{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Three-dimensional temperature and mass profiles of galaxy clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have seen in the [gas density profiles tutorial](../advanced_tutorials/cluster_gas.html) that XGA has the capacity to measure the gas density profile of a cluster, and in the [annular spectra tutorial](../advanced_tutorials/annular_spectra.html) we have seen that XGA can make (and fit models to) sets of annular spectra. That means that we can measure a projected temperature profile for a galaxy cluster\n",
    "\n",
    "This tutorial will demonstrate how to go from a set of annular spectra to a projected temperature profile, from there to a three-dimensional temperature profile, and then how to combine that with a gas density profile to measure the hydrostatic mass of a galaxy cluster. We will detail the 'manual' way of generating a hydrostatic mass profile, and then demonstrate the convenience function that has been implemented to do the same job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xga.sources import GalaxyCluster\n",
    "from xga.sourcetools.temperature import min_snr_proj_temp_prof, onion_deproj_temp_prof\n",
    "from xga.sourcetools.density import ann_spectra_apec_norm\n",
    "from xga.products.profile import HydrostaticMass\n",
    "\n",
    "from astropy.units import Quantity, Unit\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm setting up a single galaxy cluster to (eventually) measure a hydrostatic mass for, though please bear in mind that the R$_{500}$, R$_{200}$, and redshift are approximate and shouldn't be used for real scientific analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "demo_src = GalaxyCluster(149.59209, -11.05972, 0.16, r500=Quantity(1200, 'kpc'), r200=Quantity(1700, 'kpc'), \n",
    "                         name=\"A907\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convenience function for creating a projected temperature profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [annular spectra tutorial](../advanced_tutorials/annular_spectra.html) has already shown you how to generate and fit a set of annular spectra, so we won't go through that whole process again here. Instead I will demonstrate a convenient way to go through the steps necessary to make a projected temperature profile by calling a single function `min_snr_proj_temp_prof`, for which you can find documentation [here](../../xga.sourcetools.html#xga.sourcetools.temperature.min_snr_proj_temp_prof)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function will decide for itself how large the annuli should be, based on both a minimum signal to noise and a minimum annulus size. We also have to decide what the outer radius is going to be, and bear in mind that algorithm used to make the annuli alter the outer radius slightly so that it is an integer multiple of the minimum width, though it will never make the outer radius smaller, only larger. In this tutorial we are only using a single cluster, but this function is also compatible with a cluster sample. We're going to set `min_snr=35` (this is something you may have to experiment with), leave the minimum width set to 20 arcseconds, and generate the profile out to 1.3R$_{500}$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating products of type(s) spectrum: 100%|██████████| 9/9 [41:09<00:00, 274.42s/it]  \n",
      "Generating products of type(s) annular spectrum set components: 100%|██████████| 117/117 [26:58<00:00, 13.83s/it]\n",
      "Running XSPEC Fits: 100%|██████████| 13/13 [04:17<00:00, 19.82s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<Quantity [  0.        ,  21.74999258,  43.49998517,  65.24997775,\n",
       "             86.99997034, 108.74996292, 130.49995551, 152.24994809,\n",
       "            173.99994068, 239.24991843, 304.49989619, 391.49986653,\n",
       "            478.49983687, 548.09981314] arcsec>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_snr_proj_temp_prof(demo_src, min_snr=35, outer_radii=demo_src.r500*1.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving a projected temperature profile from a source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a specific get method designed to retrieve *projected* temperature profiles that have been stored in a source object, `get_proj_temp_profiles`. As with most of the other get methods implemented in XGA, if you have only made one of these profiles then you can call the method with no arguments and it will be returned. Otherwise it asks you to pass the annular radii that were used to generate the profile, or the `set_id` that was assigned to the `AnnularSpectra` from which the profile was made:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xga.products.profile.ProjectedGasTemperature1D at 0x7f73ba1d2eb0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proj_temp = demo_src.get_proj_temp_profiles()\n",
    "proj_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating profiles from a fitted `AnnularSpectra`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we have already generated a projected temperature profile using the handy `min_snr_proj_temp_prof` function, we also wanted to demonstrate how you could manually create such a profile from a fitted `AnnularSpectra`. For this we first have to retrieve the object using the get method demonstrated in the [annular spectra tutorial](../advanced_tutorials/annular_spectra.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ann_spec = demo_src.get_annular_spectra()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we've read it out into a variable, we can call the `generate_profile()` [method](../../xga.products.html#xga.products.spec.AnnularSpectra.generate_profile) to tell it to assemble a profile out of a specific parameter of the fit. When it comes to temperature profiles this is rarely going to be necessary, as the temperature profile is automatically generated after the fitting process is complete, but we demonstrate this for completeness' sake:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xga.products.profile.ProjectedGasTemperature1D at 0x7f73ba33ac70>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manual_proj_temp = ann_spec.generate_profile('constant*tbabs*apec', 'kT', 'keV')\n",
    "manual_proj_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a deprojected temperature profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Deprojection' is a process that takes us from a temperature profile that is the result of the emission of the 3D shells of a cluster being 'projected' along the line of site onto the plane that we observe with the telescope. Temperatures measured in an these annulus are a combination of the temperatures of all the 3D shells of cluster that intersect the annulus when that annulus is extended back through the cluster.\n",
    "\n",
    "There are different methods of dealing with this, but in XGA the 'onion peeling' deprojection method has been implemented in the `onion_deproj_temp_prof()` [function](../../xga.sourcetools.html#xga.sourcetools.temperature.onion_deproj_temp_prof). The function is an implementation of a fairly old technique, though it has been used recently in [this paper](https://doi.org/10.1051/0004-6361/201731748). For a more in depth discussion of this technique and its uses I would currently recommend [this paper](https://doi.org/10.1051/0004-6361:20020905)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to call `onion_deproj_temp_prof()` to generate our desired deprojected profile from the projected profile we have already measured, although it is important to note that if we hadn't already made that profile then this function would have done that for us (including the generation and fitting of annular spectra). As such for a 'real-world' use case you should just call `onion_deproj_temp_prof()` and let it do everything. So now we call this function with the same arguments we used earlier, and it will automatically fetch the profile we generated, then deproject it:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating products of type(s) spectrum: 100%|██████████| 9/9 [32:30<00:00, 216.69s/it]  \n",
      "Generating products of type(s) annular spectrum set components: 100%|██████████| 108/108 [25:21<00:00, 14.09s/it]\n",
      "Running XSPEC Fits: 100%|██████████| 12/12 [03:04<00:00, 15.41s/it]\n"
     ]
    }
   ],
   "source": [
    "deproj_temp = onion_deproj_temp_prof(demo_src, min_snr=35, outer_radii=demo_src.r500*1.2)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I do not give an explanation of how this deprojection method works here, but you can find a [detailed walk-through](../advanced_tutorials/deprojected_temp_profs.html) in the 'Under the Hood' section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving a deprojected temperature profile from a source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `get_3d_temp_profiles()` method of can be used to easily retrieve a deprojected temperature profile, behaving the same way as the `get_proj_temp_profiles()`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xga.products.profile.GasTemperature3D at 0x7f73ba08deb0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo_src.get_3d_temp_profiles()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing the profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're quickly going to look at the profiles we've just generated, using the profile view method that was introduced in the [profile tutorial](../advanced_tutorials/intro_to_profiles.html). First the original, projected, profile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "proj_temp.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then the newly deprojected temperature profile, which will help us measure the mass of cluster:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vupFXnHxMj9t17tjJfZu20TI02JNw6viRtA5vOej2yzds5YHPvbmepQ4KEbEyM6cXXcdAYEupJEk6QOvwFlqGBs/uTo4bfeQhA6lUD8OKLkCSJPWv6z74qh63WbZ+C+cuvoMTxhzFjp17mHf2JGa0jT3o9hMvurGeJaoJ2VIqSVITmTdzUo/bdI0hPXX8SCYcPYKFc6Yyd8mqA25+qvW40qEYSiVJaiIXzprc4zZrNnaycM7UvV32M9rGsnDOVNZs7Dys40qHYiiVJEn7uODMtgO66me0jeWCM9sKqkjNwFAqSZKkwhlKJUmSVDhDqSRJkgpnKJUkSVLhDKWSJEkqnKFUkiRJhTOUSpIkqXCGUkmSJBXOUCpJkqTCGUolSZJUOEOpJEmSCmcolSRJUuEMpZIkSSqcoVSSJEmFM5RKkiSpcIZSSZIkFc5QKkmSpMIZSiVJklQ4Q6kkSZIKZyiVJElS4QylkiRJKpyhVJIkSYUzlEqSJKlwhlJJkiQVzlAqSZKkwhlKJUmSVDhDqSRJkgpnKJUkSVLhDKWSJEkqnKFUkiQdYP7StSzfsJXlG7Yy8aIbmb90bdElaZCLzCy6hppMnz49V6xYUXQZkiRJPYqIlZk5veg6BgJbSiVJklQ4Q6kkSZIKZyiVJElS4QylkiRJKpyhVJIkSYUzlEqSJKlwhlJJkiQVzlAqSZKkwhlKJUmSVDhDqSRJkgpnKJUkSVLhDKWSJEkqnKFUkiRJhTOUSpIkqXCGUkmSJBXOUCpJkqTCGUolSZJUOEOpJEmSCmcolSRJUuEMpZIkSSqcoVSSJEmFM5RKkiSpcIZSSZIkFc5QKkmSpMIZSiVJklQ4Q6kkSZIKZyiVJElS4QylkiRJKlwhoTQivh4Rj0fEXRXLjomIpRGxrvz76CJqkyRJUv8rqqX0G8Ab91t2EXBzZk4Cbi6/lyRJUhMoJJRm5k+Arfstfhtwdfn11cDb+7UoSZIkFaaRxpQel5m/Bij/Hl9wPZIkSeonw4ouoFabN29m+vTpe9+3t7fT3t5eYEWSJEm/t2jRIhYtWtT1dmyRtQwkkZnFnDhiIvCDzHxx+f19wFmZ+euIeB5wS2a+YP/9pk+fnitWrOjXWiVJknojIlZm5vSet1Qjdd9/H/iL8uu/AL5XYC2SJEnqR0VNCfVt4OfACyJiY0T8JfA5YFZErANmld9LkiSpCRQypjQz33OQVTP7tRBJkiQ1hEbqvpckSVKT6rGlNCKerfJYOzKz9TDrkSRJUhOqpvt+F/CmHrYJvDFJkiRJvVRNKP1pZt7a00YR8bM61CNJkqQm1OOY0sz8o2oOlJl/fPjlSJIkqRlVdaNTRHw5Il7U18VIkiSpOVV79/0fAHdGxG0R8b6IOKovi5IkSVJzqSqUZubrgNMoTXj/eeCRiJgfEaf1ZXGSJElqDlXPU5qZ6zLzb4EJwIcptZ7eFRE/jYj39lWBkiRJGvxqnjw/M3dm5nWZeTYwDTgeuLrulUmSJKlp9OqJThFxRkRcAfykfIyL61VQRMyLiLsi4u6I+Fi9jitJkqTGVXUojYjREfFXEbEa+BkwFjgHOCUzP1uPYiLixcAHgJcDLwHeEhGT6nFsSZIkNa5qp4S6GngU+FvgO8BJmfknmfnvmZl1rOc04PbMfDozdwG3An9Sx+NLkiSpAVXzRCeAVuDPgHqH0P3dBXwmIo4FdgB/DKzow/NJkiSpAVQ7JdTbM/NHmZkRMTQiZkTEuwAiYkREDK9HMZl5D3ApsBT4d+BOYFflNp2dnbS3t9PR0VGPU0qSJNVdR0cH7e3tUGrYUxWilobPiGgDfgA8DxiWmSMj4u3An2Zm3aeFiojPAhsz8ytdy6ZPn54rVth4KkmSGl9ErMzM6UXXMRDUevf9ZcC1wDHAzvKyW4DX1qugiBhf/n0i8A7g2/U6tiRJkhpTtWNKu7wceGtm7omIBMjMJyNiTB1r+rfymNKdwF9l5m/qeGxJkiQ1oFpD6VPAGGBL14KIOB54rF4FZWbdWl0lSZI0MNTafX8D8PWImABQbtH8EqUufUmSJKlXag2lFwPbgYcotZg+DvwOqMvk+ZIkSWpONXXfZ+YOYE5EfAQ4GXgwMzf3SWWSJA0C85euZcHN6/a+nzdzEhfOmlxgRVJjqimURsTwzNyRmU8AT1Qsn5CZG+tenSRJA9yFsyZz+/2l/2Re98FXFVyN1Lhq7b6/PiKickF5Cqel9StJkiRJzabWULqb0lylAJSngvoP4If1LEqSJEnNpdZQOgd4eUT8TUSMAH4E/CIz/7r+pUmSJKlZ1BRKM/NpYDbwIeB24P7MbO+LwiRJktQ8erzRKSLmdLP4WuD9wH90rc/MJXWuTZIkSU2imrvvP3OQ5b8DPlV+nYChVJIkSb3SYyjNzJP7oxBJkiQ1r5rmKZUkSX3LyfbVrKoZU/rLzPyDKrZbnZkvPdyCIuJC4HxKQwJ+Cbw/M5853ONKkjQQONm+mlU1LaWnRsR7gOhhu4mHW0xEnAB8FDg9M3dExPXAu4FvHO6xJUmS1LiqCaWPAZ+tYrtNh1lLl2HA8IjYCYwAHq3TcSVJktSgqrnRaWI/1NF1rkci4vPAQ8AO4MeZ+eP+Or8kSZKKUesTnfpURBwNvA04GTgeeE5EvLdym87OTtrb2+no6CiiREmSpB51dHTQ3t4O0Fp0LQNFQ4VS4GxgQ2ZuzsydwA3AjMoNWltbWbRoEbNnzy6kQEmSpJ7Mnj2bRYsWAXQWXctA0Wih9CHglRExIiICmAncU3BNkiRJ6mMNFUozcznwHeC/KE0HNQRYVGhRkiRJ6nM1T54fEUOBVwDPz8zrImIEkJm5ox4FZeYngU/W41iSJEkaGGpqKY2INuAu4IfA4vLiNwBX1LkuSZIGvMtvXc+y9Vv2WbZs/RYuv3V9QRVJjavW7vvLgGuBY4Cd5WW3AK+tY02SJA1Y85eu3ft6yoRW5i5ZReeO0n8yl63fwtwlq5gyobXb7aVmVmv3/cuBt2bmnohIgMx8MiLG1L80SZIGngU3r9v7mFCA8aOO5L5N22gZGpy7+A5OHT+SBTetY8FNpefbL9+w1WfbS9TeUvoUsE8AjYjjKT31SZIk7ad1eAstQ4NndyfHjT6S1uEtRZckNaRaW0pvAL4eER8GiIhjgS9R6tKXJEnAdR981d7Xy9Zv4dzFd3DCmKPYsXMP886exIy2sXvXT7zoxiJKlBpOrS2lFwPbKc0nOgZ4HPgd8Nk61yVJ0oA0b+akva+7xpCeOn4kE44ewcI5U5m7ZNU+Nz9Vbi81s6pDaUQMA2YD5wHjKI0vfW5mvi8zn+mj+iRJGlAqx4eu2djJwjlT93bZz2gby8I5U1mzsbPb7aVmVnX3fWbuiojFmTkKeAZ4oqd9JElqZhec2Qaw96YmKAXTyu57SSW1dt//IiKm9EklkiRJalq13uj0/4COiFgEPAjs6VqRmUvqWZgkSZKaR62h9DxKQfT8/ZYnYCiVJElSr9QUSjPz5L4qRJIkSc2rplBanii/W5n56OGXI0mSpGZUa/f9Rkpd9d0Zepi1SJIkqUnVGkr3774/AfgE8O36lCNJkqRmVOuY0gf3W/RgRPwF8J/ANYdbTES8ALiuYtEpwD9m5pcO99iSJElqXLW2lHbnaeCkOhyHzLwPeClARAwFHgG+W49jS5IkqXHVeqPTnP0WPQd4D7C8bhX93kxgfTets5IkSRpkam0p/cx+77cDKyiNK623d+NYVUmSpKbQkPOURsQRwFuBv99/XWdnJ+3t7cyePZvZs2f3RzmSJPWLy29dz5QJrfssW7Z+C2s2dnLBmW0FVaXe6OjooKOjA6C1p21VMqToAg7iTcB/ZeZj+69obW1l0aJFBlJJ0qAwf+nava+nTGhl7pJVdO7YCZQC6dwlq/YG1cpt1dhmz57NokWLADqLrmWgqHVM6T2ZeVo3y3+ZmX9Qv7J4D3bdS5KawIKb13H7/U/sfT9+1JHct2kbLUODcxffwanjR7LgpnUsuGkdyzds5cJZkwusVuo7tbaUTqhxec0iYgQwC7ihXseUJGmgaB3eQsvQ4NndyXGjj6R1eEvRJUn9oqqW0oj4h67tK153ORV4uF4FZebTwLH1Op4kSY3uug++au/rZeu3cO7iOzhhzFHs2LmHeWdPYkbbWAAmXnRjUSVKfa7a7vtZ5d8tFa8B9gCbgPPqWZQkSc1i3sxJe193jSE9dfxIWoe3MO/sScxdsoqFc6Yyo23sPttKg01VoTQzXwcQEZdl5kf6tiRJkppH5RjRNRs7WThnKgtuWgfAjLaxLJwzlTUbO5nRNtbxpBrUahpTaiCVJKnvXHBm296u+i4z2sY6HZSaQq133wdwPqWnLY0DomtdZr6+vqVJkiSpWdR69/1ngEso3dj0SmAlcDqwus51SZIkqYnU+pjROcAfZeadEXF+Zv5tRPwb8Hd9UJskSXUxf+laFty8bu/7eTMn9dv4zPlL17J8w1agdPd8f55bGkhqDaXHZOad5de7I2JoZt4eEa+rd2GSJNXLhbMm752gvnL6pf46tyFU6lmtofSRiDgxMx8C7gfeFBFbgJ31L02SJEnNotZQ+lVgGvAQMB/4v5RudvpkneuSJElSE6k6lEbEMEoT5f8IIDO/HRE/BUZm5r19VJ8kSZKaQNWhNDN3RcTizBxVsWxj35QlSZKkZlLrlFC/iIgpfVKJJEmSmlatY0r/H9AREYuAB4E9XSsyc0k9C5MkSVLzqDWUnkcpiJ6/3/IEDKWSpIZz+a3rmTKhdZ9ly9ZvYc3GTh/fKTWQmrrvM/Pkg/yc0lcFSpJ0KPOXrj3k+ikTWpm7ZBWdO0qzFy5bv4W5S1YdEFSrPZ6kvlFrSykRMRR4BfD8zLwuIkYAmZk76l6dJEk9WHDzur0T4x/M+FFHct+mbbQMDc5dfAenjh/JgpvWseCmdQdsu3zDVie7lwpQU0tpRLQBdwE/BBaXF78BuKLOdUmSVDetw1toGRo8uzs5bvSRtA5vKbokSfuptaX0MuBa4BKg639LbwEW1LEmSZJq0tOjQ5et38K5i+/ghDFHsWPnHuadPYkZbWO73XbiRTf2RYmSelDrlFAvBz6TmXso3dxEZj4JjKl3YZIkVWPezEmHXN81hvTU8SOZcPQIFs6Zytwlq1i2fkuvjiepb9TaUvoUpQC699/kiDgeeKxeBUXEGOBK4MWUgu95mfnzeh1fkjS49DT+c83GThbOmbp3/OiMtrEsnDOVNRs7u20tHajjSecvXcuCm38/RnbezEkD9rPUi3+TgaXWUHoD8PWI+DBARBwLfIlSl369LAD+PTP/NCKOAEbU8diSpCbTNe1T5U1NM9rGHrT7fqC6cNbkvTd89TScoVn4NxlYau2+vxjYDjxEqcX0ceB3wGfrUUxEjAb+kPJNVJn5bHl4gCRJkgaxWucp3ZGZc4BxlMaXPjcz35eZz9SpnlOAzcBVEbEqIq6MiOdUbtDZ2Ul7ezsdHR11OqUkSVJ9dXR00N7eDtD9hLg6QK0tpUTESGAWcBbw+ogYVcd6hgEvA76amVOB3wIXVW7Q2trKokWLmD17dh1PK0mSVD+zZ89m0aJFAJ1F1zJQ1DpP6XTgfuBS4G3APwPry8vrYSOwMTOXl99/h1JIlSRJ0iBWa0vpV4AvZOZJmfnazDwJ+Dzw1XoUk5mbgIcj4gXlRTOBX9Xj2JIkSWpctd59fxrwhf2WfZHSDVD18hHgW+U77+8H3l/HY0uS1CtOLyT1rVpD6WpK84eurlj2B/u9PyyZuRqo13AASZLqwumFpL5Vayj9MfCDiLgSeBCYCJwHLIqIOV0bZeaSulUoSZKkQa/WUHoesBP4i4plu8rLuyRgKJUkNYz5S9eyfMNWoPRse7vepcZTUyjNzJP7qhBJkvrKhbMmG0KlBlfzPKWSJElSvdXUUhoRQ4BzgDOAfSbNz8z2OtYlSWoi3tkuqdYxpV8D3grcAjxd92okSU3JO9sl1RpK/xSYkpkP90UxkiRJak61jindAmzui0IkSZLUvGoNpRcDX4qIY/qiGEmSJDWnWkPp3cDZwOaIeLbypw9qkySp6XTNqbp8w1YmXnQj85euLbokqV/UOpYV7hgAABT+SURBVKb0m8DPKT2f3hudJEmqM+dUVbOqNZSeArwsM3f3RTGSJElqTrV23/8CaOuLQiRJzenyW9ezbP2WfZYtW7+Fy29dX1BFkopQayi9GeiIiL+OiDmVP31RnCRp4Kl1DOSUCa3MXbKKzh07gVIgnbtkFVMmtPbJ+SQ1plq7788v/5673/IElhx+OZKkgW7Bzev2ToRfrfGjjuS+TdtoGRqcu/gOTh0/kgU3rWPBTet63Hf5hq2OwZQGgZpCaWae3FeFSJKaV+vwFlqGBs/uTk4YcxStw1uKLklSP6u1pZSIGAq8Anh+Zl4XESOAzMwdda9OkjQg1fqo0GXrt3Du4js4YcxR7Ni5h3lnT2JG29iq9p140Y29KVFSg6lpTGlEtAF3AT8EFpcXvwG4os51SZIGqHkzJ9W0fdcY0lPHj2TC0SNYOGcqc5esOuDmp3qdT1JjqvVGp8uAa4FjgJ3lZbcAr61jTZKkAazW8Z1rNnaycM7UvV32M9rGsnDOVNZs7OyT8w1GzmCgwaDWUPpy4DOZuYfSzU1k5pPAmHoXJklqDhec2XZAV/2MtrFccKYzEO7vYDMN1DKDgbMVqFHVOqb0KUoBdO//jkXE8cBj9SxKkiQd6FAzG1Q7g4GzFahR1dpSegPw9YiYABARxwJfotSlL0mSClI5g8Fxo490BgMNOLW2lF5M6Qanh8rvH6c0P+ln61mUJEnq3sFmNqh2BgNnK1CjqnWe0h3AnIj4CHAy8GBmbq5nQRHxALAN2A3syszp9Ty+JEkD1cFmGqicwaB1eAvzzp7E3CWrWDhn6gHBdKDNVjB/6VoW3Pz7IQjzZk5y+MEgVVUojYinMnN01/vMfAKo7XEdtXldZlY3F4gkSU3iYGGsawaDrvGjlTMY7B9KB1qgu3DW5L3jaGud/1YDS7VjSqNPq5AkSb3mDAYaDKrtvs8+reLAc/04IhL4WmYu6sdzS1LTsFu0epffuv6A6ZWWrd/Cmo2dBj+pTqoNpUdFxNcPtUFmnleHegBenZmPRsR4YGlE3JuZP+la2dnZSXt7O7Nnz2b27Nl1OqUkNZ9G6hadv3QtyzdsBUo34hQZkOcvXXvAubvmAR0/qnRXe9cYzoVzpla1v5pPR0cHHR0dAAdOFqtu1XKj0+4+q6JCZj5a/v14RHyX0oT9e0Npa2srixbZeCpJg8mFsyY3TJA72FygzgOqWnQ1nl1xxRXVPZpMVYfSZzLzA31aCRARzwGGZOa28us3AP/U1+eVJKknlfOAnjDmKOcBleqs1nlK+9pxwHcjAkq1LcnMfy+2JElSs+luOIPzgEp9q6Huvs/M+zPzJeWfF2XmZ/rjvJLUbC6/dT3L1u87896y9Vu4/Nb1BVXUOLqbx7NyHtAJR49g4ZypzF2y6oC/4cH2l9SzqkJpZo7q60IkSaWbZPpD1407nTt2Ar8PXfvfYd4b/fUZ+kp340G75gHt6rKvnAe0mv0l9azRuu8lqakd7CabvlDtjTu1Gow3+nRN+1T5t5nRNrbb7ntJvVNt970kaZCpvHHnuNFHeuOOpELZUipJDaa/5gyt9sadWnmjj6TeqLmlNCImR8SrImJw9c1IUgPor5tkarlxp1be6COpN6oOpRHxtoh4CLgH+BlwT0Q8FBFv77PqJKnJ9NdYzFpu3KnVYBtPKql/VBVKI+JlwL8CPwb+CDi9/PvHwPURMa3PKpQk1d0FZ7Yd0FU/o22sz3GXVJhqx5R+DLg0My+uWHYvcFNEPFJe/756FydJkqTmUG33/QzgYA+cv6K8XpIkSeqVakPpsZn5cHcrMnMjcGz9SpIkSTo8PrVs4Kk2lPa0Xb88hlSSVD/zl65l+YatLN+wlYkX3Tjgn8Sk5tXdtVvNU8u85htLtWNKj4yIfzjE+iPqUYwkqf9cOGuyd8prUDjYk9B6emrZYHz62EBWbSi9HZjVw3pJkqSGUfnUshPGHOVTyxpcVaE0M8/af1lEjAGOB+7NzD11rkuSJKlq3T0Jraenlvn0scZS7TylEyLitojYHBEfjog/BB4GfgmsjYiJfVijJEnSQXX3FLFqnlrm08caS7U3On0RWA5cDcwHXg2cAjyfUjC9pE+qkyRJ6kF340KreWqZ40kbS7VjSv8QmAgMBT4OLM7MzQAR8RHg531SnSRJUi90PZ2s66YmKAXT/Z9kpsZRbSgdnpnPAETEU5n5eNeKzNxYHl8qSZK0j/lL17Lg5t8Hw3kzJ9lCqW5VG0qfiIhRmbkN+FDliogYB/y27pVJkqQB78JZk/dO19TdzUhSl2rHlC4BngeQmd/eb905wMp6FiVJkqTmUu2UUJ84xOqvlH8GBbsZJEmS+l+13fcHlZlZj0Iahd0MkiRJ/a/a7ntJkiSpzxhKJUmSVDhDqSRJkgpnKK1w+a3r93n8GJQeU3b5resLqkiSpOrMX7qW5Ru2snzDViZedCPzl64tuiSpJod9o9NgMmVCK3OXrGL8qCNpHd6y97m5C+dMLbo0STWav3StM2eoqVw4a7LXvAa0ARdK79/8W971tb57qun4UUdy36ZttAwNzl18B6eOH8mCm9bt85gySY1v+Yat/gdakgYQu+/30zq8hZahwbO7k+NGl1pMJUmS1LcGXEvpKeOe06fzhy5bv4VzF9/BCWOOYsfOPcw7exIz2sb22fkk9Y2JF91YdAmSpBrYUlqhawzpqeNHMuHoESycM5W5S1YdcPOTpMY3b+akokuQJNWgIUNpRAyNiFUR8YP+PO+ajZ0snDN1b5f9jLaxLJwzlTUbO/uzDEl14HhSSRpYGjKUAvOAe/r7pBec2XZAV/2MtrFccGZbf5ciSZLUVBoulEbEBODNwJVF1yJJkqT+0XChFPgS8HfAnqILkSRJUv9oqFAaEW8BHs/MlQfbprOzk/b2djo6OvqxMklSs/OJSapFR0cH7e3tAK1F1zJQNNqUUK8G3hoRfwwcBYyOiG9m5nu7NmhtbWXRokV9VkDXlw6UppSZN3OSN0xIknxikmoye/ZsZs+ezRVXXOHd0lVqqFCamX8P/D1ARJwF/E1lIO0PfulIkiT1v4bqvpckSVJzaqiW0kqZeQtwS8FlSJIkqR/YUipJkqTCGUolSVJDuvzW9Qc86nvZ+i1cfuv6gipSXzKUSpKkQh1seq0pE1qZu2QVnTt2AqVAOnfJKqZM2HeWJafnGhwadkypJElqDgtuXsft9z/R7brxo47kvk3baBkanLv4Dk4dP5IFN61jwU3r9m6zfMNWZ84ZBGwplSRJfaIe3e+tw1toGRo8uzs5bvSRtA5vqXeZahC2lEqSpMM2f+naA1oru7rfx48qhcmu7veFc6YesP91H3xVt8ddtn4L5y6+gxPGHMWOnXuYd/YkZrSN3WebiRfdWL8PosIYSiVJ0mE7WBd8Nd3vB9MVYk8dP5LW4S3MO3vS3lBbGUznzZxU18+iYth9L0mS+szhdL+v2djJwjlT9+4zo20sC+dMZc3GfZ/c6XjSwcGWUkmSVBfddcEfTvf7BWe2AezTqjqjbewB+2twsKVUkiQdtu660Cu73yccPYKFc6Yyd8mqA25+svtdYCiVJEl10F0Xut3vqoWhVJIk9YkLzmw7oKt9RtvYvd3yUiVDqSRJkgpnKJUkSVLhDKWSJEkqnKFUkiRJhTOUSpIkqXCGUkmSJBXOUCpJkqTCGUolSZJUOEOpJEmSCmcolSRJUuEMpZIkSSqcoVSSJEmFM5RKkiSpcIZSSZIkFc5QKkmSpMIZSiVJklQ4Q6kkSZIKZyiVJElS4QylkiRJKpyhVJIkSYUzlEqSJKlwhlJJkiQVzlAqSZKkwhlKJUmSVDhDqSRJkgpnKJUkSVLhDKWSJEkqnKFUkiRJhTOUSpIkqXCGUkmSJBXOUCpJkqTCNVQojYijIuKOiLgzIu6OiE8VXZMkSeq9+UvXsnzDVpZv2MrEi25k/tK1RZekBjWs6AL28zvg9Zm5PSJagNsi4keZeXvRhUmSpNpdOGsyF86aXHQZGgAaKpRmZgLby29byj9ZXEWSJEnqDw3VfQ8QEUMjYjXwOLA0M5dXru/s7KS9vZ2Ojo5iCpQkSepBR0cH7e3tAK1F1zJQRKlxsvFExBjgu8BHMvOuruXTp0/PFStWFFeYJEnqV+/62s8BuO6Dr6ppv/lL17Lg5nV738+bOanfhxJExMrMnN6vJx2gGqr7vlJmPhkRtwBvBO7qYXNJkqR9OJ51YGmo7vuIGFduISUihgNnA/cWW5UkSZL6WqO1lD4PuDoihlIKzNdn5g8KrkmSJEl9rKFCaWauAaYWXYckSZL6V0N130uSJKk5GUolSZJUOEOpJEmSCmcolSRJUuEMpZIkSSqcoVSSJEmFM5RKkiSpcIZSSZIkFc5QKkmSpMIZSiVJklQ4Q6kkSZIKZyiVJElS4QylkiRJKpyhVJIkSYUzlEqSJKlwhlJJkiQVzlAqSZKkwhlKJUmSVDhDqSRJkgpnKJUkSVLhDKWSJEkqnKFUkiRJhTOUSpIkqXCGUkmSJBXOUCpJkqTCGUolSZJUOEOpJEmSCmcolSRJUuEMpZIkqWHNX7qW5Ru2snzDViZedCPzl64tuiT1kcjMomuoyfTp03PFihVFlyFJktSjiFiZmdOLrmMgsKVUkiRJhTOUSpIkqXCGUkmSJBXOUCpJkqTCGUolSZJUOEOpJEmSCmcolSRJUuEMpZIkSSqcoVSSJEmFM5RKkiSpcIZSSZIkFc5QKkmSpMIZSiVJklQ4Q+lBLFq0qOgS1MS8/lQ0r0EVyeuvOQ3IUNrR0XHY2/S0fqD/C1HN36iRz3e4x+vN/tXuU6/tDrV+oF9/0NzXYF9ef9Vu63dg815/vd3f78D66u9rcDAwlA5SfiEP7C/kwaCZr8HBEEoHuma+/nq7v9+B9TXYP19fiMwsuoaaRMRm4Emgs4dNW3vYpqf1Y4EttVXXUHr6fI1+vsM9Xm/2r3afem13qPUD/fqD5r4G+/L6q3ZbvwOb9/rr7f5+B9ZX1+c7KTPHFV3MQDDgQqkkSZIGnwHZfS9JkqTBxVAqSZKkwhlKJUmSVDhDaRUi4jkRcXVEXBERf150PWo+EXFKRCyOiO8UXYuaU0S8vfwd+L2IeEPR9ai5RMRpEXF5RHwnIj5UdD3qG00bSiPi6xHxeETctd/yN0bEfRHx3xFxUXnxO4DvZOYHgLf2e7EalGq5BjPz/sz8y2Iq1WBV4zX4f8vfgf8DeFcB5WqQqfH6uyczLwDOAaYXUa/6XtOGUuAbwBsrF0TEUOD/AG8CTgfeExGnAxOAh8ub7e7HGjW4fYPqr0GpL3yD2q/BT5TXS4frG9Rw/UXEW4HbgJv7t0z1l6YNpZn5E2DrfotfDvx3uVXqWeBa4G3ARkrBFJr4b6b6qvEalOqulmswSi4FfpSZ/9XftWrwqfU7MDO/n5kzAIfRDVIGrH2dwO9bRKEURk8AbgDeGRFfBXxEg/pSt9dgRBwbEZcDUyPi74spTU3iYN+DHwHOBv40Ii4oojA1hYN9B54VEV+OiK8BPyymNPW1YUUX0GCim2WZmb8F3t/fxagpHewafAIwCKg/HOwa/DLw5f4uRk3nYNffLcAt/VuK+pstpfvaCDy/4v0E4NGCalFz8hpU0bwGVSSvvyZmKN3XL4BJEXFyRBwBvBv4fsE1qbl4DapoXoMqktdfE2vaUBoR3wZ+DrwgIjZGxF9m5i5gLvAfwD3A9Zl5d5F1avDyGlTRvAZVJK8/7S8ys+gaJEmS1OSatqVUkiRJjcNQKkmSpMIZSiVJklQ4Q6kkSZIKZyiVJElS4QylkiRJKpyhVJIkSYUzlEqSJKlwhlJJA15ETIiIjIiJFct+FBF/10fnOzEi/iYipkXEAxHx3j44x+sj4lMR4fe0pKbgl52kfhcRt0TE7yJie0R0RsTqiPizep4jM9+Umf9cz2NWGA8clZkr++j4ZOZ/Ai/C72lJTcIvO0lFuSQzRwLHAt8AlkTEqcWWJEkqiqFUUqEycxdwBTAMeGnX8oiYFxH3RsS2iHgoIv53RAwtr3tuRHy/3Mq6Fnjj/sctt8Z+ovx6ny72iJhY7u6fULHsoxGxoXy+RyLis7V+logYERHfi4gbI+I5Fef+x4i4rdwyvCIizqjYZ2REfD4i7i+f++6IeE2t55akgW5Y0QVIam4RcQTwofLbtRWrNgJvAh6gFFb/vfz6a8C3gKeAE4HhwHcOs4bJwOeAMzLz7ogYA7ywxmM8F+gAVgBzM3N3xeoLgNnAL4GPAz+MiLbMfApYDBwPzKT0+U4F8nA+jyQNRLaUSirK/4yIJ4EdwKeB8zNzTdfKzPy3zNyQJauAa4CZEXEC8HrgbzKzMzM3AZ86zFp2AQG8KCJGZuaTmXl7DfufDiwDvpOZH9ovkAIszsyVmfkscCmlz/yWiBgPnANcUPFZ12Xmfx/m55GkAcdQKqkon8nMMcBY4IeUguZeEfGeiPhFRDwREZ3AXwHjgK4u9wcrNt9wOIVk5v3AnwMfAB4td7W/oYZDnAc8Dfyfg6x/oOJcCTxE6XNMLC9ee+AuktRcDKWSCpWZvwHOB/44It4GEBHPB75JqQX1eZnZSinwBfBIedeTKg5zcg+n2Q48p+L98d3UcUNmzqIUkq8HvhcRI6r8GBdR6pq/KSKO7mb9xK4XERGUhh1s5PdhdVKV55GkQctQKqlwmbkV+CLw2fK8nCMpfT9tBnZGxCuB95W33QjcAvxzRIyOiOOAi3s4xQrgPeWbisbtv31EvCAi3lgOoTuBTkrjOvdU+RF2UWpp/SVwS7mmSudFxMsiogX4W2AEcGNmPk5pPOxXyjdfRUSc6iwEkpqRoVRSo1gAPA84NzPvAT4JfA94klJL5Lcrtp0DHAk8DPwU+Jcejv0JYDfwa0qB9tr91h9RPt+vy+f7KPDOzHym2uIzc09mfgC4GfhJRJxYsXoR8GXgN8C7gDdnZmd53XnAauBWYBulz/zcas8rSYNFlIY3SZKqFRHTgTdm5qer2PYB4BOZ+c1enOc7wLvL02ZJ0qBmS6kkSZIKZyiVpNptAXZFxLS+OkFEvB5YR/XjWiVpQLP7XpIkSYWzpVSSJEmFM5RKkiSpcIZSSZIkFc5QKkmSpMIZSiVJklQ4Q6kkSZIKZyiVJElS4QylkiRJKtz/DyWQByo/hd+BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "deproj_temp.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a hydrostatic mass profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will show you how to create a hydrostatic mass profile from a density and temperature profile, though we will not be explaining how hydrostatic masses work specifically as that is the subject of a another [detailed walk-through](../advanced_tutorials/how_hydro_mass_works.html) in the 'Under the Hood' section. \n",
    "\n",
    "We will define a mass profile manually here, but will also touch on a convenience function that can perform this whole process in a single line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Measuring hydrostatic masses of galaxy clusters in XGA is entirely based around the `HydrostaticMass` [profile class](../../xga.products.html#xga.products.profile.HydrostaticMass), there is no way to measure a mass without one. This profile class is a special case as you do not pass the values on declaration, but pass two other profiles (deprojected temperature and three-dimensional gas density) and allow the profile class itself to do the calculation.\n",
    "\n",
    "Speaking of which, we don't yet have the gas density profile which we need to declare the `HydrostaticMass` instance. As we've already gone to the trouble of running the generation and fitting of an `AnnularSpectra`, we're just going to run `ann_spectra_apec_norm()` (which was introduced in the [gas density profiles tutorial](../advanced_tutorials/cluster_gas.html)), with the same `min_snr` and `outer_radii` values. This will use the normalisation profile from the XSPEC model fits to measure a density profile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dt237/code/PycharmProjects/XGA/xga/xspec/run.py:186: UserWarning: All XSPEC operations had already been run.\n",
      "  warnings.warn(\"All XSPEC operations had already been run.\")\n",
      "Generating density profiles from annular spectra: 100%|██████████| 1/1 [00:06<00:00,  6.29s/it]\n"
     ]
    }
   ],
   "source": [
    "dens_prof = ann_spectra_apec_norm(demo_src, min_snr=35, outer_radii=demo_src.r500*1.2)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dens_prof.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `HydrostaticMass` class requires both the input profiles to have model fits, and if they haven't been run prior to mass profile being declared then it will do it for you. As such you also have to provide the model names (or declared model instances) when you declare the mass profile. As we have not fitted a deprojected temperature profile before in these tutorials, we can quickly run the `allowed_models()` method to check what models are available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------------+------------------------------------------+------------------------------------------------+\n",
      "| MODEL NAME            | EXPECTED PARAMETERS                      | DEFAULT START VALUES                           |\n",
      "+=======================+==========================================+================================================+\n",
      "| vikhlinin_temp        | r_cool, a_cool, t_min, t_zero, r_tran,   | 100.0 kpc, 1.0, 1.0 keV, 5.0 keV, 400.0 kpc,   |\n",
      "|                       | a_power, b_power, c_power                | 1.0, 1.0, 1.0                                  |\n",
      "+-----------------------+------------------------------------------+------------------------------------------------+\n",
      "| simple_vikhlinin_temp | r_cool, a_cool, t_min, t_zero, r_tran,   | 100.0 kpc, 1.0, 1.0 keV, 5.0 keV, 400.0 kpc,   |\n",
      "|                       | c_power                                  | 1.0                                            |\n",
      "+-----------------------+------------------------------------------+------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "deproj_temp.allowed_models('grid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to set `temperature_model='simple_vikhlinin_temp'` and `density_model='simple_vikhlinin_dens'`, change the `num_steps` value to 40000, and decide on which radii we should use. As the mass profile is generated from mathematics performed on continuous models, you could decide to set the radii to any values (within reason) and this would work. We, however, are going to use the radii at which the temperature values were measured with the AnnularSpectra:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40000/40000 [01:32<00:00, 432.27it/s]\n",
      "100%|██████████| 40000/40000 [01:31<00:00, 435.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The chain is shorter than 50 times the integrated autocorrelation time for 6 parameter(s). Use this estimate with caution and run a longer chain!\n",
      "N/50 = 800;\n",
      "tau: [1886.81022727 1984.87460631 1483.3064957  1619.67813139 2676.38323939\n",
      " 2377.75272609]\n"
     ]
    }
   ],
   "source": [
    "hym_prof = HydrostaticMass(deproj_temp, 'simple_vikhlinin_temp', dens_prof, 'simple_vikhlinin_dens', \n",
    "                           deproj_temp.radii[1:], deproj_temp.radii_err[1:], deproj_temp.deg_radii[1:], \n",
    "                           num_steps=40000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can again use the `view()` method as we could with any other profile, we shall also highlight where the R$_{500}$ that was passed in at the declaration of the `GalaxyCluster` instance is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hym_prof.view(draw_rads={'R$_{500}$': demo_src.r500})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conveniently creating mass profiles with `inv_abel_dens_onion_temp()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function wraps various other XGA abilities and allows you to generate mass profiles for a cluster (or sample of clusters) with a single line of code. This particular convenience function uses the `onion_deproj_temp_prof()` function for the creation of the temperature profiles, and the `inv_abel_fitted_model()` function to calculate gas density from surface brightness profiles. \n",
    "\n",
    "We aren't making use of this in this tutorial as here we want to detail the whole process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measuring a mass from a `HydrostaticMass` instance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course we are likely to want to measure the mass of the cluster contained within a specific radius (R$_{500}$ for instance), and of course there is a method built in to enable that, `mass()`. It works almost identically to the `gas_mass()` method built into the `GasDensity3D` class, which we showed you how to use in the [gas density profiles tutorial](../advanced_tutorials/cluster_gas.html). The only difference is that you do not need to tell the method which models were used to fit the temperature and density profiles, as the `HydrostaticMass` instance is already aware that.\n",
    "\n",
    "The method returns a single mass value (with uncertainties calculated at `conf_level`), and a mass distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[4.1269095 \\times 10^{14},~4.5371415 \\times 10^{13},~4.5234292 \\times 10^{13}] \\; \\mathrm{M_{\\odot}}$"
      ],
      "text/plain": [
       "<Quantity [4.12690950e+14, 4.53714154e+13, 4.52342923e+13] solMass>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mass_val, mass_dist = hym_prof.mass(demo_src.r500)\n",
    "mass_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[3.8027968 \\times 10^{14},~3.5735732 \\times 10^{14},~2.7504132 \\times 10^{14},~\\dots,~4.6360991 \\times 10^{14},~4.483208 \\times 10^{14},~4.1140427 \\times 10^{14}] \\; \\mathrm{M_{\\odot}}$"
      ],
      "text/plain": [
       "<Quantity [3.80279675e+14, 3.57357319e+14, 2.75041321e+14, ...,\n",
       "           4.63609914e+14, 4.48320800e+14, 4.11404267e+14] solMass>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mass_dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing the mass distribution at a given radius"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again in a very similar manner to the gas density profiles, you can view a mass distribution calculated at whatever radius you would like. It takes the secondary output of `mass()` (mass_dist in the cell above) and plots it in a histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hym_prof.view_mass_dist(demo_src.r500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can request a mass within any radius you like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hym_prof.view_mass_dist(Quantity(400, 'kpc'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measuring the baryon fraction within a radius"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we now have information both about the total mass of the baryons within the cluster (from the gas density profile that was fed into the `HydrostaticMass` declaration), and the total mass of the whole halo (from the calculation of the mass), we can easily calculate the [baryon fraction](https://iopscience.iop.org/article/10.1088/0004-637X/778/1/14) of the cluster within a given radius.\n",
    "\n",
    "Simply call `baryon_fraction()`, setting the radius within which the baryon fraction should be measured, and the method will measure both the hydrostatic and gas masses within that radius, then divide the latter by the former. This method is called in the same way as `mass()` was, and returns both a single baryon fraction measurement (with uncertainty), and a baryon fraction distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[0.22813241,~0.023563652,~0.028947003] \\; \\mathrm{}$"
      ],
      "text/plain": [
       "<Quantity [0.22813241, 0.02356365, 0.028947  ]>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfrac_val, bfrac_dist = hym_prof.baryon_fraction(demo_src.r500)\n",
    "bfrac_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[0.24699649,~0.26298398,~0.33176081,~\\dots,~0.19946204,~0.2088122,~0.2305421] \\; \\mathrm{}$"
      ],
      "text/plain": [
       "<Quantity [0.24699649, 0.26298398, 0.33176081, ..., 0.19946204, 0.2088122 ,\n",
       "           0.2305421 ]>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfrac_dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing the baryon fraction distribution within a radius"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as we can view distributions of mass at a specific radius, we can view a baryon fraction distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hym_prof.view_baryon_fraction_dist(demo_src.r500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At whatever radius we like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hym_prof.view_baryon_fraction_dist(Quantity(400, 'kpc'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating baryon fraction profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As I've just demonstrated, we can easily measure the baryon fraction of the cluster at different radii, and as such it is possible for a profile showing that behaviour to be constructed. A method, `baryon_fraction_profile()` has been implemented in the `HydrostaticMass` class. By default it measures baryon fraction values at the same points that the hydrostatic mass profile measures masses, but again (as the whole process is based upon continuous density and temperature models), you can use whatever radii you like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dt237/code/PycharmProjects/XGA/xga/products/profile.py:463: UserWarning: The outer radius you supplied is greater than or equal to the outer radius covered by the data, so you are effectively extrapolating using the model.\n",
      "  warn(\"The outer radius you supplied is greater than or equal to the outer radius covered by the data, so\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xga.products.profile.BaryonFraction at 0x7f73bf4e6c40>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfrac_prof = hym_prof.baryon_fraction_profile()\n",
    "bfrac_prof"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the method shows us the change in baryon fraction as we move away from the centre of the cluster towards the outskirts, we also use one of the user configurable options of the `view()` method to set the y-axis scale to linear:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bfrac_prof.view(yscale='linear', draw_rads={'R$_{500}$': demo_src.r500, 'R$_{200}$': demo_src.r200})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving a `HydrostaticMass` profile from a `GalaxyCluster`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yet again we have implemented a handy [get method](../../xga.sources.html#xga.sources.extended.GalaxyCluster.get_hydrostatic_mass_profiles) to do this job, `get_hydrostatic_mass_profiles()`. This get method behaves in a slightly different way to others, as it wants to know which temperature and density profiles were used to generate the mass profile, along with the chosen models. It does allow you to pass no information at all however, in which case it will return every hydrostatic mass profile associated with the cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we manually made this mass profile, it hasn't actually been stored in the source at all, so if we ran the get method now we wouldn't see any results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "NoProductAvailableError",
     "evalue": "Cannot find any combined_hydrostatic_mass profiles matching your input.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNoProductAvailableError\u001b[0m                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-0ce3d76cc5c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdemo_src\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_hydrostatic_mass_profiles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/code/PycharmProjects/XGA/xga/sources/extended.py\u001b[0m in \u001b[0;36mget_hydrostatic_mass_profiles\u001b[0;34m(self, temp_prof, temp_model_name, dens_prof, dens_model_name, radii)\u001b[0m\n\u001b[1;32m    689\u001b[0m         \"\"\"\n\u001b[1;32m    690\u001b[0m         \u001b[0;31m# Get all the hydrostatic mass profiles associated with this source\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 691\u001b[0;31m         \u001b[0mmatched_prods\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_profiles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'combined_hydrostatic_mass'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m         \u001b[0;31m# Convert the radii to degrees for comparison with deg radii later\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/code/PycharmProjects/XGA/xga/sources/base.py\u001b[0m in \u001b[0;36mget_profiles\u001b[0;34m(self, profile_type, obs_id, inst, central_coord, radii, lo_en, hi_en)\u001b[0m\n\u001b[1;32m   3003\u001b[0m             \u001b[0mmatched_prods\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatched_prods\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3004\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatched_prods\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3005\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mNoProductAvailableError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot find any {p} profiles matching your input.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprofile_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3007\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmatched_prods\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNoProductAvailableError\u001b[0m: Cannot find any combined_hydrostatic_mass profiles matching your input."
     ]
    }
   ],
   "source": [
    "demo_src.get_hydrostatic_mass_profiles()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This would not be the case if we had used the mass profile convenience function mentioned earlier, as that automatically stores each profile within the source it was generated for. Here we shall manually add the profile to the source object (which you can do for any product or profile you have defined manually): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "demo_src.update_products(hym_prof)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if we run the same get method again, we'll retrieve that profile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xga.products.profile.HydrostaticMass at 0x7f73abff66a0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo_src.get_hydrostatic_mass_profiles()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other useful features of `HydrostaticMass` instances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the whole, the `HydrostaticMass` profile behaves much like any other. Though there are a couple of useful abilities built in which are specific to this class. Just like gas density profiles have a property that allows them easily find the profile from which they were generated, the hydrostatic mass profile class has `tempererature_profile` and `density_profile` properties to easily retrieve those profiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hym_prof.temperature_profile.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hym_prof.density_profile.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also equivalent properties for the fitted models, `temperature_model` and `density_model`, though they could also be accessed through the temperature and density profiles that were just retrieved:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hym_prof.temperature_model.view(Quantity(np.linspace(1, 1000, 100), 'kpc'), xscale='linear', \n",
    "                                yscale='linear', figsize=(10, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hym_prof.density_model.view(Quantity(np.linspace(1, 1000, 100), 'kpc'), xscale='linear', \n",
    "                            yscale='linear', figsize=(10, 7))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}