def combine_flat(filename):
outname = filename.replace('.lst','.fits')
print 'run function flatcombine...'
print 'make file', outname
iraf.flatcombine(input = 'tbo//@' + filename
, output = outname, combine = 'average', reject = 'avsigclip'
, ccdtype = '', process = False, subsets = True
, delete = False, clobber = False, scale = 'mode'
, statsec = '', nlow = 1, nhigh = 1, nkeep = 1
, mclip = True, lsigma = 3.0, hsigma = 3.0
, rdnoise = 9.4, gain = 0.35, snoise = 0.0
, pclip = -0.5, blank = 1.0)
iraf.noao()
iraf.twodspec()
iraf.longslit()
print 'run function response...'
print 'make file', 're' + outname
iraf.response(calibration = outname
, normalization = outname, response = 're' + outname
, interactive = True, threshold = 'INDEF', sample = '*'
, naverage = 1, function = 'spline3', order = 7
, low_reject = 0.0, high_reject = 0.0, niterate = 1
, grow = 0.0, graphics = 'stdgraph', cursor = '')
print 'run function illumination...'
print 'make file', 'il' + outname
iraf.illumination(images = 're' + outname
, illuminations = 'il' + outname, interactive = False
, bins = '', nbins = 5, sample = '*', naverage = 1
, function = 'spline3', order = 1, low_reject = 0.0
, high_reject = 0.0, niterate = 1, grow = 0.0
, interpolator = 'poly3', graphics = 'stdgraph', cursor = '')
print 'run function imarith...'
print 'make file', 'per' + outname
iraf.imarith(operand1 = 're' + outname
, op = '/', operand2 = 'il' + outname, result = 'per' + outname
, title = '', divzero = 0.0, hparams = '', pixtype = ''
, calctype = '', verbose = True, noact = False)
return outname, 're' + outname, 'il' + outname, 'per' + outname
reject='sigclip',
lsigma=3.0,
hsigma=3.0,
weight='exposure',
expname='EXPTIME')
pyfits.setval(combineoutname, 'DISPAXIS', value=1)
# We want to make an illumination correction file
# before running response:
illumoutname = 'flats/flt%05.2fill.fits' % (ga)
iraf.unlearn(iraf.illumination)
iraf.illumination(images=combineoutname,
illuminations=illumoutname,
interactive=False,
naverage=-40,
order=11,
low_reject=3.0,
high_reject=3.0,
niterate=5,
mode='hl')
# Flag any pixels in the illumination correction< 0.1
illumhdu = pyfits.open(illumoutname, mode='update')
illumhdu[0].data[illumhdu[0].data <= 0.1] = 0.0
illumhdu[0].data[np.isnan(illumhdu[0].data)] = 0.0
illumhdu.flush()
# Get 40 pixels out of the middle of the image and
# median them to run response
combinehdu = pyfits.open(combineoutname)
ny = combinehdu[0].data.shape[0]
def makeflats(fs=None):
# Note the list of files need to not include any paths relative to
# the work directory.
# Maybe not the greatest convention, but we can update this later
iraf.cd('work')
if fs is None:
fs = glob('pysalt/bxgp*.fits')
if len(fs) == 0:
print "WARNING: No flat-fields to combine and normalize."
# Fail gracefully by going up a directory
iraf.cd('..')
return
# make a flats directory
if not os.path.exists('flats'):
os.mkdir('flats')
# Figure out which images are flats and which grating angles were used
allflats, grangles = get_ims(fs, 'flat')
# For each grating angle
for ga in np.unique(grangles):
# grab the flats for this gr angle
flats = allflats[grangles == ga]
# For each chip
for c in range(1, 7):
# run imcombine with average and sigclip, weighted by exposure time
flatlist = ''
for f in flats:
flatlist += '%s[%i],' % (f, c)
# Add the exptime keyword to each extension
pyfits.setval(f, 'EXPTIME', ext=c,
value=pyfits.getval(f, 'EXPTIME'))
# set the output combined file name
combineoutname = 'flats/flt%05.2fcomc%i.fits' % (ga, c)
if os.path.exists(combineoutname):
os.remove(combineoutname)
# initialize the iraf command
iraf.unlearn(iraf.imcombine)
print(flatlist)
# don't forget to remove the last comma in the filelist
iraf.imcombine(input=flatlist[:-1], output=combineoutname,
combine='average', reject='sigclip', lsigma=3.0,
hsigma=3.0, weight='exposure', expname='EXPTIME')
pyfits.setval(combineoutname, 'DISPAXIS', value=1)
# We want to make an illumination correction file
# before running response:
illumoutname = 'flats/flt%05.2fillc%i.fits' % (ga, c)
iraf.unlearn(iraf.illumination)
iraf.illumination(images=combineoutname,
illuminations=illumoutname, interactive=False,
naverage=-40, order=11, low_reject=3.0,
high_reject=3.0, niterate=5, mode='hl')
# Flag any pixels in the illumination correction< 0.1
illumhdu = pyfits.open(illumoutname, mode='update')
illumhdu[0].data[illumhdu[0].data <= 0.1] = 0.0
illumhdu.flush()
# Get 40 pixels out of the middle of the image and
# median them to run response
combinehdu = pyfits.open(combineoutname)
ny = combinehdu[0].data.shape[0]
# divide out the illumination correction before running response
flat1d = np.median(combinehdu[0].data[ny / 2 - 21: ny / 2 + 20, :]
/ illumhdu[0].data[ny / 2 - 21: ny / 2 + 20, :],
axis=0)
# close the illumination file because we don't need it anymore
illumhdu.close()
# File stage m1d for median 1-D
flat1dfname = 'flats/flt%05.2fm1dc%i.fits' % (ga, c)
tofits(flat1dfname, flat1d, hdr=combinehdu[0].header.copy())
# run response
# r1d = response1d
resp1dfname = 'flats/flt%05.2fr1dc%i.fits' % (ga, c)
iraf.response(flat1dfname, flat1dfname, resp1dfname, order=31,
interactive=False, naverage=-5, low_reject=3.0,
high_reject=3.0, niterate=5, mode='hl')
resp1dhdu = pyfits.open(resp1dfname)
resp1d = resp1dhdu[0].data.copy()
resp1dhdu.close()
# After response divide out the response function
# normalize the 1d resp to its median
resp1d /= np.median(resp1d)
# Chuck any outliers
flatsig = np.std(resp1d - 1.0)
resp1d[abs(resp1d - 1.0) > 5.0 * flatsig] = 1.0
resp = flat1d / resp1d
resp2dfname = 'flats/flt%05.2fresc%i.fits' % (ga, c)
resp2d = combinehdu[0].data.copy() / resp
tofits(resp2dfname, resp2d, hdr=combinehdu[0].header.copy())
combinehdu.close()
# close the combined flat because we don't need it anymore
combinehdu.close()
pyfits.setval(resp2dfname, 'DISPAXIS', value=1)
# Reset any pixels in the flat field correction< 0.1
# We could flag bad pixels here if we want, but not right now
flathdu = pyfits.open(resp2dfname, mode='update')
flathdu[0].data[flathdu[0].data <= 0.1] = 0.0
flathdu.flush()
flathdu.close()
# Step back up to the top directory
iraf.cd('..')
def makeflats(fs=None):
# Note the list of files need to not include any paths relative to
# the work directory.
# Maybe not the greatest convention, but we can update this later
iraf.cd('work')
if fs is None:
fs = glob('pysalt/bxgp*.fits')
if len(fs) == 0:
print "WARNING: No flat-fields to combine and normalize."
# Fail gracefully by going up a directory
iraf.cd('..')
return
# make a flats directory
if not os.path.exists('flats'):
os.mkdir('flats')
# Figure out which images are flats and which grating angles were used
allflats, grangles = get_ims(fs, 'flat')
# For each grating angle
for ga in np.unique(grangles):
# grab the flats for this gr angle
flats = allflats[grangles == ga]
# For each chip
for c in range(1, 7):
# run imcombine with average and sigclip, weighted by exposure time
flatlist = ''
for f in flats:
flatlist += '%s[%i],' % (f, c)
# Add the exptime keyword to each extension
pyfits.setval(f, 'EXPTIME', ext=c,
value=pyfits.getval(f, 'EXPTIME'))
# set the output combined file name
combineoutname = 'flats/flt%05.2fcomc%i.fits' % (ga, c)
if os.path.exists(combineoutname):
os.remove(combineoutname)
# initialize the iraf command
iraf.unlearn(iraf.imcombine)
print(flatlist)
# don't forget to remove the last comma in the filelist
iraf.imcombine(input=flatlist[:-1], output=combineoutname,
combine='average', reject='sigclip', lsigma=3.0,
hsigma=3.0, weight='exposure', expname='EXPTIME')
pyfits.setval(combineoutname, 'DISPAXIS', value=1)
# We want to make an illumination correction file
# before running response:
illumoutname = 'flats/flt%05.2fillc%i.fits' % (ga, c)
iraf.unlearn(iraf.illumination)
iraf.illumination(images=combineoutname,
illuminations=illumoutname, interactive=False,
naverage=-40, order=11, low_reject=3.0,
high_reject=3.0, niterate=5, mode='hl')
# Flag any pixels in the illumination correction< 0.1
illumhdu = pyfits.open(illumoutname, mode='update')
illumhdu[0].data[illumhdu[0].data <= 0.1] = 0.0
illumhdu.flush()
# Get 40 pixels out of the middle of the image and
# median them to run response
combinehdu = pyfits.open(combineoutname)
ny = combinehdu[0].data.shape[0]
# divide out the illumination correction before running response
flat1d = np.median(combinehdu[0].data[ny / 2 - 21: ny / 2 + 20, :]
/ illumhdu[0].data[ny / 2 - 21: ny / 2 + 20, :],
axis=0)
# close the illumination file because we don't need it anymore
illumhdu.close()
# File stage m1d for median 1-D
flat1dfname = 'flats/flt%05.2fm1dc%i.fits' % (ga, c)
tofits(flat1dfname, flat1d, hdr=combinehdu[0].header.copy())
# run response
# r1d = response1d
resp1dfname = 'flats/flt%05.2fr1dc%i.fits' % (ga, c)
iraf.response(flat1dfname, flat1dfname, resp1dfname, order=31,
interactive=False, naverage=-5, low_reject=3.0,
high_reject=3.0, niterate=5, mode='hl')
resp1dhdu = pyfits.open(resp1dfname)
resp1d = resp1dhdu[0].data.copy()
resp1dhdu.close()
# After response divide out the response function
# normalize the 1d resp to its median
resp1d /= np.median(resp1d)
# Chuck any outliers
flatsig = np.std(resp1d - 1.0)
resp1d[abs(resp1d - 1.0) > 5.0 * flatsig] = 1.0
resp = flat1d / resp1d
resp2dfname = 'flats/flt%05.2fresc%i.fits' % (ga, c)
resp2d = combinehdu[0].data.copy() / resp
tofits(resp2dfname, resp2d, hdr=combinehdu[0].header.copy())
combinehdu.close()
# close the combined flat because we don't need it anymore
combinehdu.close()
pyfits.setval(resp2dfname, 'DISPAXIS', value=1)
# Reset any pixels in the flat field correction< 0.1
# We could flag bad pixels here if we want, but not right now
flathdu = pyfits.open(resp2dfname, mode='update')
flathdu[0].data[flathdu[0].data <= 0.1] = 0.0
flathdu.flush()
flathdu.close()
# Step back up to the top directory
iraf.cd('..')
