def gim2d_catalog(cat, band):
'''
http://irsa.ipac.caltech.edu/data/COSMOS/tables/morphology/cosmos_morph_zurich_colDescriptions.html
'''
'''
ACS_MU_CLASS float Type of object.
1 = galaxy
2 = star
3 = spurious
'''
'''
ACS_CLEAN float Object useable flag.
0 = do not use this object
1 = use this object
'''
'''
FLUX_GIM2D float counts GIM2D total flux
R_GIM2D float arcseconds GIM2D psf-convolved half-light radius of object
ELL_GIM2D float GIM2D ellipticity = 1-b/a of object
PA_GIM2D float degrees GIM2D position angle of object - cw from +y-axis
DX_GIM2D float arcseconds x-offset of GIM2D-model center from ACS-coordinate center
DY_GIM2D float arcseconds y-offset of GIM2D-model center from ACS-coordinate center
SERSIC_N_GIM2D float GIM2D Sersic index
R_0P5_GIM2D float arcseconds GIM2D half-light radius of object without PSF convolution
TYPE float ZEST Type CLASS
1 = Early type
2 = Disk
3 = Irregular Galaxy
9 = no classification
'''
'''
BULG float ZEST "Bulgeness" CLASS - only for Type 2 (disk) galaxies.
0 = bulge dominated galaxy
1,2 = intermediate-bulge galaxies
3 = pure disk galaxy
9 = no classification
'''
'''
STELLARITY float Visual Stellarity flag.
0 if ACS_CLASS_STAR<0.6 (object is ASSUMED to be a galaxy; no visual inspection)
0 if ACS_CLASS_STAR>=0.6 AND object visually identified as a galaxy.
1 if ACS_CLASS_STAR>=0.6 AND visually identified as a star.
2 if ACS_CLASS_STAR>=0.8 (object is assumed to be a star and was not visually inspected)
3 if ACS_CLASS_STAR<0.6 but object is visually identified as a star (e.g. saturated star, etc)
JUNKFLAG float
0 = good object
1 = spurious
'''
print('Classifications:', Counter(cat.type).most_common())
cat.is_galaxy = (cat.stellarity == 0)
srcs = []
for t in cat:
pos = RaDecPos(t.ra, t.dec)
bright = NanoMaggies(
**{band: NanoMaggies.magToNanomaggies(t.acs_mag_auto)})
shape = GalaxyShape(t.r_0p5_gim2d, 1. - t.ell_gim2d, 90. + t.pa_gim2d)
is_galaxy = (t.is_galaxy * (shape.re >= 0) * (shape.ab <= 1.) *
(shape.phi > -999))
if is_galaxy and t.type == 1:
# deV
src = DevGalaxy(pos, bright, shape)
elif is_galaxy and t.type == 2:
# exp
src = ExpGalaxy(pos, bright, shape)
else:
src = PointSource(pos, bright)
srcs.append(src)
return srcs
def gim2d_catalog(cat, band):
'''
http://irsa.ipac.caltech.edu/data/COSMOS/tables/morphology/cosmos_morph_zurich_colDescriptions.html
'''
'''
ACS_MU_CLASS float Type of object.
1 = galaxy
2 = star
3 = spurious
'''
'''
ACS_CLEAN float Object useable flag.
0 = do not use this object
1 = use this object
'''
'''
FLUX_GIM2D float counts GIM2D total flux
R_GIM2D float arcseconds GIM2D psf-convolved half-light radius of object
ELL_GIM2D float GIM2D ellipticity = 1-b/a of object
PA_GIM2D float degrees GIM2D position angle of object - cw from +y-axis
DX_GIM2D float arcseconds x-offset of GIM2D-model center from ACS-coordinate center
DY_GIM2D float arcseconds y-offset of GIM2D-model center from ACS-coordinate center
SERSIC_N_GIM2D float GIM2D Sersic index
R_0P5_GIM2D float arcseconds GIM2D half-light radius of object without PSF convolution
TYPE float ZEST Type CLASS
1 = Early type
2 = Disk
3 = Irregular Galaxy
9 = no classification
'''
'''
BULG float ZEST "Bulgeness" CLASS - only for Type 2 (disk) galaxies.
0 = bulge dominated galaxy
1,2 = intermediate-bulge galaxies
3 = pure disk galaxy
9 = no classification
'''
'''
STELLARITY float Visual Stellarity flag.
0 if ACS_CLASS_STAR<0.6 (object is ASSUMED to be a galaxy; no visual inspection)
0 if ACS_CLASS_STAR>=0.6 AND object visually identified as a galaxy.
1 if ACS_CLASS_STAR>=0.6 AND visually identified as a star.
2 if ACS_CLASS_STAR>=0.8 (object is assumed to be a star and was not visually inspected)
3 if ACS_CLASS_STAR<0.6 but object is visually identified as a star (e.g. saturated star, etc)
JUNKFLAG float
0 = good object
1 = spurious
'''
print('Classifications:', Counter(cat.type).most_common())
cat.is_galaxy = (cat.stellarity == 0)
srcs = []
for t in cat:
pos = RaDecPos(t.ra, t.dec)
bright = NanoMaggies(**{band:NanoMaggies.magToNanomaggies(t.acs_mag_auto)})
shape = GalaxyShape(t.r_0p5_gim2d, 1. - t.ell_gim2d, 90. + t.pa_gim2d)
is_galaxy = (t.is_galaxy * (shape.re >= 0 ) * (shape.ab <= 1.) *
(shape.phi > -999))
if is_galaxy and t.type == 1:
# deV
src = DevGalaxy(pos, bright, shape)
elif is_galaxy and t.type == 2:
# exp
src = ExpGalaxy(pos, bright, shape)
else:
src = PointSource(pos, bright)
srcs.append(src)
return srcs
def compare_mags(TT, name, ps):
for i,T in enumerate(TT):
T.set('exp', np.zeros(len(T), np.uint8)+i)
plt.clf()
ap = 5
for i,T in enumerate(TT):
cc = 'rgb'[i]
plt.plot(T.flux, T.apflux[:, ap] / T.flux,
'.', color=cc, alpha=0.5)
ff, frac = [],[]
mags = np.arange(14, 24)
for mlo,mhi in zip(mags, mags[1:]):
flo = NanoMaggies.magToNanomaggies(mhi)
fhi = NanoMaggies.magToNanomaggies(mlo)
I = np.flatnonzero((T.flux > flo) * (T.flux <= fhi))
ff.append(np.sqrt(flo * fhi))
frac.append(np.median(T.apflux[I,ap] / T.flux[I]))
plt.plot(ff, frac, 'o-', color=cc)
plt.xscale('symlog')
plt.xlim(1., 1e3)
plt.ylim(0.9, 1.1)
plt.xlabel('Forced-phot flux')
plt.ylabel('Aperture / Forced-phot flux')
plt.axhline(1, color='k', alpha=0.1)
plt.title('%s region: Aperture %i fluxes' % (name, ap))
ps.savefig()
T = merge_tables(TT)
T.bobjid = T.brickid.astype(int) * 10000 + T.objid
bomap = {}
for i,bo in enumerate(T.bobjid):
try:
bomap[bo].append(i)
except KeyError:
bomap[bo] = [i]
II = []
for bo,ii in bomap.items():
if len(ii) != 3:
continue
II.append(ii)
II = np.array(II)
print 'II', II.shape
exps = T.exp[II]
print 'exposures:', exps
assert(np.all(T.exp[II[:,0]] == 0))
assert(np.all(T.exp[II[:,1]] == 1))
assert(np.all(T.exp[II[:,2]] == 2))
fluxes = T.flux[II]
print 'fluxes', fluxes.shape
meanflux = np.mean(fluxes, axis=1)
print 'meanfluxes', meanflux.shape
plt.clf()
for i in range(3):
plt.plot(meanflux, fluxes[:,i] / meanflux, '.',
color='rgb'[i], alpha=0.5)
#plt.yscale('symlog')
plt.xscale('symlog')
plt.xlabel('Mean flux (nanomaggies)')
plt.ylabel('Forced-phot flux / Mean')
#plt.ylim(0, 2)
plt.ylim(0.9, 1.1)
plt.xlim(0, 1e3)
plt.axhline(1, color='k', alpha=0.1)
plt.title('%s region: Forced-phot fluxes' % name)
ps.savefig()
for ap in [4,5,6]:
apfluxes = T.apflux[:,ap][II,]
print 'ap fluxes', apfluxes.shape
plt.clf()
for i in range(3):
plt.plot(meanflux, apfluxes[:,i] / meanflux, '.',
color='rgb'[i], alpha=0.5)
plt.xscale('symlog')
plt.xlabel('Mean flux (nanomaggies)')
plt.ylabel('Aperture(%i) flux / Mean' % ap)
plt.ylim(0.9, 1.1)
plt.xlim(0, 1e3)
plt.axhline(1, color='k', alpha=0.1)
plt.title('%s region: Aperture %i fluxes' % (name, ap))
ps.savefig()
plt.clf()
for i in range(3):
plt.plot(fluxes[:,i], apfluxes[:,i] / fluxes[:,i], '.',
color='rgb'[i], alpha=0.5)
plt.xscale('symlog')
plt.xlim(0, 1e3)
plt.ylim(0.9, 1.1)
plt.xlabel('Forced-phot flux')
plt.ylabel('Aperture / Forced-phot flux')
plt.axhline(1, color='k', alpha=0.1)
plt.title('%s region: Aperture %i fluxes' % (name, ap))
ps.savefig()
