04_HowTos/SPARCL/How_to_use_SPARCL.ipynb

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    "__nbid__ = '0051'\n",
    "__author__ = 'Alice Jacques <alice.jacques@noirlab.edu>, Steve Pothier <steve.pothier@noirlab.edu>, Adam Bolton <adam.bolton@noirlab.edu>'\n",
    "__version__ = '20240924' # yyyymmdd\n",
    "__datasets__ = ['sdss_dr16', 'boss_dr16', 'desi_edr']  \n",
    "__keywords__ = ['sparcl', 'spectroscopy', 'HowTo', 'sdss spectra', 'desi spectra', 'tutorial']"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "# How to use SPARCL\n",
    "SPectra Analysis and Retrievable Catalog Lab (SPARCL)"
   ]
  },
  {
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   "id": "45030923",
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   "source": [
    "### Table of contents\n",
    "* [Goals & Summary](#goalssummary)\n",
    "* [Disclaimer & attribution](#disclaimer)\n",
    "* [If necessary, install the most recent version of the SPARCL Client](#install)\n",
    "* [Imports and setup](#imports)\n",
    "* [Authentication for Data Lab](#authentication)\n",
    "* [Authentication for SPARCL](#auth_sparcl)\n",
    "* [Data sets available in SPARCL](#datasets)\n",
    "* [Get default field names](#defaultfieldnames)\n",
    "* [Get all field names](#allfieldnames)\n",
    "* [Data discovery I: using SPARCL's <tt>client.find()</tt> method](#datadiscoveryI)\n",
    "* [Data discovery II: using Data Lab's <tt>sparcl.main</tt> table](#datadiscoveryII)\n",
    "* [Retrieve records by <tt>sparcl_id</tt> using <tt>client.retrieve()</tt>](#retrieve)\n",
    "* [Retrieve records by <tt>specid</tt> using <tt>client.retrieve_by_specid()</tt>](#retrieve_specid)\n",
    "* [Reorder spectrum records](#reorder)\n",
    "* [Access fields in records](#access)\n",
    "* [Convert retrieved output to Pandas DataFrame or Spectrum1D object](#convert)\n",
    "* [Plot spectra](#plot)\n",
    "* [Use <tt>client.missing()</tt> to find missing IDs in the SPARCL database](#missing)"
   ]
  },
  {
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   "source": [
    "<a class=\"anchor\" id=\"goalssummary\"></a>\n",
    "## Goals & Summary \n",
    "\n",
    "SPARCL (SPectra Analysis and Retrievable Catalog Lab) is an online service for discovery and retrieval of one-dimensional optical-infrared spectra. SPARCL is designed to work for large survey datasets containing many millions of spectra, and to provide access to multiple different data sets through common methods. For more information, see the [SPARCL User Guide](https://astrosparcl.datalab.noirlab.edu/static/SPARCLUserManual.pdf).\n",
    "\n",
    "This notebook provides a basic introduction to using the SPARCL client (or sparclclient) to find and retrieve spectroscopic data within a Python notebook context. The sparclclient connects to the SPARCL server at [NSF NOIRLab](https://noirlab.edu/public/) and provides access to the contents of the SPARCL database.\n",
    "\n",
    "To see the current data sets and number of spectra available in the SPARCL database, please visit the [SPARCL summary page](https://astrosparcl.datalab.noirlab.edu/sparc/).\n",
    "\n",
    "To get the latest sparclclient documentation, visit the [sparclclient readthedocs site](https://sparclclient.readthedocs.io/en/latest/).\n",
    "\n",
    "Feedback on SPARCL (questions, comments, science use cases, feature requests, bug reports, confusing error messages, etc.) can be submitted to datalab-spectro@noirlab.edu. For bug reports and confusing error messages, it's helpful if you include: a log of *what you did*, *the result you got*, and *the result you expected*.\n",
    "\n",
    "See our science use-case notebooks that use SPARCL:\n",
    "- [Introduction to DESI Early Data Release (EDR) at the Astro Data Lab](https://github.com/astro-datalab/notebooks-latest/blob/master/03_ScienceExamples/DESI/01_Intro_to_DESI_EDR.ipynb)\n",
    "- [Comparing SDSS and DESI spectra using SPARCL](https://github.com/astro-datalab/notebooks-latest/blob/master/03_ScienceExamples/DESI/02_DESI_EDR_SDSS_Comparison.ipynb)\n",
    "- [Stacking SDSS Spectra of Galaxies Selected from the BPT Diagram](https://github.com/astro-datalab/notebooks-latest/blob/master/03_ScienceExamples/EmLineGalaxies/01_EmLineGalaxies_SpectraStack.ipynb)\n",
    "- [Multi-wavelength Image Cutouts and SDSS Spectra of Active Galaxies with Extreme Emission-Line Ratios](https://github.com/astro-datalab/notebooks-latest/blob/master/03_ScienceExamples/EmLineGalaxies/02_EmLineGalaxies_Outliers.ipynb)\n",
    "\n",
    "And our other How-To notebooks that use SPARCL:\n",
    "- [Obtain spectra with SPARCL and plot them with Jdaviz](https://github.com/astro-datalab/notebooks-latest/blob/master/04_HowTos/SPARCL/Plot_SPARCL_Spectra_with_Jdaviz.ipynb)\n",
    "- [Obtain spectra with SPARCL and plot them with prospect](https://github.com/astro-datalab/notebooks-latest/blob/master/04_HowTos/SPARCL/Plot_SPARCL_Spectra_with_Prospect.ipynb)"
   ]
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    "<a class=\"anchor\" id=\"attribution\"></a>\n",
    "# Disclaimer & attribution\n",
    "\n",
    "Disclaimers\n",
    "-----------\n",
    "Note that using the Astro Data Lab constitutes your agreement with our minimal [Disclaimers](https://datalab.noirlab.edu/disclaimers.php).\n",
    "\n",
    "Acknowledgments\n",
    "---------------\n",
    "If you use **Astro Data Lab** in your published research, please include the text in your paper's Acknowledgments section:\n",
    "\n",
    "_This research uses services or data provided by the Astro Data Lab, which is part of the Community Science and Data Center (CSDC) Program of NSF NOIRLab. NOIRLab is operated by the Association of Universities for Research in Astronomy (AURA), Inc. under a cooperative agreement with the U.S. National Science Foundation._\n",
    "\n",
    "If you use **SPARCL jointly with the Astro Data Lab platform** (via JupyterLab, command-line, or web interface) in your published research, please include this text below in your paper's Acknowledgments section:\n",
    "\n",
    "_This research uses services or data provided by the SPectra Analysis and Retrievable Catalog Lab (SPARCL) and the Astro Data Lab, which are both part of the Community Science and Data Center (CSDC) Program of NSF NOIRLab. NOIRLab is operated by the Association of Universities for Research in Astronomy (AURA), Inc. under a cooperative agreement with the U.S. National Science Foundation._\n",
    "\n",
    "In either case **please cite the following papers**:\n",
    "\n",
    "* Data Lab concept paper: Fitzpatrick et al., \"The NOAO Data Laboratory: a conceptual overview\", SPIE, 9149, 2014, https://doi.org/10.1117/12.2057445\n",
    "\n",
    "* Astro Data Lab overview: Nikutta et al., \"Data Lab - A Community Science Platform\", Astronomy and Computing, 33, 2020, https://doi.org/10.1016/j.ascom.2020.100411\n",
    "\n",
    "If you are referring to the Data Lab JupyterLab / Jupyter Notebooks, cite:\n",
    "\n",
    "* Juneau et al., \"Jupyter-Enabled Astrophysical Analysis Using Data-Proximate Computing Platforms\", CiSE, 23, 15, 2021, https://doi.org/10.1109/MCSE.2021.3057097\n",
    "\n",
    "If publishing in a AAS journal, also add the keyword: `\\facility{Astro Data Lab}`\n",
    "\n",
    "And if you are using SPARCL, please also add `\\software{SPARCL}` and cite:\n",
    "\n",
    "* Juneau et al., \"SPARCL: SPectra Analysis and Retrievable Catalog Lab\", Conference Proceedings for ADASS XXXIII, 2024\n",
    "https://doi.org/10.48550/arXiv.2401.05576\n",
    "\n",
    "The NOIRLab Library maintains [lists of proper acknowledgments](https://noirlab.edu/science/about/scientific-acknowledgments) to use when publishing papers using the Lab's facilities, data, or services."
   ]
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    "<a class=\"anchor\" id=\"install\"></a>\n",
    "### If necessary, install the most recent version of the SPARCL Client:\n",
    "If you are using the Astro Data Lab Jupyter notebook server, you do not need to run this cell.\n",
    "### NOTE: After installing the most recent version, please restart your kernel."
   ]
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   "source": [
    "## Uncomment the following only if SPARCL client is not already installed\n",
    "#!pip install --upgrade sparclclient"
   ]
  },
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   "source": [
    "<a class=\"anchor\" id=\"imports\"></a>\n",
    "## Imports and setup"
   ]
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   "source": [
    "# SPARCL imports\n",
    "from sparcl.client import SparclClient\n",
    "\n",
    "# 3rd party imports\n",
    "import numpy as np\n",
    "import astropy.units as u\n",
    "from specutils import Spectrum1D\n",
    "from astropy.nddata import InverseVariance\n",
    "from astropy.convolution import convolve, Gaussian1DKernel\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Data Lab imports\n",
    "from dl import queryClient as qc\n",
    "from dl import authClient as ac\n",
    "from getpass import getpass\n",
    "\n",
    "# plots default setup\n",
    "plt.rcParams['font.size'] = 14\n",
    "plt.rcParams['figure.figsize'] = (14,8)"
   ]
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    "<a class=\"anchor\" id=\"authentication\"></a>\n",
    "# Authentication\n",
    "Much of the functionality of Data Lab can be accessed without explicitly logging in (the service then uses an anonymous login). But some capacities, for instance saving the results of your queries to your virtual storage space, require a login (i.e. you will need a registered user account).\n",
    "\n",
    "If you need to log in to Data Lab, un-comment the cell below and execute it:"
   ]
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   "source": [
    "#token = ac.login(input(\"Enter user name: (+ENTER) \"),getpass(\"Enter password: (+ENTER) \"))\n",
    "#ac.whoAmI()"
   ]
  },
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   "source": [
    "#### We now create a SPARCL client instance:"
   ]
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    {
     "data": {
      "text/plain": [
       "(sparclclient:1.2.2, api:12.0, https://astrosparcl.datalab.noirlab.edu/sparc, client_hash=, verbose=False, connect_timeout=1.1, read_timeout=5400.0)"
      ]
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     "metadata": {},
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   ],
   "source": [
    "client = SparclClient()\n",
    "client"
   ]
  },
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    "<a class=\"anchor\" id=\"auth_sparcl\"></a>\n",
    "# Authentication for SPARCL\n",
    "All public SPARCL data sets can be accessed without explicitly logging in. However, some data sets are private and can only be accessed by authorized users. If you are an authorized user and wish to login to SPARCL, un-comment the cell below and enter your NOIRLab CSDC SSO user name and password. If you need to create an account, sign-up at https://sso.csdc.noirlab.edu/. If you encounter an issue, email datalab-spectro@noirlab.edu with your First Name, Last Name, and Email Address (the same one you used to create your NOIRLab CSDC SSO account).\n",
    "\n",
    "To logout of SPARCL after a session, use:\n",
    "```\n",
    "client.logout()\n",
    "```\n",
    "\n",
    "For assistance with SPARCL authentication/authorization please contact us at datalab-spectro@noirlab.edu"
   ]
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   "source": [
    "#client.login(input(\"Enter SSO user name: (+ENTER) \"),getpass(\"Enter password: (+ENTER) \"))"
   ]
  },
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    "#### View which data sets you have access to:\n",
    "**Note:** if you are not logged in or if your SSO user name is not in the authorized list of SPARCL users, you will only see public data sets"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "{'Loggedin_As': 'Anonymous',\n",
       " 'Authorized_Datasets': {'BOSS-DR16', 'DESI-EDR', 'SDSS-DR16'}}"
      ]
     },
     "execution_count": 7,
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    "client.authorized"
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    "<a class=\"anchor\" id=\"datasets\"></a>\n",
    "## Data sets available in SPARCL\n",
    "List all currently available data sets from the server/url associated with client"
   ]
  },
  {
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   "id": "da920ad2",
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    {
     "data": {
      "text/plain": [
       "{'BOSS-DR16', 'DESI-EDR', 'SDSS-DR16', 'SDSS-DR17-test'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.all_datasets"
   ]
  },
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   "source": [
    "<a class=\"anchor\" id=\"defaultfieldnames\"></a>\n",
    "## Get default field names\n",
    "Gets fields tagged as 'default' that are common to all data sets in the `dataset_list` passed to the function. If `dataset_list` is None (the default), the function returns the intersection of 'default' fields across all datasets currently available in the SPARC database."
   ]
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     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Get fields tagged as 'default' that are in DATASET_LIST.\n",
       "These are the fields used for the DEFAULT value of the include\n",
       "parameter of client.retrieve().\n",
       "\n",
       "Args:\n",
       "    dataset_list (:obj:`list`, optional): List of data sets from\n",
       "        which to get the default fields. Defaults to None, which\n",
       "        will return the intersection of default fields in all\n",
       "        data sets hosted on the SPARCL database.\n",
       "\n",
       "Returns:\n",
       "    List of fields tagged as 'default' from DATASET_LIST.\n",
       "\n",
       "Example:\n",
       "    >>> client = SparclClient()\n",
       "    >>> client.get_default_fields()\n",
       "    ['dec', 'flux', 'ra', 'sparcl_id', 'specid', 'wavelength']\n",
       "\u001b[0;31mFile:\u001b[0m      /data0/sw/anaconda3/lib/python3.10/site-packages/sparcl/client.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
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   "source": [
    "client.get_default_fields?"
   ]
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    {
     "data": {
      "text/plain": [
       "['dec', 'flux', 'ra', 'sparcl_id', 'specid', 'wavelength']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    "client.get_default_fields()"
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   "source": [
    "<a class=\"anchor\" id=\"allfieldnames\"></a>\n",
    "## Get all field names\n",
    "Gets fields tagged as 'all' that are common to all data sets in the `dataset_list` passed to the function. If `dataset_list` is None (the default), the function returns the intersection of 'all' fields across all datasets currently available in the SPARC database."
   ]
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Get fields tagged as 'all' that are in DATASET_LIST.\n",
       "These are the fields used for the ALL value of the include parameter\n",
       "of client.retrieve().\n",
       "\n",
       "Args:\n",
       "    dataset_list (:obj:`list`, optional): List of data sets from\n",
       "        which to get all fields. Defaults to None, which\n",
       "        will return the intersection of all fields in all\n",
       "        data sets hosted on the SPARCL database.\n",
       "\n",
       "Returns:\n",
       "    List of fields tagged as 'all' from DATASET_LIST.\n",
       "\n",
       "Example:\n",
       "    >>> client = SparclClient()\n",
       "    >>> client.get_all_fields()\n",
       "    ['data_release', 'datasetgroup', 'dateobs', 'dateobs_center', 'dec', 'exptime', 'flux', 'instrument', 'ivar', 'mask', 'model', 'ra', 'redshift', 'redshift_err', 'redshift_warning', 'site', 'sparcl_id', 'specid', 'specprimary', 'spectype', 'survey', 'targetid', 'telescope', 'wave_sigma', 'wavelength', 'wavemax', 'wavemin']\n",
       "\u001b[0;31mFile:\u001b[0m      /data0/sw/anaconda3/lib/python3.10/site-packages/sparcl/client.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "client.get_all_fields?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3ec96b73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:53.391291Z",
     "iopub.status.busy": "2024-09-24T20:37:53.390817Z",
     "iopub.status.idle": "2024-09-24T20:37:53.393967Z",
     "shell.execute_reply": "2024-09-24T20:37:53.393473Z",
     "shell.execute_reply.started": "2024-09-24T20:37:53.391277Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['data_release', 'datasetgroup', 'dateobs', 'dateobs_center', 'dec', 'exptime', 'flux', 'instrument', 'ivar', 'mask', 'model', 'ra', 'redshift', 'redshift_err', 'redshift_warning', 'site', 'sparcl_id', 'specid', 'specprimary', 'spectype', 'survey', 'targetid', 'telescope', 'wave_sigma', 'wavelength', 'wavemax', 'wavemin']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(client.get_all_fields()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ebd397b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:53.396494Z",
     "iopub.status.busy": "2024-09-24T20:37:53.396062Z",
     "iopub.status.idle": "2024-09-24T20:37:53.399264Z",
     "shell.execute_reply": "2024-09-24T20:37:53.398783Z",
     "shell.execute_reply.started": "2024-09-24T20:37:53.396482Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ancillary_target1', 'ancillary_target2', 'anyandmask', 'anyormask', 'bluefiber', 'boss_specobj_id', 'boss_target1', 'boss_target2', 'calibflux', 'calibflux_ivar', 'chi68p', 'chunk', 'class_noqso', 'class_person', 'comments_person', 'cx', 'cy', 'cz', 'data_release', 'datasetgroup', 'dateobs', 'dateobs_center', 'dec', 'deredsn2', 'designid', 'dof', 'eboss_target0', 'eboss_target1', 'eboss_target2', 'eboss_target_id', 'elodie_bv', 'elodie_dof', 'elodie_feh', 'elodie_filename', 'elodie_logg', 'elodie_object', 'elodie_rchi2', 'elodie_sptype', 'elodie_teff', 'elodie_z', 'elodie_z_err', 'elodie_z_modelerr', 'exptime', 'fiberid', 'firstrelease', 'flux', 'fluxobjid', 'fracnsighi', 'fracnsiglo', 'fracnsigma', 'instrument', 'ivar', 'lambda_eff', 'legacy_target1', 'legacy_target2', 'marvels_target1', 'marvels_target2', 'mask', 'mjd', 'model', 'npoly', 'nspecobs', 'nturnoff', 'objid', 'plate', 'plateid', 'platequality', 'platerun', 'platesn2', 'primtarget', 'programname', 'ra', 'rchi2', 'rchi2diff', 'rchi2diff_noqso', 'redshift', 'redshift_err', 'redshift_warning', 'run1d', 'run2d', 'sectarget', 'segue1_target1', 'segue1_target2', 'segue2_target1', 'segue2_target2', 'site', 'sky', 'sn_median', 'sn_median_all', 'snturnoff', 'sourcetype', 'sparcl_id', 'spec1_g', 'spec1_i', 'spec1_r', 'spec2_g', 'spec2_i', 'spec2_r', 'specboss', 'special_target1', 'special_target2', 'specid', 'speclegacy', 'specprimary', 'specsdss', 'specsegue', 'specsegue1', 'specsegue2', 'spectroflux', 'spectroflux_ivar', 'spectrographid', 'spectroskyflux', 'spectrosynflux', 'spectrosynflux_ivar', 'spectype', 'subclass', 'subclass_noqso', 'survey', 'targetid', 'targetobjid', 'targettype', 'tcolumn', 'telescope', 'tfile', 'theta', 'thing_id', 'thing_id_targeting', 'tile', 'vdisp', 'vdisp_err', 'vdispchi2', 'vdispdof', 'vdispnpix', 'vdispz', 'vdispz_err', 'wave_sigma', 'wavelength', 'wavemax', 'wavemin', 'wcoverage', 'xfocal', 'yfocal', 'z_conf_person', 'z_err_noqso', 'z_noqso', 'z_person', 'zoffset', 'zwarning_noqso']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(client.get_all_fields(dataset_list=['SDSS-DR16', 'BOSS-DR16'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2d9164d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:53.399979Z",
     "iopub.status.busy": "2024-09-24T20:37:53.399856Z",
     "iopub.status.idle": "2024-09-24T20:37:53.410976Z",
     "shell.execute_reply": "2024-09-24T20:37:53.410535Z",
     "shell.execute_reply.started": "2024-09-24T20:37:53.399969Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bgs_target', 'chi2', 'cmx_target', 'coadd_fiberstatus', 'coadd_numexp', 'coadd_numnight', 'coadd_numtile', 'coeff', 'data_release', 'datasetgroup', 'dateobs', 'dateobs_center', 'dec', 'deltachi2', 'desi_target', 'exptime', 'fa_target', 'fa_type', 'firstnight', 'flux', 'healpix', 'instrument', 'ivar', 'lastnight', 'mask', 'mean_delta_x', 'mean_delta_y', 'mean_fiber_dec', 'mean_fiber_ra', 'mean_mjd', 'mean_psf_to_fiber_specflux', 'model', 'mws_target', 'ncoeff', 'npixels', 'numobs_init', 'objtype', 'obsconditions', 'plate_dec', 'plate_ra', 'pmdec', 'pmra', 'priority_init', 'program', 'ra', 'redshift', 'redshift_err', 'redshift_warning', 'ref_epoch', 'rms_delta_x', 'rms_delta_y', 'scnd_target', 'site', 'sparcl_id', 'specid', 'specprimary', 'spectype', 'spgrpval', 'std_fiber_dec', 'std_fiber_ra', 'subpriority', 'subtype', 'survey', 'sv1_bgs_target', 'sv1_desi_target', 'sv1_mws_target', 'sv1_scnd_target', 'sv2_bgs_target', 'sv2_desi_target', 'sv2_mws_target', 'sv2_scnd_target', 'sv3_bgs_target', 'sv3_desi_target', 'sv3_mws_target', 'sv3_scnd_target', 'sv_nspec', 'sv_primary', 'targetid', 'telescope', 'tsnr2_bgs', 'tsnr2_elg', 'tsnr2_gpbbackup', 'tsnr2_gpbbright', 'tsnr2_gpbdark', 'tsnr2_lrg', 'tsnr2_lya', 'tsnr2_qso', 'wave_sigma', 'wavelength', 'wavemax', 'wavemin', 'zcat_nspec']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(client.get_all_fields(dataset_list=['DESI-EDR'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd9cc611",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"datadiscoveryI\"></a>\n",
    "## Data discovery I: using SPARCL's `client.find()` method\n",
    "The first way you can discover your data is by using SPARCL's `client.find()` method, which allows you to find records in the SPARCL database based on certain parameters passed to the function. Only Core fields may be in the `outfields` and `constraints` parameters. The descriptions for all fields, including Core fields, is located [here](https://astrosparcl.datalab.noirlab.edu/sparc/sfc/). The SPARCL Core fields constraint types are:\n",
    "\n",
    "\n",
    "| Field name       | Constraint type | Example |\n",
    "|:----------------|:---------------|:-------|\n",
    "| data_release     | List of allowed values<br>from [SPARCL Categoricals](https://astrosparcl.datalab.noirlab.edu/sparc/cats/) | ['DESI-EDR', 'BOSS-DR16', 'SDSS-DR16']\n",
    "| datasetgroup     | List of allowed values<br>from [SPARCL Categoricals](https://astrosparcl.datalab.noirlab.edu/sparc/cats/) | ['DESI', 'SDSS_BOSS']\n",
    "| dateobs_center   | Range of values | ['2013-03-14T10:16:17Z',<br>'2014-05-24T12:10:00Z']\n",
    "| dec              | Range of values | [2.03, 7.76]\n",
    "| exptime          | Range of values | [3603.46, 3810.12]\n",
    "| instrument       | List of allowed values<br>from [SPARCL Categoricals](https://astrosparcl.datalab.noirlab.edu/sparc/cats/) | ['SDSS', 'BOSS', 'DESI']\n",
    "| ra               | Range of values (may not<br>\"wrap\" around RA=0) | [44.53, 47.96]\n",
    "| redshift         | Range of values | [0.5, 0.9]\n",
    "| redshift_err     | Range of values | [0.000225, 0.000516]\n",
    "| redshift_warning | List of values  | [0, 3, 5]\n",
    "| sparcl_id               | List of values (but not<br>intended for data discovery) | ['00001658-460c-4da1-987d-e493d8c9b89b',<br>'000017b6-56a2-4f87-8828-3a3409ba1083']\n",
    "| site             | List of allowed values<br>from [SPARCL Categoricals](https://astrosparcl.datalab.noirlab.edu/sparc/cats/) |  ['apo', 'kpno']\n",
    "| specid           | List of values | [6988698046080241664, 6971782884823945216]\n",
    "| spectype         | List of allowed values<br>from [SPARCL Categoricals](https://astrosparcl.datalab.noirlab.edu/sparc/cats/) | ['GALAXY', 'STAR', 'QSO']\n",
    "| specprimary      | List of values (but typically<br>would only include 1 if<br>being used for data<br>discovery constraints) | [1]\n",
    "| targetid         | List of values | [1237679502171374316, 1237678619584692841]\n",
    "| telescope        | List of allowed values<br>from [SPARCL Categoricals](https://astrosparcl.datalab.noirlab.edu/sparc/cats/) | ['sloan25m', 'kp4m']\n",
    "| wavemin          | Range of values | [3607, 3608]\n",
    "| wavemax          | Range of values | [10363, 10364]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fe2e2df8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:53.411617Z",
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     "shell.execute_reply": "2024-09-24T20:37:53.420541Z",
     "shell.execute_reply.started": "2024-09-24T20:37:53.411607Z"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0moutfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mconstraints\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Find records in the SPARCL database.\n",
       "\n",
       "Args:\n",
       "    outfields (:obj:`list`, optional): List of fields to return.\n",
       "        Only CORE fields may be passed to this parameter.\n",
       "        Defaults to None, which will return only the sparcl_id\n",
       "        and _dr fields.\n",
       "\n",
       "    constraints (:obj:`dict`, optional): Key-Value pairs of\n",
       "        constraints to place on the record selection. The Key\n",
       "        part of the Key-Value pair is the field name and the\n",
       "        Value part of the Key-Value pair is a list of values.\n",
       "        Defaults to no constraints. This will return all records in the\n",
       "        database subject to restrictions imposed by the ``limit``\n",
       "        parameter.\n",
       "\n",
       "    limit (:obj:`int`, optional): Maximum number of records to\n",
       "        return. Defaults to 500.\n",
       "\n",
       "    sort (:obj:`list`, optional): Comma separated list of fields\n",
       "        to sort by. Defaults to None. (no sorting)\n",
       "\n",
       "    verbose (:obj:`bool`, optional): Set to True for in-depth return\n",
       "        statement. Defaults to False.\n",
       "\n",
       "Returns:\n",
       "    :class:`~sparcl.Results.Found`: Contains header and records.\n",
       "\n",
       "Example:\n",
       "    >>> client = SparclClient()\n",
       "    >>> outs = ['sparcl_id', 'ra', 'dec']\n",
       "    >>> cons = {'spectype': ['GALAXY'], 'redshift': [0.5, 0.9]}\n",
       "    >>> found = client.find(outfields=outs, constraints=cons)\n",
       "    >>> sorted(list(found.records[0].keys()))\n",
       "    ['_dr', 'dec', 'ra', 'sparcl_id']\n",
       "\u001b[0;31mFile:\u001b[0m      /data0/sw/anaconda3/lib/python3.10/site-packages/sparcl/client.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "client.find?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ecf1aa4",
   "metadata": {},
   "source": [
    "#### Define the fields we want returned (`outfields`) and the constraints (`constraints`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1a3fd21",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:53.421810Z",
     "iopub.status.busy": "2024-09-24T20:37:53.421640Z",
     "iopub.status.idle": "2024-09-24T20:37:53.424355Z",
     "shell.execute_reply": "2024-09-24T20:37:53.423902Z",
     "shell.execute_reply.started": "2024-09-24T20:37:53.421798Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "out = ['sparcl_id', 'ra', 'dec', 'redshift', 'spectype', 'data_release', 'redshift_err']\n",
    "cons = {'spectype': ['GALAXY'],\n",
    "        'redshift': [0.5, 0.9]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c423b17d",
   "metadata": {},
   "source": [
    "#### Execute the `client.find()` method with our parameters:\n",
    "The `limit` argument here is being used for demonstration purposes only, and simply returns only the first 20 results here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c55933c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:53.424978Z",
     "iopub.status.busy": "2024-09-24T20:37:53.424854Z",
     "iopub.status.idle": "2024-09-24T20:37:53.512857Z",
     "shell.execute_reply": "2024-09-24T20:37:53.512380Z",
     "shell.execute_reply.started": "2024-09-24T20:37:53.424967Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['00000c19-9c85-11ee-bf5c-525400ad1336, DESI-EDR, 176.814829, 0.594318, 0.000061',\n",
       " '00000f02-a2dc-11ee-ac91-525400ad1336, BOSS-DR16, 230.697100, 0.522124, 0.000107',\n",
       " '00001b3a-a41b-11ee-8210-525400ad1336, BOSS-DR16, 9.635124, 0.542893, 0.000181',\n",
       " '00001cbb-9c7e-11ee-8b67-525400ad1336, DESI-EDR, 251.669195, 0.790116, 0.000046',\n",
       " '0000321e-a4a9-11ee-bde8-525400ad1336, BOSS-DR16, 228.064370, 0.631113, 0.000165',\n",
       " '000041af-a296-11ee-9b05-525400ad1336, BOSS-DR16, 203.071870, 0.572525, 0.000165',\n",
       " '00004954-a46e-11ee-b0bd-525400ad1336, BOSS-DR16, 143.997260, 0.833832, 0.000309',\n",
       " '00004a95-9d0d-11ee-8bf1-525400ad1336, DESI-EDR, 218.983858, 0.831348, 0.000092',\n",
       " '00004bbf-a42c-11ee-9dd6-525400ad1336, BOSS-DR16, 6.668670, 0.754319, 0.000283',\n",
       " '00005931-a340-11ee-8fab-525400ad1336, BOSS-DR16, 102.062250, 0.677861, 0.408586',\n",
       " '00006e76-a4b2-11ee-a3df-525400ad1336, BOSS-DR16, 250.566970, 0.694172, 0.000191',\n",
       " '00008e21-a1ad-11ee-8e57-525400ad1336, BOSS-DR16, 333.787720, 0.568273, 0.000129',\n",
       " '0000920c-a40a-11ee-9e59-525400ad1336, BOSS-DR16, 40.835157, 0.682240, 0.000279',\n",
       " '000093ef-9e66-11ee-8b5b-525400ad1336, BOSS-DR16, 157.353360, 0.859480, 0.000064',\n",
       " '0000a486-a1dd-11ee-b9a1-525400ad1336, BOSS-DR16, 245.705360, 0.530106, 0.000146',\n",
       " '0000af3a-9c76-11ee-bec7-525400ad1336, DESI-EDR, 251.608575, 0.625994, 0.000057',\n",
       " '0000b0bb-a2b0-11ee-892f-525400ad1336, BOSS-DR16, 155.201430, 0.580323, 0.000197',\n",
       " '0000b8c4-a2c9-11ee-8a72-525400ad1336, BOSS-DR16, 202.609980, 0.619319, 0.000165',\n",
       " '0000d281-a14b-11ee-b6b8-525400ad1336, BOSS-DR16, 216.383320, 0.533600, 0.000151',\n",
       " '0000d45b-9b1f-11ee-afc7-525400ad1336, DESI-EDR, 234.809864, 0.871094, 0.000021']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "found_I = client.find(outfields=out, constraints=cons, limit=20)\n",
    "[\"%s, %s, %f, %f, %f\" % (f.sparcl_id, f.data_release, f.ra, f.redshift, f.redshift_err)\n",
    " for f in found_I.records]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8118bcc",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"datadiscoveryII\"></a>\n",
    "## Data discovery II: using Data Lab's `sparcl.main` table\n",
    "The second way you can discover your data is by querying the `sparcl.main` table hosted at Astro Data Lab. This table contains the following columns:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95acdae6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:53.513559Z",
     "iopub.status.busy": "2024-09-24T20:37:53.513423Z",
     "iopub.status.idle": "2024-09-24T20:37:53.665175Z",
     "shell.execute_reply": "2024-09-24T20:37:53.664699Z",
     "shell.execute_reply.started": "2024-09-24T20:37:53.513549Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Schema: sparcl\n",
      " Table: main\n",
      "\n",
      "     Column Name   Description\n",
      "     -----------   -----------\n",
      "            elon   Ecliptic longitude\n",
      "            elat   Ecliptic latitude\n",
      "            glon   Galactic longitude\n",
      "            glat   Galactic latitude\n",
      "        redshift   Observed redshift, or radial velocity in units of redshift\n",
      "              ra   Right Ascension in degrees\n",
      "             dec   Declination in degrees\n",
      "         wavemin   Minimum value of wavelength coverage\n",
      "         wavemax   Maximum value of wavelength coverage\n",
      "    redshift_err   Uncertainty on the observed redshift, or radial velocity i\n",
      "                   n units of redshift\n",
      "        targetid   Dataset-specific photometric target identifier\n",
      "          specid   Dataset-specific spectrum identifier (may not be unique)\n",
      "         exptime   Exposure time in seconds\n",
      "       random_id   Random ID in the range 0 to 100 (decimal)\n",
      "            htm9   HTM Level-9 index\n",
      "         ring256   Healpix ID with nside 256, ring schema\n",
      "        nest4096   Healpix ID with nside 4096, nested schema\n",
      "redshift_warning   Bitmask with warning on redshift result\n",
      "     specprimary   Whether an entry is the primary spectrum for a given targe\n",
      "                   t object\n",
      "    datasetgroup   Human-readable common name for a coherent set of spectra t\n",
      "                   hat go together\n",
      "        spectype   Spectral type of the object (STAR, GALAXY, QSO)\n",
      "      instrument   Instrument used for observation\n",
      "       sparcl_id   Universally Unique Identifier for spectrum in SPARCL\n",
      "       telescope   Telescope used for the observation\n",
      "            site   Site of observations\n",
      "    data_release   Data release of a given record\n",
      "  dateobs_center   Central value of the dateobs range\n",
      "         dateobs   Range of observational dates for a given spectrum (single \n",
      "                   value for a single date)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(qc.schema('sparcl.main'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ce3dc4",
   "metadata": {},
   "source": [
    "#### Query this table for a few rows of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9f4dba5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:53.665826Z",
     "iopub.status.busy": "2024-09-24T20:37:53.665699Z",
     "iopub.status.idle": "2024-09-24T20:37:53.788680Z",
     "shell.execute_reply": "2024-09-24T20:37:53.788162Z",
     "shell.execute_reply.started": "2024-09-24T20:37:53.665816Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sparcl_id</th>\n",
       "      <th>specid</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>redshift</th>\n",
       "      <th>spectype</th>\n",
       "      <th>data_release</th>\n",
       "      <th>redshift_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9a5016c0-a1db-11ee-b15c-525400ad1336</td>\n",
       "      <td>6802933904984788992</td>\n",
       "      <td>238.053390</td>\n",
       "      <td>43.941233</td>\n",
       "      <td>0.615950</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>414c359e-9d07-11ee-9b9b-525400ad1336</td>\n",
       "      <td>39633149608592885</td>\n",
       "      <td>237.243397</td>\n",
       "      <td>44.080806</td>\n",
       "      <td>0.618849</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>DESI-EDR</td>\n",
       "      <td>0.000064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c1156072-a1cb-11ee-953a-525400ad1336</td>\n",
       "      <td>6767971084522508288</td>\n",
       "      <td>211.972990</td>\n",
       "      <td>28.694906</td>\n",
       "      <td>0.548824</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>911ab2b7-9f3e-11ee-a597-525400ad1336</td>\n",
       "      <td>1426690603265058816</td>\n",
       "      <td>128.166450</td>\n",
       "      <td>28.023924</td>\n",
       "      <td>0.633630</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>SDSS-DR16</td>\n",
       "      <td>0.315646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53478e46-9b67-11ee-b77b-525400ad1336</td>\n",
       "      <td>39627769637766794</td>\n",
       "      <td>180.563917</td>\n",
       "      <td>-0.716587</td>\n",
       "      <td>0.863014</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>DESI-EDR</td>\n",
       "      <td>0.000049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a7a49bf4-9f60-11ee-ab7b-525400ad1336</td>\n",
       "      <td>4536428664930455552</td>\n",
       "      <td>217.293440</td>\n",
       "      <td>-1.445119</td>\n",
       "      <td>0.535872</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1f3410af-a0f5-11ee-a5bf-525400ad1336</td>\n",
       "      <td>5862600223436853248</td>\n",
       "      <td>242.419380</td>\n",
       "      <td>7.977997</td>\n",
       "      <td>0.524114</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>976845d0-a3d8-11ee-9ae7-525400ad1336</td>\n",
       "      <td>8633450365726248960</td>\n",
       "      <td>353.990980</td>\n",
       "      <td>23.535728</td>\n",
       "      <td>0.653509</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0699dae7-a508-11ee-bfb9-525400ad1336</td>\n",
       "      <td>-8071304449020899328</td>\n",
       "      <td>346.124650</td>\n",
       "      <td>-0.927300</td>\n",
       "      <td>0.654820</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1ff6f4e6-a560-11ee-bfda-525400ad1336</td>\n",
       "      <td>-7692018839834046464</td>\n",
       "      <td>143.933660</td>\n",
       "      <td>18.334093</td>\n",
       "      <td>0.785994</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cacfb6c2-9fca-11ee-9c39-525400ad1336</td>\n",
       "      <td>4911301385507852288</td>\n",
       "      <td>344.297430</td>\n",
       "      <td>-2.604723</td>\n",
       "      <td>0.566902</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3944b361-a29a-11ee-80c7-525400ad1336</td>\n",
       "      <td>7471599706856247296</td>\n",
       "      <td>186.331270</td>\n",
       "      <td>44.257821</td>\n",
       "      <td>0.614164</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5552bd48-9b66-11ee-82d6-525400ad1336</td>\n",
       "      <td>39633285986387037</td>\n",
       "      <td>177.668864</td>\n",
       "      <td>52.529184</td>\n",
       "      <td>0.688995</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>DESI-EDR</td>\n",
       "      <td>0.000081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9673af20-a31a-11ee-9be2-525400ad1336</td>\n",
       "      <td>8001969487737016320</td>\n",
       "      <td>174.226020</td>\n",
       "      <td>61.472635</td>\n",
       "      <td>0.505193</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>c6c14db9-9e52-11ee-b3e4-525400ad1336</td>\n",
       "      <td>-6882417905396068352</td>\n",
       "      <td>158.575220</td>\n",
       "      <td>33.747348</td>\n",
       "      <td>0.709140</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3bf03614-9e8f-11ee-8611-525400ad1336</td>\n",
       "      <td>-6158471691026280448</td>\n",
       "      <td>248.887370</td>\n",
       "      <td>33.071720</td>\n",
       "      <td>0.892999</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6bdd212c-a04d-11ee-8e4d-525400ad1336</td>\n",
       "      <td>5299835806479044608</td>\n",
       "      <td>201.163590</td>\n",
       "      <td>40.816210</td>\n",
       "      <td>0.507812</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67d8f85f-a4fe-11ee-8915-525400ad1336</td>\n",
       "      <td>-8111943498295128064</td>\n",
       "      <td>358.573430</td>\n",
       "      <td>2.079838</td>\n",
       "      <td>0.669183</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>35242386-a3fd-11ee-b362-525400ad1336</td>\n",
       "      <td>8717889569062932480</td>\n",
       "      <td>6.020312</td>\n",
       "      <td>34.411377</td>\n",
       "      <td>0.541011</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>291837e0-9f27-11ee-b62a-525400ad1336</td>\n",
       "      <td>4330408768305385472</td>\n",
       "      <td>184.548990</td>\n",
       "      <td>0.780102</td>\n",
       "      <td>0.570419</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>BOSS-DR16</td>\n",
       "      <td>0.000150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               sparcl_id               specid          ra  \\\n",
       "0   9a5016c0-a1db-11ee-b15c-525400ad1336  6802933904984788992  238.053390   \n",
       "1   414c359e-9d07-11ee-9b9b-525400ad1336    39633149608592885  237.243397   \n",
       "2   c1156072-a1cb-11ee-953a-525400ad1336  6767971084522508288  211.972990   \n",
       "3   911ab2b7-9f3e-11ee-a597-525400ad1336  1426690603265058816  128.166450   \n",
       "4   53478e46-9b67-11ee-b77b-525400ad1336    39627769637766794  180.563917   \n",
       "5   a7a49bf4-9f60-11ee-ab7b-525400ad1336  4536428664930455552  217.293440   \n",
       "6   1f3410af-a0f5-11ee-a5bf-525400ad1336  5862600223436853248  242.419380   \n",
       "7   976845d0-a3d8-11ee-9ae7-525400ad1336  8633450365726248960  353.990980   \n",
       "8   0699dae7-a508-11ee-bfb9-525400ad1336 -8071304449020899328  346.124650   \n",
       "9   1ff6f4e6-a560-11ee-bfda-525400ad1336 -7692018839834046464  143.933660   \n",
       "10  cacfb6c2-9fca-11ee-9c39-525400ad1336  4911301385507852288  344.297430   \n",
       "11  3944b361-a29a-11ee-80c7-525400ad1336  7471599706856247296  186.331270   \n",
       "12  5552bd48-9b66-11ee-82d6-525400ad1336    39633285986387037  177.668864   \n",
       "13  9673af20-a31a-11ee-9be2-525400ad1336  8001969487737016320  174.226020   \n",
       "14  c6c14db9-9e52-11ee-b3e4-525400ad1336 -6882417905396068352  158.575220   \n",
       "15  3bf03614-9e8f-11ee-8611-525400ad1336 -6158471691026280448  248.887370   \n",
       "16  6bdd212c-a04d-11ee-8e4d-525400ad1336  5299835806479044608  201.163590   \n",
       "17  67d8f85f-a4fe-11ee-8915-525400ad1336 -8111943498295128064  358.573430   \n",
       "18  35242386-a3fd-11ee-b362-525400ad1336  8717889569062932480    6.020312   \n",
       "19  291837e0-9f27-11ee-b62a-525400ad1336  4330408768305385472  184.548990   \n",
       "\n",
       "          dec  redshift spectype data_release  redshift_err  \n",
       "0   43.941233  0.615950   GALAXY    BOSS-DR16      0.000154  \n",
       "1   44.080806  0.618849   GALAXY     DESI-EDR      0.000064  \n",
       "2   28.694906  0.548824   GALAXY    BOSS-DR16      0.000149  \n",
       "3   28.023924  0.633630   GALAXY    SDSS-DR16      0.315646  \n",
       "4   -0.716587  0.863014   GALAXY     DESI-EDR      0.000049  \n",
       "5   -1.445119  0.535872   GALAXY    BOSS-DR16      0.000204  \n",
       "6    7.977997  0.524114   GALAXY    BOSS-DR16      0.000151  \n",
       "7   23.535728  0.653509   GALAXY    BOSS-DR16      0.000196  \n",
       "8   -0.927300  0.654820   GALAXY    BOSS-DR16      0.000182  \n",
       "9   18.334093  0.785994   GALAXY    BOSS-DR16      0.000080  \n",
       "10  -2.604723  0.566902   GALAXY    BOSS-DR16      0.000245  \n",
       "11  44.257821  0.614164   GALAXY    BOSS-DR16      0.000132  \n",
       "12  52.529184  0.688995   GALAXY     DESI-EDR      0.000081  \n",
       "13  61.472635  0.505193   GALAXY    BOSS-DR16      0.000056  \n",
       "14  33.747348  0.709140   GALAXY    BOSS-DR16      0.000151  \n",
       "15  33.071720  0.892999   GALAXY    BOSS-DR16      0.000112  \n",
       "16  40.816210  0.507812   GALAXY    BOSS-DR16      0.000099  \n",
       "17   2.079838  0.669183   GALAXY    BOSS-DR16      0.000209  \n",
       "18  34.411377  0.541011   GALAXY    BOSS-DR16      0.000335  \n",
       "19   0.780102  0.570419   GALAXY    BOSS-DR16      0.000150  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = \"\"\"SELECT \n",
    "            sparcl_id, specid, ra, dec, redshift, spectype, data_release, redshift_err\n",
    "        FROM \n",
    "            sparcl.main\n",
    "        WHERE \n",
    "            spectype='GALAXY'\n",
    "            AND redshift>=0.5 AND redshift<=0.9\n",
    "            AND random_id BETWEEN 15.1 AND 15.4\n",
    "        LIMIT 20\n",
    "    \"\"\"\n",
    "found_II = qc.query(sql=q, fmt='pandas')  # output as Pandas DataFrame\n",
    "found_II"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd77794-e1e5-4005-8cdf-97072a5e0e0b",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a class=\"anchor\" id=\"retrieve\"></a>\n",
    "## Retrieve records by `sparcl_id` using `client.retrieve()`\n",
    "In order to retrieve spectra records from SPARCL by `sparcl_id`, pass the following to the `client.retrieve()` method:\n",
    "\n",
    "`uuid_list` : List of SPARCL IDs.  \n",
    "`dataset_list` : List of data sets to search for the SPARCL IDs in (default: None, which will search all available data sets).  \n",
    "`include` : List of field names to include in each record (default: 'DEFAULT').  \n",
    "`limit` : Maximum number of records to return (default: 500). Max allowed is 24,000.\n",
    "\n",
    "\n",
    "**NOTE: A reasonable amount of records to request retrieval of is about 10,000. Exceeding this value may cause the retrieval to timeout or fail.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c271a02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:53.789358Z",
     "iopub.status.busy": "2024-09-24T20:37:53.789230Z",
     "iopub.status.idle": "2024-09-24T20:37:53.795723Z",
     "shell.execute_reply": "2024-09-24T20:37:53.795181Z",
     "shell.execute_reply.started": "2024-09-24T20:37:53.789347Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0muuid_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'DEFAULT'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdataset_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Retrieve spectra records from the SPARCL database by list of\n",
       "sparcl_ids.\n",
       "\n",
       "Args:\n",
       "    uuid_list (:obj:`list`): List of sparcl_ids.\n",
       "\n",
       "    include (:obj:`list`, optional): List of field names to include\n",
       "        in each record. Defaults to 'DEFAULT', which will return\n",
       "        the fields tagged as 'default'.\n",
       "\n",
       "    dataset_list (:obj:`list`, optional): List of data sets from\n",
       "        which to retrieve spectra data. Defaults to None, meaning all\n",
       "        data sets hosted on the SPARCL database.\n",
       "\n",
       "    limit (:obj:`int`, optional): Maximum number of records to\n",
       "        return. Defaults to 500. Maximum allowed is 24,000.\n",
       "\n",
       "    verbose (:obj:`bool`, optional): Set to True for in-depth return\n",
       "        statement. Defaults to False.\n",
       "\n",
       "Returns:\n",
       "    :class:`~sparcl.Results.Retrieved`: Contains header and records.\n",
       "\n",
       "Example:\n",
       "    >>> client = SparclClient()\n",
       "    >>> ids = client.find(limit=1).ids\n",
       "    >>> inc = ['sparcl_id', 'flux', 'wavelength', 'model']\n",
       "    >>> ret = client.retrieve(uuid_list=ids, include=inc)\n",
       "    >>> type(ret.records[0].wavelength)\n",
       "    <class 'numpy.ndarray'>\n",
       "\u001b[0;31mFile:\u001b[0m      /data0/sw/anaconda3/lib/python3.10/site-packages/sparcl/client.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "client.retrieve?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40036a51",
   "metadata": {},
   "source": [
    "#### Use the sparcl_ids from the output of using `client.find()` to retrieve records from SPARCL:\n",
    "Note that `ids` in `found_I.ids` is a property name of the Found class. It is a list of records from all records, not a field name of a record."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c93923fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:53.796497Z",
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     "shell.execute_reply": "2024-09-24T20:37:53.798668Z",
     "shell.execute_reply.started": "2024-09-24T20:37:53.796486Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the fields to include in the retrieve function\n",
    "inc = ['sparcl_id', 'specid', 'data_release', 'redshift', 'flux',\n",
    "       'wavelength', 'model', 'ivar', 'mask', 'spectype', 'ra', 'dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1b86047a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:53.800087Z",
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    },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': {'success': True,\n",
       "  'info': [\"Successfully found 20 records in dr_list={'SDSS-DR16', 'BOSS-DR16', 'DESI-EDR'}\"],\n",
       "  'warnings': []}}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids_I = found_I.ids\n",
    "results_I = client.retrieve(uuid_list=ids_I, include=inc)\n",
    "results_I.info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "316c6f45",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"retrieve_specid\"></a>\n",
    "## Retrieve records by `specid` using `client.retrieve_by_specid()`\n",
    "In order to retrieve spectra records from SPARCL by `specid`, pass the following to the `client.retrieve_by_specid()` method:\n",
    "\n",
    "`specid_list` : List of specIDs.  \n",
    "`dataset_list` : List of data sets to search for the specIDs in (default: None, which will search all available data sets).  \n",
    "`include` : List of field names to include in each record (default: 'DEFAULT').  \n",
    "`limit` : Maximum number of records to return (default: 500). Max allowed is 24,000.\n",
    "\n",
    "\n",
    "**NOTE: A reasonable amount of records to request retrieval of is about 10,000. Exceeding this value may cause the retrieval to timeout or fail.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "743a19b8",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-09-24T20:37:54.480788Z"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve_by_specid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mspecid_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msvc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'spectras'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pkl'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'DEFAULT'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdataset_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Retrieve spectra records from the SPARCL database by list of\n",
       "specids.\n",
       "\n",
       "Args:\n",
       "    specid_list (:obj:`list`): List of specids.\n",
       "\n",
       "    include (:obj:`list`, optional): List of field names to include\n",
       "        in each record. Defaults to 'DEFAULT', which will return\n",
       "        the fields tagged as 'default'.\n",
       "\n",
       "    dataset_list (:obj:`list`, optional): List of data sets from\n",
       "        which to retrieve spectra data. Defaults to None, meaning all\n",
       "        data sets hosted on the SPARCL database.\n",
       "\n",
       "    limit (:obj:`int`, optional): Maximum number of records to\n",
       "        return. Defaults to 500. Maximum allowed is 24,000.\n",
       "\n",
       "    verbose (:obj:`bool`, optional): Set to True for in-depth return\n",
       "        statement. Defaults to False.\n",
       "\n",
       "Returns:\n",
       "    :class:`~sparcl.Results.Retrieved`: Contains header and records.\n",
       "\n",
       "Example:\n",
       "    >>> client = SparclClient()\n",
       "    >>> sids = [5840097619402313728, -8985592895187431424]\n",
       "    >>> inc = ['specid', 'flux', 'wavelength', 'model']\n",
       "    >>> ret = client.retrieve_by_specid(specid_list=sids, include=inc)\n",
       "    >>> len(ret.records[0].wavelength)\n",
       "    4617\n",
       "\u001b[0;31mFile:\u001b[0m      /data0/sw/anaconda3/lib/python3.10/site-packages/sparcl/client.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "client.retrieve_by_specid?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f73a2d7",
   "metadata": {},
   "source": [
    "#### Use the specIDs from the `sparcl.main` table to retrieve records from SPARCL:\n",
    "We first need to convert the Pandas Series of specIDs into an int list to work with SPARCL. Then we can pass the int list to the `client.retrieve_by_specid()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aa1cb379-a8b4-40ce-a8c1-e9cddffb8540",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-09-24T20:37:54.488485Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "specids = [int(s) for s in found_II.specid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b751a29e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:54.491569Z",
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     "shell.execute_reply.started": "2024-09-24T20:37:54.491559Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': {'success': True,\n",
       "  'info': [\"Successfully found 20 records in dr_list={'SDSS-DR16', 'BOSS-DR16', 'DESI-EDR'}\"],\n",
       "  'warnings': []}}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_II = client.retrieve_by_specid(specid_list=specids, include=inc)\n",
    "results_II.info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96da932f",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"reorder\"></a>\n",
    "## Reorder spectrum records\n",
    "The ordering of the results of `client.retrieve()` is not necessarily sorted to match the order of the IDs in the argument passed to the method. The `reorder()` method will reorder the retrieved results to be in the same order as the original list of IDs passed to `client.retrieve()`.\n",
    "\n",
    "##### Reorder SPARCL IDs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0b59b31a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:55.153817Z",
     "iopub.status.busy": "2024-09-24T20:37:55.153675Z",
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     "shell.execute_reply.started": "2024-09-24T20:37:55.153804Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Order of SPARCL IDs originally:\n",
      "['414c359e-9d07-11ee-9b9b-525400ad1336', 'c1156072-a1cb-11ee-953a-525400ad1336', '911ab2b7-9f3e-11ee-a597-525400ad1336']\n",
      "\n",
      "Order of SPARCL IDs after passing to retrieve():\n",
      "['414c359e-9d07-11ee-9b9b-525400ad1336', '911ab2b7-9f3e-11ee-a597-525400ad1336', 'c1156072-a1cb-11ee-953a-525400ad1336']\n",
      "\n",
      "Use reorder() to obtain original order of SPARCL IDs:\n",
      "['414c359e-9d07-11ee-9b9b-525400ad1336', 'c1156072-a1cb-11ee-953a-525400ad1336', '911ab2b7-9f3e-11ee-a597-525400ad1336']\n"
     ]
    }
   ],
   "source": [
    "ids_II = list(found_II.sparcl_id[1:4])\n",
    "print(f\"Order of SPARCL IDs originally:\\n{ids_II}\\n\")\n",
    "\n",
    "res = client.retrieve(uuid_list=ids_II)\n",
    "res_ids = [f.sparcl_id for f in res.records]\n",
    "print(f\"Order of SPARCL IDs after passing to retrieve():\\n{res_ids}\\n\")\n",
    "\n",
    "res_reordered = res.reorder(ids_II)\n",
    "res_reordered_ids = [f.sparcl_id for f in res_reordered.records]\n",
    "print(f\"Use reorder() to obtain original order of SPARCL IDs:\\n{res_reordered_ids}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3332ef24",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"access\"></a>\n",
    "## Access fields in records\n",
    "You can access the fields from records by using dot notation and dictionary indexing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81856081",
   "metadata": {},
   "source": [
    "#### Accessing a record from our example using `client.find()` and `client.retrieve()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "869c4f66",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:55.307565Z",
     "iopub.status.busy": "2024-09-24T20:37:55.307417Z",
     "iopub.status.idle": "2024-09-24T20:37:55.310699Z",
     "shell.execute_reply": "2024-09-24T20:37:55.310230Z",
     "shell.execute_reply.started": "2024-09-24T20:37:55.307553Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "records_I = results_I.records[0]\n",
    "\n",
    "id_I = records_I.sparcl_id\n",
    "data_release_I = records_I.data_release\n",
    "redshift_I = records_I.redshift\n",
    "flux_I = records_I.flux\n",
    "wavelength_I = records_I.wavelength\n",
    "model_I = records_I.model\n",
    "ivar_I = records_I.ivar\n",
    "mask_I = records_I.mask\n",
    "spectype_I = records_I.spectype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020f7d50",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"convert\"></a>\n",
    "## Convert retrieved output to Pandas DataFrame or Spectrum1D object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbb5458",
   "metadata": {},
   "source": [
    "#### Pandas DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0e7e06ea",
   "metadata": {
    "execution": {
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   },
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       "      <th>4</th>\n",
       "      <td>0000321e-a4a9-11ee-bde8-525400ad1336</td>\n",
       "      <td>0.631113</td>\n",
       "      <td>[3592.5266624766173, 3593.353967553901, 3594.1...</td>\n",
       "      <td>[-2.767551898956299, -2.7674343585968018, -2.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000041af-a296-11ee-9b05-525400ad1336</td>\n",
       "      <td>0.572525</td>\n",
       "      <td>[3562.049907308445, 3562.8701940455303, 3563.6...</td>\n",
       "      <td>[6.18538236618042, 6.185926914215088, 6.186471...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00004954-a46e-11ee-b0bd-525400ad1336</td>\n",
       "      <td>0.833832</td>\n",
       "      <td>[3569.4392919884644, 3570.2612803901543, 3571....</td>\n",
       "      <td>[-1.1925723552703857, -1.1927012205123901, -1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00004a95-9d0d-11ee-8bf1-525400ad1336</td>\n",
       "      <td>0.831348</td>\n",
       "      <td>[3600.0, 3600.8, 3601.6000000000004, 3602.4000...</td>\n",
       "      <td>[2.1275572776794434, -1.5423610210418701, 2.38...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00004bbf-a42c-11ee-9dd6-525400ad1336</td>\n",
       "      <td>0.754319</td>\n",
       "      <td>[3567.795882885623, 3568.617492834813, 3569.43...</td>\n",
       "      <td>[0.38861986994743347, 3.381983995437622, 0.267...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00005931-a340-11ee-8fab-525400ad1336</td>\n",
       "      <td>0.677861</td>\n",
       "      <td>[3791.4034418330666, 3792.276545253626, 3793.1...</td>\n",
       "      <td>[-0.8654358386993408, -0.8654358386993408, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>00006e76-a4b2-11ee-a3df-525400ad1336</td>\n",
       "      <td>0.694172</td>\n",
       "      <td>[3587.566830392618, 3588.392993294867, 3589.21...</td>\n",
       "      <td>[-1.914870262145996, -1.9146013259887695, -1.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>00008e21-a1ad-11ee-8e57-525400ad1336</td>\n",
       "      <td>0.568273</td>\n",
       "      <td>[3584.2640804371426, 3585.0894827655493, 3585....</td>\n",
       "      <td>[-0.8480641841888428, 0.9721508026123047, -1.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0000920c-a40a-11ee-9e59-525400ad1336</td>\n",
       "      <td>0.682240</td>\n",
       "      <td>[3571.9058251119704, 3572.7283815192923, 3573....</td>\n",
       "      <td>[3.6436429023742676, 3.6437201499938965, 3.643...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>000093ef-9e66-11ee-8b5b-525400ad1336</td>\n",
       "      <td>0.859480</td>\n",
       "      <td>[3583.43886814277, 3584.2640804371463, 3585.08...</td>\n",
       "      <td>[-2.2581210136413574, 4.059389114379883, -4.48...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0000a486-a1dd-11ee-b9a1-525400ad1336</td>\n",
       "      <td>0.530106</td>\n",
       "      <td>[3581.789013481115, 3582.6138458386763, 3583.4...</td>\n",
       "      <td>[-2.0286598205566406, -2.0284993648529053, -2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0000af3a-9c76-11ee-bec7-525400ad1336</td>\n",
       "      <td>0.625994</td>\n",
       "      <td>[3600.0, 3600.8, 3601.6000000000004, 3602.4000...</td>\n",
       "      <td>[0.48305296897888184, 6.715146064758301, -6.40...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0000b0bb-a2b0-11ee-892f-525400ad1336</td>\n",
       "      <td>0.580323</td>\n",
       "      <td>[3563.6906696823603, 3564.5113342624427, 3565....</td>\n",
       "      <td>[6.914778232574463, 7.095730781555176, 0.41984...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0000b8c4-a2c9-11ee-8a72-525400ad1336</td>\n",
       "      <td>0.619319</td>\n",
       "      <td>[3566.9744620973356, 3567.795882885623, 3568.6...</td>\n",
       "      <td>[2.697164535522461, 2.697510242462158, 2.69785...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0000d281-a14b-11ee-b6b8-525400ad1336</td>\n",
       "      <td>0.533600</td>\n",
       "      <td>[3562.049907308445, 3562.8701940455303, 3563.6...</td>\n",
       "      <td>[-5.767019748687744, -7.26045560836792, 9.3124...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0000d45b-9b1f-11ee-afc7-525400ad1336</td>\n",
       "      <td>0.871094</td>\n",
       "      <td>[3600.0, 3600.8, 3601.6000000000004, 3602.4000...</td>\n",
       "      <td>[-2.3349931240081787, 4.351064205169678, 1.869...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               sparcl_id  redshift  \\\n",
       "0   00000c19-9c85-11ee-bf5c-525400ad1336  0.594318   \n",
       "1   00000f02-a2dc-11ee-ac91-525400ad1336  0.522124   \n",
       "2   00001b3a-a41b-11ee-8210-525400ad1336  0.542893   \n",
       "3   00001cbb-9c7e-11ee-8b67-525400ad1336  0.790116   \n",
       "4   0000321e-a4a9-11ee-bde8-525400ad1336  0.631113   \n",
       "5   000041af-a296-11ee-9b05-525400ad1336  0.572525   \n",
       "6   00004954-a46e-11ee-b0bd-525400ad1336  0.833832   \n",
       "7   00004a95-9d0d-11ee-8bf1-525400ad1336  0.831348   \n",
       "8   00004bbf-a42c-11ee-9dd6-525400ad1336  0.754319   \n",
       "9   00005931-a340-11ee-8fab-525400ad1336  0.677861   \n",
       "10  00006e76-a4b2-11ee-a3df-525400ad1336  0.694172   \n",
       "11  00008e21-a1ad-11ee-8e57-525400ad1336  0.568273   \n",
       "12  0000920c-a40a-11ee-9e59-525400ad1336  0.682240   \n",
       "13  000093ef-9e66-11ee-8b5b-525400ad1336  0.859480   \n",
       "14  0000a486-a1dd-11ee-b9a1-525400ad1336  0.530106   \n",
       "15  0000af3a-9c76-11ee-bec7-525400ad1336  0.625994   \n",
       "16  0000b0bb-a2b0-11ee-892f-525400ad1336  0.580323   \n",
       "17  0000b8c4-a2c9-11ee-8a72-525400ad1336  0.619319   \n",
       "18  0000d281-a14b-11ee-b6b8-525400ad1336  0.533600   \n",
       "19  0000d45b-9b1f-11ee-afc7-525400ad1336  0.871094   \n",
       "\n",
       "                                           wavelength  \\\n",
       "0   [3600.0, 3600.8, 3601.6000000000004, 3602.4000...   \n",
       "1   [3568.617492834813, 3569.439291988468, 3570.26...   \n",
       "2   [3570.2612803901543, 3571.0834580834608, 3571....   \n",
       "3   [3600.0, 3600.8, 3601.6000000000004, 3602.4000...   \n",
       "4   [3592.5266624766173, 3593.353967553901, 3594.1...   \n",
       "5   [3562.049907308445, 3562.8701940455303, 3563.6...   \n",
       "6   [3569.4392919884644, 3570.2612803901543, 3571....   \n",
       "7   [3600.0, 3600.8, 3601.6000000000004, 3602.4000...   \n",
       "8   [3567.795882885623, 3568.617492834813, 3569.43...   \n",
       "9   [3791.4034418330666, 3792.276545253626, 3793.1...   \n",
       "10  [3587.566830392618, 3588.392993294867, 3589.21...   \n",
       "11  [3584.2640804371426, 3585.0894827655493, 3585....   \n",
       "12  [3571.9058251119704, 3572.7283815192923, 3573....   \n",
       "13  [3583.43886814277, 3584.2640804371463, 3585.08...   \n",
       "14  [3581.789013481115, 3582.6138458386763, 3583.4...   \n",
       "15  [3600.0, 3600.8, 3601.6000000000004, 3602.4000...   \n",
       "16  [3563.6906696823603, 3564.5113342624427, 3565....   \n",
       "17  [3566.9744620973356, 3567.795882885623, 3568.6...   \n",
       "18  [3562.049907308445, 3562.8701940455303, 3563.6...   \n",
       "19  [3600.0, 3600.8, 3601.6000000000004, 3602.4000...   \n",
       "\n",
       "                                                 flux  \n",
       "0   [4.658731460571289, 3.5160093307495117, 1.9316...  \n",
       "1   [7.6649274826049805, 7.667514324188232, 7.6701...  \n",
       "2   [4.828716278076172, 4.829677581787109, 4.83063...  \n",
       "3   [4.414716720581055, 1.5143095254898071, 1.3886...  \n",
       "4   [-2.767551898956299, -2.7674343585968018, -2.7...  \n",
       "5   [6.18538236618042, 6.185926914215088, 6.186471...  \n",
       "6   [-1.1925723552703857, -1.1927012205123901, -1....  \n",
       "7   [2.1275572776794434, -1.5423610210418701, 2.38...  \n",
       "8   [0.38861986994743347, 3.381983995437622, 0.267...  \n",
       "9   [-0.8654358386993408, -0.8654358386993408, -0....  \n",
       "10  [-1.914870262145996, -1.9146013259887695, -1.9...  \n",
       "11  [-0.8480641841888428, 0.9721508026123047, -1.4...  \n",
       "12  [3.6436429023742676, 3.6437201499938965, 3.643...  \n",
       "13  [-2.2581210136413574, 4.059389114379883, -4.48...  \n",
       "14  [-2.0286598205566406, -2.0284993648529053, -2....  \n",
       "15  [0.48305296897888184, 6.715146064758301, -6.40...  \n",
       "16  [6.914778232574463, 7.095730781555176, 0.41984...  \n",
       "17  [2.697164535522461, 2.697510242462158, 2.69785...  \n",
       "18  [-5.767019748687744, -7.26045560836792, 9.3124...  \n",
       "19  [-2.3349931240081787, 4.351064205169678, 1.869...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[r.sparcl_id, r.redshift, r.wavelength, r.flux,]\n",
    "                    for r in results_I.records],\n",
    "                  columns=['sparcl_id', 'redshift', 'wavelength', 'flux'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de3baaf9",
   "metadata": {},
   "source": [
    "#### Spectrum1D:\n",
    "When applicable, the units for each field are documented on the [Fields tab of the astrosparcl website](https://astrosparcl.datalab.noirlab.edu/sparc/sfc/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1fc03e7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:55.340621Z",
     "iopub.status.busy": "2024-09-24T20:37:55.340492Z",
     "iopub.status.idle": "2024-09-24T20:37:55.369201Z",
     "shell.execute_reply": "2024-09-24T20:37:55.368723Z",
     "shell.execute_reply.started": "2024-09-24T20:37:55.340611Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Spectrum1D(flux=[4.658731460571289e-17 ... 4.4325074553489685e-18] erg / (Angstrom s cm2) (shape=(7781,), mean=0.00000 erg / (Angstrom s cm2)); spectral_axis=<SpectralAxis \n",
       "   (observer to target:\n",
       "      radial_velocity=130506.60402496679 km / s\n",
       "      redshift=0.5943176916313619)\n",
       "  [3600.  3600.8 3601.6 ... 9822.4 9823.2 9824. ] Angstrom> (length=7781); uncertainty=InverseVariance)>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specs = [Spectrum1D(spectral_axis = r.wavelength*u.AA,\n",
    "                    flux = np.array(r.flux)* 10**-17 * u.Unit('erg cm-2 s-1 AA-1'),\n",
    "                    uncertainty = InverseVariance(np.array(r.ivar)),\n",
    "                    redshift = r.redshift,\n",
    "                    mask = r.mask)\n",
    "        for r in results_I.records]\n",
    "specs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ee58cd93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:55.369877Z",
     "iopub.status.busy": "2024-09-24T20:37:55.369743Z",
     "iopub.status.idle": "2024-09-24T20:37:55.494503Z",
     "shell.execute_reply": "2024-09-24T20:37:55.493881Z",
     "shell.execute_reply.started": "2024-09-24T20:37:55.369866Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a couple\n",
    "f, ax = plt.subplots()  \n",
    "ax.step(specs[0].spectral_axis, specs[0].flux) \n",
    "ax.step(specs[1].spectral_axis, specs[1].flux)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce3ebae5",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"plot\"></a>\n",
    "## Plot spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "35e2607b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:55.495436Z",
     "iopub.status.busy": "2024-09-24T20:37:55.495277Z",
     "iopub.status.idle": "2024-09-24T20:37:55.500084Z",
     "shell.execute_reply": "2024-09-24T20:37:55.499561Z",
     "shell.execute_reply.started": "2024-09-24T20:37:55.495422Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_spec(index, results):\n",
    "    \"\"\"\n",
    "    Pass an index value and the output from using client.retrieve()\n",
    "    to plot the spectrum at the specified index.\n",
    "    \"\"\"\n",
    "    \n",
    "    record = results.records[index]\n",
    "\n",
    "    sparcl_id = record.sparcl_id\n",
    "    data_release = record.data_release\n",
    "    flux = record.flux\n",
    "    wavelength = record.wavelength\n",
    "    model = record.model\n",
    "    spectype = record.spectype\n",
    "    redshift = record.redshift\n",
    "    ra = record.ra\n",
    "    dec = record.dec\n",
    "\n",
    "    plt.title(f\"Data Set = {data_release}\\n\"\n",
    "              f\"Type = {spectype}\\n\"\n",
    "              f\"ID = {sparcl_id}\\n\"\n",
    "              f\"Redshift = {redshift}\\n\"\n",
    "              f\"RA = {ra}\\n\"\n",
    "              f\"Dec = {dec}\", loc='left')\n",
    "    plt.xlabel('$\\lambda\\ [\\AA]$')\n",
    "    plt.ylabel('$f_{\\lambda}$ $(10^{-17}$ $erg$ $s^{-1}$ $cm^{-2}$ $\\AA^{-1})$')\n",
    "    \n",
    "    # Plot unsmoothed spectrum in grey\n",
    "    plt.plot(wavelength, flux, color='k', alpha=0.2, label='Unsmoothed spectrum')\n",
    "    \n",
    "    # Overplot spectrum smoothed using a 1-D Gaussian Kernel in black\n",
    "    plt.plot(wavelength, convolve(flux, Gaussian1DKernel(5)), color='k', label='Smoothed spectrum')\n",
    "    \n",
    "    # Overplot the model spectrum in red\n",
    "    plt.plot(wavelength, model, color='r', label='Model spectrum')\n",
    "    \n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a2545c6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:55.500921Z",
     "iopub.status.busy": "2024-09-24T20:37:55.500786Z",
     "iopub.status.idle": "2024-09-24T20:37:55.729779Z",
     "shell.execute_reply": "2024-09-24T20:37:55.729055Z",
     "shell.execute_reply.started": "2024-09-24T20:37:55.500910Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spec(index=5, results=results_I)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49cbc02a",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"missing\"></a>\n",
    "## Use `client.missing()` to find missing SPARCL IDs in the SPARCL database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c404a563",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:55.730635Z",
     "iopub.status.busy": "2024-09-24T20:37:55.730488Z",
     "iopub.status.idle": "2024-09-24T20:37:55.738590Z",
     "shell.execute_reply": "2024-09-24T20:37:55.737830Z",
     "shell.execute_reply.started": "2024-09-24T20:37:55.730621Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0muuid_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdataset_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcountOnly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Return the subset of sparcl_ids in the given uuid_list that are\n",
       "NOT stored in the SPARCL database.\n",
       "\n",
       "Args:\n",
       "    uuid_list (:obj:`list`): List of sparcl_ids.\n",
       "\n",
       "    dataset_list (:obj:`list`, optional): List of data sets from\n",
       "        which to find missing sparcl_ids. Defaults to None, meaning\n",
       "        all data sets hosted on the SPARCL database.\n",
       "\n",
       "    countOnly (:obj:`bool`, optional): Set to True to return only\n",
       "        a count of the missing sparcl_ids from the uuid_list.\n",
       "        Defaults to False.\n",
       "\n",
       "    verbose (:obj:`bool`, optional): Set to True for in-depth return\n",
       "        statement. Defaults to False.\n",
       "\n",
       "Returns:\n",
       "    A list of the subset of sparcl_ids in the given uuid_list that\n",
       "    are NOT stored in the SPARCL database.\n",
       "\n",
       "Example:\n",
       "    >>> client = SparclClient()\n",
       "    >>> ids = ['ddbb57ee-8e90-4a0d-823b-0f5d97028076',]\n",
       "    >>> client.missing(ids)\n",
       "    ['ddbb57ee-8e90-4a0d-823b-0f5d97028076']\n",
       "\u001b[0;31mFile:\u001b[0m      /data0/sw/anaconda3/lib/python3.10/site-packages/sparcl/client.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "client.missing?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efc74105",
   "metadata": {},
   "source": [
    "#### When you try to retrieve records from the SPARCL database with IDs that do not exist in the SPARCL database, a warning message will appear:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e1c06d56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:55.739611Z",
     "iopub.status.busy": "2024-09-24T20:37:55.739427Z",
     "iopub.status.idle": "2024-09-24T20:37:55.839428Z",
     "shell.execute_reply": "2024-09-24T20:37:55.838873Z",
     "shell.execute_reply.started": "2024-09-24T20:37:55.739598Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1163822/2568251338.py:7: UserWarning: Some UUIDs were not found. 5 out of the 5 requested uuids have no records available in the SPARCL database associated with DataSets {'SDSS-DR16', 'BOSS-DR16', 'DESI-EDR'}.Use \"client.missing()\" to get a list of the 5 unavailable uuids.\n",
      "  res_test = client.retrieve(uuid_list=ids_test)\n"
     ]
    }
   ],
   "source": [
    "ids_test = ['000025da-914b-4b67-b9a9-97cc811b0459',\n",
    "            '0000fabc-0e15-4d76-b44c-4f152f31de09',\n",
    "            '00001c20-b7ff-4367-af08-26432cd7e01d',\n",
    "            '0000403e-51da-43b0-a0e9-e806ea31ca9e',\n",
    "            'ddbb57ee-8e90-4a0d-823b-0f5d97028076']\n",
    "\n",
    "res_test = client.retrieve(uuid_list=ids_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5b6f5c",
   "metadata": {},
   "source": [
    "#### In order to figure out which of the IDs from your list do not exist in the SPARCL database, use the `client.missing()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dd63d6b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-24T20:37:55.840361Z",
     "iopub.status.busy": "2024-09-24T20:37:55.840222Z",
     "iopub.status.idle": "2024-09-24T20:38:50.635349Z",
     "shell.execute_reply": "2024-09-24T20:38:50.634742Z",
     "shell.execute_reply.started": "2024-09-24T20:37:55.840351Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0000fabc-0e15-4d76-b44c-4f152f31de09',\n",
       " '00001c20-b7ff-4367-af08-26432cd7e01d',\n",
       " '0000403e-51da-43b0-a0e9-e806ea31ca9e',\n",
       " 'ddbb57ee-8e90-4a0d-823b-0f5d97028076',\n",
       " '000025da-914b-4b67-b9a9-97cc811b0459']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.missing(uuid_list=ids_test)"
   ]
  }
 ],
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