strace demontraing bug https://bugzilla.redhat.com/show_bug.cgi?id=1055234

strace frequent freezes.ipynb

{
 "metadata": {
  "name": "strace frequent freezes"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import matplotlib.pylab as pylab\n",
      "import pandas as pd\n",
      "import StringIO\n",
      "import matplotlib.pylab as plt\n",
      "import numpy as np\n",
      "pylab.rcParams['figure.figsize'] = 8, 8  # that's default image size for this interactive session\n",
      "pd.options.display.width = 200\n",
      "from datetime import datetime, timedelta"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_fwf('wc.log', colspecs=[(0,14),(15,173)], header=None, parse_dates=[0])\n",
      "df[0] = pd.to_datetime(df[0])\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 1948 entries, 0 to 1947\n",
        "Data columns (total 2 columns):\n",
        "0    1948  non-null values\n",
        "1    1948  non-null values\n",
        "dtypes: datetime64[ns](1), object(1)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 187,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 1948 entries, 0 to 1947\n",
        "Data columns (total 2 columns):\n",
        "0    1948  non-null values\n",
        "1    1948  non-null values\n",
        "dtypes: datetime64[ns](1), object(1)"
       ]
      }
     ],
     "prompt_number": 187
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# calls taking more than 1 second\n",
      "\n",
      "idxs = (df[0].diff() > np.timedelta64(1,'s')).values\n",
      "df.iloc[idxs.nonzero()[0] - 1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>390 </th>\n",
        "      <td>2014-01-19 19:56:05.754720</td>\n",
        "      <td> read(3, \"u|\\375i\\272h\\3308\\216\\371\\370\\17\\333\\...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>742 </th>\n",
        "      <td>2014-01-19 19:56:12.388820</td>\n",
        "      <td> read(3, \"\\352x\\351\\347&lt;\\315-Xb\\273\\22\\275\\r\\22...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>966 </th>\n",
        "      <td>2014-01-19 19:56:16.738680</td>\n",
        "      <td> read(3, \"h\\215\\273U\\316xL\\f\\177[-BE#\\356\\2D\\36...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1382</th>\n",
        "      <td>2014-01-19 19:56:23.086230</td>\n",
        "      <td> read(3, \"8}\\204\\271i\\254\\330\\313\\227\\242%]\\215...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1638</th>\n",
        "      <td>2014-01-19 19:56:28.103210</td>\n",
        "      <td> read(3, \"\\2!\\30\\275\\232\\f\\26\\265\\320@\\343O\\371...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1670</th>\n",
        "      <td>2014-01-19 19:56:29.487310</td>\n",
        "      <td> read(3, \"\\226\\3442%\\240L \\203[\\327\\347\\312\\365...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 189,
       "text": [
        "                              0                                                  1\n",
        "390  2014-01-19 19:56:05.754720  read(3, \"u|\\375i\\272h\\3308\\216\\371\\370\\17\\333\\...\n",
        "742  2014-01-19 19:56:12.388820  read(3, \"\\352x\\351\\347<\\315-Xb\\273\\22\\275\\r\\22...\n",
        "966  2014-01-19 19:56:16.738680  read(3, \"h\\215\\273U\\316xL\\f\\177[-BE#\\356\\2D\\36...\n",
        "1382 2014-01-19 19:56:23.086230  read(3, \"8}\\204\\271i\\254\\330\\313\\227\\242%]\\215...\n",
        "1638 2014-01-19 19:56:28.103210  read(3, \"\\2!\\30\\275\\232\\f\\26\\265\\320@\\343O\\371...\n",
        "1670 2014-01-19 19:56:29.487310  read(3, \"\\226\\3442%\\240L \\203[\\327\\347\\312\\365..."
       ]
      }
     ],
     "prompt_number": 189
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# overall time\n",
      "print df[0].max()-df[0].min()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0:00:30.884950\n"
       ]
      }
     ],
     "prompt_number": 190
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[0].plot()\n",
      "plt.title('Calls vs timestamp')\n",
      "plt.ylabel('time (s)')\n",
      "# y axis max is 35 seconds\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 191,
       "text": [
        "<matplotlib.text.Text at 0x7f667d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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gqvvXKtyH/AVfVZXULEANVfIHL9sKvTFGU6ZMUWZmpm699dYGj9m5c6fvr5b8\n/HwZY9SuXTu7QgKAkFZdbc2JBwLJtkv3n3zyiQYNGqQ+ffrI4/FIkh5++GFt2bJFkjR16lQ999xz\nmjNnjpo1a6bY2FjNmjVLAwcO9A+QS/cAIkR8vLR5M4PuIh33oweAMBUbK33/vdSqldORwEmu6tEj\nstEjdDfyF3z06GEHCj0AhABj6NHDHly6B4AQUF0tNW8u1dQ4HQmcxqV7AAhDVVWczcMeFHrYhh6h\nu5G/4Ap0oSd/8OJ+9AAQRMOGSbt2WT35ult1tdSypdPRIRzRoweAIKmtlaKjpS++kKKiJI/Hf0tI\nkFJTnY4STmMePQC4VFWVNVe+qsrpSBDKGIwH16BH6G7kL/CqqwM3T/5YyB+8KPQAECSBXBAHaCou\n3QNAkOzdK6WnS/v2OR0JQhmX7gHApVj5Dk6g0MM29AjdjfwFHj16OIFCDwBBEsxCD3jRoweAINm4\nURo61HoEGkOPHgBcilH3cAKFHrahR+hu5C/w6NHDCRR6AAgSRt3DCfToASDAdu6UKiqste3r3rjm\n66+lBx+01roHGhPouke3CAACqKhI6tJF6tjR/8Y13udDhjgdISINl+5hG3qE7kb+TswPP0jdu0tb\ntkiFhdKmTdYo+/XrpXXrpLlzgxMH+YMXhR4AAqiiQmrRwukogMPo0QNAAC1dKt1xh/UInAjm0QNA\nCOOMHqGGQg/b0CN0N/J3YioqpObNnY6C/OEwCj0ABNChQ5zRI7TQoweA4/Ttt9KUKdYCON658rW1\n1rZvnzR4sDR/vtNRwq2YRw8ADlu3zpoT//TTh+fIezePRzr9dKcjBA7j0j1sQ4/Q3chf4w4dkjp0\nkAYMkPr3l/r1k/r2lbKzpawsqW1bpyMkfziMQg8AxylUBtwBTUGPHgCO0x//KH32mfSnPzkdCcIR\n8+gBwGGHDnFGD/eg0MM29Ajdjfw1zg2L4pA/eDHqHgAacOiQtHbt4alzdW83u2mTFBfndIRA09Cj\nB4AGzJ4t/eY31u1mvbearbv96lfS//yP01EiHDGPHgCCYPduado06be/dToS4OTQo4dt6BG6W6Tn\nb9++0JgPf6IiPX84jDN6ABFr8WLplVf8++/eLT9f+vWvnY4QOHn06AFErBtukA4ckIYOrd+Dj4qS\nhg+XEhKcjhKRhh49AARIRYV1A5oJE5yOBLAPPXrYhh6hu0VC/srLpZYtnY7CHpGQPzQNhR5AxArn\nQg940aN72t7+AAAgAElEQVQHELGGDZNuu83qxQOhgh49AByHigprqlxt7eEV7rzP9+0L/aVsgZPF\npXvYhh6hu4VL/q6+WsrMtO4bf+650vnnS7m51kj7/fulTp2cjtAe4ZI/nDzO6AGEtf37pVdflS6+\n2OlIAGfQowcQ1gYPlu6/3zqLB9yA+9EDwHGoqpJOOcXpKADnUOhhG3qE7hYu+auslGJinI4i+MIl\nfzh5FHoAYa2ykjN6RDZ69ADC2plnSm+9JfXo4XQkQNPQoweA40CPHpGOQg/b0CN0t3DJX6Reug+X\n/OHkMY8egOtt3y59/33Dq98dPBiZhR7wokcPwPW8/fcWLQ7fS967tWol/b//x1K3cA/XrHVfVFSk\nCRMmaNeuXfJ4PLruuut088031zvu5ptv1qJFixQbG6sXX3xROTk5doUEIEyVlEhffCF17Oh0JEDo\nsa1HHxMToyeeeEKrV6/WsmXL9Nxzz+mbb77xO2bhwoVav3691q1bp7lz52ratGl2hQMH0CN0Nzfl\n7+BB68wdh7kpf7CXbYW+Q4cOys7OliS1bt1aPXr00Pbt2/2OWbBggSZOnChJGjBggEpKSrRz5067\nQgIQhoyh0ANHE5TBeIWFhSooKNCAAQP8Pr5t2zZ1qnPrqNTUVG3dulVJSUl+x02aNElpaWmSpISE\nBGVnZyv3vwtXe/9qZT/09nNzc0MqHvbdnb9t26T338+TMdLZZ+fKGCk/P09VVVJMTK6aNQutn5/T\n+6GWP/Yb3/c+LywslB1sH4x34MAB5ebm6u6779aYMWP8Xhs1apTuvPNOnXfeeZKkoUOH6tFHH1Xf\nvn0PB8hgPCDiGSO1bCmlpVmD7Y7c0tKkf/7T6SiBwHDNYDxJqqqq0tixY3X11VfXK/KSlJKSoqKi\nIt/+1q1blZKSYmdICKK8vDzfX65wn1DKX3W1VFMjffut05G4RyjlD86yrUdvjNGUKVOUmZmpW2+9\ntcFjRo8erfnz50uSli1bpoSEhHqX7QGgooLpccCJsu3S/SeffKJBgwapT58+8ng8kqSHH35YW7Zs\nkSRNnTpVkvTzn/9cixcvVqtWrTRv3jy/y/YSl+4BSLt3W2vW797tdCSA/QJd91gwB0DI27pVGjjQ\negTCHTe1gWvUHVEK9wml/B06JDVv7nQU7hJK+YOzKPQAQl5FBYUeOFFcugcQEqqrpT/9ySrqkjWl\nzvu4bZv0wQfSl186Fx8QLPToAYSlb7+VzjlHuuYaa7/uPHlJysmRJkxwLj4gWFw1jx6RjXm87hbs\n/JWWSmecIT39dNC+ZFjj9w9e9OgBhITSUikuzukogPDDpXsAjjJGqq2VFiyQXniBpWwBptcBcK1l\ny6zR89HRUlSU1X+PipJOOUUaN85asx5AYFHoYRvm8bqbHfnbsEEaM0aqrLRG2dfWWmf0NTXW/jPP\nBPxLRix+/+B11MF4u3bt0uuvv66PPvpIhYWF8ng86ty5swYNGqRx48YpMTExWHECcImKCuk//7GK\n+JHb8uVSUpJ1Rg8gOBrt0U+ZMkUbNmzQiBEj1L9/fyUnJ8sYo+LiYuXn52vx4sXKyMjQH//4R3sD\npEcPuMqcOdK991qX4aOi/DePR7rtNuusHkDDgjaPftWqVerTp89RP7kpx5wsCj3gLo8+Kn3/vfTY\nY05HArhT0AbjNVTA9+7dq1WrVh31GMCLHqG7nWj+ysulli0DGwuOH79/8DrmYLzBgwfrhx9+0N69\ne3XWWWfpZz/7mW677bZgxAbAhSj0QGg5ZqHfv3+/2rRpozfffFMTJkxQfn6+3n///WDEBpdjVS53\nO9H8lZVR6EMBv3/wOmahr6mpUXFxsV577TVdcsklkqz+AQA0hDN6ILQcs9D/5je/0bBhw5Senq7+\n/ftrw4YN6tq1azBig8vRI3S3o+XviSeklBSpY0epQwcpMVFq31469VTppZesRziL3z94HfOmNuPG\njdO4ceN8++np6fr73/9ua1AAnFdYKL3+uv/tYr3bq69KDzwgjRjhP3XO+zwhwdHQAdTR6PS6++67\nT9OmTVNSUlKDn1hcXKzf//73uv/+++0NkOl1gCOGD7f67YmJh4u497axzZpZ0+c6dHA6SiD8BO02\ntf369dP48eNVWVmpvn37+hbM2bFjh7788ks1b95cv/zlLwMWCIDQsnu3tfjN2Wc7HQmAk3HMu9cV\nFRVp6dKl2rJliySpc+fOOu+885SamhqcADmjdy3uhx36ioqknTv9L8t7t1Gj8pSfn6szznA6SpwI\nfv/cK2hn9F6dOnXS+PHjA/YFAYSOESOsot6y5eHL8t7t9NOl5GSnIwRwsrgfPRDBMjKkxYutRwCh\ngfvRAwiY6mprYB2A8EWhh22Yxxv6jlboyZ+7kT94HbPQf/fdd7rwwgvVs2dPSdYd6x588EHbAwNg\nv+pq7g0PhLtj9ugHDRqkxx57TNdff70KCgpkjFGvXr20evXq4ARIjx6wTfv20po11iOA0BD0Hn1Z\nWZkGDBjgF0BMTEzAAgDgHHr0QPg7ZqFv37691q9f79t/4403lMycGzQBPcLQR48+fJE/eB3zb/ln\nn31W1113nb799lt17NhRXbp00V/+8pdgxAbAZpzRA+GvyfPoDx48qNraWsXFxdkdkx969IB9YmKk\ngwelU05xOhIAXkFfGW/fvn2aP3++CgsLVV1d7Qvi6aefDlgQAJxRU8MZPRDujtmjHzlypDZv3qw+\nffqoX79+Ouuss3TWWWcFIza4HD3C0FZbaz1GNfK/APlzN/IHr2P+LX/o0CHNmjUrGLEACCL680Bk\nOGaPfubMmWrTpo1GjRql5s2b+z7erl0724OT6NEDdikrk047zXoEEDqC3qNv0aKFpk+froceekhR\n/73G5/F4tHHjxoAFAcA+27ZJP/xgXao35vDj/v1SebnT0QGw2zHP6Lt06aLPP/9cp512WrBi8sMZ\nvXtxP2zneW9B26WLdevZqKjDt6GtqbFuRbtoUcOfS/7cjfy5V9DP6Lt27aqWLVsG7AsCCB7vGfs3\n3zgbBwDnHPOMfsyYMVq9erWGDBni69EHc3odZ/TAidu9W+reXdqzx+lIADRV0M/ox4wZozFjxtQL\nAkDoO3hQatXK6SgAOOmYhX7SpElBCAPhiB6h806m0JM/dyN/8Gq00I8bN06vv/66evfuXe81j8ej\nVatW2RoYgKbZs0fq18/qxx85sr66WsrOdjpCAE5qtEe/fft2dezYUZs3b67XK/B4POrcuXNwAqRH\nDxzV2rXSsGHSp5/6j6z3PrZqJdVZAgNAiAva/eg7duwoSZo9e7bS0tL8ttmzZwcsAAAnp6pKio2V\nkpOlDh2kxESpfXvp1FOldu0o8kCkO+Za9++++269jy1cuNCWYBBeWGs7OCorrbvQBRr5czfyB69G\ne/Rz5szR7NmztWHDBr8+fWlpqc4777ygBAfg2Kqq7Cn0AMJDoz36/fv3a9++fbrzzjs1Y8YMX78g\nLi5Op556avACpEcPHNXSpdIdd1iPANwvaPPo4+PjFR8fr7/97W8B+2IAAs+uS/cAwsMxe/TAiaJH\nGBx2Xbonf+5G/uBFoQdcrrJSOuUUp6MAEKqOuda90+jRA0f31lvSSy9J//iH05EACISgr3UPwHnV\n1VJ+vvVojP/qd19+SY8eQOMo9LANa20HTl6eNG6c1LOnteLdkavf/fjHdnxN8udm5A9eFHrABUpL\npdxc6zI9ABwPevSAC/zlL9LChdYjgPAWtLXuT9a1116rpKSkBu9+J1mXleLj45WTk6OcnBw9+OCD\ndoUCuF5ZmbWePQAcL9sK/eTJk7V48eKjHjN48GAVFBSooKBAd999t12hwCHM4w0cJwo9+XM38gcv\n23r0F1xwgQoLC496TFMvTUyaNElpaWmSpISEBGVnZ/sGmXj/MbPPvtv3d+2SnnwyT8ZI3brlqrZW\n+uYba3/jxlx17x5a8bLPPvuB2fc+P1bNPFG29ugLCws1atQoff311/VeW7Jkia644gqlpqYqJSVF\nM2fOVGZmZv0A6dEjQjz7rLUNGHB4ZL1383ika66RuJ8UEP7CZh593759VVRUpNjYWC1atEhjxozR\n2rVrnQoHcFxFhXTJJdLjjzsdCYBw4tgSuHFxcYr9b9NxxIgRqqqq0t69e50KBzaoe1kKx3bokNS8\nudNRHEb+3I38wcuxQr9z507fpYn8/HwZY9SuXTunwgEcF2qFHkB4sO3S/VVXXaUlS5Zo9+7d6tSp\nk+6//35VVVVJkqZOnao33nhDc+bMUbNmzRQbG8vtcMOQd8AJmqaiQjr1VKejOIz8uRv5gxcL5gAh\n4pZbpC5dpFtvdToSAE5yzYI5AD3C4xNql+7Jn7uRP3ix1j0QRN99Z92gxnsHurp3oVu5Uurf3+kI\nAYQbLt0DQXTjjdKqVVJmpjU33rt558rffLPUtavTUQJwUtjMowci0b590tSp0tVXOx0JgEhBjx62\noUdYX0mJ1Lat01E0DflzN/IHL87ogQD75S+lV1+1eu9Hbj/8IHGjRgDBRI8eCLCLLpImTpSGDKm/\nZn2zZlJ8vNMRAghl9OiBEFdeLp1+upSS4nQkAECPHjaK1B5hRYXUsqXTUZy8SM1fuCB/8KLQAwFW\nXh4ehR5AeKBHDwTYGWdI770npac7HQkAN6JHD4SAZcukTZvqr25njDVXvkULpyMEAAtn9LBNXl5e\n2N5Bq0cP68w9Pt5/hTuPR2rVSnr6aSkmxukoT0445y8SkD/34oweCAFVVdKTT7JcLYDQxxk9cAK6\ndJE++MB6BIBA4ja1QAiorrYWvwGAUEehh23CeR5vJBT6cM5fJCB/8KLQAyegpkaKjnY6CgA4Nnr0\nwAlo105av956BIBAokcPhIBIuHQPIDxQ6GGbcO4RVleH/6X7cM5fJCB/8KLQAyegpoYzegDuQI8e\nOAHR0VJlZfif1QMIPnr0gMO8a9tH8dsDwAX4rwq2CdceoXdqncfjdCT2Ctf8RQryBy8KPXCcGHEP\nwE3o0QPH6cABqUMH6xEAAo271wFBcsMN0s6dh+81732srHT/LWgBRA7O6GEbN98Pu6ZGOuUU6bXX\nrEF3Ho//Y/v2Uv/+TkdpLzfnD+TPzTijB4Lg4EEpNlYaO9bpSADg5HBGDzRgxw4pK8u6dA8AwcQ8\neiAIDh6UWrVyOgoAOHlcuodtQr1HaIy0cqVUXm4NtKs76G7TJuvSfSQL9fzh6MgfvCj0iFhbt0oD\nB0pnnVV/sJ3HI40e7XSEAHDy6NEjYq1dK116qfUIAKGCHj0QIFVVzIcHEP4o9LBNqK+1zcI3Rxfq\n+cPRkT94UegRsTijBxAJ6NEjYi1dKk2fLn36qdORAMBh9OiBAOGMHkAkoNDDNqHeI6TQH12o5w9H\nR/7gRaFHxKqqsm5cAwDhjB49ItY//iHNmye9/bbTkQDAYdy9DjgOxkhbtkjV1f5L3NbWWsvfRnFN\nC0CYo9DDNqGw1vaSJdKIEVLHjv5L3EZFSQcOSNOmORpeSAuF/OHEkT94UegR1vbulYYNsy7TA0Ak\nokePsPbnP0vvvCO9/LLTkQBA0zCPHjgO3FceQKSj0MM2wZzHW1Fh3Vf+4EGr915aKv3wg3XpnkJ/\nYpiH7W7kD1706OF6f/mLNGGCtfiNx1N/e+QRpyMEAOfQo4frPfGENYXuiSecjgQATh49euAIFRVS\nixZORwEAoYlCD9sEq0dIobcHPV53I3/wotDD9crLKfQA0Bh69HC9m2+W0tOlW25xOhIAOHmu6dFf\ne+21SkpKUu/evRs95uabb1bXrl2VlZWlgoICu0JBGDBGqqyUDh06PJWurOzwdLqWLZ2OEABCk22F\nfvLkyVq8eHGjry9cuFDr16/XunXrNHfuXE1j0fGwE8ge4W9/axXzuDgpPl5q21Y69VQpMVF67TUp\nNTVgXwr/RY/X3cgfvGybR3/BBReosLCw0dcXLFigiRMnSpIGDBigkpIS7dy5U0lJSfWOnTRpktLS\n0iRJCQkJys7O9t2swfuPmf3w3t+xI1dPPSX16hUa8bDPPvvsB2rf+/xoNfNk2NqjLyws1KhRo/T1\n11/Xe23UqFG66667dO6550qShg4dqhkzZuiss87yD5AePSRNmSKdc470s585HQkA2Ms1PfqmOPIb\n8Xg8DkWCUMcUOgA4MY4V+pSUFBUVFfn2t27dqpSUFKfCgQ3qXpY6WRT64Atk/hB85A9ejhX60aNH\na/78+ZKkZcuWKSEhocH+PCBZo+0p9ABw/GwbjHfVVVdpyZIl2r17tzp16qT7779fVVVVkqSpU6dq\n5MiRWrhwoTIyMtSqVSvNmzfPrlDgEO+Ak0DgjD74Apk/BB/5gxcL5iBkvPaa9OGH1pz52trDj7W1\n0sKF0t//Lp1/vtNRAoC9wmowHsLb8fYI586VmjeXsrOlfv2k/v2lc8+VLrhAmjnT+hiChx6vu5E/\neHE/eoSMffukq6+moANAIHHpHkG1bZu0c2f9S/O1tdKVV0pLlljr1gNApAp03eOMHkE1fLhUXS3F\nxkoejxQVZW0ej3TmmVLHjk5HCADhhUIP2+Tl5dUb+XvggPTBB1KXLs7EhKZrKH9wD/IHLwbjIagq\nKqwBdwCA4KBHj6Bq105av956BADUx/Q6uBoL3wBAcFHoYZsj5/Eaw6V7N2EetruRP3hR6BE01dVS\ndLS1AQCCgx49Au6GG/znynvny1dXS0uXSj/84HSEABC6Al33KPQIqNpaqVkza936unPkvXPm27eX\nBgxwOkoACF0UeoS0gwelxETrkXm87kb+3I38uRej7hHSDh60Vr0DAIQGzugRUIWF0uDB0ubNTkcC\nAO7EWvcIOd4Bd8ZIpaWc0QNAKOHSPY7brl1Sq1aHB9pFRVlT5mJirHvJn366dRzzeN2N/Lkb+YMX\nZ/Q4bjt2WDel+eqrwyPqPR6nowIANIQePY7bxx9Ld90lffKJ05EAQPihR4+gMEYqLpaqqqy58d7N\nGGnNGik+3ukIAQBNQaFHg5YulYYOlZKS/Be+8T7/6U+P/R7M43U38udu5A9eFHo0qLRUys2VFi92\nOhIAwMmgR48G/fOf0ty51iMAIHhYGQ9BUVVlrVkPAHA3Cj0aVFVlzYs/GczjdTfy527kD14UejSo\nupozegAIB/To0aAXX5Q+/FB66SWnIwGAyEKPHkHBGT0AhAcKPRpEjx7kz93IH7wo9GhQIAo9AMB5\n9OjRoFmzpKIi6YknnI4EACILPXoEBWf0ABAeGG4Vwb75Rvr97+vftKa2ViookC666OTen7W23Y38\nuRv5gxeFPoJ9+KH0xRfSlVf637jG45HOPlu68EKnIwQAnCx69BHs6aeldeukZ55xOhIAgBc9egRM\nTQ1z5QEg3FHoI5jdi+Iwj9fdyJ+7kT94UegjGKvfAUD4o0cfwR58UKqosB4BAKGBHj0ChjN6AAh/\nFPoIRo8eR0P+3I38wYtCH8E4oweA8EePPoJNny4lJlqPAIDQQI8eAcMZPQCEPwp9BKNHj6Mhf+5G\n/uBFoY9g1dVSdLTTUQAA7ESPPoJdd53Ur5/1CAAIDYGue3Row1xtrXTwoP+taL1baSk9egAId1y6\nD3MPPCCddpp0+unSGWdI3bpJmZlSnz5SXp7UqZN9X5seobuRP3cjf/DifC7M7d0rPfqodMstTkcC\nAHACPfowd8MNUs+e0o03Oh0JAKApmEeP41JVJcXEOB0FAMApFPow5+SiOPQI3Y38uRv5gxeFPsxx\nRg8Akc3WQr948WKdeeaZ6tq1q2bMmFHv9by8PMXHxysnJ0c5OTl6kBujB5yTZ/S5ubnOfGEEBPlz\nN/IHL9tKQE1NjX7+85/r/fffV0pKis4++2yNHj1aPXr08Dtu8ODBWrBggV1hRDzO6AEgstl2Rp+f\nn6+MjAylpaUpJiZG48eP19tvv13vOEbU24sePU4U+XM38gcv20rAtm3b1KnOaiypqalavny53zEe\nj0effvqpsrKylJKSopkzZyozM7Pee02aNElpaWmSpISEBGVnZ/suS3n/MbPf8P6OHXn69ltJCo14\n2GefffbZ99/3Pi8sLJQdbJtH//e//12LFy/WH/7wB0nSyy+/rOXLl+uZZ57xHVNaWqro6GjFxsZq\n0aJFuuWWW7R27Vr/AJlHf1Iuvlj6xS+kYcOcjgQA0BSumUefkpKioqIi335RUZFSU1P9jomLi1Ns\nbKwkacSIEaqqqtLevXvtCiki0aMHgMhm26X7fv36ad26dSosLFTHjh316quv6q9//avfMTt37lRi\nYqI8Ho/y8/NljFG7du3sCilsffWVtHKldaMaYw7ftMYYqajIuUKfl5fnu0QF9yF/7kb+4GVboW/W\nrJmeffZZDRs2TDU1NZoyZYp69Oih559/XpI0depUvfHGG5ozZ46aNWum2NhY/e1vf7MrnLB2991S\nWZl1g5qoqMObxyMNHy4dMdEBABBBWOs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      }
     ],
     "prompt_number": 191
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}