notebook6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducción a Python para ciencias e ingenierías (notebook 6)\n",
    "\n",
    "\n",
    "Ing. Martín Gaitán\n",
    "\n",
    "- Email: gaitan@gmail.com\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn\n",
    "\n",
    "Antes de empezar con pandas vamos a configurar [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/index.html), un paquete que, entre otras cosas, mejora la estética de gráficos de matplotlib\n",
    "\n",
    "Para instalar, como casi siempre\n",
    "\n",
    "    conda install seaborn\n",
    "      \n",
    "o \n",
    "\n",
    "    pip install seaborn\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos un gráfico default generado por matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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36n9Eu0dfPHsBqW5xxh7p79FKcEVFrYaTORUGi/T9LeJvuFi48N7B94zTkwkY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\n1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HsYa9rUtfP2nywm+0wFdwxRV+ttVtdBMH0F5CXAmtncFeLC2apathxtYqVyaS6suBUK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\nEruiKEoHoxK7oihKB/P/3bOl/Ela7x8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6fc3de470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sinplot():\n",
    "    x = np.linspace(0, 14, 100)\n",
    "    for i in range(1, 7):\n",
    "        plt.plot(x, np.sin(x + i * .5) * (7 - i))\n",
    "        \n",
    "sinplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tin/.virtualenvs/curso/lib/python3.6/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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fv3aIkKoLJDr7URqNxH7+i0Ff1x6fh++d/Fd8oo//tvGfbtuqc/PvKYoif//z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2K3pghBITh0Gr7VWcvX22qwaNS8t+zhv40FmLU+TjIZxT37UtbTaWZe5+PVsezb0DA/mbu0pVlj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\nJPkKIYQQTvb/AZFKyjC8vZoHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6fc3baf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns   # magia !\n",
    "sinplot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn permite configurar paletas, estilos de una manera muy fácil, globalmente o para un gráfico en particular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d4LLL1q8JjnviehbCxmZkNsd4Jk97NLgoGQWgoFsx0ohPmWtKsBxO42KVyJLE\n4bYGDNN0NUbmVuKVd6jc2KQLJa5PzRL1q+xtTyx6XTdMMnNZzitLrJtyDVZoB6zDwWZlEytf6yvr\n1mLubY6jlq1ft2K/9mnQqYuusi7SY2Nhmiany6Mql0pGAZgtx0ejq7TdA3dczpXsaowS9atcn5pl\n1qWSuohPKbueNUduP7vLkRf33ZicG53GBA61NSxp9WaKGiZWo4rVcCPeW0lDyE97NMhEJs942t3G\nG+vF6r+1DYhpWm36BM5dczaqLLGvOc47o9NcmUxxoHXxSW+t+GQrCSFT1DBMc0kBrgYhQDLn475u\nPbOHc0Zmc8zki+xsiBAP+tAMg+sTsyRzBVRZQpUkJrIF/KrCvqbIqtdzW/lKQnB7RyMvXR/lzMg0\nD/S0Ob6mLFktBGeLGgXdILCK1bMctlzrWK52T6w3DulCiRvTaWJ+le5EmHxRY2w2RypXIJUrki1q\nSLJEOOhfNcsZ3Fe+YB0KRmZznBtN0rKEx2mjsymVr4H1ZXX75vc1x7g4nuTCeJI9zbFVGyGshhCC\nqF9hMlskU9SqEtLlkLGeW2eTLtoW5fxYEk03KBV1/ubVi1waTy47g/nVK0M8fHAHexqixJeILbkZ\n762kMxaiIehjYCZDulC6qd1lrUT9KrNFjXRBq1n5gvWsevnPpnXDbUHeGZ3GME2iPoUvvnyeS2PL\nh+NaIgF2Nka5t7eN7obFB8y19imvlqZwgJZIgNF0jqlsnsbQyvkUG41NuY/X4xQFVn3uvuY458aS\nXJ1MLWqhVgtRn8pktshsoeRY+ZbwlO9GYjyd49zQFCPJDGcMa+h3UzjAwV0J2mMhSobJTL7ITK5I\nplDk6liKb7x2CQHsa0vw4Tt6aArPbxhuZIMuhRCC/S0JXu0b4+L4DHfvaHZ8TXsOcbpYojnsr/k6\nlXFf50cCDzeYzuY5eWOcqdkcp65bXa16W+M0Bf3Egz5iAR8mcGUiRTKbZzSV5WTfOCf7xrm9q4n3\nHeqmcR3kGuC21gbG08OcG03yUG97HT6hfmzKfbxeyhdgX0ucSxMzXBibYU9TbMW5lNXgVyRUSZB2\n6Hr2MkM3FgPTab7y2kVSuSIBVebxg50c6mykeYH768Z0hpyms7cpQlHTuTaT4fl3Brg4muRPnn2b\nDxzZxX091tSjesr1jkSY08MK16dmOdzesGqCzGrIkiCkymRLOiXdWHFC00rYcV8vpLIxmEjn+KsX\nz5HKFZFq7oOnAAAgAElEQVSE4K7uZh7c085d+zoYH58faj+UyhINB+hJhPEpEtcmUvzz2T5OD07y\nztAU9+9u4z0HdhD0KXWV69ZIgMaQn6FUlplccUmP0kZFfuqpp55ajw/KZt0riNaBgE9GqkMrMEWS\n0AyTkdkcAVV27MoQQlhNu0s6QUXGt8ZNLxz2k80WEcJ6bgPLQthqm5T9nBsd3TB55kwf33rzKgVN\npz0e4hffdYR9bQ2EFtQ6aobBWKZAUJVpDPpRZZnDPS3c1hynORrk4liSs0NT9E+n6W2OI8qy4cP9\n9bXLn4Znsyiy5EqMzDCtpBufLBFc0Hyh2vWslGuFzSfXm0Vuq+H04CRfeuU82aJGWzzEZx89wt07\nW4kFfDc9p2GajKTzKJKgJexHEoLGcIB7drXSGg0ykMxwaWyGs0NT7G9LoPoUTOon1wFVpj+Zoajr\n7EisnlexEm6vZ3gFr9CmDLP4gbbE6s0KamVfcxxJwOWJlCtF3HNZz17J0aZG0w3+7vWLvHhlmLBf\nZU97go8d3U0ksPRpO12ugVw431QIwZ07mvmV99zJ3tY4l8Zm+K/PnmJgatb1uFglvY1RVEni8kTK\nlcHk9nOlvZKjTY2mG3z71DX+7vVLGIbJrpYYHzyyi1hwacWRLWoYphX3r6xpl4Tgjh3N/Op77+SR\nvR1MZvL82Y/PcGNq1pX63uXoiIaIB3z0JzOuNH9ZLzal8p3O5ufmotaDgCrTnYgwWyi5UsQdVKzG\n3U5LM7ySo1tHQdP50ivneWd4mp6mKHs6ErTHQ3TFl++IY9d3R5cZLh4P+vi3DxzkI3f0UNB0/vbl\n8wxNzS75s26gyhK7m6IUNN2ViUdqeYpNtqQ7aiTjHSpvHSXd4EuvXOCVa6O0RoMc7m6mJRakZ4lu\nVja2XC9XYqTIEh84souP3tVLvqTxlRff4fzgZF3uH6zD7MFydcoVF/s01JtNqXy/duIy/8c3X2Z4\nxr1hCAvZ22w1xr884Xx8lRBWaYZhOpsZ6VkIt4ZsUeO/v3iOK+Mpbmtv4N7dbQghONCSWHaKi2Ga\ncy7ZlUINQgiO727niXv2UdJ1vvLyeW5M1k8B72uJI4AL4zOueHUiZZdixkEHLfs36Mn1+qIbBl99\n/RJXxmc42N7Ah+/sRUjC8pAsE8M3TWt8oCQEwVVCaPf2tPHTxw8iSxLfOHGJl6+M1OMxANgRDxNQ\nZK5PpV3t0V9PNqXyfWhvB/mSzhdfPs90tlCXz2gMWYH84dmsY7caVGSHOuj1LMTNySke9SdX1PjL\nF87SP53maHczn7xnL9en0/gViZ7G5eNLcw0IlrF6F3Kwq4mPHrMU8F+/fK5uCjioKuxssLw6Iy54\ndeyevk7l2p7vu44T3bY1hmnyzTeucm5kmj0tMT517z6uT1syt7c5vuz7CrqBZphWo5Uq4iO9rQl+\n9uFDRP0q3zl9nTN1soAlSbC7KUrJMLiRdHeOdb3YlMr39q4mfur4fmbzJb740jmyDmOpy7GvLIRu\nWL/hclegjBf33TQYpslXT1xiNJXjvt42nrh7D0OpDCXdYHfjypnwcy7nKrr/2HWQhzubePLYPjTd\n5K9fPsegi2MuK9nfYsn1hXHnrfn8ioRSzuZ3Ykl7Xp31wzRNvv32dd4amGBnY4Sfuf8AqXyRqWyB\nzlhoxTrw5fIYlv4cS67b4mF+9oGD+GSJr5+8TL8LIY+l2N0UQ2CVQLk5n71ebErlC/D47bt4eG8H\n4+k8/+8rF1wbiFDJvCtj1rErQxKCULkhvZN79ZTv+vHPZ/u4NDbD/rYEH7mjB0kIrpZjsruXmNdr\nY5om6YKGLERVDSgqSzGOdDXx5LG9lDSDv33tYl0Olomgn9ZIgPF0npTDlpM3hVRKtatOr5Ru/fjB\n+QFeuzZKeyzEzx4/iF+R5+Y+71vB6oX5Q2W4GuVb/iMBnYkwT967D90w+fIrF5jOuD+6L6gqdMXD\nzOSLTNTh+m6zaZUvwPsP7+SOriZuTM3y9ZOXXT/tWK6MmOXKcOG0Npcd6mBD9ZKu1oc3+8Z54fIw\nzZEATx7biyQE09kCU9kCHbEQoRU2n5ymo5smEX91rrmFTQiOdDXx7gNdTGcLfP3kZYw6nOLtw8NV\nF9zbtqXkyfXG5+Jokh9eGKQx5OffPXiQoE8hV9IYSGaIBVRaIsuXVmqGQV7TCaryqjOpYXE/hoPt\nDXz4jh4yhRJfeuUCuTocLN3M1ak3m1r5SkLw8bv30NsU5ezQFK9eG3X9M/Y0RRECLk84T1AJu6F8\nvSHkdad/Os0/vHWVgCLzmeMHCKjWul2dtL7Qe1awemHeNbdclvNClmpC8O6DO9jXGp/bLN2mKxbG\nr8jcmJ51XHYUUmUkYZXS1fodqYz7enJdH1K5Il8/eRlZEnzqvv1zJXJXJlKYWFbvSodFO2RWbR7D\nUp2tju9u58E97YzN5vjqiUuuHyybwwHiAR+DMxnXxmjWi02tfMFKa/+pY/sI+RS+e+YGo6msq9cP\nqArd8QipQomxtDNXhj1oIVvudlUr9qJ5e5T7ZAolvvLqBXTD5Ml79811rCrpViJHSFVoj67coCJd\n1BCwonVss1zfW0kIfurYPhpCfn54foALo+6NBATLq9PTEKGoGww4rBqQhCCsKpTKoxNrvk75b8/6\ndR/dMPnaiUtkixofPLKLroRVImeYJtemZlFliV1L9GWuZC3xXlh+SMgHj+xiX7m+3W2DSQjBnuYY\nJvOH5Y3Kple+ALGgj48d3Y1mmHz1xGXX47+2K+OKC4tpl2Y4ieV5ySn14+lT15jNl/hXh7rZ3zbf\n27tvOo1umOxuiq5oHdgKKORTqmolulLf25BP4dP37UeWBF8/cdn1OJm7rmfnXh0vn6F+/PDCANcm\nZznc0cjx3vnJViOpLHlNZ1cismICoWGaZEoaqiTwVdFK1FxhSIgkBE/cvYegqvDMmRuMu5B1X8mu\nRARVkrg6OYuxgdPnt4TyBTjU0ci9Pa2MprJ8750+V6/dGPITD/gYSmUoOKjTBXfivt4mVR9OD05y\nZmiKXU1RHtnXOff/pmlyZTKFgBWbD0BFA4I1uuaWS8vqSoT5yB295Eo633zzqqt5DRG/SmvEmonq\nNPHKDqk4yeb3LN/6cGV8hucuDNIQ8vOxo7tvOjxeKycQ9jatLNepbBHDtGSmmjyG1UZjxgI+PnpX\nL5ph8rWTl13puGajyFYZYF7TGUzVrxeEU7aM8gX40JFdNEcCvHRlhIujzssobIQQ9DRGMU0cdwYK\nluNjTkozvKYE7pPOF3n61DVUWeLjR/fcZLVOZQvM5It0xcME1ZWVamYN2aBQ3fzee3a1cKAtwdWJ\nFCf7xqu6brXsLm+61xx21lIkiYDDbldCWL8Hr47dPfIljb8/eQUhBE8e20uwQi5zJY3hVJZE0Edi\nmVaSNlNl63StLueVcv2PdDVxtLuZoWTG9bwG26tzvY4d45yypZSvT5F58tg+ZCH4xhtXXM2m29UQ\nQQhrk3JifQghCPsUNMOkUKN73GtK4C6mafL0qetkixrvO9RN04KMTzvcsFJ5kX2dbEmbi+2v/rmW\norEn+yyHEIKfuLMXvyLz3TM3HFupldiJV9ennCde2QeOrINEF6/kyF3+5dwAqXyRxw500b3Aa3Nj\nOo0J7G5cWa4BptJ5BCwaoLEc1Y4R/PAdPSSCPp67MEifi4oyFvDRGPIzMpvbsIlXW0r5glVP9p6D\nO0gXSnz/XL9r1/UrMp0xq4YsmXO2+UVccNF5rmf3OD04ydnhKXqaohzfffNM0KKu05/MEPGptK5Q\nhgGQLekYZvVWb2Ud5GqevETIz/sPd5Mv6Xzn1PWqrl8NlYlXgzPOkhXdcD17cu0eA9NpXrk6QnMk\nwKMVYRSwDorXJmeRhKC7Yfn+5ABFTSdX1AlXmcewXBLhUgRUhU/csxeAvz95xdXWkHaIaKNav1tO\n+QI8vK+DlkiA166NMuhiq7HeRndcdG60mvTiY+5guZuvo8oSTyxwNwP0JzMYpknvKolWULvLudoh\nk/f2tNHTFOXs8JSrbfrmE6+cJRQGFSukknEQUvHk2h10w+Qf3rqKCfzknb0oCzwxE5k86WKJHYkw\nPnllCXQ7j2Ehvc0xju9uZzKT58Urw1W+a3V2JiJIQnB9Kr0hO15tSeWrSBIfubMXEyt71a1asrZo\nkIAi05dMO3LRKZJEUJGtZgw1+o0995w7fO+dfnKlpd3NADfKB63VyjDAUjoCq+61Gqp1zdlIQvDR\nu3ajSIJvv33dtbCKnXg1nsk76mNuh1RKhlnzvXl17O7wytURhmey3L2zhd0ti7tW2QbE7lUSCKE+\neQwLee/BHYR9Cj+8MMhMzp1+/aossSMeJl0sMVmnGQBOqEn5lkolfvM3f5Of/umf5hOf+AQ/+MEP\n3L4vx+xpiXNHVxMD0xlO3Bhz5ZpSOfGq5KaLrsZ4hJec4pzBZJo3+sZpi4W4v7d90euzBetL2xYJ\nrppoVdINCrpRbjhR3eDSWjaplmhwLqzyPRfDKj3lw8WNaYdenfLvaTpd+2bn1bE7I5kt8C/n+gn5\nFD5weOei10u6wUAyQ8Sn0BxeOZRimCbZkk7Iryw76WjRe8p/V2v5AgR9Cu8/vJOSbvDdM+5Vq9jD\nTzai67km5fv000+TSCT4m7/5G/7yL/+S3/u933P7vlzhg0d24Vdkvne2n4xLQ5Z7XHY9u1Ga4Vm/\na8c0Tf7x7RuYwL++fdeS7fJsRbRrhelFNmu1Duw6yGriYgt5eG8HzZEAr18bZcylpjJd8TCyJKwk\nHAenOfv5px00pPHq2J3xnbevU9QNPnh4J+ElhiT0TafRTZOextVDKbmSjgk0rKKkbUzTWjfB2uX6\n6M4WuhsinB6c5Oq4O3N5WyNBgqpMf3LjjRqsbqdYwAc+8AHe//73A9YmJq8SM7hVxII+3ntwB/90\n5gb//E4fTxzd4/iaUb9KczjAWDpHplgi7Ft+AshKBBQJSYi5+Fg1tXMLkQENS9g35gpsXE4PTnJj\napZDHQ3sKbvlDNPk2ugMb/eNMzmb4+JokmyhxKunbhAN+mlPhGlPhGhPhNnf0UhDhZt6ra331hrv\nrUSWJD54ZBdffuUC3z3bx7954GANV7kZpeyiuzGdZjJbWNUiWg5VlvDLEjPZAi1BX9VegEq8Q2Xt\nXBmf4dzIND1NUY7ubAFA0w36J2fpn0jRN5HizRvjpPMlTseChPwqAVUmEQpwqLuJwzuaCQfm9zRb\nrhsifrTc6gaMfWyrxaqThODDd/TwZz86w7ffvs4vv/v2FRt/VIMQgp6GKOfGkgzOZNhVhZt9vahJ\n+YbDVnZcOp3mV37lV/jVX/3VVd/T0BBCqWLCy1poaVn9F/mRpjCnhiY5eWOc9x/tpWeJ+MdauaPU\nwrPv9DOWL3JfV2PN15ksakykckTi1pdgOZZ7Tk03GJjMoPhkWhKhmu9jo1DNerpBQdP53vcHUCTB\npx++jUvD07x0boCXzw8yNbvYYgv5FfomZjnbPzH3f5IQ3L23ncfv6uGhQzvITs4SUGU621fujwvW\nc06nCxSyRRriQUJVjB1cSHNzhNf7xjk/NMVYocjhHU1rvsZC7pCs8pPRXIHbelpqvk7aMBiYTKOG\nfDSukiG+FKZp0jeeRsgSLU0rZ+JuBNZLblfDME3+4oWzAPz0I7eR0Q3+8fXLfP/Na6QWVGjIkmB6\nQWepf3rzKpIQHNjRyH37O/mJ+/eRTxlIAmIhP3IVazmbK5GbzROP+ImFfGt+hpaWKA+PTvP8+UHO\njCV5/PZda77GQu4O+yzlm85x7EDnqj+/XuspzBp9TMPDw3z2s5+di/uuxvi4uz73lpZo1de8Mj7D\nF148x56WOD/30G2OP1vTDZ5+5wYBReaDB7trsloBkrkiI+k8rWE/jaGli9xXe85s2X0ZYu1uno3E\nWtbTKc+eH+BfzvWzIxriYv8kQ+XGKdGgj7t727h7dxvThSKTuQKPH9xBRyxMsaQzOpNhJJlhcCrN\n61dGuDRs9Vv2KRK397Txsfv33dSScins58yVyzGcrNtQMsP/89xpWmNBfvndd9RkZVZimibfeacP\n3TT5yKGdNVsdmaJG/0yWhqCPthqUL+DK72c9WE+5XY1TAxN89fVLNPhUkqncnHzGQz7u3dPBrpYY\nugTTxRIP7+mgOx4hX9LIl3RGkxlO941zum+cS8NJDNNElSWO7mnnX93Vw+NHe6t6zoJpeeMCgFzj\numUKJf7Lv7yFYcJvvO8uQjV6Fyv54eUhJjJ5PnSwe0lXvI3b67mSIq/J8p2YmODnfu7n+NznPscD\nDzxQ842tF3ta4uxtjXN5bIbLYzPsbXVm/SqyRFcsTF8yzVS2QFONLrr5pCudWu1niXJmKCs3avCw\nmM7k+c6bV5mYSnO5MI4kBO85spP3HNnJ3vYGJEmgGQbfPnuDxkiQ9qjlUfCpMt3NMbqbY9wLfPS+\nfYxMp3n+/CA/PNvHycvDnLkxxsfv38+H7t6NbwUvz03NNRwsWmcizN07WzjZN87JG2Pc29O2+ptW\nQAjBroYIF8ZnGE5l2ZFYPda9FKHyyDmn9b4GlmzXtEltMzTd4B9OXmFgKMnlQgkh4GhvK+89sou7\nd7ehyBKmafLM+X6CwmquIkmCkF8l5FdpjAS4bUcTP/XgQbKFEi+cH+Rbr13itYtDnLg8zMkrIzxx\nbC+JVfa6WpIIFxL2q7z7gBUu/NHFIT54xLn129sYZSKT58Z0mkPtDY6v5wbyU0899dRa3/T5z3+e\ns2fPcvnyZb71rW/xrW99iw996EMoyvJfk2zWva48AOGwf03XbIuGeP36GGOzOY7taq3ZWrWRJUFf\nMo0sCTpitbl8ZUmQKpQoaDqNId+S97Tac5pYAi9R+0lzI7DW9ayFmWyBp/7+JQbHUui6waOHuvn1\njxzjscM7aYoG537//ck0/ckMe5tjtEWXX9tI0Mfh7mYO7GohFFDpH09x8uooz58bpCUWpGuJ+FI4\n7Gc2U0TDUiqKwzXrSkR4/foo16fS3NfTtqiec60EFIUrkykM02RnFeVVSyGEwJAE6XyJWECtavbr\nQjaLXK+H3K5GOl/kj79zgjPXxtB0gwf2d/IfPno/H7irl66mKFL59z+VLXBhfIbueGTFtVUVmT3t\nCe7Y00Yk5Cc5m+fNq6M8904/bfEQO5bpA22aUMJaM9XhmnXEw7zZP861iRR372wlUGX53nKEfSoX\nx2fIlTT2NMWW3f/dXs9wePm2nTUp33e96138/M//PE888cTcn5UUL9x65RsN+JiYzXF5fIa2WIi2\nGhXm3Of7FK5MpEgVSuxrWT3OtxxF3SCn6YRUZcmWhNU8p4ZlRTndyG8l9d7E3ro2xu9/42UmUjni\nkQB/+OlHePyOXXMzTSt5e2iKTFHjWHcz/lXyFDTDYCJbZF97Ax+/dx+GYfJ23zgvnh9kNlfk9p0t\nc5sfWM+ZyhTnLDqnisWvyhimyYXRpDVOzWFOQ0CVGZzJMJnJs6c5hlKj69nvV5lK5/HJUtUtCSsR\nWBs5ON/I68mtVr7nByf5na+9RN94Cr8q8+sfOcbHjx+4KWnK5sJ4kqlsgds7Gomu4HoFKwQxninS\n1RTlyeP76WiO8uqFIV68MMjwdJrD3c2LvDsGzB0qncq1LAn8qsw7w9MUdZ2DDq1VWRLM5IpMZPJ0\nxpbv0b6eyndLNtlYjsdv60YSgu+/0++4j60kBN2JCAVNZyxd+0iscHljqtVF52WGrkxR0/nrH57h\nD7/1CrP5Is2NEX7lQ3cve3rPlTRG0zmaQn6i/tUTRrJFy9EW9imEAyqfefQw/9dnHqO7Kcozb13j\nd776IhMLElucZDovxcN7O4j4VV66OuxKSV1PYwQTywNQKw0Ra9OpVa5FudmGV8e+NKZp8sxb1/jd\nr79EKlegqSHMz73vTu7d07HkzxumSX8yjV+RaFtlHjVAXjPQTZOwT0GWJD7+0EH+02ceZW97ghfO\nD/IbX3qOi0NTN73HDZdzJUe7W2iJBDh5Y4wJB3usjW3t97nY9dAJ20r5NkUC3NvTymQmz4kbzqfD\nzC2mg0lHIZ+CwGu2UQ8y+RJ/8I2X+ac3r9IWD9Hd2cChXc0c7lo+M9hWONW6XO11C1ecpHc0RfmD\nn36ER27bwaWRaf7Dl3/EqevzjV7sg5JbBp1PkXl0fydFzeCFy87b83Un7IYbtct1wKegSoJsqfZW\nk16f56Upajp/+r23+MKzpwn5VLo7G9jd2cDDe5fP5B2dzVHQDLrjkaoS8+bq1ivkuqsxyu996mGe\nfPAgyUyB3/36S7x6aWju9Vqaa6yELAkev60bw4QfnBtwfL32aAhVkuhPbox2k9tK+QK8+0AXqizx\n7PkBig5n8zaF/IR8CoMzGbQaLWlJCIKqTEEzar6Gt0ktZmo2x+e++gLnBqc4vr+Tew/twO9X57wf\ny9GfzCCAHfHVla9pmmSKGrIQ+JWbv0oBVeGXP3CU/+G9d5Arafzht17hx2WPy3JDxp1wb08b0YDK\nK1dHHFu/QVWhLRpkKltg1sG1wj4Fw4R8jd8zz6uzmFS2wO989UWeO9vPnrYE7zu2G385QWmlDlS2\ngbDmQ6XvZlUqSxIfP76f//DR+5Alwee/fYJ/euOqo+YaK3G4s5HORJi3BycZSjqbzStLgh2JMLmS\nzkSm9iYwbrHtlG804OOhPR2kCyVeuzbq6FpCCHYmImiGybCDTkPz3a6cbVJeRyCLwalZfvvvXqB/\ncpYP3NXLJx86yIXRJN0NEQ6sUAqULpSYyhZojQSrSvAo6gaaYRLyyUvG/IUQvO/OHp765IOEfCp/\n8sybfOuli4D7XzxVlnh0fxdF3eD5CmukVnaWrV8nrueQQ7n2DpU3k8zk+d2vv8SV0STvOrSDf/+T\n9/LOyDQNIT9HdzYv+z5NNxhMZQj7lGVLGivRDZNcSSegSMuWmx3tbeN3n3yYeNjPXz93hr9+7gyG\nYbre7EcIwfsOdQO4MqXOlmunc9ndYNspX4CH9nbgUySevzxMyWHLMTdcz05bTXqb1DyXR6b53N9Z\ncdZPPXSQf/fuI/zwvOWyevy2lWuy1+pyzpbK8d5V+j7v72zkd37qQWJBH//tH0/yzMnLdelIdmxX\nK7GAj1eujZJ2OPO3Kx5GEsKR8rV/LzWHVMp/e4dKq13n7379JfonZ/ng0V4++/6jvHptFM0weXR/\n54o12YOpDLphlmeSr26WZkvVtUrtbY3zB59+hB1NUb775lW+8P236hL72tsSp7c5xsXRJP0O2/q2\nRAIEFJmBmQzGLR6Gvi2Vb8incLy3nXSh5HjoQjzgIx7wMTybrdmN7Zcl5IpWk2vFTk7Z7pNg+sZT\n/ME3XiFdKPI/ve8unrh/P/3TaS6MJultirKnZeWh4f3JDJKAznh1mfD2YSlURUvJnpY4v/vkQzRG\nAnz1x+/wjZcvVPUZa0GVJR470ElJN/jxJWexX1WWaI8GSeVLzNQ4v1qWBAFFIlfSa5osZuczmGxv\nuZ6czfHU115kcCrNh+/Zw7997Mic5y4R9M21kVyONbucl4j3LkdLLMTvPfkwu9sTvPhOP1/+4WnX\n46lCCN5zoAuA5y468+oIIehOhCnqBiNpd/qi18q2VL5gZYiqssTzl4YcN9ze2RDBNGFgpraYhD2K\nTTdNCjXey3afBDOSzPD733yZTKHEL73/KO85Yk1z+eGFQWB1q3cmX2QmX6Q9Glp1vilY8d5sSUOV\npSVLxJaisyHKf3zyIVpiIf7+lQt85+SVqt63Fu7Z2Uo86OPVayOkHFq/brie7Q0869Crs12t36l0\nnqe+9iLDyQwfvW8fn3nXIYQQPH9puGz1dq1YDlbQdEZnczQEfVVl74Pl0ZEEVZeIhQMqv/6x43Q3\nx/jeqet85flzrivg3uYYuxqjnB+ZZrjGfdbGDW+lG2xb5Rv2q9zX08ZMrsgb/c4yn92II9iJDd4m\ntXam0nl+/xsvk8wU+LePHeHRcoxoKJnh4miSnqYoPc2rWL3lteuusqtTTtMxzPlSsWowgeZYmP/1\nkw/SEA7wpR+d5YXzzrM4K1Fkicf2d6EZJs87tBI6YiFkSdCfzNS8mYYqurjVwnZOusoVNf7Pb73C\n6EyWJ+7fz6cfOogQgnS+yGvXR4kHfdy9a2Wrd3Amg0n1cl3SDYq6QVBVqu5dYJoQDvj4zSeO09kQ\n4ekTl/nmq5eqem+1CCF4tNyX+UcXBx1dqyHoJ+JTGEplb+mko22rfAEe3teBIgl+fHHIUd1vyKfQ\nFPIznsmTrzG+FVK9TaoWZnNFfv8bLzM2k+WTDxzgQ3fvnnvN/pI+tr9rxWuY5RpIWRJ0Vtl8pbK+\nt1rslW2NhfjfnjhO0Kfw3555k9N9zsveKrl7VwuJoI/Xb4w5ynxWZInOWIh0sUSyRtdzUJUROD9U\nbje51nSDz3/7dW6Mp3j8jl08+eCBOWVo56o8ur9z1SYo/eUM4R2J6gZUzMV713CotOW6MRzgf//E\nA7TEQnz1pfM88+a1qq9RDftbE3TGw5wZnGJ8tva6XyEE3Q0RdMNkyKWRnLWwrZVvLODjnl2tTGcL\nnBqYdHQt+2RZq+vZdl9ma4z7bseM55Km85/+v9cYKCehfOL4/rnXJtI5zg5N0REPrdrLO5krki5q\ndMZCVbdnnIv3VhEXs6msg9zZEuO3fvI+hBD88dOvc33MnfmlAIok8dDeDkq6wctXRxxdq9uh61kS\ngpAqU9CNmqyM7ZjPYJomf/mDtzl1Y5y7e9v4+ffcPqd4M4USr16zrN57draueB27AVBjyF/16FM7\nM72aPAYbe1UloCka5HOfeIB4yM9/f+40b113llNTiRCCxw50YeLc+p3zVt7ChhvbWvkCvGtfJ7IQ\n/OjiYE1JITb2ybLfQS1aSJUxsVyaa2W7JaeYpslfPXuaC0NTPLC/k3/z2JGb3GQ/vjiEiWX1ruY+\ns3sv9mAAACAASURBVL+A1brmdMMkp+kEFHlNfYt15pUJwOHuZn75A0fJF6064IWdsJxwbFcrIZ/C\nK1dHKDioZ2+PBlFccz076+K2DcQagG+8epFnz/Sxuy3Or/7re27KZH756ggl3eCRvZ2rHhRtQ6C7\nSqvXzmOQhcC/hh7hC5trtCXC/NZP3ociSfzf/3iCQYcZypXc1tFAazTIqYEJph3U6sbKibKjs1nH\nFS+1su2VbyLk5+jOFibSec4M1m79BlWF5nCAiUyeXI2bjO3CzHp1kavyzFvXePZMH72tcX7p/Xfd\n1DgjmS3wZv8EzZEAhzpXnhdlu5xVSZqbYLQa2WUaEKz8OZby8CvyTU0IHjzQxc8+dphkpsB/fvp1\nx41fbHyKzAO728mVdE44sD5kSaIrHiZb0pjKFmq6htM69u2Uz/DShUG+9tIFWmJB/peP3k+gwgIt\najqvXB0l5FO4Z5VYL8x7K6ppGAOr160vxXLNNfZ1NPCL77uLbEHjj/7hNdI1hi0WIgnBo/u7MEz4\nscN69h2JMIYJQylnCVy1simV74/f6ee7J9zLFH3X/k4E8PylYUdZevYJc7BG13PIYV3kdnE9n+4b\n54vPnSUe8vGbP3kf/gWu3xcvD2OYJu/a17lqK73JbIFcSacrHqraiq22vrcSe038S8TSPnR0N48e\n6ubKaJK/+oF7pRrHd7fhkyVeuDxcc/c0cO56tkvpam01uV3yGfonUvzpP79FQJX5jx87vmh834kb\nY+RKGsd721YcWQmQL2mMp/NzXfiqIVODXNuruZQiefi2HXzsvn2MJDN8/jsnXEtuur2ricawn5N9\n46QcKPUdcWu/HnDYOatWNqXy/cHpPv7TN17hxBVn8SybpnCAw52NDM1kuDqRqvk6XXFnrmendZHb\nYZMaSWb4L985gRDw7z9yL80LmsRnCiVevzFGPOjjzu7lu/7YDNjWwRpm12aKGoLqSzFgfk2WUr5C\nCH7hvXfQ2xrnh2f7+P7bN6q+7kqEfCr39rSSyhc51T9R83XaIkF8slSz69kqpZPRDJNiDRvwdpDr\nbKHEHz/9OgVN55fef3TR4A/dMHjx8jCqLHF8d/uq15t3OVcv19k11K3P3Vf57+W+CU8+dJD79rZz\npn+CLz53purrroQsCd61rxPdMB3lNMQCPmIBlZFb5HrelMr3595zBJ8i89+eeZMxhzVfNg/vtaaB\nOGlM74brOeSgLlLa4pNgiiWdP376NdL5Er/w+J0cXGJAwktXKmJiq2SCmqbJwEwGVZZoi6w+6QXm\nSzFCvupLMaBS+S59Tz5V5jd+4l6iQR///YenF02MqZWH9nYgC8GPLw3VnNMgSYKueJi8VntP3Dmv\nTg1yvdWHhximyZ888ybDyQw/cWwvx/cvHpBwenCKZK7I3TtbCK8yDhDWnuU8V7cuiarr1uHmZKul\nkITglz9wNzubo/zzqeu8dMFZopTNXd3W7+G166MUaqwQAetwcqtcz5tS+e5qifM//+S9ZAolPv+d\nE5RciJN1N0bpaYpycTTJqIP0c9v1XKsrYy7u66DkyGRrJqf89XNn6JuY5X139sw10aikqOm8eq36\nmNiU7XKOhW6aubsSdrw3tJb63oq42EptAFtiIX7tX9+DYZr852+/TtKF5u/xoJ87u5uZSOc5Nzxd\n83Xm5LrGw65Tud7K+QxPv36ZE1dGONzdzKcfPrjoddM0ef7SEIJ5I2ElciWNiUye5nBg2bm1C8nb\ndetrsHphdeUL1oSrX/vwMfyqzJ9//xTDLjS3UGWJ471t5Es6J/tqz2m4la7nTal8AT50bA+PHe7m\n6ugMX/zRWVeuaY/kctKYfm4xa9yk7LpIb77vzbx4fpB/OX2DXS0xfvbRw0v+zBt94+RKGvf1WDEx\n3TCYnMlyfWSavrEkQxMpRqfTzKTzc1YvrM3lPBfvXcMmtVJcbCFHdrbwM48cYjpT4E+eedNRBr7N\nI/usnIYfXxqsOZ7cUnY9D9ToelZlCVWWHMd9t1o+w5m+cf72xXM0RgKLMpttLo/NMJLKlmOdAfJF\njanZLKPTafrHZrg6NMXQZIpcuabbViTVZjlDbSVGpmntMxKrTzLqaozyPz5+J7mixn/5zglXEgvv\n721DlSVevDyMXmOf5nnXc27dXc9rO+ZsMH7+PbdzdTTJ905d57auJh46uHIzhdU40J6gJRLg7YFJ\n/tWhncSD1bVjqySgKrSEA4yXXc/Vnjxt7BGD2ZJeU5KMDJSwNqlNvbgVjEyn+Yt/OYVflfm1Dx9b\nlGwyk8lzrm+cv3r2bUYnU1w5c53//KU8yXRuWTdlwKcQDQeIRYNc723jSE8bd+5pp6Mxuqw72R4h\nKK2xFGO1uNhCPnLPHt7pn+SNa6N858QVfuLevVV/1lK0RoMcaG/g/Mg0fVNpdi2IJ1aDJASd8TDX\np2aZzBZoXpAMVA1hVSaZN8hrxpri5bA1D5WpXIH/+t03kITg1z98jPiCiUOFksbZ62N84dlTDIyn\nGLg4wBe/+RKTK3jm/KpCMKASiQS4sb+LQ7ta2b+jmZ72hhVLk2rx6FRj9VbyyG07eGdgkh+cvsEX\nnzvDLzx+Z9WftRRhv8rdO1t49dooZ4cmuWPH6jkeS9Edj3B2dJqhVIbO/5+9846Oo7ze/2e2F/Xe\nreLeJPduY4xtTDHVtIQaOgESSoAUwpdfEkoICQmEHgKmGZtmugvGuFdJ7kWS1XtbSdt3Z35/rNaS\njaSdGdlgTJ5zfHQ4aGe1O++89733Pvd5kvrWBDiR+FHvz0a9jnvOm8CDb63lxZUF5CRFkaTgtHc8\nNILA9IEpfFhQwqbiGs4eOUDVddKirDTYXVS22hkUr/xmWvQ6HF6/qpGj022T8vr8/P2zHTg9Pu5c\nMJaU6DBEUWJ/eT3rdpexbncpRceNiFlMeuIiLAxIiCImwkyExYQoBUQefH4Rl8dHVVMblQ1tNLR0\nUFzewLK1ATJIQpSV3JxkZowawPRRmVhNXQcwryjhEyXCVfZ75W5SgiBw+/w87lv8De9s2M/w9FgG\nJkXLfr+eMH1gMgdqW9hQXKMq+EKgqlPa3E5lq11V8LXodbS6vDi8PuXBVwBB6hLbOJGesT8EJEni\nhRWFtNjdXDV9GINTYpAkicOVTawpLGHnoWr2ltXh9XU9yXWCQFJMGBOHphFmNmDQaTHotei1Whxu\nL60dTprandS0dFBV08LSbm0Gq8nA5GHpTB81gCnDM4juRlQUpYCFoFGnCcmT6I7j53vl4PozRlJU\n08LKXWUMT4vrd8I0bWAyW4/Usa6ohlGpsYqeyyDSoqzsrWuhstXO+H79Ncrwow6+ACkxYdw4ZzTP\nfpnPs1/s5P8un9ZnXy0U8tLjWLm/gq2l9ZwxJBWTwswVAptUflUTFa0dqoKv1aCj0eFWNXIkCKCR\nusgpP/ZN6s11+zhSb2P2iAySI8w89d46vs4vOXr61+s0TBiSikejQdTruOvssYzKiA/5EO6qbuJg\ng428xGg8Lg+7SmopKK6hsLiWlTuKWLmjCKNey5QRGcwdO5DpozJxdZallJTmQHnwBYiwGLlzwVj+\ntGwTz3y2gyd+PguLDKJNb8iMDScl0sq+6maa7S5iVATPxDAzeo2GSlsHuSkxijc6S+dctN3jI1aG\nr+zx0NAZfOkSKvmxYuWusqN93ryMOF75fDsrtx+mtK4VCCQCg9JisYab8el1XDN9BJMHp6APMWJU\n1Ggjv6qJYXERiG4fBysaOVTZwLaDVazOL2Z1fjGCALnZyZw/ZShnjcvBj4CEshEj6KroKFnXBr2W\nX58/ngffXMtLqwoZnBJNvExJ154Qaw3M8u+tbqa0qZ2sEBruPaF76flEzdnLgfaRRx555Pt4I4fj\nxAxZB2G1Go9eMyMuguoWOwWl9Wg1AsNVlh8gwOwUJYlDda1YDToyVGQJOq2Ghg4njQ432bHh6BWU\nKAF0GoEWpwe/KJEWF674uxM7/2kJZAw/BnS/n0EUlNbz6urdGCSRurpmXvhkK/vK6jHqtcwZm8ON\nC8bz4JVnMG54BvtaOhiTncj8UZkhg4IkSeyoakSSJCZnJZIYHc6orCTmjhvIz+bkMmdMDjHhZupt\nDgqKavg6v4QP1++j2e4iITqcAbFhsjMESQIPgQ1KL/T8OXtDYqQVj8/PjpI6mjpcTBoUmmzTGwRB\nwKDTsremGQEYnBil6hptbg+NdjfJEZY+Wyo9fU6NINDu9uL2icRYDIqDt0Rgwz+V1rWS+xlEZVM7\nTy3fht/jwdPu4LmPN7PzcDVOj5czcrO55fyJPHjlTM6ePJSChjYyk6JZNHEQWhn7yK7qZhxeH5Mz\nE0mLi2BEZgIzR2dx+RmjmDM2h+SYcDxeP4UlNazdVcrStXuoamonIsxMdmJUr0znnj5n8L8MKDvk\nh5sNRFlNbDpUTWm9jZkhHMdCIdJsYEdZA3a3V9Z4YU/w+MSAFKfVhLkfydvxsFp7P2SeFsFXEARG\nZsSx/kAVO0vqyMtMICZc3uhIT0gMt7CppJa6NgeTs5NCCjX0BJ8oUtvuxGrQEWNRlmUIgoDT68fl\nE0mMtOBxK8uAg5uUBtCeIptUKBz/cNe3dHDvKyuoqWmkqbmdRpudKcPTueuiqTx45Sxm52WTmRSN\nXqfl8z1l1Lc7uSA3W1ZG1+r0cLDBRlpkGOnHeZwKgkB0uJmxg1O5dOYIZudlE2YycLCikYLD1aza\nepDyulZS4yKIiwzd4hABH4GAoVMYfAGGp8VSWFZPQWk9SVFWBoTwJO4LceEmdpY1UN7SzqTMRMWH\nQghknBWtdgxaDYl9KIL19jk9fhGnz49Fr1M00hKEr/Nv0J0i61rp/Wx3uPnVS19SWl5HS0sHdS0d\nTBiSys3nTeR3PzuDBRMHk5UUjUGv49vDVZQ2tTN3eDqpMrx43T4/+VVNxFqMDI4/9nAVXNe5OYGM\n95xJQ7AYDRypaaGwuIY12w9TVNlIamwEiT281/GfU5QC3BItgUOlUmTGR1De2EZBaQMmvY4hqX0r\n0fWFSLOR4nobxY1tjEqNlTWKdTyMOi3FTW0Y9VoSVFSFesNpH3whIKeXGR/J2n0V7K1sYvaIDNki\n+cdDr9XQ5vJQ3NBGUoSFBBVlEbNex6EGG35JIjNGefbs7yT3WI16NAqZfAKn3iYVCsH72eH08Pbq\nAh58dQW2NgcGnZafn5XLH6+dw6JZo8hMij5mLKjF4WZ5YQmJERbOHpEh6wRd1NhGo93FiKRoIky9\nk+oEQSAmwsLEoWksnDYcnclAY2sH+UU1fLRhH8XVzWSnxBzTPzse/s5/egLZmtLNWqMRGJEex9d7\nyiksbWDmsDTMKsvPGkHAL4ocqrNhMehU9X4tBh2HG204vD4GxkX0+n339jklSaLd7UOvFRSPtQgE\nNnxQt+GfDMi9nz6/n4827OfXz39OXaeQz3mTh/LHa87k53PHMCg19hgiocfn573txRh0Gi4ZO1CW\n+lp5Swc1bQ4GxUWG7MmHW4yMH5LKJTNHEhkdRpvdxc5D1SzfdICCohqSYsJIie066B3/OYPrWoe6\nA74gCIxMj2Ptvkp2HqljfE7SdxS9lMCk17G7qglRkhiqgh9h1GmJNOkZkhaL5DtxjJmfRPAFSIi0\n4Pb62FFSh83hZnxOaCWY3hBjNbH5SB3tbi/jBvTtHtIT9FoNde0OGu1uBsZFKCIyQGCjbnV50WoE\nLCH6PD3BSyADPlU2qVAwmvQs/jKfB176kvV7ypCA7PQE3nroEmaMyiK8lx7hmgNVlDd3MH9EBiky\nyHaSJLGjshFRkhifFie7quHw+omPi+Sq2aOYODiV8vpWth6o5MN1+6hqtDE4Na7HvzF4H4KlOTVl\nyjCTgXCzgc2Ha6hs7mD60NBmEb0hIdzC5n5UdTSCgM0VKD2nRFh7LT339jl1Gg3NTg9IEKVwmkAQ\nAhu+SOAwcyrwGULdT1GUWLH9MA+9vIIvth7C5xdJSYrh9d9czLmThhLTy8F+e1kDe6ubmZaTzCCZ\nLYLdNc3YPT7Gpcdh0MrbM5w+P9ZwCxdOHcbMkRk02BxsO1jJZ1sOsq+sniHp8USHmb/zOX0E7oMB\n9S0Ao15HWkw46/ZXcqCqmdkjM1TzdeLCTBRUNFLe3MHELHVVnQiTgbho6wmNVT+Z4AswLDWWnSV1\n5JfWk5MYRbKMck2P1zfqqWzpoKShjSGJUUSoGDvy+EXqOpyEGfVEKySYaAWBVqcXt99PtEmvaLMN\nblISp84m1Rc276/gV//6hE83HUCrEYiKiSApJY6nf3EWcRG9B1S318/SHUWY9TouHpstK5DYXB4O\n1NtIi7SSES0/82tyePD4RRLDzGQkRLFw6jCGZsRTXN3ElgOVfLh+H35RZERm4jEVl+P7YmqCL0B2\nQiSHa1ooLGsgJsxEtoqeLRxb1UkIN5OoiuwiUGmzY9BpSewl6+/tc2oEgQ6PD5fPr6rve6rxGfq6\nn/vK6nng5a94b+0eHG4vcXERRMdH8di1Z5KV0Pv9EyWJ93cU4fb6uWzCQIwyDt8en5+dVY1Emw0M\nTZCf+bW6vLh8fuIsRjLiI1kwcTBThmdQ1WgLHC7X76Wlw8n4YWmI3TJCD8ceKtUiOToMm8NN/pF6\nPD4/uZnKEx0IZNKiBAfrWrHotQyIVdeeUft89nW93vCjFdnoDXqdll+ePRatRuDFlYXYXerNxKd1\nZs6bVGpIBwU31BgtCIKAxaDF6xNV6eH+GJxgapraue+Fz7n72U8pqmriwmnDmD5hMJYIK9efOYqk\nEAennRUNuH1+JmUlyq4sBAUIUmX0a4M4Kr3XKRQBgfszY1Qmix+6jP+7dg6RVhOvfL6dK//0Lut2\nlwKBvphE4F709wAkCAK3zcvDatTz+tq91PVDkWdqThICASlVNYIXSRFmtBqBytYOVa8PGrWrklDt\n/Hkqj9LZ7C4ef2ctN/z1ffaV1TNv/EAuPWsM5ogwLpo0mCEhnLYO17fS0OFiVFpsn22R7qhucyCh\nbF1D4B4IgKnb6NfIrESeu2shT958NsmxESxdu4fZd73ERxv2IYrSUXGN452M1OLqmcNJjrby2Y4S\nDvTDWW78gHgMOg2bj9Th74eRyPeF0y74QsCo/NLJQ2ixu3h9rXox75z4SBLCzeyualLlnmE16oky\nG6jrcOLxKw+DQeq/Gkm+U3mT8osiS9bs6gxUZYwdlMKnT1zHhJGZHKxpZUxmAmeN6nvGWpQkNhXX\notUITMxKlP3elTY7GkEgWaZ9IIDLJwak93qYTdVoBM6eOJglD1/Bz+bkUtdi574XvuC+Fz6nrtO0\n4UQ9ZDHhZq6fPRK318+/vypQrX4VFxYQ3ahqtVOhQupPp9GQHG6hw+PD5lL+XFj6ITV5KstMSpLE\nZ5sPcNmj7/Dh+n1kJkbz77sXctmcPDYV1ZIWG85lU78rH3k8NhYFDvvTcuSz27vU2uQHX58o4vYH\nBE+OrxoJgsCs3Cze/f3l3H3xVERR4rG313L7Mx9TWheYH1beDOsZRr2O2+ePAeD5rwpwq9TFN+l1\njE2Px+b0sK8fUqrfF07L4AtwwYSBZCVE8s3eCnaW1Km6hiAITM1Jwi9JbDmi7hppkVYkCWpsyvWi\nj25SKjKEU3WTKq5u4qa/fcjTyzag12v54zVn8u+7FxITZWXxt/swG3TcPDc3ZDnycF0rTXYXuWlx\nhMkkILW5PLS7vQGDeAU9oS71nz5Ga0wG7rp4Km/+dhHjBqewbncZP//zEr7YdADhBLoBzBiWxsSB\nyeyvauKL/BLV15na36pOP+wzj0qoqplj7/x5qlV06ls7uPeFL3h08RrcHh+/vHAyb/52EcMGJPD8\nVwVoBIE75o8JaQVY1+agqMFGVmy4LA4DBMw+6todRJj0hBvlt8eCIj59Ed/0Oi1Xzcnlq6d/wczR\nmeQX1XDNY0tZ/OUORBUJRW8YkhLDueOyqWm1s2TjAdXXmdK5rjcWqzfI+b5w2gZfnVbDHfPHHC0/\nd6g4oUPAPcNi0LG1tE6V9mdqP7Se9RoBo16L3etXXN4TOh2OgopAPzT8osgbK/K55vFl7C0NlOKW\n/OEKzpk0BIC/fbAFp8fHtbNGECtjTGxjp5XYFBn2akEEA0WawtKc/ajVWuizfnZyDM/dtZAHr5yF\nKEn89a013Pf8Z9SdADF5CBwIbz5rNOFmA++sP0CtyvJzdlwECeFm9lQ3Y1NR1UkKt6ARBFXrOiih\n6vaJisuDQYcjiVNjXQez3av+tIQNe8qYODSNd35/BVfPHYNOq2Xxt/tobHdy4cRB5CSF7tMHLfKm\nKsh6a9ociJLydS3nUBlEcmw4T958No/fNJ9wi5HXPt3KrU99yJHaE5dhXj51KMlRgfLzQZWuXnFh\nZoYkRlHe3KGqqvN94rQNvhAoPy+a0ll+/kad+YJeq2FCZgIOj48CFZ6oESYDEcaAeopSM2lBEIiy\nGBElCXc/fFB/6D2quqmN2/+xnOc+3kxUmIm/3bqA/3f9XGI6S7/f7K1g2+EacgfEM7sHt6LjUd/u\npKjeFlBtUlBmq7LZEYDkSPkl56PSe1r50nuCIHDhtOG8+tvLGT80nc37KrjyT0v4avth2e/bFyIs\nRm6YPQqPz8+LK9SVnwVBYEpOEqIksVVFVUev1ZAYbqbN5aXdraL0HLQY/BGXnlvandzy1w95dPEa\n/KLEA1fM5J+/PI/kzhGuPeUNrN5dRkZcOJdOHhzyesE9JtpiZGiyfNLU0ZJzpDJyqd3rRyOASSd/\nXc/Oy+a/v7+C+ZOGcKCigWsfX8qSNbsQVRobdIdRr+O2+XkA/PurfDwqHbCCB5dNp3j2e1oHXwiU\nn7MTA/O/+SpLx5OyAiMZm0pqVRFMUqOsiJJETbvy0nNkJ1vux1h6DmYFP/vLexQU1zA7L5u3f3s5\n00dlHv2d5nYnr6/dg8Wo4xYZ5WboeqiUZAd2j5cWp4eEcLPsMQwAl9ePhHJJSQlIjAnnb3ecy2+v\nmoUoijz82iruffYzOlRkmsdj6pAUxucksbdTqF4N8tLiMOu1bFNZ1ekiFKppqfSfdPVDlp637K/g\nZ395j1Xbixg3OIW3fncZF88YcXT9urw+XlhZiCDAbfPGyGpz7Cirx+sXmZyVKHsELCDm4yDMqCfC\nJH/+2+sX8fpFLHplOuWSBFaLkYeuPpMnbpqP2ajn6WUbuPu5T6lv7X+mOTQ1lgVjs6lpsfOuyvJz\nTnxEJ1dHXVXn+8JpH3y1Gg23zwuUn19atQunioc90mxgREoMdW0OjnQOyCtBfzwjozqDr5oM4Yfc\npDqcHv7w2ioeXbwGgIevns1jN84jMqxrkF6SJF5evQuH28ctC8YSJ2PsxenxkV/RSJTZwDAF2YHq\nkrMKtxfo5mQkCFwwbThvPLSI4QMS+PDbvVzz+FJ2H1HXaw1CEARunDMai1HHm52lTaUw6LSMz0zA\n7vGxu1J5VSclwoKAyr6vLtD3/bGRCb0+P//8YCN3PfsprR0uHvz5LJ69c+ExghQA7244QL3Nwfnj\nBsoqN/tFic0ltei1GkW6AnXtzoAMbaRVURBV42IEx5opnNF5mJ42cgBbD1Ry9WNL2bBH3UGwO66c\nNpSkKCuf7SymSAVxKsjVUVvV+b5w2gdfCJSfL5o4iKZ2J2+v26fqGsHe4mYV5K1Ik4Ewg46adofi\nHpdRr8Wg1eBU4YP6Q21S+8sD5aiVO4oYmZXIWw9dxrmTh35nc9hwsIodJXWMTI/j/InybPO2B7MD\nhQIRwewspY+54Z4QJKXI6Yt1x/FmChkJUbx874XcduFkqpvauOXpj1i8Mr9f5bqYMBPXzBqJ0+Pj\n5ZWFqqoyk7MCY0cbVVR1DDot8WFmmh1uxRmsIAhY9Fo8nU5TSqD5gfgMlQ02fvHUB7y1upD0hEhe\nve8ibl446RjFNYBD1c18sbOE5Cgrl00ZIuvaB2pbaHV6GJMeh1lBlSVYclY6YqTGvxe+a6YQG2nh\nb7cu4L7LZmB3ebjn+c955oONePthUGDU67h1bi6SBM+vKFC8PgBy+1nV+T7wkwi+ABdNHERqTBhf\nFZayv1L5LFlGTFjAFaammRaHW9FrBUEgNdKKX5SoU5GhWPRaRAlcChd0kJwSdDg62ZAkiSVrdnHj\nUx9Q2djGNXPH8OKvLyClB6eRNqeb19bswaDTyi43i1JXdjBeQXbg8vpotLuIs5qOmWeU835Onx+T\nTiNL3u+Y13b+7P6A6bRa7r9qJs/dtZDocDPPfrSZ+178AluHS9G1u2P2iHRGD4gnv7Seb/dXKn59\nlMXI8JQYamwOypraFb++P7PswY2/P1Wd7yv2fp1fzDWPL+NgRSPnTh7CGw8EmMzHw+vz8/yKAiTg\nlnl5GGSut00qCISiKFFjc2DWa4lWIAIUnFvXKvSlhp7XtSAILJo1kv/cdwkZCZG8vbqQm5/+iCoV\nVcIghqfHcdaoAVQ0tfPRNuVcCYNOy/gBnVWdfswOn0z8ZIKvXqfltnl5CMALKwsUW0cFCSoSsEVF\nyTC1P5vUj2Det8Pp4aFXVvD0sg2Emw384/ZzuePCyeh66a++/s1e2p0eLp86hESZpKkDNeqyg2DW\nq7Tk7PSqy3qDIgQaehYhGDc4lcUPLWLi0DQ27Cnj6seXsrtEXRlaEARumZuLUa/l9W/2YFN4MISu\nDX+Tir8hpZO81r91feryGTxeP397bz0PvbICvyjyx2vO5OGrz8TSS3/1/S2HqWruYF5uJsPTYmW9\nR63NzpHGtoCugALFsfoOJ15RVFxyDvpSWwxaVQpjAj1bOg5Oj+P1BxaxYOJg9pXVc+3jS/l21xFF\n1++On88cTrTVxPubD1Gp4mA4KSsRAVRzdU42fjLBF2BwSgwLxgSa+e9vPqT49aNSY7EadGwvbVAc\nvGMsRsx6LdVtDsXs1O4+qErxfQTfoqomrntyGWsKShgzMJnFD13GlBG9s5YLjtSzbn8lOYlRpv7H\nMQAAIABJREFUnDM2W/b7BIPDZAXZAXQFBuWlueCI0Yn3740Jt/DMHedxy3kTaWi1c8vfP+adr9WV\njuMjLFw5bRgdLi+vrVEuKpMZG05ypIW91c20KgzeZr2OWIuRBrsLl8LDoUmnQSOcuqSrmqZ2bvn7\nR7y3djdZSdG89ptLjo7G9YTyhjY+3naY2HAzP5sxXPb7bOpsZSnJekH9ug5+34pbKZ2Kbb0dKgEs\nJj2PXDuH3/98Nh6fn/tf/JLnPtqsqnRsMeq56azR+EWJF1YUKG7RRFtNDE2OplqlmMzJxo8y+L67\nZhf/eG+9qr7CFdOGEh9h5uNtRZTW2xS9NjB2lIjT66NQYelaEARSI6x4/CINCsuMOo0Go1aD0+tX\nHLhPtszkZ5sPcMNfP6Ci3sbVc/N49q6FxPeRybo8Pl5aVYhWI3DrvFzZQup1bQ5KGtvIjotQpEfs\n8fmp73ASbTYqDqIngpTSFzQagRsWjONfd51PpNXIP97fyO9eXYldxUz62XlZDEqKZuPBKnYozGAF\nQWBydrCqo5zTENz8q9uUZb+Bvq8Orygp7sud7EPl5n3lXPPEUvaV1bNg4mBe+80lZCf3LgspihIv\nrCzAL0rcfNZo2ZUZh8dLYWUjMRYjQ2QQs4KQJImqNgdGnSakg9HxCJIIe1Js6wtyDpVBnD9lKK/c\ndzFp8ZG8sTKfO//1CU0qWPHjc5KYMjiFQzUtfFWoPIuemt0/MZmTCdXBVxRFHn74YS6//HKuvvpq\nysr6z3KTi60HKvnnso3c/sxyxfR2k0HHzWflIkrBh0XZ4zsxKxGNoK6UkdoPVSCLQYcEirOL4AH1\nRG9SHq+fx95ey6OL16DXaXjy5rP55YVTQo5UvLNhP43tTi6YMJAB8ZGy309NTwy6NG+Vlpz9ooTL\nJ2LWfVd6L+RrO3/KfbjGD07ljQcXkZuTxOr8Yq5/8n1KapSJDGg0ArfMy0WrEXhFBas/Ny0Oi0F3\nlNCmBP3q+3YGAKVqVyeLzyCKEq98vp1f/fsznG4vD145iz9ec2ZIG8cvC45QVNvK9KGpjFEgd7q9\nrAGvX2SSQgJho92F2+cnJUJZyVmSJBwePzqNoNj5R+6hMojBaXG8/sAlnJGXxc7D1Vz9+FIKipTP\n3l4/eyRWo5631++noU1ZAM+KiyAxIiAmo0Yi+GRCdfBdtWoVHo+HJUuWcO+99/L444+fyL+rT/z5\nhrmcP20Yu0pquebxZew4VKXo9bmZCcwcnkZJnY3PdyqT6AuMHcVS1+agVGEfIs5qwqDVUGWzKw7c\n/d2kTqQiUG1zoBz30YZ9DEqN5fUHLmVWblbI1x2qbubL/COkRIdx8aTQogNBODvFB6LMBkXiA9Ct\nNBelzL3H6ZWvanU8gpuUkpAdH2Xl33cv5Kozcymra+WGJ99n5Y4iRe+bEdfJ6u9w8c76/YpeGySx\nOTw+dikcO+qPhnmXhOoPz2ew2V3c+8LnvPzZNhKjwnjpnou4aPrwkMGt3ubgnQ37CTcZuO6MkbLf\nzy9KbAmOF2XEK/pb1Y7OefwifklSPN8Lyg+VAGFmI4/fOJ+7L55Ka4eT259Zrri9EmU1ce0ZI3B7\n/byyepei1wqCwOSszrGj0lNr7Eh18N2xYwczZswAIC8vjz171BsYKIXZqOcfd53HPZdOo83u5s5/\nfcLilfmKbsq1s0YSYTawZONBxQ4xU1SWMjSCQEqkFZfPT7PC3lp/SFcnsvS8ZX8F1zy+jH1l9Zw7\naQiv3HcRaTIyWJ9f5MWVhQEW6NzckBq33bFD5XiRzy9S2+5UrHkLXQxcNWQrtU5GOq2Wuy+Zyl9+\nMQ9BEPj9f1by92Ub8CkIaEdZ/QVHFEv09YegkhrUMFeYmRi1GrSCgOMHHqU7WNHAdU8sY+PeciYP\nS+eNBwNz2aEgSRIvryrE7fVz7RkjiFBgHap2vEiSJKpsDnQagYSw0FKs3XF0XSs8VEqSpNrJSBAE\nrpqTy3N3LTzaXvn9a6twKHCcmzU8nVEZceQfqWfDQWXJVl56HCa9lq2l9ap6zycLynaWbujo6CAs\nrEvOTKvV4vP50Ol6vmR0tAWdClP4vvDLy6YxefQAfvn3j3n2o80crm7mydsX9Gq83h3xwJ0Lx/Pn\nJRt5/du9PHnDmbJPgnFxYWTsL2dfbTMas45YBQ/AcEGitLmdZq+XofHyTrvx8QG5uhq7C7vLS0ys\nVZHptMPto97mxGw1HBXtUApRlHj+o808vWQdOo2GP900jyvPkjciBPDG6t1UNLWzcNIgZo7J7PF3\ngp/z+PfdtroBvVbD/LHZWBWo+BTVtSJKEoOTY3q8dl+oaHMiCJCRHPWdOc6+4HD7cNichPfxXYf6\nW66Yn8eEkenc9tRHvLtmF0U1zTz764UkyPSmfmDRFO56cSWvfL2Ll+48R/ZBJ55w8jITyC+tp00U\nGaigBznKrGdvbQuNTg/jBwc+n9zvvMnjo7HNSXikBbNR/pbk8fmpbnZgMOmIi1AWhLpj2Te7+cPL\nK3F7fdx56VTuunSq7OdrZf4RCssaGD8omYtnDlOUTW7fchCAc8bnEK/Ad7y+zYHD62NQUhSJicp8\naxsqAlyV9MRITAoCfpBgau3Hdz0vfgi5Q1P45d+Xs2pHEWV1Lfz73gvJSZXHCn/w8qnc8I/PeH3t\nXs4cm0Wkgl73jKFprNxdRmm7nSmDUvr8XaV7hVqoDr5hYWHY7V0ZoyiKvQZegJYW5c32vhAfH05D\nQzsDYiP57/2X8rv/rOCrrYfYX1rHEzedTXYIz0yA0SmxjMlMYHtRLe9/u59Zw9Nlv/+E9HjKG9v5\nYnsJ8/tg9h4Pkyih0wgcrmlhYGRYyIc1+DkBDIJAB1BRY+vTieR4BBOKdrsHrwqj6HaHm/97YzXr\ndpeREGXlsRvnMzIrkcZGef32yqZ2Fq/ZQ7TVxMXjBx79PN3R/XN2x/6aFhrbnYGSaLsLR7t8str+\nTi3uaL2ux2v3Br8oYnd7sei1NDUp4xR4Or9rZy/fdW+f83hEGAy8dM+F/PnNb1idX8y59/+XP/9i\nLmMG9r1xACRZzczLzWRFYSkvf7ZTlo1dEOPS4sgvreeLncVcMUF+a0CSJMKNesoa26ips5GcGCn7\nO9d2slgr62xEKZpXDfy0u3xIbuWjKB6vn6eXrefD9fsINxv4yy8WMH1UJs1N8iphbQ43z366A6NO\ny3Uzh8t+HgBqbQ4O1bSQEx+B3icpWp97OvkA8SajotdJkkRLhwu9RqDd5kTJN2bqVKbzuHw0qPiu\ng9AA/7zjXJ75YBPvfbObCx58gz9cPZszx+SEfK0euGzKEBZ/u4+nP9jCL88eK/t9RydFs2p3GSsK\nSsnpY9+V+3zKRV+BXHXZeezYsXz77bcAFBQUMHiw/Af1RCM20sKzd53PVXNyKa+3ccNf5fXLBEHg\nxrNGq5qRHNVJUNlWqoygotVoSI6wYFfhharWYrA/DkeHKhu59ollrNsdcGx548FFjFRAKBEliRdX\nFuLzi9w4ZxQWmfZ/QaglWvlFkZo2B1aDjkiZhuRBqC05QzdZScWv/C6sJgN//sXco/2yO55Zztur\n5fXLrpo+jNgwEx9uPUyFArGDzNhwEiMCY0dKdHGPCslIyoVkfgg+Q3VTGzc9/SEfrg/wFv77wKXH\naI7LwX+/2UObw83l04aSoLD3uvmIunUNgX6vVhBIlOH+1R3uTl9qpax/AHfnM3EixmN0Wi33LprO\no9efhShJPPTKCp75YKOskvA5Y7PJTozk232VFJbWy37PGKuJof3wsD4ZUP1dzp07F4PBwBVXXMFj\njz3GQw89dCL/LsXQabXcffGx/bKn3lsX0hlD7Yxk0O3I6fVRqNDtSK3gRtcm9f0oAn22+QA3PvUB\nVY1tXDd/LP+441yiFT7wq3aVcrC6mcmDkpkwUL4RAgTKa8Wd3qZJCpyIAOo7XPhEiVSFAgTQbQ5S\ncV8M1X2x3tC9XxYVZuaZDzby0CsrQpozWIx6bjw6I1koe0ZSEASmZCciSijWxU1TaZ9p0GrQaQQc\nHuXWmWrENjbsKePax5dxoLyBcyfL5y10x86SOtYfqGJoWiznjJE/qw5dBMJoi5EhScoIhEc9qSPM\nsl22grArsBA8Hm7fiQu+QcwfP4jX7u9SxfrlP5fTGGLtaDUabp2bh0YIaPW7FBzYgl6/m1UK2pxo\nqP4uNRoNjz76KO+++y5LliwhJyd02eD7wJyxOZ0zedEsXbuHW//+ETUhWMln52UxKDkwI7ldAYlq\nksqxI7VeqBpBwKzT4vL58SscOFeySbk8Xv705prOMSItf73lbG5bOElRnxmgqd3JW+v2YzXqueHM\nUYpeC7Cpc/OfosC9KIhKlWxQCJDaBALi/0oQvCMnltkQwJhBKbzx4KWMGZjMmoISrn9yGUUhZPPG\nZScxdUgqh2tb+LJA/oxkd11cJQSVKLMBi15HjU2Zhnlw3tcvSXhUzvvKOY76RZEXP93KPc9/jsvj\n47dXzeIPP5+NyaCsGuP0+Hh59S60GoH7L/mutnMoBMe5JilwLwpCrbAGdJE1lc6tSxJ4fWKf4hpq\nkZ0Sw2u/uZTZednkF9Xw88eWsv1g3zKpmQmRLByfQ0Obg3c3yHc+CnpY765qpk2lv/uJxI9SZCMU\nMpOi+c/9l7Bg4mD2ltVzzRN9u21oNAK3zu2ckVy9C4dbHgsv0mxkeHIMtQrHjvrjhRrMxpwKS3Ry\nN6myuhZ+8dcP+GTTAYakx/HGg4uYOTr0GNHxCLBAA/OmV88cTpRCIQCnx0d+eUNgvEhhdiBKEtU2\nOyadlhgF7FMIWLR5/CIWvXLpPTWjGEoQF2nl2bsW8vOz8o62V5Zv3N/nwe/62SMJM+l5Z4P8GUm1\nuriB0rMFryhS1aystKe2qiOX8dxos3PXvz7lP1/sIDk2nJfvvYgLpoUeI+oJ76zfT1O7kwsmDCJb\nxdrccqROsT55EJU2O4IAyQqEZiDwPDq9Pgxajer53pO1rsPMBh67cR6/Pjq98imvfrG9z2rNpZOH\nkBxt5YudJRySyeoPVHUCY0fbTgG3o9My+EJgHOmP15zJQ1fNwuX2cc/zn/PPPtw20uMiuGTSYJo7\nXLypwPlIbSnjqDBBqzIimloTcjmb1Irth7nuifcpqm7mkhkjePnei0jtwRRBDjYcqGLnkTpGZcQx\ne6R8QloQO8q7xAeUmho02l14/KLKkrM6txc4+ZsUgE6r4c6LpvDkzWdj0Gn581vf8Mjrq3tVxYq0\nGLlmVmBG8uVV8mckJ2V3uh0VK6vqBDOykgZl6nFq+QxBh6O+xDa27K8IZFSHqpg5OpM3HlzEUIVz\ntUEcrG7mq4IjpMaEccmkQcpfX9tCi8NNnsLxIgC720ur00NimDJPagiYsoiS8qwXlItrqIEgCFwx\nezQv3nMB8VFWXvp0G7/692c09XJgNOi13Do3Dwl4YWWhbLXDU2ns6LQNvhC4oRdOG84r911EekIk\nb4Vw27hw4iDSY8NZtauMfTL7uANiArq4+2qU6eIGvVCVlp7N+qAP6olTBHK4vDy6+Gv+8NoqAP7f\n9WfxmytmYlTRG4IAC/S1NXswKnAs6g617kVBBH2T1ZTmuqT3Ts3gG8Ss3CwWP3QZIzIT+HLbYa57\nYhmHehHHmDU8ndwB8RQocD6KtgSqOtU2O+UKstg4qwmjTsuR+jZFQdug1aDXqJ/3lfgun8HnF3nh\nky3c/dyntDvc/PrSaTx589mKZnG7w+Pz80LQsWhuLnoVo5MbO9tak7OUE63U2geCegtBOPkVne4Y\nlZXE4gcXMXVERuDQ9Jf32Ly/osffHZYWy7zcTCqb2vlwqzzno2BVp8PtZU/1D+t2dFoH3yCGpMfz\n+gOXHnXbuPqxpazY/t2bpdNqAs5HAry4sjAkWQu6lzJQpKBi0GlJCDPT4nRj98gfNtcIAma9FrdP\nVCyN2VP2e6C8gWueWMpnmw8yJD2O1x+4lHnjlZ/ou+M/a/bQ7vJw5fRhilmg0JUdBCUPlSAgQGDH\noNUQH6as1A2BzEsjgFGn7NEI5WR0MpAcG86Lv77wGJb/26u/S64SBIGb5+Zi0mv575o9tMjUFg9W\ndTaVyJcEDGiYW3B22jgqgcWgQ5QCrFwl6GldVzbYuPXvH/HalztJjgmUma+YPVpVmTmIZZsPHXUs\nGipzNrU7am1d+uRKCYTQ336vOp1y6FzXgqBIsa0/iAwz8bdbz+Hui6fS5nBz97Of8q8PN/WY3V41\nfRix4WY+3HqYMpnVlu5iMj8kfhLBFwJjG49cO4c/XnMmoijyh9dW8cf/rqb9uGx1YHI054zJpqbV\nznub5DXzR6scO+piPasrPStVu+q+SflFkbdWFfCLpwKmCFfNyeXV+y4mI1G+sEJP2F5cy8aDVQxO\njubsPOW9YlDv8gLQ7HDj8vlJibAoJrN4/CJeUZ303veZ9XaHXhdg+T992zmEmY0888FG7n7u0+9o\nnsdHWPjZjOHY3V7ZEn2ZseEkRSh3O1KrYd7VUlFW1emu4CZJEp9sOsDVjy1l95E65o4bKFutqi+U\n1LWyfFsR8RFmfq7Asag7gpv91Bzl69rp9dHkcBPfWVlQAlGScHr9GLUaxQzpoJORUa/53g6VEODh\nXDUnl1fuu5j0hEjeXFXATX/7kNLalmN+z2LUc9OcLla/nIQkxmpiSFI0lS12KppP3EyvUvxkgm8Q\n50wawhsPLmLEgAS+3HaIq/68hK0Hji3FXT5tKImRFj7ZUSyrmR8cO3J0jhDIRapKL1S1FoPBR7ai\nwcZt//iYf364iQiLkWfuOJe7L56qqozWHXaXl5dX7UKn1XDrvDzFLFDoGi/6QbODfvR7T2ZfrC9M\nGzmAt393GdNHDmDrgUp+9pf3WL2z+JjfmZubybDUWLYV17LpUHXIawY9rJVWdRKsZow6LZUKNcyD\nWZnaQ2VLh4uHXlnBn95cgyAI/N+1c/jTDXNlKd71BZ9f5N9f5SNKErfMzVOkDBWE3e2loKKh071I\nGUkL+reuXV4/Ev1b10YVGfOJwLCMeN54YBHnTh7C/vIGrnl8Ke98fWx1Z2x2IjOGpVFc18qnO4r7\nuFoXggegjT9g9vuTC74AGYlRvHTvRdx83gSa2pzc+a9PeOq9dTg7Wc4mvY7b5o9BkuD5FQWyvHsn\nZQV0hzcW18jecEx6HXFWE412l6J5NbMu2PdVtklJosRHa/dw/WPvUVhcy+y8bN7+3eVMHq6cENUT\nXl+7hxa7i0smDSYtVp1Em1pRDQhkPZU2OzqNcgEC6CJbKbVag++3L9YbYsItPHXrAn5z+QzcHj+/\nfXUFD7781VErN40gcNu8gK72f77eTZuMbDZXRVVHoxEYEBeB0+unRYFQh16rwaDV4PAo7ftKrN1Z\nxLV/eoc1BSXk5STz1m8v4+yJJ0b458OthylvbGfOqAGMHqCOqLW9rB6fKCnWJw8iWB3rD4+hP2Sr\nHyr4QsAj+OGrz+Txm+ZjMer5x/sbuf2Zj6nuxt257oyRRFqMvLfxIJUyJk+CY0d7qpSJyZxIaB95\n5JFHvo83cqiQNewLVquxX9fUaATGDkph2sgBFBbXsGFvOV9tO0x6QiQZCVHER1hod3rIP1KPX5RC\nPnQmvZaGDicljW1kxkUQI3O0xusXqWt3EmbQ9zgW09PnFAQBp9eHyycSZdbLepjL61r53X9W8NG6\nvViMen7/s9ncfN6EkFZpcrGjpJa31u0nKyGS2+ePUZz1Wq1GmlodLNtZTIRJz8K8LMWl31anh4MN\nNlIjrWREKwv+kiRRb3chIBBvNSp+7+AdMtB3z7e/6zYUBEFg+IAE5ozN4XBlE5v3V/DJpgPERVoY\nmBpLuNmIXqthW3Etje0uJg/uW65SqxFweX0U1duIthhJ6cOruTusViNFda0YdVpFByG3z4/LJ2I1\n6GSNxDS02vm/xV/z+pc7ECWJ2xZO5sGrZhKhcLStN5Q12PjXFzuJsZq4/4KJ36kOybmfflHkve3F\nSEgsGjcwpO3m8XD7/ORXdXr+JihvCzU63PhEiaQws/JWDIGyc2yYCecPbMmXlRTNOZOHUNXQxub9\nFSzfuB+DXsewAQmYDTqSoqysP1BFcV0rZ4xI7/OzCoKAVqNhf20LOq1ATqfIyol+Pq19aOn/JDPf\n7hiaEc/rD17KdfPHUt9q557nP+e3r6yg0WbnqhnDSIy0sHx7EYdrWkJea2qnGMQGBZ6ValWBgiUk\newgrNpfHx4ufbuWqvyxhx6Fqpo0cwKu/u5wzxw/qF/mkOzqcHl5aWYhWI3DH2WMUby5BbCutw+sX\nmaI6O1AvrOHxi/hECYtB+Xxvf5yMThYyEqJ4/lcXcN9lM/D6/PzfG1/zq+c+o7yulXPH5jAoKSAq\ns1lG+TkoJrNRgZhMRmw4Wo1AZWuHoizWKtNi0C+KfLBuL1f86V3WFh4hb2AKr/72cq44K0+xGExv\nCJSbC/CLEjfPzVUsjRrE3uqAqMO4jARMKlj0QU9qNVlvsN9r0mkUj+x1V2xT00I6GYgJt/D4TfN5\n5No5GPQ6nvlgI9c9sYw9R+qYODCZ6UNTKapt5ZPtocvPeeldVR051c0TjZ988AUw6nXctnASix+6\nlFFZiazOL+ayR9/lw3V7uWnOaCQJ/v1VfsgblB4dRnp0GIfqWmnskKdvazHoiDYbaehwKloAXaSr\n3svVG/eWcdWfl/CfL3YQHWbm8Zvm88QtC4iNtJ4wD1SA/6zZTYvdzWVTh5Khci7YJ4psKqnDoNMw\nPlM5OSZYctYKAknhynvFwRK+mhGjU6Hk3BM0GoFFs0by9u8vZ9KwdDbvr+DKPy/h38s3c8PskRh0\nWl5etYvWEKzkSLORESkx1LUF2LpyoNNqSA630KFQw7yr79v7us4vqua6J97niXe/RZQkHrhiJs/d\ntZDU+MgTuq4/2HKII/U2Zg1PZ4wCPfPjsamkFgGYnK3uGv05VDr6oVN+MhXb+gNBEFgwcTDvPXwF\n508ZyuGqJm782wc88c5aLpk4iCirkfc2HQypaa7XapiYmYjD46NQoYf1icCptl/8oMhJieWley7i\ngStmIgjwj/c38n+vrWRwXDiVTe28tzE0+3lqThISsLlEPkElLdKKROCEKxcmnQaN0LMowf7yBu78\n1yf8+t+fU9vczlVzclnyhyuZnZeNrjM1O1Gb1NbDNaw/UMXApCgWjlcvMbqzpK5f2UGb29uleasi\n8+7Scz6153vVICU2gmfuOJfHb5pPfKSVxSsLuOtfyxmeGEGb0x3wWQ6Rnaqq6kQpr+poNRqMOg1O\nrx/xuL+ppqmd3/1nJbf+/WMOVTZy7qQhLH34Si6eMeJoVnei8pei2hY+2HKY2HAz188eqfo6lS0d\nlDd3MDgxijiF3rsQbEs5iDQZCFOReQfXtRIXtCBO1UNlEFFhZn7/89m88KsLyEyM5oP1+7j6saUM\niDDh8fo6qxZ973RBiU+lYjInAqotBU9XaDQCF88YwZwxObz65XaWrd1LZeMBwqwmlq7bR15mAiP7\nUMcZkRJDhMnAjvJ6zhqWJiuQpEZZ2F3bTKXNTmaMvF5lUA+3w+PD4xcxaDVUNth44ZOtRx2dJg9L\n586LpjCw20yiIIAgdTkc9adM2uZw89KqQvRaDbfPH6O63CdJEqv2lCPQNVuqFFWtwexAvi9q9/e3\ne/3oNAJ6FeW1E+lkdLIgCAKz87KZMjyDN1fm88bKfD7+dg9Wi5Fv7U7G5yQxZ9SAXl+fERNOenQY\nBzurOnICSXJQw7zVzsik0BafQVj1Otw+D06vH6tBR11LB//9cgfLNx3A5xcZMSCBexZNP8ZdSxBA\nI3WJyPRnXbu9Pp79IsBuvmN+nupyM3SJaqhd1zVtDkRJXckZAmQrgYA4j1Kc6ofKIMYMSmHxQ4v4\nYN1eXv1iB8vX78Ni0uPocPL+5oNcNnVYr6+NMBsYlRpLYWUjxQ02EhLUVe7U4CdLuAoFk0HHlOEZ\nzB0/kAabnUMVjXS0O1idX0JKbDhZSdE99gY1goBfFDlUZyPMqCdDRjANjGV00OJwMygu8pjeTF+f\n0y9J2D0+KmqbeXH5Fp54dx1F1U0MzYjnkWvm8ItzxhPTgwasn0BJSYf6TUqSJP71xU6O1Nv42Yxh\nih2LuqO8uYPV+ysZlhTNZBUsZ4D8qia8fj/j0+MV97bcfpEWp4dwg55wk7KNVpICpBQBMMh425O9\nbkNBp9UwdnAqCyYOxun2sre0DnuHk/W7S4kKMzEoNbbX/p5Rr2VPdTOiREi9bavViMvppdXpptHu\nJj3KKns+VZIk2tw+Wtvt/PeLHTy6+Gv2ltaTEhvOry+dxj2LppMY891DlkQgYGgJyE6qxeJv95J/\npJ5zxmYzL7fvWfW+7qfN6eajgiPEh5tZMHKAKo7F3rpm2t1exqbFKZ7v9YsS9XY3Zr2WaAU+yUF4\nCXynBn74dRsKWo2GkVmJXDR9OAKwq7iWjg4n2/ZX4PL6GJWZ2OsoZaTZwPayehweH1OHpH5vhKv/\nBd8QiLSamDtuIFOGZ7C3vIG6pja+zi/hm8ISdFoN6QmRGI67qQnhZjaX1FHf7jw6ghQKbp9IfYeL\nSJOByG4PSm+fUxQlth+s5Nn3N/L6Fzsoqm4mKymKey+bwa8vmdanRZpEIAD3Z5NavbuMT3YUMzwt\nlpvmKJeQ7I7P95TR0O7kwrxsolXMZLa7veytbSEpwkKWzMpBd7S5vNi9fmIsBkwqnIx8BL5L3Y8g\n+AYRZjYyY3QmZ08YRFFNC5V1razfXcZnmw/i9voZkBj1HSZ8XJiZ/PIGyps7mJiVgL4PfeHg55Sk\nQM/SpNcSLyNbliSJgsPVvPzJVl78eDN7S+tJig7jV5dM46GrzmBIenyvay24rgVAq3I57i5v4NWv\nd5MaE8Y9545HG6KF0df9/OZgFWXN7cwbkUFqlPKKjM8vsr2ykTCjnhEKKgdB2D0+2t2DrYc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qs8dVjKNfHKJkuoskQ4mXvc92z5DL/YdYRdRzpZ21DBJ6/dZEn+AkAwlmBvWy/lbgebluRvSWca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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6fe704160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.color_palette(\"Blues\", desat=.7):\n",
    "    sinplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al ser un paquete especialmente diseñado para **estadística**, además de gráficos más lindos, tiene **gráficos especiales** e incorpora \"datesets\" de prueba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BWy7qsX/+85/gve99P/7t3/4fFixYhOeeezrzvaeffgonTnRm9blQfmGFj4iI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\n1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8mggcXY+7ZoREREFF75hZU4erYOYwcnYnr2QKmHE5GiJvBp1AJizTpu3SYiIoogTrcX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6ec628748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "g = sns.jointplot(\"total_bill\", \"tip\", data=tips, kind=\"reg\", xlim=(0, 50), ylim=(0, 10), size=9)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Y qué ese dataset? Veamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Pandas\n",
    "\n",
    "\n",
    "[Pandas](http://pandas.pydata.org) es una biblioteca para manipulación y análisis de datos basada en Numpy. Aporta nuevas estructuras de datos de alto nivel que extienden datos almacenados en arrays, aportando más semántica y nuevas operaciones. \n",
    "\n",
    "Puede pensarse a Pandas como un **reemplazo pythonico a excel**\n",
    "\n",
    "\n",
    "Pandas introduce dos estructuras de datos principales: `Series` y `DataFrame`.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series\n",
    "\n",
    "Un Series es un objeto **unidemensional**, similar a un array, una lista o una columna en una tabla, que tiene **asociado una etiqueta** para cada elemento. Por defecto, esta etiqueta es un número de 0 a N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8]) # creamos una serie, analogo a un array de 1D\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.Series?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vemos, es simplemente un envoltorio más lindo: el verdadero contenedor es un array de numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   3.,   5.,  nan,   6.,   8.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, (6,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values.ndim, s.values.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si bien una serie se puede indizar directamente como una secuencia, la forma correcta y potente es utilizar los métodos especiales `loc` (basado en valor) e `iloc` (basado en posicion). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=6, step=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 1.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc[0], s.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Pero entonces una Serie es una secuencia?. En realidad es más parecido a un diccionario ordenado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "-1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-0e2107f91cbd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    599\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   2475\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2476\u001b[0m             return self._engine.get_value(s, k,\n\u001b[0;32m-> 2477\u001b[0;31m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m   2478\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2479\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4404)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4087)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:14031)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:13975)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: -1"
     ]
    }
   ],
   "source": [
    "s[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.ix[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hay muchas operaciones que podemos hacer sobre una serie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.mean(), s.sum(), s.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E incluso ver un resumen general de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vemos, es un poco más permisivo que Numpy con los datos faltantes (ese `nan` que hay por allí)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.values.mean(), s.values.sum(), s.values.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y hay operaciones que no están disponibles en numpy o se comportan distinto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.values.median() # np.median(s.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas es una herramienta nacida en la industria de las finanzas, y por ello intenta hacer fácil las cosas tipicas. Una de ellas es **graficar datos**. Utiliza **matplotlib** por default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como dijimos, los valores de una serie tienen una etiqueta y se pueden definir explicitamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "goles = pd.Series([10, 54, 31, 0], index=['gaitan', 'messi', 'suarez', 'ronaldo'])\n",
    "goles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuando el índice no es de enteros, se puede buscar un valor tanto por clave como por posición (con reglas de slicing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "goles['messi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "goles[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "goles.ix[1:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviamente, dado que se trata de claves y valores, se puede instanciar una serie directamente desde un diccionario. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.Series({'gaitan': 80, 'messi': 100})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero a diferencia de un diccionario, las etiquetas se pueden repetir. Es decir que pueden haber multiples valores para una clave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series(np.random.rand(4) * 100, ['gaitan', 'messi', 'maradona', 'messi'])\n",
    "s['messi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.plot('bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La series se utilizan mucho para representar datos en **función del tiempo**. Hay una función especial análoga a `range()` que permite generar índices temporales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.date_range('1/1/2010', periods=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2010', periods=1000))\n",
    "ts = ts.cumsum()\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataframe\n",
    "\n",
    "Un DataFrame es una estructura tabular de **filas y columnas** (¡como una hoja de cálculo!). También se puede pensar un DataFrame como un conjunto de Series que comparten el índice (es decir, la primera columna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000,4), index=ts.index, columns=list('ABCD'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las columnas son Series!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(df.A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"A\"].cumsum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede instanciar un DataFrame a partir de un diccionario. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({ 'A': 1.,\n",
    "                     'B': pd.Timestamp('20130102'),\n",
    "                     'C': pd.Series(1, index=range(4),dtype='float32'),\n",
    "                     'D': np.array([3, 2, 1, .9]),\n",
    "                     'E': 'foo' })\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2[['B','C']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.loads(pickle.dumps(df2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas tambien puede importar archivos CSV o Excel, locales o remotos. Por ejemplo este [dataset](https://github.com/rjtavares/football-crunching/blob/master/datasets/cl-shots-2012.csv) de remates al arco en la Champions League"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/rjtavares/football-crunching/master/datasets/cl-shots-2012.csv'\n",
    "shots = pd.read_csv(url, index_col=0, na_values='N/A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos filtrar el dataset por múltiples criterios. Por ejemplo, para calcular la distancia promedio de los goles que hizo el genio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots[(shots.player == 'Lionel Messi') & shots.goal].dist.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos separar subconjuntos basado en uno o más criterios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots.groupby('team').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots.groupby(['team', 'goal']).dist.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una alternativa aún más poderosa es hacer un pivot de datos, donde se definen una o múltples filas y columnas para agrupar datos, y la dimesiones ('values') que se quieren ver resumidas. La función de agregación por defecto es `np.mean`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt1 = pd.pivot_table(shots, index=['team'], columns=['goal'], values=['dist'])\n",
    "pt1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "como siempre, podemos graficar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt1.plot(kind='bar', figsize=(13,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En los dataframes resultantes de un pivot, índice y claves no son lo mismo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt1.index is pt1.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entonces esto no funciona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt1['Barcelona']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt2 = pd.pivot_table(shots, index=['team', 'player'], columns=['goal'], values=['dist'])\n",
    "pt2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt1.loc['Barcelona']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las tablas pivoteantes son mucho más poderosas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt2 = pd.pivot_table(shots, index=['team', 'player'], columns=['goal'], values=['dist', 'x', 'y'])\n",
    "pt2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notar que en este caso usamos un índice compuesto (team-player). Esto hace que el dataframe utilice un tipo de índice especial llamado `MultiIndex`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt2.loc['Barcelona']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos conseguir la fila particular para un índice compuesto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pt2.loc[('Barcelona', 'Lionel Messi')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y ahora podemos volver al dataset `tips` importado de Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips.pivot_table(index=['day', 'sex'], columns=['time', 'smoker'], values=['total_bill', 'tip'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y ya que estamos, veamos los gráficos facetados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sns.FacetGrid(tips, col=\"sex\", hue=\"smoker\")\n",
    "g.map(plt.scatter, \"total_bill\", \"tip\")\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas es una herramienta poderosísima y sólo vimos un poquito. ¡A estudiarlo!\n",
    "\n",
    "------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python es infinito: Instalando paquetes\n",
    "\n",
    "\n",
    "Python es un lenguaje utilizado en muchísimas áreas. El repositorio donde se suelen compartir paquetes se llama PyPi, el Python Package Index. \n",
    "\n",
    "**Desafío**: elijan un tópico cualquiera y busquemos un paquete para instalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('http://pypi.python.org/pypi', '100%', 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La herramienta oficial para instalar paquetes es [pip](https://pip.pypa.io/en/stable/). La forma canónica es\n",
    "\n",
    "    pip install --user <nombre_paquete>\n",
    "\n",
    "(el flag `--user` (opcional) instala el paquete a nivel usuario, sin requerir permisos de adminitracion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pip busca, baja, descomprime, e instala (y repite los pasos recursivamente para las dependencias). Por ejemplo, podemos instalar [pint](http://pint.readthedocs.org/en/0.6/tutorial.html), del amigo [Hernán Greco](http://www.conicet.gov.ar/hernan-grecco-la-alfabetizacion-cientifica-es-tan-importante-como-la-alfabetizacion-tradicional/)\n",
    "\n",
    "(Nota: esto no va a funcionar en `try.jupyter.org` debido a las limitaciones de salida de red por cuestiones de seguridad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pint \n",
    "ur = pint.UnitRegistry()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vel = 60 * ur.km / ur.hr\n",
    "vel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vel.to('m/s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero **¿dónde se instala un paquete?**. Si no le decimos lo contrario con algun parámetro del instalador, el paquete se descromprime y se copia a un directorio especial para Python llamado `site-packages`, que a su vez se encuentra en una **lista de directorios** (definida como una variable de entorno del sistema) en la que la maquinaria de importanción de Python busca paquetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pint.__file__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para desintalar paquetes, el subcomando de pip es `uninstall`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip freeze"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notaron ese directorio \".virtualenvs\" que se lee en mi sys.path?  Es que yo estoy usando un **entorno virtual**\n",
    "\n",
    "### Entornos virtuales\n",
    "\n",
    "Un entorno virtual (virtualenv) en python es una compartimentalización para tener multiples conjuntos de **dependencias por proyecto**, instalándolas en un directorio particular. Es decir, podemos tener un virtualenv en el que usamos numpy 1.8 y no vernos obligado a actualizarlo si comenzamos otro para el que necesitamos una feature que aparece en numpy 2.0. \n",
    "\n",
    "Python 3 incorpora built-in una herramienta llamada [pyenv](https://docs.python.org/3/library/venv.html) para manejar entornos virtuales, pero la forma canónica sigue siendo utilizar la herramienta [virtuelenv](https://virtualenv.pypa.io/en/latest/)\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero instalamos virtualenv\n",
    "\n",
    "        pip install virtualenv   #  o pip install --user virtualenv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para crear un virtualenv basta usar \n",
    "\n",
    "    virtualenv <DIR>\n",
    "    \n",
    "Donde `<DIR>` es el directorio donde queremos instalar las dependencias exclusivas para nuestro proyecto. En mi caso es `~/.virtualenvs/curso`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez creado un virtualenv, cada vez que se quiere usarlo hay que activarlo. En sistemas linux/mac:\n",
    "\n",
    "    source <DIR>/bin/activate \n",
    "    \n",
    "o en sistemas windows: \n",
    "\n",
    "    <DIR>\\Scripts\\activate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuando el virtualenv está activado, se denotará por un prefijo en el prompt. Luego, podemos instalar paquetes directamente via pip, sin permisos de adminitrador, ya que estamos instalando los paquetes en el `site-packages` definido dentro del virtualenv. \n",
    "\n",
    "\n",
    "Para facilitar la creacion y activacion de entronos con virtualenv se puede usar [virtualenvwrapper](https://virtualenvwrapper.readthedocs.org/en/latest/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para salir de un virtualenv (desactivarlo) se ejecuta\n",
    "\n",
    "    deactivate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conda \n",
    "\n",
    "Un problema del tandem virtualenv-pip es que, en general, los paquetes instalables son archivos comprimidos de **código fuente**. Eso no es ningun problema cuando se trata de paquetes python puro, porque Python se interpreta y no se compila, pero cuando es un paquete que tiene dependencias externas (fortran, cython, c, etc.) o requiere dependencias complejas en el sistema operativo, la instalación via pip requerirá compilar, eso implica compiladores, headers y un montón de cosas que complican la vida (además de ser un proceso lento). \n",
    "\n",
    "Por eso la herramienta [conda](http://conda.pydata.org/), que viene preinstalada si usamos la distribución de python Anaconda, es muy interesante. En vez de bajar código fuente, baja paquetes precompilados (binarios) en un formato y desde un [repositorio especial](https://binstar.org/) (distinto a PyPi). \n",
    "\n",
    "Además, conda sabe crear por sí mismo entornos virtuales, es decir que es una mezcla entre virtualenv y pip. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para crear un entorno virtual via conda\n",
    "\n",
    "    conda create -n <nombre_venv> <programas o paquetes basicos a instalar, opcionalmente con versiones>\n",
    "    \n",
    "Por ejemplo:\n",
    "\n",
    "    conda create -n curso python=3.4 numpy scipy matplotlib pandas sympy seaborn ipython ipython-notebook\n",
    "\n",
    "Para activar el entorno, es análogo a la manera de `virtualenv`\n",
    "\n",
    "    source activate curso   \n",
    "    \n",
    "O en Windows, directamente \n",
    "\n",
    "\n",
    "    activate curso\n",
    "    \n",
    "Para instalar paquetes dentro del entorno, \n",
    "\n",
    "    conda install <paquetes o programas>\n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribuyendo tus paquetes (o módulos)\n",
    "\n",
    "Ya vimos algo de esto. Python usa un archivo especial llamado `setup.py` que invoca a una función llamada `setup()` con diversos parámetros que especifican de qué se trata el paquete, la versión, el autor, la lista explícita de  módulos y paquetes que debe incluir, etc.\n",
    "\n",
    "Este *script* `setup.py` es llamado por python (directamente o a través de pip) y nos presenta una lista de subcomandos. \n",
    "\n",
    "\n",
    "Se nuevo sucede que Python trae incoporada lo necesario para escribir este archivo de setup, en el paquete `distutils` de la biblioteca estandar, pero la opción canónica superadora es usar el paquete [setuptools](https://setuptools.pypa.io/en/latest/index.html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%mkdir test_pack/\n",
    "%mkdir test_pack/marquesina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd test_pack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supongamos que queremos distribuir nuestra [función maquesina](https://gist.githubusercontent.com/mgaitan/6319640/raw/68d4fc1b55407ce9e358b6769699e536eed5a4e1/gistfile1.txt) de la clase 2. Vamos a crear un paquete. Como es  nuestro único código, lo podemos poner directamente en el `__init__.py` del proyecto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile marquesina/__init__.py\n",
    "\n",
    "def marquesina(cadena, ancho=60, alto=1, caracter=\"*\"):\n",
    "    \"\"\"\n",
    "    Wraps ``cadena`` in a box of ``caracter`` symbols with padding ``ancho`` x ``alto``\n",
    "    \"\"\"\n",
    "    \n",
    "    cadena = cadena.center(ancho)\n",
    "    cadena = caracter + cadena[1:-1] + caracter\n",
    "    cadena += '\\n'\n",
    "    relleno = \" \" * ancho\n",
    "    relleno = caracter + relleno[1:-1] + caracter\n",
    "    relleno += '\\n'\n",
    "    tapa = caracter * ancho\n",
    "    return tapa + '\\n' + relleno * alto + cadena + relleno * alto + tapa\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sigiendo la [doc](https://setuptools.pypa.io/en/latest/setuptools.html), una versión mínima del `setup.py` se vería así"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile setup.py\n",
    "from setuptools import setup, find_packages\n",
    "\n",
    "setup(\n",
    "    name='marquesina',\n",
    "    version = '0.1',\n",
    "    description='Marquesina, decorate your text',\n",
    "    long_description='A package with functions to decorate text',\n",
    "    author='Martín Gaitán',\n",
    "    author_email='curso@',\n",
    "    packages = find_packages(), \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Así se ve el árbol de archivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nuestro `setup.py` es un programa de linea de comandos, con ayuda y todo!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python setup.py install --help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora podemos instalar nuestro paquete, por ejemplo via `pip install -e` que instala el paquete sin copiar los archivos a `site-packages`, permitiendo que si modificamos el código impacte automaticamente en la versión importable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -e ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from marquesina import marquesina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(marquesina('hola mundo'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supongamos que lanzamos una nueva versión que incopora otra función "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile -a marquesina/__init__.py\n",
    "\n",
    "def boca():\n",
    "    print(marquesina('¡Aguante Boca!'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No haría falta cambiar el `setup.py` para importar el paquete o sus funciones, pero podemos usar una funcionalidad de `setuptools` que permite convertir funciones en simples programas de línea de comando instalables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile setup.py\n",
    "from setuptools import setup, find_packages\n",
    "\n",
    "setup(\n",
    "    name='marquesina',\n",
    "    version = '0.2',\n",
    "    description='Marquesina, decorate your text',\n",
    "    author='Martín Gaitán',\n",
    "    author_email='curso@',\n",
    "    packages = find_packages(),    # ['marquesina']\n",
    "    entry_points = {\n",
    "        'console_scripts': [           # esto crear un script 'boquita'  que ejecuta marquesina.boca()\n",
    "            'boquita = marquesina:boca',\n",
    "        ]}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -e ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entonces **boquita** ahora es un programa en nuestro sistema!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!boquita"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para escribir \"scripts\" de linea de comandos que reciban parámetros, se puede utilizar el paquete de las biblioteca estándar [argparse](https://docs.python.org/3.4/library/argparse.html) o bien usar [Docopt](http://docopt.org)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos más allá"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile marquesina/plot.py\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def marquesina_plt(text, x=5, y=2, text_size=80):\n",
    "    \"\"\"\n",
    "    return create a matplotlib's Text instance of `text`\n",
    "    \"\"\"\n",
    "    coef =  x/max(len(text), 6)\n",
    "    plt.figure(figsize=(x/coef, y/coef))\n",
    "    plt.tick_params(bottom=False, left=False, right=False, top=False, labelleft=False, labelbottom=False) \n",
    "    return plt.text(0.5, 0.5, text, size=text_size, ha=\"center\", va=\"center\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "marquesina_plt('aguante boca!', x=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile setup.py\n",
    "from setuptools import setup, find_packages\n",
    "\n",
    "setup(\n",
    "    name='marquesina',\n",
    "    version = '0.3',\n",
    "    description='Marquesina, decorate your text',\n",
    "    author='Martín Gaitán',\n",
    "    author_email='curso@',\n",
    "    packages = find_packages(),    # ['marquesina']\n",
    "    install_requires=['matplotlib'],\n",
    "    entry_points = {\n",
    "        'console_scripts': [           # esto crear un script 'boquita'  que ejecuta marquesina.boca()\n",
    "            'boquita = marquesina:boca',\n",
    "        ]}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -e ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from marquesina.plot import marquesina_plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd test_pack/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Luego, podemos armar nuestro paquete distribuíble. Es un simple archivo comprimido con todo lo necesario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python setup.py sdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ls dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setuptools también sabe registrar y subir nuestro paquete a PyPi\n",
    "\n",
    "    # solo hace falta hacerlo la primera vez\n",
    "    python setup.py register\n",
    "    \n",
    "Luego cada vez que queramos publicar una nueva versión\n",
    "\n",
    "    python setup.py sdist upload \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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