view_spectrum_BOSS_star.ipynb

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    "# Plotting a BOSS Stellar Spectrum and Best Fit\n",
    "\n",
    "## Learning Goals\n",
    "By the end of this tutorial, you will:\n",
    "- Know how to manually retrieve SDSS spectrum files\n",
    "- Understand some of the structure of a fits file containing a BOSS spectrum\n",
    "- (Optionally) have a copy of the AllStarSnowWhite file\n",
    "\n",
    "## Introduction\n",
    "SDSS V now uses the BOSS optical spectrograph to observe stars. White dwarfs are a particularly large SDSS V program, and the data are reduced by a product called Snow White.\n",
    "\n",
    "In this short tutorial we will briefly cover how to retrieve an object's sdss_id from the allStarSnowWhite file, then show how to build the file name and retrieve the BOSS spectrum of that star from the SDSS Science Archive Server (SAS).\n",
    "\n",
    "## Imports\n",
    "This tutorial requires three commonly used python packages, and an SDSS specific package\n",
    "- _numpy_ for doing large array operations\n",
    "- _matplotlib_ for plotting\n",
    "- _astropy_ for reading fits table\n",
    "\n",
    "Missing packages can be installed via `pip install numpy matplotlib astropy`\n",
    "\n",
    "## Optional Import\n",
    "\n",
    "A portion of this tutorial will demonstrate how to use `sdss_access` to download sdss data in the background, saving some manual effort. This is optional, as we first show how to find these files manually.\n",
    "\n",
    "`sdss_access` can be installed via `pip install sdss-access`\n",
    "\n",
    "The last two lines of the next block should be commented out if you choose not install `sdss_access`\n",
    "\n",
    "# Note!\n",
    "If you already have `sdss_access` installed but have trouble with these file types, try upgrading to the latest version `pip install --upgrade sdss-access`"
   ]
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.table import Table\n",
    "\n",
    "from sdss_access import Access\n",
    "access = Access(release='dr19')"
   ]
  },
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    "Let's get the summary file for SnowWhite. The file is approximately 1.6 MB and can be downloaded from \n",
    "https://data.sdss5.org/sas/dr19/spectro/astra/0.6.0/summary/astraAllStarSnowWhite-0.6.0.fits.gz\n",
    "\n",
    "The variable `wd_file` is set to just the name of the file. You may need to modify it to point to the full path if the file is not in the same location as this notebook.\n",
    "\n",
    "HDU 1 contains the parameters from BOSS spectra, which is what SnowWhite runs on.\n",
    "\n",
    "If you are running this notebook on SciServer, this file is already available. Uncomment the file path to rename the `wd_file` variable to point to this version."
   ]
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     "text": [
      "10364\n"
     ]
    }
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   "source": [
    "wd_file = \"astraAllStarSnowWhite-0.6.0.fits.gz\"\n",
    "\n",
    "# wd_file = \"/home/idies/workspace/sdss_sas/dr19/spectro/astra/0.6.0/summary/astraAllStarSnowWhite-0.6.0.fits.gz\"\n",
    "\n",
    "allStarSnowWhiteFull = Table.read(wd_file, hdu=1)\n",
    "print(len(allStarSnowWhiteFull))"
   ]
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   "source": [
    "First we'll apply a cut to remove lower Signal-to-Noise (SNR) stars and stars with higher reduced χ^2 values, leaving only the stars with highest quality data and good fits"
   ]
  },
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   "cell_type": "code",
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    {
     "name": "stdout",
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     "text": [
      "52\n"
     ]
    }
   ],
   "source": [
    "allStarSnowWhite = allStarSnowWhiteFull[np.logical_and(allStarSnowWhiteFull[\"nmf_rchi2\"] < 2, allStarSnowWhiteFull[\"snr\"] > 50)]\n",
    "print(len(allStarSnowWhite))"
   ]
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   "source": [
    "## Choosing a star and constructing the data path\n",
    "\n",
    "Let's compare the observed spectrum and best-fit SnowWhite model for a single star.\n",
    "\n",
    "First, grab the SDSS ID for a star, let's say the first. This is the info we need to grab the stacked spectrum and best-fit model."
   ]
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   "cell_type": "code",
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   "source": [
    "single_star = allStarSnowWhite[0]\n",
    "sdss_id = str(single_star[\"sdss_id\"])"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## SciServer\n",
    "\n",
    "If you are running this notebook on SciServer, the data are already available. We'll set the `mwmStar_filename` and `astraStar_filename` variables, then skip ahead to loading the files."
   ]
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   "cell_type": "code",
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   "source": [
    "file_root = \"/home/idies/workspace/sdss_sas_ipl/dr19/spectro/astra/0.6.0/spectra/star/\"\n",
    "mwmStar_filename = file_root + f\"{sdss_id[-4:-2]}/{sdss_id[-2:]}/mwmStar-0.6.0-{sdss_id}.fits\"\n",
    "astraStar_filename = file_root +  f\"{sdss_id[-4:-2]}/{sdss_id[-2:]}/astraStarSnowWhite-0.6.0-{sdss_id}.fits\""
   ]
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   "source": [
    "If running on SciServer, **skip ahead to the \"Loading Data\" section**. Running the cells between here and there will reset the file names.\n",
    "\n",
    "## Manually downloaded data\n",
    "\n",
    "If you are running this notebook yourself, SDSS Spectra can be manually downloaded from the Science Archive Server (SAS) if you carefully construct the paths. Many may find it easier to use the `sdss_access` product, if so you may skip to that section.\n",
    "\n",
    "To manually access data, the URL of the mwmStar file (containing the stacked observed spectrum) can be constructed from the SDSS ID, and the URL of the astraStarSnowWhite file (containing the best-fit model spectrum)."
   ]
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    {
     "data": {
      "text/plain": [
       "'https://data.sdss5.org/sas/dr19/spectro/astra/0.6.0/spectra/star/07/10/mwmStar-0.6.0-66090710.fits'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "mwmStar_filename = f\"mwmStar-0.6.0-{sdss_id}.fits\"\n",
    "f\"https://data.sdss5.org/sas/dr19/spectro/astra/0.6.0/spectra/star/{sdss_id[-4:-2]}/{sdss_id[-2:]}/\" + mwmStar_filename"
   ]
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   "source": [
    "Navigating to that URL (copy and paste in any browser, or possibly just click the link) should begin the download immediately. Move the file to the same directory as this notebook, or modify the mwmStar_filename with the path to the file.\n",
    "\n",
    "Now we need to grab the astraStar file in order to load the model flux. SnowWhite astraStar filenames contains WD instead of SnowWhite for legacy reasons."
   ]
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    {
     "data": {
      "text/plain": [
       "'https://data.sdss5.org/sas/dr19/spectro/astra/0.6.0/results/star/07/10/astraStarSnowWhite-0.6.0-66090710.fits'"
      ]
     },
     "execution_count": 7,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "astraStar_filename = f\"astraStarSnowWhite-0.6.0-{sdss_id}.fits\"\n",
    "f\"https://data.sdss5.org/sas/dr19/spectro/astra/0.6.0/results/star/{sdss_id[-4:-2]}/{sdss_id[-2:]}/\" + astraStar_filename"
   ]
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   "source": [
    "Download and move the astraStar file just as before.\n",
    "\n",
    "Alternatively, the files can be downloaded in the background using `sdss_access`.\n",
    "\n",
    "## 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 [.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)"
   ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Progress:   0%|                | 0/2 [00:00<?, ?files/s, n_files=2, n_streams=2]"
     ]
    },
    {
     "name": "stdout",
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     "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.add('astraStarSnowWhite', v_astra='0.6.0', component='', sdss_id=sdss_id)\n",
    "access.set_stream()\n",
    "access.commit()"
   ]
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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "Now we'll change the file names we created before to point to the new paths created by `sdss_access`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "720fc777-58a0-4515-ba94-e3f5eab289ea",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jdonor/sas/dr19/spectro/astra/0.6.0/spectra/star/07/10/mwmStar-0.6.0-66090710.fits\n",
      "/Users/jdonor/sas/dr19/spectro/astra/0.6.0/results/star/07/10/astraStarSnowWhite-0.6.0-66090710.fits\n"
     ]
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    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
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    }
   ],
   "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)\n",
    "\n",
    "astraStar_filename = access.full('astraStarSnowWhite', v_astra='0.6.0', component='', sdss_id=sdss_id)\n",
    "print(astraStar_filename)\n",
    "access.exists('', full=astraStar_filename)"
   ]
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   "source": [
    "## Loading data\n",
    "\n",
    "Now, load the data and model spectrum and make plots.\n",
    "\n",
    "Again, HDU 1 contains the data from BOSS.\n",
    "\n",
    "We want the first entry in each of these columns. They are stored this way to be as similar as possible to mwmVisit files, which contain separate spectra for each visit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "90bf06e2-0ce4-4d5d-9f1f-d0ac651c5f61",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spectrum_pk_id release filetype ... nmf_rectified_model_flux nmf_rchi2 nmf_flags\n",
      "-------------- ------- -------- ... ------------------------ --------- ---------\n",
      "      41553629   sdss5  mwmStar ... 0.97839606 .. 0.99388695 1.6908267         0\n"
     ]
    }
   ],
   "source": [
    "mwmStar = Table.read(mwmStar_filename, hdu=1)\n",
    "\n",
    "print(mwmStar)\n",
    "\n",
    "obs_wl = mwmStar[\"wavelength\"][0]\n",
    "obs_flux = mwmStar[\"flux\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370633fc-120c-4d19-afce-02c47a6f4a39",
   "metadata": {},
   "source": [
    "Now we can load the astraStar file and grab the model flux. The structure is purposefully similar to the mwmStar file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95fed49f-743a-46c6-bd99-fe7b2aafa9fc",
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    "tags": []
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   "source": [
    "astraStar = Table.read(astraStar_filename, hdu=1)\n",
    "model_flux = astraStar[\"model_flux\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfd53583-823a-4a0c-aaad-a78b5bc97889",
   "metadata": {},
   "source": [
    "## Plot the data and model\n",
    "\n",
    "The model_flux, obs_flux, and obs_wl arrays are the same length, as one might expect.\n",
    "\n",
    "However, there is some kind of ringing going on at the blue end of the spectrum, which we'll mask out. Uncomment the other line to see it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7b54e5e-5ed2-4410-949b-684c3bada476",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'flux')"
      ]
     },
     "execution_count": 12,
     "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_wl > 3800\n",
    "\n",
    "plt.plot(obs_wl[mask], obs_flux[mask])\n",
    "plt.plot(obs_wl, model_flux, zorder=10)\n",
    "plt.xlabel(\"λ [Å]\")\n",
    "plt.ylabel(\"flux\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735c250a-4022-4a8a-9ee3-c9d6c3a6e752",
   "metadata": {},
   "source": [
    "### About this notebook\n",
    "\n",
    "This notebook was created to illustrate how to use an BOSS spectrum and model fit by Snow White\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."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}