def pstarrs(obsids_table,
data_folder,
moc_folder,
nir_moc=None,
radius=15 * u.arcmin,
moc_order=16,
overwrite=True):
"""
Get Pan-STARRS data using astroquery and Vizier.
For each observation in obsids_table, saves a fits file with
name 'OBS_ID.fits' in 'data_folder/groups'.
The function sends a Vizier query and selects all sources within 'radius'
arcmin of the RA,DEC of the observation, then it filters the result
selecting the sources in the corresponding MOC stored in 'moc_folder/mocs'
(moc_order must be consistent with the order used to calculate the MOC).
If overwrite is True, always create a new fits file. If False, checks for
an existing file and uses it to calculate the number of Pan-STARRS sources
in the field. If it doesn't exist, creates the file.
The function returns obsids_table with an additional column 'NSRC_PS' with
the number of sources in the field.
"""
# Groups folder
if nir_moc is None:
groups_folder = os.path.join(data_folder, 'groups')
else:
root, _ = os.path.splitext(os.path.basename(nir_moc))
survey = root.split('_')[-1]
groups_folder = os.path.join(data_folder, 'groups_' + survey)
moc_nirsurvey = MOC()
read_moc_fits(moc_nirsurvey, nir_moc)
if not os.path.exists(groups_folder):
os.makedirs(groups_folder)
moc_folder = os.path.join(moc_folder, 'mocs')
nsources_field = np.full((len(obsids_table), ), np.nan)
hp = HEALPix(nside=2**moc_order, order='nested', frame=ICRS())
v = Vizier(columns=[
'objID', 'RAJ2000', 'DEJ2000', 'e_RAJ2000', 'e_DEJ2000', 'Nd', 'Qual'
],
column_filters={"Nd": ">1"},
row_limit=np.inf,
timeout=6000)
for i, row in enumerate(tqdm(obsids_table,
desc="Making Pan-STARRS groups")):
## Group file name
field_table_file = os.path.join(data_folder, groups_folder,
'{}.fits'.format(row['OBS_ID']))
is_field_table = os.path.exists(field_table_file)
if overwrite or (not overwrite and not is_field_table):
## Select all sources in the field
field_coords = SkyCoord(ra=row['RA'] * u.deg,
dec=row['DEC'] * u.deg)
vrsp = v.query_region_async(field_coords,
radius=radius,
catalog='II/349',
return_type='asu-tsv')
# Fix bug in the vizier response
# (returns the objID as a short int and fails to load
# properly as an astropy table)
with open('/tmp/tmp.tab', 'wb') as tmpfile:
tmpfile.write(vrsp.content)
src_table = Table.read('/tmp/tmp.tab', format='ascii.tab')
src_table = src_table[2:]
objid = np.array(src_table['objID']).astype(np.int64)
ra = np.array(src_table['RAJ2000']).astype(np.float) * u.deg
dec = np.array(src_table['DEJ2000']).astype(np.float) * u.deg
err_ra = np.array(src_table['e_RAJ2000'])
err_ra[err_ra == ' '] = '-1'
err_ra = err_ra.astype(np.float) * u.arcsec
err_ra[err_ra == -1] = np.nan
err_dec = np.array(src_table['e_DEJ2000'])
err_dec[err_dec == ' '] = '-1'
err_dec = err_dec.astype(np.float) * u.arcsec
err_dec[err_dec == -1] = np.nan
flag = np.array(src_table['Qual']).astype(np.int32)
src_table = Table([objid, ra, dec, err_ra, err_dec, flag],
names=[
'objID', 'RAJ2000', 'DEJ2000', 'e_RAJ2000',
'e_DEJ2000', 'Qual'
])
# Filter table
msk_good = (src_table['Qual'] & 16) != 0
src_table_new = src_table[msk_good]
## Select sources in the non-overlaping area
moc_field = MOC()
read_moc_fits(
moc_field,
os.path.join(moc_folder, '{}.moc'.format(row['OBS_ID'])))
if nir_moc is not None:
moc_field = moc_nirsurvey.intersection(moc_field)
inmoc_table = sources_inmoc(src_table_new,
hp,
moc_field,
moc_order=moc_order,
ra='RAJ2000',
dec='DEJ2000')
## Save sources
inmoc_table.remove_columns(['Qual'])
inmoc_table.meta['description'] = 'Pan-STARRS'
inmoc_table.write(field_table_file, overwrite=True)
else:
inmoc_table = Table.read(field_table_file)
nsources_field[i] = len(inmoc_table)
colsrc = Table.Column(nsources_field, name='NSRC_PS')
obsids_table.add_column(colsrc)
return obsids_table
def tmass(obsids_table,
data_folder,
moc_folder,
opt_moc=None,
radius=15 * u.arcmin,
moc_order=16,
overwrite=True):
"""
Get 2MASS data using astroquery and Vizier
For each observation in obsids_table, saves a fits file with
name 'OBS_ID.fits' in 'data_folder/groups'.
The function sends a Vizier query and selects all sources within 'radius'
arcmin of the RA,DEC of the observation, then it filters the result
selecting the sources in the corresponding MOC stored in 'moc_folder/mocs'
(moc_order must be consistent with the order used to calculate the moc).
If overwrite is True, always create a new fits file. If False, checks for
an existing file and uses it to calculate the number of WISE sources
in the field. If it doesn't exist, creates the file.
The function returns obsids_table with an additional column 'NSRC_2M'
with the number of sources in the field.
"""
# Groups folder
groups_folder = os.path.join(data_folder, 'groups')
if not os.path.exists(groups_folder):
os.makedirs(groups_folder)
moc_folder = os.path.join(moc_folder, 'mocs')
if opt_moc is not None:
moc_optsurvey = MOC()
read_moc_fits(moc_optsurvey, opt_moc)
nsources_field = np.full((len(obsids_table), ), np.nan)
hp = HEALPix(nside=2**moc_order, order='nested', frame=ICRS())
v = Vizier(columns=[
'Cntr', 'RAJ2000', 'DEJ2000', 'errMaj', 'errMin', 'errPA', 'Qflg'
],
row_limit=np.inf,
timeout=6000)
for i, row in enumerate(tqdm(obsids_table, desc="Making 2MASS groups")):
## Group file name
field_table_file = os.path.join(data_folder, groups_folder,
'{}.fits'.format(row['OBS_ID']))
is_field_table = os.path.exists(field_table_file)
if overwrite or (not overwrite and not is_field_table):
## Select all sources in the field
field_coords = SkyCoord(ra=row['RA'] * u.deg,
dec=row['DEC'] * u.deg)
vrsp = v.query_region_async(field_coords,
radius=radius,
catalog='II/246/out',
return_type='asu-tsv')
# Fix bug in the vizier response
# (returns the id as a short int and fails to load
# properly as an astropy table)
with open('/tmp/tmp.tab', 'wb') as tmpfile:
tmpfile.write(vrsp.content)
src_table = Table.read('/tmp/tmp.tab', format='ascii.tab')
src_table = src_table[2:]
objid = np.array(src_table['Cntr']).astype(np.int64)
ra = np.array(src_table['RAJ2000']).astype(np.float) * u.deg
dec = np.array(src_table['DEJ2000']).astype(np.float) * u.deg
errMaj = np.array(src_table['errMaj']).astype(np.float) * u.arcsec
# err_ra[err_ra == ' '] = '-1'
# err_ra = err_ra.astype(np.float) * u.arcsec
# err_ra[err_ra == -1] = np.nan
errMin = np.array(src_table['errMin']).astype(np.float) * u.arcsec
# err_dec[err_dec == ' '] = '-1'
# err_dec = err_dec.astype(np.float) * u.arcsec
# err_dec[err_dec == -1] = np.nan
errPA = np.array(src_table['errPA']).astype(np.float) * u.deg
flag = np.array(src_table['Qflg'])
src_table = Table([objid, ra, dec, errMaj, errMin, errPA, flag],
names=[
'objID', 'RAJ2000', 'DEJ2000', 'errMaj',
'errMin', 'errPA', 'Qflg'
])
# Filter table
# Sources detected with SNR>=5 in J, H or K
flgJ = [f[0] in ['A', 'B', 'C'] for f in src_table['Qflg']]
flgH = [f[1] in ['A', 'B', 'C'] for f in src_table['Qflg']]
flgK = [f[2] in ['A', 'B', 'C'] for f in src_table['Qflg']]
msk_good = np.logical_and(flgJ, np.logical_and(flgH, flgK))
src_table_new = src_table[msk_good]
## Select sources in the non-overlaping area
moc_field = MOC()
read_moc_fits(
moc_field,
os.path.join(moc_folder, '{}.moc'.format(row['OBS_ID'])))
if opt_moc is not None:
moc_field = moc_optsurvey.intersection(moc_field)
inmoc_table = sources_inmoc(src_table_new,
hp,
moc_field,
moc_order=moc_order,
ra='RAJ2000',
dec='DEJ2000')
## Save sources
field_table_file = os.path.join(data_folder, groups_folder,
'{}.fits'.format(row['OBS_ID']))
inmoc_table.meta['description'] = '2MASS'
inmoc_table.remove_column('Qflg')
inmoc_table.write(field_table_file, overwrite=True)
else:
inmoc_table = Table.read(field_table_file)
nsources_field[i] = len(inmoc_table)
colsrc = Table.Column(nsources_field, name='NSRC_2M')
obsids_table.add_column(colsrc)
return obsids_table
def query_vizier(name,
type1,
config,
RA="_RAJ2000",
DEC="_DEJ2000",
z="Redshift"):
"""
Use astroquery to query the Vizier catalogue database
Input:
name: name of the source to query region for
type1: redshift of velocity query
config: configuration
RA, DEC: optional, coordinates of RA and DEC columns; defaults to vizier
names which point to RA and DEC homogenized to deg and J2000
Return:
Final table containing 4 columns, RA, DEC, redshift and origin of
redshift measurement, compiled from all data available of Vizier
"""
# Setup the column and keywords used for the selection; calculate
# homogenized RA and DEC and return unlimited rows
if type1 == "velocity":
UCD = VELOCITY_SRC
KEYS = VELOCITY_KEYS
if type1 == "redshift":
UCD = REDSHIFT_SRC
KEYS = REDSHIFT_KEYS
v = Vizier(
columns=["**", RA, DEC],
ucd=UCD,
row_limit=-1,
timeout=timeout,
)
# Query a region using source name, return a XML response and process
# through Astropy as VOTABLE
cat_vot = parse(
BytesIO(
v.query_region_async(name, radius=config.radius).text.encode()),
pedantic=False,
invalid="mask",
)
# Make a preliminary selection of columns to keep only RA, DEC and the
# possible redshift columns
cat_list = prelim_selection(cat_vot, type1, RA, DEC, KEYS)
# Initialize a list of table to be appended; These will be tables that have
# a redshift measurement
table_list = []
# Find whether the catalogue contains relevant information and save the
# column with the relevant data
for cat in cat_list:
final_cat = process_catalog(type1, cat, config, RA, DEC)
if final_cat is not None:
table_list.append(final_cat)
# Stack all the final catalogues with the relevant data
if table_list:
grand_table = vstack(table_list)
# Remove potential photoz's by removing measurements with little precision
grand_table = remove_potential_photoz(grand_table, z)
# Return results of the vizier search
return grand_table
else:
return None
