diff --git a/Gesture Typing.ipynb b/Gesture Typing.ipynb
index debbbaa..83c56f5 100644
--- a/Gesture Typing.ipynb	
+++ b/Gesture Typing.ipynb	
@@ -50,21 +50,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "%pylab --no-import-all inline\n",
-    "from __future__ import division\n",
     "from collections import Counter\n",
     "import matplotlib.pyplot as plt\n",
     "import urllib\n",
@@ -99,9 +87,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def Keyboard(rows):\n",
@@ -130,9 +116,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class Point(complex):\n",
@@ -162,9 +146,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -222,9 +204,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -252,9 +232,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def path_length(word, kbd=qwerty):\n",
@@ -266,9 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -295,9 +271,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -326,9 +300,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -357,29 +329,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "WORDS = set(urllib.urlopen('http://norvig.com/ngrams/TWL06.txt').read().split())"
+    "! [ -e TWL06.txt ] || curl -O http://norvig.com/ngrams/TWL06.txt\n",
+    "    \n",
+    "WORDS = set(open('TWL06.txt').read().split())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "21"
+       "178691"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -398,9 +368,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -427,9 +395,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -471,9 +437,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def path_length_ratio(word, kbd=qwerty): return path_length(word, kbd) / len(word)"
@@ -482,9 +446,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -520,24 +482,22 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "10.0 PERPETUITY\n",
-      "10.0 PEPPERTREE\n",
-      "10.0 REPERTOIRE\n",
       "10.0 PREREQUIRE\n",
-      "10.0 PROPRIETOR\n",
+      "10.0 PEPPERTREE\n",
       "10.0 TYPEWRITER\n",
-      "9.0 POTPOURRI\n",
-      "9.0 TERRITORY\n",
-      "9.0 TWITTERER\n",
-      "9.0 PUTTYROOT\n"
+      "10.0 REPERTOIRE\n",
+      "10.0 PROPRIETOR\n",
+      "9.0 PROPRIETY\n",
+      "9.0 ETIQUETTE\n",
+      "9.0 PUTTYROOT\n",
+      "9.0 TWITTERER\n"
      ]
     }
    ],
@@ -565,9 +525,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def Workload(text):\n",
@@ -602,14 +560,20 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Counter({'OO': 0.55, 'GO': 0.1, 'DO': 0.05, 'HS': 0.05, 'IS': 0.05, 'AL': 0.05, 'AO': 0.05, 'HO': 0.05, 'OT': 0.05})"
+       "Counter({'AL': 0.05,\n",
+       "         'AO': 0.05,\n",
+       "         'DO': 0.05,\n",
+       "         'GO': 0.1,\n",
+       "         'HO': 0.05,\n",
+       "         'HS': 0.05,\n",
+       "         'IS': 0.05,\n",
+       "         'OO': 0.55,\n",
+       "         'OT': 0.05})"
       ]
      },
      "execution_count": 18,
@@ -630,25 +594,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "module 'urllib' has no attribute 'urlopen'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-388de13c3aca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTEXT\u001b[0m     \u001b[0;34m=\u001b[0m \u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://norvig.com/ngrams/smaller.txt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mWORKLOAD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWorkload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTEXT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: module 'urllib' has no attribute 'urlopen'"
-     ]
-    }
-   ],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "TEXT     = urllib.urlopen('http://norvig.com/ngrams/smaller.txt').read().upper()\n",
+    "! [ -e smaller.txt ] || curl -O http://norvig.com/ngrams/smaller.txt\n",
+    "    \n",
+    "TEXT     = open('smaller.txt').read().upper()\n",
     "WORKLOAD = Workload(TEXT)"
    ]
   },
@@ -662,9 +614,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -700,9 +650,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -735,23 +683,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'WORKLOAD' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-7d3b00ebb5d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mworkload_average\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkbd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworkload\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mWORKLOAD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"The average segment length over a workload of segments.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     return sum(distance(kbd[A], kbd[B]) * workload[A+B]\n\u001b[1;32m      4\u001b[0m                for (A, B) in workload)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'WORKLOAD' is not defined"
-     ]
-    }
-   ],
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def workload_average(kbd, workload=WORKLOAD):\n",
     "    \"The average segment length over a workload of segments.\"\n",
@@ -762,14 +696,12 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3.2333097802127644"
+       "3.2333097802127635"
       ]
      },
      "execution_count": 23,
@@ -800,10 +732,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_kbd(kbd, K=20):\n",
@@ -831,31 +761,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD7CAYAAABuSzNOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n248BxMHn+gAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x109fe18d0>"
+       "<matplotlib.figure.Figure at 0x108b64128>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "ename": "NameError",
-     "evalue": "name 'workload_average' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-84031174ed37>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mshow_kbd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqwerty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-10-b3c79385436d>\u001b[0m in \u001b[0;36mshow_kbd\u001b[0;34m(kbd, name)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mplot_kbd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkbd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     print('workload average = {:.1f} for {}'\n\u001b[0;32m---> 21\u001b[0;31m           .format(workload_average(kbd), name))\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'workload_average' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "workload average = 3.2 for keyboard\n"
      ]
     }
    ],
@@ -889,9 +812,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def improve(kbd, swaps=1000, scorer=workload_average):\n",
@@ -926,9 +847,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def improved(kbd, swaps=1000, scorer=workload_average): \n",
@@ -946,15 +865,13 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD7CAYAAABuSzNOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10da809d0>"
+       "<matplotlib.figure.Figure at 0x10052e6d8>"
       ]
      },
      "metadata": {},
@@ -964,7 +881,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "workload average = 1.9 for keyboard\n"
+      "workload average = 2.0 for keyboard\n"
      ]
     }
    ],
@@ -994,15 +911,13 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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TtWvXcPjwYQDArFmz4OTkZPLA7iaTyWBhIXDsGBAYaNaqiYi6LLMtuAOA+vp6CCF04xEX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\nDz4+PpgwYQLS0tLwxRdfYO3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9XtJM4EOAgGMioifrYVwFLKiWLHK/BL6bt2IGA/sB3+6uQt+SMjNrvpSnpLYClgE3VBwb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\nSUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115db4550>"
+       "<matplotlib.figure.Figure at 0x109b01d30>"
       ]
      },
      "metadata": {},
@@ -1028,7 +943,8 @@
     "def workload_plot(kbd, swaps):\n",
     "    plt.ylabel('Workload average segment length') \n",
     "    plt.xlabel('Number of swaps');  \n",
-    "    plt.plot(list(improve_scores(kbd.copy(), swaps)))                         \n",
+    "    plt.plot(list(improve_scores(kbd.copy(), swaps)))  \n",
+    "    plt.show()\n",
     "\n",
     "workload_plot(qwerty, 3000)"
    ]
@@ -1043,15 +959,13 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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2jdT8fNLk0pMQQlQrp4li//79PPzww2iaRnh4OPPmzePzzz93R2wlXb70pJQN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\na/TSU58+fVi5ciV9+/YlLS2NzMxMWrd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YxhgTVJCEUS1e7/AyEblCRL4P5CY5rkBCEYhGveeSm8snPziKymgFX9WsIiRK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\nRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1116e3d10>"
+       "<matplotlib.figure.Figure at 0x109bbb6a0>"
       ]
      },
      "metadata": {},
@@ -1059,15 +973,21 @@
     }
    ],
    "source": [
-    "for run in range(10):\n",
-    "    workload_plot(qwerty, 3000)"
+    "def workload_plot(kbd, swaps, runs=10):\n",
+    "    plt.ylabel('Workload average segment length') \n",
+    "    plt.xlabel('Number of swaps');  \n",
+    "    for _ in range(runs):\n",
+    "        plt.plot(list(improve_scores(kbd.copy(), swaps)))  \n",
+    "    plt.show()\n",
+    "    \n",
+    "workload_plot(qwerty, 3000, 10)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This plot is even more interesting. (Note: on most browsers you can drag the lower-right corner of the plot to make it bigger.) I note the following:\n",
+    "This plot is even more interesting. I note the following:\n",
     "\n",
     "* Each line follows a slightly different path (because the swaps are random).\n",
     "* The lines are grouped very tightly together; the variance is small.  Almost everywhere, the difference between the best line and the worst line is about 0.2 or less. By the end, almost all the lines are between 1.9 and 2.0.\n",
@@ -1089,9 +1009,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def repeated_improved(kbd, repeats=20, swaps=1000, scorer=workload_average):\n",
@@ -1110,15 +1028,13 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ac91510>"
+       "<matplotlib.figure.Figure at 0x109aa4748>"
       ]
      },
      "metadata": {},
@@ -1128,14 +1044,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "workload average = 2.0 for 3000 swaps repeated once\n"
+      "workload average = 1.9 for 3000 swaps repeated once\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD7CAYAAABuSzNOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSekPoAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111717450>"
+       "<matplotlib.figure.Figure at 0x109aa4a90>"
       ]
      },
      "metadata": {},
@@ -1145,14 +1061,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "workload average = 2.0 for 1000 swaps repeated three times\n"
+      "workload average = 1.9 for 1000 swaps repeated three times\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD7CAYAAABuSzNOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115d974d0>"
+       "<matplotlib.figure.Figure at 0x109a14be0>"
       ]
      },
      "metadata": {},
@@ -1162,7 +1078,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "workload average = 2.0 for 500 swaps repeated six times\n"
+      "workload average = 1.9 for 500 swaps repeated six times\n"
      ]
     }
    ],
@@ -1182,15 +1098,13 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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w1q5dq5iYGH3++efq0aOHkS273H++TNu6dauaNWumo0eP6j//+Y9tu56KHyCjSmJiYnTg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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD7CAYAAABuSzNOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMEiSmv8DOf+frVBN6RsAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10da83b50>"
+       "<matplotlib.figure.Figure at 0x109c46438>"
       ]
      },
      "metadata": {},
@@ -1200,14 +1114,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "workload average = 1.9 for 3000 swaps repeated once\n"
+      "workload average = 2.0 for 3000 swaps repeated once\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111717f90>"
+       "<matplotlib.figure.Figure at 0x1099f0c18>"
       ]
      },
      "metadata": {},
@@ -1222,9 +1136,9 @@
     },
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115dccd50>"
+       "<matplotlib.figure.Figure at 0x109a5f2e8>"
       ]
      },
      "metadata": {},
@@ -1235,8 +1149,8 @@
      "output_type": "stream",
      "text": [
       "workload average = 1.9 for 500 swaps repeated six times\n",
-      "CPU times: user 3.49 s, sys: 79.1 ms, total: 3.57 s\n",
-      "Wall time: 3.53 s\n"
+      "CPU times: user 1.86 s, sys: 25.5 ms, total: 1.89 s\n",
+      "Wall time: 1.91 s\n"
      ]
     }
    ],
@@ -1259,15 +1173,13 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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FTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115d91890>"
+       "<matplotlib.figure.Figure at 0x109bd3d68>"
       ]
      },
      "metadata": {},
@@ -1282,9 +1194,9 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115d91410>"
+       "<matplotlib.figure.Figure at 0x109b510b8>"
       ]
      },
      "metadata": {},
@@ -1299,9 +1211,9 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115db5790>"
+       "<matplotlib.figure.Figure at 0x10c8af6d8>"
       ]
      },
      "metadata": {},
@@ -1312,8 +1224,8 @@
      "output_type": "stream",
      "text": [
       "workload average = 1.9 for 500 swaps repeated sixty times\n",
-      "CPU times: user 29 s, sys: 314 ms, total: 29.3 s\n",
-      "Wall time: 29.1 s\n"
+      "CPU times: user 14.6 s, sys: 143 ms, total: 14.8 s\n",
+      "Wall time: 14.9 s\n"
      ]
     }
    ],
@@ -1339,9 +1251,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "keyboards = {\n",
@@ -1365,15 +1275,13 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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jL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S\n1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHj\nL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kd\nMv6S1CHjL0kd+n+vMapXuGgEcAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111705710>"
+       "<matplotlib.figure.Figure at 0x10c88d390>"
       ]
      },
      "metadata": {},
@@ -1388,9 +1296,9 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1117f4cd0>"
+       "<matplotlib.figure.Figure at 0x107b865c0>"
       ]
      },
      "metadata": {},
@@ -1405,9 +1313,9 @@
     },
     {
      "data": {
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gYgAAEDEAAIgYAABEDAAAIgYAABEDv9SlSxetWbOm2mPTp09XSkqKoUXSmDFjNGPGjMrP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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1117e9d10>"
+       "<matplotlib.figure.Figure at 0x107d1cbe0>"
       ]
      },
      "metadata": {},
@@ -1439,9 +1347,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def report(keyboards, scorer=workload_average):\n",
@@ -1454,15 +1360,13 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115db5150>"
+       "<matplotlib.figure.Figure at 0x107c7eeb8>"
       ]
      },
      "metadata": {},
@@ -1477,9 +1381,9 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111709790>"
+       "<matplotlib.figure.Figure at 0x107d1d550>"
       ]
      },
      "metadata": {},
@@ -1494,9 +1398,9 @@
     },
     {
      "data": {
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PXWzyjR03dUYcEwEAiAEAgBgAAEQMAAAiBgAAEQMAgIgBAEDEAAAgYgAAEDEAAIgYAABE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\nAwCAiAEAQNJ/ALhmZTZHpeyrAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111707050>"
+       "<matplotlib.figure.Figure at 0x107ce4358>"
       ]
      },
      "metadata": {},
@@ -1507,8 +1411,8 @@
      "output_type": "stream",
      "text": [
       "workload average = 1.7 for repeated improved 5-by-6\n",
-      "CPU times: user 19.3 s, sys: 221 ms, total: 19.5 s\n",
-      "Wall time: 19.4 s\n"
+      "CPU times: user 10 s, sys: 101 ms, total: 10.1 s\n",
+      "Wall time: 10.2 s\n"
      ]
     }
    ],
@@ -1548,9 +1452,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cat = ''.join    ## Function to join strings together into one big string\n",
@@ -1570,9 +1472,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1592,15 +1492,13 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD7CAYAAABuSzNOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB6K0o1BtUGyLAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115db0910>"
+       "<matplotlib.figure.Figure at 0x109a144e0>"
       ]
      },
      "metadata": {},
@@ -1610,7 +1508,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "workload average = 2.1 for recentered qwerty\n"
+      "workload average = 2.0 for recentered qwerty\n"
      ]
     }
    ],
@@ -1621,15 +1519,13 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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gG/lboiVS0tatW20tn6tGjx7N+fPnJYgIIW4KcXFxxMXFuaVsp0EkKiqKAwcO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\nuXOnxXh4PB54PB7mzJmDXbt2obKyEv7+/s2m/mYYXbEkwlg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PekHiVolZZmKMMR15WRzrUREpAYaq6qIuKFOnBAI70dC4jLkfjaFhtSASYP/9PwVRtOXD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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115dc2510>"
+       "<matplotlib.figure.Figure at 0x101e34710>"
       ]
      },
      "metadata": {},
@@ -1637,8 +1533,7 @@
     }
    ],
    "source": [
-    "for _ in range(10):\n",
-    "    workload_plot(recentered(qwerty), 3000)"
+    "workload_plot(recentered(qwerty), 3000, 10)"
    ]
   },
   {
@@ -1653,15 +1548,13 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1117f5910>"
+       "<matplotlib.figure.Figure at 0x109a14240>"
       ]
      },
      "metadata": {},
@@ -1700,9 +1593,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def workload_lower_bound(kbd=recentered(qwerty), workload=WORKLOAD):\n",
@@ -1726,23 +1617,21 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4-by-7 1.5038079626\n",
-      "qwerty 1.60656497359\n",
-      "5-by-6 1.47094590247\n"
+      "4-by-7 1.5038079625992777\n",
+      "qwerty 1.606564973591206\n",
+      "5-by-6 1.4709459024696767\n"
      ]
     }
    ],
    "source": [
     "for name in keyboards:\n",
-    "    print name, workload_lower_bound(keyboards[name])"
+    "    print(name, workload_lower_bound(keyboards[name]))"
    ]
   },
   {
@@ -1779,9 +1668,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def neighboring_keys(kbd, radius=1.5):\n",
@@ -1803,39 +1690,37 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'A': 'AQSWZ',\n",
-       " 'B': 'BGHJNV',\n",
-       " 'C': 'CDGFVX',\n",
-       " 'D': 'CEDFSRXZ',\n",
-       " 'E': 'EDSRW',\n",
-       " 'F': 'CDGFRTVX',\n",
-       " 'G': 'CBGFHTVY',\n",
-       " 'H': 'BGHJNUVY',\n",
-       " 'I': 'IKJOU',\n",
-       " 'J': 'BIHKJMNU',\n",
-       " 'K': 'IKJMLON',\n",
-       " 'L': 'KMLOP',\n",
-       " 'M': 'KJMLN',\n",
-       " 'N': 'BHKJMN',\n",
-       " 'O': 'IKLOP',\n",
-       " 'P': 'LOP',\n",
+       "{'A': 'ZASQW',\n",
+       " 'B': 'VNGBHJ',\n",
+       " 'C': 'VCGDFX',\n",
+       " 'D': 'EZCSDRFX',\n",
+       " 'E': 'ESDRW',\n",
+       " 'F': 'VCGDRFXT',\n",
+       " 'G': 'VCGYBHFT',\n",
+       " 'H': 'VNUGYBHJ',\n",
+       " 'I': 'UJOKI',\n",
+       " 'J': 'NUBHJKIM',\n",
+       " 'K': 'NLJOKIM',\n",
+       " 'L': 'PLOKM',\n",
+       " 'M': 'NLJKM',\n",
+       " 'N': 'NBHJKM',\n",
+       " 'O': 'PLOKI',\n",
+       " 'P': 'PLO',\n",
        " 'Q': 'AQW',\n",
-       " 'R': 'EDFRT',\n",
-       " 'S': 'AEDSWXZ',\n",
-       " 'T': 'GFRTY',\n",
-       " 'U': 'IHJUY',\n",
-       " 'V': 'CBGFHV',\n",
-       " 'W': 'AEQSW',\n",
-       " 'X': 'CDFSXZ',\n",
-       " 'Y': 'GHUTY',\n",
-       " 'Z': 'ADSXZ'}"
+       " 'R': 'EDRFT',\n",
+       " 'S': 'EZASDWX',\n",
+       " 'T': 'GYRFT',\n",
+       " 'U': 'UYHJI',\n",
+       " 'V': 'VCGBHF',\n",
+       " 'W': 'EASQW',\n",
+       " 'X': 'ZCSDFX',\n",
+       " 'Y': 'UGYHT',\n",
+       " 'Z': 'ZASDX'}"
       ]
      },
      "execution_count": 47,
@@ -1863,14 +1748,12 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['BGHJNUVY', 'EDSRW', 'KMLOP', 'KMLOP', 'IKLOP']"
+       "['VNUGYBHJ', 'ESDRW', 'PLOKM', 'PLOKM', 'PLOKI']"
       ]
      },
      "execution_count": 48,
@@ -1887,7 +1770,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Think of this as five \"columns\" of letters, and if we pick one letter from each column, we get a path that is formed by letters that are each confusions of letters in the original word, and so the whole path is a confusion for the original word.  So \"JELLO\" is a confusion for \"HELLO\", as would be \"BEKKI\" (formed by taking the first possible neighbor for each of the five letters) and \"YWPPP\" (formed by taking the last possible neighbors), except they are not words.\n",
+    "Think of this as five \"columns\" of letters, and if we pick one letter from each column, we get a path that is formed by letters that are each confusions of letters in the original word, and so the whole path is a confusion for the original word.  So \"JELLO\" is a confusion for \"HELLO\", as would be \"GEOOO\" and \"JRMML\", except they are not words.\n",
     "\n",
     "It turns out that there is a library function, `itertools.product`, that will take an iterable of alternative choices, and generate all possible ways of assembling a sequence consisting of one selection (letter) from each alternative choice."
    ]
@@ -1895,9 +1778,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "paths = {cat(letters) for letters in itertools.product(*choices)}"
@@ -1913,9 +1794,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1942,14 +1821,12 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['JSMML', 'JRPML', 'VWMPP', 'JSPML', 'JDLML', 'VWPPK', 'GRLLK', 'BWOMK']"
+       "['UEKKP', 'BDOOP', 'JEMMO', 'JWLLK', 'NELKK', 'VRKLO', 'UWOKI', 'BRKPP']"
       ]
      },
      "execution_count": 51,
@@ -1971,9 +1848,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2000,9 +1875,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def confusions(word, neighbors=neighboring_keys(qwerty), words=WORDS):\n",
@@ -2014,9 +1887,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2036,9 +1907,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2058,9 +1927,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2102,15 +1969,13 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3.71 s, sys: 623 ms, total: 4.33 s\n",
+      "CPU times: user 3.6 s, sys: 496 ms, total: 4.1 s\n",
       "Wall time: 4.2 s\n"
      ]
     },
@@ -2139,9 +2004,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2172,9 +2035,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def prefixes(words):\n",
@@ -2185,9 +2046,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2214,9 +2073,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2249,9 +2106,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def confusions(word, neighbors=neighboring_keys(qwerty), words=WORDS, prefixes=PREFIXES):\n",
@@ -2279,9 +2134,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2308,16 +2161,14 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 543 µs, sys: 155 µs, total: 698 µs\n",
-      "Wall time: 586 µs\n"
+      "CPU times: user 403 µs, sys: 8 µs, total: 411 µs\n",
+      "Wall time: 419 µs\n"
      ]
     },
     {
@@ -2345,9 +2196,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def words(text): \n",
@@ -2364,16 +2213,15 @@
     "    for word in sorted(set(words)):\n",
     "        others = sorted(set(confusions(word)) - {word})\n",
     "        total += len(others)\n",
-    "        print('{}({}): {}'.format(word, len(others), ' '.join(others)))\n",
-    "    print 'Total of {} confusions for {} words'.format(total, len(words))"
+    "        if others:\n",
+    "            print('{}({}): {}'.format(word, len(others), ' '.join(others)))\n",
+    "    print('Total of {} confusions for {} words'.format(total, len(words)))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 66,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2381,14 +2229,10 @@
      "text": [
       "BETWEEN(2): BERSEEM BERSERK\n",
       "CONFUSION(2): CONCISION CONTUSION\n",
-      "DISTINCT(0): \n",
-      "EVERYTHING(0): \n",
       "EXTREMES(3): ESTEEMED EXTERNES SCREENED\n",
       "HELLO(3): BROMO BROOK JELLO\n",
       "IF(7): ID IT OD OF OR OX UT\n",
       "INPUTS(6): IMPURE IMPUTE KNOUTS OBLIGE UMPIRE UNPURE\n",
-      "LENGTHS(0): \n",
-      "MULTIPLE(0): \n",
       "OF(6): ID IF IT OD OR OX\n",
       "ON(3): IN OH OM\n",
       "OR(7): ID IF IT OD OE OF PE\n",
@@ -2396,21 +2240,18 @@
       "PERFECT(2): PREFECT PRETEXT\n",
       "POORLY(1): LIKELY\n",
       "SEE(20): ADD ADS ARE ARS ASS AWE DEE DEW EDS ERE ERR ERS ESS EWE SER SEW WED WEE ZED ZEE\n",
-      "SOMETHING(0): \n",
       "SOMEWHERE(1): WINDSURFS\n",
       "TESTING(10): FEARING FRAYING GEARING GRATING GRAYING GREYING REARING REEFING RESTING TEARING\n",
       "THE(5): FUD FUR RUE RYE TYE\n",
       "VARIOUS(3): CARIOUS CAROLUS FATUOUS\n",
       "WORKS(13): AIRNS AKELA AKENE ALTOS EIDOS SIDLE SKENE SORNS SPEND WIDOW WIELD WORLD WORMS\n",
       "WORLD(7): ALTOS EIDOS SIDLE SKEPS WIELD WORKS WORMS\n",
-      "Total of 108 confusions for 25 words\n",
-      "CPU times: user 7.21 ms, sys: 2.06 ms, total: 9.27 ms\n",
-      "Wall time: 7.84 ms\n"
+      "Total of 108 confusions for 25 words\n"
      ]
     }
    ],
    "source": [
-    "%time test_confusions()"
+    "test_confusions()"
    ]
   },
   {
@@ -2432,9 +2273,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_kbd(kbd, K=20, words=()):\n",
@@ -2466,15 +2305,13 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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A7fv0zYB7e/+k3XKwGFS5Dv+zvBk4OTPfERE/As7LzLtqyJkL/AiYDdwCXJ+Zt7Vz23Fk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\nQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1117fd650>"
+       "<matplotlib.figure.Figure at 0x10c779dd8>"
       ]
      },
      "metadata": {},
@@ -2497,9 +2334,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_paths(kbd, K, words):\n",
@@ -2515,15 +2350,13 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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LeST/p7tpo1MpGDy7qjSGTFYBVwBXDOsmPgB8OYLraQRFJismYPk2RfySTK4Brmn+v1G6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\nSVLh/wNHHKptQo7cmwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1116f0c10>"
+       "<matplotlib.figure.Figure at 0x10da09b00>"
       ]
      },
      "metadata": {},
@@ -2544,15 +2377,13 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+/vpryc3N1bmOPrWcnJz0Wq++tW4/fCgvx8dL+2PHJCUrSxbuXyh2be2kZp2a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\nwDAgIiIwDIiICAwDIiIC8P8UKRu3sOtPIgAAAABJRU5ErkJggg==\n",
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6O76QTq3+RmMMIvuBxh2yYglofCEXFAwi+0k2ru/A8cfneC57tK6uvyAZUleS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\nplfS8heeHrYAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115da5150>"
+       "<matplotlib.figure.Figure at 0x109b87f98>"
       ]
      },
      "metadata": {},
@@ -2578,9 +2409,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2607,15 +2436,13 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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YRr/dROYyYmGdxbCR6z4BHAtcCByTmbePNwtyLzo4h5LFoJ4zzifL64AjMvNvI+JHwNsz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\nSSpYDJKkgsUgSSr8f6x+CYd5kPoUAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1117fde50>"
+       "<matplotlib.figure.Figure at 0x10da09748>"
       ]
      },
      "metadata": {},
@@ -2636,15 +2463,13 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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z7jBa60Vj4Pno6+D8IWMMVx2a+di9VWbVqVezRspi6BIWw9jMWte4w2iuF88clbTtlMaW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\nGCRJBYtBklSwGCRJBYtBklSwGCRJhf8HnBZ5NlvxozIAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10da809d0>"
+       "<matplotlib.figure.Figure at 0x1099f9208>"
       ]
      },
      "metadata": {},
@@ -2665,9 +2490,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2697,9 +2520,7 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def similar_segments(kbd, path, word):\n",
@@ -2723,9 +2544,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2745,9 +2564,7 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2767,9 +2584,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2789,9 +2604,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2818,9 +2631,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def confusions(word, neighbors=neighboring_keys(qwerty), \n",
@@ -2843,9 +2654,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2853,23 +2662,16 @@
      "text": [
       "BETWEEN(2): BERSEEM BERSERK\n",
       "CONFUSION(2): CONCISION CONTUSION\n",
-      "DISTINCT(0): \n",
-      "EVERYTHING(0): \n",
       "EXTREMES(1): ESTEEMED\n",
       "HELLO(1): JELLO\n",
       "IF(5): ID IT OF OR UT\n",
       "INPUTS(4): IMPURE IMPUTE KNOUTS UNPURE\n",
-      "LENGTHS(0): \n",
-      "MULTIPLE(0): \n",
       "OF(6): ID IF IT OD OR OX\n",
       "ON(3): IN OH OM\n",
       "OR(5): ID IF OD OE OF\n",
       "PATHS(6): LARGE LARVA LATHE LATHS OATHS PARGE\n",
-      "PERFECT(0): \n",
       "POORLY(1): LIKELY\n",
       "SEE(16): ADD ADS ASS AWE DEE DEW ERE ERR ERS EWE SER SEW WED WEE ZED ZEE\n",
-      "SOMETHING(0): \n",
-      "SOMEWHERE(0): \n",
       "TESTING(6): FEARING GEARING GREYING REARING RESTING TEARING\n",
       "THE(3): RUE RYE TYE\n",
       "VARIOUS(2): CARIOUS CAROLUS\n",
@@ -2893,15 +2695,13 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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LWd5gZoBaZWfv1lNPvacTJ777PQPPPjtXzZtLycneFwL8zx/+8Adt2rRJzz33XEPvCuqB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\n",
+      "image/png": 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F9AqB/tcuX+3e0x9Kehq9MvqnroKq6m7g48ApXWUMZK0B9qN3tPAvtEdG2gIWg2YkyW8D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\nRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11180a6d0>"
+       "<matplotlib.figure.Figure at 0x109b78e10>"
       ]
      },
      "metadata": {},
@@ -2922,9 +2722,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2953,9 +2751,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def similar_segments(kbd, path, word):\n",
@@ -2979,9 +2775,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2989,23 +2783,15 @@
      "text": [
       "BETWEEN(2): BERSEEM BERSERK\n",
       "CONFUSION(2): CONCISION CONTUSION\n",
-      "DISTINCT(0): \n",
-      "EVERYTHING(0): \n",
-      "EXTREMES(0): \n",
       "HELLO(1): JELLO\n",
       "IF(5): ID IT OF OR UT\n",
       "INPUTS(1): KNOUTS\n",
-      "LENGTHS(0): \n",
-      "MULTIPLE(0): \n",
       "OF(6): ID IF IT OD OR OX\n",
       "ON(3): IN OH OM\n",
       "OR(5): ID IF OD OE OF\n",
       "PATHS(6): LARGE LARVA LATHE LATHS OATHS PARGE\n",
-      "PERFECT(0): \n",
       "POORLY(1): LIKELY\n",
       "SEE(13): ADD ADS ASS AWE ERE ERR ERS SER SEW WED WEE ZED ZEE\n",
-      "SOMETHING(0): \n",
-      "SOMEWHERE(0): \n",
       "TESTING(6): FEARING GEARING GREYING REARING RESTING TEARING\n",
       "THE(3): RUE RYE TYE\n",
       "VARIOUS(2): CARIOUS CAROLUS\n",
@@ -3034,9 +2820,7 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "WORDLOAD = normalize(Counter(words(TEXT)))"
@@ -3045,9 +2829,7 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3074,9 +2856,7 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3112,9 +2892,7 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "WORDLOAD_PREFIXES = prefixes(WORDLOAD)\n",
@@ -3133,9 +2911,7 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3143,7 +2919,7 @@
      "text": [
       "3164\n",
       "12020\n",
-      "0.263227953411\n"
+      "0.2632279534109817\n"
      ]
     }
    ],
@@ -3163,14 +2939,12 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.5521549493605586"
+       "0.5521549493605558"
       ]
      },
      "execution_count": 92,
@@ -3195,9 +2969,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def confusingness(kbd, wordload=WORDLOAD, prefixes=WORDLOAD_PREFIXES):\n",
@@ -3217,9 +2989,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def show_kbd(kbd, name='keyboard'):\n",
@@ -3239,15 +3009,13 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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jL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S\n1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHj\nL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kd\nMv6S1CHjL0kd+n+vMapXuGgEcAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x113d851d0>"
+       "<matplotlib.figure.Figure at 0x10dbf6400>"
       ]
      },
      "metadata": {},
@@ -3262,9 +3030,9 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1117f6cd0>"
+       "<matplotlib.figure.Figure at 0x107cd66a0>"
       ]
      },
      "metadata": {},
@@ -3279,9 +3047,9 @@
     },
     {
      "data": {
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gYgAAEDEAAIgYAABEDAAAIgYAABEDv9SlSxetWbOm2mPTp09XSkqKoUXSmDFjNGPGjMrP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\n",
+      "image/png": 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hkCQ1hkGS1BgGSVLjGdzWiSSz+zTEOW6Cee5y04psGz1go/DTVSVJjYeSJEmNYZAkNYZB\nktQYBklSYxgkSY1hkCQ1hkGS1BgGSVJjGCRJjWGQJDWGQZLUGAZJUmMYJEmNYZAkNYZBktQYBklS\nYxgkSY1hkCQ1hkGS1BgGSVJjGCRJjWGQJDWGQZLUGAZJUmMYJEmNYZAkNYZBktQYBklSYxgkSY1h\nkCQ1hkGS1BgGSVJjGCRJjWGQJDWGQZLUGAZJUmMYJEmNYZAkNf8PeRS5GWt6wawAAAAASUVORK5C\nYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114ffa810>"
+       "<matplotlib.figure.Figure at 0x10d9b0978>"
       ]
      },
      "metadata": {},
@@ -3310,15 +3078,13 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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4neyaWpPxlyQtz8s+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh\n4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9J\nHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+\nktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktQh4y9JHTL+ktSh\n/wPYt4WFzs5u8AAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111713410>"
+       "<matplotlib.figure.Figure at 0x107d1c128>"
       ]
      },
      "metadata": {},
@@ -3328,14 +3094,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "workload average = 1.8; confusingness = 29% for repeated improved 4-by-7\n"
+      "workload average = 1.8; confusingness = 33% for repeated improved 4-by-7\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1140d81d0>"
+       "<matplotlib.figure.Figure at 0x108b3d588>"
       ]
      },
      "metadata": {},
@@ -3345,14 +3111,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "workload average = 1.9; confusingness = 35% for repeated improved qwerty\n"
+      "workload average = 1.9; confusingness = 32% for repeated improved qwerty\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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xE9zWiCSTuxriFDfBNHe5aSlbRw9YL7y6qiSp8VSSJKkxDJKkxjBIkhrDIElqDIMkqTEM\nkqTGMEiSGsMgSWoMgySpMQySpMYwSJIawyBJagyDJKkxDJKkxjBIkhrDIElqDIMkqTEMkqTGMEiS\nGsMgSWoMgySpMQySpMYwSJIawyBJagyDJKkxDJKkxjBIkhrDIElqDIMkqTEMkqTGMEiSGsMgSWoM\ngySpMQySpMYwSJIawyBJagyDJKkxDJKk5v8Aa8lihdZ5jHQAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114fdf650>"
+       "<matplotlib.figure.Figure at 0x109b5de10>"
       ]
      },
      "metadata": {},
@@ -3362,7 +3128,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "workload average = 1.7; confusingness = 35% for repeated improved 5-by-6\n"
+      "workload average = 1.7; confusingness = 37% for repeated improved 5-by-6\n"
      ]
     }
    ],
@@ -3385,22 +3151,20 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3.62 s, sys: 174 ms, total: 3.79 s\n",
-      "Wall time: 3.68 s\n"
+      "CPU times: user 2.04 s, sys: 21.1 ms, total: 2.06 s\n",
+      "Wall time: 2.08 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "0.5521549493605586"
+       "0.5521549493605558"
       ]
      },
      "execution_count": 97,
@@ -3415,22 +3179,20 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 375 µs, sys: 126 µs, total: 501 µs\n",
-      "Wall time: 419 µs\n"
+      "CPU times: user 169 µs, sys: 0 ns, total: 169 µs\n",
+      "Wall time: 171 µs\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "3.2333097802127644"
+       "3.2333097802127635"
       ]
      },
      "execution_count": 98,
@@ -3452,23 +3214,21 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 47.9 s, sys: 1.21 s, total: 49.1 s\n",
-      "Wall time: 48.3 s\n"
+      "CPU times: user 21.8 s, sys: 207 ms, total: 22 s\n",
+      "Wall time: 22.2 s\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111810290>"
+       "<matplotlib.figure.Figure at 0x10dccb080>"
       ]
      },
      "metadata": {},
@@ -3478,7 +3238,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "workload average = 3.1; confusingness = 16% for keyboard\n"
+      "workload average = 3.6; confusingness = 14% for keyboard\n"
      ]
     }
    ],
@@ -3509,9 +3269,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def combined_scorer(kbd):\n",
@@ -3529,9 +3287,7 @@
   {
    "cell_type": "code",
    "execution_count": 101,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def show_kbd(kbd, name='keyboard'):\n",
@@ -3553,15 +3309,13 @@
   {
    "cell_type": "code",
    "execution_count": 102,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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jL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S\n1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHj\nL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kdMv6S1CHjL0kd\nMv6S1CHjL0kd+n+vMapXuGgEcAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114fbd590>"
+       "<matplotlib.figure.Figure at 0x10dc68ac8>"
       ]
      },
      "metadata": {},
@@ -3576,9 +3330,9 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11180abd0>"
+       "<matplotlib.figure.Figure at 0x10dc6c160>"
       ]
      },
      "metadata": {},
@@ -3593,9 +3347,9 @@
     },
     {
      "data": {
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+      "image/png": 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hkCQ1hkGS1BgGSVLjGdzWiSSz+zTEOW6Cee5y04psGz1go/DTVSVJjYeSJEmNYZAkNYZB\nktQYBklSYxgkSY1hkCQ1hkGS1BgGSVJjGCRJjWGQJDWGQZLUGAZJUmMYJEmNYZAkNYZBktQYBklS\nYxgkSY1hkCQ1hkGS1BgGSVJjGCRJjWGQJDWGQZLUGAZJUmMYJEmNYZAkNYZBktQYBklSYxgkSY1h\nkCQ1hkGS1BgGSVJjGCRJjWGQJDWGQZLUGAZJUmMYJEmNYZAkNf8PeRS5GWt6wawAAAAASUVORK5C\nYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1140d18d0>"
+       "<matplotlib.figure.Figure at 0x10d9ce3c8>"
       ]
      },
      "metadata": {},
@@ -3623,24 +3377,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 103,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 42.3 s, sys: 725 ms, total: 43 s\n",
-      "Wall time: 42.6 s\n"
+      "CPU times: user 24 s, sys: 224 ms, total: 24.3 s\n",
+      "Wall time: 24.5 s\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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XpA4Zf0nqkPGXpA4Zf0nq0Kb9lc6tN0jSjB1VtbX1iFmbMv6SpD3zso8kdcj4S1KHjL8k\ndcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4\nS1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KH\njL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8k\ndcj4S1KHjL8kdcj4S1KHjL8kdcj4S1KHjL8kdej/AZCmhLceHWOhAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115db02d0>"
+       "<matplotlib.figure.Figure at 0x10dc79208>"
       ]
      },
      "metadata": {},
@@ -3650,7 +3402,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "workload average = 2.9; confusingness = 6%; combined = 1.6 for keyboard\n"
+      "workload average = 2.8; confusingness = 9%; combined = 1.6 for keyboard\n"
      ]
     }
    ],
@@ -3671,16 +3423,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 104,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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9QZJmbKmqDa1HzNoj4y9J2jFP+0hSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/\nJHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI\n+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtS\nh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/JHXI+EtSh4y/\nJHXI+EtSh/4XqLSuBBGekGwAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1159ad550>"
+       "<matplotlib.figure.Figure at 0x10dc4e2b0>"
       ]
      },
      "metadata": {},
@@ -3740,16 +3490,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 105,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115a44b90>"
+       "<matplotlib.figure.Figure at 0x107c0f208>"
       ]
      },
      "metadata": {},
@@ -3777,9 +3525,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }