def arma_soar(imagen_proc,out_name):
"""creates a single image for SOAR SAMI
It add to the file the exposure time
and the filter. It also copies the header
of the PRIMARY extenssion."""
output_tmp=imagen_proc[:-5]+'_tmp.fits'
iraf.blkrep(input=imagen_proc+'[1]',output=output_tmp,b1=2,b2=2)
iraf.imcopy(input=imagen_proc+'[1]',output=output_tmp+'[1:1024,1:1028]',verbose='yes')
iraf.imcopy(input=imagen_proc+'[2]',output=output_tmp+'[1025:2048,1:1028]',verbose='yes')
iraf.imcopy(input=imagen_proc+'[3]',output=output_tmp+'[1:1024,1029:2056]',verbose='yes')
iraf.imcopy(input=imagen_proc+'[4]',output=output_tmp+'[1025:2048,1029:2056]',verbose='yes')
iraf.imcopy(input=output_tmp,output=out_name,verbose='yes')
iraf.imutil.imdelete(output_tmp)
return
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):
# 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_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)
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
#!/usr/bin/env python
import sys, os, string
import pyraf
from pyraf import iraf
from iraf import images, tv, sleep, imutil, imgeom, blkrep, imcopy, imdelete
imagen = sys.argv[1]
iraf.imcopy(input=imagen + '[1]', output="test.fits")
iraf.blkrep(input="test.fits", output=imagen + '_GMOS', b1=12, b2=1)
for i in range(1, 13):
# print i
if i < 2:
# print i
iraf.imcopy(input=imagen + '[' + str(i) + ']',
output=imagen + '_GMOS.fits[1:' + str(288) + ',*]')
# if i>1 and i<12:
else:
# print i,i
iraf.imcopy(input=imagen + '[' + str(i) + ']',
output=imagen + '_GMOS.fits[' + str(
(i - 1) * 288) + ':' + str((i) * 288) + ',*]')
# if i>12:
# iraf.imcopy(input=imagen+'['+str(i)+']',output=imagen+'_GMOS.fits['+str(i*288)+':'+str((i+1)*288)+',*]')
iraf.imdelete("test.fits")
#!/usr/bin/env python
import sys,os,string
import pyraf
from pyraf import iraf
from iraf import images,tv,sleep,imutil,imgeom,blkrep,imcopy,imdelete
imagen=sys.argv[1]
iraf.imcopy(input=imagen+'[1]',output="test.fits")
iraf.blkrep(input="test.fits",output=imagen+'_GMOS',b1=12,b2=1)
for i in range (1,13):
# print i
if i < 2:
# print i
iraf.imcopy(input=imagen+'['+str(i)+']',output=imagen+'_GMOS.fits[1:'+str(288)+',*]')
# if i>1 and i<12:
else:
# print i,i
iraf.imcopy(input=imagen+'['+str(i)+']',output=imagen+'_GMOS.fits['+str((i-1)*288)+':'+str((i)*288)+',*]')
# if i>12:
# iraf.imcopy(input=imagen+'['+str(i)+']',output=imagen+'_GMOS.fits['+str(i*288)+':'+str((i+1)*288)+',*]')
iraf.imdelete("test.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)
#finding out which slits need the tilt correction
if slit_tilt != 0.0:
#here I'm using IRAF's block replicate function, confusing way of saying "enlarging", to turn 1 pixel into more
#this will allow me to make corrections to the tilt in increments smaller than a pixel (imcopy only takes integers)
#this will trick it so an integer is now less than one pixel - how small determined by how much I enlarge it by
y_exp = 5
x_exp = 5
#I have to put the formatted strings into smaller variables otherwise iraf tasks break with the long names
#orig_image = '%s[%d]' % (filename[:-5], ext_num)
#rep_image = '%s[%d, overwrite]' % (filename[:-5], ext_num)
iraf.blkrep('%s[%d]' % (filename[:-5], ext_num), '%s[%d, overwrite]' % (filename[:-5], ext_num), x_exp, y_exp)
#this is block replicating the slits in the final corrected_file so I can put the expanded slits into the final image
iraf.blkrep('%s[%d]' % (corrected_file[:-5], ext_num), '%s[%d,overwrite]' % (corrected_file[:-5], ext_num), x_exp, y_exp)
#now I'm renaming the dimensions from the MDF file to reflect the enlargement
#I only need to change the ending dimensions (x2 and y2) because the beginning will still be 0's or 1's
mod_secy2 = secy2 * y_exp
mod_secx2 = secx2 * x_exp
mod_slit_width = slit_width * 5
#setting this slit to have values of zero so when the tilt is corrected the extra space is filled w/ 0's
#this way the uncorrected image won't show through areas that corrected image doesn't cover
iraf.imarith('%s[%d]' % (corrected_file, ext_num), '*', '0', '%s[%d,overwrite]' % (corrected_file, ext_num))
mdfinfo = pyfits.getdata(filename, 'MDF')
#loading the slit tilt info from the MDF of the file
for i in range(len(mdfinfo)):
slit_tilt = mdfinfo[i]['slittilt']
ext_num = i-1
#finding out which slits had the tilt correction so we can read the imcopy logs and put the tilt back
if slit_tilt != 0.0:
#I'm using "blkrep" again to enlarge the slits that were enlarged before and retilting them using the imcopy logs
#this allows me to reverse the corrections that were made in smaller incrementsthan a pixel
y_exp = 5
x_exp = 5
iraf.blkrep('%s[%d]' % (filename[:-5], ext_num), '%s[%d, overwrite]' % (filename[:-5], ext_num), x_exp, y_exp)
#this is block replicating the slits in the final retilted_file so the dimensions will be correct for me to copy them over to that file and then I can "compact" them with block averaging in that file
iraf.blkrep('retilted_%s[%d]' % (filename[:-5], ext_num), 'retilted_%s[%d,overwrite]' % (filename[:-5], ext_num), x_exp, y_exp)
#setting this slit to have values of 0 so when it's retilted the extra space is filled w/ 0's
#this way the old image won't appear through the edges
iraf.imarith('retilted_%s[%d]' % (filename, ext_num), '*', '0', 'retilted_%s[%d,overwrite]' % (filename, ext_num))
#the reason the [18:5] is there is to get the root name of the filename and load that filename's imcopy log
imcopy_log = [line.strip() for line in open('%s[%d]_imcopy_log.txt' % (filename[18:-5], ext_num))]
#now going through each line in the imcopy log and pulling out the bits I need
for line in imcopy_log:
#I need to break up the imcopy log into more variables, can't pass imcopy that whole string, thinks its literal
#I'll first start with the by splitting up the first command in the result log line
