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    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## [Python Programming for Biologists, Tel-Aviv University / 0411-3122 / Spring 2015](http://py4life.github.io/TAU2015/)\n",
      "# Homework 6"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 1) Time-series analysis\n",
      "\n",
      "In this exercise we will do a simple time series analysis. \n",
      "The data is taken from an experiment that measures the growth of bacteria (_E. coli_) in a [96 wells microplate](http://upload.wikimedia.org/wikipedia/commons/0/07/Microplate_with_reagents.jpg). The growth is measured in OD (optical density) over time in seconds.\n",
      "\n",
      "The data file is in CSV format (comma separated values). The first row in the file is the time of measurements. The next 96 rows are the OD values in each well at each time points."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**a)** Start by loading the data using the `loadtxt` function in `numpy`. Note that in order to load a CSV file you must give it the proper `delimiter` argument (see [lecture 7](http://nbviewer.ipython.org/github/Py4Life/TAU2015/blob/master/lecture7.ipynb)).\n",
      "After you load the data, put the first row of the data in a variable called `t` (for time) and the rest of the rows in a variable called `OD`. Make sure (`assert`) that `OD` has 96 rows and that the number of columns in `OD` is equal to the length of `t`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = np.loadtxt('bacterial_growth.csv', delimiter=',')\n",
      "print(data.shape)\n",
      "print(data[0,:5])\n",
      "print (data[1:5,:5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(97, 89)\n",
        "[    0.    836.8  1676.5  2513.2  3350. ]\n",
        "[[ 0.11    0.1099  0.1105  0.1105  0.1116]\n",
        " [ 0.1077  0.107   0.1069  0.1057  0.1063]\n",
        " [ 0.1145  0.1144  0.1139  0.1139  0.1145]\n",
        " [ 0.1104  0.1097  0.1095  0.1092  0.1098]]\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = data[0,:]\n",
      "OD = data[1:,:]\n",
      "assert OD.shape[0] == 96\n",
      "assert len(t) == OD.shape[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**b)** Plot all the growth cruves - one per well, or per row in the data. Matplotlib will assign each line you plot with a different color. Note that Matplotlib expects the length of `x` and `y` to be equal, but the length of `OD` is 96. To fix this you can [_transpose_](http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.T.html) `OD`.\n",
      "Don't forget to label the x and y axes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(t, OD.T)\n",
      "plt.xlabel('time')\n",
      "plt.ylabel('OD');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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       "output_type": "display_data",
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xhW1TtDZGKYQvo1i4hISToBXVsCIaolIi1lih17UIax5GeIm0MiFDLAlHClyh\nomIAIRwlLB3VwFFCKPgo0hHS99CEJKoqRDQVTdWpizRlkacqCpiEQVrg2/iuiyvDuCKKp0QwQzmS\n5gq9rdMk3ZMk/XkMfx5pLdJs+9RaEepmmIajUhNtXK2FWutBLw6jzG2Xzuz1wl54HppdI8xR+rUj\nbIs9JK+JPMg1Vllsr3tYIZ/D6zVmN+ZpD2aJkyHmZYi3Exjz/XBgJyz3ghQgZHdkJQUCgYf0LaRb\nRvOOk1Orwupsyd1R2Za/WxvSJmMpqxwVdWkspbJi8vIsxzfl5FQyI2djWX8hlGEh2qeU48OKp8XA\nt0D63U8HBKgRhOUgF3xyp6B/xSPbapMzy3K4viiuWjgid8/fJ3rMErVwVJ7NZjmYiPFdfDGJwula\nm0pxEalooKYJu3FUL4ktE9gySve2DRWVVUIsE6JFlDAxosQIS5eGqFGkzCpN2uioaEJBKODikw8n\nyYaTjI6kSt/Yfyj/FPXuiy44rgb28KNLVW8HfH58gvyP6W5atufc848C3wS++BPvJYF3Peb5Xeda\nIPBf0v9eXtpw+MzHN2A5b32JfvPcv4vtPbe/bvDXD3/6S1tfFRLmAfnme74izjRiPLSrQ7t/I17y\n6XiJNM84dIgD+25g68T3aBlfJVY4xrOf1mRwQGNDq4NxGpQpQWpeI7UoSBZ9XEWjrYdoGyqWriDR\n6F6vUjFci7BjEnZshJSYIkxbxGj5CTyhIYVAKgJfE9gRBT/mQdL0I0rTD/stwp4lrBTi4JVD4si2\nDeJo33qmlX4W6aciegEfRfoo+GjSJe0XKfiLpOQyodoCrYU5ZovTzGsVNDNBst6HUuulUx3CXrkU\ne+USQqsuY9ZeLuE7XKfOsFltM4DDkOPiKDAVh4VeiTemIjLrUP3taLV1aLOjsNgP7SgUViDRwPcF\nXiciZT0usXWJG0J4uhC+qijnvivgEVOPy6xxP+nQA8T9SaGZsLQ+xqGdw0yu6eVsfw8z+R7OxPrk\ntDpETSsITwmD1wbXQjgSPBWpGRCJgOmh1hyUuoNTNkmU6owVF+WVK6e5sXhCrC+V6C8XSTXaNI0w\nC6mknE/Fxf26xgOpNMcaaZaWClj1IRL+AFF/APwBagxj0oOCRMUiQoUkFikEazgtDe2EZScmWtF8\n+eDnJz79rPPUpW/gxxcYvZOLLDg0upPjz6K74diD/J+T45vojjqeR/cGnAeAV9G9AeexghFH4El3\ny2d4zUf9umcMAAAgAElEQVTO8O9/Y9II/fWrJl9nfXrU5398/JI39txy8MXPVaKzX2HzSloe3BYX\npHYwPFshc69Oa98Ad5++kVNXx8l7c/RUQXRAdkBpQaQJk7Ec+91LmEzDqUyLk4USU4NF9HiDJB6J\nCkQsUHRQNVBUaCrQEBoNoeKDjOKJKC4xQLV/1KIdSLQEiZYm4x0D04/RIkVLZsnW01xe9sTl3ozc\nLKeEIzRp6jrtiEYlHRbtuCI7CSmbKZgZyYjpkQJzfT1ioTDCbHycRWWYvFwi5hbRvSqqXUW0mlj1\nBuVGnZJcRtTKaKt5nOo6RK2fcMtAb0G41aRvocLm1kOsY46tms52YI1nM6Wn2JfJMjmaYf3Cy8iW\ndqAaDrrwu6ORdgyZK8HYWcT4BIzM4EXa2J7wrcObXffAZTI2O6rrTkhxZdOLixkvK06JtJxUY3Ja\nRJUZT1NaivBQfB3qBYOTlxU4vK3X3z/a5x4qDMlZZURd8vo1Oz7Q/aY/9hPFNsH1wVdA0RC6htQU\nwo0O6UqdTKlDpuR5V8xOy0uWjiub504qoyuLaK6HkD6uEJwc6Gffxs08VNjCMS/PqkizutjAO3SY\nXCnNuL0OITcR4rT/fX5TfYq6+UU34gC4kR8tx/0Y8JfAzee+9i/nHm8BfoPuaOQjwN//lPcJgiPw\npPrsnTz/Tw7xjd/cj33N52545zP5zh/BjWbuBTsHSm9+saIqRXRXx68+wCu+to4t33UYKtzBmelt\nvEO+B5DMZuBbG5J8bcSlWdpJqbaORrTN/NgBvL4pFFvFb2bQVNAibcvWHMcXGGi2gR1HOBFf9XQU\nT0dTUkrE0Z2Y5zTCvllsRuqN1eSqcDQnR3fH1zCSc9PqOPi4mg+6j6K7aJrAEDp6RxV0kCim7quV\nsB+tazLREDLRQKTLrpZahVx5yO51c60xcEax1AEa4UGK4TQ1bSLaw0Nbhv2lsSjVoYho9sVEI5+k\nmk5TiqVZ0XMsKIOo0iZtz6LXKlg1H7Nh0zGrdJQHYclDO3MF8bn1ZFc7ZIrHGLIeYCM1LqXBdQgS\nwLSSYEXk8f0RjiY2cmRojB6RZUM5w2ApgSIkSAXFU/ATLUS6hBicQ2w6iYy08OeGfBYGhVgcEMpy\nLzJsYiZrTgurKr3V+Uta30v02yeGdKWsqWZHrUhYiUJyPVIZx2+NQGcAMZMOmfvSw/5xZVQ7467R\nVp1+pS6zSkdL4kbTyGgS3A64DfA9hB9BFRHQdaSq4KsKCBhYKnH18VPy0skZBldXRCei0Amr1KJx\nDNsmanZk2OzQlLLztm99M/YUdfWLMjieLEFwBJ40e/ey7u9PcNQ8iP/+j6y5ZIt38AvwF1uVZ1Hz\n//DGnK63Gfr+Fwkt3Mfff/6XGe//LD3FGeq1HEmjymnDdl+Y26YtvGyZSGUzTuoodryMYqs+IVfR\nFKC0gYwI+4l4eb508PlhuXBtbnhLWcmun/bjvat+JNP07XBcWTBGxaKxTi3JFFE6Mq20RVppokub\nNhHaROkQkfiup0rHVaTtuKhmSySaCp6Z8EvqoD/dG/GromxrR5szy/vHHryz3TH8HbUQ66Wg19QI\nVyI4DQPhKOhSnPtZskPQjEoqmk3FtSP1plxTUcLj1bwyVM80h1uJ9oBn+L044YJSSfSLJSPr18V8\nIsIj63M8vKOPqbEcjXSGVjpLK5lhJTHEgjJMyKlCs4LpWLiyDraLVnUIr1pE5j3yMx02Tx1n88oE\nY40267DYjo2FxiHynNZynDV6qDCMoI8hb5Q1Th9awiBCiGRLpdrfoZ3uYIVMLN2Uhtbyc1qNpGIr\nejsqqKVgtQCVNFLxIVL1dVG2w3LRijgNoTu2rvi+ofqmGpZFDLWISBbxs1XH66vb9qDjtcaQ1S2K\nMjXUE1pQB/QJq899qLhBTtl9WlM1FF+4+IqLr0lIjEGogLBMFNdHObdKTvMlUbNN3DbRpCI3TtTq\nX3/f76efou4eBMeFLiJw8du7l+QDKxz9wCEGP/0ZXvDss6deDROv5bnfW+EPX9yXkmV67vwH3vft\nY7zwZIvOkE8jrvHy+a/yYe83ONAuy19/7rOE3PIAG+IKM/ocFLcuDBSvyL7xBV8KT06v40jDlEP9\n8/StXC/WZlqsXX9ARuJtZsSwbMio15Jh0fRDSoSO1c+q30dZSaotw5aa3ZJaq+WJluNLdIWILmTI\nEL4BUpWg+lKqqoCE6iIRNPwwjgjhoUspVFkUPRxkp3dUbJs5zPZ9NqF7d5w+Pf3h227LX3XixDgw\nbCqMHOlh/XfXkvraeKh5qKBEmhEnIhVfw4lKXEOiWD56R0FKSVN0WPWbLENoXo+sX+iLbW/kmjsU\nXR1RPCOrtbWcaCh9Xl2kPJN9PSHuvizL7Gg/0ojiG2GaWkbOFfrEXK6PSrwXK5TGM2LgO+BU0atz\nZFeW2Dq1n2uP7GP0rMvwQpR8x0HDRMXFwCONQhLJAj3Mcy1n2MG02EpLDpPHo0+Y9Eioazarskw7\n6+KOQiS/KnOxRT8u2kQ7EUUJW0KJNQEBlQzM9yNW8lJUE0KYIXw3giJcDEpE5SwRZgmr856Wm3bc\n7Wdk/Zq6Ud9My4tR9yK0pY7b9IgcKfbl7yk9LXbWGsJ0PCzXFabviWaogJ3KQTZLYr4mG3/wB8pT\n1OWD4LjQRQQubnv3olYs7vgf9/PMd9zBl/+/Bz/zMFz7Pi55T4m//pXcSGuGyJ3v5/ZvnKanuI3D\nG8a45MjdbO/Zy2dKr0KIBs956Sjh7DIj48eo1fN+7IFb2m974/tiQ9mqqJoahahDo5LzDh/aLStm\n39zD24V35CRrxQ8OuaGH9jmYpoLnCQlaGJQ8kAQ3Cq4DoVXwV8BFgX4FZUiiDXooKQMZ1WkZPkvJ\nDvMj0OrNoqqDJOcLcLRfN44W1ERjwMv3D0XTl+R0Mag3xTyDzPkFf9YrsCR7vLraW6mpPbMlL3vK\nO16e23nfA/kNJ04Mb5ie7h2ol/vrCZIPZ7LlezNZ63Ra05sZMy57KjGydRVLgCMluoQQgjI+FSQ2\nYAEm5EqKsmtpQF7eSDHiSzRNCD0khKE6jLorbGrUQEjOxiOUwiGKiTRnk2u5b90Wjm5az/x4H04h\nDWYJOmfQG8uEbZuYBynXZqw9xyUnZ4nd79CY9VnbXuF6WuSR3IPKUZHiqNzAMs8mw2ZSao64Eift\nxVgjLYYwWYxrnOoRTOQcZvJNWe9ZdnOFKXc8M+2NhMpKT8dXM6JtpPS2jHhCKK2oUGZ60Y6slUyP\n4FWGhIJNWC6hSBcFB4GL1KXnxR3pZm3FT7dbythEVe44UnF2nK17KWlIRGZ+fl2hUcueeMvvPHjN\nU9Ttg+C40EUELm579/KOdz3CLcP34t3x5be/vMTvfY/Bv2zzL78czbmrFL5xK1++c5bE0tP5Xs8N\n/NLi33JT4q95X/sDRPw2u16dJ5KuEB48Sbq6pXPNUDn0um1LysxyL6cPbCHJA81/+NiNQinbMUdt\ns6L2Ir1eeuUAEbcXhz5fUPc8VlybZbuJ6XZIeBBHVUOGpjqhKJaR9Fw15Ndlm7OyIadkg7N+hBUt\nhvRD4Pvgt7vH6KkOqHEws+D0gToO+k7Q14DdE8cyt0a1mZ0Fo74+ptEjhRGziUfaZEJ1PFRm6wVK\nM5qsnrGYLvnedNlrdcpOR5SE01txtR3tTmak06lKVT343fXjq/ds6o204t564qtjZGaTgMSOWnhq\nE+HW0dodlI6KTxqdDA4ey7TEIr6yStxfITosUmJ7Pu3nw1GRklKk3Tbj7SJbKh02FQVL4Sh3jW/l\n3nXbvaPD6/x6LEk7pCutkKpW8hp+fwI0jWj7NL1yjlG7zJZig8uPLjFycoq+iQqDiy2Ucyt1fcDw\n4WFpcA+9nOQKDC5jQB1lUGQZ9iK0UTlLgiktxHLEYDWkUlRdVsMm5VzJj2TKcjS9IsfVmj+uNpU1\nvq320HTjkQqhcFPRNFMx2qYIz2uEpsMoq1nM5gZa1gY8GUfg4mPgYxDJHmxeXX5r4inq9kFwXOgi\nAhevvXtZ/81F9n/hISK3fbpn9y+v3PkdEnerfPgKJZJpiPVffjtfuuMwWukGHojdyHMqf8aXwzfw\n0vb9/K/Qlbzp1Uv0Rj3U4aMMaGH/t8YdZyzuh/7x7a+TDxwYFsvOFjR24jJKWBSl2SelErFa/ln3\n+56MnICe4xBdBNK6bg729U1dksstru3vP9PX0zM3CLJULA5MzM1tmDx58vIZ04zHgTG6hxOtARkD\nOQf2AlSrcDgMj/TCsT44mIYTBlgSMAV4OqgehMJgjsD0VXDsZji2BUKOwZBlMDq5LZ889MyxfHVz\nJKWk3YgWdpWkaJDxy6RFDVW4dJyYXLH7ONtZ1ywuD5pD+9rJ9bMLjdHl5aMbz0498JnUpcXbBm9I\nTEVjGwg1thBqDBEppcictcgfV1AdE8WdRHVqgIEkBQwLSSjqUE91kPE6kc4ZEq0FZDKfVNclE6xz\nNMYaFmtrLXJWm2xHkmtp9LY9ltUMD6a3e3dv3qRMrE2KhZEQq4MRatkkViSDNFKg6hhOg5hbJeVX\n6POWuWJxkuvuOcPV3z/DwFKDZhhWY4I5Q+G0U2C5vQaztQFDDpMWvSS0HEnSxN0wDVWwIiJMigRH\n3RRHRYJZQwHdBe1cizWkkezIwZQt10dmzCFlxu8T82qvXwmNJs8ow+lJwmoVrRxZ3Xj7Su9T1PWD\n4LjQRQQuTnv3Ir65xCMfPs62T9zOR1568pM3og+PcVvMV7c0lKd94mN86O7P4TtXMdV4A7v830bY\nce5iNx9LSr77q4cZaSZkbNtBMRAW8o82eH5iUlHf//s38xXnXazXv+JHRbI8az+tUnpn+SZuqH4R\n+CrwdnZ3Dwfau5cx4PV019hfDpwB7gG+B3x/925KP/tvIaPwjufC4htAf3p3d+5ti7C1BaMZCA+C\nNwsrp+DbR+Hdy3A6S/fo1M10DySK0F2VVaG7RfgJusev3gvs5wOXeJfOu6/IeOnfsFNrt7XyI4XO\nUK8+pC/xrMb32aofIxarI5Y16RfDsmz2ikY77ctyCKWoeUpdW3VUfV/VTX3hb/b+1fFFt/8yeg+9\nnN6D17Fmb5O+Aw7pqShGM4ZUFlDcZQQWoAlIKDAoBPE02DEbQ22idVaxnGVEVsfIFBChjC7ibphs\nzSdX6jBQ1egrx+irhhipe4xZVZbUNMfjA/JEb15M9SZY6A0zPRbj5IYszdwYJMYwnA59tWk2F8+w\nfXWBfNsi1PYJ1z0yiyZRp40WbqPJBqJRZqUZo9Lqw65ul/H6dtHLBgwZpxStsRxrMJ+wmO4dYj4y\nwnIJVhsaDSGQCPDV7jxKLQPxBhRqK0yuCYLjKRAER+Dn9pqP8RdfX+DWb3yMxeun/mC/r7z1Rfzl\nohSX18Uz//le3v3QOwlra1ic+St6Uq/DqBa4030Jf9VzgNJrT7F1ar2bvP5erceQ8ndHpRh6d4Ib\n7/sE02xDvHb/Z7xPveog8Hpuv++DFOx3AO9n9+6/2bsXhe49Sm+hu3fbp4E7gft276b2OMsXdJer\n/zbdY00/BnyW7hGmjzlZThrARuA64EXA0+kedfvoudzHYe8kPHMH8Ey64TUC9NA92U4BWnTvuTpO\nd1v5b3ALM2x46WWktl+nGD1X5FTl0n6jOTioLOkbzGP+OmfKG2RFpsINVdVd1VhUiM5CrZ2lamW9\nupNuldz8/L7Z66b2nv6lMwunN2nI1noyZ3aQnorQd3CR/keaDD6kEV8aRsgOMAVUdYGX1ElFVHql\npKdhk2x6CMPGo4EXbcFoCK03hZLNg4zASsdALujElywKDegppymsFFi7HObK5iwK8EBkHfetH+TA\n1hwTmxOUe8MIxUV3I4TcCGhRZCiMDGnYkRiVeIZ0p8FwaZlLJ05x3YFjbC8fJWHWqPmDNPxB2tYg\nzU4OafYSNfuJWDk0N4wVatKJVFlNVZgcUtm3biNlY9o99JGb9SfYjX9eQXBc6CICF5/h9/GCmsPX\n/vX7LL3rOzv3H9S/80L+YhplV5VnfHhGvuPhN4qCmmP27Edxh96MPjeA6iS4aaRD51WTbDmyuzr8\n4i+lB0Lw+rxE/tEmnnPqW7iJB3xurr2W235zjKtKv8efH1lFl03gFnbvvnfvXnbQvS9Jp3uT6+d2\n76b9BMsP0w2KDcA7gP8EHud5DjIGPAPYTvdG283AFrpnZx851x6ku83PFIghuvddPQPYSfdEuxjd\nn70SMEs3UB4Evs637mxQve9FCOV5GNlLCfePhFURGmzfxzrziL2rNeFts1boV0xVT0rdz3iKXoHw\ntIpcjEq3EulYnchKfbUwN3Nma+2OmZflH+DKbcVc+QTj355k5J4mgw9BcjaFIh89AKlfgJUQrOZc\nIlmVTCpBWGhoU6tQdKBpwNNPXs3v3f/rPLLt2yz1naGUXaYZ7dBSXfS6y9CRQUZmE2woKmyot8nZ\nJqaMYuo+rm6TlA0KbpOsY1INKcwn4UR/npN96ziw9kpOjW9ncW2eSjoJgOJ7CAmb56Z49r6HueHQ\nw6wvnkZxBZ6bwvdSKF4WKzSC6a2hGJm3XnL8Q+En2Bd+XkFwXOgiAheZPbwoovL5P9OZ+tqtawv3\nhe7O8p4lRd9e4epvzHi3fuu31AE/zOzMv3F069sZOTjI09zTXL+mj5mXn2HTwdfULn3FB1M7kj5P\nd+Don/4Sb57+KLLvQ1U58tybEOMf5HVTA2xoNtDkLcAX97I7DPwZ8JvAnwAf370b/+eovg/4D7r/\n+/4Nuh/4/0VSAEN0NxXdTndboGvojjbuoxsKDwD7QNTp7ubwNLpHI+yke972AN0Rikt3dHIKOAsc\nZ+vWKd79PyGiXoWiXYUSuhTfClM9qCrtMxNbzAOnf7lyJLxRM9Ylo3qPGlYjStxVtJiJ5nskTyCj\nJxS/PZNvzy+uNQ/NX6Puq14TaxGf7mAtlDiiTSW/u6Y9+MMY/5u98w6Potr/8Ds72zfZbHoljd57\nlbaAiIKiKCiCKCrYC4piAclVr6Co2K8NC4oNRKUISFmQJr2XUJKQ3pPNZnuZ3x8TLlx/XkUvTZ33\nec6TZLM7e2Y2OZ/51tPMk0MGQUxEoiZRJaAyq7En6vEn6dHHVydZhLL4kN8e7g/Zw9Qqj1asNNSo\nDzc6KpSoiknwIjXTIyWGI0RGqAWtwUxhWXNyDnckvyQZuz2JUH0C0XYDCS4/iWIhyWG76aDdQGfv\nUdrX1uETBcxeCadGwKEVOZDZks9792VT+46UxccjIqGSJCSVgMNgIK7GTqPyCgbs2CU999G7Sjru\neUARDoUzR+52e79ORdbjSQTnTWhsOarbKDCzQKVvVknrI7n+kV9P0lyTH+Jw+Tw+HDibiUtEunCI\n21vGs2hYGT0P3xgcdtMbYluLRNoSeOOrLOaU3oNaP3WLb8gjexhQdysZzloMgccRmYvV6rPZSECO\nWewH7rdaKf0DsxeQ2/S8D3wIPM1/uKTONpKA7LLqAXRDbkTaEXl/7L0NYzuwHnk/bpAtkUuRXWJt\nkCvbIznl8qpFvgYb6NOngJuuiyIxrB/ayLaoTdG4CuqoO6jCeTxM7SwvyCzKLxlo95laRkWmRySJ\n4do4Z8igd6pN+jpBFAKoigQ0+SKmIjEUnRsQ/IejKCtKc9RjzjXi2t2G/ZtyEhxl73dC80NjLDmR\nJAZVNA5TE2PREBOmJsYdJLHcizHdiN0sUeKsR1XrJqJGwOLWoJMEiPNYGJfTj/5SAvUdtxBqeRCv\nxkdxpYma8khyKs0cPd6J7KJmBEq7Em6Ppi6QQMhYhWgqJTliPf1CG6T+zmNCpqcWj9aPRxvAL+mI\nqTei10TgjLSwxdAqNPWneUrLkfOAIhwKZ0YWauDVMDXDpsZievbO5qZ6zUYdM3KEsBZlUpK9KNhy\n0WT1x9+72Bh8m3f7LmbMmg1keNNY1zjA5OFlXHpwHHdPeon9NXDDTJhc9pq0pGSEoI2ZddiddVM6\nsb4Qds1TNK1/Fau817TNhhm58eZ3Vut/NOQ8U8KAm4B7kf/enwIWnp2L8nuR1MjWRTvk4Hr3hnEI\nsCHHPzaBUPOzFwrIojMWGIycEWY47felmEwbGDt2J1dfLaBVNSXo6YBK15RAXZDK9UHKbIbossPF\nicUhXWGBlJjk1wm9m2fWRvdoXCslBXUWY6WlaeiY0Rh0S668WC+FBlFTKWginQ5fXGWdL+a4SzDn\nBfVqHzXIFlFRw8jblUn5GyNpsz+BrlUBMiq8RPslNGlGiNPhitFRF6vFgUfj1dl6xzTdPDQmrj5a\nvbPjEnZ3Ximok+oY6M+gjV5PoMlhagIStaXR2HNacaQyipwTTaXCynShpD4OT30SkjMB3FEQXgKx\nByA6G23UfiLq1b6KNfN05+7z+w8U4bjQk1C4yMlCC3wZpSXlmXgyJ91+ic6n/t4kzDiIpX0J5vKq\ngH7TFPW2ubXsDT3Ja70OE7n+IMO8CcTpSulxexXNj14jvfjQ28IneRKPP6PnWvtH5DhaIA1Z4mP8\nADgSNpveVVNPCgaAzYYWWIocsL7Lav1dFkJT5OD5OGAd8DqyAJ1DK+OPIOmRxcOKHIDvjuxG+wk5\nAL8b2AOC82cvFJD32mkJ3IrcLTsZEFCrvURG+sjIUNO/v4YOHTREmEGjEagpBdEdFEJVkli7Ry3Y\nD9GioCjQK7dOiPTqAzVN06ukNjFukuO8aNQGXbkQHempC083HSMpJleorY0NBl06t97js0e4nfaY\nyrr62L1+IXIXsfpiEgV5D/CiwnAqNqciHIsn4UQk0YURmCsi0NSEoXNo0dZIEJSQ9CL1OhW14SEc\nKQXJUf03jkmIDekEf3ipZMucLwnJtUJ6BjSN1AlNjCJ6gw+n3US1SwwV1hn93ryMkKcwSXfC7xHq\n60KV369eHHc+PjUU4fjTzFXhQiCLxldRGqJfSKb1nePv0AfUsw3aF7dgbF0jReTUSo4jj6h2feHG\nVT2MrEsE3Jt8TPVsIjPop8fgxtSHp0ofjlsufFzmpO1TSfyjfCGoHPheqpOobv4xxuAdTGnvO/1t\nGzKn5iJbDNdarWcUvI5Azra6BeiKHAD/F3KK7J8ESY0c9+iGbGV0BFqBlA112+HHPPiqCL7NhXoH\nWq0fq3UQYWFDcTq7kZ+vobTUh8Ohwu/XAtqTB0ar9ZCWtpdOnY7TubOTZs0iCTe2AaEJviqv2n40\naKjcoxWrd2mc/uNikkMgImAKBXUxoeqYVH+VpZk/ob6RJtIeoTXXq0SzO0CCUE7Tljukpo13STqt\nJ+RwR9RILnWFtkxVqy7U2J2VkTX1NVF1wZBaalFQEJVZXJwUa6/N0Pt8CXYztUWJuHPTUR1qhDk7\nBuMBQe3LFwShWu0Xg6AKk3DrQ4RENxp/LXqvC/QShKsR4kyQHK8iOVokI1yHvyjeM3naccMvX9ez\n/0GhCIeCwi/QIBrRWmJmxAut7rjtbZNkuElrfMOGJs0bjMquFutynmDlUoGU3AQevqQtjgMaWjvm\nMcqbyScRrfj42nLevmwH39a4iXo2idfzN2GKOyE53hWCFJgmcG/nj37+tjYbauBNZD//IKv1VwPY\nycBI4CpksVgPzAe+4KwEvs8bItAI2ZXVGDk+kggkyLUknZtAXwEGeKGtHiw6VAVBtEdFLHt8RGw+\ngm/fSvLydiBJdUAd4AdKG451GXKtSyvAgEgAAQ0hNFjUATo0tdO1Y5AWbUQSm5sQtSrBnm03VOyQ\ndFV79JLrhMEvOTVuDQhy0XggKACSqKa8U4Ccq4XImtZimsmjSossIy1zH6mZB6SUxBwiw6uECpcl\nYBc0PodaE6jDLFWHogSHPUYjlYXrLCfwppY4PE3Kyv1NqvI10S67Rit6NT6tV1OhhWqnRIEF/9F4\nak5E4HOoMdRp0Nm1aGv1CA4taqeImOBR1eW+GIo4T5+XIhwXehIKFyFyTGN+jJa4u/1i66en/xBG\nRHfR+O4PkjE6EArLrhV9h7NYu8xNZEEK03t0ILfciKPoQ95zJeIIWOgzIcArl+Sz3+tGO0fFa9vW\nok7USb73HF5JEIZxRb/VP39bmw0T8qKvBa6zWnH8wuwsyGJxI3Ks4NuGsRq5ZuJipBFyoDwKOQhu\nQq71aIwsFqlABbJb7hiylVTSMEqBHGw2O7Jraixu1QSyw7cwN309uyJaQOAqqArAinpYIsAPUeDQ\nIIuHSDgi7dHRghBGVOzDxVG8lAF+TMjpzX4aOokQHu6jc+cqBg/20rSpgcjISFQqR4Szvji9MCfU\nKH+vxVx5KK4idEy9NaZC8mgIAPu9aqEU0aLC0ziSmvZRVHSM0NUmWhIJmSwutSrCWCdFNNofjEvd\nF0xvdIhGsaVigsmrrvIinXBBgVsIlPs0Ho+k9nrQufVqbUCjjdA1qtNFdiqu06TW1oVEQ1AUDT7B\noPGptIc1IctOVSjumFd1sEPiTx23FF1ynj5PRTgu9CQULjLk7Km3ozR0HnJY3eKzT9briW8l6t9d\nSYTRHxJyKlTGHa/w44pKxJJWTBqQQpEnnuod77A+KBB0mRjQ83L6DTxCk9Y72fSVxPdLP8BNN4KL\nyh3ohEuxWrf8/G1tNuKBJcA+4A6rFf9pvxaQi+8mAFcCKzlV+Oc915fkdyAii0CzhtEZuYYjHLmS\nvBxZ3JxAJbJQHAdysdm8yAu4FjmbKh05EN4Y2XXVo+E131NR8RqjRjVFFs8hoCqFkbvhHhW0awrm\nVsA+9Pbt9Hs6hXaf9sNY8RW1LOcz9lOBCTnAbkCua2nEqWr4DCAWedtpAbnGJYTZLJKZaadFCwdN\nmwbJyBCFhIRobTAopOZme1NP7A7T1R9310s55ScMZe6ScMw+kQQE8oBDiKajBAb6yR+bSF6vTMrD\nGlOqjxO9kiax+75Akw7balo121CTmbSzPkJT6jdK9WqNEAw3iCToVYRV+AR3uVcVdAXxuYOS1xWQ\nPKTKNSYAACAASURBVK6gyiAIkjkyGDIGq01FMyc5087VB/szFOG40JNQuMjI4jGjigmtV2iTt23c\nLtI+Xq2e8ROxarvkKiwRWq5cwJJ1h3BXdOf2K9VUSWmUrXyfbWotvtpwnoy5mZzBlTw0Zi6T14fQ\nvvMwR2ruk/g6d5cUxRys1rd+/pY2G1cAbwEfAf84LRAeh9xS5HbkGof3gE+RF9CLhXTkIr/LkYPc\nNWg0uURHV5CeXkqfPoe57LIyRFGHXKUe1zDiketKEht+1iLf9fsAO3IdRx5yG5UdwGas1jhk8byh\n4fF5wCIg/9+zyUJPTXofcgeOx556NYeHV1LW1gKqkzuGbmn4ug8Ez6+cl4Ds2hqMHLTvAGQiWyVB\n5BRhLTExQdq2DdKqlYrMTJGMDBGVCt3x45LhaDaqkqP+UF22168rcXsTpVAgFiM6jLipwkUpdaZK\nqntCzWWRlPWPp7BdPJZgkNYOkWYOF03r95rbnvhxsHZFwWWscGaQK4mELMjWWyNkoUsPSmwcNICb\n/9An+PtRhONCT0LhIiKL0Wp4JWWhOipv30GJEaJGe/sBolTVUm1prnD5l9v4eOs6Ku2DGD2qHE2Z\nimM7NrFBF4a7LIpvTFbe7JHER5Ne5o0dPirfG8SWgnmonjj4UujSUFfAitX678I9m41M5N0sWyDX\naCxHXpAGIS+Qg4BvkOsvNnNhs6LCgHYIQgdMpi6IYge83kwEQUOrVqX07u2kf38tFksK8uKfi+x+\n8nJKECqRrY6T46QrqhzwnOzB9TMEtAxBzWTUtCaSr2jLMrpQgWwVJCFbOanIqb6dgAPI2WRvkUVe\nQ9C9NdCNMH9/RKkv9eoEIdLnFtOcAbF1bUDbp7xGHeX1CBIBSUArCehCKvRikJDRRV24A0d4bbAu\nUFJor8vfV19TsMdfc2JPrFRT1QdJCiBJ+5DFL52IiHjatStWtWsnaJo0iQilpZklg0EdV1AgxRQU\nSKGKsjqXp8xZ5yv2OkL5Qa9YpicMC2ZMaFBTpYPqJD+V7QNU9VNT2U0koPYRV68iGjURKidmTTWx\nnkKSa48Q685Gq97C6NFrz8Hn/ksownGhJ6FwkZBFH5XEoqgFhFce2xPkIbTR3fZLfqNa8JxYw4R5\ndl7Y/R357msYOfYAupwS7HlVLAglUFdoplCdzu1de/LFbR9SWF/O02/2pjR7HjTb/yDvSNOAnlit\nR+HfWVMPA48BLwIvW61YkKu5JyDfcb8LfIYc6D0fCMgtQToj313HAJEYDIkIQhN8vliSkty0bKkj\nPT1AQkIuKSn7SE/fh1pdzCkROIHV+ls9swSi0NCNBGLIREsmQdIJNWzNJ5KAjjgMWDBiQERAwoOK\nWgRcyIF/T8PXYuTWJfnIDRY3k/Wz2JDNlq7xcbPewxi/htRuW5HSjgrqQKHJUVdsduZWRGmP1Vmi\n40xOZ8v4ipomETXVKWpnjc6P26sjWBaPviIWXXUUJreBSLcBi8tIWH0Y2rowKegoP0Zg51ZRdaIg\nKBQXS1JZqSpkrxFVUTEBIS7BT2xMIBQbo5ISE/SkpYbUMbEBnUBIq9GofWFh2jCPh6ZFReVxtbX7\nvLg3b4ipLLY7jjTCUdwEd14KopSBXorDYwhSExekJlGgMk2ksr1IeT8V1ekQtrYY++jks/bX8Oso\nwnGhJ6FwEZBFUyHEFtN3RLjy1/hC0yL0zaO3SnmJiYJ23zwe/sbElF1fkh28kZG3/gTbj2BxRjCr\nshHeSj8mdIzsdTlzxs4nFFHGiH8OwLfvbQlhx7Ws0d8HfI/V+iKAzUY08DGy22Z0g1vqMWT3y0Lg\nHWAb59660CM3JeyN0TgYn68LohgiMbGEJk0cpKZaiI9Pxmh0ERGxn6ZN16HXbwV2YrWWIt9dJzeM\nhgwo4tFhJoZoIonEQCQqolARgZpwwtFiQcSCwMn8nzqCOPDjxYkPOwGq8VGKm+M4OEQ5u2jKNtb+\n/uuhXW5rF1XNS/YI+ly6Erpt5Uinncw1ePgGyLFyuoUjGYChwCigO0jmaKqOt2F/bVv2ubqwPdSD\nn1RNOepSIfkBXwi130O81k1ymF2TbKmMiBLLLJFSRWSEqtygU+e7a82Fzsrwcnd5WK2r3Gh3lemq\nXcUaSSWgjowjlBCHPy2JUJMMVK3booqNIWg0gEqFGAyiQpD8ogqNX3KEVKqjAZwF+GvVBOrNBJ1m\nAo4I/PUWgk4z9Z6N3P1Rvz/81/D7UITjQk9C4QKTRbQQYI92DQmB4vn+4NRUfVv/T9KhzFQhbfVn\n/GNzLSO3/shOzW2MmbCGwPJsMsVmPJKfQNB9jMRADGP6XsbL93wEejcTHxohFR+bISBuGMqq2J7I\nqaA9sVqDNhs9gC+BBaNH815pKY8AVyNbFy8ju3bONipkP3g7oC2C0AaNphOBQCMSEhx06aKlXbsA\nzZvvJCUln2CwHLu9hp07i/nmm0oOHjQgi0Pqz0YsFipIw0EakISRKKJQo8dHLT6qCVCDCjsiNaip\naggUH0fkOBryyKL2bJ/sl7E2/bwx3LO/DXfVRJI+dCnlQ5fyRkIZH1qxlvzaayW559Zo4NIy4nqt\no1/xGgZUH6KlNo90czlxsRKCEEd5fga5hV3ZVjSOuaXt2KcCzMjZYsaGr+GnDTOgBsQQiOWgOQba\nQxA4CMENENwH+jAETxhqp1+jFeqjI8NcKQnauMbNQ4bmTUV3erpQGxuHT68jwh7CbBeluDLBG1dB\nndYdsEshx9aF744Ye7av53+/VIpwKPxdyUKHm63qfbQWSt8K+h9pr21dtks63CxG6PP5Ul7M3kDL\n/VWs0U9i0q3zcH51nI5hnbntuAG3ai/NvWnc1vUmZkx9Go9X5O5Jd0tlhXcI8Y3mjiv7uPd1yNk5\n19mwlgIPAY8uX07W88/TEzmY/BZyjKP6LJ5VMnKTwZ7ImUjt0GrdpKbW06qVgVatojCZynA4jrN1\nayW7dmmx2zORxUCPnNnkQY47lAIlqCghAyct0NCISKJIRUNrBELIgevtDWMnUELWH2rA+P+wYTtp\n1Rg5lXGlBfRBFfrCFKKdJhIDauKDInGHWtL5h8E0Dajx9tqE7crF/GNcoXXrr72HJC/yVyMXTnYG\nFiBX7P8oyHuM/PwViZwqTuyBXPFejtwy5eQ1+K3AO5c9/7z2QHp6T59G09fkdjfRuFyNvHl5qa7c\n3GjPiRN6z4kT2kB5uUqTmippMzMRU1PxJyYKnqgoQpGRiEYjolZLSG9AEATaHz1RtP3eCSlnem3/\nRxThuNCTULhAZCFQwwpVBQOMJY/56+8apk8vy5YK0kLCmHc28Hz2AqiJZ4XmHt4c9y/yPjzOoIg+\nDCutx6E6QGdnE25t8xAvPzWZcpeeOx/4h1Rv7yK0G/jpI3sev3Ii8APwkA1rOPDxrl2kTZtGntNJ\nd+A15DYgZ7p/xn8jBTnrpz1ywWBrQI3JdIAmTUI0aZJAVFQ6xcXVHDlSQ1GRBpcrGTkd9giwB7m1\nxy7kLCU3j6LBSGPkhbFLw2gLlHGqQeGOhlFEluxCsmE72QbEwqm7bCPyQq9pGGpkC0hs+H0ykGw3\nk1wZg74mEnVNJGKthQhHOPG1FsLL4/B6dQRDKqSgiOQ2INRa0NSZ0apChDR+fKoQPkHCq/OSow4w\noySJJacH2iX5/1x/2nubGq7bCORMsA3I7sNFwu8unJTEhuvTBzkw3xE5FflIw3E3AhuwrS1s+Iwu\nR27m2J1TG2/lI7eZr0b+m3ADbg4eFPn66y6cONGDysrOOJ3pBAImQRCCapXKY9RqgxqjUeuOj9fH\nREY6TmzcGPn75v6HUYTjQk9C4QIxnmcFA49Hl44OVI66VxtVf0KyW4qFqbP3MPnY5+RqevAjI1l+\n/fNseLeMMZZ+dPAV4w4epVddJuOb/FN6Jetu4VBuuvTQ008LwQAMvnbJP5ZNGHkP8DBW69xFi+iz\naRPfzJ2Lt7gYH/IeGu/CLxb2nQka5Arxy5G7yKai168nMVGFwZCMx9OIqqpoXC6B8PA6RPEEDscu\nPJ7DnEptPQLUkoURWXA6I4tDK2SXlgk5I2qPKqTaEeOI2T9h1YSCQfsGRSJbJY2QLanohhELJPo0\nJJTHEczJxH20KaGcTFRV0YgNCz4hFZI6QFAdIKDxE/TooSoaXZ2Z8JBKEnQ+ya7zhpwmd8Bl9Phq\ndQF3oSFQm59YVVHTqKLCEGu3R0TW1UXG1NVpGxcVuZsUFXmjHQ6QRSoSWbC0yNaSBzmTKxo59pKA\n/P8ebBg+ZAthIbD0ly2L/wVJR6K7JxH+EbjFPpTrWmAOaGhrd9HI9RNpzq9oVbeQUZf8EUtTQBbk\naOTiyZYNIwfZ3Xk+UITjQk9C4QJwHX1ozLrYot6Biiue15jcJSFP+HHVWzO2c3POAjbGX8k+3wA2\nXzqdHz5z87i+H9qIIwTcRQyoSea2tDeZ9eIN7F7dg0fffBGNYYs0ceiaWa9PvPNW4EYb1lXLl/P8\nl18yqbaWI7W1TEWuNzjDDZP+jRo5NtEHuS16P1SqYmJiPERFReD3J1JYqCcuzkFKyhGSktZhNi/k\nww83w2nuIlkkMoF2giT0FENi36AQbGZ2m0vSy9Or259oH2xT0EabVpGmi62LDRMQLBIYai2IxUl4\ny+JxlcVTW5KIszyOQJ0ZqT4MwWVE9BhCJq9WigmKgtHo9dbE2O2l6aWlZW1zcqqaFRb6wtxuo8Hr\nNen8fpNfrdZ6tFq1R6vVGD0ebfOCAmNmcbEl0uEIU4GLU4u+F3lxP5nGe9JtVorcXj142qhDXvhr\nG56rQ7YudMh38aVAmcDv3vDq/2OzaTlVO5GBnBRwUrQsnBLTaGSL6hCwkxC7eL1JPt8lZyAJVuSC\nxkhkET+IbHWsAo6DcJE1ovxFFOG40JNQOM9YiaANpWF1SZr6QfNFrb9SCpqyhU9nrOKa3JV8k349\ntdV9Wdp2Mrs36JlFb/IjtxKgmmHl0dzX4hWeeO4WVn1+mfTSJ28KxpQXQ493PfL+1PsmjwBuvOkD\n687iYn7cto1m8fFMOXqU2Zx5hpSALBRXILtQegCFxMbmkJQUT319c8rLw2jevASLZRtq9SJ2715A\naamcspuFGdlV0trsMvdQB9VdPFpPilftNcbWxfrSy9PpcKKDtkVRi7rmRc1P6AP6o5XR5K3tj2db\nV3SFKcTWWkj06EkLqaRUMRQKmF2uitjaWmdCdXUgpaKC1LIybXxNjSHWbjfF2O3mGLtdm1hVVR5f\nXV0sSlIZ8kLuQnaHOTi1qNuRF/aTBXQuTtVz1AqcnbjIGWGznQxg65Grx83IllNcw9dITglCDKcK\nFSNoaKeObJUVcer8apGF6qTLqQKr9fTq/58hmZHrd9oA/ZBrdvzIXYxPFinuBeFXjnHBUITjQk9C\n4bwiMIrDKovYRN1itSoQU4+gPyTNnbFSGHFiJXOa3Eh4wQDeT76f2pxEpks9yfd9RXEjkVvyTTzT\n71HGPzCNN14cH1yyKks0d7k1dG1Py4sfjrj9VuDG1vdawysq+CwpiRK9nv4//XRGnWkF5ADrTcBQ\nVCovcXFFxMRY0GgSqKqKorRUICPjOKmpn5KY+DJz5zrIQtT5dM0yKjKGuLXufrXG2o5OvTMhqTqp\nrmVRS31GWQZxdXF7kqqT9qRVZOz+9lptYOWlWAoaEe7XkgykqUKh1oIkxaaWldnb5OVJLfPyQm3y\n8tSt8vKMTYqKhAiX6wSy/72IU3UapcjxjtMX/Qt3l2yzicgLezJyzOdkinACssV2knBOZYSZgHpO\n1YPUc+p8KpAX/tPF4GTfrEqs1t9rNZ4hkoAsJH2RLZJuyFbicWTL5AiQjbyZ1SEQ/ncL6o+jCMeF\nnoTCeaQfH9CF8SmGd4OFrRJFQZcrffLsYuHaE6uY3WEYTfdez3NRd6Gpb8YYywiJQ08Ki7tF8fEe\nD6/eMpouA5bw6OSZgfyCvmrT0KtD8Vfe8PaxZr2uo6DgNvO940ar1YwaMYIvx4zhpt/YP0NAXhRG\nAeMxGHR07FiHTpfM/v0WVCoX6el70Wp/pLx88VXZzXcPTRza6PuO3w/LjcsdVGmubFsZXpkQVR8l\npFekuxuXNi5pVdjqSMfcjvsNfsPhzT3YPe3ZkCahuqonMKTabO5h9HiCrfPyqtNLS/2Ni4qk5gUF\n+s5HjsRllJZuEEOhVcgB8pN3zzWA/bwJgs2m4lQAXYu86LdrGK05ZQVYkC2Ek24qGh6r4tTmSidH\nCbKFcxInsgjmIwvAn8AlJJk41ferGXIso3XD94XIG2AtA1Y3bMt73iaGIhwKfwtSaMe17GltuiF4\noNNoEW0N7z73GTcdWcmD17TgqnnP85jldgymJozVPoT/8HW8cWkK6zdVsHhyFypFA889+7KkCh4W\nxJvvCnqvenm515yawYwZL4irf5g5aBBht9zCs6NH8/x/mYEaQRiOwXANfv8ABMFIYqKTqKgYjh5V\nCSqxJtKQtObKqkuWXa0e6tnZZGfPEktJ1wpzRbPC6MKogykHhcSaREfz4ua5zUqabWtV2Gppsr3Z\nj9d+4zN2zs4epgkGL6sJD+9YFBOT4NbptM0LCmien+/vmp1dMnDnzn3tjx/fi7zYnBSHKmCvcK6a\nJNpsBuRK9FPFgf+ZcRXDKesgDtlVdXprkpMZXPuRLZyTFoCbU9lRAlD16y6hvyKSGtk6uRQ5UaIn\n8BYIU87XBFCEQ+FvgMjlVBk7NDe7us8S0Hp55sWPeWTXUsbd0Yhr3nuWrwPzOJpQyhT7SxR5bmR6\nHwtrtpdROCmGhbuu5osvHiPVOI3Kuz/y1g/64iAacyGjRu1T11ZOePZZVN2786jVyge/+N5G41jU\n6lnExEQRFRWktDRARYVGH5eW26emzZEx9VdFJYqJHTY32+xY1W6VakfmjgiT11Rn8piOCQg7KsMr\nbd1L6tfPXxjWfGHfvmO2N2vW/VBaWvLBtLRISRDEDseOeTNKSnIbVVRsbZObu+bS7dt3hHk8+cIf\nz946c2y2MOSMrNYNoxXynXECciuQk26ek4v/yf0yqpEtg0Kg9PTdDxV+L1IYEAtC7vl6Q/6EwjEE\nuWhKRG7+9vM7vP7Ad8jpaQBfA8/+wnEU4fi70IGPuEJzs77t53giNUx8c570+o/zhYn3Nydp2UAi\nD8Qzp+m/mJUzl/Vd3ua1UA6v2wvIvD3AC989zapVI+iecBV7b91d5+z/7R6CmkKGDrXEx4aav/46\nEbGxTLRa/99e3kZUqusxGJ4VdaZ4nUcKql3+YE+6V/WjXzAqPsq8L2OfZkfmjqJDyYewG+3JCOxS\nhZj//iJ2jd9NXFV4eIutLVt2OpiW1uanVq0ar+zSRTC7XNWpZWU7UsvKdnQ+cmT9xCVLdpjd7nPX\nLddm0yGnfZ7cZCkTObPoZEzBjNwj6kDDOIicTZSriMFflj+dcIjIAaJByHcr25BbBBw67Tn9kat0\nr/qNYynC8XegI71owsaE3k9LpS06CEPmLZC++/YT4an7+rApr4aHvphOVrspPHvkVT4fXcimH9+i\nT4c8Jg708NC/vmLn7pb0TL6UvbcUVNb1+2Ytfk2McM3wjEv7B6TJkynTaBhttfLvO70IVWQXXXjc\nzOr6vP5GMVzl9TmETnTx9KPPOn0L/boven/RNCc+p09ADBjN3tAPQ49QNvIgnr4nBNPRtGY9t7Rs\n2XFtx47SlpYtKY+M1EXU15eHud3ZLp1uYXlU1DdYrUVn/RrZbBagOaeyihKRrYd2yGJxglObLOVw\nKmBehGwtnKOAscJFyp9OOHoC05GtDpCbwwHMPO05/ZE7j175G8dShOOvTj80OKgWb+hpCrZ7TGi2\n8Ttp36sfCm9OGMLrgfXMePdtvkz5iJH5t7N0XBgFyx/HeUMeL7f1cOfUtRzNF7k0egib7nB47X2/\nfBchvK947dWxE8a6jddfz5vIe2f4bdhS9hqPj5srffzIYfcui0bUSQEh5G8R1sUWn6h/aemoxS0F\nuKltGelj9rF31H48aXYygypV+souXUreGzo0tLJLlwRJEJyqUGitw2RagZySeeSs++9tNhOySLQG\negGXINckHOZUZlEZsuWwFziE1XoxbRalcOE5J2un+ref8odJRvabnqQQuXT/dCTkf4g9yHdEk5H/\nCRT+blSyiMEmU6jNY4KhYC0735grLL62N/NVmxj/3UMcCd/FkBOj2HilCefu56keX8UrbT08cM8q\njpe7uTJsGGvu9ONq//IbiBEjxBtH6W4f4zZdP1K4F+ua/RI8sUQbvP6z0PwmH3s/1AQFKUiYfo8z\nrOo51W2Y6uwrx7Uo4tvln1LaLw+LLkhdeWRk+exR19nnDRrkKoyNjUAQ6oCvgK+wWo+d1fOXA9Rd\nONW7qiNykPoY8v/ET8AHwO6/X4BZ4WLjXArHmaTQ7UT2wbqQMw6+RU5f+yWyTvt+bcNQ+CvQnFE0\nZ4iu15t4nUfZ/sxcCjvF83qHfOKX9qVxRSpVhipyupooMS2lqI+DFzqV8/ytX3GwRsUwzZVsnOST\n0oKPLNkb1XqcePv4wG3Da8OvD95o81wx4SVXhGR4v+lazcrDr2sDLrskqtU/PZbkW90y3HN9qp3P\nOr5ASCXhVIdYWxwbv6bbO//U7svMHIIgDAM2IfeymobVevisnK/NlopcNNiJU+mbSchb0W5G7sr7\nKJCjuJYUfif9G8Y55VwKRxGyKJykEbLVcTqnZ5IsQ+5AGsUvdyDNOpuTU7hI0BJFLPNMl06UnGEm\n4Y2smSSYHdzSOYm6DS4eWnsrBy/7At3uK/j85npU8x3MHrmPD296n7W1KVzOALIfd4faFY2rXjl6\n8CXGKU+ox/WtjBpV8pBjjXNo+ofjN4WK5s30sN2h6wq5H2o4nCL4BuS66ZKrZ0Wig8mI+s2mZcua\nIm/MNA1YA7wErMRq/Z2N9H6GzXayDqQ3cjuS/shVzTZkF9dK5IKxXMWSUDgLrOU/b6qnn4s3OZfC\nsR1oirzPcTFwPXJw/HTikf20EnL1pcDZbVutcHEj0IafdEOaqZ1NrmbAwhe4NSebD3tcTtk+Gw9t\neofKse/TfN4d3PNuCOO0fTz66ucsnPwii2vb0ochWKa4/Jr6jtLWy8cZLbPfMFzfdmeoX2a/OQPN\n/kHSF6PSTWsrNXdD6AXwVkZgeLcz1tUZzNvSiEfpZ/MDY5AXbwPyxkyPYrWW/09nZbPFIu/tcTkw\nALnuYUPDeBU4cPq2tAoKfzbOpXAEgHuBFcgZVnOQM6ruaPj9O8B1wF0Nz3Uh76ym8HehOS8bBuma\n+tvOJPL4CpZ8sp6dvS7nY/NKLtsxBUOHvQgbLuOtB1RkPGKj54w5HPrqFhZmX00TruDKkSrX03qD\nMSZtRsi/ZoFqVMw3rnU1zZa9+9K221Nc63zT/X7tYIGypR052rY37XOj+B6YST9bEXAfsjtoPXKC\nxpo/vJjLQexLkN1PA5ALvtYgW9FTgbw/R/WzgsKZ8WfJVFKyqv5qaBgafxOLQ93fFiqTzRy+/R4C\nPSK5K8xPXFE3Rh24nIqb5lO27SF2Ot/GP7CcPpFBXnj8OwIM5/3WzsCDNxxSJ6rn1BzSFIUP2fxU\n3epDaaL5eGH4PK9XMKjJeXUo+7/sQF9J4BPgxQbBGA38E7ltx2NYrdm/e+6y+6kNcoPDy5GD2ruQ\n3U82YCNWq++/H0BB4bzxp8uqUlD4b7Rp15VvyjMnCBUZycyYPRNTWyfv13fCEyhj7Oax1L/4BDFP\nv8oXVz5HyfGePNZuC4/dvA0v9zLT4uTt2w+pI/w3Fx5ppkmMn/5ScM1BIep2/3H6ieROHstPO5sw\nBLkz6p30s5Uhbwb0HXKrjJuwWtf/rhnLbbr7A8OR6478wPfALGAtVqvzbF0cBYWLHUU4FM43cT1i\nWb9vUHuNs9NguuxYycSD29nUbhjfYOPJ1e/AIzPRvfk4b179BfVfd2HKu68w884NGILv0UV9lMJp\npf5DvqYBV8crk/33PCwEjlaJT6iwL7qOVa+2YQByj6e2ZFGEzdYd+By5UO5JYOHvchvZbF2Au4Fr\nkOsnvkMWocOK+0nh74oiHArnE013kdW+4TEWZ+cniKgrYdlzczh4Z3OyClYyfv0zuHr/QHVRS/Y0\n1ZE2/yApz5/ggxdfx1CRSzVzaT+ufenb7mMJxqaPaYIvv03M0eOh+IHYnuhNRwSqgQ5kkY/NFoON\nD5AX+enAx2fcVkNu3XEDcA9yhfbbQCus1pJzc1kUFP5cKMKhcN7oBc8P6KJr/Wyft1BpBDZNfIHC\nSfBCgYOBe+8iQgriHb4c1UtzyOv4FOKoZMQ9rSn8qTfl9GdAy9s8a9vMTDAb/0nhtn1Iq5YHSp+g\nslRLGdCVLHKw2VTYuBWYAXyGvOCfWRtruZ3HncD9yJ1enwaWKbUUCgr/iSIcCueFG2HgbRGqBwde\n+7FAoo45019C16+aBWVtsRxuTPuclmg+vo/IBz5gxr17Cc3vzvX9N/D0pHdQS4PpHzY41PauV/Xv\nqcdSQwS881SI6zmCltvI4icAbLbewIvIwcAhWK27zmhyNlt75BqOG4GlwOVYrXvOyYVQUPgLoAiH\nwjlnCcSlCSzrMe4tgTZBRi1cxhWObayI6smW/BDjtwwn9O691L8whcXXhWN8dS8jZ69k6t2bSA7c\nj5pYOt2aI70WbIY3bQTMnOjF6NtCU6xkEcJma4bcA60zchzjs99MrZVTaG8EJiLXE30AtMdqLfjV\n1ykoKCjCoXBukcB4WBD2jB3wgMY50E/6sSLmfPkBW0f1ZLa7nOnLnqHyiUepWHwpwWBzcrOXMuyp\nrcx65lMy6hZRyB669rjGs6zJu/pA5hwCc2fks6WshBCXN4jGBOA5ZEtjzG9WettsjZGD3TcDG5Hj\nHysUd5SCwpmjCIfCOUOC2CqENW/F90vYMSECgy+M3VOncOj65jzjzefxr2Zy6MqXsdtjaLlu9kaY\nAwAAIABJREFUBA9NddInu5y13z2CMSfAMbK4POwev/m2F/Q/RL9J3Lr9Rwq/2hwArsBmCyHv8dIT\n6P1f6zFsNjNycV5foB9yN4MPgC5YrXnn4zooKPzVUIRD4ZwgQdsgLJ2tTkt5fWZHVOZ27Lrzfoqu\niuC1ihDDdt9DTvpqaroX0PMfr/H4rACp8w4S1iaDbVu74PT3ZSQjpUazXtA8Y7iXRgWRjtxZE/XA\nJQ1isALIBbpjtdb/x5vL+2IPBG5HLtDbCaxD7kO16X/uP6Wg8DdHEQ6Fs44EV0nw/v1CD/Nbr1wi\nkNSPhU/MRNfezkf5vQm3J2LwqKieOo/Od3/ApxO9gcDsg+p+N+Qx561ZhNf1ogsjiJy+VHhNNQCj\nzipVPnmHH73+CpYtuwbZvfQcMPs/ailstmTgFuA25C1P3wPuwGqtPf9XQUHhr4siHApnFQk6SfD+\n3arbhLendtbRJJYn3vqGHuptrCy4kUO6bK7NGU/ZC3djfmI62d1Czp9WHzfdOH4Db8yaR6ZzKGaa\n0qZfDVtTjNSm3Ev7++cV7uzc7B2yst6V34Le/25xLrf/uAK5B1pv5P0yRgE7lAI9BYVzgyIcCmcN\nCTQSfPAyjwXeHnlpIt3yGbp8L4/um8tm80TmJH7F/bZ3WX1XFprPL6Oj1NifpSsy3XL9Kv718qe0\nU92BWsplsGEMgTvf50fzewz9oDh/SRtvNfdNfxC5tf6//p0xZbN1A14GwpH3tr/x/7mtFBQUzjqK\ncCicTabswBo7uc2tiYz+gbZH6/ni8xlsbzGOSTEfSGO3Pyms67GU0qCPmzbfyIQRpZprG9n49J1/\n0TLqWVy5i+jPdMJmvRh4THxM1WFnyG8z7o1h7N0CgjAQq3UvADZbCnL6rRW5++xcJStKQeH88Wfp\nOKt0x73IkaCVH91PyZaNYRUfbBIS6+HQA4+w/9KB3KbeJSXWNxfu/ekWvp/yAAMee5vFQ+ySJ2K/\ncGD7DejZgOPwEwxmGl2v2xOYdbleLDYPDyRurPYe6xVbQmzsJVitFdhsInIb/unIbUCeVywMBYVf\nRemOq3BxIoEYwPD5FPENQ8VrOwSjKpLtk+7nwOiWzD7skyoinMJLa+6lbNJMMmc9jCPdGTxeXyZq\navvg8xdQm/skz9MLf3wjNl09XzwaN0OivLrkWHydSDCqLVarF5utDXKwOwD0xWo9dIFPW0Hhb4vq\nQk9A4c+Pn/BHD9Gr+eyHM9SquDiWPPU8ebeaOLJoaOjrzquEfxy6D3HgOhYfNNO0rq00M7NeTLSE\nk50bT33eeOaHDBzjdnxPZ0kfRT0BdaU7KKtOZPbsviQk6LHZXkLeDvNjoJ8iGgoKFxZFOBT+J4Ko\nm0sEswZa/6HhkqPcvvR74jrm4vrkSSb1fFV11YEetLS35JUW33LTont5w1ok9EosZe2GIQhl1/Ba\nUIeNKfS4eTmPR40P4XfkEIpszcsvP8uXX/ZHbmUeAbTGan1b2XJVQeHCo7iqFP4wEqi9mL9/MGM2\nFfdsVyXVG3hy9eccSbyDadHvEUwNcfdHD7Ltyvfo89WdHG5UTlJsLou/nUSE6RKulXwk04VgoxRe\nGrw65AwbHkCwxPLog+V8+OEQZN/sVVit2y70uSooKJxCsTgU/jBukmbuEXulvjNNq1WHpbN4+kyO\nDGvCmgNednbcyUObb8fbZifL3V5aFLViVecili6cQlLza8gsy+epgMgmxlI49Wnpx5j7BBBdPP+C\nhrvvtqDRfApcooiGgoLCH0Up5LrI8GLp6CHcHzt+XUi1aI40bewN0q4HTNLipDek8BS91G58G2lV\n1HzpijtaSwt0C6Shl74SzMjcJaV2uVuKEZG2a+OkV3lAWjj2xpD+q6lBFr9XwaxZLvr0ycdmS73Q\n56eg8BfhnKydiqtK4XcjgcaD+vs7G7/jrxq2U51epWHcoeXkJt7CP5zvwDiBpxY8zK4Bn9F285Uc\niKrBr9GqXJoCHHv/xbcIJPpE7DG9+XLwK3jM14SQwqJ4+iEHgUB7rNaaC32OCgoK/x3FVaXwu7HT\n5l9bxQExXz/jN6hMGXz13EvkXJ3M6s0SB7sdD12/fxT65CLmmY7Ta/8l/HRJDZs3XY3guZ87fRKt\nDTG8wFMI0x+XNsU9KCCoBaZOLcThuAu3WxENBYWLHEU4FH4XBYzop6b41omTrhfdYW4eXPA14tAy\nvO9P4C3LB1Jmo3TV9TuGs6Dtaun+xZP4tG9+cPuPo9Bn3IOhII/7Ms3k1jcjvqeL5+JHCgRdbnZl\nb2LHjoPAFxf6/BQUFH4bRTgUzpiNzDeEc3jxxPYveI/3KRbi64LcuWUxFYWjedo1B/Uwg/DCt9PZ\nMWpByF4SKTiDBkpCMaI6bTm+fd/yjkZDZEGI18Rp2O79Go+5UwgxvpinnmqFvLmSEstSUPgToAiH\nwhlhwyYk8v3KZUJX3bqsgF7SNeLrZ18ib2QMm1ab2Dn6AK8um4an32o+k/arbl09nhWDitl/sBua\n/GncGJJo3cTIh/4JZN7zESujH0CSQvU88UQ1Pt8M5L01FBQU/gQowqFwRpg49ojETz1mP95HUyIK\njP9+CeF9Cgl8eA+zLp3DI7smkBQW4NPoHOd1G0ayrFuRf/O6kejTJiKUVfBARgaBw+FsjB3Oe31S\nJcFbVcuhorXs3KkGXr3Q56egoHDmKMKh8JvYsHVNYd4zd7XOEnb38AqRbg2PLf+aIuc1/DN6Pp39\nnRic3Z85wzfUFzvrTS1OtGWvGKnRZi7Fv2slM41m4svKeTj4GsKsZ6gO7yxJutRypkzpibzpUuBC\nn6OCgsKZ81vCMQBYCBxsGAuQW1kr/E2wYYuMZc2St8X+YsWzx4WANon5z73MiTFm9q/OZGfPPUxb\ndx+Hbn6NfflHTY9+M4XPrspl/4EeGE5M5XIBuqXFssh5Da1HbWRB/AQkn8PFY1Or8PvfAHZd6HNU\nUFD4ffyacAwF5gCLgRuBMcD3DY8NPcPjD0HuNXQUmPIrz+uKfNc54gyPq3AesGETNFR/JrAocvtk\no2q3YBGuW2cjod1xAp9M4tkRb/LcrtsIddrKshp10fAtI4StXWuDG20j0DabiL+skkciuxN+tJYt\n4Zcwd1QQwVPs4mCljd27tcCMC32OCgoKZ5d1QPtfeLwd8OMZvF4EjgHpgAbYDbT8L89bAywBrv0v\nx1KybS4ANmyTy+hTPSLtw6Bp0atS+DfzpGNNI6WlN90gDRlymdRxaKvQGsvX0o0vXOHtOqartND4\nvdSr99dSSrf3pFhRJb1lNEvFUbHSvcIr0r3vWkPC8gUSS77KRq2uAFpf6PNTUPgbcE7Wzl+zOOKB\nPb/w+F4g7gyO3Q1ZOPIAP3KO/vBfeN59yC6wijM4psJ5woatZxSbn1xDYnjdP4/hUScx98VXyLvB\nwjFbO34wruT57PuEE8M+4viBHPWURVN5f2gRhw53Rl38mNRfCnFJUldyaxqTPnAj7yTfIqjtBR4e\nnlZDIDALOHChz1FBQeGP8WvC4fqDvztJMlBw2s+FDY/9/DnDgX81/KxYFhcBNmwWEdfnKczWzLn6\nMmF1eIqq54H9tErai//zSfxz0Ctkha5C0Hr5oKXF0zV7mGpjFz9b1lyO2Go8vsIq4b6wXjQ6sY3v\ndZfx+Z1hksZhl/x7KxaTnS0BL13oc1RQUPjj/FqvqsbI8Y1fIvMMjn0mIvAK8Bintjf8tS0Os077\nfm3DUDjL2LAJwDvNmeV4zvBQSuGESlFUNee9t7PI7jmCD9JXIdW66L3mZtbc+3ao/nCFfuC+cTzX\nykdYk++o3fQj00x6TDFBFuUOh2dWh3aGT1al7686kvvcjAFAb0DZH1xB4dzQv2GcU35NOIYjL+hG\noEnDY8eQrY0zuWMsAhqd9nMjZKvjdDpzqs1EDHA5sltr0S8cL+sM3lPhf+c2I3nty6jIPPKET8iW\nMnjsy8+oH+qjdEFnFnd/hM81Y6ltsYWdJeHSyH238lHvYvJ2X4JPGi2NC4WE9rG3kJb7JdvT03mt\n1Uih9eaD0v5336hAkuYgJ0soKCicG9bynzfV08/Fm/yaq2ojcvbUJ8D4hvEJMKzhd7/FdqApcnBc\nC1zP/xeETCCjYSwA7vqF5yicJ2zYWgIzOnGv8x/pDwlrOieroh0e7tqymIq9d/Cc9XV6N9MRvfwa\nfryqMlhpTBTNdQY2bB+IN+02UqqcQr/4TkTV/8hc4SaWP1cXdEthQuZatY3iYg3w8oU+RwUFhf+d\nXxOOWUAU8qLeqWFkAhbgxTM4dgC4F1iBXAPyJXAIuKNhKFx8zExj7sIV9GtTO61Y5VLF8Nms18ge\nnsRKu4vK+hM8WjkMV7ODbCnwixOXDefLOD9hKSvx7VzPUzot+rBe6Kt8xI7YxHeRt4pXLKx3Llr7\naDvkGw+l0E9B4S/OMX5ZWE6m2Z5PlKD5OcaGreU6lpd50JRe0naRX/P9J1Lb15+VcgdrpUWtX5ai\nm5mlJ/6lkX6ImC9NvvvJ0LD7F0lTer4nRVhKJG1klPSyWi293vFFKVvbWPpIP1pqsfCeUNx7L0jp\nsd02A09c6PNTUPibct7TcUMN4+cE/8vjCn9uHm7K7F3vcGek9ORBlSSZeOnDL9jToQ2La7aQ2LWO\nnnuHEMwsklabVMJN8wS+KO6BmPoIzb0uLK37Yak4Tl0ggoKsQ6HDpkHCgK91B/MqthqRrVcFBYW/\nCL8mHIeAm3/h8ZtQApx/KWzYEiE0QsOOLnP6DhN3xrRUJVcUkZJ4COnjO5nv/Sb0+EgVuvk3Ssct\nDqFleTe+aFVNSKzCcWAeb6q1+IvvYEDRN4TiXLzQ6mZVp/XZwa+XT44GJiAnPCgoKPxF+LWsqnuQ\n+1TdCuxoeKwzcpbVNed4Xgrnl/sTWLF2Ng9eGXr4iCrkjuPpT76kuElPPlo9Vxpyl0/lW3kZoZg6\n5jfzc9MKDw9WDsMb3pUpOgM/dXgQy66VlKrieT4rPeQWIlXGucs2+/HvALZe6JNTUFA4u/yaxVEI\ndAeeRq7+zm34viv/P61W4U+KDVs4MKER70UvHdFWLDAlCwZvkPa1W7CvuprNkRt8oy6D6O9HhkJ1\n5UK6oxOfxKkJS5xHSk0R7du2x7urHSPrv8LXyC0tTr1a1W95af2G/C9TgakX+vwUFBTOPr9mcYAc\nWFndMBT+mkzQUb5uBf2vrLu5Rgh4Ajyy4Duq+iUz/5VN3PNPdGu3ZHJ1jUX16mWlDN7uYUHRpbj9\nzfhRLfJmoBMDfN9TIUQzadolktrtEirnfFeHvKNf/YU+OQUFhbOPsh/H3xgbNj0wqQ1P5r/S50bR\nEWbEL8RwzZZV+HeMZW/iilBUc8hcNjaoChQQE2zBJ+FmtNFvMlzr5psBV9LiYG+u9n7H+oyWga3J\nQ1TDv3DX7/Fs3gUsvdDnp6CgcG5QhOPvzX0Q2llB8PpjE1Uq0ZXHyFXfUXI57NosMmyMU/XmQR1t\n93URv+gapPVhDweOdMZRPIsHwyJZke3ALxbiQccL0zNV5poT5H39TQC5jYyCgsJfFEU4/qbYsMUA\nj7bi6S+ebnVfrDcxRJW6GfctXoHPM4IFzEfdWMOgA5ei1pcQjEjhEzERVeQsblF7eSX1BumKooH0\nda5gT9N46UjCANUt72jrt4Q2fQvsv9Dnp6CgcO5QhOPvy1PAF5FsvGbxPa3VTZ0bSSvJp7RtGZ7v\nrSQNPshSpx/r5qulQ0YvycVeck40x1P0BiPjMtkQ3Isj1IZe0iYeeNIqJJTtlfas/Q4J6Zz0xlFQ\nULh4UITjb4gNWzP+r737Do+iWh84/p2t6Y2EJBC69A7Si0ZBQBBFEBBRUFBUFLmAAhaIDQVEaRYU\nRKWKoDTpsBSlSkd6JyGk97L1/P6Y3V+WXPDqvYTdwPk8zzzbZmffM7tz3jln5szCk5X5dubn1Z/p\nYa1m4c+CKF5etZG8eo1ZnrKGeq187D6XqxCWFqqs7xjEGnsk+E9kmGJjXszDtoeP1VBiLFtJ8gvi\nfHRrnvre37yNbV8Clz1dPkmSSpZMHHenicDkyszr89HgPrpujh8xh9Sl7sUdlFnXh+MVN3Ig2Kbt\nf+AJhyCBBtorHDneHFviXO6PbsIS7SVtsHUAzzGHfw1/yOFbcJVTm7bZHDg+9nTBJEkqeTJx3GVM\nmNoDjasy6+uNFVq9nlVfYfdlf1ocO0hWKytHjptp3TXXcSBZR539bTWLOgZxNt0Hjc+PjCCf9W3q\nmzv+oVXStWfwc+SzqXkjTYdd18RWtn4MpHu6fJIklTyZOO4+7wLjKrK479BeY/wesK7nanADnl+z\nAT9zdxbZ5pN1j6/y7MmuGIzxRLb/nZUbnsaeOZMmIffxTYHZ0Nj6PH0cC1jcvoEo8K1M5BfZBTnk\nTPV0wSRJuj1k4riLmDC1BiqVZ9nik36V3z7fIUCTczQRTWB5QpV9ZJnaEP3gSdbk5ittt/USh6uZ\nUa4VUMBFenKcfT0irE32FCp7CaGjfRPvD26r1L60n90526YCeZ4unyRJt4dMHHeXscCk6szs9mK3\ndyPr24/yu09VHvttG9Ym0Sy6uozo5r72zolNCTbblORXf+OnRSPQFH5GT204czRGbde0x2js2Eh8\naBDxUa14YB6Woxyd4OmCSZJ0+8jEcZcwYWoINDWS/F2KJuTtPT1jdGWOHBe+Ve/hkZ2bMBx/gnMx\n21jvU6B9cOOzIjU8hQrm85y+WpEqjuUkP1hXhO+yavZam/Aa03jx1UdFSO4FknecmYtsbUjSXUUm\njrvHWODTVvS5d2Srt+uVCbrGjuQAJdiixb/8cUzH8qnZVmMrnx1JtUuVlOVjM1k5fwT4fM0ou4U5\nDYIc/U82JVO5RpiSxvamjZROO5Mce9k70tMFkyTp9pKJ4y7gHLfxIPBVrsZv9KqnGuobXNkjRLNg\nXvv5F/ThbVmY+h1Hqzl0XbcMEkmRGdRS/mDDrkcw5M8ipnJFru0P1l6yd+JN7ft83PUhh91Ylgpf\n2Vde5KJsbUjSXUYmjrvDaGDm/cRGT415oZO1upmdvxsVTZkm1IvfxOmTrajSPM9R6NDR5mAr5aPh\nUaQdrITNuJNnuMqi1uUdL26rwGb8uN++nS/6ttQ0P71PGHINL3q6YJIk3X4ycdzhTJiqA48CM8wa\n3YgvB3fSt8zcJbKaW3h8+2Zor+ObXUvJbqpoOu16SqSGW6kqDrBi1fOIghkMUWBzbgON1f4o/bVz\n2Fa7DsnhDakxO+3XCUxI8nT5JEm6/WTiuPNNBibfT6yywu/hQRmtBKdXOhRjxXb03byS9KTe5IWd\nFQn+Wvps66589nIwtU+fJiE5jHraHWxvXJEhayP4kXBednzF+Oe7UPHa76w+srK3pwsmSZJnyMRx\nBzNhigUaANMsOt1Lb/V7UdfAdoQrVXOpnhAP9S/z87YEAu9TlG6/P0VqWQUluYD9Jx7Ayiw+tBSw\nLOpB7LZu1NTvxx6osKtua4J+27E6jbQCT5dPkiTPkInjDmXCpAU+A0bfT6w4oGk4JqWbXSlYWyB8\n6lRnxLKfsYd2YtPVVVyONtJ/2xNMGh5E7Svb2Lb3Yfwd8wmKCODBTTEsEpF8Yh8j3nvmCfyzj3Ds\n612ytSFJdzGZOO5cA4EcYKlZp3tqeOybfmG+yRwxZypGYxQRym627q5OTFt4fE8/kqM1ZKXlYkuo\njs5nA8NsySxo3BV7YSeitKcpr7mmfNvlfiJObTkMyNaGJN3FZOK4A5kwBQLvAyPuJ5azxiofXuhr\nUEL3XBTaNv68tGIlomkV5u2bI1KrBtBvR28mjtARc+6yWH+hHeb8WQzU2IneWZ/FIpKveFV81e1x\nrNYU8jZtbe/p8kmS5FkycdyZRgKbYondZ9bper5XaUSkuYKdw5dTFH2Z5sQeXs2h890JrJKjPLfv\nJRLLaTmnLaTRHp2Sly/opNvH8uadhTn3ASI0F6iiuah8MPAByl3ekJRsItvThZMkybNk4rjDmDAF\nAEOB9wVor/mEf771qWpKlTN/YG8TzMA1KzE/aOfHtRsd1lrhdDrYiQkj83gsaTdbCppgtnzOxMI8\nNIfbKEtEFF9qhorvY7uTqbURlrmhuafLJ0mS58nEced5HjDFEnsmOSRk4AdRoyJymts4cSwRQ0Qb\n+m75hfOF/UjgkGbkkdFcK1fAxVo6ai2swKWsctQIXcD5ms3E1dz7iVAuUs14Tokb3IlK11YlH37H\nIv/dT5KkEk8cnYGTwBnU0cvFPQocBg4C+4EHSjieO5oJkwEYAUwUoDfbjVOW9Gup1E3cirlZKP03\nrCb7kQJWLc4SYXUq0PhCY956F4atWMmvoTFYHBOZkppL7qVOyjJRjpna1xzL7u1Ash800W6M9XT5\nJEnyDiWZOLTATNTkUQd4EqhdbJ5NQEOgMepZQF+XYDx3g37AqVhi95+KiRk+puabQfY2WZzYm4ih\nbHsGrFvGUccz7Mxcogy/PJL4GAtKuRQqRV/k4LHGRLKSSmXK80dhR8pqL1I58LgybshjVEtambHs\nucLjni6cJEneoSQTR3PgLHARsAKLUVsY7twvkBcApJZgPHc0EyYN8AbwsQCjf5b53V+eaqq0SVxB\nXtNwem9ZT2bXHA7MznfUiKlD3cTavDPRwqeZbzFt/uvY7JOZLVK4mvkIyxyVmMpI26babZX4IB33\n6dc95unySZLkPUoycZQHrrg9jnc+V9xjwAlgLTCsBOO503VDHV+xeXft2u8PbfOe0adGqvhtdy6G\nyAd4fvUS9vg+w4b8xZpXs4ZxtpqgUeAOrh5pwemLtYjRbqahMYA11kcpq71ATNBh7Vsv9qVG8i/5\ns5+xbPd04SRJ8h66Ely2+JvzLXdO7YB5QM2bzBfndn+rc5IAEyYFGANMvJ9Yw6XEyGFr36in6XTx\nG1Y3L0vfbRvJ6JLFsS8domlUS6VyfDT9voDpSUsZ9eMccEzme8NV4vP6sFRUZyG9bNvrttNdCtMz\nLGPDcyc9XD5Jkv62+51TiSrJxJEAVHB7XAG11XEzO5zxlAHSbvB63C2L7M7TFQgBlh2qVu3NN5qN\nMUQEJdg377JoDY89yMsfD2LFU0+zPetHZZb5a47UMxDpf5SkC9WIT6hOjOYgjW0OPhYdidJdoEzA\ncV3foV9xz7XvC7tXK1zymadLJ0nS37WV63eqx5fEh5RkV9UfQHWgMmAA+gAri81TDVCc95s4b2+U\nNKSbMGHSo14B9/X7idUEpeS/YepYR2l0cZ22oEUUPbdu5FrXTI7NNIiuZbsSUggfvq1h0LFvmTD9\nI3C8xw++Fzln7s+3tGKG4zXb9hYPcM03h3sV08DY2L/dcpQk6S5RkonDBrwCrAeOAz+iHssY4pwA\negJHUU/HnQb0LcF47lQvoLbu1lyMjHxheN0P/P2i0sSmk/n4RD/I4FWL2RH5JMcyVih90vqxs2UZ\nDIZLZOTUISnVSBVSaJGbwQylK+21G80iJEH3zgtPU+7qvNwFgxw/erpwkiRJ/y2513sDJkwhJkxJ\nJkwNBWiPBlZPN779h2g043WhmT1c9HvjNfH9CL24L3qYGB0+Siz3nyMCVmwWw4c3FEHBpwXG3mJ3\ncBWxnpEiUJMsTukiCz/p2Uf4LZsomk+nj6fLJ0nS/6xE6k45crx0exNYHUvs4YyAgL5vBcWFiPtS\nOX7yJMboDgz8dSG7K/YlPdlEy9zW/Ni7An6ZZ8hNjyUn7yJNrAYaZSXxhv5JxopJeefCfYwTBvQh\n4MoPmXvTWeLpwkmS5J1k4iilTJiqAIOAdwRojlPr000P1ldi4jegqVeHHtu2cK5zBsc+ixCD/QaR\nFp7ELz11tFj2KfOXvIHw/ZrFvibmMo4sh6+I9fncsOPhTtgLD6Iv/PNV4mQrT5KkG5OJoxRynn47\nFZgaS+zVQr3+yfHivQhD3zPiytmdaCu0p/+aeRy+pzdhly8oAQZ/ZgyvTvSpndjNAzFbN9Mzz4+y\neRbe0T7Nl/ah1sLIWvqZvbrhuDg7PaGQhZ4uoyRJ3ksmjtKpJ+oZa5MEGHbo2ny+v1UlRWRsV3wj\nu9Nn8zpOdc7k0uSqjgHapzldz0xCjELnI/NYv/kFROAWphvXMIGpNBL7rQ7fi/rJQ57CkPqTw1qY\nOJI4HJ4uoCRJ3ksmjlLGhCkUmA4MjiXWfC46etRY3UfBEUNNjtwLm7BXbUSPTQs4Xasn7c4HaH6r\nuYM5Q+tQ6+SPHD33CnYWMiY7D0dBCLOUzoxzvCsKG9ZQ9taPxnzlx4uFDn7wdBklSfJuMnGUPpOB\nn2OJ3Skg4Ehmk7jEZhpxKeewJsTvJZ5ev5JzPXJhXGt7lE9Zkhu3wjfXTndlN7/vfBJ80hilXcs7\nTOdx8ZNN61veMHzEIAovzbDlWy2DZWtDkqT/RCaOUsSE6QGgE+rZVJjqN/zqDe0EnTJqr2K/shVz\n9Sp0/G0x5wIH0uFqjPZI5SP80juczomTxdfzxiAMX/NVwUESrHVZrrTgCc4p65+IwKg7iyZj905H\nHCbPllCSpNJAJo5SwoTJCMwChsYSmz2/Q4dKV8/WetL+9lkyLywiUjOewat/5mrfAqqNbyd2hGzl\n4oNdCcjJoKYuTTl7LhZfRwV6KVt4iykMEFsd/tEntJ/1fYwrZ2Zasm286OkySpJUOsjEUXoMBs7G\nErsSwKfA/Muo6m9z7Z4jijUF8uoE0+LQMhLiR1Iu36iEBdZhdXctLx0fx4eTPgD/2XxrXMBBW1f2\nUpMenFE+fbkFFbN+ETpL4iLiOOHh8kmSVEqU5EUOpVvEhMkPtXuqO0CPd999Ovnjy420i3cr1sNf\nEB79A2/M/ZzMJy00HNmMX4N+4dDwZyi7+wRpmRXJSL+H8sJBVyZxH/t4lovidJt1ym+NXyHn4Idm\ni/2G/84oSZJ0Q7LFUTq8DOyOJXY/JlPEw6Z9X735yrOkJK4gsPB1IsRlQjM3kbnlQ5HZGy5VAAAg\nAElEQVRiv0LV8Ic4VsvBwPjP+PLrT1H8V/J58FTmMpJCIqhh6O+Y8sJAyqR8KbQUTiSOJE8XUJKk\n0kMmDi9nwhQIvA6Mx2RSWv755+IdiR2M9pY7FEviSWwt6vPpjI8QA0Oov6qWsjF4A7PfKkv5lfPY\ndPgxHMpFmuXVplXWCd7nX7zOV/nXutfTWgMLuHxlS1aBnYmeLqMkSaWLTBze7zVgUyyxx4DnB8/e\n1Gzdh+W0+afmUjngY55e/wtHOqSQ9eFMsVmsp1Kdp0gOyqJ71ln27h2NVvjzheED3mIKbUkgpcx0\n38kDnsacMNXuQLxGHAWeLqAkSaWLTBxezDnYbzgQh8lUvvWxY5M+ajMoINNyCIOlGeYK8NBv8zEa\n+ohqZwzKkeBDfPd6GJWmfcX89UMwhq5ksHE3mB0s5REe5knzqWf7K5UK9pCTeeayxcF8T5dRkqTS\nRyYO7zYCWB5L7BnF4RjffvE5xdolWbGcXY6xQT8+nzqBpN5lqDzlSWW8ZgxBnV5EJJwlCgPZ2Q+i\nz3iA8blzeZlvGcb3wt7Gz7Astj2Xzn1uz7U7BsjBfpIk/TeU/zyLVxCUnlhvCeexjfNAy1gT+lHf\nLdw/9+GH/TLzthBhaUznU4docnk2FTYtZWnuHNLL+rNpztO0/Hg4v//2IwEhp/gmfxnHChuxgj4M\n9G8sps3+XKlsn+U4cnHb9oy3RaynyyhJUokrkbpTno7rvV4ANscSe+7lX4btXl6uq69el4Vfei7+\n4QYe3fw1Vu3HnMo5w46APUS98h3BW1dx4uRT+AReok6GH/6ikC8Zwlc0ETsG9lXCdPHsPr3NYhbi\nOU8XTpKk0kt2VXkh5yjxEcDEiX37Diw4G9Fc2zJJSb1yGKV6L2Z8+jZptbuRfao672rGEPrqFA5W\nd1DzwD7SU5/FltWUieJ7nuUbZjLEEd8+SnzXpTPmhCmFNsQE4rjg6TJKklR6ycThnfoDR1vqH0op\ndzD3m7UvVVfO27REhN7L2AVz2NdGR8Svg3lTGQcjR5MeEkbrxc+wa8vXGHz+4COm8DojeJaF4kzw\nFr4ZOEjzQN4scTrrWqJdMMnThZMkqXSTXVVexoRJC7wBDDlWptqvL4weqfNXLhB8NYHaBb7k2TfS\ndsEC3vc5SH7NCB5IqkH8+smcSByG3vcyNfJCuEwNAimkLiPE5hdHaPyD01j359pMm2AIcZg9XUZJ\nkko32eLwPo8BGY39HlGe6fVRg1pl95F5fhu6yHr0W/YRjc+OZyMKv2um0yvgYS7s/InAYDspKQNx\n5N3LeJaygB68R1/b5d6PaTa3aox/wqdJBQ7HRuLY6OnCSZJU+snE4UWcfwk7Buwfz23UfYWuyxWO\nXTuKT+VezJwynvTqbUhJqst06wgadhnFzvPbadH2CL9vn4/CaSYxkck8yRgmi9+aV9R+8szT9DF/\nULg1PVGHesxEkiTpfyYTh3d5HDDo6gyv8e7L/QIvZZ7CEBjL6EUL2Fw/lbqrX2R65i8oXZqiOX+I\ngU+a+O67nxC+G2jmsKPBBzO+hEXOY/qoEcqzli/EjJN/Zgh4kTgSPF04SZLuDPIYh5dwnkk1SUv2\ni5906L++smMNhx1aHohPw568lM4nvuN7aw7ngzdwT504hhje4JX3pyH8LxKaE8t0xtKVaSzU3ife\nH/uG0sy+l6Wntp+1CrGWOJZ6unySJN05ZIvDe7wK/Lm357x3drYLUw5lHSM0uAOvzpuAf0B/Dhgj\nWG5+Gc2bb/FY+nu8NW0ANm0djDmtWcp4ptGNXiwSs0c/qhSU0WBNWHg13p6fgXqBREmSpFtGJg4v\nYMIUAYwpjNn4+ZRHerezXfgEv0rDmT71E36JDaDSnt58Ez+K6A69aZW3n3U7G5GW+RpGLHzAdK6i\nYxtt0D1ziP31a/OA9dOCX3Ov6oHexGHxdPkkSbqzyMThHeKAhXMHaH+yXZtFTsUBdNuznz3he+i5\nYDrfKdsgWGAZEkv4gYMcOPwVmuiFdLacpR0nGcbHvNrmdbHksYeV12zviymXL2cBg4jjkofLJUmS\n5DHC0wGUFBOmOiZMKZ89MmC6ce6bQj/jAVF22TLxQ5NA8U3zOPGvRr8Io95XhMxaJF58pbFQlHhB\nvedFeeVPcZjnRE1OinfKDXWUWb5cTJ/aSpQZF3yMOKZ4ulySJHmFEqk7b0eLozNwEjgDN/yL0qeA\nw8AR4HegwW2IyZt8IvSpn09+tPGrIuFb9FVHMHPKBM7f04L8wqZ89edz1Og+mrZZq/nmy7kotaZh\nPD2W78UiJtKRaoY94scJzZRBh35iSUbGqTRNVj4w1tOFkiTpzlXSZ1VpgZlAByAB2AesBE64zXMe\naA9koSaZr4GWJRyXVzBh6gLinrnDdtZLTfwFpd5kBqxdw8HIq7TY+TajHfOpWrYZmU9XIvWlQDTB\np7Hn1GS8ZSknyWW30pCao5coVdMM2AIPZ/2WfTIMeFge15AkqSSVdIujOXAWuAhYgcXAo8Xm2YWa\nNAD2ADElHJNXMGHSA1OuNti6fWGV0xWMMT2omZJPwz3f0WrHVD6LMJOUZuLilFeo/fmPJCX1xVpn\nPi2vRtCUP3iHt+n5yGRO1qpOn8DPHVOzj2ejMJQ4znu6bJIkSf+LXsA3bo/7AzP+Yv5RqC2O4u64\nYxwmTK9s0q3bHjnzVbvvlDYicPVqsapepJhV6wPRu8cK4aOUF9Vf+1w89K/HhaKcFZp2vUWA5ozY\nRT9RliTxSdUnRdjy5WL2pEaOsHFBx4ljmqfLJEmS1ymRurOku6r+SdCxwHNAmxKKxWuYMIUB474c\nvsOcmrVCo2/8BV9/9AFbYxpQxq8s21b+Svn2nbDWLmT3Gx0wxPyG7dRL/OyYwsuMpk/kl3wy6XFe\n3LNEfOufciVdk52MmnQlSZJKXEknjgSggtvjCkD8DeZrgNoy6Qxk3GRZcW73tzqn0mrcnvaJl1ZU\n3H+vf7m3Gbh6NabQ87RPe5dxe6MoLLsD6/OTqfTBbK4UTMNe4ws++uNP5nI/oeHxLJtem767N4rc\nqhsLdyanKKjjNayeLpQkSR53v3Mq1XTAOaAyYAAOAbWLzVMR9TjIXx0Qv2O6qkyYmvwcsjk16IcP\nHIZ5b4r7Pp0iJrQziO/qfSoq1d0v9IZyImzmQtGg6uNCo7kmDA/1Ee2ZJ6YwWlQLOyNiFi0Ww4a/\nJGbMC7Jq4pQM4rjX02WSJMlrldq6swtwCjU5uE4THeKcAGYDacBB57T3BssotYV3Z8LkswXTny2m\nfJuu/3qwqDT/B7G0XqB4q8VrosnD24S/rpXwG/2eqNx8sFCUS6LmI0+JwMCtYg1Pi9CAZFH5+8Xi\n1deGinkLguz6OM014njG02WSJMmrlUjdecv/xLyElMgfrt9uJkyfLO6f2Wv+/TsraiOaK7+88Rrf\nV2mCNboXvy/4g6SHEqkZ2o4/53egWfvv2H+5JRsu/MRTmkmET9pH0+Q9dKk1Rwy6knslR1jmEMd7\nni6TJElerUTqTnnJkdvEhOm+M9Vtz8575FJFW7nWyk/jxjOtfhhRFWM5slDhWvQGGtUewMn5zYip\nvBml8lW+u/AjE3iWskOPEsEluocvEK/FF17IEZaNwPueLpMkSXcnmThuAxOmoAIf8cObb/8ZZA2s\nqMybMIkva6bwgL4Xi5Z24JTyOhWejeP4Z6DxSeHV8e/R8gdf/qQxZ7r7kH9vFsPOzRLjrIVXEh35\nZ4CXiLszuu8kSSp9ZOK4PSa//c6xMlnhMbpZn81kadQhOuc8y9sHnyE1ZyD+w4eh+aaQfBHNtK+e\nYt+UHtQXRr5o+ig5A3OY8eNkvqmSm37Ump4BPCHPoJIkSfrPSu3etQlTbP9+3+XpVy8Wnz3WXTzV\n00fMvH+EiOq+S0TqnhaPjW8jBj7zjFCUi6L7yIri/tbDxVqeFP4NL4iAVWvEmrZtxajPyhUynkTi\nqOjp8kiSVKqU2rrzViiVhTdh8nu5x6p0w6oFIq5fPzGgh0FMbvGGaPn8UlFF94W4p+69Im58B6GQ\nLIhtKozR34iddBHBDc8J/5Vrxa8t2ojvpoQ4lPGaDOJo5enySJJU6pTKuvNWKZWFf3rQmm3G1UvE\n2AH9xfOPGsX4ZqPEUwMWihjN7yJCV0EMeHeo0GqSBLW7Ck2tqWI3rUSF+oeE74r1Yk2z1mLNJ4FC\nP86QTBz9PV0WSZJKpVJZd94qpa7wzSav+cTn1xVizLP9xfOP+okRbYaKDx9cLMobD4oAIkWbQV8L\nvf6y0Fd4WtBqjNhAfVG3yTZhXL5RrG7eVpg+9Bd+bwVfJo4PPV0WSZJKrVJ5raq7kt/qjWOOaSwj\nhy1dSnbSahyWHtybfh8jE+qRbelI73smsHD+fQRFfEZaPZi19gxvxX7MuVdt/Br3Dv4PH6ZTbkB8\nvjFlLfC2p8sjSZLkTp5VdYspmzdPt+scH8V9+x22i1tIjKlBh8QuvJXagNycbrzk8yYLrrYmOHgx\nBbH7eX9tHt/37M3Rl41sfvtVQjseoJs9/Gq2MeU34GV52q0kSdJ/x/srT5NJY1y3bm3oipVifYM2\nYnCX6uL+98PF4rrzRLXwMyJQqS36BX0lFEOiCAn9UHR/P0qMNdQSLV5eIPx+2CSOVKkkDozRirIj\na8QznpXEofd0kSRJKvW8v+4sQd5deJOpSvCqVVfu/XKW2Fu2s2j8cpRo/amP+LH2D6JG2DkRqDQS\nbUPnC0WTLqLKPyNmLtaLQRUqi5hpK0XZjzeIC1ERYv+bGlFmRLWLjGcDcfh4ukiSJN0RvLvuLGHe\nWXiTSWtcv36035q1tveeGiA2h7wgosb5is5TI8XS6OWiTug54U8zUStkg9Bo4kWdBi3FB9/7iMda\nNxYBS9aL5k8vFRmBPmLfmxoRMqLCBeJYIZOGJEm3kHfWnbeJdxXeZFIwmR4IXL36ZPPPv3T8Vqmb\nmNlgqCPkPY3oNLG1+Dlkk6jre0H48YAoH3hA6DT7RexDMWLoF/6i+2PdhN+yDeLpltNETpBR7HpL\nJ4JGRF4kjsWye0qSpFtMXh3X00FgMilAJ6PF8l6ZrNz6cd/O92m0o4pjxpC1mlWhp2lmH8KQT3vw\nYW4ZTjmG4xfyNdl5R+jVbwA599ooOPcCfzTuxvD31zEiZxabntDQ10+XaPHLXgu8QBx2TxdRkqQ7\nSonUnZ6vjP8ezyUONVk0AR7XOBxPRKVlRk34+tuA5r8HcuJeg/WD7gsNyeZw6qdNZtCcMkzKTsYc\nuZjL5q8oyJ9H7cc/4UQtMxHRcSSG+jBn9GK66g8wqVUFplS4nOPQWV8FfpBnT0mSVAJk4ritn6gm\njCeAjzV24eix5YjtteULqpe/EKwcaF4n4WiP78vPyMpX9MEP0X/NIBr+YWOCbS3h4fdwIHEwVusI\n7E/sRtOxCz4RHYnaJfjmi/FYo48yqHMAWRjO5QSndiSOC7e1XJIk3U1KpO6UAwBvxGSqpjj4wj/f\nXnPahPXpT+6a2yghuIb4+cEupwOfXFT1W+P2mHPpIWgqvMO4D6uQ4bjKqPwfCIuYwuEkA3Zjd2wT\nH0Jf5VGiT5moNM3BlyfH4B+TxJmzTTIqnIz8IL7Fr9Nk15QkSaWRbHE4VZ9l0kUlOfoVBuQMOXWP\nT4uRC9Ypry+drZyLqGv+fkCLtArRW0N2Gk77/3pNoAT0o0n6owydCcujdnPMEsKZ5OcRymxs923C\nMeARui35jdPh6xg1vzN9bKvI8vPldFbVdQr+3WLZJhOGJEm3g+yqupUL/KyhSXuhCl0zIrIGppTL\nbb+vUUiZCslJdP99t63VwfjkFQ829s++Lz2zde7ashtT03w3p1kJzWxHQfO+DP0ykFopyUz1T+f4\n+edwsB+b74eYBzSkXdKftF/cgkblv6NDYh6nytWk+vnzIkcJfqWySPziVpZBkiTpP5CJ4+/ObMKk\nAcoAkUDZrCDKnaxFrUtVChukRufWuhotos5W9QtEsdL22FFHnXOJicF5eacWP9Qy1FLVUv3xpHVW\n2Ba64bwvRyz5VMxvQeM2QTzit45grZ1tpr7MmfsO2dmZaAI/wPKIQsWAg7wxtxxaW3l6OFayqGUQ\ndQ6NpFb+pynlLRltFThdUitHkiTpJu7uxNFy5sxf/PPIrXzOL8o3Vx9osOgDDIXaQI1DMTi0doTG\nLuw6ockOVAIzg7W+KeFaR0qEIpIiDNrMQB9NdFq6aHD+HNUvJ+ZVSUxNCsnJPfVr20aZ21vXblRb\nd7xa24zfLUbr3sB917Rsz7AQbA7HR9ue59vvp7I2mY3nKrDlt1Gc2tYVcpJRbO/g3yGNqmEmhq6p\nQkhKY+7TbmR1vQIO6nvTZ19vovxmrqqev6mXAhZPr0BJku5Kd3fieO35sTarTq9LC9ULuw6HQ8Eu\nNDgUIRyA0DiEQ2+320NzsgsisjJyy2Zk5PqYzfkpISGOA9WrR+2vWatielk/czkuKMbULfqG+izR\nVHNRfz4vRbc7wSgOFxQqUQVlEcZ7uVK7O7X88xlhm8qsje+y+1AHOBwCfsfQZk/Ct8lBNK1P0+2c\nkb6HfWmXlMOaqn7MbOzLk1s60jUzs9DqqDq2lnXGVE+vOEmS7mp3d+J4r+vLHIpd4cj2zbdetAaQ\natfo8qwFWj//crZyFbvnG4xhdi0OTbCS6ROkpBrSUw8q5KWKyhoH1fVmJcyYwa9pBWxLFeiFVhQ4\nHIrOZqBqVn3sQY05X78j5pAIOO9LtcMZGHcFcuZUE+zhv0HBfLT56whv4UNH8zUGHKxDTUsiqT4G\nfg5thaJvQrztDz7I+hnfyvZVwX/SR4ECT680SZLuend34oj++V2H0Z6N0Z6tBGnMjhCtRZTRWzmf\nl689klmgtAxFNAuxszvNxh/pVgzChs5mEXmWXMVsy1KEPRedNgKj/h4cgZUpCK8JoU0gJxjNnmBC\nthuwnSiDw2InvMwq0tM2kO1YSXBNeFRno9f58lTPMBBJCquVh5guhhFLPB1w0EI/XFAzObEgmmHR\nG1nm6ZUlSZLkdHcnDs04g3pPcV18RcF1TygOwA4oKLpQjCIGo6iAoiuH8KmIOaAy5rBohEZPzOEc\nos/kYj4fSuKlSuRklKN8uT/QCxPx11ZTNvIE7coXEpoNXTL0tInXcolK/M69mGjHOiozlLV0oCXB\nmouEt594oaB+xis1ZrDGI2tGkiTp5u7uxFF7/LeYbT5Y7UbsBgWbAYRRwWEE9IBeQcnLx5AShjZZ\njyZVj0gz4sj0wZppJD8zEHNBEGXD/yTM9wzGvHNcNq8jNXsvtfx96Ki30ifbQCOrlUQfHzZV0XLo\n3EvstzTjANG0Yw5DFAflRUd8fC8LY4dFF/KeODiw5TPs8PTKkSRJuom7O3GAQFFsKIpQHypq8Fqd\nBaMhD4MhH6MxFx+fJPTaRBz2ZArzk8nOvkpeYTyRvmlE2uMJs9oJE1aqCyv3CTthIQrpjqr8nv8o\nh8rCoXsucPne3wid8z4dsk7QmyAiaIRBycGnwn6rtev2i3m9Dz0fG8s2T6+UO0AU6tUL4j0diHQd\nPyD/Bs9XBfqh7qrlOecpRG3uO1DPHjzunKx/sXwFqALUA+o67/8EbMLbroRd+t3diSMOLVnYyUQh\nB4UsFHIAMwp2wI5AASoiqIKgHD6Uw5/yBBFCCNkEcVpbmePU4qS9IVf0waQYs7HG7CKn5wQUoRB4\nLZQ6VyN49kBXaia3I0Q5LIyV95otXffmZj6cOMfhy4LYWI7ewnIZgYpAGOrGaHDeXld21I3StXHq\n3Oa7BhzkrzfS4px9fh49RTgUGA0873z8I/ABcNVjEUlGoDvwHPAgcAFY7Zz0wKtAK2ABkAH4Oycj\n6l9QawEf1GRQCTgK7AP2OqezQAugF9DTOf9R4BiQCAwGMoE4YCPq714L+AJm/v037nrNvQ7TOWNw\nTX9Vv9mcy7Q44w8EgoAA52uFzskCzipGnVxlLT4V/xtuH9SxZGVQf+921BNmCoEzwM6/iO1WKrWJ\nozMwFXXlzgYmFnu9FjAXaAy8BUy5wTJEJOewEIgdIxry8SEPX3LRYQXFdawDsvVmcvXZoE/CoU3H\noclGKHloyUVvTcVemIa+IJlAkU8FfAkjALufHk1BIEJkYNWmCSUkw6GUyzSnah1nE5M4n5iIFrWC\nr4j6A7gAXETdU3ZtML6oPx5XJS9Qf4ihQAjqxmehaAOoAEQ4l5HmfM2C+qN13+tSuP7HaXWbKgLV\nUDfQ/c4YyjonqzPGC0AK6gbdwnkrgAPAb6gbtdntM3wo2oj8nWVxxW2maIMyu8WmcS7TtSHane8N\nci7LSNGGFwEMAX4B3nMuZzRqhbUAuOQ2r83ts63OdevivgEbUFsv5Z2TEbVyywSynJ/hWo7ru3Fx\nL5Oeoo092PmePOdkcXufe7m1/Pt25J7oFef34uOMy31ejdsyNMViUVD3/P2dt8JtmcXXi8HtMwzF\n1o3R7bXi16ZzxWlH3Q6PAN8Cy4GaQDfgEefyvgQWcuOWSHGBqNtzM6C5cyqPOgh2qXP6k+u/By3q\nRUXHAeXcylJI0e8nj6LflhG1Inb/TbhXzuZir7lTnOtC75wEkANkA7kUbQeu78x9+3Nw/XpzTY5i\n5bGgbtdpqL9FxW2ZfwAzb7bybrFSmTi0wCmgA5CAWsE9CZxwmycCdQ/lMdQVfMPEsUy/BJ3DgN5h\nQCd06NFjx06hUkAeeeSSRz552IQNh/O7FVixY8aOBRs21N+hARDosGDEgkZvduj9cu3a8hmKJUSk\nFRb6nMuLD72QlxyQKxBGgdDq0Sf7438lksjLFiyOS1wqn0pquVxyw/PJz88jLzuLrMwEEiqGEnpe\nIDQCoejR5/jgkxVAQLYffg49eh8dOl8tWp0e/dna1D4RR1ze/7iOQ1D3BBuh/viTUROFHrULoDLq\nCPrjqEliP3AfaoXTFmiKuhG5NgAz6gaUjVpJaCjawIxcv0fnvhG5z6dD3chdy7FQtOFZUHcgThUr\nRznUFkiQ27yVnGVxtbDcf6+uitTuLMs11N9YgrMMwRQlbdf7Dc5luyjO51zlsVO0sWc6X3PtWRu4\nnuuzK8B1VzhWuD4hCG5embknAwfXJwAoSlr5bsvVUrSeXevawr/vIbu+F9fztVG//5vFeRk1aZeU\nANRK+T+JRW2F5KGuN1fF5/outMVe85T7ga0e/Py/q1QmjlbAeNRWB8AY5+3HN5h3POoP64aJw8Rm\nFKwo2BHoEBgACxosKNhQsKNgR4MFDVaExo5Da8NmtAqr0S4cPnb0frl2XUgWSmChzq435DosgSnk\nBKWRHpZOQvkkzD5QVEG4txx8KapAtG6vKc7nAoGg2cwOGczgm+2RuSo4i/P9gagVm0DdsF0bgXCu\nh2zURODqQy7eVWVwPi50mwrc7tu5STP6Iz5qMpax2ymqaFx77fpi87oqvXyKNlZXGVx776514d70\nt1G0R+dan8Xf55oEN2kBPMqj/1rBiklurzlu8j6FoorU4Hw9GyiIJVYAmDDpUL8rfbHPtwLWWGKF\nCZP79xnIX28fDsDem97DlrDkU25eibmvW1eCdq0HW7F16L6M4q2R4v7R+3rR619LWfqZ23zFW024\nxeXePeNKajdb1zr+vcvG/bP/Tovx/1tb3eg2fDWrPyi2TPdEqfkPy3T/fPedi+K/M/edHri+q9j9\nfe6xanD+zh/m4ZFrWPMR1+9Iib+5zMxYYhO5PUrlZdXLA1fcHsejdpf8Y2Hvd/zS6qfvjF0X5vC1\nZtkCrPl2f8woIltvJsOWR0GuGWGNIEtbhmwfHRZFUfeetc4JuGpT90gTY2P/0XGBvytuAQvi/u7M\nzorKF3VP3kWDuncWiLrXXLyp7N5V5d78dS3nRt1m1zWjs8mOQT2e4GqK2yhKCu5X7tU4lxWA2mrx\n4fpKw32D0heb3LtTKPY+9/K4V4rXtQAe4ZFg1C4s12uam7wPrk9MWuf6M5gw5TvL6GoFuV53r5R0\nJkw25zILKeqy+KuuDg2g6UCHMNQ++5txHTR2JQrXZ7oqXfcK1F3x1kjx7st/9L6HeCgItSvInXty\nwC0u13d0s8Tlvq5tXJ+I3ROawvWV581ajK7Wlk8PeoSgHk/5t+ROUXfhf1qmq+zuybH478y9Cxiu\nT0bu71OKLVMLGHrSMwB4hetbk/yNZWqBNcAIpJvqCXzj9rg/MOMm844HRt7kNWEy8anJRCuT6YZ7\nX94iztMB/E1xng7gb4r7X95swqQ3YQo2YfJxJumbzaeYMBmcrZJ/Ku6/j/C2ivN0AH9TnKcD+Jvi\nPB3A31Qq/3O8JeoKdnVVjUXN2sUPkMNfd1WdRT0ILEmSJP1954B7bvVCS7qr6g+gOuoB2qtAH9SD\n4zfyV0nslhdckiRJ8l5dUM+gOYva4gD1dMwhzvtRqMdBslDPqrqM2qcuSZIkSZIkSZJUsjoDJ1FH\nWo6+TZ/5LZAE140QD0MdzXoa2IA6NsBlrDO+k8BDbs83dS7jDDDN7Xkj6kjpM8Bu1LEK/1QFwIQ6\niOoYMMxL4/QB9gCHUMeRfOSlcbpoUUfir/LiOC+iDtQ7SNG4DG+MMwR1oN8J1O++hRfGWRN1Pbqm\nLNRtydviHIu6rR9FHYRp9MIYvYYWtXurMuppd4dQBzGVtHaoo17dE8ck4A3n/dEUjUOp44xL74zz\nLEXHavaijpgF9fQ71wkCLwOu/x7vAyz+L2KMQh3wB2q33inUdeNtcYI66hnU42m7UQcdemOcoJ4i\nuQBY6XzsjXFeQK003HljnN9TdCq1DvXUcm+M00WDeumTCl4WZ2XgPEWn7P8IDPCyGL1KK2Cd2+Mx\nFA0gLGmVuT5xnEQdxwBqpX3SeX8s17eE1qGeSRbN9aPj+wJfuc3jGsuiQx0Z/b9ajjo635vj9EO9\nckBdL40zBvUie7EUtTi8Mc4LqJdEcedtcQajVnbFeVuc7h6C/7/StTfFGYa6Y3xsxwsAAAPZSURB\nVBjqfP8qoKOnY/TmMRE3GjxY3kOxRKJ2X+G8dX1h5bj+yq6uGIs/n0BR7O7lsqE2j4vvQf4TlVFb\nSHu8NE4N6h5QEkXda94Y52fA61w/6M8b4xSoCe4Pii4S6W1xVkGtfOaiXhPtG9QR+d4Wp7u+wCLn\nfW+KMx11iMJl1DNTM1G7qDwaozcnDm+9vHLxSxd4UgCwDHgN9fIk7rwlTgdqt1oM0B51j96dN8TZ\nDfUaXwe5+Wnh3hAnQBvUHYUuwFDUrlV33hCnDmiC2v3RBHXEfvHeAm+I08WAejHHn27wmqfjrAYM\nR91BLIe6zfcvNs9tj9GbE0cCan+jSwU8978NSajNQVCbfMnO+8VjjEGNMcF5v/jzrvdUdN539f2m\n/xcx6VGTxjzUripvjdMlC/gV9QCdt8XZGvWS4hdQ9zofQF2v3hYnqP3woO7R/4LaZ+1tccY7p33O\nx0tRE8g1L4vTpQvqxT9dXTTetD7vRb0Eexpqa+Bn1G58j65Lb04c7oMHDagHbVb+1RtK0ErUA1I4\nb5e7Pd8XNb4qqPHuRf1Ss1H7DRXgaWDFDZbVC9j8X8SjAHNQz1aZ6sVxhlN0tocvat/sQS+M803U\nja2K8/O3OD/D2+L0Q70GF6hdPw+hHovztjivoXZ91HA+7oDaRbnKy+J0eZKibqriy/Z0nCdRj1G4\n/nukA+p2763r0ivcaPBgSVuE2pdoQf3xP4va37eJG5/69qYzvpNAJ7fnXae+nQWmuz1vBJZQdOpb\n5f8ixraoXUCHKDqVsLMXxlkftY/7EOoppK87n/e2ON3dR9EOirfFWQV1XR5CPQ3btU14W5wADVFb\nHIdR95KDvTROfyCVooSMF8b5BkWn436P2tvgbTFKkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJ0n8rGHjJeT+aG1+KQpIkSZL+X2WuvzqyJEmSJP2lxUA+6oj8JRQlkYGol3bYgHpdq1eA\nUaij43ehXvoa1AvTrUW9bM521D8MkiRJku5glShKFu73B6JelsEf9TpcWcALztc+Rb1KMajX+rnH\neb8Fd8C1fyTJnc7TAUiSF1Juch/U/xTJc06ZFP3p01GgAWpSac31x0UMJROmJHmGTByS9M+Y3e47\n3B47ULcnDZCB+p8ZknRH8ubLqkuSp+Rw/dVS/w5XyyQH9fhHL7fnG9yiuCTJK8jEIUn/Lg34HbX7\naRJF/65W/J/Wit93PX4KGETR5c+7l2SwkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJksT/Aa28Kxdu+4XtAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f976d0d4518>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**c)** Now we want to present an aggregated version of the previous plot. In the next plot we will plot the mean and SEM (standrad error of the mean) of the OD values across the wells at each time point. We will present the result as a line with errorbars, where the length of the errorbars is given by the SEM. Reminders:\n",
      "- SEM is calculated as the standard deviation devided by the square root of the number of samples.\n",
      "- To plot an errorbar plot you can use Matplotlib's [errobar function](http://matplotlib.org/1.2.1/examples/pylab_examples/errorbar_demo.html)\n",
      "- To calculate the mean and SEM have a look at [lecture 7](http://nbviewer.ipython.org/github/Py4Life/TAU2015/blob/master/lecture7.ipynb).\n",
      "- Use `assert` to make sure you get what you expect: becuase we want to do a mean over all the wells we expect the result of the mean to have the same length as `t`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_OD = OD.mean(axis=0)\n",
      "assert len(mean_OD) == len(t)\n",
      "std_OD = OD.std(axis=0, ddof=1)\n",
      "assert len(std_OD) == len(t)\n",
      "sem_OD = std_OD / np.sqrt(len(std_OD))\n",
      "assert (sem_OD < std_OD).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.errorbar(t, mean_OD, sem_OD)\n",
      "plt.xlabel('time')\n",
      "plt.ylabel('OD');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/Qns67leI0YayxShJkiRJkiRJkiRJkiRJkiRJ0lTNBU7O7m9O96UoJEkaM49V\nV0eWJGlSlwBPE2fkX0o7iZxALO3wXWJdq1OA04mz428ilr6GWJjuamLZnB8QFwySJA2xbWkni877\nJxDLMqxLrMP1OPCebNvniFWKIdb62TG7vz9DsPaP1GlW0QFIJdSY4D7ENUWeym6/o33RpzuB3Ymk\n8nJWrYvMySdMqRgmDqk/f+q4/1zH4+eIz9MM4LfENTOkoVTmZdWlojzBqqul9mK0Z/IEUf84uuP5\n3acpLqkUTBzS6h4FfkQMP32G9tXVxl9pbfz90cd/A5xIe/nzN+YZrCRJkiRJkiRJkiRJkiRJkiRJ\nkiRJksT/B8NJbsnvXpTtAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f976cf7ac50>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**d)** Finally, we want to check thed distributions of the maximum and minimum OD values in each well (row of data). To do this, we will calculate the maximum and minimum OD over time in each well and plot two histograms, one for the maximum OD and one for the minimum OD.\n",
      "\n",
      "- There are several ways to plot two plots in the same figure. We  recommend [`subplots`](http://matplotlib.org/examples/pylab_examples/subplots_demo.html).\n",
      "- To plot the histogram you could calculate the histogram and plot with a `bar` plot, but a better, easier way to do this is with the [`hist`](http://matplotlib.org/examples/pylab_examples/histogram_demo_extended.html) function. Make sure you use enough bins to make the plot intersting but not too much.\n",
      "- Use `assert` to make sure you aggregated on the right axis: check the length of the aggregation result against you expectation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "max_OD = OD.max(axis=1)\n",
      "min_OD = OD.min(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1, 2)\n",
      "ax[0].hist(max_OD, bins=75)\n",
      "ax[0].set_xlabel('Max OD')\n",
      "ax[0].set_ylabel('Frequency')\n",
      "ax[1].hist(min_OD, bins=75)\n",
      "ax[1].set_xlabel('Min OD')\n",
      "ax[1].set_ylabel('Frequency');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJEmSJEmSJEmSJEmSmuv/AXXNSl8icx2jAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f976cf02e10>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 2) Split-apply-combine\n",
      "\n",
      "In this question we will learn how to use the very useful [split-apply-combine](http://nbviewer.ipython.org/github/ResearchComputing/Meetup-Fall-2013/blob/master/python/lecture_12_pandas_groupby.ipynb) paradigm in _Pandas_ and how to use it to create sophisticated plots with very little coding.\n",
      "\n",
      "Start by reading this [blog post](http://bconnelly.net/2013/10/summarizing-data-in-python-with-pandas/) by Brian Connelly\n",
      "which described the data and functions we will use."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import seaborn as sns\n",
      "sns.set_style('ticks')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**0)** Start by loading the dataset from this URL: <http://bconnelly.net/wp-content/uploads/2013/10/TradeoffData.csv>.\n",
      "\n",
      "> This data set contains the fitness of a flocculated strain of Escherichia coli relative to a non-floculated strain when grown alone in either spatially-structured (dish) or spatially-unstructured (tube) environments.\n",
      "\n",
      "Use the `read_csv` function in `pandas` which can open csv files from the local filesystem using a filename or from a remote resource using a URL. After reading the file into a variable called `data` (in your research consider using a less generic name for the `DataFrame` variable), view it by calling the method `head` to see the first few rows in `data`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_csv('http://bconnelly.net/wp-content/uploads/2013/10/TradeoffData.csv')\n",
      "data.head()"
     ],
     "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>Group</th>\n",
        "      <th>Treatment</th>\n",
        "      <th>Replicate</th>\n",
        "      <th>RelativeFitness</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> BKB</td>\n",
        "      <td> Tube</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.869963</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> BKB</td>\n",
        "      <td> Tube</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1.000363</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> BKB</td>\n",
        "      <td> Tube</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0.982935</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> BAC</td>\n",
        "      <td> Tube</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.810392</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> BAC</td>\n",
        "      <td> Tube</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0.795107</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "  Group Treatment  Replicate  RelativeFitness\n",
        "0   BKB      Tube          1         0.869963\n",
        "1   BKB      Tube          2         1.000363\n",
        "2   BKB      Tube          3         0.982935\n",
        "3   BAC      Tube          1         0.810392\n",
        "4   BAC      Tube          2         0.795107"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**a)** First, you should group the data by the `Treatment` variable and call the `describe` method on the grouped data to see a textual summary of the `RelativeFitness` distribution in each `Treatment` (`Dish` and `Tube`)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytreatment = data.groupby('Treatment')\n",
      "bytreatment.describe()"
     ],
     "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></th>\n",
        "      <th>RelativeFitness</th>\n",
        "      <th>Replicate</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"8\" valign=\"top\">Dish</th>\n",
        "      <th>count</th>\n",
        "      <td> 32.000000</td>\n",
        "      <td> 32.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  1.456359</td>\n",
        "      <td>  2.031250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  0.184792</td>\n",
        "      <td>  0.822442</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>  0.955221</td>\n",
        "      <td>  1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  1.429005</td>\n",
        "      <td>  1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>  1.510884</td>\n",
        "      <td>  2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>  1.581340</td>\n",
        "      <td>  3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  1.699276</td>\n",
        "      <td>  3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"8\" valign=\"top\">Tube</th>\n",
        "      <th>count</th>\n",
        "      <td> 32.000000</td>\n",
        "      <td> 32.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  0.929589</td>\n",
        "      <td>  1.968750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  0.050153</td>\n",
        "      <td>  0.822442</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>  0.795107</td>\n",
        "      <td>  1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  0.915050</td>\n",
        "      <td>  1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>  0.939089</td>\n",
        "      <td>  2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>  0.953505</td>\n",
        "      <td>  3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  1.000363</td>\n",
        "      <td>  3.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "                 RelativeFitness  Replicate\n",
        "Treatment                                  \n",
        "Dish      count        32.000000  32.000000\n",
        "          mean          1.456359   2.031250\n",
        "          std           0.184792   0.822442\n",
        "          min           0.955221   1.000000\n",
        "          25%           1.429005   1.000000\n",
        "          50%           1.510884   2.000000\n",
        "          75%           1.581340   3.000000\n",
        "          max           1.699276   3.000000\n",
        "Tube      count        32.000000  32.000000\n",
        "          mean          0.929589   1.968750\n",
        "          std           0.050153   0.822442\n",
        "          min           0.795107   1.000000\n",
        "          25%           0.915050   1.000000\n",
        "          50%           0.939089   2.000000\n",
        "          75%           0.953505   3.000000\n",
        "          max           1.000363   3.000000"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**b)** Next, we want to plot a summary of the distribution of `RelativeFitness` in each of the `Treatment`s.\n",
      "Here we aim at getting a plot of the mean or median of the `RelativeFitness` together with some meaure of the variance in the data. This can be achieved with a _boxplot_, _violinplot_, _whiskerplot_ and a regular plot with errorbars.\n",
      "\n",
      "So - plot **either** a boxplot, violinplot or a plot with errorbars of the data. A boxplot will show the media, quartiles and outliers; the violinplot will show the entire distribution of values; the errorbar plot will show the mean and the standard deviation.\n",
      "\n",
      "Here are some references to get you started:\n",
      "\n",
      "- [Violin Plots: A Box Plot-Density Trace Synergism](http://www.sci.utah.edu/~kpotter/Library/Papers/hintze:1998:VPDT/)\n",
      "- [Violin plots](http://stanford.edu/~mwaskom/software/seaborn/examples/violinplots.html)\n",
      "- [factor plots](http://stanford.edu/~mwaskom/software/seaborn/examples/grouped_boxplot.html)\n",
      "- [Visualizing distributions of data](http://stanford.edu/~mwaskom/software/seaborn/tutorial/plotting_distributions.html)\n",
      "- [boxplot with matplotlib](http://matplotlib.org/examples/pylab_examples/boxplot_demo.html)\n",
      "- [violinplot with matplitlib](http://matplotlib.org/examples/statistics/violinplot_demo.html)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.boxplot('RelativeFitness', 'Treatment')\n",
      "plt.ylabel('Relative Fitness');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Kjjkc+GlELAWIiK4C4zEzswIU2bU1HlhS8nopMLXsmJ2BEZJ+B4wBvhMRPygw\nJjMz62dFJpKeHMeMAPYB3g5sDvxJ0s0R8ff1qfDRRx9dn9Oshs7OTpYuXTrYYZjl5vdsMWp9vhaZ\nSB4GJpa8nkhqlZRaQhpgXwGskHQjsBdQNZFIagNOqlD00KxZs7ZrKGIzM6vlIWCRpHV2DiuqNkmb\nAveRWhuPALcAMyPibyXH7EoakH8XMAr4M/DBiLhnPerbCtiqH0K3dS0Cth/sIMzq4PdscZ6KiKcG\ntEZJB0m6T9L9kr6S7fukpE+WHPNlSXdLukvSZwc0QOuTpDxdlGZDht+zZkOM/1Nas/F7duB5ZruZ\nmTXEicTMzBriRGJ9OXmwAzCrk9+zZmZmZmZmZmZmZmZmZmZmZmZmtgErbK0tG9okrQbuJK3A/AJw\nKXBmRPRImgx8NCI+V+XcacCXIuLQgYrXNm6SXgH8Nns5DlgNdJJWGZ+aPfOo99hp+P05oAp91K4N\nacsjYm8ASa8ELge2BNoiYgGwYDCDMysVEY8Dve/Xk4BnIuLbgxuV9XIiMSKiU9LRwK1AW+k3OkkH\nAGdlh/YAb8m2t5A0D2gFFkTEhwc6btuoDZN0MfDLiPgpgKRnI2KLrHxLSb8EdgJ+BxyTtbanA22k\n1cYfAI6MiOcGPvwNi2e2GwARsQgYnrVOSn2J9J9wb+BNwIps/97A54DdgB0k7T9gwZpVVrpY4xTg\nWNL7c0fgfZJagK8Cb4+IyaRW9xcHPMoNkBOJ9WU+cKakzwBbR8TqbP8tEfFIRPQAfwUmDVaAZhXc\nEhGLI2INMJf0JWgqKbH8UdLtwEeBbQcxxg2Gu7YMAEk7AKuzbq61+yPi9KyL4BBgvqR3ZUWrSk5f\njd9LNvBeIPsyLGkTYGRJWWnrZFj2ehjwm4g4fMAi3Ei4RWK9g+3/DZxdoWzHiLg7Ir5JGkPZhXX/\nk5oNlsXA5Gz73aQ7EHtNkTQpSzAfAG4Cbgb2l7QjgKSXSdp5AOPdYPlb5MZrs6x5v/b235K7YHp4\nMVl8TtJbgTXAQuAa4I28NJk4udhA6gEuAK6U9FfgWuDZkrJbSY/x3gm4ISJ+DiDpCGCupFHZsV8F\n/j6AcZuZmZmZmZmZmZmZmZmZmZmZmZmZmZkNJi8jb5aRdDNpMb+RpImXd2VFf4mI2Q1e+/PADyOi\ns7Eoh3adZmYGSNpO0ks+fCUNb+CaiyTt3lhkQ79O2zh5ZrvZS61tqUtaTFr0723AnZKOAb5OWk5/\nFOnhYJ/9FvALAAAB60lEQVSKiOckHQ58lhfXfPpyRNwg6avANsBPJK0EDgc+COwKjAEE3A58E2gH\nJgI/i4jjshheA3yXtMDgZsDciDitJL5LgHcCrwHOiIhzK9Q5MyLu7e8/lBl4rS2zvvQAYyJiakQc\nBRwPPJW9fh3wT+Ar2bHXRsQbImIfYCbpA56I+BrwCPCvEbF3RPwtO34f4EOkbjQBXwOmA3sCH+td\nE4r09MrvRsRU4PXAwZLeURLfZhHxRmAa8A1Jm1eo00nECuMWiVnfLi3ZfjcwRtL7s9ejSMvoA+wk\n6VRSS+B5YJykV0XEYxWu2UNKPM8ASLoT+Gv2yNjnJd0H7CjpUVKCaClZlXkLUmum99GzVwBExEOS\nngQmANHg72yWmxOJWd+eLXv9qYj4vwrHzQW+EBFXSRoGLAdG17hu+VL8lZbm34S0YObrS54FU25l\nhfPMBoy7tszqcxXwJUmjASSNkbRrVjaWtLQ5wGxSa6XX08BWJa9z3TGZtVhu4sXuMyRNlPTqHKeX\n12lWCH9zMaus2rL43yA98/tWSWuy49qAe4HPA7/IupeuBbpKzvsucLGk54BZrLtUf191ziI9pfLO\n7PUzwJFARx+/Q2+dy/Fgu5mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZkNpv8PzQTqoGyZ7rwA\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa02c070>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.boxplot(data.RelativeFitness, data.Treatment, widths=0.2);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x9b79d50>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.violinplot(data.RelativeFitness, data.Treatment);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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egfLNOJ0uzj777Li1KYToHNpSsnsXsAb4O3Cl1rqurY0rpZ4GzgNKtNZjmnn9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\nIYQQQgghhBBCCCGEEEJ0d/8frK+vUCCnOSYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9b01a30>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.factorplot(x='Treatment', y='RelativeFitness', hue='Treatment', data=data);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x9af66d0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**c)** We now want to check if the variance between `Group`s in the same `Treatment` is large and if the `Treatment` had the same effect on all `Group`s.\n",
      "\n",
      "Do a new grouping, this time by both `Group` and `Treatment`, and print the resut of the `describe` method."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bygroup_treatment = data.groupby(['Group', 'Treatment'])\n",
      "bygroup_treatment.describe()"
     ],
     "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></th>\n",
        "      <th></th>\n",
        "      <th>RelativeFitness</th>\n",
        "      <th>Replicate</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Group</th>\n",
        "      <th>Treatment</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"16\" valign=\"top\">BAC</th>\n",
        "      <th rowspan=\"8\" valign=\"top\">Dish</th>\n",
        "      <th>count</th>\n",
        "      <td> 2.000000</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 1.633628</td>\n",
        "      <td> 2.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 0.026313</td>\n",
        "      <td> 0.707107</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 1.615022</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 1.624325</td>\n",
        "      <td> 2.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 1.633628</td>\n",
        "      <td> 2.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 1.642931</td>\n",
        "      <td> 2.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 1.652234</td>\n",
        "      <td> 3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"8\" valign=\"top\">Tube</th>\n",
        "      <th>count</th>\n",
        "      <td> 2.000000</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 0.802749</td>\n",
        "      <td> 1.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 0.010808</td>\n",
        "      <td> 0.707107</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 0.795107</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 0.798928</td>\n",
        "      <td> 1.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 0.802749</td>\n",
        "      <td> 1.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 0.806570</td>\n",
        "      <td> 1.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 0.810392</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"14\" valign=\"top\">BKB</th>\n",
        "      <th rowspan=\"8\" valign=\"top\">Dish</th>\n",
        "      <th>count</th>\n",
        "      <td> 3.000000</td>\n",
        "      <td> 3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 1.315682</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 0.179156</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 1.152544</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 1.219815</td>\n",
        "      <td> 1.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 1.287085</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 1.397250</td>\n",
        "      <td> 2.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 1.507415</td>\n",
        "      <td> 3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">Tube</th>\n",
        "      <th>count</th>\n",
        "      <td> 3.000000</td>\n",
        "      <td> 3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 0.951087</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 0.070794</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 0.869963</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 0.926449</td>\n",
        "      <td> 1.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 0.982935</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <th>...</th>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"14\" valign=\"top\">PPP</th>\n",
        "      <th rowspan=\"6\" valign=\"top\">Dish</th>\n",
        "      <th>std</th>\n",
        "      <td> 0.036531</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 1.508969</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 1.531269</td>\n",
        "      <td> 1.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 1.553568</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 1.567477</td>\n",
        "      <td> 2.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 1.581386</td>\n",
        "      <td> 3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"8\" valign=\"top\">Tube</th>\n",
        "      <th>count</th>\n",
        "      <td> 3.000000</td>\n",
        "      <td> 3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 0.970277</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 0.020897</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 0.948767</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 0.960166</td>\n",
        "      <td> 1.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 0.971565</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 0.981033</td>\n",
        "      <td> 2.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 0.990500</td>\n",
        "      <td> 3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"16\" valign=\"top\">SWI</th>\n",
        "      <th rowspan=\"8\" valign=\"top\">Dish</th>\n",
        "      <th>count</th>\n",
        "      <td> 3.000000</td>\n",
        "      <td> 3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 1.451796</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 0.079652</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 1.362556</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 1.419848</td>\n",
        "      <td> 1.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 1.477141</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 1.496417</td>\n",
        "      <td> 2.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 1.515692</td>\n",
        "      <td> 3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"8\" valign=\"top\">Tube</th>\n",
        "      <th>count</th>\n",
        "      <td> 3.000000</td>\n",
        "      <td> 3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 0.918647</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 0.009692</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 0.909023</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 0.913768</td>\n",
        "      <td> 1.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 0.918514</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 0.923459</td>\n",
        "      <td> 2.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 0.928405</td>\n",
        "      <td> 3.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>176 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "                       RelativeFitness  Replicate\n",
        "Group Treatment                                  \n",
        "BAC   Dish      count         2.000000   2.000000\n",
        "                mean          1.633628   2.500000\n",
        "                std           0.026313   0.707107\n",
        "                min           1.615022   2.000000\n",
        "                25%           1.624325   2.250000\n",
        "                50%           1.633628   2.500000\n",
        "                75%           1.642931   2.750000\n",
        "                max           1.652234   3.000000\n",
        "      Tube      count         2.000000   2.000000\n",
        "                mean          0.802749   1.500000\n",
        "                std           0.010808   0.707107\n",
        "                min           0.795107   1.000000\n",
        "                25%           0.798928   1.250000\n",
        "                50%           0.802749   1.500000\n",
        "                75%           0.806570   1.750000\n",
        "                max           0.810392   2.000000\n",
        "BKB   Dish      count         3.000000   3.000000\n",
        "                mean          1.315682   2.000000\n",
        "                std           0.179156   1.000000\n",
        "                min           1.152544   1.000000\n",
        "                25%           1.219815   1.500000\n",
        "                50%           1.287085   2.000000\n",
        "                75%           1.397250   2.500000\n",
        "                max           1.507415   3.000000\n",
        "      Tube      count         3.000000   3.000000\n",
        "                mean          0.951087   2.000000\n",
        "                std           0.070794   1.000000\n",
        "                min           0.869963   1.000000\n",
        "                25%           0.926449   1.500000\n",
        "                50%           0.982935   2.000000\n",
        "...                                ...        ...\n",
        "PPP   Dish      std           0.036531   1.000000\n",
        "                min           1.508969   1.000000\n",
        "                25%           1.531269   1.500000\n",
        "                50%           1.553568   2.000000\n",
        "                75%           1.567477   2.500000\n",
        "                max           1.581386   3.000000\n",
        "      Tube      count         3.000000   3.000000\n",
        "                mean          0.970277   2.000000\n",
        "                std           0.020897   1.000000\n",
        "                min           0.948767   1.000000\n",
        "                25%           0.960166   1.500000\n",
        "                50%           0.971565   2.000000\n",
        "                75%           0.981033   2.500000\n",
        "                max           0.990500   3.000000\n",
        "SWI   Dish      count         3.000000   3.000000\n",
        "                mean          1.451796   2.000000\n",
        "                std           0.079652   1.000000\n",
        "                min           1.362556   1.000000\n",
        "                25%           1.419848   1.500000\n",
        "                50%           1.477141   2.000000\n",
        "                75%           1.496417   2.500000\n",
        "                max           1.515692   3.000000\n",
        "      Tube      count         3.000000   3.000000\n",
        "                mean          0.918647   2.000000\n",
        "                std           0.009692   1.000000\n",
        "                min           0.909023   1.000000\n",
        "                25%           0.913768   1.500000\n",
        "                50%           0.918514   2.000000\n",
        "                75%           0.923459   2.500000\n",
        "                max           0.928405   3.000000\n",
        "\n",
        "[176 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**d)** Now use the `sns.FacetGrid` function to create a faceted plot of the distributions of `RelativeFitness`. Each facet should be similar to the plot you made in **(b)** (but you are free to choose a different plot type if you want to practive it!). Facet on either column (`col`) or row (`row`) to make a wide or long plot. \n",
      "\n",
      "Create two figures - in the first you facet according to `Treatment` and group by `Group`, and in the second vice-versa, facet by `Group` and group by `Treatment`. \n",
      "\n",
      "For clarity and bonus points, use the `hue` argument of `FacetGrid` and set it to the same variable as you facet by. \n",
      "\n",
      "Note that you may have to set the value of the argument `size` in `FacetGrid` to a number larger than the default 3."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = sns.FacetGrid(data, col='Treatment', hue='Treatment',  size=5)\n",
      "g.map(sns.boxplot, 'RelativeFitness', 'Group')\n",
      "sns.despine()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yXkP143N4Zj4G+FTZ/nbglaU35rHARS2/y5L6ge2l7aVmyJ5jzRcD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\nkiRJkiRJkiRJkiRJkiRJkiRJkubO/wfkZ58q8TAkLQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9adc5b0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = sns.FacetGrid(data, col='Treatment', hue='Treatment', size=5)\n",
      "g.map(sns.violinplot, 'RelativeFitness', 'Group')\n",
      "sns.despine()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dO3fnbfzGxkaA7koZydtCCCGEEOka7vze84A7AUzTPKCUOgQsBrYM9gSl1B3Adwoyuzzo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\niIiIiIiIiIiIiIiIiIiIiIiIiEj5/D8K09e4kYPRdAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9ff74b0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.set_palette('Paired')\n",
      "g = sns.FacetGrid(data, col='Group', col_wrap=4, hue='Group', size=3)\n",
      "g.map(sns.violinplot, 'RelativeFitness', 'Treatment')\n",
      "sns.despine()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DTY2NbNm6nfj4GNwuDy63B5/v4Fty7TYDux0cNmhsNsnpAfGxkJYMK9ZDQQ40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\nvf9tg/5O8aX3v23Q3ym+9P63Dfo7xZfefxERERERERERERE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0AAAg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\nJEmSJEmSJEmSJEmS9P/3D0IcmXqiI8FLAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa6ccab0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**e)** Finally, we want to save a file with the mean and standard deviation of `RelativeFitness` for each of the `Group`s and `Treatment`s. Use the `aggregate` method of the `DataFrameGroupBy` object created by `groupby` an give it the names of required functions - `np.mean` for the mean and `np.std` for the standard deviation.\n",
      "Save the result to a csv file using the `to_csv` method of the `DataFrame` object created by `aggregate`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "agg = bygroup_treatment['RelativeFitness'].aggregate([np.mean, np.std])\n",
      "agg.head()"
     ],
     "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></th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Group</th>\n",
        "      <th>Treatment</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">BAC</th>\n",
        "      <th>Dish</th>\n",
        "      <td> 1.633628</td>\n",
        "      <td> 0.026313</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Tube</th>\n",
        "      <td> 0.802749</td>\n",
        "      <td> 0.010808</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">BKB</th>\n",
        "      <th>Dish</th>\n",
        "      <td> 1.315682</td>\n",
        "      <td> 0.179156</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Tube</th>\n",
        "      <td> 0.951087</td>\n",
        "      <td> 0.070794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DOS</th>\n",
        "      <th>Dish</th>\n",
        "      <td> 1.587148</td>\n",
        "      <td> 0.006740</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "                     mean       std\n",
        "Group Treatment                    \n",
        "BAC   Dish       1.633628  0.026313\n",
        "      Tube       0.802749  0.010808\n",
        "BKB   Dish       1.315682  0.179156\n",
        "      Tube       0.951087  0.070794\n",
        "DOS   Dish       1.587148  0.006740"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "agg.to_csv(\"agg.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    }
   ],
   "metadata": {}
  }
 ]
}