{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "78f2dea8",
   "metadata": {},
   "source": [
    "# Spectral Access\n",
    "\n",
    "This notebook is one of a set produced by NAVO to demonstrate data access with python tools.\n",
    "\n",
    "In this notebook, we show how to search for and retrieve spectra from VO services using the Registry and the __[Simple Spectral Access](http://www.ivoa.net/documents/SSA/)__ (SSA) protocol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "95a14c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import requests, io\n",
    "\n",
    "from astropy.table import Table\n",
    "import astropy.io.fits as fits\n",
    "from astropy.coordinates import SkyCoord\n",
    "# For downloading files\n",
    "from astropy.utils.data import download_file\n",
    "\n",
    "import pyvo as vo\n",
    "\n",
    "# There are a number of relatively unimportant warnings that show up, so for now, suppress them:\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", module=\"astropy.io.votable.*\")\n",
    "warnings.filterwarnings(\"ignore\", module=\"pyvo.utils.xml.*\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "111e6f5a",
   "metadata": {},
   "source": [
    "## Finding available Spectral Access Services\n",
    "\n",
    "First, we find out what spectral access services ('ssa') are available in the Registry offering x-ray data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98eac349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=7</i>\n",
       "<table id=\"table140066481589792\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>ivoid</th><th>short_name</th></tr></thead>\n",
       "<thead><tr><th>object</th><th>object</th></tr></thead>\n",
       "<tr><td>ivo://nasa.heasarc/chanmaster</td><td>Chandra</td></tr>\n",
       "<tr><td>ivo://nasa.heasarc/hitomaster</td><td>HITOMASTER</td></tr>\n",
       "<tr><td>ivo://nasa.heasarc/intbsc</td><td>INTEGRAL/BSC</td></tr>\n",
       "<tr><td>ivo://nasa.heasarc/xrismmastr</td><td>XRISMMASTR</td></tr>\n",
       "<tr><td>ivo://nasa.heasarc/xtemaster</td><td>RXTE</td></tr>\n",
       "<tr><td>ivo://ned.ipac/sed_data_near_position</td><td>NED_SED</td></tr>\n",
       "<tr><td>ivo://wfau.roe.ac.uk/heavens_at_isdc/light-curves</td><td>HEAVENS @ ISDC</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=7>\n",
       "                      ivoid                         short_name  \n",
       "                      object                          object    \n",
       "------------------------------------------------- --------------\n",
       "                    ivo://nasa.heasarc/chanmaster        Chandra\n",
       "                    ivo://nasa.heasarc/hitomaster     HITOMASTER\n",
       "                        ivo://nasa.heasarc/intbsc   INTEGRAL/BSC\n",
       "                    ivo://nasa.heasarc/xrismmastr     XRISMMASTR\n",
       "                     ivo://nasa.heasarc/xtemaster           RXTE\n",
       "            ivo://ned.ipac/sed_data_near_position        NED_SED\n",
       "ivo://wfau.roe.ac.uk/heavens_at_isdc/light-curves HEAVENS @ ISDC"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "services = vo.regsearch(servicetype='ssa',waveband='x-ray')\n",
    "services.to_table()['ivoid','short_name']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e9c7233",
   "metadata": {},
   "source": [
    "We can look at only the Chandra entry:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "333f0832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://heasarc.gsfc.nasa.gov/xamin/vo/ssa?table=chanmaster&'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chandra_service = [s for s in services if 'Chandra' in s.short_name][0]\n",
    "chandra_service.access_url"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e9ccf89",
   "metadata": {},
   "source": [
    "## Chandra Spectrum of Delta Ori\n",
    "\n",
    "Getting the list of spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e2ebcb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: AstropyDeprecationWarning: show_in_notebook() is deprecated as of 6.1 and to create\n",
      "         interactive tables it is recommended to use dedicated tools like:\n",
      "         - https://github.com/bloomberg/ipydatagrid\n",
      "         - https://docs.bokeh.org/en/latest/docs/user_guide/interaction/widgets.html#datatable\n",
      "         - https://dash.plotly.com/datatable [warnings]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<i>Table length=6</i>\n",
       "<table id=\"table140066482437328-823116\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>obsid</th><th>status</th><th>name</th><th>ra</th><th>dec</th><th>time</th><th>detector</th><th>grating</th><th>exposure</th><th>type</th><th>pi</th><th>public_date</th><th>datalink</th><th>SSA_start_time</th><th>SSA_tmid</th><th>SSA_stop_time</th><th>SSA_duration</th><th>SSA_coord_obs</th><th>SSA_ra</th><th>SSA_dec</th><th>SSA_fov</th><th>SSA_title</th><th>SSA_reference</th><th>SSA_datalength</th><th>SSA_datamodel</th><th>SSA_instrument</th><th>SSA_publisher</th><th>SSA_format</th><th>SSA_wavelength_min</th><th>SSA_wavelength_max</th><th>SSA_bandwidth</th><th>SSA_bandpass</th><th>cloud_access</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th>deg</th><th>deg</th><th>d</th><th></th><th></th><th>s</th><th></th><th></th><th>d</th><th></th><th>d</th><th>d</th><th>d</th><th>s</th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>m</th><th>m</th><th>m</th><th>m</th><th></th></tr></thead>\n",
       "<tr><td>0</td><td>639</td><td>archived</td><td>DELTA ORI</td><td>83.00125</td><td>-0.29917</td><td>51556.1364</td><td>ACIS-S</td><td>HETG</td><td>49680</td><td>GO</td><td>Cassinelli</td><td>52037</td><td>11724:chandra.obs.misc</td><td>51556.136400463</td><td>--</td><td>--</td><td>49680.0</td><td>--</td><td>83.00125</td><td>-0.29917</td><td>0.81</td><td>acisf00639N005_pha2.fits</td><td>https://heasarc.gsfc.nasa.gov/FTP/chandra/data/byobsid/9/639/primary/acisf00639N005_pha2.fits.gz</td><td>12</td><td>Spectrum-1.0</td><td>ACIS-S</td><td>HEASARC</td><td>application/fits</td><td>1.2398e-10</td><td>6.1992e-09</td><td>6.07522e-09</td><td>3.16159e-09</td><td>{&quot;aws&quot;:{&quot;bucket_name&quot;:&quot;nasa-heasarc&quot;,&quot;region&quot;:&quot;us-east-1&quot;,&quot;policy&quot;:&quot;open&quot;,&quot;key&quot;:&quot;chandra/data/byobsid/9/639/primary/acisf00639N005_pha2.fits.gz&quot;}}</td></tr>\n",
       "<tr><td>1</td><td>7416</td><td>archived</td><td>delta Ori (HD 36486)</td><td>83.00167</td><td>-0.29908</td><td>54413.427</td><td>HRC-S</td><td>LETG</td><td>97080</td><td>GO</td><td>Raassen</td><td>54783</td><td>11725:chandra.obs.misc</td><td>54413.4270486111</td><td>--</td><td>--</td><td>97080.0</td><td>--</td><td>83.00167</td><td>-0.29908</td><td>0.81</td><td>hrcf07416N004_pha2.fits</td><td>https://heasarc.gsfc.nasa.gov/FTP/chandra/data/byobsid/6/7416/primary/hrcf07416N004_pha2.fits.gz</td><td>2</td><td>Spectrum-1.0</td><td>HRC-S</td><td>HEASARC</td><td>application/fits</td><td>1.2398e-10</td><td>6.1992e-09</td><td>6.07522e-09</td><td>3.16159e-09</td><td>{&quot;aws&quot;:{&quot;bucket_name&quot;:&quot;nasa-heasarc&quot;,&quot;region&quot;:&quot;us-east-1&quot;,&quot;policy&quot;:&quot;open&quot;,&quot;key&quot;:&quot;chandra/data/byobsid/6/7416/primary/hrcf07416N004_pha2.fits.gz&quot;}}</td></tr>\n",
       "<tr><td>2</td><td>14567</td><td>archived</td><td>Delta Ori</td><td>83.00167</td><td>-0.29908</td><td>56280.7037</td><td>ACIS-S</td><td>HETG</td><td>116500</td><td>GO</td><td>Corcoran</td><td>56647</td><td>11726:chandra.obs.misc</td><td>56280.7036921296</td><td>--</td><td>--</td><td>116500.0</td><td>--</td><td>83.00167</td><td>-0.29908</td><td>0.81</td><td>acisf14567N003_pha2.fits</td><td>https://heasarc.gsfc.nasa.gov/FTP/chandra/data/byobsid/7/14567/primary/acisf14567N003_pha2.fits.gz</td><td>12</td><td>Spectrum-1.0</td><td>ACIS-S</td><td>HEASARC</td><td>application/fits</td><td>1.2398e-10</td><td>6.1992e-09</td><td>6.07522e-09</td><td>3.16159e-09</td><td>{&quot;aws&quot;:{&quot;bucket_name&quot;:&quot;nasa-heasarc&quot;,&quot;region&quot;:&quot;us-east-1&quot;,&quot;policy&quot;:&quot;open&quot;,&quot;key&quot;:&quot;chandra/data/byobsid/7/14567/primary/acisf14567N003_pha2.fits.gz&quot;}}</td></tr>\n",
       "<tr><td>3</td><td>14568</td><td>archived</td><td>Delta Ori</td><td>83.00167</td><td>-0.29908</td><td>56288.1612</td><td>ACIS-S</td><td>HETG</td><td>123600</td><td>GO</td><td>Corcoran</td><td>56655</td><td>11727:chandra.obs.misc</td><td>56288.1612268519</td><td>--</td><td>--</td><td>123600.0</td><td>--</td><td>83.00167</td><td>-0.29908</td><td>0.81</td><td>acisf14568N003_pha2.fits</td><td>https://heasarc.gsfc.nasa.gov/FTP/chandra/data/byobsid/8/14568/primary/acisf14568N003_pha2.fits.gz</td><td>12</td><td>Spectrum-1.0</td><td>ACIS-S</td><td>HEASARC</td><td>application/fits</td><td>1.2398e-10</td><td>6.1992e-09</td><td>6.07522e-09</td><td>3.16159e-09</td><td>{&quot;aws&quot;:{&quot;bucket_name&quot;:&quot;nasa-heasarc&quot;,&quot;region&quot;:&quot;us-east-1&quot;,&quot;policy&quot;:&quot;open&quot;,&quot;key&quot;:&quot;chandra/data/byobsid/8/14568/primary/acisf14568N003_pha2.fits.gz&quot;}}</td></tr>\n",
       "<tr><td>4</td><td>14569</td><td>archived</td><td>Delta Ori</td><td>83.00167</td><td>-0.29908</td><td>56283.254</td><td>ACIS-S</td><td>HETG</td><td>120850</td><td>GO</td><td>Corcoran</td><td>56650</td><td>11728:chandra.obs.misc</td><td>56283.2539814815</td><td>--</td><td>--</td><td>120850.0</td><td>--</td><td>83.00167</td><td>-0.29908</td><td>0.81</td><td>acisf14569N003_pha2.fits</td><td>https://heasarc.gsfc.nasa.gov/FTP/chandra/data/byobsid/9/14569/primary/acisf14569N003_pha2.fits.gz</td><td>12</td><td>Spectrum-1.0</td><td>ACIS-S</td><td>HEASARC</td><td>application/fits</td><td>1.2398e-10</td><td>6.1992e-09</td><td>6.07522e-09</td><td>3.16159e-09</td><td>{&quot;aws&quot;:{&quot;bucket_name&quot;:&quot;nasa-heasarc&quot;,&quot;region&quot;:&quot;us-east-1&quot;,&quot;policy&quot;:&quot;open&quot;,&quot;key&quot;:&quot;chandra/data/byobsid/9/14569/primary/acisf14569N003_pha2.fits.gz&quot;}}</td></tr>\n",
       "<tr><td>5</td><td>14570</td><td>archived</td><td>Delta Ori</td><td>83.00167</td><td>-0.29908</td><td>56285.5508</td><td>ACIS-S</td><td>HETG</td><td>124100</td><td>GO</td><td>Corcoran</td><td>56652</td><td>11729:chandra.obs.misc</td><td>56285.5507986111</td><td>--</td><td>--</td><td>124100.0</td><td>--</td><td>83.00167</td><td>-0.29908</td><td>0.81</td><td>acisf14570N003_pha2.fits</td><td>https://heasarc.gsfc.nasa.gov/FTP/chandra/data/byobsid/0/14570/primary/acisf14570N003_pha2.fits.gz</td><td>12</td><td>Spectrum-1.0</td><td>ACIS-S</td><td>HEASARC</td><td>application/fits</td><td>1.2398e-10</td><td>6.1992e-09</td><td>6.07522e-09</td><td>3.16159e-09</td><td>{&quot;aws&quot;:{&quot;bucket_name&quot;:&quot;nasa-heasarc&quot;,&quot;region&quot;:&quot;us-east-1&quot;,&quot;policy&quot;:&quot;open&quot;,&quot;key&quot;:&quot;chandra/data/byobsid/0/14570/primary/acisf14570N003_pha2.fits.gz&quot;}}</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140066482437328-823116').dataTable()\");\n",
       "    \n",
       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
       "    \"optionalnum-asc\": astropy_sort_num,\n",
       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
       "});\n",
       "\n",
       "    $('#table140066482437328-823116').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 4, 5, 6, 9, 12, 14, 15, 16, 17, 18, 19, 20, 21, 29, 30, 31, 32], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delori = SkyCoord.from_name(\"Delta Ori\")\n",
    "\n",
    "spec_tables = chandra_service.search(pos=delori,diameter=0.1)\n",
    "spec_tables.to_table().show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c86c1e9",
   "metadata": {},
   "source": [
    "Accessing one of the spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a0180fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "## If you only run this once, you can do it in memory in one line:\n",
    "##  This fetches the FITS as an astropy.io.fits object in memory\n",
    "# hdu_list = spec_tables[0].getdataobj()\n",
    "## But if you might run this notebook repeatedly with limited bandwidth,\n",
    "##  download it once and cache it.\n",
    "file_name = download_file(spec_tables[0].getdataurl(),cache=True)\n",
    "hdu_list = fits.open(file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b9f69c",
   "metadata": {},
   "source": [
    "## Simple example of plotting a spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4fa555de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=12</i>\n",
       "<table id=\"table140066443680576\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>SPEC_NUM</th><th>TG_M</th><th>TG_PART</th><th>TG_SRCID</th><th>X</th><th>Y</th><th>CHANNEL</th><th>COUNTS</th><th>STAT_ERR</th><th>BACKGROUND_UP</th><th>BACKGROUND_DOWN</th><th>BIN_LO</th><th>BIN_HI</th></tr></thead>\n",
       "<thead><tr><th>int16</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>int16[8192]</th><th>int16[8192]</th><th>float32[8192]</th><th>int16[8192]</th><th>int16[8192]</th><th>float64[8192]</th><th>float64[8192]</th></tr></thead>\n",
       "<tr><td>1</td><td>-3</td><td>1</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>7.159166666667378 .. 0.3333333333333333</td><td>7.160000000000712 .. 0.33416666666666667</td></tr>\n",
       "<tr><td>2</td><td>-2</td><td>1</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>10.738750000000886 .. 0.5</td><td>10.740000000000887 .. 0.50125</td></tr>\n",
       "<tr><td>3</td><td>-1</td><td>1</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>21.477500000001772 .. 1.0</td><td>21.480000000001773 .. 1.0025</td></tr>\n",
       "<tr><td>4</td><td>1</td><td>1</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>21.477500000001772 .. 1.0</td><td>21.480000000001773 .. 1.0025</td></tr>\n",
       "<tr><td>5</td><td>2</td><td>1</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>10.738750000000886 .. 0.5</td><td>10.740000000000887 .. 0.50125</td></tr>\n",
       "<tr><td>6</td><td>3</td><td>1</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>7.159166666667378 .. 0.3333333333333333</td><td>7.160000000000712 .. 0.33416666666666667</td></tr>\n",
       "<tr><td>7</td><td>-3</td><td>2</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>13.985000000001373 .. 0.3333333333333333</td><td>13.98666666666804 .. 0.33499999999999996</td></tr>\n",
       "<tr><td>8</td><td>-2</td><td>2</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>20.977500000001505 .. 0.5</td><td>20.980000000001507 .. 0.5025</td></tr>\n",
       "<tr><td>9</td><td>-1</td><td>2</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>41.95500000000301 .. 1.0</td><td>41.960000000003014 .. 1.005</td></tr>\n",
       "<tr><td>10</td><td>1</td><td>2</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>41.95500000000301 .. 1.0</td><td>41.960000000003014 .. 1.005</td></tr>\n",
       "<tr><td>11</td><td>2</td><td>2</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>20.977500000001505 .. 0.5</td><td>20.980000000001507 .. 0.5025</td></tr>\n",
       "<tr><td>12</td><td>3</td><td>2</td><td>1</td><td>4094.9138</td><td>4132.076</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.8660254 .. 1.8660254</td><td>0 .. 0</td><td>0 .. 0</td><td>13.985000000001373 .. 0.3333333333333333</td><td>13.98666666666804 .. 0.33499999999999996</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=12>\n",
       "SPEC_NUM  TG_M ...                  BIN_HI                 \n",
       " int16   int16 ...              float64[8192]              \n",
       "-------- ----- ... ----------------------------------------\n",
       "       1    -3 ... 7.160000000000712 .. 0.33416666666666667\n",
       "       2    -2 ...            10.740000000000887 .. 0.50125\n",
       "       3    -1 ...             21.480000000001773 .. 1.0025\n",
       "       4     1 ...             21.480000000001773 .. 1.0025\n",
       "       5     2 ...            10.740000000000887 .. 0.50125\n",
       "       6     3 ... 7.160000000000712 .. 0.33416666666666667\n",
       "       7    -3 ... 13.98666666666804 .. 0.33499999999999996\n",
       "       8    -2 ...             20.980000000001507 .. 0.5025\n",
       "       9    -1 ...              41.960000000003014 .. 1.005\n",
       "      10     1 ...              41.960000000003014 .. 1.005\n",
       "      11     2 ...             20.980000000001507 .. 0.5025\n",
       "      12     3 ... 13.98666666666804 .. 0.33499999999999996"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec_table = Table(hdu_list[1].data)\n",
    "spec_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68dd28d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (12, 10)\n",
    "\n",
    "for i in range(len(spec_table)):\n",
    "\n",
    "    ax = plt.subplot(6,2,i+1)\n",
    "    pha = plt.plot( spec_table['CHANNEL'][i],spec_table['COUNTS'][i])\n",
    "    ax.set_yscale('log')\n",
    "\n",
    "    if spec_table['TG_PART'][i] == 1:\n",
    "        instr='HEG'\n",
    "    if spec_table['TG_PART'][i] == 2:\n",
    "        instr='MEG'\n",
    "    if spec_table['TG_PART'][i] == 3:\n",
    "        instr='LEG'\n",
    "\n",
    "    ax.set_title(\"{grating}{order:+d}\".format(grating=instr, order=spec_table['TG_M'][i]))\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd34d78",
   "metadata": {},
   "source": [
    "This can then be analyzed in your favorite spectral analysis tool, e.g., [pyXspec](https://heasarc.gsfc.nasa.gov/xanadu/xspec/python/html/index.html).  (For the winter 2018 AAS workshop, we demonstrated this in a [notebook](https://github.com/NASA-NAVO/aas_workshop_2018/blob/master/heasarc/heasarc_Spectral_Access.md) that you can consult for how to use pyXspec, but the pyXspec documentation will have more information.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e37b38a2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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