def psfconv(df_image,psf):
print "\n************ Running the psf convolution steps ************\n"
iraf.imdel('_model.fits')
iraf.imdel('_model_4.fits')
iraf.imdel('_res*.fits')
iraf.imdel('_psf*.fits')
iraf.imdel('_df_sub')
'subtract the sky value from the dragonfly image header'
try:
df_backval = fits.getheader(df_image)['BACKVAL']
iraf.imarith('%s'%df_image,'-','%s'%df_backval,'_df_sub')
except:
print "WARNING: No BACKVAL to subtract! Skipping the background subtraction..."
iraf.imcopy('%s'%df_image,'_df_sub.fits')
##### subtract the background from the cfht image?
'convolve the model with the Dragonfly PSF'
if usemodelpsf:
makeallisonspsf()
psf = './psf/psf_static_fullframe.fits'
if verbose:
print 'VERBOSE: Using %s for the psf convolution.'%psf
'resample the PSF by a factor of 4'
iraf.magnify('%s'%psf,'_psf_4',4,4,interp="spline3")
'this is just to retain the same total flux in the psf'
iraf.imarith('_psf_4','*',16.,'_psf_4')
'Convolve with the psf'
# from scipy import signal
# fluxmoddata,fluxmodheader = fits.getdata('_fluxmod_dragonfly.fits',header=True)
# psfdata = fits.getdata('_psf_4.fits')
# fluxmodheader['COMMENT']='convolved with '+'_psf_4.fits'
# modeldata = signal.fftconvolve(fluxmoddata, psfdata)
# print ""
# print fluxmoddata.shape
# print modeldata.shape
# print ""
# writeFITS(modeldata,fluxmodheader,'_model_4.fits')
iraf.stsdas.analysis.fourier.fconvolve('_fluxmod_dragonfly','_psf_4','_model_4')
'now after the convolution we can go back to the Dragonfly resolution'
iraf.blkavg('_model_4','_model',4,4,option="average")
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):
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 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 extract(arcs, objs,ccd,mode=1):
"""
mode=1: 1D extraction
mode=2: 2D extraction
"""
#apall na vseh luckah
print " + identifing arcs"
for i in arcs:
iraf.hedit(images='tmp/arcs/'+i[1], fields='DISPAXIS', value=1, add='yes', verify='no', update='yes',Stdout="/dev/null")
iraf.apall(input='tmp/arcs/%s' % (i[1]), referen='tmp/masterflat.fits', format='multispec', interac='no', find='no', recenter='no', resize='no', edit='no', trace='no', fittrac='no', extract='yes', extras='no', review='yes', lower=-3.0, upper=3.0, nsubaps=7, pfit="fit1d", Stdout="/dev/null")
if mode==1:
pass
else:
iraf.cd("tmp/arcs")
geometry_prep(arcs,ccd)
os.system("cp transformations* ../objs/")
#sys.exit(0)
iraf.cd("../..")
for i in arcs:
os.system("rm tmp/arcs/%s.ms.fits" % (i[1][:-5]))
os.system("rm tmp/arcs/%s_cut*.fits" % (i[1][:-5]))
os.system("rm tmp/arcs/%s_t*.fits" % (i[1][:-5]))
pass
os.system("rm tmp/arcs/calibrations/idarcs_cut*")
os.system("rm tmp/arcs/arcs_cut*")
#extract 2d arcs and objects
for i in arcs:
iraf.hedit(images='tmp/arcs/'+i[1], fields='DISPAXIS', value=1, add='yes', verify='no', update='yes',Stdout="/dev/null")
iraf.apall(input='tmp/arcs/%s' % (i[1]), referen='tmp/masterflat.fits', format='multispec', interac='no', find='no', recenter='no', resize='no', edit='no', trace='no', fittrac='no', extract='yes', extras='no', review='yes', lower=-3.0, upper=3.0, nsubaps=7, pfit="fit1d", Stdout="/dev/null")
#forget apertures
for j in range(8,1000):
iraf.hedit(images='tmp/arcs/%s.ms.fits' % (i[1][:-5]), fields="APNUM%s" % (str(j)), value='', delete="yes", verify="no", Stdout="/dev/null")
for i in objs:
iraf.hedit(images='tmp/objs/'+i[1], fields='DISPAXIS', value=1, add='yes', verify='no', update='yes',Stdout="/dev/null")
iraf.apall(input='tmp/objs/%s' % (i[1]), referen='tmp/masterflat.fits', format='multispec', interac='no', find='no', recenter='no', resize='no', edit='no', trace='no', fittrac='no', extract='yes', extras='no', review='yes', lower=-3.0, upper=3.0, nsubaps=7, pfit="fit1d", Stdout="/dev/null")
#forget apertures
for j in range(8,1000):
iraf.hedit(images='tmp/objs/%s.ms.fits' % (i[1][:-5]), fields="APNUM%s" % (str(j)), value='', delete="yes", verify="no", Stdout="/dev/null")
iraf.cd("tmp/arcs")
for ii in arcs:
geometry_transform(ii)
iraf.cd("../..")
iraf.cd("tmp/objs")
for ii in objs:
geometry_transform(ii)
iraf.cd("../..")
#make normal 1d extraction and copy results into it
for i in arcs:
os.system("rm -f tmp/arcs/%s" % (i[1][:-5]+".ms.fits"))
iraf.apall(input='tmp/arcs/%s' % (i[1]), referen='tmp/masterflat.fits', format='multispec', interac='no', find='no', recenter='no', resize='no', edit='no', trace='no', fittrac='no', extract='yes', extras='no', review='yes', lower=-3.0, upper=3.0, nsubaps=1, pfit="fit1d", Stdout="/dev/null")
os.system("cp tmp/arcs/%s" % (i[1][:-5])+".ms.fits tmp/arcs/%s" % (i[1][:-5])+".ms2.fits")
if mode==1:
pass
else:
for j in range(1,393):
os.system("rm -f tmp/copy_tmp.fits")
try:
iraf.blkavg(input="tmp/arcs/"+i[1][:-5]+"_t%s.fits" % (str(j)), output="tmp/copy_tmp", option='sum', b1=1, b2=7, Stdout="/dev/null")
iraf.imcopy(input="tmp/copy_tmp", output="tmp/arcs/"+i[1][:-5]+".ms.fits[*,%s]" % (j), Stdout="/dev/null")
except:
pass
for i in objs:
os.system("rm -f tmp/objs/%s" % (i[1][:-5]+".ms.fits"))
iraf.apall(input='tmp/objs/%s' % (i[1]), referen='tmp/masterflat.fits', format='multispec', interac='no', find='no', recenter='no', resize='no', edit='no', trace='no', fittrac='no', extract='yes', extras='no', review='yes', lower=-3.0, upper=3.0, nsubaps=1, pfit="fit1d", Stdout="/dev/null")
os.system("cp tmp/objs/%s" % (i[1][:-5])+".ms.fits tmp/objs/%s" % (i[1][:-5])+".ms2.fits")
if mode==1:
pass
else:
for j in range(1,393):
os.system("rm -f tmp/copy_tmp.fits")
try:
iraf.blkavg(input="tmp/objs/"+i[1][:-5]+"_t%s.fits" % (str(j)), output="tmp/copy_tmp", option='sum', b1=1, b2=7, Stdout="/dev/null")
iraf.imcopy(input="tmp/copy_tmp", output="tmp/objs/"+i[1][:-5]+".ms.fits[*,%s]" % (j), Stdout="/dev/null")
except:
pass
imcopy_output = iraf.imcopy(image1,image2, Stdout=1)
imcopy_log.write('%s\n' % imcopy_output)
except:
print "\n\nCorrection not performed. Check slit image, correlation results, and imcopy parameters for %s[%d]\n\n" % (filename, ext_num)
IPython.embed()
sys.exit()
#here I'm reversing IRAF's 'block replicate' with 'block averaging' (re-binning basically)
#The 5's in the parameters are the number of columns to be block averaged
#as it sits in this for-loop, the blkavg should take place after each slit is finished being corrected
#this is compressing the slits in the original image that has the tilts in it
iraf.blkavg('%s[%d]' % (filename[:-5], ext_num), '%s[%d,overwrite]' % (filename[:-5], ext_num), x_exp, y_exp)
#this is compressing the slits in the tilt-corrected image
iraf.blkavg('corrected_%s[%d]' % (filename[:-5], ext_num), 'corrected_%s[%d,overwrite]' % (filename[:-5], ext_num), x_exp, y_exp)
#removing the extra files after the correction has been done
#os.system('rm %s_ext%d_line1.fits' % (filename[:-5], ext_num))
#os.system('rm %s_ext%d_line2.fits' % (filename[:-5], ext_num))
#os.system('rm %s_ext%d_corr_results.fits' % (filename[:-5], ext_num))
#os.system('rm %s' % trimmed_xcorr_result)
imcopy_log.close()
os.system('rm tmp*')
if step == 1:
source = fits.open("mystars_smeared.fits")
image = source[0]
try:
os.remove('sourcelist.txt')
except OSError:
pass
source_list = open('sourcelist.txt', 'w')
coordinates = []
for n in xrange(0, 50):
coordinates.append(makeSource(21))
for coordinate in coordinates:
temp_string = ('\t'.join(str(i) for i in coordinate) + '\n')
source_list.writelines(temp_string)
source_list.close()
savenum = 21.2
save(savenum)
iraf.blkavg('mystars_smeared_' + str(savenum) + '.fits',
'mystars_smeared_' + str(savenum) + '.fits', expansion_factor,
expansion_factor)
#for i in xrange(2,3):
# makeField()
# smear()
#recondense()
#mkNoise()
# for n in xrange(0,50):
# makeSource(21)
# save(i)
# iraf.blkavg('mystars_smeared_'+str(i)+'.fits','mystars_smeared_'+str(i)+'.fits',expansion_factor,expansion_factor)
#iraf.mknoise('mystars_smeared.fits')
cm2_root_file = command2_pieces[0].split('.')[0][10:]
cm2_x1 = int(command2_pieces[1].split(':')[0][1:])
#the reason the cm2_x2 value isn't hardcoded is because the correction method works differently on pos/neg tilt
cm2_x2 = int(command2_pieces[1].split(':')[1].split(',')[0])
cm2_y1 = int(command2_pieces[1].split(',')[1].split(':')[0])
cm2_y2 = int(command2_pieces[1].split(',')[1].split(':')[1])
#putting all the bits together to make a more usable string
#I had to wait to put the new_command1 down here becuase I needed to adjust the cm1_x2 value by subtracting cm2_x1; I needed to define the cm2_x1 before using it. The reason I need it is because I can't tell a small image piece, say x1-100 fill a x1-125 image piece <- the computer fills that extra space with extreme random values and it crashes the scripts. What I'm doing is adjusting the cm1_x2 value to be the correct length of the image piece I'm copying.
new_command1 = '%s[%d][%d:%d,%d:%d]' % (cm1_root_file, ext_num, cm1_x1, cm1_x2-cm2_x1, cm1_y1, cm1_y2)
new_command2 = '%s[%d][%d:%d,%d:%d]' % (cm2_root_file, ext_num, cm2_x1, cm2_x2, cm2_y1, cm2_y2)
#IPython.embed()
#sys.exit()
#since I've got the usable bits from the imcopy log file, I need to reverse which ever way they were corrected
iraf.imcopy('optimum_corrected_%s' % new_command2, 'retilted_optimum_corrected_%s' % new_command1)
iraf.blkavg('retilted_%s[%d]' % (filename[:-5], ext_num), 'retilted_%s[%d,overwrite]' % (filename[:-5], ext_num), x_exp, y_exp)
#log_removal = raw_input("Delete the imcopy logs created from tilt_correction.py (which takes ~4 hours)? 'yes' or 'no'.\n")
#if log_removal == 'yes':
# os.system('rm *_imcopy_log.txt')
end = time.strftime("%H:%M:%S")
print "Start time: ", start
print "End time: ", end