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 DR15. 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(search_filter='nsa.z < 0.1')
>>> qdm = query.datamodel
>>> print(qdm)
<QueryDataModel release='DR15', n_groups=5, n_parameters=987, 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)
[<QueryDataModel release='MPL-4', n_groups=4, n_parameters=309, n_total=0>,
<QueryDataModel release='MPL-5', n_groups=4, n_parameters=301, n_total=0>,
<QueryDataModel release='MPL-6', n_groups=5, n_parameters=987, n_total=0>,
<QueryDataModel release='MPL-7', n_groups=5, n_parameters=987, n_total=0>,
<QueryDataModel release='DR15', n_groups=5, n_parameters=987, n_total=0>]
Let’s select the DR15 query datamodel:
>>> qdm = datamodel['DR15']
>>> print(qdm)
<QueryDataModel release='DR15', n_groups=5, n_parameters=987, 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 (currently unavailable)
Emission Lines - DAP emission-line measurements (currently unavailable)
Kinematics - DAP kinematic measurements (currently unavailable)
Spectral Indices - DAP spectral index measurements (currently unavailable)
NSA Catalog (NASA Sloan-Atlas Catalog) - global galaxy properties
ObsInfo - parameters relevant to the observations of individual exposures
DAPall Summary - measurements from the DAPall summary file
Other - all other non-classified parameters
To access the list of groups, use the groups attribute:
>>> qdm.groups
[<ParameterGroup name=Metadata, n_parameters=11>,
<ParameterGroup name=NSA Catalog, n_parameters=158>,
<ParameterGroup name=ObsInfo, n_parameters=64>,
<ParameterGroup name=DAPall Summary, n_parameters=620>,
<ParameterGroup name=Other, n_parameters=134>]
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, n_parameters=11>
>>> # 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>,
<QueryParameter full=cube_header_keyword.label, name=label, short=label, remote=label, display=Label>,
<QueryParameter full=cube_header_value.value, name=value, short=value, remote=value, display=Value>,
<QueryParameter full=maps_header_keyword.name, name=maps_header_keyword_name, short=maps_header_keyword_name, remote=maps_header_keyword_name, display=Name>,
<QueryParameter full=maps_header_value.value, name=value, short=value, remote=value, display=Value>]
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 search_filter and return_params 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',
'cube_header_keyword.label',
'cube_header_value.value',
'maps_header_keyword.name',
'maps_header_value.value']
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 = qdm['metadata']
>>> nsa = qdm['nsa']
>>> myparams = meta.list_params('full', subset=['ra','dec']) + nsa.list_params('full', subset=['nsa.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(search_filter='nsa.z < 0.1', return_params=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(search_filter='nsa.z < 0.1', return_params=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
[<ParameterGroup name=Metadata, n_parameters=7>,
<ParameterGroup name=NSA Catalog, n_parameters=11>]
>>> # get a full list of best parameters
>>> qdm.best
[<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>,
<QueryParameter full=nsa.iauname, name=iauname, short=iauname, remote=iauname, display=IAU Name>,
<QueryParameter full=nsa.ra, name=ra, short=ra, remote=ra, display=RA>,
<QueryParameter full=nsa.dec, name=dec, short=dec, remote=dec, display=Dec>,
<QueryParameter full=nsa.z, name=z, short=z, remote=z, display=Redshift>,
<QueryParameter full=nsa.elpetro_ba, name=elpetro_ba, short=axisratio, remote=elpetro_ba, display=Elpetro axis ratio>,
<QueryParameter full=nsa.elpetro_mag_g_r, name=elpetro_mag_g_r, short=g_r, remote=elpetro_mag_g_r, display=g-r>,
<QueryParameter full=nsa.elpetro_absmag_g_r, name=elpetro_absmag_g_r, short=absmag_g_r, remote=elpetro_absmag_g_r, display=Absmag g-r>,
<QueryParameter full=nsa.elpetro_logmass, name=elpetro_logmass, short=logmass, remote=elpetro_logmass, display=Elpetro Stellar Mass>,
<QueryParameter full=nsa.elpetro_th50_r, name=elpetro_th50_r, short=th50_r, remote=elpetro_th50_r, display=r-band half-light radius>,
<QueryParameter full=nsa.sersic_logmass, name=sersic_logmass, short=sersic_logmass, remote=sersic_logmass, display=Sersic Stellar Mass>,
<QueryParameter full=nsa.sersic_ba, name=sersic_ba, short=sersic_ba, remote=sersic_ba, display=Sersic axis ratio>]
Reference/API¶
Class Inheritance Diagram
Class
ParameterGroup Object |
A Query Parameter Group Object |
A Query Parameter class |
Methods
|
Returns a list of query groups. |
|
Returns a list of parameters from all groups. |
|
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
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.