def step3(udropscat, galfitcat, outputcat):
# Now merge the GALFIT results with original U-dropout catalog
# Both catalogs should be FITS tables
# Output catalog should
if os.path.exists(outputcat):
os.remove(outputcat)
c1 = Ftable(
udropscat
) # the U-dropout catalog with the same column as the GOODS-S MW catalog
c2 = Ftable(galfitcat) # the merged GALFIT catalog
colnames = c1.Columns + c2.Columns[1:] # bypass c2.objectid (redundant)
formats = c1.d.formats + c2.d.formats[1:]
# Find matching indices
#i1 = N.arange(len(c1.d),'int') # indices in c1
i2 = N.ones(len(c1.d), 'int') * (
-1) # to store indices in c2 corresponding to i1
# e.g., c2.objectid[i2[0]] = c1.id_1[0]
for i1 in range(len(c1.d)):
j = N.arange(len(c2.d))[c2.objectid == c1.id_1[i1]]
if len(j) > 0:
i2[i1] = j[0]
# Now initialize the arrays
arrays = {}
# First collect all columns from c1
for col in c1.Columns:
arrays[col] = getattr(c1, col)
for i in range(len(c2.Columns)):
if c2.Columns[i] == 'objectid':
pass
elif c2.d.formats[i] == 'D':
arrays[c2.Columns[i]] = N.ones(len(c1.d), 'float') * (-99.0)
else:
arrays[c2.Columns[i]] = N.ones(len(c1.d), 'int') * (-1)
print arrays.keys()
# Now copy the arrays in c2
for i1 in range(len(c1.d)):
if i2[i1] >= 0:
for col in c2.Columns[1:]:
arrays[col][i1] = getattr(c2, col)[i2[i1]]
# Now convert arrays into a list
arrays2 = []
allcolumns = []
for col in colnames:
arrays2 += [arrays[col]]
# Now construct pyfits.ColDefs
allcolumns = []
for i in range(len(colnames)):
allcolumns += [
pyfits.Column(name=colnames[i],
format=formats[i],
array=arrays2[i])
]
allcols = pyfits.ColDefs(allcolumns)
tbhdu = pyfits.new_table(allcols)
tbhdu.writeto(outputcat)
def plot_RLdist_udrops(cat=catdir+'/udrops/udrops_goodss_ubvy_130517_vflags_galfit.fits',
markers=['^','o'], ax=None, ms=8**2,
colors=[pms.Bright_Blue,pms.Bright_Red]):
c = Ftable(cat)
mag_array1 = c.f606w_magout_gf[(c.udf_fit==True)&(c.f606w_magout_gf<=27.5)]
re_array1 = c.f606w_reout_gf[(c.udf_fit==True)&(c.f606w_magout_gf<=27.5)]
n_array1 = c.f606w_nout_gf[(c.udf_fit==True)&(c.f606w_magout_gf<=27.5)]
mag_array2 = c.f606w_magout_gf[(c.deep_fit==True)&(c.f606w_magout_gf<=26.5)]
re_array2 = c.f606w_reout_gf[(c.deep_fit==True)&(c.f606w_magout_gf<=26.5)]
n_array2 = c.f606w_nout_gf[(c.deep_fit==True)&(c.f606w_magout_gf<=26.5)]
mag_array3 = c.f606w_magout_gf[(c.ers_fit==True)&(c.f606w_magout_gf<=26.5)]
re_array3 = c.f606w_reout_gf[(c.ers_fit==True)&(c.f606w_magout_gf<=26.5)]
n_array3 = c.f606w_nout_gf[(c.ers_fit==True)&(c.f606w_magout_gf<=26.5)]
mag_array4 = c.f606w_magout_gf[(c.wide_fit==True)&(c.f606w_magout_gf<=26.5)]
re_array4 = c.f606w_reout_gf[(c.wide_fit==True)&(c.f606w_magout_gf<=26.5)]
n_array4 = c.f606w_nout_gf[(c.wide_fit==True)&(c.f606w_magout_gf<=26.5)]
mag_array = np.concatenate([mag_array1,mag_array2,mag_array3,mag_array4])
re_array = np.concatenate([re_array1,re_array2,re_array3,re_array4])
n_array = np.concatenate([n_array1,n_array2,n_array3,n_array4])
plot_RLdist(mag_array, re_array, n_array, 'U-dropouts', colors=colors,
markers=markers, ax=ax, ms=ms, gfband='F606W')
def use_catalog(self, catalog, objid='id', ra='ra', dec='dec', format='fits'):
if format.lower() == 'fits':
self.c = Ftable(catalog)
self.Nc = len(self.c.d)
else:
self.c = sextractor(catalog)
self.Nc = len(self.c)
self.objid = getattr(self.c, objid)
self.ra = getattr(self.c, ra)
self.dec = getattr(self.c, dec)
def plot_RLdist_vdrops(logR0,
beta,
cat1=catdir + '/vdrops/vdrops_gf_v3.fits',
cat2=catdir + '/vdrops/vdrops_udf_gf_v3.fits',
marker='o',
ax=None,
ms=8**2,
color=pms.Bright_Blue,
bootstrap=False,
nsamp=10000):
c1 = Ftable(cat1)
c2 = Ftable(cat2)
mcz = bl.mconvert('kcorr/M1500_to_f850lp_omega_m_0.3.txt')
mean_kcorr = mcz(5.0)
gfcrit1 = (c1.f850lp_gfflag == True) & (c1.magout <= 26.5)
gfcrit2 = (c2.f850lp_gfflag == True) & (c2.magout <= 28.5)
mag_array1 = c1.magout[gfcrit1]
re_array1 = c1.reout[gfcrit1]
n_array1 = c1.nout[gfcrit1]
mag_array2 = c2.magout[gfcrit2]
re_array2 = c2.reout[gfcrit2]
n_array2 = c2.nout[gfcrit2]
mag_array = np.concatenate([mag_array1, mag_array2])
re_array = np.concatenate([re_array1, re_array2])
n_array = np.concatenate([n_array1, n_array2])
plot_RLdist(mag_array,
re_array,
'V-dropouts',
logR0,
beta,
mean_kcorr,
color=color,
marker=marker,
ax=ax,
ms=ms,
gfband='F850LP')
if bootstrap == True:
p_array = bootstrap_error(mag_array, np.log10(re_array), nsamp=nsamp)
return p_array
else:
return 0
def __init__(self, catalog):
# matplotlib keyword arguments
self.c = Ftable(catalog)
# Dropout catalog as a FITS table
self.histkw = {'histtype': 'step', 'lw': 1.5}
self.errorbarkw = {
'ms': 6,
'capsize': 6,
'mec': 'blue',
'ecolor': 'blue'
}
def plot_RLdist_vdrops(cat1=catdir+'/vdrops/vdrops_gf_v3.fits',
cat2=catdir+'/vdrops/vdrops_udf_gf_v3.fits',
markers=['^','o'], ax=None, ms=8**2,
colors=[pms.Bright_Blue,pms.Bright_Red]):
c1 = Ftable(cat1)
c2 = Ftable(cat2)
gfcrit1 = (c1.f850lp_gfflag==True)&(c1.magout<=26.5)
gfcrit2 = (c2.f850lp_gfflag==True)&(c2.magout<=28.5)
mag_array1 = c1.magout[gfcrit1]
re_array1 = c1.reout[gfcrit1]
n_array1 = c1.nout[gfcrit1]
mag_array2 = c2.magout[gfcrit2]
re_array2 = c2.reout[gfcrit2]
n_array2 = c2.nout[gfcrit2]
mag_array = np.concatenate([mag_array1, mag_array2])
re_array = np.concatenate([re_array1, re_array2])
n_array = np.concatenate([n_array1, n_array2])
plot_RLdist(mag_array, re_array, n_array, 'V-dropouts', colors=colors,
markers=markers, ax=ax, ms=ms, gfband='F850LP')
def mergeoutput_gfsim(root,bands,output_root='merged_gfsim',
exclude_columns=['filename']):
"""
Make one merged catalog for each band.
exclude_columns: a kludge to skip certain columns that do not exist in all
runs. This should not have happened in the first place...
"""
timestr = time.strftime('%y%m%d')
for b in bands:
broot = root+'_'+b # the directory of the given band
mcats = glob.glob(broot+'/gfit*_matched.fits')
# print "mcats[0]", mcats[0]
c0 = Ftable(mcats[0])
colnames = c0.Columns
# colnames = c0._colnames
for col in colnames:
if col in exclude_columns:
colnames.remove(col)
colformats = c0.d.formats
colarrays_dic = {}
# initialize colarrays
for i in range(len(colnames)):
col = colnames[i]
colarrays_dic[col] = getattr(c0,col)
# Now concatenate columns from all catalogs
for i in range(1,len(mcats)): # we already added the first one
ci = Ftable(mcats[i])
# print mcats[i]
for col in colnames:
# print len(colarrays_dic[col]), len(getattr(ci, col)), col
colarrays_dic[col] = N.concatenate([colarrays_dic[col],getattr(ci,col)])
# Now write the output catalog
outname = output_root+'_'+b+'_'+timestr+'.fits'
columns = []
for i in range(len(colnames)):
columns += [pyfits.Column(name=colnames[i],format=colformats[i],
array=colarrays_dic[colnames[i]])]
ColDefs = pyfits.ColDefs(columns)
tbhdu = pyfits.new_table(ColDefs)
tbhdu.writeto(outname)
print "%s done." % outname
def __call__(self,simcatalog,field,plot_diag=False,ktest=None,
bands=['acs_f435w','acs_f606w','acs_f775w','acs_f850lp'],
sn_lolim={'acs_f850lp':5.0},
sn_hilim={},
interpolate=False,dznew=0.1):
"""
simcatalog --- the simulation catalog as a FITS table.
"""
# Initialize i-dropout color criteria
self.lcc = lcc.colorcrit(lib=lcc.cc_library)
self.lcc = self.lcc('b','g04')
# Use Giavalisco et al. 2004 criteria
c = Ftable(simcatalog)
# Now initialize the attributes
for b in bands:
bs = filter_dic[b] # short name of filter b
flux_iso = getattr(c,'%s_flux_iso'%bs)
fluxerr_iso = getattr(c,'%s_fluxerr_iso'%bs)
mag_iso = getattr(c,'%s_mag_iso'%bs)
# calculate 1-sigma upper limits on sources with S/N < 1
mag_iso = N.where(flux_iso/fluxerr_iso>1.0,mag_iso,zeropoints[b]-2.5*N.log10(fluxerr_iso))
setattr(self,b+'_mag',mag_iso)
# MAG_ISO --> for color calculation
setattr(self,b+'_flux',getattr(c,'%s_flux_auto'%bs))
# FLUX_AUTO
setattr(self,b+'_fluxerr',getattr(c,'%s_fluxerr_auto'%bs))
# FLUXERR_AUTO
setattr(self,b+'_sn',
getattr(self,'%s_flux'%b)/getattr(self,'%s_fluxerr'%b))
# S/N calculated using FLUX_AUTO
# Now construct S/N criteria
self.sncrit = N.ones(len(c.d),'bool')
for b in sn_lolim.keys(): # enforce S/N lower limits
self.sncrit = self.sncrit & (getattr(self,b+'_sn')>=sn_lolim[b])
for b in sn_hilim.keys(): # enforce S/N upper limits (veto bands)
self.sncrit = self.sncrit & (getattr(self,b+'_sn')<sn_hilim[b])
print "Total number of objects satisfying the S/N criteria:", \
sum(self.sncrit)
print "Do selections..."
self.color1 = self.acs_f435w_mag-self.acs_f606w_mag
self.color2 = self.acs_f606w_mag-self.acs_f850lp_mag
self.lcc.select(self.color1,self.color2) # do color selection!
self.lcc.crit = self.lcc.crit & self.sncrit # enforce S/N criteria
self.dropcrit = self.lcc.crit & (c.detect==True) # just in case
self.detect = c.detect.copy()
print "Selection done."
print "Total number of objects in the catalog: %d" % len(c.d)
print "Total number selected as B-dropouts: %d" % (sum(self.dropcrit))
# Now invoke zdist.zdgrid.__call__ to calculate P(z)
zdist.zdgrid.__call__(self,c,interpolate=interpolate,dznew=dznew,
plot_diag=plot_diag,ktest=ktest)
def add_filternames(self, f):
# prepend the FITS table columns by filter name
print "Adding filter names to the columns..."
c = Ftable(self.sex_fitstable[f])
old_colnames = []
new_colnames = []
for col in c.Columns:
if col.lower() in self.fixcols:
pass
else:
old_colnames += [col]
new_colnames += ['%s_%s' % (f, col)]
fitsutil.change_column_names(self.sex_fitstable[f], old_colnames,
new_colnames)
def __init__(self,
catalog,
field,
filtername,
z0,
maglimits,
logrlimits,
good_vflags=[0, 1, 2, 3, 12],
colnames=colnames_default,
re_err_lim=0.6,
chi2nu_lim=5.0,
sexmaglimit=None):
self.c = Ftable(catalog)
self.field = field
self.z0 = z0
self.colnames = colnames
self.re_err_lim = re_err_lim
self.chi2nu_lim = chi2nu_lim
self.filtername = filtername
self.maglimits = maglimits
# if sexmaglimit != None, we will include galaxies fainter than the
# magnitude limit where GALFIT is still reliable... we include these
# faint galaxies in order to better constrain the faint-end slope. This
# is desirable when the GALFIT magnitude limit is not much fainter than
# L*, e.g., for i-dropouts. In this case, we do not use the size
# information at all for these faint galaxies.
self.sexmaglimit = sexmaglimit
self.logrlimits = logrlimits
self.gfsample = np.logical_and(self.check_goodfit(),
self.check_within())
self.gfsample = np.logical_and(self.gfsample,
self.check_vflag(good_vflags))
# self.mag, self.logr only include those well-fit by GALFIT and those
# within maglimits and logrlimits
self.mag = self.get_col('mag')[self.gfsample]
self.logr = np.log10(self.get_col('re')[self.gfsample])
if sexmaglimit == None:
self.sexsample = np.zeros(len(self.c.d), 'bool')
self.mag_auto = np.array([])
else:
self.sexsample = self.check_within(
magcol='mag_auto',
recol='r_hl',
maglimits=[maglimits[1], sexmaglimit])
self.mag_auto = self.get_col('mag_auto')[self.sexsample]
self.data = np.array([self.mag, self.logr]).T # it is an N by 2 array
def __init__(self, catalog, column_names=colnames_default, disk=0,
diskfrac=0.7, re_err_lim=re_err_lim, chi2nu_lim=5.0,
shape='gaussian', expand=[0.2, 0.1], dx=0.02, dy=0.02,
xmax=2.56, ymax=2.56, xunit='dmag', yunit='dlogR',
xname=r'$\Delta$ mag', yname=r'$\Delta\log R$', Ntot=None):
# More of a template; don't expect to call this constructor directly
# dx, dy: pixel size of the kernels (in kernel units)
# xmax, ymax: extent of kernel
assert catalog.endswith('.fits'), "Please provide a FITS table as the catalog."
self.c = Ftable(catalog)
self.catalog = catalog
self.column_names = column_names
self.re_err_lim = re_err_lim
self.chi2nu_lim = chi2nu_lim
self.shape = shape
assert shape in KPDF_func.keys(), "Kernel shape should be either 'gaussian' or 'epanechnikov'."
self.expand = expand
print "self.expand = ", self.expand
assert disk in [0, 1], "disk should be either 0 or 1."
self.disk = disk
if disk == 0:
self.devauc = 1
else:
self.devauc = 0
self.diskfrac = diskfrac
# self.Ntot will be the total number of galaxies used from the simulation
# catalog, after drawing a specified fraction of disks
if Ntot==None:
self.Ntot = len(self.c.d)
self.outindex = np.arange(len(self.c.d))
else:
self.Ntot = Ntot
self.outindex = self.draw_diskfrac()
print "self.outindex = ", self.outindex
# Below are some basic kernel properties
self.dx = dx # pixel size for kernels, NOT bin width
self.dy = dy
self.xlimits = [-xmax/2., xmax/2.] # limtis of each kernel, centered around zero
self.ylimits = [-ymax/2., ymax/2.]
self.xunit = xunit
self.yunit = yunit
self.xname = xname # include LaTex syntax for use in plots
self.yname = yname
self._check_goodfit = False
self.goodfit = self.check_goodfit()
def __init__(self, catalog):
self.c = Ftable(catalog)
self.gfmeasure = (self.c.recovered_galfit == 1) & (
self.c.re_out_err / self.c.re_out <=
0.6) & (self.c.mag_auto < 99.0) & (self.c.magerr_auto > 0.)
self.dmag = (self.c.mag_out - self.c.mag_in)[self.gfmeasure]
self.dlogRe = (N.log10(self.c.re_out) -
N.log10(self.c.re_in))[self.gfmeasure]
self.mag_auto = self.c.mag_auto[self.gfmeasure]
self.magerr_auto = self.c.magerr_auto[self.gfmeasure]
self.SN_auto = (1.0857 / self.c.magerr_auto)[self.gfmeasure]
self.mag_in = (self.c.mag_in)[self.gfmeasure]
self.re_in = (self.c.re_in)[self.gfmeasure]
self.mag_out = (self.c.mag_out)[self.gfmeasure]
self.re_out = (self.c.re_out)[self.gfmeasure]
self.robust = False
self.fig = None
self.ax = None
def calc_sex_transfunc_1d(simcat,
kgridname,
limits=limits,
field='udf',
logre_lims=[0.0, 1.5],
dm=0.5):
"""
Calculate the magnitude transfer functions for each apparent magnitude bin
for the faint-end of i-dropouts.
logre_lims are the limits of log(Re) (in pixels) that are used from
the simulation catalog. This is to match the observed range of log(Re)
of i-dropouts at the faint-end in UDF.
"""
# Read simulation catalog
c = Ftable(simcatdir + '/' + simcat)
y_mag_auto = c.y_mag_auto
y_mag_in = c.y_mag_in
kw = {'logre_lims': logre_lims, 'm0_arr': m0_arr}
kgrid = kernelgrid_1d(limits, dm, pixdx=pixdx, xmax=xmax, **kw)
kgrid.grid1d = np.arange(-xmax / 2., xmax / 2. + pixdx, pixdx)
logre_crit = (np.log10(c.y_flux_radius_1)>=logre_lims[0]) & \
(np.log10(c.y_flux_radius_1)<logre_lims[1]) & \
(c.detect==True)
for m in m0_arr:
print "m=", m, m + kgrid.binwidth
bincrit = (y_mag_in >= m) & (y_mag_in < (m + kgrid.binwidth))
y_mag_in_bin = y_mag_in[(bincrit == True) & (logre_crit == True)]
y_mag_auto_bin = y_mag_auto[(bincrit == True) & (logre_crit == True)]
dmag = y_mag_auto_bin - y_mag_in_bin
h = KPDF.UPDFOptimumBandwidth(dmag)
pdf = KPDF.UPDFEpanechnikov(dmag, kgrid.grid1d, h)
pdf = pdf / sum(pdf) # normalize to 1.0
kgrid.kernels['%.1f' % m] = kernel1d(m,
m + kgrid.binwidth,
pdf,
xmax=xmax,
pixdx=pixdx)
# Write kgrid into a pickled file
f = open(kgridname, 'wb')
cPickle.dump(kgrid, f, 2)
f.close()
def drawUMag(self):
# Draw U-band magnitude from probability distribution functions calculated
# using TFIT simulation catalogs in VIMOS U-band.
# In princple, we draw an output U-band magnitude given U-band input
# magnitudes to simulate photometric errors. We do it this way because
# running enough simulations with TFIT is prohibitively expensive.
cu = Ftable(tfitsim_cat[self.field])
# mag0 = 22.0
umag_in = cu.uvimos_mag_in[cu.uvimos_fitquerr > 0.]
print "Repeat the drawing process %d times" % self.n_repeat
vimos_u_mag = uu.draw_umag(np.tile(self.c.u_mag_in, self.n_repeat),
self.umag_kpdf,
mag0=self.mag0)
vimos_u_mag_1sig = uu.draw_ulimmag(
self.n_repeat * len(self.c.u_mag_in), self.umag_1sig_kpdf)
# If the drawn U-band 1-sigma limit mag is brighter than the drawn
# U-band magnitude, use the 1-sigma limit magnitude as the measured
# U-band magnitude.
self._drawn_umag = True
return np.minimum(vimos_u_mag, vimos_u_mag_1sig)
def calc_umag_pdf(tfitsimcatalog,
umag_array=arange(21., 35.0, 0.5),
pdfname='uvimos_mag_pdf.p',
b_snlim=3.0):
"""
Calculate the PDF of output magnitude around
input value of U-band magnitudes when S/N>=1 in U-band.
"""
c = Ftable(tfitsimcatalog)
include = (c.uvimos_mag_out>0.)&(c.detect==True)&(c.uvimos_mag_out<99.)&\
(c.f435w_magerr_iso < (1.0857/b_snlim)) & \
(c.uvimos_fitqty > 0.)
print "sum(include)=", sum(include)
umag_in = c.uvimos_mag_in[include == True]
umag_out = c.uvimos_mag_out[include == True]
dmag_array = arange(-10., 6.05, 0.05) # x-coordinates of the PDF
dumag = umag_array[1] - umag_array[
0] # the bin width of U-band input magnitude
kpdf_umag = {}
kpdf_umag['h'] = {}
kpdf_umag['dmag_array'] = dmag_array
for umag in umag_array[:-1]:
mag0 = umag
mag1 = mag0 + dumag
print mag0, mag1
bincrit = (umag_in >= mag0) & (umag_in < mag1)
mag_in_bin = umag_in[bincrit]
mag_out_bin = umag_out[bincrit]
if sum(bincrit) >= 5:
h = KPDF.UPDFOptimumBandwidth(mag_out_bin)
kpdf_umag['h']['%.1f' % mag0] = h
pdf = KPDF.UPDFEpanechnikov((mag_out_bin - mag_in_bin), dmag_array,
h)
else:
pdf = ones(len(dmag_array))
pdf = pdf / sum(pdf)
kpdf_umag['%.1f' % mag0] = pdf
f = open(pdfname, 'wb')
cPickle.dump(kpdf_umag, f, 2)
f.close()
def __init__(self,
catalog,
column_names=colnames_sex_default,
disk=0,
diskfrac=0.7,
expand=0.0,
dx=0.02,
xmax=2.56,
xunit='dmag',
xname=r'$\Delta$ mag',
Ntot=None):
assert catalog.endswith(
'.fits'), "Please provide a FITS table as the catalog."
self.c = Ftable(catalog)
self.catalog = catalog
self.column_names = column_names
assert disk in [0, 1], "disk should be either 0 or 1."
self.disk = disk
if disk == 0:
self.devauc = 1
else:
self.devauc = 0
self.diskfrac = diskfrac
self.expand = expand
# self.Ntot will be the total number of galaxies used from the simulation
# catalog, after drawing a specified fraction of disks
if Ntot == None:
self.Ntot = len(self.c.d)
else:
self.Ntot = Ntot
self.outindex = self.draw_diskfrac()
# Below are some basic kernel properties
self.dx = dx # pixel size for kernels, NOT bin width
self.xlimits = [-xmax / 2., xmax / 2.
] # limtis of each kernel, centered around zero
self.xunit = xunit
self.xname = xname # include LaTex syntax for use in plots
def __init__(self,
catalog,
z0,
zlo,
zhi,
dz=0.1,
interpolate=True,
interp_dz=0.02,
n_repeat=1,
expand=[0., 0.],
cosmo=cosmo_default,
mag_in='m1500_in',
re_in='re_in_arcsec',
zeropoints=zeropoints):
# More of a template; don't expect to call this constructor directly
assert catalog.endswith(
'.fits'), "Please provide a FITS table as the catalog."
self.c = Ftable(catalog)
self.mag_in_col = mag_in
self.re_in_col = re_in
self.zeropoints = zeropoints
assert hasattr(self.c,
self.mag_in_col), "Column M1500_in not in catalog."
assert hasattr(
self.c,
self.re_in_col), "Column %s not in catalog." % self.re_in_col
self.z0 = z0 # the nominal redshift of the LBG sample
self.zlimits = [zlo, zhi]
self.dz = dz
self.Nz = int(round((zhi - zlo) / dz))
self.interpolate = interpolate
self.interp_dz = interp_dz
self.n_repeat = n_repeat
self.expand = expand
self.cosmo = cosmo
self.bands = [] # will be defined by subclass
def calc_ulimmag_pdf(tfitsimcatalog,
pdfname,
limmag_array=arange(26., 31., 0.02)):
c = Ftable(tfitsimcatalog)
uvimos_1sig_mag = uvimos_magzero - 2.5 * log10(c.uvimos_fitquerr)
umag_crit = uvimos_1sig_mag > 0
uvimos_1sig_mag = uvimos_1sig_mag[umag_crit]
kpdf_ulimmag = {}
kpdf_ulimmag['dmag_array'] = limmag_array
h = KPDF.UPDFOptimumBandwidth(uvimos_1sig_mag)
kpdf_ulimmag['h'] = h
pdf = KPDF.UPDFEpanechnikov(uvimos_1sig_mag, limmag_array, h)
kpdf_ulimmag['pdf'] = pdf
# Also calculate the fraction that have S/N < 1
dlimmag = limmag_array[1] - limmag_array[0]
limmag_bins = concatenate([limmag_array, [limmag_array[-1] + dlimmag]])
uvimos_ston = (c.uvimos_fitqty / c.uvimos_fitquerr)[umag_crit]
# n1 = histogram(uvimos_1sig_mag[uvimos_ston<1.], limmag_bins)[0]
# n2 = histogram(uvimos_1sig_mag, limmag_bins)[0]
# limfrac = n1.astype('float') / maximum(1.,n2.astype('float'))
# kpdf_ulimmag['limfrac'] = limfrac
f = open(pdfname, 'wb')
cPickle.dump(kpdf_ulimmag, f, 2)
f.close()
def __init__(self, simcatalog):
self.c = Ftable(simcatalog)
def __call__(self,
simcatalog,
field,
plot_diag=False,
ktest=None,
bands=def_bands,
key='Hua13',
sn_lolim={
'wfc3_f160w': 5.0,
'wfc3_f125w': 3.5,
'acs_f850lp': 2.0
},
sn_hilim={'acs_f435w': 2.0},
interpolate=False,
dznew=0.1,
expand=[0, 0]):
"""
simcatalog --- the simulation catalog as a FITS table.
"""
# Initialize i-dropout color criteria
print "dznew=", dznew
print "interpolate?", interpolate
self.lcc = lcc.colorcrit()
self.lcc = self.lcc('f775w_drop', key)
c = Ftable(simcatalog)
# Now initialize the attributes
for b in bands:
bs = filter_dic[b] # short name of filter b
flux_iso = getattr(c, '%s_flux_iso' % bs)
fluxerr_iso = getattr(c, '%s_fluxerr_iso' % bs)
mag_iso = getattr(c, '%s_mag_iso' % bs)
# calculate 1-sigma upper limits on sources with S/N < 1
mag_iso = N.where(flux_iso / fluxerr_iso > 1.0, mag_iso,
zeropoints[b] - 2.5 * N.log10(fluxerr_iso))
setattr(self, b + '_mag', mag_iso)
# MAG_ISO --> for color calculation
setattr(self, b + '_flux', getattr(c, '%s_flux_auto' % bs))
# FLUX_AUTO
setattr(self, b + '_flux_aper', getattr(c, '%s_flux_aper_2' % bs))
# Aperture flux within 0.18" aperture (2.94 pixels)
setattr(self, b + '_fluxerr', getattr(c, '%s_fluxerr_auto' % bs))
# FLUXERR_AUTO
setattr(self, b + '_fluxerr_aper',
getattr(c, '%s_fluxerr_aper_2' % bs))
# Aperture flux errors within 0.18" aperture
setattr(
self, b + '_sn',
getattr(self, '%s_flux' % b) / getattr(self, '%s_fluxerr' % b))
# S/N calculated using AUTO APERTURE
setattr(
self, b + '_sn_aper',
getattr(self, '%s_flux_aper' % b) /
getattr(self, '%s_fluxerr_aper' % b))
# S/N calculated using 0.18" APERTURE
# Now construct S/N criteria
#self.sncrit = N.ones(len(c.d),'bool')
self.sn_lolim_crit = N.ones(len(c.d), 'bool')
self.sn_hilim_crit = N.ones(len(c.d), 'bool')
for b in sn_lolim.keys(): # enforce S/N lower limits
self.sn_lolim_crit = self.sn_lolim_crit & \
(getattr(self,b+'_sn')>=sn_lolim[b])
for b in sn_hilim.keys():
# enforce S/N upper limits (veto bands)
# but use fixed aperture S/N
self.sn_hilim_crit = self.sn_hilim_crit & \
(getattr(self,b+'_sn_aper')<sn_hilim[b])
print "Total number of objects satisfying the S/N criteria:", \
sum((self.sn_hilim_crit)&(self.sn_lolim_crit))
self.sncrit = (self.sn_hilim_crit == True) & (self.sn_lolim_crit
== True)
print "Do selections..."
self.color1 = self.acs_f775w_mag - self.acs_f850lp_mag
self.color2 = self.acs_f850lp_mag - self.wfc3_f125w_mag
self.lcc.select(self.color1, self.color2) # do color selection!
self.colorcrit = self.lcc.crit.copy()
self.lcc.crit = self.lcc.crit & self.sncrit # enforce S/N criteria
self.dropcrit = self.lcc.crit & (c.detect == True) # just in case
self.detect = c.detect.copy()
print "Selection done."
print "Total number of objects in the catalog: %d" % len(c.d)
print "Total number selected as i-dropouts: %d" % (sum(self.dropcrit))
# Now invoke zdist.zdgrid.__call__ to calculate P(z)
zdist.zdgrid.__call__(self,
c,
interpolate=interpolate,
dznew=dznew,
plot_diag=plot_diag,
ktest=ktest,
expand=expand)
def __call__(self,
simcatalog,
bands=def_bands,
key='Hua13',
sn_lolim={
'wfc3_f160w': 5.0,
'wfc3_f125w': 3.5,
'acs_f850lp': 2.0
},
sn_hilim={'acs_f435w': 2.0},
expand=[0., 0.]):
## Most stuff is the same as the 2D case:
self.lcc = lcc.colorcrit()
self.lcc = self.lcc('f775w_drop', key)
c = Ftable(simcatalog)
# Now initialize the attributes
for b in bands:
bs = filter_dic[b] # short name of filter b
flux_iso = getattr(c, '%s_flux_iso' % bs)
fluxerr_iso = getattr(c, '%s_fluxerr_iso' % bs)
mag_iso = getattr(c, '%s_mag_iso' % bs)
# calculate 1-sigma upper limits on sources with S/N < 1
mag_iso = N.where(flux_iso / fluxerr_iso > 1.0, mag_iso,
zeropoints[b] - 2.5 * N.log10(fluxerr_iso))
setattr(self, b + '_mag', mag_iso)
# MAG_ISO --> for color calculation
setattr(self, b + '_flux', getattr(c, '%s_flux_auto' % bs))
# FLUX_AUTO
setattr(self, b + '_flux_aper', getattr(c, '%s_flux_aper_2' % bs))
# Aperture flux within 0.18" aperture (2.94 pixels)
setattr(self, b + '_fluxerr', getattr(c, '%s_fluxerr_auto' % bs))
# FLUXERR_AUTO
setattr(self, b + '_fluxerr_aper',
getattr(c, '%s_fluxerr_aper_2' % bs))
# Aperture flux errors within 0.18" aperture
setattr(
self, b + '_sn',
getattr(self, '%s_flux' % b) / getattr(self, '%s_fluxerr' % b))
# S/N calculated using AUTO APERTURE
setattr(
self, b + '_sn_aper',
getattr(self, '%s_flux_aper' % b) /
getattr(self, '%s_fluxerr_aper' % b))
# S/N calculated using 0.18" APERTURE
# Now construct S/N criteria
#self.sncrit = N.ones(len(c.d),'bool')
self.sn_lolim_crit = N.ones(len(c.d), 'bool')
self.sn_hilim_crit = N.ones(len(c.d), 'bool')
for b in sn_lolim.keys(): # enforce S/N lower limits
self.sn_lolim_crit = self.sn_lolim_crit & \
(getattr(self,b+'_sn')>=sn_lolim[b])
for b in sn_hilim.keys():
# enforce S/N upper limits (veto bands)
# but use fixed aperture S/N
self.sn_hilim_crit = self.sn_hilim_crit & \
(getattr(self,b+'_sn_aper')<sn_hilim[b])
print "Total number of objects satisfying the S/N criteria:", \
sum((self.sn_hilim_crit)&(self.sn_lolim_crit))
self.sncrit = (self.sn_hilim_crit == True) & (self.sn_lolim_crit
== True)
print "Do selections..."
self.color1 = self.acs_f775w_mag - self.acs_f850lp_mag
self.color2 = self.acs_f850lp_mag - self.wfc3_f125w_mag
self.lcc.select(self.color1, self.color2) # do color selection!
self.colorcrit = self.lcc.crit.copy()
self.lcc.crit = self.lcc.crit & self.sncrit # enforce S/N criteria
self.dropcrit = self.lcc.crit & (c.detect == True) # just in case
self.detect = c.detect.copy()
print "Selection done."
print "Total number of objects in the catalog: %d" % len(c.d)
print "Total number selected as i-dropouts: %d" % (sum(self.dropcrit))
for M in self.M1500arr:
# For each magnitude bin, calculate completeness P(z)
bincrit = (c.m1500_in >= M) & (c.m1500_in < (M + self.dM))
z_in_bin = c.z_in[bincrit == True]
z_in_bin_detect = c.z_in[(bincrit == True) & (c.detect == True)]
zhist_bin_input = N.histogram(z_in_bin, self.zarr_edges)[0]
zhist_bin_detect = N.histogram(z_in_bin_detect, self.zarr_edges)[0]
self.zdist['%.1f'%M] = zhist_bin_detect.astype('float') / \
N.maximum(zhist_bin_input.astype('float'), 1.0)
self.zdist['%.1f' % M] = self.zdist['%.1f' % M]
#!/usr/bin/env python
from numpy import *
import udropsim_uflux as uu
from pygoods import Ftable
import fitsutil
import os
rootdir = '/Users/khuang/Dropbox/Research/bivariate/udrops_fitting'
rootcat = rootdir + 'udropsim_run2m_goodss_deep_130505.fits'
c = Ftable(rootcat)
def draw_umags(niter=1):
for i in range(niter):
vimos_u_mag = uu.draw_umag(c.u_mag_in,
'simcatalogs/udrops/uvimos_mag_pdf.p')
vimos_u_mag_1sig = uu.draw_umag(
c.u_mag_in,
'simcatalogs/udrops/uvimos_mag_1sigma_pdf.p',
onesigma=True)
vimos_u_mag = minimum(vimos_u_mag, vimos_u_mag_1sig)
os.system('cp %s udropsim_umag_drawn_%d.fits' % (rootcat, i))
fitsutil.add_columns('udropsim_umag_drawn_%d.fits' % i,
['vimos_u_mag_out'], [vimos_u_mag], ['D'])
def __call__(self,
simcatalog,
field,
plot_diag=False,
ktest=None,
n_repeat=1,
sn_lolim={
'wfc3_f160w': 5.0,
'wfc3_f105w': 5.0,
'acs_f435w': 3.0
},
mode='ubvy',
interpolate=False,
dznew=0.1,
redraw=False,
drawnflux=None,
testumag=False,
testdraw=False,
mag0=22.0,
expand=[0., 0.],
mag_in_col='m1500_in',
re_in_col='re_in'):
"""
simcatalog --- the simulation catalog as a FITS table.
Because the U-band magnitudes are randomly drawn from each object, we
would like to repeat the magnitude drawing process for a few times to
average out any statistical anomalies.
mode --- the bands to use for selection (uby v.s. ubvy)
"""
# Initialize U-dropout color criteria
print "n_repeat", n_repeat
print "dznew", dznew
print "interpolate?", interpolate
print "Redraw?", redraw
self.lcc = lcc.colorcrit()
if mode == 'uby':
if field == 'ers':
self.lcc = self.lcc('uvimos_drop', 'UBY098')
else:
self.lcc = self.lcc('uvimos_drop', 'UBY105')
elif mode == 'ubvy':
if field == 'ers':
self.lcc = self.lcc('uvimos_drop', 'UBVY098')
else:
self.lcc = self.lcc('uvimos_drop', 'UBVY105')
c = Ftable(simcatalog)
#self.c = c
## First: draw U-band magnitudes, and initialize correct attributes of c
## Tile the arrays by `n_repeat` times
if field == 'ers':
bands = ['acs_f435w', 'acs_f606w', 'wfc3_f098m', 'wfc3_f160w']
# HST bands only
else:
bands = ['acs_f435w', 'acs_f606w', 'wfc3_f105w', 'wfc3_f160w']
for b in bands:
bs = filter_dic[b] # short name of filter b
flux_iso = getattr(c, '%s_flux_iso' % bs)
fluxerr_iso = getattr(c, '%s_fluxerr_iso' % bs)
mag_iso = getattr(c, '%s_mag_iso' % bs)
# calculate 1-sigma upper limits on sources with S/N < 1
mag_iso = N.where((flux_iso / fluxerr_iso) > 1.0, mag_iso,
zeropoints[b] - 2.5 * N.log10(fluxerr_iso))
setattr(self, b + '_mag', N.tile(mag_iso, n_repeat))
# MAG_ISO --> for color calculation
# setattr(self,b + '_flux',
# N.tile(getattr(c, '%s_flux_auto'%bs),n_repeat))
setattr(self, b + '_flux',
N.tile(getattr(c, '%s_flux_iso' % bs), n_repeat))
# FLUX_AUTO
# setattr(self, b + '_fluxerr',
# N.tile(getattr(c,'%s_fluxerr_auto'%bs),n_repeat))
setattr(self, b + '_fluxerr',
N.tile(getattr(c, '%s_fluxerr_iso' % bs), n_repeat))
# FLUXERR_AUTO
setattr(
self, b + '_sn',
getattr(self, '%s_flux' % b) / getattr(self, '%s_fluxerr' % b))
# S/N calculated using FLUX_ISO
print "Step 1: draw U-band magnitudes and 1-sigma limits from files \
%s and %s" % (self.umag_kpdf, self.umag_1sig_kpdf)
# Draw U-band magnitude
# Calculate the fraction that have U-band S/N >= 1
if field == 'udf':
cu = Ftable(
'/Users/khuang/Dropbox/Research/bivariate/bivariate_fit/udrops_fitting/simcatalogs/run1_udf_130625.fits'
)
umag_bins = N.arange(25., 38.5, 0.5)
else:
cu = Ftable(
'/Users/khuang/Dropbox/Research/bivariate/bivariate_fit/udrops_fitting/simcatalogs/run2_tfitsim_130322.fits'
)
umag_bins = N.arange(25., 38.5, 0.5)
umag_in = cu.uvimos_mag_in[cu.uvimos_fitquerr > 0.]
u_ston = (cu.uvimos_fitqty /
cu.uvimos_fitquerr)[cu.uvimos_fitquerr > 0.]
n1 = N.histogram(umag_in[u_ston >= 1.], umag_bins)[0]
n2 = N.histogram(umag_in, umag_bins)[0]
u_detect_frac = n1.astype('float') / N.maximum(1., n2.astype('float'))
if testumag == False:
# If not testing anything...
if redraw == True:
print "Repeat the drawing process %d times" % n_repeat
self.vimos_u_mag = uu.draw_umag(N.tile(c.u_mag_in, n_repeat),
self.umag_kpdf,
mag0=mag0)
self.vimos_u_mag_1sig = uu.draw_ulimmag(
n_repeat * len(c.u_mag_in), self.umag_1sig_kpdf)
# Draw 1-sigma magnitude upper limit
# Now replace U-band magnitude with 1-sigma upper limit by using
# the S/N > 1 fraction
# index_detfrac = (c.u_mag_in - c.u_mag_in.min()) - (c.u_mag_in % 0.5)
# index_detfrac = N.minimum(len(u_detect_frac)-1, index_detfrac)
# detfrac = u_detect_frac.take(index_detfrac.astype('int'))
# # Now randomly draws a reference level to compare with the detection
# # fraction
# rdn_level = N.random.uniform(0., 1., size=len(c.d))
# self.vimos_u_mag = N.where(detfrac>=rdn_level, self.vimos_u_mag,
# self.vimos_u_mag_1sig)
self.vimos_u_mag = N.minimum(self.vimos_u_mag,
self.vimos_u_mag_1sig)
else:
print "Read U-band magnitudes from %s." % drawnflux
# Read the drawn U-band magnitudes from file drawnflux
df = cPickle.load(open(drawnflux))
self.vimos_u_mag_1sig = df.vimos_u_mag_1sig
self.vimos_u_mag = df.vimos_u_mag
else:
self.vimos_u_mag = c.u_mag_in
self.vimos_u_mag_1sig = c.u_mag_in
# Now construct S/N criteria
#self.sncrit = N.ones(len(c.d)*n_repeat,'bool')
self.sn_lolim_crit = N.ones(len(c.d) * n_repeat, 'bool')
self.detect = c.detect.copy()
#print len(self.sncrit), len(self.acs_f435w_sn)
if len(sn_lolim.keys()) > 0:
print "sn_lolim", sn_lolim
for b in sn_lolim.keys():
self.sn_lolim_crit = self.sn_lolim_crit & \
(getattr(self,b+'_sn')>=sn_lolim[b])
#self.sncrit = self.sncrit & (getattr(self,'%s_sn'%b)>=sn_lolim[b])
self.sncrit = (self.sn_lolim_crit == True)
## Second: calculate dropcrit (do color selection)
#import select_udrops_uby as suu
print "Do selections..."
if field == 'ers':
self.color1 = self.vimos_u_mag - self.acs_f435w_mag
if mode == 'uby':
self.color2 = self.acs_f435w_mag - self.wfc3_f098m_mag
elif mode == 'ubvy':
self.color2 = self.acs_f606w_mag - self.wfc3_f098m_mag
self.lcc.select(self.color1, self.color2)
else:
self.color1 = self.vimos_u_mag - self.acs_f435w_mag
if mode == 'uby':
self.color2 = self.acs_f435w_mag - self.wfc3_f105w_mag
elif mode == 'ubvy':
self.color2 = self.acs_f606w_mag - self.wfc3_f105w_mag
self.lcc.select(self.color1, self.color2)
self.colorcrit = self.lcc.crit.copy()
self.lcc.crit = self.lcc.crit & self.sncrit # Fold in S/N criteria
#if field=='ers':
# self.udrops = suu.udrops_ubvy098(self.c,
# bands=['acs_f435w','acs_f606w','wfc3_f098m','wfc3_f160w'],
# sn_lolim={'wfc3_f160w':5.0,'acs_f435w':3.0,'wfc3_f098m':5.0})
#else:
# self.udrops = suu.udrops_ubvy105(self.c,
# bands=['acs_f435w','acs_f606w','wfc3_f105w','wfc3_f160w'],
# sn_lolim={'wfc3_f160w':5.0,'acs_f435w':3.0,'wfc3_f105w':5.0})
#dropcrit = self.udrops.crit # the dropout-selection array
dropcrit = self.lcc.crit & (N.tile(c.detect, n_repeat) == True)
#self.dropcrit = dropcrit[:len(c.d)]
self.dropcrit = dropcrit
self.detect = c.detect.copy()
print "Selections done."
print "Total number of objects in the catalog: %d" % len(c.d)
print "Total number selected as U-dropouts: %d" % (sum(dropcrit) /
float(n_repeat))
## Third: call make_Pz to calculate P(z)
if not testdraw:
# Now invoke zdist.zdgrid.__call__ to calculate P(z)
zdist.zdgrid.__call__(self,
c,
interpolate=interpolate,
dznew=dznew,
plot_diag=plot_diag,
ktest=ktest,
n_repeat=n_repeat,
expand=expand,
mag_in_col=mag_in_col,
re_in_col=re_in_col)
if self.n_repeat > 30:
delattr(self, 'color1')
delattr(self, 'color2')
delattr(self, 'sncrit')
def __init__(self, paramfile, filtername, **kwargs):
# Additional keyword arguments goes to GalaxySample.
p = yaml.load(open(paramfile))
self.filtername = filtername
# The surveyed comoving volume in each field will be factored into the
# respective dropout-selection kernels, so no need to include them here.
self.guess = p['GUESS']
(self.alpha, self.mstar, self.logr0, self.sigma,
self.beta) = p['GUESS']
self.phistar = 0.
self.fields = p['FIELDS']
self.z0 = p['Z0'] # nominal redshift of the galaxy sample
# pixel scales of the image on which the sizes are measured
self.pixscales = dict(zip(p['FIELDS'], p['PIXSCALES']))
self.datafiles = dict(zip(p['FIELDS'], p['DATAFILES']))
self.maglimits = dict(zip(p['FIELDS'], p['MAGLIMITS']))
self.logrlimits = dict(zip(p['FIELDS'], p['LOGRLIMITS']))
if p.has_key('SEXMAGLIMITS'):
self.sexmaglimits = dict(zip(p['FIELDS'], p['SEXMAGLIMITS']))
self.sex_kgrids = dict(
zip(p['FIELDS'], map(pu.load_pickle, p['SEX_KGRIDS'])))
else:
self.sexmaglimits = dict(
zip(p['FIELDS'], [None] * len(self.fields)))
self.sex_kgrids = dict(zip(p['FIELDS'], [None] * len(self.fields)))
self.galfit_kgrids = dict(
zip(p['FIELDS'], map(pu.load_pickle, p['GALFIT_KGRIDS'])))
self.re_err_lim = p['RE_ERR_LIM']
self.chi2nu_lim = p['CHI2NU_LIM']
self.technique = p['TECHNIQUE']
self.magconvert = RLDist.MagConvert(p['MCFILE'])
self.dropout_kgrids = dict(
zip(p['FIELDS'], map(pu.load_pickle, p['DROPOUT_KGRIDS'])))
if p['NPROC_MODEL'] > 0:
self.nproc_model = p['NPROC_MODEL']
else:
self.nproc_model = multiprocessing.cpu_count()
# interloper fraction parameters
self.add_interloper = p['ADD_INTERLOPER']
# self.sdfiles = dict(zip(p['FIELDS'], p['SDFILES']))
self.size_catalog = p['SIZE_CATALOG']
self.mag_size_catalog = p['MAG_SIZE_CATALOG']
self.re_size_catalog = p['RE_SIZE_CATALOG']
self.intfracfiles = dict(zip(p['FIELDS'], p['INTFRACFILES']))
self.mag_lolims = dict(zip(p['FIELDS'], p['MAG_LOLIMS']))
self.goodflags = p['GOOD_VFLAGS']
if self.add_interloper:
# Calculate the *multiplicative* interloper fractions in the RL plane
self.sd = {}
self.intfrac = {}
c = Ftable(self.size_catalog)
mag = getattr(c, self.mag_size_catalog)
re = getattr(c, self.re_size_catalog)
for f in self.fields:
# first calculate the size distribution for all sources
sd_f = ID.InterloperSizeDist(self.maglimits[f],
self.logrlimits[f], 0.02, 0.02,
0.5, 0.2, f)
x = sd_f.compute(mag, re)
self.sd[f] = sd_f
# then calculate the multiplicative interloper distribution
intdist = ID.InterloperRLDist(self.maglimits[f],
self.logrlimits[f],
0.02,
0.02,
f,
mag_lolim=self.mag_lolims[f])
intfrac = Ftable(self.intfracfiles[f])
x = intdist.compute(intfrac, sd_f)
self.intfrac[f] = intdist
### MCMC parameters?
### Define a separate class for MCMC stuff...
self.galaxy_samples = {}
self.p = p
# define galaxy samples
for f in p['FIELDS']:
self.galaxy_samples[f] = GalaxySample(
self.datafiles[f],
f,
self.filtername,
self.z0,
self.maglimits[f],
self.logrlimits[f],
re_err_lim=self.re_err_lim,
chi2nu_lim=self.chi2nu_lim,
good_vflags=self.goodflags,
sexmaglimit=self.sexmaglimits[f],
**kwargs)
print "Total number of galaxies in %s used in likelihood calculation: %d" % (
f, len(self.galaxy_samples[f].mag))
mag, logr = self.combine_samples(
flist=self.fields) # to record attributes self.mag, self.logr
self.mag = mag
self.logr = logr
self.Ntot = len(self.mag)
# Now builds RLDist factories
self.RLDist_factories = {}
factory = RLDist.RLDistributionFactory
# build RL Distribution factories for each field
for f in self.fields:
if self.sexmaglimits[f] == None:
maglimits = self.maglimits[f]
else:
maglimits = [self.maglimits[f][0], self.sexmaglimits[f]]
self.RLDist_factories[f] = factory(maglimits, self.logrlimits[f],
self.magconvert)
def plot_RLdist_idrops(logR0,
beta,
cat=catdir +
'/idrops/idrops_goodss_130623_vflags_galfit.fits',
marker='o',
ax=None,
ms=8**2,
color=pms.Bright_Blue,
bootstrap=False,
nsamp=10000):
c = Ftable(cat)
mcy = bl.mconvert('kcorr/M1500_to_f105w_omega_m_0.3.txt')
mean_kcorr = mcy(6.0)
mag_array1 = c.f105w_magout_gf[(c.udf_fit == True)
& (c.f105w_magout_gf <= 27.5)]
re_array1 = c.f105w_reout_gf[(c.udf_fit == True)
& (c.f105w_magout_gf <= 27.5)]
n_array1 = c.f105w_nout_gf[(c.udf_fit == True)
& (c.f105w_magout_gf <= 27.5)]
mag_array2 = c.f105w_magout_gf[(c.deep_fit == True)
& (c.f105w_magout_gf <= 26.5)]
re_array2 = c.f105w_reout_gf[(c.deep_fit == True)
& (c.f105w_magout_gf <= 26.5)]
n_array2 = c.f105w_nout_gf[(c.deep_fit == True)
& (c.f105w_magout_gf <= 26.5)]
mag_array3 = c.f098m_magout_gf[(c.ers_fit == True)
& (c.f098m_magout_gf <= 26.5)]
re_array3 = c.f098m_reout_gf[(c.ers_fit == True)
& (c.f098m_magout_gf <= 26.5)]
n_array3 = c.f098m_nout_gf[(c.ers_fit == True)
& (c.f098m_magout_gf <= 26.5)]
mag_array4 = c.f105w_magout_gf[(c.wide_fit == True)
& (c.f105w_magout_gf <= 26.5)]
re_array4 = c.f105w_reout_gf[(c.wide_fit == True)
& (c.f105w_magout_gf <= 26.5)]
n_array4 = c.f105w_nout_gf[(c.wide_fit == True)
& (c.f105w_magout_gf <= 26.5)]
mag_array = np.concatenate(
[mag_array1, mag_array2, mag_array3, mag_array4])
re_array = np.concatenate([re_array1, re_array2, re_array3, re_array4])
n_array = np.concatenate([n_array1, n_array2, n_array3, n_array4])
plot_RLdist(mag_array,
re_array,
'i-dropouts',
logR0,
beta,
mean_kcorr,
color=color,
marker=marker,
ax=ax,
ms=ms,
gfband='F105W/F098M')
if bootstrap == True:
p_array = bootstrap_error(mag_array, np.log10(re_array), nsamp=nsamp)
return p_array
else:
return 0
def __init__(self,
id2fit,
sexcat,
sci_image,
rms_image,
seg_image,
sexcatfmt='fits',
idcol='id_1',
racol='ra_1',
deccol='dec_1',
magcol='mag_auto',
recol='flux_radius_2',
axratiocol='axratio',
thetacol='theta_image',
isoareacol='isoarea_image',
root='images',
pixscale=0.06,
psffile="",
constraint_file="constraints3",
magzpt=25.96,
cbox=160):
"""
id2fit: array of ID numbers to run GALFIT on.
sexcat: the SExtractor catalog where the object IDs are specified.
NOTE: sexcat needs to contain ALL objects in the field, not just the objects to
be fit by GALFIT!!
sci_image: the science (drizzled) mosaic
rms_image: the RMS image
seg_image: the segmentation map
racol: column name that contains RA
deccol: column name that contains DEC
magcol: column name that contains magnitude (default to MAG_AUTO)
recol: column name for Re (half-light radius)
axratiocol: column name for axis ratio
thetacol: column name for position angle (theta)
root: the directory name for all the GALFIT cutouts and output images
pixscale: the pixel scale in arcsec
"""
self.id2fit = id2fit
if sexcatfmt == 'fits':
self.sexcat = Ftable(sexcat)
self.nobj_all = len(self.sexcat.d)
else:
self.sexcat = sextractor(sexcat)
self.nobj_all = len(self.sexcat)
if not os.path.exists(root):
os.mkdir(root)
elif not os.path.isdir(root):
raise OSError, "%s already exists and is not a directory." % root
self.ra_array = getattr(self.sexcat, racol)
self.dec_array = getattr(self.sexcat, deccol)
self.id_array = getattr(self.sexcat, idcol)
self.mag_array = getattr(self.sexcat, magcol)
self.Re_array = getattr(self.sexcat, recol)
self.axratio_array = getattr(self.sexcat, axratiocol)
self.theta_array = getattr(self.sexcat, thetacol)
self.isoarea_array = getattr(self.sexcat, isoareacol)
self.sci_image = sci_image
self.rms_image = rms_image
self.seg_image = seg_image
# figure out the WCS
hdr = pyfits.getheader(self.sci_image)
self.wcs = pywcs.WCS(hdr)
# figure out the X, Y positions of each source... will need later
skycrd = N.array([self.ra_array, self.dec_array])
skycrd = skycrd.swapaxes(0, 1)
pixcrd = self.wcs.wcs_sky2pix(skycrd,
1) # pixel coordinate starts at 1
pixcrd = pixcrd.swapaxes(0, 1)
self.x_array = pixcrd[0]
self.y_array = pixcrd[1]
self.crashes = "galfit.crashes"
self.fitted = "galfit.fitted"
self.root = root # a directory containing all the cutouts
self.xscale = pixscale
self.yscale = pixscale
self.psffile = psffile
self.constraint_file = constraint_file
self.magzpt = magzpt
self.cbox = cbox # convolution box size --- should be comparable to the PSF image size?
def merge_sexcats(self):
"""
Assume that files self.sex_catalog exist.
"""
# Match & merge photometry into one master catalog
# Note: update all magnitude errors with the scaled flux errors
merged_columns = []
for f in self.filters:
c = Ftable(self.sex_fitstable[f])
skyrms_factor = self.c['skyrms_factor'][f]
if f == self.detectband:
merged_columns += [
pyfits.Column(name='number', array=c.number, format='I')
]
merged_columns += [
pyfits.Column(name='x_image', array=c.x_image, format='D')
]
merged_columns += [
pyfits.Column(name='y_image', array=c.y_image, format='D')
]
merged_columns += [
pyfits.Column(name='alpha_j2000',
array=c.alpha_j2000,
format='D')
]
merged_columns += [
pyfits.Column(name='delta_j2000',
array=c.delta_j2000,
format='D')
]
flux_iso = c.__getitem__('%s_flux_iso' % f)
fluxerr_iso = c.__getitem__('%s_fluxerr_iso' % f)
print "Scaling fluxerr_iso..."
fluxerr_iso_scaled = fluxerr_iso * skyrms_factor
# sn_iso = flux_iso / fluxerr_iso
sn_iso = flux_iso / fluxerr_iso_scaled
flux_auto = c.__getitem__('%s_flux_auto' % f)
fluxerr_auto = c.__getitem__('%s_fluxerr_auto' % f)
sn_auto = flux_auto / fluxerr_auto
print "Scaling fluxerr_auto..."
fluxerr_auto_scaled = fluxerr_auto * skyrms_factor
for i in range(len(c.Columns)):
colname = c.Columns[i]
if colname in self.fixcols:
continue
else:
merged_columns += [
pyfits.Column(name=colname,
array=c.__getitem__(colname),
format=c.d.formats[i])
]
if colname == '%s_fluxerr_iso' % f:
# also add the scaled fluxerr
merged_columns += [
pyfits.Column(name='%s_fluxerr_iso_scaled' % f,
array=fluxerr_iso_scaled,
format='D')
]
magerr_iso_scaled = magerr_from_sn(sn_iso)
if colname == '%s_fluxerr_auto' % f:
merged_columns += [
pyfits.Column(name='%s_fluxerr_auto_scaled' % f,
array=fluxerr_auto_scaled,
format='D')
]
magerr_auto_scaled = magerr_from_sn(sn_auto)
if colname.startswith('%s_fluxerr_aper' % f):
fluxerr_aper_n = getattr(c, colname)
print "Scaling %s..." % colname
fluxerr_aper_n_scaled = fluxerr_aper_n * skyrms_factor
flux_aper_n = getattr(c, '%s_flux_aper' % f)
sn_aper = flux_aper_n / fluxerr_aper_n_scaled
merged_columns += [
pyfits.Column(name='%s_scaled' % colname,
array=fluxerr_aper_n_scaled,
format='D')
]
# Now update MAG_ISO: MAG_ISO=99.0 if S/N < 1 and MAGERR_ISO becomes
# the 1-sigma magnitude limit; MAG_ISO=-99.0 if object is not detected
if colname == '%s_mag_iso' % f:
mag_iso = np.where(sn_iso >= 1.,
c.__getitem__('%s_mag_iso' % f),
99.0)
mag_iso = np.where(fluxerr_iso == 0, -99.0, mag_iso)
merged_columns[-1].array = mag_iso # update the array
if colname == '%s_magerr_iso' % f:
magerr_iso = np.where(fluxerr_iso_scaled == 0, 0.,
2.5 * np.log10(1. + 1. / sn_iso))
# if S/N <= 1., use the 1-sigma magnitude limit as magerr_iso
magerr_iso = np.where(
sn_iso >= 1., magerr_iso, self.zeropoints[f] -
2.5 * np.log10(fluxerr_iso_scaled))
merged_columns[-1].array = magerr_iso
# Write the 1-sigma MAG_ISO for ease of color selection
if colname == '%s_mag_auto' % f:
mag_auto = np.where(sn_auto >= 1.,
c.__getitem__('%s_mag_auto' % f),
99.0)
mag_auto = np.where(fluxerr_auto == 0., -99.0,
mag_auto)
merged_columns[-1].array = mag_auto
if colname == '%s_magerr_auto' % f:
magerr_auto = np.where(
fluxerr_auto_scaled == 0, 0.,
2.5 * np.log10(1. + 1. / sn_auto))
magerr_auto = np.where(
sn_auto >= 1., magerr_auto, self.zeropoints[f] -
2.5 * np.log10(fluxerr_auto_scaled))
mag_iso_1sig = np.where(sn_iso >= 1.0, mag_iso, magerr_iso)
merged_columns += [
pyfits.Column(name='%s_mag_iso_1sig' % f,
array=mag_iso_1sig,
format='D')
]
mag_auto_1sig = np.where(sn_auto >= 1.0, mag_auto, magerr_auto)
merged_columns += [
pyfits.Column(name='%s_mag_auto_1sig' % f,
array=mag_auto_1sig,
format='D')
]
naper = 0
flux_aper = c.__getitem__('%s_flux_aper' % f)
fluxerr_aper = c.__getitem__('%s_fluxerr_aper' % f)
print "Scaling fluxerr_aper..."
fluxerr_aper_scaled = fluxerr_aper * skyrms_factor
# sn_aper = flux_aper / fluxerr_aper
sn_aper = flux_aper / fluxerr_aper_scaled
for i in range(len(merged_columns)):
if merged_columns[i].name == '%s_mag_aper' % f:
mag_aper = np.where(sn_aper >= 1.,
c.__getitem__('%s_mag_aper' % f), 99.0)
mag_aper = np.where(fluxerr_aper == 0, -99.0, mag_aper)
merged_columns[i].array = mag_aper
if merged_columns[i].name == '%s_magerr_aper' % f:
# magerr_aper = np.where(fluxerr_aper==0, 0.,
# c.__getitem__('%s_magerr_aper'%f))
# magerr_aper = np.where(sn_aper>=1., magerr_aper,
# self.zeropoints[f]-2.5*np.log10(fluxerr_aper))
magerr_aper = magerr_from_sn(sn_aper)
magerr_aper = np.where(fluxerr_aper_scaled == 0., 0.,
2.5 * np.log10(1. + 1. / sn_aper))
# update magerr_aper with 1-sigma magnitude limit
magerr_aper = np.where(
sn_aper >= 1., magerr_aper, self.zeropoints[f] -
2.5 * np.log10(fluxerr_aper_scaled))
merged_columns[i].array = magerr_aper
if merged_columns[i].name.startswith('%s_mag_aper_' % f):
naper = merged_columns[i].name.split('_')[-1]
naper = int(naper)
flux_aper_n = c.__getitem__('%s_flux_aper_%d' % (f, naper))
fluxerr_aper_n = c.__getitem__('%s_fluxerr_aper_%d' %
(f, naper))
fluxerr_aper_n_scaled = fluxerr_aper_n * skyrms_factor
# sn_aper_n = flux_aper_n / fluxerr_aper_n
sn_aper_n = flux_aper_n / fluxerr_aper_n_scaled
mag_aper_n = np.where(
sn_aper_n >= 1.,
c.__getitem__('%s_mag_aper_%d' % (f, naper)), 99.0)
mag_aper_n = np.where(fluxerr_aper_n == 0, -99.0,
mag_aper_n)
merged_columns[i].array = mag_aper_n
if merged_columns[i].name.startswith('%s_magerr_aper_' % f):
naper = merged_columns[i].name.split('_')[-1]
naper = int(naper)
flux_aper_n = c.__getitem__('%s_flux_aper_%d' % (f, naper))
fluxerr_aper_n = c.__getitem__('%s_fluxerr_aper_%d' %
(f, naper))
fluxerr_aper_n_scaled = fluxerr_aper_n * skyrms_factor
# sn_aper_n = flux_aper_n / fluxerr_aper_n
sn_aper_n = flux_aper_n / fluxerr_aper_n_scaled
# magerr_aper_n = np.where(fluxerr_aper_n==0, 0.,
# c.__getitem__('%s_magerr_aper_%d' % (f,naper)))
# magerr_aper_n = np.where(sn_aper_n>=1., magerr_aper_n,
# self.zeropoints[f]-2.5*np.log10(fluxerr_aper_n))
magerr_aper_n = np.where(
fluxerr_aper_n == 0., 0.,
2.5 * np.log10(1. + 1. / sn_aper_n))
magerr_aper_n = np.where(
sn_aper >= 1., magerr_aper, self.zeropoints[f] -
2.5 * np.log10(fluxerr_aper_n_scaled))
merged_columns[i].array = magerr_aper_n
# OK, special treatment for mag_aper_1, which should be 0.4 arcsec
# diameter aperture
for i in range(len(merged_columns)):
if merged_columns[i].name == '%s_mag_aper_1' % f:
j_mag_aper_1 = i
elif merged_columns[i].name == '%s_magerr_aper_1' % f:
j_magerr_aper_1 = i
elif merged_columns[i].name == '%s_mag_aper_3' % f:
j_mag_aper_3 = i
elif merged_columns[i].name == '%s_magerr_aper_3' % f:
j_magerr_aper_3 = i
flux_aper_1 = c.__getitem__('%s_flux_aper_1' % f)
fluxerr_aper_1 = c.__getitem__('%s_fluxerr_aper_1' % f)
fluxerr_aper_1_scaled = fluxerr_aper_1 * skyrms_factor
# sn_aper_1 = flux_aper_1 / fluxerr_aper_1
sn_aper_1 = flux_aper_1 / fluxerr_aper_1_scaled
mag_aper_1_1sig = np.where(sn_aper_1 >= 1.,
merged_columns[j_mag_aper_1].array,
merged_columns[j_magerr_aper_1].array)
merged_columns += [
pyfits.Column(name='%s_mag_aper_1_1sig' % f,
array=mag_aper_1_1sig,
format='D')
]
flux_aper_3 = c.__getitem__('%s_flux_aper_3' % f)
fluxerr_aper_3 = c.__getitem__('%s_fluxerr_aper_3' % f)
fluxerr_aper_3_scaled = fluxerr_aper_3 * skyrms_factor
# sn_aper_3 = flux_aper_3 / fluxerr_aper_3
sn_aper_3 = flux_aper_3 / fluxerr_aper_3_scaled
mag_aper_3_1sig = np.where(sn_aper_3 >= 1.,
merged_columns[j_mag_aper_3].array,
merged_columns[j_magerr_aper_3].array)
merged_columns += [
pyfits.Column(name='%s_mag_aper_3_1sig' % f,
array=mag_aper_3_1sig,
format='D')
]
print "len(merged_columns)", len(merged_columns)
assert len(
merged_columns
) <= 999, "Warning: maximum number of columns allowed in a FITS table is 999!"
coldefs = pyfits.ColDefs(merged_columns)
tbhdu = pyfits.new_table(coldefs)
tbhdu.header['PARAMS'] = self.paramfile
merged_table = os.path.join(self.homedir, self.merged_table)
print merged_table
if os.path.exists(merged_table):
os.remove(merged_table)
tbhdu.writeto(merged_table)
def __init__(self, catalog, bands=['H']):
Ftable.__init__(self, catalog) # read the FITS table
self.bands = bands
self.catalog = catalog
