"""

This serves to run flagging_dobledetections through the ALHAMBRA fields

----

import alhambra_overlap as alhov

alhov.script_alhambra_flagging_dobledetections(2)

"""

root = '/Volumes/amb22/catalogos/reduction_v4e/f0%i/'%(field)

vector1a = [2,1,2,2,1,2]

vector1b = [1,2,2,4,3,3]

vector2a = [1,2,1,1,2,1]

vector2b = [1,1,2,4,4,3]

for ss in range(6):

cat1 = root+'f0%ip0%i_colorproext_%i_ISO.cat'%(field,vector1a[ss],vector1b[ss])

cat2 = root+'f0%ip0%i_colorproext_%i_ISO.cat'%(field,vector2a[ss],vector2b[ss])

if os.path.exists(cat1) and os.path.exists(cat2):

print 'Running flagging_dobledetections on: '

print cat1

print cat2

print ''

alhov.flagging_dobledetections(cat1,cat2)

# Colapse double columns if necessary...

alhov.flagging_dobledetections_mergecolumns(cat1)

"""

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

cat2 = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p01_colorproext_2_ISO.cat'

alhov.flagging_dobledetections_mergecolumns(cat2)

"""

data = coeio.loaddata(catalog) # Loading the whole catalog content.

head = coeio.loadheader(catalog) # Loading the original header.

nc = len(data.T) # Number columns

dim = len(data[:,0]) # Number elements

print 'nc,dim',nc,dim

var1 = head[-3].split()[-1]

var2 = head[-2].split()[-1]

if var1 == var2:

print 'Duplicated columns. Merging information...'

uno = data[:,72]

dos = data[:,73]

tres = uno+dos

newdata = U.zeros((dim,nc-1),float)

for ii in range(nc-1):

for jj in range(dim):

if ii == nc-1:

print 'pepe'

newdata[jj,ii] = tres[jj]

else:

newdata[jj,ii] = data[jj,ii]

head2 = head[:-1]

head2[-1]='#'

outcat = catalog[:-4]+'.mergedcolumns.cat'

coeio.savedata(newdata,outcat, dir="",header=head2) # Saving and creating the new catalog.

# Renaming files

ap.renamefile(catalog,catalog+'.oldold.cat')

"""

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')

"""

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:

"""

-------------------------------------------------------------------------

The program matchs objects using their coordinates.

-------------------------------------------------------------------------

c10: identification number from set1

c11 & c12: coordinates to be used when matching the common objects.

c20: identification number from set2.

c21 & c22: coordinates to be used when matching the common objects.

An output matrix (outmatrix) will provide the matched elements.

outmatrix[0]: c10_matched & outmatrix[1]: c20_matched

--------------------------------------------------------------------------

Alberto Molino amb.at.iaa.es // July_09 //

--------------------------------------------------------------------------

"""

# Variable definition.

id1= c10

x1 = c11

y1 = c12

dim1 = len(c10)

id2= c20

x2 = c21

y2 = c22

dim2 = len(c20)

delta_xx = U.zeros(dim1+dim2,float)

delta_yy = U.zeros(dim1+dim2,float)

INSIDE = U.zeros((dim1+dim2,2),float)

MATCHING = U.zeros((dim1+dim2,2),float)

kmin = 0

nn = 0

prec = float(precision)

thr = 1e-30

for jj in range(dim1):

kk = 0

for ii in range(dim2):

delta_xx[ii] = (x2[ii]-x1[jj])

delta_yy[ii] = (y2[ii]-y1[jj])

circle = U.sqrt(delta_xx[ii]**2 + delta_yy[ii]**2)

if circle < prec:

INSIDE[kk,0]=float(circle)

INSIDE[kk,1]=id2[ii]

kk += 1

kmin = 0

if kk > 0:

if kk > 1:

kmin = U.argmin(INSIDE[0:kk,0])

MATCHING[nn,0]=id1[jj]

MATCHING[nn,1]=INSIDE[kmin,1] # id2

else:

MATCHING[nn,0]=id1[jj]

MATCHING[nn,1]=INSIDE[0,1] # id2

nn += 1 # Real dimension of matched objects

print '-------------------------------------'

print ' Matched elements= ',nn

if nn > 0 :

print MATCHING[0:nn,0]

print MATCHING[0:nn,1]