def lris_standard(standards, xcorr=yes):
'''Extract standard stars and calculate sensitivity functions.'''
for ofile, nstd in standards:
shutil.copy(ofile, "%s.fits" % nstd)
# Extract standard
if get_head("%s.fits" % nstd, "SKYSUB"):
iraf.apall(nstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="none")
else:
iraf.apall(nstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="fit")
if xcorr:
# Cross correlate to tweak wavelength
iraf.xcsao("%s.ms" % nstd, templates=XCTEMPLATE,
correlate="wavelength", logfiles="%s.xcsao" % ofile)
# If a solution was found, apply shift
try:
xcsaof = open("%s.xcsao" % ofile)
shift = float(xcsaof.readlines()[6].split()[14])
except:
shift = 0.0
iraf.specshift("%s.ms" % nstd, -shift)
# Create telluric
iraf.splot("%s.ms" % nstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % nstd, "/", "a%s.ms" % nstd, "telluric.%s.fits" % nstd)
iraf.imreplace("telluric.%s.fits" % nstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.%s.fits" % nstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % nstd, "s%s.ms" % nstd, 5.0)
iraf.sarith("%s.ms" % nstd, "/", "s%s.ms" % nstd, "ds%s.ms" % nstd)
# Apply telluric correction
iraf.telluric("ds%s.ms" % nstd, "tds%s.ms" % nstd, "telluric.%s.fits" % nstd,
xcorr=no, tweakrms=no, interactive=no,
sample='4000:4010,6850:6975,7150:7350,7575:7725,8050:8400,8900:9725')
# Define bandpasses for standard star calculation
obj=get_head("%s.fits" % nstd, "OBJECT")
iraf.standard("tds%s.ms" % nstd, "%s.std" % nstd,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/', observatory='Keck', interac=yes,
star_name=obj, airmass='', exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % nstd, '%s.sens' % nstd,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat', observatory='Keck',
function='legendre', order=4, interactive=yes)
return
def make_flat(images,
outflat,
gain=1.0,
rdnoise=0.0,
xwindow=50,
ywindow=50,
hmin=0,
hmax=65535,
lowclip=0.7,
highclip=1.3):
'''Construct flat field from individual frames'''
flatimages = ','.join(images)
iraf.flatcombine(flatimages,
output='flat1',
combine='median',
reject='avsigclip',
ccdtype='',
process=no,
subsets=no,
delete=no,
clobber=no,
scale='median',
lsigma=3.0,
hsigma=3.0,
gain=gain,
rdnoise=rdnoise)
iraf.fmedian('flat1', 'flat2', xwindow, ywindow, hmin=hmin, hmax=hmax)
iraf.imarith('flat1', '/', 'flat2', outflat)
iraf.imreplace(outflat, 1.0, lower=INDEF, upper=lowclip)
iraf.imreplace(outflat, 1.0, lower=highclip, upper=INDEF)
return
def run_imreplace(imageFilename, lowPixVal, uppPixVal): ''' replace all pixels in image between lower and upper with zero parameter imageFilename - the full path filename of the image on which to invoke imexam parameter lowPixVal - the lower bound of pixels to be replaced with zero parameter uppPixVal - the upper bound of pixels to be replaced with zero ''' # TODO: this could be inlines easily, only called once in main() iraf.imreplace(imageFilename, 0, lower=lowPixVal, upper=uppPixVal)
def flatten(flatFile):
"""grab any sky-subtracted images, and flatten them using flatFile"""
# first we have to clean up the flat, and get rid of any negative numbers or zeros
# just set them equal to 1 (??? why do we do this ???)
iraf.imreplace(flatFile, 1.0, imaginary=0.0, lower="INDEF", upper=1.0, radius=0.0)
# get a list of the sky subtracted images
skyims = glob.glob("scratch/s-binir*fits")
flatIms = ["s-f-" + i[10:] for i in skyims]
# organize input and output in iraf-friendly ways
inputFiles = joinStrList(skyims)
outputFiles = joinStrList(flatIms, scratch=True)
# now flatfield the sky subtracted images
iraf.ccdproc(
images=inputFiles,
output=outputFiles,
ccdtype="",
max_cache=0,
noproc="no",
fixpix="no",
overscan="no",
trim="no",
zerocor="no",
darkcor="no",
flatcor="yes",
illumcor="no",
fringecor="no",
readcor="no",
scancor="no",
readaxis="line",
fixfile="",
biassec="",
trimsec="",
zero="",
dark="",
flat=flatFile,
illum="",
fringe="",
minreplace=1.0,
scantype="shortscan",
nscan=1,
interactive="no",
function="legendre",
order=1,
sample="*",
naverage=1,
niterate=1,
low_reject=3.0,
high_reject=3.0,
grow=0.0,
)
return
def make_flat(images, outflat, gain=1.0, rdnoise=0.0, xwindow=50,
ywindow=50, hmin=0, hmax=65535, lowclip=0.7, highclip=1.3):
'''Construct flat field from individual frames'''
flatimages=','.join(images)
iraf.flatcombine(flatimages, output='flat1', combine='median',
reject='avsigclip', ccdtype='', process=no, subsets=no,
delete=no, clobber=no, scale='median', lsigma=3.0,
hsigma=3.0, gain=gain, rdnoise=rdnoise)
iraf.fmedian('flat1', 'flat2', xwindow, ywindow, hmin=hmin, hmax=hmax)
iraf.imarith('flat1', '/', 'flat2', outflat)
iraf.imreplace(outflat, 1.0, lower=INDEF, upper=lowclip)
iraf.imreplace(outflat, 1.0, lower=highclip, upper=INDEF)
return
def main(fits):
fits_skysig = fits[0:fits.rfind(".")] + "_skysig.fits"
sexpath = "/Users/nagashima/astro/default/sextractor/object/default.sex" #set sextractor configure file path
#make object mask image
fits_seg = fits[0:fits.rfind(".")] + "_seg.fits"
cat_seg = fits_seg[0:fits_seg.rfind(".")] + ".cat"
dma = 1.
os.system(
"sex %s -c %s -DETECT_MINAREA %s -CHECKIMAGE_TYPE SEGMENTATION -CATALOG_NAME %s -CHECKIMAGE_NAME %s"
% (fits, sexpath, dma, cat_seg, fits_seg))
iraf.imreplace(images=fits_seg, value=1e06, lower=1.)
#use stack image and only object image -> make skysigma image
if os.path.exists(fits_skysig) == True:
os.system("rm %s" % fits_skysig)
iraf.imarith(fits, "+", fits_seg, fits_skysig)
print "ds9 %s &" % fits_skysig
return fits_skysig
def prep(df_image, hi_res_image):
'run SExtractor to get bright sources that are easily detected in the high resolution data'
##### DEB: Can choose to run sextractor more or less aggressively
subprocess.call('sex %s' % hi_res_image, shell=True)
'copy the segmentation map to a mask'
iraf.imcopy('seg.fits', '_mask.fits')
'replace the values in the segments in the segmentation map (i.e. stars) all to 1, all the background is still 0'
iraf.imreplace('_mask.fits', 1, lower=0.5)
'multiply the mask (with 1s at the stars) by the high res image to get the star flux back - now have the flux model'
iraf.imarith('_mask.fits', '*', '%s' % hi_res_image, '_fluxmod_cfht')
'smooth the flux model'
'increase size of mask, so more is subtracted'
'the "1.5" in the next line should be a user-defined parameter that is given'
'to the script; it controls how much of the low surface brightness'
'emission in the outskirts of galaxies is subtracted. This choice'
'depends on the science application'
iraf.gauss('_fluxmod_cfht', '_fluxmod_cfht_smoothed', 2.5) ### nsigma=4
'''
imreplace _fluxmod_cfht_rs -1 lower=0 upper=0
imreplace _fluxmod_cfht_rs 1 lower=-0.5
imreplace _fluxmod_cfht_rs 0 upper=-0.5
imarith _cfht_r * _fluxmod_cfht_rs _fluxmod_cfht_r_new
'''
'''
# this is the key new step! we're registering the CFHT image
# to a frame that is 4x finer sampled than the Dragonfly image.
# this avoids all the pixelation effects we had before.
# (4x seems enough; but we could have it as a free parameter - need
# to be careful as it occurs elsewhere in the scripts too)
blkrep _df_g _df_g4 4 4
'''
'register the flux model onto the same pixel scale as the dragonfly image'
iraf.wregister('_fluxmod_cfht_smoothed',
'%s' % df_image,
'_fluxmod_dragonfly',
interpo='linear',
fluxcon='yes')
return None
def make_skysigim( fitspath ):
"""
stdout = iraf.imstat(images=fitspath,fields="npix,min,max,stddev,midpt,mode",Stdout=1)
key_ls = stdout[0].strip("#").split()
val_ls = map(float,stdout[1].split())
data = dict([(key,val) for key,val in zip(key_ls,val_ls)])
if ( data["MIDPT"] < 1000 ) and ( data["MODE"] < 1000 ):
fits_skypath = fitspath
print "Already substract sky!!"
else:
fits_skypath = fitspath[0:fitspath.rfind(".")] + "_sk.fits"
os.system("sex %s -c /mnt/data5/users/nagashima/default/default.sex -CHECKIMAGE_TYPE -BACKGROUND -CHECKIMAGE_NAME %s" % (fitspath,fits_skypath))
print "Subtract sky!!"
"""
objonlypath = fitspath[0:fitspath.rfind("/")] + "/object_only.fits"
skysigpath = fitspath[0:fitspath.rfind("/")] + "/skysig.fits"
if os.path.exists(objonlypath) == True :os.remove(objonlypath)
if os.path.exists(skysigpath) == True :os.remove(skysigpath)
os.system("sex %s -c /mnt/data5/users/nagashima/default/default.sex -CHECKIMAGE_TYPE OBJECTS -CHECKIMAGE_NAME %s" % (fitspath,objonlypath))
iraf.imreplace(images=objonlypath,value=-300000,lower=1)#not 0 not float so
iraf.imarith(fitspath,"+",objonlypath,skysigpath)
return skysigpath
def mask(res_org,upperlim=0.04):
print "\n**** Masking the residual, using upperlim of %s ****\n" % upperlim
upperlim = float(upperlim)
iraf.imdel('_res_final.fits')
iraf.imdel('_model_mask')
iraf.imdel('_model_maskb')
iraf.imcopy('_model_sc.fits','_model_mask.fits')
iraf.imreplace('_model_mask.fits',0,upper=upperlim)
iraf.imreplace('_model_mask.fits',1,lower=upperlim/2.)
iraf.boxcar('_model_mask','_model_maskb',5,5)
iraf.imreplace('_model_maskb.fits',1,lower=0.1)
iraf.imreplace('_model_maskb.fits',0,upper=0.9)
iraf.imarith(1,'-','_model_maskb','_model_maskb')
iraf.imarith('_model_maskb','*',res_org,'_res_final')
return None
def lacos(_input0, output='clean.fits', outmask='mask.fits', gain=1.3, readn=9, xorder=9, yorder=9, sigclip=4.5, sigfrac=0.5, objlim=1, verbose=True, interactive=False):
# print "LOGX:: Entering `lacos` method/function in %(__file__)s" %
# globals()
import ntt
from ntt.util import delete
import sys
import re
import os
import string
from pyraf import iraf
import numpy as np
oldoutput, galaxy, skymod, med5 = 'oldoutput.fits', 'galaxy.fits', 'skymod.fits', 'med5.fits'
blk, lapla, med3, med7, sub5, sigima, finalsel = 'blk.fits', 'lapla.fits', 'med3.fits', 'med7.fits', 'sub5.fits', 'sigima.fits', 'finalsel.fits'
deriv2, noise, sigmap, firstsel, starreject = 'deriv2.fits', 'noise.fits', 'sigmap.fits', 'firstsel.fits', 'starreject.fits'
inputmask = 'inputmask.fits'
# set some parameters in standard IRAF tasks
iraf.convolve.bilinear = 'no'
iraf.convolve.radsym = 'no'
# create Laplacian kernel
# laplkernel = np.array([[0.0, -1.0, 0.0], [-1.0, 4.0, -1.0], [0.0, -1.0, 0.0]])
f = open('_kernel', 'w')
f.write('0 -1 0;\n-1 4 -1;\n0 -1 0')
f.close()
# create growth kernel
f = open('_gkernel', 'w')
f.write('1 1 1;\n1 1 1;\n1 1 1')
f.close()
gkernel = np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
delete(galaxy)
delete(skymod)
delete(oldoutput)
if not output:
output = _input0
else:
delete(output)
iraf.imcopy(_input0, output, verbose='no')
delete('_xxx.fits,_yyy.fits')
iraf.imcopy(_input0 + '[350:550,*]', '_xxx.fits', verbose='no')
_input = '_xxx.fits'
arrayinput, headerinput = ntt.cosmics.fromfits(_input, verbose=False)
ntt.cosmics.tofits(outmask, np.float32(
arrayinput - arrayinput), headerinput, verbose=False)
# subtract object spectra if desired
iraf.fit1d(_input, galaxy, "fit", axis=2, order=9, func="leg", low=4.,
high=4., nav=1, inter='no', sample="*", niter=3, grow=0, cursor="")
iraf.imarith(_input, "-", galaxy, oldoutput)
# Subtract sky lines
iraf.fit1d(oldoutput, skymod, "fit", axis=1, order=5, func="leg", low=4., high=4.,
inter='no', sample="*", nav=1, niter=3, grow=0, cursor="")
iraf.imarith(oldoutput, "-", skymod, oldoutput)
arrayoldoutput, headeroldoutput = ntt.cosmics.fromfits(
oldoutput, verbose=False)
# add object spectra to sky model
iraf.imarith(skymod, "+", galaxy, skymod)
delete(med5)
# add median of residuals to sky model
iraf.median(oldoutput, med5, 5, 5, zlor='INDEF',
zhir='INDEF', verbose='no')
# m5 = ndimage.filters.median_filter(_inputarray, size=5, mode='mirror')
iraf.imarith(skymod, "+", med5, med5)
# take second-order derivative (Laplacian) of input image
# kernel is convolved with subsampled image, in order to remove negative
# pattern around high pixels
delete(blk)
delete(lapla)
iraf.blkrep(oldoutput, blk, 2, 2)
iraf.convolve(blk, lapla, '_kernel')
iraf.imreplace(lapla, 0, upper=0, lower='INDEF')
delete(deriv2)
delete(noise)
iraf.blkavg(lapla, deriv2, 2, 2, option="average")
# create noise model
iraf.imutil.imexpr(expr='sqrt(a*' + str(gain) + '+' + str(readn) +
'**2)/' + str(gain), a=med5, output=noise, verbose='no')
iraf.imreplace(med5, 0.00001, upper=0, lower='INDEF')
# divide Laplacian by noise model
delete(sigmap)
iraf.imutil.imexpr(expr='(a/b)/2', a=deriv2, b=noise,
output=sigmap, verbose='no')
# removal of large structure (bright, extended objects)
delete(med5)
iraf.median(sigmap, med5, 5, 5, zlo='INDEF', zhi='INDEF', verbose='no')
iraf.imarith(sigmap, "-", med5, sigmap)
# find all candidate cosmic rays
# this selection includes sharp features such as stars and HII regions
arraysigmap, headersigmap = ntt.cosmics.fromfits(sigmap, verbose=False)
arrayf = np.where(arraysigmap < sigclip, 0, arraysigmap)
arrayf = np.where(arrayf > 0.1, 1, arrayf)
ntt.cosmics.tofits(firstsel, np.float32(
arrayf), headersigmap, verbose=False)
# compare candidate CRs to median filtered image
# this step rejects bright, compact sources from the initial CR list
# subtract background and smooth component of objects
delete(med3)
iraf.median(oldoutput, med3, 3, 3, zlo='INDEF', zhi='INDEF', verbose='no')
delete(med7)
delete('_' + med3)
iraf.median(med3, med7, 7, 7, zlo='INDEF', zhi='INDEF', verbose='no')
iraf.imutil.imexpr(expr='(a-b)/c', a=med3, b=med7,
c=noise, output='_' + med3, verbose='no')
iraf.imreplace('_' + med3, 0.01, upper=0.01, lower='INDEF')
# compare CR flux to object flux
delete(starreject)
iraf.imutil.imexpr(expr='a+b+c', a=firstsel, b=sigmap,
c="_" + med3, output=starreject, verbose='no')
# discard if CR flux <= objlim * object flux
iraf.imreplace(starreject, 0, upper=objlim, lower='INDEF')
iraf.imreplace(starreject, 1, lower=0.5, upper='INDEF')
iraf.imarith(firstsel, "*", starreject, firstsel)
# grow CRs by one pixel and check in original sigma map
arrayfirst, headerfirst = ntt.cosmics.fromfits(firstsel, verbose=False)
arraygfirst = ntt.cosmics.my_convolve_with_FFT2(arrayfirst, gkernel)
arraygfirst = np.where(arraygfirst > 0.5, 1, arraygfirst)
arraygfirst = arraygfirst * arraysigmap
arraygfirst = np.where(arraygfirst < sigclip, 0, arraygfirst)
arraygfirst = np.where(arraygfirst > 0.1, 1, arraygfirst)
# grow CRs by one pixel and lower detection limit
sigcliplow = sigfrac * sigclip
# Finding neighbouring pixels affected by cosmic rays
arrayfinal = ntt.cosmics.my_convolve_with_FFT2(arraygfirst, gkernel)
arrayfinal = np.where(arrayfinal > 0.5, 1, arrayfinal)
arrayfinal = arrayfinal * arraysigmap
arrayfinal = np.where(arrayfinal < sigcliplow, 0, arrayfinal)
arrayfinal = np.where(arrayfinal > 0.1, 1, arrayfinal)
# determine number of CRs found in this iteration
arraygfirst = (1 - (arrayfinal - arrayfinal)) * arrayfinal
npix = [str(int(np.size(np.where(arraygfirst > 0.5)) / 2.))]
# create cleaned output image; use 3x3 median with CRs excluded
arrayoutmask = np.where(arrayfinal > 1, 1, arrayfinal)
ntt.cosmics.tofits(outmask, np.float32(
arrayoutmask), headerfirst, verbose=False)
delete(inputmask)
arrayinputmask = (1 - (10000 * arrayoutmask)) * arrayoldoutput
ntt.cosmics.tofits(inputmask, np.float32(
arrayinputmask), headerfirst, verbose=False)
delete(med5)
iraf.median(inputmask, med5, 5, 5, zloreject=-
9999, zhi='INDEF', verbose='no')
iraf.imarith(outmask, "*", med5, med5)
delete('_yyy.fits')
iraf.imutil.imexpr(expr='(1-a)*b+c', a=outmask, b=oldoutput,
c=med5, output='_yyy.fits', verbose='no')
# add sky and object spectra back in
iraf.imarith('_yyy.fits', "+", skymod, '_yyy.fits')
# cleanup and get ready for next iteration
if npix == 0:
stop = yes
# delete temp files
iraf.imcopy('_yyy.fits', output + '[350:550,*]', verbose='no')
delete(blk + "," + lapla + "," + deriv2 + "," + med5)
delete(med3 + "," + med7 + "," + noise + "," + sigmap)
delete(firstsel + "," + starreject)
delete(finalsel + "," + inputmask)
delete(oldoutput + "," + skymod + "," + galaxy)
delete("_" + med3 + ",_" + sigmap)
delete('_kernel' + "," + '_gkernel')
delete(outmask)
delete('_xxx.fits,_yyy.fits')
def lacos_spec(_input, output='clean.fits', outmask='mask.fits', gain=1.3, readn=9,\
xorder=9, yorder=3, sigclip=4.5, sigfrac=0.5, objlim=1, niter=4, instrument='kastr', verbose=True, interactive=False):
# print "LOGX:: Entering `lacos` method/function in %(__file__)s" %
# globals()
import lickshane
import sys
import re
import os
import string
from pyraf import iraf
import numpy as np
oldoutput, galaxy, skymod, med5 = 'oldoutput.fits', 'galaxy.fits', 'skymod.fits', 'med5.fits'
blk, lapla, med3, med7, sub5, sigima, finalsel = 'blk.fits', 'lapla.fits', 'med3.fits', 'med7.fits', 'sub5.fits', 'sigima.fits', 'finalsel.fits'
deriv2, noise, sigmap, firstsel, starreject = 'deriv2.fits', 'noise.fits', 'sigmap.fits', 'firstsel.fits', 'starreject.fits'
inputmask = 'inputmask.fits'
# set some parameters in standard IRAF tasks
iraf.convolve.bilinear = 'no'
iraf.convolve.radsym = 'no'
# create Laplacian kernel
# laplkernel = np.array([[0.0, -1.0, 0.0], [-1.0, 4.0, -1.0], [0.0, -1.0, 0.0]])
f = open('_kernel', 'w')
f.write('0 -1 0;\n-1 4 -1;\n0 -1 0')
f.close()
# create growth kernel
f = open('_gkernel', 'w')
f.write('1 1 1;\n1 1 1;\n1 1 1')
f.close()
gkernel = np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
lickshane.util.delete(galaxy)
lickshane.util.delete(skymod)
lickshane.util.delete(oldoutput)
if not output:
output = _input
else:
os.system('cp ' + _input + ' ' + output)
os.system('cp ' + _input + ' ' + oldoutput)
arrayinput, headerinput = lickshane.cosmics.fromfits(oldoutput,
verbose=False)
lickshane.cosmics.tofits(outmask,
np.float32(arrayinput - arrayinput),
headerinput,
verbose=False)
if instrument in ['kastr']:
axis1 = 1
axis2 = 2
elif instrument in ['kastb']:
axis1 = 2
axis2 = 1
# subtract object spectra if desired
if xorder > 0:
iraf.fit1d(oldoutput,
galaxy,
"fit",
axis=axis1,
order=xorder,
func="leg",
low=4.,
high=4.,
nav=1,
inter='no',
sample="*",
niter=3,
grow=0,
cursor="")
iraf.imarith(oldoutput, "-", galaxy, oldoutput)
else:
lickshane.cosmics.tofits(galaxy,
np.float32(arrayinput - arrayinput),
headerinput,
verbose=False)
# Subtract sky lines
if yorder > 0:
iraf.fit1d(oldoutput,
skymod,
"fit",
axis=axis2,
order=yorder,
func="leg",
low=4.,
high=4.,
inter='no',
sample="*",
nav=1,
niter=3,
grow=0,
cursor="")
iraf.imarith(oldoutput, "-", skymod, oldoutput)
else:
lickshane.cosmics.tofits(skymod,
np.float32(arrayinput - arrayinput),
headerinput,
verbose=False)
arrayoldoutput, headeroldoutput = lickshane.cosmics.fromfits(oldoutput,
verbose=False)
# add object spectra to sky model
iraf.imarith(skymod, "+", galaxy, skymod)
###########
## start iteration
###########
ii = 0
while ii < niter:
print ii
# add median of residuals to sky model
lickshane.util.delete(med5)
iraf.median(oldoutput,
med5,
5,
5,
zlor='INDEF',
zhir='INDEF',
verbose='no')
# m5 = ndimage.filters.median_filter(_inputarray, size=5, mode='mirror')
iraf.imarith(skymod, "+", med5, med5)
# take second-order derivative (Laplacian) of input image
# kernel is convolved with subsampled image, in order to remove negative
# pattern around high pixels
lickshane.util.delete(blk)
lickshane.util.delete(lapla)
lickshane.util.delete(deriv2)
lickshane.util.delete(noise)
lickshane.util.delete(sigmap)
iraf.blkrep(oldoutput, blk, 2, 2)
iraf.convolve(blk, lapla, '_kernel')
iraf.imreplace(lapla, 0, upper=0, lower='INDEF')
iraf.blkavg(lapla, deriv2, 2, 2, option="average")
# create noise model
iraf.imutil.imexpr(expr='sqrt(a*' + str(gain) + '+' + str(readn) +
'**2)/' + str(gain),
a=med5,
output=noise,
verbose='no')
iraf.imreplace(med5, 0.00001, upper=0, lower='INDEF')
# divide Laplacian by noise model
iraf.imutil.imexpr(expr='(a/b)/2',
a=deriv2,
b=noise,
output=sigmap,
verbose='no')
# removal of large structure (bright, extended objects)
lickshane.util.delete(med5)
iraf.median(sigmap, med5, 5, 5, zlo='INDEF', zhi='INDEF', verbose='no')
iraf.imarith(sigmap, "-", med5, sigmap)
# find all candidate cosmic rays
# this selection includes sharp features such as stars and HII regions
arraysigmap, headersigmap = lickshane.cosmics.fromfits(sigmap,
verbose=False)
arrayf = np.where(arraysigmap < sigclip, 0, arraysigmap)
arrayf = np.where(arrayf > 0.1, 1, arrayf)
lickshane.cosmics.tofits(firstsel,
np.float32(arrayf),
headersigmap,
verbose=False)
# compare candidate CRs to median filtered image
# this step rejects bright, compact sources from the initial CR list
# subtract background and smooth component of objects
lickshane.util.delete(med3)
iraf.median(oldoutput,
med3,
3,
3,
zlo='INDEF',
zhi='INDEF',
verbose='no')
lickshane.util.delete(med7)
lickshane.util.delete('_' + med3)
iraf.median(med3, med7, 7, 7, zlo='INDEF', zhi='INDEF', verbose='no')
iraf.imutil.imexpr(expr='(a-b)/c',
a=med3,
b=med7,
c=noise,
output='_' + med3,
verbose='no')
iraf.imreplace('_' + med3, 0.01, upper=0.01, lower='INDEF')
# compare CR flux to object flux
lickshane.util.delete(starreject)
iraf.imutil.imexpr(expr='(a*b)/c',
a=firstsel,
b=sigmap,
c="_" + med3,
output=starreject,
verbose='no')
# ###### ##### ###### ##### ###### FOUND A BUG ?
# iraf.imutil.imexpr(expr='a+b+c', a=firstsel, b=sigmap,
# c="_" + med3, output=starreject, verbose='no')
# discard if CR flux <= objlim * object flux
iraf.imreplace(starreject, 0, upper=objlim, lower='INDEF')
iraf.imreplace(starreject, 1, lower=0.5, upper='INDEF')
iraf.imarith(firstsel, "*", starreject, firstsel)
# grow CRs by one pixel and check in original sigma map
arrayfirst, headerfirst = lickshane.cosmics.fromfits(firstsel,
verbose=False)
arraygfirst = lickshane.cosmics.my_convolve_with_FFT2(
arrayfirst, gkernel)
arraygfirst = np.where(arraygfirst > 0.5, 1, arraygfirst)
arraygfirst = arraygfirst * arraysigmap
arraygfirst = np.where(arraygfirst < sigclip, 0, arraygfirst)
arraygfirst = np.where(arraygfirst > 0.1, 1, arraygfirst)
# grow CRs by one pixel and lower detection limit
sigcliplow = sigfrac * sigclip
# Finding neighbouring pixels affected by cosmic rays
arrayfinal = lickshane.cosmics.my_convolve_with_FFT2(
arraygfirst, gkernel)
arrayfinal = np.where(arrayfinal > 0.5, 1, arrayfinal)
arrayfinal = arrayfinal * arraysigmap
arrayfinal = np.where(arrayfinal < sigcliplow, 0, arrayfinal)
arrayfinal = np.where(arrayfinal > 0.1, 1, arrayfinal)
# determine number of CRs found in this iteration
arraygfirst = (1 - (arrayfinal - arrayfinal)) * arrayfinal
npix = [str(int(np.size(np.where(arraygfirst > 0.5)) / 2.))]
# create cleaned output image; use 3x3 median with CRs excluded
arrayoutmask = np.where(arrayfinal > 1, 1, arrayfinal)
lickshane.cosmics.tofits(outmask,
np.float32(arrayoutmask),
headerfirst,
verbose=False)
lickshane.util.delete(inputmask)
arrayinputmask = (1 - (10000 * arrayoutmask)) * arrayoldoutput
lickshane.cosmics.tofits(inputmask,
np.float32(arrayinputmask),
headerfirst,
verbose=False)
lickshane.util.delete(med5)
iraf.median(inputmask,
med5,
5,
5,
zloreject=-9999,
zhi='INDEF',
verbose='no')
iraf.imarith(outmask, "*", med5, med5)
lickshane.util.delete(output)
iraf.imutil.imexpr(expr='(1-a)*b+c',
a=outmask,
b=oldoutput,
c=med5,
output=output,
verbose='no')
lickshane.util.delete(oldoutput)
os.system('cp ' + output + ' ' + oldoutput)
# add sky and object spectra back in
iraf.imarith(output, "+", skymod, output)
# cleanup and get ready for next iteration
ii = ii + 1
if npix == 0:
ii = niter
# delete temp files
lickshane.util.delete(blk + "," + lapla + "," + deriv2 + "," + med5)
lickshane.util.delete(med3 + "," + med7 + "," + noise + "," + sigmap)
lickshane.util.delete(firstsel + "," + starreject)
lickshane.util.delete(finalsel + "," + inputmask)
lickshane.util.delete(oldoutput + "," + skymod + "," + galaxy)
lickshane.util.delete("_" + med3 + ",_" + sigmap)
lickshane.util.delete('_kernel' + "," + '_gkernel')
lickshane.util.delete(outmask)
def lacos(_input0,
output='clean.fits',
outmask='mask.fits',
gain=1.3,
readn=9,
xorder=9,
yorder=9,
sigclip=4.5,
sigfrac=0.5,
objlim=1,
verbose=True,
interactive=False):
import floyds
from floyds.util import delete
from pyraf import iraf
import numpy as np
oldoutput, galaxy, skymod, med5 = 'oldoutput.fits', 'galaxy.fits', 'skymod.fits', 'med5.fits'
blk, lapla, med3, med7, sub5, sigima, finalsel = 'blk.fits', 'lapla.fits', 'med3.fits', 'med7.fits', 'sub5.fits',\
'sigima.fits', 'finalsel.fits'
deriv2, noise, sigmap, firstsel, starreject = 'deriv2.fits', 'noise.fits', 'sigmap.fits', 'firstsel.fits',\
'starreject.fits'
inputmask = 'inputmask.fits'
# set some parameters in standard IRAF tasks
iraf.convolve.bilinear = 'no'
iraf.convolve.radsym = 'no'
# create Laplacian kernel
# laplkernel = np.array([[0.0, -1.0, 0.0], [-1.0, 4.0, -1.0], [0.0, -1.0, 0.0]])
f = open('_kernel', 'w')
f.write('0 -1 0;\n-1 4 -1;\n0 -1 0')
f.close()
# create growth kernel
f = open('_gkernel', 'w')
f.write('1 1 1;\n1 1 1;\n1 1 1')
f.close()
gkernel = np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
delete(galaxy)
delete(skymod)
delete(oldoutput)
if not output:
output = _input0
else:
delete(output)
iraf.imcopy(_input0, output, verbose='no')
delete('_xxx.fits,_yyy.fits')
iraf.imcopy(_input0 + '[*,10:80]', '_xxx.fits', verbose='no')
_input = '_xxx.fits'
arrayinput, headerinput = floyds.cosmics.fromfits(_input, verbose=False)
floyds.cosmics.tofits(outmask,
np.float32(arrayinput - arrayinput),
headerinput,
verbose=False)
# subtract object spectra if desired
# iraf.fit1d(_input,galaxy,"fit",axis=2,order=5,func="leg",low=4.,
# high=4.,nav=1,inter='yes',sample="*",niter=3,grow=0,cursor="")
# iraf.imarith(_input,"-",galaxy,oldoutput)
# iraf.display(oldoutput,1,fill='yes') #######
#Subtract sky lines
# iraf.fit1d(oldoutput,skymod,"fit",axis=2,order=5,func="leg",low=4.,high=4.,
# inter='no',sample="*",nav=1,niter=3,grow=0,cursor="")
# iraf.imarith(oldoutput,"-",skymod,oldoutput)
# iraf.display(oldoutput,2,fill='yes') #####
iraf.imcopy(_input, oldoutput)
arrayoldoutput, headeroldoutput = floyds.cosmics.fromfits(oldoutput,
verbose=False)
# add object spectra to sky model
# iraf.imarith(skymod,"+",galaxy,skymod)
delete(med5)
# add median of residuals to sky model
iraf.median(oldoutput,
med5,
5,
5,
zlor='INDEF',
zhir='INDEF',
verbose='no')
# m5 = ndimage.filters.median_filter(_inputarray, size=5, mode='mirror')
# iraf.imarith(skymod,"+",med5,med5)
# take second-order derivative (Laplacian) of input image
# kernel is convolved with subsampled image, in order to remove negative
# pattern around high pixels
delete(blk)
delete(lapla)
iraf.blkrep(oldoutput, blk, 2, 2)
iraf.convolve(blk, lapla, '_kernel')
iraf.imreplace(lapla, 0, upper=0, lower='INDEF')
delete(deriv2)
delete(noise)
iraf.blkavg(lapla, deriv2, 2, 2, option="average")
# create noise model
iraf.imutil.imexpr(expr='sqrt(a*' + str(gain) + '+' + str(readn) +
'**2)/' + str(gain),
a=med5,
output=noise,
verbose='no')
iraf.imreplace(med5, 0.00001, upper=0, lower='INDEF')
# divide Laplacian by noise model
delete(sigmap)
iraf.imutil.imexpr(expr='(a/b)/2',
a=deriv2,
b=noise,
output=sigmap,
verbose='no')
# removal of large structure (bright, extended objects)
delete(med5)
iraf.median(sigmap, med5, 5, 5, zlo='INDEF', zhi='INDEF', verbose='no')
iraf.imarith(sigmap, "-", med5, sigmap)
# find all candidate cosmic rays
# this selection includes sharp features such as stars and HII regions
arraysigmap, headersigmap = floyds.cosmics.fromfits(sigmap, verbose=False)
arrayf = np.where(arraysigmap < sigclip, 0, arraysigmap)
arrayf = np.where(arrayf > 0.1, 1, arrayf)
floyds.cosmics.tofits(firstsel,
np.float32(arrayf),
headersigmap,
verbose=False)
# compare candidate CRs to median filtered image
# this step rejects bright, compact sources from the initial CR list
# subtract background and smooth component of objects
delete(med3)
iraf.median(oldoutput, med3, 3, 3, zlo='INDEF', zhi='INDEF', verbose='no')
delete(med7)
delete('_' + med3)
iraf.median(med3, med7, 7, 7, zlo='INDEF', zhi='INDEF', verbose='no')
iraf.imutil.imexpr(expr='(a-b)/c',
a=med3,
b=med7,
c=noise,
output='_' + med3,
verbose='no')
iraf.imreplace('_' + med3, 0.01, upper=0.01, lower='INDEF')
# compare CR flux to object flux
delete(starreject)
iraf.imutil.imexpr(expr='a+b+c',
a=firstsel,
b=sigmap,
c="_" + med3,
output=starreject,
verbose='no')
# discard if CR flux <= objlim * object flux
iraf.imreplace(starreject, 0, upper=objlim, lower='INDEF')
iraf.imreplace(starreject, 1, lower=0.5, upper='INDEF')
iraf.imarith(firstsel, "*", starreject, firstsel)
# grow CRs by one pixel and check in original sigma map
arrayfirst, headerfirst = floyds.cosmics.fromfits(firstsel, verbose=False)
arraygfirst = floyds.cosmics.my_convolve_with_FFT2(arrayfirst, gkernel)
arraygfirst = np.where(arraygfirst > 0.5, 1, arraygfirst)
arraygfirst = arraygfirst * arraysigmap
arraygfirst = np.where(arraygfirst < sigclip, 0, arraygfirst)
arraygfirst = np.where(arraygfirst > 0.1, 1, arraygfirst)
# grow CRs by one pixel and lower detection limit
sigcliplow = sigfrac * sigclip
# Finding neighbouring pixels affected by cosmic rays
arrayfinal = floyds.cosmics.my_convolve_with_FFT2(arraygfirst, gkernel)
arrayfinal = np.where(arrayfinal > 0.5, 1, arrayfinal)
arrayfinal = arrayfinal * arraysigmap
arrayfinal = np.where(arrayfinal < sigcliplow, 0, arrayfinal)
arrayfinal = np.where(arrayfinal > 0.1, 1, arrayfinal)
# determine number of CRs found in this iteration
arraygfirst = (1 - (arrayfinal - arrayfinal)) * arrayfinal
npix = [str(int(np.size(np.where(arraygfirst > 0.5)) / 2.))]
# create cleaned output image; use 3x3 median with CRs excluded
arrayoutmask = np.where(arrayfinal > 1, 1, arrayfinal)
floyds.cosmics.tofits(outmask,
np.float32(arrayoutmask),
headerfirst,
verbose=False)
delete(inputmask)
arrayinputmask = (1 - (10000 * arrayoutmask)) * arrayoldoutput
floyds.cosmics.tofits(inputmask,
np.float32(arrayinputmask),
headerfirst,
verbose=False)
delete(med5)
iraf.median(inputmask,
med5,
5,
5,
zloreject=-9999,
zhi='INDEF',
verbose='no')
iraf.imarith(outmask, "*", med5, med5)
delete('_yyy.fits')
iraf.imutil.imexpr(expr='(1-a)*b+c',
a=outmask,
b=oldoutput,
c=med5,
output='_yyy.fits',
verbose='no')
# add sky and object spectra back in
#iraf.imarith('_yyy.fits',"+",skymod,'_yyy.fits')
# cleanup and get ready for next iteration
if npix == 0: stop = yes
# delete temp files
iraf.imcopy('_yyy.fits', output + '[*,10:80]', verbose='no')
delete(blk + "," + lapla + "," + deriv2 + "," + med5)
delete(med3 + "," + med7 + "," + noise + "," + sigmap)
delete(firstsel + "," + starreject)
delete(finalsel + "," + inputmask)
delete(oldoutput + "," + skymod + "," + galaxy)
delete("_" + med3 + ",_" + sigmap)
delete('_kernel' + "," + '_gkernel')
delete(outmask)
delete('_xxx.fits,_yyy.fits')
def irafflatten():
os.system('cp /Users/rfinn/clusters/spitzer/flatsexfiles/* .')
infile=open('InputImageList.txt','r')
outfile=open('FlatImageList.txt','w')
sky=[]
for line in infile:
im=line[0:(len(line)-1)]
mask='mask'+im
skyim='s'+im
outline='f'+line
iraf.imgets(im,'DRIBKGND')
t=iraf.imgets.value
sky.append(float(t))
outfile.write(outline)
#get object positions using sextractor
iraf.imarith(im,'-',t,skyim)#subtract sky before running sextractor (otherwise it doesn't detect any objects - don't know why...)
s='sex '+skyim
os.system(s)
x=[]
y=[]
catfile=open('test.cat','r')
for line in catfile:
if line.find('#') > -1:
continue
t=line.split()
x.append(float(t[10]))
y.append(float(t[11]))
catfile.close()
x=N.array(x,'f')
y=N.array(y,'f')
try:#create image of ones same size as image
iraf.imarith(im,'/',im,'mask')
except:
iraf.imdelete('mask')
iraf.imarith(im,'/',im,'mask')
print "masking objects"
for j in range(len(x)): #mask objects and radius around each position using imreplace, radius=10 pix
for k in range(11):
y1=int(y[j]+5-k)
if y1 < 1:
continue
if y1 > 128:
continue
xmin=int(x[j]-5)
if xmin < 1:
xmin=1
xmax=int(x[j]+5)
if xmax > 128:
xmax=128
s='mask['+str(xmin)+':'+str(xmax)+","+str(y1)+":"+str(y1)+"]"
iraf.imreplace(s,0.)
iraf.imrename('mask',mask)
print "updating BPM field in header"
iraf.hedit(im,fields='BPM',value=mask,add='yes',verify='no',update='yes')
outfile.close()
infile.close()
avesky=N.average(N.array(sky,'f'))
lthresh=avesky-1.
hthresh=avesky+.6
iraf.imcombine('@InputImageList.txt','flat',combine='average',reject='ccdclip',scale='none',zero='mode',lthreshold=lthresh,hthreshold=hthresh,lsigma=2.,hsigma=2.,rdnoise=5.,gain=5.,blank=1.,grow=12.,masktype='badvalue',maskvalue='0')
t=iraf.imstat('flat',fields='mean',format='no',Stdout=1)
ave=float(t[0])
iraf.imarith('flat','/',ave,'nflat')
iraf.imarith('@InputImageList.txt','/','nflat','@FlatImageList.txt')
def subract(df_image, psf):
iraf.imdel('_model*.fits')
iraf.imdel('_res*.fits')
iraf.imdel('_df_sub')
'subtract the sky value from the dragonfly image header'
df_backval = fits.getheader(df_image)['BACKVAL']
iraf.imarith('%s' % df_image, '-', '%s' % df_backval, '_df_sub')
'convolve the model with the Dragonfly PSF'
if usemodelpsf:
makeallisonspsf()
psf = './psf/psf_static_fullframe.fits'
### XXX resample the PSF by a factor of 4
if verbose:
print 'VERBOSE: Using %s for the psf convolution.' % psf
iraf.stsdas.analysis.fourier.fconvolve('_fluxmod_dragonfly', '%s' % psf,
'_model')
'shift the images so they have the same physical coordinates'
iraf.stsdas.analysis.dither.crossdriz('_df_sub.fits',
'_model.fits',
'cc_images',
dinp='no',
dref='no')
iraf.stsdas.analysis.dither.shiftfind('cc_images.fits', 'shift_values')
x_shift = 0
y_shift = 0
with open('shift_values', 'r') as datafile:
line = datafile.read().split()
x_shift = float(line[2])
y_shift = float(line[4])
print('The shift in x and y are: ' + str(x_shift) + ',' + str(y_shift))
iraf.imshift('_model', '_model_sh', 0 - x_shift, 0 - y_shift)
'scale the model so that roughly the same as the _df_sub image'
if usemodelpsf:
iraf.imarith(
'_model_sh', '/', 16., '_model_sc'
) ## just trying to match it to the original (below) approximately
else:
iraf.imarith(
'_model_sh', '/', 2422535.2, '_model_sc'
) ## difference between the flux of the star used to make the psf in both frames
iraf.imarith('_model_sc', '*', 1.5, '_model_sc')
'subtract the model frm the dragonfly cutout'
iraf.imarith('_df_sub', '-', '_model_sc', '_res')
'????'
iraf.imcopy('_model_sc', '_model_mask')
iraf.imreplace('_model_mask.fits', 0, upper=50)
iraf.imreplace('_model_mask.fits', 1, lower=0.01)
iraf.boxcar('_model_mask', '_model_maskb', 5, 5)
iraf.imreplace('_model_maskb.fits', 1, lower=0.1)
iraf.imreplace('_model_maskb.fits', 0, upper=0.9)
iraf.imarith(1, '-', '_model_maskb', '_model_maskb')
iraf.imarith('_model_maskb', '*', '_res', '_res_final')
return None
r = 555.
#template image
template = 'qopen0004.fits'
try:
iraf.imarith(template, '/', template, 'mask')
except:
iraf.imdelete('mask')
iraf.imarith(template, '/', template, 'mask')
for y in range(1, yc):
xmax = int(512. - N.sqrt(r**2 - (1. * y - yc)**2))
if xmax < 1.:
break
s = 'mask[1:' + str(xmax) + "," + str(y) + ":" + str(y) + "]"
print y, s
iraf.imreplace(s, 0.)
x1 = int(imxmax - xmax)
x2 = int(imxmax)
s = 'mask[' + str(x1) + ':' + str(x2) + ',' + str(y) + ':' + str(y) + ']'
print y, s
iraf.imreplace(s, 0.)
y1 = int(imymax - y + 1)
y2 = int(imymax)
s = 'mask[1:' + str(xmax) + "," + str(y1) + ":" + str(y1) + "]"
print y, s
iraf.imreplace(s, 0.)
s = 'mask[' + str(x1) + ':' + str(x2) + ',' + str(y1) + ':' + str(y1) + ']'
print y, s
iraf.imreplace(s, 0.)
iraf.display('mask', 2)
def imrep(inimage):
iraf.imreplace(inimage, value='1')
def prep(df_image,hi_res_image,width_mask=1.5,unmaskgal=False,galvalues = None):
print "\n************ Running the preparation steps ************\n"
iraf.imdel('_mask.fits')
iraf.imdel('_fluxmod_cfht*.fits')
iraf.imdel('_df_4*.fits')
'run SExtractor to get bright sources that are easily detected in the high resolution data'
subprocess.call('sex %s'%hi_res_image,shell=True) ##### Add in option to change sextractor threshold detect_thresh and analysis_thresh 2/3
'copy the segmentation map to a mask'
iraf.imcopy('seg.fits','_mask.fits')
##### Add step to get rid of diffraction spikes
if unmaskgal:
'Run SExtractor to get values for the central galaxy'
print '\nDoing a second sextractor run to unmask central galaxy. \n'
analysis_thresh_lg=2;back_size_lg=128;detect_thresh_lg=2;detect_minarea_lg=60
segname = run_SExtractor(hi_res_image,detect_thresh=detect_thresh_lg,analysis_thresh=analysis_thresh_lg,back_size=back_size_lg,detect_minarea=detect_minarea_lg)
print '\nSegmentation map is named: '+segname
'Open up the data'
seg2data = fits.getdata(segname)
segdata,segheader = fits.getdata('_mask.fits',header=True)
'Detect sources from the segmentation map'
from photutils import detect_sources
segrefdata = detect_sources(seg2data, 3, npixels=5)#, filter_kernel=kernel)
segrefdata = segrefdata.data
segrefname = re.sub('.fits','_ds.fits',segname)
writeFITS(segrefdata,segheader,segrefname)
print '\nSource separated segmentation map is named: '+segrefname
'Use detected source seg map to mask galaxies' #galvalues = [3531,5444,5496]
print 'Unmask the galaxies in the mask from the original segmap. \n'
segdatanew = unmaskgalaxy(segdata,'_mask.fits',segrefname=segrefname,segref=segrefdata,galvalues=galvalues)
writeFITS(segdatanew,segheader,'_mask.fits')
if verbose:
print_verbose_string('Carrying on')
'replace the values in the segments in the segmentation map (i.e. stars) all to 1, all the background is still 0'
iraf.imreplace('_mask.fits',1,lower=0.5)
'multiply the mask (with 1s at the stars) by the high res image to get the star flux back - now have the flux model'
iraf.imarith('_mask.fits','*','%s'%hi_res_image,'_fluxmod_cfht')
'smooth the flux model'
'increase size of mask, so more is subtracted'
'the "1.5" in the next line should be a user-defined parameter that is given'
'to the script; it controls how much of the low surface brightness'
'emission in the outskirts of galaxies is subtracted. This choice'
'depends on the science application'
iraf.gauss('_fluxmod_cfht','_fluxmod_cfht_smoothed',width_mask,nsigma=4.)
iraf.imreplace('_fluxmod_cfht_smoothed', -1, lower=0, upper=0)
iraf.imreplace('_fluxmod_cfht_smoothed', 1, lower=-0.5)
iraf.imreplace('_fluxmod_cfht_smoothed', 0, upper=-0.5)
iraf.imarith('%s'%hi_res_image, '*','_fluxmod_cfht_smoothed', '_fluxmod_cfht_new')
' this is the key new step! we"re registering the CFHT image'
' to a frame that is 4x finer sampled than the Dragonfly image.'
' this avoids all the pixelation effects we had before.'
' (4x seems enough; but we could have it as a free parameter - need'
' to be careful as it occurs elsewhere in the scripts too) '
iraf.blkrep('%s'%df_image,'_df_4',4,4)
'register the flux model onto the same pixel scale as the dragonfly image'
iraf.wregister('_fluxmod_cfht_new','_df_4','_fluxmod_dragonfly',interpo='linear',fluxcon='yes')
return None
def imrep(inimage):
iraf.imreplace(inimage, value='1')
def stacking(cllist,zpofflist,ref,zprefoff=0.0,stackname='stack',shiftsize=400):
"""
"""
#Reset the IRAF tasks used in this routine.
iraf.unlearn('imcalc')
iraf.unlearn('imcombine')
iraf.unlearn('imreplace')
iraf.unlearn('xyxymatch')
iraf.unlearn('geomap')
iraf.unlearn('geotran')
iraf.unlearn('imcopy')
#Find reference image in reference directory. Check to make
#sure that it is actually the image and not the mask file!
#Grab the mask for adding to the mask list now.
(refimg,refmask,expmap)=classify(ref+'/tu*.fits')
zpref=pf.open(refimg)[0].header['MAGZERO']
# zprefoff=NewfirmZPoffset[ref.split('/')[-1]]
zprefoff=float(zprefoff)
#Get 2MASS PSC positions for reference cluster image.
catalog=get2masspsc(refimg)
foo=file_check(ref+'/2mass_ref_stars.cdt',delete=True)
foo=open(ref+'/2mass_ref_stars.cdt','w')
for y in catalog:
data=y.split()
foo.write(data[6]+'\t'+data[7]+'\n')
foo.close()
#Create lists for files to be input into the stacking routine.
foo=file_check('matchlist',delete=True)
foo=file_check('scalelist',delete=True)
foo=file_check('shiftlist',delete=True)
foo=file_check('masklist',delete=True)
foo=file_check('shiftmask',delete=True)
foo=file_check('expmaplist',delete=True)
(matchlist,scalelist,shiftlist,masklist,
shiftmask,finalmasks,stacklist,stackmask,
finalmasks2,expmaplist,shiftexp,expmaplist2)=(open('matchlist','w'),open('scalelist','w'),
open('shiftlist','w'),open('masklist','w'),
open('shiftmask','w'),open('finalmasks','w'),
open('stacklist','w'),open('stackmask','w'),
open('finalmasks2','w'),open('expmaplist','w'),
open('shiftexp','w'),open('expmaplist2','w'))
(xsize,ysize)=(np.array([]),np.array([]))
#Step through all of the input cluster directories.
i=0
for x in cllist:
#Find the image, mask, and exposure map files. Get zeropoints and
#scale image to the reference image.
scaleimg=x+'/scaled_to_'+ref.split('/')[-1]+'.fits'
foo=file_check(scaleimg,delete=True)
(img,mask,expmap)=classify(x+'/tu*.fits')
imgzp=pf.open(img)[0].header['MAGZERO']
(xs,ys)=(pf.open(img)[0].header['NAXIS1'],pf.open(img)[0].header['NAXIS2'])
(xsize,ysize)=(np.append(xsize,xs),np.append(ysize,ys))
imgzpoff=float(zpofflist[i])
# imgzpoff=NewfirmZPoffset[x.split('/')[-1]]
scale=scalecounts(imgzp+imgzpoff,zpref+zprefoff)
iraf.imcalc(img,scaleimg,'im1*'+str(scale))
#Get X,Y pixel positions of 2MASS sources from the 2MASS PSC
#in the image. Use these to compute shifts relative to the
#reference image using IRAF task geomap.
foo=file_check(x+'/2mass_ref_stars.cdt',delete=True)
foo=open(x+'/2mass_ref_stars.cdt','w')
catalog=get2masspsc(scaleimg)
for y in catalog:
data=y.split()
foo.write(data[6]+'\t'+data[7]+'\n')
foo.close()
#Match the 2MASS PSC positions with stars in the reference
#image using xyxymatch. The matched source list is then fed
#into geomap to get the X and Y shifts.
foo=file_check(x+'/2mass_matched.cdt',delete=True)
iraf.xyxymatch(x+'/2mass_ref_stars.cdt',ref+'/2mass_ref_stars.cdt',
x+'/2mass_matched.cdt','200.0',verbose='no')
#Append all of the names of the files for the input and output filename
#lists to be passed to IRAF tasks further down the line.
matchlist.write(x+'/2mass_matched.cdt\n')
scalelist.write(scaleimg+'\n')
foo=file_check(x+'/scaled_and_shifted.fits',delete=True)
shiftlist.write(x+'/scaled_and_shifted.fits['+str(shiftsize)+':'+\
str(int(np.max(xsize))+shiftsize)+','+str(shiftsize)+':'+\
str(int(np.max(ysize))+shiftsize)+']\n')
stacklist.write(x+'/scaled_and_shifted.fits\n')
file_check(x+'/mask_tmp.fits',delete=True)
file_check(x+'/expmap_tmp.fits',delete=True)
iraf.imarith(mask+'[1]','*',1000.0,x+'/mask_tmp.fits',pixtype='real')
iraf.imarith(expmap+'[1]','*',1.0,x+'/expmap_tmp.fits',pixtype='real')
offset=2.558435
file_check(x+'/mask_tmp2.fits',delete=True)
iraf.imcalc(x+'/mask_tmp.fits',x+'/mask_tmp2.fits','im1+'+str(offset))
os.remove(x+'/mask_tmp.fits')
masklist.write(x+'/mask_tmp2.fits\n')
file_check(x+'/mask_shift.fits',delete=True)
shiftmask.write(x+'/mask_shift.fits['+str(shiftsize)+':'+\
str(int(np.max(xsize))+shiftsize)+','+str(shiftsize)+':'+\
str(int(np.max(ysize))+shiftsize)+']\n')
stackmask.write(x+'/mask_shift.fits\n')
finalmasks.write(x+'/mask_final.fits\n')
finalmasks2.write(x+'/mask_final.fits[0]\n')
expmaplist.write(x+'/expmap_tmp.fits[0]\n')
shiftexp.write(x+'/expmap_shift.fits['+str(shiftsize)+':'+\
str(int(np.max(xsize))+shiftsize)+','+str(shiftsize)+':'+\
str(int(np.max(ysize))+shiftsize)+']\n')
expmaplist2.write(x+'/expmap_shift.fits\n')
i += 1
#Close all of the input and output filename lists to be passed to IRAF tasks.
matchlist.close()
scalelist.close()
stacklist.close()
masklist.close()
shiftmask.close()
finalmasks.close()
shiftlist.close()
stackmask.close()
finalmasks2.close()
expmaplist.close()
expmaplist2.close()
shiftexp.close()
#Get the shifts between all input files (including the reference) and the
#reference image itself.
foo=file_check('shift.db',delete=True)
iraf.geomap('@matchlist','shift.db',1.0,np.max(xsize),
1.0,np.max(ysize),fitgeometry='shift',interactive='no',
maxiter=2,function='legendre',verbose='no')
#Shift the input images (including the reference) and associated mask files
#to a common pixel grid. Add some padding around the individual frames (-99
#in the images, 1 in the bad pixel masks) to ensure that the images will
#combine properly.
(maxx,maxy)=(np.max(xsize)+shiftsize+100.0,np.max(ysize)+shiftsize+100.0)
iraf.geotran('@scalelist','@shiftlist','shift.db','@matchlist',geometry='linear',
boundary='constant',nlines=maxy,ncols=maxx,constant=-99.0)
iraf.geotran('@masklist','@shiftmask','shift.db','@matchlist',geometry='linear',
boundary='constant',nlines=maxy,ncols=maxx,constant=1000.0,
nxblock=10000,nyblock=10000)
iraf.geotran('@expmaplist','@shiftexp','shift.db','@matchlist',geometry='linear',
boundary='constant',nlines=maxy,ncols=maxx,constant=0.)
for x in cllist:
file_check(x+'/mask_final.fits',delete=True)
shutil.copy(x+'/mask_shift.fits',x+'/mask_final.fits')
iraf.hedit(x+'/scaled_and_shifted.fits[0]','BPM',x+'/mask_final.fits[0]',
add='yes',update='yes',verify='no')
iraf.imreplace('@finalmasks2',0,upper=offset)
iraf.imreplace('@finalmasks2',1,lower=offset)
file_check(stackname,delete=True)
file_check(stackname[:-5]+'_mask.pl',delete=True)
file_check(stackname[:-5]+'_expmap.fits',delete=True)
iraf.imcombine('@stacklist',stackname,bpmasks=stackname[:-5]+'_bpm',
masktype='goodval',reject='none',mclip='yes',lthresh='INDEF',hthresh='INDEF',
hsigma=10.0,lsigma='INDEF',nrejmasks=stackname[:-5]+'_nrej',
sigmas=stackname[:-5]+'_sigma',grow=2.5,nkeep=1,blank=-99.0,gain=8.0,rdnoise=35.0)
iraf.imcombine('@expmaplist2',stackname[:-5]+'_expmap.fits',combine='sum')
hdu=pf.open(stackname,mode='update')
hdu[0].header['BPM']=stackname.split('/')[-1][:-5]+'_mask.pl'
hdu[0].header['MAGZERO']=zpref+zprefoff
hdu.close()
#Fix the WCS information in the stacked image.
copyhead(stackname,refimg,offset=shiftsize)
applywcs(stackname,stackname[:-5]+'_wcs.fits')
trash=['matchlist','scalelist','shiftlist','masklist','shiftmask','finalmasks',
'shift.db','stacklist','finalmasks2','stackmask','tmp_wcs.fits','expmaplist',
'expmaplist2','shiftexp']
for x in trash:
os.remove(x)
def deimos_standard(standard):
'''Extract standard star and calculate sensitivit function.'''
bstd = "%s_01_B" % standard
rstd = "%s_01_R" % standard
# Extract standard
if get_head("%s.fits" % bstd, "SKYSUB"):
iraf.apall(bstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none")
iraf.apall(rstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none")
else:
iraf.apall(bstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit")
iraf.apall(rstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit")
# Fit the continuum
#iraf.continuum("%s.ms" % bstd, output="s%s.ms" % bstd, lines=1, bands=1,
# type="fit", sample="*", naverage=1, function="chebyshev",
# order=15, low_reject=3.0, high_reject=5.0, niterate=10, grow=1.0,
# interac=yes)
#iraf.continuum("%s.ms" % rstd, output="s%s.ms" % rstd, lines=1, bands=1,
# type="fit", sample="*", naverage=1, function="chebyshev",
# order=15, low_reject=3.0, high_reject=5.0, niterate=10, grow=1.0,
# interac=yes)
# Create telluric
iraf.splot("%s.ms" %
bstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % bstd, "/", "a%s.ms" % bstd,
"telluric.B.%s.fits" % bstd)
iraf.imreplace("telluric.B.%s.fits" % bstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.B.%s.fits" %
bstd) # Remove stellar features and resave
iraf.splot("%s.ms" %
rstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % rstd, "/", "a%s.ms" % rstd,
"telluric.R.%s.fits" % rstd)
iraf.imreplace("telluric.R.%s.fits" % rstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.R.%s.fits" %
rstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % bstd, "s%s.ms" % bstd, 5.0)
iraf.sarith("%s.ms" % bstd, "/", "s%s.ms" % bstd, "ds%s.ms" % bstd)
iraf.gauss("a%s.ms[*,1,1]" % rstd, "s%s.ms" % rstd, 5.0)
iraf.sarith("%s.ms" % rstd, "/", "s%s.ms" % rstd, "ds%s.ms" % rstd)
# Divide through by smoothed standard
#iraf.sarith("%s.ms" % bstd, "/", "s%s.ms" % bstd, "ds%s.ms" % bstd)
#iraf.sarith("%s.ms" % rstd, "/", "s%s.ms" % rstd, "ds%s.ms" % rstd)
# Create and apply telluric correction
#iraf.splot("%s.ms" % bstd) # Remove telluric, save as a%s.ms
#iraf.splot("%s.ms" % rstd) # Remove telluric, save as a%s.ms
#iraf.sarith("%s.ms" % bstd, "/", "a%s.ms" % bstd, "telluric.B.fits")
#iraf.sarith("%s.ms" % rstd, "/", "a%s.ms" % rstd, "telluric.R.fits")
#iraf.imreplace("telluric.B.fits", 1.0, lower=0.0, upper=0.0)
#iraf.imreplace("telluric.R.fits", 1.0, lower=0.0, upper=0.0)
iraf.telluric("ds%s.ms" % bstd,
"tds%s.ms" % bstd,
"telluric.B.%s.fits" % bstd,
xcorr=no,
tweakrms=no,
interactive=no,
sample='6850:6950,7575:7700')
iraf.telluric("ds%s.ms" % rstd,
"tds%s.ms" % rstd,
"telluric.R.%s.fits" % rstd,
xcorr=no,
tweakrms=no,
interactive=no,
sample='6850:6950,7575:7700')
# Define bandpasses for standard star calculation
iraf.standard("tds%s.ms" % bstd,
"%s.B.std" % standard,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/',
observatory='Keck',
interac=yes,
star_name=standard,
airmass='',
exptime='')
iraf.standard("tds%s.ms" % rstd,
"%s.R.std" % standard,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/',
observatory='Keck',
interac=yes,
star_name=standard,
airmass='',
exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.B.std' % standard,
'%s.B.sens' % standard,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat',
observatory='Keck',
function='legendre',
order=4,
interactive=yes)
iraf.sensfunc('%s.R.std' % standard,
'%s.R.sens' % standard,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat',
observatory='Keck',
function='legendre',
order=4,
interactive=yes)
return
def deimos_standard(standard):
'''Extract standard star and calculate sensitivit function.'''
bstd = "%s_01_B" % standard; rstd = "%s_01_R" % standard
# Extract standard
if get_head("%s.fits" % bstd, "SKYSUB"):
iraf.apall(bstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="none")
iraf.apall(rstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="none")
else:
iraf.apall(bstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="fit")
iraf.apall(rstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="fit")
# Fit the continuum
#iraf.continuum("%s.ms" % bstd, output="s%s.ms" % bstd, lines=1, bands=1,
# type="fit", sample="*", naverage=1, function="chebyshev",
# order=15, low_reject=3.0, high_reject=5.0, niterate=10, grow=1.0,
# interac=yes)
#iraf.continuum("%s.ms" % rstd, output="s%s.ms" % rstd, lines=1, bands=1,
# type="fit", sample="*", naverage=1, function="chebyshev",
# order=15, low_reject=3.0, high_reject=5.0, niterate=10, grow=1.0,
# interac=yes)
# Create telluric
iraf.splot("%s.ms" % bstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % bstd, "/", "a%s.ms" % bstd, "telluric.B.%s.fits" % bstd)
iraf.imreplace("telluric.B.%s.fits" % bstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.B.%s.fits" % bstd) # Remove stellar features and resave
iraf.splot("%s.ms" % rstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % rstd, "/", "a%s.ms" % rstd, "telluric.R.%s.fits" % rstd)
iraf.imreplace("telluric.R.%s.fits" % rstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.R.%s.fits" % rstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % bstd, "s%s.ms" % bstd, 5.0)
iraf.sarith("%s.ms" % bstd, "/", "s%s.ms" % bstd, "ds%s.ms" % bstd)
iraf.gauss("a%s.ms[*,1,1]" % rstd, "s%s.ms" % rstd, 5.0)
iraf.sarith("%s.ms" % rstd, "/", "s%s.ms" % rstd, "ds%s.ms" % rstd)
# Divide through by smoothed standard
#iraf.sarith("%s.ms" % bstd, "/", "s%s.ms" % bstd, "ds%s.ms" % bstd)
#iraf.sarith("%s.ms" % rstd, "/", "s%s.ms" % rstd, "ds%s.ms" % rstd)
# Create and apply telluric correction
#iraf.splot("%s.ms" % bstd) # Remove telluric, save as a%s.ms
#iraf.splot("%s.ms" % rstd) # Remove telluric, save as a%s.ms
#iraf.sarith("%s.ms" % bstd, "/", "a%s.ms" % bstd, "telluric.B.fits")
#iraf.sarith("%s.ms" % rstd, "/", "a%s.ms" % rstd, "telluric.R.fits")
#iraf.imreplace("telluric.B.fits", 1.0, lower=0.0, upper=0.0)
#iraf.imreplace("telluric.R.fits", 1.0, lower=0.0, upper=0.0)
iraf.telluric("ds%s.ms" % bstd, "tds%s.ms" % bstd, "telluric.B.%s.fits" % bstd,
xcorr=no, tweakrms=no, interactive=no, sample='6850:6950,7575:7700')
iraf.telluric("ds%s.ms" % rstd, "tds%s.ms" % rstd, "telluric.R.%s.fits" % rstd,
xcorr=no, tweakrms=no, interactive=no, sample='6850:6950,7575:7700')
# Define bandpasses for standard star calculation
iraf.standard("tds%s.ms" % bstd, "%s.B.std" % standard,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/', observatory='Keck', interac=yes,
star_name=standard, airmass='', exptime='')
iraf.standard("tds%s.ms" % rstd, "%s.R.std" % standard,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/', observatory='Keck', interac=yes,
star_name=standard, airmass='', exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.B.std' % standard, '%s.B.sens' % standard,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat', observatory='Keck',
function='legendre', order=4, interactive=yes)
iraf.sensfunc('%s.R.std' % standard, '%s.R.sens' % standard,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat', observatory='Keck',
function='legendre', order=4, interactive=yes)
return
def mkcombmask(combimg,
inlist,
sspref='sdfr',
outpref='mc',
minpix=250,
hsigma=3,
lsigma=10,
conv='block 3 3',
bpm='none',
sigmap='none'):
# load IRAF package
iraf.nproto()
# chceck combined image
if not os.access(combimg, os.R_OK):
print >> sys.stderr, 'cannot open combined image (%s)' % combimg
return 1
# open input list and check if it exists
inimg_arr = check_input(inlist, sspref)
if isinstance(inimg_arr, int):
return 1
# check output images
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(outimg_arr, int):
return 1
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
# 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()
# check sigma map
if sigmap.lower() == 'none':
tmp_sigma = ''
tmp_mask = ''
else:
if os.access(sigmap, os.R_OK) == False:
print >> sys.stderr, 'cannot read sigma map image (%s)' % sigmap
return 1
else:
tmp_mask = tmp_prefix + 'mask' + os.path.basename(sigmap)
tmp_sigma = tmp_prefix + 'sigma' + os.path.basename(sigmap)
iraf.imcopy(sigmap, tmp_sigma, verbose='no')
ret = iraf.imstat(sigmap,
nclip=50,
field='mode',
format='no',
Stdout=1)
sigma_mode = float(ret[0])
iraf.imreplace(tmp_sigma, sigma_mode, upper=0)
iraf.imarith(sigmap, '*', -1, tmp_mask, verbose='no')
iraf.imreplace(tmp_mask, 1, lower=0)
iraf.imreplace(tmp_mask, 0, upper=0)
iraf.imarith(tmp_mask, '*', -1, tmp_mask)
iraf.imarith(tmp_mask, '+', 1, tmp_mask)
#
tmp_precombmask = tmp_prefix + 'premask' + os.path.basename(combimg)
tmp_combmask = tmp_prefix + 'mask' + os.path.basename(combimg)
iraf.unlearn('objmasks')
try:
iraf.objmasks(combimg,
tmp_precombmask,
omtype='boolean',
masks='',
convolve=conv,
lsigma=lsigma,
hsigma=hsigma,
hdetect='yes',
ldetect='yes',
minpix=minpix,
sigmas=tmp_sigma)
iraf.objmasks(combimg,
tmp_combmask,
omtype='boolean',
masks=tmp_precombmask + '[pl]',
convolve=conv,
lsigma=lsigma,
hsigma=hsigma,
hdetect='yes',
ldetect='yes',
minpix=minpix,
sigmas=tmp_sigma)
os.remove(tmp_precombmask)
except:
print >> sys.stderr, 'failed to execute iraf objmasks task for %s' % combimg
remove_temp_all(tmp_prefix)
return 1
# remove the area with no sigma value
tmp_combmask2 = tmp_prefix + 'mask2' + os.path.basename(combimg)
if sigmap.lower() == 'none':
iraf.imcopy(tmp_combmask + '[pl]', tmp_combmask2, verbose='no')
else:
iraf.imarith(tmp_combmask + '[pl]',
'*',
tmp_mask,
tmp_combmask2,
verbose='no')
os.remove(tmp_combmask)
# check if input images exist and fix bad pixel if requested
iraf.unlearn('imcopy')
iraf.unlearn('imshift')
for i in range(len(inimg_arr)):
# get offset values from header
im = pyfits.open(inimg_arr[i])
try:
dx = float(im[0].header['dx'])
dy = float(im[0].header['dy'])
dxc = float(im[0].header['dxc'])
dyc = float(im[0].header['dyc'])
nx = int(im[0].header['NAXIS1'])
ny = int(im[0].header['NAXIS2'])
except:
print >> sys.stderr, 'failed to get offset values from the header of %s' % inimg_arr[
i]
remove_temp_all(tmp_prefix)
return 1
im.close()
# shift combined mask
tmp_shiftimg = tmp_prefix + os.path.basename(outimg_arr[i])
iraf.imshift(tmp_combmask2,
tmp_shiftimg,
dxc,
dyc,
boundary_typ='constant',
constant=0.0)
tmp_shiftimg_reg = '%s[1:%d,1:%d]' % (tmp_shiftimg, nx, ny)
#iraf.imcopy(tmp_shiftimg_reg, outimg_arr[i]+'[pl]',verbose='no')
iraf.imcopy(tmp_shiftimg_reg, outimg_arr[i], verbose='no')
if bpmex == 1:
iraf.imarith(outimg_arr[i], '+', bpm, outimg_arr[i], verbose='no')
convert_maskfits_int(outimg_arr[i], outimg_arr[i])
os.remove(tmp_shiftimg)
# remove all temporary files
remove_temp_all(tmp_prefix)
return 0
def maskStars(imageName):
#read in the fits data and header
imageHeader = fits.open(imageName+".fits")[0]
#Read in the skysigma, skyv, and seeing (fwhm) from the header and set up other relevant values
sigma = imageHeader.header['SKYSIGMA']
threshold = 3.0 * sigma
skyv = imageHeader.header['SKY']
fwhm = imageHeader.header['SEEING']
aper = 3.0 * fwhm
annul = 5 * fwhm
#Read in the galcut file
galcut = open("galcut.file")
galcutString = file.read(galcut)
galcut.close()
#Parse the galcut file into its variables
galList = galcutString.split(' ')
xCenter = float(galList[0])
yCenter = float(galList[1])
a = float(galList[2]) #Semimajor Axis
eccent = float(galList[3]) #Eccentricity
posAngle = float(galList[4]) #Position angle
posAngleRad = (posAngle+90)*3.14159/180
b = a * eccent #Semiminor Axis
na = -1*a
nb = -1*b
#Create a version of the r image with the sky added back in for the daofind procedure
iraf.imdelete("withsky")
iraf.imarith(imageName,'+',skyv,"withsky")
# Load daophot package
iraf.digiphot()
iraf.daophot()
print("A. Find stars using daofind")
#DAOFIND searches the IRAF images image for local density maxima,
# with a full-width half-maxima of datapars.fwhmpsf, and a peak
# amplitude greater than findpars.threshold * datapars.sigma above
# the local background, and writes a list of detected objects in the
# file output.
# Here we set the fwhm and sigma equal to those listed in the header.
# We set the datamin to -3*sigma = -1*threshold defined above
# The findpars.threshold is set to 4.5*sigma by default. You may have
# to alter this value for images where too few/many stars are found.
#Configure 'datapars', 'findpars', and 'daofind'
iraf.datapars.fwhmpsf=fwhm
iraf.datapars.sigma=sigma
iraf.datapars.datamax='indef'
iraf.datapars.datamin=(-1)*threshold
iraf.datapars.ccdread='RDNOISE'
iraf.datapars.gain="GAIN"
iraf.datapars.exposure="EXPTIME"
iraf.datapars.airmass="AIRMASS"
iraf.datapars.filter="FILTER"
iraf.datapars.obstime="TIME-OBS"
iraf.findpars.threshold=4.5
iraf.findpars.roundhi=3
iraf.findpars.roundlo=-3
iraf.daofind.verify='no'
iraf.daofind.verbose='no'
#create the variable for the coordinate file
allStars = imageName+'.coo'
#Remove a previous coordinate file if one exists
silentDelete(imageName+'.coo')
#Find the stars in the aligned R image
iraf.daofind("withsky",allStars)
print("The file containing the data of the stars in the aligned R-image is ",allStars)
print(" ")
if os.path.getsize(allStars) > 2239 :
print("B. Separate stars inside and outside galaxy")
#Delete existing files
for f in ("temp.file","maskfile","maskfile.sat"):
silentDelete(f)
#Extract the coordinates from the allStars file, redirecting stdout to do so
sys.stdout=open("temp.file","w")
iraf.pdump(allStars,"xcenter,ycenter",'yes')
sys.stdout = sys.__stdout__
#Read the file to get the xy coordinate pairs in a list
coords = open("temp.file")
coordList = list(coords)
countout=0
#Create strings of xy pairs to write to files
outGalString = ""
for pair in coordList:
#for each entry in the list, parse it into xy value integer pairs
values = pair.split(" ")
x = float(values[0])
y = float(values[1])
#Determine if the star lies within the galaxy-centered ellipse
inGalaxy = False
deltaX = x - xCenter
deltaY = y - yCenter
cdx = abs(deltaX)
cdy = abs(deltaY)
if (cdx < a and cdy < a):
xt=(deltaX*math.cos(posAngleRad))+(deltaY*math.sin(posAngleRad))
yt=(deltaY*math.cos(posAngleRad))-(deltaX*math.sin(posAngleRad))
if(xt <= a and xt >= na and yt <= b and yt >= nb):
inGalaxy = True
if (not inGalaxy):
outGalString += str(x) + " " + str(y) + "\n"
countout=countout+1
#Write the coordinate list to a file
mask = open("maskfile","w")
mask.write(outGalString)
mask.close()
print('')
print(" ",countout," stars found outside galaxy")
print('')
if countout!=0 :
print("C. Do photometry to find saturated stars")
for f in ("maskfile.mag","maskfile.satmag","maskfile.sat"):
silentDelete(f)
#Configure 'centerpars', 'fitskypars','photpars'
iraf.datapars.datamax=150000
iraf.datapars.datamin='indef'
iraf.centerpars.calgorithm="none"
iraf.fitskypars.salgorithm="mode"
iraf.fitskypars.annulus=annul
iraf.fitskypars.dannulus=10
iraf.photpars.apertures=fwhm
iraf.phot.verify='no'
iraf.phot.verbose='no'
#Call 'phot' to get the data of the stars
iraf.phot (imageName,"maskfile","maskfile.mag")
boexpr="PIER==305"
iraf.pselect("maskfile.mag","maskfile.satmag",boexpr)
sys.stdout=open("maskfile.sat","w")
iraf.pdump("maskfile.satmag","xcenter,ycenter","yes")
sys.stdout = sys.__stdout__
print("D. Make mask of stars outside galaxy")
#Delete the rmask if one exists
silentDelete("rmask.fits")
#Configure 'imedit'
iraf.imedit.cursor="maskfile"
iraf.imedit.display='no'
iraf.imedit.autodisplay='no'
iraf.imedit.aperture="circular"
iraf.imedit.value=-1000
iraf.imedit.default="e"
#Replace the pixel values near the star coordinates with value -1000 using imedit
iraf.imedit(imageName,"redit",radius=aper)
iraf.imcopy.verbose='no'
#Do the same for the saturated stars, but with larger apertures
satMaskSize = os.path.getsize("maskfile.sat")
if satMaskSize < 1 :
print(" No saturated stars found")
if (satMaskSize!=0):
iraf.imedit.cursor="maskfile.sat"
iraf.imedit.radius=2*aper
iraf.imedit("redit","rmask")
else:
iraf.imcopy("redit","rmask")
#Replace good pixels with value 0 in 'rmask'
iraf.imreplace.lower=-999
iraf.imreplace.upper='indef'
iraf.imreplace("rmask",0)
#Replace bad pixels with value 1 in 'rmask'
iraf.imreplace.lower='indef'
iraf.imreplace.upper=-1000
iraf.imreplace("rmask",1)
#Remove the mask file if one already exists
silentDelete(imageName+"mask.fits")
iraf.imcopy("rmask",imageName+"mask")
print(" ")
print("The mask image is ",imageName+"mask")
print("The masked image is ",imageName+"Masked")
print(" ")
else :
print("No stars found outside galaxy, no mask created.")
if os.path.getsize(allStars) < 2314 :
print("No stars found, no mask created.")
#Delete intermediate images
for f in ("rmask.fits","redit.fits","maskimage.fits","maskfile.mag","maskfile.satmag","temp.file","withsky.fits","mask.fits"):
silentDelete(f)
def maskStars(imageName):
#read in the fits data and header
imageHeader = fits.open(imageName + ".fits")[0]
#Read in the skysigma, skyv, and seeing (fwhm) from the header and set up other relevant values
sigma = imageHeader.header['SKYSIGMA']
threshold = 3.0 * sigma
skyv = imageHeader.header['SKY']
fwhm = imageHeader.header['SEEING']
aper = 3.0 * fwhm
annul = 5 * fwhm
#Read in the galcut file
galcut = open("galcut.file")
galcutString = file.read(galcut)
galcut.close()
#Parse the galcut file into its variables
galList = galcutString.split(' ')
xCenter = float(galList[0])
yCenter = float(galList[1])
a = float(galList[2]) #Semimajor Axis
eccent = float(galList[3]) #Eccentricity
posAngle = float(galList[4]) #Position angle
posAngleRad = (posAngle + 90) * 3.14159 / 180
b = a * eccent #Semiminor Axis
na = -1 * a
nb = -1 * b
#Create a version of the r image with the sky added back in for the daofind procedure
iraf.imdelete("withsky")
iraf.imarith(imageName, '+', skyv, "withsky")
# Load daophot package
iraf.digiphot()
iraf.daophot()
print("A. Find stars using daofind")
#DAOFIND searches the IRAF images image for local density maxima,
# with a full-width half-maxima of datapars.fwhmpsf, and a peak
# amplitude greater than findpars.threshold * datapars.sigma above
# the local background, and writes a list of detected objects in the
# file output.
# Here we set the fwhm and sigma equal to those listed in the header.
# We set the datamin to -3*sigma = -1*threshold defined above
# The findpars.threshold is set to 4.5*sigma by default. You may have
# to alter this value for images where too few/many stars are found.
#Configure 'datapars', 'findpars', and 'daofind'
iraf.datapars.fwhmpsf = fwhm
iraf.datapars.sigma = sigma
iraf.datapars.datamax = 'indef'
iraf.datapars.datamin = (-1) * threshold
iraf.datapars.ccdread = 'RDNOISE'
iraf.datapars.gain = "GAIN"
iraf.datapars.exposure = "EXPTIME"
iraf.datapars.airmass = "AIRMASS"
iraf.datapars.filter = "FILTER"
iraf.datapars.obstime = "TIME-OBS"
iraf.findpars.threshold = 4.5
iraf.findpars.roundhi = 3
iraf.findpars.roundlo = -3
iraf.daofind.verify = 'no'
iraf.daofind.verbose = 'no'
#create the variable for the coordinate file
allStars = imageName + '.coo'
#Remove a previous coordinate file if one exists
silentDelete(imageName + '.coo')
#Find the stars in the aligned R image
iraf.daofind("withsky", allStars)
print(
"The file containing the data of the stars in the aligned R-image is ",
allStars)
print(" ")
if os.path.getsize(allStars) > 2239:
print("B. Separate stars inside and outside galaxy")
#Delete existing files
for f in ("temp.file", "maskfile", "maskfile.sat"):
silentDelete(f)
#Extract the coordinates from the allStars file, redirecting stdout to do so
sys.stdout = open("temp.file", "w")
iraf.pdump(allStars, "xcenter,ycenter", 'yes')
sys.stdout = sys.__stdout__
#Read the file to get the xy coordinate pairs in a list
coords = open("temp.file")
coordList = list(coords)
countout = 0
#Create strings of xy pairs to write to files
outGalString = ""
for pair in coordList:
#for each entry in the list, parse it into xy value integer pairs
values = pair.split(" ")
x = float(values[0])
y = float(values[1])
#Determine if the star lies within the galaxy-centered ellipse
inGalaxy = False
deltaX = x - xCenter
deltaY = y - yCenter
cdx = abs(deltaX)
cdy = abs(deltaY)
if (cdx < a and cdy < a):
xt = (deltaX * math.cos(posAngleRad)) + (deltaY *
math.sin(posAngleRad))
yt = (deltaY * math.cos(posAngleRad)) - (deltaX *
math.sin(posAngleRad))
if (xt <= a and xt >= na and yt <= b and yt >= nb):
inGalaxy = True
if (not inGalaxy):
outGalString += str(x) + " " + str(y) + "\n"
countout = countout + 1
#Write the coordinate list to a file
mask = open("maskfile", "w")
mask.write(outGalString)
mask.close()
print('')
print(" ", countout, " stars found outside galaxy")
print('')
if countout != 0:
print("C. Do photometry to find saturated stars")
for f in ("maskfile.mag", "maskfile.satmag", "maskfile.sat"):
silentDelete(f)
#Configure 'centerpars', 'fitskypars','photpars'
iraf.datapars.datamax = 150000
iraf.datapars.datamin = 'indef'
iraf.centerpars.calgorithm = "none"
iraf.fitskypars.salgorithm = "mode"
iraf.fitskypars.annulus = annul
iraf.fitskypars.dannulus = 10
iraf.photpars.apertures = fwhm
iraf.phot.verify = 'no'
iraf.phot.verbose = 'no'
#Call 'phot' to get the data of the stars
iraf.phot(imageName, "maskfile", "maskfile.mag")
boexpr = "PIER==305"
iraf.pselect("maskfile.mag", "maskfile.satmag", boexpr)
sys.stdout = open("maskfile.sat", "w")
iraf.pdump("maskfile.satmag", "xcenter,ycenter", "yes")
sys.stdout = sys.__stdout__
print("D. Make mask of stars outside galaxy")
#Delete the rmask if one exists
silentDelete("rmask.fits")
#Configure 'imedit'
iraf.imedit.cursor = "maskfile"
iraf.imedit.display = 'no'
iraf.imedit.autodisplay = 'no'
iraf.imedit.aperture = "circular"
iraf.imedit.value = -1000
iraf.imedit.default = "e"
#Replace the pixel values near the star coordinates with value -1000 using imedit
iraf.imedit(imageName, "redit", radius=aper)
iraf.imcopy.verbose = 'no'
#Do the same for the saturated stars, but with larger apertures
satMaskSize = os.path.getsize("maskfile.sat")
if satMaskSize < 1:
print(" No saturated stars found")
if (satMaskSize != 0):
iraf.imedit.cursor = "maskfile.sat"
iraf.imedit.radius = 2 * aper
iraf.imedit("redit", "rmask")
else:
iraf.imcopy("redit", "rmask")
#Replace good pixels with value 0 in 'rmask'
iraf.imreplace.lower = -999
iraf.imreplace.upper = 'indef'
iraf.imreplace("rmask", 0)
#Replace bad pixels with value 1 in 'rmask'
iraf.imreplace.lower = 'indef'
iraf.imreplace.upper = -1000
iraf.imreplace("rmask", 1)
#Remove the mask file if one already exists
silentDelete(imageName + "mask.fits")
iraf.imcopy("rmask", imageName + "mask")
print(" ")
print("The mask image is ", imageName + "mask")
print("The masked image is ", imageName + "Masked")
print(" ")
else:
print("No stars found outside galaxy, no mask created.")
if os.path.getsize(allStars) < 2314:
print("No stars found, no mask created.")
#Delete intermediate images
for f in ("rmask.fits", "redit.fits", "maskimage.fits", "maskfile.mag",
"maskfile.satmag", "temp.file", "withsky.fits", "mask.fits"):
silentDelete(f)
def ratir_doall(name, ra, dec, refdate, cat="SDSS-R9", varorder=1):
"""Full pipeline for image subtractions with RATIR data."""
filts = ["r", "i", "Z", "Y", "J", "H"]
pixscale_dict = {
'r': 0.317,
'i': 0.317,
'Z': 0.292,
'Y': 0.292,
'J': 0.292,
'H': 0.292
}
exptime_dict = {
'r': 80.0,
'i': 80.0,
'Z': 67.11,
'Y': 67.11,
'J': 67.11,
'H': 67.11
}
gain_dict = {
'r': 1.23,
'i': 1.23,
'Z': 2.20,
'Y': 2.20,
'J': 2.40,
'H': 2.40
}
readn_dict = {
'r': 13.6,
'i': 13.6,
'Z': 14.70,
'Y': 14.70,
'J': 11.25,
'H': 11.25
}
sat_dict = {
'r': 50000.0,
'i': 50000.0,
'Z': 24000.0,
'Y': 24000.0,
'J': 24000.0,
'H': 24000.0
}
dirs = glob.glob("20??????")
dirs.remove(refdate)
# Create directory structure
for filt in filts:
os.mkdir(filt)
# Rename "new" files
for dir in dirs:
if os.path.exists("%s/stack_C0_r.fits" % dir) and os.path.exists(
"%s/stack_C0_r.rms.fits" % dir):
shutil.copy("%s/stack_C0_r.fits" % dir, "r/%s_r.fits" % dir)
shutil.copy("%s/stack_C0_r.rms.fits" % dir,
"r/%s_r.rms.fits" % dir)
if os.path.exists("%s/stack_C1_i.fits" % dir) and os.path.exists(
"%s/stack_C1_i.rms.fits" % dir):
shutil.copy("%s/stack_C1_i.fits" % dir, "i/%s_i.fits" % dir)
shutil.copy("%s/stack_C1_i.rms.fits" % dir,
"i/%s_i.rms.fits" % dir)
if os.path.exists("%s/stackA_C2_ZY.fits" % dir) and os.path.exists(
"%s/stackA_C2_ZY.rms.fits" % dir):
shutil.copy("%s/stackA_C2_ZY.fits" % dir, "Z/%s_Z.fits" % dir)
shutil.copy("%s/stackA_C2_ZY.rms.fits" % dir,
"Z/%s_Z.rms.fits" % dir)
if os.path.exists("%s/stackB_C2_ZY.fits" % dir) and os.path.exists(
"%s/stackB_C2_ZY.rms.fits" % dir):
shutil.copy("%s/stackB_C2_ZY.fits" % dir, "Y/%s_Y.fits" % dir)
shutil.copy("%s/stackB_C2_ZY.rms.fits" % dir,
"Y/%s_Y.rms.fits" % dir)
if os.path.exists("%s/stackA_C3_JH.fits" % dir) and os.path.exists(
"%s/stackA_C3_JH.rms.fits" % dir):
shutil.copy("%s/stackA_C3_JH.fits" % dir, "J/%s_J.fits" % dir)
shutil.copy("%s/stackA_C3_JH.rms.fits" % dir,
"J/%s_J.rms.fits" % dir)
if os.path.exists("%s/stackB_C3_JH.fits" % dir) and os.path.exists(
"%s/stackB_C3_JH.rms.fits" % dir):
shutil.copy("%s/stackB_C3_JH.fits" % dir, "H/%s_H.fits" % dir)
shutil.copy("%s/stackB_C3_JH.rms.fits" % dir,
"H/%s_H.rms.fits" % dir)
# Rename reference files
shutil.copy("%s/stack_C0_r.fits" % refdate, "r/ref_r.fits")
shutil.copy("%s/stack_C0_r.rms.fits" % refdate, "r/ref_r.rms.fits")
shutil.copy("%s/stack_C1_i.fits" % refdate, "i/ref_i.fits")
shutil.copy("%s/stack_C1_i.rms.fits" % refdate, "i/ref_i.rms.fits")
shutil.copy("%s/stackA_C2_ZY.fits" % refdate, "Z/ref_Z.fits")
shutil.copy("%s/stackA_C2_ZY.rms.fits" % refdate, "Z/ref_Z.rms.fits")
shutil.copy("%s/stackB_C2_ZY.fits" % refdate, "Y/ref_Y.fits")
shutil.copy("%s/stackB_C2_ZY.rms.fits" % refdate, "Y/ref_Y.rms.fits")
shutil.copy("%s/stackA_C3_JH.fits" % refdate, "J/ref_J.fits")
shutil.copy("%s/stackA_C3_JH.rms.fits" % refdate, "J/ref_J.rms.fits")
shutil.copy("%s/stackB_C3_JH.fits" % refdate, "H/ref_H.fits")
shutil.copy("%s/stackB_C3_JH.rms.fits" % refdate, "H/ref_H.rms.fits")
# Create ds9 region file for OT location
coofile = open("Coords.reg", "w")
coofile.write('fk5;circle(%10.5f,%10.5f,1.0") # text={%s}' %
(ra, dec, name))
coofile.close()
# Get SDSS reference stars
os.system("getsdss.pl -r 7.0 -f sdss.reg -p %10.5f %10.5f sdss.txt" %
(ra, dec))
# Get 2MASS stars
os.system(
"getastrom.pl -d 2mass -r 7.0 -f temp.reg %10.5f %10.5f 2mass.txt" %
(ra, dec))
tmass = Starlist("temp.reg")
for star in tmass:
star.mags["YMAG"] = star.mags["JMAG"] + 0.5 * (
star.mags["JMAG"] - star.mags["HMAG"]) + 0.08
tmass.write("2mass.reg")
os.remove("temp.reg")
# If no SDSS coverage, copy 2mass.reg to sdss.reg (kludge until PS1)
if os.stat("sdss.txt")[6] == 0:
shutil.copy("ps1.reg", "sdss.reg")
# Loop over filters
for filt in filts:
iraf.chdir(filt)
# Update ref header
update_head("ref_%s.fits" % filt, ["PIXSCALE", "SATURATE"],
[pixscale_dict[filt], sat_dict[filt]])
# Generate mask for reference
iraf.imexpr("a == 0 ? 1 : 0", "ref_%s.mask.fits" % filt,
"ref_%s.fits" % filt)
# Generate variance for reference
iraf.imcopy("ref_%s.rms.fits" % filt, "ref_%s.var.fits" % filt)
iraf.imreplace("ref_%s.var.fits" % filt,
1.0e31,
lower=INDEF,
upper=0.0)
# Detect objects in reference
iqobjs("ref_%s.fits" % filt,
3.0,
sat_dict[filt],
wtimage="ref_%s.var.fits" % filt,
skyval="0.0")
# Tweak WCS in reference image
p60scamp("ref_%s.fits" % filt,
distortdeg=1,
match=yes,
rms=True,
mask=True,
cat=cat)
os.remove("ref_%s.cat" % filt)
# Get reference stars for PSF matching
sdss = Starlist("../sdss.reg")
ref = Starlist("ref_%s.fits.stars" % filt)
sdss.wcs2pix("ref_%s.fits" % filt)
a, b = sdss.match(ref, maxnum=1000)
a.write("psf_%s.reg" % filt)
# Loop over "new images"
ims = glob.glob("????????_%s.fits" % filt)
for im in ims:
# Update headers
nstack = int(get_head(im, "EXPTIME") / exptime_dict[filt])
update_head(im, ["GAIN", "READN", "SATURATE"], [
nstack * gain_dict[filt], readn_dict[filt] / np.sqrt(nstack),
sat_dict[filt]
])
# Create variance files
iraf.imcopy("%s.rms.fits" % im[:-5], "%s.var.fits" % im[:-5])
iraf.imreplace("%s.var.fits" % im[:-5],
1.0e31,
lower=INDEF,
upper=0.0)
# Create bad pixel mask
iraf.imexpr("a == 0 ? 1 : 0", "%s.mask.fits" % im[:-5], im)
# Update WCS for new images
p60scamp(im, distortdeg=1, match=yes, rms=True, mask=True, cat=cat)
os.remove("%s.cat" % im[:-5])
# Detect objects and measure seeing
iqobjs(im,
5.0,
sat_dict[filt],
wtimage="%s.var.fits" % im[:-5],
skyval="0.0")
# Match sources for PSF determination
newstars = Starlist("%s.stars" % im)
newstars.pix2wcs(im)
newstars.wcs2pix("ref_%s.fits" % filt)
c, d = newstars.match(a, maxnum=1000)
d.write("psf_%s.%s.reg" % (filt, im[:-5]))
# If there was a problem with alignment, will be no PSF stars
if not os.stat("psf_%s.%s.reg" % (filt, im[:-5]))[6] == 0:
if varorder == 0:
#if (filt=="J") or (filt=="H"):
p60sdsssub(im,
"ref_%s.fits" % filt,
"../Coords.reg",
stamps="psf_%s.%s.reg" % (filt, im[:-5]),
nsx=2,
nsy=2,
ko=0,
distortdeg=1)
else:
p60sdsssub(im,
"ref_%s.fits" % filt,
"../Coords.reg",
stamps="psf_%s.%s.reg" % (filt, im[:-5]),
nsx=5,
nsy=5,
ko=1,
distortdeg=1)
# PSF photometry
for im in ims:
if os.path.exists("%s.sub.fits" % im[:-5]):
psfphot(im,
"psf_%s.reg" % filt,
"../Coords.reg",
wtimage="ref_%s.var.fits" % filt,
varorder=varorder)
# Photometry
if (filt == "J") or (filt == "H") or (filt == "Y"):
ratircal("%s.%s.dat" % (name, filt), ims, "../2mass.reg", filt)
else:
ratircal("%s.%s.dat" % (name, filt), ims, "../sdss.reg", filt)
iraf.chdir("../")
return
def process():
ovrsc = '[1:4,*]'
trim = '[200:2046,50:200]'
imglist = glob.glob('FORS2*.fits')
# objdict = {}
# objdict.clear()
for img in imglist:
hdu = pyfits.getheader(img)
if 'OBJECT' in hdu:
obj = hdu['OBJECT']
if obj not in objdict:
objdict[obj] = [img]
else:
objdict[obj].append(img)
if 'STD' not in objdict:
os.system('cp /dark/jsamuel/agn/standards/FORS2.2016-05-12T09:15:14.678.fits ./')
objdict['STD'] = ['FORS2.2016-05-12T09:15:14.678.fits']
os.system('cp /dark/jsamuel/agn/standards/FORS2.2016-05-12T04:24:38.547.fits ./')
objdict['STD'].append('FORS2.2016-05-12T04:24:38.547.fits')
stars = {'specphot-LTT7379':'l7379','specphot-LDS749B':'lds749b','specphot-EG274':'eg274','specphot-G138-31':'g13831','specphot-C-32d9927':'cd32','specphot-LTT9491':'l9491','specphot-LTT7987':'l7987'}
i = 0
for key in objdict.keys():
if 'STD' in key:
for img in objdict[key]:
i = i + 1
numstars = len(objdict['STD'])
hds = pyfits.getheader(img)
_starname = stars[hds['HIERARCH ESO OBS NAME']]
if _starname in ['lds749b','g13831']:
print 'Bad standard, copying from 2016-05-12'
if not os.path.isdir('badstd'):
os.mkdir('badstd')
os.system('mv '+img+' ./badstd')
if i >= numstars:
objdict.pop('STD')
os.system('cp /dark/jsamuel/agn/standards/FORS2.2016-05-12T09:15:14.678.fits ./')
objdict['STD'] = ['FORS2.2016-05-12T09:15:14.678.fits']
os.system('cp /dark/jsamuel/agn/standards/FORS2.2016-05-12T04:24:38.547.fits ./')
objdict['STD'].append('FORS2.2016-05-12T04:24:38.547.fits')
if os.path.isfile('biaslist'):
os.remove('biaslist')
if os.path.isfile('masterbias.fits'):
os.remove('masterbias.fits')
f = open('biaslist','w')
for img in objdict['BIAS']:
f.write(img+'\n')
f.close()
imglist = '@biaslist'
name = 'masterbias.fits'
hdb = pyfits.getheader(objdict['BIAS'][0])
_gain = hdb['HIERARCH ESO DET OUT1 GAIN']
_ron = hdb['HIERARCH ESO DET OUT1 RON']
iraf.zerocombine(imglist,output=name,combine='average',reject='minmax',ccdtype='none',process='no',gain=_gain,rdnoise=_ron,Stdout=1)
if os.path.isfile('flatlist'):
os.remove('flatlist')
if os.path.isfile('sciflatlist'):
os.remove('sciflatlist')
if os.path.isfile('stdflatlist'):
os.remove('stdflatlist')
if os.path.isfile('masterflat.fits'):
os.remove('masterflat.fits')
if os.path.isfile('scimasterflat.fits'):
os.remove('scimasterflat.fits')
if os.path.isfile('stdmasterflat.fits'):
os.remove('stdmasterflat.fits')
f = open('sciflatlist','w')
for img in objdict['FLAT,LAMP']:
hdu = pyfits.getheader(img)
if hdu['HIERARCH ESO DPR TECH'] == 'SPECTRUM':
f.write(img+'\n')
f.close()
j = 0
f = open('stdflatlist','w')
for img in objdict['FLAT,LAMP']:
hdu = pyfits.getheader(img)
if hdu['HIERARCH ESO DPR TECH'] == 'MOS':
f.write(img+'\n')
j = j + 1
f.close()
imglist = '@sciflatlist'
name = 'scimasterflat.fits'
hdf = pyfits.getheader(objdict['FLAT,LAMP'][0])
_gain = hdf['HIERARCH ESO DET OUT1 GAIN']
_ron = hdf['HIERARCH ESO DET OUT1 RON']
iraf.flatcombine(imglist,output=name,combine='average',reject='avsigclip',ccdtype='none',process='no',subsets='yes',delete='no',clobber='no',gain=_gain,rdnoise=_ron,Stdout=1)
if j == 0:
imglist = '@sciflatlist'
elif j >= 1:
imglist = '@stdflatlist'
name = 'stdmasterflat.fits'
hdf = pyfits.getheader(objdict['FLAT,LAMP'][0])
_gain = hdf['HIERARCH ESO DET OUT1 GAIN']
_ron = hdf['HIERARCH ESO DET OUT1 RON']
iraf.flatcombine(imglist,output=name,combine='average',reject='avsigclip',ccdtype='none',process='no',subsets='yes',delete='no',clobber='no',gain=_gain,rdnoise=_ron,Stdout=1)
if os.path.isfile('tmasterbias.fits'):
os.remove('tmasterbias.fits')
fits = 'masterbias.fits'
name = 'tmasterbias.fits'
zcor = 'no'
fcor = 'no'
_zero = ''
_flat = ''
iraf.ccdproc(fits,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=_flat,Stdout=1)
if os.path.isfile('tmasterflat.fits'):
os.remove('tmasterflat.fits')
if os.path.isfile('tscimasterflat.fits'):
os.remove('tscimasterflat.fits')
if os.path.isfile('tstdmasterflat.fits'):
os.remove('tstdmasterflat.fits')
fits = 'scimasterflat.fits'
name = 'tscimasterflat.fits'
zcor = 'yes'
fcor = 'no'
_zero = 'tmasterbias.fits'
_flat = ''
iraf.ccdproc(fits,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=_flat,Stdout=1)
fits = 'stdmasterflat.fits'
name = 'tstdmasterflat.fits'
zcor = 'yes'
fcor = 'no'
_zero = 'tmasterbias.fits'
_flat = ''
iraf.ccdproc(fits,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=_flat,Stdout=1)
if os.path.isfile('ntmasterflat.fits'):
os.remove('ntmasterflat.fits')
if os.path.isfile('ntscimasterflat.fits'):
os.remove('ntscimasterflat.fits')
if os.path.isfile('ntstdmasterflat.fits'):
os.remove('ntstdmasterflat.fits')
cal = 'tscimasterflat.fits'
resp = 'ntscimasterflat.fits'
_order = 100
iraf.response(calibration=cal,normalization=cal,response=resp,interactive='no',function='legendre',order=_order,graphics='stdgraph')
cal = 'tstdmasterflat.fits'
resp = 'ntstdmasterflat.fits'
_order = 100
iraf.response(calibration=cal,normalization=cal,response=resp,interactive='no',function='legendre',order=_order,graphics='stdgraph')
##########
ovrsc = '[1:4,*]'
trim = '[200:2046,50:200]'
fimg = 'ntscimasterflat'
_area = '[1:150,*]'
_value = 1.0
iraf.imreplace(images=fimg+_area,value=_value)
fimg = 'ntstdmasterflat'
_area = '[1:150,*]'
_value = 1.0
iraf.imreplace(images=fimg+_area,value=_value)
alist = glob.glob('*AGN*.fits')
for f in alist:
os.remove(f)
slist = glob.glob('STD*.fits')
for f in slist:
os.remove(f)
llist = glob.glob('LAMP*.fits')
for f in llist:
os.remove(f)
zcor = 'yes'
fcor = 'yes'
_zero = 'tmasterbias.fits'
sciflat = 'ntscimasterflat.fits'
stdflat = 'ntstdmasterflat.fits'
for key in objdict.keys():
if 'AGN' in key:
for img in objdict[key]:
hds = pyfits.getheader(img)
if 'EXPTIME' in hds:
if hds['EXPTIME'] >= 50.0:
num = img.rsplit('.',2)[1]
name = key+'_'+num
'''
iraf.ccdproc(img,output='trim_'+name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor='no',darkcor='no',flatcor='no',illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero='',flat='',Stdout=1)
_gain = hds['HIERARCH ESO DET OUT1 GAIN']
_ron = hds['HIERARCH ESO DET OUT1 RON']
cosmics.lacos('trim_'+name+'.fits', output='c_'+name+'.fits', gain=_gain, readn=_ron, xorder=9, yorder=9, sigclip=4.5, sigfrac=0.5, objlim=1, verbose=True, interactive=False)
iraf.ccdproc('c_'+name,output=name,ccdtype='',noproc='no',fixpix='no',overscan='no',trim='no',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec='',trimsec='',zero=_zero,flat=sciflat,Stdout=1)
'''
iraf.ccdproc(img,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=sciflat,Stdout=1)
else:
pass
elif 'STD' in key:
for img in objdict[key]:
num = img.rsplit('.',2)[1]
name = key+'_'+num
iraf.ccdproc(img,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=stdflat,Stdout=1)
elif 'WAVE' in key:
for img in objdict[key]:
num = img.rsplit('.',2)[1]
name = 'LAMP_'+num
iraf.ccdproc(img,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=stdflat,Stdout=1)
continue #starting loop from top
filename_list = filter(lambda x: x[0] == unique_filter,
zip(filters, filenames))
fin = open(object_list, 'w+')
fout = open(out_object_list, 'w+')
for fn in filename_list:
print >> fin, fn[1] + '[1]'
print >> fout, 'r' + fn[1]
fin.close()
fout.close()
if do_flat:
#Making sure no division by zero occurs
iraf.imreplace(flat_file, value=0.00001, upper=0, radius=0)
iraf.noao.imred.ccdred.ccdproc.output = '@' + out_object_list
iraf.noao.imred.ccdred.ccdproc.overscan = overboo
iraf.noao.imred.ccdred.ccdproc.trim = trimboo
iraf.noao.imred.ccdred.ccdproc.zerocor = biasboo
iraf.noao.imred.ccdred.ccdproc.flatcor = flatboo
iraf.noao.imred.ccdred.ccdproc.zero = join(biasdir, bias_file)
iraf.noao.imred.ccdred.ccdproc.flat = flat_file
iraf.noao.imred.ccdred.ccdproc('@' + object_list)
print bold("\nImreduce ended successfully")
###################################################################################################
def lris_standard(standards, xcorr=yes):
'''Extract standard stars and calculate sensitivity functions.'''
for ofile, nstd in standards:
shutil.copy(ofile, "%s.fits" % nstd)
# Extract standard
if get_head("%s.fits" % nstd, "SKYSUB"):
iraf.apall(nstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none")
else:
iraf.apall(nstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit")
if xcorr:
# Cross correlate to tweak wavelength
iraf.xcsao("%s.ms" % nstd,
templates=XCTEMPLATE,
correlate="wavelength",
logfiles="%s.xcsao" % ofile)
# If a solution was found, apply shift
try:
xcsaof = open("%s.xcsao" % ofile)
shift = float(xcsaof.readlines()[6].split()[14])
except:
shift = 0.0
iraf.specshift("%s.ms" % nstd, -shift)
# Create telluric
iraf.splot("%s.ms" %
nstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % nstd, "/", "a%s.ms" % nstd,
"telluric.%s.fits" % nstd)
iraf.imreplace("telluric.%s.fits" % nstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.%s.fits" %
nstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % nstd, "s%s.ms" % nstd, 5.0)
iraf.sarith("%s.ms" % nstd, "/", "s%s.ms" % nstd, "ds%s.ms" % nstd)
# Apply telluric correction
iraf.telluric(
"ds%s.ms" % nstd,
"tds%s.ms" % nstd,
"telluric.%s.fits" % nstd,
xcorr=no,
tweakrms=no,
interactive=no,
sample='4000:4010,6850:6975,7150:7350,7575:7725,8050:8400,8900:9725'
)
# Define bandpasses for standard star calculation
obj = get_head("%s.fits" % nstd, "OBJECT")
iraf.standard("tds%s.ms" % nstd,
"%s.std" % nstd,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/',
observatory='Keck',
interac=yes,
star_name=obj,
airmass='',
exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % nstd,
'%s.sens' % nstd,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat',
observatory='Keck',
function='legendre',
order=4,
interactive=yes)
return
def combine_images(images,
output,
reference=None,
method='average',
zscale=1,
fitgeometry='general',
function='polynomial',
xxorder=3,
yyorder=3,
xyorder=3,
yxorder=3,
objects=None,
trim=0,
interactive=1,
maxiter=5,
reject=3,
sigma=None,
xrange=None,
yrange=None,
rectify=1,
thresh=2.0,
scale=0.125,
fwhm=4.0,
debug=0,
aoffset=0,
creject=None):
'''This Routine will take a list of images and use rectify_image to rectify
each with respect to referece. It will then use imcombine to combine the
images into one and output to "output". Default method is 'average', but
you can use any available to imcombine. The rest of the arguments are sent
to rectify_images. If zscale=1, all the images are scaled to a common
zmag (30). Also, if a file with _sigma.fits is found for all input files,
we combine them too. If you specify a sigma, that file will hold the
standard deviations from the files.'''
names = get_files(images)
if reference is None:
reference = names[0]
names = names[1:]
pclip = -0.5
lsigma = 3.
hsigma = 3.
nkeep = -2
if creject:
creject = "pclip"
else:
creject = "none"
# Check to see if we have _sigma files for each input file
if os.path.isfile(reference.replace('.fits', '_sigma.fits')):
do_sigmas = 1
else:
do_sigmas = 0
sigmas = []
for name in names:
if os.path.isfile(name.replace('.fits', '_sigma.fits')):
sigmas.append(name.replace('.fits', '_sigma.fits'))
else:
do_sigmas = 0
break
# Put the reference image first, to ensure that all positional header keywords are
# valid.
if do_sigmas:
ref_sig = reference.replace('.fits', '_sigma.fits')
update = 0
if zscale:
zmag = get_header(reference, "ZMAG", float)
if zmag is not None and zmag != 26:
update = 1
zmagfac = num.power(10, (zmag - 30) / 2.5)
if zmagfac != 1:
print 'zmagfac = ', zmagfac
iraf.imarith(reference, '/', zmagfac, reference)
iraf.hedit(reference, "ZMAG", 30.0, add=1, verify=0)
if do_sigmas:
iraf.imarith(ref_sig, '/', zmagfac, ref_sig)
iraf.hedit(ref_sig, "ZMAG", 30.0, add=1, verify=0)
print "Using %s as reference" % (reference)
if rectify:
nuke(reference.replace('.fits', '_rec.fits'))
iraf.imcopy(reference, reference.replace('.fits', '_rec.fits'))
if do_sigmas:
nuke(reference.replace('.fits', '_rec_sigma.fits'))
iraf.imcopy(reference.replace('.fits', '_sigma.fits'),
reference.replace('.fits', '_rec_sigma.fits'))
temps = [reference.replace('.fits', '_rec.fits')]
if do_sigmas:
sig_temps = [reference.replace('.fits', '_rec_sigma.fits')]
else:
temps = [reference]
if do_sigmas: sig_temps = [ref_sig]
i = 0
for name in names:
print name
if rectify:
temp = name.replace('.fits', '_rec.fits')
nuke(temp)
if do_sigmas:
(x0, x1, y0, y1) = rectify_image(name,
reference,
output=temp,
fitgeometry=fitgeometry,
function=function,
xxorder=xxorder,
yyorder=yyorder,
xyorder=xyorder,
yxorder=yxorder,
objects=objects,
interactive=interactive,
maxiter=maxiter,
reject=reject,
xrange=xrange,
yrange=yrange,
thresh=thresh,
scale=scale,
fwhm=fwhm,
image_sigma=sigmas[i],
reference_sigma=ref_sig,
debug=debug,
aoffset=aoffset)
else:
(x0, x1, y0, y1) = rectify_image(name,
reference,
output=temp,
fitgeometry=fitgeometry,
function=function,
xxorder=xxorder,
yyorder=yyorder,
xyorder=xyorder,
yxorder=yxorder,
objects=objects,
interactive=interactive,
maxiter=maxiter,
reject=reject,
xrange=xrange,
yrange=yrange,
thresh=thresh,
scale=scale,
fwhm=fwhm,
debug=debug,
aoffset=aoffset)
else:
temp = name
bpm = get_header(name, 'BPM', str)
if bpm and rectify:
bpm_fits = bpm.replace('.pl', '.fits')
temp_bpm = bpm.replace('.pl', '_rec.fits')
temp_bpm_pl = bpm.replace('.pl', '_rec.pl')
nuke(bpm_fits)
nuke(temp_bpm)
nuke(temp_bpm_pl)
iraf.imcopy(bpm, bpm_fits)
iraf.imarith(bpm_fits, "*", 10.0, bpm_fits)
if objects is None: sigmaobjects = 'geomap.objects'
iraf.geotran(input=bpm_fits,
output=temp_bpm,
database='geomap.db',
transforms=sigmaobjects,
geometry='geometric',
interpolant="linear")
iraf.imreplace(temp_bpm, lower=0.02, upper="INDEF", value=1.)
iraf.imcopy(temp_bpm, temp_bpm_pl)
iraf.hedit(temp, 'BPM', temp_bpm_pl, update=1, verify=0)
if do_sigmas:
if rectify:
temp_sig = sigmas[i].replace('_sigma.fits', '_rec_sigma.fits')
nuke(temp_sig)
if objects is None:
sigmaobjects = 'geomap.objects'
else:
sigmaobjects = objects
iraf.geotran(input=sigmas[i],
output=temp_sig,
database='geomap.db',
transforms=sigmaobjects,
geometry='geometric',
interpolant="linear")
else:
temp_sig = sigmas[i]
if rectify:
if i == 0:
xmin = x0
ymin = y0
xmax = x1
ymax = y1
else:
xmin = max(xmin, x0)
ymin = max(ymin, y0)
xmax = min(xmax, x1)
ymax = min(ymax, y1)
if zscale:
zmag = get_header(name, "ZMAG", float)
if zmag is not None and zmag != 26:
zmagfac = num.power(10, (zmag - 30) / 2.5)
if zmagfac != 1:
print 'zmagfac = ', zmagfac
iraf.imarith(temp, '/', zmagfac, temp)
iraf.hedit(temp, 'ZMAG', 30.0, add=1, verify=0)
if do_sigmas:
iraf.imarith(temp_sig, '/', zmagfac, temp_sig)
iraf.hedit(temp_sig, 'ZMAG', 30.0, add=1, verify=0)
temps.append(temp)
if do_sigmas: sig_temps.append(temp_sig)
i = i + 1
Nimages = len(temps)
temps = string.join(temps, ',')
print "Combining ", temps
nuke(output)
if sigma is not None:
iraf.imcombine(temps,
output,
combine=method,
sigma=sigma,
masktyp='badvalue',
maskval=1.,
reject=creject,
lsigma=lsigma,
hsigma=hsigma,
nkeep=nkeep)
else:
iraf.imcombine(temps,
output,
combine=method,
masktyp='badvalue',
maskval=1.,
reject=creject,
lsigma=lsigma,
hsigma=hsigma,
nkeep=nkeep)
if do_sigmas:
Ns = []
for this in sig_temps:
N = this.replace('.fits', '_N.fits')
nuke(N)
iraf.imcopy(this, N)
iraf.imreplace(N, lower=0.01, upper="INDEF", value=1)
Ns.append(N)
sig_temps = string.join(sig_temps, ',')
Ns = string.join(Ns, ',')
iraf.imfunction(sig_temps, sig_temps, 'square')
file = output.replace('.fits', '_sigma.fits')
nuke(file)
iraf.imcombine(sig_temps, file, combine='sum')
if method == 'average':
nuke("N.fits")
iraf.imcombine(Ns, 'N.fits', combine='sum')
iraf.imfunction('N.fits', 'N.fits', 'square')
iraf.imarith(file, '/', 'N.fits', file)
iraf.imfunction(file, file, 'sqrt')
iraf.imfunction(sig_temps, sig_temps, 'sqrt')
if update:
iraf.hedit(output, "ZMAG", 30.0, verify=0, add=1)
#nuke('temp?.fits')
# Now, let's clean up the gain and rdnoise headers, as they don't seem to
# be handled in the PANIC pipeline. NOTE: we really need a noise map, but
# for now, we'll deal with just global values.
gain = get_header(output, "gain", float)
rdnoise = get_header(output, "rdnoise", float)
if gain is None:
# have to compute it from scratch
egain = get_header(output, "egain", float)
enoise = get_header(output, "enoise", float)
nloop = get_header(output, "nloops", int)
i = 1
key = ""
# This seems to be the only safe way to compute this. Can't trust
# NDITHERS
while key is not None:
key = get_header(output, "imcmb%03d" % (i), str)
i = i + 1
ndither = i - 1
print "N, ndither, nloop, egain, enoise = ", Nimages, ndither, nloop, egain, enoise
gain = egain * Nimages * nloop * ndither
rdnoise = enoise * num.sqrt(Nimages * nloop * ndither)
else:
gain = gain * Nimages
rdnoise = rdnoise * num.sqrt(Nimages)
iraf.hedit(output, "gain", gain, verify=0, add=1)
iraf.hedit(output, "rdnoise", rdnoise, verify=0, add=1)
# If requested, trim to the common regions of the combined images
if rectify and trim and Nimages > 1:
iraf.imcopy(output + "[%d:%d,%d:%d]" % (xmin, xmax, ymin, ymax),
output.replace('.fits', '_trim.fits'))
def lacos_im(_input,
_output='clean.fits',
outmask='mask.fits',
gain=1.3,
readn=9,
xorder=9,
yorder=9,
sigclip=4.5,
sigfrac=0.5,
objlim=1,
skyval=0,
niter=2,
verbose=True,
interactive=False):
import floyds
from floyds.util import delete
import sys, re, os, string
from pyraf import iraf
import numpy as np
iraf.convolve.bilinear = 'no'
iraf.convolve.radsym = 'no'
# make temporary files
oldoutput, galaxy, skymod, med5 = 'oldoutput.fits', 'galaxy.fits', 'skymod.fits', 'med5.fits'
blk, lapla, med3, med7, sub5, sigima, finalsel = 'blk.fits', 'lapla.fits', 'med3.fits', 'med7.fits', 'sub5.fits',\
'sigima.fits', 'finalsel.fits'
deriv2, noise, sigmap, firstsel, starreject = 'deriv2.fits', 'noise.fits', 'sigmap.fits', 'firstsel.fits',\
'starreject.fits'
inputmask, gfirstsel = 'inputmask.fits', 'gfirstsel.fits'
f = open('_kernel', 'w')
f.write('0 -1 0;\n-1 4 -1;\n0 -1 0')
f.close()
# create growth kernel
f = open('_gkernel', 'w')
f.write('1 1 1;\n1 1 1;\n1 1 1')
f.close()
gkernel = np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
# initialize loop
usegain = gain
i = 1
stop = 'no'
previous = 0
if not _output: _output = _input
arrayinput, headerinput = floyds.cosmics.fromfits(_input, verbose=False)
floyds.cosmics.tofits(outmask,
np.float32(arrayinput - arrayinput),
headerinput,
verbose=False)
delete(oldoutput)
if skyval > 0:
arrayoldoutput = arrayinput + skyval
else:
arrayoldoutput = arrayinput
floyds.cosmics.tofits(oldoutput,
np.float32(arrayoldoutput),
headerinput,
verbose=False)
# start iterations
while stop == 'no':
# take second-order derivative (Laplacian) of input image
# kernel is convolved with subsampled image, in order to remove negative
# pattern around high pixels
delete(blk)
delete(lapla)
delete(deriv2)
iraf.blkrep(oldoutput, blk, 2, 2)
iraf.convolve(blk, lapla, '_kernel')
iraf.imreplace(lapla, 0, upper=0, lower='INDEF', radius=0)
iraf.blkavg(lapla, deriv2, 2, 2, option="average")
delete(med5)
# create model of background flux - 5x5 box should exclude all CRs
iraf.median(oldoutput,
med5,
5,
5,
zlo='INDEF',
zhi='INDEF',
verbose='no')
iraf.imreplace(med5, 0.0001, upper=0, lower='INDEF', radius=0)
# create noise model
delete(noise)
iraf.imutil.imexpr(expr='sqrt(a*' + str(usegain) + '+' + str(readn) +
'**2)/' + str(usegain),
a=med5,
output=noise,
verbose='no')
# divide Laplacian by noise model
delete(sigmap)
iraf.imarith(deriv2, "/", noise, sigmap)
# Laplacian of blkreplicated image counts edges twice:
iraf.imarith(sigmap, "/", 2., sigmap)
# removal of large structure (bright, extended objects)
delete(med5)
iraf.median(sigmap, med5, 5, 5, zlo='INDEF', zhi='INDEF', verbose='no')
arraysigmap, headersigmap = floyds.cosmics.fromfits(sigmap,
verbose=False)
arraymed5, headermed5 = floyds.cosmics.fromfits(med5, verbose=False)
arraysigmap = arraysigmap - arraymed5
iraf.imarith(sigmap, "-", med5, sigmap)
# find all candidate cosmic rays
# this selection includes sharp features such as stars and HII regions
delete(firstsel)
iraf.imcopy(sigmap, firstsel, verbose='no')
iraf.imreplace(firstsel, 0, upper=sigclip, lower='INDEF', radius=0)
iraf.imreplace(firstsel, 1, lower=0.1, upper='INDEF', radius=0)
# arraygfirst=arraysigmap
# arraygfirst = np.where(arraygfirst<sigclip,0,arraygfirst)
# arraygfirst = np.where(arraygfirst>0.1,1,arraygfirst)
# compare candidate CRs to median filtered image
# this step rejects bright, compact sources from the initial CR list
# subtract background and smooth component of objects
delete(med3)
delete(med7)
iraf.median(oldoutput,
med3,
3,
3,
zlo='INDEF',
zhi='INDEF',
verbose='no')
iraf.median(med3, med7, 7, 7, zlo='INDEF', zhi='INDEF', verbose='no')
iraf.imarith(med3, "-", med7, med3)
iraf.imarith(med3, "/", noise, med3)
iraf.imreplace(med3, 0.01, upper=0.01, lower='INDEF', radius=0)
# compare CR flux to object flux
delete(starreject)
iraf.imutil.imexpr(expr="(a*b)/c",
a=firstsel,
b=sigmap,
c=med3,
output=starreject,
verbose='no')
# discard if CR flux <= objlim * object flux
iraf.imreplace(starreject, 0, upper=objlim, lower='INDEF', radius=0)
iraf.imreplace(starreject, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(firstsel, "*", starreject, firstsel)
# grow CRs by one pixel and check in original sigma map
delete(gfirstsel)
iraf.convolve(firstsel, gfirstsel, '_gkernel')
iraf.imreplace(gfirstsel, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(gfirstsel, "*", sigmap, gfirstsel)
iraf.imreplace(gfirstsel, 0, upper=sigclip, lower='INDEF', radius=0)
iraf.imreplace(gfirstsel, 1, lower=0.1, upper='INDEF', radius=0)
# grow CRs by one pixel and lower detection limit
sigcliplow = sigfrac * sigclip
delete(finalsel)
iraf.convolve(gfirstsel, finalsel, '_gkernel')
iraf.imreplace(finalsel, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(finalsel, "*", sigmap, finalsel)
iraf.imreplace(finalsel, 0, upper=sigcliplow, lower='INDEF', radius=0)
iraf.imreplace(finalsel, 1, lower=0.1, upper='INDEF', radius=0)
# determine number of CRs found in this iteration
delete(gfirstsel)
iraf.imutil.imexpr(expr="(1-b)*a",
a=outmask,
b=finalsel,
output=gfirstsel,
verbose='no')
npix = iraf.imstat(gfirstsel,
fields="npix",
lower=0.5,
upper='INDEF',
Stdout=1)
# create cleaned output image; use 3x3 median with CRs excluded
delete(med5)
iraf.imarith(outmask, "+", finalsel, outmask)
iraf.imreplace(outmask, 1, lower=1, upper='INDEF', radius=0)
delete(inputmask)
iraf.imutil.imexpr(expr="(1-10000*a)",
a=outmask,
output=inputmask,
verbose='no')
iraf.imarith(oldoutput, "*", inputmask, inputmask)
delete(med5)
iraf.median(inputmask,
med5,
5,
5,
zloreject=-9999,
zhi='INDEF',
verbose='no')
iraf.imarith(outmask, "*", med5, med5)
if i > 1: delete(_output)
delete(_output)
iraf.imutil.imexpr(expr="(1.-b)*a+c",
a=oldoutput,
b=outmask,
c=med5,
output=_output,
verbose='no')
# cleanup and get ready for next iteration
delete(oldoutput)
iraf.imcopy(_output, oldoutput, verbose='no')
if npix == 0: stop = 'yes'
i = i + 1
if i > niter: stop = 'yes'
# delete temp files
delete(blk + "," + lapla + "," + deriv2 + "," + med5)
delete(med3 + "," + med7 + "," + noise + "," + sigmap)
delete(firstsel + "," + starreject + "," + gfirstsel)
delete(finalsel + "," + inputmask)
if skyval > 0: iraf.imarith(_output, "-", skyval, _output)
delete('_kernel' + "," + '_gkernel')
delete(oldoutput)
def lacos_im(_input, _output='clean.fits', outmask='mask.fits', gain=1.3, readn=9, xorder=9, yorder=9, sigclip=4.5, sigfrac=0.5, objlim=1, skyval=0, niter=2, verbose=True, interactive=False):
# print "LOGX:: Entering `lacos_im` method/function in %(__file__)s" %
# globals()
import ntt
from ntt.util import delete
import sys
import re
import os
import string
from pyraf import iraf
import numpy as np
iraf.convolve.bilinear = 'no'
iraf.convolve.radsym = 'no'
# make temporary files
oldoutput, galaxy, skymod, med5 = 'oldoutput.fits', 'galaxy.fits', 'skymod.fits', 'med5.fits'
blk, lapla, med3, med7, sub5, sigima, finalsel = 'blk.fits', 'lapla.fits', 'med3.fits', 'med7.fits', 'sub5.fits', 'sigima.fits', 'finalsel.fits'
deriv2, noise, sigmap, firstsel, starreject = 'deriv2.fits', 'noise.fits', 'sigmap.fits', 'firstsel.fits', 'starreject.fits'
inputmask, gfirstsel = 'inputmask.fits', 'gfirstsel.fits'
f = open('_kernel', 'w')
f.write('0 -1 0;\n-1 4 -1;\n0 -1 0')
f.close()
# create growth kernel
f = open('_gkernel', 'w')
f.write('1 1 1;\n1 1 1;\n1 1 1')
f.close()
gkernel = np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
# initialize loop
usegain = gain
i = 1
stop = 'no'
previous = 0
if not _output:
_output = _input
arrayinput, headerinput = ntt.cosmics.fromfits(_input, verbose=False)
ntt.cosmics.tofits(outmask, np.float32(
arrayinput - arrayinput), headerinput, verbose=False)
delete(oldoutput)
if skyval > 0:
arrayoldoutput = arrayinput + skyval
else:
arrayoldoutput = arrayinput
ntt.cosmics.tofits(oldoutput, np.float32(
arrayoldoutput), headerinput, verbose=False)
# start iterations
while stop == 'no':
# take second-order derivative (Laplacian) of input image
# kernel is convolved with subsampled image, in order to remove negative
# pattern around high pixels
delete(blk)
delete(lapla)
delete(deriv2)
iraf.blkrep(oldoutput, blk, 2, 2)
iraf.convolve(blk, lapla, '_kernel')
iraf.imreplace(lapla, 0, upper=0, lower='INDEF', radius=0)
iraf.blkavg(lapla, deriv2, 2, 2, option="average")
delete(med5)
# create model of background flux - 5x5 box should exclude all CRs
iraf.median(oldoutput, med5, 5, 5, zlo='INDEF',
zhi='INDEF', verbose='no')
iraf.imreplace(med5, 0.0001, upper=0, lower='INDEF', radius=0)
# create noise model
delete(noise)
iraf.imutil.imexpr(expr='sqrt(a*' + str(usegain) + '+' + str(readn) +
'**2)/' + str(usegain), a=med5, output=noise, verbose='no')
# divide Laplacian by noise model
delete(sigmap)
iraf.imarith(deriv2, "/", noise, sigmap)
# Laplacian of blkreplicated image counts edges twice:
iraf.imarith(sigmap, "/", 2., sigmap)
# removal of large structure (bright, extended objects)
delete(med5)
iraf.median(sigmap, med5, 5, 5, zlo='INDEF', zhi='INDEF', verbose='no')
arraysigmap, headersigmap = ntt.cosmics.fromfits(sigmap, verbose=False)
arraymed5, headermed5 = ntt.cosmics.fromfits(med5, verbose=False)
arraysigmap = arraysigmap - arraymed5
iraf.imarith(sigmap, "-", med5, sigmap)
# find all candidate cosmic rays
# this selection includes sharp features such as stars and HII regions
delete(firstsel)
iraf.imcopy(sigmap, firstsel, verbose='no')
iraf.imreplace(firstsel, 0, upper=sigclip, lower='INDEF', radius=0)
iraf.imreplace(firstsel, 1, lower=0.1, upper='INDEF', radius=0)
# arraygfirst=arraysigmap
# arraygfirst = np.where(arraygfirst<sigclip,0,arraygfirst)
# arraygfirst = np.where(arraygfirst>0.1,1,arraygfirst)
# compare candidate CRs to median filtered image
# this step rejects bright, compact sources from the initial CR list
# subtract background and smooth component of objects
delete(med3)
delete(med7)
iraf.median(oldoutput, med3, 3, 3, zlo='INDEF',
zhi='INDEF', verbose='no')
iraf.median(med3, med7, 7, 7, zlo='INDEF', zhi='INDEF', verbose='no')
iraf.imarith(med3, "-", med7, med3)
iraf.imarith(med3, "/", noise, med3)
iraf.imreplace(med3, 0.01, upper=0.01, lower='INDEF', radius=0)
# compare CR flux to object flux
delete(starreject)
iraf.imutil.imexpr(expr="(a*b)/c", a=firstsel, b=sigmap,
c=med3, output=starreject, verbose='no')
# discard if CR flux <= objlim * object flux
iraf.imreplace(starreject, 0, upper=objlim, lower='INDEF', radius=0)
iraf.imreplace(starreject, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(firstsel, "*", starreject, firstsel)
# grow CRs by one pixel and check in original sigma map
delete(gfirstsel)
iraf.convolve(firstsel, gfirstsel, '_gkernel')
iraf.imreplace(gfirstsel, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(gfirstsel, "*", sigmap, gfirstsel)
iraf.imreplace(gfirstsel, 0, upper=sigclip, lower='INDEF', radius=0)
iraf.imreplace(gfirstsel, 1, lower=0.1, upper='INDEF', radius=0)
# grow CRs by one pixel and lower detection limit
sigcliplow = sigfrac * sigclip
delete(finalsel)
iraf.convolve(gfirstsel, finalsel, '_gkernel')
iraf.imreplace(finalsel, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(finalsel, "*", sigmap, finalsel)
iraf.imreplace(finalsel, 0, upper=sigcliplow, lower='INDEF', radius=0)
iraf.imreplace(finalsel, 1, lower=0.1, upper='INDEF', radius=0)
# determine number of CRs found in this iteration
delete(gfirstsel)
iraf.imutil.imexpr(expr="(1-b)*a", a=outmask,
b=finalsel, output=gfirstsel, verbose='no')
npix = iraf.imstat(gfirstsel, fields="npix",
lower=0.5, upper='INDEF', Stdout=1)
# create cleaned output image; use 3x3 median with CRs excluded
delete(med5)
iraf.imarith(outmask, "+", finalsel, outmask)
iraf.imreplace(outmask, 1, lower=1, upper='INDEF', radius=0)
delete(inputmask)
iraf.imutil.imexpr(expr="(1-10000*a)", a=outmask,
output=inputmask, verbose='no')
iraf.imarith(oldoutput, "*", inputmask, inputmask)
delete(med5)
iraf.median(inputmask, med5, 5, 5, zloreject=-
9999, zhi='INDEF', verbose='no')
iraf.imarith(outmask, "*", med5, med5)
if i > 1:
delete(_output)
delete(_output)
iraf.imutil.imexpr(expr="(1.-b)*a+c", a=oldoutput,
b=outmask, c=med5, output=_output, verbose='no')
# cleanup and get ready for next iteration
delete(oldoutput)
iraf.imcopy(_output, oldoutput, verbose='no')
if npix == 0:
stop = 'yes'
i = i + 1
if i > niter:
stop = 'yes'
# delete temp files
delete(blk + "," + lapla + "," + deriv2 + "," + med5)
delete(med3 + "," + med7 + "," + noise + "," + sigmap)
delete(firstsel + "," + starreject + "," + gfirstsel)
delete(finalsel + "," + inputmask)
if skyval > 0:
iraf.imarith(_output, "-", skyval, _output)
delete('_kernel' + "," + '_gkernel')
delete(oldoutput)
reject='minmax',
first=no,
w1=INDEF,
w2=INDEF,
dw=INDEF,
nw=INDEF,
log=no,
scale='none',
weight='none',
zero='none',
nlow='0',
nhigh='90',
nkeep='1')
### Shouldnt be necessary, but incase there are huge spikes cut them off.
iraf.imreplace(images='fullspec*', value=50, lower=50, upper=INDEF)
iraf.imreplace(images='fullspec*', value=-10, lower=INDEF, upper=-10)
### Fix exposure times. They are off by the number of orders in the trace
### from the summing of orders part
norders = 91.0 #115.0 updated to reflect ignoring edge orders
flist = open('targets_extracted')
for ifile in flist:
ifile = 'fullspec' + ifile.strip()
exptime = iraf.hsel(ifile, 'exptime', 'yes', Stdout=1)[0]
print ifile, exptime
iraf.hedit(ifile,
'exptime',
value=float(exptime) / norders,
fields='mean',
format='no',
Stdout=1)[0]
median = iraf.imstat('master$flat-' + filter,
fields='midpt',
format='no',
Stdout=1)[0]
iraf.imarith(operand1='master$flat-' + filter,
op='/',
operand2=median,
result='master$norm-flat-' + filter)
# Replace the values up to 0.5 with 0
iraf.imreplace('master$norm-flat-' + filter,
value=0.0,
lower='INDEF',
upper=0.5)
###########################################################################
# Make comment
if not config.noReduce:
iraf.hedit('master$norm-flat-' + filter,
'OBS_I',
'Combined flats, bias removed',
add='yes',
ver='no')
###########################################################################
print