Query Datamodel¶

This page contains a general guide to the query datamodel and its usage. Marvin provides the ability to query on or return a number of different parameters. These parameteres are described in the datamodel, along with their relevant syntax and groupings. The datamodel lives in marvin.utils.datamodel.query. The full datamodel for any release is available here.

The following guide showcases the datamodel for MPL-5. Other releases behave similarly, but may contain a different number of parameters.

Accessing¶

You can access the query datamodel for a given release from within a built Marvin Query object:

query = Query(searchfilter='nsa.z < 0.1')
qdm = query.datamodel
print(qdm)
<QueryDataModel release='MPL-5', n_groups=7, n_parameters=697, n_total=0>

Or from importing the datamodel directly. Importing the datamodel directly provides a list of available datamodels to choose from.

# import the list of datamodels
from marvin.utils.datamodel.query import datamodel

print(datamodel)
QueryDataModelList([('MPL-4',
                     <QueryDataModel release='MPL-4', n_groups=7, n_parameters=565, n_total=0>),
                    ('MPL-5',
                     <QueryDataModel release='MPL-5', n_groups=7, n_parameters=697, n_total=0>),
                    ('MPL-6',
                     <QueryDataModel release='MPL-6', n_groups=7, n_parameters=1634, n_total=0>)])

Let’s select the MPL-5 query datamodel:

qdm = datamodel['MPL-5']
print(qdm)
<QueryDataModel release='MPL-5', n_groups=7, n_parameters=697, n_total=0>

Groups¶

All Manga query parameters are grouped by parameter type. We offer six groups of parameters, and have provided a toolset designed to help you search for and locate what parameters are available, and to easily input them into Queries.

Metadata - metadata associated with the galaxy
Spaxel metadata - metadata associated with an individual spaxel
Emission Lines - DAP emission-line measurements
Kinematics - DAP kinematic measurements
Spectral Indices - DAP spectral index measurements
NSA Catalog (NASA Sloan-Atlas Catalog) - global galaxy properties
Other - all other non-classified parameters

To access the list of groups, use the groups attribute:

qdm.groups

[<ParameterGroup name=Metadata, n_parameters=7>,
 <ParameterGroup name=Spaxel Metadata, n_parameters=3>,
 <ParameterGroup name=Emission Lines, n_parameters=336>,
 <ParameterGroup name=Kinematics, n_parameters=14>,
 <ParameterGroup name=Spectral Indices, n_parameters=8>,
 <ParameterGroup name=NSA Catalog, n_parameters=158>,
 <ParameterGroup name=Other, n_parameters=171>]

Shown is a list of all available query groups, with the number of parameters within each group. You can access individual groups, and list their parameters using either the parameters attribute, or the list_params method.

# access a group
meta = qdm.groups['metadata']
<ParameterGroup name=Metadata, paramcount=7>

# list parameters in the metadata group
meta.parameters
[<QueryParameter full=cube.plateifu, name=plateifu, short=plateifu, remote=plateifu, display=Plate-IFU>,
 <QueryParameter full=cube.mangaid, name=mangaid, short=mangaid, remote=mangaid, display=Manga-ID>,
 <QueryParameter full=cube.ra, name=ra, short=ra, remote=ra, display=RA>,
 <QueryParameter full=cube.dec, name=dec, short=dec, remote=dec, display=Dec>,
 <QueryParameter full=cube.plate, name=plate, short=plate, remote=plate, display=Plate>,
 <QueryParameter full=bintype.name, name=bintype_name, short=bin, remote=bintype_name, display=Bintype>,
 <QueryParameter full=template.name, name=template_name, short=template, remote=template_name, display=Template>]

Alternatively with the list_params method:

meta.list_params()

Access individual parameters with the same list indexing technique for groups.

# grab ra and dec
meta['ra']
 <QueryParameter full=cube.ra, name=ra, short=ra, remote=ra, display=RA>

meta['dec']
<QueryParameter full=cube.dec, name=dec, short=dec, remote=dec, display=Dec>

# slice it like a list
meta[0:3]
[<QueryParameter full=cube.plateifu, name=plateifu, short=plateifu, remote=plateifu, display=Plate-IFU>,
 <QueryParameter full=cube.mangaid, name=mangaid, short=mangaid, remote=mangaid, display=Manga-ID>,
 <QueryParameter full=cube.ra, name=ra, short=ra, remote=ra, display=RA>]

Parameters¶

All queryable parameters are QueryParameter objects. These provide a variety of formats for the naming of the paramter, the most important being the full attribute. full represents the unique parameter name guranteed to be queryable. Additional name formats of interest might be short, which provides a shortcut name to the paramter, and display, which provides a display name useful in plots.

To access a parameter’s full name:

# RA parameter
ra = meta['ra']

ra.full
cube.ra

Each QueryParameter also has a property attribute which references the DAP DataModel Property if it exists:

stvel = qdm.groups['kin'].stellar_vel
print(stvel)
<QueryParameter full=spaxelprop.stellar_vel, name=stellar_vel, short=stvel, remote=stellar_vel, display=Stellar Velocity>

stvel.property
<Property 'stellar_vel', channel='None', release='2.0.2', unit=u'km / s'>

Using within Marvin Queries¶

The parameter syntax Marvin prefers for all input into searchfilter and returnparams is the full attribute on the QueryParameter.

To generate a list of names that are formatted as ready-input into Marvin Queries, use the full keyword.

# format the RA query parameter
meta['ra'].full
'cube.ra'

# format the entire list of metadata parameters
meta.list_params('full')
['cube.plateifu',
 'cube.mangaid',
 'cube.ra',
 'cube.dec',
 'cube.plate',
 'bintype.name',
 'template.name']

You can combine lists. Make a list of the galaxy RA, Dec, NSA redshift, and g-r color parameters.

# make a custom list of parameters
meta = query_params['metadata']
nsa = query_params['nsa']
myparams = meta.list_params('full', subset=['ra','dec']) + nsa.list_params('full', subset=['z', 'absmag_g_r'])

myparams
['cube.ra', 'cube.dec', 'nsa.z', 'nsa.elpetro_absmag_g_r']

# input into a Marvin Query
from marvin.tools.query import Query
query = Query(searchfilter='nsa.z < 0.1', returnparams=myparams)

If you want all parameters from all groups, use the groups.list_params method with the full keyword.

# return all parameters from all groups
qdm.groups.list_params('full')
['cube.plateifu',
 'cube.mangaid',
 'cube.ra',
 'cube.dec',
 'cube.plate',
 'bintype.name',
  ...
  ...
 'nsa.z',
 'nsa.elpetro_ba',
 'nsa.elpetro_mag_g_r',
 'nsa.elpetro_absmag_g_r',
 'nsa.elpetro_logmass',
 'nsa.elpetro_th50_r',
 'nsa.sersic_logmass',
 'nsa.sersic_ba']

You can also select the parameters from individual groups. Let’s return all the Kinematic parameters.

myparams = query_params.list_params('full', groups=[kin'])
myparams
[ ...
 'spaxelprop.emline_gvel_ha_6564',
 'spaxelprop.emline_gvel_oiii_5008',
 'spaxelprop.emline_gsigma_ha_6564',
 'spaxelprop.emline_gsigma_oiii_5008',
 'spaxelprop.stellar_vel',
 'spaxelprop.stellar_sigma']

We can input these directly into a Marvin Query. Note that returning lots of parameters or a mix of spaxel and galaxy parameters may result in long query times or a large result set.

from marvin.tools.query import Query
query = Query(searchfilter='nsa.z < 0.1', returnparams=myparams)
results = query.run()

results.results[0]
ResultRow(mangaid=u'1-209232', plate=8485, plateifu=u'8485-1901', ifu_name=u'1901', emline_gvel_ha_6564=4.95878, emline_gvel_oiii_5008=0.674934, emline_gsigma_ha_6564=110.361, emline_gsigma_oiii_5008=128.882, stellar_vel=32.2628, stellar_sigma=95.9309, stellar_cont_fresid_68th_percentile=0.0358072, stellar_cont_fresid_99th_percentile=0.162992, stellar_cont_rchi2=2.03099, stellar_sigma_ivar=0.0733379, stellar_sigma_mask=0, stellar_sigmacorr=62.5568, stellar_vel_ivar=0.117477, stellar_vel_mask=0, z=0.0407447, x=6, y=15, bintype_name=u'ALL', template_name=u'GAU-MILESHC')

Best¶

We provide a small subset of most common parameters that have also been tested and vetted. We call these parameters best. You can access these within the datamodel:

# get a list of groups containing the best parameters
qdm.best_groups

# get a full list of best parameters
qdm.best

Reference/API¶

Class Inheritance Diagram

Inheritance diagram of marvin.utils.datamodel.query.base

Class

marvin.utils.datamodel.query.base.DataModel

marvin.utils.datamodel.query.base.ParameterGroupList(items) ParameterGroup Object

marvin.utils.datamodel.query.base.ParameterGroup(…) A Query Parameter Group Object

marvin.utils.datamodel.query.base.QueryParameter(full) A Query Parameter class

Methods

`marvin.utils.datamodel.query.base.ParameterGroupList.list_groups`()	Returns a list of query groups.
`marvin.utils.datamodel.query.base.ParameterGroupList.list_params`([…])	Returns a list of parameters from all groups.
`marvin.utils.datamodel.query.base.ParameterGroup.list_params`([…])	List the parameter names for a given group

Best Parameter List¶

The list of available “best” parameters and the group they belong in. For a full list of parameters, see the Query Datamodel.

Metadata¶

cube.plateifu: (default) The plate+ifudesign name for this object
cube.mangaid: (default) The mangaid for this object
cube.ra: OBJRA - Right ascension of the science object in J2000
cube.dec: OBJDEC - Declination of the science object in J2000
cube.plate: (default) The plateid
bintype.name: The type of binning used in DAP maps
template.name: The stellar libary template used in DAP maps

Spaxel Metadata¶

spaxelprop.x: The spaxel x position
spaxelprop.y: The spaxel y position
spaxelprop.spx_snr: The spaxel r-band signal-to-noise ratio

Emission Lines¶

spaxelprop.emline_gflux_ha_6564: Gaussian profile integrated flux for Ha emission line
spaxelprop.emline_gflux_hb_4862: Gaussian profile integrated flux for Hb emission line
spaxelprop.emline_gflux_nii_6549: Gaussian profile integrated flux for NII emission line
spaxelprop.emline_gflux_nii_6585: Gaussian profile integrated flux for NII emission line
spaxelprop.emline_gflux_oiid_3728: Gaussian profile integrated flux for OIId emission line
spaxelprop.emline_gflux_oiii_4960: Gaussian profile integrated flux for OIII emission line
spaxelprop.emline_gflux_oiii_5008: Gaussian profile integrated flux for OIII emission line
spaxelprop.emline_gflux_sii_6718: Gaussian profile integrated flux for SII emission line
spaxelprop.emline_gflux_sii_6732: Gaussian profile integrated flux for SII emission line
spaxelprop.nii_to_ha: The NII/Ha ratio computed from emline_gflux
spaxelprop.oiii_to_hb: The OIII/Hb ratio computed from emline_gflux
spaxelprop.sii_to_ha: The SII/Ha ratio computed from emline_gflux
spaxelprop.ha_to_hb: The Ha/Hb ratio computed from emline_gflux

Kinematics¶

spaxelprop.emline_gvel_ha_6564: Gaussian profile velocity for Ha emission line
spaxelprop.emline_gvel_oiii_5008: Gaussian profile velocity for OIII emission line
spaxelprop.emline_gsigma_ha_6564: Gaussian profile velocity dispersion for Ha emission line; must be corrected using EMLINE_INSTSIGMA
spaxelprop.emline_gsigma_oiii_5008: Gaussian profile velocity dispersion for OIII emission line; must be corrected using EMLINE_INSTSIGMA
spaxelprop.stellar_vel: Stellar velocity relative to NSA redshift
spaxelprop.stellar_sigma: Stellar velocity dispersion (must be corrected using STELLAR_SIGMACORR)

Spectral Indices¶

spaxelprop.specindex_d4000: Measurements of spectral indices

NSA Catalog¶

nsa.iauname: The accepted IAU name
nsa.ra: Right ascension of the galaxy
nsa.dec: Declination of the galaxy
nsa.z: The heliocentric redshift
nsa.elpetro_ba: Axis ratio b/a from elliptical petrosian fit.
nsa.elpetro_mag_g_r: g-r color computed from the Azimuthally-averaged SDSS-style Petrosian flux in FNugriz
nsa.elpetro_logmass: Log of the stellar mass from K-correction fit in h-2 solar masses to elliptical petrosian magnitudes.
nsa.elpetro_th50_r: Elliptical petrosian 50% light radius (derived from r band), in arcsec.
nsa.sersic_logmass: Log of the stellar mass from 2D Sersic fit
nsa.sersic_ba: Axis ratio b/a from 2D Sersic fit.