def clean_image(img, cleanimg):
import floyds
from floyds.util import readkey3, readhdr, delete
array, header = floyds.cosmics.fromfits(img, verbose=False)
import warnings
def fxn():
warnings.warn(" ", DeprecationWarning)
original_filters = warnings.filters[:]
# Ignore warnings.
warnings.simplefilter("ignore")
try:
c = floyds.cosmics.cosmicsimage(array,
gain=readkey3(header, 'gain'),
readnoise=readkey3(header, 'ron'),
sigclip=5.0,
sigfrac=0.3,
objlim=5.0,
verbose=False)
c.run(maxiter=4, verbose=False)
fxn()
finally:
warnings.filters = original_filters
if not cleanimg:
delete(img)
cleanimg = img
floyds.cosmics.tofits(cleanimg, c.cleanarray, header, verbose=False)
return cleanimg
def airmass(img, overwrite=True, _observatory='lasilla'):
import floyds
from floyds.util import readhdr, readkey3, delete
from pyraf import iraf
iraf.astutil(_doprint=0)
hdr = readhdr(img)
if readkey3(hdr, 'UTC'):
_UT = (readkey3(hdr, 'UTC') + (readkey3(hdr, 'exptime') / 2)) / 3600
_date = readkey3(hdr, 'date-obs')
_date = _date[0:4] + '-' + _date[4:6] + '-' + _date[6:8]
_RA = readkey3(hdr, 'RA') / 15
_DEC = readkey3(hdr, 'DEC')
f = file('airmass.txt', 'w')
f.write('mst = mst ("' + str(_date) + '",' + str(_UT) + ', obsdb ("' + str(_observatory) + '", "longitude"))\n')
f.write(
'air = airmass (' + str(_RA) + ',' + str(_DEC) + ',mst, obsdb ("' + str(_observatory) + '", "latitude"))\n')
f.write('print(air)\n')
f.close()
_air = iraf.astcalc(image=img, command="airmass.txt", Stdout=1)[0]
try:
_air = float(_air)
except:
_air = 999
delete('airmass.txt')
if overwrite and _air < 99.:
floyds.util.updateheader(img, 0, {'AIRMASS': [_air, 'mean airmass computed with astcalc']})
else:
_air = ''
return _air
def clean_image(img, cleanimg):
import floyds
from floyds.util import readkey3, readhdr, delete
array, header = floyds.cosmics.fromfits(img, verbose=False)
import warnings
def fxn():
warnings.warn(" ", DeprecationWarning)
original_filters = warnings.filters[:]
# Ignore warnings.
warnings.simplefilter("ignore")
try:
c = floyds.cosmics.cosmicsimage(array, gain=readkey3(header, 'gain'), readnoise=readkey3(header, 'ron'),
sigclip=5.0, sigfrac=0.3, objlim=5.0, verbose=False)
c.run(maxiter=4, verbose=False)
fxn()
finally:
warnings.filters = original_filters
if not cleanimg:
delete(img)
cleanimg = img
floyds.cosmics.tofits(cleanimg, c.cleanarray, header, verbose=False)
return cleanimg
def airmass(img, overwrite=True, _observatory='lasilla'):
import floyds
from floyds.util import readhdr, readkey3, delete
from pyraf import iraf
iraf.astutil(_doprint=0)
hdr = readhdr(img)
if readkey3(hdr, 'UTC'):
_UT = (readkey3(hdr, 'UTC') + (readkey3(hdr, 'exptime') / 2)) / 3600
_date = readkey3(hdr, 'date-obs')
_date = _date[0:4] + '-' + _date[4:6] + '-' + _date[6:8]
_RA = readkey3(hdr, 'RA') / 15
_DEC = readkey3(hdr, 'DEC')
f = file('airmass.txt', 'w')
f.write('mst = mst ("' + str(_date) + '",' + str(_UT) + ', obsdb ("' + str(_observatory) + '", "longitude"))\n')
f.write(
'air = airmass (' + str(_RA) + ',' + str(_DEC) + ',mst, obsdb ("' + str(_observatory) + '", "latitude"))\n')
f.write('print(air)\n')
f.close()
_air = iraf.astcalc(image=img, command="airmass.txt", Stdout=1)[0]
try:
_air = float(_air)
except:
_air = 999
delete('airmass.txt')
if overwrite and _air < 99.:
floyds.util.updateheader(img, 0, {'AIRMASS': [_air, 'mean airmass computed with astcalc']})
else:
_air = ''
return _air
def archivingtar(outputlist, nametar):
import os, string, re
from floyds.util import delete
print '### making a tar with pre-reduced frames ........ please wait'
stringa = ' '.join(outputlist)
delete(nametar)
os.system('tar -zcvf ' + nametar + ' ' + stringa)
print '### tar file: ' + nametar + '\n'
def archivingtar(outputlist, nametar):
import os, string, re
from floyds.util import delete
print '### making a tar with pre-reduced frames ........ please wait'
stringa = ' '.join(outputlist)
delete(nametar)
os.system('tar -zcvf ' + nametar + ' ' + stringa)
print '### tar file: ' + nametar + '\n'
def archivingtar(outputlist, nametar):
import os, string, re
from floyds.util import delete
print '\n### making a tar with pre-reduced frames ........ please wait'
stringa = ''
for img in outputlist: stringa = stringa + img + ' '
# stringa=stringa+
# delete(re.sub('raw.list','tar.gz',rawfile))
delete(nametar)
print stringa
os.system('tar -zcf ' + nametar + ' ' + stringa)
print '\n### tar file: ' + nametar
def archivingtar(outputlist, nametar):
import os, string, re
from floyds.util import delete
print '\n### making a tar with pre-reduced frames ........ please wait'
stringa = ''
for img in outputlist: stringa = stringa + img + ' '
# stringa=stringa+
# delete(re.sub('raw.list','tar.gz',rawfile))
delete(nametar)
print stringa
os.system('tar -zcf ' + nametar + ' ' + stringa)
print '\n### tar file: ' + nametar
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 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(_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 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)