{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Mission XGA\n",
    "\n",
    "This tutorial highlights the differences between XGA interaction with telescope-specific software, and recommends the best way to use XGA when interacting with data from different telescopes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Products"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When generating products for a source with multi-telescope observations associated with it, XGA will interact with each telescope specific software needed. Since different softwares are used, it is important to consider the subtlities and caveats associated with the different telescopes. In this tutorial we detail the considerations the user should take into account when comparing products from different telescopes. We do not go into full detail of the capabilities of the product related functions, for this please seek the other pages of the docs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# Let's declare a source to demonstrate with\n",
    "from astropy.units import Quantity\n",
    "from xga.sources import GalaxyCluster\n",
    "from xga.generate.esass import evtool_image, expmap, srctool_spectrum, srctool_lightcurve\n",
    "from xga.generate.sas import evselect_image, eexpmap, evselect_spectrum, evselect_lightcurve, emosaic\n",
    "from xga.xspec import single_temp_apec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n",
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n",
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n",
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n",
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n",
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n",
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n",
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n",
      "/mnt/lustre/projects/astro/general/jp735/XGA_dev/XGA/xga/sources/general.py:229: UserWarning: Generating the combined images required for this is not supported for eROSITA - we will use the highest exposure ObsID instead - associated with source A907\n",
      "  self._all_peaks(peak_find_method, 'extended')\n",
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n",
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n",
      "/mnt/lustre/projects/astro/general/jp735/XGA_dev/XGA/xga/sources/general.py:231: UserWarning: Multiple telescopes are associated with source A907 - we do not yet support combining peak information, so the XMM peak is being returned.\n",
      "  self._default_coord = self.peak\n"
     ]
    }
   ],
   "source": [
    "# And defining an individual source object for Abell 907\n",
    "src = GalaxyCluster(149.59209, -11.05972, 0.16, r500=Quantity(1200, 'kpc'), \n",
    "                    r200=Quantity(1700, 'kpc'), name=\"A907\",\n",
    "                    search_distance={'erosita': Quantity(3, 'deg'), \n",
    "                                     'xmm': Quantity(30, 'arcmin')})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### eROSITA\n",
    "Images are generated with different sizes depending on their origin, but all with a binning of 4.35 arcseconds per pixel. This was chosen to match the XCS binsize. \n",
    "\n",
    "* **Pointed** observations - The size of the image is 1.05 degrees, this is so the full eROSITA FOV (1.03 degrees) is captured.\n",
    "* **Calibration Fields** (ie. eFEDS) - Images are 7.5 by 9 degrees in the x and y direction respectively. \n",
    "* **Other Calibration Fields** (ie eta Cha) - Images are 7 by 7 degrees.\n",
    "* **eRASS sky tiles** - Images are 3.6 by 3.6 degrees, chosen to match the eROSITA team's definition of a skytile. This will have some overlapping areas between other skytiles, so the area of the sky is not completely unique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xga.sources.extended.GalaxyCluster at 0x7f96a463f970>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making images for eROSITA observations is done through the evtool_image function\n",
    "evtool_image(src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that XGA will generate a **combined** image from all seven telescope modules."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieving Images\n",
    "\n",
    "XGA generates one combined image for all seven eROSITA telescope modules. When retrieving products using the 'inst' keyword, these combined images are stored under the inst parameter 'combined'. If the user tries to retrieve images for individual instruments, an error will be thrown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<xga.products.phot.Image at 0x7f966b394e50>,\n",
       " <xga.products.phot.Image at 0x7f966b70a610>,\n",
       " <xga.products.phot.Image at 0x7f966b70aa60>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src.get_images(telescope='erosita', inst='combined', lo_en=Quantity(0.5, 'keV'), hi_en=Quantity(2.0, 'keV'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NoProductAvailableError",
     "evalue": "Cannot find any images matching your input.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNoProductAvailableError\u001b[0m                   Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msrc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_images\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtelescope\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43merosita\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTM1\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlo_en\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mQuantity\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mkeV\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhi_en\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mQuantity\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mkeV\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/mnt/lustre/projects/astro/general/jp735/XGA_dev/XGA/xga/sources/base.py:2743\u001b[0m, in \u001b[0;36mBaseSource.get_images\u001b[0;34m(self, obs_id, inst, lo_en, hi_en, psf_corr, psf_model, psf_bins, psf_algo, psf_iter, telescope)\u001b[0m\n\u001b[1;32m   2715\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_images\u001b[39m(\u001b[38;5;28mself\u001b[39m, obs_id: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, inst: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, lo_en: Quantity \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, hi_en: Quantity \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   2716\u001b[0m                psf_corr: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, psf_model: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mELLBETA\u001b[39m\u001b[38;5;124m\"\u001b[39m, psf_bins: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m, psf_algo: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrl\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   2717\u001b[0m                psf_iter: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m15\u001b[39m, telescope: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[Image, List[Image]]:\n\u001b[1;32m   2718\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   2719\u001b[0m \u001b[38;5;124;03m    A method to retrieve XGA Image objects. This supports the retrieval of both PSF corrected and non-PSF\u001b[39;00m\n\u001b[1;32m   2720\u001b[0m \u001b[38;5;124;03m    corrected images, as well as setting the energy limits of the specific image you would like. A\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2741\u001b[0m \u001b[38;5;124;03m    :rtype: Union[Image, List[Image]]\u001b[39;00m\n\u001b[1;32m   2742\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2743\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_phot_prod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mimage\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobs_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlo_en\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhi_en\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpsf_corr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpsf_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpsf_bins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpsf_algo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2744\u001b[0m \u001b[43m                               \u001b[49m\u001b[43mpsf_iter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtelescope\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/mnt/lustre/projects/astro/general/jp735/XGA_dev/XGA/xga/sources/base.py:2048\u001b[0m, in \u001b[0;36mBaseSource._get_phot_prod\u001b[0;34m(self, prod_type, obs_id, inst, lo_en, hi_en, psf_corr, psf_model, psf_bins, psf_algo, psf_iter, telescope)\u001b[0m\n\u001b[1;32m   2046\u001b[0m     matched_prods \u001b[38;5;241m=\u001b[39m matched_prods[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   2047\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(matched_prods) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 2048\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NoProductAvailableError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot find any \u001b[39m\u001b[38;5;132;01m{p}\u001b[39;00m\u001b[38;5;124ms matching your input.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(p\u001b[38;5;241m=\u001b[39mprod_type))\n\u001b[1;32m   2050\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m matched_prods\n",
      "\u001b[0;31mNoProductAvailableError\u001b[0m: Cannot find any images matching your input."
     ]
    }
   ],
   "source": [
    "src.get_images(telescope='erosita', inst='TM1', lo_en=Quantity(0.5, 'keV'), hi_en=Quantity(2.0, 'keV'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing eRASS Images\n",
    "Since eRASS is divided into large sky tiles, when viewing an image with XGA, it can be difficult to see the source within the full skytile image using the default arguments in the `view()` method. Here we reccommend some optional arguments to use to see a source in more detail.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/regions/core/compound.py:93: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = self.operator(*np.array(padded_data, dtype=np.int))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Retrieving the eROSITA images\n",
    "images = src.get_images(telescope='erosita', lo_en=Quantity(0.5, 'keV'), hi_en=Quantity(2.0, 'keV'))\n",
    "\n",
    "for img in images:\n",
    "    # get_mask returns the source and background mask, \n",
    "    # so we use [0] index to get just the source mask\n",
    "    mask = src.get_mask('r500', obs_id=img.obs_id, telescope='erosita')[0]\n",
    "    # This will then show us the r500 region of the image\n",
    "    # Zoom_in then crops the image around pixels that are non zero\n",
    "    img.view(mask=mask, zoom_in=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XMM\n",
    "All images are made with a binning of 4.35 arcseconds per pixel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xga.sources.extended.GalaxyCluster at 0x7f96a463f970>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evselect_image(src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an additional optional argument in the XMM image generation function: 'add_expr'. This allows the user to add a string to the SAS expression keyword. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xga.sources.extended.GalaxyCluster at 0x7f96a463f970>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evselect_image(src, add_expr='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exposure Maps\n",
    "### eROSITA\n",
    "XGA generates one merged exposure map for all seven telescope modules aboard eROSITA. Note that for non pointed osbervations, exposure map generation can take a longer time, as it has to calculate exposures for each snapshot of time, as the telescope slews across the sky. \n",
    "\n",
    "XGA also removes the 'REFYCRVL' keyword from the generated exposure map, as this gets occupied with a nonsense value (as confirmed with the eROSITA team), which cannot be read into memory later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating products of type(s) expmap: 100%|██████████| 3/3 [05:44<00:00, 114.90s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xga.sources.extended.GalaxyCluster at 0x7f96a463f970>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expmap(src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XMM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xga.sources.extended.GalaxyCluster at 0x7f96a463f970>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eexpmap(src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spectra\n",
    "### eROSITA\n",
    "Spectra are generated differently depending on the source type and observing mode.\n",
    "\n",
    "**Source type**:\n",
    "\n",
    "* Spectra of **extended** sources are generated using the psftype=NONE so this does not apply any PSF corrections. The exttype parameter is set to MAP, this means that the spatial extent of the source is described by a detection map. This detection map is an image generated by XGA of the observation one is generating a spectrum for, the image is of the full eROSITA energy range (0.2 keV - 10 keV). This is then used for effective area calculations.\n",
    "* Spectra of **point** sources are generated using the psftype='2D_PSF', and exttype='POINT'.\n",
    "\n",
    "**Observing Mode**:\n",
    "Since a source will move across the field of view as the telescope slews, the PSF and effective area of that source will vary. There are two parameters eSASS uses to control the accuracy of the spectra generated due to this variation. The first is **tstep**, which are the time bins that the PSF correction, vignetting correction and effective area are calculated for. The second is **xgrid**, which controls the sampling of the PSF, vignetting, and the effective area to perform the corrections with. Processing time is very sensitive to these parameters, so within XGA we have chosen these values to compromise between accuracy and computing time. \n",
    "\n",
    "* **Survey** mode - Tstep is set to 0.5s. Within this time the telescope will have scanned across 45 arcseconds (assuming the eRASS nominal scan speed) - which corresponds to about 5 detector pixels. \n",
    "* **Pointed** mode - Tstep is set to 100s. As the telescope is not moving with respect to the sky in this observing mode, we can increase the tstep, decrease processing time, and not sacrifice any spectral generation accuracy. \n",
    "* For both modes we set xgrid to be its default eSASS value (1.0). \n",
    "\n",
    "\n",
    "For both sources, the background spectrum are generated seperately from the source spectrum. This is done for two reasons, the first is so the background spectrum has its own ARF and RMF, which is appropriate for large sources. Secondly eSASS will use the GTIs of the source spectra for the background spectra - if generated together. If the source is a large extended source, this can have the effect of throwing away lots of usable data from the background. We therefore chose to generate them separately, so that we use the GTIs of the background region for background spectra generation. Lastly we increase the timestep to 2s for the background region, as this decreases the processing time, and less spatial accuracy is acceptable for background spectra. \n",
    "\n",
    "Spectra may be grouped by either a minimum count or signal to noise, this is implemented using Heasoft's ftgrouppha command. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating products of type(s) spectrum: 100%|██████████| 3/3 [01:41<00:00, 33.92s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xga.sources.extended.GalaxyCluster at 0x7f96a463f970>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making spectra for eROSITA observations is done through the srctool_spectrum function\n",
    "srctool_spectrum(src, 'r500')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "Generating products of type(s) spectrum: 100%|██████████| 21/21 [00:11<00:00,  1.86it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xga.sources.extended.GalaxyCluster at 0x7f96a463f970>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By default this generates a combined spectrum for all eROSITA instruments\n",
    "# To generate each instrument separately use the combine_tm argument\n",
    "srctool_spectrum(src, 'r500', combine_tm=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XMM\n",
    "\n",
    "#### Extra keyword arguments\n",
    "\n",
    "When generating XMM spectra, an additional keyword argument ``oversample`` is available in the `evselect_spectrum` function. If the ``one_rmf`` keyword argument is set to True (as is the default) then only one RMF per ObsID-instrument combo will be generated, and will be used by any future spectrum generation for the ObsID-instrument combo. RMFs do change slightly with position on detector, but are also very computationally expensive to produce (relative to the other spectrum generation tasks), as such this is an acceptable compromise.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ARF generation\n",
    "\n",
    "The Auxiliary Response File (ARF), is an extremely important product which describes the change of the effective area of the telescope with energy, something that must be understood to try and recover the spectrum that was actually emitted by the object rather than the one that was recieved by the telescope. Calculating the ARF is a fairly complex process which, for XMM, is fully described in the [SAS documentation](http://xmm-tools.cosmos.esa.int/external/sas/current/doc/arfgen/), and as such the SAS *arfgen* procedure has numerous configuration options for the user to set.\n",
    "\n",
    "XGA chooses to generate ARFs in two different ways, depending on whether the source being analysed is extended or point-like. For extended sources we generate a 'detector map', which is essentially an image of the source in detector coordinates, and acts to weight the calculation of the ARF curve by the emission of the source. For point sources this is not valid or necessary, so we do not use a detector map, and we set the `extendedsource` argument to False.\n",
    "\n",
    "The use of the detector map to weight ARF generation for extended sources does extend the runtime, so do not be surprised if generating many spectra takes hours."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lightcurves\n",
    "### eROSITA\n",
    "To generate eROSITA lightcurves, the srctool command is used. For all source types we set: 'exttype'='POINT', 'psftype'= '2D_PSF', tstep='0.5', 'lct'='REGULAR', and 'lc_gamma'='1.9'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[28], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Making lightcurves for eROSITA observations is done through the srctool_lightcurve function\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43msrctool_lightcurve\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr500\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/mnt/lustre/projects/astro/general/jp735/XGA_dev/XGA/xga/generate/esass/run.py:52\u001b[0m, in \u001b[0;36mesass_call.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     50\u001b[0m source_rep \u001b[38;5;241m=\u001b[39m []  \u001b[38;5;66;03m# For repr calls of each source object, needed for assigning products to sources\u001b[39;00m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(cmd_list)):\n\u001b[0;32m---> 52\u001b[0m     source \u001b[38;5;241m=\u001b[39m \u001b[43msources\u001b[49m\u001b[43m[\u001b[49m\u001b[43mind\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(cmd_list[ind]) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     54\u001b[0m         src_lookup[\u001b[38;5;28mrepr\u001b[39m(source)] \u001b[38;5;241m=\u001b[39m ind\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "# Making lightcurves for eROSITA observations is done through the srctool_lightcurve function\n",
    "srctool_lightcurve(src, 'r500')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[29], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# By default this generates a combined lightcurve for all eROSITA instruments\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# To generate each instrument separately use the combine_tm argument\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43msrctool_lightcurve\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr500\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcombine_tm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/mnt/lustre/projects/astro/general/jp735/XGA_dev/XGA/xga/generate/esass/run.py:52\u001b[0m, in \u001b[0;36mesass_call.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     50\u001b[0m source_rep \u001b[38;5;241m=\u001b[39m []  \u001b[38;5;66;03m# For repr calls of each source object, needed for assigning products to sources\u001b[39;00m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(cmd_list)):\n\u001b[0;32m---> 52\u001b[0m     source \u001b[38;5;241m=\u001b[39m \u001b[43msources\u001b[49m\u001b[43m[\u001b[49m\u001b[43mind\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(cmd_list[ind]) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     54\u001b[0m         src_lookup[\u001b[38;5;28mrepr\u001b[39m(source)] \u001b[38;5;241m=\u001b[39m ind\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "# By default this generates a combined lightcurve for all eROSITA instruments\n",
    "# To generate each instrument separately use the combine_tm argument\n",
    "srctool_lightcurve(src, 'r500', combine_tm=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Allowed pattern types can be controlled through the 'patt' argument. To see how to represent different patterns see: https://erosita.mpe.mpg.de/edr/DataAnalysis/prod_descript/EventFiles_edr.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Here we select all valid patterns (which is the XGA default)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43msrctool_lightcurve\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr500\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpatt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m15\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/mnt/lustre/projects/astro/general/jp735/XGA_dev/XGA/xga/generate/esass/run.py:52\u001b[0m, in \u001b[0;36mesass_call.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     50\u001b[0m source_rep \u001b[38;5;241m=\u001b[39m []  \u001b[38;5;66;03m# For repr calls of each source object, needed for assigning products to sources\u001b[39;00m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(cmd_list)):\n\u001b[0;32m---> 52\u001b[0m     source \u001b[38;5;241m=\u001b[39m \u001b[43msources\u001b[49m\u001b[43m[\u001b[49m\u001b[43mind\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(cmd_list[ind]) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     54\u001b[0m         src_lookup[\u001b[38;5;28mrepr\u001b[39m(source)] \u001b[38;5;241m=\u001b[39m ind\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "# Here we select all valid patterns (which is the XGA default)\n",
    "srctool_lightcurve(src, 'r500', patt=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "When fitting models and acquiring astrophysical parameters of XGA sources, some considerations must be made depending on the origin of the observations. In this section we reccommend the best way to use XGA's inbuilt functions for each telescope.\n",
    "\n",
    "## Fitting Models to Spectra\n",
    "### eROSITA\n",
    "\n",
    "When fitting models, XGA will generate a spectra for each telescope module indiviually.\n",
    "By default all models that can be fit to spectra using XGA use simultaneous fitting. Since eROSITA's instruments are mostly identical, it is reccommended to use 'stacked_spectra=True' when using these models, as this should give a more reliable fit - especially for low exposure sources. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/its/home/jp735/.conda/envs/xga_dev/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "/mnt/lustre/projects/astro/general/jp735/XGA_dev/XGA/xga/xspec/fit/general.py:92: UserWarning: Spectrum stacking is not currently supported for XMM, and so combined spectra will not be used for these XSPEC fits.\n",
      "  sources, inn_rad_vals, out_rad_vals = _pregen_spectra(sources, outer_radius, inner_radius, group_spec, min_counts,\n",
      "Generating products of type(s) spectrum: 100%|██████████| 9/9 [28:04<00:00, 187.13s/it]  \n",
      "Generating products of type(s) spectrum: 100%|██████████| 3/3 [01:43<00:00, 34.61s/it]\n",
      "Running XSPEC Fits: 100%|██████████| 2/2 [01:43<00:00, 51.54s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xga.sources.extended.GalaxyCluster at 0x7fb6f402da30>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_temp_apec(src, 'r500', stacked_spectra=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving fit values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XMM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When XSPEC performs a simultaneous fit to multiple spectra, and then a luminosity is measured, it actually measures luminosities individually for all the spectra. XGA preferentially reports a PN luminosity (because of the greater sensitivity compared to the MOS cameras). If a PN spectra was not available, then a MOS2 luminosity is taken instead, and if MOS2 wasn't available then a MOS1 luminosity will be used."
   ]
  }
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