def get_gaia_data(self, index):
# get data about star
row = self.stars.iloc[index]
ls_id = row.ls_id
# get gaia id of star from decals
q = """SELECT
ra1, dec1, id1, ra2, dec2, id2, distance
FROM
ls_dr9.x1p5__tractor__gaia_edr3__gaia_source
WHERE
id1 = {} """.format(ls_id)
res = qc.query(sql=q)
decals_data = convert(res, 'pandas')
# get gaia id (or input id = 0 if no gaia data)
gaia_id = 0 if len(decals_data.id2) == 0 else decals_data.id2
# use gaia id to get info about it
query = """SELECT
source_id, ra, ra_error, dec, dec_error, parallax, pmra, pmra_error, pmdec, pmdec_error
FROM
gaiadr2.gaia_source
WHERE
source_id = {} """.format(int(gaia_id))
gaia_data = Gaia.launch_job(query).get_results().to_pandas()
return gaia_data
def get_catalog(self,
query=None,
query_fields=None,
print_query=False,
timeout=120):
"""
Get catalog sources around the given coordinates
within self.radius.
Args:
query (str, optional): SQL query to generate the catalog
query_fields (list, optional): Over-ride list of items to query
print_query (bool): Print the SQL query generated
Returns:
astropy.table.Table: Catalog of sources obtained from the SQL query.
"""
qc.set_profile(self.qc_profile)
# Generate the query
if query is None:
self._gen_cat_query(query_fields)
query = self.query
if print_query:
print(query)
# Do it while silencing print statements
result = qc.query(self.token, sql=query, timeout=timeout)
self.catalog = convert(result, outfmt="table")
self.catalog.meta['radius'] = self.radius
self.catalog.meta['survey'] = self.survey
# Validate
self.validate_catalog()
# Return
return self.catalog.copy()
def __init__(self, file=None, ra=None, dec=None, bd=None, n_arcmin=5):
self.n_arcmin = n_arcmin
# brown dwarf can be ra/dec or data or class
if ra is not None and dec is not None:
self.ra = ra
self.dec = dec
elif isinstance(bd, (Table, pd.DataFrame)):
# first change df into what want
bd = find_info(bd)
# basic info
self.ra = bd['ra']
self.dec = bd['dec']
self.mu_a = bd['mu_alpha']
self.mu_d = bd['mu_delta']
self.pi = bd['pi']
elif isinstance(bd, BrownDwarf):
self.ra = bd.ra
self.dec = bd.dec
self.bd = bd
else:
raise Exception(
'Brown Dwarf data needs to either be ra/dec, an astropy table or pandas table of the dwarf data, or the brown dwarf class.'
)
# now to grab the star info
dl.queryClient.getClient(profile='default',
svc_url='https://datalab.noirlab.edu/query')
if file == None:
q = """SELECT
ls_id, ra, dec, dered_mag_g, dered_mag_r, dered_mag_w1, dered_mag_w2, dered_mag_w3, dered_mag_w4, dered_mag_z, gaia_phot_g_mean_mag, gaia_duplicated_source, pmdec, pmra, psfsize_g, psfsize_r, psfsize_z, ref_cat, ref_epoch, ref_id, type
FROM
ls_dr9.tractor
WHERE
't' = Q3C_RADIAL_QUERY(ra, dec, {} , {} , ({}/60)) """.format(
float(self.ra), float(self.dec), float(self.n_arcmin))
res = qc.query(sql=q)
self.stars = convert(res, 'pandas')
else:
self.file = file
self.stars = pd.read_csv(self.file)
self.stars = self.filter_stars_only(self.stars)
self.stars = self.filter_stars_mag(self.stars)
# create array of paths for each star
self.star_paths = np.zeros(len(self.stars))
def get_catalog(self,
query=None,
query_fields=None,
print_query=False,
timeout=120):
"""
Get catalog sources around the given coordinates
within self.radius.
Args:
query (str, optional): SQL query to generate the catalog
query_fields (list, optional): Over-ride list of items to query
print_query (bool): Print the SQL query generated
Returns:
astropy.table.Table: Catalog of sources obtained from the SQL query.
"""
qc.set_profile(self.qc_profile)
# Generate the query
if query is None:
self._gen_cat_query(query_fields)
query = self.query
if print_query:
print(query)
# Do it while silencing print statements
result = qc.query(self.token, sql=query, timeout=timeout)
sys.stdout = open(os.devnull, "w")
temp = convert(result)
sys.stdout = sys.__stdout__
self.catalog = Table.from_pandas(temp)
# TODO:: Dig into why the heck it doesn't want to natively
# output to a table when it was clearly intended to with 'outfmt=table'
# Finish
self.catalog.meta['radius'] = self.radius
self.catalog.meta['survey'] = self.survey
# Validate
self.validate_catalog()
# Return
return self.catalog.copy()
def test_votable_value(self):
"""Result of convert() to votable should have res['mag_auto_i'][0] == 23.168003"""
res = utils.convert(self.votable, 'votable')
self.assertEqual(int(res.array['mag_auto_i'][0]), self.intval)
def test_is_votable(self):
"""Result of convert() to votable should be a votable"""
res = utils.convert(self.votable, 'votable')
self.assertIsInstance(res, astropy.io.votable.tree.Table)
def test_astropy_table_value(self):
"""Result of convert() to table should have res['mag_auto_i'][0] == 23.168003"""
res = utils.convert(self.csvtable, 'table')
self.assertEqual(int(res['mag_auto_i'][0]), self.intval)
def test_is_astropy_table(self):
"""Result of convert() to table should be an astropy table.Table"""
res = utils.convert(self.csvtable, 'table')
self.assertIsInstance(res, astropy.table.table.Table)
def test_pandas_dataframe_value(self):
"""Result of convert() to pandas should have res['mag_auto_i'][0] == 23.168003"""
res = utils.convert(self.csvtable, 'pandas')
self.assertEqual(int(res['mag_auto_i'][0]), self.intval)
def test_is_pandas_dataframe(self):
"""Result of convert() to pandas should be a numpy array"""
res = utils.convert(self.csvtable, 'pandas')
self.assertIsInstance(res, pandas.DataFrame)
def test_structarray_value(self):
"""Result of convert() to structarray should have res['mag_auto_i'][0] == 23.168003"""
res = utils.convert(self.csvtable, 'structarray')
self.assertEqual(int(res['mag_auto_i'][0]), self.intval)
def test_is_structarray(self):
"""Result of convert() to structarray should be a numpy array"""
res = utils.convert(self.csvtable, 'structarray')
self.assertIsInstance(res, numpy.ndarray)
def test_array_value(self):
"""Result of convert() to array should have res[0,2] == 23.168003"""
res = utils.convert(self.csvtable, 'array')
self.assertEqual(int(res[0, 2]), self.intval)
def test_string(self):
"""Result of convert() to string should be identical to the input string"""
res = utils.convert(self.csvtable, 'string')
self.assertEqual(res, self.csvtable)