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 source_match(self):
'''
Uses iraf.xyxymatch to match sources between images.
'''
countlist = glob.glob(self.root + '/*counts.fits')
sourcelist = glob.glob(self.root + '/*sources.dat')
refimage = sourcelist[0]
for image, source in zip(countlist, sourcelist):
iraf.xyxymatch(input=source, reference=refimage, \
output=image[:-12] + '_match.dat', \
tolerance=self.tolerance, verbose='no')
def twomassmatch():
twomassRA, twomassDec, mag, lineno, octRA, octDec, octlineno, octflux, chipno = [], [], [], [], [], [], [], [], []
global bandline, indexjhk
with open('2mass.txt', 'r') as f:
lines = f.readlines()
i = 1
for x in lines:
line = x.split()
if x.startswith('#') == False and x.split()[21][indexjhk] == '0' \
and x.split()[23] == '0' and x.split()[24] == '0' \
and (x.split()[18][indexjhk] == 'A' or x.split()[18][indexjhk] == 'B'):
twomassRA.append(float(line[0]) * 1000)
twomassDec.append(float(line[1]) * 1000)
mag.append("#" + line[bandline])
lineno.append("#" + str(i))
i += 1
ascii.write([twomassRA, twomassDec, mag, lineno],
'2mass_RADec.txt',
names=('#RA', 'Dec', 'Mag', 'Line number'),
overwrite=True)
with open('oct_total.txt', 'r') as f:
lines = f.readlines()
for x in lines:
line = x.split(' ')
octRA.append(float(line[len(line) - 3]) * 1000)
octDec.append(float(line[len(line) - 2]) * 1000)
octlineno.append(line[0])
octflux.append(line[convergeap])
chipno.append(line[len(line) - 1])
ascii.write([octRA, octDec, octlineno, chipno, octflux],
'Oct_RADec.txt',
names=('#RA', 'Dec', 'line', 'chip', 'flux'),
overwrite=True)
iraf.xyxymatch(input='Oct_RADec.txt',
reference='2mass_RADec.txt',
output="2mass_Oct_matched.txt",
tolerance=3,
verbose="no",
xref=0,
yref=0,
xin=0,
yin=0)
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 matchOctSept():
global liststars, masteroctlines, masterseptlines
septxlist, septylist, septmatchlinelist, octmatchlinelist, septcatlinelist, octcatlinelist, oct_total, octlineno, \
septlineno, octxlist, octylist = [], [], [], [], [], [], [], [], [], [], []
# create list of acceptable sources in september
with open(septextracted[0], "r") as f:
lines = f.readlines()
RAs, Decs = [], []
for x in lines:
line = x.split(' ')
if (not x.startswith("N")) and int(line[3]) == 0 \
and uppersept > float(line[1]) > lowersept and uppersept > float(line[2]) > lowersept:
septxlist.append(line[1])
septylist.append(line[2])
septlineno.append('#%s' % line[0])
RAs.append('#%s' % line[5])
Decs.append('#%s' % line[6])
with open((userdata + 'match1.xml'), 'w') as f:
for y in range(len(septxlist)):
f.write('circle(%s,%s,%s)' % (septxlist[y], septylist[y], 30) +
'\n')
septxy = ((sept + 'xysept_full.txt'))
ascii.write([septxlist, septylist, septlineno, RAs, Decs],
septxy,
names=('#x', 'y', 'line no.', 'RA', 'Dec'),
overwrite=True)
for num in range(len(octclean)):
octfile = octclean[num]
with open(octfile, "r") as f:
lines = f.readlines()
for x in lines:
line = x.split(' ')
if int(line[3]) == 0:
octxlist.append(line[1])
octylist.append(line[2])
octlineno.append('#%s' % line[0])
octxy = oct + 'c%s_xyoct_full.txt' % (num + 1)
ascii.write([octxlist, octylist, octlineno],
octxy,
names=('#x', 'y', 'line no.'),
overwrite=True)
del octxlist[:], octylist[:], octlineno[:]
str_out = 'c%s_oct_sept_match.txt' % (num + 1)
refpnt = (reference + '%sref.txt' % (num + 1))
iraf.xyxymatch(input=(sept + 'xysept_full.txt'),
reference=octxy,
output=str_out,
xmag=1.0,
ymag=1.0,
refpoints=refpnt,
matching="tolerance",
tolerance=3,
verbose="no")
with open(str_out, "r") as f:
lines = f.readlines()
for x in lines:
x = x.strip()
if not x or x.startswith('#'):
continue
septmatchlinelist.append(int(x.split()[5]) - 1)
octmatchlinelist.append(int(x.split()[4]) - 1)
if len(septmatchlinelist) != len(
set(septmatchlinelist)) or len(octmatchlinelist) != len(
set(octmatchlinelist)):
print "Error: tweak xyxymatch parameters so that a coordinate in one set of data isn't matched to " \
"multiple coordinates in the other"
print num
sys.exit()
with open((sept + 'xysept_full.txt'), 'r') as fs:
tempsept, tempoct = np.array(septmatchlinelist), np.array(
octmatchlinelist)
with open(octxy, 'r') as f:
lines = fs.readlines()
linesoct = f.readlines()
i = 1
for x in lines:
line = x.split('#')
if x.startswith("#"):
continue
elif i in septmatchlinelist:
septcatlinelist.append(int(line[1]))
masterseptlines[num].append(int(line[1]))
RA.append(float(line[2]))
Dec.append(float(line[3]))
p = linesoct[octmatchlinelist[np.where(
tempsept == i)[0][0]]].split('#')
octcatlinelist.append(int(p[1]))
masteroctlines[num].append(int(p[1]))
i += 1
temptotal = []
with open(octfile, 'r') as f:
tempoct = np.array(octcatlinelist)
lines = f.readlines()
for x in lines:
z = int(x.split(' ')[0])
if x.startswith("#"):
continue
elif z in octcatlinelist:
a = np.where(tempoct == z)[0][0]
temptotal.append('%s %s %s %s\n' %
(x.rstrip('\n'), RA[a], Dec[a],
(num + 1)))
for item in range(len(temptotal)):
if int(temptotal[item].split(' ')[0]) in liststars[num]:
oct_total.append(temptotal[item])
del septmatchlinelist[:], octmatchlinelist[:], septcatlinelist[:], octcatlinelist[:], RA[:], Dec[:], temptotal[:]
with open('oct_total.txt', 'w') as f:
for item in oct_total:
f.write('%s' % item)
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 cat_im_match(xref, yref, xin, yin, septol, **kwargs):
'''Written by Gregory Rudnick 9 January 2018
PURPOSE:
Take two lists of x and y coordinates in the same units and match
them within some tolerance.
Plot the differences in each coordinate.
INPUT PARAMETERS:
xref, yref: the reference coordinates in pixels. Numpy arrays.
xin, yin: the input coordinates in pixels. If performing
coordinate transforms, these would be the ones to be transformed.
Numpy arrays
septol: the maximum separation allowed for a match. Units are
pixels
OPTIONAL KEYWORD PARAMETERS
icfile: The filename with the input reference coordinates
for xyxymatch. These need to be created by hand from the images
matchfile: a name of the file that will contain the reference and
input coordinates. Suitable for geomap input. Default is 'xyxy_out.txt'
OUTPUT
arrays containing row-matched (x,y) coordinates of the reference and input
coordinates that fall with septol
'''
# #loop through all coordiantes and find closest match. This is an
# #N^2 process. I can make it faster later.
# #initialize distance coordinate
# dist = np.full(xref.size, 1.e6)
# #initialize array with indices of closest match
# imatch = np.zeros(xref.size, dtype=np.int8)
# for iref,valref in enumerate(xref):
# for iin,valin in enumerate(xin):
# #print("hi",iref,iin,xref[iref],xin[iin],yref[iref],yin[iin])
# disttest = np.sqrt( (xref[iref] - xin[iin])**2 +(yref[iref] - yin[iin])**2)
# #find the closest reference point to each input point
# if disttest < dist[iref]:
# #print(disttest,iref,iin)
# dist[iref] = disttest
# imatch[iref] = iin
# #select close matches, where I assume septol is in pixels, just like the input catalog
# iclose = np.where(dist < septol)
# #convert tuple to pure array
# iclose=iclose[0]
#generate input files for xyxymatch from reference coordinates
xyxy_refin = 'xyxymatch_refcoords.txt'
fo = open(xyxy_refin, "w")
for i, val in enumerate(xref):
#print(i,iclose[i])
#print(raref[iclose[i]],decref[iclose[i]],rain[idx[iclose[i]]],decin[idx[iclose[i]]])
fo.write('{} {}\n'.format(xref[i], yref[i]))
fo.close()
xyxy_inin = 'xyxymatch_incoords.txt'
fo = open(xyxy_inin, "w")
for i, val in enumerate(xin):
fo.write('{} {}\n'.format(xin[i], yin[i]))
fo.close()
#set xyxymatch output name if keyword is given
xyxy_out = 'xyxy_match.txt'
#remove geotran output file if it already exists
if os.path.isfile(xyxy_out) is True:
cmdstr = 'rm ' + xyxy_out
os.system(cmdstr)
#run xyxymatch and include a set of reference points if given as input
if 'icfile' in kwargs.keys():
iraf.xyxymatch(xyxy_inin, xyxy_refin, xyxy_out, septol, refpoints=kwargs['icfile'], \
xcolumn=1,ycolumn=2,xrcolumn=1,yrcolumn=2,matching="tolerance")
else:
iraf.xyxymatch(xyxy_inin, xyxy_refin, xyxy_out, septol, refpoints="", \
xcolumn=1,ycolumn=2,xrcolumn=1,yrcolumn=2,matching="tolerance")
#read in xyxymatch output to get coordinate limits and to return
#ordered matched coordinates
xyxy_cat = ascii.read(xyxy_out)
xref_m = np.array(xyxy_cat['col1'])
yref_m = np.array(xyxy_cat['col2'])
xin_m = np.array(xyxy_cat['col3'])
yin_m = np.array(xyxy_cat['col4'])
#write files only if "matchfile" keyword is set
#this reformat of the xyxymatch code is just so that my other routines work
keys = sorted(kwargs.keys())
for kw in keys:
if kw == 'matchfile':
#print(kwargs[kw])
#open file for writing and write a header
fo = open(kwargs[kw], "w")
fo.write("# xref yref xin yin\n")
for i, val in enumerate(xref_m):
#print(i,iclose[i])
#print(raref[iclose[i]],decref[iclose[i]],rain[idx[iclose[i]]],decin[idx[iclose[i]]])
fo.write('{} {} {} {}\n'.format(xref_m[i], yref_m[i], xin_m[i],
yin_m[i]))
fo.close()
#store the limits of the coordinates
lims = {
'xmax': np.amax(xref_m),
'xmin': np.amin(xref_m),
'ymax': np.amax(yref_m),
'ymin': np.amin(yref_m)
}
#return all matches within the tolerance
return xref_m, yref_m, xin_m, yin_m, lims
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 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 plot_shifts(ROOT_DIRECT,
ALIGN_IMAGE,
clean=True,
verbose=True,
ALIGN_EXTENSION=0,
toler=3,
skip_swarp=False,
threshold=7,
force=False,
drz=True,
WEIGHT_IMAGE=None):
"""
Run SExtractor on two images and match the objects to plot the shifts between them.
ALIGN_IMAGE is a string that may contain wildcards, and the function will use
`align_img_list` to find ALIGN_IMAGEs
"""
import glob
from pyraf import iraf
from iraf import stsdas, dither
no = iraf.no
yes = iraf.yes
INDEF = iraf.INDEF
if os.path.exists(ROOT_DIRECT + '_align.fits') & (not force):
if verbose:
print 'Image %s_align.fits exists. Skipping SWarp.' % (
ROOT_DIRECT)
skip_swarp = True
if not skip_swarp:
if drz:
align_img_list = find_align_images_that_overlap(
ROOT_DIRECT + '_drz.fits',
ALIGN_IMAGE,
ALIGN_EXTENSION=ALIGN_EXTENSION)
else:
align_img_list = glob.glob(ALIGN_IMAGE)
if not align_img_list:
print 'threedhst.shifts.align_to_reference: no alignment images overlap.'
return 0, 0
#
try:
os.remove(ROOT_DIRECT + '_align.fits')
except:
pass
if drz:
matchImagePixels(input=align_img_list,
matchImage=ROOT_DIRECT + '_drz.fits',
output=ROOT_DIRECT + '_align.fits',
match_extension=1,
input_extension=ALIGN_EXTENSION)
ALIGN_FITS = ROOT_DIRECT + '_align.fits'
else:
ALIGN_FITS = os.path.basename(
ROOT_DIRECT.split('.fits')[0]) + '_align.fits'
matchImagePixels(input=align_img_list,
matchImage=ROOT_DIRECT,
output=ALIGN_FITS,
match_extension=0,
input_extension=ALIGN_EXTENSION)
se = threedhst.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options['CHECKIMAGE_TYPE'] = 'NONE'
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
if drz:
se.options['WEIGHT_IMAGE'] = ROOT_DIRECT + '_drz.fits[1]'
else:
if WEIGHT_IMAGE:
se.options['WEIGHT_IMAGE'] = WEIGHT_IMAGE
else:
se.options['WEIGHT_TYPE'] = 'NONE'
se.options['WEIGHT_IMAGE'] = 'NONE'
se.options['FILTER'] = 'Y'
## Detect thresholds (default = 1.5)
se.options['DETECT_THRESH'] = '%f' % (threshold)
se.options['ANALYSIS_THRESH'] = '%f' % (threshold)
se.options['MAG_ZEROPOINT'] = str(threedhst.options['MAG_ZEROPOINT'])
#### Run SExtractor on direct and alignment images
## direct image
se.options['CATALOG_NAME'] = 'direct.cat'
if drz:
status = se.sextractImage(ROOT_DIRECT + '_drz.fits[0]')
INPUT_IMAGE = ROOT_DIRECT + '_drz.fits'
else:
status = se.sextractImage(ROOT_DIRECT)
INPUT_IMAGE = ROOT_DIRECT
## alignment image
se.options['CATALOG_NAME'] = 'align.cat'
se.options['WEIGHT_TYPE'] = 'NONE'
status = se.sextractImage(ALIGN_FITS)
## Read the catalogs
directCat = threedhst.sex.mySexCat('direct.cat')
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)
while status1[-1].startswith('0'):
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)
#### Images are aligned, plot the offsets
dx, dy, ax, ay, di, ai = np.loadtxt('align.match', unpack=True)
ddx, ddy = dx - ax, dy - ay
keep = (np.abs(ddx) < 15) & (np.abs(ddy) < 15)
for i in range(5):
sx, sy = threedhst.utils.biweight(
ddx[keep], both=True), threedhst.utils.biweight(ddy[keep],
both=True)
keep = keep & (np.abs(ddx - sx[0]) < 5 * sx[1]) & (np.abs(ddy - sy[0])
< 5 * sy[1])
if USE_PLOT_GUI:
fig = plt.figure(figsize=[8, 4], dpi=100)
else:
fig = Figure(figsize=[8, 4], dpi=100)
fig.subplots_adjust(wspace=0.28,
hspace=0.0,
left=0.08,
bottom=0.14,
right=0.98,
top=0.98)
ax = fig.add_subplot(121)
ax.plot(ddx,
ddy,
marker='o',
linestyle='None',
color='black',
alpha=0.4,
ms=2,
zorder=-1)
ax.errorbar(sx[0],
sy[0],
sx[1],
sy[1],
marker='o',
ms=0.1,
color='white',
linewidth=3,
zorder=100)
ax.errorbar(sx[0],
sy[0],
sx[1],
sy[1],
marker='o',
ms=0.1,
color='red',
linewidth=2,
zorder=500)
ax.grid(alpha=0.5)
dwin = 5 * np.max([sx[1], sy[1]])
ax.set_xlim(sx[0] - dwin, sx[0] + dwin)
ax.set_ylim(sy[0] - dwin, sy[0] + dwin)
ax.set_xlabel(r'$\Delta x$ [pix]')
ax.set_ylabel(r'$\Delta y$ [pix]')
ax.text(0.5,
0.95,
os.path.basename(INPUT_IMAGE),
fontsize=9,
horizontalalignment='center',
transform=ax.transAxes)
ax.text(0.5,
0.9,
os.path.basename(ALIGN_IMAGE),
fontsize=9,
horizontalalignment='center',
transform=ax.transAxes)
ax.text(0.5,
0.1,
r'$\Delta x, \Delta y = %.2f \pm %.2f, %.2f \pm %.2f)$' %
(sx[0], sx[1], sy[0], sy[1]),
fontsize=11,
horizontalalignment='center',
transform=ax.transAxes)
ax = fig.add_subplot(122)
ax.plot(dx[keep],
dy[keep],
marker='o',
ms=1,
linestyle='None',
color='black',
alpha=0.1)
ax.quiver(dx[keep],
dy[keep],
ddx[keep],
ddy[keep],
alpha=0.5,
angles='xy',
headlength=1,
headwidth=1,
scale=30. / (dx.max() - dx.min()),
units='x',
minlength=1)
aa = np.array([1, 1])
ax.quiver(dx[keep].mean() * aa,
dy[keep].max() * 0.95 * aa,
1 * aa,
0 * aa,
alpha=0.9,
angles='xy',
headlength=0,
headwidth=1,
scale=30. / (dx.max() - dx.min()),
units='x',
color='red')
ax.set_xlabel(r'$x$ [pix]')
ax.set_ylabel(r'$y$ [pix]')
if USE_PLOT_GUI:
fig.savefig(os.path.basename(ROOT_DIRECT.split('.fits')[0]) +
'_align.pdf',
dpi=100,
transparent=False)
else:
canvas = FigureCanvasAgg(fig)
canvas.print_figure(os.path.basename(ROOT_DIRECT.split('.fits')[0]) +
'_align.pdf',
dpi=100,
transparent=False)
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
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 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 plot_shifts(
ROOT_DIRECT,
ALIGN_IMAGE,
clean=True,
verbose=True,
ALIGN_EXTENSION=0,
toler=3,
skip_swarp=False,
threshold=7,
force=False,
drz=True,
WEIGHT_IMAGE=None,
):
"""
Run SExtractor on two images and match the objects to plot the shifts between them.
ALIGN_IMAGE is a string that may contain wildcards, and the function will use
`align_img_list` to find ALIGN_IMAGEs
"""
import glob
from pyraf import iraf
from iraf import stsdas, dither
no = iraf.no
yes = iraf.yes
INDEF = iraf.INDEF
if os.path.exists(ROOT_DIRECT + "_align.fits") & (not force):
if verbose:
print "Image %s_align.fits exists. Skipping SWarp." % (ROOT_DIRECT)
skip_swarp = True
if not skip_swarp:
if drz:
align_img_list = find_align_images_that_overlap(
ROOT_DIRECT + "_drz.fits", ALIGN_IMAGE, ALIGN_EXTENSION=ALIGN_EXTENSION
)
else:
align_img_list = glob.glob(ALIGN_IMAGE)
if not align_img_list:
print "threedhst.shifts.align_to_reference: no alignment images overlap."
return 0, 0
#
try:
os.remove(ROOT_DIRECT + "_align.fits")
except:
pass
if drz:
matchImagePixels(
input=align_img_list,
matchImage=ROOT_DIRECT + "_drz.fits",
output=ROOT_DIRECT + "_align.fits",
match_extension=1,
input_extension=ALIGN_EXTENSION,
)
ALIGN_FITS = ROOT_DIRECT + "_align.fits"
else:
ALIGN_FITS = os.path.basename(ROOT_DIRECT.split(".fits")[0]) + "_align.fits"
matchImagePixels(
input=align_img_list,
matchImage=ROOT_DIRECT,
output=ALIGN_FITS,
match_extension=0,
input_extension=ALIGN_EXTENSION,
)
se = threedhst.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options["CHECKIMAGE_TYPE"] = "NONE"
se.options["WEIGHT_TYPE"] = "MAP_WEIGHT"
if drz:
se.options["WEIGHT_IMAGE"] = ROOT_DIRECT + "_drz.fits[1]"
else:
if WEIGHT_IMAGE:
se.options["WEIGHT_IMAGE"] = WEIGHT_IMAGE
else:
se.options["WEIGHT_TYPE"] = "NONE"
se.options["WEIGHT_IMAGE"] = "NONE"
se.options["FILTER"] = "Y"
## Detect thresholds (default = 1.5)
se.options["DETECT_THRESH"] = "%f" % (threshold)
se.options["ANALYSIS_THRESH"] = "%f" % (threshold)
se.options["MAG_ZEROPOINT"] = str(threedhst.options["MAG_ZEROPOINT"])
#### Run SExtractor on direct and alignment images
## direct image
se.options["CATALOG_NAME"] = "direct.cat"
if drz:
status = se.sextractImage(ROOT_DIRECT + "_drz.fits[0]")
INPUT_IMAGE = ROOT_DIRECT + "_drz.fits"
else:
status = se.sextractImage(ROOT_DIRECT)
INPUT_IMAGE = ROOT_DIRECT
## alignment image
se.options["CATALOG_NAME"] = "align.cat"
se.options["WEIGHT_TYPE"] = "NONE"
status = se.sextractImage(ALIGN_FITS)
## Read the catalogs
directCat = threedhst.sex.mySexCat("direct.cat")
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,
)
while status1[-1].startswith("0"):
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,
)
#### Images are aligned, plot the offsets
dx, dy, ax, ay, di, ai = np.loadtxt("align.match", unpack=True)
ddx, ddy = dx - ax, dy - ay
keep = (np.abs(ddx) < 15) & (np.abs(ddy) < 15)
for i in range(5):
sx, sy = threedhst.utils.biweight(ddx[keep], both=True), threedhst.utils.biweight(ddy[keep], both=True)
keep = keep & (np.abs(ddx - sx[0]) < 5 * sx[1]) & (np.abs(ddy - sy[0]) < 5 * sy[1])
if USE_PLOT_GUI:
fig = plt.figure(figsize=[8, 4], dpi=100)
else:
fig = Figure(figsize=[8, 4], dpi=100)
fig.subplots_adjust(wspace=0.28, hspace=0.0, left=0.08, bottom=0.14, right=0.98, top=0.98)
ax = fig.add_subplot(121)
ax.plot(ddx, ddy, marker="o", linestyle="None", color="black", alpha=0.4, ms=2, zorder=-1)
ax.errorbar(sx[0], sy[0], sx[1], sy[1], marker="o", ms=0.1, color="white", linewidth=3, zorder=100)
ax.errorbar(sx[0], sy[0], sx[1], sy[1], marker="o", ms=0.1, color="red", linewidth=2, zorder=500)
ax.grid(alpha=0.5)
dwin = 5 * np.max([sx[1], sy[1]])
ax.set_xlim(sx[0] - dwin, sx[0] + dwin)
ax.set_ylim(sy[0] - dwin, sy[0] + dwin)
ax.set_xlabel(r"$\Delta x$ [pix]")
ax.set_ylabel(r"$\Delta y$ [pix]")
ax.text(0.5, 0.95, os.path.basename(INPUT_IMAGE), fontsize=9, horizontalalignment="center", transform=ax.transAxes)
ax.text(0.5, 0.9, os.path.basename(ALIGN_IMAGE), fontsize=9, horizontalalignment="center", transform=ax.transAxes)
ax.text(
0.5,
0.1,
r"$\Delta x, \Delta y = %.2f \pm %.2f, %.2f \pm %.2f)$" % (sx[0], sx[1], sy[0], sy[1]),
fontsize=11,
horizontalalignment="center",
transform=ax.transAxes,
)
ax = fig.add_subplot(122)
ax.plot(dx[keep], dy[keep], marker="o", ms=1, linestyle="None", color="black", alpha=0.1)
ax.quiver(
dx[keep],
dy[keep],
ddx[keep],
ddy[keep],
alpha=0.5,
angles="xy",
headlength=1,
headwidth=1,
scale=30.0 / (dx.max() - dx.min()),
units="x",
minlength=1,
)
aa = np.array([1, 1])
ax.quiver(
dx[keep].mean() * aa,
dy[keep].max() * 0.95 * aa,
1 * aa,
0 * aa,
alpha=0.9,
angles="xy",
headlength=0,
headwidth=1,
scale=30.0 / (dx.max() - dx.min()),
units="x",
color="red",
)
ax.set_xlabel(r"$x$ [pix]")
ax.set_ylabel(r"$y$ [pix]")
if USE_PLOT_GUI:
fig.savefig(os.path.basename(ROOT_DIRECT.split(".fits")[0]) + "_align.pdf", dpi=100, transparent=False)
else:
canvas = FigureCanvasAgg(fig)
canvas.print_figure(os.path.basename(ROOT_DIRECT.split(".fits")[0]) + "_align.pdf", dpi=100, transparent=False)
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
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')
