Reexecuted to incorporate css changes.

01-g-h-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",
@@ -240,7 +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",
@@ -248,7 +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",
@@ -330,9 +330,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAx0AAAB6CAYAAADagD7sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4607c18>"
+       "<matplotlib.figure.Figure at 0x4d335f8>"
       ]
      },
      "metadata": {},
@@ -364,9 +364,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x72396d8>"
+       "<matplotlib.figure.Figure at 0x7a85a90>"
       ]
      },
      "metadata": {},
@@ -395,9 +395,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7eb5780>"
+       "<matplotlib.figure.Figure at 0x7fb8f98>"
       ]
      },
      "metadata": {},
@@ -432,7 +432,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0549\n"
+      "Average of measurements is 165.0092\n"
      ]
     }
    ],
@@ -465,7 +465,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0200\n"
+      "Average of measurements is 165.0151\n"
      ]
     }
    ],
@@ -497,9 +497,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f41358>"
+       "<matplotlib.figure.Figure at 0x7fead68>"
       ]
      },
      "metadata": {},
@@ -528,9 +528,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdc160>"
+       "<matplotlib.figure.Figure at 0x811ddd8>"
       ]
      },
      "metadata": {},
@@ -557,9 +557,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x804ef98>"
+       "<matplotlib.figure.Figure at 0x8161400>"
       ]
      },
      "metadata": {},
@@ -588,9 +588,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdc0b8>"
+       "<matplotlib.figure.Figure at 0x80793c8>"
       ]
      },
      "metadata": {},
@@ -620,9 +620,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x72b7518>"
+       "<matplotlib.figure.Figure at 0x7a66ef0>"
       ]
      },
      "metadata": {},
@@ -655,9 +655,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7236b38>"
+       "<matplotlib.figure.Figure at 0x81700f0>"
       ]
      },
      "metadata": {},
@@ -684,9 +684,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6ce80>"
+       "<matplotlib.figure.Figure at 0x8024f28>"
       ]
      },
      "metadata": {},
@@ -727,9 +727,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f17358>"
+       "<matplotlib.figure.Figure at 0x7feaf28>"
       ]
      },
      "metadata": {},
@@ -784,9 +784,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91ad390>"
+       "<matplotlib.figure.Figure at 0x81e9780>"
       ]
      },
      "metadata": {},
@@ -848,9 +848,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80807b8>"
+       "<matplotlib.figure.Figure at 0x92d1668>"
       ]
      },
      "metadata": {},
@@ -890,9 +890,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x815a710>"
+       "<matplotlib.figure.Figure at 0x81730f0>"
       ]
      },
      "metadata": {},
@@ -981,9 +981,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f816a0>"
+       "<matplotlib.figure.Figure at 0x7389470>"
       ]
      },
      "metadata": {},
@@ -1067,7 +1067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -1085,7 +1085,7 @@
        "array([1, 2, 3])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1106,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -1117,7 +1117,7 @@
        "array([4, 5, 6])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1129,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
@@ -1160,7 +1160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -1187,7 +1187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -1216,7 +1216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -1227,7 +1227,7 @@
        "array([1, 4])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1245,7 +1245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -1256,7 +1256,7 @@
        "array([4, 5, 6])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1274,7 +1274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -1319,7 +1319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -1352,7 +1352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -1402,7 +1402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 29,
    "metadata": {
     "collapsed": true
    },
@@ -1422,7 +1422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -1452,7 +1452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -1514,16 +1514,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAADTCAYAAADpoczIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e461d0>"
+       "<matplotlib.figure.Figure at 0x930ee80>"
       ]
      },
      "metadata": {},
@@ -1589,7 +1589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
@@ -1607,7 +1607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1615,9 +1615,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91b0f28>"
+       "<matplotlib.figure.Figure at 0x817bb70>"
       ]
      },
      "metadata": {},
@@ -1645,16 +1645,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8105dd8>"
+       "<matplotlib.figure.Figure at 0x801f2b0>"
       ]
      },
      "metadata": {},
@@ -1681,7 +1681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
@@ -1699,16 +1699,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8047978>"
+       "<matplotlib.figure.Figure at 0x8142518>"
       ]
      },
      "metadata": {},
@@ -1744,7 +1744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
@@ -1762,16 +1762,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x923f710>"
+       "<matplotlib.figure.Figure at 0x94afc18>"
       ]
      },
      "metadata": {},
@@ -1809,7 +1809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -1827,16 +1827,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x925e6a0>"
+       "<matplotlib.figure.Figure at 0x94f0f98>"
       ]
      },
      "metadata": {},
@@ -1886,7 +1886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
@@ -1904,7 +1904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1912,9 +1912,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x924a080>"
+       "<matplotlib.figure.Figure at 0x8120e10>"
       ]
      },
      "metadata": {},
@@ -1947,16 +1947,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 44,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc3668>"
+       "<matplotlib.figure.Figure at 0x8003160>"
       ]
      },
      "metadata": {},
@@ -1992,16 +1992,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f56f98>"
+       "<matplotlib.figure.Figure at 0x80e4550>"
       ]
      },
      "metadata": {},
@@ -2039,16 +2039,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x728de48>"
+       "<matplotlib.figure.Figure at 0x8029588>"
       ]
      },
      "metadata": {},
@@ -2087,16 +2087,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80d72e8>"
+       "<matplotlib.figure.Figure at 0x9326198>"
       ]
      },
      "metadata": {},
@@ -2133,16 +2133,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 48,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92660f0>"
+       "<matplotlib.figure.Figure at 0x80591d0>"
       ]
      },
      "metadata": {},
@@ -2169,16 +2169,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 49,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7f278>"
+       "<matplotlib.figure.Figure at 0x80cc748>"
       ]
      },
      "metadata": {},
@@ -2235,16 +2235,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 50,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92acc18>"
+       "<matplotlib.figure.Figure at 0x9312e48>"
       ]
      },
      "metadata": {},
@@ -2286,16 +2286,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 51,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92702e8>"
+       "<matplotlib.figure.Figure at 0x94b2438>"
       ]
      },
      "metadata": {},
@@ -2317,7 +2317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 52,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -2325,9 +2325,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9209358>"
+       "<matplotlib.figure.Figure at 0x939d5c0>"
       ]
      },
      "metadata": {},
@@ -2351,16 +2351,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 53,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92fcf60>"
+       "<matplotlib.figure.Figure at 0x93606a0>"
       ]
      },
      "metadata": {},
@@ -2384,7 +2384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 54,
    "metadata": {
     "collapsed": false
    },
@@ -2401,16 +2401,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 55,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92d75f8>"
+       "<matplotlib.figure.Figure at 0x8224eb8>"
       ]
      },
      "metadata": {},
@@ -2432,16 +2432,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80487b8>"
+       "<matplotlib.figure.Figure at 0x7ffcba8>"
       ]
      },
      "metadata": {},
@@ -2487,7 +2487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 57,
    "metadata": {
     "collapsed": true
    },
@@ -2508,7 +2508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 58,
    "metadata": {
     "collapsed": false
    },
@@ -2519,7 +2519,7 @@
        "(0.96, 0.24)"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2537,7 +2537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 59,
    "metadata": {
     "collapsed": false
    },
@@ -2563,7 +2563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 60,
    "metadata": {
     "collapsed": false
    },
@@ -2589,7 +2589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 61,
    "metadata": {
     "collapsed": false
    },
@@ -2618,7 +2618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 62,
    "metadata": {
     "collapsed": false
    },