def uncorrect_red_sources(phot_groups_filepath_tuple):
"""
identify the 'red' sources by comparing ch1 to ch2 energies, then find their
entries in the single-exposure catalogs and un-correct (divide) the
array location dependent correction. red = ch1_flux < 3.6/4.5 * ch2_flux
"""
ch1_file, ch2_file = phot_groups_filepath_tuple
root = os.path.abspath("../bcdphot")
fp1 = os.path.join(root, "cal", "ch1_photcorr_ap_5.fits")
fp2 = os.path.join(root, "cal", "ch2_photcorr_ap_5.fits")
arrloc1 = pyfits.open(fp1)[0].data
arrloc2 = pyfits.open(fp2)[0].data
ch1 = json.load(open(ch1_file))
ch2 = json.load(open(ch2_file))
ra1, dec1 = zip(*[i[0][:2] for i in ch1.values()])
ra2, dec2 = zip(*[i[0][:2] for i in ch2.values()])
if len(ra1) < len(ra2):
idx1, idx2, ds = spherematch(ra1, dec1, ra2, dec2, tolerance=2 / 3600.0)
else:
idx2, idx1, ds = spherematch(ra2, dec2, ra1, dec1, tolerance=2 / 3600.0)
for k1, k2 in zip(np.array(ch1.keys())[idx1], np.array(ch2.keys())[idx2]):
f1 = np.array(ch1[k1]).mean(0)[6]
f2 = np.array(ch2[k2]).mean(0)[6]
red = f1 < (3.6 / 4.5) * f2
if red:
for obs in ch1[k1]:
x, y = obs[4:6]
obs[6:] = [i / arrloc1[x, y] for i in obs[6:]]
for obs in ch2[k2]:
x, y = obs[4:6]
obs[6:] = [i / arrloc2[x, y] for i in obs[6:]]
out_path = ch1_file.replace("_arrayloc.json", "_arrayloc_cor.json")
with open(out_path, "w") as w:
json.dump(ch1, w, indent=4 * " ")
print("created file: " + out_path)
out_path = ch2_file.replace("_arrayloc.json", "_arrayloc_cor.json")
with open(out_path, "w") as w:
json.dump(ch2, w, indent=4 * " ")
print("created file: " + out_path)
def match_sdss(cat_path):
for catfile in find_files(cat_path, "*merged.txt"):
# read pipeline catalog
print("\nreading catalog: {}".format(catfile))
cat = pd.read_table(catfile, sep=' ')
# retrieve SDSS data from ViZieR if not already downloaded
ch = catfile.split('/')[-1].split('_')[1]
outpath = catfile.replace('{}_merged.txt'.format(ch), 'sdss.vot')
if not os.path.isfile(outpath):
cntr_ra = np.median(cat.ra)
cntr_dec = np.median(cat.dec)
# get source from one corner of the mosaic to calculate radius
c1 = (cat.ra.min(), cat.dec[cat.ra == cat.ra.min()].values[0])
# make radius 10% bigger just to be on safe side
radius = great_circle_distance(cntr_ra, cntr_dec, *c1) * 1.1
url = get_url(cntr_ra, cntr_dec, radius)
print("retrieving URL: {}".format(url))
handler = urllib2.urlopen(url)
raw = handler.read()
with open(outpath, 'wb') as f:
f.write(raw)
print("created file: {}".format(outpath))
# parse VOTable
print("reading VOTable: {}".format(outpath))
table = parse_single_table(outpath)
# if this is one of the southern hemisphere regions, delete and continue
if table.array.size == 0:
os.remove(outpath)
print("outside of SDSS coverage")
continue
# make sure no missing data
for name in table.array.dtype.names:
assert table.array[name].mask.sum() == 0
# get unmasked array
sdss = table.array.data
# make sure sky coverage is big enough
assert sdss['RAJ2000'].min() < cat.ra.min()
assert sdss['RAJ2000'].max() > cat.ra.max()
assert sdss['DEJ2000'].min() < cat.dec.min()
assert sdss['DEJ2000'].max() > cat.dec.max()
# match to catalog
assert cat.shape[0] < sdss.shape[0]
tol = 2 / 3600.
idx1, idx2, ds = spherematch(cat.ra,
cat.dec,
sdss['RAJ2000'],
sdss['DEJ2000'],
tolerance=tol)
print("matched {} out of {} sources with {} arcsec tolerance".format(
ds.size, cat.shape[0], tol * 3600))
# create vector of star/galaxy class (0=missing, 3=galaxy, 6=star)
cl = np.zeros(cat.shape[0]).astype('int')
cl[idx1] = sdss['cl'][idx2]
# add the column to the dataset
cat['cl'] = cl
# write to new file
outpath = catfile.replace('merged.txt', 'merged+sdss.txt')
# fmt = ['%i']+['%0.8f']*2+['%.4e']*2+['%i']*2
# hdr = ' '.join(names)+' cl'
# np.savetxt(outpath, df.to_records(index=False), fmt=fmt, header=hdr)
cat.to_csv(outpath, index=False, sep=' ', float_format='%.8f')
print("created file: {}".format(outpath))
def match_wise(cat_path, sdss=True):
if sdss:
search_pattern = "*merged+sdss.txt"
else:
search_pattern = "*merged.txt"
for catfile in find_files(cat_path, search_pattern):
# read pipeline catalog
print("\nreading catalog: {}".format(catfile))
cat = pd.read_table(catfile, sep=' ')
# retrieve WISE data from ViZieR if not already downloaded
ch = catfile.split('/')[-1].split('_')[1]
if sdss:
outpath = catfile.replace('{}_merged+sdss.txt'.format(ch),
'wise.vot')
else:
outpath = catfile.replace('{}_merged.txt'.format(ch), 'wise.vot')
if not os.path.isfile(outpath):
cntr_ra = np.median(cat.ra)
cntr_dec = np.median(cat.dec)
# get source from one corner of the mosaic to calculate radius
c1 = (cat.ra.min(), cat.dec[cat.ra == cat.ra.min()].values[0])
# make radius 10% bigger just to be on safe side
radius = great_circle_distance(cntr_ra, cntr_dec, *c1) * 1.1
url = get_url(cntr_ra, cntr_dec, radius)
print("retrieving URL: {}".format(url))
handler = urllib2.urlopen(url)
raw = handler.read()
with open(outpath, 'wb') as f:
f.write(raw)
print("created file: {}".format(outpath))
# parse VOTable
print("reading VOTable: {}".format(outpath))
table = parse_single_table(outpath)
# if this is one of the southern hemisphere regions, delete and continue
if table.array.size == 0:
os.remove(outpath)
print("no WISE coverage")
continue
# get unmasked array
wise = table.array.data
# make sure sky coverage is big enough
assert wise['RAJ2000'].min() < cat.ra.min()
assert wise['RAJ2000'].max() > cat.ra.max()
assert wise['DEJ2000'].min() < cat.dec.min()
assert wise['DEJ2000'].max() > cat.dec.max()
# match to catalog
tol = 2 / 3600.
if cat.shape[0] < wise.shape[0]:
idx1, idx2, ds = spherematch(cat.ra,
cat.dec,
wise['RAJ2000'],
wise['DEJ2000'],
tolerance=tol)
else:
idx2, idx1, ds = spherematch(wise['RAJ2000'],
wise['DEJ2000'],
cat.ra,
cat.dec,
tolerance=tol)
print("matched {} out of {} sources with {} arcsec tolerance".format(
ds.size, cat.shape[0], tol * 3600))
# add WISE to the catalog
if ch == '1':
cat['W1mag'] = np.repeat(np.nan, cat.shape[0])
cat['e_W1mag'] = np.repeat(np.nan, cat.shape[0])
cat['W1mag'][idx1] = wise['W1mag'][idx2]
cat['e_W1mag'][idx1] = wise['e_W1mag'][idx2]
elif ch == '2':
cat['W2mag'] = np.repeat(np.nan, cat.shape[0])
cat['e_W2mag'] = np.repeat(np.nan, cat.shape[0])
cat['W2mag'][idx1] = wise['W2mag'][idx2]
cat['e_W2mag'][idx1] = wise['e_W2mag'][idx2]
else:
print("unexpected error adding WISE data")
# write to new file
outpath = catfile.replace('.txt', '+wise.csv')
# fmt = ['%i']+['%0.8f']*2+['%.4e']*2+['%i']*2
# hdr = ' '.join(names)+' cl'
# np.savetxt(outpath, df.to_records(index=False), fmt=fmt, header=hdr)
cat.to_csv(outpath, index=False, float_format='%.8f')
print("created file: {}".format(outpath))
def combine_hdr_catalogs(catalog_filepaths_tuple):
"""
Takes a tuple containing the filepaths to the short and long exposure
single-channel catalogs for a given region and channel. The result is
a single catalog containing the union of all sources in both short and
long exposure catalogs, with the short exposure measurements being used
for the brighter sources, and the long exposure measurements used for
the fainter sources. The cutoff between the two is determined by the
parameter 'hdr_cutoff' in the metadata file and should be set to the
saturation limit for the long exposure data (there are actually 2
parameters, one for each channel: hdr_cutoff_ch1, hdr_cutoff_ch2).
"""
# read in the data
long_file, short_file = catalog_filepaths_tuple
work_dir = "/".join(short_file.split("/")[:-1])
meta = json.load(open(work_dir + "/metadata.json"))
header = "id ra dec flux unc n_obs"
names = header.split()
long_cat = np.recfromtxt(long_file, names=names)
short_cat = np.recfromtxt(short_file, names=names)
# fit a line to short ~ long
idx_s = short_cat.flux < meta["short_cutoff"]
idx_l = long_cat.flux < meta["long_cutoff"]
short_flux = short_cat.flux[idx_s]
long_flux = long_cat.flux[idx_l]
short_ra, short_dec = short_cat.ra[idx_s], short_cat.dec[idx_s]
long_ra, long_dec = long_cat.ra[idx_l], long_cat.dec[idx_l]
idx1, idx2, ds = spherematch(short_ra, short_dec, long_ra, long_dec, tolerance=1 / 3600.0)
y = short_flux[idx1]
X = long_flux[idx2]
slope = ordinary_least_squares(y, X)
# divide short flux/unc by the slope so that it agrees with the long flux
print("region {} correction value: {}".format(meta["name"], slope))
short_cat.flux /= slope
short_cat.unc /= slope
# get everything brighter than the cutoff in short and combine with long
idx_faint = long_cat.flux < meta["long_cutoff"]
idx_bright = short_cat.flux > meta["long_cutoff"]
# before concatenation of long and short subsets, check for any duplicates
# (if they exist they should tend to have flux very close to the cutoff)
ls, ss = long_cat[idx_faint], short_cat[idx_bright]
idx_s, idx_l, ds = spherematch(ss.ra, ss.dec, ls.ra, ls.dec, tolerance=1 / 3600.0)
dup_ids = []
for idx in idx_l:
if (ls.flux[idx] > 0.9 * meta["long_cutoff"]) & (ls.flux[idx] < meta["long_cutoff"]):
dup_ids.append(ls.id[idx])
# now use the ids of the duplicates to delete them from the long dataset
for idx in dup_ids:
ls = ls[ls.id != idx]
data = np.concatenate([ls, ss])
# eliminate sources with negative flux
good = data["flux"] >= 0
data = data[good]
# apply global sigma clip using the value from setup.yaml
snr = data["flux"] / data["unc"]
good = snr >= meta["sigma_clip"]
data = data[good]
# write to disk
header = "id ra dec flux unc n_obs"
data = data[header.split()]
idx = np.argsort(data["ra"])
data = data[idx]
data["id"] = np.arange(1, data.shape[0] + 1)
fmt = ["%i"] + ["%0.8f"] * 2 + ["%.4e"] * 2 + ["%i"]
out_name = "_".join([meta["name"], meta["channel"], "combined_hdr_catalog.txt"])
out_path = "/".join(["/".join(work_dir.split("/")[:-1]), out_name])
np.savetxt(out_path, data, fmt=fmt, header=header)
print("created file: " + out_path)
def match_sdss(cat_path):
for catfile in find_files(cat_path, "*merged.txt"):
# read pipeline catalog
print("\nreading catalog: {}".format(catfile))
cat = pd.read_table(catfile, sep=' ')
# retrieve SDSS data from ViZieR if not already downloaded
ch = catfile.split('/')[-1].split('_')[1]
outpath = catfile.replace('{}_merged.txt'.format(ch), 'sdss.vot')
if not os.path.isfile(outpath):
cntr_ra = np.median(cat.ra)
cntr_dec = np.median(cat.dec)
# get source from one corner of the mosaic to calculate radius
c1 = (cat.ra.min(), cat.dec[cat.ra==cat.ra.min()].values[0])
# make radius 10% bigger just to be on safe side
radius = great_circle_distance(cntr_ra, cntr_dec, *c1) * 1.1
url = get_url(cntr_ra, cntr_dec, radius)
print("retrieving URL: {}".format(url))
handler = urllib2.urlopen(url)
raw = handler.read()
with open(outpath,'wb') as f:
f.write(raw)
print("created file: {}".format(outpath))
# parse VOTable
print("reading VOTable: {}".format(outpath))
table = parse_single_table(outpath)
# if this is one of the southern hemisphere regions, delete and continue
if table.array.size == 0:
os.remove(outpath)
print("outside of SDSS coverage")
continue
# make sure no missing data
for name in table.array.dtype.names:
assert table.array[name].mask.sum() == 0
# get unmasked array
sdss = table.array.data
# make sure sky coverage is big enough
assert sdss['RAJ2000'].min() < cat.ra.min()
assert sdss['RAJ2000'].max() > cat.ra.max()
assert sdss['DEJ2000'].min() < cat.dec.min()
assert sdss['DEJ2000'].max() > cat.dec.max()
# match to catalog
assert cat.shape[0] < sdss.shape[0]
tol = 2/3600.
idx1, idx2, ds = spherematch(cat.ra, cat.dec,
sdss['RAJ2000'], sdss['DEJ2000'], tolerance = tol)
print("matched {} out of {} sources with {} arcsec tolerance".format(ds.size,
cat.shape[0], tol*3600))
# create vector of star/galaxy class (0=missing, 3=galaxy, 6=star)
cl = np.zeros(cat.shape[0]).astype('int')
cl[idx1] = sdss['cl'][idx2]
# add the column to the dataset
cat['cl'] = cl
# write to new file
outpath = catfile.replace('merged.txt', 'merged+sdss.txt')
# fmt = ['%i']+['%0.8f']*2+['%.4e']*2+['%i']*2
# hdr = ' '.join(names)+' cl'
# np.savetxt(outpath, df.to_records(index=False), fmt=fmt, header=hdr)
cat.to_csv(outpath, index=False, sep=' ', float_format='%.8f')
print("created file: {}".format(outpath))
def merge_subarray(vg_dir, bcdphot_dir):
out_dir = vg_dir.replace('clean','plots_catalogs')
os.mkdir(out_dir)
hdr_files = find_files(bcdphot_dir, '*combined_hdr_*xsc_cor.txt')
# hdr_file = list(hdr_files)[0]
for hdr_file in hdr_files:
reg, ch = hdr_file.split('/')[-1].split('_')[:2]
sub_file = '/'.join([vg_dir, "d{}_ch{}_agg.csv".format(reg, ch)])
hdr_names = open(hdr_file).readline().split()[1:]
hdr = np.recfromtxt(hdr_file, names=hdr_names)
sub = np.recfromcsv(sub_file)
# sub.flux *= 1e-3 # convert from uJy to mJy
idx1, idx2, ds = spherematch(sub.ra, sub.dec, hdr.ra, hdr.dec, tolerance=3/3600.)
df = pd.DataFrame({'sub_flux': sub.flux[idx1], 'hdr_flux':hdr.flux[idx2]})
slope = fit_line(df, int(ch))
with open("{}/linefits.txt".format(out_dir),'a') as f:
f.write("{} {} {}\n".format(reg, ch, slope))
fig = df.plot(x='hdr_flux',y='sub_flux', kind='scatter')
fig.plot([0, fig.get_xlim()[1]], [0, slope * fig.get_xlim()[1]], 'r-')
fig.set_title("region {} channel {}".format(reg, ch))
fig.text(fig.get_xlim()[1]*0.2, fig.get_ylim()[1]*0.8,
"slope: {0:3f}".format(slope), fontsize=24)
plt.savefig("{}/{}_{}_linefit.png".format(out_dir, reg, ch), dpi=300)
plt.close()
# now save the (uncorrected) matched data to disk
sub_matched = pd.DataFrame.from_records(sub[idx1])
# rename the columns
cols = sub_matched.columns.tolist()
cols_new = ['sub_'+i for i in cols]
sub_matched.columns = cols_new
# set hdr_matched dataframe index equal to sub_matched index, this is
# necessary for concatenation using pandas.concat
hdr_matched = pd.DataFrame.from_records(hdr[idx2]).set_index(sub_matched.index)
# rename the columns
cols = hdr_matched.columns.tolist()
cols_new = ['hdr_'+i for i in cols]
hdr_matched.columns = cols_new
# concatenate
concat = pd.concat([ sub_matched, hdr_matched ], 1)
# # convert subarray flux to mJy
# concat.sub_flux = concat.sub_flux*1e3
# concat.sub_unc = concat.sub_unc*1e3
concat.to_csv("{}/{}_{}_hdr_vs_sub.csv".format(out_dir, reg, ch),
index=False, float_format='%.8f')
# now correct all the subarray flux values with the slope
sub.flux /= slope
# now merge hdr and subarray into one dataset:
# want to keep all the hdr photometry that is not saturated, and
# keep only the subarray photometry above the hdr saturation limit
cutoff = get_cutoff(ch)
bad = hdr.flux > cutoff
hdr_subset = pd.DataFrame.from_records(hdr[~bad])
bad = sub.flux < cutoff
sub_subset = pd.DataFrame.from_records(sub[~bad])
# add n_obs column to subarray data so it has same format as hdr
sub_subset['n_obs'] = 4
# add column indicating whether if it came from subarray
hdr_subset['sub'] = np.zeros(hdr_subset.shape[0]).astype(int)
sub_subset['sub'] = np.ones(sub_subset.shape[0]).astype(int)
# concatenate them
concat = pd.concat([ hdr_subset, sub_subset ], 0, ignore_index=True)
# get rid of the 'id' field since it is no longer relevant
# but add a column indicating if it was a 2MASS XSC measurement
concat['xsc'] = np.zeros(concat.shape[0]).astype(int)
concat.xsc[concat.id < 1] = 1
concat = concat.drop('id', 1)
# apply 1% flux reduction to correct for stray light (only to >100 mJy sources)
concat.flux[concat.flux > 100] *= 0.99
concat.unc[concat.flux > 100] *= 0.99
# write to disk
concat.to_csv("{}/{}_{}_merged.txt".format(out_dir, reg, ch),
index=False, sep=' ', float_format='%.8f')
def merge_subarray(vg_dir, bcdphot_dir):
out_dir = vg_dir.replace('clean', 'plots_catalogs')
os.mkdir(out_dir)
hdr_files = find_files(bcdphot_dir, '*combined_hdr_*xsc_cor.txt')
# hdr_file = list(hdr_files)[0]
for hdr_file in hdr_files:
reg, ch = hdr_file.split('/')[-1].split('_')[:2]
sub_file = '/'.join([vg_dir, "d{}_ch{}_agg.csv".format(reg, ch)])
hdr_names = open(hdr_file).readline().split()[1:]
hdr = np.recfromtxt(hdr_file, names=hdr_names)
sub = np.recfromcsv(sub_file)
# sub.flux *= 1e-3 # convert from uJy to mJy
idx1, idx2, ds = spherematch(sub.ra,
sub.dec,
hdr.ra,
hdr.dec,
tolerance=3 / 3600.)
df = pd.DataFrame({
'sub_flux': sub.flux[idx1],
'hdr_flux': hdr.flux[idx2]
})
slope = fit_line(df, int(ch))
with open("{}/linefits.txt".format(out_dir), 'a') as f:
f.write("{} {} {}\n".format(reg, ch, slope))
fig = df.plot(x='hdr_flux', y='sub_flux', kind='scatter')
fig.plot([0, fig.get_xlim()[1]], [0, slope * fig.get_xlim()[1]], 'r-')
fig.set_title("region {} channel {}".format(reg, ch))
fig.text(fig.get_xlim()[1] * 0.2,
fig.get_ylim()[1] * 0.8,
"slope: {0:3f}".format(slope),
fontsize=24)
plt.savefig("{}/{}_{}_linefit.png".format(out_dir, reg, ch), dpi=300)
plt.close()
# now save the (uncorrected) matched data to disk
sub_matched = pd.DataFrame.from_records(sub[idx1])
# rename the columns
cols = sub_matched.columns.tolist()
cols_new = ['sub_' + i for i in cols]
sub_matched.columns = cols_new
# set hdr_matched dataframe index equal to sub_matched index, this is
# necessary for concatenation using pandas.concat
hdr_matched = pd.DataFrame.from_records(hdr[idx2]).set_index(
sub_matched.index)
# rename the columns
cols = hdr_matched.columns.tolist()
cols_new = ['hdr_' + i for i in cols]
hdr_matched.columns = cols_new
# concatenate
concat = pd.concat([sub_matched, hdr_matched], 1)
# # convert subarray flux to mJy
# concat.sub_flux = concat.sub_flux*1e3
# concat.sub_unc = concat.sub_unc*1e3
concat.to_csv("{}/{}_{}_hdr_vs_sub.csv".format(out_dir, reg, ch),
index=False,
float_format='%.8f')
# now correct all the subarray flux values with the slope
sub.flux /= slope
# now merge hdr and subarray into one dataset:
# want to keep all the hdr photometry that is not saturated, and
# keep only the subarray photometry above the hdr saturation limit
cutoff = get_cutoff(ch)
bad = hdr.flux > cutoff
hdr_subset = pd.DataFrame.from_records(hdr[~bad])
bad = sub.flux < cutoff
sub_subset = pd.DataFrame.from_records(sub[~bad])
# add n_obs column to subarray data so it has same format as hdr
sub_subset['n_obs'] = 4
# add column indicating whether if it came from subarray
hdr_subset['sub'] = np.zeros(hdr_subset.shape[0]).astype(int)
sub_subset['sub'] = np.ones(sub_subset.shape[0]).astype(int)
# concatenate them
concat = pd.concat([hdr_subset, sub_subset], 0, ignore_index=True)
# get rid of the 'id' field since it is no longer relevant
# but add a column indicating if it was a 2MASS XSC measurement
concat['xsc'] = np.zeros(concat.shape[0]).astype(int)
concat.xsc[concat.id < 1] = 1
concat = concat.drop('id', 1)
# apply 1% flux reduction to correct for stray light (only to >100 mJy sources)
concat.flux[concat.flux > 100] *= 0.99
concat.unc[concat.flux > 100] *= 0.99
# write to disk
concat.to_csv("{}/{}_{}_merged.txt".format(out_dir, reg, ch),
index=False,
sep=' ',
float_format='%.8f')
def match_wise(cat_path, sdss=True):
if sdss:
search_pattern = "*merged+sdss.txt"
else:
search_pattern = "*merged.txt"
for catfile in find_files(cat_path, search_pattern):
# read pipeline catalog
print("\nreading catalog: {}".format(catfile))
cat = pd.read_table(catfile, sep=' ')
# retrieve WISE data from ViZieR if not already downloaded
ch = catfile.split('/')[-1].split('_')[1]
if sdss:
outpath = catfile.replace('{}_merged+sdss.txt'.format(ch), 'wise.vot')
else:
outpath = catfile.replace('{}_merged.txt'.format(ch), 'wise.vot')
if not os.path.isfile(outpath):
cntr_ra = np.median(cat.ra)
cntr_dec = np.median(cat.dec)
# get source from one corner of the mosaic to calculate radius
c1 = (cat.ra.min(), cat.dec[cat.ra==cat.ra.min()].values[0])
# make radius 10% bigger just to be on safe side
radius = great_circle_distance(cntr_ra, cntr_dec, *c1) * 1.1
url = get_url(cntr_ra, cntr_dec, radius)
print("retrieving URL: {}".format(url))
handler = urllib2.urlopen(url)
raw = handler.read()
with open(outpath,'wb') as f:
f.write(raw)
print("created file: {}".format(outpath))
# parse VOTable
print("reading VOTable: {}".format(outpath))
table = parse_single_table(outpath)
# if this is one of the southern hemisphere regions, delete and continue
if table.array.size == 0:
os.remove(outpath)
print("no WISE coverage")
continue
# get unmasked array
wise = table.array.data
# make sure sky coverage is big enough
assert wise['RAJ2000'].min() < cat.ra.min()
assert wise['RAJ2000'].max() > cat.ra.max()
assert wise['DEJ2000'].min() < cat.dec.min()
assert wise['DEJ2000'].max() > cat.dec.max()
# match to catalog
tol = 2/3600.
if cat.shape[0] < wise.shape[0]:
idx1, idx2, ds = spherematch(cat.ra, cat.dec,
wise['RAJ2000'], wise['DEJ2000'], tolerance = tol)
else:
idx2, idx1, ds = spherematch(wise['RAJ2000'], wise['DEJ2000'],
cat.ra, cat.dec, tolerance = tol)
print("matched {} out of {} sources with {} arcsec tolerance".format(ds.size,
cat.shape[0], tol*3600))
# add WISE to the catalog
if ch == '1':
cat['W1mag'] = np.repeat(np.nan, cat.shape[0])
cat['e_W1mag'] = np.repeat(np.nan, cat.shape[0])
cat['W1mag'][idx1] = wise['W1mag'][idx2]
cat['e_W1mag'][idx1] = wise['e_W1mag'][idx2]
elif ch == '2':
cat['W2mag'] = np.repeat(np.nan, cat.shape[0])
cat['e_W2mag'] = np.repeat(np.nan, cat.shape[0])
cat['W2mag'][idx1] = wise['W2mag'][idx2]
cat['e_W2mag'][idx1] = wise['e_W2mag'][idx2]
else:
print("unexpected error adding WISE data")
# write to new file
outpath = catfile.replace('.txt', '+wise.csv')
# fmt = ['%i']+['%0.8f']*2+['%.4e']*2+['%i']*2
# hdr = ' '.join(names)+' cl'
# np.savetxt(outpath, df.to_records(index=False), fmt=fmt, header=hdr)
cat.to_csv(outpath, index=False, float_format='%.8f')
print("created file: {}".format(outpath))