{
 "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",
    " You can contribute with your own notebooks in this\n",
    " [GitHub repository](https://github.com/gammapy/gammapy-extra/tree/master/notebooks).\n",
    "\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](http://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 images**\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.image.SkyImage](http://docs.gammapy.org/dev/api/gammapy.image.SkyImage.html)\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](http://docs.gammapy.org/dev/api/gammapy.data.EventList.html)\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](http://docs.gammapy.org/dev/api/gammapy.catalog.SourceCatalog.html), specifically [gammapy.catalog.SourceCatalog2FHL](http://docs.gammapy.org/dev/api/gammapy.catalog.SourceCatalog2FHL.html)\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](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.SpectralModel.html), specifically the [PowerLaw2](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.PowerLaw2.html) model.\n",
    "    - [gammapy.spectrum.FluxPoints](http://docs.gammapy.org/dev/api/gammapy.spectrum.FluxPoints.html#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_EXTRA/datasets')\n",
    "\n",
    "if not os.path.exists(path):\n",
    "    raise Exception('gammapy-extra 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](http://docs.gammapy.org/dev/datasets/index.html#gammapy-extra) after you are done with this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#os.environ['GAMMAPY_EXTRA'] = 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": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "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": [
    "## Sky images\n",
    "\n",
    "The central data structure to work with image based data in Gammapy is the [SkyImage](http://docs.gammapy.org/dev/api/gammapy.image.SkyImage.html) class. It combines the raw data with world coordinate (WCS) information, FITS I/O functionality and convenience methods, that allow easy handling, processing and plotting of image based data. \n",
    "\n",
    "In this section we 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\n",
    "\n",
    "The `SkyImage` class is part of the [gammapy.image](http://docs.gammapy.org/dev/image/index.html) submodule. So we will start by importing it from there:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from gammapy.image import SkyImage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a first example, we will read a FITS file from a prepared Fermi-LAT 2FHL dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "vela_2fhl = SkyImage.read(\"$GAMMAPY_EXTRA/datasets/fermi_2fhl/fermi_2fhl_vela.fits.gz\", hdu='COUNTS')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the FITS file `fermi_2fhl_vela.fits.gz` contains mutiple image extensions and a `SkyImage` can only represent a single image, we explicitely specify to read the extension called 'Counts'. Let's print the image to get some basic information about it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Counts\n",
      "Data shape: (180, 320)\n",
      "Data type: >f8\n",
      "Data unit: ct\n",
      "Data mean: 2.720e-02\n",
      "WCS type: ['GLON-CAR', 'GLAT-CAR']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(vela_2fhl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape of the image is 320x180 pixel, the data unit is counts ('ct') and it is defined using a cartesian projection in galactic coordinates.\n",
    "\n",
    "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('Total number of counts in the image: {:.0f}'.format(vela_2fhl.data.sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To show the image on the screen we can use the [SkyImage.show()](http://docs.gammapy.org/dev/api/gammapy.image.SkyImage.html#gammapy.image.SkyImage.show) 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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QMl3lhvoplj/H6rjNl6ljbdItPo7VnQ/vdM59pmufYRiGYYwm9ezE5wxHM/gVYBJetONz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153c07f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vela_2fhl.show()"
   ]
  },
  {
   "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 `SkyImage.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 `SkyImage` object, that we can plot directly the same way we did above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UWq4bAF5WNX4JwKlIXTlpKNRBrBXeSfLtCr9lZPB+mlse4k7eiVMaV726cQRs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rX2odbaxdbaJQD/EzvWhWeKcGar6hUA3tta+0E69WcAvnj+/aWYXocAAG8A8PUx4bEA/rq19sET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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115510b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# smooth counts image with gaussian kernel of 0.5 deg\n",
    "vela_2fhl_smoothed = vela_2fhl.smooth(kernel='gauss', radius=0.5 * u.deg)\n",
    "vela_2fhl_smoothed.show()"
   ]
  },
  {
   "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 [SkyImage.cutout()](http://docs.gammapy.org/dev/api/gammapy.image.SkyImage.html#gammapy.image.SkyImage.cutout), which wraps Astropy's [Cutout2D](http://docs.astropy.org/en/stable/api/astropy.nddata.Cutout2D.html). Again it returns a `SkyImage` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gZpAtfY3ZeVqN4eyL7Pm9tEpSrZJmq/e1Pd5LoXCQ7NdSf2lb6Z5p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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116199ef0>"
      ]
     },
     "metadata": {},
     "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",
    "size = 9.0 * u.deg\n",
    "\n",
    "vela_2fhl_cutout = vela_2fhl_smoothed.cutout(center, size)\n",
    "vela_2fhl_cutout.show()"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FGsP6+VpfgJsqysfjQHNmpP5JxD3AywSPee5cNNAV8XbRV8BUN6iKwor1/nR6TF+xFbMC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\nRWJCCQkJPUViQgkJCT1FYkIJCQk9xf8BCL3JZ3a3Qn8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1161b6710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vela_wmap = SkyImage.read(\"$GAMMAPY_EXTRA/datasets/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.show(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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: None\n",
      "Data shape: (300, 300)\n",
      "Data type: >f4\n",
      "Data unit: mK\n",
      "Data mean: 1.827e+00\n",
      "WCS type: ['RA---TAN', 'DEC--TAN']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(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 [SkyImage.reproject()](http://docs.gammapy.org/dev/api/gammapy.image.SkyImage.html#gammapy.image.SkyImage.reproject) method: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BnAZw590bksPhOBPRwrN5rYicD+D5AK5EJ1wtfe63iHwvgGeHnzcB+FFVfV/I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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1163b97f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reproject WMAP image\n",
    "vela_wmap_reprojected = vela_wmap.reproject(vela_2fhl)\n",
    "\n",
    "# cutout part we're interested in\n",
    "vela_wmap_reprojected_cutout = vela_wmap_reprojected.cutout(center, size)\n",
    "vela_wmap_reprojected_cutout.show(cmap='viridis', norm=norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x11639b278>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ic+vG6/Ob0R/x6I03sO///ZE9hu1bJ33+3uDyv6vvieN7oLcQoocQIgM4EXi3nmnwaARY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\nIGjMX8WwdHCk9MPBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11639b710>"
      ]
     },
     "metadata": {},
     "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)).\n",
    "* Find the maximum brightness location in the WMAP image. The methods [np.argmax()](https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html) and [SkyImage.wcs_pixel_to_skycoord()](http://docs.gammapy.org/dev/api/gammapy.image.SkyImage.html#gammapy.image.SkyImage.wcs_pixel_to_skycoord) might be helpful. Try to identify the source.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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](http://docs.gammapy.org/dev/api/gammapy.data.EventList.html) 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](http://docs.gammapy.org/dev/data/index.html) submodule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from gammapy.data import EventList"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very similar to the `SkyImage` class an event list can be created, by passing a filename to the `.read()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_2fhl = EventList.read('$GAMMAPY_EXTRA/datasets/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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=60978&gt;\n",
       "<table id=\"table4667969608\" 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>145927.0</td><td>291.662</td><td>42.2341</td><td>74.5437</td><td>11.8678</td><td>38.0455</td><td>83.5358</td><td>55.6387</td><td>314.034</td><td>239561457.866</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>275.698088974</td><td>2.69657e-11</td><td>3238.25</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>221273.0</td><td>46.9858</td><td>-40.6389</td><td>247.489</td><td>-58.8739</td><td>34.1051</td><td>224.209</td><td>68.2524</td><td>198.319</td><td>239562739.085</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>64.7974931598</td><td>1.53573e-12</td><td>2774.2</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>57709.2</td><td>121.841</td><td>49.2288</td><td>169.868</td><td>32.3017</td><td>71.5636</td><td>34.2925</td><td>36.7173</td><td>25.5439</td><td>239563180.302</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>30.57218647</td><td>8.11096e-12</td><td>253.221</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>221224.0</td><td>83.5626</td><td>-4.21744</td><td>207.783</td><td>-19.0771</td><td>20.5089</td><td>92.1605</td><td>32.3033</td><td>239.141</td><td>239563382.213</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>27.4125095904</td><td>1.66075e-11</td><td>2980.12</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>698997.0</td><td>320.895</td><td>-1.32789</td><td>51.2218</td><td>-33.9718</td><td>35.3621</td><td>158.741</td><td>12.0867</td><td>72.2029</td><td>239566572.951</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>106.475481123</td><td>2.26543e-13</td><td>2706.14</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>119159.0</td><td>318.811</td><td>12.3028</td><td>62.6361</td><td>-24.416</td><td>26.5896</td><td>213.894</td><td>17.7156</td><td>23.9409</td><td>239572348.06</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>185.346427292</td><td>2.75677e-11</td><td>3439.29</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>56175.6</td><td>279.251</td><td>47.8835</td><td>76.6915</td><td>22.0739</td><td>29.1034</td><td>61.0048</td><td>62.1731</td><td>321.104</td><td>239572763.431</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>24.4507339597</td><td>2.08265e-10</td><td>4071.25</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1.41812e+06</td><td>100.311</td><td>-47.4481</td><td>256.468</td><td>-21.2641</td><td>61.2256</td><td>294.18</td><td>90.4753</td><td>144.149</td><td>239573788.813</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>68.271614641</td><td>3.20022e-14</td><td>1086.23</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>62164.9</td><td>331.492</td><td>-41.2264</td><td>359.42</td><td>-53.4049</td><td>28.1408</td><td>229.927</td><td>52.0142</td><td>189.054</td><td>239578601.168</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>90.332262665</td><td>1.21479e-10</td><td>3566.07</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>51296.8</td><td>79.0311</td><td>78.9327</td><td>133.868</td><td>22.2399</td><td>51.1726</td><td>267.814</td><td>99.7602</td><td>357.79</td><td>444418973.822</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>181.6889835</td><td>2.14127e-10</td><td>2586.88</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>60315.7</td><td>243.681</td><td>-50.5669</td><td>332.43</td><td>0.28268</td><td>24.8501</td><td>255.683</td><td>74.8804</td><td>195.14</td><td>444421777.761</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>57.3358902931</td><td>2.35854e-08</td><td>3541.04</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>90000.1</td><td>282.698</td><td>-33.1987</td><td>2.66219</td><td>-14.3339</td><td>8.49144</td><td>343.559</td><td>47.3965</td><td>191.033</td><td>444422182.738</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>187.602469146</td><td>3.62123e-10</td><td>3792.57</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>61988.7</td><td>247.98</td><td>-48.1091</td><td>336.146</td><td>0.0220339</td><td>33.6499</td><td>144.271</td><td>80.3966</td><td>221.396</td><td>444422758.145</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>39.2098327279</td><td>2.26421e-08</td><td>3395.07</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>54282.9</td><td>159.924</td><td>-58.3628</td><td>286.362</td><td>0.205554</td><td>24.1196</td><td>287.138</td><td>65.0847</td><td>177.524</td><td>444425794.794</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>196.209668875</td><td>4.8258e-09</td><td>3672.78</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>146728.0</td><td>244.848</td><td>-46.5876</td><td>335.754</td><td>2.60326</td><td>45.2539</td><td>5.33644</td><td>91.871</td><td>137.032</td><td>444425911.819</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>65.6327857375</td><td>4.4595e-10</td><td>2899.33</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>135433.0</td><td>83.5278</td><td>27.9053</td><td>179.529</td><td>-2.69106</td><td>17.4911</td><td>52.0858</td><td>53.306</td><td>313.822</td><td>444430968.968</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>97.6446831226</td><td>1.57989e-10</td><td>3515.6</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>61592.1</td><td>231.214</td><td>-5.45521</td><td>357.435</td><td>40.6473</td><td>46.6356</td><td>141.047</td><td>62.7584</td><td>256.631</td><td>444433607.906</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>28.7290657759</td><td>2.82028e-10</td><td>2793.45</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>80480.8</td><td>228.244</td><td>-45.044</td><td>327.561</td><td>10.9528</td><td>37.3149</td><td>193.714</td><td>83.3882</td><td>221.57</td><td>444433702.069</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>122.891819298</td><td>2.26095e-10</td><td>3176.43</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>124449.0</td><td>238.008</td><td>-51.0371</td><td>329.429</td><td>2.3006</td><td>32.4522</td><td>199.504</td><td>80.9768</td><td>214.48</td><td>444433764.433</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>185.255487204</td><td>8.54908e-10</td><td>3366.3</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",
       "   145927.0 291.662  42.2341 74.5437 ... 3238.25     0.0     0.0     0.0\n",
       "   221273.0 46.9858 -40.6389 247.489 ...  2774.2     0.0     0.0     0.0\n",
       "    57709.2 121.841  49.2288 169.868 ... 253.221     0.0     0.0     0.0\n",
       "   221224.0 83.5626 -4.21744 207.783 ... 2980.12     0.0     0.0     0.0\n",
       "   698997.0 320.895 -1.32789 51.2218 ... 2706.14     0.0     0.0     0.0\n",
       "   119159.0 318.811  12.3028 62.6361 ... 3439.29     0.0     0.0     0.0\n",
       "    56175.6 279.251  47.8835 76.6915 ... 4071.25     0.0     0.0     0.0\n",
       "1.41812e+06 100.311 -47.4481 256.468 ... 1086.23     0.0     0.0     0.0\n",
       "    62164.9 331.492 -41.2264  359.42 ... 3566.07     0.0     0.0     0.0\n",
       "        ...     ...      ...     ... ...     ...     ...     ...     ...\n",
       "    51296.8 79.0311  78.9327 133.868 ... 2586.88     0.0     0.0     0.0\n",
       "    60315.7 243.681 -50.5669  332.43 ... 3541.04     0.0     0.0     0.0\n",
       "    90000.1 282.698 -33.1987 2.66219 ... 3792.57     0.0     0.0     0.0\n",
       "    61988.7  247.98 -48.1091 336.146 ... 3395.07     0.0     0.0     0.0\n",
       "    54282.9 159.924 -58.3628 286.362 ... 3672.78     0.0     0.0     0.0\n",
       "   146728.0 244.848 -46.5876 335.754 ... 2899.33     0.0     0.0     0.0\n",
       "   135433.0 83.5278  27.9053 179.529 ...  3515.6     0.0     0.0     0.0\n",
       "    61592.1 231.214 -5.45521 357.435 ... 2793.45     0.0     0.0     0.0\n",
       "    80480.8 228.244  -45.044 327.561 ... 3176.43     0.0     0.0     0.0\n",
       "   124449.0 238.008 -51.0371 329.429 ...  3366.3     0.0     0.0     0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_2fhl.table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to find the total number of events contained int the list. This doesn't work:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "Because Gammapy objects don't redefine properties, that are accessible from the underlying Astropy of Numpy data object.\n",
    "\n",
    "So in this case of course we can directly use the `.table` attribute 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 [ 145.92724609, 221.27337646,  57.70924377,...,   61.59211731,\n",
       "             80.48078918, 124.44916534] 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(center=center, radius=0.5 * u.deg)\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": null,
   "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](http://docs.gammapy.org/dev/catalog/index.html)\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](http://docs.gammapy.org/dev/catalog/index.html) 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": [
       "&lt;Table length=360&gt;\n",
       "<table id=\"table4673084720\" 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>str18</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>str25</th><th>float32</th><th>float32</th><th>str8</th><th>float32</th><th>float32</th><th>str18</th><th>str18</th><th>str18</th></tr></thead>\n",
       "<tr><td>2FHL J0008.1+4709</td><td>2.0437</td><td>47.1642</td><td>115.339</td><td>-15.0688</td><td>0.061114</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>3.36344e-12</td><td>-1.53668e-12 .. 2.16095e-12</td><td>5.35354</td><td>4.34947e-18</td><td>nan .. 4.75389e-12</td><td>0.0</td><td>8.44871e-18</td><td>nan .. 7.29101e-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>0.99722</td><td>0.834827</td><td>bll</td><td>2.1</td><td>2.51188e+15</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>116.124</td><td>-11.7932</td><td>0.0454392</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>8.36282e-12</td><td>-2.98962e-12 .. 3.89512e-12</td><td>7.35132</td><td>2.91458e-17</td><td>nan .. 5.10003e-12</td><td>0.0</td><td>3.50875e-16</td><td>nan .. 4.87458e-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>0.999724</td><td>0.734808</td><td>bll</td><td>0.0</td><td>1.41254e+15</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>114.463</td><td>-32.5424</td><td>0.0370936</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>-5.55342e-12 .. 5.55342e-12</td><td>5.76579</td><td>1.179e-15</td><td>nan .. 5.37865e-12</td><td>0.0</td><td>1.60465e-16</td><td>nan .. 6.12008e-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>0.999868</td><td>0.978522</td><td>bll</td><td>0.1</td><td>5.91561e+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>107.172</td><td>-61.8618</td><td>0.0511852</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>1.61197e-11</td><td>-7.17255e-12 .. 9.95349e-12</td><td>5.3822</td><td>3.63922e-12</td><td>-4.38636e-12 .. 4.38636e-12</td><td>1.38539</td><td>8.42356e-16</td><td>nan .. 7.3424e-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>0.900089</td><td>bll-g</td><td>0.306</td><td>4.31519e+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>94.28</td><td>-81.2224</td><td>0.0348384</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>4.00161e-11</td><td>-1.01615e-11 .. 1.22378e-11</td><td>12.1725</td><td>2.13901e-12</td><td>nan .. 8.25812e-12</td><td>0.907958</td><td>2.43955e-17</td><td>nan .. 6.84226e-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>0.981424</td><td>bll</td><td>0.61</td><td>8.31764e+15</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>120.972</td><td>-2.98122</td><td>0.0316026</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>9.42294e-11</td><td>-1.52566e-11 .. 1.71572e-11</td><td>17.3124</td><td>2.66991e-11</td><td>-7.62818e-12 .. 9.45084e-12</td><td>10.3909</td><td>3.72609e-16</td><td>nan .. 4.67444e-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>0.999958</td><td>0.992868</td><td>bll</td><td>0.0</td><td>1.31826e+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>120.676</td><td>-21.9918</td><td>0.0355148</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>7.41577e-12</td><td>-4.0778e-12 .. 6.45182e-12</td><td>4.95442</td><td>3.00308e-12</td><td>-2.31775e-12 .. 4.46644e-12</td><td>1.70835</td><td>4.73166e-16</td><td>nan .. 5.79809e-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>0.998738</td><td>0.9347</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>121.164</td><td>-28.435</td><td>0.0592417</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>9.65762e-12</td><td>-4.31687e-12 .. 4.31687e-12</td><td>6.31152</td><td>8.75861e-17</td><td>nan .. 5.24335e-12</td><td>0.0</td><td>9.43812e-16</td><td>nan .. 5.68787e-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>0.998594</td><td>0.838844</td><td>fsrq</td><td>0.966</td><td>6.45655e+13</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>121.017</td><td>-41.3933</td><td>0.0378046</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>3.11532e-11</td><td>-8.93008e-12 .. 1.10103e-11</td><td>10.2404</td><td>2.99712e-12</td><td>-3.03054e-12 .. 3.03054e-12</td><td>2.7089</td><td>4.68348e-17</td><td>nan .. 6.42314e-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>0.99961</td><td>0.965127</td><td>bll</td><td>0.0</td><td>1.0023e+16</td><td>3FGL J0045.3+2126</td><td>1FHL J0045.2+2126</td><td></td></tr>\n",
       "<tr><td>2FHL J0048.0+5449</td><td>12.0023</td><td>54.8281</td><td>122.433</td><td>-8.04033</td><td>0.0572055</td><td>35.15</td><td>1.3</td><td>0.51</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.21e-11</td><td>6.12e-12</td><td>7.64e-12</td><td>5.72e-12</td><td>6.43137e-12</td><td>-3.56228e-12 .. 5.62894e-12</td><td>4.6401</td><td>2.94345e-12</td><td>-3.03542e-12 .. 3.03542e-12</td><td>2.27561</td><td>3.59026e-12</td><td>-2.51846e-12 .. 4.90533e-12</td><td>2.86203</td><td>4.1</td><td>605.9</td><td>0.99</td><td>4</td><td>1RXS J004754.5+544758</td><td>nan</td><td>0.906161</td><td>bcu II</td><td>0.0</td><td>7.87046e+15</td><td>3FGL J0047.9+5447</td><td></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>102.876</td><td>-24.7718</td><td>0.0717998</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>1.37062e-11</td><td>-5.99053e-12 .. 8.27325e-12</td><td>5.51109</td><td>1.50445e-15</td><td>nan .. 5.25568e-12</td><td>0.0</td><td>3.34396e-16</td><td>nan .. 6.35236e-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>0.996639</td><td>0.877901</td><td>bll-g</td><td>0.098</td><td>1.04713e+15</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>334.608</td><td>-62.0492</td><td>0.038949</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>9.13009e-12</td><td>-3.73356e-12 .. 5.01738e-12</td><td>6.05223</td><td>6.65757e-17</td><td>nan .. 5.27137e-12</td><td>0.0</td><td>6.34713e-16</td><td>nan .. 5.77243e-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>0.999434</td><td>0.937726</td><td>bll</td><td>0.0</td><td>4.46683e+15</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>111.744</td><td>-2.14031</td><td>0.0348384</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>5.93689e-11</td><td>-1.21101e-11 .. 1.38986e-11</td><td>11.6238</td><td>1.03761e-11</td><td>-4.37845e-12 .. 6.14398e-12</td><td>5.81435</td><td>2.90309e-12</td><td>-2.02416e-12 .. 3.98202e-12</td><td>3.50834</td><td>28.8</td><td>662.12</td><td>1.0</td><td>135</td><td>Cas A</td><td>nan</td><td>0.999833</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>106.059</td><td>-17.8224</td><td>0.0456016</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>1.62017e-11</td><td>-5.24708e-12 .. 6.61912e-12</td><td>8.11938</td><td>2.00018e-12</td><td>-1.41271e-12 .. 2.77927e-12</td><td>2.56849</td><td>2.18906e-18</td><td>nan .. 5.54082e-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>0.961506</td><td>bll</td><td>0.059</td><td>4.84173e+15</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>350.157</td><td>-67.5836</td><td>0.0358658</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>2.13391e-11</td><td>-7.09939e-12 .. 9.10435e-12</td><td>8.85383</td><td>2.71834e-12</td><td>-2.11714e-12 .. 4.11668e-12</td><td>1.65792</td><td>3.54717e-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>0.999467</td><td>0.985516</td><td>bll</td><td>0.173587</td><td>8.31764e+15</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>105.532</td><td>-22.1627</td><td>0.038539</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>1.11304e-11</td><td>-4.6962e-12 .. 6.5288e-12</td><td>6.31149</td><td>4.40415e-16</td><td>nan .. 5.47737e-12</td><td>0.0</td><td>2.64065e-17</td><td>nan .. 5.92547e-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>0.999792</td><td>0.956363</td><td>bll</td><td>0.264</td><td>8.91251e+15</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>119.838</td><td>17.7999</td><td>0.0348934</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>1.16398e-11</td><td>-3.81183e-12 .. 4.82791e-12</td><td>8.38745</td><td>1.25703e-16</td><td>nan .. 3.71812e-12</td><td>0.0</td><td>1.13621e-15</td><td>nan .. 4.04723e-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>0.999874</td><td>0.979435</td><td>bll</td><td>0.274</td><td>3.42768e+15</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>107.422</td><td>-26.1717</td><td>0.0715056</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>5.94254e-12</td><td>-2.73222e-12 .. 3.85247e-12</td><td>5.2347</td><td>5.08627e-17</td><td>nan .. 8.36317e-12</td><td>0.0</td><td>3.14709e-16</td><td>nan .. 5.6852e-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>0.999148</td><td>0.955429</td><td>bll</td><td>0.366</td><td>1.0292e+15</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>112.88</td><td>-9.90159</td><td>0.0335464</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>4.78884e-11</td><td>-1.15596e-11 .. 1.339e-11</td><td>11.282</td><td>2.42894e-11</td><td>-7.68104e-12 .. 9.87777e-12</td><td>8.8577</td><td>3.51388e-12</td><td>-3.55282e-12 .. 3.55282e-12</td><td>2.66572</td><td>25.2</td><td>652.31</td><td>0.99</td><td>1</td><td>1ES 2344+514</td><td>0.99986</td><td>0.988082</td><td>bll</td><td>0.044</td><td>7.07946e+15</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>-40.6111</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>-3.9133e-12 .. 5.47729e-12</td><td>5.38935</td><td>3.90782e-16</td><td>nan .. 4.95279e-12</td><td>0.0</td><td>3.23398e-17</td><td>nan .. 5.16722e-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",
       "      str18       float32 float32  ...       str18           str18     \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",
       "2FHL J0048.0+5449 12.0023  54.8281 ...                                 \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('$GAMMAPY_EXTRA/datasets/catalogs/fermi/gll_psch_v08.fit.gz')\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": [
       "&lt;Table length=5&gt;\n",
       "<table id=\"table4667789440\" 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>str18</th><th>str25</th><th>str8</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",
       "      str18          str25      str8\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 methods [SkyImage.contains()](http://docs.gammapy.org/dev/api/gammapy.image.SkyImage.html#gammapy.image.SkyImage.contains) and [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": null,
   "metadata": {
    "collapsed": true
   },
   "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",
      "ParameterList\n",
      "Parameter(name='amplitude', value=1.3099999973675835e-09, unit=Unit(\"1 / (cm2 s)\"), min=0, max=None, frozen=False)\n",
      "Parameter(name='index', value=2.130000114440918, unit=Unit(dimensionless), min=0, max=None, frozen=False)\n",
      "Parameter(name='emin', value=0.05, unit=Unit(\"TeV\"), min=None, max=None, frozen=False)\n",
      "Parameter(name='emax', value=2.0, unit=Unit(\"TeV\"), min=None, max=None, frozen=False)\n",
      "\n",
      "Covariance: [[  4.66488967e-21   0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  0.00000000e+00   4.90000004e-03   0.00000000e+00   0.00000000e+00]\n",
      " [  0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+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](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.PowerLaw2.html#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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1GPB4x9rc2awyUVEanQSDv261W8k5t82HEKOBa4BSwB7gb865d8zsBmAYEA28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\nT/4P4AsA8Gy9GAQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11692c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_crab_2fhl = crab_2fhl.spectral_model.plot(\n",
    "    energy_range=[50, 2000] * u.GeV, energy_power=0)"
   ]
  },
  {
   "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 6.870047729801166e-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 1.3099999973675835e-09 1 / (cm2 s)>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_2fhl.spectral_model.integral(\n",
    "    emin=50 * u.GeV, emax=2000 * u.GeV,\n",
    ").to('cm-2 s-1')"
   ]
  },
  {
   "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/plain": [
       "1.31e-09"
      ]
     },
     "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": [
       "$3.5295398 \\times 10^{-10} \\; \\mathrm{\\frac{erg}{s\\,cm^{2}}}$"
      ],
      "text/plain": [
       "<Quantity 3.529539814304347e-10 erg / (cm2 s)>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_2fhl.spectral_model.energy_flux(\n",
    "    emin=50 * u.GeV, emax=2000 * u.GeV,\n",
    ").to('erg cm-2 s-1')"
   ]
  },
  {
   "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": [
      "Flux points of type 'dnde'\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": [
       "&lt;Table length=3&gt;\n",
       "<table id=\"table4667474328\" 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><th>e_ref</th><th>dnde</th><th>dnde_errn</th><th>dnde_errp</th><th>dnde_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><th>TeV</th><th>GeV(9/8) / (cm2 s TeV(17/8))</th><th>GeV(9/8) / (cm2 s TeV(17/8))</th><th>GeV(9/8) / (cm2 s TeV(17/8))</th><th>GeV(9/8) / (cm2 s TeV(17/8))</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>bool</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>50.0</td><td>171.0</td><td>9.94072e-10</td><td>5.7595e-11</td><td>5.94726e-11</td><td>False</td><td>nan</td><td>0.0917326969964</td><td>2.01666069866e-05</td><td>1.16842211486e-06</td><td>1.20651355523e-06</td><td>nan</td></tr>\n",
       "<tr><td>171.0</td><td>585.0</td><td>2.43875e-10</td><td>2.82542e-11</td><td>3.04354e-11</td><td>False</td><td>nan</td><td>0.313772883446</td><td>1.44599896753e-06</td><td>1.6752658163e-07</td><td>1.80459220037e-07</td><td>nan</td></tr>\n",
       "<tr><td>585.0</td><td>2000.0</td><td>5.27536e-11</td><td>1.33401e-11</td><td>1.60478e-11</td><td>False</td><td>nan</td><td>1.07308951286</td><td>9.15157602841e-08</td><td>2.31421507433e-08</td><td>2.78392984262e-08</td><td>nan</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       " e_min   e_max  ...          dnde_errp                     dnde_ul           \n",
       "  GeV     GeV   ... GeV(9/8) / (cm2 s TeV(17/8)) GeV(9/8) / (cm2 s TeV(17/8))\n",
       "float64 float64 ...           float64                      float64           \n",
       "------- ------- ... ---------------------------- ----------------------------\n",
       "   50.0   171.0 ...            1.20651355523e-06                          nan\n",
       "  171.0   585.0 ...            1.80459220037e-07                          nan\n",
       "  585.0  2000.0 ...            2.78392984262e-08                          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": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116a8f7f0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LsTv47o2vuwL4EbE76O5z9++c0g+UIIWRtEj3T8Edqi/9PZWRGuYu3sTuA4cpyO7A9FHF\nVIRLGFqsNhcSnCYNIzP7LbHLZ0eABcRC4bvu/sMzLbS1UBhJi3T/FNi1AW6KXSl/98hRnou3uXhu\n9VYOH3UGF2VTEY7NBtE9R20upHk1dXO9Ee6+x8w+DjwJ3EwslNpNGIm0WLvfv6G0Q1oqk4b3YNLw\nHuzcf4h5S2qZFYny7/NX8d3HVjG2NJ+Z4RImDutOVka7moBFWrhEfxsz4uMz04Gfu/shMzuWxLpE\nku/47AWt2eYlse8n+Fm6AlcDV2fBG327MHvPQKreHMiX12ynkx1iUvY6Zuas5rysKC2my4XuCmy3\nEg2j3wBvEbul+3kz6wXsS1pVItKwXRs+cEbEhhdj33N7Qpfe/7B5/4xdfDX/VW7Ke5XXDvSgas8g\nHt3bn1l7BlOctpfpOWuoyFnNgA47/2FfkeZwyjcwAJhZCpAWn1WhXdCYkbRI90+JBdEdDU4TeUIH\nDx/lqRVbqKqO8vzr2zh6zBkRyqUiHGLaqGLyO3dIQsHS3jT1mNEHuPsx/nFqHxFpRTLTU5k2qphp\no4rZtvdd5iyupaq6hjvnruA7j67kooEFVIRLuGRIIZnpanMhyaURTJHWLrdn49s0oiC7A9df0Jfr\nL+jL6s17qayu4ZHqWp5ZFSE7M40pI2LTEI3p3VVt1CUpFEYird0JxojOxKCibG6ZPISbLx/M39/Y\nQWWkhjmLa3notY307NaR8tEhysMl9M3v1KTvK+1bo2FkZp2AfHffUO/1YcmY6UBEWobUFOOCAflc\nMCCfu949whPLN1NVHeV/nlvLfz+7lrJeXWJt1Ef2oEuW2qjLmWlsOqCZxPoI7SDWHO+T7h6Jr4u4\ne7hZqmwBdAODtEgBNNfbvPsgj8TbXKzespf0VOPiwbE2FxMGFarNhXxAU93AcBswxt2jZvYh4EEz\n+7q7zwF04VikHSrKzeRfL+rPpy/sx4pNe6iMRHlkUZQnlm+hS1Y600YWUxEOMbpnF7W5kIQ1FkYp\n7h4FcPe/mdnFwDwz60nsTElE2ikzY1hxLsOKc7ll8mD+unY7lZEof1qwkd+9vIF++Z0oLwsxoyxE\nz24JtlFvC9QK/rQ0Fkb7zayvu68HiJ8hjQceAYYmuzgRaR3SUlOYMKiQCYMK2XPwMI8v3cysSA0/\neOp1fvDU65zTtxszwyEmj+hBjtpcyAk0Fkafr7+Nu+82s4nAx5JWlYi0WjmZ6Xzk7J585OyebHz7\nnffGl74xaynffmQ5E4cVUVEWYtwAtbmQ9zXWdjxSd9nMsursU5WsokSkbejZLYsvXDyAz08oZdHG\nXVRVR5mzuJa5i2vJ79yBK0fFxpeGFedofKmdS+g5IzO7AbgLOAocI3bzggO9kleaiLQVZkZZr66U\n9erKt6YM5S+rY20ufv/yBu57aT2DumdTHg4xY3SIoly1uWiPEn3o9RvAKHffmsxiRKTty0hLYeKw\nIiYOK2LXO4eYtyTWRv27j63ie4+vYmz/fCrCIS4fVkSnDnouv71I9G96HbAnmYWISPvTJSuDT5zX\nm0+c15v12/dTVR2lqrqGr/xpMR3TlzFpeKyN+of655OqaYjatETD6JvAS2b2MvDu8Rfd/StJqUpE\n2p2++Z34ymUDuenSASzYsJPKSA3zlmyiqjpK95wOzBgdoiJcwqCi7KBLlSRINIx+AbwELCU2ZiQi\nkhRmxtl9unF2n27cPm0Yz6zcSlV1Dfe+uJ5fvrCOYcU5lJfF2qgXZKvNRVuRaBgdc/cvJbUSEZF6\nMtNTmTKyB1NG9mDHvneZu7iWyuoodz+6knseW8WFA/IpD5cwcWh3tblo5RINo2fM7DpgLh+8TKdx\nJBFpFnmdO3DN2L5cM7Yva7fupTISZXZ1lC89WE12hzQmjyiiIlzCOX26qc1FK5RoGH0y/v3OOq/p\n1m4RCURpYTY3TxrM1yYO4uV1O6isjvLokk38aUENoS4dKS8LUR4O0b+gc9ClSoISCiN3P/PuXSIi\nTSwlxfhQaT4fKs3n36YP48nlW6isjvKzv6zlJ8+tZXTPLrE26iOL6dpJbS5askQfev0M8JC774ov\ndwWucvdfJbM4EZFEZWWkMSM+MevWPQeZHZ+G6NuPLOeueSsYP6iQmeEQEwYX0iFN40stTaKX6T7j\n7r84vuDuO83ss4DCSERanMKcTD59YX8+fWF/VtTuoTJSwyOLa3lqxRZyO6YzdWSsjXq4l9pctBSJ\nhtEH/hlhZimApt4VkRZvaHEOQ4uH8s3Jg3npjR3MWljDrEgNf3jlLfrkZVFeVkJ5WYheee2ozUUL\nlGgYPWVmDxJ73siBzwJPJ60qEZEmlpaawkUDC7hoYAF7Dx7m8WWbqYxE+dEzr/NfT7/O2X26Ul5W\nwpSRPcjtqH9rN7dEw+jrxALoJmKTpD4J/DJZRYmIJFN2ZjpXjenJVWN6Et11gNnVUSojNfy/qqXc\nMXc5lw4ppKKshIsGFZCuNhfNItG76Y4CP4l/iYi0GaEuHfn8hFI+N74/S6O7qYzE2lzMX7qZbp0y\n3mtzMSKUq/GlJGowjMxsNrEzoKfc/Ui9db2JPX9U4+73Ja9EEZHkMzNGlnRhZEkXbp0yhBde30Zl\nJMr/vfoWD/ztTUoLO1MRb3NR3KVj0OW2OebuJ19pFgK+CswAtgDbgEygH/AW8FN3n9UMdQZuzJgx\nvmDBgqDLEJFmtvvAYeYvjbW5eO3NnZjB+f3yqAiXMGl4EZ3rt7m4f0rs+7WPNn+xLZCZLXT3MY1u\n11AY1TtgKdADOACsdve9Z1Zi66IwEpENO463uYiyYcc7ZKanMGlYEeXhEi4ojbe5UBh9QKJhlHDn\nKndfC6w9o6pERFqx3nmd+PKlA7nxkgFE3tpJZSTKvCWbmL2olsLsDswoC1F+MI8hmTuCLrXVSfjM\nqL3TmZGInMi7R47y3KpYG/XnVm/l8FFnSIftVFwyjumjiynMad9t1Jv8Ml17pzASkca8vf8Qc395\nG5V7BrH4YHdSDC4YUMDMcIiJQ4vomNH+piFqkst0ZvYz4DHgGXd/p6mKExFpi7p1yuCTXZfxya7L\neGPqH6mKxMaXbnxoEZ0yUpk8ogcV4RDn9c1Tm4t6Grub7gJgEnAxsA94Anjc3Zc3T3nBM7NpwLTS\n0tJPrVmzJuhyRKSlq3cDw7Fjzqtvvk1lpIb5Szez790jFOdmMqMsREU4RGlh226jnoy76QqBy4HJ\nwGBgAbFgqjyTQlsLXaYTkYQ0cDfdgUNHeWrlFiojNfx1zXaOHnNGluRSXhbiylHF5HVue23Ukzpm\nZLHHkM8BJrn7nY1t3xYojEQkIQne2r1170HmLKqlMhJlxaY9pKUY4wcVUF5WwiVDCttMG/Umv7W7\nLo8l2CvxLxEROUWF2ZncMK4fN4zrx6rNe94bX3p65VayM9OYOjI2DdGY3l3bxTREpxVGIiLSdAYX\n5XDLFTncPGkwf3tjO5WRKLOrozz46lv06pZFeXx8qXdep6BLTZrG7qZLjU+SKiIiSZaaYowbUMC4\nAQXcPeMIjy/bTFV1lP9+dg0/fmYNZ/XuSkU4xNQRxeRmta02F42dGdWaWSXwoLu/0BwFiYgIdOqQ\nxsyzSph5Vgmbdh9gdnUtVdU13Fq1jDvnrOCSIYWUl4UYP6iQjLTW3+aisTAaAVwFfCc+S/efiAXT\nwqRXJiIiAPTI7chnx/fnMxf1Y3ntHmZFapi7uJbHlm2ma1Y600YVUxEuYVRJ621zcSq3dvcEPgL8\nE5BLLJRuT2JtLYruphORhDTTRKmHjx7jxTXb+XOkhqdWbOHQkWP0K+hERVmIGWUhSrq2jDbqSbm1\n28yyiLWT+DrQ093zT7/E1kVhJCIJCWDW7j0HD/PY0k3MikR5df3bAJzbtxszwyVMHlFEdmZw40tN\nFkZmlgFMAT4GXAQ8DTxE7IHXw01Qa6ugMBKRhATcQmLj2+/E2qhXR1m/fT8d0lK4fFgR5eEQ40rz\nSWvmNupNNTfdb4lNB/Q3YgF0jeaoExFpuXp2y+KLlwzgCxeXsmjjLiojUeYuqWXO4lryO3d4r436\nsOKcFjW+1NgNDM8DX3L3Xc1RjIiINA0zo6xXV8p6deW2qUN5bvVWKiM1/O7lN7nvpfUM6p4da6Ne\nFqJ7C2hz0WAYufu9AGZWANwNlLj7FDMbCpzj7g8kv0QRETkTGfFLdZcPK2Ln/kPMi7dRv+exVXzv\n8VWMLc2nIhzi8mFFZGUEMxdCou/6APAH4Bvx5TXAH+Ovi4hIK9G1UwZXn9ebq8/rzbpt+94bX7rp\nj4vJyljGpOFFzAyXcF6/vFgb9WaSaBgVuvv/mdnXAdz9sJlpZgYRkVasX0FnvjJxEF++dCCvvfk2\nVdVRHl2yicpIlB65mUwfHZuGaGD35Le5SDSM9ptZN8ABzOxsYG/SqhIRkWaTkmKc2y+Pc/vlcceV\nw3h65RaqIlF+/dd1/OL5N/jc+P7cPGlwUmtINIy+BswF+pnZ80AI+HDSqhIRkUBkpqcydWQxU0cW\ns33fu8xdXMvIktykv29jt3af5+4vu/sCM5sADAEMWOHuh5JenYiIBCa/cweuHdu3Wd6rsTOjnwFh\ngHj4LE56RSIi0u60/qleRUSk1WvszKifmc052Up3v7KJ6xERkXaosTDaBvygOQoREZH2q7Ew2ufu\nzzdLJSIi0m41Nma0vlmqEBGRdq2xMPphQyvNLMfMhjdhPSIi0g41dpluppn9B/A4sJDYGFImUApM\nAHoDX01qhSIi0uY1Nmv3TWbWldhsC1cBPYADwErgl+7+YvJLFBGRtq7R6YDcfSfw6/iXiIhIk9ND\nryIiEjiFkYiIBE5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11633e2e8>"
      ]
     },
     "metadata": {},
     "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": null,
   "metadata": {
    "collapsed": true
   },
   "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](http://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": "3.6.3"
  },
  "nbsphinx": {
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 },
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}