docs/sphinx/tutorials/notebooks/Basics_of_Marvin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basics of Marvin\n",
    "\n",
    "In this notebook, you will learn the common core functionality across many of the Marvin Tools.  This includes the basics of accessing and handling MaNGA data from different locations, as well as a beginners guide of interacting with data via the core tools of Cubes and Maps.  Much of this information can be found in the [Getting Started section](https://sdss-marvin.readthedocs.io/en/stable/getting-started.html) of the Marvin documentation.  \n",
    "\n",
    "## Table of Contents\n",
    "- [Data Access Modes](#modes) <br>\n",
    "- [Interacting with Data Content](#interaction) <br>\n",
    " - [Object Datamodels](#datamodel) <br>\n",
    " - [Extracting Data](#extract) <br> \n",
    " - [Accessing Associated Objects](#associates)<br>\n",
    " - [Quality and Target Flags](#flags)<br>\n",
    " - [NSA and DAPall Metadata](#mixin)<br>\n",
    " - [Downloading Files](#download)<br>\n",
    "- [Interacting with Spaxels](#spaxels)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"common\"></a>\n",
    "## Common Core Functionality \n",
    "\n",
    "Marvin Tools provides the core functionality for accessing MaNGA data with Marvin. At the lowest level they are class wrappers around data products or elements (datacubes, maps, and spaxels). Their purpose is to provide a more natural way to interact with the data, unconstrained by specific data storage architectures such as files or databases, or by data location such as a laptop or remote server.  \n",
    "\n",
    "All Marvin Tools are built off a set of common core functionalities and features, and thus behave in very similar ways.  There are four main classes provided in Marvin: the ``Cube``, the ``RSS``, the ``Maps``, and the ``ModelCube``.\n",
    "We will start by focusing on a Marvin Cube to demonstrate the common features and handling of Marvin Tools.  Everything discussed here also applies to any of the other available Marvin Tools listed above.   \n",
    "  \n",
    "Let's start by importing the Marvin Cube class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from marvin.tools import Cube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may see the info message `No release version set. Setting default to DR15`.  The default data release Marvin uses is DR15. For this tutorial, we will be using DR15 data so there is no need to do anything further.  In general, to change which release Marvin uses, we can use the Marvin `config.setRelease` method on the `config` object.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from marvin import config\n",
    "# set the release to DR15\n",
    "config.setRelease(\"DR15\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"modes\"></a>\n",
    "### Data Access Modes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's instantiate our first cube, for galaxy with plate-IFU designation 8485-1901.  Each marvin tool takes a string id input and can one of: a string filepath and name, a plate-IFU designation, or a manga-ID designation.  Marvin will attempt to identify the input format and properly load the target data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Marvin Cube (plateifu='8485-1901', mode='local', data_origin='file')>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate a cube for 8485-1901\n",
    "cube = Cube('8485-1901')\n",
    "cube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once instantiated, each marvin tool indicates a **mode** and a **data-origin**.  Note that `mode=\"local\"` and  `data_origin=\"file\"`.  This indicates that we have accessed a file from our local filesystem.  Our cube now has a ``filename`` attribute indicating its location on disk. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/Brian/Work/sdss/sas/dr15/manga/spectro/redux/v2_4_3/8485/stack/manga-8485-1901-LOGCUBE.fits.gz'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the local filename\n",
    "cube.filename"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we won't have the MaNGa data products for our target of interest?  We can specify the **plateifu** or **mangaid** of the target and Marvin will access it remotely.  Let's open a cube for 8485-1902."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Marvin Cube (plateifu='8485-1902', mode='remote', data_origin='api')>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remote_cube = Cube('8485-1902')\n",
    "remote_cube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now note that ``mode=\"remote\"`` and ``data_origin=\"api\"``.  This indicates the cube has been remotely loaded using the built-in Marvin API.  By specifying an input **plateifu** or **mangaid**, marvin will always first try to access the data locally, and then fall back to remote access.  You can also explicitly load a file using the ``filename`` keyword argument.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Marvin Cube (plateifu='8485-1901', mode='local', data_origin='file')>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explicitly load a cube from a file on disk \n",
    "filename = '/Users/Brian/Work/sdss/sas/dr15/manga/spectro/redux/v2_4_3/8485/stack/manga-8485-1901-LOGCUBE.fits.gz'\n",
    "cube = Cube(filename=filename)\n",
    "cube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"interaction\"></a>\n",
    "### Interacting with Data Content\n",
    "\n",
    "Regardless of how you load an object, the way we interact with all the tools, and the way they behave, is the same.  They also all provide some quick access to basic metadata, like coordinates, header information, and WCS.\n",
    "<a id=\"metadata\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target Coordindates: 232.544703894 48.6902009334\n",
      "Header:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XTENSION= 'IMAGE   '           / IMAGE extension                                \n",
       "BITPIX  =                  -32 / Number of bits per data pixel                  \n",
       "NAXIS   =                    3 / Number of data axes                            \n",
       "NAXIS1  =                   34 /                                                \n",
       "NAXIS2  =                   34 /                                                \n",
       "NAXIS3  =                 4563 /                                                \n",
       "PCOUNT  =                    0 / No Group Parameters                            \n",
       "GCOUNT  =                    1 / One Data Group                                 \n",
       "AUTHOR  = 'Brian Cherinka & David Law <bcherin1@jhu.edu, dlaw@stsci.edu>' /     \n",
       "VERSDRP2= 'v2_4_3  '           / MaNGA DRP version (2d processing)              \n",
       "VERSDRP3= 'v2_4_3  '           / MaNGA DRP Version (3d processing)              \n",
       "VERSPLDS= 'v2_52   '           / Platedesign Version                            \n",
       "VERSFLAT= 'v1_31   '           / Specflat Version                               \n",
       "VERSCORE= 'v1_6_2  '           / MaNGAcore Version                              \n",
       "VERSPRIM= 'v2_5    '           / MaNGA Preimaging Version                       \n",
       "VERSUTIL= 'v5_5_32 '           / Version of idlutils                            \n",
       "VERSIDL = 'x86_64 linux unix linux 7.1.1 Aug 21 2009 64 64 ' / Version of IDL   \n",
       "BSCALE  =              1.00000 / Intensity unit scaling                         \n",
       "BZERO   =              0.00000 / Intensity zeropoint                            \n",
       "BUNIT   = '1E-17 erg/s/cm^2/Ang/spaxel' / Specific intensity (per spaxel)       \n",
       "MASKNAME= 'MANGA_DRP3PIXMASK'  / Bits in sdssMaskbits.par used by mask extension\n",
       "TELESCOP= 'SDSS 2.5-M'         / Sloan Digital Sky Survey                       \n",
       "INSTRUME= 'MaNGA   '           / SDSS-IV MaNGA IFU                              \n",
       "SRVYMODE= 'MaNGA dither'       / Survey leading this observation and its mode   \n",
       "PLATETYP= 'APOGEE-2&MaNGA'     / Type of plate (e.g. MaNGA, APOGEE-2&MANGA)     \n",
       "OBJSYS  = 'ICRS    '           / The TCC objSys                                 \n",
       "EQUINOX =              2000.00 /                                                \n",
       "RADESYS = 'FK5     '           /                                                \n",
       "LAMPLIST= 'lamphgcdne.dat'     /                                                \n",
       "TPLDATA = 'BOSZ_3000-11000A.fits' /                                             \n",
       "NEXP    =                    9 / Total number of exposures                      \n",
       "EXPTIME =              8100.87 / Total exposure time (seconds)                  \n",
       "BLUESN2 =              19.9834 / Total SN2 in blue channel                      \n",
       "REDSN2  =              42.7417 / Total SN2 in red channel                       \n",
       "AIRMSMIN=              1.03987 / Minimum airmass                                \n",
       "AIRMSMED=              1.04708 / Median airmass                                 \n",
       "AIRMSMAX=              1.08221 / Maximum airmass                                \n",
       "SEEMIN  =              1.17790 / Best guider seeing                             \n",
       "SEEMED  =              1.30425 / Median guider seeing                           \n",
       "SEEMAX  =              1.42179 / Worst guider seeing                            \n",
       "TRANSMIN=             0.802254 / Worst guider transparency                      \n",
       "TRANSMED=             0.831209 / Median guider transparency                     \n",
       "TRANSMAX=             0.839501 / Best guider transparency                       \n",
       "MJDMIN  =                57132 / MJD of first exposure                          \n",
       "MJDMED  =                57132 / MJD of median exposure                         \n",
       "MJDMAX  =                57132 / MJD of last exposure                           \n",
       "DATE-OBS= '2015-04-20'         / Date of median exposure                        \n",
       "MJDRED  =                58198 / MJD of the reduction                           \n",
       "DATERED = '2018-03-21'         / Date of the reduction                          \n",
       "MNGTARG1=                 2336 / manga_target1 maskbit                          \n",
       "MNGTARG2=                    0 / manga_target2 maskbit                          \n",
       "MNGTARG3=                    0 / manga_target3 maskbit                          \n",
       "IFURA   =        232.544700000 / IFU R.A. (J2000 deg.)                          \n",
       "IFUDEC  =        48.6902010000 / IFU Dec. (J2000 deg.)                          \n",
       "OBJRA   =        232.544703894 / Object R.A. (J2000 deg.)                       \n",
       "OBJDEC  =        48.6902009334 / Object Dec. (J2000 deg.)                       \n",
       "CENRA   =        234.064260000 / Plate center R.A. (J2000 deg.)                 \n",
       "CENDEC  =        48.5898740000 / Plate center Dec. (J2000 deg.)                 \n",
       "PLATEID =                 8485 / Current plate                                  \n",
       "DESIGNID=                 8980 / Current design                                 \n",
       "IFUDSGN =                 1901 / ifuDesign                                      \n",
       "FRLPLUG =                   29 / Plugged ferrule                                \n",
       "PLATEIFU= '8485-1901'          / PLATEID-ifuDesign                              \n",
       "CARTID  = '3       '           / Cart(s) used                                   \n",
       "HARNAME = 'ma060   '           / Harness name(s)                                \n",
       "METFILE = 'ma060-56887-1.par'  / IFU metrology file(s)                          \n",
       "MANGAID = '1-209232'           / MaNGA ID number                                \n",
       "CATIDNUM= '1       '           / Primary target input catalog                   \n",
       "PLTTARG = 'plateTargets-1.par' / plateTarget reference file                     \n",
       "DRP3QUAL=                    0 / DRP-3d quality bitmask                         \n",
       "IFUGLON =        78.9550411299 / IFU Galactic longitude (deg)                   \n",
       "IFUGLAT =        52.6212190954 / IFU Galactic latitude (deg)                    \n",
       "EBVGAL  =            0.0144335 / Galactic reddening E(B-V)                      \n",
       "GFWHM   =              2.46769 / Reconstructed FWHM in g-band (arcsec)          \n",
       "RFWHM   =              2.47181 / Reconstructed FWHM in r-band (arcsec)          \n",
       "IFWHM   =              2.45516 / Reconstructed FWHM in i-band (arcsec)          \n",
       "ZFWHM   =              2.43769 / Reconstructed FWHM in z-band (arcsec)          \n",
       "CTYPE3  = 'WAVE-LOG'           /                                                \n",
       "CRPIX3  =                    1 / Starting pixel (1-indexed)                     \n",
       "CRVAL3  =        3621.59598486 / Central wavelength of first pixel              \n",
       "CD3_3   =       0.833903304339 / Initial dispersion per pixel                   \n",
       "CUNIT3  = 'Angstrom'           /                                                \n",
       "CRPIX1  =              18.0000 /Reference pixel (1-indexed)                     \n",
       "CRPIX2  =              18.0000 /Reference pixel (1-indexed)                     \n",
       "CRVAL1  =        232.544700000 /                                                \n",
       "CRVAL2  =        48.6902010000 /                                                \n",
       "CD1_1   =         -0.000138889 /                                                \n",
       "CD2_2   =          0.000138889 /                                                \n",
       "CTYPE1  = 'RA---TAN'           /                                                \n",
       "CTYPE2  = 'DEC--TAN'           /                                                \n",
       "CUNIT1  = 'deg     '           /                                                \n",
       "CUNIT2  = 'deg     '           /                                                \n",
       "HDUCLASS= 'SDSS    '           /SDSS format class                               \n",
       "HDUCLAS1= 'CUBE    '           /                                                \n",
       "HDUCLAS2= 'DATA    '           /                                                \n",
       "ERRDATA = 'IVAR    '           / Error extension name                           \n",
       "QUALDATA= 'MASK    '           / Mask extension name                            \n",
       "EXTNAME = 'FLUX    '           /                                                \n",
       "DATASUM = '2745070007'         / data unit checksum updated 2018-03-21T06:08:47 \n",
       "CHECKSUM= 'KZaaKYRXKYXaKYXU'   / HDU checksum updated 2018-03-21T06:08:47       "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Target Coordindates:', cube.ra, cube.dec)\n",
    "print('Header:')\n",
    "\n",
    "# access the PRIMARY header for the current object\n",
    "cube.header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WCS Keywords\n",
       "\n",
       "Number of WCS axes: 3\n",
       "CTYPE : 'RA---TAN'  'DEC--TAN'  'WAVE-LOG'  \n",
       "CRVAL : 232.5447  48.690201  3.62159598486e-07  \n",
       "CRPIX : 18.0  18.0  1.0  \n",
       "CD1_1 CD1_2 CD1_3  : -0.000138889  0.0  0.0  \n",
       "CD2_1 CD2_2 CD2_3  : 0.0  0.000138889  0.0  \n",
       "CD3_1 CD3_2 CD3_3  : 0.0  0.0  8.33903304339e-11  \n",
       "NAXIS : 34  34  4563"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the WCS for the current object\n",
    "cube.wcs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"datamodel\"></a>\n",
    "### Object Datamodels\n",
    "Every Marvin object comes with a built-in **datamodel**, which shows us what extensions are available, how they are names, and what they contain.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DRPCubeDataModel release='DR15', n_datacubes=3, n_spectra=2>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look up the cube datmaodel\n",
    "datamodel = cube.datamodel\n",
    "datamodel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The datamodel for a cube contains 3 datacube extensions and 2 spectral extensions.  Let's look at them more closely.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datacubes:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<DataCube 'flux', release='DR15', unit='1e-17 erg / (Angstrom cm2 s spaxel)'>,\n",
       " <DataCube 'dispersion', release='DR15', unit='Angstrom'>,\n",
       " <DataCube 'dispersion_prepixel', release='DR15', unit='Angstrom'>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see what datacubes are available\n",
    "print('Datacubes:')\n",
    "datamodel.datacubes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectra:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<Spectrum 'spectral_resolution', release='DR15', unit='Angstrom'>,\n",
       " <Spectrum 'spectral_resolution_prepixel', release='DR15', unit='Angstrom'>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see what additional spectral extensions are available\n",
    "print('Spectra:')\n",
    "datamodel.spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us that this cube has three associated 3D datacubes, ``flux``, ``dispersion``, and ``dispersion_prepixel``, and two associated spectra, ``spectral_resolution`` and ``spectral_resolution_prepixel``, as well as their associated units. The ``description`` attribute provides information on what the extension is.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flux description: 3D rectified cube\n",
      "Spectral Resolution description: Median spectral resolution as a function of wavelength for the fibers in this IFU\n"
     ]
    }
   ],
   "source": [
    "print('Flux description:', datamodel.datacubes.flux.description)\n",
    "print('Spectral Resolution description:', datamodel.spectra.spectral_resolution.description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The names of each of the datacubes and spectral extensions are available as object attributes and can be used to access the associated data quantity.  Let's access the ``flux`` for our current cube.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  [0,~0,~0,~\\dots,~0,~0,~0]]] \\; \\mathrm{1 \\times 10^{-17}\\,\\frac{erg}{\\mathring{A}\\,s\\,spaxel\\,cm^{2}}}$"
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       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "           [[0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            ...,\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "           [[0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            ...,\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "           ...,\n",
       "\n",
       "           [[0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            ...,\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "           [[0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            ...,\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "           [[0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            ...,\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.],\n",
       "            [0., 0., 0., ..., 0., 0., 0.]]] 1e-17 erg / (Angstrom cm2 s spaxel)>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flux = cube.flux\n",
    "flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The flux is represented as a 3D array with units. We can also access the inverse variance and the mask using ``flux.ivar`` and ``flux.mask``, respectively.  Marvin uses [Astropy quantities](https://sdss-marvin.readthedocs.io/en/stable/getting-started.html#marvin-quantities) to represent all internal multidimensional data, e.g. 3-d datacube and 2-d map arrays, or 1-d spectral data.  \n",
    "\n",
    "<a id=\"extract\"></a>\n",
    "### Extracting data\n",
    "All marvin arrays can be sliced to extract subsets of data.  Slices of datacubes produce new datacubes.  Slices of spectra produce new spectra.  A single slice from a datacube extracts a spectrum.  Each spectrum can be easily displayed with its ``plot`` attribute.        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[[[-0.091659956,~-0.057862502,~-0.069440775,~\\dots,~0.053298473,~0.062018514,~0.061692294],~\n",
       "  [-0.039790876,~0.0012025207,~0.020654099,~\\dots,~0.10088551,~0.086681418,~0.077831492],~\n",
       "  [0.019458937,~0.077283047,~0.10514027,~\\dots,~0.20350538,~0.14156495,~0.095826343],~\n",
       "  \\dots,~\n",
       "  [0.11238833,~0.210729,~0.26030195,~\\dots,~0.31280893,~0.15664732,~0.045407701],~\n",
       "  [0.10175533,~0.16688073,~0.1919646,~\\dots,~0.25600749,~0.17017685,~0.085642852],~\n",
       "  [0.054798402,~0.090783022,~0.094782196,~\\dots,~0.19204016,~0.14744423,~0.092620544]],~\n",
       "\n",
       " [[0.082437925,~0.13506371,~0.15769725,~\\dots,~-0.017692961,~-0.0091365296,~-0.011535237],~\n",
       "  [0.08238145,~0.14059068,~0.20465873,~\\dots,~0.00030586062,~0.015362157,~0.02579339],~\n",
       "  [0.10742286,~0.19580711,~0.26307943,~\\dots,~0.090322539,~0.099070497,~0.10215754],~\n",
       "  \\dots,~\n",
       "  [0.13724391,~0.24727274,~0.32436091,~\\dots,~0.20887852,~0.1519153,~0.08934544],~\n",
       "  [0.071373947,~0.18353277,~0.279935,~\\dots,~0.19906537,~0.19028623,~0.14837392],~\n",
       "  [0.03953917,~0.1445726,~0.23140128,~\\dots,~0.19428748,~0.18247326,~0.14610548]],~\n",
       "\n",
       " [[0.080393434,~0.15365259,~0.17991598,~\\dots,~0.11157861,~0.14622529,~0.13381542],~\n",
       "  [0.063174374,~0.13302146,~0.19359955,~\\dots,~0.092888884,~0.12845202,~0.13867338],~\n",
       "  [0.073336095,~0.15482704,~0.22972785,~\\dots,~0.12644014,~0.12638213,~0.12386632],~\n",
       "  \\dots,~\n",
       "  [0.13358407,~0.20693749,~0.2933836,~\\dots,~0.29559252,~0.25168392,~0.22717273],~\n",
       "  [0.068491481,~0.16247286,~0.27922529,~\\dots,~0.2724455,~0.24940087,~0.24897653],~\n",
       "  [0.025780736,~0.14267682,~0.24933307,~\\dots,~0.25498557,~0.22155441,~0.18757398]],~\n",
       "\n",
       " \\dots,~\n",
       "\n",
       " [[0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  \\dots,~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0]],~\n",
       "\n",
       " [[0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  \\dots,~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0]],~\n",
       "\n",
       " [[0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  \\dots,~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       "  [0,~0,~0,~\\dots,~0,~0,~0]]] \\; \\mathrm{1 \\times 10^{-17}\\,\\frac{erg}{\\mathring{A}\\,s\\,spaxel\\,cm^{2}}}$"
      ],
      "text/plain": [
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       "              5.32984734e-02,  6.20185137e-02,  6.16922937e-02],\n",
       "            [-3.97908762e-02,  1.20252068e-03,  2.06540991e-02, ...,\n",
       "              1.00885510e-01,  8.66814181e-02,  7.78314918e-02],\n",
       "            [ 1.94589365e-02,  7.72830471e-02,  1.05140269e-01, ...,\n",
       "              2.03505382e-01,  1.41564950e-01,  9.58263427e-02],\n",
       "            ...,\n",
       "            [ 1.12388328e-01,  2.10729003e-01,  2.60301948e-01, ...,\n",
       "              3.12808931e-01,  1.56647325e-01,  4.54077013e-02],\n",
       "            [ 1.01755328e-01,  1.66880727e-01,  1.91964597e-01, ...,\n",
       "              2.56007493e-01,  1.70176849e-01,  8.56428519e-02],\n",
       "            [ 5.47984019e-02,  9.07830223e-02,  9.47821960e-02, ...,\n",
       "              1.92040160e-01,  1.47444233e-01,  9.26205441e-02]],\n",
       "\n",
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       "             -1.76929608e-02, -9.13652964e-03, -1.15352366e-02],\n",
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       "            ...,\n",
       "            [ 1.37243912e-01,  2.47272745e-01,  3.24360907e-01, ...,\n",
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       "            [ 7.13739470e-02,  1.83532774e-01,  2.79935002e-01, ...,\n",
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       "\n",
       "           [[ 8.03934336e-02,  1.53652593e-01,  1.79915980e-01, ...,\n",
       "              1.11578606e-01,  1.46225289e-01,  1.33815423e-01],\n",
       "            [ 6.31743744e-02,  1.33021459e-01,  1.93599552e-01, ...,\n",
       "              9.28888842e-02,  1.28452018e-01,  1.38673380e-01],\n",
       "            [ 7.33360946e-02,  1.54827043e-01,  2.29727849e-01, ...,\n",
       "              1.26440138e-01,  1.26382127e-01,  1.23866320e-01],\n",
       "            ...,\n",
       "            [ 1.33584067e-01,  2.06937492e-01,  2.93383598e-01, ...,\n",
       "              2.95592517e-01,  2.51683921e-01,  2.27172732e-01],\n",
       "            [ 6.84914812e-02,  1.62472859e-01,  2.79225290e-01, ...,\n",
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       "            [ 2.57807355e-02,  1.42676815e-01,  2.49333069e-01, ...,\n",
       "              2.54985571e-01,  2.21554413e-01,  1.87573984e-01]],\n",
       "\n",
       "           ...,\n",
       "\n",
       "           [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            ...,\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n",
       "\n",
       "           [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            ...,\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n",
       "\n",
       "           [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            ...,\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "            [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "              0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]] 1e-17 erg / (Angstrom cm2 s spaxel)>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a small datacube subset in a 10x10 pixel region around the center of the target\n",
    "central_pixel = int(cube.flux.shape[1]/2)\n",
    "lo = central_pixel - 5\n",
    "hi = central_pixel + 5\n",
    "subset = flux[:, lo:hi, lo:hi]\n",
    "subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[0.51865631,~0.43517455,~0.41204447,~\\dots,~0,~0,~0] \\; \\mathrm{1 \\times 10^{-17}\\,\\frac{erg}{\\mathring{A}\\,s\\,spaxel\\,cm^{2}}}$"
      ],
      "text/plain": [
       "<Spectrum [0.5186563 , 0.43517455, 0.41204447, ..., 0.        , 0.        ,\n",
       "           0.        ] 1e-17 erg / (Angstrom cm2 s spaxel)>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract a single spectrum and display it\n",
    "spectrum = flux[:, 16, 17]\n",
    "spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: AstropyDeprecationWarning: The truth value of a Quantity is ambiguous. In the future this will raise a ValueError. [astropy.units.quantity]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ceb8588>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spectrum.plot(show_std=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"associates\"></a>\n",
    "### Accessing Associated Objects\n",
    "\n",
    "For a given target galaxy, there are many associated MaNGA Data Products from both the reduction and analysis pipelines.  Marvin seamlessly connects these together so you don't have to access them individually.  Let's access the DAP Maps objects associated with our cube.  To do so we use the ``getMaps`` method on ``cube``.  All instances have ``getXXX`` methods that allow you to access associated data objects for a given target.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Marvin Maps (plateifu='8485-1901', mode='local', data_origin='file', bintype='HYB10', template='GAU-MILESHC')>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access the Maps object from our cube\n",
    "maps = cube.getMaps()\n",
    "maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When accessing associated objects, Marvin also smartly determines whether to open the file locally or remotely grab it over the API.  In our case, the **mode** is local and **data_origin** is file.  DAP Maps have different bintypes.  The default binning is **HYB10**, a hybrid binning scheme to S/N~10.  To load a different bintype, use the ``bintype`` keyword argument in the ``cube.getMaps`` method.    \n",
    "\n",
    "A ``Maps`` behaves very similarly to a ``Cube`` and everything we have discussed above will still work. Instead of datacubes and spectra, a ``Maps`` object contains a set of 2D quantities called ``Map``, each one of them representing a different property measured by the DAP. One can get a full list of all the properties available using the datamodel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Property 'spx_skycoo', channel='on_sky_x', release='2.2.1', unit='arcsec'>,\n",
       " <Property 'spx_skycoo', channel='on_sky_y', release='2.2.1', unit='arcsec'>,\n",
       " <Property 'spx_ellcoo', channel='elliptical_radius', release='2.2.1', unit='arcsec'>,\n",
       " <Property 'spx_ellcoo', channel='r_re', release='2.2.1', unit=''>,\n",
       " <Property 'spx_ellcoo', channel='elliptical_azimuth', release='2.2.1', unit='deg'>,\n",
       " <Property 'spx_mflux', channel='None', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'spx_snr', channel='None', release='2.2.1', unit=''>,\n",
       " <Property 'binid', channel='binned_spectra', release='2.2.1', unit=''>,\n",
       " <Property 'binid', channel='stellar_continua', release='2.2.1', unit=''>,\n",
       " <Property 'binid', channel='em_line_moments', release='2.2.1', unit=''>,\n",
       " <Property 'binid', channel='em_line_models', release='2.2.1', unit=''>,\n",
       " <Property 'binid', channel='spectral_indices', release='2.2.1', unit=''>,\n",
       " <Property 'bin_lwskycoo', channel='lum_weighted_on_sky_x', release='2.2.1', unit='arcsec'>,\n",
       " <Property 'bin_lwskycoo', channel='lum_weighted_on_sky_y', release='2.2.1', unit='arcsec'>,\n",
       " <Property 'bin_lwellcoo', channel='lum_weighted_elliptical_radius', release='2.2.1', unit='arcsec'>,\n",
       " <Property 'bin_lwellcoo', channel='r_re', release='2.2.1', unit=''>,\n",
       " <Property 'bin_lwellcoo', channel='lum_weighted_elliptical_azimuth', release='2.2.1', unit='deg'>,\n",
       " <Property 'bin_area', channel='None', release='2.2.1', unit='arcsec2'>,\n",
       " <Property 'bin_farea', channel='None', release='2.2.1', unit=''>,\n",
       " <Property 'bin_mflux', channel='None', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'bin_snr', channel='None', release='2.2.1', unit=''>,\n",
       " <Property 'stellar_vel', channel='None', release='2.2.1', unit='km / s'>,\n",
       " <Property 'stellar_sigma', channel='None', release='2.2.1', unit='km / s'>,\n",
       " <Property 'stellar_sigmacorr', channel='None', release='2.2.1', unit='km / s'>,\n",
       " <Property 'stellar_cont_fresid', channel='68th_percentile', release='2.2.1', unit=''>,\n",
       " <Property 'stellar_cont_fresid', channel='99th_percentile', release='2.2.1', unit=''>,\n",
       " <Property 'stellar_cont_rchi2', channel='None', release='2.2.1', unit=''>,\n",
       " <Property 'emline_sflux', channel='oiid_3728', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='oii_3729', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='hthe_3798', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='heta_3836', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='neiii_3869', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='hzet_3890', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='neiii_3968', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='heps_3971', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='hdel_4102', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='hgam_4341', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='heii_4687', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='hb_4862', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='oiii_4960', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='oiii_5008', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='hei_5877', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='oi_6302', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='oi_6365', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='nii_6549', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='ha_6564', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='nii_6585', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='sii_6718', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sflux', channel='sii_6732', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_sew', channel='oiid_3728', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='oii_3729', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='hthe_3798', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='heta_3836', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='neiii_3869', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='hzet_3890', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='neiii_3968', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='heps_3971', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='hdel_4102', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='hgam_4341', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='heii_4687', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='hb_4862', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='oiii_4960', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='oiii_5008', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='hei_5877', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='oi_6302', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='oi_6365', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='nii_6549', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='ha_6564', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='nii_6585', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='sii_6718', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_sew', channel='sii_6732', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gflux', channel='oii_3727', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='oii_3729', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='hthe_3798', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='heta_3836', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='neiii_3869', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='hzet_3890', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='neiii_3968', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='heps_3971', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='hdel_4102', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='hgam_4341', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='heii_4687', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='hb_4862', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='oiii_4960', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='oiii_5008', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='hei_5877', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='oi_6302', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='oi_6365', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='nii_6549', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='ha_6564', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='nii_6585', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='sii_6718', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gflux', channel='sii_6732', release='2.2.1', unit='1e-17 erg / (cm2 s spaxel)'>,\n",
       " <Property 'emline_gvel', channel='oii_3727', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='oii_3729', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='hthe_3798', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='heta_3836', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='neiii_3869', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='hzet_3890', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='neiii_3968', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='heps_3971', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='hdel_4102', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='hgam_4341', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='heii_4687', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='hb_4862', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='oiii_4960', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='oiii_5008', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='hei_5877', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='oi_6302', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='oi_6365', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='nii_6549', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='ha_6564', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='nii_6585', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='sii_6718', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gvel', channel='sii_6732', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gew', channel='oii_3727', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='oii_3729', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='hthe_3798', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='heta_3836', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='neiii_3869', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='hzet_3890', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='neiii_3968', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='heps_3971', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='hdel_4102', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='hgam_4341', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='heii_4687', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='hb_4862', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='oiii_4960', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='oiii_5008', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='hei_5877', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='oi_6302', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='oi_6365', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='nii_6549', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='ha_6564', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='nii_6585', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='sii_6718', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gew', channel='sii_6732', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'emline_gsigma', channel='oii_3727', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='oii_3729', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='hthe_3798', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='heta_3836', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='neiii_3869', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='hzet_3890', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='neiii_3968', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='heps_3971', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='hdel_4102', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='hgam_4341', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='heii_4687', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='hb_4862', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='oiii_4960', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='oiii_5008', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='hei_5877', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='oi_6302', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='oi_6365', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='nii_6549', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='ha_6564', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='nii_6585', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='sii_6718', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_gsigma', channel='sii_6732', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='oii_3727', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='oii_3729', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='hthe_3798', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='heta_3836', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='neiii_3869', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='hzet_3890', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='neiii_3968', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='heps_3971', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='hdel_4102', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='hgam_4341', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='heii_4687', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='hb_4862', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='oiii_4960', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='oiii_5008', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='hei_5877', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='oi_6302', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='oi_6365', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='nii_6549', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='ha_6564', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='nii_6585', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='sii_6718', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_instsigma', channel='sii_6732', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='oii_3727', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='oii_3729', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='hthe_3798', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='heta_3836', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='neiii_3869', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='hzet_3890', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='neiii_3968', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='heps_3971', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='hdel_4102', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='hgam_4341', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='heii_4687', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='hb_4862', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='oiii_4960', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='oiii_5008', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='hei_5877', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='oi_6302', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='oi_6365', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='nii_6549', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='ha_6564', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='nii_6585', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='sii_6718', release='2.2.1', unit='km / s'>,\n",
       " <Property 'emline_tplsigma', channel='sii_6732', release='2.2.1', unit='km / s'>,\n",
       " <Property 'specindex', channel='cn1', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex', channel='cn2', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex', channel='ca4227', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='g4300', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='fe4383', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='ca4455', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='fe4531', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='c24668', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='hb', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='fe5015', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='mg1', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex', channel='mg2', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex', channel='mgb', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='fe5270', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='fe5335', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='fe5406', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='fe5709', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='fe5782', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='nad', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='tio1', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex', channel='tio2', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex', channel='hdeltaa', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='hgammaa', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='hdeltaf', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='hgammaf', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='cahk', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='caii1', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='caii2', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='caii3', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='pa17', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='pa14', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='pa12', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='mgicvd', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='naicvd', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='mgiir', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='fehcvd', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='nai', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='btio', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex', channel='atio', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex', channel='cah1', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex', channel='cah2', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex', channel='naisdss', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='tio2sdss', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex', channel='d4000', release='2.2.1', unit=''>,\n",
       " <Property 'specindex', channel='dn4000', release='2.2.1', unit=''>,\n",
       " <Property 'specindex', channel='tiocvd', release='2.2.1', unit=''>,\n",
       " <Property 'specindex_corr', channel='cn1', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex_corr', channel='cn2', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex_corr', channel='ca4227', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='g4300', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='fe4383', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='ca4455', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='fe4531', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='c24668', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='hb', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='fe5015', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='mg1', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex_corr', channel='mg2', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex_corr', channel='mgb', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='fe5270', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='fe5335', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='fe5406', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='fe5709', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='fe5782', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='nad', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='tio1', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex_corr', channel='tio2', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex_corr', channel='hdeltaa', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='hgammaa', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='hdeltaf', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='hgammaf', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='cahk', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='caii1', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='caii2', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='caii3', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='pa17', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='pa14', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='pa12', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='mgicvd', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='naicvd', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='mgiir', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='fehcvd', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='nai', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='btio', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex_corr', channel='atio', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex_corr', channel='cah1', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex_corr', channel='cah2', release='2.2.1', unit='mag'>,\n",
       " <Property 'specindex_corr', channel='naisdss', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='tio2sdss', release='2.2.1', unit='Angstrom'>,\n",
       " <Property 'specindex_corr', channel='d4000', release='2.2.1', unit=''>,\n",
       " <Property 'specindex_corr', channel='dn4000', release='2.2.1', unit=''>,\n",
       " <Property 'specindex_corr', channel='tiocvd', release='2.2.1', unit=''>]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the datamodel for the maps\n",
    "maps.datamodel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that some properties such as ``spx_skycoo`` have multiple channels (in this case the ``on-sky`` ``x`` and ``y`` coordinates). To access the individual property, simply combine the property name and the channel name with an underscore, i.e. ``spx_skycoo_on_sky_x``.  As before, we can get more information about a property using the ``description`` attribute.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spx_skycoo_on_sky_x:  Offsets of each spaxel from the galaxy center. Channel = On-sky X.\n"
     ]
    }
   ],
   "source": [
    "print('spx_skycoo_on_sky_x: ', maps.datamodel.spx_skycoo_on_sky_x.description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More information about Marvin datamodels can be found [here](https://sdss-marvin.readthedocs.io/en/stable/datamodel/datamodels.html).  See the [DR15 Datamodel](https://sdss-marvin.readthedocs.io/en/stable/datamodel/dr15.html#datamodel-dr15) for a full description of the available datamodels for DR15.  \n",
    "\n",
    "We can retrieve the map associated to a specific property directly from the Maps instance. For example, let’s get the Hα emission line flux (fitted by a Gaussian).  As ``Map`` quantities are similar to ``DataCube``, we can easily plot the 2d map. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[[0,~0,~0,~\\dots,~0,~0,~0],~\n",
       " [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       " [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       " \\dots,~\n",
       " [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       " [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       " [0,~0,~0,~\\dots,~0,~0,~0]] \\; \\mathrm{1 \\times 10^{-17}\\,\\frac{erg}{s\\,spaxel\\,cm^{2}}}$"
      ],
      "text/plain": [
       "<Marvin Map (property='emline_gflux_ha_6564')>\n",
       "[[0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " ...\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]] 1e-17 erg / (cm2 s spaxel)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab the map of H-alpha emission line flux\n",
    "ha = maps.emline_gflux_ha_6564\n",
    "ha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the H-alpha map\n",
    "fig, ax = ha.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the ``plot`` method returns the matplotlib ``Figure`` and ``Axes`` for the plot. We can use those to modify or save the plot. Marvin plotting routines try to select the best parameters, colour maps, and dynamic ranges. You can modify those by passing extra arguments to plot. You can learn more in the [Map plotting section](https://sdss-marvin.readthedocs.io/en/stable/tools/utils/plot-map.html#marvin-utils-plot-map). We will talk about the Map class in detail in Working with [Astropy Quantities](https://sdss-marvin.readthedocs.io/en/stable/getting-started.html#marvin-quantities) and in [Map](https://sdss-marvin.readthedocs.io/en/stable/tools/map.html#marvin-map).\n",
    "\n",
    "Remember that all associated objects are connected together.  They can be accessed either from within an object instance or loaded independently from the class itself.  From within an instance, the core tools, ``Maps``, ``RSS``, and ``ModelCube`` are available using the ``getXXX`` methods, as well as additional auxillary tools and object data, such as ``Image``.         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the same Maps object by itself\n",
    "from marvin.tools import Maps\n",
    "my_maps = Maps('8485-1901')\n",
    "my_maps\n",
    "\n",
    "# get the stellar velocity map\n",
    "stvel = my_maps.stellar_velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<astropy.visualization.wcsaxes.core.WCSAxes at 0x1584312e8>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load an auxilliary tool, the gri color image of the target galaxy using the Marvin Image tool\n",
    "\n",
    "# get the Image of the galaxy and plot it\n",
    "image = cube.getImage()\n",
    "image.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"flags\"></a>\n",
    "### Quality and Target Flags and Maskbits\n",
    "Each MaNGA target has associated quality assurance flags as well targeting flags.  Quality flags indiciate the overall quality of the data and/or any problems with the data discovered during the reduction and analysis pipeline runs.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Maskbit 'MANGA_DRP3QUAL' []>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the overall cube quality flags\n",
    "cube.quality_flag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case the MANGA_DRP3QUAL maskbit does not have any bit activated, which means the data is safe to use. See the [Maskbits](https://sdss-marvin.readthedocs.io/en/stable/tools/utils/maskbit.html#marvin-maskbit) section for more information on how to interact with Marvin Maskbits.\n",
    "\n",
    "The targeting flags highlight the **targeting bits** set for this galaxy and tell you which sample the target belongs to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Maskbit 'MANGA_TARGET1' ['SECONDARY_v1_1_0', 'SECONDARY_COM2', 'SECONDARY_v1_2_0']>,\n",
       " <Maskbit 'MANGA_TARGET2' []>,\n",
       " <Maskbit 'MANGA_TARGET3' []>]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the targeting flags for this galaxy\n",
    "cube.target_flags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in this case the galaxy belongs to the secondary sample from the final target selection (``SECONDARY_v1_2_0``) as well as to the secondary samples from several commissioning target selections. The galaxy does not have any ancillary bit (**manga_target3**).  An **manga_target3** bit set indicates the target belongs to one of the [MaNGA Ancillary programs](https://www.sdss.org/dr15/manga/manga-target-selection/ancillary-targets/).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"mixin\"></a>\n",
    "### NSA, DAPall, and Value-Added catalog data\n",
    "For each target we can also access additional catalog data: the associated parameters from the [NASA Sloan Atlas](http://nsatlas.org/), and the [DAPall](https://www.sdss.org/dr15/manga/manga-data/catalogs/#DAPALLFile) file.  The NSA catalog contains measured global galaxy photometric and shape profile information, while the DAPall catalog contains aggregrate statistics as measured by the MaNGA DAP pipeline.  NSA properties are available on all Marvin objects under the ``nsa`` attribute.  DAPall properties are available on all Marvin ``Maps`` and ``Modelcube`` objects.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'camcol': 2,\n",
       " 'elpetro_absmag': [-14.3422,\n",
       "  -15.7994,\n",
       "  -17.0133,\n",
       "  -18.1788,\n",
       "  -18.775,\n",
       "  -19.1125,\n",
       "  -19.3132],\n",
       " 'elpetro_amivar': [1.39298,\n",
       "  52.442,\n",
       "  188.696,\n",
       "  2311.74,\n",
       "  2362.19,\n",
       "  2158.28,\n",
       "  909.175],\n",
       " 'elpetro_ba': 0.87454,\n",
       " 'elpetro_flux': [2.9988, 10.9678, 33.184, 104.279, 189.074, 266.311, 329.502],\n",
       " 'elpetro_flux_ivar': [0.183006,\n",
       "  0.560983,\n",
       "  0.382391,\n",
       "  3.32798,\n",
       "  1.41304,\n",
       "  0.262453,\n",
       "  0.0543157],\n",
       " 'elpetro_mass': 3676850000.0,\n",
       " 'elpetro_phi': 154.873,\n",
       " 'elpetro_th50_r': 1.33067,\n",
       " 'extinction': [0.11668,\n",
       "  0.115132,\n",
       "  0.0725557,\n",
       "  0.0533858,\n",
       "  0.0387198,\n",
       "  0.0293601,\n",
       "  0.0208166],\n",
       " 'field': 145,\n",
       " 'iauname': 'J153010.73+484124.8',\n",
       " 'nsaid': 221394,\n",
       " 'nsaid_v1b': -999,\n",
       " 'petro_flux': [2.42345, 7.63147, 33.5316, 103.177, 187.303, 266.833, 320.63],\n",
       " 'petro_flux_ivar': [0.261188,\n",
       "  0.978818,\n",
       "  0.357724,\n",
       "  4.59488,\n",
       "  1.37015,\n",
       "  0.156463,\n",
       "  0.0512794],\n",
       " 'petro_th50': 1.32995,\n",
       " 'run': 3225,\n",
       " 'sersic_absmag': [-14.2599,\n",
       "  -15.9543,\n",
       "  -17.0904,\n",
       "  -18.2817,\n",
       "  -18.9128,\n",
       "  -19.2697,\n",
       "  -19.5088],\n",
       " 'sersic_ba': 0.773047,\n",
       " 'sersic_flux': [2.7631, 12.5968, 35.5298, 114.376, 213.438, 306.252, 391.783],\n",
       " 'sersic_flux_ivar': [0.234907,\n",
       "  0.390875,\n",
       "  1.03359,\n",
       "  2.70403,\n",
       "  1.11227,\n",
       "  0.504806,\n",
       "  0.0975765],\n",
       " 'sersic_mass': 4259420000.0,\n",
       " 'sersic_n': 3.29617,\n",
       " 'sersic_phi': 163.295,\n",
       " 'sersic_th50': 1.27748,\n",
       " 'version': 'v1_0_1',\n",
       " 'z': 0.0407447,\n",
       " 'zdist': 0.0412014}"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the NSA properties for this galaxy from the cube instance\n",
    "nsa = cube.nsa\n",
    "nsa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adist_nsa_z': 116.28242,\n",
       " 'adist_z': 116.28242,\n",
       " 'bin_r_n_1': 21.0,\n",
       " 'bin_r_n_2': 36.0,\n",
       " 'bin_r_n_3': 40.0,\n",
       " 'bin_r_snr_1': 31.444183,\n",
       " 'bin_r_snr_2': 26.358574,\n",
       " 'bin_r_snr_3': 12.533445,\n",
       " 'bin_rmax': 3.5165226,\n",
       " 'binkey': 'HYB10',\n",
       " 'binsnr': 10.0,\n",
       " 'bintype': 'voronoi',\n",
       " 'dapbins': 393,\n",
       " 'dapdone': True,\n",
       " 'dapqual': 0,\n",
       " 'daptype': 'HYB10-GAU-MILESHC',\n",
       " 'datedap': '2018-07-21',\n",
       " 'drp3qual': 0,\n",
       " 'drpallindx': 3991,\n",
       " 'elfkey': 'EFITMDB',\n",
       " 'elmkey': 'EMOMM',\n",
       " 'emline_gew_1re_ha_6564': 27.498219,\n",
       " 'emline_gew_1re_hb_4862': 6.1061563,\n",
       " 'emline_gew_1re_hdel_4102': 1.3583332,\n",
       " 'emline_gew_1re_hei_5877': 0.7100007,\n",
       " 'emline_gew_1re_heii_4687': 0.06504237,\n",
       " 'emline_gew_1re_heps_3971': 0.80449516,\n",
       " 'emline_gew_1re_heta_3836': 0.3896089,\n",
       " 'emline_gew_1re_hgam_4341': 2.8444698,\n",
       " 'emline_gew_1re_hthe_3798': 0.65047354,\n",
       " 'emline_gew_1re_hzet_3890': 1.2700435,\n",
       " 'emline_gew_1re_neiii_3869': 0.14080524,\n",
       " 'emline_gew_1re_neiii_3968': 0.001792379,\n",
       " 'emline_gew_1re_nii_6549': 3.5805068,\n",
       " 'emline_gew_1re_nii_6585': 10.961037,\n",
       " 'emline_gew_1re_oi_6302': 0.43432698,\n",
       " 'emline_gew_1re_oi_6365': 0.13930151,\n",
       " 'emline_gew_1re_oii_3727': 4.2655497,\n",
       " 'emline_gew_1re_oii_3729': 5.457714,\n",
       " 'emline_gew_1re_oiii_4960': 0.47679856,\n",
       " 'emline_gew_1re_oiii_5008': 1.4359674,\n",
       " 'emline_gew_1re_sii_6718': 4.303934,\n",
       " 'emline_gew_1re_sii_6732': 3.0316718,\n",
       " 'emline_gew_peak_ha_6564': 38.932842,\n",
       " 'emline_gew_peak_hb_4862': 16.031044,\n",
       " 'emline_gew_peak_hdel_4102': 15.133765,\n",
       " 'emline_gew_peak_hei_5877': 12.108011,\n",
       " 'emline_gew_peak_heii_4687': 15.555817,\n",
       " 'emline_gew_peak_heps_3971': 19.180122,\n",
       " 'emline_gew_peak_heta_3836': 19.870707,\n",
       " 'emline_gew_peak_hgam_4341': 14.521507,\n",
       " 'emline_gew_peak_hthe_3798': 25.816128,\n",
       " 'emline_gew_peak_hzet_3890': 57.001114,\n",
       " 'emline_gew_peak_neiii_3869': 39.4136,\n",
       " 'emline_gew_peak_neiii_3968': 10.767518,\n",
       " 'emline_gew_peak_nii_6549': 4.0757356,\n",
       " 'emline_gew_peak_nii_6585': 12.755485,\n",
       " 'emline_gew_peak_oi_6302': 3.7761304,\n",
       " 'emline_gew_peak_oi_6365': 1.3595514,\n",
       " 'emline_gew_peak_oii_3727': 81.769936,\n",
       " 'emline_gew_peak_oii_3729': 47.940876,\n",
       " 'emline_gew_peak_oiii_4960': 3.5188925,\n",
       " 'emline_gew_peak_oiii_5008': 9.513018,\n",
       " 'emline_gew_peak_sii_6718': 8.063611,\n",
       " 'emline_gew_peak_sii_6732': 11.514327,\n",
       " 'emline_gflux_1re_ha_6564': 518.4598,\n",
       " 'emline_gflux_1re_hb_4862': 112.42022,\n",
       " 'emline_gflux_1re_hdel_4102': 21.380278,\n",
       " 'emline_gflux_1re_hei_5877': 12.862995,\n",
       " 'emline_gflux_1re_heii_4687': 1.1992174,\n",
       " 'emline_gflux_1re_heps_3971': 11.629765,\n",
       " 'emline_gflux_1re_heta_3836': 5.229859,\n",
       " 'emline_gflux_1re_hgam_4341': 47.112793,\n",
       " 'emline_gflux_1re_hthe_3798': 8.3632555,\n",
       " 'emline_gflux_1re_hzet_3890': 17.749779,\n",
       " 'emline_gflux_1re_neiii_3869': 1.9424094,\n",
       " 'emline_gflux_1re_neiii_3968': 0.024020921,\n",
       " 'emline_gflux_1re_nii_6549': 67.45055,\n",
       " 'emline_gflux_1re_nii_6585': 206.27078,\n",
       " 'emline_gflux_1re_oi_6302': 8.000183,\n",
       " 'emline_gflux_1re_oi_6365': 2.62406,\n",
       " 'emline_gflux_1re_oii_3727': 44.304893,\n",
       " 'emline_gflux_1re_oii_3729': 57.342915,\n",
       " 'emline_gflux_1re_oiii_4960': 8.812629,\n",
       " 'emline_gflux_1re_oiii_5008': 25.919497,\n",
       " 'emline_gflux_1re_sii_6718': 80.26501,\n",
       " 'emline_gflux_1re_sii_6732': 56.71939,\n",
       " 'emline_gflux_cen_ha_6564': 518.4598,\n",
       " 'emline_gflux_cen_hb_4862': 112.42022,\n",
       " 'emline_gflux_cen_hdel_4102': 21.380278,\n",
       " 'emline_gflux_cen_hei_5877': 12.862995,\n",
       " 'emline_gflux_cen_heii_4687': 1.1992174,\n",
       " 'emline_gflux_cen_heps_3971': 11.629765,\n",
       " 'emline_gflux_cen_heta_3836': 5.229859,\n",
       " 'emline_gflux_cen_hgam_4341': 47.112793,\n",
       " 'emline_gflux_cen_hthe_3798': 8.3632555,\n",
       " 'emline_gflux_cen_hzet_3890': 17.749779,\n",
       " 'emline_gflux_cen_neiii_3869': 1.9424094,\n",
       " 'emline_gflux_cen_neiii_3968': 0.024020921,\n",
       " 'emline_gflux_cen_nii_6549': 67.45055,\n",
       " 'emline_gflux_cen_nii_6585': 206.27078,\n",
       " 'emline_gflux_cen_oi_6302': 8.000183,\n",
       " 'emline_gflux_cen_oi_6365': 2.62406,\n",
       " 'emline_gflux_cen_oii_3727': 44.304893,\n",
       " 'emline_gflux_cen_oii_3729': 57.342915,\n",
       " 'emline_gflux_cen_oiii_4960': 8.812629,\n",
       " 'emline_gflux_cen_oiii_5008': 25.919497,\n",
       " 'emline_gflux_cen_sii_6718': 80.26501,\n",
       " 'emline_gflux_cen_sii_6732': 56.71939,\n",
       " 'emline_gflux_tot_ha_6564': 1361.6647,\n",
       " 'emline_gflux_tot_hb_4862': 318.3752,\n",
       " 'emline_gflux_tot_hdel_4102': 68.51021,\n",
       " 'emline_gflux_tot_hei_5877': 41.265724,\n",
       " 'emline_gflux_tot_heii_4687': 24.926678,\n",
       " 'emline_gflux_tot_heps_3971': 38.877518,\n",
       " 'emline_gflux_tot_heta_3836': 27.327614,\n",
       " 'emline_gflux_tot_hgam_4341': 132.94618,\n",
       " 'emline_gflux_tot_hthe_3798': 32.712917,\n",
       " 'emline_gflux_tot_hzet_3890': 56.146633,\n",
       " 'emline_gflux_tot_neiii_3869': 19.969725,\n",
       " 'emline_gflux_tot_neiii_3968': 5.9532337,\n",
       " 'emline_gflux_tot_nii_6549': 185.20743,\n",
       " 'emline_gflux_tot_nii_6585': 566.38385,\n",
       " 'emline_gflux_tot_oi_6302': 35.631546,\n",
       " 'emline_gflux_tot_oi_6365': 11.68715,\n",
       " 'emline_gflux_tot_oii_3727': 153.15848,\n",
       " 'emline_gflux_tot_oii_3729': 204.93419,\n",
       " 'emline_gflux_tot_oiii_4960': 28.799543,\n",
       " 'emline_gflux_tot_oiii_5008': 84.70451,\n",
       " 'emline_gflux_tot_sii_6718': 237.78206,\n",
       " 'emline_gflux_tot_sii_6732': 162.72934,\n",
       " 'emline_gsb_1re_ha_6564': 24.68856,\n",
       " 'emline_gsb_1re_hb_4862': 5.353344,\n",
       " 'emline_gsb_1re_hdel_4102': 1.0181085,\n",
       " 'emline_gsb_1re_hei_5877': 0.61252356,\n",
       " 'emline_gsb_1re_heii_4687': 0.057105593,\n",
       " 'emline_gsb_1re_heps_3971': 0.5537983,\n",
       " 'emline_gsb_1re_heta_3836': 0.2490409,\n",
       " 'emline_gsb_1re_hgam_4341': 2.2434664,\n",
       " 'emline_gsb_1re_hthe_3798': 0.39825025,\n",
       " 'emline_gsb_1re_hzet_3890': 0.84522754,\n",
       " 'emline_gsb_1re_neiii_3869': 0.09249569,\n",
       " 'emline_gsb_1re_neiii_3968': 0.0011438534,\n",
       " 'emline_gsb_1re_nii_6549': 3.2119308,\n",
       " 'emline_gsb_1re_nii_6585': 9.822418,\n",
       " 'emline_gsb_1re_oi_6302': 0.3809611,\n",
       " 'emline_gsb_1re_oi_6365': 0.12495524,\n",
       " 'emline_gsb_1re_oii_3727': 2.109757,\n",
       " 'emline_gsb_1re_oii_3729': 2.730615,\n",
       " 'emline_gsb_1re_oiii_4960': 0.41964898,\n",
       " 'emline_gsb_1re_oiii_5008': 1.2342618,\n",
       " 'emline_gsb_1re_sii_6718': 3.822143,\n",
       " 'emline_gsb_1re_sii_6732': 2.7009234,\n",
       " 'emline_gsb_peak_ha_6564': 32.130814,\n",
       " 'emline_gsb_peak_hb_4862': 6.729493,\n",
       " 'emline_gsb_peak_hdel_4102': 1.2454971,\n",
       " 'emline_gsb_peak_hei_5877': 0.81370413,\n",
       " 'emline_gsb_peak_heii_4687': 0.2812441,\n",
       " 'emline_gsb_peak_heps_3971': 0.72898835,\n",
       " 'emline_gsb_peak_heta_3836': 0.4459554,\n",
       " 'emline_gsb_peak_hgam_4341': 2.8376071,\n",
       " 'emline_gsb_peak_hthe_3798': 0.5431222,\n",
       " 'emline_gsb_peak_hzet_3890': 1.1030834,\n",
       " 'emline_gsb_peak_neiii_3869': 0.48630247,\n",
       " 'emline_gsb_peak_neiii_3968': 0.16095476,\n",
       " 'emline_gsb_peak_nii_6549': 4.100286,\n",
       " 'emline_gsb_peak_nii_6585': 12.539102,\n",
       " 'emline_gsb_peak_oi_6302': 0.45632312,\n",
       " 'emline_gsb_peak_oi_6365': 0.14967398,\n",
       " 'emline_gsb_peak_oii_3727': 2.4997134,\n",
       " 'emline_gsb_peak_oii_3729': 3.518587,\n",
       " 'emline_gsb_peak_oiii_4960': 0.5353756,\n",
       " 'emline_gsb_peak_oiii_5008': 1.5746341,\n",
       " 'emline_gsb_peak_sii_6718': 4.7994447,\n",
       " 'emline_gsb_peak_sii_6732': 3.5544295,\n",
       " 'emline_sew_1re_ha_6564': 27.390146,\n",
       " 'emline_sew_1re_hb_4862': 6.206489,\n",
       " 'emline_sew_1re_hdel_4102': 1.150524,\n",
       " 'emline_sew_1re_hei_5877': 0.64392686,\n",
       " 'emline_sew_1re_heii_4687': -0.17607133,\n",
       " 'emline_sew_1re_heps_3971': 0.94543386,\n",
       " 'emline_sew_1re_heta_3836': -0.2824071,\n",
       " 'emline_sew_1re_hgam_4341': 2.7655118,\n",
       " 'emline_sew_1re_hthe_3798': 0.48443457,\n",
       " 'emline_sew_1re_hzet_3890': 1.0155623,\n",
       " 'emline_sew_1re_neiii_3869': -0.22516635,\n",
       " 'emline_sew_1re_neiii_3968': 1.0079005,\n",
       " 'emline_sew_1re_nii_6549': 3.5470846,\n",
       " 'emline_sew_1re_nii_6585': 11.102756,\n",
       " 'emline_sew_1re_oi_6302': 0.38920906,\n",
       " 'emline_sew_1re_oi_6365': 0.07573469,\n",
       " 'emline_sew_1re_oii_3729': -999.0,\n",
       " 'emline_sew_1re_oiid_3728': 10.446977,\n",
       " 'emline_sew_1re_oiii_4960': 0.4875546,\n",
       " 'emline_sew_1re_oiii_5008': 1.631077,\n",
       " 'emline_sew_1re_sii_6718': 4.2352896,\n",
       " 'emline_sew_1re_sii_6732': 2.9410377,\n",
       " 'emline_sew_peak_ha_6564': 36.61824,\n",
       " 'emline_sew_peak_hb_4862': 16.381104,\n",
       " 'emline_sew_peak_hdel_4102': 35.858673,\n",
       " 'emline_sew_peak_hei_5877': 7.4755464,\n",
       " 'emline_sew_peak_heii_4687': 16.448965,\n",
       " 'emline_sew_peak_heps_3971': 21.757128,\n",
       " 'emline_sew_peak_heta_3836': 93.69317,\n",
       " 'emline_sew_peak_hgam_4341': 9.010911,\n",
       " 'emline_sew_peak_hthe_3798': 42.653828,\n",
       " 'emline_sew_peak_hzet_3890': 109.95577,\n",
       " 'emline_sew_peak_neiii_3869': 49.09403,\n",
       " 'emline_sew_peak_neiii_3968': 26.516947,\n",
       " 'emline_sew_peak_nii_6549': 7.722923,\n",
       " 'emline_sew_peak_nii_6585': 14.361592,\n",
       " 'emline_sew_peak_oi_6302': 27.917864,\n",
       " 'emline_sew_peak_oi_6365': 6.318035,\n",
       " 'emline_sew_peak_oii_3729': -999.0,\n",
       " 'emline_sew_peak_oiid_3728': 138.72752,\n",
       " 'emline_sew_peak_oiii_4960': 9.007727,\n",
       " 'emline_sew_peak_oiii_5008': 9.377492,\n",
       " 'emline_sew_peak_sii_6718': 8.540775,\n",
       " 'emline_sew_peak_sii_6732': 6.5237107,\n",
       " 'emline_sflux_1re_ha_6564': 516.3007,\n",
       " 'emline_sflux_1re_hb_4862': 114.247505,\n",
       " 'emline_sflux_1re_hdel_4102': 17.843182,\n",
       " 'emline_sflux_1re_hei_5877': 11.667008,\n",
       " 'emline_sflux_1re_heii_4687': -3.065474,\n",
       " 'emline_sflux_1re_heps_3971': 13.718588,\n",
       " 'emline_sflux_1re_heta_3836': -3.9439018,\n",
       " 'emline_sflux_1re_hgam_4341': 45.73389,\n",
       " 'emline_sflux_1re_hthe_3798': 6.2887335,\n",
       " 'emline_sflux_1re_hzet_3890': 14.105891,\n",
       " 'emline_sflux_1re_neiii_3869': -3.129478,\n",
       " 'emline_sflux_1re_neiii_3968': 14.486424,\n",
       " 'emline_sflux_1re_nii_6549': 66.72829,\n",
       " 'emline_sflux_1re_nii_6585': 208.84776,\n",
       " 'emline_sflux_1re_oi_6302': 7.2487226,\n",
       " 'emline_sflux_1re_oi_6365': 1.3686633,\n",
       " 'emline_sflux_1re_oii_3729': -999.0,\n",
       " 'emline_sflux_1re_oiid_3728': 109.3201,\n",
       " 'emline_sflux_1re_oiii_4960': 8.973248,\n",
       " 'emline_sflux_1re_oiii_5008': 29.46506,\n",
       " 'emline_sflux_1re_sii_6718': 78.89978,\n",
       " 'emline_sflux_1re_sii_6732': 54.994053,\n",
       " 'emline_sflux_cen_ha_6564': 516.3007,\n",
       " 'emline_sflux_cen_hb_4862': 114.247505,\n",
       " 'emline_sflux_cen_hdel_4102': 17.843182,\n",
       " 'emline_sflux_cen_hei_5877': 11.667008,\n",
       " 'emline_sflux_cen_heii_4687': -3.065474,\n",
       " 'emline_sflux_cen_heps_3971': 13.718588,\n",
       " 'emline_sflux_cen_heta_3836': -3.9439018,\n",
       " 'emline_sflux_cen_hgam_4341': 45.73389,\n",
       " 'emline_sflux_cen_hthe_3798': 6.2887335,\n",
       " 'emline_sflux_cen_hzet_3890': 14.105891,\n",
       " 'emline_sflux_cen_neiii_3869': -3.129478,\n",
       " 'emline_sflux_cen_neiii_3968': 14.486424,\n",
       " 'emline_sflux_cen_nii_6549': 66.72829,\n",
       " 'emline_sflux_cen_nii_6585': 208.84776,\n",
       " 'emline_sflux_cen_oi_6302': 7.2487226,\n",
       " 'emline_sflux_cen_oi_6365': 1.3686633,\n",
       " 'emline_sflux_cen_oii_3729': -999.0,\n",
       " 'emline_sflux_cen_oiid_3728': 109.3201,\n",
       " 'emline_sflux_cen_oiii_4960': 8.973248,\n",
       " 'emline_sflux_cen_oiii_5008': 29.46506,\n",
       " 'emline_sflux_cen_sii_6718': 78.89978,\n",
       " 'emline_sflux_cen_sii_6732': 54.994053,\n",
       " 'emline_sflux_tot_ha_6564': 1351.6582,\n",
       " 'emline_sflux_tot_hb_4862': 325.56708,\n",
       " 'emline_sflux_tot_hdel_4102': 71.18532,\n",
       " 'emline_sflux_tot_hei_5877': 11.650391,\n",
       " 'emline_sflux_tot_heii_4687': 9.703766,\n",
       " 'emline_sflux_tot_heps_3971': 28.350378,\n",
       " 'emline_sflux_tot_heta_3836': 3.1166573,\n",
       " 'emline_sflux_tot_hgam_4341': 121.193794,\n",
       " 'emline_sflux_tot_hthe_3798': 26.55513,\n",
       " 'emline_sflux_tot_hzet_3890': 59.658585,\n",
       " 'emline_sflux_tot_neiii_3869': 11.57201,\n",
       " 'emline_sflux_tot_neiii_3968': 34.55008,\n",
       " 'emline_sflux_tot_nii_6549': 189.18219,\n",
       " 'emline_sflux_tot_nii_6585': 563.47064,\n",
       " 'emline_sflux_tot_oi_6302': 12.422502,\n",
       " 'emline_sflux_tot_oi_6365': 2.6049273,\n",
       " 'emline_sflux_tot_oii_3729': -999.0,\n",
       " 'emline_sflux_tot_oiid_3728': 373.35123,\n",
       " 'emline_sflux_tot_oiii_4960': 33.708405,\n",
       " 'emline_sflux_tot_oiii_5008': 91.77788,\n",
       " 'emline_sflux_tot_sii_6718': 229.86024,\n",
       " 'emline_sflux_tot_sii_6732': 147.31511,\n",
       " 'emline_ssb_1re_ha_6564': 24.585749,\n",
       " 'emline_ssb_1re_hb_4862': 5.440357,\n",
       " 'emline_ssb_1re_hdel_4102': 0.8496753,\n",
       " 'emline_ssb_1re_hei_5877': 0.55557185,\n",
       " 'emline_ssb_1re_heii_4687': -0.14597495,\n",
       " 'emline_ssb_1re_heps_3971': 0.6532661,\n",
       " 'emline_ssb_1re_heta_3836': -0.18780485,\n",
       " 'emline_ssb_1re_hgam_4341': 2.1778042,\n",
       " 'emline_ssb_1re_hthe_3798': 0.2994635,\n",
       " 'emline_ssb_1re_hzet_3890': 0.6717091,\n",
       " 'emline_ssb_1re_neiii_3869': -0.14902276,\n",
       " 'emline_ssb_1re_neiii_3968': 0.6898297,\n",
       " 'emline_ssb_1re_nii_6549': 3.1775374,\n",
       " 'emline_ssb_1re_nii_6585': 9.945131,\n",
       " 'emline_ssb_1re_oi_6302': 0.34517726,\n",
       " 'emline_ssb_1re_oi_6365': 0.065174446,\n",
       " 'emline_ssb_1re_oii_3729': -999.0,\n",
       " 'emline_ssb_1re_oiid_3728': 5.205719,\n",
       " 'emline_ssb_1re_oiii_4960': 0.4272975,\n",
       " 'emline_ssb_1re_oiii_5008': 1.4030981,\n",
       " 'emline_ssb_1re_sii_6718': 3.7571323,\n",
       " 'emline_ssb_1re_sii_6732': 2.6187644,\n",
       " 'emline_ssb_peak_ha_6564': 31.868652,\n",
       " 'emline_ssb_peak_hb_4862': 6.8731008,\n",
       " 'emline_ssb_peak_hdel_4102': 1.1216679,\n",
       " 'emline_ssb_peak_hei_5877': 0.7711617,\n",
       " 'emline_ssb_peak_heii_4687': 0.31739423,\n",
       " 'emline_ssb_peak_heps_3971': 0.91349566,\n",
       " 'emline_ssb_peak_heta_3836': 0.33681902,\n",
       " 'emline_ssb_peak_hgam_4341': 2.769505,\n",
       " 'emline_ssb_peak_hthe_3798': 0.4718025,\n",
       " 'emline_ssb_peak_hzet_3890': 0.8520103,\n",
       " 'emline_ssb_peak_neiii_3869': 0.3048002,\n",
       " 'emline_ssb_peak_neiii_3968': 0.9720679,\n",
       " 'emline_ssb_peak_nii_6549': 4.120372,\n",
       " 'emline_ssb_peak_nii_6585': 12.650413,\n",
       " 'emline_ssb_peak_oi_6302': 0.6804554,\n",
       " 'emline_ssb_peak_oi_6365': 0.3308851,\n",
       " 'emline_ssb_peak_oii_3729': -999.0,\n",
       " 'emline_ssb_peak_oiid_3728': 6.397572,\n",
       " 'emline_ssb_peak_oiii_4960': 0.609163,\n",
       " 'emline_ssb_peak_oiii_5008': 1.8636974,\n",
       " 'emline_ssb_peak_sii_6718': 4.6782374,\n",
       " 'emline_ssb_peak_sii_6732': 3.4148548,\n",
       " 'ha_gsigma_1re': 68.02177,\n",
       " 'ha_gsigma_hi': 71.87889,\n",
       " 'ha_gsigma_hi_clip': 69.07364,\n",
       " 'ha_gvel_hi': 101.71413,\n",
       " 'ha_gvel_hi_clip': 101.71413,\n",
       " 'ha_gvel_lo': -102.10004,\n",
       " 'ha_gvel_lo_clip': -102.10004,\n",
       " 'ha_z': 0.040754627,\n",
       " 'ifudec': 48.6902,\n",
       " 'ifudesign': 1901,\n",
       " 'ifura': 232.5447,\n",
       " 'ldist_nsa_z': 125.95125,\n",
       " 'ldist_z': 125.95125,\n",
       " 'mangaid': '1-209232',\n",
       " 'mngtarg1': 2336,\n",
       " 'mngtarg2': 0,\n",
       " 'mngtarg3': 0,\n",
       " 'mode': 'CUBE',\n",
       " 'nsa_elpetro_ba': 0.87454,\n",
       " 'nsa_elpetro_phi': 154.873,\n",
       " 'nsa_elpetro_th50_r': 1.33067,\n",
       " 'nsa_sersic_ba': 0.773047,\n",
       " 'nsa_sersic_n': 3.29617,\n",
       " 'nsa_sersic_phi': 163.295,\n",
       " 'nsa_sersic_th50': 1.27748,\n",
       " 'nsa_z': 0.0407447,\n",
       " 'nsa_zdist': 0.0412014,\n",
       " 'objdec': 48.6902,\n",
       " 'objra': 232.54471,\n",
       " 'plate': 8485,\n",
       " 'plateifu': '8485-1901',\n",
       " 'rcov90': 4.593927,\n",
       " 'rdxqakey': 'SNRG',\n",
       " 'sb_1re': 0.7397315,\n",
       " 'sckey': 'GAU-MILESHC',\n",
       " 'sfr_1re': 0.052848306,\n",
       " 'sfr_tot': 0.13879897,\n",
       " 'sikey': 'INDXEN',\n",
       " 'snr_med_1': 23.818922,\n",
       " 'snr_med_2': 31.974985,\n",
       " 'snr_med_3': 36.93258,\n",
       " 'snr_med_4': 29.912338,\n",
       " 'snr_ring_1': 43.22005,\n",
       " 'snr_ring_2': 54.946964,\n",
       " 'snr_ring_3': 68.299904,\n",
       " 'snr_ring_4': 40.68281,\n",
       " 'specindex_1re_atio': 0.0055130944,\n",
       " 'specindex_1re_btio': 0.0035524892,\n",
       " 'specindex_1re_c24668': 4.1051598,\n",
       " 'specindex_1re_ca4227': 0.5814394,\n",
       " 'specindex_1re_ca4455': 0.86386544,\n",
       " 'specindex_1re_cah1': 0.0027957938,\n",
       " 'specindex_1re_cah2': 0.011549883,\n",
       " 'specindex_1re_cahk': 11.244532,\n",
       " 'specindex_1re_caii1': 1.2633883,\n",
       " 'specindex_1re_caii2': 4.1861773,\n",
       " 'specindex_1re_caii3': 2.8871956,\n",
       " 'specindex_1re_cn1': -0.08640129,\n",
       " 'specindex_1re_cn2': -0.0437025,\n",
       " 'specindex_1re_d4000': 1.5241088,\n",
       " 'specindex_1re_dn4000': 1.3374544,\n",
       " 'specindex_1re_fe4383': 2.8076222,\n",
       " 'specindex_1re_fe4531': 2.3227534,\n",
       " 'specindex_1re_fe5015': 2.7222157,\n",
       " 'specindex_1re_fe5270': 1.5268862,\n",
       " 'specindex_1re_fe5335': -999.0,\n",
       " 'specindex_1re_fe5406': 1.3877692,\n",
       " 'specindex_1re_fe5709': 0.86984617,\n",
       " 'specindex_1re_fe5782': 1.121057,\n",
       " 'specindex_1re_fehcvd': -999.0,\n",
       " 'specindex_1re_g4300': 2.198671,\n",
       " 'specindex_1re_hb': 3.240003,\n",
       " 'specindex_1re_hdeltaa': 4.0503592,\n",
       " 'specindex_1re_hdeltaf': 3.57781,\n",
       " 'specindex_1re_hgammaa': 1.6787565,\n",
       " 'specindex_1re_hgammaf': 2.7013273,\n",
       " 'specindex_1re_mg1': 0.040176533,\n",
       " 'specindex_1re_mg2': 0.11580241,\n",
       " 'specindex_1re_mgb': 2.42074,\n",
       " 'specindex_1re_mgicvd': 2.8958685,\n",
       " 'specindex_1re_mgiir': 0.6450525,\n",
       " 'specindex_1re_nad': 1.9727253,\n",
       " 'specindex_1re_nai': 0.9842906,\n",
       " 'specindex_1re_naicvd': 0.30560908,\n",
       " 'specindex_1re_naisdss': 0.4465879,\n",
       " 'specindex_1re_pa12': 0.6469185,\n",
       " 'specindex_1re_pa14': 0.13523296,\n",
       " 'specindex_1re_pa17': 0.082407184,\n",
       " 'specindex_1re_tio1': 0.02312117,\n",
       " 'specindex_1re_tio2': 0.051430404,\n",
       " 'specindex_1re_tio2sdss': 0.05941378,\n",
       " 'specindex_1re_tiocvd': 1.0534573,\n",
       " 'specindex_hi_atio': 0.18232621,\n",
       " 'specindex_hi_btio': 0.13323237,\n",
       " 'specindex_hi_c24668': 13.145877,\n",
       " 'specindex_hi_ca4227': 7.470146,\n",
       " 'specindex_hi_ca4455': 7.579963,\n",
       " 'specindex_hi_cah1': 0.18345265,\n",
       " 'specindex_hi_cah2': 0.134479,\n",
       " 'specindex_hi_cahk': 39.694824,\n",
       " 'specindex_hi_caii1': 0.0,\n",
       " 'specindex_hi_caii2': 0.0,\n",
       " 'specindex_hi_caii3': 0.0,\n",
       " 'specindex_hi_clip_atio': 0.12031085,\n",
       " 'specindex_hi_clip_btio': 0.13429171,\n",
       " 'specindex_hi_clip_c24668': 11.559466,\n",
       " 'specindex_hi_clip_ca4227': 5.70183,\n",
       " 'specindex_hi_clip_ca4455': 5.8970137,\n",
       " 'specindex_hi_clip_cah1': 0.1814796,\n",
       " 'specindex_hi_clip_cah2': 0.12494275,\n",
       " 'specindex_hi_clip_cahk': 29.477184,\n",
       " 'specindex_hi_clip_caii1': 0.0,\n",
       " 'specindex_hi_clip_caii2': 0.0,\n",
       " 'specindex_hi_clip_caii3': 0.0,\n",
       " 'specindex_hi_clip_cn1': 0.03848137,\n",
       " 'specindex_hi_clip_cn2': 0.110505216,\n",
       " 'specindex_hi_clip_d4000': 2.8179746,\n",
       " 'specindex_hi_clip_dn4000': 2.5910015,\n",
       " 'specindex_hi_clip_fe4383': 12.733747,\n",
       " 'specindex_hi_clip_fe4531': 10.478491,\n",
       " 'specindex_hi_clip_fe5015': 10.268717,\n",
       " 'specindex_hi_clip_fe5270': 6.442884,\n",
       " 'specindex_hi_clip_fe5335': -999.0,\n",
       " 'specindex_hi_clip_fe5406': 5.7971134,\n",
       " 'specindex_hi_clip_fe5709': 5.739539,\n",
       " 'specindex_hi_clip_fe5782': 7.0338426,\n",
       " 'specindex_hi_clip_fehcvd': -999.0,\n",
       " 'specindex_hi_clip_g4300': 12.858058,\n",
       " 'specindex_hi_clip_hb': 6.3344836,\n",
       " 'specindex_hi_clip_hdeltaa': 11.696167,\n",
       " 'specindex_hi_clip_hdeltaf': 7.363281,\n",
       " 'specindex_hi_clip_hgammaa': 11.439256,\n",
       " 'specindex_hi_clip_hgammaf': 7.3267484,\n",
       " 'specindex_hi_clip_mg1': 0.16603558,\n",
       " 'specindex_hi_clip_mg2': 0.18487155,\n",
       " 'specindex_hi_clip_mgb': 6.9796433,\n",
       " 'specindex_hi_clip_mgicvd': 7.97149,\n",
       " 'specindex_hi_clip_mgiir': 0.0,\n",
       " 'specindex_hi_clip_nad': 7.651409,\n",
       " 'specindex_hi_clip_nai': 0.0,\n",
       " 'specindex_hi_clip_naicvd': 0.0,\n",
       " 'specindex_hi_clip_naisdss': 0.0,\n",
       " 'specindex_hi_clip_pa12': 0.0,\n",
       " 'specindex_hi_clip_pa14': 0.0,\n",
       " 'specindex_hi_clip_pa17': 0.0,\n",
       " 'specindex_hi_clip_tio1': 0.15529612,\n",
       " 'specindex_hi_clip_tio2': 0.19362745,\n",
       " 'specindex_hi_clip_tio2sdss': 0.17072938,\n",
       " 'specindex_hi_clip_tiocvd': 1.199695,\n",
       " 'specindex_hi_cn1': 0.13124722,\n",
       " 'specindex_hi_cn2': 0.13624196,\n",
       " 'specindex_hi_d4000': 3.6134193,\n",
       " 'specindex_hi_dn4000': 3.3198805,\n",
       " 'specindex_hi_fe4383': 19.225418,\n",
       " 'specindex_hi_fe4531': 14.033874,\n",
       " 'specindex_hi_fe5015': 10.447021,\n",
       " 'specindex_hi_fe5270': 7.0438824,\n",
       " 'specindex_hi_fe5335': -999.0,\n",
       " 'specindex_hi_fe5406': 6.769316,\n",
       " 'specindex_hi_fe5709': 7.8267226,\n",
       " 'specindex_hi_fe5782': 7.715983,\n",
       " 'specindex_hi_fehcvd': -999.0,\n",
       " 'specindex_hi_g4300': 13.199309,\n",
       " 'specindex_hi_hb': 9.765947,\n",
       " 'specindex_hi_hdeltaa': 13.315697,\n",
       " 'specindex_hi_hdeltaf': 8.133986,\n",
       " 'specindex_hi_hgammaa': 12.889933,\n",
       " 'specindex_hi_hgammaf': 8.241569,\n",
       " 'specindex_hi_mg1': 0.17528483,\n",
       " 'specindex_hi_mg2': 0.18594217,\n",
       " 'specindex_hi_mgb': 7.8064184,\n",
       " 'specindex_hi_mgicvd': 11.277573,\n",
       " 'specindex_hi_mgiir': 0.0,\n",
       " 'specindex_hi_nad': 8.078163,\n",
       " 'specindex_hi_nai': 0.0,\n",
       " 'specindex_hi_naicvd': 0.0,\n",
       " 'specindex_hi_naisdss': 0.0,\n",
       " 'specindex_hi_pa12': 0.0,\n",
       " 'specindex_hi_pa14': 0.0,\n",
       " 'specindex_hi_pa17': 0.0,\n",
       " 'specindex_hi_tio1': 0.17581758,\n",
       " 'specindex_hi_tio2': 0.22457242,\n",
       " 'specindex_hi_tio2sdss': 0.22362435,\n",
       " 'specindex_hi_tiocvd': 1.2137107,\n",
       " 'specindex_lo_atio': -0.09773963,\n",
       " 'specindex_lo_btio': -0.3110191,\n",
       " 'specindex_lo_c24668': -21.919329,\n",
       " 'specindex_lo_ca4227': -6.4965906,\n",
       " 'specindex_lo_ca4455': -9.576805,\n",
       " 'specindex_lo_cah1': -0.17542453,\n",
       " 'specindex_lo_cah2': -0.2115286,\n",
       " 'specindex_lo_cahk': -40.765045,\n",
       " 'specindex_lo_caii1': 0.0,\n",
       " 'specindex_lo_caii2': 0.0,\n",
       " 'specindex_lo_caii3': 0.0,\n",
       " 'specindex_lo_clip_atio': -0.098941304,\n",
       " 'specindex_lo_clip_btio': -0.2334966,\n",
       " 'specindex_lo_clip_c24668': -10.48352,\n",
       " 'specindex_lo_clip_ca4227': -3.1533794,\n",
       " 'specindex_lo_clip_ca4455': -5.247075,\n",
       " 'specindex_lo_clip_cah1': -0.17553627,\n",
       " 'specindex_lo_clip_cah2': -0.12197615,\n",
       " 'specindex_lo_clip_cahk': -3.206794,\n",
       " 'specindex_lo_clip_caii1': 0.0,\n",
       " 'specindex_lo_clip_caii2': 0.0,\n",
       " 'specindex_lo_clip_caii3': 0.0,\n",
       " 'specindex_lo_clip_cn1': -0.28052378,\n",
       " 'specindex_lo_clip_cn2': -0.30325928,\n",
       " 'specindex_lo_clip_d4000': 2.2906063,\n",
       " 'specindex_lo_clip_dn4000': 2.1369634,\n",
       " 'specindex_lo_clip_fe4383': -7.074122,\n",
       " 'specindex_lo_clip_fe4531': -4.7090836,\n",
       " 'specindex_lo_clip_fe5015': -7.476422,\n",
       " 'specindex_lo_clip_fe5270': -4.5851026,\n",
       " 'specindex_lo_clip_fe5335': -999.0,\n",
       " 'specindex_lo_clip_fe5406': -2.3742986,\n",
       " 'specindex_lo_clip_fe5709': -4.472775,\n",
       " 'specindex_lo_clip_fe5782': -6.253817,\n",
       " 'specindex_lo_clip_fehcvd': -999.0,\n",
       " 'specindex_lo_clip_g4300': -4.7669578,\n",
       " 'specindex_lo_clip_hb': -0.5814569,\n",
       " 'specindex_lo_clip_hdeltaa': -1.1341102,\n",
       " 'specindex_lo_clip_hdeltaf': -0.19643383,\n",
       " 'specindex_lo_clip_hgammaa': -9.221306,\n",
       " 'specindex_lo_clip_hgammaf': -0.45533746,\n",
       " 'specindex_lo_clip_mg1': -0.0719978,\n",
       " 'specindex_lo_clip_mg2': -0.030249458,\n",
       " 'specindex_lo_clip_mgb': -1.8905766,\n",
       " 'specindex_lo_clip_mgicvd': -3.1088808,\n",
       " 'specindex_lo_clip_mgiir': 0.0,\n",
       " 'specindex_lo_clip_nad': -4.248341,\n",
       " 'specindex_lo_clip_nai': 0.0,\n",
       " 'specindex_lo_clip_naicvd': 0.0,\n",
       " 'specindex_lo_clip_naisdss': 0.0,\n",
       " 'specindex_lo_clip_pa12': 0.0,\n",
       " 'specindex_lo_clip_pa14': 0.0,\n",
       " 'specindex_lo_clip_pa17': 0.0,\n",
       " 'specindex_lo_clip_tio1': -0.12913258,\n",
       " 'specindex_lo_clip_tio2': 0.0031668446,\n",
       " 'specindex_lo_clip_tio2sdss': -0.017870137,\n",
       " 'specindex_lo_clip_tiocvd': 0.7384483,\n",
       " 'specindex_lo_cn1': -0.38581884,\n",
       " 'specindex_lo_cn2': -0.4855681,\n",
       " 'specindex_lo_d4000': 2.2926426,\n",
       " 'specindex_lo_dn4000': 2.1320908,\n",
       " 'specindex_lo_fe4383': -8.182995,\n",
       " 'specindex_lo_fe4531': -6.772291,\n",
       " 'specindex_lo_fe5015': -22.142231,\n",
       " 'specindex_lo_fe5270': -10.728889,\n",
       " 'specindex_lo_fe5335': -999.0,\n",
       " 'specindex_lo_fe5406': -7.101056,\n",
       " 'specindex_lo_fe5709': -11.745926,\n",
       " 'specindex_lo_fe5782': -8.231678,\n",
       " 'specindex_lo_fehcvd': -999.0,\n",
       " 'specindex_lo_g4300': -4.7642274,\n",
       " 'specindex_lo_hb': -3.283807,\n",
       " 'specindex_lo_hdeltaa': -2.2277663,\n",
       " 'specindex_lo_hdeltaf': -2.327436,\n",
       " 'specindex_lo_hgammaa': -13.2703,\n",
       " 'specindex_lo_hgammaf': -0.46250376,\n",
       " 'specindex_lo_mg1': -0.08722681,\n",
       " 'specindex_lo_mg2': -0.105767705,\n",
       " 'specindex_lo_mgb': -2.209138,\n",
       " 'specindex_lo_mgicvd': -5.943578,\n",
       " 'specindex_lo_mgiir': 0.0,\n",
       " 'specindex_lo_nad': -10.772675,\n",
       " 'specindex_lo_nai': 0.0,\n",
       " 'specindex_lo_naicvd': 0.0,\n",
       " 'specindex_lo_naisdss': 0.0,\n",
       " 'specindex_lo_pa12': 0.0,\n",
       " 'specindex_lo_pa14': 0.0,\n",
       " 'specindex_lo_pa17': 0.0,\n",
       " 'specindex_lo_tio1': -0.14582819,\n",
       " 'specindex_lo_tio2': 0.0048940494,\n",
       " 'specindex_lo_tio2sdss': -0.02482577,\n",
       " 'specindex_lo_tiocvd': 0.7096045,\n",
       " 'stellar_cont_rchi2_1re': 1.582368,\n",
       " 'stellar_sigma_1re': 76.32441,\n",
       " 'stellar_vel_hi': 51.786217,\n",
       " 'stellar_vel_hi_clip': 49.862907,\n",
       " 'stellar_vel_lo': -105.37463,\n",
       " 'stellar_vel_lo_clip': -58.098408,\n",
       " 'stellar_z': 0.040770844,\n",
       " 'tplkey': 'MILESHC',\n",
       " 'verscore': 'v1_6_2',\n",
       " 'versdap': '2.2.1',\n",
       " 'versdrp2': 'v2_4_3',\n",
       " 'versdrp3': 'v2_4_3',\n",
       " 'versutil': 'v5_5_32',\n",
       " 'z': 0.0407447}"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the DAP all properties from the maps instance\n",
    "maps.dapall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SDSS members produce Value-Added Catalogs (VACS) of MaNGA products.  These products can be incorporated into Marvin.  Any incorporated VACS in Marvin are contained in the ``vacs`` attribute attached to each tool.  More information on VACs in Marvin can be found [here](https://sdss-marvin.readthedocs.io/en/stable/tools/catalogues.html#value-added-catalogs-vacs).  Currently the MaNGA-HI VAC for DR15 has been contributed into Marvin.  Let's take a look at it.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VACContainer ('mangahi')>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access the available VACs for this object\n",
    "vacs = cube.vacs\n",
    "vacs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**vacs** is a container (list) of all names of the vacs contributed into Marvin.  These names are dottable to access to vac data.  These contributed custom products can be of any format chosen by the VAC owner, a Python class, a dictionary, an array, etc.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HI(8485-1901)\n",
      "data type: <class 'marvin.contrib.vacs.mangahi.HIData'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'No HI data exists for 8485-1901'"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access the mangahi vac\n",
    "hi = vacs.mangahi\n",
    "print(hi)\n",
    "print('data type:', type(hi))\n",
    "\n",
    "# access any HI data available for 8485-1901\n",
    "hi.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='download'></a>\n",
    "### Downloading files\n",
    "While Marvin allows you to access data remotely, frequently you will find that you want to download the file associated to an object so that you can access it more quickly in the future. We can do that using the ``download`` method attached to all marvin core tools. Let’s try to load a cube that we know we do not have in out hard drive, and download it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Marvin Cube (plateifu='8485-1902', mode='remote', data_origin='api')>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check our remote cube we loaded earlier \n",
    "remote_cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download the remote cube\n",
    "# remote_cube.download()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Marvin Cube (plateifu='8485-1902', mode='remote', data_origin='api')>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the cube a second time loads it from the new file\n",
    "new_cube = Cube('8485-1902')\n",
    "new_cube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each tool contains a ``download`` method to download that individual file.  If you want to download a list of targets in bulk, you can use the ``downloadList`` utility function.  This function uses the built-in Python product ``sdss_access`` to download files from the Utah SAS.  ``downloadList`` accepts as input a list of target ids, e.g. plateifus or mangaids.  The ``dltype`` keyword indicates the type of file you wish to download.  The default file is ``cube``.  Let's use the utility function to download a list of DAP MAPS files for three targets.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from marvin.utils.general import downloadList\n",
    "\n",
    "# create a list of target ids\n",
    "plateifus = ['8485-1901', '8485-1902', '7443-1901']\n",
    "\n",
    "# download the DAP 2d MAPS files for these targets\n",
    "# dllist = downloadList(plateifus, dltype='maps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"spaxels\"></a>\n",
    "### Interacting with Spaxels\n",
    "All of the core Marvin Galaxy Tools have the ability to extract one ore more ``Spaxel`` objects.  ``Spaxel`` objects are special Marvin objects to make it easier to deal with individual spaxels in galaxies and access their properties.  See the [Getting Started with Spaxels](https://sdss-marvin.readthedocs.io/en/stable/tools/spaxel.html#marvin-spaxel) for more information on how to interact with ``Spaxels``.  We can either use the standard array slicing notation (0-indexed, origin of coordinates in the lower left corner of the array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Marvin Spaxel (plateifu=8485-1901, x=10, y=15; x_cen=-7, y_cen=-2, loaded=cube)>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract the spaxel at array index x=10, y=15\n",
    "spaxel = cube[15, 10]\n",
    "spaxel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or we can use ``getSpaxel`` method, which accepts multiple arguments. Note that by default, (x, y) coordinates passed to ``getSpaxel`` are measured from the center of the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Marvin Spaxel (plateifu=8485-1901, x=17, y=17; x_cen=0, y_cen=0, loaded=cube)>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the spaxel at the center of the target galaxy\n",
    "central_spaxel = cube.getSpaxel(x=0, y=0)\n",
    "central_spaxel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: AstropyDeprecationWarning: The truth value of a Quantity is ambiguous. In the future this will raise a ValueError. [astropy.units.quantity]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x157f312b0>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can plot the spectrum of the spaxel\n",
    "spaxel.flux.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Resources\n",
    "\n",
    "- Go through the [Lean Tutorial](https://sdss-marvin.readthedocs.io/en/latest/tutorials/lean-tutorial.html#marvin-lean-tutorial)\n",
    "- Learn more [Plotting Techniques](https://sdss-marvin.readthedocs.io/en/latest/tutorials/plotting-tutorial.html)\n",
    "- Dig into the [Galaxy Tools](https://sdss-marvin.readthedocs.io/en/latest/tools/index.html) in more detail.\n",
    "- Try out [Marvin Queries](https://sdss-marvin.readthedocs.io/en/latest/query/index.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:marvindev]",
   "language": "python",
   "name": "conda-env-marvindev-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}