10 Fitting a PHA dataset using the object API.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting a PHA dataset using the object API\n",
    "\n",
    "The aim of this notebook is to highlight some of the ways that you can use Sherpa to analyze PHA data (i.e. Astronomical X-ray spectra) using the object API rather than the \"UI\" layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author and disclaimer\n",
    "\n",
    "This was written by \n",
    "[Douglas Burke](http://hea-www.cfa.harvard.edu/~dburke/)\n",
    "on August 8 2017. It has since been updated, most-recetly to take advantage of the changes to plotting of PHA datasets in the Sherpa 4.12.2 release, and the addition of support for Jupyter notebooks to improve the display of Sherpa objects.\n",
    "\n",
    "This notebook, and others that may be of interest, can be found on GitHub at\n",
    "https://github.com/DougBurke/sherpa-standalone-notebooks.\n",
    "\n",
    "The information in this document is placed into the Publc Domain. It is not an official product of the Chandra X-ray Center, and I make no guarantee that it is not without bugs or embarassing typos. Please contact me via the GitHub repository\n",
    "if you have any questions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Last run\n",
    "\n",
    "Let's start off by seeing when I last ran this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2024-09-30 14:17'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What version of Sherpa is being used?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'date': '2024-09-26T12:18:47-0400', 'dirty': False, 'error': None, 'full-revisionid': 'ae169c85481920960353e621865c73c8e7fe26c1', 'version': '4.17.0'}\n"
     ]
    }
   ],
   "source": [
    "import sherpa\n",
    "print(sherpa._version.get_versions())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about the \"important\" packages?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;31m\u001b[Kastropy\u001b[m\u001b[K                   6.1.4\n",
      "\u001b[01;31m\u001b[Kastropy\u001b[m\u001b[K-iers-data         0.2024.9.30.0.32.59\n",
      "\u001b[01;31m\u001b[Kmatplotlib\u001b[m\u001b[K                3.9.2\n",
      "\u001b[01;31m\u001b[Kmatplotlib\u001b[m\u001b[K-inline         0.1.7\n",
      "\u001b[01;31m\u001b[Knumpy\u001b[m\u001b[K                     1.26.4\n",
      "\u001b[01;31m\u001b[Ksherpa\u001b[m\u001b[K                    4.17.0\n"
     ]
    }
   ],
   "source": [
    "!pip list --format=columns | egrep 'astropy|matplotlib|numpy|sherpa'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in the main modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: failed to import sherpa.astro.xspec; XSPEC models will not be available\n"
     ]
    }
   ],
   "source": [
    "from sherpa.astro import ui\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What data shall we use?\n",
    "\n",
    "In this example I am going to use a PHA file - and responses - I obtained from\n",
    "[version 1.1 of the Chandra Source Catalog](https://cxc.harvard.edu/csc/). I'm not trying to use a physically-motivated model here, just show how you can set up and evaluate a model expression to include the effects of the instrument model (i.e. the Ancillary Response Function, ARF, and Redistribution Matrix Function ,RMF)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3c273.arf    \u001b[0m\u001b[38;5;9macisf01575_001N001_r0043_arf3.fits.gz\u001b[0m\n",
      "3c273_bg.pi  \u001b[38;5;9macisf01575_001N001_r0043_pha3.fits.gz\u001b[0m\n",
      "3c273.pi     \u001b[38;5;9macisf01575_001N001_r0043_rmf3.fits.gz\u001b[0m\n",
      "3c273.rmf\n"
     ]
    }
   ],
   "source": [
    "ls data/pha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A simple fit using the UI layer\n",
    "\n",
    "This section shows some common steps taken when analysing PHA data using the UI layer. I shall\n",
    "be attempting to recreate them using the object API below!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read ARF file data/pha/acisf01575_001N001_r0043_arf3.fits\n",
      "read RMF file data/pha/acisf01575_001N001_r0043_rmf3.fits\n",
      "read background file data/pha/acisf01575_001N001_r0043_pha3.fits\n"
     ]
    }
   ],
   "source": [
    "ui.load_pha('data/pha/acisf01575_001N001_r0043_pha3.fits.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As I want this notebook to be runnable with the standalone Sherpa release$^\\dagger$, not just with CIAO, \n",
    "I just use a very-simple model.\n",
    "\n",
    "---\n",
    "\n",
    "$^\\dagger$ CIAO includes XSPEC models, such as an absorption model, but here we just use a\n",
    "a non-physical fit (a straight power law with no absorption component)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset 1: 0.0073:14.9504 -> 0.4964:8.0008 Energy (keV)\n",
      "dataset 1: 0.4964:8.0008 Energy (keV) (unchanged)\n",
      "Dataset               = 1\n",
      "Method                = levmar\n",
      "Statistic             = chi2gehrels\n",
      "Initial fit statistic = 14264.5\n",
      "Final fit statistic   = 44.5589 at function evaluation 22\n",
      "Data points           = 58\n",
      "Degrees of freedom    = 56\n",
      "Probability [Q-value] = 0.864502\n",
      "Reduced statistic     = 0.795694\n",
      "Change in statistic   = 14220\n",
      "   pl.gamma       2.25578      +/- 0.0593475   \n",
      "   pl.ampl        4.60085e-05  +/- 1.51715e-06 \n"
     ]
    }
   ],
   "source": [
    "ui.set_source(ui.powlaw1d.pl)\n",
    "ui.subtract()\n",
    "pl.ampl = 1e-4\n",
    "ui.set_xlog()\n",
    "ui.set_ylog('data')\n",
    "ui.notice(0.5, 8)\n",
    "ui.group_counts(20)\n",
    "ui.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ui.plot_fit_delchi()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also do some basic error analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Confidence Method     = covariance\n",
      "Iterative Fit Method  = None\n",
      "Fitting Method        = levmar\n",
      "Statistic             = chi2gehrels\n",
      "covariance 1-sigma (68.2689%) bounds:\n",
      "   Param            Best-Fit  Lower Bound  Upper Bound\n",
      "   -----            --------  -----------  -----------\n",
      "   pl.gamma          2.25578   -0.0571239    0.0571239\n",
      "   pl.ampl       4.60085e-05 -1.51585e-06  1.51585e-06\n"
     ]
    }
   ],
   "source": [
    "ui.covar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pl.ampl lower bound:\t-1.51585e-06\n",
      "pl.ampl upper bound:\t1.51585e-06\n",
      "pl.gamma lower bound:\t-0.0564988\n",
      "pl.gamma upper bound:\t0.057749\n",
      "Dataset               = 1\n",
      "Confidence Method     = confidence\n",
      "Iterative Fit Method  = None\n",
      "Fitting Method        = levmar\n",
      "Statistic             = chi2gehrels\n",
      "confidence 1-sigma (68.2689%) bounds:\n",
      "   Param            Best-Fit  Lower Bound  Upper Bound\n",
      "   -----            --------  -----------  -----------\n",
      "   pl.gamma          2.25578   -0.0564988     0.057749\n",
      "   pl.ampl       4.60085e-05 -1.51585e-06  1.51585e-06\n"
     ]
    }
   ],
   "source": [
    "ui.conf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results of both of these are stored for later (hardly-ominous forshadowing):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "covar_ui = ui.get_covar_results()\n",
    "conf_ui = ui.get_conf_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section is finished off by a quick interval-projection plot, to see how sensitive the fit is to the `gamma` parameter of the powerlaw, and a look at the best-fit statistic value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ui.int_proj(pl.gamma, min=2, max=2.5, nloop=51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44.558875643830135\n"
     ]
    }
   ],
   "source": [
    "stat_ui = ui.calc_stat()\n",
    "print(stat_ui)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How do I recreate this with the object API?\n",
    "\n",
    "I have split up the above into several steps:\n",
    "\n",
    "  - load the data and responses\n",
    "  - adjust the data\n",
    "  - create the model\n",
    "  - fit\n",
    "  - error analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data and responses\n",
    "\n",
    "This uses the `sherpa.astro.io` module directly, which may not be a good idea (it's not clear to me yet whether this module is well suited for general use), but it's what is currently easiest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.astro.io import read_pha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read ARF file data/pha/acisf01575_001N001_r0043_arf3.fits\n",
      "read RMF file data/pha/acisf01575_001N001_r0043_rmf3.fits\n",
      "read background file data/pha/acisf01575_001N001_r0043_pha3.fits\n"
     ]
    },
    {
     "data": {
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       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
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       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
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       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
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       " the Free Software Foundation; either version 3 of the License, or\n",
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       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
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       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
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       "</details></div><details open><summary>Summary (7)</summary><div class=\"datavals\"><div class=\"dataname\">Identifier</div><div class=\"dataval\">data/pha/acisf01575_001N001_r0043_pha3.fits.gz</div><div class=\"dataname\">Exposure</div><div class=\"dataval\">37664.2 s</div><div class=\"dataname\">Number of bins</div><div class=\"dataval\">1024</div><div class=\"dataname\">Channel range</div><div class=\"dataval\">1 - 1024</div><div class=\"dataname\">Count range</div><div class=\"dataval\">0.0 - 37.0</div><div class=\"dataname\">Background</div><div class=\"dataval\">Not subtracted</div><div class=\"dataname\">Using</div><div class=\"dataval\">0.0073-14.9504 Energy (keV) with 1024 channels</div></div></details><details><summary>Metadata (8)</summary><div class=\"datavals\"><div class=\"dataname\">Mission or Satellite</div><div class=\"dataval\">CHANDRA</div><div class=\"dataname\">Instrument or Detector</div><div class=\"dataval\">ACIS</div><div class=\"dataname\">Object</div><div class=\"dataval\">CSC</div><div class=\"dataname\">Observation date</div><div class=\"dataval\">2001-10-05T00:33:43</div><div class=\"dataname\">Program that created the PHA</div><div class=\"dataval\">dmextract - Version CAT 2.7</div><div class=\"dataname\">The channel type</div><div class=\"dataval\">PI</div><div class=\"dataname\">Data stored</div><div class=\"dataval\">TOTAL</div><div class=\"dataname\">Data format</div><div class=\"dataval\">COUNT</div></div></details></div>"
      ],
      "text/plain": [
       "<DataPHA data set instance 'data/pha/acisf01575_001N001_r0043_pha3.fits.gz'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pha = read_pha('data/pha/acisf01575_001N001_r0043_pha3.fits.gz')\n",
    "\n",
    "pha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this particular case the PHA file has `ANCRFILE` and `RESPFILE` keywords set, so that the ARF and RMF can be automatically loaded. If they weren't then I would have had to say\n",
    "\n",
    "    from sherpa.astro.io import read_arf, read_rmf\n",
    "    arf = read_arf(arfname)\n",
    "    rmf = read_rmf(rmfname)\n",
    "    pha.set_arf(arf)\n",
    "    pha.set_rmf(rmf)\n",
    "    \n",
    "The object returned by `read_pha` is a an instance of the `sherpa.astro.data.DataPHA` class, which means that it can be used in the same wasy as the `Data1D` and `Data1DInt` classes in `sherpa.data`. For example, creating a plot (note that,\n",
    "unlike my \n",
    "[earlier plotting notebooks](http://nbviewer.ipython.org/github/DougBurke/sherpa-standalone-notebooks/blob/master/plotting%20using%20the%20lower-level%20routines.ipynb),\n",
    "I am using the classes from `sherpa.astro.plot` rather than `sherpa.plot`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sherpa.astro.plot import DataPHAPlot\n",
    "\n",
    "dplot = DataPHAPlot()\n",
    "dplot.prepare(pha)\n",
    "dplot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjust the data\n",
    "\n",
    "The `pha` object can be controlled using methods with similar names to the functions from the `sherpa.astro.ui` module; e.g. to subtract the background, check the analysis setting, filter an energy range, and then group$^\\dagger$, I can say:\n",
    "\n",
    "----\n",
    "\n",
    "$\\dagger$ note that the object API does not report the selected range after calls to `notice` or `group_counts`, unlike the UI version above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysis setting: 'energy'\n",
      "Number of bins  = 1024\n",
      "Number of bins  = 514\n",
      "Number of bins  = 58\n"
     ]
    }
   ],
   "source": [
    "pha.subtract()\n",
    "print(\"Analysis setting: '{}'\".format(pha.get_analysis()))\n",
    "print(\"Number of bins  = {}\".format(len(pha.get_dep(filter=True))))\n",
    "\n",
    "pha.notice(0.5, 8.0)\n",
    "print(\"Number of bins  = {}\".format(len(pha.get_dep(filter=True))))\n",
    "\n",
    "pha.group_counts(20)\n",
    "print(\"Number of bins  = {}\".format(len(pha.get_dep(filter=True))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-creating the plot now gives a grouped and filtered view of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dplot.prepare(pha)\n",
    "dplot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the plot axis preferences to get a log-log plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dplot.histo_prefs[\"xlog\"] = True\n",
    "dplot.histo_prefs[\"ylog\"] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it is just the preferences that have changed, we do not need to call `prepare` again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dplot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the model\n",
    "\n",
    "The first major difference to the `sherpa.astro.ui` layer is creating the source model, since we have to\n",
    "manually add the instrumental response (that is, include the RMF and ARF). Fortunately in this case it is\n",
    "relatively easy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<sherpa.astro.instrument.Response1D object at 0x7f694dd00a70>\n"
     ]
    }
   ],
   "source": [
    "from sherpa.astro.instrument import Response1D\n",
    "rsp = Response1D(pha)\n",
    "print(rsp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Response1D` object contains knowledge of the ARF, RMF, and PHA data sets, which means it can group and filter the evaluated model to match the data. Astute readers will note that it doesn't actually contain a model expression, so what do we have to do here? Thankfully it is simple process, in that we apply it to the model expression we want to evaluate (in this case a power law, but it can be a more-complex expression):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.models.basic import PowLaw1D\n",
    "\n",
    "pl = PowLaw1D('pl')\n",
    "mdl_expr = rsp(pl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Response1D` object can be thought of as acting as a [factory](https://en.wikipedia.org/wiki/Factory_%28object-oriented_programming%29) here, as the type of `mdl_expr` depends on whether the PHA data set contains an ARF or RMF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sherpa.astro.instrument.RSPModelPHA'>\n"
     ]
    }
   ],
   "source": [
    "print(type(mdl_expr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic model (i.e. without any knowledge of the ARF and RMF) is (you can also use\n",
    "\n",
    "    print(pl)\n",
    "    \n",
    "but we have support for Jupyter notebooks to display the common Sherpa objects such as models):    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;PowLaw1D model instance &#x27;pl&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: pl</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=3>pl</th><td>gamma</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-10.0</td><td>10.0</td><td></td></tr><tr><td>ref</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.0</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<PowLaw1D model instance 'pl'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full model expression, including the ARF, exposure time, and RMF, is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;RSPModelPHA model instance &#x27;apply_rmf(apply_arf(37664.157219191 * pl))&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: apply_rmf(apply_arf(37664.157219191 * pl))</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=3>pl</th><td>gamma</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-10.0</td><td>10.0</td><td></td></tr><tr><td>ref</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.0</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<RSPModelPHA model instance 'apply_rmf(apply_arf(37664.157219191 * pl))'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_expr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and here is what it looks like with `print`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apply_rmf(apply_arf(37664.157219191 * pl))\n",
      "   Param        Type          Value          Min          Max      Units\n",
      "   -----        ----          -----          ---          ---      -----\n",
      "   pl.gamma     thawed            1          -10           10           \n",
      "   pl.ref       frozen            1 -3.40282e+38  3.40282e+38           \n",
      "   pl.ampl      thawed            1            0  3.40282e+38           \n"
     ]
    }
   ],
   "source": [
    "print(mdl_expr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both `pl` and `mdl_expr` are standard Sherpa models, in that they have parameter values, and can be evaluated directly. However, `pl` has no knowledge of the instrument model and so will interpret the input arguments directly, for example the bins 0.1-0.2, 0.2-0.3, ..., 0.9-1.0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.69314718 0.40546511 0.28768207 0.22314355 0.18232156 0.15415068\n",
      " 0.13353139 0.11778304 0.10536052]\n"
     ]
    }
   ],
   "source": [
    "ebins = np.linspace(0.1, 1.0, 10)\n",
    "elo = ebins[:-1]\n",
    "ehi = ebins[1:]\n",
    "\n",
    "print(pl(elo, ehi))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `mdl_expr` model however knows about the instrument model, which defines the evaluation grid, and so ignores the input arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y1 and y2 are the same: True\n",
      "y1 and y3 are the same: True\n",
      "Output shape:              (1024,)\n",
      "Number of channels in PHA: 1024\n"
     ]
    }
   ],
   "source": [
    "y1 = mdl_expr(elo, ehi)\n",
    "y2 = mdl_expr([1, 2])\n",
    "y3 = mdl_expr([])\n",
    "\n",
    "print(\"y1 and y2 are the same: {}\".format((y1 == y2).all()))\n",
    "print(\"y1 and y3 are the same: {}\".format((y1 == y3).all()))\n",
    "\n",
    "print(\"Output shape:              {}\".format(y3.shape))\n",
    "print(\"Number of channels in PHA: {}\".format(len(pha.channel)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can then be used to evaluate the model directly - e.g. to create plots - but it is important to understand what the units are (as the model is integrated and then passed through the ARF and RMF)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pha.channel, y1)\n",
    "plt.xlabel(\"Channel\")\n",
    "plt.ylabel(\"Counts\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A trick\n",
    "\n",
    "A trick to determining what model expression to use is to load up a data set using the UI layer and then use the `sherpa.astro.ui.get_model` function. This can be useful for complicated cases, such as when multiple responses are in use (as you can get with Chandra and XMM grating spectra).\n",
    "\n",
    "An example, using the data we have already loaded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;RSPModelPHA model instance &#x27;apply_rmf(apply_arf(37664.157219191 * powlaw1d.pl))&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: apply_rmf(apply_arf(37664.157219191 * powlaw1d.pl))</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=3>powlaw1d.pl</th><td>gamma</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.2557765163241092</td><td>-10.0</td><td>10.0</td><td></td></tr><tr><td>ref</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>4.600846221718905e-05</td><td>0.0</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<RSPModelPHA model instance 'apply_rmf(apply_arf(37664.157219191 * powlaw1d.pl))'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_ui = ui.get_model()\n",
    "mdl_ui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sherpa.astro.instrument.RSPModelPHA'>\n"
     ]
    }
   ],
   "source": [
    "print(type(mdl_ui))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be compared to the model expression I created manually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;RSPModelPHA model instance &#x27;apply_rmf(apply_arf(37664.157219191 * pl))&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: apply_rmf(apply_arf(37664.157219191 * pl))</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=3>pl</th><td>gamma</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-10.0</td><td>10.0</td><td></td></tr><tr><td>ref</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.0</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<RSPModelPHA model instance 'apply_rmf(apply_arf(37664.157219191 * pl))'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sherpa.astro.instrument.RSPModelPHA'>\n"
     ]
    }
   ],
   "source": [
    "print(type(mdl_expr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that although both power law components are labelled as `pl` above, they refer to different objects, as shown by the fact the parameter values are different.\n",
    "\n",
    "### Fit\n",
    "\n",
    "Getting back to the analysis, I now need to create a `Fit` object to control the fit and error analysis. This is made slightly easier by my laziness above in using the default optimiser and statistic! Some of my\n",
    "[earlier notebooks](https://github.com/DougBurke/sherpa-standalone-notebooks) can be read to find out how to set up fits using non-default values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.fit import Fit\n",
    "\n",
    "f = Fit(pha, mdl_expr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit object now contains the data and model to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data      = data/pha/acisf01575_001N001_r0043_pha3.fits.gz\n",
      "model     = apply_rmf(apply_arf(37664.157219191 * pl))\n",
      "stat      = Chi2Gehrels\n",
      "method    = LevMar\n",
      "estmethod = Covariance\n"
     ]
    }
   ],
   "source": [
    "print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before starting the fit, let's just check the starting statistic (spoiler, it's not very good) and model values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting statistic = 1922880893626.27\n",
      "---------------------------------------------\n",
      "apply_rmf(apply_arf(37664.157219191 * pl))\n",
      "   Param        Type          Value          Min          Max      Units\n",
      "   -----        ----          -----          ---          ---      -----\n",
      "   pl.gamma     thawed            1          -10           10           \n",
      "   pl.ref       frozen            1 -3.40282e+38  3.40282e+38           \n",
      "   pl.ampl      thawed            1            0  3.40282e+38           \n"
     ]
    }
   ],
   "source": [
    "print(f\"Starting statistic = {f.calc_stat()}\")\n",
    "print(\"---------------------------------------------\")\n",
    "print(mdl_expr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit itself is simple to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "fres = f.fit()\n",
    "if not fres.succeeded:\n",
    "    print(\"Eek\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method                = levmar\n",
      "Statistic             = chi2gehrels\n",
      "Initial fit statistic = 1.92288e+12\n",
      "Final fit statistic   = 44.5589 at function evaluation 25\n",
      "Data points           = 58\n",
      "Degrees of freedom    = 56\n",
      "Probability [Q-value] = 0.864502\n",
      "Reduced statistic     = 0.795694\n",
      "Change in statistic   = 1.92288e+12\n",
      "   pl.gamma       2.25577      +/- 0.0593472   \n",
      "   pl.ampl        4.60084e-05  +/- 1.51715e-06 \n"
     ]
    }
   ],
   "source": [
    "print(fres.format())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does the fit look, visually?\n",
    "\n",
    "New in 4.16.1 is `DelchiPHAPlot` for displaying delchi plots for PHA data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sherpa.astro.plot import DelchiPHAPlot, ModelPHAHistogram\n",
    "from sherpa.plot import FitPlot, SplitPlot\n",
    "\n",
    "dplot = DataPHAPlot()\n",
    "mplot = ModelPHAHistogram()\n",
    "fplot = FitPlot()\n",
    "rplot = DelchiPHAPlot()\n",
    "\n",
    "# Match the axis scaling of the `plot_fit_delchi` plot earlier\n",
    "dplot.histo_prefs['xlog'] = True\n",
    "dplot.histo_prefs['ylog'] = True\n",
    "rplot.histo_prefs['xlog'] = True\n",
    "\n",
    "dplot.prepare(pha)\n",
    "mplot.prepare(pha, mdl_expr)\n",
    "fplot.prepare(dplot, mplot)\n",
    "rplot.prepare(pha, mdl_expr, stat=f.stat)\n",
    "\n",
    "# remove the title from the residual plot\n",
    "rplot.title = ''\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "splot = SplitPlot()\n",
    "splot.addplot(fplot)\n",
    "splot.addplot(rplot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error analysis\n",
    "\n",
    "The last thing left is to calculate the parameter errors. The default error estimator is `sherpa.estmethods.Covariance`, which is the same as used by the `sherpa.astro.ui.covar` routine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Covariance error-estimation method instance 'covariance'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.estmethod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "covar = f.est_errors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `format` method produces a display version of the results similar to that shown by `sherpa.astro.ui.covar`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence Method     = covariance\n",
      "Iterative Fit Method  = None\n",
      "Fitting Method        = levmar\n",
      "Statistic             = chi2gehrels\n",
      "covariance 1-sigma (68.2689%) bounds:\n",
      "   Param            Best-Fit  Lower Bound  Upper Bound\n",
      "   -----            --------  -----------  -----------\n",
      "   pl.gamma          2.25577   -0.0571235    0.0571235\n",
      "   pl.ampl       4.60084e-05 -1.51585e-06  1.51585e-06\n"
     ]
    }
   ],
   "source": [
    "print(covar.format())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.estmethods import Confidence\n",
    "f.estmethod = Confidence()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pl.ampl lower bound:\t-1.51585e-06\n",
      "pl.ampl upper bound:\t1.51585e-06\n",
      "pl.gamma lower bound:\t-0.0564984\n",
      "pl.gamma upper bound:\t0.0577486\n"
     ]
    }
   ],
   "source": [
    "conf = f.est_errors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence Method     = confidence\n",
      "Iterative Fit Method  = None\n",
      "Fitting Method        = levmar\n",
      "Statistic             = chi2gehrels\n",
      "confidence 1-sigma (68.2689%) bounds:\n",
      "   Param            Best-Fit  Lower Bound  Upper Bound\n",
      "   -----            --------  -----------  -----------\n",
      "   pl.gamma          2.25577   -0.0564984    0.0577486\n",
      "   pl.ampl       4.60084e-05 -1.51585e-06  1.51585e-06\n"
     ]
    }
   ],
   "source": [
    "print(conf.format())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the results numerically (this is just a demonstration, so only a limited number of fields are checked, but the values look pretty similar):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "covar   parvals\n",
      "(2.2557765163241092, 4.600846221718905e-05)\n",
      "(2.255765680773031, 4.600841985906586e-05)\n",
      "\n",
      "covar   parmaxes\n",
      "(0.057123909581766164, 1.5158525565437446e-06)\n",
      "(0.057123494523922576, 1.515854871880797e-06)\n",
      "\n",
      "conf    parvals\n",
      "(2.2557765163241092, 4.600846221718905e-05)\n",
      "(2.255765680773031, 4.600841985906586e-05)\n",
      "\n",
      "conf    parmaxes\n",
      "(0.057748978511570126, 1.5158525565437422e-06)\n",
      "(0.05774856345339163, 1.5158548718807987e-06)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for lbl, old, new in zip([\"covar\", \"conf\"], [covar_ui, conf_ui], [covar, conf]):\n",
    "    for field in [\"parvals\", \"parmaxes\"]:\n",
    "        print(f\"{lbl:6s}  {field}\")\n",
    "        print(getattr(old, field))\n",
    "        print(getattr(new, field))\n",
    "        print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the interval-projection is a little-more involved:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.plot import IntervalProjection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "iproj = IntervalProjection()\n",
    "iproj.prepare(min=2, max=2.5, nloop=51)\n",
    "\n",
    "# The following line performs the calculation, so can take some time if the number\n",
    "# of iterations is high or there are many free parameters.\n",
    "iproj.calc(f, pl.gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iproj.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit object is used to calculate the statistic value; fortunately it agrees with the earlier version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistic = 44.5588757909127 vs 44.558875643830135\n"
     ]
    }
   ],
   "source": [
    "stat = f.calc_stat()\n",
    "print(f\"Statistic = {stat} vs {stat_ui}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus stuff\n",
    "\n",
    "A bonus for reading this far is the following exploration of explicit model and data evaluation. The `eval_model` method of the `DataPHA` object will evaluate the model on the full grid (so all 1024 channels), whereas `eval_model_to_fit` applies any filters and grouping. This explains the difference in the number of elements shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1024,)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pha.eval_model(mdl_expr).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58,)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pha.eval_model_to_fit(mdl_expr).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl_vals = pha.eval_model_to_fit(mdl_expr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The trick is working out sensible values for the independent axis (i.e. the channels, or energy, or wavelengths) for the filtered and grouped data. The `apply_filter` method will filter an array (whose length should match the number of channels, so in this case 1024 elements) to match the data, and then apply a grouping function to calculate the per-group value. The default grouping function is to sum the values, but there are options to return the minimum, maxium, or the average of the minimum and maximum values in a group.\n",
    "\n",
    "There is also an `apply_grouping` method which just performs the grouping (i.e. does not apply any filtering), but for now I am going to stick with `apply_filter`. Here I create three arrays, which give the middle, left, and right values for each group of channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "chan_mid = pha.apply_filter(pha.channel, groupfunc=pha._middle)\n",
    "chan_min = pha.apply_filter(pha.channel, groupfunc=pha._min)\n",
    "chan_max = pha.apply_filter(pha.channel, groupfunc=pha._max)\n",
    "\n",
    "# This will be useful below, so calculate the number of channels in each group\n",
    "nchans = 1 + chan_max - chan_min"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These values can be used to create a histogram plot (using the `chan_min` and `chan_max` values) or a line plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "plt.bar(x=chan_min, height=mdl_vals, width=nchans, align='edge')\n",
    "plt.plot(chan_mid, mdl_vals, 'k')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Channel')\n",
    "plt.ylabel('Counts');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This does not match the earlier plots - e.g. created by `sherpa.astro.ui.plot_fit` or the `sherpa.astro.plot.DataPHAPlot` class - because the Y axis here has not been normalized by the bin width (as well as the more prosaic fact that the earlier plots used energy rather than channel for the X axis):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dplot_chan = DataPHAPlot()\n",
    "pha.set_analysis('channel')\n",
    "dplot_chan.prepare(pha)\n",
    "\n",
    "dplot_chan.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be able to visually compare the model values I have just calculated to the data - as plotted by `DataPlot` - I have to normalize by both the bin width (in channels) *and* the exposure time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dplot_chan.plot()\n",
    "plt.plot(chan_mid, mdl_vals / nchans / pha.exposure, '-')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the equivalent \"energy\" plot is a bit-more complicated, and I'm not convinced the following should be considered the \"official\" approach, but it works for me here! The idea is to use the `E_MIN` and `E_MAX` columns from the RMF - these give a \"hand-wavy\" approximation to the energies for each channel - to get the minimum and maximum energies for each group, and then manually create the center of each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "elo = pha.apply_filter(mdl_expr.rmf.e_min, groupfunc=pha._min)\n",
    "ehi = pha.apply_filter(mdl_expr.rmf.e_max, groupfunc=pha._max)\n",
    "\n",
    "emid = (elo + ehi) / 2.0\n",
    "ewidth = ehi - elo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives a reasonable looking model fit to the data (given that this is not meant to be a physically-motivated fit!):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dplot.plot()\n",
    "plt.plot(emid, mdl_vals / ewidth / pha.exposure, '-');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a further check, here I overplot the values I calculate - as the dotted orange line - on top of the model plot created by Sherpa's `ModelPHAHistogram` class, and we can see that there's good agreement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mplot.plot()\n",
    "plt.plot(emid, mdl_vals / ewidth / pha.exposure, linestyle='dotted', lw=4)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus bonus stuff\n",
    "\n",
    "Or should that be \"Bonus$^2$ stuff\"?\n",
    "\n",
    "So far this analysis has required a PHA file along with the ARF and RMF, but it's possible to just work with the instrument responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.astro.instrument import ARF1D, RMF1D\n",
    "from sherpa.astro.io import read_arf, read_rmf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "arf = read_arf('data/pha/acisf01575_001N001_r0043_arf3.fits.gz')\n",
    "rmf = read_rmf('data/pha/acisf01575_001N001_r0043_rmf3.fits.gz')\n",
    "\n",
    "arf_model = ARF1D(arf)\n",
    "rmf_model = RMF1D(rmf)\n",
    "full_expr = rmf_model(arf_model(pl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sherpa.astro.instrument.RMFModelNoPHA'>\n"
     ]
    }
   ],
   "source": [
    "print(type(full_expr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;RMFModelNoPHA model instance &#x27;apply_rmf(apply_arf(37664.157219191 * pl))&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: apply_rmf(apply_arf(37664.157219191 * pl))</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=3>pl</th><td>gamma</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.255765680773031</td><td>-10.0</td><td>10.0</td><td></td></tr><tr><td>ref</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>4.600841985906586e-05</td><td>0.0</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<RMFModelNoPHA model instance 'apply_rmf(apply_arf(37664.157219191 * pl))'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_expr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there is no PHA, there is no fitlering or grouping, which makes evaluation a bit simpler than above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(rmf.offset, rmf.offset + rmf.detchans)\n",
    "y = full_expr([])  # there just has to be an array-like argument, but its value is ignored"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To finish off, I plot the values calculated using this approach, and overplot the previously-calculated values (to show that you get similar results): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "plt.plot(x, y, color='black')\n",
    "plt.loglog()\n",
    "plt.xlim(5, 1100)\n",
    "plt.ylim(1e-2, 100)\n",
    "\n",
    "plt.xlabel('Channel')\n",
    "plt.ylabel('Counts per channel')\n",
    "\n",
    "# Overplot the \"grouped\" values I calculated earlier\n",
    "plt.plot(chan_mid, mdl_vals / nchans, color='orange');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks for reading this far!"
   ]
  }
 ],
 "metadata": {
  "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}