What is Sherpa?.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "inside-archives",
   "metadata": {
    "tags": []
   },
   "source": [
    "# What is Sherpa?\n",
    "\n",
    "CIAO provides the `sherpa` \"tool\", but what is it?\n",
    "\n",
    "It is a \"wrapper\" around `ipython` that\n",
    "\n",
    "- automatically starts up `matplotlib`: `ipython --matplotlib`\n",
    "\n",
    "- prints out a header welcoming you to the glories of Sherpa\n",
    "\n",
    "- automatically loads useful modules:\n",
    "\n",
    "    import numpy as np\n",
    "    from matplotlib import pyplot as plt\n",
    "    from pycrates import *  # This is the CIAO I/O module\n",
    "    from sherpa.astro.ui import *\n",
    "\n",
    "- sets up optional IPython things, such as tweaking the prompt and adding brackets (\"`str 42` -> `str(42)`)\n",
    "\n",
    "- there's a few CIAO-specific additions, such as the `ahelp` function, and we hide much of the traceback as we don't think it useful for most users\n",
    "\n",
    "If you want to you can tweak the Sherpa settings in the directory\n",
    "\n",
    "    ~/.ipython-ciao/profile_sherpa/\n",
    "    ~/.ipython-ciao/profile_sherpa/startup/\n",
    "    \n",
    "You do **not** need to use the `sherpa` \"tool\" - you can just use `python`, `ipython`, of even Jupyter notebooks!\n",
    "\n",
    "I am not going to try running sherpa from within this notebook as it'll essentially hang the process!\n",
    "\n",
    "```\n",
    "% sherpa\n",
    "sherpa\n",
    "-----------------------------------------------------\n",
    "Welcome to Sherpa: CXC's Modeling and Fitting Package\n",
    "-----------------------------------------------------\n",
    "Sherpa 4.13.0\n",
    "\n",
    "Python 3.8.2 (default, Mar 25 2020, 17:03:02)\n",
    "Type 'copyright', 'credits' or 'license' for more information\n",
    "IPython 7.21.0 -- An enhanced Interactive Python. Type '?' for help.\n",
    "\n",
    "IPython profile: sherpa\n",
    "Using matplotlib backend: Qt5Agg\n",
    "\n",
    "sherpa In [1]:\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "smooth-beginning",
   "metadata": {},
   "source": [
    "# Running Sherpa from the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "acquired-handle",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sherpa.astro.ui import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "greatest-sydney",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_arrays(1, [1, 2, 5, 10], [9, -2, 12, 14])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "differential-yugoslavia",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "biological-campbell",
   "metadata": {},
   "source": [
    "So far this matches the \"sherpa\" tool. We do have some extra support for Jupyter notebooks, taking advantage of its \"rich interface\" support to get nicer output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "protective-aruba",
   "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;Data1D data set instance &#x27;&#x27;&gt;</div><div hidden class=\"sherpa\"><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;sherpa.plot.DataPlot object at 0x7f0f9be65040&gt;</div><div hidden class=\"sherpa\"><details open><summary>Data1D Plot</summary><svg height=\"288pt\" version=\"1.1\" viewBox=\"0 0 432 288\" width=\"432pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
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       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2021-08-26T10:19:57.832279</dc:date>\n",
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       "    <dc:creator>\n",
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       " </g>\n",
       " <defs>\n",
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       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n",
       "</details></div></div>"
      ],
      "text/plain": [
       "<Data1D data set instance ''>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_data()  # rich putput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "potential-flower",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_source(polynom1d.pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "known-matrix",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Method                = levmar\n",
      "Statistic             = chi2gehrels\n",
      "Initial fit statistic = 19.355\n",
      "Final fit statistic   = 18.2628 at function evaluation 4\n",
      "Data points           = 4\n",
      "Degrees of freedom    = 3\n",
      "Probability [Q-value] = 0.000388226\n",
      "Reduced statistic     = 6.0876\n",
      "Change in statistic   = 1.09217\n",
      "   pl.c0          2.58167      +/- 1.51346     \n"
     ]
    }
   ],
   "source": [
    "fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "retired-adult",
   "metadata": {},
   "outputs": [],
   "source": [
    "pl.c1.thaw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "becoming-increase",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Method                = levmar\n",
      "Statistic             = chi2gehrels\n",
      "Initial fit statistic = 18.2628\n",
      "Final fit statistic   = 9.79605 at function evaluation 6\n",
      "Data points           = 4\n",
      "Degrees of freedom    = 2\n",
      "Probability [Q-value] = 0.0074613\n",
      "Reduced statistic     = 4.89803\n",
      "Change in statistic   = 8.46675\n",
      "   pl.c0          -2.58909     +/- 2.33419     \n",
      "   pl.c1          1.73737      +/- 0.597081    \n"
     ]
    }
   ],
   "source": [
    "fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "searching-tucson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "elegant-glossary",
   "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;Fit results instance&gt;</div><div hidden class=\"sherpa\"><details open><summary>Fit parameters</summary><table class=\"fit\"><thead><tr><th>Parameter</th><th>Best-fit value</th><th>Approximate error</th></tr></thead><tbody><tr><td>pl.c0</td><td>    -2.58909</td><td>&#177;      2.33419</td></tr><tr><td>pl.c1</td><td>     1.73737</td><td>&#177;     0.597081</td></tr></tbody></table></details><details><summary>Summary (11)</summary><div class=\"datavals\"><div class=\"dataname\">Dataset</div><div class=\"dataval\">1</div><div class=\"dataname\">Method</div><div class=\"dataval\">levmar</div><div class=\"dataname\">Statistic</div><div class=\"dataval\">chi2gehrels</div><div class=\"dataname\">Final statistic</div><div class=\"dataval\">9.79605</div><div class=\"dataname\">Number of evaluations</div><div class=\"dataval\">6</div><div class=\"dataname\">Reduced statistic</div><div class=\"dataval\">4.89803</div><div class=\"dataname\">Probability (Q-value)</div><div class=\"dataval\">0.0074613</div><div class=\"dataname\">Initial statistic</div><div class=\"dataval\">18.2628</div><div class=\"dataname\">&#916; statistic</div><div class=\"dataval\">8.46675</div><div class=\"dataname\">Number of data points</div><div class=\"dataval\">4</div><div class=\"dataname\">Degrees of freedom</div><div class=\"dataval\">2</div></div></details></div>"
      ],
      "text/plain": [
       "<Fit results instance>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_fit_results()  # rich output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "answering-reading",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ignore(1.5, 2.5)\n",
    "plot_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "greatest-drama",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset               = 1\n",
      "Method                = levmar\n",
      "Statistic             = chi2gehrels\n",
      "Initial fit statistic = 7.40466\n",
      "Final fit statistic   = 0.0196631 at function evaluation 6\n",
      "Data points           = 3\n",
      "Degrees of freedom    = 1\n",
      "Probability [Q-value] = 0.888482\n",
      "Reduced statistic     = 0.0196631\n",
      "Change in statistic   = 7.385\n",
      "   pl.c0          8.68041      +/- 4.29407     \n",
      "   pl.c1          0.558286     +/- 0.706193    \n"
     ]
    }
   ],
   "source": [
    "fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "broadband-notice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "republican-robinson",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pl.c0 lower bound:\t-4.29407\n",
      "pl.c0 upper bound:\t4.29407\n",
      "pl.c1 lower bound:\t-0.706193\n",
      "pl.c1 upper bound:\t0.706193\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.c0             8.68041     -4.29407      4.29407\n",
      "   pl.c1            0.558286    -0.706193     0.706193\n"
     ]
    }
   ],
   "source": [
    "conf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "exact-highland",
   "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;confidence results instance&gt;</div><div hidden class=\"sherpa\"><details open><summary>confidence 1&#963; (68.2689%) bounds</summary><table><thead><tr><th>Parameter</th><th>Best-fit value</th><th>Lower Bound</th><th>Upper Bound</th></tr></thead><tbody><tr><td>pl.c0</td><td>     8.68041</td><td>    -4.29407</td><td>     4.29407</td></tr><tr><td>pl.c1</td><td>    0.558286</td><td>   -0.706193</td><td>    0.706193</td></tr></tbody></table></details><details><summary>Summary (3)</summary><div class=\"datavals\"><div class=\"dataname\">Dataset</div><div class=\"dataval\">1</div><div class=\"dataname\">Fitting Method</div><div class=\"dataval\">levmar</div><div class=\"dataname\">Statistic</div><div class=\"dataval\">chi2gehrels</div></div></details></div>"
      ],
      "text/plain": [
       "<confidence results instance>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_conf_results()  # rich output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abroad-leisure",
   "metadata": {},
   "source": [
    "# Can I run CIAO tools?\n",
    "\n",
    "You can use `!<dmtool>` as with any unix command (at least from an ipython session). You can also run them using the standard Python modules, as described at https://cxc.harvard.edu/ciao/scripting/runtool.html#subprocess, but we suggest using the `ciao_contrib.runtool` module https://cxc.harvard.edu/ciao/scripting/runtool.html#runtool:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "broken-consumption",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ciao_contrib import runtool as rt  # this requires the ciao-contrib package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fatal-subscriber",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acisf04938_000N002_r0043_arf3.fits  acisf04938_000N002_r0043_rmf3.fits\n",
      "acisf04938_000N002_r0043_pha3.fits\n"
     ]
    }
   ],
   "source": [
    "!ls data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorporate-queen",
   "metadata": {},
   "source": [
    "You run CIAO tools with python functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "individual-helping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " \n",
       "--------------------------------------------------------------------------------\n",
       "Dataset: data/acisf04938_000N002_r0043_pha3.fits\n",
       "--------------------------------------------------------------------------------\n",
       " \n",
       "     Block Name                          Type         Dimensions\n",
       "--------------------------------------------------------------------------------\n",
       "Block    1: PRIMARY                        Null        \n",
       "Block    2: SPECTRUM1                      Table         4 cols x 1024     rows\n",
       "Block    3: GTI3                           Table         2 cols x 1        rows\n",
       "Block    4: GTI2                           Table         2 cols x 5        rows\n",
       "Block    5: SPECTRUM2                      Table         4 cols x 1024     rows\n",
       "Block    6: GTI3_CPT2                      Table         2 cols x 5        rows"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt.dmlist(\"data/acisf04938_000N002_r0043_pha3.fits\", \"blocks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "valuable-archive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " \n",
       "--------------------------------------------------------------------------------\n",
       "Columns for Table Block SPECRESP\n",
       "--------------------------------------------------------------------------------\n",
       " \n",
       "ColNo  Name                 Unit        Type             Range\n",
       "   1   ENERG_LO             keV          Real4          -Inf:+Inf            Min Energy\n",
       "   2   ENERG_HI             keV          Real4          -Inf:+Inf            Max Energy\n",
       "   3   SPECRESP             cm**2        Real4          -Inf:+Inf            Effective Area\n",
       "   4   PSF_FRAC                          Real8          -Inf:+Inf            Fraction of PSF enclosed in SRC region"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt.dmlist(\"data/acisf04938_000N002_r0043_arf3.fits\", \"cols\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hired-fence",
   "metadata": {},
   "source": [
    "Optional parameters are supported, and there is conversion from Python values to those supported by the tool:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "governing-wagner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SPECRESP[cm**2]\n",
       "    min:\t0.02838011831 \t      @:\t1 \n",
       "    max:\t480.51446533 \t      @:\t125 \n",
       "   mean:\t173.55324853 \n",
       " median:\t178.48394012 \n",
       "  sigma:\t131.67394595 \n",
       "    sum:\t156197.92368 \n",
       "   good:\t900 \n",
       "   null:\t0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt.dmstat(\"data/acisf04938_000N002_r0043_arf3.fits[cols specresp]\", median=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "solar-hundred",
   "metadata": {},
   "source": [
    "The \"function\" contains information taken from the parameter file for the tool:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "crude-frame",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters for dmstat:\n",
      "\n",
      "Required parameters:\n",
      "              infile = data/acisf04938_000N002_r0043_arf3.fits[cols specresp]  Input file specification\n",
      "\n",
      "Optional parameters:\n",
      "            centroid = True             Calculate centroid if image?\n",
      "              median = True             Calculate median value?\n",
      "               sigma = True             Calculate the population standard deviation?\n",
      "                clip = False            Calculate stats using sigma clipping?\n",
      "              nsigma = 3.0              Number of sigma to clip\n",
      "             maxiter = 20               Maximum number of iterations\n",
      "             verbose = 1                Verbosity level\n",
      "         out_columns = SPECRESP         Output Column Label\n",
      "             out_min = 0.02838011831    Output Minimum Value\n",
      "         out_min_loc = 1                Output Minimum Location Value\n",
      "             out_max = 480.51446533     Output Maximum Value\n",
      "         out_max_loc = 125              Output Maximum Location Value\n",
      "            out_mean = 173.55324853     Output Mean Value\n",
      "          out_median = 178.48394012     Output Median Value\n",
      "           out_sigma = 131.67394595     Output Sigma Value\n",
      "             out_sum = 156197.92368     Output Sum of Values\n",
      "            out_good = 900              Output Number Good Values\n",
      "            out_null = 0                Output Number Null Values\n",
      "          out_cnvrgd =                  Converged?\n",
      "       out_cntrd_log =                  Output Centroid Log Value\n",
      "      out_cntrd_phys =                  Output Centroid Phys Value\n",
      "     out_sigma_cntrd =                  Output Sigma Centroid Value\n"
     ]
    }
   ],
   "source": [
    "print(rt.dmstat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "former-mambo",
   "metadata": {},
   "source": [
    "The parameters can even be read (or set for the next call):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "faced-journalism",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'178.48394012'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt.dmstat.out_median"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mathematical-extent",
   "metadata": {},
   "source": [
    "Although the `runtool` module comes with functions for each tool, you can create a function for a specific tool (to avoid potential complications with parameter settings):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "searching-sensitivity",
   "metadata": {},
   "outputs": [],
   "source": [
    "dms = rt.make_tool('dmstat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "received-northwest",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters for dmstat:\n",
      "\n",
      "Required parameters:\n",
      "              infile =                  Input file specification\n",
      "\n",
      "Optional parameters:\n",
      "            centroid = True             Calculate centroid if image?\n",
      "              median = False            Calculate median value?\n",
      "               sigma = True             Calculate the population standard deviation?\n",
      "                clip = False            Calculate stats using sigma clipping?\n",
      "              nsigma = 3                Number of sigma to clip\n",
      "             maxiter = 20               Maximum number of iterations\n",
      "             verbose = 1                Verbosity level\n",
      "         out_columns =                  Output Column Label\n",
      "             out_min =                  Output Minimum Value\n",
      "         out_min_loc =                  Output Minimum Location Value\n",
      "             out_max =                  Output Maximum Value\n",
      "         out_max_loc =                  Output Maximum Location Value\n",
      "            out_mean =                  Output Mean Value\n",
      "          out_median =                  Output Median Value\n",
      "           out_sigma =                  Output Sigma Value\n",
      "             out_sum =                  Output Sum of Values\n",
      "            out_good =                  Output Number Good Values\n",
      "            out_null =                  Output Number Null Values\n",
      "          out_cnvrgd =                  Converged?\n",
      "       out_cntrd_log =                  Output Centroid Log Value\n",
      "      out_cntrd_phys =                  Output Centroid Phys Value\n",
      "     out_sigma_cntrd =                  Output Sigma Centroid Value\n"
     ]
    }
   ],
   "source": [
    "print(dms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ecological-establishment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PSF_FRAC\n",
       "    min:\t0.91909490197 \t      @:\t651 \n",
       "    max:\t0.97671880734 \t      @:\t51 \n",
       "   mean:\t0.94588493995 \n",
       " median:\t0.94405206988 \n",
       "  sigma:\t0.018654448891 \n",
       "    sum:\t851.29644595 \n",
       "   good:\t900 \n",
       "   null:\t0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dms.median = True\n",
    "dms(\"data/acisf04938_000N002_r0043_arf3.fits[cols psf_frac]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fifteen-insulation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters for dmstat:\n",
      "\n",
      "Required parameters:\n",
      "              infile = data/acisf04938_000N002_r0043_arf3.fits[cols psf_frac]  Input file specification\n",
      "\n",
      "Optional parameters:\n",
      "            centroid = True             Calculate centroid if image?\n",
      "              median = True             Calculate median value?\n",
      "               sigma = True             Calculate the population standard deviation?\n",
      "                clip = False            Calculate stats using sigma clipping?\n",
      "              nsigma = 3.0              Number of sigma to clip\n",
      "             maxiter = 20               Maximum number of iterations\n",
      "             verbose = 1                Verbosity level\n",
      "         out_columns = PSF_FRAC         Output Column Label\n",
      "             out_min = 0.91909490197    Output Minimum Value\n",
      "         out_min_loc = 651              Output Minimum Location Value\n",
      "             out_max = 0.97671880734    Output Maximum Value\n",
      "         out_max_loc = 51               Output Maximum Location Value\n",
      "            out_mean = 0.94588493995    Output Mean Value\n",
      "          out_median = 0.94405206988    Output Median Value\n",
      "           out_sigma = 0.018654448891   Output Sigma Value\n",
      "             out_sum = 851.29644595     Output Sum of Values\n",
      "            out_good = 900              Output Number Good Values\n",
      "            out_null = 0                Output Number Null Values\n",
      "          out_cnvrgd =                  Converged?\n",
      "       out_cntrd_log =                  Output Centroid Log Value\n",
      "      out_cntrd_phys =                  Output Centroid Phys Value\n",
      "     out_sigma_cntrd =                  Output Sigma Centroid Value\n"
     ]
    }
   ],
   "source": [
    "print(dms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "celtic-novelty",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters for dmstat:\n",
      "\n",
      "Required parameters:\n",
      "              infile = data/acisf04938_000N002_r0043_arf3.fits[cols specresp]  Input file specification\n",
      "\n",
      "Optional parameters:\n",
      "            centroid = True             Calculate centroid if image?\n",
      "              median = True             Calculate median value?\n",
      "               sigma = True             Calculate the population standard deviation?\n",
      "                clip = False            Calculate stats using sigma clipping?\n",
      "              nsigma = 3.0              Number of sigma to clip\n",
      "             maxiter = 20               Maximum number of iterations\n",
      "             verbose = 1                Verbosity level\n",
      "         out_columns = SPECRESP         Output Column Label\n",
      "             out_min = 0.02838011831    Output Minimum Value\n",
      "         out_min_loc = 1                Output Minimum Location Value\n",
      "             out_max = 480.51446533     Output Maximum Value\n",
      "         out_max_loc = 125              Output Maximum Location Value\n",
      "            out_mean = 173.55324853     Output Mean Value\n",
      "          out_median = 178.48394012     Output Median Value\n",
      "           out_sigma = 131.67394595     Output Sigma Value\n",
      "             out_sum = 156197.92368     Output Sum of Values\n",
      "            out_good = 900              Output Number Good Values\n",
      "            out_null = 0                Output Number Null Values\n",
      "          out_cnvrgd =                  Converged?\n",
      "       out_cntrd_log =                  Output Centroid Log Value\n",
      "      out_cntrd_phys =                  Output Centroid Phys Value\n",
      "     out_sigma_cntrd =                  Output Sigma Centroid Value\n"
     ]
    }
   ],
   "source": [
    "print(rt.dmstat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "previous-amplifier",
   "metadata": {},
   "source": [
    "# What happens if I want to use parts of Sherpa or add to it?\n",
    "\n",
    "Firstly, welcome and think about following https://github.com/sherpa/sherpa and reading the documentation at https://sherpa.readthedocs.io/.\n",
    "\n",
    "The `sherpa.astro.ui` module used above (and in the other notebooks in this repository) contains a (hidden) session class that [deals with a lot of data-management tasks](https://sherpa.readthedocs.io/en/latest/ui/index.html), such as\n",
    "\n",
    "- creating and storing data\n",
    "- creating and storing models\n",
    "- automatically applying responses when needed (e.g. PSF or X-ray responses)\n",
    "- handling a lot of the busy work when fitting data\n",
    "\n",
    "However, this class leverages a number of other classes that you are free to use. You can even use them alongside the `sherpa.astro.ui` module (mainly the classes that [represent the data objects](https://sherpa.readthedocs.io/en/latest/data/index.html)) if needed.\n",
    "\n",
    "The [tutorial on readthedocs](https://sherpa.readthedocs.io/en/latest/quick.html) gives an overview of how you can use these classes. This can be particularly helpful when you want to embed Sherpa into a larger program - e.g. fitting a number of datasets - but don't need the management-layer provided by `sherpa.astro.ui`.\n",
    "\n",
    "If you are interested in re-using parts of Sherpa then you can import just those parts - such as the [statistics](https://sherpa.readthedocs.io/en/latest/statistics/index.html) or [optimisers](https://sherpa.readthedocs.io/en/latest/optimisers/index.html). In general we prefer people to use the class interface - as it's the most stable - but you can use some very low-level pieces if you desire (e.g. the neldermead minimiser without any of the support provided by the data-class structure for filtering data). At this point it's probably easiest to contact me or any of the other Sherpa developers directly."
   ]
  }
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