def lax(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('bkg/*bkg*.fits')
if len(fs) == 0:
print "WARNING: No background-subtracted files for Lacosmicx."
iraf.cd('..')
return
if not os.path.exists('lax'):
os.mkdir('lax')
for f in fs:
outname = f.replace('bkg','lax')
hdu = pyfits.open(f)
# Add a CRM extension
hdu.append(pyfits.ImageHDU(data=hdu['BPM'].data.copy(),
header=hdu['BPM'].header.copy(),
name='CRM'))
# Set all of the pixels in the CRM mask to zero
hdu['CRM'].data[:, :] = 0
# less aggressive lacosmic on standard star observations
if not isstdstar(f):
objl = 1.0
sigc = 4.0
else:
objl = 3.0
sigc = 10.0
chipgaps = get_chipgaps(hdu)
chipedges = [[0, chipgaps[0][0]], [chipgaps[0][1] + 1,
chipgaps[1][0]], [chipgaps[1][1] + 1, chipgaps[2][0]]]
# Run each chip separately
for chip in range(3):
# Use previously subtracted sky level = 0 as we have already added
# a constant sky value in the background task
# Gain = 1, readnoise should be small so it shouldn't matter much.
# Default value seems to work.
chipinds = slice(chipedges[chip][0], chipedges[chip][1])
crmask, _cleanarr = lacosmicx.lacosmicx(hdu[1].data[:, chipinds].copy(),
inmask=np.asarray(hdu[2].data[:, chipinds].copy(), dtype = np.uint8), sigclip=sigc,
objlim=objl, sigfrac=0.1, gain=1.0, pssl=0.0)
# Update the image
hdu['CRM'].data[:, chipinds][:, :] = crmask[:,:]
# Flag the cosmic ray pixels with a large negative number
hdu['SCI'].data[:, chipinds][crmask == 1] = -1000000
# Save the file
hdu.writeto(outname, clobber=True)
hdu.close()
iraf.cd('..')
def lax(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('bkg/*bkg*.fits')
if len(fs) == 0:
print "WARNING: No background-subtracted files for Lacosmicx."
iraf.cd('..')
return
if not os.path.exists('lax'):
os.mkdir('lax')
for f in fs:
outname = f.replace('bkg','lax')
hdu = pyfits.open(f)
# Add a CRM extension
hdu.append(pyfits.ImageHDU(data=hdu['BPM'].data.copy(),
header=hdu['BPM'].header.copy(),
name='CRM'))
# Set all of the pixels in the CRM mask to zero
hdu['CRM'].data[:, :] = 0
# less aggressive lacosmic on standard star observations
if not isstdstar(f):
objl = 1.0
sigc = 4.0
else:
objl = 3.0
sigc = 10.0
chipgaps = get_chipgaps(hdu)
chipedges = [[0, chipgaps[0][0]], [chipgaps[0][1] + 1,
chipgaps[1][0]], [chipgaps[1][1] + 1, chipgaps[2][0]]]
# Run each chip separately
for chip in range(3):
# Use previously subtracted sky level = 0 as we have already added
# a constant sky value in the background task
# Gain = 1, readnoise should be small so it shouldn't matter much.
# Default value seems to work.
chipinds = slice(chipedges[chip][0], chipedges[chip][1])
crmask, _cleanarr = lacosmicx.lacosmicx(hdu[1].data[:, chipinds].copy(),
inmask=np.asarray(hdu[2].data[:, chipinds].copy(), dtype = np.uint8), sigclip=sigc,
objlim=objl, sigfrac=0.1, gain=1.0, pssl=0.0)
# Update the image
hdu['CRM'].data[:, chipinds][:, :] = crmask[:,:]
# Flag the cosmic ray pixels with a large negative number
hdu['SCI'].data[:, chipinds][crmask == 1] = -1000000
# Save the file
hdu.writeto(outname, clobber=True)
hdu.close()
iraf.cd('..')
def cosmic_ray_remove(fnlist):
"""Runs the LACosmicx routine on a series of images in order to correct
cosmic rays. Creates the header keyword 'fpcosmic' and sets its value to
'True'
Inputs:
fnlist -> A list containing the paths to fits images.
"""
for i in range(len(fnlist)):
print "Cosmic-ray correcting image "+str(i+1)+" of "+str(len(fnlist))+": "+fnlist[i]
image = openfits(fnlist[i],mode="update")
image[0].header["fpcosmic"] = "True"
_mask, image[0].data = lacosmicx(image[0].data,verbose=False,cleantype='idw')
image[0].data[np.isnan(image[0].data)]=0
image.close()
return
def prep_direct_grism_pair(direct_asn='goodss-34-F140W_asn.fits', grism_asn='goodss-34-G141_asn.fits', radec=None, raw_path='../RAW/', mask_grow=18, scattered_light=False, final_scale=None, skip_direct=False, ACS=False, jump=False, order=2, get_shift=True, align_threshold=20, column_average=True, sky_iter=3, run_acs_lacosmic=False):
"""
Process both the direct and grism observations of a given visit
"""
import threedhst.prep_flt_astrodrizzle as prep
import drizzlepac
from stwcs import updatewcs
import time
t0 = time.time()
#direct_asn='goodss-34-F140W_asn.fits'; grism_asn='goodss-34-G141_asn.fits'; radec=None; raw_path='../RAW/'
#radec = os.getenv('THREEDHST') + '/ASTRODRIZZLE_FLT/Catalog/goodss_radec.dat'
################################
#### Direct image processing
################################
#### xx add astroquery 2MASS/SDSS workaround for radec=None
if not skip_direct:
#### Get fresh FLTS from ../RAW/
asn = threedhst.utils.ASNFile(direct_asn)
if ACS:
for exp in asn.exposures:
print 'cp %s/%s_flc.fits.gz .' %(raw_path, exp)
os.system('cp %s/%s_flc.fits.gz .' %(raw_path, exp))
os.system('gunzip -f %s_flc.fits.gz' %(exp))
if run_acs_lacosmic:
try:
import lacosmicx
status = True
except:
print 'import lacosmicx failed!'
status = False
if status:
im = pyfits.open('%s_flc.fits' %(exp), mode='update')
for ext in [1,2]:
indata = im['SCI',ext].data
#inmask = im['DQ',ext].data > 0
if im['SCI',ext].header['BUNIT'] == 'ELECTRONS':
gain = 1
else:
gain = 1./im[0].header['EXPTIME']
if 'MDRIZSK0' in im['SCI',ext].header:
pssl = im['SCI',ext].header['MDRIZSK0']
else:
pssl = 0.
if 'FLASHLVL' in im[0].header:
pssl += im[0].header['FLASHLVL']
sig_scale = 1.8
else:
sig_scale = 1.
out = lacosmicx.lacosmicx(indata, inmask=None,
sigclip=3.5*sig_scale, sigfrac=0.2,
objlim=7.0, gain=gain,
readnoise=im[0].header['READNSEA'],
satlevel=np.inf, pssl=pssl, niter=5,
sepmed=True, cleantype='meanmask',
fsmode='median', psfmodel='gauss',
psffwhm=2.5,psfsize=7, psfk=None,
psfbeta=4.765, verbose=True)
crmask, cleanarr = out
im['DQ',ext].data |= 16*crmask
### Low pixels
if im[0].header['INSTRUME'] == 'WFC3':
bad = im['SCI',ext].data < -4*im['ERR',ext].data
im['DQ',ext].data |= 16*bad
im.flush()
else:
threedhst.process_grism.fresh_flt_files(direct_asn, from_path=raw_path)
if (not ACS):
#### Subtract WFC3/IR direct backgrounds
prep.subtract_flt_background(root=direct_asn.split('_asn')[0], scattered_light=scattered_light, order=order)
#### Flag IR CRs again within runTweakReg
#### Run TweakReg
if (radec is None) & (not ACS):
print len(asn.exposures)
if len(asn.exposures) > 1:
drizzlepac.astrodrizzle.AstroDrizzle(direct_asn, clean=True, final_scale=None, final_pixfrac=0.8, context=False, final_bits=576, preserve=False, driz_cr_snr='5.0 4.0', driz_cr_scale = '2.5 0.7')
else:
drizzlepac.astrodrizzle.AstroDrizzle(direct_asn, clean=True, final_scale=None, final_pixfrac=1, context=False, final_bits=576, preserve=False, driz_separate=False, driz_sep_wcs=False, median=False, blot=False, driz_cr=False, driz_cr_corr=False, driz_combine=True)
else:
if get_shift:
prep.runTweakReg(asn_file=direct_asn, master_catalog=radec, final_scale=None, ACS=ACS, threshold=align_threshold)
#### Subtract background of direct ACS images
if ACS:
for exp in asn.exposures:
flc = pyfits.open('%s_flc.fits' %(exp), mode='update')
if 'SUB' in flc[0].header['APERTURE']:
extensions = [1]
else:
extensions = [1,4]
for ext in extensions:
threedhst.showMessage('Subtract background from %s_flc.fits[%d] : %.4f' %(exp, ext, flc[ext].header['MDRIZSKY']))
flc[ext].data -= flc[ext].header['MDRIZSKY']
flc[ext].header['MDRIZSK0'] = flc[ext].header['MDRIZSKY']
flc[ext].header['MDRIZSKY'] = 0.
#
flc.flush()
else:
pass
#### Do this later, gives segfaults here???
#prep.subtract_flt_background(root=direct_asn.split('_asn')[0], scattered_light=scattered_light)
#### Flag CRs again on BG-subtracted image
#drizzlepac.astrodrizzle.AstroDrizzle(direct_asn, clean=True, final_scale=None, final_pixfrac=0.8, context=False, final_bits=576, preserve=False, driz_cr_snr='5.0 4.0', driz_cr_scale = '2.5 0.7') # ,
################################
#### Grism image processing
################################
if grism_asn:
asn = threedhst.utils.ASNFile(grism_asn)
if ACS:
for exp in asn.exposures:
print 'cp %s/%s_flc.fits.gz .' %(raw_path, exp)
os.system('cp %s/%s_flc.fits.gz .' %(raw_path, exp))
os.system('gunzip -f %s_flc.fits.gz' %(exp))
updatewcs.updatewcs('%s_flc.fits' %(exp))
prep.copy_adriz_headerlets(direct_asn=direct_asn, grism_asn=grism_asn, ACS=True)
prep.subtract_acs_grism_background(asn_file=grism_asn, final_scale=None)
else:
#### Remove the sky and flag CRs
## with mask from rough zodi-only subtraction
prep.subtract_grism_background(asn_file=grism_asn, PATH_TO_RAW='../RAW/', final_scale=None, visit_sky=True, column_average=False, mask_grow=mask_grow, first_run=True)
## Redo making mask from better combined image
prep.subtract_grism_background(asn_file=grism_asn, PATH_TO_RAW='../RAW/', final_scale=final_scale, visit_sky=True, column_average=column_average, mask_grow=mask_grow, first_run=False, sky_iter=sky_iter)
#### Copy headers from direct images
if radec is not None:
prep.copy_adriz_headerlets(direct_asn=direct_asn, grism_asn=grism_asn, ACS=False)
#### Run CR rejection with final shifts
drizzlepac.astrodrizzle.AstroDrizzle(grism_asn, clean=True, skysub=False, final_wcs=True, final_scale=final_scale, final_pixfrac=0.8, context=False, final_bits=576, driz_sep_bits=576, preserve=False, driz_cr_snr='8.0 5.0', driz_cr_scale='2.5 0.7') # driz_cr_snr='5.0 4.0', driz_cr_scale = '2.5 0.7')
if not grism_asn:
t1 = time.time()
threedhst.showMessage('direct: %s\n\nDone (%d s).' %(direct_asn, int(t1-t0)))
else:
t1 = time.time()
threedhst.showMessage('direct: %s\ngrism: %s\n\nDone (%d s).' %(direct_asn, grism_asn, int(t1-t0)))
def fix_cosmic_rays(self, rm_custom=False, flag=None, **lacosmic_param):
'''
Resets cosmic rays within the seg maps of objects and uses L.A.Cosmic
to find them again.
Parameters
----------
self : object
DashData object created from an individual IMA file.
rm_custom : bool
Specifies whether or not the user would like to remove custom flags
within the boundaries of sources, as defined by the segmentation map
created from the original FLT.
flag : int
Specifies flag the user would like the remove within the boundaries
of sources.
lacosmic_param : dic
Dictionary of the L.A.Cosmic parameters that users may want to specify.
If not set, then presets are used.
Output
------
Fixed for cosmic rays diff files : fits
Same diff files created in split_ima that have now been corrected
for cosmic ray errors.
'''
asn_exposures = sorted(glob('diff/' + self.root + '_*_diff.fits'))
seg = fits.open('segmentation_maps/{}_seg.fits'.format(self.root))
seg_data = np.cast[np.float32](seg[0].data)
flt_full = fits.open(self.flt_file_name)
flt_full_wcs = stwcs.wcsutil.HSTWCS(flt_full, ext=1)
EXPTIME = flt_full[0].header['EXPTIME']
if lacosmic_param:
gain = lacosmic_param['gain']
readnoise = lacosmic_param['readnoise']
objlim = lacosmic_param['objlim']
pssl = lacosmic_param['pssl']
verbose = lacosmic_param['verbose']
else:
gain = 1.0
readnoise = 20.
objlim = 15.0
pssl = 0.
verbose = True
#Have lacosmicx locate cosmic rays
crmask, clean = lacosmicx.lacosmicx(flt_full[1].data,
gain=gain,
readnoise=readnoise,
objlim=objlim,
pssl=pssl,
verbose=verbose)
yi, xi = np.indices((1014, 1014))
#Remove all 4096 flags within the boundaries of objects
for exp in asn_exposures:
flt = fits.open(exp, mode='update')
flagged_stars = ((flt['DQ'].data & 4096) > 0) & (seg_data > 0)
flt['DQ'].data[flagged_stars] -= 4096
new_cr = (crmask == 1) & ((flt['DQ'].data & 4096) == 0) & (
(seg_data == 0) |
((seg_data > 0) &
(flt['SCI'].data < 1.))) & (xi > 915) & (yi < 295)
flt['DQ'].data[new_cr] += 4096
flt.flush()
#Remove custom flags
if rm_custom is True:
if flag is not None:
for exp in asn_exposures:
flt = fits.open(exp, mode='update')
flagged_stars = (
(flt['DQ'].data & flag) > 0) & (seg_data > 0)
flt['DQ'].data[flagged_stars] -= flag
new_cr = (crmask == 1) & ((flt['DQ'].data & flag) == 0) & (
(seg_data == 0) |
((seg_data > 0) &
(flt['SCI'].data < 1.))) & (xi > 915) & (yi < 295)
flt['DQ'].data[new_cr] += flag
flt.flush()
else:
raise Exception('Must specify which flags to remove.')
