{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5afe77b1-d417-4b1c-b83b-ed39cc837814",
   "metadata": {},
   "source": [
    "# Accessing an APOGEE Spectrum with sdss-access\n",
    "\n",
    "## Learning Goals\n",
    "By the end of this tutorial, you will:\n",
    "- Understand how to retrieve SDSS data with sdss-access\n",
    "- Understand some of the structure of a fits file containing an APOGEE spectrum\n",
    "- (Optionally) have a copy of the AllStarASPCAP file\n",
    "\n",
    "## Introduction\n",
    "The Apache Point Observatory Galactic Evolution Experiment (APOGEE) is an infrared spectroscopic survey using two spectrographs at the two SDSS V observatories, Apache Point Observatory (APO) and Las Campanas Observatory (LCO).\n",
    "\n",
    "In this short tutorial we will briefly cover how to retrieve an object's sdss_id from the allStarASPCAP file, then show how to use the sdss-access tool to retreive the APOGEE spectrum of that star from the SDSS Science Archive Server (SAS).\n",
    "\n",
    "## Imports\n",
    "This tutorial requires two commonly used python packages, and an SDSS specific package\n",
    "- _matplotlib_ for plotting\n",
    "- _astropy_ for reading fits table\n",
    "- _sdss-access_ for accessing data on the SAS\n",
    "\n",
    "Missing packages can be installed via `pip install matplotlib astropy sdss-access` \n",
    "\n",
    "NOTE: You may want to set the `$SAS_BASE_DIR` environment variable before starting this notebook, other wise `$HOME/sas` will be created and used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3c60bdb-a26c-4e7f-a7db-b68033021c34",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from astropy.table import Table\n",
    "\n",
    "from sdss_access import Access\n",
    "access = Access(release='dr19')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ede9706d-ef85-4cd5-ac89-50d122f2fca2",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Optional Download\n",
    "\n",
    "If you have good internet access and space on your computer, you may download the summary file for ASPCAP. It is approximately 1.1 GB in size and can be downloaded from \n",
    "https://dr19.sdss.org/sas/dr19/spectro/astra/0.6.0/summary/astraAllStarASPCAP-0.6.0.fits.gz\n",
    "\n",
    "`file_name` is currently set to just the name of the file. If the file is not in the same location as this notebook, you may need to change the variable to point to the full path\n",
    "\n",
    "If you prefer to use a previously chosen example object, skip the next two cells.\n",
    "\n",
    "HDU 2 contains the parameters from APOGEE north (APO) spectra. The current data release does not have LCO spectra.\n",
    "\n",
    "### SciServer\n",
    "\n",
    "If you are running this notebook on SciServer, this file is already available. Uncomment the second `file_name` variable to use that file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce127f54-957c-434f-b802-4daf3981a7b4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1095480"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = \"astraAllStarASPCAP-0.6.0.fits.gz\"\n",
    "\n",
    "# file_name = \"/home/idies/workspace/sdss_sas/dr19/spectro/astra/0.6.0/summary/astraAllStarASPCAP-0.6.0.fits.gz\"\n",
    "\n",
    "allStarASPCAP = Table.read(file_name, hdu=2)\n",
    "len(allStarASPCAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b5bf54c-14c9-4d72-937d-f630796c7dc5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85993595"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_star = allStarASPCAP[1]\n",
    "sdss_id = single_star[\"sdss_id\"]\n",
    "sdss_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfbaee44-fabd-427c-9a4b-6a2be814f415",
   "metadata": {},
   "source": [
    "For anyone who did not download the allStar summary file, we'll set sdss_id explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0850ba4f-db5f-4864-9eac-7c41a3747a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "sdss_id = 85993595"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e33a223d-9a1e-428d-88b7-2d76bfd89d9d",
   "metadata": {},
   "source": [
    "## sdss-access\n",
    "\n",
    "sdss-access has extensive [documentation](https://sdss-access.readthedocs.io/en/latest/), but we should mention a few things. First if you are a member of the collaboration accessing proprietary data, you need to set up a a [.netrc file](https://sdss-access.readthedocs.io/en/latest/auth.html). After that usage is the same for everyone.\n",
    "\n",
    "You need specify the file species you need, in this case 'mwmStar', then a series of key word arguments that vary depending on the file species. A list of file species and their keyword arugments is [available](https://sdss-access.readthedocs.io/en/latest/paths.html).\n",
    "\n",
    "The four lines of code below show an example of retreiving an sdss spectrum. `access.commit()` will retrieve the file and save it to your local SAS, `$SAS_BASE_DIR`, with the same structure as the SAS (so it will be many files deep)\n",
    "\n",
    "### SciServer\n",
    "\n",
    "If you are running this notebook on SciServer, the next cell may fail, but it is not needed. The data are all already avaible, so skip this cell and move to the next where we'll verify the file we need is present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87238c12-e86b-46bf-9770-72f93ac1db32",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Progress:   0%|                | 0/1 [00:00<?, ?files/s, n_files=1, n_streams=1]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SDSS_ACCESS> Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "access.remote()\n",
    "access.add('mwmStar', v_astra='0.6.0', component='', sdss_id=sdss_id)\n",
    "access.set_stream()\n",
    "access.commit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd42c388-1af7-48c2-9454-661786a55a39",
   "metadata": {},
   "source": [
    "Now we'll separately build the path to our spectrum using `access.full`, print to see it on disk, then check that the file is there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1284ad17-3121-4af4-8c7f-30ec5db27d63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jdonor/sas/dr19/spectro/astra/0.6.0/spectra/star/35/95/mwmStar-0.6.0-85993595.fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mwmStar_filename = access.full('mwmStar', v_astra='0.6.0', component='', sdss_id=sdss_id)\n",
    "print(mwmStar_filename)\n",
    "access.exists('', full=mwmStar_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b363830-1d26-4bb6-8da9-a05353385918",
   "metadata": {},
   "source": [
    "## Load the spectrum\n",
    "\n",
    "Now use `astropy.Table` load the data. HDU 3 contains the APO spectrum.\n",
    "\n",
    "We then extract wavelength, flux, and ivar (inverse variance) to more convenient variables. Not we need the `[0]` entry. This is part of the file structure meant to be as similar as possible to other file species, which contain separat espectra for each visit. \n",
    "\n",
    "$ivar^{1/2}$ is a good representation of uncertainty in the flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90bf06e2-0ce4-4d5d-9f1f-d0ac651c5f61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1;33m[WARNING]: \u001b[0m/var/folders/nd/01s30xjn2z1_lx_f3bqdbwt4nm074c/T/ipykernel_54888/3069109418.py:5: RuntimeWarning: divide by zero encountered in power\n",
      "  obs_err = mwmStar[\"ivar\"][0]**(-0.5)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mwmStar = Table.read(mwmStar_filename, hdu=3)\n",
    "\n",
    "obs_wl = mwmStar[\"wavelength\"][0]\n",
    "obs_flux = mwmStar[\"flux\"][0]\n",
    "obs_err = mwmStar[\"ivar\"][0]**(-0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dbe895e-7cde-4363-a167-4cf2b6b4b054",
   "metadata": {},
   "source": [
    "Now plot the spectrum, flux vs wavelength.\n",
    "\n",
    "Note the mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7b54e5e-5ed2-4410-949b-684c3bada476",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'flux')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "\n",
    "mask = obs_err < 0.1 * obs_flux\n",
    "\n",
    "plt.errorbar(obs_wl[mask], obs_flux[mask], yerr=obs_err[mask])\n",
    "plt.xlabel(\"λ [Å]\")\n",
    "plt.ylabel(\"flux\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0343c99-7237-49b1-aced-e9daddcf0c91",
   "metadata": {},
   "source": [
    "### About this notebook\n",
    "\n",
    "This notebook was created to give an example of using the `sdss-access` tool, and illustrate how to use an APOGEE spectrum.\n",
    "\n",
    "**Authors**: Adam Wheeler, John Donor\n",
    "\n",
    "**Last Update**: 2025 April 2\n",
    "\n",
    "If you use `astropy`  for published research, please cite the\n",
    "authors. Follow this link for more information about citing `astropy`:\n",
    "\n",
    "* [Citing `astropy`](https://www.astropy.org/acknowledging.html)\n",
    "\n",
    "And of course please [cite](https://sdss.org/collaboration/citing-sdss/) SDSS when you use our data."
   ]
  }
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