{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a008d9f6",
   "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": "9e49a1d8",
   "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": "d44f9406",
   "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": "8d887252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=6</i>\n",
       "<table id=\"table140057753260368\" 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/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=6>\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/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": "22752a63",
   "metadata": {},
   "source": [
    "We can look at only the Chandra entry:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "783abb30",
   "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": "ef0b31a8",
   "metadata": {},
   "source": [
    "## Chandra Spectrum of Delta Ori\n",
    "\n",
    "Getting the list of spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eda46b65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=6</i>\n",
       "<table id=\"table140058743339280-493730\" 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>11172: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>acisf00639N004_pha2.fits</td><td>https://heasarc.gsfc.nasa.gov/FTP/chandra/data/byobsid/9/639/primary/acisf00639N004_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/acisf00639N004_pha2.fits.gz&quot;}}</td></tr>\n",
       "<tr><td>1</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>11173: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",
       "<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>11174: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>11175: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>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>11176: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>5</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>11177: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",
       "</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(\"$('#table140058743339280-493730').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",
       "    $('#table140058743339280-493730').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": "dfb50979",
   "metadata": {},
   "source": [
    "Accessing one of the spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c7280b8",
   "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": "ffcdce94",
   "metadata": {},
   "source": [
    "## Simple example of plotting a spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8cde288f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=12</i>\n",
       "<table id=\"table140057753140480\" 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>4102.815</td><td>4131.828</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>4102.815</td><td>4131.828</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>4102.815</td><td>4131.828</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>4102.815</td><td>4131.828</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>4102.815</td><td>4131.828</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>4102.815</td><td>4131.828</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>4102.815</td><td>4131.828</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>4102.815</td><td>4131.828</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>4102.815</td><td>4131.828</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>4102.815</td><td>4131.828</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>4102.815</td><td>4131.828</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>4102.815</td><td>4131.828</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": "194d3796",
   "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": "08209ef1",
   "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": "f92f5f79",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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