def gaia_in_file(infile, maglim=18):
"""Retrieve the Gaia objects from a HEALPixel-split Gaia file.
Parameters
----------
maglim : :class:`float`, optional, defaults to 18
Magnitude limit for GFAs in Gaia G-band.
Returns
-------
:class:`~numpy.ndarray`
Gaia objects in the passed Gaia file brighter than `maglim`,
formatted according to `desitarget.gfa.gfadatamodel`.
Notes
-----
- A "Gaia file" here is as made by, e.g.
:func:`~desitarget.gaiamatch.gaia_fits_to_healpix()`
"""
# ADM read in the Gaia file and limit to the passed magnitude.
objs = read_gaia_file(infile)
ii = objs['GAIA_PHOT_G_MEAN_MAG'] < maglim
objs = objs[ii]
# ADM rename GAIA_RA/DEC to RA/DEC, as that's what's used for GFAs.
for radec in ["RA", "DEC"]:
objs.dtype.names = [radec if col == "GAIA_"+radec else col
for col in objs.dtype.names]
# ADM initiate the GFA data model.
gfas = np.zeros(len(objs), dtype=gfadatamodel.dtype)
# ADM make sure all columns initially have "ridiculous" numbers
gfas[...] = -99.
for col in gfas.dtype.names:
if isinstance(gfas[col][0].item(), (bytes, str)):
gfas[col] = 'U'
if isinstance(gfas[col][0].item(), int):
gfas[col] = -1
# ADM populate the common columns in the Gaia/GFA data models.
cols = set(gfas.dtype.names).intersection(set(objs.dtype.names))
for col in cols:
gfas[col] = objs[col]
# ADM update the Gaia morphological type.
gfas["TYPE"] = gaia_morph(gfas)
# ADM populate the BRICKID columns.
gfas["BRICKID"] = bricks.brickid(gfas["RA"], gfas["DEC"])
return gfas
def gaia_in_file(infile, maglim=18, mindec=-30., mingalb=10.):
"""Retrieve the Gaia objects from a HEALPixel-split Gaia file.
Parameters
----------
infile : :class:`str`
File name of a single Gaia "healpix" file.
maglim : :class:`float`, optional, defaults to 18
Magnitude limit for GFAs in Gaia G-band.
mindec : :class:`float`, optional, defaults to -30
Minimum declination (o) to include for output Gaia objects.
mingalb : :class:`float`, optional, defaults to 10
Closest latitude to Galactic plane for output Gaia objects
(e.g. send 10 to limit to areas beyond -10o <= b < 10o)"
Returns
-------
:class:`~numpy.ndarray`
Gaia objects in the passed Gaia file brighter than `maglim`,
formatted according to `desitarget.gfa.gfadatamodel`.
Notes
-----
- A "Gaia healpix file" here is as made by, e.g.
:func:`~desitarget.gaiamatch.gaia_fits_to_healpix()`
"""
# ADM read in the Gaia file and limit to the passed magnitude.
objs = read_gaia_file(infile)
ii = objs['GAIA_PHOT_G_MEAN_MAG'] < maglim
objs = objs[ii]
# ADM rename GAIA_RA/DEC to RA/DEC, as that's what's used for GFAs.
for radec in ["RA", "DEC"]:
objs.dtype.names = [
radec if col == "GAIA_" + radec else col
for col in objs.dtype.names
]
# ADM initiate the GFA data model.
gfas = np.zeros(len(objs), dtype=gfadatamodel.dtype)
# ADM make sure all columns initially have "ridiculous" numbers
gfas[...] = -99.
for col in gfas.dtype.names:
if isinstance(gfas[col][0].item(), (bytes, str)):
gfas[col] = 'U'
if isinstance(gfas[col][0].item(), int):
gfas[col] = -1
# ADM some default special cases. Default to REF_EPOCH of Gaia DR2,
# ADM make RA/Dec very precise for Gaia measurements.
gfas["REF_EPOCH"] = 2015.5
gfas["RA_IVAR"], gfas["DEC_IVAR"] = 1e16, 1e16
# ADM populate the common columns in the Gaia/GFA data models.
cols = set(gfas.dtype.names).intersection(set(objs.dtype.names))
for col in cols:
gfas[col] = objs[col]
# ADM update the Gaia morphological type.
gfas["TYPE"] = gaia_morph(gfas)
# ADM populate the BRICKID columns.
gfas["BRICKID"] = bricks.brickid(gfas["RA"], gfas["DEC"])
# ADM limit by Dec first to speed transform to Galactic coordinates.
decgood = is_in_box(gfas, [0., 360., mindec, 90.])
gfas = gfas[decgood]
# ADM now limit to requesed Galactic latitude range.
bbad = is_in_gal_box(gfas, [0., 360., -mingalb, mingalb])
gfas = gfas[~bbad]
return gfas
def gaia_in_file(infile,
maglim=18,
mindec=-30.,
mingalb=10.,
nside=None,
pixlist=None,
addobjid=False):
"""Retrieve the Gaia objects from a HEALPixel-split Gaia file.
Parameters
----------
infile : :class:`str`
File name of a single Gaia "healpix" file.
maglim : :class:`float`, optional, defaults to 18
Magnitude limit for GFAs in Gaia G-band.
mindec : :class:`float`, optional, defaults to -30
Minimum declination (o) to include for output Gaia objects.
mingalb : :class:`float`, optional, defaults to 10
Closest latitude to Galactic plane for output Gaia objects
(e.g. send 10 to limit to areas beyond -10o <= b < 10o)"
nside : :class:`int`, optional, defaults to `None`
(NESTED) HEALPix `nside` to use with `pixlist`.
pixlist : :class:`list` or `int`, optional, defaults to `None`
Only return sources in a set of (NESTED) HEALpixels at the
supplied `nside`.
addobjid : :class:`bool`, optional, defaults to ``False``
If ``True``, include, in the output, a column "GAIA_OBJID"
that is the integer number of each row read from file.
Returns
-------
:class:`~numpy.ndarray`
Gaia objects in the passed Gaia file brighter than `maglim`,
formatted according to `desitarget.gfa.gfadatamodel`.
Notes
-----
- A "Gaia healpix file" here is as made by, e.g.
:func:`~desitarget.gaiamatch.gaia_fits_to_healpix()`
"""
# ADM read in the Gaia file and limit to the passed magnitude.
objs = read_gaia_file(infile, addobjid=addobjid)
ii = objs['GAIA_PHOT_G_MEAN_MAG'] < maglim
objs = objs[ii]
# ADM rename GAIA_RA/DEC to RA/DEC, as that's what's used for GFAs.
for radec in ["RA", "DEC"]:
objs.dtype.names = [
radec if col == "GAIA_" + radec else col
for col in objs.dtype.names
]
# ADM initiate the GFA data model.
dt = gfadatamodel.dtype.descr
if addobjid:
for tup in ('GAIA_BRICKID', '>i4'), ('GAIA_OBJID', '>i4'):
dt.append(tup)
gfas = np.zeros(len(objs), dtype=dt)
# ADM make sure all columns initially have "ridiculous" numbers
gfas[...] = -99.
for col in gfas.dtype.names:
if isinstance(gfas[col][0].item(), (bytes, str)):
gfas[col] = 'U'
if isinstance(gfas[col][0].item(), int):
gfas[col] = -1
# ADM some default special cases. Default to REF_EPOCH of Gaia DR2,
# ADM make RA/Dec very precise for Gaia measurements.
gfas["REF_EPOCH"] = 2015.5
gfas["RA_IVAR"], gfas["DEC_IVAR"] = 1e16, 1e16
# ADM populate the common columns in the Gaia/GFA data models.
cols = set(gfas.dtype.names).intersection(set(objs.dtype.names))
for col in cols:
gfas[col] = objs[col]
# ADM update the Gaia morphological type.
gfas["TYPE"] = gaia_morph(gfas)
# ADM populate the BRICKID columns.
gfas["BRICKID"] = bricks.brickid(gfas["RA"], gfas["DEC"])
# ADM limit by HEALPixel first as that's the fastest.
if pixlist is not None:
inhp = is_in_hp(gfas, nside, pixlist)
gfas = gfas[inhp]
# ADM limit by Dec first to speed transform to Galactic coordinates.
decgood = is_in_box(gfas, [0., 360., mindec, 90.])
gfas = gfas[decgood]
# ADM now limit to requesed Galactic latitude range.
if mingalb > 1e-9:
bbad = is_in_gal_box(gfas, [0., 360., -mingalb, mingalb])
gfas = gfas[~bbad]
return gfas