def remove_detections_bysegmmaps(field, pointing, ccd):
"""
It uses the segmentation-maps to remove fake detections
when masking out saturated stars.
----
import alhambra_fakedets as AF
AF.remove_detections_bysegmmaps(2,1,1)
"""
root = '/Volumes/amb22/catalogos/reduction_v4f/f0%i/' % (field)
root2images = '/Volumes/amb22/imagenes/f0%i/' % (field)
catalog = root + 'f0%ip0%i_colorproext_%i_ISO.irmsF814W.free.cat' % (
field, pointing, ccd)
ids, x, y, area = U.get_data(catalog, (0, 3, 4, 5))
dim = len(ids)
valor = U.zeros(dim)
ima1 = root2images + 'f0%ip0%i_F814W_%i.swp.seg.fits' % (field, pointing,
ccd)
ima2 = root2images + 'f0%ip0%i_F814W_%i.swp.segnomask.fits' % (
field, pointing, ccd)
segm1 = pyfits.open(ima1)[0].data
segm2 = pyfits.open(ima2)[0].data
for ii in range(dim):
xo = x[ii]
yo = y[ii]
dimx = U.shape(datos[yo - size:yo + size, xo - size:xo + size])[1]
dimy = U.shape(datos[yo - size:yo + size, xo - size:xo + size])[0]
perc[ii] = (datos[yo - size:yo + size, xo - size:xo + size].sum() /
(dimx * dimy * 1.))
# Defining the sample to be keep.
good = U.greater(valor, 0)
idr = U.compress(good, ids)
dim2 = len(idr)
print 'Dimensions: Original: %i, Final: %i, Excluded: %i detections. ' % (
dim, dim2, dim - dim2)
finalcat = root + 'f0%ip0%i_colorproext_%i_ISO.irmsF814W.free.cat' % (
field, pointing, ccd)
data1 = coeio.loaddata(catalog) # Loading the whole catalog content.
head = coeio.loadheader(catalog)
data2 = data1[good, :]
coeio.savedata(data2, finalcat, dir="",
header=head) # Saving & creating a new catalog.
def remove_fakeabsorptions_F814W(field, pointing, ccd):
"""
Using the rmsweight images, it gets rid of
detections with imrs_F814W < 0.5.
-------------------------------------------
import alhambra_fakedets as AF
AF.remove_fakeabsorptions_F814W(2,1,1)
"""
root = '/Volumes/amb22/catalogos/reduction_v4f/f0%i/' % (field)
catalog = root + 'f0%ip0%i_colorproext_%i_ISO.cat' % (field, pointing, ccd)
ids, x, y, area = U.get_data(catalog, (0, 3, 4, 5))
dim = len(ids)
perc = U.zeros(dim)
# Opening F814W Weight image
ima = alh.alhambra_invrmsimagelist(field, pointing, ccd)[-1]
datos = pyfits.open(ima)[0].data
for ii in range(dim):
if area[ii] > 1:
size = int(round(U.sqrt(area[ii]) / 2.))
xo = x[ii]
yo = y[ii]
dimx = U.shape(datos[yo - size:yo + size, xo - size:xo + size])[1]
dimy = U.shape(datos[yo - size:yo + size, xo - size:xo + size])[0]
perc[ii] = (datos[yo - size:yo + size, xo - size:xo + size].sum() /
(dimx * dimy * 1.))
# Defining the sample to be keep.
good = U.greater(perc, 0.5)
idr = U.compress(good, ids)
dim2 = len(idr)
print 'Dimensions: Original: %i, Final: %i, Excluded: %i detections. ' % (
dim, dim2, dim - dim2)
finalcat = root + 'f0%ip0%i_colorproext_%i_ISO.irmsF814W.free.cat' % (
field, pointing, ccd)
data1 = coeio.loaddata(catalog) # Loading the whole catalog content.
head = coeio.loadheader(catalog)
data2 = data1[good, :]
coeio.savedata(data2, finalcat, dir="",
header=head) # Saving & creating a new catalog.
def match_spz_sample(cluster): # TO CHECK
finalcat1 = catalog2[:-3]+'CLASH.redu.cat'
finalcat2 = catalog2[:-3]+'nada.cat'
# if not os.path.exists(finalcat1):
if not os.path.exists(finalcat2):
# print 'Final catalog does not exist yet.'
if os.path.exists(catalog1) and os.path.exists(catalog2):
# It matches up detections to its Spectroscopic Sample.
# Reading specz catalog
print 'Reading info1 before matching...'
speczsample = catalog1
idsp,xsp,ysp = U.get_data(speczsample,(0,3,4))
goodsp = U.greater_equal(xsp,1500) * U.less_equal(xsp,3500)
goodsp *= U.greater_equal(ysp,1500) * U.less_equal(ysp,3500)
idsp,xsp,ysp = U.multicompress(goodsp,(idsp,xsp,ysp))
print 'New dimension for specz catalogue: ',len(xsp)
# rasp,decsp,xsp,ysp,zsp = get_data(speczsample,(0,1,2,3,4))
# xsp,ysp,zsp = get_data(speczsample,(1,2,7))
####### idsp = U.arange(len(xsp))+1
# idsp = arange(len(rasp))+1
# Reading ColorPro catalog
print 'Reading info2 before matching...'
idcol,xcol,ycol = U.get_data(catalog2,(0,3,4))
print 'Dimension for input catalogue before compressing: ',len(idcol)
gsp = U.greater_equal(xcol,1500) * U.less_equal(xcol,3500)
gsp *= U.greater_equal(ycol,1500) * U.less_equal(ycol,3500)
idcol,xcol,ycol = U.multicompress(gsp,(idcol,xcol,ycol))
print 'Dimension for input catalogue after compressing: ',len(idcol)
# Using "matching_vects" to match up samples...
print 'Matching samples....'
pepe = CT.matching_vects(idcol,xcol,ycol,idsp,xsp,ysp,1.1) # We use now X,Y instead RA,Dec
# Compressing matches for ColorPro...
print 'Compressing matches...'
matchidcol = pepe[:,0].astype(int)
gdet_col = U.greater(matchidcol,0) # Excluding 0's (non matched detections)
matchidcol = U.compress(gdet_col,(matchidcol))
# Compressing matches for Spectroscopic...
matchidsp = pepe[:,1].astype(int)
gdet_spz = U.greater(matchidsp,0) # Excluding 0's (non matched detections)
matchidsp = U.compress(gdet_spz,(matchidsp))
print 'len(idcol)',len(idcol)
print 'len(idsp)',len(idsp)
if len(matchidcol) == len(matchidsp):
print 'Creating idredu & zsredu '
print 'Dimension of matchidsp ',len(matchidsp)
idredu = U.zeros(len(matchidsp))
idspredu = U.zeros(len(matchidsp))
for ii in range(len(matchidsp)):
colindex = A.id2pos(idcol,matchidcol[ii]) # Position for Index idcol
spzindex = A.id2pos(idsp,matchidsp[ii]) # Position for Index idsp
idredu[ii] = idcol[colindex] # ID for ColorPro
idspredu[ii] = idsp[spzindex] # Specz for Specz
# A new smaller catalog will be created containing specz info as an extra column.
print 'Selecting by rows... '
finalcat1 = catalog2[:-3]+'UDF.redu.cat'
finalcat2 = catalog2[:-3]+'CLASH.redu.cat'
U.put_data(catalog2[:-3]+'idsfrommatch.txt',(idredu,idspredu))
A.select_rows_bylist_sorted(catalog1,idspredu,finalcat1)
A.select_rows_bylist_sorted(catalog2,idredu,finalcat2)
def correct_SExt_uncertainties(cluster):
# (catalog,columns,zpts,gains,area2rms,weightimas,arinarout,finalcat):
"""
It reads the input catalogue and corrects*
its photometric errors empirically using direct area_v_sigma estimations*.
It returns the name of the new and corrected catalog.
--
*** THE POSITION OF AREA,MAGS & ERRMAGS NEED TO BE CHECK OUT BEFORE RUNNING IT.
----------------------------------------------------------------------------
REQUIREMENTS:
- An input catalogue (catalog)
- Its corresponding COLUMNS file (columns)
- A list with all ZPS (2nd column) (zpts)
- A list with all GAINS (2nd column) (gains)
- A list with all area2sigma files (1 per band) (area2rms)
- A list with all WEIGHT-MAPS (weightimas)
"""
# If weight = 1, it uses the Weight-maps to calculate photo.uncertainties.
weight = 0
# If verbose = 1, additional information is displayed during the analysis.
verbose = 0
# This factor serves to plot several check figures.
check1 = 1
check2 = 1
catalog = finalroot +'/%s/images/%s.photo.cat'%(cluster,cluster)
columns = finalroot +'/%s/images/%s.photo.columns'%(cluster,cluster)
scimas = finalroot+'/%s/images/sci.list'%(cluster)
weightimas = finalroot+'/%s/images/wht.list'%(cluster)
zpts = U.get_data(finalroot+'/%s/images/%s.zpt.cat'%(cluster,cluster),0)
gains = get_JPLUS_gains(scimas)
# Name for the final corrected catalogue
newphotcat = finalroot +'/%s/images/%s.photo.err.cat'%(cluster,cluster)
# To account for differences in exposure time, we need a list with ALL WEIGHT-maps.
if weight: wimas = U.get_str(weightimas,0)
if verbose: print 'Catalog: ',catalog
if os.path.exists(catalog):
mm = get_magnitudes(catalog,columns)
em = get_errmagnitudes(catalog,columns)
xx,yy,aper = U.get_data(catalog,(3,4,5))
data = C.loaddata(catalog) # Loading the whole catalog content.
head = C.loadheader(catalog) # Loading the original header.
zps = U.get_data(zpts,1) # Loading Zeropoint values
# gain = U.get_data(gains,1) # Loading Gain Values.
filters = get_filters(columns) # It gets the filter names for plots.
# Defining new variables
ng = len(mm[:,0]) # ng is the number of galaxies.
nf = len(mm[0,:]) # nf is the number of filters.
if verbose: print 'ng,nl',ng,nl
errmag = N.zeros((ng,nf),float) # Where the new photo errors will be saved.
# Starting the game..
for jj in range(nf):
if verbose: print 'Analyzing filter %i'%(jj+1)
# For every single band we need to read the apert_v_sigma file (area2rms)
# to interpolate the real values of area from the catalog.
# print 'area2rms[%i]'%(jj),area2rms
area2rms_bands = U.get_str(area2rms,0)
sqrtarea, sbackg, smean = U.get_data(area2rms_bands[jj],(0,1,2))
rmsfit = N.poly1d(N.polyfit(sqrtarea, sbackg, 3)) # CHECK
meanfit = N.poly1d(N.polyfit(sqrtarea, smean, 2)) # CHECK
# It reads the WEIGHT-map if requested
if weight:
if verbose: print 'Reading Weight image: ', wimas[jj]
wdata = fits.open(wimas[jj])[0].data
wdatanorm = wdata / wdata.max()
if check1:
# Sanity plot to assure the interpolated area2sigma function was right
plt.figure(0, figsize = (7,6),dpi=70, facecolor='w', edgecolor='k')
plt.clf()
plt.plot(sqrtarea,sbackg,'ko',sqrtarea,rmsfit(sqrtarea),'r-',linewidth=2)
plt.xlabel('$\sqrt{N}$',size=18)
plt.ylabel('$\sigma$',size=20)
plt.legend(['Data','Interpolation'],numpoints=1,loc='upper left')
plt.grid()
plt.savefig(catalog[:-3]+'.%s.Ar2Si.check.png'%(filters[jj]),dpi=80)
# Sanity plot to assure the interpolated area2sigma function was right
# plt.figure(2, figsize = (7,6),dpi=70, facecolor='w', edgecolor='k')
plt.clf()
plt.plot(sqrtarea, smean,'ko',sqrtarea, meanfit(sqrtarea),'r-',linewidth=2)
plt.xlabel('$\sqrt{N}$',size=18)
plt.ylabel('$mean$',size=20)
plt.legend(['Data','Interpolation'],numpoints=1,loc='upper left')
plt.grid()
plt.savefig(catalog[:-3]+'.%s.Ar2Mean.check.png'%(filters[jj]),dpi=80)
fluxgal = B.mag2flux(mm[:,jj]-zps[jj]) # -meansignal+U.sqrt(fluxcorr4aperture)
# good_sample = U.less_equal(abs(mm[:,jj]),30) # good sample
# bad_sample = U.greater(abs(mm[:,jj]),30) # bad sample
# Values to estimates the mags error.
sqar = N.sqrt(aper)
sigback = rmsfit(sqar)
meansignal = meanfit(sqar)
# There will be non-detected galaxies
# with m=99 magnitudes.
# Those numbers should not change here.
detected = U.less_equal(abs(mm[:,jj]),30) # good sample
nondetected = U.greater(abs(mm[:,jj]),30) # bad sample
# Photom. error (as defined by SExtractor) but using the new sigma value!
if weight:
pixw = N.zeros(ng)
for hhh in range(ng): pixw[hhh]=wdatanorm[int(yy[hhh])-1,int(xx[hhh])-1]
fluxcor = fluxgal*pixw
newerror=N.sqrt((aper*sigback*sigback/fluxcor**2)+(fluxcor*gain))
newerror *= 1.0857
else:
newerror=N.sqrt((aper*sigback*sigback/fluxgal**2)+(fluxgal*gain))
newerror *= 1.0857
# Assesing new uncertainties.
errmag[detected,jj] = newerror[detected]
errmag[nondetected,jj] = em[nondetected,jj]
# A new figure is create to compare SExtractor vs Empirical uncert.
if check2:
line = N.arange(16.,30.,0.25)
SExline = U.bin_stats(mm[:,jj],em[:,jj],line,stat='mean_robust')
aperline = U.bin_stats(mm[:,jj],errmag[:,jj],line,stat='mean_robust')
# plt.figure(1,figsize = (8,7),dpi=70, facecolor='w', edgecolor='k')
plt.clf()
plt.plot(mm[:,jj],em[:,jj],'r+',mm[:,jj],errmag[:,jj],'k+')
plt.plot(line,SExline,'-ro',line,aperline,'-ko',linewidth=6,alpha=0.2)
plt.legend(['$SExtractor$','$Apertures$'],numpoints=1,loc='upper left')
plt.xlabel('$Mags$',size=17)
plt.ylabel('$ErrMags$',size=17)
plt.xlim(17.,30.)
plt.ylim(0.,1.0)
plt.grid()
plt.savefig(catalog[:-3]+'.%s.uncert.comparison.png'%(filters[jj]),dpi=80)
# The new values of mags error are now overwrited in the original data.
vars,evars,posref,zpe,zpo = get_usefulcolumns(columns)
data[:,evars] = errmag[:,N.arange(nf)]
C.savedata(data,finalcat, dir="",header=head) # Saving and creating the new catalog.
def appending_ids2catalogues(field, pointing, ccd):
"""
import alhambra_3arcs as A3
A3.appending_ids2catalogues(2,1,1)
"""
catalhambra = root + 'f0%i/alhambra.F0%iP0%iC0%i.ColorProBPZ.cat' % (
field, field, pointing, ccd)
idalh = U.get_str(catalhambra, 0)
idalh2 = U.arange(len(idalh)) + 1
xalh, yalh = U.get_data(catalhambra, (6, 7))
cat3arcs = finalroot + 'f0%i/alhambra.f0%ip0%ic0%i.3arcs.cat' % (
field, field, pointing, ccd)
id3arcs, x3arcs, y3arcs = U.get_data(cat3arcs, (0, 3, 4))
print len(id3arcs)
matchfile = cat3arcs[:-3] + 'idsfrommatch.txt'
if not os.path.exists(matchfile):
idcol = idalh2
xcol = xalh
ycol = yalh
idsp = id3arcs
xsp = x3arcs
ysp = y3arcs
pepe = CT.matching_vects(idcol, xcol, ycol, idsp, xsp, ysp, 5)
# Compressing matches for ColorPro...
print 'Compressing matches...'
matchidcol = pepe[:, 0].astype(int)
gdet_col = U.greater(matchidcol,
0) # Excluding 0's (non matched detections)
matchidcol = U.compress(gdet_col, (matchidcol))
# Compressing matches for Spectroscopic...
matchidsp = pepe[:, 1].astype(int)
gdet_spz = U.greater(matchidsp,
0) # Excluding 0's (non matched detections)
matchidsp = U.compress(gdet_spz, (matchidsp))
print 'len(idcol)', len(idcol)
print 'len(idsp)', len(idsp)
if len(matchidcol) == len(matchidsp):
print 'Creating idredu & zsredu '
print 'Dimension of matchidsp ', len(matchidsp)
idredu = U.zeros(len(matchidsp))
idspredu = U.zeros(len(matchidsp))
for ii in range(len(matchidsp)):
colindex = A.id2pos(idcol,
matchidcol[ii]) # Position for Index idcol
spzindex = A.id2pos(idsp,
matchidsp[ii]) # Position for Index idsp
idredu[ii] = idcol[colindex] # ID for ColorPro
idspredu[ii] = idsp[spzindex] # Specz for Specz
matchfile = cat3arcs[:-3] + 'idsfrommatch.txt'
U.put_data(matchfile, (idredu, idspredu))
if os.path.exists(matchfile):
pepa = open(matchfile[:-3] + 'bis.cat', 'w')
idredu, idspredu = U.get_data(matchfile, (0, 1))
i11 = idredu.astype(int) - 1
i22 = idspredu.astype(int)
lista = []
for ii in range(len(i11)):
lista.append(idalh[i11[ii]])
pepa.write('%s %s \n' % (idalh[i11[ii]], i22[ii]))
pepa.close()
finalfinal = cat3arcs[:-3] + 'final.cat'
if os.path.exists(finalfinal): A.deletefile(finalfinal)
if not os.path.exists(finalfinal):
print 'Preparing ', finalfinal
idsa = U.get_str(matchfile[:-3] + 'bis.cat', 0)
append_IDs2_3arcs_catalogues(cat3arcs, idsa)
def figura33(lista):
"""
I'm using the stellar classification from the version_e.
----
import alhambra_completeness as alhc
lista = '/Volumes/amb22/catalogos/reduction_v4d/globalcats/lista.list'
alhc.figura33(lista)
"""
blue = 0
red = 1
cats = U.get_str(lista, 0)
cats2 = U.get_str(lista, 1)
nc = len(cats)
dx = 0.2
dy = 0.4
nxbins = 4
nybins = 2
ods = 0.05
mmin = 16.0
mmax = 23.75
zbmin = 0.0001
zbmax = 1.4
Mmin = -24
Mmax = -17
if red:
Tbmin = 1 # 7.
Tbmax = 5 # 11.
resolmag = 0.2 # 0.2
resolz = 0.05
if blue:
Tbmin = 7.
Tbmax = 11.
resolmag = 0.2
resolz = 0.05
resol = 0.025
areas = ([0.45, 0.47, 0.23, 0.24, 0.47, 0.47, 0.46, 2.79])
plt.figure(111, figsize=(21.5, 11.5), dpi=70, facecolor='w', edgecolor='k')
ss = 0
for jj in range(nybins):
for ii in range(nxbins):
# Reading data from catalogs.
mo, zb, tb, odds, m814 = U.get_data(cats[ss], (81, 72, 75, 76, 62))
sf = U.get_data(cats2[ss], 71)
# mo,zb,tb,sf,odds,m814 = U.get_data(cats[ss],(81,72,75,71,76,62))
g = U.greater_equal(abs(m814), mmin) * U.less_equal(
abs(m814), mmax)
# g* = U.greater_equal(odds,ods)
g *= U.greater_equal(tb, Tbmin) * U.less_equal(tb, Tbmax)
g *= U.less_equal(sf, 0.8)
yy = -0.014 * m814 + 0.38
g *= U.greater(odds, yy)
g *= U.less_equal(mo, Mmax + resol) * U.greater(mo, Mmin - resol)
g *= U.greater(zb, zbmin) * U.less_equal(zb, zbmax)
mo, zb, tb, odds = U.multicompress(g, (mo, zb, tb, odds))
print 'dimension', len(mo)
# Plotting density.
# cuadrado = plt.axes([.1+(ii*dx),.1+((nybins-jj-1)*dy),dx,dy])
if ii == nxbins - 1:
cuadrado = plt.axes([
.1 + (ii * dx), .1 + ((nybins - jj - 1) * dy),
dx + (dx * 0.2), dy
])
else:
cuadrado = plt.axes(
[.1 + (ii * dx), .1 + ((nybins - jj - 1) * dy), dx, dy])
matrix, axis2, axis1 = rs.CC_numberdensity_contour_zvolume(
zb, mo, resolz, resolmag, 1)
if blue:
plt.contourf(axis2,
axis1,
U.log10(matrix / areas[ss]),
250,
vmin=-11.,
vmax=-7.) # blue galaxies
if red:
plt.contourf(axis2,
axis1,
U.log10(matrix / areas[ss]),
250,
vmin=-12.,
vmax=-7.65) # red galaxies
if ii == nxbins - 1:
aa = plt.colorbar(pad=0., format='%.1f')
aa.set_label('Log. Density [N/Mpc$^{3}$/deg$^{2}$]', size=18)
if jj != nybins - 1: plt.setp(cuadrado, xticks=[])
if ii != 0: plt.setp(cuadrado, yticks=[])
if jj == nybins - 1:
plt.xlabel('M$_{B}$', size=27)
plt.xticks(fontsize=17)
if ii == 0:
plt.ylabel('redshift', size=28)
plt.yticks(fontsize=17)
# plotting axis manually
base1 = U.arange(Mmin, Mmax + 1., 1.)
base2 = U.arange(0, zbmax + (2. * resol), resol)
dim1 = len(base1)
dim2 = len(base2)
for rr in range(dim1):
plt.plot(base2 * 0. + base1[rr],
base2,
'k--',
linewidth=1.,
alpha=0.25)
for rr in range(dim2):
plt.plot(base1,
base1 * 0. + base2[rr],
'k--',
linewidth=1.,
alpha=0.25)
# plt.grid()
plt.ylim(zbmin + 0.0001, zbmax - 0.001)
plt.xlim(Mmin + 0.0001, Mmax - 0.0001)
if ss == 7: labelleg = 'Global'
else: labelleg = 'A%i' % (ss + 2)
xypos = (Mmax - 1.6, zbmax - 0.18)
if ss == 7: xypos = (Mmax - 3.5, zbmax - 0.18)
plt.annotate(labelleg, xy=xypos, fontsize=40, color='black')
ss += 1
plt.savefig('completeness.alhambra.png', dpi=200)
def flagging_dobledetections(cat1,cat2):
"""
This serves to append an extra column (each to both inputted catalogs)
indicating either a detection was repeated and with the lowest S/N
of the two.
Sources flagged as 1 are those detections to be excluded when combining
both catalogs into a single one.
--------
import alhambra_overlap as alhov
cat1 = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p02_colorproext_1_ISO.cat'
cat2 = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p01_colorproext_1_ISO.cat'
alhov.flagging_dobledetections(cat1,cat2)
"""
id1,ra1,dec1,x1,y1,s2n1 = U.get_data(cat1,(0,1,2,3,4,14))
id2,ra2,dec2,x2,y2,s2n2 = U.get_data(cat2,(0,1,2,3,4,14))
ne1 = len(id1)
ne2 = len(id2)
g1 = U.greater_equal(ra1,min(ra2))
g2 = U.less_equal(ra2,max(ra1))
id1r,ra1r,dec1r,x1r,y1r,s2n1r = U.multicompress(g1,(id1,ra1,dec1,x1,y1,s2n1))
id2r,ra2r,dec2r,x2r,y2r,s2n2r = U.multicompress(g2,(id2,ra2,dec2,x2,y2,s2n2))
flag1 = U.zeros(ne1)
flag2 = U.zeros(ne2)
dim1 = len(id1r)
dim2 = len(id2r)
print 'dim1,dim2',dim1,dim2
if dim1>0 and dim2>0:
print 'Matching samples....'
pepe = matching_vects_ddet(id1r,ra1r,dec1r,id2r,ra2r,dec2r,0.000312) # We use now X,Y instead RA,Dec
# Purging null elements
matchidcol = pepe[:,0].astype(int)
good_det1 = U.greater(matchidcol,0) # Excluding 0's (non matched detections)
matchidcol = U.compress(good_det1,(matchidcol))
matchidsp = pepe[:,1].astype(int)
good_det2 = U.greater(matchidsp,0) # Excluding 0's (non matched detections)
matchidsp = U.compress(good_det2,(matchidsp))
if len(matchidcol) == len(matchidsp) and len(matchidcol) >0 :
newdim = len(matchidsp)
print 'Dimension of matching',newdim
idr1 = U.zeros(newdim)
idr2 = U.zeros(newdim)
s2nr1 = U.zeros(newdim)
s2nr2 = U.zeros(newdim)
for ii in range(newdim):
idr1index = ap.id2pos(id1r,matchidcol[ii])
idr2index = ap.id2pos(id2r,matchidsp[ii])
idr1[ii] = id1r[idr1index]
s2nr1[ii] = s2n1r[idr1index]
idr2[ii] = id2r[idr2index]
s2nr2[ii] = s2n2r[idr2index]
# Select/Purge detections according to its S/N
marcador1 = U.zeros(newdim)
marcador2 = U.zeros(newdim)
for ss in range(newdim):
cociente = s2nr1[ss]/s2nr2[ss]
if cociente >= 1.: marcador1[ss] = 1.
else: marcador2[ss] = 1.
cond1 = U.less(marcador1,1)
cond2 = U.less(marcador2,1)
idr1b = U.compress(cond1,idr1)
dim1rr = len(idr1b)
idr2b = U.compress(cond2,idr2)
dim2rr = len(idr2b)
# Two new IDs (finalid1 & finalid2) are generated with
# the final elements to be included in the output catalog.
for hh1 in range(ne1):
if id1[hh1] in idr1b:
flag1[hh1] = 1
for hh2 in range(ne2):
if id2[hh2] in idr2b:
flag2[hh2] = 1
# A new smaller catalog will be created containing specz info as an extra column.
outcat1 = ap.decapfile(cat1)+'.doubledetect.cat'
outcat2 = ap.decapfile(cat2)+'.doubledetect.cat'
print 'outcat1',outcat1
print 'outcat2',outcat2
ap.appendcol(cat1,flag1,'Flag2Detected',outcat1)
ap.appendcol(cat2,flag2,'Flag2Detected',outcat2)
# Renaming files
ap.renamefile(cat1,cat1+'.old.cat')
if not os.path.exists(cat1): ap.renamefile(outcat1,cat1)
ap.renamefile(cat2,cat2+'.old.cat')
if not os.path.exists(cat2): ap.renamefile(outcat2,cat2)
else:
print 'No common sources in betwen the catalogs'
# A new smaller catalog will be created containing specz info as an extra column.
outcat1 = ap.decapfile(cat1)+'.doubledetect.cat'
outcat2 = ap.decapfile(cat2)+'.doubledetect.cat'
print 'outcat1',outcat1
print 'outcat2',outcat2
ap.appendcol(cat1,flag1*0,'Flag2Detected',outcat1)
ap.appendcol(cat2,flag2*0,'Flag2Detected',outcat2)
# Renaming files
ap.renamefile(cat1,cat1+'.old.cat')
if not os.path.exists(cat1): ap.renamefile(outcat1,cat1)
ap.renamefile(cat2,cat2+'.old.cat')
if not os.path.exists(cat2): ap.renamefile(outcat2,cat2)
def purging_dobledetections(cat1,cat2):
"""
import alhambra_overlap
from alhambra_overlap import *
cat1 = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p02_colorproext_1_ISO.cat'
cat2 = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p01_colorproext_1_ISO.cat'
purging_dobledetections(cat1,cat2)
"""
id1,ra1,dec1,x1,y1,s2n1 = U.get_data(cat1,(0,1,2,3,4,14))
id2,ra2,dec2,x2,y2,s2n2 = U.get_data(cat2,(0,1,2,3,4,14))
ne1 = len(id1)
ne2 = len(id2)
g1 = U.greater_equal(ra1,min(ra2))
g2 = U.less_equal(ra2,max(ra1))
id1r,ra1r,dec1r,x1r,y1r,s2n1r = U.multicompress(g1,(id1,ra1,dec1,x1,y1,s2n1))
id2r,ra2r,dec2r,x2r,y2r,s2n2r = U.multicompress(g2,(id2,ra2,dec2,x2,y2,s2n2))
dim1 = len(id1r)
dim2 = len(id2r)
print 'dim1,dim2',dim1,dim2
if dim1>0 and dim2>0:
print 'Matching samples....'
pepe = matching_vects_ddet(id1r,ra1r,dec1r,id2r,ra2r,dec2r,0.000312) # We use now X,Y instead RA,Dec
# Purging null elements
matchidcol = pepe[:,0].astype(int)
good_det1 = U.greater(matchidcol,0) # Excluding 0's (non matched detections)
matchidcol = U.compress(good_det1,(matchidcol))
matchidsp = pepe[:,1].astype(int)
good_det2 = U.greater(matchidsp,0) # Excluding 0's (non matched detections)
matchidsp = U.compress(good_det2,(matchidsp))
if len(matchidcol) == len(matchidsp) and len(matchidcol) >0 :
newdim = len(matchidsp)
print 'Dimension of matching',newdim
idr1 = U.zeros(newdim)
idr2 = U.zeros(newdim)
s2nr1 = U.zeros(newdim)
s2nr2 = U.zeros(newdim)
for ii in range(newdim):
idr1index = ap.id2pos(id1r,matchidcol[ii])
idr2index = ap.id2pos(id2r,matchidsp[ii])
idr1[ii] = id1r[idr1index]
s2nr1[ii] = s2n1r[idr1index]
idr2[ii] = id2r[idr2index]
s2nr2[ii] = s2n2r[idr2index]
# Select/Purge detections according to its S/N
marcador1 = U.zeros(newdim)
marcador2 = U.zeros(newdim)
for ss in range(newdim):
cociente = s2nr1[ss]/s2nr2[ss]
if cociente >= 1.: marcador1[ss] = 1.
else: marcador2[ss] = 1.
cond1 = U.less(marcador1,1)
cond2 = U.less(marcador2,1)
idr1b = U.compress(cond1,idr1)
dim1rr = len(idr1b)
idr2b = U.compress(cond2,idr2)
dim2rr = len(idr2b)
print ''
print 'Number of detections to be removed from cat1: ', dim1rr
print 'Number of detections to be removed from cat2: ', dim2rr
print ''
# Two new IDs (finalid1 & finalid2) are generated with
# the final elements to be included in the output catalog.
finalid1 = U.zeros((ne1-dim1rr))
finalid2 = U.zeros((ne2-dim2rr))
kk1 = 0
for hh1 in range(ne1):
if id1[hh1] not in idr1b:
finalid1[kk1] = id1[hh1]
kk1 += 1
print 'kk1',kk1
kk2 = 0
for hh2 in range(ne2):
if id2[hh2] not in idr2b:
if kk2 <= (ne2-dim2rr-1):
finalid2[kk2] = id2[hh2]
kk2+=1
print 'kk2',kk2
# A new smaller catalog will be created containing specz info as an extra column.
outcat1 = ap.decapfile(cat1)+'.wo2detect.cat'
outcat2 = ap.decapfile(cat2)+'.wo2detect.cat'
print 'outcat1',outcat1
print 'outcat2',outcat2
ap.select_rows_bylist(cat1,finalid1,outcat1)
ap.select_rows_bylist(cat2,finalid2,outcat2)
else:
print 'No common sources in betwen the catalogs'
def globalimage_zb(image, cat, posx, posy, posarea, poszb, shape, save,
outfile):
"""
============
from alhambra_webpage import *
image = '/Users/albertomolino/Desktop/emss2137/emss2137.png'
cat = '/Volumes/amb2/SUBARU/emss2137/catalogs/MS2137_Subaru.bpz.2.cat'
posx = 3
posy = 4
posarea = 5
poszb = 17
shape = 'circle'
save = 'yes'
outfile = '/Users/albertomolino/Desktop/emss2137/emss2137_22.png'
globalimage_zb(image,cat,posx,posy,posarea,poszb,shape,save,outfile)
-----------------
import alhambra_photools
from alhambra_photools import *
import alhambra_webpage
from alhambra_webpage import *
image = '/Users/albertomolino/Desktop/macs1206/macs1206.color.png'
cat = '/Users/albertomolino/Desktop/UDF/Molino12/catalogs/ColorPro/macs1206_UDFconf_NIR_July2012_ISO_RS.cat'
posx = 3
posy = 4
shape = 'circle'
save = 'yes'
posarea = 5
poszb = 0
outfile = '/Users/albertomolino/Desktop/macs1206/macs1206.color.RS.purged.png'
globalimage_zb(image,cat,posx,posy,posarea,poszb,shape,save,outfile)
--------
import alhambra_photools
from alhambra_photools import *
import alhambra_webpage
from alhambra_webpage import *
image = '/Users/albertomolino/Desktop/rxj2248/HST/rxj2248_acs.png'
cat = '/Users/albertomolino/Desktop/rxj2248/HST/catalogs/rxj2248_IR_RedSeq.cat'
posx = 3
posy = 4
shape = 'circle'
save = 'yes'
posarea = 5
poszb = 0
outfile = '/Users/albertomolino/Desktop/rxj2248/HST/rxj2248_acs.RS.png'
globalimage_zb(image,cat,posx,posy,posarea,poszb,shape,save,outfile)
--------
"""
colorfile = image
im = Image.open(colorfile)
imsize = nx, ny = im.size
stamp = im.crop((0, 0, nx, ny))
draw = ImageDraw.Draw(stamp)
try:
x, y, area, zb = U.get_data(cat, (posx, posy, posarea, poszb))
sf = U.get_data(cat, 74)
# good = greater(zb,0.28) * less(zb,0.34)
good = U.greater(zb, 0.001) * U.less(sf, 0.7)
x, y, area, zb = U.multicompress(good, (x, y, area, zb))
except:
print 'Impossible to read the data from catalog. Check it out!!'
for ii in range(len(x)):
xx = x[ii]
yy = y[ii]
aa = area[ii]
zzb = zb[ii]
zbval = ' %.2f ' % (zzb)
# print 'x,y',xx,yy
# print 'zbval',zbval
# print 'ii',ii
# dx = dy = 1.5 * aa
shapesize = 20 # aa * 1.05
# print 'shapesize',shapesize
# dxo = dyo = 0
colores = (255, 255, 1)
# if zzb < 0.1 : colores = (255,1,1) # red
# elif zzb >= 0.1 and zzb < 0.3 : colores = (255,255,1) # yellow
# elif zzb >= 0.3 and zzb < 1. : colores = (1,255,1) # Green
# elif zzb >= 1. and zzb < 3. : colores = (1,255,255) # Blue
# else: colores = (255,1,255) # Purple
if shape != 'None':
if shape == 'circle':
draw.ellipse((xx - shapesize, ny - yy - shapesize,
xx + shapesize, ny - yy + shapesize),
fill=None)
elif shape == 'crosshair':
draw.line((xx + shapesize, yy, xx + shapesize - 10, yy),
fill=None,
width=3)
draw.line((xx - shapesize, yy, xx - shapesize + 10, yy),
fill=None,
width=3)
draw.line((xx, yy + shapesize, xx, yy + shapesize - 10),
fill=None,
width=3)
draw.line((xx, yy - shapesize, xx, yy - shapesize + 10),
fill=None,
width=3)
elif shape == 'rectangle':
draw.rectangle((dx - shapesize, dy - shapesize, dx + shapesize,
dy + shapesize),
fill=colores) # fill=None)
else:
print 'Shape not found!. It will not be overlaid...'
draw.text((xx - (shapesize / 2.), ny - yy - (shapesize / 2.)),
zbval,
fill=(255, 255, 1))
# draw.text((xx-(1.5*shapesize),ny-yy-(2.*shapesize)),zbval,fill=(255,255,255))
# # draw.text((xx-(1.5*shapesize),ny-yy-(2.*shapesize)),zbval,fill=colores)
if save == 'yes':
if outfile != 'None':
stamp.save(outfile)
else:
stamp.save('imcutoff.png')