def isolate_real_gals(fullcat,starcat):
try:
star_matcher=htm.Matcher(16,ra=starcat['ALPHAWIN_J2000'],dec=starcat['DELTAWIN_J2000'])
except:
star_matcher=htm.Matcher(16,ra=starcat['ra'],dec=starcat['dec'])
full_ind, star_ind, dist=star_matcher.match(ra=fullcat['ALPHAWIN_J2000'],dec=fullcat['DELTAWIN_J2000'],maxmatch=1,radius=2.5E-4)
this_range=np.arange(len(fullcat))
gals=np.setdiff1d(this_range,full_ind)
gals_only = fullcat[gals]
gals_only = gals_only[(gals_only['FWHM_IMAGE']>3) & (gals_only['FLUX_RADIUS']>2.7)] # clean out the junk
return gals_only
def self_match_cat(cat, superid='SuperID', verbose=False):
id = catslist[cat['name']]['ID']
sr = cat['sr'] / 3600
if verbose:
print('Self-matching', cat['name'], 'using', id, 'field with sr',
sr * 3600, 'into new field', superid)
if not superid in cat['table'].dtype.fields:
cat['table'].add_column(
Column(dtype=cat['table'].dtype.fields.get(id)[0],
name=superid,
length=len(cat['table'][id])))
ra = cat['table'][cat.get('ra', 'RAJ2000')]
dec = cat['table'][cat.get('dec', 'DEJ2000')]
hm = htm.Matcher(10, ra, dec)
mask = np.zeros(len(cat['table']), dtype=np.bool)
for i in range(len(cat['table'])):
if mask[i]:
continue
m = hm.match(ra[i], dec[i], sr, maxmatch=0)
if len(m[1]):
cat['table'][superid][m[1]] = cat['table'][id][min(m[1])]
mask[m[1]] = True
if verbose:
print(len(np.unique(cat['table'][superid])), '/', len(cat['table']),
'records unique')
def make_annular_cat(self, hdu_ext=2, filter_g=True, clobber=True):
"""
make a catalog of positions and shapes
ready for annular.c and lensing in general
Filter_g : whether to clean failed fits or not
"""
# actually load the master coadd catalog
try:
coadd_cat = Table.read(self.coadd_catalog,hdu=hdu_ext)
except:
print("wrong coadd extension, try again?")
pdb.set_trace()
# do matching
fitvd_matcher=htm.Matcher(16,ra=self.master_fitvd['ra'],dec=self.master_fitvd['dec'])
sex_ind,fitvd_ind,dist=fitvd_matcher.match(ra=coadd_cat['ALPHAWIN_J2000'],dec=coadd_cat['DELTAWIN_J2000'],maxmatch=1,radius=5.5E-4)
for_annular=Table()
for_annular.add_columns([coadd_cat['ALPHAWIN_J2000'][sex_ind],coadd_cat[sex_ind]['DELTAWIN_J2000'],coadd_cat[sex_ind]['X_IMAGE'],
coadd_cat[sex_ind]['Y_IMAGE']])
for_annular.add_columns([self.master_fitvd[fitvd_ind]['exp_g'][:,0],self.master_fitvd[fitvd_ind]['exp_g'][:,1]],names=['g1','g2'])
self.annular_cat = for_annular
# write to file
if filter_g==True:
for_annular_clean=for_annular[for_annular['g1']>-10.]
for_annular_clean.write('fitvd-out.csv',format='ascii.csv',overwrite=clobber)
else:
for_annular.write('fitvd-out.csv',format='ascii.csv',overwrite=clobber)
def _make_table(self):
"""
- Match master metacal catalog to source extractor catalog
- Trim catalog on g_cov, T/T_psf, etc.
- Correct g1/g2_noshear for the Rinv quantity (see Huff & Mandelbaum 2017)
- Make an output table containing (corrected) ellipticities
- Convert table to a "fiat" format ascii file
"""
# This step both applies selection cuts and returns shear-calibrated
# tangential ellipticity moments
self._compute_metacal_quantities()
# Now match trimmed & shear-calibrated catalog against sextractor
gals = Table.read(self.sexcat, format='fits', hdu=2)
master_matcher = htm.Matcher(16,
ra=self.mcCat['ra'],
dec=self.mcCat['dec'])
gals_ind, master_ind, dist = master_matcher.match(
ra=gals['ALPHAWIN_J2000'],
dec=gals['DELTAWIN_J2000'],
radius=5E-4,
maxmatch=1)
match_master = self.mcCat[master_ind]
gals = gals[gals_ind]
# Write to file
newtab = Table()
newtab.add_columns(
[match_master['id'], match_master['ra'], match_master['dec']],
names=['id', 'ra', 'dec'])
newtab.add_columns([gals['X_IMAGE'], gals['Y_IMAGE']])
newtab.add_columns([match_master['g1_boot'], match_master['g2_boot']],
names=['g1_boot', 'g2_boot'])
newtab.add_columns([match_master['g1_MC'], match_master['g2_MC']],
names=['g1_MC', 'g2_MC'])
newtab.add_columns([match_master['g1_Rinv'], match_master['g2_Rinv']],
names=['g1_Rinv', 'g2_Rinv'])
try:
newtab.add_columns(
[match_master['T_gmix'], match_master['flux_gmix']],
names=['T_noshear', 'flux'])
except:
newtab.add_columns(
[match_master['T_noshear'], match_master['flux']],
names=['T_noshear', 'flux'])
newtab.write(
self.outcat, format='csv',
overwrite=True) # file gets replaced in the run_sdsscsv2fiat step
self._run_sdsscsv2fiat()
return newtab
def _make_table(self):
"""
- Match master metacal catalog to source extractor catalog
- Correct g1/g2_noshear for the Rinv quantity (see Huff & Mandelbaum 2017)
- Make an output table containing (corrected) ellipticities
- Convert table to a "fiat" format ascii file
"""
gals = Table.read(self.sexcat, format='fits',
hdu=2) #contains only analysis objects.
master_clean = self._compute_metacal_quantities()
master_matcher = htm.Matcher(16,
ra=master_clean['ra'],
dec=master_clean['dec'])
gals_ind, master_ind, dist = master_matcher.match(
ra=gals['ALPHAWIN_J2000'],
dec=gals['DELTAWIN_J2000'],
radius=5E-4,
maxmatch=1)
match_master = master_clean[master_ind]
gals = gals[gals_ind]
newtab = Table()
newtab.add_columns(
[match_master['id'], match_master['ra'], match_master['dec']],
names=['id', 'ra', 'dec'])
newtab.add_columns([gals['X_IMAGE'], gals['Y_IMAGE']])
newtab.add_columns([match_master['g1_gmix'], match_master['g2_gmix']],
names=['g1_gmix', 'g2_gmix'])
newtab.add_columns([match_master['g1_MC'], match_master['g2_MC']],
names=['g1_MC', 'g2_MC'])
newtab.add_columns([match_master['g1_Rinv'], match_master['g2_Rinv']],
names=['g1_Rinv', 'g2_Rinv'])
try:
newtab.add_columns(
[match_master['T_gmix'], match_master['flux_gmix']],
names=['T', 'flux'])
except:
newtab.add_columns([match_master['T'], match_master['flux']],
names=['T', 'flux'])
newtab.write(
self.outcat, format='csv',
overwrite=True) # file gets replaced in the run_sdsscsv2fiat step
self._run_sdsscsv2fiat()
return newtab
def get_stars(truthcats,all_cats):
"""
isolate stars from GalSim truth catalogs ("truthcats"), and match to SEXtractor
catalogs ("all_cats"), returning indicies corresponding to stars in all_cats
"""
# read in truth catalogs and isolate stars from it (they all have z<=0 by construction!)
stars=truthcats[truthcats['redshift']==0]
# Match to observed objects (airy flight cats) in RA/Dec
star_match=htm.Matcher(16,ra=stars['ra'],dec=stars['dec'])
all_ind,stars_ind, dist=star_match.match(ra=all_cats['ALPHAWIN_J2000'],dec=all_cats['DELTAWIN_J2000'],maxmatch=1,radius=1.5E-4)
master_star_cat=hstack([all_cats[all_ind],stars[stars_ind]])
return master_star_cat
def get_galaxies(truthcats,all_cats):
"""
isolate galaxies from GalSim truth catalogs ("truthcats"), and match to SEXtractor
catalogs ("all_cats"), returning indicies corresponding to galaxies in all_cats
"""
# read in truth catalogs and isolate stars from it (they all have z<=0 by construction!)
truthgals=truthcats[truthcats['redshift']>0]
# print("a total of %d gals were injected into simulations"%len(gals))
all_cats=all_cats[(all_cats['FWHM_IMAGE']>3)&(all_cats['FLUX_RADIUS']>2.7)]
# Match to observed objects (airy flight cats) in RA/Dec
gal_match=htm.Matcher(16,ra=truthgals['ra'],dec=truthgals['dec'])
all_ind,truth_ind, dist=gal_match.match(ra=all_cats['ALPHAWIN_J2000'],dec=all_cats['DELTAWIN_J2000'],maxmatch=1,radius=2.5E-4)
print("Found %d real galaxies in catalog %s" %(len(all_ind),all_cats))
master_gal_cat=hstack([all_cats[all_ind],truthgals[truth_ind]])
return master_gal_cat
fulln='/Users/jemcclea/Research/SuperBIT/superbit-ngmix/scripts/output-jitter/one_hour_obs/mock_coadd_cat_full.ldac'
full=Table.read(fulln,format='fits',hdu=2)
truthdir = '/Users/jemcclea/Research/SuperBIT/superbit-metacal/GalSim/cluster3-newpsf/round6'
truthcatn = 'truth_gaussJitter.002.dat'
#truthdir = '/Users/jemcclea/Research/SuperBIT/superbit-ngmix/scripts/output-jitter/one_hour_obs'
#truthcat = 'truth_superbit300004.dat'
truthfile=os.path.join(truthdir,truthcatn)
#truthfile = '/Users/jemcclea/Research/SuperBIT/superbit-ngmix/scripts/output-deep/truth_flight_jitter_only_oversampled_1x300.0006.dat'
truthcat = Table.read(truthfile,format='ascii')
stars=truthcat[truthcat['redshift']==0]
star_matcher = htm.Matcher(16,ra=stars['ra'],dec=stars['dec'])
matches,starmatches,dist = star_matcher.match(ra=full['ALPHAWIN_J2000'],
dec=full['DELTAWIN_J2000'],radius=2E-4,maxmatch=1)
stars = full[matches]
# Save result to file, return filename
outname = fulln.replace('.ldac','.star')
full[matches].write(outname,format='fits',overwrite=True)
# Make cute plots, if desired:
from matplotlib import colors
mcb.fit_metacal(psf_model, gal_model, max_pars, Tguess, prior=prior, ntry=ntry,metacal_pars=metacal_pars)
mcr = mcb.get_metacal_result() # this is a dict
########################################################################
##
## Do size comparisons
##
########################################################################
truth300_1=Table.read('/Users/jemcclea/Research/GalSim/examples/output-debug/truth_shear_300_1.dat',format='ascii')
gals=truth300_1[ (truth300_1['redshift']>0)]
mcal=Table.read('/Users/jemcclea/Research/SuperBIT/metacal/metacal-debug3-shear.csv',format='csv')
full_full = Table.read('/Users/jemcclea/Research/SuperBIT/superbit-ngmix/scripts/debug3/mock_empirical_debug_coadd_cat_full.ldac',format='fits',hdu=2)
gal_match=htm.Matcher(16,ra=gals['ra'],dec=gals['dec'])
all_ind,truth_ind, dist=gal_match.match(ra=mcal['ra'],dec=mcal['dec'],maxmatch=1,radius=8E-4)
plt.plot((gals[truth_ind]['mom_size'])**4,mcal[all_ind]['T_mcal'],'.',label='mcal',alpha=0.5)
plt.plot((gals[truth_ind]['mom_size'])**4,mcal[all_ind]['T_gmix'],'.',label='gmix',alpha=0.5)
#plt.plot(gals[truth_ind]['g1_nopsf'],mcal[all_ind]['g1_Rinv'],'.',label='<R11>^-1')
#plt.plot(gals[truth_ind]['g1_nopsf'],mcal[all_ind]['g1_MC'],'.',label='Projected into R')
plt.xlabel('truth hsm_sigma**4'); plt.ylabel('T size')
plt.xlim(0,20); plt.ylim(-1,10)
plt.legend()
plt.savefig('MetacalT_v_truthMomSize.png')
