{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**This is a fixed-text formatted version of a Jupyter notebook**\n",
    "\n",
    "- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.11?urlpath=lab/tree/cta_1dc_introduction.ipynb)\n",
    "- You can contribute with your own notebooks in this\n",
    "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n",
    "- **Source files:**\n",
    "[cta_1dc_introduction.ipynb](../_static/notebooks/cta_1dc_introduction.ipynb) |\n",
    "[cta_1dc_introduction.py](../_static/notebooks/cta_1dc_introduction.py)\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![CTA first data challenge logo](images/cta-1dc.png)\n",
    "\n",
    "# CTA first data challenge (1DC) with Gammapy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In 2017 and 2018, the [CTA observatory](https://www.cta-observatory.org/) did a first data challenge, called CTA DC-1, where CTA high-level data was simulated assuming a sky model and using CTA IRFs.\n",
    "\n",
    "The main page for CTA 1DC is here:\n",
    "https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki (CTA internal)\n",
    "\n",
    "There you will find information on 1DC sky models, data access, data organisation, simulation and analysis tools, simulated observations, as well as contact information if you have any questions or comments.\n",
    "\n",
    "### This tutorial notebook\n",
    "\n",
    "This notebook shows you how to get started with CTA 1DC data and what it contains.\n",
    "\n",
    "You will learn how to use Astropy and Gammapy to:\n",
    "\n",
    "* access event data\n",
    "* access instrument response functions (IRFs)\n",
    "* use index files and the ``gammapy.data.DataStore`` to access all data\n",
    "* use the observation index file to select the observations you're interested in\n",
    "* read model XML files from Python (not integrated in Gammapy yet)\n",
    "\n",
    "This is to familiarise ourselves with the data files and to get an overview.\n",
    "\n",
    "### Further information\n",
    "\n",
    "How to analyse the CTA 1DC data with Gammapy (make an image and spectrum) is shown in a second notebook [cta_data_analysis.ipynb](cta_data_analysis.ipynb). If you prefer, you can of course just skim or skip this notebook and go straight to second one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook and Gammapy Setup\n",
    "\n",
    "Before we get started, please execcute the following code cells.\n",
    "\n",
    "The first one configures the notebooks so that plots are shown inline (if you don't do this, separate windows will pop up). The second cell imports and checks the version of the packages we will use below. If you're missing some packages, install them and then select \"Kernel -> Restart\" above to restart this notebook.\n",
    "\n",
    "In case you're new to Jupyter notebooks: to execute a cell, select it, then type \"SHIFT\" + \"ENTER\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy: 1.16.2\n",
      "astropy: 3.1.1\n",
      "gammapy: 0.11\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import astropy\n",
    "import gammapy\n",
    "\n",
    "print(\"numpy:\", np.__version__)\n",
    "print(\"astropy:\", astropy.__version__)\n",
    "print(\"gammapy:\", gammapy.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DC-1 data\n",
    "\n",
    "In this and other Gammapy tutorials we will only access a few data files from CTA DC-1 from `$GAMMAPY_DATA/cta-1dc` that you will have if you followed the \"Getting started with Gammapy\" instructions and executed `gammapy download tutorials`.\n",
    "\n",
    "Information how to download more or all of the DC-1 data (in total 20 GB) is here:\n",
    "https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki#Data-access\n",
    "\n",
    "Working with that data with Gammapy is identical to what we show here, except that the recommended way to do it is to point `DataStore.read` at `$CTADATA/index/gps` or whatever dataset or files you'd like to access there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/adonath/data/gammapy-datasets/cta-1dc\r\n"
     ]
    }
   ],
   "source": [
    "!echo $GAMMAPY_DATA/cta-1dc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "README.md \u001b[34mcaldb\u001b[m\u001b[m     \u001b[34mdata\u001b[m\u001b[m      \u001b[34mindex\u001b[m\u001b[m     make.py\r\n"
     ]
    }
   ],
   "source": [
    "!ls $GAMMAPY_DATA/cta-1dc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Let's have a look around at the directories and files in `$GAMMAPY_DATA`.\n",
    "\n",
    "We will look at the `data` folder with events, the `caldb` folder with the IRFs and the `index` folder with the index files. At the end, we will also mention what the `model` and `obs` folder contains, but they aren't used with Gammapy, at least not at the moment.\n",
    "\n",
    "## EVENT data\n",
    "\n",
    "First, the EVENT data (RA, DEC, ENERGY, TIME of each photon or hadronic background event) is in the `data` folder, with one observation per file. The \"baseline\" refers to the assumed CTA array that was used to simulate the observations. The number in the filename is the observation identifier `OBS_ID` of the observation. Observations are ~ 30 minutes, pointing at a fixed location on the sky."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gps_baseline_110380.fits\r\n",
      "gps_baseline_111140.fits\r\n",
      "gps_baseline_111159.fits\r\n",
      "gps_baseline_111630.fits\r\n"
     ]
    }
   ],
   "source": [
    "!ls -1 $GAMMAPY_DATA/cta-1dc/data/baseline/gps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's open up the first event list using the Gammapy `EventList` class, which contains the ``EVENTS`` table data via the ``table`` attribute as an Astropy `Table` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapy.data import EventList\n",
    "\n",
    "path = \"$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits\"\n",
    "events = EventList.read(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gammapy.data.event_list.EventList"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.table.table.Table"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(events.table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Row index=0</i>\n",
       "<table id=\"table112204354112\">\n",
       "<thead><tr><th>EVENT_ID</th><th>TIME</th><th>RA</th><th>DEC</th><th>ENERGY</th><th>DETX</th><th>DETY</th><th>MC_ID</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th>deg</th><th>deg</th><th>TeV</th><th>deg</th><th>deg</th><th></th></tr></thead>\n",
       "<thead><tr><th>uint32</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th></tr></thead>\n",
       "<tr><td>1</td><td>664502403.0454683</td><td>-92.63541</td><td>-30.514854</td><td>0.03902182</td><td>-0.9077294</td><td>-0.2727693</td><td>2</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Row index=0>\n",
       "EVENT_ID        TIME           RA       DEC       ENERGY      DETX       DETY    MC_ID\n",
       "                 s            deg       deg        TeV        deg        deg          \n",
       " uint32       float64       float32   float32    float32    float32    float32   int32\n",
       "-------- ----------------- --------- ---------- ---------- ---------- ---------- -----\n",
       "       1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693     2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First event (using [] for \"indexing\")\n",
    "events.table[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=2</i>\n",
       "<table id=\"table112204035576\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>EVENT_ID</th><th>TIME</th><th>RA</th><th>DEC</th><th>ENERGY</th><th>DETX</th><th>DETY</th><th>MC_ID</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th>deg</th><th>deg</th><th>TeV</th><th>deg</th><th>deg</th><th></th></tr></thead>\n",
       "<thead><tr><th>uint32</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th></tr></thead>\n",
       "<tr><td>1</td><td>664502403.0454683</td><td>-92.63541</td><td>-30.514854</td><td>0.03902182</td><td>-0.9077294</td><td>-0.2727693</td><td>2</td></tr>\n",
       "<tr><td>2</td><td>664502405.2579999</td><td>-92.64103</td><td>-28.262728</td><td>0.030796371</td><td>1.3443842</td><td>-0.2838398</td><td>2</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "EVENT_ID        TIME           RA       DEC     ...    DETX       DETY    MC_ID\n",
       "                 s            deg       deg     ...    deg        deg          \n",
       " uint32       float64       float32   float32   ...  float32    float32   int32\n",
       "-------- ----------------- --------- ---------- ... ---------- ---------- -----\n",
       "       1 664502403.0454683 -92.63541 -30.514854 ... -0.9077294 -0.2727693     2\n",
       "       2 664502405.2579999 -92.64103 -28.262728 ...  1.3443842 -0.2838398     2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First few events (using [] for \"slicing\")\n",
    "events.table[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.time.core.Time"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Event times can be accessed as Astropy Time objects\n",
    "type(events.time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Time object: scale='tt' format='mjd' value=[59235.50003525 59235.50006086]>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.time[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2021-01-21T12:00:03.045' '2021-01-21T12:00:05.258']\n"
     ]
    }
   ],
   "source": [
    "# Convert event time to more human-readable format\n",
    "print(events.time[:2].fits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'astropy.coordinates.sky_coordinate.SkyCoord'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<SkyCoord (ICRS): (ra, dec) in deg\n",
       "    [(267.3646 , -30.514854), (267.35898, -28.262728)]>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Event positions can be accessed as Astropy SkyCoord objects\n",
    "print(type(events.radec))\n",
    "events.radec[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (Galactic): (l, b) in deg\n",
       "    [(359.08019367, -1.52993937), (  1.00977094, -0.36805311)]>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.galactic[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'collections.OrderedDict'>\n"
     ]
    }
   ],
   "source": [
    "# The event header information is stored\n",
    "# in the `events.table.meta` dictionary\n",
    "print(type(events.table.meta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(267.68121338, -29.6075)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# E.g. to get the observation pointing position in degrees:\n",
    "events.table.meta[\"RA_PNT\"], events.table.meta[\"DEC_PNT\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EVENT analysis example\n",
    "\n",
    "As an example how to work with EVENT data, let's look at the spatial and energy distribution of the events for a single run.\n",
    "\n",
    "Note that EVENT data in Gammapy is just stored in an Astropy Table, which is basically a dictionary mapping column names to column data, where the column data is a Numpy array. This means you can efficienly process it from Python using any of the scientific Python tools you like (e.g. Numpy, Scipy, scikit-image, scikit-learn, ...)\n",
    "\n",
    "### EVENT spatial distribution\n",
    "\n",
    "To illustrate a bit how to work with EVENT table an header data,\n",
    "let's plot the event positions as well as the pointing position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (Galactic): (l, b) in deg\n",
       "    (359.9999912, -1.29999594)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Event positions\n",
    "pos = events.galactic[::300]  # sub-sample every 100th event\n",
    "plt.scatter(pos.l.wrap_at(\"180 deg\").deg, pos.b.deg, s=10)\n",
    "# Pointing position\n",
    "pos_pnt = events.pointing_radec.galactic\n",
    "plt.scatter(\n",
    "    pos_pnt.l.wrap_at(\"180 deg\").deg, pos_pnt.b.deg, marker=\"*\", s=400, c=\"red\"\n",
    ")\n",
    "plt.xlabel(\"Galactic longitude (deg)\")\n",
    "plt.ylabel(\"Galactic latitude (deg)\")\n",
    "pos_pnt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EVENT energy distribution\n",
    "\n",
    "Let's have a look at the event energy distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy = events.table[\"ENERGY\"].data\n",
    "energy_bins = np.logspace(-2, 2, num=100)\n",
    "plt.hist(energy, bins=energy_bins)\n",
    "plt.semilogx()\n",
    "plt.xlabel(\"Event energy (TeV)\")\n",
    "plt.ylabel(\"Number of events\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A double-peak, at ~ 30 GeV and ~ 100 GeV? .... let's try to find out what's going on ...\n",
    "\n",
    "### EVENT MC_ID\n",
    "\n",
    "One idea could be to split the data into gamma-ray and hadronic background events.\n",
    "Now from looking at the FITS header, one can see that ``MC_ID == 1`` is the hadronic background, and the other values are for different gamma-ray emission components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of events:  106217\n",
      "Number of gammas:  8239\n",
      "Number of hadrons:  97978\n"
     ]
    }
   ],
   "source": [
    "is_gamma = events.table[\"MC_ID\"] != 1\n",
    "print(\"Number of events: \", len(events.table))\n",
    "print(\"Number of gammas: \", is_gamma.sum())\n",
    "print(\"Number of hadrons: \", len(events.table) - is_gamma.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy = events.table[\"ENERGY\"].data\n",
    "energy_bins = np.logspace(-2, 2, num=100)\n",
    "opts = dict(bins=energy_bins, density=True, histtype=\"step\")\n",
    "plt.hist(energy[~is_gamma], label=\"hadron\", **opts)\n",
    "plt.hist(energy[is_gamma], label=\"gamma\", **opts)\n",
    "plt.loglog()\n",
    "plt.xlabel(\"Event energy (TeV)\")\n",
    "plt.ylabel(\"Number of events\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the energy spectra are roughly power-laws. \n",
    "When plotting in log-log, one can see that the spectra are roughly power-laws (as expected), and the \"double peak\" we saw before looks more like a \"double energy threshold\" pattern.\n",
    "It's there for gammas and background, and below 100 GeV the signal to background ratio is lower.\n",
    "\n",
    "What we're seeing here is the result of a mixed-array in CTA with LSTs, MSTs and SSTs, which  have different energy thresholds:\n",
    "\n",
    "* ~ 30 GeV is the energy threshold of the LSTs\n",
    "* ~ 100 GeV is the energy threshold of the MSTs\n",
    "* the energy threshold of the SSTs is at a few TeV and doesn't show up as a clear feature in the gamma and background energy distribution (probably the rise in slope in gamma in the 1-10 TeV range is due to the SSTs).\n",
    "\n",
    "Let's look a little deeper and also check the event offset distribution in the field of view ...\n",
    "\n",
    "### EVENT FOV offset\n",
    "\n",
    "The event position and offset in the field of view (FOV) can be computed from the event RA, DEC position and the observation pointing RA, DEC position.\n",
    "\n",
    "But actually, the field of view position is stored as extra columns in the EVENT list: ``DETX`` and ``DETY`` (angles in degree, I think RA / DEC aligned field of view system).\n",
    "\n",
    "I presume (hope) this unnecessary information will be dropped from the CTA event lists in the future to save some space (make the CTA DL3 data ~25% smaller), but for now, let's use those columns to compute the field of view offset and look at the offset-energy distribution of the events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Offset (deg)')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy_bins = 10 ** np.linspace(-2, 2, 100)\n",
    "offset_bins = np.arange(0, 5.5, 0.1)\n",
    "\n",
    "t = events.table\n",
    "offset = np.sqrt(t[\"DETX\"] ** 2 + t[\"DETY\"] ** 2)\n",
    "hist = np.histogram2d(\n",
    "    x=t[\"ENERGY\"], y=offset, bins=(energy_bins, offset_bins)\n",
    ")[0].T\n",
    "\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "plt.pcolormesh(energy_bins, offset_bins, hist, norm=LogNorm())\n",
    "plt.semilogx()\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"Energy (TeV)\")\n",
    "plt.ylabel(\"Offset (deg)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the CTA field of view increases with energy in steps. The energy distribution we saw before was the combination of the energy distribution at all offsets. Even at a single offset, the double energy-threshold at ~ 30 GeV and ~ 100 GeV is present.\n",
    "\n",
    "There is also a quick-look peek method you can use to get a peek at the contents of an event list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "events.peek()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking EVENTS tables\n",
    "\n",
    "As a final example of how to work with event lists, here's now to apply arbitrary event selections and how to stack events tables from several observations into a single event list. \n",
    "\n",
    "We will just use `astropy.table.Table` directly, not go via the `gammapy.data.EventList` class. Note that you can always make an `EventList` object from a `Table` object via `event_list = EventList(table)`. One point to keep in mind is that `Table.read` doesn't resolve environment variables in filenames, so we'll use the Python standard library `os` package to construct the filenames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from astropy.table import Table\n",
    "from astropy.table import vstack as table_vstack\n",
    "\n",
    "filename = os.path.join(\n",
    "    os.environ[\"GAMMAPY_DATA\"],\n",
    "    \"cta-1dc/data/baseline/gps/gps_baseline_110380.fits\",\n",
    ")\n",
    "t1 = Table.read(filename, hdu=\"EVENTS\")\n",
    "\n",
    "filename = os.path.join(\n",
    "    os.environ[\"GAMMAPY_DATA\"],\n",
    "    \"cta-1dc/data/baseline/gps/gps_baseline_111140.fits\",\n",
    ")\n",
    "t2 = Table.read(filename, hdu=\"EVENTS\")\n",
    "tables = [t1, t2]\n",
    "table = table_vstack(tables, metadata_conflicts=\"silent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of events:  212603\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of events: \", len(table))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of events after selection: 215\n"
     ]
    }
   ],
   "source": [
    "# Let's select gamma rays with energy above 10 TeV\n",
    "mask_mc_id = table[\"MC_ID\"] != 1\n",
    "mask_energy = table[\"ENERGY\"] > 10\n",
    "mask = mask_mc_id & mask_energy\n",
    "table2 = table[mask]\n",
    "print(\"Number of events after selection:\", len(table2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When processing a lot or all of the 1DC events, you would write a for loop, and apply the event selection before putting the table in the list of tables, or you might run out of memory. An example is [here](https://github.com/gammasky/cta-dc/blob/master/data/cta_1dc_make_allsky_images.py).\n",
    "\n",
    "That's all for ``EVENTS``. You now know what every column in the event table contains, and how to work with event list tables using ``gammapy.data.EventList`` and ``astropy.table.Table``. \n",
    "\n",
    "Just in case that there is some observation parameter in the FITS EVENTS header that you're interested in, you can find the full description of the keys you can access via the ``events.table.meta`` dictionary [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/events/events.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IRFs\n",
    "\n",
    "The CTA instrument reponse functions (IRFs) are given as FITS files in the `caldb` folder.\n",
    "\n",
    "Note that this is not realistic. Real CTA data at the DL3 level (what we have here, what users get) will mostly likely have per-observation or per-time interval IRFs, and the IRFs will not be stored in a separate CALDB folder, but distributed with the EVENTS (probably in the same file, or at least in the same folder, so that it's together).\n",
    "\n",
    "For now, the EVENT to IRF association (i.e. which IRF is the right one for given EVENTS) is done by index files. We will discuss those in the next section, but before we do, let's look at the CTA IRFs for one given configuration: `South_z20_50h`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/sh: tree: command not found\r\n"
     ]
    }
   ],
   "source": [
    "!(cd $GAMMAPY_DATA/cta-1dc && tree caldb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: /Users/adonath/data/gammapy-datasets/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU       8   ()      \n",
      "  1  EFFECTIVE AREA    1 BinTableHDU     53   1R x 5C   [42E, 42E, 6E, 6E, 252E]   \n",
      "  2  POINT SPREAD FUNCTION    1 BinTableHDU     70   1R x 10C   [25E, 25E, 6E, 6E, 150E, 150E, 150E, 150E, 150E, 150E]   \n",
      "  3  ENERGY DISPERSION    1 BinTableHDU     56   1R x 7C   [500E, 500E, 300E, 300E, 6E, 6E, 900000E]   \n",
      "  4  BACKGROUND    1 BinTableHDU     59   1R x 7C   [36E, 36E, 36E, 36E, 21E, 21E, 27216E]   \n"
     ]
    }
   ],
   "source": [
    "# Let's look at the content of one of the IRF FITS files.\n",
    "# IRFs are stored in `BinTable` HDUs in a weird format\n",
    "# that you don't need to care about because it's implemented in Gammapy\n",
    "irf_filename = os.path.join(\n",
    "    os.environ[\"GAMMAPY_DATA\"],\n",
    "    \"cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\",\n",
    ")\n",
    "from astropy.io import fits\n",
    "\n",
    "hdu_list = fits.open(irf_filename)\n",
    "hdu_list.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Effective area\n",
    "\n",
    "The effective area is given as a 2-dim array with energy and field of view offset axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapy.irf import EffectiveAreaTable2D\n",
    "\n",
    "aeff = EffectiveAreaTable2D.read(irf_filename, hdu=\"EFFECTIVE AREA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gammapy.irf.effective_area.EffectiveAreaTable2D"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(aeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gammapy.utils.nddata.NDDataArray"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(aeff.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NDDataArray summary info\n",
      "energy         : size =    42, min =  0.014 TeV, max = 177.828 TeV\n",
      "offset         : size =     6, min =  0.500 deg, max =  5.500 deg\n",
      "Data           : size =   252, min =  0.000 m2, max = 5371581.000 m2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(aeff.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aeff.peek()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$[3.783587] \\; \\mathrm{km^{2}}$$"
      ],
      "text/plain": [
       "<Quantity [3.78358697] km2>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the on-axis effective area at 10 TeV?\n",
    "aeff.data.evaluate(energy=\"10 TeV\", offset=\"0 deg\").to(\"km2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gammapy.irf.effective_area.EffectiveAreaTable at 0x1c242ebbe0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is how you slice out an `EffectiveAreaTable` object\n",
    "# at a given field of view offset for analysis\n",
    "aeff.to_effective_area_table(offset=\"1 deg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy dispersion\n",
    "\n",
    "Let's have a look at the CTA energy dispersion with three axes: true energy, fov offset and migra = e_reco / e_true and has dP / dmigra as value.\n",
    "\n",
    "Similar to the event energy distribution above, we can see the mixed-telescope array reflected in the EDISP.\n",
    "At low energies the events are only detected and reconstructed by the LSTs.\n",
    "At ~100 GeV, the MSTs take over and EDISP is chaotic in the ~ 50 GeV to 100 GeV energy range.\n",
    "So it can be useful to have quick access to IRFs like with Gammapy (e.g. for spectral line searches in this case), even if for 95% of science analyses users later won't have to look at the IRFs and just trust that everything is working."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'gammapy.irf.energy_dispersion.EnergyDispersion2D'>\n",
      "<class 'gammapy.utils.nddata.NDDataArray'>\n"
     ]
    }
   ],
   "source": [
    "from gammapy.irf import EnergyDispersion2D\n",
    "\n",
    "edisp = EnergyDispersion2D.read(irf_filename, hdu=\"ENERGY DISPERSION\")\n",
    "print(type(edisp))\n",
    "print(type(edisp.data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NDDataArray summary info\n",
      "e_true         : size =   500, min =  0.005 TeV, max = 495.450 TeV\n",
      "migra          : size =   300, min =  0.005, max =  2.995\n",
      "offset         : size =     6, min =  0.500 deg, max =  5.500 deg\n",
      "Data           : size = 900000, min =  0.000, max = 10595.855\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(edisp.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "edisp.peek()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gammapy.irf.energy_dispersion.EnergyDispersion at 0x1c25cc6e10>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is how for analysis you could slice out an `EnergyDispersion`\n",
    "# object at a given offset:\n",
    "edisp.to_energy_dispersion(offset=\"0 deg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Point spread function\n",
    "\n",
    "The point spread function (PSF) in this case is given as an analytical Gaussian model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary PSF info\n",
      "----------------\n",
      "Theta          : size =     6, min =  0.000 deg, max =  5.000 deg\n",
      "Energy hi      : size =    25, min =  0.020 TeV, max = 1258.925 TeV\n",
      "Energy lo      : size =    25, min =  0.013 TeV, max = 794.328 TeV\n",
      "Safe energy threshold lo:  0.100 TeV\n",
      "Safe energy threshold hi: 100.000 TeV\n",
      "68% containment radius at theta = 0.0 deg and E =  1.0 TeV: 0.05099999 deg\n",
      "68% containment radius at theta = 0.0 deg and E = 10.0 TeV: 0.03700000 deg\n",
      "95% containment radius at theta = 0.0 deg and E =  1.0 TeV: 0.08269457 deg\n",
      "95% containment radius at theta = 0.0 deg and E = 10.0 TeV: 0.05999410 deg\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from gammapy.irf import EnergyDependentMultiGaussPSF\n",
    "\n",
    "psf = EnergyDependentMultiGaussPSF.read(\n",
    "    irf_filename, hdu=\"POINT SPREAD FUNCTION\"\n",
    ")\n",
    "print(psf.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psf.peek()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gammapy.irf.psf_table.EnergyDependentTablePSF at 0x1c24341c50>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is how for analysis you could slice out the PSF\n",
    "# at a given field of view offset\n",
    "psf.to_energy_dependent_table_psf(\"1 deg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background\n",
    "\n",
    "The hadronic background for CTA DC-1 is given as a template model with an absolute rate that depends on `energy`, `detx` and `dety`. The coordinates `detx` and `dety` are angles in the \"field of view\" coordinate frame.\n",
    "\n",
    "Note that really the background model for DC-1 and most CTA IRFs produced so far are radially symmetric, i.e. only depend on the FOV offset. The background model here was rotated to fill the FOV in a rotationally symmetric way, for no good reason."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background3D\n",
      "NDDataArray summary info\n",
      "energy         : size =    21, min =  0.016 TeV, max = 158.489 TeV\n",
      "fov_lon        : size =    36, min = -5.833 deg, max =  5.833 deg\n",
      "fov_lat        : size =    36, min = -5.833 deg, max =  5.833 deg\n",
      "Data           : size = 27216, min =  0.000 1 / (MeV s sr), max =  0.421 1 / (MeV s sr)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from gammapy.irf import Background3D\n",
    "\n",
    "bkg = Background3D.read(irf_filename, hdu=\"BACKGROUND\")\n",
    "print(bkg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: implement a peek method for Background3D\n",
    "# bkg.peek()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$[1.2053315 \\times 10^{-5}] \\; \\mathrm{\\frac{1}{MeV\\,s\\,sr}}$$"
      ],
      "text/plain": [
       "<Quantity [1.20533149e-05] 1 / (MeV s sr)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkg.data.evaluate(energy=\"3 TeV\", fov_lon=\"1 deg\", fov_lat=\"0 deg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index files and DataStore\n",
    "\n",
    "As we saw, you can access all of the CTA data using Astropy and Gammapy.\n",
    "\n",
    "But wouldn't it be nice if there were a better, simpler way?\n",
    "\n",
    "Imagine what life could be like if you had a butler that knows where all the files and HDUs are, and hands you the 1DC data on a silver platter, you just have to ask for it.\n",
    "\n",
    "![gammapy.data.DataStore - your butler for CTA data](images/gammapy_datastore_butler.png)\n",
    "\n",
    "Well, the butler exists. He's called `gammapy.data.DataStore` and he keeps track of the data using index files.\n",
    "\n",
    "### Index files\n",
    "\n",
    "The files with in the `index` folder with names `obs-index.fits.gz` and `hdu-index.fits.gz` are so called \"observation index files\" and \"HDU index files\".\n",
    "\n",
    "* The purpose of observation index files is to get a table of available observations, with the most relevant parameters commonly used for observation selection (e.g. pointing position or observation time). Their format is described in detail [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/data_storage/obs_index/index.html).\n",
    "* The purpose of HDU index files is to locate all FITS header data units (HDUs) for a given observation. At the moment, for each observation, there are six relevant HDUs: EVENTS, GTI, AEFF, EDISP, PSF and BKG. The format is described in detail [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/data_storage/hdu_index/index.html).\n",
    "\n",
    "For 1DC there is one set of index files per simulated dataset, as well as a set of index files listing all available data in the all directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/sh: tree: command not found\r\n"
     ]
    }
   ],
   "source": [
    "!(cd $GAMMAPY_DATA/cta-1dc && tree index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gammapy DataStore\n",
    "\n",
    "If you want to access data and IRFs from the CTA 1DC GPS dataset, just create a `DataStore` by pointing at a folder where the index files are located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapy.data import DataStore\n",
    "\n",
    "data_store = DataStore.from_dir(\"$GAMMAPY_DATA/cta-1dc/index/gps\")\n",
    "\n",
    "# If you want to access all CTA DC-1 data,\n",
    "# set the CTADATA env var and use this:\n",
    "# data_store = DataStore.from_dir(\"$CTADATA/index/gps\")\n",
    "# Or point at the directly with the index files directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data store:\n",
      "HDU index table:\n",
      "BASE_DIR: /Users/adonath/data/gammapy-datasets/cta-1dc/index/gps\n",
      "Rows: 24\n",
      "OBS_ID: 110380 -- 111630\n",
      "HDU_TYPE: ['aeff', 'bkg', 'edisp', 'events', 'gti', 'psf']\n",
      "HDU_CLASS: ['aeff_2d', 'bkg_3d', 'edisp_2d', 'events', 'gti', 'psf_3gauss']\n",
      "\n",
      "Observation table:\n",
      "Observatory name: 'N/A'\n",
      "Number of observations: 4\n"
     ]
    }
   ],
   "source": [
    "# Print out some basic information about the available data:\n",
    "data_store.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations:  4\n",
      "['OBS_ID', 'RA_PNT', 'DEC_PNT', 'GLON_PNT', 'GLAT_PNT', 'ZEN_PNT', 'ALT_PNT', 'AZ_PNT', 'ONTIME', 'LIVETIME', 'DEADC', 'TSTART', 'TSTOP', 'DATE-OBS', 'TIME-OBS', 'DATE-END', 'TIME-END', 'N_TELS', 'OBJECT', 'CALDB', 'IRF', 'EVENTS_FILENAME', 'EVENT_COUNT']\n",
      "2.0\n"
     ]
    }
   ],
   "source": [
    "# The observation index is loaded as a table\n",
    "print(\"Number of observations: \", len(data_store.obs_table))\n",
    "print(data_store.obs_table.colnames)\n",
    "# Compute and print total obs time in hours\n",
    "print(data_store.obs_table[\"ONTIME\"].sum() / 3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "['OBS_ID', 'HDU_TYPE', 'HDU_CLASS', 'FILE_DIR', 'FILE_NAME', 'HDU_NAME']\n"
     ]
    }
   ],
   "source": [
    "# The HDU index is loaded as a table\n",
    "print(len(data_store.hdu_table))\n",
    "print(data_store.hdu_table.colnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Of course, you can look at the tables if you like\n",
    "# data_store.obs_table[:10].show_in_browser(jsviewer=True)\n",
    "# data_store.hdu_table[:10].show_in_browser(jsviewer=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select observations\n",
    "\n",
    "With ``data_store.obs_table`` you have a table with the most common per-observation parameters that are used for observation selection. Using Python / Table methods it's easy to apply any selection you like, always with the goal of making a list or array of `OBS_ID`, which is then the input to analysis.\n",
    "\n",
    "For the current 1DC dataset it's pretty simple, because the only quantities useful for selection are:\n",
    "* pointing position\n",
    "* which irf (i.e. array  / zenith angle)\n",
    "\n",
    "With real data, there will be more parameters of interest, such as data quality, observation duration, zenith angle, time of observation, ...\n",
    "\n",
    "Let's look at one example: select observations that are at offset 1 to 2 deg from the Galactic center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of selected observations:  3\n"
     ]
    }
   ],
   "source": [
    "from astropy.coordinates import SkyCoord\n",
    "\n",
    "table = data_store.obs_table\n",
    "pos_obs = SkyCoord(\n",
    "    table[\"GLON_PNT\"], table[\"GLAT_PNT\"], frame=\"galactic\", unit=\"deg\"\n",
    ")\n",
    "pos_target = SkyCoord(0, 0, frame=\"galactic\", unit=\"deg\")\n",
    "offset = pos_target.separation(pos_obs).deg\n",
    "mask = (1 < offset) & (offset < 2)\n",
    "table = table[mask]\n",
    "print(\"Number of selected observations: \", len(table))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>ObservationTable length=3</i>\n",
       "<table id=\"table112222961904\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>OBS_ID</th><th>GLON_PNT</th><th>GLAT_PNT</th><th>IRF</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>bytes13</th></tr></thead>\n",
       "<tr><td>110380</td><td>359.9999912037958</td><td>-1.299995937905366</td><td>South_z20_50h</td></tr>\n",
       "<tr><td>111140</td><td>358.4999833830074</td><td>1.3000020211954284</td><td>South_z20_50h</td></tr>\n",
       "<tr><td>111159</td><td>1.5000056568267741</td><td>1.299940468335294</td><td>South_z20_50h</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<ObservationTable length=3>\n",
       "OBS_ID      GLON_PNT           GLAT_PNT           IRF     \n",
       "              deg                deg                      \n",
       "int64       float64            float64          bytes13   \n",
       "------ ------------------ ------------------ -------------\n",
       "110380  359.9999912037958 -1.299995937905366 South_z20_50h\n",
       "111140  358.4999833830074 1.3000020211954284 South_z20_50h\n",
       "111159 1.5000056568267741  1.299940468335294 South_z20_50h"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the first few\n",
    "table[[\"OBS_ID\", \"GLON_PNT\", \"GLAT_PNT\", \"IRF\"]][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'South_z20_50h'}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check which IRFs were used ... it's all south and 20 deg zenith angle\n",
    "set(table[\"IRF\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Galactic latitude (deg)')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dQNO34Sx6krQA9Ruw8FbgaUmeBTyx3fzFqrp4TiKTJI2cLs9pXAyYKCRJnZ4IlyQJMGlIkjaCSUOS1JlJQ5LUmUlDktSZSUOS1JlJQ5LUmUlDktSZSUOS1JlJQ5LUmUlDktSZSUOS1JlJQ5LUmUlDktTZUJJGkqOTXJfkviRjfco9N8nqJGuSnDqXMUqSHmpYVxrXAn8GXDpVgSSLgPcBhwL7AMcm2WduwpMkTWbaSZgGoaquB0jSr9iTgTVVdUNb9tPAEcD3Bh6gJGlSo9ynsQtwU8/62nbbQyQ5McnKJCvXr18/J8FJ0kI0sCuNJBcCj51k12lVdW6XQ0yyrSYrWFVnAmcCjI2NTVpGkjRzA0saVfXsGR5iLbBbz/quwLoZHlOSNAOj3Dx1BbBnkt2TbAUcA5w35JgkaUEb1i23L0yyFngq8MUkK9rtOye5AKCq7gFOAlYA1wP/WlXXDSNeSVJjWHdPfR74/CTb1wGH9axfAFwwh6FJkvoY5eYpSdKIMWlIkjozaUiSOjNpSJI6M2lIkjozaUiSOjNpSJI6M2lIkjozaUiSOjNpSJI6M2lIkjozaUiSOjNpSJI6M2lIkjobytDo0kJzzndu5h0rVrPu9g3svP0STjlkL16w36RT3ksjzaQhDdg537mZN5y9ig133wvAzbdv4A1nrwIwcWizY/OUNGDvWLH6/oQxbsPd9/KOFauHFJG06Uwa0oCtu33DRm2XRplJQxqwnbdfslHbpVFm0pAG7JRD9mLJlosetG3Jlos45ZC9hhSRtOnsCJcGbLyz27unNB+YNKQ58IL9djFJaF6weUqS1JlJQ5LUmUlDktSZSUOS1JlJQ5LUmUlDktRZqmrYMcyqJOuBG2d4mB2Bn81COMNmPUbPfKnLfKkHzJ+6zLQej6uqpdMVmndJYzYkWVlVY8OOY6asx+iZL3WZL/WA+VOXuaqHzVOSpM5MGpKkzkwakztz2AHMEusxeuZLXeZLPWD+1GVO6mGfhiSpM680JEmdLfikkeToJNcluS/JlHceJPlJklVJrk6yci5j7Goj6vLcJKuTrEly6lzG2EWSRyb5apIftj93mKLcve3v4+ok5811nP1M9xkn2TrJZ9r9lydZPvdRTq9DPY5Psr7n9/DyYcQ5nSQfTXJrkmun2J8k727r+d0k+891jF10qMdBSe7o+X387awHUVUL+gU8AdgLuAQY61PuJ8COw453pnUBFgE/AvYAtgKuAfYZduwTYjwDOLVdPhV4+xTl7hx2rJv6GQP/A/hgu3wM8Jlhx72J9TgeeO+wY+1Ql2cC+wPXTrH/MOBLQIADgcuHHfMm1uMg4AuDjGHBX2lU1fVVtXrYccyGjnV5MrCmqm6oqt8CnwaOGHx0G+UI4Kx2+SzgBUOMZVN0+Yx76/g54E+SZA5j7GJz+LfSSVVdCvy8T5EjgE9U4zJg+yQ7zU103XWox8At+KSxEQr4SpIrk5w47GBmYBfgpp71te22UfKYqroFoP356CnKLU6yMsllSUYpsXT5jO8vU1X3AHcAj5qT6Lrr+m/lyLZJ53NJdpub0Gbd5vD/oqunJrkmyZeSPHG2D74gZu5LciHw2El2nVZV53Y8zNOral2SRwNfTfL9NuvPqVmoy2R/zc75LXT96rERh1nW/k72AC5OsqqqfjQ7Ec5Il894JH4P0+gS4/nAp6rqriSvoLl6etbAI5t9m8Pvo4uraIYDuTPJYcA5wJ6zeYIFkTSq6tmzcIx17c9bk3ye5tJ9zpPGLNRlLdD71+CuwLoZHnOj9atHkp8m2amqbmmbCG6d4hjjv5MbklwC7EfTBj9sXT7j8TJrkzwM2I4hNztMYtp6VNVtPasfBt4+B3ENwkj8v5ipqvplz/IFSd6fZMeqmrWxtWye6iDJ7yR5xPgy8KfApHcvbAauAPZMsnuSrWg6YUfqziOaeI5rl48DHnIFlWSHJFu3yzsCTwe+N2cR9tflM+6t41HAxdX2ZI6Qaesxod3/cOD6OYxvNp0HvKS9i+pA4I7xJtLNSZLHjveNJXkyzXf8bf3ftZGGfTfAsF/AC2n+yrgL+Cmwot2+M3BBu7wHzZ0j1wDX0TQFDT32TalLu34Y8AOav8pHri40bfsXAT9sfz6y3T4GfKRdfhqwqv2drAJOGHbcE+rwkM8YeDNweLu8GPgssAb4NrDHsGPexHq8tf0/cQ3wNWDvYcc8RT0+BdwC3N3+HzkBeAXwinZ/gPe19VxFnzspR7weJ/X8Pi4DnjbbMfhEuCSpM5unJEmdmTQkSZ2ZNCRJnZk0JEmdmTQkSZ2ZNDRUSR6T5F+S3NAO0fKtJC+c5j3Lpxrls8P5jk+yc8/6R5Ls0/G9ByX5wqacd5rjvjnJs9vl1ybZZhOOcedGlk+Si5NsO8m+05P89cbG0L53aZIvb8p7tXkwaWho2oeQzgEurao9quoAmgfIdh3gaY+neW4FgKp6eVUN9aHAqvrbqrqwXX0tsNFJYxMcBlxTPU8Qz4aqWg/ckuTps3lcjQ6ThobpWcBvq+qD4xuq6saqeg/cf0Xx9SRXta+nTTxAvzJJXp9mDpRrkrwtyVE0Dwj+czvXwJIkl6SdeyTN3BFXteUv6hd4mjk/zmkH6rssyZPa7ae3cx5c0l49vbrnPX+T5Ptp5gj51Phf80k+nuSotuzOwNeSfK3dd2fP+49K8vF2eff2quyKJG+ZENsp7fbvJnnTFFX4S3qetE9yWpp5My6kGV5/fPvjk3y5vQr8epK9e7Zf1p7nzROudM5pj6/5aNhPOPpauC/g1cA/9Nm/DbC4Xd4TWNkuL6edT6BPmUOBbwLbtOvjT5VfQs/TvuPrwFKaUU537y0/IZ6DaOcqAN4DvLFdfhZwdbt8enverYEdaYZw2LI9x9XAEuARNE+7/3X7no8DR7XLP6Fn3hZ65gyhGW7k4+3yecBL2uVXjZejGeLmTJonnLcAvgA8c5K63Ag8ol0+gOYp6G2AbWmeUh+P7SJgz3b5KTTDndAe99h2+RUT4twFWDXsf1++BvNaEAMWavOQ5H3AM2iuPv6Q5sv2vUn2Be4Ffm+St01V5tnAx6rq1wBVNd1ggAfSNJP9uGP5ZwBHtmUvTvKoJNu1+75YVXcBdyW5FXhMW/7cqtrQ1vX8aY4/naePnx/4JA8MFPin7es77frDaZLpxME1H1lVv2qX/wj4/PhnlXYWxCQPpxmu5bN5YKqPrdufT+WBeU7+BXhnz7FvpacJUPOLSUPDdB0PfPFRVa9qBx8cn073ZJoxtP6A5q/m30xyjKnKhI0b2npTyk80/v67erbdS/P/bFMnWOqNaXGffb1xvbWqPjTNce9JskVV3dfnWFsAt1fVvt1CfVCcGzbyPdpM2KehYbqYZiKlV/Zs6+0E3g64pf1iezHN9KMTTVXmK8DLxu9ESvLIdvuvaJqHJvoW8MdJdp9QfiqX0rbbJzkI+Fn171T+BvD8JIvbv+D/6xTlJsb30yRPSLIFzYCU4/4fzU0D8OD+gxU09X54G9suaeaAmWg1zUCc43V5YdvH8wjg+XD/MNs/TnJ0e6wk+YP2PZfxQMI/hgf7PTbfUaA1DZOGhqaqiqaJ44+T/DjJt2km8fnfbZH3A8cluYzmi+g/JznMpGWq6ss07f4rk1wNjN9C+nHgg+Md4T2xrAdOBM5Ocg3wmWnCPx0YS/Jd4G08MMz5VHW9oo3nGuBsmqupOyYpeibwpfGOcJo50r9Ak2B7h+p+DfCqJFfQJM7x83yFprnoW0lW0UwlO1mS/CJNHw1VdRVNfa8G/g34ek+5vwROaD+T63hgutfXAq9rf2c7TajLwe3xNQ85yq00R5I8vJoZ1bah+ev+xPYLexix7EQzJ/ZzNvH92wAbqqqSHEPTKX5Eu+9S4Iiq+sXsRaxRYZ+GNHfOTPMg4WLgrGElDGjmXk/y4STbTtOsNpUDaG5ACHA78DJoHu4D/t6EMX95pSFJ6sw+DUlSZyYNSVJnJg1JUmcmDUlSZyYNSVJnJg1JUmf/H5GmALl2iX3HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the pointing positions\n",
    "# The grid pointing positions at GLAT = +- 1.2 deg are visible\n",
    "from astropy.coordinates import Angle\n",
    "\n",
    "plt.scatter(\n",
    "    Angle(table[\"GLON_PNT\"], unit=\"deg\").wrap_at(\"180 deg\"), table[\"GLAT_PNT\"]\n",
    ")\n",
    "plt.xlabel(\"Galactic longitude (deg)\")\n",
    "plt.ylabel(\"Galactic latitude (deg)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "Once you have selected the observations of interest, use the `DataStore` to load the data and IRF for those observations. Let's say we're interested in `OBS_ID=110380`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = data_store.obs(obs_id=110380)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info for OBS_ID = 110380\n",
      "- Start time: 59235.50\n",
      "- Pointing pos: RA 267.68 deg / Dec -29.61 deg\n",
      "- Observation duration: 1800.0 s\n",
      "- Dead-time fraction: 2.000 %\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gammapy.data.event_list.EventList at 0x1a2171bbe0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3</i>\n",
       "<table id=\"table112212694240\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>EVENT_ID</th><th>TIME</th><th>RA</th><th>DEC</th><th>ENERGY</th><th>DETX</th><th>DETY</th><th>MC_ID</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th>deg</th><th>deg</th><th>TeV</th><th>deg</th><th>deg</th><th></th></tr></thead>\n",
       "<thead><tr><th>uint32</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th></tr></thead>\n",
       "<tr><td>1</td><td>664502403.0454683</td><td>-92.63541</td><td>-30.514854</td><td>0.03902182</td><td>-0.9077294</td><td>-0.2727693</td><td>2</td></tr>\n",
       "<tr><td>2</td><td>664502405.2579999</td><td>-92.64103</td><td>-28.262728</td><td>0.030796371</td><td>1.3443842</td><td>-0.2838398</td><td>2</td></tr>\n",
       "<tr><td>3</td><td>664502408.8205513</td><td>-93.20372</td><td>-28.599625</td><td>0.04009629</td><td>1.0049409</td><td>-0.7769775</td><td>2</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "EVENT_ID        TIME           RA       DEC     ...    DETX       DETY    MC_ID\n",
       "                 s            deg       deg     ...    deg        deg          \n",
       " uint32       float64       float32   float32   ...  float32    float32   int32\n",
       "-------- ----------------- --------- ---------- ... ---------- ---------- -----\n",
       "       1 664502403.0454683 -92.63541 -30.514854 ... -0.9077294 -0.2727693     2\n",
       "       2 664502405.2579999 -92.64103 -28.262728 ...  1.3443842 -0.2838398     2\n",
       "       3 664502408.8205513 -93.20372 -28.599625 ...  1.0049409 -0.7769775     2"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.events.table[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gammapy.data.gti.GTI at 0x1c25cc65f8>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.gti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gammapy.irf.effective_area.EffectiveAreaTable2D at 0x1a21466978>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.aeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gammapy.irf.energy_dispersion.EnergyDispersion2D at 0x1a20f76ef0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.edisp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gammapy.irf.psf_gauss.EnergyDependentMultiGaussPSF at 0x1a20f6f400>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.psf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gammapy.irf.background.Background3D at 0x1a20f67828>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.bkg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's an example how to loop over many observations and analyse them: [cta_1dc_make_allsky_images.py](https://github.com/gammasky/cta-dc/blob/master/data/cta_1dc_make_allsky_images.py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model XML files\n",
    "\n",
    "The 1DC sky model is distributed as a set of XML files, which in turn link to a ton of other FITS and text files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: copy an example XML file for tutorials\n",
    "# This one is no good: $CTADATA/models/models_gps.xml\n",
    "# Too large, private in CTA, loads large diffuse model\n",
    "# We need to prepare something custom.\n",
    "# !ls $CTADATA/models/*.xml | xargs -n 1 basename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is what the XML file looks like\n",
    "# !tail -n 20 $CTADATA/models/models_gps.xml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the moment, you cannot read and write these sky model XML files with Gammapy.\n",
    "\n",
    "There are multiple reasons why this XML serialisation format isn't implemented in Gammapy yet (all variants of \"it sucks\"):\n",
    "\n",
    "* XML is tedious to read and write for humans.\n",
    "* The format is too strict in places: there are many use cases where \"min\", \"max\", \"free\" and \"error\" aren't needed, so it should be possible to omit them (see e.g. dummy values above)\n",
    "* The parameter \"scale\" is an implementation detail that very few optimisers need. There's no reason to bother all gamma-ray astronomers with separating value and scale in model input and result files.\n",
    "* The \"unit\" is missing. Pretty important piece of information, no?\n",
    "  All people working on IACTs use \"TeV\", why should CTA be forced to use \"MeV\"?\n",
    "* Ad-hoc extensions that keep being added and many models can't be put in one file (see extra ASCII and FITS files with tables or other info, e.g. pulsar models added for CTA 1DC). Admittedly complex model serialisation is an intrinsically hard problem, there is not simple and flexible solution.\n",
    "\n",
    "Also, to be honest, I also want to say / admit:\n",
    "\n",
    "* A model serialisation format is useful, even if it will never cover all use cases (e.g. energy-dependent morphology or an AGN with a variable spectrum, or complex FOV background models).\n",
    "* The Gammapy model package isn't well-implemented or well-developed yet.\n",
    "* So far users were happy to write a few lines of Python to define a model instead of XML.\n",
    "* Clearly with CTA 1DC now using the XML format there's an important reason to support it, there is the legacy of Fermi-LAT using it and ctools using / extending it for CTA.\n",
    "\n",
    "So what now?\n",
    "\n",
    "* In Gammapy, we have started to implement a modeling package in `gammapy.utils.modeling`, with the goal of supporting this XML format as one of the serialisation formats. It's not very far along, will be a main focus for us, help is welcome.\n",
    "* In addition we would like to support a second more human-friendly model format that looks something like [this](https://github.com/gammapy/gamma-cat/blob/b651de8d1d793e924764ffb13c8ec189bce9ea7d/input/data/2006/2006A%2526A...457..899A/tev-000025.yaml#L11)\n",
    "* For now, you could use Gammalib to read the XML files, or you could read them directly with Python. The Python standard library contains [ElementTree](https://docs.python.org/3/library/xml.etree.elementtree.html) and there's [xmltodict](https://github.com/martinblech/xmltodict) which simply hands you back the XML file contents as a Python dictionary (containing a very nested hierarchical structure of Python dict and list objects and strings and numbers.\n",
    "\n",
    "As an example, here's how you can read an XML sky model and access the spectral parameters of one source (the last, \"Arp200\" visible above in the XML printout) and create a [gammapy.spectrum.models.PowerLaw](..\/api/gammapy.spectrum.models.PowerLaw.rst) object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read XML file and access spectrum parameters\n",
    "# from gammapy.extern import xmltodict\n",
    "\n",
    "# filename = os.path.join(os.environ[\"CTADATA\"], \"models/models_gps.xml\")\n",
    "# data = xmltodict.parse(open(filename).read())\n",
    "# data = data[\"source_library\"][\"source\"][-1]\n",
    "# data = data[\"spectrum\"][\"parameter\"]\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a spectral model the the right units\n",
    "# from astropy import units as u\n",
    "# from gammapy.spectrum.models import PowerLaw\n",
    "\n",
    "# par_to_val = lambda par: float(par[\"@value\"]) * float(par[\"@scale\"])\n",
    "# spec = PowerLaw(\n",
    "#     amplitude=par_to_val(data[0]) * u.Unit(\"cm-2 s-1 MeV-1\"),\n",
    "#     index=par_to_val(data[1]),\n",
    "#     reference=par_to_val(data[2]) * u.Unit(\"MeV\"),\n",
    "# )\n",
    "# print(spec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "* Easy: Go over this notebook again, and change the data / code a little bit:\n",
    "    * Pick another run (any run you like)\n",
    "    * Plot the energy-offset histogram of the events separately for gammas and background\n",
    "\n",
    "* Medium difficulty: Find all runs within 1 deg of the Crab nebula.\n",
    "    * Load the \"all\" index file via the `DataStore`, then access the ``.obs_table``, then get an array-valued ``SkyCoord`` with all the observation pointing positions.\n",
    "    * You can get the Crab nebula position with `astropy.coordinates.SkyCoord` via ``SkyCoord.from_name('crab')`` \n",
    "    * Note that to compute the angular separation between two ``SkyCoord`` objects can be computed via ``separation = coord1.separation(coord2)``.\n",
    "\n",
    "* Hard: Find the PeVatrons in the 1DC data, i.e. the ~ 10 sources that are brightest at high energies (e.g. above 10 TeV).\n",
    "    * This is difficult, because it's note clear how to best do this, and also because it's time-consuming to crunch through all relevant data for any given method.\n",
    "    * One idea could be to go brute-force through **all** events, select the ones above 10 TeV and stack them all into one table. Then make an all-sky image and run a peak finder, or use an event cluster-finding method e.g. from scikit-learn.\n",
    "    * Another idea could be to first make a list of targets of interest, either from the CTA 1DC sky model or from gamma-cat, compute the model integral flux above 10 TeV and pick candidates that way, then run analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# start typing here ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What next?\n",
    "\n",
    "* This notebook gave you an overview of the 1DC files and showed you have to access and work with them using Gammapy.\n",
    "* To see how to do analysis, i.e. make a sky image and spectrum, see [cta_data_analysis.ipynb](cta_data_analysis.ipynb) next."
   ]
  }
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