def ApplyBadPixelMask(images, cal_path, outbase = '_bpm'):
"""
#####################################################################################
# Fix a list of images with a bad pixel mask
#
# PARAMETERS:
# images - list of images to fix
# cal_path - path to calibration data
# outbase - addition to the name of the image after mask has been applied
#
# RETURNS:
# list of fixed images
#####################################################################################
"""
print '########################################'
print '# Applying Bad Pixel Masks'
print '########################################'
# finds the bad pixel mask
badPixelMask = [FindPixelMask(i, cal_path) for i in images]
# Build the list of output image names
out = [os.path.splitext(i) for i in images] # Split off the extension
out = [i[0] + outbase + '.fit' for i in out] # Paste the outbase at the end of the filename
# and put the extension back on
# Set up to call the task
iraf.set(clobber='yes')
iraf.proto()
iraf.fixpix.unlearn() # Forget everything
iraf.fixpix.mode = 'h' # Don't ask for user input
for (i,o,m) in zip(images, out, badPixelMask):
# gets image data and copies it to the corresponding output file name
shutil.copy(i,o)
#runs fixpix on the output file (fixpix overwrites file data)
if not os.path.isfile(i):
print i + ": Input file does not exist."
elif not os.path.isfile(m):
print i + ": No pixel mask applied."
else:
try:
#set parameters
iraf.fixpix.images = o
iraf.fixpix.masks = m
print i + ": applying mask at " + m
iraf.fixpix()
except iraf.IrafError, e:
print "fixpix failed"
print "error #" + str(e.errno)
print "Msg: " + e.errmsg
print "Task: " + e.errtask
raise e
def fixpix(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('nrm/sci*nrm*.fits')
if len(fs) == 0:
print "WARNING: No rectified images to fix."
iraf.cd('..')
return
if not os.path.exists('fix'):
os.mkdir('fix')
for f in fs:
outname = f.replace('nrm', 'fix')
# Copy the file to the fix directory
shutil.copy(f, outname)
# Set all of the BPM pixels = 0
h = pyfits.open(outname, mode='update')
h['SCI'].data[h['BPM'].data == 1] = 0
# Grab the CRM extension from the lax file
laxhdu = pyfits.open(f.replace('nrm', 'lax'))
h.append(pyfits.ImageHDU(data=laxhdu['CRM'].data.copy(),
header=laxhdu['CRM'].header.copy(),
name='CRM'))
h.flush()
h.close()
laxhdu.close()
# Run iraf's fixpix on the cosmic rays, not ideal,
# but better than nothing because apall doesn't take a bad pixel mask
iraf.unlearn(iraf.fixpix)
iraf.flpr()
iraf.fixpix(outname + '[SCI]', outname + '[CRM]', mode='hl')
iraf.cd('..')
def fixpix(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('nrm/sci*nrm*.fits')
if len(fs) == 0:
print "WARNING: No rectified images to fix."
iraf.cd('..')
return
if not os.path.exists('fix'):
os.mkdir('fix')
for f in fs:
outname = f.replace('nrm', 'fix')
# Copy the file to the fix directory
shutil.copy(f, outname)
# Set all of the BPM pixels = 0
h = pyfits.open(outname, mode='update')
h['SCI'].data[h['BPM'].data == 1] = 0
# Grab the CRM extension from the lax file
laxhdu = pyfits.open(f.replace('nrm', 'lax'))
h.append(pyfits.ImageHDU(data=laxhdu['CRM'].data.copy(),
header=laxhdu['CRM'].header.copy(),
name='CRM'))
h.flush()
h.close()
laxhdu.close()
# Run iraf's fixpix on the cosmic rays, not ideal,
# but better than nothing because apall doesn't take a bad pixel mask
iraf.unlearn(iraf.fixpix)
iraf.flpr()
iraf.fixpix(outname + '[SCI]', outname + '[CRM]', mode='hl')
iraf.cd('..')
def makeMask(resFile):
shutil.copy(resFile, resFile+".orig")
iraf.ccdmask.setParam("image", resFile)
maskFile = resFile + ".mask"
iraf.ccdmask.setParam("mask", maskFile)
iraf.ccdmask(Stdout=1, mode="h")
iraf.fixpix.setParam("images", resFile )
iraf.fixpix.setParam("masks", maskFile+ ".pl")
iraf.fixpix(Stdout=1, mode="h")
def makeMask(resFile):
shutil.copy(resFile, resFile + ".orig")
iraf.ccdmask.setParam("image", resFile)
maskFile = resFile + ".mask"
iraf.ccdmask.setParam("mask", maskFile)
iraf.ccdmask(Stdout=1, mode="h")
iraf.fixpix.setParam("images", resFile)
iraf.fixpix.setParam("masks", maskFile + ".pl")
iraf.fixpix(Stdout=1, mode="h")
def FixBadPixels(PC,images,nightdir):
""" This will run iraf task proto.fixpix to interpolate badpixels """
PixelMask = os.path.join(nightdir,PC.BadPixelMaskName)
if not os.path.isfile(PixelMask):
print("No Bad Pixel Mask file found by the name "+ PixelMask)
print("Hence skipping Bad pixel interpolation")
return
iraf.proto(_doprint=0)
iraf.fixpix.unlearn()
iraf.fixpix(images=images,masks=PixelMask)
pssl_temp = head_temp['SKYLEVEL']
# create mask image for template
mask = np.abs(data_temp) < 1e-6
fits.writeto('tempmask.fits',
mask.astype('i'),
overwrite=True)
if args.fixpix:
try:
iraf.flpr()
iraf.flpr()
iraf.unlearn(iraf.fixpix)
cwd = os.getcwd()
iraf.fixpix(os.path.join(cwd, imgtarg),
os.path.join(cwd, targmask),
verbose='yes')
iraf.flpr()
iraf.flpr()
iraf.unlearn(iraf.fixpix)
iraf.fixpix(os.path.join(cwd, imgtemp),
os.path.join(cwd, tempmask),
verbose='yes')
iraf.flpr()
iraf.flpr()
iraf.unlearn(iraf.fixpix)
except:
print 'FIXPIX ERROR, continuing without fixpix'
# hotpants parameters
iuthresh = str(
sat_targ) # upper valid data count, image
print 'variance image already there, do not create noise image'
# if skylevel is in the header, swarp with bkg subtraction has been applyed
if 'SKYLEVEL' in head_temp:
pssl_temp = head_temp['SKYLEVEL']
# create mask image for template
data_temp, head_temp
mask = np.abs(data_temp) < 1e-6
fits.writeto('tempmask.fits',mask.astype('i'))
if _fixpix:
iraf.flpr(); iraf.flpr()
iraf.unlearn(iraf.fixpix)
cwd = os.getcwd()
iraf.fixpix(os.path.join(cwd, imgtarg),
os.path.join(cwd, targmask), verbose='yes')
iraf.flpr(); iraf.flpr()
iraf.unlearn(iraf.fixpix)
iraf.fixpix(os.path.join(cwd, imgtemp),
os.path.join(cwd, tempmask), verbose='yes')
iraf.flpr(); iraf.flpr()
iraf.unlearn(iraf.fixpix)
# hotpants parameters
iuthresh = str(sat_targ) # upper valid data count, image
iucthresh = str(0.95*sat_targ) # upper valid data count for kernel, image
tuthresh = str(sat_temp) # upper valid data count, template
tucthresh = str(0.95*sat_temp) # upper valid data count for kernel, template
rkernel = str(np.median([10, 2.*max_fwhm, 20])) # convolution kernel half width
radius = str(np.median([15, 3.0*max_fwhm, 25])) # HW substamp to extract around each centroid
sconv = '-sconv' # all regions convolved in same direction (0)
def mkobjmask(inlist,
inpref='',
outpref='m',
bpm='none',
minpix=250,
hsigma=3,
lsigma=10,
conv='block 3 3'):
# load IRAF package
iraf.nproto()
# open input list and check if it exists
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check output images
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(outimg_arr, int):
return 1
# check optional parameters
if bpm.lower() != 'none':
if not os.access(bpm, os.R_OK):
print >> sys.stderr, 'cannot read bad pixel mask (%s)' % bpm
return 1
bpmex = 1
else:
bpmex = 0
if lsigma <= 0:
print >> sy.stderr, 'invalid lsigma value (%f)' % lsigma
return 1
if hsigma <= 0:
print >> sys.stderr, 'invalid hsigma value (%f)' % hsigma
return 1
# check convolution kernel name
ret = 1
param = conv.split()
if len(param) == 1:
if os.access(param[0], os.R_OK):
conv = '@' + conv
ret = 0
else:
print >> sys.stderr, 'convolution kernel file (%s) does not exist' % param[
0]
elif param[0] == 'block' and len(param) == 3:
if param[1].isdigit() and param[2].isdigit():
ret = 0
elif param[0] == 'bilinear' and len(param) == 3:
if param[1].isdigit() and param[2].isdigit() and int(
param[1]) > 0 and int(param[2]) > 0:
ret = 0
elif param[0] == 'gauss' and len(param) == 5:
if param[1].isdigit() and param[2].isdigit() and int(
param[1]) > 0 and int(param[2]) > 0:
if isfloat(param[3]) and isfloat(
param[4]) and float(param[3]) > 0 and float(param[4]) > 0:
ret = 0
if ret > 0:
print >> sys.stderr, 'invalid convolve parameter (%s)' % conv
return 1
# prefix for temoprary images
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# check if input images exist and fix bad pixel if requested
for i in range(len(inimg_arr)):
tmp_inimg = tmp_prefix + os.path.basename(inimg_arr[i])
tmp_outimg = tmp_prefix + 'mask' + os.path.basename(outimg_arr[i])
iraf.unlearn('imcopy')
iraf.imcopy(inimg_arr[i], tmp_inimg, verbose='no')
if bpmex == 1:
iraf.unlearn('fixpix')
iraf.fixpix(tmp_inimg, bpm)
iraf.fixpix(tmp_inimg, bpm)
iraf.fixpix(tmp_inimg, bpm)
iraf.unlearn('objmasks')
iraf.objmasks(tmp_inimg,
tmp_outimg,
omtype='boolean',
masks='',
convolve=conv,
lsigma=lsigma,
hsigma=hsigma,
hdetect='yes',
ldetect='yes',
minpix=minpix)
ret = []
ret = iraf.objmasks(tmp_inimg,
outimg_arr[i],
omtype='boolean',
masks=tmp_outimg + '[pl]',
convolve=conv,
lsigma=lsigma,
hsigma=hsigma,
hdetect='yes',
ldetect='yes',
minpix=minpix,
Stdout=1)
if len(ret) != 0:
iraf.unlearn('hedit')
iraf.hedit(inimg_arr[i],
'objmask',
outimg_arr[i] + '[pl]',
add='yes',
verify='no')
os.remove(tmp_inimg)
os.remove(tmp_outimg)
if len(ret) == 0:
print >> sys.stderr, 'failed to execute iraf objmasks task for %s' % inimg_arr[
i]
return 1
return 0
def combine(do_cti=False, doreduce=True, doshifts=True):
if do_cti:
os.system('stis_cti --crds_update')
if doreduce:
# Defringing didn't seem to converge because of the low S/N
stistools.ocrreject.ocrreject('oc0102070_flc.fits','oc0102070_crc.fits')
iraf.normspflat(inflat='oc0102070_crc.fits',outflat='oc0102070_nsp.fits', do_cal='no')
iraf.imcalc(input='oc0102070_nsp.fits', output='temp_nsp.fits', equals='if(x .lt. 250) then 1 else im1')
iraf.imcopy('temp_nsp.fits[1][1:250,*]', 'oc0102070_nsp.fits[1][1:250,*]')
#iraf.defringe('oc0102060_flc.fits', 'oc0102070_nsp.fits', 'oc0102060_dfr.fits')
#for each image
for im in ['oc0102050_flc','oc0102060_flc']:
outbase = 'blue'
if im[:-4] == 'oc0102060':
outbase = 'red'
#reset the aperture table to the newer file (we maybe should check this)
pyfits.setval(im +'.fits','APERTAB',value='oref$y2r1559to_apt.fits')
pyfits.setval(im +'.fits', 'SPTRCTAB', value='oref$qa31608go_1dt.fits')
# fixpix any negative values. In principle some of this noise
# could be real, but I have found that is often not the case
hdu = fits.open(im+ '.fits')
mask1 = hdu[1].data < -20
mask2 = hdu[4].data < -20
hdu.close()
fits.writeto(outbase+'mask1.fits', mask1.astype('i'), clobber=True)
fits.writeto(outbase+'mask2.fits', mask2.astype('i'), clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[1]', outbase+'mask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[4]', outbase+'mask2.fits')
# Subtract off the median value
hdu = fits.open(im+ '.fits', mode='update')
hdu[1].data -= np.median(hdu[1].data)
hdu[4].data -= np.median(hdu[4].data)
readnoise1 = 1.4826 * np.median(np.abs(hdu[1].data))
readnoise2 = 1.4826 * np.median(np.abs(hdu[4].data))
# Cosmic ray reject both images using scrappy
# Make sure to treat the noise in a sensible way
crmask1, clean1 = detect_cosmics(hdu[1].data, readnoise=readnoise1,
sigclip=5, objlim=5, sigfrac=0.8,
fsmode='median', psfmodel='gaussy',
psffwhm=2., cleantype='idw')
crmask2, clean2 = detect_cosmics(hdu[4].data, readnoise=readnoise2,
sigclip=5, objlim=5, sigfrac=0.8,
fsmode='median', psfmodel='gaussy',
psffwhm=2., cleantype='idw')
hdu.flush()
hdu.close()
fits.writeto(outbase + '_crmask1.fits', crmask1.astype('i'), clobber=True)
fits.writeto(outbase + '_crmask2.fits', crmask2.astype('i'), clobber=True)
# Run fixpix on the frames
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[1]', outbase+'_crmask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[4]', outbase+'_crmask2.fits')
if outbase=='red':
iraf.mkfringeflat('oc0102060_flc.fits', 'oc0102070_nsp.fits', 'oc0102070_frr.fits',
beg_scale=0.6, end_scale=1.5, scale_step=0.01,
beg_shift=-3.0, end_shift=3.0,shift_step=0.05)
iraf.defringe('oc0102060_flc.fits', 'oc0102070_frr.fits', 'oc0102060_dfr.fits')
#Run x2d on the flt frame
stistools.x2d.x2d(input='oc0102060_dfr.fits',output=im[:-4]+'x2d.fits')
else:
stistools.x2d.x2d(input='oc0102050_flc.fits',output=im[:-4]+'x2d.fits')
h = pyfits.open(im[:-4]+'x2d.fits', mode='update')
#Replace all of the bad pixels in the image by -666 based on the DQ array
#save them to a new file
#Throw away bad reference file pixels and saturated pixels. None of the other error codes
#were in the first file so I haven't included them here, but we might want to
d = h[3].data
badpix = logical_and(bitwise_and(d,256) == 256,bitwise_and(d,512) == 512)
h[1].data[badpix] = -10000
d = h[6].data
badpix = logical_and(bitwise_and(d,256) == 256,bitwise_and(d,512) == 512)
h[4].data[badpix] = -10000
h.flush()
# Trim the images
for i in range(1,7):
h[i].data = h[i].data[100:-100, 100:-100]
h[i].header['CRPIX1'] -= 100
h[i].header['CRPIX2'] -= 100
h.flush()
h.close()
# Combine the images
iraf.unlearn(iraf.imcombine)
iraf.imcombine(input=im[:-4]+'x2d[1],'+im[:-4]+'x2d[4]', output=outbase+'_com.fits',
reject='crreject')
hdu = pyfits.open(outbase +'_com.fits')
mask = hdu[0].data == 0.0
hdu.close()
fits.writeto(outbase+'_mask.fits', mask.astype('i'), clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(outbase+'_com.fits', outbase+'_mask.fits')
iraf.unlearn(iraf.apall)
iraf.apall(input=outbase+'_com',output='13dh_'+outbase, review='no',
nsum = -500, b_order = 1,
b_function='legendre',b_niterate=30, b_naverage = -21,
nfind=1,t_order=3,background='median',weights='variance',
skybox=100 )
iraf.splot(outbase+'[SCI]')
# SET DISPAXIS (dispersion axis)
# you can set Dispaxis = 1 (horizontal) or 2 (vertical)
print 'Setting dispaxis...'
iraf.hedit('*.fits', 'dispaxis', '1', add=yes, verify=no, show=yes, update=yes)
# CREATE ECHELLE BAD PIXEL MASK
# Use the task TEXT2MASK to create the mask from the ASCII 'badcols' file
#print 'Creating bad pixel mask from file "badcols"...'
#iraf.text2mask('badcols','echmask',ncols=2128,nlines=2068,linterp=1,cinterp=1,
# square=1,pixel=1)
# Note: This ^ doesn't work currently on 64-bit systems. Does not produce
# a working mask. Use 32-bit IRAF to produce mask.
# Forces interpolation of bad pixel masks along the
iraf.fixpix('@objflat', 'echmask.pl', linterp=1, verbose=yes)
iraf.fixpix('@arcs', 'echmask.pl', linterp=1, verbose=yes)
# MAKE AN AVERAGE (FIDUCIAL) BIAS AND DARK (if applicable)
# by default, we skip the darks, but this can be changed if desired
iraf.imdelete('bias_fid', verify=no)
iraf.zerocombine('@biases',
output='bias_fid',
combine='median',
reject='none',
ccdtype='',
process=no,
delete=no,
scale='none')
#iraf.imdelete('dark_fid',verify=no)
#iraf.darkcombine('@darks',output='dark_fid',combine='average',
def adjustDQ(self, inIm, ext, txtMask):
print "\nADJUSTING MASK FOR " + inIm + ext.upper()
# grab dimensions of mask from header of image
# TODO: will expression for num work in one-slit case?
hdu = fits.open(inIm)
num = 3 * (int(ext[-2]) - 1) + 2
xDim = hdu[num].header["NAXIS1"]
yDim = hdu[num].header["NAXIS2"]
hdu.close()
# display input image
print "Displaying 2D spectra"
iraf.display(inIm + ext)
if ext[-2] == "1":
iraf.imdelete("x" + inIm, verify="yes")
# allow user to adjust mask iteratively
while True:
# ask user to input coordinate ranges to mask
fixDQ = bool(input("\nDoes mask need improvement? (True/False): "))
if fixDQ:
os.system("rm -iv " + txtMask)
ranges = raw_input(
"Coord ranges to be masked (space-separated): ")
cmd = "echo '" + ranges + "' >> " + txtMask
os.system(cmd)
else:
break
# transform text mask to pixel list format
plMask = txtMask[:txtMask.find(".")] + ".pl"
iraf.delete(plMask)
iraf.text2mask(txtMask, plMask, xDim, yDim)
# interpolate over bad pixels and mark them in DQ plane
tmp = "tmp_" + inIm[:inIm.find(".")] + "_" + ext[-2] + ".fits"
tmpdq = "tmpdq_" + inIm[:inIm.find(".")] + "_" + ext[-2] + ".fits"
images = tmp + "," + tmpdq
iraf.imdelete(images)
if ext[-2] == "1": iraf.copy(inIm, "x" + inIm)
iraf.imcopy(inIm + ext, tmp)
iraf.fixpix(tmp, plMask, linterp="1,2,3,4")
iraf.imcopy(tmp + "[*,*]", "x" + inIm + ext + "[*,*]")
iraf.imarith(plMask, "+", "x" + inIm + "[DQ," + ext[-2:], tmpdq)
iraf.imcopy(tmpdq + "[*,*]", "x" + inIm + "[DQ," + ext[-2:] + \
"[*,*]")
# display result
iraf.display("x" + inIm + ext)
# delete temporary files
iraf.imdelete(images)
return
def combine(do_cti=False, doreduce=True, doshifts=True):
if do_cti:
os.system('stis_cti --crds_update')
if doreduce:
# Defringing didn't seem to converge because of the low S/N
stistools.ocrreject.ocrreject('oc0102070_flc.fits',
'oc0102070_crc.fits')
iraf.normspflat(inflat='oc0102070_crc.fits',
outflat='oc0102070_nsp.fits',
do_cal='no')
iraf.imcalc(input='oc0102070_nsp.fits',
output='temp_nsp.fits',
equals='if(x .lt. 250) then 1 else im1')
iraf.imcopy('temp_nsp.fits[1][1:250,*]',
'oc0102070_nsp.fits[1][1:250,*]')
#iraf.defringe('oc0102060_flc.fits', 'oc0102070_nsp.fits', 'oc0102060_dfr.fits')
#for each image
for im in ['oc0102050_flc', 'oc0102060_flc']:
outbase = 'blue'
if im[:-4] == 'oc0102060':
outbase = 'red'
#reset the aperture table to the newer file (we maybe should check this)
pyfits.setval(im + '.fits',
'APERTAB',
value='oref$y2r1559to_apt.fits')
pyfits.setval(im + '.fits',
'SPTRCTAB',
value='oref$qa31608go_1dt.fits')
# fixpix any negative values. In principle some of this noise
# could be real, but I have found that is often not the case
hdu = fits.open(im + '.fits')
mask1 = hdu[1].data < -20
mask2 = hdu[4].data < -20
hdu.close()
fits.writeto(outbase + 'mask1.fits',
mask1.astype('i'),
clobber=True)
fits.writeto(outbase + 'mask2.fits',
mask2.astype('i'),
clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[1]', outbase + 'mask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[4]', outbase + 'mask2.fits')
# Subtract off the median value
hdu = fits.open(im + '.fits', mode='update')
hdu[1].data -= np.median(hdu[1].data)
hdu[4].data -= np.median(hdu[4].data)
readnoise1 = 1.4826 * np.median(np.abs(hdu[1].data))
readnoise2 = 1.4826 * np.median(np.abs(hdu[4].data))
# Cosmic ray reject both images using scrappy
# Make sure to treat the noise in a sensible way
crmask1, clean1 = detect_cosmics(hdu[1].data,
readnoise=readnoise1,
sigclip=5,
objlim=5,
sigfrac=0.8,
fsmode='median',
psfmodel='gaussy',
psffwhm=2.,
cleantype='idw')
crmask2, clean2 = detect_cosmics(hdu[4].data,
readnoise=readnoise2,
sigclip=5,
objlim=5,
sigfrac=0.8,
fsmode='median',
psfmodel='gaussy',
psffwhm=2.,
cleantype='idw')
hdu.flush()
hdu.close()
fits.writeto(outbase + '_crmask1.fits',
crmask1.astype('i'),
clobber=True)
fits.writeto(outbase + '_crmask2.fits',
crmask2.astype('i'),
clobber=True)
# Run fixpix on the frames
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[1]', outbase + '_crmask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[4]', outbase + '_crmask2.fits')
if outbase == 'red':
iraf.mkfringeflat('oc0102060_flc.fits',
'oc0102070_nsp.fits',
'oc0102070_frr.fits',
beg_scale=0.6,
end_scale=1.5,
scale_step=0.01,
beg_shift=-3.0,
end_shift=3.0,
shift_step=0.05)
iraf.defringe('oc0102060_flc.fits', 'oc0102070_frr.fits',
'oc0102060_dfr.fits')
#Run x2d on the flt frame
stistools.x2d.x2d(input='oc0102060_dfr.fits',
output=im[:-4] + 'x2d.fits')
else:
stistools.x2d.x2d(input='oc0102050_flc.fits',
output=im[:-4] + 'x2d.fits')
h = pyfits.open(im[:-4] + 'x2d.fits', mode='update')
#Replace all of the bad pixels in the image by -666 based on the DQ array
#save them to a new file
#Throw away bad reference file pixels and saturated pixels. None of the other error codes
#were in the first file so I haven't included them here, but we might want to
d = h[3].data
badpix = logical_and(
bitwise_and(d, 256) == 256,
bitwise_and(d, 512) == 512)
h[1].data[badpix] = -10000
d = h[6].data
badpix = logical_and(
bitwise_and(d, 256) == 256,
bitwise_and(d, 512) == 512)
h[4].data[badpix] = -10000
h.flush()
# Trim the images
for i in range(1, 7):
h[i].data = h[i].data[100:-100, 100:-100]
h[i].header['CRPIX1'] -= 100
h[i].header['CRPIX2'] -= 100
h.flush()
h.close()
# Combine the images
iraf.unlearn(iraf.imcombine)
iraf.imcombine(input=im[:-4] + 'x2d[1],' + im[:-4] + 'x2d[4]',
output=outbase + '_com.fits',
reject='crreject')
hdu = pyfits.open(outbase + '_com.fits')
mask = hdu[0].data == 0.0
hdu.close()
fits.writeto(outbase + '_mask.fits',
mask.astype('i'),
clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(outbase + '_com.fits', outbase + '_mask.fits')
iraf.unlearn(iraf.apall)
iraf.apall(input=outbase + '_com',
output='13dh_' + outbase,
review='no',
nsum=-500,
b_order=1,
b_function='legendre',
b_niterate=30,
b_naverage=-21,
nfind=1,
t_order=3,
background='median',
weights='variance',
skybox=100)
iraf.splot(outbase + '[SCI]')
def fixpix(scifiles):
# Run fixpix to interpolate over cosmic rays
for f in scifiles:
# run fixpix
iraf.unlearn(iraf.fixpix)
iraf.fixpix('t' + f[:-4] + '.fits[2]', f[:-4] + '.lamask.fits', mode='h')
def fixpix(scifiles):
# Run fixpix to interpolate over cosmic rays
for f in scifiles:
# run fixpix
iraf.unlearn(iraf.fixpix)
iraf.fixpix('t' + f[:-4] + '.fits[2]', f[:-4] + '.lamask.fits', mode='h')
pssl_temp = 0
print 'variance image already there, do not create noise image'
# if skylevel is in the header, swarp with bkg subtraction has been applyed
if 'SKYLEVEL' in head_temp:
pssl_temp = head_temp['SKYLEVEL']
# create mask image for template
data_temp, head_temp
mask = np.abs(data_temp) < 1e-6
pyfits.writeto(temp_file0+'_tempmask3.fits',mask.astype('i'))
if _fixpix:
iraf.flpr(); iraf.flpr()
iraf.unlearn(iraf.fixpix)
iraf.fixpix('./'+imgtarg, './'+targmask, verbose='no')
iraf.flpr(); iraf.flpr()
iraf.unlearn(iraf.fixpix)
iraf.fixpix('./'+imgtemp, './'+tempmask, verbose='no')
iraf.flpr(); iraf.flpr()
iraf.unlearn(iraf.fixpix)
# hotpants parameters
iuthresh = str(sat_targ) # upper valid data count, image
iucthresh = str(0.95*sat_targ) # upper valid data count for kernel, image
tuthresh = str(sat_temp) # upper valid data count, template
tucthresh = str(0.95*sat_temp) # upper valid data count for kernel, template
rkernel = str(np.median([10, 2.*max_fwhm, 20])) # convolution kernel half width
radius = str(np.median([15, 3.0*max_fwhm, 25])) # HW substamp to extract around each centroid
sconv = '-sconv' # all regions convolved in same direction (0)
normalize = _normalize #normalize to (t)emplate, (i)mage, or (u)nconvolved (t)
def flatfield(inlist,
flatimg,
inpref='',
outpref='f',
dark='none',
bpm='none'):
# open input list and check if it exists
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check flat image
if os.access(flatimg, os.R_OK):
im_flat = pyfits.open(flatimg)
flat_data = im_flat[0].data
flat_shape = flat_data.shape
im_flat.close()
else:
print >> sys.stderr, 'cannot read flat image (%s)' % flatimg
f.close()
return 1
# check dark frame
if dark.lower() != 'none':
subdark = 1
if os.access(dark, os.R_OK):
im_dark = pyfits.open(dark)
dark_data = im_dark[0].data
dark_shape = dark_data.shape
im_dark.close()
else:
print >> sys.stderr, 'cannot read dark image (%s)' % dark
return 1
else:
subdark = 0
# 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
f.close()
return 1
else:
bpmex = 0
# check output prefix
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(outimg_arr, int):
return 1
# check if file exist and record exptime, coadds, ndr, slwcnt
for i in range(len(inimg_arr)):
# read image data and fits header
im = pyfits.open(inimg_arr[i])
hdr0 = im[0].header
im_shape = im[0].data.shape
exptime = float(im[0].header['EXP1TIME'])
coadds = int(im[0].header['COADDS'])
ndr = int(im[0].header['NDR'])
# normalize by coadds and ndr values
out_data = im[0].data / (coadds * ndr)
# close input image
im.close()
# subtract dark if requested
if subdark == 1:
if im_shape[0] == dark_shape[0] and im_shape[1] == dark_shape[1]:
out_data = out_data - dark_data
else:
print >> sys.stderr, 'input image (%s) and dark (%s) have different image shape' % (
inimg, dark)
return 1
# divide by flat frame
if im_shape[0] == flat_shape[0] and im_shape[1] == flat_shape[1]:
out_data = out_data / flat_data
else:
print >> sys.stderr, 'input image (%s) and flat (%s) have different image shape' % (
inimg, flatimg)
return 1
# normalized by EXP1TIME
out_data = out_data / exptime
# write output data into fits
hdu = pyfits.PrimaryHDU(out_data)
imgout = pyfits.HDUList([hdu])
imgout[0].header = hdr0
imgout[0].header['EXPTIME'] = 1.0
imgout[0].header['EXPTORG'] = exptime * coadds
imgout.writeto(outimg_arr[i])
imgout.close()
if bpmex == 1:
iraf.unlearn('fixpix')
iraf.fixpix(outimg_arr[i], bpm, verbose='no')
iraf.fixpix(outimg_arr[i], bpm, verbose='no')
iraf.fixpix(outimg_arr[i], bpm, verbose='no')
return 0
