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    "# Exploring and Plotting BOSS Spectra with their Alternative Model Fits\n",
    "\n",
    "## Learning Goals\n",
    "By the end of this tutorial, you will:\n",
    "- Know how to retrieve SDSS spectrum files and alternive model fits (as fit by IDLSPEC2D spec1d pipeline)\n",
    "\n",
    "## Introduction\n",
    "There are some circumstances where the BOSS spec1d pipeline does not provide a very good redshift or where it could not decide between two redshift values. However, it fits a large number of templates and provides the best fit result for each template, as well as their reduced $\\chi^2$. The resultingredshifts (and corresponding models) of all of the other fits can be retreived using the ZALL extension in the specFull/specLite files or the spZall files (and the model template files stored within the IDLspec2D repository). Here we discuss how to retreive the alternative model redshift, and build corresponding the synthetic model spectra from the templates files.\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 files and tables (and optionally used to download the supplemental files)\n",
    "- `os` used for accessing environmental variables (optional if not using astropy to download the supplemental files)\n",
    "- `sdss_access` for download the sdss data and building the file paths (this can be manually down if desired, but this is not shown here)\n",
    "\n",
    "Missing packages can be installed via `pip install numpy matplotlib astropy 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 sdss-tree`"
   ]
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    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.table import Table, Row\n",
    "from astropy.io import fits\n",
    "from astropy.utils.data import download_file\n",
    "import os\n",
    "\n",
    "from sdss_access import Access\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd592590",
   "metadata": {},
   "source": [
    "## Obtaining/Building the Alternative Synthetic Model Spectra \n",
    "The Alternative Synthetic Model Spectra are not stored in any of the standard pipeline products. However, the parameters required to build them (using the model templates provided in the IDLspec2D repositiory) are stored within pipeline outputs. Below is the python port of the IDL function of the same name (from IDLSPEC2D; IDLSPEC2D > v6_2_1 includes this python function, but it was not included before that).\n",
    "\n",
    "The function, synthspec, takes a row of the spZall file, the log-lambda array of the target, and optionally the location of the templates, and returns the model spectra evaluated to the loglamda wavelenth grid of the target. If the eigendir (location of templates) is not defined, it first tries to use the IDLSPEC2D_DIR environmental variable to find them, and if thats not set, then it downloads the required template from the Github repo. "
   ]
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    "def synthspec(zans, loglam, eigendir=None):\n",
    "    \"\"\"\n",
    "    Generate a synthetic spectrum using SDSS templates and redshifted eigenspectra. \n",
    "    This is a python port of the idl (IDLSPEC2D) function of the same name\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    zans : astropy.table.Row or dict-like\n",
    "        A row from an Astropy Table (typically from SDSS fitting output), containing:\n",
    "        - 'TFILE': template filename\n",
    "        - 'TCOLUMN': array of column indices used in the eigentemplates\n",
    "        - 'NPOLY': degree of additive polynomial terms used in the fit\n",
    "        - 'THETA': coefficients used to combine eigentemplates and polynomial terms\n",
    "        - 'Z': redshift of the object\n",
    "\n",
    "    loglam : np.ndarray\n",
    "        Array of log10(wavelength) values to which the spectrum should be interpolated.\n",
    "\n",
    "    eigendir : str, optional\n",
    "        Local path to the eigentemplates directory. If not provided, the templates will be downloaded\n",
    "        from the SDSS GitHub repository.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    newflux : np.ndarray\n",
    "        The synthetic flux interpolated to the input `loglam` grid, accounting for redshift.\n",
    "        If no template is available, returns an array of zeros with the same length as `loglam`.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    - If the template file (`TFILE`) is not specified or empty, the function returns a zero flux array.\n",
    "    - Polynomial terms up to degree `NPOLY` are optionally included in the spectrum model.\n",
    "    - The final model is redshifted using the provided `Z` value and interpolated to the desired loglam grid.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    if eigendir is None:\n",
    "        # Try using the local IDLSPEC2D to get templates\n",
    "        eigendir = os.getenv('IDLSPEC2D_DIR', default=None)\n",
    "        if eigendir is not None: \n",
    "            eigendir = os.path.join(eigendir,'templates')\n",
    "\n",
    "    tfile = str(zans['TFILE']).strip()\n",
    "    if tfile == '':\n",
    "        print('No Template')\n",
    "        return np.zeros(len(loglam), dtype=np.float32)\n",
    "        \n",
    "    run1d = str(zans['RUN1D']).strip()\n",
    "\n",
    "    if eigendir is not None:\n",
    "        # Use local templates \n",
    "        model = os.path.join(eigendir, tfile)\n",
    "    else:\n",
    "        # Download requested template file\n",
    "        try:\n",
    "            baseurl = f'https://raw.githubusercontent.com/sdss/idlspec2d/refs/tags/{run1d}/templates/{tfile}'\n",
    "            model = download_file(baseurl, cache=True, pkgname='boss_drp')\n",
    "        except:\n",
    "            try:\n",
    "                baseurl = f'https://raw.githubusercontent.com/sdss/idlspec2d/refs/heads/{run1d}/templates/{tfile}'\n",
    "                model = download_file(baseurl, cache=True, pkgname='boss_drp')\n",
    "            except:\n",
    "                baseurl = f'https://raw.githubusercontent.com/sdss/idlspec2d/refs/heads/master/templates/{tfile}'\n",
    "                model = download_file(baseurl, cache=True, pkgname='boss_drp')                \n",
    "    try:\n",
    "        with fits.open(model, ignore_missing_end=True) as hdul:\n",
    "            # Read the Template file\n",
    "            starflux = hdul[0].data\n",
    "            shdr = hdul[0].header\n",
    "            has_data = starflux is not None and starflux.size > 1\n",
    "            if not has_data:\n",
    "                print('Invalid Template File')\n",
    "                return np.zeros(len(loglam), dtype=np.float32)\n",
    "\n",
    "            try:\n",
    "                starloglam0 = shdr['COEFF0']\n",
    "                stardloglam = shdr['COEFF1']\n",
    "            except:\n",
    "                print('Missing Wavelenth Coeffients in Template file')\n",
    "                return np.zeros(len(loglam), dtype=np.float32)\n",
    "    except Exception as e:\n",
    "        print('Error reading Template')\n",
    "        print(f'{type(e).__name__}: {e}')\n",
    "        return np.zeros(len(loglam), dtype=np.float32)\n",
    "\n",
    "    # Reshape and select relevant columns\n",
    "    starflux = starflux.T if starflux.ndim == 2 else starflux[:, np.newaxis]\n",
    "    starflux = starflux[:, np.array(zans['TCOLUMN'])[np.where(zans['TCOLUMN'] != -1)[0]]]\n",
    "\n",
    "    # Add polynomial terms if required\n",
    "    if zans['NPOLY'] != 0:\n",
    "        x = np.arange(starflux.shape[0], dtype=np.float32) / starflux.shape[0]\n",
    "        pa =  np.vstack([x**i for i in range(zans['NPOLY'])]).T  # shape (npix, npoly)\n",
    "        starflux = np.hstack([starflux, pa])\n",
    "\n",
    "    # Apply coefficients to get synthetic flux\n",
    "    synflux = starflux @ zans['THETA'][:starflux.shape[1]]\n",
    "    \n",
    "    # Build loglam grid for template\n",
    "    starloglam = starloglam0 + np.arange(starflux.shape[0]) * stardloglam\n",
    "\n",
    "    # Redshift and interpolate\n",
    "    newflux = np.interp(loglam, starloglam + np.log10(1 + zans['Z']), synflux)\n",
    "\n",
    "    return newflux"
   ]
  },
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   "cell_type": "markdown",
   "id": "bc148c5a-61e4-4ea7-867f-32272e6d2e0c",
   "metadata": {},
   "source": [
    "## Obtaining and Plotting the SDSS-V Spectra with Alternative model\n",
    "Below is an example of obtaining, reading, and plotting an SDSS-V (RUN2D >= 6.0.1) Spectra file with both its primary and alternative model.\n",
    "\n",
    "If running this notebook on sciserver, `access.remote` may fail. Comment out `access.remote` through `access.reset` and just use `access.full` to get the local file path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4851a036",
   "metadata": {},
   "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",
      "/uufs/chpc.utah.edu/common/home/sdss50/dr19/spectro/boss/redux/v6_1_3/spectra/lite/101202/60095/spec-101202-60095-27021597842439106.fits Exists: True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "catalogid = 27021597842439106\n",
    "mjd = 60095\n",
    "fieldid = 101202\n",
    "\n",
    "access = Access(release='DR19', verbose=False)\n",
    "run2d = 'v6_1_3' # The Run2d version of the BOSS Pipeline used for DR19\n",
    "\n",
    "access.remote()\n",
    "access.add('specLite', run2d=run2d, fieldid = fieldid, mjd = mjd, catalogid=catalogid)\n",
    "access.set_stream()\n",
    "access.commit()\n",
    "access.reset()\n",
    "\n",
    "specFile = access.full('specLite', run2d=run2d, fieldid=fieldid, mjd = mjd, catalogid=catalogid)\n",
    "print(f\"{specFile} Exists: {access.exists('',full=specFile)}\")\n"
   ]
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       "<div><i>Table length=134</i>\n",
       "<table id=\"table23434519063248\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>FIELD</th><th>TILE</th><th>MJD</th><th>TARGET_INDEX</th><th>CATALOGID</th><th>SDSS_ID</th><th>FIBERID_LIST</th><th>RUN2D</th><th>RUN1D</th><th>OBJTYPE</th><th>FIBER_RA</th><th>FIBER_DEC</th><th>PLUG_RA</th><th>PLUG_DEC</th><th>CLASS</th><th>SUBCLASS</th><th>Z</th><th>Z_ERR</th><th>RCHI2</th><th>DOF</th><th>RCHI2DIFF</th><th>TFILE</th><th>TCOLUMN</th><th>NPOLY</th><th>THETA</th><th>THETA_COVAR</th><th>VDISP</th><th>VDISP_ERR</th><th>VDISPZ</th><th>VDISPZ_ERR</th><th>VDISPCHI2</th><th>VDISPNPIX</th><th>VDISPDOF</th><th>WAVEMIN</th><th>WAVEMAX</th><th>WCOVERAGE</th><th>ZWARNING</th><th>SN_MEDIAN</th><th>SN_MEDIAN_ALL</th><th>CHI68P</th><th>FRACNSIGMA</th><th>FRACNSIGHI</th><th>FRACNSIGLO</th><th>SPECTROFLUX</th><th>SPECTROFLUX_IVAR</th><th>SPECTROSYNFLUX</th><th>SPECTROSYNFLUX_IVAR</th><th>SPECTROSKYFLUX</th><th>ANYANDMASK</th><th>ANYORMASK</th><th>SPEC1_G</th><th>SPEC1_R</th><th>SPEC1_I</th><th>SPEC2_G</th><th>SPEC2_R</th><th>SPEC2_I</th></tr></thead>\n",
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       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>QSO</td><td></td><td>1.0649701</td><td>0.0005128066</td><td>3.1263814</td><td>4513</td><td>0.00699234</td><td>spEigenQSO-58596.fits</td><td>0 .. 9</td><td>0</td><td>0.014231999 .. 0.046974313</td><td>1.2605423e-09 .. 3.007719e-06</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>4</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>QSO</td><td></td><td>1.2176032</td><td>0.00033940477</td><td>3.1333737</td><td>4513</td><td>0.030988216</td><td>spEigenQSO-58596.fits</td><td>0 .. 9</td><td>0</td><td>0.0124671785 .. 0.002895194</td><td>8.0286144e-10 .. 3.7565494e-06</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>4</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>QSO</td><td></td><td>1.6645188</td><td>0.00027568208</td><td>3.164362</td><td>4513</td><td>0.024287462</td><td>spEigenQSO-58596.fits</td><td>0 .. 9</td><td>0</td><td>0.008893823 .. 0.01691912</td><td>1.7290694e-10 .. 3.4429695e-06</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>4</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>QSO</td><td></td><td>1.7290903</td><td>0.0006354796</td><td>3.1886494</td><td>4513</td><td>0.04216528</td><td>spEigenQSO-58596.fits</td><td>0 .. 9</td><td>0</td><td>0.008159752 .. 0.032425806</td><td>1.4965262e-10 .. 2.8830323e-06</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>0</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>GALAXY</td><td></td><td>0.12214215</td><td>9.891258e-05</td><td>3.2308147</td><td>4516</td><td>0.09958577</td><td>spEigenGal-56436.fits</td><td>0 .. -1</td><td>3</td><td>0.032829024 .. 0.0</td><td>1.7460961e-05 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>0</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>GALAXY</td><td></td><td>0.7668193</td><td>0.00018733462</td><td>3.3304005</td><td>4516</td><td>0.01957202</td><td>spEigenGal-56436.fits</td><td>0 .. -1</td><td>3</td><td>0.022821821 .. 0.0</td><td>1.5428457e-05 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>4</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>GALAXY</td><td></td><td>0.7703472</td><td>0.00016685556</td><td>3.3499725</td><td>4516</td><td>0.10627031</td><td>spEigenGal-56436.fits</td><td>0 .. -1</td><td>3</td><td>0.027640253 .. 0.0</td><td>1.5233166e-05 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>0</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>STAR</td><td>K5 (110281)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>12</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>STAR</td><td>K5 (G_19-24)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>12</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>STAR</td><td>K5Ve (118100)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>12</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>STAR</td><td>M8IIIe (84748)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>12</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>STAR</td><td>O9.5Iae (30614)</td><td>-0.00013974984</td><td>0.00081630517</td><td>3.6046703</td><td>4518</td><td>0.0</td><td>spEigenStar-55734.fits</td><td>114 .. -1</td><td>4</td><td>0.538998 .. 0.0</td><td>0.2155091 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>4</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>STAR</td><td>sdF3 (140283)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>12</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>STAR</td><td>Ldwarf</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>12</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>STAR</td><td>WDcooler</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>12</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>STAR</td><td>WDhotter</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>12</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>101202</td><td>0</td><td>60095</td><td>180</td><td>27021597842439106</td><td>68995788</td><td>119 119</td><td>v6_1_3</td><td>v6_1_3</td><td>science</td><td>285.8111565410277</td><td>65.12902804991552</td><td>nan</td><td>nan</td><td>STAR</td><td>WDmagnetic</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3619.9285</td><td>10332.371</td><td>0.4523</td><td>12</td><td>5.8534417 .. 7.142995</td><td>11.357151</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.05728464 .. 0.049960647</td><td>0.0 .. 0.0</td><td>0.12136683 .. 0.07636316</td><td>0.0 .. 0.0</td><td>84410368</td><td>256835584</td><td>1.3282832</td><td>6.2918587</td><td>3.7958698</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=134>\n",
       "FIELD   TILE  MJD  TARGET_INDEX ...  SPEC1_I  SPEC2_G SPEC2_R SPEC2_I\n",
       "int32  int32 int32    int32     ...  float32  float32 float32 float32\n",
       "------ ----- ----- ------------ ... --------- ------- ------- -------\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "   ...   ...   ...          ... ...       ...     ...     ...     ...\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0\n",
       "101202     0 60095          180 ... 3.7958698     0.0     0.0     0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spectra = fits.getdata(specFile, 'COADD')\n",
    "spAll = fits.getdata(specFile,'SPALL')\n",
    "Zall_targ = fits.getdata(specFile,'ZALL')\n",
    "Table(Zall_targ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e3b5b52-8047-4a45-9408-0761e7c0e816",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-08T21:45:17.033506Z",
     "iopub.status.busy": "2025-04-08T21:45:17.032848Z",
     "iopub.status.idle": "2025-04-08T21:45:17.264615Z",
     "shell.execute_reply": "2025-04-08T21:45:17.263989Z",
     "shell.execute_reply.started": "2025-04-08T21:45:17.033482Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "znum = 1 # zero indexed\n",
    "\n",
    "plt.figure(figsize=(14,4))\n",
    "wave = np.power(10, spectra['LOGLAM'])\n",
    "mask = (spectra['AND_MASK'] == 0)\n",
    "plt.plot(wave[mask], spectra['FLUX'][mask], label ='Flux')\n",
    "\n",
    "label = \"Model: {znum}; z={z:.2f}; $\\\\chi_\\\\nu^2={rchi2:.2f}$\"\n",
    "\n",
    "plt.plot(wave[mask], spectra['MODEL'][mask],\n",
    "         label = label.format(znum=0,z=spAll['Z'][0],rchi2=spAll['RCHI2'][0]))\n",
    "\n",
    "\n",
    "plt.plot(wave, synthspec(Zall_targ[znum], spectra['LOGLAM'], eigendir=None), ls = ':', \n",
    "        label = \"Alt \"+ label.format(znum=znum,z=Zall_targ[znum]['Z'],\n",
    "                                     rchi2=Zall_targ[znum]['RCHI2']))\n",
    "\n",
    "error = np.full_like(spectra['IVAR'].data, np.nan)\n",
    "error[spectra['IVAR'] > 0]  = 1.0/np.sqrt(spectra['IVAR'][spectra['IVAR'] > 0])\n",
    "plt.plot(wave[mask], error[mask], label='Error')\n",
    "plt.ylim(np.min(spectra['FLUX'][mask][np.where(wave[mask]>3800)[0]]),\n",
    "         1.1*np.max(spectra['FLUX'][mask][np.where(wave[mask]>3800)[0]]))\n",
    "plt.ylabel(r'$f_\\lambda~(10^{-17}~ergs/s/cm^2/\\mathrm{\\AA})$')\n",
    "plt.xlabel(r'Wavelength $(\\mathrm{\\AA})$')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06bf7344",
   "metadata": {},
   "source": [
    "## Obtaining and Plotting Pre-SDSS-V Spectra with Alternative model\n",
    "Below is an example of obtaining, reading, and plotting an SDSS-IV Spectra file with both its primary and alternative model. In SDSS-IV (RUN2D <= v5_13_2), the specLite and specFull files did not contain the ZALL, so it is necessary to access it from the spZall file instead. Additionally, the `specFull` and `specLite` were called `spec` and `spec-lite`, and some of the required keywords in `sdss_access` are different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d6dfd6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Progress:   0%|          | 0/2 [00:00<?, ?files/s, n_files=2, n_streams=2]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SDSS_ACCESS> Done!\n",
      "/uufs/chpc.utah.edu/common/home/sdss50/dr17/eboss/spectro/redux/v5_13_2/spectra/lite/7340/spec-7340-57106-0010.fits Exists: True\n",
      "/uufs/chpc.utah.edu/common/home/sdss50/dr17/eboss/spectro/redux/v5_13_2/7340/v5_13_2/spZall-7340-57106.fits Exists: True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "fiberid = 10\n",
    "mjd = 57106\n",
    "plateid = 7340\n",
    "\n",
    "access = Access(release='DR17', verbose=False)\n",
    "run2d = 'v5_13_2' # The Run2d version of the BOSS Pipeline used for DR17\n",
    "\n",
    "access.remote()\n",
    "access.add('spec-lite', run2d=run2d, plateid = plateid, mjd = mjd, fiberid=fiberid)\n",
    "access.add('spZall', run2d=run2d, run1d=run2d, plateid=plateid, mjd = mjd)\n",
    "access.set_stream()\n",
    "access.commit()\n",
    "access.reset()\n",
    "\n",
    "specFile = access.full('spec-lite', run2d=run2d, plateid = plateid, mjd = mjd, fiberid=fiberid)\n",
    "print(f\"{specFile} Exists: {access.exists('',full=specFile)}\")\n",
    "\n",
    "spZallFile = access.full('spZall', run2d=run2d, run1d=run2d, plateid=plateid, mjd = mjd)\n",
    "print(f\"{spZallFile} Exists: {access.exists('',full=spZallFile)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d2e0fc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=134</i>\n",
       "<table id=\"table23434548085200\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>PLATE</th><th>TILE</th><th>MJD</th><th>FIBERID</th><th>RUN2D</th><th>RUN1D</th><th>OBJID</th><th>OBJTYPE</th><th>PLUG_RA</th><th>PLUG_DEC</th><th>CLASS</th><th>SUBCLASS</th><th>Z</th><th>Z_ERR</th><th>RCHI2</th><th>DOF</th><th>RCHI2DIFF</th><th>TFILE</th><th>TCOLUMN</th><th>NPOLY</th><th>THETA</th><th>THETA_COVAR</th><th>VDISP</th><th>VDISP_ERR</th><th>VDISPZ</th><th>VDISPZ_ERR</th><th>VDISPCHI2</th><th>VDISPNPIX</th><th>VDISPDOF</th><th>WAVEMIN</th><th>WAVEMAX</th><th>WCOVERAGE</th><th>ZWARNING</th><th>SN_MEDIAN</th><th>SN_MEDIAN_ALL</th><th>CHI68P</th><th>FRACNSIGMA</th><th>FRACNSIGHI</th><th>FRACNSIGLO</th><th>SPECTROFLUX</th><th>SPECTROFLUX_IVAR</th><th>SPECTROSYNFLUX</th><th>SPECTROSYNFLUX_IVAR</th><th>SPECTROSKYFLUX</th><th>ANYANDMASK</th><th>ANYORMASK</th><th>SPEC1_G</th><th>SPEC1_R</th><th>SPEC1_I</th><th>SPEC2_G</th><th>SPEC2_R</th><th>SPEC2_I</th></tr></thead>\n",
       "<thead><tr><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>bytes7</th><th>bytes7</th><th>int32[5]</th><th>bytes16</th><th>float64</th><th>float64</th><th>bytes6</th><th>bytes19</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th><th>float32</th><th>bytes24</th><th>int32[10]</th><th>int32</th><th>float32[10]</th><th>float32[10,10]</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th><th>float32[5]</th><th>float32</th><th>float32</th><th>float32[10]</th><th>float32[10]</th><th>float32[10]</th><th>float32[5]</th><th>float32[5]</th><th>float32[5]</th><th>float32[5]</th><th>float32[5]</th><th>int32</th><th>int32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>F3/F5V (30743)</td><td>-0.00039913345</td><td>6.546535e-06</td><td>1.8991747</td><td>4350</td><td>6.683651</td><td>spEigenStar-55734.fits</td><td>56 .. -1</td><td>4</td><td>47.61027 .. 0.0</td><td>0.07937908 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>0</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.9991111</td><td>0.31986222 .. 0.0011481056</td><td>0.17451206 .. 0.0</td><td>0.14535017 .. 0.0011481056</td><td>113.82309 .. 394.08588</td><td>1.1645232 .. 1.6072257</td><td>110.584465 .. 394.58902</td><td>5.902484 .. 2.6994438</td><td>8.837707 .. 95.84193</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>F2III (89254)</td><td>-0.00044416595</td><td>7.432444e-06</td><td>2.2103121</td><td>4350</td><td>6.372514</td><td>spEigenStar-55734.fits</td><td>54 .. -1</td><td>4</td><td>36.973557 .. 0.0</td><td>0.05012351 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>0</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>F2V (33256)</td><td>-0.0004143466</td><td>6.7580118e-06</td><td>2.2561219</td><td>4350</td><td>6.326704</td><td>spEigenStar-55734.fits</td><td>55 .. -1</td><td>4</td><td>40.67742 .. 0.0</td><td>0.06117346 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>0</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>F6II (61295)</td><td>-0.0004133646</td><td>7.017288e-06</td><td>2.260286</td><td>4350</td><td>6.3225393</td><td>spEigenStar-55734.fits</td><td>58 .. -1</td><td>4</td><td>40.04821 .. 0.0</td><td>0.059381444 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>0</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>F0IV (81937)</td><td>-0.00046872822</td><td>7.568015e-06</td><td>2.3718188</td><td>4350</td><td>6.211007</td><td>spEigenStar-55734.fits</td><td>51 .. -1</td><td>4</td><td>34.993977 .. 0.0</td><td>0.04617321 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>0</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>F6III (61064)</td><td>-0.00040342665</td><td>7.181535e-06</td><td>2.5748246</td><td>4350</td><td>6.0080013</td><td>spEigenStar-55734.fits</td><td>59 .. -1</td><td>4</td><td>37.729305 .. 0.0</td><td>0.055467244 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>0</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>F0V (90277)</td><td>-0.0004554232</td><td>8.195688e-06</td><td>2.6615121</td><td>4350</td><td>5.9213133</td><td>spEigenStar-55734.fits</td><td>53 .. -1</td><td>4</td><td>28.308561 .. 0.0</td><td>0.03167367 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>0</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>F0III (89025)</td><td>-0.00041001348</td><td>7.447895e-06</td><td>2.795751</td><td>4350</td><td>5.7870746</td><td>spEigenStar-55734.fits</td><td>50 .. -1</td><td>4</td><td>26.781986 .. 0.0</td><td>0.029121205 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>0</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>F6V (16673)</td><td>-0.00041205843</td><td>7.750305e-06</td><td>2.8770542</td><td>4350</td><td>5.7057714</td><td>spEigenStar-55734.fits</td><td>61 .. -1</td><td>4</td><td>41.170902 .. 0.0</td><td>0.069462374 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>0</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>Am (78362)</td><td>-0.00044879262</td><td>7.843921e-06</td><td>2.9499912</td><td>4350</td><td>5.6328344</td><td>spEigenStar-55734.fits</td><td>18 .. -1</td><td>4</td><td>36.323948 .. 0.0</td><td>0.05479685 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>0</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>GALAXY</td><td></td><td>0.76292914</td><td>7.101308e-05</td><td>10.145178</td><td>4348</td><td>0.0</td><td>spEigenGal-56436.fits</td><td>0 .. -1</td><td>3</td><td>0.2361783 .. 0.0</td><td>1.5689478e-05 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>4</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>G9Ib (221861)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>12</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>K3Iab: (4817)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>12</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>K3Ib... (17506)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>12</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>K4III (136726)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>12</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>K4Iab: (34255)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>12</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>K5Ve (118100)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>12</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>M8IIIe (84748)</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>12</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>Carbon</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>12</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>Ldwarf</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td></td><td>-1 .. -1</td><td>4</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>12</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=134>\n",
       "PLATE  TILE  MJD  FIBERID  RUN2D  ... SPEC1_R SPEC1_I SPEC2_G SPEC2_R SPEC2_I\n",
       "int32 int32 int32  int32   bytes7 ... float32 float32 float32 float32 float32\n",
       "----- ----- ----- ------- ------- ... ------- ------- ------- ------- -------\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       "  ...   ...   ...     ...     ... ...     ...     ...     ...     ...     ...\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spectra = fits.getdata(specFile, 'COADD')\n",
    "spAll = fits.getdata(specFile,'SPALL')\n",
    "\n",
    "spZall = Table.read(spZallFile,1)\n",
    "spZall_targ = spZall[(spZall['FIBERID']==fiberid)]\n",
    "spZall_targ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d24a5696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=1</i>\n",
       "<table id=\"table23434518129616\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>PLATE</th><th>TILE</th><th>MJD</th><th>FIBERID</th><th>RUN2D</th><th>RUN1D</th><th>OBJID</th><th>OBJTYPE</th><th>PLUG_RA</th><th>PLUG_DEC</th><th>CLASS</th><th>SUBCLASS</th><th>Z</th><th>Z_ERR</th><th>RCHI2</th><th>DOF</th><th>RCHI2DIFF</th><th>TFILE</th><th>TCOLUMN</th><th>NPOLY</th><th>THETA</th><th>THETA_COVAR</th><th>VDISP</th><th>VDISP_ERR</th><th>VDISPZ</th><th>VDISPZ_ERR</th><th>VDISPCHI2</th><th>VDISPNPIX</th><th>VDISPDOF</th><th>WAVEMIN</th><th>WAVEMAX</th><th>WCOVERAGE</th><th>ZWARNING</th><th>SN_MEDIAN</th><th>SN_MEDIAN_ALL</th><th>CHI68P</th><th>FRACNSIGMA</th><th>FRACNSIGHI</th><th>FRACNSIGLO</th><th>SPECTROFLUX</th><th>SPECTROFLUX_IVAR</th><th>SPECTROSYNFLUX</th><th>SPECTROSYNFLUX_IVAR</th><th>SPECTROSKYFLUX</th><th>ANYANDMASK</th><th>ANYORMASK</th><th>SPEC1_G</th><th>SPEC1_R</th><th>SPEC1_I</th><th>SPEC2_G</th><th>SPEC2_R</th><th>SPEC2_I</th></tr></thead>\n",
       "<thead><tr><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>bytes7</th><th>bytes7</th><th>int32[5]</th><th>bytes16</th><th>float64</th><th>float64</th><th>bytes6</th><th>bytes19</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th><th>float32</th><th>bytes24</th><th>int32[10]</th><th>int32</th><th>float32[10]</th><th>float32[10,10]</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th><th>float32[5]</th><th>float32</th><th>float32</th><th>float32[10]</th><th>float32[10]</th><th>float32[10]</th><th>float32[5]</th><th>float32[5]</th><th>float32[5]</th><th>float32[5]</th><th>float32[5]</th><th>int32</th><th>int32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
       "<tr><td>7340</td><td>15910</td><td>57106</td><td>10</td><td>v5_13_2</td><td>v5_13_2</td><td>3712 .. 100</td><td>SPECTROPHOTO_STD</td><td>215.88634000000002</td><td>52.634655</td><td>STAR</td><td>F2III (89254)</td><td>-0.00044416595</td><td>7.432444e-06</td><td>2.2103121</td><td>4350</td><td>6.372514</td><td>spEigenStar-55734.fits</td><td>54 .. -1</td><td>4</td><td>36.973557 .. 0.0</td><td>0.05012351 .. 0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>3601.6375</td><td>10337.131</td><td>0.4355</td><td>0</td><td>24.031927 .. 48.189957</td><td>53.531708</td><td>0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>0.0 .. 0.0</td><td>1.1645232 .. 1.6072257</td><td>0.0 .. 0.0</td><td>5.902484 .. 2.6994438</td><td>0.0 .. 0.0</td><td>92274688</td><td>265224192</td><td>17.8765</td><td>52.8939</td><td>42.1128</td><td>20.1358</td><td>57.2146</td><td>42.958</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=1>\n",
       "PLATE  TILE  MJD  FIBERID  RUN2D  ... SPEC1_R SPEC1_I SPEC2_G SPEC2_R SPEC2_I\n",
       "int32 int32 int32  int32   bytes7 ... float32 float32 float32 float32 float32\n",
       "----- ----- ----- ------- ------- ... ------- ------- ------- ------- -------\n",
       " 7340 15910 57106      10 v5_13_2 ... 52.8939 42.1128 20.1358 57.2146  42.958"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "znum = 1 #(zero indexed)\n",
    "display(spZall_targ[[znum]])\n",
    "plt.figure(figsize=(14,4))\n",
    "wave = np.power(10, spectra['LOGLAM'])\n",
    "mask = (spectra['AND_MASK'] == 0)\n",
    "\n",
    "label = \"Model: {znum}; z={z:.2f}; $\\\\chi_\\\\nu^2={rchi2:.2f}$\"\n",
    "\n",
    "plt.plot(wave[mask], spectra['FLUX'][mask], label ='Flux')\n",
    "plt.plot(wave[mask], spectra['MODEL'][mask], \n",
    "         label = label.format(znum=0,z=spAll['Z'][0],rchi2=spAll['RCHI2'][0]))\n",
    "error = np.full_like(spectra['IVAR'].data, np.nan)\n",
    "error[spectra['IVAR'] > 0]  = 1.0/np.sqrt(spectra['IVAR'][spectra['IVAR'] > 0])\n",
    "plt.plot(wave[mask], error[mask], label='Error')\n",
    "plt.plot(wave, synthspec(spZall_targ[znum], spectra['LOGLAM'], eigendir=None), ls = ':', \n",
    "        label = \"Alt \"+ label.format(znum=znum,z=spZall_targ[znum]['Z'],\n",
    "                                     rchi2=spZall_targ[znum]['RCHI2']))\n",
    "plt.ylim(np.min(spectra['FLUX'][mask][np.where(wave[mask]>3800)[0]]),\n",
    "         1.1*np.max(spectra['FLUX'][mask][np.where(wave[mask]>3800)[0]]))\n",
    "plt.ylabel(r'$f_\\lambda~(10^{-17}~ergs/s/cm^2/\\mathrm{\\AA})$')\n",
    "plt.xlabel(r'Rest Frame Wavelength $(\\mathrm{\\AA})$')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1dd1827",
   "metadata": {},
   "source": [
    "## About this notebook\n",
    "This notebook was created to illustrate how to find and use the various Coadds of the SDSS-V BOSS spectra\n",
    "\n",
    "**Authors**: Sean Morrison\n",
    "\n",
    "**Last Update**: 2025 April 16\n",
    "\n",
    "If you use astropy for published research, please cite the authors. Follow this link for more information about citing astropy: [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": "base",
   "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": "3.11.8"
  },
  "scenes_data": {
   "active_scene": "Default Scene",
   "init_scene": "",
   "scenes": [
    "Default Scene"
   ]
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}