def get_panstarrs_catalog_old(imgwcs, radius=0.2, verbose=False):
import astropy.units as u
from astropy.coordinates import SkyCoord
from astroquery.mast import Catalogs
ra0, dec0 = imgwcs.wcs.crval
coords = SkyCoord(ra0, dec0, unit=u.deg, frame='icrs')
if verbose:
print('Querying Pan-STARRS {:.3f} deg around RA, Dec={:.5f}, {:.5f} '.
format(radius, ra0, dec0))
cat = Catalogs.query_criteria(coordinates=coords,
radius=radius,
catalog='PANSTARRS',
data_release='dr2',
table='mean',
columns=[
'objID', 'raMean', 'decMean',
'gMeanPSFMag', 'rMeanPSFMag',
'iMeanPSFMag', 'zMeanPSFMag'
],
gMeanPSFMag=[('lte', 18), ('gte', 8)],
rMeanPSFMag=[('lte', 18), ('gte', 8)],
iMeanPSFMag=[('lte', 18), ('gte', 8)],
zMeanPSFMag=[('lte', 18), ('gte', 8)])
#sort_by=[("asc", "rMeanPSFMag")]
# http://legacysurvey.org/dr8/description/#photometry
gi = cat['gMeanPSFMag'] - cat['iMeanPSFMag']
keep = np.where((gi > 0.4) * (gi < 2.7))[0]
cat = cat[keep]
return cat
def tic_to_name(tic, ra=None, dec=None):
"""
Function to determine the common name of a TIC ID or given RA/Dec position,
if it has one. Queries the MAST and Simbad to gather this information.
!!Keysort so planet doesn't come first?!!
Parameters
----------
tic : int
The TIC ID of the object for which the common name is desired.
ra : float
The RA in decimal degrees. Optional with Dec to circumvent querying MAST.
dec : float
The Dec in decimal degrees. Optional with TA to circumvent querying MAST.
Returns
-------
name : str
The common name of the input TIC ID.
"""
if not isinstance(tic, int):
raise ValueError('TIC must be an integer')
if not ra and not dec:
cat = Catalogs.query_criteria(catalog="TIC", ID=int(tic))
ra = cat[0]['ra']
dec = cat[0]['dec']
results = Simbad.query_region(coord.SkyCoord(ra, dec, unit=(u.deg, u.deg)),
radius='0d0m5s')
name = str(results[0]['MAIN_ID'].decode('utf-8'))
return str(name)
def getting_star_mass_radius(tic_id):
target_info = Catalogs.query_criteria(catalog='TIC', ID=tic_id).to_pandas()
star_radius = target_info['rad']
star_mass = target_info['mass']
print(f'Star mass: {star_mass}')
print(f'Star radius: {star_radius}')
return star_mass, star_radius
def load_catalog(filename=False,
header=False,
wcs=False,
ra_key=False,
dec_key=False):
'''
From Anna Marini: get positions from catalog.
'''
if filename and not header:
header = get_fits_header(filename)
if header and not wcs:
wcs = WCS(header)
if ra_key and dec_key:
ra = header[ra_key]
dec = header[dec_key]
ra = wcs.wcs.crval[0]
dec = wcs.wcs.crval[1]
# Diagonal
diag_bound = wcs.pixel_to_world_values([[0, 0], wcs.pixel_shape])
radius = np.mean(diag_bound[1] - diag_bound[0]) / 2
catalog = Catalogs.query_region(
f'{ra} {dec}',
# frame='icrs',
# unit="deg",
radius=radius,
catalog='Gaia',
version=2)
return catalog
def get_tic_name(name):
with warnings.catch_warnings():
warnings.simplefilter('ignore')
customSimbad = Simbad()
customSimbad.add_votable_fields(
'ra(2;A;ICRS;J2000;2000)', 'dec(2;D;ICRS;J2000;2000)')
customSimbad.remove_votable_fields('coordinates')
result_table = customSimbad.query_object(name)
if result_table is None:
logger.error("Target name failed to resolve, please check")
sys.exit(1)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
ra_sex = result_table['RA_2_A_ICRS_J2000_2000'][0]
dec_sex = result_table['DEC_2_D_ICRS_J2000_2000'][0]
catalogData = Catalogs.query_region(SkyCoord
(ra_sex, dec_sex, unit=(u.hour, u.deg)),
catalog='Tic', radius=0.006)
try:
return catalogData['ID'][0]
except IndexError:
logger.error("No TIC target at those coordiantes")
sys.exit(1)
def get_catalog_data(tic_id):
catalogTIC = Catalogs.query_criteria(
ID=tic_id,
catalog='Tic',
provenance_name='TASOC',
)
return catalogTIC['Tmag'], catalogTIC['Teff'], catalogTIC['logg'],
def tic_list_by_magnitudes(path, lowermag, uppermag, n, filelabel):
""" Creates a fits file of the first n TICs that fall between the given
magnitude ranges.
parameters:
* path to where you want things saved
* lower magnitude limit
* upper magnitude limit
* n - number of TICs you want
* file label (what to call the fits file)
modified [lcg 07082020]
"""
catalog_data = Catalogs.query_criteria(catalog="Tic", Tmag=[uppermag, lowermag], objType="STAR")
T_mags = np.asarray(catalog_data["Tmag"], dtype= float)
TICIDS = np.asarray(catalog_data["ID"], dtype = int)
tmag_index = np.argsort(T_mags)
sorted_tmags = T_mags[tmag_index]
sorted_ticids = TICIDS[tmag_index]
hdr = fits.Header() # >> make the header
hdu = fits.PrimaryHDU(sorted_ticids[0:n], header=hdr)
hdu.writeto(path + filelabel + ".fits")
fits.append(path + filelabel + ".fits",sorted_tmags[0:n])
return sorted_ticids, sorted_tmags
def get_TIC_data(ra, dec):
radii = np.linspace(start=0.0001, stop=0.001, num=19)
#for i,row in self.df.iterrows():
tic_found = False
for rad in radii:
if tic_found == False:
# query_string = str(ra) + " " + str(dec) # make sure to have a space between the strings!#SkyCoord(ra = row['ra'], dec = row['dec'], frame = 'icrs') str(row['ra']) + " " + str(row['dec']) # make sure to have a space between the strings!
sc = SkyCoord(ra=ra * u.deg, dec=dec * u.deg)
obs_table = Catalogs.query_region(coordinates=sc,
radius=rad * u.deg,
catalog="TIC")
obs_df = obs_table.to_pandas()
if len(obs_table['ID']) == 1:
tic = obs_table['ID'][0]
tic_found = True
continue
if len(obs_df[obs_df['GAIA'].to_numpy(dtype='str') != '']) == 1:
temp_obs_df = obs_df[obs_df['GAIA'].to_numpy(
dtype='str') != '']
tic = temp_obs_df['ID'].iloc[0]
tic_found = True
continue
# if len(np.unique(obs_df[obs_df['HIP'].to_numpy(dtype = 'str') != '']['HIP'])) == 1:
# tic = obs_table['ID'][0]
# tic_found = True
# continue
if tic_found == False:
tic = np.nan
print("Didn't find TIC for this object.")
else:
print("Found TIC Data for TIC " + str(tic) + "!")
#self.tic = tic
return (obs_df, tic)
def download_tic_coord(tic):
'''
Use the MAST archive to download a SkyCoord for one
star from the TESS Input Catalog.
'''
# import Catalogs only when we need it
# (otherwise, we'll need the internet to ever run tfs)
from astroquery.mast import Catalogs
# download that TIC from the archive
t = Catalogs.query_criteria(catalog="Tic", ID=tic)[0]
# the 'ra' and 'dec' columns were propagated to J2000 (https://outerspace.stsci.edu/display/TESS/TIC+v8+and+CTL+v8.xx+Data+Release+Notes)
obstime='J2000.0'
# define a sky coord, with proper motions and a time
s = coord.SkyCoord( ra=t['ra']*u.deg,
dec=t['dec']*u.deg,
pm_ra_cosdec=t['pmRA']*u.mas/u.year,
pm_dec=t['pmDEC']*u.mas/u.year,
obstime='J2000.0')
return s
def __init__(self, ID: int, sectors: np.ndarray, search_radius: int = 10):
"""
Queries TIC for sources near the target and obtains a cutout
of the pixels enclosing the target.
Args:
ID (int): TIC ID of the target.
sectors (numpy array): Sectors in which the target
has been observed.
search_radius (int): Number of pixels from the target
star to search.
"""
self.ID = ID
self.sectors = sectors
self.search_radius = search_radius
self.N_pix = 2 * search_radius + 2
# query TIC for nearby stars
pixel_size = 20.25 * u.arcsec
df = Catalogs.query_object("TIC" + str(ID),
radius=search_radius * pixel_size,
catalog="TIC")
new_df = df["ID", "Tmag", "ra", "dec", "mass", "rad", "Teff", "plx"]
stars = new_df.to_pandas()
self.stars = stars
TESS_images = []
col0s, row0s = [], []
pix_coords = []
# for each sector, get FFI cutout and transform RA/Dec into
# TESS pixel coordinates
for j, sector in enumerate(sectors):
Tmag = stars["Tmag"].values
ra = stars["ra"].values
dec = stars["dec"].values
cutout_coord = SkyCoord(ra[0], dec[0], unit="deg")
cutout_hdu = Tesscut.get_cutouts(cutout_coord,
size=self.N_pix,
sector=sector)[0]
cutout_table = cutout_hdu[1].data
hdu = cutout_hdu[2].header
wcs = WCS(hdu)
TESS_images.append(np.mean(cutout_table["FLUX"], axis=0))
col0 = cutout_hdu[1].header["1CRV4P"]
row0 = cutout_hdu[1].header["2CRV4P"]
col0s.append(col0)
row0s.append(row0)
pix_coord = np.zeros([len(ra), 2])
for i in range(len(ra)):
RApix = np.asscalar(wcs.all_world2pix(ra[i], dec[i], 0)[0])
Decpix = np.asscalar(wcs.all_world2pix(ra[i], dec[i], 0)[1])
pix_coord[i, 0] = col0 + RApix
pix_coord[i, 1] = row0 + Decpix
pix_coords.append(pix_coord)
self.TESS_images = TESS_images
self.col0s = col0s
self.row0s = row0s
self.pix_coords = pix_coords
return
def get_IDS(number):
catalogTIC = Catalogs.query_criteria(catalog="Tic",
Tmag=[12.0, 12.5],
Teff=[3500.0, 3550.0],
logg=[4.2, 5.0])
catalogTIC = catalogTIC[catalogTIC['dec'] < 0]
return catalogTIC['ID'][:number]
def get_coord_from_ticid(ticid):
df = Catalogs.query_criteria(catalog="Tic", ID=ticid).to_pandas()
coord = SkyCoord(
ra=df.iloc[0]["ra"],
dec=df.iloc[0]["dec"],
distance=Distance(parallax=df.iloc[0]["plx"] * u.mas).pc,
unit=(u.degree, u.degree, u.pc),
)
return coord
def get_TIC(ra, dec, radius):
"""Retrieve TIC from MAST."""
cat = Catalogs.query_region(
SkyCoord(
ra=ra, dec=dec, unit=(u.deg, u.deg), frame='icrs'
), radius=radius, catalog='TIC'
)
return cat
def get_star_info(IDnumber):
tic = Catalogs.query_object("TIC {0}".format(IDnumber),
radius=0.0001,
catalog="TIC")
star = tic[np.argmin(tic["dstArcSec"])]
tic_ID = int(star["ID"])
tic_ra = float(star["ra"])
tic_dec = float(star["dec"])
return tic_ID, tic_ra, tic_dec
def check_tess(df):
"""
Verify TESS objects with MAST archive
Args:
df (pd.Dataframe): Dataframe with objects not found yet
Returns:
df with match info updated
"""
df = df.copy()
# Load TOI/TIC list
data = requests.get(TOI_URL)
tbl = Table.read(data.text, format='ascii')
df_toi = tbl.to_pandas()[['TIC ID', 'TOI']].astype(str)
# Replace TOI names with TIC
# Keep order consistent with dataframe
toi_inds = df.index[df['OBJECT'].str.startswith('TOI')]
tois = df['OBJECT'].loc[toi_inds]
# tois = tois.str.split('-').map(lambda x: x[1]) + '.01'
tois = tois.str[3:].str.strip('-') + '.01'
tois = tois.tolist()
toi_to_tic = df_toi.loc[df_toi['TOI'].isin(tois)]
toi_to_tic = toi_to_tic.set_index('TOI', drop=False)
toi_to_tic = toi_to_tic.reindex(tois).set_index(np.arange(len(tois)))
tics = toi_to_tic['TIC ID']
gaiaids = df['GAIADR2ID'].loc[toi_inds]
# Check where TIC ID matches same as local Gaia ID
if tics.size == 0:
return df
tic_gaia = Catalogs.query_criteria(catalog='TIC',
objType='STAR',
ID=tics.tolist()).to_pandas()[[
'ID', 'GAIA'
]]
tic_gaia = tic_gaia.set_index('ID', drop=False)
tic_gaia = tic_gaia.reindex(tics).set_index(np.arange(len(tics)))
mask = tic_gaia['GAIA'].values == gaiaids.values
# Record if match or not
df['FOUND'].loc[toi_inds] = mask
df['CHECKED'].loc[toi_inds] = mask
# Where no match, replace by new ID
df['GAIADR2ID'].loc[toi_inds] = df['GAIADR2ID'].loc[toi_inds].where(
mask, other=tic_gaia['GAIA'])
msg = 'Updated Gaia ID from TIC'
df['COMMENTS'].loc[toi_inds] = df['COMMENTS'].loc[toi_inds].where(
mask, other=msg)
df['FOUND'].loc[toi_inds] = True
df['CHECKED'].loc[toi_inds] = True
return df
def get_ID_from_ID(id_type, ID, new_id_type):
if id_type == 'TIC':
query_string = 'tic ' + str(ID)
obs_table = Catalogs.query_object(query_string,
radius=0.002 * u.deg,
catalog='TIC')
obs_table = obs_table[obs_table['ID'] == str(ID)]
ra = obs_table['ra'][0]
dec = obs_table['dec'][0]
return (ra, dec)
def get_tess_input_catalog_row(tic_id: int) -> pd.Series:
"""
Get the TIC row for a TIC ID.
:param tic_id: The target's TIC ID.
:return: The row of a the TIC corresponding to the TIC ID.
"""
target_observations = Catalogs.query_criteria(catalog='TIC',
ID=tic_id).to_pandas()
return target_observations.iloc[0]
def fetch_local_bright_TICs(ra_dec_string, targetname):
"""Produces a CSV of all <Tmag 10 targets w/in 2 degrees of the target for reference """
catalog_data1 = Catalogs.query_criteria(coordinates=ra_dec_string, radius=2,
catalog="TIC", Tmag = [-40, 10])
correctcols = catalog_data1['ID', 'ra', 'dec', 'Tmag']
print(correctcols)
correctcols.write("/users/conta/urop/Local_TIC_to_" + targetname +".csv")
def find_tic(target_ID, from_file = True):
if from_file == True:
try:
table_data = Table.read("Original_BANYAN_XI-III_xmatch_TIC.csv" , format='ascii.csv')
#table_data = Table.read("Original VCA Members.csv" , format='ascii.csv')
#table_data = Table.read("Original Argus members info.csv" , format='ascii.csv')
# Obtains ra and dec for object from target_ID
i = list(table_data['main_id']).index(target_ID)
ra = table_data['ra'][i]
dec = table_data['dec'][i]
tic = table_data['MatchID'][i]
except:
try:
TIC_table = Catalogs.query_object(target_ID, catalog = "TIC")
ra = TIC_table['ra'][0]
dec = TIC_table['dec'][0]
tic = TIC_table['ID'][0]
except:
table_data = Table.read('BANYAN_XI-III_combined_members.csv')
i = list(table_data['main_id']).index(target_ID)
ra = table_data['ra'][i]
dec = table_data['dec'][i]
object_coord = SkyCoord(ra, dec, unit="deg")
TIC_table = Catalogs.query_region(object_coord, radius = '1 deg', catalog = 'TIC')
tic = TIC_table['ID'][0]
else:
# Find ra, dec and tic # via the TIC (typically based on Gaia DR2)
try:
TIC_table = Catalogs.query_object(target_ID, catalog = "TIC")
ra = TIC_table['ra'][0]
dec = TIC_table['dec'][0]
tic = TIC_table['ID'][0]
except:
table_data = Table.read('BANYAN_XI-III_combined_members.csv')
i = list(table_data['main_id']).index(target_ID)
ra = table_data['ra'][i]
dec = table_data['dec'][i]
object_coord = SkyCoord(ra, dec, unit="deg")
TIC_table = Catalogs.query_region(object_coord, radius = '1 deg', catalog = 'TIC')
tic = TIC_table['ID'][0]
return ra, dec, tic
def get_catalog(self, image):
max_fov = image.fov.max() * np.sqrt(2) / 2
table = Catalogs.query_region(image.skycoord,
max_fov,
"TIC",
verbose=False)
table["ra"].unit = "deg"
table["dec"].unit = "deg"
table.rename_column('ID', 'id')
return table
def get_panstarrs_catalog(imgwcs,
imgfile,
radius=0.2,
rfaint=17,
region=False):
from astroquery.mast import Catalogs
ra0, dec0 = imgwcs.wcs.crval
print(
'Querying Pan-STARRS catalog with radius={:.3f} deg and central coordinates RA,Dec={:.5f},{:.5f}'
.format(radius, ra0, dec0))
if region:
allcat = Catalogs.query_region('{} {}'.format(ra0, dec0),
radius=radius,
catalog='PANSTARRS',
data_release='dr2',
table='mean') #, rMeanPSFMag=[12, 22])
else:
allcat = Catalogs.query_criteria(coordinates='{} {}'.format(ra0, dec0),
radius=radius,
catalog='PANSTARRS',
data_release='dr2',
table='mean',
columns=[
'objID', 'raMean', 'decMean',
'gMeanPSFMag', 'rMeanPSFMag',
'iMeanPSFMag', 'zMeanPSFMag'
],
gMeanPSFMag=[('lte', 18),
('gte', 12)],
rMeanPSFMag=[('lte', 18),
('gte', 12)],
iMeanPSFMag=[('lte', 18),
('gte', 12)],
zMeanPSFMag=[('lte', 18),
('gte', 12)],
sort_by=[("asc", "rMeanPSFMag")])
#rmag = allcat['rMeanPSFMag']
#good = np.isfinite(rmag) * rmag < rfaint
#cat = allcat[good]
#print('Keeping {}/{} Pan-STARRS sources.'.format(len(cat), len(allcat)))
return allcat
def app_catalogs():
global blc
global trc
global im
if blc is None or trc is None or im is None: load_image()
searchString = '{} {}'.format(np.mean([blc[0], trc[0]]),
np.mean([blc[1], trc[1]]))
catalogData = Catalogs.query_object(searchString,
radius=0.2,
catalog="GAIAdr2")
# get plot
p = make_base_bokeh()
source = ColumnDataSource(catalogData.to_pandas())
p.scatter('ra',
'dec',
source=source,
legend="GAIA DR2",
alpha=0.7,
size=10)
# Add hover tooltip for GAIA data
tooltip = [("RA", "@ra"), ("Dec", "@dec"), ("Desig.", "@designation"),
("parallax", "@parallax"),
("phot_g_mean_mag", "@phot_g_mean_mag")]
p.add_tools(HoverTool(tooltips=tooltip))
p.legend.click_policy = "hide"
# Table data
columns = []
for col in catalogData.to_pandas().columns:
if col not in ('ra', 'dec', 'designation', 'parallax'):
continue
columns.append(TableColumn(field=col, title=col))
data_table = DataTable(source=source,
columns=columns,
width=1200,
height=280)
# Fails to load anything
# script, div_dict = components({'plot': p, 'table': widgetbox(data_table)})
# return render_template('catalogs.html', script=script, div=div_dict)
# Fails to load table
# script1, div1 = components(p)
# script2, div2 = components(widgetbox(data_table))
# return render_template('catalogs.html', script1=script1, div1=div1, script2=script2, div2=div2)
# No table
script, div = components(p)
return render_template('base.html', script=script, plot=div)
def get_target_coordinates(tic_id: int) -> SkyCoord:
"""
Get the sky coordinates of the target by a TIC ID.
:param tic_id: The target's TIC ID.
:return: The coordinates of the target.
"""
target_observations = Catalogs.query_criteria(catalog='TIC', ID=tic_id).to_pandas()
ra = target_observations['ra'].iloc[0]
dec = target_observations['dec'].iloc[0]
return SkyCoord(ra, dec, unit='deg')
def get_tic_radec(ra, dec):
with warnings.catch_warnings():
warnings.simplefilter('ignore')
catalogData = Catalogs.query_region('{} {}'.format(
ra, dec),
catalog='Tic', radius=1 * u.arcsec)
try:
return catalogData['ID'][0]
except IndexError:
logger.error("No TIC target at those coordiantes")
sys.exit(1)
def getRaDec(self, ticid):
"""
Use astroquery catalogs to get the RA and Dec
"""
#@todo check ticid is an integer.
starName = "TIC %i" % int(ticid)
cat = Catalogs.query_criteria(ID=starName, catalog="Tic")
coord = SkyCoord(cat[0]['ra'], cat[0]['dec'], unit="deg")
return coord
def get_IDS(number1, number2):
catalogTIC = Catalogs.query_criteria(catalog="Tic",
Teff=[3450.0, 3500.0],
logg=[4.2, 5.0],
Tmag=[11.5, 12.0])
catalogTIC = catalogTIC[catalogTIC['dec'] < 0]
return catalogTIC['ID'][number1:number2]
# catalogTIC = Catalogs.query_criteria(catalog = "Tic", Teff = [3450.0, 3500.0], logg=[4.2,5.0], Tmag=[11.5,12.0])
# catalogTIC = catalogTIC[catalogTIC['dec'] < 0]
# print(catalogTIC)
def ticid_to_coord(ticid):
"""
Input:
ticid: string containing the TIC Identificaiton number.
Returns:
coord: astropy SkyCoordinate object of ra and dec
"""
info = Catalogs.query_criteria(catalog='Tic', ID=ticid)
ra = info['ra'][0] # deg
dec = info['dec'][0] # deg
coord = SkyCoord(ra, dec, unit='deg')
return coord
def coords_from_tic(tic):
"""Finds the RA, Dec, and magnitude for a given TIC source_id.
Returns
-------
coords : tuple
(RA, Dec) position [degrees].
tmag : float
TESS apparent magnitude.
"""
ticData = Catalogs.query_object('tic'+str(tic), radius=.0001, catalog="TIC")
return [ticData['ra'].data[0], ticData['dec'].data[0]], [ticData['Tmag'].data[0]], int(ticData['version'].data[0]), ticData['contratio'].data[0]
def tessobs_info(tic=None, ra=None, dec=None):
#!!Update to include exp time, pixel location, other observation-specific
# quantities!!
"""
Function to retrieve observation information for objects observed by TESS.
Parameters
----------
tic : int or None
TIC ID of target to be queried. Must not be None if ra and dec are None.
ra : float or None
RA of target to be queried. Must not be None if tic is None.
dec : float or None
Dec of target to be queried. Must not be None if tic is None.
Returns
-------
info : dict
Dictionary continaing TESS observation info.
"""
if not tic and not ra and not dec:
raise ValueError('Please provide either a TIC ID or both RA and Dec')
if not ra or not dec:
cat = Catalogs.query_criteria(catalog="TIC", ID=int(tic))
ra = cat[0]['ra']
dec = cat[0]['dec']
coords = coord.SkyCoord(ra, dec, unit='deg')
sector_table = Tesscut.get_sectors(coordinates=coords)
if len(sector_table) == 0:
print('Object not observed by TESS')
sec_name = []
sec = []
cam = []
ccd = []
for i in range(len(sector_table)):
sec_name.append(sector_table[i]['sectorName'])
sec.append(sector_table[i]['sector'])
cam.append(sector_table[i]['camera'])
ccd.append(sector_table[i]['ccd'])
info = {'sectorName' : sec_name, 'sector' : sec, 'camera' : cam,
'ccd' : ccd}
return info
def loadSingleSector(ticId, sector, size_pixels):
starname = "TIC %08i" % (ticId)
catalogData = Catalogs.query_object(starname,
radius=1 / 60.,
catalog="TIC")
ra = catalogData[0]['ra']
dec = catalogData[0]['dec']
coord = SkyCoord(ra, dec, unit='deg')
data, hdr, wcshdr = getTessCutout(coord, size_pixels, sector)
time = data['TIME']
cube = getTargetPixelArrayFromFits(data, hdr)
return time, cube, hdr, wcshdr
