def get_files(directory, args):
science = [] ; arc = [] ; flat = []
if len(args)==1:
files = util.readlist(args[0])
else:
file_pattern = os.path.join(directory, 't*.fits')
arc_pattern = os.path.join(directory, '../ARC*.fits')
resp_pattern = os.path.join(directory, '../RESP*.fits')
files = glob.glob(file_pattern)
files += glob.glob(arc_pattern)
files += glob.glob(resp_pattern)
for file in files:
hdr0 = util.readhdr(file)
_type=util.readkey3(hdr0, 'object')
if _type.startswith('arc') or os.path.basename(file).startswith('ARC'):
arc.append(file)
elif 'RESP' in file:
flat.append(file)
else:
science.append(file)
files = []
if len(arc)==0 and len(args)==1:
q = '\n# No arc available. Do you want to glob ../ARC*.fits ([y]/n)?'
inp = raw_input(q)
if q=='' or q=='y' or q=='yes':
arc_pattern = os.path.join(directory, '../ARC*.fits')
files = glob.glob(arc_pattern)
if len(flat)==0 and len(args)==1:
q = '\n# No arc available. Do you want to glob ../RESP*.fits ([y]/n)?'
inp = raw_input(q)
if q=='' or q=='y' or q=='yes':
flat_pattern = os.path.join(directory, '../RESP*.fits')
files = glob.glob(flat_pattern)
for file in files:
hdr0 = util.readhdr(file)
_type=util.readkey3(hdr0, 'object')
if _type.startswith('arc'):
arc.append(file)
elif 'RESP' in file:
flat.append(file)
else:
science.append(file)
return(science, arc, flat)
def blue_or_red(img):
hdr = fits.open(img)[0].header
# kast
if util.readkey3(hdr, 'VERSION') == 'kastb':
return 'blue', kast_blue
elif util.readkey3(hdr, 'VERSION') == 'kastr':
return 'red', kast_red
# soar
elif util.readkey3(hdr, 'WAVMODE') == '400_M1' or util.readkey3(
hdr, 'WAVMODE') == '400 m1':
return 'blue', goodman_m1
elif util.readkey3(hdr, 'WAVMODE') == '400_M2' or util.readkey3(
hdr, 'WAVMODE') == '400 m2':
return 'red', goodman_m2
# lris
elif util.readkey3(hdr, 'INSTRUME') == 'LRISBLUE':
return 'blue', lris_blue
elif util.readkey3(hdr, 'INSTRUME') == 'LRIS':
return 'red', lris_red
else:
print(util.readkey3(hdr, 'VERSION') + 'not in database')
return None, None
def blue_or_red(img):
hdu = fits.open(img)
hdr = hdu[0].header
# Add keys as you need them for different instruments
check_keys = ['VERSION', 'WAVMODE', 'INSTRUME']
check = {}
for key in check_keys:
if key in hdr.keys() and key not in check.keys():
check[key] = hdr[key]
if len(hdu) > 1:
hdr = hdu[1].header
for key in check_keys:
if key in hdr.keys() and key not in check.keys():
check[key] = hdr[key]
# kast
if util.readkey3(check, 'VERSION') == 'kastb':
return 'blue', kast_blue
elif util.readkey3(check, 'VERSION') == 'kastr':
return 'red', kast_red
# soar
elif util.readkey3(check, 'WAVMODE') == '400_M1':
return 'blue', goodman_m1
elif util.readkey3(check, 'WAVMODE') == '400_M2':
return 'red', goodman_m2
# lris
elif util.readkey3(check, 'INSTRUME') == 'LRISBLUE':
return 'blue', lris_blue
elif util.readkey3(check, 'INSTRUME') == 'LRIS':
return 'red', lris_red
elif util.readkey3(check, 'INSTRUME') == 'Binospec':
return 'only', binospec
else:
print('Current instrument not in database!')
return None, None
def reduce(imglist, files_arc, files_flat, _cosmic, _interactive_extraction,
_arc):
import string
import os
import re
import sys
import pdb
os.environ["PYRAF_BETA_STATUS"] = "1"
try:
from astropy.io import fits as pyfits
except:
import pyfits
import numpy as np
import util
import instruments
import combine_sides as cs
import cosmics
from pyraf import iraf
dv = util.dvex()
scal = np.pi / 180.
if not _interactive_extraction:
_interactive = False
else:
_interactive = True
if not _arc:
_arc_identify = False
else:
_arc_identify = True
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
iraf.disp(inlist='1', reference='1')
toforget = [
'ccdproc', 'imcopy', 'specred.apall', 'longslit.identify',
'longslit.reidentify', 'specred.standard', 'longslit.fitcoords',
'onedspec.wspectext'
]
for t in toforget:
iraf.unlearn(t)
iraf.ccdred.verbose = 'no'
iraf.specred.verbose = 'no'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
list_arc_b = []
list_arc_r = []
for arcs in files_arc:
hdr = util.readhdr(arcs)
br, inst = instruments.blue_or_red(arcs)
if br == 'blue':
list_arc_b.append(arcs)
elif br == 'red':
list_arc_r.append(arcs)
else:
errStr = '{} '.format(str(util.readkey3(hdr, 'VERSION')))
errStr += 'not in database'
print(errStr)
sys.exit()
asci_files = []
newlist = [[], []]
print('\n### images to reduce :', imglist)
#raise TypeError
for img in imglist:
if 'b' in img:
newlist[0].append(img)
elif 'r' in img:
newlist[1].append(img)
if len(newlist[1]) < 1:
newlist = newlist[:-1]
elif len(newlist[0]) < 1:
newlist = newlist[1:]
else:
sides = raw_input("Reduce which side? ([both]/b/r): ")
if sides == 'b':
newlist = newlist[:-1]
elif sides == 'r':
newlist = newlist[1:]
for imgs in newlist:
hdr = util.readhdr(imgs[0])
br, inst = instruments.blue_or_red(imgs[0])
if br == 'blue':
flat_file = '../RESP_blue'
elif br == 'red':
flat_file = '../RESP_red'
else:
errStr = 'Not in intrument list'
print(errStr)
sys.exit()
iraf.specred.dispaxi = inst.get('dispaxis')
iraf.longslit.dispaxi = inst.get('dispaxis')
_gain = inst.get('gain')
_ron = inst.get('read_noise')
iraf.specred.apall.readnoi = _ron
iraf.specred.apall.gain = _gain
_object0 = util.readkey3(hdr, 'OBJECT')
_date0 = util.readkey3(hdr, 'DATE-OBS')
_object0 = re.sub(' ', '', _object0)
_object0 = re.sub('/', '_', _object0)
nameout0 = str(_object0) + '_' + inst.get('name') + '_' + str(_date0)
nameout0 = util.name_duplicate(imgs[0], nameout0, '')
timg = nameout0
print('\n### now processing :', timg, ' for -> ', inst.get('name'))
if len(imgs) > 1:
img_str = ''
for i in imgs:
img_str = img_str + i + ','
iraf.imcombine(img_str, output=timg)
else:
img = imgs[0]
if os.path.isfile(timg):
os.system('rm -rf ' + timg)
iraf.imcopy(img, output=timg)
# should just do this by hand
iraf.ccdproc(timg,
output='',
overscan='no',
trim='no',
zerocor="no",
flatcor="yes",
readaxi='line',
flat=flat_file,
Stdout=1)
img = timg
#raw_input("Press Enter to continue...")
if _cosmic:
print('\n### starting cosmic removal')
array, header = cosmics.fromfits(img)
c = cosmics.cosmicsimage(array,
gain=inst.get('gain'),
readnoise=inst.get('read_noise'),
sigclip=5,
sigfrac=0.5,
objlim=2.0)
c.run(maxiter=5)
cosmics.tofits('cosmic_' + img, c.cleanarray, header)
img = 'cosmic_' + img
print('\n### cosmic removal finished')
else:
print(
'\n### No cosmic removal, saving normalized image for inspection???'
)
if inst.get('arm') == 'blue' and len(list_arc_b) > 0:
arcfile = list_arc_b[0]
elif inst.get('arm') == 'red' and len(list_arc_r) > 0:
arcfile = list_arc_r[0]
else:
arcfile = None
if arcfile is not None and not arcfile.endswith(".fits"):
arcfile = arcfile + '.fits'
if not os.path.isdir('database/'):
os.mkdir('database/')
if _arc_identify:
os.system('cp ' + arcfile + ' .')
arcfile = string.split(arcfile, '/')[-1]
arc_ex = re.sub('.fits', '.ms.fits', arcfile)
arcref = inst.get('archive_arc_extracted')
arcref_img = string.split(arcref, '/')[-1]
arcref_img = arcref_img.replace('.ms.fits', '')
arcrefid = inst.get('archive_arc_extracted_id')
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
os.system('cp ' + arcrefid + ' ./database')
aperture = inst.get('archive_arc_aperture')
os.system('cp ' + aperture + ' ./database')
print('\n### arcfile : ', arcfile)
print('\n### arcfile extraction : ', arc_ex)
print('\n### arc reference : ', arcref)
# read for some meta data to get the row right
tmpHDU = pyfits.open(arcfile)
header = tmpHDU[0].header
try:
spatialBin = int(header['binning'].split(',')[0])
except KeyError:
spatialBin = 1
apLine = 700 // spatialBin
iraf.specred.apall(arcfile,
output=arc_ex,
ref=arcref_img,
line=apLine,
nsum=10,
interactive='no',
extract='yes',
find='yes',
nfind=1,
format='multispec',
trace='no',
back='no',
recen='no')
iraf.longslit.reidentify(referenc=arcref,
images=arc_ex,
interac='NO',
section=inst.get('section'),
coordli=inst.get('line_list'),
shift='INDEF',
search='INDEF',
mode='h',
verbose='YES',
step=0,
nsum=5,
nlost=2,
cradius=10,
refit='yes',
overrid='yes',
newaps='no')
print('\n### extraction using apall')
result = []
hdr_image = util.readhdr(img)
_type = util.readkey3(hdr_image, 'object')
if (_type.startswith("arc") or _type.startswith("dflat")
or _type.startswith("Dflat") or _type.startswith("Dbias")
or _type.startswith("Bias")):
print('\n### warning problem \n exit ')
sys.exit()
else:
imgex = util.extractspectrum(img, dv, inst, _interactive, 'obj')
print('\n### applying wavelength solution')
print(arc_ex)
iraf.disp(inlist=imgex, reference=arc_ex)
result = result + [imgex] + [timg]
# asci_files.append(imgasci)
if not os.path.isdir(_object0 + '_ex/'):
os.mkdir(_object0 + '_ex/')
if not _arc_identify:
util.delete(arcref)
else:
util.delete(arcfile)
util.delete(arc_ex)
util.delete(img)
util.delete(imgex)
util.delete(arcref)
util.delete('logfile')
#if _cosmic:
#util.delete(img[7:])
#util.delete("cosmic_*")
os.system('mv ' + 'd' + imgex + ' ' + _object0 + '_ex/')
use_sens = raw_input('Use archival flux calibration? [y]/n ')
if use_sens != 'no':
sensfile = inst.get('archive_sens')
os.system('cp ' + sensfile + ' ' + _object0 + '_ex/')
bstarfile = inst.get('archive_bstar')
os.system('cp ' + bstarfile + ' ' + _object0 + '_ex/')
return result
def main():
description = "> Performs pre-reduction steps"
usage = "%prog \t [option] \n Recommended syntax: %prog -i -c"
parser = OptionParser(usage=usage, description=description, version="0.1")
option, args = parser.parse_args()
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
iraf.ccdred.verbose = 'no'
iraf.specred.verbose = 'no'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
mkarc = raw_input("Make arc? ([y]/n): ")
mkflat = raw_input("Make flat? ([y]/n): ")
if len(args) > 1:
files = []
sys.argv.append('--help')
option, args = parser.parse_args()
sys.exit()
elif len(args) == 1:
files = util.readlist(args[0])
sys.exit()
else:
listfile = glob.glob('*.fits')
files_science = []
files_arc = []
files_dflat = []
#print 'checking your files ...'
for img in listfile:
_type = ''
hdr0 = util.readhdr(img)
_type = util.readkey3(hdr0, 'object')
if 'flat' in _type.lower():
files_dflat.append(img)
elif 'arc' not in _type.lower() and 'arc' not in img.lower():
files_science.append(img)
if mkarc != 'n':
mkarc_b = raw_input(
"List blue arc files to combine (.fits will be added): "
).split()
mkarc_r = raw_input(
"List red arc files to combine (.fits will be added): ").split(
)
for arc in mkarc_b:
files_arc.append(arc + '.fits')
for arc in mkarc_r:
files_arc.append(arc + '.fits')
if mkarc != 'n':
list_arc_b = []
list_arc_r = []
for arcs in files_arc:
if instruments.blue_or_red(arcs)[0] == 'blue':
list_arc_b.append(arcs)
elif instruments.blue_or_red(arcs)[0] == 'red':
list_arc_r.append(arcs)
else:
sys.exit()
if mkflat != 'n':
list_flat_b = []
list_flat_r = []
for dflats in files_dflat:
if instruments.blue_or_red(dflats)[0] == 'blue':
list_flat_b.append(dflats)
elif instruments.blue_or_red(dflats)[0] == 'red':
list_flat_r.append(dflats)
else:
sys.exit()
# make pre_reduced if it doesn't exist
if not os.path.isdir('pre_reduced/'):
os.mkdir('pre_reduced/')
# log the existing processed files (need to verify this works if pre_reduced is empty...)
pfiles = []
new_files = []
for root, dirnames, filenames in os.walk('pre_reduced'):
for file in filenames:
if file.startswith('to'):
pfiles.append(file)
print(pfiles)
# loop over each image in pre_reduced
for img in listfile:
hdr = util.readhdr(img)
targ = util.readkey3(hdr, 'object')
# if file is not not a processed file, run the overscan+trim code
if 'to' + img not in pfiles:
# if the file is a science file, grab the name for later
if 'arc' not in targ.lower() and 'flat' not in targ.lower():
new_files.append(img)
print('Adding data for: ' + targ)
inst = instruments.blue_or_red(img)[1]
iraf.specred.dispaxi = inst.get('dispaxis')
iraf.longslit.dispaxi = inst.get('dispaxis')
_biassec0 = inst.get('biassec')
_trimsec0 = inst.get('trimsec')
######################################################################
#
# JB: this chunk of code needs attention
# It seems incredibly hacky for anything but Kast...
#
# overscan
if not img.startswith('o') and inst.get('observatory') == 'lick':
if os.path.isfile('pre_reduced/o' + img):
os.remove('pre_reduced/o' + img)
util.kastbias(img, 'pre_reduced/o' + img)
elif not img.startswith('o') and inst.get('observatory') != 'lick':
if os.path.isfile('pre_reduced/o' + img):
os.remove('pre_reduced/o' + img)
os.system('cp ' + img + ' ' + 'pre_reduced/' + img)
# trim
if not img.startswith('t') and inst.get('observatory') == 'lick':
if os.path.isfile('pre_reduced/to' + img):
os.remove('pre_reduced/to' + img)
iraf.ccdproc('pre_reduced/o' + img,
output='pre_reduced/to' + img,
overscan='no',
trim='yes',
zerocor="no",
flatcor="no",
readaxi='line',
trimsec=str(_trimsec0),
Stdout=1)
elif not img.startswith('t') and inst.get('observatory') != 'lick':
if os.path.isfile('pre_reduced/to' + img):
os.remove('pre_reduced/to' + img)
iraf.ccdproc('pre_reduced/' + img,
output='pre_reduced/to' + img,
overscan='yes',
trim='yes',
zerocor="no",
flatcor="no",
readaxi='line',
trimsec=str(_trimsec0),
biassec=str(_biassec0),
Stdout=1)
# combine the arcs
if mkarc != 'n':
# blue arcs
if len(list_arc_b) > 0:
if len(list_arc_b) == 1:
arc_blue = list_arc_b[0]
os.system('cp ' + 'pre_reduced/to' + arc_blue + ' ' +
'pre_reduced/ARC_blue.fits')
else:
arc_str = ''
for arc in list_arc_b:
arc_str = arc_str + 'pre_reduced/to' + arc + ','
if os.path.isfile('pre_reduced/ARC_blue.fits'):
os.remove('pre_reduced/ARC_blue.fits')
iraf.imcombine(arc_str, output='pre_reduced/ARC_blue.fits')
# red arcs
if len(list_arc_r) > 0:
if len(list_arc_r) == 1:
arc_red = list_arc_r[0]
os.system('cp ' + 'pre_reduced/to' + arc_red + ' ' +
'pre_reduced/ARC_red.fits')
else:
arc_str = ''
for arc in list_arc_r:
arc_str = arc_str + 'pre_reduced/to' + arc + ','
if os.path.isfile('pre_reduced/ARC_red.fits'):
os.remove('pre_reduced/ARC_red.fits')
iraf.imcombine(arc_str, output='pre_reduced/ARC_red.fits')
# combine the flats
if mkflat != 'n':
inter = 'yes'
# blue flats
if len(list_flat_b) > 0:
br, inst = instruments.blue_or_red(list_flat_b[0])
iraf.specred.dispaxi = inst.get('dispaxis')
if len(list_flat_b) == 1:
# Flat_blue = 'pre_reduced/to'+ list_flat_b[0]
Flat_blue = list_flat_b[0]
else:
flat_str = ''
for flat in list_flat_b:
flat_str = flat_str + 'pre_reduced/to' + flat + ','
#subsets = 'no'
if os.path.isfile('pre_reduced/toFlat_blue'):
os.remove('pre_reduced/toFlat_blue')
iraf.flatcombine(flat_str,
output='pre_reduced/toFlat_blue',
ccdtype='',
rdnoise=3.7,
subsets='no',
process='no')
Flat_blue = 'Flat_blue.fits'
#What is the output here? Check for overwrite
iraf.specred.response('pre_reduced/to' + Flat_blue,
normaliz='pre_reduced/to' + Flat_blue,
response='pre_reduced/RESP_blue',
interac=inter,
thresho='INDEF',
sample='*',
naverage=2,
function='legendre',
low_rej=3,
high_rej=3,
order=60,
niterat=20,
grow=0,
graphic='stdgraph')
# red flats
if len(list_flat_r) > 0:
br, inst = instruments.blue_or_red(list_flat_r[0])
iraf.specred.dispaxi = inst.get('dispaxis')
if len(list_flat_r) == 1:
# Flat_red = 'pre_reduced/to' + list_flat_r[0]
Flat_red = list_flat_r[0]
else:
flat_str = ''
for flat in list_flat_r:
flat_str = flat_str + 'pre_reduced/to' + flat + ','
if os.path.isfile('pre_reduced/toFlat_red'):
os.remove('pre_reduced/toFlat_red')
iraf.flatcombine(flat_str,
output='pre_reduced/toFlat_red',
ccdtype='',
rdnoise=3.8,
subsets='yes',
process='no')
Flat_red = 'Flat_red.fits'
#What is the output here? Check for overwrite
iraf.specred.response('pre_reduced/to' + Flat_red,
normaliz='pre_reduced/to' + Flat_red,
response='pre_reduced/RESP_red',
interac=inter,
thresho='INDEF',
sample='*',
naverage=2,
function='legendre',
low_rej=3,
high_rej=3,
order=80,
niterat=20,
grow=0,
graphic='stdgraph')
# science files should have 't' in front now
# this just gets the base name, to prefix assumed below
if new_files is not None:
files_science = new_files
# get all the science objects for the night
science_targets = []
for obj in files_science:
hdr = util.readhdr(obj)
_type = util.readkey3(hdr, 'object')
science_targets.append(_type)
# make a dir for each sci object
science_targets = set(science_targets)
for targ in science_targets:
if not os.path.isdir('pre_reduced/' + targ + '/'):
os.mkdir('pre_reduced/' + targ + '/')
# copy the files into the obj dir
for obj in files_science:
hdr = util.readhdr(obj)
targ = util.readkey3(hdr, 'object')
if not obj.startswith('to'):
os.system('cp ' + 'pre_reduced/to' + obj + ' ' + 'pre_reduced/' +
targ + '/')
else:
os.system('cp ' + 'pre_reduced/' + obj + ' ' + 'pre_reduced/' +
targ + '/')
rawfiles = glob.glob('*.fits')
ofiles = glob.glob('pre_reduced/o' + '*.fits')
tfiles = glob.glob('pre_reduced/to' + '*.fits')
# delete raw files from the pre_reduced dir
# there shouldn't be any there though?
# maybe if the overscan isn't implemented for that detector
for img in rawfiles:
util.delete('pre_reduced/' + img)
# delete the ofiles from pre_reduced dir
for img in ofiles:
util.delete(img)
def reduce(imglist,files_arc, _cosmic, _interactive_extraction,_arc):
import string
import os
import re
import sys
os.environ["PYRAF_BETA_STATUS"] = "1"
try: from astropy.io import fits as pyfits
except: import pyfits
import numpy as np
import util
import instruments
import combine_sides as cs
import cosmics
from pyraf import iraf
dv = util.dvex()
scal = np.pi / 180.
if not _interactive_extraction:
_interactive = False
else:
_interactive = True
if not _arc:
_arc_identify = False
else:
_arc_identify = True
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
iraf.disp(inlist='1', reference='1')
toforget = ['ccdproc', 'imcopy', 'specred.apall', 'longslit.identify', 'longslit.reidentify', 'specred.standard',
'longslit.fitcoords', 'onedspec.wspectext']
for t in toforget:
iraf.unlearn(t)
iraf.ccdred.verbose = 'no'
iraf.specred.verbose = 'no'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
list_arc_b = []
list_arc_r = []
for arcs in files_arc:
hdr = util.readhdr(arcs)
if util.readkey3(hdr, 'VERSION') == 'kastb':
list_arc_b.append(arcs)
elif util.readkey3(hdr, 'VERSION') == 'kastr':
list_arc_r.append(arcs)
else:
print util.readkey3(hdr, 'VERSION') + 'not in database'
sys.exit()
asci_files = []
newlist = [[],[]]
print '\n### images to reduce :',imglist
#raise TypeError
for img in imglist:
if 'b' in img:
newlist[0].append(img)
elif 'r' in img:
newlist[1].append(img)
if len(newlist[1]) < 1:
newlist = newlist[:-1]
for imgs in newlist:
hdr = util.readhdr(imgs[0])
if util.readkey3(hdr, 'VERSION') == 'kastb':
inst = instruments.kast_blue
elif util.readkey3(hdr, 'VERSION') == 'kastr':
inst = instruments.kast_red
else:
print util.readkey3(hdr, 'VERSION') + 'not in database'
sys.exit()
iraf.specred.dispaxi = inst.get('dispaxis')
iraf.longslit.dispaxi = inst.get('dispaxis')
_gain = inst.get('gain')
_ron = inst.get('read_noise')
iraf.specred.apall.readnoi = _ron
iraf.specred.apall.gain = _gain
_object0 = util.readkey3(hdr, 'OBJECT')
_date0 = util.readkey3(hdr, 'DATE-OBS')
_biassec0 = inst.get('biassec')
_trimsec0 = inst.get('trimsec')
_object0 = re.sub(' ', '', _object0)
_object0 = re.sub('/', '_', _object0)
nameout0 = str(_object0) + '_' + inst.get('name') + '_' + str(_date0)
nameout0 = util.name_duplicate(imgs[0], nameout0, '')
timg = nameout0
print '\n### now processing :',timg,' for -> ',inst.get('name')
if len(imgs) > 1:
img_str = ''
for i in imgs:
img_str = img_str + i + ','
iraf.imcombine(img_str, output=timg)
else:
img = imgs[0]
if os.path.isfile(timg):
os.system('rm -rf ' + timg)
iraf.imcopy(img, output=timg)
zero_file = inst.get('archive_zero_file')
os.system('cp ' + zero_file + ' .')
zero_file = string.split(zero_file, '/')[-1]
flat_file = inst.get('archive_flat_file')
os.system('cp ' + flat_file + ' .')
flat_file = string.split(flat_file, '/')[-1]
iraf.ccdproc(timg, output='', overscan='yes', trim='yes', zerocor="no", flatcor="no", readaxi='line',
trimsec=str(_trimsec0),biassec=str(_biassec0), Stdout=1)
iraf.ccdproc(timg, output='', overscan='no', trim='no', zerocor="yes", flatcor="no", readaxi='line',
zero=zero_file,order=3, Stdout=1)
iraf.ccdproc(timg, output='', overscan='no', trim='no', zerocor="no", flatcor="yes", readaxi='line',
flat=flat_file, Stdout=1)
img = timg
#raw_input("Press Enter to continue...")
print '\n### starting cosmic removal'
if _cosmic:
array, header = cosmics.fromfits(img)
c = cosmics.cosmicsimage(array, gain=inst.get('gain'), readnoise=inst.get('read_noise'), sigclip = 4.5, sigfrac = 0.5, objlim = 1.0)
c.run(maxiter = 4)
cosmics.tofits('cosmic_' + img, c.cleanarray, header)
print '\n### cosmic removal finished'
img='cosmic_' + img
if inst.get('name') == 'kast_blue':
arcfile = list_arc_b[0]
elif inst.get('name') == 'kast_red':
arcfile = list_arc_r[0]
if not arcfile.endswith(".fits"):
arcfile=arcfile+'.fits'
if os.path.isfile(arcfile):
util.delete('t' + arcfile)
iraf.ccdproc(arcfile, output= 't' + arcfile, overscan='yes', trim='yes', zerocor="no", flatcor="no",
readaxi='line', trimsec=str(_trimsec0), biassec=str(_biassec0), Stdout=1)
arcfile = 't' + arcfile
else:
print '\n### warning no arcfile \n exit '
sys.exit()
if not os.path.isdir('database/'):
os.mkdir('database/')
if _arc_identify:
arc_ex=re.sub('.fits', '.ms.fits', arcfile)
print '\n### arcfile : ',arcfile
print '\n### arcfile extraction : ',arc_ex
iraf.specred.apall(arcfile, output='', line = 'INDEF', nsum=10, interactive='no', extract='yes',find='yes', nfind=1 ,format='multispec', trace='no',back='no',recen='no')
iraf.longslit.identify(images=arc_ex, section=inst.get('section'),coordli=inst.get('line_list'),function = 'spline3',order=3, mode='h')
else:
arcref = inst.get('archive_arc_extracted')
arcrefid = inst.get('archive_arc_extracted_id')
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
os.system('cp ' + arcrefid + ' ./database')
arc_ex=re.sub('.fits', '.ms.fits', arcfile)
print '\n### arcfile : ',arcfile
print '\n### arcfile extraction : ',arc_ex
print '\n### arc referenece : ',arcref
iraf.specred.apall(arcfile, output=arc_ex, line = 'INDEF', nsum=10, interactive='no', extract='yes',find='yes', nfind=1 ,format='multispec', trace='no',back='no',recen='no')
iraf.longslit.reidentify(referenc=arcref, images=arc_ex, interac='NO', section=inst.get('section'),
coordli=inst.get('line_list'), shift='INDEF', search='INDEF',
mode='h', verbose='YES', step=0,nsum=5, nlost=2, cradius=10, refit='yes',overrid='yes',newaps='no')
#print '\n### checking sky lines '
#_skyfile = inst.get('sky_file')
#shift = util.skyfrom2d(img, _skyfile,'True')
#print '\n### I found a shift of : ',shift
print '\n### extraction using apall'
result = []
hdr_image = util.readhdr(img)
_type=util.readkey3(hdr_image, 'object')
if _type.startswith("arc") or _type.startswith("dflat") or _type.startswith("Dflat") or _type.startswith("Dbias") or _type.startswith("Bias"):
print '\n### warning problem \n exit '
sys.exit()
else:
imgex = util.extractspectrum(
img, dv, inst, _interactive, 'obj')
print '\n### applying wavelength solution'
iraf.disp(inlist=imgex, reference=arc_ex)
sensfile = inst.get('archive_sens')
os.system('cp ' + sensfile + ' .')
sensfile = string.split(sensfile, '/')[-1]
if sensfile:
print '\n### sensitivity function : ',sensfile
imgf = re.sub('.fits', '_f.fits', img)
_extinction = inst.get('extinction_file')
_observatory = inst.get('observatory')
_exptime = util.readkey3(hdr, 'EXPTIME')
_airmass = util.readkey3(hdr, 'AIRMASS')
util.delete(imgf)
dimgex='d'+imgex
iraf.specred.calibrate(input=dimgex, output=imgf, sensiti=sensfile, extinct='yes',
extinction=_extinction,flux='yes', ignorea='yes', airmass=_airmass, exptime=_exptime,
fnu='no')
imgout = imgf
imgasci = re.sub('.fits', '.asci', imgout)
errasci = re.sub('.fits', '_err.asci', imgout)
util.delete(imgasci)
iraf.onedspec.wspectext(imgout + '[*,1,1]', imgasci, header='no')
iraf.onedspec.wspectext(imgout + '[*,1,4]', errasci, header='no')
spec = np.transpose(np.genfromtxt(imgasci))
err = np.transpose(np.genfromtxt(errasci))
util.delete(errasci)
final = np.transpose([spec[0], spec[1], err[1]])
np.savetxt(imgasci, final)
result = result + [imgout, imgasci]
result = result + [imgex] + [timg]
asci_files.append(imgasci)
if not os.path.isdir(_object0 + '/'):
os.mkdir(_object0 + '/')
for img in result:
os.system('mv ' + img + ' ' + _object0 + '/')
else:
for img in result:
os.system('mv ' + img + ' ' + _object0 + '/')
if not _arc_identify:
util.delete(arcref)
util.delete(sensfile)
util.delete(zero_file)
util.delete(flat_file)
util.delete(arc_ex)
util.delete(arcfile)
util.delete('logfile')
util.delete(dimgex)
util.delete('cosmic_*')
print '\n### now i will merge ...'
if len(asci_files) > 1:
final = cs.combine_blue_red(asci_files[0], asci_files[1], _object0)
print '\n### final result in folder ',_object0,' is ',_object0+'_merged.asci'
return result
option, args = parser.parse_args()
elif len(args) == 1:
files = util.readlist(args[0])
else:
listfile = glob.glob('t*.fits')
files_science = []
files_arc = []
files_flat = []
# prep_files = glob.glob('../*.fits')
files_arc.append('../ARC_blue.fits')
files_arc.append('../ARC_red.fits')
#print 'checking your files ...'
for img in listfile:
_type = ''
hdr0 = util.readhdr(img)
_type = util.readkey3(hdr0, 'object')
if _type.startswith("arc"):
files_arc.append(img)
elif 'RESP' in img:
files_flat.append(img)
else:
files_science.append(img)
_cosmic = option.cosmic
if not _cosmic:
_cosmic = False
_interactive_extraction = option.interactive_extraction
if not _interactive_extraction:
_interactive_extraction = False
if len(args) > 1:
files = []
sys.argv.append('--help')
option, args = parser.parse_args()
elif len(args) == 1:
files = util.readlist(args[0])
else:
listfile = glob.glob('*.fits')
files_science = []
files_arc = []
files_dflat = []
#print 'checking your files ...'
for img in listfile:
_type = ''
hdr0 = util.readhdr(img)
_type = util.readkey3(hdr0, 'object')
if _type.lower().startswith("arc"):
files_arc.append(img)
elif _type.lower().startswith("dflat"):
files_dflat.append(img)
else:
files_science.append(img)
list_arc_b = []
list_arc_r = []
for arcs in files_arc:
hdr = util.readhdr(arcs)
if util.readkey3(hdr, 'VERSION') == 'kastb':
list_arc_b.append(arcs)
elif util.readkey3(hdr, 'VERSION') == 'kastr':
list_arc_r.append(arcs)
