def GeoTran():
filters = ['J', '1113', '1184']
for filt in filters:
s = 'mfm*' + filt + '*.fits'
files = glob.glob(s)
flist = 'flist' + str(filt)
glist = 'glist' + str(filt)
fout = open(flist, 'w')
gout = open(glist, 'w')
for file in files:
ffile = str(file) #+'\n'
gfile = 'g' + str(file) #+'\n'
fout.write(ffile + '\n')
gout.write(gfile + '\n')
iraf.geotran(input=ffile,
output=gfile,
database='PiscesBok',
transforms=filt,
fluxconserve='no')
iraf.rotate(input=gfile, output=gfile, rotation=90)
gfilein = 'g' + str(file) + '[-*,*]'
gfileout = 'g' + str(file)
iraf.imcopy(gfilein, gfileout)
infiles = '@' + str(flist)
outfiles = '@' + str(glist)
#infiles=str(flist)
#outfiles=str(glist)
fout.close()
gout.close()
def imtrans(impath, refimpath, dbfile, geomap_infile, lims):
'''Run geotran on an image
INPUT:
impath : the full path to the image to be transformed
refimpath : the full path to the reference image
dbfile: the geomap database file
geomap_infile: the name of the input coordinate file for the
geomap transformation, which is used as the record identifier for
the transfor
lims: a dictionary that defines the minimum and maximum
'''
#find the x and y limits of the reference image
refim = fits.open(refimpath)
xmax = refim[0].header['NAXIS1']
ymax = refim[0].header['NAXIS2']
xmin = 1
ymin = 1
refim.close()
outpath = impath.replace('.fits[1]','.trans.fits')
iraf.geotran(impath, outpath, dbfile, geomap_infile, xmin = xmin, xmax = xmax, \
ymin = ymin, ymax = ymax, xscale = 1.0, yscale = 1.0)
def geotran(self):
# Run geotran on self.input_image... is this what I want to do?
output_image = os.path.splitext(self.input_image)[0] + '_geotran.fits'
if os.path.exists(output_image):
os.remove(output_image)
h = fits.getheader(self.input_image)
iraf.geotran(self.input_image, output_image, self.database,
self.coofile,
geometry='geometric',
xscale=self.relscale, yscale=self.relscale,
interpolant='spline3')
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
def distcor(inlist, database, inpref='', outpref='d'):
# open input list and check if it exists
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check output prefix
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(inimg_arr, int):
return 1
# check database
try:
fd = open(database)
except IOError:
print >> sys.stderr, 'cannot open database file (%s)' % database
f.close()
return 1
# read geomap coordinate file
read = 0
for line in fd:
param = line[:-1].split()
if param[0] == 'begin':
gmp = param[1]
read += 1
fd.close()
if read == 0:
print >> sys.stderr, 'cannot read coordinate file in geomap database (%s)' % database
f.close
return 1
# correct distortion
for i in range(len(inimg_arr)):
# correct distortion
iraf.unlearn('geotran')
iraf.geotran(inimg_arr[i], outimg_arr[i], database, gmp)
return 0
def mygeotran(input,
output,
database,
transforms,
geometry='geometric',
interpolant="linear"):
# First, we get the shifts from the database
f = open(database, 'r')
for line in f.readlines():
if re.search('xshift', line):
xshift = float(string.split(line)[1])
elif re.search('yshift', line):
yshift = float(string.split(line)[1])
f.close()
f = open('offsets.dat', 'w')
print >> f, 0, 0
print >> f, xshift + 1, yshift + 1
f.close()
# Now we make a blank holder for the mapped image
nuke('placeholder.fits')
iraf.imcombine(input + ',' + input,
'placeholder.fits',
offsets='offsets.dat')
iraf.imarith('placeholder.fits', '*', 0.0, 'placeholder.fits')
xsize, ysize = get_image_size(input)
# next, copy the input into this place holder
iraf.imcopy(input + '[*,*]',
'placeholder.fits[1:%d,1:%d]' % (xsize, ysize))
# now run geogran on this padded image
iraf.geotran(input='placeholder.fits',
output=output,
database=database,
transforms=transforms,
geometry=geometry,
interpolant=interpolant)
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 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 combine_images(images,
output,
reference=None,
method='average',
zscale=1,
fitgeometry='general',
function='polynomial',
xxorder=3,
yyorder=3,
xyorder=3,
yxorder=3,
objects=None,
trim=0,
interactive=1,
maxiter=5,
reject=3,
sigma=None,
xrange=None,
yrange=None,
rectify=1,
thresh=2.0,
scale=0.125,
fwhm=4.0,
debug=0,
aoffset=0,
creject=None):
'''This Routine will take a list of images and use rectify_image to rectify
each with respect to referece. It will then use imcombine to combine the
images into one and output to "output". Default method is 'average', but
you can use any available to imcombine. The rest of the arguments are sent
to rectify_images. If zscale=1, all the images are scaled to a common
zmag (30). Also, if a file with _sigma.fits is found for all input files,
we combine them too. If you specify a sigma, that file will hold the
standard deviations from the files.'''
names = get_files(images)
if reference is None:
reference = names[0]
names = names[1:]
pclip = -0.5
lsigma = 3.
hsigma = 3.
nkeep = -2
if creject:
creject = "pclip"
else:
creject = "none"
# Check to see if we have _sigma files for each input file
if os.path.isfile(reference.replace('.fits', '_sigma.fits')):
do_sigmas = 1
else:
do_sigmas = 0
sigmas = []
for name in names:
if os.path.isfile(name.replace('.fits', '_sigma.fits')):
sigmas.append(name.replace('.fits', '_sigma.fits'))
else:
do_sigmas = 0
break
# Put the reference image first, to ensure that all positional header keywords are
# valid.
if do_sigmas:
ref_sig = reference.replace('.fits', '_sigma.fits')
update = 0
if zscale:
zmag = get_header(reference, "ZMAG", float)
if zmag is not None and zmag != 26:
update = 1
zmagfac = num.power(10, (zmag - 30) / 2.5)
if zmagfac != 1:
print 'zmagfac = ', zmagfac
iraf.imarith(reference, '/', zmagfac, reference)
iraf.hedit(reference, "ZMAG", 30.0, add=1, verify=0)
if do_sigmas:
iraf.imarith(ref_sig, '/', zmagfac, ref_sig)
iraf.hedit(ref_sig, "ZMAG", 30.0, add=1, verify=0)
print "Using %s as reference" % (reference)
if rectify:
nuke(reference.replace('.fits', '_rec.fits'))
iraf.imcopy(reference, reference.replace('.fits', '_rec.fits'))
if do_sigmas:
nuke(reference.replace('.fits', '_rec_sigma.fits'))
iraf.imcopy(reference.replace('.fits', '_sigma.fits'),
reference.replace('.fits', '_rec_sigma.fits'))
temps = [reference.replace('.fits', '_rec.fits')]
if do_sigmas:
sig_temps = [reference.replace('.fits', '_rec_sigma.fits')]
else:
temps = [reference]
if do_sigmas: sig_temps = [ref_sig]
i = 0
for name in names:
print name
if rectify:
temp = name.replace('.fits', '_rec.fits')
nuke(temp)
if do_sigmas:
(x0, x1, y0, y1) = rectify_image(name,
reference,
output=temp,
fitgeometry=fitgeometry,
function=function,
xxorder=xxorder,
yyorder=yyorder,
xyorder=xyorder,
yxorder=yxorder,
objects=objects,
interactive=interactive,
maxiter=maxiter,
reject=reject,
xrange=xrange,
yrange=yrange,
thresh=thresh,
scale=scale,
fwhm=fwhm,
image_sigma=sigmas[i],
reference_sigma=ref_sig,
debug=debug,
aoffset=aoffset)
else:
(x0, x1, y0, y1) = rectify_image(name,
reference,
output=temp,
fitgeometry=fitgeometry,
function=function,
xxorder=xxorder,
yyorder=yyorder,
xyorder=xyorder,
yxorder=yxorder,
objects=objects,
interactive=interactive,
maxiter=maxiter,
reject=reject,
xrange=xrange,
yrange=yrange,
thresh=thresh,
scale=scale,
fwhm=fwhm,
debug=debug,
aoffset=aoffset)
else:
temp = name
bpm = get_header(name, 'BPM', str)
if bpm and rectify:
bpm_fits = bpm.replace('.pl', '.fits')
temp_bpm = bpm.replace('.pl', '_rec.fits')
temp_bpm_pl = bpm.replace('.pl', '_rec.pl')
nuke(bpm_fits)
nuke(temp_bpm)
nuke(temp_bpm_pl)
iraf.imcopy(bpm, bpm_fits)
iraf.imarith(bpm_fits, "*", 10.0, bpm_fits)
if objects is None: sigmaobjects = 'geomap.objects'
iraf.geotran(input=bpm_fits,
output=temp_bpm,
database='geomap.db',
transforms=sigmaobjects,
geometry='geometric',
interpolant="linear")
iraf.imreplace(temp_bpm, lower=0.02, upper="INDEF", value=1.)
iraf.imcopy(temp_bpm, temp_bpm_pl)
iraf.hedit(temp, 'BPM', temp_bpm_pl, update=1, verify=0)
if do_sigmas:
if rectify:
temp_sig = sigmas[i].replace('_sigma.fits', '_rec_sigma.fits')
nuke(temp_sig)
if objects is None:
sigmaobjects = 'geomap.objects'
else:
sigmaobjects = objects
iraf.geotran(input=sigmas[i],
output=temp_sig,
database='geomap.db',
transforms=sigmaobjects,
geometry='geometric',
interpolant="linear")
else:
temp_sig = sigmas[i]
if rectify:
if i == 0:
xmin = x0
ymin = y0
xmax = x1
ymax = y1
else:
xmin = max(xmin, x0)
ymin = max(ymin, y0)
xmax = min(xmax, x1)
ymax = min(ymax, y1)
if zscale:
zmag = get_header(name, "ZMAG", float)
if zmag is not None and zmag != 26:
zmagfac = num.power(10, (zmag - 30) / 2.5)
if zmagfac != 1:
print 'zmagfac = ', zmagfac
iraf.imarith(temp, '/', zmagfac, temp)
iraf.hedit(temp, 'ZMAG', 30.0, add=1, verify=0)
if do_sigmas:
iraf.imarith(temp_sig, '/', zmagfac, temp_sig)
iraf.hedit(temp_sig, 'ZMAG', 30.0, add=1, verify=0)
temps.append(temp)
if do_sigmas: sig_temps.append(temp_sig)
i = i + 1
Nimages = len(temps)
temps = string.join(temps, ',')
print "Combining ", temps
nuke(output)
if sigma is not None:
iraf.imcombine(temps,
output,
combine=method,
sigma=sigma,
masktyp='badvalue',
maskval=1.,
reject=creject,
lsigma=lsigma,
hsigma=hsigma,
nkeep=nkeep)
else:
iraf.imcombine(temps,
output,
combine=method,
masktyp='badvalue',
maskval=1.,
reject=creject,
lsigma=lsigma,
hsigma=hsigma,
nkeep=nkeep)
if do_sigmas:
Ns = []
for this in sig_temps:
N = this.replace('.fits', '_N.fits')
nuke(N)
iraf.imcopy(this, N)
iraf.imreplace(N, lower=0.01, upper="INDEF", value=1)
Ns.append(N)
sig_temps = string.join(sig_temps, ',')
Ns = string.join(Ns, ',')
iraf.imfunction(sig_temps, sig_temps, 'square')
file = output.replace('.fits', '_sigma.fits')
nuke(file)
iraf.imcombine(sig_temps, file, combine='sum')
if method == 'average':
nuke("N.fits")
iraf.imcombine(Ns, 'N.fits', combine='sum')
iraf.imfunction('N.fits', 'N.fits', 'square')
iraf.imarith(file, '/', 'N.fits', file)
iraf.imfunction(file, file, 'sqrt')
iraf.imfunction(sig_temps, sig_temps, 'sqrt')
if update:
iraf.hedit(output, "ZMAG", 30.0, verify=0, add=1)
#nuke('temp?.fits')
# Now, let's clean up the gain and rdnoise headers, as they don't seem to
# be handled in the PANIC pipeline. NOTE: we really need a noise map, but
# for now, we'll deal with just global values.
gain = get_header(output, "gain", float)
rdnoise = get_header(output, "rdnoise", float)
if gain is None:
# have to compute it from scratch
egain = get_header(output, "egain", float)
enoise = get_header(output, "enoise", float)
nloop = get_header(output, "nloops", int)
i = 1
key = ""
# This seems to be the only safe way to compute this. Can't trust
# NDITHERS
while key is not None:
key = get_header(output, "imcmb%03d" % (i), str)
i = i + 1
ndither = i - 1
print "N, ndither, nloop, egain, enoise = ", Nimages, ndither, nloop, egain, enoise
gain = egain * Nimages * nloop * ndither
rdnoise = enoise * num.sqrt(Nimages * nloop * ndither)
else:
gain = gain * Nimages
rdnoise = rdnoise * num.sqrt(Nimages)
iraf.hedit(output, "gain", gain, verify=0, add=1)
iraf.hedit(output, "rdnoise", rdnoise, verify=0, add=1)
# If requested, trim to the common regions of the combined images
if rectify and trim and Nimages > 1:
iraf.imcopy(output + "[%d:%d,%d:%d]" % (xmin, xmax, ymin, ymax),
output.replace('.fits', '_trim.fits'))
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 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 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 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 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("$REDUCTION/runsex.pl %s 1.5" % out)
stars = Starlist("%s.stars" % out)
cands = []
[nax1, nax2] = get_head(ref, ["NAXIS1", "NAXIS2"])
stars.pix2wcs(new)
stars.wcs2pix(ref)
refu = pyfits.open(refunc)
for star in stars:
if star.xval > EDGETOL and star.xval < (nax1 - EDGETOL) and star.yval > EDGETOL and star.yval < (nax2 - EDGETOL) and refu[0].data[star.yval,star.xval] != 0 and star.fwhmw > 0.5 and star.fwhmw < 10.0:
cands.append(star)
scands = Starlist(stars=cands)
scands.write("cands_all.reg")
return
def spitzer_sub(new,
newunc,
newcov,
ref,
refunc,
refcov,
out,
stamps=None,
tmass=None):
# Remove nan from mosaics
x = pyfits.open(new)
y = np.nan_to_num(x[0].data)
x[0].data = y
new2 = "n%s" % new
x.writeto(new2)
# Find stars in new image
os.system("$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
ffile = 'fmd' + file
m999file = 'mfmd' + file
gfile = 'gmfmd' + file
if npass == 1:
iraf.imarith(mfile, "/", flat, ffile)
diminput = fname
if npass == 2:
iraf.imdelete(ffile)
iraf.imarith(mfile, "/", flat, ffile)
domask999(ffile, invmask)
# iraf.geotran(ffile,gfile,database="geodb4",trans="mmtj",geometry="geometric")
iraf.geotran(m999file,
gfile,
database=geodb,
trans=geotrans,
geometry="geometric",
interpolant="nearest",
boundary="nearest",
fluxconserve="no")
diminput = gname
makedim = 'ls ' + diminput + ' > diminlist' + str(i)
os.system(makedim)
i = i + 1
gmask = 'g' + mask
try:
iraf.geotran(
mask,
gmask,
database=geodb,
trans=geotran,
geometry="geometric",
def sigmap(inlist,
sigmap,
expmap='none',
whtmap='none',
inpref='',
ffpref='',
objmask='none',
reject='sigclip',
fscale=False,
fbase=100,
fhead='F1',
gain=5.6):
# check output image
if os.access(sigmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % sigmap
return 1
if os.access(expmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % expmap
return 1
if os.access(whtmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % whtmap
return 1
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check input image list
ffimg_arr = check_input2(inlist, ffpref)
if isinstance(ffimg_arr, int):
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 not os.access(dbs, os.R_OK):
print >> sys.stderr, 'database file (%s) does not exist' % (dbs)
return 1
if not os.access(gmp2, os.R_OK):
print >> sys.stderr, 'modified geomap file (%s) does not 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
# check object mask
if objmask.lower() == 'none':
objmask = ''
else:
objmask_arr = check_inpref(objmask, inimg_arr)
if isinstance(objmask_arr, int):
return 1
# prepare for temporary file
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# 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)
# copy image to larger format and shift image #
iraf.unlearn('geomap')
iraf.unlearn('geotran')
# for exposure time weight and flux scaling
expt_arr = []
flux_scale_arr = []
for i in range(len(inimg_arr)):
# load original frame
img = pyfits.open(inimg_arr[i])
# for exposure time weight
try:
t = float(img[0].header['EXP1TIME'])
coadd = float(img[0].header['COADDS'])
expt_arr.append(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_arr.append(fbase / flux)
else:
flux_scale_arr.append(1.0)
img.close()
# preparing weighted variance map for each image
inverse_var_list = tmp_prefix + '_obj.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)):
# 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
ffimg = pyfits.open(ffimg_arr[i])
img = pyfits.open(inimg_arr[i])
# object frame
inverse_var = np.zeros((y_size, x_size))
#print np.median(ffimg[0].data), ffimg_arr[i], gain, expt_arr[i], flux_scale_arr[i]
#print np.median(np.sqrt(ffimg[0].data / (gain * expt_arr[i]))), flux_scale_arr[i]
inverse_var[ymin - 1:ymax, xmin -
1:xmax] = (np.sqrt(ffimg[0].data / (gain * expt_arr[i])) *
flux_scale_arr[i])**-2
hdu = pyfits.PrimaryHDU(inverse_var)
inverse_var_img = pyfits.HDUList([hdu])
inverse_var_img[0].header = img[0].header
inverse_var_img[0].header['bpm'] = msktr_fits
inverse_var_img[0].header.update('EXPTIME', expt_arr[i])
#inverse_var_img[0].header.update('MASKSCAL', expt_arr[i])
#inverse_var_img[0].header.update('MASKZERO', expt_arr[i])
#print 'EXPT = %f' % (expt_arr[i])
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
finverse_var.close()
# sum weighted variance images
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)
tmp_sigma = tmp_prefix + 'sigma.fits'
if os.access(tmp_sigma, os.R_OK):
os.remove(tmp_sigma)
tmp_exp = tmp_prefix + 'exp.fits'
if os.access(tmp_exp, os.R_OK):
os.remove(tmp_exp)
if expmap != 'none':
#iraf.hselect('@'+inverse_var_list,"$I,EXPTIME,MASKSCAL,MASKZERO","yes")
iraf.imcombine('@' + inverse_var_list,
tmp_inverse_var_sum,
expmasks=tmp_exp,
combine='sum',
reject=reject,
masktype='!BPM',
maskvalue=0.0,
expname='EXPTIME')
else:
iraf.imcombine('@' + inverse_var_list,
tmp_inverse_var_sum,
combine='sum',
reject=reject,
masktype='!BPM',
maskvalue=0.0)
# calculate sigma
iraf.stsdas()
iraf.imcalc(tmp_inverse_var_sum,
tmp_sigma,
'sqrt(1.0/im1)',
pixtype='double')
# cut image
iraf.unlearn('imcopy')
cut_sig = '%s[%d:%d,%d:%d]' % (tmp_sigma, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_sig, sigmap)
if expmap != 'none':
cut_exp = '%s[%d:%d,%d:%d]' % (tmp_exp, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_exp, expmap)
# calc weight map
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
os.remove(inverse_var_list)
# remove all temporary files
remove_temp_all(tmp_prefix)
return 0
def align_norm(fnlist,uncertlist=None):
"""Aligns a set of images to each other, as well as normalizing the images
to the same average brightness.
Both the alignment and normalization are accomplished through stellar
photometry using the IRAF routine 'daophot'. The centroids of a handful
of stars are found and used to run the IRAF routine 'imalign'. The
instrumental magnitudes of the stars are used to determine by how much
each image must be scaled for the photometry to match across images.
The images are simply updated with their rescaled, shifted selves. This
overwrites the previous images and adds the header keyword 'fpphot' to
the images.
A handful of temporary files are created during this process, which should
all be deleted by the routine at the end. But if it is interrupted, they
might not be.
If the uncertainty images exist, this routine also shifts them by the same
amounts as the intensity images, as well as updating the uncertainty values
for both the new normalization and the uncertainties in normalizing the
images.
Inputs:
fnlist -> List of strings, each the path to a fits image.
uncertlist (optional) -> List of paths to uncertainty images.
"""
#Fit for the sky background level
_skyavg, skysig = fit_sky_level(fnlist)
#Get image FWHMs
fwhm = np.empty(len(fnlist))
firstimage = openfits(fnlist[0])
toggle = firstimage[0].header.get("fpfwhm")
axcen = firstimage[0].header.get("fpaxcen")
aycen = firstimage[0].header.get("fpaycen")
arad = firstimage[0].header.get("fparad")
firstimage.close()
if axcen == None:
print "Error! Images have not yet been aperture-masked! Do this first!"
crash()
if toggle == None:
print "Warning: FWHMs have not been measured! Assuming 5 pixel FWHM for all images."
for i in range(len(fnlist)): fwhm[i] = 5
else:
for i in range(len(fnlist)):
image = openfits(fnlist[i])
fwhm[i] = image[0].header["fpfwhm"]
image.close()
#Identify objects in the fields
coolist = identify_objects(fnlist,skysig,fwhm)
#Match objects between fields
coofile = match_objects(coolist)
#Do aperture photometry on the matched objects
photlist = do_phot(fnlist,coofile,fwhm,skysig)
#Read the photometry files
x, y, mag, dmag = read_phot(photlist)
#Calculate the normalizations
norm, dnorm = calc_norm(mag,dmag)
#Normalize the images (and optionally, the uncertainty images)
for i in range(len(fnlist)):
print "Normalizing image "+fnlist[i]
image = openfits(fnlist[i],mode="update")
if not (uncertlist is None):
uncimage = openfits(uncertlist[i],mode="update")
uncimage[0].data = np.sqrt(norm[i]**2*uncimage[0].data**2 + dnorm[i]**2*image[0].data**2)
uncimage.close()
image[0].data *= norm[i]
image.close()
#Calculate the shifts
for i in range(x.shape[1]):
x[:,i] = -(x[:,i] - x[0,i])
y[:,i] = -(y[:,i] - y[0,i])
xshifts = np.average(x,axis=1)
yshifts = np.average(y,axis=1)
#Shift the images (and optionally, the uncertainty images)
iraf.images(_doprint=0)
iraf.immatch(_doprint=0)
for i in range(len(fnlist)):
print "Shifting image "+fnlist[i]
iraf.geotran(input=fnlist[i],
output=fnlist[i],
geometry="linear",
xshift=xshifts[i],
yshift=yshifts[i],
database="",
verbose="no")
if not (uncertlist is None):
iraf.geotran(input=uncertlist[i],
output=uncertlist[i],
geometry="linear",
xshift=xshifts[i],
yshift=yshifts[i],
database="",
verbose="no")
#Update the image headers
for i in range(len(fnlist)):
image = openfits(fnlist[i],mode="update")
image[0].header["fpphot"]="True"
image[0].header["fpxcen"]+=xshifts[i]
image[0].header["fpycen"]+=yshifts[i]
image[0].header["fpaxcen"]+=xshifts[i]
image[0].header["fpaycen"]+=yshifts[i]
image.close()
#Clean up the coordinate file list
clean_files(fnlist)
remove(coofile)
for i in range(len(photlist)):
remove(photlist[i])
return
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
