{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**This is a fixed-text formatted version of a Jupyter notebook**\n",
    "\n",
    "- Try online[![Binder](https://static.mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.19?urlpath=lab/tree/tutorials/starting/overview.ipynb)\n",
    "- You may download all the notebooks in the documentation as a\n",
    "[tar file](../../_downloads/notebooks-0.19.tar).\n",
    "- **Source files:**\n",
    "[overview.ipynb](../../_static/notebooks/overview.ipynb) |\n",
    "[overview.py](../../_static/notebooks/overview.py)\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data structures\n",
    "\n",
    "## Introduction\n",
    "\n",
    "This is a getting started tutorial for Gammapy.\n",
    "\n",
    "In this tutorial we will use the [Second Fermi-LAT Catalog of High-Energy Sources (3FHL) catalog](http://fermi.gsfc.nasa.gov/ssc/data/access/lat/3FHL/), 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 3FHL example image. We will work with the following classes:\n",
    "    - `~gammapy.maps.WcsNDMap`\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`\n",
    "    - [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html)\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`, specifically `~gammapy.catalog.SourceCatalog3FHL`\n",
    "    - [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html)\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.modeling.models.SpectralModel`, specifically the `~gammapy.modeling.models.PowerLaw2SpectralModel`\n",
    "    - `~gammapy.estimators.FluxPoints`\n",
    "    - [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "**Important**: to run this tutorial the environment variable `GAMMAPY_DATA` must be defined and point to the directory on your machine where the datasets needed are placed. To check whether your setup is correct you can execute the following cell:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:03.953696Z",
     "iopub.status.busy": "2021-11-22T21:08:03.952804Z",
     "iopub.status.idle": "2021-11-22T21:08:03.957732Z",
     "shell.execute_reply": "2021-11-22T21:08:03.958193Z"
    }
   },
   "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 environment correctly as described [here](../../getting-started/quickstart.rst#download-tutorials) after you are done with this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:03.962996Z",
     "iopub.status.busy": "2021-11-22T21:08:03.962403Z",
     "iopub.status.idle": "2021-11-22T21:08:03.964052Z",
     "shell.execute_reply": "2021-11-22T21:08:03.964839Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:03.973814Z",
     "iopub.status.busy": "2021-11-22T21:08:03.973043Z",
     "iopub.status.idle": "2021-11-22T21:08:04.134378Z",
     "shell.execute_reply": "2021-11-22T21:08:04.134582Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:04.136442Z",
     "iopub.status.busy": "2021-11-22T21:08:04.136136Z",
     "iopub.status.idle": "2021-11-22T21:08:04.342498Z",
     "shell.execute_reply": "2021-11-22T21:08:04.342695Z"
    }
   },
   "outputs": [],
   "source": [
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Maps\n",
    "\n",
    "The `~gammapy.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:04.344595Z",
     "iopub.status.busy": "2021-11-22T21:08:04.344295Z",
     "iopub.status.idle": "2021-11-22T21:08:04.622553Z",
     "shell.execute_reply": "2021-11-22T21:08:04.622722Z"
    }
   },
   "outputs": [],
   "source": [
    "from gammapy.maps import Map\n",
    "\n",
    "gc_3fhl = Map.read(\"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts.fits.gz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The image is a `~gammapy.maps.WcsNDMap` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:04.625911Z",
     "iopub.status.busy": "2021-11-22T21:08:04.625541Z",
     "iopub.status.idle": "2021-11-22T21:08:04.627361Z",
     "shell.execute_reply": "2021-11-22T21:08:04.627535Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WcsNDMap\n",
       "\n",
       "\tgeom  : WcsGeom \n",
       " \taxes  : ['lon', 'lat']\n",
       "\tshape : (400, 200)\n",
       "\tndim  : 2\n",
       "\tunit  : \n",
       "\tdtype : >i8"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc_3fhl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape of the image is 400 x 200 pixel and it is defined using a cartesian projection in galactic coordinates.\n",
    "\n",
    "The ``geom`` attribute is a `~gammapy.maps.WcsGeom` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:04.630530Z",
     "iopub.status.busy": "2021-11-22T21:08:04.630227Z",
     "iopub.status.idle": "2021-11-22T21:08:04.631555Z",
     "shell.execute_reply": "2021-11-22T21:08:04.631773Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WcsGeom\n",
       "\n",
       "\taxes       : ['lon', 'lat']\n",
       "\tshape      : (400, 200)\n",
       "\tndim       : 2\n",
       "\tframe      : galactic\n",
       "\tprojection : CAR\n",
       "\tcenter     : 0.0 deg, 0.0 deg\n",
       "\twidth      : 20.0 deg x 10.0 deg\n",
       "\twcs ref    : 0.0 deg, 0.0 deg"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc_3fhl.geom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a closer look a the `.data` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:04.633756Z",
     "iopub.status.busy": "2021-11-22T21:08:04.633466Z",
     "iopub.status.idle": "2021-11-22T21:08:04.634756Z",
     "shell.execute_reply": "2021-11-22T21:08:04.634946Z"
    }
   },
   "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, 1],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 1]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc_3fhl.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": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:04.636754Z",
     "iopub.status.busy": "2021-11-22T21:08:04.636426Z",
     "iopub.status.idle": "2021-11-22T21:08:04.637649Z",
     "shell.execute_reply": "2021-11-22T21:08:04.637824Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of counts in the image: 32684\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total number of counts in the image: {gc_3fhl.data.sum():.0f}\")"
   ]
  },
  {
   "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 `gc_3fhl.data` attribute but in addition handles axis with world coordinates using [astropy.visualization.wcsaxes](https://docs.astropy.org/en/stable/visualization/wcsaxes/) and defines some defaults for nicer plots (e.g. the colormap 'afmhot'):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:04.639494Z",
     "iopub.status.busy": "2021-11-22T21:08:04.639211Z",
     "iopub.status.idle": "2021-11-22T21:08:04.837446Z",
     "shell.execute_reply": "2021-11-22T21:08:04.837690Z"
    }
   },
   "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": [
    "gc_3fhl.plot(stretch=\"sqrt\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make the structures in the image more visible we will smooth the data using a Gaussian kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:04.839938Z",
     "iopub.status.busy": "2021-11-22T21:08:04.839584Z",
     "iopub.status.idle": "2021-11-22T21:08:04.843656Z",
     "shell.execute_reply": "2021-11-22T21:08:04.843817Z"
    }
   },
   "outputs": [],
   "source": [
    "gc_3fhl_smoothed = gc_3fhl.smooth(kernel=\"gauss\", width=0.2 * u.deg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:04.854401Z",
     "iopub.status.busy": "2021-11-22T21:08:04.854042Z",
     "iopub.status.idle": "2021-11-22T21:08:05.025307Z",
     "shell.execute_reply": "2021-11-22T21:08:05.025729Z"
    }
   },
   "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": [
    "gc_3fhl_smoothed.plot(stretch=\"sqrt\");"
   ]
  },
  {
   "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 `~gammapy.maps.Map.cutout` to make a cutout map:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.039629Z",
     "iopub.status.busy": "2021-11-22T21:08:05.028788Z",
     "iopub.status.idle": "2021-11-22T21:08:05.093189Z",
     "shell.execute_reply": "2021-11-22T21:08:05.093439Z"
    },
    "nbsphinx-thumbnail": {
     "tooltip": "Introduction to basic data structures handling."
    }
   },
   "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(0, 0, unit=\"deg\", frame=\"galactic\")\n",
    "gc_3fhl_cutout = gc_3fhl_smoothed.cutout(center, 9 * u.deg)\n",
    "gc_3fhl_cutout.plot(stretch=\"sqrt\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a more detailed introduction to `~gammapy.maps`, take a look a the [maps.ipynb](../api/maps.ipynb) notebook.\n",
    "\n",
    "### Exercises\n",
    "\n",
    "* Add a marker and circle at the position of `Sag A*` (you can find examples in [astropy.visualization.wcsaxes](https://docs.astropy.org/en/stable/visualization/wcsaxes/))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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` 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` submodule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.095984Z",
     "iopub.status.busy": "2021-11-22T21:08:05.095504Z",
     "iopub.status.idle": "2021-11-22T21:08:05.109783Z",
     "shell.execute_reply": "2021-11-22T21:08:05.110025Z"
    }
   },
   "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 `~gammapy.data.EventList.read()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.111930Z",
     "iopub.status.busy": "2021-11-22T21:08:05.111591Z",
     "iopub.status.idle": "2021-11-22T21:08:05.171453Z",
     "shell.execute_reply": "2021-11-22T21:08:05.171701Z"
    }
   },
   "outputs": [],
   "source": [
    "events_3fhl = EventList.read(\n",
    "    \"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-events.fits.gz\"\n",
    ")"
   ]
  },
  {
   "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) object. It can be accessed with `.table` attribute: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.174176Z",
     "iopub.status.busy": "2021-11-22T21:08:05.173802Z",
     "iopub.status.idle": "2021-11-22T21:08:05.178959Z",
     "shell.execute_reply": "2021-11-22T21:08:05.179233Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=32843</i>\n",
       "<table id=\"table5063539632\" 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",
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       "<tr><td>13911.061</td><td>269.30997</td><td>-27.239439</td><td>2.7684028</td><td>-1.3365301</td><td>65.15399</td><td>224.52101</td><td>53.017742</td><td>242.62904</td><td>460197889.2652691</td><td>12049887</td><td>460192198</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>95.46356403827667</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>13226.425</td><td>265.16287</td><td>-27.344238</td><td>0.7796942</td><td>1.7680178</td><td>59.38332</td><td>126.7019</td><td>32.12299</td><td>246.97205</td><td>460203215.10816145</td><td>11716888</td><td>460198235</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. False</td><td>1</td><td>3.733097553253174</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>17445.463</td><td>266.63342</td><td>-28.807201</td><td>0.21464892</td><td>-0.1039705</td><td>55.48627</td><td>135.59155</td><td>14.227151</td><td>106.7812</td><td>460225372.9842249</td><td>1679082</td><td>460224933</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. False</td><td>1</td><td>80.52235281467438</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>13133.864</td><td>270.42474</td><td>-22.651058</td><td>7.251185</td><td>0.071358204</td><td>48.704975</td><td>134.73102</td><td>2.489122</td><td>294.48605</td><td>460225688.52486295</td><td>2879335</td><td>460224933</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. False</td><td>1</td><td>117.88173341751099</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>32095.705</td><td>266.0002</td><td>-29.77206</td><td>359.1034</td><td>-0.13615231</td><td>45.013103</td><td>236.72498</td><td>6.92107</td><td>212.86594</td><td>460231367.1387127</td><td>1113706</td><td>460231084</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>108.92976492643356</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>18465.783</td><td>266.39728</td><td>-29.105953</td><td>359.85202</td><td>-0.08294058</td><td>55.97552</td><td>135.87787</td><td>18.909636</td><td>112.137924</td><td>459939497.057684</td><td>7689831</td><td>459935572</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>70.72638684511185</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>14457.25</td><td>262.72217</td><td>-34.388405</td><td>353.7184</td><td>-0.26906812</td><td>45.683174</td><td>237.74162</td><td>25.728264</td><td>240.87035</td><td>459945845.4798405</td><td>10049680</td><td>459941302</td><td>0</td><td>0 .. 0</td><td>False .. True</td><td>False .. True</td><td>0</td><td>147.4274787902832</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=32843>\n",
       "  ENERGY      RA       DEC          L           B         THETA      PHI     ...      LIVETIME      DIFRSP0 DIFRSP1 DIFRSP2 DIFRSP3 DIFRSP4\n",
       "   MeV       deg       deg         deg         deg         deg       deg     ...         s                                                 \n",
       " float32   float32   float32     float32     float32     float32   float32   ...      float64       float32 float32 float32 float32 float32\n",
       "--------- --------- ---------- ----------- ------------ --------- ---------- ... ------------------ ------- ------- ------- ------- -------\n",
       "12186.642 260.45935 -33.553337   353.36273    1.7538676 71.977325  125.50694 ... 238.57837238907814     0.0     0.0     0.0     0.0     0.0\n",
       "25496.598 261.37506 -34.395004   353.09607    0.6520652  42.49406  278.49347 ... 176.16850754618645     0.0     0.0     0.0     0.0     0.0\n",
       "15621.498 259.56973 -33.409416   353.05673    2.4450684  64.32412  234.22194 ...  9.392075657844543     0.0     0.0     0.0     0.0     0.0\n",
       " 12816.32 273.95883 -25.340391     6.45856   -4.0548873 43.292503  142.87392 ...  4.034786552190781     0.0     0.0     0.0     0.0     0.0\n",
       "18988.387  260.8568 -36.355804   351.23734 -0.101912394 26.916113  290.39337 ... 131.60132896900177     0.0     0.0     0.0     0.0     0.0\n",
       " 11610.23 266.15518 -26.224436   2.1986027    1.6034819  35.77363  274.53387 ...  74.98110938072205     0.0     0.0     0.0     0.0     0.0\n",
       "13960.802 271.44742 -29.615316   1.6267247   -4.1431155 25.917883   238.0368 ... 106.37336817383766     0.0     0.0     0.0     0.0     0.0\n",
       "10477.372  266.3981  -28.96814   359.97003 -0.011748177 39.091587   275.5457 ... 214.62817406654358     0.0     0.0     0.0     0.0     0.0\n",
       " 13030.88 271.70428 -20.632627     9.59348  0.026241468 52.622505   161.3205 ...  94.68753063678741     0.0     0.0     0.0     0.0     0.0\n",
       "11517.904 265.00894 -30.065119   358.40112   0.43904436 41.812317  276.02448 ... 123.15007302165031     0.0     0.0     0.0     0.0     0.0\n",
       "19958.182 263.31854 -37.094856   351.71606    -2.153713 52.544586 121.415764 ... 17.113530546426773     0.0     0.0     0.0     0.0     0.0\n",
       " 23760.14  265.4694  -31.55994   357.34256     -0.68805  52.70319  248.87813 ...  225.2544113099575     0.0     0.0     0.0     0.0     0.0\n",
       "32168.988 266.35397 -30.096745   358.98682  -0.56723064 64.830696  212.93645 ...  45.64776709675789     0.0     0.0     0.0     0.0     0.0\n",
       "  10165.9 266.15427 -19.829279    7.668937    4.9320326  51.85829   207.0177 ... 204.40402576327324     0.0     0.0     0.0     0.0     0.0\n",
       "25277.545 269.68344 -24.668644   5.1628966  -0.34179303  42.77833  129.13857 ...  85.29761010408401     0.0     0.0     0.0     0.0     0.0\n",
       " 223713.7  261.1885 -31.794796    355.1619     2.239802  25.89418   284.8728 ... 166.99824661016464     0.0     0.0     0.0     0.0     0.0\n",
       "15236.758 264.52255 -31.730831    356.7694  -0.09604572 51.646976  141.44576 ...  5.334206968545914     0.0     0.0     0.0     0.0     0.0\n",
       "36314.297 264.79855 -31.116806   357.41434  0.032732587 48.567314 122.824875 ...  34.09180650115013     0.0     0.0     0.0     0.0     0.0\n",
       "      ...       ...        ...         ...          ...       ...        ... ...                ...     ...     ...     ...     ...     ...\n",
       "74033.305 268.76508  -24.43612    4.944724    0.4974049   51.1174  225.20499 ...  65.83160126209259     0.0     0.0     0.0     0.0     0.0\n",
       "47114.324 266.99127 -28.421051  0.70769924   -0.1726071  60.81272  212.27025 ...  77.02012866735458     0.0     0.0     0.0     0.0     0.0\n",
       " 14473.36 259.55792 -32.497227   353.79837    2.9774146  69.47437  136.79776 ... 13.922565162181854     0.0     0.0     0.0     0.0     0.0\n",
       "20056.527 267.50372 -21.494694    6.888726    2.9924572 46.228405  129.99919 ... 115.29787397384644     0.0     0.0     0.0     0.0     0.0\n",
       "10021.135 266.58603 -29.006046 0.023192262  -0.17183326 58.983257  139.42741 ... 115.27116513252258     0.0     0.0     0.0     0.0     0.0\n",
       "11217.242 260.67258 -32.080307   354.67896    2.4407566  76.96116  214.30273 ...  64.04110872745514     0.0     0.0     0.0     0.0     0.0\n",
       " 18494.64  267.2745 -36.705185    353.7175   -4.6370916 38.942596  226.92189 ... 111.12007641792297     0.0     0.0     0.0     0.0     0.0\n",
       "13333.997 265.57535 -31.519987   357.42413   -0.7436878  43.89681  230.49913 ... 23.238793969154358     0.0     0.0     0.0     0.0     0.0\n",
       "13160.466 269.90778 -24.671898   5.2616706  -0.52015543   60.9269  226.45708 ... 1.9516496658325195     0.0     0.0     0.0     0.0     0.0\n",
       "387834.72  270.3779  -21.56711    8.171749   0.64531475 56.755512  221.84715 ... 34.214694023132324     0.0     0.0     0.0     0.0     0.0\n",
       " 20559.74  268.5538 -26.345692    3.200638  -0.30328986 49.523575  233.67285 ... 103.17629969120026     0.0     0.0     0.0     0.0     0.0\n",
       "27209.146 266.59344  -30.52607   358.72775  -0.96718174   62.1856  140.27434 ...  43.22334712743759     0.0     0.0     0.0     0.0     0.0\n",
       "13911.061 269.30997 -27.239439   2.7684028   -1.3365301  65.15399  224.52101 ...  95.46356403827667     0.0     0.0     0.0     0.0     0.0\n",
       "13226.425 265.16287 -27.344238   0.7796942    1.7680178  59.38332   126.7019 ...  3.733097553253174     0.0     0.0     0.0     0.0     0.0\n",
       "17445.463 266.63342 -28.807201  0.21464892   -0.1039705  55.48627  135.59155 ...  80.52235281467438     0.0     0.0     0.0     0.0     0.0\n",
       "13133.864 270.42474 -22.651058    7.251185  0.071358204 48.704975  134.73102 ... 117.88173341751099     0.0     0.0     0.0     0.0     0.0\n",
       "32095.705  266.0002  -29.77206    359.1034  -0.13615231 45.013103  236.72498 ... 108.92976492643356     0.0     0.0     0.0     0.0     0.0\n",
       "18465.783 266.39728 -29.105953   359.85202  -0.08294058  55.97552  135.87787 ...  70.72638684511185     0.0     0.0     0.0     0.0     0.0\n",
       " 14457.25 262.72217 -34.388405    353.7184  -0.26906812 45.683174  237.74162 ...  147.4274787902832     0.0     0.0     0.0     0.0     0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_3fhl.table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can do *len* over event_3fhl.table to find the total number of events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.181512Z",
     "iopub.status.busy": "2021-11-22T21:08:05.181145Z",
     "iopub.status.idle": "2021-11-22T21:08:05.182581Z",
     "shell.execute_reply": "2021-11-22T21:08:05.182764Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32843"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(events_3fhl.table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can access any other attribute of the `Table` object as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.184800Z",
     "iopub.status.busy": "2021-11-22T21:08:05.184483Z",
     "iopub.status.idle": "2021-11-22T21:08:05.185873Z",
     "shell.execute_reply": "2021-11-22T21:08:05.186116Z"
    }
   },
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_3fhl.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.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": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.188148Z",
     "iopub.status.busy": "2021-11-22T21:08:05.187834Z",
     "iopub.status.idle": "2021-11-22T21:08:05.189889Z",
     "shell.execute_reply": "2021-11-22T21:08:05.190157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$[12.186643,~25.496599,~15.621499,~\\dots,~32.095707,~18.465784,~14.457251] \\; \\mathrm{GeV}$$"
      ],
      "text/plain": [
       "<Quantity [12.186643, 25.4966  , 15.621499, ..., 32.095707, 18.465784,\n",
       "           14.457251] GeV>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_3fhl.energy.to(\"GeV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.192213Z",
     "iopub.status.busy": "2021-11-22T21:08:05.191899Z",
     "iopub.status.idle": "2021-11-22T21:08:05.199567Z",
     "shell.execute_reply": "2021-11-22T21:08:05.199751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (Galactic): (l, b) in deg\n",
       "    [(353.36228879,  1.75408483), (353.09562941,  0.6522806 ),\n",
       "     (353.05628243,  2.44528685), ..., (359.10295505, -0.1359316 ),\n",
       "     (359.85157506, -0.08269984), (353.71795506, -0.26883694)]>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_3fhl.galactic\n",
    "# events_3fhl.radec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.201408Z",
     "iopub.status.busy": "2021-11-22T21:08:05.201093Z",
     "iopub.status.idle": "2021-11-22T21:08:05.206027Z",
     "shell.execute_reply": "2021-11-22T21:08:05.206269Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Time object: scale='tt' format='mjd' value=[54682.82946153 54682.89243456 54682.89709472 ... 57236.75267735\n",
       " 57233.37455141 57233.44802852]>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_3fhl.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": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.209472Z",
     "iopub.status.busy": "2021-11-22T21:08:05.209155Z",
     "iopub.status.idle": "2021-11-22T21:08:05.221195Z",
     "shell.execute_reply": "2021-11-22T21:08:05.221426Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$1917.8592 \\; \\mathrm{GeV}$$"
      ],
      "text/plain": [
       "<Quantity 1917.85925 GeV>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select all events within a radius of 0.5 deg around center\n",
    "from gammapy.utils.regions import SphericalCircleSkyRegion\n",
    "\n",
    "region = SphericalCircleSkyRegion(center, radius=0.5 * u.deg)\n",
    "events_gc_3fhl = events_3fhl.select_region(region)\n",
    "\n",
    "# sort events by energy\n",
    "events_gc_3fhl.table.sort(\"ENERGY\")\n",
    "\n",
    "# and show highest energy photon\n",
    "events_gc_3fhl.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`\n",
    "* Sort and index the underlying Astropy tables\n",
    "* Access data from individual sources\n",
    "\n",
    "Let's start with importing the 3FHL catalog object from the `~gammapy.catalog` submodule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.224396Z",
     "iopub.status.busy": "2021-11-22T21:08:05.224078Z",
     "iopub.status.idle": "2021-11-22T21:08:05.261209Z",
     "shell.execute_reply": "2021-11-22T21:08:05.261425Z"
    }
   },
   "outputs": [],
   "source": [
    "from gammapy.catalog import SourceCatalog3FHL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we initialize the Fermi-LAT 3FHL catalog and directly take a look at the `.table` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.263879Z",
     "iopub.status.busy": "2021-11-22T21:08:05.263247Z",
     "iopub.status.idle": "2021-11-22T21:08:05.341537Z",
     "shell.execute_reply": "2021-11-22T21:08:05.341768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=1556</i>\n",
       "<table id=\"table5085609600\" 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>Conf_95_SemiMajor</th><th>Conf_95_SemiMinor</th><th>Conf_95_PosAng</th><th>ROI_num</th><th>Signif_Avg</th><th>Pivot_Energy</th><th>Flux_Density</th><th>Unc_Flux_Density</th><th>Flux</th><th>Unc_Flux</th><th>Energy_Flux</th><th>Unc_Energy_Flux</th><th>Signif_Curve</th><th>SpectrumType</th><th>Spectral_Index</th><th>Unc_Spectral_Index</th><th>beta</th><th>Unc_beta</th><th>PowerLaw_Index</th><th>Unc_PowerLaw_Index</th><th>Flux_Band [5]</th><th>Unc_Flux_Band [5,2]</th><th>nuFnu [5]</th><th>Sqrt_TS_Band [5]</th><th>Npred</th><th>HEP_Energy</th><th>HEP_Prob</th><th>Variability_BayesBlocks</th><th>Extended_Source_Name</th><th>ASSOC_GAM</th><th>TEVCAT_FLAG</th><th>ASSOC_TEV</th><th>CLASS</th><th>ASSOC1</th><th>ASSOC2</th><th>ASSOC_PROB_BAY</th><th>ASSOC_PROB_LR</th><th>Redshift</th><th>NuPeak_obs</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th></th><th></th><th>GeV</th><th>1 / (cm2 GeV s)</th><th>1 / (cm2 GeV s)</th><th>1 / (cm2 s)</th><th>1 / (cm2 s)</th><th>erg / (cm2 s)</th><th>erg / (cm2 s)</th><th></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></th><th></th><th>GeV</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>Hz</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>int16</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>bytes11</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>bytes18</th><th>bytes18</th><th>bytes1</th><th>bytes21</th><th>bytes7</th><th>bytes26</th><th>bytes26</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
       "<tr><td>3FHL J0001.2-0748</td><td>0.3107</td><td>-7.8075</td><td>89.0094</td><td>-67.3118</td><td>0.0424</td><td>0.0424</td><td>--</td><td>64</td><td>5.362</td><td>23.73</td><td>5.3174e-13</td><td>2.0975e-13</td><td>2.9593e-11</td><td>1.1704e-11</td><td>1.6752e-12</td><td>1.0743e-12</td><td>1.02</td><td>PowerLaw</td><td>1.6724</td><td>0.8274</td><td>0.5916</td><td>0.7129</td><td>2.2226</td><td>0.4808</td><td>1.1127661e-11 .. 1.1422301e-22</td><td>-6.0763976e-12 .. 6.529277e-12</td><td>3.533989e-13 .. 1.1789072e-22</td><td>3.1458344 .. 0.0</td><td>7.63</td><td>86.975</td><td>0.9964</td><td>1</td><td></td><td>3FGL J0001.2-0748</td><td>N</td><td></td><td>bll</td><td>PMN J0001-0746</td><td></td><td>0.9974</td><td>0.9721</td><td>--</td><td>306196370000000.0</td></tr>\n",
       "<tr><td>3FHL J0001.9-4155</td><td>0.4849</td><td>-41.9303</td><td>334.1216</td><td>-72.0697</td><td>0.1018</td><td>0.1018</td><td>--</td><td>429</td><td>5.638</td><td>28.42</td><td>5.4253e-13</td><td>1.6839e-13</td><td>4.3230e-11</td><td>1.3428e-11</td><td>3.4900e-12</td><td>1.8276e-12</td><td>0.45</td><td>PowerLaw</td><td>1.7819</td><td>0.4941</td><td>0.1187</td><td>0.2798</td><td>1.9418</td><td>0.3100</td><td>2.1003905e-11 .. 1.9287885e-18</td><td>-8.032091e-12 .. 5.8594097e-12</td><td>6.7452245e-13 .. 2.078675e-18</td><td>4.899907 .. 0.0</td><td>12.51</td><td>266.625</td><td>0.9622</td><td>1</td><td></td><td>3FGL J0002.2-4152</td><td>N</td><td></td><td>bcu</td><td>1RXS J000135.5-415519</td><td></td><td>0.9960</td><td>0.0000</td><td>--</td><td>6309576500000000.0</td></tr>\n",
       "<tr><td>3FHL J0002.1-6728</td><td>0.5283</td><td>-67.4825</td><td>310.0868</td><td>-48.9549</td><td>0.0357</td><td>0.0357</td><td>--</td><td>386</td><td>8.470</td><td>20.82</td><td>1.2062e-12</td><td>3.2106e-13</td><td>5.0093e-11</td><td>1.3349e-11</td><td>2.3058e-12</td><td>9.5580e-13</td><td>1.53</td><td>PowerLaw</td><td>1.8109</td><td>0.6260</td><td>0.7933</td><td>0.5956</td><td>2.4285</td><td>0.3710</td><td>2.4550664e-11 .. 1.9009976e-21</td><td>-8.634195e-12 .. 4.8021903e-12</td><td>7.7340695e-13 .. 1.9026535e-21</td><td>5.900217 .. 0.0</td><td>17.11</td><td>52.152</td><td>0.9988</td><td>1</td><td></td><td>3FGL J0002.0-6722</td><td>N</td><td></td><td>bcu</td><td>SUMSS J000215-672653</td><td></td><td>0.0000</td><td>0.9395</td><td>--</td><td>4466832000000000.0</td></tr>\n",
       "<tr><td>3FHL J0003.3-5248</td><td>0.8300</td><td>-52.8150</td><td>318.9245</td><td>-62.7936</td><td>0.0425</td><td>0.0425</td><td>--</td><td>145</td><td>7.229</td><td>23.66</td><td>7.5065e-13</td><td>2.3102e-13</td><td>4.1560e-11</td><td>1.2839e-11</td><td>2.2874e-12</td><td>1.1145e-12</td><td>1.70</td><td>PowerLaw</td><td>1.6010</td><td>0.5644</td><td>0.9972</td><td>0.1721</td><td>2.2481</td><td>0.3732</td><td>2.0886386e-11 .. 7.5867555e-23</td><td>-8.143967e-12 .. 5.31299e-12</td><td>6.6265456e-13 .. 7.800202e-23</td><td>5.298393 .. 0.0</td><td>13.02</td><td>67.310</td><td>0.9636</td><td>1</td><td></td><td>3FGL J0003.2-5246</td><td>N</td><td></td><td>bcu</td><td>RBS 0006</td><td></td><td>0.9996</td><td>0.9716</td><td>--</td><td>7.079464e+16</td></tr>\n",
       "<tr><td>3FHL J0007.0+7303</td><td>1.7647</td><td>73.0560</td><td>119.6625</td><td>10.4666</td><td>0.0101</td><td>0.0101</td><td>--</td><td>277</td><td>75.265</td><td>12.80</td><td>1.7436e-10</td><td>7.5950e-12</td><td>1.5308e-09</td><td>6.1341e-11</td><td>3.6785e-11</td><td>1.5973e-12</td><td>3.24</td><td>LogParabola</td><td>3.1751</td><td>0.2103</td><td>0.9021</td><td>0.2659</td><td>3.8315</td><td>0.1141</td><td>1.3514667e-09 .. 3.839895e-18</td><td>-5.7581186e-11 .. 4.060418e-12</td><td>4.109739e-11 .. 2.9231144e-18</td><td>71.33829 .. 0.0</td><td>654.15</td><td>60.292</td><td>0.9972</td><td>1</td><td></td><td>3FGL J0007.0+7302</td><td>E</td><td>CTA 1</td><td>PSR</td><td>LAT PSR J0007+7303</td><td></td><td>1.0000</td><td>0.0000</td><td>--</td><td>--</td></tr>\n",
       "<tr><td>3FHL J0007.9+4711</td><td>1.9931</td><td>47.1920</td><td>115.3093</td><td>-15.0354</td><td>0.0196</td><td>0.0196</td><td>--</td><td>302</td><td>17.774</td><td>17.19</td><td>5.9778e-12</td><td>8.7683e-13</td><td>1.5131e-10</td><td>2.2181e-11</td><td>5.1444e-12</td><td>1.0540e-12</td><td>0.56</td><td>PowerLaw</td><td>2.6783</td><td>0.4196</td><td>0.1696</td><td>0.3282</td><td>2.8588</td><td>0.2685</td><td>1.0582407e-10 .. 1.9819723e-16</td><td>-1.7538379e-11 .. 4.823511e-12</td><td>3.278615e-12 .. 1.8668298e-16</td><td>15.209969 .. 0.0</td><td>50.95</td><td>68.152</td><td>0.9759</td><td>1</td><td></td><td>3FGL J0008.0+4713</td><td>N</td><td></td><td>bll</td><td>MG4 J000800+4712</td><td></td><td>1.0000</td><td>0.9873</td><td>0.2800</td><td>2511884200000000.0</td></tr>\n",
       "<tr><td>3FHL J0008.4-2339</td><td>2.1243</td><td>-23.6514</td><td>50.2908</td><td>-79.7021</td><td>0.0366</td><td>0.0366</td><td>--</td><td>517</td><td>9.679</td><td>16.96</td><td>3.0610e-12</td><td>7.3475e-13</td><td>7.4602e-11</td><td>1.7896e-11</td><td>2.4733e-12</td><td>8.1716e-13</td><td>0.34</td><td>PowerLaw</td><td>2.7388</td><td>0.7145</td><td>0.1737</td><td>0.5618</td><td>2.9070</td><td>0.4520</td><td>5.804992e-11 .. 1.1117311e-20</td><td>-1.4419374e-11 .. 6.10661e-12</td><td>1.7951775e-12 .. 1.0403958e-20</td><td>9.133706 .. 0.0</td><td>19.83</td><td>71.122</td><td>0.9968</td><td>1</td><td></td><td>3FGL J0008.6-2340</td><td>N</td><td></td><td>bll</td><td>RBS 0016</td><td></td><td>0.9996</td><td>0.9673</td><td>0.1470</td><td>524807800000000.0</td></tr>\n",
       "<tr><td>3FHL J0009.1+0628</td><td>2.2874</td><td>6.4814</td><td>104.4637</td><td>-54.8669</td><td>0.0385</td><td>0.0385</td><td>--</td><td>402</td><td>6.282</td><td>18.92</td><td>1.2691e-12</td><td>4.3696e-13</td><td>4.1597e-11</td><td>1.4317e-11</td><td>1.6903e-12</td><td>8.9372e-13</td><td>0.10</td><td>PowerLaw</td><td>2.5529</td><td>0.8363</td><td>0.0122</td><td>0.4477</td><td>2.5800</td><td>0.5391</td><td>2.4161059e-11 .. 6.6482124e-19</td><td>-9.546595e-12 .. 6.287476e-12</td><td>7.566492e-13 .. 6.5095056e-19</td><td>4.678369 .. 0.0</td><td>10.95</td><td>12.256</td><td>0.9721</td><td>1</td><td></td><td>3FGL J0009.1+0630</td><td>N</td><td></td><td>bll</td><td>CRATES J000903.95+062821.5</td><td></td><td>0.9993</td><td>0.9878</td><td>--</td><td>663742400000000.0</td></tr>\n",
       "<tr><td>3FHL J0009.4+5030</td><td>2.3504</td><td>50.5049</td><td>116.1257</td><td>-11.8105</td><td>0.0176</td><td>0.0176</td><td>--</td><td>302</td><td>22.402</td><td>17.04</td><td>9.8252e-12</td><td>1.3192e-12</td><td>2.2191e-10</td><td>2.6212e-11</td><td>8.7336e-12</td><td>1.2488e-12</td><td>3.15</td><td>LogParabola</td><td>1.4305</td><td>0.3505</td><td>0.7965</td><td>0.3072</td><td>2.3610</td><td>0.1611</td><td>1.16274e-10 .. 9.252794e-17</td><td>-1.8225135e-11 .. 4.417993e-12</td><td>3.8564165e-12 .. 7.0436765e-17</td><td>15.780677 .. 0.0</td><td>78.50</td><td>72.762</td><td>0.9950</td><td>2</td><td></td><td>3FGL J0009.3+5030</td><td>C</td><td></td><td>bll</td><td>NVSS J000922+503028</td><td></td><td>1.0000</td><td>0.9698</td><td>--</td><td>1412536400000000.0</td></tr>\n",
       "<tr><td>3FHL J0009.7-4319</td><td>2.4450</td><td>-43.3195</td><td>327.7563</td><td>-71.7445</td><td>0.0496</td><td>0.0496</td><td>--</td><td>429</td><td>4.957</td><td>16.77</td><td>1.0257e-12</td><td>4.2463e-13</td><td>2.4189e-11</td><td>1.0008e-11</td><td>7.8730e-13</td><td>4.3643e-13</td><td>1.00</td><td>PowerLaw</td><td>2.2044</td><td>0.9640</td><td>0.9994</td><td>0.0609</td><td>2.9442</td><td>0.7655</td><td>1.4262526e-11 .. 1.124737e-17</td><td>-6.5860017e-12 .. 5.5998656e-12</td><td>4.4044038e-13 .. 1.04738585e-17</td><td>4.1469994 .. 0.0</td><td>7.00</td><td>26.010</td><td>0.9912</td><td>1</td><td></td><td></td><td>N</td><td></td><td>bll</td><td>SUMSS J000949-431654</td><td></td><td>0.9985</td><td>0.9072</td><td>--</td><td>--</td></tr>\n",
       "<tr><td>3FHL J0013.0-3956</td><td>3.2523</td><td>-39.9381</td><td>332.3835</td><td>-74.9150</td><td>0.0517</td><td>0.0517</td><td>--</td><td>429</td><td>7.201</td><td>14.27</td><td>3.5994e-12</td><td>1.0357e-12</td><td>4.9878e-11</td><td>1.4376e-11</td><td>1.2946e-12</td><td>4.5911e-13</td><td>0.99</td><td>PowerLaw</td><td>3.0130</td><td>0.9492</td><td>0.9978</td><td>0.1128</td><td>3.6066</td><td>0.7866</td><td>4.1576055e-11 .. 4.364096e-18</td><td>-1.1673498e-11 .. 5.6003054e-12</td><td>1.2527341e-12 .. 3.750808e-18</td><td>7.4368286 .. 0.0</td><td>14.19</td><td>20.627</td><td>0.9837</td><td>1</td><td></td><td>3FGL J0013.2-3954</td><td>N</td><td></td><td>bll</td><td>PKS 0010-401</td><td></td><td>0.9998</td><td>0.9962</td><td>--</td><td>1862089000000.0</td></tr>\n",
       "<tr><td>3FHL J0013.8-1855</td><td>3.4684</td><td>-18.9169</td><td>74.5314</td><td>-78.0876</td><td>0.0485</td><td>0.0485</td><td>--</td><td>152</td><td>5.272</td><td>18.94</td><td>9.4557e-13</td><td>3.6339e-13</td><td>3.1086e-11</td><td>1.1945e-11</td><td>1.2427e-12</td><td>7.0538e-13</td><td>1.06</td><td>PowerLaw</td><td>1.9438</td><td>0.9164</td><td>0.9135</td><td>0.8288</td><td>2.6020</td><td>0.5839</td><td>1.8761324e-11 .. 2.4010398e-18</td><td>-8.03048e-12 .. 6.2478846e-12</td><td>5.8704425e-13 .. 2.343576e-18</td><td>4.4304204 .. 0.0</td><td>8.19</td><td>37.217</td><td>0.9861</td><td>1</td><td></td><td>3FGL J0013.9-1853</td><td>P</td><td>SHBL J001355.9-185406</td><td>bll</td><td>RBS 0030</td><td></td><td>0.9995</td><td>0.9772</td><td>0.0950</td><td>5.260181e+16</td></tr>\n",
       "<tr><td>3FHL J0014.0-5024</td><td>3.5038</td><td>-50.4003</td><td>317.4913</td><td>-65.6568</td><td>0.0597</td><td>0.0597</td><td>--</td><td>239</td><td>4.869</td><td>22.74</td><td>5.9817e-13</td><td>2.2478e-13</td><td>3.0263e-11</td><td>1.1370e-11</td><td>1.7404e-12</td><td>1.0928e-12</td><td>1.45</td><td>PowerLaw</td><td>1.4978</td><td>0.7488</td><td>0.9964</td><td>0.1776</td><td>2.2086</td><td>0.4499</td><td>1.0878415e-11 .. 1.7599409e-17</td><td>-5.7917993e-12 .. 5.8792246e-12</td><td>3.4567488e-13 .. 1.8203253e-17</td><td>3.097385 .. 0.0</td><td>9.23</td><td>26.054</td><td>0.9927</td><td>1</td><td></td><td>3FGL J0014.0-5025</td><td>N</td><td></td><td>bll</td><td>RBS 0032</td><td></td><td>0.9998</td><td>0.8923</td><td>--</td><td>8.413936e+16</td></tr>\n",
       "<tr><td>3FHL J0014.7+5801</td><td>3.6951</td><td>58.0244</td><td>118.0788</td><td>-4.5015</td><td>0.0266</td><td>0.0266</td><td>--</td><td>518</td><td>8.215</td><td>25.55</td><td>8.7309e-13</td><td>2.1415e-13</td><td>5.6736e-11</td><td>1.3982e-11</td><td>3.5097e-12</td><td>1.3787e-12</td><td>0.51</td><td>PowerLaw</td><td>2.0013</td><td>0.4443</td><td>0.1259</td><td>0.2631</td><td>2.1459</td><td>0.2768</td><td>3.2196253e-11 .. 6.0640007e-18</td><td>-9.896842e-12 .. 4.293739e-12</td><td>1.0256123e-12 .. 6.3322123e-18</td><td>6.1053777 .. 0.0</td><td>22.02</td><td>160.817</td><td>0.9943</td><td>1</td><td></td><td>3FGL J0014.7+5802</td><td>N</td><td></td><td>bll</td><td>1RXS J001442.2+580201</td><td></td><td>1.0000</td><td>0.9778</td><td>--</td><td>4.3651522e+16</td></tr>\n",
       "<tr><td>3FHL J0014.9+6118</td><td>3.7366</td><td>61.3025</td><td>118.5645</td><td>-1.2594</td><td>0.0339</td><td>0.0339</td><td>--</td><td>518</td><td>9.091</td><td>15.86</td><td>4.2551e-12</td><td>8.4587e-13</td><td>8.4783e-11</td><td>1.6849e-11</td><td>2.5742e-12</td><td>6.9789e-13</td><td>0.00</td><td>PowerLaw</td><td>3.0910</td><td>0.5482</td><td>0.0089</td><td>0.4114</td><td>3.1010</td><td>0.4416</td><td>6.525263e-11 .. 2.2172363e-18</td><td>-1.3828337e-11 .. 4.048499e-12</td><td>2.0031396e-12 .. 2.0232124e-18</td><td>8.736899 .. 0.0</td><td>33.68</td><td>34.407</td><td>0.9822</td><td>1</td><td></td><td>3FGL J0014.6+6119</td><td>N</td><td></td><td>bcu</td><td>4C +60.01</td><td></td><td>0.9994</td><td>0.9996</td><td>--</td><td>12971790000000.0</td></tr>\n",
       "<tr><td>3FHL J0015.7+5551</td><td>3.9337</td><td>55.8649</td><td>117.9031</td><td>-6.6579</td><td>0.0335</td><td>0.0335</td><td>--</td><td>302</td><td>8.112</td><td>31.18</td><td>5.5030e-13</td><td>1.3879e-13</td><td>5.2164e-11</td><td>1.3207e-11</td><td>4.5355e-12</td><td>1.9094e-12</td><td>0.74</td><td>PowerLaw</td><td>1.6969</td><td>0.3726</td><td>0.1531</td><td>0.2218</td><td>1.8898</td><td>0.2480</td><td>2.4335116e-11 .. 1.9521596e-17</td><td>-8.442904e-12 .. 4.4789077e-12</td><td>7.8311885e-13 .. 2.1210226e-17</td><td>5.2631125 .. 0.0</td><td>19.71</td><td>167.578</td><td>0.9820</td><td>1</td><td></td><td>3FGL J0015.7+5552</td><td>C</td><td></td><td>bll</td><td>GB6 J0015+5551</td><td></td><td>1.0000</td><td>0.9832</td><td>--</td><td>6180169000000000.0</td></tr>\n",
       "<tr><td>3FHL J0018.6+2946</td><td>4.6525</td><td>29.7821</td><td>114.4798</td><td>-32.5503</td><td>0.0312</td><td>0.0312</td><td>--</td><td>522</td><td>7.917</td><td>29.20</td><td>6.0010e-13</td><td>1.7106e-13</td><td>5.0577e-11</td><td>1.4495e-11</td><td>3.8639e-12</td><td>1.7982e-12</td><td>0.92</td><td>PowerLaw</td><td>1.6802</td><td>0.4901</td><td>0.2639</td><td>0.3270</td><td>1.9816</td><td>0.2912</td><td>2.3603335e-11 .. 0.0</td><td>-9.441283e-12 .. 5.7002103e-12</td><td>7.568024e-13 .. 0.0</td><td>4.5989604 .. 0.0</td><td>15.13</td><td>127.317</td><td>0.9988</td><td>1</td><td></td><td>3FGL J0018.4+2947</td><td>N</td><td></td><td>bll</td><td>RBS 0042</td><td></td><td>0.9985</td><td>0.9711</td><td>0.1000</td><td>5.9156075e+16</td></tr>\n",
       "<tr><td>3FHL J0019.1-8151</td><td>4.7762</td><td>-81.8621</td><td>304.3273</td><td>-35.1783</td><td>0.0439</td><td>0.0439</td><td>--</td><td>382</td><td>7.504</td><td>16.95</td><td>2.3874e-12</td><td>6.0485e-13</td><td>5.8043e-11</td><td>1.4696e-11</td><td>1.9468e-12</td><td>6.8861e-13</td><td>0.68</td><td>PowerLaw</td><td>2.5737</td><td>0.7694</td><td>0.3901</td><td>0.7378</td><td>2.8844</td><td>0.4717</td><td>3.5834426e-11 .. 3.3873322e-25</td><td>-1.0997976e-11 .. 4.837986e-12</td><td>1.1091266e-12 .. 3.1795783e-25</td><td>6.12071 .. 0.0</td><td>19.34</td><td>57.525</td><td>0.8990</td><td>1</td><td></td><td>3FGL J0018.9-8152</td><td>N</td><td></td><td>bll</td><td>PMN J0019-8152</td><td></td><td>0.9998</td><td>0.9878</td><td>--</td><td>1393156900000000.0</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><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>3FHL J2338.9+2123</td><td>354.7450</td><td>21.3966</td><td>101.2522</td><td>-38.4057</td><td>0.0284</td><td>0.0284</td><td>--</td><td>185</td><td>7.247</td><td>23.33</td><td>9.0048e-13</td><td>2.7961e-13</td><td>4.8313e-11</td><td>1.5041e-11</td><td>2.6473e-12</td><td>1.3074e-12</td><td>0.82</td><td>PowerLaw</td><td>1.9093</td><td>0.6666</td><td>0.3358</td><td>0.4978</td><td>2.2522</td><td>0.3767</td><td>2.564995e-11 .. 9.101627e-21</td><td>-9.6132095e-12 .. 5.884382e-12</td><td>8.136546e-13 .. 9.351896e-21</td><td>5.4281335 .. 0.0</td><td>13.72</td><td>75.646</td><td>0.9983</td><td>1</td><td></td><td>3FGL J2339.0+2122</td><td>N</td><td></td><td>bll</td><td>RX J2338.8+2124</td><td></td><td>0.9998</td><td>0.9769</td><td>0.2910</td><td>524807800000000.0</td></tr>\n",
       "<tr><td>3FHL J2339.2-7404</td><td>354.8159</td><td>-74.0796</td><td>309.5087</td><td>-42.1144</td><td>0.0332</td><td>0.0332</td><td>--</td><td>55</td><td>8.664</td><td>28.07</td><td>6.2505e-13</td><td>1.6352e-13</td><td>4.8749e-11</td><td>1.2787e-11</td><td>3.6711e-12</td><td>1.6064e-12</td><td>1.52</td><td>PowerLaw</td><td>1.5806</td><td>0.4650</td><td>0.4661</td><td>0.3798</td><td>1.9921</td><td>0.2731</td><td>2.6423676e-11 .. 6.5882064e-17</td><td>-8.613914e-12 .. 4.8000375e-12</td><td>8.468765e-13 .. 7.044718e-17</td><td>6.531144 .. 0.0</td><td>17.02</td><td>126.770</td><td>0.9600</td><td>1</td><td></td><td>3FGL J2338.7-7401</td><td>N</td><td></td><td>bcu</td><td>1RXS J233919.8-740439</td><td></td><td>0.9999</td><td>0.9847</td><td>--</td><td>1.56675e+16</td></tr>\n",
       "<tr><td>3FHL J2339.5-0533</td><td>354.8927</td><td>-5.5577</td><td>81.3085</td><td>-62.4702</td><td>0.0448</td><td>0.0448</td><td>--</td><td>34</td><td>9.451</td><td>12.47</td><td>1.0697e-11</td><td>2.5392e-12</td><td>8.0465e-11</td><td>1.9050e-11</td><td>1.6710e-12</td><td>4.1909e-13</td><td>1.03</td><td>PowerLaw</td><td>5.9809</td><td>1.1763</td><td>-0.7543</td><td>0.3721</td><td>5.3548</td><td>1.0701</td><td>7.264754e-11 .. 5.1358784e-16</td><td>-1.6699227e-11 .. 7.735961e-12</td><td>2.067454e-12 .. 3.8239114e-16</td><td>9.646015 .. 0.0</td><td>20.48</td><td>39.666</td><td>0.9714</td><td>1</td><td></td><td>3FGL J2339.6-0533</td><td>N</td><td></td><td>PSR</td><td>PSR J2339-0533</td><td></td><td>1.0000</td><td>0.0000</td><td>--</td><td>--</td></tr>\n",
       "<tr><td>3FHL J2340.8+8015</td><td>355.2244</td><td>80.2593</td><td>119.8457</td><td>17.8129</td><td>0.0167</td><td>0.0167</td><td>--</td><td>277</td><td>23.252</td><td>21.00</td><td>5.1759e-12</td><td>5.6611e-13</td><td>2.1901e-10</td><td>2.3964e-11</td><td>1.0479e-11</td><td>1.8366e-12</td><td>2.73</td><td>PowerLaw</td><td>1.9285</td><td>0.2600</td><td>0.5734</td><td>0.2595</td><td>2.3864</td><td>0.1503</td><td>1.2925577e-10 .. 2.6074902e-17</td><td>-1.7942073e-11 .. 3.828835e-12</td><td>4.0786077e-12 .. 2.6260199e-17</td><td>17.065054 .. 0.0</td><td>94.02</td><td>158.182</td><td>0.9509</td><td>1</td><td></td><td>3FGL J2340.7+8016</td><td>C</td><td></td><td>bll</td><td>1RXS J234051.4+801513</td><td></td><td>1.0000</td><td>0.9970</td><td>0.2740</td><td>3427676800000000.0</td></tr>\n",
       "<tr><td>3FHL J2343.6+3439</td><td>355.9012</td><td>34.6513</td><td>107.4255</td><td>-26.1696</td><td>0.0286</td><td>0.0286</td><td>--</td><td>484</td><td>8.937</td><td>24.36</td><td>1.1204e-12</td><td>2.8008e-13</td><td>6.5701e-11</td><td>1.6429e-11</td><td>4.1626e-12</td><td>1.7981e-12</td><td>1.13</td><td>PowerLaw</td><td>1.7902</td><td>0.4906</td><td>0.3600</td><td>0.4014</td><td>2.1261</td><td>0.2959</td><td>2.8448787e-11 .. 5.3683953e-21</td><td>-1.00504665e-11 .. 5.499488e-12</td><td>9.069466e-13 .. 5.622843e-21</td><td>5.1358323 .. 0.0</td><td>20.09</td><td>72.660</td><td>0.9994</td><td>1</td><td></td><td>3FGL J2343.7+3437</td><td>C</td><td></td><td>bll</td><td>1RXS J234332.5+343957</td><td></td><td>0.9998</td><td>0.9819</td><td>0.3660</td><td>1029200060000000.0</td></tr>\n",
       "<tr><td>3FHL J2345.1-1554</td><td>356.2999</td><td>-15.9151</td><td>65.6780</td><td>-70.9813</td><td>0.0166</td><td>0.0166</td><td>--</td><td>450</td><td>28.822</td><td>17.04</td><td>1.7869e-11</td><td>1.7238e-12</td><td>4.4142e-10</td><td>4.2557e-11</td><td>1.4800e-11</td><td>1.9974e-12</td><td>2.07</td><td>PowerLaw</td><td>2.4593</td><td>0.3078</td><td>0.6222</td><td>0.3633</td><td>2.8852</td><td>0.1826</td><td>3.1803404e-10 .. 5.8906217e-21</td><td>-3.5389455e-11 .. 6.2676912e-12</td><td>9.843321e-12 .. 5.528767e-21</td><td>24.107079 .. 0.0</td><td>114.62</td><td>69.571</td><td>0.9585</td><td>3</td><td></td><td>3FGL J2345.2-1554</td><td>N</td><td></td><td>FSRQ</td><td>PMN J2345-1555</td><td></td><td>0.9999</td><td>0.9978</td><td>0.6210</td><td>20535250000000.0</td></tr>\n",
       "<tr><td>3FHL J2346.6+0705</td><td>356.6726</td><td>7.0844</td><td>95.9910</td><td>-52.3676</td><td>0.0232</td><td>0.0232</td><td>--</td><td>402</td><td>13.667</td><td>25.33</td><td>2.0843e-12</td><td>3.7923e-13</td><td>1.3252e-10</td><td>2.4135e-11</td><td>8.7692e-12</td><td>2.7196e-12</td><td>2.28</td><td>PowerLaw</td><td>1.5568</td><td>0.3805</td><td>0.6154</td><td>0.3448</td><td>2.0910</td><td>0.2071</td><td>5.3996665e-11 .. 1.5570329e-16</td><td>-1.4772433e-11 .. 6.39344e-12</td><td>1.7238113e-12 .. 1.6396604e-16</td><td>7.4013906 .. 0.0</td><td>35.13</td><td>74.636</td><td>0.9967</td><td>1</td><td></td><td>3FGL J2346.7+0705</td><td>C</td><td></td><td>bll</td><td>TXS 2344+068</td><td></td><td>0.9999</td><td>0.9971</td><td>0.1720</td><td>568853050000000.0</td></tr>\n",
       "<tr><td>3FHL J2347.0+5142</td><td>356.7688</td><td>51.7008</td><td>112.8895</td><td>-9.9116</td><td>0.0129</td><td>0.0129</td><td>--</td><td>248</td><td>29.450</td><td>31.39</td><td>3.4657e-12</td><td>3.2859e-13</td><td>3.3165e-10</td><td>3.1490e-11</td><td>3.0741e-11</td><td>4.9987e-12</td><td>0.60</td><td>PowerLaw</td><td>1.7869</td><td>0.1340</td><td>0.0454</td><td>0.0730</td><td>1.8463</td><td>0.0925</td><td>1.4582405e-10 .. 5.3311014e-12</td><td>-2.0452615e-11 .. 5.501045e-12</td><td>4.700856e-12 .. 5.8319695e-12</td><td>17.46963 .. 2.4212334</td><td>120.69</td><td>652.312</td><td>0.9629</td><td>1</td><td></td><td>3FGL J2347.0+5142</td><td>P</td><td>1ES 2344+514</td><td>bll</td><td>1ES 2344+514</td><td></td><td>1.0000</td><td>0.9985</td><td>0.0440</td><td>7079464000000000.0</td></tr>\n",
       "<tr><td>3FHL J2347.9+5435</td><td>356.9805</td><td>54.5974</td><td>113.7400</td><td>-7.1368</td><td>0.0276</td><td>0.0276</td><td>--</td><td>248</td><td>10.942</td><td>22.66</td><td>1.4071e-12</td><td>2.9176e-13</td><td>7.0715e-11</td><td>1.4671e-11</td><td>3.8764e-12</td><td>1.3330e-12</td><td>1.11</td><td>PowerLaw</td><td>1.9329</td><td>0.4328</td><td>0.3293</td><td>0.3409</td><td>2.2519</td><td>0.2615</td><td>4.5147826e-11 .. 2.1944359e-17</td><td>-1.1016588e-11 .. 4.4637232e-12</td><td>1.4321772e-12 .. 2.2549025e-17</td><td>8.825034 .. 0.0</td><td>26.27</td><td>116.419</td><td>0.8977</td><td>1</td><td></td><td>3FGL J2347.9+5436</td><td>N</td><td></td><td>bcu</td><td>NVSS J234753+543627</td><td></td><td>0.0000</td><td>0.9766</td><td>--</td><td>2.5118841e+16</td></tr>\n",
       "<tr><td>3FHL J2347.9-1630</td><td>356.9978</td><td>-16.5106</td><td>65.5355</td><td>-71.8766</td><td>0.0288</td><td>0.0288</td><td>--</td><td>450</td><td>9.297</td><td>16.28</td><td>3.1279e-12</td><td>8.0896e-13</td><td>6.7585e-11</td><td>1.7478e-11</td><td>2.0267e-12</td><td>6.5608e-13</td><td>0.07</td><td>PowerLaw</td><td>3.1259</td><td>0.7781</td><td>0.0104</td><td>0.5756</td><td>3.1324</td><td>0.5259</td><td>5.2519888e-11 .. 1.0747592e-20</td><td>-1.4230782e-11 .. 6.211813e-12</td><td>1.6103665e-12 .. 9.768252e-21</td><td>8.333468 .. 0.0</td><td>17.55</td><td>50.215</td><td>0.9869</td><td>3</td><td></td><td>3FGL J2348.0-1630</td><td>N</td><td></td><td>fsrq</td><td>PKS 2345-16</td><td></td><td>0.9994</td><td>0.9999</td><td>0.5760</td><td>9332549000000.0</td></tr>\n",
       "<tr><td>3FHL J2350.5-3006</td><td>357.6354</td><td>-30.1070</td><td>16.7759</td><td>-76.3194</td><td>0.0491</td><td>0.0491</td><td>--</td><td>70</td><td>6.497</td><td>21.20</td><td>1.0879e-12</td><td>3.2997e-13</td><td>4.7039e-11</td><td>1.4274e-11</td><td>2.2909e-12</td><td>1.1390e-12</td><td>0.63</td><td>PowerLaw</td><td>2.1012</td><td>0.6173</td><td>0.2880</td><td>0.4870</td><td>2.3678</td><td>0.4234</td><td>2.1939225e-11 .. 4.5892933e-16</td><td>-8.926376e-12 .. 6.097474e-12</td><td>6.927891e-13 .. 4.63469e-16</td><td>4.0536985 .. 0.0</td><td>12.84</td><td>49.286</td><td>0.9644</td><td>1</td><td></td><td>3FGL J2350.4-3004</td><td>N</td><td></td><td>bll</td><td>NVSS J235034-300603</td><td></td><td>0.9998</td><td>0.9218</td><td>0.2237</td><td>3981075200000000.0</td></tr>\n",
       "<tr><td>3FHL J2351.5-7559</td><td>357.8926</td><td>-75.9890</td><td>307.6546</td><td>-40.5855</td><td>0.0650</td><td>0.0650</td><td>--</td><td>55</td><td>6.067</td><td>26.82</td><td>4.9826e-13</td><td>1.6350e-13</td><td>3.5689e-11</td><td>1.1769e-11</td><td>2.3897e-12</td><td>1.2622e-12</td><td>0.61</td><td>PowerLaw</td><td>1.8474</td><td>0.5802</td><td>0.2003</td><td>0.3661</td><td>2.0816</td><td>0.3532</td><td>2.3730832e-11 .. 6.9375605e-17</td><td>-8.570627e-12 .. 4.7928705e-12</td><td>7.578736e-13 .. 7.316245e-17</td><td>5.2754674 .. 0.0</td><td>12.41</td><td>134.721</td><td>0.9892</td><td>1</td><td></td><td>3FGL J2351.9-7601</td><td>N</td><td></td><td>bll</td><td>SUMSS J235115-760012</td><td></td><td>0.0000</td><td>0.9625</td><td>--</td><td>--</td></tr>\n",
       "<tr><td>3FHL J2352.1+1753</td><td>358.0415</td><td>17.8865</td><td>103.5764</td><td>-42.7466</td><td>0.0838</td><td>0.0838</td><td>--</td><td>185</td><td>4.117</td><td>16.97</td><td>9.9227e-13</td><td>4.3475e-13</td><td>2.4254e-11</td><td>1.0640e-11</td><td>7.6327e-13</td><td>4.2356e-13</td><td>0.02</td><td>PowerLaw</td><td>3.0175</td><td>1.2164</td><td>0.0100</td><td>0.8524</td><td>3.0166</td><td>0.8270</td><td>1.5997077e-11 .. 2.9107688e-20</td><td>-7.581037e-12 .. 5.821708e-12</td><td>4.926488e-13 .. 2.6849966e-20</td><td>3.5496242 .. 0.0</td><td>6.73</td><td>43.107</td><td>0.9668</td><td>1</td><td></td><td>3FGL J2352.0+1752</td><td>N</td><td></td><td>bll</td><td>CLASS J2352+1749</td><td></td><td>0.9926</td><td>0.0000</td><td>--</td><td>1737799900000000.0</td></tr>\n",
       "<tr><td>3FHL J2356.2+4035</td><td>359.0746</td><td>40.5985</td><td>111.7521</td><td>-21.0732</td><td>0.0298</td><td>0.0298</td><td>--</td><td>312</td><td>7.625</td><td>29.01</td><td>5.2427e-13</td><td>1.5104e-13</td><td>4.3400e-11</td><td>1.2511e-11</td><td>3.6677e-12</td><td>1.8547e-12</td><td>0.35</td><td>PowerLaw</td><td>2.0233</td><td>0.4242</td><td>-0.0706</td><td>0.1926</td><td>1.9095</td><td>0.2975</td><td>2.5777725e-11 .. 3.110794e-16</td><td>-8.514681e-12 .. 5.4134618e-12</td><td>8.2889175e-13 .. 3.3694582e-16</td><td>6.2127647 .. 0.0</td><td>13.81</td><td>417.861</td><td>0.9119</td><td>1</td><td></td><td>3FGL J2356.0+4037</td><td>N</td><td></td><td>bll</td><td>NVSS J235612+403648</td><td></td><td>0.9998</td><td>0.9199</td><td>0.1310</td><td>6309576500000000.0</td></tr>\n",
       "<tr><td>3FHL J2357.4-1717</td><td>359.3690</td><td>-17.2996</td><td>68.4009</td><td>-74.1285</td><td>0.0327</td><td>0.0327</td><td>--</td><td>450</td><td>6.961</td><td>29.52</td><td>5.4394e-13</td><td>1.7370e-13</td><td>4.6654e-11</td><td>1.4945e-11</td><td>3.7598e-12</td><td>1.9583e-12</td><td>1.11</td><td>PowerLaw</td><td>1.5762</td><td>0.5187</td><td>0.3513</td><td>0.3771</td><td>1.9430</td><td>0.3116</td><td>1.9003682e-11 .. 2.714288e-20</td><td>-8.131149e-12 .. 6.4742196e-12</td><td>6.1025685e-13 .. 2.92465e-20</td><td>4.552822 .. 0.0</td><td>12.30</td><td>146.757</td><td>0.9838</td><td>1</td><td></td><td>3FGL J2357.4-1716</td><td>N</td><td></td><td>bll</td><td>RBS 2066</td><td></td><td>0.9999</td><td>0.9631</td><td>--</td><td>8.912525e+16</td></tr>\n",
       "<tr><td>3FHL J2358.4-1808</td><td>359.6205</td><td>-18.1408</td><td>66.5520</td><td>-74.8501</td><td>0.0511</td><td>0.0511</td><td>--</td><td>450</td><td>6.493</td><td>18.23</td><td>1.6335e-12</td><td>4.9686e-13</td><td>4.8680e-11</td><td>1.4811e-11</td><td>1.7825e-12</td><td>7.6480e-13</td><td>1.83</td><td>PowerLaw</td><td>2.0532</td><td>0.6673</td><td>0.9999</td><td>0.0134</td><td>2.7312</td><td>0.5024</td><td>2.6735683e-11 .. 6.0349635e-21</td><td>-9.960717e-12 .. 6.2551535e-12</td><td>8.323882e-13 .. 5.7844478e-21</td><td>4.3616037 .. 0.0</td><td>12.74</td><td>28.304</td><td>0.9845</td><td>1</td><td></td><td>3FGL J2358.6-1809</td><td>N</td><td></td><td></td><td></td><td></td><td>0.0000</td><td>0.0000</td><td>--</td><td>--</td></tr>\n",
       "<tr><td>3FHL J2358.5+3829</td><td>359.6266</td><td>38.4963</td><td>111.6905</td><td>-23.2173</td><td>0.0584</td><td>0.0584</td><td>--</td><td>312</td><td>5.797</td><td>18.24</td><td>1.4104e-12</td><td>4.4534e-13</td><td>4.2106e-11</td><td>1.3321e-11</td><td>1.7404e-12</td><td>9.8271e-13</td><td>0.44</td><td>PowerLaw</td><td>2.7466</td><td>0.6917</td><td>-0.1329</td><td>0.3013</td><td>2.5576</td><td>0.5781</td><td>2.824428e-11 .. 9.750687e-17</td><td>-9.458818e-12 .. 5.2791343e-12</td><td>8.852925e-13 .. 9.5778846e-17</td><td>5.7128677 .. 0.0</td><td>13.13</td><td>57.301</td><td>0.9782</td><td>1</td><td></td><td>3FGL J2358.5+3827</td><td>N</td><td></td><td>bcu</td><td>B3 2355+382</td><td></td><td>0.0000</td><td>0.9254</td><td>--</td><td>--</td></tr>\n",
       "<tr><td>3FHL J2359.1-3038</td><td>359.7760</td><td>-30.6397</td><td>12.7909</td><td>-78.0268</td><td>0.0231</td><td>0.0231</td><td>--</td><td>70</td><td>11.551</td><td>21.21</td><td>1.8903e-12</td><td>4.1965e-13</td><td>8.1774e-11</td><td>1.8149e-11</td><td>4.2849e-12</td><td>1.6806e-12</td><td>0.08</td><td>PowerLaw</td><td>2.2865</td><td>0.4632</td><td>0.0101</td><td>0.2434</td><td>2.2944</td><td>0.3092</td><td>5.5015617e-11 .. 6.037456e-17</td><td>-1.3604539e-11 .. 8.488618e-12</td><td>1.7422797e-12 .. 6.164239e-17</td><td>9.39347 .. 0.0</td><td>22.41</td><td>111.366</td><td>0.9607</td><td>1</td><td></td><td>3FGL J2359.3-3038</td><td>P</td><td>H 2356-309</td><td>bll</td><td>H 2356-309</td><td></td><td>0.9999</td><td>0.9975</td><td>0.1650</td><td>2.818388e+17</td></tr>\n",
       "<tr><td>3FHL J2359.3-2049</td><td>359.8293</td><td>-20.8256</td><td>58.0522</td><td>-76.5411</td><td>0.0722</td><td>0.0722</td><td>--</td><td>580</td><td>4.638</td><td>19.02</td><td>9.1911e-13</td><td>3.6043e-13</td><td>3.0559e-11</td><td>1.1979e-11</td><td>1.2593e-12</td><td>7.4704e-13</td><td>0.32</td><td>PowerLaw</td><td>2.3402</td><td>0.9445</td><td>0.1851</td><td>0.6600</td><td>2.5615</td><td>0.5838</td><td>2.3253791e-11 .. 8.3778735e-21</td><td>-8.939083e-12 .. 6.2386546e-12</td><td>7.2875863e-13 .. 8.224765e-21</td><td>4.8207045 .. 0.0</td><td>8.06</td><td>64.177</td><td>0.9859</td><td>1</td><td></td><td>3FGL J2359.5-2052</td><td>N</td><td></td><td>bll</td><td>TXS 2356-210</td><td></td><td>0.9894</td><td>0.9906</td><td>0.0960</td><td>4073799600000000.0</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=1556>\n",
       "   Source_Name     RAJ2000  DEJ2000    GLON     GLAT   ...           ASSOC2           ASSOC_PROB_BAY ASSOC_PROB_LR Redshift     NuPeak_obs    \n",
       "                     deg      deg      deg      deg    ...                                                                          Hz        \n",
       "     bytes18       float32  float32  float32  float32  ...          bytes26              float32        float32    float32       float32      \n",
       "------------------ -------- -------- -------- -------- ... -------------------------- -------------- ------------- -------- ------------------\n",
       "3FHL J0001.2-0748    0.3107  -7.8075  89.0094 -67.3118 ...                                    0.9974        0.9721       --  306196370000000.0\n",
       "3FHL J0001.9-4155    0.4849 -41.9303 334.1216 -72.0697 ...                                    0.9960        0.0000       -- 6309576500000000.0\n",
       "3FHL J0002.1-6728    0.5283 -67.4825 310.0868 -48.9549 ...                                    0.0000        0.9395       -- 4466832000000000.0\n",
       "3FHL J0003.3-5248    0.8300 -52.8150 318.9245 -62.7936 ...                                    0.9996        0.9716       --       7.079464e+16\n",
       "3FHL J0007.0+7303    1.7647  73.0560 119.6625  10.4666 ...                                    1.0000        0.0000       --                 --\n",
       "3FHL J0007.9+4711    1.9931  47.1920 115.3093 -15.0354 ...                                    1.0000        0.9873   0.2800 2511884200000000.0\n",
       "3FHL J0008.4-2339    2.1243 -23.6514  50.2908 -79.7021 ...                                    0.9996        0.9673   0.1470  524807800000000.0\n",
       "3FHL J0009.1+0628    2.2874   6.4814 104.4637 -54.8669 ...                                    0.9993        0.9878       --  663742400000000.0\n",
       "3FHL J0009.4+5030    2.3504  50.5049 116.1257 -11.8105 ...                                    1.0000        0.9698       -- 1412536400000000.0\n",
       "3FHL J0009.7-4319    2.4450 -43.3195 327.7563 -71.7445 ...                                    0.9985        0.9072       --                 --\n",
       "3FHL J0013.0-3956    3.2523 -39.9381 332.3835 -74.9150 ...                                    0.9998        0.9962       --    1862089000000.0\n",
       "3FHL J0013.8-1855    3.4684 -18.9169  74.5314 -78.0876 ...                                    0.9995        0.9772   0.0950       5.260181e+16\n",
       "3FHL J0014.0-5024    3.5038 -50.4003 317.4913 -65.6568 ...                                    0.9998        0.8923       --       8.413936e+16\n",
       "3FHL J0014.7+5801    3.6951  58.0244 118.0788  -4.5015 ...                                    1.0000        0.9778       --      4.3651522e+16\n",
       "3FHL J0014.9+6118    3.7366  61.3025 118.5645  -1.2594 ...                                    0.9994        0.9996       --   12971790000000.0\n",
       "3FHL J0015.7+5551    3.9337  55.8649 117.9031  -6.6579 ...                                    1.0000        0.9832       -- 6180169000000000.0\n",
       "3FHL J0018.6+2946    4.6525  29.7821 114.4798 -32.5503 ...                                    0.9985        0.9711   0.1000      5.9156075e+16\n",
       "3FHL J0019.1-8151    4.7762 -81.8621 304.3273 -35.1783 ...                                    0.9998        0.9878       -- 1393156900000000.0\n",
       "               ...      ...      ...      ...      ... ...                        ...            ...           ...      ...                ...\n",
       "3FHL J2338.9+2123  354.7450  21.3966 101.2522 -38.4057 ...                                    0.9998        0.9769   0.2910  524807800000000.0\n",
       "3FHL J2339.2-7404  354.8159 -74.0796 309.5087 -42.1144 ...                                    0.9999        0.9847       --        1.56675e+16\n",
       "3FHL J2339.5-0533  354.8927  -5.5577  81.3085 -62.4702 ...                                    1.0000        0.0000       --                 --\n",
       "3FHL J2340.8+8015  355.2244  80.2593 119.8457  17.8129 ...                                    1.0000        0.9970   0.2740 3427676800000000.0\n",
       "3FHL J2343.6+3439  355.9012  34.6513 107.4255 -26.1696 ...                                    0.9998        0.9819   0.3660 1029200060000000.0\n",
       "3FHL J2345.1-1554  356.2999 -15.9151  65.6780 -70.9813 ...                                    0.9999        0.9978   0.6210   20535250000000.0\n",
       "3FHL J2346.6+0705  356.6726   7.0844  95.9910 -52.3676 ...                                    0.9999        0.9971   0.1720  568853050000000.0\n",
       "3FHL J2347.0+5142  356.7688  51.7008 112.8895  -9.9116 ...                                    1.0000        0.9985   0.0440 7079464000000000.0\n",
       "3FHL J2347.9+5435  356.9805  54.5974 113.7400  -7.1368 ...                                    0.0000        0.9766       --      2.5118841e+16\n",
       "3FHL J2347.9-1630  356.9978 -16.5106  65.5355 -71.8766 ...                                    0.9994        0.9999   0.5760    9332549000000.0\n",
       "3FHL J2350.5-3006  357.6354 -30.1070  16.7759 -76.3194 ...                                    0.9998        0.9218   0.2237 3981075200000000.0\n",
       "3FHL J2351.5-7559  357.8926 -75.9890 307.6546 -40.5855 ...                                    0.0000        0.9625       --                 --\n",
       "3FHL J2352.1+1753  358.0415  17.8865 103.5764 -42.7466 ...                                    0.9926        0.0000       -- 1737799900000000.0\n",
       "3FHL J2356.2+4035  359.0746  40.5985 111.7521 -21.0732 ...                                    0.9998        0.9199   0.1310 6309576500000000.0\n",
       "3FHL J2357.4-1717  359.3690 -17.2996  68.4009 -74.1285 ...                                    0.9999        0.9631       --       8.912525e+16\n",
       "3FHL J2358.4-1808  359.6205 -18.1408  66.5520 -74.8501 ...                                    0.0000        0.0000       --                 --\n",
       "3FHL J2358.5+3829  359.6266  38.4963 111.6905 -23.2173 ...                                    0.0000        0.9254       --                 --\n",
       "3FHL J2359.1-3038  359.7760 -30.6397  12.7909 -78.0268 ...                                    0.9999        0.9975   0.1650       2.818388e+17\n",
       "3FHL J2359.3-2049  359.8293 -20.8256  58.0522 -76.5411 ...                                    0.9894        0.9906   0.0960 4073799600000000.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fermi_3fhl = SourceCatalog3FHL()\n",
    "fermi_3fhl.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 `Signif_Avg` to find the top 5 most significant sources:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.343915Z",
     "iopub.status.busy": "2021-11-22T21:08:05.343618Z",
     "iopub.status.idle": "2021-11-22T21:08:05.349771Z",
     "shell.execute_reply": "2021-11-22T21:08:05.350004Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=5</i>\n",
       "<table id=\"table5085888368\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Source_Name</th><th>ASSOC1</th><th>ASSOC2</th><th>CLASS</th><th>Signif_Avg</th></tr></thead>\n",
       "<thead><tr><th>bytes18</th><th>bytes26</th><th>bytes26</th><th>bytes7</th><th>float32</th></tr></thead>\n",
       "<tr><td>3FHL J0534.5+2201</td><td>Crab Nebula</td><td></td><td>PWN</td><td>168.641</td></tr>\n",
       "<tr><td>3FHL J1104.4+3812</td><td>Mkn 421</td><td></td><td>BLL</td><td>144.406</td></tr>\n",
       "<tr><td>3FHL J0835.3-4510</td><td>PSR J0835-4510</td><td>Vela X field</td><td>PSR</td><td>138.801</td></tr>\n",
       "<tr><td>3FHL J0633.9+1746</td><td>PSR J0633+1746</td><td></td><td>PSR</td><td>99.734</td></tr>\n",
       "<tr><td>3FHL J1555.7+1111</td><td>PG 1553+113</td><td></td><td>BLL</td><td>94.411</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=5>\n",
       "   Source_Name               ASSOC1                     ASSOC2            CLASS  Signif_Avg\n",
       "     bytes18                bytes26                    bytes26            bytes7  float32  \n",
       "------------------ -------------------------- -------------------------- ------- ----------\n",
       "3FHL J0534.5+2201  Crab Nebula                                           PWN        168.641\n",
       "3FHL J1104.4+3812  Mkn 421                                               BLL        144.406\n",
       "3FHL J0835.3-4510  PSR J0835-4510             Vela X field               PSR        138.801\n",
       "3FHL J0633.9+1746  PSR J0633+1746                                        PSR         99.734\n",
       "3FHL J1555.7+1111  PG 1553+113                                           BLL         94.411"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort table by significance\n",
    "fermi_3fhl.table.sort(\"Signif_Avg\")\n",
    "\n",
    "# invert the order to find the highest values and take the top 5\n",
    "top_five_TS_3fhl = fermi_3fhl.table[::-1][:5]\n",
    "\n",
    "# print the top five significant sources with association and source class\n",
    "top_five_TS_3fhl[[\"Source_Name\", \"ASSOC1\", \"ASSOC2\", \"CLASS\", \"Signif_Avg\"]]"
   ]
  },
  {
   "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": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.371009Z",
     "iopub.status.busy": "2021-11-22T21:08:05.370583Z",
     "iopub.status.idle": "2021-11-22T21:08:05.371975Z",
     "shell.execute_reply": "2021-11-22T21:08:05.372216Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144.40611\n"
     ]
    }
   ],
   "source": [
    "mkn_421_3fhl = fermi_3fhl[\"3FHL J1104.4+3812\"]\n",
    "\n",
    "# or use any alias source name that is defined in the catalog\n",
    "mkn_421_3fhl = fermi_3fhl[\"Mkn 421\"]\n",
    "print(mkn_421_3fhl.data[\"Signif_Avg\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "* Try to load the Fermi-LAT 2FHL catalog and check the total number of sources it contains.\n",
    "* Select all the sources from the 2FHL catalog which are contained in the Galactic Center region. The methods `~gammapy.maps.WcsGeom.contains()` and `~gammapy.catalog.SourceCatalog.positions` might be helpful for this. 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 also 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": {},
   "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": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.382339Z",
     "iopub.status.busy": "2021-11-22T21:08:05.381987Z",
     "iopub.status.idle": "2021-11-22T21:08:05.383231Z",
     "shell.execute_reply": "2021-11-22T21:08:05.383406Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PowerLawSpectralModel\n",
      "\n",
      "  type      name     value         unit        error   min max frozen link\n",
      "-------- --------- ---------- -------------- --------- --- --- ------ ----\n",
      "spectral     index 2.2202e+00                2.498e-02 nan nan  False     \n",
      "spectral amplitude 1.7132e-10 cm-2 GeV-1 s-1 3.389e-12 nan nan  False     \n",
      "spectral reference 2.2726e+01            GeV 0.000e+00 nan nan   True     \n"
     ]
    }
   ],
   "source": [
    "crab_3fhl = fermi_3fhl[\"Crab Nebula\"]\n",
    "crab_3fhl_spec = crab_3fhl.spectral_model()\n",
    "print(crab_3fhl_spec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `crab_3fhl_spec` is an instance of the `~gammapy.modeling.models.PowerLaw2SpectralModel` model, with the parameter values and errors taken from the 3FHL catalog. \n",
    "\n",
    "Let's plot the spectral model in the energy range between 10 GeV and 2000 GeV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.427326Z",
     "iopub.status.busy": "2021-11-22T21:08:05.427007Z",
     "iopub.status.idle": "2021-11-22T21:08:05.739718Z",
     "shell.execute_reply": "2021-11-22T21:08:05.739963Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OS+FfX24mI0OFFkWCId8PCYqEu/g4Y0iX+swc3o3WdSvzwHsruXLCl3y7I2LKsYlEDCUPiTr1qpbjX0PO5tFBrVj94156P57Cc3M2cvSYSpyIFJd8JQ8z62xm5QM/X2NmYyNlK1qJTWbG5WfV5eMR3enepDpjPlzLJc98wZof94Y6NJGokN+exzPAgcA2tPcAm4HXghZVHsysnplNNbOXzGxkqOKQ8FezUhmeu7YtT111Jlt/OUi/8fMYO2sdh4+qxIlIUeQ3eRz1zAdCBgBPuPsTQMXCXDDwC3+bma084XxvM1tnZqn5SAhNgBnuPhhILkwcEjvMjIta1WL23d3p3/pUnvw0lb5PzmOpCi2KFFp+k8evZnYfcA0ww8zigVKFvOYrQO+sJwLtPQX0ITMZXGlmyWbW0symn/CqAXwNXGFmnwKfFTIOiTEnl09k7G/a8PKNZ7H/cGahxb9OW82B9KOhDk0k4uQ3efwGOAwMcfefyCyQ+FhhLujuKcCuE063B1LdfZO7pwOTgAHuvsLd+57w2gbcCPzZ3c8DLipMHBK7zm1ag1kjunPN2afx0vzMQovzU1VoUaQg8lsY8Sd3H+vucwPH37t7cc551Aay7heSFjiXk4+AO83sWXJ4kNHMhpnZYjNbvH379mILVKJDhdIJjB7YgjeHdSAhLo6rX1jIvZOXs+egCi2K5Ede5UlKSna1JHJ8usvdVwKX5taguz9vZj8C/RITE9sWMT6JUmc3qMqHd3Xl8Y83MGHuJj5bt43RA1vQq/kpoQ5NJKyFy3MeaUDdLMd1gK1FbVR7mEt+lCkVz8g+zXjv1s5UrVCam15fwm3/Xsr2Xw+HOjSRsBUuyWMR0NjM6ptZInAFMLWojZpZPzN7fs+ePUUOUKJfyzqVmXp7Z/7QqymzV//MBePmMGVpGrlVnhaJVXltQ1vJzMaY2etmdtUJ7z2d0/fyaHMisABoamZpZjbE3Y8CtwMzydze9i13X1WY9rNSz0MKqlR8HLed24gP7upCg2rlGfHWMm54eRE/qNCiyH/Jaz+Pd4ANwJfAYOAIcJW7Hzazpe5+ZsmEWTTaz0MK41iG8/qC73h05joMGNmnGVeffRpxcSr3LrEht/088hq2aujuI939vcCugUuBT82sarFHGQQatpKiiI8zbuicWWjxzNNO5sH3V/Gb5xewcfu+UIcmEnJ59TzWAM3dPSPLuevJLFFSwd0jor6Veh5SVO7OO0t/YPT01Rw8cozhPRozrGsDEuLDZdpQpPgVpecxDTgv6wl3fxX4HZBePOEFj3oeUlzMjEvb1mH2iG6c17QGj360joFPz2fVVt1bEpty7XlEC/U8pLh9uOJHHnx/FbsPpHNz9wbccV5jypSKD3VYIsWqKHuYH2/gJOA6ICnrd9z9zmKITyTi9GlZi44Nq/LQjDU89dlGPlz5E48OakW7pCqhDk2kROR3wPYDMhPHCmBJlldY07CVBNNJ5RL5x2WteXVwew4fyeCy5xYwauoq9h9WoUWJfvkatoqkZbnZ0bCVBNu+w0d57KO1vPblZk6tXJYxl7SkW5PqoQ5LpEiKMmF+3OtmNtTMaplZleOvYoxRJKJVKJ3AXwa04K2bOlK6VBzXvfQVv397Gb8cCPt1JSKFkt/kkU5mCfYF/GfISv+VFznBWUlV+ODOrtx2bkPe/foHeoxN4cMVP4Y6LJFil99hq43A2e4eUZsemFk/oF+jRo2GbtiwIdThSIxZtXUP90xezqqte+nd/BT+OqA5NSqVCXVYIvlWHMNWq4ADxRdSyVBtKwml5qdW5r3bOnNP76Z8um4bPcbO4a3FW1RoUaJCfvfzOAZ8Y2afkbmjIKCluiJ5KRUfx63nNKJX81MY+c5y7pm8nGnLtvK3i1tSt0q5UIcnUmj5Hba6PrvzgafNw55WW0k4yMhw/rVwM498uBYH7unVlOs6JqnQooSt3Iat8ps8ygOH3P1Y4DgeKO3uETGUpeQh4SRt9wHuf3clKeu30/a0k3lkUEsa1agY6rBE/p/imPP4BCib5bgs8HFRAws2PSQo4ajOyeV49caz+J/LWrNx+z4ufGIe//x0A0eOZeT9ZZEwkd/kUcbd/68OdeDnsB+w1YS5hCszY1DbOsy+uzsXJNfkH7PW0/+f81n5g/6jI5Ehv8ljv5n93xPmZtYW0NZqIkVUvWJpnrr6TJ67ti079x1mwFPz+fuHazl05FioQxPJVX5XWw0H3jazrYHjWsBvghKRSAzq1fwUOjSoyt9mrOHZORuZteon/j6oFe3rq5CDhKd8l2Q3s1JAU8CAte5+JJiBFSdNmEskmZ+6g5FTlrNl10Gu7XAa9/ZpRoXS+f1/nkjxKfSEuZl1Of6zux9x95XuvuJ44jCzSmbWonjDFYltnRtVY+bwbgzuXJ9/LdxMz7Fz+GzdtlCHJfJf8przGGRmX5jZn8zsIjNrb2bdzGywmb0OTOe/V2GJSDEol5jAn/olM/nmTpQrncCNLy9ixJvfsHu/Ci1KeMhz2MrMTgYuBTqTOddxEFgDzHD3eUGPsAhU20qiweGjx3jq01Se/nwjJ5UrxV/6t+DClqdgpocLJbiK/JBgpNOch0SDNT/u5Z7Jy1nxwx56Jtdk9MAW1FShRQmi4nhIUERC7PRalXj31k7c16cZc9Zvzyy0uEiFFiU0lDxEIkhCfBw3dW/IR8O7cXqtStzzznKueXEh3++MiEpBEkXyWm1Vq6QCEZH8q1+tPJOGduChgS1YtmUPvR5P4cV533IsQ70QKRl59TxeMrMvzezvZnaOmWmxuUiYiIszrulwGrPu7kbHhlUZPX01g575gvU//xrq0CQG5Jo83L0PcA7wOXAx8KWZTTGzYWZWL/jhiUheTj2pLC9e344nrmjD5p37uejJuTz5yQbSj6rQogRPgVdbmVl9oA/QGzjF3dsHI7DipNVWEit27jvMqGmrmbZsK81Oqcgjg1rRuu5JoQ5LIlTQluqaWaK7l/hTS2aWDIwCdgKfuPvk3D6v5CGxZvbqn3ngvRVs//UwQ7s2YHiPJpRNjA91WBJhgrZUtzCJw8xeMrNtZrbyhPO9zWydmaWa2cg8mukDjHf3W4DrChqDSLS7ILkms0d05zdn1eW5lE30eSKFLzftDHVYEkVK/CFBM+sG7ANec/cWgXPxwHrgAiANWARcCcQDY05oYnDgzz8DB4BO7t45t2uq5yGx7IvUHYycsoLvdx3g6rPrMbJPMyqWKRXqsCQC5NbzKPHVU+6eYmZJJ5xuD6S6+yYAM5sEDHD3MUDfHJq6LZB0pgQtWJEo0ClQaPF/Zq3jpfnf8unabTx8cQvOa1Yz1KFJBCv0sJWZfViMcdQGtmQ5Tgucy+naSWb2PPAa8FgOnxlmZovNbPH27duLMVSRyFM2MZ4H+ibzzi2dqFgmgcGvLGb4pK/ZpUKLUki59jyy7h544ltAm2KMI7sKbzmOp7n7d8Cw3Bp09+fN7EegX2JiYtuihScSHc6odzLT7+jK05+n8tRnqaRs2MGo/s3p16qWCi1KgeQ1bLUImEP2v9xPKsY40oC6WY7rAFtz+Gy+ufs0YFq7du2GFrUtkWiRmBDH8B5N6NOiFve8s5w7J37N1G9+4KGBLTmlsgotSv7kNWy1BrjJ3c898QXsKMY4FgGNzay+mSUCVwBTi9qomfUzs+f37NlT5ABFok3TUyoy5ZZOPHDR6cxL3cEFY+fwxsLvyVCJE8mHvJLHqFw+c0dhLmhmE4EFQFMzSzOzIe5+FLgdmElmwnrL3VcVpv2s3H2auw+rXLlyUZsSiUrxccZvuzZg5vButKhdmfvfXcFVL3zJdzv2hzo0CXPaz0NEAHB3Ji3awt9mrOFIRga/u6Apg7vUJz5OcyGxqih7mOe0TLZAnwkVDVuJ5J+ZcWX7eswe0Z0ujarx8AdruOTp+az7SYUW5f/LtedhZmuAq8h+wvy4V9y9VXEHVpzU8xApGHdn+vIfGTV1FXsPHeHWcxpx27mNSEzQFkCxpNC1rczsc3JZMhuwy90HFT684NEe5iJFs2t/On+dtor3vtlKk5oVePTS1rRRocWYoT3M1fMQKZJP1/7MH99dyc97DzG4c31+17OpCi3GAO1hLiJFcl6zmsy6uxtXtq/HC/O+pdfjKXyRWpyr9SXSRHXy0IS5SPGpWKYUD1/ckknDOhBncNULCxn5znL2HDwS6tAkBDRsJSIFdjD9GOM+Xs8LczdRvWJpHhrYkguSVWgx2hR52MrMypnZg2Y2IXDcOJyX6IpIcJVNjOf+C0/nvds6c3K5RIa+tpjb31jKzn2HQx2alJD8Dlu9DBwGOgaO04CHghKRiESMVnVOYurtXRhxQRNmrfqZHmPn8P43PxALIxqxLr/Jo6G7PwocAXD3g+T+7EdY0JyHSPAlJsRx5/mNmXFnF5KqleeuSd8w5NXFbP3lYKhDkyDKb/JIN7OyBJ75MLOGZPZEwppqW4mUnMY1KzL55k482DeZBRt30nNcCv/6crMKLUap/CaPPwMfAXXN7N/AJ8A9QYtKRCJSfJwxpEt9Zg7vRuu6lXngvZVcMeFLvlWhxaiT79VWZlYV6EDmcNWX7h4xi7y12kqk5Lk7by9OY/SM1aQfzWDEBU0Y0qU+CfFR/YRAVClKeZKcdhIEwN2XFjG2EqHkIRI6P+89xIPvrWTW6p9pWbsyj17aitNrVQp1WJIPRUkenwV+LAO0A5aR2fNoBSx09y7FHGuxUm0rkfDg7ny48if+9P5KfjlwhFvOacjt5zWidIJKnISzQj/nkWXXwM3Ame7ezt3bAmcAqcUfavHShLlIeDAzLmxZi9l3d6d/m1MZ/2kqFz05jyWbd4c6NCmk/A4+NnP3FccP3H0l0CYoEYlI1Dq5fCJjL2/DKzeexYHDR7n02S/4y7RV7D98NNShSQHlN3msMbMXzOwcM+seeNJ8TTADE5HodU7TGswa0Z1rO5zGy/O/o9fjKczdsD3UYUkB5Dd53AisAu4ChgOrA+dERAqlQukE/jqgBW/d1JFS8XFc++JX/OHtZew5oEKLkUCFEUUk5A4dOcYTn2zg+ZRNVCmfyOgBLejd4pRQhxXziqMwYmczm21m681s0/FX8YYpIrGqTKl47u3djPdu7Uy1CqW5+V9LuO3fS9n+a9gXsohZ+ep5mNla4G5gCXDs+Hl33xm80IpOS3VFIs+RYxk8n7KJJz7eQNnEeP7UN5lLzqyNWdiX04s6Rd6G1swWuvvZxR5ZCdGwlUjkSd32K/e+s4Ilm3fTrUl1/nZxC+qcXC7UYcWU4tiG9jMze8zMOprZmcdfxRijiMh/aVSjIm/d1JFR/ZJZ/N0ueo1L4bUF36nQYpjIb8/js2xOu7ufV/whFT/1PEQi25ZdB7j/3RXM3bCDs5JO5u+DWtGweoVQhxX1ijxsFemUPEQin7szeUkao6ev5tDRDO46vzHDujWglAotBk1uySMhjy+OyO19dx9blMBERPLLzLisXV26N63On95bxWMz1/HBih95ZFArWtRWCaKSllfKrhh4tQNuAWoHXjcDycENTUTk/6tRsQzPXtuWZ64+k5/3HmbAU/N59KO1HDpyLO8vS7HJ75zHLGCQu/8aOK4IvO3uvYMcX7HQsJVIdPrlQDoPzVjD5CVpNKhenkcHtaJdUpVQhxU1imO1VT0gPctxOpBUxLjyxcwamNmLZjY5y7nyZvaqmU0ws6tLIg4RCT8nlUvkH5e15rXB7Tl8JIPLnlvAn99fyT4VWgy6/CaP14GvzGyUmf0ZWAi8mteXzOwlM9tmZitPON/bzNaZWaqZjcytDXff5O5DTjh9CTDZ3YcC/fP5dxCRKNWtSXVm3d2N6zsm8dqXm+k1LoU561VoMZjylTzc/WEyCyHuBn4BbnT3Mfn46ivAfw1tmVk88BTQh8x5kyvNLNnMWprZ9BNeNXJotw6wJfCzBjpFhPKlExjVvzmTb+5ImVJxXP/SV/zurWX8ciA97y9LgeW62iqrwJazBdp21t1TzCzphNPtgVR33wRgZpOAAYFk1DefTaeRmUC+If+9JxGJAW1Pq8KMO7sy/tMNPDtnE3PWb2f0gOb0aVkr1KFFlVD84q3Nf3oNkJkIauf0YTOrambPAmeY2X2B01OAQWb2DDAth+8NM7PFZrZ4+3Z1X0ViSZlS8fyhVzOm3t6ZmpVKc8u/l3Lz60vY9uuhUIcWNfLd8yhG2VU3y3HJV6D44s0nnNtPHvuJuPvzwPOQudqq4GGKSKRrfmpl3r+tMxPmfsu4j9fzxf/s4MG+yVzato4KLRZRKHoeaUDdLMd1gK3BuJCZ9TOz5/fs2ROM5kUkAiTEx3HLOQ356K6uNDulEn+YvJzrXvqKLbsOhDq0iBaK5LEIaGxm9c0sEbgCmBqMC7n7NHcfVrmynj4ViXUNqldg0rAOjB7QnKWbd9Pr8RRemf8tx1RosVCCmjzMbCKwAGhqZmlmNsTdjwK3AzPJ3Af9LXdfFaTrq+chIv8nLs64tmMSs0Z056ykKoyatprLn1tA6rZfQx1axFFhRBGJSe7Oe9/8wF+mrebA4WPceX4jbureUIUWsyiOJ8wjknoeIpITM+PiM+rw8YjuXNC8Jv+YtZ7+/5zPijT9vsgP9TxERICZq37iwfdWsnN/OkO7NmB4j8aUKRUf6rBCSj0P9TxEJA+9mp/C7BHdufTMOjw7ZyN9npjLwk07Qx1W2FLPQ0TkBPNTdzByynK27DrINR3qcW/vZlQsUyrUYZW4mO15iIgURudG1Zg5vBuDO9fn3wu/p9e4FD5bty3UYYUVJQ8RkWyUS0zgT/2SeeeWTpQvncCNLy9ixJvfsHu/Ci1ClCcPzXmISFGdWe9kpt/ZhTvPb8zUZVvpMXYO05dvJRaG/HOjOQ8RkXxa8+Ne7n1nOcvT9tAzuSajB7agZqUyoQ4raDTnISJSDE6vVYkpt3Ti/gubMWf9dnqMncObi76PyV5IVCcPDVuJSHFLiI9jWLeGzBzejeRalbj3nRVc8+JCvt8ZW4UWNWwlIlJIGRnOxEXfM+aDtRzLcH7fqyk3dEoiPi46yr1r2EpEJAji4oyrzz6N2SO60bFhVUZPX82gZ75g/c/RX2hRyUNEpIhqVS7Li9e344kr2rB5534uenIuT3y8gfSjGaEOLWiUPEREioGZMaBNbT4e0Z3eLWox7uP19P/nPJZt+SXUoQVFVCcPTZiLSEmrWqE04688gwnXtWP3gXQufno+f/tgDQfTj4U6tGKlCXMRkSDZe+gIYz5Yy8SvviepajnGXNKKjg2rhjqsfNOEuYhICFQqU4oxl7TkjaFn48CVE77k/ndXsPfQkVCHVmRKHiIiQdapYTU+uqsbQ7vWZ9JX39NzbAqfrv051GEViZKHiEgJKJsYzx8vSmbKrZ2pXLYUg19ZzF2TvmZXhBZaVPIQESlBbeqexLQ7ujC8R2M+WPEjPcbOYeqyyCu0qOQhIlLCEhPiGN6jCdPv6ErdKuW4c+LXDH1tMT/tORTq0PItqpOHluqKSDhrekpFptzSiQcuOp15qTu4YOwc3lj4PRkZ4d8L0VJdEZEwsHnnfka+s4IFm3bSoUEV/n5JK5KqlQ9pTFqqKyIS5k6rWp43hp7NmEtasuqHvfR+IoUJKZs4Fqa9ECUPEZEwYWZc2b4es0d0p0uj6jz8wRoueXo+a3/aG+rQ/h8lDxGRMHNK5TJMuK4t4688g7TdB+k3fh7jZq/n8NHwKXGi5CEiEobMjH6tT2X2iO70bXUqT3yygX7j5/H197tDHRqg5CEiEtaqlE9k3G/a8PINZ/HroaNc8swXjJ6+mgPpR0Mal5KHiEgEOLdZDWbd3Y2rz67Hi/O+pffjc/kidUfI4gn75GFmDczsRTObnNs5EZFoV7FMKR4a2JJJwzoQZ3DVCwsZ+c5y9hws+UKLQU0eZvaSmW0zs5UnnO9tZuvMLNXMRubWhrtvcvcheZ0TEYkVHRpU5aPh3bipewPeWryFC8bOYdaqn0o0hmD3PF4Bemc9YWbxwFNAHyAZuNLMks2spZlNP+FVI8jxiYhEpDKl4rmvz+m8d1tnqpRPZNjrS7j9jaXs2He4RK6fEMzG3T3FzJJOON0eSHX3TQBmNgkY4O5jgL7BjEdEJNq0qnMSU2/vwnNzNjL+01Tmpe7gz/2SGdimNmYWtOuGYs6jNrAly3Fa4Fy2zKyqmT0LnGFm9+V0LpvvDTOzxWa2ePv27cUYvohIeElMiOOO8xsz484u1K9WnrvfXMbgVxax9ZeDQbtm0GtbBXoe0929ReD4MqCXu/82cHwt0N7d7whWDKptJSKx4liG8+oX3/HYzHXEGdx34elc0+G0QrUVbrWt0oC6WY7rAFuDcSFV1RWRWBMfZwzuUp9Zd3fjjHons+HnX4NynVD0PBKA9cD5wA/AIuAqd18VrBjU8xCRWOTupB/LoHRCfKG+H7Keh5lNBBYATc0szcyGuPtR4HZgJrAGeCtYiUM9DxGJZWZW6MSRZ9vaz0NERLITbnMeJUY9DxGR4Ijq5OHu09x9WOXKlUMdiohIVInq5CEiIsER1clDw1YiIsER1clDw1YiIsER1clDRESCI6iFEUPNzPoB/YC9ZrYNyGn8qnIO71UDQrfbSt5yijtc2i/M9wvynfx8Nq/P5Pa+7ovgtB3u94Xuif/Iua6Ju8fEC3i+oO8Bi0Mdd2H/TuHQfmG+X5Dv5OezeX1G90XJtx3u94Xuify9YmnYaloh3wtnwY67qO0X5vsF+U5+PpvXZ3RflHzb4X5f6J7Ih5h4wrywzGyx5/B0pcQu3Rdyoli8J2Kp51EYz4c6AAlLui/kRDF3T6jnISIiBaaeh4iIFJiSh4iIFJiSh4iIFJiSRwGYWQMze9HMJoc6FgkPZjbQzCaY2ftm1jPU8Uh4MLPTzexZM5tsZreEOp5giPnkYWYvmdk2M1t5wvneZrbOzFLNbCSAu29y9yGhiVRKSgHviffcfShwA/CbEIQrJaSA98Uad78ZuByIyiW8MZ88gFeA3llPmFk88BTQB0gGrjSz5JIPTULkFQp+TzwQeF+i1ysU4L4ws/7APOCTkg2zZMR88nD3FGDXCafbA6mBnkY6MAkYUOLBSUgU5J6wTI8AH7r70pKOVUpOQX9XuPtUd+8EXF2ykZaMmE8eOagNbMlynAbUNrOqZvYscIaZ3Rea0CREsr0ngDuAHsClZnZzKAKTkMrpd8U5ZvakmT0HfBCa0IIrqqvqFoFlc87dfSegXxCxKad74kngyZIORsJGTvfF58DnJRtKyVLPI3tpQN0sx3WArSGKRcKD7gnJTszeF0oe2VsENDaz+maWCFwBTA1xTBJauickOzF7X8R88jCzicACoKmZpZnZEHc/CtwOzATWAG+5+6pQxiklR/eEZEf3xX9TYUQRESmwmO95iIhIwSl5iIhIgSl5iIhIgSl5iIhIgSl5iIhIgSl5iIhIgSl5SEwzs2Nm9k2W18hQxwT/FdepgeMKZvaMmW00s6/NbImZDc2jjc/NrNcJ54ab2dNm1jDQ/r5g/j0keqm2lcS6g+7epjgbNLOEwMNjRXFiXC8Am4DG7p5hZtWBwXm0MZHMJ55nZjl3BfAHd98ItFHykMLSQ4IS08xsn7tXyOb8d8CrQD+gFHCZu681s/LAeKAlmf/5GuXu75vZDcBFQBmgPNCXzP0fmpH55HEScBvQGmjh7ncHrjMUON3dR+QUl5k1BGYDjdw9I4e/xx/I3HioNPCuu//ZzKoCa4E67n7YzJKAFOA0D/zDz+nvL5IXDVtJrCt7wrBV1t0Ad7j7mcAzwO8D5/4IfOruZwHnAo8FEgpAR+B6dz8PuBXY7e6tgNFA28BnJgH9zaxU4PhG4OU8YmwOLMslcfQEGpO5t0QboK2ZdQtUgf6K/2xgdAXwput/jFIMNGwlsS63YaspgT+XAJcEfu5J5i//48mkDFAv8PNsdz++WVAX4AkAd19pZssDP+83s0+Bvma2Bijl7isKErCZ/RG4DKjh7qcGYuoJfB34SAUyk0kK/xm6ej/wZ15DXSL5ouQhkrPDgT+P8Z9/KwYMcvd1WT9oZmcD+7OeyqXdF4D7yRxSyqvXAbAaaG1mce6e4e4PAw9nma8wYIy7P5fNd98DxprZmUBZ7XYoxUXDViIFMxO4w8wMwMzOyOFz88icgyCwp3XL42+4+0Iy94C4isyeQa7cPRVYDDwU2DMbMyvDfxLUTGCwmR2fI6ltZjUC391H5qZEL+XnWiL5pZ6HxLqyZvZNluOP3D235bqjgceB5YEE8h2Zk+Mnehp4NTBc9TWwHNiT5f23gDbuvjufcf4WeAxINbNdwEHgXgB3n2VmpwMLAjltH3ANsC3w3YlkDsFdkc9rieRJq61EgiDQQyjl7ocCq6U+AZq4e3rg/enAOHf/JIfvl8gqKK22ksLSsJVIcJQD5pnZMuBd4BZ3Tzezk8xsPZkT9dkmjoC9WR8SLG7HHxIEfg5G+xL91PMQEZECU89DREQKTMlDREQKTMlDREQKTMlDREQKTMlDREQKTMlDREQK7H8B6TGsg+1Fiy8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_crab_3fhl = crab_3fhl_spec.plot(\n",
    "    energy_bounds=[10, 2000] * u.GeV, energy_power=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assign the return axes object to variable called `ax_crab_3fhl`, 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": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.742716Z",
     "iopub.status.busy": "2021-11-22T21:08:05.742408Z",
     "iopub.status.idle": "2021-11-22T21:08:05.743723Z",
     "shell.execute_reply": "2021-11-22T21:08:05.743877Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$6.3848913 \\times 10^{-12} \\; \\mathrm{\\frac{1}{GeV\\,s\\,cm^{2}}}$$"
      ],
      "text/plain": [
       "<Quantity 6.38489128e-12 1 / (cm2 GeV s)>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_3fhl_spec(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 10 GeV and 2000 GeV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.746668Z",
     "iopub.status.busy": "2021-11-22T21:08:05.746296Z",
     "iopub.status.idle": "2021-11-22T21:08:05.747692Z",
     "shell.execute_reply": "2021-11-22T21:08:05.747873Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$8.6745734 \\times 10^{-9} \\; \\mathrm{\\frac{1}{s\\,cm^{2}}}$$"
      ],
      "text/plain": [
       "<Quantity 8.67457342e-09 1 / (cm2 s)>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_3fhl_spec.integral(energy_min=10 * u.GeV, energy_max=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 3FHL catalog:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.750103Z",
     "iopub.status.busy": "2021-11-22T21:08:05.749741Z",
     "iopub.status.idle": "2021-11-22T21:08:05.751269Z",
     "shell.execute_reply": "2021-11-22T21:08:05.751495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$8.6589091 \\times 10^{-9} \\; \\mathrm{\\frac{1}{s\\,cm^{2}}}$$"
      ],
      "text/plain": [
       "<Quantity 8.658909e-09 1 / (cm2 s)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_3fhl.data[\"Flux\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition we can compute the energy flux between 10 GeV and 2000 GeV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.754229Z",
     "iopub.status.busy": "2021-11-22T21:08:05.753921Z",
     "iopub.status.idle": "2021-11-22T21:08:05.755229Z",
     "shell.execute_reply": "2021-11-22T21:08:05.755501Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$5.3114892 \\times 10^{-10} \\; \\mathrm{\\frac{erg}{s\\,cm^{2}}}$$"
      ],
      "text/plain": [
       "<Quantity 5.31148917e-10 erg / (cm2 s)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_3fhl_spec.energy_flux(energy_min=10 * u.GeV, energy_max=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": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.759760Z",
     "iopub.status.busy": "2021-11-22T21:08:05.759454Z",
     "iopub.status.idle": "2021-11-22T21:08:05.771426Z",
     "shell.execute_reply": "2021-11-22T21:08:05.771822Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FluxPoints\n",
      "----------\n",
      "\n",
      "  geom                   : RegionGeom\n",
      "  axes                   : ['lon', 'lat', 'energy']\n",
      "  shape                  : (1, 1, 5)\n",
      "  quantities             : ['norm', 'norm_errp', 'norm_errn', 'norm_ul', 'sqrt_ts', 'is_ul']\n",
      "  ref. model             : pl\n",
      "  n_sigma                : 1\n",
      "  n_sigma_ul             : 2\n",
      "  sqrt_ts_threshold_ul   : 1\n",
      "  sed type init          : flux\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(crab_3fhl.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](https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/flux_points/index.html).\n",
    "\n",
    "No we can check again the underlying astropy data structure by accessing the `.table` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.787966Z",
     "iopub.status.busy": "2021-11-22T21:08:05.787530Z",
     "iopub.status.idle": "2021-11-22T21:08:05.788996Z",
     "shell.execute_reply": "2021-11-22T21:08:05.789235Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=5</i>\n",
       "<table id=\"table5087785456\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>e_ref</th><th>e_min</th><th>e_max</th><th>dnde</th><th>dnde_errp</th><th>dnde_errn</th><th>dnde_ul</th><th>sqrt_ts</th><th>is_ul</th></tr></thead>\n",
       "<thead><tr><th>GeV</th><th>GeV</th><th>GeV</th><th>1 / (cm2 GeV s)</th><th>1 / (cm2 GeV s)</th><th>1 / (cm2 GeV s)</th><th>1 / (cm2 GeV s)</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float32</th><th>bool</th></tr></thead>\n",
       "<tr><td>14.142</td><td>10.000</td><td>20.000</td><td>5.120e-10</td><td>1.321e-11</td><td>1.321e-11</td><td>nan</td><td>125.157</td><td>False</td></tr>\n",
       "<tr><td>31.623</td><td>20.000</td><td>50.000</td><td>7.359e-11</td><td>2.842e-12</td><td>2.842e-12</td><td>nan</td><td>88.715</td><td>False</td></tr>\n",
       "<tr><td>86.603</td><td>50.000</td><td>150.000</td><td>9.024e-12</td><td>5.367e-13</td><td>5.367e-13</td><td>nan</td><td>59.087</td><td>False</td></tr>\n",
       "<tr><td>273.861</td><td>150.000</td><td>500.000</td><td>7.660e-13</td><td>8.707e-14</td><td>8.097e-14</td><td>nan</td><td>33.076</td><td>False</td></tr>\n",
       "<tr><td>1000.000</td><td>500.000</td><td>2000.000</td><td>4.291e-14</td><td>1.086e-14</td><td>9.393e-15</td><td>nan</td><td>15.573</td><td>False</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=5>\n",
       " e_ref    e_min   e_max         dnde         dnde_errp       dnde_errn        dnde_ul     sqrt_ts is_ul\n",
       "  GeV      GeV     GeV    1 / (cm2 GeV s) 1 / (cm2 GeV s) 1 / (cm2 GeV s) 1 / (cm2 GeV s)              \n",
       "float64  float64 float64      float64         float64         float64         float64     float32  bool\n",
       "-------- ------- -------- --------------- --------------- --------------- --------------- ------- -----\n",
       "  14.142  10.000   20.000       5.120e-10       1.321e-11       1.321e-11             nan 125.157 False\n",
       "  31.623  20.000   50.000       7.359e-11       2.842e-12       2.842e-12             nan  88.715 False\n",
       "  86.603  50.000  150.000       9.024e-12       5.367e-13       5.367e-13             nan  59.087 False\n",
       " 273.861 150.000  500.000       7.660e-13       8.707e-14       8.097e-14             nan  33.076 False\n",
       "1000.000 500.000 2000.000       4.291e-14       1.086e-14       9.393e-15             nan  15.573 False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crab_3fhl.flux_points.to_table(sed_type=\"dnde\", formatted=True)"
   ]
  },
  {
   "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": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:08:05.844730Z",
     "iopub.status.busy": "2021-11-22T21:08:05.844340Z",
     "iopub.status.idle": "2021-11-22T21:08:05.980668Z",
     "shell.execute_reply": "2021-11-22T21:08:05.980860Z"
    }
   },
   "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_3fhl_spec.plot(energy_bounds=[10, 2000] * u.GeV, energy_power=2)\n",
    "crab_3fhl.flux_points.plot(ax=ax, sed_type=\"dnde\", 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": {},
   "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 or use the full-text search.\n",
    "* If you have any questions, ask on the mailing list."
   ]
  }
 ],
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