Spectra¶

Description¶

The spectra are stored in a series of HDF5 Groups as multi-dimension arrays (npixels x nspectra x nkeys). Each array has the following keys.

Key	Type	Description
wave	float64	Wavelength array; default is Angstroms
flux	float32	Flux array; default is unitless
sig	float32	Error array; same units as flux
co (optional)	float32	Continuum array; same units as flux

The Python software included with specdb can read these arrays into the XSpectrum1D class provided in linetools.

Retrieving Spectra¶

There are several methods to retrieve spectra from a database instantiated in the SpecDB Class. But we strongly recommend that the default approach be:

Perform a meta query.
Inspect the table returned.
Retrieve the spectra with this table (or a cut-down version).

A series of simple examples are given in the Retrieving Spectra Notebook. The main methods are also described below, with examples. The lower level methods using the InterfaceGroup class are described in Retrieving Spectra from a Group.

Spectra from a Meta data Query¶

As noted above, this is the recommended approach to retrieving spectra. One is required to first generate a meta data Table by Querying the Meta Data. One may then retrieve all of the associated spectra.:

# Meta query
qdict = {'TELESCOPE': 'Gemini-North', 'NPIX': (1580,1583), 'DISPERSER': ['B600', 'R400']}
qmeta = sdb.query_meta(qdict)

# Retrieve spectra
spectra = sdb.spectra_from_meta(qmeta)

This returns an XSpectrum1D object containing all of the spectra associated with the meta data Table, aligned to the Table. Of course, one can first slice the meta data Table to retrieve only a subset of the spectra.

specdb files with groups that contain only meta are now appearing. To retrieve spectra when the meta table is a mix of sources with and without spectra, use the subset=True option:

# Retrieve spectra
sub_spec, sub_meta = sdb.spectra_from_meta(qmeta, subset=True)

Spectra for a single Source¶

A common usage of specdb may be to grab all of the spectra related to a single source. The method spectra_from_coord takes an input coordinate (in a range of Formats), identifies the closest catalog source within a given tolerance (default is 0.5”) and returns all of the spectra and meta data within the database for that source. Here is an example call:

spec, meta = sdb.spectra_from_coord('J223438.52+005730.0')

spec and meta are a XSpectrum1D object and an astropy.Table object.

One can restrict the call to grab spectra from a subset of the groups or by querying the meta data, e.g.:

qdict = dict(DISPERSER='R400')
spec, meta = sdb.spectra_from_coord('001115.23+144601.8', meta_query=qdict)

In this example, we require the spectra have been taken with the R400 grating. Note that queries are performed on both the source catalog and the Meta Data table of each group.

A related method is to retrieve all the spectra for a source given its IDKEY:

spec, meta = sdb.spectra_from_ID(3244)

The objects returned are the same as above.

Spectra from Coordinates in a Group¶

Another common usage may be to retrieve the spectra from a list of coordinates from a single group. The spectra_in_group method accomplishes this most efficiently. Here the input must be a SkyCoord object containing the coordinates for one or more sources. An example call:

coords = SkyCoord(ra=[0.0028, 0.0019], dec=[14.9747, 17.77374], unit='deg')
spec, meta= igmsp.spectra_in_group(coords, 'BOSS_DR12')

The output is an XSpectrum1D object containing the spectra and an astropy.Table of the meta data. This returns only the first spectrum and meta row identified in the group for each source, ordered the same as the input coordinates.

For cases where one or more spectra may be present, you may wish to restrict by providing a Query dict, which will either query on the meta data or the catalog, e.g.:

coords = SkyCoord(ra=[2.8135,16.5802], dec=[14.7672, 0.8065], unit='deg')
qdict = dict(DISPERSER='R400')
spec, meta = sdb.spectra_in_group(coords, 'GGG', meta_query=qdict)

This requires the spectra returned were taken with the R400 grating.

Note: This method will raise an IOError if one or more of the input coordinates are not within the requested group to within the tolerance parameter (default = 0.5”) or if one or more sources fails to match an input Query dict.

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