{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1f70e2a6-abfd-49b1-b092-02affeb2f233",
   "metadata": {},
   "source": [
    "# Checking whether SDSS has a spectrum for me"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0130bd5f-ea21-42fa-ae5d-1ecd3e454992",
   "metadata": {},
   "source": [
    "## Learning goals\n",
    "\n",
    "Through this tutorial, you will learn:\n",
    "\n",
    " * How to use the \"allspec\" file either locally or on SciServer to check whether an object you are interested in has SDSS spectra.\n",
    " * How to track down the relevant SDSS-V BOSS (optical) parameters and spectrum for galaxies and quasars.\n",
    " * How to track down the relevant SDSS-V BOSS (optical) and APOGEE (infrared) parameters and spectrum for stars."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e854ec-1e34-40d0-ba63-f03ca94e7512",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "SDSS has observed spectra in several ways over the past 20+ years. Specifically, we have used the SDSS and BOSS optical spectrographs in single-fiber and integral-field modes, and we have used the two APOGEE spectrographs in single-fiber mode. Meanwhile, this data has been processed through a several pipelines and pipeline stages, and is processed into both individual visit and coadded spectra.\n",
    "\n",
    "To help users track down the relevant information for objects of interest, SDSS has created the `allspec` file. This file lists all spectra that SDSS has created, including all visit spectra and all coadds from every spectrograph. In this file, every object may appear once or more, if they have multiple spectra. \n",
    "\n",
    "Objects in SDSS are tracked through the `sdss_id` identifier. This is designed to correspond to unique objects on the sky, and in this notebook we will use it to identify the \"same object\" across different spectra."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d085989-2d66-4cca-a94d-83cc1427e84d",
   "metadata": {},
   "source": [
    "## Imports\n",
    "\n",
    "We will need several Python modules for our tasks:\n",
    "\n",
    " * `os` because we usually need it\n",
    " * `matplotlib.pyplot` for plotting\n",
    " * `matplotlib` because I'm persnickety about TeXing labels\n",
    " * `numpy` because we have to do things with numbers and arrays\n",
    " * `astropy.io.fits` to read in the allspec file\n",
    " * `fitsio` for efficiently reading very large files (spAll, specObjAll)\n",
    " * `astropy.coordinates` for spatial matching\n",
    " * `sdss_access` to track down files\n",
    "\n",
    " #### Caveats: \n",
    " If you are running this notebook on your own machine, there are few things you should make note of:\n",
    " \n",
    " 1. Some of the files loaded in this notebook are very large and use a lot of RAM\n",
    " 2. If you don't have a properly configured Tex distrubution, you may need to comment out the line `matplotlib.rcParams['text.usetex'] = True` in the imports cell.\n",
    " 3. The first time you check for a local file on your \"local\" \"SAS\", the filename may be created as `.fits` whereas the downloaded file may be `.fits.gz`. Re-running the cell fixes the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f4bc87f-200a-4e87-a52c-b924517f3a9d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import astropy.io.fits\n",
    "import astropy.coordinates\n",
    "import fitsio\n",
    "import sdss_access\n",
    "\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "matplotlib.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd1a60c-10e5-4da0-868a-82b1156a2938",
   "metadata": {},
   "source": [
    "## SDSS File Access\n",
    "\n",
    "`sdss_access` provides ways to track down SDSS files in the data structure. \n",
    "\n",
    "This notebook is perhaps easiest to run on SciServer. But if you are working on a machine without a full copy of the SDSS Science Archive Server (SAS), you can set it up to download and cache files locally. See https:://https://sdss-access.readthedocs.io for more information!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9243391c-2130-4bde-8d11-ea72a35a02ee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sdss_path = sdss_access.path.Path(release='dr19', verbose=True)\n",
    "access = sdss_access.Access(release='dr19', verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "886ffece-b5cf-4cd4-a9ed-fefba175a98c",
   "metadata": {},
   "source": [
    "## Finding and Reading the `allspec` file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23567a29-5808-402a-904d-b864badf4f5c",
   "metadata": {},
   "source": [
    "`sdss_path` gives us an easy way to find the path to the `allspec` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5702bab5-ccb7-4c90-ab48-623603bd77e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jdonor/sas/dr19/spectro/allspec/1.0.1/allspec-dr19-1.0.1.fits.gz\n"
     ]
    }
   ],
   "source": [
    "allspec_file = sdss_path.full('allspec', vers='1.0.1', release='dr19')\n",
    "\n",
    "if not sdss_path.exists('',full=allspec_file):\n",
    "    # if the file does not exist locally, this code will download the data.\n",
    "    access.remote()\n",
    "    access.add('allspec', vers='1.0.1', release='dr19')\n",
    "    access.set_stream()\n",
    "    access.commit()\n",
    "print(allspec_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e838f7e8-aca5-461b-9c19-ae3b42c09e25",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "allspec_hdus = astropy.io.fits.open(allspec_file)\n",
    "allspec = np.array(allspec_hdus[1].data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e60434e9-6547-400c-9b8e-3a6cca5e1167",
   "metadata": {},
   "source": [
    "## Matching by position to `allspec`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e937b269-bef7-46d5-b064-ecaba0aed4f5",
   "metadata": {},
   "source": [
    "Let's say we have some position of interest. Let's figure out if we have SDSS spectra of any sort near it!\n",
    "\n",
    "I like the location (ra, dec) = (177.78, -0.73), just because I do. Let's put this in the `astropy.coordinates.SkyCoord` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "946bb2a8-92ec-43f5-aefe-999e2d68cfaf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "center_ra = [103.40819321]\n",
    "center_dec = [-1.32137059392]\n",
    "center_coords = astropy.coordinates.SkyCoord(center_ra, center_dec, unit='deg', frame='icrs')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9046cd94-a430-4395-8b10-c8ce298e0b40",
   "metadata": {},
   "source": [
    "Each `sdss_id` has a specific RA and Dec, so we only need to match to the unique set of `sdss_id`s. We will put those coordinates into the `astropy` object too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10e1732c-bb31-46f7-90f9-eb46baf8ed00",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unique_sdss_id, unique_indx = np.unique(allspec['sdss_id'], return_index=True)\n",
    "unique_ra = allspec['ra'][unique_indx]\n",
    "unique_dec = allspec['dec'][unique_indx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d7769ed-9870-4817-a945-ec9ee8b4020f",
   "metadata": {},
   "source": [
    "There are a few NaNs as coordinates that need to be removed. This is because the RA and Dec of each row is associated with the spectrum, and sometimes (because the fiber is unplugged or a robot is broken) we don't actually know the RA and Dec of the spectrum. There are only a few of these and it is virtually always the right thing to ignore them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "23ba6c59-dff8-4caf-9c67-36c4c99d627d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "isfinite = np.isfinite(unique_ra) & np.isfinite(unique_dec)\n",
    "unique_indx = unique_indx[isfinite]\n",
    "unique_ra = unique_ra[isfinite]\n",
    "unique_dec = unique_dec[isfinite]\n",
    "unique_coords = astropy.coordinates.SkyCoord(unique_ra, unique_dec, unit='deg', frame='icrs')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e824437-ee46-4b81-ab65-22200611c97c",
   "metadata": {},
   "source": [
    "Now it is easy to match. Let's look at the closest one to our desired position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc3bc52d-37f6-43c6-85d8-f1440a6c758a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "indx, sep, s3 = unique_coords.match_to_catalog_sky(center_coords)\n",
    "sep = sep.value   # avoid units nonsense (\"value\" is in deg in this case)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "86f07312-987c-44c9-b191-eee4d413128a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "iminsep = np.argmin(sep)\n",
    "match_indx = unique_indx[iminsep]\n",
    "sdss_id = allspec['sdss_id'][match_indx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cceec22d-f61d-4ba4-afc9-74535251ed86",
   "metadata": {},
   "source": [
    "## Information in allspec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21bd2df7-0758-4805-9674-af01957e5123",
   "metadata": {},
   "source": [
    "Now that we have found a spectrum, we can take a look at the information in `allspec`. Below you can see several types of information:\n",
    "\n",
    " * The `allspec` and `multiplex` identifiers (just unique identifiers)\n",
    " * Information about the observatory, SDSS phase, and instrument\n",
    " * The `sdss_id` and `catalogid` of the target object.\n",
    " * Many columns of spectroscopic IDs. Only one set of identifiers will be filled with meaningful information.\n",
    " * The name and location of the spectrum file (useful!) and a link to SkyServer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ea324fb2-e53a-4972-a665-4b2d2028ce9e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "allspec_id :: b'sdss5-apo-boss-daily-v6_1_3-103427-59654-27021598031242844-74807581'\n",
      "multiplex_id :: b'sdss5-apo-boss-v6_1_3-103427-59654'\n",
      "sdss_phase :: 5\n",
      "observatory :: b'APO'\n",
      "instrument :: b'boss'\n",
      "sdss_id :: 74807581\n",
      "catalogid :: 27021598031242844\n",
      "fiberid :: -999\n",
      "ifudsgn :: -999\n",
      "plate :: -999\n",
      "fps_field :: 103427\n",
      "plate_or_fps_field :: 103427\n",
      "mjd :: 59654\n",
      "run2d :: b'v6_1_3'\n",
      "run1d :: b'v6_1_3'\n",
      "coadd :: b'daily'\n",
      "apred_vers :: b''\n",
      "drpver :: b''\n",
      "version :: b'v6_1_3'\n",
      "programname :: b'mwm_yso'\n",
      "survey :: b'MWM'\n",
      "sas_file :: b'spec-103427-59654-27021598031242844.fits'\n",
      "cas_url :: b'https://skyserver.sdss.org/public/VisualTools/explore/summary?sId=7480758101034275965400060103'\n",
      "sas_url :: b'https://data.sdss.org/sas/dr19/spectro/boss/redux/v6_1_3/spectra/lite/103427/59654/spec-103427-59654-27021598031242844.fits'\n",
      "ra :: 103.408290969\n",
      "dec :: -1.32150220553\n",
      "healpix :: 100755\n",
      "healpixgrp :: 100\n",
      "apogee_id :: b''\n",
      "apogee_field :: b''\n",
      "telescope :: b''\n",
      "file_spec :: b'specLite'\n",
      "apstar_id :: b''\n",
      "visit_id :: b''\n",
      "mangaid :: b''\n",
      "specobjid :: b'7480758101034275965400060103'\n"
     ]
    }
   ],
   "source": [
    "for n in allspec.dtype.names:\n",
    "    print(\"{n} :: {v}\".format(n=n, v=allspec[n][match_indx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc79f1b-7836-44ce-a28c-c28fea2aafa4",
   "metadata": {},
   "source": [
    "## Finding all spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f20eeeb5-27a1-4fe3-8fa8-4b2ff7a62c12",
   "metadata": {},
   "source": [
    "We can also find all of the spectra associated with the `sdss_id`. This is useful for objects with repeat spectroscopy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4bfa0bc-3ede-4f22-9242-3fa38ef30052",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "iallmatch = np.where(allspec['sdss_id'] == allspec['sdss_id'][match_indx])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a3e49c-8287-4d37-a312-c346fafd3e2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-09T21:03:57.516083Z",
     "iopub.status.busy": "2025-07-09T21:03:57.515339Z",
     "iopub.status.idle": "2025-07-09T21:03:59.339288Z",
     "shell.execute_reply": "2025-07-09T21:03:59.337072Z",
     "shell.execute_reply.started": "2025-07-09T21:03:57.516024Z"
    },
    "tags": []
   },
   "source": [
    "We can look at what these spectra actually are. It can take some getting used to to figure out this information. What the IDs tell us is:\n",
    "\n",
    " * There are three distinct BOSS spectra, which were taken during SDSS-V.\n",
    " * We can see that for each MJD (59654 through 59656) there is both a \"daily\" and an \"epoch\" coadd. Apparently, the BOSS pipeline considers (for this type of object) that two different days are two different epochs. In this case the \"daily\" and \"epoch\" coadds will be very very similar.\n",
    " * There are four distinct APOOGEE spectra, and one coadd."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c6ca0f9e-b9e4-43db-a3da-62dad8042e49",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[b'sdss5-apo-boss-daily-v6_1_3-103427-59654-27021598031242844-74807581'\n",
      " b'sdss5-apo-boss-daily-v6_1_3-103427-59655-27021598031242844-74807581'\n",
      " b'sdss5-apo-boss-daily-v6_1_3-103427-59656-27021598031242844-74807581'\n",
      " b'sdss5-apo-boss-epoch-v6_1_3-103427-59654-27021598031242844-74807581'\n",
      " b'sdss5-apo-boss-epoch-v6_1_3-103427-59655-27021598031242844-74807581'\n",
      " b'sdss5-apo-boss-epoch-v6_1_3-103427-59656-27021598031242844-74807581'\n",
      " b'sdss5-apo-apogee-1.3-4054-59657-185'\n",
      " b'sdss5-apo-apogee-1.3-4003-59654-185'\n",
      " b'sdss5-apo-apogee-1.3-4053-59657-144'\n",
      " b'sdss5-apo-apogee-1.3-4036-59655-185'\n",
      " b'sdss5-apo-apogee-1.3-2M06533798-0119171']\n"
     ]
    }
   ],
   "source": [
    "print(allspec['allspec_id'][iallmatch])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c296130-0879-468a-a4ae-306c9ab5b77b",
   "metadata": {},
   "source": [
    "## Looking at the spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39274240-3b5e-4c51-9211-4b21175fc533",
   "metadata": {},
   "source": [
    "We can track down the spectra on disk quite easily. The `sas_url` tells us the path. We just need to change the root of the tree to a local file path as follows.\n",
    "\n",
    "If the data don't already exist on disk (e.g., if you're not running this notebook on SciServer), we can download the data easily with `sdss_access`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49d6c33e-cc63-4ec0-af82-9a2d3c9907b1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "url_root = 'https://data.sdss.org/sas'\n",
    "local_root = os.getenv('SAS_BASE_DIR')\n",
    "spectrum_files = list()\n",
    "download_files = list()\n",
    "\n",
    "for p in allspec[\"sas_url\"][iallmatch]:\n",
    "    local_path = p.decode().replace(url_root, local_root)\n",
    "    spectrum_files.append(local_path)\n",
    "    if not os.path.exists(local_path):\n",
    "        download_files.append(local_path)\n",
    "\n",
    "if len(download_files) > 0:\n",
    "    print(\"fetching files, please stand by\")\n",
    "    access.remote()\n",
    "    for local_path in download_files:\n",
    "        access.add_file(local_path, input_type='filepath')\n",
    "\n",
    "    access.set_stream()\n",
    "\n",
    "    # disable follow_symlinks\n",
    "    access.commit(follow_symlinks=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827b0cec-f98b-46b9-9bf8-c0fd9dbc8d26",
   "metadata": {},
   "source": [
    "Here then are the paths in the local SAS directory structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "542986a1-67b7-4525-8ba7-008af82e99d7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jdonor/sas/dr19/spectro/boss/redux/v6_1_3/spectra/lite/103427/59654/spec-103427-59654-27021598031242844.fits\n",
      "/Users/jdonor/sas/dr19/spectro/boss/redux/v6_1_3/spectra/lite/103427/59655/spec-103427-59655-27021598031242844.fits\n",
      "/Users/jdonor/sas/dr19/spectro/boss/redux/v6_1_3/spectra/lite/103427/59656/spec-103427-59656-27021598031242844.fits\n",
      "/Users/jdonor/sas/dr19/spectro/boss/redux/v6_1_3/epoch/spectra/lite/103427/59654/spec-103427-59654-27021598031242844.fits\n",
      "/Users/jdonor/sas/dr19/spectro/boss/redux/v6_1_3/epoch/spectra/lite/103427/59655/spec-103427-59655-27021598031242844.fits\n",
      "/Users/jdonor/sas/dr19/spectro/boss/redux/v6_1_3/epoch/spectra/lite/103427/59656/spec-103427-59656-27021598031242844.fits\n",
      "/Users/jdonor/sas/dr19/spectro/apogee/redux/1.3/visit/apo25m/103442/4054/59657/apVisit-1.3-apo25m-4054-59657-185.fits\n",
      "/Users/jdonor/sas/dr19/spectro/apogee/redux/1.3/visit/apo25m/103442/4003/59654/apVisit-1.3-apo25m-4003-59654-185.fits\n",
      "/Users/jdonor/sas/dr19/spectro/apogee/redux/1.3/visit/apo25m/103427/4053/59657/apVisit-1.3-apo25m-4053-59657-144.fits\n",
      "/Users/jdonor/sas/dr19/spectro/apogee/redux/1.3/visit/apo25m/103442/4036/59655/apVisit-1.3-apo25m-4036-59655-185.fits\n",
      "/Users/jdonor/sas/dr19/spectro/apogee/redux/1.3/stars/apo25m/100/100755/apStar-1.3-apo25m-2M06533798-0119171.fits\n"
     ]
    }
   ],
   "source": [
    "for f in spectrum_files:\n",
    "    print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fc2a516-4689-4b58-8711-e303da904550",
   "metadata": {},
   "source": [
    "We can open one of the BOSS files up to see what it has in it. We'll first just look at what the HDUs are called."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81bdb5f5-aeb6-4b32-a6ab-0bf4ea4d4281",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "spec_hdulist = astropy.io.fits.open(spectrum_files[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c0309f85-aca6-407a-9c6d-870f0e25e749",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HDU0\n",
      "COADD\n",
      "SPALL\n",
      "ZALL\n",
      "ZLINE\n"
     ]
    }
   ],
   "source": [
    "for ihdu, spec_hdu in enumerate(spec_hdulist):\n",
    "    if('extname' in spec_hdu.header):\n",
    "        print(spec_hdu.header['extname'])\n",
    "    else:\n",
    "        print(\"HDU{i}\".format(i=ihdu))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35f603b2-602e-42ad-b2c6-b3baee34a43b",
   "metadata": {},
   "source": [
    "It looks like \"COADD\" actuall has the spectrum. This is a table, and the columns have the fluxes, wavelengths, masks, etc. Really you should look at the data model at: https://data.sdss.org/datamodel/files/BOSS_SPECTRO_REDUX/RUN2D/spectra/PLATE4/spec.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b260e118-0bb2-4733-a1ac-693d734dc6db",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "coadd = np.array(spec_hdulist['COADD'].data)\n",
    "coadd_header = spec_hdulist['COADD'].header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5cbed391-3db0-4ba2-b409-8223f267782f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('FLUX', 'LOGLAM', 'IVAR', 'AND_MASK', 'OR_MASK', 'WDISP', 'SKY', 'MODEL', 'WRESL')\n"
     ]
    }
   ],
   "source": [
    "print(coadd.dtype.names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "598f410c-f896-473b-9bbc-4a86a00b12e1",
   "metadata": {},
   "source": [
    "But if we want to know the units of FLUX or LOGLAM we can check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "94452eb8-1bbd-4438-ae33-8a56d6d7f2f7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10^-17 ergs/s/cm^2/Angs\n",
      "log10(Angs)\n"
     ]
    }
   ],
   "source": [
    "print(coadd_header['TUNIT1'])\n",
    "print(coadd_header['TUNIT2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2c74d6c-7369-477d-a2a5-892c3494536a",
   "metadata": {},
   "source": [
    "Now we can plot and label our plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "54eb9ef2-aa7d-412b-8f3c-a4ea8d5ad10e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$f_\\\\lambda$ \\\\rm ($10^{-17}$ erg cm$^{-2}$ s$^{-1}$ \\\\AA$^{-1}$)')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's set sensible limits; the model is better than the data for that\n",
    "gd = coadd['IVAR'] > 0\n",
    "gdmax = coadd['MODEL'][gd].max()\n",
    "\n",
    "plt.plot(10.**coadd['LOGLAM'], coadd['FLUX'], linewidth=1, color='black')\n",
    "plt.plot(10.**coadd['LOGLAM'], coadd['MODEL'], linewidth=1, color='red')\n",
    "plt.ylim(np.array([-0.05, 1.3]) * gdmax)\n",
    "\n",
    "plt.xlabel(r'\\rm Wavelength (Angstroms)')\n",
    "plt.ylabel(r'$f_\\lambda$ \\rm ($10^{-17}$ erg cm$^{-2}$ s$^{-1}$ \\AA$^{-1}$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9c7b59a-0eb6-4b0a-9965-3479a548f3ed",
   "metadata": {},
   "source": [
    "We have three visits so we can compare them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4da11feb-5de0-4724-8cca-ac497f655796",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "boss_visits = []\n",
    "mjds = []\n",
    "for file in spectrum_files[0:3]:\n",
    "    spec_hdulist = astropy.io.fits.open(file)\n",
    "    visit = np.array(spec_hdulist['COADD'].data)\n",
    "    boss_visits.append(visit)\n",
    "    mjds.append(spec_hdulist[0].header['MJD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "deb3757f-bf82-472d-881a-1fa3369c027c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11a6007a0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's set sensible limits; the model is better than the data for that\n",
    "allmax = 0.0\n",
    "for mjd, visit in zip(mjds, boss_visits):\n",
    "    gd = visit['IVAR'] > 0\n",
    "    gdmax = visit['MODEL'][gd].max()\n",
    "    if(gdmax > allmax):\n",
    "        allmax = gdmax\n",
    "\n",
    "    plt.plot(10.**visit['LOGLAM'], visit['FLUX'], linewidth=1, label=str(mjd))\n",
    "\n",
    "plt.ylim(np.array([-0.05, 1.3]) * allmax)\n",
    "plt.xlabel(r'\\rm Wavelength (Angstroms)')\n",
    "plt.ylabel(r'$f_\\lambda$ \\rm ($10^{-17}$ erg cm$^{-2}$ s$^{-1}$ \\AA$^{-1}$)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231d93d5-601d-4ff5-b20a-0c3c2a3a57c5",
   "metadata": {},
   "source": [
    "This is either exciting or we shouldn't trust the spectrophotometry. In this case, we should not trust the spectrophotometry. This is a deep subject for fiber spectroscopy, but in general you do need to be careful and not leap to conclusions. In this case, these are fairly early SDSS-V observations and likely the fiber positioning is not good enough for good spectrophotometry. In addition, the observing mode we used had fewer spectrophotometric standards (for example, the Reverberation Mapping plates have substantially more)>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31737e08-f933-419b-af96-3f908f5cf2a9",
   "metadata": {},
   "source": [
    "We can also look at the APOGEE spectra. Again we can check the HDUs of one of the files in our list associated with an APOGEE visit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ccd9ab22-3b0b-4e27-a462-a3b01bf29144",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HDU0\n",
      "FLUX\n",
      "ERROR\n",
      "MASK\n",
      "WAVELENGTH\n",
      "SKY FLUX\n",
      "SKY ERROR\n",
      "TELLURIC\n",
      "TELLURIC ERROR\n",
      "WAVE COEFFICIENTS\n",
      "LSF COEFFICIENTS\n",
      "HDU11\n"
     ]
    }
   ],
   "source": [
    "spec_hdulist = astropy.io.fits.open(spectrum_files[6])\n",
    "for ihdu, spec_hdu in enumerate(spec_hdulist):\n",
    "    if('extname' in spec_hdu.header):\n",
    "        print(spec_hdu.header['extname'])\n",
    "    else:\n",
    "        print(\"HDU{i}\".format(i=ihdu))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e25773e-1295-458b-806e-8010ed584e0c",
   "metadata": {},
   "source": [
    "And we can check the units of the flux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c98dddcc-30a7-438e-91d5-4a2f0dbad5a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flux (10^-17 erg/s/cm^2/Ang)\n"
     ]
    }
   ],
   "source": [
    "print(spec_hdulist['FLUX'].header['BUNIT'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62feb4d-690f-4789-8d3a-260e0ab564d7",
   "metadata": {},
   "source": [
    "In the case of APOGEE the spectra are a lot harder to interpret by eye! Most of the information is in the fine scale details. You can zoom in and explore with the notebook to try to make sense of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4a7255ea-cf68-4b3c-bc12-ca75ff1895bd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$f_\\\\lambda$ \\\\rm ($10^{-17}$ erg cm$^{-2}$ s$^{-1}$ \\\\AA$^{-1}$)')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apogee_visits = []\n",
    "mjds = []\n",
    "maxmed = 0.\n",
    "colors = ['magenta', 'green', 'black', 'cyan']\n",
    "for color, file in zip(colors, spectrum_files[6:10]):\n",
    "    spec_hdulist = astropy.io.fits.open(file)\n",
    "    wave = spec_hdulist['WAVELENGTH'].data\n",
    "    flux = spec_hdulist['FLUX'].data\n",
    "    med = np.median(flux)\n",
    "    if(med > maxmed):\n",
    "        maxmed = med\n",
    "    for cam in range(0, 3):\n",
    "        plt.plot(wave[cam, :], flux[cam, :], color=color, linewidth=1, alpha=0.5)\n",
    "\n",
    "ymax = maxmed * 1.8\n",
    "plt.ylim(np.array([- 0.05, 1.1]) * ymax)\n",
    "\n",
    "# Uncomment to zoom in in wavelength\n",
    "#plt.xlim([16035, 16070])\n",
    "\n",
    "plt.xlabel(r'\\rm Wavelength (Angstroms)')\n",
    "plt.ylabel(r'$f_\\lambda$ \\rm ($10^{-17}$ erg cm$^{-2}$ s$^{-1}$ \\AA$^{-1}$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e27013-0bb9-4d96-a058-346e9d0f1007",
   "metadata": {},
   "source": [
    "## Tracking down parameters (BOSS DRP)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57f8e4a-3d86-4b35-b1c0-d26b0ccfc3a8",
   "metadata": {},
   "source": [
    "Of course, you don't always want to go back to the spectrum! You may often find it easier to just use the catalogs we have. You can do so by matching to our catalogs by `sdss_id`.\n",
    "\n",
    "For the BOSS DRP, this is useful for looking up general classifications and redshifts, from the `spAll` file. We can again use `sdss_path` to find it, using the `run2d` version associated with DR19."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2ae82d8e-e2fa-4976-a9e6-f3dd4cb2733c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jdonor/sas/dr19/spectro/boss/redux/v6_1_3/spAll-v6_1_3.fits.gz\n"
     ]
    }
   ],
   "source": [
    "spall_file = sdss_path.full('spAll', run2d='v6_1_3')\n",
    "\n",
    "if not sdss_path.exists('',full=spall_file):\n",
    "    # if the file does not exist locally, this code will download the data.\n",
    "    access.remote()\n",
    "    access.add('spAll', run2d='v6_1_3')\n",
    "    access.set_stream()\n",
    "    access.commit()\n",
    "print(spall_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85674205-fe0a-4c0a-ae9d-7e12344fb0e5",
   "metadata": {},
   "source": [
    "We don't want to read in all of the columns, it is pretty big. Let's just read in a few important ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a98d977d-d9b9-402b-9386-6e4bb24a6f15",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "spall_columns = ['SDSS_ID', 'CARTON_TO_TARGET_PK', 'MJD', 'CLASS', 'SUBCLASS', 'Z', 'ZWARNING']\n",
    "spall = fitsio.read(spall_file, columns=spall_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebcdab0d-e9d0-452c-98bb-2741c4c60a4f",
   "metadata": {},
   "source": [
    "Now we can just find the entries associated with the `sdss_id` we have identified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1d3a8c25-c611-48af-beb2-12dbbcf905ac",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ispall = np.where(spall['SDSS_ID'] == sdss_id)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de13be1-dd06-45d2-8a3d-dfeeace525b7",
   "metadata": {},
   "source": [
    "We can output the information for each of the three spectra in the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b12c43b7-002f-4ce6-ad14-71ff93ca8fa6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MJD: 59654, CLASS: STAR, Z : 0.00025740719866007566\n",
      "MJD: 59655, CLASS: STAR, Z : 0.00021845850278623402\n",
      "MJD: 59656, CLASS: STAR, Z : 0.00021172482229303569\n"
     ]
    }
   ],
   "source": [
    "for i in ispall:\n",
    "    print(\"MJD: {m}, CLASS: {c}, Z : {z}\".format(m=spall['MJD'][i], c=spall['CLASS'][i], z=spall['Z'][i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c1d2365-05b2-468a-83ef-bd50ca01e47c",
   "metadata": {},
   "source": [
    "If you have a set of more than one `sdss_id`'s you can use `np.intersect1d()` to easily find the entries you need, or convert to `astropy.table.Table` objects and use the `join()` method for those."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b662efd-efcb-41d2-9329-43b4911e8fd9",
   "metadata": {},
   "source": [
    "## Tracking down parameters (APOGEE DRP)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6476f327-c7bd-4dde-bd94-f2aba94418b4",
   "metadata": {},
   "source": [
    "For the APOGEE DRP, there is a similar summary file. If you are looking at the DRP, you are most likely interested in the individual visits, so the `allVisit` file is what you want. \n",
    "\n",
    "In this case you do need to know which telescope the observations are in; if you are looking at many objects, this will involve some logic based on what `allspec` tells you. In our case we will just look. For the APOGEE spectra, it specifies the telescope name (for BOSS it does not, because the data is not organized by telescope)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f79ad7e5-b9f5-4ae8-b3b2-d866f5bad14f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[b'' b'' b'' b'' b'' b'' b'apo25m' b'apo25m' b'apo25m' b'apo25m' b'apo25m']\n"
     ]
    }
   ],
   "source": [
    "print(allspec['telescope'][iallmatch])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59148c9b-f780-4cc4-afc5-e5932eed16e5",
   "metadata": {},
   "source": [
    "Then `sdss_path` tells us where the file is, using the `apred` reduction version associated with DR19:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "69485ed6-ff21-4d5a-be40-eb9bc3bc65d3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jdonor/sas/dr19/spectro/apogee/redux/1.3/summary/allVisit-1.3-apo25m.fits\n"
     ]
    }
   ],
   "source": [
    "allvisit_file = sdss_path.full('allVisit', telescope='apo25m', apred='1.3')\n",
    "\n",
    "if not sdss_path.exists('',full=allvisit_file):\n",
    "    # if the file does not exist locally, this code will download the data.\n",
    "    access.remote()\n",
    "    access.add('allVisit', telescope='apo25m', apred='1.3')\n",
    "    access.set_stream()\n",
    "    access.commit()\n",
    "print(allvisit_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd7871a-6f62-46b9-83a9-038ed8dd5af4",
   "metadata": {},
   "source": [
    "Now we can read it in, search by `sdss_id`, and look (for example) at the barycentric radial velocities for each visit. Note that the formal errors reported somewhat underestimate the true errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "74717abd-9929-4ec6-a873-124a7385a1c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "allvisit = fitsio.read(allvisit_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a2f08039-24dd-43a6-a6f3-799ec09d9833",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "iallvisit = np.where(allvisit['sdss_id'] == sdss_id)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "012b240a-6be2-436f-ad51-90f581c65618",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MJD: 59657, vrad: 72.180 +/ 0.155 km/s\n",
      "MJD: 59654, vrad: 72.387 +/ 0.166 km/s\n",
      "MJD: 59657, vrad: 72.787 +/  0.11 km/s\n",
      "MJD: 59655, vrad: 72.690 +/ 0.201 km/s\n"
     ]
    }
   ],
   "source": [
    "for i in iallvisit:\n",
    "    print(\"MJD: {m}, vrad: {v:5.3f} +/ {e:5.3} km/s\".format(m=allvisit['mjd'][i], v=allvisit['vrad'][i], e=allvisit['vrelerr'][i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19aa95de-9e37-4489-a8f4-d7833df17805",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-10T16:43:29.193664Z",
     "iopub.status.busy": "2025-07-10T16:43:29.192818Z",
     "iopub.status.idle": "2025-07-10T16:43:29.203755Z",
     "shell.execute_reply": "2025-07-10T16:43:29.201638Z",
     "shell.execute_reply.started": "2025-07-10T16:43:29.193604Z"
    },
    "tags": []
   },
   "source": [
    "## Tracking down parameters (Astra results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d5e876-f8c0-4553-9ac0-62be61b56cf0",
   "metadata": {},
   "source": [
    "Finally, since this is a star, there will be results in the Astra files. This is a whole other topic, but basically any Astra result file will also have `sdss_id` in it. Note that there are Astra results based on BOSS and on APOGEE, depending on the particular pipeline in the Astra framework being used.\n",
    "\n",
    "There is a summary file with all processed stars (one row per star). Below we just read it in and show some basic results about the star.\n",
    "\n",
    "Note that in the \"lite\" file, the parameters are a best guess based on the pipelines that were run, provided for convenience, but it usually is best to be performing your analysis based on the results of one of the specific pipelines.\n",
    "\n",
    "See the Astra web documentation for more information: https://sdss.org/dr19/mwm/astra/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "87e72767-abee-49f9-9d0a-91209ebefa8c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jdonor/sas/dr19/spectro/astra/0.6.0/summary/astraMWMLite-0.6.0.fits.gz\n"
     ]
    }
   ],
   "source": [
    "mwmlite_file = sdss_path.full('astraMWMLite', v_astra='0.6.0')\n",
    "\n",
    "if not sdss_path.exists('',full=mwmlite_file):\n",
    "    # if the file does not exist locally, this code will download the data.\n",
    "    access.remote()\n",
    "    access.add('astraMWMLite', v_astra='0.6.0')\n",
    "    access.set_stream()\n",
    "    access.commit()\n",
    "print(mwmlite_file)\n",
    "\n",
    "mwmlite = fitsio.read(mwmlite_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e4aacd8f-3692-425f-892f-42d463d5cebf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "imwm = np.where(mwmlite['sdss_id'] == sdss_id)[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d1b0aaa0-5d4b-43ac-9dc2-6b045ece2e3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Teff: 5665.25 K, log g:  3.79, [Fe/H]: -0.35\n"
     ]
    }
   ],
   "source": [
    "print(\"Teff: {t:5.2f} K, log g: {l:5.2f}, [Fe/H]: {f:5.2f}\".format(t=mwmlite['teff'][imwm], l=mwmlite['logg'][imwm], f=mwmlite['fe_h'][imwm]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d04501-5c5e-4bae-96af-059c9ffc78aa",
   "metadata": {},
   "source": [
    "## A note on CASJobs\n",
    "\n",
    "The `allspec` table is also available in CASJobs in the DR19 context, as are the BOSS DRP, APOGEE DRP, and Astra results. So using SQL you can perform many of the same operations using joins with `sdss_id`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8c7ed5d-fd4a-41f4-aa5d-e73adef6192e",
   "metadata": {},
   "source": [
    "## About this notebook\n",
    "\n",
    "This notebook was created to illustrate how to use `allspec` in DR19.\n",
    "\n",
    "Authors: Michael R. Blanton\n",
    "\n",
    "Last Update: 2025 July 10\n",
    "\n",
    "If you use astropy for published research, please cite the authors. Follow this link for more information about citing astropy: https://www.astropy.org/acknowledging.html\n",
    "\n",
    "And of course please cite SDSS when you use our data!\n"
   ]
  }
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