def check_n_in_aper(radius_factor=1, k=100):
for catfile in find_files(bcdphot_out_path, "*_combined_hdr_catalog.txt"):
print
print catfile
names = open(catfile).readline().split()[1:]
cat = np.recfromtxt(catfile, names=names)
xscfile = catfile.replace('combined_hdr_catalog.txt','2mass_xsc.tbl')
print xscfile
names = open(xscfile).read().split('\n')[76].split('|')[1:-1]
xsc = np.recfromtxt(xscfile, skip_header=80, names=names)
n_in_aper = []
coords = radec_to_coords(cat.ra, cat.dec)
kdt = KDT(coords)
for i in range(xsc.size):
r_deg = xsc.r_ext[i]/3600.
idx, ds = spherematch2(xsc.ra[i], xsc.dec[i], cat.ra, cat.dec,
kdt, tolerance=radius_factor*r_deg, k=k)
n_in_aper.append(ds.size)
for i in [(i,n_in_aper.count(i)) for i in set(n_in_aper)]:
print i
def check_n_in_aper(radius_factor=1, k=100):
for catfile in find_files(bcdphot_out_path, "*_combined_hdr_catalog.txt"):
print
print catfile
names = open(catfile).readline().split()[1:]
cat = np.recfromtxt(catfile, names=names)
xscfile = catfile.replace('combined_hdr_catalog.txt', '2mass_xsc.tbl')
print xscfile
names = open(xscfile).read().split('\n')[76].split('|')[1:-1]
xsc = np.recfromtxt(xscfile, skip_header=80, names=names)
n_in_aper = []
coords = radec_to_coords(cat.ra, cat.dec)
kdt = KDT(coords)
for i in range(xsc.size):
r_deg = xsc.r_ext[i] / 3600.
idx, ds = spherematch2(xsc.ra[i],
xsc.dec[i],
cat.ra,
cat.dec,
kdt,
tolerance=radius_factor * r_deg,
k=k)
n_in_aper.append(ds.size)
for i in [(i, n_in_aper.count(i)) for i in set(n_in_aper)]:
print i
def get_k_closest_bcd_idx(ra, dec, tree, k=10):
"""
Returns the indices of the k BCDs with central coordinates closest
to the input RA/Dec coordinate pair.
"""
coords = radec_to_coords(ra, dec)
idx = tree.query(coords, k=k)[1].ravel()
return idx
def get_k_closest_bcd_idx(ra, dec, tree, k=10):
"""
Returns the indices of the k BCDs with central coordinates closest
to the input RA/Dec coordinate pair.
"""
coords = radec_to_coords(ra, dec)
idx = tree.query(coords, k=k)[1].ravel()
return idx
def get_k_closest_idx(ra, dec, tree, k=10):
"""
Converts RA/Dec to a cartesian coordinate array, then
queries the input k-d tree to return nearest neighbors.
Defaults to k=10 nearest neighbors.
"""
coords = radec_to_coords(ra, dec)
idx = tree.query(coords, k=k)[1].ravel()
return idx
def spherematch2(ra1, dec1, ra2, dec2, kdt, tolerance=1/3600., k=100): """ Uses a k-d tree to efficiently match two pairs of coordinates in spherical geometry, with a tolerance in degrees. """ coords1 = radec_to_coords(ra1, dec1) idx = kdt.query(coords1, k=k)[1].flatten() ds = great_circle_distance(ra1, dec1, ra2[idx], dec2[idx]) msk = ds < tolerance idx = idx[msk] ds = ds[msk] return idx, ds
def spherematch2(ra1, dec1, ra2, dec2, kdt, tolerance=1 / 3600., k=100):
"""
Uses a k-d tree to efficiently match two pairs of coordinates in spherical
geometry, with a tolerance in degrees.
"""
coords1 = radec_to_coords(ra1, dec1)
idx = kdt.query(coords1, k=k)[1].flatten()
ds = great_circle_distance(ra1, dec1, ra2[idx], dec2[idx])
msk = ds < tolerance
idx = idx[msk]
ds = ds[msk]
return idx, ds
def get_bcd_list(metadata):
"""
Metadata is a dict with keys:
name, radecfile, data_dir, out_dir, work_dir, aors, channel,
bcd_dict_path, max_cov
"""
radecfile = metadata["radecfile"]
work_dir = metadata["work_dir"]
aors = metadata["aors"]
max_cov = metadata["max_cov"]
# split the RA/Dec into two arrays
radec = np.genfromtxt(radecfile)
ra = radec[:, 0]
dec = radec[:, 1]
# read the region/ch/hdr specific bcd_dict in the work_dir for efficiency
bcd_dict = json.load(open(metadata["bcd_dict_path"]))
filenames, filepaths = [np.array(i) for i in unzip(bcd_dict.items())]
# extract center pixel coordinates
files_ra = np.zeros(filepaths.size)
files_dec = np.zeros(filepaths.size)
for i, fp in enumerate(filepaths):
hdr = pyfits.getheader(fp)
files_ra[i] = hdr["CRVAL1"]
files_dec[i] = hdr["CRVAL2"]
# make array of coordinates and grow the tree
kdt = KDT(radec_to_coords(files_ra, files_dec))
# spawn processes using multiprocessing to check for images containing,
# the source, using the tree to find only the closest BCDs to check
ncpus = multiprocessing.cpu_count()
pool = multiprocessing.Pool(processes=ncpus)
# print "using %i CPUs" % ncpus
max_num_images = 0
sources = []
for i in range(len(ra)):
# create internal source ID and associate with each RA/Dec pair
d = {"id": i, "ra": ra[i], "dec": dec[i]}
message = "finding files associated with source {} at ({}, {})"
print(message.format(i, ra[i], dec[i]))
# get the subset of BCDs to search
idx = get_k_closest_bcd_idx(ra[i], dec[i], kdt, k=max_cov)
n_files = filepaths[idx].size
filepaths_subset = filepaths[idx]
filenames_subset = filenames[idx]
argslist = zip([ra[i]] * n_files, [dec[i]] * n_files, filepaths_subset)
# send jobs to the pool
results = pool.map(source_in_image, argslist)
# unzip the results and extract the boolean array and pixel coordinates
results_unzipped = unzip(results)
bool_arr = np.array(results_unzipped[0])
# if none found, continue to next source
if np.sum(bool_arr) == 0:
continue
x = results_unzipped[1]
y = results_unzipped[2]
pix_coord = np.array(zip(x, y))[bool_arr].tolist()
# get the names of the files associated with the source
good_bcds = filenames_subset[bool_arr].tolist()
# compare the number of associated images to the previous maximum
num_images = len(good_bcds)
print("\t{} images".format(num_images))
if num_images > max_num_images:
max_num_images = num_images
# store results in source dict and append to source list
d["files"] = good_bcds
d["pixels"] = pix_coord
sources.append(d)
outfile = "bcd_list.json"
outfilepath = "/".join([work_dir, outfile])
with open(outfilepath, "w") as w:
json.dump(sources, w, indent=4 * " ")
print("created file: {}".format(outfilepath))
message = "maximum number of images associated with a source: {}"
print(message.format(max_num_images))
def get_bcd_list(metadata):
"""
Metadata is a dict with keys:
name, radecfile, data_dir, out_dir, work_dir, aors, channel,
bcd_dict_path, max_cov
"""
radecfile = metadata['radecfile']
work_dir = metadata['work_dir']
aors = metadata['aors']
max_cov = metadata['max_cov']
# split the RA/Dec into two arrays
radec = np.genfromtxt(radecfile)
ra = radec[:, 0]
dec = radec[:, 1]
# read the region/ch/hdr specific bcd_dict in the work_dir for efficiency
bcd_dict = json.load(open(metadata['bcd_dict_path']))
filenames, filepaths = [np.array(i) for i in unzip(bcd_dict.items())]
# extract center pixel coordinates
files_ra = np.zeros(filepaths.size)
files_dec = np.zeros(filepaths.size)
for i, fp in enumerate(filepaths):
hdr = pyfits.getheader(fp)
files_ra[i] = hdr['CRVAL1']
files_dec[i] = hdr['CRVAL2']
# make array of coordinates and grow the tree
kdt = KDT(radec_to_coords(files_ra, files_dec))
# spawn processes using multiprocessing to check for images containing,
# the source, using the tree to find only the closest BCDs to check
ncpus = multiprocessing.cpu_count()
pool = multiprocessing.Pool(processes=ncpus)
# print "using %i CPUs" % ncpus
max_num_images = 0
sources = []
for i in range(len(ra)):
# create internal source ID and associate with each RA/Dec pair
d = {'id': i, 'ra': ra[i], 'dec': dec[i]}
message = 'finding files associated with source {} at ({}, {})'
print(message.format(i, ra[i], dec[i]))
# get the subset of BCDs to search
idx = get_k_closest_bcd_idx(ra[i], dec[i], kdt, k=max_cov)
n_files = filepaths[idx].size
filepaths_subset = filepaths[idx]
filenames_subset = filenames[idx]
argslist = zip([ra[i]] * n_files, [dec[i]] * n_files, filepaths_subset)
# send jobs to the pool
results = pool.map(source_in_image, argslist)
# unzip the results and extract the boolean array and pixel coordinates
results_unzipped = unzip(results)
bool_arr = np.array(results_unzipped[0])
# if none found, continue to next source
if np.sum(bool_arr) == 0:
continue
x = results_unzipped[1]
y = results_unzipped[2]
pix_coord = np.array(zip(x, y))[bool_arr].tolist()
# get the names of the files associated with the source
good_bcds = filenames_subset[bool_arr].tolist()
# compare the number of associated images to the previous maximum
num_images = len(good_bcds)
print('\t{} images'.format(num_images))
if num_images > max_num_images:
max_num_images = num_images
# store results in source dict and append to source list
d['files'] = good_bcds
d['pixels'] = pix_coord
sources.append(d)
outfile = 'bcd_list.json'
outfilepath = '/'.join([work_dir, outfile])
with open(outfilepath, 'w') as w:
json.dump(sources, w, indent=4 * ' ')
print('created file: {}'.format(outfilepath))
message = 'maximum number of images associated with a source: {}'
print(message.format(max_num_images))
def run_xsc_phot(bcdphot_out_path, mosaic_path):
replaced = {}
for cat in find_files(bcdphot_out_path, "*_combined_hdr_catalog.txt"):
print("\n======================================================")
print("\nadjusting photometry in: {}".format(cat.split('/')[-1]))
print("------------------------------------------------------")
outpath = cat.replace('combined_hdr_catalog.txt','2mass_xsc.tbl')
# retrieve 2mass data if file doesn't already exist (from previous run)
if not os.path.isfile(outpath):
# get url and retrieve data
url = query_2mass_xsc_polygon(*get_region_corners(cat))
print("\ndownloading 2MASS photometry from: {}".format(url))
text = urllib2.urlopen(url).read()
# write to disk
with open(outpath, 'w') as f:
f.write(text)
print("\ncreated file: {}".format(outpath))
# read back in as recarray
print("\nreading: {}".format(outpath))
names = open(outpath).read().split('\n')[76].split('|')[1:-1]
da = np.recfromtxt(outpath, skip_header=80, names=names)
# write input file for xsc_phot.pro
infile_outpath = '/'.join(cat.split('/')[:-1])+'/xsc.txt'
with open(infile_outpath,'w') as w:
for i in range(da.shape[0]):
w.write("{} {} {} {}\n".format(da.designation[i], da.ra[i], da.dec[i], da.r_ext[i]))
print("\ncreated input file for xsc_phot.pro: {}".format(infile_outpath))
# locate the FITS mosaic file for xsc_phot.pro to do photometry on
reg, ch = cat.split('/')[-1].split('_')[:2]
mosaicfile = filter(lambda x: 'dirbe{}/ch{}/long/full/Combine'\
.format(reg,ch) in x, find_files(mosaic_path, '*mosaic.fits'))[0]
print("\nfound mosaic file: {}".format(mosaicfile))
# spawn IDL subprocess running xsc_phot.pro and catch stdout in file
outpath = infile_outpath.replace('xsc.txt', 'xsc_phot_out.txt')
if not os.path.isfile(outpath):
outfile = open(outpath,'w')
print("\nspawning xsc_phot.pro IDL subprocess")
cmd = "xsc_phot,'"+mosaicfile+"','"+infile_outpath+"','long'"
rc = subprocess.call(['/usr/local/itt/idl71/bin/idl','-quiet','-e',cmd],
stderr = subprocess.PIPE, stdout = outfile)
outfile.close()
# read in output to recarray
print("\nreading: {}".format(outpath))
phot = np.recfromtxt(outpath, names=['id','flux','unc','sky','skyunc'])
# make sure rows are aligned
assert (da.designation == phot.id).all()
# ignore xsc sources we got a NaN or negative flux for
bad = np.isnan(phot.flux) | (phot.flux < 0)
print("\naper.pro returned NaN or negative flux for {} sources".format(bad.sum()))
if bad.sum() > 0:
for i in phot[bad].id:
print(i)
outpath = cat.replace('combined_hdr_catalog.txt','xsc_nan_phot.csv')
with open(outpath,'w') as f:
w = csv.writer(f)
w.writerow(da.dtype.names)
w.writerows(da[bad].tolist())
print('\ncreated file: {}'.format(outpath))
phot = phot[~bad]
da = da[~bad]
# read in pipeline catalog
print("\nreading: {}".format(cat))
names = open(cat).readline().split()[1:]
c = np.recfromtxt(cat, names=names)
# loop through xsc sources and find matches in pipeline catalog
print("\nfinding records associated with XSC sources in pipeline catalog")
c_flux_total = []
n_in_aper = []
c_idx = []
coords = radec_to_coords(c.ra, c.dec)
kdt = KDT(coords)
for i in range(phot.size):
radius = da.r_ext[i]/3600.
# idx1, idx2, ds = spherematch(da.ra[i], da.dec[i],
# c.ra, c.dec, tolerance=radius)
idx, ds = spherematch2(da.ra[i], da.dec[i], c.ra, c.dec,
kdt, tolerance=radius, k=500)
# c_flux_total.append(c.flux[idx2].sum())
# n_in_aper.append(c.flux[idx2].size)
# c_idx.append(idx2.tolist())
c_flux_total.append(c.flux[idx].sum())
n_in_aper.append(ds.size)
c_idx.append(idx.tolist())
print("\nhistogram of source counts in r_ext aperture")
for i in [(i,n_in_aper.count(i)) for i in set(n_in_aper)]:
print i
# create new version of catalog file with xsc-associated entries replaced
c_idx = np.array(flatten(c_idx))
print("\nremoving {}, adding {}".format(c_idx.size, phot.size))
replaced[cat] = {'old':c_idx.size, 'new':phot.size}
replaced[cat]['hist'] = [(i,n_in_aper.count(i)) for i in set(n_in_aper)]
c = np.delete(c, c_idx)
newrows = np.rec.array([(-i, da.ra[i], da.dec[i],
phot.flux[i], phot.unc[i], 1) for i in \
range(phot.size)], dtype=c.dtype)
newcat = np.hstack((c, newrows))
# write new version of catalog to disk
fmt = ['%i']+['%0.8f']*2+['%.4e']*2+['%i']
outpath = cat.replace('catalog.txt', 'catalog_xsc_cor.txt')
np.savetxt(outpath, newcat, fmt = fmt, header = ' '.join(names))
print('\ncreated file: {}'.format(outpath))
# make plot of total old vs. new flux
plt.scatter(c_flux_total, phot.flux)
ylim = plt.gca().get_ylim()
plt.xlim(*ylim)
max_y = ylim[1]
plt.plot(ylim, ylim, 'r-')
plt.xlabel('old flux [mJy]')
plt.ylabel('new flux [mJy]')
name = ' '.join(cat.split('/')[-1].split('_')[:2])
plt.title(name)
outpath = cat.replace('combined_hdr_catalog.txt','xsc_new_vs_old_phot.png')
plt.savefig(outpath, dpi=200)
plt.close()
print('\ncreated file: {}'.format(outpath))
outfile = 'xsc_replaced.json'
json.dump(replaced, open(outfile,'w'))
print("\ncreated file: {}".format(outfile))
print("\nremoved / added")
for k,v in replaced.iteritems():
print k.split('/')[-1], v['old'], v['new']
m = np.mean([i['old']/float(i['new']) for i in replaced.values()])
print("average ratio: {}".format(m))
print("\nK mag and r_ext of sources with NaN photometry:")
for i in find_files(bcdphot_out_path, "*xsc_nan_phot.csv"):
reg = i.split('/')[-1]
rec = np.recfromcsv(i)
bad_id = rec.designation.tolist()
bad_k = rec.k_m_k20fe.tolist()
bad_r_ext = rec.r_ext.tolist()
print reg
print ("\tid\t\t\tKmag\tr_ext")
if type(bad_id) is list:
seq = sorted(zip(bad_id, bad_k, bad_r_ext), key=lambda x: x[0])
for j,k,l in seq:
print("\t{}\t{}\t{}".format(j,k,l))
else:
print("\t{}\t{}\t{}".format(bad_id, bad_k, bad_r_ext))
def run_xsc_phot(bcdphot_out_path, mosaic_path):
replaced = {}
for cat in find_files(bcdphot_out_path, "*_combined_hdr_catalog.txt"):
print("\n======================================================")
print("\nadjusting photometry in: {}".format(cat.split('/')[-1]))
print("------------------------------------------------------")
outpath = cat.replace('combined_hdr_catalog.txt', '2mass_xsc.tbl')
# retrieve 2mass data if file doesn't already exist (from previous run)
if not os.path.isfile(outpath):
# get url and retrieve data
url = query_2mass_xsc_polygon(*get_region_corners(cat))
print("\ndownloading 2MASS photometry from: {}".format(url))
text = urllib2.urlopen(url).read()
# write to disk
with open(outpath, 'w') as f:
f.write(text)
print("\ncreated file: {}".format(outpath))
# read back in as recarray
print("\nreading: {}".format(outpath))
names = open(outpath).read().split('\n')[76].split('|')[1:-1]
da = np.recfromtxt(outpath, skip_header=80, names=names)
# write input file for xsc_phot.pro
infile_outpath = '/'.join(cat.split('/')[:-1]) + '/xsc.txt'
with open(infile_outpath, 'w') as w:
for i in range(da.shape[0]):
w.write("{} {} {} {}\n".format(da.designation[i], da.ra[i],
da.dec[i], da.r_ext[i]))
print(
"\ncreated input file for xsc_phot.pro: {}".format(infile_outpath))
# locate the FITS mosaic file for xsc_phot.pro to do photometry on
reg, ch = cat.split('/')[-1].split('_')[:2]
mosaicfile = filter(lambda x: 'dirbe{}/ch{}/long/full/Combine'\
.format(reg,ch) in x, find_files(mosaic_path, '*mosaic.fits'))[0]
print("\nfound mosaic file: {}".format(mosaicfile))
# spawn IDL subprocess running xsc_phot.pro and catch stdout in file
outpath = infile_outpath.replace('xsc.txt', 'xsc_phot_out.txt')
if not os.path.isfile(outpath):
outfile = open(outpath, 'w')
print("\nspawning xsc_phot.pro IDL subprocess")
cmd = "xsc_phot,'" + mosaicfile + "','" + infile_outpath + "','long'"
rc = subprocess.call(
['/usr/local/itt/idl71/bin/idl', '-quiet', '-e', cmd],
stderr=subprocess.PIPE,
stdout=outfile)
outfile.close()
# read in output to recarray
print("\nreading: {}".format(outpath))
phot = np.recfromtxt(outpath,
names=['id', 'flux', 'unc', 'sky', 'skyunc'])
# make sure rows are aligned
assert (da.designation == phot.id).all()
# ignore xsc sources we got a NaN or negative flux for
bad = np.isnan(phot.flux) | (phot.flux < 0)
print("\naper.pro returned NaN or negative flux for {} sources".format(
bad.sum()))
if bad.sum() > 0:
for i in phot[bad].id:
print(i)
outpath = cat.replace('combined_hdr_catalog.txt',
'xsc_nan_phot.csv')
with open(outpath, 'w') as f:
w = csv.writer(f)
w.writerow(da.dtype.names)
w.writerows(da[bad].tolist())
print('\ncreated file: {}'.format(outpath))
phot = phot[~bad]
da = da[~bad]
# read in pipeline catalog
print("\nreading: {}".format(cat))
names = open(cat).readline().split()[1:]
c = np.recfromtxt(cat, names=names)
# loop through xsc sources and find matches in pipeline catalog
print(
"\nfinding records associated with XSC sources in pipeline catalog"
)
c_flux_total = []
n_in_aper = []
c_idx = []
coords = radec_to_coords(c.ra, c.dec)
kdt = KDT(coords)
for i in range(phot.size):
radius = da.r_ext[i] / 3600.
# idx1, idx2, ds = spherematch(da.ra[i], da.dec[i],
# c.ra, c.dec, tolerance=radius)
idx, ds = spherematch2(da.ra[i],
da.dec[i],
c.ra,
c.dec,
kdt,
tolerance=radius,
k=500)
# c_flux_total.append(c.flux[idx2].sum())
# n_in_aper.append(c.flux[idx2].size)
# c_idx.append(idx2.tolist())
c_flux_total.append(c.flux[idx].sum())
n_in_aper.append(ds.size)
c_idx.append(idx.tolist())
print("\nhistogram of source counts in r_ext aperture")
for i in [(i, n_in_aper.count(i)) for i in set(n_in_aper)]:
print i
# create new version of catalog file with xsc-associated entries replaced
c_idx = np.array(flatten(c_idx))
print("\nremoving {}, adding {}".format(c_idx.size, phot.size))
replaced[cat] = {'old': c_idx.size, 'new': phot.size}
replaced[cat]['hist'] = [(i, n_in_aper.count(i))
for i in set(n_in_aper)]
c = np.delete(c, c_idx)
newrows = np.rec.array([(-i, da.ra[i], da.dec[i],
phot.flux[i], phot.unc[i], 1) for i in \
range(phot.size)], dtype=c.dtype)
newcat = np.hstack((c, newrows))
# write new version of catalog to disk
fmt = ['%i'] + ['%0.8f'] * 2 + ['%.4e'] * 2 + ['%i']
outpath = cat.replace('catalog.txt', 'catalog_xsc_cor.txt')
np.savetxt(outpath, newcat, fmt=fmt, header=' '.join(names))
print('\ncreated file: {}'.format(outpath))
# make plot of total old vs. new flux
plt.scatter(c_flux_total, phot.flux)
ylim = plt.gca().get_ylim()
plt.xlim(*ylim)
max_y = ylim[1]
plt.plot(ylim, ylim, 'r-')
plt.xlabel('old flux [mJy]')
plt.ylabel('new flux [mJy]')
name = ' '.join(cat.split('/')[-1].split('_')[:2])
plt.title(name)
outpath = cat.replace('combined_hdr_catalog.txt',
'xsc_new_vs_old_phot.png')
plt.savefig(outpath, dpi=200)
plt.close()
print('\ncreated file: {}'.format(outpath))
outfile = 'xsc_replaced.json'
json.dump(replaced, open(outfile, 'w'))
print("\ncreated file: {}".format(outfile))
print("\nremoved / added")
for k, v in replaced.iteritems():
print k.split('/')[-1], v['old'], v['new']
m = np.mean([i['old'] / float(i['new']) for i in replaced.values()])
print("average ratio: {}".format(m))
print("\nK mag and r_ext of sources with NaN photometry:")
for i in find_files(bcdphot_out_path, "*xsc_nan_phot.csv"):
reg = i.split('/')[-1]
rec = np.recfromcsv(i)
bad_id = rec.designation.tolist()
bad_k = rec.k_m_k20fe.tolist()
bad_r_ext = rec.r_ext.tolist()
print reg
print("\tid\t\t\tKmag\tr_ext")
if type(bad_id) is list:
seq = sorted(zip(bad_id, bad_k, bad_r_ext), key=lambda x: x[0])
for j, k, l in seq:
print("\t{}\t{}\t{}".format(j, k, l))
else:
print("\t{}\t{}\t{}".format(bad_id, bad_k, bad_r_ext))