Hk5ATA3r17UalUAGRmZnLo0CEAypUr987qJnxaRHAhCILwkejWrRvm5ub4+flRo0YNvv32WypXrkxYWBjlypWjefPm77qKwifivX4sIgiCIBScqakpe/bsoUOHDly8eJGLFy8C4ODgwO7du1EoFO+4hsKnQgQXgiAIH5G6desSHh7Ozp07iYyMxNnZmVatWqGjo1NkZd66dYurV6/i5eVVZGUIHxYRXAiCIHxk9PX16d69+1spKzo6mgoVKmBgYMCVK1c4deoUOjo6tG/fnn79+qGvr/9W6iG8X8SYC0EQBKFAcnJy2Lp1KxMnTtSklS5dmiZNmiBJElOnTuXEiRMcPnyY4cOH07hxY9LT099hjYV3pch6LiRJsgXWANaAGlgmy/I8SZLMAR/AHggHusiynFRU9RAEQfhUpWTkEpWUQUxyZv4rJYt7SZncS87kfkomuSoZhZaEtpaEliShrZA0X1uZ6NGlui3NXa1Rauf/HZqWlkbPnj3Jy8tjyJAh2NjYAFC8eHEePXpEtWrVmDhxIunp6YwfPx5/f39mz57NpEmT3mUzCO9AUT4WyQO+l2U5SJIkYyBQkiRf4EvgiCzLMyRJ+h/wP2BsEdZDEAThvRQdHc3GjRuJj4+ncuXKdO7cGT09vULJe3tQNKO3XkGl/ntHBV1tLUqa6iJlJCM9jMFYW4uy5cphWcIKlRpUajVxDxKIiIwiIkLJfp9VEH+bsTN+p3tNO2wtLPj+++8pUaIEhoaGAGRlZbF9+3YkSWLnzp2ULl0aSZIoUaIEnp6ebNy4UQQXnyBJlt/OVh6SJO0Cfn/8aiTL8n1JkkoCx2VZdn5y3tN7i9y+ffut1E0QBOFt27dvH1OnTiUvL0+TZmNjw4IFC7Czs/tPed9OzGXi8USczHX4wskASwMFxQ0UZCY94KuvhhEZGfnM+Z06dWLMmDHMmTMHHx+fvw9oKUCtouSX89G1KktVayXNHQ2oYq0E4MEjFSFxaUyZuww9Kwcq1G5CjhrmNy9OVFQUHTt2xMLCgoMHDwJ/r8MBYm+Rj91bGdApSZI9UBXwB6xkWb4P8DjAKPE26iAIgvC+CA8P5+eff0alUtGoUSOcnZ05dOgQYWFh/PDDD6xZswZJerPP3qQsFf93NhkzPS1G1SmGie7fQ+smz5xBZGQkDg4OdO7cmfj4eDZt2sS2bduIjIzkwoUL6Ojo0KVLF0qUKMGqVatISkrCIeogtRuO5kh4JtPPJGOqq0Vmnpqc/HW6MGv0JXmpD3iUGEet8rbk5OayevVqACpVqvSf20v4AMmyXKQvwAgIBDo+/jr5H8eTnv46OTlZfvJ6ngsXLsgXLlx47jHhb6KdXk20UcGIdiqY12mn0aNHy4Dcp08fTVpqaqpsaWkpA2/c3lm5eXLHRWfk8hMOyNfvpTxzLDY2VpYkSVYqlXJsbKwmfe7cuTIga2lpyYA8cOBAzbF79+7JOjo6spaWlhwfHy/n5KnkvZdj5BEbguSf9lyXN52PkAMjEuUVazfIgAzI9vb2spWVlQzIOjo6sr+/vya/f/x+L/LPH/F6d68inS0iSZIOsA1YL8vy9sfJcY8fh/D43/iirIMgCML7JioqCoAmTZpo0oyNjalZsybAvx5bFNTk3TcIjEji/7wq4WJj8syxBw8eIMsytra2bNmyhaZNm5KRkaHZC+XJAlu9e/fWXGNjY4OdnR1qtZr4+Hh0FFq0qlSS+d2r8mNrF7rWsKOanRnevbqzfPlyrK2tCQ8PJy4uDicnJ/bs2aO5J+HTUmTBhZTfp/cncFOW5TlPHdoN9H38vi+wq6jqIAiC8D767LPPANi2bduTHlzi4uI4derUM8dfx7pzEWw8H8mwRo60rmTzzLGcnBwcHBwwMjIiNDSUpUuXcvToUTZt2sTKlSsBNOM81q9fr6nT6dOnCQ0NxcjICHt7eyB/EOqJEye4e/fuM2X079+fiIgILl68yLVr17h165ZYbvwTVpQ9F/WA3kATSZIuPX59AcwAPCRJug14PP5aEAThkzFgwAD09fXZs2cPNWrUYNCgQVSqVInU1FSaNGlCxYoVXyu/82GJTN59ncbOlnzvqRkfjyzLDB48GCsrK1JTUxkyZAgAN27coHLlysyePZsVK1agra3NTz/9hEKhYOnSpbi7u9O2bVtNz8qQIUPIycnBy8sLOzs7GjVqhKOjI56ensTExGjKUyqVVKlSBVdXV7S0xDJKn7IiG9Apy/Jp4EUjkpoWVbmCIAjvO1tbW3bs2EGPHj0IDAwkMDAQgFq1arFhw4bXyismOZNh6wOxMzfgt25VSUp8SPHixQGQJIkHDx6QnJzM4cOH+eWXX0hNTWX58uVcvnwZAEtLS5YtW0b79u3R0dFh8ODBmn1JJEmiX79+TJ06lZYtW3Ls2DGUSiXu7u5cuXIFX19fmjdvTlBQUJEuLy58eN7aVNSCenoqqqmp6b+OBwQEAGieEwrPJ9rp1UQbFYxop4J5k3bKyMhgz549mnUuPv/8c80skTyVmvspWUQlZoAEhkptDHW1MdRVYKirjYGOgjy1jNcSP8ISHrHzq7pMHTOC9evXc+nSJVxdXQG4fv06Ojo6zzxqiYqK4ty5cxgZGdGkSRN0dXWfqdPhw4dJT0+nbt262Nvb4+fnR926dTE3NycgIAAHBwfi4+OpXbs2YWFhbNu2jY4dO77yflNSUjTvxVTUj5vYW0QQBOEdMTAwoFNnL07fSSA04RFH99wg/OEjIh5mEJ2UQa7q5X/8SZlJyPqmLO9Tk3IljDX7eJw7d04TXDz592m2trbY2tq+sE5t27Z9Ju3ChQsAdOzYEQcHBwBKlChB7969+emnn/D39y9QcCF8OkRwIQiC8I6o1TIjN11k/9VYAIx0tSljYYBLSRMaljUh/FoAsXeuYWpqQoOmnpS2dyQjR8Wj7DzWzJ7EmT0bmbZsI81crACYMGECEydOpGTJkoVaTzMzMwBCQkKQZVnTuxIcHAyAubl5oZYnfPhEcCEIgvAOyLLMz/tusP9qLKM8P6NbTTssDJVIksTly5dp1qwZCQkJmvM3/z6N8ePHM23aNMLCwlgQdgW1WsWEwd05s3MNU6ZMoVq1akVS17Zt22JkZMTJkyfp378/HTt25MiRI/j4+KBQKOjatWuRlCt8uMRwXkEQhHfgz9NhrDwTTr96Dgxv4kRxI10kSUKWZXr16kVCQgL169dn1apV1K9fH4BffvmFrVu3Urt2ba5cuQKASqVi79691K9fH39//yKpq6mpKcuXL0ehULBy5UratGnDb7/9BsDcuXM101QF4QnRcyEIgvCW7bkcw9R9N/nCzZoJrSo8cywoKIhr165RokQJfH190dPTIzMzk9OnTwPw448/Eh8fT8OGDVmxYgUGBgZ89913bNy4kfHjx3PkyJEiqXPXrl1xdXVlyZIlhISEYGdnx8CBA6lVq1aRlCd82ERwIQiC8Badu/uQ7zdfpoa9GXO6VEFL69lJE9u35y9mbGxsrNkhtVevXmRlZfHtt98SHh4OwLx58yhbtiwAixYtYvPmzRw9epSMjAwMDAyKpO4VK1bk999/L5K8hY+LeCwiCILwH4SGhjJ69Gi++eYbfvnlF86fP//Cc0Pi0hi0JgA7CwP+6FMdPZ38JbefXhLgScBw9+5dzaMPXV1d9u/fD+QvVPVPb7rJmSAUFRFcCIIgvKEDBw7g6urKrFmzOHPmDDt27KBWrVrMmZO/40FsbCxjx46lcuXKVKnTkPZzfVEqJFZ516CYgZJLly7RunVrJk6cqMnzyy+/pGnTpsiyTPXq1fH09MTR0RFfX19MTU1p06YNAF9//TV3794lLi6OoUOHolKpaNy4cZH1WgjC6xCPRQRBEN5AZmYmvXv3Jjs7Gy8vL6pXr87ly5fZuHEjo0aNokqVKvTp04d79+4hKQ2w7jkT7WwVOYd/QTl4M2BAZmYm+/bt49KlS0yePBmFQoFCoWD37t0MHjyY9evX4+vrC0C5cuVYt24d1tbW+Pr6cuLECRwdHTX10dPTY/r06e+oNQThWaLnQhAE4Q0cPHiQhw8fUrVqVXx8fGjSpAnffvstffv2RZZlvv76a+4npePWaSS1JmxBR9+ItC1jCQ08wdSpUwGoXbs2y5Yt4+LFi5pdSSF/Iau1a9cSHh7Ozp07OX36NMHBwdSqVYsyZcrg5+dHz549MTQ0RKlU0rp1a06fPi0GVwrvDdFzIQiC8AYePnwIQIUKFZ4Z81DexRV9pzrEf9aU0i2qk6rQxt7MhCHVzfFeHA6Aj48P8+fPR5IkBg4c+MIy7OzsNLuVPq1s2bKsW7eucG9IEAqRCC4EQRDewJM9RPbu3UtoaChhybkcup2K7307in/xNcl+m0k+64P/2VNUcchfQTPqp5/48ccfycrKepdVF4QiJ4ILine truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4cabf98>"
+       "<matplotlib.figure.Figure at 0x1fed3e7d160>"
       ]
      },
      "metadata": {},
@@ -1177,17 +1174,16 @@
     "xs, zs = simulate_system(Q=Q, count=50)\n",
     "\n",
     "kf = FirstOrderKF(R, Q, dt=1)\n",
+    "data1 = filter_data(kf, zs)\n",
     "\n",
-    "filter_xs1, ps1 = filter_data(kf, zs)\n",
-    "\n",
-    "plot_kf_output(xs, filter_xs1, zs)"
+    "plot_kf_output(xs, data1.x, data1.z)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It looks like the filter is performing well, but it is hard to tell exactly how well. Let's look at the residuals and see if they help. You may have noticed that in the code above I saved the covariance at each step. I did that to use in the following plot. The `ConstantVelocityObject` class returns a tuple of (position, velocity) for the real object, and this is stored in the array `xs`, and the filter's estimates are in `filter_xs1`."
+    "It looks like the filter is performing well, but it is hard to tell exactly how well. Let's look at the residuals and see if they help. We'll do this a lot, so I'll write a function to plot them."
    ]
   },
   {
@@ -1198,10 +1194,10 @@
    "source": [
     "from kf_book.book_plots import plot_residual_limits, set_labels\n",
     "\n",
-    "def plot_residuals(xs, filter_xs, Ps, title, y_label, stds=1):\n",
-    "    res = xs - filter_xs\n",
+    "def plot_residuals(xs, data, col, title, y_label, stds=1):\n",
+    "    res = xs - data.x[:, col]\n",
     "    plt.plot(res)\n",
-    "    plot_residual_limits(Ps, stds)\n",
+    "    plot_residual_limits(data.P[:, col, col], stds)\n",
     "    set_labels(title, 'time (sec)', y_label)"
    ]
   },
@@ -1214,7 +1210,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4d006d8>"
+       "<matplotlib.figure.Figure at 0x1fed3f00c50>"
       ]
      },
      "metadata": {},
@@ -1222,7 +1218,7 @@
     }
    ],
    "source": [
-    "plot_residuals(xs[:, 0], filter_xs1[:, 0], ps1[:, 0], \n",
+    "plot_residuals(xs[:, 0], data1, 0, \n",
     "               title='First Order Position Residuals(1$\\sigma$)',\n",
     "               y_label='meters')   "
    ]
@@ -1245,7 +1241,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4dde908>"
+       "<matplotlib.figure.Figure at 0x1fed3eea358>"
       ]
      },
      "metadata": {},
@@ -1253,8 +1249,7 @@
     }
    ],
    "source": [
-    "\n",
-    "plot_residuals(xs[:, 1], filter_xs1[:, 1], ps1[:, 1], \n",
+    "plot_residuals(xs[:, 1], data1, 1, \n",
     "               title='First Order Velocity Residuals(1$\\sigma$)',\n",
     "               y_label='meters/sec')   "
    ]
@@ -1275,9 +1270,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4dd80f0>"
+       "<matplotlib.figure.Figure at 0x1fed507a160>"
       ]
      },
      "metadata": {},
@@ -1286,8 +1281,8 @@
    ],
    "source": [
     "kf0 = ZeroOrderKF(R, Q)\n",
-    "filter_xs0, ps0 = filter_data(kf0, zs)\n",
-    "plot_kf_output(xs, filter_xs0, zs)"
+    "data0 = filter_data(kf0, zs)\n",
+    "plot_kf_output(xs, data0.x, data0.z)"
    ]
   },
   {
@@ -1310,7 +1305,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5e667b8>"
+       "<matplotlib.figure.Figure at 0x1fed3ef6e48>"
       ]
      },
      "metadata": {},
@@ -1318,7 +1313,7 @@
     }
    ],
    "source": [
-    "plot_residuals(xs[:, 0], filter_xs0[:, 0], ps0[:, 0], \n",
+    "plot_residuals(xs[:, 0], data0, 0, \n",
     "               title='Zero Order Position Residuals(3$\\sigma$)',\n",
     "               y_label='meters',\n",
     "               stds=3)"
@@ -1342,9 +1337,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5f44ac8>"
+       "<matplotlib.figure.Figure at 0x1fed3f29438>"
       ]
      },
      "metadata": {},
@@ -1353,8 +1348,8 @@
    ],
    "source": [
     "kf2 = SecondOrderKF(R, Q, dt=1)\n",
-    "filter_xs2, ps2 = filter_data(kf2, zs)\n",
-    "plot_kf_output(xs, filter_xs2, zs)"
+    "data2 = filter_data(kf2, zs)\n",
+    "plot_kf_output(xs, data2.x, data2.z)"
    ]
   },
   {
@@ -1379,7 +1374,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbAAAAETCAYAAABXxlNqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsnXd4W9XZwH+v5CXveMROHGfYcQaEEZJAGGGmrNIWSqHARwe0QPvR0gGUDtrSAZ20lK+0BVqgC8pqyyZAIMxMEkISSOLEcRI73rIlD1m2pfP9ca5s2Za3ZMnO+T2PH0v3Xt17zpXuec/7nneIUgqDwWAwGCYatmg3wGAwGAyG0WAEmMFgMBgmJEaAGQwGg2FCYgSYwWAwGCYkRoAZDAaDYUJiBJjBYDAYJiRGgBkmHSLyeRF5axyuc5uI/CPS1xktIjJTRFpExD7IMS0iUjSe7RoLIrJDRE4fYN/pIlIRpuusEZEvhuNchshhBJhhxIjIKSLyjoi4RMQpIm+LyLJot2u4iEiiiPxMRA6IiEdESkXkZhGRKLer3GpPi4jUiMiDIpI62vMppQ4opVKVUj7r/P0GZWt/2Vjb3pc+fakWkYfG0pcASqkjlVJrwtBEwyTACDDDiBCRdOBZ4P+ALKAA+BHgjWa7RsjjwFnA+UAa8BngWuB3oQ4WTdielSHO9zGlVCpwHLAMuDVc140Cgb4cCywGvhPl9hgmGUaAGUbKPACl1CNKKZ9SyqOUekkp9X7gABG5WkQ+FJFGEVklIrOC9h0pIi9bmluNiHzX2p4oIneJyCHr7y4RSbT2nS4iFSJyo4jUikiViFwVdM5sEXlaRNwisgEoHqjxInIWcDZwsVJqu1KqSym1DrgSuF5E5lrHrRGR20XkbaANKBKROSLyuog0i8jLQE6fcy+3NNMmEdkabOoKdb7BbrJSqhJ4AVhkfX661UeniOwRkWuCzn28iGyy+l8jIr+xts8WESUicSJyO7AC+L2lFf3eOkYF9TlDRP4mInUisl9Ebg0I2oBZVkR+bX2v+0TkvMH6ENSXamAVWpAF2pxoneuA1eY/iYjD2pcjIs9a99EpIm8GtaNcRFZarx2WZtcoIh+gBX7w99HdN+v9QyLyU+v1FOsaddbnnxWRGaHaLyJzre/dJSL1IvLocPptiDxGgBlGym7AJyJ/FZHzRGRK8E4RuRD4LvBJIBd4E3jE2pcGvAK8CEwH5gKrrY9+D1iOHuSOAY6nt/aRD2SgNb4vAPcEXfseoB2YBlxt/Q3ER4D1SqmDwRuVUuuBCrRmFiCgmaUB+4GHgXfRgusnwOeC+l0APAf8FK2Z3gQ8KSK5g5xvQESkEK0hbrE2PWK1bzrwKeAOSxiD1hx/p5RKRwvvx/qeTyn1PfR38RXLbPiVEJf9P/Q9LgJOAz4LXBW0/wRgl9X/XwJ/ERna7GoJhvOAPUGbf4GeDB2L/h0UAD+w9t1o9TUXyEP/nkLlvPuh1d9i4ByCvo9hYAMeBGYBMwEP8PsBjv0J8BIwBZiBvk+GWEApZf7M34j+gIXAQ+hBpgt4Gsiz9r0AfCHoWBta45gFXA5sGeCce4Hzg96fA5Rbr09HDzBxQftr0QLPDnQCC4L23QG8NcB1/gz8a4B964DvWa/XAD8O2jfT6mtK0LaHgX9Yr28B/t7nfKuAz4U63wDXLwdagCa0gPsD4AAKAR+QFnTsz4CHrNdvoM24OX3ONxs98McFteGLfY5RaAFiR5uBjwjadx2wxnr9eWBP0L5k67P5Q/Sl2TpuNZBp7ROgFSgOOv5EYJ/1+sfAU8DcAc670npdBpwbtO9aoKJv34LePwT8dID2Hgs0Br3vvlfA34D7gBnRfvbMX+8/o4EZRoxS6kOl1OeVUjPQJq7pwF3W7lnA7yzzTxPgRA9YBeiBeO8Ap51Ob61kv7UtQINSqivofRuQip6lxwEH+3x2IOrRmloopln7AwSfczp6gGsd4DqzgEsC/bb6fkqfa/XS+gbgQqVUplJqllLqf5VSHuvaTqVUc59rF1ivv4DWZnaKyEYRuWAY1+lLDpBA/++gIOh9deCFUqrNejmYY8aFSqk09ARkAT0m11y0AHw36F69aG0H+BVaW3tJRMpE5NsDnH86w//eeyEiySJyr2UqdaMnAZkS2mPzW+jf8AbRXpCDafiGccQIMMOYUErtRM9sF1mbDgLXWYNw4M+hlHrH2jfQ+tQhtBAIMNPaNhR1aM2osM9nB+IV4ATLRNeNiBxvnePVoM3BZqsqYIqIpAxwnYNoDSy43ylKqZ8PcL6RcAjIskywwdeuBFBKlSqlLgemok1zT/Rp53CuX4/WZPt+B5WjbHPPRZV6Hf0b+XXQtTzAkUH3KkNphw+UUs1KqRuVUkXAx4BvBplLg6li8O+9DS0oA+QHvb4RmA+coLTp9VRrez+TqFKqWil1jVJqOlor/UPw2pohehgBZhgRIrJAtDPFDOt9Ido0uM465E/Ad0TkSGt/hohcYu17FsgXka9bi/hpInKCte8R4FYRyRWRHPR6yJAxVkq7iP8buM2aVR/BIGshSqlX0OasJ0U7lNhFZDnwT+CPSqnSAT63H9gE/EhEEkTkFPTgGuAfwMdE5BzrnEminU9COgaMBKXX694Bfmad92i01vVPABG5UkRylVJ+tPkRtMmxLzUM4Dxi3cfHgNut72UW8E2G8R0Mk7uAj4jIsVY77wd+KyJTrT4UiMg51usLLMcJAdxWX0L15zH0b22KdZ+/2mf/e8AV1vdxLnpdL0AaWog2iUgWej0tJCJySdD32IieCIRqj2GcMQLMMFKa0Yv560WkFS24tqNntCil/oPWAv5lmWa2oxfwsUxgH0EP/NVAKXCGdd6fogXE+8A2YLO1bTh8BW3KqkbP9B8c4viLgdfQZqsW9CD9F/oPgH25At13J3rA+1tghyVkPoF2OKhDa2Q3E75n7HL0mtYh4D/AD5VSL1v7zgV2iEgL2qHjMqVUe4hz/A74lOV1d3eI/V9Fr02VAW+h1/geCEfjlVJ16Pv1fWvTLWgz4Trrd/IKWiMCKLHetwBrgT+o0LFfP0KbDfehnSz+3mf/19C/tSbgf4D/Bu27C72+WI/+Db84SPOXoX/vLej13q8ppfYN3mPDeCBKmYKWBoPBYJh4GA3MYDAYDBMSI8AMBoPBMCExAsxgMBgMExIjwAwGg8EwIYmLdgPGisvlMl4oBoPBMMnJyMjoF6NnNDCDwWAwTEiMADMYDAbDhGTCmxCDycjIGNXnNm3aBMDSpUvD2ZwJj7kvoTH3pT/mnoTG3JfQDPe+uFyuQfcbDcxgMBgMExIjwAwGg8EwITECzGAwGAwTEiPADAaDwTAhMQLMYDAYDBMSI8AMBoPBMCExAsxgGA9aauE3R0L1tmi3xGCYNBgBZjCMB3U7wV0BFRuj3RKDYdJgBJjBMB601un/rsrotsNgmEQYAWYwjAet9fq/+1B022EwTCKMADMYxoNuAVYR3XYYDJMII8AMhvHAmBANhrBjBJjBMB4EBJi7EpQpYWcwhAMjwAyG8aCtQf/vagdPY3TbYjBMEowAMxjGg9Y6sCfq1y6zDmYwhAMjwAyG8aC1DvIX6ddusw7Wze6X4IFzwdcV7ZYYJiBGgBkMkaarA9pdMO0Y/d5oYD1s+TscWAstNdFuiWECYgSYwRBpAutfU48AW5yJBQvg98G+N/TrlurotsUwITECzGCINAEPxNQ8SJtuTIgBqt6D9ib9uqU2um0xTEjiot0Ag2HS02YFMafkQkaBiQULsPe1ntfNRgObkOx8Dhr2QuZMyCyEzFmQnA0i43J5I8AMhkgTyMKRkgPp06Hy3ei2J1YoWwO5C3SiY7MGNjF5+oaeCVqA+GTIKITpi+GT90b08kaAGQyRppcAK4APn9HBzOM0S41JOlrh4Ho44TptYjUCbOLh69TC66Qb4OhPQ9OBnj/XAZDIr1AZAWYwRJrWOu28kZQJGTPA16GFWmputFsWPfav1feh6AzYsxqajQCbcAQmZlNm6xCRQJjIOGKcOAyGSNNaB8k5WuNKL9DbDvekvmWvgT0BZp6onVuMBjbxaLUcb1KnRq0JURVgIvKAiNSKyPYB9ouI3C0ie0TkfRE5brzbaDCMmbYG7cABeg0MjCv93tdg5nJISDYCbKLSYnnXphymAgx4CDh3kP3nASXW37XAH8ehTQZDeGmt0+tfoE2IcHh7IjbXQO0ObT4ESLMEmElyPLHo1sCiZwqP6hqYUuoNEZk9yCGfAP6mlFLAOhHJFJFpSqmqUAdv2rRp1G3xer1s3LgROZwX1gdgLPd1MjPc+7LIWUFr5hHs27QJlJ/jbPHU7N5EpX3yGRSGc0+yKl6hCPjAm0fbpk3kNXZQ6Otgy9rX8CWkR76RUWAyPkP5ezYzA9i8uxJ/nHNU5xjqvpSUlAy6P9oa2FAUAAeD3ldY28KKx+Phq1+9nt/97ncoMwvspnjjD8jf83C0mzHhie9w0ZWYqd+Ijc6kHBI8ddFtVBRJr3+Xrvg02jLmAtCRmAVAvHd0g6AhOsR1NOKzJ+GPc0SvDVG78vAIpQ4NKGGWLl06qovce++9bN++ndraOn7zm98wdWr0bLoxg1L4XtDxSqO9r5OVwKxxWPel0wPPtpFXdCR5geO3FZGo2smeRPd12PdEKXh9O5ScxdJlJ+htOe2wBRbNyoGiyXNPYIS/lYnGfhuk5Y2qb8O9Ly6Xa9D9sS7AKoDCoPczgLCvfi9ZsoR77vkSp556DFOnxvotGSc8jdh97di7WqLdkolNwNU4OadnW3oBHFwXnfZEm/rd0HwIik7v2Zaap/8bV/qJRWttVD0QIfZNiE8Dn7W8EZcDroHWv8bKqacuYv78fGAdUM+6devo6jqMSzxYGdPtna1RbsgEJziNVICMAnBXgd8fnTZFk0D6qOIzerYFBJjxRJxYtNRF1QMRou9Line truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5fc3550>"
+       "<matplotlib.figure.Figure at 0x1fed51fb358>"
       ]
      },
      "metadata": {},
@@ -1387,12 +1382,12 @@
     }
    ],
    "source": [
-    "res = xs[:, 0] - filter_xs2[:, 0]\n",
-    "res1 = xs[:, 0] - filter_xs1[:, 0]\n",
+    "res2 = xs[:, 0] - data2.x[:, 0]\n",
+    "res1 = xs[:, 0] - data1.x[:, 0]\n",
     "\n",
     "plt.plot(res1, ls=\"--\", label='order 1')\n",
-    "plt.plot(res, label='order 2')\n",
-    "plot_residual_limits(ps2[:, 0])\n",
+    "plt.plot(res2, label='order 2')\n",
+    "plot_residual_limits(data2.P[:, 0, 0])\n",
     "set_labels('Second Order Position Residuals',\n",
     "           'meters', 'time (sec)')\n",
     "plt.legend();"
@@ -1416,7 +1411,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5fe4748>"
+       "<matplotlib.figure.Figure at 0x1fed5201f28>"
       ]
      },
      "metadata": {},
@@ -1424,12 +1419,12 @@
     }
    ],
    "source": [
-    "res = xs[:, 1] - filter_xs2[:, 1]\n",
-    "res1 = xs[:, 1] - filter_xs1[:, 1]\n",
+    "res2 = xs[:, 1] - data2.x[:, 1]\n",
+    "res1 = xs[:, 1] - data1.x[:, 1]\n",
     "\n",
-    "plt.plot(res, label='order 2')\n",
+    "plt.plot(res2, label='order 2')\n",
     "plt.plot(res1, ls='--', label='order 1')\n",
-    "plot_residual_limits(ps2[:, 1])\n",
+    "plot_residual_limits(data2.P[:, 1, 1])\n",
     "set_labels('Second Order Velocity Residuals', \n",
     "                      'meters/sec', 'time (sec)')\n",
     "plt.legend();"
@@ -1456,9 +1451,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5f76438>"
+       "<matplotlib.figure.Figure at 0x1fed5128128>"
       ]
      },
      "metadata": {},
@@ -1467,8 +1462,8 @@
    ],
    "source": [
     "kf2 = SecondOrderKF(R, 0, dt=1)\n",
-    "filter_xs2, ps2 = filter_data(kf2, zs)\n",
-    "plot_kf_output(xs, filter_xs2, zs)"
+    "data2 = filter_data(kf2, zs)\n",
+    "plot_kf_output(xs, data2.x, data2.z)"
    ]
   },
   {
@@ -1487,9 +1482,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5fdad68>"
+       "<matplotlib.figure.Figure at 0x1fed524f9b0>"
       ]
      },
      "metadata": {},
@@ -1499,7 +1494,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5fdaba8>"
+       "<matplotlib.figure.Figure at 0x1fed524fa20>"
       ]
      },
      "metadata": {},
@@ -1511,10 +1506,10 @@
     "xs500, zs500 = simulate_system(Q=Q, count=500)\n",
     "\n",
     "kf2 = SecondOrderKF(R, 0, dt=1)\n",
-    "filter_xs2, ps2 = filter_data(kf2, zs500)\n",
+    "data500 = filter_data(kf2, zs500)\n",
     "\n",
-    "plot_kf_output(xs500, filter_xs2, zs500)\n",
-    "plot_residuals(xs500[:, 0], filter_xs2[:, 0], ps2[:, 0], \n",
+    "plot_kf_output(xs500, data500.x, data500.z)\n",
+    "plot_residuals(xs500[:, 0], data500, 0, \n",
     "               'Second Order Position Residuals',\n",
     "               'meters') "
    ]
@@ -1537,9 +1532,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5fda7b8>"
+       "<matplotlib.figure.Figure at 0x1fed3fc4cc0>"
       ]
      },
      "metadata": {},
@@ -1549,7 +1544,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4c61828>"
+       "<matplotlib.figure.Figure at 0x1fed3fa6080>"
       ]
      },
      "metadata": {},
@@ -1561,10 +1556,10 @@
     "xs2000, zs2000 = simulate_system(Q=0.0001, count=2000)\n",
     "\n",
     "kf2 = SecondOrderKF(R, 0, dt=1)\n",
-    "filter_xs2, ps2 = filter_data(kf2, zs2000)\n",
+    "data2000 = filter_data(kf2, zs2000)\n",
     "\n",
-    "plot_kf_output(xs2000, filter_xs2, zs2000)\n",
-    "plot_residuals(xs2000[:, 0], filter_xs2[:, 0], ps2[:, 0], \n",
+    "plot_kf_output(xs2000, data2000.x, data2000.z)\n",
+    "plot_residuals(xs2000[:, 0], data2000, 0, \n",
     "               'Second Order Position Residuals',\n",
     "               'meters') "
    ]
@@ -1589,7 +1584,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5ebe978>"
+       "<matplotlib.figure.Figure at 0x1fed4fbb780>"
       ]
      },
      "metadata": {},
@@ -1623,7 +1618,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c6004160>"
+       "<matplotlib.figure.Figure at 0x1fed5415400>"
       ]
      },
      "metadata": {},
@@ -1675,9 +1670,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5fdab70>"
+       "<matplotlib.figure.Figure at 0x1fed51e64e0>"
       ]
      },
      "metadata": {},
@@ -1687,7 +1682,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4cce160>"
+       "<matplotlib.figure.Figure at 0x1fed51e6048>"
       ]
      },
      "metadata": {},
@@ -1699,10 +1694,10 @@
     "xs, zs = simulate_acc_system(R=R, Q=Q, count=80)\n",
     "\n",
     "kf2 = SecondOrderKF(R, Q, dt=1)\n",
-    "fxs2, ps2 = filter_data(kf2, zs)\n",
+    "data2 = filter_data(kf2, zs)\n",
     "\n",
-    "plot_kf_output(xs, fxs2, zs, aspect_equal=False)\n",
-    "plot_residuals(xs[:, 0], fxs2[:, 0], ps2[:, 0], \n",
+    "plot_kf_output(xs, data2.x, data2.z, aspect_equal=False)\n",
+    "plot_residuals(xs[:, 0], data2, 0, \n",
     "               'Second Order Position Residuals',\n",
     "               'meters') "
    ]
@@ -1723,9 +1718,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c3ff5e80>"
+       "<matplotlib.figure.Figure at 0x1fed3fcee80>"
       ]
      },
      "metadata": {},
@@ -1735,7 +1730,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c3ff5ba8>"
+       "<matplotlib.figure.Figure at 0x1fed5402a58>"
       ]
      },
      "metadata": {},
@@ -1744,10 +1739,10 @@
    ],
    "source": [
     "kf3 = FirstOrderKF(R, Q * 10, dt=1)\n",
-    "fxs3, ps3 = filter_data(kf3, zs)\n",
+    "data3= filter_data(kf3, zs)\n",
     "\n",
-    "plot_kf_output(xs, fxs3, zs, aspect_equal=False)\n",
-    "plot_residuals(xs[:, 0], fxs3[:, 0], ps3[:, 0], \n",
+    "plot_kf_output(xs, data3.x, data3.z, aspect_equal=False)\n",
+    "plot_residuals(xs[:, 0], data3, 0, \n",
     "               'First Order Position Residuals',\n",
     "               'meters') "
    ]
@@ -1766,9 +1761,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4cce0f0>"
+       "<matplotlib.figure.Figure at 0x1fed56830b8>"
       ]
      },
      "metadata": {},
@@ -1778,7 +1773,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5e73e10>"
+       "<matplotlib.figure.Figure at 0x1fed55f9358>"
       ]
      },
      "metadata": {},
@@ -1787,10 +1782,10 @@
    ],
    "source": [
     "kf4 = FirstOrderKF(R, Q * 10000, dt=1)\n",
-    "fxs4, ps4 = filter_data(kf4, zs)\n",
+    "data4 = filter_data(kf4, zs)\n",
     "\n",
-    "plot_kf_output(xs, fxs4, zs, aspect_equal=False)\n",
-    "plot_residuals(xs[:, 0], fxs4[:, 0], ps4[:, 0], \n",
+    "plot_kf_output(xs, data4.x, data4.z, aspect_equal=False)\n",
+    "plot_residuals(xs[:, 0], data4, 0, \n",
     "               'First Order Position Residuals',\n",
     "               'meters') "
    ]
@@ -1912,7 +1907,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c3ff5f98>"
+       "<matplotlib.figure.Figure at 0x1fed3194208>"
       ]
      },
      "metadata": {},
@@ -1967,7 +1962,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Detecting and Rejecting Bad Measurement\n",
+    "## Detecting and Rejecting Bad Measurement\n",
     "\n",
     "The Kalman filter provides no way to detect and reject a bad measurement. Suppose you are tracking aircraft, and you receive a measurement 100km away from the aircraft's current position. If you call update with that value the new estimate will be skewed radically towards the measurement.\n",
     "\n",
@@ -2011,7 +2006,6 @@
     "kf.update(z)\n",
     "p1 = kf.x[0:2]\n",
     "\n",
-    "\n",
     "# compute error of measurement from prior\n",
     "y = np.abs(z - np.dot(kf.H, prior))\n",
     "dist = np.linalg.norm(y)\n",
@@ -2035,7 +2029,7 @@
     "\n",
     "What can we do to avoid this? Our first thought might be to just add a check if the prior is far from the measurement. Why the prior and not the current estimate? Because after the update the estimate could now be quite close to the bad measurement, although it isn't in this case.\n",
     "\n",
-    "Note that while I could have just written `prior[0:2] - z` to get the error, I used the mathematically correct $\\mathbf z - \\mathbf{Hx}$ to compute the error. This is just for illustration; the Kalman filter class stores the innovation in `KalmanFilter.y` since you will normally be interested in this value. I use it instead of the value I computed above to illustrate this:"
+    "Note that while I could have just written `prior[0:2] - z` to get the error, I used the mathematically correct $\\mathbf z - \\mathbf{Hx}$ to compute the error. This is just for illustration; the Kalman filter class stores the innovation in `KalmanFilter.y`. I use it instead of the value I computed above to illustrate this:"
    ]
   },
   {
@@ -2085,7 +2079,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4d4b7b8>"
+       "<matplotlib.figure.Figure at 0x1fed3e65748>"
       ]
      },
      "metadata": {},
@@ -2115,7 +2109,7 @@
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5fe3390>"
+       "<matplotlib.figure.Figure at 0x1fed3f23f98>"
       ]
      },
      "metadata": {},
@@ -2186,7 +2180,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c60ac860>"
+       "<matplotlib.figure.Figure at 0x1fed50e1d68>"
       ]
      },
      "metadata": {},
@@ -2363,10 +2357,10 @@
    "source": [
     "from scipy.linalg import inv\n",
     "\n",
-    "def NEES(xs, est_xs, ps):\n",
+    "def NEES(xs, est_xs, Ps):\n",
     "    est_err = xs - est_xs\n",
     "    err = []\n",
-    "    for x, p in zip(est_err, ps):\n",
+    "    for x, p in zip(est_err, Ps):\n",
     "        err.append(np.dot(x.T, inv(p)).dot(x))\n",
     "    return err"
    ]
@@ -2410,7 +2404,9 @@
     "```python\n",
     "from filterpy.stats import NESS\n",
     "```\n",
-    "This is the best measure of the filter's performance, and should be used whenever possible. However, if your simulation is of limited fidelity then you need to use another approach."
+    "This is an excellent measure of the filter's performance, and should be used whenever possible, especially in production code when you need to evaluate the filter while it is running. While designing a filter I still prefer plotting the residauls as it gives me a more intuitive understand what is happening.\n",
+    "\n",
+    "However, if your simulation is of limited fidelity then you need to use another approach."
    ]
   },
   {
@@ -2447,7 +2443,9 @@
     "likelihood = multivariate_normal.pdf(z.flatten(), mean=hx, cov=S)\n",
     "```\n",
     "\n",
-    "This is computed for you when `update()` is called, and is accessible via the `likelihood` data attribute. Let's look at this: I'll run the filter with several measurements within expected range, and then inject measurements far from the expected values:"
+    "In practice it happens a bit differently. Likelihoods can be difficult to deal with mathmatically. It is common to compute and use the *log-likelihood* instead, which is just the natural log of the likelihood. This has several benefits. First, the log is strictly increasing, and it reaches it's maximum value at the same point of the function it is applied to. If you want to find the maximum of a function you normally take the derivative of it; it can be difficult to find the derivative of some arbitrary function, but finding $\\frac{d}{dx} log(f(x))$ is trivial, and the result is the same as $\\frac{d}{dx} f(x)$. We don't use this property in this book, but it is essential when performing analysis on filters.\n",
+    "\n",
+    "The likelihood and log-likelihood are computed you when `update()` is called, and is accessible via the 'log_likelihood' and `likelihood` data attribute. Let's look at this: I'll run the filter with several measurements within expected range, and then inject measurements far from the expected values:"
    ]
   },
   {
@@ -2459,7 +2457,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4d3c240>"
+       "<matplotlib.figure.Figure at 0x1fed52c4208>"
       ]
      },
      "metadata": {},
@@ -2472,12 +2470,9 @@
     "zs[-5:-1] = [100, 200, 200, 200] # bad measurements, bad!\n",
     "\n",
     "kf = SecondOrderKF(R, Q, dt=1, P=1)\n",
-    "likelihoods = []\n",
-    "for z in zs:\n",
-    "    kf.predict() \n",
-    "    kf.update(z)\n",
-    "    likelihoods.append(kf.likelihood)\n",
-    "plt.plot(likelihoods);"
+    "s = Saver(kf)\n",
+    "kf.batch_filter(zs, saver=s)\n",
+    "plt.plot(s.likelihood);"
    ]
   },
   {
@@ -2486,7 +2481,7 @@
    "source": [
     "The likelihood gets larger as the filter converges during the first few iterations. After that the likelihood bounces around until it reaches the bad measurements, at which time it goes to zero, indicating that if the measurements are valid the filter is very unlikely to be optimal.\n",
     "\n",
-    "The *log likelihood* is somewhat more convienent to use in some cases. By taking the log we guarantee that the function is monotonically increasing, and its maximum values occur at the same points as the function itself. See how starkly it illustrates where the filter goes 'bad'."
+    "See how starkly the log-likelihood illustrates where the filter goes 'bad'."
    ]
   },
   {
@@ -2498,7 +2493,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5df72b0>"
+       "<matplotlib.figure.Figure at 0x1fed505ab38>"
       ]
      },
      "metadata": {},
@@ -2506,13 +2501,7 @@
     }
    ],
    "source": [
-    "kf = SecondOrderKF(R, Q, dt=1, P=1)\n",
-    "log_likelihoods = []\n",
-    "for z in zs:\n",
-    "    kf.predict() \n",
-    "    kf.update(z)\n",
-    "    log_likelihoods.append(kf.log_likelihood)\n",
-    "plt.plot(log_likelihoods);"
+    "plt.plot(s.log_likelihood);"
    ]
   },
   {
@@ -2579,9 +2568,9 @@
     },
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5df71d0>"
+       "<matplotlib.figure.Figure at 0x1fed5394ba8>"
       ]
      },
      "metadata": {},
@@ -2671,14 +2660,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "fusion std: 0.391\n"
+      "fusion std: 1.647\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5ebd358>"
+       "<matplotlib.figure.Figure at 0x1fed55d0898>"
       ]
      },
      "metadata": {},
@@ -2701,32 +2690,22 @@
     "    kf.P *= 100\n",
     "    kf.R[0, 0] = wheel_sigma**2\n",
     "    kf.R[1, 1] = ps_sigma**2 \n",
+    "    s = Saver(kf)\n",
     "\n",
     "    random.seed(1123)\n",
-    "    xs, zs, nom = [], [], []\n",
     "    for i in range(1, 100):\n",
     "        m0 = i + randn()*wheel_sigma\n",
     "        m1 = i + randn()*ps_sigma\n",
-    "        z = array([[m0], [m1]])\n",
-    "\n",
     "        kf.predict()\n",
-    "        kf.update(z)\n",
-    "\n",
-    "        xs.append(kf.x.T[0])\n",
-    "        zs.append(z.T[0])\n",
-    "        nom.append(i)\n",
-    "\n",
-    "    xs = array(xs)\n",
-    "    zs = array(zs)\n",
-    "    nom = array(nom)\n",
-    "    \n",
-    "    res = nom - xs[:, 0]\n",
-    "    print('fusion std: {:.3f}'.format(np.std(res)))\n",
+    "        kf.update(array([[m0], [m1]]))\n",
+    "        s.save()\n",
+    "    s.to_array()\n",
+    "    print('fusion std: {:.3f}'.format(np.std(s.y[:, 0])))\n",
     "    if do_plot:\n",
     "        ts = np.arange(0.1, 10, .1)\n",
-    "        plot_measurements(ts, zs[:, 0], label='Wheel')\n",
-    "        plt.plot(ts, zs[:, 1], ls='--', label='Pos Sensor')\n",
-    "        plot_filter(ts, xs[:, 0], label='Kalman filter')\n",
+    "        plot_measurements(ts, s.z[:, 0], label='Wheel')\n",
+    "        plt.plot(ts, s.z[:, 1], ls='--', label='Pos Sensor')\n",
+    "        plot_filter(ts, s.x[:, 0], label='Kalman filter')\n",
     "        plt.legend(loc=4)\n",
     "        plt.ylim(0, 100)\n",
     "        set_labels(x='time (sec)', y='meters')\n",
@@ -2746,7 +2725,7 @@
     "\n",
     "I will position the target at (100, 100). The first radar will be at (50, 50), and the second radar at (150, 50). This will cause the first radar to measure a bearing of 45 degrees, and the second will report 135 degrees.\n",
     "\n",
-    "I will create the Kalman filter first, and then plot its initial covariance matrix. I am using a **unscented Kalman filter**."
+    "I will create the Kalman filter first, and then plot its initial covariance matrix. I am using an **unscented Kalman filter**, which is covered in a later chapter."
    ]
   },
   {
@@ -2760,7 +2739,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4cc0588>"
+       "<matplotlib.figure.Figure at 0x1fed556d160>"
       ]
      },
      "metadata": {},
@@ -2795,7 +2774,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5f419b0>"
+       "<matplotlib.figure.Figure at 0x1fed556d390>"
       ]
      },
      "metadata": {},
@@ -2846,7 +2825,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5fa9208>"
+       "<matplotlib.figure.Figure at 0x1fed54770b8>"
       ]
      },
      "metadata": {},
@@ -2931,9 +2910,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5eaad30>"
+       "<matplotlib.figure.Figure at 0x1fed4fb1470>"
       ]
      },
      "metadata": {},
@@ -2941,9 +2920,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c601b5c0>"
+       "<matplotlib.figure.Figure at 0x1fed55caa20>"
       ]
      },
      "metadata": {},
@@ -2951,9 +2930,9 @@
     },
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c3ffd4a8>"
+       "<matplotlib.figure.Figure at 0x1fed529e208>"
       ]
      },
      "metadata": {},
@@ -2980,11 +2959,11 @@
     "kf2 = SecondOrderKF(R, Q, dt=1)\n",
     "\n",
     "# Filter measurements\n",
-    "fxs0, ps0 = filter_data(kf0, zs)\n",
+    "fxs0, ps0, _, _ = kf0.batch_filter(zs)\n",
     "\n",
-    "# filter twice more, using P as error\n",
-    "fxs1, ps1, _, _ = kf1.batch_filter(fxs0[:, 0], ps0[:, 0])\n",
-    "fxs2, _, _, _ = kf2.batch_filter(fxs1[:, 0], ps1[:, 0, 0])\n",
+    "# filter twice more, using the state as the input\n",
+    "fxs1, ps1, _, _ = kf1.batch_filter(fxs0[:, 0])\n",
+    "fxs2, _, _, _ = kf2.batch_filter(fxs1[:, 0])\n",
     "\n",
     "plot_kf_output(xs, fxs0, zs, 'KF', False)\n",
     "plot_kf_output(xs, fxs1, zs, '1 iteration', False)\n",
@@ -3008,9 +2987,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5f4fc18>"
+       "<matplotlib.figure.Figure at 0x1fed5504780>"
       ]
      },
      "metadata": {},
@@ -3018,9 +2997,9 @@
     },
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5f4f9b0>"
+       "<matplotlib.figure.Figure at 0x1fed54779e8>"
       ]
      },
      "metadata": {},
@@ -3028,9 +3007,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4d00c50>"
+       "<matplotlib.figure.Figure at 0x1fed3e99f28>"
       ]
      },
      "metadata": {},
@@ -3087,8 +3066,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "fusion std: 0.391\n",
-      "fusion std: 0.438\n"
+      "fusion std: 1.647\n",
+      "fusion std: 1.647\n"
      ]
     }
    ],
@@ -3134,9 +3113,9 @@
     },
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4cc0eb8>"
+       "<matplotlib.figure.Figure at 0x1fed5277470>"
       ]
      },
      "metadata": {},
@@ -3281,9 +3260,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5ed8470>"
+       "<matplotlib.figure.Figure at 0x1fed548d828>"
       ]
      },
      "metadata": {},
@@ -3479,7 +3458,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c61670f0>"
+       "<matplotlib.figure.Figure at 0x1fed55cec88>"
       ]
      },
      "metadata": {},
@@ -3741,7 +3720,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4cb1518>"
+       "<matplotlib.figure.Figure at 0x1fed3f63080>"
       ]
      },
      "metadata": {},
@@ -3863,7 +3842,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4dd22b0>"
+       "<matplotlib.figure.Figure at 0x1fed4fb1588>"
       ]
      },
      "metadata": {},
@@ -4006,7 +3985,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c4cbd9e8>"
+       "<matplotlib.figure.Figure at 0x1fed56350b8>"
       ]
      },
      "metadata": {},
@@ -4075,7 +4054,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c60f9898>"
+       "<matplotlib.figure.Figure at 0x1fed560ce80>"
       ]
      },
      "metadata": {},
@@ -4085,7 +4064,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c5f93c88>"
+       "<matplotlib.figure.Figure at 0x1fed54b7ba8>"
       ]
      },
      "metadata": {},
@@ -4147,7 +4126,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c6196630>"
+       "<matplotlib.figure.Figure at 0x1fed53ecb00>"
       ]
      },
      "metadata": {},
@@ -4157,7 +4136,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAD1CAYAAABz79PWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3XmcjWX/wPHPNYsZMxiMXbIUyTqE0GOJnhYx9KCiXzGUVk9ST7SplFSUFtOmslUiLcaSQkmlZAo1skyyVCY0OJYxZrt+f1znzIwxy5k5y32W7/v1Oq/7rPf9veecOd9z7UprjRBCCOEuIVYHIIQQIrBIYhFCCOFWkliEEEK4lSQWIYQQbiWJRQghhFuFeWrHNptNupsJIUSAi4mJUUXvkxKLEEIIt5LEIoQQwq08VhVWWExMTLlfk5ycDECnTp3cHY7l5Nz8k5ybf5Jzcz+bzVbq41JiEUII4VaSWIQQQriVV6rCCtNac/jwYfLy8kp9XpUqVQA4dOiQN8LyKn87t5CQEGrWrIlSZ3X+EEKIs3g9sRw+fJjo6GgiIyNLfV5UVBQA0dHR3gjLq/zt3DIzMzl8+DCxsbFWhyKE8ANerwrLy8srM6kI3xIZGVlmCVMIIRykjUUIIYRbBWViUUpx44035t/Oycmhdu3a9O/f38KovGfPnj289957VochhAhQQZlYoqOjSUlJ4dSpUwCsWrWKhg0bWhJLTk6O148piUVYJiMDEhNh8mSzzciwOiLhAUGZWACuuuoqli9fDsCCBQsYNmxY/mMnT55k1KhRdO7cmQ4dOrBkyRLAfCH36NGDjh070rFjR9avXw9AWloaPXv2JC4ujjZt2vD1118DBb2/ABYvXszIkSMBuPXWW5k4cSKXXnopEyZMKPF4c+bMYdCgQQwYMICmTZsyc+ZMnn/+eTp06EDXrl05fPgwALt27eLKK6/koosuokePHmzfvh2AkSNH8t///pfu3bvTrFkzFi9eDMDEiRP5+uuviYuLY8aMGWzdupUuXboQFxdHu3btSE1N9dSfXQS72bMhLQ3y8sx29myrIxIeUGavMKVUJLAOiLA/f7HW+lGl1BygF+AYgjlSa73Z7RFmZJgPX3o6xMZCQgLYe1W54vrrr2fy5Mn079+fn3/+mVGjRuUnhClTptCnTx/efvttjh49SpcuXbjsssuoU6cOq1atIjIyktTUVIYNG0ZycjLvvfceV1xxBQ899BC5ublkOPEr7LfffmP16tWEhoby4IMPFns8gJSUFDZt2kRmZibnn38+zzzzDJs2beKee+5h3rx5jBs3jjFjxvDaa6/RvHlzNmzYwB133MEXX3wBmKT3zTffsH37duLj4xkyZAhPP/0006dPZ9myZQCMHTuWu+++mxtuuIGsrCxyc3Nd/vsKUaz0dAizf+2EhZnbIuA40934NNBHa31CKRUOfKOU+tT+2P+01os9Fx4Fv3DCwgp+4dx5p8u7bdeuHXv27GHBggX069fvjMc+//xzkpKSmD59OmC62+7bt48GDRpw1113sXnzZkJDQ9m5cycAnTt3ZtSoUWRnZzNo0CDi4uLKPP4111xDaGhoqccDuPTSS6latSpVq1YlJiaGAQMGANC2bVt+/vlnTpw4wfr16xk6dGj+vk+fPp1/fdCgQYSEhNCqVSsOHDhQbCzdunVjypQp/Pnnn/znP/+hefPmTv0NhSi3qlXhs8/g9GmIiIArrrA6IuEBZSYWrbUGTthvhtsv5ZoS3zGfDZjqoahylDiyHMXmrCxzR1oa2SdPlufwxTp58iRXXnkl9913HytWrODw4cPk5uZy8uRJcnNzmT9/Pi1atDjjNVOmTKFmzZqsX7+evLw8YmNjOXnyJBdddBErV65k5cqV3HDDDYwbN47hw4ejlOKkPVabzUZOTk7+7aioqPzrJR1v3bp1hISE5D9PKZW/j+zsbE6dOsXx48eJiYnh22+/Pev8cnJy0Frnv95x/dSpU/nnCjBw4EDatm3LypUrufzyy5k5cya9e/c+Y3/p6ens3bvX6b9v4fc80Mi5VVydP/6gms1GSFYWeZUqceyPPzjopb+nvG/uU9aPT6faWJRSoUqpzcBBYJXWeoP9oSlKqZ+VUjOUUhGuhVo8XbMmOBq4c3LMbTe56aabmDBhAm3atDnj/ssuu4zXXnsNk1Nhy5YtABw7dox69eoREhLCggUL8quM9u3bR+3atUlISGDEiBFs3mxqBOvUqcP27dvJy8sjKSmpxDhKOp4zqlWrRpMmTfjoo48Akzx++eWXUl9TtWpVjh8/nn979+7dNG3alDvuuIN+/fqxdetWp48vRHmEnjxJRtu2nLjoIjLatiXUDT8She9xauS91joXiFNKVQc+Vkq1AR4A/gYqAW8AE4DJxb2+8Mybhw4dcmrEuePXdMRtt53VxhLhhjaW6OhoWrRowf333w9A5cqVCQ0NJTo6msmTJzNu3Di6deuG1pomTZqwbNky7r77bgYPHsySJUu49NJLiY6OJjo6mh9++IFp06YRHh5OlSpVmDdvHtHR0TzzzDNce+21NGrUiDZt2nDixIkzzt1xvaTjRUREEB4env88pVT+MQs/tmDBAm6//XamT59OdnY2119/PV27diUsLIzIyMizjnnxxRcTERFB9+7dGTlyJJmZmbzzzjuEh4dTr149nnjiibPeo9jYWFq2bFnm31VmkvVDGRnsfewxwmw2GrZr57Z2zGJt2FBQtZ2TA/Xr09DDf8+Afd/w3dmNleNXsrOUUo8CJ7XW0wvd1xu4T2udPxCk8AqShafNP3ToELVr1y7zOI7E4i/TnpSHP56bs++b/BP7ocRE9v/0E4SG0qBOHahf3/l2zPJ2rvFQZ5zSBOz7hm8kluJWkHSmV1htIFtrfVQpVRm4DHhGKVVfa52mzMyEg4AU94UthPCa9HSwdyQpd0+t8nauiYpyS+cb4ducqQqrD8xVSoVi2mQWaa2XKaW+sCcdBWwGbvNgnEL4Hwt+nVdIbCzs3WuSi716ymnSfVgUw5leYT8DHYq5v49HIhLCE6z4kvdQV3m3S0gge98+wo4eNUklIcH518bGntVmIoTXp80XwhJWfMm7+mveW8kwKopD9nFQ5W5IT0goiNGZpFTGOR05Ym5GeKSPqfAWSSwiOFhRZePEr/mDB+HWW+Gnn6B6dahRo2BbY+9OGoc14ZrWWZyb5aMlnvK2mcyejd6fxu/Ha7NtazW2r97E9thL2LEDtm+Hf/4xSaVrV+jVy1y6dYPKlT13CsL9JLGI4GBFlU0Zv+aTk+Gaa+DPP81t+2QLhcQBcYxbdTU9zt3L8HYpDLkOatVy7vCnTsGLL8K2bVCzpvkT1KxZcGnVCs45x9WTdI7W8MMP8OGCDnz06zB2HSl+PFp0NJw8CV99ZS4AlSqZv9PDD0ORIWfCRwVlYqlSpQonTpjJBFasWMHdd9/NmjVrOPfcc4t9/pw5c0hOTmbmzJlei3HYsGFs3bqVhIQEjhw5Qs+ePbnsssvo3bs306dPp1OnTjz11FM8+OCDXovJr5W3ysYdSvk1P3euKamcPg3du8Orr0JurqkKclyOLv+WH3ZUY2lqS77e15iv9zVmbH0zC8rw4RAfD4XmOT3DF1+Y/f/2W8nhhYXBxx+Dp1aLyM2Fb76Bjz4yF5NAuwNQK+okHeqm0fLcDC4Y2o6WLaFlS2jQAA4fhq+/hrVrTXLZsgUWLjSXwYPhkUegfXvPxCzcIygTi8OaNWsYO3Ysn3/+eYlJxQp///0369evL3MKlYokltzc3Pw5yoKKj3Rzzc6Ge++Fl182t2+7zZQqKlUq5snDO8Ds2Rzfv5xP/ryI99L6sOqLUJYvh+XLzSnFx5skc8UVZh/p6XDffTBnjtlF69Zw112mFHD4cMFl3z74/nu47jpYt85953fwoNnv8uXwySfmtsM558B/BmQzOGIpl8RsJbR2TXsby5n7iI2FQYPMBeCPP+DZZ2HWLPjwQ3MZONAkmIsucl/swo201h65HD16VDsuhR08eFA748SJE/rEiRNOPbe8oqOj9bp163TTpk31tm3b8u9PSkrSXbp00XFxcbpv377677//1lprPXv2bH3nnXdqrbUeMWKEvu2223Tv3r1106ZN9dq1a3VCQoJu2bKlHjFiRP6+brvtNn3RRRfpVq1a6UmTJuXf37hxYz1x4kTdvn173aZNmzOO79C2bVsdGRmp27dvr9etW6dHjBihP/jgA6211r169dIbN27UEyZM0CEhIbp9+/Z6+PDhWmut58+frzt37qzbt2+vx4wZo3NycvLP95FHHtFdunTRX3/9dYX+Zs6+bxs3btQbN26s0DF8navnduCA1r16aQ1ah4dr/cYb5d/HwYNaJyZqfcklZj+OS40aWt90k9a1a5vbERFaP/mk1qdPF7+fvDzzfNC6Xj2tlyzZUu5zy8rS+scftZ45U+sbbtC6WbMzYwKtzztP6/vv1/r777XOzS3/+Rb2119ajxundWRkwf6vvtrsuzTymXS/It/vZ33/W55Yin4QXb04IywsTNeoUUNv2bLljPsPHz6s8/LytNZaz5o1S48fP15rfXZiue6663ReXp7+5JNPdNWqVfXPP/+sc3NzdceOHfWmTZu01lqnp6drrbXOycnRvXr1yj9W48aN9bRp0/SJEyd0YmKiHj169Fnx7d69W7du3Tr/dnGJRWuTMBx+/fVX3b9/f52VlaW11vr222/Xc+fOtf+N0QsXLnTuj1MCSSyundvGjVo3amQ+o/Xra71+vevx7N6t9dSpWrdte+b/QO/eWu/YUfbrT5/Wuk8f85qmTTN0UtKWsl+ktf7iC6379tW6cuWz//+iokzynDRJ682bTQJzt7Q0re+7zxzLcdz4eK0zMop/vnwm3a+sxBKUVWHh4eF0796dt956ixdffDH//j///JPrrruOtLQ0srKyaNqLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x214c625d6a0>"
+       "<matplotlib.figure.Figure at 0x1fed57e0ba8>"
       ]
      },
      "metadata": {},

book_format.py

@@ -1,129 +1,129 @@
-# -*- coding: utf-8 -*-
-
-"""Copyright 2015 Roger R Labbe Jr.
-
-
-Code supporting the book
-
-Kalman and Bayesian Filters in Python
-https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
-
-
-This is licensed under an MIT license. See the LICENSE.txt file
-for more information.
-"""
-
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-from contextlib import contextmanager
-from IPython.core.display import HTML
-import json
-import matplotlib
-import matplotlib.pylab as pylab
-import matplotlib.pyplot as plt
-import numpy as np
-import os.path
-import sys
-import warnings
-from kf_book.book_plots import set_figsize, reset_axis
-
-# version 1.4.3 of matplotlib has a bug that makes
-# it issue a spurious warning on every plot that
-# clutters the notebook output
-if matplotlib.__version__ == '1.4.3':
-    warnings.simplefilter(action="ignore", category=FutureWarning)
-
-try:
-    matplotlib.style.use('default')
-except:
-    pass
-
-def test_filterpy_version():
-
-    import filterpy
-    from distutils.version import LooseVersion
-
-    v = filterpy.__version__
-    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"
-                       "   ex: pip install filterpy --upgrade".format(
-             min_version))
-
-
-# ensure that we have the correct filterpy loaded. This is
-# called when this module is imported at the top of each book
-# chapter so the reader can see that they need to update FilterPy.
-test_filterpy_version()
-
-pylab.rcParams['figure.max_open_warning'] = 50
-pylab.rcParams['figure.figsize'] = 8, 3
-
-
-
-@contextmanager
-def numpy_precision(precision):
-	old = np.get_printoptions()['precision']
-	np.set_printoptions(precision=precision)
-	yield
-	np.set_printoptions(old)
-
-@contextmanager
-def printoptions(*args, **kwargs):
-    original = np.get_printoptions()
-    np.set_printoptions(*args, **kwargs)
-    yield
-    np.set_printoptions(**original)
-
-def _decode_list(data):
-    rv = []
-    for item in data:
-        if isinstance(item, unicode):
-            item = item.encode('utf-8')
-        elif isinstance(item, list):
-            item = _decode_list(item)
-        elif isinstance(item, dict):
-            item = _decode_dict(item)
-        rv.append(item)
-    return rv
-
-def _decode_dict(data):
-    rv = {}
-    for key, value in data.iteritems():
-        if isinstance(key, unicode):
-            key = key.encode('utf-8')
-        if isinstance(value, unicode):
-            value = value.encode('utf-8')
-        elif isinstance(value, list):
-            value = _decode_list(value)
-        elif isinstance(value, dict):
-            value = _decode_dict(value)
-        rv[key] = value
-    return rv
-
-
-def load_style(directory = '.', name='kf_book/custom.css'):
-        version = [int(version_no) for version_no in matplotlib.__version__.split('.')]
-
-        try:
-            if sys.version_info[0] >= 3:
-                style = json.load(open(os.path.join(directory, "kf_book/538.json")))
-            else:
-                style = json.load(open(directory + "/kf_book/538.json"), object_hook=_decode_dict)
-            plt.rcParams.update(style)
-        except:
-            pass
-        set_figsize()
-        reset_axis ()
-        np.set_printoptions(suppress=True, precision=3, 
-		                    threshold=10000., linewidth=70,
-                            formatter={'float':lambda x:' {:.3}'.format(x)})
-        styles = open(os.path.join(directory, name), 'r').read()
-        set_figsize()
-        # I don't know why I have to do this, but I have to call
-        # with suppress a second time or the notebook doesn't suppress
-        # exponents
-        np.set_printoptions(suppress=True)
-        return HTML(styles)
+# -*- coding: utf-8 -*-
+
+"""Copyright 2015 Roger R Labbe Jr.
+
+
+Code supporting the book
+
+Kalman and Bayesian Filters in Python
+https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
+
+
+This is licensed under an MIT license. See the LICENSE.txt file
+for more information.
+"""
+
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+from contextlib import contextmanager
+from IPython.core.display import HTML
+import json
+import matplotlib
+import matplotlib.pylab as pylab
+import matplotlib.pyplot as plt
+import numpy as np
+import os.path
+import sys
+import warnings
+from kf_book.book_plots import set_figsize, reset_axis
+
+# version 1.4.3 of matplotlib has a bug that makes
+# it issue a spurious warning on every plot that
+# clutters the notebook output
+if matplotlib.__version__ == '1.4.3':
+    warnings.simplefilter(action="ignore", category=FutureWarning)
+
+try:
+    matplotlib.style.use('default')
+except:
+    pass
+
+def test_filterpy_version():
+
+    import filterpy
+    from distutils.version import LooseVersion
+
+    v = filterpy.__version__
+    min_version = "1.3.2"
+    if LooseVersion(v) < LooseVersion(min_version):
+       raise Exception("Minimum FilterPy version supported is {}.\n"
+                       "Please install a more recent version.\n"
+                       "   ex: pip install filterpy --upgrade".format(
+             min_version))
+
+
+# ensure that we have the correct filterpy loaded. This is
+# called when this module is imported at the top of each book
+# chapter so the reader can see that they need to update FilterPy.
+test_filterpy_version()
+
+pylab.rcParams['figure.max_open_warning'] = 50
+pylab.rcParams['figure.figsize'] = 8, 3
+
+
+
+@contextmanager
+def numpy_precision(precision):
+	old = np.get_printoptions()['precision']
+	np.set_printoptions(precision=precision)
+	yield
+	np.set_printoptions(old)
+
+@contextmanager
+def printoptions(*args, **kwargs):
+    original = np.get_printoptions()
+    np.set_printoptions(*args, **kwargs)
+    yield
+    np.set_printoptions(**original)
+
+def _decode_list(data):
+    rv = []
+    for item in data:
+        if isinstance(item, unicode):
+            item = item.encode('utf-8')
+        elif isinstance(item, list):
+            item = _decode_list(item)
+        elif isinstance(item, dict):
+            item = _decode_dict(item)
+        rv.append(item)
+    return rv
+
+def _decode_dict(data):
+    rv = {}
+    for key, value in data.iteritems():
+        if isinstance(key, unicode):
+            key = key.encode('utf-8')
+        if isinstance(value, unicode):
+            value = value.encode('utf-8')
+        elif isinstance(value, list):
+            value = _decode_list(value)
+        elif isinstance(value, dict):
+            value = _decode_dict(value)
+        rv[key] = value
+    return rv
+
+
+def load_style(directory = '.', name='kf_book/custom.css'):
+        version = [int(version_no) for version_no in matplotlib.__version__.split('.')]
+
+        try:
+            if sys.version_info[0] >= 3:
+                style = json.load(open(os.path.join(directory, "kf_book/538.json")))
+            else:
+                style = json.load(open(directory + "/kf_book/538.json"), object_hook=_decode_dict)
+            plt.rcParams.update(style)
+        except:
+            pass
+        set_figsize()
+        reset_axis ()
+        np.set_printoptions(suppress=True, precision=3, 
+		                    threshold=10000., linewidth=70,
+                            formatter={'float':lambda x:' {:.3}'.format(x)})
+        styles = open(os.path.join(directory, name), 'r').read()
+        set_figsize()
+        # I don't know why I have to do this, but I have to call
+        # with suppress a second time or the notebook doesn't suppress
+        # exponents
+        np.set_printoptions(suppress=True)
+        return HTML(styles)

kf_book/book_plots.py

@@ -19,13 +19,14 @@ from __future__ import (absolute_import, division, print_function,
 
 
 from contextlib import contextmanager
+import sys
+import time
+import ipywidgets
 import matplotlib as mpl
 import matplotlib.pylab as pylab
 import matplotlib.pyplot as plt
 from matplotlib.patches import Circle
 import numpy as np
-import sys
-import time
 
 try:
     import seabornee
@@ -363,6 +364,22 @@ def plot_kf_output(xs, filter_xs, zs, title=None, aspect_equal=True):
     plt.xlim((-1, len(xs)))
     plt.show()
 
+    
+def FloatSlider(value, **kwargs):
+    """ 
+    Creates an ipwidgets FloatSlider with continuous update
+    turned off
+    """
+    return ipywidgets.FloatSlider(value, continuous_update=False, **kwargs)
+
+
+def IntSlider(value, **kwargs):
+    """ 
+    Creates an ipwidgets IntSlider with continuous update
+    turned off
+    """
+    return ipywidgets.IntSlider(value, continuous_update=False, **kwargs)
+
 
 def plot_measurements(xs, ys=None, dt=None, color='k', lw=1, label='Measurements',
                       lines=False, **kwargs):
@@ -373,7 +390,7 @@ def plot_measurements(xs, ys=None, dt=None, color='k', lw=1, label='Measurements
         ys = xs
         xs = np.arange(0, len(ys)*dt, dt)
 
-    plt.autoscale(tight=True)
+    plt.autoscale(tight=False)
     if lines:
         if ys is not None:
             return plt.plot(xs, ys, color=color, lw=lw, ls='--', label=label, **kwargs)