Ran all notebooks to use new css settings.

05-Multivariate-Gaussians.ipynb

@@ -28,7 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -42,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -123,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -349,7 +348,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv0AAADxCAYAAABYmwhGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fd4c4b00>"
+       "<matplotlib.figure.Figure at 0x72c8240>"
       ]
      },
      "metadata": {},
@@ -471,7 +470,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAADaCAYAAACFFWQ1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fdd30e10>"
+       "<matplotlib.figure.Figure at 0x7dcbc88>"
       ]
      },
      "metadata": {},
@@ -511,9 +510,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fe587048>"
+       "<matplotlib.figure.Figure at 0x8037630>"
       ]
      },
      "metadata": {},
@@ -523,8 +522,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 7.78   3.043]\n",
-      " [ 3.043  2.439]]\n"
+      "[[ 7.775  2.981]\n",
+      " [ 2.981  2.278]]\n"
      ]
     }
    ],
@@ -554,9 +553,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fe593400>"
+       "<matplotlib.figure.Figure at 0x72c8400>"
       ]
      },
      "metadata": {},
@@ -566,8 +565,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 1.002  0.   ]\n",
-      " [ 0.     1.011]]\n"
+      "[[ 1.006 -0.002]\n",
+      " [-0.002  1.004]]\n"
      ]
     }
    ],
@@ -615,9 +614,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fe55db00>"
+       "<matplotlib.figure.Figure at 0x8057d68>"
       ]
      },
      "metadata": {},
@@ -766,7 +765,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fe53d048>"
+       "<matplotlib.figure.Figure at 0x8181e48>"
       ]
      },
      "metadata": {},
@@ -807,9 +806,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fe53dc18>"
+       "<matplotlib.figure.Figure at 0x8efd9e8>"
       ]
      },
      "metadata": {},
@@ -846,7 +845,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fe5485f8>"
+       "<matplotlib.figure.Figure at 0x81846d8>"
       ]
      },
      "metadata": {},
@@ -889,7 +888,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207811c4f98>"
+       "<matplotlib.figure.Figure at 0x8e5aa90>"
       ]
      },
      "metadata": {},
@@ -923,7 +922,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fe53deb8>"
+       "<matplotlib.figure.Figure at 0x8e5a5f8>"
       ]
      },
      "metadata": {},
@@ -958,7 +957,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20781203be0>"
+       "<matplotlib.figure.Figure at 0x9423080>"
       ]
      },
      "metadata": {},
@@ -1003,7 +1002,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2078013f898>"
+       "<matplotlib.figure.Figure at 0xa110d30>"
       ]
      },
      "metadata": {},
@@ -1048,7 +1047,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207800d2438>"
+       "<matplotlib.figure.Figure at 0x940ecf8>"
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@@ -1109,7 +1108,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fe5af860>"
+       "<matplotlib.figure.Figure at 0x8043ba8>"
       ]
      },
      "metadata": {},
@@ -1142,7 +1141,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fdd3cc88>"
+       "<matplotlib.figure.Figure at 0x93ac6a0>"
       ]
      },
      "metadata": {},
@@ -1178,7 +1177,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fe58d908>"
+       "<matplotlib.figure.Figure at 0x8181c18>"
       ]
      },
      "metadata": {},
@@ -1302,7 +1301,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20780170ba8>"
+       "<matplotlib.figure.Figure at 0x89df400>"
       ]
      },
      "metadata": {},
@@ -1334,7 +1333,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207812429e8>"
+       "<matplotlib.figure.Figure at 0x8ed6908>"
       ]
      },
      "metadata": {},
@@ -1379,7 +1378,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2078005c588>"
+       "<matplotlib.figure.Figure at 0x8a3cb70>"
       ]
      },
      "metadata": {},
@@ -1434,7 +1433,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2078003feb8>"
+       "<matplotlib.figure.Figure at 0x81e92b0>"
       ]
      },
      "metadata": {},
@@ -1465,7 +1464,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20781316978>"
+       "<matplotlib.figure.Figure at 0x8e86780>"
       ]
      },
      "metadata": {},
@@ -1499,7 +1498,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207813f0ef0>"
+       "<matplotlib.figure.Figure at 0x89530f0>"
       ]
      },
      "metadata": {},
@@ -1538,7 +1537,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207813cd630>"
+       "<matplotlib.figure.Figure at 0xa1863c8>"
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      },
      "metadata": {},
@@ -1569,7 +1568,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207812ffd68>"
+       "<matplotlib.figure.Figure at 0xa197390>"
       ]
      },
      "metadata": {},
@@ -1603,7 +1602,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207813b4080>"
+       "<matplotlib.figure.Figure at 0xa181c18>"
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      "metadata": {},
@@ -1636,7 +1635,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207811d8518>"
+       "<matplotlib.figure.Figure at 0x7dfa400>"
       ]
      },
      "metadata": {},
@@ -1669,7 +1668,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20780021710>"
+       "<matplotlib.figure.Figure at 0x7ddca90>"
       ]
      },
      "metadata": {},
@@ -1704,7 +1703,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2078017a390>"
+       "<matplotlib.figure.Figure at 0x8056860>"
       ]
      },
      "metadata": {},
@@ -1745,7 +1744,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207fe537b38>"
+       "<matplotlib.figure.Figure at 0x93cf208>"
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      "metadata": {},
@@ -1775,7 +1774,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207813af550>"
+       "<matplotlib.figure.Figure at 0x93cf278>"
       ]
      },
      "metadata": {},
@@ -1811,7 +1810,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20781371908>"
+       "<matplotlib.figure.Figure at 0x8a0c860>"
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      "metadata": {},
@@ -1840,7 +1839,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2078117e8d0>"
+       "<matplotlib.figure.Figure at 0x8965a90>"
       ]
      },
      "metadata": {},
@@ -1869,7 +1868,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvcAAADxCAYAAABPuZiPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207814130f0>"
+       "<matplotlib.figure.Figure at 0x9430e10>"
       ]
      },
      "metadata": {},
@@ -1900,7 +1899,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvcAAADxCAYAAABPuZiPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x207800badd8>"
+       "<matplotlib.figure.Figure at 0x9395048>"
       ]
      },
      "metadata": {},

06-Multivariate-Kalman-Filters.ipynb

@@ -28,7 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -42,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -123,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -369,9 +368,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2938d518>"
+       "<matplotlib.figure.Figure at 0x4b235c0>"
       ]
      },
      "metadata": {},
@@ -864,7 +863,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2c3a48d0>"
+       "<matplotlib.figure.Figure at 0x895c5f8>"
       ]
      },
      "metadata": {},
@@ -1027,9 +1026,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAADlCAYAAAB3YbDsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAADlCAYAAAB3YbDsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2c33ddd8>"
+       "<matplotlib.figure.Figure at 0x8954cf8>"
       ]
      },
      "metadata": {},
@@ -1368,9 +1367,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwAAAAD+CAYAAACN8WJ6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwAAAAD+CAYAAACN8WJ6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d4af320>"
+       "<matplotlib.figure.Figure at 0x9a95cf8>"
       ]
      },
      "metadata": {},
@@ -1380,7 +1379,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt4AAADsCAYAAABDuaHQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d3e4710>"
+       "<matplotlib.figure.Figure at 0x9a9b358>"
       ]
      },
      "metadata": {},
@@ -1429,9 +1428,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d5c9a90>"
+       "<matplotlib.figure.Figure at 0x9bace80>"
       ]
      },
      "metadata": {},
@@ -1569,7 +1568,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d60a1d0>"
+       "<matplotlib.figure.Figure at 0x8993ef0>"
       ]
      },
      "metadata": {},
@@ -1618,7 +1617,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu8AAAEUCAYAAACbPtgWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2c2c8710>"
+       "<matplotlib.figure.Figure at 0x7c18048>"
       ]
      },
      "metadata": {},
@@ -1680,9 +1679,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d5cd908>"
+       "<matplotlib.figure.Figure at 0x9bac198>"
       ]
      },
      "metadata": {},
@@ -1834,7 +1833,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d4af710>"
+       "<matplotlib.figure.Figure at 0x9bac898>"
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      "metadata": {},
@@ -1844,7 +1843,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2bb2f978>"
+       "<matplotlib.figure.Figure at 0x9aa2dd8>"
       ]
      },
      "metadata": {},
@@ -1982,7 +1981,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d88ef98>"
+       "<matplotlib.figure.Figure at 0x9dc15f8>"
       ]
      },
      "metadata": {},
@@ -2072,7 +2071,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d645198>"
+       "<matplotlib.figure.Figure at 0x9a476a0>"
       ]
      },
      "metadata": {},
@@ -2101,7 +2100,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2bdb5fd0>"
+       "<matplotlib.figure.Figure at 0x9bcc780>"
       ]
      },
      "metadata": {},
@@ -2229,7 +2228,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d5905c0>"
+       "<matplotlib.figure.Figure at 0x9bb65f8>"
       ]
      },
      "metadata": {},
@@ -2239,7 +2238,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d41a550>"
+       "<matplotlib.figure.Figure at 0x9bcc550>"
       ]
      },
      "metadata": {},
@@ -2289,7 +2288,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2edb9240>"
+       "<matplotlib.figure.Figure at 0x7c20b38>"
       ]
      },
      "metadata": {},
@@ -2319,7 +2318,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d5a13c8>"
+       "<matplotlib.figure.Figure at 0x9b329b0>"
       ]
      },
      "metadata": {},
@@ -2350,7 +2349,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d857978>"
+       "<matplotlib.figure.Figure at 0x9b5a828>"
       ]
      },
      "metadata": {},
@@ -2397,7 +2396,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2f0b1128>"
+       "<matplotlib.figure.Figure at 0x9a48dd8>"
       ]
      },
      "metadata": {},
@@ -2407,7 +2406,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d5c9518>"
+       "<matplotlib.figure.Figure at 0x7f1da20>"
       ]
      },
      "metadata": {},
@@ -2451,7 +2450,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d3a94a8>"
+       "<matplotlib.figure.Figure at 0x7c2d908>"
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      },
      "metadata": {},
@@ -2461,7 +2460,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2bb41ef0>"
+       "<matplotlib.figure.Figure at 0x9beeb00>"
       ]
      },
      "metadata": {},
@@ -2495,7 +2494,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2eeef208>"
+       "<matplotlib.figure.Figure at 0x9b8d4a8>"
       ]
      },
      "metadata": {},
@@ -2529,7 +2528,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d4e5898>"
+       "<matplotlib.figure.Figure at 0x9b8af28>"
       ]
      },
      "metadata": {},
@@ -2567,7 +2566,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2f096438>"
+       "<matplotlib.figure.Figure at 0xb32f3c8>"
       ]
      },
      "metadata": {},
@@ -2577,7 +2576,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d3aebe0>"
+       "<matplotlib.figure.Figure at 0x8967160>"
       ]
      },
      "metadata": {},
@@ -2669,7 +2668,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2bdbfe10>"
+       "<matplotlib.figure.Figure at 0x9aa8cf8>"
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@@ -2806,7 +2805,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2eef43c8>"
+       "<matplotlib.figure.Figure at 0x8972be0>"
       ]
      },
      "metadata": {},
@@ -2857,7 +2856,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2d5d4588>"
+       "<matplotlib.figure.Figure at 0x810ada0>"
       ]
      },
      "metadata": {},
@@ -2900,7 +2899,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2bdd6d68>"
+       "<matplotlib.figure.Figure at 0x9b667b8>"
       ]
      },
      "metadata": {},
@@ -2959,7 +2958,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAADaCAYAAADnoPfrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ee2c3b79e8>"
+       "<matplotlib.figure.Figure at 0x9b24438>"
       ]
      },
      "metadata": {},

07-Kalman-Filter-Math.ipynb

@@ -28,7 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -42,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -123,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -1245,7 +1244,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAADaCAYAAADuS1eRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8264b70>"
+       "<matplotlib.figure.Figure at 0x82668d0>"
       ]
      },
      "metadata": {},
@@ -1279,7 +1278,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x829a9e8>"
+       "<matplotlib.figure.Figure at 0x829df60>"
       ]
      },
      "metadata": {},
@@ -1360,7 +1359,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x859cb70>"
+       "<matplotlib.figure.Figure at 0x8570128>"
       ]
      },
      "metadata": {},
@@ -1517,7 +1516,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAADaCAYAAADqvoesAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa8197f0>"
+       "<matplotlib.figure.Figure at 0xa8298d0>"
       ]
      },
      "metadata": {},

08-Designing-Kalman-Filters.ipynb

@@ -28,7 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -42,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -123,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -352,9 +351,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAADTCAYAAABDa58YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAADTCAYAAABOde9fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7208f60>"
+       "<matplotlib.figure.Figure at 0x72ee240>"
       ]
      },
      "metadata": {},
@@ -689,9 +688,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAADTCAYAAACbfYSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAADTCAYAAABp7RNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86bcc50>"
+       "<matplotlib.figure.Figure at 0x879b898>"
       ]
      },
      "metadata": {},
@@ -756,9 +755,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAADYCAYAAADGbgTlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8731a20>"
+       "<matplotlib.figure.Figure at 0x88367b8>"
       ]
      },
      "metadata": {},
@@ -854,9 +853,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAADaCAYAAAD0d7cfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86bc470>"
+       "<matplotlib.figure.Figure at 0x88362e8>"
       ]
      },
      "metadata": {},
@@ -964,9 +963,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAADWCAYAAAATpg6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAADWCAYAAAAeuH7sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86bc668>"
+       "<matplotlib.figure.Figure at 0x879b710>"
       ]
      },
      "metadata": {},
@@ -1280,9 +1279,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAADqCAYAAADJRsOsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAADqCAYAAADArWPWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x881db38>"
+       "<matplotlib.figure.Figure at 0x88f5cf8>"
       ]
      },
      "metadata": {},
@@ -1332,9 +1331,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAEACAYAAADr3HqZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwUAAAEACAYAAADiN9rjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8895400>"
+       "<matplotlib.figure.Figure at 0x897e8d0>"
       ]
      },
      "metadata": {},
@@ -1365,9 +1364,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwUAAAEACAYAAADiN9rjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86bc5c0>"
+       "<matplotlib.figure.Figure at 0x89da748>"
       ]
      },
      "metadata": {},
@@ -1398,9 +1397,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAADqCAYAAADJRsOsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAADqCAYAAADArWPWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFNfXwPHvLh1BRKWIiF2xYG9YEGtssWJBVOyJGkET\nG8bYY429JGossWDvsYsNO6gxRv1ZUWyICiqCorDz/sHLJCugoCCW83keHpk7M3fOjrB7uHOLRlEU\nBSGEEEII8VnQZnYAQgghhBAi/UhyJ4QQQgjxGZHkTgghhBDiMyLJnRBCCCHEZ0SSOyGEEEKIz4hh\nZgeQnp48eZLZIQghhPjArKysMjsEIT4q0nInhBBCCPEZkeROCCGEEOIz8lk9lv2v15vpg4ODAahQ\noUJmhPNJkXuVenKvUkfuU+rJvUod6YYjRMqk5U4IIYQQ4jMiyZ0QQgghxGdEkjshhBBCiM+IJHdC\nCCGSFR0dja+vLyVLlqRChQrMnj0bRVEyOywhxFt8tgMqhBBCvLv4+HiaNWtGQECAWnbq1CkiIiIY\nPnx4JkYmhHgbabkTQgiRxNGjRwkICMDa2pqAgAB+//13AMaNG8erV68yOTohxJtIcieEECKJsLAw\nAKpUqULt2rXp0qUL5ubmxMbG8vjx40yOTgjxJvJYVgghRBIlSpQAYPfu3fj5+RESEkJMTAx2dnbk\nzJkzk6MTQryJtNwJIYRIonjx4gwbNoz4+HgmTJjA6tWrMTY2ZtGiRWg0mswOTwjxBtJyJ4QQIllj\nxoyhcuXK7Nu3DxMTEzw9PSlVqlRmhyWEeAtJ7oQQQqSoSZMmNGnS5INdb/v27dSqVQszM7MPdk0h\nPjeZ+lh2/PjxVKpUCSsrK2xtbWnatCnnz59PctzIkSPJnTs35ubm1KpViwsXLmRCtEIIIdKboih6\no2+nTZvGli1bgITpWIQQaZepyd2hQ4f47rvvOHbsGPv378fQ0JC6devqjcSaOHEi06ZNY86cOQQH\nB2Nra0u9evWIjo7OxMiFEEKkh8GDBzNr1ix129fXl1u3blG2bFmMjIzImTMnkydPlsmThUiDTE3u\nduzYQadOnShevDglSpRg2bJlPHjwgCNHjqjHzJgxAz8/P5o3b07x4sX5448/iIqKwt/fPxMjF0II\n8S7Onj3L4sWL1e0GDRqwc+dOdbtMmTKMGzeOv/76C0VRePToEYMGDWL27NmZEa4Qn6SParTs06dP\n0el0WFtbAxASEkJYWBj16tVTjzE1NcXNzY2jR49mVphCCCHS4NatW+r3JiYmDB06VH0U6+7uzvbt\n29X9q1atIjIykpo1axIVFcWvv/4KwNy5cz9s0EJ8wj6qARW+vr6UK1cOV1dXIGESTY1Gg52dnd5x\ndnZ23L179411BQcHp6lcJCX3KvXkXqWO3KfUS8u9unfvHgsXLuTOnTs4ODjQtWtXcufOnYHRJTh2\n7Bh//PEHERERFClSBB8fH2xtbQF49uwZc+fO5a+//uL69esMGzZMHZjRt29fTp48iYmJSZI6L126\nBECuXLn43//+R7Zs2QB49OiR3j0pXLhwRr88IT5ZH01y9/3333P06FGOHDkicygJIUQqhYWF4e3t\nTWRkpFp28OBBli5dioODQ4Zd98iRI/Tv31/tCxcSEsK5c+dYunQpS5Ys4cSJE1y5ckU9ftSoUeh0\nOpo2bUr9+vVTrDdxqpV169bx+PFjzp07B0Dp0qUz7LUI8dlRPgL9+vVTHBwclMuXL+uVX79+XdFo\nNEpwcLBeeePGjZXOnTsnqefx48fq1+uCgoKUoKCg9A38MyX3KvXkXqWO3KfUS+u96tOnjwIo1apV\nU/7880/Fzc1NAZSePXtmYJSKUrlyZQVQ+vTpoxw8eFApVqyYAijTpk1TGjVqpACKg4ODsnfvXmX4\n8OEKoOTOnTtVdQ8aNEgB1K/ixYsrYWFhese86f1eiC9dpve58/X1ZfXq1ezfvz9JM3v+/Pmxt7dn\nz549atmLFy8IDAykWrVqHzpUIYT46Ny4cQMAHx8fGjduTL9+/fTKM0pi15g+ffpw7NgxdV66u3fv\nUqlSJQC++uor6tSpw+DBg4GEx8c6ne6tdU+cOJGjR48yd+5c1qxZQ1BQUJLuOUKIlGXqY9k+ffqw\nfPlyNm/ejJWVFffv3wfAwsKCLFmyANCvXz/Gjx9P0aJFKVy4MGPHjsXS0hJPT8/MDF0IIT4KiX8U\nT5gwgZcvXzJjxgwAChUqlGHXvHDhgtp9pmfPntSsWVN9fFqyZEm1v9+aNWsoUqQIJ06cAMDZ2Rmt\nNnVtCq6urmr/ayFE2mgUJfMmD9Jqtcn2rxsxYgTDhw9Xt0ePHs28efOIjIykcuXKzJkzh+LFiyc5\n78mTJ+r3VlZWevsSO+JWqFAhvcL/bMm9Sj25V6kj9yn10nqv7t+/T8WKFfVGpObOnZugoCBy5cqV\nLjG9evWKtWvX4unpiUajISYmhly5cmFoaEhERIR6XIMGDdi6dSsGBgZ07tyZpUuXqvvMzMzYtWsX\nNWrUACAmJobdu3fz+PFjKlasSIkSJdIU05ve74X40mVqy11qmucBhg8frpfsCSGESGBnZ0dwcDCT\nJ0/m2rVr5M+fn4EDB2Jvb/9e9Sb+3a/RaDAwMGDo0KEUL16cMmXKYG5uzuHDh8maNSuLFy8mPDyc\nMmXK0LVrVwwNEz5WFi9eTO3atTl69ChZs2ala9euFCtWDIDbt29Tt25ddWSsRqNhypQp9O/f/71i\nFkIk+GhGywohhHg3tra2TJ48OV3rbNeuHV26dKFBgwZotVpGjRqlt0yYi4sLkLA8ZHK0Wi3e3t54\ne3sn2de9e3cuXbpEgQIFcHFxYcuWLXz//ffUqFFDWneFSAeZPqBCCCFE5vvrr784ePCgul2nTh1W\nr16tbnt7e1OxYsV0uVZgYCAABw4cYNOmTbRv3x5Ab3UiIcS7k+ROCCG+UP/tGhMaGoqfn5+63blz\nZxYsWJAh100cMHf27FliYmK4fPmyXrkQ4v1IcieEEJ8xnU7H7Nmz+eqrr2jYsCGLFy9GURRCQkIo\nW7YsOp0OnU5H1apVady4MfHx8QAYGxur/efSW48ePQD4+uuvyZIlC0FBQdja2tK0aVP1mFv3Fe49\nzLTxfkJ80qTPnRBCfMZ8fX2ZPXu2ur1z505u3LjByJEjMTAwYNCgQcyfP5+oqCjs7e0pWbIkzZo1\ny9CYRo8ezatXr5g9ezbPnz+ndOnSLFmylJuPbJi9WeHPI/DXFfjRG8b0zNBQhPgsScudEEJ8pkJC\nQpg9ezYGBgbMnz8fZ2dnAMaOHUtkZCR9+/ZlypQpREVFYWRkRFhYGB4eHgQFBWVoXAYGBkyaNInQ\nu89YGxBLxXZnaDTchcrdYeyShMQO4E/pgifEO5GWOyGE+ExNnz4dgLJly9KjRw+KFClCu3btCAsL\n486dOyxfvhxIWBFi4MCBdO3alSVLlrB06dJ0Gzzx8LHCpVC4dgeu3v7336u3ITJKAxgle56RIdjn\ngBexCqYmst64EGkhyZ0QQnwm4uLiuHLlijqfXO3atZk5cybBwcFMmDCBmJgYwsLCMDIywsnJiZiY\nGCBh5QiNRqO27CWWvwtFUfj7Kmw5DFsPQ/D/Un9uzmzQ2BWaVIP6lcAyiyR1QrwLSe6EEOIj8ODB\nA8aMGcPx48extbVl0qRJya7E8yZXr17F3d2dmzdvYmpqSrNmzfD19WXGjBl6I2EnTpyIlZUVtWrV\n4vjx43Tu3Bl3d3e2b98OgLu7e5qu+/KVwoHTsPVIQkIXej9155mZQFEnqF8Zvq4GVUqAgYEkdEK8\nL0nuhBAik0VGRuLq6sq1a9fUsoMHD3L06FFcXFyIj49n0qRJrFq1iri4OBo3bszo0aMxMTHB19eX\nUaNGYW1tjbOzM61atSIkJERtvZs2bRpVqlRh69ataLVaWrdurY5KHT58OKdPn2bXrl1s3LgRgF69\netGhQ4e3xhwXp7A3GJbvTEjqolJo7DM0AJeCUNgRCjpCIUcomBsK5YZcOUl2CUohxPuR5E4IITLZ\nnDlzuHbtGiVLlqRz585s2rSJw4cPM3z4cDZu3Ej//v2ZNWuWevyFCxe4evUq69evJyIigkWLFvHD\nDz8AMHfuXL26NRoN7dq1o127dkmua2pqyvbt2wkMDOTWrVs4Ozu/cYUIRVE4fQmW7YRVeyE8Mvnj\nrCygkSt8XR0aVIZslpLACfEhSXInhBCZ7MaNGwD07NkTV1dXsmXLxuHDhwkJCSEiIkKdymT16tVs\n3LiRdevWsXHjRi5cuMDIkSPVdWDfhVarpWbNmm88JjRMYfkuWLEbLt5I/pj8DtC0ekJCV6M0GBlK\nQidEZpHkTgghMlnBggUBmD17Nk+ePGHLli0AFCpUiDNnzqAoCrlz56Z169aULVuWbdu2ERUVRVhY\nGHXq1MmwuOLiFMYsgXFL4f/nNtaTKwd41gev+lCmsDxiFeJjIcmdEEJksj59+rBs2TIuXrzITz/9\nBEC2bNkYO3YsZmZmANy5c4euXbtiZWVFVFQUBgYGar+6jHDrvkKHURB4Vr88ixm0qgleX0Ht8jIA\nQoiPkSR3Qgjxgdy9e5fNmzcTExNDzZo11f5tWbNm5ejRo0ycOJGjR49y6tQpNm3apE5N0q9fP2bO\nnMmSJUvUuqZMmYKDg0OGxLklUKHrOIh4+m+Za0no3RKau0EWM0nohPiYSXInhBAfQFBQEF999RWR\nkQmjEDQaDdOnT8fHx4eTJ0+SPXt2xo8fT3BwMFOnTuX8+fNqX7hp06bh5eXF+vXriYuLo1GjRtSq\nVSvdY3wRqzBoLsxe92+ZVgujusOQDtJKJ8SnQpI7IYTIYIqi0KFDByIjI6levTr58Line truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a06f98>"
+       "<matplotlib.figure.Figure at 0x9a4ec88>"
       ]
      },
      "metadata": {},
@@ -1435,9 +1434,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAEACAYAAADcN+KEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvcAAAEACAYAAAA3AFmHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a1e208>"
+       "<matplotlib.figure.Figure at 0x9b4f6a0>"
       ]
      },
      "metadata": {},
@@ -1476,9 +1475,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAADqCAYAAADJRsOsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAADqCAYAAADArWPWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b0c860>"
+       "<matplotlib.figure.Figure at 0x9be5748>"
       ]
      },
      "metadata": {},
@@ -1511,9 +1510,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAD+CAYAAABiMwlEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwUAAAD+CAYAAABr2Kk+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFNf6B/DvzAILSAcBEaQJNkQB0VBUVJQEDUY0Es1V\n0BQTDck1mnj1GjVGvZaY+0tMotFYYxJvjBpLLBRRERsgUhQVKaJIUXovu+f3x8rCuktTqryf5/Fx\nz8yZmbOzs8u8cxrHGGMghBBCCCGEdFt8RxeAEEIIIYQQ0rEoKCCEEEIIIaSbo6CAEEIIIYSQbo6C\nAkIIIYQQQro5CgoIIYQQQgjp5igoIIQQQgghpJujoIAQ0i2dP38ePM9j9erVHV2UZrGwsICVlVVH\nF6PT4HkeY8eObdE2AQEB4Hke6enpbVSql8OqVavA8zwuXLjQ7G08PDzA8x1zS/E81wIhRB4FBYR0\nE4wx7Ny5E2PGjIGBgQFUVFRgaGiIwYMHY86cOfjf//7X0UXsMo4ePQpfX1+YmppCVVUV+vr6eOWV\nV7B27VoUFha2yTE5jmuT/bYEz/My/wQCAfT09DBy5Ehs374dYrG43crCcZzcOam96W/oZlbRNh1t\n7969cudVKBTC1NQUb775JiIiItq9TM9znjrjuSWEtIxSRxeAENL2GGN4/fXXcfLkSejo6GDSpEkw\nNTVFdXU1bt26hUOHDuHq1avw8/Pr6KJ2asXFxfDz88Pp06ehpaUFb29vWFpaori4GCEhIfjiiy+w\nZcsWHD58GK6urh1d3DbBcRxWrVoFxhhqamqQnJyMI0eOICIiAqGhoe0WXCYmJkJdXV2ubI3dmK5f\nvx5Lly5F796927p4LTZ06FC88cYbAICSkhJERUXh0KFDOHLkCA4ePIgpU6a0W1kCAwMxY8YM9OnT\np92OSQjpeBQUENINHDhwACdPnoSDgwPOnz8PDQ0NmfXV1dUIDw/voNJ1DYwxTJs2DcHBwRg/fjx+\n//136OnpyeT57rvvsHDhQnh7e+PatWuwtbXtoNK2rS+++EImffPmTQwfPhx//vknIiIi4Obm1uZl\nUHRuGWONbmNkZAQjI6O2KtILGTp0KFasWCGzbN26dVi+fDk+++yzdg0K9PT05K5tQsjLj5oPEdIN\nREREgOM4+Pv7ywUEAKCsrNxgm9ywsDD4+PjA0NAQQqEQFhYWWLBgAbKzsxXmLygowBdffAF7e3to\naGhAW1sbgwcPxmeffSbXtCY1NRVz5syBmZkZhEIhjI2N4efnh/j4eLn91jazWL16NWJjYzFx4kTo\n6uqiR48e8PDwwOXLlxWWJycnB++88w6MjY2hrq4OBwcH7Nu3r6lTJue3335DcHAwrK2tceTIEYU3\nTR9//DEWLVqEoqIifPLJJzLrattp79u3DydPnsTo0aOhra0NfX19mXzff/897OzsoKamBlNTUwQG\nBqKoqKjRsh06dAienp7Q19eHqqoqbG1tsXTpUhQXF8vltbCwgEAgQFVVFVatWgVbW1sIhUJ8+umn\nLT4ntQYNGgQPDw8AwNWrV2XWxcbGYvr06TA2NoZQKESfPn3w7rvvIi0tTW4/JSUlWLNmDezt7aGj\nowNNTU1YWVlh6tSpck2Cnm1HbmlpKf1ca9u31zZxqtVYn4LDhw9jzJgx0NHRgZqaGgYOHIiVK1ei\ntLRULm/t/tPT0/HTTz/B3t4eampqMDY2xrx585r8vJrr/fffByD5nuTl5cmtZ4zh559/hru7u7Tc\ngwcPxvr161FdXS2XPzw8HD4+PujTpw9UVVVhZGQEZ2dnLF68WCZfY30KDhw4ACcnJ6irq8PIyAiz\nZ89GZmamwvLXfmcb+r4p6idTVFSETZs2Ydy4cdLfBUNDQ/j4+DT4HVekJdcSIUSCagoI6Qb09PTA\nGMPdu3dbtN2GDRuwdOlS6OvrY+LEiTA2NkZcXBy2bt2K48eP48qVKzAxMZHmT0tLg4eHBx48eAAH\nBwd88MEHAICkpCT8+OOPmDVrFuzt7QEA169fx7hx41BcXIyJEydi8ODBSE5OxuHDh3H8+HEcO3YM\nnp6eMuXhOA6RkZHYsGEDXF1d8d577yE9PR1//vknPD09cePGDdjY2Ejz5+bmwsXFBWlpaXBzc8PI\nkSORmZmJ+fPnw9PTs0VtoLdv3w6O47B48WK5Ziv1LV26FFu2bEFQUBDS09OlTTBqm7b88ccfOHPm\nDCZNmoT58+cjJydHuu0nn3yCLVu2oFevXnj//fchFApx9OhRXL16FVVVVRAKhXLHmz9/PrZt2wYz\nMzP4+vpCV1cXV65cwYYNG3Dq1ClERESgR48eMucQAKZOnYqYmBi8+uqrMDAwgKWlZbPPhSK1T+nr\nn9NTp07B19cXYrEYvr6+sLa2RlxcHHbt2oUjR44gLCxMej0AgJeXFy5fvowRI0bgnXfegYqKCjIy\nMhAeHo6QkBCMGjWqweMvXLgQu3fvRlxcHAICAmBhYSFXnoaaF61YsQJr1qyBvr4+ZsyYAR0dHQQH\nB+Orr77C8ePHER4eLncOOY7DZ599hqCgILz++uvw8vJCWFgYduzYgeTkZISEhDz3uaxVv+ZDWVlZ\nZp1IJMKUKVNw4sQJ9OvXDzNnzoSqqirOnz+PZcuW4ezZszh9+rS08+/p06cxadIkaGlpwcfHB6am\npsjPz0dSUhK2bNmCjRs3SvM2dJ7++9//YtGiRdDR0YG/vz90dHQQFBQEV1dXaGtrK3wPjX3HFK1L\nTEzE8uXLMXr0aEyaNAm6urpIT0/HsWPHcOrUKRw/fhyvvvpqk+fuRa4lQrotRgh56cXExDAVFRXG\n8zz7xz/+wf7880+Wmpra6Dbnz59nPM8zNzc3VlRUJLNu//79jOM4Nm3aNJnlLi4ujOd5tnbtWrn9\nFRYWstLSUml64MCBjOd59ssvv8jkCw0NZTzPMyMjI1ZeXi5dvmfPHsZxHON5nu3bt09mm59++olx\nHMcWLFggs/y9995jPM+zTz75RGb5jRs3pOfjyy+/bPQ8MMZYTU0NEwqFjOd5du/evSbzu7q6Mp7n\n2a+//ipdtmrVKsZxHBMIBCwoKEhum0uXLjGO45i1tTXLy8uTLq+qqmLu7u6M4zhmaWkps80vv/wi\n/RwqKytl1q1du5ZxHMcWL14ss9zCwoLxPM+GDBkic5zmqD3/z4qNjWVqamqM53kWERHBGGOstLSU\nGRgYMCUlJXb+/HmZ/Lt27WIcxzF7e3vpsvj4eMZxHPP19VV47GfLynEcGzNmjMyygIAAxvO83PGe\nXX///n3psitXrjCO41ifPn1YVlaWTH5/f3/G8zwLDAyUWe7h4cE4jmPm5ubs4cOH0uUikYiNGjWK\n8TzPIiMjFZbhWbXX9Zw5c+TWffnll4zjODZkyBC5dV999RXjOI59/PHHTCwWy6ybN28e43meff/9\n99Jlvr6+jOd5FhcXJ7evZ8/tqlWr5M5jWloaU1FRYbq6uiwtLU0m//Tp0xVeG3v27GE8z7O9e/cq\nfO8WFhZy13RRURHLzc2Vy5uRkcFMTEzYgAED5NY9ey209FoihEhQUEBIN3Hw4EFmYmLCeJ5nHMcx\njuOYjo4OmzhxIjtw4IDcjUXtTUR8fLzC/Tk6OjJlZWVWUlLCGGMsOjqacRzHhg4d2mRZIiIiGMdx\n7JVXXlG4furUqYzneXbgwAHpstqbp1GjRsnlr66uZsrKyszZ2VlmWY8ePZimpiYrKCiQ22bu3LnN\nDgpycnKkNz3P3nwr4ufnx3ieZ5s2bZIuqw0Kpk6dqnCbd999l/E8z3bu3Cm37sKFCwqDgtrPID8/\nX24bkUjEevbsyYyNjWWW1wYFx48fb/J9PKv2HKxatYqtWrWKLV++nM2YMYOpqqoynueZn5+fNO+v\nv/7KOI5jb731lsJ9OTk5MZ7n2ZUrVxhjdTdyM2fObHZZWiMoqD3v27Ztk8ufnZ3N1NXVmaamJqup\nqZEu9/DwYDzPs127dslts3v3bsZxHPvhhx+a9T5qr2sHBwfpeV28eDEbNWoU4ziO6erqSs9RLbFY\nLP1sRSKR3D4LCgoYz/NsxIgR0mW136m7d+82WSZFQcGaNWsYz/Psiy++kMufmprKlJSUWiUoaMzH\nH3/MeJ5nDx48kFneUFDQ3GuJECJBzYcI6SamTZuGKVOmICwsDBcvXkRMTAwuXryIU6dO4eTJk9i7\ndy+OHTsGJSXJz8KlS5egpKSEQ4cO4dChQ3L7q6yshEgkwt27d+Hg4IArV64AAMaPH99kWa5fvw4A\nGDNmjML1np6eOHz4MK5fvy43IpKTk5NcfiUlJRgZGSE/P1+67Pbt2ygrK2uwacPo0aOxe/fuJsva\nmjiOg7Ozs8J1MTExAKCwWYObm5v0c6lVXl6OGzduQF9fH99++63cNowxqKioIDMzE/n5+dDV1ZVZ\n31A5mqN2bgeO46ClpQVnZ2fMmjUL7777rjTP9evXwXFco59xTEwMrl+/jhEjRmDgwIEYOnQoDhw4\ngNTUVEyePBlubm5wdnZW2GyqtdSed0XlrB2yNzIyEnfv3sWAAQNk1iu6Fs3MzABA5lpsjtjYWMTG\nxsos09fXR1hYGOzs7GSW3717F0+ePEHfvn3x1Vdfye2LMQY1NTUkJiZKl7399ts4cuQIhg8fjunT\np2PMmDFwcXGBubl5s8rX2PVpYWEBMzMz3L9/v1n7akpERAS+/fZbXLlyBTk5OaiqqpKu4zgOGRkZ\nMDU1bXD7jrqWCOnqKCggpBsRCATw9PSUttVnjOHQoUOYM2cOzpw5g61btyIwMBCApD2+SCRqdHIv\njuNQUlICQNLBmOO4Zg33WFhYCI7jYGxsrHB9r169pPt8lo6OjsJtlJSUIBKJZI4BoLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a8d1d0>"
+       "<matplotlib.figure.Figure at 0x9b75cf8>"
       ]
      },
      "metadata": {},
@@ -1550,9 +1549,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAD+CAYAAABiMwlEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwUAAAD+CAYAAABr2Kk+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8911f98>"
+       "<matplotlib.figure.Figure at 0x8a41f28>"
       ]
      },
      "metadata": {},
@@ -1593,9 +1592,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAADqCAYAAADJRsOsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAADqCAYAAADArWPWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b0cd30>"
+       "<matplotlib.figure.Figure at 0x9be5940>"
       ]
      },
      "metadata": {},
@@ -1626,9 +1625,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAADtCAYAAACvZzHYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b0c6a0>"
+       "<matplotlib.figure.Figure at 0x9be16a0>"
       ]
      },
      "metadata": {},
@@ -1636,9 +1635,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwUAAAD+CAYAAABr2Kk+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b0cef0>"
+       "<matplotlib.figure.Figure at 0x9be5710>"
       ]
      },
      "metadata": {},
@@ -1678,9 +1677,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAADqCAYAAABZVbpjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8899f60>"
+       "<matplotlib.figure.Figure at 0x87a5b70>"
       ]
      },
      "metadata": {},
@@ -1688,9 +1687,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw4AAAD+CAYAAACTOFLJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a21ef0>"
+       "<matplotlib.figure.Figure at 0x87a84a8>"
       ]
      },
      "metadata": {},
@@ -1730,9 +1729,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAADaCAYAAADwgmciAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu0AAADaCAYAAAASXnxbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8812f60>"
+       "<matplotlib.figure.Figure at 0x9aee198>"
       ]
      },
      "metadata": {},
@@ -1771,9 +1770,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAADaCAYAAADuS1eRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAADaCAYAAADqvoesAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtcU3eeP/5XEgKBAIFgSIAggkUFbygoKhYrl3Yctep2\nxsGZqbb1a2cfU621bt2x7lj9VdfpfGceLvtddXbctWVotWpbp+14qVhvKFoLKkVARQEVMJH7Pdxy\nfn+g0VNUriFcXs/HIw/C53xy8j7vMs6Lw8nnSARBEEBERERERDYltXUBRERERETEYE5ERERE1Ccw\nmBMRERER9QEM5kREREREfQCDORERERFRH8BgTkRERETUBzCYExERERH1AZ0O5lu2bIFUKsWbb74p\nGt+wYQN8fHzg5OSEmTNnIisrS7S9sbERK1asgEajgbOzM+bNm4fCwsLuVU9ERERENEB0KpifP38e\nO3fuxPjx40XjH3zwAbZu3Ypt27YhNTUVnp6eiI2NRW1trWXOypUrceDAAezduxdnzpxBVVUV5syZ\nA97fiIiIiIioE8G8srISv/71r/Hhhx/Czc1NtC0+Ph5r167F/PnzERwcjISEBFRXV2P37t0AgKqq\nKuzatQt/+tOfEBUVhZCQECQmJuKHH37AsWPHevaIiIiIiIj6oQ4H89dffx0LFy7EjBkzRON5eXkw\nGAyIjY21jCkUCkRGRiIlJQUAkJqaiubmZtEcvV6PoKAgyxwiIiIiosHMriOTdu7cidzcXOzZs6fN\nNoPBAIlEAq1WKxrXarUoKioCABiNRshkMnh4eLSZYzAYulo7EREREdGA0W4wv379OtatW4ezZ89C\nKu2dRVwqKyt75X2IiIiIiKxBpVJ1+jXtJu1z586htLQUwcHBkMvlkMvlOHXqFLZt2wZ7e3t4eHhA\nEAQYjUbR64xGI3Q6HQBAp9OhpaUFpaWlT5xDRERERDSYtRvMFyxYgIyMDKSnp1seYWFhWLRoEdLT\n0zFixAjodDokJSVZXmMymZCcnIyIiAgAQGhoKOzs7ERzCgoKkJ2dbZlDRERERDSYtXspi6urK4KD\ng0VjSqUSarUaQUFBAIC33noLW7ZswciRIxEYGIhNmzbBxcUFixYtsuxj6dKlWLNmDTQaDdRqNVav\nXo2QkBBER0c/9f278mcAerzU1FQAQFhYmI0rGVjYV+tgX62DfbUO9tV62FvrYF+fTBAEpF47jc9P\n7kRdQ41lXKPywq+eX4kA71FPfG13L8fu0Ic/f0wikYi+X7NmDUwmE5YvX47y8nKEh4fj6NGjUCqV\nljnx8fGQy+WIi4tDfX09YmJikJiY2GZfRERERES2UF1Xgb3H/4Ifbp4Xjc8ImYO5016GvdzBqu/f\npWB+/PjxNmPr16/H+vXrn/gauVyO+Ph4xMfHd+UtiYiIiIis5lJOCvad+Atq66ssY2pXT/wqdgUC\n9WN7pYYuBXMiIiIiooGguq4Sn5/aiYvXz4jGI8b+BPOmL4HC3rHXamEwJyIiIqJB6VLOWew/8VfU\n1D+8NtzN2QO/jFmBUX4hvV4PgzkRERERDSpVtRXYf/K/kX7jnGg8PDga/xT5GhwdlE94pXUxmBMR\nERHRoCAIAi5eT8ZnJ3ei1lRtGXdz9kBc9BsIHjbRhtUxmBMRERHRIFBZW4Z9x/+CjNwLovFpY2Ix\nb/orNjtL/igGcyIiIiIasARBwPdXT+KLU/8rWpfc3UWDRdFv2ORa8idhMCciIiKiAamsqhj7ju9A\n1q2LonFbrLjSEQzmRERERDSgmAUzzv5wBF+d/RsamkyWcbWrJ34ZsxwjfMfZsLonYzAnIiIiogHD\nWF6IT49tw82iLMuYBBI8O34W5k57GQ597Cz5oxjMiYiIiKjfazG34Hja33H4u0/R3NJkGde667Eo\n5g0EeAfZsLqOYTAnIiIion6toDgXu4/9Fwru5VrGpBIpYsJewguTfw65nb0Nq+s4BnMiIiIi6pca\nmxvwzXf78G3aAZgFs2Vc7xmAX8Ysh14TYMPqOk/a3oTt27dj/PjxUKlUUKlUmDZtGg4dOmTZ/uqr\nr0IqlYoe06ZNE+2jsbERK1asgEajgbOzM+bNm4fCwsKePxoiIiIiGhSu38nABx+/haTUzy2hXC6z\nx4sRi7H6F/+334VyoANnzH19ffHHP/4RgYGBMJvN+OijjzB//nxcvHgRY8aMAQDExsbi448/hiAI\nAAB7e/GfC1auXImvv/4ae/fuhVqtxqpVqzBnzhxcvHgREonECodFRERERANRrakaXyZ/hPNZ34rG\nh3sHY1HMG/B097FRZd3XbjCfO3eu6PtNmzZhx44dOHfunCWYOzg4QKPRPPb1VVVV2LVrFxISEhAV\nFQUASExMhJ+fH44dO4bY2NjuHgMRERERDXCCIOBSzll8fnInqusrLeMKeyfMm74EU8fEQipp92KQ\nPq1T15ibzWbs27cPtbW1iIiIsIyfOXMGWq0Wbm5umDFjBjZv3mwJ6mlpaWhubhYFcL1ej6CgIKSk\npDCYExEREdFTlVUVY/+J/0ZmfqpofPzwKfjZc69D5ay2UWU9SyI8uP7kKa5cuYKpU6fCZDLBxcUF\nn3zyCWbNmgUA2LdvH5ycnODv74/8/HysW7cOLS0tuHjxIuRyOfbs2YMlS5agsbFRtM/o6GiMGDEC\nO3bsaPN+lZUPfwvKycnp7jESERERUT9kFsy4djcVl26dQLP54RKIjvYuCA/4CYZ6jLRhdW0FBgZa\nnqtUqk6/vkNnzEeNGoX09HRUVlbis88+w+LFi3Hq1CkEBwdj4cKFlnmjR4/GxIkT4efnh4MHD2L+\n/PmdLoiIiIiIqLTGgPM3D6K05q5ofKQuFBP8ZsLeTmGjyqynQ8Hczs4OAQGtn2ydMGECLly4gK1b\nt2Lnzp1t5np5eUGv11vOdOt0OrS0tKC0tBQeHh6WeUajEZGRke2+d1hYWIcOhNqXmtr65x/2tGex\nr9bBvloH+2od7Kv1sLfW0Zf72tBYj0Pn9+DkD/+A8MgSiDq1L+Ki30CA9ygbVvd0j1710RVdWsfc\nbDajoaHhsduKi4tRWFgILy8vAEBoaCjs7OyQlJSEuLg4AEBBQQGys7NF16kTERER0eCWkXsBn534\nK8prSixjMpkdnp/0c8SE/hPkdnIbVmd97QbztWvXYvbs2fD19UV1dTU++eQTnDp1CocOHUJtbS02\nbNiAl156CV5eXsjLy8O7774LnU6HBQsWAABcXV2xdOlSrFmzBhqNBmq1GqtXr0ZISAiio6OtfoBE\nRERE1LdV1JTi85M7kX7zvGh8hH4sFkb9c79eArEz2g3mBoMBL7/8MgwGA1QqFcaNG4cjR44gJiYG\nJpMJGRkZSExMREVFBby8vBAVFYX9+/dDqVRa9hEfHw+5XI64uDjU19cjJiYGiYmJXMOciIiIaBAz\nm1uQ/MNh/OPcJ2horLeMKx1dseDZVzFp1HODKi+2G8w//PDDJ25TKBQ4cuRIu28il8sRHx+P+Pj4\nzlVHRERERAPSbeMN7Dv+F9y+d0M0PiU4GvOmL4HS0dVGldlOl64xJyIiIiLqivqGWhw89wmSfzgi\n+nCn1l2PhVH/jED9GBtWZ1sM5kRERERkdYIgIO3aaRxI/hDVdRWWcTuZHM9P+hmiB8GHO9vDYE5E\nREREVmUsL8T+E/+N63d+EI2P8puAnz/3OjRuXjaqrG9hMCciIiIiq2hsbkDS95/hWNoBtLQ0W8ZV\nSjX+acZShDwzbVB9uLM9DOZERERE1OMy81Lx2amdKK00WsakEikiQ+ZgVngcHB2cbFhd38RgTkRE\nREQ9prTSiM9P/y+u5F4QjQ/zGolfzPxn+Gj8bVRZ38dgTkRERETd1tTciGNpB3Ds+8/R1NJoGXdS\nuODFiMWYMjoaUonUhhX2fQzmRERERNQtmXmp+PzU/6Ck0iAanzI6BnOnvQwXJ5WNKutfGMyJiIiI\nqEuedNmK3jMAP3/uN/D3GmmjyvonBnMiIiIi6pQnXrbi4Iw5036NaWNiIZXKbFhh/8RgTkREREQd\nIggCMnK/w4HTH6K0yijaNnV0LOZGvAxnR1cbVdf/tXsF/vbt2zF+/HioVCqoVCpMmzYNhw4dEs3Z\nsGEDfHx84OTkhJkzZyIrK0u0vbGxEStWrIBGo4GzszPmzZuHwsLCnj0SIiIiIrIaQ9kd7Pj7RvzP\nP/4gCuV6zwCsWvgBFsW8wVDeTe0Gc19fX/zxj3/EpUuXkJaWhqioKMyfPx9XrlwBAHzwwQfYunUr\ntm3bhtTUVHh6eiI2Nha1tbWWfaxcuRIHDhzA3r17cebMGVRVVWHOnDkQBMF6R0ZERERE3VbfUIsD\np3fhD5+8hau3L1vGnRQuWDjzn/Evv/i/vJa8h7R7KcvcuXNF32/atAk7duzAuXPnMGbMGMTHx2Pt\n2rWYP38+ACAhIQGenp7YvXs3li1bhqqqKuzatQsJCQmIiooCACQmJsLPzw/Hjh1DbGysFQ6LiIiI\niLrDLJjxffYJfHU2EdV1FZZxiUSK6WN/gp9OXQSlwsWGFQ48nbrG3Gw2Y9++faitrUVERATy8vJg\nMBhE4VqhUCAyMhIpKSlYtmwZUlNT0dzcLJqj1+sRFBSElJQUBnMiIiKiPuaW4To+O7kTt4w5ovHh\nPqPxsxn/hzcJspIOBfMrV65g6tSpMJlMcHFxwYEDBxAcHIxz585BIpFAq9WK5mu1WhQVFQEAjEYj\nZDIZPDw82swxGMRrXT5OampqR4+FOog9tQ721TrYV+tgX62DfbUe9tY6ftzXuoZqXLx1HLnFGaJx\nJ3sXhA6LwbAhwbh7qxR3b5X2Zpn9RmBgYLde36FgPmrUKKSnp6OyshKfffYZFi9ejLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x893eef0>"
+       "<matplotlib.figure.Figure at 0x88492e8>"
       ]
      },
      "metadata": {},
@@ -1827,9 +1826,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAADqCAYAAAB0gsLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b1f390>"
+       "<matplotlib.figure.Figure at 0x87a8cf8>"
       ]
      },
      "metadata": {},
@@ -1837,9 +1836,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvcAAAD+CAYAAAC+7ypaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87a8ba8>"
+       "<matplotlib.figure.Figure at 0x883d7f0>"
       ]
      },
      "metadata": {},
@@ -1877,9 +1876,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAADqCAYAAAB0gsLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99cefd0>"
+       "<matplotlib.figure.Figure at 0x892ad30>"
       ]
      },
      "metadata": {},
@@ -1887,9 +1886,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvcAAAD+CAYAAAC+7ypaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99ce400>"
+       "<matplotlib.figure.Figure at 0x74a15f8>"
       ]
      },
      "metadata": {},
@@ -1922,9 +1921,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAADqCAYAAAB0gsLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8864d68>"
+       "<matplotlib.figure.Figure at 0x8a32b00>"
       ]
      },
      "metadata": {},
@@ -1932,9 +1931,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwkAAAD+CAYAAABx5EmwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x88649e8>"
+       "<matplotlib.figure.Figure at 0x8a32860>"
       ]
      },
      "metadata": {},
@@ -2094,9 +2093,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAADaCAYAAAB+WC5eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuIAAADaCAYAAADjVSfWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9acbd30>"
+       "<matplotlib.figure.Figure at 0x87b6860>"
       ]
      },
      "metadata": {},
@@ -2135,9 +2134,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwkAAADaCAYAAADt2Q6QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw0AAADaCAYAAADkMq7qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87a1e10>"
+       "<matplotlib.figure.Figure at 0x87c78d0>"
       ]
      },
      "metadata": {},
@@ -2228,9 +2227,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAADqCAYAAADAgaGTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYVMfXwPHvsoiAAkoXNUqMvcVGxIItKvauid1YIzGx\n917Q2HvD2DX+7D1KjMaGRuy9xS69I4Kw7H3/INw3xIa4CMr5PA/Pwty7M3MZytnZuWc0iqIoCCGE\nEEIIIT4qRhndASGEEEIIIcS7k0BeCCGEEEKIj5AE8kIIIYQQQnyEJJAXQgghhBDiIySBvBBCCCGE\nEB8h44zuwIcQGRmZ0V0QQgghhBAizaysrF4qkxl5IYQQQgghPkISyAshhBBCCPERyhJLa/7tVW9L\nfAhnz54FoGLFihnSvsg4MvZZm4x/1iVjn3XJ2Gddhh77ty0Plxl5IYQQQgghPkISyAshhBBCCPER\nynJLa4QQQgghhHiTy5cvs3btWp4/f46bmxvt2rVDo9FkdLdeIoG8EEIIIYTIkh4/fszZs2exsLCg\nWrVqmJqa4u3tTZMmTYiPjwdgyZIl+Pj4MH/+/Azu7csybGnNsWPHaNq0Kfny5cPIyIg1a9a8dM74\n8ePJmzcv5ubm1KpVi+vXr6c4/uLFC/r164ednR05c+akWbNmPH369ENdghBCCCGE+Eht2LCBL774\ngpYtW1K3bl0qVKjAkydP6NGjB/Hx8bRr146JEydiYmLCggULOHPmTEZ3+SUZFsjHxMRQpkwZ5s2b\nh5mZ2UtvV/z888/Mnj2bhQsX4uvri729PXXr1uXZs2fqOf3792f79u1s2rSJ48ePExUVRePGjdHr\n9R/6coQQQgghxEfizp07dOvWjfj4eNzc3MifPz/Xr1+nY8eOPH78GCMjIzZu3MiYMWNo0qQJADdu\n3MjgXr8swwL5Bg0aMHnyZFq1aoWRUcpuKIrC3LlzGTFiBC1atKBkyZKsWbOG6OhoNm7cCCSl41m5\nciUzZ86kTp06lCtXjnXr1nH58mUOHTqUEZckhBBCCCE+AsePHychIYGGDRty9OhRNW3k8ePHsbS0\nRK/XM3fuXLy9vTl69CgATk5OGdnlV8qUWWvu379PYGAg9erVU8tMTU1xc3PDx8cHgHPnzpGQkJDi\nnHz58lG8eHH1HCGEEEIIIf4re/bsAAQEBBAXF8ejR48AMDExYcKECQAMGjSI+vXrExISQo0aNahV\nq1aG9fd1MuXNrgEBAQA4ODikKLe3t8fPz089R6vVYmNjk+IcBwcHAgMDP0xHhRBCCCHER8fd3R0H\nBwfOnz+PtbU1sbGxAHTu3Jn+/ftjbW2Nl5cXMTEx1KhRg8mTJ2NsnPnC5szXo7d439Q/yW+dZJSM\nbl9kHBn7rE3GP+uSsc+6ZOwzt1mzZjFq1CgePnyIRqOhYcOGdOrUibNnz1KiRAnmzJmjnvuu6+MN\nNfaFCxd+4/FMGcg7OjoCEBgYSL58+dTywMBA9ZijoyOJiYmEhoammJUPCAjAzc3tw3ZYCCGEEEJk\nKH9/f44dO0Z8fDwuLi4ULVr0jecXLVqULVu2EBkZiampKaamph+op4aTKQN5Z2dnHB0d8fb2pkKF\nCgDExcVx4sQJZs6cCUCFChXIli0b3t7efPvttwA8efKEmzdvUqVKldfWXbFixfS/gFdIfmWWUe2L\njCNjn7XJ+GddMvZZl4z9h3fs2DE6dOhAdHQ0AEZGRixdupSePXt+0H4YeuwjIyPfeDxD009evHiR\nixcvotfrefjwIRcvXuTx48doNBr69+/Pzz//zI4dO7h69Spdu3bFwsKC9u3bA2BlZUX37t0ZOnQo\nf/zxBxcuXKBTp06ULVuWr7/+OqMuSwghhBBCpJObN29SrVo1TExMcHBwwNPTE51OpwbxderUoUOH\nDuj1evr27cvjx4+BpIyI0dHRKIqSwVdgWBk2I+/r60vt2rWBpHXv48aNY9y4cXTt2pWVK1cydOhQ\nYmNj8fDwIDw8nMqVK+Pt7U2OHDnUOubOnYuxsTHt2rUjNjaWr7/+mvXr12fKLXSFEEIIIUTahYSE\nULt2bfz9/QEICgpi1KhRxMTE8OTJEywtLTl48CBarZbHjx9z7NgxLly4wMGDBxk6dCjh4eE4ODiw\nePFiWrZsmcFXYxgZNiNfs2ZN9Ho9er2exMRE9fOVK1eq54wbNw4/Pz9iY2M5cuQIJUqUSFGHiYkJ\n8+fPJyQkhJiYGHbt2kXevHk/9KUIIYQQQoh0tn37dvz9/alQoQLBwcGsXr0agNWrV6PRaIiKiuLw\n4cNcv35dvTn16tWr9OzZk/DwcLRaLYGBgbRp04aTJ09m4JUYTqbMIy+EEEIIIbK2u3fvcuzYMTUt\nefJ68S+//BJbW1s1r3t0dDS9evUCoF69epQsWZLg4GC++uorNWAfO3YsCQkJ9OrVC71ez6pVqzLg\nigwvU97sKoQQQgghsiadTkf37t1Zu3YtAFqtlhkzZuDq6grAmjVriI+P59y5cwC4uroyf/58LC0t\nWbduHfHx8dStW5dFixbRpk0bAEqWLIlGo6FkyZIAPH/+PAOuzPBkRl4IIYQQQqSrK1eusH79eg4e\nPEhCQsIbz506dSpr167FxMSE8uXLk5iYyMCBA3n+/Dljx45Fp9Oxbt06rl+/jrOzM8uWLcPExITp\n06fj7+9PaGgomzZtwsbGhho1agDQq1cv2rRpw/DhwwHU8o+dBPJCCCGEECLdTJgwgTJlytCpUyfc\n3d2pVq0a4eHhrz1/9+7dAGzcuJFz584xePBgAPbu3cuECRM4deoU8+bNY+3atVy8eJGCBQu+tq7h\nw4fTqFEjIiMj2bp1K7GxsXTp0uWDp6VML7K0RgghhBBCpItDhw4xfvx4tFotjRs35q+//uLMmTMM\nGDBAvVn1v4yMkuaZ4+LiUjwmZyWsXLkylStXTlX72bNnZ/fu3Rw7doyHDx9SuHBhXF1dP5kMhxLI\nCyGEEEJ8IpLzpVtYWGSKYPXPP/8EwMPDg3nz5uHr64uLi4ta/tdff7F9+3b0ej2NGjWiZs2atGnT\nhjNnztCpUyfGjh3LvXv30Gg0tGrVKk19MDIyombNmoa5oExGltYIIYQQQnwCtm/fjpOTE1ZWVtjY\n2ODl5ZUu7ezatYvu3bvTs2dPDh48mKJ9FxcXChYsSPPmzXnw4AHm5uYA3LlzB51Op6aFNDc3Z/36\n9VSpUoXp06czc+ZMatWqxaJFixg4cCD9+vVDURTu3buHqakpXl5euLm5pcv1fMxkRl4IIYQQ4iPn\n4+NDmzZt0Ov1aLVawsPD6dWrF7a2trRo0cJg7Xh6ejJq1Cj16xUrVrBw4ULy5MmTYsb84cOHnD17\nlu3bt+Pp6clvv/2GmZkZOp0OgI4dO9K7d2/0ej3du3fH3NycBQsW0L9/f1q3bs38+fMZM2YM/v7+\nODs7Y2FhYbBr+JTIjLwQQgghxEdu1apV6PV6evXqRUJCAuPHjweSAm1D8ff3Z8yYMUBSXvahQ4cC\nMHDgQCZNmgTAkCFDuHLlCuXLl+fp06ecOHGC3377DWdnZ3Q6HWZmZowbN47GjRvz/PlzChYsyIoV\nK5g/fz7ly5dHp9Nx69YtAOzs7ChTpowE8W8gM/JCCCGEEB+55LzoJUqUSLd86Xfu3EGv11OhQgUm\nTJgAwLZt2/j777/VTZu6du1KiRIlaNCgAefPnycwMJDq1atz7949oqKiyJEjB1qtlqCgICBp5n7r\n1q2YmZlx7do1ABwdHQ3W50+dBPJCCCGEEB+5mjVrsnHjRkaMGMHx48c5dOgQYNh86fnz5wfg0qVL\nrFu3jri4OP7++280Gg1lypQhICCArl274u7uzqJFi4CkXViTWVpaqp/b29vz/fffs2TJEnXTJoB2\n7dpRpEgRg/X5UyeBvBBCCCHER6579+74+PiwevVqtm3bBkCjRo0YMWKEwdpwdnamV69eLF++nM6d\nO6vlgwcPpk+fPlSrVg1fX198fX0BaNmyJe3atXttfQsWLCBv3rxs3rwZRVFo3LixuiRIpI4E8kII\nIYQQHzkjIyNWrlxJr169uH37NgUKFMDNzQ0jIyPi4+OZOnUq3t7eZMuWjU6dOvHdd9+9NT3ljRs3\nuH79Ok5OTnz11VcYGRmxePFiihQpwp49e9BoNLRp04bvv/8ejUbDxYsXWbNmDUFBQXz55Zd8++23\nak74V9FqtYwaNSrFzbPi3UggL4QQQgjxCdBoNLi6uuLq6qqWKYpChw4d2Lp1q1p29OhR/P39GT16\n9GvrGj16NFOmTFG/btCgAdu2bcPMzIxBgwYxaNCgl55jb2/PkCFDDHQ1IjUka40QQgghxCfqwoUL\nbN26FXNzc7Zu3cqCBQsAmDRpErGxscTFxbFv3z7Wr1/P9evXgaQ88VOmTEGr1VK/fn0sLS357bff\nGDt2bEZeingFmZEXQgghhEhnoaGhHDhwgLi4OKpVq0bRokU/SLtPnjwBoHLlyrRq1QpFURg3bhxh\nYWFcv36dbt26ceXKFSBpRn/mzJncv38fgOHDhzN58mQOHDhAgwYN1BtoReYhM/JCCCGEEOno8uXL\nlCxZko4dO9KjRw9KlSrFmjVr1OMPHjzgzz//5OHDh2rZ8+fPGTJkCC4uLtSsWZP169enqe3kFwxH\njx5l0qRJ9O3bl7CwMCwtLZkwYQJXrlzhs88+o0mTJgAMGjSIsLAwAB49eoSiKDx69AiA7Nmzp6kP\nIv1IIC+EEEIIkU4UReGbb74hMDCQcuXK0aRJE3Q6HT179uT+/fsMGDAAZ2dnatWqRcGCBRk2bBg6\nnY6mTZsyc+ZMfH19OXr0KJ06dWL+/Pnv3H7RokUZPnw4iYmJjB07lqVLl2JkZMSSJLine truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86c2ef0>"
+       "<matplotlib.figure.Figure at 0x9aa0c88>"
       ]
      },
      "metadata": {},
@@ -2330,9 +2329,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAADxCAYAAABLTEiyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86c2438>"
+       "<matplotlib.figure.Figure at 0x9aa0898>"
       ]
      },
      "metadata": {},
@@ -2413,9 +2412,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAADaCAYAAAB+WC5eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAADaCAYAAAAMl0zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f67898>"
+       "<matplotlib.figure.Figure at 0x9a92d68>"
       ]
      },
      "metadata": {},
@@ -2449,9 +2448,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAADaCAYAAAB3s44kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt4AAADaCAYAAACRmkVgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x88e2588>"
+       "<matplotlib.figure.Figure at 0x9b785c0>"
       ]
      },
      "metadata": {},
@@ -2501,9 +2500,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAADaCAYAAAB3s44kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt4AAADaCAYAAACRmkVgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f8f128>"
+       "<matplotlib.figure.Figure at 0x9b2b1d0>"
       ]
      },
      "metadata": {},
@@ -2587,9 +2586,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAD+CAYAAAA3fihfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9af0da0>"
+       "<matplotlib.figure.Figure at 0x9c0e6d8>"
       ]
      },
      "metadata": {},
@@ -2597,9 +2596,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f8f160>"
+       "<matplotlib.figure.Figure at 0x8960cc0>"
       ]
      },
      "metadata": {},
@@ -2607,9 +2606,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6wAAAD+CAYAAAA+lYglAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99e5e80>"
+       "<matplotlib.figure.Figure at 0x8960da0>"
       ]
      },
      "metadata": {},
@@ -2666,9 +2665,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAD+CAYAAAA3fihfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a5c3c8>"
+       "<matplotlib.figure.Figure at 0x9c31438>"
       ]
      },
      "metadata": {},
@@ -2676,9 +2675,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a5c438>"
+       "<matplotlib.figure.Figure at 0x9a3a9e8>"
       ]
      },
      "metadata": {},
@@ -2686,9 +2685,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9acfc18>"
+       "<matplotlib.figure.Figure at 0x9c78f98>"
       ]
      },
      "metadata": {},
@@ -2795,9 +2794,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x872b8d0>"
+       "<matplotlib.figure.Figure at 0x9a92ef0>"
       ]
      },
      "metadata": {},
@@ -2966,9 +2965,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAADxCAYAAABLTEiyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdcV9X/wPHX/XzYS0D2EFCR4ciBG/dKTcs9KrdpjnLk\nSjPNbamZOVrmSi2/mnvvPcCJuBDEgYCTjYzP/f1BffoRaKGgGO/n40H5Ofecc8/5XJD353ru+yiq\nqqoIIYQQQgghXluaVz0AIYQQQgghxIuRoF4IIYQQQojXnAT1QgghhBBCvOYkqBdCCCGEEOI1J0G9\nEEIIIYQQrzkJ6oUQQgghhHjNSVAvhBBCCCHEa+6VB/WHDh3i7bffxs3NDY1Gw7Jly3LUmTBhAq6u\nrpiZmdGgQQNCQ0OzHU9LS2Pw4MHY29tjYWHB22+/zZ07d17WFIQQQgghhHilXnlQn5iYSPny5fnm\nm28wMzPLcXzGjBnMmTOH+fPnExQUhIODA02aNCEpKUlf5+OPP+b333/n119/5fDhw8THx/PWW28h\n+2oJIYQQQoiiQClMO8paWloyf/58unXrpi9zcXHho48+YvTo0QCkpqbi4ODArFmz6Nu3L/Hx8djb\n27N06VI6d+4MwO3bt/Hw8GD79u00adLklcxFCCGEEEKIl+WV36l/loiICKKjo7MF5iYmJtStW5ej\nR48CEBQUREZGRrY6bm5u+Pn56esIIYQQQgjxX1aog/ro6GgURcHR0TFbuaOjI9HR0QDExMSg1Wop\nXrz4U+sIIYQQQgjxX2bwqgfwssXFxb3qIQghhBBCCPHcihUrlqOsUN+pd3JyQlVVYmJispXHxMTg\n5OSkr5OZmcmDBw+eWkcIIYQQQoj/skId1Ht5eeHk5MSuXbv0ZampqRw6dIjatWsDUKVKFQwMDLLV\nuX37NpcuXdLXEUIIIYQQ4r/slS+/SUpKIiwsDFVV0el03Lx5k3PnzmFra4u7uztDhgxh2rRp+Pj4\n4O3tzeTJk7G0tKRLly4AWFlZ0bt3b0aOHIm9vT22trYMHz6cihUr0qhRo2eeO7d/uihIQUFBAAQE\nBLzU84pXR6550STXveiRa170yDUvHJo1a8bOnTtZt24dbdq0YdWq3+jaZwrlavWjYuCH7DmVQvQj\n06e2V5RMnIpfRhO3mY87+PPZJ51ITU3VH2/YsCE7duzAwCArZH6V1/2flpC/8qA+KCiIBg0aoCgK\nAJ9//jmff/453bt3Z/HixYwcOZLU1FQGDRrEo0ePqF69Ojt37sTc3Fzfx9y5czE0NKRz586kpKTQ\nuHFjli9fru9TCCGEEEK8XBmZ6aQ8ScLSzDrHsfSMNG5EX6Gksx9a7fOHo2XKlGHn/hAGTw1j3Mrb\nXLnTAqViBy4mw8WdADkDehPNQ1KjN1C1WiSebuc5sHo/b5StyfABo2ha5wQLFizgwYMHBAQEMGTI\nEH1AX9gVqjz1L8P//5Qjd+pFQZNrXjTJdS965JoXPXLNny0+6RGLt84kLeMJQzpMw8jAONvx0BvB\nLNowCVNjc/w9q1DOqyp+npUwM7b4V/3HPlL53z5YsS2N4xcNQHn6inKtkoZZ5jnib66hSqn7HNg2\nn+HDh7Fl+0bS0zJp2fwt5s6di4XFP5+7sNypzy2GfT0+egghhBBCiNdCZPRVftwyg7jErCQmv+5Z\nyHtNP862giIk/BQAKU+SCL5ykOArB9FotDQNaE+Lml1y9JnyRCXoUga/bY3kUIgVITft0KkKYAR/\nW5jhZKvDwfQa5w4v4u2mxZk6sS02Ru64uHzFpcdmmJv/zKJFi4BFBfUWvBIS1AshhBBCiHxx/OIe\nft23kMzMDAAURYNz8RI56l2/eoPExylYWP+1PEany8TGyp7Ll6+wYm0wkQ+d0ZlV4fJtSy5cV8nI\nNABK5ehLo4EGlaFjI2haDUo4aliy5BjTr/8Pl7KVWLr1Mq66rDvrtra2BTPxQkCCeiGEEEII8cKu\n3DzHyt3z9K/NjC3o3nw4fh6VstULDg5m3MAv0el01GpQFdUshWLulUjR1uLk6dKcvGQKhn+/W5/L\nc5Lxhxjb35tBXZxwtP3reMqTZHR2sTTrlhXIp6QlsevirwAMHz48fyZbCElQL4QQQgghXlgZ9wpU\nLlOH01cP4VLcg95vjcbe2jlHvW3btqFTrAhsMw23cv3YdSKNh+eN/qpgmHv/1pa3cbS9hrPddZxN\nj3P+yF4cGYqj7Uf6OpHRV1mybRYP4v/a4yjhYQp3LiQwd+5cBg8enG/zLWwkqBdCCCGEKIJUVeXM\nmTNERUXh7+9PyZIlX6g/RVHo2ngQtpb2NKvWAWOj7JlnbsWobDgES052haojORJjCDEARjn6Kl4M\nAiuo7N04HSOCaNvTBGOj5Gx16rxTDiOj7G0zMtN5mHBP/7qaXwPa1/8Ak4lPT2v5XyFBvRBCCCFE\nEZOamkqnTp3YuHGjvmzSpEmMGzfuhfo1MjSmdWA3AJJTVZauC2PO4jPcSSpLisb/j1peOZLVqOkP\nKWEdwc0LyzDPPMX+fYtJSUliy+wJxGekk3njfU5GXKFkOSfsXLMyv8REPKbVuLez9VPKtSxNq7bn\n4NnNdGo0gMplAl9oPq8TCeqFEEIIIYqYCRMmsHHjRiwsLAgICODAgQN89tlnVK5cmRYtWuTaRqfT\nsWDBAnbu2knx0lqa1GhD1w7d9MdTnqgcC4F9wbD/DJy4qCMjszRQGnLJOKlJDiLz3u/weBvvtanA\n998vonnz8xw4cJxy5fz19dq1a8f82T/w4YcfsuSrJVhYm1Clrj+D+g/F2Tnn8p43q3eiVrkm2Fja\nv/D79DqRoF4IIYQQoojZsWMHAL/++istWrRg9OjRzJgxgx07djw1qO/Xrx8rVi2jec+qWJawY3PQ\nEq5ez8Teuwebj2SyLxjSMrT/r0X2SF4hHd2jPTQJSOHnWW2wMS9LeLgpxYsP1gfn69evZ8CAAWze\nvBmNRkOHDh34+uuvMTY2ZvHixXzzzTckJCTg6OiIRpN7bnqtRlvkAnqQoF4IIYQQosgxNMx6GjU2\nNhZVVYmNjc1W/ncXLlxgw/Y1dBzWiISMKhw+W5nIuwGsTnD7o4Y213ZqcgjvNCjGe63cuX3xF4YM\n7oVD5XdxtW8LmFGuXLls9a2trVm5cuVTx21hYfGvNokqiiSoF0IIIYQoYt59911OnTpFz549GTFi\nBPfv30er1dKpU6ccdeOTVOati8S61u/8djCAJ+lPD6pNiCTl7hYMk4/wZm0zNq37iZuaytwt2ZNv\n5swBwNfXt8DmVZRJUC+EEEII8ZpITExk7ty5XL58GTc3NwYPHoyLi0ue+xk8eDAxMTF8+eWX3L9/\nH2tra77//nuqVq3KkzSV0Btw/CJsOgx7gyEtvWXuHelSUOP2wqNtBO+eTOWynjRtuoFdu3bRuN5c\nzpx04/Tp05w+fRqAihUrMmTIkBd4B8TTSFAvhBBCCPEaSExMJDAwkHPnzunLfv75Z06cOIGHh0ee\n+tJoNEydOpVhn4xl57EE7jy2Y/NlLZO7qVy6ARmZT2+rpN9Cd38jPNrCrM9bM2rER2RkZGCo609q\nqql+KY+zszPBwcEsWLCAmzdv4uPjw6BBgzA3N3+e6Yt/IEG9EEIIIcRrYN68eZw7d46SJUsycuRI\nlixZwvHjxxk7diwrVqzIU1/hd1QW/A6LN5vxOMHsH+tX9IaWtTJIv7eWy6d+xdjLiPfeG0CrVq04\nf/YES5cupUKFCpiampKSkoKzszONGzfGxsaGCRMmPOeMRV5IUC+EEEII8Rq4cuUKAMOHD6dfv374\n+vpSv359Ll++/K/a63Qqu0/B/LWw+Sio6tPr2lk8xkIJp4zTXSYOCaB6RSeytnrt/MfXX+bPn09a\nWhqrVq0iJSUFX19ffv31V2xsbJ5vouK5SFAvhBBCCPEacHd3B2DJkiX4+vry7bff6ssfPXrEsGHD\n2LdvH2ZmZvTu3ZuhQ4eS8kQh+AocvQBLt8KVmzn79XKBhlWgQmkd8UlbuBG1krVfbyEiNpEI4PRW\ne44ePUrp0qVzHZe5uTkrV67k+++/Jzk5GXt7exRFKai3QTyFBPVCCCGEEK+IqqrcvXuXjIwM3Nzc\nnpp7HWDgwIEsXryYU6dO0ahRIwBMTU0ZPXo0b775JidPngSTMmBVnU8WmPPVzhjupziSmZl7gP1m\nDRjULuv/KU8SWLJtFjGPz2FqBq371aK8TWN+X7uBI0eO8Mknn7B+/fpnzkXSTb5aEtQLIYQQQrwC\n0dHRdOzYkUOHDgFQvnx51q5di7e3d671nZycOH78OOPGjePKlSu4uroyYtRn7AnK5OS999BW/Q2d\nYQl9/ZjEnH2oGXG4Gu5i94q2+Hpm5Za/cy+CHzdP50F8jL6efTFnBgwYSMP6jalcuTKXLl3Kx5mL\ngiBBvRBCCCHES6aqKh06dODw4cOYmJhgaGjIhQsXaNmyJRcuXMDY2DjXdu7u7nw9bwlr98OWo9B4\nDCSnguJcGV2uJ9KhpoTSIMCcWuV1LJzUiDv3b3L/9iHwDCTlSTLz1n5G8pO/PgGc2H6ZtGhT3qlx\nhp9++gngudJmipdLgnohhBBCiJfszp07HD58GDMzM8LDwzE3N8fPz49r164RHBxMrVq1crQJCVeZ\n9z/4ZUdWIJ8bQ00q1przxF7fgJ1JOPdvbMXCVGV3cBwajYYTm8qwe/dNfdpJU2MzWgd2Z/We+Rgb\nmfJOzd5smN2bmzevEBgYCIBWq2X8+PEF9l6I/CFBvRBCCCHES5aeng6AoaEhlpaWGBsbY2aWlVoy\nIyNDXy8jQ2XjYfj2f7D/TO59ebtDcc1xjm0dR1r8IWLVdMzNzVmzeTNduhwgOjqadLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87226d8>"
+       "<matplotlib.figure.Figure at 0x9b78b70>"
       ]
      },
      "metadata": {},
@@ -3156,9 +3155,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAADaCAYAAAAFfOySAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAADaCAYAAAAfQAwcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87cd160>"
+       "<matplotlib.figure.Figure at 0x886e278>"
       ]
      },
      "metadata": {},
@@ -3463,9 +3462,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAADaCAYAAADwgmciAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu4AAADaCAYAAAD5acdYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x88d1550>"
+       "<matplotlib.figure.Figure at 0x8994e80>"
       ]
      },
      "metadata": {},
@@ -3584,9 +3583,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAADaCAYAAAAIYpzVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuUAAADaCAYAAAABiTyvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a4a6a0>"
+       "<matplotlib.figure.Figure at 0x87fe828>"
       ]
      },
      "metadata": {},
@@ -3733,9 +3732,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAADaCAYAAAAIYpzVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuUAAADaCAYAAAABiTyvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a5ecf8>"
+       "<matplotlib.figure.Figure at 0x9b569b0>"
       ]
      },
      "metadata": {},
@@ -3803,9 +3802,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAADaCAYAAAB+WC5eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8752358>"
+       "<matplotlib.figure.Figure at 0x88842b0>"
       ]
      },
      "metadata": {},
@@ -3813,9 +3812,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuUAAADaCAYAAAABiTyvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f830b8>"
+       "<matplotlib.figure.Figure at 0x88d8e48>"
       ]
      },
      "metadata": {},
@@ -3877,9 +3876,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAADaCAYAAADwgmciAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x881d668>"
+       "<matplotlib.figure.Figure at 0x9f71f98>"
       ]
      },
      "metadata": {},
@@ -3887,9 +3886,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu4AAADaCAYAAAD5acdYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a2be10>"
+       "<matplotlib.figure.Figure at 0x9ba64a8>"
       ]
      },
      "metadata": {},

09-Nonlinear-Filtering.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -28,7 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -42,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -123,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -262,7 +261,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -377,16 +376,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAADaCAYAAABkZM7QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAADaCAYAAABper6XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x859fbe0>"
+       "<matplotlib.figure.Figure at 0x72eb8d0>"
       ]
      },
      "metadata": {},
@@ -435,16 +434,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu8AAADaCAYAAAAWq6xmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8555278>"
+       "<matplotlib.figure.Figure at 0x72c9470>"
       ]
      },
      "metadata": {},
@@ -468,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -476,9 +475,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAFfCAYAAAB0jrv4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x833a048>"
+       "<matplotlib.figure.Figure at 0xa7b4278>"
       ]
      },
      "metadata": {},
@@ -510,16 +509,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAFfCAYAAAB0jrv4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa8972e8>"
+       "<matplotlib.figure.Figure at 0xab40470>"
       ]
      },
      "metadata": {},
@@ -552,16 +551,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAFfCAYAAAB0jrv4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x847a160>"
+       "<matplotlib.figure.Figure at 0xaa2a908>"
       ]
      },
      "metadata": {},
@@ -584,7 +583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -593,8 +592,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "input  mean, variance: -0.0005, 0.9999\n",
-      "output mean, variance: -0.1240, 2.4029\n"
+      "input  mean, variance: 0.0000, 0.9999\n",
+      "output mean, variance: -0.1251, 2.4036\n"
      ]
     }
    ],
@@ -616,16 +615,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAFfCAYAAAB0jrv4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xad2a3c8>"
+       "<matplotlib.figure.Figure at 0xab45630>"
       ]
      },
      "metadata": {},
@@ -635,7 +634,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output mean, variance: 0.0007, 2.2498\n"
+      "output mean, variance: -0.0001, 2.2498\n"
      ]
     }
    ],
@@ -659,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -668,8 +667,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "linear    output mean, variance: 0.0404, 7481.2295\n",
-      "nonlinear output mean, variance: -9.0832, 30461.4199\n"
+      "linear    output mean, variance: -0.0043, 7481.0839\n",
+      "nonlinear output mean, variance: -9.2225, 30507.1449\n"
      ]
     }
    ],
@@ -697,16 +696,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcVPX+P/DXmWGRddgRBAQEFBUVHVDBDdc0Nc3UNi2t\n69XMyG71vZWVdcuy9dpimS1WtqjX0lxLE0RzYwD3VBQRBAEB2ZFl5vz+8OfkNIAKM3Nmhtfz8eDR\nzOdzzvm8Tozj28985hxBFEURRERERETUajKpAxARERERWToW1UREREREbcSimoiIiIiojVhUExER\nERG1EYtqIiIiIqI2YlFNRERERNRGLKqJiIiIiNqIRTVJTiaTQSaznJfiww8/DJlMht27d0sdhYiI\niMyE5VQyZNUEQZA6wm2zxMxERERkHCyqiVqJNyMlIiKi61hUk1nKzs6GTCZDQkICSkpKMGfOHPj5\n+aFDhw7o2bMnVq1apbdPcnIyZDIZZs2ahZMnT2LixInw8PCAs7MzhgwZgt9//11vn8WLF0MmkyEl\nJaXJHNczXBccHIxvvvkGAJCQkKBdumJJy1eIiIjI8GykDkDUkrKyMsTHx8Pe3h7Tpk1DXV0d1q5d\ni9mzZ0Mmk2HmzJl6+5w/fx7x8fHo06cP5s2bh4sXL2Lt2rUYM2YM1q5di7vvvvu2Mty4zGPhwoVY\ntWoVjhw5gocffhjBwcFtPUUiIiKyAiyqyawdOXIEjz76KFasWKEtbhMTE9GrVy8sXbq0yaI6JSUF\nzzzzDJYuXaptmz9/PuLj4zFnzhyMGTMGTk5OrcqTmJiIjIwMbVE9ZMiQ1p0YERERWRV+Zk1mzcnJ\nCe+9957ObHFkZCTi4uJw6tQp1NTU6O3j5uaGl156SactNjYW06ZNQ2lpKTZu3Gj03ERERNS+sKgm\nsxYeHg5nZ2e99sDAQIiiiCtXruj19e3bt8mZ6OuzyocPHzZ8UCIiImrXWFSTWXNzc2uy3cbm2sol\ntVqt1+fr69vkPtfby8vLDZSOiIiI6BoW1WR1CgsLW2xXKBTatutX7WhsbNTbvqyszAjpiIiIyBqx\nqCark56ejqqqKr3263dAjI6O1ra5u7sDAHJycvS2T01NbfL4crkcQNOz5ERERNQ+sagmq1NWVoZX\nX31Vp+3gwYNYu3YtPDw8cNddd2nbBwwYAAD44osvdGari4uL8cwzzzR5fE9PTwDAhQsXDB2diIiI\nLBQvqUdWZ/DgwVi5ciUOHTqEuLg45OXlYc2aNRAEAZ999hkcHR2128bExCAhIQFJSUlQKpUYMWIE\nSktLsXXrVowcORJHjx7VO/7o0aPxzjvv4LnnnsOxY8fg7u4OQRDwwgsvmPI0iYiIyIxwpposkiAI\nOpfZu1GXLl2wf/9+KBQKfPrpp1i/fj369++PX3/9tckbv/z888+YO3cuioqK8PHHH+PAgQN45pln\n8O233zZ5/JEjR2LZsmXw9PTE8uXL8dJLL+ldwo+IiIjaF0EURVHqEESGkJycjOHDh+Phhx/Gl19+\nKXUcIiIiakc4U01ERERE1EYsqomIiIiI2ohFNRERERFRG5l8TTXvZkdE7cmNNxsiIiLrxZlqIiIi\nIqI2YlFNRERERNRGkt78xVo+FlWpVAAApVIpcRLD4PmYN56P+eMyNyKi9ocz1UREREREbcSimoiI\niIiojVhUExERERG1EYtqIiIiIqI2YlFNRERERNRGLKqJiIiIiNqIRTW1K2VVJaitq5Y6BhEREVkZ\nSa9TTWQIDY31OHx2H8qrSqHsNhRuzp6orq3AzrSfIAhyeMlDUddYg+UbXsGpCxkQBBmCfLpgeL9J\niA6Plzo+ERERWQEW1WTRSioK8cWWpbhYlAUA2KFajxmjn8SW/d8hrzhbu50AASJEAIAoanChMBOr\ntr4Dt2leCPHrKkV0IiIisiKCKIqiKQe88U5jmZmZphyarExJVQF2nvgedY01rT5GZ89IDO02xYCp\niIDw8HDtY2u5cywREbWMM9VkkS5XXsTOEz+gQV3XpuNcKPkTO098D4WDFzycfdGgbkBRRS6uNlQh\n2Ks7wn37QhAEA6UmIiIiayVpUa1UKqUc3mBUKhUAno+pnMk9hjWHfmxVQX33kEeQlPELrlRe1rbl\nl2UhvyxLb9uC8gvw6+SHhOiJbcpraOb++7ld1nY+gO4nckRE1D5wpposxqkLh/F7+s84nXNEr294\n30kI8euGr7a9DY1G3eT+9wybgyG9x8FGbou1SZ/e0pgb9qyCv2dndO4Ygaracni6+nLmmoiIiPSw\nqCaz1qhuwOmcIzj45y4cztzX5DZ3xE7H2AH3QhAEzByzEN/t+AANjfXw9QjAfSMexx+qXfB2CcCQ\n3uMAADHdhmLzvtWoqau66fiiqMHHP78MmSCDRtSgk1cwxg64F1Gh/VlcExERkRaLajJbNVer8N7a\n/0PRlbxmt5kQNwOjYv76omHfiEHo4t8d+SUX0LljOBztnVHqr1s829s5YMaYJ/Hjrk8AAF38I+HY\nwQW1V6tgI7dFwZWLuFBwRmcfjagBAOQVZ+PzzW9C2XUo7h35GOxs7A11ukRERGTBWFST2UrO2NRs\nQe3nGYQxsdPQN2KQXp/C2QMKZ48Wj90jRIlXZ3/e7Gzz/5JXIuXIlmb3V53ejeKKAiy4+z+wtbFr\ncSwiIiKyfiyqyWw0NDYgu+AUauuq4ezghoMnf9fbxt+zM6aPeMwg15ZuafnGiH6TsffoNu0MdVOy\nL53GDtV6dPYNR0ePQHgqfNuciYiIiCwTi2oyC/WNdfho/UvILjjd7DYzxjyJvhGDIZfJjZ7H3cUL\nsd2H48CJnTrtbs6eKKsq0T7ffnANAMDOxh7/vGsRwgOijJ6NiIiIzI9M6gBEALD78JYWC2plt6GI\n6TbMJAX1dWNipsLR3hnAtTsyzh73LJ69/33Y2XbQ27a+sQ7f7/gI9Y1tu242ERERWSbOVJPkqmor\nsCP1fy1uM6D7CBOl+YunwhdPTX8Lx8+norNvGLp06gEAiOs5GskZv+htX1JRiN9VP2PsgHtNHZWI\niIgkxqKaJKHRqJFdkIlLJReQlL4RV+ubv9W4t8IPYQE9TZjuLz7u/hjufpdOW0L0BKQc2dLk9bB3\nqNajZ2gsAn1CTRWRiIiIzACLajI5URSxescHUJ3a3WT/xPiZcLB3wg7VenSwdcD9oxZAJpjPSiV3\nF29MiJuBjXtX6fU1qhvw5ZalePq+d+DUwcX04YiIiEgSLKrJ5E6cVzVbUPu4+WNon/GwtbFDXM/R\nZnuDlRH9JiEqNAaN6gZcKsnF19vf1faVVBRi7a5PMWvcMxImJCIiIlMSRFEUTTlgeXm59nFmZqYp\nhyYzoNaosSljBSqulur1+SlCEBc+Hk72CgmStc3Bc9twuiBNp21Ujwfg5xYiUSKSUnh4uPaxQmF5\nr2ciIrp95vOZOlk9jahB6vlf9Qpqb5cAJHSbhpE97rfIghoAlCGj4eHUUact+dT/UFyZL1EiIiIi\nMiVJZ6qtZQZHpVIBAJRKpcRJDMMY5yOKIr7e/h7Sz+zRaY/rOQr3jphvsHGaYqrfT3bBGby35lm9\ndn/PzhjS504M7DHKIMtZ+Hozf9b4PkdERC3jTDWZxJGz+/UKamcHBcYNeECiRIYX3DGiyUv/5Zdc\nwI+/L8fmfaslSEVERESmwC8qktHkF2cjKWMTcgozcakkR6dP4eyJeXe9CFcnN4nSGcfkIY+gtKII\nZy4e0+vboVqPsICeiOwcLUEyIiIiMiYW1WQUWw/8oL2F998JEDDvrpfg79XZxKmMz8HeEfPvfhUn\ns9OQnLEJp3OP6PR/++t/8X8PvA+Fk4dECYmIiMgYuPyDDK6gNLfZghoA+oTHWWVBfZ0gCOgRosT8\nu1/BPycugnDDNbarasvxzfb3m7xxDBEREVkuFtVkMDV1VUg/sxff/Pp+i9uNiZ1qokTS6xGixNj+\n03XaMi8eww7VTxIlIiIiImPg8g8yiNq6arzzw9MoLi9osl8us4FGo8YdA+6Fv1ewacNJbHTMPTh7\n8bjOOuvtB9cgKjTWqmfsiYiI2hMW1WQQ+47vaLKg9nT1xaKHlqOxsR4N6gY4O7hKkE5aMpkcM+5Y\niKXfLURV7bVLrak1jfj2t//iyXuWwN7OQeKERERE1FZc/kFtphE1+OPY9ib7EvpOhFwmh72dQ7ss\nqK9TOHlgasI/ddryLp/H2z/8C0fO7oda3ShRMiIiIjIEFtXUZqdzjjQ5S93JOwQDuo+UIJF5ig6P\nQ+8uA3Taisry8cWWpVj6/UKUVBRKlIyIiIjaikU1tdmeo9t0ngf4hOKfExdh4dQ3YWdrL1Eq8zRt\n+Dx09AjUay8ozcXX296DRtRIkIqIiIjaStLblGdmZppyaDKCqw01WJf6X4g3FIN3RD0EH1f9wpGu\naVDXQ3V+BzILM/T6+oeORVe/fhKkIkMKDw/XPuZtyomI2gfOVFOb5JSc0imoFQ6e8HYJkDCR+bOV\n22Fg2J0Y3/tRONnrrjNPv7AL5TXFEiUjIiKi1pL06h9KpVLK4Q1GpVIBaJ/ns3/9Rp3ncb1HIyYm\nxii5Wsucfz8x/fpjybcLUNdwFQDQoK7DnnM/YeG0N5u966I5n09rWNv5ALqfyBERUfvAmWpqtbzL\n2ci84drLANAvYpBEaSyTu4s3JsTP0GkrrSjCOz8+g6z8PyVKRURERLeLRTXdNlEUcfDk73j7x3/p\ntAf4hMLHvZNEqSzX4F7jMLDHKJ228qoSfPzTy8gtOidRKiIiIrodLKrpttQ31uHLrW/hux0fQqNR\n6/Qpuw6RKJVlEwQB0xL+iR7BussfGtT1+PH35SgozUVtXbVE6YiIiOhWsKim27JhzLine truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa8e9860>"
+       "<matplotlib.figure.Figure at 0x853e208>"
       ]
      },
      "metadata": {},
@@ -746,16 +745,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAADaCAYAAABtj26qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtkAAADaCAYAAABzRl4ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa99e438>"
+       "<matplotlib.figure.Figure at 0x84b2d30>"
       ]
      },
      "metadata": {},
@@ -776,16 +775,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtkAAADaCAYAAABzRl4ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78715c0>"
+       "<matplotlib.figure.Figure at 0x8ae94a8>"
       ]
      },
      "metadata": {},
@@ -805,16 +804,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtkAAADaCAYAAABzRl4ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86f4128>"
+       "<matplotlib.figure.Figure at 0x85f72e8>"
       ]
      },
      "metadata": {},
@@ -834,16 +833,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAADaCAYAAABtj26qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtkAAADaCAYAAABzRl4ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa8f0668>"
+       "<matplotlib.figure.Figure at 0x86150f0>"
       ]
      },
      "metadata": {},

10-Unscented-Kalman-Filter.ipynb

@@ -28,7 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -42,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -123,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -298,9 +297,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAFfCAYAAAB0jrv4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAFfCAYAAAB0jrv4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4f4e0>"
+       "<matplotlib.figure.Figure at 0x72c7cc0>"
       ]
      },
      "metadata": {},
@@ -361,7 +360,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAEPCAYAAABLO6wGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7798a20>"
+       "<matplotlib.figure.Figure at 0x72c79e8>"
       ]
      },
      "metadata": {},
@@ -409,14 +408,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in mean x=0.005, y=43.645\n"
+      "Difference in mean x=-0.004, y=41.586\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8aceb00>"
+       "<matplotlib.figure.Figure at 0x47130b8>"
       ]
      },
      "metadata": {},
@@ -484,7 +483,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4c01198>"
+       "<matplotlib.figure.Figure at 0x8309438>"
       ]
      },
      "metadata": {},
@@ -535,7 +534,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8bc79b0>"
+       "<matplotlib.figure.Figure at 0x86c5c18>"
       ]
      },
      "metadata": {},
@@ -587,7 +586,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8bf1ac8>"
+       "<matplotlib.figure.Figure at 0x86c5d30>"
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      },
      "metadata": {},
@@ -643,7 +642,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9bf3208>"
+       "<matplotlib.figure.Figure at 0x88d1278>"
       ]
      },
      "metadata": {},
@@ -871,7 +870,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9bc7128>"
+       "<matplotlib.figure.Figure at 0x88376d8>"
       ]
      },
      "metadata": {},
@@ -1002,7 +1001,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0bceb8>"
+       "<matplotlib.figure.Figure at 0x8979ac8>"
       ]
      },
      "metadata": {},
@@ -1127,7 +1126,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa9bf0f0>"
+       "<matplotlib.figure.Figure at 0x8979a20>"
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      },
      "metadata": {},
@@ -1197,7 +1196,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0a2e80>"
+       "<matplotlib.figure.Figure at 0x893c128>"
       ]
      },
      "metadata": {},
@@ -1374,7 +1373,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9bf3128>"
+       "<matplotlib.figure.Figure at 0x88d10b8>"
       ]
      },
      "metadata": {},
@@ -1384,7 +1383,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9ba0240>"
+       "<matplotlib.figure.Figure at 0x8a4ce80>"
       ]
      },
      "metadata": {},
@@ -1394,7 +1393,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9bd58d0>"
+       "<matplotlib.figure.Figure at 0x88732e8>"
       ]
      },
      "metadata": {},
@@ -1481,7 +1480,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87f3908>"
+       "<matplotlib.figure.Figure at 0x82bf828>"
       ]
      },
      "metadata": {},
@@ -1584,7 +1583,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87f3d68>"
+       "<matplotlib.figure.Figure at 0x86b7ac8>"
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      },
      "metadata": {},
@@ -1650,7 +1649,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xce58828>"
+       "<matplotlib.figure.Figure at 0x8715278>"
       ]
      },
      "metadata": {},
@@ -1733,7 +1732,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcea9a90>"
+       "<matplotlib.figure.Figure at 0x8a83c88>"
       ]
      },
      "metadata": {},
@@ -1815,7 +1814,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8a78780>"
+       "<matplotlib.figure.Figure at 0x8a9e908>"
       ]
      },
      "metadata": {},
@@ -1931,7 +1930,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xce5e908>"
+       "<matplotlib.figure.Figure at 0x878e470>"
       ]
      },
      "metadata": {},
@@ -2003,7 +2002,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcf416a0>"
+       "<matplotlib.figure.Figure at 0x8a5c898>"
       ]
      },
      "metadata": {},
@@ -2049,7 +2048,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c47208>"
+       "<matplotlib.figure.Figure at 0x8a9a630>"
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      },
      "metadata": {},
@@ -2102,7 +2101,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8ae4240>"
+       "<matplotlib.figure.Figure at 0x84b3710>"
       ]
      },
      "metadata": {},
@@ -2143,7 +2142,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xce50a58>"
+       "<matplotlib.figure.Figure at 0x8496eb8>"
       ]
      },
      "metadata": {},
@@ -2581,7 +2580,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcea9d30>"
+       "<matplotlib.figure.Figure at 0x87c07f0>"
       ]
      },
      "metadata": {},
@@ -2591,7 +2590,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b76fd0>"
+       "<matplotlib.figure.Figure at 0x89cf080>"
       ]
      },
      "metadata": {},
@@ -2601,7 +2600,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8bb65f8>"
+       "<matplotlib.figure.Figure at 0x89f3320>"
       ]
      },
      "metadata": {},
@@ -2687,7 +2686,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b866a0>"
+       "<matplotlib.figure.Figure at 0x8a58a58>"
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      },
      "metadata": {},
@@ -2697,7 +2696,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcf6edd8>"
+       "<matplotlib.figure.Figure at 0x86afba8>"
       ]
      },
      "metadata": {},
@@ -2707,7 +2706,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9c59ba8>"
+       "<matplotlib.figure.Figure at 0x847f0b8>"
       ]
      },
      "metadata": {},
@@ -2905,7 +2904,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa9a38d0>"
+       "<matplotlib.figure.Figure at 0x88d56d8>"
       ]
      },
      "metadata": {},
@@ -3340,7 +3339,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xce96668>"
+       "<matplotlib.figure.Figure at 0x82ff048>"
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      },
      "metadata": {},
@@ -3430,7 +3429,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xce231d0>"
+       "<matplotlib.figure.Figure at 0x88a8ac8>"
       ]
      },
      "metadata": {},
@@ -3471,7 +3470,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa9bb3c8>"
+       "<matplotlib.figure.Figure at 0x8a7d6d8>"
       ]
      },
      "metadata": {},

11-Extended-Kalman-Filters.ipynb

@@ -28,7 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -42,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -123,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -324,7 +323,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b6c860>"
+       "<matplotlib.figure.Figure at 0x4b126d8>"
       ]
      },
      "metadata": {},
@@ -428,7 +427,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8226320>"
+       "<matplotlib.figure.Figure at 0x886c320>"
       ]
      },
      "metadata": {},
@@ -735,9 +734,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fab048>"
+       "<matplotlib.figure.Figure at 0x8938b38>"
       ]
      },
      "metadata": {},
@@ -745,9 +744,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a733c8>"
+       "<matplotlib.figure.Figure at 0x997f7f0>"
       ]
      },
      "metadata": {},
@@ -755,9 +754,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwYAAADxCAYAAABxuaDoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9ac09e8>"
+       "<matplotlib.figure.Figure at 0x99cdac8>"
       ]
      },
      "metadata": {},
@@ -898,7 +897,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbsAAADbCAYAAAD06j/wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89cab70>"
+       "<matplotlib.figure.Figure at 0x88e4d68>"
       ]
      },
      "metadata": {},
@@ -1548,9 +1547,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa7ca278>"
+       "<matplotlib.figure.Figure at 0xa6e1a90>"
       ]
      },
      "metadata": {},
@@ -1595,9 +1594,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa82cc50>"
+       "<matplotlib.figure.Figure at 0xa6e17b8>"
       ]
      },
      "metadata": {},
@@ -1607,7 +1606,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.02   0.021  0.002]\n"
+      "Final P: [ 0.02   0.02   0.002]\n"
      ]
     }
    ],
@@ -1638,9 +1637,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa7ca080>"
+       "<matplotlib.figure.Figure at 0xa6e15c0>"
       ]
      },
      "metadata": {},
@@ -1650,7 +1649,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.021  0.045  0.   ]\n"
+      "Final P: [ 0.02   0.044  0.   ]\n"
      ]
     }
    ],
@@ -1678,9 +1677,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa956588>"
+       "<matplotlib.figure.Figure at 0xa8a91d0>"
       ]
      },
      "metadata": {},
@@ -1690,7 +1689,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [ 0.265  0.796  0.004]\n"
+      "Final P: [ 0.237  0.793  0.003]\n"
      ]
     }
    ],
@@ -1718,9 +1717,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa727a20>"
+       "<matplotlib.figure.Figure at 0xa654978>"
       ]
      },
      "metadata": {},
@@ -1782,9 +1781,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa77cfd0>"
+       "<matplotlib.figure.Figure at 0x9a60f60>"
       ]
      },
      "metadata": {},
@@ -1820,9 +1819,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuIAAADaCAYAAADjVSfWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa920ba8>"
+       "<matplotlib.figure.Figure at 0xa89c860>"
       ]
      },
      "metadata": {},
@@ -1832,7 +1831,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "actual mean=1.30, std=1.13\n",
+      "actual mean=1.30, std=1.12\n",
       "UKF    mean=1.30, std=1.08\n"
      ]
     }

12-Particle-Filters.ipynb

@@ -28,7 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -42,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -123,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -328,9 +327,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7978be0>"
+       "<matplotlib.figure.Figure at 0x7a5dcc0>"
       ]
      },
      "metadata": {},
@@ -400,9 +399,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e24ef0>"
+       "<matplotlib.figure.Figure at 0x8353e10>"
       ]
      },
      "metadata": {},
@@ -493,9 +492,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4937be0>"
+       "<matplotlib.figure.Figure at 0x8353c50>"
       ]
      },
      "metadata": {},
@@ -547,9 +546,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b77278>"
+       "<matplotlib.figure.Figure at 0x84065f8>"
       ]
      },
      "metadata": {},
@@ -583,7 +582,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8347198>"
+       "<matplotlib.figure.Figure at 0x84b0908>"
       ]
      },
      "metadata": {},
@@ -948,9 +947,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e09390>"
+       "<matplotlib.figure.Figure at 0x4b1de48>"
       ]
      },
      "metadata": {},
@@ -1075,9 +1074,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8401f98>"
+       "<matplotlib.figure.Figure at 0x7fe3518>"
       ]
      },
      "metadata": {},
@@ -1133,9 +1132,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91c98d0>"
+       "<matplotlib.figure.Figure at 0x7bd02e8>"
       ]
      },
      "metadata": {},
@@ -1185,9 +1184,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x96b47b8>"
+       "<matplotlib.figure.Figure at 0x84c35c0>"
       ]
      },
      "metadata": {},
@@ -1225,9 +1224,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x967e898>"
+       "<matplotlib.figure.Figure at 0x8f6e470>"
       ]
      },
      "metadata": {},
@@ -1360,9 +1359,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAABACAYAAAC+2bgWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAIBx/gXyNx3ze6skVAAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAABACAYAAACgEIilAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBbfSRXeeBV/hAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x83a49b0>"
+       "<matplotlib.figure.Figure at 0x7d6b470>"
       ]
      },
      "metadata": {},
@@ -1414,9 +1413,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x833c320>"
+       "<matplotlib.figure.Figure at 0x72c7518>"
       ]
      },
      "metadata": {},
@@ -1500,9 +1499,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8304828>"
+       "<matplotlib.figure.Figure at 0x83fdd68>"
       ]
      },
      "metadata": {},
@@ -1545,9 +1544,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b50978>"
+       "<matplotlib.figure.Figure at 0x9473d30>"
       ]
      },
      "metadata": {},
@@ -1620,9 +1619,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ddb198>"
+       "<matplotlib.figure.Figure at 0x8f333c8>"
       ]
      },
      "metadata": {},
@@ -1699,9 +1698,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x835ea90>"
+       "<matplotlib.figure.Figure at 0x84ddcc0>"
       ]
      },
      "metadata": {},
@@ -1709,9 +1708,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x88dcf28>"
+       "<matplotlib.figure.Figure at 0x83a4588>"
       ]
      },
      "metadata": {},
@@ -1719,9 +1718,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8950550>"
+       "<matplotlib.figure.Figure at 0x8a7fe48>"
       ]
      },
      "metadata": {},
@@ -1729,9 +1728,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAABCCAYAAACSCTtfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8460b38>"
+       "<matplotlib.figure.Figure at 0x899d3c8>"
       ]
      },
      "metadata": {},

13-Smoothing.ipynb

@@ -28,7 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -42,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -123,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -298,9 +297,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x72073c8>"
+       "<matplotlib.figure.Figure at 0x72d9438>"
       ]
      },
      "metadata": {},
@@ -347,9 +346,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x72cd400>"
+       "<matplotlib.figure.Figure at 0x72d9ac8>"
       ]
      },
      "metadata": {},
@@ -379,9 +378,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75a1470>"
+       "<matplotlib.figure.Figure at 0x77b3d68>"
       ]
      },
      "metadata": {},
@@ -522,9 +521,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x783c6d8>"
+       "<matplotlib.figure.Figure at 0x7817f60>"
       ]
      },
      "metadata": {},
@@ -597,9 +596,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75771d0>"
+       "<matplotlib.figure.Figure at 0x77d42e8>"
       ]
      },
      "metadata": {},
@@ -626,9 +625,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x77e7860>"
+       "<matplotlib.figure.Figure at 0x88027b8>"
       ]
      },
      "metadata": {},
@@ -665,9 +664,9 @@
     },
     {
      "data": {
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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7294ba8>"
+       "<matplotlib.figure.Figure at 0x8873e48>"
       ]
      },
      "metadata": {},
@@ -706,9 +705,9 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x883aba8>"
+       "<matplotlib.figure.Figure at 0x8937a90>"
       ]
      },
      "metadata": {},
@@ -755,9 +754,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99babe0>"
+       "<matplotlib.figure.Figure at 0x8985fd0>"
       ]
      },
      "metadata": {},
@@ -854,7 +853,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

14-Adaptive-Filtering.ipynb

@@ -28,7 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -42,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -123,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -417,7 +416,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a939a8a90>"
+       "<matplotlib.figure.Figure at 0x7318160>"
       ]
      },
      "metadata": {},
@@ -475,9 +474,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvcAAAD+CAYAAAC+7ypaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xlczdn/wPHXvZV2KalItiFkH4RhUrYwsu9DmLGNrzHM\n19Ys4cswjGWyzCBjJzOGMBjEkKwpawZZsqVCSpTS8vn94dHn505lMKZbeT8fD49H95zz+Xze5/rQ\n+557PudoFEVREEIIIYQQQhR6Wn0HIIQQQgghhHgzJLkXQgghhBCiiJDkXgghhBBCiCJCknshhBBC\nCCGKCEnuhRBCCCGEKCIkuRdCCCGEEKKIkOReCCFeQXJyMlqtltq1a+s7FCGEECIHSe6FEHqj1Wpf\n6c/q1av1HXKhlv3BRKvVYmRkRGxsbJ5tmzRporZdv359PkYp8kv37t3RarWcPHlS36EIId4gQ30H\nIIR4e02ePDlH2YoVK7h58yYDBgygQoUKOnV169bNn8CKOCMjIzIyMlixYgU+Pj456s+fP8/x48fV\ndqJo0mg0aDQafYchhHjDJLkXQuiNr69vjrL9+/dz8+ZNBg4ciJubmx6iKvrKly+PqakpP/30U67J\n/dKlS9FqtXTo0IEtW7boIUIhhBCvS6blCCEKnQYNGmBgYEBcXByzZs2iRo0amJiY4O3tDcCDBw/4\n9ttvcXd3x9HREWNjYxwcHOjatSthYWF5nvf8+fPqNwYmJibY29vj5ubGDz/88FJxrV69GmNjY2rW\nrMmtW7fybPfo0SPMzc0pV65cnm2GDx+OVqtl8+bNatm+ffv44IMPcHJywsTEBAcHBxo3bswXX3zx\nUvE9b8iQIVy7do19+/bplKelpbF27Vo8PDyoVKlSnscnJCTg4+ODi4sLZmZmWFlZ4eHhwW+//Zaj\nbWpqKn5+frRt25by5ctjYmKCra0tbdu2Ze/evbmePywsjJ49e1KxYkVMTEyws7OjXr16jBo1itTU\nVLXd2LFj85xakj0NqWPHjjrl2cds3ryZwMBAmjVrRvHixXP8fRw/fpzu3btTunRpjI2NKVu2LB9/\n/DE3b97Mca3np7isXLmSevXqYWZmhpOTE19++SVZWVkA/P7777i5uWFpaUnJkiX5+OOPSUpKyvU9\nuH37NiNHjqRy5cqYmJhQsmRJPvjgAw4fPpyj7aJFi9BqtcydO5cTJ07g6emJlZUVlpaWtG7dOsf7\nU6pUKTZt2gQ8+/eUPQWrePHiapuYmBhGjx5NtWrVsLCwoESJElSrVg1vb28iIyNzjVkIoX8yci+E\nKHSypxIMHjyY48eP0759ezp27IitrS0Ap06dYsqUKbi7u9O5c2esrKyIiopi27Zt7Ny5kz179uT4\nVuDXX3+lf//+ZGZm0q5dOz788EMSEhI4e/YsM2fOZMSIES+Madq0afj6+tK8eXO2bt2qkyT9laWl\nJV27dmX9+vXs3buXVq1a6dQ/ffqUX375BVtbWzUx3bRpEz169KBkyZJ4eXlRpkwZHjx4wKVLl1i4\ncCHTp09/pfewf//+jB8/Hn9/f1q2bKnzPiQmJjJkyBBOnDiR67FRUVF4eHhw69YtPDw8aN++PUlJ\nSfz222906tSJ2bNn8/nnn6vto6OjGTt2LM2aNaNt27bY2toSHR3Ntm3b8PT0ZM2aNfTt21dtHxoa\nyvvvv4+xsTFeXl5UqFCBpKQkrl27xrJly/jqq68wMTEBXm9qSfYxK1asYM+ePXh5eeHm5kZCQoLa\nxt/fnxEjRmBubo6XlxeOjo5cunSJlStX8ttvv3Ho0CGcnZ1znHP69Ons37+fjh074uHhwZYtW5gx\nYwaPHz+mYcOGDBs2jM6dO9OoUSN2797NihUrePz4MT///LNOjCdOnKBt27YkJSXRrl07unXrxt27\ndwkMDMTd3Z3169fTo0ePHP0KCQnhyy+/xMPDg2HDhnHt2jUCAwPx8PAgIiICJycnACZMmMCKFSu4\nePEiQ4cOpUyZMgAYGxsDzz6Aurq6EhMTQ5s2bejUqRNZWVncvHmTXbt20aFDB53+CyEKEEUIIQoQ\nd3d3RavVKsHBwXm2adCggaLRaJR33nlHiYmJyVGfkJCgJCYm5ii/du2aYmtrqzRs2FCn/Pbt24qZ\nmZliamqqHD9+PMdxt2/fVn9+/PixotFolFq1aimKoiiZmZnKkCFDFI1Go/Tu3Vt5+vTpS/Vz3759\nikajUfr165ej7ueff1Y0Go0yatQotaxNmzaKVqtVrl69mqN9fHz8S10zO/YqVaooiqIo/fr1U0xM\nTJT79++rbZo3b66UKlVKefr0qTJ27FhFq9Uq69at0zlPo0aNFENDQ2X79u065QkJCYqLi4tiZGSk\nXLlyRS1/8uSJEhsbmyOeBw8eKJUrV1bKlCmjZGZmquXDhg1TtFqt8scff+Q4JikpScnIyFBfZ8cY\nHh6eZ3+9vLx0yseOHatoNBrFyMhIOXToUI7jzp07pxgZGSm1atXSeW8URVF+//13RavVKu7u7jrl\n3bt3VzQajVK6dGnl1q1bavn9+/cVKysrpVixYoqdnZ1y9uxZnfflnXfeUQwMDHTer7S0NKVcuXKK\nhYWFEhYWpnOdW7duKXZ2dkqJEiWUhw8fquULFy5UNBqNotVqlc2bN+scM2fOHEWj0SgTJkzIEXNe\n711AQICi0WgUX1/fHHUZGRlKUlJSjnIhRMEg03KEEIWSRqPhq6++wsHBIUddiRIlsLKyylFesWJF\nOnbsSHh4OImJiWr5smXLSE1NZfTo0bi6uuY4ztHRMdcYUlJS8PLy4qeffuLzzz8nICAAIyOjl4q/\nRYsWlCtXjsDAQB4/fqxTt3LlSjQaDQMGDNDpr0ajUUdWn2djY/NS1/yrIUOGkJaWxqpVqwCIjIzk\n4MGDDBgwIM9+HDt2jNDQUD788EM++OADnboSJUrw1VdfkZGRQUBAgFqePcXpr6ytrfH29iY2Npaz\nZ8/mqM8enX+epaUlBgYGr9TPvPTt25emTZvmKF+wYAGZmZnMnz+fkiVL6tS1bduWVq1acfDgQW7f\nvq1Tp9FoGDduHGXLllXLSpYsSdu2bcnIyKB3797UqlVLrTMxMaFbt24oiqLT/02bNnHr1i3++9//\nUr9+fZ1rlC1bltGjR5OUlMS2bdtyxN62bVu6dOmiUzZ06FDg2Tciryq3vwMDAwMsLS1f+VxCiPwh\n03KEEIVWw4YN86zbv38/CxYs4MSJE9y9e5f09HS1TqPREB0dTYkSJYBnc6vhWWL0spKSknB3d+fU\nqVN8//33fPrpp68cv7e3N9988w2//PILH330EQBxcXEEBQVRq1Yt3n33XbXthx9+SFBQELVr16ZX\nr164u7vz3nvv6SSSr8rNzQ1nZ2f8/f35/PPP8ff3R6PRMHjw4DyPyZ7vfe/ePaZMmZKjPjo6GoAL\nFy7olJ8+fZrZs2dz5MgRYmJiSEtLU+uy/z6yV0Pq06cP/v7+tG7dmu7du9OyZUuaNGlC5cqVX7uv\nf6XRaPK8f44cOQJAUFAQwcHBOeqzp+9cuHAhx/uf24pOpUuXzrMuezrM8x8Ujhw5gkaj4fLly7m+\nx+fPn0dRlBzvMZDjwwCAhYUFVlZWOtOO/k7r1q0pVaoUvr6+HDp0iHbt2tGkSRPq1auHVivjgkIU\nZJLcCyEKrdxG7QHWrl3LgAED1IcJK1asiLm5ORqNht27d3Ps2DGd5DJ7FD+vEfrcJCQkEBsbS4kS\nJXLMmX9ZAwcOZNq0aaxatUpN7tesWUNmZqbOqD08myNfvHhx5s+fz/Lly1myZAmKolC3bl2mTp2a\nYxT9ZQ0ePJgJEyawd+9eVq9eTdOmTalatWqe7ePj4wHYtWsXu3btyrWNRqMhOTlZfb1//37atWuH\nVqulVatWdOnSBUtLS7RaLcePH2fXrl06fx/NmzcnODiYmTNn8uuvv7JmzRoURaFy5cp8+eWXOd6b\n15XX/ZPdx2+//TbPYzUaTY5vXIBcvzEyNDR8YZ2iKDofPuPj41EUhQ0bNrzy9bM/sOZ2nczMzDzP\n91clS5bkxIkTTJkyhe3bt7Nr1y4URaFkyZIMGTKEyZMnU6xYsZc+nxAi/0hyL4QotPJ6kPKrr76i\nePHinD59mvLly+vURUZGcuzYMZ2y7IQoOjqad95556WuXb58eWbOnEm3bt1o3rw5QUFB1KlT55Xi\nr1SpEu+//z6HDh0iKiqKihUrsmrVKoyMjPjwww9ztO/UqROdOnXiyZMnhIaGsmPHDn744Qe6dOnC\n0aNHcx21/TsDBw7kq6++4qOPPuL+/fvMnTv3he2trKzQaDQsWbLkhSP8z5syZQoZGRmEhobqfBsB\nz/6ucvuQ0KxZM5o1a0Z6ejrh4eHs2bOHhQsX8tFHH2FlZUXnzp0B1FHk3Nbjf37qVW7yun+srKyI\ni4sjLS1NTczzU/Z7vGfPHp2HnfNbuXLl+Omnn4Bn31Ls37+fH3/8kW+//ZaUlBS+//57vcUmhMib\nfLcmhChSMjMzuXnzJnXr1s2R2GdkZKhTLp7XuHFjFEXh999/f6VrtWvXju3bt/PkyRNatGiR5+oy\nLzJw4EAURWHVqlWcPHmS8+fP07ZtW+zs7PI8xtTUlObNmzNr1iymTp1KZmZmrvOvX4atrS2dOnUi\nOjoaa2trunfv/sL2TZo0QVEUQkJCXvoaV69excnJKUdiD+Q67eV5RkZGNG7cGF9fX/XbisDAQLXe\n2toaINelR19njjk86yPwSn18k7Lf44MHD/6r18l+duFlRvSrV6/OiBEjOHDgAFqtVufvQAhRsEhy\nL4QoUgwMDHB0dOT8+fPq9AoARVGYOHEi169fz3HM4MGDMTU1xc/PL8eoPvz/PPLctLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvcAAAD+CAYAAAC+7ypaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a939b6cf8>"
+       "<matplotlib.figure.Figure at 0x7312be0>"
       ]
      },
      "metadata": {},
@@ -553,9 +552,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvMAAAD+CAYAAAC3BIogAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvMAAAD+CAYAAAC3BIogAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a93c58a90>"
+       "<matplotlib.figure.Figure at 0x786bf60>"
       ]
      },
      "metadata": {},
@@ -598,9 +597,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a93c58470>"
+       "<matplotlib.figure.Figure at 0x7872518>"
       ]
      },
      "metadata": {},
@@ -641,9 +640,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a949d8a20>"
+       "<matplotlib.figure.Figure at 0x730b860>"
       ]
      },
      "metadata": {},
@@ -680,9 +679,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a94a55c50>"
+       "<matplotlib.figure.Figure at 0x72d5f98>"
       ]
      },
      "metadata": {},
@@ -745,9 +744,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a94a441d0>"
+       "<matplotlib.figure.Figure at 0x785d0b8>"
       ]
      },
      "metadata": {},
@@ -804,9 +803,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAADlCAYAAAB3YbDsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XlclNXiP/DPM+xLLCIuqFxAUVNc0MItN1zAXy937u26\nQWqmYqW5hUsBpmTmkvd63cgFJTP1m6HlgoCglWJZVqJeLVPTcgMFTXbO749zZ2SYGWBgAJHP+/Xi\nJXOe85znPM+YfebMec6jCCEEiIiIiIioUlQ13QEiIiIioqcBgzURERERkQkwWBMRERERmQCDNRER\nERGRCTBYExERERGZAIM1EREREZEJMFgTEREREZkAgzURERERkQkwWBMRERH9z9mzgIUFkJho3H77\n9gFWVsCvv1ZNv6h2UPjkRSIiInqa/Pgj8PnnwPjxgLu7cfsOHAgUFABJScYf97nnAA8PYM8e4/el\npwNHrImIiOipcuYMEBkJXLli3H4nTgAJCcDMmRU77vTpwN69wPnzFdufaj8GayIiojqsqAjIzq7p\nXpiWEICiGL/f2rWAqyswaFDFjjtiBGBjA6xfX7H9qfZjsCYiojorJgZQqeTX/osWya/xbW2Brl2B\n1FRZJyUF6NkTsLcH3NyAxYt128nLA6KiAB8fGaycnYEhQ+TIaXEPHwILF8r2XV0Ba2vA2xuYN083\n3ObmAhERQOvWgJ2dbLN9e2DuXO16ERHyHK5d0+2Xhwfg7697vomJwLvvAi1ayP7u3m3ceZjqulXk\nmEePAsuXy75bWwOtWgHbtj2uFxkJTJggf+/TR+6jUj0uM6SwEIiLA/r3B8zMSq9riJ2dPGdOBam7\nzGu6A0RERDUtLEyO3M6YIYPe8uVAQIAMcxMnAlOmAGPHArt2AeHhgJcXMHq03LegQNY9eRIYNw54\n/XUgMxOIjgZ69ACOHwc6dZJ1b9wANm8GRo4ExowBzM1lAF22TAbJgwcf9yk0FNi6FQgJAbp1k8e5\ndEkGy+IUxfDorKHy2bNle6++Cjg4yHBqzHmY4roZe+3U5s8HcnJk21ZWwLp1ci61t7e8TiNHAn/+\nKdtYuFB+MAGA5s0Nvv0AgNOn5QcfP7/S65WlWzcgPh64eBFo2bJybVEtJIiIiOqorVuFUBQhOncW\nIj//cfm+fbLc0lKI779/XJ6XJ0TjxkJ07/64bOVKIVQqIY4c0W77wQMh3N2F6Nv3cVl+vhAFBbr9\nePtt2ca33z4uq1dPiBdfLPscIiLkvlev6m7z8NA+vvp8W7cWIidHu64x52GK61bRY3bqpH0Nb9wQ\nwspKiNGjteuqVEKkpOheE0O2bJH77N+vf/tffwkxcqQQDg5CrFkjy7ZtE+L0ae16sbGync8+K/+x\n6enBqSBERFTnhYbK0WO1nj3ln127Ar6+j8stLOSI5qVLj8s+/liOivr6Aunpj39ycoABA4CvvpLT\nOgB5DPU0g8JC4P59WbdfPzkvWD2NAgAcHYG0NPlTFedrZaVdZsx5FG+notetosecNk17qoabmxwZ\nLtm2se7ckX/Wq6d/+/vvA927y28RbtyQI+/p6boj6i4u8r28fbty/aHaiVNBiIioTlMUwNNTu8zJ\nSf7p4aFb39lZBiq18+dlEHR11d82ANy9CzRpIn9fuxbYsEEG5qIi7br37j1+/eGHQHAw0K6dnELR\nty8weLD8qciNecWP4+2tW27seVT2upnqmIAMs/rmmBtDfTxDixC/+ebj83N3Bw4fllNgSlLvX5n3\niGovBmsiIqrzDN2sVp6b2ISQ4XfVKsOhTB0cV66U85sDA+XSbG5ugKWlHAENCdEO2kOGyOXiDhyQ\n87ATEoBNm4BeveTv6pHi0gJcQYH+clvbyp2HWmWum6mPWdmncqiPk5Ghf7s6VB85Ij8QzJ+vv15G\nhnxP9H1YoKcfgzUREVEleHvLaQR9+5ZdNzZWjrgeOKBdfviw/vpOTnLKgfqGv7Aw4IMP5OoVI0fK\nMvXUhYwM7Yeh5ObKm/j0jU5X9jxMpaqOWZHRYh8fGc5Lm1LyySdyCs8bbxiu88svj9ujuodzrImI\niCohOBi4eRNYsUL/9uJzbc3MZOgrPrpaUAC89552GCwqkqtjlNSxo9y3+Khqy5ayLCFBu+7Kldoj\n4KY8D1OpqmPa2+tep7L4+soVUk6e1L89Jka2O3bs47J583TrnTwJNGxY/g809HThiDUREdVplZ1C\nMH26nB4wd65c19nfXwa0a9fketE2NvJPAAgKklMIAgPlw0QyM+UoqKWldj8ePAAaN5bTQXx9gQYN\ngMuX5YNHXFzkPGu1/v3lcnnvvCPnI3t6ypv+UlOB+vXLf77GnIcprltVHvP55+Xa1UuWyHBtZyev\nS2lL6alU8j2JiwPy8+UNl2p798pyDw85bcfBQX77MHmydht//SWXCHzllXJfAnrKMFgTEVGdVtoa\n0OVZH9rcXE7tWLsW2L5dPrAFkPOn/fzk3Gk19cNdNm2SN741agT885/Ayy8Dbdo8btfWVt4sl5go\nfx4+lEF72DA5HaRRo8dtqlTA/v1yesKaNTKkBwTIedndu+ueg6FzMuY8THHdTHnMktuaNQO2bJEr\neYSGyqAcElL2GtVTp8qR6S++AIYPl2W3bgH//S/w2WfAb7/J9+DuXTklZ+hQ7f337JEP+ikZuKnu\nUIQwxWdOIiIiotpv0CDg0SP5wcRYnTrJB9Gon2RJdQ+DNREREdH/nDsn57IfOCCn2ZRXXBzw0kty\nfy+vqusfPdkYrImIiIiITIBzrImIiEiHEAJZWQU4e7YA16/LCcxNmwr4+JjDwcEcCp+AQqSDI9ZE\nRESkRQiB1NQchIdbID7eDIA6RAsEBBQiMjIffn7WDNdEJTBYExERkYY6VAcGWiMzU39wdnQUOHQo\nB126MFwTFccHxBAREZFGVlYBwsMtDIZqAMjMVBARYYGsLAPPTCeqoxisiYiISOPs2YL/Tf8o3eHD\nZkhLY7AmKo7BmoiIiDTkjYrlmd6h4PffOQ2EqDgGayIiIiIiE2CwJiIiIo2mTQWA8qxrINCsGdc/\nICqOwZqIiIg0fHzMMXBgYZn1AgIK0bYtH4dBVByX2yMiIiINIQROncpBQIDh5facnIpw8GAul9sj\nKoEj1kRERKShKAr8/Kxx6FAOAgIKoD0tRCAwsIChmsgAjlgTERGRDvUjzdPSCjSrfzRrJtC2LR9p\nTmQIR6yJiIhIh6IocHS0wLPP5uDLL1/F4cOhaNeuAI6OFgzVRAbwrgMiIiIyaPv27di+fTsAoEeP\nHpg4cWIN94joycURayIiIjLo1q1bmt9v3rxZgz0hevIxWBMRERERmQCDNRERERGRCTBYExERERGZ\nAIM1EREREZEJMFgTEREREZkAgzURERERkQkwWBMRERERmQCDNRERERGRCTBYExERERGZAIM1ERER\nEZEJMFgTEREREZkAgzURERERkQkwWBMRERERmQCDNRERVUpKSgpUKhW2bdtW012pcmfPnoWFhQUS\nExNLrXfx4kWj2t23bx+srKzw66+/VqZ7RFTDGKyJiKhMP/74IyIjI3Ht2jW92xVFqeYe6Sqrj6Yw\nc+ZM9OzZE/369TNYZ+PGjRg1ahQ2bdpU7naHDBmCdu3a4a233jJFN4mohjBYExFRmc6cOYPIyEhc\nuXJFZ1vv3r2RnZ2NcePGVX/Hiimtj6Zw4sQJJCQkYObMmQbrREdHIysrC6dPn8bt27exefPmcrc/\nffp07N27F+fPnzdFd4moBjBYExFRmYQQpY5KW1pa1viodVl9rKy1a9fC1dUVgwYNMlind+/emD17\nNgBg3rx56NGjR7nbHzFiBGxsbLB+/fpK99UUhBDIzMzE77//rim7evUqMjMzIYSowZ4RPbkYrImI\nyikmJgYqlQpHjx7F8uXL0aJFC1hbW6NVq1Z65xfn5eUhKioKPj4+sLGxgbOzM4YMGYIzZ85o6ly7\ndg0qlQqRkZFa+wYEBEClUmH16tVa5V26dEHbtm3L7Gt5jq2Wm5uLiIgItG7dGnZ2dnB2dkb79u0x\nd+5cAEBkZCQmTJgAAOjTpw9UKhVUKpWmTN8ca/W1SkpKwqJFi+Dh4QFbW1t07doVqampmv169uwJ\ne3t7uLm5YfHixVr9evjwIRYuXIiuXbvC1dUV1tbW8Pb2xrx585Cdna1Vt6w+GntNSiosLERcXBz6\n9+8PMzMzg/Vatmyp9bpVq1Zltq1mZ2eHnj17Ys+ePeXep6oIIZCamop//OMf2L59u6Y8OjoaL730\nEk6dOsVwTaSHeU13gIiotpk/fz5ycnIwZcoUWFlZYd26dRg/fjy8vb3RrVs3AEBBQQECAgJw8uRJ\njBs3Dq+//joyMzMRHR2NHj164Pjx4+jUqRPc3d3h5eWFpKQkhIeHAwDy8/Px9ddfw8zMDElJSZg+\nfToA4MGDB/j+++8xderUUvtX3mOrhYaGYuvWrQgJCUG3bt1QUFCAS5cu4ejRowDkSOqff/6J6Oho\nLFy4EK1btwYANG/eXNOGoZHisLAwFBUVYcaMGcjLy8Py5csREBCAmJgYTJw4EVOmTMHYsWOxa9cu\nhIeHw8vLC6NHjwYA3LhxA5s3b8bIkSMxZswYmJubIyUlBcuWLcOZM2dw8OBBzXFGjhxZah+NvSYl\nnT59Gg8fPoSfn1+p176yunXrhvj4eFy8eFEnpJvST7/eQtqV2xje81lYW2pHAXWoDgwMRGZmps6+\nhw8fxsmTJ3Ho0CF06dKlxr+pIHqiCCIiKpetW7cKRVFEp06dREFBgab8xo0bwsrKSowePVpTtnLl\nSqFSqcSRI0e02njw4IFwd3cXffv21ZRNmjRJWFlZiezsbCGEEMeOHROKoojg4GDh6Line truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAADlCAYAAAB3YbDsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a949ab4e0>"
+       "<matplotlib.figure.Figure at 0x88bde48>"
       ]
      },
      "metadata": {},
@@ -834,9 +833,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a95cb4048>"
+       "<matplotlib.figure.Figure at 0x730b5c0>"
       ]
      },
      "metadata": {},
@@ -944,9 +943,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv0AAAD+CAYAAACpzbqTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv0AAAD+CAYAAACpzbqTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a95ce6748>"
+       "<matplotlib.figure.Figure at 0x9b4a9b0>"
       ]
      },
      "metadata": {},
@@ -995,9 +994,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAAD+CAYAAABhoVBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAAD+CAYAAABhoVBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a95ccab38>"
+       "<matplotlib.figure.Figure at 0x9b4ab38>"
       ]
      },
      "metadata": {},
@@ -1080,9 +1079,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a95be7630>"
+       "<matplotlib.figure.Figure at 0x9b086d8>"
       ]
      },
      "metadata": {},
@@ -1179,9 +1178,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a95bfa128>"
+       "<matplotlib.figure.Figure at 0x730b5f8>"
       ]
      },
      "metadata": {},
@@ -1208,9 +1207,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a94adc898>"
+       "<matplotlib.figure.Figure at 0x9b2f5f8>"
       ]
      },
      "metadata": {},
@@ -1237,9 +1236,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a95d532b0>"
+       "<matplotlib.figure.Figure at 0x8898be0>"
       ]
      },
      "metadata": {},
@@ -1247,9 +1246,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xt8T/UfwPHXOd/d7xu2mftm5jqXJtVyTy6lkGvkGoou\nRCREiPSrVEi6UkIqlxSKCOVSUu7bwpjMhpld7fb9fn5/rH352p3Nd+P9fDz28P1+Pp9zzvt8N+e8\nv5/zOZ+jKaUUQgghhBBCiHJLt3YAQgghhBBCiJsjSb0QQgghhBDlnCT1QgghhBBClHOS1AshhBBC\nCFHOSVIvhBBCCCFEOSdJvRBCCCGEEOWcJPWizFqyZAm6rvP5558Xazld12nXrl0pRVU07du3p169\nelhjxtg2bdqg62Xjv/bo0aPx9PTk0qVL1g5FCFHKrHHsqVmzJv7+/rdkW8ePH8fBwYHXXnvtlmzv\nWqdPn0bXdYYOHXrLt3291NRUfHx8eOKJJ6wdirhO2TjzC5EHTdPQNC1Xua7rBR7E81vuVlm7di3b\ntm1jxowZNxxHYftYkNLa/6ysLN59912GDh1K06ZNsbe3R9d1Pvzww3yXmTJlCunp6UydOrXE4xFC\nlC3WOPbmtc3p06ffUIdQYcaPH4+7uztjxoy5oeWXLl2KruvMmDGjROO6WWFhYUybNo1u3bpRo0YN\ndF3HYDCQkZGRZ3snJycmTZrEihUr+P33329xtKIgktSLMqtHjx4cO3aM7t27F2u5Y8eOsXTp0lKK\nqnAvv/wyAQEB9OrVy2oxlIaUlBTGjh3L0qVLiY2NpXLlyoWewCtXrszgwYP56KOPOH369C2KVAhx\np9i6dStbtmyxKCuNLxd79+7lu+++49lnn8XZ2fmG12PNDqf8/Pjjj8ycOZMNGzbg4uKCg4NDocs8\n/fTTuLm5MXny5FsQoSgqSepFmeXq6kqdOnVwdXUt1nJ16tShatWqpRRVwbZs2UJYWBiDBg2yyvZL\nk5OTExs3biQ6Opro6GiGDBlSpOUGDRpEVlYWixcvLuUIhRB3mlq1alGrVi2LstIY9jh//nx0XWfg\nwIE3vA6llFWGZBamS5cu7N27l6SkJI4cOYKPj0+hy9jb29OnTx+2bt1KeHj4LYhSFIUk9cJCzri9\ndu3aER0dTf/+/fH29sbJyYnmzZuzatWqPJf7+OOPuffee3Fzc8PZ2ZlmzZrx9ttvk5WVlavtoUOH\nePzxx/H398fR0ZFKlSrRuHFjRo8eTVJSkrldzqXKnEuo27dvR9d1NE3j1KlT6Lpu/rl2nGF+Y+qT\nk5OZMmUK9erVw9HREU9PT9q3b893331X4OcQFxfHiBEj8PPzw8HBgYYNG7JkyZJ8PwdN0+jTp0+u\nuszMTObPn09ISAgVK1bEycmJGjVq0KVLF9asWVOsfQRYuXIld911F05OTvj4+DBw4EDOnTuXZ1wl\nwdbWlo4dOxbpgH+tFi1aULNmTT777LNSikwIUZi9e/ei6zqPPvpovm1CQkKwsbGxuKq2bds2Hnnk\nEby9vbG3t6dmzZqMHj2a2NjYYm2/OOcIgOjoaMaMGUNQUBBOTk54eXkREhLCtGnTMBqN5nbXj6lv\n27ateXjL4MGDzcdPg8FAVFQUL7/8Mrqu88UXX+S53fDwcHRdp1WrVuaypKQkvvnmG1q0aEH16tVz\nLRMZGcnIkSOpU6eOOdb69eszbNgw/v33XwCGDBnC0KFD0TTNPDwoJ64dO3aY15WcnMwLL7xAtWrV\ncHR0pF69esybNw+TyVSMT7t4AgMDad68Ofb29sVarl+/fiil+PTTT0spMlFcNtYOQJRN8fHxhIaG\n4uXlxbBhw4iPj2fVqlX07duXc+fO8fzzz5vbDhw4kGXLllG1alWGDh2Kra0t69evZ/z48WzevJkf\nfvjBfPPUoUOHaNGiBbqu8/DDDxMQEEBycjKRkZEsXbqUcePGWfTMX3upsmbNmkyfPp3p06fj4eHB\n2LFjzb0eTZo0KXB/EhMTCQ0N5ciRIzRr1owxY8Zw+fJlvv76a7p168aMGTOYMmVKruUuX75MaGgo\n9vb29OrVi/T0dL7++muGDh2KwWDIdaPQtm3bqFSpEoGBgbnWNWjQIFauXEmDBg0YMGAAzs7OREdH\n8/vvv7NmzRq6d+9e5H2cN28e48aNw8PDg0GDBuHh4cFPP/3Efffdh7u7e4GfhTWEhoayfPlyDh48\nSHBwsLXDEeKO06JFC+rWrcumTZu4ePEiFStWtKg/evQo+/fvp23bttSoUQOAuXPnMmnSJCpUqMBD\nDz2Er68vBw8eZNGiRaxfv549e/bg5+dX6LaLc44A2LdvH506dSI+Pp6WLVvSvXt30tLSCAsL4/XX\nX2fcuHG4ubkBuYez5FxB3LFjB926dTMfNzVNw8PDg5EjR/LGG2+wePHiPG/0/OCDD9A0jaefftpc\ntmPHDjIyMrj//vtztY+JiSEkJITk5GQ6depEjx49yMjIICoqitWrV9O/f3+qVq1K9+7dSUhIYO3a\ntbRp04Y2bdqY11GzZk0AMjIyaNeuHfv27SM4OJgBAwaQkJDAa6+9xvbt2wv9nG+1u+++G1tbWzZv\n3szcuXOtHY4AUEJc49SpU0rTNKXruurXr59F3cmTJ5WHh4dycHBQUVFRSimlVq5cqTRNU02bNlXJ\nycnmtpmZmap9+/ZK13X11ltvmcvHjRundF1X3333Xa5tJycnq4yMDPP7JUuWKF3X1dKlSy3aaZqm\natWqle8+aJqm2rZta1H21FNPKU3T1JNPPmlRfvbsWVW5cmVlMBjUvn378vwcRowYoUwmk7nu6NGj\nysbGRjVo0MBiXWFhYUrTNNWlS5dcMSUkJChd11Xz5s0t1pUjLi6uyPt46tQpZWdnpzw9PdWpU6cs\n6nr37m2O+3rTpk1T06dPL/LP2rVr89x+junTpytd19XixYsLbKeUUu+++67SdV3Nnz+/0LZCiNIx\nd+5cpeu6euedd3LVvfjii0rXdfX5558rpZTavn270nVdhYaGqsTERIu2y5YtU5qmqZ49e1qUt2nT\nJtexp7jniIyMDFWzZk2l67patmxZrjhjY2OV0Wg0v69Zs2auY2XOsen6c0eOhx9+WOm6rg4dOmRR\nnpaWpry8vFSlSpUszkUvvfSS0nVdrVq1Kte65s+fr3RdV++9916uuoyMDJWSkmJ+v2TJEqVpmnr1\n1VfzjOu1115TmqapHj16WJRHRUWpihUrKl3X1ZAhQyzq/v7772Id16dPn65Onz6d5/Zz5Hz+6enp\nBbZTSqmmTZsqg8GgEhISCm0rSp8k9cJCTjJra2ubK2FUSqmXX35Z6bquZs+erZRS6oEHHlC6rqtN\nmzblanvw4EGlaZqqW7euuSwnqf/pp58KjaWkkvrMzEzl7OysXFxcciXPSmUflDVNU0899ZS5LOdz\ncHFxUUlJSbmWad26tdJ13eKA/dNPPylN09SwYcNytU9MTFSapqnQ0NCCd7oI+zhr1iyl67qaOnVq\nrrrIyEhlY2OTZ1Kfk+wX9ef6k8f1ipPUf/XVV0rTNPXiiy8W2lYIUTqio6OVjY2Natq0qUW50WhU\nfn5+ytXV1XxM69GjR56Jb45mzZopW1tbi0Q9r6S+uOeIb7/9Vmmaprp27VqkfbqRpH7Dhg1K0zT1\nzDPPWJQvXbpUaZqmJkyYYFH++OOPK13X1c6dO3OtK+f88eGHHxYaa2FJfe3atZXBYFD//PNPrroZ\nM2YoTdNyHZdzzpPF+dm+fXuBcRYnqe/cubPSdV0dOXKk0Lai9MmYepGn6tWrmy/BXqt169Yopfjr\nr78AzP9eeykxR6NGjfD29iYiIoLU1FQA+vTpYx7X+cQTT7BkyZJSv8kmLCyM1NRUGjVqhJeXV676\nBx54AID9+/fnqgsMDMTFxSVXebVq1YDsYUo54uLiAPD09MzV3tXVla5du7J7926Cg4OZPn06W7Zs\nISUlpdj7k/OZXzvmM0fNmjXNsV3PZDJhNBqL/FOS4yRzPveLFy+W2DqFEMVTuXJlOnTowIEDBzh4\n8KC5/Mcff+TcuXP06tULJycnAHbt2oWNjQ3ffvstr776aq6f9PR0jEYjERERBW6zuOeIPXv2oGka\nnTp1KqG9zq1Tp074+/uzbNkyrly5Yi5fvHgxuq4zYsQIi/YFHdsfeeQRXFxcGD16ND169GDx4sUc\nPHiw2DfEJicnc+LECXx9faldu3au+tatW+e53KBBg4p1XDcajXmeO26UHNvLFknqRZ7yuxkypzwh\nIcH8r7u7e7432FSuXNmiffPmzfntt9948MEHWbt2LcOGDaNevXr4+/uX2uwoOdv29fUtMMbLly/n\nqvPw8MhzGRub7NtRrr1hy9HREYC0tLQ8l1m1ahWvvvoqWVlZzJw5kwcffBAvLy969uxZrOkec/an\nsN9RWZJz4sz5jIQQ1jFkyBCUUhbT/i5duhRN0yxm7YqLiyMrK4sZM2bk+XPs2DE0TSM5ObnA7RX3\nHJFzHK5SpcpN7WdBNE1j5MiRJCYmsmLFCiD7fq/du3fTvn17AgICLNoXdGyvXr06+/bto1evXmzf\nvp1Ro0bRpEkT/Pz8mD17dpFvcC2Px3WQY3tZIzfKijzlN7NBTnnOzZju7u7Ex8eTnp6e50E7ZzaW\na2/evPvuu1m7di1ZWVn89ddfbN68mQULFjBq1CicnZ0ZMGBAie5LzrZjYmLyrM8rxhvh7e0NXO3V\nuZ69vT1TpkxhypQpxMTEsHPnTpYvX87q1as5evQohw4dwmAwFLqdnDhjY2Np1KhRrvr8fnfTp08v\n1hzJTZo0KXCmjOLI+UxyPiMhhHU8+uijeHp6snz5cv73v/+RlJTEd999R61atSx6cLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a93c33dd8>"
+       "<matplotlib.figure.Figure at 0x88716a0>"
       ]
      },
      "metadata": {},
@@ -1279,9 +1278,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a94ad84e0>"
+       "<matplotlib.figure.Figure at 0x785dda0>"
       ]
      },
      "metadata": {},
@@ -1289,9 +1288,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a94a7c7f0>"
+       "<matplotlib.figure.Figure at 0x89bf6a0>"
       ]
      },
      "metadata": {},
@@ -1299,9 +1298,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a95c14be0>"
+       "<matplotlib.figure.Figure at 0x9c38d30>"
       ]
      },
      "metadata": {},
@@ -1309,9 +1308,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a94a91320>"
+       "<matplotlib.figure.Figure at 0x9b527b8>"
       ]
      },
      "metadata": {},
@@ -1319,9 +1318,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a94b1e3c8>"
+       "<matplotlib.figure.Figure at 0x895fa58>"
       ]
      },
      "metadata": {},
@@ -1392,9 +1391,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAADoCAYAAAAgyBgxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAADoCAYAAAAgyBgxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a94a868d0>"
+       "<matplotlib.figure.Figure at 0x9a2df98>"
       ]
      },
      "metadata": {},
@@ -1549,9 +1548,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a949bc9b0>"
+       "<matplotlib.figure.Figure at 0x9a7eb00>"
       ]
      },
      "metadata": {},
@@ -1580,9 +1579,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a949f5518>"
+       "<matplotlib.figure.Figure at 0x9a746a0>"
       ]
      },
      "metadata": {},
@@ -1637,9 +1636,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAD+CAYAAAC6GvpnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a95f25f28>"
+       "<matplotlib.figure.Figure at 0x887a828>"
       ]
      },
      "metadata": {},
@@ -1689,9 +1688,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt4AAADTCAYAAAC2lRSoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4TPf+B/D3bMlkEyGbJGQhVCyxBCVBg1CU6m1p0Vra\na7mltv5o1XK1Rbi3pZbQVjdLFdXW1mrFEkJiSe1CYktsSQTZ15k55/eH25NOlTBJ5swk79fzeB4+\nc2bmXTWffHJyzverEEVRBBERERERVSml3AGIiIiIiGoCDt5ERERERGbAwZuIiIiIyAw4eBMRERER\nmQEHbyIiIiIiM1Cb+w1zcnLM/ZZERLJwdnaWO4JZsb8TUU1han/nGW8iIiIiIjPg4E1EREREZAZm\nv9Tkzyztx7AJCQkAgJCQEJmTPIjZTGfJ+ZjNNJacjZdb3Mf+/viYzTSWnA2w7HzMZprK6O88401E\nREREZAYcvImIiIiIzICDNxERERGRGXDwJiIiIiIyAw7eRERERERmwMGbiIiIiMgMOHgTEREREZkB\nB28iIiIiIjPg4E1EREREZAblDt6CIGDWrFkICAiAnZ0dAgICMGvWLAiCYHTcnDlz4O3tDXt7e4SH\nhyMxMbHKQhMRUcWxvxNRdZR8TZQ7wkOVO3gvWLAAK1euxPLly5GUlISlS5dixYoViIyMlI5ZuHAh\nFi9ejKioKCQkJMDd3R0REREoKCio0vBERKYo1Zdg26G1KCqp2T2K/Z2IqpsPvy5C81cNWL/LIHeU\nv6Uu74D4+Hj069cPffr0AQA0aNAA/fr1w5EjR6RjlixZgunTp2PAgAEAgNWrV8Pd3R3r16/HqFGj\nqig6EdGTy86/iy92LMC1jItIu5OKUf2mQ6lUyR1LFuzvRFSdfLQ+E//+whUA8NoHIpwdRfTtpJA5\nlbFyz3iHhYVh3759SEpKAgAkJiZi79696Nu3LwDg6tWrSE9PR0REhPQcrVaLLl26IC4uropiExE9\nudOXj2Dht5NwLeMiAOBcSgLOXDkqcyr5sL8TUXUgCCJGLbiAaVGuUs3L9RyC/K/LmOrvlXvG+513\n3kFeXh6CgoKgUqlgMBgwY8YMjBkzBgCQnp4OhUIBDw8Po+d5eHjg1q1bj3zthISECkSvOpaaC2C2\nirDkfMxmmsfNptOXICFlNy5mnJBqCijQLqAXSrPUlfrfGBgYWGmvVdXY3y0Ls5nGkrMBlp2vOmRL\nuV2KD7+zx5krwVLN3eUqFryehLs3/XD35u1Ky1QZ/b3cwXvDhg1Yu3YtNmzYgKCgIJw8eRITJkyA\nv78/Ro4cWeEARERVKSMnFYcubkd+SbZUs7dxQufGA+Dh7CtjMvmxvxORNfv811J8s6s19AatVPPz\nvIDFo2/Cu66ffMEeodzBe9q0aZg2bRoGDhwIAGjWrBlSUlIQGRmJkSNHwtPTE6IoIiMjAz4+PtLz\nMjIy4Onp+cjXDgkJqWD8yvXHd1eWlgtgtoqw5HzMZprHyabTl2JH3DrEnN0OEWV3uAc36ohXur8J\nB61TlWTLycmpktetCuzvloHZTGPJ2QDLzmft2fILczHsw1PYciDMqN49JAlbFjSEg13TKslWGf29\n3Gu8CwsLoVQaH6ZUKqXlpvz9/eHp6Yno6Gjp8eLiYsTGxiI0NLTCAYmIntS1jEv473dvY9+JbdLQ\nbWfrgNd6TcbrfaZV2dBtbdjficiaiKKIr3YkofWIq0ZDt5vLTayZfQXRS56Cg52NjAnLV+4Z7379\n+mHBggXw8/NDs2bNcPz4cSxevBgjRoyQjpk0aRIiIyPRpEkTBAYGYu7cuXBycsLgwYOrMjsRkRGD\nQY9dxzbjt2PfQxDKlpJ6qkErDO4xHi5Oro94ds3D/k5E1uJ2VhH6Tb2JY+cbG9VbNLyGPUvd4Frb\nXqZkT6bcwXv58uWYNWsWxo0bh9u3b6NevXoYM2YMZs2aJR0zbdo0FBcXY/z48cjKykKHDh2wa9cu\nODg4VGl4IqI/pN29jm93LcG125ekmo3aFs93HoGwFs9CobCsJaUsAfs7EVmD73ZfwYRFdrib09Co\n3r9zJjZ+0AC2NtbT38sdvB0cHLBo0SIsWrTokcfNnj0bs2fPrrRgRESPQxAFxJzYjh1x66A36KR6\nQL2mGNpzAtxq15MxnWVjfyciS3bmcgmGvn8HZy/7G9VDW57B0in+aB3oLlMy05U7eBMRWaq7ORlY\nF70Ul2+ek2oqlRp9nx6Cbm2er7Eb4xARWbsvtt/CWx/XRonOS6ppVMWY/cZVvDeshdX+FJODNxFZ\nHVEUcSnjJDYe/QglumKp7u3mj9d6ToSXq5984YiIyGR6vQHzNxuwI77sp5UKhQHtm53Birf90Lpx\nkIzpKo6DNxFZlZyCe9h7fiNuZpVdy61UKBHR7kX0aj8IapVGxnRERGSq86k3MPITWyRdL9sMp5bD\nbXwy6TKG9+5otWe5/4yDNxFZjePJB7Fp32coLM6Tau4u3nit50T4ejZ+xDOJiMhSCYIB89Ycw8K1\njVFYXDZ0B9a/hG3/cUKTBp1kTFe5OHgTkcUrKMrF9zGf43jyQaN611bPoV+n12CjsZUpGRERVcTh\nc5l4Y/5dnE/pYFR/qdslrJsVABub6nWvDgdvIrJoF1JPYl30EuQWZEk1B9taCG3UH891fUnGZERE\nZCpBEDE1KhlLNgVAEMr2WHCwy8HbL17GnH+1lTFd1eHgTUQWSW/QYUfct9h7fItR/emg7vBzagMb\nNc9yExFZo+u38zFo5jUcOffnrd0F9Gx/FW/3z4ZLNd5cmIM3EVmc21k38c2vH+PG7StSzcnOGa/0\nGIcWAe2RkJAgYzoiIjKFXi9i5qoMLNnkhJLSsqHbyy0Vn01Tom+nRtW+v3PwJiKLIYoiDifuwQ8x\nq1CqL5HqTX3bYGjEBNRyqC1jOiIiMtWVWwb0m3oX51M8jOrdQs7hx/mNUMtBK1My8+LgTUQWobA4\nHxv3rsSJi4ekmkqlRv/QYeja6jkoFUoZ0xERkakOnLyL3lO0KCpxk2outdIwc3gOJr/SXMZk5sfB\nm4hkd+nmOaz97RNk5WVKNQ8XHwzvPQU+bgEyJiMiIlOJoogDp+LwwrsBKCqpA+D+Zjh9Ou3Fl9Nb\nw92laTmvUP1w8CYi2dzNycC2Q2uMznIDQGjzXnihy+tcJpCIyAoVl4iYu/oudh6+hVt36iI7zxMA\noFEXYcGbhzFxYHcoldVrmcDHxcGbiGTxe1IsvtsThdI/bflub+uIwT3GIbhRRxmTERGRqa7cFPHs\nlCxculEXQF2jx/4zPhsTB3aTJ5iF4OBNRGZVqi/B1tjViD39i1G9VWAnvND5dbg4uT7kmUREZMl2\nxhdg4EwFCotdHnhsVH89Jg70kiGVZeHgTURmc/32Zaz5bTEy7t2Qam7O9TC050QEeD0lYzIiIqqI\nyLVXMetzHwjC/dFSqdShR7v9CG3ZDn6ezhgSwZET4OBNRGYgCAbs/v0n7Dy8AQZBL9WDGz6NIRFv\nwc7WQcZ0RERkqlJdCcYvPowvtnaRavbae5g58hCmDu4DlYqj5p/xb4OIqtTdnAys3fUJrtw6L9Vs\nNFr8o8vr6NgsAgqFQsZ0RERkqpS0i5i05Ai2xQ6Wam4u17H+/Wx0b9tfxmSWi4M3EVUJURRx9Pxe\nbN7/BUpKi6S6r2djDOs1GW6168mYjoiITKU3GDDz8yP4Yrs37uUMleq+nmk4+GlteLs1kDGdZePg\nTUSVLr8oFxv3rMCpy4elmlKhRK8OL6Nnu5egqqHLSBERWbtTlzIw+N/3cCHFePWpRj55iP+8Huo6\n86eYj8LBm4gq1alLh7Fp70rkFeVINbfaXhjWaxJ8PRvLmIyIiEwliAK+2BaHyUtaoKik7GZ4W00J\nRvTVY/4YJ7jU4tBdHg7eRFQp8otysTlmFY4nxxrVQ1s8iwGdR8BWo5UpGRERVURmdhpmf/krvtr2\nMnR6u/9VBTzf+TK+nN4QdZzZ3x8XB28iqrBTl+Kxae+nRme5nR3q4JXub6KZf4iMyYiIyFSCKODn\nuGjM+EyFs5dHSHU72wJ8PTMbg7rxp5hPioM3EZns/lnuz3E8+aBRvUPTbnihy+uw1zrKlIyIiCri\n9KUMTFh8EfFnO//pLDfg6lyA3Utt0bKRj4zprBcHbyIySfL101jz22LkFmRJNZ7lJiKybqIoYtaq\nC/hofQOU6kKNHuvZPh9fvecILzdey20qDt5E9EQMBj12HtmA6GM/QIQo1Ts07YYXur4Oe1ue5SYi\nskZ5hUWImHgNRxONdxJu4JGNj95ywkvhTjIlqz6Uj3NQeno6RowYAXd3d9jZ2aF58+aIjTW+gWrO\nnDnw9vaGvb09wsPDkZiYWCWBiUg+d3LSseSHGdh1bLM0dDvY1cLofjMwtOcEDt1WiP2diAAg+fpF\nPD3qAo4mll23Xdc5A1/PSMPVH2rjpXCeq60M5Q7eOTk5CA0NhUKhwM6dO3HhwgUsW7YM7u7u0jEL\nFy7E4sWLERUVhYSEBLi7uyMiIgIFBQVVGp6IzEMQBew/uQML1k1ESlqSVG/s0wLvDvkEzQPayZiO\nTMX+TkQ/x5Wi0aA7CBrii/MpraR6eNszSNrgjOF9vLjDcCUq99uXhQsXwsvLC19//bVU8/X1NTpm\nyZIlmD59OgYMGAAAWL16Ndzd3bF+/XqMGjWqkiMTkTnlFmRj7W+LkXT9lFRTKpTo23EourcdACU3\nw7Fa7O9ENdvW2Nt46T0XGIS6RvWhva5jzawWHLirQLlnvLdu3YoOHTrglVdegYeHB1q3bo2oqCjp\n8atXryI9PR0RERFSTavVokuXLoiLi6ua1ERkFuk5KfjP+slGQ3e9ug0wedBCRLR7kLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt4AAADTCAYAAAC2lRSoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a95c326d8>"
+       "<matplotlib.figure.Figure at 0x887a940>"
       ]
      },
      "metadata": {},
@@ -1764,9 +1763,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAD+CAYAAAD1R/m3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuIAAAD+CAYAAAB/aGD2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a95bc8c50>"
+       "<matplotlib.figure.Figure at 0x9bbe668>"
       ]
      },
      "metadata": {},
@@ -1816,7 +1815,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEuCAYAAAB7xO7rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x23a95eaab70>"
+       "<matplotlib.figure.Figure at 0x7491ef0>"
       ]
      },
      "metadata": {},
@@ -1891,7 +1890,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

Appendix-A-Installation.ipynb

@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -37,7 +37,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
        ".CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
@@ -51,7 +51,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -132,11 +132,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -271,7 +270,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -374,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -582,7 +581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -596,7 +595,7 @@
        "2⋅t + 2"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -619,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -637,7 +636,7 @@
        "⎣╲╱  x  + z       ╲╱  x  + z  ⎦"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -668,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -695,7 +694,7 @@
        "⎣    6           2                 ⎦"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }

Appendix-B-Symbols-and-Notations.ipynb

@@ -21,8 +21,11 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
+       ".CodeMirror pre {\n",
+       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
+       "}\n",
        "    div.cell{\n",
        "        width: 900px;\n",
        "        margin-left: 0% !important;\n",
@@ -32,7 +35,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -42,7 +45,7 @@
        "\t\n",
        "    div.input_area {\n",
        "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;\n",
+       "        border: 1px solid #586e75;      \n",
        "    }\n",
        "\n",
        "    .text_cell_render h1 {\n",
@@ -101,6 +104,7 @@
        "    h5 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "    }\n",
+       "\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 200;\n",
        "        font-style: normal;\n",
@@ -112,11 +116,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -135,7 +138,8 @@
        "}\n",
        "\n",
        "    code{\n",
-       "      font-size: 70%;\n",
+       "        font-size: 6pt;\n",
+       "\n",
        "    }\n",
        "    .rendered_html code{\n",
        "    background-color: transparent;\n",
@@ -228,7 +232,8 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
+       "                           extensions: [\"AMSmath.js\"],\n",
+       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -236,7 +241,7 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:100,\n",
+       "                    scale:95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",

Appendix-D-HInfinity-Filters.ipynb

@@ -28,8 +28,11 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
+       ".CodeMirror pre {\n",
+       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
+       "}\n",
        "    div.cell{\n",
        "        width: 900px;\n",
        "        margin-left: 0% !important;\n",
@@ -39,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -49,7 +52,7 @@
        "\t\n",
        "    div.input_area {\n",
        "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;\n",
+       "        border: 1px solid #586e75;      \n",
        "    }\n",
        "\n",
        "    .text_cell_render h1 {\n",
@@ -108,6 +111,7 @@
        "    h5 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "    }\n",
+       "\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 200;\n",
        "        font-style: normal;\n",
@@ -119,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -142,7 +145,8 @@
        "}\n",
        "\n",
        "    code{\n",
-       "      font-size: 70%;\n",
+       "        font-size: 6pt;\n",
+       "\n",
        "    }\n",
        "    .rendered_html code{\n",
        "    background-color: transparent;\n",
@@ -235,7 +239,8 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
+       "                           extensions: [\"AMSmath.js\"],\n",
+       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -243,7 +248,7 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:100,\n",
+       "                    scale:95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",
@@ -292,9 +297,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7378f98>"
+       "<matplotlib.figure.Figure at 0x7458940>"
       ]
      },
      "metadata": {},
@@ -362,7 +367,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

Appendix-E-Ensemble-Kalman-Filters.ipynb

@@ -28,8 +28,11 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
+       ".CodeMirror pre {\n",
+       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
+       "}\n",
        "    div.cell{\n",
        "        width: 900px;\n",
        "        margin-left: 0% !important;\n",
@@ -39,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -49,7 +52,7 @@
        "\t\n",
        "    div.input_area {\n",
        "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;\n",
+       "        border: 1px solid #586e75;      \n",
        "    }\n",
        "\n",
        "    .text_cell_render h1 {\n",
@@ -108,6 +111,7 @@
        "    h5 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "    }\n",
+       "\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 200;\n",
        "        font-style: normal;\n",
@@ -119,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -142,7 +145,8 @@
        "}\n",
        "\n",
        "    code{\n",
-       "      font-size: 70%;\n",
+       "        font-size: 6pt;\n",
+       "\n",
        "    }\n",
        "    .rendered_html code{\n",
        "    background-color: transparent;\n",
@@ -235,7 +239,8 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
+       "                           extensions: [\"AMSmath.js\"],\n",
+       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -243,7 +248,7 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:100,\n",
+       "                    scale:95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",
@@ -296,9 +301,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7248f98>"
+       "<matplotlib.figure.Figure at 0x72e9d68>"
       ]
      },
      "metadata": {},
@@ -579,9 +584,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7efa470>"
+       "<matplotlib.figure.Figure at 0x8045208>"
       ]
      },
      "metadata": {},
@@ -747,7 +752,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

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

@@ -28,7 +28,11 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
+       ".CodeMirror pre {\n",
+       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
+       "}\n",
        "    div.cell{\n",
        "        width: 900px;\n",
        "        margin-left: 0% !important;\n",
@@ -38,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -48,7 +52,7 @@
        "\t\n",
        "    div.input_area {\n",
        "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;\n",
+       "        border: 1px solid #586e75;      \n",
        "    }\n",
        "\n",
        "    .text_cell_render h1 {\n",
@@ -107,6 +111,7 @@
        "    h5 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "    }\n",
+       "\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 200;\n",
        "        font-style: normal;\n",
@@ -118,10 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 120%;\n",
-       "        text-align:justify;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
+       "        line-height: 150%;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -140,7 +145,8 @@
        "}\n",
        "\n",
        "    code{\n",
-       "      font-size: 70%;\n",
+       "        font-size: 6pt;\n",
+       "\n",
        "    }\n",
        "    .rendered_html code{\n",
        "    background-color: transparent;\n",
@@ -233,7 +239,8 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
+       "                           extensions: [\"AMSmath.js\"],\n",
+       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -241,7 +248,7 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:100,\n",
+       "                    scale:95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",
@@ -379,9 +386,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEPCAYAAACeKXhLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAADTCAYAAADAr6YjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl0HNWdL/Bv9a7WvliWtVmWLW/yLtl43w0xW4CAE0hs\n3vDCQJI5bDOZycsLASYhGc4Mw3vkEIfHOwmGCS9AGMIQwmIcG9vYxpaxZVve5FW2tVnW2pJa3V1d\n7w+wULvK1narq6v6+znH58C19Ksfpn3rp1u/e0tSFEUBERERERFFhc3oBIiIiIiI4gkLcCIiIiKi\nKGIBTkREREQURSzAiYiIiIiiiAU4EREREVEUOYy8eFtbm5GXJyIiIiIaltTU1EF/D1fAiYiIiIii\niAU4EREREVEUGdqC0tdQlu+NoigK/unFj3GkpkmX+P+wZh6WzCjSJTYNXUVFBQCgvLzc4EzI7BRF\nwf9+6zP844sbEQyFe8cfufM6PPO3q+By2iO+vuO//gD/vs9UcdLuexjOgiLNa7z6p034Px8dR1pa\nmpCcMwLtuO/sn1HcVdc7Zh8xEpn/+Au4J08Xcg2Kb5xjzUsJBtDywr9AbmuJGLenpiH9B/8DktNl\nUGb6GW4bNVfAh2D3kQu6Fd/TirOxePpoXWITkfFaOrpx++Ov49EXPuwtvlMT3Xj7Z9/Ecz/4mqr4\nDtWd1yy+PTPmXLX4BoBJ+amYVpQuLO9mVwr+fdw3sXHEV8WRfLEBjf/0t2h/6xUo4fA1vpuIrExy\nupB4w22qcbmtFd07NhuQUexjAT5I4bCCVz6q1CW23SbhwVvLIUmSLvGJyFifHT6Pmfe/iHc+PdY7\nNntiLva99ABuWzhR83s6N72nGpM8HiSuvKXf6902pwDuKwr64QhLdryduwTri25Dl8Pz5aCMtt8+\nj6Z/fgxye6uwaxGRubgmToVrrHoe69qxGeFOnwEZxTYW4IO0ed9p1DS26xL71vkTUJBtnlYcIhoY\nRVHwv/64C4se/h3ONnz12PKRO6/D9ufvw5hR2ivVgdPVCJw8qhpPXLoatsSkfq+bnuTGmqWlQ0/8\nKg6mjsUvStbClzuud8y/ZzsaHvo2eg7rs0BBRLFNkiQkfu12SLbI0lIJ9KBr+8cGZRW7WIAPQiAo\n4/cfH9QldkZyAu5eMUWX2ERknMG2nFymKAo6N/1ZNW5Pz4SnfP6Ar3/7oonIzey/WB+sZlcKns6/\nA55b7+kdY0sKUXxzZGXDM2ueaty/Zzvk1mYDMopdLMAH4aOKk7jY1qVL7PtWz0CC26lLbCIyxlBa\nTi4LHDmA0IUa1bh3+Y2Q7APfP+902PG3N5cNPOlBaOuR8fG4Vcj66b/DlpTyxSBbUojimnfx9ZCc\nkfWMIsvo2vKBQRnFJhbgAxSSw/jPrUd0iT1lzAhuvCSykKG2nPR+vyyjc/NfVOOOnDy4S2cOOp+y\nCbmYNzl/0N83EO/uOA5p5nyM/NXv4Zo4tXecLSlE8cmWnIKEuUtV4/7KPQg11qm/IU6xAB+gT/af\n0WX1W5KAB27hxksiqxhqy0lfPQf2QG5qVI0nLr9pyHPFd2+aBYdd/JTf0R3Ah7tPwJE9CtnPvITk\nO9b2/h5bUojiU8L8ZbAleFXjnX9VbyqPVyzAB0BRFLyl0+r30ulFKMoRc04vERlrOC0nlynBIDq3\nfKgad44eC+e4gcXQkp2eiBuvG9f/Fw7B29uPIhiSITkcSPvvD7MlhSjO2TwJSFi0SjUeOFaFYM1p\nAzKKPSzAB2DX4fM4d1H8yScOuw3fXjm1/y8kopg23JaTvvyVexDWKFQTV9487Cdla5aWwuMS//61\nS+3d2LL/TO+/J1y3mC0pRHEuoXwBbCnqBcau7RsNyCb2sADvh6IoeHPLYV1ir54zDiMzxJ9OQETR\nI6Ll5DJFltG946+qcfeEKXDmFw0719QkD+5YNPRV9Gv54yeHEQ4rvf/OlhSi+CY5nUhc+jXVeKD6\nCEL1FwzIKLawAO/HgZMNqL4g/ugcj8uBby4Tfz4vEUWPiJaTvnqOVEJuuaQa9y65YVh59nXbwolI\nTXQLi3dZ7SUfdlSdixhjSwpRfHNPK4c9LUM13vXpJgOyiS0swPvx5if6rH7fvnAiUpM8usQmIn2J\nbDnpG7N7m/plFa5xE+EYJe4EkwS3U7cf/t/cUgVFUVTjbEkhik+S3Y6E+ctU4z2H9kFubjIgo9gh\nvAD/5S9/iTlz5iA1NRXZ2dm49dZbUVVVJfoyUXHyQjMqTzYIj5videN2nR4DE5G+RLac9BWoPqx5\nRFfCghXDylfL1+aMQ3aa+oSC4TpV13rVOZMtKUTxyTPjOs0393ZptNvFE+EF+NatW/F3f/d32Llz\nJzZv3gyHw4GVK1eitdV8jxrf21WtS9w7l0ziS3eITEh0y0lf3RqvanYWFME5euyw4mpxOuy4e4U+\nG8D/co15ky0pRPFHcjqRMHeJarxn/27IHW0a3xEfhBfg77//PtatW4fJkyejtLQUr776Ki5evIhP\nP/1U9KV05esO4JPKs8LjJiW48LU5+hwFRkT60KPlpK/g2ZMInjujGk9YuFK3dwQsnVGErFTxq+C7\njpxHUz/vTGBLClF88ZQvhOSJbLtVZBndO7cYk1AM0L0HvL29HeFwGOnpw7tBRdumvacQCMnC4948\nt4Sr30QmolfLSV9dn6ofxTqyR8FVMnnYsa/GYbfh9mGu2mtRFODD3Sf6vz5bUojihs3jQcLshapx\nf8UOhP1+AzIynqRo7ZgRaM2aNTh16hT27NmjWslpa/tqJam6Wp92j6FQFAW/fOsQLraL/VC4HDb8\ndM10JHrEn8NLROIdqmnBj/9jH+paunvHJhek4hffmYW8DDGrxzZfO1L+6z++qFz76FywCsGi8UKu\ncTU9QRk/e+MAOntCQuOmJDjx+JppA37zpuvYAaS+/TJs3V+tnPeMn4q22++F4uVRrURWIPm7kfKn\nVyDJkfNN1+zFCIw33ztRSkpKev85NTV10N+v6wr4Y489hh07duCtt94y1avWj9e2Cy++AWDehBEs\nvolMQFEUvLbtNO7/9c6I4vvuRWPwf78/X1jxDQCuE1Wq4jucmIxgof6tam6nHYtLRwqP294dxKGa\ngfdzByZMw6UH/ycC+WO+yu34QWT+5mk4a04Kz4+Iok/xJCBQrH7q5j5+UDUHxgPdqsFHH30Ub7zx\nBrZs2YLRo0f3+/Xl5eV6pTJoHx3dhrQ0sa+Hd9hteOQ7X9Ol55Kio6KiAkBsfVZJvJaObvzNM+9E\nbLRMTXTj5R/dNuyNlldSgkE0b/4TwlfMN4krboJ3zpxhxR7o53ViaQD7zr8Df0DsKvjJVhu+O8i/\nK8rSFWjb8AI6/vNVAIC9rQUZv/t3pP63HyD59u9AsvHkXCvjHGt9ocJ8tKx/5opRBWNGpMNVZK79\ncX27OIZCl9ns4Ycfxuuvv47NmzdHLNGbQVNbF3YdOS887jKdNjwRkTh6nnKipedwJcJdnRFjkt0O\nz8y5wq91NUkJLqzWYWP4odMXUdMwuBsUT0khsjZHdo7myU7+PdsNyMZYwgvwH/zgB3j55Zfx2muv\nITU1FQ0NDWhoaEBnZ2f/3xwDPtx9QpcnIXcsniQ+KBEJofcpJ1fjr1CfDuWaPEPzzFw93bZwIuw2\n8W2C7382tL09PCWFyLq0NmMGjh6MuyMJhRfg69evh8/nw4oVK5Cbm9v769lnnxV9KeEURcHm/WeE\nx50xbiTyR6QIj0tEwxeNU060hOrOI3j+jGo8oXyBLte7loyUBMwvLRAe95PKswjJQzvNhKekEFmT\na+IU2JKSI8aUcBg9n+8yKCNjCC/Aw+EwZFlW/frpT38q+lLCHa1pQkOL+JX6m+bqe5IBEQ1NtFtO\n+urWWP12jMyFo6BI1+tezY1zxbcLdnQHsK9a/XbPgWJLCpH1SHYHPLPmqca79+6AIovdixLLuKOl\njy06rH5npXoxe0Ku8LhENHRGtZz0Xj/Qg56Dn6vGPbMXGnZiVGnRCBRmi39St3nfmWHHYEsKkbV4\nZs0Drpjrwh3tCFQfNSij6GMB/qWQHMa2AzXC435t9ljYB3gWLhHpz6iWk756jhyEEgxEjEluDzxT\nZ+l+7auRJEmXp3WfHbmALn9w2HHYkkJkHfbUNLgnTFGN9xysMCAbY7Ay/NLnx+vQ0R3o/wsHwW6T\ncP1s9W5fIjKGkS0nffUc2qsac0+ZCcnljloOWpbOKILHJfZ02kBIxs6qc0JisSWFyDrcM69TjQWO\nVyHs79b4authAf4lPdpP5pcWID05QXhcIhoco1tO+gr7OhA4oX7M6plm/NnHXo8Ty2cWCY+7pfKM\n0HhsSSEyP9fYCbB5EyPGlFAIgSMHDMoouliAA+jyB/HZkQvC4+qxqYmIBicWWk766jmk7v22p6bD\nUTBG46uj78brxM9blScb0NwudlWLLSlE5ibZHXCXzlCN+w/ERxsKC3AAO6vOIRCShcYcmZ6I0qIR\nQmMS0eDESstJX/6DGu0n08oM23x5pdE5aRibK/aJgKIAWw+cFRoTYEsKkdm5NZ78Bc+ciIu/uyzA\nAXx6SEx/Yl9LZxTFzA2VKN7EUstJX6GmRoRq1fONe2qZAdlc3bIZRcJj7tBhnr2MLSlE5uTIGw17\nepZqvOfQPgOyia64L8D9gRAqTzYIj7tUhxsYEfUv1lpO+urRWP125OTBMSLHgGyubtG00VeeEDZs\nR881oc3nFxu0D7akEJmPJElwa5z+1BMHbShxX4BXnqgX3n4yLi+db74kMkAstpxcpiiKZv+31iNY\no2WkJGD62JFCYyoKsOdYrdCYV2JLCpH5aD0BDDXUItQkfnE0lsR9Aa7H5sul04uExLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b75fd0>"
+       "<matplotlib.figure.Figure at 0x72f8f28>"
       ]
      },
      "metadata": {},
@@ -499,7 +506,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAASBAMAAAB/WzlGAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAdolEMpnNqyK7EO/d\nVGbrAyG9AAAAX0lEQVQIHWNgYGBgBGIgYEKhjCE8ARDFEppeAaQqFyh0NzBwFjAbsBYwyC9gYmA6\nwHAUqI1zAsNHhoUM6y8wJDJcYShlYLBhUGAPAGqLm6sC0s7xAEQy8DWAKag1YEsBkk8P0x6j9zMA\nAAAASUVORK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAATBAMAAAC0B+rjAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIokQdkQymVRmzbur\n3e+SS/cOAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAYElEQVQIHWNgYGBgBGIgYEKhTCC8MAilAKLY\nAgsbgVQF4wQuAQbuCZwCTAcY5gK1sV9g2A6kuA4wvGSQZJBvYFjCMBEowhDLoMBdANRWuaUMpJ1x\nAohk4BQAU1DbOEEcAFCSDm4gARWbAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "\\phi"
       ]
@@ -534,7 +541,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAACkAAAAwBAMAAAB+l06WAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZjKJ\nRFRer8KoAAABNklEQVQ4EWNggAMhOAuJYfIJiQNjqoRhE2VgH76irBk/s2C+H3r0fyzgA328YQ+x\nGs2yXDjfNMQbxmYWALLYQTymBIbzF6DCrA+ADB4Qh02BgaMBxAACTRABFuWYwMD2G8QDgjgQcRdE\nMH6CizJNAAkUgAgg4ISmUZ4FDAzcoWLJYEGG+wcgNMjYFIUDFgZg7hSIIEMVAwNLAu8F1gQQn9WB\ngYEX5DigsfUKPAw8QJqBoQyIKwSAzi9gYJgKdBiLA1CAL4DBmFn9L9BSIOc7gzqD/gMgYysDQzED\n0xcGhjVAjjjDa4ZUIM0850wY0KB4A4ZJQN5NhgNsAUCaERh4QFH7AK4DQB53lN9pIAUDbJ84FUBs\n5gUgEga4/zwFM7kMYCJgWkIITIFDDCHxHmQJNMwRoozIWgFe5Vu10Z1KpwAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAACkAAAAwBAMAAAB+l06WAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAVO8Qq5l2zWYiuzKJ\nRN0MreaOAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABN0lEQVQ4EWNgwA8YlbHIm4R8xiLKwDZ8RVmd\nvntg8/LQEPuPBXygj9PnQ6xGs8wXyOcEi02NWg2T4ywAsnhAPGYHhvMXoMKsAjBRFgMGJgWoqB2I\nNgMRTA8YWH6DGEAQCCJugwj2z3BR5gcggQMgAgj4oGmUp4GBgftmpCRYkOH+AQgNMjaQs4F9Apj7\nBCLIkMHAwNXAMYHHAcRnDWBg4AU57gEDQx/QuaxAmoEhFYiTChgY2BIYGJYCRdkdgAKMAQwzOc3+\nAi0FcjQYZjDMFwAytjIwZDIw/2Jg6AFyyhg6geqBgfHmTMgDBob4CQyPgbx7DAe4gNYwsAMDDyg6\nP4DjAJDHHbEsGkjBAMtnPgMQm7MBJgKiuf+IgrkcE5BFGYqKwVxw6CIk5EGWMDBwIERALKjvIYIA\nUQJebishGGcAAAAASUVORK5CYII=\n",
       "text/plain": [
        "   1   \n",
        "───────\n",
@@ -567,7 +574,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIsAAAAcBAMAAACjVFE3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIsAAAAcBAMAAACjVFE3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ng/w/6i9+jIWXI72KpwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "        ⎛ 2    ⎞\n",
        "(φ - 1)⋅⎝φ  + 1⎠"
@@ -598,7 +605,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ4AAABLCAMAAAB3JhEVAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nc844NmVfmwAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ4AAABLCAMAAAB3JhEVAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nZfMfmA4D/WMrfyLnD+Kk/1SSWdR/72DNtOJLnxsAAAAASUVORK5CYII=\n",
       "text/plain": [
        "⎡           x - xₐ                           y - yₐ              ⎤\n",
        "⎢ ──────────────────────────   0   ──────────────────────────   0⎥\n",
@@ -754,9 +761,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e90748>"
+       "<matplotlib.figure.Figure at 0x57647f0>"
       ]
      },
      "metadata": {},
@@ -867,9 +874,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e45240>"
+       "<matplotlib.figure.Figure at 0x5733c18>"
       ]
      },
      "metadata": {},
@@ -1008,7 +1015,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

Appendix-H-Least-Squares-Filters.ipynb

@@ -28,8 +28,11 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
        "\n",
+       ".CodeMirror pre {\n",
+       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
+       "}\n",
        "    div.cell{\n",
        "        width: 900px;\n",
        "        margin-left: 0% !important;\n",
@@ -39,7 +42,7 @@
        "        background: transparent;\n",
        "        color: #000000;\n",
        "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
+       "        font-size: 12pt;\n",
        "        font-style: bold;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "   }\n",
@@ -49,7 +52,7 @@
        "\t\n",
        "    div.input_area {\n",
        "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;\n",
+       "        border: 1px solid #586e75;      \n",
        "    }\n",
        "\n",
        "    .text_cell_render h1 {\n",
@@ -108,6 +111,7 @@
        "    h5 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "    }\n",
+       "\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 200;\n",
        "        font-style: normal;\n",
@@ -119,11 +123,10 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
        "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
@@ -142,7 +145,8 @@
        "}\n",
        "\n",
        "    code{\n",
-       "      font-size: 70%;\n",
+       "        font-size: 6pt;\n",
+       "\n",
        "    }\n",
        "    .rendered_html code{\n",
        "    background-color: transparent;\n",
@@ -235,7 +239,8 @@
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
+       "                           extensions: [\"AMSmath.js\"],\n",
+       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@@ -243,7 +248,7 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:100,\n",
+       "                    scale:95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",
@@ -317,7 +322,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

Supporting_Notebooks/Interactions.ipynb

@@ -1,5 +1,265 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
+       "\n",
+       ".CodeMirror pre {\n",
+       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
+       "}\n",
+       "    div.cell{\n",
+       "        width: 900px;\n",
+       "        margin-left: 0% !important;\n",
+       "        margin-right: auto;\n",
+       "    }\n",
+       "    div.text_cell code {\n",
+       "        background: transparent;\n",
+       "        color: #000000;\n",
+       "        font-weight: 600;\n",
+       "        font-size: 12pt;\n",
+       "        font-style: bold;\n",
+       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
+       "   }\n",
+       "    h1 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "\t}\n",
+       "\t\n",
+       "    div.input_area {\n",
+       "        background: #F6F6F9;\n",
+       "        border: 1px solid #586e75;      \n",
+       "    }\n",
+       "\n",
+       "    .text_cell_render h1 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 30pt;\n",
+       "        line-height: 100%;\n",
+       "        color:#c76c0c;\n",
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+       "        margin-top: 1em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
+       "    } \n",
+       "    h2 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "    .text_cell_render h2 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 16pt;\n",
+       "        font-style: italic;\n",
+       "        line-height: 100%;\n",
+       "        color:#c76c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 1.5em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
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+       "    h3 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
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+       "    .text_cell_render h3 {\n",
+       "        font-weight: 200;\n",
+       "        font-size: 14pt;\n",
+       "        line-height: 100%;\n",
+       "        color:#d77c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 2em;\n",
+       "        display: block;\n",
+       "        white-space: wrap;\n",
+       "        text-align: left;\n",
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+       "    h4 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "    .text_cell_render h4 {\n",
+       "        font-weight: 100;\n",
+       "        font-size: 14pt;\n",
+       "        color:#d77c0c;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 0.5em;\n",
+       "        display: block;\n",
+       "        white-space: nowrap;\n",
+       "    }\n",
+       "    h5 {\n",
+       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "    }\n",
+       "\n",
+       "    .text_cell_render h5 {\n",
+       "        font-weight: 200;\n",
+       "        font-style: normal;\n",
+       "        color: #1d3b84;\n",
+       "        font-size: 16pt;\n",
+       "        margin-bottom: 0em;\n",
+       "        margin-top: 0.5em;\n",
+       "        display: block;\n",
+       "        white-space: nowrap;\n",
+       "    }\n",
+       "    div.text_cell_render{\n",
+       "        /*font-family: 'Vollkorn', verdana,arial,sans-serif;*/\n",
+       "        line-height: 150%;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
+       "        text-justify:inter-word;\n",
+       "    }\n",
+       "    div.output_subarea.output_text.output_pyout {\n",
+       "        overflow-x: auto;\n",
+       "        overflow-y: scroll;\n",
+       "        max-height: 50000px;\n",
+       "    }\n",
+       "    div.output_subarea.output_stream.output_stdout.output_text {\n",
+       "        overflow-x: auto;\n",
+       "        overflow-y: scroll;\n",
+       "        max-height: 50000px;\n",
+       "    }\n",
+       "    div.output_wrapper{\n",
+       "        margin-top:0.2em;\n",
+       "        margin-bottom:0.2em;\n",
+       "}\n",
+       "\n",
+       "    code{\n",
+       "        font-size: 6pt;\n",
+       "\n",
+       "    }\n",
+       "    .rendered_html code{\n",
+       "    background-color: transparent;\n",
+       "    }\n",
+       "    ul{\n",
+       "        margin: 2em;\n",
+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li li{\n",
+       "        padding-left: 0.2em; \n",
+       "        margin-bottom: 0.2em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    ol{\n",
+       "        margin: 2em;\n",
+       "    }\n",
+       "    ol li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.5em; \n",
+       "    }\n",
+       "    ul li{\n",
+       "        padding-left: 0.5em; \n",
+       "        margin-bottom: 0.5em; \n",
+       "        margin-top: 0.2em; \n",
+       "    }\n",
+       "    a:link{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:visited{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:hover{\n",
+       "       font-weight: bold;\n",
+       "       color: #1d3b84;\n",
+       "    }\n",
+       "    a:focus{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    a:active{\n",
+       "       font-weight: bold;\n",
+       "       color:#447adb;\n",
+       "    }\n",
+       "    .rendered_html :link {\n",
+       "       text-decoration: underline; \n",
+       "    }\n",
+       "    .rendered_html :hover {\n",
+       "       text-decoration: none; \n",
+       "    }\n",
+       "    .rendered_html :visited {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :focus {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .rendered_html :active {\n",
+       "      text-decoration: none;\n",
+       "    }\n",
+       "    .warning{\n",
+       "        color: rgb( 240, 20, 20 )\n",
+       "    } \n",
+       "    hr {\n",
+       "      color: #f3f3f3;\n",
+       "      background-color: #f3f3f3;\n",
+       "      height: 1px;\n",
+       "    }\n",
+       "    blockquote{\n",
+       "      display:block;\n",
+       "      background: #fcfcfc;\n",
+       "      border-left: 5px solid #c76c0c;\n",
+       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
+       "      width:680px;\n",
+       "      padding: 10px 10px 10px 10px;\n",
+       "      text-align:justify;\n",
+       "      text-justify:inter-word;\n",
+       "      }\n",
+       "      blockquote p {\n",
+       "        margin-bottom: 0;\n",
+       "        line-height: 125%;\n",
+       "        font-size: 100%;\n",
+       "      }\n",
+       "</style>\n",
+       "<script>\n",
+       "    MathJax.Hub.Config({\n",
+       "                        TeX: {\n",
+       "                           extensions: [\"AMSmath.js\"],\n",
+       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
+       "                           },\n",
+       "                tex2jax: {\n",
+       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
+       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
+       "                },\n",
+       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
+       "                \"HTML-CSS\": {\n",
+       "                    scale:95,\n",
+       "                        availableFonts: [],\n",
+       "                        preferredFont:null,\n",
+       "                        webFont: \"TeX\",\n",
+       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                }\n",
+       "        });\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#format the book\n",
+    "%matplotlib inline\n",
+    "from __future__ import division, print_function\n",
+    "import sys;sys.path.insert(0,'..')\n",
+    "from  book_format import load_style;load_style('..')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -272,7 +532,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

code/pf_internal.py

@@ -17,7 +17,7 @@ from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
 
-from filterpy.monte_carlo import stratified_resample
+from filterpy.monte_carlo import stratified_resample, residual_resample
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 
@@ -25,7 +25,7 @@ import numpy as np
 from numpy.random import randn, random, uniform, multivariate_normal, seed
 #from nonlinear_plots import plot_monte_carlo_mean
 import scipy.stats
-from RobotLocalizationParticleFilter import *
+#from RobotLocalizationParticleFilter import *
 
 
 

table_of_contents.ipynb

@@ -124,6 +124,10 @@
     "Describes how I implemented the plotting of various pdfs in the book.\n",
     "\n",
     "\n",
+    "[**Interactions**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Supporting_Notebooks/Interactions.ipynb)\n",
+    "\n",
+    "Interactive simulations of various algorithms. Use sliders to change the output in real time.\n",
+    "\n",
     "\n",
     "[**Iterative Least Squares for Sensor Fusion**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Supporting_Notebooks/Iterative-Least-Squares-for-Sensor-Fusion.ipynb)\n",
     "\n",