{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<script type=\"text/javascript\" src=\"../_static/linksdl.js\"></script>\n",
    "<div class='alert alert-info'>\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/v0.9?urlpath=lab/tree/first_steps.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",
    "[first_steps.ipynb](../_static/notebooks/first_steps.ipynb) |\n",
    "[first_steps.py](../_static/notebooks/first_steps.py)\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with Gammapy\n",
    "\n",
    "## Introduction\n",
    "\n",
    "This is a getting started tutorial for [Gammapy](https://docs.gammapy.org/).\n",
    "\n",
    "In this tutorial we will use the [Second Fermi-LAT Catalog of High-Energy Sources (2FHL) catalog](http://fermi.gsfc.nasa.gov/ssc/data/access/lat/2FHL/), corresponding event list and images to learn how to work with some of the central Gammapy data structures.\n",
    "\n",
    "We will cover the following topics:\n",
    "\n",
    "* **Sky maps**\n",
    "  * We will learn how to handle image based data with gammapy using a Fermi-LAT 2FHL example image. We will work with the following classes:\n",
    "    - [gammapy.maps.WcsNDMap](..\/api/gammapy.maps.WcsNDMap.rst)\n",
    "    - [astropy.coordinates.SkyCoord](http://astropy.readthedocs.io/en/latest/coordinates/index.html)\n",
    "    - [numpy.ndarray](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html)\n",
    "\n",
    "* **Event lists**\n",
    "  * We will learn how to handle event lists with Gammapy. Important for this are the following classes: \n",
    "    - [gammapy.data.EventList](..\/api/gammapy.data.EventList.rst)\n",
    "    - [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html#astropy.table.Table)\n",
    "\n",
    "* **Source catalogs**\n",
    "  * We will show how to load source catalogs with Gammapy and explore the data using the following classes:\n",
    "    - [gammapy.catalog.SourceCatalog](..\/api/gammapy.catalog.SourceCatalog.rst), specifically [gammapy.catalog.SourceCatalog2FHL](..\/api/gammapy.catalog.SourceCatalog2FHL.rst)\n",
    "    - [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html#astropy.table.Table)\n",
    "\n",
    "* **Spectral models and flux points**\n",
    "  * We will pick an example source and show how to plot its spectral model and flux points. For this we will use the following classes:\n",
    "    - [gammapy.spectrum.SpectralModel](..\/api/gammapy.spectrum.models.SpectralModel.rst), specifically the [PowerLaw2](..\/api/gammapy.spectrum.models.PowerLaw2.rst) model.\n",
    "    - [gammapy.spectrum.FluxPoints](..\/api/gammapy.spectrum.FluxPoints.rst#gammapy.spectrum.FluxPoints)\n",
    "    - [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html#astropy.table.Table)\n",
    "\n",
    "If you're not yet familiar with the listed Astropy classes, maybe check out the [Astropy introduction for Gammapy users](astropy_introduction.ipynb) first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "**Important**: to run this tutorial the environment variable `GAMMAPY_EXTRA` must be defined and point to the directory, where the gammapy-extra is respository located on your machine. To check whether your setup is correct you can execute the following cell:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Great your setup is correct!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "path = os.path.expandvars(\"$GAMMAPY_DATA\")\n",
    "\n",
    "if not os.path.exists(path):\n",
    "    raise Exception(\"gammapy-data repository not found!\")\n",
    "else:\n",
    "    print(\"Great your setup is correct!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case you encounter an error, you can un-comment and execute the following cell to continue. But we recommend to set up your enviroment correctly as decribed [here](..\/datasets/index.rst#gammapy-extra) after you are done with this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.environ['GAMMAPY_DATA'] = os.path.join(os.getcwd(), '..')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can continue with the usual IPython notebooks and Python imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.visualization import simple_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Maps\n",
    "\n",
    "The [gammapy.maps](https://docs.gammapy.org/dev/maps) package contains classes to work with sky images and cubes.\n",
    "\n",
    "In this section, we will use a simple 2D sky image and will learn how to:\n",
    "\n",
    "* Read sky images from FITS files\n",
    "* Smooth images\n",
    "* Plot images\n",
    "* Cutout parts from images\n",
    "* Reproject images to different WCS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapy.maps import Map\n",
    "\n",
    "vela_2fhl = Map.read(\n",
    "    \"$GAMMAPY_DATA/fermi_2fhl/fermi_2fhl_vela.fits.gz\", hdu=\"COUNTS\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the FITS file `fermi_2fhl_vela.fits.gz` contains mutiple image extensions and a map can only represent a single image, we explicitely specify to read the extension called 'COUNTS'.\n",
    "\n",
    "The image is a ``WCSNDMap`` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WcsNDMap\n",
       "\n",
       "\tgeom  : WcsGeom \n",
       " \taxes  : lon, lat\n",
       "\tshape : (320, 180)\n",
       "\tndim  : 2\n",
       "\tunit  : '' \n",
       "\tdtype : >f8 "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vela_2fhl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape of the image is 320x180 pixel and it is defined using a cartesian projection in galactic coordinates.\n",
    "\n",
    "The ``geom`` attribute is a ``WcsGeom`` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WcsGeom\n",
       "\n",
       "\taxes       : lon, lat\n",
       "\tshape      : (320, 180)\n",
       "\tndim       : 2\n",
       "\tcoordsys   : GAL\n",
       "\tprojection : CAR\n",
       "\tcenter     : 266.0 deg, -1.2 deg\n",
       "\twidth      : 32.0 x 18.0 deg"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vela_2fhl.geom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a closer look a the `.data` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vela_2fhl.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks familiar! It just an *ordinary* 2 dimensional numpy array,  which means you can apply any known numpy method to it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of counts in the image: 1567\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Total number of counts in the image: {:.0f}\".format(vela_2fhl.data.sum())\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To show the image on the screen we can use the ``plot`` method. It basically calls [plt.imshow](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.imshow), passing the `vela_2fhl.data` attribute but in addition handles axis with world coordinates using [wcsaxes](https://wcsaxes.readthedocs.io/en/latest/) and defines some defaults for nicer plots (e.g. the colormap 'afmhot'):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aX/trhgNWma8rtz6F1LarPqU9CTj3msF1DL3onrPfSzXz6T3NEklHIrHBOVx3aO1D6tcR++Ziu7QO3T41Bj7LlOT3MdpL7lfM/kRLJMBHgAc0vj+QxrtDcrC1DKacbZevlxKLzYxCt9Ztvm/3U2lPGL78EdOvvtrqu3NxaBtt+8X0RSm+3hmLiInk8iHrckwkS7VYg8xsdxyUex4YtxPLxkLG0Hd9Ptsbsm9s/Sl8lWMfYyaSzwKPanw/EtiTOnnklkh04EDJcTAN7d+cckPLj/HhXF/OKb9JHtt3a7W1oVtlQuqKZb5iYJbWYiaSk4CvAe+o7avAj6VOHjETiXYszcqyoXHrirNufe7aPuaurD5NQ/VO3b8EW2KfcupztERSH6iPAH4c+JfAEakTR1siaXN+X0CGBitkUH3V7aOfPtoN2VZq063lWL/78E1o/3bVn0tcc9GxJAueSIDH1cvj2ix18uiakUwZbDkM0LkacuhDm6axuubuv6t8LD+NmZWUHufcbK6P1uTjGInk9+vlx1rsgtTJoyuR5Do4zfL0dV9bQ3UM2W9un7rKt60PlcRKtzE+nBuvUix4ImkcpA8Zsm7piWTNpgttK7WN7asP3+QSy9zjnLO+XX4doz1mIrnHHVpt63JKJCkGQVsAd+nIebCu3YbExlf8uurRxra+fXz3K6YPU2ssyUL4JXgiAR4KPAG4Bng8+66PnAhcmzp5dCWSNQ3C2H2N2Z72tBdSR1+7ofwzpdwcnaHjFiNOOdjUuKXW3bQYieRUqushtwAXsO/6yPuB56VOHn0zkpQDau5AGVs+t4G5NL05+aYE34XUOLTuEvyUi4+DJ5LGQfqU1IliaCJZ+gAqcaAuwWL4Y2obWpurfgiTyqb2b0hf5d6/1PpiJpL/yj2ftfXrqZNHWyLJ3eYOmtSDbumat7Xu0q4D9tXGNu0o09fOkHqBnf9hnyomQ9oa6/ddvkjRzyF6Qseir66ubTETSduztrK82J5q4JjFt7mxnlve+jtNw1QdS+h/jhYzkVwJHNz4fh/g6tTJoy2RzB1IQwfbWgdljH4vybcx+6ID29Otpc9+LOn/V2LGLmW/YiaSVwCfAv4t8LP151ekTh5jE8lSLOUAT9F2yv7mYLv6rxn4aKNhc3ptiJ65mkP3eWj9qX0/t0++jsEHsQPn3NkichXwdECA/+Kc+6td5QzDMIyVkHo2EXpGohn/5TBF09gyOfY7le9ybd93Xdv17frus+2hp7f6NLRtSx3v0HFLpcPbMXjAQfrJwKXArcB3gbuA76ROHn2JJJcAl2hjfDfFz7HK5OAj37p163NX/dvrt8uF0j7mzrEcY56izaFth9IWM5FcBjwWuBw4EHgJ8NrUySNlIkk54HI3802e/krRTqw2U/a3dIuaSOrllY11n0mdPPoSiQ3KvCy1/0q9m2jbb11+jO1fbbSpCdoP7efU9Uxpc2rbMRPJJ4F7A28HzgbOAK5InTx2JZI2B+sCBn3pNsX/S4vZlMSmLcu2z6n8pdjvq0SLmUiOpPrfkfsBrwZ+C3hs6uQRc0ayMe34PGT/KdtjW256YsaqNFPm/zUay/+lWtPHJferT3O0RFKC5TyQSz2tYjbcdOT6XduGtKUe6kjhk9x0Ls10pN+CJxLgKqr/am+11MkjxYxkSlBNQ3iNufXRd3+0sWzbNqQ97am/BCtRcwl+iJFIjuyz1MmjhESS40Br2+5rcPoe5Ln5bko5xc9B3Kdvfd6mW7Ll5INYWrbbCZ5ISrIQDh9zSirFgMzpR5Czbfspd78N0Zd7H9ZoIWLiq86+eqIlEgr8h0QzsximGWjIvc99+3dtW6NfU1nMRFLcPySahTP1vK2vTGi9sfyVWkOJZj4L56NmuaiJpF6u9h8SUwZ9rbq6tG6sNO0pY9VVbs2+NKssZiIp8h8SzcxSmGagIdf+6sD9l+xDn33zUVfMRHIkcAgr+YfEpQziof0opb8pdMZss5Q4LM2G+N1nbLSxzCHm0RJJhCRwIvD3wN7azmxseyGwB3j5rkSSQ1BCDsAS28/VpvpFJ5TXBcYhRH9y+MfdKfEt3YInEuA5wGmN7xcDX6nt+Z4TyQc7tr2P6gL/u4DDY8xI1miagYZStOWmJ3ddPvoVs2+5+9G3P2Ikkk8Dj2x83wt8D/Ao4KOREsn7qRLJHwH3XWIiCTVwQ9W7NsvBj9rxuTTTiPq72pnafk5+96klRiK5dOv7OY3PF3lOJH8HXAH8JXBMY9upVLcd/9KOOqIPAM1scOUwYPv2X7qvzNKYetonB0uhM0Yiua5n25c9JpL7UZ+2Ak4GvjSgjAC3A7fVlnwQlGopBm8O2obWnbN/dmnu015iv3K1wn25OYbeDsjU4/gBdHOxiLx0e6WI/DxwSU+5nYjIaSKyV0T2UiWRWwGccx8C7iUiR/SVdxWHOucOc84dNkeLYRjGWtkcQ+vjqZtTUddf/Q8GPgN8DHh9bR8HLgQe4nFG8lDqTAicAHyNkZkR+8vDLLGpp31y1G2almvBT201DtJPA06v7Wm+Gm7U/zLgaqprJBcBT51QR/KATDXNQEOpFst3u9rxqaOtrq51umOf0s1Hn5boF58WLZGUYDbQlmu5x2eXPk3chxht5x4js+54WSLxlEj6nNy3zmf9ZubLtVno2IWufylmicRTIrEBt0y/5a6vZMvVt7nqytknvo7Bm4vcRSMi5XfCMCaitRn5olvLXHDOiY96+m7/XQWaWoCRLZpRW7u2h2ZI+0P2SYUmbkcjamhru+2zV1Kflkp9asusPNMMNKzZhvjfR4xyjnNKbT7b9nYMTp0EQiaSnAdiqIFSap9L8O3SLBdf7NIxVWcu/cvZfB2D7RqJYSRCSX86KAcNY1DK0psSZbevnF0jyRNNLSAQmlpAAnRmuV3lp9bvA91aloKmFlAQGrEtm5GsDMV+jD5QwvoxdP197TaXhl+UvHzra0ZiicRYBUpeP+DScQqi08srFo8csFNbhjECTS1gYcxJIjAuHkP3HVNnqWhqAR0sLpFoagED0dQCjF504jafZZaERig7p41S0JHrY2GntrZQ0gdlaSjm0yWgzLsBYWpZIxx2aisQuvD2hqCZ12cMRz2Wm1KXbi3XhqYWEAmbkRjGDJT1HCxyRbeWxnBsRpIYTdym03uuM4ajI9ePrccIg3asa1vvu52+9WvHZiSGYURHsYNySJRh/rUZiWEYhpEFlkgKQ1MLMAwPaGoBkdHUAgJjiaQHTS2gBU1cvjQ0tYDC0cD7rwXNpL2u9XOxayRbKPZjMPyhW8vSUMrVviSUMHHwdY0k+btEQr6PZEmmnvczyzuOpVmIfi3VVzn5wd5H0mCtd20p93yNprbsZ8xDMb8ay8Se/ttgrYnEMDYoluxKQMkrTnb770g0tQAjCzS1gECo5/2MMGhqAYGwRGJMYvOf9U1aVg1iarnc20qFTtxmGFNZTSIx/KHMfx/Fdn1ztqdCyXMmMKatMfsa7Wjh9Xsh9R1XJdy1pRncuWF9MSslvjloKN1i+dDbMTh1EighkZjlbbrSts38xmIJsdRGP4b0x9cx2O7aMrJHKWR67wHFz9ML5tZh9KMsw8fO7tpaHxq5XMq2tOPz0tFM6jD60Znbl4bNSBaMsr4BbRjGcHzNSCyRGIZhrBQ7tRUJTS0gQzS1gIBoJm1rxz5LQ1MLKAAduT4FNiPJBCWvgWGEQbE4G/lgM5JM0MzqCY2mFlA4OnBdrmhqAUaW2IzEMAxjpdiMZOVoagGGVzS1gELR1AIyQRO3b4nEMAzDmIUlkkzQreXc/XwTu71QaGoBHejK25+KethnSB25o6kFpH5Olj1rK3/ThWjIoR9zNOWoPwfThfgmRR+8HYNTJ4HcE8kSBqj1uwwL4fOUcUzZttkw83UMtru2AqFkMN00oqNY3OeglOM/pRytXThPd23FnDU8DrgQuAP45a1tJwFfAK4DXtlYf0xd5m3AAXZqazmmGWiw/pmFNM1Awy4r7tQW8GDgicBraSQS4EDgy8BjgHsDVwBH19veAjwIOB04aWgiSRFALWTgpPBLag3WT+u/9b3dfB3fo9215Zy72Tl3KfD/tjadAFznnPuKc+67wLuA59TbDqw7/I/A4CmYzpc7Gk3Ubu5oagGe0Jnbl44mbjt1+1PKTCmXKznc/vsI4OuN7zfW6wDeAPwF8BTgfF8NauD9jTRooXWXhKYW0IKSp64+lPI095FDImmbaTgA59zlzrknOef+tXPurrsLVNwuIreJyG1jG9St5dD9jd3oStteC5pawErQSO1sjqH18XTyhfeDfIraRkROA15afz3ZOffNlt1uBB7Z+P69QNt+d+OqCyOHNtpxxx13XOu+Pw+8qaOeDwDtpYypmE+XT99vyvBDjN/Rnj17cM4d5qOu6Lf/iogCtzrnXld/Pwj4IvB04BvApcBPO+euHlFn3E4YhmEsAF+3/0ZLJCLyUOAy4H5UF89vpbo76zsicjLwO1QX189zzr02iqgeROQ2X9na8I/FJ38sRnnjMz6L+IfEENiPIG8sPvljMcobn/HJ4WJ7rvj5j0/DMIyFYzOSDkREnDnHMIyF4vMYZ4nEMAzDmMWqTm2JyCNF5GMico2IXC0i/6Fef6yIXCQie0XkMhE5oV4vIvK7InKdiFwpIsc16jpDRPaIyAtS9Wdp9MTnj+vY7BWRG0Rkb6PMq+r4fEFEfqyx/oV1fF6eoi9LpCs+9bbT6xhcLSJnN9ZbfCLR8/tREflG4zd0cqOMn/jEetZWDgY8DDiu/nxfqtuOj6b6r/ln1+tPBj7e+PyXVNdLngxcXK8/HHgn1f/hvD91v5ZiXfHZ2uf1wJn156Opns12MPBoqme2HVhvex/VXYDvAg5P3bclWM/v558Dfw0cXG97sMUnq/goWw/K9R2fVc1InHM3Oef21J9vAa6hehyLo7otGeD+7PuHyOcAb3cVFwEPEJGHse9CvJ0X9EhPfIBqhgj8K+CP6lXPAd7lnLvDOXc91dOjT9jsvqkWu3HCCz3x+UXgLOfcHfW2m+siFp+I7Pr9tOAtPqtKJE1E5Cjg8cDFwMuB3xSRrwOvA15V79b6HLA6SFdR/V/MH0eSvCq24rPhR4BvOee+VH/ve07be6jic1kdL8MjW/H5AeBHRORiEfmEiDyx3s3ik4iW38/L6tPz54nIA+t13uKzykQiIocDfwa83Dn3Haq/qM5wzj0SOIPq8fXQ/xyw33DOPd45984YmtdES3w2/BT7ZiPQH5+31fF5fTil66QlPgcBD6Q6/fsfgT+pZ48WnwS0xOeNwPcBxwI3UZ0eBo/xWV0iEZF7UTn5D51z76lXn0qVgQHezb7p3ejngBnz6IjP5lE6z2P/GaDFJzId8bkReE99CvgSqidXHIHFJzpt8XHOfcs5d5dz7h+B/0GA49uqEkn9V9JbgGucc7/V2PRN4Efrz08DNqdOPgD8m/rurScDf++cuyma4JXREx+AZwDXOudubKz7APBCETlYRB4NfD9wSRy166MnPu+j+t0gIj9A9YK6b2PxiUpXfOrruhueC3yu/uwtPkGf/psh/wx4EXBV4xbSX6F6QvEb6r96/wH4uXrbh6ju3LoOuB14SVy5q6M1Ps65DwEvZP/TWjjnrhaRPwE+D9wJnOYarxswvNP1+zkPOE9EPgd8FzjVVbf+WHzi0hWfnxKRY6lOW91A9QBnr78f+4dEwzAMYxarOrVlGIZh+McSiWEYhjELSySGYRjGLCyRGIZhGLOwRGIYhmHMwhKJYRiGMQtLJEYRiMhDROSdIvIVEfmsiFwoIs/dUeao+n8bprT3YhF5eOP7m0Xk6IFlTxSRD05pdygi8pl6eZSI/PSE8i8WkXP8KzPWiCUSI3vq/9h9H/BJ59xjnHNPoPoHxe8N2OyLgbsTiXPu3znnPh+wvVE4555afzwKGJ1IDMMnlkiMEnga8F3n3LmbFc65rzrnfg/u/qv8b+oX8ewRkaduV9C3j4i8QkSuEpErROQsEXk+cDzwh/WLgO4jIh8XkePr/U+q67hCRD46tBMi8nQRubxu6zwRObhef4OIvKau8yoReVy9/kEi8pF6/ZtE5KsickS97da62rOonrxQva4wAAAC70lEQVS7V6qXre030xCRD4rIifXnl4jIF0XkE1T/BU2jnT8TkUtru3ubYQzBEolRAscAe3q23ww80zl3HPAC4HeH7iMizwZ+AniSc+6HgLOdc39K9Qjtn3HOHeuc+7+bSkTkQVQPvjul3v8nh3RARA4B3gq8wDn3g1SPJ/rFxi7frrW9Efjlet2rgQvq9e8FHtVS9SuBv6l1/nZP+w8DXkOVQJ5J9VKjDW8Afts590TgFODNQ/pkGBvW9qwtYwGIyH8DfphqlvJE4F7AOfXzhO6iej/GNl37PAP4n8652wGcc/97R/NPpjrFdv3A/Tf8U+B659wX6+9vA04Dfqf+vnmS7mepnnJM3cfn1u18WET+z8C22ngS1Zs//xaq1xezvw+Ors4gAnA/EbmvvSfEGIolEqMErqb6SxkA59xp9Smey+pVZwDfAn6Iapb9Dy11dO0jjHvT5dj9m+X6uKNe3sW+3+WUNwfeyf5nGg5pfO7SfQDwlObMyzDGYKe2jBK4ADhERJqngg5tfL4/cFP9voUXUb1repuufc4HflZEDgUQkX9Sr7+F6r3X21wI/Gj92O3m/ru4FjhKRB5bf38R8IkdZT5F9WphRORZVC+P2mZb5w3AsSJygIg8kn3vnrgYOFFEvkeqd1Y0T8mdD7xs86WetRnGYCyRGNlTP5L8J6gO4NeLyCVUp4b+U73LfwdOFZGLqE7X3NZSTes+zrkPU72X4bL60dub6xNvBc7dXGxvaPlbqtcMvEdErqD7VctPF5EbN0b12tOXAO8WkauoXv50bkfZDa8BniUie4BnU73dbvt005XAnfWF/zOATwPXU70K+nXU15bq9+goVSL8a/a/5vTvgeOlehXr54Ff2KHLMPbDHiNvGJlS39V1l3PuThF5CvBG55zNFozssGskhpEvj6J6//kBVC+MemliPYbRis1IDMMwjFnYNRLDMAxjFpZIDMMwjFlYIjEMwzBmYYnEMAzDmIUlEsMwDGMW/x98/eoHu3UIaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vela_2fhl.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make the structures in the image more visible we will smooth the data using a Gausian kernel with a radius of 0.5 deg. Again `smooth()` is a wrapper around existing functionality from the scientific Python libraries. In this case it is Scipy's [gaussian_filter](https://docs.scipy.org/doc/scipy-0.16.1/reference/generated/scipy.ndimage.filters.gaussian_filter.html) method. For convenience the kernel shape can be specified with as string and the smoothing radius with a quantity. It returns again a map object, that we can plot directly the same way we did above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "vela_2fhl_smoothed = vela_2fhl.smooth(kernel=\"gauss\", width=0.2 * u.deg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "vela_2fhl_smoothed.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The smoothed plot already looks much nicer, but still the image is rather large. As we are mostly interested in the inner part of the image, we will cut out a quadratic region of the size 9 deg x 9 deg around Vela. Therefore we use ``Map.cutout`` to make a cutout map:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "# define center and size of the cutout region\n",
    "center = SkyCoord(265.0, -2.0, unit=\"deg\", frame=\"galactic\")\n",
    "vela_2fhl_cutout = vela_2fhl_smoothed.cutout(center, 9 * u.deg)\n",
    "vela_2fhl_cutout.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make this exercise a bit more scientifically useful, we will load a second image containing WMAP data from the same region:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "vela_wmap = Map.read(\"$GAMMAPY_DATA/images/Vela_region_WMAP_K.fits\")\n",
    "\n",
    "# define a norm to stretch the data, so it is better visible\n",
    "norm = simple_norm(vela_wmap.data, stretch=\"sqrt\", max_percent=99.9)\n",
    "vela_wmap.plot(cmap=\"viridis\", norm=norm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to make the structures in the data better visible we used the [simple_norm()](http://docs.astropy.org/en/stable/api/astropy.visualization.mpl_normalize.simple_norm.html#astropy.visualization.mpl_normalize.simple_norm) method, which allows to stretch the data for plotting. This is very similar to the methods that e.g. `ds9` provides. In addition we used a different colormap called 'viridis'. An overview of different colomaps can be found [here](http://matplotlib.org/examples/color/colormaps_reference.html). \n",
    "\n",
    "Now let's check the details of the WMAP image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WcsNDMap\n",
       "\n",
       "\tgeom  : WcsGeom \n",
       " \taxes  : lon, lat\n",
       "\tshape : (300, 300)\n",
       "\tndim  : 2\n",
       "\tunit  : 'mK' \n",
       "\tdtype : >f4 "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vela_wmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see it is defined using a tangential projection and ICRS coordinates, which is different from the projection used for the `vela_2fhl` image. To compare both images we have to reproject one image to the WCS of the other. This can be achieved with the ``reproject`` method: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "# reproject and cut out the part we're interested in:\n",
    "vela_wmap_reprojected = vela_wmap.reproject(vela_2fhl.geom)\n",
    "vela_wmap_reprojected_cutout = vela_wmap_reprojected.cutout(center, 9 * u.deg)\n",
    "vela_wmap_reprojected_cutout.plot(cmap=\"viridis\", norm=norm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we will combine both images in single plot, by plotting WMAP contours on top of the smoothed Fermi-LAT 2FHL image:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "fig, ax, _ = vela_2fhl_cutout.plot()\n",
    "ax.contour(vela_wmap_reprojected_cutout.data, cmap=\"Blues\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "* Add a marker and circle at the Vela pulsar position (you can find examples in the WCSAxes [documentation](https://wcsaxes.readthedocs.io/en/latest/overlays.html))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Event lists\n",
    "\n",
    "Almost any high-level gamma-ray data analysis starts with the raw measured counts data, which is stored in event lists. In Gammapy event lists are represented by the [gammapy.data.EventList](..\/api/gammapy.data.EventList.rst) class. \n",
    "\n",
    "In this section we will learn how to:\n",
    "\n",
    "* Read event lists from FITS files\n",
    "* Access and work with the `EventList` attributes such as `.table` and `.energy` \n",
    "* Filter events lists using convenience methods\n",
    "\n",
    "Let's start with the import from the [gammapy.data](..\/data/index.rst) submodule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapy.data import EventList"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very similar to the sky map class an event list can be created, by passing a filename to the `.read()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_2fhl = EventList.read(\"$GAMMAPY_DATA/fermi_2fhl/2fhl_events.fits.gz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time the actual data is stored as an [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html#astropy.table.Table) object. It can be accessed with `.table` attribute: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=60978</i>\n",
       "<table id=\"table120663740768\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>ENERGY</th><th>RA</th><th>DEC</th><th>L</th><th>B</th><th>THETA</th><th>PHI</th><th>ZENITH_ANGLE</th><th>EARTH_AZIMUTH_ANGLE</th><th>TIME</th><th>EVENT_ID</th><th>RUN_ID</th><th>RECON_VERSION</th><th>CALIB_VERSION [3]</th><th>EVENT_CLASS [32]</th><th>EVENT_TYPE [32]</th><th>CONVERSION_TYPE</th><th>LIVETIME</th><th>DIFRSP0</th><th>DIFRSP1</th><th>DIFRSP2</th><th>DIFRSP3</th><th>DIFRSP4</th></tr></thead>\n",
       "<thead><tr><th>MeV</th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th>s</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>s</th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float64</th><th>int32</th><th>int32</th><th>int16</th><th>int16</th><th>bool</th><th>bool</th><th>int16</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
       "<tr><td>145927.23</td><td>291.6619</td><td>42.234055</td><td>74.54367</td><td>11.86778</td><td>38.045475</td><td>83.53583</td><td>55.638725</td><td>314.03378</td><td>239561457.86561257</td><td>4851437</td><td>239559565</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>275.6980889737606</td><td>2.6965721e-11</td><td>3238.2458</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>221273.36</td><td>46.985752</td><td>-40.638943</td><td>247.48888</td><td>-58.873917</td><td>34.10511</td><td>224.20937</td><td>68.2524</td><td>198.31926</td><td>239562739.08500722</td><td>7521432</td><td>239559565</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>64.79749315977097</td><td>1.5357299e-12</td><td>2774.1985</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>57709.242</td><td>121.84143</td><td>49.22879</td><td>169.86801</td><td>32.301655</td><td>71.56363</td><td>34.292484</td><td>36.717308</td><td>25.543858</td><td>239563180.30194944</td><td>8690693</td><td>239559565</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>30.57218647003174</td><td>8.110957e-12</td><td>253.2212</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>221223.75</td><td>83.56263</td><td>-4.217438</td><td>207.7828</td><td>-19.077145</td><td>20.50894</td><td>92.16046</td><td>32.303345</td><td>239.14058</td><td>239563382.21257913</td><td>9208424</td><td>239559565</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>27.412509590387344</td><td>1.6607507e-11</td><td>2980.1243</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>698996.9</td><td>320.89468</td><td>-1.327888</td><td>51.221825</td><td>-33.97183</td><td>35.36213</td><td>158.74147</td><td>12.086709</td><td>72.20295</td><td>239566572.9507265</td><td>2480483</td><td>239565645</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>106.47548112273216</td><td>2.2654334e-13</td><td>2706.1436</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>119158.54</td><td>318.81128</td><td>12.30284</td><td>62.63608</td><td>-24.41602</td><td>26.589584</td><td>213.89417</td><td>17.7156</td><td>23.940882</td><td>239572348.06027594</td><td>1725276</td><td>239571670</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>185.3464272916317</td><td>2.7567707e-11</td><td>3439.2861</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>56175.598</td><td>279.25122</td><td>47.883488</td><td>76.6915</td><td>22.07387</td><td>29.103403</td><td>61.004787</td><td>62.173084</td><td>321.10422</td><td>239572763.4307742</td><td>76017</td><td>239572736</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>24.450733959674835</td><td>2.0826456e-10</td><td>4071.2454</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1418123.2</td><td>100.31091</td><td>-47.44805</td><td>256.468</td><td>-21.264088</td><td>61.225597</td><td>294.17953</td><td>90.475266</td><td>144.14915</td><td>239573788.81279647</td><td>1781569</td><td>239572736</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>68.27161464095116</td><td>3.200215e-14</td><td>1086.2307</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>62164.945</td><td>331.49234</td><td>-41.22639</td><td>359.41953</td><td>-53.404858</td><td>28.140808</td><td>229.92653</td><td>52.014217</td><td>189.05397</td><td>239578601.16829255</td><td>2000700</td><td>239577663</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>90.33226266503334</td><td>1.2147898e-10</td><td>3566.0723</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>51296.766</td><td>79.031075</td><td>78.93273</td><td>133.86821</td><td>22.239853</td><td>51.17257</td><td>267.81366</td><td>99.76019</td><td>357.79028</td><td>444418973.82216597</td><td>1284853</td><td>444418590</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>181.68898350000381</td><td>2.1412723e-10</td><td>2586.8826</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>60315.69</td><td>243.6808</td><td>-50.56688</td><td>332.42966</td><td>0.2826802</td><td>24.8501</td><td>255.68343</td><td>74.880394</td><td>195.14026</td><td>444421777.7614562</td><td>6582949</td><td>444418590</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. False</td><td>1</td><td>57.33589029312134</td><td>2.3585365e-08</td><td>3541.045</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>90000.086</td><td>282.69763</td><td>-33.19874</td><td>2.6621876</td><td>-14.333867</td><td>8.491435</td><td>343.55902</td><td>47.39651</td><td>191.0325</td><td>444422182.73806727</td><td>7507466</td><td>444418590</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>187.60246914625168</td><td>3.6212314e-10</td><td>3792.568</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>61988.746</td><td>247.97972</td><td>-48.109093</td><td>336.146</td><td>0.022033876</td><td>33.649937</td><td>144.27061</td><td>80.396614</td><td>221.39618</td><td>444422758.1452106</td><td>8699147</td><td>444418590</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>39.20983272790909</td><td>2.2642107e-08</td><td>3395.0696</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>54282.88</td><td>159.92404</td><td>-58.362797</td><td>286.36215</td><td>0.20555383</td><td>24.119644</td><td>287.13782</td><td>65.084694</td><td>177.52434</td><td>444425794.79357773</td><td>2522237</td><td>444424619</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>196.2096688747406</td><td>4.825799e-09</td><td>3672.7822</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>146728.25</td><td>244.84776</td><td>-46.58761</td><td>335.75424</td><td>2.6032612</td><td>45.25394</td><td>5.3364415</td><td>91.87099</td><td>137.03206</td><td>444425911.81856924</td><td>2705210</td><td>444424619</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. False</td><td>1</td><td>65.6327857375145</td><td>4.4594994e-10</td><td>2899.333</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>135432.88</td><td>83.52781</td><td>27.905334</td><td>179.52916</td><td>-2.6910558</td><td>17.491108</td><td>52.085804</td><td>53.306004</td><td>313.8216</td><td>444430968.96848625</td><td>939188</td><td>444430599</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. False</td><td>1</td><td>97.64468312263489</td><td>1.5798855e-10</td><td>3515.5964</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>61592.113</td><td>231.21422</td><td>-5.4552135</td><td>357.4355</td><td>40.647316</td><td>46.63564</td><td>141.04663</td><td>62.758415</td><td>256.6312</td><td>444433607.90634906</td><td>5443944</td><td>444430599</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. False</td><td>1</td><td>28.729065775871277</td><td>2.8202804e-10</td><td>2793.452</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>80480.79</td><td>228.24373</td><td>-45.04397</td><td>327.56085</td><td>10.952821</td><td>37.314854</td><td>193.71402</td><td>83.3882</td><td>221.57036</td><td>444433702.0691026</td><td>5612443</td><td>444430599</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. False</td><td>1</td><td>122.89181929826736</td><td>2.2609459e-10</td><td>3176.4265</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>124449.16</td><td>238.00813</td><td>-51.03712</td><td>329.42947</td><td>2.3006005</td><td>32.452194</td><td>199.50433</td><td>80.97682</td><td>214.4802</td><td>444433764.4327705</td><td>5723361</td><td>444430599</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>185.25548720359802</td><td>8.549079e-10</td><td>3366.3015</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=60978>\n",
       "  ENERGY      RA       DEC         L     ...  DIFRSP1  DIFRSP2 DIFRSP3 DIFRSP4\n",
       "   MeV       deg       deg        deg    ...                                  \n",
       " float32   float32   float32    float32  ...  float32  float32 float32 float32\n",
       "--------- --------- ---------- --------- ... --------- ------- ------- -------\n",
       "145927.23  291.6619  42.234055  74.54367 ... 3238.2458     0.0     0.0     0.0\n",
       "221273.36 46.985752 -40.638943 247.48888 ... 2774.1985     0.0     0.0     0.0\n",
       "57709.242 121.84143   49.22879 169.86801 ...  253.2212     0.0     0.0     0.0\n",
       "221223.75  83.56263  -4.217438  207.7828 ... 2980.1243     0.0     0.0     0.0\n",
       " 698996.9 320.89468  -1.327888 51.221825 ... 2706.1436     0.0     0.0     0.0\n",
       "119158.54 318.81128   12.30284  62.63608 ... 3439.2861     0.0     0.0     0.0\n",
       "56175.598 279.25122  47.883488   76.6915 ... 4071.2454     0.0     0.0     0.0\n",
       "1418123.2 100.31091  -47.44805   256.468 ... 1086.2307     0.0     0.0     0.0\n",
       "62164.945 331.49234  -41.22639 359.41953 ... 3566.0723     0.0     0.0     0.0\n",
       "      ...       ...        ...       ... ...       ...     ...     ...     ...\n",
       "51296.766 79.031075   78.93273 133.86821 ... 2586.8826     0.0     0.0     0.0\n",
       " 60315.69  243.6808  -50.56688 332.42966 ...  3541.045     0.0     0.0     0.0\n",
       "90000.086 282.69763  -33.19874 2.6621876 ...  3792.568     0.0     0.0     0.0\n",
       "61988.746 247.97972 -48.109093   336.146 ... 3395.0696     0.0     0.0     0.0\n",
       " 54282.88 159.92404 -58.362797 286.36215 ... 3672.7822     0.0     0.0     0.0\n",
       "146728.25 244.84776  -46.58761 335.75424 ...  2899.333     0.0     0.0     0.0\n",
       "135432.88  83.52781  27.905334 179.52916 ... 3515.5964     0.0     0.0     0.0\n",
       "61592.113 231.21422 -5.4552135  357.4355 ...  2793.452     0.0     0.0     0.0\n",
       " 80480.79 228.24373  -45.04397 327.56085 ... 3176.4265     0.0     0.0     0.0\n",
       "124449.16 238.00813  -51.03712 329.42947 ... 3366.3015     0.0     0.0     0.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_2fhl.table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can do *len* over event_2fhl.table to find the total number of events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of events: 60978\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of events: {}\".format(len(events_2fhl.table)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can access any other attribute of the `Table` object as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ENERGY',\n",
       " 'RA',\n",
       " 'DEC',\n",
       " 'L',\n",
       " 'B',\n",
       " 'THETA',\n",
       " 'PHI',\n",
       " 'ZENITH_ANGLE',\n",
       " 'EARTH_AZIMUTH_ANGLE',\n",
       " 'TIME',\n",
       " 'EVENT_ID',\n",
       " 'RUN_ID',\n",
       " 'RECON_VERSION',\n",
       " 'CALIB_VERSION',\n",
       " 'EVENT_CLASS',\n",
       " 'EVENT_TYPE',\n",
       " 'CONVERSION_TYPE',\n",
       " 'LIVETIME',\n",
       " 'DIFRSP0',\n",
       " 'DIFRSP1',\n",
       " 'DIFRSP2',\n",
       " 'DIFRSP3',\n",
       " 'DIFRSP4']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_2fhl.table.colnames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience we can access the most important event parameters as properties on the `EventList` objects. The attributes will return corresponding Astropy objects to represent the data, such as [astropy.units.Quantity](http://docs.astropy.org/en/stable/api/astropy.units.Quantity.html#astropy.units.Quantity), [astropy.coordinates.SkyCoord](http://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html) or [astropy.time.Time](http://docs.astropy.org/en/stable/api/astropy.time.Time.html#astropy.time.Time) objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$[145.92725,~221.27338,~57.709244,~\\dots,~61.592117,~80.480789,~124.44917] \\; \\mathrm{GeV}$$"
      ],
      "text/plain": [
       "<Quantity [145.92725 , 221.27338 ,  57.709244, ...,  61.592117,  80.48079 ,\n",
       "           124.449165] GeV>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_2fhl.energy.to(\"GeV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (Galactic): (l, b) in deg\n",
       "    [( 74.54326949,  11.86794629), (247.48874019, -58.87409431),\n",
       "     (169.86765712,  32.30144389), ..., (357.43496217,  40.64753602),\n",
       "     (327.56037412,  10.95297324), (329.42901812,   2.30075607)]>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_2fhl.galactic\n",
    "# events_2fhl.radec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Time object: scale='tt' format='mjd' value=[54682.7028015  54682.71763043 54682.72273711 ... 57053.90824179\n",
       " 57053.90933163 57053.91005343]>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_2fhl.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition `EventList` provides convenience methods to filter the event lists. One possible use case is to find the highest energy event within a radius of 0.5 deg around the vela position:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$342.76625 \\; \\mathrm{GeV}$$"
      ],
      "text/plain": [
       "<Quantity 342.76625 GeV>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select all events within a radius of 0.5 deg around center\n",
    "events_vela_2fhl = events_2fhl.select_sky_cone(\n",
    "    center=center, radius=0.5 * u.deg\n",
    ")\n",
    "\n",
    "# sort events by energy\n",
    "events_vela_2fhl.table.sort(\"ENERGY\")\n",
    "\n",
    "# and show highest energy photon\n",
    "events_vela_2fhl.energy[-1].to(\"GeV\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "* Make a counts energy spectrum for the galactic center region, within a radius of 10 deg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Source catalogs\n",
    "\n",
    "Gammapy provides a convenient interface to access and work with catalog based data. \n",
    "\n",
    "In this section we will learn how to:\n",
    "\n",
    "* Load builtins catalogs from [gammapy.catalog](..\/catalog/index.rst)\n",
    "* Sort and index the underlying Astropy tables\n",
    "* Access data from individual sources\n",
    "\n",
    "Let's start with importing the 2FHL catalog object from the [gammapy.catalog](..\/catalog/index.rst) submodule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapy.catalog import SourceCatalog2FHL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we initialize the Fermi-LAT 2FHL catalog and directly take a look at the `.table` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=360</i>\n",
       "<table id=\"table120705767464\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Source_Name</th><th>RAJ2000</th><th>DEJ2000</th><th>GLON</th><th>GLAT</th><th>Pos_err_68</th><th>TS</th><th>Spectral_Index</th><th>Unc_Spectral_Index</th><th>Intr_Spectral_Index_D11</th><th>Unc_Intr_Spectral_Index_D11</th><th>Intr_Spectral_Index_G12</th><th>Unc_Intr_Spectral_Index_G12</th><th>Flux50</th><th>Unc_Flux50</th><th>Energy_Flux50</th><th>Unc_Energy_Flux50</th><th>Flux50_171GeV</th><th>Unc_Flux50_171GeV [2]</th><th>Sqrt_TS50_171GeV</th><th>Flux171_585GeV</th><th>Unc_Flux171_585GeV [2]</th><th>Sqrt_TS171_585GeV</th><th>Flux585_2000GeV</th><th>Unc_Flux585_2000GeV [2]</th><th>Sqrt_TS585_2000GeV</th><th>Npred</th><th>HEP_Energy</th><th>HEP_Prob</th><th>ROI</th><th>ASSOC</th><th>ASSOC_PROB_BAY</th><th>ASSOC_PROB_LR</th><th>CLASS</th><th>Redshift</th><th>NuPeak_obs</th><th>3FGL_Name</th><th>1FHL_Name</th><th>TeVCat_Name</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>1 / (cm2 s)</th><th>1 / (cm2 s)</th><th>erg / (cm2 s)</th><th>erg / (cm2 s)</th><th>1 / (cm2 s)</th><th>1 / (cm2 s)</th><th></th><th>1 / (cm2 s)</th><th>1 / (cm2 s)</th><th></th><th>1 / (cm2 s)</th><th>1 / (cm2 s)</th><th></th><th></th><th>GeV</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>Hz</th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>bytes18</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>bytes25</th><th>float32</th><th>float32</th><th>bytes8</th><th>float32</th><th>float32</th><th>bytes18</th><th>bytes18</th><th>bytes18</th></tr></thead>\n",
       "<tr><td>2FHL J0008.1+4709</td><td>2.0437</td><td>47.1642</td><td>115.339355</td><td>-15.068757</td><td>0.06111402</td><td>28.64</td><td>6.24</td><td>2.75</td><td>3.96</td><td>3.19</td><td>2.16</td><td>4.21</td><td>1.23e-11</td><td>6.71e-12</td><td>1.21e-12</td><td>6.71e-13</td><td>3.3634447e-12</td><td>-1.5366806e-12 .. 2.1609453e-12</td><td>5.353536</td><td>4.3494674e-18</td><td>nan .. 4.7538935e-12</td><td>0.0</td><td>8.448714e-18</td><td>nan .. 7.291014e-12</td><td>0.0</td><td>4.0</td><td>68.15</td><td>0.99</td><td>1</td><td>MG4 J000800+4712</td><td>0.99721974</td><td>0.8348271</td><td>bll</td><td>2.1</td><td>2511884200000000.0</td><td>3FGL J0008.0+4713</td><td>1FHL J0007.7+4709</td><td></td></tr>\n",
       "<tr><td>2FHL J0009.3+5031</td><td>2.3435</td><td>50.5217</td><td>116.12411</td><td>-11.793202</td><td>0.045439195</td><td>53.97</td><td>5.08</td><td>1.66</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.91e-11</td><td>7.82e-12</td><td>2.03e-12</td><td>8.79e-13</td><td>8.362822e-12</td><td>-2.9896227e-12 .. 3.895122e-12</td><td>7.351317</td><td>2.9145808e-17</td><td>nan .. 5.1000337e-12</td><td>0.0</td><td>3.5087458e-16</td><td>nan .. 4.874578e-12</td><td>0.0</td><td>6.4</td><td>72.76</td><td>1.0</td><td>1</td><td>NVSS J000922+503028</td><td>0.99972373</td><td>0.7348077</td><td>bll</td><td>0.0</td><td>1412536400000000.0</td><td>3FGL J0009.3+5030</td><td>1FHL J0009.2+5032</td><td></td></tr>\n",
       "<tr><td>2FHL J0018.5+2947</td><td>4.6355</td><td>29.7879</td><td>114.46349</td><td>-32.54235</td><td>0.037093636</td><td>30.89</td><td>2.58</td><td>0.99</td><td>2.41</td><td>1.04</td><td>2.4</td><td>1.04</td><td>1.06e-11</td><td>6.15e-12</td><td>2.05e-12</td><td>1.72e-12</td><td>9.65438e-12</td><td>-5.5534162e-12 .. 5.5534162e-12</td><td>5.765786</td><td>1.1790023e-15</td><td>nan .. 5.378652e-12</td><td>0.0</td><td>1.6046534e-16</td><td>nan .. 6.120081e-12</td><td>0.0</td><td>3.0</td><td>127.32</td><td>1.0</td><td>3</td><td>RBS 0042</td><td>0.9998682</td><td>0.97852194</td><td>bll</td><td>0.1</td><td>5.9156075e+16</td><td>3FGL J0018.4+2947</td><td>1FHL J0018.6+2946</td><td></td></tr>\n",
       "<tr><td>2FHL J0022.0+0006</td><td>5.5001</td><td>0.1059</td><td>107.171715</td><td>-61.86175</td><td>0.05118522</td><td>29.96</td><td>1.86</td><td>0.57</td><td>0.95</td><td>0.72</td><td>0.88</td><td>0.71</td><td>1.97e-11</td><td>9.56e-12</td><td>6.86e-12</td><td>5.29e-12</td><td>1.6119727e-11</td><td>-7.1725507e-12 .. 9.9534886e-12</td><td>5.3821964</td><td>3.6392152e-12</td><td>-4.3863624e-12 .. 4.3863624e-12</td><td>1.3853942</td><td>8.423558e-16</td><td>nan .. 7.342399e-12</td><td>0.0</td><td>4.8</td><td>180.13</td><td>0.86</td><td>2</td><td>5BZGJ0022+0006</td><td>0.99928</td><td>0.90008944</td><td>bll-g</td><td>0.306</td><td>4.315193e+16</td><td></td><td></td><td></td></tr>\n",
       "<tr><td>2FHL J0033.6-1921</td><td>8.4115</td><td>-19.3575</td><td>94.28002</td><td>-81.22237</td><td>0.034838412</td><td>148.31</td><td>3.32</td><td>0.69</td><td>2.56</td><td>0.88</td><td>2.33</td><td>0.92</td><td>5.46e-11</td><td>1.5e-11</td><td>7.62e-12</td><td>2.69e-12</td><td>4.0016098e-11</td><td>-1.0161534e-11 .. 1.223782e-11</td><td>12.172502</td><td>2.1390131e-12</td><td>nan .. 8.258118e-12</td><td>0.9079581</td><td>2.439548e-17</td><td>nan .. 6.8422585e-12</td><td>0.0</td><td>13.8</td><td>170.01</td><td>0.99</td><td>2</td><td>KUV 00311-1938</td><td>0.999759</td><td>0.9814242</td><td>bll</td><td>0.61</td><td>8317639000000000.0</td><td>3FGL J0033.6-1921</td><td>1FHL J0033.6-1921</td><td>TeV J0033-1921</td></tr>\n",
       "<tr><td>2FHL J0035.8+5949</td><td>8.9625</td><td>59.8312</td><td>120.97197</td><td>-2.9812155</td><td>0.031602595</td><td>402.4</td><td>2.23</td><td>0.21</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.25e-10</td><td>1.9e-11</td><td>3.11e-11</td><td>7.23e-12</td><td>9.4229395e-11</td><td>-1.5256577e-11 .. 1.7157154e-11</td><td>17.312449</td><td>2.6699127e-11</td><td>-7.628179e-12 .. 9.450845e-12</td><td>10.390878</td><td>3.7260905e-16</td><td>nan .. 4.6744392e-12</td><td>0.0</td><td>46.5</td><td>247.62</td><td>0.96</td><td>4</td><td>1ES 0033+595</td><td>0.99995834</td><td>0.9928678</td><td>bll</td><td>0.0</td><td>1.3182593e+17</td><td>3FGL J0035.9+5949</td><td>1FHL J0035.9+5950</td><td>TeV J0035+5950</td></tr>\n",
       "<tr><td>2FHL J0040.3+4049</td><td>10.0949</td><td>40.8315</td><td>120.676285</td><td>-21.991812</td><td>0.035514843</td><td>26.76</td><td>2.12</td><td>0.81</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.05e-11</td><td>6.3e-12</td><td>2.84e-12</td><td>2.67e-12</td><td>7.415768e-12</td><td>-4.077804e-12 .. 6.4518156e-12</td><td>4.954423</td><td>3.0030787e-12</td><td>-2.317754e-12 .. 4.466438e-12</td><td>1.7083547</td><td>4.7316626e-16</td><td>nan .. 5.7980946e-12</td><td>0.0</td><td>3.2</td><td>258.77</td><td>0.85</td><td>3</td><td>B3 0037+405</td><td>0.9987379</td><td>0.9346999</td><td>bcu I</td><td>0.0</td><td>1.0</td><td>3FGL J0040.3+4049</td><td>1FHL J0040.3+4049</td><td></td></tr>\n",
       "<tr><td>2FHL J0043.9+3424</td><td>10.9755</td><td>34.4109</td><td>121.16442</td><td>-28.434984</td><td>0.059241667</td><td>39.5</td><td>4.57</td><td>1.61</td><td>3.46</td><td>1.85</td><td>2.95</td><td>1.91</td><td>1.83e-11</td><td>8.24e-12</td><td>2.03e-12</td><td>9.93e-13</td><td>9.657617e-12</td><td>-4.316866e-12 .. 4.316866e-12</td><td>6.3115244</td><td>8.758609e-17</td><td>nan .. 5.2433453e-12</td><td>0.0</td><td>9.438122e-16</td><td>nan .. 5.687869e-12</td><td>0.0</td><td>5.4</td><td>109.97</td><td>0.9</td><td>3</td><td>GB6 J0043+3426</td><td>0.99859357</td><td>0.8388443</td><td>fsrq</td><td>0.966</td><td>64565484000000.0</td><td>3FGL J0043.8+3425</td><td>1FHL J0043.7+3425</td><td></td></tr>\n",
       "<tr><td>2FHL J0045.2+2127</td><td>11.3161</td><td>21.4555</td><td>121.01704</td><td>-41.393295</td><td>0.03780455</td><td>110.43</td><td>3.07</td><td>0.64</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>4.14e-11</td><td>1.26e-11</td><td>6.29e-12</td><td>2.45e-12</td><td>3.1153188e-11</td><td>-8.930081e-12 .. 1.101035e-11</td><td>10.240447</td><td>2.9971193e-12</td><td>-3.030543e-12 .. 3.030543e-12</td><td>2.708902</td><td>4.683478e-17</td><td>nan .. 6.4231385e-12</td><td>0.0</td><td>11.4</td><td>246.75</td><td>0.99</td><td>5</td><td>GB6 J0045+2127</td><td>0.9996096</td><td>0.9651269</td><td>bll</td><td>0.0</td><td>1.002304e+16</td><td>3FGL J0045.3+2126</td><td>1FHL J0045.2+2126</td><td></td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>2FHL J2322.5+3436</td><td>350.628</td><td>34.613</td><td>102.87579</td><td>-24.771763</td><td>0.071799815</td><td>28.77</td><td>2.33</td><td>0.8</td><td>2.13</td><td>0.85</td><td>2.11</td><td>0.85</td><td>1.42e-11</td><td>7.46e-12</td><td>3.24e-12</td><td>2.48e-12</td><td>1.3706217e-11</td><td>-5.990534e-12 .. 8.273254e-12</td><td>5.511089</td><td>1.5044521e-15</td><td>nan .. 5.2556817e-12</td><td>0.0</td><td>3.3439588e-16</td><td>nan .. 6.3523587e-12</td><td>0.0</td><td>4.1</td><td>166.2</td><td>0.99</td><td>133</td><td>TXS 2320+343</td><td>0.9966389</td><td>0.87790126</td><td>bll-g</td><td>0.098</td><td>1047129700000000.0</td><td>3FGL J2322.5+3436</td><td>1FHL J2322.5+3436</td><td></td></tr>\n",
       "<tr><td>2FHL J2322.7-4916</td><td>350.698</td><td>-49.2706</td><td>334.60815</td><td>-62.049187</td><td>0.038949013</td><td>36.84</td><td>4.58</td><td>1.69</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.75e-11</td><td>8.3e-12</td><td>1.94e-12</td><td>9.96e-13</td><td>9.1300864e-12</td><td>-3.7335608e-12 .. 5.0173784e-12</td><td>6.0522294</td><td>6.65757e-17</td><td>nan .. 5.2713697e-12</td><td>0.0</td><td>6.347128e-16</td><td>nan .. 5.772431e-12</td><td>0.0</td><td>5.1</td><td>106.33</td><td>0.9</td><td>138</td><td>SUMSS J232254-491629</td><td>0.99943393</td><td>0.9377265</td><td>bll</td><td>0.0</td><td>4466832000000000.0</td><td>3FGL J2322.9-4917</td><td></td><td></td></tr>\n",
       "<tr><td>2FHL J2323.4+5848</td><td>350.874</td><td>58.808</td><td>111.74413</td><td>-2.1403134</td><td>0.034838412</td><td>183.62</td><td>2.45</td><td>0.3</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>7.81e-11</td><td>1.53e-11</td><td>1.64e-11</td><td>4.68e-12</td><td>5.936891e-11</td><td>-1.2110057e-11 .. 1.38986435e-11</td><td>11.623798</td><td>1.0376127e-11</td><td>-4.3784516e-12 .. 6.143983e-12</td><td>5.8143535</td><td>2.9030862e-12</td><td>-2.0241562e-12 .. 3.9820235e-12</td><td>3.508337</td><td>28.8</td><td>662.12</td><td>1.0</td><td>135</td><td>Cas A</td><td>nan</td><td>0.9998325</td><td>snr</td><td>nan</td><td>nan</td><td>3FGL J2323.4+5849</td><td>1FHL J2323.3+5849</td><td>TeV J2323+588</td></tr>\n",
       "<tr><td>2FHL J2323.8+4209</td><td>350.972</td><td>42.1629</td><td>106.05886</td><td>-17.822372</td><td>0.045601647</td><td>70.61</td><td>4.21</td><td>1.16</td><td>4.14</td><td>1.15</td><td>4.13</td><td>1.15</td><td>3.05e-11</td><td>1.05e-11</td><td>3.55e-12</td><td>1.35e-12</td><td>1.620174e-11</td><td>-5.247084e-12 .. 6.6191224e-12</td><td>8.119378</td><td>2.0001782e-12</td><td>-1.4127128e-12 .. 2.779269e-12</td><td>2.5684886</td><td>2.1890578e-18</td><td>nan .. 5.540821e-12</td><td>0.0</td><td>9.3</td><td>230.9</td><td>0.92</td><td>133</td><td>1ES 2321+419</td><td>0.99956</td><td>0.9615062</td><td>bll</td><td>0.059</td><td>4841728400000000.0</td><td>3FGL J2323.9+4211</td><td>1FHL J2323.8+4210</td><td></td></tr>\n",
       "<tr><td>2FHL J2324.7-4041</td><td>351.195</td><td>-40.6934</td><td>350.1568</td><td>-67.58365</td><td>0.03586584</td><td>80.93</td><td>3.18</td><td>0.83</td><td>2.94</td><td>0.87</td><td>2.9</td><td>0.87</td><td>2.97e-11</td><td>1.08e-11</td><td>4.34e-12</td><td>2.03e-12</td><td>2.1339108e-11</td><td>-7.099395e-12 .. 9.104349e-12</td><td>8.853827</td><td>2.7183436e-12</td><td>-2.1171383e-12 .. 4.116685e-12</td><td>1.6579158</td><td>3.547171e-17</td><td>nan .. 6.3514e-12</td><td>0.0</td><td>8.0</td><td>118.48</td><td>1.0</td><td>138</td><td>1ES 2322-409</td><td>0.99946654</td><td>0.9855157</td><td>bll</td><td>0.173587</td><td>8317639000000000.0</td><td>3FGL J2324.7-4040</td><td>1FHL J2324.6-4041</td><td></td></tr>\n",
       "<tr><td>2FHL J2329.2+3754</td><td>352.308</td><td>37.9157</td><td>105.53183</td><td>-22.16268</td><td>0.038538978</td><td>39.4</td><td>3.29</td><td>1.19</td><td>2.99</td><td>1.28</td><td>2.92</td><td>1.28</td><td>1.43e-11</td><td>7.17e-12</td><td>2.02e-12</td><td>1.25e-12</td><td>1.1130363e-11</td><td>-4.6961983e-12 .. 6.5287966e-12</td><td>6.3114944</td><td>4.4041454e-16</td><td>nan .. 5.477368e-12</td><td>0.0</td><td>2.6406502e-17</td><td>nan .. 5.9254654e-12</td><td>0.0</td><td>4.3</td><td>142.13</td><td>0.98</td><td>133</td><td>NVSS J232914+375414</td><td>0.99979174</td><td>0.95636314</td><td>bll</td><td>0.264</td><td>8912505400000000.0</td><td>3FGL J2329.2+3754</td><td>1FHL J2329.1+3754</td><td></td></tr>\n",
       "<tr><td>2FHL J2340.8+8014</td><td>355.203</td><td>80.2447</td><td>119.83777</td><td>17.79993</td><td>0.034893442</td><td>69.79</td><td>4.2</td><td>1.28</td><td>4.0</td><td>1.36</td><td>3.94</td><td>1.37</td><td>1.95e-11</td><td>7.24e-12</td><td>2.26e-12</td><td>9.72e-13</td><td>1.1639776e-11</td><td>-3.811828e-12 .. 4.8279145e-12</td><td>8.387453</td><td>1.2570346e-16</td><td>nan .. 3.7181217e-12</td><td>0.0</td><td>1.1362121e-15</td><td>nan .. 4.047232e-12</td><td>0.0</td><td>7.9</td><td>80.85</td><td>0.86</td><td>125</td><td>1RXS J234051.4+801513</td><td>0.9998745</td><td>0.97943515</td><td>bll</td><td>0.274</td><td>3427676800000000.0</td><td>3FGL J2340.7+8016</td><td>1FHL J2341.3+8016</td><td></td></tr>\n",
       "<tr><td>2FHL J2343.5+3438</td><td>355.898</td><td>34.6484</td><td>107.42183</td><td>-26.171658</td><td>0.071505584</td><td>27.43</td><td>4.99</td><td>2.36</td><td>4.88</td><td>2.43</td><td>4.79</td><td>2.44</td><td>1.33e-11</td><td>7.32e-12</td><td>1.41e-12</td><td>8.56e-13</td><td>5.9425394e-12</td><td>-2.732224e-12 .. 3.8524665e-12</td><td>5.234698</td><td>5.086273e-17</td><td>nan .. 8.363173e-12</td><td>0.0</td><td>3.1470887e-16</td><td>nan .. 5.6851962e-12</td><td>0.0</td><td>3.9</td><td>72.66</td><td>1.0</td><td>133</td><td>1RXS J234332.5+343957</td><td>0.99914765</td><td>0.9554292</td><td>bll</td><td>0.366</td><td>1029200060000000.0</td><td>3FGL J2343.7+3437</td><td></td><td></td></tr>\n",
       "<tr><td>2FHL J2347.1+5142</td><td>356.75</td><td>51.7081</td><td>112.879944</td><td>-9.90159</td><td>0.03354639</td><td>209.84</td><td>1.85</td><td>0.25</td><td>1.74</td><td>0.26</td><td>1.73</td><td>0.26</td><td>7.48e-11</td><td>1.54e-11</td><td>2.62e-11</td><td>8.56e-12</td><td>4.788841e-11</td><td>-1.1559573e-11 .. 1.3389954e-11</td><td>11.282044</td><td>2.4289425e-11</td><td>-7.681043e-12 .. 9.877774e-12</td><td>8.8576975</td><td>3.5138778e-12</td><td>-3.5528204e-12 .. 3.5528204e-12</td><td>2.6657157</td><td>25.2</td><td>652.31</td><td>0.99</td><td>1</td><td>1ES 2344+514</td><td>0.99986017</td><td>0.98808235</td><td>bll</td><td>0.044</td><td>7079464000000000.0</td><td>3FGL J2347.0+5142</td><td>1FHL J2347.0+5142</td><td>TeV J2346+5142</td></tr>\n",
       "<tr><td>2FHL J2352.0-7558</td><td>358.019</td><td>-75.9718</td><td>307.623</td><td>-40.611088</td><td>0.0733733</td><td>28.49</td><td>3.37</td><td>1.24</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.18e-11</td><td>6.14e-12</td><td>1.63e-12</td><td>1.03e-12</td><td>9.00308e-12</td><td>-3.9132994e-12 .. 5.477288e-12</td><td>5.389353</td><td>3.9078217e-16</td><td>nan .. 4.95279e-12</td><td>0.0</td><td>3.2339844e-17</td><td>nan .. 5.167223e-12</td><td>0.0</td><td>3.9</td><td>134.72</td><td>1.0</td><td>137</td><td></td><td>nan</td><td>nan</td><td>unk</td><td>nan</td><td>nan</td><td>3FGL J2351.9-7601</td><td></td><td></td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=360>\n",
       "   Source_Name    RAJ2000 DEJ2000  ...     1FHL_Name      TeVCat_Name  \n",
       "                    deg     deg    ...                                 \n",
       "     bytes18      float32 float32  ...      bytes18         bytes18    \n",
       "----------------- ------- -------- ... ----------------- --------------\n",
       "2FHL J0008.1+4709  2.0437  47.1642 ... 1FHL J0007.7+4709               \n",
       "2FHL J0009.3+5031  2.3435  50.5217 ... 1FHL J0009.2+5032               \n",
       "2FHL J0018.5+2947  4.6355  29.7879 ... 1FHL J0018.6+2946               \n",
       "2FHL J0022.0+0006  5.5001   0.1059 ...                                 \n",
       "2FHL J0033.6-1921  8.4115 -19.3575 ... 1FHL J0033.6-1921 TeV J0033-1921\n",
       "2FHL J0035.8+5949  8.9625  59.8312 ... 1FHL J0035.9+5950 TeV J0035+5950\n",
       "2FHL J0040.3+4049 10.0949  40.8315 ... 1FHL J0040.3+4049               \n",
       "2FHL J0043.9+3424 10.9755  34.4109 ... 1FHL J0043.7+3425               \n",
       "2FHL J0045.2+2127 11.3161  21.4555 ... 1FHL J0045.2+2126               \n",
       "              ...     ...      ... ...               ...            ...\n",
       "2FHL J2322.5+3436 350.628   34.613 ... 1FHL J2322.5+3436               \n",
       "2FHL J2322.7-4916 350.698 -49.2706 ...                                 \n",
       "2FHL J2323.4+5848 350.874   58.808 ... 1FHL J2323.3+5849  TeV J2323+588\n",
       "2FHL J2323.8+4209 350.972  42.1629 ... 1FHL J2323.8+4210               \n",
       "2FHL J2324.7-4041 351.195 -40.6934 ... 1FHL J2324.6-4041               \n",
       "2FHL J2329.2+3754 352.308  37.9157 ... 1FHL J2329.1+3754               \n",
       "2FHL J2340.8+8014 355.203  80.2447 ... 1FHL J2341.3+8016               \n",
       "2FHL J2343.5+3438 355.898  34.6484 ...                                 \n",
       "2FHL J2347.1+5142  356.75  51.7081 ... 1FHL J2347.0+5142 TeV J2346+5142\n",
       "2FHL J2352.0-7558 358.019 -75.9718 ...                                 "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fermi_2fhl = SourceCatalog2FHL(\n",
    "    \"$GAMMAPY_DATA/catalogs/fermi/gll_psch_v08.fit.gz\"\n",
    ")\n",
    "fermi_2fhl.table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks very familiar again. The data is just stored as an [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html#astropy.table.Table) object. We have all the methods and attributes of the `Table` object available. E.g. we can sort the underlying table by `TS` to find the top 5 most significant sources:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=5</i>\n",
       "<table id=\"table120704849400\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Source_Name</th><th>ASSOC</th><th>CLASS</th></tr></thead>\n",
       "<thead><tr><th>bytes18</th><th>bytes25</th><th>bytes8</th></tr></thead>\n",
       "<tr><td>2FHL J1104.4+3812</td><td>Mkn 421</td><td>bll</td></tr>\n",
       "<tr><td>2FHL J0534.5+2201</td><td>Crab</td><td>pwn</td></tr>\n",
       "<tr><td>2FHL J1653.9+3945</td><td>Mkn 501</td><td>bll</td></tr>\n",
       "<tr><td>2FHL J1555.7+1111</td><td>PG 1553+113</td><td>bll</td></tr>\n",
       "<tr><td>2FHL J2158.8-3013</td><td>PKS 2155-304</td><td>bll</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=5>\n",
       "   Source_Name       ASSOC     CLASS \n",
       "     bytes18        bytes25    bytes8\n",
       "----------------- ------------ ------\n",
       "2FHL J1104.4+3812      Mkn 421    bll\n",
       "2FHL J0534.5+2201         Crab    pwn\n",
       "2FHL J1653.9+3945      Mkn 501    bll\n",
       "2FHL J1555.7+1111  PG 1553+113    bll\n",
       "2FHL J2158.8-3013 PKS 2155-304    bll"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort table by TS\n",
    "fermi_2fhl.table.sort(\"TS\")\n",
    "\n",
    "# invert the order to find the highest values and take the top 5\n",
    "top_five_TS_2fhl = fermi_2fhl.table[::-1][:5]\n",
    "\n",
    "# print the top five significant sources with association and source class\n",
    "top_five_TS_2fhl[[\"Source_Name\", \"ASSOC\", \"CLASS\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are interested in the data of an individual source you can access the information from catalog using the name of the source or any alias source name that is defined in the catalog:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5600.98\n"
     ]
    }
   ],
   "source": [
    "mkn_421_2fhl = fermi_2fhl[\"2FHL J1104.4+3812\"]\n",
    "\n",
    "# or use any alias source name that is defined in the catalog\n",
    "mkn_421_2fhl = fermi_2fhl[\"Mkn 421\"]\n",
    "print(mkn_421_2fhl.data[\"TS\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "* Try to load the Fermi-LAT 3FHL catalog and check the total number of sources it contains.\n",
    "* Select all the sources from the 2FHL catalog which are contained in the Vela region. Add markers for all these sources and try to add labels with the source names. The function [ax.text()](http://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text) might be helpful.\n",
    "* Try to find the source class of the object at position ra=68.6803, dec=9.3331\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spectral models and flux points\n",
    "\n",
    "In the previous section we learned how access basic data from individual sources in the catalog. Now we will go one step further and explore the full spectral information of sources. We will learn how to:\n",
    "\n",
    "* Plot spectral models\n",
    "* Compute integral and energy fluxes\n",
    "* Read and plot flux points\n",
    "\n",
    "As a first example we will start with the Crab Nebula:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PowerLaw2\n",
      "\n",
      "Parameters: \n",
      "\n",
      "\t   name     value     error     unit   min max frozen\n",
      "\t--------- --------- --------- -------- --- --- ------\n",
      "\tamplitude 1.310e-09 6.830e-11 cm-2 s-1 nan nan  False\n",
      "\t    index 2.130e+00 7.000e-02          nan nan  False\n",
      "\t     emin 5.000e-02 0.000e+00      TeV nan nan   True\n",
      "\t     emax 2.000e+00 0.000e+00      TeV nan nan   True\n",
      "\n",
      "Covariance: \n",
      "\n",
      "\t   name   amplitude   index      emin      emax  \n",
      "\t--------- --------- --------- --------- ---------\n",
      "\tamplitude 4.665e-21 0.000e+00 0.000e+00 0.000e+00\n",
      "\t    index 0.000e+00 4.900e-03 0.000e+00 0.000e+00\n",
      "\t     emin 0.000e+00 0.000e+00 0.000e+00 0.000e+00\n",
      "\t     emax 0.000e+00 0.000e+00 0.000e+00 0.000e+00\n"
     ]
    }
   ],
   "source": [
    "crab_2fhl = fermi_2fhl[\"Crab\"]\n",
    "print(crab_2fhl.spectral_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `crab_2fhl.spectral_model` is an instance of the [gammapy.spectrum.models.PowerLaw2](..\/api/gammapy.spectrum.models.PowerLaw2.rst#gammapy.spectrum.models.PowerLaw2) model, with the parameter values and errors taken from the 2FHL catalog. \n",
    "\n",
    "Let's plot the spectral model in the energy range between 50 GeV and 2000 GeV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "ax_crab_2fhl = crab_2fhl.spectral_model.plot(\n",
    "    energy_range=[50, 2000] * u.GeV, energy_power=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assign the return axes object to variable called `ax_crab_2fhl`, because we will re-use it later to plot the flux points on top.\n",
    "\n",
    "To compute the differential flux at 100 GeV we can simply call the model like normal Python function and convert to the desired units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$6.8700477 \\times 10^{-12} \\; \\mathrm{\\frac{1}{GeV\\,s\\,cm^{2}}}$$"
      ],
      "text/plain": [
       "<Quantity 6.87004773e-12 1 / (cm2 GeV s)>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_2fhl.spectral_model(100 * u.GeV).to(\"cm-2 s-1 GeV-1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can compute the integral flux of the Crab between 50 GeV and 2000 GeV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$1.31 \\times 10^{-9} \\; \\mathrm{\\frac{1}{s\\,cm^{2}}}$$"
      ],
      "text/plain": [
       "<Quantity 1.31e-09 1 / (cm2 s)>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_2fhl.spectral_model.integral(emin=50 * u.GeV, emax=2000 * u.GeV).to(\n",
    "    \"cm-2 s-1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily convince ourself, that it corresponds to the value given in the Fermi-LAT 2FHL catalog:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$1.31 \\times 10^{-9} \\; \\mathrm{\\frac{1}{s\\,cm^{2}}}$$"
      ],
      "text/plain": [
       "<Quantity 1.31e-09 1 / (cm2 s)>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_2fhl.data[\"Flux50\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition we can compute the energy flux between 50 GeV and 2000 GeV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$3.5295399 \\times 10^{-10} \\; \\mathrm{\\frac{erg}{s\\,cm^{2}}}$$"
      ],
      "text/plain": [
       "<Quantity 3.52953994e-10 erg / (cm2 s)>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_2fhl.spectral_model.energy_flux(emin=50 * u.GeV, emax=2000 * u.GeV).to(\n",
    "    \"erg cm-2 s-1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will access the flux points data of the Crab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FluxPoints(sed_type=\"flux\", n_points=3)\n"
     ]
    }
   ],
   "source": [
    "print(crab_2fhl.flux_points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to learn more about the different flux point formats you can read the specification [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/results/flux_points/index.html#flux-points).\n",
    "\n",
    "No we can check again the underlying astropy data structure by accessing the `.table` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3</i>\n",
       "<table id=\"table120656129272\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>e_min</th><th>e_max</th><th>flux</th><th>flux_errn</th><th>flux_errp</th><th>is_ul</th><th>flux_ul</th></tr></thead>\n",
       "<thead><tr><th>GeV</th><th>GeV</th><th>1 / (cm2 s)</th><th>1 / (cm2 s)</th><th>1 / (cm2 s)</th><th></th><th>1 / (cm2 s)</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float32</th><th>float32</th><th>bool</th><th>float64</th></tr></thead>\n",
       "<tr><td>50.0</td><td>171.0</td><td>9.94072046900385e-10</td><td>5.7595e-11</td><td>5.947264e-11</td><td>False</td><td>nan</td></tr>\n",
       "<tr><td>171.0</td><td>585.0</td><td>2.4387539210302123e-10</td><td>2.8254246e-11</td><td>3.0435404e-11</td><td>False</td><td>nan</td></tr>\n",
       "<tr><td>585.0</td><td>2000.0</td><td>5.275358622158777e-11</td><td>1.3340122e-11</td><td>1.6047759e-11</td><td>False</td><td>nan</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       " e_min   e_max           flux          ...   flux_errp   is_ul   flux_ul  \n",
       "  GeV     GeV        1 / (cm2 s)       ...  1 / (cm2 s)        1 / (cm2 s)\n",
       "float64 float64        float64         ...    float32     bool   float64  \n",
       "------- ------- ---------------------- ... ------------- ----- -----------\n",
       "   50.0   171.0   9.94072046900385e-10 ...  5.947264e-11 False         nan\n",
       "  171.0   585.0 2.4387539210302123e-10 ... 3.0435404e-11 False         nan\n",
       "  585.0  2000.0  5.275358622158777e-11 ... 1.6047759e-11 False         nan"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_2fhl.flux_points.table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally let's combine spectral model and flux points in a single plot and scale with `energy_power=2` to obtain the spectral energy distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "ax = crab_2fhl.spectral_model.plot(\n",
    "    energy_range=[50, 2000] * u.GeV, energy_power=2\n",
    ")\n",
    "crab_2fhl.flux_points.plot(ax=ax, energy_power=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "* Plot the spectral model and flux points for PKS 2155-304 for the 3FGL and 2FHL catalogs. Try to plot the error of the model (aka \"Butterfly\") as well. Note this requires the [uncertainties package](https://pythonhosted.org/uncertainties/) to be installed on your machine.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What next?\n",
    "\n",
    "This was a quick introduction to some of the high-level classes in Astropy and Gammapy.\n",
    "\n",
    "* To learn more about those classes, go to the API docs (links are in the introduction at the top).\n",
    "* To learn more about other parts of Gammapy (e.g. Fermi-LAT and TeV data analysis), check out the other tutorial notebooks.\n",
    "* To see what's available in Gammapy, browse the [Gammapy docs](https://docs.gammapy.org/) or use the full-text search.\n",
    "* If you have any questions, ask on the mailing list."
   ]
  }
 ],
 "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": "3.6.0"
  },
  "nbsphinx": {
   "orphan": true
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 },
 "nbformat": 4,
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}