def runall(prefixes,cat):
for prefix in prefixes:
images=glob.glob(prefix)
iraf.imgets(images[0],'FILTER')
filter=str(iraf.imgets.value)
for im in images:
runsextractor(im)
t=im.split('.')
if cat < 0.1:
out='sdssmatch-'+str(t[0])
refcat='sdsscoords.dat'
s='cat '+out+' >> sdssmatch'+filter
plotsdsspos(im)
if cat > .1:
out='2massmatch-'+str(t[0])
refcat='2masscoords.dat'
s='cat '+out+' >> 2massmatch'+filter
plot2masspos(im)
#iraf.xyxymatch(input='testxy.cat',reference='sdsscoords.dat',output=out,tolerance=20.,refpoints='refpoints',interactive='yes')
iraf.xyxymatch(input='testxy.cat',reference=refcat,output=out,tolerance=10.,refpoints='refpoints',interactive='no')
os.system(s)
infile='sdssmatch'+filter
infile='2massmatch'+filter
iraf.geomap(input=infile,database='PiscesBok',transform=filter,xmin=1.,xmax=1024,ymin=1.,ymax=1024.)
def geomap(self, xmax=-1, ymax=-1, fitgeometry='rscale'):
if not self.coofile:
raise ValueError, "Specify self.coofile first!"
if os.path.exists(self.database):
os.remove(self.database)
if xmax < 0: xmax = self.hdr_ref['naxis1']
if ymax < 0: ymax = self.hdr_ref['naxis2']
iraf.geomap(self.coofile, self.database, 1, xmax, 1, ymax,
interactive='no', fitgeometry=fitgeometry)
def geomap(self, database, xmax=-1, ymax=-1):
if not self.coofile:
raise ValueError, "Specify self.coofile first!"
if os.path.exists(database):
os.remove(database)
if xmax < 0: xmax = self.hdr_in['naxis1']
if ymax < 0: ymax = self.hdr_in['naxis2']
iraf.geomap(self.coofile, database, 1, xmax, 1, ymax, interactive='no')
self.database = database
def georun_sky(clustname, lims):
#run geomap and geoxytran. I have to run this within this module
#as geomap crashes when run from an external module.
#in this version
catpath = '.'
geomap_infile = catpath + '/geomap_coords_sky.' + clustname + '.in'
dbfile = catpath + '/' + clustname + '_geomap_sky.db'
iraf.geomap.xmin = lims['ramin']
iraf.geomap.xmax = lims['ramax']
iraf.geomap.ymin = lims['decmin']
iraf.geomap.ymax = lims['decmax']
iraf.geomap.maxiter = 3
iraf.geomap.reject = 3.0
iraf.geomap.fitgeometry = 'rxyscale'
iraf.geomap(geomap_infile, dbfile)
geotran_outfile = catpath + '/geomap_coords_sky.' + clustname + '.out'
#remove geotran output file if it already exists
if os.path.isfile(geotran_outfile) is True:
cmdstr = 'rm ' + geotran_outfile
os.system(cmdstr)
#transform the input coordinates in the geomap input file.
iraf.geoxytran(geomap_infile,
geotran_outfile,
dbfile,
geomap_infile,
direction="backward",
xcolumn=3,
ycolumn=4)
#read in the geomap input catalog that was transformed using geoxytran.
trans_ggdat = ascii.read(geotran_outfile)
ratrans = trans_ggdat['rain']
dectrans = trans_ggdat['decin']
raref = trans_ggdat['raref']
decref = trans_ggdat['decref']
#make a plot of the new residuals
posttrans_plotfile = catpath + '/posttrans.' + clustname + '_coordiff_sky.pdf'
cmts.match_diff_sky_plot(raref,
decref,
ratrans,
dectrans,
plotfile=posttrans_plotfile)
return dbfile, geomap_infile
def AlignImage(TargetImg,Template,AlignedImageName,fitgeometry="general"):
"""Align Template Image to TargetImage to create output image AlignedImageName.
fitgeometry can be set to 'rotate' when the Template is also TIRSPEC data
Otherwise if Template is 2MASS or other instrument set it as 'general' """
MatchingXYcoordsFile = os.path.splitext(AlignedImageName)[0]+'.XYXY'
if not os.path.isfile(MatchingXYcoordsFile) :
CreateMatchingXYCoords(TargetImg,Template,MatchingXYcoordsFile)
else:
print('Using Old {0} file'.format(MatchingXYcoordsFile))
XYtranformdb = os.path.splitext(AlignedImageName)[0]+'rtran.db'
iraf.geomap(input=MatchingXYcoordsFile, database=XYtranformdb, xmin=1, xmax=1024, ymin=1, ymax=1024, interactive=0,fitgeometry=fitgeometry)
#Now geotran the Template image to match the TargetImg
iraf.geotran(input=Template,output=AlignedImageName,database=XYtranformdb,transforms=MatchingXYcoordsFile)
#return the transfgorm .db filename just incase it is needed for the user
return XYtranformdb
pox_ini=j
l=i
m=j
print 500,i+1,j+1-4,i+1
#se ve que se este en el otro
if (imagen[i+1,j+1] != 0) and (imagen[i+1,j+2] == 0):
pox_fin=j
print 526,i+1,j+4+1,i+1
else:
pos_ini=pos_ini
pos_fin=pos_fin
sys.stdout.close()
sys.stdout=temp
####
#
# Ejecuta geomap, mapea el x e y del orden curvo a uno nuevo recto
# running geomap
#
####
import pyraf
from pyraf import iraf
from iraf import immatch, geomap
iraf.geomap(input=file_out,database=file_out+"_database.txt",fitgeometry="general",function="polynomial",xxorder=3,xyorder=3,yxorder=3,yyorder=3,maxiter=10)
workdir, "geodatabase"
) # better, it prevents pbs when doing simultaneous alignments with same pipe.
if os.path.isfile(databasename):
os.remove(databasename)
iraf.unlearn(iraf.geomap)
iraf.geomap.fitgeom = "rscale" # shift, xyscale, rotate, rscale
iraf.geomap.function = "polynomial"
iraf.geomap.transfo = "broccoli"
iraf.geomap.interac = "no"
#iraf.geomap.verbose="no"
mapblabla = iraf.geomap(input=geomapin,
database=databasename,
xmin=1,
xmax=dimx,
ymin=1,
ymax=dimy,
Stdout=1)
for line in mapblabla:
if "X and Y scale:" in line:
mapscale = line.split()[4:6]
if "Xin and Yin fit rms:" in line:
maprmss = line.split()[-2:]
if "X and Y axis rotation:" in line:
mapangles = line.split()[-4:-2]
if "X and Y shift:" in line:
mapshifts = line.split()[-4:-2]
geomaprms = math.sqrt(
def rectify_image(image,
reference,
output,
refout=None,
fitgeometry='general',
function='polynomial',
xxorder=3,
yyorder=3,
xyorder=3,
yxorder=3,
clobber=1,
objects=None,
interactive=1,
maxiter=5,
reject=3,
xrange=None,
yrange=None,
thresh=2.0,
scale=0.125,
fwhm=4,
image_sigma=None,
reference_sigma=None,
debug=0,
aoffset=0):
'''This task will take an image and a reference image and apply geomap/
geotran to aligned them. image is registered and output to output.
You can specify the fit geometry (shift, xyscale, rotate, rscale,
rxyscale, general). You can also choose the geenral function
(legendre, chebyshev, polynomial). You can specfy the order of all
the terms (xxorder, yyorder, xyorder, yxorder). If you choose
clobber=1 (the default), new images overwrite older ones. Lastly, if
you want to use a pre-existing list of objects, specify them as the
objects keyword.'''
# First, if we already have objects to use, save time by not finding them
# again!
if objects is None:
sex(image,
output='image.cat',
thresh=thresh,
scale=scale,
fwhm=fwhm,
sigma_map=image_sigma)
sex(reference,
output='reference.cat',
thresh=thresh,
scale=scale,
fwhm=fwhm,
sigma_map=reference_sigma)
im_masks1 = get_mask(image)
im_masks2 = get_mask(reference)
if aoffset == 0:
# Check the ROTANG header field to see if we need to give a rotation
# offset to the object matcher.
rotang1 = get_header(reference, "ROTANG", float)
rotang2 = get_header(image, "ROTANG", float)
if rotang1 is not None and rotang2 is not None:
if abs(rotang1 - rotang2) > 0.1:
aoffset = rotang1 - rotang2
# Run the matchum routine to get corresponding objects:
(x2, y2, x1, y1, o1, o2) = matchum('image.cat',
'reference.cat',
im_masks1=im_masks1,
im_masks2=im_masks2,
xrange=xrange,
yrange=yrange,
debug=debug,
aoffset=aoffset)
f = open('geomap.objects', 'w')
for i in range(len(x1)):
f.write('%.2f %.2f %.2f %.2f\n' % (x1[i], y1[i], x2[i], y2[i]))
f.close()
objects = "geomap.objects"
else:
data = pygplot.columns(objects)
x1 = data[0]
y1 = data[1]
x2 = data[2]
y2 = data[3]
xshift = int(stats.bwt(x2 - x1)[0])
yshift = int(stats.bwt(y2 - y1)[0])
# We now have some selected objects to use with geomap. Let's figure out
# the area of validity for the data using ths shifts computed above.
(r_xmax, r_ymax) = get_image_size(reference)
if xshift < 0:
xmin = -xshift
xmax = r_xmax
else:
xmin = 1
xmax = r_xmax - xshift
if yshift < 0:
ymin = -yshift
ymax = r_ymax
else:
ymin = 1
ymax = r_ymax - yshift
#xmin = min(x1); xmax = max(x1)
#ymin = min(y1); ymax = max(y1)
print xshift, yshift, xmin, xmax, ymin, ymax
iraf.images()
if os.path.isfile('geomap.db'): os.unlink('geomap.db')
# We may not have enough points to do a general fit, so we do them in order of
# complexity. Hopefully, we have at least ONE point (for the shift)
fitgeometries = [fitgeometry, 'rxyscale', 'rotate', 'shift']
success = 0
for fitgeo in fitgeometries:
print "using fitgeometry = ", fitgeo
res = iraf.geomap(input=objects,
database='geomap.db',
xmin=1,
ymin=1,
xmax=r_xmax,
ymax=r_ymax,
fitgeometry=fitgeo,
function=function,
xxorder=xxorder,
yyorder=yyorder,
xyorder=xyorder,
yxorder=yxorder,
results='',
transforms=objects,
interactive=interactive,
maxiter=maxiter,
reject=reject,
Stdout=1)
# assume all went well
success = 1
for line in res:
print line
if re.search('Too few points', line):
success = 0
if success:
break
if clobber and os.path.isfile(output): iraf.imdel(output)
iraf.geotran(input=image,
output='temp.rectify',
database='geomap.db',
transforms=objects,
geometry='geometric',
interpolant="linear")
section = "[%d:%d,%d:%d]" % (xmin, xmax, ymin, ymax)
print 'Valid section of data:', section
iraf.imcopy(input='temp.rectify', output=output)
iraf.imdel('temp.rectify')
return (xmin, xmax, ymin, ymax)
def mscimage(input=None, output=None, format='image', pixmask=no,verbose=')_.verbose',wcssource='image',reference='',ra=INDEF,dec=INDEF,scale=INDEF,rotation=INDEF,blank=0.0,interpolant='poly5',minterpolant='linear',boundary='reflect',constant=0.0,fluxconserve=no,ntrim=8,nxblock=INDEF,nyblock=INDEF,interactive=no,nx=10,ny=20,fitgeometry='general',xxorder=4,xyorder=4,xxterms='half',yxorder=4,yyorder=4,yxterms='half',fd_in='',fd_ext='',fd_coord='',mode='ql',DOLLARnargs=0,taskObj=None):
Vars = IrafParList('mscimage')
Vars.addParam(makeIrafPar(input, datatype='string', name='input', mode='a',prompt='List of input mosaic exposures'))
Vars.addParam(makeIrafPar(output, datatype='string', name='output',mode='a',prompt='List of output images'))
Vars.addParam(makeIrafPar(format, datatype='string', name='format',enum=['image', 'mef'],mode='h',prompt='Output format (image|mef)'))
Vars.addParam(makeIrafPar(pixmask, datatype='bool', name='pixmask',mode='h',prompt='Create pixel mask?'))
Vars.addParam(makeIrafPar(verbose, datatype='bool', name='verbose',mode='h',prompt='Verbose output?\n\n# Output WCS parameters'))
Vars.addParam(makeIrafPar(wcssource, datatype='string', name='wcssource',enum=['image', 'parameters', 'match'],mode='h',prompt='Output WCS source (image|parameters|match)'))
Vars.addParam(makeIrafPar(reference, datatype='file', name='reference',mode='h',prompt='Reference image'))
Vars.addParam(makeIrafPar(ra, datatype='real', name='ra', max=24.0,min=0.0,mode='h',prompt='RA of tangent point (hours)'))
Vars.addParam(makeIrafPar(dec, datatype='real', name='dec', max=90.0,min=-90.0,mode='h',prompt='DEC of tangent point (degrees)'))
Vars.addParam(makeIrafPar(scale, datatype='real', name='scale', mode='h',prompt='Scale (arcsec/pixel)'))
Vars.addParam(makeIrafPar(rotation, datatype='real', name='rotation',max=360.0,min=-360.0,mode='h',prompt='Rotation of DEC from N to E (degrees)\n\n# Resampling parmeters'))
Vars.addParam(makeIrafPar(blank, datatype='real', name='blank', mode='h',prompt='Blank value'))
Vars.addParam(makeIrafPar(interpolant, datatype='string',name='interpolant',mode='h',prompt='Interpolant for data'))
Vars.addParam(makeIrafPar(minterpolant, datatype='string',name='minterpolant',mode='h',prompt='Interpolant for mask'))
Vars.addParam(makeIrafPar(boundary, datatype='string', name='boundary',enum=['nearest', 'constant', 'reflect', 'wrap'],mode='h',prompt='Boundary extension'))
Vars.addParam(makeIrafPar(constant, datatype='real', name='constant',mode='h',prompt='Constant boundary extension value'))
Vars.addParam(makeIrafPar(fluxconserve, datatype='bool',name='fluxconserve',mode='h',prompt='Preserve flux per unit area?'))
Vars.addParam(makeIrafPar(ntrim, datatype='int', name='ntrim', min=0,mode='h',prompt='Edge trim in each extension'))
Vars.addParam(makeIrafPar(nxblock, datatype='int', name='nxblock',mode='h',prompt='X dimension of working block size in pixels'))
Vars.addParam(makeIrafPar(nyblock, datatype='int', name='nyblock',mode='h',prompt='Y dimension of working block size in pixels\n\n# Geometric mapping parameters'))
Vars.addParam(makeIrafPar(interactive, datatype='bool', name='interactive',mode='h',prompt='Fit mapping interactively?'))
Vars.addParam(makeIrafPar(nx, datatype='int', name='nx', mode='h',prompt='Number of x grid points'))
Vars.addParam(makeIrafPar(ny, datatype='int', name='ny', mode='h',prompt='Number of y grid points'))
Vars.addParam(makeIrafPar(fitgeometry, datatype='string',name='fitgeometry',enum=['shift', 'xyscale', 'rotate', 'rscale', 'rxyscale', 'general'],mode='h',prompt='Fitting geometry'))
Vars.addParam(makeIrafPar(xxorder, datatype='int', name='xxorder', min=2,mode='h',prompt='Order of x fit in x'))
Vars.addParam(makeIrafPar(xyorder, datatype='int', name='xyorder', min=2,mode='h',prompt='Order of x fit in y'))
Vars.addParam(makeIrafPar(xxterms, datatype='string', name='xxterms',mode='h',prompt='X fit cross terms type'))
Vars.addParam(makeIrafPar(yxorder, datatype='int', name='yxorder', min=2,mode='h',prompt='Order of y fit in x'))
Vars.addParam(makeIrafPar(yyorder, datatype='int', name='yyorder', min=2,mode='h',prompt='Order of y fit in y'))
Vars.addParam(makeIrafPar(yxterms, datatype='string', name='yxterms',mode='h',prompt='Y fit cross terms type\n\n'))
Vars.addParam(makeIrafPar(fd_in, datatype='struct', name='fd_in',list_flag=1,mode='h',prompt=''))
Vars.addParam(makeIrafPar(fd_ext, datatype='struct', name='fd_ext',list_flag=1,mode='h',prompt=''))
Vars.addParam(makeIrafPar(fd_coord, datatype='struct', name='fd_coord',list_flag=1,mode='h',prompt=''))
Vars.addParam(makeIrafPar(mode, datatype='string', name='mode', mode='h',prompt=''))
Vars.addParam(makeIrafPar(DOLLARnargs, datatype='int', name='$nargs',mode='h'))
Vars.addParam(makeIrafPar(None, datatype='file', name='in', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='out', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='ref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pl', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='image', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='trimsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='outsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='plsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='inlists', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='extlist', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pllist', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='coord', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='db', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='wcsref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='outtemp', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pltemp', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nc', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nl', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='ncref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nlref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='cmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='cmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='lmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='lmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nimage', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nimages', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nxblk', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nyblk', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='x', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='y', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='rval', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='xmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='xmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='ymin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='ymax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='crpix1', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='crpix2', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='string', name='extname',mode='u'))
Vars.addParam(makeIrafPar(None, datatype='string', name='str', mode='u'))
iraf.cache('mscextensions', 'mscgmask')
Vars.inlists = iraf.mktemp('tmp$iraf')
Vars.extlist = iraf.mktemp('tmp$iraf')
Vars.pllist = iraf.mktemp('tmp$iraf')
Vars.coord = iraf.mktemp('tmp$iraf')
Vars.db = iraf.mktemp('tmp$iraf')
Vars.outtemp = iraf.mktemp('tmp')
Vars.wcsref = iraf.mktemp('tmp')
Vars.pltemp = iraf.mktemp('tmp')
iraf.joinlists(Vars.input, Vars.output, output = Vars.inlists, delim = ' ',short=yes,type = 'image')
Vars.fd_in = Vars.inlists
while (iraf.fscan(locals(), 'Vars.fd_in', 'Vars.PYin', 'Vars.out') != EOF):
if (iraf.imaccess(Vars.out)):
iraf.printf('Warning: Image already exists (%s)\n', Vars.out)
continue
if (Vars.pixmask):
Vars.pl = Vars.out
Vars.nc = iraf.strlen(Vars.pl)
if (Vars.nc > 5 and iraf.substr(Vars.pl, Vars.nc - 4, Vars.nc) == '.fits'):
Vars.pl = iraf.substr(Vars.pl, 1, Vars.nc - 5)
elif (Vars.nc > 4 and iraf.substr(Vars.out, Vars.nc - 3, Vars.nc) == '.imh'):
Vars.pl = iraf.substr(Vars.pl, 1, Vars.nc - 4)
Vars.pl = Vars.pl + '_bpm'
if (Vars.format == 'image' and iraf.imaccess(Vars.pl)):
iraf.printf('Warning: Mask already exists (%s)\n', Vars.pl)
continue
else:
Vars.pl = ''
iraf.mscextensions(Vars.PYin, output = 'file', index = '0-',extname = '',extver = '',lindex = no,lname = yes,lver = no,ikparams = '',Stdout=Vars.extlist)
Vars.nimages = int(iraf.mscextensions.nimages)
Vars.nimage = 0
if (Vars.nimages < 1):
iraf.printf("WARNING: No input image data found in `%s'.\
",Vars.PYin)
iraf.delete(Vars.extlist, verify = no)
continue
if (not iraf.imaccess(Vars.wcsref)):
Vars.ref = Vars.reference
if (Vars.wcssource == 'match'):
Vars.wcsref = Vars.ref
else:
iraf.mscwtemplate('@' + Vars.extlist, Vars.wcsref,wcssource = Vars.wcssource,reference = Vars.ref,ra = Vars.ra,dec = Vars.dec,scale = Vars.scale,rotation = Vars.rotation,projection = '',verbose = Vars.verbose)
Vars.fd_ext = Vars.extlist
while (iraf.fscan(locals(), 'Vars.fd_ext', 'Vars.image') != EOF):
Vars.nimage = Vars.nimage + 1
if (Vars.nimages > 1):
Pipe1 = iraf.hselect(Vars.image, 'extname', yes, Stdout=1)
iraf.scan(locals(), 'Vars.extname', Stdin=Pipe1)
del Pipe1
if (iraf.nscan() == 0):
Vars.extname = 'im' + str(Vars.nimage)
Pipe1 = iraf.printf('%s[%s,append]\n', Vars.outtemp,Vars.extname,Stdout=1)
iraf.scan(locals(), 'Vars.outsec', Stdin=Pipe1)
del Pipe1
Pipe1 = iraf.printf('%s%s\n', Vars.pl, Vars.extname, Stdout=1)
iraf.scan(locals(), 'Vars.plsec', Stdin=Pipe1)
del Pipe1
else:
Vars.extname = ''
Vars.outsec = Vars.outtemp
Vars.plsec = Vars.pl
if (Vars.pixmask and iraf.imaccess(Vars.plsec)):
iraf.delete(Vars.coord, verify = no)
iraf.delete(Vars.db, verify = no)
iraf.printf('Warning: Mask already exists (%s)\n', Vars.plsec)
continue
if (Vars.verbose):
iraf.printf('Resampling %s ...\n', Vars.image)
Pipe1 = iraf.hselect(Vars.image, 'naxis1,naxis2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.nc', 'Vars.nl', Stdin=Pipe1)
del Pipe1
Vars.cmin = 1 + Vars.ntrim
Vars.cmax = Vars.nc - Vars.ntrim
Vars.lmin = 1 + Vars.ntrim
Vars.lmax = Vars.nl - Vars.ntrim
Pipe1 = iraf.printf('[%d:%d,%d:%d]\n', Vars.cmin, Vars.cmax,Vars.lmin,Vars.lmax,Stdout=1)
iraf.scan(locals(), 'Vars.trimsec', Stdin=Pipe1)
del Pipe1
if (Vars.wcssource == 'match'):
Pipe1 = iraf.hselect(Vars.ref, 'naxis1,naxis2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.ncref', 'Vars.nlref', Stdin=Pipe1)
del Pipe1
Vars.xmin = (Vars.ncref - 1.) / (Vars.nx - 1.)
Vars.ymin = (Vars.nlref - 1.) / (Vars.ny - 1.)
Vars.ymax = 1
while (Vars.ymax <= Vars.nlref + 1):
Vars.xmax = 1
while (Vars.xmax <= Vars.ncref + 1):
iraf.clPrint(Vars.xmax, Vars.ymax, Vars.xmax,Vars.ymax,StdoutAppend=Vars.coord)
Vars.xmax = Vars.xmax + Vars.xmin
Vars.ymax = Vars.ymax + Vars.ymin
iraf.mscctran(Vars.coord, Vars.db, Vars.ref, 'logical','world',columns = '3 4',units = '',formats = '%.4H %.3h',min_sigdigit = 10,verbose = no)
iraf.delete(Vars.coord, verify=no)
iraf.wcsctran(Vars.db, Vars.coord, Vars.image + Vars.trimsec,inwcs = 'world',outwcs = 'logical',columns = '3 4',units = 'hours native',formats = '',min_sigdigit = 10,verbose = no)
iraf.delete(Vars.db, verify=no)
else:
Vars.nc = Vars.cmax - Vars.cmin + 1
Vars.nl = Vars.lmax - Vars.lmin + 1
Vars.xmin = (Vars.nc - 1.) / (Vars.nx - 1.)
Vars.ymin = (Vars.nl - 1.) / (Vars.ny - 1.)
Vars.ymax = 1
while (Vars.ymax <= Vars.nl + 1):
Vars.xmax = 1
while (Vars.xmax <= Vars.nc + 1):
iraf.clPrint(Vars.xmax, Vars.ymax, Vars.xmax,Vars.ymax,StdoutAppend=Vars.coord)
Vars.xmax = Vars.xmax + Vars.xmin
Vars.ymax = Vars.ymax + Vars.ymin
iraf.mscctran(Vars.coord, Vars.db, Vars.image + Vars.trimsec,'logical','world',columns = '1 2',units = '',formats = '%.4H %.3h',min_sigdigit = 10,verbose = no)
iraf.delete(Vars.coord, verify=no)
iraf.wcsctran(Vars.db, Vars.coord, Vars.wcsref,inwcs = 'world',outwcs = 'logical',columns = '1 2',units = 'hours native',formats = '',min_sigdigit = 10,verbose = no)
iraf.delete(Vars.db, verify=no)
Vars.xmax = 0.
Vars.xmin = 1.
Vars.ymax = 0.
Vars.ymin = 1.
Vars.fd_coord = Vars.coord
while (iraf.fscan(locals(), 'Vars.fd_coord', 'Vars.x', 'Vars.y') != EOF):
if (iraf.nscan() < 2):
continue
if (Vars.xmax < Vars.xmin):
Vars.xmin = Vars.x
Vars.xmax = Vars.x
Vars.ymin = Vars.y
Vars.ymax = Vars.y
else:
Vars.xmin = float(iraf.minimum(Vars.x, Vars.xmin))
Vars.xmax = float(iraf.maximum(Vars.x, Vars.xmax))
Vars.ymin = float(iraf.minimum(Vars.y, Vars.ymin))
Vars.ymax = float(iraf.maximum(Vars.y, Vars.ymax))
Vars.fd_coord = ''
if (Vars.xmax <= Vars.xmin or Vars.ymax <= Vars.ymin):
iraf.error(1, 'No overlap for matching reference')
Vars.cmin = int(iraf.nint(Vars.xmin - 1.5))
Vars.cmax = int(iraf.nint(Vars.xmax + 1.5))
Vars.lmin = int(iraf.nint(Vars.ymin - 1.5))
Vars.lmax = int(iraf.nint(Vars.ymax + 1.5))
iraf.geomap(Vars.coord, Vars.db, Vars.cmin, Vars.cmax, Vars.lmin,Vars.lmax,transforms = '',results = '',fitgeometry = Vars.fitgeometry,function = 'chebyshev',xxorder = Vars.xxorder,xyorder = Vars.xyorder,xxterms = Vars.xxterms,yxorder = Vars.yxorder,yyorder = Vars.yyorder,yxterms = Vars.yxterms,reject = INDEF,calctype = 'double',verbose = no,interactive = Vars.interactive,graphics = 'stdgraph',cursor = '')
if (Vars.wcssource == 'match'):
Vars.cmin = 1
Vars.lmin = 1
Vars.cmax = Vars.ncref
Vars.lmax = Vars.nlref
if (Vars.nxblock == INDEF):
Vars.nxblk = Vars.cmax - Vars.cmin + 3
else:
Vars.nxblk = Vars.nxblock
if (Vars.nyblock == INDEF):
Vars.nyblk = Vars.lmax - Vars.lmin + 3
else:
Vars.nyblk = Vars.nyblock
iraf.geotran(Vars.image + Vars.trimsec, Vars.outsec, Vars.db,Vars.coord,geometry = 'geometric',xin = INDEF,yin = INDEF,xshift = INDEF,yshift = INDEF,xout = INDEF,yout = INDEF,xmag = INDEF,ymag = INDEF,xrotation = INDEF,yrotation = INDEF,xmin = Vars.cmin,xmax = Vars.cmax,ymin = Vars.lmin,ymax = Vars.lmax,xsample = 10.,ysample = 10.,xscale = 1.,yscale = 1.,ncols = INDEF,nlines = INDEF,interpolant = Vars.interpolant,boundary = 'constant',constant = Vars.constant,fluxconserve = Vars.fluxconserve,nxblock = Vars.nxblk,nyblock = Vars.nyblk,verbose = no)
iraf.wcscopy(Vars.outsec, Vars.wcsref, verbose=no)
Vars.xmin = 0.
Vars.ymin = 0.
Pipe1 = iraf.hselect(Vars.outsec, 'crpix1,crpix2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.xmin', 'Vars.ymin', Stdin=Pipe1)
del Pipe1
Vars.xmin = Vars.xmin - Vars.cmin + 1
Vars.ymin = Vars.ymin - Vars.lmin + 1
if (Vars.nimage == 1):
Vars.crpix1 = Vars.xmin
Vars.crpix2 = Vars.ymin
else:
Vars.crpix1 = float(iraf.maximum(Vars.crpix1, Vars.xmin))
Vars.crpix2 = float(iraf.maximum(Vars.crpix2, Vars.ymin))
iraf.hedit(Vars.outsec, 'crpix1', Vars.xmin, add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.outsec, 'crpix2', Vars.ymin, add=yes, verify=no,show=no,update=yes)
if (Vars.pixmask):
Pipe1 = iraf.printf('%s%s\n', Vars.pl, Vars.extname, Stdout=1)
iraf.scan(locals(), 'Vars.plsec', Stdin=Pipe1)
del Pipe1
iraf.mscgmask(Vars.image + Vars.trimsec, Vars.pltemp + '.pl','BPM',mval = 10000)
iraf.geotran(Vars.pltemp, Vars.plsec + '.fits', Vars.db,Vars.coord,geometry = 'geometric',xin = INDEF,yin = INDEF,xshift = INDEF,yshift = INDEF,xout = INDEF,yout = INDEF,xmag = INDEF,ymag = INDEF,xrotation = INDEF,yrotation = INDEF,xmin = Vars.cmin,xmax = Vars.cmax,ymin = Vars.lmin,ymax = Vars.lmax,xsample = 10.,ysample = 10.,interpolant = Vars.minterpolant,boundary = 'constant',constant = 20000.,fluxconserve = no,nxblock = Vars.nxblk,nyblock = Vars.nyblk,verbose = no)
iraf.imdelete(Vars.pltemp, verify=no)
iraf.mscpmask(Vars.plsec + '.fits', Vars.plsec + '.pl')
iraf.imdelete(Vars.plsec + '.fits', verify=no)
iraf.hedit(Vars.outsec, 'BPM', Vars.plsec + '.pl', add=yes,show=no,verify=no,update=yes)
iraf.wcscopy(Vars.plsec, Vars.outsec, verbose=no)
iraf.clPrint(Vars.plsec, StdoutAppend=Vars.pllist)
else:
iraf.hedit(Vars.outsec, 'BPM', PYdel=yes, add=no, addonly=no,show=no,verify=no,update=yes)
iraf.delete(Vars.coord, verify = no)
iraf.delete(Vars.db, verify = no)
Vars.fd_ext = ''
iraf.delete(Vars.extlist, verify = no)
if (Vars.nimages > 1 and Vars.format == 'image'):
if (Vars.verbose):
iraf.printf('Creating image %s ...\n', Vars.out)
iraf.mscextensions(Vars.outtemp, output = 'file', index = '',extname = '',extver = '',lindex = no,lname = yes,lver = no,ikparams = '',Stdout=Vars.extlist)
if (Vars.pixmask):
iraf.combine('@' + Vars.pllist, Vars.pltemp + '.pl',headers = '',bpmasks = Vars.pl,rejmasks = '',nrejmasks = '',expmasks = '',sigmas = '',imcmb = '',ccdtype = '',amps = no,subsets = no,delete = no,combine = 'average',reject = 'none',project = no,outtype = 'real',outlimits = '',offsets = 'wcs',masktype = 'none',maskvalue = '0',blank = 0.,scale = 'none',zero = 'none',weight = 'none',statsec = '',lthreshold = INDEF,hthreshold = 0.99,nlow = 1,nhigh = 1,nkeep = 1,mclip = yes,lsigma = 3.,hsigma = 3.,rdnoise = '0.',gain = '1.',snoise = '0.',sigscale = 0.1,pclip = - 0.5,grow = 0.,Stdout='dev$null')
iraf.imdelete(Vars.pltemp, verify=no)
iraf.combine('@' + Vars.extlist, Vars.out, headers = '',bpmasks = '',rejmasks = '',nrejmasks = '',expmasks = '',sigmas = '',imcmb = '',ccdtype = '',amps = no,subsets = no,delete = no,combine = 'average',reject = 'none',project = no,outtype = 'real',outlimits = '',offsets = 'wcs',masktype = 'badvalue',maskvalue = '2',blank = 0.,scale = 'none',zero = 'none',weight = 'none',statsec = '',lthreshold = INDEF,hthreshold = INDEF,nlow = 1,nhigh = 1,nkeep = 1,mclip = yes,lsigma = 3.,hsigma = 3.,rdnoise = '0.',gain = '1.',snoise = '0.',sigscale = 0.1,pclip = - 0.5,grow = 0.,Stdout='dev$null')
iraf.hedit(Vars.out, 'BPM', Vars.pl, add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.pl, 'IMCMB???,PROCID??', add=no, addonly=no,PYdel=yes,update=yes,verify=no,show=no)
else:
iraf.combine('@' + Vars.extlist, Vars.out, headers = '',bpmasks = '',rejmasks = '',nrejmasks = '',expmasks = '',sigmas = '',imcmb = '',ccdtype = '',amps = no,subsets = no,delete = no,combine = 'average',reject = 'none',project = no,outtype = 'real',outlimits = '',offsets = 'wcs',masktype = 'none',maskvalue = '2',blank = 0.,scale = 'none',zero = 'none',weight = 'none',statsec = '',lthreshold = INDEF,hthreshold = INDEF,nlow = 1,nhigh = 1,nkeep = 1,mclip = yes,lsigma = 3.,hsigma = 3.,rdnoise = '0.',gain = '1.',snoise = '0.',sigscale = 0.1,pclip = - 0.5,grow = 0.,Stdout='dev$null')
Pipe2 = iraf.hselect('@' + Vars.extlist, 'gain', yes, Stdout=1)
Pipe1 = iraf.average(data_value = 0., Stdin=Pipe2, Stdout=1)
del Pipe2
iraf.scan(locals(), 'Vars.rval', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.out, 'gain', Vars.rval, add=yes, PYdel=no,update=yes,verify=no,show=no)
Pipe2 = iraf.hselect('@' + Vars.extlist, 'rdnoise', yes, Stdout=1)
Pipe1 = iraf.average(data_value = 0., Stdin=Pipe2, Stdout=1)
del Pipe2
iraf.scan(locals(), 'Vars.rval', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.out, 'rdnoise', Vars.rval, add=yes, PYdel=no,update=yes,verify=no,show=no)
iraf.hedit(Vars.out, 'IMCMB???,PROCID??', add=no, addonly=no,PYdel=yes,update=yes,verify=no,show=no)
iraf.hedit(Vars.out,'NEXTEND,DETSEC,CCDSEC,AMPSEC,IMAGEID,DATASEC,TRIMSEC,BIASSEC',add=no,addonly=no,PYdel=yes,update=yes,verify=no,show=no)
iraf.imdelete(Vars.outtemp, verify=no)
if (iraf.access(Vars.pllist)):
iraf.imdelete('@' + Vars.pllist, verify=no)
iraf.delete(Vars.pllist, verify=no)
iraf.delete(Vars.extlist, verify = no)
elif (Vars.nimages > 1):
iraf.imrename(Vars.outtemp, Vars.out, verbose=no)
iraf.mscextensions(Vars.out, output = 'file', index = '',extname = '',extver = '',lindex = no,lname = yes,lver = no,ikparams = '',Stdout=Vars.extlist)
Vars.fd_ext = Vars.extlist
while (iraf.fscan(locals(), 'Vars.fd_ext', 'Vars.image') != EOF):
Pipe1 = iraf.hselect(Vars.image, 'naxis1,naxis2,crpix1,crpix2',yes,Stdout=1)
iraf.scan(locals(), 'Vars.nc', 'Vars.nl', 'Vars.xmin','Vars.ymin',Stdin=Pipe1)
del Pipe1
Vars.cmin = int(iraf.nint(Vars.crpix1 - Vars.xmin + 1))
Vars.lmin = int(iraf.nint(Vars.crpix2 - Vars.ymin + 1))
Vars.cmax = Vars.nc + Vars.cmin - 1
Vars.lmax = Vars.nl + Vars.lmin - 1
Pipe1 = iraf.printf('[%d:%d,%d:%d]\n', Vars.cmin, Vars.cmax,Vars.lmin,Vars.lmax,Stdout=1)
iraf.scan(locals(), 'Vars.str', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.image, 'DETSEC', Vars.str, add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.image, 'DTM1_1', 1., add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.image, 'DTM2_2', 1., add=yes, verify=no,show=no,update=yes)
Vars.cmin = Vars.cmin - 1
Vars.lmin = Vars.lmin - 1
iraf.hedit(Vars.image, 'DTV1', Vars.cmin, add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.image, 'DTV2', Vars.lmin, add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.image,'CCDSUM,CCDSEC,AMPSEC,ATM1_1,ATM2_2,ATV1,ATV2',PYdel=yes,add=no,addonly=no,verify=no,show=no,update=yes)
Vars.fd_ext = ''
iraf.delete(Vars.extlist, verify=no)
else:
iraf.imrename(Vars.outsec, Vars.out, verbose=no)
if (iraf.access(Vars.pllist)):
iraf.delete(Vars.pllist, verify=no)
Vars.fd_in = ''
iraf.delete(Vars.inlists, verify = no)
if (Vars.wcssource != 'match' and iraf.imaccess(Vars.wcsref)):
iraf.imdelete(Vars.wcsref, verify=no)
def get_align_to_subaru(sci='M0416_Ks_c1_mp_avg.fits',
wht='M0416_Ks_c1_mp_exp.fits',
field='',
clean=True,
toler=3,
verbose=False,
fitgeometry='shift',
shift_max=20,
rms_max=1.1,
rot_max=2,
rot_only=True,
THRESH=2,
align_data=None):
"""
Align HAWK-I images to the FF Subaru astrometric reference catalogs
"""
#sci='M0416_Ks_c1_mp_avg.fits'; wht='M0416_Ks_c1_mp_exp.fits'
### Make object catalog
se = threedhst.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options['CHECKIMAGE_TYPE'] = 'NONE'
if wht is None:
se.options['WEIGHT_TYPE'] = 'NONE'
else:
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
se.options['WEIGHT_IMAGE'] = wht
se.options['FILTER'] = 'Y'
se.options['DETECT_THRESH'] = '%d' % (THRESH)
se.options['ANALYSIS_THRESH'] = '%d' % (THRESH)
se.options['MAG_ZEROPOINT'] = '26.0'
#### Run SExtractor on direct and alignment images
## direct image
se.options['CATALOG_NAME'] = 'direct.cat'
status = se.sextractImage(sci)
threedhst.sex.sexcatRegions('direct.cat', 'direct.reg', format=2)
directCat = threedhst.sex.mySexCat('direct.cat')
#### Get the X/Y coords of the reference catalog
#head = pyfits.getheader(sci, 0)
#wcs = pywcs.WCS(head)
if 'M0416' in sci:
ra_list, dec_list, mag = np.loadtxt(
os.getenv('HAWKI') +
'/FrontierFields/HST/hlsp_frontier_subaru_suprimecam_macs0416-astrom_R_v1_cat.txt',
unpack=True)
if ('c4' in sci):
ra_list, dec_list, mag = np.loadtxt(
os.getenv('HAWKI') +
'/FrontierFields/HST/M0416/macs0416_f814w_radec.cat',
unpack=True)
#
if 'M0717' in sci:
ra_list, dec_list, mag = np.loadtxt('subaru.radec', unpack=True)
if ('M1149' in sci) | (field == 'M1149'):
ra_list, dec_list, mag = np.loadtxt(
'/Users/brammer/Research/VLT/HAWKI/MACS1149/hlsp_frontier_subaru_suprimecam_macs1149-astrom_R_v1_cat.txt',
unpack=True)
if 'A2744' in sci:
ra_list, dec_list, mag = np.loadtxt(
os.getenv('HAWKI') +
'/FrontierFields/HST/hlsp_frontier_subaru_suprimecam_abell2744-astrom_i_v1_cat.txt',
unpack=True)
if ('c1' in sci) | ('c4' in sci):
ra_list, dec_list, mag = np.loadtxt(
os.getenv('HAWKI') +
'/FrontierFields/HST/abell2744_f814w_radec.cat',
unpack=True)
if align_data is not None:
ra_list, dec_list, mag = align_data
im = pyfits.open(sci)
print sci
sh = im[0].shape
head = im[0].header
head['CUNIT1'] = 'deg'
head['CUNIT2'] = 'deg'
wcs = pywcs.WCS(head)
x_image, y_image = wcs.wcs_sky2pix(ra_list, dec_list, 1)
try:
x_image, y_image = wcs.wcs_sky2pix(ra_list, dec_list, 1)
except:
x_image, y_image = wcs.wcs_world2pix(ra_list, dec_list, 1)
ok = (x_image > 0) & (y_image > 0) & (x_image < sh[1]) & (y_image < sh[1])
x_image, y_image = x_image[ok], y_image[ok]
fpr = open('align.reg', 'w')
fpr.write('image\n')
for i in range(ok.sum()):
fpr.write('circle(%.6f, %.6f,0.3") # color=magenta\n' %
(x_image[i], y_image[i]))
fpr.close()
# x_image, y_image = [], []
#
# for ra, dec in zip(ra_list, dec_list):
# x, y = wcs.wcs_sky2pix([[ra, dec]], 1)[0]
# if (x > 0) & (y > 0) & (x < sh[1]) & (y < sh[1]):
# x_image.append(x)
# y_image.append(y)
alignCat = catIO.EmptyCat()
alignCat['X_IMAGE'] = np.array(x_image)
alignCat['Y_IMAGE'] = np.array(y_image)
xshift = 0
yshift = 0
rot = 0
scale = 1.
xrms = 2
yrms = 2
NITER = 5
IT = 0
while (IT < NITER):
IT = IT + 1
#### Get x,y coordinates of detected objects
## direct image
fp = open('direct.xy', 'w')
for i in range(len(directCat.X_IMAGE)):
fp.write('%s %s\n' % (directCat.X_IMAGE[i], directCat.Y_IMAGE[i]))
fp.close()
## alignment image
fp = open('align.xy', 'w')
for i in range(len(alignCat.X_IMAGE)):
fp.write('%s %s\n' % (np.float(alignCat.X_IMAGE[i]) + xshift,
np.float(alignCat.Y_IMAGE[i]) + yshift))
fp.close()
iraf.flpr()
iraf.flpr()
iraf.flpr()
#### iraf.xyxymatch to find matches between the two catalogs
pow = toler * 1.
try:
os.remove('align.match')
except:
pass
status1 = iraf.xyxymatch(input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2**pow,
separation=0,
verbose=iraf.yes,
Stdout=1)
nmatch = 0
while status1[-1].startswith('0') | (nmatch < 10) | (float(
status1[-3].split()[1]) > 40):
pow += 1
os.remove('align.match')
status1 = iraf.xyxymatch(input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2**pow,
separation=0,
verbose=iraf.yes,
Stdout=1)
#
nmatch = 0
for line in open('align.match').xreadlines():
nmatch += 1
if verbose:
for line in status1:
print line
#### Compute shifts with iraf.geomap
iraf.flpr()
iraf.flpr()
iraf.flpr()
try:
os.remove("align.map")
except:
pass
status2 = iraf.geomap(input="align.match",
database="align.map",
fitgeometry=fitgeometry,
interactive=iraf.no,
xmin=iraf.INDEF,
xmax=iraf.INDEF,
ymin=iraf.INDEF,
ymax=iraf.INDEF,
maxiter=10,
reject=2.0,
Stdout=1)
if verbose:
for line in status2:
print line
#fp = open(root+'.iraf.log','a')
#fp.writelines(status1)
#fp.writelines(status2)
#fp.close()
#### Parse geomap.output
fp = open("align.map", "r")
for line in fp.readlines():
spl = line.split()
if spl[0].startswith('xshift'):
xshift += float(spl[1])
if spl[0].startswith('yshift'):
yshift += float(spl[1])
if spl[0].startswith('xrotation'):
rot = float(spl[1])
if spl[0].startswith('xmag'):
scale = float(spl[1])
if spl[0].startswith('xrms'):
xrms = float(spl[1])
if spl[0].startswith('yrms'):
yrms = float(spl[1])
fp.close()
#os.system('wc align.match')
print 'Shift iteration #%d, xshift=%f, yshift=%f, rot=%f, scl=%f (rms: %5.2f,%5.2f)' % (
IT, xshift, yshift, rot, scale, xrms, yrms)
os.system(
'cat align.match | grep -v "\#" | grep [0-9] | awk \'{print "circle(", $1, ",", $2, ",4) # color=green"}\' > d.reg'
)
os.system(
'cat align.match | grep -v "\#" | grep [0-9] | awk \'{print "circle(", $3, ",", $4, ",4) # color=magenta"}\' > a.reg'
)
shutil.copy('align.map', sci.replace('.fits', '.align.map'))
shutil.copy('align.match', sci.replace('.fits', '.align.match'))
#### Cleanup
if clean:
rmfiles = [
'align.cat', 'align.map', 'align.match', 'align.reg', 'align.xy',
'direct.cat', 'direct.reg', 'direct.xy'
]
for file in rmfiles:
try:
os.remove(file)
except:
pass
fp = open(sci.replace('.fits', '.align.info'), 'w')
fp.write('# image xshift yshift rot scale xrms yrms\n')
fp.write('%s %.3f %.3f %.4f %.4f %.3f %.3f\n' %
(sci, xshift, yshift, rot, scale, xrms, yrms))
if (np.abs(xshift) > shift_max) | (np.abs(yshift) > shift_max) | (
xrms > rms_max) | (yrms > rms_max):
print 'Shifts out of allowed range. Run again with increased shift_max to accept.'
#return xshift, yshift, rot, scale, xrms, yrms
## Add a small shift that should come out easily with another
## shift iteration
xshift, yshift, rot, scale, xrms, yrms = 2, 2, 0, 1.0, -99, -99
for file in [sci, wht]:
if ('r' in fitgeometry) & rot_only:
xshift, yshift = 0, 0
#apply_offsets(file, [[xshift, yshift, rot, scale]])
from drizzlepac import updatehdr
updatehdr.updatewcs_with_shift(file,
sci,
wcsname='DRZWCS',
rot=rot,
scale=scale,
xsh=xshift,
ysh=yshift,
fit=None,
xrms=xrms,
yrms=yrms,
verbose=False,
force=True,
sciext=0)
if '_dr' in sci:
im = pyfits.open(sci)
h = im[0].header
for i in range(h['NDRIZIM']):
flt_str = h['D%03dDATA' % (i + 1)]
if 'sci,2' in flt_str:
continue
#
flt_im = flt_str.split('[')[0]
ext = int(flt_str.split('[')[1][:-1].split(',')[1])
updatehdr.updatewcs_with_shift(flt_im,
sci,
wcsname='GTWEAK',
rot=rot,
scale=scale,
xsh=xshift,
ysh=yshift,
fit=None,
xrms=xrms,
yrms=yrms,
verbose=False,
force=True,
sciext='SCI')
# im = pyfits.open(file, mode='update')
# wcs = pywcs.WCS(im[0].header)
# wcs.rotateCD(-rot)
# wcs.wcs.cd /= scale
# #
# im[0].header['CRPIX1'] += xshift
# im[0].header['CRPIX2'] += yshift
# #
# for i in [0,1]:
# for j in [0,1]:
# im[0].header['CD%d_%d' %(i+1, j+1)] = wcs.wcs.cd[i,j]
# #
# im.flush()
return xshift, yshift, rot, scale, xrms, yrms
def spitzer_sub(new,
newunc,
newcov,
ref,
refunc,
refcov,
out,
stamps=None,
tmass=None):
# Remove nan from mosaics
x = pyfits.open(new)
y = np.nan_to_num(x[0].data)
x[0].data = y
new2 = "n%s" % new
x.writeto(new2)
# Find stars in new image
os.system("$REDUCTION/runsex.pl %s 5.0 -weight %s" % (new2, newcov))
# Find stars in reference
os.system("$REDUCTION/runsex.pl %s 5.0 -weight %s" % (ref, refcov))
# Create file for geotran
stars = Starlist("%s.stars" % new2)
refstars = Starlist("%s.stars" % ref)
refstars.pix2wcs(ref)
refstars.wcs2pix(new2)
a, b = stars.match(refstars, maxnum=1000)
# Create file for geotran
b.pix2wcs(new2)
b.wcs2pix(ref)
refroot = ref.split(".")[0]
outf = open("%s.match" % refroot, "w")
for i in range(len(a)):
outf.write("%10.3f%10.3f%10.3f%10.3f\n" %
(a[i].xval, a[i].yval, b[i].xval, b[i].yval))
outf.close()
# Geomap and geotran
[naxis1, naxis2] = get_head(new, ["NAXIS1", "NAXIS2"])
iraf.geomap("%s.match" % refroot,
"%s.db" % refroot,
1,
naxis1,
1,
naxis2,
fitgeometry="rotate",
interactive=no)
iraf.geotran(ref, "t%s" % ref, "%s.db" % refroot, "%s.match" % refroot)
iraf.geotran(refunc, "t%s" % refunc, "%s.db" % refroot,
"%s.match" % refroot)
# Get stars for PSF matching
if stamps != None:
psfstars = Starlist(stamps)
psfstars.pix2wcs(ref)
psfstars.wcs2pix(new2)
outf = open("stamps.lis", "w")
for star in psfstars:
outf.write("%10.3f%10.3f\n" % (star.xval, star.yval))
outf.close()
# Appropriate parameters
iraf.iterstat(ref)
update_head(ref, ["MEDSKY", "SKYSIG"],
[iraf.iterstat.median, iraf.iterstat.sigma])
[refskybkg, refskysig] = get_head(ref, ["MEDSKY", "SKYSIG"])
tl = refskybkg - 10 * refskysig
tu = 30000.0
iraf.iterstat(new)
update_head(new, ["MEDSKY", "SKYSIG"],
[iraf.iterstat.median, iraf.iterstat.sigma])
[newskybkg, newskysig] = get_head(new, ["MEDSKY", "SKYSIG"])
il = newskybkg - 10 * newskysig
iu = 30000.0
# Run hotpants
hpcmd = "hotpants -inim %s -tmplim t%s -outim %s -tni t%s -ini %s -nsx 3 -nsy 3 -savexy %s.xy -ko 0 -bgo 0 -oni u%s -n t -tl %.2f -tu %.2f -il %.2f -iu %.2f -r 7.5 -rss 18.0" % (
new2, ref, out, refunc, newunc, new2, out, tl, tu, il, iu)
if stamps != None:
hpcmd += " -ssf stamps.lis -afssc 0"
os.system(hpcmd)
#iraf.imarith(new2, "-", "t%s" % ref, out)
# Create appropriate weight image
#iraf.imexpr("sqrt(a**2 + b**2)", "u%s" % out, a=newunc, b=refunc)
iraf.imarith(1, "/", "u%s" % out, "temp1.fits")
iraf.imarith("temp1.fits", "/", "u%s" % out, "w%s" % out)
os.remove("temp1.fits")
# Pick up candidates
os.system("$REDUCTION/runsex.pl %s 2.0 -weight w%s" % (out, out))
stars = Starlist("%s.stars" % out)
cands = []
[nax1, nax2] = get_head(ref, ["NAXIS1", "NAXIS2"])
stars.pix2wcs(new)
stars.wcs2pix(ref)
refu = pyfits.open(refunc)
for star in stars:
if star.xval > EDGETOL and star.xval < (
nax1 - EDGETOL) and star.yval > EDGETOL and star.yval < (
nax2 - EDGETOL) and refu[0].data[
star.yval, star.
xval] != 0 and star.fwhmw > 0.5 and star.fwhmw < 10.0:
cands.append(star)
scands = Starlist(stars=cands)
# Filter out bright stars
if tmass == None:
scands.write("cands.reg")
else:
stmass = Starlist(tmass)
stmass.wcs2pix(ref)
s2cands = scands.nomatch(stmass)
s2cands.write("cands.reg")
# More comprehensive list
os.system("$REDUCTION/runsex.pl %s 1.5" % out)
stars = Starlist("%s.stars" % out)
cands = []
[nax1, nax2] = get_head(ref, ["NAXIS1", "NAXIS2"])
stars.pix2wcs(new)
stars.wcs2pix(ref)
refu = pyfits.open(refunc)
for star in stars:
if star.xval > EDGETOL and star.xval < (
nax1 - EDGETOL) and star.yval > EDGETOL and star.yval < (
nax2 - EDGETOL) and refu[0].data[
star.yval, star.
xval] != 0 and star.fwhmw > 0.5 and star.fwhmw < 10.0:
cands.append(star)
scands = Starlist(stars=cands)
scands.write("cands_all.reg")
return
def irafalign(filepath,
uknstarlist,
refstarlist,
shape,
alifilepath=None,
outdir="alipy_out",
makepng=False,
hdu=0,
verbose=True):
"""
Uses iraf geomap and gregister to align the image. Three steps :
* Write the matched source lists into an input file for geomap
* Compute a geomap transform from these stars.
* Run gregister
:param filepath: FITS file to be aligned
:type filepath: string
:param uknstarlist: A list of stars from the "unknown" image to be aligned,
that matches to ...
:type uknstarlist: list of Star objects
:param refstarlist: ... the list of corresponding stars in the reference
image.
:type refstarlist: list of Star objects
:param shape: Output shape (width, height)
:type shape: tuple
:param alifilepath: where to save the aligned image. If None, I put it in
the default directory.
:type alifilepath: string
:param makepng: If True I make a png of the aligned image as well.
:type makepng: boolean
:param hdu: The hdu of the fits file that you want me to use.
0 is primary. If multihdu, 1 is usually science.
"""
try:
from pyraf import iraf
except ImportError:
print("Couldn't import pyraf !")
return
assert len(uknstarlist) == len(refstarlist)
if len(uknstarlist) < 2:
if verbose:
print("Not enough stars for using geomap !")
return
basename = os.path.splitext(os.path.basename(filepath))[0]
geomapinpath = basename + ".geomapin"
geodatabasepath = basename + ".geodatabase"
if os.path.isfile(geomapinpath):
os.remove(geomapinpath)
if os.path.isfile(geodatabasepath):
os.remove(geodatabasepath)
# Step 1, we write the geomap input.
table = []
for (uknstar, refstar) in zip(uknstarlist, refstarlist):
table.append([refstar.x, refstar.y, uknstar.x, uknstar.y])
geomap = open(geomapinpath, "w")
writer = csv.writer(geomap, delimiter="\t")
writer.writerows(table)
geomap.close()
# Step 2, geomap
iraf.unlearn(iraf.geomap)
iraf.geomap.fitgeom = "rscale"
# You can change this to:
# shift, xyscale, rotate, rscale
iraf.geomap.function = "polynomial" # Surface type
iraf.geomap.maxiter = 3 # Maximum number of rejection iterations
iraf.geomap.reject = 3.0 # Rejection limit in sigma units
# other options you could specify :
# (xxorder= 2) Order of x fit in x
# (xyorder= 2) Order of x fit in y
# (xxterms= half) X fit cross terms type
# (yxorder= 2) Order of y fit in x
# (yyorder= 2) Order of y fit in y
# (yxterms= half) Y fit cross terms type
# (calctyp= real) Computation type
iraf.geomap.transfo = "broccoli" # keep it
iraf.geomap.interac = "no" # keep it
iraf.geomap.verbose = "yes" # keep it
# iraf.geomap.results = "bla.summary" # The optional results summary files
geomapblabla = iraf.geomap(input=geomapinpath,
database=geodatabasepath,
xmin=1,
xmax=shape[0],
ymin=1,
ymax=shape[1],
Stdout=1)
# We read this output ...
for line in geomapblabla:
if "X and Y scale:" in line:
mapscale = line.split()[4:6]
if "Xin and Yin fit rms:" in line:
maprmss = line.split()[-2:]
if "X and Y axis rotation:" in line:
mapangles = line.split()[-4:-2]
if "X and Y shift:" in line:
mapshifts = line.split()[-4:-2]
# not used?
geomaprms = math.sqrt(
float(maprmss[0]) * float(maprmss[0]) +
float(maprmss[1]) * float(maprmss[1]))
geomapangle = float(mapangles[0]) # % 360.0
geomapscale = 1.0 / float(mapscale[0])
if mapscale[0] != mapscale[1]:
raise RuntimeError("Error reading geomap scale")
if verbose:
print(("IRAF geomap : Rotation %+11.6f [deg], "
"scale %8.6f, RMS %.3f [pixel]") %
(geomapangle, geomapscale, geomaprms))
# Step 3
if alifilepath is None:
alifilepath = os.path.join(outdir, basename + "_gregister.fits")
else:
outdir = os.path.split(alifilepath)[0]
if not os.path.isdir(outdir):
os.makedirs(outdir)
if os.path.isfile(alifilepath):
os.remove(alifilepath)
iraf.unlearn(iraf.gregister)
iraf.gregister.geometry = "geometric" # linear, distortion, geometric
iraf.gregister.interpo = "spline3" # linear, spline3
iraf.gregister.boundary = "constant" # padding with zero
iraf.gregister.constant = 0.0
iraf.gregister.fluxconserve = "yes"
if verbose:
print("IRAF gregister ...")
regblabla = iraf.gregister(input='%s[%s]' % (filepath, hdu),
output=alifilepath,
database=geodatabasepath,
transform="broccoli",
Stdout=1)
# not used?
if verbose:
print("IRAF gregister done !")
if os.path.isfile(geomapinpath):
os.remove(geomapinpath)
if os.path.isfile(geodatabasepath):
os.remove(geodatabasepath)
if makepng:
try:
import f2n
except ImportError:
print("Couldn't import f2n -- install it !")
return
myimage = f2n.fromfits(alifilepath, verbose=False)
myimage.setzscale("auto", "auto")
myimage.makepilimage("log", negative=False)
myimage.writetitle(os.path.basename(alifilepath))
if not os.path.isdir(outdir):
os.makedirs(outdir)
myimage.tonet(
os.path.join(outdir,
os.path.basename(alifilepath) + ".png"))
def get_align_to_subaru(sci='M0416_Ks_c1_mp_avg.fits', wht='M0416_Ks_c1_mp_exp.fits', field='', clean=True, toler=3, verbose=False, fitgeometry='shift', shift_max=20, rms_max=1.1, rot_max=2, rot_only=True, THRESH=2, align_data=None):
"""
Align HAWK-I images to the FF Subaru astrometric reference catalogs
"""
#sci='M0416_Ks_c1_mp_avg.fits'; wht='M0416_Ks_c1_mp_exp.fits'
### Make object catalog
se = threedhst.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options['CHECKIMAGE_TYPE'] = 'NONE'
if wht is None:
se.options['WEIGHT_TYPE'] = 'NONE'
else:
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
se.options['WEIGHT_IMAGE'] = wht
se.options['FILTER'] = 'Y'
se.options['DETECT_THRESH'] = '%d' %(THRESH)
se.options['ANALYSIS_THRESH'] = '%d' %(THRESH)
se.options['MAG_ZEROPOINT'] = '26.0'
#### Run SExtractor on direct and alignment images
## direct image
se.options['CATALOG_NAME'] = 'direct.cat'
status = se.sextractImage(sci)
threedhst.sex.sexcatRegions('direct.cat', 'direct.reg', format=2)
directCat = threedhst.sex.mySexCat('direct.cat')
#### Get the X/Y coords of the reference catalog
#head = pyfits.getheader(sci, 0)
#wcs = pywcs.WCS(head)
if 'M0416' in sci:
ra_list, dec_list, mag = np.loadtxt(os.getenv('HAWKI')+'/FrontierFields/HST/hlsp_frontier_subaru_suprimecam_macs0416-astrom_R_v1_cat.txt', unpack=True)
if ('c4' in sci):
ra_list, dec_list, mag = np.loadtxt(os.getenv('HAWKI')+'/FrontierFields/HST/M0416/macs0416_f814w_radec.cat', unpack=True)
#
if 'M0717' in sci:
ra_list, dec_list, mag = np.loadtxt('subaru.radec', unpack=True)
if ('M1149' in sci) | (field == 'M1149'):
ra_list, dec_list, mag = np.loadtxt('/Users/brammer/Research/VLT/HAWKI/MACS1149/hlsp_frontier_subaru_suprimecam_macs1149-astrom_R_v1_cat.txt', unpack=True)
if 'A2744' in sci:
ra_list, dec_list, mag = np.loadtxt(os.getenv('HAWKI')+'/FrontierFields/HST/hlsp_frontier_subaru_suprimecam_abell2744-astrom_i_v1_cat.txt', unpack=True)
if ('c1' in sci) | ('c4' in sci):
ra_list, dec_list, mag = np.loadtxt(os.getenv('HAWKI')+'/FrontierFields/HST/abell2744_f814w_radec.cat', unpack=True)
if align_data is not None:
ra_list, dec_list, mag = align_data
im = pyfits.open(sci)
print sci
sh = im[0].shape
head = im[0].header
head['CUNIT1'] = 'deg'; head['CUNIT2'] = 'deg'
wcs = pywcs.WCS(head)
x_image, y_image = wcs.wcs_sky2pix(ra_list, dec_list, 1)
try:
x_image, y_image = wcs.wcs_sky2pix(ra_list, dec_list, 1)
except:
x_image, y_image = wcs.wcs_world2pix(ra_list, dec_list, 1)
ok = (x_image > 0) & (y_image > 0) & (x_image < sh[1]) & (y_image < sh[1])
x_image, y_image = x_image[ok], y_image[ok]
fpr = open('align.reg','w')
fpr.write('image\n')
for i in range(ok.sum()): fpr.write('circle(%.6f, %.6f,0.3") # color=magenta\n' %(x_image[i], y_image[i]))
fpr.close()
# x_image, y_image = [], []
#
# for ra, dec in zip(ra_list, dec_list):
# x, y = wcs.wcs_sky2pix([[ra, dec]], 1)[0]
# if (x > 0) & (y > 0) & (x < sh[1]) & (y < sh[1]):
# x_image.append(x)
# y_image.append(y)
alignCat = catIO.EmptyCat()
alignCat['X_IMAGE'] = np.array(x_image)
alignCat['Y_IMAGE'] = np.array(y_image)
xshift = 0
yshift = 0
rot = 0
scale = 1.
xrms = 2
yrms = 2
NITER = 5
IT = 0
while (IT < NITER):
IT = IT+1
#### Get x,y coordinates of detected objects
## direct image
fp = open('direct.xy','w')
for i in range(len(directCat.X_IMAGE)):
fp.write('%s %s\n' %(directCat.X_IMAGE[i],directCat.Y_IMAGE[i]))
fp.close()
## alignment image
fp = open('align.xy','w')
for i in range(len(alignCat.X_IMAGE)):
fp.write('%s %s\n' %(np.float(alignCat.X_IMAGE[i])+xshift,
np.float(alignCat.Y_IMAGE[i])+yshift))
fp.close()
iraf.flpr()
iraf.flpr()
iraf.flpr()
#### iraf.xyxymatch to find matches between the two catalogs
pow = toler*1.
try:
os.remove('align.match')
except:
pass
status1 = iraf.xyxymatch(input="direct.xy", reference="align.xy",
output="align.match",
tolerance=2**pow, separation=0, verbose=iraf.yes, Stdout=1)
nmatch = 0
while status1[-1].startswith('0') | (nmatch < 10) | (float(status1[-3].split()[1]) > 40):
pow+=1
os.remove('align.match')
status1 = iraf.xyxymatch(input="direct.xy", reference="align.xy",
output="align.match",
tolerance=2**pow, separation=0, verbose=iraf.yes, Stdout=1)
#
nmatch = 0
for line in open('align.match').xreadlines( ): nmatch += 1
if verbose:
for line in status1:
print line
#### Compute shifts with iraf.geomap
iraf.flpr()
iraf.flpr()
iraf.flpr()
try:
os.remove("align.map")
except:
pass
status2 = iraf.geomap(input="align.match", database="align.map",
fitgeometry=fitgeometry, interactive=iraf.no,
xmin=iraf.INDEF, xmax=iraf.INDEF, ymin=iraf.INDEF, ymax=iraf.INDEF,
maxiter = 10, reject = 2.0, Stdout=1)
if verbose:
for line in status2:
print line
#fp = open(root+'.iraf.log','a')
#fp.writelines(status1)
#fp.writelines(status2)
#fp.close()
#### Parse geomap.output
fp = open("align.map","r")
for line in fp.readlines():
spl = line.split()
if spl[0].startswith('xshift'):
xshift += float(spl[1])
if spl[0].startswith('yshift'):
yshift += float(spl[1])
if spl[0].startswith('xrotation'):
rot = float(spl[1])
if spl[0].startswith('xmag'):
scale = float(spl[1])
if spl[0].startswith('xrms'):
xrms = float(spl[1])
if spl[0].startswith('yrms'):
yrms = float(spl[1])
fp.close()
#os.system('wc align.match')
print 'Shift iteration #%d, xshift=%f, yshift=%f, rot=%f, scl=%f (rms: %5.2f,%5.2f)' %(IT, xshift, yshift, rot, scale, xrms, yrms)
os.system('cat align.match | grep -v "\#" | grep [0-9] | awk \'{print "circle(", $1, ",", $2, ",4) # color=green"}\' > d.reg')
os.system('cat align.match | grep -v "\#" | grep [0-9] | awk \'{print "circle(", $3, ",", $4, ",4) # color=magenta"}\' > a.reg')
shutil.copy('align.map', sci.replace('.fits', '.align.map'))
shutil.copy('align.match', sci.replace('.fits', '.align.match'))
#### Cleanup
if clean:
rmfiles = ['align.cat', 'align.map','align.match','align.reg','align.xy', 'direct.cat','direct.reg','direct.xy']
for file in rmfiles:
try:
os.remove(file)
except:
pass
fp = open(sci.replace('.fits', '.align.info'), 'w')
fp.write('# image xshift yshift rot scale xrms yrms\n')
fp.write('%s %.3f %.3f %.4f %.4f %.3f %.3f\n' %(sci, xshift, yshift, rot, scale, xrms, yrms))
if (np.abs(xshift) > shift_max) | (np.abs(yshift) > shift_max) | (xrms > rms_max) | (yrms > rms_max):
print 'Shifts out of allowed range. Run again with increased shift_max to accept.'
#return xshift, yshift, rot, scale, xrms, yrms
## Add a small shift that should come out easily with another
## shift iteration
xshift, yshift, rot, scale, xrms, yrms = 2,2,0,1.0,-99,-99
for file in [sci, wht]:
if ('r' in fitgeometry) & rot_only:
xshift, yshift = 0, 0
#apply_offsets(file, [[xshift, yshift, rot, scale]])
from drizzlepac import updatehdr
updatehdr.updatewcs_with_shift(file, sci, wcsname='DRZWCS',
rot=rot,scale=scale,
xsh=xshift, ysh=yshift,
fit=None,
xrms=xrms, yrms = yrms,
verbose=False, force=True, sciext=0)
if '_dr' in sci:
im = pyfits.open(sci)
h = im[0].header
for i in range(h['NDRIZIM']):
flt_str = h['D%03dDATA' %(i+1)]
if 'sci,2' in flt_str:
continue
#
flt_im = flt_str.split('[')[0]
ext = int(flt_str.split('[')[1][:-1].split(',')[1])
updatehdr.updatewcs_with_shift(flt_im, sci, wcsname='GTWEAK', rot=rot, scale=scale, xsh=xshift, ysh=yshift,
fit=None, xrms=xrms, yrms = yrms, verbose=False, force=True, sciext='SCI')
# im = pyfits.open(file, mode='update')
# wcs = pywcs.WCS(im[0].header)
# wcs.rotateCD(-rot)
# wcs.wcs.cd /= scale
# #
# im[0].header['CRPIX1'] += xshift
# im[0].header['CRPIX2'] += yshift
# #
# for i in [0,1]:
# for j in [0,1]:
# im[0].header['CD%d_%d' %(i+1, j+1)] = wcs.wcs.cd[i,j]
# #
# im.flush()
return xshift, yshift, rot, scale, xrms, yrms
def mscimage(input=None,
output=None,
format='image',
pixmask=no,
verbose=')_.verbose',
wcssource='image',
reference='',
ra=INDEF,
dec=INDEF,
scale=INDEF,
rotation=INDEF,
blank=0.0,
interpolant='poly5',
minterpolant='linear',
boundary='reflect',
constant=0.0,
fluxconserve=no,
ntrim=8,
nxblock=INDEF,
nyblock=INDEF,
interactive=no,
nx=10,
ny=20,
fitgeometry='general',
xxorder=4,
xyorder=4,
xxterms='half',
yxorder=4,
yyorder=4,
yxterms='half',
fd_in='',
fd_ext='',
fd_coord='',
mode='ql',
DOLLARnargs=0,
taskObj=None):
Vars = IrafParList('mscimage')
Vars.addParam(
makeIrafPar(input,
datatype='string',
name='input',
mode='a',
prompt='List of input mosaic exposures'))
Vars.addParam(
makeIrafPar(output,
datatype='string',
name='output',
mode='a',
prompt='List of output images'))
Vars.addParam(
makeIrafPar(format,
datatype='string',
name='format',
enum=['image', 'mef'],
mode='h',
prompt='Output format (image|mef)'))
Vars.addParam(
makeIrafPar(pixmask,
datatype='bool',
name='pixmask',
mode='h',
prompt='Create pixel mask?'))
Vars.addParam(
makeIrafPar(verbose,
datatype='bool',
name='verbose',
mode='h',
prompt='Verbose output?\n\n# Output WCS parameters'))
Vars.addParam(
makeIrafPar(wcssource,
datatype='string',
name='wcssource',
enum=['image', 'parameters', 'match'],
mode='h',
prompt='Output WCS source (image|parameters|match)'))
Vars.addParam(
makeIrafPar(reference,
datatype='file',
name='reference',
mode='h',
prompt='Reference image'))
Vars.addParam(
makeIrafPar(ra,
datatype='real',
name='ra',
max=24.0,
min=0.0,
mode='h',
prompt='RA of tangent point (hours)'))
Vars.addParam(
makeIrafPar(dec,
datatype='real',
name='dec',
max=90.0,
min=-90.0,
mode='h',
prompt='DEC of tangent point (degrees)'))
Vars.addParam(
makeIrafPar(scale,
datatype='real',
name='scale',
mode='h',
prompt='Scale (arcsec/pixel)'))
Vars.addParam(
makeIrafPar(
rotation,
datatype='real',
name='rotation',
max=360.0,
min=-360.0,
mode='h',
prompt=
'Rotation of DEC from N to E (degrees)\n\n# Resampling parmeters'))
Vars.addParam(
makeIrafPar(blank,
datatype='real',
name='blank',
mode='h',
prompt='Blank value'))
Vars.addParam(
makeIrafPar(interpolant,
datatype='string',
name='interpolant',
mode='h',
prompt='Interpolant for data'))
Vars.addParam(
makeIrafPar(minterpolant,
datatype='string',
name='minterpolant',
mode='h',
prompt='Interpolant for mask'))
Vars.addParam(
makeIrafPar(boundary,
datatype='string',
name='boundary',
enum=['nearest', 'constant', 'reflect', 'wrap'],
mode='h',
prompt='Boundary extension'))
Vars.addParam(
makeIrafPar(constant,
datatype='real',
name='constant',
mode='h',
prompt='Constant boundary extension value'))
Vars.addParam(
makeIrafPar(fluxconserve,
datatype='bool',
name='fluxconserve',
mode='h',
prompt='Preserve flux per unit area?'))
Vars.addParam(
makeIrafPar(ntrim,
datatype='int',
name='ntrim',
min=0,
mode='h',
prompt='Edge trim in each extension'))
Vars.addParam(
makeIrafPar(nxblock,
datatype='int',
name='nxblock',
mode='h',
prompt='X dimension of working block size in pixels'))
Vars.addParam(
makeIrafPar(
nyblock,
datatype='int',
name='nyblock',
mode='h',
prompt=
'Y dimension of working block size in pixels\n\n# Geometric mapping parameters'
))
Vars.addParam(
makeIrafPar(interactive,
datatype='bool',
name='interactive',
mode='h',
prompt='Fit mapping interactively?'))
Vars.addParam(
makeIrafPar(nx,
datatype='int',
name='nx',
mode='h',
prompt='Number of x grid points'))
Vars.addParam(
makeIrafPar(ny,
datatype='int',
name='ny',
mode='h',
prompt='Number of y grid points'))
Vars.addParam(
makeIrafPar(fitgeometry,
datatype='string',
name='fitgeometry',
enum=[
'shift', 'xyscale', 'rotate', 'rscale', 'rxyscale',
'general'
],
mode='h',
prompt='Fitting geometry'))
Vars.addParam(
makeIrafPar(xxorder,
datatype='int',
name='xxorder',
min=2,
mode='h',
prompt='Order of x fit in x'))
Vars.addParam(
makeIrafPar(xyorder,
datatype='int',
name='xyorder',
min=2,
mode='h',
prompt='Order of x fit in y'))
Vars.addParam(
makeIrafPar(xxterms,
datatype='string',
name='xxterms',
mode='h',
prompt='X fit cross terms type'))
Vars.addParam(
makeIrafPar(yxorder,
datatype='int',
name='yxorder',
min=2,
mode='h',
prompt='Order of y fit in x'))
Vars.addParam(
makeIrafPar(yyorder,
datatype='int',
name='yyorder',
min=2,
mode='h',
prompt='Order of y fit in y'))
Vars.addParam(
makeIrafPar(yxterms,
datatype='string',
name='yxterms',
mode='h',
prompt='Y fit cross terms type\n\n'))
Vars.addParam(
makeIrafPar(fd_in,
datatype='struct',
name='fd_in',
list_flag=1,
mode='h',
prompt=''))
Vars.addParam(
makeIrafPar(fd_ext,
datatype='struct',
name='fd_ext',
list_flag=1,
mode='h',
prompt=''))
Vars.addParam(
makeIrafPar(fd_coord,
datatype='struct',
name='fd_coord',
list_flag=1,
mode='h',
prompt=''))
Vars.addParam(
makeIrafPar(mode, datatype='string', name='mode', mode='h', prompt=''))
Vars.addParam(
makeIrafPar(DOLLARnargs, datatype='int', name='$nargs', mode='h'))
Vars.addParam(makeIrafPar(None, datatype='file', name='in', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='out', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='ref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pl', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='image', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='trimsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='outsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='plsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='inlists', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='extlist', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pllist', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='coord', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='db', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='wcsref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='outtemp', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pltemp', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nc', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nl', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='ncref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nlref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='cmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='cmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='lmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='lmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nimage', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nimages', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nxblk', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nyblk', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='x', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='y', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='rval', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='xmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='xmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='ymin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='ymax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='crpix1', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='crpix2', mode='u'))
Vars.addParam(
makeIrafPar(None, datatype='string', name='extname', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='string', name='str', mode='u'))
iraf.cache('mscextensions', 'mscgmask')
Vars.inlists = iraf.mktemp('tmp$iraf')
Vars.extlist = iraf.mktemp('tmp$iraf')
Vars.pllist = iraf.mktemp('tmp$iraf')
Vars.coord = iraf.mktemp('tmp$iraf')
Vars.db = iraf.mktemp('tmp$iraf')
Vars.outtemp = iraf.mktemp('tmp')
Vars.wcsref = iraf.mktemp('tmp')
Vars.pltemp = iraf.mktemp('tmp')
iraf.joinlists(Vars.input,
Vars.output,
output=Vars.inlists,
delim=' ',
short=yes,
type='image')
Vars.fd_in = Vars.inlists
while (iraf.fscan(locals(), 'Vars.fd_in', 'Vars.PYin', 'Vars.out') != EOF):
if (iraf.imaccess(Vars.out)):
iraf.printf('Warning: Image already exists (%s)\n', Vars.out)
continue
if (Vars.pixmask):
Vars.pl = Vars.out
Vars.nc = iraf.strlen(Vars.pl)
if (Vars.nc > 5
and iraf.substr(Vars.pl, Vars.nc - 4, Vars.nc) == '.fits'):
Vars.pl = iraf.substr(Vars.pl, 1, Vars.nc - 5)
elif (Vars.nc > 4
and iraf.substr(Vars.out, Vars.nc - 3, Vars.nc) == '.imh'):
Vars.pl = iraf.substr(Vars.pl, 1, Vars.nc - 4)
Vars.pl = Vars.pl + '_bpm'
if (Vars.format == 'image' and iraf.imaccess(Vars.pl)):
iraf.printf('Warning: Mask already exists (%s)\n', Vars.pl)
continue
else:
Vars.pl = ''
iraf.mscextensions(Vars.PYin,
output='file',
index='0-',
extname='',
extver='',
lindex=no,
lname=yes,
lver=no,
ikparams='',
Stdout=Vars.extlist)
Vars.nimages = int(iraf.mscextensions.nimages)
Vars.nimage = 0
if (Vars.nimages < 1):
iraf.printf("WARNING: No input image data found in `%s'.\
", Vars.PYin)
iraf.delete(Vars.extlist, verify=no)
continue
if (not iraf.imaccess(Vars.wcsref)):
Vars.ref = Vars.reference
if (Vars.wcssource == 'match'):
Vars.wcsref = Vars.ref
else:
iraf.mscwtemplate('@' + Vars.extlist,
Vars.wcsref,
wcssource=Vars.wcssource,
reference=Vars.ref,
ra=Vars.ra,
dec=Vars.dec,
scale=Vars.scale,
rotation=Vars.rotation,
projection='',
verbose=Vars.verbose)
Vars.fd_ext = Vars.extlist
while (iraf.fscan(locals(), 'Vars.fd_ext', 'Vars.image') != EOF):
Vars.nimage = Vars.nimage + 1
if (Vars.nimages > 1):
Pipe1 = iraf.hselect(Vars.image, 'extname', yes, Stdout=1)
iraf.scan(locals(), 'Vars.extname', Stdin=Pipe1)
del Pipe1
if (iraf.nscan() == 0):
Vars.extname = 'im' + str(Vars.nimage)
Pipe1 = iraf.printf('%s[%s,append]\n',
Vars.outtemp,
Vars.extname,
Stdout=1)
iraf.scan(locals(), 'Vars.outsec', Stdin=Pipe1)
del Pipe1
Pipe1 = iraf.printf('%s%s\n', Vars.pl, Vars.extname, Stdout=1)
iraf.scan(locals(), 'Vars.plsec', Stdin=Pipe1)
del Pipe1
else:
Vars.extname = ''
Vars.outsec = Vars.outtemp
Vars.plsec = Vars.pl
if (Vars.pixmask and iraf.imaccess(Vars.plsec)):
iraf.delete(Vars.coord, verify=no)
iraf.delete(Vars.db, verify=no)
iraf.printf('Warning: Mask already exists (%s)\n', Vars.plsec)
continue
if (Vars.verbose):
iraf.printf('Resampling %s ...\n', Vars.image)
Pipe1 = iraf.hselect(Vars.image, 'naxis1,naxis2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.nc', 'Vars.nl', Stdin=Pipe1)
del Pipe1
Vars.cmin = 1 + Vars.ntrim
Vars.cmax = Vars.nc - Vars.ntrim
Vars.lmin = 1 + Vars.ntrim
Vars.lmax = Vars.nl - Vars.ntrim
Pipe1 = iraf.printf('[%d:%d,%d:%d]\n',
Vars.cmin,
Vars.cmax,
Vars.lmin,
Vars.lmax,
Stdout=1)
iraf.scan(locals(), 'Vars.trimsec', Stdin=Pipe1)
del Pipe1
if (Vars.wcssource == 'match'):
Pipe1 = iraf.hselect(Vars.ref, 'naxis1,naxis2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.ncref', 'Vars.nlref', Stdin=Pipe1)
del Pipe1
Vars.xmin = (Vars.ncref - 1.) / (Vars.nx - 1.)
Vars.ymin = (Vars.nlref - 1.) / (Vars.ny - 1.)
Vars.ymax = 1
while (Vars.ymax <= Vars.nlref + 1):
Vars.xmax = 1
while (Vars.xmax <= Vars.ncref + 1):
iraf.clPrint(Vars.xmax,
Vars.ymax,
Vars.xmax,
Vars.ymax,
StdoutAppend=Vars.coord)
Vars.xmax = Vars.xmax + Vars.xmin
Vars.ymax = Vars.ymax + Vars.ymin
iraf.mscctran(Vars.coord,
Vars.db,
Vars.ref,
'logical',
'world',
columns='3 4',
units='',
formats='%.4H %.3h',
min_sigdigit=10,
verbose=no)
iraf.delete(Vars.coord, verify=no)
iraf.wcsctran(Vars.db,
Vars.coord,
Vars.image + Vars.trimsec,
inwcs='world',
outwcs='logical',
columns='3 4',
units='hours native',
formats='',
min_sigdigit=10,
verbose=no)
iraf.delete(Vars.db, verify=no)
else:
Vars.nc = Vars.cmax - Vars.cmin + 1
Vars.nl = Vars.lmax - Vars.lmin + 1
Vars.xmin = (Vars.nc - 1.) / (Vars.nx - 1.)
Vars.ymin = (Vars.nl - 1.) / (Vars.ny - 1.)
Vars.ymax = 1
while (Vars.ymax <= Vars.nl + 1):
Vars.xmax = 1
while (Vars.xmax <= Vars.nc + 1):
iraf.clPrint(Vars.xmax,
Vars.ymax,
Vars.xmax,
Vars.ymax,
StdoutAppend=Vars.coord)
Vars.xmax = Vars.xmax + Vars.xmin
Vars.ymax = Vars.ymax + Vars.ymin
iraf.mscctran(Vars.coord,
Vars.db,
Vars.image + Vars.trimsec,
'logical',
'world',
columns='1 2',
units='',
formats='%.4H %.3h',
min_sigdigit=10,
verbose=no)
iraf.delete(Vars.coord, verify=no)
iraf.wcsctran(Vars.db,
Vars.coord,
Vars.wcsref,
inwcs='world',
outwcs='logical',
columns='1 2',
units='hours native',
formats='',
min_sigdigit=10,
verbose=no)
iraf.delete(Vars.db, verify=no)
Vars.xmax = 0.
Vars.xmin = 1.
Vars.ymax = 0.
Vars.ymin = 1.
Vars.fd_coord = Vars.coord
while (iraf.fscan(locals(), 'Vars.fd_coord', 'Vars.x', 'Vars.y') !=
EOF):
if (iraf.nscan() < 2):
continue
if (Vars.xmax < Vars.xmin):
Vars.xmin = Vars.x
Vars.xmax = Vars.x
Vars.ymin = Vars.y
Vars.ymax = Vars.y
else:
Vars.xmin = float(iraf.minimum(Vars.x, Vars.xmin))
Vars.xmax = float(iraf.maximum(Vars.x, Vars.xmax))
Vars.ymin = float(iraf.minimum(Vars.y, Vars.ymin))
Vars.ymax = float(iraf.maximum(Vars.y, Vars.ymax))
Vars.fd_coord = ''
if (Vars.xmax <= Vars.xmin or Vars.ymax <= Vars.ymin):
iraf.error(1, 'No overlap for matching reference')
Vars.cmin = int(iraf.nint(Vars.xmin - 1.5))
Vars.cmax = int(iraf.nint(Vars.xmax + 1.5))
Vars.lmin = int(iraf.nint(Vars.ymin - 1.5))
Vars.lmax = int(iraf.nint(Vars.ymax + 1.5))
iraf.geomap(Vars.coord,
Vars.db,
Vars.cmin,
Vars.cmax,
Vars.lmin,
Vars.lmax,
transforms='',
results='',
fitgeometry=Vars.fitgeometry,
function='chebyshev',
xxorder=Vars.xxorder,
xyorder=Vars.xyorder,
xxterms=Vars.xxterms,
yxorder=Vars.yxorder,
yyorder=Vars.yyorder,
yxterms=Vars.yxterms,
reject=INDEF,
calctype='double',
verbose=no,
interactive=Vars.interactive,
graphics='stdgraph',
cursor='')
if (Vars.wcssource == 'match'):
Vars.cmin = 1
Vars.lmin = 1
Vars.cmax = Vars.ncref
Vars.lmax = Vars.nlref
if (Vars.nxblock == INDEF):
Vars.nxblk = Vars.cmax - Vars.cmin + 3
else:
Vars.nxblk = Vars.nxblock
if (Vars.nyblock == INDEF):
Vars.nyblk = Vars.lmax - Vars.lmin + 3
else:
Vars.nyblk = Vars.nyblock
iraf.geotran(Vars.image + Vars.trimsec,
Vars.outsec,
Vars.db,
Vars.coord,
geometry='geometric',
xin=INDEF,
yin=INDEF,
xshift=INDEF,
yshift=INDEF,
xout=INDEF,
yout=INDEF,
xmag=INDEF,
ymag=INDEF,
xrotation=INDEF,
yrotation=INDEF,
xmin=Vars.cmin,
xmax=Vars.cmax,
ymin=Vars.lmin,
ymax=Vars.lmax,
xsample=10.,
ysample=10.,
xscale=1.,
yscale=1.,
ncols=INDEF,
nlines=INDEF,
interpolant=Vars.interpolant,
boundary='constant',
constant=Vars.constant,
fluxconserve=Vars.fluxconserve,
nxblock=Vars.nxblk,
nyblock=Vars.nyblk,
verbose=no)
iraf.wcscopy(Vars.outsec, Vars.wcsref, verbose=no)
Vars.xmin = 0.
Vars.ymin = 0.
Pipe1 = iraf.hselect(Vars.outsec, 'crpix1,crpix2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.xmin', 'Vars.ymin', Stdin=Pipe1)
del Pipe1
Vars.xmin = Vars.xmin - Vars.cmin + 1
Vars.ymin = Vars.ymin - Vars.lmin + 1
if (Vars.nimage == 1):
Vars.crpix1 = Vars.xmin
Vars.crpix2 = Vars.ymin
else:
Vars.crpix1 = float(iraf.maximum(Vars.crpix1, Vars.xmin))
Vars.crpix2 = float(iraf.maximum(Vars.crpix2, Vars.ymin))
iraf.hedit(Vars.outsec,
'crpix1',
Vars.xmin,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.outsec,
'crpix2',
Vars.ymin,
add=yes,
verify=no,
show=no,
update=yes)
if (Vars.pixmask):
Pipe1 = iraf.printf('%s%s\n', Vars.pl, Vars.extname, Stdout=1)
iraf.scan(locals(), 'Vars.plsec', Stdin=Pipe1)
del Pipe1
iraf.mscgmask(Vars.image + Vars.trimsec,
Vars.pltemp + '.pl',
'BPM',
mval=10000)
iraf.geotran(Vars.pltemp,
Vars.plsec + '.fits',
Vars.db,
Vars.coord,
geometry='geometric',
xin=INDEF,
yin=INDEF,
xshift=INDEF,
yshift=INDEF,
xout=INDEF,
yout=INDEF,
xmag=INDEF,
ymag=INDEF,
xrotation=INDEF,
yrotation=INDEF,
xmin=Vars.cmin,
xmax=Vars.cmax,
ymin=Vars.lmin,
ymax=Vars.lmax,
xsample=10.,
ysample=10.,
interpolant=Vars.minterpolant,
boundary='constant',
constant=20000.,
fluxconserve=no,
nxblock=Vars.nxblk,
nyblock=Vars.nyblk,
verbose=no)
iraf.imdelete(Vars.pltemp, verify=no)
iraf.mscpmask(Vars.plsec + '.fits', Vars.plsec + '.pl')
iraf.imdelete(Vars.plsec + '.fits', verify=no)
iraf.hedit(Vars.outsec,
'BPM',
Vars.plsec + '.pl',
add=yes,
show=no,
verify=no,
update=yes)
iraf.wcscopy(Vars.plsec, Vars.outsec, verbose=no)
iraf.clPrint(Vars.plsec, StdoutAppend=Vars.pllist)
else:
iraf.hedit(Vars.outsec,
'BPM',
PYdel=yes,
add=no,
addonly=no,
show=no,
verify=no,
update=yes)
iraf.delete(Vars.coord, verify=no)
iraf.delete(Vars.db, verify=no)
Vars.fd_ext = ''
iraf.delete(Vars.extlist, verify=no)
if (Vars.nimages > 1 and Vars.format == 'image'):
if (Vars.verbose):
iraf.printf('Creating image %s ...\n', Vars.out)
iraf.mscextensions(Vars.outtemp,
output='file',
index='',
extname='',
extver='',
lindex=no,
lname=yes,
lver=no,
ikparams='',
Stdout=Vars.extlist)
if (Vars.pixmask):
iraf.combine('@' + Vars.pllist,
Vars.pltemp + '.pl',
headers='',
bpmasks=Vars.pl,
rejmasks='',
nrejmasks='',
expmasks='',
sigmas='',
imcmb='',
ccdtype='',
amps=no,
subsets=no,
delete=no,
combine='average',
reject='none',
project=no,
outtype='real',
outlimits='',
offsets='wcs',
masktype='none',
maskvalue='0',
blank=0.,
scale='none',
zero='none',
weight='none',
statsec='',
lthreshold=INDEF,
hthreshold=0.99,
nlow=1,
nhigh=1,
nkeep=1,
mclip=yes,
lsigma=3.,
hsigma=3.,
rdnoise='0.',
gain='1.',
snoise='0.',
sigscale=0.1,
pclip=-0.5,
grow=0.,
Stdout='dev$null')
iraf.imdelete(Vars.pltemp, verify=no)
iraf.combine('@' + Vars.extlist,
Vars.out,
headers='',
bpmasks='',
rejmasks='',
nrejmasks='',
expmasks='',
sigmas='',
imcmb='',
ccdtype='',
amps=no,
subsets=no,
delete=no,
combine='average',
reject='none',
project=no,
outtype='real',
outlimits='',
offsets='wcs',
masktype='badvalue',
maskvalue='2',
blank=0.,
scale='none',
zero='none',
weight='none',
statsec='',
lthreshold=INDEF,
hthreshold=INDEF,
nlow=1,
nhigh=1,
nkeep=1,
mclip=yes,
lsigma=3.,
hsigma=3.,
rdnoise='0.',
gain='1.',
snoise='0.',
sigscale=0.1,
pclip=-0.5,
grow=0.,
Stdout='dev$null')
iraf.hedit(Vars.out,
'BPM',
Vars.pl,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.pl,
'IMCMB???,PROCID??',
add=no,
addonly=no,
PYdel=yes,
update=yes,
verify=no,
show=no)
else:
iraf.combine('@' + Vars.extlist,
Vars.out,
headers='',
bpmasks='',
rejmasks='',
nrejmasks='',
expmasks='',
sigmas='',
imcmb='',
ccdtype='',
amps=no,
subsets=no,
delete=no,
combine='average',
reject='none',
project=no,
outtype='real',
outlimits='',
offsets='wcs',
masktype='none',
maskvalue='2',
blank=0.,
scale='none',
zero='none',
weight='none',
statsec='',
lthreshold=INDEF,
hthreshold=INDEF,
nlow=1,
nhigh=1,
nkeep=1,
mclip=yes,
lsigma=3.,
hsigma=3.,
rdnoise='0.',
gain='1.',
snoise='0.',
sigscale=0.1,
pclip=-0.5,
grow=0.,
Stdout='dev$null')
Pipe2 = iraf.hselect('@' + Vars.extlist, 'gain', yes, Stdout=1)
Pipe1 = iraf.average(data_value=0., Stdin=Pipe2, Stdout=1)
del Pipe2
iraf.scan(locals(), 'Vars.rval', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.out,
'gain',
Vars.rval,
add=yes,
PYdel=no,
update=yes,
verify=no,
show=no)
Pipe2 = iraf.hselect('@' + Vars.extlist, 'rdnoise', yes, Stdout=1)
Pipe1 = iraf.average(data_value=0., Stdin=Pipe2, Stdout=1)
del Pipe2
iraf.scan(locals(), 'Vars.rval', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.out,
'rdnoise',
Vars.rval,
add=yes,
PYdel=no,
update=yes,
verify=no,
show=no)
iraf.hedit(Vars.out,
'IMCMB???,PROCID??',
add=no,
addonly=no,
PYdel=yes,
update=yes,
verify=no,
show=no)
iraf.hedit(
Vars.out,
'NEXTEND,DETSEC,CCDSEC,AMPSEC,IMAGEID,DATASEC,TRIMSEC,BIASSEC',
add=no,
addonly=no,
PYdel=yes,
update=yes,
verify=no,
show=no)
iraf.imdelete(Vars.outtemp, verify=no)
if (iraf.access(Vars.pllist)):
iraf.imdelete('@' + Vars.pllist, verify=no)
iraf.delete(Vars.pllist, verify=no)
iraf.delete(Vars.extlist, verify=no)
elif (Vars.nimages > 1):
iraf.imrename(Vars.outtemp, Vars.out, verbose=no)
iraf.mscextensions(Vars.out,
output='file',
index='',
extname='',
extver='',
lindex=no,
lname=yes,
lver=no,
ikparams='',
Stdout=Vars.extlist)
Vars.fd_ext = Vars.extlist
while (iraf.fscan(locals(), 'Vars.fd_ext', 'Vars.image') != EOF):
Pipe1 = iraf.hselect(Vars.image,
'naxis1,naxis2,crpix1,crpix2',
yes,
Stdout=1)
iraf.scan(locals(),
'Vars.nc',
'Vars.nl',
'Vars.xmin',
'Vars.ymin',
Stdin=Pipe1)
del Pipe1
Vars.cmin = int(iraf.nint(Vars.crpix1 - Vars.xmin + 1))
Vars.lmin = int(iraf.nint(Vars.crpix2 - Vars.ymin + 1))
Vars.cmax = Vars.nc + Vars.cmin - 1
Vars.lmax = Vars.nl + Vars.lmin - 1
Pipe1 = iraf.printf('[%d:%d,%d:%d]\n',
Vars.cmin,
Vars.cmax,
Vars.lmin,
Vars.lmax,
Stdout=1)
iraf.scan(locals(), 'Vars.str', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.image,
'DETSEC',
Vars.str,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.image,
'DTM1_1',
1.,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.image,
'DTM2_2',
1.,
add=yes,
verify=no,
show=no,
update=yes)
Vars.cmin = Vars.cmin - 1
Vars.lmin = Vars.lmin - 1
iraf.hedit(Vars.image,
'DTV1',
Vars.cmin,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.image,
'DTV2',
Vars.lmin,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.image,
'CCDSUM,CCDSEC,AMPSEC,ATM1_1,ATM2_2,ATV1,ATV2',
PYdel=yes,
add=no,
addonly=no,
verify=no,
show=no,
update=yes)
Vars.fd_ext = ''
iraf.delete(Vars.extlist, verify=no)
else:
iraf.imrename(Vars.outsec, Vars.out, verbose=no)
if (iraf.access(Vars.pllist)):
iraf.delete(Vars.pllist, verify=no)
Vars.fd_in = ''
iraf.delete(Vars.inlists, verify=no)
if (Vars.wcssource != 'match' and iraf.imaccess(Vars.wcsref)):
iraf.imdelete(Vars.wcsref, verify=no)
def psfphot(inlist, ra, dec, reffilt, interact, fwhm, readnoise, gain,
threshold,refimage=None,starfile=None,maxnpsf=5,
clobber=globclob,verbose=globver,skykey='SKYBKG',
filtkey='FILTER',pixtol=3.0):
""" perform PSF-based photometry on a single target star (SN?) at RA, Dec and
also on a set of comparison stars, using daophot. simultaneously
perform aperture photometry on all the comparison stars (after
subtracting off contributions from neighbors) to enable absolute
photometry by comparison to aperture photometry of standard stars
observed in other fields """
# Defaults / constants
psfmult=5.0 #standard factor (multiplied by fwhm to get psfradius)
psfmultsmall=3.0 #similar to psfmult, adjusted for nstar and substar
# Necessary package
iraf.imutil()
# Parse inputs
infiles=iraffiles(inlist)
# Which file is reffilt? call it refimage
if refimage==None:
for image in infiles:
if check_head(image, filtkey):
try:
imgfilt = get_head(image, filtkey)
if imgfilt == reffilt:
refimage = image
break
except:
pass
if not refimage:
print "BAD USER! No image corresponds to the filter: %s" % reffilt
return
else:
refroot='s'+refimage.split('.')[0]
#first make sure to add back in background of sky
iraf.iqsubsky(inlist, sub=no, skykey=skykey)
#put reference image first on list
infiles.remove(refimage)
infiles.insert(0,refimage)
#setup for keywords
if gain == "!GAIN":
try: gainval = float(get_head(image, gain))
except:
print "Bad header keyword for gain."
else:
gainval = float(gain)
if readnoise == "!READNOISE":
try: readval = float(get_head(image, readnoise))
except:
print "Bad header keyword for readnoise."
else:
readval = float(readnoise)
# Process each file in turn
for image in infiles:
# Check that the image is there
check_exist(image,"r")
# Grab image root name
root=image.split('.')[0]
# Map image to reference image
if not (image==refimage):
[nx,ny]=get_head(image,['NAXIS1','NAXIS2'])
stars=Starlist(get_head(image,'STARFILE'))
refstars=Starlist(get_head(refimage,'STARFILE'))
refstars.pix2wcs(refimage)
refstars.wcs2pix(image)
match,refmatch=stars.match(refstars,useflags=yes,tol=10.0)
nstars=len(match)
if not (nstars>2):
print 'Could not find star matches between reference and %s' % image
infiles.remove(image)
continue
refmatch.pix2wcs(image)
refmatch.wcs2pix(refimage)
matchfile=open('%s.match' % root, 'w')
for i in range(len(match)):
matchfile.write('%10.3f%10.3f%10.3f%10.3f\n' %
(refmatch[i].xval,refmatch[i].yval,
match[i].xval,match[i].yval))
matchfile.close()
check_exist('%s.geodb' % root, 'w', clobber=clobber)
iraf.geomap('%s.match' % root,'%s.geodb' % root,1.0,nx,1.0,ny,
verbose=no,interactive=no)
check_exist('s%s.fits' % root, 'w', clobber=clobber)
iraf.geotran(image,'s%s' % root,'%s.geodb' % root,
'%s.match' % root,geometry="geometric",
boundary="constant",verbose=no)
else:
iraf.imcopy(image,'s%s' % root)
root='s%s' % root
#get sky level and calculate sigma
#if check_head(image, skykey):
# try:
# sky=float(get_head(image, skykey))
# except:
# print "No sky levels in header."
#sigma= (((sky * gainval) + readval**2)**.5) / gainval
iraf.iterstat(image)
# Saturation level
if not check_head(image, "SATURATE"):
saturate = 60000.0
else:
saturate = get_head(image, "SATURATE")
# Update datapars and daopars
iraf.datapars.fwhmpsf=fwhm
iraf.datapars.sigma=iraf.iterstat.sigma
iraf.datapars.datamin=iraf.iterstat.median-10*iraf.iterstat.sigma
iraf.datapars.datamax=0.90*saturate
iraf.datapars.readnoise=readval
iraf.datapars.epadu=gainval
iraf.datapars.filter=filtkey
iraf.daopars.psfrad=psfmult*fwhm
iraf.daopars.fitrad=fwhm
iraf.daopars.function="gauss,moffat15,moffat25,lorentz,penny1"
#find stars in image unless a starlist is given
if image==refimage and starfile==None:
iraf.daophot.daofind(root,'refimage.coo.1',threshold=threshold,verify=no,
verbose=verbose)
elif image==refimage:
shutil.copy(starfile,'refimage.coo.1')
#initial photometry
iraf.daophot.phot(root,'refimage.coo.1','default',aperture=fwhm,verify=no,
verbose=verbose)
#select stars for psf the first time
refstarsfile = "refimage.pst.1"
if image == refimage:
iraf.pstselect(root,'default',refstarsfile,maxnpsf,
interactive=yes,verify=no,verbose=verbose)
#fit the psf
iraf.psf(root,'default',refstarsfile,'default','default','default',
interactive=interact,verify=no,verbose=verbose)
#identify neighboring/interfering stars to selected stars
groupingfile = root+".psg.1"
iraf.nstar(root,groupingfile,'default','default','default',
psfrad= psfmultsmall * fwhm,verify=no,verbose=verbose)
#subtract out neighboring stars from image
iraf.substar(root,'default',refstarsfile,'default','default',
psfrad=psfmultsmall*fwhm,verify=no,verbose=verbose)
#repeat psf to get better psf model
#IRAF's interactive version usually crashes
subtractedimage = root+".sub.1"
iraf.psf(subtractedimage,root+".nst.1",refstarsfile,'%s.psf.2' % root,
'%s.pst.2' % root,'%s.psg.2' % root,interactive=interact,
verify=no,verbose=verbose)
#Need to make sure SN was detected by daofind
stars=Starlist('%s.mag.1' % root)
SN=Star(name='SN',radeg=ra,dcdeg=dec,fwhm=2.0,fwhmw=2.0)
SNlis=Starlist(stars=[SN])
SNlis.wcs2pix(image)
if (len(stars.match(SNlis)[0])==0):
#No match - need to add to daofind file
print "No match!"
coofile=open('refimage.coo.1', 'a+')
coofile.write('%10.3f%10.3f%9.3f%8.3f%13.3f%12.3f%8i\n' % (SNlis[0].xval, SNlis[0].yval,99.999,0.500,0.000,0.000,999))
coofile.close()
#repeat aperture photometry to get good comparisons to standard fields
iraf.daophot.phot(root,'refimage.coo.1','default',aperture=psfmult*fwhm,
verify=no,verbose=verbose)
# allstar run
iraf.allstar(root,'default','default','default','default','default',
verify=no,verbose=verbose)
def align_to_reference(
ROOT_DIRECT,
ALIGN_IMAGE,
fitgeometry="shift",
clean=True,
verbose=False,
ALIGN_EXTENSION=0,
toler=3,
skip_swarp=False,
align_sdss_ds9=False,
catalog=None,
):
"""
xshift, yshift, rot, scale, xrms, yrms = align_to_reference()
"""
import os
import glob
import shutil
from pyraf import iraf
from iraf import stsdas, dither
import threedhst
from threedhst import catIO
no = iraf.no
yes = iraf.yes
INDEF = iraf.INDEF
#### Clean slate
rmfiles = [
"SCI.fits",
"WHT.fits",
"align.cat",
"direct.cat" "align.map",
"align.match",
"align.reg",
"align.xy",
"direct.reg",
"direct.xy",
"ds9_align.tsv",
]
for file in rmfiles:
try:
os.remove(file)
except:
pass
if catalog is not None:
align_sdss_ds9 = True
#### Get only images that overlap from the ALIGN_IMAGE list
if not align_sdss_ds9:
align_img_list = find_align_images_that_overlap(
ROOT_DIRECT + "_drz.fits", ALIGN_IMAGE, ALIGN_EXTENSION=ALIGN_EXTENSION
)
if not align_img_list:
print "threedhst.shifts.align_to_reference: no alignment images overlap."
return 0, 0
#### Use swarp to combine the alignment images to the same image
#### dimensions as the direct mosaic
if (not skip_swarp) & (not align_sdss_ds9):
try:
os.remove(ROOT_DIRECT + "_align.fits")
except:
pass
matchImagePixels(
input=align_img_list,
matchImage=ROOT_DIRECT + "_drz.fits",
output=ROOT_DIRECT + "_align.fits",
match_extension=1,
input_extension=ALIGN_EXTENSION,
)
#### Run SExtractor on the direct image, with the WHT
#### extension as a weight image
se = threedhst.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options["CHECKIMAGE_TYPE"] = "NONE"
se.options["WEIGHT_TYPE"] = "MAP_WEIGHT"
se.options["WEIGHT_IMAGE"] = "WHT.fits"
se.options["FILTER"] = "Y"
## Detect thresholds (default = 1.5)
THRESH = 10
if align_sdss_ds9:
if "Vizier" not in REFERENCE_CATALOG:
THRESH = 20
se.options["DETECT_THRESH"] = "%d" % (THRESH)
se.options["ANALYSIS_THRESH"] = "%d" % (THRESH)
se.options["MAG_ZEROPOINT"] = str(threedhst.options["MAG_ZEROPOINT"])
#### Run SExtractor on direct and alignment images
## direct image
se.options["CATALOG_NAME"] = "direct.cat"
iraf.imcopy(ROOT_DIRECT + "_drz.fits[1]", "SCI.fits")
iraf.imcopy(ROOT_DIRECT + "_drz.fits[2]", "WHT.fits")
status = se.sextractImage("SCI.fits")
## Read the catalog
directCat = threedhst.sex.mySexCat("direct.cat")
if align_sdss_ds9:
### Use ds9 SDSS catalog to refine alignment
import threedhst.dq
import pywcs
import threedhst.catIO as catIO
wcs = pywcs.WCS(pyfits.getheader("SCI.fits", 0))
# wcs = pywcs.WCS(pyfits.getheader('Q0821+3107-F140W_drz.fits', 1))
if "Vizier" in REFERENCE_CATALOG:
#### Use (unstable) astroquery Vizier search
#### CFHTLS-Deep: 'Vizier.II/317'
VIZIER_CAT = REFERENCE_CATALOG.split("Vizier.")[1]
print "Align to Vizier catalog: http://vizier.u-strasbg.fr/viz-bin/VizieR?-source=%s" % (VIZIER_CAT)
import astroquery
if astroquery.__version__ < "0.0.dev1078":
from astroquery import vizier
query = {}
query["-source"] = VIZIER_CAT
# query["-out"] = ["_r", "CFHTLS", "rmag"]
query["-out"] = ["_RAJ2000", "_DEJ2000"] ### Just RA/Dec.
#### Center position and query radius
r0, d0 = wcs.wcs_pix2sky([[wcs.naxis1 / 2.0, wcs.naxis2 / 2.0]], 1)[0]
rll, dll = wcs.wcs_pix2sky([[0, 0]], 1)[0]
corner_radius = (
np.sqrt((r0 - rll) ** 2 * np.cos(d0 / 360.0 * 2 * np.pi) ** 2 + (d0 - dll) ** 2) * 60.0 * 1.5
)
h = query["-c"] = "%.6f %.6f" % (r0, d0)
query["-c.rm"] = "%.3f" % (corner_radius) ### xxx check image size
#### Run the query
vt = vizier.vizquery(query)
else:
#### Newer astroquery
from astroquery.vizier import Vizier
import astropy.coordinates as coord
import astropy.units as u
Vizier.ROW_LIMIT = -1
r0, d0 = wcs.wcs_pix2sky([[wcs.naxis1 / 2.0, wcs.naxis2 / 2.0]], 1)[0]
rll, dll = wcs.wcs_pix2sky([[0, 0]], 1)[0]
corner_radius = (
np.sqrt((r0 - rll) ** 2 * np.cos(d0 / 360.0 * 2 * np.pi) ** 2 + (d0 - dll) ** 2) * 60.0 * 1.5
)
#
c = coord.ICRSCoordinates(ra=r0, dec=d0, unit=(u.deg, u.deg))
#### something with astropy.coordinates
# c.icrs.ra.degree = c.icrs.ra.degrees
# c.icrs.dec.degree = c.icrs.dec.degrees
#
vt = Vizier.query_region(c, width=u.Quantity(corner_radius, u.arcminute), catalog=[VIZIER_CAT])[0]
#### Make a region file
ra_list, dec_list = vt["RAJ2000"], vt["DEJ2000"]
print "Vizier, found %d objects." % (len(ra_list))
fp = open("%s.vizier.reg" % (ROOT_DIRECT), "w")
fp.write("# %s, r=%.1f'\nfk5\n" % (VIZIER_CAT, corner_radius))
for ra, dec in zip(ra_list, dec_list):
fp.write('circle(%.6f, %.6f, 0.5")\n' % (ra, dec))
#
fp.close()
else:
#### Use DS9 catalog
ds9 = threedhst.dq.myDS9()
ds9.set("file SCI.fits")
# ds9.set('file Q0821+3107-F140W_drz.fits')
ds9.set("catalog %s" % (REFERENCE_CATALOG))
### Can't find XPA access point for "copy to regions"
ds9.set("catalog export tsv ds9_align.tsv")
lines = open("ds9_align.tsv").readlines()
ra_list, dec_list = [], []
for line in lines[1:]:
spl = line.split()
ra, dec = float(spl[0]), float(spl[1])
ra_list.append(ra)
dec_list.append(dec)
#
del (ds9)
x_image, y_image = [], []
for ra, dec in zip(ra_list, dec_list):
x, y = wcs.wcs_sky2pix([[ra, dec]], 1)[0]
x_image.append(x)
y_image.append(y)
alignCat = catIO.EmptyCat()
alignCat["X_IMAGE"] = np.array(x_image)
alignCat["Y_IMAGE"] = np.array(y_image)
else:
## alignment image
se.options["CATALOG_NAME"] = "align.cat"
status = se.sextractImage(ROOT_DIRECT + "_align.fits")
alignCat = threedhst.sex.mySexCat("align.cat")
xshift = 0
yshift = 0
rot = 0
scale = 1.0
xrms = 2
yrms = 2
NITER = 5
IT = 0
while IT < NITER:
IT = IT + 1
#### Get x,y coordinates of detected objects
## direct image
fp = open("direct.xy", "w")
for i in range(len(directCat.X_IMAGE)):
fp.write("%s %s\n" % (directCat.X_IMAGE[i], directCat.Y_IMAGE[i]))
fp.close()
## alignment image
fp = open("align.xy", "w")
for i in range(len(alignCat.X_IMAGE)):
fp.write("%s %s\n" % (np.float(alignCat.X_IMAGE[i]) + xshift, np.float(alignCat.Y_IMAGE[i]) + yshift))
fp.close()
iraf.flpr()
iraf.flpr()
iraf.flpr()
#### iraf.xyxymatch to find matches between the two catalogs
pow = toler * 1.0
try:
os.remove("align.match")
except:
pass
status1 = iraf.xyxymatch(
input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2 ** pow,
separation=0,
verbose=yes,
Stdout=1,
)
nmatch = 0
while status1[-1].startswith("0") | (nmatch < 10):
pow += 1
os.remove("align.match")
status1 = iraf.xyxymatch(
input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2 ** pow,
separation=0,
verbose=yes,
Stdout=1,
)
#
nmatch = 0
for line in open("align.match").xreadlines():
nmatch += 1
if verbose:
for line in status1:
print line
#### Compute shifts with iraf.geomap
iraf.flpr()
iraf.flpr()
iraf.flpr()
try:
os.remove("align.map")
except:
pass
status2 = iraf.geomap(
input="align.match",
database="align.map",
fitgeometry=fitgeometry,
interactive=no,
xmin=INDEF,
xmax=INDEF,
ymin=INDEF,
ymax=INDEF,
maxiter=10,
reject=2.0,
Stdout=1,
)
if verbose:
for line in status2:
print line
# fp = open(root+'.iraf.log','a')
# fp.writelines(status1)
# fp.writelines(status2)
# fp.close()
#### Parse geomap.output
fp = open("align.map", "r")
for line in fp.readlines():
spl = line.split()
if spl[0].startswith("xshift"):
xshift += float(spl[1])
if spl[0].startswith("yshift"):
yshift += float(spl[1])
if spl[0].startswith("xrotation"):
rot = float(spl[1])
if spl[0].startswith("xmag"):
scale = float(spl[1])
if spl[0].startswith("xrms"):
xrms = float(spl[1])
if spl[0].startswith("yrms"):
yrms = float(spl[1])
fp.close()
# os.system('wc align.match')
print "Shift iteration #%d, xshift=%f, yshift=%f, rot=%f, scl=%f (rms: %5.2f,%5.2f)" % (
IT,
xshift,
yshift,
rot,
scale,
xrms,
yrms,
)
im = pyfits.open("SCI.fits")
shutil.copy("align.map", ROOT_DIRECT + "_align.map")
shutil.copy("align.match", ROOT_DIRECT + "_align.match")
#### Cleanup
if clean:
rmfiles = [
"SCI.fits",
"WHT.fits",
"align.cat",
"align.map",
"align.match",
"align.reg",
"align.xy",
"direct.cat",
"direct.reg",
"direct.xy",
"drz_sci.fits",
"drz_wht.fits",
"bg.fits",
]
for file in rmfiles:
try:
os.remove(file)
except:
pass
return xshift, yshift, rot, scale, xrms, yrms
def spitzer_sub(new, newunc, newcov, ref, refunc, refcov, out,
stamps=None, tmass=None):
# Remove nan from mosaics
x = pyfits.open(new)
y = np.nan_to_num(x[0].data)
x[0].data = y
new2 = "n%s" % new
x.writeto(new2)
# Find stars in new image
os.system("$REDUCTION/runsex.pl %s 5.0 -weight %s" % (new2, newcov))
# Find stars in reference
os.system("$REDUCTION/runsex.pl %s 5.0 -weight %s" % (ref, refcov))
# Create file for geotran
stars = Starlist("%s.stars" % new2)
refstars = Starlist("%s.stars" % ref)
refstars.pix2wcs(ref)
refstars.wcs2pix(new2)
a,b = stars.match(refstars, maxnum=1000)
# Create file for geotran
b.pix2wcs(new2)
b.wcs2pix(ref)
refroot = ref.split(".")[0]
outf = open("%s.match" % refroot, "w")
for i in range(len(a)):
outf.write("%10.3f%10.3f%10.3f%10.3f\n" % (a[i].xval, a[i].yval,
b[i].xval, b[i].yval))
outf.close()
# Geomap and geotran
[naxis1, naxis2] = get_head(new, ["NAXIS1", "NAXIS2"])
iraf.geomap("%s.match" % refroot, "%s.db" % refroot, 1, naxis1, 1, naxis2,
fitgeometry="rotate", interactive=no)
iraf.geotran(ref, "t%s" % ref, "%s.db" % refroot, "%s.match" % refroot)
iraf.geotran(refunc, "t%s" % refunc, "%s.db" % refroot,
"%s.match" % refroot)
# Get stars for PSF matching
if stamps != None:
psfstars = Starlist(stamps)
psfstars.pix2wcs(ref)
psfstars.wcs2pix(new2)
outf = open("stamps.lis", "w")
for star in psfstars:
outf.write("%10.3f%10.3f\n" % (star.xval, star.yval))
outf.close()
# Appropriate parameters
iraf.iterstat(ref)
update_head(ref, ["MEDSKY", "SKYSIG"], [iraf.iterstat.median,
iraf.iterstat.sigma])
[refskybkg, refskysig] = get_head(ref, ["MEDSKY", "SKYSIG"])
tl = refskybkg - 10 * refskysig; tu = 30000.0
iraf.iterstat(new)
update_head(new, ["MEDSKY", "SKYSIG"], [iraf.iterstat.median,
iraf.iterstat.sigma])
[newskybkg, newskysig] = get_head(new, ["MEDSKY", "SKYSIG"])
il = newskybkg - 10 * newskysig; iu = 30000.0
# Run hotpants
hpcmd = "hotpants -inim %s -tmplim t%s -outim %s -tni t%s -ini %s -nsx 3 -nsy 3 -savexy %s.xy -ko 0 -bgo 0 -oni u%s -n t -tl %.2f -tu %.2f -il %.2f -iu %.2f -r 7.5 -rss 18.0" % (new2, ref, out, refunc, newunc, new2, out, tl, tu, il, iu)
if stamps != None:
hpcmd += " -ssf stamps.lis -afssc 0"
os.system(hpcmd)
#iraf.imarith(new2, "-", "t%s" % ref, out)
# Create appropriate weight image
#iraf.imexpr("sqrt(a**2 + b**2)", "u%s" % out, a=newunc, b=refunc)
iraf.imarith(1, "/", "u%s" % out, "temp1.fits")
iraf.imarith("temp1.fits", "/", "u%s" % out, "w%s" % out)
os.remove("temp1.fits")
# Pick up candidates
os.system("$REDUCTION/runsex.pl %s 2.0 -weight w%s" % (out, out))
stars = Starlist("%s.stars" % out)
cands = []
[nax1, nax2] = get_head(ref, ["NAXIS1", "NAXIS2"])
stars.pix2wcs(new)
stars.wcs2pix(ref)
refu = pyfits.open(refunc)
for star in stars:
if star.xval > EDGETOL and star.xval < (nax1 - EDGETOL) and star.yval > EDGETOL and star.yval < (nax2 - EDGETOL) and refu[0].data[star.yval,star.xval] != 0 and star.fwhmw > 0.5 and star.fwhmw < 10.0:
cands.append(star)
scands = Starlist(stars=cands)
# Filter out bright stars
if tmass==None:
scands.write("cands.reg")
else:
stmass = Starlist(tmass)
stmass.wcs2pix(ref)
s2cands = scands.nomatch(stmass)
s2cands.write("cands.reg")
# More comprehensive list
os.system("$REDUCTION/runsex.pl %s 1.5" % out)
stars = Starlist("%s.stars" % out)
cands = []
[nax1, nax2] = get_head(ref, ["NAXIS1", "NAXIS2"])
stars.pix2wcs(new)
stars.wcs2pix(ref)
refu = pyfits.open(refunc)
for star in stars:
if star.xval > EDGETOL and star.xval < (nax1 - EDGETOL) and star.yval > EDGETOL and star.yval < (nax2 - EDGETOL) and refu[0].data[star.yval,star.xval] != 0 and star.fwhmw > 0.5 and star.fwhmw < 10.0:
cands.append(star)
scands = Starlist(stars=cands)
scands.write("cands_all.reg")
return
def alignimages(im1, im2):
runsex = 1 #run sextractor to get object catalogs
auto = 0.
t = im1.split('.')
prefixim1 = t[0]
t = prefixim1.split('pre')
fieldid = t[0] #saves cl1018 or whatever as fieldid
t = im2.split('.')
prefixim2 = t[0]
filexy1 = prefixim1 + '-xy.cat'
filexy2 = prefixim2 + '-xy.cat'
print "aligning images for ", fieldid
if runsex > 0:
runsextractor(im1, auto)
infile = open('test.cat', 'r') #keep only sources w/in useable field
outfile = open(filexy1, 'w')
for line in infile: #keep only targets w/in 500 pixels of center
if line.find('#') > -1:
continue
t = line.split()
out = '%8.4f %8.4f \n' % (float(t[10]), float(t[11]))
outfile.write(out)
infile.close()
outfile.close()
print "running sextractor on ", im2
runsextractor(im2, auto)
infile = open('test.cat', 'r') #keep only sources w/in useable field
outfile = open(filexy2, 'w')
for line in infile: #keep only targets w/in 500 pixels of center
if line.find('#') > -1:
continue
t = line.split()
out = '%8.4f %8.4f \n' % (float(t[10]), float(t[11]))
outfile.write(out)
infile.close()
outfile.close()
match = fieldid + '-match'
dbase = fieldid + '-match-geomap'
iraf.display(im1, 1, fill='yes')
iraf.display(im2, 2, fill='yes')
reffile = fieldid + 'refpoints' #make these individually. First line w/3points in gmos image (x1 y1 x2 y2 x3 y3). 2nd line w/same points in ediscs I-band
flag = raw_input("Should we create a refpoints file (0=no, 1=yes)?\n")
flag = int(flag)
if flag > 0.1:
s = 'cp cl1018refpoints ' + reffile
os.system(s)
print "open ", reffile, " in emacs, delete 2 lines"
print "Enter x1 y1 x2 y2 x3 y3 for ref image (gmos)"
print "Enter x1 y1 x2 y2 x3 y3 for 2nd image (vlt)"
iraf.imexam()
iraf.xyxymatch(filexy2,
filexy1,
match,
tolerance=15,
refpoints=reffile,
matching='tolerance',
interactive='no')
iraf.imgets(image=im1, param='i_naxis1')
t = iraf.imgets.value
im1xmax = (float(t))
iraf.imgets(image=im1, param='i_naxis2')
t = iraf.imgets.value
im1ymax = (float(t))
iraf.geomap(input=match,
database=dbase,
function='polynomial',
xmin=1,
xmax=im1xmax,
ymin=1,
ymax=im1ymax,
xxorder=4,
xyorder=4,
yyorder=4,
yxorder=4,
transform='gmos',
interactive='yes')
transformim = 1
if transformim > 0:
outim2 = 'g' + im2
iraf.geotran(im2, output=outim2, database=dbase, transform='gmos')
iraf.display(im2, 1, fill='yes')
iraf.display(outim2, 2, fill='yes')
iraf.display(im1, 3, fill='yes')
def imshiftcomb(inlist,
outimg,
fitgeom='shift',
inpref='',
objmask='none',
combine='average',
reject='none',
fscale=False,
fbase=100,
fhead='F1',
second=False,
first_pref='sdfr',
second_pref='sdf2r',
indep=False,
sigmap='none',
expmap='none',
whtmap='none',
gain=5.6,
ffpref=''):
# check output image
if os.access(outimg, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % outimg
return 1
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check input image list
if sigmap != 'none':
ffimg_arr = check_input2(inlist, ffpref)
if isinstance(ffimg_arr, int):
return 1
# check optional output image
if sigmap != 'none':
if os.access(sigmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % sigmap
return 1
if expmap != 'none':
if os.access(expmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % expmap
return 1
if whtmap != 'none':
if os.access(whtmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % whtmap
return 1
# get array size
im = pyfits.open(inimg_arr[0])
nx = im[0].header['NAXIS1']
ny = im[0].header['NAXIS2']
im.close()
# check geomap data
dx = []
dy = []
gmp_arr = []
gmp2_arr = []
dbs_arr = []
for i in range(len(inimg_arr)):
fname, ext = os.path.splitext(inimg_arr[i])
gmp = fname + '.gmp'
gmp2 = fname + '.gmp2'
dbs = fname + '.dbs'
gmp_arr.append(gmp)
gmp2_arr.append(gmp2)
dbs_arr.append(dbs)
if not os.access(gmp, os.R_OK):
print >> sys.stderr, 'geomap file (%s) does not exist' % (gmp)
return 1
if os.access(dbs, os.R_OK):
print >> sys.stderr, 'database file (%s) is already exist' % (dbs)
return 1
if os.access(gmp2, os.R_OK):
print >> sys.stderr, 'modified geomap file (%s) is already exist' % (
gmp2)
return 1
fgmp = open(gmp)
nl = 1
dx_ave = 0.0
dy_ave = 0.0
for line in fgmp:
if not line.startswith('#'):
param = line[:-1].split()
if len(param) != 4:
print >> sys.stderr, 'Invalid format in line %d of %s: %s' % (
nl, gmp, line[:-1])
fgmp.close()
return 1
else:
if isfloat(param[0]) == False or isfloat(
param[1]) == False or isfloat(
param[2]) == False or isfloat(
param[3]) == False:
print >> sys.stderr, 'failed to decode line %d of %s: %s' % (
nl, gmp, line[:-1])
fgmp.close()
return 1
else:
dx_ave += float(param[0]) - float(param[2])
dy_ave += float(param[1]) - float(param[3])
nl += 1
#print inimg_arr[i],nl
dx.append(dx_ave / (nl - 1))
dy.append(dy_ave / (nl - 1))
if len(inimg_arr) != len(dx):
print >> sys.stderr, 'number of input images does not match with that of offsets'
return 1
#print 'debug'
#print dx, max(dx), min(dx)
#print dy, max(dy), min(dy)
# check object mask
if objmask.lower() == 'none':
objmask = ''
else:
objmask_arr = check_inpref(objmask, inimg_arr)
if isinstance(objmask_arr, int):
return 1
# independent run flag
if indep:
second = True
# prepare for temporary file
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# calculate image median for zero shift
iraf.unlearn('imstat')
iraf.unlearn('mimstat')
bgmed = []
for i in range(len(inimg_arr)):
if objmask == '':
ret = iraf.imstat(inimg_arr[i],
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
else:
if not second:
ret = iraf.mimstat(inimg_arr[i],
imasks=objmask_arr[i] + '[pl]',
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
else:
ret = iraf.mimstat(inimg_arr[i],
imasks=objmask_arr[i],
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
if len(ret) == 1:
bgmed.append(-1.0 * float(ret[0]))
else:
fout.close()
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the background in %s' % inimg_arr[
i]
return 1
# get large array size and combined image size
ret = get_large_region(nx, ny, dx, dy)
if len(ret) != 6:
print >> sys.stderr, 'failed to get large array size'
return 1
x_size = ret[0]
y_size = ret[1]
xcmin = ret[2]
xcmax = ret[3]
ycmin = ret[4]
ycmax = ret[5]
# calculate image region in the large format
xmin = int((x_size - nx) / 2) + 1
xmax = nx + int((x_size - nx) / 2)
ymin = int((y_size - ny) / 2) + 1
ymax = ny + int((y_size - ny) / 2)
#print 'debug'
#print x_size, y_size, xcmin, xcmax, ycmin, ycmax
#print xmin, xmax, ymin, ymax
# copy image to larger format and shift image #
iraf.unlearn('geomap')
iraf.unlearn('geotran')
obj_list = tmp_prefix + '_obj.lst'
if os.access(obj_list, os.R_OK):
os.remove(obj_list)
fobj = open(obj_list, 'w')
# for exposure time weight
expweight = tmp_prefix + '_exp.lst'
if os.access(expweight, os.R_OK):
os.remove(expweight)
fexp = open(expweight, 'w')
# for zero offset
zeroshift = tmp_prefix + '_zeroshift.dat'
if os.access(zeroshift, os.R_OK):
os.remove(zeroshift)
fzero = open(zeroshift, 'w')
# save the original fit geometry
fitgeom_org = fitgeom
# preparing for the sigma list and mask
if sigmap == 'none':
tmp_rejmask = ''
else:
tmp_rejmask = tmp_prefix + 'rejmask.fits'
if os.access(tmp_rejmask, os.R_OK):
os.remove(tmp_rejmask)
inverse_var_list = tmp_prefix + '_var.lst'
if os.access(inverse_var_list, os.R_OK):
os.remove(inverse_var_list)
finverse_var = open(inverse_var_list, 'w')
for i in range(len(inimg_arr)):
# restore the original fit geometry
fitgeom = fitgeom_org
# geometry transformation
fgmp = open(gmp_arr[i])
fgmp2 = open(gmp2_arr[i], 'w')
nobj = 0
for line in fgmp:
if not line.startswith('#'):
param = line[:-1].split()
xref = float(param[0]) + xmin - 1
yref = float(param[1]) + ymin - 1
xin = float(param[2]) + xmin - 1
yin = float(param[3]) + ymin - 1
fgmp2.write('%.3f %.3f %.3f %.3f\n' % (xref, yref, xin, yin))
nobj += 1
fgmp.close()
fgmp2.close()
# check number of objects
if i == 0 and nobj == 1 and fitgeom == 'rotate':
print 'Warning: Number of reference objects is not enought to measure the rotation'
print 'Warning: Only shift applied for all images'
fitgeom = 'shift'
fitgeom_org = 'shift'
if nobj == 1 and fitgeom == 'rotate':
print 'Warning: Number of objects in %s is not enought to measure the rotation' % (
inimg_arr[i])
print 'Warning: Only shift applied for %s' % (inimg_arr[i])
fitgeom = 'shift'
# mapping geometry
iraf.geomap(gmp2_arr[i],
dbs_arr[i],
1,
x_size,
1,
y_size,
fitgeom=fitgeom,
interac='no')
# mask frame
msk = np.ones((y_size, x_size))
msk[ymin - 1:ymax, xmin - 1:xmax] = 0
hdu = pyfits.PrimaryHDU(msk)
msk_img = pyfits.HDUList([hdu])
msk_fits = tmp_prefix + 'mask' + os.path.basename(inimg_arr[i])
msktr_fits = tmp_prefix + 'masktr' + os.path.basename(inimg_arr[i])
if os.access(msk_fits, os.R_OK):
os.remove(msk_fits)
if os.access(msktr_fits, os.R_OK):
os.remove(msktr_fits)
msk_img.writeto(msk_fits)
msk_img.close()
# transform mask geometry
iraf.geotran(msk_fits,
msktr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=1)
os.remove(msk_fits)
convert_maskfits_int(msktr_fits, msktr_fits)
# load original frame
img = pyfits.open(inimg_arr[i])
if sigmap != 'none':
ffimg = pyfits.open(ffimg_arr[i])
# for exposure time weight
try:
t = float(img[0].header['EXP1TIME'])
coadd = float(img[0].header['COADDS'])
expt = t * coadd
except KeyError:
print >> sys.stderr, 'can not read exposure time from the header of %s' % inimg_arr[
i]
img.close()
return 1
# for flux scaling and weight
if fscale:
try:
flux = float(img[0].header[fhead])
except KeyError:
print >> sys.stderr, 'can not read flux keyword (%s) from the header of %s' % (
fhead, inimg_arr[i])
img.close()
return 1
flux_scale = fbase / flux
weight = expt * (1.0 / flux_scale)**2
else:
flux_scale = 1.0
weight = expt
fzero.write('%f\n' % (bgmed[i] * flux_scale))
fexp.write('%f\n' % weight)
# object frame
obj = np.zeros((y_size, x_size))
obj[ymin - 1:ymax, xmin - 1:xmax] = img[0].data * flux_scale
hdu = pyfits.PrimaryHDU(obj)
obj_img = pyfits.HDUList([hdu])
obj_img[0].header = img[0].header
obj_img[0].header['bpm'] = msktr_fits
obj_img[0].header['expmap'] = expt
obj_fits = tmp_prefix + 'obj' + os.path.basename(inimg_arr[i])
objtr_fits = tmp_prefix + 'objtr' + os.path.basename(inimg_arr[i])
if os.access(obj_fits, os.R_OK):
os.remove(obj_fits)
obj_img.writeto(obj_fits)
obj_img.close()
iraf.geotran(obj_fits,
objtr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=0)
fobj.write('%s\n' % objtr_fits)
img.close()
if sigmap != 'none':
inverse_var = np.zeros((y_size, x_size))
inverse_var[ymin - 1:ymax, xmin -
1:xmax] = (np.sqrt(ffimg[0].data / (gain * expt)) *
flux_scale)**-2
hdu_var = pyfits.PrimaryHDU(inverse_var)
inverse_var_img = pyfits.HDUList([hdu_var])
inverse_var_img[0].header = img[0].header
inverse_var_img[0].header['bpm2'] = '%s[*,*,%d]' % (tmp_rejmask,
i + 1)
inverse_var_fits = tmp_prefix + 'var' + os.path.basename(
inimg_arr[i])
inverse_vartr_fits = tmp_prefix + 'vartr' + os.path.basename(
inimg_arr[i])
if os.access(inverse_var_fits, os.R_OK):
os.remove(inverse_var_fits)
if os.access(inverse_vartr_fits, os.R_OK):
os.remove(inverse_vartr_fits)
inverse_var_img.writeto(inverse_var_fits)
inverse_var_img.close()
iraf.geotran(inverse_var_fits,
inverse_vartr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=0)
finverse_var.write('%s\n' % inverse_vartr_fits)
ffimg.close()
# close file handlers
fobj.close()
fexp.close()
fzero.close()
if sigmap != 'none':
finverse_var.close()
# combine image
comb_img = tmp_prefix + '_comb.fits'
if os.access(comb_img, os.R_OK):
os.remove(comb_img)
if expmap == 'none':
tmp_expmap = ''
else:
tmp_expmap = tmp_prefix + 'expmap.fits'
if os.access(tmp_expmap, os.R_OK):
os.remove(tmp_expmap)
iraf.unlearn('imcombine')
try:
iraf.imcombine('@' + obj_list,
comb_img,
sigma='',
rejmask=tmp_rejmask,
expmasks=tmp_expmap,
combine=combine,
reject=reject,
masktype='!BPM',
maskvalue=0.0,
zero='@' + zeroshift,
weight='@' + expweight,
expname='EXPMAP')
except:
if os.access(comb_img, os.R_OK):
os.remove(comb_img)
if expmap != 'none':
if os.access(tmp_expmap, os.R_OK):
os.remove(tmp_expmap)
iraf.imcombine('@' + obj_list,
comb_img,
sigma='',
rejmask='',
expmasks=tmp_expmap,
combine=combine,
reject=reject,
masktype='!BPM',
maskvalue=0.0,
zero='@' + zeroshift,
weight='@' + expweight,
expname='EXPMAP')
if sigmap != 'none':
tmp_inverse_var_sum = tmp_prefix + 'inverse_var.fits'
if os.access(tmp_inverse_var_sum, os.R_OK):
os.remove(tmp_inverse_var_sum)
iraf.imcombine('@' + inverse_var_list,
tmp_inverse_var_sum,
combine='sum',
reject='none',
masktype='!BPM',
maskvalue=0.0)
iraf.stsdas()
tmp_sigma = tmp_prefix + 'sigma.fits'
if os.access(tmp_sigma, os.R_OK):
os.remove(tmp_sigma)
iraf.imcalc(tmp_inverse_var_sum,
tmp_sigma,
'sqrt(1.0/im1)',
pixtype='double')
# cut image
iraf.unlearn('imcopy')
cut_img = '%s[%d:%d,%d:%d]' % (comb_img, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_img, outimg)
if expmap != 'none':
cut_exp = '%s[%d:%d,%d:%d]' % (tmp_expmap, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_exp, expmap)
if sigmap != 'none':
cut_sigma = '%s[%d:%d,%d:%d]' % (tmp_sigma, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_sigma, sigmap)
if whtmap != 'none':
cut_wht = '%s[%d:%d,%d:%d]' % (tmp_inverse_var_sum, xcmin, xcmax,
ycmin, ycmax)
iraf.imcopy(cut_wht, whtmap)
# delete temporary object files
remove_temp_all(tmp_prefix + 'obj')
os.remove(obj_list)
os.remove(comb_img)
# record relative offset between input images and combined image and rotation
for i in range(len(inimg_arr)):
im = pyfits.open(inimg_arr[i], mode='update')
if second:
if indep:
# calculate offset
dxc = xcmin - xmin - dx[i]
dyc = ycmin - ymin - dy[i]
# retrieve rotation
rot = 0.0
fdbs = open(dbs_arr[i])
for line in fdbs:
param = line[:-1].split()
if param[0] == 'xrotation':
rot = float(param[1])
if rot > 180.0:
rot = rot - 360.0
im[0].header['dx'] = dx[i]
im[0].header['dy'] = dy[i]
im[0].header['dxc'] = dxc
im[0].header['dyc'] = dyc
im[0].header['rotation'] = rot
else:
# check number of objects in the geomap file
nobj = 0
fgmp = open(gmp_arr[0])
for line in fgmp:
nobj += 1
im1 = pyfits.open(inimg_arr[i].replace(second_pref,
first_pref),
mode='update')
for j in range(nobj):
key = 'XC%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'YC%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'PEAK%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'FWHM%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'DX'
im[0].header[key] = float(im1[0].header[key])
key = 'DY'
im[0].header[key] = float(im1[0].header[key])
key = 'DXC'
im[0].header[key] = float(im1[0].header[key])
key = 'DYC'
im[0].header[key] = float(im1[0].header[key])
key = 'ROTATION'
im[0].header[key] = float(im1[0].header[key])
im1.close()
else:
# calculate offset
dxc = xcmin - xmin - dx[i]
dyc = ycmin - ymin - dy[i]
# retrieve rotation
rot = 0.0
fdbs = open(dbs_arr[i])
for line in fdbs:
param = line[:-1].split()
if param[0] == 'xrotation':
rot = float(param[1])
if rot > 180.0:
rot = rot - 360.0
im[0].header['dx'] = dx[i]
im[0].header['dy'] = dy[i]
im[0].header['dxc'] = dxc
im[0].header['dyc'] = dyc
im[0].header['rotation'] = rot
im.close()
# remove all temporary files
remove_temp_all(tmp_prefix)
return 0
def align_to_reference(ROOT_DIRECT,
ALIGN_IMAGE,
fitgeometry="shift",
clean=True,
verbose=False,
ALIGN_EXTENSION=0,
toler=3,
skip_swarp=False,
align_sdss_ds9=False,
catalog=None):
"""
xshift, yshift, rot, scale, xrms, yrms = align_to_reference()
"""
import os
import glob
import shutil
from pyraf import iraf
from iraf import stsdas, dither
import threedhst
from threedhst import catIO
no = iraf.no
yes = iraf.yes
INDEF = iraf.INDEF
#### Clean slate
rmfiles = [
'SCI.fits', 'WHT.fits', 'align.cat', 'direct.cat'
'align.map', 'align.match', 'align.reg', 'align.xy', 'direct.reg',
'direct.xy', 'ds9_align.tsv'
]
for file in rmfiles:
try:
os.remove(file)
except:
pass
if catalog is not None:
align_sdss_ds9 = True
#### Get only images that overlap from the ALIGN_IMAGE list
if not align_sdss_ds9:
align_img_list = find_align_images_that_overlap(
ROOT_DIRECT + '_drz.fits',
ALIGN_IMAGE,
ALIGN_EXTENSION=ALIGN_EXTENSION)
if not align_img_list:
print 'threedhst.shifts.align_to_reference: no alignment images overlap.'
return 0, 0
#### Use swarp to combine the alignment images to the same image
#### dimensions as the direct mosaic
if (not skip_swarp) & (not align_sdss_ds9):
try:
os.remove(ROOT_DIRECT + '_align.fits')
except:
pass
matchImagePixels(input=align_img_list,
matchImage=ROOT_DIRECT + '_drz.fits',
output=ROOT_DIRECT + '_align.fits',
match_extension=1,
input_extension=ALIGN_EXTENSION)
#### Run SExtractor on the direct image, with the WHT
#### extension as a weight image
se = threedhst.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options['CHECKIMAGE_TYPE'] = 'NONE'
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
se.options['WEIGHT_IMAGE'] = 'WHT.fits'
se.options['FILTER'] = 'Y'
## Detect thresholds (default = 1.5)
THRESH = 10
if align_sdss_ds9:
if 'Vizier' not in REFERENCE_CATALOG:
THRESH = 20
se.options['DETECT_THRESH'] = '%d' % (THRESH)
se.options['ANALYSIS_THRESH'] = '%d' % (THRESH)
se.options['MAG_ZEROPOINT'] = str(threedhst.options['MAG_ZEROPOINT'])
#### Run SExtractor on direct and alignment images
## direct image
se.options['CATALOG_NAME'] = 'direct.cat'
iraf.imcopy(ROOT_DIRECT + '_drz.fits[1]', "SCI.fits")
iraf.imcopy(ROOT_DIRECT + '_drz.fits[2]', "WHT.fits")
status = se.sextractImage('SCI.fits')
## Read the catalog
directCat = threedhst.sex.mySexCat('direct.cat')
if align_sdss_ds9:
### Use ds9 SDSS catalog to refine alignment
import threedhst.dq
import pywcs
import threedhst.catIO as catIO
wcs = pywcs.WCS(pyfits.getheader('SCI.fits', 0))
#wcs = pywcs.WCS(pyfits.getheader('Q0821+3107-F140W_drz.fits', 1))
if 'Vizier' in REFERENCE_CATALOG:
#### Use (unstable) astroquery Vizier search
#### CFHTLS-Deep: 'Vizier.II/317'
VIZIER_CAT = REFERENCE_CATALOG.split('Vizier.')[1]
print 'Align to Vizier catalog: http://vizier.u-strasbg.fr/viz-bin/VizieR?-source=%s' % (
VIZIER_CAT)
import astroquery
if astroquery.__version__ < '0.0.dev1078':
from astroquery import vizier
query = {}
query["-source"] = VIZIER_CAT
#query["-out"] = ["_r", "CFHTLS", "rmag"]
query["-out"] = ["_RAJ2000", "_DEJ2000"] ### Just RA/Dec.
#### Center position and query radius
r0, d0 = wcs.wcs_pix2sky([[wcs.naxis1 / 2., wcs.naxis2 / 2.]],
1)[0]
rll, dll = wcs.wcs_pix2sky([[0, 0]], 1)[0]
corner_radius = np.sqrt(
(r0 - rll)**2 * np.cos(d0 / 360. * 2 * np.pi)**2 +
(d0 - dll)**2) * 60. * 1.5
h = query["-c"] = "%.6f %.6f" % (r0, d0)
query["-c.rm"] = "%.3f" % (corner_radius
) ### xxx check image size
#### Run the query
vt = vizier.vizquery(query)
else:
#### Newer astroquery
from astroquery.vizier import Vizier
import astropy.coordinates as coord
import astropy.units as u
Vizier.ROW_LIMIT = -1
r0, d0 = wcs.wcs_pix2sky([[wcs.naxis1 / 2., wcs.naxis2 / 2.]],
1)[0]
rll, dll = wcs.wcs_pix2sky([[0, 0]], 1)[0]
corner_radius = np.sqrt(
(r0 - rll)**2 * np.cos(d0 / 360. * 2 * np.pi)**2 +
(d0 - dll)**2) * 60. * 1.5
#
c = coord.ICRSCoordinates(ra=r0, dec=d0, unit=(u.deg, u.deg))
#### something with astropy.coordinates
# c.icrs.ra.degree = c.icrs.ra.degrees
# c.icrs.dec.degree = c.icrs.dec.degrees
#
vt = Vizier.query_region(c,
width=u.Quantity(
corner_radius, u.arcminute),
catalog=[VIZIER_CAT])[0]
#### Make a region file
ra_list, dec_list = vt['RAJ2000'], vt['DEJ2000']
print 'Vizier, found %d objects.' % (len(ra_list))
fp = open('%s.vizier.reg' % (ROOT_DIRECT), 'w')
fp.write('# %s, r=%.1f\'\nfk5\n' % (VIZIER_CAT, corner_radius))
for ra, dec in zip(ra_list, dec_list):
fp.write('circle(%.6f, %.6f, 0.5")\n' % (ra, dec))
#
fp.close()
else:
#### Use DS9 catalog
ds9 = threedhst.dq.myDS9()
ds9.set('file SCI.fits')
#ds9.set('file Q0821+3107-F140W_drz.fits')
ds9.set('catalog %s' % (REFERENCE_CATALOG))
### Can't find XPA access point for "copy to regions"
ds9.set('catalog export tsv ds9_align.tsv')
lines = open('ds9_align.tsv').readlines()
ra_list, dec_list = [], []
for line in lines[1:]:
spl = line.split()
ra, dec = float(spl[0]), float(spl[1])
ra_list.append(ra)
dec_list.append(dec)
#
del (ds9)
x_image, y_image = [], []
for ra, dec in zip(ra_list, dec_list):
x, y = wcs.wcs_sky2pix([[ra, dec]], 1)[0]
x_image.append(x)
y_image.append(y)
alignCat = catIO.EmptyCat()
alignCat['X_IMAGE'] = np.array(x_image)
alignCat['Y_IMAGE'] = np.array(y_image)
else:
## alignment image
se.options['CATALOG_NAME'] = 'align.cat'
status = se.sextractImage(ROOT_DIRECT + '_align.fits')
alignCat = threedhst.sex.mySexCat('align.cat')
xshift = 0
yshift = 0
rot = 0
scale = 1.
xrms = 2
yrms = 2
NITER = 5
IT = 0
while (IT < NITER):
IT = IT + 1
#### Get x,y coordinates of detected objects
## direct image
fp = open('direct.xy', 'w')
for i in range(len(directCat.X_IMAGE)):
fp.write('%s %s\n' % (directCat.X_IMAGE[i], directCat.Y_IMAGE[i]))
fp.close()
## alignment image
fp = open('align.xy', 'w')
for i in range(len(alignCat.X_IMAGE)):
fp.write('%s %s\n' % (np.float(alignCat.X_IMAGE[i]) + xshift,
np.float(alignCat.Y_IMAGE[i]) + yshift))
fp.close()
iraf.flpr()
iraf.flpr()
iraf.flpr()
#### iraf.xyxymatch to find matches between the two catalogs
pow = toler * 1.
try:
os.remove('align.match')
except:
pass
status1 = iraf.xyxymatch(input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2**pow,
separation=0,
verbose=yes,
Stdout=1)
nmatch = 0
while status1[-1].startswith('0') | (nmatch < 10):
pow += 1
os.remove('align.match')
status1 = iraf.xyxymatch(input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2**pow,
separation=0,
verbose=yes,
Stdout=1)
#
nmatch = 0
for line in open('align.match').xreadlines():
nmatch += 1
if verbose:
for line in status1:
print line
#### Compute shifts with iraf.geomap
iraf.flpr()
iraf.flpr()
iraf.flpr()
try:
os.remove("align.map")
except:
pass
status2 = iraf.geomap(input="align.match",
database="align.map",
fitgeometry=fitgeometry,
interactive=no,
xmin=INDEF,
xmax=INDEF,
ymin=INDEF,
ymax=INDEF,
maxiter=10,
reject=2.0,
Stdout=1)
if verbose:
for line in status2:
print line
#fp = open(root+'.iraf.log','a')
#fp.writelines(status1)
#fp.writelines(status2)
#fp.close()
#### Parse geomap.output
fp = open("align.map", "r")
for line in fp.readlines():
spl = line.split()
if spl[0].startswith('xshift'):
xshift += float(spl[1])
if spl[0].startswith('yshift'):
yshift += float(spl[1])
if spl[0].startswith('xrotation'):
rot = float(spl[1])
if spl[0].startswith('xmag'):
scale = float(spl[1])
if spl[0].startswith('xrms'):
xrms = float(spl[1])
if spl[0].startswith('yrms'):
yrms = float(spl[1])
fp.close()
#os.system('wc align.match')
print 'Shift iteration #%d, xshift=%f, yshift=%f, rot=%f, scl=%f (rms: %5.2f,%5.2f)' % (
IT, xshift, yshift, rot, scale, xrms, yrms)
im = pyfits.open('SCI.fits')
shutil.copy('align.map', ROOT_DIRECT + '_align.map')
shutil.copy('align.match', ROOT_DIRECT + '_align.match')
#### Cleanup
if clean:
rmfiles = [
'SCI.fits', 'WHT.fits', 'align.cat', 'align.map', 'align.match',
'align.reg', 'align.xy', 'direct.cat', 'direct.reg', 'direct.xy',
'drz_sci.fits', 'drz_wht.fits', 'bg.fits'
]
for file in rmfiles:
try:
os.remove(file)
except:
pass
return xshift, yshift, rot, scale, xrms, yrms
def kaitsub(refimage,inlist,kernel=9,satval=50000.0,dspace=0,dbkg=0,
nstamps=2,clobber=globclob):
infiles = iraffiles(inlist)
# RA and Dec of center pixel in reference image
[nx, ny] = get_head(refimage, ['NAXIS1', 'NAXIS2'])
xcenpix = (nx / 2.0)
ycenpix = (ny / 2.0)
[[ra, dec]] = impix2wcs(refimage, xcenpix, ycenpix)
# Big loop
for image in infiles:
# Separate image rootname from extension
root,ext = image.split('.')
# Make sure WCS fit was successful
if not check_head(image, 'IQWCS'):
print 'Skipping image %s: IQWCS not successful' % image
infiles.remove(image)
continue
# Remap image using objects detected on both frames and
# create stamp list
stars=Starlist(get_head(image,'STARFILE'))
refstars=Starlist(get_head(refimage,'STARFILE'))
refstars.pix2wcs(refimage)
refstars.wcs2pix(image)
match,refmatch=stars.match(refstars,useflags=yes,tol=10.0)
nstars=len(match)
if not (nstars>2):
print 'Could not find star matches between reference and %s' % image
infiles.remove(image)
continue
refmatch.pix2wcs(image)
refmatch.wcs2pix(refimage)
matchfile=open('%s.match' % root, 'w')
stampfile=open('%s.stamps' % root, 'w')
for i in range(len(match)):
matchfile.write('%10.3f%10.3f%10.3f%10.3f\n' % (refmatch[i].xval,
refmatch[i].yval,match[i].xval,match[i].yval))
stampfile.write('%10.3f%10.3f\n' % (refmatch[i].xval,
refmatch[i].yval))
matchfile.close()
stampfile.close()
check_exist('%s.geodb' % root, 'w', clobber=clobber)
iraf.geomap('%s.match' % root,'%s.geodb' % root,1.0,nx,1.0,ny,
fitgeom="general",verbose=no,interactive=no)
check_exist('%s.shift.fits' % root, 'w', clobber=clobber)
iraf.geotran(image,'%s.shift' % root,'%s.geodb' % root,
'%s.match' % root,geometry="geometric",
boundary="constant",verbose=no)
# Run the subtraction
check_exist('%s.sub.fits' % root, 'w', clobber=clobber)
if os.path.exists("stamps.lis"):
cmd = '$REDUCTION/hotpants -inim %s.shift.fits -tmplim %s -outim %s.sub.fits -tu %.2f -tg %.2f -tr %.2f -iu %.2f -ig %.2f -ir %.2f -r %.2f -ssf stamps.lis -afssc 0 -n t -ko %i -bgo %i -savexy %s.st -nsx %i -nsy %i' % (root, refimage, root, satval, AP_GAIN, AP_READN, satval, FL_GAIN, FL_READN, kernel, dspace, dbkg, root, nstamps, nstamps)
else:
cmd = '$REDUCTION/hotpants -inim %s.shift.fits -tmplim %s -outim %s.sub.fits -tu %.2f -tg %.2f -tr %.2f -iu %.2f -ig %.2f -ir %.2f -r %.2f -ssf %s.stamps -afssc 0 -n t -ko %i -bgo %i -savexy %s.st -nsx %i -nsy %i' % (root, refimage, root, satval, AP_GAIN, AP_READN, satval, FL_GAIN, FL_READN, kernel, root, dspace, dbkg, root, nstamps, nstamps)
#cmd = '$REDUCTION/pois -k %i %i -s %.2f %.2f -S %s.stamps %s %s.shift.fits %s.sub.fits' % (kernel,kernel,satval,satval,root,refimage,root,root)
fcmd = os.popen(cmd, 'r')
sublines = fcmd.readlines()
fcmd.close()
# Return
print 'Exiting successfully'
return
def psfphot(inlist,
ra,
dec,
reffilt,
interact,
fwhm,
readnoise,
gain,
threshold,
refimage=None,
starfile=None,
maxnpsf=5,
clobber=globclob,
verbose=globver,
skykey='SKYBKG',
filtkey='FILTER',
pixtol=3.0):
""" perform PSF-based photometry on a single target star (SN?) at RA, Dec and
also on a set of comparison stars, using daophot. simultaneously
perform aperture photometry on all the comparison stars (after
subtracting off contributions from neighbors) to enable absolute
photometry by comparison to aperture photometry of standard stars
observed in other fields """
# Defaults / constants
psfmult = 5.0 #standard factor (multiplied by fwhm to get psfradius)
psfmultsmall = 3.0 #similar to psfmult, adjusted for nstar and substar
# Necessary package
iraf.imutil()
# Parse inputs
infiles = iraffiles(inlist)
# Which file is reffilt? call it refimage
if refimage == None:
for image in infiles:
if check_head(image, filtkey):
try:
imgfilt = get_head(image, filtkey)
if imgfilt == reffilt:
refimage = image
break
except:
pass
if not refimage:
print "BAD USER! No image corresponds to the filter: %s" % reffilt
return
else:
refroot = 's' + refimage.split('.')[0]
#first make sure to add back in background of sky
iraf.iqsubsky(inlist, sub=no, skykey=skykey)
#put reference image first on list
infiles.remove(refimage)
infiles.insert(0, refimage)
#setup for keywords
if gain == "!GAIN":
try:
gainval = float(get_head(image, gain))
except:
print "Bad header keyword for gain."
else:
gainval = float(gain)
if readnoise == "!READNOISE":
try:
readval = float(get_head(image, readnoise))
except:
print "Bad header keyword for readnoise."
else:
readval = float(readnoise)
# Process each file in turn
for image in infiles:
# Check that the image is there
check_exist(image, "r")
# Grab image root name
root = image.split('.')[0]
# Map image to reference image
if not (image == refimage):
[nx, ny] = get_head(image, ['NAXIS1', 'NAXIS2'])
stars = Starlist(get_head(image, 'STARFILE'))
refstars = Starlist(get_head(refimage, 'STARFILE'))
refstars.pix2wcs(refimage)
refstars.wcs2pix(image)
match, refmatch = stars.match(refstars, useflags=yes, tol=10.0)
nstars = len(match)
if not (nstars > 2):
print 'Could not find star matches between reference and %s' % image
infiles.remove(image)
continue
refmatch.pix2wcs(image)
refmatch.wcs2pix(refimage)
matchfile = open('%s.match' % root, 'w')
for i in range(len(match)):
matchfile.write('%10.3f%10.3f%10.3f%10.3f\n' %
(refmatch[i].xval, refmatch[i].yval,
match[i].xval, match[i].yval))
matchfile.close()
check_exist('%s.geodb' % root, 'w', clobber=clobber)
iraf.geomap('%s.match' % root,
'%s.geodb' % root,
1.0,
nx,
1.0,
ny,
verbose=no,
interactive=no)
check_exist('s%s.fits' % root, 'w', clobber=clobber)
iraf.geotran(image,
's%s' % root,
'%s.geodb' % root,
'%s.match' % root,
geometry="geometric",
boundary="constant",
verbose=no)
else:
iraf.imcopy(image, 's%s' % root)
root = 's%s' % root
#get sky level and calculate sigma
#if check_head(image, skykey):
# try:
# sky=float(get_head(image, skykey))
# except:
# print "No sky levels in header."
#sigma= (((sky * gainval) + readval**2)**.5) / gainval
iraf.iterstat(image)
# Saturation level
if not check_head(image, "SATURATE"):
saturate = 60000.0
else:
saturate = get_head(image, "SATURATE")
# Update datapars and daopars
iraf.datapars.fwhmpsf = fwhm
iraf.datapars.sigma = iraf.iterstat.sigma
iraf.datapars.datamin = iraf.iterstat.median - 10 * iraf.iterstat.sigma
iraf.datapars.datamax = 0.90 * saturate
iraf.datapars.readnoise = readval
iraf.datapars.epadu = gainval
iraf.datapars.filter = filtkey
iraf.daopars.psfrad = psfmult * fwhm
iraf.daopars.fitrad = fwhm
iraf.daopars.function = "gauss,moffat15,moffat25,lorentz,penny1"
#find stars in image unless a starlist is given
if image == refimage and starfile == None:
iraf.daophot.daofind(root,
'refimage.coo.1',
threshold=threshold,
verify=no,
verbose=verbose)
elif image == refimage:
shutil.copy(starfile, 'refimage.coo.1')
#initial photometry
iraf.daophot.phot(root,
'refimage.coo.1',
'default',
aperture=fwhm,
verify=no,
verbose=verbose)
#select stars for psf the first time
refstarsfile = "refimage.pst.1"
if image == refimage:
iraf.pstselect(root,
'default',
refstarsfile,
maxnpsf,
interactive=yes,
verify=no,
verbose=verbose)
#fit the psf
iraf.psf(root,
'default',
refstarsfile,
'default',
'default',
'default',
interactive=interact,
verify=no,
verbose=verbose)
#identify neighboring/interfering stars to selected stars
groupingfile = root + ".psg.1"
iraf.nstar(root,
groupingfile,
'default',
'default',
'default',
psfrad=psfmultsmall * fwhm,
verify=no,
verbose=verbose)
#subtract out neighboring stars from image
iraf.substar(root,
'default',
refstarsfile,
'default',
'default',
psfrad=psfmultsmall * fwhm,
verify=no,
verbose=verbose)
#repeat psf to get better psf model
#IRAF's interactive version usually crashes
subtractedimage = root + ".sub.1"
iraf.psf(subtractedimage,
root + ".nst.1",
refstarsfile,
'%s.psf.2' % root,
'%s.pst.2' % root,
'%s.psg.2' % root,
interactive=interact,
verify=no,
verbose=verbose)
#Need to make sure SN was detected by daofind
stars = Starlist('%s.mag.1' % root)
SN = Star(name='SN', radeg=ra, dcdeg=dec, fwhm=2.0, fwhmw=2.0)
SNlis = Starlist(stars=[SN])
SNlis.wcs2pix(image)
if (len(stars.match(SNlis)[0]) == 0):
#No match - need to add to daofind file
print "No match!"
coofile = open('refimage.coo.1', 'a+')
coofile.write('%10.3f%10.3f%9.3f%8.3f%13.3f%12.3f%8i\n' %
(SNlis[0].xval, SNlis[0].yval, 99.999, 0.500, 0.000,
0.000, 999))
coofile.close()
#repeat aperture photometry to get good comparisons to standard fields
iraf.daophot.phot(root,
'refimage.coo.1',
'default',
aperture=psfmult * fwhm,
verify=no,
verbose=verbose)
# allstar run
iraf.allstar(root,
'default',
'default',
'default',
'default',
'default',
verify=no,
verbose=verbose)
def irafalign(filepath, uknstarlist, refstarlist, shape,
alifilepath=None, outdir="alipy_out",
makepng=False, hdu=0, verbose=True):
"""
Uses iraf geomap and gregister to align the image. Three steps :
* Write the matched source lists into an input file for geomap
* Compute a geomap transform from these stars.
* Run gregister
:param filepath: FITS file to be aligned
:type filepath: string
:param uknstarlist: A list of stars from the "unknown" image to be aligned,
that matches to ...
:type uknstarlist: list of Star objects
:param refstarlist: ... the list of corresponding stars in the reference
image.
:type refstarlist: list of Star objects
:param shape: Output shape (width, height)
:type shape: tuple
:param alifilepath: where to save the aligned image. If None, I put it in
the default directory.
:type alifilepath: string
:param makepng: If True I make a png of the aligned image as well.
:type makepng: boolean
:param hdu: The hdu of the fits file that you want me to use.
0 is primary. If multihdu, 1 is usually science.
"""
try:
from pyraf import iraf
except ImportError:
print("Couldn't import pyraf !")
return
assert len(uknstarlist) == len(refstarlist)
if len(uknstarlist) < 2:
if verbose:
print("Not enough stars for using geomap !")
return
basename = os.path.splitext(os.path.basename(filepath))[0]
geomapinpath = basename + ".geomapin"
geodatabasepath = basename + ".geodatabase"
if os.path.isfile(geomapinpath):
os.remove(geomapinpath)
if os.path.isfile(geodatabasepath):
os.remove(geodatabasepath)
# Step 1, we write the geomap input.
table = []
for (uknstar, refstar) in zip(uknstarlist, refstarlist):
table.append([refstar.x, refstar.y, uknstar.x, uknstar.y])
geomap = open(geomapinpath, "wb") # b needed for csv
writer = csv.writer(geomap, delimiter="\t")
writer.writerows(table)
geomap.close()
# Step 2, geomap
iraf.unlearn(iraf.geomap)
iraf.geomap.fitgeom = "rscale"
# You can change this to:
# shift, xyscale, rotate, rscale
iraf.geomap.function = "polynomial" # Surface type
iraf.geomap.maxiter = 3 # Maximum number of rejection iterations
iraf.geomap.reject = 3.0 # Rejection limit in sigma units
# other options you could specify :
# (xxorder= 2) Order of x fit in x
# (xyorder= 2) Order of x fit in y
# (xxterms= half) X fit cross terms type
# (yxorder= 2) Order of y fit in x
# (yyorder= 2) Order of y fit in y
# (yxterms= half) Y fit cross terms type
# (calctyp= real) Computation type
iraf.geomap.transfo = "broccoli" # keep it
iraf.geomap.interac = "no" # keep it
iraf.geomap.verbose = "yes" # keep it
# iraf.geomap.results = "bla.summary" # The optional results summary files
geomapblabla = iraf.geomap(input=geomapinpath,
database=geodatabasepath,
xmin=1, xmax=shape[0],
ymin=1, ymax=shape[1],
Stdout=1)
# We read this output ...
for line in geomapblabla:
if "X and Y scale:" in line:
mapscale = line.split()[4:6]
if "Xin and Yin fit rms:" in line:
maprmss = line.split()[-2:]
if "X and Y axis rotation:" in line:
mapangles = line.split()[-4:-2]
if "X and Y shift:" in line:
mapshifts = line.split()[-4:-2]
# not used?
geomaprms = math.sqrt(float(maprmss[0]) * float(maprmss[0]) +
float(maprmss[1]) * float(maprmss[1]))
geomapangle = float(mapangles[0]) # % 360.0
geomapscale = 1.0 / float(mapscale[0])
if mapscale[0] != mapscale[1]:
raise RuntimeError("Error reading geomap scale")
if verbose:
print(("IRAF geomap : Rotation %+11.6f [deg], "
"scale %8.6f, RMS %.3f [pixel]") % (geomapangle,
geomapscale,
geomaprms))
# Step 3
if alifilepath is None:
alifilepath = os.path.join(outdir, basename + "_gregister.fits")
else:
outdir = os.path.split(alifilepath)[0]
if not os.path.isdir(outdir):
os.makedirs(outdir)
if os.path.isfile(alifilepath):
os.remove(alifilepath)
iraf.unlearn(iraf.gregister)
iraf.gregister.geometry = "geometric" # linear, distortion, geometric
iraf.gregister.interpo = "spline3" # linear, spline3
iraf.gregister.boundary = "constant" # padding with zero
iraf.gregister.constant = 0.0
iraf.gregister.fluxconserve = "yes"
if verbose:
print("IRAF gregister ...")
regblabla = iraf.gregister(input='%s[%s]' % (filepath, hdu),
output=alifilepath,
database=geodatabasepath,
transform="broccoli",
Stdout=1)
# not used?
if verbose:
print("IRAF gregister done !")
if os.path.isfile(geomapinpath):
os.remove(geomapinpath)
if os.path.isfile(geodatabasepath):
os.remove(geodatabasepath)
if makepng:
try:
import f2n
except ImportError:
print("Couldn't import f2n -- install it !")
return
myimage = f2n.fromfits(alifilepath, verbose=False)
myimage.setzscale("auto", "auto")
myimage.makepilimage("log", negative=False)
myimage.writetitle(os.path.basename(alifilepath))
if not os.path.isdir(outdir):
os.makedirs(outdir)
myimage.tonet(
os.path.join(outdir, os.path.basename(alifilepath) + ".png"))
def align_direct_to_reference(verbose=True, n_iter=20, drizzled_image=True):
"""
Use iraf software geomap to get shift solutions between images.
n_iter = number of times to iterate over the alignment routines
drizzled_image = boolean to distinguish between a drizzled image used for alignment,
in which case the shifts must be transferred back to oiginal flt.
"""
### get the root name:
root = wfc3_grism.options['ROOT_DIRECT']
### get the alignment image which has been produced
### with run_sregister_to_cutout_CANDELS_region()
align_image = '%s_align_reference.fits' %(root)
### now run SExtractor on the direct and refereance
### images to build up 2 catalogs:
se = wfc3_grism.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options['CHECKIMAGE_TYPE'] = 'NONE'
se.options['FILTER'] = 'Y'
se.options['DETECT_THRESH'] = '%.1f' % wfc3_grism.options['ALIGN_DETECT_THRESH']
se.options['ANALYSIS_THRESH'] = '3'
se.options['MAG_ZEROPOINT'] = '%.2f' % wfc3_grism.options['MAG_ZEROPOINT']
se.options['DETECT_MINAREA'] = '%.1f' % wfc3_grism.options['ALIGN_DETECT_MINAREA']
### generate the direct image catalog:
se.options['CATALOG_NAME'] = 'direct.cat'
iraf.imcopy('%s_drz.fits[SCI]' %(root), "SCI.fits", verbose=False)
## if not using drizzled image for alignment done't include
### a weight image:
if drizzled_image:
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
se.options['WEIGHT_IMAGE'] = 'WHT.fits'
iraf.imcopy('%s_drz.fits[WHT]' %(root), "WHT.fits", verbose=False)
else:
se.options['WEIGHT_TYPE'] = 'NONE'
se.options['WEIGHT_IMAGE'] = 'WHT.fits'
status = se.sextractImage('SCI.fits')
### generate the alignment image catalog:
se.options['CATALOG_NAME'] = 'align.cat'
se.options['WEIGHT_TYPE'] = 'NONE'
se.options['WEIGHT_IMAGE'] = 'WHT.fits'
status = se.sextractImage(align_image)
### Read the catalogs
direct_cat = wfc3_grism.sex.mySexCat('direct.cat')
align_cat = wfc3_grism.sex.mySexCat('align.cat')
### initialize x,y shift parameters so can be
### updated with each iteration, the x,y shifts
### will converge twoard an optimal value over the
### iterations:
xshift = 0
yshift = 0
rot = 0
scale = 1.
### empty line in the output to make things clearer:
print ""
### now loop through the process until xrms and yrms both < 0.5
### or run out of number of iterations:
xrms, yrms = 100, 100
toler = wfc3_grism.options['ALIGN_TOLERANCE']
iteration = 0
max_iter = wfc3_grism.options['ALIGN_ITERATIONS']
while ((xrms > 0.1) | (yrms > 0.1)) & (iteration <= max_iter):
print "Running matching algorithm on iteration #%d" %(iteration)
### Get x,y coordinates of detected objects direct image
fp = open('direct.xy','w')
for i in range(len(direct_cat.X_IMAGE)):
fp.write('%-15s%-15s\n' %(direct_cat.X_IMAGE[i],direct_cat.Y_IMAGE[i]))
fp.close()
### Get x,y coordinates of detected objects alignment image
fp = open('align.xy','w')
for i in range(len(align_cat.X_IMAGE)):
fp.write('%-15s%-15s\n' %(np.float(align_cat.X_IMAGE[i])+xshift, np.float(align_cat.Y_IMAGE[i])+yshift))
fp.close()
### iraf flpr()
wfc3_grism.utils.iraf_flpr()
### remove previous solution:
if iteration > 0:
os.remove('align.match')
### get the alignment catalog:
status = iraf.xyxymatch(input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2**toler,
separation=0,
verbose=True,
Stdout=1)
### iraf flpr()
wfc3_grism.utils.iraf_flpr()
### now get shifts with geomap:
if iteration > 0:
os.remove('align.map')
iraf.geomap(input="align.match",
database="align.map",
fitgeometry="shift",
interactive=False,
xmin=iraf.INDEF,
xmax=iraf.INDEF,
ymin=iraf.INDEF,
ymax=iraf.INDEF,
maxiter = 10,
reject = 2.0,
Stdout=1)
### get the output from geomap:
fp = open("align.map", "r")
for line in fp.readlines():
spl = line.split()
if spl[0].startswith('xshift'):
xshift += float(spl[1])
if spl[0].startswith('yshift'):
yshift += float(spl[1])
if spl[0].startswith('xrotation'):
rot = float(spl[1])
if spl[0].startswith('xmag'):
scale = float(spl[1])
if spl[0].startswith('xrms'):
xrms = float(spl[1])
if spl[0].startswith('yrms'):
yrms = float(spl[1])
fp.close()
### update iteration counter:
iteration += 1
toler += 1
print 'Shift iteration #%d, xshift=%f, yshift=%f, rot=%f, scl=%f (rms: %5.2f,%5.2f)' %(iteration, xshift, yshift, rot, scale, xrms, yrms)
### copy the final align.map for posterity:
shutil.copy('align.map', '%s_align.map' %(root))
## cleanup from the alignment process:
remvfiles = ['SCI.fits','WHT.fits','align.cat',
'align.map','align.match','align.reg','align.xy',
'direct.cat','direct.reg','direct.xy',
'drz_sci.fits','drz_wht.fits','bg.fits', 'imxymatch.1', 'sex_stderr',
'wfc3_grism_auto.sex', 'wfc3_grism_auto.param', 'default.nnw', 'default.conv']
for file in remvfiles:
try:
os.remove(file)
except:
pass
if drizzled_image:
#### shifts measured in drz frame. Translate to the flt frame:
drz = fits.open('%s_drz.fits' %(root))
#### Get reference angle from first image in the ASN file:
asn = wfc3_grism.utils.ASNFile('%s_asn.fits' %(root))
alpha = (180. - fits.getheader(asn.exposures[0]+'_flt.fits', 1)['PA_APER']) / 360. * 2 * np.pi
### Get the drizzle scale from the MultiDrizzle '.run' file:
for line in open('%s.run' %(root),'r'):
if line.startswith('drizzle.scale'):
drizzle_scale = line.split()[2]
print drizzle_scale
### get the equivalent shifts in the FLT frames:
xsh = (xshift*np.cos(alpha) - yshift*np.sin(alpha))*np.float(drizzle_scale)
ysh = (xshift*np.sin(alpha) + yshift*np.cos(alpha))*np.float(drizzle_scale)
print 'Final shift:', xsh, ysh, drz[1].header['PA_APER']
else:
xsh = xshift
ysh = yshift
fp = open('%s_align.info' %(root),'w')
fp.write('%s %8.3f %8.3f %8.3f\n' %(align_image, xsh, ysh, rot))
fp.close()
#### Read the shiftfile
if drizzled_image:
shiftF = ShiftFile('%s_initial_shifts.txt' %(root))
shiftF.xshift = list(np.array(shiftF.xshift)-xsh)
shiftF.yshift = list(np.array(shiftF.yshift)-ysh)
shiftF.rotate = list((np.array(shiftF.rotate)+rot) % 360)
shiftF.scale = list(np.array(shiftF.scale)*scale)
shiftF.write('%s_final_shifts.txt' %(root))
else:
### use the default shift file in wfc3_grism data folder:
shiftF = ShiftFile('/disk1/fc/FIGS/wfc3_grism/data/default_shift_file.txt')
### add the reference image as needed by multidrizzle:
shiftF.headerlines[1] = '# refimage: %s \n' %(align_image)
#### Apply the alignment shifts to the shiftfile
shiftF.xshift = [np.array(xsh)]
shiftF.yshift = [np.array(ysh)]
shiftF.rotate = [np.array(rot) % 360]
shiftF.scale = [np.array(scale)]
shiftF.write('%s_final_shifts.txt' %(root))
def stacking(cllist,zpofflist,ref,zprefoff=0.0,stackname='stack',shiftsize=400):
"""
"""
#Reset the IRAF tasks used in this routine.
iraf.unlearn('imcalc')
iraf.unlearn('imcombine')
iraf.unlearn('imreplace')
iraf.unlearn('xyxymatch')
iraf.unlearn('geomap')
iraf.unlearn('geotran')
iraf.unlearn('imcopy')
#Find reference image in reference directory. Check to make
#sure that it is actually the image and not the mask file!
#Grab the mask for adding to the mask list now.
(refimg,refmask,expmap)=classify(ref+'/tu*.fits')
zpref=pf.open(refimg)[0].header['MAGZERO']
# zprefoff=NewfirmZPoffset[ref.split('/')[-1]]
zprefoff=float(zprefoff)
#Get 2MASS PSC positions for reference cluster image.
catalog=get2masspsc(refimg)
foo=file_check(ref+'/2mass_ref_stars.cdt',delete=True)
foo=open(ref+'/2mass_ref_stars.cdt','w')
for y in catalog:
data=y.split()
foo.write(data[6]+'\t'+data[7]+'\n')
foo.close()
#Create lists for files to be input into the stacking routine.
foo=file_check('matchlist',delete=True)
foo=file_check('scalelist',delete=True)
foo=file_check('shiftlist',delete=True)
foo=file_check('masklist',delete=True)
foo=file_check('shiftmask',delete=True)
foo=file_check('expmaplist',delete=True)
(matchlist,scalelist,shiftlist,masklist,
shiftmask,finalmasks,stacklist,stackmask,
finalmasks2,expmaplist,shiftexp,expmaplist2)=(open('matchlist','w'),open('scalelist','w'),
open('shiftlist','w'),open('masklist','w'),
open('shiftmask','w'),open('finalmasks','w'),
open('stacklist','w'),open('stackmask','w'),
open('finalmasks2','w'),open('expmaplist','w'),
open('shiftexp','w'),open('expmaplist2','w'))
(xsize,ysize)=(np.array([]),np.array([]))
#Step through all of the input cluster directories.
i=0
for x in cllist:
#Find the image, mask, and exposure map files. Get zeropoints and
#scale image to the reference image.
scaleimg=x+'/scaled_to_'+ref.split('/')[-1]+'.fits'
foo=file_check(scaleimg,delete=True)
(img,mask,expmap)=classify(x+'/tu*.fits')
imgzp=pf.open(img)[0].header['MAGZERO']
(xs,ys)=(pf.open(img)[0].header['NAXIS1'],pf.open(img)[0].header['NAXIS2'])
(xsize,ysize)=(np.append(xsize,xs),np.append(ysize,ys))
imgzpoff=float(zpofflist[i])
# imgzpoff=NewfirmZPoffset[x.split('/')[-1]]
scale=scalecounts(imgzp+imgzpoff,zpref+zprefoff)
iraf.imcalc(img,scaleimg,'im1*'+str(scale))
#Get X,Y pixel positions of 2MASS sources from the 2MASS PSC
#in the image. Use these to compute shifts relative to the
#reference image using IRAF task geomap.
foo=file_check(x+'/2mass_ref_stars.cdt',delete=True)
foo=open(x+'/2mass_ref_stars.cdt','w')
catalog=get2masspsc(scaleimg)
for y in catalog:
data=y.split()
foo.write(data[6]+'\t'+data[7]+'\n')
foo.close()
#Match the 2MASS PSC positions with stars in the reference
#image using xyxymatch. The matched source list is then fed
#into geomap to get the X and Y shifts.
foo=file_check(x+'/2mass_matched.cdt',delete=True)
iraf.xyxymatch(x+'/2mass_ref_stars.cdt',ref+'/2mass_ref_stars.cdt',
x+'/2mass_matched.cdt','200.0',verbose='no')
#Append all of the names of the files for the input and output filename
#lists to be passed to IRAF tasks further down the line.
matchlist.write(x+'/2mass_matched.cdt\n')
scalelist.write(scaleimg+'\n')
foo=file_check(x+'/scaled_and_shifted.fits',delete=True)
shiftlist.write(x+'/scaled_and_shifted.fits['+str(shiftsize)+':'+\
str(int(np.max(xsize))+shiftsize)+','+str(shiftsize)+':'+\
str(int(np.max(ysize))+shiftsize)+']\n')
stacklist.write(x+'/scaled_and_shifted.fits\n')
file_check(x+'/mask_tmp.fits',delete=True)
file_check(x+'/expmap_tmp.fits',delete=True)
iraf.imarith(mask+'[1]','*',1000.0,x+'/mask_tmp.fits',pixtype='real')
iraf.imarith(expmap+'[1]','*',1.0,x+'/expmap_tmp.fits',pixtype='real')
offset=2.558435
file_check(x+'/mask_tmp2.fits',delete=True)
iraf.imcalc(x+'/mask_tmp.fits',x+'/mask_tmp2.fits','im1+'+str(offset))
os.remove(x+'/mask_tmp.fits')
masklist.write(x+'/mask_tmp2.fits\n')
file_check(x+'/mask_shift.fits',delete=True)
shiftmask.write(x+'/mask_shift.fits['+str(shiftsize)+':'+\
str(int(np.max(xsize))+shiftsize)+','+str(shiftsize)+':'+\
str(int(np.max(ysize))+shiftsize)+']\n')
stackmask.write(x+'/mask_shift.fits\n')
finalmasks.write(x+'/mask_final.fits\n')
finalmasks2.write(x+'/mask_final.fits[0]\n')
expmaplist.write(x+'/expmap_tmp.fits[0]\n')
shiftexp.write(x+'/expmap_shift.fits['+str(shiftsize)+':'+\
str(int(np.max(xsize))+shiftsize)+','+str(shiftsize)+':'+\
str(int(np.max(ysize))+shiftsize)+']\n')
expmaplist2.write(x+'/expmap_shift.fits\n')
i += 1
#Close all of the input and output filename lists to be passed to IRAF tasks.
matchlist.close()
scalelist.close()
stacklist.close()
masklist.close()
shiftmask.close()
finalmasks.close()
shiftlist.close()
stackmask.close()
finalmasks2.close()
expmaplist.close()
expmaplist2.close()
shiftexp.close()
#Get the shifts between all input files (including the reference) and the
#reference image itself.
foo=file_check('shift.db',delete=True)
iraf.geomap('@matchlist','shift.db',1.0,np.max(xsize),
1.0,np.max(ysize),fitgeometry='shift',interactive='no',
maxiter=2,function='legendre',verbose='no')
#Shift the input images (including the reference) and associated mask files
#to a common pixel grid. Add some padding around the individual frames (-99
#in the images, 1 in the bad pixel masks) to ensure that the images will
#combine properly.
(maxx,maxy)=(np.max(xsize)+shiftsize+100.0,np.max(ysize)+shiftsize+100.0)
iraf.geotran('@scalelist','@shiftlist','shift.db','@matchlist',geometry='linear',
boundary='constant',nlines=maxy,ncols=maxx,constant=-99.0)
iraf.geotran('@masklist','@shiftmask','shift.db','@matchlist',geometry='linear',
boundary='constant',nlines=maxy,ncols=maxx,constant=1000.0,
nxblock=10000,nyblock=10000)
iraf.geotran('@expmaplist','@shiftexp','shift.db','@matchlist',geometry='linear',
boundary='constant',nlines=maxy,ncols=maxx,constant=0.)
for x in cllist:
file_check(x+'/mask_final.fits',delete=True)
shutil.copy(x+'/mask_shift.fits',x+'/mask_final.fits')
iraf.hedit(x+'/scaled_and_shifted.fits[0]','BPM',x+'/mask_final.fits[0]',
add='yes',update='yes',verify='no')
iraf.imreplace('@finalmasks2',0,upper=offset)
iraf.imreplace('@finalmasks2',1,lower=offset)
file_check(stackname,delete=True)
file_check(stackname[:-5]+'_mask.pl',delete=True)
file_check(stackname[:-5]+'_expmap.fits',delete=True)
iraf.imcombine('@stacklist',stackname,bpmasks=stackname[:-5]+'_bpm',
masktype='goodval',reject='none',mclip='yes',lthresh='INDEF',hthresh='INDEF',
hsigma=10.0,lsigma='INDEF',nrejmasks=stackname[:-5]+'_nrej',
sigmas=stackname[:-5]+'_sigma',grow=2.5,nkeep=1,blank=-99.0,gain=8.0,rdnoise=35.0)
iraf.imcombine('@expmaplist2',stackname[:-5]+'_expmap.fits',combine='sum')
hdu=pf.open(stackname,mode='update')
hdu[0].header['BPM']=stackname.split('/')[-1][:-5]+'_mask.pl'
hdu[0].header['MAGZERO']=zpref+zprefoff
hdu.close()
#Fix the WCS information in the stacked image.
copyhead(stackname,refimg,offset=shiftsize)
applywcs(stackname,stackname[:-5]+'_wcs.fits')
trash=['matchlist','scalelist','shiftlist','masklist','shiftmask','finalmasks',
'shift.db','stacklist','finalmasks2','stackmask','tmp_wcs.fits','expmaplist',
'expmaplist2','shiftexp']
for x in trash:
os.remove(x)
def kaitsub(refimage,
inlist,
kernel=9,
satval=50000.0,
dspace=0,
dbkg=0,
nstamps=2,
clobber=globclob):
infiles = iraffiles(inlist)
# RA and Dec of center pixel in reference image
[nx, ny] = get_head(refimage, ['NAXIS1', 'NAXIS2'])
xcenpix = (nx / 2.0)
ycenpix = (ny / 2.0)
[[ra, dec]] = impix2wcs(refimage, xcenpix, ycenpix)
# Big loop
for image in infiles:
# Separate image rootname from extension
root, ext = image.split('.')
# Make sure WCS fit was successful
if not check_head(image, 'IQWCS'):
print 'Skipping image %s: IQWCS not successful' % image
infiles.remove(image)
continue
# Remap image using objects detected on both frames and
# create stamp list
stars = Starlist(get_head(image, 'STARFILE'))
refstars = Starlist(get_head(refimage, 'STARFILE'))
refstars.pix2wcs(refimage)
refstars.wcs2pix(image)
match, refmatch = stars.match(refstars, useflags=yes, tol=10.0)
nstars = len(match)
if not (nstars > 2):
print 'Could not find star matches between reference and %s' % image
infiles.remove(image)
continue
refmatch.pix2wcs(image)
refmatch.wcs2pix(refimage)
matchfile = open('%s.match' % root, 'w')
stampfile = open('%s.stamps' % root, 'w')
for i in range(len(match)):
matchfile.write('%10.3f%10.3f%10.3f%10.3f\n' %
(refmatch[i].xval, refmatch[i].yval, match[i].xval,
match[i].yval))
stampfile.write('%10.3f%10.3f\n' %
(refmatch[i].xval, refmatch[i].yval))
matchfile.close()
stampfile.close()
check_exist('%s.geodb' % root, 'w', clobber=clobber)
iraf.geomap('%s.match' % root,
'%s.geodb' % root,
1.0,
nx,
1.0,
ny,
fitgeom="general",
verbose=no,
interactive=no)
check_exist('%s.shift.fits' % root, 'w', clobber=clobber)
iraf.geotran(image,
'%s.shift' % root,
'%s.geodb' % root,
'%s.match' % root,
geometry="geometric",
boundary="constant",
verbose=no)
# Run the subtraction
check_exist('%s.sub.fits' % root, 'w', clobber=clobber)
if os.path.exists("stamps.lis"):
cmd = '$REDUCTION/hotpants -inim %s.shift.fits -tmplim %s -outim %s.sub.fits -tu %.2f -tg %.2f -tr %.2f -iu %.2f -ig %.2f -ir %.2f -r %.2f -ssf stamps.lis -afssc 0 -n t -ko %i -bgo %i -savexy %s.st -nsx %i -nsy %i' % (
root, refimage, root, satval, AP_GAIN, AP_READN, satval,
FL_GAIN, FL_READN, kernel, dspace, dbkg, root, nstamps,
nstamps)
else:
cmd = '$REDUCTION/hotpants -inim %s.shift.fits -tmplim %s -outim %s.sub.fits -tu %.2f -tg %.2f -tr %.2f -iu %.2f -ig %.2f -ir %.2f -r %.2f -ssf %s.stamps -afssc 0 -n t -ko %i -bgo %i -savexy %s.st -nsx %i -nsy %i' % (
root, refimage, root, satval, AP_GAIN, AP_READN, satval,
FL_GAIN, FL_READN, kernel, root, dspace, dbkg, root, nstamps,
nstamps)
#cmd = '$REDUCTION/pois -k %i %i -s %.2f %.2f -S %s.stamps %s %s.shift.fits %s.sub.fits' % (kernel,kernel,satval,satval,root,refimage,root,root)
fcmd = os.popen(cmd, 'r')
sublines = fcmd.readlines()
fcmd.close()
# Return
print 'Exiting successfully'
return