{
 "metadata": {
  "name": "",
  "signature": "sha256:afa5035e94dc7c4b2890a235b822b79e465d72dafc26f5d6ca751b3548ff6738"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot a equirectangular plot of the stars.\n",
      "\n",
      "Reference: https://github.com/brandon-rhodes/astronomy-notebooks/blob/master/Stars.ipynb"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tau = 2.0 * pi\n",
      "import ephem\n",
      "from ephem.stars import stars\n",
      "print('there are {} stars in PyEphem'.format(len(stars)))\n",
      "starlist = stars.values()\n",
      "for star in starlist:\n",
      "    star.compute('2014/11/4')\n",
      "degree = tau / 360.0\n",
      "hour = tau / 24.0\n",
      "ra_list = [star.ra / hour for star in starlist]\n",
      "dec_list = [star.dec / degree for star in starlist]\n",
      "scatter(ra_list, dec_list)\n",
      "gca().invert_xaxis()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "there are 94 stars in PyEphem\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd8U9X7wPFPd5N0AG0pZbWssmWoTIEiQwREBEVxgCKI\nP/kqKiCCq3z9qogbFBRFRRFlL1mlSlGQjZQpe5VZKKVAOpPz++MGKMhsxk3S5/168Wpyeu85zyXN\nk5tzzz0HhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIUQRfAscBzYXKisFLAF2AklAiUK/GwbsAv4B\n2rsoRiGEEHZoATTg8kQ/CnjF9ngoMNL2uBawEQgA4oDdgK9LohRCCGGXOC5P9P8A0bbHZWzPQTub\nH1pou0VAE2cHJ4QQ4ursOdOORuvOwfbzQtIvC6QV2i4NKGdHO0IIIezgqC4VZft3vd8LIYTQgb8d\n+x5H67I5BsQAJ2zlh4EKhbYrbyu7TJUqVdSePXvsaF4IIYqlPUDVW9nBnjP6uUBv2+PewOxC5Y8A\ngUAloBqw5sqd9+zZg1LKa/+99dZbuscgxyfHVxyPz5uPTSkFUOVWk/XNntH/DLQCIoFDwJtoo2ym\nAk8D+4Eetm232cq3AQXAc0jXjRBC6OZmE33Pa5S3vUb5u7Z/QgghdCbj250kISFB7xCcSo7Ps3nz\n8XnzsRWVj45tK1t/kxBCiJvk4+MDt5i75YxeCCG8nCR6IYTwcpLohRDCy0miF0IILyeJXgjhEKmp\nqTRo0JKoqErcd98jnDp1Su+QhI2MuhFC2O3EiRPEx9fjzJl3gJYEBHxGvXqbWbs2Re/QvE5RRt3Y\nM9eNEEIAsGLFCpS6HegDQH7+Z2zaVILTp09TsmRJfYMT0nUjhLCfyWTCaj0KWG0lJ7Fa8zEYDHqG\nJWwk0Qsh7Hb33XdTq1Y4BkMX4D1MprsZNGgIwcHBeocmkD56IYSD5ObmMn78ePbtO0Tz5o3p1q3b\nhf5k4UBF6aOXRC+EEB5EpkAQQgjxL5LohRDCy0miF0IIL+eIRD8M2ApsBiYDQUApYAmwE0gCSjig\nHSGEEEVgb6KPA/oBDYG6gB/aerGvoiX6eOA323MhhBA6sDfRZwH5gBHtLlsjcAToAky0bTMR6Gpn\nO0IIIYrI3kSfAXwEHERL8JloZ/LRwHHbNsdtz4UQQujA3rluqgAvonXhnAGmAY9fsY2y/fuXxMTE\ni48TEhJkrUchhLhCSkoKKSkpdtVh7w1TDwPtgL62508ATYC7gdbAMSAGWArUuGJfuWFKCCFukR43\nTP2DltgNtobbAtuAeUBv2za9gdl2tiOEEKKIHDEFwitoydwKbEA7uw8FpgIVgf1AD7T++8LkjF4I\nIW6RzHUjXEopJZNWCeFiMteNcImzZ8/SseNDBAQEExISwdixX+kdkhDiOiTRi1v29NPP8/vvwVgs\npzh/fjlDhrxLcnKy3mEJIa5BEr24Zb/99ju5uf8FQoCamM1Pk5y8VO+whBDXIIle3LKIiChgk+2Z\nIjh4M2XKROkZkhDiOuRibCFWq5XRo8cyd24yMTGRvPPO68TFxekdlttJSUmhU6eHUKorvr4HKV/+\nJOvWLSMkJETv0ITwejLqxk6DBw9n3LhkzOah+PpuITx8PNu3byA6WmZwuNLOnTtJTk4mNDSU7t27\nYzQa9Q5JiGJBEr2djMYSZGdvAcoDYDA8zscf38Wzzz6rb2BCCGEjwyvtpH3w+BUq8cPdPoyEEOJW\nSaIvpF+/ZzAauwO/4uv7PoGBSXTtKjMsCyE8m3TdFGK1Whk16hPmzk2mTJkI3n//LapVq6Z3WEII\ncZH00QshhJeTPnohhBD/IoleOEVqaip33nk35crV4JFH+pCVlaV3SEIUW9J1Ixzu2LFjVK9en6ys\n/wFNCQr6kCZNTpCSMl/v0ITweNJ14yCZmZk8+mhfqla9nQ4dHuTAgQN6h+RRli5ditXaDG1pgtrk\n5n7NihW/Yzab9Q5NiGLJ3jVjvY5SivbtHyA1tSp5eePYvz+JJk3uZufOjYSGhuodnkfQ7pI9gbZU\nsA9wClAEBAS4PJZly5YxdepswsJMDBjwLOXLl3d5DELozRFn9CWA6cB2tGUEGwOlgCXATiDJto1H\nOHLkCJs3byUv70ugERbL65jNZVm9erXeoXmMDh06EBubT3Dww8BHGI1tGDx4qMsT/cyZM+nYsSdj\nx8bwwQfnue22xqSlpbk0BiHcgSMS/WfAAqAmcBvaOrKvoiX6eOA323OPEBgYiNWaC2TbSqxYrVkE\nBgbqGZZHCQoKYvXq30lMvIP+/Q/y3Xdv8u67iS6PY+jQdzCbJwKvYLF8QlZWd8aP/8blcQihN3u7\nbsKBFlxaCLwAOAN0AVrZyiYCKXhIso+KiqJ79weZM6cjZvPjBAcnEx8fTtOmTfUOzaOYTCaGDn1F\n1xi0awKlLz63WKI5d+6MfgEJp1BKMXnyZFasWEu1anE899z/ERQUpHdYbsXeUTf1ga/QumzqAeuB\nF4E0oGShNjIKPb/AbUfdWCwWxo79khUrNlCrVmUGD35JZmf0QK+++iZjxvyO2fwZcBSjsS/JybPk\nQ9vLPP/8YL777nfOn3+U4OBl1KtnZvnyxfj7e+clSD3ujL0DWAk0A9YCnwJngf9weWLPQOu3L0y9\n9dZbF58kJCSQkJBgZzhCXGKxWHjjjbf56acZmEwmRo16nc6dO+sdlnCgrKwsIiNjyM+/cG5pISSk\nIfPmfeY1+SQlJYWUlJSLz0eMGAEuTvRl0BJ9Jdvzu4BhQGWgNXAMiAGWAjWu2Ndtz+iF+zl//jyv\nvprI2rWbqFWrKh9++DalSl157iC83dGjR1m9ejUlS5akRYsWpKenExtbi9zcdC5ccgwLa8fkyS/S\nqVMnfYN1Ej3G0R8DDqFddAVoC2wF5nGp3743MNvOdkQxoJRi8eLFjB8/ng0bNlxW3r79A3zzzWFW\nrx7ITz9B06ZtycvLu2Gd06fPoFy56oSHl6Fnz6dlLL8H++uvv4iPr0fv3t/QufP/0anTQ0RERFCr\nVi0CAl4EtuPjMxZ//+3SPecE9dC6bVKBmWgXaEsByVx/eKUSxVdGRoZasGCB2rFjh1JKKavVqnr2\n7KNMptrKaOyjjMYYNW7ceKWUUnv37lUGQxkF+QqUAqsKDb1NrVix4rptrFq1ShkM0Qr+UHBIBQd3\nV48+2tfpxyacIza2toIZtr+BXGUyNVU//fSTOnnypOrSpacqU6aaaty4rdq2bZveoToV2g0qt8QR\nVytSgTuvUt7WAXULLzRt2jQefvgplIoGjtKyZQveeecN5s79g/PnNwEGYDcDB9bnqad62b6qXv73\nrZT1wlfYa1q4cBG5uX3QBoZBTs7HzJ/fxElHJZzt2LGDaD3CAIHk5jbnwIEDREREMGfOZD1Dc3sy\nBYJwqdzcXHr27INSvwJ7gK388ccqJkyYgJ9fLbQkD1AVH58gzpw5Q2xsLI0a3Y7B8Agwk8DAvsTG\nGvn774306TOAjz76mNzc3H+1VbJkCQID9xYq2UtoaLjTj1E4R716d+LnNxrtA/8wgYEzaNSokd5h\neQSZ1Ey41MGDB4mNrY82EOuCNrRtG8Bff63HbJ4JNMfH5wsqVBjL/v3b8PHxITs7m8TEd1mzZhO1\na1fl2LETLFy4D7O5BwZDErffXsCyZQvw9b107nLmzBnq12/GsWO1ycurTHDw90yaNI4HHnjA1Yct\nHCAtLY02bbpw4MABrNYcEhMTGT58iN5huZwsPCLcXm5uLkZjKazWxWiDtA4Ct/HmmwNp2rQpPXs+\nxZkz6VStWpf586dedYWvo0ePUqlSbXJzDwEmoACTqTZLl07izjsv70XMyspi4sSJnD6dyb33dvjX\n74VnUUpx4sQJQkNDi+29LUVJ9HrS9YKGJxo3brwKDy+jgoJC1UMP9VZms1nvkIrkq6++UmBUUEOB\nSVWuXEfl5eVd/H3hx1eze/duZTSWV2C1XZhTKiysiUpJSXF26ELojiJcjJUzeg+xePFiunV7BrP5\nV6AswcHP8Mgj0Xz33Vi9QyuS3bt3M2/ePOLi4ujSpQt+fn43va/FYuG225qya9dd5Oc/iZ/ffEqX\nnsCuXamYTCYnRi2E/qTrxou9+OIQPvssgktTBv1DdPR9HDu2S8+wblpmZiZHjhyhYsWKhISE2F1f\neno6ffsOZMOGVOLjqzJhwmfExcXZH6gQbq4oid47J4PwQqVLlyIwcBuX7hHaRkREhJ4h3bRJkybT\nr98AAgJKo1QGs2b9TNu29o2+jYqKkiF1QtwkOaP3EJmZmdSv34z09OoUFJTF338q8+dPc/v5PA4d\nOkT16g3Izl4G1AaWERLyIMePHyi2F9OEsIec0XuxEiVKsGnTKqZMmcL58+fp0OFPatS4cvog97Nj\nxw4CA+uSnV3bVtIKCCMtLY34+Pjr7SqEcBA5oxdOtXfvXurUaUJ29logFvgbg+FuTpw45JC+enFj\nGRkZHD9+nEqVKhEcHKx3OMJOsji4i2zcuJFp06axdetWvUNxe5UrV+a9997CYLiD8PAWGI3tmDjx\nG0nyLvLpp59TtmwlGje+n7Jlq7Bu3Tq9QxLFjH4DUe2QmPiuMhrLqrCwB5TBEK3GjBmnd0geYd++\nfWrp0qXqyJEjeofiVbZt26aaNbtHlStXQz34YG+VkZFx8XcbN25URmOMgv22+w2mqdKlY5XVatUx\nYmEvZBy9c13qhtgMRAP7CApqwOHDezxmBIzwHqdOnaJatdvIzByGUgkEBo6hfv1drFr1Gz4+Pvz0\n0088++w8zp375eI+AQEhpKcfJjxc5vzxVNJ142SHDh0iMLAaWpIHqERgYBmOHTumZ1iimFq+fDkW\nS12U+g9Qh7y8sWzcuIGMDG0eoapVq2K1rgRO2fZIwWAwERYWplfIQieS6G9BrVq1KCjYCfxpK5mP\nj08mlSpVut5uQjiF0WjEaj0JWG0lWViteRcvuDZu3JgBA3phMNQmPLwlISE9mDlz8g2nd3amvXv3\n8thjfWnbthvjxo3H077VeyrpurlFSUlJdO/+KPn5VozGYH79dTrNmjXTOyxRDOXn59OkSRu2bYsk\nJ6clRuOP9OnTmjFjPrxsux07dnDkyBFq165N6dKldYoWWwx3kJX1LFZrDYzG93j55Qd4++03dYvJ\nE+k5BYIfsA5IA+5DW2FqCtp4uv1ADyDzin08MtGDNtfKqVOniIyMxNfXlzNnzmC1WilZsuSNdxbC\ngbKzsxk9egy7dx+kRYtGPPHEEy49Y58zZw79+g0kMzOdu+66m2nTvr/m9arRo0czdOhGcnK+tZXs\nITS0KVlZJ1wWrzfQs49+ILCNS1eDXwWWoK0l+xuXJmjxCn5+fpQuXRqr1UqPHr2JiipHdHRF7r23\nOzk5OXqHJ4oRg8HA0KGv8PXXn9OrVy+XJvnNmzfTs2c/0tMnkZ9/hOXL4+jWrdc1t1dKoVThyev8\npevGRRyR6MsDHYFvuPQp0wWYaHs8EejqgHbczsiRH/Hrr0fIzz9Bfv5JUlIUw4ePsKvOjIwM/vrr\nLw4cOOCgKIVwjmXLlqFUd7R1BcLJzx/FihVLrpm8u3XrRlDQPHx8PgLmYzQ+TP/+/VwZcrHliET/\nCTCES1eEQBuWctz2+DiXhql4lWXL1pCd3Q8wAkHk5PTnjz/WFLm+3377jdjYGtx774vUqHE777zz\ngcNiFcLRIiIi8PPbzqW3/jZCQiKu+a2iQoUKrFq1lM6d19G48WjeeqsHo0b9z2XxFmf2znXTGTgB\n/A0kXGObaw7wT0xMvPg4ISHB7SfoulK1ahVZtiyF/PyHAB/8/ZdRtWpskeqyWCw88EBPzp2bgrYA\n8hHeffdOOnduT7169RwZthAO0b17dz7++Cu2b29Hfn4d/PymMG7cp9fdp2bNmsyd+7OLIvQOKSkp\npKSk2FWHvR167wJPAAVAMBAGzATuREv8x4AYYClw5QxcHnsx9oKMjAzuvDOB9PRQIJDQ0DTWrl1G\n2bJlb7mu9PR0KlSoTm7upbVUw8K68c03PXnooYccGLUQjpOXl8eUKVNIT0+nZcuW3HHHHXqH5PX0\nXnikFTAYbdTNKLS7NN5HuxBbgn9fkPX4RA/aqIdly5ZhsVho2bIloaGhRarHYrEQGVmezMzvgXuA\ngxiNjVm9egl16tRxZMhCuD2lFElJSRw9epRGjRpRq1YtvUNyG+6Q6AehXYgtBUwFKuKFwyud5c8/\n/6RTpwfx8SlNXl4a7777X1566Xm9wxLCpZRSdOv2OMnJm1CqPlbrYr777nMefriH3qG5Bb0T/a2S\nRH8VZ8+eZc+ePcTExBAd7ZXXsIW4Lu2mxEGcO7cOCAI2YjAkcP78aV3v6nUXsvCIh8rIyOD777/n\n7NlzdOrUUfo5RbF29OhRoB5akgeoR15eNtnZ2bIqWRHJXDc6ys7OZuDAwcTEVOOVV9by3/9m06pV\nJ+bPn693aELoplGjRlgsSWiD+RS+vh9SpUptSfJ2kK4bnVitVlq16sjKlYexWBpy6f6yxVSp8iq7\nd/+tZ3jCBaZNm8a8ecnExEQyZMhLREZG6h2S25g2bTpPPtmP3FwzVavWYeHC6TJ5oI300XuQnTt3\n0qBBG8zmRwETcGFip51ERXXgxIm9OkYnnG3kyA95++1vMJufJyBgK1FRS9i6dS0lSpTQOzS3oZQi\nJycHg8GgdyhuReaj9yAWiwUfHz+02SO+RJv6eD9BQQN54IH79A1OON3//vceZvN8YAD5+WPJzLyN\n6dOn6x2WW/Hx8ZEk7yCS6HUSHx9P9eoVCQqaCDwJPISvbz169qzE6NGjdI5OOFteXg5wabZTq7Uk\nubm5+gXk4U6ePMnixYtZu3atTJR2FV7VdZOXl4evry/+/p4xmCgrK4vBg19nw4at1K1bnY8/fkem\nOi4mHnusL7NmHSU7OxHYQkjIUDZtWu2wfmir1cqECd+yfPk6qleP46WXBnrt2fGaNWto164LUBuL\nZT/t2zdl+vQf8PX1zvPYonTd6Mlhi+Xm5OSo7t2fUH5+gcrfP0g999xLymKxOKx+e2VkZKgePZ5U\nFSvWUa1adVI7d+7UOyShk9zcXPXCC6+oSpXqq+joeFW2bLxq0qSdWrt2rUPb6dPnOWU0NlYwRgUH\nd1MNG7ZQeXl5Dm3DXVSqVFfBVNsC6NnKZLpTTZ06Ve+wnIYiLA6uJ4cd+MsvD1MGQycF5xWcUkZj\nYzV69BcOq98eVqtV3XlnggoMfEbB38rX9yMVGVlBnT59Wu/QriszM1OlpqZeN86pU6eqJ554Rg0e\nPEydOHHChdF5rl69+iuDoYOC1Qq+VyZTpNq9e7dD28jIyFABASYFZ2zJz6JCQuqppUuXOrQddxEU\nFKLgtO1YlfL3H6RGjhypd1hOQxESvVd8t0lOXk529iC06YJLYTYPIClpud5hAdpkZZs2pZKXNxao\nj9X6Mnl58fz11196h3ZNM2fOIiamEnfd1ZOyZSszY8bMf20zatTHPPnk6/z4Yz0++yyLevWacvr0\naR2i9SxTp/5MdvZEoBHQm4KC7g6/byI3Nxdf3yC00VwAvvj6liI7O9uh7biLWrUa4Os73vbsOEFB\nc2jYsKGuMbkbr0j05cvH4Ot7aR74gIC1xMXF6BjRJUFBQVitecA5W4kVq/WU2/aXnjx5kiee6Et2\ndjJnz24lOzuZXr36cerUqcu2+9//RmI2zwOeIz//czIzGzBt2jR9gvYgAQFBwKUPRD+/DIKCgq69\nQxFER0dTt25dAgOfA/7G1/dDAgP30LRpU4e24y5mzJhIhQoTMRrLERhYlUGDnqRdu3Z6hyVsHPZV\nZvfu3apUqXIqJKSrCgm5R5UvH+9WXQl9+/5HGY2NFIx2+/7SNWvWqLCwBhe/BoNSYWEN1OrVqy/b\nLigoVMGJi9sEBz+jRo8erVPUnmPUqI+V0VhNwRcqIGCAiomprDIyMhzezoXrQrGxdVVCQme1a9eu\nIteVm5urnnvuOdW+fXv1xRfu0SV6pYKCArV//3637xJ1BIrQdeM1o25OnjxJUlIS/v7+3HvvvUWe\nLtgZlFJ8++13rFixjmrVYnnxxRfc9oz+xIkTxMXVIDt7BVAT2I7B0Jz9+/+hdOnSF7fr3ftZpk07\nQHb2CGArJtMrbNq0msqVK+sVuseYPn06c+cuITo6giFDXrzs/9Xd7Nq1i/j4+rZnVsCHli2bsGzZ\n70Wq7/Dhw6SmplKuXDlZUKeIiu2oG+FY33//gzIYIlR4eFNlMJRS3333w7+2yc3NVQMHDlVVqjR0\nyqgR4R60b24mBb/Zvr3NVmBQR48eveW6FixYoIzGSBUe3l4ZjeXV888PcULE3o/ifEYvHOvIkSPs\n2bOHypUrU65cOb3DETrQph8IR/tmt7HQb8qzZMn3tG3b9qbrslqthIeX5ty5uUAzIBOTqSFJSZNo\n1qyZYwP3cnpMgVABbZnArcAW4AVbeSlgCbATSEJbYUp4kLJly9KiRQtJ8sWYdpHYB9iLtjQ0wEHg\nFHXr1r2lus6ePUtubg5akgcogY9PI/bt2+eocMV12Jvo84GXgNpAE2AA2sf/q2iJPh74jX8vIyiE\ncHM+Pj6MGPEa2pLQtYB7gXq0anXXLS+KExYWRkRENPCTrWQ3FkuK9NO7iL2J/hiXvtOdA7YD5dCW\nE7ww7+5EoKud7QjhFOvWrePppwfQp89zrFu3Tu9w3M6bb77BrFmTqVYtgooVt/Pxx2+SkrLkluvx\n8fFh0aKZREUNx2gsT1BQQz755H+yHrKLOLKPPg5YBtRB+353YdIWHyCDwjM4aaSP3gstW7aMQYNG\ncOZMFg8/3IURI17Dz89P77CuauXKlbRt2wWzeQgARuMHLFkyR/qMnSg/P58jR44QGRmJyWS68Q7i\nX/Scjz4ELcm/DcxGuyOkcGLPQOu3L0wSvZfZtGkTTZu2wWweA8RiNL7Cs8+24KOP3tU7tKvq1Olh\nFiy4G+hvK/mSjh2XMn/+FD3DEuK69FozNgCYAfyIluQBjgNl0Lp2Yrh0JecyiYmJFx8nJCSQkJDg\ngHCEXqZPn0lOTl/gEQDM5m/44Yd73DbRZ2fnAuGFSkqQnZ2nVzhCXFVKSgopKSl21WFvovcBJgDb\ngE8Llc8FegPv237O/veulyd64fkMhmD8/I5htV4oOU1gYLCeIV3Xs88+xurVQzCbtS+fRuMwnn3W\n/dYCsFgszJ49m2PHjtG0aVOHzuOyZs0avvpqIj4+PgwY8DQNGjRwWN3CMa48CR4xYoTLY7gL7Xa5\njWgr+f4NdEDrpknm+sMrHX4jQWZmpttOLVAcHD58WJUqVU75+Q1SMEYZjbHqm2++1Tus65o48UdV\np05zVadOczVx4o96h/MvBQUFqm3b+1VISCMVHNxfGY1l1HffTXRI3X/88YcyGCIVjFLwnjIaI9Wa\nNWscUrerZWdnq/z8fL3DcAmK6zTFhw8fVnXqNFYBASYVEGBQH38sc67o5eDBg2rgwMGqV6/+6tdf\nf3V6ewUFBerkyZNutf6AI82fP1+FhDRQkG+7M3WrCg4OU1ar1e6627Z9QMGEQvMajVZduz7mgKhd\n58yZM6p16862tSiC1Ztvvq13SE5HcZ2m+MEHn2T79jbk558lP387r7/+IcuWLdM7LJfKz8+nf/+B\nhIZGERFRgdGjv9AljgoVKvDppx8wceKXdOrUyaltLVq0iBIloilbtgrR0XGsXbvWqe3p4cSJEyhV\ni0u9rDXIz89xyLKD3nCN4plnXuSvvyKwWM5RULCXDz+cLGvvuhmHfcIVt4UHrmbQoOHKaGyj4JCC\nTcporKxmzZqld1hOc/ToUWUyRSpYbnvdp6uSJcuqnJwcvUNzqB07diijMUrBnwpylJ/fm6pu3aYO\nqfvHH39SRmNlBUkKFiijsaLH/c3ExMQr2FroW8kH6v/+b6DeYTkVxfWMPiqqPLDC9qyAoKDVlC9f\nXs+QXCI1NZUePZ6kY8eHmTTpZ8zmd4DyQF3M5peZNWuR3iE6zZYtW/D3rwM0t5V0Jz8/iIMHD+oZ\nlsPFx8czZcq3lCr1CL6+JurVS2HhQsfM+//4448yZswb1K6dSJ067/D11yPp2tWz7m2MiYkBLqxF\noQgKWktsbFk9QxJXcNgnXEpKijKZIlVoaDcVEnKbat26s9dfmNmyZYvtjPYjBROVr28ZBf0Lfat5\nQQ0aNFTvMJ1m+/btymCIVpBuO+bdKigoTGVmZhapvrlz56pq1W5X5crVUC+/PMwt/34c0S/vbTZs\n2KBCQ0urkJCHVEhIC1Wr1p3q3LlzeoflVBTXi7FKKXXgwAH1yy+/qKSkJFVQUODQuovqwIEDaubM\nmWrFihUOf5O+8MIgBW8V+sr6m/LxKakCAp5XwcGPqdKlY4s0laxekpKS1FNP/Z8aOHCw2r9//03t\nM3Tom8porKBCQ7srgyFajR37VZHa/uuvv5TBUFrBQgWpymhspV566dUi1SVcLy0tTU2aNEnNnDlT\nZWdn6x2O0yHTFBdNamoqS5cuJSIigh49ejhkabfFixfTrdvj+Ps3xWLZzv33t2LSpK8v3NVmtwED\nXmLs2EjgNVvJn1SsOIDnn+9FUFAQPXv2JDIy0iFtOdvPP/9C376DMZsH4+t7lLCwH0lNXUXFihVv\nuO+6devYs2cPderUoXbt2kVqf8iQV/nwQyPwpq1kK2XKdOXo0V1Fqk8IZ5KFR4pg5syZymCIUkFB\nA5TJ1EbVr9/cIRf0wsPLKPhJwWEF55XJVFstWrTIARFr1q9fr4zGSAXfKJijjMbqasyYsQ6r35Uq\nVaqn4PeL3078/F5Qr7/+psvaT0wcofz9ny307WiJqly5nsvaF+JWUFwvxtqjf/+XyM6eRW7u55w/\nv4Rduwz8/PPPdtV58OBBzpw5CwxDm+PtZazWOzlw4IAjQgagYcOGJCXNpk2bX2nSZCyfffYKAwY8\n67D6XUmbp/zSVEgWSwRmc47L2n/mmX6UKDEff///AO9iMDzBqFFvuKx9IZzNEXPdeLQzZ06iTacP\n4EN+fm3CyCVRAAAdVUlEQVROnTplV519+74I/B8wCm325ruxWHZx++3/saveKzVv3pzk5OY33tDN\nPflkTz79tD9m8yfAEYzGz3n44V9d1n5MTAybNq1m3LivyMo6TffuU2nRooXL2reXUoo1a9aQkZHB\n7bff7tZr0IriR+9vQEoppdq2vV8FBvZXkKVgtTIYotW6devsqrN06coKdhTqChilWrVq76CInS83\nN9elIzwsFot6663/qSpVGqp69VqoxYsXu6xtT2exWFS3bo8rk6mKCg9vp0JDS6uVK1de9nsZreNd\nKM6jbooqIyNDtWnTRfn7B6uSJcupX36ZYld9OTk5qmTJWNv8IUpBrjIY7lZffPGFgyJ2nkOHDqnb\nbmumfH39ldFYUk2e/LPeIYkbmDZtmjKZ7lCQffHGsdjY2urs2bOqU6eHlJ9foDIYSsi0IF4EGXWj\nv+HDE/n44z/Izd0LRANpxMWVYufODQQEBOgd3nU1aNCCzZvbYLG8AWzGYLiHVauWcNttt+kdmtdZ\nv34906bNxGgM5umn+xR5bd4PPviA1147Sn7+x7aSLAICYnjwwZ7MnGkmN/dr4ChGY3umTfucjh07\nOuwYhD70WBxcXOGPP9aSmzsQba30D4BBlC1bwaFJ/vTp0zzxxDPUqdOchx9+ivT0dLvrLCgoIDV1\nJRbL64AfUB8fn/tYuXKl3XWLyyUnJ9Oy5b28/74/b799nLp1G3Ho0KEi1dWwYUMCA+cARwHw9f2K\n2rUbkpz8O7m5iYAJqIrZ3I/k5BQHHcG/7d27l5SUFI4dO+a0NkTRSaJ3sPj4OAICfkd7g7UgIOAg\n8fFxDqvfYrGQkNCJqVP92Lr1PWbNKkmzZu3snuTK39+f0NAItJmmAfLx9d1ImTJl7I5ZXG7w4P9i\nNn8JjKCg4HOysnry2WdFm4SuTZs2DBnSl4CAahgMMVSo8D0zZ/5AVFRpINW2lSIoKJUyZaIcdQiX\nGTnyI+rUaUzXrm9QpUod5syZ65R2hGfSu6vLKU6cOKHi4mqp0NBmKjS0mYqLq6VOnDjhsPq3bdum\nTKY4BRZbn6xVhYbWccg84tOnz1AGQ5QymXqrkJCGqn37rm5zl7E3qVy5gYJVhS7Wf6L69HnOrjrP\nnDmjDh48ePH1Wr58uTKZIpXB8JQyme5R1arVV1lZWY4I/zLbtm2zTUWRZjuW1cpoLFks7lDVC0Xo\noy/2wysdLSoqiq1b17J8+XKUUrRo0QKj0eiw+gMCArBac4ECIBCwYrVm4+9v/0vZvXs3ataswcqV\nK4mOfpCOHTvi6ytf+hytZ8+ufPLJy5jNXwGnMBo/4pFHvrWrzrCwMMLCwi4+b968Oampq0hOTsZk\nMtGtWzeH/h1esGfPHgIDG5CdfeEaQyPAwPHjx4mNjXV4e6JonHkxtgPa8oJ+wDdoywoWZvtwErdC\nKUX79l1ZscJKdvZDBAfPo169DFasSMLPz0/v8MRNsFgsDB+eyMSJvxAcHMw77wzjscce1TusItmz\nZw916zYhO3s5UB1IIizsCdLTDxEYGKh3eF6pKBdjnZXo/YAdQFvgMLAW6AlsL7SNJPoiys3NZdSo\nj1m3bgv16lVn2LAhGAwGvcMSxdS3305kwICBBAREA5nMmzeVVq1a6R2W13KnRN8UeAvtrB7gVdvP\nkYW20SXR5+bmMmTIGyxY8BvR0VF8/vl7siCy8Crnzp1jyJA3WLXqb2rUqMKnn75LdHS0U9vMzMzk\nyJEjxMbGYjKZnNpWcVeURO+sPvpyQOHxYmlAYye1dUv69BnArFlHyc4ey549m2nZ8h42b15DXFyc\n3qEJYTelFLff3pzdu/OxWmuzebMfq1bdzbZt65z6ra9EiRKUKFHCafUL+zjrSptb9skopZg2bTLZ\n2ZPQPnf6YrF0ZuHChXqHJoRD9Oz5ODt3nsRqfRGohMWygPT0QNasWXPDfYX3ctYZ/WGgQqHnFdDO\n6i+TmJh48XFCQgIJCQlOCkfj4+ODv38Q+fmngZK2skyHzD8vhN6mTZvOlCnzgD+BerbSdAoK5EK9\nJ0tJSSElJcWuOpzVR++PdjG2DXAEbVFHt7gY+7//vc97732P2fwCAQGbiYpawtata+Vrp/B4jRq1\nY+3ajWhvt0q20gFER8/j4MHdMgrGS7hTH30B8B9gMdoInAlcnuR18/rrQ6laNY5ff/2NmJhIhg5d\nKUleeAXtxKkl0Bt4B9iDj89E5s9fJkm+mJNJzYTwElOnTuPJJ18iO7sZsBlf3xN8+eVI+vXrp3do\nwoHcaXjlzZBEL4SDzZ49mzFjJhIQ4M/w4c/TsmVLvUMSDiaJXgghvJxMUyyEEOJfJNELcRPS09NZ\nuHAhq1evxpO+iZrNZh58sBfBwWGUKlWOCRO+0zskoQOZvVKIG1izZg3t2nXBx6cuBQX7aNeuMTNm\n/Oh2M3sWFBRw+PBhIiMjL05D8MwzLzJ/fg65ufvIzT3ECy/cR6VKsdx99906Rytcyb3+UoVwQ488\n0pesrDGcObOE8+e3sGTJLqZPn653WJdJTU2lbNmq1KrVnIiIGMaPnwDA4sVJ5OS8B0QA9TGb+7F4\ncbKusQrXk0QvdHH27Fk2b95MRkaG3qHc0JEj+4B2tmfB5Oa2ZN++fXqGdBmlFB06dCM9/R3M5jRy\nc9fz4ouvsWXLFkqUKEXhW1iCgv6hdOkI/YIVupBEL1wuKSmJmJhKNG/+MOXKVeGHHybpHdJ11a7d\nEF/fr2zPjhEUNJuGDRvqGlNhWVlZnDp1HHjMVlINf/8EUlNTGTduFEbjUwQGPo/R2JWyZTfJuPpi\nSIZXCpc6f/480dGxnD8/C2gBbMNgaMk//2ygYsWKeod3Vfv376d1686cOJFBQcFZhg17lcTE1/QO\n6yKr1Up4eGnOnZuHNkN4JiZTQ5KSJtGsWTO2bt1KUlISISEhPPLII4SGhuodsrCDO02BIMRVpaWl\n4eNTAi3JA9QiMLAOO3fudNtEHxcXx+7dqRw+fJjw8HDCw8P1Dukyvr6+TJnyAw891IWAgIbk52+j\nT5+eNGvWDIDatWtTu3btW643MzOTuXPnkp+fT6dOnWSheA8mZ/TCpc6ePUt0dEWys38DGgL7MRju\nZMuW1VSuXFnv8DxaWloamzZtoly5ctSrV+/GO1zH8ePHadCgOVlZdVDKQEBACqtWLaVGjRoOilYU\nldwZKzzC9Okz6d37GQIC4snL28n777/N88//n95hiUIGDHiZ8eMVBQWfAODj8wnt269g0SL3Gm1U\nHEnXjfAIDz7YjbvuasauXbuIi4ujQoUKN95JuFRa2nEKCu65+Fyp+hw5MlPHiIQ9JNELXZQpU0b6\nfG9CZmYmycnJ+Pj40L59e5ddSO3YMYHk5M8wm9sDBgyG9+nQIcElbQvHk64b4TH279/P3r17iY+P\np3z58nqH43RpaWnccUdLzObqgIWwsP2sX//nTS/0nZeXV+R56JVSvPLK63z22ScoZaVHj8f57rux\nMq+9GyhK142elBA3a/TosSo4OEKFh7dUBkOE+vHHn/QOyel69HhS+fm9rkApUMrf/2XVrt19qnbt\npiou7jY1bNhbqqCg4F/7/fHHHyoysqLy8fFTFSrUUKmpqUWOwWq1KovFYs9hCAfDxWtyf4B2y10q\nMBMoPOZsGLAL+Adof4399f7/Eh7iwIEDymCIULDXlvQ2q+DgEur06dN6h+ZUjRq1U7DgYqKH6crX\nN0LBHAVrldHYXA0Z8vpl+6Snp6uQkCjbfhYFP6jIyAoqJydHp6MQjkYREr09d8YmAbXRViHeiZbc\nAWoBD9t+dgDG2tmOKOb2799PYGB1Lq2DWoeAgDKkpf1rvXmv0qZNMwyG0YAZOIu//ydYrXcAXYA7\nMJvHMWnS1Mv22bx5M35+1YF70d52T5CTE+RWUzYI17MnAS8BrLbHq4ELnab3Az8D+cB+YDfQyI52\nRDFXrVo18vL+ATbaSv7Eak0nNjZWz7CcLjFxOJ07l8bPrxR+flHUrJmPr2+VQlucIDjYcNk+ZcqU\nIS9vD5BpKzlMXt5xoqKiXBW2cEOOOtPuAyywPS4LFD7VSgPKOagdUQzFxMTw/fdfYTC0JiSkGibT\nA8yY8ZPX38ofGBjI1KkTOXv2NOfOZbJw4UzCw2fj5/cS8CEGwxO8997wy/apWbMmffr0xM+vLtAT\naIjV6suKFSv0OAThJm40vHIJcLUxcMOBebbHrwF5wOTr1HPVPqXExMSLjxMSEkhISLhBOKK46tHj\nQTp0aM/hw4epUKECISEheofkMgaDdtZerlw5UlNX8fnnX3LmzCF69Jh01Xnl7767ORMmzMdiaQUM\npKDAypNPPkRGRhcXRy4cISUlhZSUFLvqsHeIzpNAP6ANkGMre9X2c6Tt5yLgLbTuncJs1xWEuDnb\nt2+nW7de7NmzldjY6kyf/r3dt/p7o7FjxzJ4cCrZ2Rdm3MzD19dIfn6e2y2WIm6dq9eM7QAMQeuT\nzylUPhd4BAhEu3pWDVhjRztCkJ2dTUJCR3bs6Et+/nF2736Ju+/uRFZWlt6huZ0mTZoAc4CtgBU/\nv3epV6+ZJPlizJ5XfgwQgta98zfa6BqAbcBU28+FwHO4eNyn8D67du0iO9uAUv2BUKAXBQUxbN26\nVe/Q3E7Dhg358ssPCQ5uhp+fgVq1kpg793o9q8LbyZ2xwiOkpaVRrVo9cnJ2AaWAsxgM8WzcuIz4\n+Hi9w3NLSilyc3MJDg7WOxThQDKpmQc6fvw4EyZ8y/nzZh544H7uuOMOp7WllGLNmjWcPHmS22+/\n3aPmmilfvjz9+/flm2+ak5fXgcDAZHr2fNDrkvyuXbtYt24dMTExtGrV6sKbukh8fHwkyQtAzuh1\ndfToUerVa0Jm5j0UFERjMIxnxoyJdOjQweFtKaV4+OEnWbBgBf7+VbBY/mbRolk0b97c4W05i1KK\nRYsWsWXLFmrUqEHnzp3tSoTuZtas2Tz2WD/8/VtjtW6mU6cm/PLLt151jMJ+MteNhxk+/A3l7z+g\n0C3uc1StWk2c0tasWbNUSEgDBWZbW3NVuXLxTmlL3Dqr1apMplIK1tpeH7MKCamlkpKS9A5NuBlc\nPAWCsFNm5lkKCgovn1eRs2fPOqWt/fv3k5fXHLhwJ2Vbjh2T2+LdRXZ2Njk554DbbSUGrNb67Nmz\nR8+whJfwqkR//PhxRo8ezYcffugRb5Du3e/DYPgM+APYgdH4Mg895JybWho2bIi//zzgMAC+vuOo\nWfP26+8kXMZoNBIXVwMfn9FoJ2xbMZt/5aWXhjJv3q96hydEkTn068zBgwdVRER5FRTUWwUEPKdC\nQqLU+vXrHdqGM0ye/LOqUKGWioyMU88/P0Tl5eU5ra133/1ABQSYlNEYoypWrKF2797ttLbErdu1\na5eqWLGmgiAFIQomKVipTKYIr5+pU9w8itB14zUXY//v/17k66+DsFjet5V8TcuWc1i2TM6GCjt7\n9iyZmZmULVsWPz8/vcMRV1i5ciUdOjxHVtY6QHt9wsLqsXTpdzRs2FDf4IRbcPWdsW7l+PEMLJbq\nhUriOXXqtG7xuKvQ0FAqVKggSd5NxcXFkZd3ALhw/WQH+fmHZF1dYRevSfTdu9+LyXRhLZRDGI1v\n8sADjh+mKIQzxcTE8NlnH2AwNCE8vBUGQ3M+//wTmWZY2MVrum4A3n//I0aO/JiCgjx69+7Fp5++\nj7+/3BMmPM+BAwfYs2cPVatWpWLFijfeQRQbRem68apE70127NjB7NmzCQoK4tFHH6V06dJ6hySE\ncAOS6L3EqlWraNv2PnJzH8XPL5PQ0KWkpq6ibNmyeocmhNCZJHov0aRJe1avfgzoDYC//yAGDPDl\n008/0DcwIYTuivWoG2+SkXEabRp/TUFBNdLTM6+6bW5uLidOnEA+NIUQ1yKJ3g1169YRo/E14ACw\nCaPxQ7p3v/df23311TeEhUVQsWJN4uJqsWvXLpfHKoRwf9J144YKCgp44YUhTJo0mYCAQN58cygD\nB/7nsm3Wr19Py5b3YTb/AVTFx2c0Vat+z86dG/QJWgjhEnr10Q8CPgAigQxb2TCgD2ABXgCSrrKf\nJHo7jB8/npdeWo3ZPMFWYsXHJ5Dc3GwCAgJ0jU0I4Tx6LDxSAWiH1sdwQS3gYdvPckAyEA9Y7WxL\nFFKxYkV8fccA2WgzUi4nPLy0JHk3ZLVamTVrFvv27aNBgwa0adNG75BEMWNvH/3HwCtXlN0P/Azk\nA/uB3UAjO9vxeBaLhREj3qVOnea0aNGR1atX21XfPffcQ+fOd2Iy1SMs7H6Mxu78/PO3DopWOIpS\nih49nqR37/cYPvww99//DCNGvKd3WKKYsafr5n4gAXgJbWKO29G6bsYAq4CfbNt9g7ZI+Iwr9i9W\nXTeDBg3nyy+XYTa/C+zFZHqFdev+pEaNGkWuUynFX3/9xfHjx7njjjvkDko3tHbtWlq3foTz57cC\nwcBRAgPjOXEijfDwcL3DEx7IGV03S4CrLSz6Glo/fPvC7V+nnqtm9MTExIuPExISSEhIuEE4nuu7\n737AbE4BqgKtyMnZzIwZM3ntteFFrtPHx8ejlgIsjk6dOoW/f2W0JA8Qg79/GJmZmZLoxU1JSUkh\nJSXFrjpulOjbXaO8DlAJSLU9Lw+sBxqjrWxReKq98lxY7eIKhRO9t/P3DwDOXXzu63uWoCC509Xb\nNWzYEKt1E9oX2vb4+n5FZGQ45cuX1zs04SGuPAkeMWLELddR1D76LUA0WrKvBKQBDYHjwFzgESDQ\n9rtqwJoituM1Xn99MEbjg8DX+Pm9SkjIAh577DG9wxJOVrp0aZYsmUts7FsEBERTt+5sli79VaaJ\nFi7lqHH0e4E7uDS8cjja8MoCYCCw+Cr7FKs+eovFQpMmLVi3bgtQQIcO7Zg3b4bMrimEuCUy140b\n69v3GSZMmIp2eeM0MIb//KcvY8Z8onNkQghPIonejYWGxnLu3CdAN1vJm5Qu/RPHj7v/IuZCCPch\nk5rdhF9+mUJERAUCA03cc083MjOvPlmYo/n5+QARhUoiCQ4OvtbmQgjhMMUq0a9du5Y+fQaSkTGD\n/PwjpKRE0LNnX5e0/fzzTwH9gBXAAuAt3nlnmEvaFkIUb8XqSuDSpUvJz3+UCzfq5uW9T0pKJZe0\n/d//vomfnx/jxj2Fv78f77zzCY8//rhL2hZCFG/Fqo9+woQJvPDCNMzmhWiHvpzSpXtx/Phel8Yh\nhBBFJRdjbyAnJ4dGjVqzd28o+fnV8fObwuTJ4+natatL4xBCiKKSRH8TcnJymDJlChkZGbRu3Zr6\n9eu7PAYhhCgqSfRCCOHlZHilEF5u2rTpNG7cniZN7mHOnDl6hyM8hJzRC+EhZsyYSa9eL2I2fwZY\nMRheYNq08XTq1Env0IQLSdeNEF6sVasu/PHHY2gLuAFMpEOH+SxcOFXPsISLSdeNEF7M398PyClU\nkmMrE+L6itUNU0J4suHD/8OqVY9iNp8HLBgM/2Xo0Fl6hyU8gJzR22H9+vU0adKOypXr89xzL5OT\nk3PjnYQoojZt2rBgwVS6d1/Ngw+uJzl5DnfddZfeYdlNKcX27dtZvXo1ZrNZ73C8kvTRF9GBAweo\nU+dOzp0bCdyGwfBfunSJ4JdfvtM7NCE8htVq5aGHerNo0VL8/UtjMGSyfHkSVatW1Ts0tyV99C60\nYMECLJbOaOur3EF29o/MmPELnvzhJYSr/fjjjyxevAezeRdZWRtIT3+exx7rr3dYXsfeRP88sB1t\nacH3C5UPA3YB/3D5AuJeIzg4GB+fjEIlGQQEBOkWj7exWq16hyBcYNu2HZw/fy9gAMBq7cauXTv0\nDcoL2ZPoWwNdgNvQFgv/0FZeC238Vy2gAzDWznbcUvfu3SlVajsBAf2BLzCZOvL668MvfK0SwKlT\np/jnn39u6drF33//TVxcbfz9A6hYsSbr1693YoRCb7fdVhuTaR5wDgBf35+pWbO2vkGJy0wF7r5K\n+TBgaKHni4AmV9lOebqTJ0+qV199XfXq1V9NmTJF73DcysiRH6mgoDAVElJVRUSUV3///fcN9zl3\n7pwqVaqcgp8U5Cv4RZUoEaOysrJcELHQg8ViUU888YwKDo5SoaE1VPny1dW+ffv0DsutAbfcP2zP\n6effwBy0s/YcYDCwDhgDrAJ+sm33DbAQmHHF/raYhbdZs2YNrVt3x2xeCZQHfqJcuf+Slnb9r+Qb\nNmygdeunyMpKvVgWFnY7S5aMo1GjRs4NWuhq//79ZGVlUb16dYKCpAv0eopyMfZG4+iXAGWuUv6a\nbd+SaGfrd6Kd4Ve+Rj1XzeiJiYkXHyckJJCQkHCDcIQn2Lx5Mz4+bdCSPMCjHD36JDk5OdddPjEq\nKoq8vDTgJBAJZJCXd5CoqCjnBy10FRcXp3cIbislJYWUlBS76rDnjH4hMBJYZnu+Gy3pX1ibb6Tt\n5yLgLWD1FfvLGb2XWrZsGZ06Pc3582vRzgUWERHRj5MnD91w36FD3+SLLyZjsbTFz+93+vd/kI8+\netfpMQvhKVw9101/oCxaEo8HkoGKaBdhJ6Ot11fOVl6Vf5/VS6L3YgMHDuXrr38gMLAqFssO5s+f\nTsuWLW9q36VLl7Jt2zZq1KhBmzZtnBypEJ7F1Yk+APgWqA/kAYOAFNvvhqMNMC8ABgKLr7K/JHov\n988//3Ds2DHq1KlDZGSk3uEI4RVk9kohhPBycmesEEKIf5FEL4QQXk4SvRBCeDlJ9EII4eUk0Qsh\nhJeTRC+EEF5OEr0QQng5SfRCCOHlJNELIYSXk0QvhBBeThK9EEJ4OUn0Qgjh5STRCyGEl5NEL4QQ\nXk4SvRBCeDl7En0jYA3aIuFr0daNvWAYsAv4B2hvRxtCCCHsZE+iHwW8ATQA3rQ9B20pwYdtPzsA\nY+1sxyPZu5ivu5Pj82zefHzefGxFZU8CPgqE2x6XAA7bHt8P/AzkA/vRFg1vZEc7Hsnb/9jk+Dyb\nNx+fNx9bUfnbse+rwHLgQ7QPjKa28rLAqkLbpaEtEi6EEEIHN0r0S4AyVyl/DXjB9m8W8BDaQuHt\nrlGPLA4rhBA6sWdx8CwgrFA9mWhdOa/aykbafi4C3gJWX7H/bqCKHe0LIURxtAeo6qrGNgCtbI/b\noI28Ae0i7EYgEKhkC8qeDxQhhBA6uQPtLH0jsBJt9M0Fw9HO2P8B7nF9aEIIIYQQQgiHqgAsBbYC\nW9Au4gIkoo3K+dv2r4MewTnAtY6vFNoF7Z1AEtowVE/0LXAc2FyoLBHveO2udmze8rpdzX5gE9pr\ntkbfUJyiA1pPwi5gqM6xOFMibvj+KwPUtz0OAXYANdEu0r6sV1AOdK3jGwW8YisfyqUL1J6mBVrX\nXOFk6C2v3dWOzVtet6vZh/ZB5o380LqM44AAtG7lmnoG5ES39P5z1R2rx9D+0wHOAdu5NLbeGy7U\nXuv4ugATbeUTga6uD80h/gROX6XcG167qx2bt7xu1+INr9vVNEJL9PvRbtj8Be0GTm9106+jHlMT\nxKGdQV24qep5IBWYgHd8RY5DO77VQDRatwC2n9E6xeQs3vbaXeDNr5sCkoF1QD+dY3G0csChQs+9\n/WbNm37/uTrRhwDTgYFoZ77j0IZg1kebUuEjF8fjaCHADLTjO3vF7xTedeOYt7121+Jtr1tztBOR\ne4EBaF1X3sKbXifQrhNtvsq/Lrjx+y8AWAy8eI3fx3F5P6mnudrx/cOlO4tjbM89VRzXfn2u9ztP\nEMfl8XvT63Y9bwGD9A7CgZqg3aB5wTC8+4LsBXHc4P3nqjN6H7SvF9uATwuVxxR6/ACemyyudXxz\ngd62x72B2S6Oy5m85bW7Gm993YxAqO2xCW0KcW963dYB1dASXyDaLLpz9QzIidzy/XcXYEW7YHlh\nONC9wA9oQ71S0d5MntoXerXj64A2uiEZzx+m9zNwBMhD6wPtg/e8dlce21N4z+t2pUpof6Mb0YYB\nD9M3HKe4F23U22688/gu8Jb3nxBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBC3Jr/B01h\nqW/KYRp5AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb2c97b3b50>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# zoom to orion\n",
      "scatter(ra_list, dec_list)\n",
      "axis([7.5, 3.5, -20, 20])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[7.5, 3.5, -20, 20]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndvisYHOZsIAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb2c9829d10>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "orion_axes = [7.5, 3.5, -20, 20]\n",
      "print('{} is magnitude {} and has spectral type {}'.format(\n",
      "    starlist[0].name, starlist[0].mag, starlist[0]._spect))\n",
      "[star.mag for star in starlist[:5]]\n",
      "mag_array = np.array([ star.mag for star in starlist ])\n",
      "size_array = (5 - mag_array) ** 1.5 * 4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Gienah Corvi is magnitude 2.58 and has spectral type B8\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# resize to display magnitudes of stars\n",
      "scatter(ra_list, dec_list, size_array)\n",
      "axis(orion_axes)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[7.5, 3.5, -20, 20]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb2c923ec50>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "spectral_list = [star._spect for star in starlist]\n",
      "spectral_list[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "['B8', 'A2', 'B2', 'K5', 'B6', 'B3', 'K3', 'M1', 'B9', 'B9']"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Brandon Rhodes uses a [starcolors.txt](https://github.com/brandon-rhodes/astronomy-notebooks/blob/master/starcolors.txt) (from [here](http://www.vendian.org/mncharity/dir3/starcolor/)) to change the colors which looks extremely pretty.\n",
      "\n",
      "```\n",
      "from spectral_classification import build_color_chart\n",
      "color_chart = build_color_chart('starcolors.txt')\n",
      "```\n",
      "\n",
      "I want to revisit this, because some historical descriptions describe the colors in the asterisms, like `the ruddy red one` ([Betelguese](https://en.wikipedia.org/wiki/Betelgeuse)) by Ptolemy, but described as yellow by the Chinese, suggesting before it was a red super giant, it has doubled in size from a yellow supergiant in the beginning of the common era. ([A Brief on Astrophysics in China Today - 1981](http://www.sciencedirect.com/science/article/pii/0275106281900643)) And Sima Qian mentions in *Historical record* `yellow is like Betelgeuse` ([Ancient Chinese Suggest Betelgeuse is a Young Star - 1981](http://books.google.com/books?id=L4NTyHivbV8C&pg=PA238#v=onepage&q&f=false)). Let's continue on with his example and prettify the graph."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def pretty_hours(h, pos=None):\n",
      "    if h % 1.0 == 0.0:\n",
      "        return '{:.0g}h'.format(h)\n",
      "    else:\n",
      "        return '{:.2g}h'.format(h)\n",
      "\n",
      "def pretty_degrees(d, pos=None):\n",
      "    return u'{}\u00b0'.format(d)\n",
      "\n",
      "from matplotlib.ticker import FuncFormatter\n",
      "hours_formatter = FuncFormatter(pretty_hours)\n",
      "degrees_formatter = FuncFormatter(pretty_degrees)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(ra_list, dec_list, size_array)\n",
      "axis(orion_axes)\n",
      "gca().xaxis.set_major_formatter(hours_formatter)\n",
      "gca().yaxis.set_major_formatter(degrees_formatter)\n",
      "gca().xaxis.grid(True)\n",
      "gca().yaxis.grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEACAYAAABGYoqtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb2c97b3a10>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Well.. let's go ahead and do it anyway. I want things to be pretty too! [Here](http://www.vendian.org/mncharity/dir3/starcolor/UnstableURLs/starcolors.txt) is the original source. You'll also need [spectral_classification.py](https://github.com/brandon-rhodes/astronomy-notebooks/blob/master/spectral_classification.py)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"Convert stellar spectral classes to RGB values.\"\"\"\n",
      "\n",
      "from numpy import array\n",
      "\n",
      "def without_v(s):\n",
      "    return s.replace('(V)', '')\n",
      "\n",
      "def without_parens(s):\n",
      "    return s.replace('(', '').replace(')', '')\n",
      "\n",
      "def build_color_chart(path):\n",
      "    chart = {}\n",
      "\n",
      "    with open(path) as f:\n",
      "        for line in f:\n",
      "            if line.startswith('#'):\n",
      "                continue\n",
      "            fields = line.split()\n",
      "            spectral_class = fields[0]\n",
      "            rgb = array([ float(field) / 255.0 for field in fields[3:6] ])\n",
      "            chart[spectral_class] = rgb\n",
      "            chart[without_v(spectral_class)] = rgb\n",
      "            chart[without_parens(spectral_class)] = rgb\n",
      "\n",
      "    return chart"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "color_chart = build_color_chart('../data/starcolors.txt')\n",
      "color_list = [color_chart[spectral_class + '(V)']\n",
      "              for spectral_class in spectral_list]\n",
      "scatter(ra_list, dec_list, size_array, color_list)\n",
      "axis(orion_axes)\n",
      "gca().xaxis.set_major_formatter(hours_formatter)\n",
      "gca().yaxis.set_major_formatter(degrees_formatter)\n",
      "gca().xaxis.grid(True)\n",
      "gca().yaxis.grid(True)\n",
      "#savefig('stars.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEACAYAAABGYoqtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERERERERERExB/+HzMahN3W7KxvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb2c93c8a50>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}