def fringe_sub(inlist, inpref='', outpref='s', mskpref='none', iternum=50, nsigma=3, bpm='none', second=False, fr_x0=0.0, fr_y0=0.0, fr_step=5, surfit=False, xsorder=4, ysorder=4):
# load IRAF package
iraf.proto()
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check output
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(outimg_arr, int):
return 1
# check mask images
if mskpref.lower() == 'none' or mskpref == '':
mask = 0
else:
mask = 1
mskimg_arr = check_inpref(mskpref, inimg_arr)
if isinstance(mskimg_arr, int):
return 1
# check bad pixel mask
if bpm.lower() != 'none':
bpmex = 1
if os.access(bpm, os.R_OK) == False:
print >> sys.stderr, 'cannot read bad pixel mask (%s)' % bpm
return 1
else:
bpmex = 0
# prefix for temoprary images
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# subtract sky
for i in range(len(inimg_arr)):
if mask == 1:
iraf.unlearn('mimstat')
if not second:
ret = iraf.mimstat(inimg_arr[i], imasks=mskimg_arr[i]+'[pl]', format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
else:
if bpmex == 1:
tmp_mask = tmp_prefix + os.path.basename(mask_img_arr[i])
iraf.imarith(maskimg_arr[i], '+', bpm, tmp_mask, verbose='no')
convert_maskfits_int(tmp_mask, tmp_mask)
ret = iraf.mimstat(inimg_arr[i], imasks=tmp_mask, format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
else:
ret = iraf.mimstat(inimg_arr[i], imasks=mskimg_arr[i], format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
else:
iraf.unlearn('imstat')
ret = iraf.imstat(inimg_arr[i], format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
if len(ret) == 1:
objmed = float(ret[0])
else :
print >> sys.stderr, 'failed to calculate median of the object background'
return 1
# record input fits data into memory
im = pyfits.open(inimg_arr[i])
im_hdr = im[0].header
im_data = im[0].data
out_data = im_data - objmed
if surfit:
out_data_org = np.copy(out_data)
im.close()
# fringe subtraction for NB imaging
if mask == 1:
if bpmex == 1:
tmp_mask = tmp_prefix + os.path.basename(mask_img_arr[i])
mask_im = pyfits.open(tmp_mask)
else:
mask_im = pyfits.open(mskimg_arr[i])
if not second:
mask_data = mask_im[1].data
else:
mask_data = mask_im[0].data
mask_im.close()
x = np.ones(out_data.shape[0]*out_data.shape[1]).reshape(out_data.shape[0],out_data.shape[1])* np.arange(out_data.shape[1]) + 1
y = (np.ones(out_data.shape[1]*out_data.shape[0]).reshape(out_data.shape[1],out_data.shape[0]) * np.arange(out_data.shape[0]) + 1).T
r = np.sqrt((x-fr_x0)*(x-fr_x0) + (y-fr_y0)*(y-fr_y0))
rmax = np.max(r)
rmin = np.min(r)
r1 = rmin
while r1 <= rmax:
r2 = r1 + fr_step
idx = np.logical_and(r>=r1, r<r2)
out_subset = out_data[idx]
if mask == 1:
mask_subset = mask_data[idx]
idx_sky = np.where(mask_subset == 0)
#if len(out_subset[idx_sky]) != len(out_subset):
# print "masked:",inimg_arr[i],r1,r2,fr_x0,fr_y0,len(out_subset[idx_sky]),"/",len(out_subset),meanclip(out_subset[idx_sky],median=1)[0],meanclip(out_subset, median=1)[0]
med, sigma = meanclip(out_subset[idx_sky],median=1)
else:
med, sigma = meanclip(out_data[idx],median=1)
out_data[idx] = out_data[idx] - med
r1 += fr_step
# save output fits file
hdu = pyfits.PrimaryHDU(out_data)
imgout = pyfits.HDUList([hdu])
imgout[0].header = im_hdr
if surfit:
tmp_fsub = tmp_prefix + "fsub" + os.path.basename(inimg_arr[i])
tmp_fsub_fit = tmp_prefix + "fsub_fit" + os.path.basename(inimg_arr[i])
imgout.writeto(tmp_fsub)
imgout.close()
iraf.imsurfit(tmp_fsub, tmp_fsub_fit, xsorder, ysorder)
imfit = pyfits.open(tmp_fsub_fit)
imfit_data = imfit[0].data
out_data_fs = np.copy(out_data_org - imfit_data)
r1 = rmin
while r1 <= rmax:
r2 = r1 + fr_step
idx = np.logical_and(r>=r1, r<r2)
out_subset = out_data_fs[idx]
if mask == 1:
mask_subset = mask_data[idx]
idx_sky = np.where(mask_subset == 0)
med, sigma = meanclip(out_subset[idx_sky],median=1)
else:
med, sigma = meanclip(out_data_fs[idx],median=1)
out_data_org[idx] = out_data_org[idx] - med
r1 += fr_step
hdu = pyfits.PrimaryHDU(out_data_org)
imgout = pyfits.HDUList([hdu])
imgout[0].header = im_hdr
imgout.writeto(outimg_arr[i])
imgout.close()
remove_temp_all(tmp_prefix)
else:
imgout.writeto(outimg_arr[i])
imgout.close()
if bpmex == 1:
remove_temp_all(tmp_prefix)
return 0
def skysub(inlist,
inpref='',
outpref='s',
mskpref='none',
ndthpos=9,
iternum=50,
nsigma=3,
second=False):
# load IRAF package
iraf.proto()
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check output
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(outimg_arr, int):
return 1
# check mask images
if mskpref.lower() == 'none' or mskpref == '':
mask = 0
else:
mask = 1
mskimg_arr = check_inpref(mskpref, inimg_arr)
if isinstance(mskimg_arr, int):
return 1
# check dithering width
if ndthpos != 5 and ndthpos != 9:
print >> sys.stderr, 'number of dithering position should be 5 or 9'
if iternum <= 0:
print >> sys.stderr, 'invalid iternum value (%d)' % iternum
return 1
if nsigma <= 0:
print >> sys.stderr, 'invalid nsigma value (%f)' % nsigma
return 1
# check input, output, and mask files and record mask frame into header
pre_pos = 100
in_array = []
out_array = []
mask_array = []
iraf.unlearn('hedit')
for i in range(len(inimg_arr)):
if mask == 1:
if mskpref[0] != ':':
if not second:
iraf.hedit(inimg_arr[i],
'objmask',
mskimg_arr[i] + '[pl]',
add='yes',
verify='no')
else:
iraf.hedit(inimg_arr[i],
'objmask',
mskimg_arr[i],
add='yes',
verify='no')
# get dithering position from header
im = pyfits.open(inimg_arr[i])
pos = int(im[0].header['I_DTHPOS'])
im.close()
# record each dithering set into subset
if pos < pre_pos:
if i != 0:
in_array.append(in_subset)
out_array.append(out_subset)
if mask == 1:
mask_array.append(mask_subset)
in_subset = []
out_subset = []
in_subset.append(inimg_arr[i])
out_subset.append(outimg_arr[i])
if mask == 1:
mask_subset = []
mask_subset.append(mskimg_arr[i])
pre_pos = pos
else:
in_subset.append(inimg_arr[i])
out_subset.append(outimg_arr[i])
if mask == 1:
mask_subset.append(mskimg_arr[i])
pre_pos = pos
# record rest of frames
in_array.append(in_subset)
out_array.append(out_subset)
if mask == 1:
mask_array.append(mask_subset)
# number of subsets
n_subset = len(in_array)
# sky subtraction
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
for i in range(n_subset):
tmp_sky = '%s_sky_%03d.fits' % (tmp_prefix, int(i))
tmp_subset = '%s_subset_%03d.lst' % (tmp_prefix, int(i))
# make subset list
f = open(tmp_subset, 'w')
for j in range(len(in_array[i])):
f.write(in_array[i][j] + '\n')
f.close()
# combine subset
if mask == 1:
ret = zcombine(tmp_subset,
tmp_sky,
combine='median',
reject='sigclip',
masktype='!OBJMASK')
else:
ret = zcombine(tmp_subset,
tmp_sky,
combine='median',
reject='sigclip',
masktype='none')
if ret != 0:
remove_temp_all(tmp_prefix)
return 1
os.remove(tmp_subset)
# calculate median of combined sky background
iraf.unlearn('imstat')
ret = iraf.imstat(tmp_sky,
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(ret) == 1:
skymed = float(ret[0])
else:
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the combined sky background'
return 1
# save sky data into memory
sky = pyfits.open(tmp_sky)
sky_data = sky[0].data
sky.close()
os.remove(tmp_sky)
# subtract sky
for j in range(len(in_array[i])):
if mask == 1:
iraf.unlearn('mimstat')
if not second:
ret = iraf.mimstat(in_array[i][j],
imasks=mask_array[i][j] + '[pl]',
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
else:
ret = iraf.mimstat(in_array[i][j],
imasks=mask_array[i][j],
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
else:
iraf.unlearn('imstat')
ret = iraf.imstat(in_array[i][j],
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(ret) == 1:
objmed = float(ret[0])
else:
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the object background'
return 1
# record input fits data into memory
im = pyfits.open(in_array[i][j])
im_hdr = im[0].header
im_data = im[0].data
im.close()
# subtract sky in memory
out_data = im_data - (sky_data * objmed / skymed)
# save output fits file
hdu = pyfits.PrimaryHDU(out_data)
imgout = pyfits.HDUList([hdu])
imgout[0].header = im_hdr
imgout.writeto(out_array[i][j])
imgout.close()
remove_temp_all(tmp_prefix)
return 0
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 skysub_mod(inlist,
inpref='',
outpref='s',
mskpref='none',
ndthpos=9,
iternum=50,
nsigma=3,
second=False,
step=0.05):
# load IRAF package
iraf.proto()
# check step size
if step >= 0.5:
print >> sys.stderr, 'step size should be smaller than 0.5'
return 1
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check output
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(outimg_arr, int):
return 1
# check mask images
if mskpref.lower() == 'none' or mskpref == '':
mask = 0
else:
mask = 1
mskimg_arr = check_inpref(mskpref, inimg_arr)
if isinstance(mskimg_arr, int):
return 1
# check dithering width
if ndthpos != 5 and ndthpos != 9:
print >> sys.stderr, 'number of dithering position should be 5 or 9'
if iternum <= 0:
print >> sys.stderr, 'invalid iternum value (%d)' % iternum
return 1
if nsigma <= 0:
print >> sys.stderr, 'invalid nsigma value (%f)' % nsigma
return 1
# check input, output, and mask files and record mask frame into header
pre_pos = 100
in_array = []
out_array = []
mask_array = []
iraf.unlearn('hedit')
for i in range(len(inimg_arr)):
if mask == 1:
if mskpref[0] != ':':
if not second:
iraf.hedit(inimg_arr[i],
'objmask',
mskimg_arr[i] + '[pl]',
add='yes',
verify='no')
else:
iraf.hedit(inimg_arr[i],
'objmask',
mskimg_arr[i],
add='yes',
verify='no')
# get dithering position from header
im = pyfits.open(inimg_arr[i])
pos = int(im[0].header['I_DTHPOS'])
im.close()
# record each dithering set into subset
if pos < pre_pos:
if i != 0:
in_array.append(in_subset)
out_array.append(out_subset)
if mask == 1:
mask_array.append(mask_subset)
in_subset = []
out_subset = []
in_subset.append(inimg_arr[i])
out_subset.append(outimg_arr[i])
if mask == 1:
mask_subset = []
mask_subset.append(mskimg_arr[i])
pre_pos = pos
else:
in_subset.append(inimg_arr[i])
out_subset.append(outimg_arr[i])
if mask == 1:
mask_subset.append(mskimg_arr[i])
pre_pos = pos
# record rest of frames
in_array.append(in_subset)
out_array.append(out_subset)
if mask == 1:
mask_array.append(mask_subset)
# number of subsets
n_subset = len(in_array)
# sky subtraction
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
for i in range(n_subset):
tmp_sky = '%s_sky_%03d.fits' % (tmp_prefix, int(i))
tmp_subset = '%s_subset_%03d.lst' % (tmp_prefix, int(i))
# make subset list
f = open(tmp_subset, 'w')
for j in range(len(in_array[i])):
f.write(in_array[i][j] + '\n')
f.close()
# combine subset
if mask == 1:
ret = zcombine(tmp_subset,
tmp_sky,
combine='median',
reject='sigclip',
masktype='!OBJMASK')
else:
ret = zcombine(tmp_subset,
tmp_sky,
combine='median',
reject='sigclip',
masktype='none')
if ret != 0:
remove_temp_all(tmp_prefix)
return 1
os.remove(tmp_subset)
# calculate median of combined sky background
iraf.unlearn('imstat')
ret = iraf.imstat(tmp_sky,
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(ret) == 1:
skymed = float(ret[0])
else:
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the combined sky background'
return 1
# save sky data into memory
sky = pyfits.open(tmp_sky)
sky_data = sky[0].data
sky_arr = np.reshape(np.array(sky[0].data - skymed), sky[0].data.size)
sky.close()
os.remove(tmp_sky)
# subtract sky
for j in range(len(in_array[i])):
if mask == 1:
iraf.unlearn('mimstat')
if not second:
ret = iraf.mimstat(in_array[i][j],
imasks=mask_array[i][j] + '[pl]',
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
else:
ret = iraf.mimstat(in_array[i][j],
imasks=mask_array[i][j],
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
else:
iraf.unlearn('imstat')
ret = iraf.imstat(in_array[i][j],
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(ret) == 1:
objmed = float(ret[0])
else:
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the object background'
return 1
# record input fits data into memory
im = pyfits.open(in_array[i][j])
im_hdr = im[0].header
im_data = im[0].data
im_arr = np.reshape(np.array(im[0].data - objmed), im[0].data.size)
im.close()
# check if minimum sigma is obtained at scale_factor = 1.0
m1, s1 = meanclip(im_arr - sky_arr)
m2, s2 = meanclip(im_arr - (1.0 + step) * sky_arr)
m3, s3 = meanclip(im_arr - (1.0 - step) * sky_arr)
if s1 < s2 and s1 < s3:
print 'scale factor = 1.0'
# subtract sky in memory
out_data = (im_data - objmed) - (sky_data - skymed)
else:
# find minimum sigma with coarse sampling
n = 1
min_i = 0
while min_i - 1 < 0 or min_i + 1 >= len(s_list):
s = -1.0 * n
ds = 0.5
sigma_list = []
s_list = []
while s <= 1.0 * n:
sub = im_arr - (s * sky_arr)
mean, sigma = meanclip(sub)
sigma_list.append(sigma)
s_list.append(s)
s += ds
min_i, min_sigma = min(enumerate(sigma_list),
key=itemgetter(1))
n += 1
# find minimum sigma with fine sampling
s = s_list[min_i - 1]
ds = step
s2_list = []
sigma2_list = []
while s <= s_list[min_i + 1]:
sub = im_arr - (s * sky_arr)
mean, sigma = meanclip(sub)
sigma2_list.append(sigma)
s2_list.append(s)
s += ds
min_i, min_sigma = min(enumerate(sigma2_list),
key=itemgetter(1))
print 'scale factor = %f' % s2_list[min_i]
# subtract sky in memory
out_data = (im_data -
objmed) - (sky_data - skymed) * s2_list[min_i]
# save output fits file
hdu = pyfits.PrimaryHDU(out_data)
imgout = pyfits.HDUList([hdu])
imgout[0].header = im_hdr
imgout.writeto(out_array[i][j])
imgout.close()
remove_temp_all(tmp_prefix)
return 0
def mkskyflat_with_mask(inlist,
output,
inpref='',
mskpref='m',
bpm='none',
iternum=50,
nsigma=3):
# load IRAF package
iraf.proto()
iraf.nproto()
# open input list and check if it exists
inimg_arr = check_input(inlist, inpref)
if len(inimg_arr) == 1 and inimg_arr[0] == 1:
return 1
# check if output image is already exist
if os.access(output, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing file (%s)' % output
return 1
# check mask images
if mskpref.lower() == 'none' or mskpref == '':
print >> sys.stderr, 'mask frames are required for this task'
return 1
else:
mskimg_arr = check_inpref(mskpref, inimg_arr)
if isinstance(mskimg_arr, int):
return 1
# check optional parameters
if bpm.lower() == 'none' or bpm == '':
bpmex = 0
else:
bpmex = 1
if not os.access(bpm, os.R_OK):
print >> sys.stderr, 'cannot read bad pixel mask (%s)' % bpm
return 1
if iternum <= 0:
print >> sys.stderr, 'invalid iternum value (%d)' % iternum
return 1
if nsigma <= 0:
print >> sys.stderr, 'invalid nsigma value (%f)' % nsigma
return 1
# prefix for temoprary images
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# nomalize by object masked average
tmp_list = tmp_prefix + '.lst'
fout = open(tmp_list, 'w')
for i in range(len(inimg_arr)):
im = pyfits.open(inimg_arr[i], mode='update')
try:
pattern = im[0].header['PATTERN']
except KeyError:
pattern = 0
im.close()
if pattern == 0:
tmpimg = tmp_prefix + os.path.basename(inimg_arr[i])
maskimg = mskimg_arr[i]
tmpmask = tmp_prefix + maskimg
fout.write('%s\n' % tmpimg)
if os.access(tmpimg, os.R_OK):
os.remove(tmpimg)
if os.access(tmpmask, os.R_OK):
os.remove(tmpmask)
if bpmex == 1:
iraf.unlearn('imarith')
#iraf.imarith(maskimg+'[pl]','+',bpm,tmpmask,verbose='no')
iraf.imarith(maskimg, '+', bpm, tmpmask, verbose='no')
else:
iraf.unlearn('imcopy')
#iraf.imcopy(maskimg+'[pl]',tmpmask,verbose='no')
iraf.imcopy(maskimg, tmpmask, verbose='no')
iraf.unlearn('mimstat')
res = iraf.mimstat(inimg_arr[i],
imasks=tmpmask,
format='no',
fields='mean',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate masked avarage from the frame (%s)' % inimg_arr[
i]
remove_temp_all(tmp_prefix)
return 1
# normalize each frame by average
iraf.unlearn('imarith')
iraf.imarith(inimg_arr[i], '/', ave, tmpimg, verbose='no')
# add mask into header
iraf.unlearn('hedit')
if mskpref[0] != ':':
iraf.hedit(tmpimg, 'objmask', maskimg, add='yes', verify='no')
# close output file handler
fout.close()
# combine
ret = zcombine(tmp_list,
output,
combine='median',
reject='sigclip',
masktype='!objmask')
if ret != 0:
print >> sys.stderr, 'failed to combine normalized frames'
remove_temp_all(tmp_prefix)
return 1
# remove temoprary files
remove_temp_all(tmp_prefix)
return 0
def mkcalflat(calonlst, calofflst, outimg, mask='none'):
# load IRAF package
iraf.proto()
# open cal off list and check if files exist
on_list = check_input(calonlst, '')
if len(on_list) == 1 and on_list[0] == 1:
return 1
# open cal off list and check if files exist
off_list = check_input(calofflst, '')
if len(off_list) == 1 and off_list[0] == 1:
return 1
# check if output file exists
if os.access(outimg, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % outimg
return 1
# prefix for temporary images and files
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# check exptime, coadds, ndr, slwcnt for off frames
tmp_off_list = tmp_prefix + '_off.lst'
fout = open(tmp_off_list, 'w')
err = 0
for i in range(len(off_list)):
im = pyfits.open(off_list[i])
im_exptime = float(im[0].header['EXP1TIME'])
im_coadds = int(im[0].header['COADDS'])
im_ndr = int(im[0].header['NDR'])
im_slwcnt = int(im[0].header['I_SLWCNT'])
if i == 0:
off_exptime = im_exptime
off_coadds = im_coadds
off_ndr = im_ndr
off_slwcnt = im_slwcnt
else:
if im_exptime != off_exptime:
print >> sys.stderr, 'exptime for %s is not consistent with the first frame (%s)' % (
off_list[i], off_list[0])
err += 1
if im_coadds != off_coadds:
print >> sys.stderr, 'coadds for %s is not consistent with the first frame (%s)' % (
off_list[i], off_list[0])
err += 1
if im_ndr != off_ndr:
print >> sys.stderr, 'ndr for %s is not consistent with the first frame (%s)' % (
off_list[i], off_list[0])
err += 1
if im_slwcnt != off_slwcnt:
print >> sys.stderr, 'slwcnt for %s is not consistent with the first frame (%s)' % (
off_list[i], off_list[0])
err += 1
im.close()
fout.write('%s\n' % off_list[i])
fout.close()
# check error
if err != 0:
remove_temp_all(tmp_prefix)
return 1
# check exptime, coadds, ndr, slwcnt for on frames
tmp_on_list = tmp_prefix + '_on.lst'
fout = open(tmp_on_list, 'w')
for i in range(len(on_list)):
im = pyfits.open(on_list[i])
im_exptime = float(im[0].header['EXP1TIME'])
im_coadds = int(im[0].header['COADDS'])
im_ndr = int(im[0].header['NDR'])
im_slwcnt = int(im[0].header['I_SLWCNT'])
if i == 0:
on_exptime = im_exptime
on_coadds = im_coadds
on_ndr = im_ndr
on_slwcnt = im_slwcnt
else:
if im_exptime != on_exptime:
print >> sys.stderr, 'exptime for %s is not consistent with the first frame (%s)' % (
on_list[i], on_list[0])
err += 1
if im_coadds != on_coadds:
print >> sys.stderr, 'coadds for %s is not consistent with the first frame (%s)' % (
on_list[i], on_list[0])
err += 1
if im_ndr != on_ndr:
print >> sys.stderr, 'ndr for %s is not consistent with the first frame (%s)' % (
on_list[i], on_list[0])
err += 1
if im_slwcnt != on_slwcnt:
print >> sys.stderr, 'slwcnt for %s is not consistent with the first frame (%s)' % (
on_list[i], on_list[0])
err += 1
im.close()
fout.write('%s\n' % on_list[i])
fout.close()
# check error
if err != 0:
remove_temp_all(tmp_prefix)
return 1
# check consistency of EXPTIME, COADDS, NDR and SLWCNT between on and off frames
if on_exptime != off_exptime:
print >> sys.stderr, 'exptime for on and off frames are not consistent'
err += 1
if on_coadds != off_coadds:
print >> sys.stderr, 'coadds for on and off frames are not consistent'
err += 1
if on_ndr != off_ndr:
print >> sys.stderr, 'ndr for on and off frames are not consistent'
err += 1
if on_slwcnt != off_slwcnt:
print >> sys.stderr, 'slwcnt for on and off frames are not consistent'
err += 1
# check error
if err != 0:
remove_temp_all(tmp_prefix)
return 1
# combine off frames
tmpoff = tmp_prefix + '_off.fits'
iraf.unlearn('imcombine')
iraf.imcombine.combine = 'median'
iraf.imcombine.reject = 'sigclip'
iraf.imcombine('@' + tmp_off_list, tmpoff)
# combine on frames
tmpon = tmp_prefix + '_on.fits'
tmp.close()
iraf.unlearn('imcombine')
iraf.imcombine.combine = 'median'
iraf.imcombine.reject = 'sigclip'
iraf.imcombine('@' + tmp_on_list, tmpon)
# subtract off from on
iraf.unlearn('imarith')
iraf.imarith(tmpon, "-", tmpoff, outimg, verbose='no')
# delete temporary files
remove_temp_all(tmp_prefix)
# calculate average
if mask == 'none':
iraf.unlearn('imstat')
res = iraf.imstat(outimg,
format='no',
fields='mean',
nclip=50,
Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate avarage of the combined frame'
os.remove(outimg)
return 1
else:
if os.access(mask, os.R_OK):
iraf.unlearn('mimstat')
res = iraf.mimstat(outimg,
imasks=mask,
format='no',
fields='mean',
nclip=50,
Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate masked avarage of the combined frame'
os.remove(outimg)
return 1
else:
print >> sys.stderr, 'mask frame (%s) does not exist' % mask
os.remove(outimg)
return 1
# divide by average count
iraf.unlearn('imarith')
iraf.imarith(outimg, "/", ave, outimg, verbose='no')
return 0
def mkskyflat(inlist, output, inpref='', mskpref='m', bpm='none', iternum=50, nsigma=3, minpix=250, hsigma=3, lsigma=10, conv='block 3 3'):
# load IRAF package
iraf.proto()
iraf.nproto()
# open input list and check if it exists
inimg_arr = check_input(inlist, inpref)
if len(inimg_arr) == 1 and inimg_arr[0] == 1:
return 1
# check if output image is already exist
if os.access(output, os.R_OK) :
print >> sys.stderr, 'operation would overwrite existing file (%s)' % output
return 1
# check optional parameters
if bpm.lower() == 'none' or bpm == '':
bpmex = 0
else:
bpmex = 1
if not os.access(bpm, os.R_OK):
print >> sys.stderr, 'cannot read bad pixel mask (%s)' % bpm
return 1
if iternum <= 0:
print >> sys.stderr, 'invalid iternum value (%d)' % iternum
return 1
if nsigma <= 0:
print >> sys.stderr, 'invalid nsigma value (%f)' % nsigma
return 1
# prefix for temoprary images
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# temporary normalize raw frames by average
tmp_list = tmp_prefix+'.lst'
fout = open(tmp_list, 'w')
for i in range(len(inimg_arr)):
tmpimg = tmp_prefix+os.path.basename(inimg_arr[i])
fout.write('%s\n' % tmpimg)
# remove temporary files if it exists
if os.access(tmpimg, os.R_OK):
os.remove(tmpimg)
# calculate average counts in each frame
if bpmex == 0:
iraf.unlearn('imstat')
res = iraf.imstat(inimg_arr[i], format='no', fields='mean', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate avarage from the frame (%s)' % inimg
remove_temp_all(tmp_prefix)
fout.close()
return 1
else:
iraf.unlearn('mimstat')
res = iraf.mimstat(inimg_arr[i], imasks=bpm, format='no', fields='mean', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate masked avarage from the frame (%s)'% inimg
remove_temp_all(tmp_prefix)
fout.close()
return 1
# normalize each frame by average
iraf.unlearn('imarith')
iraf.imarith(inimg_arr[i], '/', ave, tmpimg, verbose='no')
# close output file handler
fout.close()
# make temporary sky flat frame
tmp_flat = tmp_prefix+'_sflat.fits'
ret = zcombine(tmp_list, tmp_flat, combine='median', reject='sigclip')
if ret != 0:
remove_temp_all(tmp_prefix)
return 1
# temporary flat field
iraf.unlearn('imarith')
tmpff_list = tmp_prefix+'_ff.lst'
fout = open(tmpff_list, 'w')
for i in range(len(inimg_arr)):
tmpimg = tmp_prefix+'_ff_'+os.path.basename(inimg_arr[i])
fout.write('%s\n' % tmpimg)
# remove temporary files if it exists
if os.access(tmpimg, os.R_OK):
os.remove(tmpimg)
# divided by temporary flat
iraf.imarith(inimg_arr[i], '/', tmp_flat, tmpimg, verbose='no')
# read image fits header
im = pyfits.open(inimg_arr[i])
exptime = float(im[0].header['EXP1TIME'])
coadds = int(im[0].header['COADDS'])
ndr = int(im[0].header['NDR'])
im.close()
# normalize by coadds, ndr, and exptime values
iraf.imarith(tmpimg, '/', (coadds*ndr*exptime), tmpimg, verbose='no')
# close output file handler
fout.close()
# remove temporary flat
os.remove(tmp_flat)
# calculate standard deviation of the temporary flat divided images
try:
iraf.unlearn('imstat')
stddev = iraf.imstat('@'+tmpff_list, format='no', fields='stddev', nclip=50, Stdout=1)
except:
remove_temp_all(tmp_prefix)
return 1
# calculate mean and sigma of the stddev of the image
y = map(float, stddev)
mean, sigma = meanclip(np.array(y, dtype=float))
#print mean, sigma
# flag bad frames
frame_flag = []
for i in range(len(stddev)):
im = pyfits.open(inimg_arr[i], mode='update')
if math.fabs(y[i] - mean) < nsigma * sigma:
frame_flag.append(0)
im[0].header['PATTERN'] = 0
else:
frame_flag.append(1)
im[0].header['PATTERN'] = 1
im.close()
# make object mask
ret = mkobjmask(tmpff_list, inpref='', outpref=mskpref, bpm=bpm, minpix=minpix, hsigma=hsigma, lsigma=lsigma, conv=conv)
# rename mask file
mskimg_arr = []
for i in range(len(inimg_arr)):
base = os.path.basename(inimg_arr[i])
tmpimg = tmp_prefix+'_ff_'+base
maskimg = mskpref+base
tmpmask = mskpref+os.path.basename(tmpimg)
move(tmpmask, maskimg)
mskimg_arr.append(maskimg)
# delete temporary flat fielded files
remove_temp_all(tmp_prefix)
# check mkobjmask result
if ret != 0:
return 1
# nomalize by object masked average
fout = open(tmp_list, 'w')
for i in range(len(inimg_arr)):
if frame_flag[i] == 0:
tmpimg = tmp_prefix+os.path.basename(inimg_arr[i])
maskimg = mskimg_arr[i]
tmpmask = tmp_prefix+maskimg
fout.write('%s\n' % tmpimg)
if os.access(tmpimg, os.R_OK):
os.remove(tmpimg)
if os.access(tmpmask, os.R_OK):
os.remove(tmpmask)
if bpmex == 1:
iraf.unlearn('imarith')
iraf.imarith(maskimg+'[pl]','+',bpm,tmpmask,verbose='no')
else :
iraf.unlearn('imcopy')
iraf.imcopy(maskimg+'[pl]',tmpmask,verbose='no')
iraf.unlearn('mimstat')
# calculate average of the raw images with mask
res = iraf.mimstat(inimg_arr[i], imasks=tmpmask, format='no', fields='mean', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate masked avarage from the frame (%s)' % inimg_arr[i]
remove_temp_all(tmp_prefix)
return 1
# normalize each frame by average
iraf.unlearn('imarith')
iraf.imarith(inimg_arr[i], '/', ave, tmpimg, verbose='no')
# add mask into header
iraf.unlearn('hedit')
iraf.hedit(tmpimg, 'objmask', maskimg+'[pl]', add='yes', verify='no')
# close output file handler
fout.close()
# combine
ret = zcombine(tmp_list, output, combine='median', reject='sigclip', masktype='!objmask')
if ret != 0:
print >> sys.stderr, 'failed to combine normalized frames'
remove_temp_all(tmp_prefix)
return 1
# remove temoprary files
remove_temp_all(tmp_prefix)
return 0
def mkskyflat_quick(inlist,
output,
inpref='',
bpm='none',
iternum=50,
nsigma=3):
# load IRAF package
iraf.proto()
iraf.nproto()
# open input list and check if it exists
inimg_arr = check_input(inlist, inpref)
if len(inimg_arr) == 1 and inimg_arr[0] == 1:
return 1
# check if output image is already exist
if os.access(output, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing file (%s)' % output
return 1
# check optional parameters
if bpm.lower() == 'none' or bpm == '':
bpmex = 0
else:
bpmex = 1
if not os.access(bpm, os.R_OK):
print >> sys.stderr, 'cannot read bad pixel mask (%s)' % bpm
return 1
if iternum <= 0:
print >> sys.stderr, 'invalid iternum value (%d)' % iternum
return 1
if nsigma <= 0:
print >> sys.stderr, 'invalid nsigma value (%f)' % nsigma
return 1
# prefix for temoprary images
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# temporary normalize raw frames by average
tmp_list = tmp_prefix + '.lst'
fout = open(tmp_list, 'w')
for i in range(len(inimg_arr)):
tmpimg = tmp_prefix + os.path.basename(inimg_arr[i])
fout.write('%s\n' % tmpimg)
# remove temporary files if it exists
if os.access(tmpimg, os.R_OK):
os.remove(tmpimg)
# calculate average counts in each frame
if bpmex == 0:
iraf.unlearn('imstat')
res = iraf.imstat(inimg_arr[i],
format='no',
fields='mean',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate avarage from the frame (%s)' % inimg
remove_temp_all(tmp_prefix)
fout.close()
return 1
else:
iraf.unlearn('mimstat')
res = iraf.mimstat(inimg_arr[i],
imasks=bpm,
format='no',
fields='mean',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate masked avarage from the frame (%s)' % inimg
remove_temp_all(tmp_prefix)
fout.close()
return 1
# normalize each frame by average
iraf.unlearn('imarith')
iraf.imarith(inimg_arr[i], '/', ave, tmpimg, verbose='no')
# close output file handler
fout.close()
# make temporary sky flat frame
tmp_flat = tmp_prefix + '_sflat.fits'
ret = zcombine(tmp_list, output, combine='median', reject='sigclip')
if ret != 0:
remove_temp_all(tmp_prefix)
return 1
# remove temoprary files
remove_temp_all(tmp_prefix)
return 0
