{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GBM Science Data: Time History Spectra\n",
    "\n",
    "The primary science data produced by GBM can be summarized as a time history of spectra, which is provided temporally pre-binned (CTIME and CSPEC) or temporally unbinned (TTE). These data types are produced as \"snippets\" for every single trigger and are also provided continuously.  CTIME and CSPEC are provided in daily chunks, and TTE are provided in hourly chunks (since late 2012).  One of the most common things that a user of GBM data wants to do is look at this data (what we call a lightcurve) for one or more detectors over some energy range. The GBM Data Tools provides high-level functions to enable easy reading and plotting of the GBM data as well as fine-grained functions that expose a variety of attributes and operations for the more advanced user.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CTIME/CSPEC data\n",
    "The CTIME and CSPEC data are temporally pre-binned data, which have 8 and 128 energy channels respectively.  Our \"Hello, World!\" for the GBM Data Tools is successfully opening/reading one of these files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glg_ctime_nb_bn120415958_v00.pha\n"
     ]
    }
   ],
   "source": [
    "#### our test data directory\n",
    "from gbm import test_data_dir\n",
    "# import the CTIME and CSPEC data classes\n",
    "from gbm.data import Ctime, Cspec\n",
    "\n",
    "# read a ctime file\n",
    "ctime = Ctime.open(test_data_dir+'/glg_ctime_nb_bn120415958_v00.pha')\n",
    "print(ctime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since GBM uses the FITS format, the data files have multiple data extensions, each with metadata information in a \"header.\"  There is also a primary header that contains metadata relevant to the overall file. You can access this metadata information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['PRIMARY', 'EBOUNDS', 'SPECTRUM', 'GTI'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list the headers in the file\n",
    "ctime.headers.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / file does conform to FITS standard             \n",
       "BITPIX  =                    8 / number of bits per data pixel                  \n",
       "NAXIS   =                    0 / number of data axes                            \n",
       "EXTEND  =                    T / FITS dataset may contain extensions            \n",
       "COMMENT   FITS (Flexible Image Transport System) format is defined in 'Astronomy\n",
       "COMMENT   and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H \n",
       "CREATOR = 'GBM_SCI_Reader.pl v1.19' / Software and version creating file        \n",
       "FILETYPE= 'PHAII   '           / Name for this type of FITS file                \n",
       "FILE-VER= '1.0.0   '           / Version of the format for this filetype        \n",
       "TELESCOP= 'GLAST   '           / Name of mission/satellite                      \n",
       "INSTRUME= 'GBM     '           / Specific instrument used for observation       \n",
       "DETNAM  = 'NAI_11  '           / Individual detector name                       \n",
       "OBSERVER= 'Meegan  '           / GLAST Burst Monitor P.I.                       \n",
       "ORIGIN  = 'GIOC    '           / Name of organization making file               \n",
       "DATE    = '2012-04-16T02:15:21' / file creation date (YYYY-MM-DDThh:mm:ss UT)   \n",
       "DATE-OBS= '2012-04-15T22:44:21' / Date of start of observation                  \n",
       "DATE-END= '2012-04-15T23:16:01' / Date of end of observation                    \n",
       "TIMESYS = 'TT      '           / Time system used in time keywords              \n",
       "TIMEUNIT= 's       '           / Time since MJDREF, used in TSTART and TSTOP    \n",
       "MJDREFI =                51910 / MJD of GLAST reference epoch, integer part     \n",
       "MJDREFF = 7.428703703703703D-4 / MJD of GLAST reference epoch, fractional part  \n",
       "TSTART  =     356222661.790904 / [GLAST MET] Observation start time             \n",
       "TSTOP   =     356224561.991216 / [GLAST MET] Observation stop time              \n",
       "FILENAME= 'glg_ctime_nb_bn120415958_v00.pha' / Name of this file                \n",
       "DATATYPE= 'CTIME   '           / GBM datatype used for this file                \n",
       "TRIGTIME=     356223561.133346 / Trigger time relative to MJDREF, double precisi\n",
       "OBJECT  = 'GRB120415958'       / Burst name in standard format, yymmddfff       \n",
       "RADECSYS= 'FK5     '           / Stellar reference frame                        \n",
       "EQUINOX =               2000.0 / Equinox for RA and Dec                         \n",
       "RA_OBJ  =              30.0000 / Calculated RA of burst                         \n",
       "DEC_OBJ =              -15.000 / Calculated Dec of burst                        \n",
       "ERR_RAD =                3.000 / Calculated Location Error Radius               \n",
       "INFILE01= 'glg_lutct_nai_100716346_v00.fit' / Level 1 input lookup table file   \n",
       "INFILE02= 'GLAST_2012106_220700_VC09_GTIME.0.00' / Level 0 input data file      \n",
       "INFILE03= 'GLAST_2012107_014000_VC09_GTIME.0.00' / Level 0 input data file      \n",
       "CHECKSUM= 'GJaaGGaTGGaZGGaZ'   / HDU checksum updated 2012-04-16T02:24:24       \n",
       "DATASUM = '         0'         / data unit checksum updated 2012-04-16T02:15:21 "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the metadata in the PRIMARY header\n",
    "ctime.headers['PRIMARY']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access the values in the headers using the traditional [```astropy.fits.io```](https://docs.astropy.org/en/stable/io/fits/) syntax, There is also easy access for certain important properties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GTI: [(-899.3424419760704, 1000.8578699827194)]\n",
      "Trigger time: 356223561.133346\n",
      "Time Range: (-899.3424419760704, 1000.8578699827194)\n",
      "Energy Range: (4.323754, 2000.0)\n",
      "# of Energy Channels: 8\n"
     ]
    }
   ],
   "source": [
    "# certain useful properties are easily accessible\n",
    "print(\"GTI: {}\".format(ctime.gti))  # the good time intervals for the data\n",
    "print(\"Trigger time: {}\".format(ctime.trigtime))\n",
    "print(\"Time Range: {}\".format(ctime.time_range))\n",
    "print(\"Energy Range: {}\".format(ctime.energy_range))\n",
    "print('# of Energy Channels: {}'.format(ctime.numchans))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CTIME/CSPEC data objects are modeled as wrappers around the [Data Primitives](./DataPrimitives.ipynb) in ```gbm.data.primitives```, and the underlying data can be accessed via the ```.data``` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gbm.data.primitives.TimeEnergyBins at 0x120a5aa90>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctime.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most casual users need not directly worry about the Data Primitives, though.  The CTIME/CSPEC data objects contain the high-level functionality to perform a lot of common data reduction.  For example, if we only want to work with a short time segment of data in the file, we can take a slice of the data and return a new fully-functional data object with the time-sliced data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.240202009677887, 10.048128008842468)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slice from approx. -10 to +10 s\n",
    "time_sliced_ctime = ctime.slice_time((-10.0, 10.0))\n",
    "time_sliced_ctime.time_range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can take a slice of the data in energy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49.60019, 538.1436)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slice from ~50 keV to ~300 keV\n",
    "energy_sliced_ctime = ctime.slice_energy((50.0, 300.0))\n",
    "energy_sliced_ctime.energy_range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned, this data is 2-dimensional, so what do we do if we want a lightcurve covering a particular energy range?  You would integrate (sum) over energy, and you can easily do this with the ```to_lightcurve()``` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-899.21444198, -898.95853901, -898.70263603, ..., 1000.21786997,\n",
       "        1000.47386995, 1000.72986996]),\n",
       " array([   0.    , 1186.1779, 1596.2815, ..., 1168.0736, 1136.9364,\n",
       "        1160.156 ], dtype=float32))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# integrate over the full energy range\n",
    "lightcurve = ctime.to_lightcurve()\n",
    "\n",
    "# the lightcurve bin centroids and count rates\n",
    "lightcurve.centroids, lightcurve.rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-899.21444198, -898.95853901, -898.70263603, ..., 1000.21786997,\n",
       "        1000.47386995, 1000.72986996]),\n",
       " array([  0.     , 223.88126, 439.27155, ..., 466.4455 , 380.28564,\n",
       "        437.49994], dtype=float32))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# integrate over 50-300 keV\n",
    "lightcurve = ctime.to_lightcurve(energy_range=(50.0, 300.0))\n",
    "# the lightcurve bin centroids and count rates\n",
    "lightcurve.centroids, lightcurve.rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Ok, that's cool, but how do I make a plot of the lightcurve?\"  To do that, you need to import the ```Lightcurve``` class from the ```gbm.plot``` module:"
   ]
  },
  {
   "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": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from gbm.plot import Lightcurve\n",
    "lcplot = Lightcurve(data=lightcurve)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What...is going on there?  Well, we are reading a trigger CTIME file, so normally the CTIME data is accumulated in 256 ms duration bins, but starting at trigger time, the data switches to 64 ms duration bins for several hundred seconds, and then it goes back to 256 ms bins.\n",
    "\n",
    "So maybe the native CTIME resolution is overkill because it's hard to see the signal.  If only we could rebin the data to a lower resolution..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 data binning module\n",
    "from gbm.binning.binned import rebin_by_time\n",
    "\n",
    "# rebin the data to 2048 ms resolution\n",
    "rebinned_ctime = ctime.rebin_time(rebin_by_time, 2.048)\n",
    "\n",
    "# and replot\n",
    "lcplot = Lightcurve(data=rebinned_ctime.to_lightcurve())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And voila!  We can now easily see the GRB signal in the lightcurve,  Of course you'd probably want to zoom in to see it better, but we will talk about the plotting functionality a little later.  If you are working in ipython, you can make these plots interactively, zooming and panning to your heart's content."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also just as easily create a count spectrum (integrating over time):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   7.04047 ,   17.340723,   36.069298,   70.78455 ,  171.29333 ,\n",
       "         395.36002 ,  732.5708  , 1412.2628  ], dtype=float32),\n",
       " array([ 8.58324638, 21.62204574,  9.88407582,  3.26465078,  1.09189102,\n",
       "         0.2609349 ,  0.10644744,  0.07470517]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# integrate over the full time range\n",
    "spectrum = ctime.to_spectrum()\n",
    "# the energy channel centroids and differential count rates\n",
    "spectrum.centroids, spectrum.rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   7.04047 ,   17.340723,   36.069298,   70.78455 ,  171.29333 ,\n",
       "         395.36002 ,  732.5708  , 1412.2628  ], dtype=float32),\n",
       " array([ 8.63882267, 22.40915926, 10.84130467,  3.48270155,  1.08284141,\n",
       "         0.23386863,  0.10268272,  0.06447497]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# integrate over the time range (-10.0, +10.0)\n",
    "spectrum = ctime.to_spectrum(time_range=(-10.0, 10.0))\n",
    "# the energy channel centroids and differential count rates\n",
    "spectrum.centroids, spectrum.rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the corresponding plot for the count spectrum can be made using the ```Spectrum``` class from the ```gbm.plot``` model thusly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqcAAAGmCAYAAABBUS08AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deZxddX3/8debxGTCEiwqi4AWTVFpGnAp1tr602IqWmjVgtjf71cW69aGulFT11qxPrCu2BKpRcW0WutScfkhxaCi9FdrCpXNqJUqu2JxISCZxMCnf9w7OI53JjN37sw5M/f1fDzuI3PPOfec90SOvPmec743VYUkSZLUBrs1HUCSJEkaYzmVJElSa1hOJUmS1BqWU0mSJLWG5VSSJEmtYTmVJElSa1hOJUmS1BpLmw7QZkkC3B+4vekskiRJC9xewM21i0n2LadTuz9wY9MhJEmSFomDgJum2sByOrXbAW644QZWrlzZdBZJkqQFaevWrRx88MEwjavRltNpWLlypeVUkiRpHvhAlCRJklrDkVO1UlUxOrqj6RgL3sjIMjrP9UmStDBYTtU6VcXzX/iXXPWV/2o6yoK3ZvUqzj5zvQVVkrRgeFlfrTM6usNiOiBXXn2NI9CSpAXFkVO12v/7yFtYMbK86RgLzrbR7Rxz3GlNx5AkacYsp2q1FSPLWbHCcipJ0rDwsr4kSZJaw3IqSZKk1rCcSpIkqTUsp5IkSWoNy2kPSdYl2QJsbjqLJEnSMLGc9lBVG6rqMODIprNIkiQNE8upJEmSWsNyKkmSpNawnEqSJKk1LKeSJElqDcupJEmSWsNyKkmSpNawnEqSJKk1LKeSJElqDcupJEmSWsNyKkmSpNawnEqSJKk1LKeSJElqDcupJEmSWsNyKkmSpNawnEqSJKk1LKeSJElqDcupJEmSWsNyKkmSpNawnEqSJKk1LKeSJElqDcupJEmSWsNyKkmSpNawnEqSJKk1LKeSJElqDcupJEmSWsNyKkmSpNawnEqSJKk1LKeSJElqDcupJEmSWsNyKkmSpNawnEqSJKk1ljYdQNLc2ja6vekI825kZBlJmo4hSeqD5VRa5I457rSmI8y7NatXcfaZ6y2okrQADcVl/STnJflBko80nUWaDyMjy1izelXTMRpz5dXXMDq6o+kYkqQ+DMvI6duB9wAnNR1Emg9JOPvM9UNX0LaNbh/KkWJJWkyGopxW1cVJHt90Dmk+JWHFiuVNx5AkaUZaf1k/yeOSfDLJzUkqyVN7bLMuybVJRpN8KcmRTWSVJEnS7LS+nAJ7AFcA63qtTHIC8FbgtcAjuttemGTfmR4oyfIkK8dewF79x5YkSdJMtb6cVtUFVfWqqjpvkk1eApxTVedW1Rbg+cCdwLP6ONzLgdvGvW7sJ7MkSZL60/pyOpUky4BHAheNLauqu7vvH9PHLs8A9h73OmgAMSVJkjRNC/2BqPsCS4BbJiy/BXjo2JskFwGHA3skuRE4vqq+OHFnVbUd2D7uc3ORWZIkSZNY6OV0WqrqiU1nkCRJ0q4t6Mv6wK3AXcB+E5bvB3xn/uNIkiRpNhZ0Oa2qHcBlwFFjy5Ls1n3/M5ftJUmS1G6tv6yfZE9g/PcwHpLkCOD7VXU9nWmkNia5FNgMvIjO9FPnzuKY6+hMXbWgy7skSdJC0/pyCjwK+Ny492/t/rkROLmqPpjkfsDpwP7A5cDRVTXxIalpq6oNwIbuXKe3bdu2nXvda/uuPtYqO+/ayaZNmwBYu3YtS5cshP+pO7aNLqy/a0mSNDitbyxVdTEw5WPzVXUWcNZcZTj2GX/C0qXL5mr3c+7NZ53fdARJkqRp8bK1WmvN6lWMjCzc/yiQJEkz1/qR0zb45IfezMqVK5uOMXRGRpY516wkSUPGcjoNK1YsZ8WK5U3HkCRJWvS8rC9JkqTWsJz2kGRdki10pqaSJEnSPLGc9lBVG6rqMODIprNIkiQNE8upJEmSWsNyKkmSpNawnEqSJKk1LKeSJElqDcupJEmSWsNyKkmSpNawnPbgPKeSJEnNsJz24DynkiRJzbCcSpIkqTUsp5IkSWoNy6kkSZJaw3IqSZKk1rCcSpIkqTUsp5IkSWoNy6kkSZJaw3Lag5PwS5IkNcNy2oOT8EuSJDXDcipJkqTWsJxKkiSpNSynkiRJag3LqSRJklrDcipJkqTWsJxKkiSpNSynkiRJag3LqSRJklrDcipJkqTWsJz24NeXSpIkNcNy2oNfXypJktQMy6kkSZJaw3IqSZKk1rCcSpIkqTUsp5IkSWoNy6kkSZJaw3IqSZKk1rCcSpIkqTUsp5IkSWoNy6kkSZJaw3IqSZKk1rCcSpIkqTUspz0kWZdkC7C56SySJEnDxHLaQ1VtqKrDgCObziJJkjRMLKeSJElqDcupJEmSWsNyKkmSpNawnEqSJKk1ljYdQJLmwrbR7U1HaJWRkWUkaTqGJO2S5VTSonTMcac1HaFV1qxexdlnrregSmo9L+tLWjRGRpaxZvWqpmO00pVXX8Po6I6mY0jSLvU1cppkOfBo4IHA7sB/A1+uqm8NMJskzUgSzj5zvSVsnG2j2x1FlrSgzKicJnks8ELgWOBewG3ANmAfYHmSbwJ/C/xNVd0+4KyStEtJWLFiedMxJEl9mvZl/SSfAD4IXAv8JrBXVd2nqg6qqt2BXwD+AjgK+M8ka+cgryRJkhaxmYycng/8blX9uNfKqvom8E1gY5LDgAMGkE+SJElDZNrltKremWTJNLfdAmzpO5UkSZKG0kyf1r8pyRuSHDonaSRJkjTUZlpONwDHAV9NckmSk5PsPge5JEmSNIRmVE6r6nVVtYrOQ0/fBM4Cvp3knCSPnouAkiRJGh59TcJfVRdX1UnA/sBpwMOALyb5SpKXDDJgE5KsS7IF2Nx0FkmSpGEyq2+Iqqo7qupdVfVrdOY+3R9400CSNaiqNlTVYcCRTWeRJEkaJrMqp0l27953+nngE8D3gFcOJJkkSZKGTr9fX/qrwLOA47v7+Ajw6qr6wgCzSZIkacjM9OtL1wOnAIcClwIvBT7gV5VKkiRpEGY6cvpS4H3A8VV19RzkkSRJ0hCbaTm9/8SvL00yUlWjA8wkSZKkITXTeU5/DJBktySvTnITcEeSB3WXvy7JH8xBTkmSJA2Bfp/WfxVwMrAe2DFu+dXAs2eZSZIkSUOq33J6IvDcqno/cNe45VcAD511KkmSJA2lfsvpgcA1k+zvXv3HkSRJ0jDrt5xuAX69x/LjgC/3H0eSJEnDrK9J+IHTgY1JDqRTcJ+e5CF0LvcfM6hwkiRJGi59jZxW1ceBY4EnAj+iU1YfBhxbVZsGF0+SJEnDpN+vLz2oqi4B1vZY9ytV9W+zTiZJkqSh0+89p59Oss/EhUkeC/zz7CJJkiRpWPVbTv+NTkHda2xBkscBnwJeO4hgkiRJGj79ltNnA9cDn0yyPMkTgPOBP6uqtw0snSRJkoZKvw9E3Q08E/gx8FngE8DLq+rtA8wmSZKkITPtB6KSrOmx+M+BDwDvA74wtk1VXTmQdJIkSRoqM3la/3KggIxbNvb+ecBzuz8XsGRQASVJkjQ8ZlJOD5mzFJIkSRIzKKdVdd1cBpEkSZL6fVr/Hkm2JnnQIMJIkiRpuM26nPLT96AuCknWJdkCbG46iyRJ0jAZRDlddKpqQ1UdBhzZdBZJkqRhMohy+j5g6wD2I0mSpCE3k6f1e6qqPwRIcu+q+uHsI0mSJGlY9TVymuRPk5ww7v2HgO8luSnJ4QNLJ0mSpKHS72X95wM3ACRZC6wFngxcALxpMNEkSZI0bPq9rL8/3XIKHAN8qKo+neRa4EuDCCZJkqTh0+/I6Q+Ag7s/Hw1c1P05+NWlkiRJ6lO/I6cfBf4hyTeA+9C5nA/wcOCaQQSTJEnS8Om3nL4YuJbO6On6qrqju/wA4B0DyCVJkqQhNKNymuR04ONVdRnw5onrq+ptgwomSZKk4TPTe04PAi5IcmOSs5McnWTZXASTJEnS8JlROa2qZ9F5Uv/3gNuBtwO3JvmnJCcm2WcOMkqSJGlIzPhp/aq6u6ouqar1VfUQ4NF0po96HnBzki8k+ZMkBw46rCRJkha3QXx96VeBrwJvTHI/4Fjgd7qrf+a+VEmSJGkyfZXTJO8BXlhVt09YdSfwa1X1Oz0+JkmSJE2p30n4TwJW9Fi+Ajix/ziSJEkaZjOdSmolnW+BCrBXktFxq5cATwG+O7h4kiRJGiYzvaz/Q6C6r//ssb6A18w2lCRJkobTTMvpE+iMmn4W+F3g++PW7QCuq6qbB5RNkiRJQ2ZG5bSqPg+Q5BDghqq6e05SSZIkaSj19bR+VV2X5N5JjgT2ZcKDVVX1d4MIJ0mSpOHS71RSxwLvB/YEttK513RMAZZTSZIkzVi/U0m9BXgPsGdV3buqfm7cy68wlSRJUl/6LacHAn9VVXcOMowkSZKGW7/l9ELgUYMMIkmSJPV1zylwPvCmJIcBVwE/Hr+yqj4x22CSJEkaPv2W03O6f/5Zj3VF59uiJEmSpBnpdyqpfm8HkCRJkiZlyZQkSVJr9DvPaa/L+feoqtP7iyNJkqRh1u89p0+b8P5ewCHATuC/AMupJEmSZqzfe04fPnFZkpXAe4HzZplJkiRJQ2pg95xW1VbgNcDrBrVPSZIkDZd+L+tPZu/uS5LUMttGtzcdQT2MjCwjSdMxpNbo94GoF0xcBBwA/D5wwWxDDVKSY4C30Bkl/suqelfDkSSpEcccd1rTEdTDmtWrOPvM9RZUqavfkdMXT3h/N/DfwEbgjFklGqAkS4G3Ak8AbgMuS3JeVX2v2WSSND9GRpaxZvUqrrz6mqajaBJXXn0No6M7WLFiedNRpFbo94GoQwYdZI4cCXylqm4CSHIB8JvABxpNJUnzJAlnn7me0dEdTUfRBNtGtzuaLfUw63tOkxwEUFU3zj7Oz+z7ccBLgUfSuW3gaVX1sQnbrOtusz9wBfDHVbW5u/r+wE3jNr8JOHDQOSWpzZI4Kidpwejraf0kuyX5syS3AdcB1yX5YZJXJxnkt07tQadwrpskxwl0Ltu/FnhEd9sLk+zbz8GSLE+ycuwF7NVfbEmSJPWj3yL5euBU4GXAw7uvVwB/zACnkqqqC6rqVVU12dypLwHOqapzq2oL8HzgTuBZ3fU389MjpQd2l03m5XTuTR17DXw0WJIkSZPrt5yeBDy7qs6uqiu7r3cAzwFOHli6KSRZRudy/0Vjy6rq7u77x3QXbQZWJzkwyZ7Ak4ELp9jtGfxkOqy9gYPmILokSZIm0e89p/sAX+ux/GvddfPhvsAS4JYJy28BHgpQVTuTnAZ8jk4Rf+NUT+pX1XbgnokAndZDkiRpfvU7cnoFncv6E53aXdcaVfWJqjq0qlZV1d82nUeSJEmT63fkdD1wfpInAl/sLnsMcDDwlEEEm4ZbgbuA/SYs3w/4zjxlkCRJ0gD1NXJaVZ8HHgKcB9y7+/oo8JCqumRw8abMsAO4DDhqbFl3poCj+ElhliRJ0gLS9zyn3YntXznALD+j+xDTqnGLDklyBPD9qrqezjRSG5NcSufhpxfRmX7q3Fkedx2d6asGOS2WJEmSdqGvcprkFOCOqvrwhOXHA7tX1cZBhAMeRedhpjFv7f65ETi5qj6Y5H7A6XQm4b8cOLqqJj4kNSNVtQHY0J3r9LbZ7EuSJEnT1+/I6cuB5/VY/l3gb+mUx1mrqouBKR+Zr6qzgLMGcTxJkiQ1q9/L1g8AvtVj+XXddZIkSdKM9VtOvwus6bH8cGDSeUQlSZKkqfR7Wf8DwF8luR34QnfZ/wLeDvzjIIJJkiRp+PRbTl8N/DzwGWBnd9luwN8Br5h9LEmSJA2jvsppd47RE5K8CjgC2AZcVVXXDTJcU5xKSpIkqRl9z3MKUFXfAL4xoCyt4VRSkiRJzZj2yGCSlyVZMc1tH53kt/qPJUmSpGE0k8vWhwHXJ3lHkid3J78HIMnSJGuS/FGSfwU+CNw+6LCSJEla3KZ9Wb+qTkxyOHAq8A/AyiR3AduB3bubfRl4F/DeqhoddFhJkiQtbjO657SqrgCek+R5dOY5fSCwArgVuLyqbh18REmSJA2Lfp/Wv5vO99hfPtg4kiRJGmZOlSRJkqTWsJz2kGRdki3A5qazSJIkDRPLaQ9VtaGqDgOObDqLJEnSMLGcSpIkqTVmVU6TrErypLHJ+ZNkMLEkSZI0jPoqp0nuk+Qi4D+BTwEHdFe9O8lbBhVOkiRJw6XfkdO3ATuBBwB3jlv+QeDo2YaSJEnScOprnlPgN4EnVdWNE67kf4POxPySJEnSjPU7croHPz1iOmYfOl9nKkmSJM1Yv+X0EuDEce8ryW7AeuBzs04lSZKkodTvZf31wGeSPApYBrwR+EU6I6ePHVC2xiRZB6zDqbYkSZLmVV/lq6quBg4F/gX4OJ3L/B8FHl5V/zW4eM1wEn5JkqRm9DVymuQBwA1V9fpe66rq+lknkyRJ0tDp97L1t4D7TVyY5D7ddZIkSdKM9VtOA1SP5XsCo/3HkSRJ0jCb0WX9JG/t/ljA65KMn05qCfBo4PIBZZMkSdKQmek9pw/v/hngl4Ad49btAK4A3jyAXJIkSRpCMyqnVfUEgCTnAi+sqq1zkkqSJElDqa+n9avqlEEHkSRJkvqdhJ/uBPzPAB5AZyL+e1TV02eZS5IkSUOor6f1kzwT+FfgYcDTgHvR+Yao3wBuG1g6SZIkDZV+p5J6BfDiqjqWzoNQLwQeCnwIWPAT8CdZl2QLsLnpLJIkScOk33L6YOD87s87gD2qqoC3Ac8dRLAm+fWlkiRJzei3nP4A2Kv7803A6u7P9wZ2n20oSZIkDad+H4j6ArAWuAr4MPD2JL/RXfaZAWWTJEnSkOm3nJ4KjHR/fj3wY+BXgX8C/mIAuSRJkjSE+p3n9Pvjfr4beMPY+yQrBpBLkiRJQ6jfe05/RpLlSV4CfGtQ+5QkSdJwmVE57RbQM5JcmuRfkzy1u/wUOqX0xXSe2JckSZJmbKaX9U8HngdcROce0w8nORf4FeAlwIer6q7BRpQkSdKwmGk5PR44sao+kWQ1cGV3H4d35zmVJEmS+jbTe04PAi4DqKqrge3A2yymkiRJGoSZltMldL4RasxO4I7BxZEkSdIwm+ll/QDvTbK9+34E+JskPxq/UVU9fRDhJEmSNFxmWk43Tnj/vkEFaZMk64B1DHCqLUmSJO3ajMppVZ0yV0HapKo2ABuSrARuazqPJEnSsHBkUJIkSa1hOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrWE4lSZLUGpbTHpKsS7IF2Nx0FkmSpGFiOe2hqjZU1WHAkU1nkSRJGiaWU0mSJLWG5VSSJEmtYTmVJElSa1hOJUmS1BqWU0mSJLWG5VSSJEmtYTmVJElSa1hOJUmS1BqWU0mSJLWG5VSSJEmtsbTpAJIkDbtto9ubjjBvRkaWkaTpGGoxy6kkSQ075rjTmo4wb9asXsXZZ663oGpSXtaXJKkBIyPLWLN6VdMx5t2VV1/D6OiOpmOoxRw5lSSpAUk4+8z1817Udt61k02bNgGwdu1ali6ZnyqwbXT7UI0Qq3+WU0mSGpKEFSuWz/NRl/P0pz11no8pTZ+X9SVJktQallNJkiS1huVUkiRJrWE5lSRJUmtYTiVJktQallNJkiS1huVUkiRJrWE57SHJuiRbgM1NZ5EkSRomltMeqmpDVR0GHNl0FkmSpGFiOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrDEU5TXJekh8k+UjTWSRJkjS5oSinwNuBE5sOIUmSpKktbTrAfKiqi5M8vukckiQJto1un7dj7bxrJ5s2bQJg7dq1LF0yv9VnZGQZSeb1mAtd4+U0yeOAlwKPBA4AnlZVH5uwzbruNvsDVwB/XFWb5zurJEmavWOOO62R4775rPPn/ZhrVq/i7DPXW1BnoA2X9fegUzjX9VqZ5ATgrcBrgUd0t70wyb7jtrk8ydU9XvefSZAky5OsHHsBe/X7S0mSpJ8YGVnGmtWrmo4x7668+hpGR3c0HWNBaXzktKouAC4AJvuvipcA51TVud1tng/8FvAs4A3dfRwxoDgvB14zoH1JkqSuJJx95vp5L2pNXdbfNrq9sRHiha7xcjqVJMvoXO4/Y2xZVd2d5CLgMXNwyDPojNKO2Qu4cQ6OI0nS0EnCihXL5/moy3n60546z8fUbLS6nAL3BZYAt0xYfgvw0OnupFtmDwf2SHIjcHxVfXHidlW1Hdg+7nP9ZJYkSVKf2l5OB6Kqnth0BkmSJO1aGx6ImsqtwF3AfhOW7wd8Z/7jSJIkaS61upxW1Q7gMuCosWVJduu+/5nL8pIkSVrYGr+sn2RPYPzcEockOQL4flVdT+cBpY1JLgU2Ay+iM/3UuXOYaR2dqa1aXd4lSZIWm8bLKfAo4HPj3o89Lb8ROLmqPpjkfsDpdCbhvxw4uqomPiQ1MFW1AdjQnev0trk6jiRJkn5a4+W0qi4GpnwsvqrOAs6al0CSJElqjJetJUmS1BqWU0mSJLWG5VSSJEmtYTmVJElSa1hOe0iyLskWOlNXSZIkaZ5YTnuoqg1VdRhwZNNZJEmShonlVJIkSa1hOZUkSVJrWE4lSZLUGpZTSZIktYblVJIkSa1hOZUkSVJrWE57cJ5TSZKkZlhOe3CeU0mSpGZYTiVJktQaS5sOIEmStJhtG93eyHF33rWTTZs2AbB27VqWLmmu9m3bNv2/A8upJEnSHDrmuNOajsCbzzq/0ePv3Llj2tt6WV+SJGnARkaWsWb1qqZjLEipqqYztFaSlcBtt912GytXrmw6jiRJWkCqitHR6Y8YDlqbLutv3bqV/fffF2Dvqto61baW0ylYTiVJkmZv69at7L333jCNcuplfUmSJLWG5bQHJ+GXJElqhpf1pzB2Wf+GG27wsr4kSVKftm7dysEHHwzeczo7SQ4Ebmw6hyRJ0iJxUFXdNNUGltMpJAlwf+D2Pnexmdl/Beps9tHPZ2f6melsvxedkn8Q/f9dLiaD+OdiLs13vrk43qD26fm3+Hj+zf3xPP9+wvPvp10KPKR2UT6dhH8K3b+8Kdv9VJLcvauh67ncRz+fnelnprN9p+MDcPts/z4Wg0H8czGX5jvfXBxvUPv0/Ft8PP/m/niefz+1zdiPnn9Akp27KqbgA1FzbUPD++jnszP9zCB+x2HT9r+z+c43F8cb1D49/xaftv+def4NZj+ef+00rb8zL+trzo09WMY0boKWNFief1JzPP/648ip5sN24LXdPyXNL88/qTmef31w5FSSJEmt4cipJEmSWsNyKkmSpNawnEqSJKk1LKeSJElqDcupJEmSWsNyqkYlOS/JD5J8pOks0jBJcnCSi5NsSXJlkuObziQNiyT3TnJpksuTXJ3kOU1nahOnklKjkjyezncPn1RVxzUcRxoaSQ4A9quqy5PsD1wGHFpVP2o4mrToJVkCLK+qO5PsAVwNPKqqvtdwtFZw5FSNqqqLgdubziENm6r6dlVd3v35O8CtwD7NppKGQ1XdVVV3dt8uB9J9CcupZiHJ45J8MsnNSSrJU3tssy7JtUlGk3wpyZFNZJUWm0Gef0keCSypqhvmPLi0CAzi/Ote2r8CuBF4U1XdOl/5285yqtnYA7gCWNdrZZITgLfS+eq2R3S3vTDJvvOWUFq8BnL+JdkH+DvguXOaVlpcZn3+VdUPq+pw4BDgfyfZb85TLxDec6qBSFLA06rqY+OWfQn496o6tft+N+AG4K+r6g3jtns8cKr3nEr96ff8S7Ic2AScU1V/P//JpYVvNv/+G7f9O4DPVpUPB+PIqeZIkmXAI4GLxpZV1d3d949pKpc0DKZz/iUJ8F46/0K0mEoDMs3zb78ke3V/3ht4HPD1+U/bTpZTzZX7AkuAWyYsvwXYf+xNkouADwNPSXJjEourNHvTOf8eC5wAPLU7nc3lSX5pHjNKi9V0zr8HApd07zm9hM6I6lXzF7HdljYdQMOtqp7YdAZpGFXVv+AAhdSIqtoMHNF0jrby/5g0V24F7gIm3uC9H/Cd+Y8jDRXPP6k5nn+zZDnVnKiqHXQm9T5qbFn3hvCjgC82lUsaBp5/UnM8/2bPy/rqW5I9gVXjFh2S5Ajg+1V1PZ1pNDYmuRTYDLyIzvQb5857WGmR8fyTmuP5N7ecSkp9604B9bkeqzZW1cndbU4FXkrnJvDLgRdU1ZfmK6O0WHn+Sc3x/JtbllNJkiS1hvecSpIkqTUsp5IkSWoNy6kkSZJaw3IqSZKk1rCcSpIkqTUsp5IkSWoNy6kkSZJaw3IqSZKk1rCcSpIkqTUsp5I0RJLcJ8l3k/x89/3jk1SSe8/xcd+Q5K/n8hiSFgfLqST1kOS93dI28fXPTWebpVcCH6+qa2e7oyT7JflxkmdOsv7dSf6j+/bNwElJHjTb40pa3CynkjS5fwYOmPD6vbk8YJJlc7jv3YE/AN49iP1V1S3A+cCzehxrD+AZY8eqqluBC4E/HMSxJS1ellNJmtz2qvrOhNcPxlZ2R1KfneS8JHcm+UaS3x6/gySrk1yQ5I4ktyT5+yT3Hbf+4iRnJTkzyViBI8lvd/c3muRzSU4au/yeZI8kW5McN+FYT03yoyR7TfL7PKX7O/3bZL9wkt27ef//2KX+7u/41W6WryX5o3EfeTdwVJIHTNjV8cBS4P3jln0S6DnKKkljLKeSNDuvAT4ErA1vj9MAAAQbSURBVAE+Bbw/yT4A3XL3WeDLwKOAo4H9utuPdxKwA3gs8PwkhwAfAT4GHA68E3j92MZV9SPgH4FTJuznFOAjVXX7JFl/Hbhssl+km3cTnX83rK2qHyb5P8DpdG4HeBjwCuB1SU7qfuxTwC3AyT2yfLSqfjhu2WbgoLH7XSWpF8upJE3umO6I5/jXKyZs896q+kBVXUOnuO0JHNlddyrw5ap6RVV9raq+TOcS+BOSHDpuH9+oqvVV9fWq+jrwPODrVfXS7rJ/BN474bjvAp6U5ACAJPvSGRl9zxS/zwOBmydZtz/weeDbwLFVdWd3+WuB06rqo1X1rar6KPC2bkaq6i5gI3ByknSzPJhOEZ6YZezYD5wio6QhZzmVpMl9DjhiwutvJmxz5dgP3RHNrcC+3UWH0ymi95Rb4GvddQ8et4+Jo5kPAf59wrLN499U1WbgK3RGXQH+L3Ad8IUpfp8VwOgk6zYB1wAnVNUOuOe+0QcD757wO7xqQv73AIcAT+i+PwW4ls6o8Xjbun/uPkVGSUNuadMBJKnFftQdEZ3Kjye8L37yH/570rnP8k97fO7b44/TXzzeBawD3kCnEJ5bVTXF9rcCPzfJuvOB3wUOA67qLtuz++dzgC9N2P6usR+q6htJLgFOSXIxcCJwTo8s+3T//O8pMkoacpZTSZo7/0Gn8F1bVTtn8Lmv07lEP94v99jufcAbk7yATqncuIv9fpnOCGsvLwPuAD6T5PFVtaWqbklyM/Cgqnr/JJ8b827gbOATwIH87G0IAKvplPmv7GJfkoaYl/UlaXLLk+w/4XXfXX/sHhvojBZ+IMkvJ3lwkiclOTfJkik+907goUn+MsmhSZ7BTx44umc0sjtzwEeBNwGfrqobd5HnQuAXk/QcPa2qP6HzdP1nkzy0u/g1wMuTvKCb5ZeSnJLkJRM+/mE6xfOd3Sw39DjErwOXVNW2HuskCbCcStJUjqZz+X3861+m++GqupnOE/hLgE/TuVx+JvBD4O4pPvct4Djg6XTuaf1DfvK0/vYJm78bWMbUD0KN7fcqOqO5z5himxfTmU3gs0kOrap3Ac+mc9vAVXQemjoZ+NaEz91JZwaBn5siyzOBc3aVU9Jwy9S3J0mS2iDJK4HnV9XBE5b/Pp2n5+8/9iDTLvbzW3RGWldX1aQFedCSPBl4C7Bmhrc4SBoy3nMqSS3Unej+34Hv0Rl9fSlw1rj1u9P5xqqXAe+cTjEFqKrzk/wCnftCe116nyt7AKdYTCXtiiOnktRCSd4GnEDnntXrgb8Hzhgrd0n+nM7E+F8Afqeq7mgoqiQNlOVUkiRJreEDUZIkSWoNy6kkSZJaw3IqSZKk1rCcSpIkqTUsp5IkSWoNy6kkSZJaw3IqSZKk1rCcSpIkqTX+B6Tqdiz+lsoLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 770x470 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gbm.plot import Spectrum\n",
    "specplot = Spectrum(data=spectrum)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so maybe not quite as interesting as the lightcurve, since CTIME is only 8-channel data after all.  The count spectra for CSPEC and TTE are much prettier. It's worth noting here that this spectrum is integrated over the full duration of the data, so perhaps you are interesting in a particular time range of data.  You can make a single-spectrum PHA object like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# integrate over a single time selection\n",
    "pha1 = ctime.to_pha(time_ranges=[(-10.0, 10.0)])\n",
    "# integrate over multiple time selections\n",
    "pha2 = ctime.to_pha(time_ranges=[(-10.0, 10.0), (20.0, 30.0)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "Let's revisit our time-sliced CTIME object.  Maybe we are doing some sort of analysis using the GBM continuous data and we don't want to save the full day's worth of data for our analysis.  We can write our sliced CTIME object to a fully-qualified FITS file following:\n",
    "```python \n",
    "time_sliced_ctime.write('./', filename='my_first_custom_ctime.pha')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything that we've done here with a CTIME data can be done with CSPEC data by simply using ```Cspec.open()``` instead of ```Ctime.open()``` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to learn about GBM TTE data head, on over to [here](./TteData.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}