def makecorr(fname, allowed_corr):
"""
Purpose
=======
Applies corrections to the WCS of a single file
:Parameters:
`fname`: string
file name
`acorr`: list
list of corrections to be applied
"""
logger.info("Allowed corrections: {0}".format(allowed_corr))
f = fits.open(fname, mode='update')
#Determine the reference chip and create the reference HSTWCS object
nrefchip, nrefext = getNrefchip(f)
wcsutil.restoreWCS(f, nrefext, wcskey='O')
rwcs = HSTWCS(fobj=f, ext=nrefext)
rwcs.readModel(update=True, header=f[nrefext].header)
if 'DET2IMCorr' in allowed_corr:
kw2update = det2im.DET2IMCorr.updateWCS(f)
for kw in kw2update:
f[1].header[kw] = kw2update[kw]
for i in range(len(f))[1:]:
extn = f[i]
if 'extname' in extn.header:
extname = extn.header['extname'].lower()
if extname == 'sci':
wcsutil.restoreWCS(f, ext=i, wcskey='O')
sciextver = extn.header['extver']
ref_wcs = rwcs.deepcopy()
hdr = extn.header
ext_wcs = HSTWCS(fobj=f, ext=i)
### check if it exists first!!!
# 'O ' can be safely archived again because it has been restored first.
wcsutil.archiveWCS(f,
ext=i,
wcskey="O",
wcsname="OPUS",
reusekey=True)
ext_wcs.readModel(update=True, header=hdr)
for c in allowed_corr:
if c != 'NPOLCorr' and c != 'DET2IMCorr':
corr_klass = corrections.__getattribute__(c)
kw2update = corr_klass.updateWCS(ext_wcs, ref_wcs)
for kw in kw2update:
hdr[kw] = kw2update[kw]
# give the primary WCS a WCSNAME value
idcname = f[0].header.get('IDCTAB', " ")
if idcname.strip() and 'idc.fits' in idcname:
wname = ''.join([
'IDC_',
utils.extract_rootname(idcname, suffix='_idc')
])
else:
wname = " "
hdr['WCSNAME'] = wname
elif extname in ['err', 'dq', 'sdq', 'samp', 'time']:
cextver = extn.header['extver']
if cextver == sciextver:
hdr = f[('SCI', sciextver)].header
w = pywcs.WCS(hdr, f)
copyWCS(w, extn.header)
else:
continue
if 'NPOLCorr' in allowed_corr:
kw2update = npol.NPOLCorr.updateWCS(f)
for kw in kw2update:
f[1].header[kw] = kw2update[kw]
# Finally record the version of the software which updated the WCS
if 'HISTORY' in f[0].header:
f[0].header.set('UPWCSVER',
value=stwcs.__version__,
comment="Version of STWCS used to updated the WCS",
before='HISTORY')
f[0].header.set('PYWCSVER',
value=astropy.__version__,
comment="Version of PYWCS used to updated the WCS",
before='HISTORY')
elif 'ASN_MTYP' in f[0].header:
f[0].header.set('UPWCSVER',
value=stwcs.__version__,
comment="Version of STWCS used to updated the WCS",
after='ASN_MTYP')
f[0].header.set('PYWCSVER',
value=astropy.__version__,
comment="Version of PYWCS used to updated the WCS",
after='ASN_MTYP')
else:
# Find index of last non-blank card, and insert this new keyword after that card
for i in range(len(f[0].header) - 1, 0, -1):
if f[0].header[i].strip() != '':
break
f[0].header.set('UPWCSVER',
stwcs.__version__,
"Version of STWCS used to updated the WCS",
after=i)
f[0].header.set('PYWCSVER',
astropy.__version__,
"Version of PYWCS used to updated the WCS",
after=i)
# add additional keywords to be used by headerlets
distdict = utils.construct_distname(f, rwcs)
f[0].header['DISTNAME'] = distdict['DISTNAME']
f[0].header['SIPNAME'] = distdict['SIPNAME']
# Make sure NEXTEND keyword remains accurate
f[0].header['NEXTEND'] = len(f) - 1
f.close()
def _align_1image(resample,
image,
image_ext,
primary_cutouts,
seg,
image_sky=None,
wcslin=None,
fitgeom='general',
nclip=3,
sigma=3.0,
use_weights=True,
cc_type='NCC',
combine_seg_mask=True):
img_info = {
'file_name': image,
'wcs_info': [], # a list of: [extension, original WCS, corrected WCS]
'fits_ext': [], # image ext. from which an image cutout was extracted
'image_cutouts': [],
'driz_cutouts': [],
'blotted_cutouts': [], # non-shifted blot images of drizzled cutouts
'ICC': [] # interlaced (oversampled) cross-correlation images
}
drz_sci_fname, drz_sci_ext = parse_file_name(resample.output_sci)
with fits.open(drz_sci_fname) as hdulist:
drz_sci = hdulist[drz_sci_ext].data
drz_wcs = HSTWCS(hdulist, ext=drz_sci_ext)
if 'EXPTIME' in hdulist[drz_sci_ext].header:
drz_exptime = hdulist[drz_sci_ext].header['EXPTIME']
else:
drz_exptime = hdulist[0].header['EXPTIME']
drz_units = hdulist[drz_sci_ext].header['BUNIT']
drz_units = 'rate' if '/' in drz_units else 'counts'
# get image data, info and create cutouts:
with fits.open(image) as hdulist:
for ext in image_ext:
img_sci = hdulist[ext].data
img_wcs = HSTWCS(hdulist, ext=ext)
orig_img_wcs = img_wcs.deepcopy()
img_exptime = hdulist[0].header['EXPTIME']
img_units = hdulist[ext].header['BUNIT']
img_units = 'rate' if '/' in img_units else 'counts'
if image_sky is not None and image_sky[ext] is not None:
img_sci -= image_sky[ext]
imgct_ext, drzct_ext = cutout.create_cutouts(
primary_cutouts,
seg,
drz_sci,
drz_wcs,
img_sci,
img_wcs,
drz_data_units=drz_units,
drz_exptime=drz_exptime,
flt_data_units=img_units,
flt_exptime=img_exptime,
combine_seg_mask=combine_seg_mask)
img_info['wcs_info'].append([ext, orig_img_wcs, img_wcs])
img_info['fits_ext'].extend(len(imgct_ext) * [ext])
img_info['image_cutouts'].extend(imgct_ext)
img_info['driz_cutouts'].extend(drzct_ext)
# find linear fit:
fit, interlaced_cc, nonshifted_blts = find_linear_fit(
img_cutouts=imgct_ext,
drz_cutouts=drzct_ext,
wcslin=wcslin,
fitgeom=fitgeom,
nclip=nclip,
sigma=sigma,
use_weights=use_weights,
cc_type=cc_type)
img_info['blotted_cutouts'].extend(nonshifted_blts)
img_info['ICC'].extend(interlaced_cc)
print("\nComputed '{:s}' fit for image {:s}:".format(fitgeom, image))
if fitgeom == 'shift':
print("XSH: {:.4f} YSH: {:.4f}".format(fit['offset'][0],
fit['offset'][1]))
elif fitgeom == 'rscale' and fit['proper']:
print(
"XSH: {:.4f} YSH: {:.4f} ROT: {:.10g} SCALE: {:.6f}".format(
fit['offset'][0], fit['offset'][1], fit['rot'],
fit['scale'][0]))
elif (fitgeom == 'general' or (fitgeom == 'rscale' and not fit['proper'])):
print("XSH: {:.4f} YSH: {:.4f} PROPER ROT: {:.10g} ".format(
fit['offset'][0], fit['offset'][1], fit['rot']))
print("<ROT>: {:.10g} SKEW: {:.10g} ROT_X: {:.10g} "
"ROT_Y: {:.10g}".format(fit['rotxy'][2], fit['skew'],
fit['rotxy'][0], fit['rotxy'][1]))
print("<SCALE>: {:.10g} SCALE_X: {:.10g} SCALE_Y: {:.10g}".format(
fit['scale'][0], fit['scale'][1], fit['scale'][2]))
print('FIT RMSE: {:.3g} FIT MAE: {:.3g} IMAGE FIT RMSE: {:.3g}\n'.
format(fit['rmse'], fit['mae'], fit['irmse']))
nmatch = fit['resids'].shape[0]
print('Final solution based on {:d} objects.'.format(nmatch))
# correct WCS:
for ext, owcs, wcs in img_info['wcs_info']:
correct_wcs(imwcs=wcs,
wcslin=drz_wcs,
rotmat=fit['matrix'],
shifts=fit['offset'],
fitgeom=fitgeom)
print("\n------- ORIGINAL WCS for '{:s}[{}]': ------".format(
image, ext))
print(owcs)
print("\n------- CORRECTED WCS for '{:s}[{}]': ------".format(
image, ext))
print(wcs)
return fit, img_info
