{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ac38b786",
   "metadata": {},
   "source": [
    "# XGA luminosity, temperature, and radius pipeline for galaxy clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05d5080c",
   "metadata": {},
   "source": [
    "This notebook serves to demonstrate the use of an XGA pipeline (or 'tool'). These pipelines do not necessarily take advantage of the interactive and highly customisable nature of XGA sources, samples, and general analyses, but will take information on a sample of objects and provide a set output of information without further interaction by the user. \n",
    "\n",
    "The XGA luminosity, temperature, and radius measurement pipeline for galaxy clusters will take information about the positions, redshifts, and names of a sample of galaxy clusters, and provide the user with a set of overdensity radius, temperature, and luminosity measurements (for those clusters which have available observations). You do not need to know a priori whether a cluster in the input sample has X-ray data available, XGA will determine that for you and simply ignore those clusters which do not.\n",
    "\n",
    "We explain how the pipeline works, demonstrate its use, and explain the information that it returns and saves to disk (if requested by the user). We also demonstrate that $R_{500}$ measurements using this XGA pipeline are consistent with previous XCS $R_{500}$ measurements. XGA measurements of temperature and luminosity within a specific radius value have been shown to be consistent with multiple previous works, including XCS, LoCuSS, and XXL (Turner et al. in prep). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe4432c",
   "metadata": {},
   "source": [
    "## Import Statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "de11b9c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from astropy.units import Quantity\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import xga\n",
    "xga.NUM_CORES = 80\n",
    "from xga.tools import luminosity_temperature_pipeline\n",
    "from xga.relations.clusters.RT import arnaud_r500, arnaud_r2500, arnaud_r200\n",
    "from xga.relations.clusters.TL import xcs_sdss_r500_52_TL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c392d4c",
   "metadata": {},
   "source": [
    "## How does the pipeline work?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4315d3",
   "metadata": {},
   "source": [
    "The pipeline can work in two different ways, the 'standard mode' which is the default behaviour, and 'frozen temperature' mode, which can be activated by the user. The default behaviour allows the temperature to be a free parameter in the spectral fits performed by the pipeline, but this requires a certain minimum data quality. The 'frozen temperature' mode does **not** allow the temperature to vary in the spectral fits, but rather keeps it at a fixed value; the measured luminosity from each iteration is used with a temperature-luminosity relation to predict the temperature which the next spectral fit will be frozen at. This predicted temperature is also used for the predicted overdensity radius at each step."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b29caa5",
   "metadata": {},
   "source": [
    "### Step 1 - Initial aperture and scaling relation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4cd87b1",
   "metadata": {},
   "source": [
    "Once the pipeline has created an XGA ClusterSample from the data provided, the pipeline will measure an initial temperature and luminosity within the aperture specified by the user (using the `start_aperture` argument). Temperatures are measured (if not in frozen-temperature mode), alongside other parameters, from the simultaneous fitting of all observations that are available for a particular cluster, with an absorbed (with tbabs) plasma emission model (apec). See the documentation of the `single_temp_apec` XGA function for more information. The temperature result (or prediction from a temperature-luminosity relation) is used to estimate an $R_{\\Delta}$ (where $\\Delta$ might equal 2500, 500, or 200) from the supplied $R_{\\Delta}$-$T_{\\rm{X}}$ relation.\n",
    "\n",
    "The scaling relation is what tells the pipeline which overdensity radius you are measuring for, and must be implemented using the XGA ScalingRelation product class. The y-axis label is used to determine which overdensity radius is relevant, and as such any custom relations defined by the user must contain 'R2500', 'R500', or 'R200'. The default relation is the [Arnaud et al. 2005](https://ui.adsabs.harvard.edu/abs/2005A&A...441..893A/abstract) $R_{500}$-$T_{\\rm{X}}$, though XGA also has the corresponding $R_{2500}$ and $R_{200}$ relations available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7985c884",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/pact/dt237/code/PycharmProjects/XGA/xga/products/relation.py:1168: UserWarning: Not all of these ScalingRelations have the same y-axis names.\n",
      "  warn('Not all of these ScalingRelations have the same y-axis names.')\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(arnaud_r2500 + arnaud_r500 + arnaud_r200).view(figsize=(7, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583906b5",
   "metadata": {},
   "source": [
    "### Step 2 - Iterate until convergence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8954a203",
   "metadata": {},
   "source": [
    "Now that we have used the starting fixed aperture to measure/predict a temperature and estimate a radius, we will iterate and repeat this process, using the new radius estimate to generate/fit a new set of spectra for each cluster, and then using the resulting new temperature measurement to estimate a radius value.\n",
    "\n",
    "A cluster radius is considered to be converged (which is when it is accepted and will not change anymore) if the new radius estimate is within a certain percentage of the last estimate (the convergence fraction can be set by the user, and the default value is 0.1 / 10%). \n",
    "\n",
    "Convergence can only occur once a minimum number of iterations has taken place (the user can set this value, and the default is 3), and the iterative process will exit either when all clusters have 'accepted' radii, or when the maximum number of iterations is reached (again can be set by the user, the default is 10).\n",
    "\n",
    "It is quite possible that spectral fitting will fail for some of the clusters during one of these iterations. If that is the case then a temperature estimate has not been produced and the pipeline cannot continue to analyse that particular cluster. Such clusters are removed from the XGA sample, though whatever progress they made through the iterations will still be recorded in the radii history dataframe (see step 4)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d376c3",
   "metadata": {},
   "source": [
    "### Step 3 - Measure results for the final radii"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "809c7b85",
   "metadata": {},
   "source": [
    "Once the iterative process has concluded, hopefully (but not necessarily) with no clusters dropping out and all of the radii successfully converging, there is another call to the XGA `single_temp_apec` function. As some clusters may only have achieved radius convergence in the very last iteration, they may not yet have temperature/luminosity measurements for those radii. Calling `single_temp_apec` after the iterative process is over ensures that there are measurements in those cases.\n",
    "\n",
    "Finally, if the user has indicated (through setting `core_excised=True` in the pipeline call) that they wish to also measure core-excised results (where spectra are generated in the [0.15-1]$R_{\\Delta}$ region, and then fit), there will be another call to `single_temp_apec`. If the user sets `core_excised=True` when the scaling relation is for $R_{2500}$, there will be a warning shown at the beginning to ensure that they meant for this behaviour.\n",
    "\n",
    "**Note** - Setting `core_excised=True` will provide core-excised results _in addition_ to global results, not instead of. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b878d0",
   "metadata": {},
   "source": [
    "### Step 4 - Record results and radius history"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "059e1703",
   "metadata": {},
   "source": [
    "Once all spectral generation and fitting are complete, the final results (in the form of a Pandas dataframe) will be put together for the user. Not every entry in the input `sample_data` dataframe will necessarily be present in the output results dataframe; if a cluster was found to have no X-ray data by the ClusterSample declaration, then it will not be included, but objects that failed part way through the iterative process **will** be included (though with NaN entries for the measurements). \n",
    "\n",
    "A dataframe containing the history of the radii through the various iterative steps will also be produced. Again this will only contain entries for those clusters which have some X-ray data, but will contain clusters that failed part way through the iterative process. For those that failed part way through, all entries for after their failure will be NaN values. The final column in this dataframe indicates whether the radius is considered to be converged or not, as it is possible to have entries for all iteration steps and it still not be converged because it reached the iteration limit set by `max_iter`.\n",
    "\n",
    "If the pipeline is being used interactively, the ClusterSample created for the analysis will be the first entry in the tuple returned by the pipeline function, the results dataframe will be the second entry, and the radius history dataframe the last. \n",
    "\n",
    "The user may instead (or additionally) choose to **write the results and radius history dataframes to csv**, by setting the `save_samp_results_path` and `save_rad_history_path` variables with the paths to save the files to."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16393cc4",
   "metadata": {},
   "source": [
    "## Demonstrating the pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed721a8",
   "metadata": {},
   "source": [
    "In this case we will use the pipeline interactively, and capture the returned ClusterSample, results dataframe, and radius history dataframe, as well as setting the variables necessary to write the results/radius history dataframes to disk. \n",
    "\n",
    "There are quite a few configuration options, but we will largely leave things on the default settings for this demonstration. To give you an idea of what you can change, take a look at the docstring of the function printed below (you can access this in Jupyter by hitting `shift+tab` while your caret is inside the function brackets, or you can look at it in the XGA API documentation entry for [luminosity_temperature_pipeline](../../xga.tools.rst#xga.tools.clusters.LT.luminosity_temperature_pipeline)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "711ec53f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    This is the XGA pipeline for measuring overdensity radii, and the temperatures and luminosities within the\n",
      "    radii, for a sample of clusters. No knowledge of the overdensity radii of the clusters is required\n",
      "    beforehand, only the position and redshift of the objects. A name is also required for each of them.\n",
      "\n",
      "    The pipeline works by measuring a temperature from a spectrum generated with radius equal to the\n",
      "    'start_aperture', and the using the radius temperature relation ('rad_temp_rel') to infer a value for the\n",
      "    overdensity radius you are targeting. The cluster's overdensity radius is set equal to the new radius estimate\n",
      "    and we repeat the process.\n",
      "\n",
      "    A cluster radius measurement is accepted if the 'current' estimate of the radius is considered to be converged\n",
      "    with the last estimate. For instance if 'convergence_frac' is set to 0.1, convergence occurs when a change of\n",
      "    less than 10% from the last radius estimate is measured. The radii cannot be assessed for convergence until\n",
      "    at least 'min_iter' iterations have been passed, and the iterative process will end if the number of iterations\n",
      "    reaches 'max_iter'.\n",
      "\n",
      "    In its standard mode the pipeline will only work for clusters that we can successfully measure temperatures\n",
      "    for, which requires a minimum data quality - as such you may find that some do not achieve successful radius\n",
      "    measurements with this pipeline. In these cases the pipeline should not error, but the failure will be recorded\n",
      "    in the results and radius history dataframes returned from the function (and optionally written to CSV files).\n",
      "    The pipeline will also gracefully handle SAS spectrum generation failures, removing the offending clusters from\n",
      "    the sample being analysed and warning the user of the failure.\n",
      "\n",
      "    If YOUR DATA ARE OF A LOW QUALITY, you may wish to run the pipeline in 'frozen-temperature' mode, where the\n",
      "    temperature is not allowed to vary during the spectral fits, instead staying at the initial value. Each iteration\n",
      "    the luminosity from the model is read out and, with the help of a temperature-luminosity relation supplied through\n",
      "    'temp_lum_rel', used to estimate the temperature that the next spectral fit will be frozen at. To activate this\n",
      "    mode, set 'freeze_temp=True'. Please note that we do not currently\n",
      "\n",
      "    As with all XGA sources and samples, the XGA luminosity-temperature pipeline DOES NOT require all objects\n",
      "    passed in the sample_data to have X-ray observations. Those that do not will simply be filtered out.\n",
      "\n",
      "    This pipeline will not read in previous XSPEC fits in its current form, though previously generated spectra\n",
      "    will be read in.\n",
      "\n",
      "    :param pd.DataFrame sample_data: A dataframe of information on the galaxy clusters. The columns 'ra', 'dec',\n",
      "        'name', and 'redshift' are required for this pipeline to work.\n",
      "    :param Quantity start_aperture: This is the radius used to generate the first set of spectra for each\n",
      "        cluster, which in turn are fit to produce the first temperature estimate.\n",
      "    :param bool use_peak: If True then XGA will measure an X-ray peak coordinate and use that as the centre for\n",
      "        spectrum generation and fitting, and the peak coordinate will be included in the results dataframe/csv.\n",
      "        If False then the coordinate in sample_data will be used. Default is False.\n",
      "    :param str peak_find_method: Which peak finding method should be used (if use_peak is True). Default is\n",
      "        'hierarchical' (uses XGA's hierarchical clustering peak finder), 'simple' may also be passed in which\n",
      "        case the brightest unmasked pixel within the source region will be selected.\n",
      "    :param float convergence_frac: This defines how close a current radii estimate must be to the last\n",
      "        radii measurement for it to count as converged. The default value is 0.1, which means the current-to-last\n",
      "        estimate ratio must be between 0.9 and 1.1.\n",
      "    :param int min_iter: The minimum number of iterations before a radius can converge and be accepted. The\n",
      "        default is 3.\n",
      "    :param int max_iter: The maximum number of iterations before the loop exits and the pipeline moves on. This\n",
      "        makes sure that the loop has an exit condition and won't continue on forever. The default is 10.\n",
      "    :param ScalingRelation rad_temp_rel: The scaling relation used to convert a cluster temperature measurement\n",
      "        for into an estimate of an overdensity radius. The y-axis must be radii, and the x-axis must be temperature.\n",
      "        The pipeline will attempt to determine the overdensity radius you are attempting to measure for by checking\n",
      "        the name of the y-axis; it must contain 2500, 500, or 200 to indicate the overdensity. The default is the\n",
      "        R500-Tx Arnaud et al. 2005 relation.\n",
      "    :param Quantity lum_en: The energy bands in which to measure luminosity. The default is\n",
      "        Quantity([[0.5, 2.0], [0.01, 100.0]], 'keV'), corresponding to the 0.5-2.0keV and bolometric bands.\n",
      "    :param bool core_excised: Should final measurements of temperature and luminosity be made with core-excision in\n",
      "        addition to measurements within the overdensity radius specified by the scaling relation. This will involve\n",
      "        multiplying the radii by 0.15 to determine the inner radius. Default is False.\n",
      "    :param bool freeze_nh: Controls whether the hydrogen column density (nH) should be frozen during XSPEC fits to\n",
      "        spectra, the default is True.\n",
      "    :param bool freeze_met: Controls whether metallicity should be frozen during XSPEC fits to spectra, the default\n",
      "        is False. Leaving metallicity free to vary tends to require more photons to achieve a good fit.\n",
      "    :param bool freeze_temp:\n",
      "    :param bool start_temp:\n",
      "    :param ScalingRelation temp_lum_rel:\n",
      "    :param Quantity lo_en: The lower energy limit for the data to be fitted by XSPEC. The default is 0.3 keV.\n",
      "    :param Quantity hi_en: The upper energy limit for the data to be fitted by XSPEC. The default is 7.9 keV, but\n",
      "        reducing this value may help achieve a good fit for suspected lower temperature systems.\n",
      "    :param bool group_spec: A boolean flag that sets whether generated spectra are grouped or not.\n",
      "    :param float min_counts: If generating a grouped spectrum, this is the minimum number of counts per channel.\n",
      "        To disable minimum counts set this parameter to None.\n",
      "    :param float min_sn: If generating a grouped spectrum, this is the minimum signal to noise in each channel.\n",
      "        To disable minimum signal to noise set this parameter to None.\n",
      "    :param float over_sample: The minimum energy resolution for each group, set to None to disable. e.g. if\n",
      "        over_sample=3 then the minimum width of a group is 1/3 of the resolution FWHM at that energy.\n",
      "    :param str save_samp_results_path: The path to save the final results (temperatures, luminosities, radii) to.\n",
      "        The default is None, in which case no file will be created. This information is also returned from this\n",
      "        function.\n",
      "    :param str save_rad_history_path: The path to save the radii history for all clusters. This specifies what the\n",
      "        estimated radius was for each cluster at each iteration step, in kpc. The default is None, in which case no\n",
      "        file will be created. This information is also returned from this function.\n",
      "    :param Cosmology cosmo: The cosmology to use for sample declaration, and thus for all analysis. The default\n",
      "        cosmology is a flat LambdaCDM concordance model.\n",
      "    :param Quantity timeout: This sets the amount of time an XSPEC fit can run before it is timed out, the default\n",
      "        is 1 hour.\n",
      "    :param int num_cores: The number of cores that can be used for spectrum generation and fitting. The default is\n",
      "        90% of the cores detected on the system.\n",
      "    :return: The GalaxyCluster sample object used for this analysis, the dataframe of results for all input\n",
      "        objects (even those for which the pipeline was unsuccessful), and the radius history dataframe for the\n",
      "        clusters.\n",
      "    :rtype: Tuple[ClusterSample, pd.DataFrame, pd.DataFrame]\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(luminosity_temperature_pipeline.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3429550a",
   "metadata": {},
   "source": [
    "### Loading the example sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6037dba7",
   "metadata": {},
   "source": [
    "In this case we use a subset of a sample of 150 clusters selected from SDSS, analysed by the XMM Cluster Survey (XCS), and that have been found to have well constrained temperature measurements ([Giles et al. 2022](https://arxiv.org/abs/2202.11107)). The subset consists of 40 clusters, selected randomly. \n",
    "\n",
    "The sample file contains coordinates, which are derived from XCS X-ray centroid measurements using the XAPA source finder. It also contains redshift values, which come from the redMaPPer catalogue that the SDSSRM-XCS sample was selected from initially. The names in the sample combine an XCSSDSS prefix with the 'MEM_MATCH_ID' entry in the original SDSS redMaPPeR catalogue. XCS measurements of $R_{500}$ are present in the sample, though only to provide a point of comparison, **they are not used by the pipeline**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d8e907e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40 entries, 0 to 39\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   name      40 non-null     object \n",
      " 1   ra        40 non-null     float64\n",
      " 2   dec       40 non-null     float64\n",
      " 3   redshift  40 non-null     float64\n",
      " 4   xcs_r500  40 non-null     float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 1.7+ KB\n"
     ]
    },
    {
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       "            name          ra        dec  redshift     xcs_r500\n",
       "0    SDSSXCS-487  323.799200  -1.049468  0.333912  1103.195128\n",
       "1  SDSSXCS-17923  350.470410  19.893604  0.314943   488.754689\n",
       "2   SDSSXCS-5977   35.868932  -8.868981  0.167958   778.068206\n",
       "3    SDSSXCS-890  126.489460   4.246014  0.234921   960.169165\n",
       "4  SDSSXCS-30950  251.929730  34.936056  0.249875   556.325349"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samp = pd.read_csv('lt_examp_samp.csv')\n",
    "samp.info()\n",
    "samp.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae33e24",
   "metadata": {},
   "source": [
    "### Calling the pipeline function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419955aa",
   "metadata": {},
   "source": [
    "Here we run the pipeline, telling it to use a 500kpc aperture for the starting measurements, and to produce core-excised measurements as well as global measurements. As already stated we have largely used the default settings for this run, but it is important to note that you can easily select the cosmology that you wish to use for analysis by setting the `cosmo=` argument with an Astropy cosmology object (the default is a flat LambdaCDM concordance model).\n",
    "\n",
    "We're using the RA and Dec supplied by the user as the central coordinates of our spectra, but it is also possible to set `use_peak=True`, to let XGA identify an X-ray peak coordinate for each of the clusters and use that instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21389db6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Declaring BaseSource Sample: 100%|████████████████████████████████████████████| 40/40 [00:32<00:00,  1.21it/s]\n",
      "Generating products of type(s) ccf: 100%|█████████████████████████████████████| 93/93 [00:35<00:00,  2.59it/s]\n",
      "Generating products of type(s) image: 100%|███████████████████████████████████| 40/40 [00:05<00:00,  7.03it/s]\n",
      "Generating products of type(s) expmap: 100%|██████████████████████████████████| 40/40 [00:04<00:00,  8.36it/s]\n",
      "Setting up Galaxy Clusters: 100%|█████████████████████████████████████████████| 40/40 [00:54<00:00,  1.36s/it]\n",
      "Generating products of type(s) image: 100%|███████████████████████████████████| 13/13 [00:00<00:00, 17.61it/s]\n",
      "Generating products of type(s) expmap: 100%|██████████████████████████████████| 13/13 [00:00<00:00, 16.95it/s]\n",
      "/mnt/pact/dt237/code/PycharmProjects/XGA/xga/samples/extended.py:246: UserWarning: Non-fatal warnings occurred during the declaration of some sources, to access them please use the suppressed_warnings property of this sample.\n",
      "  self._check_source_warnings()\n",
      "Generating products of type(s) spectrum: 100%|██████████████████████████████| 168/168 [22:24<00:00,  8.01s/it]\n",
      "Running XSPEC Fits: 100%|█████████████████████████████████████████████████████| 40/40 [04:49<00:00,  7.23s/it]\n",
      "/mnt/pact/dt237/code/PycharmProjects/XGA/xga/products/relation.py:783: UserWarning: Some of the x values you have passed are outside the validity range of this relation (1.0-12.0keV).\n",
      "  warn(\"Some of the x values you have passed are outside the validity range of this relation \"\n",
      "Generating products of type(s) spectrum: 100%|██████████████████████████████| 168/168 [47:44<00:00, 17.05s/it]\n",
      "Running XSPEC Fits: 100%|█████████████████████████████████████████████████████| 40/40 [04:26<00:00,  6.65s/it]\n",
      "Generating products of type(s) spectrum: 100%|██████████████████████████████| 168/168 [47:00<00:00, 16.79s/it]\n",
      "Running XSPEC Fits: 100%|█████████████████████████████████████████████████████| 40/40 [04:41<00:00,  7.04s/it]\n",
      "/mnt/pact/dt237/code/PycharmProjects/XGA/xga/products/relation.py:783: UserWarning: Some of the x values you have passed are outside the validity range of this relation (1.0-12.0keV).\n",
      "  warn(\"Some of the x values you have passed are outside the validity range of this relation \"\n",
      "Generating products of type(s) spectrum: 100%|██████████████████████████████| 168/168 [48:56<00:00, 17.48s/it]\n",
      "Running XSPEC Fits: 100%|█████████████████████████████████████████████████████| 40/40 [04:58<00:00,  7.45s/it]\n",
      "/mnt/pact/dt237/code/PycharmProjects/XGA/xga/products/relation.py:783: UserWarning: Some of the x values you have passed are outside the validity range of this relation (1.0-12.0keV).\n",
      "  warn(\"Some of the x values you have passed are outside the validity range of this relation \"\n",
      "Generating products of type(s) spectrum: 100%|██████████████████████████████| 168/168 [48:02<00:00, 17.16s/it]\n",
      "Running XSPEC Fits: 100%|█████████████████████████████████████████████████████| 40/40 [05:02<00:00,  7.56s/it]\n",
      "/mnt/pact/dt237/code/PycharmProjects/XGA/xga/products/relation.py:783: UserWarning: Some of the x values you have passed are outside the validity range of this relation (1.0-12.0keV).\n",
      "  warn(\"Some of the x values you have passed are outside the validity range of this relation \"\n",
      "Generating products of type(s) spectrum: 100%|████████████████████████████████| 18/18 [20:05<00:00, 66.99s/it]\n",
      "Running XSPEC Fits: 100%|███████████████████████████████████████████████████████| 4/4 [01:00<00:00, 15.16s/it]\n",
      "Generating products of type(s) spectrum: 100%|██████████████████████████████| 168/168 [47:45<00:00, 17.05s/it]\n",
      "Running XSPEC Fits: 100%|█████████████████████████████████████████████████████| 40/40 [04:04<00:00,  6.11s/it]\n"
     ]
    }
   ],
   "source": [
    "srcs, res, rad_hist = luminosity_temperature_pipeline(samp, Quantity(500, 'kpc'), core_excised=True,\n",
    "                                                      save_samp_results_path='sdssrm_results.csv',\n",
    "                                                      save_rad_history_path='sdssrm_radii_history.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea43b0a",
   "metadata": {},
   "source": [
    "### Looking at the pipeline output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "532bc304",
   "metadata": {},
   "source": [
    "The first thing returned by the pipeline function is the ClusterSample object which was used for the analysis - this contains the GalaxyCluster objects which were not removed from the sample during the iterative process (or could be single GalaxyCluster object if only one was passed through the pipeline).\n",
    "\n",
    "Here we know it will be a sample, because we are looking at a sample of 40 clusters, and we can just look at some basic summary information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56370750",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------------------------------------------------\n",
      "Number of Sources - 40\n",
      "Redshift Information - True\n",
      "Sources with ≥1 detection - 40 [100%]\n",
      "-----------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "srcs.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c8d171",
   "metadata": {},
   "source": [
    "The next return is the results dataframe, which contains the columns from the original input dataframe, as well as the measured temperatures, luminosities, and radii. As we passed a value to `save_samp_results_path` in the pipeline function call, this dataframe is also being saved as a csv.\n",
    "\n",
    "**Note** - if we had chosen to set `use_peak=True` when we called the pipeline function, there would be an entry for peak RA and peak Dec in the results dataframe, alongside the temperature, luminosity, and radius values.\n",
    "\n",
    "First off, we'll take a look at the top five rows of our output results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20e65bc6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.342160e+42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SDSSXCS-5977</td>\n",
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       "      <td>-8.868981</td>\n",
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       "      <td>1.60072</td>\n",
       "      <td>0.132661</td>\n",
       "      <td>0.198595</td>\n",
       "      <td>6.642926e+42</td>\n",
       "      <td>7.034819e+41</td>\n",
       "      <td>5.962644e+41</td>\n",
       "      <td>1.382094e+43</td>\n",
       "      <td>1.570607e+42</td>\n",
       "      <td>1.265908e+42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            name          ra        dec  redshift     xcs_r500         r500  \\\n",
       "0    SDSSXCS-487  323.799200  -1.049468  0.333912  1103.195128  1167.001129   \n",
       "1  SDSSXCS-17923  350.470410  19.893604  0.314943   488.754689   443.195494   \n",
       "2   SDSSXCS-5977   35.868932  -8.868981  0.167958   778.068206   707.035947   \n",
       "3    SDSSXCS-890  126.489460   4.246014  0.234921   960.169165   959.116962   \n",
       "4  SDSSXCS-30950  251.929730  34.936056  0.249875   556.325349   518.753784   \n",
       "\n",
       "     Tx500    Tx500-    Tx500+  Lx500_0.5-2.0  ...  Lx500_0.01-100.0+  \\\n",
       "0  7.38030  0.461906  0.495718   3.510844e+44  ...       3.420190e+43   \n",
       "1  1.42470  0.138240  0.196310   6.783900e+42  ...       2.308638e+42   \n",
       "2  2.81196  0.395739  0.486902   2.659669e+43  ...       5.661870e+42   \n",
       "3  4.78606  0.200513  0.220079   1.661848e+44  ...       1.188377e+43   \n",
       "4  1.57751  0.131621  0.162688   6.839353e+42  ...       1.408927e+42   \n",
       "\n",
       "   Tx500ce  Tx500ce-  Tx500ce+  Lx500ce_0.5-2.0  Lx500ce_0.5-2.0-  \\\n",
       "0  7.35291  0.587741  0.636899     2.614441e+44      7.118180e+42   \n",
       "1  1.40173  0.129780  0.184037     6.394102e+42      8.902959e+41   \n",
       "2  3.06433  0.555269  0.672173     1.837119e+43      2.087220e+42   \n",
       "3  4.92848  0.291353  0.312509     1.087360e+44      2.237170e+42   \n",
       "4  1.60072  0.132661  0.198595     6.642926e+42      7.034819e+41   \n",
       "\n",
       "   Lx500ce_0.5-2.0+  Lx500ce_0.01-100.0  Lx500ce_0.01-100.0-  \\\n",
       "0      6.511809e+42        1.038449e+45         3.906718e+43   \n",
       "1      1.103822e+42        1.253464e+43         2.149948e+42   \n",
       "2      2.514942e+42        4.797792e+43         6.262681e+42   \n",
       "3      2.686739e+42        3.528936e+44         1.254143e+43   \n",
       "4      5.962644e+41        1.382094e+43         1.570607e+42   \n",
       "\n",
       "   Lx500ce_0.01-100.0+  \n",
       "0         4.005130e+43  \n",
       "1         2.342160e+42  \n",
       "2         7.989428e+42  \n",
       "3         9.215954e+42  \n",
       "4         1.265908e+42  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481df4ff",
   "metadata": {},
   "source": [
    "Then we can see some summary statistics for all of the columns in the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa872abc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>redshift</th>\n",
       "      <th>xcs_r500</th>\n",
       "      <th>r500</th>\n",
       "      <th>Tx500</th>\n",
       "      <th>Tx500-</th>\n",
       "      <th>Tx500+</th>\n",
       "      <th>Lx500_0.5-2.0</th>\n",
       "      <th>Lx500_0.5-2.0-</th>\n",
       "      <th>...</th>\n",
       "      <th>Lx500_0.01-100.0+</th>\n",
       "      <th>Tx500ce</th>\n",
       "      <th>Tx500ce-</th>\n",
       "      <th>Tx500ce+</th>\n",
       "      <th>Lx500ce_0.5-2.0</th>\n",
       "      <th>Lx500ce_0.5-2.0-</th>\n",
       "      <th>Lx500ce_0.5-2.0+</th>\n",
       "      <th>Lx500ce_0.01-100.0</th>\n",
       "      <th>Lx500ce_0.01-100.0-</th>\n",
       "      <th>Lx500ce_0.01-100.0+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>170.845067</td>\n",
       "      <td>14.129411</td>\n",
       "      <td>0.226732</td>\n",
       "      <td>853.116880</td>\n",
       "      <td>839.470056</td>\n",
       "      <td>4.016504</td>\n",
       "      <td>0.197688</td>\n",
       "      <td>0.234105</td>\n",
       "      <td>1.754471e+44</td>\n",
       "      <td>2.523387e+42</td>\n",
       "      <td>...</td>\n",
       "      <td>1.158209e+43</td>\n",
       "      <td>4.052853</td>\n",
       "      <td>0.237782</td>\n",
       "      <td>0.290976</td>\n",
       "      <td>1.192408e+44</td>\n",
       "      <td>2.434959e+42</td>\n",
       "      <td>2.408247e+42</td>\n",
       "      <td>4.343402e+44</td>\n",
       "      <td>1.095071e+43</td>\n",
       "      <td>1.054230e+43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>109.002087</td>\n",
       "      <td>14.427955</td>\n",
       "      <td>0.071969</td>\n",
       "      <td>238.719870</td>\n",
       "      <td>248.748739</td>\n",
       "      <td>2.094706</td>\n",
       "      <td>0.133090</td>\n",
       "      <td>0.172728</td>\n",
       "      <td>2.709387e+44</td>\n",
       "      <td>3.416941e+42</td>\n",
       "      <td>...</td>\n",
       "      <td>1.592950e+43</td>\n",
       "      <td>2.070156</td>\n",
       "      <td>0.161917</td>\n",
       "      <td>0.257692</td>\n",
       "      <td>1.572231e+44</td>\n",
       "      <td>3.187407e+42</td>\n",
       "      <td>2.893337e+42</td>\n",
       "      <td>6.538151e+44</td>\n",
       "      <td>1.495431e+43</td>\n",
       "      <td>1.461325e+43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.406325</td>\n",
       "      <td>-8.868981</td>\n",
       "      <td>0.100436</td>\n",
       "      <td>340.505584</td>\n",
       "      <td>329.424037</td>\n",
       "      <td>0.976955</td>\n",
       "      <td>0.020285</td>\n",
       "      <td>0.026422</td>\n",
       "      <td>2.550508e+42</td>\n",
       "      <td>3.659965e+41</td>\n",
       "      <td>...</td>\n",
       "      <td>9.732723e+41</td>\n",
       "      <td>0.927612</td>\n",
       "      <td>0.050021</td>\n",
       "      <td>0.050025</td>\n",
       "      <td>2.208393e+42</td>\n",
       "      <td>3.189711e+41</td>\n",
       "      <td>2.821091e+41</td>\n",
       "      <td>3.683191e+42</td>\n",
       "      <td>1.346079e+42</td>\n",
       "      <td>9.739682e+41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>104.022664</td>\n",
       "      <td>2.516330</td>\n",
       "      <td>0.166746</td>\n",
       "      <td>753.451777</td>\n",
       "      <td>705.347318</td>\n",
       "      <td>2.636328</td>\n",
       "      <td>0.119683</td>\n",
       "      <td>0.122921</td>\n",
       "      <td>2.294106e+43</td>\n",
       "      <td>7.568225e+41</td>\n",
       "      <td>...</td>\n",
       "      <td>2.392147e+42</td>\n",
       "      <td>2.755397</td>\n",
       "      <td>0.131244</td>\n",
       "      <td>0.143492</td>\n",
       "      <td>1.818083e+43</td>\n",
       "      <td>7.711982e+41</td>\n",
       "      <td>7.825187e+41</td>\n",
       "      <td>4.604337e+43</td>\n",
       "      <td>2.432083e+42</td>\n",
       "      <td>2.324675e+42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>184.878080</td>\n",
       "      <td>11.986612</td>\n",
       "      <td>0.229464</td>\n",
       "      <td>846.063105</td>\n",
       "      <td>832.495652</td>\n",
       "      <td>3.523850</td>\n",
       "      <td>0.158468</td>\n",
       "      <td>0.188769</td>\n",
       "      <td>6.914379e+43</td>\n",
       "      <td>1.278640e+42</td>\n",
       "      <td>...</td>\n",
       "      <td>5.159838e+42</td>\n",
       "      <td>3.777330</td>\n",
       "      <td>0.182779</td>\n",
       "      <td>0.218441</td>\n",
       "      <td>4.903966e+43</td>\n",
       "      <td>1.425523e+42</td>\n",
       "      <td>1.522321e+42</td>\n",
       "      <td>1.482090e+44</td>\n",
       "      <td>5.224442e+42</td>\n",
       "      <td>4.826215e+42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>227.912807</td>\n",
       "      <td>26.541525</td>\n",
       "      <td>0.282091</td>\n",
       "      <td>962.179869</td>\n",
       "      <td>951.189710</td>\n",
       "      <td>4.623258</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.285795</td>\n",
       "      <td>2.108054e+44</td>\n",
       "      <td>2.677140e+42</td>\n",
       "      <td>...</td>\n",
       "      <td>1.201834e+43</td>\n",
       "      <td>4.616825</td>\n",
       "      <td>0.292304</td>\n",
       "      <td>0.309565</td>\n",
       "      <td>1.520532e+44</td>\n",
       "      <td>2.300037e+42</td>\n",
       "      <td>2.542027e+42</td>\n",
       "      <td>4.382001e+44</td>\n",
       "      <td>1.300986e+43</td>\n",
       "      <td>1.048910e+43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>350.470410</td>\n",
       "      <td>47.052785</td>\n",
       "      <td>0.349364</td>\n",
       "      <td>1424.488110</td>\n",
       "      <td>1380.260297</td>\n",
       "      <td>9.087740</td>\n",
       "      <td>0.591217</td>\n",
       "      <td>0.728980</td>\n",
       "      <td>1.355846e+45</td>\n",
       "      <td>1.685934e+43</td>\n",
       "      <td>...</td>\n",
       "      <td>7.047270e+43</td>\n",
       "      <td>8.897410</td>\n",
       "      <td>0.762056</td>\n",
       "      <td>1.493671</td>\n",
       "      <td>6.101462e+44</td>\n",
       "      <td>1.317662e+43</td>\n",
       "      <td>1.218105e+43</td>\n",
       "      <td>2.545512e+45</td>\n",
       "      <td>6.082700e+43</td>\n",
       "      <td>6.744091e+43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               ra        dec   redshift     xcs_r500         r500      Tx500  \\\n",
       "count   40.000000  40.000000  40.000000    40.000000    40.000000  40.000000   \n",
       "mean   170.845067  14.129411   0.226732   853.116880   839.470056   4.016504   \n",
       "std    109.002087  14.427955   0.071969   238.719870   248.748739   2.094706   \n",
       "min      4.406325  -8.868981   0.100436   340.505584   329.424037   0.976955   \n",
       "25%    104.022664   2.516330   0.166746   753.451777   705.347318   2.636328   \n",
       "50%    184.878080  11.986612   0.229464   846.063105   832.495652   3.523850   \n",
       "75%    227.912807  26.541525   0.282091   962.179869   951.189710   4.623258   \n",
       "max    350.470410  47.052785   0.349364  1424.488110  1380.260297   9.087740   \n",
       "\n",
       "          Tx500-     Tx500+  Lx500_0.5-2.0  Lx500_0.5-2.0-  ...  \\\n",
       "count  40.000000  40.000000   4.000000e+01    4.000000e+01  ...   \n",
       "mean    0.197688   0.234105   1.754471e+44    2.523387e+42  ...   \n",
       "std     0.133090   0.172728   2.709387e+44    3.416941e+42  ...   \n",
       "min     0.020285   0.026422   2.550508e+42    3.659965e+41  ...   \n",
       "25%     0.119683   0.122921   2.294106e+43    7.568225e+41  ...   \n",
       "50%     0.158468   0.188769   6.914379e+43    1.278640e+42  ...   \n",
       "75%     0.214286   0.285795   2.108054e+44    2.677140e+42  ...   \n",
       "max     0.591217   0.728980   1.355846e+45    1.685934e+43  ...   \n",
       "\n",
       "       Lx500_0.01-100.0+    Tx500ce   Tx500ce-   Tx500ce+  Lx500ce_0.5-2.0  \\\n",
       "count       4.000000e+01  40.000000  40.000000  40.000000     4.000000e+01   \n",
       "mean        1.158209e+43   4.052853   0.237782   0.290976     1.192408e+44   \n",
       "std         1.592950e+43   2.070156   0.161917   0.257692     1.572231e+44   \n",
       "min         9.732723e+41   0.927612   0.050021   0.050025     2.208393e+42   \n",
       "25%         2.392147e+42   2.755397   0.131244   0.143492     1.818083e+43   \n",
       "50%         5.159838e+42   3.777330   0.182779   0.218441     4.903966e+43   \n",
       "75%         1.201834e+43   4.616825   0.292304   0.309565     1.520532e+44   \n",
       "max         7.047270e+43   8.897410   0.762056   1.493671     6.101462e+44   \n",
       "\n",
       "       Lx500ce_0.5-2.0-  Lx500ce_0.5-2.0+  Lx500ce_0.01-100.0  \\\n",
       "count      4.000000e+01      4.000000e+01        4.000000e+01   \n",
       "mean       2.434959e+42      2.408247e+42        4.343402e+44   \n",
       "std        3.187407e+42      2.893337e+42        6.538151e+44   \n",
       "min        3.189711e+41      2.821091e+41        3.683191e+42   \n",
       "25%        7.711982e+41      7.825187e+41        4.604337e+43   \n",
       "50%        1.425523e+42      1.522321e+42        1.482090e+44   \n",
       "75%        2.300037e+42      2.542027e+42        4.382001e+44   \n",
       "max        1.317662e+43      1.218105e+43        2.545512e+45   \n",
       "\n",
       "       Lx500ce_0.01-100.0-  Lx500ce_0.01-100.0+  \n",
       "count         4.000000e+01         4.000000e+01  \n",
       "mean          1.095071e+43         1.054230e+43  \n",
       "std           1.495431e+43         1.461325e+43  \n",
       "min           1.346079e+42         9.739682e+41  \n",
       "25%           2.432083e+42         2.324675e+42  \n",
       "50%           5.224442e+42         4.826215e+42  \n",
       "75%           1.300986e+43         1.048910e+43  \n",
       "max           6.082700e+43         6.744091e+43  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecaaf438",
   "metadata": {},
   "source": [
    "The last return from the pipeline function is the radius history dataframe, which tells you what the estimated radius was at each iterative step for each galaxy cluster which had X-ray data. It will also tell you if the radius was considered to be converged or not, which can provide context for the information given in the results dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2981edf2",
   "metadata": {},
   "outputs": [
    {
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       "      <th>SDSSXCS-487</th>\n",
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       "      <td>1167.001129</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDSSXCS-17923</th>\n",
       "      <td>500.0</td>\n",
       "      <td>476.645964</td>\n",
       "      <td>464.262874</td>\n",
       "      <td>456.537207</td>\n",
       "      <td>443.195494</td>\n",
       "      <td>443.195494</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDSSXCS-5977</th>\n",
       "      <td>500.0</td>\n",
       "      <td>784.721678</td>\n",
       "      <td>688.894894</td>\n",
       "      <td>742.935950</td>\n",
       "      <td>707.035947</td>\n",
       "      <td>707.035947</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDSSXCS-890</th>\n",
       "      <td>500.0</td>\n",
       "      <td>928.603453</td>\n",
       "      <td>974.196493</td>\n",
       "      <td>950.556860</td>\n",
       "      <td>959.116962</td>\n",
       "      <td>959.116962</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDSSXCS-30950</th>\n",
       "      <td>500.0</td>\n",
       "      <td>522.166575</td>\n",
       "      <td>494.996261</td>\n",
       "      <td>509.986735</td>\n",
       "      <td>518.753784</td>\n",
       "      <td>518.753784</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0            1            2            3            4  \\\n",
       "SDSSXCS-487    500.0  1124.165421  1149.921395  1162.776447  1167.001129   \n",
       "SDSSXCS-17923  500.0   476.645964   464.262874   456.537207   443.195494   \n",
       "SDSSXCS-5977   500.0   784.721678   688.894894   742.935950   707.035947   \n",
       "SDSSXCS-890    500.0   928.603453   974.196493   950.556860   959.116962   \n",
       "SDSSXCS-30950  500.0   522.166575   494.996261   509.986735   518.753784   \n",
       "\n",
       "                         5  converged  \n",
       "SDSSXCS-487    1167.001129       True  \n",
       "SDSSXCS-17923   443.195494       True  \n",
       "SDSSXCS-5977    707.035947       True  \n",
       "SDSSXCS-890     959.116962       True  \n",
       "SDSSXCS-30950   518.753784       True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rad_hist.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afaf559f",
   "metadata": {},
   "source": [
    "### How did the radius evolve with iterative steps?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed59e05",
   "metadata": {},
   "source": [
    "We can use the radius history to produce a little diagnostic plot that shows the value of radius measured for the clusters (no legend is included here because of the large number of clusters that we are working with) at each iterative step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf038a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "clust_rad_arr = rad_hist.values[:, :-1]\n",
    "for cl_ind in range(0, len(clust_rad_arr)):\n",
    "    plt.plot(np.arange(0, clust_rad_arr.shape[1]), clust_rad_arr[cl_ind, :])\n",
    "\n",
    "plt.xlim(0, clust_rad_arr.shape[1]-1)\n",
    "plt.ylabel('Radius [kpc]', fontsize=15)\n",
    "plt.xlabel('Iteration Step', fontsize=15)\n",
    "plt.title(\"Evolution of radius estimates through iteration\", fontsize=(16))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "666b8c7c",
   "metadata": {},
   "source": [
    "### Comparing XGA-LT radii to XCS3P radii"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b46cd9b3",
   "metadata": {},
   "source": [
    "As we have the $R_{500}$ values measured for these clusters for the [Giles et al. (2022)](https://arxiv.org/abs/2202.11107) analysis, we will compare our measurements of radius to those. We do not compare the measurements of temperature and luminosity, as that has already been done as part of a paper on cluster hydrostatic masses by Turner et al. (in prep); they were found to be very consistent.\n",
    "\n",
    "We do not expect to find that the radii measured by the XGA pipeline to be identical to the previous measurements, as there are slight differences in the approach (such as we use a fixed starting aperture, but the past analysis used a starting aperture based on the size of the detection region), and XGA is an entirely separate code-base to the previous XCS pipelines, but we do expect the results to be very similar. XGA has been developed by members of XCS, and there are numerous similarities in the approaches it takes to previous XCS work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "144cead7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 7))\n",
    "\n",
    "plt.plot(res['xcs_r500'].values, res['r500'], 'x', color='black', label='Data')\n",
    "plt.plot([300, 1800], [300, 1800], color='red', linestyle='dashed', label='1:1')\n",
    "\n",
    "plt.xlim(300, 1800)\n",
    "plt.ylim(300, 1800)\n",
    "\n",
    "plt.xlabel(\"XCS3P $R_{500}$ [kpc]\", fontsize=15)\n",
    "plt.ylabel(\"XGA-LT $R_{500}$ [kpc]\", fontsize=15)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14590317",
   "metadata": {},
   "source": [
    "## Demonstrating the pipeline - 'frozen temperature' mode"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "146b3ce7",
   "metadata": {},
   "source": [
    "As has been mentioned in other parts of this tutorial, frozen-temperature mode does not allow the temperature to be a free parameter in the spectral fits - instead we leave the fit to essentially constrain the normalisation of the model, and use the luminosity calculated from that model to predict a temperature to be used as the fixed temperature for the next fit.\n",
    "\n",
    "This method is intrinsically less sophisticated than the standard method, but it can help when dealing with very low surface-brightness systems, or with low quality data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d426a6b8",
   "metadata": {},
   "source": [
    "### A temperature-luminosity relation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0bfb3f7",
   "metadata": {},
   "source": [
    "An extra relation is required for 'frozen temperature' mode, which allows us to convert from the luminosities measured by each iteration into an estimate of the temperature of the system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "caaeacc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xcs_sdss_r500_52_TL.view(figsize=(7, 7), x_lims=Quantity([2e+42, 2e+45], 'erg/s'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4181ca44",
   "metadata": {},
   "source": [
    "### Calling the pipeline function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0bd8ed9",
   "metadata": {},
   "source": [
    "Here we call the pipeline much as we did earlier, but instead we set `freeze_temp=True`, and `temp_lum_rel=xcs_sdss_r500_52_TL` (this gives the pipeline the temperature-luminosity relation, though the default value is the relation that we have passed here):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "93826ae7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_25265/1013608433.py:1: UserWarning: Core-excised temperatures will not be reported when running in frozen-temperature mode.\n",
      "  tfr_srcs, tfr_res, tfr_rad_hist = luminosity_temperature_pipeline(samp, Quantity(500, 'kpc'), core_excised=True,\n",
      "Declaring BaseSource Sample: 100%|████████████████████████████████████████████| 40/40 [00:34<00:00,  1.16it/s]\n",
      "Setting up Galaxy Clusters: 100%|█████████████████████████████████████████████| 40/40 [01:07<00:00,  1.69s/it]\n",
      "/mnt/pact/dt237/code/PycharmProjects/XGA/xga/samples/extended.py:246: UserWarning: Non-fatal warnings occurred during the declaration of some sources, to access them please use the suppressed_warnings property of this sample.\n",
      "  self._check_source_warnings()\n",
      "Generating products of type(s) spectrum: 100%|██████████████████████████████| 168/168 [40:37<00:00, 14.51s/it]\n",
      "Running XSPEC Fits: 100%|█████████████████████████████████████████████████████| 40/40 [01:41<00:00,  2.54s/it]\n",
      "Generating products of type(s) spectrum: 100%|██████████████████████████████| 168/168 [41:59<00:00, 15.00s/it]\n",
      "Running XSPEC Fits: 100%|█████████████████████████████████████████████████████| 40/40 [01:40<00:00,  2.52s/it]\n",
      "Generating products of type(s) spectrum: 100%|██████████████████████████████| 168/168 [41:35<00:00, 14.86s/it]\n",
      "Running XSPEC Fits: 100%|█████████████████████████████████████████████████████| 40/40 [01:40<00:00,  2.52s/it]\n",
      "Generating products of type(s) spectrum: 100%|██████████████████████████████| 168/168 [41:23<00:00, 14.78s/it]\n",
      "Running XSPEC Fits: 100%|█████████████████████████████████████████████████████| 40/40 [01:38<00:00,  2.47s/it]\n",
      "Generating products of type(s) spectrum: 100%|██████████████████████████████| 168/168 [40:52<00:00, 14.60s/it]\n",
      "Running XSPEC Fits: 100%|█████████████████████████████████████████████████████| 40/40 [01:40<00:00,  2.52s/it]\n"
     ]
    }
   ],
   "source": [
    "tfr_srcs, tfr_res, tfr_rad_hist = luminosity_temperature_pipeline(samp, Quantity(500, 'kpc'), core_excised=True, \n",
    "                                                                  freeze_temp=True, \n",
    "                                                                  temp_lum_rel=xcs_sdss_r500_52_TL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c822453",
   "metadata": {},
   "source": [
    "### Comparing XGA-LT frozen-temperature radii to XCS3P radii"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd88928",
   "metadata": {},
   "source": [
    "Much as we did before, we compare the radii measured from the frozen-temperature mode measurements to the published values for this sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7756f7b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 7))\n",
    "\n",
    "plt.minorticks_on()\n",
    "plt.tick_params(which='both', direction='in', top=True, right=True)\n",
    "\n",
    "plt.plot(tfr_res['xcs_r500'].values, tfr_res['r500'], 'x', color='black', label='Data')\n",
    "plt.plot([300, 1800], [300, 1800], color='red', linestyle='dashed', label='1:1')\n",
    "\n",
    "plt.xlim(300, 1800)\n",
    "plt.ylim(300, 1800)\n",
    "\n",
    "plt.xlabel(\"XCS3P $R_{500}$ [kpc]\", fontsize=15)\n",
    "plt.ylabel(\"XGA-LT FT $R_{500}$ [kpc]\", fontsize=15)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db84ae28",
   "metadata": {},
   "source": [
    "### Comparing XGA-LT frozen-temperature radii to standard-mode XGA-LT radii"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ad26c4a",
   "metadata": {},
   "source": [
    "We also compare to the values we measured using 'standard mode', where the temperature were allowed to vary during the spectral fitting process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a269741f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 7))\n",
    "\n",
    "plt.minorticks_on()\n",
    "plt.tick_params(which='both', direction='in', top=True, right=True)\n",
    "\n",
    "plt.plot(res['r500'], tfr_res['r500'], 'x', color='black', label='Data')\n",
    "plt.plot([300, 1800], [300, 1800], color='red', linestyle='dashed', label='1:1')\n",
    "\n",
    "plt.xlim(300, 1800)\n",
    "plt.ylim(300, 1800)\n",
    "\n",
    "plt.xlabel(\"XGA-LT $R_{500}$ [kpc]\", fontsize=15)\n",
    "plt.ylabel(\"XGA-LT FT $R_{500}$ [kpc]\", fontsize=15)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}