def make_master(obj, file_type=None):
"""
This routine takes the name of a file containing the raw calibration images in the **current** directory that are to
be combined to form a master calibration image. The type of calibration images (i.e. bias, dark or flat) is specified
by the file_type keyword; this is necessary because the type of calibration image determines how the multiple raw
calibration images are to be combined (eg. pixel averages vs medians).
"""
# Get the name of the master calibration file:
if file_type=='bias':
list_file = obj.bias_list
elif file_type=='dark':
list_file = obj.dark_list
elif file_type=='flat':
list_file = obj.flat_list
dirpath = os.path.dirname(list_file)+'/'
filename = 'master_'+file_type+'.fits'
master_filename = dirpath+filename
if os.path.exists(master_filename): os.remove(master_filename)
# Now make the master calibration file:
if file_type=='bias':
iraf.zerocombine(input='@'+list_file, output=master_filename, combine='average', ccdtype='', process='no', delete='no')
obj.master_bias = master_filename
elif file_type=='dark':
iraf.darkcombine(input='@'+list_file, output=master_filename, combine='average', ccdtype='', process='no', delete='no')
obj.master_dark = master_filename
elif file_type=='flat':
iraf.flatcombine(input='@'+list_file, output=master_filename, combine='median', ccdtype='', process='no', delete='no')
obj.master_flat = master_filename
return None
def mkbiasINTWFC(filelist, type='median'):
"""
Creates a master bias from given list of bias frames.
Saves each extension to a different FITS file.
Reads readnoise and gain from the FITS header, as each WFC chip has
different values.
:note: This function has been written specifically for INT WFC.
:param filelist:
:type filelist:
:param type: combining parameter (default = median)
:type type: string
:return: None
"""
input1 = ''
for line in filelist:
input1 += line[0] + '[1],'
input2 = input1.replace('[1]', '[2]')
input3 = input1.replace('[1]', '[3]')
input4 = input1.replace('[1]', '[4]')
inputs = [input1, input2, input3, input4]
#note there are four SCI extensions
for ext, input in enumerate(inputs):
iraf.unlearn('zerocombine')
iraf.zerocombine(input=input[:-1],
output='BIAS%i.fits' % (ext+1),
combine=type,
rdnoise='READNOIS',
gain='GAIN')
def mkbiasINTWFC(filelist, type='median'):
"""
Creates a master bias from given list of bias frames.
Saves each extension to a different FITS file.
Reads readnoise and gain from the FITS header, as each WFC chip has
different values.
:note: This function has been written specifically for INT WFC.
:param filelist:
:type filelist:
:param type: combining parameter (default = median)
:type type: string
:return: None
"""
input1 = ''
for line in filelist:
input1 += line[0] + '[1],'
input2 = input1.replace('[1]', '[2]')
input3 = input1.replace('[1]', '[3]')
input4 = input1.replace('[1]', '[4]')
inputs = [input1, input2, input3, input4]
#note there are four SCI extensions
for ext, input in enumerate(inputs):
iraf.unlearn('zerocombine')
iraf.zerocombine(input=input[:-1],
output='BIAS%i.fits' % (ext + 1),
combine=type,
rdnoise='READNOIS',
gain='GAIN')
def combinebias(lstfn):
"""
call iraf command zerocombine, combine bias fits.
lstfn : lst file name
type : string
output file : Zero.fits
"""
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.zerocombine(input='o//@' + lstfn,
output='Zero',
combine='average',
reject='minmax',
ccdtype='',
process=False,
delete=False,
clobber=False,
scale='none',
statsec='',
nlow=0,
nhigh=1,
nkeep=1,
mclip=True,
lsigma=3.0,
hsigma=3.0,
rdnoise='rdnoise',
gain='gain',
snoise=0.0,
pclip=-0.5,
blank=0.0)
def create_masterbias(WORK_DIR):
print "\n + Creating masterbias\n"
try: os.remove('masterbias.fits')
except: pass
iraf.zerocombine(input=','.join([observations[WORK_DIR]['ORIG_DIR']+i for i in observations[WORK_DIR]['biases']]), output='masterbias.fits', combine='median', reject='none', ccdtype='', process='no', Stdout="/dev/null")
def combinebias(filename):
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.zerocombine(input = 'o//@' + filename
, output = 'Zero', combine = 'average', reject = 'minmax'
, ccdtype = '', process = False, delete = False
, clobber = False, scale = 'none', statsec = ''
, nlow = 0, nhigh = 1, nkeep = 1, mclip = True
, lsigma = 3.0, hsigma = 3.0, rdnoise = 'rdnoise'
, gain = 'gain', snoise = 0.0, pclip = -0.5, blank = 0.0)
def combine_bias(filename):
if os.path.isfile('bias_spec.fits'):
print "bias_spec.fits already exist, skip zerocombine..."
return
iraf.zerocombine(input = 'o//@' + filename
, output = 'bias_spec.fits', combine = 'average', reject = 'minmax'
, ccdtype = '', process = False, delete = False
, clobber = False, scale = 'none', statsec = ''
, nlow = 0, nhigh = 1, nkeep = 1, mclip = True
, lsigma = 3.0, hsigma = 3.0, rdnoise = 9.4
, gain = 0.35, snoise = 0.0, pclip = -0.5, blank = 0.0)
def create_bias(self):
"""Creating Combined Bias Frame"""
self.log.debug("Running iraf.zerocombine on image list...")
iraf.unlearn(iraf.zerocombine)
iraf.zerocombine(self.bias.iinat(),
output=self.bias.iout("Bias"),
combine=self.config["Bias.Combine"],
ccdtype="zero",
reject=self.config["Bias.Reject"],
scale="none", nlow=0, nhigh=1, nkeep=1, mclip="yes", lsigma=3.0, hsigma=3.0, rdnoise="0.", gain ="1."
)
self.bias.idone()
def _makeBiasPyraf(self, combine='median', biaslist=[], output='zeroim.fits'):
'''
Creates a master bias from given list of bias frames
:param: combine, either median or mean
:param: biaslist, a list of bias frames to be combined
:param: output: the name of the output file
'''
from pyraf import iraf
from pyraf.iraf import imred, ccdred
iraf.zerocombine(input=biaslist, output=output,
combine=combine)#, rdnoise=noise, gain=gain)
def main(raw_dir):
if not os.path.exists("{}/step1.tmp".format(os.getcwd())):
print "Setting up directory"
list_dict = setup_dir(raw_dir)
print "OT'ing"
iraf.ccdproc(
"@all_raw.lst",
output="@all_ot.lst",
overscan=True,
trim=True,
zerocor=False,
darkcor=False,
flatcor=False,
illumco=False,
biassec="image",
trimsec="image",
zero="",
interact=True,
order=100,
)
cull_bias(list_dict["zeros"], "zeros.lst")
os.system("touch {}/step1.tmp".format(os.getcwd()))
return
print "Making master bias"
iraf.zerocombine("@zeros.lst", output="Zero.fits", combine="average", reject="crreject")
print "Subtracting bias"
iraf.ccdproc("@all_otz.lst", overscan=False, trim=False, zerocor=True, zero="Zero.fits")
detect_flats(os.getcwd())
flats_to_make = glob("dflat*.lst")
for flat in flats_to_make:
name = "dFlat_{}.fits".format(flat.split(".lst")[0].split("_")[1])
print "Making flat {}".format(name)
iraf.flatcombine("@{}".format(flat), output=name, combine="average", reject="crreject")
try:
print "Making master dark"
iraf.darkcombine("@darks.lst", output="Dark.fits", combine="average", reject="crreject")
except Exception as e:
print "\t{}".format(e)
print "\tNO DARK FOUND"
print "Making master comp"
iraf.imcombine("@comps.lst", "Comp.fits", combine="average", reject="crreject")
return
def makeZero(fileList='zeros.lst',
inputType=INPUT_LISTFNAME,
output="Zero.fits",
combine='average',
reject='minmax',
ccdtype='zero'):
'''create a master Zero.fits from the frames in fileList'''
iraf.zerocombine(_genIRAFString(fileList, inputType),
output=output,
combine=combine,
reject=reject,
ccdtype=ccdtype)
print "zerocombine successful"
return
def _makeBiasPyraf(self,
combine='median',
biaslist=[],
output='zeroim.fits'):
'''
Creates a master bias from given list of bias frames
:param: combine, either median or mean
:param: biaslist, a list of bias frames to be combined
:param: output: the name of the output file
'''
from pyraf import iraf
from pyraf.iraf import imred, ccdred
iraf.zerocombine(input=biaslist, output=output,
combine=combine) #, rdnoise=noise, gain=gain)
def BIAS(bias):
#iraf.unlearn(iraf.zerocombine)
path_bias = os.path.dirname(bias)
bias = bias.replace(path_bias + '/','')
iraf.chdir(path_bias)
os.chdir(path_bias)
iraf.zerocombine.input = '@' + bias
if os.path.isfile('MasterBias.fits'):
subprocess.call(['rm','MasterBias.fits'])
iraf.zerocombine.output = 'MasterBias.fits'
iraf.zerocombine.rdnoise = 'HIERARCH ESO DET OUT1 RON'
iraf.zerocombine.gain = 'HIERARCH ESO DET OUT1 GAIN'
iraf.zerocombine.ccdtype = ''
#iraf.lpar(iraf.zerocombine)
iraf.zerocombine(mode='h')
def create_masterflat(WORK_DIR):
print '\n + Creating masterflat\n'
try: os.remove('masterflat.fits')
except: pass
# subtract masterbias from flats
for flat in observations[WORK_DIR]['flats']:
try: os.remove(flat+'.fits')
except: pass
iraf.imarith(operand1=observations[WORK_DIR]['ORIG_DIR']+flat, op='-', operand2='masterbias.fits', result=flat, Stdout="/dev/null")
iraf.zerocombine(input=','.join([flat for flat in observations[WORK_DIR]['flats']]), output='masterflat.fits', combine='median', reject='none', ccdtype='', process='no')
for flat in observations[WORK_DIR]['flats']:
os.remove(flat+'.fits')
def makebiasPyraf(filelist, output, noise, gain):
"""
Creates a master bias from given list of bias frames
@param filelist: list of names of bias frames
@type: list
@param
"""
from pyraf import iraf
from pyraf.iraf import imred, ccdred
iraf.zerocombine(input=filelist,
output=output,
combine="median",
rdnoise=noise,
gain=gain)
def the_zerocombine(self,
in_file_list,
out_file,
method="median",
rejection="minmax",
ccdtype="",
gain="",
rdnoise=""):
self.etc.log(
"Zerocombine started for {} files using combine({}) and rejection({})"
.format(len(in_file_list), method, rejection))
try:
if self.fop.is_file(out_file):
self.fop.rm(out_file)
files = []
for file in in_file_list:
if self.fit.is_fit(file):
files.append(file)
if not len(files) == 0:
biases = ",".join(files)
# Load packages
# Unlearn settings
iraf.imred.unlearn()
iraf.ccdred.unlearn()
iraf.ccdred.ccdproc.unlearn()
iraf.ccdred.combine.unlearn()
iraf.ccdred.zerocombine.unlearn()
ccdred.instrument = "ccddb$kpno/camera.dat"
iraf.imred()
iraf.ccdred()
iraf.zerocombine(input=biases,
output=out_file,
combine=method,
reject=rejection,
ccdtype=ccdtype,
Stdout="/dev/null")
else:
self.etc.log("No files to combine")
except Exception as e:
self.etc.log(e)
def optdomecomb(date, fwheel=['bias','B','V','R','I']):
'''
#combine biases and optical domes
#Requires: the uncombined fits images
# if you are combining a dome, you must have a bias from the same night as the dome to preform appropriate bias subtraction
#Input: the date the domes were observed YYMMDD, and fwheel, a list that contains the filters of the domes to be combined
#Outupt: combined dome fits frame for each color where uncombined frames are in the directory
'''
#convert date to string incase it was entered as an int of float
date=str(date)
if len(glob.glob('*bias*')) < 1:
print "no biases found, exiting"
return
else:
for color in fwheel:
if color=='bias':
biaslist=glob.glob('*bias.[0-9]*')
if len(biaslist) > 10:
print "only "+str(len(biaslist))+" biases found. you need at least 10"
else:
with open("bias.list",'w') as BILIS:
for i in biaslist:
BILIS.write(i+'\n')
iraf.zerocombine("@bias.list",output="ccd"+str(date)+".bias.fits",combine="average",reject="minmax",scale="none",ccdtype="",process="no",delete="no",clobber="no",nlow=1,nhigh=1,nkeep=1)
print "created ccd"+str(date)+".bias.fits"
os.system('rm bias.list')
elif color in ['B','V','R','I']:
domelist=glob.glob('*dome'+color+'.[0-9]*')
if len(domelist) < 1:
print 'no '+color+' domes found'
elif len(domelist) > 10:
print 'only '+str(len(domelist))+' domes found. you need at least 10'
else:
with open('flat'+color+'.list', 'w') as flist:
for i in domelist:
flist.write(i+'\n')
iraf.ccdproc("@flat"+color+".list", output="z@flat"+color+".list",ccdtype=" ",noproc="no", fixpix="no",overscan="yes", trim="no", zerocor="yes",darkcor="no",flatcor="no", illumcor="no", fringec="no", readcor="no", scancor="no", readaxis="line", biassec="[3:14,1:1024]", zero="ccd"+str(date)+".bias.fits", interactive="no", functio="spline3", order=11)
iraf.flatcombine("z@flat"+color+".list", output="ccd"+str(date)+".dome"+color+".fits",combine="average", reject="crreject", ccdtype="", process="no", subsets="no", delete="no", clobber="no", scale="mode", rdnoise=6.5, gain=2.3)
os.system('rm z*dome'+color+'*fits')
print "created ccd"+str(date)+".dome"+color+".fits"
os.system('rm flat'+color+'.list')
else:
print "your input for the filter was not recognized. Please use either 'bias', 'B', 'V', 'R', or 'I' and try again"
return
def zerocombine(self,
files,
output,
method="median",
rejection="minmax",
ccdtype="",
overwrite=True):
"""IRAF zerocombine"""
self.logger.info("Zerocombine Started")
try:
iraf.imred.unlearn()
iraf.ccdred.unlearn()
iraf.ccdred.ccdproc.unlearn()
iraf.ccdred.combine.unlearn()
iraf.ccdred.zerocombine.unlearn()
iraf.ccdred.zerocombine.unlearn()
ccdred.instrument = self.instrument_path
biases = ",".join(files)
out_file = "{}/myraf_biases.flist".format(self.fop.tmp_dir)
with open(out_file, "w") as f2w:
for i in files:
f2w.write("{}\n".format(i))
biases = "@{}".format(out_file)
if self.fop.is_file(output) and overwrite:
self.logger.warning("Over Writing file({})".format(output))
self.fop.rm(output)
iraf.zerocombine(input=biases,
output=output,
combine=method,
reject=rejection,
ccdtype=ccdtype,
Stdout="/dev/null")
return True
except Exception as e:
self.logger.error(e)
return False
def biascom(input_bias='bias.list'):
"""
1. Description
: This function makes a master bias image of SAO 1-m using Pyraf. Put bias images to 201XXXXX/bias/ directory. Run this code on 201XXXXX directory, then pyraf chdir task enter bias directory and makes process. Output image is zero.fits and it will be copied on upper directory. Due to iraf.chdir task, you should reset python when this code is finished in order to make confusion of current directory between iraf and python!
2. Usage
: Start on 2018XXXX directory. Make bias directory which contains each bias frame. Naming of each bias images should be cal*bias.fit. Then just use SAO_biascom()
>>> SAO_biascom()
3. History
2018.03 Created by G.Lim.
2018.12.17 Edited by G.Lim. Define SAO_biascom function.
2019.02.07 Assign archive of masterbias in each date by G. Lim
"""
import glob
import os, sys
import numpy as np
from pyraf import iraf
from astropy.io import fits
curdir = os.getcwd()
curdate = curdir.split('/')[-1]
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.ccdred.setinst(instrume='camera', directo='/iraf/iraf/noao/imred/ccdred/ccddb/', query='q', review='no')
#input_name = 'bias.list'
output_name = curdate+'_zero.fits'
#calibrations = input_bias
#f = open(input_name,'w+')
#for i in range(len(calibrations)):
# f.write(calibrations[i]+'\n')
#f.close()
print('Zerocombine is running...')
iraf.imstat(images='@'+input_bias)
iraf.zerocombine(input='@'+input_bias, output=output_name, combine='median', reject='minmax', process='no', scale='none', ccdtype='' )
print('Output master '+output_name+' is created.')
os.system('/usr/bin/cp '+output_name+' /data1/KHAO/MDFTS/red/masterbias/')
iraf.dir('.')
def biascom(inim_list,
outim_list,
combine='median',
reject='minmax',
scale='none'):
import os, sys
from pyraf import iraf
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.ccdred.setinst(instrume='camera',
directo='/iraf/iraf/noao/imred/ccdred/ccddb/',
query='q',
review='no')
iraf.zerocombine(input=inim_list,
output=outim_list,
combine=combine,
reject=reject,
process='no',
scale=scale,
ccdtype='')
print('Output masterbias is created.')
def combine_bias(blst):
iraf.zerocombine(input='@' + blst,
output='Zero',
combine='average',
reject='minmax',
ccdtype='',
process=False,
delete=False,
clobber=False,
scale='none',
statsec='',
nlow=0,
nhigh=1,
nkeep=1,
mclip=True,
lsigma=3.0,
hsigma=3.0,
rdnoise='CCDRON',
gain='CCDGAIN',
snoise=0.0,
pclip=-0.5,
blank=0.0)
iraf.flpr()
print '<<<<<combine bias successfully>>>>>'
def optdomecomb(date, fwheel=['bias', 'B', 'V', 'R', 'I']):
'''
#combine biases and optical domes
#Requires: the uncombined fits images
# if you are combining a dome, you must have a bias from the same night as the dome to preform appropriate bias subtraction
#Input: the date the domes were observed YYMMDD, and fwheel, a list that contains the filters of the domes to be combined
#Outupt: combined dome fits frame for each color where uncombined frames are in the directory
'''
#convert date to string incase it was entered as an int of float
date = str(date)
if len(glob.glob('*bias*')) < 1:
print "no biases found, exiting"
return
else:
for color in fwheel:
if color == 'bias':
biaslist = glob.glob('*bias.[0-9]*')
if len(biaslist) > 10:
print "only " + str(
len(biaslist)) + " biases found. you need at least 10"
else:
with open("bias.list", 'w') as BILIS:
for i in biaslist:
BILIS.write(i + '\n')
iraf.zerocombine("@bias.list",
output="ccd" + str(date) + ".bias.fits",
combine="average",
reject="minmax",
scale="none",
ccdtype="",
process="no",
delete="no",
clobber="no",
nlow=1,
nhigh=1,
nkeep=1)
print "created ccd" + str(date) + ".bias.fits"
os.system('rm bias.list')
elif color in ['B', 'V', 'R', 'I']:
domelist = glob.glob('*dome' + color + '.[0-9]*')
if len(domelist) < 1:
print 'no ' + color + ' domes found'
elif len(domelist) > 10:
print 'only ' + str(
len(domelist)) + ' domes found. you need at least 10'
else:
with open('flat' + color + '.list', 'w') as flist:
for i in domelist:
flist.write(i + '\n')
iraf.ccdproc("@flat" + color + ".list",
output="z@flat" + color + ".list",
ccdtype=" ",
noproc="no",
fixpix="no",
overscan="yes",
trim="no",
zerocor="yes",
darkcor="no",
flatcor="no",
illumcor="no",
fringec="no",
readcor="no",
scancor="no",
readaxis="line",
biassec="[3:14,1:1024]",
zero="ccd" + str(date) + ".bias.fits",
interactive="no",
functio="spline3",
order=11)
iraf.flatcombine("z@flat" + color + ".list",
output="ccd" + str(date) + ".dome" +
color + ".fits",
combine="average",
reject="crreject",
ccdtype="",
process="no",
subsets="no",
delete="no",
clobber="no",
scale="mode",
rdnoise=6.5,
gain=2.3)
os.system('rm z*dome' + color + '*fits')
print "created ccd" + str(date) + ".dome" + color + ".fits"
os.system('rm flat' + color + '.list')
else:
print "your input for the filter was not recognized. Please use either 'bias', 'B', 'V', 'R', or 'I' and try again"
return
def biascom(imlist_name, camera='STX16803'):
"""
1. Description
: This function makes a master bias image of KCT using Pyraf. Put bias images to 20XX-XX-XX/bias/ directory. Run this code on 20XX-XX-XX directory, then pyraf chdir task enter bias directory and makes process. Output image is zero.fits and it will be copied on upper directory. Due to iraf.chdir task, you should reset python when this code is finished in order to make confusion of current directory between iraf and python!
2. Usage
: Start on 2018-XX-XX directory. Make bias directory which contains each bias frame. Naming of each bias images should be zero*.fit. Then just use biascom()
>>> biascom()
3. History
2018.03 Created by G.Lim.
2018.12.17 Edited by G.Lim. Define SAO_biascom function.
2019.02.07 Assign archive of masterbias in each date by G. Lim
2020.03.01 Modified for KL4040 process
2020.03.06 Modified for KCT STX16803 process
"""
import glob
import os, sys
import numpy as np
from pyraf import iraf
from astropy.io import fits
savedir = '/data1/KCT/'
curdir = os.getcwd()
'''
yy = curdir.split('/')[-1].split('-')[0]
mm = curdir.split('/')[-1].split('-')[1]
dd = curdir.split('/')[-1].split('-')[2]
curdate = yy+mm+dd
'''
curdate = curdir.split('/')[-1]
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.ccdred.setinst(instrume='camera',
directo='/iraf/iraf/noao/imred/ccdred/ccddb/',
query='q',
review='no')
iraf.chdir('./bias')
input_name = 'bias.list'
output_name = curdate + '_zero.fits'
#os.system('ls cal*bias.fit > '+input_name)
calibrations = glob.glob(imlist_name)
f = open(input_name, 'w+')
for i in range(len(calibrations)):
hdr = fits.getheader(calibrations[i])
IMAGETYP = hdr['IMAGETYP']
if IMAGETYP == 'Bias Frame':
f.write(calibrations[i] + '\n')
f.close()
print('Zerocombine is running...')
iraf.zerocombine(input='@' + input_name,
output=output_name,
combine='median',
reject='minmax',
process='no',
scale='none',
ccdtype='')
print('Output master ' + output_name + ' is created.')
os.system('/usr/bin/cp ' + output_name + ' ../')
os.system('mkdir ' + savedir + 'masterbias')
os.system('/usr/bin/cp ' + output_name + ' ' + savedir + 'masterbias/')
iraf.chdir('../')
iraf.dir('.')
def makeZero(fileList='zeros.lst',inputType=INPUT_LISTFNAME,output="Zero.fits",combine='average',reject='minmax',ccdtype='zero'): '''create a master Zero.fits from the frames in fileList''' iraf.zerocombine(_genIRAFString(fileList,inputType),output=output,combine=combine,reject=reject,ccdtype=ccdtype) print "zerocombine successful" return
def createAverageBias(biasImagesList):
biasString = getIrafString(biasImagesList)
iraf.zerocombine(input=biasString,
output="averagedBias",
process="No",
reject="none")
def main(argv):
import getopt, os
import param as p
try:
# accept options -h, -m, -t, -b, -d, -f, -r
opts, argv = getopt.getopt(argv, 'hm:t:b:d:f:r', ['help', 'master=', 'telescope=', 'bias=', 'dark=', 'flat=', 'reduce'])
except getopt.GetoptError:
usage()
sys.exit(2)
for opt, arg in opts:
if opt in ('-h', '--help'):
usage()
sys.exit(2)
# need to select the telescope, see param.py
if opt in ('-r', '--reduce') or opt in ('-m', '--master'):
for opt2, arg2 in opts:
if opt2 in ('-t', '--telescope'):
telescope = p.telescopes[arg2]
try: telescope
except:
usage()
sys.exit(2)
# option -r, reduce frames
if opt in ('-r', '--reduce'):
from pyraf import iraf
import pyfits
# inizialise variables
p = {}
framelist = []
filters = []
if arg:
# one can specify a list of frames, divided by a space
# @todo not tested!
framelist.append(arg.split(' '))
else:
# get list of frames, using function list_frames
frameType = 'Light Frame'
frames = list_frames('.')[0]
for date in frames[frameType]: # select just frameType
for temperature in frames[frameType][date]:
for bin in frames[frameType][date][temperature]:
framesProc = frames[frameType][date][temperature][bin]
for frameProc in framesProc:
# exclude master frames (shouldn't be necessary!)
if not os.path.basename(frameProc['path'])[0:6] == 'Master':
# add frame path to framelist
if not frameProc['filter'] in filters: filters.append(frameProc['filter'])
f = {}
f['path'] = os.path.abspath(frameProc['path'])
f['filter'] = frameProc['filter']
framelist.append(f)
# set bias, dark and flats.
# if nothing is specified, use default Master*.fits
for opt1, arg1 in opts:
if opt1 in ('-b', '--bias'):
if arg1: p['bias'] = arg1
if opt1 in ('-d', '--dark'):
if arg1: p['dark'] = arg1
if opt1 in ('-f', '--flat'):
if arg1: p['flat'] = arg1
if not 'bias' in p: p['bias'] = 'MasterBias.fits'
if not 'dark' in p: p['dark'] = 'MasterDark.fits'
# set flat fields, divided by filter
flatfiles = []
if not 'flats' in p:
import glob
for file in glob.glob('./MasterFlat*'):
f = {}
f['path'] = os.path.abspath(file)
header=pyfits.getheader(file)
f['filter'] = header['FILTER']
flatfiles.append(f)
# check that files exist
try:
with open(p['dark']): pass
except IOError:
print '%s not found' % p['dark']
try:
with open(p['bias']): pass
except IOError:
print '%s not found' % p['bias']
# create copies of original files, with prefix CORR_
import shutil
for f in framelist:
shutil.copy(f['path'], os.path.splitext(f['path'])[0]+'_reduced.fits')
# reduce images by filter
for filter in filters:
# create text list of files for iraf
tmplistcorr = open('tmplistcorr', 'w')
for frame in framelist:
if frame['filter'] == filter:
tmplistcorr.write("%s_reduced.fits\n" % (os.path.splitext(frame['path'])[0]))
tmplistcorr.close()
# select flat field
for flat in flatfiles:
if flat['filter'] == filter: p['flat'] = flat['path']
# reduce frames
print 'Calibrate images with %s filter' % filter
iraf.ccdproc(images='@tmplistcorr',output='', ccdtype='', fixpix='no', \
overscan='no', trim='No', zerocor='yes', zero=p['bias'], \
darkcor='Yes', dark=p['dark'], flatcor='yes', flat=p['flat'], readaxi='column', \
biassec='', trimsec='')
# option -m type: create master frames
elif opt in ('-m', '--master'):
from pyraf import iraf
import pyfits
p = {}
framelist = []
# select frame type
if arg == 'bias' or arg == 'dark' or arg == 'flat':
if arg == 'bias': frameType = 'Bias Frame' # we need 'Bias Frame', not just 'bias'!
elif arg == 'dark': frameType = 'Dark Frame' # same
elif arg == 'flat':
frameType = 'Flat Field' # same
frameflat = [] # frameflat is used to divide files by filter
filters = []
# use function list_frames from reduceall, to list all the frames
# divided by frametype, date, temperature, bin
# and add them to a single list
frames = list_frames('.')[0]
for date in frames[frameType]: # select just frameType
for temperature in frames[frameType][date]:
for bin in frames[frameType][date][temperature]:
framesProc = frames[frameType][date][temperature][bin]
for frameProc in framesProc:
# exclude master frames
if not os.path.basename(frameProc['path'])[0:6] == 'Master':
# add frame path to framelist
framelist.append(os.path.abspath(frameProc['path']))
# for flat fields, create a second list (frameflat)
# that specifies the filter name
if arg == 'flat':
if not frameProc['filter'] in filters: filters.append(frameProc['filter'])
flat = {}
flat['path'] = os.path.abspath(frameProc['path'])
flat['filter'] = frameProc['filter']
frameflat.append(flat)
# set bias frame and/or dark frame.
# if nothing is specified, use default Master*.fits
# check that files actually exist
if arg == 'dark' or arg == 'flat':
for opt1, arg1 in opts:
if opt1 in ('-b', '--bias'):
if arg1: p['bias'] = arg1
if opt1 in ('-d', '--dark') and arg == 'flat':
if arg1: p['dark'] = arg1
if not 'bias' in p: p['bias'] = 'MasterBias.fits'
if not 'dark' in p and arg == 'flat': p['dark'] = 'MasterDark.fits'
if 'dark' in p:
try:
with open(p['dark']): pass
except IOError:
print '%s not found' % p['dark']
if 'bias' in p:
try:
with open(p['bias']): pass
except IOError:
print '%s not found' % p['bias']
# create copies of original files, with prefix CORR_
import shutil
for f in framelist:
shutil.copy(f, os.path.join(os.path.dirname(f), 'CORR_'+os.path.basename(f)))
# create text lists of original and copied files
tmplistcorr = open('tmplistcorr', 'w')
tmplist = open('tmplist', 'w')
for f in framelist:
tmplistcorr.write("%s/CORR_%s\n" % (os.path.dirname(f), os.path.basename(f)))
tmplist.write("%s\n" % f)
tmplistcorr.close()
tmplist.close()
# create MasterBias
if arg == 'bias':
print 'Trim bias frames'
iraf.ccdproc (images='@tmplistcorr',output ='', ccdtype = '', fixpix='no', \
oversca = 'no', trim = 'yes', zerocor = 'no', zero = '', \
flatcor = 'no', darkcor = 'no', readaxi = 'column', \
biassec = '', trimsec=telescope.trimsection)
print 'Creating MasterBias.fits'
iraf.zerocombine (PYinput='@tmplistcorr', output='MasterBias.fits', ccdtype='', \
combine='median', reject='none', rdnoise=telescope.rdnoise, gain=telescope.egain)
# create MasterDark
if arg == 'dark':
print 'Bias subtract dark frames'
iraf.ccdproc(images='@tmplistcorr',output='', ccdtype='', fixpix='no', \
overscan='no', trim='yes', zerocor='yes', zero=p['bias'], \
darkcor='No', flatcor='no', readaxi='column', \
biassec='', trimsec=telescope.trimsection)
print 'Creating MasterDark.fits'
iraf.darkcombine (PYinput='@tmplistcorr', output='MasterDark.fits', ccdtype='', \
combine='median', reject='none', process='no', delete='no', \
clobber='no', scale='exposure', statsec='', nlow='0', nhigh='1', \
nkeep='1', mclip='yes', lsigma='5.', hsigma='4.',rdnoise=telescope.rdnoise,\
gain=telescope.egain, snoise='0.', pclip='-0.5', blank='0.')
# create FlatFields, divided by filter
if arg == 'flat':
print 'Bias and dark subtract flat fields'
iraf.ccdproc(images='@tmplistcorr',output='', ccdtype='', fixpix='no', \
overscan='no', trim='yes', zerocor='yes', zero=p['bias'], \
darkcor='Yes', dark=p['dark'], flatcor='no', readaxi='column', \
biassec='', trimsec=telescope.trimsection)
# divide images by filter and flat combine
for filter in filters:
print 'Combine flat fields - Filter %s' % filter
# create text list for iraf
flatlist = open('flatlist', 'w')
for frame in frameflat:
if frame['filter'] == filter:
flatlist.write("%s\n" % frame['path'])
flatlist.close()
iraf.flatcombine(PYinput = '@flatlist', output='MasterFlat-%s.fits' % filter, ccdtype='', \
combine='median', reject='none', process='no', subsets='no', \
delete='no', clobber='no', scale='mode', statsec=' ', nlow='1', \
nhigh='1', nkeep='1', mclip='yes', lsigma='5.', hsigma='5.', \
rdnoise=telescope.rdnoise, gain=telescope.egain, snoise='0.', pclip='-0.5',blank='1.')
os.remove('flatlist')
# remove working files & temp lists
for f in framelist:
os.remove(os.path.join(os.path.dirname(f), 'CORR_'+os.path.basename(f)))
os.remove('tmplist')
os.remove('tmplistcorr')
def createAverageBias(self):
iraf.zerocombine(input=self.inputFolder,output=self.avaragedBias)
def run_zerocom(input):
iraf.noao.imred(_doprint=0)
iraf.noao.imred.ccdred(_doprint=0)
iraf.zerocombine(input[0],output=input[1],combine="average",reject="minmax",ccdtype="",process="no",delete="no",clobber="no",scale="none",statsec="[100:1000,100:1000]",nlow="1",nhigh="1",nkeep="3",mclip="no",lsigma="3",hsigma="3",rdnoise="6",gain="7",snoise="0")
def process():
ovrsc = '[1:4,*]'
trim = '[200:2046,50:200]'
imglist = glob.glob('FORS2*.fits')
# objdict = {}
# objdict.clear()
for img in imglist:
hdu = pyfits.getheader(img)
if 'OBJECT' in hdu:
obj = hdu['OBJECT']
if obj not in objdict:
objdict[obj] = [img]
else:
objdict[obj].append(img)
if 'STD' not in objdict:
os.system('cp /dark/jsamuel/agn/standards/FORS2.2016-05-12T09:15:14.678.fits ./')
objdict['STD'] = ['FORS2.2016-05-12T09:15:14.678.fits']
os.system('cp /dark/jsamuel/agn/standards/FORS2.2016-05-12T04:24:38.547.fits ./')
objdict['STD'].append('FORS2.2016-05-12T04:24:38.547.fits')
stars = {'specphot-LTT7379':'l7379','specphot-LDS749B':'lds749b','specphot-EG274':'eg274','specphot-G138-31':'g13831','specphot-C-32d9927':'cd32','specphot-LTT9491':'l9491','specphot-LTT7987':'l7987'}
i = 0
for key in objdict.keys():
if 'STD' in key:
for img in objdict[key]:
i = i + 1
numstars = len(objdict['STD'])
hds = pyfits.getheader(img)
_starname = stars[hds['HIERARCH ESO OBS NAME']]
if _starname in ['lds749b','g13831']:
print 'Bad standard, copying from 2016-05-12'
if not os.path.isdir('badstd'):
os.mkdir('badstd')
os.system('mv '+img+' ./badstd')
if i >= numstars:
objdict.pop('STD')
os.system('cp /dark/jsamuel/agn/standards/FORS2.2016-05-12T09:15:14.678.fits ./')
objdict['STD'] = ['FORS2.2016-05-12T09:15:14.678.fits']
os.system('cp /dark/jsamuel/agn/standards/FORS2.2016-05-12T04:24:38.547.fits ./')
objdict['STD'].append('FORS2.2016-05-12T04:24:38.547.fits')
if os.path.isfile('biaslist'):
os.remove('biaslist')
if os.path.isfile('masterbias.fits'):
os.remove('masterbias.fits')
f = open('biaslist','w')
for img in objdict['BIAS']:
f.write(img+'\n')
f.close()
imglist = '@biaslist'
name = 'masterbias.fits'
hdb = pyfits.getheader(objdict['BIAS'][0])
_gain = hdb['HIERARCH ESO DET OUT1 GAIN']
_ron = hdb['HIERARCH ESO DET OUT1 RON']
iraf.zerocombine(imglist,output=name,combine='average',reject='minmax',ccdtype='none',process='no',gain=_gain,rdnoise=_ron,Stdout=1)
if os.path.isfile('flatlist'):
os.remove('flatlist')
if os.path.isfile('sciflatlist'):
os.remove('sciflatlist')
if os.path.isfile('stdflatlist'):
os.remove('stdflatlist')
if os.path.isfile('masterflat.fits'):
os.remove('masterflat.fits')
if os.path.isfile('scimasterflat.fits'):
os.remove('scimasterflat.fits')
if os.path.isfile('stdmasterflat.fits'):
os.remove('stdmasterflat.fits')
f = open('sciflatlist','w')
for img in objdict['FLAT,LAMP']:
hdu = pyfits.getheader(img)
if hdu['HIERARCH ESO DPR TECH'] == 'SPECTRUM':
f.write(img+'\n')
f.close()
j = 0
f = open('stdflatlist','w')
for img in objdict['FLAT,LAMP']:
hdu = pyfits.getheader(img)
if hdu['HIERARCH ESO DPR TECH'] == 'MOS':
f.write(img+'\n')
j = j + 1
f.close()
imglist = '@sciflatlist'
name = 'scimasterflat.fits'
hdf = pyfits.getheader(objdict['FLAT,LAMP'][0])
_gain = hdf['HIERARCH ESO DET OUT1 GAIN']
_ron = hdf['HIERARCH ESO DET OUT1 RON']
iraf.flatcombine(imglist,output=name,combine='average',reject='avsigclip',ccdtype='none',process='no',subsets='yes',delete='no',clobber='no',gain=_gain,rdnoise=_ron,Stdout=1)
if j == 0:
imglist = '@sciflatlist'
elif j >= 1:
imglist = '@stdflatlist'
name = 'stdmasterflat.fits'
hdf = pyfits.getheader(objdict['FLAT,LAMP'][0])
_gain = hdf['HIERARCH ESO DET OUT1 GAIN']
_ron = hdf['HIERARCH ESO DET OUT1 RON']
iraf.flatcombine(imglist,output=name,combine='average',reject='avsigclip',ccdtype='none',process='no',subsets='yes',delete='no',clobber='no',gain=_gain,rdnoise=_ron,Stdout=1)
if os.path.isfile('tmasterbias.fits'):
os.remove('tmasterbias.fits')
fits = 'masterbias.fits'
name = 'tmasterbias.fits'
zcor = 'no'
fcor = 'no'
_zero = ''
_flat = ''
iraf.ccdproc(fits,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=_flat,Stdout=1)
if os.path.isfile('tmasterflat.fits'):
os.remove('tmasterflat.fits')
if os.path.isfile('tscimasterflat.fits'):
os.remove('tscimasterflat.fits')
if os.path.isfile('tstdmasterflat.fits'):
os.remove('tstdmasterflat.fits')
fits = 'scimasterflat.fits'
name = 'tscimasterflat.fits'
zcor = 'yes'
fcor = 'no'
_zero = 'tmasterbias.fits'
_flat = ''
iraf.ccdproc(fits,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=_flat,Stdout=1)
fits = 'stdmasterflat.fits'
name = 'tstdmasterflat.fits'
zcor = 'yes'
fcor = 'no'
_zero = 'tmasterbias.fits'
_flat = ''
iraf.ccdproc(fits,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=_flat,Stdout=1)
if os.path.isfile('ntmasterflat.fits'):
os.remove('ntmasterflat.fits')
if os.path.isfile('ntscimasterflat.fits'):
os.remove('ntscimasterflat.fits')
if os.path.isfile('ntstdmasterflat.fits'):
os.remove('ntstdmasterflat.fits')
cal = 'tscimasterflat.fits'
resp = 'ntscimasterflat.fits'
_order = 100
iraf.response(calibration=cal,normalization=cal,response=resp,interactive='no',function='legendre',order=_order,graphics='stdgraph')
cal = 'tstdmasterflat.fits'
resp = 'ntstdmasterflat.fits'
_order = 100
iraf.response(calibration=cal,normalization=cal,response=resp,interactive='no',function='legendre',order=_order,graphics='stdgraph')
##########
ovrsc = '[1:4,*]'
trim = '[200:2046,50:200]'
fimg = 'ntscimasterflat'
_area = '[1:150,*]'
_value = 1.0
iraf.imreplace(images=fimg+_area,value=_value)
fimg = 'ntstdmasterflat'
_area = '[1:150,*]'
_value = 1.0
iraf.imreplace(images=fimg+_area,value=_value)
alist = glob.glob('*AGN*.fits')
for f in alist:
os.remove(f)
slist = glob.glob('STD*.fits')
for f in slist:
os.remove(f)
llist = glob.glob('LAMP*.fits')
for f in llist:
os.remove(f)
zcor = 'yes'
fcor = 'yes'
_zero = 'tmasterbias.fits'
sciflat = 'ntscimasterflat.fits'
stdflat = 'ntstdmasterflat.fits'
for key in objdict.keys():
if 'AGN' in key:
for img in objdict[key]:
hds = pyfits.getheader(img)
if 'EXPTIME' in hds:
if hds['EXPTIME'] >= 50.0:
num = img.rsplit('.',2)[1]
name = key+'_'+num
'''
iraf.ccdproc(img,output='trim_'+name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor='no',darkcor='no',flatcor='no',illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero='',flat='',Stdout=1)
_gain = hds['HIERARCH ESO DET OUT1 GAIN']
_ron = hds['HIERARCH ESO DET OUT1 RON']
cosmics.lacos('trim_'+name+'.fits', output='c_'+name+'.fits', gain=_gain, readn=_ron, xorder=9, yorder=9, sigclip=4.5, sigfrac=0.5, objlim=1, verbose=True, interactive=False)
iraf.ccdproc('c_'+name,output=name,ccdtype='',noproc='no',fixpix='no',overscan='no',trim='no',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec='',trimsec='',zero=_zero,flat=sciflat,Stdout=1)
'''
iraf.ccdproc(img,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=sciflat,Stdout=1)
else:
pass
elif 'STD' in key:
for img in objdict[key]:
num = img.rsplit('.',2)[1]
name = key+'_'+num
iraf.ccdproc(img,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=stdflat,Stdout=1)
elif 'WAVE' in key:
for img in objdict[key]:
num = img.rsplit('.',2)[1]
name = 'LAMP_'+num
iraf.ccdproc(img,output=name,ccdtype='',noproc='no',fixpix='no',overscan='yes',trim='yes',zerocor=zcor,darkcor='no',flatcor=fcor,illumcor='no',fringecor='no',readcor='no',scancor='no',biassec=ovrsc,trimsec=trim,zero=_zero,flat=stdflat,Stdout=1)
def efoscreduction(imglist, _interactive, _doflat, _dobias, listflat, listbias, _dobadpixel, badpixelmask,
fringingmask, _archive, typefile, filenameobjects, _system, _cosmic, _verbose=False, method='iraf'):
# print "LOGX:: Entering `efoscreduction` method/function in %(__file__)s"
# % globals()
import ntt
from ntt.efoscphotredudef import searchbias
from ntt.util import delete, readhdr, readkey3, display_image, searchflat, rangedata, correctcard
from numpy import argmin, min, abs, sqrt
import string
import os
import re
import math
import sys
from pyraf import iraf
# ## Call and set parameters for useful iraf tasks
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.proto(_doprint=0)
toforget = ['ccdproc', 'zerocombine',
'flatcombine', 'imreplace', 'proto.fixpix']
for t in toforget:
iraf.unlearn(t)
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.overscan = 'no'
iraf.ccdproc.ccdtype = ''
iraf.ccdproc.biassec = ''
iraf.ccdred.instrument = "/dev/null"
if _verbose:
iraf.ccdred.verbose = 'yes'
else:
iraf.ccdred.verbose = 'no'
import datetime
import time
# starttime=time.time()
now = datetime.datetime.now()
datenow = now.strftime('20%y%m%d%H%M')
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
outputfile = []
reduceddata = rangedata(imglist)
img = re.sub('\n', '', imglist[0])
hdr = readhdr(img)
_gain = readkey3(hdr, 'gain')
_rdnoise = readkey3(hdr, 'ron')
_instrume = readkey3(hdr, 'instrume')
_trimsec = '[3:1010,1:1015]'
biaslist = {}
flatlist1 = {}
flatlist2 = {}
objectlist = {}
filterlist1 = []
filterlist2 = []
for img in imglist:
_type = ''
img = re.sub('\n', '', img)
hdr = readhdr(img)
_naxis1 = readkey3(hdr, 'NAXIS1')
_naxis2 = readkey3(hdr, 'NAXIS2')
if _naxis1 != 1030 or _naxis2 != 1030:
ntt.util.writeinthelog(
'image ' + str(img) + ' different dimension =\n', './logNTT.txt')
_type = 'not good'
if not _type and readkey3(hdr, 'speed') != 'fastL':
_type = 'not good'
if not _type and readkey3(hdr, 'instrume') != 'efosc':
_type = 'not good'
_imagetype = readkey3(hdr, 'tech')
if not _type and _imagetype == 'SPECTRUM':
_type = 'spectroscopic data'
if not _type:
_exptime = readkey3(hdr, 'exptime')
_date = readkey3(hdr, 'date-night')
_filter = readkey3(hdr, 'filter')
if float(_exptime) == 0.0:
if _date not in biaslist:
biaslist[_date] = []
biaslist[_date].append(img)
_type = 'bias'
if not _type:
_object = readkey3(hdr, 'object')
if _filter.lower() in ['g782', 'r784', 'z623', 'u640', 'b639', 'v641', 'r642',
'i705'] and _imagetype == 'IMAGE':
if 'sky,flat' in _object.lower():
_type = 'flat'
elif 'dome' in _object.lower() or 'flat' in _object.lower():
_type = 'flat dome'
if _type == 'flat':
if _filter not in filterlist1:
filterlist1.append(_filter)
flatlist1[_filter] = []
flatlist1[_filter].append(img)
if _type == 'flat dome':
if _filter not in filterlist2:
filterlist2.append(_filter)
flatlist2[_filter] = []
flatlist2[_filter].append(img)
if not _type:
_catg = readkey3(hdr, 'catg')
if 'science' in _catg.lower() or 'acquisition' in _catg.lower():
_type = 'object'
if _filter not in objectlist:
objectlist[_filter] = []
objectlist[_filter].append(img)
if 'acquisition' in _catg.lower():
try:
correctcard(img)
_ra1, _dec1, _name = ntt.util.correctobject(
img, 'standard_efosc_mab.txt')
_ra1, _dec1, _name = ntt.util.correctobject(
img, filenameobjects)
except:
pass
elif 'focus' in _object.lower():
_type = 'not good'
if not _type:
print '\n### warning: object not recognized '
_object = readkey3(hdr, 'object')
print img, _object, _imagetype
answ = raw_input(
'what is it: bias [1], flat [3], object[4], test [5] ? [5] ')
if not answ:
answ = '5'
if answ == '1':
if _date not in biaslist:
biaslist[_date] = ()
biaslist[_date].append(img)
elif answ == '4':
if _filter not in objectlist:
objectlist[_filter] = []
objectlist[_filter].append(img)
elif answ == '3':
tt = raw_input('dome or sky [d/[s]] ? ')
if tt == 's':
_type = 'flat'
_filter = readkey3(hdr, 'filter')
if _filter not in filterlist1:
filterlist1.append(_filter)
flatlist1[_filter] = []
flatlist1[_filter].append(img)
elif tt == 'd':
_type = 'flat dome'
_filter = readkey3(hdr, 'filter')
if _filter not in filterlist2:
filterlist2.append(_filter)
flatlist2[_filter] = []
flatlist2[_filter].append(img)
elif answ == '5':
_type = 'not good'
filterlist = list(set(filterlist1 + filterlist2))
if _verbose:
print filterlist1
print filterlist2
print flatlist1
print flatlist2
flatlist = {}
for _filt in filterlist:
if _filt not in flatlist1.keys():
if _filt in flatlist2.keys():
if len(flatlist2[_filt]) >= 3:
flatlist[_filt] = flatlist2[_filt]
elif len(flatlist1[_filt]) < 3:
if _filt in flatlist2.keys():
if len(flatlist2[_filt]) >= 3:
flatlist[_filt] = flatlist2[_filt]
elif _filt in flatlist1.keys():
if len(flatlist1[_filt]) >= 3:
flatlist[_filt] = flatlist1[_filt]
listaout = []
if _verbose:
print '\n### flat ', str(flatlist), '\n'
print '\n### bias ', str(biaslist), '\n'
print '\n### object ', str(objectlist), '\n'
###### masterbias #################
if _dobias:
if not _archive:
if listbias:
masterbiaslist = listbias
else:
masterbiaslist = []
if biaslist:
for _date in biaslist:
print '\n do bias ' + str(_date) + '\n'
biaslist[_date] = rejectbias(
biaslist[_date], False, 10)
if len(biaslist[_date]) >= 3:
masterbiasfile = 'bias_' + \
str(_date) + '_' + str(MJDtoday) + '.fits'
delete(masterbiasfile)
f = open('biaslist', 'w')
h = open('obiaslist', 'w')
for img in biaslist[_date]:
f.write(img + '\n')
h.write('o' + img + '\n')
delete('o' + img)
f.close()
h.close()
try:
print 'processing bias .....'
iraf.ccdproc('@biaslist', output='@obiaslist', overscan="no", trim="yes", zerocor='no',
fixpix='no', ccdtype='', flatcor='no', darkcor='no', biassec='',
trimsec=str(_trimsec), readaxi='column', Stdout=1)
iraf.zerocombine('@obiaslist', output=masterbiasfile, combine='median',
reject='ccdclip', ccdtype='', process='no',
rdnoise=_rdnoise, gain=_gain, Stdout=1)
correctcard(masterbiasfile)
num = 0
for img in biaslist[_date]:
num = num + 1
ntt.util.updateheader(masterbiasfile, 0, {
'PROV' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
ntt.util.updateheader(masterbiasfile, 0, {
'TRACE' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
delete('o' + img)
ntt.util.updateheader(masterbiasfile, 0,
{'M_EPOCH': [False, 'TRUE if resulting from multiple epochs']})
ntt.util.updateheader(masterbiasfile, 0,
{'SINGLEXP': [False, 'TRUE if resulting from single exposure']})
ntt.util.updateheader(masterbiasfile, 0, {
'FILETYPE': [11201, 'bias']})
masterbiaslist.append(masterbiasfile)
if masterbiasfile not in outputfile:
outputfile.append(masterbiasfile)
except:
ntt.util.writeinthelog(
'Warning ' +
str(biaslist[_date]) +
' problem with this list of bias \n',
'./logNTT.txt')
if masterbiasfile and _interactive:
aa, bb, cc = display_image(
masterbiasfile, 1, '', '', False)
answ = raw_input(
'is the masterbias ok [[y]/n] ?')
if not answ:
answ = 'y'
if answ in ['n', 'no']:
sys.exit(
'remove bad bias from input list and restart')
else:
masterbiaslist = []
########## masterflat #########################
if _doflat:
if not _archive:
if listflat:
masterflatlist = listflat
else:
masterflatlist = []
if flatlist:
for _filter in flatlist:
print '\n do flat ' + str(_filter) + '\n'
flatlist[_filter] = rejectflat(
flatlist[_filter], False)
if len(flatlist[_filter]) >= 3:
_date = readkey3(
readhdr(flatlist[_filter][0]), 'date-night')
masterflat = 'flat_' + \
str(_date) + '_' + str(_filter) + \
'_' + str(MJDtoday) + '.fits'
listaflat = 'flatlist_' + \
str(_date) + '_' + str(_filter)
_bias = ''
if masterbiaslist:
_bias = searchbias(flatlist[_filter][
0], masterbiaslist)[0]
if not _bias:
_bias = searchbias(flatlist[_filter][0], '')[0]
if _bias:
if _bias[0] == '/':
os.system('cp ' + _bias + ' .')
_bias = string.split(_bias, '/')[-1]
_zerocor = 'yes'
else:
_zerocor = 'yes'
else:
_zerocor = 'no'
answ0 = 'n'
while answ0 != 'y':
f = open(listaflat, 'w')
h = open('o' + listaflat, 'w')
for img in flatlist[_filter]:
f.write(img + '\n')
h.write('o' + img + '\n')
delete('o' + img)
f.close()
h.close()
try:
print 'processing flat .....'
iraf.ccdproc('@' + listaflat, output='@o' + listaflat, overscan='no', trim='yes',
darkcor='no', fixpix='no',
zerocor=_zerocor, flatcor='no', trimsec=str(_trimsec), biassec='',
zero=_bias, readaxi='column', ccdtype='', Stdout=1)
delete(masterflat)
iraf.flatcombine('@o' + listaflat, output=masterflat, combine='average',
reject='avsigclip', ccdtype='', process='no',
rdnoise=_rdnoise, gain=_gain, statsec='[100:800,100:800]',
lsigma=3, hsigma=2, Stdout=1)
masterflatlist.append(masterflat)
correctcard(masterflat)
num = 0
for img in flatlist[_filter]:
num = num + 1
ntt.util.updateheader(masterflat, 0, {
'PROV' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
ntt.util.updateheader(masterflat, 0, {
'TRACE' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
delete('o' + img)
ntt.util.updateheader(
masterflat, 0, {'ZEROCOR': [_bias, '']})
ntt.util.updateheader(masterflat, 0, {
'M_EPOCH': [False, 'TRUE if resulting from multiple epochs']})
ntt.util.updateheader(masterflat, 0, {
'SINGLEXP': [False, 'TRUE if resulting from single exposure']})
ntt.util.updateheader(
masterflat, 0, {'FILETYPE': [11202, 'flat field']})
if masterflat not in outputfile:
outputfile.append(masterflat)
except:
ntt.util.writeinthelog(
'Warning ' +
str(flatlist[
_filter]) + ' problem with this list of flat \n',
'./logNTT.txt')
aa, bb, cc = display_image(
masterflat, 1, '', '', False)
if masterflat and _interactive:
answ = raw_input(
'is the masterflat ok [[y]/n] ?')
if not answ:
answ = 'y'
if answ.lower() in ['n', 'no']:
answ1 = raw_input(
'try again [[y]/n] ?')
if not answ1:
answ1 = 'y'
if answ1.lower() in ['y', 'yes']:
flatlist[_filter] = ntt.efoscphotredudef.rejectflat(
flatlist[_filter], True)
else:
sys.exit(
'error: problem with flat .... exit')
else:
answ0 = 'y'
else:
answ0 = 'y'
else:
masterflatlist = []
##########################################################################
if len(masterbiaslist) == 0:
masterbiaslist = ''
if len(masterflatlist) == 0:
masterflatlist = ''
######################################
if _verbose:
print ''
print '#############################'
print masterflatlist
print masterbiaslist
print '#############################'
print ''
if masterflatlist:
listaout = listaout + masterflatlist
if masterbiaslist:
listaout = listaout + masterbiaslist
if typefile == 'calib':
objectlist = {}
for _filter in objectlist:
for img in objectlist[_filter]:
hdr = readhdr(img)
print '\n#####################################################################\n'
_object = readkey3(hdr, 'object')
_object = re.sub(' ', '', _object)
_object = re.sub('/', '_', _object)
_object = re.sub('\n', '', _object)
_exptime = readkey3(hdr, 'exptime')
_date = readkey3(hdr, 'date-night')
nameout = ntt.util.name_duplicate(img, str(_object) + '_' + str(_date) + '_' + str(_filter) + '_' + str(
MJDtoday), '')
_bias = ''
if _dobias:
if masterbiaslist:
_bias = searchbias(img, masterbiaslist)[0]
if not _bias:
_bias = searchbias(img, '')[0]
_flat = ''
if _doflat:
if masterflatlist:
_flat = searchflat(img, masterflatlist)[0]
if not _flat:
_flat = searchflat(img, '')[0]
if _bias: # bias ###
if _bias[0] == '/':
os.system('cp ' + _bias + ' .')
_bias = string.split(_bias, '/')[-1]
_zerocor = 'yes'
else:
_zerocor = 'no'
else:
_zerocor = 'no'
if _flat: # flat ###
if _flat[0] == '/':
os.system('cp ' + _flat + ' .')
_flat = string.split(_flat, '/')[-1]
_flatcor = 'yes'
else:
_flatcor = 'no'
sss = str(_object) + '_' + str(_date) + '_' + str(_filter)
print '### input', img, sss
print '### bias ', _zerocor, _bias
print '### flat ', _flatcor, _flat
print '### name ', nameout
delete(nameout)
try:
iraf.ccdproc(img, output=nameout, overscan="no", trim="yes", zerocor=_zerocor, flatcor='no',
darkcor='no', trimsec=str(_trimsec), zero=_bias, biassec='', readaxi='column', Stdout=1)
try:
iraf.ccdproc(nameout, output='', overscan="no", trim="no", zerocor='no', flatcor=_flatcor,
darkcor='no', flat=_flat, readaxi='column', ccdtype='', Stdout=1)
except:
iraf.imrepla(images=_flat, value=0.01,
lower='INDEF', upper=0.01, radius=0)
iraf.ccdproc(nameout, output='', overscan="no", trim="no", zerocor='no', flatcor=_flatcor,
darkcor='no', flat=_flat, readaxi='column', ccdtype='', Stdout=1)
correctcard(nameout)
ntt.util.updateheader(nameout, 0, {'FILTER': [readkey3(readhdr(nameout), 'filter'), 'Filter name'],
'SINGLEXP': [True, 'TRUE if resulting from single exposure'],
'M_EPOCH': [False, 'TRUE if resulting from multiple epochs'],
'FLATCOR': [_flat, ''],
'ZEROCOR': [_bias, ''], 'FILETYPE': [12204, 'pre-reduced image'],
'PROV1': [readkey3(readhdr(nameout), 'ARCFILE'), 'Originating file'],
'NCOMBINE': [1, 'Number of raw science data'],
'TRACE1': [readkey3(readhdr(nameout), 'ARCFILE'),
'Originating file']})
ntt.util.airmass(nameout) # phase 3 definitions
ntt.util.writeinthelog('\n', './logNTT.txt')
ntt.util.writeinthelog(
'image= ' + str(img) + ' output= ' + str(nameout) + '\n', './logNTT.txt')
ntt.util.writeinthelog(
'bias= ' + str(_bias) + ', flat= ' + str(_flat) + '\n', './logNTT.txt')
ntt.util.writeinthelog('\n', './logNTT.txt')
if nameout not in outputfile:
outputfile.append(nameout)
except:
ntt.util.writeinthelog(
'image ' + str(img) + ' probably corrupted\n', './logNTT.txt')
if _dobadpixel:
if not badpixelmask:
badpixelmask = 'bad_pixel_mask.fits'
delete(badpixelmask)
os.system('cp ' + ntt.__path__[0] + '/archive/' + str(
_instrume) + '/badpixels/badpixel.fits ' + badpixelmask)
iraf.proto.fixpix(images=nameout, masks=badpixelmask,
linterp='INDEF', cinterp='INDEF', verbose='no')
ntt.util.updateheader(
nameout, 0, {'FIXPIX': [badpixelmask, '']})
ntt.util.writeinthelog('image ' + str(nameout) + ' bad pixel corrected with ' + badpixelmask + '\n',
'./logNTT.txt')
print '\n### bad pixel mask correction ..... done'
else:
ntt.util.writeinthelog(
'image ' + str(nameout) + ' bad pixel NOT corrected\n', './logNTT.txt')
if _cosmic:
try:
print '\n### cosmic ..... '
ntt.cosmics.lacos_im(nameout, _output='', gain=_gain, readn=_rdnoise, xorder=9, yorder=9,
sigclip=4.5, sigfrac=0.5, objlim=1, skyval=0, niter=0, verbose=True,
interactive=False)
ntt.util.updateheader(nameout, 0, {
'LACOSMIC': [True, 'TRUE if Laplacian cosmic ray rejection has been applied to the image']})
print '\n### cosmic ..... removed '
except Exception, e:
print e
else:
ntt.util.updateheader(nameout, 0, {
'LACOSMIC': [False, 'TRUE if Laplacian cosmic ray rejection has been applied to the image']})
try:
##########################
sexvec = ntt.efoscastrodef.sextractor(nameout)
for cat in ['2mass', 'usnoa2', 'usnob1']:
rmsx3, rmsy3, num3, fwhmgess, ellgess, ccc, rasys3, decsys3, magsat3 = ntt.efoscastrodef.efoscastroloop(
[nameout], cat, False, 40, 40, 100, 'rxyscale', 100, 30, sexvec, True, 10, method)
if rmsx3 <= 2 and rmsy3 <= 2:
break
if rmsx3 > 2 and rmsy3 > 2:
for cat in ['2mass', 'usnoa2', 'usnob1']:
rmsx3, rmsy3, num3, fwhmgess, ellgess, ccc, rasys3, decsys3, magsat3 = ntt.efoscastrodef.efoscastroloop(
[nameout], cat, False, 20, int(20), int(50), 'rxyscale', 100, 30, sexvec, True, 5, method)
if rmsx3 <= 2 and rmsy3 <= 2:
break
if rmsx3 > 2 and rmsy3 > 2:
for cat in ['2mass', 'usnoa2', 'usnob1']:
rmsx3, rmsy3, num3, fwhmgess, ellgess, ccc, rasys3, decsys3, magsat3 = ntt.efoscastrodef.efoscastroloop(
[nameout], cat, False, int(10), int(10),
int(25), 'rxyscale', 100, 30, sexvec, True, int(3), method)
##########################
astrostring = str(rmsx3) + ' ' + str(rmsy3) + ' ' + str(num3)
ntt.util.updateheader(
nameout, 0, {'ASTROMET': [astrostring, 'rmsx rmsy nstars']})
print '\n### check astrometry: fine \n### rmsx rmsy nstars: ' + astrostring
except Exception, e:
print e
rmsx3, rmsy3, num3, fwhmgess, ellgess, ccc, rasys3, decsys3, magsat3 = '', '', '', '', '', '', '', '', ''
print '\n### problem with astrometry, do you have network ? '
if fwhmgess and fwhmgess < 99:
ntt.util.updateheader(nameout, 0, {'PSF_FWHM': [fwhmgess, 'Spatial resolution (arcsec)'],
'ELLIPTIC': [ellgess, 'Average ellipticity of point sources'],
'CRDER1': [(1 / sqrt(2.)) * float(rmsx3) * (1. / 3600.),
'Random error (degree)'],
'CRDER2': [(1 / sqrt(2.)) * float(rmsy3) * (1. / 3600.),
'Random error (degree)'],
'CUNIT1': ['deg', 'unit of the coord. trans.'],
'CUNIT2': ['deg', 'unit of the coord. trans.'],
'CSYER1': [rasys3, 'Systematic error (RA_m - Ra_ref)'],
'CSYER2': [decsys3, 'Systematic error (DEC_m - DEC_ref)']})
else:
ntt.util.updateheader(nameout, 0, {'PSF_FWHM': [9999., 'FHWM (arcsec) - computed with sectractor'],
'ELLIPTIC': [9999., 'ellipticity of point sources (1-b/a)'],
'CRDER1': [9999., 'Random error in axis 1'],
'CRDER2': [9999., 'Random error in axis 2'],
'CUNIT1': ['deg', 'unit of the coord. trans.'],
'CUNIT2': ['deg', 'unit of the coord. trans.'],
'CSYER1': [9999., 'Systematic error (RA_m - Ra_ref)'],
'CSYER2': [9999., 'Systematic error (DEC_m - DEC_ref)']})
try:
result = ntt.efoscastrodef.zeropoint(
nameout, _system, method, False, False)
except:
result = ''
if result:
if os.path.isfile(re.sub('.fits', '.ph', nameout)):
if re.sub('.fits', '.ph', nameout) not in outputfile:
outputfile.append(
re.sub('.fits', '.ph', nameout))
print '\n### zeropoint ..... done'
for ll in result:
valore = '%3.3s %6.6s %6.6s' % (
str(ll), str(result[ll][1]), str(result[ll][0]))
print '### ', valore
ntt.util.updateheader(
nameout, 0, {'zp' + ll: [str(valore), '']})
if magsat3:
if readkey3(readhdr(nameout), 'FLUXCAL') == 'ABSOLUTE':
try:
ntt.util.updateheader(nameout, 0, {
'ABMAGSAT': [float(magsat3) + float(readkey3(readhdr(nameout)), 'PHOTZP'),
'Saturation limit for point sources (AB mags)']})
except:
ntt.util.updateheader(nameout, 0, {
'ABMAGSAT': [float(magsat3), 'Saturation limit for point sources (AB mags)']})
else:
ntt.util.updateheader(nameout, 0, {
'ABMAGSAT': [float(magsat3), 'Saturation limit for point sources (AB mags)']})
else:
ntt.util.updateheader(nameout, 0, {'ABMAGSAT': [
9999., 'Saturation limit for point sources (AB mags)']})
maglim = ntt.util.limmag(nameout)
if maglim:
ntt.util.updateheader(nameout, 0,
{'ABMAGLIM': [maglim, '5-sigma limiting AB magnitude for point sources']})
else:
ntt.util.updateheader(nameout, 0,
{'ABMAGLIM': [9999., '5-sigma limiting AB magnitude for point sources']})
if readkey3(readhdr(nameout), 'filter') in ['i705']:
try:
nameout, maskname = ntt.efoscphotredudef.fringing2(
nameout, fringingmask, _interactive, False)
if nameout not in outputfile:
outputfile.append(nameout)
if maskname not in outputfile:
outputfile.append(maskname)
except:
ntt.util.writeinthelog(
'image ' + str(nameout) + ' probably corrupted\n', './logNTT.txt')
print '\n### problem with fringing correction'
def run_zerocombine(images, output, ccdtype="none"):
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.zerocombine(input=images, output=output, ccdtype=ccdtype, nhigh=0)
def createAverageBias(biasImagesList):
biasString = getIrafString(biasImagesList)
iraf.zerocombine(input=biasString,output="averagedBias",process="No",reject="none")
if(verbose == True):
print sys.argv[0],"STATUS: Output master bias image as",biasfits
# Edit the images headers by changing IMAGETYP to "zero"
if(verbose == True):
print sys.argv[0],"STATUS: Running hedit to set IMAGETYP=zero"
if(verbose == True):
iraf.hedit(atlist, fields="IMAGETYP", value="zero", add="yes", verify="no", update="yes", show="yes")
else:
iraf.hedit(atlist, fields="IMAGETYP", value="zero", add="yes", verify="no", update="yes", show="no")
# Run the zerocombine
if(verbose == True):
print sys.argv[0],"STATUS: Running zerocombine on imagelist =",biaslist,"and output as",biasfits,"with combine =",combinetype,"and reject =",rejecttype
# hardwired some of the parameters at the moment
iraf.zerocombine(atlist, output=biasfits, combine=combinetype, ccdtype="zero", reject=rejecttype, scale="none", nlow=0, nhigh=1, nkeep=1, mclip="yes", lsigma=3.0, hsigma=3.0, rdnoise="0.", gain ="1.")
# Rename the logfile
outlog=biasfits.rstrip('fits')+'log'
os.rename("logfile",outlog)
# Finally print the ending message
print sys.argv[0],"STATUS: zerocombine done"
if __name__ == "__main__":
main()
#----------------------------------------------------
# Date Version Note
#----------------------------------------------------
#iraf.text2mask('badcols','echmask',ncols=2128,nlines=2068,linterp=1,cinterp=1,
# square=1,pixel=1)
# Note: This ^ doesn't work currently on 64-bit systems. Does not produce
# a working mask. Use 32-bit IRAF to produce mask.
# Forces interpolation of bad pixel masks along the
iraf.fixpix('@objflat', 'echmask.pl', linterp=1, verbose=yes)
iraf.fixpix('@arcs', 'echmask.pl', linterp=1, verbose=yes)
# MAKE AN AVERAGE (FIDUCIAL) BIAS AND DARK (if applicable)
# by default, we skip the darks, but this can be changed if desired
iraf.imdelete('bias_fid', verify=no)
iraf.zerocombine('@biases',
output='bias_fid',
combine='median',
reject='none',
ccdtype='',
process=no,
delete=no,
scale='none')
#iraf.imdelete('dark_fid',verify=no)
#iraf.darkcombine('@darks',output='dark_fid',combine='average',
# reject='avsigclip',ccdtype='',process=no,delete=no,
# scale='none')
# CALIBRATE ARCS, OBJECT AND FLAT FIELD SPECTRA
# Calibrate object images and flat field images with the task ccdproc. The
# calibrations applied at this step are the bias level subtraction
# and trimming. The trim section is hardcoded here.
trimsec = '[200:1850,1:2048]'
print 'Calibrating object images and flats (bias, and trimming)...'
iraf.ccdproc('@objflat',
flatcup = str(raw_input('Flat cupula: '))
flati = str(raw_input('Flat interno: '))
bias = str(raw_input('Zero level: '))
linelist = str(raw_input('Lista de linhas (linelists$thar.dat): '))
iraf.imred()
iraf.ccdred()
iraf.specred()
ds9()
iraf.zerocombine.unlearn()
iraf.zerocombine(input=bias,
reject='avsigclip',
ccdtype='',
rdnoise='rdnoise',
gain='gain')
iraf.imstat('bias*')
iraf.imstat('Zero')
iraf.imexamine('Zero')
iraf.flatcombine.unlearn()
iraf.flatcombine(input=flatcup,
output='Flat',
ccdtype='',
process=False,
subsets=False,
rdnoise='rdnoise',
def preprocess(path): #, mode='EABA'):
""" Pipeline that applies the basic CCD reduction tasks.
Asks for the path were all files are present, and
performs Bias combination, Dark combination, Flat
combination and the inherent substractions
Works in TORITOS mode, and EABA mode, the latter using UBVRI
filters.
Pipeline que aplica una reducción básica a imágenes de tipo CCD
Realiza combinación de Bias, de Darks, y Flats.
Luego realiza las restas inherentes
Funciona en dos modos, TORITOS y EABA, el último usando filtros UBVRI
"""
bornpath = os.path.abspath('.')
workdir = os.path.abspath(path)
biasdir = os.path.join(workdir, "bias")
flatdir = os.path.join(workdir, "flat")
darkdir = os.path.join(workdir, "dark")
scidir = os.path.join(workdir, "science")
for x in [biasdir, flatdir, darkdir, scidir]:
print "creating {} directory".format(str(x))
mkdir(x)
cd(workdir)
#-------------------------------------------------------------------------#
fitslist = []
for root, dirs, files in os.walk(path, topdown=True):
for name in fnmatch.filter(files, '*.fit*'):
fitslist.append(os.path.join(root, name))
#fitslist = glob.glob('*.fits') + glob.glob('*.fit')
#load_ccdred()
#-------------------------------------------------------------------------#
imagetypes = {}
for img in fitslist:
imghead = fits.getheader(img)
imgty = imghead['IMAGETYP']
imagetypes[str(img)] = imgty
#-------------------------------------------------------------------------#
# now we get the image types in the dictionary imagetypes
# now we make different lists for different image types
#-------------------------------------------------------------------------#
biaslist = []
sciencelist = []
flatlist = []
darklist = []
#-------------------------------------------------------------------------#
for k, v in imagetypes.iteritems():
print k, v
if v.upper() == 'LIGHT' or v.upper() == 'OBJECT' or v.upper(
) == 'LIGHT FRAME':
# print str(k)+' is a '+str(v)+' file'
sciencelist.append(str(k))
elif v.upper() == 'BIAS' or v.upper() == 'ZERO' or v.upper(
) == 'BIAS FRAME':
# print str(k)+' is a '+str(v)+' file'
biaslist.append(str(k))
elif v.upper() == 'FLAT' or v.upper() == 'FLAT FRAME' or v.upper(
) == 'FLAT FIELD':
# print str(k)+' is a '+str(v)+' file'
flatlist.append(str(k))
elif v.upper() == 'DARK' or v.upper() == 'DARK FRAME':
# print str(k)+' is a '+str(v)+' file'
darklist.append(str(k))
#-------------------------------------------------------------------------#
insp = raw_input("Inspeccionar imagenes de correccion con ds9? (si/NO)\n")
if insp.upper() in ('S', 'Y', 'SI', 'YES'):
print('\n')
print(u"""Comienza la inspeccion visual de bias
ante cualquier anomalia en alguna imagen se la vetará
Starting visual inspection of Bias frames. If there exists any
anomalies image will be discarded""")
for idx, img in enumerate(list(biaslist)):
I = raw_input("inspeccionar {} mediante ds9? (si/NO)\n".format(
str(img)))
if I.upper() in ('S', 'Y', 'SI', 'YES'):
print(""" inspeccionando {} mediante ds9""".format(str(img)))
print("\n")
command = shlex.split('ds9 {}'.format(str(img)))
subprocess.call(command)
V = raw_input(" Vetar la imagen? (si/NO)")
print("\n")
if V.upper() in ('S', 'Y', 'SI', 'YES'):
S = raw_input('Es una imagen de ciencia? (LIGHT) (SI/no)')
print("\n")
if S.upper() in ('S', 'Y', 'SI', 'YES'):
hdu = fits.open(img, mode='update')
hdr = hdu[0].header
hdr.set('IMAGETYP', 'LIGHT')
hdu.close()
sciencelist.append(img)
elif S.upper() in ('N', 'NO'):
os.rename(img, img + '.vet')
biaslist.remove(img)
#-------------------------------------------------------------------------#
print('\n')
print(u"""Comienza la inspeccion visual de flats
ante cualquier anomalia en alguna imagen se la vetará \n""")
for idx, img in enumerate(list(flatlist)):
I = raw_input("inspeccionar {} mediante ds9? (si/NO)\n".format(
str(img)))
if I.upper() in ('S', 'Y', 'SI', 'YES'):
print(""" inspeccionando {} mediante ds9""".format(str(img)))
print("\n")
command = shlex.split('ds9 {}'.format(str(img)))
subprocess.call(command)
V = raw_input(" Vetar la imagen? (si/NO) ")
print("\n")
if V.upper() in ('S', 'Y', 'SI', 'YES'):
S = raw_input(
'Es una imagen de ciencia? (LIGHT) (SI/no) ')
print("\n")
if S.upper() in ('S', 'Y', 'SI', 'YES'):
hdu = fits.open(img, mode='update')
hdr = hdu[0].header
hdr.set('IMAGETYP', 'LIGHT')
hdu.close()
sciencelist.append(img)
elif S.upper() in ('N', 'NO'):
os.rename(img, img + '.vet')
flatlist.remove(img)
#-------------------------------------------------------------------------#
print("\n")
print(u"""Comienza la inspeccion visual de darks
ante cualquier anomalia en alguna imagen se la vetará \n""")
for idx, img in enumerate(list(darklist)):
I = raw_input("inspeccionar {} mediante ds9? (si/NO)\n".format(
str(img)))
if I.upper() in ('S', 'Y', 'SI', 'YES'):
print(""" inspeccionando {} mediante ds9""".format(str(img)))
print("\n")
command = shlex.split('ds9 {}'.format(str(img)))
subprocess.call(command)
V = raw_input(" Vetar la imagen? (si/NO)")
print("\n")
if V.upper() in ('S', 'Y', 'SI', 'YES'):
S = raw_input('Es una imagen de ciencia? (LIGHT) (SI/no)')
print("\n")
if S.upper() in ('S', 'Y', 'SI', 'YES'):
hdu = fits.open(img, mode='update')
hdr = hdu[0].header
hdr.set('IMAGETYP', 'LIGHT')
hdu.close()
sciencelist.append(img)
elif S.upper() in ('N', 'NO'):
os.rename(img, img + '.vet')
darklist.remove(img)
#-------------------------------------------------------------------------#
#posee listas de todos los files.
#comienzo por los bias:
#primero creo una lista (file) para darle a zerocombine
write_tolist(biaslist, 'lbias', biasdir) #, workdir)
#-------------------------------------------------------------------------#
iraf.ccdhedit('@lbias', parameter='imagetype', value='zero')
iraf.zerocombine.unlearn()
iraf.zerocombine('@lbias', output='Zero.fits')
#baseflat = [os.path.basename(x) for x in flatlist]
#basesci = [os.path.basename(x) for x in sciencelist]
#basedark = [os.path.basename(x) for x in darklist]
#-------------------------------------------------------------------------#
#ahora corrijo las imagenes de flat, dark y objetos por bias.
load_ccdproc()
iraf.ccdproc.zero = 'Zero.fits'
for names in flatlist:
iraf.ccdproc(names,
output=os.path.join(flatdir,
'fz' + os.path.basename(names)))
for names in darklist:
iraf.ccdproc(names,
output=os.path.join(darkdir,
'dz' + os.path.basename(names)))
#-------------------------------------------------------------------------#
#recreate lists of new corrected objects
flatlist = []
darklist = []
for root, dirs, files in os.walk(path, topdown=True):
for name in fnmatch.filter(files, 'fz*'):
flatlist.append(os.path.join(flatdir, name))
for root, dirs, files in os.walk(path, topdown=True):
for name in fnmatch.filter(files, 'dz*'):
darklist.append(os.path.join(darkdir, name))
#-------------------------------------------------------------------------#
#combino darks tal como hice con los bias
#-------------------------------------------------------------------------#
write_tolist(darklist, 'ldark')
#write_tolist(sciencelist, 'lsci')
iraf.darkcombine.unlearn()
iraf.ccdhedit('@ldark', parameter='imagetype', value='dark')
iraf.darkcombine('@ldark', output='Dark.fits')
#-------------------------------------------------------------------------#
# discrimino por filtro los datos. es importante esta parte!
#.------------------------------------------------------------------------#
#SE USARON FILTROS?
filteruse = True
for v in flatlist:
try:
hdr = fits.getheader(v)
FILTER = hdr['FILTER']
print 'FILTROS HALLADOS'
except KeyError:
filteruse = False
print 'NO SE USARON FILTROS'
#---------------------------IF FILTERS USED ------------------------------#
if filteruse:
FD = {'U': [], 'B': [], 'V': [], 'R': [], 'I': []}
for idx, img in enumerate(list(flatlist)):
hdr = fits.getheader(img)
FR = hdr['FILTER']
#creo un diccionario de filtros
FD[str(FR)].append(img)
print(FD)
#-------------------------------------------------------------------------#
#combino los flats
#-------------------------------------------------------------------------#
for k, v in FD.iteritems():
if not v == []:
print('writing to list for {} filter'.format(k))
print(v)
lname = str(k) + 'flat'
#print(lname)
write_tolist(v, lname)
iraf.flatcombine.unlearn()
iraf.flatcombine.combine = 'median'
iraf.ccdhedit('@' + lname, parameter='imagetype', value='flat')
iraf.flatcombine('@' + lname, output='Flat' + k + '.fits')
#-------------------------------------------------------------------------#
SD = {'U': [], 'B': [], 'V': [], 'R': [], 'I': []}
for idx, img in enumerate(list(sciencelist)):
hdr = fits.getheader(img)
FR = hdr['FILTER']
#creo un diccionario de filtros
SD[str(FR)].append(img)
print(SD)
#-------------------------------------------------------------------------#
for k, v in SD.iteritems():
iraf.ccdproc.flatcor = 'yes'
iraf.ccdproc.darkcor = 'yes'
iraf.ccdproc.dark = 'Dark.fits'
for img in v:
if os.path.isfile('Flat' + k + '.fits'):
iraf.ccdproc.flat = 'Flat' + k + '.fits'
iraf.ccdproc(img, output='reduced_' + img)
#----------------------IF NO FILTERS--------------------------------------#
else:
lname = 'flist'
write_tolist(flatlist, lname)
iraf.flatcombine.unlearn()
iraf.flatcombine.combine = 'median'
iraf.ccdhedit('@' + lname, parameter='imagetype', value='flat')
iraf.flatcombine('@' + lname, output='Flat.fits')
iraf.ccdproc.flatcor = 'yes'
iraf.ccdproc.darkcor = 'yes'
iraf.ccdproc.dark = 'Dark.fits'
iraf.ccdproc.flat = 'Flat.fits'
iraf.ccdproc.ccdtype = ''
for sciname in sciencelist:
print 'ccdproc of ', sciname, '\n', os.path.join(
scidir, 'reduced_' + os.path.basename(sciname))
iraf.ccdproc(sciname, output=os.path.join(scidir,\
'reduced_'+os.path.basename(sciname)))
#[os.remove(x) for x in fitslist]
aux = glob.glob('sz*.fits') + glob.glob('dz*.fits') + glob.glob('fz*.fits')
[os.remove(x) for x in aux]
aux = glob.glob('reduced*.fits')
[os.rename(x, x[10:-4].upper() + x[-4:].lower()) for x in aux]
#-------------------------------------------------------------------------#
print(u""" Imágenes reducidas exitosamente APARENTEMENTE,
chequea todo obsesivamente, porque ni el desarrollador de
esto confia en que ande bien \n
""")
#-------------------------------------------------------------------------#
cd(bornpath)
#print sys.argv[:]
return ()
def zerocombine(self):
'''Combine all of the bias images into a single bias flat'''
iraf.zerocombine()
# <codecell>
cd ~/Documents/HERMES/reductions/iraf/bias/140821/1
# <codecell>
biases = glob.glob('*.fits')`
# <codecell>
biases = ",".join(biases)
# <codecell>
iraf.zerocombine(input=biases, output='masterbias', combine='median', reject='none', ccdtype='',Stdout="/dev/null")
# <codecell>
cd ~/Documents/HERMES/reductions/iraf/HD1581/0_20aug/1/
# <codecell>
a = pf.open('20aug10052.fits')
a[0].header['RUNCMD']
# <headingcell level=3>
# FLAT
# <codecell>