def matching(master, cat, masteridskey=None,
angular=False, radius=1.5, masked=False):
"""Function to match stars between frames.
"""
if masteridskey is None:
masterids = np.arange(len(master))
master['masterindex'] = masterids
idkey = 'masterindex'
else:
idkey = masteridskey
if angular:
masterRaDec = np.empty((len(master), 2), dtype=np.float64)
try:
masterRaDec[:, 0] = master['RA']
masterRaDec[:, 1] = master['Dec']
except KeyError:
masterRaDec[:, 0] = master['ra']
masterRaDec[:, 1] = master['dec']
imRaDec = np.empty((len(cat), 2), dtype=np.float64)
try:
imRaDec[:, 0] = cat['RA']
imRaDec[:, 1] = cat['Dec']
except KeyError:
imRaDec[:, 0] = cat['ra']
imRaDec[:, 1] = cat['dec']
radius2 = radius/3600.
dist, ind = cx.crossmatch_angular(masterRaDec, imRaDec,
max_distance=radius2/2.)
dist_, ind_ = cx.crossmatch_angular(imRaDec, masterRaDec,
max_distance=radius2/2.)
else:
masterXY = np.empty((len(master), 2), dtype=np.float64)
masterXY[:, 0] = master['x']
masterXY[:, 1] = master['y']
imXY = np.empty((len(cat), 2), dtype=np.float64)
imXY[:, 0] = cat['x']
imXY[:, 1] = cat['y']
dist, ind = cx.crossmatch(masterXY, imXY, max_distance=radius)
dist_, ind_ = cx.crossmatch(imXY, masterXY, max_distance=radius)
IDs = np.zeros_like(ind_) - 13133
for i in range(len(ind_)):
if dist_[i] != np.inf:
# dist_o = dist_[i]
ind_o = ind_[i]
if dist[ind_o] != np.inf:
# dist_s = dist[ind_o]
ind_s = ind[ind_o]
if ind_s == i:
IDs[i] = master[idkey][ind_o]
# print((len(IDs), len(ind_), len(ind)))
# print(("Matching result:: IDs > 0. => {}".format(sum(IDs > 0))))
if masked:
mask = IDs > 0
return(IDs, mask)
return(IDs)
def _matching(
master, cat, masteridskey=None, angular=False, radius=1.5, masked=False
):
"""
Function to match stars between frames.
"""
if masteridskey is None:
masterids = np.arange(len(master))
master["masterindex"] = masterids
idkey = "masterindex"
else:
idkey = masteridskey
if angular:
masterRaDec = np.empty((len(master), 2), dtype=np.float64)
try:
masterRaDec[:, 0] = master["RA"]
masterRaDec[:, 1] = master["Dec"]
except KeyError:
masterRaDec[:, 0] = master["ra"]
masterRaDec[:, 1] = master["dec"]
imRaDec = np.empty((len(cat), 2), dtype=np.float64)
try:
imRaDec[:, 0] = cat["RA"]
imRaDec[:, 1] = cat["Dec"]
except KeyError:
imRaDec[:, 0] = cat["ra"]
imRaDec[:, 1] = cat["dec"]
radius2 = radius / 3600.0
dist, ind = cx.crossmatch_angular(
masterRaDec, imRaDec, max_distance=radius2 / 2.0
)
dist_, ind_ = cx.crossmatch_angular(
imRaDec, masterRaDec, max_distance=radius2 / 2.0
)
else:
masterXY = np.empty((len(master), 2), dtype=np.float64)
masterXY[:, 0] = master["x"]
masterXY[:, 1] = master["y"]
imXY = np.empty((len(cat), 2), dtype=np.float64)
imXY[:, 0] = cat["x"]
imXY[:, 1] = cat["y"]
dist, ind = cx.crossmatch(masterXY, imXY, max_distance=radius)
dist_, ind_ = cx.crossmatch(imXY, masterXY, max_distance=radius)
IDs = np.zeros_like(ind_) - 13133
for i in range(len(ind_)):
if dist_[i] != np.inf:
ind_o = ind_[i]
if dist[ind_o] != np.inf:
ind_s = ind[ind_o]
if ind_s == i:
IDs[i] = master[idkey][ind_o]
if masked:
mask = IDs > 0
return (IDs, mask)
return IDs
def do_crossmatch(ra_im, dec_im, ra_std, dec_std):
'''
Do the crossmatching
params: ra,dec of data set 1 and 2
returns:
index of the match
'''
# get imaging data
try:
imX = np.empty((len(ra_im), 2), dtype=np.float64)
except TypeError:
imX = np.empty((1, 2), dtype=np.float64)
imX[:, 0] = ra_im
imX[:, 1] = dec_im
# get standard stars
stX = np.empty((len(ra_std), 2), dtype=np.float64)
stX[:, 0] = ra_std
stX[:, 1] = dec_std
# crossmatch catalogs
max_radius = 1.5 / 3600 # 1 arcsec
dist, ind_im = crossmatch_angular(imX, stX, max_radius)
match_im = ~np.isinf(dist)
return ind_im, match_im
def crossmatch_skybot(sources, moving_objects, radius=5):
"""
crossmatch list of detected sources with list of moving objects in this field from skybot
parameters: sources, moving_objects, radius:
sources: astropy.table containing list of unknown sources detected
moving_objects: astropy.table containing list of moving objects from skybot
in the same field of view
radius in arcsecond
returns: astropy.table object
NOT WORKING WITH PYTHON2.7, seems ok WITH PYTHON3
"""
#sources=ascii.read('/home/corre/codes/Tests/%s.fits.oc' % filename)
#moving_objects=ascii.read('/home/corre/codes/gmadet/gmadet/moving_objects.dat')
cat1 = np.empty((len(sources), 2), dtype=np.float64)
cat2 = np.empty((len(moving_objects), 2), dtype=np.float64)
cat1[:, 0] = sources['_RAJ2000']
cat1[:, 1] = sources['_DEJ2000']
cat2[:, 0] = moving_objects['RA']
cat2[:, 1] = moving_objects['DEC']
dist, ind = crossmatch_angular(cat1, cat2, radius / 3600)
match = ~np.isinf(dist)
#print (match)
dist_match = dist[match]
# Convert in arcseconds
dist_match *= 3600
#print (dist_match)
if dist_match:
mov_match = sources[ind[np.unique(match)]]
print(mov_match)
candidates = sources[match == False]
return candidates
def get_assigned_targets(cat, ps_cat):
ra_e = cat[:,0]
dec_e = cat[:,1]
X_e = np.empty((ra_e.size, 2), dtype = np.float64)
X_e[:, 0] = ra_e
X_e[:, 1] = dec_e
data_m = ps_cat
ra_m = data_m[:,0]
dec_m = data_m[:,1]
X_m = np.empty((ra_m.size, 2), dtype = np.float64)
X_m[:, 0] = ra_m
X_m[:, 1] = dec_m
print "Out of:", ra_m.size
max_radius = 0.3 / 3600 # 0.01 arcsec
dist, ind = crossmatch_angular(X_m, X_e, max_radius)
idx = (~np.isinf(dist))
return data_m[idx], dist, ind
def crossmatch_skybot(sources, moving_objects, radius=10):
"""
crossmatch list of detected sources with list of moving objects
in this field using skybot
parameters: sources, moving_objects, radius:
sources: astropy.table containing list of unknown
sources detected
moving_objects: astropy.table containing list of moving
objects from skybot in the same field of view
radius in arcsecond
returns: astropy.table object
NOT WORKING WITH PYTHON2.7, seems ok WITH PYTHON3
"""
cat1 = np.empty((len(sources), 2), dtype=np.float64)
cat2 = np.empty((len(moving_objects), 2), dtype=np.float64)
cat1[:, 0] = sources["_RAJ2000"]
cat1[:, 1] = sources["_DEJ2000"]
cat2[:, 0] = moving_objects["RA"]
cat2[:, 1] = moving_objects["DEC"]
dist, ind = crossmatch_angular(cat1, cat2, radius / 3600)
match = ~np.isinf(dist)
dist_match = dist[match]
# Convert in arcseconds
dist_match *= 3600
if len(dist_match) > 0:
mov_match = moving_objects[ind[match]]
movingObjMatch_list = []
movingObjSep_list = []
movingObjName = []
for j in range(len(mov_match)):
movingObjMatch_list.append("Y")
movingObjSep_list.append(dist_match[j])
movingObjName.append(mov_match["Name"][j])
sources["movingObjMatch"][match] = movingObjMatch_list
sources["movingObjSep"][match] = movingObjSep_list
sources["movingObjName"][match] = movingObjName
return sources
#ra1 = []
#for i in ra:
#ra1.append(i/15)
first_cat = np.empty((len(ra1), 2), dtype=np.float64)
first_cat[:, 0] = ra1
first_cat[:, 1] = dec1
new_cat = np.empty((int(len(ra2)), 2), dtype=np.float64)
new_cat[:, 0] = ra2
new_cat[:, 1] = dec2
print(print(new_cat))
print(print(first_cat))
max_radius = 1 / 3600 #.5 arc second
dist_between, ind_row = crossmatch_angular(first_cat, new_cat, max_radius)
match = ~np.isinf(dist_between)
match_table = Table()
match_table['matched_true_false'] = match
match_table['match_ID'] = ind_row
match_table['matched_firstcat_RA'] = first_cat[:, 0]
match_table['matched_firstcat_DEC'] = first_cat[:, 1]
new_cat_line_ID = range(len(match))
#print('LENGTH OF MATCH: ' + str(len(match)))
match_table['match_table_false_id'] = new_cat_line_ID
new_cat_match_true = []
new_cat_match_false = []
new_cat_ID_true = []
# get standard stars ##HASH catalog
# standards_data = fetch_sdss_S82standards() ## HASH PN
stara = ascii.read("ra-pne-HASH.txt")
stadec = ascii.read("dec-pne-HASH.txt")
c2 = SkyCoord(stara, stadec, unit=(u.hourangle, u.deg))
RA1 = c2.ra.degree.reshape(len(c2.ra.degree), )
DEC1 = c2.dec.degree.reshape(len(c2.dec.degree), )
stX = np.empty((len(c2), 2), dtype=np.float64)
stX[:, 0] = RA1
stX[:, 1] = DEC1
#HAST cata
# crossmatch catalogs
max_radius = 3600. / 3600 # 1 arcsec
dist, ind = crossmatch_angular(imX, stX, max_radius)
#dist, ind = crossmatch_angular(c1, c2, max_radius)
match = ~np.isinf(dist)
dist_match = dist[match]
dist_match *= 3600
print('Number with match:', np.sum(match))
print('PN:', imX[match])
sys.exit()
ax = plt.axes()
hist(dist_match,
bins='knuth',
ax=ax,
histtype='stepfilled',
ec='k',
fc='#AAAAAA')
open("{}.csv".format(trackletPaths[midIdx].split('.')[0]),
'r'))
notNAN = np.asarray([
num for num, (v, ve) in enumerate(
zip(apass['Johnson_V'].values, apass['Verr'].values))
if (str(v) != 'nan') and (str(ve) != 'nan')
])
apassRA = apass['radeg'].values[notNAN]
apassDec = apass['decdeg'].values[notNAN]
apassMag = apass['Johnson_V'].values[notNAN]
apassMagerr = abs(apass['Verr'].values[notNAN])
# Cross-match the sources between SExtractor catalogs and APASS catalogs.
apassTable = np.column_stack((apassRA, apassDec))
sexTable = np.column_stack((sexRA, sexDec))
dist, ind = crossmatch_angular(apassTable, sexTable,
coaddFWHM * 1.22 * 2 / 3600)
match = ~np.isinf(dist)
sexMag_mat = sexMag[ind[match]]
sexMagerr_mat = sexMagerr[ind[match]]
# Calculate the zero point and its error.
zp = np.mean(apassMag[match] - sexMag_mat)
zperr = (np.sum(apassMagerr[match]**2) +
np.sum(sexMagerr_mat**2))**(0.5) / len(sexMag_mat)
# Store the zero point as new headers.
hdu = fits.open(stkPath, mode='append')
hdu[0].header.set("S-ZEROPT", str(zp),
" sexInstMag+ZP=apassCalibMag")
hdu[0].header.set("S-ZEROER", str(zperr),
" sexZPerr+apassZPerr")
def crossmatch(a, b, keys, name, sname, arcsec=5., bijective =False,
plot=False, union=False):
'''
Match targets from catalog c2 into reference catalog c1
in a cone with a maximum search radius.
Parameters:
------------
a : DataFrame
b : DataFrame
keys : list with length 2
Contains extensions into RA, Dec, and PMem in a and b,
respectively.
arcsec : 5. or float
Maximum search radius in arcsec.
plot : False or bool
Export a plot of matching distances.
bijective : True or bool
Force 1-on-1 matching
Return:
--------
DataFrame of length c1 with c2 matched if possible. Otherwise
the row is filled with the last entry in c2 and the distance is
filled with inf.
'''
c1 = copy(a).astype(float)
c2 = copy(b).astype(float)
# get imaging data
imX = np.empty((c1.shape[0], 2), dtype=np.float64)
imX[:, 0] = c1['RAJ2000_{}'.format(keys[0])].values
imX[:, 1] = c1['DEJ2000_{}'.format(keys[0])].values
# get standard stars
stX = np.empty((c2.shape[0], 2), dtype=np.float64)
stX[:, 0] = c2['RAJ2000_{}'.format(keys[1])].values
stX[:, 1] = c2['DEJ2000_{}'.format(keys[1])].values
# crossmatch catalogs
max_radius = arcsec / 3600
dist, ind = crossmatch_angular(imX, stX, max_radius)
match = ~np.isinf(dist)#indices into imX
#matching distances
dist_match = dist[match]
dist_match *= 3600
#merge data frames
ind = list(filter(lambda x: x != c2.shape[0], ind))#throw out fill values
for colname in c2.columns.values:
c1[colname] = np.nan
c1.iloc[match, c1.columns.get_loc(colname)] = c2.iloc[ind, c2.columns.get_loc(colname)].tolist()
#add distances to results
distcol = 'dist_{}_{}'.format(keys[0], keys[1])
c1[distcol] = np.nan
c1.iloc[match, c1.columns.get_loc(distcol)] = dist_match
#Force 1-on-1 matching if requested
if bijective == True:
c1 = select_min_dist(c1, keys)
#append the rest of c2 if required
if union == True:
allid = list(range(c2.shape[0]))
ids, counts = np.unique(allid+ind, return_counts=True)
unmatched_ids = ids[np.where(counts==1)]
c1 = c1.append(c2.iloc[unmatched_ids,:],ignore_index=True, sort=False)
#plotting optional
if plot == True:
plot_radius_of_match(dist_match,name, sname,
keys, arcsec, match, imX, stX)
return c1
found_clusters = get_clusters(galaxy_data, p, z, h, minz, linking,
'found_clusters.txt')
f_ra = [x[0] for x in found_clusters if str(x) != 'nan']
f_dec = [x[1] for x in found_clusters if str(x) != 'nan']
f_list = np.transpose([f_ra, f_dec])
n_ra = [x[0] for x in natural_clusters]
n_dec = [x[1] for x in natural_clusters]
n_list = np.transpose([n_ra, n_dec])
c_list = np.transpose(cluster_locs)
tol = 0.1 #tolerance value for matches (degrees)
l_dist, l_ind = crossmatch_angular(f_list, c_list, tol)
l_matches[run] = len(l_dist[~np.isinf(l_dist)])
n_dist, n_ind = crossmatch_angular(f_list, n_list, tol)
n_matches[run] = len(n_dist[~np.isinf(n_dist)])
fc[run] = len(found_clusters)
print('done' + str(run + 1))
run += 1
nm_avg = np.mean(n_matches)
nm_std = np.std(n_matches)
lm_avg = np.mean(l_matches)
lm_std = np.std(l_matches)
tmass= pd.read_csv("/users/alex/Data/tmassF.min.db")
print("imported 2mass")
tmass = tmass.apply(lambda x: pd.to_numeric(x, errors='coerce')).dropna()
print("fixed 2mass")
vvv = pd.read_csv("/users/alex/Data/vvvEQUF.min.db")
print("imported vvv")
vvv = vvv.apply(lambda x: pd.to_numeric(x, errors='coerce')).dropna()
print("fixed vvv")
X1 = vvv[["RA2000", "DEC2000"]].to_numpy()
X2 = tmass[["ra", "dec"]].to_numpy()
dist, ind = crossmatch_angular(X1, X2, max_distance=(1.0/36000))
print("Done crossmatching")
vvv['DIST'] = dist
vvv["IND"] = ind
vvv = vvv[vvv['DIST']!=np.inf]
print(vvv)
print("Max of gen ind: ", np.max(vvv["IND"]))
print("Length of tmass: ", len(tmass['k_m']))
vvv["K2MASS"] = np.array([tmass['k_m'][i] for i in vvv["IND"]])
print("Subscripted K2MASS vals")
del tmass
# Set arrays of MPA-JHU SFG
SDSSX = np.empty((len(table_SFG_sdss), 2), dtype=np.float64)
SDSSX[:, 0] = table_SFG_sdss['RA']
SDSSX[:, 1] = table_SFG_sdss['DEC']
# Set arrays of MaNGA DR14
Table_drp = Table.read('/home/qliu/MaNGA/drpall-v2_1_2.fits')
table_drp = Table(Table_drp.columns["plateifu", "objra", "objdec"]).to_pandas()
MANGAX = np.empty((len(table_drp), 2), dtype=np.float64)
MANGAX[:, 0] = table_drp['objra']
MANGAX[:, 1] = table_drp['objdec']
# Crossmatch catalogs
print "Start Crossmatching..."
max_radius = 1. / 3600 # 1 arcsec
dist, ind = crossmatch_angular(SDSSX, MANGAX, max_radius)
match = ~np.isinf(dist)
dist_match = dist[match]
dist_match *= 3600
# Plot crossmatching results
ax = plt.axes()
hist(dist_match,
bins='knuth',
ax=ax,
histtype='stepfilled',
ec='k',
fc='#AAAAAA')
ax.set_xlabel('radius of match (arcsec)')
ax.set_ylabel('N(r, r+dr)')
def check():
''' Check if any median separations with the reference stars is an outlier '''
science_path = os.path.join(os.getcwd(), 'science-imaging')
nights = [
n for n in sorted(os.listdir(science_path))
if os.path.isdir(os.path.join(science_path, n))
]
outliers = {} # Median outliers
notsolved = {} # Not solved by Astrometry
medianseps = {} # Quartiles of separations
# Check outliers inside each night using a boxplot
for n in tqdm(nights, desc='Checking calibrations'):
img_path = os.path.join(science_path, n)
name = []
mediansep = []
notsol = []
sci_path = [
sci for sci in sorted(os.listdir(img_path))
if os.path.isdir(os.path.join(img_path, sci))
and sci.startswith('science-imaging')
]
for science in sci_path:
folder = os.path.join(science_path, n, science)
if not os.path.isfile(os.path.join(folder, 'xxx.new')):
notsol.append(science)
continue
else:
dat_df = pd.read_csv(os.path.join(folder, 'full_1.cat'),
delim_whitespace=True,
header=None,
comment='#')
ref_data = dat_df[dat_df[1] == 0]
ref = np.empty((len(ref_data), 2), dtype=np.float64)
ref[:, 0] = ref_data[10]
ref[:, 1] = ref_data[11]
sci_data = dat_df[dat_df[1] == 1]
sci = np.empty((len(sci_data), 2), dtype=np.float64)
sci[:, 0] = sci_data[10]
sci[:, 1] = sci_data[11]
max_radius = 1. / 3600 # 1 arcsec
dist, ind = crossmatch_angular(sci, ref, max_radius)
match = ~np.isinf(dist)
dist_match = dist[match]
dist_match *= 3600
name.append(science)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
mediansep.append(np.median(dist_match))
sep = pd.DataFrame([name, mediansep]).T
out = ['NaN ' + str(i) for i in list(sep[0][np.isnan(list(sep[1]))])]
sep_aux = sep.dropna().reset_index(drop=True)
sep_aux[1] = [float("%.50f" % elem) for elem in list(sep_aux[1])]
fl = plt.boxplot(sep_aux[1], showfliers=True)
plt.close()
fl_data = [i.get_data() for i in fl['fliers']]
for i in [float("%.50f" % elem) for elem in fl_data[0][1]]:
if i > fl['medians'][0].get_ydata()[0]:
out.append(
str(i) + ' ' + str(sep_aux[0][list(sep_aux[1]).index(i)]))
outliers[n] = out
notsolved[n] = notsol
medianseps[n] = 'Median_of_seps: ' + str(
fl['medians'][0].get_ydata()[0]) + ' | Q1: ' + str(
fl['whiskers'][0].get_ydata()[0]) + ' | Q3: ' + str(
fl['whiskers'][1].get_ydata()[0])
if any(notsolved.values()):
print("\n Astrometry couldn't find astrometric solution for:")
NS = pd.DataFrame(notsolved.items(), columns=['Night', 'Images'])
NS.to_csv(os.path.join(os.getcwd(), "list_notsolved.csv"),
index=False,
header=True)
pprint(notsolved, width=1)
else:
print(" - Astrometry solved all the images.")
if any(outliers.values()):
print('\n Bad astrometric solutions found in:')
OL = pd.DataFrame(outliers.items(), columns=['Night', 'Images'])
OL.to_csv(os.path.join(os.getcwd(), "list_outliers.csv"),
index=False,
header=True)
pprint(outliers, width=1)
else:
print(" - There are no outliers.")
pprint(medianseps, width=1)
return [outliers, notsolved, medianseps]
def correct_photom_SkyMapper(year, month, field, exp_time, verbose=False, debugmode=False):
input_path = '/fred/oz100/NOAO_archive/archive_NOAO_data/data_outputs/'+year+'/'+month+'/'+field+'/g_band/single/*/SE_cats/'
input_cats = '/fred/oz100/NOAO_archive/archive_NOAO_data/scripts/correct_photom/skymapper/shortlisted_stars_for_photom/'+field+'_SM_NEW.csv_SHORTLISTED.ascii'
print('\n#########################')
print('# Corrections to SkyMapper have started #')
print('#########################\n')
path_list = glob.glob(input_path)
for i in path_list:
print(i)
av_correction = []
odd_differences = []
filenames_good = []
filenames_odd = []
master_filenames = []
num_sources_ccd = []
master_mean_diff = []
master_median_diff = []
num_photom_sources = []
for filename in os.listdir(i):
#av_correction = np.zeros(len(os.listdir(i)))
#print(av_correction)
#print( filename)
#filenames_for_av = []
#filenames_for_av.append(filename)
#num_sources_ccd = []
if filename.endswith('.cat'):
MAG_APER, MAGERR_APER, MAG_AUTO, MAGERR_AUTO, XPEAK_IMAGE, YPEAK_IMAGE, X_IMAGE, Y_IMAGE, ALPHA_J2000, DELTA_J2000 = np.loadtxt(i + filename, unpack = True)
master_filenames.append(filename)
zp_exp_correction = 2.5*np.log10(exp_time)
ccd_SE_cat = Table()
num_sources_ccd.append(len(MAG_APER))
print('Filename:' + str(filename))
print('Number sources ccd: '+ str(len(MAG_APER)))
ccd_SE_cat['mag_aper'] = MAG_APER + zp_exp_correction
ccd_SE_cat['magerr_aper'] = MAGERR_APER
ccd_SE_cat['mag_auto'] = MAG_AUTO + zp_exp_correction
ccd_SE_cat['mag_auto_err'] = MAGERR_AUTO
ccd_SE_cat['xpeak_image'] = XPEAK_IMAGE
ccd_SE_cat['ypeak_image'] = YPEAK_IMAGE
ccd_SE_cat['x_image'] = X_IMAGE
ccd_SE_cat['y_image'] = Y_IMAGE
ccd_SE_cat['RA'] = ALPHA_J2000
ccd_SE_cat['DEC'] = DELTA_J2000
DWF_X = np.empty((len(ccd_SE_cat), 2), dtype=np.float64)
DWF_X[:, 0] = ALPHA_J2000
DWF_X[:, 1] = DELTA_J2000
ra, dec, ngood, class_star, g_psf, e_g_psf, r_psf, e_r_psf, i_psf, e_i_psf, z_psf, e_z_psf = np.loadtxt(input_cats , unpack = True, skiprows = 1)
SM_X = np.empty((len(ra), 6), dtype=np.float64)
SM_X[:, 0] = ra
SM_X[:, 1] = dec
SM_X[:, 2] = g_psf
SM_X[:, 3] = r_psf
SM_X[:, 4] = i_psf
SM_X[:, 5] = z_psf
os.chdir(i)
os.chdir("..")
filename_directory = os.path.abspath(os.curdir)
#plt.plot(DWF_X[:, 0], DWF_X[:, 1], 'b')
#plt.plot(ra, dec)
#plt.show()
#break
photom_correction_path = filename_directory + '/photom_correction_files/'
#print(photom_correction_path)
final_source_cat_path = filename_directory + '/final_source_cats/'
#print(final_source_cat_path)
photom_correction_path_images = photom_correction_path + 'skymapper_checkphotom_plots/'
#print(photom_correction_path_images)
if not os.path.exists(photom_correction_path):
os.makedirs(photom_correction_path, 0o755)
else:
pass
if not os.path.exists(final_source_cat_path):
os.makedirs(final_source_cat_path, 0o755)
else:
pass
if not os.path.exists(photom_correction_path_images):
os.makedirs(photom_correction_path_images, 0o755)
else:
pass
max_radius = 1./3600 #1 arc second
dist_between, ind_row = crossmatch_angular(DWF_X, SM_X, max_radius)
match = ~np.isinf(dist_between)
if len(match) != 0:
match_table = Table()
match_table['matched_true_false'] = match
match_table['matched_ID'] = ind_row
match_table['matched_DWF_data_gmag'] = ccd_SE_cat['mag_auto']
match_table['matched_DWF_RA'] = ccd_SE_cat['RA']
match_table['matched_DWF_DEC'] = ccd_SE_cat['DEC']
SM_match_true = []
SM_row_matched = []
DWF_g_mags_matched = []
DWF_g_mags_error_matched = []
DWF_obs_ra_matched = []
DWF_obs_dec_matched = []
for row in match_table:
if row['matched_true_false'] == True:
SM_match_true.append(row['matched_true_false'])
SM_row_matched.append(row['matched_ID'])
DWF_g_mags_matched.append(row['matched_DWF_data_gmag'])
DWF_obs_ra_matched.append(row['matched_DWF_RA'])
DWF_obs_dec_matched.append(row['matched_DWF_DEC'])
SM_RA = []
SM_DEC = []
SM_g_mag = []
SM_r_mag = []
SM_i_mag = []
SM_z_mag = []
for j in SM_row_matched:
RA = SM_X[j, 0]
DEC = SM_X[j, 1]
g_mag = SM_X[j, 2]
r_mag = SM_X[j, 3]
i_mag = SM_X[j, 4]
z_mag = SM_X[j, 5]
SM_RA.append(RA)
SM_DEC.append(DEC)
SM_g_mag.append(g_mag)
SM_r_mag.append(r_mag)
SM_i_mag.append(i_mag)
SM_z_mag.append(z_mag)
source_match_table = Table()
source_match_table['SM_match_true'] = SM_match_true
print('SKY MATCH: ' + str(len(source_match_table['SM_match_true'])))
source_match_table['SM_match_row_ID'] = SM_row_matched
source_match_table['SM_g_mag'] = SM_g_mag
source_match_table['SM_r_mag'] = SM_r_mag
source_match_table['SM_i_mag'] = SM_i_mag
source_match_table['SM_z_mag'] = SM_z_mag
source_match_table['SM_RA'] = SM_RA
source_match_table['SM_DEC'] = SM_DEC
source_match_table['DWF_gmag'] = DWF_g_mags_matched
source_match_table['DWF_RA'] = DWF_obs_ra_matched
source_match_table['DWF_DEC'] = DWF_obs_dec_matched
diff_mags = []
num_stars = []
for row, row1 in zip(source_match_table['DWF_gmag'], source_match_table['SM_g_mag']):
if row < 24:
num_stars.append(1)
diff = row - row1
if diff < 1.5:
diff_mags.append(diff)
filenames_good.append(filename)
#print(diff)
elif diff > 1.5:
odd_differences.append(diff)
filenames_odd.append(filename)
elif row > 24:
pass
print('num photom stars:' + str(len(num_stars)))
num_photom_sources.append(len(num_stars))
if len(diff_mags) > 0:
mean_diff = np.mean(diff_mags)
median_diff= np.median(diff_mags)
print('mean correction (mag):' +str(mean_diff))
print('median correction (mag):' +str(median_diff))
master_mean_diff.append(mean_diff)
master_median_diff.append(median_diff)
#print('got to step 3: found mean: ' + str(mean_diff))
source_match_table['DWF_g_mag_ZPoffset'] = source_match_table['DWF_gmag'] - mean_diff
#a = [10, 23]
#b = [10, 23]
#plt.scatter(source_match_table['DWF_gmag'], source_match_table['SM_g_mag'])
#plt.ylim(10, 23)
#plt.xlim(10, 23)
#plt.plot(a, b, 'r')
#plt.xlabel('DWF_g_mag')
#plt.ylabel('SM_g_mag')
#plt.title('uncorrected ' + str(filename))
#plt.savefig(photom_correction_path_images + filename + 'skymapper_uncorrection.png', overwrite = True)
#plt.show()
#plt.close()
#plt.scatter(source_match_table['DWF_g_mag_ZPoffset'], source_match_table['SM_g_mag'])
#plt.ylim(10, 23)
#plt.xlim(10, 23)
#plt.plot(a, b, 'r')
#plt.xlabel('DWF_g_mag')
#plt.ylabel('SM_g_mag')
#plt.title('corrected ' + str(filename))
#plt.savefig(photom_correction_path_images + filename + 'skymapper_corrected.png', overwrite = True)
#plt.close()
corrected_g_mags = ccd_SE_cat['mag_auto'] - mean_diff
corrected_g_mags_aper = ccd_SE_cat['mag_aper'] - mean_diff
#print(corrected_g_mags)
#print('Matching Sky Mapper sources found! Correcting to average deviation for this ccd.')
new_cat = Table()
new_cat['RA'] = ccd_SE_cat['RA']
new_cat['DEC'] = ccd_SE_cat['DEC']
new_cat['g_mag'] = corrected_g_mags
new_cat['g_mag_err'] = ccd_SE_cat['mag_auto_err']
new_cat['g_mag_APER'] = corrected_g_mags_aper
new_cat['g_mag_err_APER'] = ccd_SE_cat['magerr_aper']
#print(new_cat)
av_correction.append(mean_diff)
#print('HELOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO')
#print('average correction ' + str(av_correction))
t = new_cat
output= final_source_cat_path + '/'+ filename + '_NEWCORRECTED.ascii'
#print(output)
t.write(output, format= 'ascii' , overwrite = True)
'''new_tab = Table()
new_tab['filename'] = str(filename)
new_tab['num ccd sources'] = num_sources_ccd
new_tab['num SM source'] = len(num_stars)
new_tab['mean diff '] = mean_diff
new_tab['median diff '] = median_diff
output_name = field + '_'+year+'_' +month+'_CHECK_VALUES.ascii'
new_tab.write(output_name, format='ascii', overwrite=True)'''
print('FILENAME' )
else:
print('No gband from SkyMapper for this ccd')
if len(av_correction) != 0:
mean_of_means = np.mean(av_correction)
print('mean_of_means: ' + str(mean_of_means))
if mean_of_means == 0:
print('No matching Skymapper sources, NOT correcting for this ccd')
corrected_g_mags = ccd_SE_cat['mag_auto'] - mean_of_means
corrected_g_mags_aper = ccd_SE_cat['mag_aper'] - mean_of_means
new_cat = Table()
new_cat['RA'] = ccd_SE_cat['RA']
new_cat['DEC'] = ccd_SE_cat['DEC']
new_cat['g_mag_AUTO'] = corrected_g_mags
new_cat['g_mag_err_AUTO'] = ccd_SE_cat['mag_auto_err']
new_cat['g_mag_APER'] = corrected_g_mags_aper
new_cat['g_mag_err_APER'] = ccd_SE_cat['magerr_aper']
t = new_cat
output= final_source_cat_path + '/' + filename + '_NOT_NEWCORRECTED.ascii'
t.write(output, format= 'ascii' , overwrite = True)
elif mean_of_means > 0:
#print('No matching Skymapper sources, correcting to average of average corrections for this field')
corrected_g_mags = ccd_SE_cat['mag_auto'] - mean_of_means
corrected_g_mags_aper = ccd_SE_cat['mag_aper'] - mean_of_means
new_cat = Table()
new_cat['RA'] = ccd_SE_cat['RA']
new_cat['DEC'] = ccd_SE_cat['DEC']
new_cat['g_mag_AUTO'] = corrected_g_mags
new_cat['g_mag_err_AUTO'] = ccd_SE_cat['mag_auto_err']
new_cat['g_mag_APER'] = corrected_g_mags_aper
new_cat['g_mag_err_APER'] = ccd_SE_cat['magerr_aper']
t = new_cat
output= final_source_cat_path + '/' + filename + '_NEWCORRECTED.ascii'
t.write(output, format= 'ascii' , overwrite = True)
#new_table =Table()
#new_table['filename'] =
else:
print('No matching Skymapper sources, NOT correcting for this ccd')
corrected_g_mags = ccd_SE_cat['mag_auto']
corrected_g_mags_aper = ccd_SE_cat['mag_aper']
new_cat = Table()
new_cat['RA'] = ccd_SE_cat['RA']
new_cat['DEC'] = ccd_SE_cat['DEC']
new_cat['g_mag_AUTO'] = corrected_g_mags
new_cat['g_mag_err_AUTO'] = ccd_SE_cat['mag_auto_err']
new_cat['g_mag_APER'] = corrected_g_mags_aper
new_cat['g_mag_err_APER'] = ccd_SE_cat['magerr_aper']
t = new_cat
output= final_source_cat_path + '/' + filename + '_NOT_NEWCORRECTED.ascii'
t.write(output, format= 'ascii' , overwrite = True)
try:
av_tab = Table()
av_tab['average correction'] = av_correction
#av_tab['filenames'] = filenames_good
print('HELLOOOOOOOOOOOOOOOOOOOOOOOOOOOO')
print(av_tab)
#av_tab['filename'] = filenames_for_av
output2 = photom_correction_path + '/average_correction_per_ccd.ascii'
av_tab.write(output2, format ='ascii', overwrite = True)
new_tab = Table()
new_tab['filename'] = master_filenames
new_tab['num ccd sources'] = num_sources_ccd
new_tab['num SM source'] = num_photom_sources
new_tab['mean diff '] = master_mean_diff
new_tab['median diff '] = master_median_diff
output_name = photom_correction_path + filename[0:17]+'_NEW_CHECK_VALUES.ascii'
new_tab.write(output_name, format='ascii', overwrite=True)
print(output_name)
#print(av_tab)
except:
pass
try:
odd_tab = Table()
odd_tab['odd differences'] = odd_differences
odd_tab['filenames'] = filenames_odd
output3 = photom_correction_path + '/odd_values_found_per_ccd.ascii'
odd_tab.write(output3, format ='ascii', overwrite = True)
except:
pass
filenames_good.clear()
av_correction.clear()
return print('finshed')
MAG_APP_OBS_band3, MAGERR_APP_OBS_band3, MAG_AUTO_OBS_band3, MAGERR_AUTO_OBS_band3, XPEAK_OBS_band3, YPEAK_OBS_band3, X_IMG_OBS_band3, Y_IMG_OBS_band3, RA_OBS_band3, DEC_OBS_band3 = np.loadtxt(field_name +'_' + filter_band3 + '_band_' + date +'.cat', unpack = True) Coords_obs_band3 = np.empty((len(MAG_APP_OBS_band3), 4), dtype = np.float64) Coords_obs_band3[:, 0] = RA_OBS_band3 Coords_obs_band3[:, 1] = DEC_OBS_band3 Coords_obs_band3[:, 2] = MAG_AUTO_OBS_band3 Coords_obs_band3[:, 3] = MAGERR_AUTO_OBS_band3 #print(Coords_obs_band3) #---------------Cross match between sources between the two bands -------------- # max_radius = 1./3600 # 1 arcsec dist_between, ind_row = crossmatch_angular(Coords_obs_band1, Coords_obs_band2, max_radius) match = ~np.isinf(dist_between) #print(match) match_table = Table() match_table['band_' + filter_band2+ '_match_TF'] = match match_table['band_' + filter_band2+ '_match_ID'] = ind_row match_table['band_' + filter_band1+ '_obs_mag'] = MAG_AUTO_OBS_band1 match_table['band_' + filter_band1+ '_obs_mags_err'] = MAGERR_AUTO_OBS_band1 match_table['band_' + filter_band1+ '_obs_ra'] = RA_OBS_band1 match_table['band_' + filter_band1+ '_obs_dec'] = DEC_OBS_band1 print(match_table) band2_match_true = []
data_table['band_' + filter_band3 + '_mag_err'] = data['band_' + filter_band3 +
'_mag_err']
data_table['band_' + filter_band3 + '_ra'] = data['band_' + filter_band3 +
'_ra']
data_table['band_' + filter_band3 + '_dec'] = data['band_' + filter_band3 +
'_dec']
#----------- make coords_cat for the the first band to cross match with the entred coords ----------------#
coords_cat = np.empty((len(data), 12), dtype=np.float64)
coords_cat[:, 0] = data['band_' + filter_band1 + '_ra']
coords_cat[:, 1] = data['band_' + filter_band1 + '_dec']
coords_cat[:, 2] = data['band_' + filter_band1 + '_mag']
max_radius = sep_arcsec / 3600. # 5 arcsec
dist_between_can, ind_row_can = crossmatch_angular(coords_cat, can_entry,
max_radius)
match_can = ~np.isinf(dist_between_can)
print(match_can)
coords_matched_band1_mag = []
coords_matched_band1_mag_err = []
coords_matched_band2_mag = []
coords_matched_band2_mag_err = []
coords_matched_band3_mag = []
coords_matched_band3_mag_err = []
coords_matched_table = Table()
coords_matched_table['match_true_false'] = match_can
coords_matched_table['band_' + filter_band1 + '_mag'] = coords_cat[:, 2]
coords_matched_table['band_' + filter_band1 +
'_mag_err'] = data_table['band_' + filter_band1 +
# get imaging data
image_data = fetch_imaging_sample()
imX = np.empty((len(image_data), 2), dtype=np.float64)
imX[:, 0] = image_data['ra']
imX[:, 1] = image_data['dec']
# get standard stars
standards_data = fetch_sdss_S82standards()
stX = np.empty((len(standards_data), 2), dtype=np.float64)
stX[:, 0] = standards_data['RA']
stX[:, 1] = standards_data['DEC']
# crossmatch catalogs
max_radius = 1. / 3600 # 1 arcsec
dist, ind = crossmatch_angular(imX, stX, max_radius)
match = ~np.isinf(dist)
dist_match = dist[match]
dist_match *= 3600
ax = plt.axes()
hist(dist_match, bins='knuth', ax=ax,
histtype='stepfilled', ec='k', fc='#AAAAAA')
ax.set_xlabel('radius of match (arcsec)')
ax.set_ylabel('N(r, r+dr)')
ax.text(0.95, 0.95,
"Total objects: %i\nNumber with match: %i" % (imX.shape[0],
np.sum(match)),
ha='right', va='top', transform=ax.transAxes)
ax.set_xlim(0, 0.2)
if not os.path.exists(photom_correction_path):
os.makedirs(photom_correction_path, 0o755)
else:
pass
if not os.path.exists(final_source_cat_path):
os.makedirs(final_source_cat_path, 0o755)
else:
pass
if not os.path.exists(photom_correction_path_images):
os.makedirs(photom_correction_path_images, 0o755)
else:
pass
max_radius = 5. / 3600 #1 arc second
dist_between, ind_row = crossmatch_angular(DWF_X, SM_X, max_radius)
match = ~np.isinf(dist_between)
if len(match) != 0:
match_table = Table()
match_table['matched_true_false'] = match
match_table['matched_ID'] = ind_row
match_table['matched_DWF_data_gmag'] = ccd_SE_cat['mag_auto']
match_table['matched_DWF_RA'] = ccd_SE_cat['RA']
match_table['matched_DWF_DEC'] = ccd_SE_cat['DEC']
SM_match_true = []
SM_row_matched = []
DWF_g_mags_matched = []
DWF_g_mags_error_matched = []
DWF_obs_ra_matched = []
DWF_obs_dec_matched = []
for row in match_table:
def create_finding_chart_inputs(RA,
DEC,
source_name,
field,
template,
verbose=False,
debugmode=False):
print('#############################################')
print('# CANVIS Finding Chart version has started #')
print('#############################################')
print(f'Finding chart image for RA: {RA} DEC: {DEC}')
temp_path = '/fred/oz100/pipes/DWF_PIPE/field_stacks/' + template
print(f'Template path being used is: {temp_path}')
output_path = '/fred/oz100/FINDING_CHARTS/'
output_files_path = '/fred/oz100/FINDING_CHARTS/' + field + '/' + source_name + '/'
print(f'Your files will be saved here: {output_files_path}')
if not os.path.exists(output_files_path):
os.makedirs(output_files_path, 0o755)
else:
pass
mydic = SortedDict()
with fits.open(temp_path) as hdu:
print('## Template opened ##')
size = 1370
w = WCS(hdu[0].header)
#print(w)
head = hdu[0].header
date = dt.datetime.strptime(head['DATE'], '%Y-%m-%dT%H:%M:%S')
xlim = head['NAXIS1']
ylim = head['NAXIS2']
print(xlim, ylim)
pixcrd_im = np.array([[xlim, ylim]], np.float_)
world_im = w.wcs_pix2world(pixcrd_im, 1)
pixx_im, pixy_im = world_im[0][0], world_im[0][1]
pixcrd = np.array([[RA, DEC]], np.float_)
worldpix = w.wcs_world2pix(pixcrd, 1)
pixx, pixy = worldpix[0][0], worldpix[0][1]
cutout = Cutout2D(hdu[0].data, (pixx, pixy), size, wcs=w)
hdu[0].data = cutout.data
hdu[0].header['CRPIX1'] = cutout.wcs.wcs.crpix[0]
hdu[0].header['CRPIX2'] = cutout.wcs.wcs.crpix[1]
hdu.writeto(output_files_path + source_name + '.fits', overwrite=True)
print('## Large Fits CUTOUT saved ##')
small_size = 457
with fits.open(temp_path) as hdu:
size = 457
w = WCS(hdu[0].header)
#print(w)
head = hdu[0].header
date = dt.datetime.strptime(head['DATE'], '%Y-%m-%dT%H:%M:%S')
xlim = head['NAXIS1']
ylim = head['NAXIS2']
print(xlim, ylim)
pixcrd_im = np.array([[xlim, ylim]], np.float_)
world_im = w.wcs_pix2world(pixcrd_im, 1)
pixx_im, pixy_im = world_im[0][0], world_im[0][1]
pixcrd = np.array([[RA, DEC]], np.float_)
worldpix = w.wcs_world2pix(pixcrd, 1)
pixx, pixy = worldpix[0][0], worldpix[0][1]
cutout = Cutout2D(hdu[0].data, (pixx, pixy), size, wcs=w)
hdu[0].data = cutout.data
hdu[0].header['CRPIX1'] = cutout.wcs.wcs.crpix[0]
hdu[0].header['CRPIX2'] = cutout.wcs.wcs.crpix[1]
hdu.writeto(output_files_path + source_name + 'SMALL.fits',
overwrite=True)
print('## Small Fits CUTOUT saved ##')
print('## Starting Source Extracting for SMALL image ##')
os.system('sex ' + output_files_path + source_name + 'SMALL.fits' +
' -c default_params.sex -CATALOG_NAME ' + output_files_path +
source_name + '_SE.cat -DETECT_THRESH 1.5 -MAG_ZEROPOINT 25 ')
print('## Source extractor done! ##')
print('#############################################')
print('## Cross matching with SkyMapper DR2 ##')
print('#############################################')
try:
skymapper_cat_path = '/fred/oz100/SM_cats/' + field + '_SM.csv'
sm_cat = pd.read_csv(skymapper_cat_path).to_dict(orient='row')
sm_ra = []
sm_dec = []
sm_ngood = []
sm_class_star = []
sm_g_psf = []
sm_g_psf_error = []
sm_r_psf = []
sm_r_psf_error = []
sm_i_psf = []
sm_i_psf_error = []
sm_z_psf = []
sm_z_psf_error = []
if debugmode:
print('Numebr of sources in SkyMapper Catalouge: ' +
str(len(sm_cat)))
for i in range(len(sm_cat)):
sm_ra.append(sm_cat[i]['raj2000'])
sm_dec.append(sm_cat[i]['dej2000'])
sm_ngood.append(sm_cat[i]['ngood'])
sm_class_star.append(sm_cat[i]['class_star'])
sm_g_psf.append(sm_cat[i]['g_psf'])
sm_g_psf_error.append(sm_cat[i]['e_g_psf'])
sm_r_psf.append(sm_cat[i]['r_psf'])
sm_r_psf_error.append(sm_cat[i]['e_r_psf'])
sm_i_psf.append(sm_cat[i]['i_psf'])
sm_i_psf_error.append(sm_cat[i]['e_i_psf'])
sm_z_psf.append(sm_cat[i]['z_psf'])
sm_z_psf_error.append(sm_cat[i]['e_z_psf'])
SM_table = Table()
SM_table['RA'] = sm_ra
SM_table['DEC'] = sm_dec
SM_table['ngood'] = sm_ngood
SM_table['class_star'] = sm_class_star
SM_table['g_psf'] = sm_g_psf
SM_table['e_g_psf'] = sm_g_psf_error
SM_table['r_psf'] = sm_r_psf
SM_table['e_r_psf'] = sm_r_psf_error
SM_table['i_psf'] = sm_i_psf
SM_table['e_i_psf'] = sm_i_psf_error
SM_table['z_psf'] = sm_z_psf
SM_table['e_z_psf'] = sm_z_psf_error
SM_filtered_ra = []
SM_filtered_dec = []
SM_filtered_ngood = []
SM_filtered_class_star = []
SM_filtered_g_psf = []
SM_filtered_g_psf_err = []
SM_filtered_r_psf = []
SM_filtered_r_psf_err = []
SM_filtered_i_psf = []
SM_filtered_i_psf_err = []
SM_filtered_z_psf = []
SM_filtered_z_psf_err = []
for row in SM_table:
if 0.90 <= row['class_star'] <= 1.0:
SM_filtered_ra.append(row['RA'])
SM_filtered_dec.append(row['DEC'])
SM_filtered_ngood.append(row['ngood'])
SM_filtered_class_star.append(row['class_star'])
SM_filtered_g_psf.append(row['g_psf'])
SM_filtered_g_psf_err.append(row['e_g_psf'])
SM_filtered_r_psf.append(row['r_psf'])
SM_filtered_r_psf_err.append(row['e_r_psf'])
SM_filtered_i_psf.append(row['i_psf'])
SM_filtered_i_psf_err.append(row['e_i_psf'])
SM_filtered_z_psf.append(row['z_psf'])
SM_filtered_z_psf_err.append(row['e_z_psf'])
if debugmode:
print('Number of SkyMapper Sources pass S/G cuts: ' +
str(len(SM_filtered_ra)))
SM_X = np.empty((len(SM_filtered_ra), 7), dtype=np.float64)
SM_X[:, 0] = SM_filtered_ra
SM_X[:, 1] = SM_filtered_dec
SM_X[:, 2] = SM_filtered_ngood
SM_X[:, 3] = SM_filtered_g_psf
SM_X[:, 4] = SM_filtered_r_psf
SM_X[:, 5] = SM_filtered_i_psf
SM_X[:, 6] = SM_filtered_z_psf
MAG_APER, MAGERR_APER, MAG_AUTO, MAGERR_AUTO, XPEAK_IMAGE, YPEAK_IMAGE, X_IMAGE, Y_IMAGE, ALPHA_J2000, DELTA_J2000 = np.loadtxt(
output_files_path + source_name + '_SE.cat', unpack=True)
DWF_X = np.empty((len(MAG_APER), 2), dtype=np.float64)
DWF_X[:, 0] = ALPHA_J2000
DWF_X[:, 1] = DELTA_J2000
max_radius = 2 / 3600 #1 arc second
dist_between, ind_row = crossmatch_angular(DWF_X, SM_X, max_radius)
match = ~np.isinf(dist_between)
if debugmode:
print('Length of Match Table: ' + str(len(match)))
print(match)
if len(match) != 0:
match_table = Table()
match_table['matched_true_false'] = match
match_table['matched_ID'] = ind_row
SM_match_true = []
SM_row_matched = []
DWF_g_mags_matched = []
DWF_g_mags_error_matched = []
DWF_obs_ra_matched = []
DWF_obs_dec_matched = []
for row in match_table:
if row['matched_true_false'] == True:
if debugmode:
print('Found match source: ' + str(row['matched_ID']))
SM_match_true.append(row['matched_true_false'])
SM_row_matched.append(row['matched_ID'])
SM_RA = []
SM_DEC = []
SM_g_mag = []
SM_r_mag = []
SM_i_mag = []
SM_z_mag = []
for j in SM_row_matched:
RA = SM_X[j, 0]
DEC = SM_X[j, 1]
g_mag = SM_X[j, 3]
r_mag = SM_X[j, 4]
i_mag = SM_X[j, 5]
z_mag = SM_X[j, 6]
SM_RA.append(RA)
SM_DEC.append(DEC)
SM_g_mag.append(g_mag)
SM_r_mag.append(r_mag)
SM_i_mag.append(i_mag)
SM_z_mag.append(z_mag)
SM_final_table = Table()
SM_final_table['RA'] = SM_RA
SM_final_table['DEC'] = SM_DEC
SM_final_table['g_mag'] = SM_g_mag
SM_final_table['r_mag'] = SM_r_mag
SM_final_table['i_mag'] = SM_i_mag
SM_final_table['z_mag'] = SM_z_mag
output = output_files_path + source_name + '_skymapper_star_cat.ascii'
SM_final_table.write(output, format='ascii', overwrite=True)
print('#############################################')
print('SkyMapper Sources Found and Saved')
print('#############################################')
except:
print('SKY MAPPER SOURCE CHECK FAILED')
print('#############################################')
print('Starting to search for GAIA sources')
GAIA_DR2 = "I/345"
field_RA_DEC = str(f'{RA} {DEC}')
result = Vizier.query_region(field_RA_DEC,
radius=Angle('120"'),
catalog=GAIA_DR2)
print(field_RA_DEC)
GAIA_DR2 = Table()
GAIA_DR2['RA'] = result[0]['RA_ICRS']
GAIA_DR2['DEC'] = result[0]['DE_ICRS']
GAIA_DR2['Gmag'] = result[0]['Gmag']
GAIA_DR2['BPmag'] = result[0]['BPmag']
GAIA_DR2['RPmag'] = result[0]['RPmag']
output = output_files_path + source_name + '_gaia_star_cat.ascii'
GAIA_DR2.write(output, format='ascii', overwrite=True)
print('#############################################')
print('# YOUR FINDING CHART INPUTS ARE DONE#')
print(f'# FIND THEM HERE: {output_files_path}')
print('#############################################')
