def _get_catalog(self):
"""Returns GAIA catalog for current telescope coordinates."""
if self._catalog_coords is None or self._catalog_coords.separation(
self.telescope.real_pos) > 10. * u.arcmin:
self._catalog = Gaia.query_object_async(
coordinate=self.telescope.real_pos, radius=1. * u.deg)
return self._catalog
def get_gaia_cat(ims, cat_name='gaia'):
"""Get the Gaia catalog for the area of input images"""
from calc_bounds import bounds, get_footprints
print 'Calculating coordinate ranges for Gaia query:'
footprint_list = map(get_footprints, ims)
ras, decs = bounds(footprint_list)
ra_midpt = (np.amax(ras)+np.amin(ras))/2.
dec_midpt = (np.amax(decs)+np.amin(decs))/2.
ra_width = (np.amax(ras)-np.amin(ras))
dec_height = (np.amax(decs)-np.amin(decs))
print '\nPerforming Gaia query:'
coord = SkyCoord(ra=ra_midpt, dec=dec_midpt, unit=(u.degree, u.degree), frame='icrs')
width = Quantity(ra_width, u.deg)
height = Quantity(dec_height, u.deg)
r = Gaia.query_object_async(coordinate=coord, width=width, height=height)
print 'Sources returned: {}'.format(len(r))
assert len(r) > 0, 'No sources found in Gaia query\n'
cat_file_name = '{}.cat'.format(cat_name)
print 'Writing Gaia source catalog: {}\n'.format(cat_file_name)
Table([r['ra'], r['dec']]).write(cat_file_name, format='ascii.fast_commented_header')
return cat_file_name
def radec_gaia_stars(ra,
dec,
radius=1.0,
width=None,
height=None,
verbose=False,
tap_url=None):
"""Search for bright stars using GAIA catalog.
TODO:
Should be absorbed by the object for image later.
TODO:
Should have a version that just uses the local catalog.
"""
# Central coordinate
radec = SkyCoord(ra, dec, unit=('deg', 'deg'), frame='icrs')
# Default searching radius is 1.0 deg
r_search = Quantity(radius, u.degree)
if width is not None:
w_search = Quantity(width, u.degree)
if height is None:
# Search in a square
h_search = w_search
else:
h_search = Quantity(height, u.degree)
# Search for stars
if tap_url is not None:
from astroquery.gaia import TapPlus, GaiaClass
Gaia = GaiaClass(TapPlus(url=tap_url))
else:
from astroquery.gaia import Gaia
if width is not None:
gaia_results = Gaia.query_object_async(coordinate=radec,
width=w_search,
height=h_search,
verbose=verbose)
else:
gaia_results = Gaia.query_object_async(coordinate=radec,
radius=r_search,
verbose=verbose)
return gaia_results
def get_gaia_stars(name, width):
result_table = Simbad.query_object(name)
ra, dec = result_table["RA"][0], result_table["DEC"][0]
coord = SkyCoord(ra=ra, dec=dec, unit=(u.degree, u.degree), frame='icrs')
r = Gaia.query_object_async(coordinate=coord, width=width, height=width)
return r
def query_gaia():
# Westerlund 1
coord = SkyCoord(ra="16 47 04.00", dec="-45 51 04.9", unit=(u.degree, u.degree), frame='icrs')
width = 1 * u.deg
height = 1 * u.deg
r = Gaia.query_object_async(coordinate=coord, width=width, height=height)
return r
def searchGaiaArchives(ra, dec, height, width):
coord = SkyCoord(ra=ra * u.deg, dec=dec * u.deg)
width = u.Quantity(width * u.deg)
height = u.Quantity(height * u.deg)
results = Gaia.query_object_async(coordinate=coord,
width=width,
height=height)
return np.array(list(results['ra'])), np.array(list(results['dec']))
def gaia_xmatch_results():
# get a sample of stars to cross match
center_coord = SkyCoord(ra=280, dec=-60, unit=(u.degree, u.degree), frame='icrs')
width = u.Quantity(0.05, u.deg)
height = width
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
sample_of_stars = Gaia.query_object_async(coordinate=center_coord, width=width, height=height)
# cross match the coordinates and check we get back the same thing
coordinates_of_stars = pd.DataFrame({'ra': sample_of_stars['ra'], 'de': sample_of_stars['dec']})
xmatch_results = mwtools.xmatch.Gaia_DR2_Xmatch(coordinates_of_stars)
return xmatch_results
def get_gaia_cat(input_images, cat_name='gaia'):
"""
Get the Gaia catalog for the area of input images.
This function queries Gaia for a table of sources. It determines
the dimensions to use for the query by finding the sky positions
of the corners of each of the images.
"""
print('Calculating coordinate ranges for Gaia query:')
footprint_list = map(get_footprints, input_images)
merged = []
for im in footprint_list:
for ext in im:
merged.append(ext)
merged = np.vstack(merged)
ras = merged[:,0]
decs = merged[:,1]
ra_midpt = (max(ras)+min(ras))/2.
dec_midpt = (max(decs)+min(decs))/2.
ra_width = (np.amax(ras)-np.amin(ras))
dec_height = (np.amax(decs)-np.amin(decs))
coord = SkyCoord(ra=ra_midpt, dec=dec_midpt, unit=(u.degree, u.degree), frame='icrs')
width = Quantity(ra_width, u.deg) * 1.1
height = Quantity(dec_height, u.deg) * 1.1
r = Gaia.query_object_async(coordinate=coord, width=width, height=height)
assert len(r) > 0, 'No sources found in Gaia query\n'
print('Sources returned: {}'.format(len(r)))
cat = r['ra', 'dec']
cat.write('{}.cat'.format(cat_name), format='ascii.commented_header')
return '{}.cat'.format(cat_name)
def gaia_results(image,saveas=None): # image wcs and frame, for conversions pixels/skycoord wcs,frame=WCS(image.header),image.header['RADESYS'].lower() # coord of strong lensing galaxy ra=image.header['CAT-RA'] dec=image.header['CAT-DEC'] coord = SkyCoord(ra,dec,unit=(u.hourangle,u.deg)) # the pixscale is same along x&y and rotated to default (N up, E left) cdi_j ~ delta_ij cdi_i = image.header['CD1_1'] # deg/pixel naxis = image.header['NAXIS1'] # naxis1=naxis2 radius = 3600*cdi_i*naxis/2 # approx 800 arcsec entire image radius*=.75 # do 3/4 of that # do the search r = Gaia.query_object_async(coordinate=coord, radius=radius*u.arcsec) if saveas: if not os.path.exists(saveas): os.makedirs(saveas) pickle.dump(r,open(saveas+"/gaia_results.pkl","wb")) return r,image
def get_gaia_star(coord_center, radius_arcsec, g_mag_cut=15.0):
'''
Retruns list of coordinates of GAIA stars in the field.
'''
# set up coordinates
coord = SkyCoord(ra=coord_center[0][0],
dec=coord_center[0][1],
unit=(u.degree, u.degree),
frame='icrs')
radius = u.Quantity(radius_arcsec, u.arcsec)
# query GAIA stars
gaia_stars = Gaia.query_object_async(coordinate=coord, radius=radius)
# choose stars with proper motion and mag cut
prop_motion = np.sqrt(gaia_stars['pmra'].data.data**2 +
gaia_stars['pmdec'].data.data**2)
idx_good_stars = (
(gaia_stars['astrometric_excess_noise'].data.data < 1.0) &
(prop_motion < 20.0).data &
(gaia_stars['phot_g_mean_mag'].data.data > g_mag_cut)).astype(bool)
print('number of stars found in GAIA :', np.sum(idx_good_stars))
coord_gaia = SkyCoord(ra=gaia_stars[idx_good_stars]['ra'],
dec=gaia_stars[idx_good_stars]['dec'],
unit=(u.deg, u.deg))
return (coord_gaia)
def query_one_star(ra_deg, dec_deg, radius_arcsec=10):
# get gaia data
coord = SkyCoord(ra=ra_deg,
dec=dec_deg,
unit=(u.degree, u.degree),
frame='icrs')
rad = u.Quantity(float(radius_arcsec), u.arcsec)
rG = Gaia.query_object_async(coordinate=coord, radius=rad)
# return NaN if no GAIA star is found
if len(rG) == 0:
return np.repeat(np.nan, 18).reshape(9, 2)
# get 2MASS data
r2 = Vizier.query_region(coord, radius=rad, catalog='II/246')
# return NaN if no 2MASS star is found
if (r2 == None) or (len(r2) == 0):
return np.repeat(np.nan, 18).reshape(9, 2)
# step through possible matches
for i in range(len(rG)):
for j in range(len(r2)):
# get giaa parameters
par, epar = rG['parallax'][i] + .03, rG['parallax_error'][i]
Gmag = rG['phot_g_mean_mag'][i]
GBPmag = rG['phot_bp_mean_mag'][i]
FBP = rG['phot_bp_mean_flux'][i]
eFBP = rG['phot_bp_mean_flux_error'][i]
eGBPmag = -2.5 * np.log10(FBP / (FBP + eFBP))
GRPmag = rG['phot_rp_mean_mag'][i]
FRP = rG['phot_rp_mean_flux'][i]
eFRP = rG['phot_rp_mean_flux_error'][i]
eGRPmag = -2.5 * np.log10(FRP / (FRP + eFRP))
# get 2MASS photometry
Hmag, Kmag, eKmag = r2[j]['Hmag'][0], r2[j]['Kmag'][0], \
r2[j]['e_Kmag'][0]
# change bad values to NaN
if np.ma.is_masked(par) or par <= 0: par, epar = np.nan, np.nan
if np.ma.is_masked(epar) or epar <= 0: par, epar = np.nan, np.nan
if np.ma.is_masked(Gmag): Gmag = np.nan
if np.ma.is_masked(GBPmag): GBPmag, eGBPmag = np.nan, np.nan
if np.ma.is_masked(eGBPmag): GBPmag, eGBPmag = np.nan, np.nan
if np.ma.is_masked(GRPmag): GRPmag, eGRPmag = np.nan, np.nan
if np.ma.is_masked(eGRPmag): GRPmag, eGRPmag = np.nan, np.nan
if np.ma.is_masked(Hmag): Hmag = np.nan
if np.ma.is_masked(Kmag): Kmag, eKmag = np.nan, np.nan
if np.ma.is_masked(eKmag): Kmag, eKmag = np.nan, np.nan
# check that GAIA and 2MASS photometry aproximately match
if np.ma.is_masked(par) or np.ma.is_masked(Gmag):
match = False
else:
matches = np.zeros(4).astype(bool)
matches[0], _, _ = does_G_K_match(Gmag, Hmag, Kmag)
matches[1], _, _ = does_GBP_K_match(GBPmag, Hmag, Kmag)
matches[2], _, _ = does_GRP_K_match(GRPmag, Hmag, Kmag)
matches[3], _, _ = does_GBP_GRP_match(GBPmag, GRPmag, Hmag,
Kmag)
match = np.all(matches)
if match:
dist, mu = compute_distance_modulus(unp.uarray(par, epar))
print unp.nominal_values(dist)
l, b = coord.galactic.l.deg, coord.galactic.b.deg
AK = compute_AK_mwdust(l, b, unp.nominal_values(dist),
unp.std_devs(dist))
MK = compute_MK(unp.uarray(Kmag, eKmag), mu, AK)
Rs = MK2Rs(MK)
Teff = gaia2Teff(unp.uarray(GBPmag, eGBPmag),
unp.uarray(GRPmag, eGRPmag))
Ms = MK2Ms(MK)
return [par,epar], [Kmag,eKmag], \
[unp.nominal_values(dist), unp.std_devs(dist)], \
[unp.nominal_values(mu), unp.std_devs(mu)], \
[unp.nominal_values(AK), unp.std_devs(AK)], \
[unp.nominal_values(MK), unp.std_devs(MK)], \
[unp.nominal_values(Rs), unp.std_devs(Rs)], \
[unp.nominal_values(Teff), unp.std_devs(Teff)], \
[unp.nominal_values(Ms), unp.std_devs(Ms)], \
# return NaN if not found a match by now
return np.repeat(np.nan, 18).reshape(9, 2)
print(" - RA = {:0.2f} +/- {:0.2f}; Dec = {:0.2f} +/- {:0.2f}".format(
RA, dRA, DEC, dDEC))
print(GAIAcoord) #; sys.exit()
print("")
#Check if we already have the Gaia catalog
if os.path.exists(GaiaTable):
gaia_cat = ascii.read(GaiaTable, format="ipac")
print("## Already have a Gaia catalog with " + str(len(gaia_cat)) +
" sources.")
print("## To get a new one rename or remove " + GaiaTable)
else:
#Get the GAIA catalog
print(">> Querying the GAIA catalog ....")
gaia_cat = Gaia.query_object_async(coordinate=GAIAcoord,
width=dRA,
height=dDEC)
print(" - Downloaded GAIA-DR2 catalog with " + str(len(gaia_cat)) +
' sources')
#Add fluxes in uJy
gaia_cat['g'] = 10**((gaia_cat['phot_g_mean_mag'] - 23.9) / -2.5)
gaia_cat['bp'] = 10**((gaia_cat['phot_bp_mean_mag'] - 23.9) / -2.5)
gaia_cat['rp'] = 10**((gaia_cat['phot_rp_mean_mag'] - 23.9) / -2.5)
ascii.write(gaia_cat, GaiaTable, format="ipac",
overwrite=True) #save the catalog
print(" ==> Wrote GAIA-DR2 catalog to ", GaiaTable)
#fix a bug in the ipac table writer
fixcmd = "sed -i -e \"s/'null'/ null /g\" " + GaiaTable
print(i, name)
sleep(1)
if True:
# try:
radec = dat["pos"][sysi]
sep = dat["sep"][sysi]
sp = dat["sp"][sysi]
print(radec)
c = SkyCoord("J" + radec, unit=(u.hourangle, u.deg))
width = u.Quantity(5, u.arcsec)
height = u.Quantity(5, u.arcsec)
#GAIA
r = Gaia.query_object_async(coordinate=c, width=width, height=height)
plx = None
if len(r["parallax"]) == 0:
sw = False
elif type(r["parallax"][0]) == np.float64:
plx = r["parallax"][0]
sw = True
else:
sw = False
print("GAIA", plx)
Simbad.SIMBAD_URL = "http://simbad.u-strasbg.fr/simbad/sim-script"
Simbad.add_votable_fields("parallax", "flux(V)", "flux(R)", "flux(J)",
"flux(H)", "flux(K)", "pmra", "pmdec")
result_table = Simbad.query_region(c, radius='0d0m5s')
def query_one_TIC(theta, radius_arcsec=10):
# get gaia data
assert len(theta) == 9
ra_deg, dec_deg, GAIAmag, Jmag, e_Jmag, Hmag, e_Hmag, Kmag, e_Kmag = theta
coord = SkyCoord(ra=ra_deg,
dec=dec_deg,
unit=(u.degree, u.degree),
frame='icrs')
rad = u.Quantity(float(radius_arcsec), u.arcsec)
rG = Gaia.query_object_async(coordinate=coord, radius=rad)
# return NaN if no GAIA star is found
if len(rG) == 0:
return np.repeat(np.nan, 20).reshape(10, 2)
# step through possible matches
for i in range(len(rG)):
# get gaia parameters
par, epar = rG['parallax'][i] + .029, rG['parallax_error'][i]
GAIAmag_dr2 = rG['phot_g_mean_mag'][i]
GBPmag = rG['phot_bp_mean_mag'][i]
FBP = rG['phot_bp_mean_flux'][i]
eFBP = rG['phot_bp_mean_flux_error'][i]
eGBPmag = -2.5 * np.log10(FBP / (FBP + eFBP))
GRPmag = rG['phot_rp_mean_mag'][i]
FRP = rG['phot_rp_mean_flux'][i]
eFRP = rG['phot_rp_mean_flux_error'][i]
eGRPmag = -2.5 * np.log10(FRP / (FRP + eFRP))
# change bad values to NaN
if np.ma.is_masked(par) or par <= 0: par, epar = np.nan, np.nan
if np.ma.is_masked(epar) or epar <= 0: par, epar = np.nan, np.nan
if np.ma.is_masked(GBPmag): GBPmag, eGBPmag = np.nan, np.nan
if np.ma.is_masked(eGBPmag): GBPmag, eGBPmag = np.nan, np.nan
if np.ma.is_masked(GRPmag): GRPmag, eGRPmag = np.nan, np.nan
if np.ma.is_masked(eGRPmag): GRPmag, eGRPmag = np.nan, np.nan
# check that GAIA and 2MASS photometry aproximately match
if np.isnan(par) or np.isnan(GBPmag) or np.isnan(GRPmag):
match = False
elif GAIAmag == GAIAmag_dr2:
match = True
else:
matches = np.zeros(4).astype(bool)
matches[0], _, _ = does_G_K_match(GAIAmag_dr2, Hmag, Kmag)
matches[1], _, _ = does_GBP_K_match(GBPmag, Hmag, Kmag)
matches[2], _, _ = does_GRP_K_match(GRPmag, Hmag, Kmag)
matches[3], _, _ = does_GBP_GRP_match(GBPmag, GRPmag, Hmag, Kmag)
match = np.all(matches)
if match:
dist, mu = compute_distance_modulus(unp.uarray(par, epar))
l, b = coord.galactic.l.deg, coord.galactic.b.deg
AK = compute_AK_mwdust(l, b, unp.nominal_values(dist),
unp.std_devs(dist))
MK = compute_MK(unp.uarray(Kmag, e_Kmag), mu, AK)
Rs = MK2Rs(MK)
Teff = gaia2Teff(unp.uarray(GBPmag, eGBPmag),
unp.uarray(GRPmag, eGRPmag),
unp.uarray(Jmag, e_Jmag),
unp.uarray(Hmag, e_Hmag))
Ms = MK2Ms(MK)
# ***gbp and grp used to have switched positions here
return [par,epar], [GBPmag,eGBPmag], [GRPmag,eGRPmag], \
[unp.nominal_values(dist), unp.std_devs(dist)], \
[unp.nominal_values(mu), unp.std_devs(mu)], \
[unp.nominal_values(AK), unp.std_devs(AK)], \
[unp.nominal_values(MK), unp.std_devs(MK)], \
[unp.nominal_values(Rs), unp.std_devs(Rs)], \
[unp.nominal_values(Teff), unp.std_devs(Teff)], \
[unp.nominal_values(Ms), unp.std_devs(Ms)], \
# return NaN if not found a match by now
return np.repeat(np.nan, 20).reshape(10, 2)
ixf = 0 for line in lines: cos = line.split() ra1 = float(cos[1]) dec1 = float(cos[2]) ra2 = float(cos[12]) dec2 = float(cos[13]) bp1 = float(cos[-4]) bp2 = float(cos[-2]) #print ra1,dec1,ra2,dec2 if dec1 < 30 and dec2 < 30: coord1 = SkyCoord(ra=ra1, dec=dec1, unit=(u.deg, u.deg), frame='icrs') coord2 = SkyCoord(ra=ra2, dec=dec2, unit=(u.deg, u.deg), frame='icrs') r1 = Gaia.query_object_async(coordinate=coord1, radius=u.Quantity(10./3600., u.deg)) print len(r1) pmra = r1[0]['pmra'] pmdec = r1[0]['pmdec'] parallax = r1[0]['parallax'] epmra = r1[0]['pmra_error'] epmdec = r1[0]['pmdec_error'] eparallax = r1[0]['parallax_error'] drop1,drop2 = False, False drop3,drop4 = False, False if len(r1)>1: for r in r1[1:]: if (np.absolute(r['pmra']-pmra)/max(epmra,r['pmra_error']) < 5) and (np.absolute(r['pmdec']-pmdec)/max(epmdec,r['pmdec_error'])<5) and (np.absolute(r['parallax']-parallax)/eparallax < 5): drop1 = True
def querycat_gaia(ralist=None, declist=None, cone_search=False,
width=10.0, height=10.0, radius=5.0, dr2=False,
test=False, debug=False):
"""
"""
import time
from astropy.table import Table, vstack
import astropy.units as u
from astropy.coordinates import SkyCoord
from astroquery.gaia import Gaia
if dr2:
help(Gaia)
gaiadr2_table = Gaia.load_table('gaiadr2.gaia_source')
for column in (gaiadr1_table.get_columns()):
print(column.get_name())
if debug:
help(Gaia)
width = u.Quantity(width/3600.0, u.degree)
height = u.Quantity(height/3600.0, u.degree)
radius = u.Quantity(radius/3600.0, u.degree)
if test is True:
ralist = [180.0]
declist = [0.0]
width = u.Quantity(30.0, u.arcsec)
height = u.Quantity(30.0, u.arcsec)
radius = u.Quantity(15.0, u.arcsec)
result_nrows = 0
for isource, (ra, dec) in enumerate(zip(ralist, declist)):
coord = SkyCoord(ra=ra, dec=dec,
unit=(u.degree, u.degree),
frame='icrs')
t0 = time.time()
if not cone_search:
result = Gaia.query_object_async(coordinate=coord,
width=width, height=height)
# help(result)
if debug:
result.pprint()
result.info('stats')
if cone_search:
job = Gaia.cone_search_async(coord, radius)
# help(job)
result = job.get_results()
print('Number of rows:', len(result))
print('Elapsed time(secs):',time.time() - t0)
if debug:
help(result)
result.pprint()
result.info('stats')
result_nrows = result_nrows + len(result)
if isource == 0:
result_all = result
if isource > 0:
result_all = vstack([result_all, result])
#def fix_vot_object():
table = result_all
print('icol, format, dtype')
# help(table)
# help(table.columns)
for (icol, column) in enumerate(table.columns):
print(icol, table.columns[icol].name,
table.columns[icol].format,
table.columns[icol].dtype)
# convert the columns for dtype = object which is not
# supported by FITs to bool
if table.columns[icol].dtype == 'object':
colname = table.columns[icol].name
NewColumn = Table.Column(table[colname].data, dtype='bool')
table.replace_column(colname, NewColumn)
print()
result_all = table
print('Number of Gaia sources returned:', result_nrows, len(result_all))
return result_all
def search(image, headinfo, target_coords, syntax, catalog_syntax, filter_):
"""
Search area around transient/target location in photometric catalogs
Current catalog (selectable in syntax):
- Skymapper: Southern Hemisphere
- Pan Starrs: North of declination -30 degree
- Apass: All-sky survey
- 2MASS: JHK all sky survey
Future:
- SDSS: Future implemtation
- Ability to make custom catalog from different surveys
Input:
- Image: Numpy 2D array
- headinfo: astropy.io.fits.header.Header
- target_coords: astropy.coordinates.sky_coordinate.SkyCoord
- syntax: dict
- catalog_syntax: dict
- filter_: str
Output:
- data: pandas DataFrame
"""
import warnings
if not syntax['catalog_warnings'] or syntax['master_warnings']:
warnings.filterwarnings("ignore")
import numpy as np
import os,sys
import requests
import pathlib
import shutil
import os.path
import logging
from functools import reduce
from astropy.table import Table
from astropy.wcs import wcs
from astroquery.vizier import Vizier
from astropy.io.votable import parse_single_table
from astropy.coordinates import Angle
from autophot.packages.functions import r_dist
logger = logging.getLogger(__name__)
try:
# Get wxs information
w1 = wcs.WCS(headinfo)
# Radius around target
radius = float(syntax['radius'])
# Target name, if applicable
target = syntax['target_name']
# Get workdirectory location,, create directory if needed
dirname = os.path.join(syntax['wdir'],'catalog_queries')
pathlib.Path(dirname).mkdir(parents = True, exist_ok=True)
'''
Getting target Ra and Dec
- if target is none but a Ra and DEC is given, create new target name
- if ra and dec not given us center of image as location - for quick reduction of image
'''
# if target or it's ra/dec - set target name
if target == None:
if syntax['target_ra'] != None and syntax['target_dec'] != None:
target = 'target_ra_'+str(round(syntax['target_ra']))+'_dec_'+str(round(syntax['target_dec']))
logger.info('New target name: %s' %target)
else:
# if not just call target
target = 'target'
# Search limitation with Pan Starrs rlimited to 0.5 deg
if radius > 0.5 and syntax['catalog'] == 'pan_starrs' :
logger.warning('Search Limitation with PanStarrs API -> Radius = 0.5 [deg] ')
radius = 0.5
# Choosen catalog for input.yml, create directory for catalog if needed
catalog_dir = syntax['catalog']
pathlib.Path(os.path.join(dirname ,catalog_dir)).mkdir(parents = True, exist_ok=True)
# Folder for target, create directory if needed
target_dir = reduce(os.path.join,[dirname,catalog_dir,target.lower()])
pathlib.Path(target_dir).mkdir(parents = True, exist_ok=True)
# Filename of fetchec catalog
fname = str(target) + '_r_' + str(radius)
# Can force to use certain catalog - untested 03-10-19
if syntax['force_catalog_csv']:
logger.info('Using '+syntax['force_catalog_csv_name']+' as catalog')
fname = str(syntax['force_catalog_csv_name']) + '_r_' + str(radius)
# if catalog is found via it's filename - use this and return data
if os.path.isfile(os.path.join(target_dir,fname+'.csv')):
logger.info('Catalog found for Target: %s\nCatalog: %s \nFile: %s' % (target,str(catalog_dir).upper(),fname))
data = Table.read(os.path.join(target_dir,fname+'.csv'),format = 'csv')
data = data.to_pandas()
else:
# If no previously catalog found - look for one
logger.info('Searching for new catalog: %s ' % syntax['catalog'])
if syntax['catalog'] in ['gaia']:
import astropy.units as u
from astroquery.gaia import Gaia
import warnings
warnings.filterwarnings('ignore')
width = u.Quantity(radius, u.deg)
height = u.Quantity(radius, u.deg)
data = Gaia.query_object_async(coordinate=target_coords, width=width, height=height)
data = data.to_pandas()
data.to_csv(fname+'.csv', sep=',',index = False)
# Move file to new location - 'catalog queries'
shutil.move(os.path.join(os.getcwd(), fname+'.csv'),
os.path.join(target_dir, fname+'.csv'))
warnings.filterwarnings('default')
if syntax['catalog'] in ['apass','2mass']:
# No row limit
Vizier.ROW_LIMIT = -1
catalog_search = Vizier.query_region(target_coords,
radius = Angle(radius,'deg'),
catalog = syntax['catalog'])
# Select first catalog from list
data = catalog_search[0].to_pandas()
data.to_csv(fname+'.csv', sep=',',index = False)
# Move file to new location - 'catalog queries'
shutil.move(os.path.join(os.getcwd(), fname+'.csv'),
os.path.join(target_dir, fname+'.csv'))
# some catalogs need specific download path using 'requests'
if syntax['catalog'] in ['pan_starrs','skymapper']:
mindet=1
if syntax['catalog'] == 'pan_starrs':
server=('https://archive.stsci.edu/'+'panstarrs/search.php')
params = {'RA': target_coords.ra.degree, 'DEC': target_coords.dec.degree,
'SR': radius, 'max_records': 10000,
'outputformat': 'VOTable',
'ndetections': ('>%d' % mindet)}
if syntax['catalog'] == 'skymapper':
server=('http://skymapper.anu.edu.au/sm-cone/public/query?')
params = {'RA': target_coords.ra.degree, 'DEC': target_coords.dec.degree,
'SR': radius,
'RESPONSEFORMAT': 'VOTABLE'}
with open('temp.xml', "wb") as f:
logger.info('Downloading from %s' % syntax['catalog'] )
response = requests.get(server,params = params)
f.write(response.content)
# Parse local file into astropy.table object
data = parse_single_table('temp.xml')
# Delete temporary file
os.remove('temp.xml')
# Convert table to dataframe
data_table = data.to_table(use_names_over_ids=True)
data = data_table.to_pandas()
# invalid entries in panstarrs are -999 - change to nans
if syntax['catalog'] == 'pan_starrs':
data = data.replace(-999,np.nan)
# No sources in field - temporary fix - will add "check different catalog"
if len(data) == 0:
logging.critical('Catalog: %s : does not cover field' % syntax['catalog'])
sys.exit()
# Save to csv and move to 'catalog_queries'
data.to_csv(fname+'.csv',index = False)
shutil.move(os.path.join(os.getcwd(), fname+'.csv'),
os.path.join(target_dir, fname+'.csv'))
# Add in x and y pixel locatins under wcs
x_pix,y_pix = w1.wcs_world2pix(data[catalog_syntax['RA']], data[catalog_syntax['DEC']],1)
data.insert(loc = 5, column = 'x_pix', value = x_pix)
data.insert(loc = 6, column = 'y_pix', value = y_pix)
# Remove boundary sources
data = data[data.x_pix < image.shape[1] - syntax['pix_bound']]
data = data[data.x_pix > syntax['pix_bound']]
data = data[data.y_pix < image.shape[0] - syntax['pix_bound']]
data = data[data.y_pix > syntax['pix_bound']]
logger.info('Catalog length: %d' % len(data))
warnings.filterwarnings("default")
except Exception as e:
logger.exception(e)
data = None
return data
def query_nearby_gaia(tic,
ra_deg,
dec_deg,
Npixsearch=5,
Npixplt=3,
pltt=True):
'''Search nearby stars within some number of TESS pixels.'''
# download TPF file
bjds, tpfs = read_TESS_TPF(tic)
TPFfname = glob.glob('MAST/TESS/TIC%i/tess*_tp.fits' % tic)
assert len(TPFfname) > 0
hdu = fits.open(TPFfname[0])
# get reference pixel and pixelscale
pixscale_ra, pixscale_dec = abs(hdu[1].header['1CDLT4']), \
abs(hdu[1].header['2CDLT4'])
refra, refdec = hdu[1].header['1CRVL4'], hdu[1].header['2CRVL4']
#R = np.matrix([[hdu[1].header['11PC4'],hdu[1].header['12PC4']],
# [hdu[1].header['21PC4'],hdu[1].header['22PC4']]])
# search gaia data
coord = SkyCoord(ra=ra_deg,
dec=dec_deg,
unit=(u.degree, u.degree),
frame='icrs')
radius_arcsec = float(Npixsearch * pixscale_ra * 3600)
rad = u.Quantity(radius_arcsec, u.arcsec)
rG = Gaia.query_object_async(coordinate=coord, radius=rad)
assert len(rG) > 0
# get source data
ras = np.array(rG['ra'])
e_ras = np.array(rG['ra_error'])
decs = np.array(rG['dec'])
e_decs = np.array(rG['dec_error'])
Gmags = np.array(rG['phot_g_mean_mag'])
# plotting
if pltt:
fig, ax = plt.subplots(1)
ax.plot(ra_deg, dec_deg, 'bd', ms=8, label='Input source position')
cax = ax.scatter(ras,
decs,
c=Gmags,
s=50 * 10**(-.4 * (Gmags - 15)),
edgecolor='k',
alpha=.9,
cmap=truncate_colormap(plt.get_cmap('hot_r'), .1, .9))
ax.plot(ras, decs, 'k.')
cbar_axes = fig.add_axes([.1, .1, .85, .04])
cbar = fig.colorbar(cax, cax=cbar_axes, orientation='horizontal')
cbar.set_label('GAIA mag')
ax.set_xlabel('RA [deg]'), ax.set_ylabel('Dec [deg]')
# plot search annulus
r = np.repeat(radius_arcsec, 100) / 3600
theta = np.linspace(0, 2 * np.pi, r.size)
ax.plot(r * np.cos(theta) + ra_deg,
r * np.sin(theta) + dec_deg,
'k--',
lw=.9)
# add patches of a TESS pixels
assert Npixplt % 2 == 1
Xcorners = range(-int(np.floor(Npixplt / 2.)),
int(np.floor(Npixplt / 2.)) + 1)
Ycorners = range(-int(np.floor(Npixplt / 2.)),
int(np.floor(Npixplt / 2.)) + 1)
for i in Xcorners:
for j in Ycorners:
lowercorner_ra,lowercorner_dec = (-.5+i)*pixscale_ra + refra, \
(-.5+j)*pixscale_dec + refdec
rect = Rectangle((lowercorner_ra, lowercorner_dec),
pixscale_ra,
pixscale_dec,
lw=1,
edgecolor='k',
facecolor='none')
ax.add_patch(rect)
ax.set_xlim((ras.min() - pixscale_ra, ras.max() + pixscale_ra))
ax.set_ylim((decs.min() - pixscale_dec, decs.max() + pixscale_dec))
ax.set_title('TIC %i (%i sources found)' % (tic, len(rG)), fontsize=10)
ax.legend(loc='upper left', fontsize=10)
fig.subplots_adjust(bottom=.22, top=.95)
plt.savefig('plots/gaiasources_tic%i.png' % tic)
plt.show()
plt.close('all')
return ras, e_ras, decs, e_decs, Gmags, radius_arcsec
# get image center
ra_targ = table['CRVAL1'][0]
print(table)
dec_targ = table['CRVAL2'][0]
logger.info(
"Looking Gaia sources around (%.3f, %.3f) with radius %.3f with less than %.1f mas/yr"
% (ra_targ, dec_targ, search_radius.value, proper_motion_threshold))
# improve this using a box like the image https://astroquery.readthedocs.io/en/latest/gaia/gaia.html (width and height)
coord = SkyCoord(ra=ra_targ, dec=dec_targ, unit=(u.deg, u.deg))
# query Gaia sources and write the result
ref_cat = 'gaia_hst.csv'
max_rows = 3000
Gaia.ROW_LIMIT = max_rows
gaia_query = Gaia.query_object_async(coordinate=coord,
radius=search_radius)
#https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html
reduced_query = gaia_query['ra', 'dec', 'ra_error', 'dec_error',
'phot_g_mean_flux', 'ref_epoch', 'pmra',
'pmdec', 'pmra_error', 'pmdec_error',
'solution_id']
ngaia = len(reduced_query)
# np.abs was giving problems here so just filter twice with 10 and -10 proper motions are in mas
filter = ((np.abs(reduced_query['pmdec']) < proper_motion_threshold) &
((np.abs(reduced_query['pmra']) < proper_motion_threshold)))
reduced_query = reduced_query[filter]
reduced_query.write(ref_cat,
format='ascii.commented_header',
delimiter='\t',
def hst_button(
galaxies,
skymethod='globalmin+match',
instruments="ACS/WFC",
prop_ids=None,
filters=None,
radius=None,
filepath=None,
download_data=True,
correct_astrometry=True,
create_mosaic=True,
jy_conversion=True,
verbose=False,
log_filename='hst.log',
):
"""Create a HST mosaic, given a galaxy name.
Using a galaxy name and radius, queries around that object,
downloads available HST data and mosaics into a final product. It
will create separate mosaics for each proposal ID, and the file structure
will look like ``/galaxy/HST/proposal_id/galaxy_instrument_filter_proposal_id.fits``.
N.B. I must confess to not being well-versed with HST data, so if
anyone can help improve this please let me know.
This data button uses a number of tools included in the drizzlepac
Python package. This includes alignimages/tweakreg and astrodrizzle,
which correct astrometry and are specifically tailored for the setup
of HST data. This means that 1) creating mosaics with this will likely
take a long time and 2) you will need a beefy computer (especially with
regards to hard drive space).
Args:
galaxies (str or list): Names of galaxies to create mosaics for.
Resolved by NED.
skymethod (str, optional): Method used for AstroDrizzle's background
matching step. In general, this can be left untouched but for
mosaics with little overlap, it may be worth playing around
with this. For instance, I've had some luck when there isn't
much overlap between exposures using 'globalmin'. Options are
'localmin', 'globalmin+match', 'globalmin', and 'match'. Defaults
to 'globalmin+match'.
instruments (str or list, optional): Instrument to download data
for. Can be any combination of 'ACS/WFC', 'WFC3/IR',
'WFC3/UVIS', 'WFPC2/PC', or 'WFPC2/WFC'. If you want all
available data for all these instruments, select 'all', but
this is not recommended! Defaults to 'ACS/WFC'.
prop_ids (str or list, optional): Proposal IDs to download data for.
Defaults to None, which will pull out all proposal IDs for each
instrument.
filters (str or list, optional): Filters to download data for.
The script will look for each filter, for each instrument.
Defaults to None, which will pull out all applicable filters
for each instrument, for each proposal ID.
radius (astropy.units.Quantity, optional): Radius around the
galaxy to search for observations. Defaults to None, where
it will query Ned to get size.
filepath (str, optional): Path to save the working and output
files to. If not specified, saves to current working
directory.
download_data (bool, optional): If True, will download data from
MAST. Defaults to True.
correct_astrometry (bool, optional): If True, will perform astrometric
corrections to the downloaded data using alignimages. Defaults
to True.
create_mosaic (bool, optional): Switching this to True will
mosaic the data using astrodrizzle as appropriate. Defaults
to True.
jy_conversion (bool, optional): Convert the mosaicked file from
raw units to Jy/pix. Defaults to True.
verbose (bool, optional): Can be used to suppress most of the
output messages produced during the process. Mainly useful
for debugging. Defaults to False.
log_filename (str, optional): Will produce a stripped down log
of what data the code is reducing. By default, will save to
galaxy/hst.log.
"""
if isinstance(galaxies, str):
galaxies = [galaxies]
if isinstance(instruments,str):
instruments = [instruments]
if instruments == 'all':
instruments = ['ACS/WFC',
'WFC3/IR','WFC3/UVIS',
'WFPC2/PC','WFPC2/WFC']
if isinstance(filters,str):
filters = [filters]
if isinstance(prop_ids,str):
prop_ids = [prop_ids]
if filepath is not None:
os.chdir(filepath)
orig_dir = os.getcwd()
if radius is not None:
original_radius = radius.copy()
else:
original_radius = None
steps = []
if download_data:
steps.append(1)
if correct_astrometry:
steps.append(2)
if create_mosaic:
steps.append(3)
if jy_conversion:
steps.append(4)
# Set up folders for various corrections
os.environ['CRDS_SERVER_URL'] = 'https://hst-crds.stsci.edu'
os.environ['CRDS_PATH'] = orig_dir+'/reference_files'
os.environ['iref'] = orig_dir+'/reference_files/references/hst/wfc3/'
os.environ['jref'] = orig_dir+'/reference_files/references/hst/acs/'
os.environ['uref'] = orig_dir+'/reference_files/references/hst/wfpc2/'
# For large proposals, astrodrizzle can run into file open
# issues so raise the max file open amount.
_, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE,(hard,hard))
# Change the temp directory -- if this gets filled up it can cause
# problems.
orig_tmpdir = os.environ['TMPDIR']
if not os.path.exists('tmp'):
os.mkdir('tmp')
os.environ['TMPDIR'] = orig_dir+'/tmp'
for galaxy in galaxies:
if not os.path.exists(galaxy):
os.mkdir(galaxy)
if not os.path.exists(galaxy+'/HST'):
os.mkdir(galaxy+'/HST')
if not verbose:
# Various packages used here put out a lot of messages. Silence info messages.
loggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict]
for logger in loggers:
logger.setLevel(logging.ERROR)
# Even if verbose is not True, still print out some useful messages to the
# console.
hst_logger = logging.getLogger('data_buttons')
handler = logging.FileHandler(galaxy+'/'+log_filename,mode='w')
hst_logger.addHandler(handler)
hst_logger.addHandler(logging.StreamHandler())
hst_logger.setLevel(logging.INFO)
hst_logger.info('Beginning '+galaxy)
hst_logger.info(' ')
hst_logger.info(' ')
if radius is None:
try:
size_query = Ned.get_table(galaxy,table='diameters')
radius = np.max(size_query['NED Major Axis'])/2*u.arcsec
radius = radius.to(u.deg)
except:
hst_logger.warning(galaxy+' not resolved by Ned, using 0.2deg radius.')
radius = 0.2*u.degree
obs_table = Observations.query_criteria(objectname=galaxy,
radius=radius,
obs_type='all',
obs_collection='HST')
# Ignore any calibration observations.
obs_table = obs_table[obs_table['intentType'] == 'science']
for instrument in instruments:
# Pixel sizes for final mosaics selected to match the HLA.
pix_size = {'ACS/HRC':0.025,
'ACS/SBC':0.03,
'ACS/WFC':0.05,
'NICMOS/NIC1':0.025,
'NICMOS/NIC2':0.05,
'NICMOS/NIC3':0.1,
'WFC3/IR':0.09,
'WFC3/UVIS':0.04,
'WFPC2/PC':0.05,
'WFPC2/WFC':0.1}[instrument]
# Bits to consider good for drizzling.
bits = {'ACS/HRC':256,
'ACS/SBC':256,
'ACS/WFC':256,
'NICMOS/NIC1':0,
'NICMOS/NIC2':0,
'NICMOS/NIC3':0,
'WFC3/IR':768,
'WFC3/UVIS':256,
'WFPC2/PC':'8,1024',
'WFPC2/WFC':'8,1024'}[instrument]
# Filename extension, in order of preference.
suffixes = {'ACS/WFC':['FLC','FLT'],
'WFC3/IR':['FLT'],
'WFC3/UVIS':['FLC','FLT'],
'WFPC2/PC':[['C0M','C1M']],
'WFPC2/WFC':[['C0M','C1M']],
}[instrument]
# The instruments often have / in the name, so account for
# this in making folders and files.
hst_logger.info('Beginning '+instrument)
if not os.path.exists(galaxy+'/HST/'+instrument.replace('/','_')):
os.mkdir(galaxy+'/HST/'+instrument.replace('/','_'))
reset_filters = False
instrument_table = obs_table[obs_table['instrument_name'] == instrument]
reset_prop_ids = False
if not prop_ids:
prop_ids = list(np.unique(instrument_table['proposal_id']))
reset_prop_ids = True
hst_logger.info('Available proposal IDs: '+','.join(prop_ids))
hst_logger.info(' ')
for prop_id in prop_ids:
hst_logger.info('Proposal ID: '+str(prop_id))
prop_table = instrument_table[instrument_table['proposal_id'] == prop_id]
if not filters:
filters = list(np.unique(prop_table['filters']))
reset_filters = True
hst_logger.info('Available filters: '+','.join(filters))
for hst_filter in filters:
# If we have a highly illegal filter, just skip.
# TODO: This needs to be sorted for some fringe
# cases, probably.
if not hst_filter[0] == 'F':
continue
hst_logger.info('Filter: '+str(hst_filter))
# Pull out available data and download.
filter_table = prop_table[prop_table['filters'] == hst_filter]
if len(filter_table) == 0:
hst_logger.warning('No available data to download. Skipping...')
continue
data_products_id = Observations.get_product_list(filter_table)
for suffix in suffixes:
download_table = Observations.filter_products(data_products_id,
productSubGroupDescription=suffix,
mrp_only=False)
if len(download_table) > 0:
break
if isinstance(suffix,list):
filename_exts = [ext.lower() for ext in suffix]
else:
filename_exts = [suffix.lower()]
hst_logger.info(instrument+'/'+prop_id+'/'+hst_filter)
if not os.path.exists(galaxy+
'/HST/'+
instrument.replace('/','_')+
'/'+
hst_filter):
os.mkdir(galaxy+
'/HST/'+
instrument.replace('/','_')+
'/'+
hst_filter)
if not os.path.exists(galaxy+'/HST/'+prop_id):
os.mkdir(galaxy+'/HST/'+prop_id)
full_filepath = (galaxy+
'/HST/'+
instrument.replace('/','_')+
'/'+
hst_filter+
'/'
+prop_id)
if not os.path.exists(full_filepath):
os.mkdir(full_filepath)
if 1 in steps:
# Download files
download_mast(download_table,
download_dir="hst_temp/" + galaxy)
if not os.path.exists(full_filepath+'/raw'):
os.mkdir(full_filepath+'/raw')
if not os.path.exists(full_filepath+'/outputs'):
os.mkdir(full_filepath+'/outputs')
# Pull out the relevant files, and move to base folder.
for filename_ext in filename_exts:
matches = []
for root, _, filenames in os.walk("hst_temp/" + galaxy):
for filename in fnmatch.filter(
filenames, "*_"+filename_ext+".fits"
):
matches.append(os.path.join(root, filename))
for match in matches:
filename = match.split('/')
os.rename(match,full_filepath+'/raw/'+filename[-1])
# Clean up any temporary files.
shutil.rmtree("hst_temp/" + galaxy, ignore_errors=True)
filename_ext = filename_exts[0]
hst_files = glob.glob(full_filepath+'/raw/*_'+filename_ext+'.fits')
if 2 in steps:
# First, update the WCS information in case it's
# required.
for filename_ext in filename_exts:
hst_files = glob.glob(full_filepath+'/raw/*_'+filename_ext+'.fits')
crds.assign_bestrefs(hst_files,
sync_references=True)
# For WFPC2, the CRDS doesn't download everything
# needed. Download the GEIS data files and
# rerun the bestrefs assignment.
if 'WFPC2' in instrument:
geis_hdrs = glob.glob(os.environ['uref']+'/*h')
for geis_hdr in geis_hdrs:
geis_data = geis_hdr[:-1]+'d'
if not os.path.exists(geis_data):
geis_data = geis_data.split('/')[-1]
print(geis_data)
print(os.environ['uref'])
wget.download(
os.environ['CRDS_SERVER_URL']+'/unchecked_get/references/hst/'+geis_data,
out=os.environ['uref'])
crds.assign_bestrefs(hst_files,sync_references=True)
for hst_file in hst_files:
stwcs.updatewcs.updatewcs(hst_file,
use_db=False)
os.chdir(full_filepath+'/raw')
filename_ext = filename_exts[0]
hst_files = glob.glob('*_'+filename_ext+'.fits')
# Normalize all files.
photeq.photeq(', '.join(hst_files),readonly=False)
os.rename('photeq.log','../outputs/photeq.log')
if 'WFPC' in instrument:
# Using tweakreg, align each frame to GAIA.
gaia_table = Gaia.query_object_async(coordinate=galaxy,
radius=2*radius)
ras = gaia_table['ra']
decs = gaia_table['dec']
source_table = Table([ras,decs])
source_table.write('gaia.cat',
format='ascii.fast_commented_header')
tweakreg.TweakReg(hst_files,
imagefindcfg={'threshold':5,'conv_width':3},
refcat='gaia.cat',
#expand_refcat=True,
enforce_user_order=False,
shiftfile=True,
outshifts='shifts.txt',
searchrad=10,
minobj=5,
separation=0,
updatehdr=True,
reusename=True,
wcsname='TWEAK',
interactive=False,
fitgeometry='general',
clean=True,
see2dplot=False
)
# Update the c1m files to use the TWEAK
# wcs
for hst_file in hst_files:
dq_file = hst_file.replace('c0','c1')
tweakback.tweakback(hst_file,
dq_file,
newname='TWEAK')
plot_files = glob.glob('*.png')
for plot_file in plot_files:
os.remove(plot_file)
cat_files = glob.glob('*.coo')
for cat_file in cat_files:
os.remove(cat_file)
os.rename('shifts_wcs.fits','../outputs/shifts_wcs.fits')
os.rename('tweakreg.log','../outputs/tweakreg.log')
os.rename('shifts.txt','../outputs/shifts.txt')
elif 'ACS' in instrument or 'WFC3' in instrument:
# Correct astrometry using alignimages. First,
# correct each frame separately.
pool = mp.Pool(mp.cpu_count())
suitable_hst_files = pool.map(astrometric_correction,
hst_files)
pool.close()
suitable_hst_files = [x for x in suitable_hst_files
if x is not None]
if len(suitable_hst_files) == 0:
hst_logger.warning('Failure with astrometry corrections. Skipping')
os.chdir(orig_dir)
continue
# Now, align every suitable frame simultaneously.
output_table = astrometric_correction(suitable_hst_files)
with open('../outputs/astrometry.pkl','wb') as table_file:
pickle.dump(output_table,table_file)
else:
raise Exception('Unknown instrument!')
os.chdir(orig_dir)
os.chdir(full_filepath)
if 3 in steps:
os.chdir('raw')
if 'WFPC2' in instrument:
hst_files = glob.glob('*_c0m.fits')
wcskey = 'TWEAK'
elif 'ACS' in instrument or 'WFC3' in instrument:
with open('../outputs/astrometry.pkl','rb') as table_file:
output_table = pickle.load(table_file)
# We only want fits where an acceptable astrometric
# solution has been found.
suitable_fits = np.where(output_table['fit_qual'] < 5)
# (output_table['fit_qual'] >= 1)
hst_files = list(output_table[suitable_fits]['imageName'])
if len(output_table[suitable_fits]) == 0:
hst_logger.warning('Failure with astrometry corrections. Skipping')
os.chdir(orig_dir)
continue
wcskey = ' '
else:
raise Exception('Unknown instrument!')
# Following Dalcanton+ (2012), group exposures into
# long (>50s) and short (<=50s), and process for cosmic
# rays separately
exp_times = []
for hst_file in hst_files:
hdu = fits.open(hst_file)[0]
exp_time = hdu.header['EXPTIME']
exp_times.append(exp_time)
for exp_group in ['short','long']:
hst_files_group = []
for i in range(len(exp_times)):
if exp_times[i] > 50 and exp_group == 'long':
hst_files_group.append(hst_files[i])
elif exp_times[i] <= 50 and exp_group == 'short':
hst_files_group.append(hst_files[i])
if len(hst_files_group) == len(hst_files):
exp_group = ''
if len(hst_files_group) == 0:
continue
if len(exp_group) > 0:
output_name = '../outputs/'+galaxy+'_'+exp_group
drizzle_log_name = '../outputs/astrodrizzle_'+exp_group+'.log'
else:
output_name = '../outputs/'+galaxy
drizzle_log_name = '../outputs/astrodrizzle.log'
# Perform the mosaicking. Generally, use iminmed.
# However, sometimes iminmed will fail so
# for the other instruments we'll use imedian as
# a fallback.
combine_types = ['iminmed','imedian']
if 'WFPC2' in instrument:
combine_nhigh = 1
else:
combine_nhigh = 0
for combine_type in combine_types:
try:
astrodrizzle.AstroDrizzle(
input=hst_files_group,
output=output_name,
preserve=False,
clean=True,
combine_type=combine_type,
combine_nhigh=combine_nhigh,
skymethod=skymethod,
sky_bits=bits,
driz_sep_bits=bits,
driz_sep_fillval=99999,
combine_hthresh=90000,
final_scale=pix_size,
final_bits=bits,
final_fillval=0,
wcskey=wcskey,
final_rot=0,
)
break
except ValueError:
pass
# Move the AstroDrizzle log.
os.rename('astrodrizzle.log',
drizzle_log_name)
# Move back to the original directory.
os.chdir(orig_dir)
if 4 in steps:
mosaic_outputs = glob.glob(full_filepath+'/outputs/*_sci.fits')
for mosaic_output in mosaic_outputs:
# Replace any fillvals with NaNs.
hdu = fits.open(mosaic_output)[0]
hdu.data[hdu.data == 0] = np.nan
fits.writeto(mosaic_output,
hdu.data,hdu.header,
overwrite=True)
if '_long_' in mosaic_output.split('/')[-1]:
new_filename = (galaxy+
'/HST/'
+prop_id
+'/'
+galaxy
+'_'
+instrument.replace('/','_')
+'_'
+hst_filter
+'_'
+prop_id
+'_long.fits')
elif '_short_' in mosaic_output.split('/')[-1]:
new_filename = (galaxy+
'/HST/'
+prop_id
+'/'
+galaxy
+'_'
+instrument.replace('/','_')
+'_'
+hst_filter
+'_'
+prop_id
+'_short.fits')
else:
new_filename = (galaxy+
'/HST/'
+prop_id
+'/'
+galaxy
+'_'
+instrument.replace('/','_')
+'_'
+hst_filter
+'_'
+prop_id
+'.fits')
convert_to_jy(mosaic_output,
new_filename)
if reset_filters:
filters = None
hst_logger.info(' ')
if reset_prop_ids:
prop_ids = None
hst_logger.info(' ')
if original_radius is None:
radius = None
else:
radius = original_radius.copy()
# Clear out the tmp folder and reset to the original.
shutil.rmtree('tmp/', ignore_errors=True)
os.environ['TMPDIR'] = orig_tmpdir
import astropy.units as u
from astropy.coordinates import SkyCoord
from astroquery.gaia import Gaia
import numpy as np
Gaia.ROW_LIMIT = -1
coord = SkyCoord(ra=301,
dec=-60,
unit=(u.degree, u.degree, u.pc),
frame='icrs')
width = u.Quantity(0.1, u.deg)
height = u.Quantity(0.1, u.deg)
radius = u.Quantity(0.02, u.deg)
r = Gaia.query_object_async(coordinate=coord,
width=width,
height=height,
radius=radius)
print((r['dist']))
print(type(r['designation']))
print(r.colnames)
# print(r['source_id'])
arr = np.array(r)
print(arr.shape)
def image_gaia_stars(image,
wcs,
radius=None,
center=None,
pixel=0.168,
mask_a=694.7,
mask_b=4.04,
verbose=False,
visual=False,
size_buffer=1.4,
tap_url=None,
img_size=(8, 8)):
"""Search for bright stars using GAIA catalog.
TODO:
Should be absorbed by the object for image later.
TODO:
Should have a version that just uses the local catalog.
"""
# Central coordinate
if center is None:
ra_cen, dec_cen = wcs.wcs_pix2world(image.shape[0] / 2,
image.shape[1] / 2, 0)
img_cen_ra_dec = SkyCoord(ra_cen,
dec_cen,
unit=('deg', 'deg'),
frame='icrs')
if verbose:
print("# The center of the search: RA={:9.5f}, DEC={:9.5f}".format(
ra_cen, dec_cen))
else:
if not isinstance(center, SkyCoord):
raise TypeError(
"# The center coordinate should be a SkyCoord object")
img_cen_ra_dec = center
if verbose:
print("# The center of the search: RA={:9.5f}, DEC={:9.5f}".format(
center.ra, center.dec))
# Width and height of the search box
if radius is None:
img_search_x = Quantity(pixel * (image.shape)[0] * size_buffer,
u.arcsec)
img_search_y = Quantity(pixel * (image.shape)[1] * size_buffer,
u.arcsec)
if verbose:
print("# The width of the search: {:7.1f}".format(img_search_x))
print("# The height of the search: {:7.1f}".format(img_search_y))
else:
if not isinstance(radius, Quantity):
raise TypeError(
"# Searching radius needs to be an Astropy Quantity.")
if verbose:
print("# The searching radius is: {:7.2f}".format(radius))
# Search for stars
if tap_url is not None:
with suppress_stdout():
from astroquery.gaia import TapPlus, GaiaClass
Gaia = GaiaClass(TapPlus(url=tap_url))
if radius is not None:
gaia_results = Gaia.query_object_async(
coordinate=img_cen_ra_dec, radius=radius, verbose=verbose)
else:
gaia_results = Gaia.query_object_async(
coordinate=img_cen_ra_dec,
width=img_search_x,
height=img_search_y,
verbose=verbose)
else:
with suppress_stdout():
from astroquery.gaia import Gaia
if radius is not None:
gaia_results = Gaia.query_object_async(
coordinate=img_cen_ra_dec, radius=radius, verbose=verbose)
else:
gaia_results = Gaia.query_object_async(
coordinate=img_cen_ra_dec,
width=img_search_x,
height=img_search_y,
verbose=verbose)
if gaia_results:
# Convert the (RA, Dec) of stars into pixel coordinate
ra_gaia = np.asarray(gaia_results['ra'])
dec_gaia = np.asarray(gaia_results['dec'])
x_gaia, y_gaia = wcs.wcs_world2pix(ra_gaia, dec_gaia, 0)
# Generate mask for each star
rmask_gaia_arcsec = mask_a * np.exp(
-gaia_results['phot_g_mean_mag'] / mask_b)
# Update the catalog
gaia_results.add_column(Column(data=x_gaia, name='x_pix'))
gaia_results.add_column(Column(data=y_gaia, name='y_pix'))
gaia_results.add_column(
Column(data=rmask_gaia_arcsec, name='rmask_arcsec'))
if visual:
fig = plt.figure(figsize=img_size)
ax1 = fig.add_subplot(111)
ax1 = display_single(image, ax=ax1)
# Plot an ellipse for each object
for star in gaia_results:
smask = mpl_ellip(xy=(star['x_pix'], star['y_pix']),
width=(2.0 * star['rmask_arcsec'] / pixel),
height=(2.0 * star['rmask_arcsec'] / pixel),
angle=0.0)
smask.set_facecolor('coral')
smask.set_edgecolor('coral')
smask.set_alpha(0.3)
ax1.add_artist(smask)
# Show stars
ax1.scatter(gaia_results['x_pix'],
gaia_results['y_pix'],
c='orangered',
s=100,
alpha=0.9,
marker='+')
ax1.set_xlim(0, image.shape[0])
ax1.set_ylim(0, image.shape[1])
return gaia_results, fig
return gaia_results
return None