{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis Workflow: Reduction and Export\n",
    "\n",
    "GBM was designed with high temporal and spectral resolution primarily to study GRB spectra and spectral evolution throughout a GRB.  So, ideally we would like to reduce the data and model the background so that we can get at the thing we're really interested in: the source spectrum.  The following workflow will show you how you can do this and export the relevant data so that it can be used with [XSPEC](https://heasarc.gsfc.nasa.gov/xanadu/xspec/) to fit the source spectrum.\n",
    "\n",
    "To start, we need to be able to read in some data.  Let's use TTE. Because we're using TTE, we'll need a binning algorithm.  And we need some response files.  Luckily these are already generated for us if it's a GRB that was triggered onboard GBM.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gbm import test_data_dir\n",
    "from gbm.data import TTE\n",
    "from gbm.binning.unbinned import bin_by_time\n",
    "\n",
    "# open a TTE file\n",
    "tte = TTE.open(test_data_dir+'/glg_tte_n9_bn090131090_v00.fit')\n",
    "# bin to 1.024 s resolution, reference time is trigger time\n",
    "phaii = tte.to_phaii(bin_by_time, 1.024, time_ref=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course we'll want to visualize what we're doing, so we need some classes from the ```gbm.plot``` module. Also, what we're going to do here is set the energy range we're interested in.  Generally, for the NaIs, you don't want to go below 8 keV.  Yes, GBM data goes below that, but the response is poorly calibrated below 8 keV. And you don't want to include the overflow channel, so going up to ~900 keV should be sufficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 770x470 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from gbm.plot import Lightcurve, Spectrum\n",
    "\n",
    "erange = (8.0, 900.0)\n",
    "\n",
    "lc_data = phaii.to_lightcurve(energy_range=erange)\n",
    "lcplot = Lightcurve(data=lc_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice looking lightcurve!  Next, we'll want to define regions outside the obvious source region to identify as background.  The TTE data early in the mission unfortunately didn't have very much pre-trigger background, but we'll try our best.  We haven't actually talked about background modeling yet, but I'll walk you through it; it's not very hard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we want two background intervals, one before and one after the source.\n",
    "# by eye, let's try:\n",
    "bkgd_times = [(-20.0, -5.0), (75.0, 200.0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The background module is in ```gbm.background```, and similar to the binning algorithms, background modeling algorithms can be divded between binned and unbinned data.  Now TTE data *is* temporally unbinned, but for simplicitly let's use an algorithm for pre-binned data.  In fact we'll use the same polynomial background algorithm that [RMfit](https://fermi.gsfc.nasa.gov/ssc/data/analysis/rmfit/) uses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the background fitter interface\n",
    "from gbm.background import BackgroundFitter\n",
    "# our fitting algorithm\n",
    "from gbm.background.binned import Polynomial\n",
    "\n",
    "# we initialize our background fitter with the phaii object, the algorithm, and the time ranges to fit.\n",
    "# if we were using an unbinned algorithm, we'd call .from_tte() and give it tte instead of phaii\n",
    "backfitter = BackgroundFitter.from_phaii(phaii, Polynomial, time_ranges=bkgd_times)\n",
    "\n",
    "# and once initialized, we can run the fit with the fitting parameters appropriate for our algorithm.\n",
    "# here, we'll do a 1st order polynomial\n",
    "backfitter.fit(order=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, the fit is done, but how do we know if it is a good fit?  You can return the fit statistic and degrees-of-freedom (DoF) for each energy channel fit, and try to figure out if it's a good fit based on that (Note: not always the best way to go)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.05253464, 0.97225381, 0.91227241, 0.98050048, 0.84681665,\n",
       "       1.0109339 , 0.83987198, 0.99393676, 0.93546538, 0.97676709,\n",
       "       1.06159651, 1.13014594, 1.02241299, 0.87300405, 0.93111584,\n",
       "       0.77749972, 0.88585415, 1.24709709, 1.00078239, 1.18972735,\n",
       "       1.08504497, 0.86004   , 1.09627527, 1.26029689, 1.02805267,\n",
       "       1.09511262, 1.09453753, 0.84877554, 1.03829697, 1.18797705,\n",
       "       1.17876657, 1.0435771 , 0.87277942, 1.1235214 , 0.75154049,\n",
       "       1.16525405, 0.96174605, 0.86952962, 1.05345248, 1.06671789,\n",
       "       0.98715977, 0.87106808, 0.81729816, 1.01830519, 0.96619755,\n",
       "       0.96130275, 1.26006097, 1.10956129, 0.7465444 , 1.14112326,\n",
       "       1.17921618, 0.99306683, 0.94085532, 0.75801103, 0.93522882,\n",
       "       0.93297998, 1.34048725, 0.97457337, 0.92851687, 1.13544663,\n",
       "       1.03582596, 0.86974828, 0.95736557, 0.90115499, 0.97121474,\n",
       "       0.99067311, 1.03314964, 1.05197093, 0.67085108, 0.86989996,\n",
       "       1.13958071, 0.79637932, 0.83125611, 0.96961894, 0.70648981,\n",
       "       1.28318981, 0.96054971, 0.95883523, 0.98814164, 0.79250393,\n",
       "       0.98667504, 0.85922942, 1.07602863, 0.89644774, 1.04385212,\n",
       "       0.74181608, 0.88999557, 0.89085185, 0.85543955, 0.74622679,\n",
       "       0.84810177, 1.00566535, 0.7757027 , 0.77445155, 0.83288842,\n",
       "       0.81179572, 1.03735701, 0.78808631, 0.77720697, 0.68643965,\n",
       "       0.76911826, 0.82697149, 0.94950339, 0.69566148, 0.84312887,\n",
       "       0.83848112, 0.72023423, 0.64302101, 0.87508165, 0.95623795,\n",
       "       0.90862106, 0.74499819, 0.85851114, 0.82726985, 1.10998919,\n",
       "       1.16072684, 0.85533812, 1.0301769 , 0.55424838, 0.6925533 ,\n",
       "       0.73817561, 0.71850524, 0.86838555, 0.62815818, 0.869434  ,\n",
       "       0.91485833, 0.93208495, 1.21406148])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backfitter.statistic/backfitter.dof"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also plot it.  If you want to plot the background model, it's a good idea to plot it not just during the background segments you fit, but also during the source interval. This is important because you want to see how well the background is modeled *during* the source interval.  This means that the model needs to be interpolated over the source interval. For simplicity, and to make the background look smooth, we'll go ahead and interpolate across every time bin in the lightcurve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gbm.background.background.BackgroundRates"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkgd = backfitter.interpolate_bins(phaii.data.tstart, phaii.data.tstop)\n",
    "type(bkgd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what is ```bkgd```?  It's similar to the same data structure that contains the PHAII data: a 2D Time-Energy histogram, but instead of containing observed counts, it contains the modeled background rates for each time bin and energy channel.  So if we want to plot the background model on our lightcurve, we need to integrate over energy.  Reminder: we don't want to integrate over *all* energy channels, only the ones we set in `erange`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5qgm0VwJbgQXAteGyDQRXCltem2qJiIiIiJSnmnloHegEOs1sYrhsa60rJiIiIiJSjooDrZnNABrdfU00yJrZwUCvuz9bw/qJiIiIiAyompPCvg28t8jyd4frRERERERGTDWB9p3A74ss/wPFZz8QERERERk21QRaByYWWT4ZaBhadUREREREKlNNoP0tcJGZ5cJreP8i4D9rVTEZXslkkra2Ntra2kgmk6NdHREREZGqVTNt1wUEofZpM/tduOz9wCTgw7WqmIiIiIhIOSruoXX3VcDbgH8GphIMP7gHONTdV9S2ejJc3J10JrgFM7GJiIiI1Kdqemhx9w3A12pcFxkh7s4553Wyck0KgHkLO1l2Xcco10pERESkOmX10JrZ9Eo2amb7VVcdGQmJRA8rV3flHq9Y1UUi0TOKNRIRERGpXrlDDv5oZneY2ZGlCpjZZDP7spmtAE6pTfVERERERAZW7pCDOcDFwM/MLAE8CmwAEsBu4fq5wGPA+e7+4DDUVURERESkn7J6aN39dXc/F9gHOBtYA+wJHBwW+R5whLsfpTArIiIiIiOpopPC3L0b+FF4ExEREREZddVcWEFEREREZMxQoBURERGRuqZAKyIiIiJ1TYFWREREROqaAq2IiIiI1LWqAq2Z/YOZ/d7MNpjZgeGydjP7m9pWT0RERERkYBUHWjM7C1gKPAhMARrCVZuA9tpVTURERERkcNX00J4DfNndrwbSkeWPAIfXpFYiIiIiImWqJtDOAP5UZHkSGD+06oiIiIiIVKaaQLsOeEeR5R8DVleyITM7y8yeMLMt4e1hM/vryPoWM7vFzF43s21mdq+ZTSvYxnQze8DMdpjZK2a2xMwaC8p8yMweM7OkmT1jZqdXUk8RERERGbsquvRtaClwi5m1AAa8y8z+D3AR8KUKt7UeuBBYE27rc8C/mtk73X0l0Al8HPgUsBm4GbgP+CsAM2sAHgBeBt4L7APcA/QCXwvLzAjL3A58BjgGuNPMXnL3h6rYfxEREREZQyoOtO5+p5l1A1cB44DvAxuA+e7+wwq39e8Fiy4OTzp7j5mtB74InObuvwQws88Dq83sPe7+B+AjwBzgWHffCDxuZl8HFpvZZe7eA5wJrHP3BeFrrDaz9wEdgAKtiIiISJ2ratoud/+eux8MTAD2dvf93f2uoVTEzBrM7FSCcbgPA0cATcDPI6/7FPA8cFS46CjgyTDMZj0ETALmRsr8nHwPRbZRrC5xM5uUvQETq94xERERERlW1Uzb9UszmwLg7jvc/ZVw+SQz+2UV2zvczLYRnFR2O3CSu68C9gZ63H1TwVM2husIf24ssp4yykwys9YS1bqIYIhD9ra+/D0SERERkZFUTQ/th4DmIstbgPdXsb2nCU4yezdwG/AdM5tTxXZq6VpgcuS2/+hWR0RERERKKXsMrZm9LfJwjpntHXncQDDLwYuVViAc5/pM+PBRMzsSmA/8X6DZzKYU9NJOIzgJjPDnuwo2OS2yLvtzWpEyW9y9u0SdkgQ9xgCYWfk7JCIiIiIjqpKTwh4HPLwVG1rQTXDRhaGKAXHgUYLZCo4B7gUws0OA6QRjbAl/XmxmU7NDH4DjgC3AqkiZ4wte47jINkRERESkjlUSaGcQTK3VRdAr+mpkXQ/wiruniz2xFDO7FvgJwYleE4HTCIY0fNTdN5vZXcBSM3uDIKTeBDwcznAA8FOC4PpdMzufYLzsVcAtYS8rBONyzzaz64C7gQ8DnyaYDkxERERE6lzZgdbdnwvvVjUzQglTCeaN3Yfg5KsnCMLsz8L1HUCGoIc2TjA7QVukTmkzO4Fg7O3DwHbgO8ClkTLrzOzjBHPazic4wetLmoNWREREZOdQzYUVAAhP3JpOwQli7v5v5W7D3b84yPoE8NXwVqrMc/QfUlBY5tfAO8utl4iIiIjUj4oDrZnNBH4MHE4wnjZ7xpSHPxtqUzURERERkcFVM3zgRmAdwXCBHQQXMPgA8AjB+FcRERERkRFTzZCDo4APu/trZpYBMu7+n2Z2EbAcHdoXERERkRFUTQ9tA7A1vP8asG94/zngkFpUSkRERESkXNX00K4A3k4w7OC/gfPNrAf4CsGUXiIiIiIiI6aaHtqrIs+7lGB+2t8RzDQwv0b1kl1Ye0c7yWRy8IIiIiIiVNFDG52/1d2fAQ41s92BN93dSz9TpDyZDOirJCIiIuWqyUUS3P0NYG8zu7kW25Nd28q1KeYt7FSoFRERkbJUFGjNbK6ZnW1mXzGzKeGyPc1sGcH42aOHo5Kya5g9c//c/RWrukgkekaxNiIiIlIvyg60ZvYJ4E8EU3PdDjxiZkcDq4FDgZPcfe6w1FJ2CcuXdDB3VtUXrxMREZFdVCU9tJcAtwCTgHOBmQTh9nh3/5i7/8cw1E92JQaxmgyCERERkV1JJfHhEOAWd98G3ARkgA53/+Ow1ExEREREpAyVBNqJwBYAd08D3WjeWamheHOcZZ3LRrsaIiIiUmcqHbD4UTPbHN6PAceY2WHRAu7+bzWpmYiIiIhIGSoNtN8peHxHwWMnuDSuiIiIiMiIKDvQurtO1xERERGRMUchVURERETqmgKtiIiIiNQ1BVoRERERqWsKtCIiIiJS1xRoRURERKSuVRxozazLzPYosnyKmelCCyIiIiIyoqrpoT2I4nPNxoH9hlQbEREREZEKlT0PrZl9IvIwesUwCALuMcCzNaqXiIiIiEhZKrlS2L+EP53+VwzrJQizC2pQJxERERGRslV8pTAzWwcc6e6vDVutRERERETKVEkPLQDuPmM4KiIiIiIiUo2KAy2AmR1DMGZ2KgUnlrn7F2pQLxERERGRslQcaM3sG8ClwCPASwRjakVERERERkU1PbRnAqe7+3drXRkRERERkUpVMw9tM/Bfta6IiIiIiEg1qgm0dwKn1boiIiIiIiLVqGbIQQvwFTM7FniCYA7aHHc/txYVExEREREpRzWB9m3A4+H9wwrW6QQxERERERlR1cxDe/RwVEREREREpBrVjKEVERERERkzqpmH9lcMMLTA3T88pBqJiIiIiFSgmjG0jxc8bgLeQTCe9jtDrpGIiIiISAWqGUPbUWy5mV0GTBhqhWR4JZNJ2jvaR7saIiIiIjVTyzG0/wR8oYbbExEREREZVC0D7VFAoobbk2Hg7mQyo10LERERkdqp5qSw+woXAfsAfwlcWYtKyfBwd845r5OVa1OjXRURERGRmqmmh3Zzwe0N4NfA8e5+ee2qJrWWSPSwcnVX7vHsmfuPYm1EREREaqOak8I+PxwVkZE1d1YjS67r4PhTFox2VURERESGpJppuwAwsyOAt4YPV7r7n2pTJRkJsRjBYBERERGROlfNGNqpwA+BDwGbwsVTwgsunOrur9auejJclnUuG+0qiIiIiNRENWNobwImAnPdfXd3353gogqTgOW1rJyIiIiIyGCqGXLwMeBYd1+dXeDuq8zsq8BPa1YzEREREZEyVNNDGwN6iyzvrXJ7IkOWTCZpa2ujra2NZDI52tURERGREVRNAP0lcKOZ7ZtdYGb7AZ3AL2pVMZFKuDvpTHBz99GujoiIiIygagLt2QTjZZ81s7VmthZYFy47p5aVEylH9oIRT65J8eSaFPMWdirUioiI7EKqmYf2BTP7C+BY4NBw8Wp3/3lNayZSpsILRqxY1UUi0UNra3wUayUiIiIjpap5aD3o/vpZeBMRERERGTVlDzkwsw+b2Sozm1Rk3WQzW2lm769t9UREREREBlbJGNp24FvuvqVwhbtvBu4Azq1VxUREREREylFJoH078B8DrP8pcMTQqiMiIiIiUplKAu00is8/m5UC9hpadUREREREKlNJoH2R4BK3pbwNeGlo1RERERERqUwlgfZB4EozaylcYWatwOXA/bWqmIiIiIhIOSqZtusq4GTgz2Z2M/B0uPxQ4KtAA3B1basnIiIiIjKwsgOtu280s/cCtwHXApZdBTwEfNXdN9a+iiIiIiIipVV06Vt3f87djwf2BN4NvAfY092Pd/d1lb64mV1kZn80s61m9oqZ/YuZHVJQpsXMbjGz181sm5nda2bTCspMN7MHzGxHuJ0lZtZYUOZDZvaYmSXN7BkzO73S+oqIiIjI2FNRoM1y9zfd/Y/u/j/u/uYQXv+DwC0Ewfg4oAn4qZmNj5TpBE4EPhWW3xe4L7vSzBqAB4Bm4L3A54DTgSsiZWaEZX4FvANYBtxpZh8dQt1FREREZAyo6tK3teLuH4s+DntNXyGYz/a3ZjYZ+CJwmrv/MizzeWC1mb3H3f8AfASYAxwbDnl43My+Diw2s8vcvQc4E1jn7gvCl1ptZu8DOgiGS4iIiIhInaqqh3YYTQ5/vhH+PIKg1/bn2QLu/hTwPHBUuOgo4MmC8bsPAZOAuZEyPyffQ5Ft5DGzuJlNyt6AidXtjoiIiIgMtzETaM0sRjAU4PfuviJcvDfQ4+6bCopvDNdlyxSejLYxsm6gMpPCKccKXQRsjtzWV7ArMkySySRtbW20tbWRTCYHLNve0T5oGREREdk5jJlASzCW9jDg1NGuCMEsDpMjt/1HtzoC4O6kM8HN3Qcsm8kwaBkRERHZOYyJQBvOa3sCcLS7R3tDXwaazWxKwVOmheuyZaYVWU8ZZba4e3dhfdw96e5bsjdga0U7JDUR7WV1d845r5Mn16R4ck2KeQs7BwysK9cOXkZERER2DqMaaC1wM3AS8OEiU389CvQCx0SecwgwHXg4XPQwcLiZTY087zhgC7AqUuYY8h0X2YaMQdFe1kSih5Wru3LrVqzqIpHo6fec2TP3H7SMiIiI7FxGu4f2FuDvgdOArWa2d3hrBXD3zcBdwFIzO9rMjgD+EXg4nOEA4KcEwfW7Zvb2cCquq4Bb3D07iPJ2YKaZXWdmh5pZG/BpginBZIyqppd1+ZIO5s4a1ck7REREZISNdqA9i2CM6q+BlyK3v4uU6QDuB+4FfkswfODk7Ep3TxMMV0gT9Lj+E3APcGmkzDrg4wS9sv8LLAC+5O6asmuMaWlp5rA5M3OPK+5lNYiN9rdaRERERtSo/ut3dytx+3akTMLdv+ruu7v7eHc/2d1fLtjOc+HVysa5+17ufp67pwrK/Nrd3+nucXefFX0NGTvMbEi9rPHmOMs6l9W4ViIiIjKWqS9LxhwzUy+riIiIlE2xQcaceFy9rCIiIlI+BVoRERERqWsKtCIiIiJS1xRoRURERKSuKdDKmJfsSdLe0T7a1RAREZExSoFWxj4PrhomIiIiUowCrYx55yxcysq1qX7Lkz3JIqVFRERkV6NAK2Pe2q4Xc/dnzdi3b0X5V8QVERGRnZgCrdSNubMauf7qs3OP5y3sxH3gVJvsSdLW1kZbWxvJpHp0RUREdkYKtFI3YjFoaWmmNR48fqZrPYlEz8BPckhnnHTGBw2/IiIiUp8UaKVuLOtcRktLC7OnN5b9nJNPW8CTa1I8uSZVVo+uiIiI1B8FWhlUe0d73R6uT0SqvWJV1+A9uiIiIlJ3FGhlUJkMI96z2dLSzGFzZuYeHzZnJi0tzcTjcZZ1LhvRuoiIiMjYpkArg1q5duQP15sZy5d0cPjBjRx+cCPLl3RgZmU9tzAMZ8fcioiIyM5JgVaKKgyFo3G43sxoiAW3csNs9nnRMHzv928YxlqKiIjIaCv/7BrZpWRD4TnzOope1GAkxONxbr311qqemw3DwYMaVkpERETGHAVaKcnMiI3xPvxkT5ILLrig3/JoGO7urs8T2kRERKQ8YzyuyGiqixOwPDhpTURERHZd6qGVunbOwqWs7RqdIREiIiIyNqiHVgCKHravB2u7Xszdz07tNZB6nlNXREREilOgFaD+D9vPnVXe1F6jMaeuiIiIDC8FWgFg9br6Pmy//MbgsriDWbk2xYmfnEcikRiBWomIiMhIUKDdhRXONbuzKtzP7d2uS+CKiIjsRBRod2HZuWbnztq5zw0stp8jfeUzERERGT4KtLu4ephrthbMjMaGvsvgPtO1Xr20IiIiO4ldIMrIQOLxOIsXLx7tagy7eDzOsmXLmD29r5dWMx6IiIjsHBRoZZelGQ9ERER2Dgq0sssovPLZyrUpjaUVEXKg2awAACAASURBVBHZCSjQCi3xZsa3BvO3lnNxgtFWOGtBJXUufO6KVV0aSysiIlLnFGgFM2P2AQ0cfnB5FycYbdlZCw4/uLHiOu8qMzuIiIjsSvRfXYAg6DUYYz7MZpkZDTHL3a/0ubvCzA4iIiK7CgVaIR6Pc+utt452NSoylDpnx9Iec8LZNa6ViIiIjAb1U4mIiIhIXVOglV3evPntJBKJ0a6GiIiIVEmBVnZ5mr5LRESkvinQyi5J03eJiIjsPBRoZZek6btERER2Hgq0Urb2jnaSyeRoV6NmNH2XiIjIzkH/zqVsmQw71TjTwkvhioiISH1SoN3JJRIJzjjzLM4486wh967q5CkREREZixRod2LuzjnndfLkmhRPrgnCaKV2pZOnkslkLvxrGi8REZH6oUC7E0skeli5uiv3+Jmu9RVvYzhPnkomk7R3tNd8u9U6+bQFeeFfPdEiIiL1QYFWBtXS0sLyG2s/1tTdyWRqvtmqJSIjMnbmnmgREZGdjQLtTmqs9X4Wyg6HWLk2NdpVGVQymaStrY22tradapYHERGRnYUC7U5qrPV+FiocDnHYnJm0tDSPeD0Kxwi3xvuXcXfSGdcQBBERkTFKgXYnVE+9nwBzZzWyfEkHZjbir50dI3z4wY0cfnAj937/hrz10RPrnnkhrVArIiIyBukySTuhwt7P2TP3r+qEsFKiwxluXNZJS0vLkLa3/MZltLQU6RodIS0tLdxx+20AdHfnDymIvpfbu51EsmfI+zuYRCLB/PYOoDbvr4iIyM5OPbQ7uWzvZy2ds3DpLjsbwMKFF+RN6VXO+NroXMBnnXXWgGWLTbW2K72/IiIi1VAP7RAkk0k6OoKw2NnZSTw+er2MpQzH7ARru17M3V+xqoszz2pj+Y3Lytr/7HuWzoz9kJbsSXLBBRfkLVu5NsWJn5zH7AMaKNyFYsEzN/xjTTD8ozUOs6c3Mm9+8R7uwt717GwLrQWDe6vpxa3l97UevvsiIrLrUA/tEGRPFhrKCUPR3rtsz1+xZUNReOJTrU/AymRg3vz2vLqW2ofsezaWT1jLcYrWc3u389S6VK4XtVhvanb/v3JGW15A7U5S8jllV6vKXtyhfF8LP8/otubNbx/W72+taLaKnctY/Z6JyOhQoK1SLQ4NF9tGJpOp+SHnwhOfan0C1sq1QV1P/OQ8zjor+OdSuA+JRIKzzjqLE0+Zx5NrUnVxwtpJpy7Iq+dbZ/Qd0Ej29i+/YlUXmzdv5Ywzz+LjJ5/Tbz/jTcWfk53vttyp1kr14g6k2Hetu7u7rEBQ+NwTT5mX+xwLw/lA399aB5DBAupAIXwov1OVDCEZq8r5LMZyYNTQnPKoESe7Eg05qEIikeCceR15YWXFqi6+ckYbNy0v/ySeYsHklVffKOuQc6XMjIaY5e5XKtvLu2JVULe5bw16fKN13d7tpNLF92t+eweZDGxP1M8/nWhonXPoQTRl1jO+1djeHexDdvhAJkPuu3DSqQuKhl2AtxzU9+sWfc68+e3ctLwzt7yquhY5US86LAHIDXuA8Ps6bzFru4JlHz/5HGZPbyRmwfdj0aJFXHjhhQBcc+2i/M+5xGe4YlUXmzZvK/r9bWlpzht6MW9hJ3csvzD3XaxmCEU2oGbvF66Lvt4553XmvQeFr1+uUkNIxlKYKvzcYwbLlvUNCSrch+h7EX1uumF/VlX4fiWTSdrb28l45Sc0lhrGEl2eVfidrOXQnJ3JQL8jUlsahlVa4XszXBRoy7RPYyOvP/44V95+By+8nKa719gTcAwHMGPDU3Bh2+VcfdlZWEMDmGFmWCwGsVjwzyDyM5XspTmTwiPbueSiizHPQGS7WQP9cY7+I1m8aHG/+sfjcW699daq9z/by5t7/euDn9mgmg1nK9cGvSWF/vxsqmTQG0sKg3s2sNx4w4J+ITEb/hYvXsTxJy8A8kNwvCn/8eLFi5kyeRLJZDI3hhaC9ywXuIr0XCeTyUEbNCeftiB3pbOzF9xAU2Y9zzyfzoXPYr3D0bHQ2aEQrXF4y4GNef8IGeD/YPaSyNl6L1x4QdFypXqVYzFob2/nz8+lyE4wkQ3XAwWQ7u5uTjhlXu45/QNy/utF72dfv5Kx36X2I/u+zVvYyfIlHZx77rnA4P/QhhK0Sj03u/yZ5/veS4DxrZYXZkp9FoWNDng2r0w5DXZ3z33vBgrBxf75lwpfed/F3MLS70/0eQM1ooYSvuvBYPsvtVXPjYfhbviN1Htj9fbGjwYzm9S2556bP7jnNDwVTWVGQ0MwaiOdzuT+xk6ZPCH3R8OBbdu25Z7hgNH39ziVChYM9CnssccUNm/eTG8aMmHQNTMaG42pU/cKwjPw/PqNpB2a480keoJ6Hjz7QBoag3BNNlyHP6P3KQjeuWUNDbn7uceRYJ5x5w9//CNbtjs9qTCYW19Ad7Pc/QyGm7HbbhN5482tZMJ1Xzz9b2iONwfbjMVyr5F77SKNAcvej9S7XwOiyP5ln0P2dSC/rBk9qRRLrr8BN+PCCy8IgklBmehjgAsuu42n/vw8bkZLMxy0fyOYsXJtCg/L3PuDRUyZMjn3Xp517hJWrFqX/ZLlfeazZuzL2nUbgGDate9889Lcd6q7O8kxJ5w94Hd27qzGsod1FAbvljg0mOXCcHTat9Y4ubB02JyZuUbOk2tKv9bhB+f3Ymf94v6bMXM+fvI5dBc5GnrYnJlF/wG7O5/7yhX9pqJ78L4b+NpFF+IeHCkYbP9b4/DAfTfR0tJSdu9K9L0vfN8evPcGLrgwCPTRfwrZbbs7GYdlnUuZt3BZLlTmGk1Fwin0NZw6Oztpbm7mK+f09U6ObzVmH9BAZ2dnEF4KgnvWj79/DZd+41IgaPAef8qCvPW/uP9mgEG/V8U+k8Ie4eh34Rf331y0MVb4HAh7hJ96Nu91gLz9zSo2FWH2+1SqLoX1SSQSnHjKPLYnvOR3rVqlPr/KGk9DCxnF/k6U+jxkaNw973ta6+/TcKpV3Ut9X4ttf8lVbUyZMgVgsrtvqdW+qIe2TJvSaXakndbwsQENMWdcaxzc2dHdTSYdBIAdW6Kfj2GZoA82+7zo16SBYJWZFW25OLDjtddpcA/KRqVg8/MvYAbNzc1M7A3HUaa6GRcW2bGuq19YGlCZZXOlzJhJ8KVNJBJ5h8wdiMViZMKFZhAzoyXdwvbt3blyz915Z/6bMkh9BqxhqfpHl5vl1b9YmXdv3QrAo1/6Ut8vdrG6hMtOdOf9mzf3rVgPU6ZM4QObt9Lbm8YMHj35pCAEhz7pzjFvbqI3FbZcLQj+TU3Gnpm92PDSa2Qc/EX45Ud/z8sbN+Z680/vgdy3KtJoyK63jXC4B8stBmkP1o8b18q2HQkwIxOWnzyxATfYst1JZ4xM2CCBsMxrTzMjXPY3J36Q3/3nf+JmHLvPDNbddBNnz5jBP/3PQ5EGDOy++2Ref2MLbkbDn6A3bRwZries4ws//AGpdIpDXnsRBxobDGLQkwr35X9e5oq/PYnPfe6zNDU3Y7EYvakUd939bWxjhtkYDQ2QSgfbu+4LX2S3mPHiK2mSvXBA+L5kXy9bv6l7TmHja5vwpLHhj49y6+234Wa0huu3rVtHbzze1+iLNKSSyV7Gp5K4GT+48wq+ccUVPLUujZvR0bGYrmeTONB+7hJuWDSfBectzO3zMy+k6U7CGfOuywtj0V7eG69rp73j3Lxe1myvOfTvXS01zKc1DjP372vULPjazXQ929dTV4loI6bw0H5hL2DhkYDCoTBA0V7kwLO5e9Fx4cVCerF5tefNb6drfd92ix2ViEoke3KNtqEO7Yr+M881WAYZljJQI6pY72r2u5FVbVAeisLQApQM3cNxFGI4RHvqC1+v3HqUOzvNWDTUuhc7MpQ/jKnycz6qpR7aMpjZJGDzXx/1SaaQ4aB9GmhoCILVGWecAe7cdvsdPPdi8MfHgOYm2HuPGM+/nA7+pYZvc+7fa/i+x4AD9tuLf/i7j/BP3/s+Mc+EZZ2XXk4DHp65F/xsanTMIZ0KthHEDqcpBulMWNaDf98xh0+fdDQNDQ14JgPuwc9MJgiZ2Zs7mXQaMhk8ncYzmVw5z4Q9z9mk6uEJNdnl7rlbOp3hscefygvth8+Zybp1XeAwc+YMDMhkMqxavS73Xhz6lgOJmeW2k/tOZu8XWY5HDl8UK19qXUGdc+Uiy3P7FfnpBY+LLe/uDkJ6Szye1wufSCQwIF7kj2FhIwBg3LiWfuvGjWspWjbaYCglkqFpaWlhx45E3hGB8eNaSCSTwWc4yBjeCRPGFV2+oztJJp2BIPfR2trKtm3d/co1NMRIh+UmThjHjh07cq85ccI4MGPH9u25ZQ25uhsYpNOeV/fGmJHKHY7ua3Bkl+ReL2Ly5Ils2rwVMKzg6IgDTY2NTJ48IbdsU9hQmTx5cnCkJOz022vP3cDglVffLPKOGI1NDfT2pvOWeq6xEP40woZFsKypuZlkb29+A8WMmMGhh86k69ln2d7dtzzXgIlBJmy0TBgfC9IOsGlbtmw21Pc9x8N9z5hxwsfex+8ffpjXN3veOsf49CnH8otf/YpX3gze+8995uM0NTeRzmS4/8EHeem1TC60Z7J/kcLnNzYaveGRm3322Qs3ePGl13KvkX0f8usUPL78kq9gsRiXXPlNHJi+TwNuxnMvpUs+h4LH+UeK4K5bL6altYXeVIqrrr6Gp58LtpUxOOSgRq644vLg9zRyZKent5dLLrkEN+PaRYuC9dGjPWac2bGElavXgVnJC9lke76SyWS/3unCsFSsd3XWzP3yhgkV69nPKnaeBwRHbmKx4mHUzPoF7GjoXrp0ad5RgMLzKKI9e0Pp9avkueUcWRmsTOHwpVJHBwaqRzm94cWOShSOb69kv4ptd6R78gs/q8JtxGJBQ7PwSMmPv38Ne+89FWrcQ6tAW4ZsoD3yA5+lsbE5mCmgYOydu3PGvEW5sZeFsn9ICnteSv1BKvYlmzurkcawmzbjlOjlyDeSh5jcnTPOuTb3Hhw2Zwa3dZ4fBvq+ANjdneT4k4JeGwPu/9H1tLbEgxDdt7G+sBgu92JBtFT4zW6nMIgXhuMi64qF1X7PLRZso8/NZPKCv6fTkE6TcYd0Ote4SCZ7uPSK23MNk5g7lyw8HXC+e889vBg2iKbuOZnXX32TWNiIiXmGvaftwSc+dhQP3H8/5s6xxxzLD//5oVxDJ+bOSSe8n+amxqA+6TSp3hQP/uR3wWsB5s6H3/8XNDTE8EyGx/70FFu2bMOA8eNa2bFjR/A5hY2s9xx5GLGY5X8mmQzpTIY/P/00BsyePZuYGX9e8zw7dgTpqyHmtMaNA6cfyFNPPws4LfFmksmeXBSZPXN/YsD69S+SSGZy3w/C98bCtzvbYDJzmpsMaKCnJ0VfLAwaio2NsMfuu/PGG1vo7Q3GBjQ1NTJl8gRee+3NvO3jfY1NgN2mTMSArVt3kE6n87adLTZxQivunne0oVINDUHyKzYvcy5se+SxF6wv8ud7/PjWXKgou27RFljBMZDxE1rp7u4mkw6KTAgfZ6XTA9dnoEMq2QZLsYbUxInjAdi6dTsAkyaOBzM2bwkel/zPlQ3KweELGhr7GjW77TaJreERNCfo3c9qbBw8bE2ZMqUvyJJ9Hc9r1OQaLMGXNPizEC4/4IC9Wf9iMDSs7/yLoKE5ffrePP/CC0F4P/BAVj79HOQCeX5DKLpswsTxvOPth2Bh4/bxJ55g2w6nN1OqEWPsttskHOP1TcF7sdseUzj6A0fwq9/8JmgghXXPRJ6Xwdj4hke2ld/ocmCfqY3Ba7ix/tVMePQoKL+w/e/5wQ9/gJvx+c+fTlM8njdELJVK8a077yTtsG5DfqNlxv4NzJ8/D8xYftNNYEZ7RwcOXN8ZzLVuQZc1F1xwAc2RYWLJnh6uuvoa3IxvfCMYuvWNy6/Aw+esXZ+muye/8fPv/+8GzIzjP7kgb/9+9u830zqupWijZKDhLaWCX2sc7r93Oa2trXnLywmp2Z7l6LkSgzUcivWwF2v4FOaGYgG7WKMpOgxLgXaMKgy0g40LKwya0S9ZuWOrCgNydqzcsmXBL290TN5AwXakx0yV84tYGNZ35XFdA7WOS42VLdUQKqelPViZ6OdXaqxlsc+q1B+8wu/6Ndf2nUAXFe0VcXfOnLeIlau6iAbJbMPIgDkzG7lhyaLc61xwwYW5NBX+LyYGXHXVVXgmw8WXXAIOV11xOc3NzZx/yc08/efnMDwY87xvA56BNc8Ff3jvvvVrLFq0mGee7zvqAp4LvtMPmMa13ziTZLKXL7VdlRugYR68rpEh5hDDOWifGDFzSDsvvJwilgmWG/C1Bf8AmQxLlt4DOA0eHHnZd0+jwYCM8/JraWL0HX2JAVP3mMxxHzqCX/7iF2zenCGdDo7c7LHbRD74V28Hd3qTPfzHzx4OXit3NAfyopETeX+DxxMmjCPRvYN02pk0oZU5h84Ijr78aRUADWF7JtofbsDcQw/iuWefxR26E31Hj4hsP/v+tcZhv333BWDDixtI9HhBfZzp+08D4IX1GzFgv332xMx49dU36OlJYUBTYwOpVN9n1NQYhE53Z/OmTRgwceJE3nwzDG5TJrJt69ZcCI/WP/Kw7/2xvmXZF4mFATRqx47i05plj45k29WxWOmZTGKxvvc0Ho+zo8Qf9WLbGDeuJdcmKTySU+woTrH/+sWGh2XcizdSSik85BHdfoORCY+wNDTEcnWO1inbgI42cILyxoTxQQNn2/agQTNhwgS2besmFX6YZkEDacKECQVldpBKpcMjBkGPUCqVJneEpv8BHnbfbTIAr78ZGUYG7LH7ZMyMTZu30RtuM/viuXAfbmi//aby6muv4cC0aXvz7AsvFz2KEI83M2vWAUEjFOhat47upJPKBOXGjx/HWw+dkTtnZdVTT4EZb50zh5WrVrNlh+eF/6M/eAQNjY1k3Hn00UfzGjNbtpM7F6ehIWgUpdLZoyV9DZhTP/WR3FGYh3760/AITHBE6eMnnkhjUxP3/utveOnVN3Lb33uv4LD1i686brBg3mewhhidN3+PZC95R3EWdnyGOf/nVNAY2rErOzXWWw5szLuK1I2ReV9bWlq44/bbytpW3qwCBeHl1ltvzYWI6OsVnlhRywsolGOo04PtagpnVRjsM8v20lv25LSa16fv+9k9WPd/RLFZNIp914ttM3t55uz+mBnLrz83rwck12iz4D26LdIL0dTYiDcHAyZLHZ676e678x4vuenifr9b0bB//o3/zNqXmqEp+CyiPQ+tcbj+rmtobW2luzvJq/GJA743k/YKjuhkMv1Putr3xE/Q0tJM5vdded+BiyKHbqMN22xj5vKwzm+7+OK8hsNFkb8T3d1J5j2e/3rRw+GFh4yz275ucTBlW/Ts/+7uJOcPcMLYYXNm0laizuNbgs9peyI4gjG+1Th4/xg3LF1KvKmJTCbD2ecuYdXqdbTGIZEMIsGP7rmab1x6KavTBwDw4x8spiXeRDKR4OJLLsEcrrj8Mr5+5Td5+s/PM74F7r7tsuDzzx5NyWRIJHr47Jcvw4DmRqenJdvLH2kARcJ2dDnZx5m+oV8z940FDTR3YgSB77kXU31HPcJ4EcsEDRZ3uG7pd3LbNg+OxOyzZ4wYzsZX07kjENnGR1MMvDGIF7Ew6MfCIzh7TjYsPFK0ZXNwmvCH338EMYPHHlvFlsz2XCCfNM449JBDeOSxp3KvUdhItEhizZXxvnLRvzKGBw0aj2zD+7ZFwXsaM/BcOHUsciLI5ClTMIIhaK+/9hqpFLg3Em1IZLeZTsMbW3eEdQui+6Yt2/LDswfltm/bEc4UBJneXry3N3f+ifUGy7PDq6P7HiM48mlAattWenp6aCkI56ktW+np7aHRg/A0UNZPbtxIS08wVjSx4UWmpIL74b/HvozQu53XnthEPB78rZmU7GFCtBWR3MLLf9hIvLmZZE8vu4VPfPl3v2cPYI/cuxt47Te/yT11WjIZjCrMPh6gvrGYkQm3vf4H3ycRPvfAaCGDdd/6FgCzdySYHVke2xL8zT8kHNL2xBWX09LSwgfDxl60wbTu2ms4oKmJF3prO/WRAm0NRf+pR3usqg0eg4XD7OtFX+u6a87m/AuCnq4ba3wBhXJUMz1Ysmfwaal2Vv0aLnmNn/5hd6DDSeWE40oCdKVhu1rLb1xGS0v+518YhvN6/gu+19V85wb73YqOVcweHcn+A4pZ8Me5lGhoPGzOTG4PP7NSDYSBvgOF67I93dE6l2okF/v8oicWRafeK9y2WdBDXOq7mA2/WQPVebCTh2LAsqULc+uzhycvuvxKaGgi0RBst3nKFOKtceLkN1CW3HQJ89s7iBmM22+/fg2aWHeSN5siY7/Dr3B2HxYvupYLLryo3/sXPfIVbdCsjwyZzp58t3ZT8Rk19vnE39DS0oz/fi1PRoajjW81rv/RcuLx+IBD1YopdgTnrKuvYeH5F7CydRbZM5ejY3av/OQ8tnd73gl+RbkTb4JgkpwgtI5rhlkH9CXRqy6/glM+E7xf2aMmMw/al2ef3QA4rc0wY7+GXOB9qiuV33gIw9rdN3+N669bzHMb0iT22J/oEZBs+QP3n8YL618u2tiINkKaGqE3lX9UwBziTU7POPoth6BxE3buc+AB0/jiP5zAZVd9C3D2nbYbr2x8A+gbmmXuTN1zCq+/+mZwBAbYe/cYr7yeDo+49B0JaXBntykT2PTmlmD4U8zJNAV1OPGj7+V//vAwnnE2bQ56l3ebaLzrL4/gkUdWsXnLtnA7fY2oyeOC3tGtYVDv++sTvl7MyaSD/RzfbBhOIuFkGpqgoX/DJGaeG74VM2hphv3224+1a9cDzg5roicWjM3Pyu5bjztT95rKKy9uBILfjb322ivXONmReC3XEOlJOalY8Hdizz0m8/rrm8GdSRMmEh+GbDKqQw7M7APAQuAIYB/gJHf/l8h6Ay4HvgxMAX4PnOXuayJldgduAk4kOL/iXmC+u2+LlHkbcAtwJPAqcJO7X1dBPcsaciDlKTzs/eB9NzBl8qRRrNHYVemA/3LKV7LNWp9tXNh7N5amtyk1bn2guVeL7U9eaCzoMR3paZRq+fkN55nn2UZ5OuMlp4Cr9r0q1ctdbC7v6LAZ974pwJZ1LmX++csGDZ6FjZliQ80g/2SgwnXRIF1qerLCQNsSJzcPNeR/bwvnKC8cRlQoOv1cYV2h+Pc4+pxswwiC97431nfUMLo/D957QxDCS4zBjG43+p5k276JyElcN17Xzrz55w44VV/hZ1P4e+puRYd4DXSS37LrOjj2hHNKvmYxpYaUFe577nXDjBY0NIJQGW+Cnp6ggTLrgEauuuJKTjkt6MjKNjL6QmwwrMqAZNIZ12LM2DeWG2oRw4mZsXDh+XzhzKvJ65n34LkHTGtg7Qt9Q3um7zeV9euDQHvw9AbOW3AuzU1NZNJpFt1wD+vWvZjbTvAcZ9HlbVx06a0YzhWXfJm3n3A83e471ZCD8cD/AncD9xVZfz4wD/gcsA64EnjIzOa4e3bQ0vcIwvBxBEcS/hH4JnAa5MLoT4GfA2cChwN3m9kmd//mMO2XSE1UOoSjnCEt5Q57qbRsOQbqjRyLivUeR5Xan7Ey7KaWn1+tvwtR2V72UifXDuXowGDDtwrrEBXd3+g2oP+5C/1CUplDzaLfl/5B+vy8IF3qfUiUOGcju1+33Ra8trsPeMXHw+bMZPLkiQN+zsV6/gd6Tv64/I5coC68quLcWY1cdeUVnHTa13LL4vE4d9x+28DThS3poKWlhZuWd3Ji2BNdGA4h+Pxy80SHz4nWudRwqCXXdfRrBGSHSbkP/vsdrUv08yt8Hwvfi+U35n/2yRS5sbqzw8uwZ7/L7s5bDntLv9+baOMt+57FDBYXmVmhuzvJpqb8k9OyDaPCIwmvvroDwqFW378rv6F5xZ1vLXqi2eQj/pLnW3cDYNIRR9A9DJ2poxpo3f0nwE+g/x/+sHe2HbjK3f81XPZZYCPwt8APzeytwMeAI939kbDMOcCDZnaeu28APkNwkOkL7t4DrDSzdwDnEgRfGWHRX+TxrUZLfGTH+daToV7hbSwazmA0FNUOsSi2P8U+s5EawlHPCsNn1lAbPrVoZEQ/58K5S7N1rOa7XUmQHux9KByPXqhfuC8YdlLO+1xpo7TUuPxogBvfajQ2BL8j2cuL54e//u9r4WOzvqFBhT3q1QT1bJ0osmvBjG5GPD7wJeEHauRE38do4yjaICm2fs6hBxHLrM8belTy96ag8VZpQyXaMFq+pKNoUO2/nRaW37hs0Iu0DIcxM8uBmTmRIQdmNhNYC7zT3R+PlPsN8Li7zzezLwA3uPtukfWNQAL4lLv/2MzuASa5+99GyhwN/BLY3d37TSJpZnEg2nyZCKzXkIPayba4S83DJzIahntC95GcMF52bmN5+E4pgw3/qOXliKv5XSv2nMHe58EuNlFsft9K61ruNF7lzl1byf5HlTtsqli5+390Ayd8MujpHq5pu0Z7yMFA9g5/bixYvjGybm/glehKd0+Z2RsFZdYV2UZ2XbFZ0S8CvlFFnaVMY7WXTnZtw/291PdeaqXehu/A4MM/osMjhqqa37ViPfmDvc+Fr5NMJvO2Uc5RtsHqWs6+1OJo3uD1KO8o02gdjRrLgXY0XQssjTyeCPQfFS4iIjJK6rGBNJbrXCoUVlLnnXGYWFa5jahi5coZbzxUYznQvhz+nAa8FFk+DXg8UmZq9EnhkIPdI89/mf7Tr02LrOvH3ZNAbrBP9APTuDcRERHZFVUyl360p3okRreO5UC7jiBwHkMYYMMZC94NZN/Nh4EpZnaEuz8aLvswwTRt/x0pc7WZNbl7dhj6ccDTxcbPDmSwAfciIiIiu7rCnupK/ED2fgAADEhJREFULtRTrdKzg48AM5tgZu8IZx0AmBE+nu7B2WrLgEvM7BNmdjhwD7AB+BcAd18N/AfwLTN7l5n9FXAz8MNwhgOA7wM9wF1mNtfM/g6YT/6QgrLoJA4RERGRsWe0e2j/EvhV5HE2ZH4HOB24jmCu2v/f3v3H3lXXdxx/vigr5cdaAmo7x0pdRTYp0ggDmQqCQSFxCxg2ICTDbFmC07GYLYgmc9oskg0zCcLYRLcuRCVuMyzMgVoM7gdNpxVp/QHj98JKC6X2F0L59dkfn/Odp3ffH/f77ffe+z1fno/kk9tzPuec+znvfPr5vu+5n3PuZ6k/rPDvwLmtZ9BCfSzX9cCd/PSHFa4Yqyyl7EryLuoPK2wEtgNrfAatJEnS/DDq59DexbhPefu/+gJ8rCkTbbOD5kcUJtlmE/D2mbVSkiRJc9lIpxxIkiRJB8qEVpIkSZ1mQitJkqROM6GVJElSp5nQSpIkqdNMaCVJktRpJrSSJEnqNBNaSZIkdZoJrSRJkjrNhFaSJEmdZkIrSZKkTjOhlSRJUqeZ0EqSJKnTTGglSZLUaSa0kiRJ6jQTWkmSJHWaCa0kSZKG4soPXzWQ45rQSpIkaSh+9MiLAzmuCa0kSZIGZtGihax64y8O9D1MaCVJkjQwSbjumg9x4nEHc8LKgwfzHqWUgRx4PkmyGNi1a9cuFi9ePOrmSJIkddLu3btZsmQJwJJSyu7ZOq5XaCVJktRpg7nuO0/t3j1rHyQkSZJecQaVSznloA9Jfh54fNTtkCRJmieOKaX8z2wdzIS2D0kCvBbYM+q2zMDPUpPxY+hm+7vGeA+PsR4eYz1cxnt4jPXwtGMNsKXMYhLqlIM+NAGftU8Rw1RzcQD2zObka43PeA+PsR4eYz1cxnt4jPXwDDrW3hQmSZKkTjOhlSRJUqeZ0M5/+4BPNK8aPOM9PMZ6eIz1cBnv4THWwzPQWHtTmCRJkjrNK7SSJEnqNBNaSZIkdZoJrSRJkjrNhFaSJEmdZkI7zyX5QJJHkzyXZEOSU0fdpq5L8vEkpafc16pflOSGJE8n2ZvkH5MsHWWbuyLJGUluS7Kliev5PfVJsibJE0meTbIuyXE92xyV5AtJdifZmeTzSY4Y7pl0Qx/xXjtOX7+jZxvjPYUkH0ny7SR7kjyZ5NYkx/dsM+W4kWR5kq8m+UlznGuS+ANJPfqM913j9O2/6tnGeE8hyfuTbGr+/+9Osj7Jea36ofVrE9p5LMlFwF9QH5PxZuBe4GtJXjPShs0PPwB+rlXe1qr7NPBrwG8AZ1J/Nvkrw25gRx1O7acfmKD+SuAK4HLgNOAZap9e1NrmC8AJwDnAe4AzgM8OqsEdN1W8Ae5g/75+SU+98Z7amcANwFuocfoZ4OtJDm9tM+m4kWQB8FVgIfCrwGXA+4A1g29+5/QTb4Cb2L9vXzlWYbz79jhwFXAycArwTeCfkpzQ1A+vX5dSLPO0ABuA61vLB1F/wveqUbetywX4OPC9CeqWAM8DF7bW/RJQgLeMuu1dKk3Mzm8tB3gC+KOeeD8HXNws/3Kz3ymtbc4FXgZeO+pzmsulN97NurXArZPsY7xnFutXN3E7o1mectwAzgNeApa2trkc2AUsHPU5zeXSG+9m3V3AtZPsY7xnHu8dwO8Mu197hXaeSrKQ+olp3di6UsrLzfLpo2rXPHJc8zXtw83Xrcub9SdTrwa0434f8N8Y9wP1OmAZ+8d2F/WD21hsTwd2llK+09pvHTXBOm1I7Zxv3tF8DXh/khuTHN2qM94zs6R53dG89jNunA5sLqVsax3na8Bi6hVyTaw33mMuTbI9yfeTXJ3ksFad8Z6mJAuSXEz95mc9Q+7XzgWZv14FLAC29azfRv2EpJnbQP1K5H7q11R/AvxbklXUhOv5UsrOnn22NXWaubH4jdenl7W2ebJdWUp5MckOjP9M3EH9evARYCXwSeD2JKeXUl7CeE9bkoOAa4H/KKV8v1ndz7ixjPH7PhjrCU0Qb4AvAo8BW4A3AX8GHA+8t6k33n1KciI1gV0E7AUuKKX8MMlqhtivTWilaSql3N5a3JRkA3Vg/E3g2dG0Spp9pZRbWoubk2wCHgLeAdw5kkZ13w3AKvafd6/BGTfepZT2PO/NSZ4A7kyyspTy0DAbOA/cD6ymXgm/EPi7JGcOuxFOOZi/ttPMS+lZvxTYOvzmzF/Np8//Al5Pje3CJEf2bGbcD9xY/Cbr01uB/W56bO6WPQrjf8BKKQ9Tx5bXN6uM9zQkuZ5649xZpZTHW1X9jBtbGb/vg7Ee1yTxHs+G5rXdt413H0opz5dSHiylbCylfIR6o+kfMOR+bUI7T5VSngc2Au8cW9d89fJO6lcDmiXNI4pWUm9Y2gi8wP5xPx5YjnE/UI9QB7h2bBdT52qOxXY9cGSSk1v7nU0d6zagA5LkGOBoal8H492XVNcDFwBnl1Ie6dmkn3FjPXBiz1NqzgF2Az8cVNu7qI94j2d189ru28Z7Zg4CDmHY/XrUd8NZBleAi6h3gF9GvRv5r4Ef07qb0DKjuH6K+viRFdTHjHwDeAp4dVN/I3UKwlnUSfF3A3ePut1dKMAR1D8sq6l3wn6o+ffypv7DTR/+deBE4FbgYWBR6xi3A98FTgXeSr16/sVRn9tcLJPFu6m7hvrooxXUP0obm3geYrynFee/BHY248ayVjm0tc2k4wb1nojN1BtmTgLeTZ2//MlRn99cK1PFm3oB4o+bOK9oxpOHgG8Z72nH+mrqo/pWNGPy1dSbQs9p6ofWr0ceDMtgC/DBpjPto14xOW3Ubep6AW6h3kiwj/oMvluAla36RdR5Wzuoz0n9CrBs1O3uQqHOzSzjlLVNfajPJ9xK/bC2DnhDzzGOot7wsYf66Je/AY4Y9bnNxTJZvIFDmz8yT1IfvfMo9fmyS3uOYbynjvN4MS7A+1rbTDluAMcC/wL8hPoh+lPAwaM+v7lWpoo38AvAt4Cnm3HkAeDPgcXGe9qx/nwzNuxrxop1NMlsUz+0fp3mYJIkSVInOYdWkiRJnWZCK0mSpE4zoZUkSVKnmdBKkiSp00xoJUmS1GkmtJIkSeo0E1pJkiR1mgmtJEmSOs2EVpJGLMnaJLeO8P1vTvLRPre9JckfDrpNkjQd/lKYJA1QkqkG2U8An6aOxzuH0KT9JDkJ+CZwbCllbx/brwL+FXhdKWXXoNsnSf0woZWkAUqyrLV4EbAGOL61bm8/ieSgJPkc8GIp5fJp7PNtYG0p5YbBtUyS+ueUA0kaoFLK1rEC7KqrfrqulLK3d8pBkruSfCbJtUl+nGRbkt9NcniSv02yJ8mDSc5rv1eSVUluT7K32efmJK+aqG1JFgAXArf1rP+9JA8kea45zj/07HobcPGBxkaSZosJrSTNTZcB24FTgc8ANwJ/D9wNvBn4OnBzksMAkhxJnTpwD3AKcC6wFPjyJO/xJmAJ8J2xFUlOAa4DPka9knwudYpB238CpyY55IDOUJJmiQmtJM1N95ZS/rSU8gBwNfAcsL2UclOzbg1wNDUpBfggcE8p5aOllPtKKfcAvw2cleQNE7zHscBLwJOtdcuBZ4B/LqU8Vkq5p5RyXc9+W4CFwDIkaQ4woZWkuWnT2D9KKS8BTwObW/XbmtfXNK8nUZPXvWMFuK+pWznBexwK7Cv730zxDeAx4OFmysKlY1eBW55tXnvXS9JImNBK0tz0Qs9yaa9rJaFj4/gR1Lmtq3vKcfz/KQNjtgOHJVnYOu4e6pSGS4AnqFeC722mNIw5qnl9anqnJEmDYUIrSfPDd4ETgEdLKQ/2lGcm2Od7zesb2ytLKS+WUtaVUq6kTmlYAZzd2mQV8HgpZfvsnoIkzYwJrSTNDzdQr5x+KcmvJFmZ5N3NUxEWjLdDKeUpaiL8trF1Sd6T5Iokq5McC/wW9W/F/a1d3069KU2S5gQTWkmaB0opW4C3AguoyeZm4FpgJ/DyJLt+Dri0tbwTeC/1iQk/Ai4HLiml/AAgySLgfOCmWT4FSZoxf1hBkl7BkhxKvfp6USllfR/bvx+4oJTyroE3TpL65BVaSXoFK6U8S51WMOEPMPR4Afj9wbVIkqbPK7SSJEnqNK/QSpIkqdNMaCVJktRpJrSSJEnqNBNaSZIkdZoJrSRJkjrNhFaSJEmdZkIrSZKkTjOhlSRJUqeZ0EqSJKnT/hdKCeTFjx07nAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 770x470 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lc_bkgd = bkgd.integrate_energy(*erange)\n",
    "lcplot = Lightcurve(data=lc_data, background=lc_bkgd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And look at that! The red line shows our background model interpolated at each lightcurve bin.  If you find your fit to be unacceptable, you can try refitting with a different polynomial order like this:\n",
    "```python\n",
    "backfitter.fit(order=2)\n",
    "```\n",
    "Or you can try adjusting the background selection ranges.\n",
    "\n",
    "Now, we are currently viewing the full time-extent of the data in the file, but if we want to identify a particular time interval for analysis, it's kind of hard when we're zoomed out so far.  So you should zoom in appropriately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 770x470 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lcplot = Lightcurve(data=lc_data, background=lc_bkgd)\n",
    "# zoom in to 5 seconds before to 20 s after the trigger time\n",
    "view_range = (-5.0, 20.0)\n",
    "lcplot.xlim = view_range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need to define a time interval of interest.  It could be a single bin, or it could be multiple bins.  Let's select the brightest two bins in this view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 770x470 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# our lightcurve source selection\n",
    "src_time = (7.0, 8.0)\n",
    "src_lc = phaii.to_lightcurve(time_range=src_time, energy_range=erange)\n",
    "\n",
    "lcplot = Lightcurve(data=lc_data, background=lc_bkgd)\n",
    "lcplot.add_selection(src_lc)\n",
    "lcplot.xlim = view_range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The orange shading indicates the time bins you've selected as source signal.  You can also make a plot of the count spectrum during the selection to see what the background model looks like in comparison to the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 770x470 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the observed count spectrum during the source selection\n",
    "spec_data = phaii.to_spectrum(time_range=src_time)\n",
    "# the background model integrated over the source selection time\n",
    "spec_bkgd = bkgd.integrate_time(*src_time)\n",
    "# and the energy range selection that was made\n",
    "spec_selection = phaii.to_spectrum(time_range=src_time, energy_range=erange)\n",
    "\n",
    "specplot = Spectrum(data=spec_data, background=spec_bkgd)\n",
    "specplot.add_selection(spec_selection)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there you have it!  The background model, integrated over the source window, as a function of energy is red, while the orange shaded region indicates the part of the spectrum you're interested in.\n",
    "\n",
    "If we are satisifed with everything we've done, it's now time to export our selections. XSPEC uses PHA and BAK files containing a single observed count spectrum and the background model during the exposure of the spectrum, respectively.  To get from where we are to where we need to be, it is really just two function calls:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(gbm.data.pha.PHA, gbm.data.pha.BAK)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the single-spectrum PHA object over our source time interval and energy range\n",
    "pha = phaii.to_pha(time_ranges=src_time, energy_range=erange) \n",
    "\n",
    "# the background spectrum\n",
    "bak = bkgd.to_bak(time_range=src_time)\n",
    "\n",
    "type(pha), type(bak)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now you have a PHA and BAK object which can be written as fully-formed FITS files using the `.write()` methods:\n",
    "```python\n",
    "bak.write('./', filename='my_first_custom.bak')\n",
    "pha.write('./', filename='my_first_custom.pha', backfile='my_first_custom.bak')\n",
    "```\n",
    "You can omit the `filename` keywords if the default naming convention is good enough for you (although beware of naming conflicts if you are writing multiple files from one event). The `backfile` keyword is necessary if you want to use the PHA and BAK files in XSPEC.\n",
    "\n",
    "Now there is one thing missing; we forgot about the response file!  You can't do any spectral fitting without the response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gbm.data import RSP\n",
    "rsp = RSP.open(test_data_dir+'/glg_cspec_n9_bn090131090_v00.rsp2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an RSP2 file, meaning that it is a time sequence of DRMs.  We want to choose the DRM that is most appropriate for our time selection.  We have a couple of options: we could simply return the DRM that is closest to our interval of interest, or we can interpolate the time series of DRMs to provide a response at the time of our interval (more specifically at the center of our time interval)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the DRM that is nearest to our time of interest\n",
    "nearest_drm = rsp.extract_drm(atime=pha.tcent)\n",
    "\n",
    "# the interpolated DRM\n",
    "interp_drm = rsp.interpolate(pha.tcent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose one, and you can write it to a file just like the PHA and BAK:\n",
    "```python\n",
    "interp_drm.write('./', filename='my_first_custom.rsp')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're done!  You have successfully exported your source selection, background model, and an appropriate detector response. Looking back, it may seem like we've done a lot, but most of the commands we issued were for the sake of plotting!  Once you're more familiar with the workflow, you don't have to make quite so many plots along the way.\n",
    "\n",
    "In any case, you can now XSPEC to your heart's content!\n",
    "\n",
    "Of course, maybe XSPEC isn't your thing.  That's alright, too.  In fact, the GBM Data Tools have the functionality to perform maximum likelihood fits of the spectra, and if you are interested, [continue on!](./SpectralFitting.ipynb)"
   ]
  }
 ],
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